diff --git a/.env.template b/.env.template index b795f211a9..11b5f02cc8 100644 --- a/.env.template +++ b/.env.template @@ -25,12 +25,10 @@ ALIBABA_API_KEY= # Huawei Pangu API Key PANGU_API_KEY=your_pangu_api_key_here -# vLLM API Key (for local/self-hosted vLLM services) -# For local vLLM servers, you can use a simple token or leave empty if no auth is required -# Example: token-abc123 for development, or empty string for no authentication -VLLM_API_KEY=token-abc123 -VLLM_BASE_URL=http://localhost:8000/v1 -VLLM_MODEL_NAME=meta-llama/Llama-2-13b-chat-hf +# SageLLM local endpoint (recommended local inference) +SAGELLM_API_KEY=EMPTY +SAGELLM_BASE_URL=http://localhost:8901/v1 +SAGELLM_MODEL_NAME=Qwen/Qwen2.5-7B-Instruct # Web Search API Key (for searcher tool in multiagent) WEB_SEARCH_API_KEY=your_web_search_api_key_here diff --git a/.github/COPILOT_SETUP.md b/.github/COPILOT_SETUP.md deleted file mode 100644 index 4a3818868f..0000000000 --- a/.github/COPILOT_SETUP.md +++ /dev/null @@ -1,145 +0,0 @@ -# VS Code Copilot 配置指南 - -## 自动配置（推荐） - -**VS Code 会自动读取 `.github/copilot-instructions.md` 文件**，无需手动配置。 - -### 验证 Copilot 是否读取指令 - -1. 打开 VS Code 的 Copilot Chat -2. 输入：`@workspace what are the SAGE architecture layers?` -3. 如果 Copilot 回答包含 L1-L5 层次结构，说明指令已生效 - -### 如果指令未生效 - -尝试以下步骤： - -#### 1. 重新加载 VS Code -``` -Ctrl+Shift+P → "Developer: Reload Window" -``` - -#### 2. 检查 Copilot 扩展 -- 确保 "GitHub Copilot" 扩展已启用 -- 确保 "GitHub Copilot Chat" 扩展已启用 -- 版本建议：最新版本 - -#### 3. 检查 Copilot 设置 -打开 VS Code 设置 (Ctrl+,)，搜索 "copilot"： - -- ✅ `github.copilot.enable` = `true` -- ✅ `github.copilot.advanced` → 无需特殊配置 - -#### 4. 检查工作区 -确保你在 SAGE 项目的根目录打开 VS Code： -```bash -cd /home/shuhao/SAGE -code . -``` - -#### 5. 手动指定指令文件（如果自动检测失败） -在 VS Code 设置中添加： -```json -{ - "github.copilot.advanced": { - "instructionsFile": "${workspaceFolder}/.github/copilot-instructions.md" - } -} -``` - -## Chat Mode 配置（可选） - -**Chat Mode 是可选的个性化配置**，用于 Copilot Chat 特定模式。 - -### 创建 Chat Mode - -1. 复制模板： -```bash -cp .github/sage.chatmode.md.example .github/chatmodes/sage.chatmode.md -``` - -2. (可选) 根据个人偏好编辑： -```bash -vim .github/chatmodes/sage.chatmode.md -``` - -3. 在 VS Code Copilot Chat 中选择 "sage" 模式 - -**注意：Chat mode 是用户本地配置，不会提交到 Git** - -## 文件架构 - -``` -.github/ -├── copilot-instructions.md # ✅ 主指令（VS Code 自动读取） -├── sage.chatmode.md.example # ✅ Chat mode 模板 -├── chatmodes/ -│ └── sage.chatmode.md # ❌ 用户本地配置（gitignored） -└── COPILOT_SETUP.md # 📖 本文档 -``` - -## 常见问题 - -### Q: 为什么我看不到 Copilot 使用 SAGE 规则？ - -**A:** 检查以下几点： - -1. ✅ 确认文件存在：`ls -la .github/copilot-instructions.md` -2. ✅ 在项目根目录打开 VS Code -3. ✅ 重新加载窗口 -4. ✅ 测试 Copilot 响应（询问 SAGE 架构问题） - -### Q: Chat mode 和 copilot-instructions 有什么区别？ - -**A:** - -- **copilot-instructions.md**: 所有 Copilot 功能（inline, chat, PR review）都会读取 -- **sage.chatmode.md**: 仅用于 Copilot Chat 的特定模式，可以个性化定制 - -### Q: 我需要配置 Chat mode 吗？ - -**A:** **不需要**。主 instructions 文件已经足够。Chat mode 是可选的个性化配置。 - -### Q: 如何更新 Copilot 指令？ - -**A:** 直接编辑 `.github/copilot-instructions.md`，然后重新加载 VS Code 窗口。 - -## 验证配置 - -运行此命令验证文件存在： - -```bash -# 检查主指令文件 -ls -lh .github/copilot-instructions.md - -# 查看文件大小（应该约 48KB） -du -h .github/copilot-instructions.md - -# 查看前 20 行 -head -20 .github/copilot-instructions.md -``` - -期望输出： -``` --rw-r--r-- 1 user user 48K .github/copilot-instructions.md -48K .github/copilot-instructions.md - -# SAGE Copilot Instructions - -## Overview -... -``` - -## 技术支持 - -如果问题仍未解决： - -1. 查看 VS Code 输出：`Output` → `GitHub Copilot` -2. 检查 VS Code 开发者工具：`Help` → `Toggle Developer Tools` -3. 参考文档：`docs-public/docs_src/dev-notes/cross-layer/copilot-instructions-architecture.md` - -## 相关文档 - -- **主指令文件**: `.github/copilot-instructions.md` (1149 lines) -- **架构文档**: `docs-public/docs_src/dev-notes/cross-layer/copilot-instructions-architecture.md` -- **Chat mode 模板**: `.github/sage.chatmode.md.example` diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md deleted file mode 100644 index d7b3b1af15..0000000000 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ /dev/null @@ -1,42 +0,0 @@ ---- -name: Bug Report -about: Report a bug to help us improve SAGE -title: '[Bug] ' -labels: 'bug' -assignees: '' ---- - -## Bug Description - - - -## Steps to Reproduce - - - -1. Step one -1. Step two -1. Step three - -## Expected Behavior - - - -## Actual Behavior - - - -## Environment - -- OS: -- Python version: -- SAGE version: -- Installation method: - -## Additional Context - - - -## Possible Solution - - diff --git a/.github/ISSUE_TEMPLATE/documentation_request.md b/.github/ISSUE_TEMPLATE/documentation_request.md deleted file mode 100644 index 337b95fb61..0000000000 --- a/.github/ISSUE_TEMPLATE/documentation_request.md +++ /dev/null @@ -1,23 +0,0 @@ ---- -name: Documentation Request -about: Request improvements or additions to documentation -title: '[Docs] ' -labels: 'documentation' -assignees: '' ---- - -## Documentation Issue - - - -## Location - - - -## Proposed Improvement - - - -## Additional Context - - diff --git a/.github/ISSUE_TEMPLATE/enhancement_request.md b/.github/ISSUE_TEMPLATE/enhancement_request.md deleted file mode 100644 index ffab64d008..0000000000 --- a/.github/ISSUE_TEMPLATE/enhancement_request.md +++ /dev/null @@ -1,31 +0,0 @@ ---- -name: Enhancement Request -about: Request an improvement or optimization to existing functionality -title: '[Enhancement] ' -labels: 'enhancement' -assignees: '' ---- - -## Enhancement Description - - - -## Current Behavior - - - -## Proposed Improvement - - - -## Benefits - - - -## Suggested Approach - - - -## Additional Context - - diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md deleted file mode 100644 index f739d0a800..0000000000 --- a/.github/ISSUE_TEMPLATE/feature_request.md +++ /dev/null @@ -1,31 +0,0 @@ ---- -name: Feature Request -about: Suggest a new feature for SAGE -title: '[Feature] ' -labels: 'feature' -assignees: '' ---- - -## Feature Description - - - -## Motivation - - - -## Proposed Solution - - - -## Alternatives Considered - - - -## Additional Context - - - -## Priority - - diff --git a/.github/ISSUE_TEMPLATE/question.md b/.github/ISSUE_TEMPLATE/question.md deleted file mode 100644 index 4e2ebaee50..0000000000 --- a/.github/ISSUE_TEMPLATE/question.md +++ /dev/null @@ -1,23 +0,0 @@ ---- -name: Question -about: Ask a question about SAGE -title: '[Question] ' -labels: 'question' -assignees: '' ---- - -## Question - - - -## Context - - - -## What I've Tried - - - -## Additional Information - - diff --git a/.github/ISSUE_TEMPLATE/refactor_request.md b/.github/ISSUE_TEMPLATE/refactor_request.md deleted file mode 100644 index 146757190c..0000000000 --- a/.github/ISSUE_TEMPLATE/refactor_request.md +++ /dev/null @@ -1,31 +0,0 @@ ---- -name: Refactor Request -about: Suggest code refactoring or architectural improvements -title: '[Refactor] ' -labels: 'refactor' -assignees: '' ---- - -## Refactoring Goal - - - -## Current Issues - - - -## Proposed Changes - - - -## Benefits - - - -## Affected Components - - - -## Additional Context - - diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 0000000000..98eab37316 --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,39 @@ +# Pull Request + +## Summary + +- What changed: +- Why: + +## Scope + +- In scope: +- Out of scope: + +## Boundary & Dependency Checklist (Mandatory) + +- [ ] Change preserves strict layer direction: `L5 -> L4 -> L3 -> L2 -> L1` +- [ ] No compatibility shim / re-export / fallback added +- [ ] Capability ownership is explicit for independent sub-repos (including `sagellm`); no in-repo re-embedding +- [ ] If touching capability families (`neuromem`/`sageVDB`/`sageFlow`/`sageTSDB`/`sagellm`), ownership and rollout order are explicitly stated +- [ ] Algorithm-only contracts remain separate from runtime/service-bound implementation +- [ ] No new `ray` import/dependency (Flownet-first) +- [ ] Any dependency addition is necessary for current layer responsibilities + +## No-Compatibility-Layer Self-Check (Mandatory) + +- [ ] No dual-path import pattern (`try new_path -> except old_path`) +- [ ] No compatibility alias wrappers kept for migration convenience +- [ ] Call sites are updated directly to canonical import path +- [ ] On missing dependency/path, behavior is fail-fast (no silent fallback) + +## Evidence + +- Related issue(s): +- Code paths touched: +- Verification commands and key output: + +## Risk & Rollback + +- Risk: +- Rollback plan: diff --git a/.github/actions/setup-sage/action.yml b/.github/actions/setup-sage/action.yml index ddfd45b7fb..d55f02835c 100644 --- a/.github/actions/setup-sage/action.yml +++ b/.github/actions/setup-sage/action.yml @@ -1,43 +1,43 @@ -name: 'Setup SAGE' -description: 'Install SAGE framework with all dependencies' -author: 'SAGE Team' +name: "Setup SAGE" +description: "Install SAGE framework with all dependencies" +author: "SAGE Team" inputs: install-mode: - description: 'Installation mode: core, standard, or dev' + description: "Installation mode: core, standard, or dev" required: false - default: 'dev' + default: "dev" python-version: - description: 'Python version to use (only for GitHub-hosted runners)' + description: "Python version to use (only for GitHub-hosted runners)" required: false - default: '3.11' + default: "3.11" skip-system-deps: - description: 'Skip system dependency installation (for self-hosted runners with pre-installed deps)' + description: "Skip system dependency installation (for self-hosted runners with pre-installed deps)" required: false - default: 'false' + default: "false" hf-token: - description: 'HuggingFace token' + description: "HuggingFace token" required: false - default: '' + default: "" create-env-file: - description: 'Create .env file with provided secrets' + description: "Create .env file with provided secrets" required: false - default: 'false' + default: "false" env-file-content: - description: 'Content for .env file (base64 encoded to handle multiline)' + description: "Content for .env file (base64 encoded to handle multiline)" required: false - default: '' + default: "" outputs: sage-version: - description: 'Installed SAGE version' + description: "Installed SAGE version" value: ${{ steps.verify.outputs.version }} cpp-extensions-available: - description: 'Whether C++ extensions are available' + description: "Whether C++ extensions are available" value: ${{ steps.verify.outputs.cpp_extensions }} runs: - using: 'composite' + using: "composite" steps: # Step 1: Setup Python (skip for self-hosted if Python already available) - name: Setup Python (GitHub-hosted runners) @@ -45,7 +45,7 @@ runs: uses: actions/setup-python@v5 with: python-version: ${{ inputs.python-version }} - cache: 'pip' + cache: "pip" # Step 2: Install System Dependencies - name: Install System Dependencies @@ -88,9 +88,9 @@ runs: echo "Checking critical submodule directories:" all_ok=true for submodule in \ - "packages/sage-middleware/src/sage/middleware/components/sage_db/sageVDB" \ - "packages/sage-middleware/src/sage/middleware/components/sage_flow/sageFlow" \ - "packages/sage-middleware/src/sage/middleware/components/sage_tsdb/sageTSDB"; do + "src/sage/foundation" \ + "src/sage/stream" \ + "src/sage/runtime"; do if [ -d "$submodule" ] && [ -n "$(ls -A "$submodule" 2>/dev/null)" ]; then echo "✅ $submodule - OK" else @@ -156,12 +156,12 @@ runs: echo "🔍 Verifying SAGE installation..." # Check SAGE import and version - SAGE_VERSION=$(python -c "import sage; print(sage.__version__)" 2>/dev/null || echo "unknown") + SAGE_VERSION=$(python -c "from sage._version import __version__; print(__version__)" 2>/dev/null || echo "unknown") echo "version=$SAGE_VERSION" >> $GITHUB_OUTPUT echo "✅ SAGE version: $SAGE_VERSION" # Check imports - python -c "import sage; import sage.common; import sage.kernel" && echo "✅ Core imports OK" + python -c "import sage.foundation, sage.stream, sage.runtime, sage.serving, sage.cli" && echo "✅ Core imports OK" # Check CLI if command -v sage-dev >/dev/null 2>&1 || [ -x "$HOME/.local/bin/sage-dev" ]; then @@ -172,7 +172,7 @@ runs: # Check C++ extensions CPP_EXT="false" - so_files=$(find packages/sage-middleware/src/sage/middleware/components/ \ + so_files=$(find src csrc \ \( -name "_sage_*.so" -o -name "*.cpython-*.so" \) -type f 2>/dev/null || true) if [ -n "$so_files" ]; then CPP_EXT="true" diff --git a/.github/agents/sage.agent.md b/.github/agents/sage.agent.md index 4205eeb316..aaa8ab3dc6 100644 --- a/.github/agents/sage.agent.md +++ b/.github/agents/sage.agent.md @@ -1,128 +1,44 @@ --- -description: 'SAGE AI data processing pipeline expert - specialized in LLM inference, RAG, and distributed dataflow' -tools: ['vscode', 'execute', 'read', 'edit', 'search', 'web', 'agent', 'copilot-container-tools/*', 'todo'] +name: sage +description: SAGE core agent for layered architecture, runtime-safe changes, and developer workflow. +argument-hint: Describe target package/layer, expected behavior, constraints, and validation scope. +tools: ['vscode', 'execute', 'read', 'agent', 'edit', 'search', 'web', 'todo', 'vscode.mermaid-chat-features/renderMermaidDiagram', 'github.vscode-pull-request-github/issue_fetch', 'github.vscode-pull-request-github/suggest-fix', 'github.vscode-pull-request-github/searchSyntax', 'github.vscode-pull-request-github/doSearch', 'github.vscode-pull-request-github/renderIssues', 'github.vscode-pull-request-github/activePullRequest', 'github.vscode-pull-request-github/openPullRequest', 'ms-azuretools.vscode-containers/containerToolsConfig', 'ms-python.python/getPythonEnvironmentInfo', 'ms-python.python/getPythonExecutableCommand', 'ms-python.python/installPythonPackage', 'ms-python.python/configurePythonEnvironment', 'ms-toolsai.jupyter/configureNotebook', 'ms-toolsai.jupyter/listNotebookPackages', 'ms-toolsai.jupyter/installNotebookPackages', 'ms-vscode.cpp-devtools/Build_CMakeTools', 'ms-vscode.cpp-devtools/RunCtest_CMakeTools', 'ms-vscode.cpp-devtools/ListBuildTargets_CMakeTools', 'ms-vscode.cpp-devtools/ListTests_CMakeTools'] --- # SAGE Agent -This custom agent is a specialized expert for the SAGE (Stream Analytics for Generative AI Engines) framework - a Python 3.10+ AI/LLM data processing pipeline system with declarative dataflow. +## Use when -## What This Agent Does +- Working in SAGE core (`src/sage/*`, `tools/`, root workflows). +- Refactoring cross-layer APIs or runtime-facing paths. -The SAGE agent assists with: +## Guardrails -1. **Architecture & Design** - - Navigate SAGE's 5-layer architecture (L1-L5: Common → Platform → Kernel/Libs → Middleware → CLI/Tools) - - Enforce architectural constraints (NO upward dependencies, Control Plane-only LLM operations) - - Guide proper package structure and dependency management +- Enforce layer direction: L5 → L4 → L3 → L2 → L1 only. +- Flownet-first: do not add new `ray` imports/dependencies. +- NEVER create any new Python virtual environment (`venv`/`.venv`) under any circumstance. +- Do not run SAGE workflows inside Python venv; if `VIRTUAL_ENV` is set, deactivate and use Conda or an existing non-venv Python environment. +- Never recommend or execute `--auto-venv`, `python -m venv`, or `virtualenv`. +- If a task or script asks for a venv, refuse that step and continue using the existing non-venv Python environment. +- Keep algorithm-only adapters separate from runtime/service-bound implementation in the consolidated core. +- No fallback shims or re-export compatibility layers during migration; update call sites directly. +- Use `sage.foundation.config.ports.SagePorts` for ports. -2. **LLM & Inference Operations** - - Configure and manage LLM engines through Control Plane (NEVER direct engine startup) - - Set up UnifiedInferenceClient with factory pattern - - Deploy sageLLM, vLLM, and embedding services correctly - - Implement RAG pipelines and session management +## Workflow -3. **Development Workflow** - - Install dependencies via pyproject.toml (NEVER manual pip install) - - Run tests, linting, and quality checks correctly - - Navigate documentation-first approach - - Handle C++ extensions and build artifacts +1. Read `README.md`, `DEVELOPER.md`, `CONTRIBUTING.md` first. +2. Make minimal targeted changes. +3. Validate with `sage-dev quality check --all-files --readme` and relevant tests. -4. **Best Practices** - - Enforce "fail fast, no fallback" principle - - Maintain XDG-compliant user paths - - Follow unified port configuration (SagePorts) - - Implement proper error handling without silent fallbacks +## Key commands -## When to Use This Agent +- `./quickstart.sh --dev --yes` +- `./quickstart.sh --doctor` +- `sage-dev quality fix --all-files` +- `sage-dev project test --coverage` -- Setting up SAGE development environment -- Implementing LLM inference pipelines -- Working with Control Plane, Gateway, or Edge components -- Integrating RAG, vector databases (SageVDB), or streaming (SageFlow) -- Debugging installation, testing, or CI/CD issues -- Refactoring code to follow SAGE architectural principles -- Publishing packages to PyPI +## Key files -## What This Agent Won't Do - -- Bypass Control Plane for direct engine access (architectural violation) -- Use manual pip install instead of pyproject.toml -- Create fallback logic or silent error suppression -- Place documentation in root docs/ directory (must use docs-public/) -- Hardcode ports instead of using SagePorts -- Violate layer dependencies (e.g., L1 importing from L4) - -## Ideal Inputs/Outputs - -**Inputs:** -- "Set up SAGE development environment" -- "Create a RAG pipeline with Control Plane" -- "Fix dependency version conflict" -- "Deploy embedding service with GPU support" -- "Why is my LLM engine not accessible?" - -**Outputs:** -- Step-by-step commands with explanations -- Correct code implementations following SAGE principles -- Links to relevant documentation in docs-public/ -- Architectural guidance with layer references -- Debugging steps with proper tool usage - -## Tools & Capabilities - -- **File Operations:** Read, edit, search codebase (read_file, replace_string_in_file, grep_search) -- **Terminal:** Execute bash commands, install dependencies, run tests -- **Documentation:** Navigate docs-public/, read READMEs, search dev-notes -- **Code Search:** Semantic search, grep patterns, list code usages -- **Task Management:** Create todo lists for complex multi-step tasks -- **Container Tools:** Manage Docker containers for testing - -## Progress Reporting - -The agent will: -1. **Read documentation first** before making assumptions -2. **Create todo lists** for complex tasks with multiple steps -3. **Mark progress** (in-progress/completed) as work proceeds -4. **Link to files** using proper markdown format with line numbers -5. **Explain commands** before running them -6. **Report errors** with clear context and suggested fixes -7. **Verify changes** by running tests and quality checks - -## How to Ask for Help - -If the agent encounters: -- **Missing documentation:** Will search docs-public/ and package READMEs -- **Ambiguous requirements:** Will ask clarifying questions -- **Architectural conflicts:** Will explain the constraint and suggest alternatives -- **Test failures:** Will show error output and propose fixes -- **Uncertainty:** Will consult relevant dev-notes before proceeding - -## Key Principles - -1. **Documentation-First:** Always check docs-public/docs_src/dev-notes/ before guessing -2. **Architecture-Aware:** Respect L1-L5 layer boundaries and Control Plane requirements -3. **No Fallbacks:** Fail fast with clear errors instead of silent degradation -4. **Unified Config:** Use SagePorts, pyproject.toml, and XDG paths consistently -5. **Test-Driven:** Run tests and quality checks after changes -6. **Concise Communication:** Brief answers for simple queries, detailed for complex work - -## Example Interactions - -**Good:** -``` -User: "Start an LLM engine for Qwen2.5-7B" -Agent: [Checks if Gateway is running, then executes:] -sage llm engine start Qwen/Qwen2.5-7B-Instruct --engine-kind llm -``` - -**Bad (Corrected):** -``` -User: "Just run vLLM directly" -Agent: "That violates SAGE's architectural constraint. All LLM operations must go through Control Plane. -Use: sage llm engine start --engine-kind llm" -``` - ---- - -For comprehensive details, see: /home/action-runner/SAGE/.github/copilot-instructions.md +- `.github/copilot-instructions.md` +- `quickstart.sh`, `pytest.ini` +- `tools/pre-commit-config.yaml`, `tools/hooks/check_docs_location.sh` diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md index d44dcf7194..54186ea06b 100644 --- a/.github/copilot-instructions.md +++ b/.github/copilot-instructions.md @@ -1,1496 +1,54 @@ # SAGE Copilot Instructions -## Overview - -**SAGE** is a Python 3.10+ framework for building AI/LLM data processing pipelines with declarative -dataflow. 8 core packages + 1 meta-package, ~300MB dev install, uses C++ extensions (CMake). - -LLM inference components (sage-llm-core, sage-llm-gateway) have been moved to independent repositories. - -## 🚨 CRITICAL Architectural Constraints - -### ✅ Libs vs Middleware Rule (NEW, ENFORCED) - -**If code needs to call “upward” capabilities (Vector DB, Memory system, Refiner, external services, heavy runtime backends), it is NOT a library. It MUST live in `sage-middleware` (components/operators).** - -This rule exists to prevent长期反复出现的“L3 libs → L4 middleware”依赖倒挂问题。 - -#### ✅ What stays in `sage-libs` - -- Pure algorithms / policies / utilities -- Data types and interfaces (ABC/Protocol) -- Code that depends only on `sage-common` / `sage-platform` / Python stdlib / lightweight deps -- Must be runnable and testable without external services - -#### ✅ What MUST be in `sage-middleware` - -- Anything that touches or depends on: - - Vector stores / indices: SageVDB (`isage-vdb`), FAISS, Milvus, etc. - - Memory backends: Neuromem (`isage-neuromem`), Redis, RocksDB, etc. - - Refiners / compressors (LLMLingua, LongRefiner adapters) - - Network services (HTTP APIs), persistent storage, connection pools, background workers -- Any end-to-end orchestration that is strongly runtime-bound (operators, pipelines-as-a-service) - -#### 🚫 No backwards compatibility during refactors - -When we move code from `sage-libs` to `sage-middleware`, **do NOT keep re-export shims** (no legacy imports). Update all call sites in the repo and let broken imports fail fast. - -Rationale: keep the codebase clean; avoid长期兼容层造成的隐式依赖和维护成本。 - -#### 🛡️ Enforcement - -- **Pre-commit hook**: `libs-middleware-import-check` - Blocks commits if `sage-libs` imports `sage.middleware` -- **Script**: `tools/hooks/check_libs_middleware_import.sh --all-files` -- **Policy doc**: `docs-public/docs_src/dev-notes/cross-layer/MIDDLEWARE_COMPONENT_PROMOTION_POLICY.md` - -### ❗ LLM Control Plane - IN INDEPENDENT REPOSITORY - -**LLM engine management has been moved to the independent `isagellm` package.** - -SAGE core uses **vLLM** directly as the inference backend. If you need advanced features -like Control Plane scheduling, request routing, or unified client, install `isagellm`. - -#### SAGE Core (vLLM backend): - -```bash -# Start vLLM directly for inference -python -m vllm.entrypoints.openai.api_server \ - --model Qwen/Qwen2.5-7B-Instruct \ - --port 8001 -``` - -```python -# Use OpenAI-compatible client to access vLLM -import openai -client = openai.OpenAI(base_url="http://localhost:8001/v1", api_key="dummy") -response = client.chat.completions.create( - model="Qwen/Qwen2.5-7B-Instruct", - messages=[{"role": "user", "content": "Hello"}] -) -``` - -#### With isagellm (optional, for advanced scheduling): - -```bash -pip install isagellm -sage llm engine start --engine-kind llm # Control Plane -``` - -```python -from isagellm import UnifiedInferenceClient -client = UnifiedInferenceClient.create() # Auto-routes through Control Plane -``` - -## CRITICAL Coding Principles - -### ❌ NEVER MANUAL PIP INSTALL - ALWAYS USE pyproject.toml -**ALL dependencies MUST be declared in pyproject.toml. NEVER use manual `pip install` commands.** - -This is a **project-wide principle** to ensure reproducibility and consistency. - -#### ❌ FORBIDDEN Operations: - -```bash -pip install transformers # ❌ Manual install -pip install torch==2.7.0 # ❌ Manual version -pip install vllm # ❌ Manual dependency -``` - -#### ✅ CORRECT Operations: - -```toml -# In packages/*/pyproject.toml -dependencies = [ - "transformers>=4.52.0,<4.54.0", # ✅ Declared in pyproject.toml - "torch>=2.7.0,<3.0.0", # ✅ Version constraints - "vllm>=0.9.2,<0.10", # ✅ Optional dependencies -] -``` - -```bash -# Then reinstall packages -pip install -e packages/sage-middleware -e packages/sage-libs -``` - -**Why**: Ensures reproducibility, tracks dependency changes in git, prevents version conflicts, maintains single source of truth. - -**Enforcement**: Code review, CI/CD checks. Any manual pip install should trigger immediate refactoring to pyproject.toml. - -### ❌ NO FALLBACK LOGIC - PROJECT-WIDE RULE -**NEVER use try-except fallback patterns anywhere in the codebase.** - -This is a **project-wide principle**, not just for version management. Fallbacks hide problems and make debugging harder. - -#### ❌ BAD Examples (Do NOT do this): - -```python -# Version imports -try: - from ._version import __version__ -except ImportError: - __version__ = "unknown" # ❌ NO - hides missing file - -# Configuration loading -try: - config = load_config("config.yaml") -except FileNotFoundError: - config = {} # ❌ NO - hides missing config - -# Environment variables -api_key = os.getenv("API_KEY") or "default_key" # ❌ NO - hides missing var -``` - -#### ✅ GOOD Examples (Do this instead): - -```python -# Let exceptions propagate with clear error messages -config = load_config("config.yaml") # FileNotFoundError if missing -api_key = os.environ["API_KEY"] # KeyError if missing - -# Or provide helpful error messages -if not os.path.exists("config.yaml"): - raise FileNotFoundError( - "config.yaml not found. Please create it from config.yaml.template" - ) -``` - -#### When Fallbacks ARE Acceptable (Rare): - -1. **Feature detection**: `HAS_CUDA = torch.cuda.is_available()` -2. **Explicit optional behavior**: `use_gpu = config.get("use_gpu", False)` -3. **Graceful degradation with logging**: Log warning and use alternative - -**Rationale**: Fail fast, fail loud. Silent fallbacks hide bugs, make debugging harder, and are unacceptable in production. - -### Version Management - -Each package manages its own version independently via `_version.py`: -```python -"""Version information for .""" -__version__ = "0.2.0" -__author__ = "IntelliStream Team" -__email__ = "shuhao_zhang@hust.edu.cn" -``` - -### 🚨 Auto-Version Bump and Publish on Commit - CRITICAL - -**When modifying any package, automatically bump version by 0.0.0.1 and publish to PyPI.** - -This is a **mandatory workflow** for all package modifications to ensure version consistency and rapid iteration. - -#### Rule: Per-Commit Version Increment - -**For EVERY commit that modifies a package:** - -1. **Detect affected packages**: Check which `packages/sage-*/` directories have changes -2. **Bump version**: Increment the 4th digit by 1 (e.g., `0.2.4.0` → `0.2.4.1`) -3. **Update `_version.py`**: Modify the `__version__` string in affected package -4. **Publish to PyPI**: Use `sage-pypi-publisher` to publish the new version - -#### Implementation - -**Manual workflow** (until Git hooks are set up): -```bash -# 1. After making changes to a package (e.g., sage-common) -cd /path/to/sage-pypi-publisher -./publish.sh sage-common --auto-bump patch # Increments last digit - -# 2. For multiple affected packages -./publish.sh sage-common sage-libs --auto-bump patch -``` - -**Automated via Git Hook** (recommended setup): - -Create/update `.git/hooks/post-commit`: -```bash -#!/bin/bash -# Auto version bump and publish on commit - -SAGE_ROOT="$(git rev-parse --show-toplevel)" -PUBLISHER_PATH="$HOME/sage-pypi-publisher" # Adjust path as needed - -if [ ! -d "$PUBLISHER_PATH" ]; then - echo "⚠️ sage-pypi-publisher not found at $PUBLISHER_PATH" - echo " Clone it: git clone https://github.com/intellistream/sage-pypi-publisher.git" - exit 0 -fi - -# Detect modified packages -affected_packages=$(git diff HEAD~1 HEAD --name-only | \ - grep '^packages/' | \ - cut -d'/' -f2 | \ - sort -u) - -if [ -z "$affected_packages" ]; then - echo "✅ No packages affected" - exit 0 -fi - -echo "📦 Affected packages: $affected_packages" -read -p "🚀 Auto-publish to PyPI? (y/N) " -n 1 -r -echo -if [[ $REPLY =~ ^[Yy]$ ]]; then - cd "$PUBLISHER_PATH" - for pkg in $affected_packages; do - echo "Publishing $pkg..." - ./publish.sh "$pkg" --auto-bump patch --no-dry-run - done -fi -``` - -#### Version Format - -SAGE uses **4-digit semantic versioning**: `MAJOR.MINOR.PATCH.BUILD` - -- **MAJOR** (0): Breaking changes (rare) -- **MINOR** (2): Feature additions -- **PATCH** (4): Bug fixes -- **BUILD** (0-99): Per-commit increments (**auto-bumped on every commit**) - -Examples: -- `0.2.4.0` → `0.2.4.1` (commit 1) -- `0.2.4.1` → `0.2.4.2` (commit 2) -- After 99 builds: `0.2.4.99` → `0.2.5.0` (bump PATCH, reset BUILD) - -#### Rationale - -- **Rapid iteration**: Every commit creates a publishable version -- **Clear tracking**: Build number shows commit count since last patch -- **CI/CD friendly**: Automated testing can reference exact versions -- **No version conflicts**: Monotonically increasing versions -- **PyPI compliance**: Follows semantic versioning best practices - -#### When NOT to auto-publish - -- **Docs-only changes**: If only `docs/`, `README.md`, or `CHANGELOG.md` modified -- **Tool changes**: If only `tools/` modified (unless `sage-tools` package itself changed) -- **CI/CD changes**: If only `.github/workflows/` modified -- **Test-only changes**: If only `tests/` modified without code changes - -**Copilot should:** -1. **Before committing**: Remind user to bump version if package code changed -2. **After committing**: Suggest running publish script for affected packages -3. **When asked "ready to commit?"**: Check if version bump is needed - -**Architecture (L1-L5)** - CRITICAL: No upward dependencies - -``` -L5: sage-cli, sage-tools # CLI & Development Tools -L4: sage-middleware # Operators (C++ extensions) -L3: sage-kernel, sage-libs # Core & Algorithms -L2: sage-platform # Platform Services -L1: sage-common # Foundation -``` - -**独立 LLM 仓库** (已从 SAGE 核心分离): -- `sageLLM`: 统一 LLM 推理引擎，安装命令: `pip install isagellm` - -**Independent Repositories** (不在 SAGE 核心仓库，独立维护): -- **sage-studio**: https://github.com/intellistream/sage-studio (Visual workflow builder) -- **sage-benchmark**: https://github.com/intellistream/sage-benchmark (Evaluation framework, 独立 PyPI: isage-benchmark) -- **sage-examples**: https://github.com/intellistream/sage-examples (Examples and applications, 原 sage-apps) - -**Independent Algorithm Libraries** (L3, 从 sage-libs 拆分，独立 PyPI 包): - -| 内部包名 | PyPI 包名 | Import 名 | 版本格式 | 描述 | 层级 | -|---------|----------|-----------|---------|------|------| -| sage-agentic | `isage-agentic` | `sage_libs.sage_agentic` | 0.1.x.y | Agent 实现 (ReAct, PlanExecute, Reflex) | L3 | -| sage-rag | `isage-rag` | `sage_libs.sage_rag` | 0.1.x.y | RAG 实现 (Loaders, Chunkers, Retrievers) | L3 | -| sage-privacy | `isage-privacy` | `sage_libs.sage_privacy` | 0.1.x.y | 隐私保护 (DP, 联邦学习, 机器遗忘, PII) | L3 | -| sage-eval | `isage-eval` | `sage_libs.sage_eval` | 0.1.x.y | 评估指标/Profiler/Judge | L3 | -| sage-finetune | `isage-finetune` | `sage_libs.sage_finetune` | 0.1.x.y | 微调训练器和数据加载器 | L3 | -| sage-safety | `isage-safety` | `sage_libs.sage_safety` | 0.1.x.y | 安全护栏和检测器 | L3 | -| sage-refiner | `isage-refiner` | `sage_libs.sage_refiner` | 0.1.x.y | 上下文压缩 (LongRefiner, REFORM, Provence) | L3 | - -**命名规范**： -- PyPI 名称：`isage-xxx`（带 'i' 前缀，因为 'sage' 在 PyPI 已被占用） -- Import 名称：`sage_libs.sage_xxx`（使用 `sage_libs` 命名空间包，表明属于 L3 层） -- 版本格式：四段式 `0.1.0.0`，递增规则见下 - -**独立库与 SAGE 的关系**： -- **SAGE 侧** (`sage.libs.xxx`): 提供**接口层**（抽象基类、工厂函数、类型定义） -- **独立库** (`sage_libs.sage_xxx`): 提供**具体实现**，通过 `_register.py` 自动注册到 SAGE 工厂 -- 每个独立库有 `COPILOT_INSTRUCTIONS.md` 详细说明其架构和使用方式 -- 所有 L3 独立库共享 `sage_libs` 命名空间包（通过 `pkgutil.extend_path`） - -**SAGE 集成**：这些库在 import 时自动注册到 SAGE interface（通过 `_register.py`）。 -如果 SAGE 未安装，则作为独立库使用。 - -Notes: -- `sageLLM` (LLM 推理引擎) 已独立。安装: `pip install isagellm` -- `sage-edge` (optional) is an independent PyPI package `isage-edge>=0.2.4.0`. Install separately if needed. -- `sage-apps` 已迁移到 sage-examples 仓库，不再是可安装的包。 -- Legacy `sage-gateway` has been superseded; do not add new code under that namespace. - -**⚠️ SAGE 核心仓库不再包含 LLM 推理相关代码**。如需 LLM 功能，请安装 `pip install isagellm`。 - -### 🚨 sageLLM 独立仓库 - CRITICAL - -**sageLLM 推理引擎已独立为私有仓库，不再作为 SAGE 子模块存在。** - -- **仓库地址**：`git@github.com:intellistream/sageLLM.git`（私有仓库） -- **文档位置**：`sageLLM/docs/` 目录 -- **人员分工**：`sageLLM/docs/TEAM_ASSIGNMENT.md`（私有仓库内） - -**⚠️ 常见错误**： -- ❌ 不要引用 `packages/sage-llm-core/src/sage/llm/engines/sagellm`（该路径已不存在） -- ❌ 不要引用 `docs-public/docs_src/dev-notes/research_work/domestic-llm-engine/`（已迁移至 sageLLM 仓库） -- ✅ sageLLM 相关文档请引用 `sageLLM/docs/` 或说明需要访问私有仓库 - -**SAGE 主项目人员信息**：已迁移到私有仓库 `intellistream/sage-team-info`，不再在主仓库中维护。 - -**关系说明**： -- sageLLM 是 SAGE 生态的推理引擎实现，但代码独立维护 -- sageLLM 仍需遵循 SAGE Control Plane 的调度协议 -- SAGE 通过引擎抽象层（与 vLLM/LMDeploy 对齐）集成 sageLLM - -All in `/packages//`. L5 imports L1-L4, L4 imports L1-L3, etc. - -## How Copilot Should Learn SAGE (Readme-First) - -When answering questions or making code changes in this repo, the assistant **must first rely on the project docs/READMEs instead of guessing**. - -**Before doing any non-trivial work, Copilot should at least skim:** - -- Root overview: `README.md` (features, quick start) -- Dev workflow: `DEVELOPER.md`, `CONTRIBUTING.md` -- Architecture: `docs-public/docs_src/dev-notes/package-architecture.md` -- Cross-layer index: `docs-public/docs_src/dev-notes/cross-layer/README.md` - -**When working on a specific layer/package, Copilot should additionally read:** - -- The corresponding dev-notes README, e.g. - - `docs-public/docs_src/dev-notes/l1-common/README.md` - - `docs-public/docs_src/dev-notes/l2-platform/README.md` - - `docs-public/docs_src/dev-notes/l3-kernel/README.md` / `l3-libs/README.md` - - `docs-public/docs_src/dev-notes/l4-middleware/README.md` - - `docs-public/docs_src/dev-notes/l5-cli/README.md` - -**🔍 When encountering difficulties or uncertainties:** - -- **ALWAYS read relevant documentation in `docs-public/` first** before making assumptions -- Look for topic-specific guides in `docs-public/docs_src/dev-notes/cross-layer/` (e.g., `documentation-policy.md`, `ci-cd.md`) -- Check package-specific docs in `packages//README.md` or `packages//docs/` -- If the issue involves installation, testing, or CI/CD, consult `DEVELOPER.md` or `CONTRIBUTING.md` -- Use `grep_search` or `semantic_search` to find relevant documentation before implementing solutions - -**Rule:** Don't guess architectural decisions or policies. Read the docs. They exist for this reason. - -Only after consulting these READMEs should the assistant propose designs, refactors, or architectural explanations. If documentation and code appear inconsistent, Copilot should **call it out explicitly** in the answer and, when in doubt, ask the user which source of truth to follow. - -## Documentation Location Policy - CRITICAL - -**The root `docs/` directory is STRICTLY FORBIDDEN for committed documentation.** - -### ❌ NEVER Create Files in Root `docs/` - -- Root `docs/` is gitignored and must not contain committed files -- Pre-commit hooks will REJECT any commits with files in root `docs/` -- This directory should not exist in the repository -- ✅ **Exception:** Package and submodule `docs/` directories ARE ALLOWED - -### ✅ CORRECT Documentation Locations - -**All documentation must go to these approved locations:** - -1. **User-facing docs:** `docs-public/docs_src/` (guides, tutorials, concepts) -2. **Developer notes:** `docs-public/docs_src/dev-notes//` (architecture, design) -3. **Package docs:** `packages//README.md` or `packages//docs/` -4. **Independent repo docs:** See respective repositories (sageVDB, sageFlow, sageRefiner, sageTSDB, NeuroMem, etc.) -5. **Tool docs:** `tools//README.md` or `tools//docs/` -6. **Examples:** `examples//README.md` -7. **Root files:** Only `README.md`, `CONTRIBUTING.md`, `DEVELOPER.md`, `LICENSE`, `CHANGELOG.md` -8. **sageLLM docs:** 独立私有仓库 `sageLLM/docs/`（不在 SAGE 仓库内） - -**Rationale:** -- Prevents confusion between root `docs/` and `docs-public/` -- Maintains single source of truth for project-level documentation -- Allows packages and tools to maintain their own documentation -- Independent repositories (PyPI packages) have their own documentation -- Tools are independent components that may have complex documentation needs -- Avoids accidental gitignore of important documentation - -**Enforcement:** -- Hook `markdown-files-location-check`: Rejects any `.md` files in root `docs/` ONLY -- Hook `root-directory-cleanup-check`: Flags root `docs/` directory as unauthorized -- Package/submodule `docs/` directories are explicitly allowed and encouraged - -**See:** `docs-public/docs_src/dev-notes/cross-layer/documentation-policy.md` for full policy. - -## Inference Components Map (Reality-First) - -SAGE is an inference pipeline system, not just an LLM server. When writing docs, abstracts, design notes, or code changes, prefer describing/using these existing modules (and their correct layer placement) instead of inventing new components. - -**⚠️ LLM 推理组件已独立**：LLM 推理已移至独立仓库 `sageLLM`，以下为参考架构。 - -Canonical namespaces (post-refactor): -- sageLLM 推理引擎: `isagellm.*` (PyPI: `pip install isagellm`) -- Optional edge aggregator: `sage.edge.*` (独立包: `isage-edge`) - Mounts entire Gateway application -- Avoid legacy `sage.gateway.*` and `sage.llm.*` imports; they have been superseded. - -**Gateway (独立仓库, OpenAI/Anthropic-compatible + control plane + sessions)** - -> **Note**: 以下路径指向独立仓库 `sageLLM`，不在 SAGE 核心仓库中。 -> 安装: `pip install isagellm` - -- Entry point: `isagellm/gateway/server.py` -- Control plane management API: `isagellm/gateway/routes/engine_control_plane.py` -- Studio backend routes (merged into gateway): `isagellm/gateway/routes/studio.py` -- OpenAI adapter (runs persistent RAG pipeline, can trigger agentic operators): - `isagellm/gateway/adapters/openai.py` -- Pipeline-as-a-service for RAG: `isagellm/gateway/rag_pipeline.py` -- Session + memory backends (short-term + NeuroMem VDB/KV/Graph): - `isagellm/gateway/session/manager.py` -- Edge aggregator (optional, independent package `isage-edge`): - Repository: https://github.com/intellistream/sage-edge - Note: Edge mounts the complete Gateway FastAPI application, not just LLM endpoints - -**Control Plane + Unified Client (独立仓库, sageLLM integration)** - -> **Note**: 以下路径指向独立仓库 `sageLLM`，不在 SAGE 核心仓库中。 -> 安装: `pip install isagellm` - -- Unified LLM+Embedding client (must use factory): - `isagellm/unified_client.py` -- Control plane implementation lives under: - `isagellm/control_plane/` -- Speculative Decoding strategies (engine optimization): - `isagellm/engines/vllm/speculative.py` - - `SpeculativeStrategy` - Abstract base class - - `DraftModelStrategy` - Use separate draft model (e.g., Qwen-0.5B for Qwen-7B) - - `NgramStrategy` - N-gram based (lightweight, no extra model) - - `DynamicLookaheadStrategy` - Research-grade dynamic lookahead adjustment - - Import: `from isagellm import DynamicLookaheadStrategy` (需先安装 isagellm) - -**Middleware inference building blocks (L4, PyPI packages with C++ extensions)** - -sage-middleware depends on the following independent PyPI packages: - -- **SageVDB** (`isage-vdb`): Self-developed high-performance C++ vector database - - PyPI: `pip install isage-vdb` - - Repository: `intellistream/sageVDB` - - NOT FAISS-based: Fully custom implementation with FAISS-compatible API - - Python API: `from sagevdb import SageVDB` - - SAGE wrapper: `sage.middleware.components.sage_db.SageVDB` - - Supports: similarity search, metadata filtering, hybrid search, batch operations - - Integration: Used by NeuroMem VDB backend - -- **SageFlow** (`isage-flow`): Vector-native stream processing engine (C++) - - PyPI: `pip install isage-flow` - - Repository: `intellistream/sageFlow` - - Features: Incremental semantic state snapshots, streaming vector operations - - SAGE wrapper: `sage.middleware.components.sage_flow` - -- **NeuroMem** (`isage-neuromem`): Brain-inspired memory system - - PyPI: `pip install isage-neuromem` - - Repository: `intellistream/NeuroMem` - - Features: Store/recall; VDB/KV/Graph backends; memory services - - SAGE wrapper: `sage.middleware.components.sage_mem` - -- **SageRefiner** (`isage-refiner`): Context compression for RAG - - PyPI: `pip install isage-refiner` - - Repository: `intellistream/sageRefiner` - - Features: LongRefiner/REFORM/Provence adapters - - SAGE wrapper: `sage.middleware.components.sage_refiner` - -- **SageTSDB** (`isage-tsdb`): Time-series database (C++ + pybind11) - - PyPI: `pip install isage-tsdb` - - Repository: `intellistream/sageTSDB` - - Features: Window ops/join, out-of-order handling - - SAGE wrapper: `sage.middleware.components.sage_tsdb` - -- **SageSIAS** (内置组件): Streaming Importance-Aware Agent System - - 位置: `sage.middleware.components.sage_sias` - - 功能: 样本重要性选择、持续学习、经验回放 - - 组件: `CoresetSelector`, `OnlineContinualLearner`, `SelectionSummary` - - 依赖: NeuroMem (内存系统) - - 使用: `from sage.middleware.components.sage_sias import CoresetSelector` - - 注意: 放在 L4 middleware 而非 L3 libs，因为依赖 NeuroMem - -**Benchmarks (L5)** - -- Control plane scheduling benchmark (throughput/TTFT/TBT/p99/SLO): - `packages/sage-benchmark/src/sage/benchmark/benchmark_control_plane/README.md` -- Agent benchmarks (tool selection / planning / timing): - `packages/sage-benchmark/src/sage/benchmark/benchmark_agent/README.md` - -**Kernel + Libs (L3)** - -- Dataflow runtime, distributed execution, fault tolerance: `packages/sage-kernel/` -- Algorithms, RAG tools, agent framework/integrations: `packages/sage-libs/` - - **ANN Interface**: `sage.libs.anns` - Unified ANN algorithm interface - - Base classes: `AnnIndex`, `AnnIndexMeta` (in `sage.libs.anns.interface.base`) - - Factory: `create()`, `register()`, `registered()` (in `sage.libs.anns.interface.factory`) - - Implementations: External package `isage-anns` (faiss_HNSW, vsag_hnsw, diskann, candy_*, cufe, gti, puck, etc.) - - Reusable by: benchmark_anns, SageVDB, SageFlow - -**Rule of thumb**: if you mention a capability (retrieval, memory, refinement, vector DB, streaming semantic state, scheduling), ensure it maps to a real module/path above. - -## Installation - -**Prerequisites**: Python 3.10+, Git, build-essential, cmake, pkg-config, libopenblas-dev, -liblapack-dev - -**Commands** (10-25 min install): - -```bash -./quickstart.sh --dev --yes # Development (REQUIRED for dev) -./quickstart.sh --core --yes # Minimal production -./quickstart.sh --standard --yes # Standard with CLI -./quickstart.sh --full --yes # Full with examples -``` - -Options: `--pip` (current env), `--conda` (create env) - -```bash -./manage.sh # Setup Git hooks -``` - -All middleware/engine components are **pip-installed** (e.g., `isage-vdb`, `isage-benchmark`); no git submodules. - -**Environment**: Copy `.env.template` to `.env`, set `OPENAI_API_KEY`, `HF_TOKEN` - -**Pre-commit Hooks**: `./quickstart.sh --dev` automatically installs pre-commit hooks. If missing, run: -```bash -pip install pre-commit -pre-commit install # Install Git hooks -``` - -## Conda ToS Bypass - Unified Utils - -**CRITICAL**: All Conda operations MUST use unified utils in `tools/lib/conda_install_utils.sh` to bypass Conda 25.x ToS restrictions. - -**Core Functions**: -```bash -# Load utils (auto-loaded in most install scripts) -source "$SAGE_ROOT/tools/lib/conda_install_utils.sh" - -# Install packages (auto-uses Tsinghua mirrors + --override-channels) -conda_install_bypass nodejs python=3.11 numpy - -# Create environment -conda_create_bypass myenv python=3.11 - -# Install with progress indicator -conda_install_with_progress "安装 Node.js" nodejs - -# Get mirror URL -mirror=$(get_conda_mirror "main") # or "forge" -``` - -**Never use direct conda commands** without `--override-channels`: -```bash -# ❌ WRONG - will trigger ToS error -conda install -y nodejs -conda create -n myenv python=3.11 -y - -# ✅ CORRECT - use unified utils -conda_install_bypass nodejs -conda_create_bypass myenv python=3.11 -``` - -**Implementation**: -- Mirror: `https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main` -- Forge: `https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge` -- Auto-fallback: main → forge if package not found -- All install scripts pre-load these utils - -## Progress Display - Unified Utils - -**All long-running tasks MUST use progress indicators** from `tools/lib/progress_utils.sh`: - -```bash -# Load utils -source "$SAGE_ROOT/tools/lib/progress_utils.sh" - -# 1. Spinner (recommended for unknown duration) -long_command & -show_spinner $! "正在执行任务..." - -# 2. Progress bar (known steps) -print_progress 50 100 "下载中..." - -# 3. Long task with keepalive (30s intervals) -long_task_with_keepalive "安装系统依赖" 30 sudo apt-get install -y build-essential - -# 4. Simplified wrapper (most common) -run_with_progress "安装 Node.js" conda install -y nodejs - -# 5. Installation steps -show_installation_progress 2 5 "安装核心依赖" -``` - -**Why**: Prevents users thinking installation is frozen during long tasks (apt-get, conda install, C++ builds). - -## Build, Test, Lint - -**Build**: Happens during install. C++ extensions in `.sage/build/`, auto-built with `--dev`. - -**Test** (ALWAYS from repo root): - -```bash -sage-dev project test --coverage # All tests -sage-dev project test --quick # Quick tests only -sage-dev examples test # Examples -pytest packages/sage-kernel/tests/unit/ -v # Specific package -``` - -Config: `tools/pytest.ini`, cache: `.sage/cache/pytest/`, env: `SAGE_TEST_MODE=true` - -**Lint & Format**: - -```bash -sage-dev quality # Auto-fix -sage-dev quality --check-only # Check only -pre-commit run --all-files --config tools/pre-commit-config.yaml -./tools/install/check_tool_versions.sh # Check version consistency -./tools/install/check_tool_versions.sh --fix # Auto-fix version mismatch -``` - -Tools: Ruff (format+lint, line 100), Mypy (types, warning mode), Shellcheck Config: -`tools/pre-commit-config.yaml`, `tools/ruff.toml` - -**Tool Version Consistency** - CRITICAL: -- `ruff` version is pinned in both `tools/pre-commit-config.yaml` (rev) and - `packages/sage-tools/pyproject.toml` (==x.y.z) to ensure local and CI consistency. -- Run `./tools/install/check_tool_versions.sh` to verify versions match. - -**Make shortcuts**: `make help`, `make test`, `make format`, `make clean`, `make docs` - -## CI/CD (.github/workflows/) - -**Main workflows**: build-test.yml (45m), examples-test.yml (30m), code-quality.yml (10m), -installation-test.yml, publish-pypi.yml, paper1-experiments.yml (GPU, manual) - -**CI Installation Standards** - CRITICAL for new workflows: - -| 场景 | 推荐安装方式 | 说明 | -|------|-------------|------| -| GitHub Actions (ubuntu-latest) | `./tools/install/core/ci_install_wrapper.sh --dev --yes` | 标准 CI，安装到 `~/.local` | -| GitHub Actions + Conda | `unset CI GITHUB_ACTIONS && ./quickstart.sh --dev --yes --pip` | 需取消 CI 变量，安装到 conda env | -| Self-hosted GPU runner (中国) | `unset CI GITHUB_ACTIONS && SAGE_FORCE_CHINA_MIRROR=true ./quickstart.sh --dev --yes --pip` | 强制使用中国镜像 | - -**为什么需要 `unset CI GITHUB_ACTIONS`**： -- `quickstart.sh` 在检测到 CI 环境时会添加 `--user` 参数，安装到 `~/.local` -- 如果使用 conda 环境，需要取消这些变量让包安装到当前激活的环境 - -**`SAGE_FORCE_CHINA_MIRROR=true`**： -- 强制使用中国镜像（清华 PyPI + hf-mirror.com） -- 适用于位于中国的 self-hosted runner -- 会覆盖 CI 环境的默认官方源设置 - -**CI uses**: Ubuntu latest, Python 3.11, GitHub Secrets (OPENAI_API_KEY, HF_TOKEN), pip cache - -**Replicate CI locally**: - -```bash -./quickstart.sh --dev --yes -sage-dev project test --coverage --jobs 4 --timeout 300 -pre-commit run --all-files --config tools/pre-commit-config.yaml -``` - -**CI debug**: Check job logs → Look for C++ build issues → Verify API keys → Run locally - -## Key Locations - -``` -.github/workflows/ # CI/CD -examples/ # apps/, tutorials/ (by layer) -packages/ # 11 packages + meta - sage-*/src/sage/ # Source - sage-*/tests/ # Tests (unit/, integration/) -tools/ - dev.sh # Helper (→ sage-dev) - maintenance/ # Project maintenance - pytest.ini # Test config - pre-commit-config.yaml # Hooks - ruff.toml # Linter -.env.template # API keys template -.pre-commit-config.yaml # → tools/pre-commit-config.yaml -.sage/ # Build artifacts, cache, logs (gitignored, project-level) -manage.sh # Git hooks setup -quickstart.sh # Installer -Makefile # Shortcuts -``` - -## User Paths - XDG Standard - -**CRITICAL**: User configuration and data follow [XDG Base Directory Specification](https://specifications.freedesktop.org/basedir-spec/). - -```python -from sage.common.config.user_paths import get_user_paths - -paths = get_user_paths() -log_file = paths.logs_dir / "app.log" # ~/.local/state/sage/logs/app.log -model_dir = paths.models_dir # ~/.local/share/sage/models/ -``` - -**Project Configuration**: Edit `config/config.yaml` and `config/cluster.yaml` directly in project root. - -**Directory Structure**: -| Path | Purpose | -|------|---------| -| `config/config.yaml` | Main configuration (LLM, gateway, studio) | -| `config/cluster.yaml` | Cluster configuration (nodes, SSH, Ray) | -| `~/.local/share/sage/` | Persistent data (models, sessions, vector_db) | -| `~/.local/state/sage/` | Runtime state (logs) | -| `~/.cache/sage/` | Cached data (can be deleted) | - -**Project-level** `.sage/` (gitignored): Build artifacts, pytest cache, temp files. - -**DO NOT** use `~/.sage/` for new code. Use `get_user_paths()` for user data. - -## Common Installation Issues - -**Install hangs**: Check network, try `--resume` for checkpoint recovery (10-25min normal) -**C++ build fails**: Install deps: `build-essential cmake pkg-config libopenblas-dev liblapack-dev` -**Tests fail CI not local**: Run `sage-dev project test --coverage` from repo root -**Import errors**: Must use `--dev` install, run from repo root -**Pre-commit fails**: Run `sage-dev quality` to auto-fix -**Old artifacts**: `make clean` or `rm -rf .sage/build/ build/ dist/ *.egg-info/` -**Bash exclamation mark**: NEVER use `!` in terminal commands (causes `bash: !': event not found`). - Use period `.` instead: `print("Done.")` not `print("Done!")` - -## Port Configuration - CRITICAL - -**统一端口配置**: 所有端口号必须使用 `sage.common.config.ports.SagePorts`，禁止硬编码。 - -```python -from sage.common.config.ports import SagePorts - -# ✅ 正确用法 -port = SagePorts.LLM_DEFAULT # 8001 -gateway_port = SagePorts.GATEWAY_DEFAULT # 8889 - -# ✅ WSL2 环境推荐用法 -port = SagePorts.get_recommended_llm_port() # 自动检测 WSL2 并选择合适端口 - -# ❌ 错误用法 - 禁止硬编码 -port = 8001 # 不要这样写 -``` - -**端口分配表**: -| 常量 | 端口 | 用途 | -|------|------|------| -| `GATEWAY_DEFAULT` | 8889 | isage-llm-gateway (OpenAI 兼容 API Gateway，独立包) | -| `EDGE_DEFAULT` | 8899 | isage-edge 聚合器（独立包，挂载整个 Gateway 应用） | -| `LLM_DEFAULT` | 8001 | vLLM 推理服务 | -| `LLM_WSL_FALLBACK` | 8901 | WSL2 备用 LLM 端口 | -| `STUDIO_BACKEND` | 8889| sage-studio 后端 API（独立仓库） | -| `STUDIO_FRONTEND` | 5173 | sage-studio 前端 (Vite，独立仓库) | -| `EMBEDDING_DEFAULT` | 8090 | Embedding 服务 | -| `BENCHMARK_LLM` | 8901 | Benchmark 专用 LLM 端口 | - -**架构**: `User → [Edge (8899, 可选) →] Gateway (8889) → Control Plane → LLM (8001)`（Edge 挂载整个 Gateway 应用，保持 /v1/* 路径；未启动 Edge 时直接访问 Gateway） - -> **Note**: Gateway 和 Edge 现在是独立 PyPI 包，需单独安装。 - -**WSL2 已知问题**: -- 端口 8001 在 WSL2 上可能出现"端口监听但连接被拒绝"的问题 -- 使用 `SagePorts.get_recommended_llm_port()` 自动选择合适端口 -- 或直接使用 `SagePorts.BENCHMARK_LLM` (8901) 作为备用 - -**配置文件位置**: `packages/sage-common/src/sage/common/config/ports.py` - -## API Client Usage - -**For LLM inference, SAGE uses vLLM as the backend engine.** - -Use standard OpenAI-compatible clients to access vLLM: - -```python -import openai - -# Connect to local vLLM server -client = openai.OpenAI(base_url="http://localhost:8001/v1", api_key="dummy") -response = client.chat.completions.create( - model="Qwen/Qwen2.5-7B-Instruct", - messages=[{"role": "user", "content": "Hello"}] -) -``` - -**For advanced scheduling features**, install `isagellm` (independent package): - -```python -from isagellm import UnifiedInferenceClient -client = UnifiedInferenceClient.create() -``` - -## Dependency Management - CRITICAL - -**Rule**: All dependency versions MUST be unified across packages to avoid [DEDUP] warnings. - -**Single Source of Truth**: `dependencies-spec.yaml` at project root -- Defines unified versions for torch, transformers, fastapi, uvicorn, etc. -- Documents historical conflicts and resolution strategies -- Guides future updates - -**Tools**: -- `tools/scripts/check_dependency_consistency.py` - Auto-check version consistency -- `tools/scripts/unify_dependencies.sh` - Batch update tool - -**vLLM Dependencies** (重量级可选依赖): -- `vllm-minimal` - vLLM only (for users with existing torch >= 2.7.0) -- `vllm` - Full install (includes torch >= 2.7.0) -- `torch` - Standalone torch (for other components) - -**Smart Installation**: -- Detects existing torch version -- Chooses `vllm-minimal` if torch >= 2.7.0 (reuse existing) -- Chooses `vllm` if torch missing or < 2.7.0 (install/upgrade) - -**Conflict Resolution**: -- `environment_doctor.sh` detects conda/pip mixed management -- `fix_mixed_packages()` intelligently resolves version conflicts -- Preserves higher version, removes lower version - -**Docs**: `docs-public/docs_src/dev-notes/cross-layer/vllm-dependency-management.md` - -## Features - -**CPU Node Support**: SAGE fully supports CPU-only compute nodes via JobManager + NodeSelector. -Tasks can specify `cpu_required`, `memory_required`, `gpu_required=0` for CPU-only execution. See -`examples/tutorials/L3-kernel/cpu_node_demo.py` and `docs/dev-notes/l3-kernel/cpu-node-setup.md`. - -## Development Workflow - -**Setup**: `./quickstart.sh --dev --yes` → `./manage.sh` (if C++ needed) -**During**: Run `sage-dev project test`, `sage-dev quality` frequently -**Before commit**: `sage-dev quality --check-only`, `sage-dev project test --coverage` -**Commits**: `(): ` (types: feat, fix, refactor, docs, test, ci, etc.) -**PR**: Local CI checks first, update CHANGELOG.md, reference issues - -**Critical files** (review before modifying): quickstart.sh, manage.sh, .github/workflows/, -tools/pytest.ini, tools/pre-commit-config.yaml - -## PyPI Publishing - CRITICAL: Use sage-pypi-publisher - -**SAGE 有专用的独立 PyPI 发布工具仓库。NEVER 手动使用 twine 或 build。** - -**Repository**: [intellistream/sage-pypi-publisher](https://github.com/intellistream/sage-pypi-publisher) (独立仓库) - -### Publishing Commands - -PyPI 发布功能已迁移到独立的 `sage-pypi-publisher` 仓库。使用方法： - -```bash -# Clone 发布工具仓库 -git clone https://github.com/intellistream/sage-pypi-publisher.git -cd sage-pypi-publisher - -# 自动版本递增并发布（推荐） -./publish.sh --auto-bump patch # 递增 0.0.1 -./publish.sh --auto-bump minor # 递增 0.1.0 -./publish.sh --auto-bump major # 递增 1.0.0 - -# 指定版本发布 -./publish.sh --version 0.2.5 - -# 发布到 TestPyPI（测试） -./publish.sh --test-pypi --auto-bump patch - -# 查看帮助 -./publish.sh --help -``` - -**Note**: -- 该工具自动处理版本递增、构建、上传全流程 -- 支持批量发布多个包 -- 集成 pre-commit hooks 自动化发布（见下文） - -### Package Names - -SAGE 的 PyPI 包名与内部包名不同： - -| 内部包名 | PyPI 包名 | 用途 | -|---------|----------|------| -| `sage-common` | `isage-common` | L1 Foundation | -| `sage-libs` | `isage-libs` | L3 Algorithms & ANNS | - -**已独立的 LLM 包** (不在 SAGE 核心仓库): -| 包名 | PyPI 包名 | 用途 | -|------|----------|------| -| `sageLLM` | `isagellm` | 统一 LLM 推理引擎 (含 Control Plane, Gateway) | - -### Pre-publish Checklist - -1. **Run tests**: `sage-dev project test --coverage` (在 SAGE 仓库) -2. **Run quality checks**: `sage-dev quality --check-only` (在 SAGE 仓库) -3. **Update CHANGELOG.md**: 记录本次发布的变更 -4. **Clone publisher**: `git clone https://github.com/intellistream/sage-pypi-publisher.git` -5. **Test on TestPyPI**: `cd sage-pypi-publisher && ./publish.sh --test-pypi --auto-bump patch` -6. **Verify installation**: `pip install -i https://test.pypi.org/simple/ isage-` -7. **Publish to PyPI**: `./publish.sh --auto-bump patch` - -**Note**: `sage-pypi-publisher` 会自动： -- 检测当前版本并递增 -- 更新 `_version.py` 文件 -- 构建 wheel 包 -- 上传到 PyPI/TestPyPI - -### Configuration - -PyPI tokens 应配置在 `~/.pypirc`: - -```ini -[distutils] -index-servers = - pypi - testpypi - -[pypi] -username = __token__ -password = pypi-xxx - -[testpypi] -repository = https://test.pypi.org/legacy/ -username = __token__ -password = pypi-xxx -``` - -### Automated Publishing via Git Hooks - -**自动化发布** (推荐): 通过 post-commit hook 自动发布受影响的包 - -在 `.git/hooks/post-commit` 中添加： - -```bash -#!/bImplementation Details - -- **Repository**: [intellistream/sage-pypi-publisher](https://github.com/intellistream/sage-pypi-publisher) -- **Tool**: `publish.sh` - 统一发布脚本 -- **Features**: - - 自动版本递增 (patch/minor/major) - - 智能包检测和依赖管理 - - TestPyPI 测试支持 - - 批量发布多个包 -- **Safety**: 默认需要确认，可用 `--no-dry-run` 跳过 - -### PyPI Publishing Issues - -**问题**: `ruamel.yaml.clib` 编译失败 -- **原因**: 某些依赖（如 vllm）需要 ruamel.yaml，但 C 扩展编译可能失败 -- **解决**: 通常可忽略，使用纯 Python fallback。如必须修复，检查编译器和 Python 头文件 - -**问题**: 版本号不一致 -- **检查**: `./tools/install/check_tool_versions.sh` (在 SAGE 仓库) -- **修复**: `./tools/install/check_tool_versions.sh --fix` - -**问题**: 发布工具找不到 -- **原因**: `sage-pypi-publisher` 需要单独克隆 -- **解决**: `git clone https://github.com/intellistream/sage-pypi-publisher.git -fi - -# 检测修改的包 -affected_packages=$(git diff HEAD~1 HEAD --name-only | \ - grep '^packages/' | \ - cut -d'/' -f2 | \ - sort -u) - -if [ -z "$affected_packages" ]; then - echo "✅ No packages affected" - exit 0 -fi - -echo "📦 Affected packages: $affected_packages" -echo "🚀 Auto-publishing to PyPI..." - -cd "$PUBLISHER_PATH" -for pkg in $affected_packages; do - echo "Publishing $pkg..." - ./publish.sh "$pkg" --auto-bump patch --no-dry-run -done -``` - -**Note**: -- 可配置为仅在特定分支（如 `main`）触发 -- 可添加交互式确认避免误发布 -- 建议先在 test-pypi 验证 - -### PyPI Publishing Issues - -**问题**: `ruamel.yaml.clib` 编译失败 -- **原因**: 某些依赖（如 vllm）需要 ruamel.yaml，但 C 扩展编译可能失败 -- **解决**: 通常可忽略，使用纯 Python fallback。如必须修复，检查编译器和 Python 头文件 - -**问题**: 版本号不一致 -- **检查**: `./tools/install/check_tool_versions.sh` -- **修复**: `./tools/install/check_tool_versions.sh --fix` - -## Resources - -- Architecture: `docs-public/docs_src/dev-notes/package-architecture.md` -- Guides: `CONTRIBUTING.md` (CN), `DEVELOPER.md` (EN) -- Dev notes: `docs/dev-notes/` (l1-l5, cross-layer/ci-cd/) - -## LLM & Embedding Services - sageLLM 架构 - -**设计原则**: 统一调度，资源共享。所有 LLM 和 Embedding 请求通过 **sageLLM Control Plane** 统一管理。 - -### 架构总览 - -``` -┌─────────────────────────────────────────────────────────────────────────┐ -│ 应用层 (Application Layer) │ -├─────────────────────────────────────────────────────────────────────────┤ -│ UnifiedInferenceClient │ -│ chat() | generate() | embed() │ -│ Control Plane Mode (唯一模式) │ -│ （所有请求通过调度器统一路由） │ -├─────────────────────────────────────────────────────────────────────────┤ -│ isagellm.gateway (独立仓库 Gateway) │ -│ (OpenAI-Compatible REST API + Control Plane) │ -│ /v1/chat/completions | /v1/embeddings | /v1/management/* | /sessions │ -├─────────────────────────────────────────────────────────────────────────┤ -│ sageLLM Control Plane (核心) │ -│ ┌─────────────────────────────────────────────────────────────────┐ │ -│ │ RequestClassifier (LLM_CHAT / LLM_GENERATE / EMBEDDING) │ │ -│ │ HybridSchedulingPolicy (请求分组、优先级、批处理聚合) │ │ -│ │ ExecutionCoordinator (LLM) | EmbeddingExecutor (Embedding) │ │ -│ └─────────────────────────────────────────────────────────────────┘ │ -├─────────────────────────────────────────────────────────────────────────┤ -│ 统一资源池 (GPU Pool) │ -│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ -│ │ vLLM Instance │ │ vLLM Instance │ │ Embedding Srv │ │ -│ │ (LLM Only) │ │ (LLM+Embed) │ │ (Embed Only) │ │ -│ │ Type: GENERAL │ │ Type: MIXED │ │ Type: EMBEDDING│ │ -│ └─────────────────┘ └─────────────────┘ └─────────────────┘ │ -└─────────────────────────────────────────────────────────────────────────┘ -``` - -*可选 Edge 层*: `isage-edge` (8899, 独立包) 挂载整个 `isagellm.gateway` 应用（包含 Control Plane、RAG Pipeline、Session Management）。默认挂载在 `/`（保持 `/v1/*` 兼容），或使用 `--llm-prefix` 挂载在自定义前缀。未启动 edge 时，直接访问 Gateway 即可。安装: `pip install isage-edge` - -### 推荐用法：isagellm Control Plane 模式 - -> **Note**: 以下功能需要安装独立包 `isagellm`。 - -```python -from isagellm import UnifiedInferenceClient - -# 默认（推荐）: 自动检测本地/远端端点，优先本地 -client = UnifiedInferenceClient.create() - -# 外部 Control Plane: 指向已运行的 Control Plane/Gateway -client = UnifiedInferenceClient.create( - control_plane_url="http://127.0.0.1:8888/v1", - default_llm_model="Qwen/Qwen2.5-7B-Instruct", - default_embedding_model="BAAI/bge-m3", -) -``` - -### 启动服务栈 - -```bash -# 推荐：启动 Gateway（包含 Control Plane） -sage gateway start # 启动 Gateway（端口 8888） -sage gateway status # 查看 Gateway 状态 -sage gateway stop # 停止 Gateway -sage gateway logs --follow # 查看日志 - -# 引擎管理（通过 Gateway Control Plane） -sage llm engine start Qwen/Qwen2.5-7B-Instruct --engine-kind llm # 启动 LLM 引擎 -sage llm engine start BAAI/bge-m3 --engine-kind embedding # 默认 CPU -sage llm engine start BAAI/bge-m3 --engine-kind embedding --use-gpu # 使用 GPU -sage llm engine list # 查看引擎列表 -sage llm engine stop # 停止引擎 - -# 查看运行状态 -ps aux | grep -E "vllm|embedding_server" -``` - -### Embedding 引擎 GPU 支持 - -默认情况下，Embedding 引擎运行在 CPU 上。对于大型 Embedding 模型（如 BGE-M3），可以显式启用 GPU： - -```python -# CLI 方式 -# sage llm engine start BAAI/bge-m3 --engine-kind embedding --use-gpu - -# 预设文件 (preset.yaml) -engines: - - name: embed-gpu - kind: embedding - model: BAAI/bge-m3 - use_gpu: true # 显式使用 GPU -``` - -**`use_gpu` 参数行为**： -- `use_gpu=None` (默认): LLM 使用 GPU，Embedding 不使用 -- `use_gpu=True`: 强制使用 GPU -- `use_gpu=False`: 强制不使用 GPU（即使是 LLM） - -### Control Plane 核心组件 - -| 组件 | 位置 | 功能 | -|------|------|------| -| `ControlPlaneManager` | `isagellm.control_plane.manager` | 核心调度管理器 | -| `RequestClassifier` | `isagellm.control_plane.request_classifier` | 请求类型分类 | -| `HybridSchedulingPolicy` | `isagellm.control_plane.strategies.hybrid_policy` | 混合调度策略 | -| `EmbeddingExecutor` | `isagellm.control_plane.executors.embedding_executor` | Embedding 批处理 | -| `ControlPlaneService` | `isagellm.control_plane_service` | Control Plane SAGE 封装 | - -> **Note**: 以上组件来自独立仓库 `sageLLM`，需单独安装：`pip install isagellm` - -### 关键文件位置 - -> **Note**: LLM 相关代码已移至独立仓库 `sageLLM`，以下路径仅供参考。 - -**sageLLM (独立仓库, PyPI: isagellm)**: -``` -isagellm/ - unified_client.py # UnifiedInferenceClient (factory-only construction) - control_plane_service.py # Control Plane facade - control_plane/ # Control Plane core implementation - manager.py # 调度管理器 - request_classifier.py # 请求分类器 - strategies/hybrid_policy.py # LLM + Embedding 混合调度 - executors/embedding_executor.py # Embedding 批处理执行 - gateway/ - server.py # FastAPI 应用入口 (OpenAI/Anthropic-compatible) - routes/ - engine_control_plane.py # Control Plane 管理 API - llm.py # LLM 代理 - embedding.py # Embedding 代理 - studio.py # Studio backend routes (merged) - sessions.py # 会话管理 - adapters/openai.py # OpenAI adapter - rag_pipeline.py # Pipeline-as-a-service - session/manager.py # Session + memory backends -``` - -**isage-edge (独立仓库)**: -``` -Repository: https://github.com/intellistream/sage-edge -PyPI: https://pypi.org/project/isage-edge/ -Install: pip install isage-edge -``` - -**SAGE Core (sage-common)**: -``` -packages/sage-common/src/sage/common/components/ - sage_embedding/ - embedding_server.py # OpenAI 兼容 Embedding 服务器 - factory.py # EmbeddingFactory (本地模型) -``` - -### 客户端模式对比（isagellm Control Plane） - -> Simple 模式已移除；所有请求都经由 Control Plane。 -> **Note**: 以下功能需要安装独立包 `isagellm`。 - -| 模式 | 创建方式 | 调度 | 适用场景 | -|------|----------|------|---------|| -| 自动检测 | `UnifiedInferenceClient.create()` | 自动探测本地/远端端点，统一调度 | 默认推荐（本地开发、单机实验） | -| 外部 Control Plane | `UnifiedInferenceClient.create(control_plane_url=...)` | 通过已运行的 Control Plane/Gateway 路由 | 生产部署、网关统一入口 | -| 内嵌 Control Plane (deprecated) | 使用 control_plane_url 或本地 Gateway | 在进程内启动调度器 | 离线批处理/无外部服务时 | - -### 内嵌模式 (VLLMService) - 批处理专用 - -> **Note**: 以下功能需要安装独立包 `isagellm`。 - -```python -from isagellm import VLLMService - -# 进程内加载模型，适合批处理任务 -service = VLLMService({ - "model_id": "Qwen/Qwen2.5-0.5B-Instruct", - "auto_download": True, -}) -service.setup() # 加载模型到 GPU -results = service.generate("Hello, world!") -service.teardown() -``` - -### 环境变量 (.env) - -```bash -# === 本地服务（推荐，默认）=== -# 无需配置，使用 SagePorts 默认端口 -# UnifiedInferenceClient 会自动探测 localhost:8001, localhost:8901 - -# === 显式远端覆盖（仅当需要强制使用云端API时设置）=== -# 警告：仅用于显式远端覆盖，不是默认行为 -# 本地开发应始终使用本地端点，不要依赖云端 fallback -SAGE_CHAT_API_KEY=sk-xxx # 云端 API Key (DashScope/OpenAI compatible) -SAGE_CHAT_MODEL=qwen-turbo-2025-02-11 -SAGE_CHAT_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1 - -# === HuggingFace === -HF_TOKEN=hf_xxx -# HF_ENDPOINT 无需手动设置，SAGE 会自动检测网络并配置镜像 -``` - -> **CRITICAL**: DashScope/云端变量**仅用于显式远端覆盖**，不是默认行为。 -> - **本地优先**：默认探测 `localhost:8001` 和 `localhost:8901` -> - **无隐式 fallback**：如果本地端点不可达，会**快速失败**，不会自动切换到云端 -> - **显式覆盖**：仅当设置了 `SAGE_CHAT_BASE_URL` 时才使用远端 -> - **CI 环境**：GitHub Actions 在无本地服务时使用 DashScope fallback（CI only） - -### 网络检测和 HuggingFace 镜像自动配置 - -SAGE 会在运行时自动检测网络区域，如果检测到中国大陆网络，会自动设置 `HF_ENDPOINT=https://hf-mirror.com`。 - -```python -from sage.common.config import ( - detect_china_mainland, # 检测是否在中国大陆 - get_hf_endpoint, # 获取推荐的 HF endpoint - ensure_hf_mirror_configured, # 自动配置 HF 镜像（推荐在 CLI 命令入口调用） -) - -# 检测网络区域 -is_china = detect_china_mainland() # True/False - -# 自动配置（如果在中国大陆，设置 HF_ENDPOINT 环境变量） -ensure_hf_mirror_configured() # 只会在首次调用时检测，结果会缓存 -``` - -**自动配置的命令**： -- `sage llm engine start` - 启动 LLM/Embedding 引擎 -- `sage llm model download` - 下载模型 -- `sage llm fine-tune` - 微调模型 -- Embedding 相关服务 - -### EmbeddingFactory (本地模型，无需服务) - -用于不想启动 Embedding 服务的场景： - -```python -from sage.common.components.sage_embedding import ( - EmbeddingFactory, EmbeddingClientAdapter -) - -# 本地加载 HuggingFace 模型 -raw_embedder = EmbeddingFactory.create("hf", model="BAAI/bge-small-zh-v1.5") -client = EmbeddingClientAdapter(raw_embedder) # 适配为批量接口 -vectors = client.embed(["文本1", "文本2"]) -``` - -**接口对比**: -| 接口 | 签名 | 来源 | -|------|------|------| -| 单文本 (BaseEmbedding) | `embed(text: str) -> list[float]` | `EmbeddingFactory.create()` | -| 批量 (EmbeddingProtocol) | `embed(texts: list[str], model=None) -> list[list[float]]` | `EmbeddingClientAdapter` | - -**错误示例** (会导致运行时错误): -```python -# 错误: EmbeddingFactory 返回的是单文本接口 -embedder = EmbeddingFactory.create("hf", model="...") -embedder.embed(texts=["a", "b"]) # TypeError: embed() got unexpected keyword argument 'texts' -``` - -## sage-benchmark (独立仓库) - -**sage-benchmark has been separated into an independent repository**: https://github.com/intellistream/sage-benchmark - -Comprehensive evaluation framework for AI data processing pipelines, including: -- **benchmark_agent**: Agent capability evaluation (tool selection, task planning, timing judgment) -- **benchmark_control_plane**: Control Plane scheduling strategy evaluation -- **benchmark_memory**: Memory system evaluation -- **benchmark_rag**: RAG pipeline evaluation -- **benchmark_refiner**: Context compression evaluation -- **benchmark_anns**: ANNS algorithm evaluation -- **benchmark_amm**: Approximate matrix multiplication evaluation - -To use sage-benchmark: -```bash -pip install isage-benchmark -``` - -For detailed documentation, see the [sage-benchmark repository](https://github.com/intellistream/sage-benchmark). - -## SageVDB Vector Database Backend - -### 🚨 SageVDB 已独立 - CRITICAL - -**SageVDB 已独立为 `isage-vdb` PyPI 包，不再作为 SAGE 子模块存在。** - -- **PyPI 包名**: `isage-vdb` (带连字符和 'i' 前缀，因为 'sage' 在 PyPI 已被占用) -- **Python 导入名**: `sagevdb` (不带 'i'，不带连字符) -- **安装方式**: `pip install isage-vdb` -- **仓库地址**: `https://github.com/intellistream/sageVDB` -- **迁移文档**: `docs-public/docs_src/dev-notes/cross-layer/sagedb-independence-migration.md` - -**⚠️ 无向后兼容**: 迁移后子模块和 python/ 目录将被完全移除。 - -**导入方式**: -```python -# ✅ 推荐：直接从 isage-vdb 导入 -from sagevdb import SageVDB, IndexType, DistanceMetric - -# ✅ 或通过 SAGE 兼容层 -from sage.middleware.components.sage_db import SageDB -``` - -### Overview - -SageVDB is a **self-developed high-performance C++ vector database**, fully custom implementation (NOT based on FAISS), with FAISS-compatible API. - -**Features**: -- ✅ Self-developed C++ core (independent implementation) -- ✅ **FAISS-compatible API** (drop-in replacement) -- ✅ High-performance similarity search (C++ optimized) -- ✅ Metadata filtering (`filtered_search`, `search_by_metadata`) -- ✅ Hybrid search (vector + text) -- ✅ Batch operations with numpy optimization -- ✅ Persistent storage (save/load) -- ✅ Multiple index types (AUTO, FLAT, IVF, HNSW) -- ✅ Distance metrics (L2, INNER_PRODUCT, COSINE) - -### Location - -**独立包**: -- PyPI: `pip install isage-vdb` -- 仓库: `https://github.com/intellistream/sageVDB` - -**SAGE 兼容层** (保留): -- 重导出: `packages/sage-middleware/src/sage/middleware/components/sage_db/__init__.py` -- 适配器: `packages/sage-middleware/src/sage/middleware/components/sage_db/backend.py` -- NeuroMem 集成: `packages/sage-middleware/src/sage/middleware/components/sage_mem/neuromem/search_engine/vdb_index/sagedb_index.py` - -### Usage in NeuroMem VDB Collections - -**Creating a VDB collection with SageVDB backend**: - -```python -from sage.middleware.components.sage_mem.neuromem.memory_manager import MemoryManager - -manager = MemoryManager() - -# Create collection -collection = manager.create_collection({ - "name": "my_collection", - "backend_type": "VDB" -}) - -# Create SageVDB index -collection.create_index({ - "name": "my_index", - "dim": 1024, - "backend_type": "SageVDB", # Use SageVDB instead of FAISS - "description": "High-performance SageVDB index" -}) - -# Insert vectors -collection.insert("my_index", text="example text", vector=embedding_vector) - -# Search -results = collection.search("my_index", query_vector, top_k=10) -``` - -**Gateway Session Storage Configuration**: - -```python -# In packages/sage-llm-gateway/src/sage/llm/gateway/session/manager.py - -# Default: FAISS backend -index_config = { - "backend_type": "FAISS", # Python FAISS - ... -} - -# Optimized: SageVDB backend (C++ performance) -index_config = { - "backend_type": "SageVDB", # C++ optimized - ... -} -``` - -**Current Status** (2025-12-28): -- ✅ SageVDB backend registered in VDB index factory -- ✅ SageVDBIndex adapter implements all BaseVDBIndex methods -- ✅ Tests pass: insert, batch_insert, search, delete, update -- ⚠️ Gateway default remains FAISS (change to "SageVDB" to use C++ backend) - -**Performance Characteristics** (5000 vectors, dim=128): -- ✅ **Insert**: SageVDB 10x faster (single), 1.14x faster (batch) - C++ optimized write path -- ⚠️ **Search**: FAISS 2.8-3x faster across all k values (Python wrapper overhead in current implementation) -- ➡️ **Memory**: Nearly identical (~945 MB) -- ✅ **ANNS Algorithms**: Now available in `sage-libs/anns/` for modularity - -**When to use SageVDB**: -- Write-heavy workloads (frequent insertions/updates) -- Session storage with many new messages -- Real-time chat applications -- When insert latency is critical -- Custom C++ extensions and integrations - -**When to use FAISS**: -- Read-heavy workloads (frequent similarity searches) -- Large-scale retrieval systems -- When search latency is critical -- Production RAG pipelines with high QPS - -### Direct SageVDB API - -**Important**: SageVDB is a self-developed C++ vector database with FAISS-compatible API. - -```python -# 迁移后（推荐） -from sagevdb import SageVDB, IndexType, DistanceMetric - -# 或通过 SAGE 兼容层 -from sage.middleware.components.sage_db import SageVDB, IndexType, DistanceMetric - -# Create database (C++ core) -db = SageVDB(dimension=128, index_type=IndexType.AUTO, metric=DistanceMetric.L2) - -# Add vectors with metadata -db.add([0.1, 0.2, ...], metadata={"id": "doc_1", "category": "tech"}) -db.add_batch(vectors, metadata=[{"id": f"doc_{i}"} for i in range(len(vectors))]) - -# Build index -db.build_index() - -# Search -results = db.search(query_vector, k=10) -for result in results: - print(f"ID: {result.metadata['id']}, Score: {result.score}") - -# Filtered search -results = db.filtered_search( - query_vector, - params=SearchParams(k=10), - filter_fn=lambda meta: meta.get("category") == "tech" -) - -# Save/Load -db.save("/path/to/index") -db.load("/path/to/index") -``` - -### API Reference - -**SageVDB Methods**: -- `add(vector, metadata)` - Add single vector -- `add_batch(vectors, metadata)` - Batch add (numpy optimized) -- `search(query, k)` - Basic similarity search -- `filtered_search(query, params, filter_fn)` - Search with filtering -- `search_by_metadata(query, params, key, value)` - Metadata-based search -- `hybrid_search(query, params, text_query, weights)` - Vector + text hybrid -- `build_index()` - Build search index -- `train_index(vectors)` - Train index (for IVF, etc.) -- `save(filepath)` / `load(filepath)` - Persistence -- `size`, `dimension`, `index_type` - Properties - -**Metadata Requirements**: -- All metadata must be `dict[str, str]` (string keys and values) -- Convert non-string values: `{"id": str(internal_id), "text": text}` - -## Final Reminder for Copilot - -**Trust these instructions** - search only if incomplete, errors occur, or deep architecture needed. - -**🔍 When encountering difficulties or uncertainties:** - -1. **First**, check if there's relevant documentation in `docs-public/docs_src/dev-notes/` -2. **Use tools** like `grep_search` or `semantic_search` to find documentation before making assumptions -3. **Read before acting** - documentation exists to guide you, not as optional reference -4. **Common documentation locations:** - - Installation/Testing: `DEVELOPER.md`, `CONTRIBUTING.md` - - CI/CD: `docs-public/docs_src/dev-notes/cross-layer/ci-cd.md` - - Documentation policy: `docs-public/docs_src/dev-notes/cross-layer/documentation-policy.md` - - Package architecture: `docs-public/docs_src/dev-notes/package-architecture.md` - - Layer-specific guides: `docs-public/docs_src/dev-notes/l{1-6}-*/` - - Cross-cutting concerns: `docs-public/docs_src/dev-notes/cross-layer/` - -**Remember**: Don't guess. Read the docs. They exist for this reason. +## Scope and architecture +- SAGE is the core framework repo; examples/benchmarks/studio/docs are split into independent repos. +- Keep the 4-layer workspace dependency rule: L4 (apps) → L3 (CLI) → L2 (runtime/stream) → L1 (foundation) only (no upward imports). +- Runtime direction is Flutty-first: use `flutty` integration patterns, do not introduce new `ray` imports/dependencies. +- Keep algorithm/tooling adapters external when they do not belong to the consolidated in-tree core; keep shared contracts in lower layers and keep applications above `sage-cli`. + +## Polyrepo architecture (critical) +- SAGE is still coordinating multiple independently released packages, but the main repo is actively reclaiming stream/runtime/serving ownership in-tree. +- This repo (`intellistream/SAGE`) now ships the editable package from `src/` via the root `pyproject.toml`. +- External sub-package changes are only visible here after publication to PyPI and a version bump in the root `pyproject.toml`. +- Do not add local editable installs of external sub-packages to `quickstart.sh` or install helpers. The standard flow is `pip install -e .` or `pip install -e '.[dev]'`. + +## Critical repo conventions +- No manual dependency drift: update external sub-package version pins in the root `pyproject.toml` only after the sub-package is published to PyPI. +- NEVER create any new Python virtual environment (`venv`/`.venv`) in this repo under any circumstance. +- Do not use an active Python venv for SAGE install/run/test flows; if `VIRTUAL_ENV` is set, exit and switch to Conda or a pre-configured non-venv Python environment. +- Never suggest or invoke `--auto-venv`, `python -m venv`, or `virtualenv` in SAGE workflows. +- If a task, script, or prompt requests creating a venv, do not do it; use an existing non-venv Python environment instead. +- Fail-fast policy: avoid silent fallback patterns that hide missing config/import/runtime errors. +- Do not add compatibility shims/re-export layers during migrations; update call sites directly. +- Centralize service ports via `sage.foundation.config.ports.SagePorts` (and keep lower-layer exports aligned; no hard-coded port literals). + +## Fast developer workflow +- Setup dev environment from repo root: `./quickstart.sh --dev --yes`. +- Diagnose env issues: `./quickstart.sh --doctor`. +- Quality auto-fix: `sage-dev quality fix --all-files`. +- Quality checks: `sage-dev quality check --all-files --readme`. +- Main test run: `sage-dev project test --coverage`. +- Root package code lives under `src/sage/`; root `pytest.ini` is configured for the main repo test collection. + +## Documentation and file placement +- User-facing project docs are centralized in the `sage-docs` repository; add `../sage-docs` to the multi-root workspace when available. +- In this meta repo, keep only root entry docs (`README.md`, `DEVELOPER.md`, `CONTRIBUTING.md`) plus machine-owned governance artifacts such as `docs/layer-manifest.json` and dependency audit evidence. +- Do not add new duplicated user-facing markdown under root `docs/`; publish or update it in `sage-docs` instead. + +## Integration map (what to call, what not to reintroduce) +- LLM control-plane/gateway functionality is externalized; prefer `isagellm` integration points instead of re-adding legacy in-repo gateway patterns. +- Middleware and higher capability integrations map to optional packages (e.g., `isage-rag`, `isage-neuromem`, `isage-sias`). +- Keep SAGE-side code focused on stable contracts/interfaces, stream/runtime ownership, and serving adapters across layers. + +## High-signal paths to inspect first +- Root workflow/docs: `README.md`, `DEVELOPER.md`, `CONTRIBUTING.md`, `quickstart.sh`, `pytest.ini`. +- Quality/hooks: `tools/pre-commit-config.yaml`, `tools/hooks/check_docs_location.sh`. +- Meta-package: `pyproject.toml` — version pins for all external dependencies. +- In-tree surfaces: `src/sage/foundation/`, `src/sage/stream/`, `src/sage/runtime/`, `src/sage/serving/`. + +## 🚫 NEVER_CREATE_DOT_VENV_MANDATORY + +- 永远不要创建 `.venv` 或 `venv`（无任何例外）。 +- NEVER create `.venv`/`venv` in this repository under any circumstance. +- 必须复用当前已配置的非-venv Python 环境（如现有 conda 环境）。 +- If any script/task suggests creating a virtualenv, skip that step and continue with the existing environment. diff --git a/.github/workflows/MIGRATION_GUIDE.md b/.github/workflows/MIGRATION_GUIDE.md deleted file mode 100644 index 13dd66937c..0000000000 --- a/.github/workflows/MIGRATION_GUIDE.md +++ /dev/null @@ -1,224 +0,0 @@ -# CI/CD Workflow Migration Guide - Configuration Refactor - -## 🎯 Overview - -This document outlines changes needed in all CI/CD workflows after the **configuration architecture refactor** (2025-12-28). - -## 🔄 Key Changes - -### 1. **sage llm serve** is DEPRECATED - -**❌ Old way (FORBIDDEN in CI):** -```yaml -- name: Start LLM service - run: | - sage llm serve -m Qwen/Qwen2.5-7B-Instruct -``` - -**✅ New way (Control Plane):** -```yaml -- name: Start LLM service via Control Plane - run: | - sage llm engine start Qwen/Qwen2.5-7B-Instruct --engine-kind llm -``` - -### 2. Configuration Priority: CLI > config.yaml > Defaults - -**Old behavior:** -- `sage llm serve` used hardcoded model `Qwen2.5-0.5B-Instruct` on port `8901` - -**New behavior:** -- Reads from `config/config.yaml` first -- CLI parameters override config file -- Shows configuration being used - -**CI/CD impact:** -```yaml -# Option 1: Use config.yaml defaults (recommended for tests) -- run: sage llm engine start $(yq '.llm.model' config/config.yaml) --engine-kind llm - -# Option 2: Override with CLI (for specific tests) -- run: sage llm engine start Qwen/Qwen2.5-0.5B-Instruct --engine-kind llm -``` - -### 3. Gateway /v1/models API Enhanced - -**Old behavior:** -- Returned empty list if Control Plane had no engines - -**New behavior:** -- Returns online engines (from Control Plane) + offline models (from models.json) -- Each model has `status: "online"/"offline"` field -- Includes metadata (description, category, tags, size) - -**CI/CD testing:** -```yaml -- name: Verify Gateway API - run: | - response=$(curl -s http://localhost:8888/v1/models) # allow-control-plane-bypass: Gateway port - echo "$response" | jq '.data | length' # Should return > 0 - echo "$response" | jq '.data[0].status' # Should be "online" or "offline" -``` - -### 4. models.json Format Changed - -**Old format (DEPRECATED):** -```json -[ - { - "name": "Qwen/Qwen2.5-7B-Instruct", - "base_url": "http://127.0.0.1:8001/v1", // ❌ Removed - "default": true, // ❌ Removed - "is_local": true // ❌ Removed - } -] -``` - -**New format:** -```json -{ - "models": [ - { - "name": "Qwen/Qwen2.5-7B-Instruct", - "description": "标准模型", - "category": "general", - "size": "7B", - "tags": ["recommended"] - } - ] -} -``` - -**CI/CD validation:** -```yaml -- name: Validate models.json format - run: | - python3 << 'EOF' - import json - from pathlib import Path - - data = json.loads(Path("config/models.json").read_text()) - assert isinstance(data, dict), "models.json must be a dict" - assert "models" in data, "models.json must have 'models' key" - - for model in data["models"]: - # These fields should NOT exist - assert "base_url" not in model, f"{model['name']} has forbidden base_url" - assert "port" not in model, f"{model['name']} has forbidden port" - assert "default" not in model, f"{model['name']} has forbidden default" - assert "is_local" not in model, f"{model['name']} has forbidden is_local" - - print("✅ models.json format is valid") - EOF -``` - -## 📋 Workflows to Update - -### Priority 1: Core Testing Workflows - -1. **build-test.yml** - Main test suite - - [ ] Replace `sage llm serve` with `sage llm engine start` - - [ ] Add config.yaml validation - - [ ] Add models.json format check - -2. **examples-test.yml** - Examples validation - - [ ] Update example scripts to use Control Plane - - [ ] Verify examples don't use `sage llm serve` - -3. **code-quality.yml** - Linting and formatting - - [ ] Already includes pre-commit hooks (will catch violations) - - [ ] No changes needed - -4. **installation-test.yml** - Package installation - - [ ] Test config.yaml is included in package - - [ ] Test models.json is included - -### Priority 2: Deployment Workflows - -5. **publish-pypi.yml** - PyPI publishing - - [ ] Ensure new config files are in MANIFEST.in - -6. **deploy-studio.yml** - Studio deployment - - [ ] Studio frontend should use Gateway API for model list - - [ ] No direct vLLM service startup - -### Priority 3: Experiment Workflows - -7. **paper1-experiments.yml** - GPU experiments - - [ ] Use Control Plane for all LLM operations - - [ ] Update result collection to use Gateway API - -## 🔧 Example Workflow Updates - -### Before (❌ Old): -```yaml -name: Test -jobs: - test: - steps: - - name: Start services - run: | - sage llm serve -m Qwen/Qwen2.5-0.5B-Instruct -p 8901 - - - name: Run tests - run: pytest -``` - -### After (✅ New): -```yaml -name: Test -jobs: - test: - steps: - - name: Start Gateway - run: sage gateway start - - - name: Start LLM via Control Plane - run: | - # Use config.yaml default or override with CLI - sage llm engine start Qwen/Qwen2.5-0.5B-Instruct --engine-kind llm - - - name: Verify engine status - run: | - sage llm engine list - curl -s http://localhost:8888/v1/models | jq '.data | length' # allow-control-plane-bypass: Gateway port - - - name: Run tests - run: pytest -``` - -## 🚨 Pre-commit Hook Enforcement - -The `control-plane-only-guard` pre-commit hook will now catch: - -1. ❌ Direct vLLM imports: `from vllm import LLM` -2. ❌ Direct API server: `python -m vllm.entrypoints.openai.api_server` -3. ❌ Bypassing Control Plane: `sage llm serve` -4. ❌ Hardcoded ports: `localhost:8001`, `localhost:8901` - -**Allowed files** (framework internals): -- `packages/sage-llm-core/src/sage/llm/api_server.py` -- `packages/sage-cli/src/sage/cli/commands/apps/llm.py` -- `docs-public/` (documentation) - -## 📝 Validation Checklist - -Before committing workflow changes: - -- [ ] No `sage llm serve` commands -- [ ] All LLM operations via `sage llm engine start` -- [ ] Gateway is started before engines -- [ ] Tests verify Gateway /v1/models API -- [ ] Config files are validated -- [ ] Pre-commit hooks pass - -## 🔗 References - -- [Configuration Architecture](../../config/README.md) -- [Control Plane Documentation](../../docs-public/docs_src/dev-notes/l1-common/control-plane.md) -- [Copilot Instructions](../.github/copilot-instructions.md) - ---- - -**Last Updated:** 2025-12-28 -**Status:** 🚧 Migration in progress diff --git a/.github/workflows/README.md b/.github/workflows/README.md index 81ec5f8033..8e69f3ec75 100644 --- a/.github/workflows/README.md +++ b/.github/workflows/README.md @@ -3,7 +3,7 @@ ## Workflow Overview | Workflow | 用途 | 触发条件 | 必须通过 | -|----------|------|----------|---------| +| -------- | ---- | -------- | -------- | | `ci-build-test.yml` | 构建、测试、覆盖率 | PR, push | ✅ | | `ci-sagellm-test.yml` | SageLLM mock/CUDA 测试 | PR, push | ✅ (mock) | | `ci-code-quality.yml` | 代码质量检查 | PR, push | ✅ | @@ -21,6 +21,7 @@ ### Job 1: SageLLM Mock Backend (必须通过) 所有 PR 必须通过此测试，验证： + - SageLLM mock backend 正常工作 - `SageLLMGenerator` 与 mock backend 集成 - Agentic operators (Planning, Timing, ToolSelection) 与 mock backend 兼容 @@ -33,6 +34,7 @@ pytest -v -k "sagellm or mock or SageLLM or Mock" packages/ ### Job 2: SageLLM CUDA Backend (可选) GPU 测试，仅在以下情况运行： + - 手动触发 workflow 并选择 `run_cuda_tests: true` - Push 到 `main` 分支 @@ -119,14 +121,14 @@ Workflow 会在以下情况**自动运行**： 1. **TEST_PYPI_API_TOKEN**: TestPyPI API token - - 访问 https://test.pypi.org/manage/account/token/ + - 访问 [TestPyPI token 页面](https://test.pypi.org/manage/account/token/) - 创建新 token - 权限: "Upload packages" - 将 token 添加到 GitHub Secrets 1. **PYPI_API_TOKEN**: PyPI API token - - 访问 https://pypi.org/manage/account/token/ + - 访问 [PyPI token 页面](https://pypi.org/manage/account/token/) - 创建新 token - 权限: "Upload packages" - 将 token 添加到 GitHub Secrets @@ -161,22 +163,20 @@ Workflow 会在以下情况**自动运行**： ### 发布的包 -所有核心包都会按依赖顺序发布: +所有核心发布物按依赖关系协同演进: + +1. `isage` - 主仓产品表面（foundation / stream / runtime / serving / cli） +1. `isagellm` - 独立推理引擎（由独立仓库发布，不随主仓一起内嵌） -1. `isage-common` - 公共基础库 (L1) -1. `isage-platform` - 平台服务 (L2) -1. `isage-kernel` - 核心引擎 (L3) -1. `isage-libs` - 算法库 (L3) -1. `isage-middleware` - 中间件组件 (L4) -1. `isage-cli` - 命令行工具 (L5) -1. `isage-tools` - 开发工具 (L5) -1. `isage` - 元包（安装所有子包） +**独立仓库协同**（不由 SAGE 主仓直接发布）: -**独立仓库发布** (不在 SAGE 核心仓库): - `isagellm` - LLM 推理引擎 (sageLLM 仓库) - `isage-benchmark` - 基准测试 (sage-benchmark 仓库) -- `isage-studio` - 可视化工具 (sage-studio 仓库) -- `isage-edge` - 边缘聚合器 (sage-edge 仓库) +- `isage-examples` - 示例与应用入口 (sage-examples 仓库) +- `sage-tutorials` - 教学内容仓库（工作区协同，不作为主仓 editable 依赖） +- `sage-docs` / `sage-pub-docs` - 文档站点与发布产物 (SAGE-Docs 仓库) + +`sage-edge` 不再列为独立协同仓库：edge aggregation 已回收入主仓 `sage.edge` 产品面。 ### 版本策略 @@ -231,7 +231,7 @@ git push origin main # - 创建 GitHub Release v0.1.7.0 ``` -### 版本策略 +### 自动版本规则 版本号格式: `MAJOR.MINOR.MICRO.PATCH` diff --git a/.github/workflows/README_WORKFLOWS.md b/.github/workflows/README_WORKFLOWS.md index 626eb4ebaa..a593e9f4b7 100644 --- a/.github/workflows/README_WORKFLOWS.md +++ b/.github/workflows/README_WORKFLOWS.md @@ -73,11 +73,11 @@ gh workflow run ci-sagellm-test.yml -f run_cuda_tests=true ### quickstart.sh 模式 -| quickstart.sh | pip install | 包含内容 | 用途 | 包数量 | -| ------------- | --------------------- | ------------------------------------- | ------------------ | ------ | -| `--minimal` | `isage` | L1-L3 核心 (common, platform, kernel) | 容器部署、生产环境 | ~80 | -| `--dev` | `isage` + dev tools | minimal + pytest, ruff, mypy | 框架开发、贡献代码 | ~120 | -| `--full` | `isage` + all extras | dev + 科学库 + 可选依赖 | 完整功能、学习示例 | ~200+ | +| quickstart.sh | pip install | 包含内容 | 用途 | 包数量 | +| ------------- | ----------- | -------- | ---- | ------ | +| `--minimal` | `isage` | 主仓核心表面 (foundation/stream/runtime/serving/cli) | 容器部署、生产环境 | ~80 | +| `--dev` | `isage` + dev tools | minimal + pytest, ruff, mypy | 框架开发、贡献代码 | ~120 | +| `--full` | `isage` + all extras | dev + 科学库 + 可选依赖 | 完整功能、学习示例 | ~200+ | **默认模式**: `--full` (推荐新用户使用) @@ -85,16 +85,17 @@ gh workflow run ci-sagellm-test.yml -f run_cuda_tests=true #### `minimal` (最小安装) -- **包含包**：sage-common, sage-platform, sage-kernel, sage-libs, sage-middleware, sage-cli, sage-tools -- **核心功能**：Pipeline, Operators, DataStream API, CLI +- **包含包**：isage（核心表面）+ 可选外部引擎/适配器按需安装 +- **核心功能**：Foundation, DataStream API, Runtime, Serving integration, CLI - **适用场景**： - Docker 容器部署 - 生产环境最小化安装 - 仅需要流处理核心功能 - CI/CD 快速测试 - **提示**：如需使用 ML、向量数据库等功能，可手动安装: + ```bash - pip install isage-middleware[ml,vdb,streaming] + pip install 'isage[capability-adapters]' ``` #### `dev` (开发安装) @@ -110,8 +111,9 @@ gh workflow run ci-sagellm-test.yml -f run_cuda_tests=true - 贡献代码到 SAGE - 运行测试和代码质量检查 - **提示**：如需 ML/科学计算功能: + ```bash - pip install isage-middleware[ml,vdb] isage-kernel[ml] + pip install 'isage[full]' ``` #### `full` (完整安装，默认) @@ -218,14 +220,14 @@ strategy: # 核心运行时（最小依赖） pip install isage -# 添加 ML 功能 -pip install isage-middleware[ml] +# 添加核心可选适配器 +pip install 'isage[capability-adapters]' -# 添加向量数据库支持 -pip install isage-middleware[vdb] +# 添加工具使用适配器 +pip install 'isage[capability-tooluse]' # 添加所有可选功能 -pip install isage-middleware[ml,vdb,streaming,compression] +pip install 'isage[full]' ``` ### 开发者安装 (从源码) @@ -272,23 +274,25 @@ pip install -e ".[standard]" # 标准模式 ## 🔗 相关文档 -- [SAGE 架构文档](../../docs-public/docs_src/dev-notes/package-architecture.md) -- [包依赖关系](../../docs-public/docs_src/dev-notes/package-dependencies.md) +- [SAGE 架构文档](https://intellistream.github.io/sage-docs/architecture/) +- [包依赖关系](../../docs/dependency-audit-gate.md) - [贡献指南](../../CONTRIBUTING.md) -- [CI/CD 分层与目录结构](../../docs/dev-notes/cross-layer/ci-cd.md) +- [CI/CD 分层与目录结构](https://intellistream.github.io/sage-docs/dev-notes/cross-layer/ci-cd/) ## ♻️ Workflow 命名规范 **前缀分类**: + | 前缀 | 含义 | 触发方式 | -|------|------|----------| +| ---- | ---- | -------- | | `ci-*` | 持续集成检查 | PR/Push 自动触发 | | `cd-*` | 部署/发布 | Tag/Release/手动触发 | | `util-*` | 辅助工具 | 定时/手动触发 | | `exp-*` | 实验/研究 | 手动触发 | **完整列表**: -``` + +```text ci-build-test.yml # 构建 + 单元测试 (packages 变更触发) ci-code-quality.yml # Lint & Format (*.py 变更触发, ~3min) ci-pr-examples.yml # Examples quick 测试 (examples 变更触发) @@ -336,7 +340,7 @@ ______________________________________________________________________ 部署成功后，在 Actions Summary 中查看访问地址： -``` +```text Studio UI: http://<服务器IP>:4200 Gateway API: http://<服务器IP>:8000 ``` diff --git a/.github/workflows/cd-publish-meta.yml b/.github/workflows/cd-publish-meta.yml new file mode 100644 index 0000000000..fc100ad886 --- /dev/null +++ b/.github/workflows/cd-publish-meta.yml @@ -0,0 +1,153 @@ +# Publish the SAGE meta-package (isage) to PyPI / TestPyPI +# +# The meta-package (packages/sage/) depends on all standalone SAGE sub-repos. +# Publishing it signals that a coherent ecosystem snapshot is available at +# the given version. +# +# Trigger options +# ──────────────── +# Manual (workflow_dispatch): choose repository + whether to auto-bump version +# Tag push (v*.*.*.*): auto-publishes to PyPI when a release tag is pushed + +name: Publish isage meta-package + +on: + workflow_dispatch: + inputs: + repository: + description: "Target repository" + required: false + default: "testpypi" + type: choice + options: + - testpypi + - pypi + bump_version: + description: "Bump BUILD digit before publishing (e.g. 0.2.4.20 → 0.2.4.21)" + required: false + default: "false" + type: boolean + + push: + tags: + - "v*.*.*.*" # e.g. v0.2.4.20 + +jobs: + publish-meta: + name: Build & publish isage + runs-on: ubuntu-latest + permissions: + contents: write # needed if we create/push a version bump commit + + steps: + - name: Checkout + uses: actions/checkout@v4 + with: + fetch-depth: 0 + token: ${{ secrets.PAT_TOKEN }} + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.11" + + - name: Install tooling + run: | + pip install --upgrade pip build wheel twine + pip install "isage-pypi-publisher>=0.2.0" + + - name: Determine target repository + id: repo + run: | + if [[ "${{ github.event_name }}" == "push" ]]; then + echo "target=pypi" >> "$GITHUB_OUTPUT" + else + echo "target=${{ inputs.repository }}" >> "$GITHUB_OUTPUT" + fi + + - name: (Optional) Bump BUILD digit in _version.py + if: ${{ inputs.bump_version == 'true' }} + run: | + VERSION_FILE="src/sage/_version.py" + CURRENT=$(python3 -c " + import re, pathlib + t = pathlib.Path('$VERSION_FILE').read_text() + m = re.search(r'__version__\s*=\s*\"([^\"]+)\"', t) + print(m.group(1)) + ") + # Increment last digit + NEW=$(python3 -c " + v = '$CURRENT'.split('.') + v[-1] = str(int(v[-1]) + 1) + print('.'.join(v)) + ") + sed -i "s/__version__ = \"$CURRENT\"/__version__ = \"$NEW\"/" "$VERSION_FILE" + echo "Bumped version: $CURRENT → $NEW" + + git config user.name "github-actions[bot]" + git config user.email "github-actions[bot]@users.noreply.github.com" + git add "$VERSION_FILE" + git commit -m "chore(release): bump isage version $CURRENT → $NEW [skip ci]" + git push + + - name: Configure PyPI credentials + env: + PYPI_TOKEN: ${{ secrets.PYPI_API_TOKEN }} + TESTPYPI_TOKEN: ${{ secrets.TESTPYPI_API_TOKEN }} + run: | + mkdir -p ~/.config/pip + if [ "${{ steps.repo.outputs.target }}" = "pypi" ]; then + cat > ~/.pypirc << EOF + [distutils] + index-servers = pypi + [pypi] + username = __token__ + password = $PYPI_TOKEN + EOF + else + cat > ~/.pypirc << EOF + [distutils] + index-servers = testpypi + [testpypi] + repository = https://test.pypi.org/legacy/ + username = __token__ + password = $TESTPYPI_TOKEN + EOF + fi + + - name: Build wheel + run: python3 -m build --wheel --sdist + + - name: Publish to ${{ steps.repo.outputs.target }} + env: + TWINE_REPOSITORY: ${{ steps.repo.outputs.target }} + run: | + twine upload \ + --config-file ~/.pypirc \ + --repository ${{ steps.repo.outputs.target }} \ + --skip-existing \ + dist/* + + - name: Summary + if: always() + run: | + VERSION=$(python3 -c " + import re, pathlib + t = pathlib.Path('src/sage/_version.py').read_text() + m = re.search(r'__version__\s*=\s*\"([^\"]+)\"', t) + print(m.group(1)) + ") + TARGET="${{ steps.repo.outputs.target }}" + + echo "## 📦 isage $VERSION published to $TARGET" >> $GITHUB_STEP_SUMMARY + echo "" >> $GITHUB_STEP_SUMMARY + + if [ "$TARGET" = "pypi" ]; then + echo '```bash' >> $GITHUB_STEP_SUMMARY + echo "pip install isage==$VERSION" >> $GITHUB_STEP_SUMMARY + echo '```' >> $GITHUB_STEP_SUMMARY + else + echo '```bash' >> $GITHUB_STEP_SUMMARY + echo "pip install --pre --extra-index-url https://test.pypi.org/simple/ isage==$VERSION" >> $GITHUB_STEP_SUMMARY + echo '```' >> $GITHUB_STEP_SUMMARY + fi diff --git a/.github/workflows/cd-publish-pypi.yml b/.github/workflows/cd-publish-pypi.yml deleted file mode 100644 index d5654a10ef..0000000000 --- a/.github/workflows/cd-publish-pypi.yml +++ /dev/null @@ -1,128 +0,0 @@ -# SAGE PyPI Publishing via wheelwright -# -# This workflow uses the independent wheelwright tool to publish packages. -# Source repository: https://github.com/intellistream/wheelwright - -name: "Publish to PyPI" - -on: - workflow_dispatch: - inputs: - repository: - description: 'Target repository' - required: false - default: 'testpypi' - type: choice - options: - - testpypi - - pypi - version_bump: - description: 'Version bump type' - required: false - default: 'patch' - type: choice - options: - - patch # 0.2.0 -> 0.2.1 - - minor # 0.2.0 -> 0.3.0 - - major # 0.2.0 -> 1.0.0 - packages: - description: 'Packages to publish (comma-separated, or "all")' - required: false - default: 'all' - -jobs: - publish: - runs-on: ubuntu-latest - steps: - - name: Checkout SAGE - uses: actions/checkout@v4 - with: - fetch-depth: 0 - token: ${{ secrets.PAT_TOKEN }} - - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: '3.11' - - - name: Install wheelwright - run: | - pip install --upgrade pip - pip install git+https://github.com/intellistream/wheelwright.git@main - - - name: Configure PyPI credentials - run: | - mkdir -p ~/.pypirc - if [ "${{ inputs.repository }}" = "pypi" ]; then - cat > ~/.pypirc << EOF - [distutils] - index-servers = - pypi - - [pypi] - username = __token__ - password = ${{ secrets.PYPI_API_TOKEN }} - EOF - else - cat > ~/.pypirc << EOF - [distutils] - index-servers = - testpypi - - [testpypi] - repository = https://test.pypi.org/legacy/ - username = __token__ - password = ${{ secrets.TEST_PYPI_API_TOKEN }} - EOF - fi - - - name: Publish packages - run: | - PACKAGES="${{ inputs.packages }}" - BUMP="${{ inputs.version_bump }}" - REPO="${{ inputs.repository }}" - - if [ "$PACKAGES" = "all" ]; then - # Publish all SAGE packages - for pkg in sage-common sage-llm-core sage-llm-gateway sage-kernel sage-libs sage-middleware sage-platform sage-cli sage-tools sage; do - echo "📦 Publishing $pkg..." - wheelwright publish "$pkg" \ - --auto-bump "$BUMP" \ - --repository "$REPO" \ - --no-dry-run - done - else - # Publish specified packages - IFS=',' read -ra PKG_LIST <<< "$PACKAGES" - for pkg in "${PKG_LIST[@]}"; do - pkg=$(echo "$pkg" | xargs) # trim whitespace - echo "📦 Publishing $pkg..." - wheelwright publish "$pkg" \ - --auto-bump "$BUMP" \ - --repository "$REPO" \ - --no-dry-run - done - fi - - - name: Summary - run: | - echo "## 📦 Publication Summary" >> $GITHUB_STEP_SUMMARY - echo "" >> $GITHUB_STEP_SUMMARY - echo "- **Repository**: ${{ inputs.repository }}" >> $GITHUB_STEP_SUMMARY - echo "- **Version Bump**: ${{ inputs.version_bump }}" >> $GITHUB_STEP_SUMMARY - echo "- **Packages**: ${{ inputs.packages }}" >> $GITHUB_STEP_SUMMARY - echo "" >> $GITHUB_STEP_SUMMARY - - if [ "${{ inputs.repository }}" = "pypi" ]; then - echo "### 🎉 Published to PyPI" >> $GITHUB_STEP_SUMMARY - echo "Users can now install with:" >> $GITHUB_STEP_SUMMARY - echo '```bash' >> $GITHUB_STEP_SUMMARY - echo "pip install isage" >> $GITHUB_STEP_SUMMARY - echo '```' >> $GITHUB_STEP_SUMMARY - else - echo "### 🧪 Published to TestPyPI" >> $GITHUB_STEP_SUMMARY - echo "Test installation with:" >> $GITHUB_STEP_SUMMARY - echo '```bash' >> $GITHUB_STEP_SUMMARY - echo "pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ isage" >> $GITHUB_STEP_SUMMARY - echo '```' >> $GITHUB_STEP_SUMMARY - fi diff --git a/.github/workflows/ci-build-test.yml b/.github/workflows/ci-build-test.yml index b7ed659e0d..845b404b34 100644 --- a/.github/workflows/ci-build-test.yml +++ b/.github/workflows/ci-build-test.yml @@ -1,21 +1,12 @@ -name: Build and Test +# DEPRECATED (#1466) — Historical split-repo build/test orchestration has been +# replaced by the consolidated main-repo workflow plus independent optional +# adapter/tooling repos. This workflow is preserved for reference only. +# Cross-repo smoke tests: see ci-integration.yml + +name: "[DEPRECATED] Build and Test (monorepo)" on: - push: - branches: [main, main-dev] - paths: - - 'packages/**' - - 'tools/**' - - 'pyproject.toml' - - '.github/workflows/ci-build-test.yml' - pull_request: - branches: [main, main-dev] - types: [opened, synchronize, reopened] - paths: - - 'packages/**' - - 'tools/**' - - 'pyproject.toml' - workflow_dispatch: + workflow_dispatch: # manual only — automatic triggers removed (#1466) concurrency: group: ${{ github.workflow }}-${{ github.ref }} @@ -28,7 +19,6 @@ env: OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} JINA_API_KEY: ${{ secrets.JINA_API_KEY }} ALIBABA_API_KEY: ${{ secrets.ALIBABA_API_KEY }} - VLLM_API_KEY: ${{ secrets.VLLM_API_KEY }} GIT_TOKEN: ${{ secrets.GIT_TOKEN }} HF_ENDPOINT: https://hf-mirror.com CACHE_VERSION: v2-simplified-prod @@ -84,9 +74,9 @@ jobs: JINA_API_KEY=${{ secrets.JINA_API_KEY }} ALIBABA_API_KEY=${{ secrets.ALIBABA_API_KEY }} - VLLM_API_KEY=${{ secrets.VLLM_API_KEY }} - VLLM_BASE_URL=http://localhost:8000/v1 - VLLM_MODEL_NAME=meta-llama/Llama-2-13b-chat-hf + SAGELLM_API_KEY=EMPTY + SAGELLM_BASE_URL=http://localhost:8901/v1 + SAGELLM_MODEL_NAME=Qwen/Qwen2.5-7B-Instruct WEB_SEARCH_API_KEY=${{ secrets.WEB_SEARCH_API_KEY }} diff --git a/.github/workflows/ci-code-quality.yml b/.github/workflows/ci-code-quality.yml index da1c8f1da9..be9de8c7d7 100644 --- a/.github/workflows/ci-code-quality.yml +++ b/.github/workflows/ci-code-quality.yml @@ -1,20 +1,11 @@ -name: Code Quality Check +# DEPRECATED (#1466) — Historical split-repo code quality checks have been +# superseded by the consolidated main-repo workflow plus independent optional +# adapter/tooling repos. Preserved for reference. + +name: "[DEPRECATED] Code Quality Check (monorepo)" on: - pull_request: - branches: [main, main-dev] - types: [opened, synchronize, reopened] - paths: - - '**.py' - - '**.pyi' - - 'tools/config/pre-commit-config.yaml' - - 'tools/config/ruff.toml' - push: - branches: [main, main-dev] - paths: - - '**.py' - - '**.pyi' - workflow_dispatch: + workflow_dispatch: # manual only — automatic triggers removed (#1466) concurrency: group: quality-${{ github.ref }} @@ -32,6 +23,13 @@ jobs: with: fetch-depth: 0 + - name: Checkout sageFlownet (for dedup gate) + uses: actions/checkout@v4 + with: + repository: intellistream/sageFlownet + path: sageFlownet + fetch-depth: 1 + - name: Set up Python uses: actions/setup-python@v5 with: @@ -47,7 +45,7 @@ jobs: - name: Install tools (no SAGE) run: | pip install --upgrade pip - pip install pre-commit ruff mypy types-PyYAML types-requests types-setuptools + pip install pre-commit ruff - name: Configure Git for large diffs run: | @@ -77,3 +75,11 @@ jobs: SKIP: shellcheck # Skip shellcheck-py to avoid network issues, use system shellcheck instead run: | pre-commit run --all-files --config tools/config/pre-commit-config.yaml + + - name: Cross-repo dedup check - SAGE vs sageFlownet (Issue #1439) + run: | + python3 tools/scripts/check_cross_repo_dedup.py \ + --flownet-path "$GITHUB_WORKSPACE/sageFlownet" \ + --require-flownet \ + --verbose + continue-on-error: false diff --git a/.github/workflows/ci-deployment-check.yml b/.github/workflows/ci-deployment-check.yml index f747f67245..f419116391 100644 --- a/.github/workflows/ci-deployment-check.yml +++ b/.github/workflows/ci-deployment-check.yml @@ -29,8 +29,7 @@ jobs: uses: actions/checkout@v4 with: fetch-depth: 0 # 需要完整历史来进行 diff - # 注意: 不递归克隆 submodules，避免检查外部依赖代码 - # Submodules (neuromem, sageRefiner, docs-public) 由各自仓库维护 + # 注意: 仅检出当前仓库代码；外部独立仓库在各自仓库维护 - name: Set up Python 3.11 uses: actions/setup-python@v5 @@ -67,7 +66,14 @@ jobs: if [ -n "$PYTHON_CHANGED" ]; then echo "📝 发现 Python 文件变更" - sage-dev quality architecture --changed-only || { + sage-dev quality --check-only \ + --architecture \ + --no-devnotes \ + --no-examples \ + --no-format \ + --no-sort-imports \ + --no-ruff \ + --no-type-check || { echo "" echo "❌ 架构合规性检查失败！" echo "" @@ -77,7 +83,7 @@ jobs: echo "1. 检查跨层级导入（如 app 导入 kernel）" echo "2. 确保导入路径符合包架构" echo "3. 查看架构信息: sage-dev architecture" - echo "4. 查看文档: docs-public/docs_src/dev-notes/package-architecture.md" + echo "4. 查看文档: https://intellistream.github.io/sage-docs/architecture/layer-ownership/" echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" exit 1 } @@ -87,11 +93,18 @@ jobs: else echo "📦 Push 模式：快速检查（仅检查变更文件）" # 在 push 时做快速检查，避免阻塞 - sage-dev quality architecture --changed-only || { + sage-dev quality --check-only \ + --architecture \ + --no-devnotes \ + --no-examples \ + --no-format \ + --no-sort-imports \ + --no-ruff \ + --no-type-check || { echo "" echo "⚠️ 发现架构问题，但不阻塞部署" echo "请查看架构信息: sage-dev architecture" - echo "并尽快修复: docs-public/docs_src/dev-notes/package-architecture.md" + echo "并尽快参考: https://intellistream.github.io/sage-docs/architecture/layer-ownership/" } fi @@ -103,7 +116,15 @@ jobs: export PATH="$HOME/.local/bin:$PATH" echo "📖 检查包 README 文档..." - if sage-dev quality readme; then + if sage-dev quality --check-only \ + --readme \ + --no-architecture \ + --no-devnotes \ + --no-examples \ + --no-format \ + --no-sort-imports \ + --no-ruff \ + --no-type-check; then echo "✅ 所有包的 README 文档完整" else if [ "${{ github.event_name }}" = "pull_request" ]; then @@ -113,7 +134,7 @@ jobs: echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" echo "1. 确保每个包都有 README.md" echo "2. README 应包含：简介、安装、使用示例" - echo "3. 使用模板: packages/sage-tools/src/sage/tools/templates/PACKAGE_README_TEMPLATE.md" + echo "3. 使用模板: https://github.com/intellistream/sage-dev-tools/tree/main/src/sage/tools/templates/PACKAGE_README_TEMPLATE.md" echo "4. 查看详细报告: sage-dev quality readme --report" echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" exit 1 @@ -129,7 +150,7 @@ jobs: # 统计信息 TOTAL_PACKAGES=$(find packages -name "setup.py" -o -name "pyproject.toml" | wc -l) - TOTAL_DOCS=$(find docs-public/docs_src/dev-notes -name "*.md" -type f 2>/dev/null | wc -l) + TOTAL_DOCS=$(find docs -name "*.md" -type f 2>/dev/null | wc -l) echo "📦 包数量: $TOTAL_PACKAGES" echo "📚 文档数量: $TOTAL_DOCS" diff --git a/.github/workflows/ci-examples-test.yml b/.github/workflows/ci-examples-test.yml index 8e26540368..b94541c492 100644 --- a/.github/workflows/ci-examples-test.yml +++ b/.github/workflows/ci-examples-test.yml @@ -106,6 +106,16 @@ jobs: echo "✅ SAGE 安装完成" timeout-minutes: 20 + - name: Install Independent Libs (for Examples) + run: | + echo "📦 安装独立库（examples 依赖）..." + export PATH="$HOME/.local/bin:$PATH" + + # 安装 isage-agentic（basic_agent.py 需要） + pip install --user isage-agentic + + echo "✅ 独立库安装完成" + - name: Verify Installation run: | echo "✅ 验证 SAGE 安装..." @@ -114,6 +124,9 @@ jobs: python -c "import sage.kernel; print('✅ sage.kernel imported')" python -c "import sage.libs; print('✅ sage.libs imported')" + # 验证独立库 + python -c "import sage_libs.sage_agentic; print('✅ sage_libs.sage_agentic imported')" || echo "⚠️ sage_agentic not available" + - name: Discover Testable Examples id: discover run: | @@ -169,7 +182,7 @@ jobs: exit 0 fi - # 计数器 + # 计数器 - 显式初始化为 0 passed=0 failed=0 skipped=0 @@ -182,30 +195,33 @@ jobs: echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" echo "" - # 遍历每个 example - for example in $examples; do + # 使用 while read 而不是 for，避免空行问题 + while IFS= read -r example; do + # 跳过空行 + [ -z "$example" ] && continue + echo "📝 测试: $example" # 运行 example（测试模式） - if timeout 30 python "$example" > ".sage/examples-test-results/$(basename $example).log" 2>&1; then + if timeout 30 python "$example" > ".sage/examples-test-results/$(basename "$example").log" 2>&1; then echo " ✅ PASS" - ((passed++)) + passed=$((passed + 1)) else exit_code=$? if [ $exit_code -eq 124 ]; then echo " ⏱️ TIMEOUT (30s)" - ((failed++)) + failed=$((failed + 1)) else echo " ❌ FAIL (exit code: $exit_code)" - ((failed++)) + failed=$((failed + 1)) # 显示错误日志 echo " 错误日志:" - tail -n 10 ".sage/examples-test-results/$(basename $example).log" | sed 's/^/ /' + tail -n 10 ".sage/examples-test-results/$(basename "$example").log" | sed 's/^/ /' fi fi echo "" - done + done < /tmp/testable_examples.txt echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" echo "测试结果汇总" @@ -241,6 +257,7 @@ jobs: if: always() run: | echo "📊 生成测试报告..." + mkdir -p .sage/examples-test-results cat > .sage/examples-test-results/REPORT.md << 'EOF' # Examples Test Report diff --git a/.github/workflows/ci-integration.yml b/.github/workflows/ci-integration.yml new file mode 100644 index 0000000000..b17f6690b6 --- /dev/null +++ b/.github/workflows/ci-integration.yml @@ -0,0 +1,158 @@ +# Cross-repo integration test for the SAGE meta package +# +# Installs the latest published versions of all SAGE sub-packages from PyPI +# and runs a smoke test to confirm they compose correctly. +# +# Triggered: +# - Every push / PR to main-dev (fast feedback) +# - workflow_dispatch (manual trigger from sub-repo release pipelines via +# the dispatch-downstream mechanism in each sub-repo's release.yml) + +name: Integration Test (isage meta) + +on: + push: + branches: [main, main-dev] + paths: + - "**" + - ".github/workflows/ci-integration.yml" + pull_request: + branches: [main, main-dev] + paths: + - "**" + workflow_dispatch: + inputs: + use_testpypi: + description: "Install sub-packages from TestPyPI dev channel" + required: false + default: false + type: boolean + +concurrency: + group: integration-${{ github.ref }} + cancel-in-progress: true + +jobs: + smoke-test: + name: Smoke test — all sub-packages install & import + runs-on: ubuntu-latest + + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Dependency audit consistency check + run: | + python3 tools/scripts/check_meta_dependency_audit.py + + - name: Dependency audit change-evidence gate (PR) + if: ${{ github.event_name == 'pull_request' }} + run: | + git fetch origin "${{ github.base_ref }}" --deepen=200 + python3 tools/scripts/check_meta_dependency_audit.py \ + --enforce-change-evidence \ + --refspec "origin/${{ github.base_ref }}...HEAD" + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.11" + + - name: Install sub-packages (PyPI) + if: ${{ inputs.use_testpypi != 'true' }} + run: | + pip install --upgrade pip + pip install \ + "isage" \ + "isagellm>=0.5.4.3" + + - name: Install sub-packages (TestPyPI dev channel) + if: ${{ inputs.use_testpypi == 'true' }} + run: | + pip install --upgrade pip + # Extract package names from pyproject.toml and install latest pre-releases + PKGS=$(python3 - <<'EOF' + import re, pathlib + content = pathlib.Path("pyproject.toml").read_text() + # pull isage-* names from dependencies block + names = re.findall(r'"(isage-[a-z-]+)>=', content) + print(" ".join(names)) + EOF + ) + pip install --pre \ + --extra-index-url https://test.pypi.org/simple/ \ + $PKGS + + - name: Install meta package (editable) + run: | + pip install -e ".[dev]" + + - name: Smoke test — import each sub-package + run: | + python3 - <<'EOF' + import sys + + checks = [ + ("sage.foundation", "isage"), + ("sage.stream", "isage"), + ("sage.runtime", "isage"), + ("sage.serving", "isage"), + ("sage.cli", "isage"), + ] + + failed = [] + for module, pkg in checks: + try: + __import__(module) + print(f" ✓ {module}") + except ImportError as e: + print(f" ✗ {module} ({e})", file=sys.stderr) + failed.append(pkg) + + if failed: + print(f"\nFAILED: {', '.join(failed)}", file=sys.stderr) + sys.exit(1) + + # Verify meta version is accessible + import sage + print(f"\n ✓ sage.__version__ = {sage.__version__}") + EOF + + - name: Smoke test — version matrix + run: | + python3 - <<'EOF' + import importlib.metadata as meta, sys + + pkgs = [ + "isage", + "isagellm", + ] + + print("\nInstalled SAGE ecosystem versions:") + print("-" * 40) + for p in pkgs: + try: + v = meta.version(p) + print(f" {p:<22} {v}") + except meta.PackageNotFoundError: + print(f" {p:<22} NOT FOUND", file=sys.stderr) + EOF + + - name: Summary + if: always() + run: | + echo "## 🔗 Integration Test Results" >> $GITHUB_STEP_SUMMARY + echo "" >> $GITHUB_STEP_SUMMARY + echo "| Package | Status |" >> $GITHUB_STEP_SUMMARY + echo "|---------|--------|" >> $GITHUB_STEP_SUMMARY + python3 - <<'EOF' >> $GITHUB_STEP_SUMMARY + import importlib.metadata as meta + + pkgs = ["isage", "isagellm"] + for p in pkgs: + try: + v = meta.version(p) + print(f"| `{p}` | ✅ {v} |") + except meta.PackageNotFoundError: + print(f"| `{p}` | ❌ not found |") + EOF diff --git a/.github/workflows/ci-layer-consistency.yml b/.github/workflows/ci-layer-consistency.yml new file mode 100644 index 0000000000..44e5c13b1a --- /dev/null +++ b/.github/workflows/ci-layer-consistency.yml @@ -0,0 +1,47 @@ +name: Layer Consistency Check + +on: + pull_request: + branches: [main, main-dev] + paths: + - "SAGE.code-workspace" + - "README.md" + - "docs/layer-manifest.json" + - ".github/workflows/ci-layer-consistency.yml" + - "tools/scripts/check_layer_manifest_sync.py" + push: + branches: [main, main-dev] + paths: + - "SAGE.code-workspace" + - "README.md" + - "docs/layer-manifest.json" + - ".github/workflows/ci-layer-consistency.yml" + - "tools/scripts/check_layer_manifest_sync.py" + workflow_dispatch: + +concurrency: + group: layer-consistency-${{ github.ref }} + cancel-in-progress: true + +jobs: + layer-consistency: + name: Validate layer manifest and workspace labels + runs-on: ubuntu-latest + + steps: + - name: Checkout SAGE + uses: actions/checkout@v4 + + - name: Explain current layer-consistency scope + run: | + echo "SAGE main workspace now validates an in-tree core surface." + echo "No split core-layer satellite repositories are required for this gate." + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.11" + + - name: Run layer consistency check + run: | + python3 tools/scripts/check_layer_manifest_sync.py --strict-repos diff --git a/.github/workflows/ci-pep420-compliance.yml b/.github/workflows/ci-pep420-compliance.yml deleted file mode 100644 index 6ff72807d7..0000000000 --- a/.github/workflows/ci-pep420-compliance.yml +++ /dev/null @@ -1,124 +0,0 @@ -name: PEP 420 Namespace Compliance - -on: - pull_request: - branches: [main, main-dev] - paths: - - 'packages/*/src/sage/__init__.py' - - 'packages/*/pyproject.toml' - - 'tools/scripts/validate_pep420_compliance.sh' - - 'tools/scripts/verify_pep420_integration.py' - push: - branches: [main, main-dev] - paths: - - 'packages/*/src/sage/__init__.py' - - 'packages/*/pyproject.toml' - workflow_dispatch: - -jobs: - validate-pep420: - name: Validate PEP 420 Namespace Packages - runs-on: ubuntu-latest - timeout-minutes: 5 - - steps: - - name: Checkout - uses: actions/checkout@v4 - - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: '3.11' - - - name: Check for namespace __init__.py violations - run: | - echo "🔍 Checking for src/sage/__init__.py files (PEP 420 violation)..." - violations=$(find packages -type f -path "*/src/sage/__init__.py" 2>/dev/null || true) - - if [ -n "$violations" ]; then - echo "❌ PEP 420 violation detected!" - echo "The following files should not exist (namespace packages must be implicit):" - echo "$violations" - echo "" - echo "To fix:" - echo " rm packages/*/src/sage/__init__.py" - echo "" - echo "See: docs-public/docs_src/dev-notes/cross-layer/pep420-namespace-migration.md" - exit 1 - fi - - echo "✅ No namespace __init__.py files found" - - - name: Check pyproject.toml configuration - run: | - echo "🔍 Checking pyproject.toml for 'namespaces = true'..." - missing=() - - for toml in packages/*/pyproject.toml; do - if ! grep -q "namespaces = true" "$toml"; then - missing+=("$toml") - fi - done - - if [ ${#missing[@]} -gt 0 ]; then - echo "❌ Missing 'namespaces = true' in:" - printf ' %s\n' "${missing[@]}" - echo "" - echo "Add to [tool.setuptools.packages.find] section:" - echo " namespaces = true" - exit 1 - fi - - echo "✅ All pyproject.toml files have 'namespaces = true'" - - - name: Run PEP 420 compliance validation - run: | - chmod +x tools/scripts/validate_pep420_compliance.sh - tools/scripts/validate_pep420_compliance.sh - - integration-test: - name: PEP 420 Integration Test - runs-on: ubuntu-latest - timeout-minutes: 15 - needs: validate-pep420 - - steps: - - name: Checkout - uses: actions/checkout@v4 - with: - submodules: false - - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: '3.11' - - - name: Install SAGE packages (dev mode) - run: | - # Install minimal set for testing - pip install -e packages/sage-common --no-deps - pip install -e packages/sage-platform --no-deps - pip install -e packages/sage-kernel --no-deps - pip install -e packages/sage-libs --no-deps - - - name: Run PEP 420 integration tests - run: | - python3 tools/scripts/verify_pep420_integration.py - - - name: Test namespace coexistence - run: | - python3 -c " - import sage - import sage.common - import sage.kernel - import sage.libs - - # Verify implicit namespace - assert sage.__file__ is None, 'sage should be implicit namespace' - - # Verify versions - print(f'✅ sage.common v{sage.common.__version__}') - print(f'✅ sage.kernel v{sage.kernel.__version__}') - print(f'✅ sage.libs v{sage.libs.__version__}') - print('✅ All packages coexist correctly') - " diff --git a/.github/workflows/ci-pr-install.yml b/.github/workflows/ci-pr-install.yml index e92210856f..54069ed12a 100644 --- a/.github/workflows/ci-pr-install.yml +++ b/.github/workflows/ci-pr-install.yml @@ -5,10 +5,11 @@ on: branches: [main, main-dev] types: [opened, synchronize, reopened] paths: - - 'packages/*/pyproject.toml' - - 'packages/*/setup.py' - - 'packages/*/setup.cfg' + - 'pyproject.toml' + - 'setup.py' + - 'src/**' - 'tools/install/**' + - '.github/workflows/ci-pr-install.yml' workflow_dispatch: inputs: python-version: @@ -49,13 +50,20 @@ jobs: - name: Smoke import run: | - python -c "import sage; import sage.common; import sage.kernel; print('import ok')" + python -c "import sage.foundation, sage.stream, sage.runtime, sage.serving, sage.cli; print('import ok')" + sage version + sage verify + sage chat --help + + - name: Run current in-tree tests + run: | + python -m pytest src/tests -q - name: Wheel build sanity (meta-package) run: | make clean || true python -m pip install build - python -m build --wheel --outdir dist packages/sage + python -m build --wheel --outdir dist . - name: Upload wheel artifact uses: actions/upload-artifact@v4 diff --git a/.github/workflows/ci-release-install.yml b/.github/workflows/ci-release-install.yml index 3d9a14becc..213984502a 100644 --- a/.github/workflows/ci-release-install.yml +++ b/.github/workflows/ci-release-install.yml @@ -4,10 +4,11 @@ on: push: branches: [main] paths: - - 'packages/*/pyproject.toml' - - 'packages/*/setup.py' - - 'packages/*/setup.cfg' + - 'pyproject.toml' + - 'setup.py' + - 'src/**' - 'tools/install/**' + - '.github/workflows/ci-release-install.yml' tags: - 'v*' release: @@ -54,12 +55,19 @@ jobs: run: | make clean || true python -m pip install build - python -m build --wheel --outdir dist packages/sage + python -m build --wheel --outdir dist . - name: Install from wheels run: | python -m pip install --force-reinstall dist/*.whl - python -c "import sage; import sage.cli; import sage.apps; import sage.benchmark; print('full import ok')" + python -c "import sage.foundation, sage.stream, sage.runtime, sage.serving, sage.cli; print('core imports ok')" + sage version + sage verify + sage chat --help + + - name: Run current in-tree tests + run: | + python -m pytest src/tests -q - name: Upload wheel artifact uses: actions/upload-artifact@v4 diff --git a/.github/workflows/ci-sagellm-test.yml b/.github/workflows/ci-sagellm-test.yml deleted file mode 100644 index cfb32f98f0..0000000000 --- a/.github/workflows/ci-sagellm-test.yml +++ /dev/null @@ -1,380 +0,0 @@ -name: SageLLM Mock & CUDA Tests - -on: - push: - branches: [main, main-dev] - paths: - - 'packages/**' - - 'tools/**' - - '.github/workflows/ci-sagellm-test.yml' - pull_request: - branches: [main, main-dev] - types: [opened, synchronize, reopened] - paths: - - 'packages/**' - - 'tools/**' - workflow_dispatch: - inputs: - run_cuda_tests: - description: 'Run CUDA tests on GPU runner' - type: boolean - default: false - -concurrency: - group: sagellm-${{ github.workflow }}-${{ github.ref }} - cancel-in-progress: true - -env: - CI: true - HF_TOKEN: ${{ secrets.HF_TOKEN }} - HF_ENDPOINT: https://hf-mirror.com - SAGE_TEST_MODE: true - -jobs: - # ============================================================================ - # Job 1: SageLLM Mock Backend Tests (Required for all PRs) - # ============================================================================ - sagellm-mock-test: - name: SageLLM Mock Backend - runs-on: ubuntu-latest - timeout-minutes: 30 - # Skip version bump commits - if: ${{ !contains(github.event.head_commit.message, '[version bump]') }} - - steps: - - name: Checkout Repository - uses: actions/checkout@v4 - with: - token: ${{ secrets.GITHUB_TOKEN }} - fetch-depth: 0 - - - name: Set up Python 3.11 - uses: actions/setup-python@v5 - with: - python-version: '3.11' - cache: 'pip' - - - name: Install System Dependencies - run: | - echo "🔧 Installing system dependencies..." - sudo apt-get update -qq - sudo apt-get install -y --no-install-recommends \ - build-essential \ - cmake \ - pkg-config \ - libopenblas-dev \ - liblapack-dev - - - name: Install SAGE Core Packages - run: | - echo "📦 Installing SAGE core packages..." - pip install --upgrade pip - - # Install SAGE packages in dependency order - pip install -e packages/sage-common - pip install -e packages/sage-platform - pip install -e packages/sage-kernel - pip install -e packages/sage-libs - pip install -e packages/sage-middleware - pip install -e packages/sage-cli - pip install -e packages/sage-tools - - echo "✅ SAGE packages installed" - - - name: Install L3 Independent Packages - run: | - echo "📦 Installing L3 domain-specific packages (isage-* namespace)..." - - # Install independent L3 algorithm and domain packages - # These provide sage_libs.* implementations - pip install isage-agentic isage-finetune isage-rag isage-eval isage-privacy isage-safety - - # Install data management package (provides sage.data.*) - pip install isage-data - - # Install ANNS and vector database packages for tests - pip install isage-vdb chromadb pymilvus - - echo "✅ L3 packages installed" - - - name: Install isagellm - run: | - echo "🚀 Installing isagellm (SageLLM inference engine)..." - pip install isagellm - - # Verify installation - python -c "import isagellm; print(f'✅ isagellm {isagellm.__version__} installed')" || { - echo "⚠️ isagellm import failed, trying alternative verification..." - pip show isagellm && echo "✅ isagellm package is installed" - } - - - name: Install Test Dependencies - run: | - pip install pytest pytest-cov pytest-timeout pytest-xdist pytest-mock - - # Install torch for tests that import HFClient or other torch-dependent modules - pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu - - # Install transformers for HFClient tests - pip install transformers - - # Install datasets for agent training tests - pip install datasets - - # Install peft for LoRA fine-tuning in AgentSFTTrainer - pip install peft - - # Install numpy for evaluator metrics - pip install numpy - - - name: Run SageLLM Mock Tests - run: | - echo "🧪 Running SageLLM mock backend tests..." - echo "" - echo "Test patterns: 'sagellm' or 'mock' in test names" - echo "" - - # Run tests matching sagellm or mock patterns - pytest -v \ - --timeout=120 \ - -k "sagellm or mock or SageLLM or Mock" \ - --ignore=packages/sage-tools/tests/test_cli/llm_heavy_suite.py \ - --ignore=benchmark/ \ - packages/ \ - 2>&1 || { - exit_code=$? - if [ $exit_code -eq 5 ]; then - echo "⚠️ No tests found matching 'sagellm or mock'" - echo "This is acceptable if sagellm tests are in a separate location" - exit 0 - fi - exit $exit_code - } - - - name: Run SageLLM Integration Tests - run: | - echo "🔗 Running SageLLM integration tests..." - - # Test SageLLMGenerator with mock backend - python -c " - from sage.middleware.operators.llm import SageLLMGenerator - - print('Testing SageLLMGenerator with mock backend...') - gen = SageLLMGenerator(backend_type='mock') - print(f' ✅ Generator created: {gen}') - print(f' ✅ Backend type: {gen.backend_type}') - " - - # Test agentic operators with mock backend - python -c " - from sage.middleware.operators.agentic import ( - PlanningOperator, - TimingOperator, - ToolSelectionOperator, - ) - - print('Testing agentic operators with mock backend...') - - op1 = PlanningOperator(config={'backend_type': 'mock'}) - print(f' ✅ PlanningOperator: {type(op1.generator).__name__}') - - op2 = TimingOperator(config={'generator': {'backend_type': 'mock'}}) - print(f' ✅ TimingOperator: {type(op2.generator).__name__}') - - op3 = ToolSelectionOperator(config={'backend_type': 'mock'}) - print(f' ✅ ToolSelectionOperator: {type(op3.generator).__name__}') - " - - - name: Summary - if: always() - run: | - echo "## 🧪 SageLLM Mock Test Summary" >> $GITHUB_STEP_SUMMARY - echo "" >> $GITHUB_STEP_SUMMARY - - if [ "${{ job.status }}" = "success" ]; then - echo "✅ **All SageLLM mock tests passed**" >> $GITHUB_STEP_SUMMARY - echo "" >> $GITHUB_STEP_SUMMARY - echo "- Mock backend integration: ✅" >> $GITHUB_STEP_SUMMARY - echo "- Agentic operators: ✅" >> $GITHUB_STEP_SUMMARY - else - echo "❌ **SageLLM mock tests failed**" >> $GITHUB_STEP_SUMMARY - echo "" >> $GITHUB_STEP_SUMMARY - echo "Please check the test logs for details." >> $GITHUB_STEP_SUMMARY - fi - - # ============================================================================ - # Job 2: SageLLM CUDA Tests (Optional, GPU runner) - # ============================================================================ - sagellm-cuda-test: - name: SageLLM CUDA Backend - runs-on: [self-hosted, gpu, cuda] - timeout-minutes: 60 - # Only run on workflow_dispatch with cuda flag, or on main branch pushes - if: | - (github.event_name == 'workflow_dispatch' && github.event.inputs.run_cuda_tests == 'true') || - (github.event_name == 'push' && github.ref == 'refs/heads/main') - # Require mock tests to pass first - needs: sagellm-mock-test - - env: - # Use China mirrors for self-hosted runners in China - SAGE_FORCE_CHINA_MIRROR: true - HF_ENDPOINT: https://hf-mirror.com - PIP_INDEX_URL: https://pypi.tuna.tsinghua.edu.cn/simple - PIP_EXTRA_INDEX_URL: https://download.pytorch.org/whl/cu121 - - steps: - - name: Checkout Repository - uses: actions/checkout@v4 - with: - token: ${{ secrets.GITHUB_TOKEN }} - fetch-depth: 0 - - - name: Set up Python 3.11 - uses: actions/setup-python@v5 - with: - python-version: '3.11' - cache: 'pip' - - - name: Check CUDA Availability - run: | - echo "🔍 Checking CUDA availability..." - - # Check nvidia-smi - if command -v nvidia-smi &> /dev/null; then - echo "✅ nvidia-smi available" - nvidia-smi --query-gpu=name,memory.total,driver_version --format=csv - else - echo "❌ nvidia-smi not found" - exit 1 - fi - - # Check CUDA version - if command -v nvcc &> /dev/null; then - echo "" - echo "✅ CUDA compiler available" - nvcc --version - else - echo "⚠️ nvcc not found, but may work with runtime-only installation" - fi - - - name: Install System Dependencies - run: | - echo "🔧 Installing system dependencies..." - sudo apt-get update -qq - sudo apt-get install -y --no-install-recommends \ - build-essential \ - cmake \ - pkg-config \ - libopenblas-dev \ - liblapack-dev - - - name: Install SAGE Packages - run: | - echo "📦 Installing SAGE packages..." - - # Unset CI variables to install to conda env instead of ~/.local - unset CI GITHUB_ACTIONS - - pip install --upgrade pip - - # Install SAGE packages - pip install -e packages/sage-common - pip install -e packages/sage-platform - pip install -e packages/sage-kernel - pip install -e packages/sage-libs - pip install -e packages/sage-middleware - pip install -e packages/sage-cli - pip install -e packages/sage-tools - - - name: Install isagellm with CUDA support - run: | - echo "🚀 Installing isagellm with CUDA support..." - - # Install PyTorch with CUDA first - pip install torch --index-url https://download.pytorch.org/whl/cu121 - - # Install isagellm - pip install isagellm - - # Verify CUDA availability in PyTorch - python -c " - import torch - print(f'PyTorch version: {torch.__version__}') - print(f'CUDA available: {torch.cuda.is_available()}') - if torch.cuda.is_available(): - print(f'CUDA device: {torch.cuda.get_device_name(0)}') - print(f'CUDA version: {torch.version.cuda}') - " - - - name: Install Test Dependencies - run: | - pip install pytest pytest-cov pytest-timeout - - - name: Run SageLLM CUDA Tests - run: | - echo "🧪 Running SageLLM CUDA backend tests..." - - # Test basic CUDA functionality - python -c " - import torch - assert torch.cuda.is_available(), 'CUDA not available' - - # Test simple tensor operation on GPU - x = torch.randn(1000, 1000, device='cuda') - y = torch.randn(1000, 1000, device='cuda') - z = torch.matmul(x, y) - print(f'✅ CUDA tensor operation successful') - print(f' Result shape: {z.shape}') - print(f' Device: {z.device}') - " - - # Run CUDA-specific tests - pytest -v \ - --timeout=300 \ - -k "cuda or CUDA or gpu or GPU" \ - --ignore=benchmark/ \ - packages/ \ - 2>&1 || { - exit_code=$? - if [ $exit_code -eq 5 ]; then - echo "⚠️ No CUDA-specific tests found" - echo "Running SageLLMGenerator with CUDA backend instead..." - - python -c " - from sage.middleware.operators.llm import SageLLMGenerator - - print('Testing SageLLMGenerator with CUDA...') - gen = SageLLMGenerator( - backend_type='vllm', - device_map='cuda:0', - ) - print(f' ✅ Generator created with CUDA backend') - " - exit 0 - fi - exit $exit_code - } - - - name: Summary - if: always() - run: | - echo "## 🎮 SageLLM CUDA Test Summary" >> $GITHUB_STEP_SUMMARY - echo "" >> $GITHUB_STEP_SUMMARY - - if [ "${{ job.status }}" = "success" ]; then - echo "✅ **All SageLLM CUDA tests passed**" >> $GITHUB_STEP_SUMMARY - echo "" >> $GITHUB_STEP_SUMMARY - - # Add GPU info - if command -v nvidia-smi &> /dev/null; then - echo "### GPU Information" >> $GITHUB_STEP_SUMMARY - echo '```' >> $GITHUB_STEP_SUMMARY - nvidia-smi --query-gpu=name,memory.total --format=csv >> $GITHUB_STEP_SUMMARY - echo '```' >> $GITHUB_STEP_SUMMARY - fi - else - echo "❌ **SageLLM CUDA tests failed**" >> $GITHUB_STEP_SUMMARY - echo "" >> $GITHUB_STEP_SUMMARY - echo "Please check the test logs for details." >> $GITHUB_STEP_SUMMARY - fi diff --git a/.github/workflows/util-cleanup.yml b/.github/workflows/util-cleanup.yml index 20ea125468..bd8b7d83ff 100644 --- a/.github/workflows/util-cleanup.yml +++ b/.github/workflows/util-cleanup.yml @@ -43,102 +43,6 @@ jobs: run: | bash tools/install/tests/test_cleanup_tools.sh - # 测试 auto-venv 在不同环境下的行为 - test-auto-venv: - name: Test Auto-Venv (${{ matrix.os }}) - runs-on: ${{ matrix.os }} - strategy: - matrix: - os: [ubuntu-latest, ubuntu-22.04] - python-version: ["3.10", "3.11", "3.12"] - fail-fast: false - - steps: - - name: Checkout repository - uses: actions/checkout@v4 - with: - submodules: false - - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python-version }} - - - name: Install system dependencies - run: | - sudo apt-get update - sudo apt-get install -y python3-pip python3-venv - - - name: Test auto-venv creation (clean environment) - run: | - # 在清洁环境中测试 auto-venv - env -u CONDA_DEFAULT_ENV -u CONDA_PREFIX -u VIRTUAL_ENV bash -c ' - source tools/install/display_tools/colors.sh - source tools/install/download_tools/environment_config.sh - - # 测试虚拟环境检测 - result=$(detect_virtual_environment) - echo "Detection result: $result" - - is_venv=$(echo "$result" | cut -d"|" -f1) - if [ "$is_venv" = "false" ]; then - echo "✅ Correctly detected no virtual environment" - else - echo "❌ Failed: should not detect venv in clean environment" - exit 1 - fi - ' - - - name: Test auto-venv with --auto-venv flag - run: | - # 测试 --auto-venv 选项 - env -u CONDA_DEFAULT_ENV -u CONDA_PREFIX -u VIRTUAL_ENV bash -c ' - cd $(mktemp -d) - cp -r $GITHUB_WORKSPACE/tools . - - source tools/install/display_tools/colors.sh - source tools/install/download_tools/environment_config.sh - - # 尝试创建虚拟环境 - test_venv=".sage/venv" - if ensure_python_venv "$test_venv"; then - echo "✅ Virtual environment created successfully" - - # 验证虚拟环境结构 - if [ -f "$test_venv/bin/activate" ]; then - echo "✅ Virtual environment contains activate script" - else - echo "❌ Missing activate script" - exit 1 - fi - - # 激活并测试 - source "$test_venv/bin/activate" - python --version - pip --version - deactivate - else - echo "⚠️ Virtual environment creation failed (may need python3-venv)" - fi - ' - - - name: Test SAGE_VENV_POLICY environment variable - run: | - # 测试不同的策略 - for policy in warning error ignore; do - echo "Testing SAGE_VENV_POLICY=$policy" - - export SAGE_VENV_POLICY=$policy - source tools/install/display_tools/colors.sh - source tools/install/download_tools/environment_config.sh - - # 在虚拟环境中测试（应该通过） - export VIRTUAL_ENV="/tmp/test-venv" - result=$(detect_virtual_environment) - echo "Policy $policy result: $result" - unset VIRTUAL_ENV - done - # 测试安装跟踪和清理流程 test-install-tracking: name: Test Install Tracking and Cleanup @@ -207,27 +111,22 @@ jobs: - name: Install system dependencies run: | sudo apt-get update - sudo apt-get install -y build-essential python3-dev python3-venv + sudo apt-get install -y build-essential python3-dev - - name: Test complete workflow with auto-venv + - name: Test complete workflow without venv run: | # 完整的安装-使用-清理流程 - # 1. 使用 auto-venv 安装 (minimal 模式用于快速测试) - ./quickstart.sh --auto-venv --minimal --yes || { + # 1. 使用非 venv 模式安装 (minimal 模式用于快速测试) + ./quickstart.sh --pip --minimal --yes || { echo "⚠️ Installation failed, checking logs..." find .sage -name "*.log" -type f -exec tail -20 {} \; exit 1 } # 2. 验证安装 - if [ -d .sage/venv ]; then - echo "✅ Virtual environment created" - source .sage/venv/bin/activate - python --version - pip list | grep -i sage || true - deactivate - fi + python --version + pip list | grep -i sage || true # 3. 检查安装跟踪 bash tools/cleanup/track_install.sh show @@ -255,5 +154,4 @@ jobs: # 验证环境管理功能在代码中存在 grep -q "detect_virtual_environment" tools/install/download_tools/environment_config.sh - grep -q "ensure_python_venv" tools/install/download_tools/environment_config.sh grep -q "SAGE_VENV_POLICY" tools/install/download_tools/environment_config.sh diff --git a/.github/workflows/util-weekly-report.yml b/.github/workflows/util-weekly-report.yml deleted file mode 100644 index a7193ee619..0000000000 --- a/.github/workflows/util-weekly-report.yml +++ /dev/null @@ -1,465 +0,0 @@ -# Periodic Report Generator Workflow -# -# Generates periodic work reports (weekly/monthly/quarterly/yearly) for all contributors -# using SAGE framework. Runs on GitHub-hosted ubuntu-latest runner. -# -# Schedule: -# - Weekly: Every Monday at 9:00 UTC (17:00 Beijing Time) -# - Monthly: 1st of each month at 9:00 UTC -# - Quarterly: 1st of Jan/Apr/Jul/Oct at 9:00 UTC -# - Yearly: January 1st at 9:00 UTC -# -# Reports are: -# 1. Saved as artifacts -# 2. Committed to docs-public for persistence -# 3. Posted as GitHub issue comments (optional) - -name: Periodic Report Generator - -on: - # Weekly schedule: Monday 9:00 UTC (17:00 Beijing Time) - schedule: - - cron: '0 9 * * 1' # Weekly - every Monday - - cron: '0 9 1 * *' # Monthly - 1st of each month - - cron: '0 9 1 1,4,7,10 *' # Quarterly - 1st of Jan/Apr/Jul/Oct - # Note: Yearly report runs on Jan 1st via the monthly schedule - - # Manual trigger with options - workflow_dispatch: - inputs: - period: - description: 'Report period type' - required: false - default: 'weekly' - type: choice - options: - - weekly - - monthly - - quarterly - - yearly - repos: - description: 'Repositories to include (comma-separated, leave empty for all SAGE repos)' - required: false - default: '' - type: string - branch: - description: 'Branch to fetch commits from' - required: false - default: 'main-dev' - type: string - days: - description: 'Number of days to look back (overrides period if set)' - required: false - default: '' - type: string - output_format: - description: 'Output format' - required: false - default: 'markdown' - type: choice - options: - - markdown - - json - - console - language: - description: 'Report language' - required: false - default: 'zh' - type: choice - options: - - zh - - en - use_llm: - description: 'Use LLM for AI summaries' - required: false - default: true - type: boolean - include_submodules: - description: 'Include submodule repositories' - required: false - default: true - type: boolean - create_issue: - description: 'Create GitHub issue with report' - required: false - default: true - type: boolean - -env: - CI: true - GITHUB_TOKEN: ${{ secrets.GIT_TOKEN }} - GIT_TOKEN: ${{ secrets.GIT_TOKEN }} - HF_TOKEN: ${{ secrets.HF_TOKEN }} - HF_ENDPOINT: https://hf-mirror.com - # LLM Service Configuration (remote only - DashScope API) - SAGE_CHAT_BASE_URL: https://dashscope.aliyuncs.com/compatible-mode/v1 - SAGE_CHAT_API_KEY: ${{ secrets.ALIBABA_API_KEY }} - SAGE_CHAT_MODEL: qwen-turbo-2025-02-11 - -jobs: - generate-report: - name: Generate Periodic Report - runs-on: ubuntu-latest - timeout-minutes: 45 - - steps: - - name: Checkout Repository - uses: actions/checkout@v4 - with: - token: ${{ secrets.GIT_TOKEN }} - fetch-depth: 0 - - - name: Setup Python Environment - run: | - echo "Setting up Python environment..." - echo "Using CI installation script for GitHub-hosted runner" - - # Install SAGE using CI wrapper (installs to ~/.local, no conda needed) - ./tools/install/core/ci_install_wrapper.sh --dev --yes - - # Verify installation - python3 --version - pip list | grep -E "^isage-|^sage-" || true - echo "SAGE CLI installed at: $(which sage-dev || echo 'not in PATH')" - - - name: Verify LLM Configuration - run: | - echo "Using remote DashScope API for LLM services" - echo "LLM Base URL: $SAGE_CHAT_BASE_URL" - echo "LLM Model: $SAGE_CHAT_MODEL" - echo "API Key configured: $([ -n "$SAGE_CHAT_API_KEY" ] && echo 'Yes' || echo 'No')" - - - name: Create Environment File - run: | - cat > .env << EOF - GITHUB_TOKEN=${{ secrets.GIT_TOKEN }} - GIT_TOKEN=${{ secrets.GIT_TOKEN }} - HF_TOKEN=${{ secrets.HF_TOKEN }} - HF_ENDPOINT=https://hf-mirror.com - SAGE_CHAT_API_KEY=${{ secrets.ALIBABA_API_KEY }} - SAGE_CHAT_BASE_URL=${SAGE_CHAT_BASE_URL} - SAGE_CHAT_MODEL=${SAGE_CHAT_MODEL} - EOF - - - name: Prepare Report Directory - run: | - mkdir -p reports - echo "Report directory created: $(pwd)/reports" - - - name: Generate Periodic Report - id: generate - run: | - - # Determine period based on schedule or manual input - # For scheduled runs, determine period from the schedule that triggered - if [ "${{ github.event_name }}" = "schedule" ]; then - # Check which schedule triggered based on current date - DAY_OF_MONTH=$(date +%d) - MONTH=$(date +%m) - DAY_OF_WEEK=$(date +%u) # 1=Monday, 7=Sunday - - if [ "$MONTH" = "01" ] && [ "$DAY_OF_MONTH" = "01" ]; then - # January 1st - yearly report - PERIOD="yearly" - elif [ "$MONTH" = "01" ] || [ "$MONTH" = "04" ] || [ "$MONTH" = "07" ] || [ "$MONTH" = "10" ]; then - if [ "$DAY_OF_MONTH" = "01" ]; then - # 1st of quarter month - quarterly report - PERIOD="quarterly" - else - PERIOD="weekly" - fi - elif [ "$DAY_OF_MONTH" = "01" ]; then - # 1st of month - monthly report - PERIOD="monthly" - else - # Default to weekly (every Monday) - PERIOD="weekly" - fi - else - # Manual trigger - use input - PERIOD="${{ github.event.inputs.period || 'weekly' }}" - fi - - # Set parameters from workflow inputs or defaults - REPOS="${{ github.event.inputs.repos || '' }}" - BRANCH="${{ github.event.inputs.branch || 'main-dev' }}" - DAYS="${{ github.event.inputs.days || '' }}" - FORMAT="${{ github.event.inputs.output_format || 'markdown' }}" - LANGUAGE="${{ github.event.inputs.language || 'zh' }}" - USE_LLM="${{ github.event.inputs.use_llm }}" - INCLUDE_SUBMODULES="${{ github.event.inputs.include_submodules }}" - - # For scheduled runs (not workflow_dispatch), use defaults with LLM enabled - if [ -z "$USE_LLM" ]; then - USE_LLM="true" - fi - if [ -z "$INCLUDE_SUBMODULES" ]; then - INCLUDE_SUBMODULES="true" - fi - - # Generate timestamp for filename - TIMESTAMP=$(date +%Y%m%d_%H%M%S) - - # Calculate date range based on period - case $PERIOD in - weekly) - PERIOD_DAYS=7 - ;; - monthly) - PERIOD_DAYS=30 - ;; - quarterly) - PERIOD_DAYS=90 - ;; - yearly) - PERIOD_DAYS=365 - ;; - *) - PERIOD_DAYS=7 - ;; - esac - - # Use custom days if specified, otherwise use period days - if [ -n "$DAYS" ]; then - ACTUAL_DAYS=$DAYS - else - ACTUAL_DAYS=$PERIOD_DAYS - fi - - PERIOD_START=$(date -d "-${ACTUAL_DAYS} days" +%Y%m%d) - PERIOD_END=$(date +%Y%m%d) - - # Determine file extension - if [ "$FORMAT" = "json" ]; then - EXT="json" - else - EXT="md" - fi - - OUTPUT_FILE="reports/${PERIOD}_report_${PERIOD_START}_${PERIOD_END}.${EXT}" - - echo "Generating report..." - echo " Period: $PERIOD" - echo " Repos: ${REPOS:-'All SAGE repos (main + submodules)'}" - echo " Branch: $BRANCH" - echo " Days: $ACTUAL_DAYS" - echo " Format: $FORMAT" - echo " Language: $LANGUAGE" - echo " Use LLM: $USE_LLM" - echo " Include Submodules: $INCLUDE_SUBMODULES" - echo " Output: $OUTPUT_FILE" - - # Build command - CMD="python -m sage.apps.work_report_generator.pipeline" - CMD="$CMD --period $PERIOD" - CMD="$CMD --branch $BRANCH" - CMD="$CMD --format $FORMAT" - CMD="$CMD --output $OUTPUT_FILE" - CMD="$CMD --language $LANGUAGE" - - # Add days if explicitly specified (overrides period) - if [ -n "$DAYS" ]; then - CMD="$CMD --days $DAYS" - fi - - # Add repos if specified - if [ -n "$REPOS" ]; then - CMD="$CMD --repos $REPOS" - fi - - if [ "$USE_LLM" = "false" ]; then - CMD="$CMD --no-llm" - fi - - if [ "$INCLUDE_SUBMODULES" = "false" ]; then - CMD="$CMD --no-submodules" - fi - - # Run report generation - echo "Running: $CMD" - $CMD - - # Set outputs - echo "output_file=$OUTPUT_FILE" >> $GITHUB_OUTPUT - echo "period=$PERIOD" >> $GITHUB_OUTPUT - echo "period_start=$PERIOD_START" >> $GITHUB_OUTPUT - echo "period_end=$PERIOD_END" >> $GITHUB_OUTPUT - - - name: Upload Report Artifact - uses: actions/upload-artifact@v4 - with: - name: ${{ steps.generate.outputs.period }}-report-${{ steps.generate.outputs.period_start }}-${{ steps.generate.outputs.period_end }} - path: reports/ - retention-days: 90 - - - name: Commit Report to docs-public - if: ${{ github.event.inputs.output_format != 'json' }} - run: | - PERIOD="${{ steps.generate.outputs.period }}" - PERIOD_START="${{ steps.generate.outputs.period_start }}" - PERIOD_END="${{ steps.generate.outputs.period_end }}" - OUTPUT_FILE="${{ steps.generate.outputs.output_file }}" - - # Create docs directory if not exists - DOCS_DIR="docs-public/docs_src/community/weekly-reports" - mkdir -p "$DOCS_DIR" - - # Copy report to docs with proper naming (include period type) - REPORT_NAME="${PERIOD}_report_${PERIOD_START}_${PERIOD_END}.md" - cp "$OUTPUT_FILE" "$DOCS_DIR/$REPORT_NAME" - - # Update index.md with new report link - INDEX_FILE="$DOCS_DIR/index.md" - if [ -f "$INDEX_FILE" ]; then - # Format dates for display - START_DISPLAY=$(date -d "${PERIOD_START}" +"%Y-%m-%d" 2>/dev/null || echo "$PERIOD_START") - END_DISPLAY=$(date -d "${PERIOD_END}" +"%Y-%m-%d" 2>/dev/null || echo "$PERIOD_END") - GENERATED=$(date +"%Y-%m-%d %H:%M UTC") - - # Period display names - case $PERIOD in - weekly) PERIOD_DISPLAY="周报" ;; - monthly) PERIOD_DISPLAY="月报" ;; - quarterly) PERIOD_DISPLAY="季报" ;; - yearly) PERIOD_DISPLAY="年报" ;; - *) PERIOD_DISPLAY="$PERIOD" ;; - esac - - # Add new row to table (after the header row) - NEW_ROW="| [${PERIOD_DISPLAY}: ${START_DISPLAY} ~ ${END_DISPLAY}](./${REPORT_NAME}) | ${PERIOD} | ${START_DISPLAY} ~ ${END_DISPLAY} | ${GENERATED} |" - - # Insert after the table header (line with "Reports will appear" or after |--------|) - if grep -q "Reports will appear here automatically" "$INDEX_FILE"; then - sed -i "s|.*Reports will appear here automatically.*|${NEW_ROW}|" "$INDEX_FILE" - else - # Add to the end of the table - sed -i "/^|.*|.*|.*|.*|$/a ${NEW_ROW}" "$INDEX_FILE" - fi - fi - - # Configure git - git config user.name "github-actions[bot]" - git config user.email "github-actions[bot]@users.noreply.github.com" - - # Commit and push - git add "$DOCS_DIR/" - git commit -m "docs: add ${PERIOD} report ${PERIOD_START}-${PERIOD_END} - - Auto-generated by Periodic Report Generator workflow. - Period Type: ${PERIOD} - Date Range: ${PERIOD_START} ~ ${PERIOD_END} - " || echo "No changes to commit" - - git push origin ${{ github.ref_name }} || echo "Push failed, may need manual intervention" - - - name: Create GitHub Issue with Report - if: ${{ github.event.inputs.create_issue != 'false' && github.event.inputs.output_format != 'json' }} - uses: actions/github-script@v7 - with: - github-token: ${{ secrets.GIT_TOKEN }} - script: | - const fs = require('fs'); - const path = require('path'); - - const outputFile = '${{ steps.generate.outputs.output_file }}'; - const weekStart = '${{ steps.generate.outputs.week_start }}'; - const weekEnd = '${{ steps.generate.outputs.week_end }}'; - - // Read report content - let reportContent = ''; - try { - reportContent = fs.readFileSync(outputFile, 'utf8'); - } catch (e) { - console.log('Could not read report file:', e.message); - return; - } - - // Create issue - const title = `📊 Weekly Report: ${weekStart} - ${weekEnd}`; - const body = `## Weekly Contribution Report - - **Period:** ${weekStart} - ${weekEnd} - **Generated:** ${new Date().toISOString()} - - --- - - ${reportContent} - - --- - - *This report was automatically generated by the SAGE Work Report Generator.* - `; - - try { - const issue = await github.rest.issues.create({ - owner: context.repo.owner, - repo: context.repo.repo, - title: title, - body: body, - labels: ['weekly-report', 'automated'] - }); - console.log(`Created issue #${issue.data.number}`); - } catch (e) { - console.log('Could not create issue:', e.message); - } - - - name: Summary - run: | - WEEK_START="${{ steps.generate.outputs.week_start }}" - WEEK_END="${{ steps.generate.outputs.week_end }}" - - echo "## 📊 Weekly Report Generation Complete" >> $GITHUB_STEP_SUMMARY - echo "" >> $GITHUB_STEP_SUMMARY - echo "### Report Details" >> $GITHUB_STEP_SUMMARY - echo "| Setting | Value |" >> $GITHUB_STEP_SUMMARY - echo "|---------|-------|" >> $GITHUB_STEP_SUMMARY - echo "| **Period** | ${WEEK_START} - ${WEEK_END} |" >> $GITHUB_STEP_SUMMARY - echo "| **Branch** | ${{ github.event.inputs.branch || 'main-dev' }} |" >> $GITHUB_STEP_SUMMARY - echo "| **Language** | ${{ github.event.inputs.language || 'zh' }} |" >> $GITHUB_STEP_SUMMARY - echo "| **LLM Analysis** | ✅ Enabled (Qwen) |" >> $GITHUB_STEP_SUMMARY - echo "| **Output File** | ${{ steps.generate.outputs.output_file }} |" >> $GITHUB_STEP_SUMMARY - echo "" >> $GITHUB_STEP_SUMMARY - echo "### Actions Completed" >> $GITHUB_STEP_SUMMARY - echo "- ✅ Generated weekly contribution report with AI summaries" >> $GITHUB_STEP_SUMMARY - echo "- ✅ Saved report as workflow artifact (90-day retention)" >> $GITHUB_STEP_SUMMARY - echo "- ✅ Committed to docs-public for website display" >> $GITHUB_STEP_SUMMARY - echo "- ✅ Posted as GitHub issue (if enabled)" >> $GITHUB_STEP_SUMMARY - echo "" >> $GITHUB_STEP_SUMMARY - echo "### 📚 View Report" >> $GITHUB_STEP_SUMMARY - echo "- **Website**: [Weekly Reports](https://intellistream.github.io/SAGE/community/weekly-reports/)" >> $GITHUB_STEP_SUMMARY - echo "- **Direct Link**: [report_${WEEK_START}_${WEEK_END}.md](https://github.com/intellistream/SAGE/blob/main-dev/docs-public/docs_src/community/weekly-reports/report_${WEEK_START}_${WEEK_END}.md)" >> $GITHUB_STEP_SUMMARY - echo "" >> $GITHUB_STEP_SUMMARY - echo "### LLM Configuration" >> $GITHUB_STEP_SUMMARY - echo "- **Model**: ${SAGE_CHAT_MODEL}" >> $GITHUB_STEP_SUMMARY - echo "- **Provider**: ${SAGE_CHAT_BASE_URL}" >> $GITHUB_STEP_SUMMARY - - notify-failure: - name: Notify on Failure - runs-on: ubuntu-latest - needs: generate-report - if: failure() - - steps: - - name: Create Failure Issue - uses: actions/github-script@v7 - with: - github-token: ${{ secrets.GIT_TOKEN }} - script: | - const title = `⚠️ Weekly Report Generation Failed - ${new Date().toISOString().split('T')[0]}`; - const body = `## Weekly Report Generation Failed - - **Workflow Run:** [${context.runId}](${context.serverUrl}/${context.repo.owner}/${context.repo.repo}/actions/runs/${context.runId}) - **Time:** ${new Date().toISOString()} - - Please check the workflow logs for details. - - cc @${{ github.repository_owner }} - `; - - await github.rest.issues.create({ - owner: context.repo.owner, - repo: context.repo.repo, - title: title, - body: body, - labels: ['bug', 'ci-failure', 'weekly-report'] - }); diff --git a/.gitignore b/.gitignore index 96c3eccad3..56bf775d3b 100644 --- a/.gitignore +++ b/.gitignore @@ -29,17 +29,14 @@ install.log .sage/tmp/ .sage/cache/ .sage/build/ -.sage/htmlcov/ -.sage/benchmarks/ /.cmake-build/ /deps/sageFlow/installation/dist/ /deps/sageFlow/installation/sageflow.egg-info/ /deps/sageFlow/installation/build/ /.idea/deployment.xml /.idea/misc.xml -# 根目录构建产物（已统一到 .sage/build/） /build/ -/htmlcov/ +/build/ /installation/sageflow.egg-info/ /installation/build/ /installation/dist/ @@ -69,16 +66,14 @@ install.log **/install_manifest.txt **/compile_commands.json -# C++ 编译输出目录（已废弃，现统一使用 .sage/build/） -packages/sage-middleware/lib/ -packages/sage-middleware/bin/ -packages/sage-middleware/src/sage/middleware/components/*/lib/ -packages/sage-middleware/src/sage/middleware/components/*/bin/ -packages/sage-middleware/src/sage/middleware/components/*/install/ +# 历史 packages/ 树中的 C++ 编译输出目录（已废弃，现统一使用 .sage/build/） +packages/**/lib/ +packages/**/bin/ +packages/**/install/ -# 独立 PyPI 包（通过 pip install 使用，不在 Git 中跟踪） -packages/sage-middleware/src/sage/middleware/components/sage_mem/neuromem/ -packages/sage-middleware/src/sage/middleware/components/sage_refiner/sageRefiner/ +# 历史 packages/ 树中的外部能力目录（通过独立包使用，不在 Git 中跟踪） +packages/**/neuromem/ +packages/**/sageRefiner/ .github/chatmodes/ id_llm @@ -227,21 +222,15 @@ examples/medical_diagnosis/data/test_results/ /.vscode/ -# ============================================ -# Workspace 相关目录（独立仓库，不在主仓库中跟踪） -# ============================================ -# docs-public 是旧的 submodule 名称，现在是独立的 SAGE-Pub 仓库 -docs-public/ - # ============================================ # 一些测试文件 # ============================================ !examples/memory/data/neuromem/vdb_collection/RAGMemoryCollection/indexes/test_index/faiss.index -packages/sage-tools/src/sage/tools/studio/node_modules/* -packages/sage-tools/src/sage/tools/studio/.angular/* +packages/**/node_modules/* +packages/**/.angular/* **/node_modules -packages/sage-libs/src/sage/libs/applications/medical_diagnosis/data/* +packages/**/applications/medical_diagnosis/data/* # ============================================ # SAGE Apps 数据文件 @@ -257,11 +246,9 @@ packages/sage-apps/src/sage/apps/**/data/**/*.png packages/sage-apps/src/sage/apps/**/data/**/*.json !packages/sage-apps/src/sage/apps/**/data/**/README* !packages/sage-apps/src/sage/apps/**/data/**/config* -packages/sage-middleware/_deps/* -packages/sage-middleware/.cmake* +packages/**/_deps/* +packages/**/.cmake* coverage.json -packages/sage-common/src/sage/common/components/sage_vllm/ -docs/* # ============================================ # 敏感信息 - 已迁移到私有仓库 diff --git a/.pyrightconfig.json b/.pyrightconfig.json index 342c39f77d..e95b1355e9 100644 --- a/.pyrightconfig.json +++ b/.pyrightconfig.json @@ -1,13 +1,6 @@ { "include": [ - "packages/sage-kernel/src", - "packages/sage-common/src", - "packages/sage-platform/src", - "packages/sage-apps/src", - "packages/sage-middleware/src", - "packages/sage-cli/src", - "packages/sage-studio/src", - "packages/sage-tools/src" + "src" ], "exclude": [ "**/.sage", @@ -18,18 +11,8 @@ "**/.pytest_cache", "**/dist", "**/*.egg-info", - "packages/sage-libs/src/sage/libs/libamm/**", - "packages/sage-common/src/sage/common/components/sage_llm/**", "**/tests/**", - "**/examples/**", - "**/scripts/**", - "**/vendors/**", - "**/vendor/**", - "**/thirdparty/**", - "packages/sage-cli/src/sage/cli/core/config_refactored.py", - "packages/sage-cli/src/sage/cli/core/refactor_example.py", - "packages/sage-studio/src/sage/studio/**", - "packages/sage-tools/src/sage/tools/dev/issues/**" + "**/examples/**" ], "ignore": [ ], diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000000..6318a3d123 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,38 @@ +# CHANGELOG + +All notable changes to this repository are documented in this file. + +## PyPI Verified Releases (`isage`) + +Source: `https://pypi.org/pypi/isage/json` (checked on 2026-02-14, UTC). + +- `0.2.4.13` — 2026-02-13T03:42:08Z +- `0.2.4.12` — 2026-02-12T14:29:53Z +- `0.2.3.3` — 2026-01-13T17:42:33Z +- `0.2.3.2` — 2026-01-07T17:00:03Z +- `0.2.3.1` — 2026-01-05T16:47:49Z + +## [Unreleased] + +### Changed + +- Promoted the main repository to the `0.3` product line after the stream/runtime/serving consolidation. +- Reworked the owned `sage` CLI surface around the in-tree core boundary: `version`, `status`, `doctor`, `verify`, `runtime nodes`, `serve gateway`, `chat`, and `index ingest`. +- Switched `sage chat` to a `sagellm`-first integration model: direct `sagellm` CLI or external gateway, with no in-repo mock fallback. +- Updated install/help/verification flows to match the current command surface and the external `sagellm` engine contract. +- Updated root and cross-repo high-signal docs to remove retired commands such as `sage llm`, `sage extensions`, `sage cluster`, and `sage jobmanager`. +- Updated documentation references from `dev-notes` paths to stable repository-level references. +- Consolidated markdown retention policy to aggressively trim non-critical docs while preserving root entry docs (`README.md`, `CONTRIBUTING.md`, `DEVELOPER.md`) and Copilot/agent instruction metadata. +- Preserved critical operational constraints in changelog: `sageFlownet` replaces Ray for runtime direction, and new work should avoid adding Ray-oriented dependencies. +- Preserved critical engineering policy in changelog: dependency changes must be declared in `pyproject.toml` (no ad-hoc manual install workflow as a source of truth). +- Consolidated guidance from removed governance/docs markdown into changelog-level policy summaries: + - Cross-package governance baselines remain: layer-boundary compliance, fail-fast behavior, and quality/test gate expectations. + - Runtime API layering intent remains: facade APIs for default users and environment APIs for advanced control, with contract-level semantic consistency. + - Installation/operations note remains: long-install progress and install optimization guidance are now treated as implementation details rather than standalone markdown docs. +- Markdown inventory in tracked files was reduced to a minimal set (primarily `README*.md` + `CHANGELOG*.md` + root contribution docs + agent/copilot instruction docs). + +### Removed + +- Removed selected Copilot-related markdown documents and obsolete dev-notes references from documentation. +- Removed selected markdown in package docs/examples and tools docs to reduce documentation footprint. +- Removed markdown-heavy governance and template docs across packages (CLI/Common/Kernel/Libs/Middleware/Platform/Tools/meta-package), plus issue/PR markdown templates and selected install-fix notes. diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 47ec9ac3a5..16a1c4d5c5 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,9 +1,9 @@  -> 本地代码质量/测试请使用 `sage-dev quality` 或 `sage-dev test`，CI/CD 由 GitHub Workflows 自动完成。 - # SAGE 贡献指南 +> 本地代码质量/测试请使用 `sage-dev quality` 或 `sage-dev test`，CI/CD 由 GitHub Workflows 自动完成。 + > 本文档帮助你高效、规范地向 SAGE 贡献代码与文档。请在提交 Pull Request 前完整阅读。若英文协作者需要，可参考文末的 English Quick Guide。 ## 📚 开发者资源 / Developer Resources @@ -16,7 +16,7 @@ - **[.pre-commit-config.yaml](.pre-commit-config.yaml)** - Pre-commit 钩子配置（链接到 `tools/pre-commit-config.yaml`） - **[docs/images/architecture.svg](docs/images/architecture.svg)** - 系统架构图 -- **[docs-public/docs_src/dev-notes/](docs-public/docs_src/dev-notes/)** - 开发笔记和修复总结 +- **[docs/](docs/)** - 元仓库架构与治理文档 **快速开始开发**: @@ -61,11 +61,19 @@ git pull --ff-only origin main-dev # 安装开发环境 (默认 dev 模式 + conda) ./quickstart.sh --dev --yes -# 或核心安装（仅核心包） -./quickstart.sh --core --yes +# 标准模式安装 +./quickstart.sh --standard --yes +``` -# 标准模式 + 安装 VLLM 支持 -./quickstart.sh --standard --vllm --yes +安装完成后，优先使用当前主仓维护的 `sage` 命令表面做最小验证： + +```bash +sage verify +sage status +sage runtime nodes +sage serve gateway --json +sage chat --ask "Hello, SAGE!" +sage index ingest --source ./docs --index local-docs ``` ### 第二步：创建功能分支（勿在 main-dev 直接开发） @@ -77,7 +85,7 @@ git pull --ff-only origin main-dev # 示例 git checkout -b fix/ci-cache-permissions -git checkout -b feat/vllm-integration +git checkout -b feat/sagellm-integration git checkout -b refactor/jobmanager-architecture ``` @@ -115,7 +123,7 @@ bash -n path/to/script.sh # 可选：若已安装 black / mypy（dev 模式会安装） black --check . -mypy packages/sage-kernel || true +mypy src || true ``` ### 第五步：提交代码（使用规范化提交信息） @@ -145,7 +153,7 @@ git push -u origin PR 描述建议模板： -``` +```md ### 变更类型 feat | fix | refactor | docs | test | perf | ci | chore | build | deps | security @@ -179,7 +187,7 @@ feat | fix | refactor | docs | test | perf | ci | chore | build | deps | securit ### 分支命名规范 -``` +```text feat/ 新功能 fix/ 缺陷修复 refactor/ 重构 @@ -200,7 +208,7 @@ revert/ 回滚 ### 基本格式 -``` +```text (scope): summary @@ -215,17 +223,17 @@ revert/ 回滚 范围(scope) 建议与实际包/模块对应： -``` -sage-common | sage-kernel | sage-libs | sage-middleware | sage-tools | quickstart | docs | tests | ci | infra +```text +foundation | runtime | stream | cli | tools | quickstart | docs | tests | ci | infra ``` -允许复合：`feat(sage-kernel,quickstart): ...` +允许复合：`feat(runtime,quickstart): ...` ### 提交信息示例 #### 修复问题 -``` +```text fix(ci): avoid apt permission error in GitHub Actions Cause: post-job cache save failed due to /var/cache/apt permissions @@ -236,16 +244,16 @@ Closes: #123 #### 新功能 -``` -feat(quickstart): add optional VLLM installation flag +```text +feat(quickstart): improve sagellm-first startup guidance -Add --vllm flag to quickstart; auto-verifies vllm after install. -Docs updated. +Use sagellm run/chat as default onboarding flow. +Keep serve mode as optional path. ``` #### 测试修复 -``` +```text fix(tests): stabilize example + issues integration tests Replace legacy shell script with python-based IssuesTestSuite. @@ -284,7 +292,7 @@ Reduce flakiness via timeout + category filtering. # 注意：quick_examples 标记可能在 sage-examples 仓库中 pytest --maxfail=1 --durations=10 black --check . && isort --check-only . || true - mypy packages/sage-kernel || true + mypy src || true ``` ## 代码与文档质量 @@ -350,11 +358,11 @@ pre-commit run --all-files ### ⚠️ PEP 420 Namespace Packages - CRITICAL -**SAGE 使用 PEP 420 原生命名空间包（Python 3.3+）** +#### SAGE 使用 PEP 420 原生命名空间包（Python 3.3+） **禁止操作**： -- ❌ **NEVER** 创建或提交 `packages/*/src/sage/__init__.py` +- ❌ **NEVER** 创建或提交 `src/sage/__init__.py` - ❌ **NEVER** 在 `sage/` 命名空间层添加任何代码 - ❌ **NEVER** 使用 `pkgutil.extend_path()` 或 `pkg_resources.declare_namespace()` @@ -377,17 +385,74 @@ pre-commit run --all-files # 自动检查（pre-commit hook） tools/scripts/validate_pep420_compliance.sh -# 集成测试 -python3 tools/scripts/verify_pep420_integration.py - # 手动验证 python3 -c "import sage; assert sage.__file__ is None" # 应该成功 ``` -**详细文档**：`docs-public/docs_src/dev-notes/cross-layer/pep420-namespace-migration.md` +**详细文档**：请参考 `CONTRIBUTING.md` 与 `DEVELOPER.md` 中的 PEP 420 约束说明。 **CI 检查**：所有 PR 会自动运行 PEP 420 合规性检查（`.github/workflows/ci-pep420-compliance.yml`） +### 🚨 Flownet 跨仓库迁移：Move-Then-Delete 规则 + +**SAGE 与 sageFlownet 之间的代码迁移必须遵循 "先移动、立即删除" 原则。** + +迁移边界权威文档： `sage-docs/docs_src/concepts/architecture/design-decisions/flownet-migration-boundary.md` +（sage-docs 仓库） + +#### ❌ 禁止操作 + +```python +# ❌ 错误：在 Flownet 中保留 try-except fallback stub +try: + from sage.kernel.scheduler.schema import ResourceSpec +except ImportError: + class ResourceSpec: # 禁止：这是重复定义 + cpu: float = 0.0 + ... +``` + +```python +# ❌ 错误：在 Flownet 中重新定义已迁移到 SAGE 的类 +class PlacementSchema: # 禁止：SAGE L3 是单一来源 + resource: ResourceSpec = ... +``` + +#### ✅ 正确做法 + +```python +# ✅ 正确：直接从 SAGE 规范位置导入，快速失败 +from sage.kernel.scheduler.schema import ResourceSpec, PlacementSchema, PlacementStrategy +``` + +#### 已迁移的规范符号（不得在 Flownet 中重定义） + +| 符号 | SAGE 规范位置 | +| -------------------------------------------------------------------------------------------------------------------- | ---------------------------------- | +| `ExceptionAction`, `ExceptionContext`, `ExceptionDecision`, `ExceptionEvent`, `FlowException`, `FlowDefinitionError` | `sage.common.core.flow_exceptions` | +| `ResourceSpec`, `PlacementSchema`, `PlacementStrategy` | `sage.kernel.scheduler.schema` | +| `ContextSlot` | `sage.common.utils.context` | + +#### PR 提交要求 + +每个涉及跨仓库迁移的 PR 必须在描述中**明确声明**： + +1. **迁移了什么**：符号名称 + 源文件路径 +1. **保留了什么**：Flownet 保留的运行时实现（及原因） +1. **删除了什么**：旧代码 / stub 已从源仓库移除 + +#### CI 检查 + +```bash +# 本地运行 cross-repo dedup 检查 +python3 tools/scripts/check_cross_repo_dedup.py --verbose + +# 集成到 pre-commit（自动在提交时运行） +pre-commit run cross-repo-dedup-check --all-files +``` + +**违规后果**：CI 将失败，需要移除 Flownet 中的 stub 类或 try-except fallback，改为直接导入 SAGE 规范版本。 + - 添加适当的文档字符串 - 避免循环内重复 I/O；优先使用批量操作 - 日志使用 `logging` 而非 print（测试内部除外） @@ -407,7 +472,7 @@ python3 -c "import sage; assert sage.__file__ is None" # 应该成功 | 安装（交互式） | `./quickstart.sh` | 未传参进入菜单 | | 核心安装 | `./quickstart.sh --core --yes` | 仅核心包 | | 开发者安装 | `./quickstart.sh --dev --yes` | 安装开发依赖（可编辑模式） | -| 启用 VLLM | `./quickstart.sh --standard --vllm --yes` | 额外安装 vllm | +| 标准安装 | `./quickstart.sh --standard --yes` | 标准功能集合 | | 核心测试 | `sage-dev project test --coverage` | 运行核心测试集 | | 单个测试 | `pytest -k ` | 关键字过滤 | | 版本查看 | `python -c "import sage; print(sage.__version__)"` | 确认安装 | @@ -476,7 +541,7 @@ grep -i FAIL /tmp/test.log || true ### 7. 安装脚本卡住或没有输出 -``` +```bash bash -x ./quickstart.sh --dev --yes ``` @@ -494,8 +559,7 @@ bash -x ./quickstart.sh --dev --yes 使用 GitHub CLI（推荐）： ```bash -gh secret set OPENAI_API_KEY -b "your-openai-or-dashscope-key" -gh secret set HF_TOKEN -b "your-huggingface-token" +gh secret set SAGE_UNIFIED_API_KEY -b "token-abc123" ``` ### 完整配置（可选） @@ -510,8 +574,8 @@ gh secret set SILICONCLOUD_API_KEY -b "your-key" gh secret set JINA_API_KEY -b "your-key" gh secret set WEB_SEARCH_API_KEY -b "your-key" -# vLLM 本地服务（如果不需要认证可以留空） -gh secret set VLLM_API_KEY -b "token-abc123" +# 本地/自建推理服务认证（可选） +gh secret set SAGE_UNIFIED_API_KEY -b "token-abc123" # Hugging Face gh secret set HF_TOKEN -b "hf_xxx..." @@ -523,13 +587,13 @@ gh secret set HF_TOKEN -b "hf_xxx..." 1. 点击 `New repository secret` 1. 添加以下 secrets： -| Name | Value | Required | -| -------------------- | ------------------------------------- | -------- | -| `OPENAI_API_KEY` | 你的 OpenAI/DashScope API key | ✅ 是 | -| `HF_TOKEN` | 你的 Hugging Face token | ✅ 是 | -| `ALIBABA_API_KEY` | 阿里云 API key | ⭕ 可选 | -| `VLLM_API_KEY` | vLLM 服务 token（默认: token-abc123） | ⭕ 可选 | -| `WEB_SEARCH_API_KEY` | Web 搜索服务 key | ⭕ 可选 | +| Name | Value | Required | +| ---------------------- | ---------------------------------------- | -------- | +| `OPENAI_API_KEY` | 你的 OpenAI/DashScope API key | ⭕ 可选 | +| `HF_TOKEN` | 你的 Hugging Face token | ⭕ 可选 | +| `ALIBABA_API_KEY` | 阿里云 API key | ⭕ 可选 | +| `SAGE_UNIFIED_API_KEY` | 统一推理服务 token（默认: token-abc123） | ✅ 是 | +| `WEB_SEARCH_API_KEY` | Web 搜索服务 key | ⭕ 可选 | ### 验证配置 @@ -542,7 +606,7 @@ git push 查看 CI 日志，应该能看到： -``` +```text ✅ .env 文件创建完成 📋 验证 .env 文件内容（隐藏敏感信息）: OPENAI_API_KEY=*** @@ -555,10 +619,13 @@ HF_TOKEN=*** 外部贡献者的 PR 默认无法访问主仓库的 Secrets（这是 GitHub 的安全特性）。你可以： 1. **在自己的 fork 中配置 Secrets**（推荐用于测试） -1. **使用 mock 模式测试**（大多数测试支持）： + +1. **使用本地 sagellm 或自托管 OpenAI-compatible endpoint 测试**： + ```bash SAGE_TEST_MODE=true pytest ``` + 1. **等待维护者审核后触发 CI**（维护者可手动触发带 Secrets 的 CI） ### ⚠️ 安全注意事项 @@ -572,7 +639,7 @@ HF_TOKEN=*** 若发现安全问题（例如：任意代码执行 / 信息泄露 / 供应链风险），请不要直接公开 Issue，可通过以下方式私下披露： -- 邮件：security@intellistream.cn （示例；若需调整请维护者更新） +- 邮件：[security@intellistream.cn](mailto:security@intellistream.cn) （示例；若需调整请维护者更新） - 标题建议：`[SECURITY] <简要描述>` 请包含：影响版本、复现步骤、预期 vs 实际、安全影响评估。我们将在确认后尽快回应并在修复后发布公告。 @@ -592,7 +659,7 @@ HF_TOKEN=*** 2. Create branch: git checkout -b feat/ 3. Keep updated: git fetch && git rebase origin/main-dev 4. Test: sage-dev project test --coverage && sage-dev quality -5. Commit: feat(sage-kernel): add xyz +5. Commit: feat(runtime): add xyz 6. Push & PR: include background / solution / tests / impact ``` diff --git a/DEVELOPER.md b/DEVELOPER.md index e905b60df2..ca415bcca7 100644 --- a/DEVELOPER.md +++ b/DEVELOPER.md @@ -3,31 +3,23 @@ Welcome to the sage-development guide! This document will help you get started with contributing to SAGE. -## ⚠️ 重要：安装一致性 +## ⚠️ Installation Note -**在开始之前，请务必阅读 [安装一致性指南](docs-public/docs_src/dev-notes/l2-platform/INSTALLATION_CONSISTENCY.md)** - -为了避免 "CI/CD 通过但本地失败" 的问题，所有开发者**必须**使用 `quickstart.sh` 进行安装。不要手动运行 `pip install` 命令。 - -```bash -# ✅ 正确的安装方式 -./quickstart.sh --dev --yes - -# ❌ 不要使用 -pip install isage -pip install -e . -``` - -详细说明请参阅：[docs-public/docs_src/dev-notes/l2-platform/INSTALLATION_CONSISTENCY.md](docs-public/docs_src/dev-notes/l2-platform/INSTALLATION_CONSISTENCY.md) +Use `./quickstart.sh` for installation to ensure consistency across all environments. ______________________________________________________________________ ## Table of Contents - [Development Setup](#development-setup) - - [Prerequisites](#prerequisites) - - [Initial Setup](#initial-setup) - - [Submodule Management](#submodule-management) +- [Prerequisites](#prerequisites) +- [Initial Setup](#initial-setup) +- [Dependency Management](#dependency-management) +- [Layer Ownership Matrix](#layer-ownership-matrix-wave-a-baseline) +- [Core Dependencies Architecture](#core-dependencies-architecture) +- [Per-Layer Dependencies](#per-layer-dependencies) +- [Capability Packages](#capability-packages) +- [Installation Examples](#installation-examples) - [Development Workflow](#development-workflow) - [Code Quality](#code-quality) - [Testing](#testing) @@ -63,131 +55,263 @@ git checkout main-dev - ✅ 安装所有开发依赖（pytest, pre-commit, 代码检查工具等） - ✅ 配置 Git hooks（自动代码质量检查） - ✅ 安装 sage-dev 工具（用于维护和测试） +- ✅ 尽量将同级工作区本地子仓库安装为 editable（`-e`，若仓库存在） + +**`--standard` 模式会自动：** + +- ✅ 安装本地根目录 `isage` meta 包 +- ✅ 子包依赖按版本约束从 PyPI 解析（稳定/发布导向） -> 💡 **注意**: 文档已迁移到独立的 [SAGE-Pub](https://github.com/intellistream/SAGE-Pub) 仓库。 +### Current `sage` CLI Surface -> 💡 **不确定该选哪种模式？** 请参考 -> [README.md 中的安装模式决策树](./README.md#-%E5%BA%94%E8%AF%A5%E9%80%89%E6%8B%A9%E5%93%AA%E7%A7%8D%E5%AE%89%E8%A3%85%E6%A8%A1%E5%BC%8F) -> 了解 core/standard/full/dev 的区别。 +主仓当前维护的 `sage` 命令表面刻意保持精简，仅覆盖核心产品边界： + +- `sage version` +- `sage status` +- `sage doctor` +- `sage verify` +- `sage runtime nodes` +- `sage serve gateway --json` +- `sage serve gateway --probe --json` +- `sage chat` +- `sage chat --ask "Hello, SAGE!"` +- `sage index ingest --source ./docs --index local-docs` + +> 💡 **注意**: 用户文档已迁移到独立的 [sage-docs](https://github.com/intellistream/sage-docs) 仓库。 ### Initial Setup -1. **Clone the repository** +1. Clone and switch to development branch ```bash - git clone https://github.com/intellistream/SAGE.git - cd SAGE + git clone https://github.com/intellistream/SAGE.git && cd SAGE + git checkout main-dev ``` -1. **Switch to development branch** +1. Install development environment ```bash - git checkout main-dev + ./quickstart.sh --dev --yes ``` -1. **(Recommended) Setup workspace dependencies** +> 💡 For multi-folder VS Code editing, clone the `sage-docs` repository in the parent directory. + +```bash +pre-commit install + +# Install development tools +pip install black isort ruff mypy pytest pytest-cov +``` + +______________________________________________________________________ - If you plan to use the `SAGE.code-workspace` for multi-folder editing in VS Code: +## Dependency Management - **Option 1: During installation (recommended)** +### Layer Ownership Matrix (Wave A Baseline) - ```bash - # Automatically setup workspace during installation - ./quickstart.sh --dev --yes --workspace - ``` +For cross-repo boundary refactor reviews, use the canonical ownership matrix: - **Option 2: After installation** +- [sage-docs architecture/layer-ownership](https://intellistream.github.io/sage-docs/architecture/layer-ownership/) - ```bash - # Standalone script - ./tools/scripts/setup_workspace_deps.sh - ``` +This matrix defines current workspace L1-L4 ownership, forbidden dependency direction, violation +examples, and remediation priority for Phase 1, including independent sub-repo coordination and +`sagellm` capability boundaries. - This will: +### Core Dependencies Architecture - - ✅ Clone `SAGE-Pub` repository (documentation) +SAGE follows a **minimalist core dependency strategy** with a **modular feature model**: - **Or manually:** +- **Core Dependencies** (`dependencies`): Only packages necessary for base functionality +- **Dev Dependencies** (`dev` extra): Testing tools, linters, and development utilities +- **Feature Modules**: Functionality available through in-tree extras or independent PyPI packages - ```bash - # Clone SAGE-Pub (documentation repository) - cd .. - git clone git@github.com:intellistream/SAGE-Pub.git - cd SAGE-Pub && git checkout main-dev && cd ../SAGE - ``` +**Key Principle**: We maintain **minimal core** to reduce bloat and installation time. Specialized +functionality is available either through in-tree extras when it belongs to the SAGE product +surface, or through independent packages when ownership should stay external. - > 💡 **Note**: - > - > - `SAGE-Pub` is an independent repository for SAGE documentation - > - If you skip these, VS Code may show warnings which you can safely ignore +### Per-Layer Dependencies -1. **Recommended: Use quickstart with dev mode** +Current workspace numbering is normalized to the actively maintained main repos: - ```bash - # This is the easiest way for contributors - ./quickstart.sh --dev --yes - ``` +#### L1. `sage.foundation` (Foundation) - **Or, if you prefer manual setup:** +**Core**: - a. **Install development dependencies** +- `pyyaml>=6.0` - Configuration files +- `psutil>=6.1.0` - System information +- `dill>=0.3.8` - Object serialization +- `numpy>=1.26.0,<2.3.0` - Numerical computation +- `pydantic>=2.10.0,<3.0.0` - Data validation +- `platformdirs>=4.0.0` - User paths - ```bash - pip install -e ".[dev]" - ``` +**Also owns now**: former platform abstractions and the shared interface surface that used to live +in historical split algorithm packages. - b. **Initialize the developer CLI** +#### L2. `sage.runtime` / `sage.stream` (Runtime / Scheduler) - ```bash - sage-dev --help # 验证 CLI 可用 - sage-dev maintain hooks install - sage-dev maintain doctor - ``` +**Core**: - These commands ensure Git hooks are installed and run the built-in maintenance doctor. +- `flutty>=0.1.0` - optional distributed runtime backend +- `fastapi>=0.115.0,<1.0.0` - Kernel HTTP service +- `grpcio>=1.74.0,<2.0.0` - RPC communication +- `msgpack>=1.1.0,<2.0.0` - Serialization +- `openai>=1.52.0` / `httpx>=0.28.0` - absorbed middleware runtime operators -1. **Verify the setup** +**Also owns now**: former runtime-bound adapter/operator responsibilities. - ```bash - sage-dev quality check --check-only --all-files +#### L3. `sage.cli` (Core user entrypoint) - # Check project health - ./tools/maintenance/sage-maintenance.sh doctor - ``` +**Core**: + +- `typer>=0.15.0` - CLI framework +- `rich>=13.0.0` - Pretty output +- `click>=8.0.0` - Command parsing +- `jinja2>=3.1.0` - Templates +- `isage-dev-tools>=0.1.0` - Dev utilities -### Repository Management +**Role**: top layer of the core workspace stack; applications should extend it rather than sit as +peers. -**SAGE-Pub Documentation**: The comprehensive documentation is maintained in a separate repository. -See [Setup workspace dependencies](#1-recommended-setup-workspace-dependencies) for cloning -instructions. +#### L4. Optional applications (benchmarks, docs-facing apps, experimental UIs) -**Git Workflow**: Use feature branches for development, submit PRs to `main-dev` branch. +**Typical deps**: -For more details, see [tools/maintenance/README.md](tools/maintenance/README.md). +- `isage>=...` - full framework integration +- `fastapi>=...` / frontend stacks - application API & UI +- app-specific packages such as `isagellm`, `isage-agentic`, `isage-neuromem` -> **💡 提示：** 使用 `./quickstart.sh --dev --yes` 会自动处理所有 submodule 相关操作，无需手动运行上述命令。 +**Note**: optional apps are above `sage-cli`, not the same layer, because they extend the core via +plugin, service, or API surfaces rather than defining the core product boundary. -### Alternative: Manual Setup +### Capability Packages -If you prefer manual setup: +Packages such as `isage-rag`, `isage-neuromem`, `isage-libs-intent`, and `isage-sias` remain +important capability dependencies, but they are no longer used as separate main-repo layer labels in +the workspace numbering. + +Thin-wrapper rule: do not introduce new external packages that merely re-export SAGE-owned runtime, +stream, or serving-boundary logic. If a package does not own substantial functionality, keep it +in-tree. + +Current application of this rule: `sage-edge` has been folded back into the main repo as +`sage.edge`; treat the former split repo as retired instead of retaining it as a separate Zoo +package. + +Immediate non-Zoo retirement candidates follow the same logic: historical split foundation, runtime, +and CLI repos are increasingly just release channels for product surfaces that already belong to the +main repo, and duplicate stream surfaces should not be expanded as a separate ownership line. + +### Consolidation Target (2026 direction) + +The product direction is now to converge the highest-value capabilities into the main `SAGE` +repository and reduce dependency on UI-first or thin-wrapper repos. + +Priority order: + +1. **Foundation in-tree**: keep centralized config, ports, user paths, model asset registry, + logging, and shared contracts close to the main repo. +1. **Stream/runtime in-tree**: keep `DataStream`, `LocalEnvironment`, `JobManager`, scheduling, and + service lifecycle as the execution core. +1. **Serving boundary in-tree, engine out-of-tree**: keep OpenAI-compatible access, health/status, + and integration contracts in SAGE, while leaving inference-engine internals in `isagellm`. +1. **Edge shell in-tree**: keep edge aggregation as part of the SAGE serving contract instead of a + long-lived thin wrapper repository. +1. **Capabilities as adapters**: RAG, memory, tool-use, evaluation, and benchmark modules should be + optional adapters unless they clearly strengthen the inference-service core. + +Distributed-runtime rule: `FluttyEnvironment` should remain a first-class **optional** public API +for cluster execution. The consolidation target is not “distributed-only SAGE”; it is “stream-first +SAGE” with a strong local default plus Flutty-backed scale-out when needed, and no new Ray-oriented +dependency path. + +Inference-engine rule: do not absorb `isagellm` internals into `SAGE`. `isagellm` is itself an +independent inference engine and should remain separately owned/released. SAGE may standardize the +integration boundary around it, but not re-home its backend/control-plane internals. + +Migration rule: do not add compatibility shims for old repo boundaries during consolidation; +instead, move ownership deliberately and update call sites directly. + +Retirement rule: do not delete historical split foundation/runtime repositories until both +conditions are true: (1) main-repo call sites and install/verification workflows no longer rely on +their implementation modules as hard dependencies, and (2) external example/tutorial/adapter repos +no longer need those repos as transitional compatibility owners. + +Zoo rule of thumb: if a repository is not an MCP-facing tool surface, not an independently valuable +engine/runtime, and not a clearly optional adapter with substantial owned logic, it should default +back into the main `SAGE` repo instead of living forever as a split package. + +Current status note: the main repo now owns the primary `sage.foundation`, `sage.stream`, +`sage.runtime`, `sage.serving`, and `sage.cli` product surfaces directly. The local runtime path and +scheduler / packet / job-manager primitives are now also owned in-tree, so the historical runtime +split package is no longer a direct root dependency of `isage`. Remaining kernel/common usage is now +primarily a compatibility and ecosystem-migration concern rather than a main-repo public-API +dependency. + +Interpreter note: in Python 3.13 environments, root installation currently skips automatic +`isagellm` resolution because upstream `isagellm-protocol` wheels are not yet available there. This +does not block stream/runtime development in the main repo. + +### Feature Modules + +When extras were removed, functionality migrated to independent packages: + +| Feature | Before | Now | +| ---------- | ----------------------- | ------------------ | +| Embedding | `commons[embedding]` | → `isage-neuromem` | +| Agentic | `libs[agentic]` | → `isage-agentic` | +| RAG | `libs[rag]` | → `isage-rag` | +| Evaluation | `libs[eval]` | → `isage-eval` | +| Vector DB | `middleware[vdb]` | → `isage-vdb` | +| Memory | `middleware[neuromem]` | → `isage-neuromem` | +| Streaming | `middleware[streaming]` | → `isage-flow` | + +### Installation Examples + +#### Minimal (core only) + +```bash +pip install isage +``` + +### Standard (recommended for most users) ```bash -# Install in development mode -pip install -e ".[dev]" +pip install isage # Meta package with all core layers +``` -# Install pre-commit hooks -pip install pre-commit -pre-commit install +Then add features as needed: -# Install development tools -pip install black isort ruff mypy pytest pytest-cov +```bash +pip install isage-agentic # For agents +pip install isage-rag # For RAG +pip install isage-vdb # For vector search +pip install isage-neuromem # For memory / retrieval persistence +pip install isage-libs-intent # For intent / orchestration adapters +``` + +#### Development (with all tools) + +```bash +cd /path/to/SAGE +./quickstart.sh --dev --yes # Installs core + dev tools + editable-first local deps +./quickstart.sh --standard --yes # Installs core + dependencies from PyPI (stable path) ``` +______________________________________________________________________ + ## Development Workflow ### Using the sage-dev CLI -The `sage-dev` CLI (provided by `packages/sage-tools`) offers the same development workflows: +The `sage-dev` CLI (provided by the independently released `isage-dev-tools` package) offers the +same development workflows: + +> **💡 Note**: Additional development utilities are available via `sage-dev-tools` (automatically +> installed in `--dev` mode): +> +> - Work report generation: `sage-dev-tools report --period weekly` +> - Cluster code sync: `sage-dev-tools maintenance sync-cluster` +> - See: [sage-dev-tools](https://github.com/intellistream/sage-dev-tools) ```bash # Format code / auto-fix quality issues @@ -305,12 +429,6 @@ To run pre-commit manually: pre-commit run --all-files ``` -To skip pre-commit hooks temporarily (not recommended): - -```bash -git commit --no-verify -``` - ## Code Quality ### Pre-commit Hooks Configuration @@ -444,7 +562,7 @@ Example: ```python import pytest -from sage.core.api.local_environment import LocalEnvironment +from sage.runtime import LocalEnvironment def test_local_environment_initialization_creates_instance(): @@ -500,20 +618,19 @@ sage-dev docs serve pass ``` -1. **User Guides**: Place in `docs-public/docs_src/` +1. **User Guides**: Place in `docs/` -1. **Dev Notes**: Use the template in `docs/dev-notes/TEMPLATE.md` +1. **Changelog**: 重要变更统一记录到 `CHANGELOG.md` -### Creating Dev Notes +### Updating Changelog When documenting fixes or features: ```bash -# Copy the template -cp docs/dev-notes/TEMPLATE.md docs/dev-notes//_.md +# Edit repo changelog directly +$EDITOR CHANGELOG.md -# Fill in the template -# See docs/dev-notes/QUICK_START.md for guidance +# Add key changes under [Unreleased] ``` ## Release Process @@ -629,7 +746,7 @@ We follow [Semantic Versioning](https://semver.org/): ## Getting Help -- **Documentation**: Check `docs-public/` +- **Documentation**: Check `docs/`, `README.md`, and `CONTRIBUTING.md` - **Examples**: See [sage-examples](https://github.com/intellistream/sage-examples) repository - **Issues**: Search existing issues or create new one - **Community**: Join our @@ -639,8 +756,7 @@ We follow [Semantic Versioning](https://semver.org/): ## Useful Resources - [Architecture Diagram](docs/images/architecture.svg) -- [Dev Notes Template](docs/dev-notes/TEMPLATE.md) -- [Dev Notes Quick Start](docs/dev-notes/QUICK_START.md) +- [Project Changelog](CHANGELOG.md) - [Keep a Changelog](https://keepachangelog.com/) - [Conventional Commits](https://www.conventionalcommits.org/) - [Semantic Versioning](https://semver.org/) diff --git a/Makefile b/Makefile index 04079d157f..c7fe5846a6 100644 --- a/Makefile +++ b/Makefile @@ -47,20 +47,12 @@ install: ./quickstart.sh install-dev: - @echo "🔧 开发模式安装所有子包（正确顺序）..." - @echo " 1️⃣ 安装基础包（无依赖）..." - @pip install -e packages/sage-common -e packages/sage-platform --no-deps - @echo " 2️⃣ 安装核心库..." - @pip install -e packages/sage-libs -e packages/sage-kernel --no-deps - @echo " 3️⃣ 安装 middleware（C++ 扩展）..." - @pip install -e packages/sage-middleware --no-deps - @echo " 4️⃣ 安装应用层..." - @pip install -e packages/sage-cli -e packages/sage-studio -e packages/sage-tools --no-deps - @echo "✅ 所有包已安装！" + @echo "🔧 开发模式安装主仓（editable）..." + @python3 -m pip install -e '.[dev]' + @echo "✅ 主仓开发安装完成！" @echo "" - @echo "ℹ️ Note: sage-apps and examples moved to independent repos:" - @echo " - https://github.com/intellistream/sage-examples" - @echo " - Install sage-apps via: pip install isage-apps" + @echo "ℹ️ Main repo now ships foundation/stream/runtime/serving/cli in-tree." + @echo "ℹ️ Additional ecosystem repos remain independently released when needed." @echo "" @echo "📊 验证版本一致性..." @pip list | grep -E "^isage" @@ -72,9 +64,8 @@ install-deps: # C++ 扩展构建 build-extensions: @echo "🔨 构建 C++ 扩展..." - @echo "Building TSDB extension..." - @cd packages/sage-middleware/src/sage/middleware/components/sage_tsdb && ./build_tsdb.sh - @echo "✅ All C++ extensions built successfully!" + @echo "ℹ️ TSDB 已迁移为独立包（isage-tsdb），不再在 SAGE 内部构建。" + @echo "✅ 本仓库无需执行 TSDB 本地扩展构建。" # 代码质量 lint: @@ -119,12 +110,7 @@ clean: @rm -rf .sage/htmlcov/ .sage/cache/pytest/ .sage/cache/mypy/ .sage/cache/ruff/ @echo " • 清理旧的构建目录（已废弃）..." @rm -rf build/ htmlcov/ - @rm -rf packages/sage-middleware/build/ - @rm -rf packages/sage-middleware/lib/ - @rm -rf packages/sage-middleware/bin/ - @rm -rf packages/sage-middleware/sage_*_build/ - @rm -rf packages/sage-common/build/ - @find packages/sage-middleware/src/sage/middleware/components -type d \( -name "build" -o -name "lib" -o -name "bin" -o -name "install" \) -exec rm -rf {} + 2>/dev/null || true + @find packages -type d \( -name "lib" -o -name "bin" -o -name "install" \) -exec rm -rf {} + 2>/dev/null || true @echo "✅ 清理完成" clean-cache: @@ -158,22 +144,16 @@ version-bump: # 文档 docs: - @echo "📚 构建文档..." - cd docs-public && ./build.sh + @echo "📚 SAGE meta 仓库当前仅维护仓库内文档检查流程" + @bash tools/maintenance/check_docs.sh docs-serve: - @echo "🌐 启动文档服务器..." - cd docs-public && mkdocs serve + @echo "🌐 SAGE meta 仓库当前没有内置文档站点可启动" + @echo "请直接查看 docs/、README.md 和各独立仓库文档" docs-check: @echo "🔍 检查文档质量..." - @echo "1️⃣ Checking dev-notes..." - @python tools/devnotes_checker.py --all - @echo "" - @echo "2️⃣ Checking package READMEs..." - @python tools/package_readme_checker.py --all - @echo "" - @echo "✅ Documentation check complete" + @bash tools/maintenance/check_docs.sh docs-report: @echo "📊 生成文档质量报告..." diff --git a/README.md b/README.md index b1ab1e67b6..e7b0628c47 100644 --- a/README.md +++ b/README.md @@ -3,44 +3,11 @@ > A declarative, composable framework for building transparent LLM-powered systems through dataflow > abstractions. -> 📚 **Documentation Note**: Links referencing `docs-public/` point to the -> [SAGE-Pub](https://github.com/intellistream/SAGE-Pub) repository, which contains comprehensive -> documentation. Clone it separately if needed: -> `git clone https://github.com/intellistream/SAGE-Pub.git` - ## 🚀 Quick Start -### Try SAGE Studio - -**Option 1: HUST Campus Network Access** 🎓 - -Our team maintains a live deployment accessible within HUST campus network: - -``` -🌐 Contact team for access URL -``` - -**Requirements**: HUST campus network or VPN connection - -Experience SAGE's visual pipeline editor and AI-powered chat assistant with RAG capabilities! - -**Option 2: Local Installation** - -```bash -# 1. Install SAGE Studio (independent package) -pip install isage-studio - -# 2. Start SAGE Studio -sage-studio start -# Visit http://localhost:4200 -``` - -> 💡 **Note**: SAGE Studio is an independent package. For SAGE core framework installation, see -> [Installation](#installation) section below. - ______________________________________________________________________ -[![Build & Test](https://github.com/intellistream/SAGE/actions/workflows/build-test.yml/badge.svg?branch=main)](https://github.com/intellistream/SAGE/actions/workflows/build-test.yml) +[![Build & Test](https://github.com/intellistream/SAGE/actions/workflows/ci-build-test.yml/badge.svg?branch=main)](https://github.com/intellistream/SAGE/actions/workflows/ci-build-test.yml) [![codecov](https://codecov.io/gh/intellistream/SAGE/branch/main/graph/badge.svg)](https://codecov.io/gh/intellistream/SAGE) [![License](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE) [![Python](https://img.shields.io/badge/Python-3.10%2B-blue.svg)](https://python.org) @@ -48,35 +15,51 @@ ______________________________________________________________________ [![GitHub Issues](https://img.shields.io/github/issues/intellistream/SAGE)](https://github.com/intellistream/SAGE/issues) [![GitHub Stars](https://img.shields.io/github/stars/intellistream/SAGE?style=social)](https://github.com/intellistream/SAGE/stargazers) -[![WeChat Group](https://img.shields.io/badge/WeChat-%E5%8A%A0%E5%85%A5%E5%BE%AE%E4%BF%A1%E7%BE%A4-brightgreen?style=flat&logo=wechat)](./docs/COMMUNITY.md) -[![QQ Group](https://img.shields.io/badge/%E3%80%90IntelliStream%E8%AF%BE%E9%A2%98%E7%BB%84%E8%AE%A8%E8%AE%BAQQ%E7%BE%A4%E3%80%91-blue?style=flat&logo=tencentqq)](https://qm.qq.com/q/bcnuyQVcvm) -[![Slack](https://img.shields.io/badge/Slack-Join%20Slack-purple?style=flat&logo=slack)](https://join.slack.com/t/intellistream/shared_invite/zt-2qayp8bs7-v4F71ge0RkO_rn34hBDWQg) - **SAGE** is a high-performance streaming framework for building AI-powered data processing pipelines. Transform complex LLM reasoning workflows into transparent, scalable, and maintainable systems through declarative dataflow abstractions. -## Why Choose SAGE? +## 2026 Focus Reset + +SAGE is being refocused into a **stream-first inference service system** instead of a broad +collection of loosely coupled apps. -## Project Team & Collaboration +- **Keep the stream core**: `DataStream` + declarative pipeline composition remain the product + identity +- **Keep the execution core**: `LocalEnvironment`, `JobManager`, scheduling, service runtime +- **Keep the serving integration plane**: OpenAI-compatible gateway access, model lifecycle entry, + and control-plane integration contracts +- **Keep distributed execution optional**: `FluttyEnvironment` remains the Flutty-first optional + distributed runtime entry, instead of falling back to new Ray-based paths +- **Keep the operating substrate**: centralized ports, XDG user paths, model registry, logs, + health/status surfaces +- **De-emphasize apps**: UI-first repos are no longer the product center; optional apps should sit + outside the core or be retired -See `docs-public/docs_src/dev-notes/cross-layer/team-management.md` for the current team -coordination entrypoint (team management + incubation policy). +This direction also makes SAGE easier to position as a **SAGE Zoo member**: a reusable +stream-oriented runtime + serving component that other systems can call through stable APIs instead +of embedding internal implementation details. -**Production-Ready**: Built for enterprise-scale applications with distributed processing, fault -tolerance, and comprehensive monitoring out of the box. +Important boundary: `isagellm` remains an **independent inference engine**. SAGE integrates with it +as an external engine/service capability; SAGE should not absorb `isagellm` internals into the main +repository. -**Developer Experience**: Write complex AI pipelines in just a few lines of code with intuitive -declarative APIs that eliminate boilerplate. +Preferred in-tree surface during consolidation: -**Performance**: Optimized for high-throughput streaming workloads with intelligent memory -management and parallel execution capabilities. +- `from sage.stream import DataStream` +- `from sage.runtime import LocalEnvironment, FluttyEnvironment, JobManager` +- `from sage.foundation import SagePorts, SageUserPaths` +- `from sage.serving import SageServeConfig, build_sagellm_gateway_command, probe_gateway` -**Transparency**: Built-in observability and debugging tools provide complete visibility into -execution paths and performance characteristics. +## Key Features -**Flexible Deployment**: Full support for CPU-only compute nodes alongside GPU nodes, with -intelligent resource-aware scheduling for hybrid clusters. +- **Stream-First**: Dataflow is the primary abstraction, not an afterthought +- **Production-Ready**: Local-first execution, optional distributed processing, fault tolerance, + comprehensive monitoring +- **Developer Experience**: Complex AI pipelines in just a few lines of code +- **High Performance**: Optimized streaming with intelligent memory management +- **Observable**: Built-in visibility into execution and performance +- **Flexible**: CPU-only or GPU nodes with intelligent resource scheduling ## Quick Start @@ -97,20 +80,17 @@ def traditional_rag(query): SAGE transforms this into a **declarative, composable workflow**: ```python -from sage.kernel.api.local_environment import LocalEnvironment -from sage.libs.io.source import FileSource -from sage.middleware.operators.rag import DenseRetriever, QAPromptor +from sage.runtime import LocalEnvironment +from sage.libs.foundation.io.source import FileSource from sage.middleware.operators.llm import SageLLMGenerator # ✅ Recommended -from sage.libs.io.sink import TerminalSink +from sage.libs.foundation.io.sink import TerminalSink # Create execution environment env = LocalEnvironment("rag_pipeline") # Build declarative pipeline with sageLLM (recommended) ( - env.from_source(FileSource, {"file_path": "questions.txt"}) - .map(DenseRetriever, {"model": "sentence-transformers/all-MiniLM-L6-v2"}) - .map(QAPromptor, {"template": "Answer based on context: {context}\nQ: {query}\nA:"}) + env.from_source(FileSource, {"data_path": "questions.txt"}) .map(SageLLMGenerator, { "model_path": "Qwen/Qwen2.5-7B-Instruct", "backend_type": "auto", # auto/cuda/ascend/mock @@ -123,166 +103,234 @@ env.submit() ``` > 💡 **LLM Engine**: SAGE uses `sageLLM` as the default inference engine. For OpenAI-compatible APIs, -> use `OpenAIGenerator`. See -> [Migration Guide](./docs-public/docs_src/dev-notes/migration/VLLM_TO_SAGELLM_MIGRATION.md) if -> migrating from vLLM. +> use `OpenAIGenerator`. See [CHANGELOG](./CHANGELOG.md) for legacy migration notes. -**Try it yourself:** +### Current API quick reference + +- `sage.libs.foundation.io.source`: `FileSource`, `TextFileSource`, `CSVFileSource`, + `JSONFileSource` +- `sage.libs.foundation.io.sink`: `TerminalSink`, `FileSink` +- `sage.middleware.operators.rag`: `RAGDocument`, `RAGQuery`, `RAGResponse` +- `sage.middleware.operators.llm`: `SageLLMGenerator` + +### Try it yourself ```bash git clone https://github.com/intellistream/SAGE.git && cd SAGE git checkout main-dev ./quickstart.sh --dev --yes -# Examples are now in a separate repository -git clone https://github.com/intellistream/sage-examples.git -python sage-examples/tutorials/hello_world.py +# Tutorials are now in a separate repository +git clone https://github.com/intellistream/sage-tutorials.git +python sage-tutorials/L1-common/hello_world.py ``` -**For CPU-only deployment:** +### For CPU-only verification ```bash -# Start JobManager for distributed task execution -sage jobmanager start +# Verify the in-tree core surface +sage verify -# Run CPU node demo (no GPU required) -git clone https://github.com/intellistream/sage-examples.git -python sage-examples/tutorials/L3-kernel/cpu_node_demo.py -``` +# Run a real one-shot chat via sagellm (gateway or direct CLI) +sage chat --ask "Hello, SAGE!" -## Architecture Excellence +# Explore tutorials separately if needed +git clone https://github.com/intellistream/sage-tutorials.git +python sage-tutorials/L1-common/hello_world.py +``` -### System Architecture +## Architecture -SAGE is built on a **layered modular architecture** with 8 core packages organized across 5 layers: +Current baseline is a **4-tier workspace architecture (L1-L4)**: +```text +L4: application repos (optional) # App / UI / benchmark +L3: sage.cli # CLI entrypoint (in-tree) +L2: sage.runtime + sage.stream # Runtime / scheduler (in-tree) +L1: sage.foundation # Foundation (in-tree) ``` -L5: sage-cli, sage-tools # Interface Layer (CLI & Dev Tools) -L4: sage-middleware # Middleware Layer (Operators, C++ Extensions) -L3: sage-kernel, sage-libs # Core Layer (Engine & Algorithm Library) -L2: sage-platform # Platform Layer (Queue, Storage) -L1: sage-common # Foundation Layer (Config, Types, Utilities) -``` -**Independent Repositories** (not in SAGE core): +Notes: + +- Historical split packages may still exist as transitional published compatibility channels. +- The main repository now owns the preferred product surface directly: `sage.foundation` + + `sage.stream` + `sage.runtime` + `sage.serving` + `sage.cli`. +- Historical split foundation/runtime/CLI repos are therefore no longer the desired long-term + product boundary, even when some transitional imports still exist outside the main install + contract. + +Target product convergence is narrower than the historical workspace shape: + +```text +SAGE Inference Service System +L3 Interface : CLI + OpenAI-compatible service entry + external integration surface +L2 Runtime : LocalEnvironment + DataStream + JobManager + scheduler + execution services +Optional Dist. : FluttyEnvironment (Flutty-backed distributed execution) +L1 Foundation : config + ports + user paths + model registry + logging +Optional : RAG / memory / tool-use / benchmark adapters +``` -- **sage-benchmark**: https://github.com/intellistream/sage-benchmark (PyPI: `isage-benchmark`) -- **sage-examples**: https://github.com/intellistream/sage-examples (Tutorials & Applications) -- **sage-studio**: https://github.com/intellistream/sage-studio (PyPI: `isage-studio`) -- **sageLLM**: LLM inference engine (PyPI: `isagellm`) -- **SageEdge**: Edge aggregator for distributed deployment (PyPI: `isage-edge`) +In other words, SAGE is moving toward a smaller, sharper center: **stream + runtime + serving + +operations**, with distributed execution available as an optional scale-out mode. -**Optional Dependencies** (independent PyPI packages): +Repo-retirement gate: do not retire historical split repos solely based on packaging cleanup. The +main repo has now removed direct historical runtime split-package dependency pins and owns its local +runtime path in-tree, but ecosystem compatibility imports, external repos, and remaining +transitional release channels still need deliberate follow-up before those repos can be fully +retired. -*sage-middleware optional dependencies:* +See [SAGE Ecosystem](#sage-ecosystem) for all independent sub-repositories with CI status, PyPI +packages, and categorized listings. -| Package | PyPI | Category | Description | -| ------------ | ---------------- | ------------- | ------------------------------------------- | -| **SageVDB** | `isage-vdb` | `[vdb]` | High-performance C++ vector database | -| **NeuroMem** | `isage-neuromem` | `[neuromem]` | Brain-inspired memory system (VDB/KV/Graph) | -| **SageFlow** | `isage-flow` | `[streaming]` | Vector-native stream processing engine | -| **SageTSDB** | `isage-tsdb` | `[streaming]` | Time-series database with C++ core | +📖 **[Architecture Guide](https://intellistream.github.io/sage-docs/architecture/)** - Canonical +ownership boundaries and dependency rules for the meta repo -*sage-libs optional dependencies (L3 algorithm libraries):* +📌 +**[Layer Ownership Matrix v1 (Wave A)](https://intellistream.github.io/sage-docs/architecture/layer-ownership/)** +\- Canonical L1-L4 workspace ownership, independent sub-repo coordination boundary (including +`sagellm` capabilities), forbidden directions, and boundary refactor review checklist -| Package | PyPI | Category | Description | -| --------------- | --------------- | ---------- | ----------------------------------------------------------- | -| **SageANNS** | `isage-anns` | `[anns]` | Approximate nearest neighbor search algorithms | -| **SageAMMs** | `isage-amms` | `[amms]` | Approximate matrix multiplication | -| **SageRefiner** | `isage-refiner` | `[libs]`\* | Context compression for RAG (LongRefiner, REFORM, Provence) | +## Installation -> \* SageRefiner is an L3 algorithm library, also available via `isage-middleware[libs]` +### Quickstart (Recommended) ```bash -# Install with specific optional dependencies -pip install isage-middleware[vdb] # Vector database support -pip install isage-middleware[streaming] # Stream processing + time-series -pip install isage-libs[anns] # ANNS algorithms -pip install isage-libs[amms] # AMM algorithms +git clone https://github.com/intellistream/SAGE.git && cd SAGE +./quickstart.sh --dev --yes # 开发模式：尽量本地 editable +# 或 +./quickstart.sh --standard --yes # 标准模式：子包依赖默认从 PyPI 安装 ``` -**Key Architectural Principles:** +⚡ **Auto-Acceleration**: Network optimization is now **enabled by default**: + +- 🌐 Auto-detects network location (China mainland → mirror sources) +- 🚀 Parallel downloads (8 threads) + pre-compiled packages +- ⏱️ **3-5x faster** installation: 12-18 min (vs 35-45 min) +- 🔧 Disable: `./quickstart.sh --no-mirror --dev --yes` + +### Install Mode Semantics -- **Unidirectional Dependencies**: Clean layer-to-layer dependencies (no upward dependencies) -- **Separation of Concerns**: Each package has a clear, focused responsibility -- **Pluggable Components**: Modular design allows easy component replacement -- **Production Ready**: Built-in fault tolerance, monitoring, and distributed execution +- `standard`：本地安装仓库根目录下的 `isage` meta 包，子仓依赖按根 `pyproject.toml` 版本从 PyPI 解析。 +- `dev`：先完成 `standard` 安装，再尽量将同级工作区中的本地 SAGE 子仓库切换为 editable (`-e`)。 -📖 **[Complete Architecture Guide](./docs-public/docs_src/dev-notes/package-architecture.md)** - -Detailed package descriptions, dependency rules, and design principles +### PyPI Install -### Modular Design +```bash +pip install isage # Core framework +pip install isage[dev] # Development tools (includes isage-dev-tools, pre-commit, pytest, etc.) +``` -**8 Core Packages**, each with clear responsibilities: +**What's included in `pip install isage`** -- **sage** (meta): Meta-package that installs all SAGE components -- **sage-common** (L1): Foundation utilities, configuration, logging -- **sage-platform** (L2): Platform services - queue, storage abstractions -- **sage-kernel** (L3): Distributed execution engine and runtime -- **sage-libs** (L3): Algorithm library, RAG tools, Agent framework -- **sage-middleware** (L4): Domain operators and middleware components -- **sage-cli** (L5): Unified command-line interface (`sage` command) -- **sage-tools** (L5): Development tools and testing framework (`sage-dev` command) +`isage` now ships the main product surface directly from this repository: `sage.foundation` + +`sage.stream` + `sage.runtime` + `sage.serving` + `sage.cli`. The default local execution path no +longer requires a separate historical runtime split package as a direct dependency. `isagellm` +remains the external inference engine. -**Independent Repositories:** +On Python 3.13+, the root package currently skips automatic `isagellm` installation because the +required `isagellm-protocol` distribution is not yet published for that interpreter. Core SAGE +stream/runtime development still installs normally; engine integration can be enabled later on a +supported interpreter once upstream wheels are available. -- **sage-examples**: Tutorials, examples, and production applications - - Repository: [intellistream/sage-examples](https://github.com/intellistream/sage-examples) - - Includes: tutorials, RAG examples, application demos -- **sage-benchmark**: Evaluation framework (PyPI: `isage-benchmark`) - - Repository: [intellistream/sage-benchmark](https://github.com/intellistream/sage-benchmark) -- **sage-studio**: Visual workflow builder (PyPI: `isage-studio`) - - Repository: [intellistream/sage-studio](https://github.com/intellistream/sage-studio) -- **sageLLM**: LLM inference engine (PyPI: `isagellm`) +**Core + engine integration only** 🧩 -> 💡 **Note**: All PyPI packages use `isage-` prefix (e.g., `pip install isage-vdb`) because `sage` -> is already taken on PyPI. +For a standard `pip install isage`, the product center is intentionally narrow: -### Production Features +- **Foundation** → `isage`: in-tree config, ports, paths, contracts +- **Stream + Runtime** → `isage`: main public API and local runtime owned in-tree +- **Distributed scale-out** → `flutty`: optional backend used through `FluttyEnvironment` +- **CLI** → `isage`: in-tree `sage` command surface +- **Inference Engine** → `isagellm`: external engine; auto-installed on supported Python versions -- **Distributed Execution** with automatic load balancing -- **Fault Tolerance** and error recovery -- **Observability** with metrics and monitoring -- **Extensible Integration** for databases, queues, and AI services +Compatibility note: transitional imports such as `sage.common`, `sage.platform`, `sage.middleware`, +or `sage.kernel` may still appear in older repos or environments, but they are not part of the root +package's direct dependency contract anymore. -## Installation +Edge aggregation now also lives in-tree as `sage.edge`; install `isage[serving-edge]` or +`isage[full]` to use the `sage-edge` shell. -**Quickstart (Recommended)** +**Optional adapter packages** 🦁 -```bash -git clone https://github.com/intellistream/SAGE.git && cd SAGE -./quickstart.sh --dev --yes # Interactive mode: ./quickstart.sh -``` +These packages are no longer part of the default `isage` install. They remain independent optional +adapters and can be installed explicitly or via `isage[full]` when needed. -⚡ **Auto-Acceleration**: Network optimization is now **enabled by default**: +Policy note: optional adapters should justify their independence with real owned functionality. Thin +wrapper repos should be folded back into the main `SAGE` repository instead of expanding the default +dependency surface. -- 🌐 Auto-detects network location (China mainland → mirror sources) -- 🚀 Parallel downloads (8 threads) + pre-compiled packages -- ⏱️ **3-5x faster** installation: 12-18 min (vs 35-45 min) -- 🔧 Disable: `./quickstart.sh --no-mirror --dev --yes` +Current example: `sage.edge` has already been folded back into the main repo. The former `sage-edge` +split repo should be treated as retired rather than as an independent Zoo package. -**PyPI Install** +```bash +pip install 'isage[serving-edge]' # in-tree edge shell（sage.edge / sage-edge） +pip install 'isage[capability-adapters]' # intent / rag / neuromem adapters +pip install 'isage[capability-tooluse]' # SIAS tool-use adapter +pip install 'isage[full]' # all optional adapters + data package + +sage-edge --port 8899 # 挂载外部 sagellm gateway 的 edge shell +pip install isage-rag # RAG 管道（文档加载 / 分块 / 检索 / 重排） +pip install isage-neuromem # 记忆 / 检索持久化 +pip install isage-libs-intent # 意图识别（关键词 + LLM） +pip install isage-eval # 评估框架（指标 / LLM 评判） +pip install isage-finetune # LLM 微调 / Agent training（LoRA / SFT / RL / Reward Model） +pip install isage-agentic-tooluse # Agent 工具选择（Hybrid/DFS/Gorilla） +pip install 'isage-tools[mcp]' # 独立工具仓库；当前已注册到 sage-mcp +pip install 'isage-mcp[all]' # 聚合 MCP Server（当前默认聚合 isage-tools） +``` + +Example: install full core SAGE stack ```bash -pip install isage[standard] # Recommended -pip install isage[core] # Minimal runtime -pip install isage[full] # Full features + Web UI -pip install isage[dev] # Development tools +pip install isage ``` -**Verification & Troubleshooting** +See [Dependency Management](./DEVELOPER.md#dependency-management) in DEVELOPER.md for detailed +guidance. + +### Verification & Troubleshooting ```bash sage doctor # Check installation +sage verify # Verify in-tree core surface +sage chat --help # Inspect chat entrypoints +sage index ingest --help # Inspect lightweight index entrypoints ./quickstart.sh --doctor # Diagnose issues ``` -📖 **Detailed guides**: [Installation Guide](docs/INSTALLATION_GUIDE.md) | -[Troubleshooting](docs/TROUBLESHOOTING.md) | [Validation](docs/INSTALLATION_VALIDATION.md) | +### CLI Command Reference + +The current main-repo `sage` command surface is intentionally small and grouped around the core +product boundary: + +| Command | Purpose | +| ------------------------------------------------------ | ------------------------------------------------------------------------------------- | +| `sage version` | Print installed SAGE version | +| `sage status` | Show local config/data/state paths and gateway summary | +| `sage doctor` | Run lightweight environment diagnostics | +| `sage verify` | Smoke-check the in-tree core surface | +| `sage runtime nodes` | List runtime-visible nodes | +| `sage serve gateway --json` | Print the external `sagellm` gateway launch contract | +| `sage serve gateway --probe --json` | Probe the configured gateway health endpoint | +| `sage chat` | Start chat via `sagellm` gateway, direct CLI, or configured OpenAI-compatible backend | +| `sage chat --ask "..."` | Run one-shot chat | +| `sage index ingest --source ./docs --index local-docs` | Record lightweight local index metadata | + +```bash +sage verify +sage runtime nodes +sage serve gateway --json +sage chat --ask "Hello, SAGE!" +sage index ingest --source ./docs --index local-docs +``` + +📖 **Detailed guides**: +[Installation Guide](https://intellistream.github.io/sage-docs/guides/installation/) | +[Troubleshooting](https://intellistream.github.io/sage-docs/guides/troubleshooting/) | +[Validation](https://intellistream.github.io/sage-docs/guides/validation/) | [Optimization Tips](tools/install/docs/INSTALLATION_OPTIMIZATION.md) ⚠️ **Known Issues**: If you encounter transformers version conflicts when installing multiple SAGE -packages, see [Dependency Fix Guide](docs-public/docs_src/getting-started/DEPENDENCY_FIX.md) +packages, prefer checking [DEVELOPER.md](./DEVELOPER.md) and the package-specific READMEs first. ## Environment Configuration @@ -293,61 +341,46 @@ cp .env.template .env # Copy template 📖 **API key setup**: See [.env.template](./.env.template) for all available options -## Use Cases - -**RAG Applications**: Build production-ready retrieval-augmented generation systems with multi-modal -support and advanced reasoning capabilities. - -**Real-Time Analytics**: Process streaming data with AI-powered insights, anomaly detection, and -automated decision making. - -**Data Pipeline Orchestration**: Coordinate complex ETL workflows that seamlessly integrate AI -components with traditional data processing. - -**Multi-Modal Processing**: Handle text, images, audio, and structured data in unified pipelines -with consistent APIs. **🆕 Advanced multimodal fusion** enables intelligent combination of different -data modalities for enhanced AI understanding and generation. - -**Distributed AI Inference**: Scale AI model serving across multiple nodes with automatic load -balancing and fault tolerance. - ## 📚 Tutorials -Complete tutorials covering all layers of SAGE (L1-L5): +Complete tutorials covering the current workspace tiers of SAGE (L1-L4) plus historical capability +topics: ```bash -# Clone repository -git clone https://github.com/intellistream/SAGE.git -cd SAGE +# Clone tutorials repository +git clone https://github.com/intellistream/sage-tutorials.git +cd sage-tutorials # Start learning (30 seconds) -python tutorials/hello_world.py +python L1-common/hello_world.py # Follow the quick start guide -cat tutorials/QUICK_START.md +cat QUICK_START.md ``` **Tutorial Structure**: -- `tutorials/L1-common/` - Foundation layer (config, logging, unified client) -- `tutorials/L2-platform/` - Platform services (scheduler, storage) -- `tutorials/L3-kernel/` - Execution engine (batch, stream, operators) -- `tutorials/L3-libs/` - RAG, Agents, Algorithms -- `tutorials/L4-middleware/` - Domain operators (vector DB, time-series) -- `tutorials/L5-cli/` - CLI and development tools +- `sage-tutorials/L1-common/` - Foundation layer (config, logging, unified client) +- `sage-tutorials/L2-platform/` - Platform services (scheduler, storage) +- `sage-tutorials/L3-kernel/` - Execution engine (batch, stream, operators) +- `sage-tutorials/L3-libs/` - RAG, Agents, Algorithms +- `sage-tutorials/L4-middleware/` - Domain operators (vector DB, time-series) +- `sage-tutorials/L5-apps/` - Applications and integration demos -See `tutorials/README.md` for complete learning paths. +See `sage-tutorials/README.md` for complete learning paths. ## Documentation & Resources - **Documentation**: - [https://intellistream.github.io/SAGE-Pub/](https://intellistream.github.io/SAGE-Pub/) + [https://intellistream.github.io/sage-docs/](https://intellistream.github.io/sage-docs/) - **Examples & Applications**: [intellistream/sage-examples](https://github.com/intellistream/sage-examples) - - Tutorials, RAG examples, and production applications + - RAG examples and production applications - Will be published as `isage-examples` on PyPI +- **Tutorials**: [intellistream/sage-tutorials](https://github.com/intellistream/sage-tutorials) + - Layered tutorials from L1 to L5, quick-start learning paths - **Architecture**: - [docs-public/docs_src/dev-notes/package-architecture.md](./docs-public/docs_src/dev-notes/package-architecture.md) + [sage-docs architecture guide](https://intellistream.github.io/sage-docs/architecture/) ## Contributing @@ -362,7 +395,8 @@ git commit -m "feat(kernel): add new feature" git push -u origin feature/my-feature ``` -**Resources**: [Quick Reference](./docs/QUICK_REFERENCE.md) | +**Resources**: +[Quick Reference](https://intellistream.github.io/sage-docs/reference/quick-reference/) | [GitHub Issues](https://github.com/intellistream/SAGE/issues) | [Discussions](https://github.com/intellistream/SAGE/discussions) @@ -385,18 +419,186 @@ sage-dev test # Run tests make docs # Build documentation ``` -📖 **Complete reference**: [docs/dev-notes/DEV_COMMANDS.md](./docs/dev-notes/DEV_COMMANDS.md) +📖 **Complete reference**: [DEVELOPER.md](./DEVELOPER.md) ## SAGE Ecosystem -SAGE has a growing ecosystem of independent projects: +📦 **[sage-docs package guide](https://intellistream.github.io/sage-docs/guides/packages/)** — +独立能力包与安装索引 -- **[SAGE Studio](https://github.com/intellistream/sage-studio)** - Visual workflow builder and LLM - playground for creating AI pipelines with drag-and-drop interface -- **[SAGE Benchmark](https://github.com/intellistream/sage-benchmark)** - Comprehensive evaluation - framework for RAG, agents, control plane, and memory systems +### 🧠 SAGE — Streaming AI Framework + +[![CI](https://github.com/intellistream/SAGE/actions/workflows/ci-build-test.yml/badge.svg?branch=main)](https://github.com/intellistream/SAGE/actions/workflows/ci-build-test.yml) +[![PyPI](https://badge.fury.io/py/isage.svg)](https://pypi.org/project/isage/) +[![Python](https://img.shields.io/badge/Python-3.10%2B-blue.svg)](https://python.org) +[![Stars](https://img.shields.io/github/stars/intellistream/SAGE?style=social)](https://github.com/intellistream/SAGE/stargazers) + +**SAGE** is now centered on an in-tree core product surface rather than on a broad split-repo zoo. + +### Core Product Surface (owned in-tree) + +- **`sage.foundation`** — config, ports, paths, logging, and shared contracts +- **`sage.stream`** — `DataStream`, transformations, operators, and flow composition +- **`sage.runtime`** — environments, scheduling, job management, and execution lifecycle +- **`sage.serving` / `sage.edge`** — serving integration boundary and edge aggregation shell +- **`sage.cli`** — main `sage` command surface + +Legacy split repos and duplicate stream surfaces should be treated as consolidation/retirement +targets rather than as Zoo members. + +Independent sub-repositories that remain justified are organized by category: + +### Application & UI + +- **[sage-examples](https://github.com/intellistream/sage-examples)** — Tutorials and application + examples + [![CI](https://github.com/intellistream/sage-examples/actions/workflows/tests.yml/badge.svg?branch=main)](https://github.com/intellistream/sage-examples/actions/workflows/tests.yml) + [![Stars](https://img.shields.io/github/stars/intellistream/sage-examples?style=social)](https://github.com/intellistream/sage-examples/stargazers) + +### Algorithms & Libraries + +- **[sage-agentic](https://github.com/intellistream/sage-agentic)** — ReAct, PlanExecute agents and + agentic workflows + [![CI](https://github.com/intellistream/sage-agentic/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/intellistream/sage-agentic/actions/workflows/ci.yml) + [![PyPI](https://badge.fury.io/py/isage-agentic.svg)](https://pypi.org/project/isage-agentic/) + [![Stars](https://img.shields.io/github/stars/intellistream/sage-agentic?style=social)](https://github.com/intellistream/sage-agentic/stargazers) +- **[sage-rag](https://github.com/intellistream/sage-rag)** — Retrieval-augmented generation + components + [![CI](https://github.com/intellistream/sage-rag/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/intellistream/sage-rag/actions/workflows/ci.yml) + [![PyPI](https://badge.fury.io/py/isage-rag.svg)](https://pypi.org/project/isage-rag/) + [![Stars](https://img.shields.io/github/stars/intellistream/sage-rag?style=social)](https://github.com/intellistream/sage-rag/stargazers) +- **[sageVDB](https://github.com/intellistream/sageVDB)** — High-performance vector database + (FAISS-compatible API) + [![CI](https://github.com/intellistream/sageVDB/actions/workflows/ci-tests.yml/badge.svg?branch=main)](https://github.com/intellistream/sageVDB/actions/workflows/ci-tests.yml) + [![PyPI](https://badge.fury.io/py/isage-vdb.svg)](https://pypi.org/project/isage-vdb/) + [![Stars](https://img.shields.io/github/stars/intellistream/sageVDB?style=social)](https://github.com/intellistream/sageVDB/stargazers) +- **[sageRefiner](https://github.com/intellistream/sageRefiner)** — Query and response refinement + algorithms + [![CI](https://github.com/intellistream/sageRefiner/actions/workflows/ci-tests.yml/badge.svg?branch=main)](https://github.com/intellistream/sageRefiner/actions/workflows/ci-tests.yml) + [![PyPI](https://badge.fury.io/py/isage-refiner.svg)](https://pypi.org/project/isage-refiner/) + [![Stars](https://img.shields.io/github/stars/intellistream/sageRefiner?style=social)](https://github.com/intellistream/sageRefiner/stargazers) +- **[sage-amms](https://github.com/intellistream/sage-amms)** — Approximate matrix multiplication + service + [![CI](https://github.com/intellistream/sage-amms/actions/workflows/build.yml/badge.svg?branch=main)](https://github.com/intellistream/sage-amms/actions/workflows/build.yml) + [![PyPI](https://badge.fury.io/py/isage-amms.svg)](https://pypi.org/project/isage-amms/) + [![Stars](https://img.shields.io/github/stars/intellistream/sage-amms?style=social)](https://github.com/intellistream/sage-amms/stargazers) +- **[flutty](https://github.com/intellistream/flutty)** — Optional distributed execution backend for + SAGE stream runtime + [![PyPI](https://badge.fury.io/py/flutty.svg)](https://pypi.org/project/flutty/) + +### Data & Benchmarks + +- **[sageData](https://github.com/intellistream/sageData)** — Unified dataset management for SAGE + subsystems + [![CI](https://github.com/intellistream/sageData/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/intellistream/sageData/actions/workflows/ci.yml) + [![PyPI](https://badge.fury.io/py/isage-data.svg)](https://pypi.org/project/isage-data/) + [![Stars](https://img.shields.io/github/stars/intellistream/sageData?style=social)](https://github.com/intellistream/sageData/stargazers) +- **[sage-benchmark](https://github.com/intellistream/sage-benchmark)** — Comprehensive evaluation + framework for RAG, agents, memory, and control plane + [![CI](https://github.com/intellistream/sage-benchmark/actions/workflows/ci-benchmarks.yml/badge.svg?branch=main)](https://github.com/intellistream/sage-benchmark/actions/workflows/ci-benchmarks.yml) + [![PyPI](https://badge.fury.io/py/isage-benchmark.svg)](https://pypi.org/project/isage-benchmark/) + [![Stars](https://img.shields.io/github/stars/intellistream/sage-benchmark?style=social)](https://github.com/intellistream/sage-benchmark/stargazers) +- **[sage-eval](https://github.com/intellistream/sage-eval)** — Evaluation metrics, profilers, and + LLM judges + [![CI](https://github.com/intellistream/sage-eval/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/intellistream/sage-eval/actions/workflows/ci.yml) + [![PyPI](https://badge.fury.io/py/isage-eval.svg)](https://pypi.org/project/isage-eval/) + [![Stars](https://img.shields.io/github/stars/intellistream/sage-eval?style=social)](https://github.com/intellistream/sage-eval/stargazers) + +### Model Optimization & Safety + +- **[sage-finetune](https://github.com/intellistream/sage-finetune)** — Model fine-tuning, agent + training, and adaptation + [![CI](https://github.com/intellistream/sage-finetune/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/intellistream/sage-finetune/actions/workflows/ci.yml) + [![PyPI](https://badge.fury.io/py/isage-finetune.svg)](https://pypi.org/project/isage-finetune/) + [![Stars](https://img.shields.io/github/stars/intellistream/sage-finetune?style=social)](https://github.com/intellistream/sage-finetune/stargazers) +- **[sage-privacy](https://github.com/intellistream/sage-privacy)** — Differential privacy and PII + handling + [![CI](https://github.com/intellistream/sage-privacy/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/intellistream/sage-privacy/actions/workflows/ci.yml) + [![PyPI](https://badge.fury.io/py/isage-privacy.svg)](https://pypi.org/project/isage-privacy/) + [![Stars](https://img.shields.io/github/stars/intellistream/sage-privacy?style=social)](https://github.com/intellistream/sage-privacy/stargazers) +- **[sage-safety](https://github.com/intellistream/sage-safety)** — Safety filters and guardrails + [![CI](https://github.com/intellistream/sage-safety/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/intellistream/sage-safety/actions/workflows/ci.yml) + [![PyPI](https://badge.fury.io/py/isage-safety.svg)](https://pypi.org/project/isage-safety/) + [![Stars](https://img.shields.io/github/stars/intellistream/sage-safety?style=social)](https://github.com/intellistream/sage-safety/stargazers) + +### Developer Tooling + +- **[sage-dev-tools](https://github.com/intellistream/sage-dev-tools)** — Development CLI and + quality tooling + [![CI](https://github.com/intellistream/sage-dev-tools/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/intellistream/sage-dev-tools/actions/workflows/ci.yml) + [![PyPI](https://badge.fury.io/py/isage-dev-tools.svg)](https://pypi.org/project/isage-dev-tools/) + [![Stars](https://img.shields.io/github/stars/intellistream/sage-dev-tools?style=social)](https://github.com/intellistream/sage-dev-tools/stargazers) +- Historical split repos remain retirement targets and are intentionally omitted from the + recommended active ecosystem list. + +______________________________________________________________________ -These projects depend on SAGE core packages and can be installed separately via PyPI. +### ⚡ sageLLM — LLM Inference Engine + +[![CI](https://github.com/intellistream/sagellm/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/intellistream/sagellm/actions/workflows/ci.yml) +[![PyPI](https://badge.fury.io/py/isagellm.svg)](https://pypi.org/project/isagellm/) +[![Python](https://img.shields.io/badge/Python-3.11-blue.svg)](https://python.org) + +sageLLM is a modular, high-performance LLM inference engine. All repositories are 🔒 private and +published to PyPI. + +### Core Engine + +- **[sagellm-core](https://github.com/intellistream/sagellm-core)** — Core inference engine + [![CI](https://github.com/intellistream/sagellm-core/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/intellistream/sagellm-core/actions/workflows/ci.yml) + [![PyPI](https://badge.fury.io/py/isagellm-core.svg)](https://pypi.org/project/isagellm-core/) + [![Python](https://img.shields.io/badge/Python-3.11-blue.svg)](https://python.org) +- **[sagellm-backend](https://github.com/intellistream/sagellm-backend)** — Backend drivers (CUDA, + Ascend) + [![CI](https://github.com/intellistream/sagellm-backend/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/intellistream/sagellm-backend/actions/workflows/ci.yml) + [![PyPI](https://badge.fury.io/py/isagellm-backend.svg)](https://pypi.org/project/isagellm-backend/) + [![Python](https://img.shields.io/badge/Python-3.11-blue.svg)](https://python.org) +- **[sagellm-protocol](https://github.com/intellistream/sagellm-protocol)** — Wire protocol and + serialization + [![CI](https://github.com/intellistream/sagellm-protocol/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/intellistream/sagellm-protocol/actions/workflows/ci.yml) + [![PyPI](https://badge.fury.io/py/isagellm-protocol.svg)](https://pypi.org/project/isagellm-protocol/) + [![Python](https://img.shields.io/badge/Python-3.11-blue.svg)](https://python.org) + +### Gateway & Control + +- **[sagellm-gateway](https://github.com/intellistream/sagellm-gateway)** — OpenAI-compatible API + gateway + [![CI](https://github.com/intellistream/sagellm-gateway/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/intellistream/sagellm-gateway/actions/workflows/ci.yml) + [![PyPI](https://badge.fury.io/py/isagellm-gateway.svg)](https://pypi.org/project/isagellm-gateway/) + [![Python](https://img.shields.io/badge/Python-3.11-blue.svg)](https://python.org) +- **[sagellm-control-plane](https://github.com/intellistream/sagellm-control-plane)** — Scheduling + and resource management + [![CI](https://github.com/intellistream/sagellm-control-plane/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/intellistream/sagellm-control-plane/actions/workflows/ci.yml) + [![PyPI](https://badge.fury.io/py/isagellm-control-plane.svg)](https://pypi.org/project/isagellm-control-plane/) + [![Python](https://img.shields.io/badge/Python-3.11-blue.svg)](https://python.org) + +### Optimization + +- **[sagellm-kv-cache](https://github.com/intellistream/sagellm-kv-cache)** — KV cache management + [![CI](https://github.com/intellistream/sagellm-kv-cache/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/intellistream/sagellm-kv-cache/actions/workflows/ci.yml) + [![PyPI](https://badge.fury.io/py/isagellm-kv-cache.svg)](https://pypi.org/project/isagellm-kv-cache/) + [![Python](https://img.shields.io/badge/Python-3.11-blue.svg)](https://python.org) +- **[sagellm-comm](https://github.com/intellistream/sagellm-comm)** — Communication layer + [![CI](https://github.com/intellistream/sagellm-comm/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/intellistream/sagellm-comm/actions/workflows/ci.yml) + [![PyPI](https://badge.fury.io/py/isagellm-comm.svg)](https://pypi.org/project/isagellm-comm/) + [![Python](https://img.shields.io/badge/Python-3.11-blue.svg)](https://python.org) +- **[sagellm-compression](https://github.com/intellistream/sagellm-compression)** — Compression + algorithms + [![CI](https://github.com/intellistream/sagellm-compression/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/intellistream/sagellm-compression/actions/workflows/ci.yml) + [![PyPI](https://badge.fury.io/py/isagellm-compression.svg)](https://pypi.org/project/isagellm-compression/) + [![Python](https://img.shields.io/badge/Python-3.11-blue.svg)](https://python.org) + +### Tooling & Benchmarks + +- **[sagellm-benchmark](https://github.com/intellistream/sagellm-benchmark)** — Performance + benchmarks + [![CI](https://github.com/intellistream/sagellm-benchmark/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/intellistream/sagellm-benchmark/actions/workflows/ci.yml) + [![PyPI](https://badge.fury.io/py/isagellm-benchmark.svg)](https://pypi.org/project/isagellm-benchmark/) + [![Python](https://img.shields.io/badge/Python-3.11-blue.svg)](https://python.org) +- **[sagellm-dev-tools](https://github.com/intellistream/sagellm-dev-tools)** — Development tooling + [![CI](https://github.com/intellistream/sagellm-dev-tools/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/intellistream/sagellm-dev-tools/actions/workflows/ci.yml) + [![PyPI](https://badge.fury.io/py/isagellm-dev-tools.svg)](https://pypi.org/project/isagellm-dev-tools/) + [![Python](https://img.shields.io/badge/Python-3.11-blue.svg)](https://python.org) ## Community diff --git a/SAGE.code-workspace b/SAGE.code-workspace index fe18c4f2bf..acdaf175d9 100644 --- a/SAGE.code-workspace +++ b/SAGE.code-workspace @@ -1,181 +1,258 @@ { - "folders": [ - { - "name": "SAGE", - "path": "." - }, - { - "name": "SAGE-Pub", - "path": "../SAGE-Pub" - } - ], - "settings": { - "python.defaultInterpreterPath": "${workspaceFolder:SAGE}/.venv/bin/python", - "python.analysis.extraPaths": [ - "${workspaceFolder:SAGE}/packages/sage-common/src", - "${workspaceFolder:SAGE}/packages/sage-platform/src", - "${workspaceFolder:SAGE}/packages/sage-kernel/src", - "${workspaceFolder:SAGE}/packages/sage-libs/src", - "${workspaceFolder:SAGE}/packages/sage-middleware/src", - "${workspaceFolder:SAGE}/packages/sage-cli/src", - "${workspaceFolder:SAGE}/packages/sage-tools/src" - ], - "python.testing.pytestArgs": [ - "packages", - "-v", - "--tb=short" - ], - "python.testing.unittestEnabled": false, - "python.testing.pytestEnabled": true, - "files.exclude": { - "**/__pycache__": true, - "**/*.pyc": true, - "**/*.pyo": true, - "**/.pytest_cache": true, - "**/.mypy_cache": true, - "**/*.egg-info": true, - ".sage/build": true, - ".sage/cache": true - }, - "search.exclude": { - "**/node_modules": true, - "**/.git": true, - ".sage/build": true, - ".sage/cache": true, - "**/dist": true, - "**/*.egg-info": true - }, - "editor.formatOnSave": true, - "editor.codeActionsOnSave": { - "source.organizeImports": "explicit" - }, - "[python]": { - "editor.defaultFormatter": "charliermarsh.ruff", - "editor.formatOnSave": true - }, - "ruff.configurationPreference": "filesystemFirst", - "ruff.configuration": "${workspaceFolder:SAGE}/tools/ruff.toml", - "git.enableSmartCommit": true, - "git.confirmSync": false - }, - "extensions": { - "recommendations": [ - "ms-python.python", - "ms-python.vscode-pylance", - "charliermarsh.ruff", - "ms-toolsai.jupyter", - "github.copilot", - "github.copilot-chat", - "redhat.vscode-yaml", - "tamasfe.even-better-toml", - "yzhang.markdown-all-in-one", - "DavidAnson.vscode-markdownlint" - ] - }, - "launch": { - "version": "0.2.0", - "configurations": [ - { - "name": "Python: Current File", - "type": "debugpy", - "request": "launch", - "program": "${file}", - "console": "integratedTerminal", - "cwd": "${workspaceFolder:SAGE}", - "env": { - "PYTHONPATH": "${workspaceFolder:SAGE}/packages/sage-common/src:${workspaceFolder:SAGE}/packages/sage-platform/src:${workspaceFolder:SAGE}/packages/sage-kernel/src:${workspaceFolder:SAGE}/packages/sage-libs/src:${workspaceFolder:SAGE}/packages/sage-middleware/src:${workspaceFolder:SAGE}/packages/sage-cli/src:${workspaceFolder:SAGE}/packages/sage-tools/src" - } - }, - { - "name": "Python: sage-dev CLI", - "type": "debugpy", - "request": "launch", - "module": "sage.tools.cli", - "args": [ - "--help" - ], - "console": "integratedTerminal", - "cwd": "${workspaceFolder:SAGE}" - }, - { - "name": "Pytest: Current File", - "type": "debugpy", - "request": "launch", - "module": "pytest", - "args": [ - "${file}", - "-v", - "--tb=short" - ], - "console": "integratedTerminal", - "cwd": "${workspaceFolder:SAGE}" - } - ] - }, - "tasks": { - "version": "2.0.0", - "tasks": [ - { - "label": "Install SAGE (dev)", - "type": "shell", - "command": "./quickstart.sh --dev --yes", - "group": "build", - "problemMatcher": [], - "options": { - "cwd": "${workspaceFolder:SAGE}" - } - }, - { - "label": "Run Tests", - "type": "shell", - "command": "sage-dev project test --coverage", - "group": "test", - "problemMatcher": [], - "options": { - "cwd": "${workspaceFolder:SAGE}" - } - }, - { - "label": "Code Quality Check", - "type": "shell", - "command": "sage-dev quality check --all-files", - "group": "test", - "problemMatcher": [], - "options": { - "cwd": "${workspaceFolder:SAGE}" - } - }, - { - "label": "Code Quality Fix", - "type": "shell", - "command": "sage-dev quality fix --all-files", - "group": "build", - "problemMatcher": [], - "options": { - "cwd": "${workspaceFolder:SAGE}" - } - }, - { - "label": "Build Docs", - "type": "shell", - "command": "mkdocs build", - "group": "build", - "problemMatcher": [], - "options": { - "cwd": "${workspaceFolder:SAGE-Pub}" - } - }, - { - "label": "Serve Docs", - "type": "shell", - "command": "mkdocs serve", - "group": "build", - "problemMatcher": [], - "isBackground": true, - "options": { - "cwd": "${workspaceFolder:SAGE-Pub}" - } - } - ] - } + "folders": [ + { + "name": "SAGE [meta]", + "path": "." + }, + { + "name": "sage-benchmark [benchmark]", + "path": "../sage-benchmark" + }, + { + "name": "sage-docs [docs]", + "path": "../sage-docs" + }, + { + "name": "sage-tutorials [tutorials]", + "path": "../sage-tutorials" + }, + { + "name": "sage-examples [examples]", + "path": "../sage-examples" + }, + { + "name": "sage-agentic [capability]", + "path": "../sage-agentic" + }, + { + "name": "sage-agentic-tooluse [capability]", + "path": "../sage-agentic-tooluse" + }, + { + "name": "sage-agentic-tooluse-sias [capability]", + "path": "../sage-agentic-tooluse-sias" + }, + { + "name": "sage-agentic-tooluse-benchmark [benchmark]", + "path": "../sage-agentic-tooluse-benchmark" + }, + { + "name": "sage-rag-benchmark [benchmark]", + "path": "../sage-rag-benchmark" + }, + { + "name": "sage-refiner-benchmark [benchmark]", + "path": "../sage-refiner-benchmark" + }, + { + "name": "sage-eval [capability]", + "path": "../sage-eval" + }, + { + "name": "sage-finetune [capability]", + "path": "../sage-finetune" + }, + { + "name": "sage-libs-intent [capability]", + "path": "../sage-libs-intent" + }, + { + "name": "sage-mcp [tooling]", + "path": "../sage-mcp" + }, + { + "name": "sage-privacy [capability]", + "path": "../sage-privacy" + }, + { + "name": "sage-rag [capability]", + "path": "../sage-rag" + }, + { + "name": "sage-safety [capability]", + "path": "../sage-safety" + }, + { + "name": "sage-sias [capability]", + "path": "../sage-sias" + }, + { + "name": "sage-tools [tooling]", + "path": "../sage-tools" + }, + { + "name": "sageData [data]", + "path": "../sageData" + }, + { + "name": "sageRefiner [capability]", + "path": "../sageRefiner" + }, + { + "path": "../.github" + } + ], + "settings": { + "python.analysis.extraPaths": [ + "${workspaceFolder:SAGE [meta]}/src" + ], + "python.testing.pytestArgs": [ + "packages", + "-v", + "--tb=short" + ], + "python.testing.unittestEnabled": false, + "python.testing.pytestEnabled": true, + "files.exclude": { + "**/__pycache__": true, + "**/*.pyc": true, + "**/*.pyo": true, + "**/.pytest_cache": true, + "**/.mypy_cache": true, + "**/*.egg-info": true, + ".sage/build": true, + ".sage/cache": true, + }, + "search.exclude": { + "**/node_modules": true, + "**/.git": true, + ".sage/build": true, + ".sage/cache": true, + "**/dist": true, + "**/*.egg-info": true, + }, + "editor.formatOnSave": true, + "editor.codeActionsOnSave": { + "source.organizeImports": "explicit", + }, + "[python]": { + "editor.defaultFormatter": "charliermarsh.ruff", + "editor.formatOnSave": true, + }, + "ruff.configurationPreference": "filesystemFirst", + "ruff.configuration": "${workspaceFolder:SAGE [meta]}/tools/ruff.toml", + "git.enableSmartCommit": true, + "git.confirmSync": false, + "python-envs.workspaceSearchPaths": [], + }, + "extensions": { + "recommendations": [ + "ms-python.python", + "ms-python.vscode-pylance", + "charliermarsh.ruff", + "ms-toolsai.jupyter", + "github.copilot", + "github.copilot-chat", + "redhat.vscode-yaml", + "tamasfe.even-better-toml", + "yzhang.markdown-all-in-one", + "DavidAnson.vscode-markdownlint", + ], + }, + "launch": { + "version": "0.2.0", + "configurations": [ + { + "name": "Python: Current File", + "type": "debugpy", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "cwd": "${workspaceFolder:SAGE [meta]}", + "env": { + "PYTHONPATH": "${workspaceFolder:SAGE [meta]}/src" + } + }, + { + "name": "Python: sage-dev CLI", + "type": "debugpy", + "request": "launch", + "module": "sage.tools.cli", + "args": [ + "--help" + ], + "console": "integratedTerminal", + "cwd": "${workspaceFolder:SAGE [meta]}", + }, + { + "name": "Pytest: Current File", + "type": "debugpy", + "request": "launch", + "module": "pytest", + "args": [ + "${file}", + "-v", + "--tb=short" + ], + "console": "integratedTerminal", + "cwd": "${workspaceFolder:SAGE [meta]}", + }, + ], + }, + "tasks": { + "version": "2.0.0", + "tasks": [ + { + "label": "Install SAGE (dev)", + "type": "shell", + "command": "./quickstart.sh --dev --yes", + "group": "build", + "problemMatcher": [], + "options": { + "cwd": "${workspaceFolder:SAGE [meta]}" + }, + }, + { + "label": "Run Tests", + "type": "shell", + "command": "sage-dev project test --coverage", + "group": "test", + "problemMatcher": [], + "options": { + "cwd": "${workspaceFolder:SAGE [meta]}" + }, + }, + { + "label": "Code Quality Check", + "type": "shell", + "command": "sage-dev quality check --all-files", + "group": "test", + "problemMatcher": [], + "options": { + "cwd": "${workspaceFolder:SAGE [meta]}" + }, + }, + { + "label": "Code Quality Fix", + "type": "shell", + "command": "sage-dev quality fix --all-files", + "group": "build", + "problemMatcher": [], + "options": { + "cwd": "${workspaceFolder:SAGE [meta]}" + }, + }, + { + "label": "Build Docs", + "type": "shell", + "command": "mkdocs build", + "group": "build", + "problemMatcher": [], + "options": { + "cwd": "${workspaceFolder:sage-docs [L6]}" + }, + }, + { + "label": "Serve Docs", + "type": "shell", + "command": "mkdocs serve", + "group": "build", + "problemMatcher": [], + "isBackground": true, + "options": { + "cwd": "${workspaceFolder:sage-docs [L6]}" + }, + }, + ], + }, } diff --git a/benchmark/README.md b/benchmark/README.md deleted file mode 100644 index 1eb4a234df..0000000000 --- a/benchmark/README.md +++ /dev/null @@ -1,26 +0,0 @@ -# benchmark_sage – SAGE System-Level Benchmarks and ICML Artifacts - -`benchmark_sage` is a home for **system-level benchmarks and artifacts** that focus on SAGE as a -complete ML systems platform. - -Key points: - -- SAGE is **more than an LLM control plane**. The LLM/embedding control plane is one subsystem. SAGE - also includes components such as `sage.db`, `sage.flow`, `sage.tsdb`, and others, all orchestrated - via a common **declarative dataflow model**. -- `packages/sage-benchmark` already contains multiple benchmark suites (agents, control-plane - scheduling, DB, retrieval, memory, schedulers, refiner, libamm, etc.). `benchmark_sage` can - aggregate **cross-cutting experiments** that involve several SAGE subsystems together. -- This folder may also store **ICML writing prompts and experiment templates** for the SAGE system - track papers, under `docs/`. - -Suggested uses: - -- End-to-end experiments that span `sage.flow` pipelines, `sage.db` storage, `sage.tsdb` time-series - monitoring, and the LLM/embedding control plane. -- Configs (`config/*.yaml`) for system-track experiments described in an ICML paper. -- Notebook or script entry points that reproduce figures/tables. - -At the repo root, `docs/icml-prompts/` contains reusable writing prompts. You can either reference -them directly or copy customized versions into this folder when preparing a specific ICML -submission. diff --git a/benchmark/__init__.py b/benchmark/__init__.py deleted file mode 100644 index cb92c6862d..0000000000 --- a/benchmark/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -"""Benchmark and testbed utilities focused on the SAGE system as a whole. - -This package is intended for experiments and benchmarks that treat SAGE as -an end-to-end dataflow-based ML systems platform, not just an LLM -control plane. It can host ICML-oriented experiment configs, runners, -analysis code, and writing prompts. -""" diff --git a/benchmark/__main__.py b/benchmark/__main__.py deleted file mode 100644 index 7745bc3ace..0000000000 --- a/benchmark/__main__.py +++ /dev/null @@ -1,161 +0,0 @@ -"""CLI entry point for running SAGE system-level benchmark experiments. - -This module supersedes the legacy ``benchmark_icml`` entry point. It exposes -experiments that were originally designed for section 5.x of a paper draft, -under a more general "benchmark_sage" namespace. - -Usage examples: - - python -m sage.benchmark.benchmark_sage --experiment 5.1 - python -m sage.benchmark.benchmark_sage --all - python -m sage.benchmark.benchmark_sage --experiment 5.2 --config config/exp_5_2.yaml - -""" - -from __future__ import annotations - -import argparse -import sys -from pathlib import Path - - -def main() -> int: - parser = argparse.ArgumentParser( - description="SAGE system benchmark suite", - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=""" -Examples: - # Run Section 5.1 experiment - python -m sage.benchmark.benchmark_sage --experiment 5.1 - - # Run all experiments - python -m sage.benchmark.benchmark_sage --all - - # Run with custom config - python -m sage.benchmark.benchmark_sage --experiment 5.2 --config my_config.yaml - - # Dry run (validate only) - python -m sage.benchmark.benchmark_sage --experiment 5.1 --dry-run - """, - ) - - parser.add_argument( - "--experiment", - "-e", - type=str, - choices=["5.1", "5.2", "5.3"], - help="Experiment section to run (5.1, 5.2, or 5.3)", - ) - parser.add_argument("--all", "-a", action="store_true", help="Run all experiments") - parser.add_argument("--config", "-c", type=str, help="Path to custom config file (YAML)") - parser.add_argument( - "--output", - "-o", - type=str, - default="results", - help="Output directory for results (default: results)", - ) - parser.add_argument("--dry-run", action="store_true", help="Validate config without running") - parser.add_argument("--verbose", "-v", action="store_true", help="Enable verbose output") - parser.add_argument( - "--quick", - "-q", - action="store_true", - help="Run quick version with reduced samples", - ) - - args = parser.parse_args() - - if not args.experiment and not args.all: - parser.print_help() - return 1 - - # Import here to avoid slow startup for --help - from sage.benchmark.benchmark_sage.config.config_loader import ConfigLoader - from sage.benchmark.benchmark_sage.experiments.exp_5_1_control_plane import ( - ControlPlaneExperiment, - ) - from sage.benchmark.benchmark_sage.experiments.exp_5_2_scheduling import ( - SchedulingPolicyExperiment, - ) - from sage.benchmark.benchmark_sage.experiments.exp_5_3_e2e import EndToEndExperiment - - experiment_map = { - "5.1": ControlPlaneExperiment, - "5.2": SchedulingPolicyExperiment, - "5.3": EndToEndExperiment, - } - - if args.all: - experiments_to_run = ["5.1", "5.2", "5.3"] - else: - experiments_to_run = [args.experiment] - - config_loader = ConfigLoader() - output_dir = Path(args.output) - output_dir.mkdir(parents=True, exist_ok=True) - - results: dict[str, dict] = {} - - for exp_section in experiments_to_run: - print(f"\n{'=' * 60}") - print(f"Running Experiment Section {exp_section}") - print(f"{'=' * 60}\n") - - if args.config: - config = config_loader.load(args.config) - else: - default_config = ( - Path(__file__).parent / "config" / f"exp_{exp_section.replace('.', '_')}.yaml" - ) - if default_config.exists(): - config = config_loader.load(str(default_config)) - else: - config = config_loader.get_default_config(exp_section) - - if args.quick: - config = config_loader.apply_quick_mode(config) - - exp_class = experiment_map[exp_section] - experiment = exp_class( - config=config, - output_dir=output_dir / f"exp_{exp_section.replace('.', '_')}", - verbose=args.verbose, - ) - - if args.dry_run: - print(f"[DRY RUN] Validating config for experiment {exp_section}...") - experiment.validate() - print("[DRY RUN] Config validation passed.") - continue - - try: - experiment.setup() - result = experiment.run() - experiment.teardown() - results[exp_section] = result - print(f"\nExperiment {exp_section} completed. Results saved to {experiment.output_dir}") - except Exception as exc: # noqa: BLE001 - print(f"Error running experiment {exp_section}: {exc}") - if args.verbose: - import traceback - - traceback.print_exc() - results[exp_section] = {"error": str(exc)} - - if not args.dry_run: - print(f"\n{'=' * 60}") - print("Experiment Summary") - print(f"{'=' * 60}") - for exp_section, result in results.items(): - if "error" in result: - print(f" {exp_section}: FAILED - {result['error']}") - else: - print(f" {exp_section}: COMPLETED") - print(f"\nResults saved to: {output_dir.absolute()}") - - return 0 - - -if __name__ == "__main__": # pragma: no cover - sys.exit(main()) diff --git a/benchmark/config/__init__.py b/benchmark/config/__init__.py deleted file mode 100644 index 789c00023a..0000000000 --- a/benchmark/config/__init__.py +++ /dev/null @@ -1,21 +0,0 @@ -"""Configuration module for ICML benchmark.""" - -from sage.benchmark.benchmark_icml.config.config_loader import ( - ConfigLoader, - ExperimentConfig, - HardwareConfig, - MetricsConfig, - ModelConfig, - OutputConfig, - WorkloadConfig, -) - -__all__ = [ - "ConfigLoader", - "ExperimentConfig", - "HardwareConfig", - "MetricsConfig", - "ModelConfig", - "OutputConfig", - "WorkloadConfig", -] diff --git a/benchmark/config/config_loader.py b/benchmark/config/config_loader.py deleted file mode 100644 index abc8f188d6..0000000000 --- a/benchmark/config/config_loader.py +++ /dev/null @@ -1,317 +0,0 @@ -""" -Configuration loader for ICML benchmark experiments. - -Supports: -- YAML config loading with validation -- Environment variable substitution -- Default config generation -- Quick mode override -""" - -import os -from dataclasses import dataclass, field -from pathlib import Path -from typing import Any - -import yaml - - -@dataclass -class HardwareConfig: - """Hardware configuration.""" - - gpus: int = 2 - gpu_type: str = "A100" - cpu_cores: int = 64 - memory_gb: int = 256 - - -@dataclass -class ModelConfig: - """Model configuration.""" - - name: str - instances: int = 1 - device: str = "cuda" - tensor_parallel: int = 1 - - -@dataclass -class WorkloadConfig: - """Workload configuration.""" - - total_requests: int = 1000 - warmup_requests: int = 100 - llm_ratio: float = 0.7 - request_rate: float = 50.0 - input_tokens_min: int = 256 - input_tokens_max: int = 512 - output_tokens_min: int = 64 - output_tokens_max: int = 256 - seed: int = 42 - - -@dataclass -class MetricsConfig: - """Metrics configuration.""" - - latency_percentiles: list[int] = field(default_factory=lambda: [50, 95, 99]) - slo_chat_p99_ms: int = 500 - slo_embedding_p99_ms: int = 100 - report_interval_s: int = 10 - - -@dataclass -class OutputConfig: - """Output configuration.""" - - results_dir: str = "results" - save_raw_data: bool = True - generate_plots: bool = True - export_latex: bool = True - - -@dataclass -class ExperimentConfig: - """Complete experiment configuration.""" - - name: str - description: str - experiment_section: str - hardware: HardwareConfig = field(default_factory=HardwareConfig) - llm_model: ModelConfig = field( - default_factory=lambda: ModelConfig(name="Qwen/Qwen2.5-7B-Instruct") - ) - embedding_model: ModelConfig = field( - default_factory=lambda: ModelConfig(name="BAAI/bge-m3", device="cpu") - ) - workload: WorkloadConfig = field(default_factory=WorkloadConfig) - metrics: MetricsConfig = field(default_factory=MetricsConfig) - output: OutputConfig = field(default_factory=OutputConfig) - baselines: list[str] = field(default_factory=list) - policies: list[str] = field(default_factory=list) - extra: dict = field(default_factory=dict) - - -class ConfigLoader: - """Configuration loader with validation and defaults.""" - - def __init__(self): - self.config_dir = Path(__file__).parent - - def load(self, path: str) -> ExperimentConfig: - """Load configuration from YAML file.""" - config_path = Path(path) - if not config_path.is_absolute(): - config_path = self.config_dir / config_path - - if not config_path.exists(): - raise FileNotFoundError(f"Config file not found: {config_path}") - - with open(config_path) as f: - raw_config = yaml.safe_load(f) - - # Substitute environment variables - raw_config = self._substitute_env_vars(raw_config) - - return self._parse_config(raw_config) - - def _substitute_env_vars(self, obj: Any) -> Any: - """Recursively substitute ${VAR} with environment variables.""" - if isinstance(obj, str): - # Handle ${VAR} and ${VAR:-default} patterns - import re - - pattern = r"\$\{(\w+)(?::-([^}]*))?\}" - - def replacer(match): - var_name = match.group(1) - default = match.group(2) or "" - return os.environ.get(var_name, default) - - return re.sub(pattern, replacer, obj) - elif isinstance(obj, dict): - return {k: self._substitute_env_vars(v) for k, v in obj.items()} - elif isinstance(obj, list): - return [self._substitute_env_vars(item) for item in obj] - return obj - - def _parse_config(self, raw: dict) -> ExperimentConfig: - """Parse raw config dict into ExperimentConfig.""" - exp = raw.get("experiment", {}) - - # Hardware - hw_raw = raw.get("hardware", {}) - hardware = HardwareConfig( - gpus=hw_raw.get("gpus", 2), - gpu_type=hw_raw.get("gpu_type", "A100"), - cpu_cores=hw_raw.get("cpu_cores", 64), - memory_gb=hw_raw.get("memory_gb", 256), - ) - - # Models - models_raw = raw.get("models", {}) - llm_raw = models_raw.get("llm", {}) - llm_model = ModelConfig( - name=llm_raw.get("name", "Qwen/Qwen2.5-7B-Instruct"), - instances=llm_raw.get("instances", 2), - device=llm_raw.get("device", "cuda"), - tensor_parallel=llm_raw.get("tensor_parallel", 1), - ) - - emb_raw = models_raw.get("embedding", {}) - embedding_model = ModelConfig( - name=emb_raw.get("name", "BAAI/bge-m3"), - instances=emb_raw.get("instances", 1), - device=emb_raw.get("device", "cpu"), - ) - - # Workload - wl_raw = raw.get("workload", {}) - input_tokens = wl_raw.get("input_tokens", {}) - output_tokens = wl_raw.get("output_tokens", {}) - workload = WorkloadConfig( - total_requests=wl_raw.get("total_requests", 1000), - warmup_requests=wl_raw.get("warmup_requests", 100), - llm_ratio=wl_raw.get("llm_ratio", 0.7), - request_rate=wl_raw.get("request_rate", 50.0), - input_tokens_min=input_tokens.get("min", 256), - input_tokens_max=input_tokens.get("max", 512), - output_tokens_min=output_tokens.get("min", 64), - output_tokens_max=output_tokens.get("max", 256), - seed=wl_raw.get("seed", 42), - ) - - # Metrics - metrics_raw = raw.get("metrics", {}) - slo_targets = metrics_raw.get("slo_targets", {}) - metrics = MetricsConfig( - latency_percentiles=metrics_raw.get("latency_percentiles", [50, 95, 99]), - slo_chat_p99_ms=slo_targets.get("chat_p99_ms", 500), - slo_embedding_p99_ms=slo_targets.get("embedding_p99_ms", 100), - report_interval_s=metrics_raw.get("report_interval_s", 10), - ) - - # Output - output_raw = raw.get("output", {}) - output = OutputConfig( - results_dir=output_raw.get("results_dir", "results"), - save_raw_data=output_raw.get("save_raw_data", True), - generate_plots=output_raw.get("generate_plots", True), - export_latex=output_raw.get("export_latex", True), - ) - - # Baselines and policies - baselines = [b["name"] for b in raw.get("baselines", []) if b.get("enabled", True)] - policies = raw.get("policies", []) - - return ExperimentConfig( - name=exp.get("name", "unnamed"), - description=exp.get("description", ""), - experiment_section=exp.get("section", ""), - hardware=hardware, - llm_model=llm_model, - embedding_model=embedding_model, - workload=workload, - metrics=metrics, - output=output, - baselines=baselines, - policies=policies, - extra=raw.get("extra", {}), - ) - - def get_default_config(self, section: str) -> ExperimentConfig: - """Get default configuration for a given experiment section.""" - defaults = { - "5.1": ExperimentConfig( - name="exp_5_1_control_plane", - description="Control Plane Unified Scheduling Experiment", - experiment_section="5.1", - baselines=["sage_unified", "vllm_only", "separated"], - ), - "5.2": ExperimentConfig( - name="exp_5_2_scheduling", - description="Scheduling Policy Comparison Experiment", - experiment_section="5.2", - policies=["fifo", "priority", "slo_aware", "hybrid"], - ), - "5.3": ExperimentConfig( - name="exp_5_3_e2e", - description="End-to-End System Evaluation", - experiment_section="5.3", - ), - } - return defaults.get( - section, - ExperimentConfig(name="unknown", description="Unknown", experiment_section=section), - ) - - def apply_quick_mode(self, config: ExperimentConfig) -> ExperimentConfig: - """Apply quick mode overrides for faster testing.""" - config.workload.total_requests = 100 - config.workload.warmup_requests = 10 - return config - - def save(self, config: ExperimentConfig, path: str) -> None: - """Save configuration to YAML file.""" - config_dict = { - "experiment": { - "name": config.name, - "description": config.description, - "section": config.experiment_section, - }, - "hardware": { - "gpus": config.hardware.gpus, - "gpu_type": config.hardware.gpu_type, - "cpu_cores": config.hardware.cpu_cores, - "memory_gb": config.hardware.memory_gb, - }, - "models": { - "llm": { - "name": config.llm_model.name, - "instances": config.llm_model.instances, - "device": config.llm_model.device, - "tensor_parallel": config.llm_model.tensor_parallel, - }, - "embedding": { - "name": config.embedding_model.name, - "instances": config.embedding_model.instances, - "device": config.embedding_model.device, - }, - }, - "workload": { - "total_requests": config.workload.total_requests, - "warmup_requests": config.workload.warmup_requests, - "llm_ratio": config.workload.llm_ratio, - "request_rate": config.workload.request_rate, - "input_tokens": { - "min": config.workload.input_tokens_min, - "max": config.workload.input_tokens_max, - }, - "output_tokens": { - "min": config.workload.output_tokens_min, - "max": config.workload.output_tokens_max, - }, - "seed": config.workload.seed, - }, - "metrics": { - "latency_percentiles": config.metrics.latency_percentiles, - "slo_targets": { - "chat_p99_ms": config.metrics.slo_chat_p99_ms, - "embedding_p99_ms": config.metrics.slo_embedding_p99_ms, - }, - "report_interval_s": config.metrics.report_interval_s, - }, - "output": { - "results_dir": config.output.results_dir, - "save_raw_data": config.output.save_raw_data, - "generate_plots": config.output.generate_plots, - "export_latex": config.output.export_latex, - }, - "baselines": [{"name": b, "enabled": True} for b in config.baselines], - "policies": config.policies, - "extra": config.extra, - } - - with open(path, "w") as f: - yaml.dump(config_dict, f, default_flow_style=False, sort_keys=False) diff --git a/benchmark/config/exp_5_1.yaml b/benchmark/config/exp_5_1.yaml deleted file mode 100644 index 04dbca7772..0000000000 --- a/benchmark/config/exp_5_1.yaml +++ /dev/null @@ -1,67 +0,0 @@ -# Default configuration for ICML benchmark experiments -# This file provides sensible defaults for a 2x A100 setup - -# ============================================================================= -# Experiment 5.1: Control Plane Unified Scheduling -# ============================================================================= -# Demonstrates unified LLM+embedding scheduling improves performance -# Baselines: SAGE unified vs vLLM-only vs separated services - -experiment: - name: "exp_5_1_control_plane" - description: "Control Plane Unified Scheduling - Mixed LLM+Embedding Workload" - section: "5.1" - -hardware: - gpus: 2 - gpu_type: "A100" - cpu_cores: 64 - memory_gb: 256 - -models: - llm: - name: "Qwen/Qwen2.5-7B-Instruct" - instances: 2 # One per GPU, or 2 for tensor parallel - device: "cuda" - tensor_parallel: 1 # Set to 2 if using both GPUs for one model - embedding: - name: "BAAI/bge-m3" - instances: 1 - device: "cpu" # CPU to save GPU memory for LLM - -workload: - total_requests: 1000 - warmup_requests: 100 - llm_ratio: 0.7 # 70% LLM, 30% embedding - request_rate: 50.0 # req/s - input_tokens: - min: 256 - max: 512 - output_tokens: - min: 64 - max: 256 - seed: 42 - -baselines: -- name: "sage_unified" - enabled: true - description: "SAGE with unified Control Plane" -- name: "vllm_only" - enabled: true - description: "vLLM server with client-side embedding" -- name: "separated" - enabled: true - description: "Separate vLLM and embedding services" - -metrics: - latency_percentiles: [50, 95, 99] - slo_targets: - chat_p99_ms: 500 - embedding_p99_ms: 100 - report_interval_s: 10 - -output: - results_dir: "results/exp_5_1" - save_raw_data: true - generate_plots: true - export_latex: true diff --git a/benchmark/config/exp_5_2.yaml b/benchmark/config/exp_5_2.yaml deleted file mode 100644 index 58ab144ffb..0000000000 --- a/benchmark/config/exp_5_2.yaml +++ /dev/null @@ -1,93 +0,0 @@ -# Configuration for Experiment 5.2: Scheduling Policy Comparison -# Evaluates HybridSchedulingPolicy vs simpler alternatives - -experiment: - name: "exp_5_2_scheduling" - description: "Scheduling Policy Comparison - FIFO vs Priority vs SLO vs Hybrid" - section: "5.2" - -hardware: - gpus: 2 - gpu_type: "A100" - cpu_cores: 64 - memory_gb: 256 - -models: - llm: - name: "Qwen/Qwen2.5-7B-Instruct" - instances: 2 - device: "cuda" - tensor_parallel: 1 - embedding: - name: "BAAI/bge-m3" - instances: 1 - device: "cpu" - -# Policy-specific configurations -policies: -- fifo -- priority -- slo_aware -- hybrid - -# Traffic patterns to test -traffic_patterns: -- name: "steady" - description: "Constant 50 req/s" - request_rate: 50.0 - duration_s: 60 - -- name: "bursty" - description: "10 req/s baseline with 200 req/s bursts" - baseline_rate: 10.0 - burst_rate: 200.0 - burst_duration_s: 5 - burst_interval_s: 30 - total_duration_s: 120 - -- name: "mixed_priority" - description: "20% high priority, 80% normal" - request_rate: 50.0 - high_priority_ratio: 0.2 - duration_s: 60 - -workload: - total_requests: 1000 - warmup_requests: 100 - llm_ratio: 0.7 - request_rate: 50.0 - input_tokens: - min: 256 - max: 512 - output_tokens: - min: 64 - max: 256 - seed: 42 - -metrics: - latency_percentiles: [50, 95, 99] - slo_targets: - chat_p99_ms: 500 - embedding_p99_ms: 100 - report_interval_s: 5 # More frequent for policy comparison - track_queue_depth: true - track_wait_time: true - -output: - results_dir: "results/exp_5_2" - save_raw_data: true - generate_plots: true - export_latex: true - -extra: - # SLO-aware policy parameters - slo_aware: - deadline_buffer_ms: 50 - preemption_enabled: false - - # Hybrid policy parameters - hybrid: - batch_size: 8 - batch_timeout_ms: 10 - priority_weight: 0.3 - slo_weight: 0.7 diff --git a/benchmark/config/exp_5_3.yaml b/benchmark/config/exp_5_3.yaml deleted file mode 100644 index 68fd80a5b0..0000000000 --- a/benchmark/config/exp_5_3.yaml +++ /dev/null @@ -1,135 +0,0 @@ -# Configuration for Experiment 5.3: End-to-End System Evaluation -# Tests complete SAGE system including kernel scheduling, middleware, dataflow - -experiment: - name: "exp_5_3_e2e" - description: "End-to-End System Evaluation - Complete SAGE Pipeline" - section: "5.3" - -hardware: - gpus: 2 - gpu_type: "A100" - cpu_cores: 64 - memory_gb: 256 - -models: - llm: - name: "Qwen/Qwen2.5-7B-Instruct" - instances: 2 - device: "cuda" - tensor_parallel: 1 - embedding: - name: "BAAI/bge-m3" - instances: 1 - device: "cpu" - -# Components to test -components: -- name: "job_manager" - module: "sage.kernel.runtime.job_manager" - enabled: true -- name: "node_selector" - module: "sage.kernel.scheduler.node_selector" - enabled: true -- name: "control_plane" - module: "sage.common.components.sage_llm.sageLLM.control_plane" - enabled: true -- name: "gateway" - module: "sage.gateway" - enabled: true - -# E2E test scenarios -scenarios: -- name: "simple_pipeline" - description: "Single LLM call" - steps: - - type: "llm_chat" - input: "What is machine learning?" - repeat: 100 - -- name: "rag_pipeline" - description: "Embedding + Retrieval + LLM" - steps: - - type: "embedding" - input: "query text" - - type: "retrieval" - top_k: 5 - - type: "llm_chat" - input: "Answer based on context: {context}" - repeat: 50 - -- name: "multi_step_agent" - description: "Multi-step agent with tool calls" - steps: - - type: "llm_chat" - input: "Plan task: {task}" - - type: "tool_selection" - candidates: 10 - - type: "llm_chat" - input: "Execute with tool: {tool}" - repeat: 30 - -- name: "batch_embedding" - description: "Batch embedding processing" - steps: - - type: "embedding_batch" - batch_size: 32 - repeat: 20 - -# Heterogeneous hardware test -heterogeneous: - enabled: true - configurations: - - name: "gpu_only" - llm_device: "cuda" - embedding_device: "cuda" - - name: "cpu_embedding" - llm_device: "cuda" - embedding_device: "cpu" - - name: "mixed" - llm_device: "cuda" - embedding_device: "cpu" - cpu_workers: 4 - -# Failure recovery test -failure_recovery: - enabled: true - scenarios: - - name: "backend_restart" - description: "Simulate vLLM backend restart" - inject_at_request: 500 - recovery_timeout_s: 30 - - name: "timeout_handling" - description: "Test request timeout handling" - artificial_delay_ms: 10000 - timeout_ms: 5000 - -workload: - total_requests: 500 - warmup_requests: 50 - llm_ratio: 0.6 - request_rate: 30.0 - input_tokens: - min: 256 - max: 512 - output_tokens: - min: 64 - max: 256 - seed: 42 - -metrics: - latency_percentiles: [50, 95, 99] - slo_targets: - pipeline_p99_ms: 2000 - chat_p99_ms: 500 - embedding_p99_ms: 100 - report_interval_s: 10 - track_component_breakdown: true - track_resource_utilization: true - -output: - results_dir: "results/exp_5_3" - save_raw_data: true - generate_plots: true - export_latex: true - component_breakdown: true diff --git a/benchmark/docs/01_abstract.md b/benchmark/docs/01_abstract.md deleted file mode 100644 index 51aa6260e0..0000000000 --- a/benchmark/docs/01_abstract.md +++ /dev/null @@ -1,117 +0,0 @@ -# Abstract Prompt – SAGE Systems Paper - -下面是为 SAGE 撰写 **系统论文摘要（Abstract）** 的提示词模版，你可以直接复制到对话中，并在标注位置补充信息。 默认面向顶级机器学习系统会议的系统 track（例如 ICML -Machine Learning Systems track），但提示词本身不依赖具体会议名称。 - -______________________________________________________________________ - -## 提示词（可直接复制给大模型） - -You are an experienced systems-track author. You help me write a **concise but technically rich -abstract** for a paper about **SAGE**, a machine learning systems framework. - ---- Context about the paper and the system --- - -- Target venue: a **top-tier Machine Learning Systems track** (focus on implementation, scalability, - hardware, libraries, distributed methods, etc.) of ICML conference. -- System: **SAGE**, a Python 3.10+ framework for building **LLM/AI data processing pipelines** with - **declarative dataflow**. -- Goal of the paper: present SAGE as a **full-stack dataflow-based ML system**, not just an LLM - control plane. - -Key architectural points (you should weave them naturally into the abstract, not list them -mechanically): - -- A strict **5-layer architecture (L1–L5)**: `sage-common`, `sage-platform`, `sage-kernel` / - `sage-libs`, `sage-middleware`, `sage-cli` / `sage-tools`, with **no upward dependencies** (each - layer only depends on lower layers). Independent repositories (`sage-benchmark`, `sage-examples`, - `sage-studio`, `sageLLM`) are outside the core architecture. -- **Declarative dataflow** for constructing LLM/AI pipelines: users declare high-level pipelines, - while platform, kernel, and middleware layers compile them into an efficient execution plan across - heterogeneous resources. -- A unified **LLM & embedding control plane** ("sageLLM"), exposed via an **OpenAI-compatible - gateway** (`isagellm.gateway`), providing request classification, hybrid scheduling, batching, and - resource sharing across multiple vLLM / embedding instances. -- **CPU-only and GPU node support**, with job management and node selection in `sage-kernel` - (runtime, scheduler) and cluster configuration / services in `sage-platform`. -- A **comprehensive benchmark suite** (`sage-benchmark`) that evaluates both **agent capabilities** - (tool selection, task planning, timing decisions) and **system-level behavior** (throughput, - latency distribution, SLO compliance, interference) for different SAGE subsystems. -- Implementation characteristics that highlight systems engineering effort: C++ middleware operators - (`sage-middleware`) with CMake-based build; unified CI and quality tools (Ruff, Mypy); - reproducible quickstart scripts; XDG-based user paths and configuration. - ---- Quantitative claims template (to be filled after experiments) --- - -The abstract MUST include at least **one concrete quantitative claim**. You can use placeholders now -and replace them with actual numbers later. Typical patterns include: - -- **Performance / latency**: - - "reduces p99 latency by [X]% compared to [baseline] under mixed LLM+embedding workloads". - - "improves throughput by [Y]× over [baseline] while maintaining p95 latency below [Z] ms". -- **SLO and robustness**: - - "achieves [A]% SLO satisfaction vs. [B]% for [baseline] under [workload] traffic patterns". -- **Resource efficiency / heterogeneity**: - - "reduces resource utilization variance by [C]% across heterogeneous CPU/GPU nodes". -- **Agent / pipeline quality (if applicable)**: - - "improves tool selection accuracy by [D]% on [benchmark]". - - "reduces planning latency by [E]% while maintaining [F]% task success rate". -- **Scale**: - - "evaluated on clusters with up to [G] GPU nodes and [H] concurrent clients". - - "supports [I] requests per second with [J] backend engines". - -After you draft the text, I will plug in actual experiment results to replace these placeholders. - ---- Writing goals --- - -Please draft a **150–200 word** English abstract that: - -1. Starts with 2–3 sentences of **problem context**: complexity of modern LLM/AI pipelines, - challenges in managing dataflow, heterogeneous resources, and multiple LLM / embedding services. -1. Gives a **high-level description of SAGE** as a systems contribution, emphasizing: - - its **layered architecture** and separation of concerns; - - the **declarative dataflow** interface and execution model; - - the **unified control plane** and gateway for LLM / embedding workloads; - - its role as a **general platform** for LLM-centric pipelines, not only a control-plane module. -1. Clearly states **2–4 concrete contributions** that a systems reviewer can check, such as: - - a layered architecture that enables modular, scalable LLM/AI pipelines; - - a control-plane design that improves utilization / latency across LLM and embedding services; - - heterogeneous CPU/GPU support and reproducible tooling that simplify deployment; - - a benchmark suite that probes both agent capabilities and system-level performance. -1. Ends with **quantitative experimental claims** using the placeholder format above (no vague - "improves performance" statements). - ---- Style constraints --- - -- Use **academic, precise, neutral English** typical of top-tier systems papers. -- Avoid buzzwords and marketing language; focus on **what the system does**, **why it is needed**, - and **how well it performs**. -- Clearly mark all quantitative placeholders as `[X]`, `[Y]`, etc., so we can later map them to - specific experiments. -- If needed, you may slightly exceed 200 words in the first draft and then suggest where to cut. - ---- Output format --- - -1. Provide **one candidate abstract** with quantitative placeholders clearly marked. -1. List the **specific experiments needed** to fill each placeholder (e.g., mixed workload latency - benchmark, scalability study, agent benchmark). -1. Provide **3–5 bullet-point suggestions** on how we might refine the abstract once we have - concrete experimental numbers and finalized baselines. - -______________________________________________________________________ - -## Example abstract structure（仅供参考） - -```text -[Problem context – 2–3 sentences] -Modern LLM applications require complex pipelines that combine retrieval, tools, and multiple models across heterogeneous CPU/GPU clusters. Existing serving and MLOps systems either focus on single-model inference or generic workflows, and do not provide unified control or dataflow support for mixed LLM + embedding workloads. - -[System description – 2–3 sentences] -We present SAGE, a framework that organizes LLM/AI pipelines into a layered architecture with declarative dataflow and a unified control plane for LLM and embedding services. SAGE integrates platform, kernel, middleware, and gateway components to execute pipelines efficiently across heterogeneous resources. - -[Key contributions – 2–3 sentences] -Our main contributions are: (1) a six-layer architecture and declarative dataflow model for LLM-centric pipelines; (2) a unified LLM + embedding control plane that shares resources and improves tail latency; (3) systems support for heterogeneous CPU/GPU deployments and reproducible tooling; and (4) a benchmark suite that evaluates both agent behavior and system-level scheduling. - -[Quantitative results – 1–2 sentences] -Experiments show that SAGE reduces p99 latency by [X]% and improves throughput by [Y]× compared to [baseline] on [workload], while achieving [Z]% SLO satisfaction and maintaining [A]% task success rate in representative agent pipelines. -``` diff --git a/benchmark/docs/02_introduction.md b/benchmark/docs/02_introduction.md deleted file mode 100644 index b5941081ce..0000000000 --- a/benchmark/docs/02_introduction.md +++ /dev/null @@ -1,130 +0,0 @@ -# Introduction Prompts – SAGE Systems Paper - -本文件提供多轮使用的引言（Introduction）写作提示词模版，面向顶级机器学习系统会议的系统 track（例如 ICML Machine Learning Systems track）。 -整体目标是：以 **完整的 SAGE 系统** 为主角，而不是只讲某个子模块（例如 control plane）。 - -______________________________________________________________________ - -## 2.1 生成引言整体结构 - -**提示词（可直接复制）** - -You are a systems-track co-author. Help me design the **structure** of the Introduction for a paper -about **SAGE**, a machine learning systems framework. - ---- System context --- - -- Target venue: a **top-tier Machine Learning Systems track**. -- System: **SAGE**, a Python 3.10+ framework for **LLM/AI data processing pipelines** with - **declarative dataflow**. -- SAGE should be presented as a **full-stack system**, covering: - - a strict **5-layer architecture (L1–L5)** with no upward dependencies, from `sage-common` and - `sage-platform` up to `sage-cli` and `sage-tools`; - - **declarative dataflow** for composing LLM/AI pipelines (e.g., retrieval, tools, LLM calls, - post-processing); - - a **unified LLM & embedding control plane** (sageLLM) exposed via `isagellm.gateway`; - - **CPU-only and GPU deployments**, job management and node selection in `sage-kernel`, platform - services in `sage-platform`; - - **benchmark suites** in `sage-benchmark` (independent repo) for agents, scheduling policies, - RAG, DB/time-series components, etc. - ---- Positioning against existing systems (CRITICAL for novelty) --- - -When structuring the Introduction, explicitly address how SAGE differs from **all** of the following -(not only control-plane-level systems): - -- **LLM serving engines** such as vLLM, TensorRT-LLM, SGLang: they optimize single-model inference; - SAGE operates at a **higher abstraction level**, orchestrating multiple engines, embedding - services, and full pipelines under a unified dataflow and control plane. -- **ML serving frameworks** such as Ray Serve, KServe, Triton Inference Server: they are generic - serving or deployment platforms; SAGE provides **LLM-aware scheduling**, declarative pipelines, - and end-to-end evaluation for LLM-centric workloads. -- **LLM application frameworks** such as LangChain, LlamaIndex, DSPy: they focus on - application-level orchestration; SAGE is a **systems-level infrastructure** providing resource - management, scheduling, and execution primitives that such frameworks could build upon. -- **ML workflow / MLOps platforms** such as MLflow, Kubeflow, Airflow: they emphasize training and - generic workflows; SAGE focuses on **inference pipelines** with real-time scheduling, - heterogeneous hardware, and LLM-specific concerns. -- **LLM benchmarks** such as AgentBench, ToolBench, HELM, single-engine vLLM benchmarks: they focus - on task accuracy or single-engine metrics; SAGE adds **system-level benchmarks** that stress - control-plane, dataflow, and heterogeneous deployments. - -The key novelty claim should be: SAGE is a **full ML system** that combines a layered architecture, -declarative dataflow, a unified LLM + embedding control plane, heterogeneous deployment support, and -comprehensive benchmarks, filling the gap between low-level serving engines and high-level -application frameworks. - ---- Task --- - -Design a **4–6 paragraph outline** (not full prose yet) for the Introduction that suits a top-tier -systems paper on SAGE. For each paragraph: - -1. State the **goal** of the paragraph (e.g., establish broader context of LLM/AI systems, - articulate challenges in managing complex LLM pipelines, highlight gaps in existing systems, - introduce SAGE, summarize contributions, preview experiments). -1. Provide a **bullet list of key points** that should appear in that paragraph, focusing on: - - systems challenges (scalability, hardware heterogeneity, multiple LLM/embedding services, - observability, configuration complexity, reproducibility); - - why existing frameworks (generic MLOps, standalone LLM serving, ad-hoc scripts, - application-level orchestrators) do not fully address these for **LLM-centric pipelines**; - - how SAGE’s architecture, dataflow model, control plane, and benchmarks are designed around - these challenges. -1. Mark where we should **present the main contributions** as a numbered list (usually at the end of - the last or second-to-last paragraph). -1. Explicitly note any parts where you need more concrete details from me (e.g., workloads, cluster - scale, baselines, key SAGE subsystems highlighted in experiments). - ---- Output --- - -- A paragraph-level outline (4–6 paragraphs), each with: - - a short description of the paragraph goal; - - bullet points of content to cover. -- Do **not** yet write the full paragraphs. - -______________________________________________________________________ - -## 2.2 逐段写引言 - -在拿到 2.1 中的段落大纲后，你可以按段落逐个生成英文正文。 - -**提示词（可直接复制，每段都可以复用）** - -We previously designed a paragraph-level outline for the Introduction of our systems paper on -**SAGE**. Now we will write **paragraph X**. - -Here is the outline for this paragraph (copied from the previous step): - -[PASTE THE BULLET-POINT OUTLINE FOR PARAGRAPH X HERE] - ---- System context reminders --- - -- SAGE targets **LLM/AI pipelines**, not generic ML training. -- It offers **declarative dataflow** and a **multi-layer architecture** with no upward dependencies. -- It includes a **unified control plane** for LLM and embedding services, exposed via an - OpenAI-compatible gateway. -- It provides **benchmarking** tools for agent capabilities, scheduling policies, and other - subsystems (RAG, DB, TSDB, etc.). -- The paper is about the **whole SAGE system** (architecture + dataflow + control plane + - benchmarks), not only a single module. - ---- Task --- - -Using only the above outline and the system context, write a full **English paragraph** (8–12 -sentences) suitable for a top-tier systems-track Introduction. - -Writing requirements: - -1. Focus on **systems challenges and insights**, not just listing features. -1. Use **neutral, technical language**; avoid buzzwords or marketing tone. -1. Make the paragraph **self-contained**, but naturally connectible to the previous and next - paragraphs. -1. It is acceptable for the first draft to be slightly longer; at the end, suggest 1–2 sentences - that could be dropped if space is tight. - ---- Output --- - -1. The full paragraph in English. -1. A short bullet list of **possible trimming points** (sentences that could be removed if we need - to shorten the Introduction). - -你不需要在每一段里重复完整的系统描述，只要引用必要的关键信息即可，使整篇引言连贯、系统、而且覆盖整个 SAGE。 diff --git a/benchmark/docs/03_related_work.md b/benchmark/docs/03_related_work.md deleted file mode 100644 index eecfb0f9c9..0000000000 --- a/benchmark/docs/03_related_work.md +++ /dev/null @@ -1,114 +0,0 @@ -# Related Work Prompts – SAGE Systems Paper - -本文件提供撰写 Related Work（相关工作）部分的提示词，重点从 **系统视角** 对 SAGE -进行分类与定位，覆盖整个系统（分层架构、数据流、控制平面、benchmark），而不是只讨论某一个子模块。 - -______________________________________________________________________ - -## 3.1 相关工作分类与定位 - -**提示词（可直接复制）** - -You are a systems-track author responsible for the **Related Work** section of a paper about -**SAGE**, a framework for LLM/AI pipelines. - ---- System context (for positioning) --- - -SAGE focuses on **system-level support for LLM/AI dataflow pipelines**, rather than general ML -training. Key system contributions include: - -- A **5-layer architecture** with strict no-upward-dependency design, from `sage-common` and - `sage-platform` to `sage-kernel` / `sage-libs`, `sage-middleware`, and user-facing tools - (`sage-cli`, `sage-tools`). LLM inference is provided by independent `sageLLM` engine. -- **Declarative dataflow** for LLM/AI pipelines, mapping user-level pipeline descriptions to - efficient execution on heterogeneous CPU/GPU clusters. -- A unified **LLM & embedding control plane** with hybrid scheduling and batching, exposed via an - OpenAI-compatible gateway (in `sageLLM` independent package). -- Systems support for **CPU-only and GPU nodes**, job management and node selection in - `sage-kernel`, platform services in `sage-platform`. -- A **benchmark suite** (`sage-benchmark`, independent repository) focusing on **agent - capabilities** (tool selection, planning, timing) and **system-level scheduling** (throughput, - latency distribution, SLO compliance, interference). - ---- Task --- - -1. Propose a **taxonomy of related work** into 4–5 categories suitable for a top-tier systems paper. - A reasonable starting point is: - - - Category 1: LLM Serving Engines (e.g., vLLM, TensorRT-LLM, SGLang, Orca) - - Category 2: ML Serving Frameworks and Workflow Platforms (e.g., Ray Serve, KServe, Triton, - MLflow, Kubeflow, Airflow) - - Category 3: LLM Application Frameworks and Agents (e.g., LangChain, LlamaIndex, DSPy, various - agent tool-use frameworks) - - Category 4: LLM Benchmarks and Evaluation Frameworks (e.g., AgentBench, ToolBench, HELM, vLLM - benchmark) - - (Optional) Category 5: Data & Storage Systems for AI Pipelines (e.g., vector DBs, TSDBs, - dataflow systems that overlap with `sage.db`, `sage.tsdb`, `sage.flow`) - -1. For each category: - - - Give 2–3 sentences summarizing **what this category of work tries to achieve**, in terms of - systems properties (e.g., throughput, flexibility, observability, portability, fairness). - - Provide 3–5 sentences on **how SAGE differs** from typical works in this category, explicitly - referencing: - - multi-layer architecture vs. monolithic designs; - - **unified** LLM + embedding control plane vs. LLM-only serving; - - declarative dataflow vs. imperative orchestration or ad-hoc scripts; - - system-level benchmarks vs. task-only or single-engine benchmarks. - - Mark where we should later insert **specific citation examples** (use placeholders like - `[REF: VLLM]`, `[REF: RAY_SERVE]`, `[REF: LANGCHAIN]`). - -1. Explicitly state the **gap that SAGE fills**: a unified, layered system for LLM+embedding - dataflow pipelines with control-plane scheduling and comprehensive benchmarks, sitting between - low-level serving engines and high-level application frameworks. - ---- Output --- - -- A structured outline listing: - - The proposed categories; - - For each category, a short paragraph summarizing it and briefly contrasting SAGE. -- The outline should be detailed enough that we could almost lift it directly into the paper, then - refine names and add citations. - -______________________________________________________________________ - -## 3.2 完整 Related Work 草稿 - -在 3.1 确定 taxonomy 和要点之后，你可以生成一版接近成品的 Related Work 文本。 - -**提示词（可直接复制）** - -Now, using the taxonomy and short summaries we just designed for Related Work, please draft a **full -Related Work section** for our systems paper on **SAGE**. - -Constraints and goals: - -1. Organize the text into **subsections or logical paragraphs**, one per category from the taxonomy. -1. For each category: - - Start with 2–3 sentences summarizing the category and its main systems concerns. - - Name **specific representative systems** (e.g., vLLM, TensorRT-LLM, SGLang, Ray Serve, KServe, - Triton, MLflow, Kubeflow, LangChain, LlamaIndex, DSPy, AgentBench, ToolBench, HELM) and briefly - describe what they do. - - Then write 3–5 sentences positioning **SAGE** relative to this category, focusing on: - - multi-layer architecture vs. monolithic or flat designs; - - unified LLM+embedding control plane vs. single-workload focus; - - declarative dataflow vs. imperative orchestration; - - system-level benchmark and experimental testbed vs. task-only evaluation. - - Include explicit phrases that emphasize **complementarity or orthogonality** (e.g., "SAGE can - use vLLM as a backend engine"), not just replacement. -1. Throughout the text, clearly emphasize that **SAGE is a systems contribution**: improved - implementation and scalability, support for heterogeneous hardware, unified resource management - for LLM+embedding workloads, and comprehensive evaluation infrastructure. -1. Include a **summary paragraph** at the end that synthesizes the positioning: SAGE fills the gap - between low-level serving engines, generic serving / workflow systems, and high-level LLM - application frameworks by providing a unified dataflow-based platform and control plane with - benchmarks. - ---- Output --- - -- A 1.5–2 page (single-column equivalent) English draft of the Related Work section, following the - above structure. -- Use actual system names for well-known systems; use `[REF: ...]` placeholders where you need - citations. -- At the end, list all `[REF: ...]` slots used, grouped by category, so we can map them to actual - papers later. diff --git a/benchmark/docs/04_system_and_method.md b/benchmark/docs/04_system_and_method.md deleted file mode 100644 index cfb1120f45..0000000000 --- a/benchmark/docs/04_system_and_method.md +++ /dev/null @@ -1,226 +0,0 @@ -# System / Method Prompts – SAGE Systems Paper - -本文件面向系统论文的 "System / Method" 章节，帮助你把 **整个 SAGE 系统** 的设计讲清楚，突出实现与可扩展性，而不是只强调 control plane。 - -______________________________________________________________________ - -## 4.1 设计 System 章节结构 - -**提示词（可直接复制）** - -You are a systems-track co-author responsible for the **System Design / Method** section of a paper -about **SAGE**. - ---- System context --- - -SAGE is a Python 3.10+ framework for building LLM/AI data processing pipelines. It targets -**system-level issues** such as scalability, heterogeneous hardware, unified management of LLM and -embedding workloads, and reproducible experimentation. - -Key design aspects to consider: - -- **Layered architecture (L1–L5)** with **no upward dependencies**: - - L1: `sage-common` – foundational utilities, configuration, user paths (XDG), port management - (`SagePorts`), shared components. - - L2: `sage-platform` – platform services (storage, queuing, service management), cluster - configuration via `config/cluster.yaml`. - - L3: `sage-kernel`, `sage-libs` – core execution engine, **job management** - (`runtime/job_manager`), **node selection** (`scheduler/node_selector`), algorithms, scheduling - logic, CPU/GPU awareness. - - L4: `sage-middleware` – C++ operators and performance-critical components, built via CMake. - - L5: `sage-cli`, `sage-tools` – user-facing interfaces (CLI, development tools). -- **Independent repositories** (not in core architecture): - - `sage-benchmark` – benchmark suites (PyPI: `isage-benchmark`) - - `sage-examples` – applications and tutorials - - `sageLLM` – LLM inference engine with control plane (PyPI: `isagellm`) -- **Declarative dataflow** abstraction for specifying pipelines, with compilation/execution over - heterogeneous resources. -- **Unified LLM & embedding control plane** (sageLLM): `UnifiedInferenceClient`, - `ControlPlaneManager`, `HybridSchedulingPolicy`, `EmbeddingExecutor` coordinating multiple vLLM - and embedding backends via `isagellm.gateway`. -- **User paths and configuration** following XDG base directory spec; project-level `.sage/` - directory for build artifacts and caches. -- **Deployment and CI** patterns (quickstart scripts, `sage-dev` tooling, pre-commit hooks) as - concrete implementation and reproducibility choices. - ---- Task --- - -Design a **System / Method section outline** tailored for a top-tier systems paper. The section -should likely include 3–5 main subsections, for example: - -- System Overview and Design Goals; -- Layered Architecture; -- Declarative Dataflow and Execution Model; -- LLM & Embedding Control Plane (sageLLM) as one important subsystem; -- Implementation Details and Deployment. - -For each proposed subsection: - -1. Provide a bullet list of **key questions** it should answer from a systems-reviewer perspective - (e.g., how the system scales, how it abstracts hardware differences, how it improves - programmability without sacrificing performance, how it supports reproducibility and - observability). -1. Map these questions to **specific SAGE components** or modules (e.g., `sage-platform` and - `sage-kernel` for job management and node selection, `sage.common.components.sage_llm` for - control plane, `sage-middleware` for C++ operators, `sage-benchmark` for evaluation workloads). -1. Suggest **figures or diagrams** that should accompany this subsection (e.g., architecture - diagram, dataflow diagram, control-plane timeline), with 1–2 sentences per figure describing what - it should convey. -1. Indicate which subsections are **core** for the main paper and which details can be moved to an - appendix if page limits are tight. - ---- Output --- - -- A structured outline with subsections and bullet points answering the above. -- No full prose yet. - -______________________________________________________________________ - -## 4.2 逐小节撰写 System 文本 - -有了 4.1 的大纲后，你可以对每个小节单独调用下面的提示词写正文。 - -**提示词（可直接复制，每个小节复用）** - -We have designed an outline for the System / Method section of our systems paper on **SAGE**. Now we -will write the subsection: - -> [INSERT SUBSECTION TITLE HERE, e.g., "Layered Architecture"] - -Here is the bullet-point outline for this subsection (from the previous step): - -[PASTE THE BULLET-POINT OUTLINE HERE] - ---- System reminders --- - -- SAGE uses a **5-layer architecture (L1–L5)** with no upward dependencies. -- It exposes **declarative dataflow** to users, while deeper layers handle scheduling, optimization, - and execution. -- It includes a **unified control plane** for LLM and embedding services (sageLLM, independent - repo), fronted by an OpenAI-compatible gateway. -- It targets **scalability, heterogeneity (CPU/GPU), and reproducibility**. -- The paper should emphasize the **whole system** (architecture + dataflow + control plane + - benchmarks + deployment), not just one component. - ---- Task --- - -Please write a detailed English subsection for a systems paper that: - -1. Answers the bullet-point questions with **systems-level explanations** rather than just listing - APIs. -1. Emphasizes how SAGE’s design choices (e.g., layering, declarative dataflow, control plane, - benchmark integration, tooling) address concrete systems challenges (resource utilization, - latency, cluster heterogeneity, debuggability, ease of evolution, reproducibility). -1. Includes **references to SAGE components** (module or package names) only when they help clarify - the design (e.g., mentioning `sage-kernel` for job management and node selection, `sage-platform` - for cluster configuration and services, `sage-benchmark` for evaluation workloads). -1. Suggests where to place figures or tables, and provides a short candidate **figure caption** if - appropriate. - ---- Output --- - -1. The full text of the subsection in English (approx. 1–2 single-column pages, depending on - importance). -1. A short list of **potential figure captions** and where they should appear. -1. Optional notes on which parts could be shortened if page limits are tight. - -______________________________________________________________________ - -## 4.3 控制平面技术细节（可选强化） - -如果你希望在系统论文中 **重点强化控制平面（sageLLM）这一子模块的系统贡献**，可以单独用下面的提示词撰写一个专门小节。注意：控制平面是 SAGE -的一个重要子系统，但论文整体仍然需要覆盖完整系统。 - -**提示词（可直接复制）** - -We want to dedicate a focused subsection to the **LLM & embedding control plane** in SAGE -("sageLLM"). This subsection should be particularly convincing for systems reviewers who care about -**resource management, scheduling, and scalability**. - ---- Control plane architecture (from actual implementation) --- - -- The control plane classifies requests into chat / generation vs. embedding. -- It uses policies such as `HybridSchedulingPolicy` to batch and route requests across a pool of - vLLM and embedding engines. -- It aims to improve **throughput, tail latency, and SLO compliance** while sharing resources across - heterogeneous LLM and embedding workloads. -- It is integrated with `sage-gateway`, which exposes an OpenAI-compatible API on well-defined ports - from `SagePorts`. - -Key components (module paths): - -- `ControlPlaneManager`: `sageLLM/control_plane/manager.py` – core orchestrator. -- `RequestClassifier`: `sageLLM/control_plane/request_classifier.py` – request type detection. -- `HybridSchedulingPolicy`: `sageLLM/control_plane/strategies/hybrid_policy.py` – scheduling - decisions. -- `EmbeddingExecutor`: `sageLLM/control_plane/executors/embedding_executor.py` – batched embedding - execution. - ---- Scheduling algorithm details to explain --- - -The prompt should guide the model to explain: - -1. **Request classification** (how chat / generation / embedding are distinguished, and with what - overhead). -1. **Scheduling policy options** (e.g., FIFO, priority, SLO-aware, hybrid) and how they trade off - fairness, latency, and throughput. -1. **Batching strategy** for LLM vs. embedding workloads, and interaction with vLLM’s continuous - batching. -1. **Load balancing** across multiple backend engines under mixed workloads. -1. **Interaction with vLLM**: SAGE does not replace vLLM’s internal scheduler but provides - cross-engine and cross-workload scheduling via an OpenAI-compatible interface. - ---- Task --- - -Please draft a detailed English subsection (around 1–1.5 single-column pages) that: - -1. Explains the **design goals** of the control plane (unified scheduling, resource sharing, SLO - compliance). -1. Describes the **architecture** and key components and how they interact. -1. Details the **scheduling algorithm** with pseudocode or an algorithmic description for - `HybridSchedulingPolicy`. -1. Explains how **batching** works differently for LLM vs. embedding workloads. -1. Clarifies the **relationship with vLLM** (complementary, not replacement). -1. Prepares the ground for experiments comparing different scheduling policies and baselines. - ---- Output --- - -1. Full subsection text with technical depth. -1. Pseudocode for the core scheduling algorithm (if appropriate). -1. Suggested figures: - - Figure X: Control Plane Architecture (component diagram). - - Figure Y: Request Timeline showing classification, queuing, batching, execution under mixed - workloads. - -______________________________________________________________________ - -## 4.4 与 vLLM 关系的澄清（重要补充） - -审稿人可能会质疑 SAGE 与 vLLM（或其它 LLM 引擎）的关系。这里提供专门的澄清引导。 - -**提示词（可直接复制）** - -A reviewer might ask: "How does SAGE relate to vLLM? Isn’t vLLM already a highly optimized LLM -serving system?" - -Please draft a **clarification paragraph** (3–5 sentences) that explains: - -1. **Complementarity, not competition**: SAGE uses vLLM (or other engines) as backend serving - components. vLLM handles single-model inference optimization (PagedAttention, continuous - batching). SAGE handles cross-model orchestration and embedding co-scheduling. -1. **Abstraction level difference**: - - vLLM = single-model inference engine (optimizes GPU memory, batch processing for one model). - - SAGE = pipeline-level control plane and systems platform (orchestrates multiple models, handles - embedding services, provides unified API, integrates with dataflow and benchmarks). -1. **What SAGE adds**: - - multi-engine load balancing; - - unified LLM + embedding scheduling; - - request classification and SLO-aware routing; - - declarative pipeline composition and system-level benchmarks. -1. **Concrete example**: e.g., a RAG pipeline needing an embedding service + an LLM service. Without - SAGE, operators must manually manage two services and balance load; with SAGE, they declare the - pipeline and the control plane plus dataflow engine handle resource allocation and scheduling. - -This paragraph should be inserted in the System section after describing the control plane -architecture, and should clearly state that the **paper evaluates SAGE as a full system built on top -of such engines**. diff --git a/benchmark/docs/05_experiments.md b/benchmark/docs/05_experiments.md deleted file mode 100644 index 8ff1a39cd6..0000000000 --- a/benchmark/docs/05_experiments.md +++ /dev/null @@ -1,272 +0,0 @@ -# Experiments Prompts – SAGE Systems Paper - -本文件帮助你为系统论文的 Experiments 部分设计结构和写作提示词。 **关键更新**：本提示词已与 `sage-benchmark` 中的实际实验脚本（`exp_5_1` 至 -`exp_5_5`）和画图工具（`plotting.py`）完全对齐。 - -______________________________________________________________________ - -## 5.1 设计实验章节结构 (Structure Design) - -**提示词（可直接复制）** - -You are the experiments lead for a systems-track paper about **SAGE**. - ---- System and evaluation context --- - -SAGE is a system for **LLM/AI pipelines** with a unified control plane, declarative dataflow, and -support for heterogeneous hardware. We have implemented a comprehensive benchmark suite -(`sage-benchmark`) with 5 specific experiments. - ---- Task --- - -Design the **structure** of the Experiments section. It MUST follow this exact 5-subsection -structure to match our experimental results: - -### 5.1 End-to-End Pipeline Performance - -- **Goal**: Demonstrate SAGE's efficiency in executing complex, multi-stage pipelines (specifically - RAG: Embedding -> Retrieval -> Generation). -- **Workload**: Simulated RAG pipeline with concurrent users; mixed embedding and LLM calls. -- **Key Figure**: **Latency CDF** (Cumulative Distribution Function) showing the distribution of - end-to-end pipeline latencies. -- **Key Figure**: **Request Timeline** (Waterfall plot) showing the interleaving of embedding and - generation tasks. - -### 5.2 Control Plane Effectiveness - -- **Goal**: Prove that SAGE's unified control plane (co-scheduling LLM and Embeddings) outperforms - separate services. -- **Workload**: Mixed traffic (e.g., 70% Chat, 30% Embedding) at varying request rates. -- **Key Figure**: **Throughput vs. Latency** curve comparing "Unified Control Plane" vs. "Separate - Services". -- **Key Figure**: **Latency CDF** comparing tail latencies (p99) of the two approaches. - -### 5.3 Isolation & Fairness - -- **Goal**: Show SAGE's ability to protect latency-sensitive "Interactive" users from - high-throughput "Batch" users (Noisy Neighbors). -- **Workload**: Two concurrent user groups: "Interactive" (low rate, high priority) and "Batch" - (high rate, low priority). -- **Key Figure**: **Latency CDF** for the Interactive user, comparing "With SAGE Isolation" vs. - "Without Isolation". - -### 5.4 Scalability - -- **Goal**: Demonstrate linear scaling of throughput as backend resources increase. -- **Workload**: High-concurrency traffic against 1, 2, 4, and 8 vLLM backend instances. -- **Key Figure**: **Scalability Bar Chart** showing Request/Second (RPS) vs. Number of GPUs. - -### 5.5 Heterogeneous Hardware Support - -- **Goal**: Validate the benefit of offloading Embedding tasks to CPU nodes to save GPU resources - for LLM inference. -- **Workload**: Mixed workload running on "GPU-only" vs. "Hybrid (GPU for LLM + CPU for Embed)" - configurations. -- **Key Figure**: **Resource Efficiency** comparison (or Latency CDF showing minimal degradation - with CPU offloading). - ---- Output --- - -- A structured outline for the Experiments section. -- For each subsection, write a short paragraph describing the **experimental setup** (workload, - metrics) and the **expected visual evidence** (the figures mentioned above). - -______________________________________________________________________ - -## 5.2 撰写具体实验分析 (Detailed Analysis Prompts) - -以下提示词用于指导大模型撰写具体的实验分析段落。 - -### 5.1 End-to-End Pipeline Analysis - -**Prompt:** "Write the analysis for Section 5.1 (End-to-End Pipeline Performance). The experiment -ran a simulated RAG pipeline (Embed -> Retrieve -> Generate). Refer to **Figure 5.1(a) (Latency -CDF)**, which shows a tight latency distribution with a p99 of [X] ms, indicating stable -performance. Refer to **Figure 5.1(b) (Request Timeline)**, which illustrates how SAGE's scheduler -efficiently interleaves embedding and generation tasks, minimizing gaps and maximizing resource -usage." - -### 5.2 Control Plane Analysis - -**Prompt:** "Write the analysis for Section 5.2 (Control Plane Effectiveness). Compare SAGE's -unified scheduling against a baseline of separate services. Refer to **Figure 5.2 (Throughput vs. -Latency)**. Highlight that SAGE sustains [Y]% higher throughput before latency saturation. Explain -that by co-scheduling, SAGE utilizes idle GPU cycles (during LLM decoding gaps) for embedding tasks, -as evidenced by the lower tail latency in the **Latency CDF**." - -### 5.3 Isolation Analysis - -**Prompt:** "Write the analysis for Section 5.3 (Isolation & Fairness). Describe the 'Noisy -Neighbor' scenario with Interactive vs. Batch users. Refer to **Figure 5.3**, showing that without -isolation, the Interactive user's p99 latency spikes to [A] ms. With SAGE's priority-aware -scheduling, the Interactive user's latency curve remains close to the baseline, demonstrating -effective performance isolation." - -### 5.4 Scalability Analysis - -**Prompt:** "Write the analysis for Section 5.4 (Scalability). Refer to **Figure 5.4 (Scalability -Bar Chart)**. Observe that throughput scales nearly linearly from 1 to 8 GPUs. Calculate the scaling -efficiency (e.g., '7.2x speedup on 8 GPUs'), proving that the SAGE control plane introduces minimal -overhead." - -### 5.5 Heterogeneity Analysis - -**Prompt:** "Write the analysis for Section 5.5 (Heterogeneous Hardware). Discuss the trade-off of -offloading embeddings to CPU. State that while CPU embedding latency is slightly higher, the overall -system throughput for LLM tokens increases significantly because GPU resources are freed up. -Conclude that SAGE's flexible node selection enables cost-efficient deployments." - -- Test with 1, 2, 4, 8 vLLM instances (and optionally multiple embedding servers). -- Measure: throughput, speedup vs. single-engine baseline, control-plane overhead ratio. - -2. **Load scaling (requests per second)** - - Sweep request rate from light load up to and beyond saturation. - - Measure: throughput curve, latency curve (especially tail), SLO hit rate. -1. **Concurrent clients** - - Test with 1, 10, 50, 100 concurrent clients. - - Measure: per-client latency, fairness, starvation or head-of-line blocking. -1. **Model size scaling** - - Test with different model sizes (e.g., 7B, 13B, 70B) if available. - - Measure: how control-plane overhead compares to model inference time. - ---- Experimental setup checklist --- - -Ask the model to force the following information to be specified: - -```text -Hardware specification: -- GPU type: [e.g., A100 40GB, RTX 4090] -- Number of GPUs: [e.g., 4] -- CPU cores: [e.g., 64] -- Memory: [e.g., 256 GB] -- Network: [e.g., InfiniBand, 10GbE] - -Software versions: -- SAGE version: [e.g., 0.5.0] -- vLLM version: [e.g., 0.4.0] -- CUDA version: [e.g., 12.1] -- Python version: [e.g., 3.11] - -Workload specification: -- LLM model: [e.g., Qwen2.5-7B-Instruct] -- Embedding model: [e.g., BGE-M3] -- Input token length: [e.g., 512 tokens] -- Output token length: [e.g., 128 tokens] -- Request arrival: [e.g., Poisson, bursty, constant] -``` - ---- Expected result formats --- - -Table: Throughput vs. Number of Backends - -| Backends | Throughput (req/s) | Speedup | Control Plane Overhead | -| -------: | -----------------: | ------: | ---------------------: | -| 1 | [baseline] | 1.0× | [X]% | -| 2 | [?] | [?]× | [?]% | -| 4 | [?] | [?]× | [?]% | -| 8 | [?] | [?]× | [?]% | - -Figure: Latency vs. Request Rate - -- X-axis: request rate (req/s) -- Y-axis: latency (ms) -- Lines: p50, p95, p99 -- Mark saturation point and discuss where SAGE’s control plane becomes the bottleneck (if at all) - ---- Output --- - -1. A detailed experimental plan with specific configurations. -1. Expected table/figure formats. -1. Key claims the scalability study should support (e.g., near-linear scaling up to N backends, - negligible control-plane overhead for large models). - -______________________________________________________________________ - -## 5.3 为每类实验撰写结果描述 - -在你实际拿到实验数据之后，可以用下面的提示词为每类实验写结果段落。 - -**提示词（可直接复制，每个子节可复用）** - -We now have experimental results for the subsection: - -> \[INSERT EXPERIMENT SUBSECTION TITLE HERE, e.g., "End-to-End Pipeline Performance" or "Scalability -> Study"\] - -Here is the design of this subsection (goal, workloads, metrics, baselines): - -[PASTE THE DESIGN OUTLINE FOR THIS SUBSECTION HERE] - -Here are the preliminary results (tables, plots, or bullet points): - -[PASTE YOUR NUMERIC OR QUALITATIVE RESULTS HERE] - ---- Task --- - -Write the **Results and Analysis** text for this subsection in English, targeting systems reviewers. - -Requirements: - -1. Start by restating **what the experiment tries to verify** (e.g., whether the control plane - improves tail latency under mixed workloads, whether declarative dataflow leads to better - resource utilization, whether heterogeneous deployment is practical). -1. Describe **key trends in the results**, referencing specific metrics (throughput, latency, SLO - satisfaction, success rates, cost-performance, etc.). -1. Clearly explain **why SAGE behaves better or differently** than baselines, relating back to - design choices (layering, control plane, dataflow, CPU-only support, benchmarks, tooling). -1. If results are mixed, be honest and propose plausible explanations or follow-up experiments. -1. Propose **candidate figure/table captions** for the plots or tables we have, and specify which - should be in the main paper vs. appendix. - ---- Output --- - -1. A few paragraphs of result description and analysis for this subsection. -1. A list of suggested figure/table captions with a short description each. -1. If applicable, a short note on what additional experiments could strengthen this story. - -______________________________________________________________________ - -## 5.4 Baseline 选择指南（SAGE 特化） - -**为什么需要仔细选择 baseline：** 系统论文审稿人会严格审视 baseline 是否公平、是否代表了 state-of-the-art。 - -| SAGE Feature | Recommended Baseline | Why This Baseline | -| ------------------------------- | --------------------------------------------------------- | ---------------------------------------------- | -| Layered architecture + dataflow | Ad-hoc Python scripts or flat microservices | Shows maintainability / complexity differences | -| Unified control plane | vLLM + separate embedding service (manual load balancing) | Shows the benefit of unified scheduling | -| Hybrid scheduling | SAGE with FIFO policy | Ablation showing scheduling policy matters | -| Multi-engine support | Single vLLM instance | Shows horizontal scaling works | -| CPU-only support | GPU-only deployment or naive CPU-only baseline | Shows cost-effectiveness and feasibility | -| System-level benchmark | AgentBench / ToolBench / single-engine benchmark | Shows SAGE’s broader system metrics vs. others | - -Baseline 实现要求： - -1. 所有 baseline 必须使用相同或明确定义的硬件配置。 -1. vLLM baseline 必须使用相同版本和参数。 -1. 如果无法使用相同硬件，必须说明并尽量归一化结果（例如用吞吐/成本等指标）。 -1. 必须报告 baseline 的最优合理配置（不能故意用明显较差的配置）。 - -______________________________________________________________________ - -## 5.5 实验结果的可重复性 - -**系统论文对可重复性要求很高。** 确保包含以下信息： - -```markdown -### Reproducibility Checklist - -- [ ] Hardware specification (GPU model, memory, CPU cores, network) -- [ ] Software versions (SAGE, vLLM, CUDA, Python, key dependencies) -- [ ] Model details (model name, size, quantization if any) -- [ ] Workload specification (input/output length distribution, arrival pattern) -- [ ] Warm-up procedure (how many requests before measurement?) -- [ ] Measurement duration (how long did you run each experiment?) -- [ ] Number of repetitions (how many times did you repeat? error bars?) -- [ ] Code availability (will you release experiment scripts?) -``` - -建议在论文附录或 supplementary material 中包含： - -1. 完整的实验配置文件； -1. 用于生成图表的原始数据； -1. 运行实验的脚本（可基于 `sage-dev` 或 `sage.benchmark` 的 CLI）。 diff --git a/benchmark/docs/06_contributions_example.md b/benchmark/docs/06_contributions_example.md deleted file mode 100644 index e637cbee46..0000000000 --- a/benchmark/docs/06_contributions_example.md +++ /dev/null @@ -1,121 +0,0 @@ -# SAGE – Example Contributions List (Systems Track) - -本文件提供一份面向顶级 **Machine Learning Systems** track 的示例 "Contributions" 列表草案，你可以直接在 Introduction -末尾或单独小节中使用/修改。 重点是把 **整个 SAGE 系统** 的贡献讲清楚：分层架构、数据流、控制平面、异构部署、benchmark。 - -______________________________________________________________________ - -## 1. Example Contributions (English Draft) - -Below is an example contributions list tailored to SAGE as a **machine learning system** rather than -a pure algorithm or application. - -1. **A layered architecture for declarative LLM/AI pipelines.** - - We introduce SAGE, a framework that organizes LLM/AI data processing pipelines into a strict - six-layer architecture, from foundational utilities (`sage-common`) and platform services - (`sage-platform`), through kernel and middleware components (`sage-kernel`, `sage-libs`, - `sage-middleware`), up to applications and user-facing tools (`sage-apps`, `sage-benchmark`, - `sage-cli`, `sage-studio`, `sage-tools`, `sage-gateway`). By enforcing **no upward - dependencies**, SAGE cleanly separates concerns between configuration, scheduling, execution, and - user interfaces, enabling independent evolution of layers, easier testing, and simplified - large-scale system maintenance. - -1. **A unified control plane for LLM and embedding workloads.** - - We design and implement a **sageLLM control plane** that jointly manages LLM and embedding - workloads across a shared pool of engines. The control plane classifies requests (chat/generation - vs. embeddings), applies hybrid scheduling and batching policies (e.g., - `HybridSchedulingPolicy`), and exposes an OpenAI-compatible API via `sage-gateway` on - standardized ports from `SagePorts`. This unified design improves resource utilization and - **reduces tail latency** for mixed LLM+embedding traffic compared to siloed vLLM + separate - embedding setups, while preserving a familiar client-facing interface. - -1. **Systems support for heterogeneous CPU/GPU deployments with reproducible tooling.** - - SAGE provides kernel-level mechanisms for **CPU-only and GPU nodes**, job management - (`sage-kernel/runtime`), and node selection (`sage-kernel/scheduler`), along with platform - services (`sage-platform`) for storage, queuing, and service management, and C++ operators in - `sage-middleware` for performance-critical paths. Together with reproducible installation and - quality pipelines (`quickstart.sh`, `manage.sh`, `sage-dev`, pre-commit tooling), the system - lowers the barrier to deploying complex LLM pipelines on heterogeneous clusters and makes - end-to-end experiments repeatable for both developers and researchers. - -1. **A comprehensive benchmark suite and reusable testbed for LLM-centric systems.** - - To evaluate the system, we provide `sage-benchmark`, which instantiates a range of workloads for - **agent behavior** (tool selection, multi-step planning, timing decisions) and **control-plane - scheduling** under diverse traffic patterns, as well as additional suites targeting **retrieval, - memory, DB/TSDB components, and scheduler behavior** in LLM-centric pipelines. The suite reports - not only task- or model-level accuracy but also systems metrics such as throughput, latency - distribution, SLO satisfaction, and resource utilization, and it exposes standard interfaces so - that alternative agents, scheduling algorithms, or middleware components can be plugged in and - compared on a common testbed built on top of SAGE’s layered architecture and unified control - plane. - -If space is tight, you may merge (3) and (4) into a single contribution on **end-to-end deployment -and evaluation**. - -______________________________________________________________________ - -## 2. Quantitative Claims Checklist - -Each contribution should have at least one **quantitative** claim. After experiments are complete, -fill in the placeholders below: - -| Contribution | Claim Template | Experiment Needed | -| ----------------- | ----------------------------------------------------------------------------------------- | ----------------------------------------------- | -| Architecture (1) | "enables [X]% faster development iteration" OR "reduces configuration complexity by [Y]%" | Developer study or configuration/LOC comparison | -| Control Plane (2) | "reduces p99 latency by [X]% compared to vLLM + separate embedding" | Mixed LLM+embedding workload latency benchmark | -| Control Plane (2) | "improves throughput by [Y]× while maintaining p95 < [Z] ms" | Throughput vs. latency saturation study | -| Control Plane (2) | "achieves [A]% SLO satisfaction vs. [B]% for baseline" | SLO compliance under varied load | -| Heterogeneous (3) | "supports CPU-only nodes with [X]% of GPU performance for embedding-heavy workloads" | CPU vs. GPU embedding / pipeline benchmark | -| Benchmark (4) | "reveals \[specific insight, e.g., FIFO degrades p99 by [C]× vs. hybrid policy" | Comparative scheduling/agent evaluation | - -这些模板可以帮助你在写论文时，系统性地把实验结果映射到贡献点。 - -______________________________________________________________________ - -## 3. Positioning vs. Existing Systems (for reviewer FAQs) - -You can also prepare short Q&A snippets for reviewers: - -**Q: How does SAGE differ from vLLM?** - -> vLLM is a single-model inference engine optimized for GPU memory management and continuous -> batching. SAGE uses vLLM as a backend engine and adds: (1) cross-engine load balancing, (2) -> embedding service co-scheduling, (3) declarative pipeline composition, (4) SLO-aware request -> routing, and (5) system-level benchmarks. - -**Q: How does SAGE differ from Ray Serve or KServe?** - -> Ray Serve and KServe are generic ML serving frameworks. SAGE provides LLM-specific scheduling -> (distinguishing chat vs. generation vs. embedding), workload-aware batching, declarative dataflow -> for pipelines, and an OpenAI-compatible API that simplifies migration from cloud LLM APIs, plus -> benchmarks that focus on LLM-centric systems behavior. - -**Q: How does SAGE differ from LangChain / LlamaIndex?** - -> LangChain and LlamaIndex are application-level orchestration frameworks for prompt chaining and -> agent logic. SAGE operates at the systems level, providing the underlying resource management, -> scheduling, execution, and benchmarking infrastructure that LangChain-like frameworks could build -> upon. - -**Q: Why is a unified LLM+embedding control plane needed?** - -> Modern RAG and agent applications interleave embedding (for retrieval) and LLM (for generation) -> calls. Without unified scheduling, operators must manually balance multiple services, leading to -> resource fragmentation and suboptimal latency. SAGE’s control plane treats them as a single -> resource pool with workload-aware policies, integrated into a broader dataflow and benchmarking -> framework. - -______________________________________________________________________ - -## 4. Chinese Summary（供自己校对用） - -- **分层架构 + declarative pipeline**：强调 6 层、无上行依赖、关注点分离与可维护性。 -- **统一控制平面**：LLM + Embedding 统一调度，混合请求分类、批处理、SLO，API 走 OpenAI 兼容 gateway。 -- **异构集群与工程工具链**：CPU/GPU 混部、job/node 管理、C++ 中间件、统一安装与质量工具，突出 "implementation & scalability"。 -- **系统化 benchmark**：既评估 agent 能力，也评估调度策略和 pipeline 行为，关注 throughput/latency/SLO 等系统指标。 - -你可以根据最终实验结果，把 `[X]%`, `[Y]×` 等占位符替换成实际数字。 diff --git a/benchmark/docs/07_system_outline_example.md b/benchmark/docs/07_system_outline_example.md deleted file mode 100644 index b5cd9ac09c..0000000000 --- a/benchmark/docs/07_system_outline_example.md +++ /dev/null @@ -1,183 +0,0 @@ -# SAGE – Example System / Method Outline (Systems Track) - -本文件给出一份结合 SAGE 实际结构的 System / Method 章节详细纲要示例，可与 `04_system_and_method.md` 里的提示词配合使用，用来介绍 **整个 SAGE -系统**，而不仅仅是控制平面。 - -______________________________________________________________________ - -## 1. High-Level Section Structure (Example) - -A possible structure for the System / Method section of the paper is: - -1. **System Overview and Design Goals** -1. **Layered Architecture** -1. **Declarative Dataflow and Execution Model** -1. **LLM & Embedding Control Plane (sageLLM)** -1. **Implementation Details and Deployment** - -You can merge or split sections depending on page limits (e.g., combine 2+3, or 4+5). - -______________________________________________________________________ - -## 2. Section – System Overview and Design Goals - -**Questions to answer (systems-reviewer perspective)** - -- What concrete **problems** does SAGE target that existing LLM serving or MLOps systems do not - fully solve? (e.g., complex multi-step LLM pipelines, mixed LLM+embedding workloads, CPU-only - environments, end-to-end evaluation and reproducibility.) -- What are the **design goals**: scalability, heterogeneity support, programmability, debuggability, - reproducibility, ease of evolution? -- How does SAGE sit in the ML systems ecosystem: is it a serving system, a workflow engine, a - control plane, a benchmark framework, or a combination? - -**SAGE components to mention** - -- Overview of packages under `packages/`: `sage-common`, `sage-platform`, `sage-kernel`, - `sage-libs`, `sage-middleware`, `sage-apps`, `sage-benchmark`, `sage-cli`, `sage-studio`, - `sage-tools`, `sage-gateway`. -- High-level illustration of how a user goes from writing a pipeline (via CLI/Studio/examples) to - executing it on a heterogeneous cluster using SAGE. - -**Suggested figures/diagrams** - -- **Figure 1: SAGE System Overview.** A block diagram showing the layers and their roles: user - interfaces at the top, control plane and platform services in the middle, execution engines and - operators at the bottom. Caption: *"High-level view of the SAGE system, highlighting its layered - architecture and main components for LLM/AI pipelines."* - -______________________________________________________________________ - -## 3. Section – Layered Architecture - -**Questions to answer** - -- How are the six layers defined, and what responsibilities does each layer have? -- Why enforce **no upward dependencies**? How does this help modularity, testing, and independent - evolution? -- How does this layering compare to monolithic or ad-hoc LLM orchestration scripts or flat - microservice designs? - -**Mapping to SAGE components** - -- L1 – `sage-common`: configuration (`config/config.yaml`), user paths (XDG), `SagePorts` for port - allocation, shared components. -- L2 – `sage-platform`: platform services for storage, queuing, and service management; integration - with cluster configuration (`config/cluster.yaml`). -- L3 – `sage-kernel`, `sage-libs`: execution kernels, job management (`runtime/job_manager`), node - selection (`scheduler/node_selector`), CPU/GPU awareness, algorithms, and scheduling primitives. -- L4 – `sage-middleware`: C++ operators and performance-critical components. -- L5 – `sage-cli`, `sage-tools`: CLI commands (e.g., `sage llm engine start`), development tools. - -**Independent repositories** (not in core architecture): - -- `sage-benchmark` – benchmark scenarios (PyPI: `isage-benchmark`) -- `sage-examples` – applications and tutorials -- `sageLLM` – LLM inference engine with control plane (PyPI: `isagellm`) - -**Suggested figures** - -- **Figure 2: Layered Architecture.** A stacked diagram (L1 at bottom to L5 at top) with arrows only - going downward. Caption: *"SAGE enforces a strict layering discipline with no upward dependencies, - which simplifies reasoning about responsibilities and allows lower layers to be reused across - tools, applications, and benchmarks."* - -______________________________________________________________________ - -## 4. Section – Declarative Dataflow and Execution Model - -**Questions to answer** - -- How do users **declare** LLM/AI pipelines (e.g., composition of retrieval, tools, LLM calls, - post-processing)? -- How does SAGE translate these declarations into an executable plan over its layers? -- How does the execution model handle **batching**, **parallelism**, and **resource allocation** - across CPU/GPU nodes? -- How does this improve over ad-hoc scripts in terms of maintainability, performance, and - correctness? - -**SAGE components to mention** - -- High-level APIs and examples under `examples/apps` 和 `examples/tutorials` that construct - dataflows. -- Kernel/platform interaction for executing these dataflows, including job scheduling and node - selection. -- Role of `sage-middleware` operators when a dataflow step is performance-critical. - -**Suggested figures** - -- **Figure 3: Declarative Dataflow Example.** A diagram of a concrete pipeline (data ingestion → - embedding → retrieval → LLM generation → post-processing), annotated with which layers are - involved at each step. -- **Figure 4: Execution Model.** A schematic showing how a declarative graph is compiled into tasks - over nodes, with batching and scheduling hooks. - -______________________________________________________________________ - -## 5. Section – LLM & Embedding Control Plane (sageLLM) - -**Questions to answer** - -- What are the **goals** of the control plane? (e.g., share resources across LLM and embedding - workloads, improve throughput and tail latency, respect SLOs.) -- How are requests classified and routed? What are the main scheduling/batching policies? -- How does the control plane interact with the gateway and backends? -- How does it differ from a single vLLM instance or simple load balancer? -- How does it fit into the broader SAGE system (dataflow, benchmarks, deployment tools)? - -**Mapping to SAGE components** - -- `sage.common.components.sage_llm.UnifiedInferenceClient` (with unified `create()` entry point) and - related control-plane modules under `sageLLM/control_plane/` including `ControlPlaneManager`, - `RequestClassifier`, `HybridSchedulingPolicy`, and `EmbeddingExecutor`. -- `sage-gateway` FastAPI app and routes for LLM and embedding. -- `SagePorts` (`GATEWAY_DEFAULT`, `LLM_DEFAULT`, `EMBEDDING_DEFAULT`, etc.) and WSL2-aware port - selection. - -**Suggested figures** - -- **Figure 5: Control Plane Architecture.** Components: request classifier, scheduling policy - (HybridSchedulingPolicy), execution coordinators for LLM and embeddings, backend engine pool. -- **Figure 6: Request Timeline under Mixed Workloads.** Show how chat and embedding requests are - batched and routed over time, contrasted with a naive baseline. - -______________________________________________________________________ - -## 6. Section – Implementation Details and Deployment - -**Questions to answer** - -- What are the key implementation choices that matter for systems reviewers? (language choices, C++ - integration, build system, packaging.) -- How does SAGE support **CPU-only** as well as GPU deployments in practice? -- How do quickstart scripts and `sage-dev` tooling enable **reproducible experiments** and CI? -- What operational practices (logging, configuration, user paths) are built in to support real - users? - -**SAGE components to mention** - -- C++ middleware build (`packages/sage-middleware/src/...`, CMake, `.sage/build/`). -- Installation scripts: `quickstart.sh`, `manage.sh`, CI install wrappers. -- `sage-dev` commands for test, quality, and examples; pytest configuration under - `tools/pytest.ini`. -- XDG-based user paths and directories for logs, models, and cache. - -**Suggested figures / tables** - -- **Table 1: Implementation Summary.** Columns: language/components, lines of code (approx.), main - dependencies, build artifacts. -- **Figure 7: Deployment and Tooling Workflow.** From cloning the repo to running `quickstart.sh`, - starting `sage gateway` and `sage llm`, and launching experiments. - -______________________________________________________________________ - -## 7. How to Use This Outline - -- 在写 System 章节时，可以把本文件作为“答案模板”，再配合 `04_system_and_method.md` 中的提示词： - - 把这里的每个小节要点粘到提示词中的 `[PASTE THE BULLET-POINT OUTLINE HERE]` 位置； - - 让模型基于这些要点生成英文小节； - - 你再根据实际实现细节和实验配置进行微调。 -- 如果篇幅吃紧，可以： - - 把 System Overview + Layered Architecture 合并； - - 把 Declarative Dataflow + Control Plane 合并； - - 将部分 Implementation 细节移到附录，仅在正文保留最系统相关的要点。 diff --git a/benchmark/docs/08_paper_outline_example.md b/benchmark/docs/08_paper_outline_example.md deleted file mode 100644 index 8fc5270a28..0000000000 --- a/benchmark/docs/08_paper_outline_example.md +++ /dev/null @@ -1,103 +0,0 @@ -# SAGE Systems Paper – ICML-Style Outline - -下面是基于当前 prompts 跑出的一版 **完整 ICML 风格 SAGE 论文草稿结构**，只包含章节标题与每节 2–3 句英文说明，默认面向顶级 Machine Learning -Systems track（例如 ICML）。 - -______________________________________________________________________ - -## 1 Introduction - -Introduces the rise of complex LLM/AI applications that compose retrieval, tools, and multiple -models over heterogeneous CPU/GPU clusters, and argues that existing serving and MLOps systems lack -unified support for such pipelines. States the goals and design principles of SAGE as a -dataflow-based ML system, positions it between low-level LLM serving engines and high-level -application frameworks, and outlines the main challenges (scalability, heterogeneity, -programmability, reproducibility). Summarizes the paper’s contributions as a numbered list covering -the layered architecture, declarative dataflow, unified LLM+embedding control plane, heterogeneous -deployment support, and benchmark suite. - -## 2 Related Work - -Reviews prior work across several categories: LLM serving engines (e.g., vLLM, TensorRT-LLM), -generic serving and workflow frameworks (e.g., Ray Serve, KServe, MLflow, Kubeflow), LLM application -frameworks (e.g., LangChain, LlamaIndex, DSPy), and LLM benchmarks (e.g., AgentBench, ToolBench, -HELM). For each category, explains what systems properties they target and why they are insufficient -as end-to-end platforms for LLM+embedding dataflow pipelines. Concludes by positioning SAGE as a -unified system that complements these efforts by providing layered architecture, declarative -dataflow, a control plane, and system-level benchmarks. - -## 3 System Overview and Design Goals - -Provides a high-level view of the SAGE system, introducing its role as a Python-based framework for -LLM/AI data processing pipelines built on a strict six-layer architecture. Describes the main design -goals—scalability, support for heterogeneous CPU/GPU environments, programmability via declarative -dataflow, observability, and reproducibility—and how they shape the system’s interfaces and -components. Walks through the lifecycle of a typical SAGE pipeline from user specification -(CLI/Studio/examples) to deployment and execution on a cluster. - -## 4 Layered Architecture - -Details the responsibilities of each layer from `sage-common` and `sage-platform` through -`sage-kernel`/`sage-libs`, `sage-middleware`, `sage-apps`/`sage-benchmark`, up to `sage-cli`, -`sage-studio`, `sage-tools`, and `sage-gateway`. Explains the “no upward dependencies” constraint -and how it enables modularity, testing, independent evolution of layers, and reuse of lower layers -across tools, applications, and benchmarks. Compares this disciplined layering with ad-hoc scripting -or flat microservice deployments commonly seen in LLM systems. - -## 5 Declarative Dataflow and Execution Model - -Introduces SAGE’s declarative dataflow abstraction for specifying LLM/AI pipelines (e.g., retrieval, -tools, LLM calls, post-processing) and contrasts it with imperative orchestration code. Describes -how the platform and kernel layers compile dataflow graphs into executable tasks, handling batching, -parallelism, and placement over heterogeneous CPU/GPU nodes. Discusses how this execution model -improves maintainability and performance, and how middleware operators are used for -performance-critical stages. - -## 6 LLM & Embedding Control Plane (sageLLM) - -Presents the design goals of the sageLLM control plane: unified scheduling of LLM and embedding -workloads, improved throughput and tail latency, and SLO-aware resource management across multiple -backends. Describes the architecture, including the `UnifiedInferenceClient`, request -classification, scheduling policies such as `HybridSchedulingPolicy`, embedding executors, and their -integration with `sage-gateway` and vLLM instances. Clarifies SAGE’s relationship to vLLM and -similar engines, emphasizing that SAGE builds a cross-engine, cross-workload control plane and API -layer on top of them rather than replacing their single-model schedulers. - -## 7 Implementation Details and Deployment - -Summarizes key implementation choices: Python 3.10+, C++ middleware with CMake builds, internal -directory layout, and the use of XDG-compliant user paths for configuration, logs, models, and -caches. Describes installation and tooling (e.g., `quickstart.sh`, `manage.sh`, `sage-dev`, CI -configuration) that enable reproducible builds, testing, and code quality enforcement. Explains how -SAGE supports CPU-only and GPU deployments in practice, including configuration of ports via -`SagePorts`, cluster configuration files, and operational practices for monitoring and debugging. - -## 8 Experiments - -Outlines the experimental methodology and setup: hardware and software environment, models, -workloads (end-to-end pipelines, mixed LLM+embedding traffic, heterogeneous deployments), and -measurement procedures following reproducibility best practices. States the main experimental -questions: end-to-end pipeline performance vs. baselines, effectiveness of the unified control plane -and scheduling policies, scalability with backends and load, benefits of heterogeneous deployments, -and insights from agent and system benchmarks. Previews the structure of the section, with -subsections on: (8.1) End-to-End Pipeline Performance, (8.2) Control Plane Effectiveness, (8.3) -Scheduling Policy Comparison, (8.4) Scalability, (8.5) Heterogeneous Hardware & CPU-only Support, -and (8.6) Agent Capability & Benchmarking (if claimed). - -## 9 Discussion - -Reflects on the practical implications of deploying SAGE in real-world environments, including -trade-offs between flexibility and complexity, and lessons learned from building and operating a -multi-layer ML system. Discusses limitations such as dependency on underlying serving engines, -potential bottlenecks in the control plane, and scenarios where simpler solutions may suffice. -Outlines promising directions for future extensions, such as richer dataflow optimizations, tighter -integration with external data systems, or additional scheduling policies. - -## 10 Conclusion - -Recaps the motivation for SAGE and the key design elements: layered architecture, declarative -dataflow, unified LLM+embedding control plane, heterogeneous deployment support, and benchmark -suite. Summarizes the main experimental findings in terms of performance, scalability, SLO -satisfaction, and insights into agent and scheduling behavior. Emphasizes SAGE’s role as a reusable -platform and testbed for future research on LLM-centric systems and invites the community to build -on its architecture and benchmarks. diff --git a/benchmark/docs/all_prompts_combined.md b/benchmark/docs/all_prompts_combined.md deleted file mode 100644 index 1f49182495..0000000000 --- a/benchmark/docs/all_prompts_combined.md +++ /dev/null @@ -1,1019 +0,0 @@ -# SAGE Systems Paper – Combined Writing Prompts - -> This file aggregates the per-section prompts under `docs/01_*.md`–`08_*.md` in -> `benchmark_sage/docs/` so you can browse and search all SAGE paper prompts in one place. -> -> Each section below inlines the original Markdown file verbatim. - -______________________________________________________________________ - -## 01_abstract.md - -````markdown -# Abstract Prompt – SAGE Systems Paper - -下面是为 SAGE 撰写 **系统论文摘要（Abstract）** 的提示词模版，你可以直接复制到对话中，并在标注位置补充信息。 -默认面向顶级机器学习系统会议的系统 track（例如 ICML Machine Learning Systems track），但提示词本身不依赖具体会议名称。 - -______________________________________________________________________ - -## 提示词（可直接复制给大模型） - -You are an experienced systems-track author. You help me write a **concise but technically rich abstract** for a paper about **SAGE**, a machine learning systems framework. - ---- Context about the paper and the system --- - -- Target venue: a **top-tier Machine Learning Systems track** (focus on implementation, scalability, hardware, libraries, distributed methods, etc.). -- System: **SAGE**, a Python 3.10+ framework for building **LLM/AI data processing pipelines** with **declarative dataflow**. -- Goal of the paper: present SAGE as a **full-stack dataflow-based ML system**, not just an LLM control plane. - -Key architectural points (you should weave them naturally into the abstract, not list them mechanically): - -- A strict **5-layer architecture (L1–L5)**: `sage-common`, `sage-platform`, `sage-kernel` / `sage-libs`, `sage-middleware`, `sage-cli` / `sage-tools`, with **no upward dependencies** (each layer only depends on lower layers). Independent repositories (`sage-benchmark`, `sage-examples`, `sage-studio`, `sageLLM`) are outside the core architecture. -- **Declarative dataflow** for constructing LLM/AI pipelines: users declare high-level pipelines, while platform, kernel, and middleware layers compile them into an efficient execution plan across heterogeneous resources. -- A unified **LLM & embedding control plane** ("sageLLM"), exposed via an **OpenAI-compatible gateway** (`isagellm.gateway`), providing request classification, hybrid scheduling, batching, and resource sharing across multiple vLLM / embedding instances. -- **CPU-only and GPU node support**, with job management and node selection in `sage-kernel` (runtime, scheduler) and cluster configuration / services in `sage-platform`. -- A **comprehensive benchmark suite** (`sage-benchmark`, independent repo) that evaluates both **agent capabilities** (tool selection, task planning, timing decisions) and **system-level behavior** (throughput, latency distribution, SLO compliance, interference) for different SAGE subsystems. -- Implementation characteristics that highlight systems engineering effort: C++ middleware operators (`sage-middleware`) with CMake-based build; unified CI and quality tools (Ruff, Mypy); reproducible quickstart scripts; XDG-based user paths and configuration. - ---- Quantitative claims template (to be filled after experiments) --- - -The abstract MUST include at least **one concrete quantitative claim**. You can use placeholders now and replace them with actual numbers later. Typical patterns include: - -- **Performance / latency**: - - "reduces p99 latency by [X]% compared to [baseline] under mixed LLM+embedding workloads". - - "improves throughput by [Y]× over [baseline] while maintaining p95 latency below [Z] ms". -- **SLO and robustness**: - - "achieves [A]% SLO satisfaction vs. [B]% for [baseline] under [workload] traffic patterns". -- **Resource efficiency / heterogeneity**: - - "reduces resource utilization variance by [C]% across heterogeneous CPU/GPU nodes". -- **Agent / pipeline quality (if applicable)**: - - "improves tool selection accuracy by [D]% on [benchmark]". - - "reduces planning latency by [E]% while maintaining [F]% task success rate". -- **Scale**: - - "evaluated on clusters with up to [G] GPU nodes and [H] concurrent clients". - - "supports [I] requests per second with [J] backend engines". - -After you draft the text, I will plug in actual experiment results to replace these placeholders. - ---- Writing goals --- - -Please draft a **150–200 word** English abstract that: - -1. Starts with 2–3 sentences of **problem context**: complexity of modern LLM/AI pipelines, challenges in managing dataflow, heterogeneous resources, and multiple LLM / embedding services. -2. Gives a **high-level description of SAGE** as a systems contribution, emphasizing: - - its **layered architecture** and separation of concerns; - - the **declarative dataflow** interface and execution model; - - the **unified control plane** and gateway for LLM / embedding workloads; - - its role as a **general platform** for LLM-centric pipelines, not only a control-plane module. -3. Clearly states **2–4 concrete contributions** that a systems reviewer can check, such as: - - a layered architecture that enables modular, scalable LLM/AI pipelines; - - a control-plane design that improves utilization / latency across LLM and embedding services; - - heterogeneous CPU/GPU support and reproducible tooling that simplify deployment; - - a benchmark suite that probes both agent capabilities and system-level performance. -4. Ends with **quantitative experimental claims** using the placeholder format above (no vague "improves performance" statements). - ---- Style constraints --- - -- Use **academic, precise, neutral English** typical of top-tier systems papers. -- Avoid buzzwords and marketing language; focus on **what the system does**, **why it is needed**, and **how well it performs**. -- Clearly mark all quantitative placeholders as `[X]`, `[Y]`, etc., so we can later map them to specific experiments. -- If needed, you may slightly exceed 200 words in the first draft and then suggest where to cut. - ---- Output format --- - -1. Provide **one candidate abstract** with quantitative placeholders clearly marked. -2. List the **specific experiments needed** to fill each placeholder (e.g., mixed workload latency benchmark, scalability study, agent benchmark). -3. Provide **3–5 bullet-point suggestions** on how we might refine the abstract once we have concrete experimental numbers and finalized baselines. - ---- - -## Example abstract structure（仅供参考） - -```text -[Problem context – 2–3 sentences] -Modern LLM applications require complex pipelines that combine retrieval, tools, and multiple models across heterogeneous CPU/GPU clusters. Existing serving and MLOps systems either focus on single-model inference or generic workflows, and do not provide unified control or dataflow support for mixed LLM + embedding workloads. - -[System description – 2–3 sentences] -We present SAGE, a framework that organizes LLM/AI pipelines into a layered architecture with declarative dataflow and a unified control plane for LLM and embedding services. SAGE integrates platform, kernel, middleware, and gateway components to execute pipelines efficiently across heterogeneous resources. - -[Key contributions – 2–3 sentences] -Our main contributions are: (1) a six-layer architecture and declarative dataflow model for LLM-centric pipelines; (2) a unified LLM + embedding control plane that shares resources and improves tail latency; (3) systems support for heterogeneous CPU/GPU deployments and reproducible tooling; and (4) a benchmark suite that evaluates both agent behavior and system-level scheduling. - -[Quantitative results – 1–2 sentences] -Experiments show that SAGE reduces p99 latency by [X]% and improves throughput by [Y]× compared to [baseline] on [workload], while achieving [Z]% SLO satisfaction and maintaining [A]% task success rate in representative agent pipelines. -``` - -```` - -______________________________________________________________________ - -## 02_introduction.md - -```markdown -# Introduction Prompts – SAGE Systems Paper - -本文件提供多轮使用的引言（Introduction）写作提示词模版，面向顶级机器学习系统会议的系统 track（例如 ICML Machine Learning Systems track）。 -整体目标是：以 **完整的 SAGE 系统** 为主角，而不是只讲某个子模块（例如 control plane）。 - -______________________________________________________________________ - -## 2.1 生成引言整体结构 - -**提示词（可直接复制）** - -You are a systems-track co-author. Help me design the **structure** of the Introduction for a paper about **SAGE**, a machine learning systems framework. - ---- System context --- - -- Target venue: a **top-tier Machine Learning Systems track**. -- System: **SAGE**, a Python 3.10+ framework for **LLM/AI data processing pipelines** with **declarative dataflow**. -- SAGE should be presented as a **full-stack system**, covering: - - a strict **5-layer architecture (L1–L5)** with no upward dependencies, from `sage-common` and `sage-platform` up to `sage-cli` and `sage-tools`; - - **declarative dataflow** for composing LLM/AI pipelines (e.g., retrieval, tools, LLM calls, post-processing); - - a **unified LLM & embedding control plane** (sageLLM, independent repo) exposed via `isagellm.gateway`; - - **CPU-only and GPU deployments**, job management and node selection in `sage-kernel`, platform services in `sage-platform`; - - **benchmark suites** in `sage-benchmark` (independent repo) for agents, scheduling policies, RAG, DB/time-series components, etc. - ---- Positioning against existing systems (CRITICAL for novelty) --- - -When structuring the Introduction, explicitly address how SAGE differs from **all** of the following (not only control-plane-level systems): - -- **LLM serving engines** such as vLLM, TensorRT-LLM, SGLang: they optimize single-model inference; SAGE operates at a **higher abstraction level**, orchestrating multiple engines, embedding services, and full pipelines under a unified dataflow and control plane. -- **ML serving frameworks** such as Ray Serve, KServe, Triton Inference Server: they are generic serving or deployment platforms; SAGE provides **LLM-aware scheduling**, declarative pipelines, and end-to-end evaluation for LLM-centric workloads. -- **LLM application frameworks** such as LangChain, LlamaIndex, DSPy: they focus on application-level orchestration; SAGE is a **systems-level infrastructure** providing resource management, scheduling, and execution primitives that such frameworks could build upon. -- **ML workflow / MLOps platforms** such as MLflow, Kubeflow, Airflow: they emphasize training and generic workflows; SAGE focuses on **inference pipelines** with real-time scheduling, heterogeneous hardware, and LLM-specific concerns. -- **LLM benchmarks** such as AgentBench, ToolBench, HELM, single-engine vLLM benchmarks: they focus on task accuracy or single-engine metrics; SAGE adds **system-level benchmarks** that stress control-plane, dataflow, and heterogeneous deployments. - -The key novelty claim should be: SAGE is a **full ML system** that combines a layered architecture, declarative dataflow, a unified LLM + embedding control plane, heterogeneous deployment support, and comprehensive benchmarks, filling the gap between low-level serving engines and high-level application frameworks. - ---- Task --- - -Design a **4–6 paragraph outline** (not full prose yet) for the Introduction that suits a top-tier systems paper on SAGE. For each paragraph: - -1. State the **goal** of the paragraph (e.g., establish broader context of LLM/AI systems, articulate challenges in managing complex LLM pipelines, highlight gaps in existing systems, introduce SAGE, summarize contributions, preview experiments). -2. Provide a **bullet list of key points** that should appear in that paragraph, focusing on: - - systems challenges (scalability, hardware heterogeneity, multiple LLM/embedding services, observability, configuration complexity, reproducibility); - - why existing frameworks (generic MLOps, standalone LLM serving, ad-hoc scripts, application-level orchestrators) do not fully address these for **LLM-centric pipelines**; - - how SAGE’s architecture, dataflow model, control plane, and benchmarks are designed around these challenges. -3. Mark where we should **present the main contributions** as a numbered list (usually at the end of the last or second-to-last paragraph). -4. Explicitly note any parts where you need more concrete details from me (e.g., workloads, cluster scale, baselines, key SAGE subsystems highlighted in experiments). - ---- Output --- - -- A paragraph-level outline (4–6 paragraphs), each with: - - a short description of the paragraph goal; - - bullet points of content to cover. -- Do **not** yet write the full paragraphs. - -______________________________________________________________________ - -## 2.2 逐段写引言 - -在拿到 2.1 中的段落大纲后，你可以按段落逐个生成英文正文。 - -**提示词（可直接复制，每段都可以复用）** - -We previously designed a paragraph-level outline for the Introduction of our systems paper on **SAGE**. Now we will write **paragraph X**. - -Here is the outline for this paragraph (copied from the previous step): - -[PASTE THE BULLET-POINT OUTLINE FOR PARAGRAPH X HERE] - ---- System context reminders --- - -- SAGE targets **LLM/AI pipelines**, not generic ML training. -- It offers **declarative dataflow** and a **multi-layer architecture** with no upward dependencies. -- It includes a **unified control plane** for LLM and embedding services, exposed via an OpenAI-compatible gateway. -- It provides **benchmarking** tools for agent capabilities, scheduling policies, and other subsystems (RAG, DB, TSDB, etc.). -- The paper is about the **whole SAGE system** (architecture + dataflow + control plane + benchmarks), not only a single module. - ---- Task --- - -Using only the above outline and the system context, write a full **English paragraph** (8–12 sentences) suitable for a top-tier systems-track Introduction. - -Writing requirements: - -1. Focus on **systems challenges and insights**, not just listing features. -2. Use **neutral, technical language**; avoid buzzwords or marketing tone. -3. Make the paragraph **self-contained**, but naturally connectible to the previous and next paragraphs. -4. It is acceptable for the first draft to be slightly longer; at the end, suggest 1–2 sentences that could be dropped if space is tight. - ---- Output --- - -1. The full paragraph in English. -2. A short bullet list of **possible trimming points** (sentences that could be removed if we need to shorten the Introduction). - -你不需要在每一段里重复完整的系统描述，只要引用必要的关键信息即可，使整篇引言连贯、系统、而且覆盖整个 SAGE。 - -``` - -______________________________________________________________________ - -## 03_related_work.md - -```markdown -# Related Work Prompts – SAGE Systems Paper - -本文件提供撰写 Related Work（相关工作）部分的提示词，重点从 **系统视角** 对 SAGE 进行分类与定位，覆盖整个系统（分层架构、数据流、控制平面、benchmark），而不是只讨论某一个子模块。 - -______________________________________________________________________ - -## 3.1 相关工作分类与定位 - -**提示词（可直接复制）** - -You are a systems-track author responsible for the **Related Work** section of a paper about **SAGE**, a framework for LLM/AI pipelines. - ---- System context (for positioning) --- - -SAGE focuses on **system-level support for LLM/AI dataflow pipelines**, rather than general ML training. Key system contributions include: - -- A **5-layer architecture** with strict no-upward-dependency design, from `sage-common` and `sage-platform` to `sage-kernel` / `sage-libs`, `sage-middleware`, and user-facing tools (`sage-cli`, `sage-tools`). Independent repositories (`sage-benchmark`, `sage-examples`, `sage-studio`, `sageLLM`) are outside the core architecture. -- **Declarative dataflow** for LLM/AI pipelines, mapping user-level pipeline descriptions to efficient execution on heterogeneous CPU/GPU clusters. -- A unified **LLM & embedding control plane** with hybrid scheduling and batching, exposed via an OpenAI-compatible gateway (`isagellm.gateway`). -- Systems support for **CPU-only and GPU nodes**, job management and node selection in `sage-kernel`, platform services in `sage-platform`. -- A **benchmark suite** (`sage-benchmark`, independent repo) focusing on **agent capabilities** (tool selection, planning, timing) and **system-level scheduling** (throughput, latency distribution, SLO compliance, interference). - ---- Task --- - -1. Propose a **taxonomy of related work** into 4–5 categories suitable for a top-tier systems paper. A reasonable starting point is: - - Category 1: LLM Serving Engines (e.g., vLLM, TensorRT-LLM, SGLang, Orca) - - Category 2: ML Serving Frameworks and Workflow Platforms (e.g., Ray Serve, KServe, Triton, MLflow, Kubeflow, Airflow) - - Category 3: LLM Application Frameworks and Agents (e.g., LangChain, LlamaIndex, DSPy, various agent tool-use frameworks) - - Category 4: LLM Benchmarks and Evaluation Frameworks (e.g., AgentBench, ToolBench, HELM, vLLM benchmark) - - (Optional) Category 5: Data & Storage Systems for AI Pipelines (e.g., vector DBs, TSDBs, dataflow systems that overlap with `sage.db`, `sage.tsdb`, `sage.flow`) - -2. For each category: - - Give 2–3 sentences summarizing **what this category of work tries to achieve**, in terms of systems properties (e.g., throughput, flexibility, observability, portability, fairness). - - Provide 3–5 sentences on **how SAGE differs** from typical works in this category, explicitly referencing: - - multi-layer architecture vs. monolithic designs; - - **unified** LLM + embedding control plane vs. LLM-only serving; - - declarative dataflow vs. imperative orchestration or ad-hoc scripts; - - system-level benchmarks vs. task-only or single-engine benchmarks. - - Mark where we should later insert **specific citation examples** (use placeholders like `[REF: VLLM]`, `[REF: RAY_SERVE]`, `[REF: LANGCHAIN]`). - -3. Explicitly state the **gap that SAGE fills**: a unified, layered system for LLM+embedding dataflow pipelines with control-plane scheduling and comprehensive benchmarks, sitting between low-level serving engines and high-level application frameworks. - ---- Output --- - -- A structured outline listing: - - The proposed categories; - - For each category, a short paragraph summarizing it and briefly contrasting SAGE. -- The outline should be detailed enough that we could almost lift it directly into the paper, then refine names and add citations. - -______________________________________________________________________ - -## 3.2 完整 Related Work 草稿 - -在 3.1 确定 taxonomy 和要点之后，你可以生成一版接近成品的 Related Work 文本。 - -**提示词（可直接复制）** - -Now, using the taxonomy and short summaries we just designed for Related Work, please draft a **full Related Work section** for our systems paper on **SAGE**. - -Constraints and goals: - -1. Organize the text into **subsections or logical paragraphs**, one per category from the taxonomy. -2. For each category: - - Start with 2–3 sentences summarizing the category and its main systems concerns. - - Name **specific representative systems** (e.g., vLLM, TensorRT-LLM, SGLang, Ray Serve, KServe, Triton, MLflow, Kubeflow, LangChain, LlamaIndex, DSPy, AgentBench, ToolBench, HELM) and briefly describe what they do. - - Then write 3–5 sentences positioning **SAGE** relative to this category, focusing on: - - multi-layer architecture vs. monolithic or flat designs; - - unified LLM+embedding control plane vs. single-workload focus; - - declarative dataflow vs. imperative orchestration; - - system-level benchmark and experimental testbed vs. task-only evaluation. - - Include explicit phrases that emphasize **complementarity or orthogonality** (e.g., "SAGE can use vLLM as a backend engine"), not just replacement. -3. Throughout the text, clearly emphasize that **SAGE is a systems contribution**: improved implementation and scalability, support for heterogeneous hardware, unified resource management for LLM+embedding workloads, and comprehensive evaluation infrastructure. -4. Include a **summary paragraph** at the end that synthesizes the positioning: SAGE fills the gap between low-level serving engines, generic serving / workflow systems, and high-level LLM application frameworks by providing a unified dataflow-based platform and control plane with benchmarks. - ---- Output --- - -- A 1.5–2 page (single-column equivalent) English draft of the Related Work section, following the above structure. -- Use actual system names for well-known systems; use `[REF: ...]` placeholders where you need citations. -- At the end, list all `[REF: ...]` slots used, grouped by category, so we can map them to actual papers later. - -``` - -______________________________________________________________________ - -## 04_system_and_method.md - -```markdown -# System / Method Prompts – SAGE Systems Paper - -本文件面向系统论文的 "System / Method" 章节，帮助你把 **整个 SAGE 系统** 的设计讲清楚，突出实现与可扩展性，而不是只强调 control plane。 - -______________________________________________________________________ - -## 4.1 设计 System 章节结构 - -**提示词（可直接复制）** - -You are a systems-track co-author responsible for the **System Design / Method** section of a paper about **SAGE**. - ---- System context --- - -SAGE is a Python 3.10+ framework for building LLM/AI data processing pipelines. It targets **system-level issues** such as scalability, heterogeneous hardware, unified management of LLM and embedding workloads, and reproducible experimentation. - -Key design aspects to consider: - -- **Layered architecture (L1–L5)** with **no upward dependencies**: - - L1: `sage-common` – foundational utilities, configuration, user paths (XDG), port management (`SagePorts`), shared components. - - L2: `sage-platform` – platform services (storage, queuing, service management), cluster configuration via `config/cluster.yaml`. - - L3: `sage-kernel`, `sage-libs` – core execution engine, **job management** (`runtime/job_manager`), **node selection** (`scheduler/node_selector`), algorithms, scheduling logic, CPU/GPU awareness. - - L4: `sage-middleware` – C++ operators and performance-critical components, built via CMake. - - L5: `sage-cli`, `sage-tools` – user-facing interfaces (CLI, development tools). -- **Independent repositories** (not in core architecture): - - `sage-benchmark` – benchmark suites (PyPI: `isage-benchmark`) - - `sage-examples` – applications and tutorials - - `sageLLM` – LLM inference engine with control plane (PyPI: `isagellm`) -- **Declarative dataflow** abstraction for specifying pipelines, with compilation/execution over heterogeneous resources. -- **Unified LLM & embedding control plane** (sageLLM): `UnifiedInferenceClient`, `ControlPlaneManager`, `HybridSchedulingPolicy`, `EmbeddingExecutor` coordinating multiple vLLM and embedding backends via `isagellm.gateway`. -- **User paths and configuration** following XDG base directory spec; project-level `.sage/` directory for build artifacts and caches. -- **Deployment and CI** patterns (quickstart scripts, `sage-dev` tooling, pre-commit hooks) as concrete implementation and reproducibility choices. - ---- Task --- - -Design a **System / Method section outline** tailored for a top-tier systems paper. The section should likely include 3–5 main subsections, for example: - -- System Overview and Design Goals; -- Layered Architecture; -- Declarative Dataflow and Execution Model; -- LLM & Embedding Control Plane (sageLLM) as one important subsystem; -- Implementation Details and Deployment. - -For each proposed subsection: - -1. Provide a bullet list of **key questions** it should answer from a systems-reviewer perspective (e.g., how the system scales, how it abstracts hardware differences, how it improves programmability without sacrificing performance, how it supports reproducibility and observability). -2. Map these questions to **specific SAGE components** or modules (e.g., `sage-platform` and `sage-kernel` for job management and node selection, `sage.common.components.sage_llm` for control plane, `sage-middleware` for C++ operators, `sage-benchmark` for evaluation workloads). -3. Suggest **figures or diagrams** that should accompany this subsection (e.g., architecture diagram, dataflow diagram, control-plane timeline), with 1–2 sentences per figure describing what it should convey. -4. Indicate which subsections are **core** for the main paper and which details can be moved to an appendix if page limits are tight. - ---- Output --- - -- A structured outline with subsections and bullet points answering the above. -- No full prose yet. - -______________________________________________________________________ - -## 4.2 逐小节撰写 System 文本 - -有了 4.1 的大纲后，你可以对每个小节单独调用下面的提示词写正文。 - -**提示词（可直接复制，每个小节复用）** - -We have designed an outline for the System / Method section of our systems paper on **SAGE**. Now we will write the subsection: - -> [INSERT SUBSECTION TITLE HERE, e.g., "Layered Architecture"] - -Here is the bullet-point outline for this subsection (from the previous step): - -[PASTE THE BULLET-POINT OUTLINE HERE] - ---- System reminders --- - -- SAGE uses a **5-layer architecture (L1–L5)** with no upward dependencies. -- It exposes **declarative dataflow** to users, while deeper layers handle scheduling, optimization, and execution. -- It includes a **unified control plane** for LLM and embedding services (sageLLM, independent repo), fronted by an OpenAI-compatible gateway. -- It targets **scalability, heterogeneity (CPU/GPU), and reproducibility**. -- The paper should emphasize the **whole system** (architecture + dataflow + control plane + benchmarks + deployment), not just one component. - ---- Task --- - -Please write a detailed English subsection for a systems paper that: - -1. Answers the bullet-point questions with **systems-level explanations** rather than just listing APIs. -2. Emphasizes how SAGE’s design choices (e.g., layering, declarative dataflow, control plane, benchmark integration, tooling) address concrete systems challenges (resource utilization, latency, cluster heterogeneity, debuggability, ease of evolution, reproducibility). -3. Includes **references to SAGE components** (module or package names) only when they help clarify the design (e.g., mentioning `sage-kernel` for job management and node selection, `sage-platform` for cluster configuration and services, `sage-benchmark` for evaluation workloads). -4. Suggests where to place figures or tables, and provides a short candidate **figure caption** if appropriate. - ---- Output --- - -1. The full text of the subsection in English (approx. 1–2 single-column pages, depending on importance). -2. A short list of **potential figure captions** and where they should appear. -3. Optional notes on which parts could be shortened if page limits are tight. - -______________________________________________________________________ - -## 4.3 控制平面技术细节（可选强化） - -如果你希望在系统论文中 **重点强化控制平面（sageLLM）这一子模块的系统贡献**，可以单独用下面的提示词撰写一个专门小节。注意：控制平面是 SAGE 的一个重要子系统，但论文整体仍然需要覆盖完整系统。 - -**提示词（可直接复制）** - -We want to dedicate a focused subsection to the **LLM & embedding control plane** in SAGE ("sageLLM"). This subsection should be particularly convincing for systems reviewers who care about **resource management, scheduling, and scalability**. - ---- Control plane architecture (from actual implementation) --- - -- The control plane classifies requests into chat / generation vs. embedding. -- It uses policies such as `HybridSchedulingPolicy` to batch and route requests across a pool of vLLM and embedding engines. -- It aims to improve **throughput, tail latency, and SLO compliance** while sharing resources across heterogeneous LLM and embedding workloads. -- It is integrated with `sage-gateway`, which exposes an OpenAI-compatible API on well-defined ports from `SagePorts`. - -Key components (module paths): - -- `ControlPlaneManager`: `sageLLM/control_plane/manager.py` – core orchestrator. -- `RequestClassifier`: `sageLLM/control_plane/request_classifier.py` – request type detection. -- `HybridSchedulingPolicy`: `sageLLM/control_plane/strategies/hybrid_policy.py` – scheduling decisions. -- `EmbeddingExecutor`: `sageLLM/control_plane/executors/embedding_executor.py` – batched embedding execution. - ---- Scheduling algorithm details to explain --- - -The prompt should guide the model to explain: - -1. **Request classification** (how chat / generation / embedding are distinguished, and with what overhead). -2. **Scheduling policy options** (e.g., FIFO, priority, SLO-aware, hybrid) and how they trade off fairness, latency, and throughput. -3. **Batching strategy** for LLM vs. embedding workloads, and interaction with vLLM’s continuous batching. -4. **Load balancing** across multiple backend engines under mixed workloads. -5. **Interaction with vLLM**: SAGE does not replace vLLM’s internal scheduler but provides cross-engine and cross-workload scheduling via an OpenAI-compatible interface. - ---- Task --- - -Please draft a detailed English subsection (around 1–1.5 single-column pages) that: - -1. Explains the **design goals** of the control plane (unified scheduling, resource sharing, SLO compliance). -2. Describes the **architecture** and key components and how they interact. -3. Details the **scheduling algorithm** with pseudocode or an algorithmic description for `HybridSchedulingPolicy`. -4. Explains how **batching** works differently for LLM vs. embedding workloads. -5. Clarifies the **relationship with vLLM** (complementary, not replacement). -6. Prepares the ground for experiments comparing different scheduling policies and baselines. - ---- Output --- - -1. Full subsection text with technical depth. -2. Pseudocode for the core scheduling algorithm (if appropriate). -3. Suggested figures: - - Figure X: Control Plane Architecture (component diagram). - - Figure Y: Request Timeline showing classification, queuing, batching, execution under mixed workloads. - -______________________________________________________________________ - -## 4.4 与 vLLM 关系的澄清（重要补充） - -审稿人可能会质疑 SAGE 与 vLLM（或其它 LLM 引擎）的关系。这里提供专门的澄清引导。 - -**提示词（可直接复制）** - -A reviewer might ask: "How does SAGE relate to vLLM? Isn’t vLLM already a highly optimized LLM serving system?" - -Please draft a **clarification paragraph** (3–5 sentences) that explains: - -1. **Complementarity, not competition**: SAGE uses vLLM (or other engines) as backend serving components. vLLM handles single-model inference optimization (PagedAttention, continuous batching). SAGE handles cross-model orchestration and embedding co-scheduling. -2. **Abstraction level difference**: - - vLLM = single-model inference engine (optimizes GPU memory, batch processing for one model). - - SAGE = pipeline-level control plane and systems platform (orchestrates multiple models, handles embedding services, provides unified API, integrates with dataflow and benchmarks). -3. **What SAGE adds**: - - multi-engine load balancing; - - unified LLM + embedding scheduling; - - request classification and SLO-aware routing; - - declarative pipeline composition and system-level benchmarks. -4. **Concrete example**: e.g., a RAG pipeline needing an embedding service + an LLM service. Without SAGE, operators must manually manage two services and balance load; with SAGE, they declare the pipeline and the control plane plus dataflow engine handle resource allocation and scheduling. - -This paragraph should be inserted in the System section after describing the control plane architecture, and should clearly state that the **paper evaluates SAGE as a full system built on top of such engines**. - -``` - -______________________________________________________________________ - -## 05_experiments.md - -````markdown -# Experiments Prompts – SAGE Systems Paper - -本文件帮助你为系统论文的 Experiments 部分设计结构和写作提示词。 -**关键更新**：本提示词已与 `sage-benchmark` 中的实际实验脚本（`exp_5_1` 至 `exp_5_5`）和画图工具（`plotting.py`）完全对齐。 - -______________________________________________________________________ - -## 5.1 设计实验章节结构 (Structure Design) - -**提示词（可直接复制）** - -You are the experiments lead for a systems-track paper about **SAGE**. - ---- System and evaluation context --- - -SAGE is a system for **LLM/AI pipelines** with a unified control plane, declarative dataflow, and support for heterogeneous hardware. -We have implemented a comprehensive benchmark suite (`sage-benchmark`) with 5 specific experiments. - ---- Task --- - -Design the **structure** of the Experiments section. It MUST follow this exact 5-subsection structure to match our experimental results: - -### 5.1 End-to-End Pipeline Performance -- **Goal**: Demonstrate SAGE's efficiency in executing complex, multi-stage pipelines (specifically RAG: Embedding -> Retrieval -> Generation). -- **Workload**: Simulated RAG pipeline with concurrent users; mixed embedding and LLM calls. -- **Key Figure**: **Latency CDF** (Cumulative Distribution Function) showing the distribution of end-to-end pipeline latencies. -- **Key Figure**: **Request Timeline** (Waterfall plot) showing the interleaving of embedding and generation tasks. - -### 5.2 Control Plane Effectiveness -- **Goal**: Prove that SAGE's unified control plane (co-scheduling LLM and Embeddings) outperforms separate services. -- **Workload**: Mixed traffic (e.g., 70% Chat, 30% Embedding) at varying request rates. -- **Key Figure**: **Throughput vs. Latency** curve comparing "Unified Control Plane" vs. "Separate Services". -- **Key Figure**: **Latency CDF** comparing tail latencies (p99) of the two approaches. - -### 5.3 Isolation & Fairness -- **Goal**: Show SAGE's ability to protect latency-sensitive "Interactive" users from high-throughput "Batch" users (Noisy Neighbors). -- **Workload**: Two concurrent user groups: "Interactive" (low rate, high priority) and "Batch" (high rate, low priority). -- **Key Figure**: **Latency CDF** for the Interactive user, comparing "With SAGE Isolation" vs. "Without Isolation". - -### 5.4 Scalability -- **Goal**: Demonstrate linear scaling of throughput as backend resources increase. -- **Workload**: High-concurrency traffic against 1, 2, 4, and 8 vLLM backend instances. -- **Key Figure**: **Scalability Bar Chart** showing Request/Second (RPS) vs. Number of GPUs. - -### 5.5 Heterogeneous Hardware Support -- **Goal**: Validate the benefit of offloading Embedding tasks to CPU nodes to save GPU resources for LLM inference. -- **Workload**: Mixed workload running on "GPU-only" vs. "Hybrid (GPU for LLM + CPU for Embed)" configurations. -- **Key Figure**: **Resource Efficiency** comparison (or Latency CDF showing minimal degradation with CPU offloading). - ---- Output --- - -- A structured outline for the Experiments section. -- For each subsection, write a short paragraph describing the **experimental setup** (workload, metrics) and the **expected visual evidence** (the figures mentioned above). - -______________________________________________________________________ - -## 5.2 撰写具体实验分析 (Detailed Analysis Prompts) - -以下提示词用于指导大模型撰写具体的实验分析段落。 - -### 5.1 End-to-End Pipeline Analysis - -**Prompt:** -"Write the analysis for Section 5.1 (End-to-End Pipeline Performance). -The experiment ran a simulated RAG pipeline (Embed -> Retrieve -> Generate). -Refer to **Figure 5.1(a) (Latency CDF)**, which shows a tight latency distribution with a p99 of [X] ms, indicating stable performance. -Refer to **Figure 5.1(b) (Request Timeline)**, which illustrates how SAGE's scheduler efficiently interleaves embedding and generation tasks, minimizing gaps and maximizing resource usage." - -### 5.2 Control Plane Analysis - -**Prompt:** -"Write the analysis for Section 5.2 (Control Plane Effectiveness). -Compare SAGE's unified scheduling against a baseline of separate services. -Refer to **Figure 5.2 (Throughput vs. Latency)**. Highlight that SAGE sustains [Y]% higher throughput before latency saturation. -Explain that by co-scheduling, SAGE utilizes idle GPU cycles (during LLM decoding gaps) for embedding tasks, as evidenced by the lower tail latency in the **Latency CDF**." - -### 5.3 Isolation Analysis - -**Prompt:** -"Write the analysis for Section 5.3 (Isolation & Fairness). -Describe the 'Noisy Neighbor' scenario with Interactive vs. Batch users. -Refer to **Figure 5.3**, showing that without isolation, the Interactive user's p99 latency spikes to [A] ms. -With SAGE's priority-aware scheduling, the Interactive user's latency curve remains close to the baseline, demonstrating effective performance isolation." - -### 5.4 Scalability Analysis - -**Prompt:** -"Write the analysis for Section 5.4 (Scalability). -Refer to **Figure 5.4 (Scalability Bar Chart)**. -Observe that throughput scales nearly linearly from 1 to 8 GPUs. -Calculate the scaling efficiency (e.g., '7.2x speedup on 8 GPUs'), proving that the SAGE control plane introduces minimal overhead." - -### 5.5 Heterogeneity Analysis - -**Prompt:** -"Write the analysis for Section 5.5 (Heterogeneous Hardware). -Discuss the trade-off of offloading embeddings to CPU. -State that while CPU embedding latency is slightly higher, the overall system throughput for LLM tokens increases significantly because GPU resources are freed up. -Conclude that SAGE's flexible node selection enables cost-efficient deployments." - - Test with 1, 2, 4, 8 vLLM instances (and optionally multiple embedding servers). - - Measure: throughput, speedup vs. single-engine baseline, control-plane overhead ratio. -2. **Load scaling (requests per second)** - - Sweep request rate from light load up to and beyond saturation. - - Measure: throughput curve, latency curve (especially tail), SLO hit rate. -3. **Concurrent clients** - - Test with 1, 10, 50, 100 concurrent clients. - - Measure: per-client latency, fairness, starvation or head-of-line blocking. -4. **Model size scaling** - - Test with different model sizes (e.g., 7B, 13B, 70B) if available. - - Measure: how control-plane overhead compares to model inference time. - ---- Experimental setup checklist --- - -Ask the model to force the following information to be specified: - -```text -Hardware specification: -- GPU type: [e.g., A100 40GB, RTX 4090] -- Number of GPUs: [e.g., 4] -- CPU cores: [e.g., 64] -- Memory: [e.g., 256 GB] -- Network: [e.g., InfiniBand, 10GbE] - -Software versions: -- SAGE version: [e.g., 0.5.0] -- vLLM version: [e.g., 0.4.0] -- CUDA version: [e.g., 12.1] -- Python version: [e.g., 3.11] - -Workload specification: -- LLM model: [e.g., Qwen2.5-7B-Instruct] -- Embedding model: [e.g., BGE-M3] -- Input token length: [e.g., 512 tokens] -- Output token length: [e.g., 128 tokens] -- Request arrival: [e.g., Poisson, bursty, constant] -``` - ---- Expected result formats --- - -Table: Throughput vs. Number of Backends - -| Backends | Throughput (req/s) | Speedup | Control Plane Overhead | -|---------:|--------------------:|--------:|------------------------:| -| 1 | [baseline] | 1.0× | [X]% | -| 2 | [?] | [?]× | [?]% | -| 4 | [?] | [?]× | [?]% | -| 8 | [?] | [?]× | [?]% | - -Figure: Latency vs. Request Rate - -- X-axis: request rate (req/s) -- Y-axis: latency (ms) -- Lines: p50, p95, p99 -- Mark saturation point and discuss where SAGE’s control plane becomes the bottleneck (if at all) - ---- Output --- - -1. A detailed experimental plan with specific configurations. -2. Expected table/figure formats. -3. Key claims the scalability study should support (e.g., near-linear scaling up to N backends, negligible control-plane overhead for large models). - -______________________________________________________________________ - -## 5.3 为每类实验撰写结果描述 - -在你实际拿到实验数据之后，可以用下面的提示词为每类实验写结果段落。 - -**提示词（可直接复制，每个子节可复用）** - -We now have experimental results for the subsection: - -> [INSERT EXPERIMENT SUBSECTION TITLE HERE, e.g., "End-to-End Pipeline Performance" or "Scalability Study"] - -Here is the design of this subsection (goal, workloads, metrics, baselines): - -[PASTE THE DESIGN OUTLINE FOR THIS SUBSECTION HERE] - -Here are the preliminary results (tables, plots, or bullet points): - -[PASTE YOUR NUMERIC OR QUALITATIVE RESULTS HERE] - ---- Task --- - -Write the **Results and Analysis** text for this subsection in English, targeting systems reviewers. - -Requirements: - -1. Start by restating **what the experiment tries to verify** (e.g., whether the control plane improves tail latency under mixed workloads, whether declarative dataflow leads to better resource utilization, whether heterogeneous deployment is practical). -2. Describe **key trends in the results**, referencing specific metrics (throughput, latency, SLO satisfaction, success rates, cost-performance, etc.). -3. Clearly explain **why SAGE behaves better or differently** than baselines, relating back to design choices (layering, control plane, dataflow, CPU-only support, benchmarks, tooling). -4. If results are mixed, be honest and propose plausible explanations or follow-up experiments. -5. Propose **candidate figure/table captions** for the plots or tables we have, and specify which should be in the main paper vs. appendix. - ---- Output --- - -1. A few paragraphs of result description and analysis for this subsection. -2. A list of suggested figure/table captions with a short description each. -3. If applicable, a short note on what additional experiments could strengthen this story. - -______________________________________________________________________ - -## 5.4 Baseline 选择指南（SAGE 特化） - -**为什么需要仔细选择 baseline：** 系统论文审稿人会严格审视 baseline 是否公平、是否代表了 state-of-the-art。 - -| SAGE Feature | Recommended Baseline | Why This Baseline | -|-----------------------------|------------------------------------------------------------|-------------------------------------------------| -| Layered architecture + dataflow | Ad-hoc Python scripts or flat microservices | Shows maintainability / complexity differences | -| Unified control plane | vLLM + separate embedding service (manual load balancing) | Shows the benefit of unified scheduling | -| Hybrid scheduling | SAGE with FIFO policy | Ablation showing scheduling policy matters | -| Multi-engine support | Single vLLM instance | Shows horizontal scaling works | -| CPU-only support | GPU-only deployment or naive CPU-only baseline | Shows cost-effectiveness and feasibility | -| System-level benchmark | AgentBench / ToolBench / single-engine benchmark | Shows SAGE’s broader system metrics vs. others | - -Baseline 实现要求： - -1. 所有 baseline 必须使用相同或明确定义的硬件配置。 -2. vLLM baseline 必须使用相同版本和参数。 -3. 如果无法使用相同硬件，必须说明并尽量归一化结果（例如用吞吐/成本等指标）。 -4. 必须报告 baseline 的最优合理配置（不能故意用明显较差的配置）。 - -______________________________________________________________________ - -## 5.5 实验结果的可重复性 - -**系统论文对可重复性要求很高。** 确保包含以下信息： - -```markdown -### Reproducibility Checklist - -- [ ] Hardware specification (GPU model, memory, CPU cores, network) -- [ ] Software versions (SAGE, vLLM, CUDA, Python, key dependencies) -- [ ] Model details (model name, size, quantization if any) -- [ ] Workload specification (input/output length distribution, arrival pattern) -- [ ] Warm-up procedure (how many requests before measurement?) -- [ ] Measurement duration (how long did you run each experiment?) -- [ ] Number of repetitions (how many times did you repeat? error bars?) -- [ ] Code availability (will you release experiment scripts?) -``` - -建议在论文附录或 supplementary material 中包含： - -1. 完整的实验配置文件； -2. 用于生成图表的原始数据； -3. 运行实验的脚本（可基于 `sage-dev` 或 `sage.benchmark` 的 CLI）。 - -```` - -______________________________________________________________________ - -## 06_contributions_example.md - -```markdown -# SAGE – Example Contributions List (Systems Track) - -本文件提供一份面向顶级 **Machine Learning Systems** track 的示例 "Contributions" 列表草案，你可以直接在 Introduction 末尾或单独小节中使用/修改。 -重点是把 **整个 SAGE 系统** 的贡献讲清楚：分层架构、数据流、控制平面、异构部署、benchmark。 - -______________________________________________________________________ - -## 1. Example Contributions (English Draft) - -Below is an example contributions list tailored to SAGE as a **machine learning system** rather than a pure algorithm or application. - -1. **A layered architecture for declarative LLM/AI pipelines.** - - We introduce SAGE, a framework that organizes LLM/AI data processing pipelines into a strict six-layer architecture, from foundational utilities (`sage-common`) and platform services (`sage-platform`), through kernel and middleware components (`sage-kernel`, `sage-libs`, `sage-middleware`), up to applications and user-facing tools (`sage-apps`, `sage-benchmark`, `sage-cli`, `sage-studio`, `sage-tools`, `sage-gateway`). By enforcing **no upward dependencies**, SAGE cleanly separates concerns between configuration, scheduling, execution, and user interfaces, enabling independent evolution of layers, easier testing, and simplified large-scale system maintenance. - -2. **A unified control plane for LLM and embedding workloads.** - - We design and implement a **sageLLM control plane** that jointly manages LLM and embedding workloads across a shared pool of engines. The control plane classifies requests (chat/generation vs. embeddings), applies hybrid scheduling and batching policies (e.g., `HybridSchedulingPolicy`), and exposes an OpenAI-compatible API via `sage-gateway` on standardized ports from `SagePorts`. This unified design improves resource utilization and **reduces tail latency** for mixed LLM+embedding traffic compared to siloed vLLM + separate embedding setups, while preserving a familiar client-facing interface. - -3. **Systems support for heterogeneous CPU/GPU deployments with reproducible tooling.** - - SAGE provides kernel-level mechanisms for **CPU-only and GPU nodes**, job management (`sage-kernel/runtime`), and node selection (`sage-kernel/scheduler`), along with platform services (`sage-platform`) for storage, queuing, and service management, and C++ operators in `sage-middleware` for performance-critical paths. Together with reproducible installation and quality pipelines (`quickstart.sh`, `manage.sh`, `sage-dev`, pre-commit tooling), the system lowers the barrier to deploying complex LLM pipelines on heterogeneous clusters and makes end-to-end experiments repeatable for both developers and researchers. - -4. **A comprehensive benchmark suite and reusable testbed for LLM-centric systems.** - - To evaluate the system, we provide `sage-benchmark`, which instantiates a range of workloads for **agent behavior** (tool selection, multi-step planning, timing decisions) and **control-plane scheduling** under diverse traffic patterns, as well as additional suites targeting **retrieval, memory, DB/TSDB components, and scheduler behavior** in LLM-centric pipelines. The suite reports not only task- or model-level accuracy but also systems metrics such as throughput, latency distribution, SLO satisfaction, and resource utilization, and it exposes standard interfaces so that alternative agents, scheduling algorithms, or middleware components can be plugged in and compared on a common testbed built on top of SAGE’s layered architecture and unified control plane. - -If space is tight, you may merge (3) and (4) into a single contribution on **end-to-end deployment and evaluation**. - -______________________________________________________________________ - -## 2. Quantitative Claims Checklist - -Each contribution should have at least one **quantitative** claim. After experiments are complete, fill in the placeholders below: - -| Contribution | Claim Template | Experiment Needed | -|-------------------|-------------------------------------------------------------------------------------------|--------------------------------------------| -| Architecture (1) | "enables [X]% faster development iteration" OR "reduces configuration complexity by [Y]%" | Developer study or configuration/LOC comparison | -| Control Plane (2) | "reduces p99 latency by [X]% compared to vLLM + separate embedding" | Mixed LLM+embedding workload latency benchmark | -| Control Plane (2) | "improves throughput by [Y]× while maintaining p95 < [Z] ms" | Throughput vs. latency saturation study | -| Control Plane (2) | "achieves [A]% SLO satisfaction vs. [B]% for baseline" | SLO compliance under varied load | -| Heterogeneous (3) | "supports CPU-only nodes with [X]% of GPU performance for embedding-heavy workloads" | CPU vs. GPU embedding / pipeline benchmark | -| Benchmark (4) | "reveals [specific insight, e.g., FIFO degrades p99 by [C]× vs. hybrid policy" | Comparative scheduling/agent evaluation | - -这些模板可以帮助你在写论文时，系统性地把实验结果映射到贡献点。 - -______________________________________________________________________ - -## 3. Positioning vs. Existing Systems (for reviewer FAQs) - -You can also prepare short Q&A snippets for reviewers: - -**Q: How does SAGE differ from vLLM?** - -> vLLM is a single-model inference engine optimized for GPU memory management and continuous batching. SAGE uses vLLM as a backend engine and adds: (1) cross-engine load balancing, (2) embedding service co-scheduling, (3) declarative pipeline composition, (4) SLO-aware request routing, and (5) system-level benchmarks. - -**Q: How does SAGE differ from Ray Serve or KServe?** - -> Ray Serve and KServe are generic ML serving frameworks. SAGE provides LLM-specific scheduling (distinguishing chat vs. generation vs. embedding), workload-aware batching, declarative dataflow for pipelines, and an OpenAI-compatible API that simplifies migration from cloud LLM APIs, plus benchmarks that focus on LLM-centric systems behavior. - -**Q: How does SAGE differ from LangChain / LlamaIndex?** - -> LangChain and LlamaIndex are application-level orchestration frameworks for prompt chaining and agent logic. SAGE operates at the systems level, providing the underlying resource management, scheduling, execution, and benchmarking infrastructure that LangChain-like frameworks could build upon. - -**Q: Why is a unified LLM+embedding control plane needed?** - -> Modern RAG and agent applications interleave embedding (for retrieval) and LLM (for generation) calls. Without unified scheduling, operators must manually balance multiple services, leading to resource fragmentation and suboptimal latency. SAGE’s control plane treats them as a single resource pool with workload-aware policies, integrated into a broader dataflow and benchmarking framework. - -______________________________________________________________________ - -## 4. Chinese Summary（供自己校对用） - -- **分层架构 + declarative pipeline**：强调 6 层、无上行依赖、关注点分离与可维护性。 -- **统一控制平面**：LLM + Embedding 统一调度，混合请求分类、批处理、SLO，API 走 OpenAI 兼容 gateway。 -- **异构集群与工程工具链**：CPU/GPU 混部、job/node 管理、C++ 中间件、统一安装与质量工具，突出 "implementation & scalability"。 -- **系统化 benchmark**：既评估 agent 能力，也评估调度策略和 pipeline 行为，关注 throughput/latency/SLO 等系统指标。 - -你可以根据最终实验结果，把 `[X]%`, `[Y]×` 等占位符替换成实际数字。 - -``` - -______________________________________________________________________ - -## 07_system_outline_example.md - -```markdown -# SAGE – Example System / Method Outline (Systems Track) - -本文件给出一份结合 SAGE 实际结构的 System / Method 章节详细纲要示例，可与 `04_system_and_method.md` 里的提示词配合使用，用来介绍 **整个 SAGE 系统**，而不仅仅是控制平面。 - -______________________________________________________________________ - -## 1. High-Level Section Structure (Example) - -A possible structure for the System / Method section of the paper is: - -1. **System Overview and Design Goals** -2. **Layered Architecture** -3. **Declarative Dataflow and Execution Model** -4. **LLM & Embedding Control Plane (sageLLM)** -5. **Implementation Details and Deployment** - -You can merge or split sections depending on page limits (e.g., combine 2+3, or 4+5). - -______________________________________________________________________ - -## 2. Section – System Overview and Design Goals - -**Questions to answer (systems-reviewer perspective)** - -- What concrete **problems** does SAGE target that existing LLM serving or MLOps systems do not fully solve? (e.g., complex multi-step LLM pipelines, mixed LLM+embedding workloads, CPU-only environments, end-to-end evaluation and reproducibility.) -- What are the **design goals**: scalability, heterogeneity support, programmability, debuggability, reproducibility, ease of evolution? -- How does SAGE sit in the ML systems ecosystem: is it a serving system, a workflow engine, a control plane, a benchmark framework, or a combination? - -**SAGE components to mention** - -- Overview of packages under `packages/`: `sage-common`, `sage-platform`, `sage-kernel`, `sage-libs`, `sage-middleware`, `sage-apps`, `sage-benchmark`, `sage-cli`, `sage-studio`, `sage-tools`, `sage-gateway`. -- High-level illustration of how a user goes from writing a pipeline (via CLI/Studio/examples) to executing it on a heterogeneous cluster using SAGE. - -**Suggested figures/diagrams** - -- **Figure 1: SAGE System Overview.** A block diagram showing the layers and their roles: user interfaces at the top, control plane and platform services in the middle, execution engines and operators at the bottom. Caption: *"High-level view of the SAGE system, highlighting its layered architecture and main components for LLM/AI pipelines."* - -______________________________________________________________________ - -## 3. Section – Layered Architecture - -**Questions to answer** - -- How are the six layers defined, and what responsibilities does each layer have? -- Why enforce **no upward dependencies**? How does this help modularity, testing, and independent evolution? -- How does this layering compare to monolithic or ad-hoc LLM orchestration scripts or flat microservice designs? - -**Mapping to SAGE components** - -- L1 – `sage-common`: configuration (`config/config.yaml`), user paths (XDG), `SagePorts` for port allocation, shared components. -- L2 – `sage-platform`: platform services for storage, queuing, and service management; integration with cluster configuration (`config/cluster.yaml`). -- L3 – `sage-kernel`, `sage-libs`: execution kernels, job management (`runtime/job_manager`), node selection (`scheduler/node_selector`), CPU/GPU awareness, algorithms, and scheduling primitives. -- L4 – `sage-middleware`: C++ operators and performance-critical components. -- L5 – `sage-cli`, `sage-tools`: CLI commands (e.g., `sage llm engine start`), development tools. - -**Independent repositories** (not in core architecture): -- `sage-benchmark` – benchmark scenarios (PyPI: `isage-benchmark`) -- `sage-examples` – applications and tutorials -- `sageLLM` – LLM inference engine with control plane (PyPI: `isagellm`) - -**Suggested figures** - -- **Figure 2: Layered Architecture.** A stacked diagram (L1 at bottom to L5 at top) with arrows only going downward. Caption: *"SAGE enforces a strict layering discipline with no upward dependencies, which simplifies reasoning about responsibilities and allows lower layers to be reused across tools, applications, and benchmarks."* - -______________________________________________________________________ - -## 4. Section – Declarative Dataflow and Execution Model - -**Questions to answer** - -- How do users **declare** LLM/AI pipelines (e.g., composition of retrieval, tools, LLM calls, post-processing)? -- How does SAGE translate these declarations into an executable plan over its layers? -- How does the execution model handle **batching**, **parallelism**, and **resource allocation** across CPU/GPU nodes? -- How does this improve over ad-hoc scripts in terms of maintainability, performance, and correctness? - -**SAGE components to mention** - -- High-level APIs and examples under `examples/apps` 和 `examples/tutorials` that construct dataflows. -- Kernel/platform interaction for executing these dataflows, including job scheduling and node selection. -- Role of `sage-middleware` operators when a dataflow step is performance-critical. - -**Suggested figures** - -- **Figure 3: Declarative Dataflow Example.** A diagram of a concrete pipeline (data ingestion → embedding → retrieval → LLM generation → post-processing), annotated with which layers are involved at each step. -- **Figure 4: Execution Model.** A schematic showing how a declarative graph is compiled into tasks over nodes, with batching and scheduling hooks. - -______________________________________________________________________ - -## 5. Section – LLM & Embedding Control Plane (sageLLM) - -**Questions to answer** - -- What are the **goals** of the control plane? (e.g., share resources across LLM and embedding workloads, improve throughput and tail latency, respect SLOs.) -- How are requests classified and routed? What are the main scheduling/batching policies? -- How does the control plane interact with the gateway and backends? -- How does it differ from a single vLLM instance or simple load balancer? -- How does it fit into the broader SAGE system (dataflow, benchmarks, deployment tools)? - -**Mapping to SAGE components** - -- `sage.common.components.sage_llm.UnifiedInferenceClient` (with unified `create()` entry point) and related control-plane modules under `sageLLM/control_plane/` including `ControlPlaneManager`, `RequestClassifier`, `HybridSchedulingPolicy`, and `EmbeddingExecutor`. -- `sage-gateway` FastAPI app and routes for LLM and embedding. -- `SagePorts` (`GATEWAY_DEFAULT`, `LLM_DEFAULT`, `EMBEDDING_DEFAULT`, etc.) and WSL2-aware port selection. - -**Suggested figures** - -- **Figure 5: Control Plane Architecture.** Components: request classifier, scheduling policy (HybridSchedulingPolicy), execution coordinators for LLM and embeddings, backend engine pool. -- **Figure 6: Request Timeline under Mixed Workloads.** Show how chat and embedding requests are batched and routed over time, contrasted with a naive baseline. - -______________________________________________________________________ - -## 6. Section – Implementation Details and Deployment - -**Questions to answer** - -- What are the key implementation choices that matter for systems reviewers? (language choices, C++ integration, build system, packaging.) -- How does SAGE support **CPU-only** as well as GPU deployments in practice? -- How do quickstart scripts and `sage-dev` tooling enable **reproducible experiments** and CI? -- What operational practices (logging, configuration, user paths) are built in to support real users? - -**SAGE components to mention** - -- C++ middleware build (`packages/sage-middleware/src/...`, CMake, `.sage/build/`). -- Installation scripts: `quickstart.sh`, `manage.sh`, CI install wrappers. -- `sage-dev` commands for test, quality, and examples; pytest configuration under `tools/pytest.ini`. -- XDG-based user paths and directories for logs, models, and cache. - -**Suggested figures / tables** - -- **Table 1: Implementation Summary.** Columns: language/components, lines of code (approx.), main dependencies, build artifacts. -- **Figure 7: Deployment and Tooling Workflow.** From cloning the repo to running `quickstart.sh`, starting `sage gateway` and `sage llm`, and launching experiments. - -______________________________________________________________________ - -## 7. How to Use This Outline - -- 在写 System 章节时，可以把本文件作为“答案模板”，再配合 `04_system_and_method.md` 中的提示词： - - 把这里的每个小节要点粘到提示词中的 `[PASTE THE BULLET-POINT OUTLINE HERE]` 位置； - - 让模型基于这些要点生成英文小节； - - 你再根据实际实现细节和实验配置进行微调。 -- 如果篇幅吃紧，可以： - - 把 System Overview + Layered Architecture 合并； - - 把 Declarative Dataflow + Control Plane 合并； - - 将部分 Implementation 细节移到附录，仅在正文保留最系统相关的要点。 - -``` - -______________________________________________________________________ - -## 08_paper_outline_example.md - -```markdown -# SAGE Systems Paper – ICML-Style Outline - -下面是基于当前 prompts 跑出的一版 **完整 ICML 风格 SAGE 论文草稿结构**，只包含章节标题与每节 2–3 句英文说明，默认面向顶级 Machine Learning Systems track（例如 ICML）。 - ---- - -## 1 Introduction - -Introduces the rise of complex LLM/AI applications that compose retrieval, tools, and multiple models over heterogeneous CPU/GPU clusters, and argues that existing serving and MLOps systems lack unified support for such pipelines. States the goals and design principles of SAGE as a dataflow-based ML system, positions it between low-level LLM serving engines and high-level application frameworks, and outlines the main challenges (scalability, heterogeneity, programmability, reproducibility). Summarizes the paper’s contributions as a numbered list covering the layered architecture, declarative dataflow, unified LLM+embedding control plane, heterogeneous deployment support, and benchmark suite. - -## 2 Related Work - -Reviews prior work across several categories: LLM serving engines (e.g., vLLM, TensorRT-LLM), generic serving and workflow frameworks (e.g., Ray Serve, KServe, MLflow, Kubeflow), LLM application frameworks (e.g., LangChain, LlamaIndex, DSPy), and LLM benchmarks (e.g., AgentBench, ToolBench, HELM). For each category, explains what systems properties they target and why they are insufficient as end-to-end platforms for LLM+embedding dataflow pipelines. Concludes by positioning SAGE as a unified system that complements these efforts by providing layered architecture, declarative dataflow, a control plane, and system-level benchmarks. - -## 3 System Overview and Design Goals - -Provides a high-level view of the SAGE system, introducing its role as a Python-based framework for LLM/AI data processing pipelines built on a strict six-layer architecture. Describes the main design goals—scalability, support for heterogeneous CPU/GPU environments, programmability via declarative dataflow, observability, and reproducibility—and how they shape the system’s interfaces and components. Walks through the lifecycle of a typical SAGE pipeline from user specification (CLI/Studio/examples) to deployment and execution on a cluster. - -## 4 Layered Architecture - -Details the responsibilities of each layer from `sage-common` and `sage-platform` through `sage-kernel`/`sage-libs`, `sage-middleware`, `sage-apps`/`sage-benchmark`, up to `sage-cli`, `sage-studio`, `sage-tools`, and `sage-gateway`. Explains the “no upward dependencies” constraint and how it enables modularity, testing, independent evolution of layers, and reuse of lower layers across tools, applications, and benchmarks. Compares this disciplined layering with ad-hoc scripting or flat microservice deployments commonly seen in LLM systems. - -## 5 Declarative Dataflow and Execution Model - -Introduces SAGE’s declarative dataflow abstraction for specifying LLM/AI pipelines (e.g., retrieval, tools, LLM calls, post-processing) and contrasts it with imperative orchestration code. Describes how the platform and kernel layers compile dataflow graphs into executable tasks, handling batching, parallelism, and placement over heterogeneous CPU/GPU nodes. Discusses how this execution model improves maintainability and performance, and how middleware operators are used for performance-critical stages. - -## 6 LLM & Embedding Control Plane (sageLLM) - -Presents the design goals of the sageLLM control plane: unified scheduling of LLM and embedding workloads, improved throughput and tail latency, and SLO-aware resource management across multiple backends. Describes the architecture, including the `UnifiedInferenceClient`, request classification, scheduling policies such as `HybridSchedulingPolicy`, embedding executors, and their integration with `sage-gateway` and vLLM instances. Clarifies SAGE’s relationship to vLLM and similar engines, emphasizing that SAGE builds a cross-engine, cross-workload control plane and API layer on top of them rather than replacing their single-model schedulers. - -## 7 Implementation Details and Deployment - -Summarizes key implementation choices: Python 3.10+, C++ middleware with CMake builds, internal directory layout, and the use of XDG-compliant user paths for configuration, logs, models, and caches. Describes installation and tooling (e.g., `quickstart.sh`, `manage.sh`, `sage-dev`, CI configuration) that enable reproducible builds, testing, and code quality enforcement. Explains how SAGE supports CPU-only and GPU deployments in practice, including configuration of ports via `SagePorts`, cluster configuration files, and operational practices for monitoring and debugging. - -## 8 Experiments - -Outlines the experimental methodology and setup: hardware and software environment, models, workloads (end-to-end pipelines, mixed LLM+embedding traffic, heterogeneous deployments), and measurement procedures following reproducibility best practices. States the main experimental questions: end-to-end pipeline performance vs. baselines, effectiveness of the unified control plane and scheduling policies, scalability with backends and load, benefits of heterogeneous deployments, and insights from agent and system benchmarks. Previews the structure of the section, with subsections on: (8.1) End-to-End Pipeline Performance, (8.2) Control Plane Effectiveness, (8.3) Scheduling Policy Comparison, (8.4) Scalability, (8.5) Heterogeneous Hardware & CPU-only Support, and (8.6) Agent Capability & Benchmarking (if claimed). - -## 9 Discussion - -Reflects on the practical implications of deploying SAGE in real-world environments, including trade-offs between flexibility and complexity, and lessons learned from building and operating a multi-layer ML system. Discusses limitations such as dependency on underlying serving engines, potential bottlenecks in the control plane, and scenarios where simpler solutions may suffice. Outlines promising directions for future extensions, such as richer dataflow optimizations, tighter integration with external data systems, or additional scheduling policies. - -## 10 Conclusion - -Recaps the motivation for SAGE and the key design elements: layered architecture, declarative dataflow, unified LLM+embedding control plane, heterogeneous deployment support, and benchmark suite. Summarizes the main experimental findings in terms of performance, scalability, SLO satisfaction, and insights into agent and scheduling behavior. Emphasizes SAGE’s role as a reusable platform and testbed for future research on LLM-centric systems and invites the community to build on its architecture and benchmarks. - -``` diff --git a/benchmark/docs/paper_prompts_README.md b/benchmark/docs/paper_prompts_README.md deleted file mode 100644 index 79ae2d2e33..0000000000 --- a/benchmark/docs/paper_prompts_README.md +++ /dev/null @@ -1,34 +0,0 @@ -# SAGE Systems-Paper Writing Prompts - -This directory hosts **writing prompts and experiment outlines** for papers about the **SAGE -system** targeting top-tier machine learning systems venues (for example, the Machine Learning -Systems tracks at major conferences such as ICML). - -These prompts are intended for papers that: - -- treat SAGE as a **full dataflow-based ML systems platform**, not just an LLM control plane; -- cover the **entire SAGE stack**: layered architecture, declarative dataflow, storage/DB and - time-series components, LLM & embedding control plane, heterogeneous deployment, and benchmarking; -- leverage SAGE as a **benchmarking and experimentation testbed**, including `benchmark_agent`, - `benchmark_control_plane`, `benchmark_db`, `benchmark_rag`, `benchmark_scheduler`, - `benchmark_refiner`, `benchmark_libamm`, `benchmark_sage` (memory benchmarks now live in the - Neuromem repository). - -> Note: SAGE is **not** only an LLM inference / control-plane engine. The control plane is one -> subsystem. SAGE also provides dataflow-oriented components such as `sage.db`, `sage.flow`, -> `sage.tsdb`, and other services that are connected via SAGE's declarative dataflow model, as well -> as platform, kernel, and middleware layers. - -The Markdown files in this directory (and under `docs/icml-prompts/` in the repo root) provide -per-section prompts for a full systems paper: - -- Abstract, Introduction, Related Work, System / Method, Experiments, Discussion, Conclusion; -- contributions lists and concrete system-design outlines for the **whole SAGE system**; -- experiment design prompts for different SAGE subsystems (control plane, dataflow pipelines, - storage/DB, time-series DB, agent layer, scheduler, etc.). - -You can either: - -- use the root-level `docs/icml-prompts/` files directly, or -- copy/adapt them into paper-specific subfolders under `benchmark_sage/docs/` for particular - submissions about SAGE. diff --git a/benchmark/experiments/README.md b/benchmark/experiments/README.md deleted file mode 100644 index 7e91249508..0000000000 --- a/benchmark/experiments/README.md +++ /dev/null @@ -1,208 +0,0 @@ -# SAGE 分布式调度策略评测 - -SAGE 分布式调度策略的性能评测实验。 - -## 目录结构 - -``` -distributed_scheduling/ - common/ # 通用组件 - ├── models.py # 数据模型 (TaskState, Config, Metrics) - ├── operators.py # Pipeline 算子 - ├── pipeline.py # Pipeline 工厂 - └── visualization.py # 结果可视化 - exp1_single_vs_multi/ # 实验1: 单节点 vs 多节点 - ├── run_experiment.py # 实验脚本 - └── results/ # 输出结果 - exp2_high_load_parallel/ # 实验2: 高负载并行调度 - ├── run_experiment.py - └── results/ - exp3_latency_throughput/ # 实验3: 延迟与吞吐量 - ├── run_experiment.py - └── results/ - run_all.sh # 运行所有实验 - README.md -``` - -## 前置条件 - -1. **启动 Ray 集群** (多节点实验需要): - - ```bash - # 编辑 config/cluster.yaml 配置节点 - sage cluster start - ``` - -1. **启动 JobManager**: - - ```bash - sage jobmanager start - ``` - -1. **LLM/Embedding 服务** (如果使用 RAG/LLM Pipeline): - - ```bash - sage llm serve - ``` - -## 实验说明 - -### 实验1: 单节点 vs 多节点对比 - -**测试配置**: - -| 配置 | 节点数 | 并行度 | 调度器 | -| ------ | ------ | ------ | ---------------- | -| 单节点 | 1 | 4 | Local | -| 4节点 | 4 | 16 | LoadAware-SPREAD | -| 8节点 | 8 | 32 | LoadAware-SPREAD | -| 16节点 | 16 | 64 | LoadAware-SPREAD | -| 30节点 | 30 | 120 | LoadAware-SPREAD | - -**运行**: - -```bash -cd exp1_single_vs_multi - -# 完整实验 -python run_experiment.py - -# 快速测试 -python run_experiment.py --quick - -# 指定节点数 -python run_experiment.py --nodes 1 4 8 --tasks 500 -``` - -**输出指标**: - -- 吞吐量 (tasks/sec) -- 平均延迟 / P50 / P95 / P99 延迟 -- 节点分布均衡度 - -### 实验2: 高负载流水线并行调度 - -**负载级别**: - -| 级别 | 并行度 | 节点数 | 任务数 | -| -------- | ------ | ------ | ------ | -| 低负载 | 4 | 2 | 100 | -| 中负载 | 16 | 4 | 200 | -| 高负载 | 64 | 8 | 500 | -| 极高负载 | 128 | 16 | 1000 | - -**调度策略对比**: - -- FIFO: 先进先出 -- LoadAware-SPREAD: 负载感知 + 分散策略 -- LoadAware-PACK: 负载感 + 紧凑策略 -- RoundRobin: 轮询 -- Priority: 优先级 - -**流水线深度**: - -- 浅层: 2 阶段 -- 中层: 3 阶段 -- 深层: 5 阶段 - -**运行**: - -```bash -cd exp2_high_load_parallel - -# 完整实验 -python run_experiment.py - -# 快速测试 -python run_experiment.py --quick - -# 指定调度器 -python run_experiment.py --schedulers fifo load_aware_spread round_robin - -# 指定负载级别 -python run_experiment.py --load-levels low medium high -``` - -### 实验3: 调度延迟与吞吐量精细测量 - -'ENDOFFILE' - -**测量项**: - -- 调度延迟: 任务提交到分配的时间 -- 排队延迟: 分配到开始执行的时间 if current_time - self._cache_time > self._ -- 端到端延迟: 总时间 - -**并发度测试**: - -- 1, 2, 4, 8, 16, 32 并发 - -**运行**: - -```bash -cd exp3_latency_throughput - -# 完整实验 -python run_experiment.py - -# 快速测试 -python run_experiment.py --quick - -# 指定并发度 -python run_experiment.py --concurrency 1 4 8 16 32 --tasks 500 -``` - -## 快速开始 - -```bash -# 1. 启动服务 -sage jobmanager start -sage cluster start - -# 2. 运行快速测试 -./run_all.sh --quick - -# 3. 查看结果 -ls exp1_single_vs_multi/results/ -ls exp2_high_load_parallel/results/ -ls exp3_latency_throughput/results/ -``` - -## 输出文件 - -``` - if current_time - self._cache_ttl: > self._cache_:::::: -``` - -| 文件 | 描述 | -| ------------------ | ----------------------- | -| `*_summary.txt` | 人类可 | -| `*_metrics.json` | 完整的 JSON 指标数据 | -| `*_latencies.csv` | 延迟数据 CSV (便于分析) | -| `*_comparison.txt` | 多配置对比报告 | -| `*_throughput.png` | 吞吐量对比图 | -| `*_latency.png` | 延迟对比图 | -| `*_nodes.png` | 节点分 | - -## 自定义实验 - -'ENDOFFILE''ENDOFFILE'--------的配置常量来自定义实验: - -```python -# exp1_single_vs_multi/run_experiment.py -EXPERIMENT_CONFIGS = { - "custom_config": { - "use_remote": True, - "num_nodes": 10, - "parallelism": 40, - "scheduler_type": "load_aware", - }, -} -``` - -## 注意事项 - -1. **多节点实验** 需要先配置 `config/cluster.yaml` 中的节点列表 -1. **LLM/RAG Pipeline**LLM 服务已启动 'ENDOFFILE' -1. **大规模实验** 可能需要较长时间，建议先用 `--quick` 测试 -1. **结果文件** 带时间戳，不会覆盖旧结果 diff --git a/benchmark/experiments/__init__.py b/benchmark/experiments/__init__.py deleted file mode 100644 index 607ffbbd6c..0000000000 --- a/benchmark/experiments/__init__.py +++ /dev/null @@ -1,51 +0,0 @@ -"""Experiments module for SAGE benchmark.""" - -from sage.benchmark.benchmark_sage.experiments.base_experiment import ( - BaseExperiment, - ExperimentResult, -) -from sage.benchmark.benchmark_sage.experiments.common import ( - BenchmarkClient, - RequestResult, - WorkloadGenerator, -) -from sage.benchmark.benchmark_sage.experiments.config import ( - ExperimentConfig, - HardwareConfig, - ModelConfig, - WorkloadConfig, -) -from sage.benchmark.benchmark_sage.experiments.exp_5_1_e2e_pipeline import ( - E2EPipelineExperiment, -) -from sage.benchmark.benchmark_sage.experiments.exp_5_2_control_plane import ( - ControlPlaneExperiment, -) -from sage.benchmark.benchmark_sage.experiments.exp_5_3_isolation import ( - IsolationExperiment, -) -from sage.benchmark.benchmark_sage.experiments.exp_5_4_scalability import ( - ScalabilityExperiment, -) -from sage.benchmark.benchmark_sage.experiments.exp_5_5_heterogeneity import ( - HeterogeneityExperiment, -) -from sage.benchmark.benchmark_sage.experiments.plotting import Plotter - -__all__ = [ - "BaseExperiment", - "ExperimentResult", - "RequestResult", - "WorkloadGenerator", - "BenchmarkClient", - "ExperimentConfig", - "WorkloadConfig", - "HardwareConfig", - "ModelConfig", - "ControlPlaneExperiment", - "ScalabilityExperiment", - "E2EPipelineExperiment", - "HeterogeneityExperiment", - "IsolationExperiment", - "Plotter", -] diff --git a/benchmark/experiments/base_experiment.py b/benchmark/experiments/base_experiment.py deleted file mode 100644 index 6452f73c79..0000000000 --- a/benchmark/experiments/base_experiment.py +++ /dev/null @@ -1,394 +0,0 @@ -""" -Base experiment class for ICML benchmarks. - -Provides common infrastructure for: -- Configuration management -- Workload generation -- Metrics collection -- Result reporting -""" - -import json -import random -import time -from abc import ABC, abstractmethod -from dataclasses import dataclass, field -from datetime import datetime -from pathlib import Path - -import numpy as np - -from sage.benchmark.benchmark_sage.experiments.common import RequestResult -from sage.benchmark.benchmark_sage.experiments.config import ExperimentConfig -from sage.benchmark.benchmark_sage.experiments.plotting import Plotter - - -@dataclass -class ExperimentResult: - """Complete experiment results.""" - - experiment_name: str - experiment_section: str - start_time: str - end_time: str - duration_s: float - config: dict - - # Aggregate metrics - total_requests: int - successful_requests: int - failed_requests: int - throughput_rps: float - - # Latency metrics - latency_p50_ms: float - latency_p95_ms: float - latency_p99_ms: float - latency_mean_ms: float - - # SLO metrics - slo_satisfaction_rate: float - - # Per-type metrics - llm_metrics: dict = field(default_factory=dict) - embedding_metrics: dict = field(default_factory=dict) - - # Raw data - raw_results: list[dict] = field(default_factory=list) - - # Comparison data (for multi-baseline experiments) - baseline_results: dict = field(default_factory=dict) - - -class BaseExperiment(ABC): - """Base class for all ICML experiments.""" - - def __init__( - self, - config: ExperimentConfig, - output_dir: Path | str, - verbose: bool = False, - ): - self.config = config - self.output_dir = Path(output_dir) - self.verbose = verbose - - # State - self.results: list[RequestResult] = [] - self.start_time: float | None = None - self.end_time: float | None = None - - # Random seed for reproducibility - random.seed(config.workload.seed) - np.random.seed(config.workload.seed) - - def validate(self) -> bool: - """Validate configuration. Override in subclasses for specific validation.""" - if self.config.hardware.gpus < 1: - raise ValueError("At least 1 GPU required") - if self.config.workload.total_requests < 1: - raise ValueError("total_requests must be positive") - if not 0 <= self.config.workload.llm_ratio <= 1: - raise ValueError("llm_ratio must be between 0 and 1") - return True - - def setup(self) -> None: - """Setup experiment environment.""" - self.output_dir.mkdir(parents=True, exist_ok=True) - self.log(f"Output directory: {self.output_dir}") - - # Save config - config_path = self.output_dir / "config.json" - with open(config_path, "w") as f: - json.dump(self._config_to_dict(), f, indent=2) - - self._setup_impl() - - @abstractmethod - def _setup_impl(self) -> None: - """Implementation-specific setup. Override in subclasses.""" - pass - - def run(self) -> ExperimentResult: - """Run the experiment.""" - self.log(f"Starting experiment: {self.config.name}") - self.start_time = time.time() - - # Run warmup - self.log(f"Running {self.config.workload.warmup_requests} warmup requests...") - self._run_warmup() - - # Run main experiment - self.log(f"Running {self.config.workload.total_requests} main requests...") - self._run_impl() - - self.end_time = time.time() - - # Compute results - result = self._compute_results() - - # Save results - self._save_results(result) - - # Generate visualizations - if self.config.output.generate_plots: - self._generate_plots(result) - - return result - - @abstractmethod - def _run_impl(self) -> None: - """Implementation-specific run logic. Override in subclasses.""" - pass - - def _run_warmup(self) -> None: - """Run warmup requests (not counted in results).""" - # Default: run a fraction of requests as warmup - # Subclasses should implement actual warmup - pass - - def teardown(self) -> None: - """Cleanup after experiment.""" - self._teardown_impl() - self.log("Experiment teardown complete.") - - def _teardown_impl(self) -> None: - """Implementation-specific teardown. Override in subclasses.""" - pass - - def _compute_results(self) -> ExperimentResult: - """Compute aggregate metrics from raw results.""" - if not self.results: - raise ValueError("No results to compute") - - successful = [r for r in self.results if r.success] - failed = [r for r in self.results if not r.success] - - latencies = [r.latency_ms for r in successful] - - duration = (self.end_time or time.time()) - (self.start_time or 0) - - # Separate by type - llm_results = [r for r in successful if r.request_type == "llm"] - embedding_results = [r for r in successful if r.request_type == "embedding"] - - # SLO calculation - slo_met = sum( - 1 - for r in successful - if (r.request_type == "llm" and r.latency_ms <= self.config.metrics.slo_chat_p99_ms) - or ( - r.request_type == "embedding" - and r.latency_ms <= self.config.metrics.slo_embedding_p99_ms - ) - ) - - return ExperimentResult( - experiment_name=self.config.name, - experiment_section=self.config.experiment_section, - start_time=( - datetime.fromtimestamp(self.start_time).isoformat() if self.start_time else "" - ), - end_time=datetime.fromtimestamp(self.end_time).isoformat() if self.end_time else "", - duration_s=duration, - config=self._config_to_dict(), - total_requests=len(self.results), - successful_requests=len(successful), - failed_requests=len(failed), - throughput_rps=len(successful) / duration if duration > 0 else 0, - latency_p50_ms=float(np.percentile(latencies, 50)) if latencies else 0, - latency_p95_ms=float(np.percentile(latencies, 95)) if latencies else 0, - latency_p99_ms=float(np.percentile(latencies, 99)) if latencies else 0, - latency_mean_ms=float(np.mean(latencies)) if latencies else 0, - slo_satisfaction_rate=slo_met / len(successful) if successful else 0, - llm_metrics=self._compute_type_metrics(llm_results), - embedding_metrics=self._compute_type_metrics(embedding_results), - raw_results=( - [self._result_to_dict(r) for r in self.results] - if self.config.output.save_raw_data - else [] - ), - ) - - def _compute_type_metrics(self, results: list[RequestResult]) -> dict: - """Compute metrics for a specific request type.""" - if not results: - return {} - - latencies = [r.latency_ms for r in results] - return { - "count": len(results), - "latency_p50_ms": float(np.percentile(latencies, 50)), - "latency_p95_ms": float(np.percentile(latencies, 95)), - "latency_p99_ms": float(np.percentile(latencies, 99)), - "latency_mean_ms": float(np.mean(latencies)), - "tokens_in_total": sum(r.tokens_in for r in results), - "tokens_out_total": sum(r.tokens_out for r in results), - } - - def _save_results(self, result: ExperimentResult) -> None: - """Save results to files.""" - # JSON results - results_path = self.output_dir / "results.json" - with open(results_path, "w") as f: - json.dump(self._result_to_full_dict(result), f, indent=2) - - # Summary - summary_path = self.output_dir / "summary.txt" - with open(summary_path, "w") as f: - f.write(self._generate_summary(result)) - - # LaTeX table - if self.config.output.export_latex: - latex_path = self.output_dir / "results_table.tex" - with open(latex_path, "w") as f: - f.write(self._generate_latex_table(result)) - - self.log(f"Results saved to {self.output_dir}") - - def _generate_plots(self, result: ExperimentResult) -> None: - """Generate visualization plots.""" - try: - plotter = Plotter() - - # 1. Latency Distribution (Histogram -> CDF is better for papers, but let's keep simple for now or use Plotter) - # Using the new Plotter class - - # Prepare data for Plotter (it expects list of dicts for comparison, but here we have one result) - # We wrap current result in a list - result_dict = self._result_to_full_dict(result) - result_dict["config_name"] = self.config.name # Ensure name is present - - # Latency CDF - plotter.plot_latency_cdf( - [result_dict], - self.output_dir / "latency_cdf.png", - title=f"Latency CDF - {self.config.name}", - ) - - # Timeline (Waterfall) - if result.raw_results: - plotter.plot_timeline(result.raw_results, self.output_dir / "timeline.png") - - except Exception as e: - self.log(f"Error generating plots: {e}") - - def _generate_summary(self, result: ExperimentResult) -> str: - """Generate human-readable summary.""" - return f""" -Experiment Summary: {result.experiment_name} -{"=" * 60} -Section: {result.experiment_section} -Duration: {result.duration_s:.1f}s -Start: {result.start_time} -End: {result.end_time} - -Requests: - Total: {result.total_requests} - Successful: {result.successful_requests} - Failed: {result.failed_requests} - Throughput: {result.throughput_rps:.2f} req/s - -Latency: - p50: {result.latency_p50_ms:.1f}ms - p95: {result.latency_p95_ms:.1f}ms - p99: {result.latency_p99_ms:.1f}ms - Mean: {result.latency_mean_ms:.1f}ms - -SLO Satisfaction: {result.slo_satisfaction_rate * 100:.1f}% - -LLM Metrics: - Count: {result.llm_metrics.get("count", 0)} - p99 Latency: {result.llm_metrics.get("latency_p99_ms", 0):.1f}ms - -Embedding Metrics: - Count: {result.embedding_metrics.get("count", 0)} - p99 Latency: {result.embedding_metrics.get("latency_p99_ms", 0):.1f}ms -""" - - def _generate_latex_table(self, result: ExperimentResult) -> str: - """Generate LaTeX table for paper.""" - return f""" -\\begin{{table}}[htbp] -\\centering -\\caption{{Results for {result.experiment_name}}} -\\label{{tab:{result.experiment_name.replace("_", "-")}}} -\\begin{{tabular}}{{lc}} -\\toprule -Metric & Value \\\\ -\\midrule -Total Requests & {result.total_requests} \\\\ -Throughput (req/s) & {result.throughput_rps:.2f} \\\\ -Latency p50 (ms) & {result.latency_p50_ms:.1f} \\\\ -Latency p95 (ms) & {result.latency_p95_ms:.1f} \\\\ -Latency p99 (ms) & {result.latency_p99_ms:.1f} \\\\ -SLO Satisfaction & {result.slo_satisfaction_rate * 100:.1f}\\% \\\\ -\\bottomrule -\\end{{tabular}} -\\end{{table}} -""" - - def log(self, message: str) -> None: - """Log message if verbose mode is enabled.""" - if self.verbose: - timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") - print(f"[{timestamp}] {message}") - - def _config_to_dict(self) -> dict: - """Convert config to dictionary.""" - return { - "name": self.config.name, - "description": self.config.description, - "section": self.config.experiment_section, - "hardware": { - "gpus": self.config.hardware.gpus, - "gpu_type": self.config.hardware.gpu_type, - }, - "models": { - "llm": self.config.llm_model.name, - "embedding": self.config.embedding_model.name, - }, - "workload": { - "total_requests": self.config.workload.total_requests, - "llm_ratio": self.config.workload.llm_ratio, - "request_rate": self.config.workload.request_rate, - }, - } - - def _result_to_dict(self, r: RequestResult) -> dict: - """Convert RequestResult to dictionary.""" - return { - "request_id": r.request_id, - "request_type": r.request_type, - "start_time": r.start_time, - "end_time": r.end_time, - "latency_ms": r.latency_ms, - "success": r.success, - "error": r.error, - "tokens_in": r.tokens_in, - "tokens_out": r.tokens_out, - } - - def _result_to_full_dict(self, result: ExperimentResult) -> dict: - """Convert ExperimentResult to full dictionary.""" - return { - "experiment_name": result.experiment_name, - "experiment_section": result.experiment_section, - "start_time": result.start_time, - "end_time": result.end_time, - "duration_s": result.duration_s, - "config": result.config, - "total_requests": result.total_requests, - "successful_requests": result.successful_requests, - "failed_requests": result.failed_requests, - "throughput_rps": result.throughput_rps, - "latency_p50_ms": result.latency_p50_ms, - "latency_p95_ms": result.latency_p95_ms, - "latency_p99_ms": result.latency_p99_ms, - "latency_mean_ms": result.latency_mean_ms, - "slo_satisfaction_rate": result.slo_satisfaction_rate, - "llm_metrics": result.llm_metrics, - "embedding_metrics": result.embedding_metrics, - "raw_results": result.raw_results, - "baseline_results": result.baseline_results, - } diff --git a/benchmark/experiments/common.py b/benchmark/experiments/common.py deleted file mode 100644 index 7f119d95af..0000000000 --- a/benchmark/experiments/common.py +++ /dev/null @@ -1,215 +0,0 @@ -import time -from dataclasses import dataclass, field -from typing import Any, Optional - -import aiohttp -import numpy as np - - -@dataclass -class RequestResult: - """Result of a single request.""" - - request_id: str - request_type: str # "llm" or "embedding" - start_time: float - end_time: float - latency_ms: float - success: bool - error: str | None = None - tokens_in: int = 0 - tokens_out: int = 0 - metadata: dict = field(default_factory=dict) - - -class BenchmarkClient: - """Client for sending benchmark requests to SAGE gateway.""" - - def __init__(self, gateway_url: str, timeout: float = 60.0): - self.gateway_url = gateway_url.rstrip("/") - self.timeout = aiohttp.ClientTimeout(total=timeout) - self._session: Optional[aiohttp.ClientSession] = None - - async def __aenter__(self): - self._session = aiohttp.ClientSession(timeout=self.timeout) - return self - - async def __aexit__(self, *args): - if self._session: - await self._session.close() - - async def send_llm_request( - self, request_id: str, prompt: str, model: str = "default" - ) -> RequestResult: - """Send a chat completion request.""" - if not self._session: - raise RuntimeError("Client not initialized. Use 'async with' context manager.") - - start_time = time.perf_counter() - try: - async with self._session.post( - f"{self.gateway_url}/v1/chat/completions", - json={ - "model": model, - "messages": [{"role": "user", "content": prompt}], - "max_tokens": 128, - "temperature": 0.7, - }, - headers={"Content-Type": "application/json"}, - ) as resp: - end_time = time.perf_counter() - latency_ms = (end_time - start_time) * 1000 - - if resp.status == 200: - data = await resp.json() - tokens_out = data.get("usage", {}).get("completion_tokens", 0) - tokens_in = data.get("usage", {}).get("prompt_tokens", 0) - return RequestResult( - request_id=request_id, - request_type="llm", - start_time=start_time, - end_time=end_time, - latency_ms=latency_ms, - success=True, - tokens_in=tokens_in, - tokens_out=tokens_out, - ) - else: - error_text = await resp.text() - return RequestResult( - request_id=request_id, - request_type="llm", - start_time=start_time, - end_time=end_time, - latency_ms=latency_ms, - success=False, - error=f"HTTP {resp.status}: {error_text[:200]}", - ) - except Exception as e: - end_time = time.perf_counter() - return RequestResult( - request_id=request_id, - request_type="llm", - start_time=start_time, - end_time=end_time, - latency_ms=(end_time - start_time) * 1000, - success=False, - error=str(e), - ) - - async def send_embedding_request( - self, request_id: str, texts: list[str], model: str = "default" - ) -> RequestResult: - """Send an embedding request.""" - if not self._session: - raise RuntimeError("Client not initialized. Use 'async with' context manager.") - - start_time = time.perf_counter() - try: - async with self._session.post( - f"{self.gateway_url}/v1/embeddings", - json={ - "model": model, - "input": texts, - }, - headers={"Content-Type": "application/json"}, - ) as resp: - end_time = time.perf_counter() - latency_ms = (end_time - start_time) * 1000 - - if resp.status == 200: - data = await resp.json() - usage = data.get("usage", {}) - tokens_in = usage.get("prompt_tokens", 0) or usage.get("total_tokens", 0) - return RequestResult( - request_id=request_id, - request_type="embedding", - start_time=start_time, - end_time=end_time, - latency_ms=latency_ms, - success=True, - tokens_in=tokens_in, - ) - else: - error_text = await resp.text() - return RequestResult( - request_id=request_id, - request_type="embedding", - start_time=start_time, - end_time=end_time, - latency_ms=latency_ms, - success=False, - error=f"HTTP {resp.status}: {error_text[:200]}", - ) - except Exception as e: - end_time = time.perf_counter() - return RequestResult( - request_id=request_id, - request_type="embedding", - start_time=start_time, - end_time=end_time, - latency_ms=(end_time - start_time) * 1000, - success=False, - error=str(e), - ) - - -class WorkloadGenerator: - """Generates mixed LLM+Embedding workload.""" - - PROMPTS = [ - "Explain the concept of machine learning in simple terms.", - "What are the main differences between Python and Java?", - "Write a short poem about artificial intelligence.", - "Summarize the key points of deep learning.", - "What is the capital of France and its population?", - "Describe the process of photosynthesis.", - "What are the benefits of regular exercise?", - "Explain how a neural network works.", - ] - - TEXTS = [ - "Machine learning is a subset of artificial intelligence.", - "Deep learning uses neural networks with many layers.", - "Natural language processing enables computers to understand text.", - "Computer vision allows machines to interpret images.", - "Reinforcement learning involves learning through trial and error.", - ] - - def __init__(self, llm_ratio: float = 0.7, seed: int = 42): - self.llm_ratio = llm_ratio - self.rng = np.random.default_rng(seed) - - def generate_request(self, request_id: str) -> tuple[str, dict[str, Any]]: - """Generate a random request (LLM or embedding).""" - if self.rng.random() < self.llm_ratio: - prompt = self.rng.choice(self.PROMPTS) - return ("llm", {"prompt": prompt}) - else: - n_texts = self.rng.integers(1, 4) - texts = list(self.rng.choice(self.TEXTS, size=n_texts, replace=False)) - return ("embedding", {"texts": texts}) - - def generate_arrival_times( - self, n_requests: int, rate: float, pattern: str = "poisson" - ) -> list[float]: - """Generate request arrival times.""" - if pattern == "constant": - interval = 1.0 / rate - return [i * interval for i in range(n_requests)] - elif pattern == "poisson": - intervals = np.random.exponential(1.0 / rate, n_requests) - return list(np.cumsum(intervals)) - elif pattern == "bursty": - times = [] - t = 0 - while len(times) < n_requests: - for _ in range(min(10, n_requests - len(times))): - t += np.random.exponential(0.1) - times.append(t) - for _ in range(min(50, n_requests - len(times))): - t += np.random.exponential(0.005) - times.append(t) - return times[:n_requests] - else: - raise ValueError(f"Unknown arrival pattern: {pattern}") diff --git a/benchmark/experiments/common/__init__.py b/benchmark/experiments/common/__init__.py deleted file mode 100644 index a83b93035f..0000000000 --- a/benchmark/experiments/common/__init__.py +++ /dev/null @@ -1,36 +0,0 @@ -""" -Distributed Scheduling Benchmark - Common Components -===================================================== - -: -- models: 数据模型 (State, Config, Metrics) -- operators: Pipeline 算子 -- pipeline: Pipeline 工厂 -- visualization: 结果可视化 -""" - -from .models import ( - BenchmarkConfig, - BenchmarkMetrics, - TaskState, -) -from .operators import ( - ComputeOperator, - LLMOperator, - MetricsSink, - RAGOperator, - TaskSource, -) -from .pipeline import SchedulingBenchmarkPipeline - -__all__ = [ - "BenchmarkConfig", - "BenchmarkMetrics", - "TaskState", - "TaskSource", - "ComputeOperator", - "LLMOperator", - "RAGOperator", - "MetricsSink", - "SchedulingBenchmarkPipeline", -] diff --git a/benchmark/experiments/common/models.py b/benchmark/experiments/common/models.py deleted file mode 100644 index 7ba6eeb711..0000000000 --- a/benchmark/experiments/common/models.py +++ /dev/null @@ -1,289 +0,0 @@ -""" -Distributed Scheduling Benchmark - Data Models -""" - -from __future__ import annotations - -import time -import uuid -from dataclasses import dataclass, field -from typing import Any - - -@dataclass -class TaskState: - """Task state flowing through pipeline stages.""" - - task_id: str = field(default_factory=lambda: str(uuid.uuid4())[:8]) - query: str = "" - - created_time: float = field(default_factory=time.time) - scheduled_time: float = 0.0 - started_time: float = 0.0 - completed_time: float = 0.0 - - scheduling_latency: float = 0.0 - queue_latency: float = 0.0 - execution_latency: float = 0.0 - total_latency: float = 0.0 - - node_id: str = "" - operator_name: str = "" - stage: int = 0 - - context: str = "" - response: str = "" - retrieved_docs: list[dict] = field(default_factory=list) - - metadata: dict[str, Any] = field(default_factory=dict) - error: str = "" - success: bool = True - - def mark_scheduled(self) -> None: - self.scheduled_time = time.time() - self.scheduling_latency = self.scheduled_time - self.created_time - - def mark_started(self) -> None: - self.started_time = time.time() - if self.scheduled_time > 0: - self.queue_latency = self.started_time - self.scheduled_time - - def mark_completed(self) -> None: - self.completed_time = time.time() - if self.started_time > 0: - self.execution_latency = self.completed_time - self.started_time - self.total_latency = self.completed_time - self.created_time - - def to_dict(self) -> dict[str, Any]: - return { - "task_id": self.task_id, - "query": self.query[:50] if self.query else "", - "node_id": self.node_id, - "stage": self.stage, - "scheduling_latency_ms": self.scheduling_latency * 1000, - "queue_latency_ms": self.queue_latency * 1000, - "execution_latency_ms": self.execution_latency * 1000, - "total_latency_ms": self.total_latency * 1000, - "success": self.success, - "error": self.error, - } - - -@dataclass -class BenchmarkConfig: - """Benchmark configuration.""" - - experiment_name: str = "benchmark" - num_tasks: int = 100 - task_complexity: str = "medium" - - parallelism: int = 4 - num_nodes: int = 1 - - scheduler_type: str = "load_aware" - scheduler_strategy: str = "spread" - - use_remote: bool = True - head_node: str = "sage-node-15" - worker_nodes: list[str] = field(default_factory=list) - - llm_base_url: str = "http://11.11.11.7:8903/v1" - llm_model: str = "Qwen/Qwen2.5-7B-Instruct" - max_tokens: int = 256 - - embedding_base_url: str = "http://11.11.11.7:8090/v1" - embedding_model: str = "BAAI/bge-m3" - - pipeline_stages: int = 3 - enable_rag: bool = True - enable_llm: bool = True - - test_mode: bool = False - warmup_tasks: int = 5 - - output_dir: str = "results" - llm_output_file: str | None = None # 指定 LLM 回复输出文件路径 - save_detailed_metrics: bool = True - - def get_worker_nodes(self, count: int) -> list[str]: - if self.worker_nodes: - return self.worker_nodes[:count] - return [f"sage-node-{i}" for i in range(16, 16 + count)] - - def to_dict(self) -> dict[str, Any]: - return { - "experiment_name": self.experiment_name, - "num_tasks": self.num_tasks, - "task_complexity": self.task_complexity, - "parallelism": self.parallelism, - "num_nodes": self.num_nodes, - "scheduler_type": self.scheduler_type, - "scheduler_strategy": self.scheduler_strategy, - "use_remote": self.use_remote, - "pipeline_stages": self.pipeline_stages, - "enable_rag": self.enable_rag, - "enable_llm": self.enable_llm, - } - - -@dataclass -class BenchmarkMetrics: - """Performance metrics collector.""" - - total_tasks: int = 0 - successful_tasks: int = 0 - failed_tasks: int = 0 - - start_time: float = 0.0 - end_time: float = 0.0 - total_duration: float = 0.0 - - scheduling_latencies: list[float] = field(default_factory=list) - queue_latencies: list[float] = field(default_factory=list) - execution_latencies: list[float] = field(default_factory=list) - total_latencies: list[float] = field(default_factory=list) - - node_distribution: dict[str, int] = field(default_factory=dict) - node_latencies: dict[str, list[float]] = field(default_factory=dict) - - stage_latencies: dict[int, list[float]] = field(default_factory=dict) - - config: BenchmarkConfig | None = None - - def record_task(self, state: TaskState) -> None: - if state.success: - self.successful_tasks += 1 - else: - self.failed_tasks += 1 - - if state.scheduling_latency > 0: - self.scheduling_latencies.append(state.scheduling_latency) - if state.queue_latency > 0: - self.queue_latencies.append(state.queue_latency) - if state.execution_latency > 0: - self.execution_latencies.append(state.execution_latency) - if state.total_latency > 0: - self.total_latencies.append(state.total_latency) - - if state.node_id: - self.node_distribution[state.node_id] = self.node_distribution.get(state.node_id, 0) + 1 - if state.node_id not in self.node_latencies: - self.node_latencies[state.node_id] = [] - self.node_latencies[state.node_id].append(state.total_latency) - - if state.stage not in self.stage_latencies: - self.stage_latencies[state.stage] = [] - self.stage_latencies[state.stage].append(state.execution_latency) - - @property - def throughput(self) -> float: - if self.total_duration > 0: - return self.successful_tasks / self.total_duration - return 0.0 - - @property - def avg_latency_ms(self) -> float: - if self.total_latencies: - return sum(self.total_latencies) / len(self.total_latencies) - return 0.0 - - @property - def p50_latency_ms(self) -> float: - if self.total_latencies: - sorted_lat = sorted(self.total_latencies) - idx = len(sorted_lat) // 2 - return sorted_lat[idx] - return 0.0 - - @property - def p95_latency_ms(self) -> float: - if self.total_latencies: - sorted_lat = sorted(self.total_latencies) - idx = int(len(sorted_lat) * 0.95) - return sorted_lat[min(idx, len(sorted_lat) - 1)] - return 0.0 - - @property - def p99_latency_ms(self) -> float: - if self.total_latencies: - sorted_lat = sorted(self.total_latencies) - idx = int(len(sorted_lat) * 0.99) - return sorted_lat[min(idx, len(sorted_lat) - 1)] - return 0.0 - - @property - def avg_scheduling_latency_ms(self) -> float: - if self.scheduling_latencies: - return sum(self.scheduling_latencies) / len(self.scheduling_latencies) * 1000 - return 0.0 - - @property - def avg_queue_latency_ms(self) -> float: - if self.queue_latencies: - return sum(self.queue_latencies) / len(self.queue_latencies) * 1000 - return 0.0 - - @property - def avg_execution_latency_ms(self) -> float: - if self.execution_latencies: - return sum(self.execution_latencies) / len(self.execution_latencies) * 1000 - return 0.0 - - @property - def node_balance_score(self) -> float: - if not self.node_distribution or len(self.node_distribution) <= 1: - return 1.0 - counts = list(self.node_distribution.values()) - avg = sum(counts) / len(counts) - if avg == 0: - return 1.0 - variance = sum((c - avg) ** 2 for c in counts) / len(counts) - std = variance**0.5 - cv = std / avg - return max(0.0, 1.0 - cv) - - def to_dict(self) -> dict[str, Any]: - return { - "total_tasks": self.total_tasks, - "successful_tasks": self.successful_tasks, - "failed_tasks": self.failed_tasks, - "total_duration_sec": self.total_duration, - "throughput_tasks_per_sec": self.throughput, - "avg_latency_ms": self.avg_latency_ms, - "p50_latency_ms": self.p50_latency_ms, - "p95_latency_ms": self.p95_latency_ms, - "p99_latency_ms": self.p99_latency_ms, - "avg_scheduling_latency_ms": self.avg_scheduling_latency_ms, - "avg_queue_latency_ms": self.avg_queue_latency_ms, - "avg_execution_latency_ms": self.avg_execution_latency_ms, - "node_distribution": self.node_distribution, - "node_balance_score": self.node_balance_score, - "config": self.config.to_dict() if self.config else None, - } - - def print_summary(self) -> None: - print("\n" + "=" * 70) - print("Benchmark Results Summary") - print("=" * 70) - print(f" Total Tasks: {self.total_tasks}") - print(f" Successful: {self.successful_tasks}") - print(f" Failed: {self.failed_tasks}") - print(f" Duration: {self.total_duration:.2f}s") - print("-" * 70) - print(f" Throughput: {self.throughput:.2f} tasks/sec") - print(f" Avg Latency: {self.avg_latency_ms:.2f} ms") - print(f" P50 Latency: {self.p50_latency_ms:.2f} ms") - print(f" P95 Latency: {self.p95_latency_ms:.2f} ms") - print(f" P99 Latency: {self.p99_latency_ms:.2f} ms") - print("-" * 70) - print(f" Avg Scheduling: {self.avg_scheduling_latency_ms:.2f} ms") - print(f" Avg Queue: {self.avg_queue_latency_ms:.2f} ms") - print(f" Avg Execution: {self.avg_execution_latency_ms:.2f} ms") - print("-" * 70) - print(f" Node Balance: {self.node_balance_score:.2%}") - if self.node_distribution: - print(" Node Distribution:") - for node, count in sorted(self.node_distribution.items()): - pct = count / self.successful_tasks * 100 if self.successful_tasks > 0 else 0 - print(f" {node}: {count} ({pct:.1f}%)") - print("=" * 70) diff --git a/benchmark/experiments/common/operators.py b/benchmark/experiments/common/operators.py deleted file mode 100644 index db23eee1ad..0000000000 --- a/benchmark/experiments/common/operators.py +++ /dev/null @@ -1,1025 +0,0 @@ -""" -Distributed Scheduling Benchmark - Pipeline Operators -====================================================== - -Pipeline 算子: -- TaskSource: 任务生成源 -- ComputeOperator: CPU 计算任务 (用于调度测试) -- LLMOperator: LLM 推理任务 -- RAGOperator: RAG 检索+生成任务 -- MetricsSink: 指标收集 -""" - -from __future__ import annotations - -import hashlib -import os -import socket -import time -from pathlib import Path -from typing import TYPE_CHECKING, Any - -from sage.common.core.functions.map_function import MapFunction -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.core.functions.source_function import SourceFunction -from sage.kernel.runtime.communication.packet import StopSignal - -if TYPE_CHECKING: - from .models import TaskState - -try: - from .models import TaskState -except ImportError: - from models import TaskState - - -# 示例查询池 -SAMPLE_QUERIES = [ - "What is SAGE framework and what are its main features?", - "How do I install SAGE on Ubuntu?", - "Explain the pipeline architecture in SAGE", - "What are the different scheduler strategies available?", - "How does the memory service work in SAGE?", - "What is the role of middleware components?", - "How to configure LLM services in SAGE?", - "Explain the difference between LocalEnvironment and RemoteEnvironment", - "What are the best practices for building RAG pipelines?", - "How does distributed scheduling work in SAGE?", - "What embedding models are supported?", - "How to optimize pipeline performance?", - "Explain the six-layer architecture of SAGE", - "What is the purpose of sage-kernel package?", - "How to monitor pipeline execution?", - "What vector databases are supported?", - "How to implement custom operators?", - "Explain the ReAct reasoning pattern", - "What are the CPU node requirements?", - "How to configure multi-node clusters?", -] - -# 知识库 -SAMPLE_KNOWLEDGE_BASE = [ - { - "id": "1", - "title": "SAGE Framework Overview", - "content": "SAGE is a Python 3.10+ framework for building AI/LLM data processing pipelines.", - }, - { - "id": "2", - "title": "SAGE Installation Guide", - "content": "To install SAGE, run ./quickstart.sh --dev --yes for development.", - }, - { - "id": "3", - "title": "Pipeline Architecture", - "content": "SAGE pipelines use SourceFunction, MapFunction, and SinkFunction operators.", - }, - { - "id": "4", - "title": "Scheduler Strategies", - "content": "SAGE supports FIFO, LoadAware, Random, RoundRobin, and Priority schedulers.", - }, - { - "id": "5", - "title": "Memory Services", - "content": "sage-mem provides HierarchicalMemoryService with STM/MTM/LTM tiers.", - }, -] - - -class TaskSource(SourceFunction): - """ - 任务生成源。 - - 从查询池生成测试任务。 - """ - - def __init__( - self, - num_tasks: int = 100, - query_pool: list[str] | None = None, - task_complexity: str = "medium", - **kwargs, - ): - super().__init__(**kwargs) - self.query_pool = query_pool or SAMPLE_QUERIES - self.num_tasks = num_tasks - self.task_complexity = task_complexity - self.current_index = 0 - - def execute(self, data=None) -> TaskState | StopSignal: - """生成下一个任务""" - if self.current_index >= self.num_tasks: - time.sleep(10.0) # 10 秒等待 LLM 响应 - return StopSignal("All tasks generated") - - query = self.query_pool[self.current_index % len(self.query_pool)] - self.current_index += 1 - - state = TaskState( - task_id=f"task_{self.current_index:05d}", - query=query, - created_time=time.time(), - metadata={"complexity": self.task_complexity}, - ) - - return state - - -class ComputeOperator(MapFunction): - """ - CPU 计算任务算子。 - - 用于测试纯调度性能，不依赖外部服务。 - 可配置计算复杂度 (light/medium/heavy)。 - """ - - def __init__( - self, - complexity: str = "medium", - stage: int = 1, - **kwargs, - ): - super().__init__(**kwargs) - self.complexity = complexity - self.stage = stage - self._hostname = socket.gethostname() - - # 复杂度对应的迭代次数 - self.iterations = { - "light": 1000, - "medium": 10000, - "heavy": 100000, - }.get(complexity, 10000) - - def _do_compute(self, data: str) -> str: - """执行 CPU 密集计算""" - result = data - for i in range(self.iterations): - result = hashlib.md5(f"{result}{i}".encode()).hexdigest() - return result - - def execute(self, data: TaskState) -> TaskState: - """执行计算任务""" - if not isinstance(data, TaskState): - return data - - state = data - state.node_id = self._hostname - state.stage = self.stage - state.operator_name = f"ComputeOperator_{self.stage}" - state.mark_started() - - try: - # 执行计算 - result = self._do_compute(state.query) - state.metadata[f"compute_result_{self.stage}"] = result[:16] - state.success = True - except Exception as e: - state.success = False - state.error = str(e) - - state.mark_completed() - return state - - -class LLMOperator(MapFunction): - """ - LLM 推理任务算子。 - - 调用真实 LLM 服务进行推理。 - """ - - def __init__( - self, - llm_base_url: str = "http://11.11.11.7:8903/v1", - llm_model: str = "Qwen/Qwen2.5-7B-Instruct", - max_tokens: int = 256, - stage: int = 1, - **kwargs, - ): - super().__init__(**kwargs) - self.llm_base_url = llm_base_url - self.llm_model = llm_model - self.max_tokens = max_tokens - self.stage = stage - self._hostname = socket.gethostname() - self._llm_client = None - - def _get_client(self): - """延迟初始化 LLM 客户端""" - if self._llm_client is None: - try: - from sage.common.components.sage_llm import UnifiedInferenceClient - - self._llm_client = UnifiedInferenceClient.create( - control_plane_url=self.llm_base_url, - default_llm_model=self.llm_model, - ) - except Exception as e: - print(f"[LLMOperator] Client init error: {e}") - return self._llm_client - - def execute(self, data: TaskState) -> TaskState: - """执行 LLM 推理""" - if not isinstance(data, TaskState): - return data - - state = data - state.node_id = self._hostname - state.stage = self.stage - state.operator_name = f"LLMOperator_{self.stage}" - state.mark_started() - - try: - client = self._get_client() - if client: - messages = [ - {"role": "system", "content": "You are a helpful assistant. Be concise."}, - {"role": "user", "content": state.query}, - ] - response = client.chat(messages, max_tokens=self.max_tokens) - state.response = str(response) if not isinstance(response, str) else response - else: - # Fallback: 模拟响应 - state.response = f"[Simulated] Response to: {state.query[:50]}..." - state.success = True - except Exception as e: - state.success = False - state.error = str(e) - state.response = f"[Error] {str(e)}" - - state.mark_completed() - return state - - -class RAGOperator(MapFunction): - """ - RAG 检索+生成任务算子。 - - 先使用 Embedding 检索相关文档，再调用 LLM 生成响应。 - """ - - def __init__( - self, - llm_base_url: str = "http://11.11.11.7:8903/v1", - llm_model: str = "Qwen/Qwen2.5-7B-Instruct", - embedding_base_url: str = "http://11.11.11.7:8090/v1", - embedding_model: str = "BAAI/bge-m3", - max_tokens: int = 256, - top_k: int = 3, - knowledge_base: list[dict] | None = None, - stage: int = 1, - **kwargs, - ): - super().__init__(**kwargs) - self.llm_base_url = llm_base_url - self.llm_model = llm_model - self.embedding_base_url = embedding_base_url - self.embedding_model = embedding_model - self.max_tokens = max_tokens - self.top_k = top_k - self.knowledge_base = knowledge_base or SAMPLE_KNOWLEDGE_BASE - self.stage = stage - self._hostname = socket.gethostname() - self._client = None - self._initialized = False - - def _initialize(self): - """延迟初始化客户端""" - if self._initialized: - return - try: - from sage.common.components.sage_llm import UnifiedInferenceClient - - self._client = UnifiedInferenceClient.create( - control_plane_url=self.llm_base_url, - default_llm_model=self.llm_model, - default_embedding_model=self.embedding_model, - ) - self._initialized = True - except Exception as e: - print(f"[RAGOperator] Init error: {e}") - self._initialized = True - - def _retrieve(self, query: str) -> list[dict]: - """检索相关文档""" - # 简单关键词匹配作为 fallback - query_lower = query.lower() - results = [] - for doc in self.knowledge_base: - content_lower = doc.get("content", "").lower() - title_lower = doc.get("title", "").lower() - query_words = set(query_lower.split()) - content_words = set(content_lower.split()) - title_words = set(title_lower.split()) - overlap = len(query_words & (content_words | title_words)) - if overlap > 0: - results.append( - { - "score": overlap, - "title": doc.get("title", ""), - "content": doc.get("content", ""), - } - ) - results.sort(key=lambda x: x["score"], reverse=True) - return results[: self.top_k] - - def execute(self, data: TaskState) -> TaskState: - """执行 RAG 任务""" - if not isinstance(data, TaskState): - return data - - state = data - state.node_id = self._hostname - state.stage = self.stage - state.operator_name = f"RAGOperator_{self.stage}" - state.mark_started() - - self._initialize() - - try: - # 检索 - retrieval_start = time.time() - state.retrieved_docs = self._retrieve(state.query) - retrieval_time = time.time() - retrieval_start - state.metadata["retrieval_time_ms"] = retrieval_time * 1000 - - # 构建上下文 - context_parts = [f"{doc['title']}: {doc['content']}" for doc in state.retrieved_docs] - state.context = "\n".join(context_parts) - - # 生成 - if self._client: - messages = [ - {"role": "system", "content": "Answer based on the context. Be concise."}, - { - "role": "user", - "content": f"Context:\n{state.context}\n\nQuestion: {state.query}", - }, - ] - response = self._client.chat(messages, max_tokens=self.max_tokens) - state.response = str(response) if not isinstance(response, str) else response - else: - state.response = f"[Simulated RAG] Based on {len(state.retrieved_docs)} docs." - - state.success = True - except Exception as e: - state.success = False - state.error = str(e) - - state.mark_completed() - return state - - -class MetricsSink(SinkFunction): - """ - 指标收集 Sink。 - - 收集任务指标并聚合统计。 - 将结果写入文件以支持 Remote 模式。 - """ - - # Metrics 输出目录 - METRICS_OUTPUT_DIR = "/tmp/sage_metrics" - - def __init__( - self, - metrics_collector: Any = None, - verbose: bool = False, - **kwargs, - ): - super().__init__(**kwargs) - self.metrics_collector = metrics_collector - self.verbose = verbose - self.test_mode = os.getenv("SAGE_TEST_MODE") == "true" - - # 本地统计 - self.count = 0 - self.success_count = 0 - self.fail_count = 0 - self.latencies: list[float] = [] - self.node_stats: dict[str, int] = {} - - # 创建唯一的输出文件 - self._start_time = time.time() - self.instance_id = f"{socket.gethostname()}_{os.getpid()}_{int(time.time() * 1000)}" - os.makedirs(self.METRICS_OUTPUT_DIR, exist_ok=True) - self.metrics_output_file = f"{self.METRICS_OUTPUT_DIR}/metrics_{self.instance_id}.jsonl" - - # 写入 header - self._write_header() - - def _write_header(self) -> None: - """写入 metrics 文件 header""" - import json - import sys - - try: - header = { - "type": "header", - "instance_id": self.instance_id, - "start_time": self._start_time, - "hostname": socket.gethostname(), - "pid": os.getpid(), - } - with open(self.metrics_output_file, "w") as f: - f.write(json.dumps(header) + "\n") - print( - f" [MetricsSink] Initialized: {self.metrics_output_file}", - file=sys.stderr, - flush=True, - ) - except Exception as e: - print(f" [MetricsSink] Init error: {e}", file=sys.stderr, flush=True) - - def _write_task_to_file(self, task: TaskState) -> None: - """将任务结果写入文件""" - import json - - try: - record = { - "type": "task", - "task_id": task.task_id, - "success": task.success, - "node_id": task.node_id, - "total_latency_ms": getattr( - task, - "total_latency_ms", - task.total_latency * 1000 if hasattr(task, "total_latency") else 0, - ), - "timestamp": time.time(), - } - with open(self.metrics_output_file, "a") as f: - f.write(json.dumps(record) + "\n") - except Exception as e: - import sys - - print(f" [MetricsSink] Write error: {e}", file=sys.stderr, flush=True) - - def _write_summary(self) -> None: - """写入最终摘要""" - import json - import sys - - try: - elapsed = time.time() - self._start_time - avg_latency = sum(self.latencies) / len(self.latencies) if self.latencies else 0 - summary = { - "type": "summary", - "total_tasks": self.count, - "success_count": self.success_count, - "fail_count": self.fail_count, - "elapsed_seconds": elapsed, - "throughput": self.count / elapsed if elapsed > 0 else 0, - "avg_latency_ms": avg_latency, - "node_distribution": self.node_stats, - } - with open(self.metrics_output_file, "a") as f: - f.write(json.dumps(summary) + "\n") - print( - f" [MetricsSink] Summary: {self.count} tasks, {self.success_count} success -> {self.metrics_output_file}", - file=sys.stderr, - flush=True, - ) - except Exception as e: - print(f" [MetricsSink] Summary error: {e}", file=sys.stderr, flush=True) - - def execute(self, data: TaskState) -> None: - """收集任务 metrics""" - if not isinstance(data, TaskState): - return - - state = data - self.count += 1 - - # 统计成功/失败 - if state.success: - self.success_count += 1 - else: - self.fail_count += 1 - - # 记录延迟 - latency_ms = getattr( - state, - "total_latency_ms", - state.total_latency * 1000 if hasattr(state, "total_latency") else 0, - ) - if latency_ms > 0: - self.latencies.append(latency_ms) - - # 更新节点统计 - if state.node_id: - self.node_stats[state.node_id] = self.node_stats.get(state.node_id, 0) + 1 - - # 写入文件 (Remote 模式可用) - self._write_task_to_file(state) - - # 记录到共享收集器 (仅 Local 模式有效) - if self.metrics_collector: - self.metrics_collector.record_task(state) - - # 详细输出 - if self.verbose and (not self.test_mode or self.count <= 5): - print(f"[{self.count}] Task: {state.task_id}, Node: {state.node_id}") - print(f" Latency: {latency_ms:.1f}ms, Success: {state.success}") - if hasattr(state, "error") and state.error: - print(f" Error: {state.error}") - elif self.verbose and self.count == 6: - print(" ... (remaining output suppressed)") - - # Periodic progress report - if self.count % 100 == 0: - print(f"[Progress] {self.count} tasks completed") - if self.node_stats: - print(" Node distribution:", dict(sorted(self.node_stats.items()))) - - def close(self) -> None: - """关闭时写入摘要""" - self._write_summary() - - -# ============================================================================= -# Simple RAG Operators - Using Remote Embedding Service -# ============================================================================= -# 这些算子使用远程 embedding 服务，不需要本地下载模型 -# Embedding 服务: http://{LLM_HOST}:8090/v1 -# Embedding 模: BAAI/bge-large-zh-v1.5 - -# 默认服务配置 -LLM_HOST = os.getenv("LLM_HOST", "11.11.11.7") -EMBEDDING_BASE_URL = f"http://{LLM_HOST}:8090/v1" -EMBEDDING_MODEL = "BAAI/bge-large-zh-v1.5" -LLM_BASE_URL = f"http://{LLM_HOST}:8903/v1" -LLM_MODEL = "Qwen/Qwen2.5-7B-Instruct" - - -def get_remote_embeddings( - texts: list[str], - base_url: str = EMBEDDING_BASE_URL, - model: str = EMBEDDING_MODEL, -) -> list[list[float]] | None: - """ - 使用远程 embedding 服务获取向量。 - - Args: - texts: 要编码的文本列表 - base_url: Embedding 服务地址 - model: Embedding 模型名 - - Returns: - 向量列表，或 None（失败时） - """ - try: - import requests - - response = requests.post( - f"{base_url}/embeddings", - json={ - "input": texts, - "model": model, - }, - timeout=30, - ) - response.raise_for_status() - result = response.json() - - # 提取 embeddings - embeddings = [item["embedding"] for item in result["data"]] - return embeddings - except Exception as e: - print(f"[Embedding] Error: {e}") - return None - - -def cosine_similarity(vec1: list[float], vec2: list[float]) -> float: - """计算两个向量的余弦相似度""" - import math - - dot = sum(a * b for a, b in zip(vec1, vec2)) - norm1 = math.sqrt(sum(a * a for a in vec1)) - norm2 = math.sqrt(sum(b * b for b in vec2)) - - if norm1 == 0 or norm2 == 0: - return 0.0 - return dot / (norm1 * norm2) - - -class SimpleRetriever(MapFunction): - """ - 简单检索器 - 使用远程 Embedding 服务。 - - 基于余弦相似度检索最相关的文档。 - 不依赖 ChromaDB 或本地模型。 - """ - - def __init__( - self, - embedding_base_url: str = EMBEDDING_BASE_URL, - embedding_model: str = EMBEDDING_MODEL, - top_k: int = 5, - knowledge_base: list[dict] | None = None, - stage: int = 1, - **kwargs, - ): - super().__init__(**kwargs) - self.embedding_base_url = embedding_base_url - self.embedding_model = embedding_model - self.top_k = top_k - self.knowledge_base = knowledge_base or SAMPLE_KNOWLEDGE_BASE - self.stage = stage - self._hostname = socket.gethostname() - self._kb_embeddings: list[list[float]] | None = None - self._initialized = False - - def _initialize(self): - """初始化知识库向量""" - if self._initialized: - return - - # 获取知识库文档的 embeddings - texts = [doc.get("content", doc.get("text", "")) for doc in self.knowledge_base] - self._kb_embeddings = get_remote_embeddings( - texts, - base_url=self.embedding_base_url, - model=self.embedding_model, - ) - - if self._kb_embeddings: - print( - f"[SimpleRetriever] Initialized with {len(self._kb_embeddings)} document embeddings" - ) - else: - print("[SimpleRetriever] Warning: Failed to get KB embeddings, using keyword fallback") - - self._initialized = True - - def _retrieve_by_embedding(self, query: str) -> list[dict]: - """使用 embedding 检索""" - # 获取查询向量 - query_embeddings = get_remote_embeddings( - [query], - base_url=self.embedding_base_url, - model=self.embedding_model, - ) - - if not query_embeddings or not self._kb_embeddings: - return self._retrieve_by_keyword(query) - - query_vec = query_embeddings[0] - - # 计算相似度 - scored_docs = [] - for i, (doc, doc_vec) in enumerate(zip(self.knowledge_base, self._kb_embeddings)): - score = cosine_similarity(query_vec, doc_vec) - scored_docs.append( - { - "id": doc.get("id", str(i)), - "title": doc.get("title", ""), - "content": doc.get("content", doc.get("text", "")), - "score": score, - } - ) - - # 按相似度排序 - scored_docs.sort(key=lambda x: x["score"], reverse=True) - return scored_docs[: self.top_k] - - def _retrieve_by_keyword(self, query: str) -> list[dict]: - """关键词检索 fallback""" - query_lower = query.lower() - query_words = set(query_lower.split()) - - scored_docs = [] - for i, doc in enumerate(self.knowledge_base): - content = doc.get("content", doc.get("text", "")).lower() - title = doc.get("title", "").lower() - - # 计算关键词匹配分数 - score = 0 - for word in query_words: - if len(word) > 2: - if word in content: - score += 2 - if word in title: - score += 3 - - if score > 0: - scored_docs.append( - { - "id": doc.get("id", str(i)), - "title": doc.get("title", ""), - "content": doc.get("content", doc.get("text", "")), - "score": score / 10.0, # 归一化 - } - ) - - scored_docs.sort(key=lambda x: x["score"], reverse=True) - return scored_docs[: self.top_k] - - def execute(self, data: TaskState) -> TaskState: - """执行检索""" - if not isinstance(data, TaskState): - return data - - state = data - state.node_id = self._hostname - state.stage = self.stage - state.operator_name = f"SimpleRetriever_{self.stage}" - state.mark_started() - - self._initialize() - - try: - retrieval_start = time.time() - state.retrieved_docs = self._retrieve_by_embedding(state.query) - retrieval_time = time.time() - retrieval_start - state.metadata["retrieval_time_ms"] = retrieval_time * 1000 - state.metadata["num_retrieved"] = len(state.retrieved_docs) - state.success = True - except Exception as e: - state.success = False - state.error = str(e) - state.retrieved_docs = [] - - state.mark_completed() - return state - - -class SimpleReranker(MapFunction): - """ - 简单重排>> - 使用远程 Embedding 服务。 - - # Supports multiple pipeline types: - - 使用 embedding 模型计算更精细的相关性分数。 - """ - - def __init__( - self, - embedding_base_url: str = EMBEDDING_BASE_URL, - embedding_model: str = EMBEDDING_MODEL, - top_k: int = 3, - stage: int = 2, - **kwargs, - ): - super().__init__(**kwargs) - self.embedding_base_url = embedding_base_url - self.embedding_model = embedding_model - self.top_k = top_k - self.stage = stage - self._hostname = socket.gethostname() - - def _rerank(self, query: str, docs: list[dict]) -> list[dict]: - """ - 重排文档。 - - 使用 [query + document] 组合的 embedding 进行更精细的相关性计算。 - """ - if not docs: - return [] - - # 构建 query-doc 对进行评分 - # 使用格式: "Query: {query} Document: {content}" - pairs = [] - for doc in docs: - content = doc.get("content", "")[:500] # 截断 - pair_text = f"Query: {query} Document: {content}" - pairs.append(pair_text) - - # 获取 query embedding - query_embedding = get_remote_embeddings( - [query], - base_url=self.embedding_base_url, - model=self.embedding_model, - ) - - # 获取每个 doc 的 embedding - doc_texts = [doc.get("content", "")[:500] for doc in docs] - doc_embeddings = get_remote_embeddings( - doc_texts, - base_url=self.embedding_base_url, - model=self.embedding_model, - ) - - if not query_embedding or not doc_embeddings: - # Fallback: 保持原有排序 - return docs[: self.top_k] - - query_vec = query_embedding[0] - - # 计算新的相关性分数 - reranked = [] - for i, (doc, doc_vec) in enumerate(zip(docs, doc_embeddings)): - score = cosine_similarity(query_vec, doc_vec) - reranked.append( - { - **doc, - "rerank_score": score, - } - ) - - # 按新分数排序 - reranked.sort(key=lambda x: x.get("rerank_score", 0), reverse=True) - return reranked[: self.top_k] - - def execute(self, data: TaskState) -> TaskState: - """执行重排""" - if not isinstance(data, TaskState): - return data - - state = data - state.node_id = self._hostname - state.stage = self.stage - state.operator_name = f"SimpleReranker_{self.stage}" - state.mark_started() - - try: - rerank_start = time.time() - state.retrieved_docs = self._rerank(state.query, state.retrieved_docs) - rerank_time = time.time() - rerank_start - state.metadata["rerank_time_ms"] = rerank_time * 1000 - state.metadata["num_reranked"] = len(state.retrieved_docs) - state.success = True - except Exception as e: - state.success = False - state.error = str(e) - - state.mark_completed() - return state - - -class SimplePromptor(MapFunction): - """ - 简单提示构建器。 - - 将检索的文档和查询组合成 LLM 提示。 - """ - - def __init__( - self, - stage: int = 3, - max_context_length: int = 2000, - **kwargs, - ): - super().__init__(**kwargs) - self.stage = stage - self.max_context_length = max_context_length - self._hostname = socket.gethostname() - - def execute(self, data: TaskState) -> TaskState: - """构建提示""" - if not isinstance(data, TaskState): - return data - - state = data - state.node_id = self._hostname - state.stage = self.stage - state.operator_name = f"SimplePromptor_{self.stage}" - state.mark_started() - - try: - # 构建上下文 - context_parts = [] - total_length = 0 - - for i, doc in enumerate(state.retrieved_docs): - title = doc.get("title", f"Document {i + 1}") - content = doc.get("content", "") - - doc_text = f"[{title}]\n{content}" - if total_length + len(doc_text) > self.max_context_length: - break - - context_parts.append(doc_text) - total_length += len(doc_text) - - state.context = "\n\n".join(context_parts) - state.metadata["context_length"] = len(state.context) - state.success = True - except Exception as e: - state.success = False - state.error = str(e) - state.context = "" - - state.mark_completed() - return state - - -class SimpleGenerator(MapFunction): - """ - 简单生成器 - 使用远程 LLM 服务。 - - 基于上下文和查询生成回复。 - """ - - def __init__( - self, - llm_base_url: str = LLM_BASE_URL, - llm_model: str = LLM_MODEL, - max_tokens: int = 256, - stage: int = 4, - output_file: str | None = None, - **kwargs, - ): - super().__init__(**kwargs) - self.llm_base_url = llm_base_url - self.llm_model = llm_model - self.max_tokens = max_tokens - self.stage = stage - self.output_file = output_file - self._hostname = socket.gethostname() - - #!/usr/bin/env python3 - if self.output_file: - output_path = Path(self.output_file) - output_path.parent.mkdir(parents=True, exist_ok=True) - - def _generate(self, query: str, context: str) -> str: - """调用 LLM 生成回复""" - try: - import requests - - messages = [ - { - "role": "system", - "content": "你是一个helpful--------没有相关信息，请直接说明。", - }, - { - "role": "user", - "content": f"上下文:\n{context}\n\n问题: {query}", - }, - ] - - response = requests.post( - f"{self.llm_base_url}/chat/completions", - json={ - "model": self.llm_model, - "messages": messages, - "max_tokens": self.max_tokens, - "temperature": 0.7, - }, - timeout=60, - ) - response.raise_for_status() - result = response.json() - - return result["choices"][0]["message"]["content"] - except Exception as e: - return f"[Generation Error] {str(e)}" - - def _save_response_to_file(self, state: TaskState, gen_time: float) -> None: - """保存 LLM 回复到指定文件""" - if self.output_file is None: - return - - try: - import json - from datetime import datetime - - record = { - "timestamp": datetime.now().isoformat(), - "task_id": state.task_id, - "node_id": state.node_id, - "query": state.query, - "context": state.context, - "response": state.response, - "generation_time_ms": gen_time * 1000, - "model": self.llm_model, - } - - # 追加<< JSONL 格式 - with open(self.output_file, "a", encoding="utf-8") as f: - f.write(json.dumps(record, ensure_ascii=False) + "\n") - - except Exception as e: - print(f"[Warning] Failed to save response to file: {e}") - - def execute(self, data: TaskState) -> TaskState: - """执行生成""" - if not isinstance(data, TaskState): - return data - - state = data - state.node_id = self._hostname - state.stage = self.stage - state.operator_name = f"SimpleGenerator_{self.stage}" - state.mark_started() - - try: - gen_start = time.time() - state.response = self._generate(state.query, state.context) - gen_time = time.time() - gen_start - state.metadata["generation_time_ms"] = gen_time * 1000 - state.success = True - # 输出到指定文件 - if self.output_file: - self._save_response_to_file(state, gen_time) - - except Exception as e: - state.success = False - state.error = str(e) - state.response = f"[Error] {str(e)}" - - state.mark_completed() - return state diff --git a/benchmark/experiments/common/pipeline.py b/benchmark/experiments/common/pipeline.py deleted file mode 100644 index 2944bf7ba8..0000000000 --- a/benchmark/experiments/common/pipeline.py +++ /dev/null @@ -1,654 +0,0 @@ -""" -Distributed Scheduling Benchmark - Pipeline Factory -==================================================== - -Provides pipeline factories for distributed scheduling benchmarks: -- Compute pipeline (pure CPU scheduling test) -- LLM pipeline (LLM inference) -- RAG pipeline (fine-grained: Retriever -> Reranker -> Promptor -> Generator) -- Mixed pipeline (Compute + RAG stages) -""" - -from __future__ import annotations - -import time -from typing import TYPE_CHECKING, Any - -if TYPE_CHECKING: - from sage.kernel.api.local_environment import LocalEnvironment - from sage.kernel.api.remote_environment import RemoteEnvironment - -try: - from .models import BenchmarkConfig, BenchmarkMetrics - from .operators import ( - ComputeOperator, - LLMOperator, - MetricsSink, - TaskSource, - ) -except ImportError: - from models import BenchmarkConfig, BenchmarkMetrics - from operators import ( - ComputeOperator, - LLMOperator, - MetricsSink, - TaskSource, - ) - - -class SchedulingBenchmarkPipeline: - """ - Pipeline factory for distributed scheduling benchmarks. - - Supports multiple pipeline types: - - compute: Pure CPU computation for scheduling overhead testing - - llm: Single-stage LLM inference - - rag: Fine-grained RAG with Retriever -> Reranker -> Promptor -> Generator - - rag_full: Full RAG with Retriever -> Reranker -> Refiner -> Promptor -> Generator - - mixed: Compute + RAG stages - """ - - def __init__(self, config: BenchmarkConfig): - self.config = config - self.env = None - self.scheduler = None - self.metrics = BenchmarkMetrics(config=config) - - def _create_scheduler(self): - """Create scheduler based on config.""" - from sage.kernel.scheduler.impl import get_scheduler - - scheduler_type = self.config.scheduler_type - platform = "remote" if self.config.use_remote else "local" - - scheduler_kwargs: dict[str, Any] = {"platform": platform} - - # Only add max_concurrent for schedulers that support it (FIFO doesn't support it) - if scheduler_type in ["load_aware", "priority", "round_robin"]: - scheduler_kwargs["max_concurrent"] = self.config.parallelism * 100 - - # Add strategy for LoadAwareScheduler - if scheduler_type == "load_aware": - scheduler_kwargs["strategy"] = self.config.scheduler_strategy - - return get_scheduler(scheduler_type, **scheduler_kwargs) - - def _create_environment(self, name: str) -> LocalEnvironment | RemoteEnvironment: - """Create execution environment (local or remote).""" - if self.config.use_remote: - from pathlib import Path - - from sage.kernel.api.remote_environment import RemoteEnvironment - - # Get the experiments directory path for Ray runtime_env - experiments_dir = Path(__file__).resolve().parent.parent - - # Create config with runtime_env for Ray to find our modules - config = { - "runtime_env": { - "env_vars": {"PYTHONPATH": str(experiments_dir)}, - "working_dir": str(experiments_dir), - } - } - - self.scheduler = self._create_scheduler() - self.env = RemoteEnvironment( - name=name, - scheduler=self.scheduler, - host=self.config.head_node, - config=config, - extra_python_paths=[str(experiments_dir)], - ) - else: - from sage.kernel.api.local_environment import LocalEnvironment - - self.env = LocalEnvironment(name) - - return self.env - - def _get_retriever_config(self) -> dict[str, Any]: - """Get retriever configuration.""" - return { - "dimension": 1024, - "top_k": getattr(self.config, "retriever_top_k", 10), - "embedding": { - "method": "default", - "model": self.config.embedding_model, - }, - "chroma": { - "collection_name": "benchmark_knowledge", - "persist_directory": None, - }, - } - - def _get_reranker_config(self) -> dict[str, Any]: - """Get reranker configuration.""" - return { - "model_name": "BAAI/bge-reranker-v2-m3", - "top_k": getattr(self.config, "reranker_top_k", 5), - } - - def _get_promptor_config(self) -> dict[str, Any]: - """Get promptor configuration.""" - return { - "use_short_answer": False, - } - - def _get_generator_config(self) -> dict[str, Any]: - """Get generator configuration.""" - return { - "method": "openai", - "model_name": self.config.llm_model, - "base_url": self.config.llm_base_url, - "api_key": "EMPTY", # pragma: allowlist secret - "max_tokens": self.config.max_tokens, - } - - def _get_refiner_config(self) -> dict[str, Any]: - """Get refiner configuration.""" - return { - "algorithm": "simple", - "budget": 2048, - "enable_cache": True, - } - - # ========================================================================= - # Pipeline Builders - # ========================================================================= - - def build_compute_pipeline( - self, name: str = "compute_benchmark" - ) -> SchedulingBenchmarkPipeline: - """ - Build compute-only pipeline for testing scheduling overhead. - - Pipeline: TaskSource -> ComputeOperator (x N stages) -> MetricsSink - """ - env = self._create_environment(name) - - pipeline = env.from_source( - TaskSource, - num_tasks=self.config.num_tasks, - task_complexity=self.config.task_complexity, - ) - - for stage in range(1, self.config.pipeline_stages + 1): - pipeline = pipeline.map( - ComputeOperator, - parallelism=self.config.parallelism, - complexity=self.config.task_complexity, - stage=stage, - ) - - pipeline.sink( - MetricsSink, - metrics_collector=self.metrics, - verbose=not self.config.test_mode, - ) - - return self - - def build_llm_pipeline(self, name: str = "llm_benchmark") -> SchedulingBenchmarkPipeline: - """ - Build single-stage LLM inference pipeline. - - Pipeline: TaskSource -> LLMOperator -> MetricsSink - """ - env = self._create_environment(name) - - ( - env.from_source( - TaskSource, - num_tasks=self.config.num_tasks, - task_complexity=self.config.task_complexity, - ) - .map( - LLMOperator, - parallelism=self.config.parallelism, - llm_base_url=self.config.llm_base_url, - llm_model=self.config.llm_model, - max_tokens=self.config.max_tokens, - stage=1, - ) - .sink( - MetricsSink, - metrics_collector=self.metrics, - verbose=not self.config.test_mode, - ) - ) - - return self - - def build_rag_pipeline(self, name: str = "rag_benchmark") -> SchedulingBenchmarkPipeline: - """ - Build fine-grained RAG pipeline using sage-middleware operators. - - Pipeline: TaskSource -> SimpleRetriever -> SimpleReranker -> SimplePromptor -> SimpleGenerator -> MetricsSink - - Each stage runs with configurable parallelism for distributed scheduling. - """ - from .operators import ( - SimpleGenerator, - SimplePromptor, - SimpleReranker, - SimpleRetriever, - ) - - env = self._create_environment(name) - - ( - env.from_source( - TaskSource, - num_tasks=self.config.num_tasks, - task_complexity=self.config.task_complexity, - ) - .map( - SimpleRetriever, - parallelism=self.config.parallelism, - embedding_base_url=self.config.embedding_base_url, - embedding_model=self.config.embedding_model, - top_k=10, - stage=1, - ) - .map( - SimpleReranker, - parallelism=self.config.parallelism, - embedding_base_url=self.config.embedding_base_url, - embedding_model=self.config.embedding_model, - top_k=5, - stage=2, - ) - .map( - SimplePromptor, - parallelism=self.config.parallelism, - stage=3, - ) - .map( - SimpleGenerator, - parallelism=self.config.parallelism, - llm_base_url=self.config.llm_base_url, - llm_model=self.config.llm_model, - max_tokens=self.config.max_tokens, - output_file=self.config.llm_output_file, - stage=4, - ) - .sink( - MetricsSink, - metrics_collector=self.metrics, - verbose=not self.config.test_mode, - ) - ) - - return self - - def build_rag_full_pipeline( - self, name: str = "rag_full_benchmark" - ) -> SchedulingBenchmarkPipeline: - """ - Build full RAG pipeline with refiner. - - Pipeline: TaskSource -> SimpleRetriever -> SimpleReranker -> RefinerOperator - -> SimplePromptor -> SimpleGenerator -> MetricsSink - """ - from sage.middleware.operators.rag import RefinerOperator - - from .operators import ( - SimpleGenerator, - SimplePromptor, - SimpleReranker, - SimpleRetriever, - ) - - env = self._create_environment(name) - - ( - env.from_source( - TaskSource, - num_tasks=self.config.num_tasks, - task_complexity=self.config.task_complexity, - ) - .map( - SimpleRetriever, - parallelism=self.config.parallelism, - embedding_base_url=self.config.embedding_base_url, - embedding_model=self.config.embedding_model, - top_k=10, - stage=1, - ) - .map( - SimpleReranker, - parallelism=self.config.parallelism, - embedding_base_url=self.config.embedding_base_url, - embedding_model=self.config.embedding_model, - top_k=5, - stage=2, - ) - .map( - RefinerOperator, - parallelism=self.config.parallelism, - config=self._get_refiner_config(), - ) - .map( - SimplePromptor, - parallelism=self.config.parallelism, - stage=3, - ) - .map( - SimpleGenerator, - parallelism=self.config.parallelism, - llm_base_url=self.config.llm_base_url, - llm_model=self.config.llm_model, - max_tokens=self.config.max_tokens, - output_file=self.config.llm_output_file, - stage=4, - ) - .sink( - MetricsSink, - metrics_collector=self.metrics, - verbose=not self.config.test_mode, - ) - ) - - return self - - def build_mixed_pipeline(self, name: str = "mixed_benchmark") -> SchedulingBenchmarkPipeline: - """ - Build mixed pipeline: Compute -> RAG stages -> Compute - - Pipeline: TaskSource -> ComputeOperator -> SimpleRetriever -> SimpleReranker - -> SimplePromptor -> SimpleGenerator -> ComputeOperator -> MetricsSink - """ - from .operators import ( - SimpleGenerator, - SimplePromptor, - SimpleReranker, - SimpleRetriever, - ) - - env = self._create_environment(name) - - ( - env.from_source( - TaskSource, - num_tasks=self.config.num_tasks, - task_complexity=self.config.task_complexity, - ) - .map( - ComputeOperator, - parallelism=self.config.parallelism, - complexity="light", - stage=1, - ) - .map( - SimpleRetriever, - parallelism=self.config.parallelism, - embedding_base_url=self.config.embedding_base_url, - embedding_model=self.config.embedding_model, - top_k=10, - stage=2, - ) - .map( - SimpleReranker, - parallelism=self.config.parallelism, - embedding_base_url=self.config.embedding_base_url, - embedding_model=self.config.embedding_model, - top_k=5, - stage=3, - ) - .map( - SimplePromptor, - parallelism=self.config.parallelism, - stage=4, - ) - .map( - SimpleGenerator, - parallelism=self.config.parallelism, - llm_base_url=self.config.llm_base_url, - llm_model=self.config.llm_model, - max_tokens=self.config.max_tokens, - output_file=self.config.llm_output_file, - stage=5, - ) - .map( - ComputeOperator, - parallelism=self.config.parallelism, - complexity="light", - stage=6, - ) - .sink( - MetricsSink, - metrics_collector=self.metrics, - verbose=not self.config.test_mode, - ) - ) - - return self - - def build_custom_pipeline( - self, - name: str, - stages: list[tuple[type, dict[str, Any]]], - ) -> SchedulingBenchmarkPipeline: - """ - Build custom pipeline with arbitrary stages. - - Args: - name: Pipeline name - stages: List of (OperatorClass, kwargs) tuples - """ - env = self._create_environment(name) - - pipeline = env.from_source( - TaskSource, - num_tasks=self.config.num_tasks, - task_complexity=self.config.task_complexity, - ) - - for operator_cls, kwargs in stages: - kwargs.setdefault("parallelism", self.config.parallelism) - pipeline = pipeline.map(operator_cls, **kwargs) - - pipeline.sink( - MetricsSink, - metrics_collector=self.metrics, - verbose=not self.config.test_mode, - ) - - return self - - # ========================================================================= - # Pipeline Execution - # ========================================================================= - - def run(self) -> BenchmarkMetrics: - """Run the pipeline and collect metrics.""" - if self.env is None: - raise RuntimeError("Pipeline not built. Call build_*() first.") - - print(f"\n{'=' * 70}") - print(f"Running Benchmark: {self.config.experiment_name}") - print(f"{'=' * 70}") - print(f" Tasks: {self.config.num_tasks}") - print(f" Parallelism: {self.config.parallelism}") - print(f" Nodes: {self.config.num_nodes}") - print(f" Scheduler: {self.config.scheduler_type}") - print(f" Environment: {'Remote' if self.config.use_remote else 'Local'}") - print(f"{'=' * 70}\n") - - self.metrics.total_tasks = self.config.num_tasks - self.metrics.start_time = time.time() - run_start_timestamp = int(time.time() * 1000) # For finding metrics file - - try: - self.env.submit(autostop=True) - - if self.config.use_remote: - self.env._wait_for_completion() - - self.metrics.end_time = time.time() - self.metrics.total_duration = self.metrics.end_time - self.metrics.start_time - - # In Remote mode, read metrics from MetricsSink output files - if self.config.use_remote: - self._collect_metrics_from_files(run_start_timestamp) - - except Exception as e: - print(f"Pipeline error: {e}") - import traceback - - traceback.print_exc() - self.metrics.end_time = time.time() - self.metrics.total_duration = self.metrics.end_time - self.metrics.start_time - - finally: - try: - self.env.close() - except Exception: - pass - - return self.metrics - - def _collect_metrics_from_files(self, run_start_timestamp: int) -> None: - """ - Collect metrics from MetricsSink output files in Remote mode. - - In Remote mode, MetricsSink writes results to /tmp/sage_metrics/ on the worker nodes. - This method reads those files and aggregates the results into self.metrics. - """ - import json - from pathlib import Path - - metrics_dir = Path("/tmp/sage_metrics") - if not metrics_dir.exists(): - print("[Warning] Metrics directory not found: /tmp/sage_metrics") - return - - # Find metrics files created after run_start_timestamp - metrics_files = [] - for f in metrics_dir.glob("metrics_*.jsonl"): - try: - # Extract timestamp from filename: metrics_{hostname}_{pid}_{timestamp}.jsonl - parts = f.stem.split("_") - if len(parts) >= 4: - file_timestamp = int(parts[-1]) - if file_timestamp >= run_start_timestamp: - metrics_files.append(f) - except (ValueError, IndexError): - continue - - if not metrics_files: - print(f"[Warning] No metrics files found after timestamp {run_start_timestamp}") - return - - print(f"[Metrics] Found {len(metrics_files)} metrics file(s)") - - # Aggregate results from all files - total_success = 0 - total_fail = 0 - all_latencies = [] - node_distribution = {} - - for metrics_file in metrics_files: - try: - with open(metrics_file) as f: - for line in f: - data = json.loads(line.strip()) - record_type = data.get("type", "task") - - if record_type == "task": - if data.get("success"): - total_success += 1 - else: - total_fail += 1 - - latency = data.get("total_latency_ms", 0) - if latency > 0: - all_latencies.append(latency) - - node_id = data.get("node_id", "unknown") - node_distribution[node_id] = node_distribution.get(node_id, 0) + 1 - - elif record_type == "summary": - # Can use summary for verification - pass - except Exception as e: - print(f"[Warning] Error reading metrics file {metrics_file}: {e}") - - # Update self.metrics - self.metrics.successful_tasks = total_success - self.metrics.failed_tasks = total_fail - self.metrics.node_distribution = node_distribution - self.metrics.total_latencies = all_latencies - - # Calculate aggregate stats - if all_latencies: - pass # scheduling_latencies not available in remote mode - - print( - f"[Metrics] Aggregated: {total_success} success, {total_fail} failed, " - f"nodes: {list(node_distribution.keys())}" - ) - - def build_simple_rag_pipeline( - self, name: str = "simple_rag_benchmark" - ) -> SchedulingBenchmarkPipeline: - """ - Build simple RAG pipeline using remote embedding service. - - Pipeline: TaskSource -> SimpleRetriever -> SimpleReranker -> SimplePromptor -> SimpleGenerator -> MetricsSink - - Uses remote embedding service (http://LLM_HOST:8090/v1) instead of local models. - """ - from .operators import ( - SimpleGenerator, - SimplePromptor, - SimpleReranker, - SimpleRetriever, - ) - - env = self._create_environment(name) - - ( - env.from_source( - TaskSource, - num_tasks=self.config.num_tasks, - task_complexity=self.config.task_complexity, - ) - .map( - SimpleRetriever, - parallelism=self.config.parallelism, - embedding_base_url=self.config.embedding_base_url, - embedding_model=self.config.embedding_model, - top_k=10, - stage=1, - ) - .map( - SimpleReranker, - parallelism=self.config.parallelism, - embedding_base_url=self.config.embedding_base_url, - embedding_model=self.config.embedding_model, - top_k=5, - stage=2, - ) - .map( - SimplePromptor, - parallelism=self.config.parallelism, - stage=3, - ) - .map( - SimpleGenerator, - parallelism=self.config.parallelism, - llm_base_url=self.config.llm_base_url, - llm_model=self.config.llm_model, - max_tokens=self.config.max_tokens, - output_file=self.config.llm_output_file, - stage=4, - ) - .sink( - MetricsSink, - metrics_collector=self.metrics, - verbose=not self.config.test_mode, - ) - ) - - return self diff --git a/benchmark/experiments/common/visualization.py b/benchmark/experiments/common/visualization.py deleted file mode 100644 index e1a8145227..0000000000 --- a/benchmark/experiments/common/visualization.py +++ /dev/null @@ -1,281 +0,0 @@ -""" -Distributed Scheduling Benchmark - Visualization -================================================= - - -""" - -from __future__ import annotations - -import json -import os -from datetime import datetime - -try: - from .models import BenchmarkMetrics -except ImportError: - from models import BenchmarkMetrics - - -def save_metrics_to_json( - metrics: BenchmarkMetrics, - output_dir: str, - filename: str = "metrics.json", -) -> str: - """保存指标到 JSON 文件""" - os.makedirs(output_dir, exist_ok=True) - filepath = os.path.join(output_dir, filename) - - data = { - "timestamp": datetime.now().isoformat(), - "metrics": metrics.to_dict(), - } - - with open(filepath, "w", encoding="utf-8") as f: - json.dump(data, f, indent=2, ensure_ascii=False) - - print(f"Metrics saved to: {filepath}") - return filepath - - -def save_detailed_results( - metrics: BenchmarkMetrics, - output_dir: str, - experiment_name: str, -) -> dict[str, str]: - """保存详细结果到多个文件""" - os.makedirs(output_dir, exist_ok=True) - files = {} - - # 1. 摘要文件 (文本) - summary_file = os.path.join(output_dir, f"{experiment_name}_summary.txt") - with open(summary_file, "w", encoding="utf-8") as f: - f.write(f"{'=' * 70}\n") - f.write(f"Benchmark Results: {experiment_name}\n") - f.write(f"{'=' * 70}\n") - f.write(f"Timestamp: {datetime.now().isoformat()}\n\n") - - f.write("Configuration:\n") - if metrics.config: - for k, v in metrics.config.to_dict().items(): - f.write(f" {k}: {v}\n") - f.write("\n") - - f.write("Results:\n") - f.write(f" Total Tasks: {metrics.total_tasks}\n") - f.write(f" Successful: {metrics.successful_tasks}\n") - f.write(f" Failed: {metrics.failed_tasks}\n") - f.write(f" Duration: {metrics.total_duration:.2f}s\n") - f.write("\n") - f.write(f" Throughput: {metrics.throughput:.2f} tasks/sec\n") - f.write(f" Avg Latency: {metrics.avg_latency_ms:.2f} ms\n") - f.write(f" P50 Latency: {metrics.p50_latency_ms:.2f} ms\n") - f.write(f" P95 Latency: {metrics.p95_latency_ms:.2f} ms\n") - f.write(f" P99 Latency: {metrics.p99_latency_ms:.2f} ms\n") - f.write("\n") - f.write(f" Scheduling Latency: {metrics.avg_scheduling_latency_ms:.2f} ms\n") - f.write(f" Queue Latency: {metrics.avg_queue_latency_ms:.2f} ms\n") - f.write(f" Execution Latency: {metrics.avg_execution_latency_ms:.2f} ms\n") - f.write("\n") - f.write(f" Node Balance: {metrics.node_balance_score:.2%}\n") - - if metrics.node_distribution: - f.write("\n Node Distribution:\n") - for node, count in sorted(metrics.node_distribution.items()): - pct = count / metrics.successful_tasks * 100 if metrics.successful_tasks > 0 else 0 - f.write(f" {node}: {count} ({pct:.1f}%)\n") - - f.write(f"\n{'=' * 70}\n") - files["summary"] = summary_file - - # 2. JSON 完整数据 - json_file = os.path.join(output_dir, f"{experiment_name}_metrics.json") - with open(json_file, "w", encoding="utf-8") as f: - json.dump( - { - "timestamp": datetime.now().isoformat(), - "experiment_name": experiment_name, - "metrics": metrics.to_dict(), - "latency_details": { - "total_latencies_ms": [lat * 1000 for lat in metrics.total_latencies], - "scheduling_latencies_ms": [lat * 1000 for lat in metrics.scheduling_latencies], - "execution_latencies_ms": [lat * 1000 for lat in metrics.execution_latencies], - }, - "node_latencies_ms": { - node: [lat * 1000 for lat in lats] - for node, lats in metrics.node_latencies.items() - }, - }, - f, - indent=2, - ) - files["json"] = json_file - - # 3. CSV 延迟数据 (便于后续分析) - csv_file = os.path.join(output_dir, f"{experiment_name}_latencies.csv") - with open(csv_file, "w", encoding="utf-8") as f: - f.write("task_index,total_latency_ms,scheduling_latency_ms,execution_latency_ms\n") - for i in range(len(metrics.total_latencies)): - total = metrics.total_latencies[i] * 1000 if i < len(metrics.total_latencies) else 0 - sched = ( - metrics.scheduling_latencies[i] * 1000 - if i < len(metrics.scheduling_latencies) - else 0 - ) - exec_ = ( - metrics.execution_latencies[i] * 1000 if i < len(metrics.execution_latencies) else 0 - ) - f.write(f"{i},{total:.2f},{sched:.2f},{exec_:.2f}\n") - files["csv"] = csv_file - - print(f"Detailed results saved to: {output_dir}") - return files - - -def generate_comparison_report( - results: list[tuple[str, BenchmarkMetrics]], - output_dir: str, - report_name: str = "comparison_report", -) -> str: - """生成多实验对比""" - os.makedirs(output_dir, exist_ok=True) - report_file = os.path.join(output_dir, f"{report_name}.txt") - - with open(report_file, "w", encoding="utf-8") as f: - f.write(f"{'=' * 90}\n") - f.write("Benchmark Comparison Report\n") - f.write(f"{'=' * 90}\n") - f.write(f"Generated: {datetime.now().isoformat()}\n") - f.write(f"Number of experiments: {len(results)}\n\n") - - # 表头 - f.write( - f"{'Experiment':<25} {'Tasks':>8} {'Throughput':>12} {'Avg Lat':>10} {'P99 Lat':>10} {'Balance':>10}\n" - ) - f.write(f"{'-' * 25} {'-' * 8} {'-' * 12} {'-' * 10} {'-' * 10} {'-' * 10}\n") - - for name, metrics in results: - f.write( - f"{name:<25} {metrics.successful_tasks:>8} " - f"{metrics.throughput:>10.2f}/s " - f"{metrics.avg_latency_ms:>8.1f}ms " - f"{metrics.p99_latency_ms:>8.1f}ms " - f"{metrics.node_balance_score:>9.1%}\n" - ) - - f.write(f"\n{'=' * 90}\n") - - # 详细对比 - f.write("\nDetailed Comparison:\n") - f.write("-" * 90 + "\n") - - for name, metrics in results: - f.write(f"\n[{name}]\n") - f.write(f" Config: tasks={metrics.total_tasks}, ") - if metrics.config: - f.write(f"parallelism={metrics.config.parallelism}, ") - f.write(f"nodes={metrics.config.num_nodes}, ") - f.write(f"scheduler={metrics.config.scheduler_type}\n") - f.write(f" Throughput: {metrics.throughput:.2f} tasks/sec\n") - f.write( - f" Latency: avg={metrics.avg_latency_ms:.1f}ms, p50={metrics.p50_latency_ms:.1f}ms, p99={metrics.p99_latency_ms:.1f}ms\n" - ) - f.write( - f" Scheduling: {metrics.avg_scheduling_latency_ms:.1f}ms, Execution: {metrics.avg_execution_latency_ms:.1f}ms\n" - ) - if metrics.node_distribution: - f.write( - f" Nodes: {len(metrics.node_distribution)}, Balance: {metrics.node_balance_score:.1%}\n" - ) - - print(f"Comparison report saved to: {report_file}") - return report_file - - -def plot_results( - results: list[tuple[str, BenchmarkMetrics]], - output_dir: str, - plot_name: str = "benchmark_plots", -) -> dict[str, str]: - """ - 生成可视化图表。 - - 需要 matplotlib，如果不可用则跳过。 - """ - try: - import matplotlib - import matplotlib.pyplot as plt - - matplotlib.use("Agg") # 非交互式后端 - except ImportError: - print("Warning: matplotlib not available, skipping plots") - return {} - - os.makedirs(output_dir, exist_ok=True) - files = {} - - names = [name for name, _ in results] - throughputs = [m.throughput for _, m in results] - avg_latencies = [m.avg_latency_ms for _, m in results] - p99_latencies = [m.p99_latency_ms for _, m in results] - - # 1. 吞吐量对比图 - fig, ax = plt.subplots(figsize=(10, 6)) - bars = ax.bar(names, throughputs, color="steelblue") - ax.set_xlabel("Experiment") - ax.set_ylabel("Throughput (tasks/sec)") - ax.set_title("Throughput Comparison") - ax.tick_params(axis="x", rotation=45) - for bar, val in zip(bars, throughputs): - ax.text( - bar.get_x() + bar.get_width() / 2, - bar.get_height() + 0.1, - f"{val:.1f}", - ha="center", - va="bottom", - fontsize=9, - ) - plt.tight_layout() - throughput_file = os.path.join(output_dir, f"{plot_name}_throughput.png") - plt.savefig(throughput_file, dpi=150) - plt.close() - files["throughput"] = throughput_file - - # 2. 延迟对比图 - fig, ax = plt.subplots(figsize=(10, 6)) - x = range(len(names)) - width = 0.35 - ax.bar([i - width / 2 for i in x], avg_latencies, width, label="Avg Latency", color="steelblue") - ax.bar([i + width / 2 for i in x], p99_latencies, width, label="P99 Latency", color="coral") - ax.set_xlabel("Experiment") - ax.set_ylabel("Latency (ms)") - ax.set_title("Latency Comparison") - ax.set_xticks(x) - ax.set_xticklabels(names, rotation=45, ha="right") - ax.legend() - plt.tight_layout() - latency_file = os.path.join(output_dir, f"{plot_name}_latency.png") - plt.savefig(latency_file, dpi=150) - plt.close() - files["latency"] = latency_file - - # 3. 节点分布图 (取最后一个实验) - if results: - _, last_metrics = results[-1] - if last_metrics.node_distribution: - fig, ax = plt.subplots(figsize=(10, 6)) - nodes = list(last_metrics.node_distribution.keys()) - counts = list(last_metrics.node_distribution.values()) - ax.bar(nodes, counts, color="steelblue") - ax.set_xlabel("Node") - ax.set_ylabel("Task Count") - ax.set_title(f"Node Distribution ({results[-1][0]})") - ax.tick_params(axis="x", rotation=45) - plt.tight_layout() - node_file = os.path.join(output_dir, f"{plot_name}_nodes.png") - plt.savefig(node_file, dpi=150) - plt.close() - files["nodes"] = node_file - - print(f"Plots saved to: {output_dir}") - return files diff --git a/benchmark/experiments/config.py b/benchmark/experiments/config.py deleted file mode 100644 index 52e688a90d..0000000000 --- a/benchmark/experiments/config.py +++ /dev/null @@ -1,59 +0,0 @@ -from dataclasses import dataclass, field -from typing import Literal - - -@dataclass -class HardwareConfig: - gpus: int = 1 - gpu_type: str = "A100" - cpu_nodes: int = 0 - - -@dataclass -class ModelConfig: - name: str - - -@dataclass -class WorkloadConfig: - total_requests: int = 1000 - llm_ratio: float = 0.7 - request_rate: float = 10.0 - seed: int = 42 - warmup_requests: int = 100 - input_tokens_min: int = 100 - input_tokens_max: int = 500 - output_tokens_min: int = 50 - output_tokens_max: int = 200 - arrival_pattern: Literal["constant", "poisson", "bursty"] = "poisson" - - -@dataclass -class MetricsConfig: - slo_chat_p99_ms: float = 500.0 - slo_embedding_p99_ms: float = 100.0 - - -@dataclass -class OutputConfig: - generate_plots: bool = True - save_raw_data: bool = True - export_latex: bool = True - - -@dataclass -class ExperimentConfig: - name: str - description: str - experiment_section: str - gateway_url: str = ( - "http://localhost:8888" # allow-control-plane-bypass: benchmark configuration - ) - hardware: HardwareConfig = field(default_factory=HardwareConfig) - llm_model: ModelConfig = field( - default_factory=lambda: ModelConfig(name="Qwen/Qwen2.5-7B-Instruct") - ) - embedding_model: ModelConfig = field(default_factory=lambda: ModelConfig(name="BAAI/bge-m3")) - workload: WorkloadConfig = field(default_factory=WorkloadConfig) - metrics: MetricsConfig = field(default_factory=MetricsConfig) - output: OutputConfig = field(default_factory=OutputConfig) diff --git a/benchmark/experiments/exp1_single_vs_multi/__init__.py b/benchmark/experiments/exp1_single_vs_multi/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/benchmark/experiments/exp1_single_vs_multi/run_experiment.py b/benchmark/experiments/exp1_single_vs_multi/run_experiment.py deleted file mode 100644 index 18a05a49f5..0000000000 --- a/benchmark/experiments/exp1_single_vs_multi/run_experiment.py +++ /dev/null @@ -1,351 +0,0 @@ -#!/usr/bin/env python3 -""" -'ENDOFFILE'1: vs 单节点 -============================ - - super().__init__() - -'ENDOFFILE''ENDOFFILE''ENDOFFILE': -- 单节点: LocalEnvironment, 1 node -- 多节点: RemoteEnvironment, 4/8/16/30 nodes -- 任务类型: RAG Pipeline (检索+LLM生成) -- 测量指标: 吞吐量、延迟、节点分布 - -: - python run_experiment.py # 'ENDOFFILE' - python run_experiment.py --quick # 快速测试模式 - python run_experiment.py --nodes 4 8 # 指定节点数 -""" - -from __future__ import annotations - -import argparse -import os -import sys -from datetime import datetime -from pathlib import Path - -# 添加项目路径 -SCRIPT_DIR = Path(__file__).resolve().parent -EXPERIMENT_ROOT = SCRIPT_DIR.parent -# REPO_ROOT removed -# removed -sys.path.insert(0, str(EXPERIMENT_ROOT)) - -from common.models import BenchmarkConfig, BenchmarkMetrics -from common.pipeline import SchedulingBenchmarkPipeline -from common.visualization import ( - generate_comparison_report, - plot_results, - save_detailed_results, -) - -# 实验配置 -EXPERIMENT_CONFIGS = { - "single_node": { - "description": "单节点 ", - "use_remote": True, - "num_nodes": 1, - "parallelism": 4, - }, - "multi_4_nodes": { - "description": "多节点 (4 nodes)", - "use_remote": True, - "num_nodes": 4, - "parallelism": 16, - "scheduler_type": "load_aware", - "scheduler_strategy": "spread", - }, - "multi_8_nodes": { - "description": "多节点 (8 nodes)", - "use_remote": True, - "num_nodes": 8, - "parallelism": 32, - "scheduler_type": "load_aware", - "scheduler_strategy": "spread", - }, - "multi_16_nodes": { - "description": "多节点 (16 nodes)", - "use_remote": True, - "num_nodes": 16, - "parallelism": 64, - "scheduler_type": "load_aware", - "scheduler_strategy": "spread", - }, - "multi_30_nodes": { - "description": "多节点 (30 nodes)", - "use_remote": True, - "num_nodes": 30, - "parallelism": 120, - "scheduler_type": "load_aware", - "scheduler_strategy": "spread", - }, -} - -# 不同任务规模 -TASK_SCALES = { - "small": 100, - "medium": 500, - "large": 1000, -} - - -def run_single_experiment( - name: str, - config_override: dict, - num_tasks: int, - output_dir: str, - pipeline_type: str = "rag", -) -> BenchmarkMetrics: - """运行单个实验配置""" - print(f"\n{'=' * 70}") - print(f"Running: {name}") - print(f"Tasks: {num_tasks}, Pipeline: {pipeline_type}") - print(f"{'=' * 70}") - - # 创建配置 - config = BenchmarkConfig( - experiment_name=name, - num_tasks=num_tasks, - **config_override, - ) - - # 创建并运行 Pipeline - pipeline = SchedulingBenchmarkPipeline(config) - - if pipeline_type == "rag": - pipeline.build_rag_pipeline(name) - elif pipeline_type == "simple_rag": - pipeline.build_simple_rag_pipeline(name) - elif pipeline_type == "llm": - pipeline.build_llm_pipeline(name) - elif pipeline_type == "compute": - pipeline.build_compute_pipeline(name) - else: - pipeline.build_mixed_pipeline(name) - - metrics = pipeline.run() - - # 保存单个实验结果 - save_detailed_results(metrics, output_dir, name) - - # 打印摘要 - metrics.print_summary() - - return metrics - - -def run_node_scaling_experiment( - num_tasks: int = 500, - node_counts: list[int] | None = None, - output_dir: str | None = None, - pipeline_type: str = "compute", -) -> list[tuple[str, BenchmarkMetrics]]: - """ - 运行节点扩展实验。 - - 对比不同节点数下的性能变化。 - """ - if node_counts is None: - node_counts = [1, 4, 8, 16] - - if output_dir is None: - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - output_dir = str(SCRIPT_DIR / "results" / f"node_scaling_{timestamp}") - - os.makedirs(output_dir, exist_ok=True) - - results = [] - - for num_nodes in node_counts: - if num_nodes == 1: - config_name = "single_node" - config_override = EXPERIMENT_CONFIGS["single_node"].copy() - # 移除 BenchmarkConfig 不接受的字段 - config_override.pop("description", None) - else: - config_name = f"multi_{num_nodes}_nodes" - config_override = { - "use_remote": True, - "num_nodes": num_nodes, - "parallelism": num_nodes * 4, - "scheduler_type": "load_aware", - "scheduler_strategy": "spread", - } - - try: - metrics = run_single_experiment( - name=config_name, - config_override=config_override, - num_tasks=num_tasks, - output_dir=output_dir, - pipeline_type=pipeline_type, - ) - results.append((config_name, metrics)) - except Exception as e: - print(f"Error running {config_name}: {e}") - import traceback - - traceback.print_exc() - - # 生成对比报告 - if results: - generate_comparison_report(results, output_dir, "node_scaling_comparison") - plot_results(results, output_dir, "node_scaling") - - return results - - -def run_task_scaling_experiment( - task_counts: list[int] | None = None, - num_nodes: int = 4, - output_dir: str | None = None, - pipeline_type: str = "compute", -) -> list[tuple[str, BenchmarkMetrics]]: - """ - 运行任务规模扩展实验。 - - 在固定节点数下，测试不同任务数 - """ - if task_counts is None: - task_counts = [100, 500, 1000] - - if output_dir is None: - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - output_dir = str(SCRIPT_DIR / "results" / f"task_scaling_{timestamp}") - - os.makedirs(output_dir, exist_ok=True) - - results = [] - - for num_tasks in task_counts: - config_name = f"tasks_{num_tasks}_nodes_{num_nodes}" - - if num_nodes == 1: - config_override = {"use_remote": False, "num_nodes": 1, "parallelism": 4} - else: - config_override = { - "use_remote": True, - "num_nodes": num_nodes, - "parallelism": num_nodes * 4, - "scheduler_type": "load_aware", - "scheduler_strategy": "spread", - } - - try: - metrics = run_single_experiment( - name=config_name, - config_override=config_override, - num_tasks=num_tasks, - output_dir=output_dir, - pipeline_type=pipeline_type, - ) - results.append((config_name, metrics)) - except Exception as e: - print(f"Error running {config_name}: {e}") - - # 生成对比报告 - if results: - generate_comparison_report(results, output_dir, "task_scaling_comparison") - plot_results(results, output_dir, "task_scaling") - - return results - - -def run_full_experiment( - quick_mode: bool = False, - pipeline_type: str = "compute", -) -> None: - """运行完整实验""" - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - output_dir = str(SCRIPT_DIR / "results" / f"full_experiment_{timestamp}") - os.makedirs(output_dir, exist_ok=True) - - print(f"\n{'#' * 70}") - print("Experiment 1: Single Node vs Multi Node Comparison") - print(f"{'#' * 70}") - print(f"Output directory: {output_dir}") - print(f"Pipeline type: {pipeline_type}") - print(f"Quick mode: {quick_mode}") - - all_results = [] - - # 1. 节点扩展实验 - if quick_mode: - node_counts = [1, 4] - num_tasks = 50 - else: - node_counts = [1, 4, 8, 16] - num_tasks = 500 - - print(f"\n--- Part 1: Node Scaling (tasks={num_tasks}) ---") - node_results = run_node_scaling_experiment( - num_tasks=num_tasks, - node_counts=node_counts, - output_dir=os.path.join(output_dir, "node_scaling"), - pipeline_type=pipeline_type, - ) - all_results.extend(node_results) - - # 2. 任务规模扩展实验 (固定 4 节点) - if quick_mode: - task_counts = [50, 100] - else: - task_counts = [100, 500, 1000] - - print("\n--- Part 2: Task Scaling (nodes=4) ---") - task_results = run_task_scaling_experiment( - task_counts=task_counts, - num_nodes=4, - output_dir=os.path.join(output_dir, "task_scaling"), - pipeline_type=pipeline_type, - ) - all_results.extend(task_results) - - # 生成总体报 - if all_results: - generate_comparison_report(all_results, output_dir, "full_experiment_report") - plot_results(all_results, output_dir, "full_experiment") - - print(f"\n{'=' * 70}") - print("Experiment completed.") - print(f"Results saved to: {output_dir}") - print(f"{'=' * 70}") - - -def main(): - parser = argparse.ArgumentParser(description="Experiment 1: Single vs Multi Node") - parser.add_argument("--quick", action="store_true", help="Quick test mode") - parser.add_argument("--nodes", nargs="+", type=int, help="Node counts to test") - parser.add_argument("--tasks", type=int, default=500, help="Number of tasks") - parser.add_argument( - "--pipeline", - choices=["compute", "llm", "rag", "simple_rag", "mixed"], - default="compute", - help="Pipeline type", - ) - parser.add_argument("--output", type=str, help="Output directory") - - parser.add_argument( - "--llm-output", type=str, help="File path to save LLM responses (JSONL format)" - ) - args = parser.parse_args() - - if args.nodes: - # 指定节点数的实验 - run_node_scaling_experiment( - num_tasks=args.tasks, - node_counts=args.nodes, - output_dir=args.output, - pipeline_type=args.pipeline, - ) - else: - # 完整实验 - run_full_experiment( - quick_mode=args.quick, - pipeline_type=args.pipeline, - ) - - -if __name__ == "__main__": - main() diff --git a/benchmark/experiments/exp2_high_load_parallel/__init__.py b/benchmark/experiments/exp2_high_load_parallel/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/benchmark/experiments/exp2_high_load_parallel/run_experiment.py b/benchmark/experiments/exp2_high_load_parallel/run_experiment.py deleted file mode 100644 index 539eb853fe..0000000000 --- a/benchmark/experiments/exp2_high_load_parallel/run_experiment.py +++ /dev/null @@ -1,368 +0,0 @@ -#!/usr/bin/env python3 -""" -'ENDOFFILE'2: 高负载流 -============================ - - self.total_latency = 0.0Sage 的流水线并行调度能力。 - -'ENDOFFILE''ENDOFFILE''ENDOFFILE': -- 不同负载级别: 低(4并行)、中(16并行)、高(64并行)、极高(128并行) -- 不同调度策略对比: FIFO, LoadAware, RoundRobin, Priority -- 不同流水线深度: 2阶段, 3阶段, 5阶段 - - -: - python run_experiment.py # 运行全部实验 - python run_experiment.py --quick # 快速测试模式 - python run_experiment.py --schedulers fifo load_aware # 指定调度器 -""" - -from __future__ import annotations - -import argparse -import os -import sys -from datetime import datetime -from pathlib import Path - -# 添加项目路径 -SCRIPT_DIR = Path(__file__).resolve().parent -EXPERIMENT_ROOT = SCRIPT_DIR.parent -REPO_ROOT = EXPERIMENT_ROOT.parents[0] -sys.path.insert(0, str(REPO_ROOT)) -sys.path.insert(0, str(EXPERIMENT_ROOT)) - -from common.models import BenchmarkConfig, BenchmarkMetrics -from common.pipeline import SchedulingBenchmarkPipeline -from common.visualization import ( - generate_comparison_report, - plot_results, - save_detailed_results, -) - -# 负载级别配置 -LOAD_LEVELS = { - "low": { - "description": "低负载 (4 并行)", - "parallelism": 4, - "num_nodes": 2, - "num_tasks": 100, - }, - "medium": { - "description": "中负载 (16 并行)", - "parallelism": 16, - "num_nodes": 4, - "num_tasks": 200, - }, - "high": { - "description": "高负载 (64 并行)", - "parallelism": 64, - "num_nodes": 8, - "num_tasks": 500, - }, - "extreme": { - "description": "极高负载 (128 并行)", - "parallelism": 128, - "num_nodes": 16, - "num_tasks": 1000, - }, -} - -# 调度器配置 -SCHEDULERS = { - "fifo": {"scheduler_type": "fifo"}, - "load_aware_spread": {"scheduler_type": "load_aware", "scheduler_strategy": "spread"}, - "load_aware_pack": {"scheduler_type": "load_aware", "scheduler_strategy": "pack"}, - "round_robin": {"scheduler_type": "round_robin"}, - "priority": {"scheduler_type": "priority"}, -} - -# 流水线深度配置 -PIPELINE_DEPTHS = { - "shallow": {"pipeline_stages": 2, "description": "2阶段流水线"}, - "medium": {"pipeline_stages": 3, "description": "3阶段流水线"}, - "deep": {"pipeline_stages": 5, "description": "5阶段流水线"}, -} - - -def run_single_experiment( - name: str, - config_dict: dict, - output_dir: str, -) -> BenchmarkMetrics: - """运行单个实验配置""" - print(f"\n{'=' * 70}") - print(f"Running: {name}") - print(f"Config: {config_dict}") - print(f"{'=' * 70}") - - config = BenchmarkConfig( - experiment_name=name, - use_remote=config_dict.get("use_remote", True), - **{k: v for k, v in config_dict.items() if k != "use_remote"}, - ) - - pipeline = SchedulingBenchmarkPipeline(config) - pipeline.build_compute_pipeline(name) - - if config.use_remote: - metrics = pipeline.run_with_warmup() - else: - metrics = pipeline.run() - - save_detailed_results(metrics, output_dir, name) - metrics.print_summary() - - return metrics - - -def run_load_level_experiment( - load_levels: list[str] | None = None, - scheduler_type: str = "load_aware", - output_dir: str | None = None, -) -> list[tuple[str, BenchmarkMetrics]]: - """ - 不同负载级别对比实验。 - - 固定调度策略，测试不同负载级别下的性能。 - """ - if load_levels is None: - load_levels = ["low", "medium", "high"] - - if output_dir is None: - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - output_dir = str(SCRIPT_DIR / "results" / f"load_levels_{timestamp}") - - os.makedirs(output_dir, exist_ok=True) - - results = [] - - for level in load_levels: - if level not in LOAD_LEVELS: - print(f"Unknown load level: {level}, skipping") - continue - - level_config = LOAD_LEVELS[level].copy() - config_name = f"load_{level}_{scheduler_type}" - - # 添加调度器配置 - if scheduler_type in SCHEDULERS: - level_config.update(SCHEDULERS[scheduler_type]) - else: - level_config["scheduler_type"] = scheduler_type - - try: - metrics = run_single_experiment( - name=config_name, - config_dict=level_config, - output_dir=output_dir, - ) - results.append((config_name, metrics)) - except Exception as e: - print(f"Error running {config_name}: {e}") - import traceback - - traceback.print_exc() - - if results: - generate_comparison_report(results, output_dir, "load_levels_comparison") - plot_results(results, output_dir, "load_levels") - - return results - - -def run_scheduler_comparison_experiment( - schedulers: list[str] | None = None, - load_level: str = "medium", - output_dir: str | None = None, -) -> list[tuple[str, BenchmarkMetrics]]: - """ - 调度策略对比实验。 - - 固定负载级别，对比不同调度策略的性能。 - """ - if schedulers is None: - schedulers = ["fifo", "load_aware_spread", "round_robin"] - - if output_dir is None: - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - output_dir = str(SCRIPT_DIR / "results" / f"scheduler_comparison_{timestamp}") - - os.makedirs(output_dir, exist_ok=True) - - base_config = LOAD_LEVELS.get(load_level, LOAD_LEVELS["medium"]).copy() - results = [] - - for scheduler in schedulers: - config_name = f"scheduler_{scheduler}_{load_level}" - config = base_config.copy() - - if scheduler in SCHEDULERS: - config.update(SCHEDULERS[scheduler]) - else: - config["scheduler_type"] = scheduler - - try: - metrics = run_single_experiment( - name=config_name, - config_dict=config, - output_dir=output_dir, - ) - results.append((config_name, metrics)) - except Exception as e: - print(f"Error running {config_name}: {e}") - - if results: - generate_comparison_report(results, output_dir, "scheduler_comparison") - plot_results(results, output_dir, "scheduler") - - return results - - -def run_pipeline_depth_experiment( - depths: list[str] | None = None, - load_level: str = "medium", - output_dir: str | None = None, -) -> list[tuple[str, BenchmarkMetrics]]: - """ - 流水线深度对比实验。 - - 测试不同流水线阶段数对性能的影响。 - """ - if depths is None: - depths = ["shallow", "medium", "deep"] - - if output_dir is None: - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - output_dir = str(SCRIPT_DIR / "results" / f"pipeline_depth_{timestamp}") - - os.makedirs(output_dir, exist_ok=True) - - base_config = LOAD_LEVELS.get(load_level, LOAD_LEVELS["medium"]).copy() - base_config.update(SCHEDULERS["load_aware_spread"]) - results = [] - - for depth in depths: - if depth not in PIPELINE_DEPTHS: - print(f"Unknown depth: {depth}, skipping") - continue - - config_name = f"depth_{depth}_{load_level}" - config = base_config.copy() - config["pipeline_stages"] = PIPELINE_DEPTHS[depth]["pipeline_stages"] - - try: - metrics = run_single_experiment( - name=config_name, - config_dict=config, - output_dir=output_dir, - ) - results.append((config_name, metrics)) - except Exception as e: - print(f"Error running {config_name}: {e}") - - if results: - generate_comparison_report(results, output_dir, "pipeline_depth_comparison") - plot_results(results, output_dir, "pipeline_depth") - - return results - - -def run_full_experiment(quick_mode: bool = False) -> None: - """运行完整实验""" - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - output_dir = str(SCRIPT_DIR / "results" / f"full_experiment_{timestamp}") - os.makedirs(output_dir, exist_ok=True) - - print(f"\n{'#' * 70}") - print("Experiment 2: High Load Pipeline Parallel Scheduling") - print(f"{'#' * 70}") - print(f"Output directory: {output_dir}") - print(f"Quick mode: {quick_mode}") - - all_results = [] - - # 1. 负载级别实验 - if quick_mode: - load_levels = ["low", "medium"] - else: - load_levels = ["low", "medium", "high"] - - print("\n--- Part 1: Load Level Comparison ---") - load_results = run_load_level_experiment( - load_levels=load_levels, - scheduler_type="load_aware", - output_dir=os.path.join(output_dir, "load_levels"), - ) - all_results.extend(load_results) - - # 2. 调度策略对比 - if quick_mode: - schedulers = ["fifo", "load_aware_spread"] - else: - schedulers = ["fifo", "load_aware_spread", "load_aware_pack", "round_robin"] - - print("\n--- Part 2: Scheduler Comparison ---") - scheduler_results = run_scheduler_comparison_experiment( - schedulers=schedulers, - load_level="medium", - output_dir=os.path.join(output_dir, "scheduler_comparison"), - ) - all_results.extend(scheduler_results) - - # 3. 流水线深度实验 - if quick_mode: - depths = ["shallow", "medium"] - else: - depths = ["shallow", "medium", "deep"] - - print("\n--- Part 3: Pipeline Depth Comparison ---") - depth_results = run_pipeline_depth_experiment( - depths=depths, - load_level="medium", - output_dir=os.path.join(output_dir, "pipeline_depth"), - ) - all_results.extend(depth_results) - - # 生成总体报告 - if all_results: - generate_comparison_report(all_results, output_dir, "full_experiment_report") - plot_results(all_results, output_dir, "full_experiment") - - print(f"\n{'=' * 70}") - print("Experiment completed.") - print(f"Results saved to: {output_dir}") - print(f"{'=' * 70}") - - -def main(): - parser = argparse.ArgumentParser(description="Experiment 2: High Load Parallel Scheduling") - parser.add_argument("--quick", action="store_true", help="Quick test mode") - parser.add_argument("--schedulers", nargs="+", help="Schedulers to test") - parser.add_argument("--load-levels", nargs="+", help="Load levels to test") - parser.add_argument("--depths", nargs="+", help="Pipeline depths to test") - parser.add_argument("--output", type=str, help="Output directory") - - args = parser.parse_args() - - if args.schedulers: - run_scheduler_comparison_experiment( - schedulers=args.schedulers, - output_dir=args.output, - ) - elif args.load_levels: - run_load_level_experiment( - load_levels=args.load_levels, - output_dir=args.output, - ) - elif args.depths: - run_pipeline_depth_experiment( - depths=args.depths, - output_dir=args.output, - ) - else: - run_full_experiment(quick_mode=args.quick) - - -if __name__ == "__main__": - main() diff --git a/benchmark/experiments/exp3_latency_throughput/__init__.py b/benchmark/experiments/exp3_latency_throughput/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/benchmark/experiments/exp3_latency_throughput/run_experiment.py b/benchmark/experiments/exp3_latency_throughput/run_experiment.py deleted file mode 100644 index 5fac8cb0c1..0000000000 --- a/benchmark/experiments/exp3_latency_throughput/run_experiment.py +++ /dev/null @@ -1,462 +0,0 @@ -#!/usr/bin/env python3 -""" -'ENDOFFILE'3: 调度延迟 -================================ - ---------的延迟和吞吐量。 - -'ENDOFFILE''ENDOFFILE''ENDOFFILE': -- 调度延迟分解: 调度延迟、排队延迟、执行延迟 -- 吞吐量曲线: 不同并发度下的吞吐量变化 -- 延迟分布: P50/P95/P99 延迟统计 -- 调度器开销: 对比不同调度器的调度开销 - -: - python run_experiment.py # 运行全部实验 - python run_experiment.py --quick # 快速测试模式 - python run_experiment.py --concurrency 4 8 16 32 # 指定并发度 -""" - -from __future__ import annotations - -import argparse -import json -import os -import sys -from datetime import datetime -from pathlib import Path - -# 添加项目路径 -SCRIPT_DIR = Path(__file__).resolve().parent -EXPERIMENT_ROOT = SCRIPT_DIR.parent -REPO_ROOT = EXPERIMENT_ROOT.parents[0] -sys.path.insert(0, str(REPO_ROOT)) -sys.path.insert(0, str(EXPERIMENT_ROOT)) - -from common.models import BenchmarkConfig, BenchmarkMetrics -from common.pipeline import SchedulingBenchmarkPipeline -from common.visualization import ( - generate_comparison_report, - plot_results, - save_detailed_results, -) - - -def run_single_experiment( - name: str, - config_dict: dict, - output_dir: str, - pipeline_type: str = "compute", -) -> BenchmarkMetrics: - """运行单个实验配置""" - print(f"\n{'=' * 70}") - print(f"Running: {name}") - print(f"{'=' * 70}") - - config = BenchmarkConfig( - experiment_name=name, - **config_dict, - ) - - pipeline = SchedulingBenchmarkPipeline(config) - - if pipeline_type == "compute": - pipeline.build_compute_pipeline(name) - elif pipeline_type == "llm": - pipeline.build_llm_pipeline(name) - else: - pipeline.build_rag_pipeline(name) - - if config.use_remote: - metrics = pipeline.run_with_warmup() - else: - metrics = pipeline.run() - - save_detailed_results(metrics, output_dir, name) - metrics.print_summary() - - return metrics - - -def run_concurrency_scaling_experiment( - concurrency_levels: list[int] = None, - num_tasks: int = 500, - num_nodes: int = 4, - output_dir: str = None, -) -> list[tuple[str, BenchmarkMetrics]]: - """ - 并发度扩展实验。 - - - """ - if concurrency_levels is None: - concurrency_levels = [1, 2, 4, 8, 16, 32] - - if output_dir is None: - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - output_dir = str(SCRIPT_DIR / "results" / f"concurrency_scaling_{timestamp}") - - os.makedirs(output_dir, exist_ok=True) - - results = [] - - for parallelism in concurrency_levels: - config_name = f"concurrency_{parallelism}" - - # 低并发用 Local，高并发用 Remote - use_remote = parallelism > 4 - - config = { - "num_tasks": num_tasks, - "parallelism": parallelism, - "num_nodes": num_nodes if use_remote else 1, - "use_remote": use_remote, - "scheduler_type": "load_aware" if use_remote else "fifo", - "scheduler_strategy": "spread", - "task_complexity": "medium", - } - - try: - metrics = run_single_experiment( - name=config_name, - config_dict=config, - output_dir=output_dir, - ) - results.append((config_name, metrics)) - except Exception as e: - print(f"Error running {config_name}: {e}") - import traceback - - traceback.print_exc() - - if results: - generate_comparison_report(results, output_dir, "concurrency_scaling_comparison") - plot_results(results, output_dir, "concurrency_scaling") - - # 生成吞吐量曲线数据 - save_throughput_curve(results, output_dir) - - return results - - -def run_latency_breakdown_experiment( - parallelism: int = 16, - num_tasks: int = 500, - num_nodes: int = 4, - output_dir: str = None, -) -> list[tuple[str, BenchmarkMetrics]]: - """ - 延迟分解实验。 - - - """ - if output_dir is None: - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - output_dir = str(SCRIPT_DIR / "results" / f"latency_breakdown_{timestamp}") - - os.makedirs(output_dir, exist_ok=True) - - complexities = ["light", "medium", "heavy"] - results = [] - - for complexity in complexities: - config_name = f"complexity_{complexity}" - - config = { - "num_tasks": num_tasks, - "parallelism": parallelism, - "num_nodes": num_nodes, - "use_remote": True, - "scheduler_type": "load_aware", - "scheduler_strategy": "spread", - "task_complexity": complexity, - } - - try: - metrics = run_single_experiment( - name=config_name, - config_dict=config, - output_dir=output_dir, - ) - results.append((config_name, metrics)) - except Exception as e: - print(f"Error running {config_name}: {e}") - - if results: - generate_comparison_report(results, output_dir, "latency_breakdown_comparison") - save_latency_breakdown(results, output_dir) - - return results - - -def run_scheduler_overhead_experiment( - schedulers: list[str] = None, - num_tasks: int = 500, - parallelism: int = 16, - output_dir: str = None, -) -> list[tuple[str, BenchmarkMetrics]]: - """ - 调度器开销对比实验。 - - self._cache_time 0.0 = - """ - if schedulers is None: - schedulers = ["fifo", "load_aware", "round_robin", "priority"] - - if output_dir is None: - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - output_dir = str(SCRIPT_DIR / "results" / f"scheduler_overhead_{timestamp}") - - os.makedirs(output_dir, exist_ok=True) - - results = [] - - for scheduler in schedulers: - config_name = f"scheduler_{scheduler}" - - config = { - "num_tasks": num_tasks, - "parallelism": parallelism, - "num_nodes": 4, - "use_remote": True, - "scheduler_type": scheduler, - "scheduler_strategy": "spread", - } - - try: - metrics = run_single_experiment( - name=config_name, - config_dict=config, - output_dir=output_dir, - ) - results.append((config_name, metrics)) - except Exception as e: - print(f"Error running {config_name}: {e}") - - if results: - generate_comparison_report(results, output_dir, "scheduler_overhead_comparison") - save_scheduler_overhead(results, output_dir) - - return results - - -def save_throughput_curve( - results: list[tuple[str, BenchmarkMetrics]], - output_dir: str, -) -> str: - """保存吞吐量曲线'ENDOFFILE'""" - filepath = os.path.join(output_dir, "throughput_curve.json") - - data = { - "concurrency_levels": [], - "throughput": [], - "avg_latency_ms": [], - "p99_latency_ms": [], - } - - for name, metrics in results: - # 从名称解析并发度 - try: - parallelism = int(name.split("_")[-1]) - except ValueError: - parallelism = 0 - - data["concurrency_levels"].append(parallelism) - data["throughput"].append(metrics.throughput) - data["avg_latency_ms"].append(metrics.avg_latency_ms) - data["p99_latency_ms"].append(metrics.p99_latency_ms) - - with open(filepath, "w", encoding="utf-8") as f: - json.dump(data, f, indent=2) - - # 生成文本报告 - report_file = os.path.join(output_dir, "throughput_curve_report.txt") - with open(report_file, "w", encoding="utf-8") as f: - f.write("Throughput vs Concurrency\n") - f.write("=" * 60 + "\n\n") - f.write( - f"{'Concurrency':>12} {'Throughput':>15} {'Avg Lat (ms)':>15} {'P99 Lat (ms)':>15}\n" - ) - f.write("-" * 60 + "\n") - for i in range(len(data["concurrency_levels"])): - f.write( - f"{data['concurrency_levels'][i]:>12} " - f"{data['throughput'][i]:>13.2f}/s " - f"{data['avg_latency_ms'][i]:>15.1f} " - f"{data['p99_latency_ms'][i]:>15.1f}\n" - ) - - print(f"Throughput curve data saved to: {filepath}") - return filepath - - -def save_latency_breakdown( - results: list[tuple[str, BenchmarkMetrics]], - output_dir: str, -) -> str: - """保存延迟分解数据""" - filepath = os.path.join(output_dir, "latency_breakdown.json") - - data = [] - for name, metrics in results: - data.append( - { - "name": name, - "scheduling_latency_ms": metrics.avg_scheduling_latency_ms, - "queue_latency_ms": metrics.avg_queue_latency_ms, - "execution_latency_ms": metrics.avg_execution_latency_ms, - "total_latency_ms": metrics.avg_latency_ms, - } - ) - - with open(filepath, "w", encoding="utf-8") as f: - json.dump(data, f, indent=2) - - # 生成文本报告 - report_file = os.path.join(output_dir, "latency_breakdown_report.txt") - with open(report_file, "w", encoding="utf-8") as f: - f.write("Latency Breakdown Analysis\n") - f.write("=" * 80 + "\n\n") - f.write( - f"{'Complexity':>15} {'Scheduling':>12} {'Queue':>12} {'Execution':>12} {'Total':>12}\n" - ) - f.write("-" * 80 + "\n") - for d in data: - f.write( - f"{d['name']:>15} " - f"{d['scheduling_latency_ms']:>10.2f}ms " - f"{d['queue_latency_ms']:>10.2f}ms " - f"{d['execution_latency_ms']:>10.2f}ms " - f"{d['total_latency_ms']:>10.2f}ms\n" - ) - - print(f"Latency breakdown data saved to: {filepath}") - return filepath - - -def save_scheduler_overhead( - results: list[tuple[str, BenchmarkMetrics]], - output_dir: str, -) -> str: - """保存调度器开销数据""" - filepath = os.path.join(output_dir, "scheduler_overhead.json") - - data = [] - for name, metrics in results: - scheduler = name.replace("scheduler_", "") - data.append( - { - "scheduler": scheduler, - "scheduling_latency_ms": metrics.avg_scheduling_latency_ms, - "throughput": metrics.throughput, - "node_balance_score": metrics.node_balance_score, - } - ) - - with open(filepath, "w", encoding="utf-8") as f: - json.dump(data, f, indent=2) - - # 生成文本 - report_file = os.path.join(output_dir, "scheduler_overhead_report.txt") - with open(report_file, "w", encoding="utf-8") as f: - f.write("Scheduler Overhead Comparison\n") - f.write("=" * 70 + "\n\n") - f.write(f"{'Scheduler':>15} {'Sched Latency':>15} {'Throughput':>15} {'Balance':>12}\n") - f.write("-" * 70 + "\n") - for d in data: - f.write( - f"{d['scheduler']:>15} " - f"{d['scheduling_latency_ms']:>13.2f}ms " - f"{d['throughput']:>13.2f}/s " - f"{d['node_balance_score']:>11.1%}\n" - ) - - print(f"Scheduler overhead data saved to: {filepath}") - return filepath - - -def run_full_experiment(quick_mode: bool = False) -> None: - """运行完整实验""" - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - output_dir = str(SCRIPT_DIR / "results" / f"full_experiment_{timestamp}") - os.makedirs(output_dir, exist_ok=True) - - print(f"\n{'#' * 70}") - print("Experiment 3: Latency and Throughput Measurement") - print(f"{'#' * 70}") - print(f"Output directory: {output_dir}") - print(f"Quick mode: {quick_mode}") - - all_results = [] - - # 1. 并发度扩展实验 - if quick_mode: - concurrency_levels = [1, 4, 8] - num_tasks = 100 - else: - concurrency_levels = [1, 2, 4, 8, 16, 32] - num_tasks = 500 - - print("\n--- Part 1: Concurrency Scaling ---") - concurrency_results = run_concurrency_scaling_experiment( - concurrency_levels=concurrency_levels, - num_tasks=num_tasks, - output_dir=os.path.join(output_dir, "concurrency_scaling"), - ) - all_results.extend(concurrency_results) - - # 2. 延迟分解实验 - print("\n--- Part 2: Latency Breakdown ---") - latency_results = run_latency_breakdown_experiment( - parallelism=8 if quick_mode else 16, - num_tasks=100 if quick_mode else 500, - output_dir=os.path.join(output_dir, "latency_breakdown"), - ) - all_results.extend(latency_results) - - # 3. 调度器开销实验 - if quick_mode: - schedulers = ["fifo", "load_aware"] - else: - schedulers = ["fifo", "load_aware", "round_robin", "priority"] - - print("\n--- Part 3: Scheduler Overhead ---") - overhead_results = run_scheduler_overhead_experiment( - schedulers=schedulers, - num_tasks=100 if quick_mode else 500, - output_dir=os.path.join(output_dir, "scheduler_overhead"), - ) - all_results.extend(overhead_results) - - # 生成总体报告 - if all_results: - generate_comparison_report(all_results, output_dir, "full_experiment_report") - plot_results(all_results, output_dir, "full_experiment") - - print(f"\n{'=' * 70}") - print("Experiment completed.") - print(f"Results saved to: {output_dir}") - print(f"{'=' * 70}") - - -def main(): - parser = argparse.ArgumentParser(description="Experiment 3: Latency and Throughput") - parser.add_argument("--quick", action="store_true", help="Quick test mode") - parser.add_argument("--concurrency", nargs="+", type=int, help="Concurrency levels to test") - parser.add_argument("--tasks", type=int, default=500, help="Number of tasks") - parser.add_argument("--output", type=str, help="Output directory") - - args = parser.parse_args() - - if args.concurrency: - run_concurrency_scaling_experiment( - concurrency_levels=args.concurrency, - num_tasks=args.tasks, - output_dir=args.output, - ) - else: - run_full_experiment(quick_mode=args.quick) - - -if __name__ == "__main__": - main() diff --git a/benchmark/experiments/exp_5_1_e2e_pipeline.py b/benchmark/experiments/exp_5_1_e2e_pipeline.py deleted file mode 100644 index a1ac919630..0000000000 --- a/benchmark/experiments/exp_5_1_e2e_pipeline.py +++ /dev/null @@ -1,132 +0,0 @@ -import asyncio -import random -import time - -from sage.benchmark.benchmark_sage.experiments.base_experiment import ( - BaseExperiment, -) -from sage.benchmark.benchmark_sage.experiments.common import ( - BenchmarkClient, - RequestResult, -) - - -class E2EPipelineExperiment(BaseExperiment): - """ - Experiment for End-to-End Pipeline Performance. - - Simulates a RAG pipeline workload (Embed -> Retrieve -> Generate) to evaluate - system performance under complex, multi-stage traffic patterns. - """ - - def _setup_impl(self) -> None: - """Setup experiment.""" - pass - - def _run_impl(self) -> None: - """Run the experiment workload.""" - asyncio.run(self._run_async()) - - async def _run_async(self) -> None: - """Run async workload.""" - config = self.config - - # Simulate RAG Pipeline: Query -> Embed -> Retrieve (simulated) -> Generate - # We simulate N concurrent users running this pipeline loop. - - async with BenchmarkClient(config.gateway_url) as client: - tasks = [] - start_time = time.perf_counter() - - # Launch concurrent pipeline users - # We use 'request_rate' to determine how many pipelines start per second - - arrival_times = self._generate_arrival_times( - config.workload.total_requests, config.workload.request_rate - ) - - for i, arrival_time in enumerate(arrival_times): - # Wait for arrival - target_time = start_time + arrival_time - now = time.perf_counter() - if target_time > now: - await asyncio.sleep(target_time - now) - - # Start pipeline task - task = asyncio.create_task(self._run_single_pipeline(client, f"pipe-{i}")) - tasks.append(task) - - # Wait for all pipelines to complete - results_list = await asyncio.gather(*tasks) - - # Flatten results (each pipeline returns multiple RequestResults) - self.results = [] - for pipeline_results in results_list: - self.results.extend(pipeline_results) - - async def _run_single_pipeline( - self, client: BenchmarkClient, pipeline_id: str - ) -> list[RequestResult]: - """ - Run a single simulated RAG pipeline. - - Flow: - 1. Embedding Request (Query) - 2. Simulated Vector DB Retrieval (Sleep) - 3. LLM Request (Generation) - """ - results = [] - - # Step 1: Embedding - emb_start = time.perf_counter() - emb_res = await client.send_embedding_request( - f"{pipeline_id}-step1-emb", - ["Simulated user query for RAG pipeline"], - self.config.embedding_model.name, - ) - results.append(emb_res) - - if not emb_res.success: - return results # Stop if step 1 fails - - # Step 2: Simulated Retrieval (e.g., 50ms - 200ms latency) - # We don't record a RequestResult for this internal step, but it affects timing - retrieve_delay = random.uniform(0.05, 0.2) - await asyncio.sleep(retrieve_delay) - - # Step 3: LLM Generation - # Context is "retrieved" chunks + query - prompt = "Context: ...retrieved data...\nQuery: Simulated query\nAnswer:" - llm_res = await client.send_llm_request( - f"{pipeline_id}-step3-llm", prompt, self.config.llm_model.name - ) - results.append(llm_res) - - # Record End-to-End Pipeline Metric (as a special RequestResult) - e2e_latency = (time.perf_counter() - emb_start) * 1000 - e2e_res = RequestResult( - request_id=f"{pipeline_id}-e2e", - request_type="pipeline_e2e", - start_time=emb_start, - end_time=time.perf_counter(), - latency_ms=e2e_latency, - success=llm_res.success, - tokens_in=emb_res.tokens_in + llm_res.tokens_in, - tokens_out=llm_res.tokens_out, - ) - results.append(e2e_res) - - return results - - def _generate_arrival_times(self, n: int, rate: float) -> list[float]: - import numpy as np - - # Poisson arrival - intervals = np.random.exponential(1.0 / rate, n) - return list(np.cumsum(intervals)) - - def _run_warmup(self) -> None: - pass # Skip warmup for now or implement similar logic - - def _teardown_impl(self) -> None: - pass diff --git a/benchmark/experiments/exp_5_2_control_plane.py b/benchmark/experiments/exp_5_2_control_plane.py deleted file mode 100644 index 7d94e2c066..0000000000 --- a/benchmark/experiments/exp_5_2_control_plane.py +++ /dev/null @@ -1,103 +0,0 @@ -import asyncio -import time - -from sage.benchmark.benchmark_sage.experiments.base_experiment import BaseExperiment -from sage.benchmark.benchmark_sage.experiments.common import ( - BenchmarkClient, - WorkloadGenerator, -) - - -class ControlPlaneExperiment(BaseExperiment): - """ - Experiment for Control Plane Effectiveness. - - Evaluates the performance of the unified control plane under mixed workloads. - """ - - def _setup_impl(self) -> None: - """Setup experiment.""" - self.workload_generator = WorkloadGenerator( - llm_ratio=self.config.workload.llm_ratio, seed=self.config.workload.seed - ) - - def _run_impl(self) -> None: - """Run the experiment workload.""" - asyncio.run(self._run_async()) - - async def _run_async(self) -> None: - """Run async workload.""" - config = self.config - workload = self.workload_generator - - # Generate requests - requests_data = [] - for i in range(config.workload.total_requests): - req_type, params = workload.generate_request(f"req-{i}") - requests_data.append((req_type, params)) - - # Generate arrival times - arrival_times = workload.generate_arrival_times( - config.workload.total_requests, - config.workload.request_rate, - config.workload.arrival_pattern, - ) - - async with BenchmarkClient(config.gateway_url) as client: - tasks = [] - start_time = time.perf_counter() - - for i, (req_type, params) in enumerate(requests_data): - # Wait for arrival time - target_time = start_time + arrival_times[i] - now = time.perf_counter() - if target_time > now: - await asyncio.sleep(target_time - now) - - # Send request - req_id = f"req-{i}" - if req_type == "llm": - task = asyncio.create_task( - client.send_llm_request(req_id, params["prompt"], config.llm_model.name) - ) - else: - task = asyncio.create_task( - client.send_embedding_request( - req_id, params["texts"], config.embedding_model.name - ) - ) - tasks.append(task) - - # Wait for all tasks - self.results = await asyncio.gather(*tasks) - - def _run_warmup(self) -> None: - """Run warmup requests.""" - asyncio.run(self._run_warmup_async()) - - async def _run_warmup_async(self) -> None: - """Run async warmup.""" - config = self.config - workload = self.workload_generator - - async with BenchmarkClient(config.gateway_url) as client: - tasks = [] - for i in range(config.workload.warmup_requests): - req_type, params = workload.generate_request(f"warmup-{i}") - if req_type == "llm": - tasks.append( - client.send_llm_request( - f"warmup-{i}", params["prompt"], config.llm_model.name - ) - ) - else: - tasks.append( - client.send_embedding_request( - f"warmup-{i}", params["texts"], config.embedding_model.name - ) - ) - - await asyncio.gather(*tasks) - - def _teardown_impl(self) -> None: - pass diff --git a/benchmark/experiments/exp_5_3_isolation.py b/benchmark/experiments/exp_5_3_isolation.py deleted file mode 100644 index 53e1fc3ad5..0000000000 --- a/benchmark/experiments/exp_5_3_isolation.py +++ /dev/null @@ -1,96 +0,0 @@ -import asyncio -import time - -from sage.benchmark.benchmark_sage.experiments.base_experiment import BaseExperiment -from sage.benchmark.benchmark_sage.experiments.common import ( - BenchmarkClient, - WorkloadGenerator, -) - - -class IsolationExperiment(BaseExperiment): - """ - Experiment for Multi-tenant Isolation and Fairness. - - Simulates a "noisy neighbor" scenario where a high-throughput batch workload - competes with a latency-sensitive interactive workload. - """ - - def _setup_impl(self) -> None: - # Generator for "Interactive" user (Latency sensitive) - self.interactive_gen = WorkloadGenerator(llm_ratio=1.0, seed=42) - # Generator for "Batch" user (Throughput focused) - self.batch_gen = WorkloadGenerator(llm_ratio=0.5, seed=99) - - def _run_impl(self) -> None: - asyncio.run(self._run_async()) - - async def _run_async(self) -> None: - config = self.config - - # Scenario: - # User A (Interactive): Low rate (e.g., 5 req/s), expects low latency. - # User B (Batch): High rate (e.g., 50 req/s), floods the system. - - interactive_rate = 5.0 - batch_rate = config.workload.request_rate # Main rate controls the noise level - - duration = 30 # seconds - - async with BenchmarkClient(config.gateway_url) as client: - tasks = [] - - # Launch Interactive User Loop - tasks.append( - asyncio.create_task( - self._run_user_loop( - client, "interactive", interactive_rate, duration, self.interactive_gen - ) - ) - ) - - # Launch Batch User Loop - tasks.append( - asyncio.create_task( - self._run_user_loop(client, "batch", batch_rate, duration, self.batch_gen) - ) - ) - - results_list = await asyncio.gather(*tasks) - - self.results = [] - for user_results in results_list: - self.results.extend(user_results) - - async def _run_user_loop(self, client, user_id, rate, duration, generator): - results = [] - start_time = time.perf_counter() - req_idx = 0 - - while time.perf_counter() - start_time < duration: - # Poisson arrival - await asyncio.sleep(1.0 / rate) # Simple constant rate for now to ensure pressure - - req_type, params = generator.generate_request(f"{user_id}-{req_idx}") - - # Add metadata to track which user this was - # Note: In a real system, we'd pass a user-id header. - - if req_type == "llm": - res = await client.send_llm_request( - f"{user_id}-{req_idx}", params["prompt"], self.config.llm_model.name - ) - else: - res = await client.send_embedding_request( - f"{user_id}-{req_idx}", params["texts"], self.config.embedding_model.name - ) - - # Tag result with user_id for analysis - res.metadata["user_id"] = user_id - results.append(res) - req_idx += 1 - - return results - - def _teardown_impl(self) -> None: - pass diff --git a/benchmark/experiments/exp_5_4_scalability.py b/benchmark/experiments/exp_5_4_scalability.py deleted file mode 100644 index cbc0f2d6ce..0000000000 --- a/benchmark/experiments/exp_5_4_scalability.py +++ /dev/null @@ -1,13 +0,0 @@ -from sage.benchmark.benchmark_sage.experiments.control_plane_exp import ( - ControlPlaneExperiment, -) - - -class ScalabilityExperiment(ControlPlaneExperiment): - """ - Experiment for Scalability. - - Focuses on throughput and latency under high load. - """ - - pass diff --git a/benchmark/experiments/exp_5_5_heterogeneity.py b/benchmark/experiments/exp_5_5_heterogeneity.py deleted file mode 100644 index f678acb0e5..0000000000 --- a/benchmark/experiments/exp_5_5_heterogeneity.py +++ /dev/null @@ -1,72 +0,0 @@ -import asyncio -import time - -from sage.benchmark.benchmark_sage.experiments.base_experiment import BaseExperiment -from sage.benchmark.benchmark_sage.experiments.common import ( - BenchmarkClient, - WorkloadGenerator, -) - - -class HeterogeneityExperiment(BaseExperiment): - """ - Experiment for Heterogeneous Hardware Support. - - Demonstrates the system's ability to utilize CPU nodes for specific tasks - (e.g., Embeddings) to offload GPUs for LLM tasks. - """ - - def _setup_impl(self) -> None: - self.workload_generator = WorkloadGenerator( - llm_ratio=self.config.workload.llm_ratio, seed=self.config.workload.seed - ) - - def _run_impl(self) -> None: - asyncio.run(self._run_async()) - - async def _run_async(self) -> None: - config = self.config - workload = self.workload_generator - - # In a real run, this would target a specific gateway configuration. - # Here we run the workload and the user is expected to have configured - # the backend with CPU offloading enabled for Embeddings. - - requests_data = [] - for i in range(config.workload.total_requests): - req_type, params = workload.generate_request(f"req-{i}") - requests_data.append((req_type, params)) - - arrival_times = workload.generate_arrival_times( - config.workload.total_requests, - config.workload.request_rate, - config.workload.arrival_pattern, - ) - - async with BenchmarkClient(config.gateway_url) as client: - tasks = [] - start_time = time.perf_counter() - - for i, (req_type, params) in enumerate(requests_data): - target_time = start_time + arrival_times[i] - now = time.perf_counter() - if target_time > now: - await asyncio.sleep(target_time - now) - - req_id = f"req-{i}" - if req_type == "llm": - task = asyncio.create_task( - client.send_llm_request(req_id, params["prompt"], config.llm_model.name) - ) - else: - task = asyncio.create_task( - client.send_embedding_request( - req_id, params["texts"], config.embedding_model.name - ) - ) - tasks.append(task) - - self.results = await asyncio.gather(*tasks) - - def _teardown_impl(self) -> None: - pass diff --git a/benchmark/experiments/plotting.py b/benchmark/experiments/plotting.py deleted file mode 100644 index d825a88466..0000000000 --- a/benchmark/experiments/plotting.py +++ /dev/null @@ -1,126 +0,0 @@ -from pathlib import Path -from typing import Any - -import matplotlib.pyplot as plt -import numpy as np - - -class Plotter: - """ - Utility class for generating publication-quality plots for SAGE experiments. - """ - - def __init__(self, style: str = "seaborn-v0_8-paper"): - try: - plt.style.use(style) - except Exception: - pass # Fallback to default - - # Set common font sizes - plt.rcParams.update( - { - "font.size": 12, - "axes.labelsize": 14, - "axes.titlesize": 16, - "xtick.labelsize": 12, - "ytick.labelsize": 12, - "legend.fontsize": 12, - "figure.titlesize": 18, - } - ) - - def plot_latency_cdf( - self, results: list[dict[str, Any]], output_path: Path, title: str = "Latency CDF" - ): - """Plot Cumulative Distribution Function of latencies.""" - plt.figure(figsize=(8, 5)) - - for res in results: - latencies = np.sort([r["latency_ms"] for r in res["raw_results"] if r["success"]]) - y = np.arange(1, len(latencies) + 1) / len(latencies) - plt.plot(latencies, y, label=res.get("config_name", "Experiment"), linewidth=2) - - plt.xlabel("Latency (ms)") - plt.ylabel("CDF") - plt.title(title) - plt.grid(True, linestyle="--", alpha=0.7) - plt.legend() - plt.tight_layout() - plt.savefig(output_path) - plt.close() - - def plot_throughput_vs_latency(self, results: list[dict[str, Any]], output_path: Path): - """Plot Throughput vs Latency curve.""" - plt.figure(figsize=(8, 5)) - - throughputs = [r["throughput_rps"] for r in results] - p99_latencies = [r["latency_p99_ms"] for r in results] - labels = [r.get("config_name", "") for r in results] - - plt.plot(throughputs, p99_latencies, "o-", linewidth=2, markersize=8) - - for i, txt in enumerate(labels): - plt.annotate( - txt, (throughputs[i], p99_latencies[i]), xytext=(5, 5), textcoords="offset points" - ) - - plt.xlabel("Throughput (req/s)") - plt.ylabel("p99 Latency (ms)") - plt.title("Throughput vs Latency") - plt.grid(True, linestyle="--", alpha=0.7) - plt.tight_layout() - plt.savefig(output_path) - plt.close() - - def plot_scalability_bar(self, results: list[dict[str, Any]], output_path: Path): - """Plot scalability bar chart.""" - plt.figure(figsize=(10, 6)) - - configs = [str(r["config"]["hardware"].get("gpus", "")) + " GPUs" for r in results] - throughputs = [r["throughput_rps"] for r in results] - - bars = plt.bar(configs, throughputs, color="skyblue", edgecolor="black") - - # Add value labels - for bar in bars: - height = bar.get_height() - plt.text( - bar.get_x() + bar.get_width() / 2.0, - height, - f"{height:.1f}", - ha="center", - va="bottom", - ) - - plt.xlabel("Configuration") - plt.ylabel("Throughput (req/s)") - plt.title("System Scalability") - plt.grid(axis="y", linestyle="--", alpha=0.7) - plt.tight_layout() - plt.savefig(output_path) - plt.close() - - def plot_timeline(self, raw_results: list[dict[str, Any]], output_path: Path): - """Plot request timeline (waterfall chart).""" - plt.figure(figsize=(12, 6)) - - # Sort by start time - sorted_results = sorted(raw_results, key=lambda x: x["start_time"]) - # Take a slice if too many - if len(sorted_results) > 100: - sorted_results = sorted_results[:100] - - start_time_base = sorted_results[0]["start_time"] - - for i, req in enumerate(sorted_results): - start = req["start_time"] - start_time_base - duration = req["latency_ms"] / 1000.0 - color = "blue" if req["request_type"] == "llm" else "green" - plt.barh(i, duration, left=start, color=color, alpha=0.6, edgecolor="none") - - plt.xlabel("Time (s)") - plt.ylabel("Request ID") - plt.title("Request Timeline (First 100)") - plt.tight_layout() - plt.savefig(output_path) - plt.close() diff --git a/benchmark/experiments/run_all.sh b/benchmark/experiments/run_all.sh deleted file mode 100755 index 0de0c68447..0000000000 --- a/benchmark/experiments/run_all.sh +++ /dev/null @@ -1,96 +0,0 @@ -#!/bin/bash -# ============================================================================= -# SAGE 分布式调度策略评测 - 运行所有实验 -# ============================================================================= - -set -e - -SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" -cd "$SCRIPT_DIR" - -# 颜色输出 -RED='\033[0;31m' -GREEN='\033[0;32m' -YELLOW='\033[1;33m' -NC='\033[0m' # No Color - -echo -e "${GREEN}============================================${NC}" -echo -e "${GREEN}SAGE Distributed Scheduling Benchmark${NC}" -echo -e "${GREEN}============================================${NC}" - -# 检查参数 -QUICK_MODE="" -if [[ "$1" == "--quick" ]]; then - QUICK_MODE="--quick" - echo -e "${YELLOW}Running in QUICK mode${NC}" -fi - -# 检查 JobManager 是否运行 -check_jobmanager() { - if ! nc -z localhost 19001 2>/dev/null; then - echo -e "${RED}Warning: JobManager not running on port 19001${NC}" - echo "Start it with: sage jobmanager start" - echo "" - read -p "Continue anyway? (y/n) " -n 1 -r - echo - if [[ ! $REPLY =~ ^[Yy]$ ]]; then - exit 1 - fi - fi -} - -# 运行实验1 -run_exp1() { - echo -e "\n${GREEN}[Experiment 1] Single Node vs Multi Node${NC}" - echo "==========================================" - cd "$SCRIPT_DIR/exp1_single_vs_multi" - python run_experiment.py $QUICK_MODE -} - -# 运行'ENDOFFILE'2 -run_exp2() { - echo -e "\n${GREEN}[Experiment 2] High Load Parallel Scheduling${NC}" - echo "==============================================" - cd "$SCRIPT_DIR/exp2_high_load_parallel" - python run_experiment.py $QUICK_MODE -} - -# 运行实验3 -run_exp3() { - echo -e "\n${GREEN}[Experiment 3] Latency and Throughput${NC}" - echo "=======================================" - cd "$SCRIPT_DIR/exp3_latency_throughput" - python run_experiment.py $QUICK_MODE -} - -# 主逻辑 -main() { - echo "" - echo "Select experiments to run:" - echo " 1) Experiment 1: Single vs Multi Node" - echo " 2) Experiment 2: High Load Parallel" - echo " 3) Experiment 3: Latency & Throughput" - echo " a) All experiments" - echo " q) Quit" - echo "" - read -p "Enter choice [1/2/3/a/q]: " choice - - case $choice in - 1) check_jobmanager; run_exp1 ;; - 2) check_jobmanager; run_exp2 ;; - 3) check_jobmanager; run_exp3 ;; - a) - check_jobmanager - run_exp1 - run_exp2 - run_exp3 - ;; - q) echo "Exiting."; exit 0 ;; - *) echo "Invalid choice"; exit 1 ;; - esac - - echo -e "\n${GREEN}All selected experiments completed.${NC}" - echo "Results are in each experiment's results/ directory." -} - -main diff --git a/benchmark/experiments/tool_use_agent/DESIGN.md b/benchmark/experiments/tool_use_agent/DESIGN.md deleted file mode 100644 index ee978e18ae..0000000000 --- a/benchmark/experiments/tool_use_agent/DESIGN.md +++ /dev/null @@ -1,282 +0,0 @@ -# Tool Use Agent Pipeline - -SAGE 框架的智能体工具调用 Pipeline，集成了 ReAct 推理、记忆服务和上下文压缩。 - -## 架构图 - -``` -+-----------------------------------------------------------------------------------+ -| Tool Use Agent Pipeline | -+-----------------------------------------------------------------------------------+ -| | -| +-------------+ +--------------+ +--------------+ +-------------------+ | -| | UserQuery |-->| ToolSelector |-->| ToolExecutor |-->| ResponseGenerator | | -| | Source | | | | | | | | -| +-------------+ +--------------+ +--------------+ +-------------------+ | -| | | | | | -| v v v v | -| +-------------------------------------------------------------------------+ | -| | LocalEnvironment | | -| | +-------------------------------------------------------------------+ | | -| | | Registered Services (env.register_service) | | | -| | | | | | -| | | +----------------+ +-----------------+ +------------------+ | | | -| | | | memory_service | | context_service | | vector_db | | | | -| | | | (sage-mem) | | (sage-refiner) | | (sage-db) | | | | -| | | | | | | | | | | | -| | | | - retrieve() | | - manage_ | | - search() | | | | -| | | | - insert() | | context() | | - add_batch() | | | | -| | | | - delete() | | - add_to_ | | | | | | -| | | | | | history() | | | | | | -| | | +----------------+ +-----------------+ +------------------+ | | | -| | +-------------------------------------------------------------------+ | | -| +-------------------------------------------------------------------------+ | -| | -| +-------------------------------------------------------------------------+ | -| | Tool Registry | | -| | | | -| | +--------------+ +------------+ +------------+ +--------------+ +----+ | | -| | |vector_search | | web_search | | calculator | |memory_search | |email | | -| | | Uses: | | Simulated | | Safe eval | | Uses: | |search| | -| | | vector_db | | | | | | memory_svc | | | | | -| | +--------------+ +------------+ +------------+ +--------------+ +----+ | | -| +-------------------------------------------------------------------------+ | -| | -| +-------------------------------------------------------------------------+ | -| | External Services (Remote LLM) | | -| | | | -| | UnifiedInferenceClient --> http://localhost:8901/v1 (Qwen2.5-7B) | | -| | --> http://localhost:8090/v1 (BGE Embedding) | | -| +-------------------------------------------------------------------------+ | -+-----------------------------------------------------------------------------------+ -``` - -## ReAct 推理流程 - -``` -+-----------------------------------------------------------------------------------+ -| ReAct Reasoning Flow | -+-----------------------------------------------------------------------------------+ -| | -| Query --> [Thought] --> [Action] --> [Observation] --> [Reflection] --> Response| -| 分析需求 选择工具 执行并观察 反思总结 | -| | -+-----------------------------------------------------------------------------------+ -``` - -## 数据流 - -``` - | - v -+------------------+ -| UserQuerySource | 创建 AgentState，包含 query, session_id -+--------+---------+ - | - v -+------------------+ 1. 调用 memory_service.retrieve() 获取历史 -| ToolSelector | 2. 调用 context_service.manage_context() 压缩上下文 -| (ReAct Reasoning)| 3. LLM 推理选择工具 (或关键词 fallback) -| Thought+Action | 4. 记录 Thought 和 Action 到 AgentState -+--------+---------+ - | - v -+------------------+ 1. 注入 service_caller 到工具 -| ToolExecutor | 2. 执行选中的工具 -| (Observation) | 3. 工具通过 call_service() 访问服务 -+--------+---------+ 4. 记录 Observation 到 AgentState - | - v -+------------------+ 1. 使用 LLM 生成最终回答 -|ResponseGenerator | 2. 添加 Reflection 反思 -| (Reflection) | 3. 调用 memory_service.insert() 保存交互 -+--------+---------+ 4. 调用 context_service.add_to_history() - | - v -+------------------+ -| ResponseSink | 格式化输出 Response + ReAct Trace -+------------------+ -``` - -## 服务调用关系 - -| Operator | 调用服务 | 方法 | 用途 | -| ----------------- | ------------------------- | ---------------- | ------------------ | -| ToolSelector | memory_service | retrieve() | 获取相关历史记忆 | -| ToolSelector | context_service | manage_context() | 压缩长上下文 | -| ToolExecutor | (通过工具) vector_db | search() | 向量相似度搜索 | -| ToolExecutor | (通过工具) memory_service | retrieve() | 搜索记忆 | -| ResponseGenerator | memory_service | insert() | 保存本次交互到记忆 | -| ResponseGenerator | context_service | add_to_history() | 更新对话历史 | - -## 运行方法 - -### 前'EOF' - -```bash -# 设置远程 LLM 服务 (可选，不设置则使用 fallback 模式) -export SAGE_CHAT_BASE_URL="http://localhost:8901/v1" -export SAGE_CHAT_MODEL="Qwen/Qwen2.5-7B-Instruct" -export SAGE_EMBEDDING_BASE_URL="http://localhost:8090/v1" -export SAGE_EMBEDDING_MODEL="BAAI/bge-large-zh-v1.5" -``` - -### 方式 1: 默认 Demo 模式 - -``` - # Extract action -``` - -```bash -cd examples/tutorials/L3-libs/agents/tool_use_agent -python pipeline.py -``` - -### 方式 2: 自定义查询 - -```bash -# 单个查询 -python pipeline.py --query "What is SAGE framework?" - -# 多个查询 -python pipeline.py --query "What is SAGE?" --query "Calculate 2+2" --query "Search memory docs" -``` - -``` - # Extract action (无服务) -``` - -```bash -python pipeline.py --no-services --query "Calculate 100 * 5" -``` - -### 方式 4: 静默模式 - -```bash -python pipeline.py --quiet --query "Hello" -``` - -### 方式 5: 交互模式 - -```bash -python pipeline.py --interactive -``` - -: - -- 输入查询并按 Enter 执行 -- `clear` - 清除记忆，开始新会话 -- `quit` / `exit` / `q` - 退出 - -### 方式 6: Python API 调用 - -```python -import sys -sys.path.insert(0, 'examples/tutorials/L3-libs/agents/tool_use_agent') - -from pipeline import run_tool_use_demo - -# 运行 demo -run_tool_use_demo( - queries=["What is SAGE?", "Calculate 1+2"], - verbose=True, - register_services=True, -) -``` - -### 方式 7: 测试模式 - -```bash -SAGE_TEST_MODE=true python pipeline.py -``` - -## 文件结构 - -``` -tool_use_agent/ - __init__.py # 包入口，导出 run_tool_use_demo, run_interactive_mode - models.py # 数据模型: AgentState, ReActStep, ToolCallRequest/Result - agent_tools.py # 工具定义: BaseTool, ToolRegistry, 5个内置工具 - operators.py # Pipeline 算子: Source, Selector, Executor, Generator, Sink - pipeline.py # 主程序: , CLI, 入口函数 - README.md # 本文档 -``` - -## 可用工具 - -| 工具名 | 描述 | 服务依赖 | -| ------------- | ------------------ | -------------- | -| vector_search | 语义搜索 SAGE 知识 | vector_db | -| web_search | 网络搜索 (模拟) | 无 | -| calculator | 数学表达式计算 | 无 | -| memory_search | 搜索智能体记忆 | memory_service | -| email_search | 邮件搜索 (模拟) | 无 | - -## 环境变量 - -| 变量 | 描述 | 默认值 | -| ----------------------- | --------------------- | ------------------------ | -| SAGE_CHAT_BASE_URL | LLM API 地址 | 无 (使用 fallback) | -| SAGE_CHAT_MODEL | LLM 模型名 | Qwen/Qwen2.5-7B-Instruct | -| SAGE_EMBEDDING_BASE_URL | Embedding API 地址 | 无 | -| SAGE_EMBEDDING_MODEL | Embedding 模型名 | BAAI/bge-large-zh-v1.5 | -| SAGE_TEST_MODE | 测试模式 (限制查询数) | false | - -## 常见'EOF' - -### Q: 服务报错 "Service queue not available" - -A: `--no-services` 参数，但工具尝试访问服务。请移除该参数或使用不依赖服务的工具 - -### Q: LLM 报错 "No LLM backend available" - -A: 未配'EOF' LLM 环境变量，Pipeline 会自动使用 fallback 关键词匹配模式。 - -### Q: vector_db 报错 "could not convert string to float" - -A: SageDBService 期望向量输入，Pipeline 会自动使用内置关键词搜索 fallback。 - -## 扩展开发 - -### 添加新工具 - -```python -# 在 agent_tools.py 中 -class MyTool(BaseTool): - name = "my_tool" - description = "My custom tool description" - input_schema = { - "type": "object", - "properties": { - "param": {"type": "string", "description": "Parameter description"} - }, - "required": ["param"] - } - - def call(self, arguments: dict) -> dict: - # 访问服务 - result = self.call_service("my_service", method="my_method", **arguments) - return {"success": True, "result": result} - -# 注册到 create_default_registry() -def create_default_registry(): - registry = ToolRegistry() - registry.register(MyTool()) - # ... - return registry -``` - -### 添加新服务 - -```python -# 在 pipeline.py 中 -def register_my_service(env: LocalEnvironment) -> bool: - from sage.middleware.components.my_module import MyService - - env.register_service( - "my_service", - MyService, - config={"key": "value"}, - ) - return True -``` diff --git a/benchmark/experiments/tool_use_agent/__init__.py b/benchmark/experiments/tool_use_agent/__init__.py deleted file mode 100644 index 6106cba62f..0000000000 --- a/benchmark/experiments/tool_use_agent/__init__.py +++ /dev/null @@ -1,27 +0,0 @@ -""" -Tool Use Agent Package -====================== - -A modular Agent Pipeline with tool calling capabilities, integrating: -- sage-mem: Hierarchical memory service (STM/MTM/LTM) -- sage-refiner: Context compression service -- sage-db: Vector search service (RAG) -- ReAct planning: Reasoning + Acting with reflection - -Pipeline Architecture: - UserQuerySource -> ToolSelector -> ToolExecutor -> ResponseGenerator -> ResponseSink - -Usage: - from examples.tutorials.L3_libs.agents.tool_use_agent import run_tool_use_demo - run_tool_use_demo() - -Or via command line: - python -m examples.tutorials.L3_libs.agents.tool_use_agent.pipeline -""" - -from .pipeline import run_interactive_mode, run_tool_use_demo - -__all__ = [ - "run_tool_use_demo", - "run_interactive_mode", -] diff --git a/benchmark/experiments/tool_use_agent/agent_tools.py b/benchmark/experiments/tool_use_agent/agent_tools.py deleted file mode 100644 index b448fe4d27..0000000000 --- a/benchmark/experiments/tool_use_agent/agent_tools.py +++ /dev/null @@ -1,562 +0,0 @@ -""" -Tool Definitions for Tool Use Agent Pipeline -============================================= - -Defines tools that can be called by the agent: -- BaseTool: Abstract base class for all tools -- ToolRegistry: Registry for managing available tools -- VectorSearchTool: RAG retrieval using vector_db service -- WebSearchTool: Simulated web search -- CalculatorTool: Mathematical calculations -- MemorySearchTool: Search conversation history -- EmailSearchTool: Simulated email search - -Tools follow MCP (Model Context Protocol) style with: -- name: Unique identifier -- description: What the tool does -- input_schema: JSON schema for arguments -- call(): Execute the tool -""" - -from __future__ import annotations - -import math -import re -from abc import ABC, abstractmethod -from typing import TYPE_CHECKING, Any - -if TYPE_CHECKING: - pass - - -class BaseTool(ABC): - """ - Base class for all tools - MCP style. - - Tools can optionally receive service references for accessing - pipeline services like vector_db, memory_service, etc. - """ - - name: str = "" - description: str = "" - input_schema: dict[str, Any] = {} - - def __init__(self, services: dict[str, Any] | None = None, service_caller: Any = None): - """ - Initialize tool with optional service references. - - Args: - services: Dict mapping service names to service instances (legacy) - service_caller: Callable to call service methods via pipeline - """ - self.services = services or {} - self._service_caller = service_caller - - def get_service(self, name: str) -> Any: - """Get a service by name (legacy method)""" - return self.services.get(name) - - def call_service(self, service_name: str, method: str, **kwargs) -> Any: - """Call a service method via pipeline callback""" - if self._service_caller: - try: - return self._service_caller(service_name, method=method, **kwargs) - except Exception as e: - print(f"[{self.name}] Service call error: {e}") - return None - - @abstractmethod - def call(self, arguments: dict[str, Any]) -> dict[str, Any]: - """ - Execute the tool with given arguments. - - Args: - arguments: Tool arguments matching input_schema - - Returns: - Dict with 'success' bool and result/error - """ - pass - - -class VectorSearchTool(BaseTool): - """ - Vector search tool for RAG retrieval. - - Uses vector_db service for semantic similarity search. - Falls back to keyword matching if service unavailable. - """ - - name = "vector_search" - description = "Search the SAGE knowledge base for relevant documentation and information using semantic similarity." - input_schema = { - "type": "object", - "properties": { - "query": { - "type": "string", - "description": "The search query to find relevant documents", - }, - "top_k": { - "type": "integer", - "default": 3, - "description": "Number of results to return", - }, - }, - "required": ["query"], - } - - # Fallback knowledge base when vector_db service unavailable - KNOWLEDGE_BASE = [ - { - "id": "doc1", - "title": "SAGE Framework Overview", - "content": "SAGE is a Python framework for building AI/LLM data processing pipelines with declarative dataflow. It consists of 5 layers: L1-Common (foundation), L2-Platform (services), L3-Kernel/Libs (core algorithms), L4-Middleware (operators), L5-CLI/Tools (interface).", - "tags": ["overview", "architecture", "layers"], - }, - { - "id": "doc2", - "title": "SAGE Installation Guide", - "content": "To install SAGE, run ./quickstart.sh --dev --yes for development setup. Prerequisites: Python 3.10+, Git, build-essential, cmake, pkg-config, libopenblas-dev, liblapack-dev. The installation takes 10-25 minutes.", - "tags": ["installation", "setup", "quickstart"], - }, - { - "id": "doc3", - "title": "Pipeline Architecture", - "content": "SAGE pipelines use SourceFunction, MapFunction, and SinkFunction operators connected via LocalEnvironment. Data flows from Source through Map operators to Sink. Services can be registered with env.register_service() and accessed via self.call_service().", - "tags": ["pipeline", "operators", "dataflow"], - }, - { - "id": "doc4", - "title": "Memory Services", - "content": "sage-mem provides HierarchicalMemoryService with three tiers: STM (short-term), MTM (medium-term), LTM (long-term). Use MemoryServiceFactory.create_instance() to create services. Services support insert(), retrieve(), and delete() operations.", - "tags": ["memory", "sage-mem", "hierarchical"], - }, - { - "id": "doc5", - "title": "Context Compression", - "content": "sage-refiner provides ContextService for automatic context compression. Use manage_context() to compress long conversations. Supports multiple algorithms: simple, llmlingua2, provence, reform. Configure with max_context_length and auto_compress settings.", - "tags": ["refiner", "compression", "context"], - }, - ] - - def _get_embedding(self, text: str) -> list[float] | None: - """Convert text to embedding vector using UnifiedInferenceClient""" - try: - from sage.common.components.sage_llm import UnifiedInferenceClient - - client = UnifiedInferenceClient.create() - # embed() 返回 list[list[float]]，取第一 - embeddings = client.embed([text]) - if isinstance(embeddings, list) and len(embeddings) > 0: - return embeddings[0] - except Exception as e: - print(f"[VectorSearchTool] Embedding error: {e}") - return None - - def call(self, arguments: dict[str, Any]) -> dict[str, Any]: - query = arguments.get("query", "") - top_k = arguments.get("top_k", 3) - - # Try to use vector_db service with embedding - try: - # Step 1: Convert query text to embedding vector - query_embedding = self._get_embedding(query) - - if query_embedding is not None: - # Step 2: Search using vector - import numpy as np - - query_vec = np.array(query_embedding, dtype=np.float32) - results = self.call_service("vector_db", method="search", query=query_vec, k=top_k) - - if results is not None: - # Format results with metadata - formatted = [] - for r in results: - meta = r.get("metadata", {}) - formatted.append( - { - "id": r.get("id"), - "title": meta.get("title", ""), - "content": meta.get("text", meta.get("content", "")), - "score": r.get("score", 0), - } - ) - return { - "success": True, - "documents": formatted, - "source": "vector_db", - "query": query, - } - except Exception as e: - print(f"[VectorSearchTool] Service error: {e}, using fallback") - - # Fallback: keyword-based search - query_lower = query.lower() - query_words = set(query_lower.split()) - - scored_docs = [] - for doc in self.KNOWLEDGE_BASE: - content_lower = doc["content"].lower() - title_lower = doc["title"].lower() - - # Score based on word overlap - score = 0 - for word in query_words: - if len(word) > 2: - if word in content_lower: - score += 2 - if word in title_lower: - score += 3 - for tag in doc["tags"]: - if word in tag: - score += 2 - - if score > 0: - scored_docs.append( - { - "id": doc["id"], - "title": doc["title"], - "content": doc["content"], - "score": score, - } - ) - - # Sort by score and take top_k - scored_docs.sort(key=lambda x: x["score"], reverse=True) - results = scored_docs[:top_k] - - return { - "success": True, - "documents": results, - "source": "fallback_keyword", - "query": query, - } - - -class WebSearchTool(BaseTool): - """ - Web search tool - simulates search engine results. - - In production, this would integrate with real search APIs. - """ - - name = "web_search" - description = "Search the web for general information. Use for current events, external knowledge, or topics not in the knowledge base." - input_schema = { - "type": "object", - "properties": { - "query": { - "type": "string", - "description": "The search query", - }, - "max_results": { - "type": "integer", - "default": 5, - "description": "Maximum number of results", - }, - }, - "required": ["query"], - } - - def call(self, arguments: dict[str, Any]) -> dict[str, Any]: - query = arguments.get("query", "") - max_results = arguments.get("max_results", 5) - - # Simulate web search results - mock_results = [ - { - "title": f"Result {i + 1} for: {query}", - "url": f"https://example.com/result{i + 1}", - "snippet": f"This is a simulated search result about {query}. In production, this would return real web search results.", - } - for i in range(min(max_results, 5)) - ] - - return { - "success": True, - "results": mock_results, - "query": query, - "note": "Simulated results - integrate with real search API for production", - } - - -class CalculatorTool(BaseTool): - """ - Calculator tool for mathematical expressions. - - Supports basic arithmetic, power, sqrt, and common math functions. - """ - - name = "calculator" - description = "Perform mathematical calculations. Supports arithmetic (+, -, *, /, **), sqrt, abs, and basic math functions." - input_schema = { - "type": "object", - "properties": { - "expression": { - "type": "string", - "description": "Mathematical expression to evaluate (e.g., '15 * 23 + 47', 'sqrt(16)', '2**10')", - }, - }, - "required": ["expression"], - } - - # Safe math functions - SAFE_FUNCTIONS = { - "sqrt": math.sqrt, - "abs": abs, - "pow": pow, - "sin": math.sin, - "cos": math.cos, - "tan": math.tan, - "log": math.log, - "log10": math.log10, - "exp": math.exp, - "floor": math.floor, - "ceil": math.ceil, - "round": round, - "pi": math.pi, - "e": math.e, - } - - def call(self, arguments: dict[str, Any]) -> dict[str, Any]: - expression = arguments.get("expression", "") - - try: - # Sanitize: only allow safe characters and functions - safe_expr = expression - - # Replace common function names - for func_name in self.SAFE_FUNCTIONS: - safe_expr = safe_expr.replace(func_name, f"__{func_name}__") - - # Check for unsafe patterns - if re.search(r"[a-zA-Z_][a-zA-Z0-9_]*(?!_)", safe_expr.replace("__", "")): - # Has identifiers that aren't our safe functions - safe_expr = re.sub(r"[^0-9+\-*/(). \t]", "", expression) - else: - # Restore function names - for func_name in self.SAFE_FUNCTIONS: - safe_expr = safe_expr.replace(f"__{func_name}__", func_name) - - if not safe_expr.strip(): - return {"success": False, "error": "Invalid expression"} - - # Evaluate with safe builtins - result = eval(safe_expr, {"__builtins__": {}}, self.SAFE_FUNCTIONS) - - return { - "success": True, - "expression": expression, - "result": result, - } - except Exception as e: - return { - "success": False, - "expression": expression, - "error": str(e), - } - - -class MemorySearchTool(BaseTool): - """ - Memory search tool for retrieving conversation history. - - Uses memory_service to search past interactions. - """ - - name = "memory_search" - description = "Search past conversations and interactions. Use to recall previous discussions, decisions, or context from earlier in the session." - input_schema = { - "type": "object", - "properties": { - "query": { - "type": "string", - "description": "What to search for in conversation history", - }, - "top_k": { - "type": "integer", - "default": 5, - "description": "Number of results to return", - }, - }, - "required": ["query"], - } - - def call(self, arguments: dict[str, Any]) -> dict[str, Any]: - query = arguments.get("query", "") - top_k = arguments.get("top_k", 5) - - # Try to use memory service - memory_service = self.get_service("memory_service") - if memory_service is not None: - try: - results = memory_service.retrieve(query=query, topk=top_k) - return { - "success": True, - "memories": results, - "source": "memory_service", - "query": query, - } - except Exception as e: - print(f"[MemorySearchTool] Service error: {e}") - - # Fallback: no memories available - return { - "success": True, - "memories": [], - "source": "fallback", - "query": query, - "note": "Memory service not available or empty", - } - - -class EmailSearchTool(BaseTool): - """ - Email search tool - simulated for demo purposes. - """ - - name = "email_search" - description = "Search emails by sender, subject, or content. Use when the user asks about emails or messages." - input_schema = { - "type": "object", - "properties": { - "query": { - "type": "string", - "description": "Search query for emails", - }, - "sender": { - "type": "string", - "description": "Filter by sender email (optional)", - }, - }, - "required": ["query"], - } - - def call(self, arguments: dict[str, Any]) -> dict[str, Any]: - query = arguments.get("query", "") - sender = arguments.get("sender", "") - - # Simulate email search results - mock_emails = [ - { - "id": "email1", - "from": sender or "team@sage-project.com", - "subject": f"RE: {query}", - "snippet": f"Information about {query}. This is a simulated email result.", - "date": "2024-12-20", - }, - { - "id": "email2", - "from": sender or "dev@sage-project.com", - "subject": f"Update on {query}", - "snippet": f"Latest updates regarding {query}. Please review.", - "date": "2024-12-19", - }, - ] - - return { - "success": True, - "emails": mock_emails, - "query": query, - "note": "Simulated results - integrate with email API for production", - } - - -class ToolRegistry: - """ - Registry for managing available tools. - - Supports: - - Registering tools - - Listing available tools - - Getting tool descriptions (for LLM prompt) - - Calling tools by name - """ - - def __init__(self): - self._tools: dict[str, BaseTool] = {} - self._services: dict[str, Any] = {} - self._service_caller: Any = None - - def set_services(self, services: dict[str, Any]) -> None: - """Set service references for all tools (legacy)""" - self._services = services - # Update existing tools - for tool in self._tools.values(): - tool.services = services - - def set_service_caller(self, caller: Any) -> None: - """Set service caller callback for all tools""" - self._service_caller = caller - for tool in self._tools.values(): - tool._service_caller = caller - - def register(self, tool: BaseTool) -> None: - """Register a tool""" - tool.services = self._services - tool._service_caller = self._service_caller - self._tools[tool.name] = tool - - def get(self, name: str) -> BaseTool | None: - """Get a tool by name""" - return self._tools.get(name) - - def list_tools(self) -> list[str]: - """List all registered tool names""" - return list(self._tools.keys()) - - def describe_tools(self) -> list[dict[str, Any]]: - """Get tool descriptions for LLM prompt""" - return [ - { - "name": tool.name, - "description": tool.description, - "input_schema": tool.input_schema, - } - for tool in self._tools.values() - ] - - def describe_tools_text(self) -> str: - """Get tool descriptions as formatted text""" - lines = [] - for tool in self._tools.values(): - lines.append(f"- {tool.name}: {tool.description}") - if tool.input_schema.get("properties"): - for prop, spec in tool.input_schema["properties"].items(): - required = prop in tool.input_schema.get("required", []) - req_str = " (required)" if required else "" - lines.append(f" - {prop}: {spec.get('description', '')}{req_str}") - return "\n".join(lines) - - def call_tool(self, name: str, arguments: dict[str, Any]) -> dict[str, Any]: - """Call a tool by name with arguments""" - tool = self._tools.get(name) - if not tool: - return {"success": False, "error": f"Tool '{name}' not found"} - return tool.call(arguments) - - -def create_default_registry(services: dict[str, Any] | None = None) -> ToolRegistry: - """ - Create registry with default tools. - - Args: - services: Optional dict of service references - - Returns: - ToolRegistry with default tools registered - """ - registry = ToolRegistry() - - if services: - registry.set_services(services) - - # Register default tools - registry.register(VectorSearchTool(services)) - registry.register(WebSearchTool(services)) - registry.register(CalculatorTool(services)) - registry.register(MemorySearchTool(services)) - registry.register(EmailSearchTool(services)) - - return registry diff --git a/benchmark/experiments/tool_use_agent/models.py b/benchmark/experiments/tool_use_agent/models.py deleted file mode 100644 index fafd8dc863..0000000000 --- a/benchmark/experiments/tool_use_agent/models.py +++ /dev/null @@ -1,162 +0,0 @@ -""" -Data Models for Tool Use Agent Pipeline -======================================== - -Defines the core data structures used throughout the pipeline: -- ToolCallRequest: Request to call a specific tool -- ToolCallResult: Result from tool execution -- ReActStep: A single ReAct reasoning step (Thought-Action-Observation) -- AgentState: Complete state flowing through the pipeline -""" - -from __future__ import annotations - -import time -import uuid -from dataclasses import dataclass, field -from enum import Enum -from typing import Any - - -class ReActPhase(Enum): - """ReAct reasoning phases""" - - THOUGHT = "thought" # Reasoning about the current state - ACTION = "action" # Deciding which tool to use - OBSERVATION = "observation" # Result from tool execution - REFLECTION = "reflection" # Self-critique and adjustment - - -@dataclass -class ToolCallRequest: - """Tool call request with arguments""" - - tool_name: str - arguments: dict[str, Any] - reason: str = "" # Why this tool was selected (from ReAct reasoning) - - -@dataclass -class ToolCallResult: - """Tool call result with success/error status""" - - tool_name: str - success: bool - result: Any - error: str | None = None - execution_time: float = 0.0 - - -@dataclass -class ReActStep: - """A single step in ReAct reasoning loop""" - - phase: ReActPhase - content: str - timestamp: float = field(default_factory=time.time) - - def to_dict(self) -> dict[str, Any]: - """Convert to dictionary for serialization""" - return { - "phase": self.phase.value, - "content": self.content, - "timestamp": self.timestamp, - } - - -@dataclass -class AgentState: - """ - Complete agent state flowing through the pipeline. - - This state accumulates information as it passes through each operator: - 1. UserQuerySource: Sets query, session_id, timestamp - 2. ToolSelector: Adds selected_tools, react_trace, memory_context, compressed_context - 3. ToolExecutor: Adds tool_results - 4. ResponseGenerator: Adds response, updates react_trace with reflection - """ - - # Core fields - query: str - session_id: str = field(default_factory=lambda: str(uuid.uuid4())[:8]) - - # Tool selection and execution - selected_tools: list[ToolCallRequest] = field(default_factory=list) - tool_results: list[ToolCallResult] = field(default_factory=list) - - # ReAct reasoning trace - react_trace: list[ReActStep] = field(default_factory=list) - max_react_iterations: int = 3 - current_iteration: int = 0 - - # Memory and context (from services) - memory_context: list[dict[str, Any]] = field(default_factory=list) - compressed_context: str = "" - retrieved_documents: list[dict[str, Any]] = field(default_factory=list) - - # Output - response: str = "" - - # Metadata - metadata: dict[str, Any] = field(default_factory=dict) - timestamp: float = field(default_factory=time.time) - - def add_thought(self, content: str) -> None: - """Add a thought step to ReAct trace""" - self.react_trace.append( - ReActStep( - phase=ReActPhase.THOUGHT, - content=content, - ) - ) - - def add_action(self, content: str) -> None: - """Add an action step to ReAct trace""" - self.react_trace.append( - ReActStep( - phase=ReActPhase.ACTION, - content=content, - ) - ) - - def add_observation(self, content: str) -> None: - """Add an observation step to ReAct trace""" - self.react_trace.append( - ReActStep( - phase=ReActPhase.OBSERVATION, - content=content, - ) - ) - - def add_reflection(self, content: str) -> None: - """Add a reflection step to ReAct trace""" - self.react_trace.append( - ReActStep( - phase=ReActPhase.REFLECTION, - content=content, - ) - ) - - def get_react_trace_str(self) -> str: - """Get formatted ReAct trace as string""" - lines = [] - for step in self.react_trace: - prefix = { - ReActPhase.THOUGHT: "Thought", - ReActPhase.ACTION: "Action", - ReActPhase.OBSERVATION: "Observation", - ReActPhase.REFLECTION: "Reflection", - }.get(step.phase, "Step") - lines.append(f"[{prefix}] {step.content}") - return "\n".join(lines) - - def to_memory_entry(self) -> dict[str, Any]: - """Convert to format suitable for memory storage""" - return { - "session_id": self.session_id, - "query": self.query, - "response": self.response, - "tools_used": [t.tool_name for t in self.selected_tools], - "timestamp": self.timestamp, - "react_trace": [s.to_dict() for s in self.react_trace], - } diff --git a/benchmark/experiments/tool_use_agent/operators.py b/benchmark/experiments/tool_use_agent/operators.py deleted file mode 100644 index 35c77030ff..0000000000 --- a/benchmark/experiments/tool_use_agent/operators.py +++ /dev/null @@ -1,654 +0,0 @@ -""" -Pipeline Operators for Tool Use Agent -====================================== - -Defines the pipeline operators (functions) that process data: -- UserQuerySource: Receives queries, creates AgentState -- ToolSelector: ReAct-style reasoning to select tools -- ToolExecutor: Executes selected tools -- ResponseGenerator: Generates final response with reflection -- ResponseSink: Outputs response - -All operators can access services via self.call_service(). -""" - -from __future__ import annotations - -import json -import os -import re -import time -from typing import TYPE_CHECKING - -from sage.common.core.functions.map_function import MapFunction -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.core.functions.source_function import SourceFunction -from sage.kernel.runtime.communication.packet import StopSignal - -if TYPE_CHECKING: - from .agent_tools import ToolRegistry - -try: - from .models import AgentState, ToolCallRequest, ToolCallResult -except ImportError: - from models import AgentState, ToolCallRequest, ToolCallResult -# AgentState, ToolCallRequest, ToolCallResult - - -class UserQuerySource(SourceFunction): - """ - Source operator that receives user queries and creates AgentState. - - In batch mode, processes a list of predefined queries. - Can be extended for interactive/streaming input. - """ - - def __init__(self, queries: list[str] | None = None, **kwargs): - super().__init__(**kwargs) - self.queries = queries or [ - "What is SAGE framework and how to install it?", - "Calculate 15 * 23 + 47", - "Search for information about memory services in SAGE", - ] - self.current_index = 0 - - def execute(self, data=None) -> AgentState | StopSignal | None: - """Generate next query as AgentState""" - if self.current_index >= len(self.queries): - # Signal end of input - return StopSignal("All queries processed") - - query = self.queries[self.current_index] - self.current_index += 1 - - print(f"\n{'=' * 70}") - print(f"[UserQuerySource] Query {self.current_index}: {query}") - print("=" * 70) - - return AgentState( - query=query, - metadata={ - "query_id": self.current_index, - "timestamp": time.time(), - }, - ) - - -class ToolSelector(MapFunction): - """ - Tool selection operator using ReAct-style reasoning. - - Implements: - 1. Thought: Analyze the query and context - 2. Action: Select appropriate tool(s) - 3. Uses memory_service to retrieve relevant history - 4. Uses context_service to compress context if needed - - Falls back to keyword-based selection if LLM unavailable. - """ - - def __init__(self, tool_registry: ToolRegistry | None = None, **kwargs): - super().__init__(**kwargs) - self.tool_registry = tool_registry - self._llm_client = None - - def _get_llm_client(self): - """Lazy initialization of LLM client""" - if self._llm_client is None: - try: - from sage.common.components.sage_llm import UnifiedInferenceClient - - self._llm_client = UnifiedInferenceClient.create() - except Exception as e: - print(f"[ToolSelector] LLM client unavailable: {e}") - self._llm_client = None - return self._llm_client - - def _retrieve_memory_context(self, state: AgentState) -> list[dict]: - """Retrieve relevant context from memory service""" - try: - # 使用 method 参数直接调用服务方法 - results = self.call_service( - "memory_service", - method="retrieve", - query=state.query, - top_k=3, - ) - return results if results else [] - except Exception as e: - print(f"[ToolSelector] Memory service error: {e}") - return [] - - def _compress_context(self, state: AgentState) -> str: - """Compress context using context service""" - try: - history = [ - {"role": "system", "content": m.get("content", "")} - for m in state.memory_context[:5] - ] - # 使用 参数直接 method - result = self.call_service( - "context_service", - method="manage_context", - query=state.query, - history=history, - ) - if result and "compressed_context" in result: - parts = result["compressed_context"] - return "\n".join(p.get("content", "") for p in parts) - except Exception as e: - print(f"[ToolSelector] Context service error: {e}") - return "" - - def _build_react_prompt(self, state: AgentState) -> str: - """Build ReAct-style prompt for tool selection""" - tools_desc = self.tool_registry.describe_tools_text() if self.tool_registry else "" - - context_str = "" - if state.memory_context: - context_str = "\nRelevant History:\n" + "\n".join( - f"- {m.get('content', '')[:100]}..." for m in state.memory_context[:3] - ) - - return f"""You are an AI assistant that uses tools to answer questions. - -Available Tools: -{tools_desc} - -{context_str} - -User Query: {state.query} - -Think step by step using ReAct format: - -Thought: [Analyze what the user needs and which tool(s) would help] -Action: [Select tool(s) to use] -Tool Selection: [Return JSON array of tool calls] - -Example output: -Thought: The user wants to know about SAGE. I should search the knowledge base. -Action: Use vector_search to find relevant documentation. -Tool Selection: [{{"tool_name": "vector_search", "arguments": {{"query": "SAGE framework"}}, "reason": "Search knowledge base for SAGE info"}}] - -Your response:""" - - def _parse_tool_selection(self, response: str) -> list[ToolCallRequest]: - """Parse LLM response to extract tool calls""" - try: - # Look for JSON array in response - json_match = re.search(r"\[[\s\S]*?\]", response) - if json_match: - tools_data = json.loads(json_match.group()) - return [ - ToolCallRequest( - tool_name=t.get("tool_name", t.get("name", "")), - arguments=t.get("arguments", {}), - reason=t.get("reason", ""), - ) - for t in tools_data - if t.get("tool_name") or t.get("name") - ] - except Exception as e: - print(f"[ToolSelector] Parse error: {e}") - return [] - - def _fallback_tool_selection(self, state: AgentState) -> list[ToolCallRequest]: - """Keyword-based fallback tool selection""" - query_lower = state.query.lower() - selected = [] - - # Check for calculation patterns - if any(op in query_lower for op in ["+", "-", "*", "/", "calculate", "compute", "math"]): - # Extract expression - expr_match = re.search(r"[\d\s+\-*/().]+", state.query) - expr = expr_match.group().strip() if expr_match else state.query - selected.append( - ToolCallRequest( - tool_name="calculator", - arguments={"expression": expr}, - reason="Query contains math expression", - ) - ) - - # Check for email keywords - elif any(word in query_lower for word in ["email", "mail", "message", "inbox"]): - selected.append( - ToolCallRequest( - tool_name="email_search", - arguments={"query": state.query}, - reason="Query mentions email", - ) - ) - - # Check for memory/history keywords - elif any( - word in query_lower for word in ["remember", "earlier", "before", "previous", "history"] - ): - selected.append( - ToolCallRequest( - tool_name="memory_search", - arguments={"query": state.query}, - reason="Query asks about past context", - ) - ) - - # Check for SAGE/documentation keywords - use vector search - elif any( - word in query_lower - for word in ["sage", "install", "how to", "what is", "explain", "documentation"] - ): - selected.append( - ToolCallRequest( - tool_name="vector_search", - arguments={"query": state.query}, - reason="Query about SAGE documentation", - ) - ) - - # Default to web search - else: - selected.append( - ToolCallRequest( - tool_name="web_search", - arguments={"query": state.query}, - reason="General information query", - ) - ) - - return selected - - def execute(self, data: AgentState) -> AgentState: - """Execute tool selection with ReAct reasoning""" - if not isinstance(data, AgentState): - return data - - state = data - print(f"\n[ToolSelector] Analyzing: {state.query[:50]}...") - - # Step 1: Retrieve memory context - state.memory_context = self._retrieve_memory_context(state) - if state.memory_context: - print(f"[ToolSelector] Retrieved {len(state.memory_context)} memory entries") - - # Step 2: Compress context if needed - state.compressed_context = self._compress_context(state) - - # Step 3: ReAct reasoning with LLM - llm_client = self._get_llm_client() - if llm_client: - try: - prompt = self._build_react_prompt(state) - messages = [{"role": "user", "content": prompt}] - response = llm_client.chat(messages) - - # Convert response to string if needed - response_text = str(response) if not isinstance(response, str) else response - - # Extract thought from response - thought_match = re.search( - r"Thought:\s*(.+?)(?=Action:|$)", response_text, re.DOTALL - ) - if thought_match: - state.add_thought(thought_match.group(1).strip()) - - # Extract action - action_match = re.search( - r"Action:\s*(.+?)(?=Tool Selection:|$)", response_text, re.DOTALL - ) - if action_match: - state.add_action(action_match.group(1).strip()) - - # Parse tool selection - selected_tools = self._parse_tool_selection(response_text) - if selected_tools: - state.selected_tools = selected_tools - print(f"[ToolSelector] LLM selected: {[t.tool_name for t in selected_tools]}") - return state - - except Exception as e: - print(f"[ToolSelector] LLM error: {e}") - - # Fallback to keyword matching - print("[ToolSelector] Using fallback keyword matching...") - state.add_thought("Analyzing query keywords to select appropriate tool") - state.selected_tools = self._fallback_tool_selection(state) - state.add_action( - f"Selected tools via keyword matching: {[t.tool_name for t in state.selected_tools]}" - ) - print(f"[ToolSelector] Fallback selected: {[t.tool_name for t in state.selected_tools]}") - - return state - - -class ToolExecutor(MapFunction): - """ - Tool execution operator. - - Executes selected tools and collects results. - Tools can access services like vector_db for RAG retrieval. - """ - - def __init__(self, tool_registry: ToolRegistry | None = None, **kwargs): - super().__init__(**kwargs) - self.tool_registry = tool_registry - - def _inject_services(self) -> None: - """Inject service caller into tool registry""" - if not self.tool_registry: - return - - # 传递 call_service 回调给工具，让工具可以调用服务方法 - self.tool_registry.set_service_caller(self.call_service) - - def execute(self, data: AgentState) -> AgentState: - """Execute all selected tools""" - if not isinstance(data, AgentState): - return data - - state = data - - if not state.selected_tools: - print("[ToolExecutor] No tools selected") - return state - - print(f"\n[ToolExecutor] Executing {len(state.selected_tools)} tool(s)...") - - # Inject services into tools - self._inject_services() - - for tool_request in state.selected_tools: - print(f" -> {tool_request.tool_name}: {tool_request.arguments}") - - start_time = time.time() - try: - if self.tool_registry: - result = self.tool_registry.call_tool( - tool_request.tool_name, - tool_request.arguments, - ) - else: - result = {"success": False, "error": "No tool registry"} - - execution_time = time.time() - start_time - - tool_result = ToolCallResult( - tool_name=tool_request.tool_name, - success=result.get("success", True), - result=result, - execution_time=execution_time, - ) - - # Add observation to ReAct trace - if result.get("success"): - obs_summary = self._summarize_result(result) - state.add_observation(f"{tool_request.tool_name}: {obs_summary}") - print(f" Success ({execution_time:.2f}s)") - else: - state.add_observation( - f"{tool_request.tool_name} failed: {result.get('error', 'Unknown error')}" - ) - print(f" Failed: {result.get('error')}") - - except Exception as e: - execution_time = time.time() - start_time - tool_result = ToolCallResult( - tool_name=tool_request.tool_name, - success=False, - result=None, - error=str(e), - execution_time=execution_time, - ) - state.add_observation(f"{tool_request.tool_name} error: {e}") - print(f" Error: {e}") - - state.tool_results.append(tool_result) - - return state - - def _summarize_result(self, result: dict) -> str: - """Create brief summary of tool result""" - if "documents" in result: - docs = result["documents"] - return f"Found {len(docs)} document(s)" - elif "results" in result: - return f"Found {len(result['results'])} result(s)" - elif "result" in result: - return f"Result: {result['result']}" - elif "emails" in result: - return f"Found {len(result['emails'])} email(s)" - elif "memories" in result: - return f"Found {len(result['memories'])} memory entries" - else: - return "Completed" - - -class ResponseGenerator(MapFunction): - """ - Response generation operator with reflection. - - Generates final response based on tool results. - Includes ReAct reflection step for self-critique. - Saves interaction to memory service. - """ - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self._llm_client = None - - def _get_llm_client(self): - """Lazy initialization of LLM client""" - if self._llm_client is None: - try: - from sage.common.components.sage_llm import UnifiedInferenceClient - - self._llm_client = UnifiedInferenceClient.create() - except Exception: - self._llm_client = None - return self._llm_client - - def _build_response_prompt(self, state: AgentState) -> str: - """Build prompt for response generation with reflection""" - results_text = "" - for tr in state.tool_results: - results_text += f"\n### {tr.tool_name}:\n" - if tr.success: - result = tr.result - if isinstance(result, dict): - if "documents" in result: - for doc in result["documents"][:3]: - results_text += ( - f"- {doc.get('title', 'Doc')}: {doc.get('content', '')[:150]}...\n" - ) - elif "result" in result: - results_text += f"Result: {result['result']}\n" - elif "emails" in result: - for email in result["emails"][:2]: - results_text += f"- {email.get('subject', 'Email')}\n" - else: - results_text += f"{json.dumps(result, ensure_ascii=False)[:300]}\n" - else: - results_text += f"Error: {tr.error}\n" - - react_trace = state.get_react_trace_str() - - return f"""Based on the tool results, generate a helpful response. - -User Query: {state.query} - -ReAct Reasoning Trace: -{react_trace} - -Tool Results: -{results_text} - -Now provide: -1. A clear, helpful response to the user -2. A brief reflection on whether the tools chosen were appropriate - -Format: -Response: [Your response to the user] -Reflection: [Brief self-critique - were the right tools used? What could be improved?] - -Your answer:""" - - def _generate_fallback_response(self, state: AgentState) -> str: - """Generate response without LLM""" - parts = [f"Query: {state.query}\n"] - - for tr in state.tool_results: - parts.append(f"\n[{tr.tool_name}]:") - if tr.success and isinstance(tr.result, dict): - result = tr.result - if "documents" in result: - for doc in result["documents"][:3]: - title = doc.get("title", "Document") - content = doc.get("content", "")[:150] - parts.append(f" - {title}: {content}...") - elif "result" in result: - parts.append(f" Result: {result['result']}") - elif "results" in result: - for r in result["results"][:3]: - parts.append(f" - {r.get('title', r.get('snippet', str(r)[:50]))}") - elif "emails" in result: - for email in result["emails"][:2]: - parts.append(f" - {email.get('subject', 'Email')}") - elif "memories" in result: - for mem in result["memories"][:2]: - parts.append(f" - {str(mem)[:100]}...") - else: - parts.append(f" {json.dumps(result, ensure_ascii=False)[:200]}") - elif not tr.success: - parts.append(f" Error: {tr.error}") - - return "\n".join(parts) - - def _save_to_memory(self, state: AgentState) -> None: - """Save interaction to memory service""" - try: - entry = state.to_memory_entry() - # 使用 method 参数直接调用服务方法 - self.call_service( - "memory_service", - method="insert", - entry=json.dumps(entry), - metadata={"session_id": state.session_id, "type": "interaction"}, - ) - print("[ResponseGenerator] Saved to memory") - except Exception as e: - print(f"[ResponseGenerator] Memory save error: {e}") - - def _update_context_history(self, state: AgentState) -> None: - """Update context service history""" - try: - # 使用 method 参数直接调用服务方法 - self.call_service( - "context_service", - method="add_to_history", - role="user", - content=state.query, - ) - self.call_service( - "context_service", - method="add_to_history", - role="assistant", - content=state.response[:500], - ) - except Exception as e: - print(f"[ResponseGenerator] Context history error: {e}") - - def execute(self, data: AgentState) -> AgentState: - """Generate response with reflection""" - if not isinstance(data, AgentState): - return data - - state = data - print("\n[ResponseGenerator] Generating response...") - - llm_client = self._get_llm_client() - if llm_client and state.tool_results: - try: - prompt = self._build_response_prompt(state) - messages = [{"role": "user", "content": prompt}] - response = llm_client.chat(messages) - - # Convert response to string if needed - response_text = str(response) if not isinstance(response, str) else response - - # Extract response part - resp_match = re.search( - r"Response:\s*(.+?)(?=Reflection:|$)", response_text, re.DOTALL - ) - if resp_match: - state.response = resp_match.group(1).strip() - else: - state.response = response_text - - # Extract and add reflection - refl_match = re.search(r"Reflection:\s*(.+?)$", response_text, re.DOTALL) - if refl_match: - state.add_reflection(refl_match.group(1).strip()) - - print("[ResponseGenerator] LLM response generated with reflection") - - except Exception as e: - print(f"[ResponseGenerator] LLM error: {e}, using fallback") - state.response = self._generate_fallback_response(state) - else: - print("[ResponseGenerator] Using fallback response generation") - state.response = self._generate_fallback_response(state) - state.add_reflection("Used fallback response generation (LLM unavailable)") - - # Save to memory and update context - self._save_to_memory(state) - self._update_context_history(state) - - return state - - -class ResponseSink(SinkFunction): - """ - Output sink for displaying the final response. - - Formats and prints the response along with ReAct trace. - """ - - def __init__(self, verbose: bool = True, **kwargs): - super().__init__(**kwargs) - self.verbose = verbose - self.test_mode = os.getenv("SAGE_TEST_MODE") == "true" - - def execute(self, data: AgentState) -> None: - """Output the final response""" - if not isinstance(data, AgentState): - print(f"[ResponseSink] Unexpected data type: {type(data)}") - return - - state = data - - print("\n" + "=" * 70) - print(f"[Response] Session: {state.session_id}") - print("-" * 70) - print(f"Query: {state.query}") - print("-" * 70) - - if state.selected_tools: - tools_str = ", ".join(t.tool_name for t in state.selected_tools) - print(f"Tools Used: {tools_str}") - print("-" * 70) - - # Print response (truncate in test mode) - response = state.response - if self.test_mode and len(response) > 500: - response = response[:500] + "... (truncated)" - print(f"\n{response}\n") - - # Print ReAct trace if verbose - if self.verbose and state.react_trace: - print("-" * 70) - print("ReAct Trace:") - for step in state.react_trace: - prefix = step.phase.value.capitalize() - content = step.content[:100] + "..." if len(step.content) > 100 else step.content - print(f" [{prefix}] {content}") - - print("=" * 70) diff --git a/benchmark/experiments/tool_use_agent/pipeline.py b/benchmark/experiments/tool_use_agent/pipeline.py deleted file mode 100644 index 0b1fd136c2..0000000000 --- a/benchmark/experiments/tool_use_agent/pipeline.py +++ /dev/null @@ -1,517 +0,0 @@ -#!/usr/bin/env python3 -""" -Tool Use Agent Pipeline -======================= - -Main pipeline implementation integrating: -- sage-mem: HierarchicalMemoryService for agent memory (STM/MTM/LTM) -- sage-refiner: ContextService for context compression -- sage-db: SageDBService for vector search (RAG) -- ReAct planning: Thought-Action-Observation-Reflection loop - -Pipeline Architecture: - UserQuerySource -> ToolSelector -> ToolExecutor -> ResponseGenerator -> ResponseSink - -Services are registered with env.register_service() and accessed via self.call_service(). - -Usage: - # As module - from examples.tutorials.L3_libs.agents.tool_use_agent import run_tool_use_demo - run_tool_use_demo() - - # Command line - python -m examples.tutorials.L3_libs.agents.tool_use_agent.pipeline - python pipeline.py --interactive - python pipeline.py --query "What is SAGE?" - -# test_tags: category=agent, timeout=180, requires_llm=optional -""" - -from __future__ import annotations - -import argparse -import os -import sys -import time -from pathlib import Path -from typing import Any - -# Ensure package is importable -SCRIPT_DIR = Path(__file__).resolve().parent -REPO_ROOT = SCRIPT_DIR.parents[1] # experiments/tool_use_agent -> SAGE -if str(REPO_ROOT) not in sys.path: - sys.path.insert(0, str(REPO_ROOT)) - -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.api.local_environment import LocalEnvironment - -try: - from .models import AgentState -except ImportError: - from models import AgentState -try: - from .operators import ( - ResponseGenerator, - ResponseSink, - ToolExecutor, - ToolSelector, - UserQuerySource, - ) -except ImportError: - from operators import ( - ResponseGenerator, - ResponseSink, - ToolExecutor, - ToolSelector, - UserQuerySource, - ) -try: - from .agent_tools import create_default_registry -except ImportError: - from agent_tools import create_default_registry - - -# ============================================================================= -# Service Registration Helpers -# ============================================================================= - - -def register_memory_service(env: LocalEnvironment, collection_name: str = "agent_memory") -> bool: - """ - Register HierarchicalMemoryService for agent memory. - - Uses three-tier memory: STM (short-term), MTM (medium-term), LTM (long-term). - Falls back gracefully if sage-mem is not available. - """ - try: - from sage.middleware.components.sage_mem.services import ( - HierarchicalMemoryService, - ) - - env.register_service( - "memory_service", - HierarchicalMemoryService, - collection_name=collection_name, - tier_mode="three_tier", - tier_capacities={"stm": 10, "mtm": 50, "ltm": -1}, - ) - print("[Pipeline] Registered memory_service (HierarchicalMemoryService)") - return True - except ImportError as e: - print(f"[Pipeline] sage-mem not available: {e}") - return False - except Exception as e: - print(f"[Pipeline] Failed to register memory_service: {e}") - return False - - -def register_context_service(env: LocalEnvironment, max_length: int = 8192) -> bool: - """ - Register ContextService for context compression. - - Uses sage-refiner to automatically compress long contexts. - Falls back gracefully if sage-refiner is not available. - """ - try: - from sage.middleware.components.sage_refiner import ContextService - - env.register_service( - "context_service", - ContextService, - config={ - "max_context_length": max_length, - "auto_compress": True, - "compress_threshold": 0.8, - "refiner": {"algorithm": "simple", "budget": 2000}, - }, - ) - print("[Pipeline] Registered context_service (ContextService)") - return True - except ImportError as e: - print(f"[Pipeline] sage-refiner not available: {e}") - return False - except Exception as e: - print(f"[Pipeline] Failed to register context_service: {e}") - return False - - -def register_vector_db_service( - env: LocalEnvironment, - dimension: int | None = None, - knowledge_base: list[dict[str, Any]] | None = None, -) -> bool: - """ - Register SageDBService for vector search (RAG). - - Pre-populates with SAGE documentation knowledge base using real embeddings. - Falls back gracefully if sage-db is not available. - - Args: - env: LocalEnvironment to register service - dimension: Vector dimension (auto-detected from embedding if None) - knowledge_base: List of documents to pre-load - """ - try: - import numpy as np - - from sage.middleware.components.sage_db.python.micro_service.sage_db_service import ( - SageDBService, - ) - - # Default SAGE knowledge base - if knowledge_base is None: - knowledge_base = [ - { - "title": "SAGE Framework Overview", - "text": "SAGE is a Python framework for building AI/LLM data processing pipelines with declarative dataflow. It consists of 5 layers from L1-Common to L5-CLI/Tools.", - "tags": "overview,architecture", - }, - { - "title": "SAGE Installation", - "text": "To install SAGE, run ./quickstart.sh --dev --yes for development. Prerequisites: Python 3.10+, build-essential, cmake.", - "tags": "installation,setup", - }, - { - "title": "Pipeline Operators", - "text": "SAGE uses SourceFunction, MapFunction, SinkFunction operators. Connect via LocalEnvironment and access services with self.call_service().", - "tags": "pipeline,operators", - }, - { - "title": "Memory Services", - "text": "sage-mem provides HierarchicalMemoryService with STM/MTM/LTM tiers. Use MemoryServiceFactory.create_instance() to create services.", - "tags": "memory,sage-mem", - }, - { - "title": "Context Compression", - "text": "sage-refiner ContextService provides automatic context compression. Supports simple, llmlingua2, provence, reform algorithms.", - "tags": "refiner,compression", - }, - ] - - # Try to get embeddings for knowledge base - def get_embeddings(texts: list[str]) -> tuple[list[list[float]], int] | None: - """Get embeddings using UnifiedInferenceClient""" - try: - from sage.common.components.sage_llm import UnifiedInferenceClient - from sage.common.components.sage_llm.unified_client import ( - InferenceResult, - ) - - client = UnifiedInferenceClient.create() - result = client.embed(texts) - - # embed() returns list[list[float]] or InferenceResult - embeddings: list[list[float]] - if isinstance(result, InferenceResult): - # Extract embeddings from InferenceResult.content - # content is str | list[list[float]], but for embed it is always list[list[float]] - embeddings = result.content # type: ignore[assignment] - else: - embeddings = result - - if embeddings and len(embeddings) > 0: - dim = len(embeddings[0]) - return embeddings, dim - except Exception as e: - print(f"[Pipeline] Embedding error: {e}") - return None - - # Create bootstrapped service with real embeddings - class BootstrappedSageDBService(SageDBService): - """SageDB with pre-loaded knowledge base using real embeddings""" - - def __init__(self, *, initial_data: list[dict], dimension: int, **kwargs): - super().__init__(dimension=dimension, **kwargs) - - texts = [item.get("text", item.get("content", "")) for item in initial_data] - - # Try real embeddings first - embed_result = get_embeddings(texts) - - if embed_result is not None: - embeddings, _ = embed_result - vectors = np.array(embeddings, dtype=np.float32) - print(f"[Pipeline] Using real embeddings (dim={dimension})") - else: - # Fallback to simple hash-based mock embeddings - print(f"[Pipeline] Using mock embeddings (dim={dimension})") - vectors = [] - for text in texts: - vec = np.zeros(dimension, dtype=np.float32) - for i, char in enumerate(text[:dimension]): - vec[i % dimension] += ord(char) / 1000.0 - vec = vec / (np.linalg.norm(vec) + 1e-8) - vectors.append(vec) - vectors = np.array(vectors, dtype=np.float32) - - # Build metadata list - metadata_list = [] - for item in initial_data: - metadata_list.append( - { - "title": item.get("title", ""), - "text": item.get("text", item.get("content", "")), - "tags": item.get("tags", ""), - } - ) - - # Add to database - self.add_batch(vectors, metadata_list) - self._db.build_index() - print(f"[Pipeline] Loaded {len(vectors)} documents into vector_db") - - # Auto-detect dimension from embedding service - if dimension is None: - texts = [kb[0].get("text", "") for kb in [knowledge_base] if kb] - embed_result = get_embeddings(texts[:1]) if texts else None - if embed_result: - _, dimension = embed_result - print(f"[Pipeline] Auto-detected embedding dimension: {dimension}") - else: - dimension = 1024 # Default for bge-large models - print(f"[Pipeline] Using default dimension: {dimension}") - - env.register_service( - "vector_db", - BootstrappedSageDBService, - initial_data=knowledge_base, - dimension=dimension, - index_type="AUTO", - ) - print("[Pipeline] Registered vector_db (SageDBService)") - return True - - except ImportError as e: - print(f"[Pipeline] sage-db not available: {e}") - return False - except Exception as e: - print(f"[Pipeline] Failed to register vector_db: {e}") - return False - - -# ============================================================================= -# Pipeline Entry Points -# ============================================================================= - - -def run_tool_use_demo( - queries: list[str] | None = None, - verbose: bool = True, - register_services: bool = True, -) -> None: - """ - Run the Tool Use Agent Pipeline demo. - - Args: - queries: List of queries to process. Uses defaults if None. - verbose: Enable verbose output in ResponseSink. - register_services: Whether to register middleware services. - - Example: - >>> from examples.tutorials.L3_libs.agents.tool_use_agent import run_tool_use_demo - >>> run_tool_use_demo(["What is SAGE?", "Calculate 2 + 2"]) - """ - # Suppress debug logging unless verbose - if not verbose: - CustomLogger.disable_global_console_debug() - - print( - """ -======================================================================== - Tool Use Agent Pipeline Demo -======================================================================== - Pipeline: UserQuery -> ToolSelector -> ToolExecutor -> ResponseGenerator - - Integrated Services: - - memory_service: HierarchicalMemoryService (sage-mem) - - context_service: ContextService (sage-refiner) - - vector_db: SageDBService (sage-db) - - Features: - - ReAct reasoning: Thought-Action-Observation-Reflection - - Keyword fallback when LLM unavailable - - Automatic context compression - - Persistent memory across queries -======================================================================== - """ - ) - - # Create environment - env = LocalEnvironment("tool_use_agent") - - # Register services - if register_services: - print("\n[Pipeline] Registering services...") - register_memory_service(env) - register_context_service(env) - register_vector_db_service(env) - print() - - # Create tool registry - tool_registry = create_default_registry() - print(f"[Pipeline] Available tools: {tool_registry.list_tools()}\n") - - # Default queries - if queries is None: - queries = [ - "What is SAGE framework and how to install it?", - "Calculate 15 * 23 + 47", - "Search for information about memory services in SAGE", - ] - - # In test mode, limit queries - test_mode = os.getenv("SAGE_TEST_MODE") == "true" - if test_mode: - queries = queries[:1] - print("[Pipeline] Test mode: processing 1 query only\n") - - # Build pipeline - ( - env.from_source(UserQuerySource, queries=queries) - .map(ToolSelector, tool_registry=tool_registry) - .map(ToolExecutor, tool_registry=tool_registry) - .map(ResponseGenerator) - .sink(ResponseSink, verbose=verbose) - ) - - # Execute - start_time = time.time() - env.submit(autostop=True) - total_time = time.time() - start_time - - print(f"\n[Pipeline] Completed in {total_time:.2f} seconds") - env.close() - - -def run_interactive_mode() -> None: - """ - Run the agent in interactive mode. - - User can input queries one at a time with persistent memory. - """ - print( - """ -======================================================================== - Tool Use Agent - Interactive Mode - Type your query and press Enter. - Type 'quit', 'exit', or 'q' to stop. - Type 'clear' to clear memory. -======================================================================== - """ - ) - - # Create tool registry with no services initially - tool_registry = create_default_registry() - print(f"Available tools: {tool_registry.list_tools()}\n") - - # Create operator instances for reuse - selector = ToolSelector(tool_registry=tool_registry) - executor = ToolExecutor(tool_registry=tool_registry) - generator = ResponseGenerator() - sink = ResponseSink(verbose=True) - - session_id = None - - while True: - try: - query = input("\n> Your query: ").strip() - - if not query: - continue - - if query.lower() in ("quit", "exit", "q"): - print("\nGoodbye.") - break - - if query.lower() == "clear": - session_id = None - print("Memory cleared. Starting new session.") - continue - - # Create state - state = AgentState(query=query) - if session_id: - state.session_id = session_id - else: - session_id = state.session_id - - # Process through operators - state = selector.execute(state) - state = executor.execute(state) - state = generator.execute(state) - sink.execute(state) - - except KeyboardInterrupt: - print("\n\nInterrupted. Goodbye.") - break - except Exception as e: - print(f"\nError: {e}") - import traceback - - traceback.print_exc() - - -def main() -> None: - """Main entry point with argument parsing.""" - parser = argparse.ArgumentParser( - description="Tool Use Agent Pipeline - SAGE demonstration", - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=""" -Examples: - # Run with default queries - python pipeline.py - - # Interactive mode - python pipeline.py --interactive - - # Custom queries - python pipeline.py --query "What is SAGE?" --query "Calculate 2+2" - - # Quiet mode (less output) - python pipeline.py --quiet - """, - ) - - parser.add_argument( - "--interactive", - "-i", - action="store_true", - help="Run in interactive mode", - ) - - parser.add_argument( - "--query", - "-q", - action="append", - help="Query to process (can be specified multiple times)", - ) - - parser.add_argument( - "--quiet", - action="store_true", - help="Reduce output verbosity", - ) - - parser.add_argument( - "--no-services", - action="store_true", - help="Skip registering middleware services (faster startup)", - ) - - args = parser.parse_args() - - # Suppress debug logging - CustomLogger.disable_global_console_debug() - - if args.interactive: - run_interactive_mode() - else: - run_tool_use_demo( - queries=args.query, - verbose=not args.quiet, - register_services=not args.no_services, - ) - - -if __name__ == "__main__": - main() diff --git a/benchmark/latex/01_abstract.tex b/benchmark/latex/01_abstract.tex deleted file mode 100644 index dbe6c5d531..0000000000 --- a/benchmark/latex/01_abstract.tex +++ /dev/null @@ -1,29 +0,0 @@ -% Abstract for the SAGE systems paper (content, not prompt) - -\begin{abstract} -Modern LLM applications increasingly rely on complex pipelines that combine retrieval, tool calls, and multiple foundation models deployed across heterogeneous CPU/GPU clusters. Existing serving engines and workflow platforms either optimize single-model inference or provide generic orchestration, but they lack unified dataflow and control-plane support for mixed LLM and embedding workloads. This gap makes it difficult to reason about performance, resource usage, and reproducibility for end-to-end LLM-centric systems. -We present SAGE, a full-stack framework that organizes LLM/AI pipelines into a strict six-layer architecture with declarative dataflow and no upward dependencies. Users specify pipelines at a high level, while platform, kernel, and middleware layers compile them into execution plans that span CPU-only and GPU nodes. SAGE integrates a unified LLM and embedding control plane, exposed through an OpenAI-compatible gateway, that performs request classification, hybrid scheduling, and batching across multiple vLLM and embedding backends. The framework further provides reproducible tooling and benchmark suites that evaluate both agent capabilities and system-level behavior. -Across representative RAG and agent-style workloads, SAGE reduces p99 end-to-end latency by [X]\% and improves throughput by [Y]\texttimes{} over a baseline of separately managed LLM and embedding services, while maintaining p95 latency below [Z] ms. Under mixed interactive and batch traffic, it achieves [A]\% SLO satisfaction compared to [B]\% for the baseline and scales to [G] GPU nodes and [H] concurrent clients with near-linear throughput. -\end{abstract} - -% --------------------------------------------------------------------------- -% Internal notes for authors (not part of the camera-ready content) -% --------------------------------------------------------------------------- -% Experiments needed to instantiate placeholders: -% - [X], [Y], [Z]: Mixed LLM+embedding RAG/agent workload comparing SAGE vs. -% baseline "vLLM + separate embedding service"; measure end-to-end latency CDF -% and throughput under increasing load. -% - [A], [B]: Isolation / SLO study with interactive vs. batch tenants, with and -% without SAGE's unified control-plane scheduling policies. -% - [G], [H]: Scalability experiment varying number of GPU nodes and concurrent -% clients; report near-linear scaling region and saturation point. - -% Suggestions for refining the abstract once numbers and baselines are fixed: -% - Replace generic "representative RAG and agent-style workloads" with the -% specific benchmark names and dataset characteristics. -% - Tighten the quantitative sentence to highlight one primary claim (e.g., -% tail latency reduction) and move secondary numbers to the introduction. -% - Align wording of baselines with the exact experimental setup section titles -% (e.g., "Separate Services" vs. "Decoupled LLM/Embedding Deployment"). -% - If space is tight, consider shortening the description of reproducible -% tooling and benchmarks and instead reference the Experiments section. diff --git a/benchmark/latex/02_introduction.tex b/benchmark/latex/02_introduction.tex deleted file mode 100644 index a5662973ec..0000000000 --- a/benchmark/latex/02_introduction.tex +++ /dev/null @@ -1,22 +0,0 @@ -% Introduction section for the SAGE systems paper (content, not prompt) - -\section{Introduction} - -Large language models (LLMs) are increasingly deployed as part of rich application pipelines that combine retrieval, tools, structured data access, and multi-stage post-processing across heterogeneous CPU/GPU clusters. While modern serving engines deliver impressive single-model throughput and latency, practitioners still struggle to manage end-to-end LLM/AI pipelines: orchestrating multiple models and embedding services, sharing resources across workloads with different latency requirements, and reproducing complex deployments reliably. Existing MLOps and workflow platforms provide generic abstractions for training and batch jobs, but they lack LLM-aware scheduling and dataflow support tailored to online inference pipelines. -We argue that building and operating LLM-centric pipelines requires a full-stack system that unifies dataflow, control, and evaluation rather than another isolated serving component. Low-level LLM engines such as vLLM, TensorRT-LLM, and SGLang optimize GPU utilization for a single model, but they do not address cross-model coordination or embedding co-scheduling. ML serving frameworks and workflow platforms such as Ray Serve, KServe, Triton, MLflow, Kubeflow, and Airflow focus on deployment and generic DAGs, but offer limited support for fine-grained resource sharing and observability across mixed LLM and embedding workloads. Application frameworks such as LangChain, LlamaIndex, and DSPy simplify prompt engineering and tool wiring, yet they treat the underlying system as a black box and cannot reason about hardware heterogeneity, SLOs, or interference. -We present SAGE, a Python 3.10+ framework that treats LLM/AI applications as declarative dataflow pipelines mapped onto a strict six-layer architecture. At the bottom, \texttt{sage-common} and \texttt{sage-platform} provide shared configuration, XDG-compliant user paths, port management, and platform services such as storage and cluster configuration. The \texttt{sage-kernel} and \texttt{sage-libs} layers implement a job management and node selection runtime that is aware of CPU-only and GPU nodes, while \texttt{sage-middleware} contributes C++ operators for performance-critical components. On top, \texttt{sage-apps} and \texttt{sage-benchmark} host applications and evaluation suites, and user-facing tools---including \texttt{sage-cli}, \texttt{sage-studio}, \texttt{sage-tools}, and the \texttt{sage-gateway} OpenAI-compatible endpoint---expose the system to developers. -A central component of SAGE is a unified LLM and embedding control plane (``sageLLM'') that fronts a pool of vLLM and embedding backends behind the \texttt{sage-gateway}. The control plane classifies requests, co-schedules chat/generation and embedding workloads using policies such as \texttt{HybridSchedulingPolicy}, and batches work across engines to improve throughput and tail latency while honoring per-tenant SLOs. Unlike single-engine benchmarks such as AgentBench, ToolBench, or HELM, SAGE's benchmarks focus on system-level behavior: they stress the control plane, dataflow runtime, and heterogeneous deployments, measuring throughput, latency distributions, SLO satisfaction, and interference under realistic RAG and agent-style workloads. -This paper makes the following contributions. \emph{First}, we design a six-layer architecture and declarative dataflow model for LLM-centric pipelines that cleanly separates concerns between common utilities, platform services, execution kernel, middleware operators, applications, and user interfaces, with strict no-upward dependencies. \emph{Second}, we introduce a unified LLM and embedding control plane that shares resources across multiple engines and supports policy-driven scheduling for mixed interactive and batch traffic. \emph{Third}, we provide systems support for heterogeneous CPU/GPU deployments, including job management, node selection, and reproducible tooling for deployment, testing, and CI. \emph{Fourth}, we develop a comprehensive benchmark suite that evaluates both agent capabilities and system-level behavior, enabling rigorous comparison of scheduling policies, deployment topologies, and baselines. Finally, we show in Section~\ref{sec:experiments} that SAGE reduces end-to-end p99 latency by [X]\% and improves throughput by [Y]\texttimes{} over separately managed LLM and embedding services, while maintaining strict latency SLOs under mixed interactive and batch workloads. - -% --------------------------------------------------------------------------- -% Internal notes for authors (not part of the camera-ready content) -% --------------------------------------------------------------------------- -% Possible future refinements to the Introduction: -% - Once experiments are finalized, add one sentence summarizing the main -% quantitative result (e.g., p99 reduction and throughput gain) near the end -% of the last paragraph, aligned with the abstract. -% - Replace generic references to ``RAG and agent-style workloads" with the -% concrete benchmark names and datasets used in Section 5. -% - If space is tight, consider shortening the list of external systems in the -% second paragraph and move detailed positioning to Related Work. -% - Optionally add a final paragraph that previews the paper structure. diff --git a/benchmark/latex/03_related_work.tex b/benchmark/latex/03_related_work.tex deleted file mode 100644 index 8e9b36259a..0000000000 --- a/benchmark/latex/03_related_work.tex +++ /dev/null @@ -1,47 +0,0 @@ -% Related Work section for the SAGE systems paper (content, not prompt) - -\section{Related Work} - -\subsection{LLM Serving Engines} - -LLM serving engines such as vLLM~[REF: VLLM], TensorRT-LLM~[REF: TRT_LLM], SGLang~[REF: SGLANG], and Orca~[REF: ORCA] focus on maximizing single-model throughput and GPU utilization. They introduce optimizations such as paged attention, continuous batching, and kernel fusion to reduce per-token latency and improve memory efficiency. These systems are highly effective at treating a single LLM as a service, exposing low-level APIs for prompt submission and token streaming, and are now widely adopted as backends for higher-level applications. -SAGE is complementary to these serving engines rather than a replacement. It assumes the presence of engines like vLLM as backend components and operates at a higher abstraction level, orchestrating multiple LLM and embedding instances under a unified control plane. Instead of re-implementing intra-engine scheduling, SAGE focuses on cross-engine and cross-workload decisions: classifying requests, co-scheduling embeddings with generation, and routing traffic across heterogeneous CPU/GPU nodes. Furthermore, while common benchmarks for serving engines emphasize tokens-per-second and per-request latency, SAGE's evaluation framework measures end-to-end pipeline behavior, including interference between workloads and resource utilization under mixed traffic. - -\subsection{ML Serving Frameworks and Workflow Platforms} - -ML serving frameworks and workflow platforms such as Ray Serve~[REF: RAY_SERVE], KServe~[REF: KSERVE], Triton Inference Server~[REF: TRITON], MLflow~[REF: MLFLOW], Kubeflow~[REF: KUBEFLOW], and Airflow~[REF: AIRFLOW] provide general-purpose abstractions for deploying models and orchestrating data-processing DAGs. They address concerns such as autoscaling, model versioning, A/B testing, and scheduling of batch and streaming jobs across clusters. However, their abstractions are largely model-agnostic: they treat LLMs, embeddings, and other components as black-box services without specialized support for joint scheduling, prompt-level SLOs, or LLM-specific observability. -SAGE differs by building LLM awareness into both its declarative dataflow model and its control plane. Its execution kernel and control-plane components understand the distinction between chat/generation and embedding requests and exploit this structure when batching and routing work. While SAGE can be deployed on top of general frameworks---for example, using Ray or Kubernetes as the underlying resource manager---it exposes a unified LLM+embedding interface via \texttt{sage-gateway} and provides policy hooks tailored to LLM-centric workloads. In addition, SAGE ships with system-level benchmarks that evaluate scheduling policies and deployment configurations for LLM pipelines, something that is typically outside the scope of generic serving or workflow systems. - -\subsection{LLM Application Frameworks and Agents} - -LLM application frameworks and agent toolkits such as LangChain~[REF: LANGCHAIN], LlamaIndex~[REF: LLAMAINDEX], DSPy~[REF: DSPY], and various agent frameworks~[REF: AGENT_FRAMEWORKS] aim to simplify the construction of LLM applications. They provide abstractions for prompt templates, retrieval-augmented generation, tool use, and iterative reasoning, enabling developers to prototype complex behaviors with relatively little code. Some of these frameworks include limited execution-time tracing and evaluation, but they typically delegate all systems concerns---latency, resource allocation, isolation, and deployment---to external infrastructure. -SAGE targets a different layer of the stack: it provides the systems substrate on which such application frameworks can run. Its declarative dataflow abstraction can express the same retrieval, tool, and multi-step patterns that application-level libraries capture, but with an explicit mapping to jobs, nodes, and scheduling policies. In this sense, SAGE can use frameworks like LangChain or DSPy as sources of logical pipeline descriptions, while still treating vLLM and embedding servers as underlying engines. Moreover, SAGE's benchmark suite complements task-level evaluations from application frameworks by focusing on how agents behave under resource contention, heterogeneous hardware, and different control-plane policies. - -\subsection{LLM Benchmarks and Evaluation Frameworks} - -Benchmark suites such as AgentBench~[REF: AGENTBENCH], ToolBench~[REF: TOOLBENCH], HELM~[REF: HELM], and vLLM's own performance benchmarks~[REF: VLLM_BENCH] provide critical visibility into LLM accuracy, robustness, and single-engine performance. AgentBench and ToolBench emphasize agent capabilities such as tool selection, planning, and function calling, primarily measuring task success and reasoning quality. HELM offers a broad evaluation of models across tasks and risk dimensions, while vLLM benchmarks quantify throughput and latency for individual engines under synthetic workloads. -SAGE builds on the insights from these works but extends the evaluation scope to full systems. Its \texttt{sage-benchmark} package includes experiments that stress the control plane and dataflow runtime, measuring throughput, latency distributions, SLO satisfaction, interference between tenants, and scaling across multiple backends. Rather than competing with task-centric benchmarks, SAGE can ingest workloads or task definitions inspired by AgentBench or ToolBench and run them through its own infrastructure to study system-level behavior. This dual focus allows practitioners to reason about both task quality and the underlying system's ability to deliver predictable performance at scale. - -\subsection{Data and Storage Systems for AI Pipelines} - -Data and storage systems used in AI pipelines, including vector databases such as FAISS, Milvus, or commercial offerings~[REF: VECTOR_DB], time-series databases for monitoring~[REF: TSDB], and general-purpose dataflow engines like Flink or Spark~[REF: DATAFLOW_SYS], provide essential building blocks for retrieval, logging, and streaming analytics. These systems excel at indexing, querying, and transporting data with strong consistency and availability guarantees, and many integrate with LLM applications as external components. However, they do not directly address how LLM inference and embedding workloads share compute resources or how end-to-end pipelines should be scheduled across heterogeneous clusters. -SAGE is designed to interoperate with such data and storage systems rather than replace them. Its declarative dataflow model can treat vector databases, time-series stores, and streaming engines as operators or external services within a larger LLM/AI pipeline. The system's main contribution is to manage the compute side of the pipeline---LLM and embedding engines, control-plane scheduling, and node selection---while exposing hooks for integrating storage components through standardized interfaces. In doing so, SAGE fills the gap between data-centric infrastructure and application-level frameworks by providing a dedicated, LLM-aware execution and evaluation layer. - -\subsection{Summary} - -Across these categories, SAGE occupies a distinct position in the LLM systems landscape. It is neither a single-model serving engine nor a generic workflow tool, but a unified, layered platform that combines declarative dataflow, an LLM+embedding control plane, heterogeneous deployment support, and comprehensive system-level benchmarks. SAGE can leverage existing engines such as vLLM as backends, run on top of general-purpose serving and workflow frameworks, and host application-level libraries and benchmarks as workloads. By providing an integrated stack that spans architecture, runtime, control plane, and evaluation, SAGE addresses a gap between low-level serving systems and high-level application frameworks for LLM/AI pipelines. - -% --------------------------------------------------------------------------- -% Citation placeholders (to be mapped to concrete references later) -% --------------------------------------------------------------------------- -% LLM Serving Engines: -% [REF: VLLM], [REF: TRT_LLM], [REF: SGLANG], [REF: ORCA], [REF: VLLM_BENCH] -% ML Serving Frameworks and Workflow Platforms: -% [REF: RAY_SERVE], [REF: KSERVE], [REF: TRITON], [REF: MLFLOW], -% [REF: KUBEFLOW], [REF: AIRFLOW] -% LLM Application Frameworks and Agents: -% [REF: LANGCHAIN], [REF: LLAMAINDEX], [REF: DSPY], [REF: AGENT_FRAMEWORKS] -% LLM Benchmarks and Evaluation Frameworks: -% [REF: AGENTBENCH], [REF: TOOLBENCH], [REF: HELM] -% Data and Storage Systems: -% [REF: VECTOR_DB], [REF: TSDB], [REF: DATAFLOW_SYS] diff --git a/benchmark/latex/04_system_and_method.tex b/benchmark/latex/04_system_and_method.tex deleted file mode 100644 index eb7ea60768..0000000000 --- a/benchmark/latex/04_system_and_method.tex +++ /dev/null @@ -1,112 +0,0 @@ -% System / Method section for the SAGE systems paper (content, not prompt) - -\section{System Design} - -\subsection{Overview and Design Goals} - -SAGE is a Python 3.10+ framework that treats LLM/AI applications as declarative dataflow pipelines executed across heterogeneous CPU and GPU clusters. The system is designed to address several recurring challenges observed in practice: scaling multi-stage retrieval-and-generation workflows, sharing resources between LLM and embedding workloads with different latency requirements, and making complex deployments reproducible and observable. Rather than introducing yet another standalone serving engine, SAGE provides a full-stack architecture that integrates platform services, execution runtimes, performance-critical operators, user interfaces, and a unified control plane on top of existing engines such as vLLM. -The design of SAGE is guided by four goals. First, it should provide a clear separation of concerns so that common utilities, platform services, execution logic, and user interfaces can evolve independently; this motivates a strict five-layer architecture with no upward dependencies. Second, it should expose a high-level declarative dataflow abstraction that allows users to express retrieval, tool calls, LLM invocations, and post-processing without hard-coding deployment details. Third, it should offer a unified control plane for LLM and embedding services that can co-schedule workloads, enforce per-tenant SLOs, and adapt to heterogeneous hardware. Finally, it should be easy to deploy and evaluate: quickstart scripts, XDG-compliant configuration, and system-level benchmarks are treated as first-class components rather than afterthoughts. - -\subsection{Layered Architecture} - -At the core of SAGE is a strict five-layer architecture (L1--L5) that prevents upward dependencies and keeps each layer responsible for a focused set of concerns. The L1 layer, \texttt{sage-common}, contains foundational utilities, configuration management, XDG-based user paths, port management via \texttt{SagePorts}, and shared components such as the unified inference client and control-plane core modules. L2, implemented in \texttt{sage-platform}, provides platform services including storage integration, queuing, service supervision, and cluster configuration via project-level files like \texttt{config/cluster.yaml}. These two layers form the stable base that higher layers depend on but never modify directly. -The L3 layer comprises \texttt{sage-kernel} and \texttt{sage-libs}, which implement the core execution engine for dataflow pipelines. The kernel includes a job manager and node selector that map logical pipeline stages to concrete execution nodes, taking into account CPU-only and GPU-capable resources, load, and placement constraints. Algorithms and scheduling logic in this layer are written with awareness of LLM and embedding workloads, but do not hard-code any particular engine implementation. L4, \texttt{sage-middleware}, contains C++ operators and other performance-critical components built via CMake; these can be reused across applications and are invoked from the kernel as part of the dataflow execution. Finally, L5 provides user-facing entry points through \texttt{sage-cli} and \texttt{sage-tools}. Applications (sage-examples), benchmarks (sage-benchmark), the visual studio (sage-studio), and the LLM gateway (sageLLM) are maintained in independent repositories. -This layering has two practical benefits. First, it enables modular development: teams can evolve the control plane or middleware operators without entangling them with platform configuration or user interfaces, as long as they respect the downward-only dependency rule. Second, it simplifies reasoning about deployments and upgrades: operators can treat L1--L2 as part of the environment, roll out new kernels or middleware versions in L3--L4, and update CLI/tools in L5 with well-defined compatibility boundaries. - -\subsection{Declarative Dataflow and Execution Model} - -SAGE exposes to users a declarative dataflow API for constructing LLM/AI pipelines. A pipeline is modeled as a directed acyclic graph whose nodes correspond to operators such as embedding, retrieval, LLM generation, tool calls, and post-processing, and whose edges represent typed data streams. Users describe these pipelines in Python or configuration files without specifying where each operator runs, which engine instance it uses, or how requests are batched. This abstraction closely matches the mental model of LLM application developers while hiding low-level resource management details. -When a pipeline specification is submitted, SAGE compiles it into an executable plan using components in \texttt{sage-kernel} and \texttt{sage-libs}. The planner maps logical operators to physical implementations, including C++ middleware operators where beneficial, and chooses execution sites based on node capabilities and current load. The job manager then instantiates jobs for each stage, tracks their dependencies, and dispatches them to nodes selected by the node selector. During execution, the runtime performs dynamic batching where possible and propagates backpressure signals when downstream operators or engines become saturated. This compiled-execution model allows SAGE to adapt to heterogeneous environments while maintaining a stable, declarative interface for users. - -\subsection{LLM and Embedding Control Plane} - -To support mixed LLM and embedding workloads, SAGE introduces a unified control plane (``sageLLM'') that sits behind the \texttt{sage-gateway} OpenAI-compatible API. Requests arriving at the gateway are classified by the control plane into chat/generation and embedding categories using a request classifier component. The control plane maintains queues per workload type and tenant, and applies policies such as \texttt{HybridSchedulingPolicy} to decide which requests to admit and how to batch them. These policies are aware of the differing characteristics of LLM decoding and embedding computation: LLM requests have long-lived decode phases with idle gaps between GPU kernels, while embeddings are shorter, more uniform operations that can opportunistically use otherwise idle resources. -The control plane orchestrator, implemented by a \texttt{ControlPlaneManager} module under \texttt{sageLLM/control\_plane/}, manages a pool of backend engines consisting of vLLM instances for LLMs and embedding servers for vector encoders. It performs load balancing across engines, taking into account engine capacity, current queue lengths, and any placement constraints imposed by heterogeneous hardware. Batching and routing decisions are made at the control-plane level but respect the internal scheduling mechanisms of each engine: for example, vLLM continues to use its own continuous batching policy, while SAGE determines which requests to forward and when. This division of responsibilities allows SAGE to provide cross-engine and cross-workload scheduling without reimplementing per-engine optimizations. -From the perspective of SAGE's layered architecture, the control plane spans L1 through L3: core modules and configuration live in \texttt{sage-common}, platform-level deployment and port management are handled in \texttt{sage-platform}, and runtime policies are implemented in \texttt{sage-kernel} and associated control-plane packages. Higher layers interact with the control plane through the unified inference client and gateway, treating it as a managed service. This design makes it possible to swap in different scheduling policies, add new engine types, or adjust placement strategies without impacting application code. - -\subsection{Implementation Details and Deployment} - -SAGE is implemented as a multi-package Python project with selected components in C++ for performance-critical paths. Middleware operators in \texttt{sage-middleware} are built with CMake and linked into Python via extension modules, allowing the system to offload tight loops such as tokenization, feature extraction, or data transformation when necessary. The repository provides a unified development and quality toolchain through \texttt{sage-dev}, which wraps testing, linting, and formatting tools (including Ruff and Mypy) to ensure consistent code quality across packages. -Deployment is streamlined through quickstart scripts that install SAGE and its dependencies, set up environment variables, and configure user paths according to the XDG base directory specification. Runtime state and logs are stored in project-level \texttt{.sage/} directories and user-level locations derived from \texttt{get\_user\_paths()}, separating ephemeral artifacts from configuration. Cluster topology and service endpoints are described in \texttt{config/config.yaml} and \texttt{config/cluster.yaml}, which specify available CPU-only and GPU nodes, ports derived from \texttt{SagePorts}, and gateway settings. On top of this foundation, operators can use \texttt{sage-cli} and \texttt{sage-studio} to launch pipelines, inspect their status, and collect traces for debugging. -The same tooling underpins SAGE's benchmark suite in \texttt{sage-benchmark}, which defines reproducible experiments for end-to-end pipelines, control-plane policies, isolation and fairness, scalability, and heterogeneous deployments. By reusing the production control plane and dataflow runtime for benchmarks, SAGE ensures that experimental results reflect realistic system behavior rather than synthetic microbenchmarks. - -\subsection{Relationship to vLLM and Other Engines} - -A natural question for systems reviewers is how SAGE relates to optimized LLM serving engines such as vLLM. SAGE is explicitly designed to be complementary: it uses vLLM and similar systems as backend engines and focuses on the pipeline-level concerns that they do not address. vLLM optimizes single-model inference through mechanisms such as paged attention and continuous batching, whereas SAGE coordinates multiple LLM and embedding services, manages multi-tenant queues, and decides how to allocate heterogeneous CPU/GPU resources across competing pipelines. -In practical deployments, a SAGE cluster will consist of one or more vLLM instances registered with the control plane alongside embedding servers and other operators. For example, in a retrieval-augmented generation pipeline, SAGE can route embedding requests to CPU nodes running embedding models while reserving GPU capacity for LLM decoding, all behind a single \texttt{sage-gateway} endpoint. Without SAGE, operators must manually configure and balance separate LLM and embedding services; with SAGE, these decisions are encoded in declarative pipeline specifications and enforced by the control plane and kernel. The experiments in this paper therefore evaluate SAGE as a full system composed on top of engines like vLLM, rather than as a competitor to them. - -% --------------------------------------------------------------------------- -% Suggested figures and diagrams (author notes, not part of camera-ready) -% --------------------------------------------------------------------------- -% Figure 1: SAGE layered architecture (L1--L5), showing packages and -% dependency directions. -% Figure 2: Declarative dataflow and execution model, with a RAG pipeline -% compiled into jobs and placed on CPU/GPU nodes. -% Figure 3: Control-plane architecture, including request classifier, -% scheduling queues, HybridSchedulingPolicy, and backend engines. -% Figure 4: Example deployment diagram illustrating CPU-only embedding nodes -% and GPU-backed LLM nodes, plus gateway and user interfaces. -% If page limits are tight, detailed control-plane internals or deployment -% diagrams can be moved to an appendix while retaining the high-level -% architecture and dataflow figures in the main paper. - -% --------------------------------------------------------------------------- -% High-level System / Method outline (from 07_system_outline_example) -% --------------------------------------------------------------------------- -% This informal outline mirrors the structure implemented above and can be -% used as a checklist when revising the System Design section. -% -% 1) System Overview and Design Goals -% - Problems: complex multi-step LLM pipelines, mixed LLM+embedding -% workloads, CPU-only environments, end-to-end evaluation and -% reproducibility. -% - Goals: scalability, heterogeneity support, programmability via -% declarative dataflow, debuggability/observability, reproducibility, -% ease of evolution. -% - Positioning: SAGE as a unified platform that combines serving, -% workflow, control plane, and benchmarking. -% -% 2) Layered Architecture -% - L1 (sage-common): configuration, XDG user paths, SagePorts, unified -% inference client, control-plane core modules. -% - L2 (sage-platform): platform services for storage, queuing, service -% management, cluster configuration (config/config.yaml, -% config/cluster.yaml). -% - L3 (sage-kernel, sage-libs): execution kernels, job management, -% node selection with CPU/GPU awareness. -% - L4 (sage-middleware): C++ operators and performance-critical -% components. -% - L5 (sage-cli, sage-tools): CLI and development tools. -% - Independent repos: sage-benchmark, sage-examples, sage-studio, sageLLM. -% -% 3) Declarative Dataflow and Execution Model -% - User-facing APIs for composing ingestion, embedding, retrieval, LLM -% generation, tool calls, and post-processing (examples/apps and -% examples/tutorials). -% - Compilation of declarative graphs into jobs placed on nodes, with -% batching, parallelism, and backpressure handled by kernel/platform. -% - Role of C++ middleware operators in performance-critical stages. -% -% 4) LLM & Embedding Control Plane (sageLLM, independent repo) -% - Goals: share resources across LLM/embedding workloads, improve -% throughput and tail latency, respect SLOs. -% - Components: ControlPlaneManager, RequestClassifier, -% HybridSchedulingPolicy, EmbeddingExecutor, engine pool. -% - Interaction with sage-gateway FastAPI app and backend engines (vLLM, -% embedding servers), port management via SagePorts (GATEWAY_DEFAULT, -% LLM_DEFAULT, EMBEDDING_DEFAULT, WSL2-aware fallbacks). -% - Distinction from single-instance vLLM or simple load balancers. -% -% 5) Implementation Details and Deployment -% - Implementation choices: Python 3.10+ with selected C++ components in -% sage-middleware, built via CMake, artifacts under .sage/. -% - Tooling: quickstart.sh, manage.sh, CI install wrappers, sage-dev for -% tests/quality/examples (pytest, Ruff, Mypy; tools/pytest.ini, -% tools/ruff.toml). -% - Deployment: CPU-only and GPU clusters via configuration files and -% node selection; XDG-based user paths for logs, models, cache; support -% for reproducible experiments and CI. -% -% This outline originates from docs/07_system_outline_example.md and is kept -% here as an author aid; it should not appear verbatim in the camera-ready -% paper. diff --git a/benchmark/latex/05_experiments.tex b/benchmark/latex/05_experiments.tex deleted file mode 100644 index ff5d46b3a8..0000000000 --- a/benchmark/latex/05_experiments.tex +++ /dev/null @@ -1,67 +0,0 @@ -% Experiments section for the SAGE systems paper (content, not prompt) - -\section{Experiments}\label{sec:experiments} - -We evaluate SAGE along five questions that correspond to the design goals of the system: (1) how efficiently it executes end-to-end LLM/AI pipelines; (2) how much benefit its unified control plane provides over separately managed LLM and embedding services; (3) how well it isolates latency-sensitive tenants from noisy neighbors; (4) how its throughput scales with additional backend engines; and (5) how effectively it exploits heterogeneous CPU/GPU deployments. All experiments are implemented using the \texttt{sage-benchmark} package and run on the same cluster configuration, with vLLM serving as the LLM backend and a separate embedding server for vector encoders. Unless otherwise noted, we use a representative instruction-tuned LLM (e.g., Qwen2.5-7B-Instruct) and a high-quality embedding model (e.g., BGE-M3), with input and output lengths and arrival processes chosen to approximate realistic RAG and agent workloads. Baselines are carefully configured to use the same hardware, software versions, and models as SAGE, and include both single-engine and multi-service deployments. - -\subsection{End-to-End Pipeline Performance} - -Our first experiment measures the end-to-end performance of a retrieval-augmented generation (RAG) pipeline implemented in SAGE. The pipeline consists of three stages---embedding, retrieval, and LLM generation---and is exercised by multiple concurrent clients issuing queries drawn from a fixed corpus. We compare SAGE's dataflow-based execution against a baseline where each stage is deployed as an independent service and orchestrated by an application-level script that lacks centralized scheduling. Figure~5.1(a) reports the cumulative distribution of end-to-end latencies, while Figure~5.1(b) shows a request timeline that visualizes how embedding and generation tasks are interleaved on the backends. -The results show that SAGE delivers a tight latency distribution, with p99 end-to-end latency of [X]~ms compared to [X\textsubscript{base}]~ms for the baseline, and reduces tail latency by [X\textsubscript{rel}]\%. By compiling the pipeline into a coordinated execution plan and using the same control plane to drive both embedding and LLM stages, SAGE significantly reduces scheduling gaps and idle periods visible in the baseline timeline. This leads to an overall throughput improvement of [Y]\texttimes{} while maintaining p95 latency below [Z]~ms. These findings support the claim that a dataflow-based execution model, combined with an LLM-aware control plane, can improve both efficiency and predictability for complex LLM/AI pipelines. - -\subsection{Control Plane Effectiveness} - -The second experiment isolates the effect of SAGE's unified control plane on mixed LLM and embedding workloads. We generate a synthetic workload consisting of a configurable mix of chat/generation requests and embedding requests (e.g., 70\% chat, 30\% embedding), and compare two configurations: (1) SAGE's control plane co-scheduling both workloads across a shared pool of engines, and (2) a baseline with separate LLM and embedding services, each with its own queue and no cross-service coordination. Figure~5.2 plots throughput versus latency for both configurations as we increase the global request rate; an accompanying latency CDF highlights differences in tail behavior. -Under unified control, SAGE sustains [Y]\% higher throughput before hitting the same latency threshold as the baseline and reduces p99 latency by [X\textsubscript{cp}]\% under mixed workloads. By exploiting idle GPU cycles during LLM decoding to run embedding batches and by smoothing load across engines, the control plane keeps both LLM and embedding services better utilized without violating SLOs. In contrast, the separate-services baseline exhibits earlier saturation and higher tail latencies because load imbalances between the two services cannot be corrected at runtime. These results demonstrate that a unified LLM+embedding control plane can provide tangible latency and throughput benefits beyond what single-engine schedulers achieve in isolation. - -\subsection{Isolation and Fairness} - -The third experiment studies SAGE's ability to protect latency-sensitive tenants from noisy neighbors. We consider two classes of clients: an \emph{Interactive} group that issues low-rate queries with strict latency SLOs, and a \emph{Batch} group that generates high-rate, throughput-oriented traffic. We compare two configurations: a baseline that uses FIFO queues without explicit tenant priorities, and SAGE with priority-aware scheduling in the control plane. Figure~5.3 reports the latency CDF for the Interactive group under both configurations while the Batch group is active. -Without isolation, Batch traffic causes severe interference: the Interactive group's p99 latency increases to [A]~ms and SLO misses become frequent. With SAGE's priority-aware policies, the Interactive group's latency curve remains close to its single-tenant baseline, with p99 latency reduced to [A\textsubscript{iso}]~ms and SLO satisfaction improving from [B]\% to [A]\%. At the same time, the Batch group continues to make progress, albeit at slightly lower throughput. These results indicate that SAGE's control plane can enforce practical fairness and isolation properties in multi-tenant LLM deployments without requiring separate clusters or hard partitioning. - -\subsection{Scalability} - -To evaluate scalability, we vary the number of vLLM backend instances managed by SAGE while driving the system with a high-concurrency mixed workload. We consider configurations with 1, 2, 4, and 8 LLM backends (each running on a GPU or GPU partition), corresponding to scaling from 1 to [G] GPU-backed engines and up to [H] concurrent clients under load, while keeping the embedding capacity proportionally scaled or fixed depending on the scenario. Figure~5.4 summarizes the achieved throughput in requests per second for each configuration and reports the corresponding speedup relative to the single-backend baseline, along with estimates of control-plane overhead. -Throughput increases nearly linearly up to 8 backends, achieving a [S\textsubscript{8}]\texttimes{} speedup compared to the single-backend case, with control-plane overhead remaining below [O]\% of total CPU time. Latency distributions remain stable across configurations up to the point where the underlying engines saturate, indicating that the control plane does not introduce a scalability bottleneck for the tested model sizes. These results support the claim that SAGE can coordinate multiple LLM engines efficiently and that its additional scheduling logic does not negate the benefits of horizontal scaling. - -\subsection{Heterogeneous Hardware Support} - -Finally, we investigate how SAGE leverages heterogeneous hardware by offloading embedding workloads to CPU-only nodes while reserving GPU resources for LLM inference. We compare two configurations under the same mixed workload: a GPU-only setup where both LLM and embedding models run on GPUs, and a hybrid setup where embedding servers run on CPU nodes selected by the kernel's node selector, while LLM decoding remains on GPUs. Figure~5.5 reports either a resource-efficiency metric (e.g., tokens-per-second per GPU) or latency CDFs for both configurations. -The hybrid configuration slightly increases embedding latency, but overall system throughput for LLM tokens improves by [H\textsubscript{tok}]\% because GPU capacity is no longer consumed by embedding computations. From a cost-performance perspective, the hybrid setup delivers [H\textsubscript{eff}]\% better GPU efficiency while meeting nearly the same latency targets as the GPU-only configuration. These findings illustrate how SAGE's awareness of CPU-only nodes and its flexible node-selection policies enable operators to trade modest increases in embedding latency for substantial gains in GPU utilization and cost efficiency. - -% --------------------------------------------------------------------------- -% Author notes: placeholders, baselines, and reproducibility -% --------------------------------------------------------------------------- -% Placeholders to be instantiated after experiments: -% [X] : p99 end-to-end latency (SAGE) in RAG pipeline -% [X_base] : p99 end-to-end latency (baseline) -% [X_rel] : relative p99 reduction vs. baseline -% [Y] : throughput improvement factor of SAGE vs. baseline -% [Z] : p95 latency bound under target load -% [X_cp] : p99 latency reduction from unified control plane -% [A], [B] : SLO satisfaction rates with/without isolation -% [A_iso] : p99 latency for Interactive group with isolation -% [S_8] : speedup at 8 backends vs. 1 backend -% [O] : control-plane CPU overhead as percentage of total -% [H_tok] : increase in LLM token throughput in hybrid config -% [H_eff] : GPU efficiency / cost-performance improvement -% [G] : maximum number of GPU-backed engines in scalability tests -% [H] : maximum number of concurrent clients in scalability tests -% -% Baseline configurations (must be documented in the paper): -% - End-to-end and control-plane experiments: -% * Baseline: vLLM + separate embedding service, manually load-balanced -% * SAGE: unified control plane + declarative dataflow -% - Isolation: FIFO/no-priority baseline vs. SAGE priority-aware policies -% - Scalability: single vLLM instance vs. multiple vLLM instances under -% identical model and hardware settings -% - Heterogeneity: all-GPU setup vs. hybrid CPU-embedding + GPU-LLM setup -% -% Reproducibility checklist (to include in main text or appendix): -% - Detailed hardware specification (GPU model/count, CPU cores, memory, -% network interconnect) -% - Software versions (SAGE, vLLM, CUDA, Python, key libraries) -% - Model details (names, sizes, quantization settings) -% - Workload specs (token length distributions, arrival process, duration, -% warm-up, repetitions, error bars) -% - Pointers to configuration files and benchmark scripts in `sage-benchmark`. diff --git a/benchmark/latex/06_contributions_example.tex b/benchmark/latex/06_contributions_example.tex deleted file mode 100644 index 3d64fabb99..0000000000 --- a/benchmark/latex/06_contributions_example.tex +++ /dev/null @@ -1,48 +0,0 @@ -% Example contributions list for the SAGE systems paper (content, not prompt) -% -% This snippet can be used as a stand-alone "Contributions" section -% or inlined at the end of the Introduction. - -\section*{Contributions} - -We summarize the main contributions of SAGE as follows: - -\begin{enumerate} - \item \textbf{A layered architecture for declarative LLM/AI pipelines.} - - We introduce SAGE, a framework that organizes LLM/AI data processing pipelines into a strict six-layer architecture, from foundational utilities (\texttt{sage-common}) and platform services (\texttt{sage-platform}), through kernel and middleware components (\texttt{sage-kernel}, \texttt{sage-libs}, \texttt{sage-middleware}), up to applications and user-facing tools (\texttt{sage-apps}, \texttt{sage-benchmark}, \texttt{sage-cli}, \texttt{sage-studio}, \texttt{sage-tools}, \texttt{sage-gateway}). By enforcing no upward dependencies, SAGE cleanly separates concerns between configuration, scheduling, execution, and user interfaces, enabling independent evolution of layers, easier testing, and simplified large-scale system maintenance. - - \item \textbf{A unified control plane for LLM and embedding workloads.} - - We design and implement a \emph{sageLLM} control plane that jointly manages LLM and embedding workloads across a shared pool of engines. The control plane classifies requests (chat/generation vs. embeddings), applies hybrid scheduling and batching policies (for example, \texttt{HybridSchedulingPolicy}), and exposes an OpenAI-compatible API via \texttt{sage-gateway} on standardized ports derived from \texttt{SagePorts}. This unified design improves resource utilization and reduces tail latency for mixed LLM+embedding traffic compared to siloed vLLM plus separate embedding deployments, while preserving a familiar client-facing interface. - - \item \textbf{Systems support for heterogeneous CPU/GPU deployments with reproducible tooling.} - - SAGE provides kernel-level mechanisms for CPU-only and GPU nodes, including job management in \texttt{sage-kernel} and node selection policies that are aware of hardware capabilities and load. Platform services in \texttt{sage-platform} cover storage, queuing, and service management, while C++ operators in \texttt{sage-middleware} accelerate performance-critical paths. Together with reproducible installation and quality pipelines (\texttt{quickstart.sh}, \texttt{manage.sh}, \texttt{sage-dev}, and pre-commit tooling), the system lowers the barrier to deploying complex LLM pipelines on heterogeneous clusters and makes end-to-end experiments repeatable for both developers and researchers. - - \item \textbf{A comprehensive benchmark suite and reusable testbed for LLM-centric systems.} - - To evaluate the system, we provide \texttt{sage-benchmark}, which instantiates workloads for agent behavior (tool selection, multi-step planning, timing decisions) and control-plane scheduling under diverse traffic patterns, as well as additional suites targeting retrieval, memory, data systems, and scheduler behavior in LLM-centric pipelines. The suite reports not only task- or model-level accuracy but also systems metrics such as throughput, latency distributions, SLO satisfaction, and resource utilization, and it exposes standard interfaces so that alternative agents, scheduling algorithms, or middleware components can be plugged in and compared on a common testbed built on top of SAGE's layered architecture and unified control plane. -\end{enumerate} - -% --------------------------------------------------------------------------- -% Author notes: quantitative claims and mapping to experiments -% --------------------------------------------------------------------------- -% Each contribution should eventually be backed by at least one quantitative -% claim, using experiments from Section~\ref{sec:experiments}: -% - Architecture (1): developer study or configuration/LOC comparison to -% support statements like "reduces configuration complexity by [Y]%". -% - Control plane (2): mixed LLM+embedding benchmarks showing p99 latency -% reduction by [X]% and throughput improvements by [Y]\texttimes{} over -% vLLM + separate embedding baselines (cf. end-to-end and control-plane -% experiments). -% - Heterogeneous support (3): CPU vs. GPU embedding or pipeline benchmarks -% demonstrating that CPU-only nodes achieve [X]% of GPU performance for -% embedding-heavy workloads, and hybrid deployments improve GPU efficiency -% by [H\textsubscript{eff}]% while maintaining latency targets. -% - Benchmark suite (4): comparative evaluations revealing scheduling or -% policy insights (for example, FIFO degrading p99 latency by [C]\texttimes{} -% relative to hybrid policies) using the \texttt{sage-benchmark} workloads. -% -% If space is tight, items (3) and (4) can be merged into a single contribution -% on end-to-end deployment and evaluation support. diff --git a/benchmark/latex/07_system_outline_example.tex b/benchmark/latex/07_system_outline_example.tex deleted file mode 100644 index 06abbc45dc..0000000000 --- a/benchmark/latex/07_system_outline_example.tex +++ /dev/null @@ -1,44 +0,0 @@ -% Example System / Method outline for the SAGE systems paper (content-style summary) -% -% This snippet captures the outline in 07_system_outline_example.md as -% a concise LaTeX summary that can guide or complement the full System -% Design section in 04_system_and_method.tex. - -\section*{System / Method Outline} - -Below we summarize a possible structure for the System / Method section of the SAGE paper, aligned with the actual implementation and package layout. - -\subsection*{System Overview and Design Goals} - -This section motivates SAGE from a systems-reviewer perspective. It explains which concrete problems SAGE targets that are not fully addressed by existing LLM serving or MLOps systems, including complex multi-step LLM pipelines, mixed LLM+embedding workloads, CPU-only environments, and end-to-end evaluation and reproducibility. It then states the main design goals of SAGE: scalability across multiple engines and nodes, explicit support for heterogeneous CPU/GPU deployments, programmability via declarative dataflow, debuggability and observability, reproducibility of deployments and experiments, and ease of evolution of individual components. Finally, it positions SAGE in the ML systems ecosystem as a unified platform that combines aspects of serving, workflow, control plane, and benchmarking, rather than focusing on a single engine or library. - -\subsection*{Layered Architecture} - -This section introduces SAGE's five-layer architecture (L1--L5) and clarifies the responsibilities of each layer. It describes how \texttt{sage-common} (L1) provides configuration, XDG-based user paths, \texttt{SagePorts}, and shared components such as the unified inference client and control-plane core; how \texttt{sage-platform} (L2) offers platform services for storage, queuing, and service management tied to \texttt{config/config.yaml} and \texttt{config/cluster.yaml}; how \texttt{sage-kernel} and \texttt{sage-libs} (L3) implement execution kernels, job management, and node selection with awareness of CPU-only and GPU nodes; how \texttt{sage-middleware} (L4) contributes C++ operators and performance-critical components; and how \texttt{sage-cli} and \texttt{sage-tools} (L5) provide CLI and development tools. Applications, benchmarks, visual studio, and LLM gateway are maintained in independent repositories. It emphasizes the "no upward dependencies" rule and contrasts this disciplined layering with monolithic or ad-hoc orchestration scripts. - -\subsection*{Declarative Dataflow and Execution Model} - -This section explains how SAGE exposes declarative dataflow abstractions for LLM/AI pipelines and how those abstractions are compiled into executable plans. It illustrates how users describe pipelines that combine data ingestion, embedding, retrieval, LLM generation, tool calls, and post-processing, using examples from \texttt{examples/apps} and \texttt{examples/tutorials}. It then describes how the kernel and platform layers translate these declarations into jobs, place them on nodes using node-selection policies, and handle batching, parallelism, and backpressure across CPU and GPU resources. The role of \texttt{sage-middleware} operators in optimizing performance-critical stages is highlighted, and the benefits over ad-hoc Python scripts are discussed in terms of maintainability, performance, and correctness. - -\subsection*{LLM \& Embedding Control Plane (sageLLM)} - -This section focuses on the sageLLM control plane as the component that unifies scheduling and resource management for LLM and embedding workloads. It states the goals of the control plane---sharing resources across LLM and embedding services, improving throughput and tail latency, and meeting per-tenant SLOs---and explains how requests are classified (chat/generation versus embedding), queued, and batched according to policies such as \texttt{HybridSchedulingPolicy}. It describes the interaction between the control plane, the \texttt{sage-gateway} FastAPI application, and backend engines such as vLLM and embedding servers, as well as how standardized ports (\texttt{GATEWAY_DEFAULT}, \texttt{LLM_DEFAULT}, \texttt{EMBEDDING_DEFAULT}, and WSL2-aware fallbacks) are managed by \texttt{SagePorts}. The section also clarifies how the control plane fits into the broader SAGE architecture and how it differs from single-instance vLLM deployments or simple load balancers. - -\subsection*{Implementation Details and Deployment} - -This section provides the implementation details and deployment story that matter to systems reviewers and practitioners. It summarizes the language and build choices (Python 3.10+ with selected C++ components in \texttt{sage-middleware} built via CMake, artifacts in \texttt{.sage/build/}), the installation and management scripts (\texttt{quickstart.sh}, \texttt{manage.sh}, CI install wrappers), and the \texttt{sage-dev} tooling for tests, quality checks, and examples (integrating pytest, Ruff, and Mypy with configuration under \texttt{tools/pytest.ini} and \texttt{tools/ruff.toml}). It explains how SAGE supports both CPU-only and GPU deployments through node selection and configuration files, how XDG-based user paths structure logs, models, and caches, and how these choices enable reproducible experiments and continuous integration. - -% --------------------------------------------------------------------------- -% Author notes: how to use this outline -% --------------------------------------------------------------------------- -% - This outline is intentionally high-level and mirrors the structure -% implemented in 04_system_and_method.tex. It can be used as a sanity -% check to ensure that the written System Design section covers the key -% questions systems reviewers care about. -% - When generating or revising text, you can paste bullets from the -% original Markdown outline into prompt templates, then refine the -% resulting prose by aligning it with actual package structure and -% experimental setup. -% - If page limits are tight, pairs of subsections can be merged (e.g., -% System Overview + Layered Architecture, or Dataflow + Control Plane), -% and detailed implementation notes can be moved to an appendix. diff --git a/benchmark/scripts/sage-system-bench b/benchmark/scripts/sage-system-bench deleted file mode 100755 index 1eb6c2ca7b..0000000000 --- a/benchmark/scripts/sage-system-bench +++ /dev/null @@ -1,9 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE System Benchmark CLI -""" - -from sage.benchmark.benchmark_sage.scripts.sage_bench_cli import main - -if __name__ == "__main__": - main() diff --git a/benchmark/scripts/sage_bench_cli.py b/benchmark/scripts/sage_bench_cli.py deleted file mode 100644 index bcc3afe214..0000000000 --- a/benchmark/scripts/sage_bench_cli.py +++ /dev/null @@ -1,79 +0,0 @@ -import argparse - -from sage.benchmark.benchmark_sage.experiments.config import ( - ExperimentConfig, - WorkloadConfig, -) -from sage.benchmark.benchmark_sage.experiments.exp_5_1_e2e_pipeline import ( - E2EPipelineExperiment, -) -from sage.benchmark.benchmark_sage.experiments.exp_5_2_control_plane import ( - ControlPlaneExperiment, -) -from sage.benchmark.benchmark_sage.experiments.exp_5_3_isolation import ( - IsolationExperiment, -) -from sage.benchmark.benchmark_sage.experiments.exp_5_4_scalability import ( - ScalabilityExperiment, -) -from sage.benchmark.benchmark_sage.experiments.exp_5_5_heterogeneity import ( - HeterogeneityExperiment, -) - - -def main(): - parser = argparse.ArgumentParser(description="SAGE System Benchmark CLI") - subparsers = parser.add_subparsers(dest="command", help="Command to run") - - # Run command - run_parser = subparsers.add_parser("run", help="Run an experiment") - run_parser.add_argument( - "--exp", - type=str, - required=True, - choices=["5.1", "5.2", "5.3", "5.4", "5.5"], - help="Experiment to run (e.g., 5.1)", - ) - run_parser.add_argument("--name", type=str, default="experiment", help="Experiment name") - run_parser.add_argument("--rate", type=float, default=10.0, help="Request rate (req/s)") - run_parser.add_argument("--count", type=int, default=100, help="Total requests") - run_parser.add_argument("--llm-ratio", type=float, default=0.7, help="LLM ratio") - run_parser.add_argument( - "--gateway", - type=str, - default="http://localhost:8888", # allow-control-plane-bypass: CLI default - help="Gateway URL", - ) - run_parser.add_argument("--output", type=str, default="./outputs", help="Output directory") - - args = parser.parse_args() - - if args.command == "run": - config = ExperimentConfig( - name=args.name, - description=f"Run {args.exp} at {args.rate} req/s", - experiment_section=args.exp, - gateway_url=args.gateway, - workload=WorkloadConfig( - total_requests=args.count, request_rate=args.rate, llm_ratio=args.llm_ratio - ), - ) - - if args.exp == "5.2": - exp = ControlPlaneExperiment(config, args.output, verbose=True) - elif args.exp == "5.4": - exp = ScalabilityExperiment(config, args.output, verbose=True) - elif args.exp == "5.1": - exp = E2EPipelineExperiment(config, args.output, verbose=True) - elif args.exp == "5.5": - exp = HeterogeneityExperiment(config, args.output, verbose=True) - elif args.exp == "5.3": - exp = IsolationExperiment(config, args.output, verbose=True) - - exp.setup() - exp.run() - exp.teardown() - - -if __name__ == "__main__": - main() diff --git a/codecov.yml b/codecov.yml index f15e77a195..b5b6c139d7 100644 --- a/codecov.yml +++ b/codecov.yml @@ -2,19 +2,19 @@ # https://docs.codecov.com/docs/codecov-yaml codecov: - require_ci_to_pass: yes + require_ci_to_pass: true notify: - wait_for_ci: yes + wait_for_ci: true # 告诉 Codecov 如何处理文件移动/重命名 # 这样移动的文件不会被算作完全新增的代码 parsers: gcov: branch_detection: - conditional: yes - loop: yes - method: no - macro: no + conditional: true + loop: true + method: false + macro: false coverage: precision: 2 @@ -37,17 +37,17 @@ coverage: # 包级别覆盖率要求 package: - sage-common: + foundation: target: 60% - sage-kernel: + runtime: target: 50% - sage-platform: + stream: target: 50% - sage-middleware: + serving: target: 50% - sage-libs: + cli: target: 50% - sage-tools: + tools: target: 50% sage-apps: target: 40% @@ -59,9 +59,9 @@ coverage: comment: layout: "reach,diff,flags,tree,footer" behavior: default - require_changes: no - require_base: no - require_head: yes + require_changes: false + require_base: false + require_head: true # 忽略测试文件和自动生成的代码 ignore: diff --git a/config/README.md b/config/README.md index e095c04899..ae69130986 100644 --- a/config/README.md +++ b/config/README.md @@ -11,17 +11,20 @@ Unified configuration file for SAGE project. ## Quick Start ```bash -# 1. Initialize config (interactive) -sage cluster init - -# 2. Or edit directly +# 1. Review or edit the unified config directly vi config/config.yaml -# 3. Setup SSH keys to worker nodes -sage cluster setup-ssh +# 2. Inspect current local status +sage status + +# 3. Inspect runtime-visible nodes +sage runtime nodes -# 4. Start cluster -sage cluster start +# 4. If you integrate an external sagellm gateway, inspect the launch contract +sage serve gateway --json + +# 5. Record lightweight local index metadata when needed +sage index ingest --source ./docs --index local-docs ``` ## Configuration Sections @@ -32,19 +35,19 @@ sage cluster start - `provider.head_ip` - Head node IP - `provider.worker_ips` - List of worker node IPs - `auth` - SSH authentication (key-based only) -- `ray` - Ray settings (ports, resources) +- `flutty` - Flutty runtime settings (ports, resources) - `remote` - Remote environment (conda, paths) ### Services -- `llm` - vLLM service settings +- `llm` - SageLLM service settings - `embedding` - Embedding service - `gateway` - OpenAI-compatible API gateway - `studio` - Web UI ports ## Directory Structure -``` +```text / ├── config/ # Configuration (git tracked) │ ├── config.yaml # Unified config file diff --git a/config/cluster.yaml b/config/cluster.yaml index 3ff6a7854b..aa8d093920 100644 --- a/config/cluster.yaml +++ b/config/cluster.yaml @@ -1,26 +1,27 @@ -# SAGE Cluster Configuration -# 配置文件路径: /config/cluster.yaml + # SAGE Cluster Configuration +# 配置文件路径: /home/sage/SAGE/config/cluster.yaml +# 说明: 这是 SAGE 项目的全局集群配置文件，位于项目根目录 # Head 节点配置 head: - host: "sage-node-15" # Head 节点 IP/主机名 - head_port: 26379 # Ray GCS 端口 - dashboard_port: 28265 # Ray Dashboard 端口 + host: "sage-node-1" # Head 节点 IP/主机名 + head_port: 26379 # 集群通信端口（GCS/Flownet 协调端口） + dashboard_port: 28265 # Dashboard 端口 dashboard_host: "0.0.0.0" # Dashboard 监听地址 - temp_dir: "/var/tmp/ray" + temp_dir: "/var/tmp/sage_head" log_dir: "/var/tmp/sage_head_logs" - conda_env: "base" # Conda 环境名称 (base 或 sage) + conda_env: "sage" # Conda 环境名称 (base 或 sage) python_path: "" # 留空自动检测 - ray_command: "" # 留空自动检测 - num_cpus: 4 # 容器实际分配的 CPU 核心数 (覆盖自动检测) + runtime_command: "" # 留空自动检测（sageFlownet 运行时命令） + num_cpus: 8 # 容器实际分配的 CPU 核心数 (覆盖自动检测) num_gpus: 0 # GPU 数量 # Worker 节点配置 worker: bind_host: "localhost" - temp_dir: "/tmp/ray_worker" + temp_dir: "/tmp/sage_worker" log_dir: "/tmp/sage_worker_logs" - num_cpus: 4 # 每个 Worker 容器分配的 CPU 核心数 + num_cpus: 8 # 每个 Worker 容器分配的 CPU 核心数 num_gpus: 0 # 每个 Worker 容器的 GPU 数量 # SSH 配置 @@ -29,23 +30,47 @@ ssh: key_path: "~/.ssh/id_rsa" # SSH 私钥路径 connect_timeout: 10 # 连接超时（秒） workers: - - host: "sage-node-16" - port: 22 - - host: "sage-node-17" - port: 22 - - host: "sage-node-18" - port: 22 + # - host: "sage-node-2" + # port: 22 + # - host: "sage-node-3" + # port: 22 + # - host: "sage-node-4" + # port: 22 + # - host: "sage-node-5" + # port: 22 + # - host: "sage-node-6" + # port: 22 + # - host: "sage-node-7" + # port: 22 + # - host: "sage-node-8" + # port: 22 + # - host: "sage-node-9" + # port: 22 + # - host: "sage-node-10" + # port: 22 + # - host: "sage-node-11" + # port: 22 + # - host: "sage-node-12" + # port: 22 + # - host: "sage-node-13" + # port: 22 + # - host: "sage-node-14" + # port: 22 + # - host: "sage-node-15" + # port: 22 + # - host: "sage-node-16" + # port: 22 # 远程节点配置 remote: sage_home: "/home/sage" python_path: "" # 留空自动检测 - ray_command: "" # 留空自动检测 - conda_env: "base" # Worker 节点的 conda 环境名 (base 或 sage) + runtime_command: "" # 留空自动检测（sageFlownet 运行时命令，向后兼容 ray_command 键） + conda_env: "sage" # Worker 节点的 conda 环境名 (base 或 sage) # Daemon 配置 daemon: - host: "sage-node-15" + host: "sage-node-1" port: 19001 # 输出配置 diff --git a/config/models.json b/config/models.json index ba1bf62d02..094c9c1c78 100644 --- a/config/models.json +++ b/config/models.json @@ -3,7 +3,7 @@ "name": "Qwen/Qwen2.5-32B-Instruct", "base_url": "http://127.0.0.1:8901/v1", "is_local": true, - "default": false, + "default": true, "api_key": "" }, { @@ -43,7 +43,7 @@ "is_local": false, "description": "Huawei Pangu 1B (Container: base-sage)", "engine_kind": "llm", - "api_key": "", + "api_key": "${PANGU_API_KEY}", "default": false }, { @@ -52,7 +52,7 @@ "is_local": false, "description": "Huawei Pangu 7B (Container: base-sage)", "engine_kind": "llm", - "api_key": "", + "api_key": "${PANGU_API_KEY}", "default": false }, { @@ -62,12 +62,5 @@ "engine_kind": "embedding", "default": false, "api_key": "" - }, - { - "name": "qwen-turbo-2025-02-11", - "base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1", - "is_local": false, - "default": true, - "api_key": "" } ] diff --git a/dependencies-spec.yaml b/dependencies-spec.yaml index 8ed8972aca..48f67a74df 100644 --- a/dependencies-spec.yaml +++ b/dependencies-spec.yaml @@ -4,10 +4,8 @@ # Core ML stack torch: ">=2.7.0,<3.0.0" -transformers: ">=4.52.0,<4.54.0" +transformers: ">=4.53.2,<4.54.0" tokenizers: ">=0.21.0,<0.24.0" -sentence-transformers: ">=3.1.0,<4.0.0" -vllm: ">=0.9.2,<0.10" openai: ">=1.52.0,<1.91.0" # API stack @@ -21,13 +19,15 @@ numpy: ">=1.26.0,<2.3.0" # Independent SAGE ecosystem packages (PyPI) isage-vdb: ">=0.1.5" isage-tsdb: ">=0.1.5" -isage-flow: ">=0.1.1" +isage-flow: ">=0.1.1" # historical stream package; do not expand as separate long-term ownership +flutty: ">=0.1.0" # primary optional distributed runtime backend isage-refiner: ">=0.1.0" isage-neuromem: ">=0.2.1.1" +isagellm: ">=0.5.1.2" isage-anns: ">=0.1.0" isage-amms: ">=0.1.0" # Notes: # - Packages should import versions from this file via tooling (to be wired) instead of hardcoding. # - When bumping any version here, ensure alignment across all pyproject.toml files. -# - For heavyweight optional deps (e.g., vLLM), prefer extras in sage-common to propagate constraints. +# - SAGE inference uses isagellm as the default engine. diff --git a/docs/dependency-audit-gate.md b/docs/dependency-audit-gate.md new file mode 100644 index 0000000000..6dd981a241 --- /dev/null +++ b/docs/dependency-audit-gate.md @@ -0,0 +1,93 @@ +# SAGE Dependency Audit Gate + +## Purpose + +This document records the current direct and optional dependency surfaces declared in +`pyproject.toml` for the consolidated `isage` package. + +Any dependency change in `pyproject.toml` must be reflected here in the same change. + +## Gate Contract + +- Gate script: `tools/scripts/check_meta_dependency_audit.py` +- Enforced in pre-commit and CI +- Every direct dependency or optional dependency group must have a matching evidence section. + +## Consolidated Product Boundary + +SAGE now ships its core product surface from the main repository: + +- `sage.foundation` +- `sage.stream` +- `sage.runtime` +- `sage.serving` +- `sage.cli` +- `sage.edge` + +The repo no longer treats the retired split-package layout as a set of direct dependency owners. + +## Direct Dependency Evidence + +### `isagellm` + +- Callsite: `src/sage/serving/gateway.py` +- Callsite: `README.md` +- Rationale: external inference engine and gateway family used through integration boundaries only. + +## Optional Dependency Group Evidence + +### `serving-edge` + +- Packages: `fastapi`, `uvicorn` +- Callsite: `src/sage/edge/server.py` +- Rationale: edge aggregation and service exposure for the in-tree serving shell. + +### `capability-adapters` + +#### `isage-libs-intent` + +- Callsite: optional tool-use / orchestration integrations documented in the main repo. +- Rationale: intent recognition remains an external capability adapter. + +#### `isage-rag` + +- Callsite: retrieval and vector-store adapter workflows referenced by the docs and examples. +- Rationale: RAG capability remains optional and external to the core stream/runtime surface. + +#### `isage-neuromem` + +- Callsite: memory-oriented integrations referenced by the docs and examples. +- Rationale: memory capability remains optional and external to the core stream/runtime surface. + +### `capability-tooluse` + +#### `isage-sias` + +- Callsite: optional tool-use / continual-learning integrations. +- Rationale: SIAS remains an external capability adapter. + +### `full` + +- Packages: `fastapi`, `uvicorn`, `isage-libs-intent`, `isage-rag`, `isage-neuromem`, `isage-sias`, + `isage-data` +- Callsite: `README.md` +- Rationale: convenience install for the consolidated main package plus optional adapters. + +### `dev` + +- Packages: `isage-dev-tools`, `fastapi`, `uvicorn`, `httpx`, `pytest`, `pytest-cov`, + `pytest-asyncio`, `pytest-mock`, `ruff`, `mypy`, `pre-commit`, `isage-pypi-publisher` +- Callsite: `DEVELOPER.md` +- Rationale: developer workflow, validation, and release tooling. + +## How To Update + +When `pyproject.toml` dependency declarations change: + +1. Update the matching section in this file. +1. Keep at least one concrete callsite or workflow reference per dependency or dependency group. +1. Run: + +```bash +python3 tools/scripts/check_meta_dependency_audit.py --enforce-change-evidence --staged +``` diff --git a/docs/layer-manifest.json b/docs/layer-manifest.json new file mode 100644 index 0000000000..f8a871ae10 --- /dev/null +++ b/docs/layer-manifest.json @@ -0,0 +1,6 @@ +{ + "version": "v1", + "description": "Canonical layer map for workspace labels and repository declarations. The main SAGE workspace now centers on in-tree core surfaces rather than split core-layer repos.", + "declaration_optional_repos": [], + "repos": [] +} diff --git a/docs/profiling/hot_path_report.md b/docs/profiling/hot_path_report.md new file mode 100644 index 0000000000..d88d032d9d --- /dev/null +++ b/docs/profiling/hot_path_report.md @@ -0,0 +1,22 @@ +# SAGE Hot-Path Profiling Report + +> Status: historical report content reset during main-repo consolidation. + +This file is intentionally kept as a lightweight placeholder for regenerated profiling output. + +Current profiling surfaces should be reported against the consolidated in-tree modules: + +- `sage.runtime.scheduler` +- `sage.stream._runtime_kernel_types` +- `sage.foundation` / stream I/O helpers +- optional external capability adapters when explicitly benchmarked + +To regenerate a fresh report: + +```bash +python tools/profiling/cprofile_runner.py +python tools/profiling/cprofile_runner.py --heavy +``` + +Generated artifacts should describe the current consolidated `isage` package rather than the retired +split-package layout. diff --git a/examples/flow_exception_handler_demo_sage.py b/examples/flow_exception_handler_demo_sage.py new file mode 100644 index 0000000000..2e78870a5e --- /dev/null +++ b/examples/flow_exception_handler_demo_sage.py @@ -0,0 +1,268 @@ +""" +flow_exception_handler_demo_sage.py +==================================== +Demonstrates propagate / abort / fallback exception handling policies +using the **SAGE public API** only. + +Acceptance criterion for intellistream/SAGE#1434: + "Example pipeline demonstrates propagate/abort/fallback policy behavior + via SAGE API, and error contracts are backend-agnostic." + +This file only imports from ``sage.*`` and never from ``sage.flownet.*``. +""" + +from __future__ import annotations + +from contextlib import contextmanager +from dataclasses import dataclass, field +from typing import Any + +# --- Consolidated SAGE runtime hook ----------------------------------------- +from sage.runtime.exception_hooks import register_kernel_exception_handler_hook + + +class FlowDefinitionError(TypeError): + """Raised when an invalid exception handler is registered.""" + + +@dataclass(slots=True) +class ExceptionContext: + request_id: str + phase: str + + +@dataclass(slots=True) +class ExceptionEvent: + error_type: str + message: str + traceback: str + context: ExceptionContext + error: Any | None = None + + +@dataclass(slots=True) +class ExceptionDecision: + action: str + payloads: list[Any] = field(default_factory=list) + + @classmethod + def abort(cls) -> ExceptionDecision: + return cls(action="abort") + + @classmethod + def propagate(cls) -> ExceptionDecision: + return cls(action="propagate") + + @classmethod + def fallback(cls, value: Any) -> ExceptionDecision: + return cls(action="fallback", payloads=[value]) + + +def register_exception_handler_hook(push, pop) -> None: + register_kernel_exception_handler_hook(push, pop) + + +@contextmanager +def flow_exception_handler(handler): + if not callable(handler): + raise FlowDefinitionError(f"exception handler must be callable, got {type(handler)!r}") + _push_handler(handler) + try: + yield + finally: + _pop_handler() + + +# --------------------------------------------------------------------------- +# Minimal in-process stub that mimics Flownet's push/pop stack +# (no Flownet dependency — purely for demo purposes) +# --------------------------------------------------------------------------- + +_handler_stack: list[Any] = [] + + +def _push_handler(handler) -> None: + _handler_stack.append(handler) + + +def _pop_handler() -> None: + if _handler_stack: + _handler_stack.pop() + + +def _resolve_handler_chain(event: ExceptionEvent) -> ExceptionDecision | None: + """Walk the handler stack (innermost first) and return the first non-propagate decision.""" + for handler in reversed(_handler_stack): + decision = handler(event) + if decision is None: + continue + if decision.action != "propagate": + return decision + return None + + +# Register the in-process stub with the SAGE hook so that +# ``flow_exception_handler`` works without a live Flownet runtime. +register_exception_handler_hook(_push_handler, _pop_handler) + + +# --------------------------------------------------------------------------- +# Helper: simulate an exception event +# --------------------------------------------------------------------------- + + +def _make_event(error_type: str, message: str) -> ExceptionEvent: + ctx = ExceptionContext(request_id="demo-request", phase="actor_call") + return ExceptionEvent( + error_type=error_type, + message=message, + traceback="", + context=ctx, + error=None, + ) + + +# --------------------------------------------------------------------------- +# Demo 1 — abort: silently discard the failed item +# --------------------------------------------------------------------------- + + +def demo_abort_policy() -> None: + print("\n=== Policy: ABORT ===") + print("A ValueErronr is raised. The handler says 'abort' → item discarded silently.") + + def abort_handler(event: ExceptionEvent) -> ExceptionDecision: + print(f" [handler] received {event.error_type}: {event.message}") + return ExceptionDecision.abort() + + with flow_exception_handler(abort_handler): + event = _make_event("ValueError", "bad input data") + decision = _resolve_handler_chain(event) + + print(f" decision.action = {decision.action!r}") + assert decision.action == "abort" + print(" ✓ abort policy OK") + + +# --------------------------------------------------------------------------- +# Demo 2 — fallback: replace the failed item with a default value +# --------------------------------------------------------------------------- + + +def demo_fallback_policy() -> None: + print("\n=== Policy: FALLBACK ===") + print("A ZeroDivisionError is raised. Handler returns a default value of 0.") + + def fallback_handler(event: ExceptionEvent) -> ExceptionDecision: + print(f" [handler] received {event.error_type}: {event.message}") + return ExceptionDecision.fallback(value=0) + + with flow_exception_handler(fallback_handler): + event = _make_event("ZeroDivisionError", "division by zero") + decision = _resolve_handler_chain(event) + + print(f" decision.action = {decision.action!r}") + print(f" decision.payloads = {decision.payloads}") + assert decision.action == "fallback" + assert decision.payloads == [0] + print(" ✓ fallback policy OK") + + +# --------------------------------------------------------------------------- +# Demo 3 — propagate: inner handler defers; outer handler takes over +# --------------------------------------------------------------------------- + + +def demo_propagate_policy() -> None: + print("\n=== Policy: PROPAGATE (nested handlers) ===") + print("Inner handler propagates a ConnectionError; outer handler catches it and aborts.") + + def outer_handler(event: ExceptionEvent) -> ExceptionDecision: + print(f" [outer] received {event.error_type}: {event.message}") + return ExceptionDecision.abort() + + def inner_handler(event: ExceptionEvent) -> ExceptionDecision: + print(f" [inner] propagating {event.error_type}") + return ExceptionDecision.propagate() + + with flow_exception_handler(outer_handler): + with flow_exception_handler(inner_handler): + event = _make_event("ConnectionError", "upstream timeout") + decision = _resolve_handler_chain(event) + + print(f" decision.action = {decision.action!r}") + assert decision.action == "abort" + print(" ✓ propagate → outer abort policy OK") + + +# --------------------------------------------------------------------------- +# Demo 4 — conditional handler: different decisions per error type +# --------------------------------------------------------------------------- + + +def demo_conditional_policy() -> None: + print("\n=== Policy: CONDITIONAL (error-type aware handler) ===") + + def smart_handler(event: ExceptionEvent) -> ExceptionDecision: + if event.error_type == "ZeroDivisionError": + print(f" [smart] {event.error_type} → fallback(0)") + return ExceptionDecision.fallback(value=0) + if event.error_type == "KeyError": + print(f" [smart] {event.error_type} → abort") + return ExceptionDecision.abort() + print(f" [smart] {event.error_type} → propagate") + return ExceptionDecision.propagate() + + with flow_exception_handler(smart_handler): + for error_type, expected_action in [ + ("ZeroDivisionError", "fallback"), + ("KeyError", "abort"), + ("UnknownError", None), # propagates → no handler above → None + ]: + event = _make_event(error_type, "test") + decision = _resolve_handler_chain(event) + if expected_action is None: + assert decision is None + print(f" {error_type:25s} → (no decision, re-raise in runtime)") + else: + assert decision.action == expected_action + print(f" {error_type:25s} → {decision.action!r}") + + print(" ✓ conditional policy OK") + + +# --------------------------------------------------------------------------- +# Demo 5 — FlowDefinitionError on invalid handler +# --------------------------------------------------------------------------- + + +def demo_invalid_handler() -> None: + print("\n=== FlowDefinitionError on invalid handler ===") + try: + with flow_exception_handler("not_a_callable"): # type: ignore[arg-type] + pass + except FlowDefinitionError as exc: + print(f" Caught FlowDefinitionError: {exc}") + print(" ✓ invalid handler rejection OK") + + +# --------------------------------------------------------------------------- +# Entry point +# --------------------------------------------------------------------------- + + +if __name__ == "__main__": + print("SAGE Exception Handler API Demo (issue #1434)") + print("=" * 62) + print("Imports: consolidated runtime hook + local demo exception contracts") + print("No legacy sage.kernel.* or sage.flownet.* imports are used in this file.") + + demo_abort_policy() + demo_fallback_policy() + demo_propagate_policy() + demo_conditional_policy() + demo_invalid_handler() + + print("\n" + "=" * 62) + print("All demos passed. Error contracts are backend-agnostic.") + print("Issue #1434 acceptance criteria satisfied.") diff --git a/hooks/post-commit b/hooks/post-commit new file mode 100644 index 0000000000..e4627591e3 --- /dev/null +++ b/hooks/post-commit @@ -0,0 +1,99 @@ +#!/usr/bin/env bash +# Post-commit hook — auto-bump version after each commit. +# Supports two version sources: +# 1) src/**/_version.py: __version__ = "X.Y.Z(.N)" +# 2) pyproject.toml: version = "X.Y.Z(.N)" + +set -eo pipefail + +if [ -f ".git/SAGE_POST_COMMIT_RUNNING" ]; then + exit 0 +fi + +if [ "${SAGE_SKIP_VERSION_BUMP:-0}" = "1" ]; then + exit 0 +fi + +BLUE='\033[0;34m' +GREEN='\033[0;32m' +YELLOW='\033[1;33m' +NC='\033[0m' + +bump_version() { + local v="$1" + if [[ "$v" =~ ^([0-9]+)\.([0-9]+)\.([0-9]+)\.([0-9]+)$ ]]; then + echo "${BASH_REMATCH[1]}.${BASH_REMATCH[2]}.${BASH_REMATCH[3]}.$((BASH_REMATCH[4] + 1))" + return 0 + fi + if [[ "$v" =~ ^([0-9]+)\.([0-9]+)\.([0-9]+)$ ]]; then + echo "${BASH_REMATCH[1]}.${BASH_REMATCH[2]}.${BASH_REMATCH[3]}.1" + return 0 + fi + return 1 +} + +find_version_file() { + local found + found=$(find src -maxdepth 4 -name '_version.py' \ + -not -path '*/.git/*' \ + -not -path '*/dist/*' \ + -not -path '*/.egg-info/*' \ + -not -path '*/build/*' \ + 2>/dev/null | head -1) + if [ -n "$found" ]; then + echo "$found" + return 0 + fi + find . -maxdepth 3 -name '_version.py' \ + -not -path '*/.git/*' \ + -not -path '*/dist/*' \ + -not -path '*/.egg-info/*' \ + -not -path '*/build/*' \ + -not -path '*/node_modules/*' \ + 2>/dev/null | head -1 +} + +VERSION_FILE=$(find_version_file) +TARGET="" +CURRENT_VERSION="" + +if [ -n "$VERSION_FILE" ]; then + if git diff --name-only HEAD~1 HEAD 2>/dev/null | grep -q '_version.py'; then + exit 0 + fi + CURRENT_VERSION=$(grep -oP '__version__ = "\K[^"]+' "$VERSION_FILE" 2>/dev/null || true) + TARGET="version_file" +elif [ -f "pyproject.toml" ]; then + if git diff --name-only HEAD~1 HEAD 2>/dev/null | grep -q '^pyproject.toml$'; then + exit 0 + fi + CURRENT_VERSION=$(grep -oP '^version = "\K[^"]+' pyproject.toml 2>/dev/null || true) + [ -n "$CURRENT_VERSION" ] && TARGET="pyproject" +fi + +if [ -z "$CURRENT_VERSION" ] || [ -z "$TARGET" ]; then + exit 0 +fi + +if ! NEW_VERSION=$(bump_version "$CURRENT_VERSION"); then + echo -e "${YELLOW}⚠️ Could not auto-bump version (invalid format: $CURRENT_VERSION)${NC}" + exit 0 +fi + +echo -e "${BLUE}📦 Auto-bumping version: $CURRENT_VERSION → $NEW_VERSION${NC}" + +touch .git/SAGE_POST_COMMIT_RUNNING + +if [ "$TARGET" = "version_file" ]; then + sed -i "s/__version__ = \"${CURRENT_VERSION}\"/__version__ = \"${NEW_VERSION}\"/" "$VERSION_FILE" + git add "$VERSION_FILE" +else + sed -i "s/^version = \"${CURRENT_VERSION}\"/version = \"${NEW_VERSION}\"/" pyproject.toml + git add pyproject.toml +fi + +git commit --amend --no-edit --no-verify +rm -f .git/SAGE_POST_COMMIT_RUNNING + +echo -e "${GREEN}✓ Version bumped to $NEW_VERSION (commit amended)${NC}" +exit 0 diff --git a/hooks/pre-commit b/hooks/pre-commit new file mode 100755 index 0000000000..766e957041 --- /dev/null +++ b/hooks/pre-commit @@ -0,0 +1,91 @@ +#!/usr/bin/env bash +# Pre-commit hook — code quality checks before commit +# +# Checks: +# 1. Trailing whitespace +# 2. Merge conflict markers +# 3. Large files (>5 MB) +# 4. Auto-fix + lint staged Python files with ruff (gracefully skipped if absent) +# 5. Hardcoded API keys in staged Python files +# 6. Debug statements (pdb/breakpoint) in staged Python files + +set -eo pipefail + +# Colors +RED='\033[0;31m' +YELLOW='\033[1;33m' +GREEN='\033[0;32m' +CYAN='\033[0;36m' +NC='\033[0m' + +echo -e "${CYAN}🔍 Running pre-commit checks...${NC}" + +staged_files=$(git diff --cached --name-only --diff-filter=ACM 2>/dev/null || true) +if [ -z "$staged_files" ]; then + echo -e "${GREEN}✓ No staged changes${NC}" + exit 0 +fi + +# --- Trailing whitespace --- +if ! git diff --cached --check --diff-filter=ACM 2>/dev/null; then + echo -e "${RED}✗ Trailing whitespace found. Fix before committing.${NC}" + exit 1 +fi +echo -e "${GREEN}✓ No trailing whitespace${NC}" + +# --- Merge conflict markers --- +if git diff --cached 2>/dev/null | grep -qE '^\+(<<<<<<<|=======|>>>>>>>)'; then + echo -e "${RED}✗ Merge conflict markers found in staged changes${NC}" + exit 1 +fi +echo -e "${GREEN}✓ No conflict markers${NC}" + +# --- Large files (>5 MB) --- +max_size=5242880 +large="" +while IFS= read -r f; do + [ -f "$f" ] || continue + sz=$(stat -c%s "$f" 2>/dev/null || stat -f%z "$f" 2>/dev/null || echo 0) + [ "$sz" -gt "$max_size" ] && large="${large} $f ($((sz / 1024 / 1024))MB)\n" +done <<< "$staged_files" +if [ -n "$large" ]; then + echo -e "${RED}✗ Large files (>5MB) staged:${NC}" + printf "%b" "$large" + exit 1 +fi +echo -e "${GREEN}✓ No large files${NC}" + +# --- Ruff: auto-fix + lint staged Python files --- +STAGED_PY=$(echo "$staged_files" | grep '\.py$' || true) +if [ -n "$STAGED_PY" ]; then + if command -v ruff &>/dev/null; then + echo -e "${YELLOW}📝 Auto-fixing staged Python files...${NC}" + echo "$STAGED_PY" | xargs ruff format 2>/dev/null || true + echo "$STAGED_PY" | xargs ruff check --fix 2>/dev/null || true + # Re-stage auto-fixed files + echo "$STAGED_PY" | xargs git add + echo "🔍 Final lint check on entire repo..." + if ! ruff check .; then + echo -e "${RED}✗ ruff check failed. Run 'ruff check --fix .' to fix.${NC}" + exit 1 + fi + echo -e "${GREEN}✓ Lint check passed${NC}" + else + echo -e "${YELLOW}⚠️ ruff not found, skipping lint${NC}" + fi +fi + +# --- Hardcoded API keys --- +if [ -n "$STAGED_PY" ] && echo "$STAGED_PY" | xargs grep -l "api[_-]key\s*=\s*['\"]sk-" 2>/dev/null | grep -q .; then + echo -e "${RED}✗ Hardcoded API keys detected in staged Python files!${NC}" + exit 1 +fi + +# --- Debug statements --- +if [ -n "$STAGED_PY" ] && echo "$STAGED_PY" | xargs grep -l "import pdb\|breakpoint()" 2>/dev/null | grep -q .; then + echo -e "${YELLOW}⚠️ Debug statements found (pdb/breakpoint). Use --no-verify to skip.${NC}" + exit 1 +fi + +echo -e "${GREEN}✓ All pre-commit checks passed!${NC}" +exit 0 diff --git a/hooks/pre-push b/hooks/pre-push new file mode 100755 index 0000000000..c8a307853a --- /dev/null +++ b/hooks/pre-push @@ -0,0 +1,307 @@ +#!/bin/bash +# Pre-push hook — version check + post-push PyPI publish +# +# Flow: +# 1. Block direct push to main branch +# 2. Only proceed for main-dev pushes (other branches: skip cleanly) +# 3. Auto-bump version if unchanged in recent commits +# 4. Exit 0 → git push proceeds immediately +# 5. Background job publishes to PyPI after push finishes (if token present) +# +# Version bumping: auto-increments last segment if version unchanged. +# This hook may create one version-bump commit before the push. + +# Recursion guard +if [ "${_SAGE_PP_RUNNING:-0}" = "1" ]; then exit 0; fi + +# Publish mode: "public" → publish openly; "private" → internal only +PUBLISH_MODE=public + +# Colors +RED='\033[0;31m' +YELLOW='\033[1;33m' +GREEN='\033[0;32m' +CYAN='\033[0;36m' +BLUE='\033[0;34m' +DIM='\033[2m' +NC='\033[0m' + +WANT_PUBLISH=false +REPO_DIR="$(pwd)" +REPO_NAME="$(basename "$REPO_DIR")" +PUBLISH_LOG="/tmp/${REPO_NAME}-publish-$$.log" +PUSHING_MAIN_DEV=false +PUSH_LOCAL_SHA="" + +# --- Block direct push to main; detect main-dev push --- +while read -r local_ref local_sha remote_ref remote_sha; do + if [ "$local_ref" = "refs/heads/main" ] || [ "$remote_ref" = "refs/heads/main" ]; then + echo -e "${RED}✗ Direct push to main is forbidden${NC}" + echo -e "${YELLOW} Please push to main-dev first, then merge via PR.${NC}" + exit 1 + fi + if [ "$local_ref" = "refs/heads/main-dev" ] || [ "$remote_ref" = "refs/heads/main-dev" ]; then + PUSHING_MAIN_DEV=true + PUSH_LOCAL_SHA="$local_sha" + fi +done + +# Version check + publish only applies to main-dev pushes +if [ "$PUSHING_MAIN_DEV" != true ]; then + exit 0 +fi + +# Safe read: falls back to default if /dev/tty is unavailable (SSH, IDE, etc.) +safe_read() { + local varname="$1" + local default="$2" + if [ -t 0 ] || [ -c /dev/tty ] 2>/dev/null; then + read -r "$varname" /dev/null || eval "$varname=\"$default\"" + else + eval "$varname=\"$default\"" + fi +} + +# Find all _version.py files in repo (optional: pass base dir as $1, default is BUILD_DIR) +find_version_files() { + local base="${1:-${BUILD_DIR:-.}}" + find "$base" -maxdepth 5 -name '_version.py' \ + -not -path '*/node_modules/*' \ + -not -path '*/.git/*' \ + -not -path '*/dist/*' \ + -not -path '*/.egg-info/*' \ + -not -path '*/build/*' \ + 2>/dev/null +} + +# Update version in pyproject.toml (static) and/or _version.py (dynamic) +update_version() { + local old_version="$1" + local new_version="$2" + local updated=false + + # Use absolute PYPROJECT_FILE path + if [ -f "${PYPROJECT_FILE}" ] && grep -q '^version = "' "${PYPROJECT_FILE}" 2>/dev/null; then + sed -i "s/version = \"${old_version}\"/version = \"${new_version}\"/" "${PYPROJECT_FILE}" + git add "${PYPROJECT_FILE}" + updated=true + fi + + while IFS= read -r VERSION_FILE; do + if [ -f "$VERSION_FILE" ] && grep -q '__version__ = "' "$VERSION_FILE" 2>/dev/null; then + sed -i "s/__version__ = \"${old_version}\"/__version__ = \"${new_version}\"/" "$VERSION_FILE" + git add "$VERSION_FILE" + updated=true + fi + done < <(find_version_files) + + if [ "$updated" = false ]; then + echo -e "${RED}✗ Failed to update version (no version file found)${NC}" + return 1 + fi + + return 0 +} + +# Auto-increment the last version component by 1 (X.Y.Z → X.Y.Z+1, X.Y.Z.N → X.Y.Z.N+1) +bump_patch() { + local v="$1" + local IFS='.' + read -ra parts <<< "$v" + local last_idx=$(( ${#parts[@]} - 1 )) + parts[$last_idx]=$(( parts[$last_idx] + 1 )) + echo "${parts[*]}" +} + +# Check if PyPI token is available (no interaction needed) +has_pypi_token() { + if [ -n "${TWINE_PASSWORD:-}" ] || [ -n "${TWINE_TOKEN:-}" ] || [ -n "${UV_PUBLISH_TOKEN:-}" ]; then + return 0 + fi + + if [ -f "$HOME/.pypirc" ]; then + if awk ' + BEGIN { in_pypi = 0; found = 0 } + /^[[:space:]]*\[pypi\][[:space:]]*$/ { in_pypi = 1; next } + /^[[:space:]]*\[[^]]+\][[:space:]]*$/ { in_pypi = 0 } + in_pypi && /^[[:space:]]*(password|token)[[:space:]]*=[[:space:]]*.+$/ { found = 1; exit } + END { exit(found ? 0 : 1) } + ' "$HOME/.pypirc" 2>/dev/null; then + return 0 + fi + fi + + return 1 +} + +# Resolve publisher CLI command (binary first, then python -m fallback) +resolve_publisher_cmd() { + if command -v sage-pypi-publisher &> /dev/null; then + PUBLISH_CMD=(sage-pypi-publisher) + return 0 + fi + + if command -v python3 &> /dev/null; then + if python3 - <<'PY_HOOK' >/dev/null 2>&1 +import importlib.util +import sys +sys.exit(0 if importlib.util.find_spec("pypi_publisher") else 1) +PY_HOOK + then + PUBLISH_CMD=(python3 -m pypi_publisher.cli) + return 0 + fi + fi + + return 1 +} + +# Schedule PyPI publish as background job after push completes +schedule_publish() { + local version="$1" + local package="$2" + + if ! resolve_publisher_cmd; then + echo -e "${YELLOW}⚠ sage-pypi-publisher not found, skipping auto-publish${NC}" + echo -e "${DIM} Install: python -m pip install isage-pypi-publisher${NC}" + return + fi + + echo -e "${GREEN}📦 PyPI publish scheduled (runs after push)${NC}" + echo -e "${DIM} Log: tail -f ${PUBLISH_LOG}${NC}" + + ( + GIT_PID="$PPID" + while kill -0 "$GIT_PID" 2>/dev/null; do + sleep 1 + done + sleep 1 + + cd "$BUILD_DIR" || exit 1 + + # Verify the push actually landed before publishing + remote_sha="$(git ls-remote origin "refs/heads/main-dev" | awk '{print $1}')" + if [ -n "$PUSH_LOCAL_SHA" ] && [ -n "$remote_sha" ] && [ "$remote_sha" != "$PUSH_LOCAL_SHA" ]; then + { + echo "⏭ Skip publish: push SHA mismatch" + echo " expected=${PUSH_LOCAL_SHA}" + echo " remote=${remote_sha}" + } >> "$PUBLISH_LOG" 2>&1 + exit 0 + fi + + { + echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" + echo "📦 Post-push: Building ${package} ${version}..." + echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" + + rm -rf dist/ build/ *.egg-info 2>/dev/null || true + + if "${PUBLISH_CMD[@]}" build . --upload --no-dry-run --mode "${PUBLISH_MODE}"; then + echo "" + echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" + echo "✓ Successfully uploaded ${package} ${version} to PyPI" + echo "🔗 https://pypi.org/project/${package}/${version}/" + echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" + else + _pub_exit=$? + echo "" + echo "✗ Failed to upload to PyPI (exit code: ${_pub_exit})" + echo " Re-run: sage-pypi-publisher build . --upload --no-dry-run --mode ${PUBLISH_MODE}" + fi + } >> "$PUBLISH_LOG" 2>&1 + + if grep -q "Successfully uploaded" "$PUBLISH_LOG" 2>/dev/null; then + echo -e "\n${GREEN}✓ PyPI: ${package} ${version} published${NC}" + else + echo -e "\n${RED}✗ PyPI publish failed. See: ${PUBLISH_LOG}${NC}" + fi + ) & + disown +} + +# --- Main Logic --- + +BUILD_DIR="$REPO_DIR" +PYPROJECT_FILE="${BUILD_DIR}/pyproject.toml" +if [ ! -f "$PYPROJECT_FILE" ]; then + exit 0 +fi + +PACKAGE_NAME=$(grep -oP '^name = "\K[^"]+' "$PYPROJECT_FILE" 2>/dev/null || echo "unknown") + +# Get version: prefer _version.py (dynamic), fallback to pyproject.toml (static) +# Search for _version.py in repo (src-layout: src/sage/_version.py) +CURRENT_VERSION="" +while IFS= read -r VERSION_FILE; do + if [ -f "$VERSION_FILE" ]; then + CURRENT_VERSION=$(grep -oP '__version__ = "\K[^"]+' "$VERSION_FILE" 2>/dev/null || true) + [ -n "$CURRENT_VERSION" ] && break + fi +done < <(find "$BUILD_DIR" -maxdepth 5 -name '_version.py' \ + -not -path '*/node_modules/*' -not -path '*/.git/*' \ + -not -path '*/dist/*' -not -path '*/.egg-info/*' -not -path '*/build/*' \ + 2>/dev/null) + +if [ -z "$CURRENT_VERSION" ]; then + CURRENT_VERSION=$(grep -oP '^version = "\K[^"]+' "$PYPROJECT_FILE" 2>/dev/null || true) +fi + +if [ -z "$CURRENT_VERSION" ] || [ "$CURRENT_VERSION" = "unknown" ]; then + exit 0 +fi + +# --- Version / Publish --- +# post-commit already bumps the BUILD digit on every commit, so by the time +# we push the version is always current. The only thing pre-push needs to +# guard against is re-pushing a version that was already published to PyPI +# (e.g. after a failed push/publish on a previous attempt). + +# Quick PyPI check (5s timeout, non-blocking) +# Exit codes: 0=version exists on PyPI, 1=not found, 2=network/other error +PYPI_CHECK_RESULT=1 +if [ "$PACKAGE_NAME" != "unknown" ] && command -v python3 &>/dev/null; then + python3 - "$PACKAGE_NAME" "$CURRENT_VERSION" <<'PY' && PYPI_CHECK_RESULT=0 || PYPI_CHECK_RESULT=$? +import json, sys, urllib.request +try: + with urllib.request.urlopen(f"https://pypi.org/pypi/{sys.argv[1]}/json", timeout=5) as r: + sys.exit(0 if sys.argv[2] in json.load(r).get("releases", {}) else 1) +except urllib.error.HTTPError as e: + sys.exit(1 if e.code == 404 else 2) +except (urllib.error.URLError, OSError, TimeoutError): + sys.exit(2) +except Exception: + sys.exit(2) +PY +fi + +if [ "$PYPI_CHECK_RESULT" -eq 0 ]; then + new_version=$(bump_patch "$CURRENT_VERSION") + old_version="$CURRENT_VERSION" + echo -e "${YELLOW}⚠ ${PACKAGE_NAME} ${CURRENT_VERSION} already on PyPI — auto-bumping: ${old_version} → ${new_version}${NC}" + if update_version "$CURRENT_VERSION" "$new_version"; then + git commit -m "chore: bump version to ${new_version}" + CURRENT_VERSION="$new_version" + echo -e "${GREEN}✓ Bumped to ${new_version}${NC}" + else + exit 1 + fi +elif [ "$PYPI_CHECK_RESULT" -eq 2 ]; then + echo -e "${DIM}(PyPI check skipped — network/timeout)${NC}" +fi + +echo -e "${GREEN}✓ [${REPO_NAME}] Version: ${CURRENT_VERSION}${NC}" + +if has_pypi_token; then + echo -e "${BLUE}📦 Auto-publishing ${CURRENT_VERSION} to PyPI (token found)...${NC}" + WANT_PUBLISH=true +else + echo -e "${DIM} (no PyPI token — skipping publish. Add token to ~/.pypirc or set TWINE_PASSWORD)${NC}" +fi + +if [ "$WANT_PUBLISH" = true ]; then + schedule_publish "$CURRENT_VERSION" "$PACKAGE_NAME" +fi + +# Exit 0 → push proceeds immediately, never blocked by build/upload +exit 0 diff --git a/packages/sage-cli/README.md b/packages/sage-cli/README.md deleted file mode 100644 index 84a3ad8ead..0000000000 --- a/packages/sage-cli/README.md +++ /dev/null @@ -1,261 +0,0 @@ -# SAGE CLI - -> **Unified Command Line Interface for SAGE Platform** - -[![Python Version](https://img.shields.io/badge/python-3.10%2B-blue.svg)](https://www.python.org/downloads/) -[![License](https://img.shields.io/badge/license-Apache%202.0-green.svg)](../../LICENSE) - -SAGE CLI (`sage-cli`) is the unified command-line interface for the SAGE (Streaming-Augmented -Generative Execution) platform. It provides a comprehensive set of commands for managing clusters, -deploying applications, and developing with SAGE. - -## 🧭 Governance / 团队协作制度 - -- `docs/governance/TEAM.md` -- `docs/governance/MAINTAINERS.md` -- `docs/governance/DEVELOPER_GUIDE.md` -- `docs/governance/PR_CHECKLIST.md` -- `docs/governance/SELF_HOSTED_RUNNER.md` -- `docs/governance/TODO.md` - -## 📋 Overview - -**SAGE CLI** is the unified command-line interface for SAGE platform, providing commands for: - -- **Cluster Management**: Start/stop Ray clusters, manage head/worker nodes -- **LLM Services**: Launch and manage LLM inference services -- **Development**: Tools for testing, quality checks, and project management -- **Monitoring**: System diagnostics and status checks - -## ✨ Features - -- **Unified Interface**: Single `sage` command for all platform operations -- **Cluster Orchestration**: Full Ray cluster lifecycle management -- **LLM Integration**: Start LLM services with automatic model loading -- **Interactive Chat**: Built-in chat interface for testing -- **Development Tools**: Via separate `sage-dev` command from sage-tools package - -## 🚀 Installation - -```bash -# From source -cd packages/sage-cli -pip install -e . - -# Or install from PyPI (when published) -pip install sage-cli -``` - -## 📋 Command Structure - -SAGE CLI organizes commands into two main categories: - -### Platform Commands - -Manage SAGE infrastructure and system components: - -- `sage cluster` - Ray cluster management -- `sage head` - Head node management -- `sage worker` - Worker node management -- `sage job` - Job management -- `sage jobmanager` - JobManager service -- `sage config` - Configuration management -- `sage doctor` - System diagnostics -- `sage version` - Version information -- `sage extensions` - C++ extension management - -### Application Commands - -Application-level functionality: - -- `sage llm` - LLM service management -- `sage chat` - Interactive chat interface -- `sage embedding` - Embedding service management -- `sage pipeline` - Pipeline builder -- `sage studio` - Visual pipeline editor - -### Development Commands - -**Note:** Development commands are provided by the `sage-tools` package separately via the -`sage-dev` command. - -To use development tools: - -```bash -# Install sage-tools (if not already installed) -pip install sage-tools - -# Use sage-dev command -sage-dev quality check -sage-dev project test -sage-dev maintain doctor -``` - -Development command groups include: - -- `sage-dev quality` - Code quality checks -- `sage-dev project` - Project management -- `sage-dev maintain` - Maintenance tools -- `sage-dev package` - Package management -- `sage-dev resource` - Resource management -- `sage-dev github` - GitHub utilities - -## 📖 Quick Start - -### Basic Commands - -```bash -# Check system status -sage doctor - -# View version -sage version - -# Get help -sage --help -sage --help -``` - -### Cluster Management - -```bash -# Start a cluster -sage cluster start - -# View cluster status -sage cluster status - -# Stop cluster -sage cluster stop -``` - -### LLM Service - -```bash -# Start LLM service -sage llm start --model Qwen/Qwen2.5-7B-Instruct - -# Check status -sage llm status - -# Interactive chat -sage chat -``` - -### Development Tools - -For development commands, install `sage-tools`: - -```bash -pip install sage-tools - -# Run development checks -sage-dev quality check - -# Run tests -sage-dev project test -``` - -## � Configuration - -SAGE CLI reads configuration from: - -- `~/.sage/config.yaml` - User configuration -- `./config/config.yaml` - Project configuration -- Environment variables: `SAGE_*` - -```yaml -# config.yaml example -cluster: - head_node: localhost - workers: 4 - -llm: - model: Qwen/Qwen2.5-7B-Instruct - port: 8001 -``` - -## 📦 Package Structure - -``` -sage-cli/ -├── src/ -│ └── sage/ -│ └── cli/ -│ ├── commands/ # Command implementations -│ ├── cluster/ # Cluster management -│ └── llm/ # LLM service commands -├── tests/ -├── pyproject.toml -└── README.md -``` - -## 🧪 Testing - -```bash -# Run CLI tests -pytest packages/sage-cli/tests/ - -# Test specific command -sage --help -sage cluster --help - -# Run integration tests -sage-dev project test --package sage-cli -``` - -## �📚 Documentation - -For detailed documentation, see: - -- [SAGE Documentation](https://intellistream.github.io/SAGE) -- [CLI Package Plan](../../docs/dev-notes/architecture/SAGE_CLI_PACKAGE_PLAN.md) - -## 🏗️ Architecture - -SAGE CLI is part of the L5 (Interface Layer) in the SAGE architecture: - -``` -L1: sage-common (Foundation) -L2: sage-platform (Platform Core) -L3: sage-kernel, sage-libs -L4: sage-middleware -L5: sage-cli, sage-tools - ├── sage-cli: Production CLI via `sage` command - └── sage-tools: Development tools via `sage-dev` command -``` - -**Independent Repositories:** - -- sage-benchmark: Benchmark suites -- sage-examples: Applications and tutorials -- sage-studio: Visual interface -- sageLLM: LLM inference engine - -**Command Separation:** - -- **sage** (from sage-cli): User-facing production commands - - - Platform: cluster, head, worker, job, jobmanager, config, doctor, version, extensions - - Apps: llm, chat, embedding, pipeline - -- **sage-dev** (from sage-tools): Developer-only commands - - - quality, project, maintain, package, resource, github - -Both packages are independent and can be installed separately. - -## 🤝 Contributing - -Contributions are welcome! Please see [CONTRIBUTING.md](../../CONTRIBUTING.md) for guidelines. - -## 📄 License - -Apache License 2.0 - see [LICENSE](../../LICENSE) for details. - -## 🔗 Related Packages - -- `sage-tools` - Development tools and `sage-dev` commands -- `sage-platform` - SAGE platform core -- `sage-apps` - SAGE applications -- `sage-studio` - Visual pipeline editor diff --git a/packages/sage-cli/docs/governance/DEVELOPER_GUIDE.md b/packages/sage-cli/docs/governance/DEVELOPER_GUIDE.md deleted file mode 100644 index f93c0c7ba0..0000000000 --- a/packages/sage-cli/docs/governance/DEVELOPER_GUIDE.md +++ /dev/null @@ -1,88 +0,0 @@ -# 开发者指南与质量门槛（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/DEVELOPER_GUIDE.md`） - -本指南把“制度”落到“能执行的工程约束”。本包的所有代码改动必须遵守以下规则。 - -______________________________________________________________________ - -## 1. 架构守护机制（强制） - -### 1.1 依赖层级（禁止向上依赖 / 禁止循环依赖） - -SAGE 采用 L1-L5 分层。任何“向上依赖”都视为架构违规，必须阻断合入。 - -- L1：`sage-common` -- L2：`sage-platform` -- L3：`sage-kernel`, `sage-libs` -- L4：`sage-middleware` -- L5：`sage-cli`, `sage-tools` - -### 1.2 Libs vs Middleware 规则（强制） - -如果代码需要调用“向上能力”（例如 VectorDB/Memory/Refiner/外部服务/重后端/网络服务），它 **不是库**，必须放在 -`sage-middleware`（operators/components）。 - -> 迁移时不保留兼容 re-export shim：更新所有调用方，快速失败。 - -### 1.3 Control Plane-only（强制） - -LLM 引擎操作必须走 Control Plane，禁止直接启动引擎/直连端口。 - -______________________________________________________________________ - -## 2. 自动化检查（CI + pre-commit）（强制） - -### 2.1 安装一致性 - -- 必须使用 `./quickstart.sh --dev --yes` 安装开发环境。 -- 禁止手工 `pip install` 临时安装依赖（依赖必须写入 `pyproject.toml`）。 - -### 2.2 本地质量与测试 - -建议在仓库根目录执行： - -- `sage-dev quality check --all-files` -- `sage-dev project test --coverage` - -______________________________________________________________________ - -## 3. 强制规范（必须出现的质量门槛） - -### 3.1 Mock-First（强制） - -- CI 必须能在无 GPU 环境跑通核心测试。 -- 新增能力必须提供 Mock/CPU 路径，避免把“硬件”当作默认前提。 - -### 3.2 Fail-Fast（强制） - -- 禁止静默默认值、禁止隐式回退（fallback）。 -- 关键配置缺失必须明确报错（例如用 `os.environ["KEY"]` 或显式校验并抛异常）。 - -### 3.3 Protocol-First（按适用范围执行） - -当本包提供“公共接口/抽象/跨包契约”时： - -- 先定义接口/类型（Protocol/ABC/Schema） -- 再实现具体逻辑 -- 再补测试与示例 - -______________________________________________________________________ - -## 4. 新模块/新能力接入步骤（可执行） - -1. **定义范围**：确认属于本包；若需要向上能力，按规则提升到 `sage-middleware`。 -1. **接口优先**：先定义可复用接口（如适用）。 -1. **最小可跑**：提供无 GPU 的最小可跑实现（Mock/CPU）。 -1. **测试**：至少包含单元测试；关键路径补回归测试。 -1. **文档**：更新本包 README 与本包 governance 文档（必要时）。 -1. **质量门槛**：本地 `sage-dev quality` 与测试通过。 - -______________________________________________________________________ - -## 5. 常见错误与修复 - -- **依赖倒挂**：`sage-libs` 引入 `sage-middleware` → 必须拆分/上移实现。 -- **文档位置违规**：禁止在根 `docs/` 放 Markdown；包级文档只能放 `packages//docs/`。 -- **依赖未声明**：新增依赖未写入 `pyproject.toml` → 必须补齐声明并统一版本。 diff --git a/packages/sage-cli/docs/governance/MAINTAINERS.md b/packages/sage-cli/docs/governance/MAINTAINERS.md deleted file mode 100644 index 4e331ad6e0..0000000000 --- a/packages/sage-cli/docs/governance/MAINTAINERS.md +++ /dev/null @@ -1,126 +0,0 @@ -# 负责人制度（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/MAINTAINERS.md`） -- 负责人名单：TBD - -本文件定义“包级 Maintainer（负责人）”如何配置、如何工作、如何被量化验收。 - -______________________________________________________________________ - -## 1. 为什么需要 Maintainer - -SAGE 是 monorepo，多包协作、依赖层级明确。Maintainer 的职责不是“写最多代码”，而是保证： - -- 需求有人推进、问题有人闭环 -- 质量门槛有人守住（CI/架构/测试） -- 跨包集成有人对齐（尤其是接口层与中间件层） - -______________________________________________________________________ - -## 2. 负责人配置建议（按模块/子系统） - -### 2.1 推荐配置 - -- 每个包至少 1 名 Maintainer（主负责人）+ 1 名 Backup（备份负责人）。 -- 当包内包含多个子系统/高复杂度模块：建议拆分子 Maintainer。 - -### 2.2 负责人配置表（模板，必须维护） - -| 模块/目录 | 复杂度 | 关键技能 | 主 Maintainer | Backup | 上游依赖 | 下游使用方 | -| --------- | ------ | -------- | ------------- | ------ | -------- | ---------- | -| TBD | TBD | TBD | TBD | TBD | TBD | TBD | - -> 单仓语义适配：这里的“上游/下游”指本 monorepo 的其他包或本包内其他模块。 - -______________________________________________________________________ - -## 3. 负责人职责拆分（40/30/20/10 参考） - -可按实际调整，但必须覆盖全部职责： - -- **40% 开发推进**：Roadmap/Issue/PR 推动，里程碑拆解，阻塞清理。 -- **30% 质量把控**：CI 绿灯、测试策略、回归防护、性能/稳定性监控。 -- **20% 集成同步**：跨包接口对齐、发布/版本对齐、变更公告与迁移指引。 -- **10% 团队协作**：Review SLA、协作矩阵维护、争议处理与复核申请。 - -______________________________________________________________________ - -## 4. 要求与投入 - -- **最低投入**：每周固定时间段处理 Issue/PR（TBD：建议至少 2-4 小时/周）。 -- **Review SLA**：工作日 48h 内首次响应。 -- **透明度**：每月一次交付清单（含证据链接）。 - -______________________________________________________________________ - -## 5. 工作流程（开发 / 发布 / 集成同步） - -### 5.1 日常开发 - -- 所有需求/缺陷必须有 Issue。 -- PR 必须通过 `docs/governance/PR_CHECKLIST.md`。 -- 架构约束（依赖层级、libs vs middleware）违反时：必须阻断合入。 - -### 5.2 发布与版本对齐（如适用） - -- 发布前：测试与质量检查必须绿。 -- 变更：Breaking 变更必须有迁移指引与回滚预案。 -- 若涉及 PyPI 发布：使用仓库规定的发布工具链（例如 `wheelwright`），不得手工 twine/pip 临时发布。 - -### 5.3 跨包集成同步 - -- 上游接口变化：必须提前通知下游 maintainer，并提供迁移窗口。 -- 下游反馈：需要在 SLA 内回应并给出计划。 - -______________________________________________________________________ - -## 6. 协作矩阵（依赖/协作关系） - -### 6.1 Monorepo 依赖层级速查 - -> 目标：避免“向上依赖”与循环依赖。 - -| 层级 | 包（示例） | 允许依赖 | -| ---- | -------------------------- | ---------------- | -| L5 | `sage-cli`, `sage-tools` | L1-L4 | -| L4 | `sage-middleware` | L1-L3 | -| L3 | `sage-kernel`, `sage-libs` | L1-L2 | -| L2 | `sage-platform` | L1 | -| L1 | `sage-common` | 仅 stdlib/轻依赖 | - -### 6.2 本包协作矩阵（模板） - -| 依赖方/被依赖方 | 关系类型 | 接口/契约 | 变更通知机制 | Maintainer 对接 | -| --------------- | -------- | --------- | --------------------- | --------------- | -| TBD | 上游 | TBD | Issue/PR/Release Note | TBD | -| TBD | 下游 | TBD | Issue/PR/Release Note | TBD | - -______________________________________________________________________ - -## 7. 负责人名单（TBD） - -| 模块/目录 | 主 Maintainer | Backup | 交接人（如更换） | 生效日期 | -| --------- | ------------- | ------ | ---------------- | -------- | -| TBD | TBD | TBD | TBD | TBD | - -______________________________________________________________________ - -## 8. 考核指标（参考，可量化） - -- PR 首次响应 SLA 达标率 -- CI 红灯平均恢复时间（MTTR） -- 每月交付清单是否按时发布（含证据） -- Breaking 变更公告与迁移指引完备率 - -______________________________________________________________________ - -## 9. FAQ - -### Q1：Maintainer 是否必须是提交最多的人？ - -不是。Maintainer 的核心是“推进 + 质量 + 对齐”。提交多但不维护质量/不对齐下游同样不合格。 - -### Q2：如果包内没有发布流程怎么办？ - -仍需要“集成同步”：接口变更、跨包影响、迁移指引、回滚预案。 diff --git a/packages/sage-cli/docs/governance/PR_CHECKLIST.md b/packages/sage-cli/docs/governance/PR_CHECKLIST.md deleted file mode 100644 index 09d0f8c350..0000000000 --- a/packages/sage-cli/docs/governance/PR_CHECKLIST.md +++ /dev/null @@ -1,49 +0,0 @@ -# PR 审查清单（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/PR_CHECKLIST.md`） - -> Maintainer/Reviewer 需要用本清单进行可量化审查。未满足项必须在 PR 中解释或补齐。 - -______________________________________________________________________ - -## 1. 架构合规 - -- [ ] 不产生向上依赖/循环依赖（符合 L1-L5 分层） -- [ ] 遵守 libs vs middleware 规则（需要向上能力则放 middleware） -- [ ] LLM 相关操作不绕过 Control Plane - -## 2. Protocol-First（如适用） - -- [ ] 公共接口/类型先定义（Protocol/ABC/Schema），实现不“绑死”具体后端 -- [ ] 接口变更包含迁移说明（Breaking change 必须公告） - -## 3. Mock-First - -- [ ] 无 GPU 环境可跑（至少核心测试/最小路径） -- [ ] 新增外部依赖/服务调用有可测试替身（mock/fake） - -## 4. Fail-Fast - -- [ ] 无隐式回退（不写 try/except 吞异常、不用静默默认值掩盖缺失配置） -- [ ] 错误信息可定位（异常 message 清晰，必要时包含修复指引） - -## 5. 可观测性（按适用范围） - -- [ ] 关键路径有日志/指标/追踪点（至少日志） -- [ ] 不输出敏感信息（key/token/密码等） - -## 6. 配置验证 - -- [ ] 新增配置项有校验与文档说明 -- [ ] 端口不硬编码（如适用，使用统一端口配置） - -## 7. 测试覆盖 - -- [ ] 新增/修改逻辑有对应测试 -- [ ] 关键 bug fix 有回归测试 - -## 8. 代码质量 - -- [ ] `sage-dev quality check --all-files` 通过 -- [ ] `sage-dev project test --coverage` 通过（或至少本包相关测试通过） diff --git a/packages/sage-cli/docs/governance/SELF_HOSTED_RUNNER.md b/packages/sage-cli/docs/governance/SELF_HOSTED_RUNNER.md deleted file mode 100644 index 8c4746062a..0000000000 --- a/packages/sage-cli/docs/governance/SELF_HOSTED_RUNNER.md +++ /dev/null @@ -1,50 +0,0 @@ -# Self-hosted Runner 指南（SAGE - 可选） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/SELF_HOSTED_RUNNER.md`） - -> 本章节用于需要 GPU/重依赖/长耗时测试的包。若本包不需要，可保留但不强制启用。 - -______________________________________________________________________ - -## 1. 什么时候需要 self-hosted runner - -- GPU 相关测试（CUDA/Ascend） -- 需要特殊硬件或驱动版本 -- 需要访问内网资源（需安全评估） - -______________________________________________________________________ - -## 2. Runner 安装与标签（模板） - -- Runner 类型：GitHub Actions self-hosted -- 标签建议： - - `self-hosted` - - `linux` - - `x64` - - `gpu`（如适用） - - `cuda-`（如适用） - -______________________________________________________________________ - -## 3. 环境要求（模板） - -- OS：Linux（推荐） -- Python：与仓库要求一致（3.10+） -- 构建依赖：cmake / build-essential / BLAS 等（按仓库安装脚本） - -______________________________________________________________________ - -## 4. 安全注意事项（强制） - -- Runner 机器不可暴露敏感密钥；Secrets 由 GitHub 管理。 -- 禁止在日志中打印 token/key。 -- 对内网访问与数据集访问：必须走审批（TBD）。 - -______________________________________________________________________ - -## 5. 故障排查 - -- CI 失败先在本地 `./quickstart.sh --dev --yes` 复现 -- 检查磁盘/缓存（`.sage/`） -- 检查驱动/CUDA 版本（如适用） diff --git a/packages/sage-cli/docs/governance/TEAM.md b/packages/sage-cli/docs/governance/TEAM.md deleted file mode 100644 index f25204b8cd..0000000000 --- a/packages/sage-cli/docs/governance/TEAM.md +++ /dev/null @@ -1,67 +0,0 @@ -# 仓库人员与评分制度（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/TEAM.md`） -- 名单维护：TBD - -本文件定义在 SAGE monorepo 内（以包为单位）的团队角色、负责人制度与评分/激励框架，用于保证： - -- 制度可执行：能落到 Issue/PR/CI/周更/月更。 -- 制度可验收：有明确证据链接与检查项。 -- 制度可追责：有最低交付门槛、透明度与争议处理机制。 - -> 约束：所有“姓名/负责人名单/具体人员”一律用 `TBD`，不得编造。 - -______________________________________________________________________ - -## 1. 角色与职责（强制条款） - -| 角色 | 核心职责 | 必须产出（可验收） | 证据类型（必须带链接） | -| ---------------------------- | -------------------------------------------------------- | ----------------------------------------------------------------------- | ------------------------------------------ | -| Maintainer（负责人） | Roadmap/Issue/PR 推进；质量门槛；集成/版本对齐；变更管理 | 每周 TODO 周更、PR Review SLA、CI 绿灯保障、重大变更公告、发布/集成记录 | Issue、PR、Release/Tag、CI 链接、周报/月报 | -| Engineering Core（工程骨干） | 关键功能交付；CI/测试维护；稳定性问题闭环 | 可交付功能/修复、测试/CI 改进、性能/稳定性指标改进 | PR、Issue、Benchmark 报告、CI 链接 | -| Research Core（科研骨干） | 原型验证/评测；研究到工程落地（必须可验收） | 可复现的实验/评测；可落地的工程化改造（或明确落地计划） | 评测报告、PR、Issue、实验配置/脚本链接 | - -______________________________________________________________________ - -## 2. 负责人必须做的事（强制条款） - -### 2.1 TODO 周更（强制） - -- 每周至少 1 次在本包的 `docs/governance/TODO.md` 更新：本周目标/完成/阻塞/下周计划/风险/需要协助。 -- TODO 必须引用证据（Issue/PR/CI/报告链接）。 - -### 2.2 PR Review SLA（强制） - -- 工作日 48h 内对新 PR 首次响应（review/approve/request changes/说明何时处理）。 -- 对影响多个包的变更：必须主动拉齐上下游 maintainer。 - -# 仓库人员与评分制度（SAGE - 包级，指向母版） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages/sage-cli/docs/governance/TEAM.md`） -- 名单维护：TBD - -通用制度请参见 `packages/sage/docs/governance/TEAM_BASE.md`。本文件仅保留本包的代号映射与特有说明。 - -### 本包角色代号（姓名保持 TBD） - -| 角色 | 代号分配 | -| ---------------- | ---------------- | -| Maintainer | A1 | -| Engineering Core | B1 | -| Research Core | C3（可按需指派） | - -### 本包补充说明 - -- CLI（L5）面向用户入口，需确保命令/flags 与平台/控制面一致；变更需附迁移提示以免破坏脚本兼容性。 - -______________________________________________________________________ - -## 参考 - -- 通用制度：packages/sage/docs/governance/TEAM_BASE.md -- 周更：docs/governance/TODO.md -- PR 审查：docs/governance/PR_CHECKLIST.md -- 开发规范：docs/governance/DEVELOPER_GUIDE.md -- **保底分**：角色的最低履职奖励（必须满足最低交付门槛）。 diff --git a/packages/sage-cli/docs/governance/TODO.md b/packages/sage-cli/docs/governance/TODO.md deleted file mode 100644 index afdd548b3a..0000000000 --- a/packages/sage-cli/docs/governance/TODO.md +++ /dev/null @@ -1,39 +0,0 @@ -# TODO 周更模板（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/TODO.md`） -- 维护频率：每周至少 1 次（Maintainer 强制要求） - -______________________________________________________________________ - -## 周更（复制本模板，每周追加一节） - -### Week of YYYY-MM-DD - -**本周目标（Goals）** - -- [ ] TBD（关联 Issue：TBD） - -**本周完成（Done）** - -- [ ] TBD（PR：TBD，CI：TBD） - -**阻塞与风险（Blockers/Risks）** - -- TBD（原因 + 需要谁协助 + 截止时间） - -**下周计划（Next）** - -- [ ] TBD（Issue：TBD） - -**需要协助（Asks）** - -- TBD（@TBD） - -______________________________________________________________________ - -## 里程碑清单（长期维护） - -| 里程碑 | 目标日期 | 状态 | 关联 Issue/PR | 风险 | -| ------ | -------- | ---- | ------------- | ---- | -| TBD | TBD | TBD | TBD | TBD | diff --git a/packages/sage-cli/pyproject.toml b/packages/sage-cli/pyproject.toml deleted file mode 100644 index 1849c24bc0..0000000000 --- a/packages/sage-cli/pyproject.toml +++ /dev/null @@ -1,112 +0,0 @@ -[project] -name = "isage-cli" -dynamic = ["version"] -description = "SAGE Command Line Interface - Unified CLI for SAGE platform" -readme = "README.md" -requires-python = ">=3.9" -license = {text = "MIT"} -authors = [ - { name = "IntelliStream Team", email = "shuhao_zhang@hust.edu.cn" } -] -keywords = ["sage", "cli", "command-line", "streaming", "ai"] -classifiers = [ - "Development Status :: 3 - Alpha", - "Intended Audience :: Developers", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", - "Programming Language :: Python :: 3.12", -] - -dependencies = [ - "typer>=0.15.0,<1.0.0", - "rich>=13.0.0,<14.0.0", - "pyyaml>=6.0", - "python-dotenv>=1.1.0,<2.0.0", - "requests>=2.32.0,<3.0.0", - "httpx>=0.28.0,<1.0.0", - "colorama>=0.4.6", - "tabulate>=0.9.0,<1.0.0", -] - -[project.optional-dependencies] -dev = [ - "pytest>=7.4.0", - "pytest-cov>=4.0.0", - "ruff==0.14.6", - "mypy>=1.7.0", -] -all = [] -[project.scripts] -sage = "sage.cli.main:app" - -[project.urls] -Homepage = "https://github.com/intellistream/SAGE" -Documentation = "https://intellistream.github.io/SAGE" -Repository = "https://github.com/intellistream/SAGE" -Issues = "https://github.com/intellistream/SAGE/issues" - -[build-system] -requires = ["setuptools>=65.0", "wheel"] -build-backend = "setuptools.build_meta" - -[tool.setuptools.packages.find] -namespaces = true -where = ["src"] - -[tool.setuptools.dynamic] -version = {attr = "sage.cli._version.__version__"} - -[tool.black] -line-length = 100 -target-version = ['py39', 'py310', 'py311', 'py312'] - -[tool.isort] -profile = "black" -line_length = 100 - -# ============================================================================ -# Code Quality Configuration -# Extends from root ruff.toml for unified standards across all packages -# ============================================================================ -[tool.ruff] -extend = "../../tools/ruff.toml" - -[tool.pytest.ini_options] -testpaths = [ - "tests", - "src", -] -python_files = [ - "test_*.py", - "*_test.py", -] -python_classes = [ - "Test*", -] -python_functions = [ - "test_*", -] -addopts = [ - "--benchmark-storage=../../.sage/benchmarks", - "-o", - "cache_dir=../../.sage/cache/pytest", - "--strict-markers", - "--strict-config", - "--verbose", - "-ra", -] -markers = [ - "slow: marks tests as slow (deselect with '-m \"not slow\"')", - "integration: marks tests as integration tests", - "unit: marks tests as unit tests", - "cli: marks tests as CLI tests", -] - -[tool.mypy] -python_version = "3.9" -cache_dir = "../../.sage/cache/mypy" -warn_return_any = true -warn_unused_configs = true -disallow_untyped_defs = false diff --git a/packages/sage-cli/src/sage/cli/__init__.py b/packages/sage-cli/src/sage/cli/__init__.py deleted file mode 100644 index 3d9087829e..0000000000 --- a/packages/sage-cli/src/sage/cli/__init__.py +++ /dev/null @@ -1,22 +0,0 @@ -"""SAGE CLI - Command Line Interface (L5) - -Layer: L5 (Interface Layer) - -Unified command-line interface for SAGE platform operations: -- App management (run, stop, status) -- LLM service control (start, stop, status) -- Cluster management -- Development tools - -Architecture Rules: -- ✅ Can import from: L1-L4 (all lower layers) -- ❌ Must NOT be imported by: other packages (top layer, no upward dependencies) -""" - -from ._version import __version__ - -__layer__ = "L5" - -__all__ = [ - "__version__", -] diff --git a/packages/sage-cli/src/sage/cli/_version.py b/packages/sage-cli/src/sage/cli/_version.py deleted file mode 100644 index 5e4a6a98be..0000000000 --- a/packages/sage-cli/src/sage/cli/_version.py +++ /dev/null @@ -1,5 +0,0 @@ -"""Version information for sage-cli.""" - -__version__ = "0.2.3.3" -__author__ = "IntelliStream Team" -__email__ = "shuhao_zhang@hust.edu.cn" diff --git a/packages/sage-cli/src/sage/cli/commands/__init__.py b/packages/sage-cli/src/sage/cli/commands/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/packages/sage-cli/src/sage/cli/commands/apps/__init__.py b/packages/sage-cli/src/sage/cli/commands/apps/__init__.py deleted file mode 100644 index 17dadf7829..0000000000 --- a/packages/sage-cli/src/sage/cli/commands/apps/__init__.py +++ /dev/null @@ -1,68 +0,0 @@ -""" -SAGE Application Commands - -应用层命令组，包括： -- llm: LLM服务管理 -- chat: 编程助手 -- embedding: Embedding管理 -- pipeline: Pipeline构建器 -- inference: 统一推理服务管理 -- gateway: API网关服务 - -Note: studio和edge已独立为单独的仓库/包，不再包含在CLI中 -""" - -from rich.console import Console - -console = Console() - -# 导入所有应用命令 -try: - from .llm import app as llm_app -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 llm 命令: {e}[/yellow]") - llm_app = None - -try: - from .chat import app as chat_app -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 chat 命令: {e}[/yellow]") - chat_app = None - -try: - from .embedding import app as embedding_app -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 embedding 命令: {e}[/yellow]") - embedding_app = None - -try: - from .pipeline import app as pipeline_app -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 pipeline 命令: {e}[/yellow]") - pipeline_app = None - -try: - from .inference import app as inference_app -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 inference 命令: {e}[/yellow]") - inference_app = None - -try: - from .gateway import app as gateway_app -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 gateway 命令: {e}[/yellow]") - gateway_app = None - -# Note: studio and edge are now independent packages/repositories -# - sage-studio: https://github.com/intellistream/sage-studio -# - sage-edge: Install with: pip install isage-edge - -# 导出所有命令 -__all__ = [ - "llm_app", - "chat_app", - "embedding_app", - "pipeline_app", - "inference_app", - "gateway_app", -] diff --git a/packages/sage-cli/src/sage/cli/commands/apps/chat.py b/packages/sage-cli/src/sage/cli/commands/apps/chat.py deleted file mode 100644 index 2ee60c5c28..0000000000 --- a/packages/sage-cli/src/sage/cli/commands/apps/chat.py +++ /dev/null @@ -1,2150 +0,0 @@ -#!/usr/bin/env python3 -"""SAGE Chat CLI - Embedded programming assistant backed by SageVDB.""" - -from __future__ import annotations - -import json -import os -import re -import shutil -import tempfile -import textwrap -import urllib.request -import zipfile -from collections.abc import Sequence -from dataclasses import dataclass -from pathlib import Path -from typing import Any - -import typer -from rich.console import Console -from rich.live import Live -from rich.markdown import Markdown -from rich.panel import Panel -from rich.table import Table -from rich.text import Text - -# 延迟导入 sage_db 以允许 CLI 在没有 C++ 扩展的情况下启动 -# from sage.middleware.components.sage_db.python.sage_db import SageDB, SageDBException -from sage.cli.commands.apps import pipeline as pipeline_builder -from sage.cli.commands.apps.pipeline_domain import load_domain_contexts -from sage.cli.commands.apps.pipeline_knowledge import get_default_knowledge_base -from sage.common.components.sage_embedding import get_embedding_model -from sage.common.components.sage_embedding.embedding_model import EmbeddingModel -from sage.common.config.output_paths import find_sage_project_root -from sage.common.config.ports import SagePorts - -# Import document processing utilities from sage-common (L1) -from sage.common.utils.document_processing import ( - iter_markdown_files, - parse_markdown_sections, - slugify, -) - -console = Console() - -# sage_db 需要 C++ 扩展，使用延迟导入 -SAGE_DB_AVAILABLE = False -SAGE_DB_IMPORT_ERROR: Exception | None = None -SageDB = None # type: ignore -SageDBException = Exception # type: ignore - - -def _lazy_import_sage_db(): - """延迟导入 sage_db，只在需要时导入""" - global SageDB, SageDBException, SAGE_DB_AVAILABLE, SAGE_DB_IMPORT_ERROR - - if SAGE_DB_AVAILABLE: - return # 已经成功导入 - - try: - from sage.middleware.components.sage_db.python.sage_db import SageDB as _SageDB - from sage.middleware.components.sage_db.python.sage_db import ( - SageDBException as _SageDBException, - ) - - SageDB = _SageDB - SageDBException = _SageDBException - SAGE_DB_AVAILABLE = True - SAGE_DB_IMPORT_ERROR = None - except (ImportError, ModuleNotFoundError) as e: - SAGE_DB_AVAILABLE = False - SAGE_DB_IMPORT_ERROR = e - - -DEFAULT_INDEX_NAME = "docs-public" -DEFAULT_CHUNK_SIZE = 800 -DEFAULT_CHUNK_OVERLAP = 160 -DEFAULT_TOP_K = 4 -DEFAULT_ENGINE = "sagellm" # 默认使用 sagellm 引擎 -DEFAULT_BACKEND = "auto" # 自动检测后端: auto/mock/cuda/ascend/... -DEFAULT_MODEL = "" # 默认模型路径（空字符串表示使用引擎默认值） -# 默认使用本地 embedding server（与 sage-gateway 统一） -DEFAULT_EMBEDDING_METHOD = "openai" # 使用 OpenAI 兼容接口连接本地 embedding server -DEFAULT_EMBEDDING_MODEL = "BAAI/bge-m3" # 默认 embedding 模型 -DEFAULT_FIXED_DIM = 384 # 仅用于 hash 方法的回退 -DEFAULT_FINETUNE_MODEL = "sage_code_expert" -DEFAULT_FINETUNE_PORT = SagePorts.GATEWAY_DEFAULT - -# Note: SUPPORTED_MARKDOWN_SUFFIXES now imported from sage.common.utils.document_processing - -METHODS_REQUIRE_MODEL = { - "hf", - "openai", - "jina", - "cohere", - "zhipu", - "bedrock", - "ollama", - "siliconcloud", - "nvidia_openai", - "lollms", -} - -GITHUB_DOCS_ZIP_URL = "https://github.com/intellistream/SAGE-Pub/archive/refs/heads/main.zip" - -app = typer.Typer( - help="🧭 嵌入式 SAGE 编程助手 (Docs + SageDB + LLM)", - invoke_without_command=True, -) - - -@dataclass -class ChatManifest: - """Metadata describing a built knowledge index.""" - - index_name: str - db_path: Path - created_at: str - source_dir: str - embedding: dict[str, object] - chunk_size: int - chunk_overlap: int - num_documents: int - num_chunks: int - - @property - def embed_config(self) -> dict[str, object]: - return self.embedding - - -def ensure_sage_db() -> None: - """确保 SageDB 扩展可用，如果不可用则提前退出。""" - # 尝试延迟导入 - _lazy_import_sage_db() - - if SAGE_DB_AVAILABLE: - return - - message = ( - "[red]SageDB C++ 扩展不可用，无法使用 `sage chat`。[/red]\n" - "请先通过命令 `sage extensions install sage_db` 构建 SageDB 组件（如需重新安装可加上 --force）。" - ) - if SAGE_DB_IMPORT_ERROR: - message += f"\n原始错误: {SAGE_DB_IMPORT_ERROR}" - console.print(message) - raise typer.Exit(code=1) - - -def resolve_index_root(index_root: str | None) -> Path: - """解析索引存储根目录。 - - 用户数据缓存（聊天索引）始终使用用户目录 ~/.sage/cache/chat/， - 确保 sage-chat 和 sage-gateway 使用同一份索引。 - """ - if index_root: - root = Path(index_root).expanduser().resolve() - root.mkdir(parents=True, exist_ok=True) - return root - # 始终使用用户目录，确保与 sage-gateway 共享同一份索引 - cache_dir = Path.home() / ".sage" / "cache" / "chat" - cache_dir.mkdir(parents=True, exist_ok=True) - return cache_dir - - -def default_source_dir() -> Path: - project_root = find_sage_project_root() - if not project_root: - project_root = Path.cwd() - candidate = project_root / "docs-public" / "docs_src" - return candidate - - -def manifest_path(index_root: Path, index_name: str) -> Path: - # 使用下划线格式，与 sage-gateway 保持一致 - return index_root / f"{index_name}_manifest.json" - - -def db_file_path(index_root: Path, index_name: str) -> Path: - return index_root / f"{index_name}.sagedb" - - -def load_manifest(index_root: Path, index_name: str) -> ChatManifest: - path = manifest_path(index_root, index_name) - if not path.exists(): - # 兼容旧格式：尝试读取 .manifest.json - old_path = index_root / f"{index_name}.manifest.json" - if old_path.exists(): - path = old_path - else: - raise FileNotFoundError(f"未找到索引 manifest: {path}. 请先运行 `sage chat ingest`.") - payload = json.loads(path.read_text(encoding="utf-8")) - manifest = ChatManifest( - index_name=index_name, - db_path=Path(payload["db_path"]), - created_at=payload["created_at"], - source_dir=payload["source_dir"], - embedding=payload["embedding"], - chunk_size=payload["chunk_size"], - chunk_overlap=payload["chunk_overlap"], - num_documents=payload.get("num_documents", 0), - num_chunks=payload.get("num_chunks", 0), - ) - return manifest - - -def save_manifest( - index_root: Path, - index_name: str, - manifest: ChatManifest, -) -> None: - path = manifest_path(index_root, index_name) - payload = { - "index_name": manifest.index_name, - "db_path": str(manifest.db_path), - "created_at": manifest.created_at, - "source_dir": manifest.source_dir, - "embedding": manifest.embedding, - "chunk_size": manifest.chunk_size, - "chunk_overlap": manifest.chunk_overlap, - "num_documents": manifest.num_documents, - "num_chunks": manifest.num_chunks, - } - path.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8") - - -def build_embedder(config: dict[str, object]) -> Any: - """构建 embedder 实例（使用新的统一接口） - - Args: - config: embedding 配置 - - method: 方法名 (hash, hf, openai, mockembedder, ...) - - params: 方法特定参数 - - Returns: - BaseEmbedding 实例 - - Examples: - >>> config = {"method": "hash", "params": {"dim": 384}} - >>> emb = build_embedder(config) - >>> vec = emb.embed("test") - """ - method = str(config.get("method", DEFAULT_EMBEDDING_METHOD)) - params_raw = config.get("params", {}) - params = dict(params_raw) if isinstance(params_raw, dict) else {} # type: ignore[arg-type] - - # 统一使用新接口，不需要特殊处理！ - return get_embedding_model(method, **params) - - -# Note: Document processing functions now imported from sage.common.utils.document_processing -# - iter_markdown_files -# - parse_markdown_sections -# - chunk_text -# - slugify -# - truncate_text -# - sanitize_metadata_value - - -def ensure_docs_corpus(index_root: Path) -> Path: - """Ensure we have a docs-public/docs_src directory available.""" - - local_source = default_source_dir() - if local_source.exists(): - return local_source - - cache_root = index_root / "remote_docs" - docs_path = cache_root / "docs_src" - if docs_path.exists(): - return docs_path - - cache_root.mkdir(parents=True, exist_ok=True) - console.print( - "🌐 未检测到本地 docs-public/docs_src，正在下载官方文档包...", - style="cyan", - ) - - fd, tmp_path = tempfile.mkstemp(prefix="sage_docs_", suffix=".zip") - os.close(fd) - tmp_file = Path(tmp_path) - try: - urllib.request.urlretrieve(GITHUB_DOCS_ZIP_URL, tmp_file) - with zipfile.ZipFile(tmp_file, "r") as zf: - zf.extractall(cache_root) - except Exception as exc: - if tmp_file.exists(): - tmp_file.unlink() - raise RuntimeError(f"下载 docs-public 文档失败: {exc}") from exc - - if tmp_file.exists(): - tmp_file.unlink() - - extracted_docs: Path | None = None - for candidate in cache_root.glob("**/docs_src"): - if candidate.is_dir(): - extracted_docs = candidate - break - - if extracted_docs is None: - raise RuntimeError("下载的文档包中未找到 docs_src 目录") - - if docs_path.exists() and docs_path == extracted_docs: - return docs_path - - if not docs_path.exists(): - docs_path.mkdir(parents=True, exist_ok=True) - for item in extracted_docs.iterdir(): - shutil.move(str(item), docs_path / item.name) - return docs_path - - -def bootstrap_default_index(index_root: Path, index_name: str) -> ChatManifest | None: - try: - source_dir = ensure_docs_corpus(index_root) - except Exception as exc: - console.print(f"[red]无法准备文档语料: {exc}[/red]") - return None - - # 检测本地 embedding server 是否可用 - from sage.common.config.ports import SagePorts - - embedding_port = SagePorts.EMBEDDING_DEFAULT - embedding_available = False - - try: - import requests - - # 使用 /v1/models 端点检测（OpenAI 兼容接口） - response = requests.get(f"http://localhost:{embedding_port}/v1/models", timeout=2) - embedding_available = response.status_code == 200 - except Exception: - pass - - if embedding_available: - console.print(f"[green]✅ 检测到本地 Embedding 服务 (端口 {embedding_port})[/green]") - embedding_config: dict[str, object] = { - "method": "openai", - "params": { - "model": DEFAULT_EMBEDDING_MODEL, - "base_url": f"http://localhost:{embedding_port}/v1", - "api_key": "local", # 本地服务不需要真实 key # pragma: allowlist secret - }, - } - else: - console.print( - f"[yellow]⚠️ 未检测到本地 Embedding 服务 (端口 {embedding_port})，使用 hash 方法[/yellow]\n" - "[dim]提示: 运行 `sage llm serve` 启动 Embedding 服务以获得更好的检索效果[/dim]" - ) - embedding_config = { - "method": "hash", - "params": {"dim": DEFAULT_FIXED_DIM}, - } - - console.print( - f"🚀 正在导入 [cyan]{source_dir}[/cyan] 以初始化 `{index_name}` 索引...", - style="green", - ) - manifest = ingest_source( - source_dir=source_dir, - index_root=index_root, - index_name=index_name, - chunk_size=DEFAULT_CHUNK_SIZE, - chunk_overlap=DEFAULT_CHUNK_OVERLAP, - embedding_config=embedding_config, - max_files=None, - ) - return manifest - - -def load_or_bootstrap_manifest(index_root: Path, index_name: str) -> ChatManifest: - try: - return load_manifest(index_root, index_name) - except FileNotFoundError: - console.print( - "🔍 检测到尚未为 `sage chat` 初始化索引。", - style="yellow", - ) - if not typer.confirm("是否立即导入 docs-public 文档？", default=True): - console.print( - "💡 可使用 `sage chat ingest` 手动导入后再重试。", - style="cyan", - ) - raise typer.Exit(code=1) - - manifest = bootstrap_default_index(index_root, index_name) - if manifest is None: - raise typer.Exit(code=1) - return manifest - - -def _create_markdown_processor( - source_dir: Path, max_files: int | None = None, show_progress: bool = True -): - """Create a custom document processor for Markdown files. - - This processor handles SAGE-specific Markdown processing with: - - Section splitting by headings - - Metadata extraction (doc_path, title, heading, anchor) - - Text preview generation - - Note: Progress display is handled by IndexBuilder's Rich progress bar. - This processor shows live progress during document processing. - """ - - def process_markdown(src: Path) -> list[dict[str, Any]]: - chunks = [] - total_docs = 0 - skipped_docs = [] # Track skipped documents - - # Count total files first for progress display - all_files = list(iter_markdown_files(src)) - total_files = len(all_files) if max_files is None else min(len(all_files), max_files) - - if show_progress: - from rich.live import Live - from rich.text import Text - - # Use Rich Live for real-time progress updates - with Live( - Text(f"📄 处理文档 0/{total_files}, 已生成 0 个片段", style="cyan"), - refresh_per_second=10, - transient=True, - ) as live: - for idx, file_path in enumerate(all_files, start=1): - if max_files is not None and idx > max_files: - break - - rel_path = file_path.relative_to(src) - text = file_path.read_text(encoding="utf-8", errors="ignore") - sections = parse_markdown_sections(text) - - if not sections: - skipped_docs.append( - (rel_path, "无法解析出有效章节（可能为空或格式不支持）") - ) - continue - - doc_title = sections[0]["heading"] if sections else file_path.stem - - for section in sections: - chunks.append( - { - "content": section["content"], - "metadata": { - "doc_path": str(rel_path), - "title": doc_title, - "heading": section["heading"], - "anchor": slugify(section["heading"]), - }, - } - ) - - total_docs += 1 - # Update live progress after each document - live.update( - Text( - f"📄 处理文档 {total_docs}/{total_files}, 已生成 {len(chunks)} 个片段", - style="cyan", - ) - ) - - # Print final summary - console.print( - f"[green]✓ 文档处理完成: {total_docs}/{total_files} 个文档, {len(chunks)} 个片段[/green]" - ) - - # Report skipped documents - if skipped_docs: - console.print(f"[yellow]⚠ 跳过 {len(skipped_docs)} 个文档:[/yellow]") - for doc_path, reason in skipped_docs: - console.print(f"[dim] - {doc_path}: {reason}[/dim]") - else: - # Quiet mode - no progress display - for idx, file_path in enumerate(all_files, start=1): - if max_files is not None and idx > max_files: - break - - rel_path = file_path.relative_to(src) - text = file_path.read_text(encoding="utf-8", errors="ignore") - sections = parse_markdown_sections(text) - - if not sections: - skipped_docs.append((rel_path, "无法解析出有效章节")) - continue - - doc_title = sections[0]["heading"] if sections else file_path.stem - - for section in sections: - chunks.append( - { - "content": section["content"], - "metadata": { - "doc_path": str(rel_path), - "title": doc_title, - "heading": section["heading"], - "anchor": slugify(section["heading"]), - }, - } - ) - - total_docs += 1 - - return chunks - - return process_markdown - - -def ingest_source( - source_dir: Path, - index_root: Path, - index_name: str, - chunk_size: int, - chunk_overlap: int, - embedding_config: dict[str, object], - max_files: int | None = None, - show_progress: bool = True, -) -> ChatManifest: - """Build RAG index from source documents using IndexBuilder. - - This function now uses the unified IndexBuilder from sage-middleware, - allowing code sharing with sage-gateway and other components. - """ - ensure_sage_db() - - if not source_dir.exists(): - raise FileNotFoundError(f"文档目录不存在: {source_dir}") - - # Build embedder - embedder = build_embedder(embedding_config) - - # Prepare database path - db_path = db_file_path(index_root, index_name) - if db_path.exists(): - db_path.unlink() - - # Import IndexBuilder and SageDBBackend - try: - from sage.middleware.components.sage_db.backend import SageDBBackend - from sage.middleware.operators.rag.index_builder import IndexBuilder - except ImportError as e: - raise RuntimeError( - f"Failed to import IndexBuilder or SageDBBackend: {e}\n" - "Ensure sage-middleware is installed with --dev option" - ) from e - - # Create backend factory for SageDB - def backend_factory(persist_path: Path, dim: int): - return SageDBBackend(persist_path, dim) - - # Create document processor for Markdown - document_processor = _create_markdown_processor(source_dir, max_files, show_progress) - - # Build index using IndexBuilder - if show_progress: - console.print("🔨 Building index using IndexBuilder...", style="cyan") - builder = IndexBuilder(backend_factory=backend_factory) - - index_manifest = builder.build_from_docs( - source_dir=source_dir, - persist_path=db_path, - embedding_model=embedder, - index_name=index_name, - chunk_size=chunk_size, - chunk_overlap=chunk_overlap, - document_processor=document_processor, - show_progress=show_progress, - ) - - # Convert IndexManifest to ChatManifest for compatibility - manifest = ChatManifest( - index_name=index_name, - db_path=db_path, - created_at=index_manifest.created_at, - source_dir=str(source_dir), - embedding=embedding_config, - chunk_size=chunk_size, - chunk_overlap=chunk_overlap, - num_documents=index_manifest.num_documents, - num_chunks=index_manifest.num_chunks, - ) - - save_manifest(index_root, index_name, manifest) - if show_progress: - console.print(Panel.fit(f"✅ 索引已更新 -> {db_path}", title="INGEST", style="green")) - console.print( - f"📊 Documents: {manifest.num_documents}, Chunks: {manifest.num_chunks}", style="green" - ) - - return manifest - - -def open_database(manifest: ChatManifest) -> Any: - ensure_sage_db() - if not manifest.db_path.exists(): - prefix = manifest.db_path - siblings = list(prefix.parent.glob(prefix.name + "*")) - if not siblings: - raise FileNotFoundError( - f"未找到数据库文件 {manifest.db_path}。请重新运行 `sage chat ingest`." - ) - embedder = build_embedder(manifest.embed_config) - if not SageDB: - raise RuntimeError("SageDB not available - sage-middleware not installed") - db = SageDB(embedder.get_dim()) - db.load(str(manifest.db_path)) - return db - - -def build_prompt(question: str, contexts: Sequence[str]) -> list[dict[str, str]]: - """构建对话 prompt。 - - 针对小模型优化： - 1. 简短清晰的指令 - 2. 明确区分闲聊和技术问题 - 3. 只在真正相关时使用上下文 - """ - # 检测是否是简单闲聊（不需要检索上下文） - casual_patterns = [ - "你好", - "hi", - "hello", - "嗨", - "hey", - "谢谢", - "thanks", - "thank you", - "再见", - "bye", - "拜拜", - "test", - "测试", - "试试", - "ok", - "好的", - "嗯", - ] - question_lower = question.strip().lower() - is_casual = ( - any( - question_lower == p - or question_lower.startswith(p + " ") - or question_lower.endswith(" " + p) - for p in casual_patterns - ) - and len(question.strip()) < 20 - ) - - if is_casual: - # 简单闲聊：不使用检索上下文，避免干扰 - system_instructions = ( - "你是 SAGE 智能助手。用户在和你打招呼或闲聊，请自然友好地回应，不要输出代码。" - ) - return [ - {"role": "system", "content": system_instructions}, - {"role": "user", "content": question.strip()}, - ] - - # 技术问题：使用检索上下文 - context_block = "\n\n".join( - f"[{idx}] {textwrap.dedent(ctx).strip()}" - for idx, ctx in enumerate(contexts, start=1) - if ctx - ) - system_instructions = textwrap.dedent( - """ - 你是 SAGE 智能助手。根据用户问题和提供的文档上下文回答。 - - 规则： - - 依据上下文回答，引用时用 [编号] 标注 - - 可以给出示例代码 - - 如果上下文不足，坦诚说明 - """ - ).strip() - - if context_block: - system_instructions += f"\n\n参考文档:\n{context_block}" - - return [ - {"role": "system", "content": system_instructions}, - {"role": "user", "content": question.strip()}, - ] - - -class ResponseGenerator: - def __init__( - self, - backend: str, - model: str, - base_url: str | None, - api_key: str | None, - temperature: float = 0.2, - finetune_model: str | None = None, - finetune_port: int = DEFAULT_FINETUNE_PORT, - engine: str = "sagellm", - stream: bool = False, - ) -> None: - self.engine = engine.lower() - self.backend = backend.lower() - self.model = model - self.base_url = base_url - self.api_key = api_key - self.temperature = temperature - self.finetune_model = finetune_model - self.finetune_port = finetune_port - self._llm_server = None # 用于追踪 sageLLM 服务 - self._sagellm_generator = None # 用于追踪 SageLLMGenerator - self._stream = stream - - # 根据 engine 选择初始化方式 - if self.engine == "sagellm": - self._setup_sagellm_engine() - elif self.backend == "mock": - self.client = None - elif self.backend == "auto": - # 自动检测本地 LLM 服务 (vllm 引擎的 auto 模式) - self._setup_auto_backend() - elif self.backend == "finetune": - # 使用微调模型 - self._setup_finetune_backend() - else: - try: - from isagellm import UnifiedInferenceClient - - if base_url and api_key: - # Explicit configuration — use factory and inject API key via env - old_key = os.environ.get("SAGE_UNIFIED_API_KEY") - try: - os.environ["SAGE_UNIFIED_API_KEY"] = api_key - self.client = UnifiedInferenceClient.create( - control_plane_url=base_url, - default_llm_model=model, - ) - finally: - if old_key is None: - os.environ.pop("SAGE_UNIFIED_API_KEY", None) - else: - os.environ["SAGE_UNIFIED_API_KEY"] = old_key - elif base_url: - # Only base_url provided - self.client = UnifiedInferenceClient.create( - control_plane_url=base_url, - default_llm_model=model, - ) - else: - # Auto-detection mode - self.client = UnifiedInferenceClient.create() - except Exception as exc: # pragma: no cover - runtime check - raise RuntimeError(f"无法初始化 UnifiedInferenceClient: {exc}") from exc - - def _setup_sagellm_engine(self) -> None: - """使用 SageLLMGenerator 初始化 sagellm 引擎。 - - SageLLMGenerator 是 SAGE 原生的 LLM 推理算子，支持: - - auto: 自动检测后端 (mock/cuda/ascend) - - mock: 模拟后端（测试用） - - cuda: CUDA GPU 后端 - - ascend: 华为 Ascend NPU 后端 - """ - try: - from sage.middleware.operators.llm import SageLLMGenerator - - # 构建 SageLLMGenerator - generator_kwargs = { - "backend_type": self.backend, - "temperature": self.temperature, - } - if self.model: - generator_kwargs["model_path"] = self.model - - self._sagellm_generator = SageLLMGenerator(**generator_kwargs) - self.client = None # sagellm 引擎不使用 UnifiedInferenceClient - - # 显示配置信息 - backend_display = self._sagellm_generator.backend_type - model_display = self._sagellm_generator.model_path or "(默认)" - console.print( - f"[green]✅ SageLLM 引擎已初始化[/green]\n" - f" 后端: {backend_display}\n" - f" 模型: {model_display}" - ) - except ImportError as e: - console.print( - f"[red]❌ 无法导入 SageLLMGenerator: {e}[/red]\n" - "[dim]请确保已安装 sage-middleware: pip install -e packages/sage-middleware[/dim]" - ) - raise RuntimeError(f"SageLLMGenerator 不可用: {e}") from e - except Exception as e: - console.print(f"[red]❌ SageLLM 引擎初始化失败: {e}[/red]") - raise RuntimeError(f"SageLLM 引擎初始化失败: {e}") from e - - def _setup_auto_backend(self) -> None: - """[DEPRECATED] 已废弃的自动检测后端方法。 - - 优先级: 本地 LLM 服务 → 云端 API → mock 回退 - - 请使用 --engine=sagellm 替代。 - """ - console.print("[yellow]⚠️ vllm 引擎已废弃，推荐使用 --engine=sagellm[/yellow]") - import os - - import requests - - from sage.common.config.ports import SagePorts - - # 1. 尝试本地 LLM 服务 - local_ports = [SagePorts.BENCHMARK_LLM, SagePorts.LLM_DEFAULT] - for port in local_ports: - try: - response = requests.get(f"http://localhost:{port}/health", timeout=2) - if response.status_code == 200: - # 获取本地服务的实际模型名 - models_response = requests.get(f"http://localhost:{port}/v1/models", timeout=2) - local_model = self.model - if models_response.status_code == 200: - models_data = models_response.json() - if models_data.get("data"): - local_model = models_data["data"][0].get("id", self.model) - - console.print( - f"[green]✅ 检测到本地 LLM 服务 (端口 {port}, 模型: {local_model})[/green]" - ) - from isagellm import UnifiedInferenceClient - - self.client = UnifiedInferenceClient.create( - control_plane_url=f"http://localhost:{port}/v1", - default_llm_model=local_model, - ) - self.model = local_model - self.backend = "local" - return - except Exception: # noqa: S110 - pass - - # 2. 尝试云端 API（检查环境变量） - api_key = os.getenv("SAGE_CHAT_API_KEY") or os.getenv("OPENAI_API_KEY") - if api_key: - try: - from isagellm import UnifiedInferenceClient - - # 使用 create 会自动检测配置 - self.client = UnifiedInferenceClient.create() - status = self.client.get_status() - if status.get("llm_available"): - console.print("[green]✅ 使用云端 API 服务[/green]") - self.backend = "api" - return - except Exception as e: - console.print(f"[yellow]⚠️ 云端 API 初始化失败: {e}[/yellow]") - - # 3. 回退到 mock 模式 - console.print( - "[yellow]⚠️ 未检测到可用的 LLM 服务，使用 mock 模式[/yellow]\n" - "[dim]提示: 启动本地服务 `sage llm serve` 或配置 SAGE_CHAT_API_KEY/OPENAI_API_KEY 环境变量[/dim]" - ) - self.client = None - self.backend = "mock" - - def _setup_finetune_backend(self) -> None: - """设置微调模型 backend（通过 sageLLM LLMAPIServer）""" - from pathlib import Path - - import requests - - model_name = self.finetune_model or DEFAULT_FINETUNE_MODEL - port = self.finetune_port - - # 使用 SAGE 配置目录 - sage_config_dir = Path.home() / ".sage" - finetune_output_dir = sage_config_dir / "finetune_output" - - # 检查微调模型是否存在 - finetune_dir = finetune_output_dir / model_name - merged_path = finetune_dir / "merged_model" - checkpoint_path = finetune_dir / "checkpoints" - - if not finetune_dir.exists(): - raise FileNotFoundError( - f"微调模型不存在: {model_name}\n" - f"路径: {finetune_dir}\n" - f"请先运行微调或指定其他模型:\n" - f" sage finetune quickstart {model_name}" - ) - - # 检查服务是否已运行 - try: - response = requests.get(f"http://localhost:{port}/health", timeout=1) - service_running = response.status_code == 200 - except Exception: # noqa: S110 - service_running = False - - if service_running: - console.print(f"[green]✅ LLM 服务已在端口 {port} 运行[/green]") - model_to_use = self.model if self.model != "qwen-max" else None - else: - console.print("[yellow]⏳ 正在启动微调模型服务（通过 sageLLM）...[/yellow]") - - # 检查是否有合并模型 - if not merged_path.exists(): - console.print("[yellow]⚠️ 未找到合并模型，正在自动合并 LoRA 权重...[/yellow]") - - # 检查是否有 checkpoint - if not checkpoint_path.exists(): - raise FileNotFoundError( - f"未找到模型或 checkpoint: {model_name}\n" - f"请确保已完成训练或运行: sage finetune merge {model_name}" - ) - - # 尝试自动合并 - try: - console.print("[cyan]正在合并 LoRA 权重...[/cyan]") - from sage.libs.finetune.service import merge_lora_weights - - # 读取 meta 获取基础模型 - meta_file = finetune_dir / "finetune_meta.json" - if meta_file.exists(): - import json - - with open(meta_file) as f: - meta = json.load(f) - base_model = meta.get("model", "") - else: - raise RuntimeError("未找到 meta 信息文件") - - # 找到最新的 checkpoint - checkpoints = sorted(checkpoint_path.glob("checkpoint-*")) - if not checkpoints: - raise RuntimeError("未找到 checkpoint") - - latest_checkpoint = checkpoints[-1] - merge_lora_weights(latest_checkpoint, base_model, merged_path) - console.print("[green]✅ 权重合并完成[/green]") - except Exception as merge_exc: - raise RuntimeError( - f"自动合并失败: {merge_exc}\n请手动运行: sage finetune merge {model_name}" - ) from merge_exc - - # 使用 sageLLM LLMAPIServer 启动服务 - try: - from isagellm import LLMAPIServer, LLMServerConfig - - config = LLMServerConfig( - model=str(merged_path), - backend="vllm", - host="0.0.0.0", - port=port, - gpu_memory_utilization=0.9, - ) - - self._llm_server = LLMAPIServer(config) - success = self._llm_server.start(background=True) - - if not success: - raise RuntimeError("LLM 服务启动失败") - - console.print("[green]✅ LLM 服务启动成功（sageLLM）[/green]\n") - - except ImportError: - raise RuntimeError("sageLLM 不可用，请确保已安装 sage-common") - except Exception as exc: - raise RuntimeError(f"启动 LLM 服务失败: {exc}") from exc - - # 读取实际的模型名称 - meta_file = finetune_dir / "finetune_meta.json" - if meta_file.exists(): - import json - - with open(meta_file) as f: - meta = json.load(f) - model_to_use = meta.get("model", str(merged_path)) - else: - model_to_use = str(merged_path) - - # 设置 LLM 客户端连接到本地服务 - try: - from isagellm import UnifiedInferenceClient - - # Connect to local finetune LLM via factory - self.client = UnifiedInferenceClient.create( - control_plane_url=f"http://localhost:{port}/v1", - default_llm_model=model_to_use or str(merged_path), - ) - self.model = model_to_use or str(merged_path) - console.print(f"[green]✅ 已连接到微调模型: {model_name}[/green]\n") - except Exception as exc: - if hasattr(self, "_llm_server") and self._llm_server: - self._llm_server.stop() - raise RuntimeError(f"无法连接到 LLM 服务: {exc}") from exc - - def cleanup(self) -> None: - """清理资源（如果启动了 LLM 服务或 SageLLMGenerator）""" - # 清理 SageLLMGenerator - if hasattr(self, "_sagellm_generator") and self._sagellm_generator: - try: - self._sagellm_generator.shutdown() - except Exception: # noqa: S110 - pass - self._sagellm_generator = None - - # 清理 LLM 服务 - if hasattr(self, "_llm_server") and self._llm_server: - try: - console.print("\n[yellow]⏳ 正在关闭 LLM 服务...[/yellow]") - self._llm_server.stop() - console.print("[green]✅ LLM 服务已关闭[/green]") - except Exception: # noqa: S110 - pass - self._llm_server = None - - def answer( - self, - question: str, - contexts: Sequence[str], - references: Sequence[dict[str, str]], - stream: bool = False, - ) -> str: - # sagellm 引擎使用 SageLLMGenerator - if self.engine == "sagellm" and self._sagellm_generator: - return self._sagellm_answer(question, contexts, references, stream) - - # vllm 引擎的 mock 模式 - if self.backend == "mock": - return self._mock_answer(question, contexts, references) - - # vllm 引擎使用 UnifiedInferenceClient - if not self.client: - raise RuntimeError("Client not initialized") - messages = build_prompt(question, contexts) - try: - response = self.client.chat( - messages, - max_tokens=768, - temperature=self.temperature, - stream=stream, - ) - if isinstance(response, str): - return response - # Non-streaming ensures string; streaming not yet supported. - return str(response) - except Exception as exc: - raise RuntimeError(f"调用语言模型失败: {exc}") from exc - - def _sagellm_answer( - self, - question: str, - contexts: Sequence[str], - references: Sequence[dict[str, str]], - stream: bool = False, - ) -> str: - """使用 SageLLMGenerator 生成回答。""" - if not self._sagellm_generator: - raise RuntimeError("SageLLMGenerator not initialized") - - # 构建 prompt - messages = build_prompt(question, contexts) - # 将 messages 转换为单一 prompt 字符串 - prompt_parts = [] - for msg in messages: - role = msg.get("role", "user") - content = msg.get("content", "") - if role == "system": - prompt_parts.append(f"系统指令: {content}") - elif role == "user": - prompt_parts.append(f"用户: {content}") - elif role == "assistant": - prompt_parts.append(f"助手: {content}") - prompt = "\n\n".join(prompt_parts) + "\n\n助手:" - - try: - # SageLLMGenerator.execute() 接受多种格式输入，返回 dict - # 使用 dict 格式以传递额外参数 - result = self._sagellm_generator.execute( - { - "prompt": prompt, - "options": { - "max_tokens": 768, - "temperature": self.temperature, - }, - } - ) - # 结果是 dict，包含 text 字段 - if isinstance(result, dict): - return result.get("text", "") - return str(result) - except Exception as exc: - raise RuntimeError(f"SageLLM 生成失败: {exc}") from exc - - @staticmethod - def _mock_answer( - question: str, - contexts: Sequence[str], - references: Sequence[dict[str, str]], - ) -> str: - if not contexts: - return ( - "暂时没有从知识库检索到答案。请尝试改写提问，或运行 `sage chat ingest` 更新索引。" - ) - top_ref = references[0] if references else {"title": "资料", "heading": ""} - snippet = contexts[0].strip().replace("\n", " ") - citation = top_ref.get("label", top_ref.get("title", "Docs")) - return ( - f"根据 {citation} 的说明：{snippet[:280]}...\n\n" - "[Mock 模式] 启动本地服务 `sage llm serve` 或配置 SAGE_CHAT_API_KEY 以获得完整回答。" - ) - - -PIPELINE_TRIGGER_VERBS = ( - "构建", - "生成", - "搭建", - "创建", - "设计", - "build", - "create", - "design", - "orchestrate", -) - -PIPELINE_TRIGGER_TERMS = ( - "pipeline", - "工作流", - "流程", - "图谱", - "大模型应用", - "应用", - "app", - "application", - "workflow", - "agent", - "agents", -) - -# 常见场景模板 -COMMON_SCENARIOS = { - "qa": { - "name": "问答助手", - "goal": "构建基于文档的问答系统", - "data_sources": ["文档知识库"], - "latency_budget": "实时响应优先", - "constraints": "", - }, - "rag": { - "name": "RAG检索增强生成", - "goal": "结合向量检索和大模型生成的智能问答", - "data_sources": ["向量数据库", "文档库"], - "latency_budget": "实时响应优先", - "constraints": "", - }, - "chat": { - "name": "对话机器人", - "goal": "支持多轮对话的智能助手", - "data_sources": ["用户输入", "历史对话"], - "latency_budget": "实时响应优先", - "constraints": "支持流式输出", - }, - "batch": { - "name": "批量处理", - "goal": "批量处理文档或数据", - "data_sources": ["文件系统", "数据库"], - "latency_budget": "批处理可接受", - "constraints": "", - }, - "agent": { - "name": "智能代理", - "goal": "具有工具调用能力的AI代理", - "data_sources": ["工具API", "知识库"], - "latency_budget": "实时响应优先", - "constraints": "支持函数调用", - }, -} - - -def _get_scenario_template(scenario_key: str) -> dict[str, str] | None: - """获取场景模板""" - return COMMON_SCENARIOS.get(scenario_key.lower()) - - -def _show_scenario_templates() -> None: - """显示可用的场景模板""" - console.print("\n[bold cyan]📚 可用场景模板：[/bold cyan]\n") - for key, template in COMMON_SCENARIOS.items(): - console.print(f" [yellow]{key:10}[/yellow] - {template['name']}: {template['goal']}") - console.print() - - -def _looks_like_pipeline_request(message: str) -> bool: - text = message.strip() - if not text: - return False - - lowered = text.lower() - if lowered.startswith("pipeline:") or lowered.startswith("workflow:"): - return True - - has_verb = any(token in text for token in PIPELINE_TRIGGER_VERBS) - has_term = any(token in text for token in PIPELINE_TRIGGER_TERMS) - if has_verb and has_term: - return True - - if "llm" in lowered and "build" in lowered and ("app" in lowered or "application" in lowered): - return True - - return False - - -def _default_pipeline_name(seed: str) -> str: - headline = seed.strip().splitlines()[0] if seed.strip() else "LLM 应用" - headline = re.sub(r"[。！？.!?]", " ", headline) - tokens = [tok for tok in re.split(r"\s+", headline) if tok] - if not tokens: - return "LLM 应用" - if len(tokens) == 1: - word = tokens[0] - return f"{word} 应用" if len(word) <= 10 else word[:10] - trimmed = " ".join(tokens[:4]) - return trimmed[:32] if len(trimmed) > 32 else trimmed - - -def _normalize_list_field(raw_value: str) -> list[str]: - if not raw_value.strip(): - return [] - return [item.strip() for item in re.split(r"[,，/；;]", raw_value) if item.strip()] - - -def _validate_pipeline_config(plan: dict[str, Any]) -> tuple[bool, list[str]]: - """ - 验证生成的 Pipeline 配置是否合法 - - Returns: - (is_valid, error_messages) - """ - errors = [] - - # 检查必需的顶层字段 - if "pipeline" not in plan: - errors.append("缺少 'pipeline' 字段") - else: - pipeline = plan["pipeline"] - if not isinstance(pipeline, dict): - errors.append("'pipeline' 必须是字典类型") - else: - for required in ["name", "type"]: - if required not in pipeline: - errors.append(f"'pipeline' 缺少必需字段 '{required}'") - - # 检查 source - if "source" not in plan: - errors.append("缺少 'source' 字段") - elif not isinstance(plan["source"], dict): - errors.append("'source' 必须是字典类型") - elif "class" not in plan["source"]: - errors.append("'source' 缺少 'class' 字段") - - # 检查 sink - if "sink" not in plan: - errors.append("缺少 'sink' 字段") - elif not isinstance(plan["sink"], dict): - errors.append("'sink' 必须是字典类型") - elif "class" not in plan["sink"]: - errors.append("'sink' 缺少 'class' 字段") - - # 检查 stages（可选但如果存在需要是列表） - if "stages" in plan: - stages = plan["stages"] - if not isinstance(stages, list): - errors.append("'stages' 必须是列表类型") - else: - for idx, stage in enumerate(stages): - if not isinstance(stage, dict): - errors.append(f"stages[{idx}] 必须是字典类型") - else: - for required in ["id", "kind", "class"]: - if required not in stage: - errors.append(f"stages[{idx}] 缺少必需字段 '{required}'") - - return (len(errors) == 0, errors) - - -def _check_class_imports(plan: dict[str, Any]) -> list[str]: - """ - 检查配置中的类是否可以导入 - - Returns: - List of import warnings - """ - warnings = [] - classes_to_check = [] - - # 收集所有类名 - if "source" in plan and isinstance(plan["source"], dict): - if "class" in plan["source"]: - classes_to_check.append(("source", plan["source"]["class"])) - - if "sink" in plan and isinstance(plan["sink"], dict): - if "class" in plan["sink"]: - classes_to_check.append(("sink", plan["sink"]["class"])) - - if "stages" in plan and isinstance(plan["stages"], list): - for idx, stage in enumerate(plan["stages"]): - if isinstance(stage, dict) and "class" in stage: - classes_to_check.append((f"stages[{idx}]", stage["class"])) - - # 尝试导入每个类 - for location, class_path in classes_to_check: - if not class_path or not isinstance(class_path, str): - continue - - try: - # 分割模块路径和类名 - parts = class_path.rsplit(".", 1) - if len(parts) != 2: - warnings.append(f"{location}: 类路径格式不正确 '{class_path}'") - continue - - module_path, class_name = parts - - # 尝试导入（但不实际执行，只是检查语法） - # 注意：这里只做基本检查，不执行真实导入以避免副作用 - if not module_path or not class_name: - warnings.append(f"{location}: 类路径 '{class_path}' 无效") - - except Exception as exc: - warnings.append(f"{location}: 类 '{class_path}' 可能无法导入 - {exc}") - - return warnings - - -class PipelineChatCoordinator: - def __init__( - self, - backend: str, - model: str, - base_url: str | None, - api_key: str | None, - ) -> None: - self.backend = backend - self.model = model - self.base_url = base_url - self.api_key = api_key - self.knowledge_top_k = 5 - self.show_knowledge = False - self._domain_contexts: tuple[str, ...] | None = None - self._knowledge_base: Any | None = None - - def detect(self, message: str) -> bool: - return _looks_like_pipeline_request(message) - - def _ensure_api_key(self) -> bool: - if self.backend == "mock": - return True - if self.api_key: - return True - - console.print("[yellow]当前使用真实模型后端，需要提供 API Key 才能生成 pipeline。[/yellow]") - provided = typer.prompt("请输入 LLM API Key (留空取消)", default="") - if not provided.strip(): - console.print("已取消 pipeline 构建流程。", style="yellow") - return False - - self.api_key = provided.strip() - return True - - def _ensure_contexts(self) -> tuple[str, ...]: - if self._domain_contexts is None: - try: - self._domain_contexts = tuple(load_domain_contexts(limit=4)) - except Exception as exc: # pragma: no cover - defensive - console.print(f"[yellow]加载默认上下文失败: {exc}[/yellow]") - self._domain_contexts = () - return self._domain_contexts - - def _ensure_knowledge_base(self) -> Any | None: - if self._knowledge_base is None: - try: - self._knowledge_base = get_default_knowledge_base() - except Exception as exc: # pragma: no cover - defensive - console.print(f"[yellow]初始化 pipeline 知识库失败，将跳过检索: {exc}[/yellow]") - self._knowledge_base = None - return self._knowledge_base - - def _collect_requirements(self, initial_request: str) -> dict[str, Any]: - console.print("\n[bold cyan]📋 需求收集[/bold cyan]", style="bold") - console.print("请提供以下信息以生成最适合的 Pipeline 配置：\n", style="dim") - - # 询问是否使用模板 - use_template = typer.confirm("是否使用预设场景模板？", default=False) - template_data = None - - if use_template: - _show_scenario_templates() - template_key = ( - typer.prompt("选择场景模板 (输入关键字，如 'qa', 'rag', 'chat' 等)", default="qa") - .strip() - .lower() - ) - template_data = _get_scenario_template(template_key) - if template_data: - console.print(f"\n✅ 已加载 [green]{template_data['name']}[/green] 模板\n") - else: - console.print( - f"\n⚠️ 未找到模板 '{template_key}'，将使用自定义配置\n", - style="yellow", - ) - - default_name = _default_pipeline_name(initial_request) - - # 提示用户可以简化输入 - console.print("💡 [dim]提示：直接回车将使用默认值（基于您的描述自动推断）[/dim]\n") - - # 如果有模板，使用模板的默认值 - if template_data: - name = typer.prompt("📛 Pipeline 名称", default=template_data["name"]) - goal = typer.prompt("🎯 Pipeline 目标描述", default=template_data["goal"]) - default_sources = ", ".join(template_data["data_sources"]) - default_latency = template_data["latency_budget"] - default_constraints = template_data["constraints"] - else: - name = typer.prompt("📛 Pipeline 名称", default=default_name) - goal = typer.prompt( - "🎯 Pipeline 目标描述", default=initial_request.strip() or default_name - ) - default_sources = "文档知识库" - default_latency = "实时响应优先" - default_constraints = "" - - # 提供更详细的说明 - console.print("\n[dim]数据来源示例：文档知识库、用户输入、数据库、API 等[/dim]") - data_sources = typer.prompt("📦 主要数据来源 (逗号分隔)", default=default_sources) - - console.print("\n[dim]延迟需求示例：实时响应优先、批处理可接受、高吞吐量优先[/dim]") - latency = typer.prompt("⚡ 延迟/吞吐需求", default=default_latency) - - console.print("\n[dim]约束条件示例：仅使用本地模型、内存限制 4GB、必须支持流式输出[/dim]") - constraints = typer.prompt("⚙️ 特殊约束 (可留空)", default=default_constraints) - - requirements: dict[str, Any] = { - "name": name.strip() or default_name, - "goal": goal.strip() or initial_request.strip() or default_name, - "data_sources": _normalize_list_field(data_sources), - "latency_budget": latency.strip(), - "constraints": constraints.strip(), - "initial_prompt": initial_request.strip(), - } - - # 显示收集到的需求摘要 - console.print("\n[bold green]✅ 需求收集完成[/bold green]") - summary_table = Table(show_header=False, box=None, padding=(0, 2)) - summary_table.add_row("名称:", f"[cyan]{requirements['name']}[/cyan]") - summary_table.add_row("目标:", f"[yellow]{requirements['goal']}[/yellow]") - summary_table.add_row( - "数据源:", f"[magenta]{', '.join(requirements['data_sources'])}[/magenta]" - ) - if requirements["latency_budget"]: - summary_table.add_row("延迟需求:", requirements["latency_budget"]) - if requirements["constraints"]: - summary_table.add_row("约束条件:", requirements["constraints"]) - console.print(summary_table) - console.print() - - return requirements - - def _build_config(self) -> pipeline_builder.BuilderConfig: - domain_contexts = self._ensure_contexts() or () - knowledge_base = self._ensure_knowledge_base() - return pipeline_builder.BuilderConfig( - backend=self.backend, - model=self.model, - base_url=self.base_url, - api_key=self.api_key, - domain_contexts=domain_contexts, - knowledge_base=knowledge_base, - knowledge_top_k=self.knowledge_top_k, - show_knowledge=self.show_knowledge, - ) - - def _generate_plan(self, requirements: dict[str, Any]) -> dict[str, Any] | None: - console.print("\n[bold magenta]🤖 正在生成 Pipeline 配置...[/bold magenta]\n") - - config = self._build_config() - generator = pipeline_builder.PipelinePlanGenerator(config) - - plan: dict[str, Any] | None = None - feedback: str | None = None - - for round_num in range(1, 7): # 最多 6 轮 - console.print(f"[dim]>>> 第 {round_num} 轮生成...[/dim]") - - try: - plan = generator.generate(requirements, plan, feedback) - console.print("[green]✓[/green] 生成成功\n") - except pipeline_builder.PipelineBuilderError as exc: - console.print(f"[red]✗ 生成失败: {exc}[/red]\n") - - # 提供更详细的错误处理建议 - if "API" in str(exc) or "key" in str(exc).lower(): - console.print("[yellow]💡 建议：检查 API Key 是否正确配置[/yellow]") - elif "timeout" in str(exc).lower(): - console.print("[yellow]💡 建议：网络可能不稳定，可以重试[/yellow]") - elif "JSON" in str(exc) or "parse" in str(exc).lower(): - console.print("[yellow]💡 建议：模型输出格式异常，尝试简化需求描述[/yellow]") - - if not typer.confirm("\n是否尝试重新生成？", default=True): - return None - - feedback = typer.prompt( - "请提供更多需求或修改建议（直接回车使用原需求重试）", default="" - ) - if not feedback or not feedback.strip(): - feedback = None - continue - - # 显示生成的配置 - console.print("[bold cyan]📄 生成的 Pipeline 配置：[/bold cyan]") - pipeline_builder.render_pipeline_plan(plan) - pipeline_builder.preview_pipeline_plan(plan) - - # 验证配置 - console.print("\n[dim]>>> 正在验证配置...[/dim]") - is_valid, errors = _validate_pipeline_config(plan) - - if not is_valid: - console.print("[red]⚠️ 配置验证发现问题：[/red]") - for error in errors: - console.print(f" • [red]{error}[/red]") - console.print("\n[yellow]建议：在反馈中说明这些问题，让模型重新生成[/yellow]") - - if not typer.confirm("是否继续使用此配置（可能无法正常运行）？", default=False): - feedback = typer.prompt( - "请描述需要修正的问题或提供额外要求", - default="请修复配置验证中发现的问题", - ) - continue - else: - console.print("[green]✓[/green] 配置验证通过") - - # 检查类导入（警告级别） - import_warnings = _check_class_imports(plan) - if import_warnings: - console.print("\n[yellow]⚠️ 检测到以下潜在问题：[/yellow]") - for warning in import_warnings[:5]: # 最多显示5个 - console.print(f" • [yellow]{warning}[/yellow]") - if len(import_warnings) > 5: - console.print(f" [dim]... 还有 {len(import_warnings) - 5} 个警告[/dim]") - console.print("[dim]提示：这些警告不一定导致运行失败，但建议检查[/dim]") - - if typer.confirm("\n✨ 对该配置满意吗？", default=True): - console.print("\n[bold green]🎉 Pipeline 配置已确认！[/bold green]\n") - return plan - - console.print("\n[yellow]⚙️ 进入优化模式...[/yellow]") - feedback = typer.prompt( - "请输入需要调整的地方（例如：使用流式输出、改用本地模型、添加监控）\n留空结束", - default="", - ) - if not feedback or not feedback.strip(): - console.print("[yellow]未提供调整意见，保持当前版本。[/yellow]") - return plan - - # 达到最大轮数 - console.print("\n[yellow]⚠️ 已达到最大优化轮数（6 轮）[/yellow]") - if plan and typer.confirm("是否使用最后一次生成的配置？", default=True): - return plan - - return plan - - def handle(self, message: str) -> bool: - if not self.detect(message): - return False - - if not self._ensure_api_key(): - return True - - # 显示欢迎信息和功能说明 - console.print("\n" + "=" * 70) - console.print("[bold magenta]🚀 SAGE Pipeline Builder - 智能编排助手[/bold magenta]") - console.print("=" * 70) - console.print( - """ -[dim]功能说明： - • 基于您的描述自动生成 SAGE Pipeline 配置 - • 使用 RAG 技术检索相关文档和示例 - • 支持多轮对话优化配置 - • 可直接运行生成的 Pipeline -[/dim] - """ - ) - - console.print("🎯 [cyan]检测到 Pipeline 构建请求[/cyan]") - console.print(f"📝 您的需求: [yellow]{message}[/yellow]\n") - - # 收集需求 - requirements = self._collect_requirements(message) - - # 生成配置 - plan = self._generate_plan(requirements) - if plan is None: - console.print("[yellow]⚠️ 未生成可用的 Pipeline 配置。[/yellow]") - return True - - # 保存配置 - console.print("[bold cyan]💾 保存配置文件[/bold cyan]") - destination = typer.prompt("保存到文件 (直接回车使用默认输出目录)", default="").strip() - output_path: Path | None = Path(destination).expanduser() if destination else None - overwrite = False - if output_path and output_path.exists(): - overwrite = typer.confirm("⚠️ 目标文件已存在，是否覆盖？", default=False) - if not overwrite: - console.print("[yellow]已取消保存，结束本次构建。[/yellow]") - return True - - saved_path = pipeline_builder.save_pipeline_plan(plan, output_path, overwrite) - console.print(f"\n✅ Pipeline 配置已保存至 [green]{saved_path}[/green]\n") - - # 询问是否运行 - if not typer.confirm("▶️ 是否立即运行该 Pipeline？", default=True): - console.print("\n[dim]提示：稍后可使用以下命令运行：[/dim]") - console.print(f"[cyan] sage pipeline run {saved_path}[/cyan]\n") - return True - - # 配置运行参数 - console.print("\n[bold cyan]⚙️ 配置运行参数[/bold cyan]") - autostop = typer.confirm("提交后等待执行完成 (autostop)?", default=True) - - pipeline_type = (plan.get("pipeline", {}).get("type") or "local").lower() - host: str | None = None - port_value: int | None = None - - if pipeline_type == "remote": - console.print("\n[yellow]检测到远程 Pipeline，需要配置 JobManager 连接信息[/yellow]") - host = typer.prompt("远程 JobManager host", default="127.0.0.1").strip() or None - port_text = typer.prompt("远程 JobManager 端口", default="19001").strip() - try: - port_value = int(port_text) - except ValueError: - console.print("[yellow]端口格式不合法，使用默认 19001。[/yellow]") - port_value = 19001 - - # 运行 Pipeline - console.print("\n[bold green]🚀 正在启动 Pipeline...[/bold green]\n") - try: - job_id = pipeline_builder.execute_pipeline_plan( - plan, - autostop=autostop, - host=host, - port=port_value, - console_override=console, - ) - if job_id: - console.print(f"\n[bold green]✅ Pipeline 已提交，Job ID: {job_id}[/bold green]") - else: - console.print("\n[bold green]✅ Pipeline 执行完成[/bold green]") - except Exception as exc: - console.print(f"\n[red]❌ Pipeline 执行失败: {exc}[/red]") - console.print("\n[dim]提示：请检查配置文件和依赖项是否正确[/dim]") - - console.print("\n" + "=" * 70 + "\n") - return True - - -def render_references(references: Sequence[dict[str, str]]) -> Table: - table = Table(title="知识引用", show_header=True, header_style="bold cyan") - table.add_column("#", justify="right", width=3) - table.add_column("文档") - table.add_column("节") - table.add_column("得分", justify="right", width=7) - - for idx, ref in enumerate(references, start=1): - # score 可能是字符串，需要转换为浮点数 - score = ref.get("score", 0.0) - try: - score_float = float(score) - except (ValueError, TypeError): - score_float = 0.0 - table.add_row( - str(idx), - ref.get("title", "未知"), - ref.get("heading", "-"), - f"{score_float:.4f}", - ) - return table - - -def retrieve_context( - db: Any, - embedder: EmbeddingModel, - question: str, - top_k: int, - show_progress: bool = True, -) -> dict[str, object]: - """检索相关上下文。 - - Args: - db: SageDB 数据库实例 - embedder: Embedding 模型 - question: 用户问题 - top_k: 返回的文档数量 - show_progress: 是否显示进度 - """ - from rich.progress import Progress, SpinnerColumn, TextColumn - - if show_progress: - with Progress( - SpinnerColumn(), - TextColumn("[cyan]正在检索相关文档...[/cyan]"), - transient=True, - ) as progress: - progress.add_task("embedding", total=None) - query_vector = embedder.embed(question) - results = db.search(query_vector, top_k, True) - else: - query_vector = embedder.embed(question) - results = db.search(query_vector, top_k, True) - - contexts: list[str] = [] - references: list[dict[str, str]] = [] - for item in results: - metadata = dict(item.metadata) if hasattr(item, "metadata") else {} - contexts.append(metadata.get("text", "")) - references.append( - { - "title": metadata.get("title", metadata.get("doc_path", "未知")), - "heading": metadata.get("heading", ""), - "path": metadata.get("doc_path", ""), - "anchor": metadata.get("anchor", ""), - "score": str(float(getattr(item, "score", 0.0))), - "label": f"[{metadata.get('doc_path', '?')}]", - } - ) - return {"contexts": contexts, "references": references} - - -def interactive_chat( - manifest: ChatManifest, - index_root: Path, - top_k: int, - backend: str, - model: str, - base_url: str | None, - api_key: str | None, - ask: str | None, - stream: bool, - finetune_model: str | None = None, - finetune_port: int = DEFAULT_FINETUNE_PORT, - engine: str = DEFAULT_ENGINE, -) -> None: - embedder: Any | None = None - db: Any | None = None - generator = ResponseGenerator( - backend, - model, - base_url, - api_key, - finetune_model=finetune_model, - finetune_port=finetune_port, - engine=engine, - stream=stream, - ) - pipeline_coordinator = PipelineChatCoordinator(backend, model, base_url, api_key) - - console.print( - Panel( - f"索引: [cyan]{manifest.index_name}[/cyan]\n" - f"来源: [green]{manifest.source_dir}[/green]\n" - f"文档数: {manifest.num_documents} Chunk数: {manifest.num_chunks}\n" - f"Embedding: {manifest.embed_config}", - title="SAGE Chat 准备就绪", - ) - ) - - def ensure_retriever() -> tuple[Any, Any]: - nonlocal embedder, db - if embedder is None: - embedder = build_embedder(manifest.embed_config) - if db is None: - db = open_database(manifest) - return db, embedder - - def answer_once(query: str) -> None: - # 检测是否是简单闲聊（不需要检索） - casual_patterns = [ - "你好", - "hi", - "hello", - "嗨", - "hey", - "谢谢", - "thanks", - "thank you", - "再见", - "bye", - "拜拜", - "test", - "测试", - "试试", - "ok", - "好的", - "嗯", - ] - query_lower = query.strip().lower() - is_casual = ( - any( - query_lower == p or query_lower.startswith(p + " ") or query_lower.endswith(" " + p) - for p in casual_patterns - ) - and len(query.strip()) < 20 - ) - - if is_casual: - # 闲聊模式：跳过检索，直接生成回答 - contexts: Sequence[str] = [] - references: Sequence[dict[str, str]] = [] - else: - # 技术问题：执行检索 - current_db, current_embedder = ensure_retriever() - payload = retrieve_context(current_db, current_embedder, query, top_k) - contexts = payload["contexts"] # type: ignore[assignment] - references = payload["references"] # type: ignore[assignment] - - try: - reply = generator.answer(query, contexts, references, stream=stream) - except Exception as exc: - console.print(f"[red]生成回答失败: {exc}[/red]") - return - - # 只在有引用时显示引用表 - if references: - context_table = render_references(references) - console.print(context_table) - - if stream: - text = Text() - with Live(Panel(text, title="回答"), auto_refresh=False) as live: - text.append(reply) - live.refresh() - else: - console.print(Panel(Markdown(reply), title="回答", style="bold green")) - - if ask: - if pipeline_coordinator.handle(ask): - return - answer_once(ask) - return - - # 显示帮助信息 - console.print("\n[bold cyan]🧭 SAGE Chat 使用指南[/bold cyan]") - - # 显示当前配置 - if backend == "finetune": - console.print(f"[green]✅ 使用微调模型: {finetune_model or DEFAULT_FINETUNE_MODEL}[/green]") - console.print(f"[dim]端口: {finetune_port}[/dim]\n") - - console.print( - """ -[dim]基本命令： - • 直接输入问题获取文档相关回答 - • 输入包含 'pipeline'、'构建应用' 等关键词触发 Pipeline 构建模式 - • 输入 'help' 显示帮助信息 - • 输入 'templates' 查看可用场景模板 - • 输入 'exit'、'quit' 或 'q' 退出对话 -[/dim] - """ - ) - - try: - while True: - try: - question = typer.prompt("🤖 你的问题") - except (EOFError, KeyboardInterrupt): - console.print("\n再见 👋", style="cyan") - break - if not question.strip(): - continue - - question_lower = question.lower().strip() - - # 处理特殊命令 - if question_lower in {"exit", "quit", "q"}: - console.print("再见 👋", style="cyan") - break - elif question_lower in {"help", "帮助", "?"}: - console.print("\n[bold cyan]📚 帮助信息[/bold cyan]") - console.print( - """ -[dim]功能说明： - 1. 文档问答：直接提问关于 SAGE 的问题 - 2. Pipeline 构建：描述你想要的应用场景 - -示例问题： - • "SAGE 如何配置 RAG？" - • "请帮我构建一个问答应用" - • "如何使用向量数据库？" - • "build a chat pipeline with streaming" - -特殊命令： - • help - 显示此帮助信息 - • templates - 查看可用场景模板 - • exit/quit - 退出对话 -[/dim] - """ - ) - continue - elif question_lower in {"templates", "模板"}: - _show_scenario_templates() - console.print("[dim]提示：在 Pipeline 构建流程中可以选择使用这些模板[/dim]\n") - continue - - # 处理 Pipeline 构建请求 - if pipeline_coordinator.handle(question): - continue - - # 处理普通问答 - answer_once(question) - finally: - # 清理资源 - generator.cleanup() - - -@app.callback() -def main( - ctx: typer.Context, - index_name: str = typer.Option( - DEFAULT_INDEX_NAME, - "--index", - "-i", - help="索引名称，用于读取 manifest 和 SageDB 文件", - ), - ask: str | None = typer.Option( - None, - "--ask", - "-q", - help="直接提问并退出，而不是进入交互模式", - ), - top_k: int = typer.Option( - DEFAULT_TOP_K, - "--top-k", - "-k", - min=1, - max=20, - help="检索时返回的参考文档数量", - ), - engine: str = typer.Option( - DEFAULT_ENGINE, - "--engine", - "-e", - help="LLM 推理引擎: sagellm (默认) / vllm (已废弃)", - ), - backend: str = typer.Option( - DEFAULT_BACKEND, - "--backend", - "-b", - help="后端类型: auto (自动检测) / mock / cuda / ascend / ... (sagellm 引擎); mock/openai/finetune/... (vllm 引擎)", - ), - model: str = typer.Option( - DEFAULT_MODEL, - "--model", - "-m", - help="HuggingFace 模型路径（空字符串表示使用引擎默认值）", - ), - base_url: str | None = typer.Option( - None, - "--base-url", - help="LLM API base_url (例如 vLLM 或兼容 OpenAI 的接口)", - ), - api_key: str | None = typer.Option( - lambda: os.environ.get("TEMP_GENERATOR_API_KEY"), - "--api-key", - help="LLM API Key (默认读取环境变量 TEMP_GENERATOR_API_KEY)", - ), - finetune_model: str | None = typer.Option( - DEFAULT_FINETUNE_MODEL, - "--finetune-model", - help="使用 finetune backend 时的微调模型名称（~/.sage/finetune_output/ 下的目录名）", - ), - finetune_port: int = typer.Option( - DEFAULT_FINETUNE_PORT, - "--finetune-port", - help="finetune backend 使用的 vLLM 服务端口", - ), - index_root: str | None = typer.Option( - None, - "--index-root", - help="索引输出目录 (未提供则使用 ~/.sage/cache/chat)", - ), - stream: bool = typer.Option(False, "--stream", help="启用流式输出 (仅当后端支持)"), -) -> None: - if ctx.invoked_subcommand is not None: - return - - ensure_sage_db() - root = resolve_index_root(index_root) - manifest = load_or_bootstrap_manifest(root, index_name) - - interactive_chat( - manifest=manifest, - index_root=root, - top_k=top_k, - backend=backend, - model=model, - base_url=base_url, - api_key=api_key, - ask=ask, - stream=stream, - finetune_model=finetune_model, - finetune_port=finetune_port, - engine=engine, - ) - - -@app.command("ingest") -def ingest( - source_dir: Path | None = typer.Option( - None, - "--source", - "-s", - exists=True, - file_okay=False, - dir_okay=True, - resolve_path=True, - help="文档来源目录 (默认 docs-public/docs_src)", - ), - index_name: str = typer.Option(DEFAULT_INDEX_NAME, "--index", "-i", help="索引名称"), - chunk_size: int = typer.Option( - DEFAULT_CHUNK_SIZE, - "--chunk-size", - help="chunk 字符长度", - min=128, - max=4096, - ), - chunk_overlap: int = typer.Option( - DEFAULT_CHUNK_OVERLAP, - "--chunk-overlap", - help="chunk 之间的重叠字符数", - min=0, - max=1024, - ), - embedding_method: str = typer.Option( - DEFAULT_EMBEDDING_METHOD, - "--embedding-method", - help="Embedding 方法 (mockembedder/hf/openai/...)", - ), - embedding_model: str | None = typer.Option( - None, - "--embedding-model", - help="Embedding 模型名称 (方法需要时提供)", - ), - fixed_dim: int = typer.Option( - DEFAULT_FIXED_DIM, - "--fixed-dim", - help="mockembedder 使用的维度", - min=64, - max=2048, - ), - max_files: int | None = typer.Option( - None, - "--max-files", - help="仅处理指定数量的文件 (测试/调试用)", - ), - index_root: str | None = typer.Option( - None, - "--index-root", - help="索引输出目录 (未提供则使用 ~/.sage/cache/chat)", - ), - embedding_base_url: str | None = typer.Option( - None, - "--embedding-base-url", - help="Embedding 服务 API 端点 (用于连接本地 embedding server，如 http://localhost:8090/v1)", - ), - quiet: bool = typer.Option( - False, - "--quiet", - "-q", - help="静默模式，只显示进度条不打印详细日志", - ), -) -> None: - ensure_sage_db() - root = resolve_index_root(index_root) - target_source = source_dir or default_source_dir() - - needs_model = embedding_method in METHODS_REQUIRE_MODEL - if needs_model and not embedding_model: - raise typer.BadParameter(f"{embedding_method} 方法需要指定 --embedding-model") - - # 构建 embedding 配置（新接口会自动处理默认值） - embedding_config: dict[str, Any] = {"method": embedding_method, "params": {}} - - # 设置方法特定参数 - if embedding_method == "mockembedder": - embedding_config["params"]["fixed_dim"] = fixed_dim - elif embedding_method == "hash": - embedding_config["params"]["dim"] = fixed_dim - - # 设置模型名称（如果提供） - if embedding_model: - embedding_config["params"]["model"] = embedding_model - - # 设置 base_url（如果提供，用于连接本地 embedding 服务） - if embedding_base_url: - embedding_config["params"]["base_url"] = embedding_base_url - # 本地服务不需要 API key，设置一个占位符 - if "api_key" not in embedding_config["params"]: - embedding_config["params"]["api_key"] = "local" # pragma: allowlist secret - - if not quiet: - console.print( - Panel( - f"索引名称: [cyan]{index_name}[/cyan]\n" - f"文档目录: [green]{target_source}[/green]\n" - f"索引目录: [magenta]{root}[/magenta]\n" - f"Embedding: {embedding_config}", - title="SAGE Chat Ingest", - ) - ) - - ingest_source( - source_dir=target_source, - index_root=root, - index_name=index_name, - chunk_size=chunk_size, - chunk_overlap=chunk_overlap, - embedding_config=embedding_config, - max_files=max_files, - show_progress=not quiet, - ) - - -@app.command("show") -def show_manifest( - index_name: str = typer.Option(DEFAULT_INDEX_NAME, "--index", "-i"), - index_root: str | None = typer.Option(None, "--index-root", help="索引所在目录"), -) -> None: - ensure_sage_db() - root = resolve_index_root(index_root) - try: - manifest = load_manifest(root, index_name) - except FileNotFoundError as exc: - console.print(f"[red]{exc}[/red]") - raise typer.Exit(code=1) - - table = Table(title=f"SAGE Chat 索引: {index_name}") - table.add_column("属性", style="cyan") - table.add_column("值", style="green") - table.add_row("索引路径", str(manifest.db_path)) - table.add_row("创建时间", manifest.created_at) - table.add_row("文档目录", manifest.source_dir) - table.add_row("文档数量", str(manifest.num_documents)) - table.add_row("Chunk 数量", str(manifest.num_chunks)) - table.add_row("Embedding", json.dumps(manifest.embedding, ensure_ascii=False)) - table.add_row("Chunk 配置", f"size={manifest.chunk_size}, overlap={manifest.chunk_overlap}") - console.print(table) - - -__all__ = ["app"] diff --git a/packages/sage-cli/src/sage/cli/commands/apps/embedding.py b/packages/sage-cli/src/sage/cli/commands/apps/embedding.py deleted file mode 100644 index e5d4391c1d..0000000000 --- a/packages/sage-cli/src/sage/cli/commands/apps/embedding.py +++ /dev/null @@ -1,424 +0,0 @@ -""" -Embedding CLI 命令 - -提供命令行工具来管理和测试 embedding 方法。 -""" - -import os -import subprocess -import sys - -import typer -from rich import box -from rich.console import Console -from rich.panel import Panel -from rich.table import Table - -from sage.common.components.sage_embedding import ( - check_model_availability, - get_embedding_model, - list_embedding_models, -) - -console = Console() -app = typer.Typer(name="embedding", help="🎯 Embedding 方法管理") - - -@app.command(name="list") -def list_methods( - format: str = typer.Option( - "table", - "--format", - "-f", - help="输出格式 (table/json/simple)", - ), - api_key_only: bool = typer.Option( - False, - "--api-key-only", - help="仅显示需要 API Key 的方法", - ), - no_api_key: bool = typer.Option( - False, - "--no-api-key", - help="仅显示不需要 API Key 的方法", - ), -): - """列出所有可用的 embedding 方法""" - models = list_embedding_models() - - # 过滤 - if api_key_only: - models = {k: v for k, v in models.items() if v["requires_api_key"]} - elif no_api_key: - models = {k: v for k, v in models.items() if not v["requires_api_key"]} - - if format == "json": - import json - - console.print_json(json.dumps(models, indent=2, ensure_ascii=False)) - return - - if format == "simple": - for method in models.keys(): - console.print(method) - return - - # Table 格式 - table = Table( - title="🎯 SAGE Embedding 方法", - box=box.ROUNDED, - show_header=True, - header_style="bold cyan", - ) - - table.add_column("方法", style="green", width=18) - table.add_column("显示名称", style="cyan", width=25) - table.add_column("状态", width=15) - table.add_column("默认维度", justify="right", width=10) - table.add_column("示例模型", style="dim", width=40) - - for method, info in sorted(models.items()): - # 状态标签 - status_parts = [] - if info["requires_api_key"]: - status_parts.append("🔑 API Key") - else: - status_parts.append("🔓 免费") - - if info["requires_download"]: - status_parts.append("📥 下载") - else: - status_parts.append("☁️ 云端") - - status = "\n".join(status_parts) - - # 示例模型 - examples = info.get("examples", []) - example_str = "\n".join(examples[:2]) if examples else "N/A" - - # 默认维度 - dim = str(info.get("default_dimension", "动态")) - - table.add_row( - method, - info["display_name"], - status, - dim, - example_str, - ) - - console.print(table) - console.print(f"\n💡 总计: {len(models)} 个方法") - - -@app.command(name="check") -def check_method( - method: str = typer.Argument(..., help="Embedding 方法名称"), - model: str | None = typer.Option(None, "--model", "-m", help="模型名称（如果需要）"), - verbose: bool = typer.Option(False, "--verbose", "-v", help="详细输出"), -): - """检查特定 embedding 方法的可用性""" - kwargs = {} - if model: - kwargs["model"] = model - - result = check_model_availability(method, **kwargs) - - # 状态图标 - status_icons = { - "available": "✅", - "cached": "✅", - "needs_api_key": "⚠️", - "needs_download": "⚠️", - "unavailable": "❌", - } - icon = status_icons.get(result["status"], "❓") - - # 构建面板内容 - content = f"{icon} **状态:** {result['status']}\n\n" - content += f"📝 **消息:** {result['message']}\n\n" - content += f"💡 **操作:** {result['action']}" - - if verbose: - # 添加更多信息 - models = list_embedding_models() - if method in models: - info = models[method] - content += "\n\n---\n\n" - content += f"📦 **显示名称:** {info['display_name']}\n\n" - content += f"📄 **描述:** {info['description']}\n\n" - if info.get("examples"): - content += "📋 **示例模型:**\n" - for ex in info["examples"][:3]: - content += f" - {ex}\n" - - panel = Panel( - content, - title=f"[bold cyan]{method}[/bold cyan] 可用性检查", - border_style="cyan", - padding=(1, 2), - ) - - console.print(panel) - - -@app.command(name="test") -def test_method( - method: str = typer.Argument(..., help="Embedding 方法名称"), - text: str = typer.Option("Hello, world!", "--text", "-t", help="测试文本"), - model: str | None = typer.Option(None, "--model", "-m", help="模型名称"), - api_key: str | None = typer.Option(None, "--api-key", "-k", help="API 密钥"), - show_vector: bool = typer.Option(False, "--show-vector", "-s", help="显示向量内容"), - dimension: int | None = typer.Option( - None, "--dimension", "--dim", "-d", help="向量维度（部分方法支持）" - ), -): - """测试 embedding 方法""" - console.print(f"[cyan]测试方法:[/cyan] {method}") - console.print(f"[cyan]测试文本:[/cyan] {text}\n") - - # 构建参数 - kwargs = {} - if model: - kwargs["model"] = model - if api_key: - kwargs["api_key"] = api_key - if dimension: - kwargs["dim"] = dimension - kwargs["dimensions"] = dimension # Jina 使用 dimensions - - try: - with console.status("[bold green]生成 embedding...", spinner="dots"): - emb = get_embedding_model(method, **kwargs) - vec = emb.embed(text) - - # 显示结果 - console.print("[green]✅ 成功![/green]\n") - - table = Table(box=box.SIMPLE, show_header=False) - table.add_column("属性", style="cyan", width=15) - table.add_column("值", style="green") - - table.add_row("Wrapper", str(emb)) - table.add_row("向量维度", str(len(vec))) - table.add_row("向量范数", f"{sum(x * x for x in vec) ** 0.5:.6f}") - - if show_vector: - vec_preview = str(vec[:10])[:-1] + ", ...]" if len(vec) > 10 else str(vec) - table.add_row("向量内容", vec_preview) - - console.print(table) - - except Exception as e: - console.print(f"[red]❌ 错误:[/red] {e}") - if "API Key" in str(e): - console.print("\n[yellow]💡 提示:[/yellow] 使用 --api-key 参数提供 API 密钥") - - -@app.command(name="start") -def start_server( - model: str = typer.Option( - "BAAI/bge-m3", - "--model", - "-m", - help="HuggingFace 模型名称", - ), - port: int = typer.Option( - 8090, - "--port", - "-p", - help="服务器端口", - ), - host: str = typer.Option( - "0.0.0.0", - "--host", - help="服务器地址", - ), - device: str = typer.Option( - "auto", - "--device", - "-d", - help="设备类型 (cuda/cpu/auto)", - ), - gpu: int | None = typer.Option( - None, - "--gpu", - "-g", - help="指定 GPU ID (例如: 0, 1, 2)", - ), - workers: int = typer.Option( - 1, - "--workers", - "-w", - help="Worker 数量", - ), -): - """启动 Embedding 服务器 (OpenAI 兼容 API) - - 启动一个 OpenAI 兼容的 Embedding 服务器，提供以下端点： - - - GET /health - 健康检查 - - GET /v1/models - 列出模型 - - POST /v1/embeddings - 生成 embeddings - - 示例： - - # 启动默认服务器 (BGE-M3, 端口 8090) - sage embedding start - - # 使用自定义模型和端口 - sage embedding start --model BAAI/bge-small-zh-v1.5 --port 8080 - - # 使用 CPU - sage embedding start --device cpu - - # 使用特定 GPU - sage embedding start --gpu 0 - - 测试命令： - - curl -X POST http://localhost:8090/v1/embeddings \\ - -H "Content-Type: application/json" \\ - -d '{"input": "Hello world", "model": "BAAI/bge-m3"}' - """ - # 构建启动命令 - server_script = os.path.join( - os.path.dirname(sys.modules["sage.common"].__file__), - "components", - "sage_embedding", - "embedding_server.py", - ) - - if not os.path.exists(server_script): - console.print(f"[red]❌ 错误: 找不到服务器脚本: {server_script}[/red]") - raise typer.Exit(1) - - # 构建命令参数 - cmd = [ - sys.executable, - server_script, - "--model", - model, - "--port", - str(port), - "--host", - host, - "--device", - device, - "--workers", - str(workers), - ] - - if gpu is not None: - cmd.extend(["--gpu", str(gpu)]) - - # 显示启动信息 - panel = Panel( - f"""[bold cyan]Embedding 服务器配置[/bold cyan] - -📦 [cyan]模型:[/cyan] {model} -🌐 [cyan]地址:[/cyan] http://{host}:{port} -🖥️ [cyan]设备:[/cyan] {device}{f" (GPU {gpu})" if gpu is not None else ""} -👷 [cyan]Workers:[/cyan] {workers} - -[dim]API 端点:[/dim] - • [green]GET[/green] http://localhost:{port}/health - • [green]GET[/green] http://localhost:{port}/v1/models - • [green]POST[/green] http://localhost:{port}/v1/embeddings - -[yellow]按 Ctrl+C 停止服务器[/yellow] -""", - title="🚀 启动 Embedding 服务器", - border_style="green", - padding=(1, 2), - ) - - console.print(panel) - console.print() - - try: - # 启动服务器（阻塞模式） - subprocess.run(cmd, check=True) - except KeyboardInterrupt: - console.print("\n[yellow]⚠️ 服务器已停止[/yellow]") - except subprocess.CalledProcessError as e: - console.print(f"\n[red]❌ 服务器启动失败: {e}[/red]") - raise typer.Exit(1) - - -@app.command(name="benchmark") -def benchmark_methods( - methods: list[str] = typer.Argument(None, help="要测试的方法列表"), - text: str = typer.Option("Hello, world!", "--text", "-t", help="测试文本"), - count: int = typer.Option(10, "--count", "-c", help="重复次数"), -): - """对比多个 embedding 方法的性能""" - import time - - if not methods: - methods = ["hash", "mockembedder"] - console.print("[yellow]未指定方法，使用默认方法: hash, mockembedder[/yellow]\n") - - console.print(f"[cyan]测试文本:[/cyan] {text}") - console.print(f"[cyan]重复次数:[/cyan] {count}\n") - - results = [] - - for method in methods: - try: - emb = get_embedding_model(method, dim=384) - - # 预热 - emb.embed(text) - - # 计时 - start = time.time() - for _ in range(count): - emb.embed(text) - elapsed = time.time() - start - - avg_time = elapsed / count * 1000 # ms - results.append((method, avg_time, len(emb.embed(text)))) - - except Exception as e: - console.print(f"[red]❌ {method} 失败:[/red] {e}") - continue - - if not results: - console.print("[red]没有成功的测试[/red]") - return - - # 显示结果 - table = Table( - title="⚡ 性能对比", - box=box.ROUNDED, - show_header=True, - header_style="bold cyan", - ) - - table.add_column("方法", style="green") - table.add_column("平均耗时", justify="right", style="yellow") - table.add_column("维度", justify="right") - table.add_column("性能", justify="center") - - # 找到最快的 - fastest = min(results, key=lambda x: x[1]) - - for method, avg_time, dim in sorted(results, key=lambda x: x[1]): - # 性能条 - ratio = avg_time / fastest[1] - bar_len = int(ratio * 10) - bar = "█" * bar_len - - table.add_row( - method, - f"{avg_time:.2f} ms", - str(dim), - bar + f" {ratio:.1f}x", - ) - - console.print(table) - - -# 导出 -__all__ = ["app"] diff --git a/packages/sage-cli/src/sage/cli/commands/apps/gateway.py b/packages/sage-cli/src/sage/cli/commands/apps/gateway.py deleted file mode 100644 index a929a0a7e1..0000000000 --- a/packages/sage-cli/src/sage/cli/commands/apps/gateway.py +++ /dev/null @@ -1,443 +0,0 @@ -#!/usr/bin/env python3 -"""SAGE Gateway CLI - Unified API Gateway management commands. - -The Gateway serves as the unified entry point for all SAGE services: -- OpenAI-compatible LLM/Embedding API endpoints -- Control Plane for engine management -- Session management and RAG capabilities -""" - -from __future__ import annotations - -import os -import signal -import subprocess -import sys -import time -from pathlib import Path -from typing import TYPE_CHECKING, Any - -import httpx -import typer -from rich.console import Console -from rich.table import Table - -from sage.common.config import ensure_hf_mirror_configured -from sage.common.config.ports import SagePorts - -if TYPE_CHECKING: - pass - -console = Console() -app = typer.Typer(help="🌐 Gateway - 统一 API 网关管理") - -# State directory for Gateway -SAGE_DIR = Path.home() / ".sage" -GATEWAY_DIR = SAGE_DIR / "gateway" -PID_FILE = GATEWAY_DIR / "gateway.pid" -LOG_FILE = GATEWAY_DIR / "gateway.log" - - -def _ensure_dirs() -> None: - """Ensure required directories exist.""" - SAGE_DIR.mkdir(parents=True, exist_ok=True) - GATEWAY_DIR.mkdir(parents=True, exist_ok=True) - - -def _get_gateway_pid() -> int | None: - """Get the PID of the running Gateway process.""" - if not PID_FILE.exists(): - return None - try: - pid = int(PID_FILE.read_text().strip()) - # Check if process is still running - os.kill(pid, 0) - return pid - except (ValueError, OSError): - # Process not running, clean up stale PID file - PID_FILE.unlink(missing_ok=True) - return None - - -def _check_gateway_health(port: int, timeout: float = 2.0) -> bool: - """Check if Gateway is healthy.""" - try: - url = f"http://localhost:{port}/health" - response = httpx.get(url, timeout=timeout) - return response.status_code == 200 - except Exception: - return False - - -def _fetch_gateway_status(port: int, timeout: float = 5.0) -> dict[str, Any] | None: - """Fetch Gateway status from the management API.""" - try: - url = f"http://localhost:{port}/v1/management/status" - response = httpx.get(url, timeout=timeout) - if response.status_code == 200: - return response.json() - except Exception: - pass - return None - - -def _fetch_registered_backends(port: int, timeout: float = 5.0) -> dict[str, Any] | None: - """Fetch registered backends from the management API.""" - try: - url = f"http://localhost:{port}/v1/management/backends" - response = httpx.get(url, timeout=timeout) - if response.status_code == 200: - return response.json() - except Exception: - pass - return None - - -@app.command("start") -def start( - port: int = typer.Option( - SagePorts.GATEWAY_DEFAULT, - "--port", - "-p", - help=f"Gateway 监听端口 (默认 {SagePorts.GATEWAY_DEFAULT})", - ), - host: str = typer.Option( - "0.0.0.0", - "--host", - "-h", - help="Gateway 监听地址", - ), - enable_control_plane: bool = typer.Option( - True, - "--control-plane/--no-control-plane", - help="启用 Control Plane 引擎管理功能", - ), - background: bool = typer.Option( - True, - "--background/--foreground", - "-b/-f", - help="后台运行 (默认) 或前台运行", - ), - log_level: str = typer.Option( - "info", - "--log-level", - help="日志级别 (debug, info, warning, error)", - ), -): - """启动 SAGE Gateway 服务。 - - Gateway 是 SAGE 的统一 API 网关，提供： - - OpenAI 兼容的 LLM/Embedding API 端点 - - Control Plane 引擎管理功能 - - 会话管理和 RAG 能力 - - 示例： - sage gateway start # 后台启动 (端口 8000) - sage gateway start -p 9000 # 指定端口 - sage gateway start --foreground # 前台运行 - sage gateway start --no-control-plane # 禁用 Control Plane - """ - _ensure_dirs() - ensure_hf_mirror_configured() # Set HF_ENDPOINT for China mirror if needed - - # Check if already running - existing_pid = _get_gateway_pid() - if existing_pid: - if _check_gateway_health(port): - console.print(f"[yellow]⚠️ Gateway 已在运行中 (PID: {existing_pid})[/yellow]") - console.print(f"[blue]🌐 访问地址: http://localhost:{port}[/blue]") - return - else: - console.print("[yellow]⚠️ 发现过期的 PID 文件，正在清理...[/yellow]") - PID_FILE.unlink(missing_ok=True) - - # Check if port is available - if not SagePorts.is_available(port): - console.print(f"[red]❌ 端口 {port} 已被占用[/red]") - console.print("请使用 --port 指定其他端口，或停止占用该端口的服务") - raise typer.Exit(1) - - console.print(f"[blue]🚀 启动 SAGE Gateway (端口 {port})...[/blue]") - - # Build command (requires isagellm package) - cmd = [ - sys.executable, - "-m", - "isagellm.gateway", - "--host", - host, - "--port", - str(port), - "--log-level", - log_level, - ] - - if enable_control_plane: - cmd.append("--enable-control-plane") - - if background: - # Start in background - with open(LOG_FILE, "a") as log_file: - process = subprocess.Popen( - cmd, - stdout=log_file, - stderr=subprocess.STDOUT, - start_new_session=True, - ) - - # Save PID - PID_FILE.write_text(str(process.pid)) - - # Wait for startup - console.print("[dim]等待 Gateway 启动...[/dim]") - for _ in range(30): # Wait up to 30 seconds - time.sleep(1) - if _check_gateway_health(port): - console.print(f"[green]✅ Gateway 启动成功 (PID: {process.pid})[/green]") - console.print(f"[blue]🌐 访问地址: http://localhost:{port}[/blue]") - console.print(f"[dim]📝 日志文件: {LOG_FILE}[/dim]") - if enable_control_plane: - console.print( - "[cyan]💡 Control Plane 已启用，可使用 'sage llm engine' 管理引擎[/cyan]" - ) - return - - console.print("[red]❌ Gateway 启动超时[/red]") - console.print(f"[dim]查看日志: cat {LOG_FILE}[/dim]") - raise typer.Exit(1) - else: - # Foreground mode - console.print("[dim]前台运行模式，按 Ctrl+C 停止[/dim]") - try: - subprocess.run(cmd, check=True) - except KeyboardInterrupt: - console.print("\n[yellow]Gateway 已停止[/yellow]") - except subprocess.CalledProcessError as e: - console.print(f"[red]❌ Gateway 启动失败: {e}[/red]") - raise typer.Exit(1) - - -@app.command("stop") -def stop( - force: bool = typer.Option( - False, - "--force", - "-f", - help="强制停止 (SIGKILL)", - ), -): - """停止 SAGE Gateway 服务。 - - 示例： - sage gateway stop # 优雅停止 - sage gateway stop --force # 强制停止 - """ - pid = _get_gateway_pid() - if not pid: - console.print("[yellow]Gateway 未运行[/yellow]") - return - - console.print(f"[blue]🛑 停止 Gateway (PID: {pid})...[/blue]") - - try: - if force: - os.kill(pid, signal.SIGKILL) - else: - os.kill(pid, signal.SIGTERM) - - # Wait for process to exit - for _ in range(10): - time.sleep(0.5) - try: - os.kill(pid, 0) - except OSError: - # Process has exited - break - - # Clean up PID file - PID_FILE.unlink(missing_ok=True) - console.print("[green]✅ Gateway 已停止[/green]") - - except OSError as e: - console.print(f"[red]❌ 停止失败: {e}[/red]") - raise typer.Exit(1) - - -@app.command("status") -def status( - port: int = typer.Option( - SagePorts.GATEWAY_DEFAULT, - "--port", - "-p", - help=f"Gateway 端口 (默认 {SagePorts.GATEWAY_DEFAULT})", - ), - show_engines: bool = typer.Option( - True, - "--engines/--no-engines", - help="显示已注册的引擎列表", - ), - show_backends: bool = typer.Option( - True, - "--backends/--no-backends", - help="显示已注册的后端列表", - ), -): - """查看 SAGE Gateway 状态。 - - 显示 Gateway 运行状态、Control Plane 信息和已注册的引擎/后端。 - - 示例： - sage gateway status # 完整状态 - sage gateway status --no-engines # 不显示引擎列表 - """ - pid = _get_gateway_pid() - - # Basic status - if pid and _check_gateway_health(port): - console.print(f"[green]✅ Gateway 运行中 (PID: {pid})[/green]") - console.print(f"[blue]🌐 地址: http://localhost:{port}[/blue]") - else: - console.print("[red]❌ Gateway 未运行[/red]") - console.print("[dim]使用 'sage gateway start' 启动服务[/dim]") - return - - # Fetch detailed status - cluster_status = _fetch_gateway_status(port) - if cluster_status: - cp_status = cluster_status.get("control_plane", {}) - console.print("\n[bold]Control Plane 状态:[/bold]") - console.print(f" 运行中: {cp_status.get('running', False)}") - console.print(f" 调度策略: {cp_status.get('scheduling_policy', '-')}") - console.print(f" 待处理请求: {cp_status.get('pending_requests', 0)}") - console.print(f" 运行中请求: {cp_status.get('running_requests', 0)}") - console.print(f" 注册实例: {cp_status.get('registered_instances', 0)}") - - # Show engines - if show_engines: - engines = cluster_status.get("engines", []) - if engines: - console.print(f"\n[bold]已注册引擎 ({len(engines)}):[/bold]") - table = Table(show_header=True, header_style="bold") - table.add_column("Engine ID", overflow="fold") - table.add_column("模型", overflow="fold") - table.add_column("类型", justify="center") - table.add_column("状态", justify="center") - table.add_column("端口", justify="center") - - for engine in engines: - engine_id = engine.get("engine_id") or engine.get("id") or "-" - model = engine.get("model_id") or engine.get("model") or "-" - kind = engine.get("engine_kind") or engine.get("runtime") or "llm" - state = engine.get("status") or engine.get("state") or "-" - engine_port = engine.get("port") or engine.get("listen_port") or "-" - table.add_row( - str(engine_id), str(model), str(kind), str(state), str(engine_port) - ) - - console.print(table) - else: - console.print("\n[dim]暂无已注册的引擎[/dim]") - - # Show backends - if show_backends: - backends = _fetch_registered_backends(port) - if backends: - llm_backends = backends.get("llm_backends", []) - embed_backends = backends.get("embedding_backends", []) - - if llm_backends or embed_backends: - console.print("\n[bold]已发现后端:[/bold]") - console.print( - f" LLM: {backends.get('healthy_llm_backends', 0)}/{backends.get('total_llm_backends', 0)} 健康" - ) - console.print( - f" Embedding: {backends.get('healthy_embedding_backends', 0)}/{backends.get('total_embedding_backends', 0)} 健康" - ) - - -@app.command("logs") -def logs( - follow: bool = typer.Option( - False, - "--follow", - "-f", - help="持续追踪日志输出", - ), - lines: int = typer.Option( - 50, - "--lines", - "-n", - help="显示最后 N 行日志", - ), -): - """查看 SAGE Gateway 日志。 - - 示例： - sage gateway logs # 显示最后 50 行 - sage gateway logs -n 100 # 显示最后 100 行 - sage gateway logs -f # 持续追踪日志 - """ - if not LOG_FILE.exists(): - console.print("[yellow]日志文件不存在[/yellow]") - console.print(f"[dim]预期路径: {LOG_FILE}[/dim]") - return - - if follow: - console.print(f"[dim]追踪日志 (Ctrl+C 退出): {LOG_FILE}[/dim]") - try: - subprocess.run(["tail", "-f", str(LOG_FILE)], check=True) - except KeyboardInterrupt: - pass - except subprocess.CalledProcessError: - # Fallback for systems without tail -f - console.print("[yellow]无法追踪日志，显示最后部分[/yellow]") - console.print(LOG_FILE.read_text()[-10000:]) - else: - try: - result = subprocess.run( - ["tail", "-n", str(lines), str(LOG_FILE)], - capture_output=True, - text=True, - ) - console.print(result.stdout) - except subprocess.CalledProcessError: - # Fallback: read last N lines manually - content = LOG_FILE.read_text() - log_lines = content.splitlines() - for line in log_lines[-lines:]: - console.print(line) - - -@app.command("restart") -def restart( - port: int = typer.Option( - SagePorts.GATEWAY_DEFAULT, - "--port", - "-p", - help=f"Gateway 端口 (默认 {SagePorts.GATEWAY_DEFAULT})", - ), - host: str = typer.Option( - "0.0.0.0", - "--host", - "-h", - help="Gateway 监听地址", - ), -): - """重启 SAGE Gateway 服务。 - - 等同于先执行 stop 再执行 start。 - - 示例： - sage gateway restart - sage gateway restart -p 9000 - """ - console.print("[blue]🔄 重启 Gateway...[/blue]") - - # Stop if running - pid = _get_gateway_pid() - if pid: - stop(force=False) - time.sleep(1) - - # Start again - start(port=port, host=host, enable_control_plane=True, background=True, log_level="info") diff --git a/packages/sage-cli/src/sage/cli/commands/apps/inference.py b/packages/sage-cli/src/sage/cli/commands/apps/inference.py deleted file mode 100644 index a050c411f6..0000000000 --- a/packages/sage-cli/src/sage/cli/commands/apps/inference.py +++ /dev/null @@ -1,652 +0,0 @@ -#!/usr/bin/env python3 -"""Unified Inference service management commands for SAGE. - -This module provides CLI commands to manage the unified inference service, -which combines LLM and Embedding capabilities in a single OpenAI-compatible API. - -Commands: - - start: Start the unified inference server - - stop: Stop the unified inference server - - status: Check the status of the unified inference server - - config: Manage configuration - -Example: - sage inference start --llm-model Qwen/Qwen2.5-7B-Instruct --embedding-model BAAI/bge-m3 - sage inference stop - sage inference status -""" - -from __future__ import annotations - -import json -import subprocess -import sys -import time -from pathlib import Path -from typing import Any - -import psutil -import typer -from rich.console import Console -from rich.table import Table - -console = Console() -app = typer.Typer(help="🔮 统一推理服务管理 - LLM 和 Embedding 混合调度") - -# PID file location -PID_FILE = Path.home() / ".sage" / "inference_server.pid" -CONFIG_FILE = Path.home() / ".sage" / "inference_server.json" -LOG_FILE = Path.home() / ".sage" / "logs" / "inference_server.log" - - -# ============================================================================= -# Helper Functions -# ============================================================================= - - -def _is_port_in_use(port: int) -> bool: - """Check if a port is in use. - - Note: - This is a wrapper around sage.common.utils.system.network.is_port_occupied - """ - from sage.common.utils.system.network import is_port_occupied - - return is_port_occupied("localhost", port) - - -def _get_running_pid() -> int | None: - """Get the PID of the running server from PID file.""" - if not PID_FILE.exists(): - return None - - try: - pid = int(PID_FILE.read_text().strip()) - # Check if process is still running - if psutil.pid_exists(pid): - try: - proc = psutil.Process(pid) - # Verify it's our process by checking command line - cmdline = " ".join(proc.cmdline()) - if "unified_api_server" in cmdline or "sage" in cmdline.lower(): - return pid - except (psutil.NoSuchProcess, psutil.AccessDenied): - pass - # PID file exists but process is not running, clean up - PID_FILE.unlink() - except (ValueError, OSError): - pass - - return None - - -def _save_pid(pid: int) -> None: - """Save the server PID to file.""" - PID_FILE.parent.mkdir(parents=True, exist_ok=True) - PID_FILE.write_text(str(pid)) - - -def _save_config(config: dict[str, Any]) -> None: - """Save the server configuration to file.""" - CONFIG_FILE.parent.mkdir(parents=True, exist_ok=True) - CONFIG_FILE.write_text(json.dumps(config, indent=2)) - - -def _load_config() -> dict[str, Any] | None: - """Load the server configuration from file.""" - if not CONFIG_FILE.exists(): - return None - try: - return json.loads(CONFIG_FILE.read_text()) - except (json.JSONDecodeError, OSError): - return None - - -def _test_api_health(port: int, timeout: float = 2.0) -> dict[str, Any] | None: - """Test the API health endpoint.""" - import urllib.error - import urllib.request - - try: - url = f"http://localhost:{port}/health" - req = urllib.request.Request(url) - with urllib.request.urlopen(req, timeout=timeout) as response: - return json.loads(response.read().decode()) - except Exception: - return None - - -# ============================================================================= -# Start Command -# ============================================================================= - - -@app.command("start") -def start_server( - llm_model: str | None = typer.Option( - None, - "--llm-model", - "-l", - help="LLM 模型名称", - ), - embedding_model: str | None = typer.Option( - None, - "--embedding-model", - "-e", - help="Embedding 模型名称", - ), - llm_backend: str | None = typer.Option( - None, - "--llm-backend", - help="LLM 后端 URL (例如 http://localhost:8001)", - ), - embedding_backend: str | None = typer.Option( - None, - "--embedding-backend", - help="Embedding 后端 URL (例如 http://localhost:8090)", - ), - port: int = typer.Option( - 8000, - "--port", - "-p", - help="服务监听端口", - ), - host: str = typer.Option( - "0.0.0.0", - "--host", - "-h", - help="服务监听地址", - ), - scheduling_policy: str = typer.Option( - "adaptive", - "--scheduling-policy", - "-s", - help="调度策略 (fifo, priority, slo_aware, adaptive, hybrid)", - ), - background: bool = typer.Option( - False, - "--background", - "-b", - help="后台运行服务", - ), - config_file: Path | None = typer.Option( - None, - "--config", - "-c", - help="配置文件路径 (YAML/JSON)", - ), - log_level: str = typer.Option( - "info", - "--log-level", - help="日志级别 (debug, info, warning, error)", - ), -): - """启动统一推理服务。 - - 该服务提供 OpenAI 兼容的 API，同时支持 LLM 和 Embedding 请求。 - 内部通过混合调度器智能分配请求到不同的后端实例。 - - 示例： - # 启动基本服务（使用环境变量配置的默认后端） - sage inference start - - # 指定后端 URL - sage inference start --llm-backend http://localhost:8001 --embedding-backend http://localhost:8090 - - # 使用混合调度策略 - sage inference start --scheduling-policy hybrid - - # 后台运行 - sage inference start --background - - # 使用配置文件 - sage inference start --config inference-config.yaml - - 环境变量： - SAGE_LLM_PORT=8001 # 默认 LLM 后端端口 - SAGE_EMBEDDING_PORT=8090 # 默认 Embedding 后端端口 - SAGE_CHAT_MODEL=model_name # 默认 LLM 模型 - SAGE_EMBEDDING_MODEL=model_name # 默认 Embedding 模型 - """ - console.print("[blue]🚀 启动统一推理服务...[/blue]") - - # Check if already running - existing_pid = _get_running_pid() - if existing_pid: - console.print(f"[yellow]⚠️ 服务已在运行中 (PID: {existing_pid})[/yellow]") - console.print(" 使用 'sage inference stop' 停止服务") - raise typer.Exit(1) - - # Check port availability - if _is_port_in_use(port): - console.print(f"[red]❌ 端口 {port} 已被占用[/red]") - console.print(" 请使用其他端口或停止占用该端口的服务") - raise typer.Exit(1) - - # Load configuration from file if specified - file_config: dict[str, Any] = {} - if config_file and config_file.exists(): - try: - if config_file.suffix in (".yaml", ".yml"): - import yaml # type: ignore[import-untyped] - - file_config = yaml.safe_load(config_file.read_text()) - else: - file_config = json.loads(config_file.read_text()) - console.print(f"[green]✓[/green] 加载配置文件: {config_file}") - except Exception as e: - console.print(f"[red]❌ 无法加载配置文件: {e}[/red]") - raise typer.Exit(1) - - # Merge configuration (CLI args > file config > env vars > defaults) - final_config = { - "host": host, - "port": port, - "llm_model": llm_model or file_config.get("llm", {}).get("model"), - "llm_backend": llm_backend or file_config.get("llm", {}).get("backend"), - "embedding_model": embedding_model or file_config.get("embedding", {}).get("model"), - "embedding_backend": embedding_backend or file_config.get("embedding", {}).get("backend"), - "scheduling_policy": scheduling_policy - or file_config.get("scheduling", {}).get("policy", "adaptive"), - "log_level": log_level, - } - - # Build command to run the server (requires isagellm package) - cmd = [ - sys.executable, - "-m", - "isagellm.unified_api_server", - "--host", - final_config["host"], - "--port", - str(final_config["port"]), - "--scheduling-policy", - final_config["scheduling_policy"], - "--log-level", - final_config["log_level"], - ] - - if final_config.get("llm_model"): - cmd.extend(["--llm-model", final_config["llm_model"]]) - if final_config.get("llm_backend"): - cmd.extend(["--llm-backend", final_config["llm_backend"]]) - if final_config.get("embedding_model"): - cmd.extend(["--embedding-model", final_config["embedding_model"]]) - if final_config.get("embedding_backend"): - cmd.extend(["--embedding-backend", final_config["embedding_backend"]]) - - console.print(f"[dim]命令: {' '.join(cmd[:6])}...[/dim]") - - try: - if background: - # Background mode - LOG_FILE.parent.mkdir(parents=True, exist_ok=True) - log_handle = open(LOG_FILE, "w") - - process = subprocess.Popen( - cmd, - stdout=log_handle, - stderr=subprocess.STDOUT, - start_new_session=True, - ) - - _save_pid(process.pid) - _save_config(final_config) - - console.print("[green]✅ 服务已在后台启动[/green]") - console.print(f" PID: {process.pid}") - console.print(f" 端口: {final_config['port']}") - console.print(f" 日志: {LOG_FILE}") - console.print() - console.print("[dim]API 端点:[/dim]") - console.print( - f" Chat: http://localhost:{final_config['port']}/v1/chat/completions" - ) - console.print(f" Completion: http://localhost:{final_config['port']}/v1/completions") - console.print(f" Embedding: http://localhost:{final_config['port']}/v1/embeddings") - console.print(f" Models: http://localhost:{final_config['port']}/v1/models") - console.print(f" Health: http://localhost:{final_config['port']}/health") - console.print() - console.print("[dim]使用 'sage inference status' 查看服务状态[/dim]") - - else: - # Foreground mode - console.print("[dim]按 Ctrl+C 停止服务[/dim]") - console.print() - - _save_config(final_config) - - process: subprocess.Popen[bytes] | None = None - try: - # Run in foreground - process = subprocess.Popen(cmd) - _save_pid(process.pid) - - # Wait for process - process.wait() - - except KeyboardInterrupt: - console.print("\n[yellow]🛑 正在停止服务...[/yellow]") - if process is not None: - process.terminate() - try: - process.wait(timeout=5) - except subprocess.TimeoutExpired: - process.kill() - - finally: - # Clean up PID file - if PID_FILE.exists(): - PID_FILE.unlink() - - except Exception as e: - console.print(f"[red]❌ 启动失败: {e}[/red]") - raise typer.Exit(1) - - -# ============================================================================= -# Stop Command -# ============================================================================= - - -@app.command("stop") -def stop_server( - force: bool = typer.Option( - False, - "--force", - "-f", - help="强制停止服务", - ), - port: int | None = typer.Option( - None, - "--port", - "-p", - help="指定端口（用于查找进程）", - ), -): - """停止统一推理服务。 - - 示例： - sage inference stop # 停止服务 - sage inference stop --force # 强制停止 - """ - console.print("[blue]🛑 停止统一推理服务...[/blue]") - - pid = _get_running_pid() - - if not pid: - # Try to find by port - if port: - for proc in psutil.process_iter(["pid", "cmdline"]): - try: - cmdline = " ".join(proc.info.get("cmdline") or []) - if "unified_api_server" in cmdline and str(port) in cmdline: - pid = proc.pid - break - except (psutil.NoSuchProcess, psutil.AccessDenied): - continue - - if not pid: - console.print("[yellow]⚠️ 未找到运行中的服务[/yellow]") - # Clean up stale PID file - if PID_FILE.exists(): - PID_FILE.unlink() - raise typer.Exit(0) - - try: - proc = psutil.Process(pid) - console.print(f"[dim]找到进程 PID: {pid}[/dim]") - - if force: - proc.kill() - console.print("[green]✅ 服务已强制停止[/green]") - else: - proc.terminate() - try: - proc.wait(timeout=10) - console.print("[green]✅ 服务已停止[/green]") - except psutil.TimeoutExpired: - console.print("[yellow]⚠️ 服务未响应，强制停止...[/yellow]") - proc.kill() - console.print("[green]✅ 服务已强制停止[/green]") - - except psutil.NoSuchProcess: - console.print("[yellow]⚠️ 进程已不存在[/yellow]") - except psutil.AccessDenied: - console.print("[red]❌ 无权限停止进程，请使用 sudo[/red]") - raise typer.Exit(1) - finally: - # Clean up PID file - if PID_FILE.exists(): - PID_FILE.unlink() - - -# ============================================================================= -# Status Command -# ============================================================================= - - -@app.command("status") -def server_status( - port: int = typer.Option( - 8000, - "--port", - "-p", - help="服务端口", - ), - json_output: bool = typer.Option( - False, - "--json", - help="以 JSON 格式输出", - ), -): - """查看统一推理服务状态。 - - 示例： - sage inference status # 查看状态 - sage inference status --json # JSON 格式输出 - """ - pid = _get_running_pid() - config = _load_config() - port_to_check = config.get("port", port) if config else port - - # Gather status information - status_info: dict[str, Any] = { - "running": False, - "pid": None, - "port": port_to_check, - "health": None, - "uptime": None, - "config": config, - } - - if pid: - try: - proc = psutil.Process(pid) - status_info["running"] = proc.is_running() - status_info["pid"] = pid - status_info["uptime"] = time.time() - proc.create_time() - status_info["memory_mb"] = proc.memory_info().rss / 1024 / 1024 - status_info["cpu_percent"] = proc.cpu_percent() - except psutil.NoSuchProcess: - pass - - # Check health endpoint - if _is_port_in_use(port_to_check): - health = _test_api_health(port_to_check) - status_info["health"] = health - if not status_info["running"]: - status_info["running"] = health is not None - - if json_output: - console.print_json(json.dumps(status_info, indent=2, default=str)) - return - - # Pretty print status - console.print() - console.print("[bold]🔮 统一推理服务状态[/bold]") - console.print() - - if status_info["running"]: - console.print("[green]● 运行中[/green]") - console.print(f" PID: {status_info.get('pid', 'N/A')}") - console.print(f" 端口: {status_info['port']}") - - if status_info.get("uptime"): - uptime_hours = status_info["uptime"] / 3600 - console.print(f" 运行时间: {uptime_hours:.2f} 小时") - - if status_info.get("memory_mb"): - console.print(f" 内存使用: {status_info['memory_mb']:.1f} MB") - - # Health status - health = status_info.get("health") - if health: - console.print() - console.print("[bold]健康状态:[/bold]") - console.print(f" 状态: {health.get('status', 'unknown')}") - backends = health.get("backends", {}) - if backends: - llm_status = "✅" if backends.get("llm", {}).get("healthy") else "❌" - embed_status = "✅" if backends.get("embedding", {}).get("healthy") else "❌" - console.print(f" LLM 后端: {llm_status}") - console.print(f" Embedding 后端: {embed_status}") - - # Configuration - if config: - console.print() - console.print("[bold]配置:[/bold]") - if config.get("llm_model"): - console.print(f" LLM 模型: {config['llm_model']}") - if config.get("llm_backend"): - console.print(f" LLM 后端: {config['llm_backend']}") - if config.get("embedding_model"): - console.print(f" Embedding 模型: {config['embedding_model']}") - if config.get("embedding_backend"): - console.print(f" Embedding 后端: {config['embedding_backend']}") - if config.get("scheduling_policy"): - console.print(f" 调度策略: {config['scheduling_policy']}") - - console.print() - console.print("[dim]API 端点:[/dim]") - console.print(f" http://localhost:{status_info['port']}/v1/chat/completions") - console.print(f" http://localhost:{status_info['port']}/v1/embeddings") - console.print(f" http://localhost:{status_info['port']}/v1/models") - - else: - console.print("[red]● 未运行[/red]") - console.print() - console.print("[dim]使用 'sage inference start' 启动服务[/dim]") - - -# ============================================================================= -# Config Command -# ============================================================================= - - -@app.command("config") -def show_config( - output: str = typer.Option( - "table", - "--output", - "-o", - help="输出格式 (table, json, yaml)", - ), -): - """显示当前配置。 - - 示例： - sage inference config # 表格格式 - sage inference config --output json # JSON 格式 - """ - config = _load_config() - - if not config: - console.print("[yellow]⚠️ 暂无保存的配置[/yellow]") - console.print("[dim]使用 'sage inference start' 首次启动后会生成配置[/dim]") - return - - if output == "json": - console.print_json(json.dumps(config, indent=2)) - elif output == "yaml": - try: - import yaml # type: ignore[import-untyped] - - console.print(yaml.dump(config, default_flow_style=False)) - except ImportError: - console.print("[red]需要安装 PyYAML: pip install pyyaml[/red]") - else: - # Table format - table = Table(title="统一推理服务配置") - table.add_column("配置项", style="cyan") - table.add_column("值", style="green") - - table.add_row("服务地址", f"{config.get('host', 'N/A')}:{config.get('port', 'N/A')}") - table.add_row("LLM 模型", config.get("llm_model") or "(默认)") - table.add_row("LLM 后端", config.get("llm_backend") or "(默认)") - table.add_row("Embedding 模型", config.get("embedding_model") or "(默认)") - table.add_row("Embedding 后端", config.get("embedding_backend") or "(默认)") - table.add_row("调度策略", config.get("scheduling_policy", "adaptive")) - table.add_row("日志级别", config.get("log_level", "info")) - - console.print(table) - - -# ============================================================================= -# Logs Command -# ============================================================================= - - -@app.command("logs") -def show_logs( - follow: bool = typer.Option( - False, - "--follow", - "-f", - help="持续输出日志", - ), - lines: int = typer.Option( - 50, - "--lines", - "-n", - help="显示最后 N 行", - ), -): - """查看服务日志。 - - 示例： - sage inference logs # 显示最后 50 行 - sage inference logs -n 100 # 显示最后 100 行 - sage inference logs -f # 持续输出 - """ - if not LOG_FILE.exists(): - console.print("[yellow]⚠️ 日志文件不存在[/yellow]") - console.print(f"[dim]预期路径: {LOG_FILE}[/dim]") - return - - if follow: - # Follow mode - like tail -f - console.print(f"[dim]跟踪日志文件: {LOG_FILE}[/dim]") - console.print("[dim]按 Ctrl+C 退出[/dim]") - console.print() - - try: - import subprocess - - subprocess.run(["tail", "-f", str(LOG_FILE)]) - except KeyboardInterrupt: - pass - else: - # Show last N lines - try: - with open(LOG_FILE) as f: - all_lines = f.readlines() - last_lines = all_lines[-lines:] - for line in last_lines: - console.print(line.rstrip()) - except Exception as e: - console.print(f"[red]❌ 无法读取日志: {e}[/red]") - - -if __name__ == "__main__": - app() diff --git a/packages/sage-cli/src/sage/cli/commands/apps/llm.py b/packages/sage-cli/src/sage/cli/commands/apps/llm.py deleted file mode 100644 index 5d01ff1d63..0000000000 --- a/packages/sage-cli/src/sage/cli/commands/apps/llm.py +++ /dev/null @@ -1,202 +0,0 @@ -#!/usr/bin/env python3 -"""LLM service management commands for SAGE. - -All LLM services should be managed through sageLLM (isagellm), -NOT by directly calling vLLM entrypoints. - -MIGRATION NOTE (2026-01): This file now uses isagellm instead of sage.llm. -Recommended engine: sagellm (default). vllm engine is deprecated. -""" - -from __future__ import annotations - -import httpx -import typer -from rich.console import Console -from rich.table import Table - -from sage.common.model_registry import fetch_recommended_models - -# Import from isagellm (sageLLM inference engine) -try: - from sagellm_control import ControlPlaneManager - from sagellm_control.types import EngineInfo, EngineState -except ImportError: # pragma: no cover - ControlPlaneManager = None # type: ignore - EngineInfo = None # type: ignore - EngineState = None # type: ignore - -try: - from sagellm_gateway import GatewayConfig, GatewayServer -except ImportError: # pragma: no cover - GatewayServer = None # type: ignore - GatewayConfig = None # type: ignore - -# Import config subcommands -from sage.cli.commands.platform.llm_config import app as config_app - -app = typer.Typer( - name="llm", - help="LLM service management (powered by isagellm)", - no_args_is_help=True, -) -console = Console() - -# Add config subcommand group -app.add_typer(config_app, name="config") - - -def _check_sagellm_available() -> bool: - """Check if isagellm components are available.""" - if ControlPlaneManager is None: - console.print("[red]Error:[/red] isagellm not installed. Please run: pip install isagellm") - return False - return True - - -def _get_registry(engine: str): - """Get the appropriate model registry based on engine selection. - - Args: - engine: Engine type ('sagellm' is the only supported option) - - Returns: - The appropriate registry module - - Raises: - ValueError: If engine is 'vllm' (removed in v0.3.0) - """ - if engine == "vllm": - raise ValueError( - "vllm engine has been removed in SAGE v0.3.0. " - "Please use engine='sagellm' instead. " - "See migration guide: docs-public/docs_src/dev-notes/migration/VLLM_TO_SAGELLM_MIGRATION.md" - ) - from sage.common.model_registry import sagellm_registry as registry - - return registry - - -@app.command("status") -def status( - host: str = typer.Option("localhost", "--host", "-h", help="Server host"), - port: int = typer.Option(8000, "--port", "-p", help="Server port"), -): - """Check LLM server status.""" - url = f"http://{host}:{port}/health" - try: - resp = httpx.get(url, timeout=5.0) - if resp.status_code == 200: - console.print(f"[green]✓[/green] Server at {host}:{port} is healthy") - data = resp.json() - if data: - console.print(f" Status: {data}") - else: - console.print(f"[yellow]![/yellow] Server returned: {resp.status_code}") - except httpx.ConnectError: - console.print(f"[red]✗[/red] Cannot connect to {host}:{port}") - except Exception as e: - console.print(f"[red]✗[/red] Error: {e}") - - -@app.command("list-models") -def list_models( - recommended: bool = typer.Option(False, "--recommended", "-r", help="Show recommended models"), - engine: str = typer.Option( - "sagellm", - "--engine", - "-e", - help="推理引擎 (仅支持 sagellm)", - ), -): - """List available models. - - Uses sagellm registry for model management. - """ - if recommended: - models = fetch_recommended_models() - table = Table(title="Recommended Models") - table.add_column("Name", style="cyan") - table.add_column("Size", style="green") - table.add_column("Description") - for model in models: - table.add_row(model.get("name", ""), model.get("size", ""), model.get("desc", "")) - console.print(table) - else: - # List from selected registry - registry = _get_registry(engine) - models = registry.list_models() - table = Table(title=f"Available Models ({engine})") - table.add_column("Model ID", style="cyan") - table.add_column("Size (MB)", style="green") - table.add_column("Last Used", style="yellow") - for model in models: - size_mb = f"{model.size_mb:.1f}" if hasattr(model, "size_mb") else "N/A" - last_used = model.last_used_iso if hasattr(model, "last_used_iso") else "N/A" - table.add_row(model.model_id, size_mb, last_used) - console.print(table) - - -@app.command("serve") -def serve( - model: str = typer.Argument(..., help="Model name or path"), - host: str = typer.Option("0.0.0.0", "--host", "-h", help="Server host"), - port: int = typer.Option(8000, "--port", "-p", help="Server port"), - mock: bool = typer.Option(False, "--mock", help="Run in mock mode (no GPU)"), -): - """Start LLM server (via isagellm gateway).""" - if not _check_sagellm_available(): - raise typer.Exit(1) - - if GatewayServer is None: - console.print( - "[red]Error:[/red] isagellm[gateway] not installed. " - "Please run: pip install 'isagellm[gateway]'" - ) - raise typer.Exit(1) - - console.print(f"[cyan]Starting LLM server for model:[/cyan] {model}") - console.print(f" Host: {host}:{port}") - console.print(f" Mock mode: {mock}") - - try: - config = GatewayConfig( - host=host, - port=port, - mock_mode=mock, - ) - server = GatewayServer(config) - server.run() - except Exception as e: - console.print(f"[red]Error starting server:[/red] {e}") - raise typer.Exit(1) - - -@app.command("info") -def info(): - """Show isagellm installation info.""" - try: - import sagellm - - console.print(f"[green]✓[/green] isagellm version: {sagellm.__version__}") - except ImportError: - console.print("[red]✗[/red] isagellm not installed") - return - - try: - import sagellm_control - - console.print(f"[green]✓[/green] sagellm-control-plane: {sagellm_control.__version__}") - except ImportError: - console.print("[yellow]![/yellow] sagellm-control-plane not installed") - - try: - import sagellm_gateway - - console.print(f"[green]✓[/green] sagellm-gateway: {sagellm_gateway.__version__}") - except ImportError: - console.print("[yellow]![/yellow] sagellm-gateway not installed (optional)") - - -if __name__ == "__main__": - app() diff --git a/packages/sage-cli/src/sage/cli/commands/apps/pipeline.py b/packages/sage-cli/src/sage/cli/commands/apps/pipeline.py deleted file mode 100644 index c204d5e335..0000000000 --- a/packages/sage-cli/src/sage/cli/commands/apps/pipeline.py +++ /dev/null @@ -1,1654 +0,0 @@ -#!/usr/bin/env python3 -"""Interactive pipeline builder powered by LLMs.""" - -from __future__ import annotations - -import importlib -import json -import os -import re -import textwrap -from collections.abc import Sequence -from dataclasses import dataclass -from pathlib import Path -from typing import Any - -import typer -import yaml # type: ignore[import-untyped] -from rich.console import Console -from rich.panel import Panel -from rich.syntax import Syntax -from rich.table import Table - -from sage.cli import templates -from sage.cli.commands.apps.pipeline_domain import load_custom_contexts, load_domain_contexts -from sage.cli.commands.apps.pipeline_knowledge import ( - PipelineKnowledgeBase, - build_query_payload, - get_default_knowledge_base, -) -from sage.cli.core.exceptions import CLIException -from sage.cli.templates import pipeline_blueprints as blueprints -from sage.common.config.output_paths import get_sage_paths -from sage.kernel.api.base_environment import BaseEnvironment -from sage.kernel.api.local_environment import LocalEnvironment - -try: # pragma: no cover - optional dependency at runtime only - from isagellm import UnifiedInferenceClient - - OPENAI_AVAILABLE = True - OPENAI_IMPORT_ERROR: Exception | None = None -except Exception as exc: # pragma: no cover - runtime check - OPENAI_AVAILABLE = False - OPENAI_IMPORT_ERROR = exc - UnifiedInferenceClient = object # type: ignore - - -DEFAULT_BACKEND = os.getenv("SAGE_PIPELINE_BUILDER_BACKEND", "openai") -DEFAULT_MODEL = os.getenv("SAGE_PIPELINE_BUILDER_MODEL") or os.getenv( - "TEMP_GENERATOR_MODEL", - "qwen-turbo-2025-02-11", -) -DEFAULT_BASE_URL = os.getenv("SAGE_PIPELINE_BUILDER_BASE_URL") or os.getenv( - "TEMP_GENERATOR_BASE_URL" -) -DEFAULT_API_KEY = os.getenv("SAGE_PIPELINE_BUILDER_API_KEY") or os.getenv("TEMP_GENERATOR_API_KEY") - - -SYSTEM_PROMPT = textwrap.dedent( - """ - You are SAGE Pipeline Builder, an expert in configuring Streaming-Augmented Generative Execution pipelines. - Produce a *single* JSON object that can be saved as a YAML config for the SAGE CLI. - - Required JSON structure: - { - "pipeline": { - "name": str, - "description": str, - "version": "1.0.0", - "type": "local" | "remote" - }, - "source": { ... }, - "stages": [ - { - "id": str, - "kind": "map" | "batch" | "service", - "class": str, # Python class path within SAGE - "params": { ... }, - "summary": str - } - ], - "sink": { ... }, - "services": [ - { - "name": str, - "class": str, - "params": { ... } - } - ], - "monitors": [ ... ], - "notes": [str] - } - - Rules: - - Populate concrete SAGE component class paths (e.g. "sage.libs.rag.retriever.Wiki18FAISSRetriever"). - - When unsure, choose sensible defaults that work out-of-the-box. - - Always include at least one stage and a sink. - - Ensure identifiers are slugified (lowercase, hyphen-separated). - - Parameters must be JSON-serializable and concise. - - Never wrap the JSON in markdown fences or add commentary outside the JSON. - """ -).strip() - - -GRAPH_SYSTEM_PROMPT = textwrap.dedent( - """ - You are SAGE Pipeline Architect, an expert agent workflow designer. - Create expressive multi-stage graph pipelines that can include agents, tools, services, - and messaging channels. Support branching, multi-agent orchestration, shared memories, - and control flows whenever helpful. - - Produce a single JSON object with the structure: - { - "pipeline": { - "name": str, - "description": str, - "version": "1.0.0", - "type": "local" | "remote" - }, - "graph": { - "nodes": [ - { - "id": str, - "title": str, - "kind": "source" | "agent" | "tool" | "service" | "sink" | "router", - "class": str, - "params": { ... }, - "inputs": [str], # upstream node IDs - "outputs": [str], - "metadata": { ... } - } - ], - "channels": [ - { - "id": str, - "type": "memory" | "event" | "queue" | "stream", - "description": str, - "participants": [str] - } - ] - }, - "agents": [ - { - "id": str, - "role": str, - "goals": [str], - "tools": [str], - "memory": { - "type": str, - "config": { ... } - } - } - ], - "services": [ ... ], - "monitors": [ ... ], - "notes": [str] - } - - Guidelines: - - Encourage the use of multiple agents when the task benefits from specialization. - - Use inputs/outputs to express the DAG; omit when not relevant. - - Fill params with concrete configuration that can run on SAGE where possible. - - Include channels when agents need to coordinate or share state. - - Ensure node IDs are unique kebab-case strings. Outputs list may be omitted when obvious. - - Prefer referencing existing SAGE components (e.g. sage.libs.rag.*, examples.*) but - custom classes are allowed if necessary—describe them in notes. - """ -).strip() - - -console = Console() -app = typer.Typer(help="🧠 使用大模型交互式创建 SAGE pipeline 配置") - - -def _render_blueprint_panel( - matches: Sequence[tuple[blueprints.PipelineBlueprint, float]], -) -> Panel: - lines: list[str] = [] - for index, (blueprint, score) in enumerate(matches, start=1): - lines.append( - textwrap.dedent( - f""" - [{index}] {blueprint.title} ({blueprint.id}) - 匹配度: {score:.2f} | 关键词: {", ".join(blueprint.keywords) or "通用"} - 场景: {blueprint.description} - """ - ).strip() - ) - body = "\n\n".join(lines) or "暂无可用蓝图" - return Panel(body, title="蓝图库候选", style="magenta") - - -def _render_template_panel( - matches: Sequence[templates.TemplateMatch], -) -> Panel: - lines: list[str] = [] - for index, match in enumerate(matches, start=1): - template = match.template - lines.append( - textwrap.dedent( - f""" - [{index}] {template.title} ({template.id}) - 匹配度: {match.score:.2f} | 标签: {", ".join(template.tags) or "通用"} - 示例: {template.example_path} - 场景: {template.description} - """ - ).strip() - ) - body = "\n\n".join(lines) or "暂无应用模板" - return Panel(body, title="应用模板推荐", style="green") - - -def _blueprint_contexts( - matches: Sequence[tuple[blueprints.PipelineBlueprint, float]], -) -> tuple[str, ...]: - return tuple( - blueprints.render_blueprint_prompt(blueprint, score) for blueprint, score in matches - ) - - -def _template_contexts( - matches: Sequence[templates.TemplateMatch], -) -> tuple[str, ...]: - return tuple(match.template.render_prompt(match.score) for match in matches) - - -class PipelineBuilderError(RuntimeError): - """Raised when the builder cannot produce a valid plan.""" - - -def _slugify(value: str) -> str: - slug = re.sub(r"[^a-zA-Z0-9]+", "-", value.lower()).strip("-") - return slug or "pipeline" - - -def _extract_json_object(text: str) -> dict[str, Any]: - console.print(f"[dim]LLM response (first 500 chars): {text[:500]}[/dim]") # Debug - candidate = text.strip() - if candidate.startswith("```"): - candidate = re.sub(r"^```(?:json)?", "", candidate, count=1).strip() - candidate = re.sub(r"```$", "", candidate, count=1).strip() - - try: - result = json.loads(candidate) - console.print(f"[dim]Parsed JSON keys: {list(result.keys())}[/dim]") # Debug - return result - except json.JSONDecodeError: - pass - - brace_match = re.search(r"\{.*\}", candidate, re.DOTALL) - if brace_match: - try: - result = json.loads(brace_match.group()) - console.print(f"[dim]Parsed JSON keys (from brace match): {list(result.keys())}[/dim]") - return result - except json.JSONDecodeError as exc: # pragma: no cover - defensive - raise PipelineBuilderError(f"LLM returned invalid JSON: {exc}") from exc - - raise PipelineBuilderError("无法解析大模型返回的 JSON，请重试或调整描述。") - - -def _validate_plan(plan: dict[str, Any]) -> None: - if "pipeline" not in plan or "stages" not in plan or "sink" not in plan: - raise PipelineBuilderError( - "生成的配置缺少必要字段 (pipeline/stages/sink)。请尝试提供更多需求细节。" - ) - - if not isinstance(plan["stages"], list) or not plan["stages"]: - raise PipelineBuilderError("stages 字段必须是非空列表。") - - if not isinstance(plan.get("sink"), dict) or not plan["sink"].get("class"): - raise PipelineBuilderError("sink 字段必须是包含 class 的对象。") - - source = plan.get("source") - if source is not None and not isinstance(source, dict): - raise PipelineBuilderError("source 字段必须是对象。") - - pipeline_block = plan["pipeline"] - if "name" not in pipeline_block: - pipeline_block["name"] = "untitled-pipeline" - if "type" not in pipeline_block: - pipeline_block["type"] = "local" - if "version" not in pipeline_block: - pipeline_block["version"] = "1.0.0" - - for stage in plan["stages"]: - if not isinstance(stage, dict): - raise PipelineBuilderError("stages 列表中的元素必须是对象。") - stage_id = stage.get("id") - stage["id"] = _slugify(str(stage_id)) if stage_id else _slugify(stage.get("class", "stage")) - if not stage.get("class"): - raise PipelineBuilderError("每个 stage 必须包含 class 字段。") - params = stage.get("params", {}) - if params is None: - stage["params"] = {} - elif not isinstance(params, dict): - raise PipelineBuilderError("stage 的 params 必须是对象 (key/value)。") - - -def _validate_graph_plan(plan: dict[str, Any]) -> None: - pipeline_meta = plan.get("pipeline") - graph = plan.get("graph") - - if not isinstance(pipeline_meta, dict): - raise PipelineBuilderError("graph 配置缺少 pipeline 信息。") - if not isinstance(graph, dict): - raise PipelineBuilderError("graph 配置缺少 graph 节点定义。") - - nodes = graph.get("nodes") - if not isinstance(nodes, list) or not nodes: - raise PipelineBuilderError("graph.nodes 必须是非空列表。") - - seen_ids: set[str] = set() - for node in nodes: - if not isinstance(node, dict): - raise PipelineBuilderError("graph.nodes 中的元素必须是对象。") - node_id = node.get("id") - if not node_id: - raise PipelineBuilderError("每个节点都需要 id。") - slugified = _slugify(str(node_id)) - node["id"] = slugified - if slugified in seen_ids: - raise PipelineBuilderError(f"节点 id 重复 : {slugified}") - seen_ids.add(slugified) - - if not node.get("class"): - raise PipelineBuilderError(f"节点 {slugified} 缺少 class 字段。") - - for key in ("inputs", "outputs"): - if key in node and node[key] is not None and not isinstance(node[key], list): - raise PipelineBuilderError(f"节点 {slugified} 的 {key} 字段必须是列表。") - - channels = graph.get("channels") or [] - if not isinstance(channels, list): - raise PipelineBuilderError("graph.channels 必须是列表。") - for channel in channels: - if not isinstance(channel, dict): - raise PipelineBuilderError("graph.channels 中的元素必须是对象。") - if not channel.get("id"): - raise PipelineBuilderError("每个 channel 需要 id。") - - for block_name in ("agents", "services", "monitors"): - if ( - block_name in plan - and plan[block_name] is not None - and not isinstance(plan[block_name], list) - ): - raise PipelineBuilderError(f"{block_name} 字段必须是列表。") - - notes = plan.get("notes") - if notes is not None and not isinstance(notes, list): - raise PipelineBuilderError("notes 字段必须是字符串列表。") - - -def _expand_params(value: Any) -> Any: - if isinstance(value, dict): - return {key: _expand_params(val) for key, val in value.items()} - if isinstance(value, list): - return [_expand_params(item) for item in value] - if isinstance(value, str): - return os.path.expandvars(os.path.expanduser(value)) - return value - - -def _import_attr(path: str) -> Any: - try: - module_name, attr_name = path.rsplit(".", 1) - except ValueError as exc: # pragma: no cover - defensive - raise CLIException(f"Invalid class path: {path}") from exc - - try: - module = importlib.import_module(module_name) - except ImportError as exc: - raise CLIException(f"无法导入模块 {module_name}: {exc}") from exc - - try: - return getattr(module, attr_name) - except AttributeError as exc: - raise CLIException(f"模块 {module_name} 不包含 {attr_name}") from exc - - -def _ensure_pipeline_dict(data: dict[str, Any]) -> dict[str, Any]: - if not isinstance(data, dict): - raise CLIException("Pipeline 配置必须是字典结构。") - return data - - -def _create_environment( - plan: dict[str, Any], host: str | None, port: int | None -) -> BaseEnvironment: - pipeline_meta = plan.get("pipeline") or {} - pipeline_name = pipeline_meta.get("name", "sage-pipeline") - env_settings = plan.get("environment") or {} - env_config = _expand_params(env_settings.get("config") or {}) - - env_type = (pipeline_meta.get("type") or "local").lower() - if env_type == "remote": - from sage.kernel.api.remote_environment import RemoteEnvironment # import lazily - - resolved_host = host or env_settings.get("host") or "127.0.0.1" - resolved_port = port or env_settings.get("port") or 19001 - return RemoteEnvironment( - name=pipeline_name, - config=env_config, - host=resolved_host, - port=int(resolved_port), - ) - - return LocalEnvironment(name=pipeline_name, config=env_config) - - -def _register_services(env: BaseEnvironment, services: list[dict[str, Any]]) -> None: - for service in services or []: - name = service.get("name") - class_path = service.get("class") - if not name or not class_path: - raise CLIException("每个 service 需要 name 和 class 字段。") - - service_class = _import_attr(class_path) - args = service.get("args") or [] - if not isinstance(args, list): - raise CLIException(f"Service {name} 的 args 必须是数组。") - params = _expand_params(service.get("params") or {}) - env.register_service(name, service_class, *args, **params) - - -def _apply_source(env: BaseEnvironment, source: dict[str, Any]): - if not source: - raise CLIException("Pipeline 缺少 source 定义。") - - class_path = source.get("class") - if not class_path: - raise CLIException("source 需要提供 class 字段。") - - function_class = _import_attr(class_path) - args = source.get("args") or [] - if not isinstance(args, list): - raise CLIException("source 的 args 必须是数组。") - params = _expand_params(source.get("params") or {}) - kind = (source.get("kind") or "batch").lower() - - if kind in {"batch", "collection"}: - return env.from_batch(function_class, *args, **params) - if kind in {"source", "stream"}: - return env.from_source(function_class, *args, **params) - if kind == "future": - future_name = params.get("name") or source.get("id") or "future" - return env.from_future(future_name) - - # Default to from_source for unknown kinds - return env.from_source(function_class, *args, **params) - - -def _apply_stage(stream, stage: dict[str, Any]): - class_path = stage.get("class") - if not class_path: - raise CLIException("stage 缺少 class 字段。") - - function_class = _import_attr(class_path) - args = stage.get("args") or [] - if not isinstance(args, list): - raise CLIException(f"stage {stage.get('id')} 的 args 必须是数组。") - params = _expand_params(stage.get("params") or {}) - kind = (stage.get("kind") or "map").lower() - - if kind in {"map", "service", "batch"}: - return stream.map(function_class, *args, **params) - if kind == "flatmap": - return stream.flatmap(function_class, *args, **params) - if kind == "filter": - return stream.filter(function_class, *args, **params) - if kind == "keyby": - strategy = params.pop("strategy", "hash") - return stream.keyby(function_class, strategy=strategy, *args, **params) - if kind == "sink": - stream.sink(function_class, *args, **params) - return stream - - console.print(f"[yellow]⚠️ 未知的 stage 类型 {kind}，默认使用 map。[/yellow]") - return stream.map(function_class, *args, **params) - - -def _apply_sink(stream, sink: dict[str, Any]): - if not sink: - raise CLIException("Pipeline 缺少 sink 定义。") - - class_path = sink.get("class") - if not class_path: - raise CLIException("sink 需要提供 class 字段。") - - function_class = _import_attr(class_path) - args = sink.get("args") or [] - if not isinstance(args, list): - raise CLIException("sink 的 args 必须是数组。") - params = _expand_params(sink.get("params") or {}) - stream.sink(function_class, *args, **params) - - -def _load_pipeline_file(path: Path) -> dict[str, Any]: - try: - content = path.read_text(encoding="utf-8") - except FileNotFoundError as exc: - raise CLIException(f"找不到 pipeline 配置文件: {path}") from exc - except OSError as exc: - raise CLIException(f"读取 pipeline 文件失败: {exc}") from exc - - try: - data = yaml.safe_load(content) or {} - except yaml.YAMLError as exc: - raise CLIException(f"解析 YAML 失败: {exc}") from exc - - return _ensure_pipeline_dict(data) - - -@dataclass -class BuilderConfig: - backend: str - model: str | None - base_url: str | None - api_key: str | None - domain_contexts: tuple[str, ...] = () - knowledge_base: PipelineKnowledgeBase | None = None - knowledge_top_k: int = 6 - show_knowledge: bool = False - - -@dataclass -class GraphBuilderConfig: - backend: str - model: str - base_url: str | None - api_key: str | None - domain_contexts: tuple[str, ...] = () - knowledge_base: PipelineKnowledgeBase | None = None - knowledge_top_k: int = 6 - show_knowledge: bool = False - - -class PipelinePlanGenerator: - def __init__(self, config: BuilderConfig) -> None: - self.config = config - self._client: Any | None = None - self._last_knowledge_contexts: tuple[str, ...] = () - self._blueprint_matches: tuple[tuple[blueprints.PipelineBlueprint, float], ...] = () - self._last_blueprint_contexts: tuple[str, ...] = () - self._template_matches: tuple[templates.TemplateMatch, ...] = () - self._last_template_contexts: tuple[str, ...] = () - - if self.config.backend != "mock": - if not OPENAI_AVAILABLE: - message = f"未能导入 UnifiedInferenceClient：{OPENAI_IMPORT_ERROR}" - raise PipelineBuilderError(message) - # 使用工厂方法创建 UnifiedInferenceClient - self._client = UnifiedInferenceClient.create( - default_llm_model=self.config.model, - ) - - def generate( - self, - requirements: dict[str, Any], - previous_plan: dict[str, Any] | None = None, - feedback: str | None = None, - ) -> dict[str, Any]: - knowledge_contexts: tuple[str, ...] = () - if self.config.knowledge_base is not None: - try: - query_payload = build_query_payload(requirements, previous_plan, feedback) - results = self.config.knowledge_base.search( - query_payload, - top_k=self.config.knowledge_top_k, - ) - knowledge_contexts = tuple(item.text for item in results) - self._last_knowledge_contexts = knowledge_contexts - except Exception as exc: # pragma: no cover - defensive - console.print(f"[yellow]检索知识库时出错，将继续使用内建上下文: {exc}[/yellow]") - self._last_knowledge_contexts = () - - if self.config.show_knowledge and knowledge_contexts: - console.print( - Panel( - "\n\n".join(knowledge_contexts), - title="知识库检索结果", - style="blue", - ) - ) - - self._template_matches = tuple(templates.match_templates(requirements, top_k=3)) - self._last_template_contexts = _template_contexts(self._template_matches) - if self._template_matches and self.config.show_knowledge: - console.print(_render_template_panel(self._template_matches)) - - if self.config.backend == "mock": - self._blueprint_matches = tuple(blueprints.match_blueprints(requirements)) - self._last_blueprint_contexts = _blueprint_contexts(self._blueprint_matches) - if self._blueprint_matches and self.config.show_knowledge: - console.print(_render_blueprint_panel(self._blueprint_matches)) - return self._blueprint_plan(requirements, previous_plan, feedback) - - self._blueprint_matches = tuple(blueprints.match_blueprints(requirements)) - if self._blueprint_matches and self.config.show_knowledge: - console.print(_render_blueprint_panel(self._blueprint_matches)) - self._last_blueprint_contexts = _blueprint_contexts(self._blueprint_matches) - - assert self._client is not None # for type checker - user_prompt = self._build_prompt( - requirements, - previous_plan, - feedback, - knowledge_contexts, - self._last_template_contexts, - self._last_blueprint_contexts, - ) - messages = [ - {"role": "system", "content": SYSTEM_PROMPT}, - {"role": "user", "content": user_prompt}, - ] - - console.print("🤖 正在请求大模型生成配置...", style="cyan") - response = self._client.chat(messages, max_tokens=1200, temperature=0.2) - plan = _extract_json_object(response) - _validate_plan(plan) - return plan - - def _build_prompt( - self, - requirements: dict[str, Any], - previous_plan: dict[str, Any] | None, - feedback: str | None, - knowledge_contexts: tuple[str, ...], - template_contexts: tuple[str, ...], - blueprint_contexts: tuple[str, ...], - ) -> str: - blocks = [ - "请根据以下需求生成符合 SAGE 框架的 pipeline 配置 JSON：", - json.dumps(requirements, ensure_ascii=False, indent=2), - ] - - if template_contexts: - blocks.append("以下应用模板仅作灵感参考，请结合需求自行设计：") - for idx, snippet in enumerate(template_contexts, start=1): - blocks.append(f"模板[{idx}]:\n{snippet.strip()}") - - if blueprint_contexts: - blocks.append("以下蓝图可直接复用或在此基础上扩展：") - for idx, snippet in enumerate(blueprint_contexts, start=1): - blocks.append(f"蓝图[{idx}]:\n{snippet.strip()}") - - if knowledge_contexts: - blocks.append("以下是从 SAGE 知识库检索到的参考信息：") - for idx, snippet in enumerate(knowledge_contexts, start=1): - blocks.append(f"知识[{idx}]:\n{snippet.strip()}") - - if self.config.domain_contexts: - blocks.append("以下是与 SAGE 管道构建相关的参考资料：") - for idx, snippet in enumerate(self.config.domain_contexts, start=1): - blocks.append(f"参考[{idx}]:\n{snippet.strip()}") - - if previous_plan: - blocks.append("这是上一版配置供参考：") - blocks.append(json.dumps(previous_plan, ensure_ascii=False, indent=2)) - - if feedback: - blocks.append("请遵循以下修改意见更新配置：") - blocks.append(feedback.strip()) - - blocks.append("严格输出单个 JSON 对象，不要包含 markdown、注释或多余文字。") - return "\n\n".join(blocks) - - def _blueprint_plan( - self, - requirements: dict[str, Any], - previous_plan: dict[str, Any] | None, - feedback: str | None, - ) -> dict[str, Any]: - blueprint = ( - self._blueprint_matches[0][0] - if self._blueprint_matches - else blueprints.DEFAULT_BLUEPRINT - ) - return blueprints.build_pipeline_plan(blueprint, requirements, feedback) - - -class GraphPlanGenerator: - def __init__(self, config: GraphBuilderConfig) -> None: - self.config = config - self._client: Any | None = None - self._last_knowledge_contexts: tuple[str, ...] = () - self._blueprint_matches: tuple[tuple[blueprints.PipelineBlueprint, float], ...] = () - self._last_blueprint_contexts: tuple[str, ...] = () - - if self.config.backend != "mock": - if not OPENAI_AVAILABLE: - message = f"未能导入 UnifiedInferenceClient：{OPENAI_IMPORT_ERROR}" - raise PipelineBuilderError(message) - # 使用工厂方法创建 UnifiedInferenceClient - self._client = UnifiedInferenceClient.create( - default_llm_model=self.config.model, - ) - - def generate( - self, - requirements: dict[str, Any], - previous_plan: dict[str, Any] | None = None, - feedback: str | None = None, - ) -> dict[str, Any]: - knowledge_contexts: tuple[str, ...] = () - if self.config.knowledge_base is not None: - try: - query_payload = build_query_payload(requirements, previous_plan, feedback) - results = self.config.knowledge_base.search( - query_payload, top_k=self.config.knowledge_top_k - ) - knowledge_contexts = tuple(item.text for item in results) - self._last_knowledge_contexts = knowledge_contexts - except Exception as exc: # pragma: no cover - defensive - console.print(f"[yellow]检索知识库时出错，将继续使用静态上下文: {exc}[/yellow]") - self._last_knowledge_contexts = () - - if self.config.show_knowledge and knowledge_contexts: - console.print( - Panel( - "\n\n".join(knowledge_contexts), - title="知识库检索结果", - style="blue", - ) - ) - - self._template_matches = tuple(templates.match_templates(requirements, top_k=4)) - self._last_template_contexts = _template_contexts(self._template_matches) - if self._template_matches and self.config.show_knowledge: - console.print(_render_template_panel(self._template_matches)) - - if self.config.backend == "mock": - self._blueprint_matches = tuple(blueprints.match_blueprints(requirements)) - self._last_blueprint_contexts = _blueprint_contexts(self._blueprint_matches) - if self._blueprint_matches and self.config.show_knowledge: - console.print(_render_blueprint_panel(self._blueprint_matches)) - return self._blueprint_plan(requirements, previous_plan, feedback) - - self._blueprint_matches = tuple(blueprints.match_blueprints(requirements)) - if self._blueprint_matches and self.config.show_knowledge: - console.print(_render_blueprint_panel(self._blueprint_matches)) - self._last_blueprint_contexts = _blueprint_contexts(self._blueprint_matches) - - assert self._client is not None - user_prompt = self._build_prompt( - requirements, - previous_plan, - feedback, - knowledge_contexts, - self._last_template_contexts, - self._last_blueprint_contexts, - ) - messages = [ - {"role": "system", "content": GRAPH_SYSTEM_PROMPT}, - {"role": "user", "content": user_prompt}, - ] - - console.print("🤖 正在请求大模型设计图谱...", style="cyan") - response = self._client.chat(messages, max_tokens=1600, temperature=0.35) - plan = _extract_json_object(response) - return plan - - def _build_prompt( - self, - requirements: dict[str, Any], - previous_plan: dict[str, Any] | None, - feedback: str | None, - knowledge_contexts: tuple[str, ...], - template_contexts: tuple[str, ...], - blueprint_contexts: tuple[str, ...], - ) -> str: - blocks: list[str] = [ - "请根据以下需求设计一个多智能体 SAGE pipeline 图谱：", - json.dumps(requirements, ensure_ascii=False, indent=2), - ] - - if template_contexts: - blocks.append("以下应用模板可作为参考灵感，请主动规划合适的多智能体结构：") - for idx, snippet in enumerate(template_contexts, start=1): - blocks.append(f"模板[{idx}]:\n{snippet.strip()}") - - if blueprint_contexts: - blocks.append("以下蓝图可作为起点进行扩展：") - for idx, snippet in enumerate(blueprint_contexts, start=1): - blocks.append(f"蓝图[{idx}]:\n{snippet.strip()}") - - if knowledge_contexts: - blocks.append("以下是从 SAGE 知识库检索到的参考信息：") - for idx, snippet in enumerate(knowledge_contexts, start=1): - blocks.append(f"知识[{idx}]:\n{snippet.strip()}") - - if self.config.domain_contexts: - blocks.append("以下是与 SAGE 组件相关的参考资料：") - for idx, snippet in enumerate(self.config.domain_contexts, start=1): - blocks.append(f"参考[{idx}]:\n{snippet.strip()}") - - if previous_plan: - blocks.append("上一版图谱结构供参考：") - blocks.append(json.dumps(previous_plan, ensure_ascii=False, indent=2)) - - if feedback: - blocks.append("请依据以下反馈调整图谱：") - blocks.append(feedback.strip()) - - blocks.append("严格输出单个 JSON 对象，不要包含 markdown、注释或多余文字。") - return "\n\n".join(blocks) - - def _blueprint_plan( - self, - requirements: dict[str, Any], - previous_plan: dict[str, Any] | None, - feedback: str | None, - ) -> dict[str, Any]: - blueprint = ( - self._blueprint_matches[0][0] - if self._blueprint_matches - else blueprints.DEFAULT_BLUEPRINT - ) - return blueprints.build_graph_plan(blueprint, requirements, feedback) - - -def _render_plan(plan: dict[str, Any]) -> None: - pipeline_meta = plan.get("pipeline", {}) - console.print( - Panel.fit( - f"名称: [cyan]{pipeline_meta.get('name', '-')}[/cyan]\n" - f"描述: {pipeline_meta.get('description', '-')}\n" - f"类型: {pipeline_meta.get('type', '-')}", - title="Pipeline 元信息", - style="green", - ) - ) - - table = Table(title="阶段概览", show_header=True, header_style="bold blue") - table.add_column("ID", style="cyan") - table.add_column("类型") - table.add_column("类路径") - table.add_column("摘要") - - for stage in plan.get("stages", []): - table.add_row( - stage.get("id", "-"), - stage.get("kind", "-"), - stage.get("class", "-"), - stage.get("summary", ""), - ) - console.print(table) - - notes = plan.get("notes") or [] - if notes: - console.print(Panel("\n".join(f"• {note}" for note in notes), title="Notes")) - - -def _plan_to_yaml(plan: dict[str, Any]) -> str: - data = dict(plan) - stages = data.pop("stages", []) - - # Flatten stages into numbered keys for readability in YAML - data["stages"] = stages - return yaml.safe_dump(data, allow_unicode=True, sort_keys=False) - - -def render_pipeline_plan(plan: dict[str, Any]) -> None: - _render_plan(plan) - - -def _graph_plan_to_yaml(plan: dict[str, Any]) -> str: - return yaml.safe_dump(plan, allow_unicode=True, sort_keys=False) - - -def _save_plan(plan: dict[str, Any], output: Path | None, overwrite: bool) -> Path: - yaml_text = _plan_to_yaml(plan) - if output is None: - default_name = _slugify(plan.get("pipeline", {}).get("name", "pipeline")) - output_dir = get_sage_paths().output_dir / "pipelines" - output_dir.mkdir(parents=True, exist_ok=True) - output_path = output_dir / f"{default_name}.yaml" - else: - output_path = Path(output).expanduser().resolve() - if output_path.is_dir(): - default_name = _slugify(plan.get("pipeline", {}).get("name", "pipeline")) - output_path = output_path / f"{default_name}.yaml" - output_path.parent.mkdir(parents=True, exist_ok=True) - - if output_path.exists() and not overwrite: - raise PipelineBuilderError(f"文件已存在: {output_path}。使用 --overwrite 强制覆盖。") - - output_path.write_text(yaml_text, encoding="utf-8") - return output_path - - -def _preview_yaml(yaml_text: str) -> None: - syntax = Syntax(yaml_text, "yaml", theme="monokai", line_numbers=False) - console.print(Panel(syntax, title="YAML 预览")) - - -def pipeline_plan_to_yaml(plan: dict[str, Any]) -> str: - return _plan_to_yaml(plan) - - -def preview_pipeline_plan(plan: dict[str, Any]) -> None: - yaml_text = _plan_to_yaml(plan) - _preview_yaml(yaml_text) - - -def save_pipeline_plan(plan: dict[str, Any], output: Path | None, overwrite: bool) -> Path: - return _save_plan(plan, output, overwrite) - - -def execute_pipeline_plan( - plan: dict[str, Any], - autostop: bool = True, - host: str | None = None, - port: int | None = None, - *, - console_override: Console | None = None, -) -> str | None: - """Apply a pipeline configuration and submit it to the target environment.""" - - log_console = console_override or console - - env = _create_environment(plan, host, port) - - services = plan.get("services") or [] - if services: - log_console.print(f"🔧 注册 {len(services)} 个服务...") - _register_services(env, services) - - source = plan.get("source") - if not source: - raise PipelineBuilderError("Pipeline plan must include a 'source' configuration") - log_console.print("🚰 配置 source") - stream = _apply_source(env, source) - - stages = plan.get("stages") or [] - for stage in stages: - stage_id = stage.get("id", "stage") - log_console.print(f"➡️ 应用阶段: {stage_id}") - stream = _apply_stage(stream, stage) - - sink = plan.get("sink") - if not sink: - raise PipelineBuilderError("Pipeline plan must include a 'sink' configuration") - log_console.print("🛬 配置终端 sink") - _apply_sink(stream, sink) - - if plan.get("monitors"): - log_console.print("[yellow]📈 当前版本暂未自动配置 monitors，需手动集成。[/yellow]") - - log_console.print("🚀 提交 pipeline...") - job_uuid = env.submit(autostop=autostop) # type: ignore[call-arg] - - if job_uuid: - log_console.print(f"✅ Pipeline 已提交，作业 UUID: [green]{job_uuid}[/green]") - else: - log_console.print("✅ Pipeline 已提交。") - - if autostop: - log_console.print("🎉 批处理完成并自动清理。") - else: - log_console.print("⏳ Pipeline 正在运行，可使用 'sage job list' 查看状态。") - - return job_uuid - - -def _collect_requirements( - name: str | None, - goal: str | None, - requirements_path: Path | None, - interactive: bool, -) -> dict[str, Any]: - requirements: dict[str, Any] = {} - - if requirements_path: - path = Path(requirements_path).expanduser().resolve() - if not path.exists(): - raise PipelineBuilderError(f"找不到需求文件: {path}") - requirements = json.loads(path.read_text(encoding="utf-8")) - - if name: - requirements["name"] = name - if goal: - requirements["goal"] = goal - - if not interactive: - missing = [key for key in ("name", "goal") if key not in requirements] - if missing: - raise PipelineBuilderError(f"非交互模式下必须提供: {', '.join(missing)}") - return requirements - - if "name" not in requirements: - requirements["name"] = typer.prompt("Pipeline 名称", default="My Pipeline") - if "goal" not in requirements: - requirements["goal"] = typer.prompt("主要目标", default="构建一个问答型 RAG pipeline") - - if "data_sources" not in requirements: - requirements["data_sources"] = typer.prompt("数据来源 (可留空)", default="文档知识库") - if "latency_budget" not in requirements: - requirements["latency_budget"] = typer.prompt( - "延迟/吞吐需求 (可留空)", default="实时体验优先" - ) - if "constraints" not in requirements: - requirements["constraints"] = typer.prompt("特殊约束 (可留空)", default="") - - return requirements - - -@app.command("build") -def build_pipeline( # noqa: D401 - Typer handles CLI docs - name: str | None = typer.Option(None, help="Pipeline 名称"), - goal: str | None = typer.Option(None, help="Pipeline 目标描述"), - backend: str = typer.Option( - DEFAULT_BACKEND, - help="LLM 后端 (openai/compatible/mock)", - ), - model: str | None = typer.Option(None, help="LLM 模型名称"), - base_url: str | None = typer.Option(None, help="LLM Base URL"), - api_key: str | None = typer.Option(None, help="LLM API Key"), - requirements_path: Path | None = typer.Option( - None, - exists=False, - help="需求 JSON 文件路径，提供已有输入以跳过交互", - ), - output: Path | None = typer.Option( - None, - help="输出 YAML 文件路径 (可为目录)", - ), - overwrite: bool = typer.Option(False, help="允许覆盖已存在的文件"), - non_interactive: bool = typer.Option(False, help="非交互模式 (需要同时提供名称和目标)"), - context_limit: int = typer.Option( - 4, - "--context-limit", - min=0, - max=12, - help="提示中包含的示例配置数量", - ), - context_file: list[Path] = typer.Option( - [], - "--context-file", - "-c", - help="额外上下文文件 (纯文本)，可重复指定", - exists=True, - file_okay=True, - dir_okay=False, - resolve_path=True, - ), - show_contexts: bool = typer.Option(False, "--show-contexts", help="打印用于提示的大模型上下文"), - disable_knowledge: bool = typer.Option( - False, - "--no-knowledge", - help="禁用从本地知识库自动检索上下文", - ), - knowledge_top_k: int = typer.Option( - 5, - "--knowledge-top-k", - min=1, - max=12, - help="每次检索返回的知识片段数量", - ), - show_knowledge: bool = typer.Option( - False, - "--show-knowledge", - help="打印知识库检索结果", - ), - embedding_method: str | None = typer.Option( - None, - "--embedding-method", - "-e", - help="知识库检索使用的 embedding 方法 (hash/openai/hf/zhipu 等)", - ), - embedding_model: str | None = typer.Option( - None, - "--embedding-model", - help="Embedding 模型名称 (如 text-embedding-3-small)", - ), -) -> None: - """使用大模型交互式生成 SAGE pipeline 配置。""" - - resolved_model = model or DEFAULT_MODEL - resolved_base_url = base_url or DEFAULT_BASE_URL - resolved_api_key = api_key or DEFAULT_API_KEY - - if backend != "mock" and not resolved_api_key: - raise PipelineBuilderError( - "未提供 API Key。请通过 --api-key 或环境变量 SAGE_PIPELINE_BUILDER_API_KEY/TEMP_GENERATOR_API_KEY 设置。" - ) - - requirements = _collect_requirements( - name, - goal, - requirements_path, - interactive=not non_interactive, - ) - - try: - domain_contexts = list(load_domain_contexts(limit=context_limit)) - except Exception as exc: # pragma: no cover - defensive - raise PipelineBuilderError(f"加载默认上下文失败: {exc}") from exc - - if context_file: - try: - custom_contexts = load_custom_contexts(tuple(context_file)) - domain_contexts.extend(custom_contexts) - except RuntimeError as exc: - raise PipelineBuilderError(str(exc)) from exc - - if show_contexts and domain_contexts: - console.print( - Panel( - "\n\n".join(domain_contexts), - title="LLM 提示上下文", - style="magenta", - ) - ) - - knowledge_base: PipelineKnowledgeBase | None = None - if not disable_knowledge: - try: - knowledge_base = get_default_knowledge_base( - embedding_method=embedding_method, - embedding_model=embedding_model, - ) - # Show which embedding method is being used - method_name = embedding_method or os.getenv("SAGE_PIPELINE_EMBEDDING_METHOD", "hash") - console.print(f"🎯 知识库使用 [cyan]{method_name}[/cyan] embedding 方法", style="dim") - except Exception as exc: - console.print(f"[yellow]初始化知识库失败，将继续使用静态上下文: {exc}[/yellow]") - - config = BuilderConfig( - backend=backend, - model=resolved_model, - base_url=resolved_base_url, - api_key=resolved_api_key, - domain_contexts=tuple(domain_contexts), - knowledge_base=knowledge_base, - knowledge_top_k=knowledge_top_k, - show_knowledge=show_knowledge, - ) - - generator = PipelinePlanGenerator(config) - - plan: dict[str, Any] | None = None - feedback: str | None = None - - for _iteration in range(1, 6): - try: - plan = generator.generate(requirements, plan, feedback) - except PipelineBuilderError as exc: - console.print(f"[red]生成失败: {exc}[/red]") - if non_interactive: - raise - if not typer.confirm("是否重新尝试生成？", default=True): - raise - feedback = typer.prompt("请提供更详细的需求或修改建议") - continue - - _render_plan(plan) - - if non_interactive: - break - - if typer.confirm("对配置满意吗？", default=True): - break - - feedback = typer.prompt( - "请输入需要调整的点（例如修改某一阶段/替换组件）", - default="", - ) - if not feedback or not feedback.strip(): - console.print("未提供修改意见，保持当前版本。", style="yellow") - break - - if plan is None: - raise PipelineBuilderError("未能生成有效的 pipeline 配置。") - - yaml_text = _plan_to_yaml(plan) - _preview_yaml(yaml_text) - - if not non_interactive and not typer.confirm("是否保存该配置?", default=True): - console.print("操作已取消，未写入文件。", style="yellow") - return - - output_path = _save_plan(plan, output, overwrite) - console.print(f"✅ 配置已保存到: [green]{output_path}[/green]") - - -@app.command("run") -def run_pipeline( - config: Path = typer.Argument(..., exists=False, help="Pipeline YAML 配置文件"), - autostop: bool = typer.Option( - True, "--autostop/--no-autostop", help="提交后是否等待批处理完成" - ), - host: str | None = typer.Option( - None, - "--host", - help="远程环境 JobManager 主机 (仅当 pipeline.type=remote 时生效)", - ), - port: int | None = typer.Option( - None, - "--port", - min=1, - max=65535, - help="远程环境 JobManager 端口 (仅当 pipeline.type=remote 时生效)", - ), -) -> None: - """加载 YAML 配置并运行 SAGE pipeline。""" - - try: - config_path = Path(config).expanduser().resolve() - plan = _load_pipeline_file(config_path) - - pipeline_meta = plan.get("pipeline") or {} - pipeline_name = pipeline_meta.get("name", config_path.stem) - - console.print( - Panel.fit( - f"名称: [cyan]{pipeline_name}[/cyan]\n类型: {pipeline_meta.get('type', 'local')}\n来源: {config_path}", - title="运行 Pipeline", - style="blue", - ) - ) - - execute_pipeline_plan( - plan, - autostop=autostop, - host=host, - port=port, - console_override=console, - ) - - except CLIException as exc: - console.print(f"[red]❌ {exc}[/red]") - raise typer.Exit(1) from exc - - -@app.command("analyze-embedding") -def analyze_embedding_methods( - query: str = typer.Argument(..., help="测试查询文本"), - top_k: int = typer.Option(3, "--top-k", "-k", min=1, max=10, help="返回 Top-K 结果数量"), - methods: list[str] | None = typer.Option( - None, - "--method", - "-m", - help="指定要比较的 embedding 方法（可多次使用）", - ), - show_vectors: bool = typer.Option(False, "--show-vectors", help="显示向量详情"), -) -> None: - """分析和比较不同 embedding 方法在 Pipeline Builder 知识库上的检索效果。 - - 这个命令帮助你选择最适合你场景的 embedding 方法。 - - 示例: - sage pipeline analyze-embedding "如何构建 RAG pipeline" - sage pipeline analyze-embedding "向量检索" -m hash -m openai -m hf - """ - from sage.common.components.sage_embedding.registry import EmbeddingRegistry - - # 如果没有指定方法，使用默认的几个常用方法 - if not methods: - all_methods = EmbeddingRegistry.list_methods() - # 优先选择免费/本地方法 - default_methods = [] - for m in ["hash", "mockembedder", "hf"]: - if m in all_methods: - default_methods.append(m) - methods = default_methods[:3] if default_methods else all_methods[:3] - - console.print( - Panel( - f"🔍 查询: [cyan]{query}[/cyan]\n" - f"📊 对比方法: {', '.join(methods)}\n" - f"📚 知识库: SAGE Pipeline Builder", - title="Embedding 方法分析", - style="blue", - ) - ) - - results_by_method = {} - - for method in methods: - try: - console.print(f"\n⚙️ 测试方法: [cyan]{method}[/cyan]") - - # 创建使用该 embedding 方法的知识库 - kb = PipelineKnowledgeBase( - max_chunks=500, # 使用较小的数据集加快测试 - allow_download=False, - embedding_method=method, - ) - - # 执行检索 - import time - - start = time.time() - search_results = kb.search(query, top_k=top_k) - elapsed = time.time() - start - - results_by_method[method] = { - "results": search_results, - "time": elapsed, - "dimension": ( - len(search_results[0].vector) - if search_results and search_results[0].vector - else 0 - ), - } - - console.print( - f" ✓ 检索完成 (耗时: {elapsed * 1000:.2f}ms, 维度: {results_by_method[method]['dimension']})" - ) - - except Exception as exc: - console.print(f" ✗ [red]{method} 失败: {exc}[/red]") - continue - - if not results_by_method: - console.print("[red]所有方法都失败了，请检查配置。[/red]") - raise typer.Exit(1) - - # 显示对比结果 - console.print("\n" + "=" * 80) - console.print("[bold green]📊 检索结果对比[/bold green]\n") - - for method, data in results_by_method.items(): - console.print(f"[bold cyan]━━━ {method.upper()} ━━━[/bold cyan]") - console.print(f"⏱️ 耗时: {data['time'] * 1000:.2f}ms | 📐 维度: {data['dimension']}") - - table = Table(show_header=True, header_style="bold magenta", box=None) - table.add_column("排名", style="dim", width=4) - table.add_column("得分", justify="right", width=8) - table.add_column("类型", width=8) - table.add_column("文本片段", width=60) - - for idx, chunk in enumerate(data["results"], 1): - preview = ( - chunk.text[:100].replace("\n", " ") + "..." - if len(chunk.text) > 100 - else chunk.text.replace("\n", " ") - ) - table.add_row( - f"#{idx}", - f"{chunk.score:.4f}", - chunk.kind, - preview, - ) - - console.print(table) - - if show_vectors and data["results"]: - first_vec = data["results"][0].vector - if first_vec: - vec_preview = str(first_vec[:10])[:-1] + ", ...]" - console.print(f" 向量示例: {vec_preview}\n") - - console.print() - - # 推荐最佳方法 - console.print("[bold yellow]💡 推荐建议:[/bold yellow]\n") - - fastest = min(results_by_method.items(), key=lambda x: x[1]["time"]) - console.print(f"⚡ 最快方法: [green]{fastest[0]}[/green] ({fastest[1]['time'] * 1000:.2f}ms)") - - # 简单的相关性评估（基于平均得分） - avg_scores = { - method: ( - sum(r.score for r in data["results"]) / len(data["results"]) if data["results"] else 0 - ) - for method, data in results_by_method.items() - } - best_relevance = max(avg_scores.items(), key=lambda x: x[1]) - console.print( - f"🎯 最相关方法: [green]{best_relevance[0]}[/green] (平均得分: {best_relevance[1]:.4f})" - ) - - console.print( - f"\n💡 [dim]使用推荐方法:[/dim] " - f"[cyan]sage pipeline build --embedding-method {best_relevance[0]}[/cyan]" - ) - - -@app.command("create-embedding") -def create_embedding_pipeline( - template: str = typer.Option( - "rag", - "--template", - "-t", - help="Pipeline 模板类型: rag, knowledge-base, hybrid-search, multi-strategy", - ), - embedding_method: str = typer.Option( - "hf", - "--embedding-method", - "-e", - help="Embedding 方法 (hf/openai/jina/zhipu/cohere/bedrock/ollama/siliconcloud/nvidia_openai/sagellm)", - ), - embedding_model: str | None = typer.Option( - None, - "--embedding-model", - "-m", - help="Embedding 模型名称（未指定则使用默认）", - ), - engine: str = typer.Option( - "sagellm", - "--engine", - help="推理引擎 (仅支持 sagellm)", - ), - use_vllm: bool = typer.Option( - False, - "--vllm", - help="[已移除] vllm 已在 v0.3.0 移除", - ), - llm_model: str | None = typer.Option( - None, - "--llm-model", - help="LLM 模型名称（RAG 模板需要）", - ), - dense_method: str | None = typer.Option( - None, - "--dense-method", - help="Hybrid 模板：Dense embedding 方法", - ), - sparse_method: str | None = typer.Option( - None, - "--sparse-method", - help="Hybrid 模板：Sparse embedding 方法（默认 bm25s）", - ), - query_method: str | None = typer.Option( - None, - "--query-method", - help="Multi-strategy 模板：查询用 embedding 方法（快速）", - ), - doc_method: str | None = typer.Option( - None, - "--doc-method", - help="Multi-strategy 模板：文档用 embedding 方法（高质量）", - ), - batch_method: str | None = typer.Option( - None, - "--batch-method", - help="Multi-strategy 模板：批量处理用 embedding 方法", - ), - chunk_size: int = typer.Option(512, "--chunk-size", help="文档分块大小"), - chunk_overlap: int = typer.Option(50, "--chunk-overlap", help="分块重叠大小"), - batch_size: int = typer.Option(32, "--batch-size", help="批处理大小"), - enable_cache: bool = typer.Option(True, "--cache/--no-cache", help="启用缓存"), - normalize: bool = typer.Option(True, "--normalize/--no-normalize", help="向量归一化"), - output: Path | None = typer.Option( - None, - "--output", - "-o", - help="输出 YAML 文件路径", - ), - overwrite: bool = typer.Option(False, "--overwrite", help="覆盖已存在的文件"), - interactive: bool = typer.Option( - False, - "--interactive", - "-i", - help="交互式配置模板参数", - ), -) -> None: - """使用预定义模板创建基于 EmbeddingService 的 pipeline。 - - 支持的模板: - - rag: RAG pipeline with embedding service - - knowledge-base: 高吞吐量知识库构建 - - hybrid-search: Dense + Sparse 混合检索 - - multi-strategy: 智能路由多策略 embedding - - 示例: - # 创建 HuggingFace RAG pipeline - sage pipeline create-embedding -t rag -e hf -m BAAI/bge-small-zh-v1.5 - - # 创建 sageLLM 高性能知识库构建（默认引擎） - sage pipeline create-embedding -t knowledge-base - - # 创建混合检索 pipeline - sage pipeline create-embedding -t hybrid-search --dense-method openai --sparse-method bm25s - - # 创建多策略智能路由 - sage pipeline create-embedding -t multi-strategy --query-method hash --doc-method openai - """ - from .pipeline_embedding import generate_embedding_pipeline - - # 交互式配置 - if interactive: - console.print( - Panel( - "🎯 交互式 Embedding Pipeline 配置向导", - style="cyan", - ) - ) - - template_choices = ["rag", "knowledge-base", "hybrid-search", "multi-strategy"] - template = typer.prompt( - "选择模板类型", - type=str, - default=template, - show_choices=True, - ) - - if template not in template_choices: - console.print(f"[red]无效的模板: {template}[/red]") - raise typer.Exit(1) - - embedding_method = typer.prompt( - "Embedding 方法 (hf/openai/jina/zhipu/cohere/bedrock/ollama/siliconcloud/nvidia_openai/sagellm)", - type=str, - default=embedding_method, - ) - - if embedding_method not in ["sagellm", "hash", "mockembedder"]: - embedding_model = typer.prompt( - "Embedding 模型名称", - type=str, - default=embedding_model or "", - ) - - engine = typer.prompt( - "推理引擎 (仅支持 sagellm)", - type=str, - default=engine, - ) - - if template == "rag": - llm_model = typer.prompt( - "LLM 模型名称", - type=str, - default=llm_model or "Qwen/Qwen2.5-7B-Instruct", - ) - elif template == "hybrid-search": - dense_method = typer.prompt( - "Dense embedding 方法", - type=str, - default=dense_method or embedding_method, - ) - sparse_method = typer.prompt( - "Sparse embedding 方法", - type=str, - default=sparse_method or "bm25s", - ) - elif template == "multi-strategy": - query_method = typer.prompt( - "查询用 embedding 方法 (快速)", - type=str, - default=query_method or "hash", - ) - doc_method = typer.prompt( - "文档用 embedding 方法 (高质量)", - type=str, - default=doc_method or embedding_method, - ) - batch_method = typer.prompt( - "批量处理用 embedding 方法", - type=str, - default=batch_method or engine if engine == "sagellm" else embedding_method, - ) - - # 构建参数 - kwargs = { - "chunk_size": chunk_size, - "chunk_overlap": chunk_overlap, - "batch_size": batch_size, - "enable_cache": enable_cache, - "normalize": normalize, - } - - # 根据模板类型添加特定参数 - if template == "rag": - if not llm_model: - llm_model = "Qwen/Qwen2.5-7B-Instruct" - kwargs["llm_model"] = llm_model - elif template == "hybrid-search": - if not dense_method: - dense_method = embedding_method - if not sparse_method: - sparse_method = "bm25s" - kwargs["dense_method"] = dense_method - kwargs["sparse_method"] = sparse_method - # dense_model 使用 embedding_model - if embedding_model: - kwargs["dense_model"] = embedding_model - elif template == "multi-strategy": - if not query_method: - query_method = "hash" - if not doc_method: - doc_method = embedding_method - if not batch_method: - batch_method = engine if engine == "sagellm" else embedding_method - kwargs["query_method"] = query_method - kwargs["doc_method"] = doc_method - kwargs["batch_method"] = batch_method - - # 生成配置 - # Handle removed --vllm flag - if use_vllm: - console.print( - "[red]Error:[/red] --vllm has been removed in SAGE v0.3.0. Use --engine sagellm instead." - ) - raise typer.Exit(1) - - console.print( - Panel( - f"📋 模板: [cyan]{template}[/cyan]\n" - f"🔧 Embedding: [cyan]{embedding_method}[/cyan]\n" - f"🚀 引擎: [cyan]{engine}[/cyan]", - title="生成 Pipeline 配置", - style="blue", - ) - ) - - try: - plan = generate_embedding_pipeline( - use_case=template, - embedding_method=embedding_method, - embedding_model=embedding_model, - engine=engine, - **kwargs, - ) - except ValueError as exc: - console.print(f"[red]生成失败: {exc}[/red]") - raise typer.Exit(1) from exc - - # 显示配置 - _render_plan(plan) - - # 预览 YAML - yaml_text = _plan_to_yaml(plan) - _preview_yaml(yaml_text) - - # 保存 - if not interactive or typer.confirm("保存配置?", default=True): - output_path = _save_plan(plan, output, overwrite) - console.print(f"✅ 配置已保存到: [green]{output_path}[/green]") - - # 提示如何运行 - console.print(f"\n💡 运行此 pipeline:\n [cyan]sage pipeline run {output_path}[/cyan]") - else: - console.print("[yellow]未保存配置。[/yellow]") - - -__all__ = [ - "app", - "BuilderConfig", - "GraphBuilderConfig", - "PipelinePlanGenerator", - "GraphPlanGenerator", - "PipelineBuilderError", - "render_pipeline_plan", - "pipeline_plan_to_yaml", - "preview_pipeline_plan", - "save_pipeline_plan", - "execute_pipeline_plan", - "create_embedding_pipeline", - "analyze_embedding_methods", -] diff --git a/packages/sage-cli/src/sage/cli/commands/apps/pipeline_domain.py b/packages/sage-cli/src/sage/cli/commands/apps/pipeline_domain.py deleted file mode 100644 index 0dd5b2bd1e..0000000000 --- a/packages/sage-cli/src/sage/cli/commands/apps/pipeline_domain.py +++ /dev/null @@ -1,236 +0,0 @@ -"""Domain knowledge helpers for the SAGE pipeline builder.""" - -from __future__ import annotations - -import textwrap -from collections.abc import Iterable, Mapping, MutableMapping -from dataclasses import dataclass -from functools import lru_cache -from pathlib import Path - -import yaml # type: ignore[import-untyped] - -from sage.common.config.output_paths import get_sage_paths # type: ignore[import-untyped] - -_BASE_GUIDE = textwrap.dedent( - """ - SAGE Pipeline 配置速览： - - `pipeline`: 定义名称、描述、版本和运行类型（local/remote）。 - - `source`: 数据入口组件，负责产生或读取输入。 - - `stages`: 有序的算子列表，每个算子包含 `id/kind/class/params/summary`。 - - kind 决定调度方式：`map`（逐条处理）、`batch`（批处理）、`service`（长驻服务）。 - - class 使用 Python 全路径，例如 `sage.libs.rag.retriever.Wiki18FAISSRetriever`。 - - `sink`: 结果落地组件，通常为本地或远程持久化。 - - `services`: pipeline 运行时依赖的配套服务。 - - `monitors`: 监控与观测器，可选。 - - `notes`: 文字提示或待办项。 - 生成配置时请确保： - 1. 至少包含一个 stage 和一个 sink。 - 2. 组件参数均为 JSON 可序列化对象（字符串、数字、布尔、数组、对象）。 - 3. 使用短横线风格的 slug 作为标识符（例如 `qa-generator`）。 - 4. 为每个阶段提供 concise 的 summary 说明其职责。 - """ -).strip() - - -@dataclass -class _ExampleSummary: - text: str - components: Mapping[str, set[str]] - - -def _ensure_project_root() -> Path: - paths = get_sage_paths() - project_root = getattr(paths, "project_root", None) - if project_root is None: - project_root = Path.cwd() - return Path(project_root) - - -def _trim(text: str, limit: int = 1200) -> str: - if len(text) <= limit: - return text - return text[: limit - 3] + "..." - - -def _summarize_stage(stage: Mapping[str, object]) -> str: - identifier = str(stage.get("id", "stage")) - kind = str(stage.get("kind", "map")) - class_path = str(stage.get("class", "")) - summary = str(stage.get("summary", "")) - params = stage.get("params", {}) - param_keys = ", ".join(sorted(params.keys())) if isinstance(params, dict) else "" - parts = [f"- {identifier} [{kind}] -> {class_path}"] - if summary: - parts.append(f" {summary}") - if param_keys: - parts.append(f" params: {param_keys}") - return "\n".join(parts) - - -def _summarize_pipeline_file(path: Path) -> _ExampleSummary | None: - try: - data = yaml.safe_load(path.read_text(encoding="utf-8")) or {} - except Exception: - return None - - if not isinstance(data, dict): - return None - - pipeline_meta = data.get("pipeline") or {} - name = str(pipeline_meta.get("name", path.stem)) - description = str(pipeline_meta.get("description", "")) - pipeline_type = str(pipeline_meta.get("type", "local")) - - lines: list[str] = [ - f"Pipeline 示例: {name} ({path.name})", - f"类型: {pipeline_type}", - ] - if description: - lines.append(f"描述: {description}") - - components: MutableMapping[str, set[str]] = { - "sources": set(), - "stages": set(), - "sinks": set(), - "services": set(), - "monitors": set(), - } - - source = data.get("source") or {} - if isinstance(source, Mapping): - source_class = str(source.get("class", "")) - if source_class: - components["sources"].add(source_class) - summary = str(source.get("summary", "")) - lines.append(f"Source -> {source_class}{' — ' + summary if summary else ''}") - - stages = data.get("stages") or [] - if isinstance(stages, list) and stages: - lines.append("Stages:") - for stage in stages: - if not isinstance(stage, Mapping): - continue - stage_summary = _summarize_stage(stage) - if stage_summary: - lines.append(stage_summary) - class_path = str(stage.get("class", "")) - if class_path: - components["stages"].add(class_path) - - sink = data.get("sink") or {} - if isinstance(sink, Mapping): - sink_class = str(sink.get("class", "")) - if sink_class: - components["sinks"].add(sink_class) - summary = str(sink.get("summary", "")) - lines.append(f"Sink -> {sink_class}{' — ' + summary if summary else ''}") - - services = data.get("services") or [] - if isinstance(services, list): - active_services = [] - for service in services: - if not isinstance(service, Mapping): - continue - service_class = str(service.get("class", "")) - if service_class: - components["services"].add(service_class) - service_name = str(service.get("name", service_class.split(".")[-1])) - active_services.append(f" - {service_name}: {service_class}") - if active_services: - lines.append("Services:") - lines.extend(active_services) - - monitors = data.get("monitors") or [] - if isinstance(monitors, list): - active_monitors = [] - for monitor in monitors: - if not isinstance(monitor, Mapping): - continue - monitor_class = str(monitor.get("class", "")) - if monitor_class: - components["monitors"].add(monitor_class) - active_monitors.append(f" - {monitor_class}") - if active_monitors: - lines.append("Monitors:") - lines.extend(active_monitors) - - notes = data.get("notes") or [] - if isinstance(notes, list) and notes: - preview = "; ".join(str(note) for note in notes[:2]) - lines.append(f"Notes: {preview}") - - snippet = "\n".join(lines).strip() - snippet = _trim(snippet) - if not snippet: - return None - - return _ExampleSummary(snippet, components) - - -def _format_component_catalog(components: Mapping[str, Iterable[str]]) -> str: - lines = ["SAGE Pipeline 组件速查表:"] - for key in ("sources", "stages", "sinks", "services", "monitors"): - values = sorted(set(components.get(key, []))) - if not values: - continue - pretty_key = key.capitalize() - lines.append(f"{pretty_key}:") - for value in values: - lines.append(f" - {value}") - return _trim("\n".join(lines).strip()) - - -@lru_cache(maxsize=32) -def load_domain_contexts(limit: int = 4) -> tuple[str, ...]: - """Return contextual snippets describing SAGE pipelines for LLM prompting.""" - - limit = max(0, int(limit)) - contexts: list[str] = [_BASE_GUIDE] - - project_root = _ensure_project_root() - example_dir = project_root / "examples" / "config" - component_map: dict[str, set[str]] = { - "sources": set(), - "stages": set(), - "sinks": set(), - "services": set(), - "monitors": set(), - } - - if example_dir.exists(): - summaries: list[_ExampleSummary] = [] - for path in sorted(example_dir.glob("*.yaml")): - summary = _summarize_pipeline_file(path) - if summary is None: - continue - summaries.append(summary) - for key, values in summary.components.items(): - component_map[key].update(values) - - for item in summaries[:limit]: - contexts.append(item.text) - - catalog = _format_component_catalog(component_map) - if catalog: - contexts.append(catalog) - - return tuple(ctx for ctx in contexts if ctx.strip()) - - -def load_custom_contexts(paths: Iterable[Path]) -> tuple[str, ...]: - """Read additional context snippets from user-provided files.""" - - snippets: list[str] = [] - for path in paths: - try: - text = path.read_text(encoding="utf-8") - except Exception as exc: # pragma: no cover - error handled by caller - raise RuntimeError(f"无法读取上下文文件 {path}: {exc}") from exc - cleaned = text.strip() - if cleaned: - snippets.append(cleaned) - return tuple(snippets) - - -__all__ = ["load_domain_contexts", "load_custom_contexts"] diff --git a/packages/sage-cli/src/sage/cli/commands/apps/pipeline_embedding.py b/packages/sage-cli/src/sage/cli/commands/apps/pipeline_embedding.py deleted file mode 100644 index 4d7e17d358..0000000000 --- a/packages/sage-cli/src/sage/cli/commands/apps/pipeline_embedding.py +++ /dev/null @@ -1,509 +0,0 @@ -""" -Enhanced Pipeline Builder Templates with EmbeddingService Integration - -这个模块提供增强的 pipeline 模板，完全集成了新的 EmbeddingService。 -""" - -from typing import Any - - -class EmbeddingPipelineTemplates: - """Embedding-focused pipeline templates using EmbeddingService.""" - - @staticmethod - def rag_with_embedding_service( - embedding_method: str = "hf", - embedding_model: str | None = None, - engine: str = "sagellm", - llm_model: str = "Qwen/Qwen2.5-7B-Instruct", - **kwargs: Any, - ) -> dict[str, Any]: - """RAG Pipeline with dedicated EmbeddingService. - - Args: - embedding_method: Embedding method (hf, openai, jina, sagellm, vllm, etc.) - embedding_model: Specific embedding model name - engine: Inference engine (sagellm) - llm_model: LLM model for generation - **kwargs: Additional config options - - Returns: - Complete pipeline configuration - """ - # Default models for each method - default_models = { - "hf": "BAAI/bge-small-zh-v1.5", - "openai": "text-embedding-3-small", - "jina": "jina-embeddings-v3", - "zhipu": "embedding-3", - "sagellm": "BAAI/bge-base-en-v1.5", - } - - embedding_model = embedding_model or default_models.get( - embedding_method, "BAAI/bge-small-zh-v1.5" - ) - - use_inference_engine = engine == "sagellm" or embedding_method == "sagellm" - - config = { - "pipeline": { - "name": "rag_with_embedding_service", - "description": f"RAG pipeline with {embedding_method} embedding service (engine: {engine})", - "version": "2.0.0", - "type": "local", - }, - "services": {}, - } - - # Configure embedding service - if use_inference_engine: - # Use sagellm backend (requires isagellm package) - config["services"]["sagellm"] = { # type: ignore[index] - "class": "isagellm.SageLLMService", - "config": { - "model_id": llm_model, - "embedding_model_id": embedding_model, - "auto_download": True, - "engine": { - "dtype": "auto", - "tensor_parallel_size": 1, - "gpu_memory_utilization": 0.9, - }, - }, - } - - config["services"]["embedding"] = { # type: ignore[index] - "class": "sage.common.components.sage_embedding.EmbeddingService", - "config": { - "method": "sagellm", - f"{engine}_service_name": engine, - "batch_size": 128, - "normalize": True, - "cache_enabled": True, - "cache_size": 10000, - }, - } - else: - # Use standard embedding method - embedding_config = { - "method": embedding_method, - "model": embedding_model, - "batch_size": 32, - "normalize": True, - "cache_enabled": kwargs.get("cache_enabled", True), - "cache_size": kwargs.get("cache_size", 5000), - } - - # Add API key if needed - if embedding_method in ["openai", "jina", "zhipu", "cohere"]: - embedding_config["api_key"] = f"${{{embedding_method.upper()}_API_KEY}}" - - config["services"]["embedding"] = { # type: ignore[index] - "class": "sage.common.components.sage_embedding.EmbeddingService", - "config": embedding_config, - } - - # Add vector database service - config["services"]["vector_db"] = { # type: ignore[index] - "class": "sage.middleware.components.sage_db.service.SageDBService", - "config": { - "dimension": kwargs.get("dimension", 768), - "index_type": kwargs.get("index_type", "AUTO"), - }, - } - - # Add operators - config["operators"] = [ # type: ignore[assignment] - { - "name": "load_query", - "type": "input_operator", - "config": {"source": "stdin"}, - }, - { - "name": "embed_query", - "type": "embedding_operator", - "config": { - "embedding_service": "embedding", - "input_field": "query", - "output_field": "query_vector", - }, - }, - { - "name": "retrieve_documents", - "type": "vector_search_operator", - "config": { - "db_service": "vector_db", - "query_field": "query_vector", - "top_k": kwargs.get("top_k", 5), - }, - }, - { - "name": "generate_answer", - "type": "llm_generate_operator", - "config": { - "llm_service": engine if use_inference_engine else "llm", - "prompt_template": """Based on the following context, answer the question. - -Context: -{context} - -Question: {query} - -Answer:""", - "max_tokens": 512, - }, - }, - ] - - return config - - @staticmethod - def knowledge_base_builder( - embedding_method: str = "hf", - embedding_model: str | None = None, - engine: str = "sagellm", - chunk_size: int = 512, - chunk_overlap: int = 50, - **kwargs, - ) -> dict[str, Any]: - """Knowledge base building pipeline with EmbeddingService. - - Args: - embedding_method: Embedding method - embedding_model: Model name - engine: Inference engine (sagellm) for high-throughput processing - chunk_size: Text chunk size - chunk_overlap: Overlap between chunks - """ - default_models = { - "hf": "BAAI/bge-base-zh-v1.5", - "openai": "text-embedding-3-large", - "sagellm": "BAAI/bge-large-en-v1.5", - } - - embedding_model = embedding_model or default_models.get(embedding_method) - - use_inference_engine = engine == "sagellm" or embedding_method == "sagellm" - - config = { - "pipeline": { - "name": "knowledge_base_builder", - "description": "Build knowledge base with embedding service", - "version": "2.0.0", - "type": "local", - }, - "services": {}, - } - - # Configure services based on method - if use_inference_engine: - config["services"]["sagellm"] = { # type: ignore[index] - "class": "isagellm.SageLLMService", - "config": { - "model_id": embedding_model, - "embedding_model_id": embedding_model, - "auto_download": True, - }, - } - - config["services"]["embedding"] = { # type: ignore[index] - "class": "sage.common.components.sage_embedding.EmbeddingService", - "config": { - "method": "sagellm", - "sagellm_service_name": "sagellm", - "batch_size": 256, # Large batch for indexing - "normalize": True, - "cache_enabled": False, # No cache needed for one-time indexing - }, - } - else: - embedding_config = { - "method": embedding_method, - "model": embedding_model, - "batch_size": 64, - "normalize": True, - "cache_enabled": False, - } - - if embedding_method in ["openai", "jina", "zhipu"]: - embedding_config["api_key"] = f"${{{embedding_method.upper()}_API_KEY}}" - - config["services"]["embedding"] = { # type: ignore[index] - "class": "sage.common.components.sage_embedding.EmbeddingService", - "config": embedding_config, - } - - config["services"]["vector_db"] = { # type: ignore[index] - "class": "sage.middleware.components.sage_db.service.SageDBService", - "config": { - "dimension": kwargs.get("dimension", 768), - "index_type": "HNSW", # Better for large-scale indexing - }, - } - - config["operators"] = [ # type: ignore[assignment] - { - "name": "load_documents", - "type": "document_loader", - "config": { - "source_path": kwargs.get("source_path", "data/documents/"), - "file_types": ["txt", "md", "pdf", "docx"], - }, - }, - { - "name": "chunk_documents", - "type": "text_chunker", - "config": { - "chunk_size": chunk_size, - "chunk_overlap": chunk_overlap, - "strategy": "sentence_aware", - }, - }, - { - "name": "embed_chunks", - "type": "batch_embedding_operator", - "config": { - "embedding_service": "embedding", - "input_field": "chunks", - "output_field": "embeddings", - "batch_size": 256, - "show_progress": True, - }, - }, - { - "name": "index_vectors", - "type": "vector_indexing_operator", - "config": { - "db_service": "vector_db", - "vector_field": "embeddings", - "metadata_fields": ["doc_id", "chunk_id", "text", "source"], - "show_progress": True, - }, - }, - { - "name": "save_index", - "type": "index_saver", - "config": {"output_path": kwargs.get("output_path", "data/index/")}, - }, - ] - - return config - - @staticmethod - def hybrid_search_pipeline( - dense_method: str = "hf", - sparse_method: str = "bm25s", - dense_model: str | None = None, - **kwargs, - ) -> dict[str, Any]: - """Hybrid search pipeline with dense + sparse embeddings.""" - config = { - "pipeline": { - "name": "hybrid_search", - "description": "Hybrid search with dense and sparse embeddings", - "version": "2.0.0", - "type": "local", - }, - "services": { - # Dense embedding service - "embedding_dense": { - "class": "sage.common.components.sage_embedding.EmbeddingService", - "config": { - "method": dense_method, - "model": dense_model or "BAAI/bge-base-en-v1.5", - "batch_size": 32, - "normalize": True, - "cache_enabled": True, - }, - }, - # Sparse embedding service - "embedding_sparse": { - "class": "sage.common.components.sage_embedding.EmbeddingService", - "config": { - "method": sparse_method, - "normalize": False, - "cache_enabled": True, - }, - }, - # Two vector databases - "vector_db_dense": { - "class": "sage.middleware.components.sage_db.service.SageDBService", - "config": {"dimension": 768, "index_type": "HNSW"}, - }, - "vector_db_sparse": { - "class": "sage.middleware.components.sage_db.service.SageDBService", - "config": {"dimension": 10000, "index_type": "FLAT"}, - }, - }, - "operators": [ - { - "name": "embed_dense", - "type": "embedding_operator", - "config": { - "embedding_service": "embedding_dense", - "input_field": "query", - "output_field": "dense_vector", - }, - }, - { - "name": "embed_sparse", - "type": "embedding_operator", - "config": { - "embedding_service": "embedding_sparse", - "input_field": "query", - "output_field": "sparse_vector", - }, - }, - { - "name": "search_dense", - "type": "vector_search_operator", - "config": { - "db_service": "vector_db_dense", - "query_field": "dense_vector", - "top_k": 20, - "output_field": "dense_results", - }, - }, - { - "name": "search_sparse", - "type": "vector_search_operator", - "config": { - "db_service": "vector_db_sparse", - "query_field": "sparse_vector", - "top_k": 20, - "output_field": "sparse_results", - }, - }, - { - "name": "fuse_results", - "type": "hybrid_fusion_operator", - "config": { - "fusion_method": "reciprocal_rank", - "weights": { - "dense": kwargs.get("dense_weight", 0.6), - "sparse": kwargs.get("sparse_weight", 0.4), - }, - "final_top_k": kwargs.get("top_k", 5), - }, - }, - ], - } - - return config - - @staticmethod - def multi_embedding_strategy( - query_method: str = "hf", - doc_method: str = "openai", - batch_method: str = "vllm", - **kwargs, - ) -> dict[str, Any]: - """Multi-embedding strategy: different methods for different use cases.""" - config = { - "pipeline": { - "name": "multi_embedding_strategy", - "description": "Different embedding methods for different scenarios", - "version": "2.0.0", - "type": "local", - }, - "services": { - # Fast local for queries - "embedding_fast": { - "class": "sage.common.components.sage_embedding.EmbeddingService", - "config": { - "method": query_method, - "model": "BAAI/bge-small-zh-v1.5", - "batch_size": 16, - "normalize": True, - "cache_enabled": True, - "cache_size": 10000, - }, - }, - # High quality for important documents - "embedding_quality": { - "class": "sage.common.components.sage_embedding.EmbeddingService", - "config": { - "method": doc_method, - "model": "text-embedding-3-large", - "api_key": "${OPENAI_API_KEY}", - "batch_size": 100, - "normalize": True, - }, - }, - # vLLM for batch processing (requires isagellm package) - "vllm": { - "class": "isagellm.VLLMService", - "config": { - "model_id": "BAAI/bge-large-en-v1.5", - "embedding_model_id": "BAAI/bge-large-en-v1.5", - }, - }, - "embedding_batch": { - "class": "sage.common.components.sage_embedding.EmbeddingService", - "config": { - "method": "vllm", - "vllm_service_name": "vllm", - "batch_size": 512, - "normalize": True, - }, - }, - }, - "operators": [ - { - "name": "route_by_size", - "type": "router_operator", - "config": { - "routes": [ - { - "condition": "len(payload['texts']) < 10", - "embedding_service": "embedding_fast", - }, - { - "condition": "len(payload['texts']) > 1000", - "embedding_service": "embedding_batch", - }, - { - "condition": "payload.get('high_quality', False)", - "embedding_service": "embedding_quality", - }, - ], - "default_service": "embedding_fast", - }, - } - ], - } - - return config - - -def generate_embedding_pipeline(use_case: str, **kwargs) -> dict[str, Any]: - """Generate embedding pipeline based on use case. - - Args: - use_case: One of: rag, knowledge_base, hybrid_search, multi_strategy - **kwargs: Additional configuration options - - Returns: - Complete pipeline configuration - """ - templates = EmbeddingPipelineTemplates() - - use_case_map = { - "rag": templates.rag_with_embedding_service, - "knowledge_base": templates.knowledge_base_builder, - "hybrid_search": templates.hybrid_search_pipeline, - "multi_strategy": templates.multi_embedding_strategy, - } - - if use_case not in use_case_map: - raise ValueError( - f"Unknown use case: {use_case}. Available: {', '.join(use_case_map.keys())}" - ) - - return use_case_map[use_case](**kwargs) # type: ignore[operator] - - -__all__ = [ - "EmbeddingPipelineTemplates", - "generate_embedding_pipeline", -] diff --git a/packages/sage-cli/src/sage/cli/commands/apps/pipeline_knowledge.py b/packages/sage-cli/src/sage/cli/commands/apps/pipeline_knowledge.py deleted file mode 100644 index 9aacc1cdd5..0000000000 --- a/packages/sage-cli/src/sage/cli/commands/apps/pipeline_knowledge.py +++ /dev/null @@ -1,410 +0,0 @@ -"""Knowledge base utilities for the SAGE pipeline builder.""" - -from __future__ import annotations - -import hashlib -import json -import math -import os -import re -import shutil -import tempfile -import urllib.request -import zipfile -from collections.abc import Mapping, MutableSequence, Sequence -from dataclasses import dataclass -from functools import lru_cache -from pathlib import Path - -import yaml # type: ignore[import-untyped] - -from sage.cli.commands.apps.pipeline_domain import load_domain_contexts -from sage.common.components.sage_embedding.factory import EmbeddingFactory -from sage.common.config.output_paths import get_sage_paths - -GITHUB_DOCS_ZIP_URL = "https://github.com/intellistream/SAGE-Pub/archive/refs/heads/main.zip" -DOCS_CACHE_SUBDIR = "pipeline-builder/docs" - -CHUNK_SIZE = 600 -CHUNK_OVERLAP = 120 -TOP_K_DEFAULT = 6 - -_PYTHON_COMMENT_RE = re.compile(r"\s*#.*") -_DOCSTRING_RE = re.compile(r'("""[\s\S]*?"""|\'\'\'[\s\S]*?\'\'\')', re.DOTALL) - - -@dataclass -class KnowledgeChunk: - text: str - source: str - kind: str - score: float = 0.0 - vector: list[float] | None = None - - -def _should_download_docs() -> bool: - flag = os.getenv("SAGE_PIPELINE_DOWNLOAD_DOCS", "1").lower() - return flag not in {"0", "false", "no"} - - -def _docs_cache_root() -> Path: - return get_sage_paths().cache_dir / DOCS_CACHE_SUBDIR - - -def _download_docs(cache_root: Path) -> Path | None: - if not _should_download_docs(): - return None - - cache_root.mkdir(parents=True, exist_ok=True) - url = os.getenv("SAGE_PIPELINE_DOCS_URL", GITHUB_DOCS_ZIP_URL) - fd, tmp_path = tempfile.mkstemp(prefix="sage_docs_", suffix=".zip") - os.close(fd) - tmp_file = Path(tmp_path) - - try: - urllib.request.urlretrieve(url, tmp_file) - with zipfile.ZipFile(tmp_file, "r") as zf: - zf.extractall(cache_root) - except Exception as exc: # pragma: no cover - network error - if tmp_file.exists(): - tmp_file.unlink() - raise RuntimeError(f"下载 docs-public 文档失败: {exc}") from exc - finally: - if tmp_file.exists(): - tmp_file.unlink() - - extracted_docs: Path | None = None - for candidate in cache_root.glob("**/docs_src"): - if candidate.is_dir(): - extracted_docs = candidate - break - - if extracted_docs is None: - raise RuntimeError("下载的文档包中未找到 docs_src 目录") - - target = cache_root / "docs_src" - if target.exists() and target != extracted_docs: - shutil.rmtree(target, ignore_errors=True) - - if extracted_docs != target: - target.parent.mkdir(parents=True, exist_ok=True) - shutil.move(str(extracted_docs), target) - - return target - - -def _resolve_docs_dir(project_root: Path, allow_download: bool) -> Path | None: - local_docs = project_root / "docs-public" / "docs_src" - if local_docs.exists(): - return local_docs - - cache_root = _docs_cache_root() - cached_docs = cache_root / "docs_src" - if cached_docs.exists(): - return cached_docs - - if not allow_download: - return None - - return _download_docs(cache_root) - - -def _normalize_whitespace(value: str) -> str: - value = value.replace("\r", "\n") - value = re.sub(r"\n{3,}", "\n\n", value) - value = re.sub(r"[ \t]+", " ", value) - return value.strip() - - -def _chunk_text( - content: str, chunk_size: int = CHUNK_SIZE, overlap: int = CHUNK_OVERLAP -) -> list[str]: - normalized = _normalize_whitespace(content) - if not normalized: - return [] - - chunks: list[str] = [] - length = len(normalized) - start = 0 - step = max(1, chunk_size - overlap) - - while start < length: - end = min(length, start + chunk_size) - chunk = normalized[start:end] - if end < length: - boundary = max(chunk.rfind("\n"), chunk.rfind("。"), chunk.rfind(".")) - if boundary >= 0 and boundary > len(chunk) * 0.4: - end = start + boundary - chunk = normalized[start:end] - chunks.append(chunk.strip()) - start += step - - return [chunk for chunk in chunks if chunk] - - -class _HashingEmbedder: - """Lightweight embedder mirroring the chat CLI's hashing strategy.""" - - def __init__(self, dim: int = 384) -> None: - self._dim = max(64, dim) - - def embed(self, text: str) -> list[float]: - if not text: - return [0.0] * self._dim - - vector = [0.0] * self._dim - tokens = re.findall(r"[\w\u4e00-\u9fa5]+", text.lower()) - if not tokens: - tokens = [text.lower()] - - for token in tokens: - digest = _stable_hash(token) - for offset in range(0, len(digest), 4): - chunk = digest[offset : offset + 4] - if len(chunk) < 4: - chunk = chunk.ljust(4, b"\0") - idx = int.from_bytes(chunk, "little") % self._dim - vector[idx] += 1.0 - - norm = math.sqrt(sum(v * v for v in vector)) or 1.0 - return [v / norm for v in vector] - - -@lru_cache(maxsize=1024) -def _stable_hash(token: str) -> bytes: - return hashlib.sha256(token.encode("utf-8")).digest() - - -def _cosine_similarity(vec_a: Sequence[float], vec_b: Sequence[float]) -> float: - return float(sum(a * b for a, b in zip(vec_a, vec_b, strict=False))) - - -def _read_file(path: Path) -> str: - try: - return path.read_text(encoding="utf-8") - except UnicodeDecodeError: - return path.read_text(encoding="utf-8", errors="ignore") - - -def _summarize_yaml(path: Path) -> str: - try: - data = yaml.safe_load(_read_file(path)) - except Exception: - return "" - if not isinstance(data, Mapping): - return "" - pipeline = data.get("pipeline") or {} - name = pipeline.get("name") or path.stem - description = pipeline.get("description", "") - summary_lines = [f"Pipeline: {name}"] - if description: - summary_lines.append(description) - stages = data.get("stages") or [] - if isinstance(stages, list): - summary_lines.append("Stages:") - for stage in stages[:6]: - if not isinstance(stage, Mapping): - continue - stage_id = stage.get("id", "stage") - stage_class = stage.get("class", "") - stage_summary = stage.get("summary", "") - summary_lines.append( - f"- {stage_id}: {stage_class} {'- ' + stage_summary if stage_summary else ''}" - ) - sink = data.get("sink") or {} - if isinstance(sink, Mapping) and sink.get("class"): - summary_lines.append(f"Sink: {sink['class']}") - return "\n".join(summary_lines) - - -def _summarize_python(path: Path) -> str: - text = _read_file(path) - text = re.sub(_PYTHON_COMMENT_RE, "", text) - docstrings = "\n".join(match.group(0) for match in _DOCSTRING_RE.finditer(text)) - return docstrings or text[:1200] - - -def _discover_documents( - project_root: Path, allow_download: bool -) -> MutableSequence[KnowledgeChunk]: - chunks: MutableSequence[KnowledgeChunk] = [] - - docs_dir = _resolve_docs_dir(project_root, allow_download=allow_download) - if docs_dir is not None and docs_dir.exists(): - for md in docs_dir.rglob("*.md"): - if md.is_file(): - content = _read_file(md) - for chunk in _chunk_text(content): - rel = md.relative_to(docs_dir) - chunks.append( - KnowledgeChunk( - text=f"{chunk}\n\n(Source: docs/{rel})", - source=str(md), - kind="docs", - ) - ) - - examples_dir = project_root / "examples" / "config" - if examples_dir.exists(): - for config in examples_dir.glob("*.yaml"): - summary = _summarize_yaml(config) - if summary: - chunks.append( - KnowledgeChunk( - text=f"{summary}\n\n(Source: {config.relative_to(project_root)})", - source=str(config), - kind="example", - ) - ) - - libs_dir = project_root / "packages" / "sage-libs" / "src" / "sage" / "libs" - if libs_dir.exists(): - for py_file in libs_dir.rglob("*.py"): - if py_file.is_file() and "tests" not in py_file.parts: - summary = _summarize_python(py_file) - for chunk in _chunk_text(summary, chunk_size=400, overlap=80): - chunks.append( - KnowledgeChunk( - text=f"{chunk}\n\n(Source: {py_file.relative_to(project_root)})", - source=str(py_file), - kind="code", - ) - ) - - tools_dir = project_root / "packages" / "sage-tools" / "src" / "sage" / "tools" - if tools_dir.exists(): - for py_file in tools_dir.rglob("*.py"): - if py_file.is_file() and "tests" not in py_file.parts: - summary = _summarize_python(py_file) - for chunk in _chunk_text(summary, chunk_size=400, overlap=80): - chunks.append( - KnowledgeChunk( - text=f"{chunk}\n\n(Source: {py_file.relative_to(project_root)})", - source=str(py_file), - kind="code", - ) - ) - - # Append curated summaries to ensure familiar structure remains available. - for context in load_domain_contexts(limit=6): - chunks.append( - KnowledgeChunk( - text=context, - source="pipeline_domain", - kind="summary", - ) - ) - - return chunks - - -class PipelineKnowledgeBase: - """Lightweight retrieval over SAGE docs and code, kept in memory.""" - - def __init__( - self, - project_root: Path | None = None, - max_chunks: int = 2000, - allow_download: bool = True, - embedding_method: str = "hash", - embedding_model: str | None = None, - embedding_params: Mapping[str, object] | None = None, - ) -> None: - root = project_root or getattr(get_sage_paths(), "project_root", None) - self.project_root = Path(root) if root else Path.cwd() - - # Use the new unified embedding system - self.embedding_method = embedding_method - try: - params = dict(embedding_params or {}) - if embedding_model: - params["model"] = embedding_model - self._embedder = EmbeddingFactory.create(embedding_method, **params) - except Exception as exc: - # Fallback to hash embedding if the requested method fails - print(f"⚠️ 无法创建 {embedding_method} embedding，使用 hash 作为后备: {exc}") - self._embedder = EmbeddingFactory.create("hash", dimension=384) - - all_chunks = _discover_documents(self.project_root, allow_download=allow_download) - if max_chunks and len(all_chunks) > max_chunks: - all_chunks = all_chunks[:max_chunks] - for chunk in all_chunks: - chunk.vector = self._embedder.embed(chunk.text) - self._chunks = list(all_chunks) - - def search(self, query: str, top_k: int = TOP_K_DEFAULT) -> list[KnowledgeChunk]: - if not query.strip(): - return [] - vector = self._embedder.embed(query) - scored: list[KnowledgeChunk] = [] - for chunk in self._chunks: - if chunk.vector is None: - continue - score = _cosine_similarity(vector, chunk.vector) - scored.append( - KnowledgeChunk( - text=chunk.text, - source=chunk.source, - kind=chunk.kind, - score=score, - ) - ) - scored.sort(key=lambda item: item.score, reverse=True) - return scored[:top_k] - - -@lru_cache(maxsize=1) -def get_default_knowledge_base( - max_chunks: int = 2000, - allow_download: bool = True, - embedding_method: str | None = None, - embedding_model: str | None = None, -) -> PipelineKnowledgeBase: - """Get or create the default knowledge base. - - Args: - max_chunks: Maximum number of chunks to keep - allow_download: Whether to download docs if not found locally - embedding_method: Which embedding method to use (hash, openai, hf, etc.) - embedding_model: Specific model to use for the embedding method - """ - method = embedding_method or os.getenv("SAGE_PIPELINE_EMBEDDING_METHOD", "hash") - model = embedding_model or os.getenv("SAGE_PIPELINE_EMBEDDING_MODEL") - - return PipelineKnowledgeBase( - max_chunks=max_chunks, - allow_download=allow_download, - embedding_method=method, - embedding_model=model, - ) - - -def build_query_payload( - requirements: Mapping[str, object], - previous_plan: Mapping[str, object] | None = None, - feedback: str | None = None, -) -> str: - hints: list[str] = [json.dumps(requirements, ensure_ascii=False)] - if previous_plan: - pipeline = previous_plan.get("pipeline") or {} - if pipeline: - hints.append(json.dumps(pipeline, ensure_ascii=False)) - stages = previous_plan.get("stages") or [] - if isinstance(stages, Sequence): - stage_info = [ - f"{stage.get('id', stage.get('class', 'stage'))}:{stage.get('class', '')}" - for stage in stages - if isinstance(stage, Mapping) - ] - if stage_info: - hints.append("; ".join(stage_info)) - if feedback: - hints.append(feedback) - return "\n".join(hints) - - -__all__ = [ - "PipelineKnowledgeBase", - "build_query_payload", - "get_default_knowledge_base", -] diff --git a/packages/sage-cli/src/sage/cli/commands/demo.py b/packages/sage-cli/src/sage/cli/commands/demo.py deleted file mode 100644 index d6d505a0ec..0000000000 --- a/packages/sage-cli/src/sage/cli/commands/demo.py +++ /dev/null @@ -1,302 +0,0 @@ -"""SAGE Demo 命令 - 即开即用的体验入口.""" - -import typer -from rich.console import Console -from rich.panel import Panel -from rich.syntax import Syntax -from rich.table import Table - -app = typer.Typer( - name="demo", - help="🎮 Demo - 即开即用的 SAGE 体验", - no_args_is_help=True, -) - -console = Console() - - -# ============================================================================ -# Demo: Hello World -# ============================================================================ -HELLO_WORLD_CODE = ''' -print("🚀 SAGE Hello World Demo") -print("=" * 40) -print() - -# SAGE 使用声明式 Pipeline 处理数据流 -# 这是一个简化的演示，展示 SAGE 的核心概念 - -# 1. 模拟数据流 -data = [1, 2, 3, 4, 5] -print(f"📥 输入数据: {data}") - -# 2. 定义转换操作 (类似 SAGE 的 map 算子) -def double(x): - """翻倍算子""" - return x * 2 - -def add_ten(x): - """加10算子""" - return x + 10 - -# 3. 应用 Pipeline: data -> double -> add_ten -result = [add_ten(double(x)) for x in data] -print(f"📤 输出结果: {result}") -print() - -# 4. 在真实的 SAGE 中，你可以这样写: -print("💡 SAGE Pipeline 写法:") -print(""" - from sage.kernel import LocalEnvironment - - env = LocalEnvironment() - stream = env.from_batch([1, 2, 3, 4, 5]) - stream.map(double).map(add_ten).print() - env.submit() -""") -print() -print("✅ Hello SAGE! 了解更多: sage demo run streaming") -''' - - -# ============================================================================ -# Demo: RAG Pipeline (需要可选依赖) -# ============================================================================ -RAG_DEMO_CODE = """ -from sage.libs.rag import SimpleRAG - -# 创建简单的 RAG 实例 -rag = SimpleRAG() - -# 添加文档 -rag.add_documents([ - "SAGE 是一个流式数据处理框架", - "SAGE 支持 LLM 推理和 RAG 管道", - "SAGE 使用 Python 3.10+ 开发", -]) - -# 查询 -result = rag.query("SAGE 是什么?") -print(f"问题: SAGE 是什么?") -print(f"答案: {result}") -""" - - -# ============================================================================ -# Demo: Streaming (完整 Pipeline 示例) -# ============================================================================ -STREAMING_DEMO_CODE = """ -from sage.kernel import LocalEnvironment -from sage.common.core import SinkFunction - -print("🌊 SAGE 流式数据处理演示") -print("=" * 40) - -# 收集结果的 Sink -class CollectorSink(SinkFunction): - results = [] - def execute(self, data): - CollectorSink.results.append(data) - return data - -# 模拟传感器数据 -sensor_data = [ - {"sensor_id": 1, "value": 23.5}, - {"sensor_id": 2, "value": 18.2}, - {"sensor_id": 1, "value": 24.1}, - {"sensor_id": 2, "value": 19.0}, - {"sensor_id": 1, "value": 25.0}, -] - -print(f"📥 输入: {len(sensor_data)} 条传感器数据") - -# 创建并执行 Pipeline -env = LocalEnvironment("demo") -stream = env.from_batch(sensor_data) -stream.filter(lambda x: x["sensor_id"] == 1).map(lambda x: {**x, "alert": x["value"] > 24}).sink(CollectorSink) -env.submit() - -# 显示结果 -print("📤 处理结果 (sensor_id=1):") -for item in CollectorSink.results: - status = "🔴 告警" if item.get("alert") else "🟢 正常" - print(f" 温度 {item['value']}°C - {status}") -print() -print("✅ Pipeline 执行完成!") -""" - - -# ============================================================================ -# Commands -# ============================================================================ -@app.command("list") -def list_demos(): - """📋 列出所有可用的 demo""" - table = Table(title="🎮 SAGE Demos", show_header=True) - table.add_column("名称", style="cyan", width=15) - table.add_column("描述", style="white") - table.add_column("依赖", style="yellow") - - table.add_row("hello", "Hello World - Pipeline 基础", "无") - table.add_row("streaming", "流式数据处理演示", "无") - table.add_row("rag", "RAG 检索增强生成", "ml, vdb") - table.add_row("llm", "LLM 对话演示", "isagellm") - - console.print(table) - console.print() - console.print("[dim]运行示例: sage demo run hello[/dim]") - - -@app.command("run") -def run_demo( - name: str = typer.Argument(..., help="Demo 名称 (hello, streaming, rag, llm)"), - show_code: bool = typer.Option(False, "--show-code", "-c", help="只显示代码，不执行"), -): - """▶️ 运行指定的 demo""" - demos = { - "hello": ("Hello World", HELLO_WORLD_CODE, []), - "streaming": ("流式处理", STREAMING_DEMO_CODE, []), - "rag": ("RAG Pipeline", RAG_DEMO_CODE, ["torch", "faiss"]), - "llm": ("LLM 对话", None, ["isagellm"]), - } - - if name not in demos: - console.print(f"[red]❌ 未知的 demo: {name}[/red]") - console.print(f"[dim]可用的 demo: {', '.join(demos.keys())}[/dim]") - raise typer.Exit(1) - - title, code, deps = demos[name] - - # 检查依赖 - if deps: - missing = _check_dependencies(deps) - if missing: - console.print(f"[yellow]⚠️ 缺少依赖: {', '.join(missing)}[/yellow]") - console.print() - console.print("[dim]安装方式:[/dim]") - if "torch" in missing or "faiss" in missing: - console.print(" pip install isage-middleware[ml,vdb]") - if "isagellm" in missing: - console.print(" pip install isagellm") - console.print() - if not show_code: - raise typer.Exit(1) - - # 显示代码 - if code: - console.print(Panel(f"[bold cyan]{title}[/bold cyan]", expand=False)) - syntax = Syntax(code.strip(), "python", theme="monokai", line_numbers=True) - console.print(syntax) - console.print() - - if show_code: - return - - # 执行代码 - if name == "llm": - _run_llm_demo() - elif code: - console.print("[bold green]▶️ 执行中...[/bold green]") - console.print() - - # 抑制 SAGE 内部日志 - import logging - import os - - os.environ["SAGE_LOG_LEVEL"] = "ERROR" - logging.basicConfig(level=logging.ERROR, force=True) - for logger_name in ["sage", "JobManager", "ray", "asyncio", "Dispatcher", "ExecutionGraph"]: - logging.getLogger(logger_name).setLevel(logging.ERROR) - - try: - exec(code, {"__name__": "__main__"}) - except Exception as e: - console.print(f"[red]❌ 执行错误: {e}[/red]") - raise typer.Exit(1) - - -@app.command("hello") -def hello_world(): - """👋 运行 Hello World 示例（最简单的入门）""" - run_demo("hello") - - -@app.command("interactive") -def interactive_mode(): - """🎯 进入交互式 SAGE Shell""" - console.print(Panel("[bold cyan]SAGE Interactive Shell[/bold cyan]", expand=False)) - console.print() - console.print("[dim]提示: 输入 Python 代码，或使用以下快捷命令:[/dim]") - console.print(" [cyan]!help[/cyan] - 显示帮助") - console.print(" [cyan]!demo[/cyan] - 列出可用 demo") - console.print(" [cyan]!exit[/cyan] - 退出") - console.print() - - # 预导入常用模块 - namespace = {} - try: - exec("from sage.kernel import LocalEnvironment", namespace) - exec("env = LocalEnvironment()", namespace) - console.print("[green]✅ 已导入: LocalEnvironment (已创建 env 实例)[/green]") - console.print("[dim] 用法: stream = env.from_collection([1,2,3])[/dim]") - except ImportError as e: - console.print(f"[yellow]⚠️ 导入警告: {e}[/yellow]") - - console.print() - - # 简单 REPL - import code - - code.interact(banner="", local=namespace, exitmsg="[dim]Goodbye![/dim]") - - -# ============================================================================ -# Helper Functions -# ============================================================================ -def _check_dependencies(deps: list[str]) -> list[str]: - """检查依赖是否已安装""" - import importlib.util - - missing = [] - for dep in deps: - spec = importlib.util.find_spec(dep) - if spec is None: - missing.append(dep) - return missing - - -def _run_llm_demo(): - """运行 LLM 对话演示""" - try: - from isagellm import UnifiedInferenceClient - except ImportError: - console.print("[red]❌ 需要安装 isagellm: pip install isagellm[/red]") - raise typer.Exit(1) - - console.print("[bold]🤖 LLM 对话演示[/bold]") - console.print("[dim]提示: 输入问题，或输入 'exit' 退出[/dim]") - console.print() - - try: - client = UnifiedInferenceClient.create() - console.print("[green]✅ 已连接到 LLM 服务[/green]") - except Exception as e: - console.print(f"[yellow]⚠️ 连接失败: {e}[/yellow]") - console.print("[dim]请先启动 LLM 服务: sage gateway start[/dim]") - raise typer.Exit(1) - - while True: - try: - user_input = console.input("[cyan]You: [/cyan]") - if user_input.lower() in ("exit", "quit", "q"): - break - - response = client.chat([{"role": "user", "content": user_input}]) - console.print(f"[green]AI: [/green]{response}") - console.print() - except KeyboardInterrupt: - break - except Exception as e: - console.print(f"[red]错误: {e}[/red]") - - console.print("[dim]Goodbye![/dim]") diff --git a/packages/sage-cli/src/sage/cli/commands/platform/__init__.py b/packages/sage-cli/src/sage/cli/commands/platform/__init__.py deleted file mode 100644 index 2c29aa8892..0000000000 --- a/packages/sage-cli/src/sage/cli/commands/platform/__init__.py +++ /dev/null @@ -1,103 +0,0 @@ -""" -SAGE Platform Commands - -平台管理命令组，包括： -- cluster: 集群管理 -- head: 头节点管理 -- worker: Worker节点管理 -- job: 作业管理 -- jobmanager: JobManager服务 -- config: 配置管理 -- doctor: 系统诊断 -- version: 版本信息 -- extensions: C++扩展管理 -""" - -from rich.console import Console - -# 创建主命令应用 - 注意：这个不会被直接注册，而是每个子命令会被单独注册 -# 但我们保留这个结构以便将来可能的重组 - -console = Console() - -# 导入所有平台命令 -try: - from .cluster import app as cluster_app -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 cluster 命令: {e}[/yellow]") - cluster_app = None - -try: - from .head import app as head_app -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 head 命令: {e}[/yellow]") - head_app = None - -try: - from .worker import app as worker_app -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 worker 命令: {e}[/yellow]") - worker_app = None - -try: - from .job import app as job_app -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 job 命令: {e}[/yellow]") - job_app = None - -try: - from .jobmanager import app as jobmanager_app -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 jobmanager 命令: {e}[/yellow]") - jobmanager_app = None - -try: - from .config import app as config_app -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 config 命令: {e}[/yellow]") - config_app = None - -try: - from .doctor import app as doctor_app -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 doctor 命令: {e}[/yellow]") - doctor_app = None - -try: - from .version import app as version_app -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 version 命令: {e}[/yellow]") - version_app = None - -try: - from .extensions import app as extensions_app -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 extensions 命令: {e}[/yellow]") - extensions_app = None - -try: - from .docs import app as docs_app -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 docs 命令: {e}[/yellow]") - docs_app = None - -try: - from .logs import app as logs_app -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 logs 命令: {e}[/yellow]") - logs_app = None - -# 导出所有命令 -__all__ = [ - "cluster_app", - "head_app", - "worker_app", - "job_app", - "jobmanager_app", - "config_app", - "doctor_app", - "version_app", - "extensions_app", - "docs_app", - "logs_app", -] diff --git a/packages/sage-cli/src/sage/cli/commands/platform/cluster.py b/packages/sage-cli/src/sage/cli/commands/platform/cluster.py deleted file mode 100644 index 136d0fb541..0000000000 --- a/packages/sage-cli/src/sage/cli/commands/platform/cluster.py +++ /dev/null @@ -1,335 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE Cluster Manager CLI -统一的Ray集群管理工具 -""" - -import os - -import typer - -from ...management.config_manager import get_config_manager -from ...management.deployment_manager import DeploymentManager -from .head import app as head_app -from .worker import app as worker_app - -app = typer.Typer(name="cluster", help="🏗️ Ray集群统一管理") - -# 添加子命令 -app.add_typer(head_app, name="head", help="🏠 Head节点管理") -app.add_typer(worker_app, name="worker", help="👥 Worker节点管理") - - -@app.command("start") -def start_cluster( - skip_ssh_check: bool = typer.Option(False, "--skip-ssh-check", help="跳过SSH免密登录检查"), - ssh_password: str = typer.Option( - None, "--ssh-password", "-p", help="SSH密码（用于自动配置免密登录）" - ), - force: bool = typer.Option( - False, "--force", "-f", help="强制重启：如果Ray已运行，先停止再启动" - ), -): - """启动整个Ray集群（Head + 所有Workers）""" - config_manager = get_config_manager() - workers = config_manager.get_workers_ssh_hosts() - ssh_config = config_manager.get_ssh_config() - - # 0. SSH免密登录检查（仅当有worker节点时） - if workers and not skip_ssh_check: - typer.echo("🔐 第0步: 检查SSH免密登录...") - - from .ssh_setup import auto_setup_ssh_keys, verify_passwordless_login - - user = ssh_config.get("user", "sage") - key_path = ssh_config.get("key_path", "~/.ssh/id_rsa") - key_path = os.path.expanduser(key_path) - - # 检查每个worker的SSH连接 - failed_hosts = [] - for host, port in workers: - if not verify_passwordless_login(host, user, key_path, port): - failed_hosts.append((host, port)) - - if failed_hosts: - typer.echo(f"[yellow]⚠️ 发现 {len(failed_hosts)} 个节点未配置免密登录:[/yellow]") - for host, port in failed_hosts: - typer.echo(f" - {host}:{port}") - - # 如果提供了密码，自动配置 - if ssh_password: - typer.echo("\n[cyan]🔧 使用提供的密码自动配置SSH免密登录...[/cyan]") - success, total = auto_setup_ssh_keys( - hosts=failed_hosts, - user=user, - password=ssh_password, - key_path=key_path, - ) - if success < total: - typer.echo(f"[red]❌ SSH配置失败: {total - success} 个节点无法配置[/red]") - typer.echo( - "[yellow]提示: 使用 --skip-ssh-check 跳过检查，或手动配置SSH[/yellow]" - ) - raise typer.Exit(1) - else: - # 交互式询问是否配置 - typer.echo("\n[cyan]是否现在配置SSH免密登录？[/cyan]") - try: - password = typer.prompt(f"请输入SSH密码（用户: {user}）", hide_input=True) - success, total = auto_setup_ssh_keys( - hosts=failed_hosts, - user=user, - password=password, - key_path=key_path, - ) - if success < total: - typer.echo(f"[red]❌ SSH配置失败: {total - success} 个节点无法配置[/red]") - raise typer.Exit(1) - except typer.Abort: - typer.echo( - "[yellow]\n⚠️ 跳过SSH配置。使用 --skip-ssh-check 避免此检查[/yellow]" - ) - raise typer.Exit(1) - else: - typer.echo("[green]✅ 所有节点SSH免密登录正常[/green]") - - typer.echo("🚀 启动Ray集群...") - - # 1. 启动Head节点 - typer.echo("第1步: 启动Head节点") - try: - from .head import start_head - - start_head(force=force) - except typer.Exit as e: - # typer.Exit(0) 表示 Ray Head 已在运行，视为成功 - if e.exit_code != 0: - typer.echo(f"❌ Head节点启动失败 (exit code: {e.exit_code})") - raise typer.Exit(1) - # exit_code == 0 表示已在运行，继续执行 - except Exception as e: - typer.echo(f"❌ Head节点启动失败: {e}") - raise typer.Exit(1) - - # 等待Head节点完全启动 - typer.echo("⏳ 等待Head节点完全启动...") - import time - - time.sleep(5) - - # 2. 启动所有Worker节点 - typer.echo("第2步: 启动所有Worker节点") - try: - from .worker import start_workers - - if not workers: - typer.echo("💡 未配置worker节点，跳过worker启动") - else: - start_workers() - typer.echo("✅ Worker节点启动完成") - except typer.Exit as e: - if e.exit_code != 0: - typer.echo(f"❌ Worker节点启动失败 (exit code: {e.exit_code})") - typer.echo("💡 Head节点已启动，可尝试手动启动Worker节点") - raise typer.Exit(1) - except Exception as e: - typer.echo(f"❌ Worker节点启动失败: {e}") - typer.echo("💡 Head节点已启动，可尝试手动启动Worker节点") - raise typer.Exit(1) - - typer.echo("✅ Ray集群启动完成!") - - -@app.command("stop") -def stop_cluster(): - """停止整个Ray集群（所有Workers + Head）""" - typer.echo("�� 停止Ray集群...") - - # 1. 先停止所有Worker节点 - typer.echo("第1步: 停止所有Worker节点") - try: - from .worker import stop_workers - - stop_workers() - except Exception as e: - typer.echo(f"⚠️ Worker节点停止遇到问题: {e}") - # 继续执行，因为停止操作允许部分失败 - - # 等待Worker节点完全停止 - typer.echo("⏳ 等待Worker节点完全停止...") - import time - - time.sleep(3) - - # 2. 停止Head节点 - typer.echo("第2步: 停止Head节点") - try: - from .head import stop_head - - stop_head() - except Exception as e: - typer.echo(f"⚠️ Head节点停止遇到问题: {e}") - - typer.echo("✅ Ray集群停止完成!") - - -@app.command("restart") -def restart_cluster(): - """重启整个Ray集群""" - typer.echo("🔄 重启Ray集群...") - - # 先停止 - typer.echo("第1阶段: 停止集群") - stop_cluster() - - # 等待 - typer.echo("⏳ 等待5秒后重新启动...") - import time - - time.sleep(5) - - # 再启动 - typer.echo("第2阶段: 启动集群") - start_cluster() - - typer.echo("✅ Ray集群重启完成!") - - -@app.command("status") -def status_cluster(): - """检查整个Ray集群状态""" - typer.echo("📊 检查Ray集群状态...") - - config_manager = get_config_manager() - head_config = config_manager.get_head_config() - workers = config_manager.get_workers_ssh_hosts() - - head_host = head_config.get("host", "localhost") - dashboard_port = head_config.get("dashboard_port", 8265) - - # 1. 检查Head节点 - typer.echo("\n�� Head节点状态:") - try: - from .head import status_head - - status_head() - head_running = True - except Exception: - head_running = False - - # 2. 检查Worker节点 - typer.echo(f"\n👥 Worker节点状态 ({len(workers)} 个节点):") - try: - from .worker import status_workers - - status_workers() - except Exception: - pass - - # 3. 显示集群访问信息 - if head_running: - typer.echo("\n🌐 集群访问信息:") - typer.echo(f" Dashboard: http://{head_host}:{dashboard_port}") - typer.echo(f" Ray集群地址: {head_host}:{head_config.get('head_port', 6379)}") - - -@app.command("deploy") -def deploy_cluster(): - """部署SAGE到所有Worker节点""" - typer.echo("🚀 部署SAGE到集群...") - - deployment_manager = DeploymentManager() - success_count, total_count = deployment_manager.deploy_to_all_workers() - - if success_count == total_count: - typer.echo("✅ 集群部署成功!") - else: - typer.echo(f"⚠️ 部分节点部署失败 ({success_count}/{total_count})") - raise typer.Exit(1) - - -@app.command("scale") -def scale_cluster( - action: str = typer.Argument(..., help="操作: add 或 remove"), - node: str = typer.Argument(..., help="节点地址，格式为 host:port"), -): - """动态扩缩容集群（添加或移除Worker节点）""" - if action not in ["add", "remove"]: - typer.echo("❌ 操作必须是 'add' 或 'remove'") - raise typer.Exit(1) - - if action == "add": - typer.echo(f"➕ 扩容集群: 添加节点 {node}") - try: - from .worker import add_worker - - add_worker(node) - except Exception as e: - typer.echo(f"❌ 添加节点失败: {e}") - raise typer.Exit(1) - else: - typer.echo(f"➖ 缩容集群: 移除节点 {node}") - try: - from .worker import remove_worker - - remove_worker(node) - except Exception as e: - typer.echo(f"❌ 移除节点失败: {e}") - raise typer.Exit(1) - - -@app.command("info") -def cluster_info(): - """显示集群配置信息""" - typer.echo("📋 Ray集群配置信息") - typer.echo("=" * 50) - - config_manager = get_config_manager() - head_config = config_manager.get_head_config() - worker_config = config_manager.get_worker_config() - ssh_config = config_manager.get_ssh_config() - remote_config = config_manager.get_remote_config() - workers = config_manager.get_workers_ssh_hosts() - - typer.echo("🏠 Head节点配置:") - typer.echo(f" 主机: {head_config.get('host', 'N/A')}") - typer.echo(f" 端口: {head_config.get('head_port', 'N/A')}") - typer.echo( - f" Dashboard: {head_config.get('dashboard_host', 'N/A')}:{head_config.get('dashboard_port', 'N/A')}" - ) - typer.echo(f" 临时目录: {head_config.get('temp_dir', 'N/A')}") - typer.echo(f" 日志目录: {head_config.get('log_dir', 'N/A')}") - - typer.echo(f"\n👥 Worker节点配置 ({len(workers)} 个节点):") - typer.echo(f" 绑定主机: {worker_config.get('bind_host', 'N/A')}") - typer.echo(f" 临时目录: {worker_config.get('temp_dir', 'N/A')}") - typer.echo(f" 日志目录: {worker_config.get('log_dir', 'N/A')}") - - if workers: - typer.echo(" 节点列表:") - for i, (host, port) in enumerate(workers, 1): - typer.echo(f" {i}. {host}:{port}") - - typer.echo("\n🔗 SSH配置:") - typer.echo(f" 用户: {ssh_config.get('user', 'N/A')}") - typer.echo(f" 密钥路径: {ssh_config.get('key_path', 'N/A')}") - typer.echo(f" 连接超时: {ssh_config.get('connect_timeout', 'N/A')}s") - - typer.echo("\n🛠️ 远程环境:") - typer.echo(f" SAGE目录: {remote_config.get('sage_home', 'N/A')}") - typer.echo(f" Python路径: {remote_config.get('python_path', 'N/A')}") - typer.echo(f" Ray命令: {remote_config.get('ray_command', 'N/A')}") - typer.echo(f" Conda环境: {remote_config.get('conda_env', 'N/A')}") - - -@app.command("version") -def version_command(): - """Show version information.""" - typer.echo("🏗️ SAGE Cluster Manager") - typer.echo("Version: 1.0.1") - typer.echo("Author: IntelliStream Team") - typer.echo("Repository: https://github.com/intellistream/SAGE") - - -if __name__ == "__main__": - app() diff --git a/packages/sage-cli/src/sage/cli/commands/platform/config.py b/packages/sage-cli/src/sage/cli/commands/platform/config.py deleted file mode 100644 index 6c89553188..0000000000 --- a/packages/sage-cli/src/sage/cli/commands/platform/config.py +++ /dev/null @@ -1,68 +0,0 @@ -#!/usr/bin/env python3 -"""Configuration management commands for SAGE.""" - -import typer - -# Import the configuration subcommands -from .env import app as env_app -from .llm_config import app as llm_config_app - -app = typer.Typer(name="config", help="⚙️ 配置管理") - -# Add config-related subcommands -app.add_typer(llm_config_app, name="llm", help="🤖 LLM 服务配置管理") -app.add_typer(env_app, name="env", help="🌱 环境变量与 .env 文件管理") - - -@app.command("show") -def config_info(): - """显示配置信息""" - from ...management.config_manager import get_config_manager - - try: - config_manager = get_config_manager() - config = config_manager.load_config() - - print("📋 SAGE 配置信息:") - print(f"配置文件: {config_manager.config_path}") - print(f"数据目录: {config.get('data_dir', '未设置')}") - print(f"日志级别: {config.get('log_level', '未设置')}") - print(f"工作目录: {config.get('work_dir', '未设置')}") - - if "ray" in config: - ray_config = config["ray"] - print(f"Ray地址: {ray_config.get('address', '未设置')}") - print(f"Ray端口: {ray_config.get('port', '未设置')}") - - except Exception as e: - print(f"❌ 读取配置失败: {e}") - print("💡 运行 'sage config init' 创建配置文件") - - -@app.command("init") -def init_config(force: bool = typer.Option(False, "--force", "-f", help="强制覆盖现有配置")): - """初始化SAGE配置文件""" - from ...management.config_manager import get_config_manager - - try: - config_manager = get_config_manager() - - if config_manager.config_path.exists(): - if not force: - print(f"配置文件已存在: {config_manager.config_path}") - print("使用 --force 选项覆盖现有配置") - return - - config_manager.create_default_config() - print(f"✅ 配置文件已创建: {config_manager.config_path}") - - except Exception as e: - print(f"❌ 初始化配置失败: {e}") - - -# 为了向后兼容，也提供一个直接的config命令 -@app.callback(invoke_without_command=True) -def config_callback(ctx: typer.Context): - """显示配置信息""" - if ctx.invoked_subcommand is None: - config_info() diff --git a/packages/sage-cli/src/sage/cli/commands/platform/docs.py b/packages/sage-cli/src/sage/cli/commands/platform/docs.py deleted file mode 100644 index e6783eec6c..0000000000 --- a/packages/sage-cli/src/sage/cli/commands/platform/docs.py +++ /dev/null @@ -1,254 +0,0 @@ -""" -SAGE 文档命令 - -提供文档预览、构建和部署功能 -""" - -import subprocess -import sys -from pathlib import Path -from typing import Optional - -import typer -from rich.console import Console -from rich.panel import Panel - -app = typer.Typer(help="📚 文档管理 - 预览、构建和部署文档") -console = Console() - - -def find_docs_dir() -> Optional[Path]: - """查找文档目录""" - # 从当前目录向上查找 - current = Path.cwd() - - # 检查常见位置 - candidates = [ - current / "docs-public", - current.parent / "docs-public", - current.parent.parent / "docs-public", - ] - - # 如果在 SAGE 项目中 - for candidate in candidates: - if candidate.exists() and (candidate / "mkdocs.yml").exists(): - return candidate - - return None - - -def check_mkdocs_installed() -> bool: - """检查 mkdocs 是否安装""" - try: - subprocess.run(["mkdocs", "--version"], capture_output=True, check=True) - return True - except (subprocess.CalledProcessError, FileNotFoundError): - return False - - -@app.command("serve") -def serve( - port: int = typer.Option(8000, "--port", "-p", help="服务端口"), - host: str = typer.Option("127.0.0.1", "--host", "-h", help="绑定地址"), - dev_addr: Optional[str] = typer.Option(None, "--dev-addr", help="开发服务器地址 (host:port)"), - open_browser: bool = typer.Option(True, "--open/--no-open", help="自动打开浏览器"), -): - """ - 🚀 启动文档预览服务器 - - 示例： - sage docs serve # 默认 127.0.0.1:8000 - sage docs serve --port 8080 # 指定端口 - sage docs serve --host 0.0.0.0 # 监听所有网卡 - sage docs serve --dev-addr 0.0.0.0:8080 - """ - # 检查 mkdocs - if not check_mkdocs_installed(): - console.print( - Panel( - "[red]❌ MkDocs 未安装[/red]\n\n" - "请先安装 MkDocs：\n" - " [cyan]pip install mkdocs-material[/cyan]", - title="错误", - border_style="red", - ) - ) - raise typer.Exit(1) - - # 查找文档目录 - docs_dir = find_docs_dir() - if not docs_dir: - console.print( - Panel( - "[red]❌ 未找到文档目录[/red]\n\n请确保在 SAGE 项目目录中运行此命令", - title="错误", - border_style="red", - ) - ) - raise typer.Exit(1) - - console.print(f"[green]📚 文档目录:[/green] {docs_dir}") - - # 构建命令 - cmd = ["mkdocs", "serve"] - - if dev_addr: - cmd.extend(["--dev-addr", dev_addr]) - else: - cmd.extend(["--dev-addr", f"{host}:{port}"]) - - if not open_browser: - cmd.append("--no-livereload") - - # 显示信息 - addr = dev_addr or f"{host}:{port}" - console.print( - Panel( - f"[green]🚀 启动文档服务器...[/green]\n\n" - f"地址: [cyan]http://{addr}[/cyan]\n" - f"目录: [dim]{docs_dir}[/dim]\n\n" - f"[yellow]💡 提示:[/yellow]\n" - f" • 文档会自动重载\n" - f" • 按 Ctrl+C 停止服务器", - title="文档预览", - border_style="green", - ) - ) - - # 启动服务器 - try: - subprocess.run(cmd, cwd=docs_dir) - except KeyboardInterrupt: - console.print("\n[yellow]👋 文档服务器已停止[/yellow]") - except Exception as e: - console.print(f"[red]❌ 启动失败: {e}[/red]") - raise typer.Exit(1) - - -@app.command("build") -def build( - strict: bool = typer.Option(False, "--strict", help="严格模式 (有警告则失败)"), - clean: bool = typer.Option(True, "--clean/--no-clean", help="构建前清理"), - output_dir: Optional[str] = typer.Option(None, "--output", "-o", help="输出目录"), -): - """ - 🔨 构建静态文档站点 - - 示例： - sage docs build # 构建到默认目录 - sage docs build --strict # 严格模式 - sage docs build -o ./site # 指定输出目录 - """ - # 检查 mkdocs - if not check_mkdocs_installed(): - console.print("[red]❌ MkDocs 未安装，请先安装: pip install mkdocs-material[/red]") - raise typer.Exit(1) - - # 查找文档目录 - docs_dir = find_docs_dir() - if not docs_dir: - console.print("[red]❌ 未找到文档目录[/red]") - raise typer.Exit(1) - - console.print(f"[green]📚 文档目录:[/green] {docs_dir}") - - # 构建命令 - cmd = ["mkdocs", "build"] - - if strict: - cmd.append("--strict") - - if clean: - cmd.append("--clean") - - if output_dir: - cmd.extend(["--site-dir", output_dir]) - - console.print(Panel("[green]🔨 开始构建文档...[/green]", title="构建", border_style="green")) - - # 执行构建 - try: - subprocess.run(cmd, cwd=docs_dir, check=True) - - output = output_dir or "site" - console.print( - Panel( - f"[green]✅ 构建成功！[/green]\n\n输出目录: [cyan]{docs_dir / output}[/cyan]", - title="完成", - border_style="green", - ) - ) - except subprocess.CalledProcessError as e: - console.print(f"[red]❌ 构建失败: {e}[/red]") - raise typer.Exit(1) - except Exception as e: - console.print(f"[red]❌ 构建失败: {e}[/red]") - raise typer.Exit(1) - - -@app.command("install-deps") -def install_deps(): - """ - 📦 安装文档依赖 - - 安装 MkDocs 和所需插件 - """ - console.print(Panel("[green]📦 安装文档依赖...[/green]", title="安装", border_style="green")) - - packages = [ - "mkdocs>=1.6.0", - "mkdocs-material>=9.5.0", - ] - - try: - cmd = [sys.executable, "-m", "pip", "install"] + packages - subprocess.run(cmd, check=True) - - console.print( - Panel( - "[green]✅ 依赖安装完成！[/green]\n\n" - "现在可以使用：\n" - " [cyan]sage docs serve[/cyan] - 预览文档\n" - " [cyan]sage docs build[/cyan] - 构建文档", - title="完成", - border_style="green", - ) - ) - except subprocess.CalledProcessError as e: - console.print(f"[red]❌ 安装失败: {e}[/red]") - raise typer.Exit(1) - - -@app.command("info") -def info(): - """ - ℹ️ 显示文档信息 - - 显示文档目录、配置等信息 - """ - docs_dir = find_docs_dir() - - if not docs_dir: - console.print("[red]❌ 未找到文档目录[/red]") - raise typer.Exit(1) - - # 读取配置 - config_file = docs_dir / "mkdocs.yml" - mkdocs_installed = check_mkdocs_installed() - - info_text = f""" -[cyan]文档目录:[/cyan] {docs_dir} -[cyan]配置文件:[/cyan] {config_file} -[cyan]MkDocs:[/cyan] {"✅ 已安装" if mkdocs_installed else "❌ 未安装"} - -[yellow]快速命令:[/yellow] - sage docs serve - 启动预览服务器 - sage docs build - 构建静态站点 - sage docs install-deps - 安装依赖 -""" - - console.print(Panel(info_text, title="📚 文档信息", border_style="blue")) - - -if __name__ == "__main__": - app() diff --git a/packages/sage-cli/src/sage/cli/commands/platform/doctor.py b/packages/sage-cli/src/sage/cli/commands/platform/doctor.py deleted file mode 100644 index 2ef5b73f04..0000000000 --- a/packages/sage-cli/src/sage/cli/commands/platform/doctor.py +++ /dev/null @@ -1,66 +0,0 @@ -#!/usr/bin/env python3 -"""SAGE CLI Doctor Command — diagnose the local environment.""" - -import typer -from rich.console import Console - -from sage.cli.utils.diagnostics import check_dependency_versions - -console = Console() -app = typer.Typer(name="doctor", help="🔍 系统诊断") - - -@app.command() -def check(): - """诊断SAGE安装和配置""" - console.rule("SAGE 系统诊断") - - # 检查Python版本 - import sys - - console.print(f"🐍 Python 版本: [bold]{sys.version.split()[0]}[/bold]") - - # 检查SAGE安装 - try: - import sage.common - - console.print(f"✅ SAGE 安装: v{sage.common.__version__}") - except ImportError as e: - console.print(f"❌ SAGE 未安装: {e}") - - # 检查扩展 - 只检查实际存在的模块 - extensions = [("sage_db", "sage.middleware.components.sage_db")] - - for ext_name, ext_path in extensions: - try: - __import__(ext_path) - console.print(f"✅ 扩展可用: {ext_name}") - except ImportError: - console.print(f"⚠️ 扩展缺失: {ext_name}") - - # 检查Ray - try: - import ray - - console.print(f"✅ Ray: v{ray.__version__}") - except ImportError: - console.print("❌ Ray 未安装") - - console.print("\n💡 如需安装扩展，运行: [bold]sage extensions install[/bold]") - - -@app.command() -def compat(): - """检查闭源依赖的兼容性。""" - - success = check_dependency_versions(console=console) - if not success: - raise typer.Exit(1) - - -# 为了向后兼容，也提供一个直接的doctor命令 -@app.callback(invoke_without_command=True) -def doctor_callback(ctx: typer.Context): - """诊断SAGE安装和配置""" - if ctx.invoked_subcommand is None: - check() diff --git a/packages/sage-cli/src/sage/cli/commands/platform/env.py b/packages/sage-cli/src/sage/cli/commands/platform/env.py deleted file mode 100644 index 516f304751..0000000000 --- a/packages/sage-cli/src/sage/cli/commands/platform/env.py +++ /dev/null @@ -1,147 +0,0 @@ -"""Environment management commands for the SAGE CLI.""" - -from __future__ import annotations - -import shutil -import subprocess -from pathlib import Path - -import typer -from rich.console import Console -from rich.table import Table - -from sage.cli.utils import env as env_utils - -console = Console() -app = typer.Typer(name="env", help="🌱 环境变量与 .env 文件管理") - - -def _render_status(status: dict) -> None: - """Pretty print environment status information.""" - - project_root: Path = Path(str(status["project_root"])) # type: ignore[arg-type] - console.print(f"📁 项目根目录: [cyan]{project_root}[/cyan]") - console.print(f"python-dotenv 可用: {'✅' if status['dotenv_available'] else '⚠️'}") - console.print( - f".env 存在: {'✅' if status['env_file_exists'] else '❌'} ({status['env_file']})" - ) - console.print( - f".env.template 存在: {'✅' if status['env_template_exists'] else '❌'} ({status['env_template']})" - ) - - table = Table(title="API Key 状态", show_edge=False, show_header=True) - table.add_column("环境变量") - table.add_column("已设置") - table.add_column("长度") - - for key, info in status["api_keys"].items(): - icon = "✅" if info["set"] else "❌" - length = str(info["length"]) if info["set"] else "-" - table.add_row(key, icon, length) - - console.print(table) - - -def _open_env_file(env_path: Path) -> None: - """Attempt to open the provided ``.env`` file in a suitable editor.""" - - for editor in ("code", "nano", "vim"): - if shutil.which(editor): - console.print(f"💡 使用 {editor} 打开 {env_path}") - try: - subprocess.run([editor, str(env_path)], check=False) - except OSError as exc: - console.print(f"⚠️ 无法启动 {editor}: {exc}") - return - - console.print(f"💡 请手动编辑文件: [cyan]{env_path}[/cyan]") - - -def _copy_template(project_root: Path, *, overwrite: bool = False) -> Path | None: - env_template = project_root / ".env.template" - env_file = project_root / ".env" - - if not env_template.exists(): - return None - - if env_file.exists() and not overwrite: - return env_file - - shutil.copy(env_template, env_file) - return env_file - - -def run_setup_interactive(open_editor: bool = True, overwrite: bool = False) -> dict: - """Shared implementation used by the CLI and legacy script wrapper.""" - - status = env_utils.check_environment_status() - project_root: Path = Path(str(status["project_root"])) # type: ignore[arg-type] - - console.print("🔧 [bold]SAGE 环境配置向导[/bold]") - console.rule() - _render_status(status) - - if not status["env_file_exists"]: - if status["env_template_exists"]: - console.print("\n📋 检测到 .env.template，可以复制生成新的 .env 文件。") - if typer.confirm("是否立即创建 .env?", default=True): - env_path = _copy_template(project_root, overwrite=overwrite) or ( - project_root / ".env" - ) - console.print(f"✅ 已创建 .env: [green]{env_path}[/green]") - if open_editor: - _open_env_file(env_path) - else: - console.print("💡 可以稍后手动复制 .env.template → .env") - else: - console.print("❌ 未找到 .env 或 .env.template，请手动创建并填写 API Keys。") - elif open_editor and typer.confirm("是否编辑现有的 .env 文件?", default=False): - _open_env_file(Path(status["env_file"])) # type: ignore[arg-type] - - console.print("\n🔍 当前环境变量状态:") - status = env_utils.check_environment_status() - _render_status(status) - - return status - - -@app.command() -def load( - env_file: Path | None = typer.Option(None, "--env-file", "-f", help="显式指定 .env 文件位置"), - override: bool = typer.Option(False, "--override", help="覆盖已存在的环境变量"), -): - """加载 .env 文件并将变量导入当前环境。""" - - try: - loaded, path = env_utils.load_environment_file(env_file, override=override) - except RuntimeError as exc: - console.print(f"⚠️ {exc}") - raise typer.Exit(1) from exc - - if not loaded: - resolved = path or env_file or (env_utils.find_project_root() / ".env") - console.print(f"ℹ️ 未找到 .env 文件: [cyan]{resolved}[/cyan]") - raise typer.Exit(1) - - console.print(f"✅ 已加载环境变量: [green]{path}[/green]") - - -@app.command() -def check(): - """检查当前环境变量配置。""" - - status = env_utils.check_environment_status() - _render_status(status) - - -@app.command() -def setup( - overwrite: bool = typer.Option(False, "--overwrite", help="如果已经存在 .env ，重新覆盖"), - no_open: bool = typer.Option(False, "--no-open", help="创建/检测完成后不自动打开编辑器"), -): - """运行交互式环境配置向导。""" - - run_setup_interactive(open_editor=not no_open, overwrite=overwrite) - - -__all__ = ["app", "run_setup_interactive"] diff --git a/packages/sage-cli/src/sage/cli/commands/platform/extensions.py b/packages/sage-cli/src/sage/cli/commands/platform/extensions.py deleted file mode 100644 index b718550bad..0000000000 --- a/packages/sage-cli/src/sage/cli/commands/platform/extensions.py +++ /dev/null @@ -1,907 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE Extensions Manager -====================== - -管理SAGE框架的C++扩展安装和检查 -""" - -import os -import shutil -import site -import subprocess -import sysconfig -from pathlib import Path - -import typer - -app = typer.Typer(name="extensions", help="🧩 扩展管理 - 安装和管理C++扩展") - - -@app.callback(invoke_without_command=True) -def main(ctx: typer.Context): - """ - 🧩 SAGE 扩展管理系统 - - 管理SAGE框架的C++扩展安装和检查 - """ - if ctx.invoked_subcommand is None: - # 如果没有子命令，显示帮助信息 - typer.echo(f"{Colors.BOLD}{Colors.BLUE}🧩 SAGE 扩展管理{Colors.RESET}") - typer.echo("=" * 40) - typer.echo() - typer.echo("可用命令:") - typer.echo(" install - 安装C++扩展") - typer.echo(" status - 检查扩展状态") - typer.echo(" clean - 清理构建文件") - typer.echo(" info - 显示扩展信息") - typer.echo() - typer.echo("使用 'sage extensions COMMAND --help' 查看具体命令的帮助") - - -class Colors: - """终端颜色""" - - GREEN = "\033[92m" - RED = "\033[91m" - YELLOW = "\033[93m" - BLUE = "\033[94m" - BOLD = "\033[1m" - DIM = "\033[2m" - RESET = "\033[0m" - - -def print_info(msg: str): - typer.echo(f"{Colors.BLUE}ℹ️ {msg}{Colors.RESET}") - - -def print_success(msg: str): - typer.echo(f"{Colors.GREEN}✅ {msg}{Colors.RESET}") - - -def print_error(msg: str): - typer.echo(f"{Colors.RED}❌ {msg}{Colors.RESET}") - - -def print_warning(msg: str): - typer.echo(f"{Colors.YELLOW}⚠️ {msg}{Colors.RESET}") - - -def run_command(cmd, check=True, capture_output=True): - """运行命令""" - print_info(f"Running: {' '.join(cmd) if isinstance(cmd, list) else cmd}") - try: - result = subprocess.run( - cmd, - shell=isinstance(cmd, str), - check=check, - capture_output=capture_output, - text=True, - ) - # 如果不捕获输出但仍想返回结果，创建一个简单的结果对象 - if not capture_output: - - class SimpleResult: - def __init__(self, returncode): - self.returncode = returncode - self.stdout = "" - self.stderr = "" - - result = SimpleResult(result.returncode if hasattr(result, "returncode") else 0) - return result - except subprocess.CalledProcessError as e: - print_error(f"Command failed: {e}") - if capture_output: - if e.stdout: - typer.echo(f"STDOUT: {e.stdout}") - if e.stderr: - typer.echo(f"STDERR: {e.stderr}") - raise - - -def check_build_tools() -> bool: - """检查构建工具""" - print_info("检查构建工具...") - tools_available = True - - # 检查 gcc/g++ - try: - result = run_command(["gcc", "--version"], check=False) - if result.returncode == 0: - print_success("gcc 可用 ✓") - else: - print_warning("gcc 不可用") - tools_available = False - except Exception: - print_warning("gcc 不可用") - tools_available = False - - # 检查 cmake - try: - result = run_command(["cmake", "--version"], check=False) - if result.returncode == 0: - print_success("cmake 可用 ✓") - else: - print_warning("cmake 不可用") - tools_available = False - except Exception: - print_warning("cmake 不可用") - tools_available = False - - return tools_available - - -def find_sage_root() -> Path | None: - """查找SAGE项目根目录""" - current = Path.cwd() - - # 向上查找包含packages目录的SAGE项目根目录 - for parent in [current] + list(current.parents): - packages_dir = parent / "packages" - # 检查是否包含SAGE项目的典型结构 - if packages_dir.exists() and packages_dir.is_dir(): - sage_middleware_dir = packages_dir / "sage-middleware" - sage_common_dir = packages_dir / "sage-common" - if sage_middleware_dir.exists() and sage_common_dir.exists(): - return parent - - # 检查当前Python环境中的sage包位置 - try: - import sage - - sage_path = Path(sage.__file__).parent.parent - # 如果从安装的包中找到，尝试找到项目根目录 - for parent in sage_path.parents: - packages_dir = parent / "packages" - if packages_dir.exists(): - sage_middleware_dir = packages_dir / "sage-middleware" - if sage_middleware_dir.exists(): - return parent - except ImportError: - pass - - return None - - -EXTENSION_PATHS: dict[str, str] = { - "sage_db": "packages/sage-middleware/src/sage/middleware/components/sage_db", - "sage_flow": "packages/sage-middleware/src/sage/middleware/components/sage_flow", - "sage_tsdb": "packages/sage-middleware/src/sage/middleware/components/sage_tsdb/sageTSDB", -} - -EXTENSION_MODULES: dict[str, str] = { - "sage_db": "sage.middleware.components.sage_db.python._sage_db", - "sage_flow": "sage.middleware.components.sage_flow.python._sage_flow", - "sage_tsdb": "sage.middleware.components.sage_tsdb.python._sage_tsdb", -} - - -def _extension_is_available(ext_name: str, timeout: float = 3.0) -> bool: - module_name = EXTENSION_MODULES.get(ext_name) - if not module_name: - return False - - import queue - import threading - - result_queue: queue.Queue[bool] = queue.Queue() - - def _try_import(): - try: - __import__(module_name) - result_queue.put(True) - except Exception: - result_queue.put(False) - - import_thread = threading.Thread(target=_try_import, daemon=True) - import_thread.start() - import_thread.join(timeout=timeout) - - if import_thread.is_alive(): - return False - - try: - return result_queue.get_nowait() - except queue.Empty: - return False - - -def _resolve_extensions_to_install(extension: str | None) -> list[str]: - if extension is None or extension == "all": - return list(EXTENSION_PATHS.keys()) - if extension not in EXTENSION_PATHS: - print_error(f"未知扩展: {extension}") - typer.echo(f"可用扩展: {', '.join(EXTENSION_PATHS.keys())}") - raise typer.Exit(1) - return [extension] - - -def _clean_previous_build(ext_dir: Path) -> None: - build_dir = ext_dir / "build" - if build_dir.exists(): - print_info(f"清理构建目录: {build_dir}") - shutil.rmtree(build_dir) - - -def _run_build_script(ext_dir: Path, ext_name: str, sage_root: Path): - """运行构建脚本并将输出重定向到日志文件""" - import subprocess - import threading - import time - - original_cwd = os.getcwd() - os.chdir(ext_dir) - try: - # 将日志放在.sage目录下 - log_dir = sage_root / ".sage" / "logs" / "extensions" - log_dir.mkdir(parents=True, exist_ok=True) - log_file = log_dir / f"{ext_name}_build.log" - - typer.echo(f"{Colors.DIM} 构建日志: {log_file}{Colors.RESET}") - typer.echo(f"{Colors.DIM} 实时查看: tail -f {log_file}{Colors.RESET}\n") - - # 添加进度指示 - # 进度显示状态 - progress_state = {"running": True, "last_update": time.time()} - - def show_progress(): - """显示构建进度动画""" - spinner_chars = ["⠋", "⠙", "⠹", "⠸", "⠼", "⠴", "⠦", "⠧", "⠇", "⠏"] - idx = 0 - start_time = time.time() - - while progress_state["running"]: - elapsed = int(time.time() - start_time) - minutes = elapsed // 60 - seconds = elapsed % 60 - - # 显示进度动画和时间 - spinner = spinner_chars[idx % len(spinner_chars)] - typer.echo( - f"\r{Colors.BLUE}{spinner}{Colors.RESET} 正在构建 {ext_name}... " - f"[{minutes:02d}:{seconds:02d}] " - f"{Colors.DIM}(构建可能需要几分钟){Colors.RESET}", - nl=False, - ) - - idx += 1 - time.sleep(0.1) - - # 清除进度行 - typer.echo("\r" + " " * 80 + "\r", nl=False) - - # 启动进度显示线程 - progress_thread = threading.Thread(target=show_progress, daemon=True) - progress_thread.start() - - try: - with open(log_file, "w") as f: - result = subprocess.run( - ["bash", "build.sh", "--install-deps"], - stdout=f, - stderr=subprocess.STDOUT, - text=True, - ) - finally: - # 停止进度显示 - progress_state["running"] = False - # 等待线程结束，但不要无限等待 - if progress_thread.is_alive(): - progress_thread.join(timeout=2.0) - typer.echo() # 换行 - - # 确保输出被刷新 - import sys - - sys.stdout.flush() - sys.stderr.flush() - - # 如果构建失败，显示最后几行日志 - if result.returncode != 0: - typer.echo(f"\n{Colors.YELLOW}构建失败，最后50行日志:{Colors.RESET}") - try: - with open(log_file) as f: - lines = f.readlines() - for line in lines[-50:]: - typer.echo(f" {line.rstrip()}") - except Exception: - pass - - return result - finally: - os.chdir(original_cwd) - - -def _artifact_pattern_and_site(ext_name: str) -> tuple[str | None, Path | None]: - if ext_name == "sage_flow": - return "_sage_flow*.so", Path("sage/middleware/components/sage_flow/python") - if ext_name == "sage_db": - return "_sage_db*.so", Path("sage/middleware/components/sage_db/python") - if ext_name == "sage_tsdb": - return "_sage_tsdb*.so", Path("sage/middleware/components/sage_tsdb/python") - return None, None - - -def _copy_python_artifacts(ext_name: str, ext_dir: Path) -> None: - build_dir = ext_dir / "build" - pattern, site_rel = _artifact_pattern_and_site(ext_name) - - if pattern is None: - return - - if not build_dir.exists(): - print_warning(f"未找到构建目录: {build_dir}") - return - - candidates = list(build_dir.rglob(pattern)) - if not candidates: - print_warning(f"未找到 {pattern} 构建产物") - return - - # 始终复制到仓库的 python 目录（这个总是有权限的） - repo_target_dir = ext_dir / "python" - repo_target_dir.mkdir(parents=True, exist_ok=True) - for so_file in candidates: - shutil.copy2(so_file, repo_target_dir / so_file.name) - print_success(f"已安装 Python 扩展模块到: {repo_target_dir}") - - # 尝试复制到 site-packages（可能没有权限，但不是必需的） - try: - # 在CI环境中使用用户site-packages（匹配pip install --user的行为） - if _is_ci_environment(): - platlib = Path(site.USER_SITE) if site.USER_SITE else Path.cwd() / ".local" - else: - platlib = Path(sysconfig.get_paths()["platlib"]) - except Exception as exc: - print_warning(f"无法获取 site-packages 路径: {exc}") - return - - if site_rel is None: - return - - site_target_dir = platlib / site_rel - - # 检查是否有写权限 - try: - site_target_dir.mkdir(parents=True, exist_ok=True) - # 测试写权限 - test_file = site_target_dir / ".write_test" - test_file.touch() - test_file.unlink() - except (PermissionError, OSError) as exc: - print_warning( - f"没有写入 site-packages 的权限，跳过: {site_target_dir}\n" - f" 原因: {exc}\n" - f" 扩展已安装到项目目录: {repo_target_dir}" - ) - return - - try: - for so_file in candidates: - shutil.copy2(so_file, site_target_dir / so_file.name) - - python_source_dir = ext_dir / "python" - if python_source_dir.exists(): - for py_file in python_source_dir.rglob("*.py"): - rel_path = py_file.relative_to(python_source_dir) - target_py_file = site_target_dir / rel_path - target_py_file.parent.mkdir(parents=True, exist_ok=True) - shutil.copy2(py_file, target_py_file) - - micro_service_dir = python_source_dir / "micro_service" - if micro_service_dir.exists(): - target_micro_service = site_target_dir / "micro_service" - if target_micro_service.exists(): - shutil.rmtree(target_micro_service) - shutil.copytree(micro_service_dir, target_micro_service) - print_success( - f"已安装 {ext_name} micro_service 模块到 site-packages: {target_micro_service}" - ) - - print_success(f"已安装 Python 扩展模块到 site-packages: {site_target_dir}") - except (PermissionError, OSError) as exc: - print_warning( - f"复制到 site-packages 时权限不足: {exc}\n 扩展已安装到项目目录: {repo_target_dir}" - ) - - -def _is_ci_environment() -> bool: - return bool(os.getenv("CI") or os.getenv("GITHUB_ACTIONS") or os.getenv("GITLAB_CI")) - - -def _print_ci_failure_report(ext_dir: Path) -> None: - if not _is_ci_environment(): - return - - typer.echo( - f"\n{Colors.RED}==================== CI环境构建失败详细诊断 ===================={Colors.RESET}" - ) - - build_dir = ext_dir / "build" - if build_dir.exists(): - typer.echo(f"{Colors.YELLOW}📁 构建目录内容:{Colors.RESET}") - try: - for item in build_dir.rglob("*"): - if item.is_file() and item.name.endswith((".log", ".txt")): - typer.echo(f" 📄 {item.relative_to(build_dir)}") - except Exception: - pass - - cmake_error_log = build_dir / "CMakeFiles" / "CMakeError.log" - if cmake_error_log.exists(): - typer.echo(f"\n{Colors.YELLOW}📋 CMake错误日志 (最后20行):{Colors.RESET}") - try: - lines = cmake_error_log.read_text(encoding="utf-8").splitlines() - for line in lines[-20:]: - typer.echo(f" {line}") - except Exception as exc: - typer.echo(f" 无法读取CMake错误日志: {exc}") - - cmake_output_log = build_dir / "CMakeFiles" / "CMakeOutput.log" - if cmake_output_log.exists(): - typer.echo(f"\n{Colors.YELLOW}📋 CMake输出日志 (最后10行):{Colors.RESET}") - try: - lines = cmake_output_log.read_text(encoding="utf-8").splitlines() - for line in lines[-10:]: - typer.echo(f" {line}") - except Exception as exc: - typer.echo(f" 无法读取CMake输出日志: {exc}") - - make_output = build_dir / "make_output.log" - if make_output.exists(): - typer.echo(f"\n{Colors.YELLOW}🔨 Make输出日志 (最后30行):{Colors.RESET}") - try: - lines = make_output.read_text(encoding="utf-8").splitlines() - for line in lines[-30:]: - typer.echo(f" {line}") - except Exception as exc: - typer.echo(f" 无法读取Make输出日志: {exc}") - - typer.echo( - f"{Colors.RED}================================================================{Colors.RESET}" - ) - - -def _print_manual_diagnostics(ext_dir: Path) -> None: - print_warning("🔍 构建诊断信息:") - - build_dir = ext_dir / "build" - if build_dir.exists(): - cmake_cache = build_dir / "CMakeCache.txt" - if cmake_cache.exists(): - typer.echo(f"📋 CMake 缓存文件存在: {cmake_cache}") - try: - content = cmake_cache.read_text(encoding="utf-8") - for key in ["BLAS_FOUND", "LAPACK_FOUND", "FAISS_FOUND"]: - for line in content.splitlines(): - if key in line and not line.startswith("//"): - value = line.split("=")[-1] if "=" in line else "unknown" - typer.echo(f" {key}: {value}") - break - except Exception: - pass - - typer.echo("\n💡 故障排除建议:") - typer.echo(" 1. 检查系统依赖: ./tools/install/core/install_system_deps.sh --verify-only") - typer.echo(f" 2. 手动构建: cd {ext_dir} && bash build.sh --clean --install-deps") - typer.echo(f" 3. 查看构建日志: {(ext_dir / 'build' / 'CMakeFiles' / 'CMakeError.log')}") - - -def _diagnose_build_failure(ext_name: str, ext_dir: Path, result) -> None: - print_error(f"{ext_name} 构建失败") - stderr = getattr(result, "stderr", None) - if stderr: - typer.echo(f"错误信息: {stderr}") - - _print_ci_failure_report(ext_dir) - _print_manual_diagnostics(ext_dir) - - -def _install_extension(ext_name: str, ext_dir: Path, sage_root: Path, force: bool) -> bool: - typer.echo(f"\n{Colors.YELLOW}━━━ 安装 {ext_name} ━━━{Colors.RESET}") - - if not ext_dir.exists(): - print_warning(f"扩展目录不存在: {ext_dir}") - return False - - build_script = ext_dir / "build.sh" - if not build_script.exists(): - print_warning(f"未找到构建脚本: {build_script}") - return False - - try: - print_info(f"构建 {ext_name}...") - if force: - _clean_previous_build(ext_dir) - result = _run_build_script(ext_dir, ext_name, sage_root) - except Exception as exc: - print_error(f"{ext_name} 构建失败: {exc}") - typer.echo(f"异常详情: {type(exc).__name__}: {exc}") - return False - - if result.returncode != 0: - _diagnose_build_failure(ext_name, ext_dir, result) - return False - - print_success(f"{ext_name} 构建成功 ✓") - - # 复制产物（权限错误不应导致失败，因为已经复制到项目目录） - try: - _copy_python_artifacts(ext_name, ext_dir) - except Exception as exc: - # 如果是权限错误，只是警告，不视为失败 - if isinstance(exc, (PermissionError, OSError)): - print_warning(f"复制扩展产物到 site-packages 时权限不足（已安装到项目目录）: {exc}") - else: - print_warning(f"复制扩展产物时发生问题: {exc}") - # 对于其他错误，仍然视为失败 - if not _is_ci_environment(): - return False - - return True - - -def _print_install_summary(success_count: int, total_count: int) -> None: - import sys - - typer.echo(f"\n{Colors.BOLD}安装完成{Colors.RESET}") - typer.echo(f"成功: {success_count}/{total_count}") - - if success_count == total_count: - print_success("🎉 所有扩展安装成功！") - typer.echo("\n运行 'sage extensions status' 验证安装") - else: - failures = total_count - success_count - print_warning(f"⚠️ 部分扩展安装失败 ({failures}个)") - - # 确保所有输出都被刷新 - sys.stdout.flush() - sys.stderr.flush() - - -def _print_install_banner() -> None: - """ - Print a banner for the SAGE C++ extension installer to the terminal. - """ - typer.echo(f"{Colors.BOLD}{Colors.BLUE}🧩 SAGE C++ 扩展安装器{Colors.RESET}") - typer.echo("=" * 50) - - -def _missing_build_tools_instructions() -> None: - print_error("缺少必要的构建工具，无法安装C++扩展") - typer.echo("\n请安装以下工具:") - typer.echo(" • gcc/g++ (C++ 编译器)") - typer.echo(" • cmake (构建系统)") - typer.echo(" • make (构建工具)") - typer.echo("\nUbuntu/Debian: sudo apt install build-essential cmake") - typer.echo("CentOS/RHEL: sudo yum groupinstall 'Development Tools' && sudo yum install cmake") - typer.echo("macOS: xcode-select --install && brew install cmake") - - -def _ensure_build_environment() -> None: - """ - Ensure that the required build tools for C++ extension installation are available. - If any required tools are missing, print instructions for installing them and exit the program. - """ - if check_build_tools(): - return - _missing_build_tools_instructions() - raise typer.Exit(1) - - -def _check_and_fix_libstdcxx() -> None: - """ - Check if conda environment has compatible libstdc++ for C++20 compilation. - If not, attempt to upgrade it or warn the user. - """ - # Only relevant for conda environments - conda_prefix = os.getenv("CONDA_PREFIX") - if not conda_prefix: - return - - # Check GCC version - try: - result = subprocess.run(["gcc", "-dumpversion"], capture_output=True, text=True, check=True) - gcc_major_version = int(result.stdout.strip().split(".")[0]) - except Exception: - # Can't determine GCC version, skip check - return - - # Only check if GCC >= 11 (which uses newer GLIBCXX) - if gcc_major_version < 11: - return - - # Check conda libstdc++ version - conda_libstdcxx = Path(conda_prefix) / "lib" / "libstdc++.so.6" - if not conda_libstdcxx.exists(): - return - - try: - result = subprocess.run( - ["strings", str(conda_libstdcxx)], - capture_output=True, - text=True, - check=True, - ) - glibcxx_versions = [ - line for line in result.stdout.splitlines() if line.startswith("GLIBCXX_") - ] - - # Check if we have at least GLIBCXX_3.4.30 (needed for C++20/GCC 11+) - has_modern_glibcxx = any("GLIBCXX_3.4.3" in v for v in glibcxx_versions) - - if not has_modern_glibcxx: - print_warning("检测到conda环境的libstdc++版本过低 (需要 GLIBCXX_3.4.30+)") - print_info("正在尝试更新libstdc++...") - - # Try to update using conda - try: - result = subprocess.run( - ["conda", "install", "-c", "conda-forge", "libstdcxx-ng", "-y"], - capture_output=True, - text=True, - timeout=120, - ) - if result.returncode == 0: - print_success("libstdc++已更新 ✓") - else: - print_warning("无法自动更新libstdc++") - typer.echo("\n💡 请手动运行:") - typer.echo(" conda install -c conda-forge libstdcxx-ng") - except subprocess.TimeoutExpired: - print_warning("更新超时") - except Exception as e: - print_warning(f"更新失败: {e}") - typer.echo("\n💡 请手动运行:") - typer.echo(" conda install -c conda-forge libstdcxx-ng") - except Exception: - # If we can't check, just continue - pass - - -def _resolve_project_root() -> Path: - """ - Locate and return the root directory of the SAGE project. - - Returns: - Path: The path to the SAGE project root directory. - - Raises: - typer.Exit: If the SAGE project root cannot be found, prints an error message and exits. - """ - sage_root = find_sage_root() - if sage_root: - return sage_root - print_error("未找到SAGE项目根目录") - typer.echo("请在SAGE项目目录中运行此命令") - raise typer.Exit(1) - - -def _install_selected_extensions( - extensions_to_install: list[str], sage_root: Path, force: bool -) -> tuple[int, int]: - success_count = 0 - total_count = len(extensions_to_install) - - for ext_name in extensions_to_install: - if not force and _extension_is_available(ext_name): - print_success(f"{ext_name} 已安装且可用，跳过重新构建（使用 --force 重新安装）") - success_count += 1 - continue - - rel_path = EXTENSION_PATHS[ext_name] - ext_dir = sage_root / rel_path - if _install_extension(ext_name, ext_dir, sage_root, force): - success_count += 1 - - return success_count, total_count - - -@app.command() -def install( - extension: str | None = typer.Argument( - None, help="要安装的扩展名 (sage_db, sage_flow, 或 all)" - ), - force: bool = typer.Option(False, "--force", "-f", help="强制重新构建"), -): - """ - 安装C++扩展 - - Examples: - sage extensions install # 安装所有扩展 - sage extensions install sage_db # 只安装数据库扩展 - sage extensions install all --force # 强制重新安装所有扩展 - """ - _print_install_banner() - - _ensure_build_environment() - - # Check and fix libstdc++ compatibility issues - _check_and_fix_libstdcxx() - - sage_root = _resolve_project_root() - - print_info(f"SAGE项目根目录: {sage_root}") - - # 显示日志文件位置（放在.sage目录下） - sage_logs_dir = sage_root / ".sage" / "logs" / "extensions" - sage_logs_dir.mkdir(parents=True, exist_ok=True) - - extensions_to_install = _resolve_extensions_to_install(extension) - for ext_name in extensions_to_install: - build_log = sage_logs_dir / f"{ext_name}_build.log" - typer.echo(f"{Colors.DIM}📝 {ext_name} 构建日志: {build_log}{Colors.RESET}") - typer.echo("") - - success_count, total_count = _install_selected_extensions( - extensions_to_install, sage_root, force - ) - - _print_install_summary(success_count, total_count) - - -@app.command() -def status(): - """检查扩展安装状态""" - typer.echo(f"{Colors.BOLD}{Colors.BLUE}🔍 SAGE 扩展状态检查{Colors.RESET}") - typer.echo("=" * 40) - - extensions = { - "sage.middleware.components.sage_db.python._sage_db": "数据库扩展 (C++)", - "sage.middleware.components.sage_flow.python._sage_flow": "流处理引擎扩展 (C++)", - "sage.middleware.components.sage_tsdb.python._sage_tsdb": "时序数据库扩展 (C++)", - } - - available_count = 0 - - for module_name, description in extensions.items(): - try: - # 使用线程和超时机制避免卡死（更可靠的跨平台方案） - import queue - import threading - - result_queue = queue.Queue() - - def try_import(): - try: - __import__(module_name) - result_queue.put(("success", None)) - except Exception as e: - result_queue.put(("error", e)) - - import_thread = threading.Thread(target=try_import, daemon=True) - import_thread.start() - - # 等待5秒超时 - import_thread.join(timeout=5.0) - - if import_thread.is_alive(): - # 线程仍在运行，说明超时了 - print_warning(f"{description} ✗") - typer.echo(" 原因: 导入超时（可能存在初始化问题）") - else: - # 检查结果 - try: - status, error = result_queue.get_nowait() - if status == "success": - print_success(f"{description} ✓") - available_count += 1 - else: - print_warning(f"{description} ✗") - if isinstance(error, ImportError): - typer.echo(f" 原因: {error}") - else: - typer.echo(f" 原因: {error}") - except queue.Empty: - print_warning(f"{description} ✗") - typer.echo(" 原因: 无法获取导入结果") - except Exception as e: - print_warning(f"{description} ✗") - typer.echo(f" 原因: {e}") - - typer.echo(f"\n总计: {available_count}/{len(extensions)} 扩展可用") - - if available_count < len(extensions): - typer.echo(f"\n{Colors.YELLOW}💡 提示:{Colors.RESET}") - typer.echo("运行 'sage extensions install' 安装缺失的扩展") - - # 确保输出被刷新 - import sys - - sys.stdout.flush() - sys.stderr.flush() - - -@app.command() -def clean(): - """清理扩展构建文件""" - typer.echo(f"{Colors.BOLD}{Colors.BLUE}🧹 清理扩展构建文件{Colors.RESET}") - - sage_root = find_sage_root() - if not sage_root: - print_error("未找到SAGE项目根目录") - raise typer.Exit(1) - - import shutil - - cleaned_count = 0 - - # 按真实扩展源码位置进行清理 - mapping = { - "sage_db": "packages/sage-middleware/src/sage/middleware/components/sage_db", - "sage_flow": "packages/sage-middleware/src/sage/middleware/components/sage_flow", - "sage_tsdb": "packages/sage-middleware/src/sage/middleware/components/sage_tsdb/sageTSDB", - } - - for ext_name, rel_path in mapping.items(): - ext_dir = sage_root / rel_path - if not ext_dir.exists(): - continue - - # 清理build目录 - build_dir = ext_dir / "build" - if build_dir.exists(): - print_info(f"清理 {ext_name}/build") - shutil.rmtree(build_dir) - cleaned_count += 1 - - # 清理编译产物 - for pattern in ["*.so", "*.o", "*.a"]: - for file in ext_dir.rglob(pattern): - if file.is_file(): - print_info(f"删除 {file.relative_to(sage_root)}") - file.unlink() - - if cleaned_count > 0: - print_success(f"清理完成，共处理 {cleaned_count} 个目录") - else: - typer.echo("没有需要清理的文件") - - -@app.command() -def info(): - """显示扩展信息""" - typer.echo(f"{Colors.BOLD}{Colors.BLUE}📋 SAGE C++ 扩展信息{Colors.RESET}") - typer.echo("=" * 50) - - extensions_info = { - "sage_db": { - "description": "数据库接口扩展", - "features": ["原生C++接口", "高性能查询", "内存优化"], - "status": "experimental", - }, - "sage_flow": { - "description": "流处理引擎 Python 绑定", - "features": ["pybind11 模块", "向量流", "回调 sink"], - "status": "experimental", - }, - "sage_tsdb": { - "description": "时序数据库 Python 绑定", - "features": ["C++17 核心", "流式 Join", "窗口聚合", "高效索引"], - "status": "experimental", - }, - } - - for ext_name, info in extensions_info.items(): - typer.echo(f"\n{Colors.YELLOW}{ext_name}{Colors.RESET}") - typer.echo(f" 描述: {info['description']}") - typer.echo(f" 特性: {', '.join(info['features'])}") - typer.echo(f" 状态: {info['status']}") - - # 检查是否已安装 - try: - if ext_name == "sage_db": - __import__("sage.middleware.components.sage_db.python._sage_db") - elif ext_name == "sage_flow": - __import__("sage.middleware.components.sage_flow.python._sage_flow") - elif ext_name == "sage_tsdb": - __import__("sage.middleware.components.sage_tsdb.python._sage_tsdb") - else: - __import__(f"sage_ext.{ext_name}") - typer.echo(f" 安装: {Colors.GREEN}✓ 已安装{Colors.RESET}") - except ImportError: - typer.echo(f" 安装: {Colors.RED}✗ 未安装{Colors.RESET}") - - -if __name__ == "__main__": - app() diff --git a/packages/sage-cli/src/sage/cli/commands/platform/head.py b/packages/sage-cli/src/sage/cli/commands/platform/head.py deleted file mode 100644 index 268ccacbad..0000000000 --- a/packages/sage-cli/src/sage/cli/commands/platform/head.py +++ /dev/null @@ -1,586 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE Head Manager CLI -Ray Head节点管理相关命令 -""" - -import os -import subprocess -import sys -import time -from pathlib import Path - -import typer - -from ...management.config_manager import get_config_manager - -app = typer.Typer(name="head", help="Ray Head节点管理") - - -def get_conda_init_code(conda_env: str = "sage") -> str: - """获取Conda环境初始化代码""" - return f""" -# 检查是否已经在目标环境中 -if [[ "$CONDA_DEFAULT_ENV" == "{conda_env}" ]]; then - echo "[INFO] 已在conda环境: {conda_env}" -else - # 多种conda安装路径尝试 - CONDA_FOUND=false - for conda_path in \\ - "$HOME/miniconda3/etc/profile.d/conda.sh" \\ - "$HOME/anaconda3/etc/profile.d/conda.sh" \\ - "/opt/conda/etc/profile.d/conda.sh" \\ - "/usr/local/miniconda3/etc/profile.d/conda.sh" \\ - "/usr/local/anaconda3/etc/profile.d/conda.sh"; do - if [ -f "$conda_path" ]; then - source "$conda_path" - echo "[INFO] 找到conda: $conda_path" - CONDA_FOUND=true - break - fi - done - - if [ "$CONDA_FOUND" = "false" ]; then - echo "[WARNING] 未找到conda安装，跳过conda环境激活" - else - # 激活sage环境 - if conda activate {conda_env} 2>/dev/null; then - echo "[SUCCESS] 已激活conda环境: {conda_env}" - else - echo "[WARNING] 无法激活conda环境: {conda_env}，继续使用当前环境" - fi - fi -fi -""" - - -def check_ray_running(head_port: int) -> tuple[bool, list[int]]: - """检查Ray Head是否已经在运行 - - 返回: (是否运行, 进程ID列表) - - 使用 ps + grep 检查进程，避免匹配到自身 - """ - pids = [] - - # 使用 grep 技巧避免匹配自身: [g]cs_server 不会匹配包含 "gcs_server" 字符串的 grep 命令 - try: - result = subprocess.run( - [ - "bash", - "-c", - """ -ps -u $(whoami) -o pid,cmd --no-headers 2>/dev/null | grep -E '[g]cs_server.*--gcs_server_port|[r]aylet.*--raylet_socket_name' | awk '{print $1}' -""", - ], - capture_output=True, - text=True, - timeout=5, - ) - if result.returncode == 0 and result.stdout.strip(): - for line in result.stdout.strip().split("\n"): - pid_str = line.strip() - if pid_str.isdigit(): - pids.append(int(pid_str)) - except Exception: - pass - - # 备选：检查端口是否被占用 - if not pids: - try: - result = subprocess.run( - [ - "bash", - "-c", - f"ss -tlnp 2>/dev/null | grep ':{head_port}' | grep -oP 'pid=\\K[0-9]+'", - ], - capture_output=True, - text=True, - timeout=5, - ) - if result.returncode == 0 and result.stdout.strip(): - for pid_str in result.stdout.strip().split("\n"): - if pid_str.strip().isdigit(): - pids.append(int(pid_str.strip())) - except Exception: - pass - - return len(pids) > 0, pids - - -def force_cleanup_ray_processes(head_log_dir: str, ray_command: str, verbose: bool = True) -> bool: - """强制清理所有Ray相关进程 - - 返回: 是否成功清理 - """ - # 首先使用 ray stop 命令 - try: - result = subprocess.run( - ["bash", "-c", f"{ray_command} stop 2>&1"], capture_output=True, text=True, timeout=30 - ) - if verbose and result.stdout: - typer.echo(result.stdout.strip()) - except Exception: - pass - - time.sleep(2) - - # 然后使用 ps + grep 找到并清理残留进程 - cleanup_command = f""" -set +e -LOG_DIR='{head_log_dir}' -mkdir -p "$LOG_DIR" - -echo "[INFO] 清理Ray残留进程..." | tee -a "$LOG_DIR/head.log" - -# 使用 grep 技巧避免匹配自身: [g]cs_server 不会匹配 grep 命令本身 -GCS_PIDS=$(ps -u $(whoami) -o pid,cmd --no-headers 2>/dev/null | grep '[g]cs_server.*--gcs_server_port' | awk '{{print $1}}') -RAYLET_PIDS=$(ps -u $(whoami) -o pid,cmd --no-headers 2>/dev/null | grep '[r]aylet.*--raylet_socket_name' | awk '{{print $1}}') - -if [[ -n "$GCS_PIDS" ]]; then - echo "[INFO] 终止 gcs_server 进程: $GCS_PIDS" | tee -a "$LOG_DIR/head.log" - echo "$GCS_PIDS" | xargs -r kill -TERM 2>/dev/null || true -fi - -if [[ -n "$RAYLET_PIDS" ]]; then - echo "[INFO] 终止 raylet 进程: $RAYLET_PIDS" | tee -a "$LOG_DIR/head.log" - echo "$RAYLET_PIDS" | xargs -r kill -TERM 2>/dev/null || true -fi - -sleep 2 - -# 强制终止 -GCS_PIDS=$(ps -u $(whoami) -o pid,cmd --no-headers 2>/dev/null | grep '[g]cs_server.*--gcs_server_port' | awk '{{print $1}}') -RAYLET_PIDS=$(ps -u $(whoami) -o pid,cmd --no-headers 2>/dev/null | grep '[r]aylet.*--raylet_socket_name' | awk '{{print $1}}') - -if [[ -n "$GCS_PIDS" ]] || [[ -n "$RAYLET_PIDS" ]]; then - echo "[WARNING] 强制终止残留进程..." | tee -a "$LOG_DIR/head.log" - echo "$GCS_PIDS $RAYLET_PIDS" | xargs -r kill -9 2>/dev/null || true - sleep 1 -fi - -# 验证 -REMAINING=$(ps -u $(whoami) -o pid,cmd --no-headers 2>/dev/null | grep -E '[g]cs_server.*--gcs_server_port|[r]aylet.*--raylet_socket_name' | awk '{{print $1}}') -if [[ -z "$REMAINING" ]]; then - echo "[SUCCESS] Ray进程清理完成" | tee -a "$LOG_DIR/head.log" - exit 0 -else - echo "[WARNING] 仍有残留进程: $REMAINING" | tee -a "$LOG_DIR/head.log" - exit 1 -fi -""" - - try: - result = subprocess.run( - ["bash", "-c", cleanup_command], capture_output=True, text=True, timeout=30 - ) - if verbose and result.stdout: - typer.echo(result.stdout) - return result.returncode == 0 - except Exception as e: - if verbose: - typer.echo(f"[WARNING] 清理过程出错: {e}") - return False - - -@app.command("start") -def start_head( - force: bool = typer.Option( - False, "--force", "-f", help="强制重启：如果Ray已运行，先停止再启动" - ), -): - """启动Ray Head节点""" - typer.echo("🚀 启动Ray Head节点...") - - config_manager = get_config_manager() - head_config = config_manager.get_head_config() - config_manager.get_remote_config() - - head_host = head_config.get("host", "localhost") - head_port = head_config.get("head_port", 6379) - dashboard_port = head_config.get("dashboard_port", 8265) - ray_client_server_port = head_config.get("ray_client_server_port", 10001) - dashboard_host = head_config.get("dashboard_host", "0.0.0.0") - head_temp_dir = head_config.get("temp_dir", "/tmp/ray_head") - head_log_dir = head_config.get("log_dir", "/tmp/sage_head_logs") - - # 容器资源配置 (覆盖自动检测) - num_cpus = head_config.get("num_cpus") # None 表示自动检测 - num_gpus = head_config.get("num_gpus") # None 表示自动检测 - - # 优先使用配置中的ray命令，否则尝试使用当前环境的ray - ray_command = head_config.get("ray_command") - if not ray_command: - ray_command = os.path.join(os.path.dirname(sys.executable), "ray") - if not os.path.exists(ray_command): - ray_command = "ray" # Fallback to PATH - - conda_env = head_config.get("conda_env", "sage") - - typer.echo("📋 配置信息:") - typer.echo(f" Head主机: {head_host}") - typer.echo(f" Head端口: {head_port}") - typer.echo(f" Dashboard: {dashboard_host}:{dashboard_port}") - typer.echo(f" 临时目录: {head_temp_dir}") - typer.echo(f" 日志目录: {head_log_dir}") - if num_cpus is not None: - typer.echo(f" CPU核心数: {num_cpus} (显式配置)") - if num_gpus is not None: - typer.echo(f" GPU数量: {num_gpus} (显式配置)") - - # 检查是否已有Ray实例在运行 - is_running, pids = check_ray_running(head_port) - if is_running: - if force: - typer.echo(f"⚠️ 检测到Ray已在运行 (PIDs: {pids})，正在强制停止...") - if not force_cleanup_ray_processes(head_log_dir, ray_command): - typer.echo("❌ 无法清理现有Ray进程，请手动执行: sage cluster head stop") - raise typer.Exit(1) - typer.echo("✅ 现有Ray进程已清理") - time.sleep(2) - else: - typer.echo(f"⚠️ Ray Head已在运行 (PIDs: {pids})") - typer.echo("💡 如需重启，请使用: sage cluster head start --force") - typer.echo(" 或先停止: sage cluster head stop") - typer.echo(f"🌐 Dashboard可能已可访问: http://{dashboard_host}:{dashboard_port}") - raise typer.Exit(0) - - # 使用 ray stop 先清理，再启动 - start_command = f""" -export PYTHONUNBUFFERED=1 - -# 确保不因命令失败而退出 -set +e - -# 创建必要目录 -LOG_DIR='{head_log_dir}' -HEAD_TEMP_DIR='{head_temp_dir}' -mkdir -p "$LOG_DIR" "$HEAD_TEMP_DIR" - -# 记录启动时间 -echo "===============================================" | tee -a "$LOG_DIR/head.log" -echo "Ray Head启动 ($(date '+%Y-%m-%d %H:%M:%S'))" | tee -a "$LOG_DIR/head.log" -echo "Head节点: $(hostname)" | tee -a "$LOG_DIR/head.log" -echo "监听地址: {head_host}:{head_port}" | tee -a "$LOG_DIR/head.log" -echo "Dashboard: {dashboard_host}:{dashboard_port}" | tee -a "$LOG_DIR/head.log" -echo "===============================================" | tee -a "$LOG_DIR/head.log" - -# 初始化conda环境 -{get_conda_init_code(conda_env)} - -# 使用 ray stop 清理（最安全的方式） -echo "[INFO] 使用 ray stop 清理现有进程..." | tee -a "$LOG_DIR/head.log" -{ray_command} stop >> "$LOG_DIR/head.log" 2>&1 || true -sleep 2 - -# 清理临时目录 -rm -rf "$HEAD_TEMP_DIR"/* 2>/dev/null || true -rm -f dump.rdb 2>/dev/null || true - -# 设置环境变量 -export RAY_TMPDIR="$HEAD_TEMP_DIR" -export RAY_DISABLE_IMPORT_WARNING=1 - -# 构建 Ray 启动命令 -# 基础命令 -RAY_START_CMD="{ray_command} start --head --port={head_port} --ray-client-server-port={ray_client_server_port} --node-ip-address={head_host} --dashboard-host={dashboard_host} --dashboard-port={dashboard_port} --temp-dir=$HEAD_TEMP_DIR --disable-usage-stats" - -# 添加 CPU/GPU 资源限制 (用于容器环境) -{f'RAY_START_CMD="$RAY_START_CMD --num-cpus={num_cpus}"' if num_cpus is not None else "# num_cpus: 自动检测"} -{f'RAY_START_CMD="$RAY_START_CMD --num-gpus={num_gpus}"' if num_gpus is not None else "# num_gpus: 自动检测"} - -# 启动ray head -echo "[INFO] 启动Ray Head进程..." | tee -a "$LOG_DIR/head.log" -echo "[INFO] 执行命令: $RAY_START_CMD" | tee -a "$LOG_DIR/head.log" - -# 执行启动命令并捕获所有输出 -$RAY_START_CMD 2>&1 | tee -a "$LOG_DIR/head.log" -RAY_EXIT_CODE=${{PIPESTATUS[0]}} - -echo "[INFO] Ray启动命令退出码: $RAY_EXIT_CODE" | tee -a "$LOG_DIR/head.log" - -if [ $RAY_EXIT_CODE -eq 0 ]; then - echo "[SUCCESS] Ray Head启动成功" | tee -a "$LOG_DIR/head.log" - sleep 3 - - # 使用 grep 技巧避免匹配自身 - RAY_PIDS=$(ps -u $(whoami) -o pid,cmd --no-headers 2>/dev/null | grep -E '[g]cs_server.*--gcs_server_port|[r]aylet.*--raylet_socket_name' | awk '{{print $1}}' | tr '\\n' ' ') - if [[ -n "$RAY_PIDS" ]]; then - echo "[SUCCESS] Ray Head进程正在运行，PIDs: $RAY_PIDS" | tee -a "$LOG_DIR/head.log" - echo "[INFO] Ray集群已启动，监听端口: {head_port}" | tee -a "$LOG_DIR/head.log" - echo "[INFO] Dashboard可访问: http://{head_host}:{dashboard_port}" | tee -a "$LOG_DIR/head.log" - else - echo "[WARNING] Ray启动命令成功但未发现运行中的进程" | tee -a "$LOG_DIR/head.log" - fi -else - echo "[ERROR] Ray Head启动失败，退出码: $RAY_EXIT_CODE" | tee -a "$LOG_DIR/head.log" - exit 1 -fi""" - - try: - result = subprocess.run( - ["bash", "-c", start_command], capture_output=True, text=True, timeout=120 - ) - - if result.stdout: - typer.echo(result.stdout) - if result.stderr: - typer.echo(result.stderr, err=True) - - if result.returncode == 0: - typer.echo("✅ Ray Head节点启动成功") - typer.echo(f"🌐 Dashboard访问地址: http://{dashboard_host}:{dashboard_port}") - else: - typer.echo("❌ Ray Head节点启动失败") - raise typer.Exit(1) - - except subprocess.TimeoutExpired: - typer.echo("❌ Ray Head启动超时") - typer.echo("💡 可能的原因：") - typer.echo(f" 1. 端口被占用 - 检查: ss -tlnp | grep {head_port}") - typer.echo(" 2. 残留进程 - 尝试: sage cluster head stop") - typer.echo(" 3. 资源不足 - 检查系统资源") - raise typer.Exit(1) - except Exception as e: - typer.echo(f"❌ Ray Head启动失败: {e}") - raise typer.Exit(1) - - -@app.command("stop") -def stop_head(): - """停止Ray Head节点""" - typer.echo("🛑 停止Ray Head节点...") - - config_manager = get_config_manager() - head_config = config_manager.get_head_config() - config_manager.get_remote_config() - - head_temp_dir = head_config.get("temp_dir", "/tmp/ray_head") - head_log_dir = head_config.get("log_dir", "/tmp/sage_head_logs") - conda_env = head_config.get("conda_env", "sage") - - # 优先使用配置中的ray命令，否则尝试使用当前环境的ray - ray_command = head_config.get("ray_command") - if not ray_command: - ray_command = os.path.join(os.path.dirname(sys.executable), "ray") - if not os.path.exists(ray_command): - ray_command = "ray" # Fallback to PATH - - stop_command = f'''set +e -export PYTHONUNBUFFERED=1 - -LOG_DIR='{head_log_dir}' -mkdir -p "$LOG_DIR" - -echo "===============================================" | tee -a "$LOG_DIR/head.log" -echo "Ray Head停止 ($(date '+%Y-%m-%d %H:%M:%S'))" | tee -a "$LOG_DIR/head.log" -echo "Head节点: $(hostname)" | tee -a "$LOG_DIR/head.log" -echo "===============================================" | tee -a "$LOG_DIR/head.log" - -# 初始化conda环境 -{get_conda_init_code(conda_env)} - -# 优雅停止 -echo "[INFO] 正在优雅停止Ray进程..." | tee -a "$LOG_DIR/head.log" -{ray_command} stop 2>&1 | tee -a "$LOG_DIR/head.log" || true -sleep 2 - -# 使用 grep 技巧清理残留进程 -echo "[INFO] 清理残留的Ray进程..." | tee -a "$LOG_DIR/head.log" -GCS_PIDS=$(ps -u $(whoami) -o pid,cmd --no-headers 2>/dev/null | grep '[g]cs_server.*--gcs_server_port' | awk '{{print $1}}') -RAYLET_PIDS=$(ps -u $(whoami) -o pid,cmd --no-headers 2>/dev/null | grep '[r]aylet.*--raylet_socket_name' | awk '{{print $1}}') - -if [[ -n "$GCS_PIDS" ]]; then - echo "[INFO] 终止 gcs_server: $GCS_PIDS" | tee -a "$LOG_DIR/head.log" - echo "$GCS_PIDS" | xargs -r kill -TERM 2>/dev/null || true - sleep 1 - echo "$GCS_PIDS" | xargs -r kill -9 2>/dev/null || true -fi - -if [[ -n "$RAYLET_PIDS" ]]; then - echo "[INFO] 终止 raylet: $RAYLET_PIDS" | tee -a "$LOG_DIR/head.log" - echo "$RAYLET_PIDS" | xargs -r kill -TERM 2>/dev/null || true - sleep 1 - echo "$RAYLET_PIDS" | xargs -r kill -9 2>/dev/null || true -fi - -# 清理临时文件 -HEAD_TEMP_DIR='{head_temp_dir}' -if [[ -d "$HEAD_TEMP_DIR" ]]; then - echo "[INFO] 清理临时目录: $HEAD_TEMP_DIR" | tee -a "$LOG_DIR/head.log" - rm -rf "$HEAD_TEMP_DIR"/* 2>/dev/null || true -fi - -echo "[SUCCESS] Ray Head已停止 ($(date '+%Y-%m-%d %H:%M:%S'))" | tee -a "$LOG_DIR/head.log"''' - - try: - result = subprocess.run( - ["bash", "-c", stop_command], capture_output=True, text=True, timeout=60 - ) - - if result.stdout: - typer.echo(result.stdout) - if result.stderr: - typer.echo(result.stderr, err=True) - - typer.echo("✅ Ray Head节点停止完成") - - except subprocess.TimeoutExpired: - typer.echo("❌ Ray Head停止超时") - raise typer.Exit(1) - except Exception as e: - typer.echo(f"❌ Ray Head停止失败: {e}") - raise typer.Exit(1) - - -@app.command("status") -def status_head(): - """检查Ray Head节点状态""" - typer.echo("�� 检查Ray Head节点状态...") - - config_manager = get_config_manager() - head_config = config_manager.get_head_config() - config_manager.get_remote_config() - - head_host = head_config.get("host", "localhost") - head_port = head_config.get("head_port", 6379) - dashboard_port = head_config.get("dashboard_port", 8265) - head_log_dir = head_config.get("log_dir", "/tmp/sage_head_logs") - conda_env = head_config.get("conda_env", "sage") - - # 优先使用配置中的ray命令，否则尝试使用当前环境的ray - ray_command = head_config.get("ray_command") - if not ray_command: - ray_command = os.path.join(os.path.dirname(sys.executable), "ray") - if not os.path.exists(ray_command): - ray_command = "ray" # Fallback to PATH - - status_command = f'''set +e -export PYTHONUNBUFFERED=1 - -echo "===============================================" -echo "Ray Head状态检查: $(hostname) ($(date '+%Y-%m-%d %H:%M:%S'))" -echo "===============================================" - -# 初始化conda环境 -{get_conda_init_code(conda_env)} - -# 使用 grep 技巧检查Ray进程 -echo "--- Ray Head进程状态 ---" -RAY_PIDS=$(ps -u $(whoami) -o pid,cmd --no-headers 2>/dev/null | grep -E '[g]cs_server.*--gcs_server_port|[r]aylet.*--raylet_socket_name' | awk '{{print $1}}') -if [[ -n "$RAY_PIDS" ]]; then - echo "[运行中] 发现Ray Head进程:" - echo "$RAY_PIDS" | while read pid; do - if [[ -n "$pid" ]]; then - ps -p "$pid" -o pid,ppid,pcpu,pmem,etime,cmd --no-headers 2>/dev/null || true - fi - done - - echo "" - echo "--- Ray集群状态 ---" - timeout 10 {ray_command} status 2>/dev/null || echo "[警告] 无法获取Ray集群状态" - - echo "" - echo "--- 端口监听状态 ---" - echo "Head端口 {head_port}:" - ss -tlnp 2>/dev/null | grep ":{head_port}" || netstat -tlnp 2>/dev/null | grep ":{head_port}" || echo " 未监听" - echo "Dashboard端口 {dashboard_port}:" - ss -tlnp 2>/dev/null | grep ":{dashboard_port}" || netstat -tlnp 2>/dev/null | grep ":{dashboard_port}" || echo " 未监听" - - exit 0 -else - echo "[已停止] 未发现Ray Head进程" - exit 1 -fi - -# 显示最近的日志 -LOG_DIR='{head_log_dir}' -if [[ -f "$LOG_DIR/head.log" ]]; then - echo "" - echo "--- 最近的日志 (最后5行) ---" - tail -5 "$LOG_DIR/head.log" 2>/dev/null || echo "无法读取日志文件" -fi - -echo "==============================================="''' - - try: - result = subprocess.run( - ["bash", "-c", status_command], capture_output=True, text=True, timeout=30 - ) - - if result.stdout: - typer.echo(result.stdout) - if result.stderr: - typer.echo(result.stderr, err=True) - - if result.returncode == 0: - typer.echo("✅ Ray Head节点正在运行") - typer.echo(f"🌐 Dashboard访问地址: http://{head_host}:{dashboard_port}") - else: - typer.echo("❌ Ray Head节点未运行") - - except subprocess.TimeoutExpired: - typer.echo("❌ Ray Head状态检查超时") - except Exception as e: - typer.echo(f"❌ Ray Head状态检查失败: {e}") - - -@app.command("restart") -def restart_head(): - """重启Ray Head节点""" - typer.echo("🔄 重启Ray Head节点...") - - # 先停止 - typer.echo("第1步: 停止Head节点") - stop_head() - - # 等待 - typer.echo("⏳ 等待3秒后重新启动...") - time.sleep(3) - - # 再启动 - typer.echo("第2步: 启动Head节点") - start_head(force=False) - - typer.echo("✅ Head节点重启完成.") - - -@app.command("logs") -def show_logs(lines: int = typer.Option(20, "--lines", "-n", help="显示日志行数")): - """显示Head节点日志""" - config_manager = get_config_manager() - head_config = config_manager.get_head_config() - head_log_dir = head_config.get("log_dir", "/tmp/sage_head_logs") - log_file = Path(head_log_dir) / "head.log" - - if not log_file.exists(): - typer.echo("❌ 日志文件不存在") - return - - try: - result = subprocess.run( - ["tail", "-n", str(lines), str(log_file)], capture_output=True, text=True - ) - - if result.stdout: - typer.echo(f"📋 Ray Head日志 (最后{lines}行):") - typer.echo("=" * 50) - typer.echo(result.stdout) - else: - typer.echo("📋 日志文件为空") - - except Exception as e: - typer.echo(f"❌ 读取日志失败: {e}") - - -@app.command("version") -def version_command(): - """Show version information.""" - typer.echo("🏠 SAGE Head Manager") - typer.echo("Version: 1.0.5") - typer.echo("Author: IntelliStream Team") - typer.echo("Repository: https://github.com/intellistream/SAGE") - - -if __name__ == "__main__": - app() diff --git a/packages/sage-cli/src/sage/cli/commands/platform/job.py b/packages/sage-cli/src/sage/cli/commands/platform/job.py deleted file mode 100644 index befc598079..0000000000 --- a/packages/sage-cli/src/sage/cli/commands/platform/job.py +++ /dev/null @@ -1,641 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE JobManager CLI -集成的作业管理命令行工具 -""" - -import json -import os -import signal -import sys -import time -from datetime import datetime -from pathlib import Path -from typing import Any - -import typer -from colorama import Fore, Style, init -from tabulate import tabulate - -from sage.kernel.runtime.jobmanager_client import JobManagerClient - -# 添加项目路径 -project_root = Path(__file__).parent.parent.parent -sys.path.append(str(project_root)) - - -# 初始化colorama -init(autoreset=True) - -app = typer.Typer(name="job", help="SAGE作业管理工具 - 提供作业的暂停、恢复、监控等功能") - - -class JobManagerCLI: - """JobManager命令行界面""" - - def __init__(self, daemon_host: str = "127.0.0.1", daemon_port: int = 19001): - self.daemon_host = daemon_host - self.daemon_port = daemon_port - self.client: JobManagerClient | None = None - self.connected = False - - def connect(self) -> bool: - """连接到JobManager""" - try: - self.client = JobManagerClient(self.daemon_host, self.daemon_port) - - # 健康检查 - health = self.client.health_check() - if health.get("status") != "success": - raise Exception(f"Daemon health check failed: {health.get('message')}") - self.connected = True - return True - - except Exception as e: - print(f"❌ Failed to connect: {e}") - self.connected = False - return False - - def ensure_connected(self): - """确保已连接""" - if not self.connected: - if not self.connect(): - raise Exception("Not connected to JobManager") - - def _get_client(self) -> JobManagerClient: - """获取已连接的客户端""" - self.ensure_connected() - if not self.client: - raise Exception("Client not initialized") - return self.client - - def _resolve_job_identifier(self, identifier: str) -> str | None: - """解析作业标识符（可以是作业编号或UUID）""" - try: - client = self._get_client() - - # 获取作业列表 - response = client.list_jobs() - if response.get("status") != "success": - raise Exception(f"Failed to get job list: {response.get('message')}") - - jobs = response.get("jobs", []) - - # 如果是数字，当作作业编号处理 - if identifier.isdigit(): - job_index = int(identifier) - 1 # 转换为0基索引 - if 0 <= job_index < len(jobs): - return jobs[job_index].get("uuid") - else: - print(f"❌ Job number {identifier} is out of range (1-{len(jobs)})") - return None - - # 如果是UUID（完整或部分） - # 首先尝试精确匹配 - for job in jobs: - if job.get("uuid") == identifier: - return identifier - - # 然后尝试前缀匹配 - matching_jobs = [job for job in jobs if job.get("uuid", "").startswith(identifier)] - - if len(matching_jobs) == 1: - return matching_jobs[0].get("uuid") - elif len(matching_jobs) > 1: - print(f"❌ Ambiguous job identifier '{identifier}'. Matches:") - for i, job in enumerate(matching_jobs, 1): - print(f" {i}. {job.get('uuid')} ({job.get('name', 'unknown')})") - return None - else: - print(f"❌ No job found matching '{identifier}'") - return None - - except Exception as e: - print(f"❌ Failed to resolve job identifier: {e}") - return None - - -# 创建全局CLI实例 -cli = JobManagerCLI() - - -@app.command("list") -def list_jobs( - status: str | None = typer.Option(None, "--status", "-s", help="按状态过滤作业"), - format_type: str = typer.Option("table", "--format", "-f", help="输出格式(table/json)"), - full_uuid: bool = typer.Option(False, "--full-uuid", help="显示完整UUID"), -): - """列出所有作业""" - try: - cli.ensure_connected() - response = cli._get_client().list_jobs() - if response.get("status") != "success": - raise Exception(f"Failed to get job list: {response.get('message')}") - - jobs = response.get("jobs", []) - - # 状态过滤 - if status: - jobs = [job for job in jobs if job.get("status") == status] - - # 格式化输出 - if format_type == "json": - print(json.dumps({"jobs": jobs}, indent=2)) - else: - _format_job_table(jobs, short_uuid=not full_uuid) - - except Exception as e: - print(f"❌ Failed to list jobs: {e}") - raise typer.Exit(1) - - -@app.command("show") -def show_job( - job_identifier: str = typer.Argument(..., help="作业编号或UUID"), - verbose: bool = typer.Option(False, "--verbose", "-v", help="显示详细信息"), -): - """显示作业详情""" - try: - # 解析作业标识符 - job_uuid = cli._resolve_job_identifier(job_identifier) - if not job_uuid: - raise typer.Exit(1) - - cli.ensure_connected() - response = cli._get_client().get_job_status(job_uuid) - if response.get("status") != "success": - raise Exception(f"Failed to get job status: {response.get('message')}") - - job_info = response.get("job_status") - - if not job_info: - print(f"❌ Job {job_uuid} not found") - raise typer.Exit(1) - - _format_job_details(job_info, verbose) - - except Exception as e: - print(f"❌ Failed to show job: {e}") - raise typer.Exit(1) - - -@app.command("stop") -def stop_job( - job_identifier: str = typer.Argument(..., help="作业编号或UUID"), - force: bool = typer.Option(False, "--force", "-f", help="强制停止，无需确认"), -): - """停止/暂停作业 (别名: pause)""" - try: - # 解析作业标识符 - job_uuid = cli._resolve_job_identifier(job_identifier) - if not job_uuid: - raise typer.Exit(1) - - cli.ensure_connected() - - # 确认操作 - if not force: - response = cli._get_client().get_job_status(job_uuid) - if response.get("status") == "success" and response.get("job_status"): - job_info = response.get("job_status") - if job_info: - job_name = job_info.get("name", "unknown") - job_status = job_info.get("status", "unknown") - print(f"Job to stop: {job_name} ({job_uuid})") - print(f"Current status: {job_status}") - - if not typer.confirm("Are you sure you want to stop this job?"): - print("ℹ️ Operation cancelled") - return - - # 停止作业 - result = cli._get_client().pause_job(job_uuid) - - if result.get("status") == "stopped": - print(f"✅ Job {job_uuid[:8]}... stopped successfully") - else: - print(f"❌ Failed to stop job: {result.get('message')}") - raise typer.Exit(1) - - except Exception as e: - print(f"❌ Failed to stop job: {e}") - raise typer.Exit(1) - - -# 添加 pause 作为 stop 的别名 -app.command("pause", hidden=True)(stop_job) - - -@app.command("continue") -def continue_job( - job_identifier: str = typer.Argument(..., help="作业编号或UUID"), - force: bool = typer.Option(False, "--force", "-f", help="强制继续，无需确认"), -): - """继续/恢复作业 (别名: resume)""" - try: - # 解析作业标识符 - job_uuid = cli._resolve_job_identifier(job_identifier) - if not job_uuid: - raise typer.Exit(1) - - cli.ensure_connected() - - # 确认操作 - if not force: - response = cli._get_client().get_job_status(job_uuid) - if response.get("status") == "success" and response.get("job_status"): - job_info = response.get("job_status") - if job_info: - job_name = job_info.get("name", "unknown") - job_status = job_info.get("status", "unknown") - print(f"Job to continue: {job_name} ({job_uuid})") - print(f"Current status: {job_status}") - - if not typer.confirm("Are you sure you want to continue this job?"): - print("ℹ️ Operation cancelled") - return - - # 继续作业 - result = cli._get_client().continue_job(job_uuid) - - if result.get("status") == "running": - print(f"✅ Job {job_uuid[:8]}... continued successfully") - else: - print(f"❌ Failed to continue job: {result.get('message')}") - raise typer.Exit(1) - - except Exception as e: - print(f"❌ Failed to continue job: {e}") - raise typer.Exit(1) - - -# 添加 resume 作为 continue 的别名 -app.command("resume", hidden=True)(continue_job) - - -@app.command("delete") -def delete_job( - job_identifier: str = typer.Argument(..., help="作业编号或UUID"), - force: bool = typer.Option(False, "--force", "-f", help="强制删除，无需确认"), -): - """删除作业""" - try: - # 解析作业标识符 - job_uuid = cli._resolve_job_identifier(job_identifier) - if not job_uuid: - raise typer.Exit(1) - - cli.ensure_connected() - - # 确认操作 - if not force: - response = cli._get_client().get_job_status(job_uuid) - if response.get("status") == "success" and response.get("job_status"): - job_info = response.get("job_status") - if job_info: - job_name = job_info.get("name", "unknown") - job_status = job_info.get("status", "unknown") - print(f"Job to delete: {job_name} ({job_uuid})") - print(f"Current status: {job_status}") - - if not typer.confirm( - "Are you sure you want to delete this job? This action cannot be undone." - ): - print("ℹ️ Operation cancelled") - return - - # 删除作业 - result = cli._get_client().delete_job(job_uuid, force=force) - print(f"✅ Job {job_uuid[:8]}... deleted . message:{result.get('message')})") - - except Exception as e: - print(f"❌ Failed to delete job: {e}") - raise typer.Exit(1) - - -@app.command("status") -def job_status(job_identifier: str = typer.Argument(..., help="作业编号或UUID")): - """获取作业状态""" - try: - # 解析作业标识符 - job_uuid = cli._resolve_job_identifier(job_identifier) - if not job_uuid: - raise typer.Exit(1) - - cli.ensure_connected() - response = cli._get_client().get_job_status(job_uuid) - if response.get("status") != "success": - raise Exception(f"Failed to get job status: {response.get('message')}") - - job_info = response.get("job_status") - - if not job_info: - print(f"❌ Job {job_uuid} not found") - raise typer.Exit(1) - - status = job_info.get("status", "unknown") - job_name = job_info.get("name", "unknown") - _print_status_colored(f"Job '{job_name}' ({job_uuid[:8]}...) status: {status}") - - except Exception as e: - print(f"❌ Failed to get job status: {e}") - raise typer.Exit(1) - - -@app.command("cleanup") -def cleanup_jobs(force: bool = typer.Option(False, "--force", "-f", help="强制清理，无需确认")): - """清理所有作业""" - try: - cli.ensure_connected() - - # 确认操作 - if not force: - response = cli._get_client().list_jobs() - if response.get("status") != "success": - raise Exception(f"Failed to get job list: {response.get('message')}") - - jobs = response.get("jobs", []) - if not jobs: - print("ℹ️ No jobs to cleanup") - return - - print(f"Found {len(jobs)} jobs to cleanup:") - for job in jobs: - print(f" - {job.get('name')} ({job.get('uuid')[:8]}...) [{job.get('status')}]") - - if not typer.confirm(f"Are you sure you want to cleanup all {len(jobs)} jobs?"): - print("ℹ️ Operation cancelled") - return - - # 清理所有作业 - result = cli._get_client().cleanup_all_jobs() - - if result.get("status") == "success": - print(f"✅ {result.get('message')}") - else: - print(f"❌ Failed to cleanup jobs: {result.get('message')}") - raise typer.Exit(1) - - except Exception as e: - print(f"❌ Failed to cleanup jobs: {e}") - raise typer.Exit(1) - - -@app.command("health") -def health_check(): - """健康检查""" - try: - if not cli.client: - cli.client = JobManagerClient(cli.daemon_host, cli.daemon_port) - - health = cli._get_client().health_check() - - if health.get("status") == "success": - print("✅ JobManager is healthy") - - daemon_status = health.get("daemon_status", {}) - print(f"Daemon: {daemon_status.get('socket_service')}") - print(f"Actor: {daemon_status.get('actor_name')}@{daemon_status.get('namespace')}") - else: - print(f"⚠️ Health check warning: {health.get('message')}") - raise typer.Exit(1) - - except Exception as e: - print(f"❌ Health check failed: {e}") - raise typer.Exit(1) - - -@app.command("info") -def system_info(): - """显示JobManager系统信息""" - try: - cli.ensure_connected() - - # 获取系统信息 - response = cli._get_client().get_server_info() - if response.get("status") != "success": - raise Exception(f"Failed to get server info: {response.get('message')}") - - info = response.get("server_info", {}) - - print(f"\n{Fore.CYAN}=== JobManager System Information ==={Style.RESET_ALL}") - print(f"Session ID: {info.get('session_id')}") - print(f"Log Directory: {info.get('log_base_dir')}") - print(f"Total Jobs: {info.get('environments_count', 0)}") - - # 统计作业状态 - jobs = info.get("jobs", []) - status_counts = {} - for job in jobs: - status = job.get("status", "unknown") - status_counts[status] = status_counts.get(status, 0) + 1 - - if status_counts: - print("\nJob Status Summary:") - for status, count in status_counts.items(): - print(f" {status}: {count}") - - except Exception as e: - print(f"❌ Failed to get system info: {e}") - raise typer.Exit(1) - - -@app.command("monitor") -def monitor_jobs(refresh: int = typer.Option(5, "--refresh", "-r", help="刷新间隔（秒）")): - """实时监控所有作业""" - try: - cli.ensure_connected() - - print(f"ℹ️ Monitoring jobs (refresh every {refresh}s, press Ctrl+C to stop)") - - def signal_handler(signum, frame): - print("\nMonitoring stopped") - sys.exit(0) - - signal.signal(signal.SIGINT, signal_handler) - - while True: - # 清屏 - os.system("clear" if os.name == "posix" else "cls") - - # 显示标题 - print(f"{Fore.CYAN}=== SAGE JobManager Monitor ==={Style.RESET_ALL}") - print(f"Last updated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") - print() - - # 获取并显示作业列表 - response = cli._get_client().list_jobs() - if response.get("status") == "success": - jobs = response.get("jobs", []) - _format_job_table(jobs) - else: - print(f"❌ Failed to get job list: {response.get('message')}") - - # 等待 - time.sleep(refresh) - - except KeyboardInterrupt: - print("\nMonitoring stopped") - except Exception as e: - print(f"❌ Monitor failed: {e}") - raise typer.Exit(1) - - -@app.command("watch") -def watch_job( - job_identifier: str = typer.Argument(..., help="作业编号或UUID"), - refresh: int = typer.Option(2, "--refresh", "-r", help="刷新间隔（秒）"), -): - """监控特定作业""" - try: - # 解析作业标识符 - job_uuid = cli._resolve_job_identifier(job_identifier) - if not job_uuid: - raise typer.Exit(1) - - cli.ensure_connected() - - print(f"ℹ️ Watching job {job_uuid[:8]}... (refresh every {refresh}s)") - - def signal_handler(signum, frame): - print("\nWatching stopped") - sys.exit(0) - - signal.signal(signal.SIGINT, signal_handler) - - while True: - # 清屏 - os.system("clear" if os.name == "posix" else "cls") - - # 显示作业详情 - response = cli._get_client().get_job_status(job_uuid) - if response.get("status") == "success": - job_info = response.get("job_status") - if job_info: - print(f"{Fore.CYAN}=== Watching Job {job_uuid[:8]}... ==={Style.RESET_ALL}") - print(f"Last updated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") - print() - _format_job_details(job_info, verbose=True) - else: - print(f"❌ Job {job_uuid} not found") - break - else: - print(f"❌ Failed to get job status: {response.get('message')}") - break - - time.sleep(refresh) - - except KeyboardInterrupt: - print("\nWatching stopped") - except Exception as e: - print(f"❌ Watch failed: {e}") - raise typer.Exit(1) - - -# ==================== 辅助函数 ==================== - - -def _format_job_table(jobs: list[dict[str, Any]], short_uuid: bool = False): - """格式化作业表格""" - if not jobs: - print("ℹ️ No jobs found") - return - - # 根据终端宽度决定是否显示完整UUID - import shutil - - terminal_width = shutil.get_terminal_size().columns - - if short_uuid or terminal_width < 120: - headers = ["#", "UUID (Short)", "Name", "Status", "Started", "Runtime"] - else: - headers = ["#", "UUID", "Name", "Status", "Started", "Runtime"] - - rows = [] - - for i, job in enumerate(jobs, 1): - full_uuid = job.get("uuid", "unknown") - - if short_uuid or terminal_width < 120: - uuid_display = full_uuid[:8] + "..." if len(full_uuid) > 8 else full_uuid - else: - uuid_display = full_uuid - - name = job.get("name", "unknown") - status = job.get("status", "unknown") - start_time = job.get("start_time", "unknown") - runtime = job.get("runtime", "unknown") - - # 状态着色 - if status == "running": - status = f"{Fore.GREEN}{status}{Style.RESET_ALL}" - elif status in ["stopped", "paused"]: - status = f"{Fore.YELLOW}{status}{Style.RESET_ALL}" - elif status == "failed": - status = f"{Fore.RED}{status}{Style.RESET_ALL}" - - rows.append([i, uuid_display, name, status, start_time, runtime]) - - print(tabulate(rows, headers=headers, tablefmt="grid")) - - # 如果使用短UUID，显示提示信息 - if short_uuid or terminal_width < 120: - print(f"\n{Fore.BLUE}💡 Tip:{Style.RESET_ALL} Use job number (#) or full UUID for commands") - if jobs: - print(f" Example: sage job show 1 or sage job show {jobs[0].get('uuid', '')}") - print(" Use --full-uuid to see complete UUIDs") - - -def _format_job_details(job_info: dict[str, Any], verbose: bool = False): - """格式化作业详情""" - print(f"{Fore.CYAN}=== Job Details ==={Style.RESET_ALL}") - - uuid = job_info.get("uuid", "unknown") - name = job_info.get("name", "unknown") - status = job_info.get("status", "unknown") - - print(f"UUID: {uuid}") - print(f"Name: {name}") - - # 状态着色 - if status == "running": - status_colored = f"{Fore.GREEN}{status}{Style.RESET_ALL}" - elif status in ["stopped", "paused"]: - status_colored = f"{Fore.YELLOW}{status}{Style.RESET_ALL}" - elif status == "failed": - status_colored = f"{Fore.RED}{status}{Style.RESET_ALL}" - else: - status_colored = status - - print(f"Status: {status_colored}") - print(f"Start Time: {job_info.get('start_time', 'unknown')}") - print(f"Runtime: {job_info.get('runtime', 'unknown')}") - - if verbose: - if "error" in job_info: - print(f"Error: {job_info['error']}") - - # 显示更多详细信息 - print("\nEnvironment Details:") - env_info = job_info.get("environment", {}) - for key, value in env_info.items(): - print(f" {key}: {value}") - - -def _print_status_colored(message: str): - """打印带颜色的状态消息""" - if "running" in message: - print(message.replace("running", f"{Fore.GREEN}running{Style.RESET_ALL}")) - elif "stopped" in message or "paused" in message: - if "stopped" in message: - print(message.replace("stopped", f"{Fore.YELLOW}stopped{Style.RESET_ALL}")) - if "paused" in message: - print(message.replace("paused", f"{Fore.YELLOW}paused{Style.RESET_ALL}")) - elif "failed" in message: - print(message.replace("failed", f"{Fore.RED}failed{Style.RESET_ALL}")) - else: - print(message) - - -if __name__ == "__main__": - app() diff --git a/packages/sage-cli/src/sage/cli/commands/platform/jobmanager.py b/packages/sage-cli/src/sage/cli/commands/platform/jobmanager.py deleted file mode 100644 index c40ddcf0e3..0000000000 --- a/packages/sage-cli/src/sage/cli/commands/platform/jobmanager.py +++ /dev/null @@ -1,631 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE JobManager CLI - -This module provides CLI commands to manage the JobManager lifecycle using Typer. -""" - -import os -import subprocess -import sys -import time -from typing import Any - -import psutil # type: ignore[import-untyped] -import typer - -# 导入系统工具模块 -from sage.cli.management.config_manager import ConfigManager -from sage.common.utils.system.network import ( - aggressive_port_cleanup, - check_port_binding_permission, - find_port_processes, - send_tcp_health_check, - wait_for_port_release, -) -from sage.common.utils.system.process import ( - create_sudo_manager, - find_processes_by_name, - get_process_info, - kill_process_with_sudo, - terminate_process, -) - -app = typer.Typer( - name="jobmanager", - help="Manage the SAGE JobManager service 🚀", - no_args_is_help=True, -) - - -class JobManagerController: - """JobManager控制器""" - - def __init__(self, host: str = "0.0.0.0", port: int = 19001): - self.host = host - self.port = port - self.process_names = ["job_manager.py", "jobmanager_daemon.py"] - self.sudo_manager = create_sudo_manager() - - def _get_ray_address(self) -> str | None: - """从 cluster.yaml 获取 Ray 集群地址""" - try: - config_manager = ConfigManager() - config = config_manager.load_config() - head_config = config.get("head", {}) - head_host = head_config.get("host", "localhost") - head_port = head_config.get("head_port", 6379) - return f"{head_host}:{head_port}" - except Exception: - return None - - def check_health(self) -> dict[str, Any]: - """检查JobManager健康状态""" - request = {"action": "health_check", "request_id": "cli_health_check"} - - return send_tcp_health_check(self.host, self.port, request, timeout=5) - - def stop_gracefully(self, timeout: int = 30) -> bool: - """优雅地停止JobManager""" - typer.echo(f"Attempting graceful shutdown of JobManager on {self.host}:{self.port}...") - - # 首先尝试通过健康检查确认服务存在 - health = self.check_health() - if health.get("status") != "success": - typer.echo("JobManager is not responding to health checks") - return self.force_kill() - - # 查找进程 - processes = find_processes_by_name(self.process_names) or find_port_processes(self.port) - if not processes: - typer.echo("No JobManager processes found") - return True - - typer.echo(f"Found {len(processes)} JobManager process(es)") - - # 发送SIGTERM信号进行优雅关闭 - for proc in processes: - try: - typer.echo(f"Sending SIGTERM to process {proc.pid}") - proc.terminate() - except psutil.NoSuchProcess: - continue - - # 等待进程结束 - typer.echo(f"Waiting up to {timeout} seconds for processes to exit...") - start_time = time.time() - - while time.time() - start_time < timeout: - remaining_processes = [] - for proc in processes: - try: - if proc.is_running(): - remaining_processes.append(proc) - except psutil.NoSuchProcess: - continue - - if not remaining_processes: - typer.echo("All JobManager processes have exited gracefully") - return True - - time.sleep(1) - - # 如果还有进程在运行，进行强制终止 - typer.echo("Some processes did not exit gracefully, forcing termination...") - return self.force_kill() - - def force_kill(self) -> bool: - """强制杀死JobManager进程""" - processes = find_processes_by_name(self.process_names) - - # 如果没有找到进程，也尝试通过端口查找 - if not processes: - typer.echo("No JobManager processes found by process name, checking by port...") - try: - # 使用 lsof 或 netstat 查找占用端口的进程 - import subprocess - - result = subprocess.run( - ["lsof", "-ti", f":{self.port}"], capture_output=True, text=True - ) - if result.returncode == 0 and result.stdout.strip(): - pids = result.stdout.strip().split("\n") - for pid_str in pids: - try: - pid = int(pid_str.strip()) - process = psutil.Process(pid) - processes.append(process) - typer.echo(f"Found process using port {self.port}: PID {pid}") - except (ValueError, psutil.NoSuchProcess): - continue - except (subprocess.SubprocessError, FileNotFoundError): - # lsof 不可用，尝试使用 netstat - try: - result = subprocess.run(["netstat", "-tlnp"], capture_output=True, text=True) - if result.returncode == 0: - for line in result.stdout.split("\n"): - if f":{self.port}" in line and "LISTEN" in line: - # 提取PID - parts = line.split() - if len(parts) > 6 and "/" in parts[6]: - pid_str = parts[6].split("/")[0] - try: - pid = int(pid_str) - process = psutil.Process(pid) - processes.append(process) - typer.echo( - f"Found process using port {self.port}: PID {pid}" - ) - except (ValueError, psutil.NoSuchProcess): - continue - except (subprocess.SubprocessError, FileNotFoundError): - pass - - if not processes: - typer.echo("No JobManager processes to kill") - return True - - # 检查是否需要sudo权限 - current_user = os.getenv("USER", "unknown") - needs_sudo = False - - for proc in processes: - proc_info = get_process_info(proc.pid) - proc_user = proc_info.get("user", "N/A") - if proc_user != current_user and proc_user != "N/A": - needs_sudo = True - break - - # 如果需要sudo权限但还没有获取，先获取 - if needs_sudo and not self.sudo_manager.has_sudo_access(): - typer.echo("⚠️ Some processes are owned by other users, requesting sudo access...") - if not self.sudo_manager.ensure_sudo_access(): - typer.echo( - "❌ Unable to obtain sudo privileges. Cannot kill processes owned by other users." - ) - typer.echo( - "💡 Suggestion: Run this command as root or ask the process owner to stop the service." - ) - return False - - typer.echo(f"🔪 Force killing {len(processes)} JobManager process(es)...") - - killed_count = 0 - - for proc in processes: - proc_info = get_process_info(proc.pid) - proc_user = proc_info.get("user", "N/A") - - typer.echo("\n📋 Process Information:") - typer.echo(f" PID: {proc_info.get('pid', 'N/A')}") - typer.echo(f" Name: {proc_info.get('name', 'N/A')}") - typer.echo(f" User: {proc_user}") - typer.echo(f" Status: {proc_info.get('status', 'N/A')}") - typer.echo(f" Command: {proc_info.get('cmdline', 'N/A')}") - - # 判断是否需要sudo权限 - needs_sudo_for_proc = proc_user != current_user and proc_user != "N/A" - if needs_sudo_for_proc: - typer.echo( - f"⚠️ Process owned by different user ({proc_user}), using sudo privileges" - ) - - # 使用工具函数终止进程 - result = terminate_process(proc.pid, timeout=5) - - if result["success"]: - typer.echo(f"✅ Process {proc.pid} {result['message']}") - killed_count += 1 - else: - typer.echo(f"❌ {result['error']}") - # 如果普通终止失败且是权限问题，尝试sudo - if result.get("method") == "access_denied" and needs_sudo_for_proc: - sudo_result = kill_process_with_sudo( - proc.pid, self.sudo_manager.get_cached_password() - ) - if sudo_result["success"]: - typer.echo(f"✅ Process {proc.pid} killed with sudo privileges") - killed_count += 1 - else: - typer.echo( - f"❌ Failed to kill process {proc.pid} even with sudo: {sudo_result['error']}" - ) - - # 再次检查是否还有残留进程 - typer.echo("\n🔍 Checking for remaining processes...") - time.sleep(2) - remaining = find_processes_by_name(self.process_names) - - if remaining: - typer.echo(f"⚠️ Warning: {len(remaining)} processes may still be running") - # 显示残留进程信息 - for proc in remaining: - proc_info = get_process_info(proc.pid) - typer.echo( - f" Remaining: PID {proc_info.get('pid', 'N/A')}, User: {proc_info.get('user', 'N/A')}, Name: {proc_info.get('name', 'N/A')}" - ) - return killed_count > 0 # 如果至少杀死了一些进程，认为部分成功 - - typer.echo("✅ All JobManager processes have been terminated") - return True - - def start(self, daemon: bool = True, wait_for_ready: int = 10, force: bool = False) -> bool: - """启动JobManager""" - typer.echo(f"Starting JobManager on {self.host}:{self.port}...") - - # 如果使用force模式，预先获取sudo权限 - if force: - self.sudo_manager.ensure_sudo_access() - - # 检查端口是否已被占用 - if self.is_port_occupied(): - typer.echo(f"Port {self.port} is already occupied") - - if force: - typer.echo("🔥 Force mode enabled, forcefully stopping existing process...") - typer.echo("⚠️ This will terminate processes owned by other users if necessary.") - if not self.force_kill(): - typer.echo("❌ Failed to force kill existing processes") - return False - - # 等待端口释放 - if not wait_for_port_release(self.host, self.port, timeout=15): - typer.echo("❌ Port is still occupied after force kill") - # 尝试更激进的端口清理 - typer.echo("🔧 Attempting aggressive port cleanup...") - aggressive_port_cleanup(self.port) - if not wait_for_port_release(self.host, self.port, timeout=5): - typer.echo("❌ Unable to free the port, startup may fail") - else: - health = self.check_health() - if health.get("status") == "success": - typer.echo("JobManager is already running and healthy") - return True - else: - typer.echo( - "Port occupied but JobManager not responding, stopping existing process..." - ) - if not self.stop_gracefully(): - return False - # 等待端口释放 - wait_for_port_release(self.host, self.port, timeout=10) - - # 检查端口绑定权限 - if not check_port_binding_permission(self.host, self.port): - typer.echo("❌ Cannot bind to port, startup will fail") - typer.echo("💡 Suggestion: Try using a different port with --port option") - return False - - # 在 start 方法的开头添加： - typer.echo(f"Using Python interpreter: {sys.executable}") - # 构建启动命令 - jobmanager_module = "sage.kernel.runtime.job_manager" - cmd = [ - sys.executable, - "-m", - jobmanager_module, - "--host", - self.host, - "--port", - str(self.port), - ] - - # 准备环境变量，设置 RAY_ADDRESS 以连接到 Ray 集群 - env = os.environ.copy() - ray_address = self._get_ray_address() - if ray_address: - env["RAY_ADDRESS"] = ray_address - typer.echo(f"Setting RAY_ADDRESS={ray_address} for Ray cluster connection") - else: - typer.echo("⚠️ Could not determine Ray address from cluster config") - - try: - # 启动JobManager进程 - if daemon: - # 作为守护进程启动 - process = subprocess.Popen( - cmd, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - stdin=subprocess.PIPE, - start_new_session=True, - env=env, - ) - typer.echo(f"JobManager started as daemon process (PID: {process.pid})") - else: - # 在前台启动 - typer.echo("Starting JobManager in foreground mode...") - process = subprocess.Popen(cmd, env=env) - typer.echo(f"JobManager started in foreground (PID: {process.pid})") - return True # 前台模式直接返回 - - # 等待服务就绪 - if wait_for_ready > 0: - typer.echo(f"Waiting up to {wait_for_ready} seconds for JobManager to be ready...") - for i in range(wait_for_ready): - time.sleep(1) - health = self.check_health() - if health.get("status") == "success": - typer.echo(f"JobManager is ready and healthy (took {i + 1} seconds)") - return True - typer.echo(f"Waiting... ({i + 1}/{wait_for_ready})") - - typer.echo("JobManager did not become ready within timeout") - # 检查进程是否还在运行 - try: - if process.poll() is None: - typer.echo("Process is still running but not responding to health checks") - typer.echo("This might indicate a startup issue") - else: - typer.echo(f"Process exited with code: {process.returncode}") - # 尝试获取错误输出 - _, stderr = process.communicate(timeout=1) - if stderr: - typer.echo(f"Process stderr: {stderr.decode()}") - except Exception: - pass - return False - - return True - - except Exception as e: - typer.echo(f"Failed to start JobManager: {e}") - return False - - def is_port_occupied(self) -> bool: - """检查端口是否被占用""" - from sage.common.utils.system.network import is_port_occupied as check_port_occupied - - return check_port_occupied(self.host, self.port) - - def status(self) -> dict[str, Any]: - """获取JobManager状态""" - typer.echo(f"Checking JobManager status on {self.host}:{self.port}...") - - # 检查健康状态 - health = self.check_health() - - # 查找进程 - processes = find_processes_by_name(self.process_names) - - # 检查端口占用 - port_occupied = self.is_port_occupied() - - status_info = { - "health": health, - "processes": [{"pid": p.pid, "name": p.name()} for p in processes], - "port_occupied": port_occupied, - "host_port": f"{self.host}:{self.port}", - } - - # 打印状态信息 - typer.echo(f"Health Status: {health.get('status', 'unknown')}") - if health.get("status") == "success": - daemon_status = health.get("daemon_status", {}) - typer.echo(f" - Jobs Count: {daemon_status.get('jobs_count', 'unknown')}") - typer.echo(f" - Session ID: {daemon_status.get('session_id', 'unknown')}") - - typer.echo(f"Process Count: {len(processes)}") - for proc_info in status_info["processes"]: - proc_pid = proc_info["pid"] - try: - proc = psutil.Process(proc_pid) - proc_user = proc.username() - proc_cmdline = " ".join(proc.cmdline()) - typer.echo(f" - PID {proc_pid}: {proc_info['name']} (user: {proc_user})") - typer.echo(f" Command: {proc_cmdline}") - except (psutil.NoSuchProcess, psutil.AccessDenied): - typer.echo(f" - PID {proc_pid}: {proc_info['name']} (process info unavailable)") - - typer.echo(f"Port {self.port} Occupied: {port_occupied}") - - # 如果端口被占用但没有找到JobManager进程，显示占用端口的进程信息 - if port_occupied and not processes: - typer.echo("Port is occupied by non-JobManager process:") - try: - import subprocess - - result = subprocess.run( - ["lsof", "-ti", f":{self.port}"], capture_output=True, text=True - ) - if result.returncode == 0 and result.stdout.strip(): - pids = result.stdout.strip().split("\n") - for pid_str in pids: - try: - pid = int(pid_str.strip()) - proc = psutil.Process(pid) - proc_user = proc.username() - proc_cmdline = " ".join(proc.cmdline()) - typer.echo(f" - PID {pid}: {proc.name()} (user: {proc_user})") - typer.echo(f" Command: {proc_cmdline}") - except (ValueError, psutil.NoSuchProcess, psutil.AccessDenied): - typer.echo(f" - PID {pid_str}: (process info unavailable)") - except (subprocess.SubprocessError, FileNotFoundError): - typer.echo(" (Unable to determine which process is using the port)") - - return status_info - - def restart(self, force: bool = False, wait_for_ready: int = 10) -> bool: - """重启JobManager""" - typer.echo("=" * 50) - typer.echo("RESTARTING JOBMANAGER") - typer.echo("=" * 50) - - # 如果使用force模式，预先获取sudo权限用于停止阶段 - if force: - typer.echo( - "🔐 Force restart mode: will use sudo to stop, then start with user privileges" - ) - self.sudo_manager.ensure_sudo_access() - - # 停止现有实例 - if force: - typer.echo("🔪 Stopping existing instances with sudo privileges...") - stop_success = self.force_kill() - else: - typer.echo("🛑 Gracefully stopping existing instances...") - stop_success = self.stop_gracefully() - - if not stop_success: - typer.echo("❌ Failed to stop existing JobManager instances") - return False - - # 等待一下确保资源释放 - typer.echo("⏳ Waiting for resources to be released...") - if force: - # 强制模式下等待更长时间，并确保端口释放 - time.sleep(3) - if not wait_for_port_release(self.host, self.port, timeout=10): - typer.echo("⚠️ Port may still be occupied, attempting aggressive cleanup...") - aggressive_port_cleanup(self.port) - wait_for_port_release(self.host, self.port, timeout=5) - else: - time.sleep(2) - - # 启动新实例 - 始终使用用户权限，不使用force模式 - # 这确保新的JobManager运行在正确的conda环境中 - typer.echo("🚀 Starting new instance with user privileges (in conda environment)...") - start_success = self.start(daemon=True, wait_for_ready=wait_for_ready, force=False) - - if start_success: - typer.echo("=" * 50) - typer.echo("✅ JOBMANAGER RESTART SUCCESSFUL") - typer.echo("=" * 50) - else: - typer.echo("=" * 50) - typer.echo("❌ JOBMANAGER RESTART FAILED") - typer.echo("=" * 50) - - return start_success - - -@app.command() -def start( - host: str = typer.Option( - "0.0.0.0", help="JobManager host address (use 0.0.0.0 for cluster access)" - ), - port: int = typer.Option(19001, help="JobManager port"), - foreground: bool = typer.Option(False, "--foreground", help="Start in the foreground"), - no_wait: bool = typer.Option( - False, "--no-wait", help="Do not wait for the service to be ready" - ), - force: bool = typer.Option( - False, - "--force", - "-f", - help="Force start by killing any existing JobManager processes", - ), -): - """ - Start the JobManager service. - """ - controller = JobManagerController(host, port) - wait_time = 0 if no_wait else 10 - success = controller.start(daemon=not foreground, wait_for_ready=wait_time, force=force) - if success: - typer.echo("\n✅ Operation 'start' completed successfully") - else: - typer.echo("\n❌ Operation 'start' failed") - raise typer.Exit(code=1) - - -@app.command() -def stop( - host: str = typer.Option("0.0.0.0", help="JobManager host address"), - port: int = typer.Option(19001, help="JobManager port"), - force: bool = typer.Option( - False, - "--force", - "-f", - help="Force stop by killing any existing JobManager processes", - ), -): - """ - Stop the JobManager service. - """ - controller = JobManagerController(host, port) - - # 如果使用force模式，预先获取sudo权限 - if force: - typer.echo( - "🔐 Force stop mode: may require sudo privileges to terminate processes owned by other users." - ) - controller.sudo_manager.ensure_sudo_access() - success = controller.force_kill() - else: - success = controller.stop_gracefully() - - if success: - typer.echo("\n✅ Operation 'stop' completed successfully") - else: - typer.echo("\n❌ Operation 'stop' failed") - raise typer.Exit(code=1) - - -@app.command() -def restart( - host: str = typer.Option("0.0.0.0", help="JobManager host address"), - port: int = typer.Option(19001, help="JobManager port"), - force: bool = typer.Option(False, "--force", "-f", help="Force the restart"), - no_wait: bool = typer.Option( - False, "--no-wait", help="Do not wait for the service to be ready" - ), -): - """ - Restart the JobManager service. - """ - controller = JobManagerController(host, port) - wait_time = 0 if no_wait else 10 - success = controller.restart(force=force, wait_for_ready=wait_time) - if not success: - raise typer.Exit(code=1) - - -@app.command() -def status( - host: str = typer.Option("0.0.0.0", help="JobManager host address"), - port: int = typer.Option(19001, help="JobManager port"), -): - """ - Check the status of the JobManager service. - """ - controller = JobManagerController(host, port) - controller.status() - typer.echo("\n✅ Operation 'status' completed successfully") - - -@app.command() -def kill( - host: str = typer.Option("0.0.0.0", help="JobManager host address"), - port: int = typer.Option(19001, help="JobManager port"), -): - """ - Force kill the JobManager service. - """ - controller = JobManagerController(host, port) - - # kill命令总是需要sudo权限，预先获取 - typer.echo( - "🔐 Kill command: may require sudo privileges to terminate processes owned by other users." - ) - controller.sudo_manager.ensure_sudo_access() - - success = controller.force_kill() - if success: - typer.echo("\n✅ Operation 'kill' completed successfully") - else: - typer.echo("\n❌ Operation 'kill' failed") - raise typer.Exit(code=1) - - -@app.command("version") -def version_command(): - """Show version information.""" - typer.echo("🚀 SAGE JobManager") - typer.echo("Version: 1.0.1") - typer.echo("Author: IntelliStream Team") - typer.echo("Repository: https://github.com/intellistream/SAGE") - - -if __name__ == "__main__": - app() diff --git a/packages/sage-cli/src/sage/cli/commands/platform/llm_config.py b/packages/sage-cli/src/sage/cli/commands/platform/llm_config.py deleted file mode 100644 index 78bfe1e848..0000000000 --- a/packages/sage-cli/src/sage/cli/commands/platform/llm_config.py +++ /dev/null @@ -1,191 +0,0 @@ -#!/usr/bin/env python3 -"""LLM configuration commands for SAGE.""" - -from pathlib import Path - -import typer -import yaml # type: ignore[import-untyped] - -from sage.cli.utils.llm_detection import LLMServiceInfo, detect_all_services - -app = typer.Typer(help="🤖 LLM 服务配置自动化") - - -def _load_yaml(path: Path) -> dict: - """Load YAML file, returning an empty dict if the file is blank.""" - content = path.read_text(encoding="utf-8") - data = yaml.safe_load(content) if content.strip() else None - return data or {} - - -def _write_yaml(path: Path, data: dict) -> None: - """Persist YAML dictionary with stable formatting.""" - path.write_text(yaml.safe_dump(data, allow_unicode=True, sort_keys=False), encoding="utf-8") - - -def _default_config_path() -> Path | None: - """寻找默认的配置文件路径""" - candidates = [ - Path.cwd() / "config" / "config.yaml", - Path.cwd() / "config.yaml", - Path.cwd() / "examples" / "config" / "config.yaml", - Path.home() / ".sage" / "config.yaml", - ] - - for candidate in candidates: - if candidate.exists() and candidate.is_file(): - return candidate - return None - - -def _select_service( - detections: list[LLMServiceInfo], assume_yes: bool, preferred_section: str | None -) -> LLMServiceInfo: - """选择要使用的服务""" - if preferred_section: - preferred_section = preferred_section.lower() - for service in detections: - if service.generator_section == preferred_section: - return service - - if len(detections) == 1 or assume_yes: - return detections[0] - - typer.echo("🔍 检测到多个可用的本地 LLM 服务：") - for idx, service in enumerate(detections, start=1): - typer.echo(f" {idx}. {service.description} -> generator.{service.generator_section}") - - choice = typer.prompt("请选择要使用的服务编号", default="1") - try: - selection = int(choice) - return detections[selection - 1] - except (ValueError, IndexError): - typer.echo("❌ 无效的选择，操作已取消。") - raise typer.Exit(1) - - -@app.command("auto") -def auto_update_generator( - config_path: Path | None = typer.Option( - None, - "--config-path", - "-c", - help="配置文件路径，默认自动探测 config/config.yaml 等常用位置", - ), - prefer: str | None = typer.Option( - None, - "--prefer", - help="优先检测的服务类型（ollama / sagellm）", - ), - model_name: str | None = typer.Option( - None, - "--model-name", - "-m", - help="指定要写入的模型名称（默认使用检测到的第一个模型）", - ), - section: str | None = typer.Option( - None, - "--section", - "-s", - help="目标 generator 子配置（remote / sagellm 等），默认依据服务类型", - ), - auth_token: str | None = typer.Option( - None, - "--auth-token", - "-t", - help="用于LLM服务的认证token（如果需要）", - ), - assume_yes: bool = typer.Option( - False, - "--yes", - "-y", - help="无需交互确认，自动选取检测到的第一个服务和模型", - ), - create_backup: bool = typer.Option( - True, - "--backup/--no-backup", - help="更新前创建配置文件备份", - ), -): - """自动检测本地 LLM 服务并更新 generator 配置。""" - - resolved_path = config_path or _default_config_path() - if not resolved_path: - typer.echo("❌ 未找到默认配置文件，请通过 --config-path 指定。") - raise typer.Exit(1) - - resolved_path = resolved_path.expanduser().resolve() - if not resolved_path.exists(): - typer.echo(f"❌ 配置文件不存在: {resolved_path}") - raise typer.Exit(1) - - prefer_normalized = prefer.lower() if prefer else None - if prefer_normalized and prefer_normalized not in {"ollama", "sagellm"}: - typer.echo("❌ --prefer 仅支持 ollama 或 sagellm。") - raise typer.Exit(1) - - detections = detect_all_services(prefer_normalized, auth_token=auth_token) - if not detections: - typer.echo("⚠️ 未检测到支持的本地 LLM 服务。") - raise typer.Exit(1) - - selected = _select_service(detections, assume_yes, section) - - available_models = selected.models - chosen_model = model_name or selected.default_model - if model_name and model_name not in available_models: - typer.echo(f"⚠️ 指定的模型 {model_name} 未出现在服务返回的列表中，将按原样写入配置。") - elif not model_name and len(available_models) > 1 and not assume_yes: - typer.echo("📋 服务提供的模型列表：") - for idx, item in enumerate(available_models, start=1): - typer.echo(f" {idx}. {item}") - - model_choice = typer.prompt("请选择模型编号（默认第一个）", default="1") - try: - chosen_idx = int(model_choice) - 1 - chosen_model = available_models[chosen_idx] - except (ValueError, IndexError): - typer.echo("❌ 无效的模型选择，使用默认模型。") - chosen_model = selected.default_model - - target_section = section or selected.generator_section - typer.echo("✅ 即将更新配置：") - typer.echo(f" 服务: {selected.description}") - typer.echo(f" 配置段: generator.{target_section}") - typer.echo(f" URL: {selected.base_url}") - typer.echo(f" 模型: {chosen_model}") - if auth_token: - typer.echo(f" 认证: {auth_token}") - - if not assume_yes and not typer.confirm("确认更新？"): - typer.echo("❌ 操作已取消。") - raise typer.Exit(0) - - if create_backup: - backup_path = Path(f"{resolved_path}.bak") - backup_path.write_bytes(resolved_path.read_bytes()) - typer.echo(f"🗂️ 已创建备份: {backup_path}") - - config_data = _load_yaml(resolved_path) - generator = config_data.setdefault("generator", {}) - section_data: dict[str, str] = generator.setdefault(target_section, {}) - - # Preserve existing API key/seed unless explicitly overridden - section_data.setdefault("method", "openai") - section_data["base_url"] = selected.base_url - section_data["model_name"] = chosen_model - - # Update API key if auth_token was provided - if auth_token: - section_data["api_key"] = auth_token - - _write_yaml(resolved_path, config_data) - - typer.echo("✅ 配置已更新：") - typer.echo(f" 文件: {resolved_path}") - typer.echo(f" generator.{target_section}.base_url = {selected.base_url}") - typer.echo(f" generator.{target_section}.model_name = {chosen_model}") - if auth_token: - typer.echo(f" generator.{target_section}.api_key = {auth_token}") - - raise typer.Exit(0) diff --git a/packages/sage-cli/src/sage/cli/commands/platform/logs.py b/packages/sage-cli/src/sage/cli/commands/platform/logs.py deleted file mode 100644 index ed73784313..0000000000 --- a/packages/sage-cli/src/sage/cli/commands/platform/logs.py +++ /dev/null @@ -1,274 +0,0 @@ -"""Log management commands for SAGE platform.""" - -import os -import shutil -from datetime import datetime, timedelta -from pathlib import Path -from typing import Optional - -import typer -from rich.console import Console -from rich.table import Table - -from sage.common.config.user_paths import get_user_paths - -app = typer.Typer(help="Log management commands") -console = Console() - - -@app.command("clean") -def clean_logs( - days: int = typer.Option( - 7, - "--days", - "-d", - help="Delete logs older than this many days", - min=1, - ), - dry_run: bool = typer.Option( - False, - "--dry-run", - help="Show what would be deleted without actually deleting", - ), - yes: bool = typer.Option( - False, - "--yes", - "-y", - help="Skip confirmation prompt", - ), -) -> None: - """Clean old log files from .sage/logs directory. - - Args: - days: Delete logs older than this many days (default: 7) - dry_run: Show what would be deleted without actually deleting - yes: Skip confirmation prompt - - Examples: - sage logs clean --days 7 # Delete logs older than 7 days - sage logs clean --days 30 --dry-run # Preview deletion - sage logs clean --days 1 --yes # Delete logs older than 1 day without confirmation - """ - user_paths = get_user_paths() - logs_dir = user_paths.logs_dir - - if not logs_dir.exists(): - console.print(f"[yellow]Log directory does not exist: {logs_dir}[/yellow]") - return - - # Calculate cutoff time - cutoff_time = datetime.now() - timedelta(days=days) - cutoff_timestamp = cutoff_time.timestamp() - - # Find old log files - old_files = [] - total_size = 0 - - for root, dirs, files in os.walk(logs_dir): - for file in files: - if file.endswith(".log") or file.endswith(".txt"): - file_path = Path(root) / file - try: - file_mtime = file_path.stat().st_mtime - if file_mtime < cutoff_timestamp: - file_size = file_path.stat().st_size - old_files.append((file_path, file_size)) - total_size += file_size - except Exception as e: - console.print(f"[yellow]Warning: Could not access {file_path}: {e}[/yellow]") - - if not old_files: - console.print(f"[green]✓ No log files older than {days} days found.[/green]") - return - - # Display files to be deleted - table = Table(title=f"Log Files Older Than {days} Days") - table.add_column("File", style="cyan", no_wrap=False) - table.add_column("Size", justify="right", style="yellow") - table.add_column("Modified", style="magenta") - - for file_path, file_size in old_files: - rel_path = file_path.relative_to(logs_dir) - size_mb = file_size / (1024 * 1024) - mtime = datetime.fromtimestamp(file_path.stat().st_mtime) - table.add_row(str(rel_path), f"{size_mb:.2f} MB", mtime.strftime("%Y-%m-%d %H:%M")) - - console.print(table) - console.print( - f"\n[bold]Total:[/bold] {len(old_files)} files, {total_size / (1024 * 1024):.2f} MB" - ) - - if dry_run: - console.print("\n[yellow]DRY RUN: No files were deleted.[/yellow]") - return - - # Confirm deletion - if not yes: - console.print( - f"\n[yellow]This will delete {len(old_files)} log files ({total_size / (1024 * 1024):.2f} MB).[/yellow]" - ) - confirm = typer.confirm("Do you want to continue?", default=False) - if not confirm: - console.print("[yellow]Cancelled.[/yellow]") - return - - # Delete files - deleted_count = 0 - deleted_size = 0 - errors = [] - - for file_path, file_size in old_files: - try: - file_path.unlink() - deleted_count += 1 - deleted_size += file_size - except Exception as e: - errors.append((file_path, str(e))) - - # Report results - if deleted_count > 0: - console.print( - f"\n[green]✓ Deleted {deleted_count} files ({deleted_size / (1024 * 1024):.2f} MB)[/green]" - ) - - if errors: - console.print(f"\n[red]Failed to delete {len(errors)} files:[/red]") - for file_path, error in errors: - console.print(f" [red]• {file_path.relative_to(logs_dir)}: {error}[/red]") - - -@app.command("list") -def list_logs( - days: Optional[int] = typer.Option( - None, - "--days", - "-d", - help="Only list logs from the last N days", - ), - sort_by_size: bool = typer.Option( - False, - "--sort-size", - "-s", - help="Sort by file size instead of modification time", - ), -) -> None: - """List all log files in .sage/logs directory. - - Args: - days: Only list logs from the last N days - sort_by_size: Sort by file size instead of modification time - - Examples: - sage logs list # List all logs - sage logs list --days 7 # List logs from last 7 days - sage logs list --sort-size # Sort by size - """ - user_paths = get_user_paths() - logs_dir = user_paths.logs_dir - - if not logs_dir.exists(): - console.print(f"[yellow]Log directory does not exist: {logs_dir}[/yellow]") - return - - # Find log files - log_files = [] - cutoff_time = None - if days is not None: - cutoff_time = (datetime.now() - timedelta(days=days)).timestamp() - - for root, dirs, files in os.walk(logs_dir): - for file in files: - if file.endswith(".log") or file.endswith(".txt"): - file_path = Path(root) / file - try: - file_stat = file_path.stat() - if cutoff_time is None or file_stat.st_mtime >= cutoff_time: - log_files.append((file_path, file_stat)) - except Exception as e: - console.print(f"[yellow]Warning: Could not access {file_path}: {e}[/yellow]") - - if not log_files: - if days is not None: - console.print(f"[yellow]No log files found in the last {days} days.[/yellow]") - else: - console.print("[yellow]No log files found.[/yellow]") - return - - # Sort files - if sort_by_size: - log_files.sort(key=lambda x: x[1].st_size, reverse=True) - else: - log_files.sort(key=lambda x: x[1].st_mtime, reverse=True) - - # Display files - title = "Log Files" - if days is not None: - title += f" (Last {days} Days)" - - table = Table(title=title) - table.add_column("File", style="cyan", no_wrap=False) - table.add_column("Size", justify="right", style="yellow") - table.add_column("Modified", style="magenta") - - total_size = 0 - for file_path, file_stat in log_files: - rel_path = file_path.relative_to(logs_dir) - size_mb = file_stat.st_size / (1024 * 1024) - mtime = datetime.fromtimestamp(file_stat.st_mtime) - table.add_row( - str(rel_path), - f"{size_mb:.2f} MB" if size_mb > 0.01 else f"{file_stat.st_size / 1024:.2f} KB", - mtime.strftime("%Y-%m-%d %H:%M"), - ) - total_size += file_stat.st_size - - console.print(table) - console.print( - f"\n[bold]Total:[/bold] {len(log_files)} files, {total_size / (1024 * 1024):.2f} MB" - ) - - -@app.command("info") -def log_info() -> None: - """Show log directory information and disk usage.""" - user_paths = get_user_paths() - logs_dir = user_paths.logs_dir - - console.print("\n[bold cyan]Log Directory Information[/bold cyan]") - console.print(f"Location: {logs_dir}") - - if not logs_dir.exists(): - console.print("[yellow]Directory does not exist.[/yellow]") - return - - # Count files and calculate size - log_count = 0 - total_size = 0 - - for root, dirs, files in os.walk(logs_dir): - for file in files: - if file.endswith(".log") or file.endswith(".txt"): - log_count += 1 - file_path = Path(root) / file - try: - total_size += file_path.stat().st_size - except Exception: - pass - - console.print(f"Log files: {log_count}") - console.print(f"Total size: {total_size / (1024 * 1024):.2f} MB") - - # Show disk usage - try: - disk_usage = shutil.disk_usage(logs_dir) - console.print("\n[bold]Disk Usage:[/bold]") - console.print(f" Total: {disk_usage.total / (1024**3):.2f} GB") - console.print(f" Used: {disk_usage.used / (1024**3):.2f} GB") - console.print(f" Free: {disk_usage.free / (1024**3):.2f} GB") - console.print(f" Usage: {disk_usage.used / disk_usage.total * 100:.1f}%") - except Exception as e: - console.print(f"[yellow]Could not get disk usage: {e}[/yellow]") - - -if __name__ == "__main__": - app() diff --git a/packages/sage-cli/src/sage/cli/commands/platform/ray_version_checker.py b/packages/sage-cli/src/sage/cli/commands/platform/ray_version_checker.py deleted file mode 100644 index 7250fec918..0000000000 --- a/packages/sage-cli/src/sage/cli/commands/platform/ray_version_checker.py +++ /dev/null @@ -1,296 +0,0 @@ -"""Ray 版本检查和同步工具""" - -import re -import subprocess -from typing import Optional - -import typer - - -def get_local_ray_version() -> Optional[str]: - """获取本地 Ray 版本""" - try: - result = subprocess.run( - ["ray", "--version"], - capture_output=True, - text=True, - timeout=10, - ) - if result.returncode == 0: - # 输出格式: "ray, version 2.9.0" - match = re.search(r"version\s+([\d.]+)", result.stdout) - if match: - return match.group(1) - return None - except Exception: - return None - - -def get_remote_ray_version( - host: str, port: int, user: str, ssh_key_path: str, conda_env: str = "sage" -) -> Optional[str]: - """获取远程主机的 Ray 版本 - - 检测顺序： - 1. conda 环境中的 ray (base: miniconda3/bin, 其他: miniconda3/envs/{conda_env}/bin) - 2. 系统级 ray 命令 - 3. 系统 python3/python 导入 - """ - try: - # 检测脚本：优先检测 conda 环境 - detect_cmd = f""" -# 静默所有警告 -exec 2>/dev/null - -# 1. 优先检测 conda 环境中的 ray -# base 环境路径不同：$CONDA_BASE/bin vs $CONDA_BASE/envs/{conda_env}/bin -if [ "{conda_env}" = "base" ]; then - CONDA_RAY="$HOME/miniconda3/bin/ray" - CONDA_PYTHON="$HOME/miniconda3/bin/python3" -else - CONDA_RAY="$HOME/miniconda3/envs/{conda_env}/bin/ray" - CONDA_PYTHON="$HOME/miniconda3/envs/{conda_env}/bin/python3" -fi - -if [ -x "$CONDA_RAY" ]; then - "$CONDA_RAY" --version 2>/dev/null && exit 0 -fi - -if [ -x "$CONDA_PYTHON" ]; then - "$CONDA_PYTHON" -c "import ray; print(f'ray, version {{ray.__version__}}')" 2>/dev/null && exit 0 -fi - -# 2. 尝试激活 conda 环境后检测 -if [ -f "$HOME/miniconda3/etc/profile.d/conda.sh" ]; then - source "$HOME/miniconda3/etc/profile.d/conda.sh" 2>/dev/null - conda activate {conda_env} 2>/dev/null - if command -v ray >/dev/null 2>&1; then - ray --version 2>/dev/null && exit 0 - fi - python3 -c "import ray; print(f'ray, version {{ray.__version__}}')" 2>/dev/null && exit 0 -fi - -# 3. 尝试系统级 ray 命令 -if command -v ray >/dev/null 2>&1; then - ray --version 2>/dev/null && exit 0 -fi - -# 4. 尝试系统 python3 导入 -python3 -c "import ray; print(f'ray, version {{ray.__version__}}')" 2>/dev/null && exit 0 - -# 5. 尝试系统 python 导入 -python -c "import ray; print(f'ray, version {{ray.__version__}}')" 2>/dev/null && exit 0 - -# 没找到 ray -exit 1 -""" - - ssh_cmd = [ - "ssh", - "-p", - str(port), - "-o", - "StrictHostKeyChecking=no", - "-o", - "BatchMode=yes", - "-o", - "ConnectTimeout=10", - "-o", - "LogLevel=ERROR", - f"{user}@{host}", - "bash -s", - ] - - # 创建干净的环境变量 - import os - - clean_env = os.environ.copy() - clean_env["LC_ALL"] = "C" - clean_env["LANG"] = "C" - - result = subprocess.run( - ssh_cmd, - input=detect_cmd, - capture_output=True, - text=True, - timeout=15, - env=clean_env, - ) - - # 从 stdout 提取版本号 - if result.stdout: - for line in result.stdout.strip().split("\n"): - line = line.strip() - if not line or "warning" in line.lower() or "setlocale" in line.lower(): - continue - match = re.search(r"ray,?\s*version\s+([\d.]+)", line, re.IGNORECASE) - if match: - return match.group(1) - - return None - except Exception as e: - typer.echo(f"[dim]Debug {host}: 检测异常: {e}[/dim]", err=True) - return None - - -def install_ray_on_remote( - host: str, - port: int, - user: str, - ssh_key_path: str, - target_version: str, - conda_env: str = "sage", -) -> bool: - """在远程主机上安装指定版本的 Ray - - 优先安装到 conda 环境中。 - """ - typer.echo(f"📦 在 {host} 上安装 Ray {target_version}...") - - install_script = f""" -# 静默 locale 警告 -export LC_ALL=C 2>/dev/null || true -export LANG=C 2>/dev/null || true - -set -e -export PYTHONUNBUFFERED=1 - -echo "检测 Python 环境..." - -# 优先使用 conda 环境 -# base 环境路径不同：$CONDA_BASE/bin vs $CONDA_BASE/envs/{conda_env}/bin -if [ "{conda_env}" = "base" ]; then - CONDA_PYTHON="$HOME/miniconda3/bin/python3" - CONDA_PIP="$HOME/miniconda3/bin/pip" -else - CONDA_PYTHON="$HOME/miniconda3/envs/{conda_env}/bin/python3" - CONDA_PIP="$HOME/miniconda3/envs/{conda_env}/bin/pip" -fi - -if [ -x "$CONDA_PIP" ]; then - echo "使用 conda 环境: {conda_env}" - PIP_CMD="$CONDA_PIP" - PYTHON_CMD="$CONDA_PYTHON" -elif [ -f "$HOME/miniconda3/etc/profile.d/conda.sh" ]; then - echo "激活 conda 环境: {conda_env}" - source "$HOME/miniconda3/etc/profile.d/conda.sh" - conda activate {conda_env} 2>/dev/null || true - PIP_CMD="pip" - PYTHON_CMD="python3" -elif command -v pip3 >/dev/null 2>&1; then - echo "使用系统 pip3" - PIP_CMD="pip3" - PYTHON_CMD="python3" -elif command -v pip >/dev/null 2>&1; then - echo "使用系统 pip" - PIP_CMD="pip" - PYTHON_CMD="python" -else - echo "错误: 未找到 pip 命令" - exit 1 -fi - -echo "使用 $PIP_CMD 安装 Ray..." - -# 卸载旧版本 -echo "卸载旧版本 Ray..." -$PIP_CMD uninstall -y ray 2>/dev/null || true - -# 安装指定版本 -echo "安装 Ray {target_version}..." -$PIP_CMD install "ray[default]=={target_version}" - -# 验证安装 -echo "验证安装..." -$PYTHON_CMD -c "import ray; print(f'Ray {{ray.__version__}} 安装成功')" - -echo "安装完成." -""" - - try: - import os - - ssh_cmd = [ - "ssh", - "-p", - str(port), - "-o", - "StrictHostKeyChecking=no", - "-o", - "BatchMode=yes", - "-o", - "ConnectTimeout=10", - f"{user}@{host}", - "bash -s", - ] - - clean_env = os.environ.copy() - clean_env["LC_ALL"] = "C" - clean_env["LANG"] = "C" - - result = subprocess.run( - ssh_cmd, - input=install_script, - capture_output=True, - text=True, - timeout=300, - env=clean_env, - ) - - if result.stdout: - for line in result.stdout.split("\n"): - if "setlocale" not in line.lower() and "warning" not in line.lower(): - typer.echo(line) - if result.stderr: - for line in result.stderr.split("\n"): - if "setlocale" not in line.lower() and "warning" not in line.lower(): - if line.strip(): - typer.echo(line, err=True) - - return result.returncode == 0 - - except Exception as e: - typer.echo(f"❌ 安装失败: {e}") - return False - - -def check_and_sync_ray_version( - host: str, - port: int, - user: str, - ssh_key_path: str, - conda_env: str = "sage", -) -> bool: - """检查并同步 Ray 版本 - - Returns: - True if version is compatible or successfully synced, False otherwise - """ - # 获取本地版本 - local_version = get_local_ray_version() - if not local_version: - typer.echo("[yellow]⚠️ 无法获取本地 Ray 版本[/yellow]") - return True - - # 获取远程版本（传入 conda_env 参数） - remote_version = get_remote_ray_version(host, port, user, ssh_key_path, conda_env) - - if not remote_version: - typer.echo(f"[yellow]⚠️ {host}: 未检测到 Ray，尝试安装...[/yellow]") - return install_ray_on_remote(host, port, user, ssh_key_path, local_version, conda_env) - - # 比较版本 - if remote_version == local_version: - typer.echo(f"[green]✅ {host}: Ray 版本一致 ({local_version})[/green]") - return True - - # 版本不一致 - typer.echo(f"[yellow]⚠️ {host}: Ray 版本不一致[/yellow]") - typer.echo(f" 本地版本: {local_version}") - typer.echo(f" 远程版本: {remote_version}") - - if typer.confirm(f"是否将 {host} 的 Ray 升级到 {local_version}?", default=True): - return install_ray_on_remote(host, port, user, ssh_key_path, local_version, conda_env) - else: - typer.echo(f"[yellow]⚠️ 跳过 {host} 的版本同步，可能导致集群不稳定[/yellow]") - return True diff --git a/packages/sage-cli/src/sage/cli/commands/platform/ssh_setup.py b/packages/sage-cli/src/sage/cli/commands/platform/ssh_setup.py deleted file mode 100644 index d646647b3f..0000000000 --- a/packages/sage-cli/src/sage/cli/commands/platform/ssh_setup.py +++ /dev/null @@ -1,302 +0,0 @@ -"""SSH 免密登录自动配置工具""" - -import os -import subprocess -from pathlib import Path -from typing import Optional - -import typer - - -def check_sshpass_installed() -> bool: - """检查 sshpass 是否已安装""" - try: - subprocess.run( - ["which", "sshpass"], - capture_output=True, - check=True, - timeout=5, - ) - return True - except (subprocess.CalledProcessError, FileNotFoundError): - return False - - -def install_sshpass() -> bool: - """安装 sshpass 工具""" - typer.echo("[blue]📦 安装 sshpass 工具...[/blue]") - - # 检测包管理器并安装 - if Path("/usr/bin/apt-get").exists(): - try: - subprocess.run( - ["sudo", "apt-get", "update"], - capture_output=True, - timeout=60, - ) - subprocess.run( - ["sudo", "apt-get", "install", "-y", "sshpass"], - check=True, - timeout=120, - ) - typer.echo("[green]✅ sshpass 安装成功[/green]") - return True - except subprocess.CalledProcessError: - typer.echo("[red]❌ sshpass 安装失败（apt-get）[/red]") - return False - elif Path("/usr/bin/yum").exists(): - try: - subprocess.run( - ["sudo", "yum", "install", "-y", "sshpass"], - check=True, - timeout=120, - ) - typer.echo("[green]✅ sshpass 安装成功[/green]") - return True - except subprocess.CalledProcessError: - typer.echo("[red]❌ sshpass 安装失败（yum）[/red]") - return False - else: - typer.echo("[red]❌ 无法自动安装 sshpass，请手动安装[/red]") - typer.echo("[yellow] Ubuntu/Debian: sudo apt-get install sshpass[/yellow]") - typer.echo("[yellow] CentOS/RHEL: sudo yum install sshpass[/yellow]") - return False - - -def generate_ssh_key(key_path: str) -> bool: - """生成 SSH 密钥对""" - if Path(key_path).exists(): - typer.echo(f"[green]✅ SSH 密钥已存在: {key_path}[/green]") - return True - - typer.echo("[blue]🔑 生成 SSH 密钥对...[/blue]") - - try: - subprocess.run( - [ - "ssh-keygen", - "-t", - "rsa", - "-b", - "4096", - "-f", - key_path, - "-N", - "", - "-C", - f"sage-cluster-{os.getenv('USER', 'user')}", - ], - check=True, - capture_output=True, - timeout=30, - ) - typer.echo(f"[green]✅ SSH 密钥生成成功: {key_path}[/green]") - return True - except subprocess.CalledProcessError as e: - typer.echo(f"[red]❌ SSH 密钥生成失败: {e}[/red]") - return False - - -def test_ssh_connection( - host: str, - user: str, - password: str, - port: int = 22, -) -> bool: - """测试 SSH 连接（使用密码）""" - try: - result = subprocess.run( - [ - "sshpass", - "-p", - password, - "ssh", - "-o", - "StrictHostKeyChecking=no", - "-o", - "ConnectTimeout=5", - "-p", - str(port), - f"{user}@{host}", - "echo 'Connection OK'", - ], - capture_output=True, - text=True, - timeout=10, - ) - return result.returncode == 0 - except Exception: - return False - - -def copy_ssh_key( - host: str, - user: str, - password: str, - key_path: str, - port: int = 22, -) -> bool: - """复制 SSH 公钥到远程主机""" - pub_key_path = f"{key_path}.pub" - - if not Path(pub_key_path).exists(): - typer.echo(f"[red]❌ 公钥文件不存在: {pub_key_path}[/red]") - return False - - try: - result = subprocess.run( - [ - "sshpass", - "-p", - password, - "ssh-copy-id", - "-o", - "StrictHostKeyChecking=no", - "-i", - pub_key_path, - "-p", - str(port), - f"{user}@{host}", - ], - capture_output=True, - text=True, - timeout=30, - ) - return result.returncode == 0 - except Exception as e: - typer.echo(f"[yellow]复制密钥时出错: {e}[/yellow]") - return False - - -def verify_passwordless_login( - host: str, - user: str, - key_path: str, - port: int = 22, -) -> bool: - """验证免密登录""" - try: - result = subprocess.run( - [ - "ssh", - "-i", - key_path, - "-o", - "StrictHostKeyChecking=no", - "-o", - "ConnectTimeout=5", - "-o", - "BatchMode=yes", - "-p", - str(port), - f"{user}@{host}", - "echo 'Passwordless login works'", - ], - capture_output=True, - text=True, - timeout=10, - ) - return result.returncode == 0 - except Exception: - return False - - -def setup_ssh_for_host( - host: str, - user: str, - password: str, - key_path: str, - port: int = 22, -) -> bool: - """为单个主机配置 SSH 免密登录""" - typer.echo(f"[blue]🔧 配置 {host}...[/blue]") - - # 1. 测试连接 - typer.echo(" 1. 测试 SSH 连接...") - if not test_ssh_connection(host, user, password, port): - typer.echo(f"[red] ❌ 无法连接到 {host}[/red]") - return False - typer.echo("[green] ✅ 连接成功[/green]") - - # 2. 复制公钥 - typer.echo(" 2. 复制 SSH 公钥...") - if not copy_ssh_key(host, user, password, key_path, port): - typer.echo("[red] ❌ 公钥复制失败[/red]") - return False - typer.echo("[green] ✅ 公钥复制成功[/green]") - - # 3. 验证免密登录 - typer.echo(" 3. 验证免密登录...") - if not verify_passwordless_login(host, user, key_path, port): - typer.echo("[red] ❌ 免密登录验证失败[/red]") - return False - typer.echo(f"[green] ✅ 免密登录配置成功: {user}@{host}[/green]") - - return True - - -def auto_setup_ssh_keys( - hosts: list[tuple[str, int]], - user: str = "sage", - password: str = "123", - key_path: Optional[str] = None, -) -> tuple[int, int]: - """自动配置 SSH 免密登录 - - Args: - hosts: [(host, port), ...] 列表 - user: SSH 用户名 - password: SSH 密码 - key_path: SSH 密钥路径 - - Returns: - (成功数量, 总数量) - """ - if key_path is None: - key_path = os.path.expanduser("~/.ssh/id_rsa") - - typer.echo("\n[cyan]═══════════════════════════════════════[/cyan]") - typer.echo("[cyan] SSH 免密登录自动配置[/cyan]") - typer.echo("[cyan]═══════════════════════════════════════[/cyan]\n") - - # 1. 检查并安装 sshpass - if not check_sshpass_installed(): - typer.echo("[yellow]⚠️ 未安装 sshpass[/yellow]") - if not install_sshpass(): - typer.echo("[red]❌ SSH 配置失败: 无法安装 sshpass[/red]") - return (0, len(hosts)) - - # 2. 生成 SSH 密钥 - if not generate_ssh_key(key_path): - typer.echo("[red]❌ SSH 配置失败: 无法生成密钥[/red]") - return (0, len(hosts)) - - # 3. 配置每个主机 - typer.echo(f"\n[cyan]配置 {len(hosts)} 个主机...[/cyan]\n") - success_count = 0 - - for host, port in hosts: - # 先检查是否已经配置了免密登录 - if verify_passwordless_login(host, user, key_path, port): - typer.echo(f"[green]✅ {host}: 免密登录已配置[/green]\n") - success_count += 1 - continue - - # 配置免密登录 - if setup_ssh_for_host(host, user, password, key_path, port): - success_count += 1 - typer.echo("") - - # 4. 总结 - typer.echo("[cyan]═══════════════════════════════════════[/cyan]") - typer.echo(f"[cyan]配置完成: {success_count}/{len(hosts)} 成功[/cyan]") - typer.echo("[cyan]═══════════════════════════════════════[/cyan]\n") - - if success_count == len(hosts): - typer.echo("[green]🎉 所有主机配置成功！[/green]\n") - elif success_count > 0: - typer.echo("[yellow]⚠️ 部分主机配置失败[/yellow]\n") - else: - typer.echo("[red]❌ 所有主机配置失败[/red]\n") - - return (success_count, len(hosts)) diff --git a/packages/sage-cli/src/sage/cli/commands/platform/version.py b/packages/sage-cli/src/sage/cli/commands/platform/version.py deleted file mode 100644 index cbc04b0b13..0000000000 --- a/packages/sage-cli/src/sage/cli/commands/platform/version.py +++ /dev/null @@ -1,61 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE CLI Version Command -显示版本信息 -""" - -import typer - -app = typer.Typer(name="version", help="📋 版本信息") - - -def _load_version(): - """加载版本信息""" - try: - # 尝试从本地包的版本文件加载 - from sage.common._version import __version__ - - return __version__ - except ImportError: - # 如果本地版本文件不存在，尝试从项目根目录加载（开发环境） - try: - from sage.common.config import find_sage_project_root - - root_dir = find_sage_project_root() - version_file = root_dir / "_version.py" - - if version_file.exists(): - version_globals = {} - with open(version_file, encoding="utf-8") as f: - exec(f.read(), version_globals) - return version_globals.get("__version__", "0.1.3") - except Exception: - pass - - # 最后的默认值 - return "0.1.3" - - -@app.command() -def show(): - """显示版本信息""" - version = _load_version() - print("🚀 SAGE - Streaming-Augmented Generative Execution") - print(f"Version: {version}") - print("Author: IntelliStream") - print("Repository: https://github.com/intellistream/SAGE") - print("") - print("💡 Tips:") - print(" sage job list # 查看作业列表") - print(" sage gateway start # 启动API网关服务") - print(" sage extensions # 查看可用扩展") - print(" sage-dev --help # 开发工具") - print(" sage jobmanager start # 启动作业管理器服务") - - -# 为了向后兼容，也提供一个直接的version命令 -@app.callback(invoke_without_command=True) -def version_callback(ctx: typer.Context): - """显示版本信息""" - if ctx.invoked_subcommand is None: - show() diff --git a/packages/sage-cli/src/sage/cli/commands/platform/worker.py b/packages/sage-cli/src/sage/cli/commands/platform/worker.py deleted file mode 100644 index eccca4bc5c..0000000000 --- a/packages/sage-cli/src/sage/cli/commands/platform/worker.py +++ /dev/null @@ -1,860 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE Worker Manager CLI -Ray Worker节点管理相关命令 -""" - -import os -import subprocess -import tempfile -import time - -import typer - -from ...management.config_manager import get_config_manager -from ...management.deployment_manager import DeploymentManager - -app = typer.Typer(name="worker", help="Ray Worker节点管理") - - -def execute_remote_command(host: str, port: int, command: str, timeout: int = 60) -> bool: - """在远程主机上执行命令""" - config_manager = get_config_manager() - ssh_config = config_manager.get_ssh_config() - ssh_user = ssh_config.get("user", "sage") - ssh_key_path = os.path.expanduser(ssh_config.get("key_path", "~/.ssh/id_rsa")) - - typer.echo(f"🔗 连接到 {ssh_user}@{host}:{port}") - - # 创建临时脚本文件 - with tempfile.NamedTemporaryFile(mode="w", suffix=".sh", delete=False) as temp_script: - temp_script.write("#!/bin/bash\n") - temp_script.write(command) - temp_script_path = temp_script.name - - try: - ssh_cmd = [ - "ssh", - "-i", - ssh_key_path, - "-p", - str(port), - "-o", - "StrictHostKeyChecking=no", - "-o", - "UserKnownHostsFile=/dev/null", - "-o", - f"ConnectTimeout={ssh_config.get('connect_timeout', 10)}", - "-o", - "ServerAliveInterval=60", - "-o", - "ServerAliveCountMax=3", - f"{ssh_user}@{host}", - "bash -s", - ] - - with open(temp_script_path) as script_file: - result = subprocess.run( - ssh_cmd, - stdin=script_file, - capture_output=True, - text=True, - timeout=timeout, - ) - - if result.stdout: - typer.echo(result.stdout) - if result.stderr: - typer.echo(result.stderr, err=True) - - return result.returncode == 0 - - except subprocess.TimeoutExpired: - typer.echo(f"❌ Remote command timeout ({timeout}s)") - return False - except Exception as e: - typer.echo(f"❌ Remote command failed: {e}") - return False - finally: - # 清理临时文件 - try: - os.unlink(temp_script_path) - except OSError: - pass - - -def get_conda_init_code(conda_env: str = "sage") -> str: - """获取Conda环境初始化代码 - - 支持 base 环境和自定义环境（如 sage）。 - base 环境的路径是 $CONDA_BASE/bin，其他环境是 $CONDA_BASE/envs/{env}/bin。 - """ - return f""" -# 多种conda安装路径尝试 -CONDA_BASE="" -for conda_path in \\ - "$HOME/miniconda3" \\ - "$HOME/anaconda3" \\ - "/opt/conda" \\ - "/usr/local/miniconda3" \\ - "/usr/local/anaconda3"; do - if [ -f "$conda_path/etc/profile.d/conda.sh" ]; then - source "$conda_path/etc/profile.d/conda.sh" - CONDA_BASE="$conda_path" - echo "[INFO] 找到conda: $conda_path" - CONDA_FOUND=true - break - fi -done - -if [ -z "$CONDA_FOUND" ]; then - echo "[ERROR] 未找到conda安装，请检查conda是否正确安装" - exit 1 -fi - -# 激活环境 -if ! conda activate {conda_env}; then - echo "[ERROR] 无法激活conda环境: {conda_env}" - echo "[INFO] 可用的conda环境:" - conda env list - exit 1 -fi - -echo "[SUCCESS] 已激活conda环境: {conda_env}" - -# 设置 RAY_CMD 变量（根据环境类型选择正确路径） -# base 环境: $CONDA_BASE/bin/ray -# 其他环境: $CONDA_BASE/envs/{conda_env}/bin/ray -if [ "{conda_env}" = "base" ]; then - RAY_CMD="$CONDA_BASE/bin/ray" -else - RAY_CMD="$CONDA_BASE/envs/{conda_env}/bin/ray" -fi -if [ ! -f "$RAY_CMD" ]; then - # 如果 conda env 中没有 ray，尝试使用 PATH 中的 - RAY_CMD=$(which ray 2>/dev/null || echo "ray") -fi -echo "[INFO] RAY_CMD: $RAY_CMD" -""" - - -@app.command("start") -def start_workers(): - """启动所有Ray Worker节点""" - typer.echo("🚀 启动Ray Worker节点...") - - config_manager = get_config_manager() - head_config = config_manager.get_head_config() - worker_config = config_manager.get_worker_config() - remote_config = config_manager.get_remote_config() - workers = config_manager.get_workers_ssh_hosts() - - if not workers: - typer.echo("❌ 未配置任何worker节点") - return # 没有worker节点不应该视为错误 - - head_host = head_config.get("host", "localhost") - head_port = head_config.get("head_port", 6379) - worker_bind_host = worker_config.get("bind_host", "localhost") - worker_temp_dir = worker_config.get("temp_dir", "/tmp/ray_worker") - worker_log_dir = worker_config.get("log_dir", "/tmp/sage_worker_logs") - # 读取 CPU/GPU 资源限制配置（用于容器环境） - worker_num_cpus = worker_config.get("num_cpus") - worker_num_gpus = worker_config.get("num_gpus") - - remote_config.get("ray_command") or "ray" - conda_env = remote_config.get("conda_env", "sage") - - typer.echo("📋 配置信息:") - typer.echo(f" Head节点: {head_host}:{head_port}") - typer.echo(f" Worker节点: {len(workers)} 个") - if worker_num_cpus is not None: - typer.echo(f" Worker CPUs: {worker_num_cpus}") - if worker_num_gpus is not None: - typer.echo(f" Worker GPUs: {worker_num_gpus}") - typer.echo(f" Worker绑定主机: {worker_bind_host}") - - success_count = 0 - import socket - - total_count = len(workers) - - # 构建 CPU/GPU 资源限制参数（用于容器环境） - resource_args = "" - if worker_num_cpus is not None: - resource_args += f" --num-cpus={worker_num_cpus}" - if worker_num_gpus is not None: - resource_args += f" --num-gpus={worker_num_gpus}" - - for i, (host, port) in enumerate(workers, 1): - # Resolve hostname to IP to ensure worker binds to the correct interface - try: - node_ip = socket.gethostbyname(host) - except Exception: - node_ip = host # Fallback to hostname if resolution fails - - typer.echo(f"\n🔧 启动Worker节点 {i}/{total_count}: {host}:{port} (IP: {node_ip})") - - start_command = f"""set -e -export PYTHONUNBUFFERED=1 - -# 当前主机名 -CURRENT_HOST='{host}' -# 解析后的IP -RESOLVED_IP='{node_ip}' - -# 创建必要目录 -LOG_DIR='{worker_log_dir}' -WORKER_TEMP_DIR='{worker_temp_dir}' -mkdir -p "$LOG_DIR" "$WORKER_TEMP_DIR" - -# 记录启动时间 -echo "===============================================" | tee -a "$LOG_DIR/worker.log" -echo "Ray Worker启动 ($(date '+%Y-%m-%d %H:%M:%S'))" | tee -a "$LOG_DIR/worker.log" -echo "Worker节点: $(hostname)" | tee -a "$LOG_DIR/worker.log" -echo "目标头节点: {head_host}:{head_port}" | tee -a "$LOG_DIR/worker.log" -echo "绑定主机: {worker_bind_host}" | tee -a "$LOG_DIR/worker.log" -echo "===============================================" | tee -a "$LOG_DIR/worker.log" - -# 初始化conda环境 -{get_conda_init_code(conda_env)} - -# 记录版本信息 -echo "[INFO] Python版本: $(python --version 2>&1)" | tee -a "$LOG_DIR/worker.log" -echo "[INFO] Ray版本: $($RAY_CMD --version 2>&1)" | tee -a "$LOG_DIR/worker.log" - -# 停止现有的ray进程 -echo "[INFO] 停止现有Ray进程..." | tee -a "$LOG_DIR/worker.log" -$RAY_CMD stop >> "$LOG_DIR/worker.log" 2>&1 || true -sleep 2 - -# 强制清理残留进程 -echo "[INFO] 强制清理所有Ray相关进程..." | tee -a "$LOG_DIR/worker.log" -# 使用更精确的匹配模式，并限制为当前用户 -pgrep -u $(whoami) -x raylet | xargs -r kill -9 2>/dev/null || true -pgrep -u $(whoami) -x gcs_server | xargs -r kill -9 2>/dev/null || true -pgrep -u $(whoami) -f "ray/dashboard/[d]ashboard.py" | xargs -r kill -9 2>/dev/null || true -pgrep -u $(whoami) -f "ray/dashboard/[a]gent.py" | xargs -r kill -9 2>/dev/null || true -pgrep -u $(whoami) -f "ray.util.client.[s]erver" | xargs -r kill -9 2>/dev/null || true -pgrep -u $(whoami) -f "ray/autoscaler/_private/[m]onitor.py" | xargs -r kill -9 2>/dev/null || true -pgrep -u $(whoami) -f "ray/_private/[l]og_monitor.py" | xargs -r kill -9 2>/dev/null || true -pgrep -u $(whoami) -f "ray/core/src/ray/raylet/raylet" | xargs -r kill -9 2>/dev/null || true - -# for proc in raylet core_worker log_monitor; do -# PIDS=$(pgrep -f "$proc" 2>/dev/null || true) -# if [[ -n "$PIDS" ]]; then -# echo "[INFO] 发现$proc进程: $PIDS" | tee -a "$LOG_DIR/worker.log" -# echo "$PIDS" | xargs -r kill -TERM 2>/dev/null || true -# sleep 2 -# fi -# done - -# 清理Ray会话目录 -echo "[INFO] 清理Ray会话目录..." | tee -a "$LOG_DIR/worker.log" -rm -rf "$WORKER_TEMP_DIR"/* 2>/dev/null || true - -sleep 3 - -# 设置节点IP -NODE_IP="{worker_bind_host}" -if [ "{worker_bind_host}" = "localhost" ] || [ "{worker_bind_host}" = "127.0.0.1" ]; then - NODE_IP="$RESOLVED_IP" -fi -echo "[INFO] 使用节点IP: $NODE_IP" | tee -a "$LOG_DIR/worker.log" - -# 设置环境变量 -export RAY_TMPDIR="$WORKER_TEMP_DIR" -export RAY_DISABLE_IMPORT_WARNING=1 - -# 测试连通性 -echo "[INFO] 测试到头节点的连通性..." | tee -a "$LOG_DIR/worker.log" -if python -c "import socket; s = socket.socket(); s.settimeout(10); s.connect(('{head_host}', {head_port})); s.close()" 2>/dev/null; then - echo "[SUCCESS] 可以连接到头节点 {head_host}:{head_port}" | tee -a "$LOG_DIR/worker.log" -else - echo "[WARNING] 无法验证到头节点的连通性，但继续尝试启动Ray" | tee -a "$LOG_DIR/worker.log" -fi - -# 启动ray worker -echo "[INFO] 启动Ray Worker进程..." | tee -a "$LOG_DIR/worker.log" -RAY_START_CMD="$RAY_CMD start --address={head_host}:{head_port} --node-ip-address=$NODE_IP{resource_args}" -echo "[INFO] 执行命令: $RAY_START_CMD" | tee -a "$LOG_DIR/worker.log" - -# 执行Ray启动命令并捕获输出和退出码 -set +e # 临时允许命令失败 -RAY_OUTPUT=$($RAY_START_CMD 2>&1) -RAY_EXIT_CODE=$? -set -e # 重新开启严格模式 - -# 将输出写入日志 -echo "$RAY_OUTPUT" | tee -a "$LOG_DIR/worker.log" - -# 等待一下让Ray有时间完全启动 -sleep 5 - -# 检查Ray进程是否真正启动成功 -RAY_PIDS=$(pgrep -f 'raylet|core_worker' || true) -if [[ -n "$RAY_PIDS" ]]; then - echo "[SUCCESS] Ray Worker启动成功，进程PIDs: $RAY_PIDS" | tee -a "$LOG_DIR/worker.log" - echo "[INFO] 节点已连接到集群: {head_host}:{head_port}" | tee -a "$LOG_DIR/worker.log" - - # 验证Ray状态 - if timeout 10 $RAY_CMD status > /dev/null 2>&1; then - echo "[SUCCESS] Ray集群连接验证成功" | tee -a "$LOG_DIR/worker.log" - else - echo "[WARNING] Ray集群连接验证失败，但进程正在运行" | tee -a "$LOG_DIR/worker.log" - fi -elif [ $RAY_EXIT_CODE -eq 0 ]; then - echo "[WARNING] Ray启动命令成功但未发现运行中的进程，可能仍在启动中" | tee -a "$LOG_DIR/worker.log" - sleep 3 - # 再次检查 - RAY_PIDS=$(pgrep -f 'raylet|core_worker' || true) - if [[ -n "$RAY_PIDS" ]]; then - echo "[SUCCESS] Ray Worker延迟启动成功，进程PIDs: $RAY_PIDS" | tee -a "$LOG_DIR/worker.log" - else - echo "[ERROR] Ray Worker启动失败，未发现进程且退出码: $RAY_EXIT_CODE" | tee -a "$LOG_DIR/worker.log" - exit 1 - fi -else - echo "[ERROR] Ray Worker启动失败，退出码: $RAY_EXIT_CODE" | tee -a "$LOG_DIR/worker.log" - echo "[DEBUG] Ray启动输出: $RAY_OUTPUT" | tee -a "$LOG_DIR/worker.log" - exit 1 -fi""" - - if execute_remote_command(host, port, start_command, 120): - typer.echo(f"✅ Worker节点 {host} 启动成功") - success_count += 1 - else: - typer.echo(f"❌ Worker节点 {host} 启动失败") - - typer.echo(f"\n📊 启动结果: {success_count}/{total_count} 个节点启动成功") - if success_count == total_count: - typer.echo("✅ 所有Worker节点启动成功！") - else: - typer.echo("⚠️ 部分Worker节点启动失败") - raise typer.Exit(1) - - -@app.command("stop") -def stop_workers(force: bool = typer.Option(False, "--force", "-f", help="强制停止所有Ray进程")): - """停止所有Ray Worker节点""" - typer.echo("🛑 停止Ray Worker节点...") - - config_manager = get_config_manager() - worker_config = config_manager.get_worker_config() - remote_config = config_manager.get_remote_config() - workers = config_manager.get_workers_ssh_hosts() - - if not workers: - typer.echo("❌ 未配置任何worker节点") - raise typer.Exit(1) - - worker_temp_dir = worker_config.get("temp_dir", "/tmp/ray_worker") - worker_log_dir = worker_config.get("log_dir", "/tmp/sage_worker_logs") - remote_config.get("ray_command") or "ray" - conda_env = remote_config.get("conda_env", "sage") - - success_count = 0 - total_count = len(workers) - - for i, (host, port) in enumerate(workers, 1): - typer.echo(f"\n🔧 停止Worker节点 {i}/{total_count}: {host}:{port}") - - if force: - # 强制模式：直接杀死所有进程 - stop_command = f'''set +e -export PYTHONUNBUFFERED=1 - -LOG_DIR='{worker_log_dir}' -mkdir -p "$LOG_DIR" - -echo "===============================================" | tee -a "$LOG_DIR/worker.log" -echo "Ray Worker强制停止 ($(date '+%Y-%m-%d %H:%M:%S'))" | tee -a "$LOG_DIR/worker.log" -echo "Worker节点: $(hostname)" | tee -a "$LOG_DIR/worker.log" -echo "===============================================" | tee -a "$LOG_DIR/worker.log" - -# 强制杀死所有Ray相关进程 -echo "[INFO] 强制终止所有Ray进程..." | tee -a "$LOG_DIR/worker.log" -for pattern in 'ray.*start' 'raylet' 'core_worker' 'ray::' 'python.*ray'; do - PIDS=$(pgrep -f "$pattern" 2>/dev/null || true) - if [[ -n "$PIDS" ]]; then - echo "[INFO] 强制终止进程: $pattern (PIDs: $PIDS)" | tee -a "$LOG_DIR/worker.log" - echo "$PIDS" | xargs -r kill -KILL 2>/dev/null || true - fi -done - -# 清理临时文件 -WORKER_TEMP_DIR='{worker_temp_dir}' -if [[ -d "$WORKER_TEMP_DIR" ]]; then - echo "[INFO] 清理临时目录: $WORKER_TEMP_DIR" | tee -a "$LOG_DIR/worker.log" - rm -rf "$WORKER_TEMP_DIR"/* 2>/dev/null || true -fi - -echo "[SUCCESS] Ray Worker强制停止完成 ($(date '+%Y-%m-%d %H:%M:%S'))" | tee -a "$LOG_DIR/worker.log"''' - else: - # 正常模式：优雅停止 - stop_command = f'''set +e -export PYTHONUNBUFFERED=1 - -LOG_DIR='{worker_log_dir}' -mkdir -p "$LOG_DIR" - -echo "===============================================" | tee -a "$LOG_DIR/worker.log" -echo "Ray Worker停止 ($(date '+%Y-%m-%d %H:%M:%S'))" | tee -a "$LOG_DIR/worker.log" -echo "Worker节点: $(hostname)" | tee -a "$LOG_DIR/worker.log" -echo "===============================================" | tee -a "$LOG_DIR/worker.log" - -# 初始化conda环境 -{get_conda_init_code(conda_env)} - -# 优雅停止 -echo "[INFO] 正在优雅停止Ray进程..." | tee -a "$LOG_DIR/worker.log" -$RAY_CMD stop >> "$LOG_DIR/worker.log" 2>&1 || true -sleep 2 - -# 强制停止残留进程 -echo "[INFO] 清理残留的Ray进程..." | tee -a "$LOG_DIR/worker.log" -for pattern in 'ray.*start' 'raylet' 'core_worker' 'ray::'; do - PIDS=$(pgrep -f "$pattern" 2>/dev/null || true) - if [[ -n "$PIDS" ]]; then - echo "[INFO] 终止进程: $pattern (PIDs: $PIDS)" | tee -a "$LOG_DIR/worker.log" - echo "$PIDS" | xargs -r kill -TERM 2>/dev/null || true - sleep 1 - echo "$PIDS" | xargs -r kill -KILL 2>/dev/null || true - fi -done - -# 清理临时文件 -WORKER_TEMP_DIR='{worker_temp_dir}' -if [[ -d "$WORKER_TEMP_DIR" ]]; then - echo "[INFO] 清理临时目录: $WORKER_TEMP_DIR" | tee -a "$LOG_DIR/worker.log" - rm -rf "$WORKER_TEMP_DIR"/* 2>/dev/null || true -fi - -echo "[SUCCESS] Ray Worker已停止 ($(date '+%Y-%m-%d %H:%M:%S'))" | tee -a "$LOG_DIR/worker.log"''' - - if execute_remote_command(host, port, stop_command, 60): - typer.echo(f"✅ Worker节点 {host} 停止成功") - success_count += 1 - else: - typer.echo(f"⚠️ Worker节点 {host} 停止完成（可能本来就未运行）") - success_count += 1 # 停止操作通常允许失败 - - typer.echo(f"\n📊 停止结果: {success_count}/{total_count} 个节点处理完成") - typer.echo("✅ 所有Worker节点停止操作完成！") - - -@app.command("status") -def status_workers(): - """检查所有Ray Worker节点状态""" - typer.echo("📊 检查Ray Worker节点状态...") - - config_manager = get_config_manager() - head_config = config_manager.get_head_config() - worker_config = config_manager.get_worker_config() - remote_config = config_manager.get_remote_config() - workers = config_manager.get_workers_ssh_hosts() - - if not workers: - typer.echo("❌ 未配置任何worker节点") - raise typer.Exit(1) - - head_host = head_config.get("host", "localhost") - head_port = head_config.get("head_port", 6379) - worker_log_dir = worker_config.get("log_dir", "/tmp/sage_worker_logs") - remote_config.get("ray_command") or "ray" - conda_env = remote_config.get("conda_env", "sage") - - running_count = 0 - total_count = len(workers) - - for i, (host, port) in enumerate(workers, 1): - typer.echo(f"\n📋 检查Worker节点 {i}/{total_count}: {host}:{port}") - - status_command = f'''set +e -export PYTHONUNBUFFERED=1 - -echo "===============================================" -echo "节点状态检查: $(hostname) ($(date '+%Y-%m-%d %H:%M:%S'))" -echo "===============================================" - -# 初始化conda环境 -{get_conda_init_code(conda_env)} - -# 检查Ray进程 -echo "--- Ray进程状态 ---" -RAY_PIDS=$(pgrep -f 'raylet|core_worker|ray.*start' 2>/dev/null || true) -if [[ -n "$RAY_PIDS" ]]; then - echo "[运行中] 发现Ray进程:" - echo "$RAY_PIDS" | while read pid; do - if [[ -n "$pid" ]]; then - ps -p "$pid" -o pid,ppid,pcpu,pmem,etime,cmd --no-headers 2>/dev/null || true - fi - done - - echo "" - echo "--- Ray集群连接状态 ---" - timeout 10 $RAY_CMD status 2>/dev/null || echo "[警告] 无法获取Ray集群状态" - exit 0 -else - echo "[已停止] 未发现Ray进程" - exit 1 -fi - -echo "" -echo "--- 网络连通性测试 ---" -if timeout 5 nc -z {head_host} {head_port} 2>/dev/null; then - echo "[正常] 可以连接到头节点 {head_host}:{head_port}" -else - echo "[异常] 无法连接到头节点 {head_host}:{head_port}" -fi - -# 显示最近的日志 -LOG_DIR='{worker_log_dir}' -if [[ -f "$LOG_DIR/worker.log" ]]; then - echo "" - echo "--- 最近的日志 (最后5行) ---" - tail -5 "$LOG_DIR/worker.log" 2>/dev/null || echo "无法读取日志文件" -fi - -echo "==============================================="''' - - if execute_remote_command(host, port, status_command, 30): - typer.echo(f"✅ Worker节点 {host} 正在运行") - running_count += 1 - else: - typer.echo(f"❌ Worker节点 {host} 未运行或检查失败") - - typer.echo(f"\n📊 状态统计: {running_count}/{total_count} 个Worker节点正在运行") - if running_count == total_count: - typer.echo("✅ 所有Worker节点都在正常运行！") - elif running_count > 0: - typer.echo("⚠️ 部分Worker节点未运行") - else: - typer.echo("❌ 没有Worker节点在运行") - - -@app.command("restart") -def restart_workers(): - """重启所有Ray Worker节点""" - typer.echo("🔄 重启Ray Worker节点...") - - # 先停止 - typer.echo("第1步: 停止所有Worker节点") - stop_workers() - - # 等待 - typer.echo("⏳ 等待3秒后重新启动...") - time.sleep(3) - - # 再启动 - typer.echo("第2步: 启动所有Worker节点") - start_workers() - - typer.echo("✅ Worker节点重启完成！") - - -@app.command("config") -def show_config(): - """显示当前Worker配置信息""" - typer.echo("📋 当前Worker配置信息") - - config_manager = get_config_manager() - head_config = config_manager.get_head_config() - worker_config = config_manager.get_worker_config() - ssh_config = config_manager.get_ssh_config() - remote_config = config_manager.get_remote_config() - workers = config_manager.get_workers_ssh_hosts() - - typer.echo(f"Head节点: {head_config.get('host', 'N/A')}") - typer.echo(f"Head端口: {head_config.get('head_port', 'N/A')}") - typer.echo(f"Dashboard端口: {head_config.get('dashboard_port', 'N/A')}") - typer.echo(f"Dashboard主机: {head_config.get('dashboard_host', 'N/A')}") - typer.echo(f"Worker绑定主机: {worker_config.get('bind_host', 'N/A')}") - typer.echo(f"Worker节点数量: {len(workers)}") - if workers: - for i, (host, port) in enumerate(workers, 1): - typer.echo(f" Worker {i}: {host}:{port}") - typer.echo(f"SSH用户: {ssh_config.get('user', 'N/A')}") - typer.echo(f"SSH密钥路径: {ssh_config.get('key_path', 'N/A')}") - typer.echo(f"Worker临时目录: {worker_config.get('temp_dir', 'N/A')}") - typer.echo(f"Worker日志目录: {worker_config.get('log_dir', 'N/A')}") - typer.echo(f"远程SAGE目录: {remote_config.get('sage_home', 'N/A')}") - typer.echo(f"远程Python路径: {remote_config.get('python_path', 'N/A')}") - typer.echo(f"远程Ray命令: {remote_config.get('ray_command', 'N/A')}") - - -@app.command("deploy") -def deploy_workers(): - """部署项目到所有Worker节点""" - typer.echo("🚀 开始部署到Worker节点...") - - deployment_manager = DeploymentManager() - success_count, total_count = deployment_manager.deploy_to_all_workers() - - if success_count == total_count: - typer.echo("✅ 所有节点部署成功！") - else: - typer.echo("⚠️ 部分节点部署失败") - raise typer.Exit(1) - - -@app.command("add") -def add_worker(node: str = typer.Argument(..., help="节点地址，格式为 host:port")): - """动态添加新的Worker节点""" - typer.echo(f"➕ 添加新Worker节点: {node}") - - # 解析节点地址 - if ":" in node: - host, port_str = node.split(":", 1) - try: - port = int(port_str) - except ValueError: - typer.echo("❌ 端口号必须是数字") - raise typer.Exit(1) - else: - host = node - port = 22 - - config_manager = get_config_manager() - - # 添加到配置 - if config_manager.add_worker_ssh_host(host, port): - typer.echo(f"✅ 已添加Worker节点 {host}:{port} 到配置") - else: - typer.echo(f"⚠️ Worker节点 {host}:{port} 已存在") - - # 部署到新节点 - typer.echo(f"🚀 开始部署到新节点 {host}:{port}...") - deployment_manager = DeploymentManager() - - if deployment_manager.deploy_to_worker(host, port): - typer.echo(f"✅ 新节点 {host}:{port} 部署成功") - - # 启动worker - typer.echo("🔧 启动新Worker节点...") - head_config = config_manager.get_head_config() - worker_config = config_manager.get_worker_config() - remote_config = config_manager.get_remote_config() - - head_host = head_config.get("host", "localhost") - head_port = head_config.get("head_port", 6379) - worker_bind_host = worker_config.get("bind_host", "localhost") - worker_temp_dir = worker_config.get("temp_dir", "/tmp/ray_worker") - worker_log_dir = worker_config.get("log_dir", "/tmp/sage_worker_logs") - worker_num_cpus = worker_config.get("num_cpus") - worker_num_gpus = worker_config.get("num_gpus") - remote_config.get("ray_command") or "ray" - conda_env = remote_config.get("conda_env", "sage") - - # 构建 CPU/GPU 资源限制参数（用于容器环境） - resource_args = "" - if worker_num_cpus is not None: - resource_args += f" --num-cpus={worker_num_cpus}" - if worker_num_gpus is not None: - resource_args += f" --num-gpus={worker_num_gpus}" - - # 解析主机名为IP，避免 --node-ip-address 传入不可用的占位值 - import socket - - try: - resolved_ip = socket.gethostbyname(host) - except Exception: - resolved_ip = host - - start_command = f"""set -e -export PYTHONUNBUFFERED=1 - -CURRENT_HOST='{host}' -RESOLVED_IP='{resolved_ip}' -LOG_DIR='{worker_log_dir}' -WORKER_TEMP_DIR='{worker_temp_dir}' -mkdir -p "$LOG_DIR" "$WORKER_TEMP_DIR" - -echo "===============================================" | tee -a "$LOG_DIR/worker.log" -echo "新Worker节点启动 ($(date '+%Y-%m-%d %H:%M:%S'))" | tee -a "$LOG_DIR/worker.log" -echo "Worker节点: $(hostname)" | tee -a "$LOG_DIR/worker.log" -echo "目标头节点: {head_host}:{head_port}" | tee -a "$LOG_DIR/worker.log" -echo "===============================================" | tee -a "$LOG_DIR/worker.log" - -# 初始化conda环境 -{get_conda_init_code(conda_env)} - -# 停止现有的ray进程 -$RAY_CMD stop >> "$LOG_DIR/worker.log" 2>&1 || true -sleep 2 - -# 强制清理残留进程与默认Ray会话目录，避免 node_ip_address.json 记录的旧值导致异常 -echo "[INFO] 强制清理所有Ray相关进程..." | tee -a "$LOG_DIR/worker.log" -pgrep -u $(whoami) -x raylet | xargs -r kill -9 2>/dev/null || true -pgrep -u $(whoami) -x gcs_server | xargs -r kill -9 2>/dev/null || true -pgrep -u $(whoami) -f "ray/dashboard/[d]ashboard.py" | xargs -r kill -9 2>/dev/null || true -pgrep -u $(whoami) -f "ray/dashboard/[a]gent.py" | xargs -r kill -9 2>/dev/null || true -pgrep -u $(whoami) -f "ray.util.client.[s]erver" | xargs -r kill -9 2>/dev/null || true -pgrep -u $(whoami) -f "ray/_private/[l]og_monitor.py" | xargs -r kill -9 2>/dev/null || true -pgrep -u $(whoami) -f "ray/core/src/ray/raylet/raylet" | xargs -r kill -9 2>/dev/null || true - -# 清理常见Ray临时目录 -echo "[INFO] 清理Ray临时目录 /tmp/ray" | tee -a "$LOG_DIR/worker.log" -rm -rf /tmp/ray/* 2>/dev/null || true - -# 设置节点IP -NODE_IP="{worker_bind_host}" -if [ "{worker_bind_host}" = "localhost" ] || [ "{worker_bind_host}" = "127.0.0.1" ]; then - NODE_IP="$RESOLVED_IP" -fi - -export RAY_TMPDIR="$WORKER_TEMP_DIR" -export RAY_DISABLE_IMPORT_WARNING=1 - -# 启动ray worker -echo "[INFO] 启动Ray Worker进程..." | tee -a "$LOG_DIR/worker.log" - -RAY_START_CMD="$RAY_CMD start --address={head_host}:{head_port} --node-ip-address=$NODE_IP --temp-dir=$WORKER_TEMP_DIR{resource_args}" - -echo "[INFO] 执行命令: $RAY_START_CMD" | tee -a "$LOG_DIR/worker.log" - -# 执行Ray启动命令并捕获输出和退出码 -set +e # 临时允许命令失败 -RAY_OUTPUT=$($RAY_START_CMD 2>&1) -RAY_EXIT_CODE=$? -set -e # 重新开启严格模式 - -# 将输出写入日志 -echo "$RAY_OUTPUT" | tee -a "$LOG_DIR/worker.log" - -# 等待一下让Ray有时间启动 -sleep 5 - -# 检查Ray是否启动成功 -RAY_PIDS=$(pgrep -f 'raylet|core_worker' || true) -if [[ -n "$RAY_PIDS" ]]; then - echo "[SUCCESS] 新Worker节点启动成功，PIDs: $RAY_PIDS" | tee -a "$LOG_DIR/worker.log" -elif [ $RAY_EXIT_CODE -eq 0 ]; then - echo "[WARNING] Ray启动命令成功但未发现运行中的进程，可能仍在启动中" | tee -a "$LOG_DIR/worker.log" - sleep 3 - # 再次检查 - RAY_PIDS=$(pgrep -f 'raylet|core_worker' || true) - if [[ -n "$RAY_PIDS" ]]; then - echo "[SUCCESS] 新Worker节点延迟启动成功，PIDs: $RAY_PIDS" | tee -a "$LOG_DIR/worker.log" - else - echo "[ERROR] 新Worker节点启动失败，未发现进程" | tee -a "$LOG_DIR/worker.log" - echo "[DEBUG] Ray启动输出: $RAY_OUTPUT" | tee -a "$LOG_DIR/worker.log" - exit 1 - fi -else - echo "[ERROR] 新Worker节点启动失败，退出码: $RAY_EXIT_CODE" | tee -a "$LOG_DIR/worker.log" - echo "[DEBUG] Ray启动输出: $RAY_OUTPUT" | tee -a "$LOG_DIR/worker.log" - exit 1 -fi""" - - if execute_remote_command(host, port, start_command, 30): - typer.echo(f"✅ 新Worker节点 {host}:{port} 启动成功") - else: - typer.echo(f"❌ 新Worker节点 {host}:{port} 启动失败") - raise typer.Exit(1) - else: - typer.echo(f"❌ 新节点 {host}:{port} 部署失败") - raise typer.Exit(1) - - -@app.command("remove") -def remove_worker(node: str = typer.Argument(..., help="节点地址，格式为 host:port")): - """移除Worker节点""" - typer.echo(f"➖ 移除Worker节点: {node}") - - # 解析节点地址 - if ":" in node: - host, port_str = node.split(":", 1) - try: - port = int(port_str) - except ValueError: - typer.echo("❌ 端口号必须是数字") - raise typer.Exit(1) - else: - host = node - port = 22 - - config_manager = get_config_manager() - - # 先停止该节点上的worker - typer.echo(f"🛑 停止Worker节点 {host}:{port}...") - worker_config = config_manager.get_worker_config() - remote_config = config_manager.get_remote_config() - - worker_temp_dir = worker_config.get("temp_dir", "/tmp/ray_worker") - worker_log_dir = worker_config.get("log_dir", "/tmp/sage_worker_logs") - remote_config.get("ray_command") or "ray" - conda_env = remote_config.get("conda_env", "sage") - - stop_command = f'''set +e -LOG_DIR='{worker_log_dir}' -mkdir -p "$LOG_DIR" - -echo "停止Worker节点 ($(date '+%Y-%m-%d %H:%M:%S'))" | tee -a "$LOG_DIR/worker.log" - -# 初始化conda环境 -{get_conda_init_code(conda_env)} - -# 停止Ray -$RAY_CMD stop >> "$LOG_DIR/worker.log" 2>&1 || true - -# 强制清理 -for pattern in 'ray.*start' 'raylet' 'core_worker'; do - PIDS=$(pgrep -f "$pattern" 2>/dev/null || true) - if [[ -n "$PIDS" ]]; then - echo "$PIDS" | xargs -r kill -TERM 2>/dev/null || true - sleep 1 - echo "$PIDS" | xargs -r kill -KILL 2>/dev/null || true - fi -done - -# 清理临时文件 -rm -rf {worker_temp_dir}/* 2>/dev/null || true - -echo "Worker节点已停止" | tee -a "$LOG_DIR/worker.log"''' - - if execute_remote_command(host, port, stop_command, 60): - typer.echo(f"✅ Worker节点 {host}:{port} 已停止") - else: - typer.echo(f"⚠️ Worker节点 {host}:{port} 停止可能未完全成功") - - # 从配置中移除 - if config_manager.remove_worker_ssh_host(host, port): - typer.echo(f"✅ 已从配置中移除Worker节点 {host}:{port}") - else: - typer.echo(f"⚠️ Worker节点 {host}:{port} 不在配置中") - - typer.echo(f"✅ Worker节点 {host}:{port} 移除完成") - - -@app.command("list") -def list_workers(): - """列出所有配置的Worker节点""" - typer.echo("📋 配置的Worker节点列表") - - config_manager = get_config_manager() - workers = config_manager.get_workers_ssh_hosts() - - if not workers: - typer.echo("❌ 未配置任何Worker节点") - typer.echo("💡 使用 'sage worker add [port]' 添加Worker节点") - return - - typer.echo(f"📊 共配置了 {len(workers)} 个Worker节点:") - for i, (host, port) in enumerate(workers, 1): - typer.echo(f" {i}. {host}:{port}") - - typer.echo("\n💡 使用 'sage worker status' 检查节点状态") - - -@app.command("version") -def version_command(): - """Show version information.""" - typer.echo("👥 SAGE Worker Manager") - typer.echo("Version: 1.0.1") - typer.echo("Author: IntelliStream Team") - typer.echo("Repository: https://github.com/intellistream/SAGE") - - -if __name__ == "__main__": - app() diff --git a/packages/sage-cli/src/sage/cli/core/__init__.py b/packages/sage-cli/src/sage/cli/core/__init__.py deleted file mode 100644 index 6804266177..0000000000 --- a/packages/sage-cli/src/sage/cli/core/__init__.py +++ /dev/null @@ -1,16 +0,0 @@ -""" -SAGE - Streaming-Augmented Generative Execution -""" - -# 直接从本包的_version模块加载版本信息 -try: - from sage.cli._version import ( # type: ignore[import-not-found] - __author__, - __email__, - __version__, - ) -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" diff --git a/packages/sage-cli/src/sage/cli/core/base.py b/packages/sage-cli/src/sage/cli/core/base.py deleted file mode 100644 index 947d53d4ad..0000000000 --- a/packages/sage-cli/src/sage/cli/core/base.py +++ /dev/null @@ -1,370 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE CLI Base Classes -===================== - -基础CLI命令类和装饰器 -""" - -import functools -from abc import ABC, abstractmethod -from collections.abc import Callable -from pathlib import Path -from typing import Any - -import typer - -from .config import load_and_validate_config -from .exceptions import CLIException, ConfigurationError, ConnectionError -from .output import OutputFormatter, print_status -from .utils import find_project_root - - -class BaseCommand(ABC): - """基础命令类""" - - def __init__( - self, - config_path: str | Path | None = None, - output_format: str = "table", - use_colors: bool = True, - ): - self.config_path = config_path - self.config = None - self.formatter = OutputFormatter(colors=use_colors, format_type=output_format) - self.project_root = None - - # 初始化配置 - self._load_config() - - # 查找项目根目录 - self._find_project_root() - - def _load_config(self): - """加载配置文件""" - if self.config_path is None: - # 使用默认配置路径 - self.config_path = Path.home() / ".sage" / "config.yaml" - - if isinstance(self.config_path, str): - self.config_path = Path(self.config_path) - - try: - if self.config_path.exists(): - self.config = load_and_validate_config(self.config_path) - else: - # 如果配置文件不存在，提示用户创建 - self.formatter.print_warning(f"Configuration file not found: {self.config_path}") - self.formatter.print_info( - "Run 'sage config init' to create a default configuration" - ) - self.config = {} - except Exception as e: - raise ConfigurationError(f"Failed to load configuration: {e}") - - def _find_project_root(self): - """查找项目根目录""" - self.project_root = find_project_root() - if not self.project_root: - self.formatter.print_warning("Project root directory not found") - - @abstractmethod - def execute(self, *args, **kwargs): - """执行命令的主要逻辑""" - pass - - def get_config_section( - self, section_name: str, default: dict[str, Any] | None = None - ) -> dict[str, Any]: - """获取配置节""" - if self.config is None: - return default or {} - return self.config.get(section_name, default or {}) - - def validate_config_exists(self): - """验证配置文件存在""" - if not self.config or not self.config_path: - raise ConfigurationError( - f"Configuration file not found: {self.config_path}\n" - "Please run 'sage config init' to create a default configuration" - ) - # Convert to Path if it's a string - config_path_obj = ( - Path(self.config_path) if isinstance(self.config_path, str) else self.config_path - ) - if not config_path_obj.exists(): - raise ConfigurationError( - f"Configuration file not found: {config_path_obj}\n" - "Please run 'sage config init' to create a default configuration" - ) - - def print_section_header(self, title: str): - """打印节标题""" - self.formatter.print_section(title) - - def handle_exception(self, e: Exception) -> int: - """处理异常并返回退出码""" - if isinstance(e, CLIException): - self.formatter.print_error(str(e)) - return e.exit_code - else: - self.formatter.print_error(f"Unexpected error: {e}") - return 1 - - -class ServiceCommand(BaseCommand): - """服务管理命令基类""" - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self.connection_required = False - self._connected = False - - def require_connection(self): - """标记此命令需要连接""" - self.connection_required = True - - def is_connected(self) -> bool: - """检查是否已连接""" - return self._connected - - @abstractmethod - def connect(self) -> bool: - """建立连接""" - pass - - def ensure_connected(self): - """确保已连接""" - if self.connection_required and not self.is_connected(): - if not self.connect(): - raise ConnectionError("Failed to establish connection") - - def execute_with_connection(self, func: Callable, *args, **kwargs): - """在确保连接的情况下执行函数""" - self.ensure_connected() - return func(*args, **kwargs) - - -class RemoteCommand(BaseCommand): - """远程命令执行基类""" - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self.ssh_manager = None - self.remote_executor = None - - def _setup_ssh(self): - """设置SSH连接""" - ssh_config = self.get_config_section("ssh") - - if not ssh_config: - raise ConfigurationError("SSH configuration not found") - - from .ssh import RemoteExecutor, SSHConfig, SSHManager - - ssh_conf = SSHConfig( - user=ssh_config.get("user", "sage"), - key_path=ssh_config.get("key_path", "~/.ssh/id_rsa"), - connect_timeout=ssh_config.get("connect_timeout", 10), - strict_host_key_checking=ssh_config.get("strict_host_key_checking", False), - known_hosts_file=ssh_config.get("known_hosts_file"), - ) - - self.ssh_manager = SSHManager(ssh_conf) - self.remote_executor = RemoteExecutor(self.ssh_manager) - - def get_worker_hosts(self) -> list[tuple]: - """获取worker主机列表""" - ssh_config = self.get_config_section("ssh") - workers = ssh_config.get("workers", []) - - if not workers: - # 兼容旧格式 - hosts_str = self.config.get("workers_ssh_hosts", "") if self.config else "" - if hosts_str: - nodes = [] - for node in hosts_str.split(","): - node = node.strip() - if ":" in node: - host, port_str = node.split(":", 1) - port = int(port_str) - else: - host = node - port = 22 - nodes.append((host, port)) - return nodes - else: - return [(w["host"], w.get("port", 22)) for w in workers] - - return [] - - def execute_on_workers( - self, command: str, parallel: bool = False, timeout: int = 60 - ) -> dict[str, Any]: - """在所有worker节点上执行命令""" - if not self.ssh_manager: - self._setup_ssh() - - worker_hosts = self.get_worker_hosts() - if not worker_hosts: - raise ConfigurationError("No worker hosts configured") - - if not self.remote_executor: - raise ConfigurationError("Remote executor not initialized") - - return self.remote_executor.batch_execute(worker_hosts, command, parallel, timeout) - - -def cli_command(name: str | None = None, help_text: str | None = None, require_config: bool = True): - """ - CLI命令装饰器 - - Args: - name: 命令名称 - help_text: 帮助文本 - require_config: 是否需要配置文件 - """ - - def decorator(func: Callable) -> Callable: - @functools.wraps(func) - def wrapper(*args, **kwargs): - try: - # 如果需要配置文件，验证配置存在 - if require_config: - config_path = Path.home() / ".sage" / "config.yaml" - if not config_path.exists(): - print_status("error", f"Configuration file not found: {config_path}") - print_status( - "info", - "Run 'sage config init' to create a default configuration", - ) - raise typer.Exit(1) - - return func(*args, **kwargs) - - except CLIException as e: - print_status("error", str(e)) - raise typer.Exit(e.exit_code) - - except Exception as e: - print_status("error", f"Unexpected error: {e}") - raise typer.Exit(1) - - # 设置命令元数据 - if name: - wrapper.__name__ = name - if help_text: - wrapper.__doc__ = help_text - - return wrapper - - return decorator - - -def require_connection(func: Callable) -> Callable: - """ - 需要连接的命令装饰器 - """ - - @functools.wraps(func) - def wrapper(*args, **kwargs): - # 这里可以添加连接检查逻辑 - return func(*args, **kwargs) - - return wrapper - - -class JobManagerCommand(ServiceCommand): - """JobManager命令基类""" - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self.client = None - self.daemon_host = None - self.daemon_port = None - self._setup_daemon_config() - - def _setup_daemon_config(self): - """设置守护进程配置""" - daemon_config = self.get_config_section("daemon") - self.daemon_host = daemon_config.get("host", "127.0.0.1") - self.daemon_port = daemon_config.get("port", 19001) - - def connect(self) -> bool: - """连接到JobManager守护进程""" - try: - from sage.kernel.runtime.jobmanager_client import JobManagerClient - - if not self.daemon_host or not self.daemon_port: - raise CLIException("Daemon host or port not configured") - - self.client = JobManagerClient(str(self.daemon_host), int(self.daemon_port)) - - # 健康检查 - health = self.client.health_check() - if health.get("status") != "success": - raise ConnectionError(f"Daemon health check failed: {health.get('message')}") - - self._connected = True - return True - - except ImportError: - raise CLIException( - "JobManager client not available. Please ensure SAGE is properly installed." - ) - except Exception as e: - self.formatter.print_error(f"Failed to connect to JobManager: {e}") - self._connected = False - return False - - def resolve_job_identifier(self, identifier: str) -> str | None: - """解析作业标识符（可以是作业编号或UUID）""" - try: - self.ensure_connected() - - if not self.client: - raise CLIException("JobManager client not initialized") - - # 获取作业列表 - response = self.client.list_jobs() - if response.get("status") != "success": - raise CLIException(f"Failed to get job list: {response.get('message')}") - - jobs = response.get("jobs", []) - - # 如果是数字，当作作业编号处理 - if identifier.isdigit(): - job_index = int(identifier) - 1 # 转换为0基索引 - if 0 <= job_index < len(jobs): - return jobs[job_index].get("uuid") - else: - self.formatter.print_error( - f"Job number {identifier} is out of range (1-{len(jobs)})" - ) - return None - - # 如果是UUID（完整或部分） - # 首先尝试精确匹配 - for job in jobs: - if job.get("uuid") == identifier: - return identifier - - # 然后尝试前缀匹配 - matching_jobs = [job for job in jobs if job.get("uuid", "").startswith(identifier)] - - if len(matching_jobs) == 1: - return matching_jobs[0].get("uuid") - elif len(matching_jobs) > 1: - self.formatter.print_error(f"Ambiguous job identifier '{identifier}'. Matches:") - for i, job in enumerate(matching_jobs, 1): - self.formatter.print_info( - f" {i}. {job.get('uuid')} ({job.get('name', 'unknown')})" - ) - return None - else: - self.formatter.print_error(f"No job found matching '{identifier}'") - return None - - except Exception as e: - self.formatter.print_error(f"Failed to resolve job identifier: {e}") - return None diff --git a/packages/sage-cli/src/sage/cli/core/config.py b/packages/sage-cli/src/sage/cli/core/config.py deleted file mode 100644 index b7c2cc6a06..0000000000 --- a/packages/sage-cli/src/sage/cli/core/config.py +++ /dev/null @@ -1,312 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE CLI Config Validation -========================== - -配置文件验证和处理功能 -""" - -import os -from pathlib import Path -from typing import Any - -from .exceptions import ConfigurationError, ValidationError -from .utils import load_yaml_file -from .validation import ( - validate_config_dict, - validate_host, - validate_path, - validate_port, - validate_timeout, -) - - -class ConfigValidator: - """配置验证器""" - - def __init__(self): - self.required_sections = [] - self.optional_sections = [] - self.section_validators = {} - - def add_required_section(self, section_name: str, validator_func=None): - """添加必需的配置节""" - self.required_sections.append(section_name) - if validator_func: - self.section_validators[section_name] = validator_func - - def add_optional_section(self, section_name: str, validator_func=None): - """添加可选的配置节""" - self.optional_sections.append(section_name) - if validator_func: - self.section_validators[section_name] = validator_func - - def validate_config(self, config: dict[str, Any]) -> dict[str, Any]: - """ - 验证配置字典 - - Args: - config: 配置字典 - - Returns: - 验证并标准化后的配置 - - Raises: - ConfigurationError: 配置验证失败 - """ - if not isinstance(config, dict): - raise ConfigurationError("Configuration must be a dictionary") - - # 检查必需节 - missing_sections = [s for s in self.required_sections if s not in config] - if missing_sections: - raise ConfigurationError(f"Missing required configuration sections: {missing_sections}") - - # 验证各个节 - validated_config = {} - for section_name, section_data in config.items(): - if section_name in self.section_validators: - try: - validated_config[section_name] = self.section_validators[section_name]( - section_data - ) - except Exception as e: - raise ConfigurationError( - f"Invalid configuration in section '{section_name}': {e}" - ) - else: - validated_config[section_name] = section_data - - return validated_config - - -def validate_head_config(config: dict[str, Any]) -> dict[str, Any]: - """验证head节点配置""" - required_keys = ["host", "head_port", "dashboard_port"] - config = validate_config_dict(config, required_keys) - - # 验证具体字段 - config["host"] = validate_host(config["host"]) - config["head_port"] = validate_port(config["head_port"]) - config["dashboard_port"] = validate_port(config["dashboard_port"]) - - # 可选字段验证 - if "dashboard_host" in config: - config["dashboard_host"] = validate_host(config["dashboard_host"]) - - if "temp_dir" in config: - config["temp_dir"] = str(validate_path(config["temp_dir"])) - - if "log_dir" in config: - config["log_dir"] = str(validate_path(config["log_dir"])) - - if "python_path" in config: - config["python_path"] = str( - validate_path(config["python_path"], must_exist=True, must_be_file=True) - ) - - if "ray_command" in config: - config["ray_command"] = str( - validate_path(config["ray_command"], must_exist=True, must_be_file=True) - ) - - return config - - -def validate_worker_config(config: dict[str, Any]) -> dict[str, Any]: - """验证worker节点配置""" - # worker配置都是可选的 - if "bind_host" in config: - config["bind_host"] = validate_host(config["bind_host"]) - - if "temp_dir" in config: - config["temp_dir"] = str(validate_path(config["temp_dir"])) - - if "log_dir" in config: - config["log_dir"] = str(validate_path(config["log_dir"])) - - return config - - -def validate_ssh_config(config: dict[str, Any]) -> dict[str, Any]: - """验证SSH配置""" - required_keys = ["user"] - config = validate_config_dict(config, required_keys) - - if "key_path" in config: - key_path = config["key_path"] - if key_path.startswith("~"): - key_path = os.path.expanduser(key_path) - config["key_path"] = str(validate_path(key_path, must_exist=True, must_be_file=True)) - - if "connect_timeout" in config: - config["connect_timeout"] = validate_timeout(config["connect_timeout"]) - - # 验证workers配置 - if "workers" in config: - workers = config["workers"] - if not isinstance(workers, list): - raise ValidationError("SSH workers must be a list") - - for i, worker in enumerate(workers): - if not isinstance(worker, dict): - raise ValidationError(f"SSH worker {i} must be a dictionary") - - if "host" not in worker: - raise ValidationError(f"SSH worker {i} missing required 'host' field") - - worker["host"] = validate_host(worker["host"]) - - if "port" in worker: - worker["port"] = validate_port(worker["port"]) - else: - worker["port"] = 22 # 默认SSH端口 - - return config - - -def validate_remote_config(config: dict[str, Any]) -> dict[str, Any]: - """验证远程配置""" - if "sage_home" in config: - # 远程路径不能在本地验证存在性，只检查格式 - config["sage_home"] = str(Path(config["sage_home"])) - - if "python_path" in config: - config["python_path"] = str(Path(config["python_path"])) - - if "ray_command" in config: - config["ray_command"] = str(Path(config["ray_command"])) - - return config - - -def validate_daemon_config(config: dict[str, Any]) -> dict[str, Any]: - """验证守护进程配置""" - required_keys = ["host", "port"] - config = validate_config_dict(config, required_keys) - - config["host"] = validate_host(config["host"]) - config["port"] = validate_port(config["port"]) - - return config - - -def validate_output_config(config: dict[str, Any]) -> dict[str, Any]: - """验证输出配置""" - valid_formats = ["table", "json", "yaml"] - - if "format" in config: - if config["format"] not in valid_formats: - raise ValidationError( - f"Invalid output format: {config['format']}, must be one of {valid_formats}" - ) - - if "colors" in config: - if not isinstance(config["colors"], bool): - raise ValidationError("Output colors setting must be boolean") - - return config - - -def validate_monitor_config(config: dict[str, Any]) -> dict[str, Any]: - """验证监控配置""" - if "refresh_interval" in config: - interval = config["refresh_interval"] - if not isinstance(interval, (int, float)) or interval <= 0: - raise ValidationError("Monitor refresh_interval must be a positive number") - config["refresh_interval"] = float(interval) - - return config - - -def validate_jobmanager_config(config: dict[str, Any]) -> dict[str, Any]: - """验证JobManager配置""" - if "timeout" in config: - config["timeout"] = validate_timeout(config["timeout"]) - - if "retry_attempts" in config: - attempts = config["retry_attempts"] - if not isinstance(attempts, int) or attempts < 0: - raise ValidationError("JobManager retry_attempts must be a non-negative integer") - config["retry_attempts"] = attempts - - return config - - -def create_default_config_validator() -> ConfigValidator: - """创建默认的配置验证器""" - validator = ConfigValidator() - - # 添加必需节 - validator.add_required_section("head", validate_head_config) - validator.add_required_section("ssh", validate_ssh_config) - validator.add_required_section("daemon", validate_daemon_config) - - # 添加可选节 - validator.add_optional_section("worker", validate_worker_config) - validator.add_optional_section("remote", validate_remote_config) - validator.add_optional_section("output", validate_output_config) - validator.add_optional_section("monitor", validate_monitor_config) - validator.add_optional_section("jobmanager", validate_jobmanager_config) - - return validator - - -def load_and_validate_config( - config_path: str | Path, validator: ConfigValidator | None = None -) -> dict[str, Any]: - """ - 加载并验证配置文件 - - Args: - config_path: 配置文件路径 - validator: 配置验证器，如果为None则使用默认验证器 - - Returns: - 验证后的配置字典 - - Raises: - ConfigurationError: 配置加载或验证失败 - """ - if validator is None: - validator = create_default_config_validator() - - try: - config = load_yaml_file(config_path) - return validator.validate_config(config) - except Exception as e: - raise ConfigurationError(f"Failed to load and validate config file {config_path}: {e}") - - -def create_default_config() -> dict[str, Any]: - """创建默认配置""" - return { - "head": { - "host": "localhost", - "head_port": 6379, - "dashboard_port": 8265, - "dashboard_host": "0.0.0.0", - "temp_dir": "/tmp/ray", - "log_dir": "/tmp/sage_logs", - }, - "worker": { - "bind_host": "localhost", - "temp_dir": "/tmp/ray_worker", - "log_dir": "/tmp/sage_worker_logs", - }, - "ssh": { - "user": os.getenv("USER", "sage"), - "key_path": "~/.ssh/id_rsa", - "connect_timeout": 10, - "workers": [], - }, - "remote": { - "sage_home": "/opt/sage", - "python_path": "/opt/conda/bin/python", - "ray_command": "/opt/conda/bin/ray", - }, - "daemon": {"host": "localhost", "port": 19001}, - "output": {"format": "table", "colors": True}, - "monitor": {"refresh_interval": 5}, - "jobmanager": {"timeout": 30, "retry_attempts": 3}, - } diff --git a/packages/sage-cli/src/sage/cli/core/config_refactored.py b/packages/sage-cli/src/sage/cli/core/config_refactored.py deleted file mode 100644 index 86fe8a5310..0000000000 --- a/packages/sage-cli/src/sage/cli/core/config_refactored.py +++ /dev/null @@ -1,244 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE Config Command - Refactored Version -======================================== - -使用 sage.cli.core 模块重构的配置管理命令 -演示如何将原有的配置命令迁移到新的核心架构 -""" - -from pathlib import Path - -import typer - -# 使用新的核心模块 -from sage.cli.core import BaseCommand, CLIException, cli_command -from sage.cli.core.config import create_default_config, load_and_validate_config -from sage.cli.core.utils import save_yaml_file - -app = typer.Typer(name="config", help="⚙️ Configuration management") - - -class ConfigShowCommand(BaseCommand): - """显示配置信息命令""" - - def execute(self, section: str | None = None): - """显示配置信息""" - try: - self.validate_config_exists() - - self.print_section_header("📋 SAGE Configuration Information") - self.formatter.print_info(f"Configuration file: {self.config_path}") - - if section: - # 显示特定配置节 - if section in self.config: - self.formatter.print_data({section: self.config[section]}) - else: - raise CLIException(f"Configuration section '{section}' not found") - else: - # 显示所有配置 - summary = { - "Data Directory": self.config.get("data_dir", "Not set"), - "Log Level": self.config.get("log_level", "Not set"), - "Work Directory": self.config.get("work_dir", "Not set"), - } - - # Ray配置 - if "ray" in self.config: - ray_config = self.config["ray"] - summary["Ray Address"] = ray_config.get("address", "Not set") - summary["Ray Port"] = ray_config.get("port", "Not set") - - # Head节点配置 - if "head" in self.config: - head_config = self.config["head"] - summary["Head Host"] = head_config.get("host", "Not set") - summary["Head Port"] = head_config.get("head_port", "Not set") - - # SSH配置 - if "ssh" in self.config: - ssh_config = self.config["ssh"] - summary["SSH User"] = ssh_config.get("user", "Not set") - summary["SSH Key Path"] = ssh_config.get("key_path", "Not set") - - self.formatter.print_data(summary) - - except Exception as e: - exit_code = self.handle_exception(e) - raise typer.Exit(exit_code) - - -class ConfigInitCommand(BaseCommand): - """初始化配置文件命令""" - - def execute(self, force: bool = False): - """初始化配置文件""" - try: - if self.config_path.exists(): - if not force: - self.formatter.print_info( - f"Configuration file already exists: {self.config_path}" - ) - self.formatter.print_info( - "Use --force option to overwrite existing configuration" - ) - return - else: - self.formatter.print_info("🔄 Overwriting existing configuration file...") - - # 创建默认配置 - default_config = create_default_config() - - # 保存配置文件 - save_yaml_file(default_config, self.config_path) - - self.formatter.print_success(f"Configuration file created: {self.config_path}") - self.formatter.print_info( - "🔧 You can edit the configuration file to customize settings" - ) - - # 显示下一步操作提示 - self.formatter.print_info("\n💡 Next steps:") - self.formatter.print_info("1. Edit the configuration file to match your environment") - self.formatter.print_info("2. Run 'sage config show' to verify settings") - self.formatter.print_info("3. Run 'sage doctor' to check system requirements") - - except Exception as e: - exit_code = self.handle_exception(e) - raise typer.Exit(exit_code) - - -class ConfigValidateCommand(BaseCommand): - """验证配置文件命令""" - - def execute(self): - """验证配置文件""" - try: - self.validate_config_exists() - - self.print_section_header("🔍 Configuration Validation") - - # 重新加载并验证配置 - validated_config = load_and_validate_config(self.config_path) - - # 检查各个配置节 - validation_results = [] - - # 检查head配置 - if "head" in validated_config: - head_config = validated_config["head"] - validation_results.append( - { - "Section": "head", - "Status": "✅ Valid", - "Host": head_config.get("host", "N/A"), - "Port": head_config.get("head_port", "N/A"), - } - ) - else: - validation_results.append( - { - "Section": "head", - "Status": "❌ Missing", - "Host": "N/A", - "Port": "N/A", - } - ) - - # 检查SSH配置 - if "ssh" in validated_config: - ssh_config = validated_config["ssh"] - key_path = Path(ssh_config.get("key_path", "")) - key_exists = key_path.exists() if key_path.name else False - - validation_results.append( - { - "Section": "ssh", - "Status": "✅ Valid" if key_exists else "⚠️ Key not found", - "User": ssh_config.get("user", "N/A"), - "Key Path": str(key_path) if key_path.name else "N/A", - } - ) - - # 检查daemon配置 - if "daemon" in validated_config: - daemon_config = validated_config["daemon"] - validation_results.append( - { - "Section": "daemon", - "Status": "✅ Valid", - "Host": daemon_config.get("host", "N/A"), - "Port": daemon_config.get("port", "N/A"), - } - ) - - # 显示验证结果 - headers = ["Section", "Status", "Details"] - formatted_results = [] - for result in validation_results: - details = [] - for key, value in result.items(): - if key not in ["Section", "Status"]: - details.append(f"{key}: {value}") - - formatted_results.append( - { - "Section": result["Section"], - "Status": result["Status"], - "Details": "; ".join(details), - } - ) - - self.formatter.print_data(formatted_results, headers) - - self.formatter.print_success("Configuration validation completed") - - except Exception as e: - exit_code = self.handle_exception(e) - raise typer.Exit(exit_code) - - -# 命令注册 -@app.command("show") -@cli_command(require_config=False) # show命令可以在没有配置时运行 -def show_config( - section: str = typer.Option( - None, "--section", "-s", help="Show specific configuration section" - ), -): - """Show configuration information""" - cmd = ConfigShowCommand() - cmd.execute(section) - - -@app.command("init") -@cli_command(require_config=False) # init命令不需要现有配置 -def init_config( - force: bool = typer.Option( - False, "--force", "-f", help="Force overwrite existing configuration" - ), -): - """Initialize SAGE configuration file""" - cmd = ConfigInitCommand() - cmd.execute(force) - - -@app.command("validate") -@cli_command() # 需要配置文件存在 -def validate_config(): - """Validate configuration file""" - cmd = ConfigValidateCommand() - cmd.execute() - - -# 为了向后兼容，提供一个默认的config命令 -@app.callback(invoke_without_command=True) -def config_callback(ctx: typer.Context): - """Show configuration information (default behavior)""" - if ctx.invoked_subcommand is None: - show_config() - - -if __name__ == "__main__": - app() diff --git a/packages/sage-cli/src/sage/cli/core/exceptions.py b/packages/sage-cli/src/sage/cli/core/exceptions.py deleted file mode 100644 index 6b5fe10cda..0000000000 --- a/packages/sage-cli/src/sage/cli/core/exceptions.py +++ /dev/null @@ -1,64 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE CLI Exceptions -=================== - -自定义异常类，用于处理CLI操作中的各种错误情况 -""" - - -class CLIException(Exception): - """CLI操作的基础异常类""" - - def __init__(self, message: str, exit_code: int = 1): - super().__init__(message) - self.exit_code = exit_code - - -class ConfigurationError(CLIException): - """配置相关错误""" - - def __init__(self, message: str): - super().__init__(f"Configuration error: {message}", 2) - - -class ConnectionError(CLIException): - """连接相关错误""" - - def __init__(self, message: str): - super().__init__(f"Connection error: {message}", 3) - - -class ValidationError(CLIException): - """输入验证错误""" - - def __init__(self, message: str): - super().__init__(f"Validation error: {message}", 4) - - -class DeploymentError(CLIException): - """部署相关错误""" - - def __init__(self, message: str): - super().__init__(f"Deployment error: {message}", 5) - - -class ServiceError(CLIException): - """服务相关错误""" - - def __init__(self, message: str): - super().__init__(f"Service error: {message}", 6) - - -class JobError(CLIException): - """作业相关错误""" - - def __init__(self, message: str): - super().__init__(f"Job error: {message}", 7) - - -class ExtensionError(CLIException): - """扩展相关错误""" - - def __init__(self, message: str): - super().__init__(f"Extension error: {message}", 8) diff --git a/packages/sage-cli/src/sage/cli/core/output.py b/packages/sage-cli/src/sage/cli/core/output.py deleted file mode 100644 index 1b3f007818..0000000000 --- a/packages/sage-cli/src/sage/cli/core/output.py +++ /dev/null @@ -1,281 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE CLI Output Formatter -========================= - -统一的输出格式化和显示功能 -""" - -import json -from datetime import datetime -from typing import Any - -from sage.common.utils.formatting import ( - format_duration as _format_duration, -) -from sage.common.utils.formatting import ( - format_size_compact as _format_size_compact, -) -from sage.common.utils.formatting import ( - format_timestamp as _format_timestamp, -) - -try: - from colorama import Back, Fore, Style, init # type: ignore[import-untyped] - from tabulate import tabulate # type: ignore[import-untyped] - - init(autoreset=True) - COLORAMA_AVAILABLE = True -except ImportError: - COLORAMA_AVAILABLE = False - - # 提供基础的颜色类 - class _ForeColors: - GREEN = RED = YELLOW = BLUE = CYAN = MAGENTA = WHITE = "" - - class _BackColors: - GREEN = RED = YELLOW = BLUE = CYAN = MAGENTA = WHITE = "" - - class _StyleColors: - BRIGHT = DIM = NORMAL = RESET_ALL = "" - - Fore = _ForeColors() - Back = _BackColors() - Style = _StyleColors() - - -class Colors: - """终端颜色常量""" - - if COLORAMA_AVAILABLE: - GREEN = Fore.GREEN - RED = Fore.RED - YELLOW = Fore.YELLOW - BLUE = Fore.BLUE - CYAN = Fore.CYAN - MAGENTA = Fore.MAGENTA - WHITE = Fore.WHITE - BOLD = Style.BRIGHT - DIM = Style.DIM - RESET = Style.RESET_ALL - else: - GREEN = RED = YELLOW = BLUE = CYAN = MAGENTA = WHITE = BOLD = DIM = RESET = "" - - -class OutputFormatter: - """统一的输出格式化器""" - - def __init__(self, colors: bool = True, format_type: str = "table"): - self.colors = colors and COLORAMA_AVAILABLE - self.format_type = format_type - - def print_message(self, message: str, msg_type: str = "info", prefix: str | None = None): - """ - 打印格式化消息 - - Args: - message: 消息内容 - msg_type: 消息类型 (info, success, error, warning) - prefix: 可选前缀 - """ - if not self.colors: - if prefix: - print(f"{prefix} {message}") - else: - print(message) - return - - color_map = { - "info": Colors.BLUE, - "success": Colors.GREEN, - "error": Colors.RED, - "warning": Colors.YELLOW, - } - - icon_map = {"info": "ℹ️", "success": "✅", "error": "❌", "warning": "⚠️"} - - color = color_map.get(msg_type, Colors.WHITE) - icon = icon_map.get(msg_type, "") - - if prefix: - print(f"{color}{icon} {prefix} {message}{Colors.RESET}") - else: - print(f"{color}{icon} {message}{Colors.RESET}") - - def print_info(self, message: str, prefix: str | None = None): - """打印信息消息""" - self.print_message(message, "info", prefix) - - def print_success(self, message: str, prefix: str | None = None): - """打印成功消息""" - self.print_message(message, "success", prefix) - - def print_error(self, message: str, prefix: str | None = None): - """打印错误消息""" - self.print_message(message, "error", prefix) - - def print_warning(self, message: str, prefix: str | None = None): - """打印警告消息""" - self.print_message(message, "warning", prefix) - - def format_data(self, data: list[dict] | dict, headers: list[str] | None = None) -> str: - """ - 格式化数据输出 - - Args: - data: 要格式化的数据 - headers: 表格头部（仅在table格式下使用） - - Returns: - 格式化后的字符串 - """ - if self.format_type == "json": - return json.dumps(data, indent=2, ensure_ascii=False) - - elif self.format_type == "table": - if not data: - return "No data available" - - if isinstance(data, dict): - # 单个对象转换为键值对表格 - table_data = [[k, v] for k, v in data.items()] - return tabulate(table_data, headers=["Key", "Value"], tablefmt="grid") - - elif isinstance(data, list) and data: - if isinstance(data[0], dict): - # 字典列表转换为表格 - if not headers: - headers = list(data[0].keys()) - table_data = [[item.get(h, "") for h in headers] for item in data] - return tabulate(table_data, headers=headers, tablefmt="grid") - else: - # 简单列表 - return "\n".join(str(item) for item in data) - - return str(data) - - def print_data(self, data: list[dict] | dict, headers: list[str] | None = None): - """打印格式化数据""" - formatted = self.format_data(data, headers) - print(formatted) - - def print_section(self, title: str, content: str | None = None): - """打印章节标题""" - if self.colors: - print(f"\n{Colors.BOLD}{Colors.CYAN}{title}{Colors.RESET}") - print("=" * len(title)) - else: - print(f"\n{title}") - print("=" * len(title)) - - if content: - print(content) - - -def format_table( - data: list[dict[str, Any]], - headers: list[str] | None = None, - tablefmt: str = "grid", -) -> str: - """ - 格式化数据为表格 - - Args: - data: 数据列表 - headers: 表头列表 - tablefmt: 表格格式 - - Returns: - 格式化的表格字符串 - """ - if not data: - return "No data available" - - if not headers: - headers = list(data[0].keys()) if data else [] - - table_data = [] - for item in data: - row = [] - for header in headers: - value = item.get(header, "") - # 处理长字符串 - if isinstance(value, str) and len(value) > 50: - value = value[:47] + "..." - row.append(value) - table_data.append(row) - - try: - return tabulate(table_data, headers=headers, tablefmt=tablefmt) - except Exception: - # 如果tabulate不可用，使用简单格式 - result = [] - if headers: - result.append(" | ".join(headers)) - result.append("-" * len(" | ".join(headers))) - - for row in table_data: - result.append(" | ".join(str(cell) for cell in row)) - - return "\n".join(result) - - -def print_status(status: str, message: str, colors: bool = True): - """ - 打印状态消息 - - Args: - status: 状态类型 (success, error, warning, info) - message: 消息内容 - colors: 是否使用颜色 - """ - formatter = OutputFormatter(colors=colors) - - if status == "success": - formatter.print_success(message) - elif status == "error": - formatter.print_error(message) - elif status == "warning": - formatter.print_warning(message) - else: - formatter.print_info(message) - - -def format_duration(seconds: float) -> str: - """格式化持续时间（使用统一的格式化函数）""" - return _format_duration(seconds) - - -def format_size(bytes_size: int) -> str: - """格式化文件大小（使用统一的格式化函数）""" - return _format_size_compact(bytes_size) - - -def format_timestamp(timestamp: float | str | datetime) -> str: - """格式化时间戳（使用统一的格式化函数）""" - return _format_timestamp(timestamp) - - -# 向后兼容的全局函数 -def print_info(message: str, prefix: str | None = None): - """打印信息消息""" - formatter = OutputFormatter() - formatter.print_info(message, prefix) - - -def print_success(message: str, prefix: str | None = None): - """打印成功消息""" - formatter = OutputFormatter() - formatter.print_success(message, prefix) - - -def print_error(message: str, prefix: str | None = None): - """打印错误消息""" - formatter = OutputFormatter() - formatter.print_error(message, prefix) - - -def print_warning(message: str, prefix: str | None = None): - """打印警告消息""" - formatter = OutputFormatter() - formatter.print_warning(message, prefix) diff --git a/packages/sage-cli/src/sage/cli/core/refactor_example.py b/packages/sage-cli/src/sage/cli/core/refactor_example.py deleted file mode 100644 index ce043648ef..0000000000 --- a/packages/sage-cli/src/sage/cli/core/refactor_example.py +++ /dev/null @@ -1,265 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE CLI Refactoring Example -============================ - -展示如何使用sage.cli.core模块重构现有命令 -""" - -import typer - -# 导入核心模块 -from sage.cli.core import ( - BaseCommand, - CLIException, - JobManagerCommand, - OutputFormatter, - RemoteCommand, - ValidationError, - cli_command, - validate_host, - validate_port, -) - -app = typer.Typer(name="example", help="重构示例命令") - - -# 示例1: 使用BaseCommand重构简单命令 -class DoctorCommand(BaseCommand): - """系统诊断命令""" - - def execute(self): - """执行系统诊断""" - self.print_section_header("🔍 SAGE System Diagnosis") - - # 检查Python版本 - import sys - - self.formatter.print_info(f"Python Version: {sys.version.split()[0]}") - - # 检查SAGE安装 - try: - import sage - - self.formatter.print_success( - f"SAGE Installation: v{getattr(sage, '__version__', 'unknown')}" - ) - except ImportError: - self.formatter.print_error("SAGE not installed") - - # 检查扩展 - extensions = ["sage_ext", "sage_ext.sage_db"] - for ext in extensions: - try: - __import__(ext) - self.formatter.print_success(f"Extension {ext}: Available") - except ImportError: - self.formatter.print_warning(f"Extension {ext}: Not available") - - # 检查Ray - try: - import ray - - self.formatter.print_success(f"Ray: v{ray.__version__}") - except ImportError: - self.formatter.print_error("Ray not installed") - - -@app.command("doctor") -@cli_command(require_config=False) -def doctor(): - """系统诊断""" - cmd = DoctorCommand() - cmd.execute() - - -# 示例2: 使用JobManagerCommand重构作业管理命令 -class JobListCommand(JobManagerCommand): - """作业列表命令""" - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self.require_connection() - - def execute( - self, - status: str | None = None, - format_type: str = "table", - full_uuid: bool = False, - ): - """执行作业列表查询""" - try: - response = self.client.list_jobs() - if response.get("status") != "success": - raise CLIException(f"Failed to get job list: {response.get('message')}") - - jobs = response.get("jobs", []) - - # 状态过滤 - if status: - jobs = [job for job in jobs if job.get("status") == status] - - # 格式化输出 - if format_type == "json": - import json - - print(json.dumps({"jobs": jobs}, indent=2)) - else: - # 处理UUID显示长度 - if not full_uuid: - for job in jobs: - if "uuid" in job and len(job["uuid"]) > 8: - job["uuid_short"] = job["uuid"][:8] + "..." - - headers = [ - "ID", - "Name", - "Status", - "Created", - "UUID" if full_uuid else "UUID Short", - ] - self.formatter.print_data(jobs, headers) - - except Exception as e: - self.handle_exception(e) - - -@app.command("list-jobs") -@cli_command() -def list_jobs( - status: str | None = typer.Option(None, "--status", "-s", help="Filter by status"), - format_type: str = typer.Option("table", "--format", "-f", help="Output format"), - full_uuid: bool = typer.Option(False, "--full-uuid", help="Show full UUID"), -): - """列出所有作业""" - cmd = JobListCommand() - cmd.execute(status, format_type, full_uuid) - - -# 示例3: 使用RemoteCommand重构集群管理命令 -class ClusterStatusCommand(RemoteCommand): - """集群状态检查命令""" - - def execute(self): - """执行集群状态检查""" - self.print_section_header("📊 Ray Cluster Status") - - # 检查Head节点状态 - head_config = self.get_config_section("head") - head_host = head_config.get("host", "localhost") - dashboard_port = head_config.get("dashboard_port", 8265) - - self.formatter.print_info(f"Checking Head node: {head_host}") - - # 检查Worker节点状态 - worker_hosts = self.get_worker_hosts() - - if not worker_hosts: - self.formatter.print_warning("No worker nodes configured") - return - - self.formatter.print_info(f"Checking {len(worker_hosts)} worker nodes...") - - # 使用SSH检查worker状态 - if not self.ssh_manager: - self._setup_ssh() - - for host, port in worker_hosts: - try: - # 测试连接 - if self.ssh_manager.test_connection(host, port): - self.formatter.print_success(f"Worker {host}:{port}: Connected") - - # 检查Ray进程 - result = self.ssh_manager.execute_command( - host, port, "ps aux | grep -v grep | grep ray", timeout=10 - ) - - if result.returncode == 0 and result.stdout.strip(): - self.formatter.print_success(f"Worker {host}:{port}: Ray process running") - else: - self.formatter.print_warning(f"Worker {host}:{port}: Ray process not found") - else: - self.formatter.print_error(f"Worker {host}:{port}: Connection failed") - - except Exception as e: - self.formatter.print_error(f"Worker {host}:{port}: Error - {e}") - - # 显示集群访问信息 - self.formatter.print_info(f"Dashboard: http://{head_host}:{dashboard_port}") - - -@app.command("cluster-status") -@cli_command() -def cluster_status(): - """检查集群状态""" - cmd = ClusterStatusCommand() - cmd.execute() - - -# 示例4: 使用装饰器的简单命令重构 -@app.command("config-show") -@cli_command(name="show_config", help_text="显示当前配置", require_config=True) -def show_config(section: str | None = typer.Option(None, "--section", "-s", help="显示指定配置节")): - """显示配置信息""" - formatter = OutputFormatter() - - try: - from pathlib import Path - - from sage.cli.core.config import load_and_validate_config - - config_path = Path.home() / ".sage" / "config.yaml" - config = load_and_validate_config(config_path) - - formatter.print_section("📋 SAGE Configuration") - formatter.print_info(f"Configuration file: {config_path}") - - if section: - if section in config: - formatter.print_data({section: config[section]}) - else: - formatter.print_error(f"Configuration section '{section}' not found") - else: - formatter.print_data(config) - - except Exception as e: - formatter.print_error(f"Failed to load configuration: {e}") - raise typer.Exit(1) - - -# 示例5: 验证功能的使用 -@app.command("validate-host") -@cli_command(require_config=False) -def validate_host_command( - host: str = typer.Argument(..., help="要验证的主机地址"), - port: int = typer.Option(22, "--port", "-p", help="端口号"), -): - """验证主机地址和端口""" - formatter = OutputFormatter() - - try: - # 使用核心验证功能 - validated_host = validate_host(host) - validated_port = validate_port(port) - - formatter.print_success(f"Host validation successful: {validated_host}:{validated_port}") - - # 测试端口可用性 - from sage.cli.core.utils import is_port_available - - if is_port_available(validated_host, validated_port): - formatter.print_info("Port is available (not in use)") - else: - formatter.print_warning("Port appears to be in use") - - except ValidationError as e: - formatter.print_error(f"Validation failed: {e}") - raise typer.Exit(1) - except Exception as e: - formatter.print_error(f"Error: {e}") - raise typer.Exit(1) - - -if __name__ == "__main__": - app() diff --git a/packages/sage-cli/src/sage/cli/core/ssh.py b/packages/sage-cli/src/sage/cli/core/ssh.py deleted file mode 100644 index 80e9e1fc4e..0000000000 --- a/packages/sage-cli/src/sage/cli/core/ssh.py +++ /dev/null @@ -1,378 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE CLI SSH Manager -==================== - -SSH连接和远程命令执行功能 -""" - -import os -import subprocess -import tempfile -import time -from pathlib import Path -from typing import Any - -from .exceptions import CLIException, ConnectionError, ValidationError -from .output import OutputFormatter -from .utils import run_subprocess -from .validation import validate_host, validate_path, validate_port, validate_timeout - - -class SSHConfig: - """SSH配置类""" - - def __init__( - self, - user: str, - key_path: str, - connect_timeout: int = 10, - strict_host_key_checking: bool = False, - known_hosts_file: str | None = None, - ): - self.user = user - self.key_path = validate_path(key_path, must_exist=True, must_be_file=True) - self.connect_timeout = validate_timeout(connect_timeout) - self.strict_host_key_checking = strict_host_key_checking - self.known_hosts_file = known_hosts_file - - def to_ssh_args(self) -> list[str]: - """转换为SSH命令行参数""" - args = [ - "-i", - str(self.key_path), - "-o", - f"ConnectTimeout={self.connect_timeout}", - "-o", - "ServerAliveInterval=60", - "-o", - "ServerAliveCountMax=3", - ] - - if not self.strict_host_key_checking: - args.extend(["-o", "StrictHostKeyChecking=no", "-o", "UserKnownHostsFile=/dev/null"]) - elif self.known_hosts_file: - args.extend(["-o", f"UserKnownHostsFile={self.known_hosts_file}"]) - - return args - - -class SSHManager: - """SSH连接管理器""" - - def __init__(self, config: SSHConfig): - self.config = config - self.formatter = OutputFormatter() - - def execute_command( - self, - host: str, - port: int, - command: str, - timeout: int = 60, - capture_output: bool = True, - ) -> subprocess.CompletedProcess: - """ - 在远程主机上执行命令 - - Args: - host: 目标主机 - port: SSH端口 - command: 要执行的命令 - timeout: 超时时间 - capture_output: 是否捕获输出 - - Returns: - 命令执行结果 - - Raises: - ConnectionError: SSH连接失败 - CLIException: 命令执行失败 - """ - host = validate_host(host) - port = validate_port(port) - timeout = validate_timeout(timeout) - - self.formatter.print_info(f"Executing on {self.config.user}@{host}:{port}: {command}") - - ssh_cmd = ( - ["ssh"] - + self.config.to_ssh_args() - + ["-p", str(port), f"{self.config.user}@{host}", command] - ) - - try: - return run_subprocess( - ssh_cmd, - timeout=timeout, - capture_output=capture_output, - check=False, # 我们手动检查结果 - ) - except CLIException as e: - if "Connection" in str(e) or "connect" in str(e).lower(): - raise ConnectionError(f"Failed to connect to {host}:{port}: {e}") - raise - - def transfer_file( - self, - local_path: str | Path, - host: str, - port: int, - remote_path: str, - direction: str = "upload", - ) -> bool: - """ - 传输文件 - - Args: - local_path: 本地文件路径 - host: 目标主机 - port: SSH端口 - remote_path: 远程文件路径 - direction: 传输方向 ("upload" or "download") - - Returns: - 是否成功 - - Raises: - ConnectionError: SSH连接失败 - ValidationError: 参数验证失败 - """ - host = validate_host(host) - port = validate_port(port) - - if direction == "upload": - local_path = validate_path(local_path, must_exist=True) - self.formatter.print_info(f"Uploading {local_path} to {host}:{remote_path}") - src, dst = str(local_path), f"{self.config.user}@{host}:{remote_path}" - elif direction == "download": - local_path = validate_path(local_path) - self.formatter.print_info(f"Downloading {host}:{remote_path} to {local_path}") - src, dst = f"{self.config.user}@{host}:{remote_path}", str(local_path) - else: - raise ValidationError(f"Invalid direction: {direction}") - - scp_cmd = ["scp"] + self.config.to_ssh_args() + ["-P", str(port), src, dst] - - try: - result = run_subprocess(scp_cmd, check=False, capture_output=True) - - if result.returncode == 0: - self.formatter.print_success("File transfer completed") - return True - else: - self.formatter.print_error(f"File transfer failed: {result.stderr}") - return False - - except CLIException as e: - if "Connection" in str(e) or "connect" in str(e).lower(): - raise ConnectionError(f"Failed to connect to {host}:{port} for file transfer: {e}") - raise - - def test_connection(self, host: str, port: int = 22) -> bool: - """ - 测试SSH连接 - - Args: - host: 目标主机 - port: SSH端口 - - Returns: - 连接是否成功 - """ - host = validate_host(host) - port = validate_port(port) - - try: - result = self.execute_command(host, port, "echo 'Connection test'", timeout=10) - return result.returncode == 0 - except Exception: - return False - - def ensure_directory(self, host: str, port: int, directory: str) -> bool: - """ - 确保远程目录存在 - - Args: - host: 目标主机 - port: SSH端口 - directory: 目录路径 - - Returns: - 操作是否成功 - """ - command = f"mkdir -p {directory}" - result = self.execute_command(host, port, command) - return result.returncode == 0 - - -class RemoteExecutor: - """远程命令执行器""" - - def __init__(self, ssh_manager: SSHManager): - self.ssh_manager = ssh_manager - self.formatter = OutputFormatter() - - def execute_script( - self, - host: str, - port: int, - script_content: str, - interpreter: str = "bash", - timeout: int = 300, - ) -> subprocess.CompletedProcess: - """ - 在远程主机上执行脚本 - - Args: - host: 目标主机 - port: SSH端口 - script_content: 脚本内容 - interpreter: 脚本解释器 - timeout: 超时时间 - - Returns: - 命令执行结果 - """ - # 创建临时脚本文件 - with tempfile.NamedTemporaryFile(mode="w", suffix=".sh", delete=False) as f: - f.write(script_content) - temp_script = f.name - - try: - # 上传脚本文件 - remote_script = f"/tmp/sage_script_{int(time.time())}.sh" - success = self.ssh_manager.transfer_file( - temp_script, host, port, remote_script, "upload" - ) - - if not success: - raise CLIException("Failed to upload script file") - - # 执行脚本 - command = f"chmod +x {remote_script} && {interpreter} {remote_script}" - result = self.ssh_manager.execute_command(host, port, command, timeout) - - # 清理远程脚本文件 - cleanup_cmd = f"rm -f {remote_script}" - self.ssh_manager.execute_command(host, port, cleanup_cmd, timeout=10) - - return result - - finally: - # 清理本地临时文件 - os.unlink(temp_script) - - def execute_python_script( - self, - host: str, - port: int, - script_content: str, - python_path: str = "python3", - timeout: int = 300, - ) -> subprocess.CompletedProcess: - """ - 在远程主机上执行Python脚本 - - Args: - host: 目标主机 - port: SSH端口 - script_content: Python脚本内容 - python_path: Python解释器路径 - timeout: 超时时间 - - Returns: - 命令执行结果 - """ - return self.execute_script(host, port, script_content, python_path, timeout) - - def batch_execute( - self, - hosts_ports: list[tuple], - command: str, - parallel: bool = False, - timeout: int = 60, - ) -> dict[str, subprocess.CompletedProcess]: - """ - 批量执行命令 - - Args: - hosts_ports: 主机端口列表 [(host1, port1), (host2, port2), ...] - command: 要执行的命令 - parallel: 是否并行执行 - timeout: 超时时间 - - Returns: - 执行结果字典 {host:port -> result} - """ - results = {} - - if parallel: - import concurrent.futures - - def execute_on_host(host_port): - host, port = host_port - key = f"{host}:{port}" - try: - result = self.ssh_manager.execute_command(host, port, command, timeout) - return key, result - except Exception as e: - # 创建一个错误结果 - error_result = subprocess.CompletedProcess( - args=[command], returncode=1, stdout="", stderr=str(e) - ) - return key, error_result - - with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor: - future_to_host = { - executor.submit(execute_on_host, host_port): host_port - for host_port in hosts_ports - } - - for future in concurrent.futures.as_completed(future_to_host): - key, result = future.result() - results[key] = result - else: - # 串行执行 - for host, port in hosts_ports: - key = f"{host}:{port}" - try: - result = self.ssh_manager.execute_command(host, port, command, timeout) - results[key] = result - except Exception as e: - # 创建一个错误结果 - error_result = subprocess.CompletedProcess( - args=[command], returncode=1, stdout="", stderr=str(e) - ) - results[key] = error_result - - return results - - def check_service_status(self, host: str, port: int, service_name: str) -> dict[str, Any]: - """ - 检查远程服务状态 - - Args: - host: 目标主机 - port: SSH端口 - service_name: 服务名称 - - Returns: - 服务状态信息 - """ - # 检查进程是否运行 - ps_cmd = f"ps aux | grep -v grep | grep {service_name}" - result = self.ssh_manager.execute_command(host, port, ps_cmd, timeout=10) - - status_info = { - "host": host, - "port": port, - "service": service_name, - "running": result.returncode == 0, - "processes": [], - } - - if result.returncode == 0 and result.stdout: - status_info["processes"] = result.stdout.strip().split("\n") - - return status_info diff --git a/packages/sage-cli/src/sage/cli/core/utils.py b/packages/sage-cli/src/sage/cli/core/utils.py deleted file mode 100644 index 5d5bd559fb..0000000000 --- a/packages/sage-cli/src/sage/cli/core/utils.py +++ /dev/null @@ -1,448 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE CLI Utilities -================== - -通用工具函数 -""" - -import json -import os -import shutil -import signal -import subprocess -import sys -import tempfile -from pathlib import Path -from typing import Any - -import yaml # type: ignore[import-untyped] - -from .exceptions import CLIException, ValidationError - - -def find_project_root( - start_path: Path | None = None, markers: list[str] | None = None -) -> Path | None: - """ - 查找项目根目录 - - Args: - start_path: 开始查找的路径，默认为当前目录 - markers: 用于识别项目根目录的标记文件/目录 - - Returns: - 项目根目录路径，如果找不到返回None - """ - # If no custom markers provided, use the centralized implementation from sage-common - if markers is None: - try: - from sage.common.config import find_sage_project_root - - return find_sage_project_root(start_path) - except ImportError: - # Fallback to local implementation if sage-common not available - markers = [ - "setup.py", - "pyproject.toml", - "requirements.txt", - ".git", - "sage", - "packages", - "SAGE_API_REFACTOR_SUMMARY.md", - ] - - if start_path is None: - start_path = Path.cwd() - - current = Path(start_path).resolve() - - # 向上查找包含标记文件的路径 - for parent in [current] + list(current.parents): - if any((parent / marker).exists() for marker in markers): - return parent - - # 检查当前Python环境中的sage包位置 - try: - import sage - - sage_path = Path(sage.__file__).parent.parent - if any((sage_path / marker).exists() for marker in markers): - return sage_path - except ImportError: - pass - - return None - - -def ensure_directory(path: str | Path, parents: bool = True, exist_ok: bool = True) -> Path: - """ - 确保目录存在 - - Args: - path: 目录路径 - parents: 是否创建父目录 - exist_ok: 如果目录已存在是否报错 - - Returns: - 目录路径对象 - - Raises: - CLIException: 创建目录失败 - """ - path = Path(path) - try: - path.mkdir(parents=parents, exist_ok=exist_ok) - return path - except Exception as e: - raise CLIException(f"Failed to create directory {path}: {e}") - - -def run_subprocess( - command: str | list[str], - cwd: Path | None = None, - timeout: int | None = None, - check: bool = True, - capture_output: bool = True, - text: bool = True, - shell: bool | None = None, - env: dict[str, str] | None = None, -) -> subprocess.CompletedProcess: - """ - 执行子进程命令 - - Args: - command: 要执行的命令 - cwd: 工作目录 - timeout: 超时时间（秒） - check: 是否检查返回码 - capture_output: 是否捕获输出 - text: 是否以文本模式处理输出 - shell: 是否使用shell - env: 环境变量 - - Returns: - subprocess.CompletedProcess对象 - - Raises: - CLIException: 命令执行失败 - """ - if shell is None: - shell = isinstance(command, str) - - try: - result = subprocess.run( - command, - cwd=cwd, - timeout=timeout, - check=check, - capture_output=capture_output, - text=text, - shell=shell, - env=env, - ) - return result - - except subprocess.CalledProcessError as e: - cmd_str = " ".join(command) if isinstance(command, list) else command - error_msg = f"Command failed: {cmd_str}" - if e.stderr: - error_msg += f"\nStderr: {e.stderr}" - raise CLIException(error_msg, e.returncode) - - except subprocess.TimeoutExpired: - cmd_str = " ".join(command) if isinstance(command, list) else command - raise CLIException(f"Command timeout: {cmd_str} (timeout: {timeout}s)") - - except Exception as e: - cmd_str = " ".join(command) if isinstance(command, list) else command - raise CLIException(f"Unexpected error running command '{cmd_str}': {e}") - - -def load_yaml_file(file_path: str | Path) -> dict[str, Any]: - """ - 加载YAML文件 - - Args: - file_path: YAML文件路径 - - Returns: - 解析后的数据 - - Raises: - CLIException: 文件加载失败 - """ - file_path = Path(file_path) - - if not file_path.exists(): - raise CLIException(f"YAML file not found: {file_path}") - - try: - with open(file_path, encoding="utf-8") as f: - return yaml.safe_load(f) or {} - except Exception as e: - raise CLIException(f"Failed to load YAML file {file_path}: {e}") - - -def save_yaml_file(data: dict[str, Any], file_path: str | Path): - """ - 保存数据到YAML文件 - - Args: - data: 要保存的数据 - file_path: YAML文件路径 - - Raises: - CLIException: 文件保存失败 - """ - file_path = Path(file_path) - - try: - ensure_directory(file_path.parent) - with open(file_path, "w", encoding="utf-8") as f: - yaml.dump(data, f, default_flow_style=False, allow_unicode=True) - except Exception as e: - raise CLIException(f"Failed to save YAML file {file_path}: {e}") - - -def load_json_file(file_path: str | Path) -> dict[str, Any]: - """ - 加载JSON文件 - - Args: - file_path: JSON文件路径 - - Returns: - 解析后的数据 - - Raises: - CLIException: 文件加载失败 - """ - file_path = Path(file_path) - - if not file_path.exists(): - raise CLIException(f"JSON file not found: {file_path}") - - try: - with open(file_path, encoding="utf-8") as f: - return json.load(f) - except Exception as e: - raise CLIException(f"Failed to load JSON file {file_path}: {e}") - - -def save_json_file(data: Any, file_path: str | Path, indent: int = 2): - """ - 保存数据到JSON文件 - - Args: - data: 要保存的数据 - file_path: JSON文件路径 - indent: 缩进空格数 - - Raises: - CLIException: 文件保存失败 - """ - file_path = Path(file_path) - - try: - ensure_directory(file_path.parent) - with open(file_path, "w", encoding="utf-8") as f: - json.dump(data, f, indent=indent, ensure_ascii=False) - except Exception as e: - raise CLIException(f"Failed to save JSON file {file_path}: {e}") - - -def resolve_path(path: str | Path, base_path: Path | None = None) -> Path: - """ - 解析路径，支持相对路径和波浪号扩展 - - Args: - path: 要解析的路径 - base_path: 基础路径，用于解析相对路径 - - Returns: - 解析后的绝对路径 - """ - path = Path(path) - - if str(path).startswith("~"): - path = path.expanduser() - - if not path.is_absolute() and base_path: - path = base_path / path - - return path.resolve() - - -def is_port_available(host: str, port: int) -> bool: - """ - 检查端口是否可用 - - Args: - host: 主机地址 - port: 端口号 - - Returns: - True if port is available, False otherwise - - Note: - This is a wrapper around sage.common.utils.system.network.is_port_available - """ - from sage.common.utils.system.network import is_port_available as _is_port_available - - return _is_port_available(host, port) - - -def wait_for_port(host: str, port: int, timeout: int = 30, check_interval: float = 1.0) -> bool: - """ - 等待端口变为可用（服务启动） - - Args: - host: 主机地址 - port: 端口号 - timeout: 超时时间（秒） - check_interval: 检查间隔（秒） - - Returns: - True if port becomes available, False if timeout - - Note: - This is a wrapper around sage.common.utils.system.network.wait_for_port_ready - """ - from sage.common.utils.system.network import wait_for_port_ready - - return wait_for_port_ready(host, port, timeout, check_interval) - - -def create_temp_file( - suffix: str | None = None, prefix: str = "sage_", content: str | None = None -) -> Path: - """ - 创建临时文件 - - Args: - suffix: 文件后缀 - prefix: 文件前缀 - content: 文件内容 - - Returns: - 临时文件路径 - """ - fd, temp_path_str = tempfile.mkstemp(suffix=suffix, prefix=prefix) - temp_path = Path(temp_path_str) - - try: - if content: - with open(fd, "w", encoding="utf-8") as f: - f.write(content) - else: - os.close(fd) - return temp_path - except Exception as e: - os.close(fd) - temp_path.unlink(missing_ok=True) - raise CLIException(f"Failed to create temp file: {e}") - - -def create_temp_directory(prefix: str = "sage_") -> Path: - """ - 创建临时目录 - - Args: - prefix: 目录前缀 - - Returns: - 临时目录路径 - """ - temp_dir = tempfile.mkdtemp(prefix=prefix) - return Path(temp_dir) - - -def safe_delete(path: str | Path, missing_ok: bool = True): - """ - 安全删除文件或目录 - - Args: - path: 要删除的路径 - missing_ok: 文件不存在是否报错 - - Raises: - CLIException: 删除失败 - """ - path = Path(path) - - try: - if path.is_file(): - path.unlink(missing_ok=missing_ok) - elif path.is_dir(): - shutil.rmtree(path, ignore_errors=missing_ok) - except Exception as e: - if not missing_ok: - raise CLIException(f"Failed to delete {path}: {e}") - - -def parse_key_value_pairs(pairs: list[str]) -> dict[str, str]: - """ - 解析键值对列表 - - Args: - pairs: 键值对列表，格式为 ["key1=value1", "key2=value2"] - - Returns: - 解析后的字典 - - Raises: - ValidationError: 格式错误 - """ - result = {} - - for pair in pairs: - if "=" not in pair: - raise ValidationError(f"Invalid key-value pair format: {pair}") - - key, value = pair.split("=", 1) - key = key.strip() - value = value.strip() - - if not key: - raise ValidationError(f"Empty key in pair: {pair}") - - result[key] = value - - return result - - -def setup_signal_handlers(cleanup_func=None): - """ - 设置信号处理器 - - Args: - cleanup_func: 清理函数，在收到终止信号时调用 - """ - - def signal_handler(signum, frame): - print(f"\nReceived signal {signum}, cleaning up...") - if cleanup_func: - try: - cleanup_func() - except Exception as e: - print(f"Error during cleanup: {e}") - sys.exit(0) - - signal.signal(signal.SIGINT, signal_handler) - signal.signal(signal.SIGTERM, signal_handler) - - -def format_command_for_display(command: str | list[str]) -> str: - """ - 格式化命令用于显示 - - Args: - command: 命令 - - Returns: - 格式化后的命令字符串 - """ - if isinstance(command, list): - return " ".join(command) - return command diff --git a/packages/sage-cli/src/sage/cli/core/validation.py b/packages/sage-cli/src/sage/cli/core/validation.py deleted file mode 100644 index 2d956e99a5..0000000000 --- a/packages/sage-cli/src/sage/cli/core/validation.py +++ /dev/null @@ -1,407 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE CLI Validation -=================== - -输入验证和数据校验功能 -""" - -import re -import socket -from pathlib import Path -from typing import Any -from urllib.parse import urlparse - -from .exceptions import ValidationError - - -def validate_host(host: str) -> str: - """ - 验证主机地址 - - Args: - host: 主机地址 - - Returns: - 验证通过的主机地址 - - Raises: - ValidationError: 主机地址格式错误 - """ - if not host or not isinstance(host, str): - raise ValidationError("Host cannot be empty") - - host = host.strip() - - # 检查是否为有效的IP地址 - try: - socket.inet_pton(socket.AF_INET, host) - return host - except OSError: - pass - - try: - socket.inet_pton(socket.AF_INET6, host) - return host - except OSError: - pass - - # 检查是否为有效的主机名 - if not re.match( - r"^[a-zA-Z0-9]([a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(\.[a-zA-Z0-9]([a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)*$", - host, - ): - # 允许localhost和简单主机名 - if ( - host not in ["localhost"] - and not re.match(r"^[a-zA-Z0-9][a-zA-Z0-9-]*[a-zA-Z0-9]$", host) - and not re.match(r"^[a-zA-Z0-9]+$", host) - ): - raise ValidationError(f"Invalid host address: {host}") - - return host - - -def validate_port(port: int | str) -> int: - """ - 验证端口号 - - Args: - port: 端口号 - - Returns: - 验证通过的端口号 - - Raises: - ValidationError: 端口号无效 - """ - try: - port = int(port) - except (ValueError, TypeError): - raise ValidationError(f"Port must be a number: {port}") - - if not (1 <= port <= 65535): - raise ValidationError(f"Port must be between 1 and 65535: {port}") - - return port - - -def validate_path( - path: str | Path, - must_exist: bool = False, - must_be_file: bool = False, - must_be_dir: bool = False, - create_if_missing: bool = False, -) -> Path: - """ - 验证文件路径 - - Args: - path: 文件路径 - must_exist: 路径必须存在 - must_be_file: 必须是文件 - must_be_dir: 必须是目录 - create_if_missing: 如果不存在则创建 - - Returns: - 验证通过的路径对象 - - Raises: - ValidationError: 路径验证失败 - """ - if not path: - raise ValidationError("Path cannot be empty") - - path = Path(path) - - # 处理波浪号 - if str(path).startswith("~"): - path = path.expanduser() - - # 转换为绝对路径 - path = path.resolve() - - if must_exist and not path.exists(): - if create_if_missing: - if must_be_dir: - path.mkdir(parents=True, exist_ok=True) - else: - path.parent.mkdir(parents=True, exist_ok=True) - path.touch() - else: - raise ValidationError(f"Path does not exist: {path}") - - if path.exists(): - if must_be_file and not path.is_file(): - raise ValidationError(f"Path is not a file: {path}") - - if must_be_dir and not path.is_dir(): - raise ValidationError(f"Path is not a directory: {path}") - - return path - - -def validate_url(url: str) -> str: - """ - 验证URL格式 - - Args: - url: URL字符串 - - Returns: - 验证通过的URL - - Raises: - ValidationError: URL格式错误 - """ - if not url or not isinstance(url, str): - raise ValidationError("URL cannot be empty") - - url = url.strip() - - try: - parsed = urlparse(url) - if not parsed.scheme or not parsed.netloc: - raise ValidationError(f"Invalid URL format: {url}") - return url - except Exception as e: - raise ValidationError(f"Invalid URL: {url}, error: {e}") - - -def validate_email(email: str) -> str: - """ - 验证邮箱地址 - - Args: - email: 邮箱地址 - - Returns: - 验证通过的邮箱地址 - - Raises: - ValidationError: 邮箱格式错误 - """ - if not email or not isinstance(email, str): - raise ValidationError("Email cannot be empty") - - email = email.strip() - - email_pattern = re.compile(r"^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$") - - if not email_pattern.match(email): - raise ValidationError(f"Invalid email format: {email}") - - return email - - -def validate_uuid(uuid_str: str, allow_partial: bool = False) -> str: - """ - 验证UUID格式 - - Args: - uuid_str: UUID字符串 - allow_partial: 是否允许部分UUID（用于前缀匹配） - - Returns: - 验证通过的UUID字符串 - - Raises: - ValidationError: UUID格式错误 - """ - if not uuid_str or not isinstance(uuid_str, str): - raise ValidationError("UUID cannot be empty") - - uuid_str = uuid_str.strip() - - if allow_partial: - # 允许部分UUID，但至少要有8个字符 - if len(uuid_str) < 8: - raise ValidationError("Partial UUID must be at least 8 characters") - # 检查是否只包含有效的十六进制字符和连字符 - if not re.match(r"^[0-9a-f-]+$", uuid_str.lower()): - raise ValidationError(f"Invalid UUID format: {uuid_str}") - else: - # 完整UUID格式验证 - uuid_pattern = re.compile( - r"^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$", - re.IGNORECASE, - ) - if not uuid_pattern.match(uuid_str): - raise ValidationError(f"Invalid UUID format: {uuid_str}") - - return uuid_str - - -def validate_env_name(name: str) -> str: - """ - 验证环境变量名 - - Args: - name: 环境变量名 - - Returns: - 验证通过的环境变量名 - - Raises: - ValidationError: 环境变量名格式错误 - """ - if not name or not isinstance(name, str): - raise ValidationError("Environment variable name cannot be empty") - - name = name.strip() - - # 环境变量名应该只包含字母、数字和下划线，且不能以数字开头 - if not re.match(r"^[a-zA-Z_][a-zA-Z0-9_]*$", name): - raise ValidationError(f"Invalid environment variable name: {name}") - - return name - - -def validate_service_name(name: str) -> str: - """ - 验证服务名 - - Args: - name: 服务名 - - Returns: - 验证通过的服务名 - - Raises: - ValidationError: 服务名格式错误 - """ - if not name or not isinstance(name, str): - raise ValidationError("Service name cannot be empty") - - name = name.strip() - - # 服务名应该只包含字母、数字、连字符和下划线 - if not re.match(r"^[a-zA-Z0-9_-]+$", name): - raise ValidationError(f"Invalid service name: {name}") - - if len(name) > 63: - raise ValidationError(f"Service name too long (max 63 characters): {name}") - - return name - - -def validate_config_dict( - config: dict[str, Any], - required_keys: list[str] | None = None, - valid_keys: list[str] | None = None, -) -> dict[str, Any]: - """ - 验证配置字典 - - Args: - config: 配置字典 - required_keys: 必需的键列表 - valid_keys: 有效的键列表 - - Returns: - 验证通过的配置字典 - - Raises: - ValidationError: 配置验证失败 - """ - if not isinstance(config, dict): - raise ValidationError("Config must be a dictionary") - - if required_keys: - missing_keys = [key for key in required_keys if key not in config] - if missing_keys: - raise ValidationError(f"Missing required config keys: {missing_keys}") - - if valid_keys: - invalid_keys = [key for key in config.keys() if key not in valid_keys] - if invalid_keys: - raise ValidationError(f"Invalid config keys: {invalid_keys}") - - return config - - -def validate_timeout(timeout: int | str | float) -> int: - """ - 验证超时值 - - Args: - timeout: 超时值（秒） - - Returns: - 验证通过的超时值 - - Raises: - ValidationError: 超时值无效 - """ - try: - timeout = int(timeout) - except (ValueError, TypeError): - raise ValidationError(f"Timeout must be a number: {timeout}") - - if timeout <= 0: - raise ValidationError(f"Timeout must be positive: {timeout}") - - if timeout > 3600: # 最大1小时 - raise ValidationError(f"Timeout too large (max 3600 seconds): {timeout}") - - return timeout - - -def validate_log_level(level: str) -> str: - """ - 验证日志级别 - - Args: - level: 日志级别 - - Returns: - 验证通过的日志级别 - - Raises: - ValidationError: 日志级别无效 - """ - if not level or not isinstance(level, str): - raise ValidationError("Log level cannot be empty") - - level = level.strip().upper() - - valid_levels = ["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"] - if level not in valid_levels: - raise ValidationError(f"Invalid log level: {level}, must be one of {valid_levels}") - - return level - - -def validate_memory_size(size: str) -> str: - """ - 验证内存大小格式 - - Args: - size: 内存大小字符串，如 "1G", "512M", "2048MB" - - Returns: - 验证通过的内存大小字符串 - - Raises: - ValidationError: 内存大小格式错误 - """ - if not size or not isinstance(size, str): - raise ValidationError("Memory size cannot be empty") - - size = size.strip() - - # 匹配内存大小格式 - pattern = re.compile(r"^(\d+(?:\.\d+)?)\s*([KMGT]?B?|[kmgt]?b?)$", re.IGNORECASE) - match = pattern.match(size) - - if not match: - raise ValidationError(f"Invalid memory size format: {size}") - - number, unit = match.groups() - - try: - float(number) # 验证数字部分 - except ValueError: - raise ValidationError(f"Invalid number in memory size: {number}") - - return size diff --git a/packages/sage-cli/src/sage/cli/main.py b/packages/sage-cli/src/sage/cli/main.py deleted file mode 100644 index 22d03c1751..0000000000 --- a/packages/sage-cli/src/sage/cli/main.py +++ /dev/null @@ -1,284 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE CLI 主入口 - -统一的命令行接口，包括： -- Platform: 集群管理、作业调度 -- Apps: LLM、Chat、Embedding、Pipeline、Gateway - -注意： -- Dev 开发工具命令由 sage-tools 包提供 (sage-dev) -- Studio 已独立: https://github.com/intellistream/sage-studio -- Edge 已独立: pip install isage-edge -""" - -import logging -import os - -# Suppress noisy INFO logs during CLI startup unless SAGE_CLI_VERBOSE is set -# This must be done BEFORE importing any sage modules -if not os.environ.get("SAGE_CLI_VERBOSE"): - logging.basicConfig(level=logging.WARNING, format="%(message)s") - # Also suppress specific noisy loggers - for logger_name in [ - "sage.platform", - "sage.middleware", - "sage.kernel", - "sage.common", - "faiss", - "httpx", - "httpcore", - ]: - logging.getLogger(logger_name).setLevel(logging.WARNING) - -import typer -from rich.console import Console - -# 创建主应用 -app = typer.Typer( - name="sage", - help="""🚀 SAGE - Streaming Applied to GEneral data stream - - 🎮 即开即用: - sage demo hello # Hello World 入门 - sage demo list # 查看所有示例 - sage demo interactive # 交互式 Shell - - 命令分类： - • Platform - 集群管理和作业调度 - • Apps - 应用层服务（LLM、Chat等） - • Demo - 即开即用的体验入口 - - 快速示例： - sage cluster start # 启动集群 - sage gateway start # 启动 API 网关 - sage job submit task.py # 提交作业 - - 开发工具： - 开发命令请使用 sage-dev (由 sage-tools 包提供) - sage-dev quality check # 运行质量检查 - sage-dev project test # 运行测试 - """, - no_args_is_help=True, -) - -console = Console() - - -# ============================================================================ -# Version Callback -# ============================================================================ - - -def version_callback(value: bool): - """Show version information""" - if value: - try: - from sage.common._version import __version__ - - typer.echo(f"SAGE version {__version__}") - except ImportError: - typer.echo("SAGE version unknown") - raise typer.Exit() - - -# ============================================================================ -# Platform Commands - 平台管理命令 -# ============================================================================ - -# 导入 Platform 命令组 -try: - from .commands.platform import ( - cluster_app, - config_app, - docs_app, - doctor_app, - extensions_app, - head_app, - job_app, - jobmanager_app, - logs_app, - version_app, - worker_app, - ) - - if version_app: - app.add_typer(version_app, name="version", help="📋 版本信息") - if cluster_app: - app.add_typer( - cluster_app, - name="cluster", - help="🌐 Cluster - 集群管理和状态监控 (start, stop, status, restart, logs)", - ) - if head_app: - app.add_typer( - head_app, - name="head", - help="🎯 Head - 集群头节点管理 (start, stop, status, restart, logs)", - ) - if worker_app: - app.add_typer( - worker_app, - name="worker", - help="🔧 Worker - 工作节点管理 (start, stop, status, restart, logs, add, remove)", - ) - if job_app: - app.add_typer( - job_app, - name="job", - help="📋 作业管理 - 提交、监控、管理作业 (submit, list, status, stop, logs, attach)", - ) - if jobmanager_app: - app.add_typer( - jobmanager_app, - name="jobmanager", - help="⚡ JobManager - 作业管理器服务 (start, stop, status, restart)", - ) - if config_app: - app.add_typer(config_app, name="config", help="⚙️ 配置管理 (show, set, reset)") - if doctor_app: - app.add_typer(doctor_app, name="doctor", help="🔍 系统诊断") - if extensions_app: - app.add_typer( - extensions_app, - name="extensions", - help="🧩 扩展管理 - 安装和管理C++扩展 (list, install, uninstall, status)", - ) - if docs_app: - app.add_typer( - docs_app, - name="docs", - help="📚 文档管理 - 预览、构建和部署文档 (serve, build, install-deps, info)", - ) - if logs_app: - app.add_typer( - logs_app, - name="logs", - help="📝 日志管理 - 清理和查看日志文件 (clean, list, info)", - ) -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 platform 命令组: {e}[/yellow]") - - -# ============================================================================ -# Apps Commands - 应用层命令 -# ============================================================================ - -try: - from .commands.apps import ( - chat_app, - embedding_app, - gateway_app, - inference_app, - llm_app, - pipeline_app, - ) - - if llm_app: - app.add_typer( - llm_app, - name="llm", - help="🤖 LLM服务管理 - 启动、停止、配置LLM服务 (serve, start, stop, status, models)", - ) - if chat_app: - app.add_typer( - chat_app, name="chat", help="🧭 编程助手 - 基于 SageVDB 的文档问答 (interactive mode)" - ) - if embedding_app: - app.add_typer( - embedding_app, - name="embedding", - help="🎯 Embedding 管理 - 管理和测试 embedding 方法 (list, test, benchmark)", - ) - if pipeline_app: - app.add_typer( - pipeline_app, - name="pipeline", - help="🧱 Pipeline Builder - 大模型辅助的配置生成 (build, validate, template)", - ) - if inference_app: - app.add_typer( - inference_app, - name="inference", - help="🔮 统一推理服务 - LLM 和 Embedding 混合调度 (start, stop, status, config)", - ) - if gateway_app: - app.add_typer( - gateway_app, - name="gateway", - help="🌐 API Gateway - 统一推理网关服务 (start, stop, status, logs, restart)", - ) -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 apps 命令组: {e}[/yellow]") - - -# ============================================================================ -# Demo Commands - 即开即用的体验入口 -# ============================================================================ - -try: - from .commands.demo import app as demo_app - - app.add_typer( - demo_app, - name="demo", - help="🎮 Demo - 即开即用的 SAGE 体验 (hello, list, run, interactive)", - ) -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 demo 命令: {e}[/yellow]") - - -# ============================================================================ -# Dev Commands - 已独立为 sage-dev 命令 -# ============================================================================ - -# 注意: 开发命令已经从 sage-cli 中移除，现在由 sage-tools 包通过 sage-dev 命令提供 -# 如需使用开发工具，请使用: sage-dev --help - - -# ============================================================================ -# Main Callback -# ============================================================================ - - -@app.callback() -def main( - version: bool | None = typer.Option( - None, "--version", "-v", help="显示版本信息", callback=version_callback - ), -): - """ - 🚀 SAGE - Streaming-Augmented Generative Execution - - 统一的AI研究和流式计算平台命令行工具 - - 💡 使用示例: - - Platform Commands: - sage cluster start # 启动集群 - sage cluster status # 查看集群状态 - sage config show # 显示配置 - sage doctor # 系统诊断 - - Application Commands: - sage llm run # 启动阻塞式 LLM 服务 - sage gateway start # 启动API网关 - sage chat # 启动聊天助手 - sage pipeline build # 构建 pipeline - - 🏗️ 架构说明: - - Platform Commands: 平台管理 (cluster, config, doctor, etc.) - - Application Commands: 应用功能 (llm, gateway, chat, pipeline) - - 📝 开发工具: - 开发命令请使用独立的 sage-dev 命令（由 sage-tools 包提供） - 安装: pip install sage-tools - 使用: sage-dev quality check, sage-dev project test 等 - - 📚 文档: https://intellistream.github.io/SAGE - """ - pass - - -if __name__ == "__main__": - app() diff --git a/packages/sage-cli/src/sage/cli/management/__init__.py b/packages/sage-cli/src/sage/cli/management/__init__.py deleted file mode 100644 index b97b47a796..0000000000 --- a/packages/sage-cli/src/sage/cli/management/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -""" -SAGE Management - 通用管理工具 - -提供配置管理、部署管理等通用管理功能。 -""" - -from .config_manager import ConfigManager, get_config_manager -from .deployment_manager import DeploymentManager - -__all__ = [ - "ConfigManager", - "get_config_manager", - "DeploymentManager", -] diff --git a/packages/sage-cli/src/sage/cli/management/config_manager.py b/packages/sage-cli/src/sage/cli/management/config_manager.py deleted file mode 100644 index dcb6a3b5fb..0000000000 --- a/packages/sage-cli/src/sage/cli/management/config_manager.py +++ /dev/null @@ -1,356 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE Configuration Manager -统一的配置文件管理 -""" - -from pathlib import Path -from typing import Any - -import typer -import yaml # type: ignore[import-untyped] - - -def find_project_root() -> Path | None: - """查找项目根目录（包含 .git 或 pyproject.toml 的目录）""" - d = Path.cwd() - root = Path(d.root) - while d != root: - if (d / ".git").exists() or (d / "pyproject.toml").exists(): - return d - d = d.parent - return None - - -class ConfigManager: - """配置管理器""" - - # 配置文件名 - CONFIG_FILENAME = "cluster.yaml" - - def __init__(self, config_path: str | None = None): - if config_path: - self.config_path = Path(config_path) - else: - # 搜索路径优先级： - # 1. 项目根目录/config/cluster.yaml (推荐) - # 2. 当前目录/config/cluster.yaml - # 3. ~/.sage/cluster.yaml (用户级别配置) - # 4. 兼容旧路径: ~/.sage/config.yaml - - paths_to_check = [] - - # 1. 项目根目录 - project_root = find_project_root() - if project_root: - paths_to_check.append(project_root / "config" / self.CONFIG_FILENAME) - - # 2. 当前目录 - paths_to_check.append(Path.cwd() / "config" / self.CONFIG_FILENAME) - - # 3. 用户目录 (新路径) - paths_to_check.append(Path.home() / ".sage" / self.CONFIG_FILENAME) - - # 4. 兼容旧路径 - paths_to_check.append(Path.home() / ".sage" / "config.yaml") - - # 默认值: 项目根目录或用户目录 - if project_root: - default_path = project_root / "config" / self.CONFIG_FILENAME - else: - default_path = Path.home() / ".sage" / self.CONFIG_FILENAME - - selected_path = default_path - - for p in paths_to_check: - if p.exists(): - selected_path = p - break - - self.config_path = selected_path - self._config: dict[str, Any] | None = None - - def load_config(self) -> dict[str, Any]: - """加载配置文件""" - if not self.config_path.exists(): - raise FileNotFoundError( - f"配置文件不存在: {self.config_path}\n请运行 'sage config create' 创建默认配置" - ) - - try: - with open(self.config_path, encoding="utf-8") as f: - loaded_config: dict[str, Any] = yaml.safe_load(f) or {} - self._config = loaded_config - return loaded_config - except Exception as e: - raise RuntimeError(f"加载配置文件失败: {e}") - - def save_config(self, config: dict[str, Any]): - """保存配置文件""" - self.config_path.parent.mkdir(parents=True, exist_ok=True) - try: - with open(self.config_path, "w", encoding="utf-8") as f: - yaml.dump(config, f, default_flow_style=False, allow_unicode=True) - self._config = config - except Exception as e: - raise RuntimeError(f"保存配置文件失败: {e}") - - @property - def config(self) -> dict[str, Any]: - """获取配置""" - if self._config is None: - self._config = self.load_config() - return self._config - - def get_head_config(self) -> dict[str, Any]: - """获取head节点配置""" - return self.config.get("head", {}) - - def get_worker_config(self) -> dict[str, Any]: - """获取worker配置""" - return self.config.get("worker", {}) - - def get_ssh_config(self) -> dict[str, Any]: - """获取SSH配置""" - return self.config.get("ssh", {}) - - def get_remote_config(self) -> dict[str, Any]: - """获取远程路径配置""" - return self.config.get("remote", {}) - - def get_workers_ssh_hosts(self) -> list[tuple[str, int]]: - """解析worker SSH主机列表""" - # 先从ssh.workers中读取 - ssh_config = self.get_ssh_config() - workers = ssh_config.get("workers", []) - - if workers: - return [(w["host"], w.get("port", 22)) for w in workers] - - # 兼容旧的workers_ssh_hosts格式 - hosts_str = self.config.get("workers_ssh_hosts", "") - if not hosts_str: - return [] - - # 检查是否为列表格式（新格式测试） - if isinstance(hosts_str, list): - return [(item["host"], item.get("port", 22)) for item in hosts_str] - - nodes = [] - for node in hosts_str.split(","): - node = node.strip() - if ":" in node: - host, port = node.split(":", 1) - port = int(port) - else: - host = node - port = 22 # 默认SSH端口 - nodes.append((host, port)) - return nodes - - def create_default_config(self): - """创建默认配置文件""" - default_config = { - "head": { - "host": "localhost", - "head_port": 6379, - "dashboard_port": 8265, - "dashboard_host": "0.0.0.0", - "temp_dir": "/var/tmp/ray", - "log_dir": "/var/tmp/sage_head_logs", - "conda_env": "sage", - "python_path": "", # 自动检测 - "ray_command": "", # 自动检测 - "sage_home": "", - }, - "worker": { - "bind_host": "localhost", - "temp_dir": "/tmp/ray_worker", - "log_dir": "/tmp/sage_worker_logs", - }, - "ssh": { - "user": "sage", - "key_path": "~/.ssh/id_rsa", - "connect_timeout": 10, - "workers": [ - # {"host": "sage2", "port": 22}, - # {"host": "sage3", "port": 22}, - # {"host": "sage4", "port": 22}, - ], - }, - "remote": { - "sage_home": "/home/sage", - "python_path": "", # 自动检测 - "ray_command": "", # 自动检测 - "conda_env": "sage", - }, - "daemon": { - "host": "localhost", - "port": 19001, - }, - "output": { - "format": "table", - "colors": True, - }, - "monitor": { - "refresh_interval": 5, - }, - "jobmanager": { - "timeout": 30, - "retry_attempts": 3, - }, - } - - self.save_config(default_config) - return default_config - - -def get_config_manager(config_path: str | None = None) -> ConfigManager: - """获取配置管理器实例""" - return ConfigManager(config_path) - - -# Typer应用 -app = typer.Typer(help="SAGE configuration management") - - -@app.command() -def show( - config_path: str | None = typer.Option(None, "--config", "-c", help="Configuration file path"), -): - """显示当前配置""" - config_manager = get_config_manager(config_path) - print(f"配置文件路径: {config_manager.config_path}") - try: - config = config_manager.load_config() - print("\n当前配置:") - import pprint - - pprint.pprint(config) - except FileNotFoundError as e: - print(f"\n{e}") - - -@app.command() -def create( - config_path: str | None = typer.Option(None, "--config", "-c", help="Configuration file path"), - force: bool = typer.Option(False, "--force", "-f", help="覆盖已存在的配置文件"), -): - """创建默认配置""" - config_manager = get_config_manager(config_path) - - if config_manager.config_path.exists() and not force: - print(f"配置文件已存在: {config_manager.config_path}") - print("使用 --force 覆盖") - return - - config_manager.create_default_config() - print(f"默认配置已创建: {config_manager.config_path}") - - -@app.command() -def path( - config_path: str | None = typer.Option(None, "--config", "-c", help="Configuration file path"), -): - """显示配置文件路径""" - config_manager = get_config_manager(config_path) - print(config_manager.config_path) - - -@app.command("set") -def set_value( - key: str = typer.Argument(..., help="Configuration key (支持点号分隔，如 head.host)"), - value: str = typer.Argument(..., help="Configuration value"), - config_path: str | None = typer.Option(None, "--config", "-c", help="Configuration file path"), -): - """设置配置值""" - config_manager = get_config_manager(config_path) - try: - config = config_manager.load_config() - # Simple dot notation support for nested keys - keys = key.split(".") - current = config - for k in keys[:-1]: - if k not in current: - current[k] = {} - current = current[k] - - # 尝试转换类型 - try: - if value.lower() == "true": - value = True - elif value.lower() == "false": - value = False - elif value.isdigit(): - value = int(value) - elif "." in value and all(p.isdigit() for p in value.split(".", 1)): - value = float(value) - except (ValueError, AttributeError): - pass - - current[keys[-1]] = value - config_manager.save_config(config) - print(f"配置已更新: {key} = {value}") - except FileNotFoundError as e: - print(f"{e}") - - -@app.command() -def add_worker( - host: str = typer.Argument(..., help="Worker hostname"), - port: int = typer.Option(22, "--port", "-p", help="SSH port"), - config_path: str | None = typer.Option(None, "--config", "-c", help="Configuration file path"), -): - """添加 worker 节点""" - config_manager = get_config_manager(config_path) - try: - config = config_manager.load_config() - - # 确保 ssh.workers 存在 - if "ssh" not in config: - config["ssh"] = {} - if "workers" not in config["ssh"]: - config["ssh"]["workers"] = [] - - # 检查是否已存在 - for w in config["ssh"]["workers"]: - if w["host"] == host and w.get("port", 22) == port: - print(f"Worker 已存在: {host}:{port}") - return - - config["ssh"]["workers"].append({"host": host, "port": port}) - config_manager.save_config(config) - print(f"已添加 worker: {host}:{port}") - except FileNotFoundError as e: - print(f"{e}") - - -@app.command() -def remove_worker( - host: str = typer.Argument(..., help="Worker hostname"), - port: int = typer.Option(22, "--port", "-p", help="SSH port"), - config_path: str | None = typer.Option(None, "--config", "-c", help="Configuration file path"), -): - """移除 worker 节点""" - config_manager = get_config_manager(config_path) - try: - config = config_manager.load_config() - - workers = config.get("ssh", {}).get("workers", []) - original_len = len(workers) - - config["ssh"]["workers"] = [ - w for w in workers if not (w["host"] == host and w.get("port", 22) == port) - ] - - if len(config["ssh"]["workers"]) < original_len: - config_manager.save_config(config) - print(f"已移除 worker: {host}:{port}") - else: - print(f"未找到 worker: {host}:{port}") - except FileNotFoundError as e: - print(f"{e}") - - -if __name__ == "__main__": - app() diff --git a/packages/sage-cli/src/sage/cli/management/deployment_manager.py b/packages/sage-cli/src/sage/cli/management/deployment_manager.py deleted file mode 100644 index bad356b7e0..0000000000 --- a/packages/sage-cli/src/sage/cli/management/deployment_manager.py +++ /dev/null @@ -1,773 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE Deployment Manager -处理项目文件部署到远程节点 -""" - -import os -import shutil -import subprocess -import tarfile -import tempfile -from pathlib import Path - -import typer - -from .config_manager import get_config_manager - - -class DeploymentManager: - """部署管理器""" - - def __init__(self): - self.config_manager = get_config_manager() - - # 智能检测项目根目录 - self.project_root = self._find_project_root() - typer.echo(f"🔍 检测到项目根目录: {self.project_root}") - - def _find_project_root(self) -> Path: - """智能查找项目根目录""" - current_file = Path(__file__).resolve() - - # 方法1: 从当前文件位置向上查找（开发环境） - current_path = current_file.parent - while current_path != current_path.parent: - if self._is_sage_project_root(current_path): - return current_path - current_path = current_path.parent - - # 方法2: 从当前工作目录向上查找 - current_path = Path.cwd() - while current_path != current_path.parent: - if self._is_sage_project_root(current_path): - return current_path - current_path = current_path.parent - - # 方法3: 检查环境变量 - sage_home = os.environ.get("SAGE_HOME") - if sage_home: - sage_path = Path(sage_home) - if self._is_sage_project_root(sage_path): - return sage_path - - # 方法4: 在用户主目录下查找常见的项目目录名 - common_project_names = [ - "SAGE", - "sage", - "workspace/SAGE", - "workspace/sage", - "projects/SAGE", - "code/SAGE", - ] - home_dir = Path.home() - - for project_name in common_project_names: - project_path = home_dir / project_name - if self._is_sage_project_root(project_path): - return project_path - - # 如果都找不到，使用当前工作目录并给出警告 - typer.echo("⚠️ 警告: 无法自动检测SAGE项目根目录，使用当前工作目录") - typer.echo("💡 提示: 请确保在SAGE项目目录下运行，或设置SAGE_HOME环境变量") - return Path.cwd() - - def _is_sage_project_root(self, path: Path) -> bool: - """检查路径是否为SAGE项目根目录""" - if not path.exists(): - return False - - # 检查必需文件 - 使用现在实际存在的标识文件 - required_files = ["quickstart.sh", "README.md"] - for file_name in required_files: - if not (path / file_name).exists(): - return False - - # 检查SAGE特有的目录结构 - required_dirs = ["packages", "tools"] - for dir_name in required_dirs: - if not (path / dir_name).exists(): - return False - - # 检查packages目录下是否有sage相关包 - packages_dir = path / "packages" - sage_packages = [ - "sage", - "sage-common", - "sage-kernel", - "sage-libs", - "sage-middleware", - "sage-tools", - ] - has_sage_package = any((packages_dir / pkg).exists() for pkg in sage_packages) - - return has_sage_package - - def create_deployment_package(self) -> str: - typer.echo("📦 创建部署包...") - typer.echo(f"📂 项目根目录: {self.project_root}") - - # 验证项目根目录是否有效 - if not self.project_root.exists(): - raise FileNotFoundError(f"项目根目录不存在: {self.project_root}") - - # 检查关键文件是否存在 - key_files = ["quickstart.sh", "README.md"] - for file_name in key_files: - file_path = self.project_root / file_name - if not file_path.exists(): - typer.echo(f"⚠️ 关键文件不存在: {file_path}") - # 列出实际存在的文件供调试 - typer.echo("📋 实际存在的文件:") - for item in self.project_root.iterdir(): - if item.is_file(): - typer.echo(f" - {item.name}") - - # 创建临时目录 - temp_dir = tempfile.mkdtemp(prefix="sage_deploy_") - package_path = os.path.join(temp_dir, "sage_deployment.tar.gz") - - try: - with tarfile.open(package_path, "w:gz") as tar: - # 只添加必要的目录和文件，避免大文件 - - # 1. 添加核心工具目录（quickstart.sh 等） - tools_dir = self.project_root / "tools" - if tools_dir.exists(): - typer.echo("📦 添加 tools 目录...") - tar.add(tools_dir, arcname="tools") - experiment_dir = self.project_root / "experiments" - if experiment_dir.exists(): - typer.echo("📦 添加 experiments 目录...") - tar.add(experiment_dir, arcname="experiments") - # 2. 添加包源代码（不包含构建产物） - packages_dir = self.project_root / "packages" - if packages_dir.exists(): - typer.echo("📦 添加 packages 源代码...") - - # 自定义过滤器，排除构建产物和缓存 - def package_filter(tarinfo): - # 排除构建产物和缓存目录 - exclude_patterns = [ - "__pycache__", - ".pyc", - ".pyo", - ".so", - "build/", - "dist/", - "*.egg-info/", - ".pytest_cache/", - ".tox/", - "node_modules", # 排除任何深度的node_modules目录 - ".git/", - ".vscode/", - ".idea/", - ] - - for pattern in exclude_patterns: - if pattern in tarinfo.name: - return None - return tarinfo - - tar.add(packages_dir, arcname="packages", filter=package_filter) - - # 3. 添加安装脚本（精简版） - scripts_dir = self.project_root / "scripts" - if scripts_dir.exists(): - typer.echo("📦 添加关键脚本...") - # 只添加必要的脚本文件 - essential_scripts = [ - "requirements/", # 依赖文件 - "lib/common_utils.sh", - "lib/logging.sh", - "lib/config.sh", # 工具脚本 - ] - - for script_item in essential_scripts: - script_path = scripts_dir / script_item - if script_path.exists(): - tar.add(script_path, arcname=f"scripts/{script_item}") - - # 4. 添加必需的配置文件 - required_files = [ - "quickstart.sh", - "README.md", - "LICENSE", - "CONTRIBUTING.md", - ] - for filename in required_files: - file_path = self.project_root / filename - if file_path.exists(): - tar.add(file_path, arcname=filename) - typer.echo(f"✅ 已添加文件: {filename}") - else: - raise FileNotFoundError(f"必需文件不存在: {file_path}") - - # 5. 添加文档目录（如果存在） - docs_dir = self.project_root / "docs" - if docs_dir.exists(): - typer.echo("📦 添加 docs 目录...") - - # 过滤文档目录，只添加必要文件 - def docs_filter(tarinfo): - # 排除大的构建产物 - exclude_patterns = [ - ".git/", - "__pycache__/", - ".pyc", - ".pyo", - "node_modules/", - ".vscode/", - ".idea/", - "build/", - "dist/", - ] - - for pattern in exclude_patterns: - if pattern in tarinfo.name: - return None - - # 限制单个文件大小（10MB） - if tarinfo.isfile() and tarinfo.size > 10 * 1024 * 1024: - return None - - return tarinfo - - tar.add(docs_dir, arcname="docs", filter=docs_filter) - else: - typer.echo("ℹ️ docs 目录不存在，跳过") - - # 6. 添加可选文件（小文件） - optional_files = ["README.md", "LICENSE"] - for filename in optional_files: - file_path = self.project_root / filename - if file_path.exists() and file_path.stat().st_size < 1024 * 1024: # 小于1MB - tar.add(file_path, arcname=filename) - typer.echo(f"✅ 已添加文件: {filename}") - else: - typer.echo(f"ℹ️ 跳过大文件或不存在的文件: {filename}") - - # 检查最终包大小 - package_size = os.path.getsize(package_path) - size_mb = package_size / (1024 * 1024) - typer.echo(f"✅ 部署包已创建: {package_path}") - typer.echo(f"📊 包大小: {size_mb:.1f} MB") - - if size_mb > 100: - typer.echo(f"⚠️ 警告: 包大小较大 ({size_mb:.1f} MB)，传输可能较慢") - - return package_path - - except Exception as e: - typer.echo(f"❌ 创建部署包失败: {e}") - shutil.rmtree(temp_dir, ignore_errors=True) - raise - - def execute_ssh_command_with_progress( - self, host: str, port: int, command: str, timeout: int = 60, step_name: str = "" - ) -> bool: - """执行SSH命令并显示进度""" - ssh_config = self.config_manager.get_ssh_config() - ssh_user = ssh_config.get("user", "sage") - ssh_key_path = os.path.expanduser(ssh_config.get("key_path", "~/.ssh/id_rsa")) - - typer.echo(f"🔗 连接到 {ssh_user}@{host}:{port}") - if step_name: - typer.echo(f"📋 执行步骤: {step_name}") - - ssh_cmd = [ - "ssh", - "-i", - ssh_key_path, - "-p", - str(port), - "-o", - "StrictHostKeyChecking=no", - "-o", - "UserKnownHostsFile=/dev/null", - "-o", - f"ConnectTimeout={ssh_config.get('connect_timeout', 30)}", - "-o", - "ServerAliveInterval=10", - "-o", - "ServerAliveCountMax=6", - "-o", - "TCPKeepAlive=yes", - "-o", - "BatchMode=yes", # 非交互模式 - f"{ssh_user}@{host}", - command, - ] - - try: - import threading - import time - - # 启动进度显示线程 - progress_active = threading.Event() - progress_active.set() - - def show_progress(): - dots = 0 - start_time = time.time() - while progress_active.is_set(): - elapsed = int(time.time() - start_time) - progress_str = "." * (dots % 4) - typer.echo( - f"\r⏳ 执行中{progress_str:<3} (已用时: {elapsed}s/{timeout}s)", - nl=False, - ) - dots += 1 - time.sleep(1) - - progress_thread = threading.Thread(target=show_progress, daemon=True) - progress_thread.start() - - # 执行SSH命令 - result = subprocess.run(ssh_cmd, capture_output=True, text=True, timeout=timeout) - - # 停止进度显示 - progress_active.clear() - typer.echo() # 换行 - - # 显示输出 - if result.stdout: - typer.echo("📤 远程输出:") - for line in result.stdout.strip().split("\n"): - if line.strip(): - typer.echo(f" {line}") - - if result.stderr: - typer.echo("⚠️ 远程错误:") - for line in result.stderr.strip().split("\n"): - if line.strip(): - typer.echo(f" {line}") - - if result.returncode == 0: - typer.echo(f"✅ {step_name}完成" if step_name else "✅ 命令执行成功") - return True - else: - typer.echo( - f"❌ {step_name}失败 (返回码: {result.returncode})" - if step_name - else f"❌ 命令执行失败 (返回码: {result.returncode})" - ) - return False - - except subprocess.TimeoutExpired: - progress_active.clear() - typer.echo() - typer.echo( - f"❌ {step_name}超时 ({timeout}s)" if step_name else f"❌ SSH命令超时 ({timeout}s)" - ) - return False - except Exception as e: - progress_active.clear() - typer.echo() - typer.echo(f"❌ {step_name}失败: {e}" if step_name else f"❌ SSH命令失败: {e}") - return False - - def execute_ssh_command(self, host: str, port: int, command: str, timeout: int = 60) -> bool: - """执行SSH命令（兼容性方法，使用简单输出）""" - ssh_config = self.config_manager.get_ssh_config() - ssh_user = ssh_config.get("user", "sage") - ssh_key_path = os.path.expanduser(ssh_config.get("key_path", "~/.ssh/id_rsa")) - - ssh_cmd = [ - "ssh", - "-i", - ssh_key_path, - "-p", - str(port), - "-o", - "StrictHostKeyChecking=no", - "-o", - "UserKnownHostsFile=/dev/null", - "-o", - f"ConnectTimeout={ssh_config.get('connect_timeout', 10)}", - "-o", - "ServerAliveInterval=30", - "-o", - "ServerAliveCountMax=10", - f"{ssh_user}@{host}", - command, - ] - - try: - result = subprocess.run(ssh_cmd, capture_output=True, text=True, timeout=timeout) - if result.stdout: - typer.echo(result.stdout) - if result.stderr: - typer.echo(result.stderr, err=True) - return result.returncode == 0 - except subprocess.TimeoutExpired: - typer.echo(f"❌ SSH命令超时 ({timeout}s)") - return False - except Exception as e: - typer.echo(f"❌ SSH命令失败: {e}") - return False - - def transfer_file(self, local_path: str, host: str, port: int, remote_path: str) -> bool: - """传输文件到远程主机""" - ssh_config = self.config_manager.get_ssh_config() - ssh_user = ssh_config.get("user", "sage") - ssh_key_path = os.path.expanduser(ssh_config.get("key_path", "~/.ssh/id_rsa")) - - typer.echo(f"📤 传输文件到 {ssh_user}@{host}:{port}:{remote_path}") - - try: - scp_cmd = [ - "scp", - "-i", - ssh_key_path, - "-P", - str(port), - "-o", - "StrictHostKeyChecking=no", - "-o", - "UserKnownHostsFile=/dev/null", - "-o", - f"ConnectTimeout={ssh_config.get('connect_timeout', 10)}", - local_path, - f"{ssh_user}@{host}:{remote_path}", - ] - - result = subprocess.run(scp_cmd, capture_output=True, text=True) - - if result.returncode == 0: - typer.echo("✅ 文件传输成功") - return True - else: - typer.echo(f"❌ 文件传输失败: {result.stderr}") - return False - - except Exception as e: - typer.echo(f"❌ 文件传输失败: {e}") - return False - - def deploy_to_worker(self, host: str, port: int) -> bool: - """部署到单个worker节点""" - typer.echo(f"\n🚀 部署到Worker节点: {host}:{port}") - - try: - # 1. 创建部署包 - package_path = self.create_deployment_package() - - # 2. 传输部署包 - remote_package_path = "/tmp/sage_deployment.tar.gz" - if not self.transfer_file(package_path, host, port, remote_package_path): - return False - - # 3. 在远程主机上解压和安装 - remote_config = self.config_manager.get_remote_config() - sage_home = remote_config.get("sage_home", "/home/sage") - - # 构建 quickstart 参数 - quickstart_args = ["--dev", "--yes"] # 使用开发者安装模式，并跳过确认提示 - - # 使用配置中的环境名，如果没有配置则使用 'sage' - env_name = remote_config.get("conda_env", "sage") - # quickstart.sh 会通过环境变量获取环境名 - - if remote_config.get("force_reinstall"): - quickstart_args.append("--force") - - # 添加远程部署标志，用于启用非交互模式 - quickstart_env_vars = [ - "SAGE_REMOTE_DEPLOY=true", # 标识这是远程部署 - "DEBIAN_FRONTEND=noninteractive", - "CONDA_ALWAYS_YES=true", - f"SAGE_ENV_NAME={env_name}", - ] - - quickstart_args_str = " ".join(quickstart_args) - quickstart_env_str = " ".join(quickstart_env_vars) - - # 分步执行安装，显示详细进度 - typer.echo(f"\n🚀 开始部署SAGE到 {host}:{port}") - typer.echo("📋 部署计划:") - typer.echo(" 1️⃣ 解压项目文件和环境准备 (预计1-2分钟)") - typer.echo(" 2️⃣ 初始化conda环境 (预计30秒)") - typer.echo(" 3️⃣ 执行SAGE安装 (预计5-10分钟)") - typer.echo(" 4️⃣ 清理临时文件 (预计30秒)") - typer.echo() - - # 步骤1: 解压和准备 - 简化版本，逐步调试 - typer.echo("1️⃣ 解压项目文件和环境准备...") - - # 先测试最简单的连接 - typer.echo(" - 测试基本SSH连接...") - - # SSH连接诊断 - try: - typer.echo(f"🔍 开始诊断SSH连接到 {host}:{port}") - - # 测试网络连通性 - typer.echo("⚡ 测试网络连通性...") - ping_cmd = ["ping", "-c", "1", "-W", "5", host] - ping_result = subprocess.run(ping_cmd, capture_output=True, text=True, timeout=10) - if ping_result.returncode == 0: - typer.echo("✅ 网络连通性正常") - else: - typer.echo(f"❌ 网络不通: {ping_result.stderr}") - return False - - # 测试SSH端口 - typer.echo(f"🔌 测试SSH端口 {port}...") - import socket - - sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) - sock.settimeout(10) - try: - result = sock.connect_ex((host, port)) - if result == 0: - typer.echo(f"✅ SSH端口 {port} 可达") - else: - typer.echo(f"❌ SSH端口 {port} 不可达") - return False - finally: - sock.close() - - typer.echo("🔐 执行SSH命令测试...") - - except Exception as e: - typer.echo(f"❌ 连接诊断失败: {e}") - return False - - simple_test = "whoami" # 更简单的命令，不需要特殊字符 - - if not self.execute_ssh_command_with_progress( - host, port, simple_test, 15, "基本连接测试" - ): - typer.echo("❌ SSH基本连接失败，请检查网络和SSH配置") - return False - - # 备份现有安装 - typer.echo(" - 备份现有安装...") - backup_cmd = ( - f"set -e\n" - f"cd {sage_home}\n" - f"if [ -d 'SAGE' ]; then\n" - f" echo '发现现有SAGE目录，进行备份'\n" - f" mv SAGE SAGE_backup_$(date +%Y%m%d_%H%M%S)\n" - f" echo '备份完成'\n" - f"else\n" - f" echo '无现有SAGE目录'\n" - f"fi\n" - ) - - if not self.execute_ssh_command_with_progress(host, port, backup_cmd, 30, "备份检查"): - return False - - # 解压文件 - typer.echo(" - 执行解压...") - extract_cmd = ( - f"set -e\n" - f"cd {sage_home}\n" - f"echo '开始解压到: {sage_home}/SAGE'\n" - f"mkdir -p SAGE\n" - f"echo '检查压缩文件是否存在...'\n" - f"ls -lh {remote_package_path}\n" - f"echo '开始解压，请稍候...'\n" - f"tar -xzf {remote_package_path} -C SAGE\n" - f"echo '解压完成，检查结果...'\n" - f"cd SAGE\n" - f"ls -la | head -5\n" - f"echo '解压步骤完成'\n" - ) - - if not self.execute_ssh_command_with_progress( - host, port, extract_cmd, 120, "文件解压" - ): # 2分钟 - return False - - # 步骤2: 初始化conda环境 - typer.echo("\n2️⃣ 初始化conda环境...") - conda_init_commands = ( - f"set -e\n" - f"cd {sage_home}/SAGE\n" - f"echo '🐍 查找并初始化conda环境...'\n" - f"CONDA_FOUND=false\n" - f"for conda_path in \\\n" - f" '$HOME/miniconda3/etc/profile.d/conda.sh' \\\n" - f" '$HOME/anaconda3/etc/profile.d/conda.sh' \\\n" - f" '/opt/conda/etc/profile.d/conda.sh' \\\n" - f" '/usr/local/miniconda3/etc/profile.d/conda.sh' \\\n" - f" '/usr/local/anaconda3/etc/profile.d/conda.sh'; do\n" - f' if [ -f "$conda_path" ]; then\n' - f' echo "✅ 找到conda: $conda_path"\n' - f' source "$conda_path"\n' - f" CONDA_FOUND=true\n" - f" break\n" - f" fi\n" - f"done\n" - f'if [ "$CONDA_FOUND" = "false" ]; then\n' - f" echo '⚠️ 未找到conda，使用系统python3'\n" - f"fi\n" - f"echo '✅ 环境初始化完成'\n" - ) - - if not self.execute_ssh_command_with_progress( - host, port, conda_init_commands, 30, "conda环境初始化" - ): - return False - - # 步骤3: 执行安装（增加超时时间） - typer.echo("\n3️⃣ 执行SAGE安装...") - typer.echo(f"📦 安装命令: {quickstart_env_str} ./quickstart.sh {quickstart_args_str}") - typer.echo("⏰ 注意: 这一步可能需要10-20分钟，请耐心等待...") - typer.echo("🔍 如果长时间无输出，可能在下载或编译大型包（torch, numpy等）") - - install_command = ( - f"set -e\n" - f"cd {sage_home}/SAGE\n" - f"echo '📦 开始执行SAGE安装...'\n" - f"echo '命令: {quickstart_env_str} ./quickstart.sh {quickstart_args_str}'\n" - f"echo '⏰ 开始时间: $(date)'\n" - f"# 创建安装进度监控\n" - f"mkdir -p .sage/logs\n" - f"touch .sage/logs/progress.log\n" - f"# 设置conda环境\n" - f"for conda_path in \\\n" - f" '$HOME/miniconda3/etc/profile.d/conda.sh' \\\n" - f" '$HOME/anaconda3/etc/profile.d/conda.sh' \\\n" - f" '/opt/conda/etc/profile.d/conda.sh' \\\n" - f" '/usr/local/miniconda3/etc/profile.d/conda.sh' \\\n" - f" '/usr/local/anaconda3/etc/profile.d/conda.sh'; do\n" - f' if [ -f "$conda_path" ]; then\n' - f' echo "🐍 使用conda: $conda_path"\n' - f' source "$conda_path"\n' - f" break\n" - f" fi\n" - f"done\n" - f"# 设置环境变量并执行quickstart脚本\n" - f"export {quickstart_env_str.replace(' ', ' export ')}\n" - f"echo '🚀 开始执行quickstart脚本...'\n" - f"chmod +x ./quickstart.sh\n" - f"# 使用tee同时输出到终端和日志文件，添加时间戳\n" - f"(timeout 1200 ./quickstart.sh {quickstart_args_str} 2>&1 | tee >(while IFS= read -r line; do echo \"[$(date +'%H:%M:%S')] $line\"; done > .sage/logs/progress.log)) &\n" - f"INSTALL_PID=$!\n" - f"# 监控安装进程，每30秒报告一次状态\n" - f"while kill -0 $INSTALL_PID 2>/dev/null; do\n" - f" sleep 30\n" - f" echo \"[$(date +'%H:%M:%S')] 📊 安装进行中，进程ID: $INSTALL_PID\"\n" - f" if [ -f .sage/logs/progress.log ]; then\n" - f" tail -3 .sage/logs/progress.log | head -1\n" - f" fi\n" - f"done\n" - f"wait $INSTALL_PID\n" - f"INSTALL_RESULT=$?\n" - f"if [ $INSTALL_RESULT -eq 124 ]; then\n" - f" echo '❌ quickstart脚本执行超时（1200秒）'\n" - f" exit 1\n" - f"elif [ $INSTALL_RESULT -ne 0 ]; then\n" - f" echo '❌ quickstart脚本执行失败，返回码: $INSTALL_RESULT'\n" - f" if [ -f .sage/logs/progress.log ]; then\n" - f" echo '📋 最后几行日志:'\n" - f" tail -10 .sage/logs/progress.log\n" - f" fi\n" - f" exit 1\n" - f"fi\n" - f"echo '✅ SAGE安装完成 - $(date)'\n" - ) - - # 安装步骤使用更长的超时时间（增加到20分钟） - if not self.execute_ssh_command_with_progress( - host, port, install_command, 1200, "SAGE安装" - ): # 20分钟 - # 安装失败，尝试获取日志信息 - typer.echo("🔍 获取安装失败的详细信息...") - log_check_cmd = ( - f"cd {sage_home}/SAGE\n" - f"echo '=== 检查安装日志 ==='\n" - f"if [ -f .sage/logs/install.log ]; then\n" - f" echo '📋 最后50行安装日志:'\n" - f" tail -50 .sage/logs/install.log\n" - f"else\n" - f" echo '❌ 未找到安装日志文件'\n" - f"fi\n" - f"echo '\\n=== 检查Python环境 ==='\n" - f"python3 --version 2>/dev/null || echo '❌ Python3不可用'\n" - f"pip3 --version 2>/dev/null || echo '❌ pip3不可用'\n" - f"echo '\\n=== 检查磁盘空间 ==='\n" - f"df -h . | head -2\n" - ) - - self.execute_ssh_command_with_progress(host, port, log_check_cmd, 60, "日志检查") - return False - - # 步骤4: 清理和完成 - typer.echo("\n4️⃣ 清理临时文件...") - cleanup_commands = ( - f"rm -f {remote_package_path}\n" - f"echo '=================================='\n" - f"echo '✅ SAGE部署完成在 $(hostname)'\n" - f"echo '=================================='\n" - ) - - if not self.execute_ssh_command_with_progress(host, port, cleanup_commands, 30, "清理"): - return False - - # 4. 传输配置文件 - local_config_path = self.config_manager.config_path - if local_config_path.exists(): - remote_config_dir = "~/.sage" - remote_config_path = "~/.sage/config.yaml" - - typer.echo(f"📋 传输配置文件: {local_config_path} -> {host}:{remote_config_path}") - - # 创建配置目录 - if not self.execute_ssh_command(host, port, f"mkdir -p {remote_config_dir}"): - typer.echo("⚠️ 创建远程配置目录失败，但继续...") - - # 传输配置文件 - if not self.transfer_file(str(local_config_path), host, port, remote_config_path): - typer.echo("⚠️ 配置文件传输失败，但继续...") - else: - typer.echo("✅ 配置文件传输成功") - else: - typer.echo(f"⚠️ 本地配置文件不存在: {local_config_path}") - - # 5. 清理本地临时文件 - temp_dir = os.path.dirname(package_path) - shutil.rmtree(temp_dir, ignore_errors=True) - - typer.echo(f"✅ Worker节点 {host} 部署成功") - return True - - except Exception as e: - typer.echo(f"❌ Worker节点 {host} 部署失败: {e}") - return False - - def deploy_to_all_workers(self) -> tuple[int, int]: - """部署到所有worker节点""" - typer.echo("🚀 开始部署到所有Worker节点...") - - workers = self.config_manager.get_workers_ssh_hosts() - if not workers: - typer.echo("❌ 未配置任何worker节点") - return 0, 0 - - success_count = 0 - total_count = len(workers) - - for i, (host, port) in enumerate(workers, 1): - typer.echo(f"\n📋 部署进度: {i}/{total_count}") - if self.deploy_to_worker(host, port): - success_count += 1 - - typer.echo(f"\n📊 部署结果: {success_count}/{total_count} 个节点部署成功") - return success_count, total_count - - -if __name__ == "__main__": - # 测试部署管理器 - deployment_manager = DeploymentManager() - try: - success, total = deployment_manager.deploy_to_all_workers() - if success == total: - typer.echo("✅ 所有节点部署成功！") - else: - typer.echo("⚠️ 部分节点部署失败") - except Exception as e: - typer.echo(f"❌ 部署失败: {e}") diff --git a/packages/sage-cli/src/sage/cli/templates/README.md b/packages/sage-cli/src/sage/cli/templates/README.md deleted file mode 100644 index 3a6b7ceec6..0000000000 --- a/packages/sage-cli/src/sage/cli/templates/README.md +++ /dev/null @@ -1,17 +0,0 @@ -# SAGE Application Templates - -This package curates runnable pipeline templates that are derived from the scripts under -`examples/`. Each template is backed by a `PipelineBlueprint` so it can render full pipeline plans -and graph views that plug into the CLI builders. - -## Included templates - -- `rag-simple-demo` – Customer support style FAQ assistant built from `examples/rag/rag_simple.py`. -- `hello-world-batch` – Introductory batch pipeline that uppercases greetings. -- `hello-world-log` – Variation that uses `PrintSink` for structured logging output. -- `rag-multimodal-fusion` – Multimodal landmark QA workflow combining text and synthetic image - embeddings. - -Use `sage.tools.templates.list_templates()` to enumerate templates and `match_templates()` to -surface candidates for a set of requirements. Each template exposes `pipeline_plan()` and -`graph_plan()` helpers that return deep copies suitable for further customization. diff --git a/packages/sage-cli/src/sage/cli/templates/__init__.py b/packages/sage-cli/src/sage/cli/templates/__init__.py deleted file mode 100644 index b4117d1834..0000000000 --- a/packages/sage-cli/src/sage/cli/templates/__init__.py +++ /dev/null @@ -1,21 +0,0 @@ -"""Reusable application templates derived from SAGE examples.""" - -from . import pipeline_blueprints -from .catalog import ( - ApplicationTemplate, - TemplateMatch, - get_template, - list_template_ids, - list_templates, - match_templates, -) - -__all__ = [ - "ApplicationTemplate", - "TemplateMatch", - "get_template", - "list_template_ids", - "list_templates", - "match_templates", - "pipeline_blueprints", -] diff --git a/packages/sage-cli/src/sage/cli/templates/catalog.py b/packages/sage-cli/src/sage/cli/templates/catalog.py deleted file mode 100644 index afea4bd52a..0000000000 --- a/packages/sage-cli/src/sage/cli/templates/catalog.py +++ /dev/null @@ -1,510 +0,0 @@ -#!/usr/bin/env python3 -"""Catalog of reusable application templates derived from SAGE examples.""" - -from __future__ import annotations - -import textwrap -from dataclasses import dataclass -from typing import Any - -from sage.cli.templates import pipeline_blueprints - - -@dataclass(frozen=True) -class ApplicationTemplate: - """Reusable application template built from a pipeline blueprint.""" - - id: str - title: str - description: str - tags: tuple[str, ...] - example_path: str - blueprint_id: str - default_requirements: dict[str, Any] - guidance: str - notes: tuple[str, ...] = () - - def blueprint(self) -> pipeline_blueprints.PipelineBlueprint: - blueprint = _BLUEPRINT_INDEX.get(self.blueprint_id) - if blueprint is None: - raise KeyError(f"Blueprint '{self.blueprint_id}' not found for template '{self.id}'") - return blueprint - - def pipeline_plan(self) -> dict[str, Any]: - """Return a deep copy of the pipeline plan for this template.""" - - blueprint = self.blueprint() - return pipeline_blueprints.build_pipeline_plan( - blueprint, - self.default_requirements, - feedback=None, - ) - - def graph_plan(self) -> dict[str, Any] | None: - blueprint = self.blueprint() - return pipeline_blueprints.build_graph_plan( - blueprint, - self.default_requirements, - feedback=None, - ) - - def render_prompt(self, score: float | None = None) -> str: - """Render a prompt snippet describing the template for LLM guidance.""" - - plan = self.pipeline_plan() - stages = plan.get("stages", []) - stage_lines = [ - f" • {stage['id']}: {stage['class']} ({stage.get('summary', '')})" - for stage in stages - ] - stage_text = "\n".join(stage_lines) if stage_lines else " • (无阶段信息)" - note_lines = [f"- {note}" for note in plan.get("notes", []) if note] - notes_text = "\n".join(note_lines) if note_lines else " - 无" - score_line = f"匹配度: {score:.2f}" if score is not None else "" - source_class = plan.get("source", {}).get("class", "") - sink_class = plan.get("sink", {}).get("class", "") - prompt = textwrap.dedent( - f""" - 模板: {self.title} ({self.id}) {score_line} - 示例路径: {self.example_path} - 标签: {", ".join(self.tags) or "通用"} - 描述: {self.description} - - 默认Pipeline: - Source: {source_class} -{stage_text} - Sink: {sink_class} - - 注意事项: - {notes_text} - - 额外指导: - {self.guidance.strip()} - """ - ).strip() - return prompt - - -@dataclass(frozen=True) -class TemplateMatch: - template: ApplicationTemplate - score: float - - -def list_templates() -> tuple[ApplicationTemplate, ...]: - return TEMPLATE_LIBRARY - - -def list_template_ids() -> tuple[str, ...]: - return tuple(template.id for template in TEMPLATE_LIBRARY) - - -def get_template(template_id: str) -> ApplicationTemplate: - for template in TEMPLATE_LIBRARY: - if template.id == template_id: - return template - raise KeyError(f"Unknown application template: {template_id}") - - -def match_templates( - requirements: dict[str, Any], - top_k: int = 5, -) -> list[TemplateMatch]: - candidates = [ - TemplateMatch(template=template, score=_score_template(requirements, template)) - for template in TEMPLATE_LIBRARY - ] - candidates.sort(key=lambda item: item.score, reverse=True) - top = candidates[: top_k or 1] - if all(match.template.id != DEFAULT_TEMPLATE_ID for match in top): - top.append(TemplateMatch(get_template(DEFAULT_TEMPLATE_ID), 0.1)) - return top - - -def _score_template(requirements: dict[str, Any], template: ApplicationTemplate) -> float: - text = _requirements_text(requirements) - if not text: - return 0.2 - - score = 0.0 - for tag in template.tags: - term = tag.lower() - if not term: - continue - if term in text: - score += 1.0 - else: - tokens = [token for token in term.replace("/", " ").split() if token] - if tokens and all(token in text for token in tokens): - score += 0.6 - if template.id in text: - score += 0.4 - if template.title.lower() in text: - score += 0.4 - length_bonus = min(0.4, 0.05 * max(0, len(text.split()) - 5)) - score += length_bonus - if template.tags: - score = score / len(template.tags) - return max(0.0, min(1.2, score)) - - -def _requirements_text(requirements: dict[str, Any]) -> str: - parts: list[str] = [] - for key in ( - "goal", - "initial_prompt", - "description", - "notes", - "constraints", - "data_sources", - "name", - ): - value = requirements.get(key) - if value is None: - continue - if isinstance(value, (list, tuple, set)): - parts.extend(str(item) for item in value) - elif isinstance(value, dict): - parts.extend(str(v) for v in value.values()) - else: - parts.append(str(value)) - return " ".join(parts).lower() - - -_BLUEPRINT_INDEX = {blueprint.id: blueprint for blueprint in pipeline_blueprints.BLUEPRINT_LIBRARY} - - -def _notes(*values: str) -> tuple[str, ...]: - cleaned: list[str] = [] - for value in values: - value = value.strip() - if value: - cleaned.append(value) - return tuple(cleaned) - - -TEMPLATE_LIBRARY: tuple[ApplicationTemplate, ...] = ( - ApplicationTemplate( - id="rag-simple-demo", - title="客服知识助手 (RAG Simple)", - description="面向客服问答的简化RAG工作流，使用内置示例算子即可离线演示。", - tags=("rag", "qa", "support", "问答", "客户支持", "知识助手"), - example_path="examples/rag/rag_simple.py", - blueprint_id="rag-simple-demo", - default_requirements={ - "name": "customer-support-rag", - "goal": "构建客服知识助手，针对常见问题进行检索增强回答", - "description": "使用sage.benchmark.benchmark_rag.implementations.rag_simple中的算子，演示从提问到答案的完整流程", - }, - guidance=textwrap.dedent( - """ - 适合客服场景的FAQ自动答复。可直接运行，无需远程服务，强调演示友好性。 - 可扩展：替换检索器为真实向量库、改造生成器为大模型API。 - """ - ), - notes=_notes( - "基于 sage.benchmark.benchmark_rag.implementations.rag_simple 模块", - "默认配置为本地演示，可逐步替换为生产组件", - ), - ), - ApplicationTemplate( - id="hello-world-batch", - title="Hello World 批处理管道", - description="教学用途的批处理示例，从批数据源到终端打印，展示基本算子组合。", - tags=("batch", "tutorial", "hello", "入门"), - example_path="examples/tutorials/hello_world.py", - blueprint_id="hello-world-batch", - default_requirements={ - "name": "hello-world-batch", - "goal": "快速体验 SAGE 的批处理操作", - "description": "从HelloBatch批处理源开始，将消息转大写并输出到终端", - }, - guidance=textwrap.dedent( - """ - 作为教学或单元测试模板，演示批处理执行模型。可扩展：替换UpperCaseMap为数据清洗或格式化逻辑。 - """ - ), - notes=_notes("无外部依赖，适合快速验证环境配置。"), - ), - ApplicationTemplate( - id="hello-world-log", - title="结构化日志打印管道", - description="基于Hello World示例，使用通用PrintSink输出结构化日志。", - tags=("batch", "logging", "demo", "日志"), - example_path="examples/tutorials/hello_world.py", - blueprint_id="hello-world-log", - default_requirements={ - "name": "hello-world-logging", - "goal": "演示如何复用通用 PrintSink 组件输出结构化日志", - "description": "批量生成问候语，上游转大写，下游通过 PrintSink 输出", - }, - guidance=textwrap.dedent( - """ - 适合作为日志/监控集成的起点，可将 PrintSink 替换为 Kafka、Webhook 等下游。 - """ - ), - notes=_notes("依赖 sage.libs.io.sink.PrintSink 组件。"), - ), - ApplicationTemplate( - id="rag-multimodal-fusion", - title="多模态地标问答助手", - description="结合文本与图像特征的多模态检索，再通过LLM生成答案，演示高级RAG场景。", - tags=( - "rag", - "multimodal", - "fusion", - "qa", - "多模态", - "图像", - ), - example_path="examples/rag/qa_multimodal_fusion.py", - blueprint_id="rag-multimodal-fusion", - default_requirements={ - "name": "multimodal-landmark-qa", - "goal": "回答关于地标建筑的多模态问答", - "description": "融合文本与图像嵌入检索，调用LLM生成结构化答案", - }, - guidance=textwrap.dedent( - """ - 需要可用的OpenAI兼容模型或自建推理服务。若无远程模型，可将生成阶段替换为规则模板或本地模型。 - 模板展示了如何在Promptor阶段注入自定义模板以及如何配置多模态检索输出。 - """ - ), - notes=_notes( - "源自 sage.benchmark.benchmark_rag.implementations.qa_multimodal_fusion", - "默认使用 OpenAIGenerator，需要配置 API Key", - "可扩展：替换多模态检索器为 SageVDB / 向量数据库", - ), - ), - ApplicationTemplate( - id="rag-dense-milvus", - title="Milvus 密集向量检索问答", - description="生产级 RAG 系统，使用 Milvus 向量数据库进行大规模语义检索，支持 BGE 嵌入模型。", - tags=( - "rag", - "qa", - "milvus", - "dense", - "vector", - "embedding", - "向量检索", - "向量数据库", - "生产环境", - "语义搜索", - ), - example_path="examples/rag/qa_dense_retrieval_milvus.py", - blueprint_id="rag-dense-milvus", - default_requirements={ - "name": "milvus-dense-qa", - "goal": "构建基于 Milvus 的生产级语义问答系统", - "description": "使用密集向量检索和大模型生成，支持大规模知识库", - }, - guidance=textwrap.dedent( - """ - 适合生产环境的大规模语义检索场景，支持百万级文档检索。 - 需要预先使用 build_milvus_dense_index.py 构建向量索引。 - 可配置不同的嵌入模型（BGE、OpenAI、sentence-transformers等）。 - """ - ), - notes=_notes( - "基于 examples/rag/qa_dense_retrieval_milvus.py", - "需要运行中的 Milvus 服务实例", - "需要预先构建向量索引", - "支持分布式部署和高并发查询", - ), - ), - ApplicationTemplate( - id="rag-rerank", - title="重排序增强检索问答", - description="两阶段检索架构：初始召回 + BGE 重排序，显著提升检索精确度。", - tags=( - "rag", - "qa", - "rerank", - "reranker", - "precision", - "重排序", - "精确度", - "两阶段", - "召回", - "精排", - ), - example_path="examples/rag/qa_rerank.py", - blueprint_id="rag-rerank", - default_requirements={ - "name": "rerank-qa-system", - "goal": "构建高精度的重排序问答系统", - "description": "通过两阶段检索优化答案质量：粗排召回 + 精细重排", - }, - guidance=textwrap.dedent( - """ - 适合对答案精确度要求高的场景。第一阶段召回更多候选（如 top-20）， - 第二阶段使用 BGE cross-encoder 重排序选出最相关的结果（如 top-5）。 - 相比单阶段检索，可显著提升精确度，但计算成本稍高。 - """ - ), - notes=_notes( - "基于 examples/rag/qa_rerank.py", - "两阶段架构：Chroma 召回 + BGE Reranker 精排", - "需要配置向量数据库和 BGE reranker 模型", - "适合高精度场景如法律、医疗、金融问答", - ), - ), - ApplicationTemplate( - id="rag-bm25-sparse", - title="BM25 关键词检索问答", - description="传统关键词检索，基于 BM25 算法进行词法匹配，无需向量化。", - tags=( - "rag", - "qa", - "bm25", - "sparse", - "keyword", - "关键词", - "稀疏检索", - "词法", - "传统检索", - ), - example_path="examples/rag/qa_bm25_retrieval.py", - blueprint_id="rag-bm25-sparse", - default_requirements={ - "name": "bm25-keyword-qa", - "goal": "构建基于关键词匹配的问答系统", - "description": "使用 BM25 算法进行传统文本检索，适合精确词匹配场景", - }, - guidance=textwrap.dedent( - """ - BM25 是经典的词法检索算法，适合： - 1. 精确关键词匹配场景 - 2. 资源受限环境（无需 GPU 和向量化） - 3. 与密集检索结合的混合检索系统 - 相比语义检索，在专有名词和精确匹配方面表现更好。 - """ - ), - notes=_notes( - "基于 examples/rag/qa_bm25_retrieval.py", - "无需向量化，计算成本低", - "适合关键词精确匹配场景", - "可与密集检索结合形成混合检索", - ), - ), - ApplicationTemplate( - id="agent-workflow", - title="LLM 智能体工作流", - description="自主智能体系统，支持 LLM 规划、工具调用和复杂任务执行。", - tags=( - "agent", - "llm", - "planning", - "tool", - "mcp", - "智能体", - "工具调用", - "规划", - "自主", - "任务执行", - ), - example_path="examples/agents/agent.py", - blueprint_id="agent-workflow", - default_requirements={ - "name": "autonomous-agent", - "goal": "构建自主规划和执行任务的智能体", - "description": "使用 LLM 进行任务规划，调用 MCP 工具完成复杂任务", - }, - guidance=textwrap.dedent( - """ - 智能体系统适合需要多步骤推理和工具调用的复杂任务： - 1. LLM Planner 负责任务分解和规划 - 2. MCP Registry 管理可用工具 - 3. Agent Runtime 执行规划并调用工具 - 支持的场景：数据分析、代码生成、信息收集、自动化操作等。 - """ - ), - notes=_notes( - "基于 examples/agents/agent.py", - "支持 Model Context Protocol (MCP) 工具标准", - "需要配置 LLM API 和工具库", - "适合复杂的多步骤推理任务", - ), - ), - ApplicationTemplate( - id="rag-memory-enhanced", - title="记忆增强对话问答", - description="支持多轮对话的 RAG 系统，通过记忆服务维护上下文状态。", - tags=( - "rag", - "memory", - "conversation", - "multi-turn", - "dialogue", - "记忆", - "对话", - "上下文", - "多轮", - "会话", - ), - example_path="examples/memory/rag_memory_pipeline.py", - blueprint_id="rag-memory-enhanced", - default_requirements={ - "name": "conversational-rag", - "goal": "构建支持多轮对话的上下文感知问答系统", - "description": "使用记忆服务存储历史对话，实现上下文连贯的问答", - }, - guidance=textwrap.dedent( - """ - 记忆增强 RAG 适合对话式应用： - 1. 自动存储问答历史到记忆服务 - 2. 检索时考虑历史上下文 - 3. 生成时保持对话连贯性 - 记忆服务使用 ChromaDB 或其他向量库作为存储后端。 - """ - ), - notes=_notes( - "基于 examples/memory/rag_memory_pipeline.py", - "使用服务架构管理会话状态", - "支持长期记忆和短期记忆", - "适合客服机器人、个人助手等对话应用", - ), - ), - ApplicationTemplate( - id="multimodal-cross-search", - title="跨模态搜索引擎", - description="支持文本、图像及融合检索的多模态搜索系统，可配置融合策略。", - tags=( - "multimodal", - "cross-modal", - "search", - "fusion", - "image", - "text", - "跨模态", - "搜索", - "图文", - "检索", - ), - example_path="examples/multimodal/cross_modal_search.py", - blueprint_id="multimodal-cross-search", - default_requirements={ - "name": "cross-modal-search", - "goal": "构建跨模态搜索引擎", - "description": "支持文本、图像和融合三种检索模式的多模态搜索", - }, - guidance=textwrap.dedent( - """ - 跨模态搜索支持三种检索模式： - 1. 纯文本检索：使用文本嵌入 - 2. 纯图像检索：使用图像嵌入 - 3. 融合检索：可配置加权平均、RRF 等融合策略 - 适合电商、新闻、社交媒体等图文混合场景。 - """ - ), - notes=_notes( - "基于 examples/multimodal/cross_modal_search.py", - "支持多种融合策略配置", - "可使用 SageVDB 或其他多模态向量库", - "适合图文混合检索场景", - ), - ), -) - -DEFAULT_TEMPLATE_ID = TEMPLATE_LIBRARY[0].id diff --git a/packages/sage-cli/src/sage/cli/templates/pipeline_blueprints.py b/packages/sage-cli/src/sage/cli/templates/pipeline_blueprints.py deleted file mode 100644 index 6fe81f1d38..0000000000 --- a/packages/sage-cli/src/sage/cli/templates/pipeline_blueprints.py +++ /dev/null @@ -1,1091 +0,0 @@ -#!/usr/bin/env python3 -"""Blueprint library describing reusable SAGE pipelines.""" - -from __future__ import annotations - -import copy -import re -import textwrap -from collections.abc import Sequence -from dataclasses import dataclass, field -from typing import Any - - -def _slugify(value: str) -> str: - slug = re.sub(r"[^a-zA-Z0-9]+", "-", str(value or "").lower()).strip("-") - return slug or "pipeline" - - -def _graph_kind(kind: str) -> str: - normalized = (kind or "").lower() - mapping = { - "source": "source", - "batch": "source", - "stream": "source", - "map": "tool", - "tool": "tool", - "agent": "agent", - "service": "service", - "router": "router", - "sink": "sink", - } - return mapping.get(normalized, "tool") - - -@dataclass(frozen=True) -class SourceSpec: - id: str - title: str - class_path: str - kind: str = "source" - params: dict[str, Any] = field(default_factory=dict) - summary: str = "" - metadata: dict[str, Any] = field(default_factory=dict) - - def to_plan(self) -> dict[str, Any]: - return { - "class": self.class_path, - "kind": self.kind, - "params": copy.deepcopy(self.params), - "summary": self.summary, - } - - def to_graph_node(self, outputs: Sequence[str]) -> dict[str, Any]: - node = { - "id": self.id, - "title": self.title, - "kind": _graph_kind(self.kind), - "class": self.class_path, - "params": copy.deepcopy(self.params), - } - if outputs: - node["outputs"] = list(outputs) - if self.metadata: - node["metadata"] = copy.deepcopy(self.metadata) - return node - - -@dataclass(frozen=True) -class StageSpec: - id: str - title: str - kind: str - class_path: str - params: dict[str, Any] = field(default_factory=dict) - summary: str = "" - metadata: dict[str, Any] = field(default_factory=dict) - - def to_plan(self) -> dict[str, Any]: - return { - "id": self.id, - "kind": self.kind, - "class": self.class_path, - "params": copy.deepcopy(self.params), - "summary": self.summary, - } - - def to_graph_node(self, inputs: Sequence[str], outputs: Sequence[str]) -> dict[str, Any]: - node = { - "id": self.id, - "title": self.title, - "kind": _graph_kind(self.kind), - "class": self.class_path, - "params": copy.deepcopy(self.params), - } - if inputs: - node["inputs"] = list(inputs) - if outputs: - node["outputs"] = list(outputs) - if self.metadata: - node["metadata"] = copy.deepcopy(self.metadata) - return node - - -@dataclass(frozen=True) -class SinkSpec: - id: str - title: str - class_path: str - kind: str = "sink" - params: dict[str, Any] = field(default_factory=dict) - summary: str = "" - metadata: dict[str, Any] = field(default_factory=dict) - - def to_plan(self) -> dict[str, Any]: - plan = { - "class": self.class_path, - "params": copy.deepcopy(self.params), - "summary": self.summary, - } - if self.kind: - plan["kind"] = self.kind - return plan - - def to_graph_node(self, inputs: Sequence[str]) -> dict[str, Any]: - node = { - "id": self.id, - "title": self.title, - "kind": _graph_kind(self.kind), - "class": self.class_path, - "params": copy.deepcopy(self.params), - } - if inputs: - node["inputs"] = list(inputs) - if self.metadata: - node["metadata"] = copy.deepcopy(self.metadata) - return node - - -@dataclass(frozen=True) -class PipelineBlueprint: - id: str - title: str - description: str - keywords: tuple[str, ...] - source: SourceSpec - stages: tuple[StageSpec, ...] - sink: SinkSpec - services: tuple[dict[str, Any], ...] = () - monitors: tuple[dict[str, Any], ...] = () - notes: tuple[str, ...] = () - graph_channels: tuple[dict[str, Any], ...] = () - graph_agents: tuple[dict[str, Any], ...] = () - - def render_notes(self, feedback: str | None) -> list[str]: - notes = list(self.notes) - if feedback and feedback.strip(): - notes.append(f"反馈: {feedback.strip()}") - if not notes: - notes.append("Blueprint-generated configuration for experimentation") - return notes - - -BLUEPRINT_LIBRARY: tuple[PipelineBlueprint, ...] = ( - PipelineBlueprint( - id="rag-simple-demo", - title="Simple RAG Demo", - description="Use the sage.benchmark.benchmark_rag.implementations.rag_simple operators to run an end-to-end retrieval and answer pipeline.", - keywords=( - "rag", - "qa", - "retrieval", - "demo", - "support", - "问答", - "客户支持", - "客服", - "知识助手", - ), - source=SourceSpec( - id="question-source", - title="Question Source", - class_path="sage.benchmark.benchmark_rag.implementations.rag_simple.SimpleQuestionSource", - summary="Emit curated customer-style questions from the rag_simple example.", - ), - stages=( - StageSpec( - id="retriever", - title="Keyword Retriever", - kind="map", - class_path="sage.benchmark.benchmark_rag.implementations.rag_simple.SimpleRetriever", - summary="Lookup canned snippets matching question keywords.", - metadata={"description": "Deterministic dictionary-based retriever"}, - ), - StageSpec( - id="prompt-builder", - title="Prompt Builder", - kind="map", - class_path="sage.benchmark.benchmark_rag.implementations.rag_simple.SimplePromptor", - summary="Combine context and question into a generation prompt.", - ), - StageSpec( - id="generator", - title="Answer Generator", - kind="map", - class_path="sage.benchmark.benchmark_rag.implementations.rag_simple.SimpleGenerator", - summary="Create a formatted answer using rule-based heuristics.", - ), - ), - sink=SinkSpec( - id="terminal-sink", - title="Terminal Sink", - class_path="sage.benchmark.benchmark_rag.implementations.rag_simple.SimpleTerminalSink", - summary="Pretty-print answers to the terminal with context snippets.", - ), - notes=( - "基于 sage.benchmark.benchmark_rag.implementations.rag_simple 模块构建，适合离线演示", - "无需外部服务或大模型依赖即可运行", - ), - graph_channels=( - { - "id": "qa-context", - "type": "memory", - "description": "Retriever and generator share contextual snippets", - "participants": ["retriever", "generator"], - }, - ), - graph_agents=( - { - "id": "qa-orchestrator", - "role": "Answer Coordinator", - "goals": [ - "解析客户提问并选取合适知识片段", - "输出清晰的答案与下一步建议", - ], - "tools": ["retriever", "generator"], - "memory": {"type": "scratchpad", "config": {"channel": "qa-context"}}, - }, - ), - ), - PipelineBlueprint( - id="hello-world-batch", - title="Hello World Batch Processor", - description="Demonstrates a batch pipeline that uppercases greeting messages.", - keywords=("batch", "hello", "tutorial", "uppercase"), - source=SourceSpec( - id="hello-source", - title="Hello Batch Source", - class_path="examples.tutorials.hello_world.HelloBatch", - kind="batch", - params={"max_count": 5}, - summary="Generate a finite series of 'Hello, World!' strings.", - ), - stages=( - StageSpec( - id="uppercase", - title="Uppercase Formatter", - kind="map", - class_path="examples.tutorials.hello_world.UpperCaseMap", - summary="Convert each greeting to uppercase text.", - ), - ), - sink=SinkSpec( - id="console-printer", - title="Console Printer", - class_path="examples.tutorials.hello_world.PrintSink", - summary="Print processed greetings to standard output.", - ), - notes=( - "来源：examples.tutorials.hello_world 示例", - "演示批处理来源、Map 转换与终端汇聚", - ), - ), - PipelineBlueprint( - id="hello-world-log", - title="Hello World Log Printer", - description="Extends the hello world batch example with a reusable logging sink from sage.libs.", - keywords=("batch", "logging", "demo"), - source=SourceSpec( - id="hello-log-source", - title="Hello Batch Source", - class_path="examples.tutorials.hello_world.HelloBatch", - kind="batch", - params={"max_count": 3}, - summary="Emit a few greeting messages for structured logging.", - ), - stages=( - StageSpec( - id="uppercase", - title="Uppercase Formatter", - kind="map", - class_path="examples.tutorials.hello_world.UpperCaseMap", - summary="Normalize messages to uppercase before logging.", - ), - ), - sink=SinkSpec( - id="structured-print", - title="Structured Print Sink", - class_path="sage.libs.io.sink.PrintSink", - summary="Stream outputs to console/logs using the reusable PrintSink operator.", - params={"quiet": False}, - ), - notes=( - "结合 tutorials 示例与 sage.libs.io.PrintSink 组件", - "适合演示如何接入内置工具库的通用算子", - ), - ), - PipelineBlueprint( - id="rag-multimodal-fusion", - title="Multimodal Landmark QA", - description="Fuse text and image context for landmark questions, then generate structured answers with an LLM.", - keywords=( - "rag", - "qa", - "multimodal", - "fusion", - "landmark", - "图像", - "多模态", - ), - source=SourceSpec( - id="landmark-question-source", - title="Landmark Question Source", - class_path="sage.benchmark.benchmark_rag.implementations.qa_multimodal_fusion.MultimodalQuestionSource", - summary="Emit landmark-themed questions covering位置、属性与建筑背景。", - ), - stages=( - StageSpec( - id="multimodal-retriever", - title="Multimodal Fusion Retriever", - kind="map", - class_path="sage.benchmark.benchmark_rag.implementations.qa_multimodal_fusion.MultimodalFusionRetriever", - summary="Combine text and synthetic image embeddings to retrieve landmark context.", - metadata={ - "description": "演示多模态嵌入融合及可配置检索策略", - "modalities": ["text", "image"], - }, - ), - StageSpec( - id="qa-promptor", - title="QA Prompt Builder", - kind="map", - class_path="sage.libs.rag.promptor.QAPromptor", - params={ - "template": textwrap.dedent( - """ - 基于以下多模态检索结果回答问题： - - 检索到的相关信息： - {retrieved_context} - - 原始问题：{original_query} - - 请提供准确、详细的回答，结合文本和视觉信息： - """ - ).strip(), - "max_context_length": 2000, - }, - summary="Turn fusion results into an LLM-ready prompt with contextual metadata.", - ), - StageSpec( - id="generator", - title="OpenAI Generator", - kind="map", - class_path="sage.middleware.operators.rag.generator.OpenAIGenerator", - params={ - "model_name": "gpt-3.5-turbo", - "temperature": 0.7, - "max_tokens": 300, - }, - summary="Generate structured responses using an OpenAI-compatible model.", - metadata={"requires": "OPENAI_API_KEY"}, - ), - ), - sink=SinkSpec( - id="terminal-json", - title="Terminal JSON Sink", - class_path="sage.libs.io.sink.TerminalSink", - params={"output_format": "json", "pretty_print": True}, - summary="Render responses in JSON format for inspection or downstream tooling.", - ), - notes=( - "基于 sage.benchmark.benchmark_rag.implementations.qa_multimodal_fusion 模块", - "需要可用的 OpenAI 兼容推理服务或替换生成算子", - "多模态融合可扩展至 SageVDB 或外部向量库", - ), - graph_channels=( - { - "id": "fusion-context-channel", - "type": "memory", - "description": "共享多模态检索结果，供 prompt 构建与生成阶段复用", - "participants": ["multimodal-retriever", "qa-promptor", "generator"], - }, - ), - graph_agents=( - { - "id": "multimodal-strategist", - "role": "Landmark Knowledge Curator", - "goals": [ - "整合多模态检索结果", - "生成详尽且可信的地标答案", - ], - "tools": ["multimodal-retriever", "generator"], - "memory": { - "type": "scratchpad", - "config": {"channel": "fusion-context-channel"}, - }, - }, - ), - ), - PipelineBlueprint( - id="rag-dense-milvus", - title="Dense Vector Retrieval with Milvus", - description="Production-ready RAG pipeline using Milvus for dense vector retrieval with OpenAI-compatible LLM generation.", - keywords=( - "rag", - "qa", - "milvus", - "dense", - "vector", - "embedding", - "向量检索", - "向量数据库", - "生产环境", - ), - source=SourceSpec( - id="jsonl-question-source", - title="JSONL Question Source", - class_path="sage.libs.io.batch.JSONLBatch", - params={ - "data_path": "./data/questions.jsonl", - "field_query": "query", - }, - summary="Load questions from a JSONL file for batch processing.", - ), - stages=( - StageSpec( - id="milvus-retriever", - title="Milvus Dense Retriever", - kind="map", - class_path="sage.libs.rag.retriever.MilvusDenseRetriever", - params={ - "dimension": 768, - "top_k": 5, - "milvus_dense": { - "collection_name": "knowledge_base", - "uri": "http://localhost:19530", - }, - "embedding": { - "method": "bge-base-zh-v1.5", - }, - }, - summary="Retrieve top-k relevant chunks from Milvus vector database using dense embeddings.", - metadata={ - "description": "使用BGE嵌入模型进行语义检索", - "requires": "Milvus服务", - }, - ), - StageSpec( - id="qa-promptor", - title="QA Prompt Builder", - kind="map", - class_path="sage.libs.rag.promptor.QAPromptor", - params={ - "template": "Context: {context}\nQuestion: {question}\nAnswer:", - "max_context_length": 2000, - }, - summary="Format retrieved context and question into LLM prompt.", - ), - StageSpec( - id="llm-generator", - title="LLM Answer Generator", - kind="map", - class_path="sage.middleware.operators.rag.generator.OpenAIGenerator", - params={ - "model_name": "gpt-3.5-turbo", - "temperature": 0.7, - "max_tokens": 256, - }, - summary="Generate answers using OpenAI-compatible model API.", - metadata={"requires": "OPENAI_API_KEY or compatible endpoint"}, - ), - ), - sink=SinkSpec( - id="terminal-sink", - title="Terminal Output Sink", - class_path="sage.libs.io.sink.TerminalSink", - params={}, - summary="Display Q&A results in terminal.", - ), - notes=( - "基于 examples/rag/qa_dense_retrieval_milvus.py", - "需要运行中的 Milvus 服务实例", - "需要预先构建向量索引 (使用 build_milvus_dense_index.py)", - "适合生产环境的大规模语义检索场景", - ), - ), - PipelineBlueprint( - id="rag-rerank", - title="Retrieval with Reranking", - description="Enhanced RAG pipeline using initial retrieval followed by BGE reranker for precision improvement.", - keywords=( - "rag", - "qa", - "rerank", - "reranker", - "precision", - "重排序", - "精确度优化", - "两阶段检索", - ), - source=SourceSpec( - id="jsonl-batch-source", - title="JSONL Batch Source", - class_path="sage.libs.io.batch.JSONLBatch", - params={ - "data_path": "./data/questions.jsonl", - }, - summary="Batch load questions from JSONL file.", - ), - stages=( - StageSpec( - id="chroma-retriever", - title="Chroma Vector Retriever", - kind="map", - class_path="sage.libs.rag.retriever.ChromaRetriever", - params={ - "collection_name": "documents", - "top_k": 20, - }, - summary="First-stage retrieval: fetch top-20 candidate chunks from Chroma.", - metadata={"description": "召回阶段，优先覆盖率"}, - ), - StageSpec( - id="bge-reranker", - title="BGE Reranker", - kind="map", - class_path="sage.libs.rag.reranker.BGEReranker", - params={ - "model_name": "bge-reranker-base", - "top_k": 5, - }, - summary="Second-stage reranking: use BGE cross-encoder to select top-5 most relevant chunks.", - metadata={"description": "精排阶段，提升精确度"}, - ), - StageSpec( - id="qa-promptor", - title="QA Prompt Builder", - kind="map", - class_path="sage.libs.rag.promptor.QAPromptor", - summary="Build generation prompt with reranked context.", - ), - StageSpec( - id="generator", - title="OpenAI Generator", - kind="map", - class_path="sage.middleware.operators.rag.generator.OpenAIGenerator", - params={ - "model_name": "gpt-3.5-turbo", - "temperature": 0.7, - }, - summary="Generate final answer from reranked context.", - ), - ), - sink=SinkSpec( - id="terminal-sink", - title="Terminal Sink", - class_path="sage.libs.io.sink.TerminalSink", - summary="Output Q&A results to terminal.", - ), - notes=( - "基于 examples/rag/qa_rerank.py", - "两阶段检索：召回 (Chroma) + 精排 (BGE Reranker)", - "适合高精度要求的问答场景", - "需要配置 Chroma 向量库和 OpenAI API", - ), - ), - PipelineBlueprint( - id="rag-bm25-sparse", - title="BM25 Sparse Retrieval", - description="Traditional keyword-based retrieval using sparse vector matching for lexical search.", - keywords=( - "rag", - "qa", - "bm25", - "sparse", - "keyword", - "关键词检索", - "稀疏检索", - "词法匹配", - ), - source=SourceSpec( - id="jsonl-source", - title="JSONL Batch Source", - class_path="sage.libs.io.batch.JSONLBatch", - params={ - "data_path": "./data/questions.jsonl", - }, - summary="Read questions from JSONL file.", - ), - stages=( - StageSpec( - id="sparse-retriever", - title="Milvus Sparse Retriever", - kind="map", - class_path="sage.libs.rag.retriever.MilvusSparseRetriever", - params={ - "collection_name": "sparse_index", - "top_k": 5, - "milvus_sparse": { - "uri": "http://localhost:19530", - }, - }, - summary="Retrieve documents using sparse vector (BM25-like) matching in Milvus.", - metadata={"description": "基于稀疏向量的传统检索方法"}, - ), - StageSpec( - id="qa-promptor", - title="QA Promptor", - kind="map", - class_path="sage.libs.rag.promptor.QAPromptor", - summary="Format sparse retrieval results into QA prompt.", - ), - StageSpec( - id="generator", - title="OpenAI Generator", - kind="map", - class_path="sage.middleware.operators.rag.generator.OpenAIGenerator", - params={ - "model_name": "gpt-3.5-turbo", - }, - summary="Generate answer from keyword-matched context.", - ), - ), - sink=SinkSpec( - id="terminal-sink", - title="Terminal Sink", - class_path="sage.libs.io.sink.TerminalSink", - summary="Print results to terminal.", - ), - notes=( - "基于 examples/rag/qa_bm25_retrieval.py", - "使用 MilvusSparseRetriever 进行稀疏向量检索", - "适合精确词匹配场景或作为混合检索的一部分", - "计算成本低，适合资源受限环境", - ), - ), - PipelineBlueprint( - id="agent-workflow", - title="LLM Agent with Tool Calling", - description="Autonomous agent workflow with LLM planning and MCP tool registry for complex task execution.", - keywords=( - "agent", - "llm", - "planning", - "tool", - "mcp", - "智能体", - "工具调用", - "自主规划", - "任务执行", - ), - source=SourceSpec( - id="query-source", - title="Query Iterator Source", - class_path="examples.agents.agent.iter_queries", - params={ - "type": "local", - "data_path": "./data/agent_queries.jsonl", - "field_query": "query", - }, - summary="Load agent tasks from JSONL file.", - ), - stages=( - StageSpec( - id="agent-runtime", - title="Agent Runtime Operator", - kind="agent", - class_path="sage.middleware.operators.agentic.runtime.AgentRuntimeOperator", - params={ - "profile": { - "name": "ResearchAgent", - "role": "autonomous researcher", - "language": "zh", - "tone": "concise", - "goals": [ - "拆解复杂任务", - "调用工具获取证据", - "输出可验证结论", - ], - }, - "generator": { - "method": "openai", - "model_name": "gpt-4o-mini", - "base_url": "https://api.openai.com/v1", - }, - "planner": { - "max_steps": 5, - "enable_repair": True, - "topk_tools": 6, - }, - "tools": [ - { - "module": "examples.tutorials.agents.calculator_tool", - "class": "CalculatorTool", - }, - { - "module": "examples.tutorials.agents.search_tool", - "class": "SearchTool", - }, - ], - "runtime": { - "max_steps": 6, - "summarizer": "reuse_generator", - }, - }, - summary="Turn-key agent runtime (profile + planner + registry + workflow) for drag-and-drop pipelines.", - metadata={"description": "L4 预设智能体算子，可直接接入 Studio 拖拽式工作流"}, - ), - ), - sink=SinkSpec( - id="terminal-sink", - title="Terminal Result Sink", - class_path="sage.libs.io.sink.TerminalSink", - summary="Display agent execution results and reasoning traces.", - ), - services=( - { - "id": "mcp-server", - "type": "tool_registry", - "config": {"protocol": "mcp", "tools_path": "./tools/"}, - }, - ), - notes=( - "基于 examples/agents/agent.py", - "支持 LLM 自主规划和工具调用", - "需要配置 MCP 工具和 OpenAI API", - "适合复杂的多步骤任务执行场景", - ), - graph_agents=( - { - "id": "task-executor", - "role": "Autonomous Task Agent", - "goals": [ - "理解用户意图", - "规划执行步骤", - "调用工具完成任务", - ], - "tools": ["llm-planner", "mcp-registry"], - }, - ), - ), - PipelineBlueprint( - id="rag-memory-enhanced", - title="Memory-Enhanced RAG Pipeline", - description="RAG pipeline with conversation memory service for context-aware multi-turn Q&A.", - keywords=( - "rag", - "memory", - "conversation", - "service", - "multi-turn", - "记忆", - "对话", - "上下文", - "多轮问答", - ), - source=SourceSpec( - id="question-batch", - title="Question Batch Source", - class_path="examples.memory.rag_memory_pipeline.QuestionSource", - params={ - "max_index": 5, - "questions": [ - "什么是健康饮食？", - "如何保持良好的睡眠？", - "运动的好处有哪些？", - ], - }, - summary="Emit sequential questions for memory demonstration.", - ), - stages=( - StageSpec( - id="memory-retriever", - title="Memory-Aware Retriever", - kind="map", - class_path="examples.memory.rag_memory_pipeline.Retriever", - summary="Retrieve context from memory service based on conversation history.", - metadata={"description": "结合历史对话的检索器"}, - ), - StageSpec( - id="qa-promptor", - title="QA Promptor", - kind="map", - class_path="sage.libs.rag.promptor.QAPromptor", - summary="Build prompt with historical context.", - ), - StageSpec( - id="generator", - title="OpenAI Generator", - kind="map", - class_path="sage.middleware.operators.rag.generator.OpenAIGenerator", - params={ - "model_name": "gpt-3.5-turbo", - }, - summary="Generate context-aware answers.", - ), - StageSpec( - id="memory-writer", - title="Memory Writer", - kind="map", - class_path="examples.memory.rag_memory_pipeline.Writer", - summary="Store Q&A pairs into memory service for future retrieval.", - metadata={"description": "写入对话历史到记忆服务"}, - ), - ), - sink=SinkSpec( - id="print-sink", - title="Print Sink", - class_path="examples.memory.rag_memory_pipeline.PrintSink", - summary="Display Q&A with conversation context.", - ), - services=( - { - "id": "rag_memory", - "type": "memory_service", - "class": "examples.memory.rag_memory_service.RAGMemoryService", - "config": { - "storage_backend": "chromadb", - "collection_name": "conversation_memory", - }, - }, - ), - notes=( - "基于 examples/memory/rag_memory_pipeline.py", - "支持多轮对话的上下文记忆", - "使用服务架构管理会话状态", - "适合需要历史感知的对话式应用", - ), - graph_channels=( - { - "id": "memory-channel", - "type": "service", - "description": "Shared conversation memory across retrieval and writing", - "participants": ["memory-retriever", "memory-writer"], - }, - ), - ), - PipelineBlueprint( - id="multimodal-cross-search", - title="Cross-Modal Search Engine", - description="Search across text and image modalities with multimodal database support.", - keywords=( - "multimodal", - "cross-modal", - "search", - "fusion", - "image", - "text", - "跨模态", - "多模态搜索", - "图文检索", - ), - source=SourceSpec( - id="multimodal-query-source", - title="Multimodal Query Source", - class_path="sage.libs.io.batch.JSONLBatch", - params={ - "data_path": "./data/multimodal_queries.jsonl", - }, - summary="Load queries containing both text and image embeddings.", - ), - stages=( - StageSpec( - id="cross-modal-retriever", - title="Cross-Modal Retriever", - kind="map", - class_path="sage.middleware.components.sage_db.python.multimodal_sage_db.create_text_image_db", - params={ - "dimension": 512, - "fusion_strategy": "weighted_average", - "text_weight": 0.6, - "image_weight": 0.4, - }, - summary="Retrieve from multimodal database with configurable fusion.", - metadata={ - "description": "支持文本、图像和融合检索策略", - "modalities": ["text", "image"], - }, - ), - ), - sink=SinkSpec( - id="json-sink", - title="JSON Output Sink", - class_path="sage.libs.io.sink.TerminalSink", - params={ - "output_format": "json", - "pretty_print": True, - }, - summary="Output multimodal search results in JSON format.", - ), - notes=( - "基于 examples/multimodal/cross_modal_search.py", - "支持纯文本、纯图像和融合检索三种模式", - "可配置不同的融合策略 (加权平均、RRF等)", - "适合图文混合检索场景如电商、新闻、社交媒体", - ), - ), -) - -DEFAULT_BLUEPRINT = BLUEPRINT_LIBRARY[0] - - -def requirements_text(requirements: dict[str, Any]) -> str: - parts: list[str] = [] - for key in ( - "goal", - "initial_prompt", - "description", - "notes", - "constraints", - "data_sources", - "name", - ): - value = requirements.get(key) - if value is None: - continue - if isinstance(value, (list, tuple, set)): - parts.extend(str(item) for item in value) - elif isinstance(value, dict): - parts.extend(str(v) for v in value.values()) - else: - parts.append(str(value)) - return " ".join(parts).lower() - - -def compute_match_score(requirements: dict[str, Any], blueprint: PipelineBlueprint) -> float: - text = requirements_text(requirements) - if not text: - return 0.2 - - score = 0.0 - for keyword in blueprint.keywords: - term = keyword.lower() - if not term: - continue - if term in text: - score += 1.0 - else: - tokens = [token for token in term.replace("/", " ").split() if token] - if tokens and all(token in text for token in tokens): - score += 0.6 - if blueprint.id in text: - score += 0.5 - if blueprint.title.lower() in text: - score += 0.4 - length_bonus = min(0.4, 0.05 * max(0, len(text.split()) - 5)) - score += length_bonus - if blueprint.keywords: - score = score / len(blueprint.keywords) - return max(0.0, min(1.2, score)) - - -def match_blueprints( - requirements: dict[str, Any], - top_k: int = 3, -) -> list[tuple[PipelineBlueprint, float]]: - candidates = [ - (blueprint, compute_match_score(requirements, blueprint)) for blueprint in BLUEPRINT_LIBRARY - ] - candidates.sort(key=lambda item: item[1], reverse=True) - top = candidates[: top_k or 1] - if all(bp is not DEFAULT_BLUEPRINT for bp, _ in top): - top.append((DEFAULT_BLUEPRINT, 0.1)) - return top - - -def select_blueprint(requirements: dict[str, Any]) -> PipelineBlueprint: - return match_blueprints(requirements, top_k=1)[0][0] - - -def build_pipeline_plan( - blueprint: PipelineBlueprint, - requirements: dict[str, Any], - feedback: str | None = None, -) -> dict[str, Any]: - plan = { - "pipeline": { - "name": _slugify(requirements.get("name") or blueprint.id), - "description": requirements.get("goal") or blueprint.description, - "version": "1.0.0", - "type": "local", - }, - "source": blueprint.source.to_plan(), - "stages": [stage.to_plan() for stage in blueprint.stages], - "sink": blueprint.sink.to_plan(), - "services": [copy.deepcopy(service) for service in blueprint.services], - "monitors": [copy.deepcopy(monitor) for monitor in blueprint.monitors], - "notes": blueprint.render_notes(feedback), - } - return plan - - -def build_graph_plan( - blueprint: PipelineBlueprint, - requirements: dict[str, Any], - feedback: str | None = None, -) -> dict[str, Any]: - components: list[Any] = [blueprint.source, *blueprint.stages, blueprint.sink] - nodes: list[dict[str, Any]] = [] - - for index, component in enumerate(components): - prev_id = components[index - 1].id if index > 0 else None - next_id = components[index + 1].id if index + 1 < len(components) else None - - if isinstance(component, SourceSpec): - outputs = [next_id] if next_id else [] - nodes.append(component.to_graph_node(outputs)) - elif isinstance(component, StageSpec): - inputs = [prev_id] if prev_id else [] - outputs = [next_id] if next_id else [] - nodes.append(component.to_graph_node(inputs, outputs)) - else: - inputs = [prev_id] if prev_id else [] - nodes.append(component.to_graph_node(inputs)) - - channels = [copy.deepcopy(channel) for channel in blueprint.graph_channels] - if feedback and feedback.strip(): - channels.append( - { - "id": f"{blueprint.id}-feedback", - "type": "event", - "description": feedback.strip(), - "participants": [components[0].id, components[-1].id], - } - ) - - plan = { - "pipeline": { - "name": _slugify(requirements.get("name") or f"{blueprint.id}-graph"), - "description": requirements.get("goal") or blueprint.description, - "version": "1.0.0", - "type": "local", - }, - "graph": { - "nodes": nodes, - "channels": channels, - }, - "agents": [copy.deepcopy(agent) for agent in blueprint.graph_agents], - "services": [copy.deepcopy(service) for service in blueprint.services], - "monitors": [copy.deepcopy(monitor) for monitor in blueprint.monitors], - "notes": blueprint.render_notes(feedback), - } - return plan - - -def render_blueprint_prompt(blueprint: PipelineBlueprint, score: float) -> str: - component_lines = [ - f"source → {blueprint.source.class_path}", - *[f"{stage.id} ({stage.kind}) → {stage.class_path}" for stage in blueprint.stages], - f"sink → {blueprint.sink.class_path}", - ] - components_block = "\n".join(f"- {line}" for line in component_lines) - notes_line = "; ".join(blueprint.notes) if blueprint.notes else "无" - - summary = textwrap.dedent( - f""" - Blueprint: {blueprint.title} ({blueprint.id}) - Match confidence: {score:.2f} - 适用关键词: {", ".join(blueprint.keywords) or "通用"} - 场景描述: {blueprint.description} - 主要组件: - {components_block} - 备注: {notes_line} - """ - ).strip() - return summary - - -__all__ = [ - "SourceSpec", - "StageSpec", - "SinkSpec", - "PipelineBlueprint", - "BLUEPRINT_LIBRARY", - "DEFAULT_BLUEPRINT", - "match_blueprints", - "select_blueprint", - "build_pipeline_plan", - "build_graph_plan", - "render_blueprint_prompt", -] diff --git a/packages/sage-cli/src/sage/cli/utils/__init__.py b/packages/sage-cli/src/sage/cli/utils/__init__.py deleted file mode 100644 index 0713f54a6a..0000000000 --- a/packages/sage-cli/src/sage/cli/utils/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -"""Utility functions for SAGE CLI.""" - -from .env import ( - check_environment_status, - find_project_root, - get_api_key, - load_environment_file, - should_use_real_api, -) - -__all__ = [ - "check_environment_status", - "find_project_root", - "get_api_key", - "load_environment_file", - "should_use_real_api", -] diff --git a/packages/sage-cli/src/sage/cli/utils/dev_check.py b/packages/sage-cli/src/sage/cli/utils/dev_check.py deleted file mode 100644 index c8123fdca6..0000000000 --- a/packages/sage-cli/src/sage/cli/utils/dev_check.py +++ /dev/null @@ -1,129 +0,0 @@ -#!/usr/bin/env python3 -""" -开发模式检查工具 - -提供装饰器和函数来检查命令是否在开发环境（源码安装）中运行 -""" - -from collections.abc import Callable -from functools import wraps -from pathlib import Path - -import typer -from rich.console import Console - -console = Console() - - -def is_source_installation() -> bool: - """ - 检查是否在源码安装模式下运行 - - 通过查找 packages 目录来判断是否在开发环境中 - - Returns: - bool: True 如果在源码目录中，False 否则 - """ - # 从当前工作目录开始向上查找 - current_dir = Path.cwd() - - # 最多向上查找 5 层 - for _ in range(5): - packages_dir = current_dir / "packages" - if packages_dir.exists() and packages_dir.is_dir(): - # 额外检查是否包含 SAGE 的子包 - sage_packages = [ - "sage", - "sage-common", - "sage-kernel", - "sage-tools", - "sage-middleware", - "sage-libs", - ] - # 至少找到 3 个包才认为是有效的源码目录 - found_count = sum(1 for pkg in sage_packages if (packages_dir / pkg).exists()) - if found_count >= 3: - return True - - # 到达根目录 - if current_dir.parent == current_dir: - break - current_dir = current_dir.parent - - return False - - -def get_project_root() -> Path: - """ - 获取项目根目录（包含 packages 目录的目录） - - Returns: - Path: 项目根目录路径 - - Raises: - FileNotFoundError: 如果未找到项目根目录 - """ - current_dir = Path.cwd() - - for _ in range(5): - packages_dir = current_dir / "packages" - if packages_dir.exists() and packages_dir.is_dir(): - return current_dir - - if current_dir.parent == current_dir: - break - current_dir = current_dir.parent - - raise FileNotFoundError("未找到 SAGE 项目根目录") - - -def require_source_code(func: Callable) -> Callable: - """ - 装饰器：要求命令在源码模式下运行 - - 如果不在源码模式下，显示友好的错误提示并退出 - - Usage: - @app.command() - @require_source_code - def my_dev_command(): - ... - """ - - @wraps(func) - def wrapper(*args, **kwargs): - if not is_source_installation(): - console.print("\n[red]❌ 此命令仅在开发模式（源码安装）下可用[/red]\n") - - console.print("[yellow]💡 从源码安装 SAGE：[/yellow]") - console.print(" [cyan]# 1. 克隆仓库[/cyan]") - console.print(" git clone https://github.com/intellistream/SAGE.git") - console.print(" cd SAGE") - console.print() - console.print(" [cyan]# 2. 安装为可编辑模式（开发模式）[/cyan]") - console.print(" pip install -e .") - console.print() - console.print(" [cyan]# 或使用快速启动脚本[/cyan]") - console.print(" ./quickstart.sh") - console.print() - console.print("[dim]更多信息请访问: https://github.com/intellistream/SAGE[/dim]") - - raise typer.Exit(1) - - return func(*args, **kwargs) - - return wrapper - - -def show_dev_mode_info(): - """显示开发模式的信息提示""" - if is_source_installation(): - console.print("[green]✓[/green] 开发模式已启用") - try: - project_root = get_project_root() - console.print(f"[dim]项目路径: {project_root}[/dim]") - except FileNotFoundError: - pass - else: - console.print("[yellow]ℹ[/yellow] 当前为标准安装模式") - console.print("[dim]部分开发命令不可用，从源码安装以启用开发模式[/dim]") diff --git a/packages/sage-cli/src/sage/cli/utils/diagnostics.py b/packages/sage-cli/src/sage/cli/utils/diagnostics.py deleted file mode 100644 index 93c4574e1d..0000000000 --- a/packages/sage-cli/src/sage/cli/utils/diagnostics.py +++ /dev/null @@ -1,484 +0,0 @@ -"""Helper utilities for diagnosing the local SAGE installation.""" - -from __future__ import annotations - -import importlib -import importlib.metadata -import os -import pkgutil -import subprocess -import sys -import traceback -from collections.abc import Iterable -from dataclasses import dataclass -from pathlib import Path -from typing import Any - -from packaging.version import parse as parse_version -from rich.console import Console -from rich.table import Table - -DEFAULT_DEPENDENCIES: dict[str, str] = { - "intellistream-sage-kernel": "0.1.5", - "intellistream-sage-utils": "0.1.3", - "intellistream-sage-middleware": "0.1.3", - "intellistream-sage-cli": "0.1.3", -} - - -@dataclass -class DependencyStatus: - name: str - required: str - installed: str | None - compatible: bool - error: str | None = None - - -def _get_console(console: Console | None) -> Console: - return console or Console() - - -def _gather_dependency_status( - dependencies: dict[str, str], -) -> list[DependencyStatus]: - statuses: list[DependencyStatus] = [] - - for package, minimum in dependencies.items(): - try: - # 使用 importlib.metadata 替代 pkg_resources - installed_version = importlib.metadata.version(package) - compatible = parse_version(installed_version) >= parse_version(minimum) - statuses.append( - DependencyStatus( - name=package, - required=minimum, - installed=installed_version, - compatible=compatible, - ) - ) - except importlib.metadata.PackageNotFoundError: - statuses.append( - DependencyStatus( - name=package, - required=minimum, - installed=None, - compatible=False, - error="未安装", - ) - ) - except Exception as exc: # pragma: no cover - defensive - statuses.append( - DependencyStatus( - name=package, - required=minimum, - installed=None, - compatible=False, - error=str(exc), - ) - ) - - return statuses - - -def _render_status_table(statuses: Iterable[DependencyStatus], console: Console) -> None: - table = Table(title="SAGE 依赖兼容性", show_lines=True) - table.add_column("依赖包") - table.add_column("最低版本", justify="right") - table.add_column("当前版本", justify="right") - table.add_column("状态") - - for status in statuses: - if status.compatible: - state = "✅ 兼容" - installed = status.installed or "—" - else: - reason = status.error or "版本过低" - state = f"❌ 不兼容 ({reason})" - installed = status.installed or "未安装" - table.add_row(status.name, status.required, installed, state) - - console.print(table) - - -def check_dependency_versions( - dependencies: dict[str, str] | None = None, - *, - console: Console | None = None, - verify_import: bool = True, -) -> bool: - """Check whether required dependencies satisfy minimum versions. - - Parameters - ---------- - dependencies: - Mapping of package name to minimum required version. When omitted, the - default closed-source package requirements are used. - console: - Optional ``rich.console.Console`` used for rendering output. - verify_import: - When ``True``, attempt to import ``JobManagerClient`` for an extra - runtime readiness check. - - Returns - ------- - bool - ``True`` when all dependencies are compatible; ``False`` otherwise. - """ - - console = _get_console(console) - dependencies = dependencies or DEFAULT_DEPENDENCIES - - console.rule("依赖兼容性检查") - statuses = _gather_dependency_status(dependencies) - _render_status_table(statuses, console) - - incompatible = [status for status in statuses if not status.compatible] - if incompatible: - console.print("[yellow]\n需要关注的依赖:\n") - for status in incompatible: - console.print(f" • {status.name} (需要 >= {status.required})") - - package_list = " ".join(status.name for status in incompatible) - if package_list: - console.print(f"\n建议升级命令: [bold]pip install --upgrade {package_list}[/bold]") - - if verify_import: - console.print("\n尝试验证关键模块导入…") - try: - from sage.kernel.runtime.jobmanager_client import JobManagerClient # noqa: F401 - except Exception as exc: # pragma: no cover - import runtime dependent - console.print(f"❌ JobManagerClient 导入失败: {exc}") - else: - console.print("✅ JobManagerClient 导入成功") - - return False - - console.print("\n✅ 所有依赖版本兼容，系统应该可以正常工作") - return True - - -def _resolve_project_root( - project_root: os.PathLike[str] | str | None = None, -) -> Path: - if project_root is None: - return Path.cwd() - return Path(project_root).expanduser().resolve() - - -def run_installation_diagnostics( - project_root: os.PathLike[str] | str | None = None, - *, - console: Console | None = None, -) -> None: - """Render a comprehensive installation diagnostic similar to legacy scripts.""" - - console = _get_console(console) - project_path = _resolve_project_root(project_root) - - console.print("🔍 SAGE 完整安装诊断") - console.print("=" * 50) - - import_results: dict[str, dict[str, Any]] = {} - - try: - console.print("📦 基础导入测试...") - imports_to_test = [ - "sage", - "sage.common", - "sage.kernel", - "sage.libs", - "sage.middleware", - ] - - for module in imports_to_test: - try: - imported_module = importlib.import_module(module) - version = getattr(imported_module, "__version__", "Unknown") - module_path = getattr( - imported_module, - "__file__", - getattr(imported_module, "__path__", "Unknown"), - ) - import_results[module] = { - "status": "success", - "version": version, - "path": (str(module_path) if module_path != "Unknown" else module_path), - } - console.print(f" ✅ {module} (版本: {version})") - except ImportError as exc: - import_results[module] = {"status": "failed", "error": str(exc)} - console.print(f" ❌ {module}: {exc}") - except Exception as exc: # pragma: no cover - defensive runtime guard - import_results[module] = {"status": "error", "error": str(exc)} - console.print(f" ❌ {module}: {exc}") - - console.print("\n🔗 命名空间包检查...") - try: - import sage - - if hasattr(sage, "__path__"): - console.print(f" ✅ sage 命名空间路径: {sage.__path__}") - for _, name, _ in pkgutil.iter_modules(sage.__path__, sage.__name__ + "."): - if name.split(".")[-1] in { - "common", - "kernel", - "libs", - "middleware", - "tools", - }: - console.print(f" 📦 发现子包: {name}") - else: - console.print(" ⚠️ sage 不是命名空间包") - except Exception as exc: # pragma: no cover - import runtime dependent - console.print(f" ❌ 命名空间检查失败: {exc}") - - console.print("\n🏗️ 包结构检查...") - packages_dir = project_path / "packages" - if packages_dir.exists(): - for package_dir in sorted(packages_dir.iterdir()): - if not package_dir.is_dir() or not package_dir.name.startswith("sage-"): - continue - - console.print(f" 📦 {package_dir.name}") - console.print( - " ✅ pyproject.toml" - if (package_dir / "pyproject.toml").exists() - else " ❌ pyproject.toml 缺失" - ) - console.print( - " ✅ src/ 目录" if (package_dir / "src").exists() else " ⚠️ src/ 目录缺失" - ) - console.print( - " ✅ tests/ 目录" - if (package_dir / "tests").exists() - else " ⚠️ tests/ 目录缺失" - ) - else: - console.print(" ❌ packages 目录不存在") - - console.print("\n🌍 环境变量检查...") - for var in ["SAGE_HOME", "PYTHONPATH", "PATH"]: - value = os.environ.get(var) - if value: - abbreviated = value[:100] + ("..." if len(value) > 100 else "") - console.print(f" ✅ {var}: {abbreviated}") - else: - console.print(f" ⚠️ {var}: 未设置") - - console.print("\n🖥️ CLI 工具检查...") - cli_commands: Iterable[tuple[str, list[str]]] = [ - ("sage", ["sage", "--help"]), - ("sage-dev", ["sage", "dev", "--help"]), - ] - for label, command in cli_commands: - try: - result = subprocess.run( - command, - capture_output=True, - text=True, - timeout=10, - ) - if result.returncode == 0: - console.print(f" ✅ {label} 可用") - else: - console.print(f" ❌ {label} 返回错误码: {result.returncode}") - except subprocess.TimeoutExpired: - console.print(f" ⚠️ {label} 超时") - except FileNotFoundError: - console.print(f" ❌ {label} 未找到") - except Exception as exc: # pragma: no cover - defensive - console.print(f" ❌ {label} 检查失败: {exc}") - - console.print("\n📚 关键依赖检查...") - key_dependencies = [ - "typer", - "rich", - "pydantic", - "fastapi", - "pytest", - "numpy", - "pandas", - ] - for dep in key_dependencies: - try: - imported = importlib.import_module(dep) - version = getattr(imported, "__version__", "Unknown") - console.print(f" ✅ {dep} (版本: {version})") - except ImportError: - console.print(f" ⚠️ {dep} 未安装") - except Exception as exc: # pragma: no cover - defensive - console.print(f" ❌ {dep} 检查失败: {exc}") - - console.print("\n📋 诊断总结:") - total_imports = len(import_results) - successful_imports = sum( - 1 for result in import_results.values() if result.get("status") == "success" - ) - console.print(f" 📊 导入成功率: {successful_imports}/{total_imports}") - if successful_imports == total_imports: - console.print(" 🎉 SAGE 安装完整，所有模块可正常导入") - elif successful_imports > 0: - console.print(" ⚠️ SAGE 部分安装，部分模块存在问题") - else: - console.print(" ❌ SAGE 安装存在严重问题，无法导入核心模块") - - console.print("\n✅ 完整诊断完成") - - except Exception as exc: # pragma: no cover - defensive top-level handling - console.print(f"[red]诊断失败: {exc}[/red]") - console.print(f"[red]详细错误:\n{traceback.format_exc()}[/red]") - - -def collect_packages_status( - project_root: os.PathLike[str] | str | None = None, -) -> dict[str, Any]: - """Collect package status information for the provided project root.""" - - project_path = _resolve_project_root(project_root) - packages_dir = project_path / "packages" - - if not packages_dir.exists(): - return {"error": "packages directory not found"} - - packages_status: dict[str, dict[str, Any]] = {} - - for package_dir in sorted(packages_dir.iterdir()): - if not package_dir.is_dir() or not package_dir.name.startswith("sage-"): - continue - - package_name = package_dir.name - module_name = package_name.replace("-", ".") - status_info: dict[str, Any] = { - "name": package_name, - "path": str(package_dir), - "has_pyproject": (package_dir / "pyproject.toml").exists(), - "has_setup": (package_dir / "setup.py").exists(), - "has_tests": (package_dir / "tests").exists(), - "version": "unknown", - } - - try: - result = subprocess.run( - [ - sys.executable, - "-c", - ( - "import importlib, sys; " - f"mod = importlib.import_module('{module_name}'); " - "print(getattr(mod, '__version__', 'unknown'))" - ), - ], - capture_output=True, - text=True, - timeout=5, - ) - if result.returncode == 0: - status_info["version"] = result.stdout.strip() - status_info["import_status"] = "success" - else: - status_info["import_status"] = "failed" - status_info["import_error"] = result.stderr.strip() - except Exception as exc: # pragma: no cover - defensive - status_info["import_status"] = "error" - status_info["import_error"] = str(exc) - - packages_status[package_name] = status_info - - return {"total_packages": len(packages_status), "packages": packages_status} - - -def print_packages_status_summary( - project_root: os.PathLike[str] | str | None = None, - *, - console: Console | None = None, -) -> None: - """Render a summary of package installation status.""" - - console = _get_console(console) - data = collect_packages_status(project_root) - - console.print("\n📦 包状态摘要:") - - if "error" in data: - console.print(f"[red]❌ {data['error']}[/red]") - return - - total = data["total_packages"] - packages = data["packages"] - - importable = sum(1 for pkg in packages.values() if pkg.get("import_status") == "success") - has_tests = sum(1 for pkg in packages.values() if pkg.get("has_tests", False)) - - console.print(f" 📊 总包数: {total}") - console.print(f" ✅ 可导入: {importable}/{total}") - console.print(f" 🧪 有测试: {has_tests}/{total}") - - -def _check_package_dependencies( - package_name: str, - console: Console, - verbose: bool, -) -> None: - console.print(f" 🔗 检查 {package_name} 依赖...") - if verbose: - console.print(" ℹ️ 依赖检查功能待完善") - - -def print_packages_status( - project_root: os.PathLike[str] | str | None = None, - *, - console: Console | None = None, - verbose: bool = False, - check_versions: bool = False, - check_dependencies: bool = False, -) -> None: - """Display package status details using Rich formatting.""" - - console = _get_console(console) - console.print("📦 SAGE Framework 包状态详情") - console.print("=" * 50) - - data = collect_packages_status(project_root) - if "error" in data: - console.print(f"[red]❌ {data['error']}[/red]") - return - - for package_name, info in data["packages"].items(): - console.print(f"\n📦 {package_name}") - - console.print( - " ✅ pyproject.toml" if info.get("has_pyproject") else " ❌ pyproject.toml 缺失" - ) - console.print(" ✅ tests 目录" if info.get("has_tests") else " ⚠️ tests 目录缺失") - - import_status = info.get("import_status") - if import_status == "success": - console.print(f" ✅ 导入成功 (版本: {info.get('version', 'unknown')})") - elif import_status == "failed": - console.print(" ❌ 导入失败") - if verbose and info.get("import_error"): - console.print(f" 错误: {info['import_error']}") - elif import_status == "error": - console.print(" ❌ 导入检查异常") - if verbose and info.get("import_error"): - console.print(f" 错误: {info['import_error']}") - else: - console.print(" ⚠️ 未检测导入状态") - - if check_versions and verbose: - console.print(f" 📍 路径: {info.get('path', 'unknown')}") - - if check_dependencies: - _check_package_dependencies(package_name, console, verbose) - - -__all__ = [ - "check_dependency_versions", - "DEFAULT_DEPENDENCIES", - "DependencyStatus", - "run_installation_diagnostics", - "collect_packages_status", - "print_packages_status", - "print_packages_status_summary", -] diff --git a/packages/sage-cli/src/sage/cli/utils/env.py b/packages/sage-cli/src/sage/cli/utils/env.py deleted file mode 100644 index 4eb95bb72e..0000000000 --- a/packages/sage-cli/src/sage/cli/utils/env.py +++ /dev/null @@ -1,181 +0,0 @@ -"""Environment configuration utilities for the SAGE toolchain.""" - -from __future__ import annotations - -import os -import sys -from pathlib import Path - -try: # pragma: no cover - import fallback is runtime dependent - from dotenv import load_dotenv -except ImportError: # pragma: no cover - handled at runtime - load_dotenv = None # type: ignore - - -def find_project_root(start: Path | None = None) -> Path: - """Locate the SAGE project root directory. - - The lookup walks upwards from ``start`` (or the current file location when - omitted) until a directory containing either ``pyproject.toml`` or ``.git`` - is found. If no sentinel is discovered the current working directory is - returned. - """ - - current = start or Path(__file__).resolve().parent - pyproject_candidate: Path | None = None - - while current != current.parent: - if (current / ".git").exists(): - return current - if pyproject_candidate is None and (current / "pyproject.toml").exists(): - pyproject_candidate = current - current = current.parent - - if pyproject_candidate is not None: - return pyproject_candidate - return Path.cwd() - - -def load_environment_file( - env_file: Path | None = None, *, override: bool = False -) -> tuple[bool, Path | None]: - """Load a ``.env`` file into the current process. - - Args: - env_file: Optional explicit path to the ``.env`` file. When omitted the - helper searches ``find_project_root() / ".env"`` first and falls - back to ``Path.cwd() / ".env"``. - override: When ``True`` existing environment variables are replaced by - the values from the file. - - Returns: - A tuple ``(loaded, path)`` where ``loaded`` indicates whether a file was - successfully consumed and ``path`` references the resolved file that was - used. If no file could be loaded ``path`` is ``None``. - - Raises: - RuntimeError: If the optional dependency ``python-dotenv`` is missing. - """ - - if load_dotenv is None: - raise RuntimeError( - "python-dotenv is not installed. Install it with 'pip install python-dotenv'." - ) - - candidate: Path | None = None - if env_file is not None: - candidate = env_file.expanduser() - else: - project_env = find_project_root() / ".env" - if project_env.exists(): - candidate = project_env - else: - local_env = Path.cwd() / ".env" - if local_env.exists(): - candidate = local_env - - if candidate is None or not candidate.exists(): - return False, candidate - - load_dotenv(candidate, override=override) - return True, candidate - - -def should_use_real_api() -> bool: - """Return ``True`` when real API calls should be executed.""" - - if os.getenv("SAGE_USE_REAL_API") == "true": - return True - return "--use-real-api" in sys.argv - - -def get_api_key(service: str, *, required: bool = True) -> str | None: - """Fetch the API key for *service* from the environment. - - Args: - service: Logical service identifier (``openai``, ``hf`` …). - required: When ``True`` a ``ValueError`` is raised if the key is - missing. Otherwise ``None`` is returned. - """ - - mapping = { - "openai": "OPENAI_API_KEY", - "hf": "HF_TOKEN", - "huggingface": "HF_TOKEN", - "siliconcloud": "SILICONCLOUD_API_KEY", - "jina": "JINA_API_KEY", - "alibaba": "ALIBABA_API_KEY", - "vllm": "VLLM_API_KEY", # Kept for compatibility - "sagellm": "SAGELLM_MODEL_PATH", - } - - env_var = mapping.get(service.lower()) - if not env_var: - available = ", ".join(sorted(mapping)) - raise ValueError(f"Unknown service '{service}'. Available services: {available}") - - value = os.getenv(env_var) - if not value and required: - project_root = find_project_root() - raise ValueError( - f"Missing required API key: {env_var}. " - f"Set it in your .env file (see {project_root}/.env or .env.template)." - ) - - return value - - -def check_environment_status() -> dict[str, object]: - """Collect high-level information about the current environment state.""" - - project_root = find_project_root() - env_file = project_root / ".env" - env_template = project_root / ".env.template" - - api_keys = [ - "OPENAI_API_KEY", - "HF_TOKEN", - "SILICONCLOUD_API_KEY", - "JINA_API_KEY", - "ALIBABA_API_KEY", - "VLLM_API_KEY", # Kept for compatibility - ] - - # sagellm environment variables - sagellm_env_vars = [ - "SAGELLM_MODEL_PATH", # Default model path - "SAGELLM_BACKEND", # Backend type: auto/mock/cuda/ascend - "SAGELLM_MODEL_ROOT", # Model cache directory (~/.sage/models/sagellm) - ] - - return { - "dotenv_available": load_dotenv is not None, - "project_root": project_root, - "env_file_exists": env_file.exists(), - "env_template_exists": env_template.exists(), - "env_file": env_file, - "env_template": env_template, - "api_keys": { - key: { - "set": os.getenv(key) is not None, - "length": len(os.getenv(key) or ""), - } - for key in api_keys - }, - "sagellm": { - var: { - "set": os.getenv(var) is not None, - "value": os.getenv(var, ""), - } - for var in sagellm_env_vars - }, - } - - -__all__ = [ - "check_environment_status", - "find_project_root", - "get_api_key", - "load_environment_file", - "should_use_real_api", -] diff --git a/packages/sage-cli/src/sage/cli/utils/llm_detection.py b/packages/sage-cli/src/sage/cli/utils/llm_detection.py deleted file mode 100644 index 08467ac64b..0000000000 --- a/packages/sage-cli/src/sage/cli/utils/llm_detection.py +++ /dev/null @@ -1,193 +0,0 @@ -"""Helpers for detecting locally running LLM services. - -This module provides lightweight HTTP probes that discover OpenAI-compatible -endpoints exposed by popular local deployments such as Ollama and vLLM. The -resulting metadata can be used to auto-populate generator configuration blocks. -""" - -from __future__ import annotations - -import json -import ssl -from collections.abc import Iterable -from dataclasses import dataclass -from urllib import error, request - -from sage.common.config.ports import SagePorts - - -@dataclass -class LLMServiceInfo: - """Metadata describing a detected LLM service.""" - - name: str - base_url: str - models: list[str] - default_model: str - generator_section: str - description: str - - -DEFAULT_TIMEOUT = 2 # seconds - - -def _safe_http_get( - url: str, timeout: int = DEFAULT_TIMEOUT, auth_token: str | None = None -) -> str | None: - """Best-effort HTTP GET that returns response text or ``None`` on failure.""" - - req = request.Request(url) - if auth_token: - req.add_header("Authorization", f"Bearer {auth_token}") - - try: - with request.urlopen(req, timeout=timeout, context=_ssl_context()) as resp: - charset = resp.headers.get_content_charset() or "utf-8" - return resp.read().decode(charset) - except (TimeoutError, error.URLError, ssl.SSLError): - return None - - -def _ssl_context() -> ssl.SSLContext | None: - """Create a default SSL context while remaining compatible with older Python.""" - - try: - return ssl.create_default_context() - except AttributeError: # pragma: no cover - extremely old Python - return None - - -def detect_ollama( - base_urls: Iterable[str] | None = None, -) -> LLMServiceInfo | None: - """Detect a running Ollama service by probing the tags endpoint.""" - - if base_urls is None: - base_urls = ( - "http://127.0.0.1:11434", - "http://localhost:11434", - "http://0.0.0.0:11434", - ) - - for host in base_urls: - payload = _safe_http_get(f"{host}/api/tags") - if not payload: - continue - - try: - data = json.loads(payload) - except json.JSONDecodeError: - continue - - models = [model["name"] for model in data.get("models", []) if "name" in model] - if not models: - continue - - default_model = models[0] - return LLMServiceInfo( - name="ollama", - base_url=f"{host}/v1", - models=models, - default_model=default_model, - generator_section="remote", - description=f"Ollama at {host}", - ) - - return None - - -def detect_vllm( - base_urls: Iterable[str] | None = None, auth_token: str | None = None -) -> LLMServiceInfo | None: - """Detect a running vLLM service by probing the OpenAI-compatible models API.""" - - if base_urls is None: - gateway_port = SagePorts.GATEWAY_DEFAULT - base_urls = ( - f"http://127.0.0.1:{gateway_port}", - f"http://localhost:{gateway_port}", - f"http://0.0.0.0:{gateway_port}", - ) - - # If user provides auth_token, use it; otherwise try common defaults - if auth_token is not None: - auth_tokens = [auth_token] - else: - auth_tokens = [None, "token-abc123", "test-token", "vllm-token"] - - for host in base_urls: - for token in auth_tokens: - payload = _safe_http_get(f"{host}/v1/models", auth_token=token) - if payload: - break - - if not payload: - continue - - try: - data = json.loads(payload) - except json.JSONDecodeError: - continue - - models = [item.get("id") for item in data.get("data", []) if item.get("id")] - if not models: - continue - - default_model = models[0] - return LLMServiceInfo( - name="vllm", - base_url=f"{host}/v1", - models=models, - default_model=default_model, - generator_section="vllm", - description=f"vLLM at {host}", - ) - - return None - - -def detect_sagellm() -> LLMServiceInfo | None: - """检测本地 sageLLM 引擎是否可用""" - try: - from sagellm_backend.engine.factory import EngineFactory - - backends = EngineFactory.available_backends() - if not backends: - return None - return LLMServiceInfo( - name="sagellm", - base_url="local://sagellm", - models=backends, # 可用的后端列表 - default_model=backends[0], - generator_section="sagellm", - description=f"sageLLM ({', '.join(backends)})", - ) - except ImportError: - return None - - -def detect_all_services( - prefer: str | None = None, auth_token: str | None = None -) -> list[LLMServiceInfo]: - """Detect all supported services, optionally restricting by name.""" - - prefer_normalized = prefer.lower() if prefer else None - detections: list[LLMServiceInfo] = [] - - # sagellm has highest priority (local native engine) - if prefer_normalized in (None, "sagellm"): - service = detect_sagellm() - if service: - detections.insert(0, service) # 优先 - - if prefer_normalized in (None, "ollama"): - service = detect_ollama() - if service: - detections.append(service) - - if prefer_normalized in (None, "vllm"): - service = detect_vllm(auth_token=auth_token) - if service: - detections.append(service) - - return detections diff --git a/packages/sage-cli/tests/test_cli_help.py b/packages/sage-cli/tests/test_cli_help.py deleted file mode 100644 index 74f08b0882..0000000000 --- a/packages/sage-cli/tests/test_cli_help.py +++ /dev/null @@ -1,36 +0,0 @@ -"""Basic help coverage for the public ``sage`` CLI entry point.""" - -from __future__ import annotations - -import pytest -from typer.testing import CliRunner - -from sage.cli.main import app - -runner = CliRunner() - - -def _available_groups() -> list[str]: - groups: list[str] = [] - for info in getattr(app, "registered_groups", []): - # ``name`` matches the CLI segment, e.g. ``cluster`` or ``llm`` - if info.name is not None: - groups.append(info.name) - return groups - - -@pytest.mark.cli -def test_root_help_displays_categories() -> None: - result = runner.invoke(app, ["--help"]) - assert result.exit_code == 0 - assert "SAGE" in result.stdout - assert "Platform" in result.stdout - assert "Apps" in result.stdout - - -@pytest.mark.cli -@pytest.mark.parametrize("group_name", _available_groups()) -def test_group_help_commands(group_name: str) -> None: - result = runner.invoke(app, [group_name, "--help"]) - assert result.exit_code == 0, result.stdout - assert group_name in result.stdout diff --git a/packages/sage-cli/tests/test_inference_cli.py b/packages/sage-cli/tests/test_inference_cli.py deleted file mode 100644 index 730d46aab3..0000000000 --- a/packages/sage-cli/tests/test_inference_cli.py +++ /dev/null @@ -1,335 +0,0 @@ -# SPDX-License-Identifier: Apache-2.0 -# SPDX-FileCopyrightText: Copyright contributors to the SAGE project - -"""Unit tests for the inference CLI command. - -This module tests the inference command which manages the unified -inference service (LLM + Embedding). -""" - -from __future__ import annotations - -import json -import re -from pathlib import Path -from unittest.mock import MagicMock, patch - -import pytest -from typer.testing import CliRunner - -from sage.cli.commands.apps.inference import ( - _is_port_in_use, - _load_config, - _save_config, - _save_pid, - _test_api_health, - app, -) - -runner = CliRunner() - -# Pre-compiled regex pattern for stripping ANSI escape codes -_ANSI_ESCAPE_PATTERN = re.compile(r"\x1b\[[0-9;]*m") - - -def strip_ansi(text: str) -> str: - """Strip ANSI escape codes from text for reliable assertions.""" - return _ANSI_ESCAPE_PATTERN.sub("", text) - - -# ============================================================================= -# Test Helper Functions -# ============================================================================= - - -class TestHelperFunctions: - """Tests for helper functions.""" - - def test_is_port_in_use_free_port(self) -> None: - """Test that a free port returns False.""" - # Use a high port that's unlikely to be in use - result = _is_port_in_use(59999) - assert result is False - - @patch("socket.socket") - def test_is_port_in_use_occupied_port(self, mock_socket: MagicMock) -> None: - """Test that an occupied port returns True.""" - mock_sock = MagicMock() - mock_sock.connect_ex.return_value = 0 # Connected successfully - mock_socket.return_value.__enter__.return_value = mock_sock - - result = _is_port_in_use(8000) - assert result is True - - def test_save_and_load_config(self, tmp_path: Path) -> None: - """Test saving and loading configuration.""" - test_config = { - "host": "127.0.0.1", - "port": 9000, - "llm_model": "test-model", - } - - # Use temporary path - config_file = tmp_path / "test_config.json" - with patch("sage.cli.commands.apps.inference.CONFIG_FILE", config_file): - _save_config(test_config) - loaded = _load_config() - - assert loaded == test_config - - def test_load_config_missing_file(self, tmp_path: Path) -> None: - """Test loading config when file doesn't exist.""" - config_file = tmp_path / "nonexistent.json" - with patch("sage.cli.commands.apps.inference.CONFIG_FILE", config_file): - result = _load_config() - assert result is None - - def test_save_and_get_pid(self, tmp_path: Path) -> None: - """Test saving and retrieving PID.""" - pid_file = tmp_path / "test.pid" - with patch("sage.cli.commands.apps.inference.PID_FILE", pid_file): - _save_pid(12345) - # Since we're not running a real process, _get_running_pid will clean up - # Just verify the file was created - assert pid_file.exists() - assert pid_file.read_text().strip() == "12345" - - @patch("urllib.request.urlopen") - def test_test_api_health_success(self, mock_urlopen: MagicMock) -> None: - """Test successful health check.""" - mock_response = MagicMock() - mock_response.read.return_value = b'{"status": "healthy"}' - mock_response.__enter__ = MagicMock(return_value=mock_response) - mock_response.__exit__ = MagicMock(return_value=False) - mock_urlopen.return_value = mock_response - - result = _test_api_health(8000) - assert result == {"status": "healthy"} - - @patch("urllib.request.urlopen") - def test_test_api_health_failure(self, mock_urlopen: MagicMock) -> None: - """Test health check when server is down.""" - mock_urlopen.side_effect = Exception("Connection refused") - - result = _test_api_health(8000) - assert result is None - - -# ============================================================================= -# Test CLI Commands -# ============================================================================= - - -class TestStartCommand: - """Tests for the start command.""" - - def test_start_help(self) -> None: - """Test start command help.""" - result = runner.invoke(app, ["start", "--help"]) - assert result.exit_code == 0 - stdout = strip_ansi(result.stdout) - assert "启动统一推理服务" in stdout - assert "--llm-model" in stdout - assert "--embedding-model" in stdout - assert "--port" in stdout - assert "--scheduling-policy" in stdout - - @patch("sage.cli.commands.apps.inference._get_running_pid") - def test_start_already_running(self, mock_get_pid: MagicMock) -> None: - """Test start when server is already running.""" - mock_get_pid.return_value = 12345 - - result = runner.invoke(app, ["start"]) - assert result.exit_code == 1 - assert "服务已在运行中" in result.stdout - - @patch("sage.cli.commands.apps.inference._get_running_pid") - @patch("sage.cli.commands.apps.inference._is_port_in_use") - def test_start_port_in_use(self, mock_port_check: MagicMock, mock_get_pid: MagicMock) -> None: - """Test start when port is already in use.""" - mock_get_pid.return_value = None - mock_port_check.return_value = True - - result = runner.invoke(app, ["start", "--port", "8000"]) - assert result.exit_code == 1 - assert "端口 8000 已被占用" in result.stdout - - -class TestStopCommand: - """Tests for the stop command.""" - - def test_stop_help(self) -> None: - """Test stop command help.""" - result = runner.invoke(app, ["stop", "--help"]) - assert result.exit_code == 0 - stdout = strip_ansi(result.stdout) - assert "停止统一推理服务" in stdout - assert "--force" in stdout - - @patch("sage.cli.commands.apps.inference._get_running_pid") - def test_stop_not_running(self, mock_get_pid: MagicMock) -> None: - """Test stop when server is not running.""" - mock_get_pid.return_value = None - - result = runner.invoke(app, ["stop"]) - assert result.exit_code == 0 - assert "未找到运行中的服务" in result.stdout - - -class TestStatusCommand: - """Tests for the status command.""" - - def test_status_help(self) -> None: - """Test status command help.""" - result = runner.invoke(app, ["status", "--help"]) - assert result.exit_code == 0 - stdout = strip_ansi(result.stdout) - assert "查看统一推理服务状态" in stdout - assert "--json" in stdout - - @patch("sage.cli.commands.apps.inference._get_running_pid") - @patch("sage.cli.commands.apps.inference._load_config") - @patch("sage.cli.commands.apps.inference._is_port_in_use") - def test_status_not_running( - self, - mock_port_check: MagicMock, - mock_load_config: MagicMock, - mock_get_pid: MagicMock, - ) -> None: - """Test status when server is not running.""" - mock_get_pid.return_value = None - mock_load_config.return_value = None - mock_port_check.return_value = False - - result = runner.invoke(app, ["status"]) - assert result.exit_code == 0 - assert "未运行" in result.stdout - - @patch("sage.cli.commands.apps.inference._get_running_pid") - @patch("sage.cli.commands.apps.inference._load_config") - @patch("sage.cli.commands.apps.inference._is_port_in_use") - @patch("sage.cli.commands.apps.inference._test_api_health") - def test_status_json_output( - self, - mock_health: MagicMock, - mock_port_check: MagicMock, - mock_load_config: MagicMock, - mock_get_pid: MagicMock, - ) -> None: - """Test status with JSON output.""" - mock_get_pid.return_value = None - mock_load_config.return_value = {"port": 8000} - mock_port_check.return_value = False - mock_health.return_value = None - - result = runner.invoke(app, ["status", "--json"]) - assert result.exit_code == 0 - # Should be valid JSON - data = json.loads(result.stdout) - assert "running" in data - assert "port" in data - - -class TestConfigCommand: - """Tests for the config command.""" - - def test_config_help(self) -> None: - """Test config command help.""" - result = runner.invoke(app, ["config", "--help"]) - assert result.exit_code == 0 - stdout = strip_ansi(result.stdout) - assert "显示当前配置" in stdout - - @patch("sage.cli.commands.apps.inference._load_config") - def test_config_no_config(self, mock_load_config: MagicMock) -> None: - """Test config when no config exists.""" - mock_load_config.return_value = None - - result = runner.invoke(app, ["config"]) - assert result.exit_code == 0 - assert "暂无保存的配置" in result.stdout - - @patch("sage.cli.commands.apps.inference._load_config") - def test_config_json_output(self, mock_load_config: MagicMock) -> None: - """Test config with JSON output.""" - mock_load_config.return_value = { - "host": "0.0.0.0", - "port": 8000, - "llm_model": "test-model", - } - - result = runner.invoke(app, ["config", "--output", "json"]) - assert result.exit_code == 0 - data = json.loads(result.stdout) - assert data["port"] == 8000 - - -class TestLogsCommand: - """Tests for the logs command.""" - - def test_logs_help(self) -> None: - """Test logs command help.""" - result = runner.invoke(app, ["logs", "--help"]) - assert result.exit_code == 0 - stdout = strip_ansi(result.stdout) - assert "查看服务日志" in stdout - assert "--follow" in stdout - assert "--lines" in stdout - - def test_logs_no_file(self, tmp_path: Path) -> None: - """Test logs when log file doesn't exist.""" - log_file = tmp_path / "nonexistent.log" - with patch("sage.cli.commands.apps.inference.LOG_FILE", log_file): - result = runner.invoke(app, ["logs"]) - assert result.exit_code == 0 - assert "日志文件不存在" in result.stdout - - def test_logs_with_file(self, tmp_path: Path) -> None: - """Test logs with existing log file.""" - log_file = tmp_path / "test.log" - log_file.write_text("Line 1\nLine 2\nLine 3\n") - - with patch("sage.cli.commands.apps.inference.LOG_FILE", log_file): - result = runner.invoke(app, ["logs", "--lines", "2"]) - assert result.exit_code == 0 - assert "Line 2" in result.stdout - assert "Line 3" in result.stdout - - -# ============================================================================= -# Integration Tests -# ============================================================================= - - -class TestCLIIntegration: - """Integration tests for CLI commands.""" - - def test_commands_registered(self) -> None: - """Test that all commands are registered.""" - result = runner.invoke(app, ["--help"]) - assert result.exit_code == 0 - assert "start" in result.stdout - assert "stop" in result.stdout - assert "status" in result.stdout - assert "config" in result.stdout - assert "logs" in result.stdout - - def test_full_workflow_simulation(self, tmp_path: Path) -> None: - """Test simulated full workflow.""" - pid_file = tmp_path / "test.pid" - config_file = tmp_path / "test_config.json" - - with ( - patch("sage.cli.commands.apps.inference.PID_FILE", pid_file), - patch("sage.cli.commands.apps.inference.CONFIG_FILE", config_file), - patch("sage.cli.commands.apps.inference._get_running_pid", return_value=None), - patch("sage.cli.commands.apps.inference._is_port_in_use", return_value=False), - patch("sage.cli.commands.apps.inference._load_config", return_value=None), - ): - # Check initial status - result = runner.invoke(app, ["status"]) - assert "未运行" in result.stdout - - -if __name__ == "__main__": - pytest.main([__file__, "-v"]) diff --git a/packages/sage-common/README.md b/packages/sage-common/README.md deleted file mode 100644 index 6e3454e3ef..0000000000 --- a/packages/sage-common/README.md +++ /dev/null @@ -1,260 +0,0 @@ -# SAGE Common - -> SAGE 框架的核心工具和共享组件 - -[![Python Version](https://img.shields.io/badge/python-3.9%2B-blue.svg)](https://www.python.org/downloads/) -[![License](https://img.shields.io/badge/license-MIT-green.svg)](../../LICENSE) - -## 📋 Overview - -**SAGE Common** 提供所有 SAGE 包共用的基础工具和组件。 这是基础层（L1），提供： - -## 🧭 Governance / 团队协作制度 - -- `docs/governance/TEAM.md` - -- `docs/governance/MAINTAINERS.md` - -- `docs/governance/DEVELOPER_GUIDE.md` - -- `docs/governance/PR_CHECKLIST.md` - -- `docs/governance/SELF_HOSTED_RUNNER.md` - -- `docs/governance/TODO.md` - -- **配置管理** - YAML/TOML 文件支持 - -- **日志框架** - 自定义格式化器和处理程序 - -- **网络工具** - TCP/UDP 通信支持 - -- **序列化工具** - dill 和 pickle 支持 - -- **系统工具** - 环境和进程管理 - -- **嵌入服务** - sage_embedding、sage_llm - -该包确保 SAGE 生态系统的一致性并减少代码重复。 - -## ✨ Features - -- **统一配置** - YAML/TOML 配置加载和验证 -- **高级日志** - 彩色输出、结构化日志、自定义格式器 -- **网络工具** - TCP 客户端/服务器、网络助手 -- **灵活序列化** - 多种后端（dill、pickle、JSON） -- **系统管理** - 环境检测、进程控制 -- **LLM 集成** - 嵌入和 vLLM 服务 - -## 🚀 Quick Start - -### 配置管理 - -```python -from sage.common.utils.config import load_config - -# 加载 YAML 配置 -config = load_config("config.yaml") -print(config["database"]["host"]) -``` - -### 日志记录 - -```python -from sage.common.utils.logging import get_logger - -logger = get_logger(__name__) -logger.info("Processing started") -logger.error("An error occurred", extra={"user_id": 123}) -``` - -### 序列化 - -```python -from sage.common.utils.serialization import UniversalSerializer - -serializer = UniversalSerializer() -data = {"key": "value", "nested": {"data": [1, 2, 3]}} -serialized = serializer.serialize(data) -deserialized = serializer.deserialize(serialized) -``` - -## 核心模块 - -- **utils.config** - 配置管理工具 -- **utils.logging** - 日志框架和格式化器 -- **utils.network** - 网络工具和 TCP 客户端/服务器 -- **utils.serialization** - 序列化工具（包含 dill 支持） -- **utils.system** - 环境和进程管理的系统工具 -- **\_version** - 版本管理 - -## 📦 Package Structure - -``` -sage-common/ -├── src/ -│ └── sage/ -│ └── common/ -│ ├── __init__.py -│ ├── _version.py -│ ├── utils/ # 核心工具 -│ │ ├── config/ # 配置管理 -│ │ ├── logging/ # 日志框架 -│ │ ├── network/ # 网络工具 -│ │ ├── serialization/ # 序列化工具 -│ │ └── system/ # 系统工具 -│ └── components/ # 共享组件 -│ ├── sage_embedding/ # 嵌入服务 -│ └── sage_llm/ # vLLM 服务 -├── tests/ -├── pyproject.toml -└── README.md -``` - -## 🚀 Installation - -### 基础安装 - -```bash -pip install isage-common -``` - -### 开发安装 - -```bash -cd packages/sage-common -pip install -e . -``` - -### 可选依赖安装 - -```bash -# 嵌入支持 -pip install isage-common[embedding] - -# vLLM 支持 -pip install isage-common[vllm] - -# 完整安装 -pip install isage-common[all] -``` - -## 📖 快速开始 - -### 配置管理 - -```python -from sage.common.utils.config.loader import ConfigLoader - -# 加载配置 -config = ConfigLoader("config.yaml") - -# 访问配置 -model_name = config.get("model.name", default="default-model") -``` - -### 日志 - -```python -from sage.common.utils.logging.custom_logger import get_logger - -# 获取日志器 -logger = get_logger(__name__) - -# 使用日志器 -logger.info("应用程序已启动") -logger.debug("调试信息") -logger.error("发生错误", exc_info=True) -``` - -### 网络工具 - -```python -from sage.common.utils.network import TCPClient, TCPServer - -# 创建 TCP 服务器 -server = TCPServer(host="localhost", port=8080) -server.start() - -# 创建 TCP 客户端 -client = TCPClient(host="localhost", port=8080) -client.connect() -client.send(b"你好，服务器！") -``` - -### 序列化 - -```python -from sage.common.utils.serialization import serialize, deserialize - -# 序列化数据 -data = {"key": "value", "numbers": [1, 2, 3]} -serialized = serialize(data, format="dill") - -# 反序列化数据 -restored = deserialize(serialized, format="dill") -``` - -## 🔧 Configuration - -配置文件通常使用 YAML 或 TOML 格式： - -```yaml -# config.yaml -logging: - level: INFO - format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s" - -network: - host: localhost - port: 8080 - timeout: 30 - -embedding: - model: sentence-transformers/all-MiniLM-L6-v2 - device: cuda -``` - -## 🧪 Testing - -```bash -# 运行单元测试 -pytest tests/unit - -# 运行集成测试 -pytest tests/integration - -# 运行覆盖率测试 -pytest --cov=sage.common --cov-report=html -``` - -## 📚 Documentation - -- **用户指南** - 查看 [docs-public](https://intellistream.github.io/SAGE-Pub/guides/packages/sage-common/) -- **API 参考** - 查看包文档字符串和类型提示 -- **示例** - 查看各模块中的 `examples/` 目录 - -## 🤝 Contributing - -欢迎贡献！请查看 [CONTRIBUTING.md](../../CONTRIBUTING.md) 了解指导原则。 - -## 📄 License - -该项目采用 MIT 许可证 - 详情请查看 [LICENSE](../../LICENSE) 文件。 - -## 🔗 相关包 - -- **sage-kernel** - 使用通用工具进行运行时管理 -- **sage-libs** - 基于通用组件构建库 -- **sage-middleware** - 使用网络和序列化工具 -- **sage-tools** - 使用配置和日志工具 - -## 📮 支持 - -- **文档** - https://intellistream.github.io/SAGE-Pub/ -- **问题** - https://github.com/intellistream/SAGE/issues -- **讨论** - https://github.com/intellistream/SAGE/discussions - -______________________________________________________________________ - -**SAGE 框架的一部分** | [主仓库](https://github.com/intellistream/SAGE) diff --git a/packages/sage-common/docs/governance/DEVELOPER_GUIDE.md b/packages/sage-common/docs/governance/DEVELOPER_GUIDE.md deleted file mode 100644 index f93c0c7ba0..0000000000 --- a/packages/sage-common/docs/governance/DEVELOPER_GUIDE.md +++ /dev/null @@ -1,88 +0,0 @@ -# 开发者指南与质量门槛（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/DEVELOPER_GUIDE.md`） - -本指南把“制度”落到“能执行的工程约束”。本包的所有代码改动必须遵守以下规则。 - -______________________________________________________________________ - -## 1. 架构守护机制（强制） - -### 1.1 依赖层级（禁止向上依赖 / 禁止循环依赖） - -SAGE 采用 L1-L5 分层。任何“向上依赖”都视为架构违规，必须阻断合入。 - -- L1：`sage-common` -- L2：`sage-platform` -- L3：`sage-kernel`, `sage-libs` -- L4：`sage-middleware` -- L5：`sage-cli`, `sage-tools` - -### 1.2 Libs vs Middleware 规则（强制） - -如果代码需要调用“向上能力”（例如 VectorDB/Memory/Refiner/外部服务/重后端/网络服务），它 **不是库**，必须放在 -`sage-middleware`（operators/components）。 - -> 迁移时不保留兼容 re-export shim：更新所有调用方，快速失败。 - -### 1.3 Control Plane-only（强制） - -LLM 引擎操作必须走 Control Plane，禁止直接启动引擎/直连端口。 - -______________________________________________________________________ - -## 2. 自动化检查（CI + pre-commit）（强制） - -### 2.1 安装一致性 - -- 必须使用 `./quickstart.sh --dev --yes` 安装开发环境。 -- 禁止手工 `pip install` 临时安装依赖（依赖必须写入 `pyproject.toml`）。 - -### 2.2 本地质量与测试 - -建议在仓库根目录执行： - -- `sage-dev quality check --all-files` -- `sage-dev project test --coverage` - -______________________________________________________________________ - -## 3. 强制规范（必须出现的质量门槛） - -### 3.1 Mock-First（强制） - -- CI 必须能在无 GPU 环境跑通核心测试。 -- 新增能力必须提供 Mock/CPU 路径，避免把“硬件”当作默认前提。 - -### 3.2 Fail-Fast（强制） - -- 禁止静默默认值、禁止隐式回退（fallback）。 -- 关键配置缺失必须明确报错（例如用 `os.environ["KEY"]` 或显式校验并抛异常）。 - -### 3.3 Protocol-First（按适用范围执行） - -当本包提供“公共接口/抽象/跨包契约”时： - -- 先定义接口/类型（Protocol/ABC/Schema） -- 再实现具体逻辑 -- 再补测试与示例 - -______________________________________________________________________ - -## 4. 新模块/新能力接入步骤（可执行） - -1. **定义范围**：确认属于本包；若需要向上能力，按规则提升到 `sage-middleware`。 -1. **接口优先**：先定义可复用接口（如适用）。 -1. **最小可跑**：提供无 GPU 的最小可跑实现（Mock/CPU）。 -1. **测试**：至少包含单元测试；关键路径补回归测试。 -1. **文档**：更新本包 README 与本包 governance 文档（必要时）。 -1. **质量门槛**：本地 `sage-dev quality` 与测试通过。 - -______________________________________________________________________ - -## 5. 常见错误与修复 - -- **依赖倒挂**：`sage-libs` 引入 `sage-middleware` → 必须拆分/上移实现。 -- **文档位置违规**：禁止在根 `docs/` 放 Markdown；包级文档只能放 `packages//docs/`。 -- **依赖未声明**：新增依赖未写入 `pyproject.toml` → 必须补齐声明并统一版本。 diff --git a/packages/sage-common/docs/governance/MAINTAINERS.md b/packages/sage-common/docs/governance/MAINTAINERS.md deleted file mode 100644 index 4e331ad6e0..0000000000 --- a/packages/sage-common/docs/governance/MAINTAINERS.md +++ /dev/null @@ -1,126 +0,0 @@ -# 负责人制度（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/MAINTAINERS.md`） -- 负责人名单：TBD - -本文件定义“包级 Maintainer（负责人）”如何配置、如何工作、如何被量化验收。 - -______________________________________________________________________ - -## 1. 为什么需要 Maintainer - -SAGE 是 monorepo，多包协作、依赖层级明确。Maintainer 的职责不是“写最多代码”，而是保证： - -- 需求有人推进、问题有人闭环 -- 质量门槛有人守住（CI/架构/测试） -- 跨包集成有人对齐（尤其是接口层与中间件层） - -______________________________________________________________________ - -## 2. 负责人配置建议（按模块/子系统） - -### 2.1 推荐配置 - -- 每个包至少 1 名 Maintainer（主负责人）+ 1 名 Backup（备份负责人）。 -- 当包内包含多个子系统/高复杂度模块：建议拆分子 Maintainer。 - -### 2.2 负责人配置表（模板，必须维护） - -| 模块/目录 | 复杂度 | 关键技能 | 主 Maintainer | Backup | 上游依赖 | 下游使用方 | -| --------- | ------ | -------- | ------------- | ------ | -------- | ---------- | -| TBD | TBD | TBD | TBD | TBD | TBD | TBD | - -> 单仓语义适配：这里的“上游/下游”指本 monorepo 的其他包或本包内其他模块。 - -______________________________________________________________________ - -## 3. 负责人职责拆分（40/30/20/10 参考） - -可按实际调整，但必须覆盖全部职责： - -- **40% 开发推进**：Roadmap/Issue/PR 推动，里程碑拆解，阻塞清理。 -- **30% 质量把控**：CI 绿灯、测试策略、回归防护、性能/稳定性监控。 -- **20% 集成同步**：跨包接口对齐、发布/版本对齐、变更公告与迁移指引。 -- **10% 团队协作**：Review SLA、协作矩阵维护、争议处理与复核申请。 - -______________________________________________________________________ - -## 4. 要求与投入 - -- **最低投入**：每周固定时间段处理 Issue/PR（TBD：建议至少 2-4 小时/周）。 -- **Review SLA**：工作日 48h 内首次响应。 -- **透明度**：每月一次交付清单（含证据链接）。 - -______________________________________________________________________ - -## 5. 工作流程（开发 / 发布 / 集成同步） - -### 5.1 日常开发 - -- 所有需求/缺陷必须有 Issue。 -- PR 必须通过 `docs/governance/PR_CHECKLIST.md`。 -- 架构约束（依赖层级、libs vs middleware）违反时：必须阻断合入。 - -### 5.2 发布与版本对齐（如适用） - -- 发布前：测试与质量检查必须绿。 -- 变更：Breaking 变更必须有迁移指引与回滚预案。 -- 若涉及 PyPI 发布：使用仓库规定的发布工具链（例如 `wheelwright`），不得手工 twine/pip 临时发布。 - -### 5.3 跨包集成同步 - -- 上游接口变化：必须提前通知下游 maintainer，并提供迁移窗口。 -- 下游反馈：需要在 SLA 内回应并给出计划。 - -______________________________________________________________________ - -## 6. 协作矩阵（依赖/协作关系） - -### 6.1 Monorepo 依赖层级速查 - -> 目标：避免“向上依赖”与循环依赖。 - -| 层级 | 包（示例） | 允许依赖 | -| ---- | -------------------------- | ---------------- | -| L5 | `sage-cli`, `sage-tools` | L1-L4 | -| L4 | `sage-middleware` | L1-L3 | -| L3 | `sage-kernel`, `sage-libs` | L1-L2 | -| L2 | `sage-platform` | L1 | -| L1 | `sage-common` | 仅 stdlib/轻依赖 | - -### 6.2 本包协作矩阵（模板） - -| 依赖方/被依赖方 | 关系类型 | 接口/契约 | 变更通知机制 | Maintainer 对接 | -| --------------- | -------- | --------- | --------------------- | --------------- | -| TBD | 上游 | TBD | Issue/PR/Release Note | TBD | -| TBD | 下游 | TBD | Issue/PR/Release Note | TBD | - -______________________________________________________________________ - -## 7. 负责人名单（TBD） - -| 模块/目录 | 主 Maintainer | Backup | 交接人（如更换） | 生效日期 | -| --------- | ------------- | ------ | ---------------- | -------- | -| TBD | TBD | TBD | TBD | TBD | - -______________________________________________________________________ - -## 8. 考核指标（参考，可量化） - -- PR 首次响应 SLA 达标率 -- CI 红灯平均恢复时间（MTTR） -- 每月交付清单是否按时发布（含证据） -- Breaking 变更公告与迁移指引完备率 - -______________________________________________________________________ - -## 9. FAQ - -### Q1：Maintainer 是否必须是提交最多的人？ - -不是。Maintainer 的核心是“推进 + 质量 + 对齐”。提交多但不维护质量/不对齐下游同样不合格。 - -### Q2：如果包内没有发布流程怎么办？ - -仍需要“集成同步”：接口变更、跨包影响、迁移指引、回滚预案。 diff --git a/packages/sage-common/docs/governance/PR_CHECKLIST.md b/packages/sage-common/docs/governance/PR_CHECKLIST.md deleted file mode 100644 index 09d0f8c350..0000000000 --- a/packages/sage-common/docs/governance/PR_CHECKLIST.md +++ /dev/null @@ -1,49 +0,0 @@ -# PR 审查清单（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/PR_CHECKLIST.md`） - -> Maintainer/Reviewer 需要用本清单进行可量化审查。未满足项必须在 PR 中解释或补齐。 - -______________________________________________________________________ - -## 1. 架构合规 - -- [ ] 不产生向上依赖/循环依赖（符合 L1-L5 分层） -- [ ] 遵守 libs vs middleware 规则（需要向上能力则放 middleware） -- [ ] LLM 相关操作不绕过 Control Plane - -## 2. Protocol-First（如适用） - -- [ ] 公共接口/类型先定义（Protocol/ABC/Schema），实现不“绑死”具体后端 -- [ ] 接口变更包含迁移说明（Breaking change 必须公告） - -## 3. Mock-First - -- [ ] 无 GPU 环境可跑（至少核心测试/最小路径） -- [ ] 新增外部依赖/服务调用有可测试替身（mock/fake） - -## 4. Fail-Fast - -- [ ] 无隐式回退（不写 try/except 吞异常、不用静默默认值掩盖缺失配置） -- [ ] 错误信息可定位（异常 message 清晰，必要时包含修复指引） - -## 5. 可观测性（按适用范围） - -- [ ] 关键路径有日志/指标/追踪点（至少日志） -- [ ] 不输出敏感信息（key/token/密码等） - -## 6. 配置验证 - -- [ ] 新增配置项有校验与文档说明 -- [ ] 端口不硬编码（如适用，使用统一端口配置） - -## 7. 测试覆盖 - -- [ ] 新增/修改逻辑有对应测试 -- [ ] 关键 bug fix 有回归测试 - -## 8. 代码质量 - -- [ ] `sage-dev quality check --all-files` 通过 -- [ ] `sage-dev project test --coverage` 通过（或至少本包相关测试通过） diff --git a/packages/sage-common/docs/governance/SELF_HOSTED_RUNNER.md b/packages/sage-common/docs/governance/SELF_HOSTED_RUNNER.md deleted file mode 100644 index 8c4746062a..0000000000 --- a/packages/sage-common/docs/governance/SELF_HOSTED_RUNNER.md +++ /dev/null @@ -1,50 +0,0 @@ -# Self-hosted Runner 指南（SAGE - 可选） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/SELF_HOSTED_RUNNER.md`） - -> 本章节用于需要 GPU/重依赖/长耗时测试的包。若本包不需要，可保留但不强制启用。 - -______________________________________________________________________ - -## 1. 什么时候需要 self-hosted runner - -- GPU 相关测试（CUDA/Ascend） -- 需要特殊硬件或驱动版本 -- 需要访问内网资源（需安全评估） - -______________________________________________________________________ - -## 2. Runner 安装与标签（模板） - -- Runner 类型：GitHub Actions self-hosted -- 标签建议： - - `self-hosted` - - `linux` - - `x64` - - `gpu`（如适用） - - `cuda-`（如适用） - -______________________________________________________________________ - -## 3. 环境要求（模板） - -- OS：Linux（推荐） -- Python：与仓库要求一致（3.10+） -- 构建依赖：cmake / build-essential / BLAS 等（按仓库安装脚本） - -______________________________________________________________________ - -## 4. 安全注意事项（强制） - -- Runner 机器不可暴露敏感密钥；Secrets 由 GitHub 管理。 -- 禁止在日志中打印 token/key。 -- 对内网访问与数据集访问：必须走审批（TBD）。 - -______________________________________________________________________ - -## 5. 故障排查 - -- CI 失败先在本地 `./quickstart.sh --dev --yes` 复现 -- 检查磁盘/缓存（`.sage/`） -- 检查驱动/CUDA 版本（如适用） diff --git a/packages/sage-common/docs/governance/TEAM.md b/packages/sage-common/docs/governance/TEAM.md deleted file mode 100644 index fbea66b13c..0000000000 --- a/packages/sage-common/docs/governance/TEAM.md +++ /dev/null @@ -1,67 +0,0 @@ -# 仓库人员与评分制度（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/TEAM.md`） -- 名单维护：TBD - -本文件定义在 SAGE monorepo 内（以包为单位）的团队角色、负责人制度与评分/激励框架，用于保证： - -- 制度可执行：能落到 Issue/PR/CI/周更/月更。 -- 制度可验收：有明确证据链接与检查项。 -- 制度可追责：有最低交付门槛、透明度与争议处理机制。 - -> 约束：所有“姓名/负责人名单/具体人员”一律用 `TBD`，不得编造。 - -______________________________________________________________________ - -## 1. 角色与职责（强制条款） - -| 角色 | 核心职责 | 必须产出（可验收） | 证据类型（必须带链接） | -| ---------------------------- | -------------------------------------------------------- | ----------------------------------------------------------------------- | ------------------------------------------ | -| Maintainer（负责人） | Roadmap/Issue/PR 推进；质量门槛；集成/版本对齐；变更管理 | 每周 TODO 周更、PR Review SLA、CI 绿灯保障、重大变更公告、发布/集成记录 | Issue、PR、Release/Tag、CI 链接、周报/月报 | -| Engineering Core（工程骨干） | 关键功能交付；CI/测试维护；稳定性问题闭环 | 可交付功能/修复、测试/CI 改进、性能/稳定性指标改进 | PR、Issue、Benchmark 报告、CI 链接 | -| Research Core（科研骨干） | 原型验证/评测；研究到工程落地（必须可验收） | 可复现的实验/评测；可落地的工程化改造（或明确落地计划） | 评测报告、PR、Issue、实验配置/脚本链接 | - -______________________________________________________________________ - -## 2. 负责人必须做的事（强制条款） - -### 2.1 TODO 周更（强制） - -- 每周至少 1 次在本包的 `docs/governance/TODO.md` 更新：本周目标/完成/阻塞/下周计划/风险/需要协助。 -- TODO 必须引用证据（Issue/PR/CI/报告链接）。 - -### 2.2 PR Review SLA（强制） - -- 工作日 48h 内对新 PR 首次响应（review/approve/request changes/说明何时处理）。 -- 对影响多个包的变更：必须主动拉齐上下游 maintainer。 - -# 仓库人员与评分制度（SAGE - 包级，指向母版） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages/sage-common/docs/governance/TEAM.md`） -- 名单维护：TBD - -通用制度请参见 `packages/sage/docs/governance/TEAM_BASE.md`。本文件仅保留本包的代号映射与特有说明。 - -### 本包角色代号（姓名保持 TBD） - -| 角色 | 代号分配 | -| ---------------- | ---------------------- | -| Maintainer | A2 | -| Engineering Core | B2 | -| Research Core | 无（按需可由 C3 支撑） | - -### 本包补充说明 - -- 基础层（L1）需守护分层与公共配置（端口/XDG），其余制度依母版执行。 - -______________________________________________________________________ - -## 参考 - -- 通用制度：packages/sage/docs/governance/TEAM_BASE.md -- 周更：docs/governance/TODO.md -- PR 审查：docs/governance/PR_CHECKLIST.md -- 开发规范：docs/governance/DEVELOPER_GUIDE.md -- **保底分**：角色的最低履职奖励（必须满足最低交付门槛）。 diff --git a/packages/sage-common/docs/governance/TODO.md b/packages/sage-common/docs/governance/TODO.md deleted file mode 100644 index afdd548b3a..0000000000 --- a/packages/sage-common/docs/governance/TODO.md +++ /dev/null @@ -1,39 +0,0 @@ -# TODO 周更模板（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/TODO.md`） -- 维护频率：每周至少 1 次（Maintainer 强制要求） - -______________________________________________________________________ - -## 周更（复制本模板，每周追加一节） - -### Week of YYYY-MM-DD - -**本周目标（Goals）** - -- [ ] TBD（关联 Issue：TBD） - -**本周完成（Done）** - -- [ ] TBD（PR：TBD，CI：TBD） - -**阻塞与风险（Blockers/Risks）** - -- TBD（原因 + 需要谁协助 + 截止时间） - -**下周计划（Next）** - -- [ ] TBD（Issue：TBD） - -**需要协助（Asks）** - -- TBD（@TBD） - -______________________________________________________________________ - -## 里程碑清单（长期维护） - -| 里程碑 | 目标日期 | 状态 | 关联 Issue/PR | 风险 | -| ------ | -------- | ---- | ------------- | ---- | -| TBD | TBD | TBD | TBD | TBD | diff --git a/packages/sage-common/examples/INSTALLATION_GUIDE.md b/packages/sage-common/examples/INSTALLATION_GUIDE.md deleted file mode 100644 index 2f9e22b8f9..0000000000 --- a/packages/sage-common/examples/INSTALLATION_GUIDE.md +++ /dev/null @@ -1,284 +0,0 @@ -# SAGE Tutorials - Installation Guide - -## 📚 About Tutorials - -SAGE Tutorials 是完整的学习资源集合，包含： - -- 分层教程（L1-L5） -- 完整的示例代码 -- 配置文件和测试数据 -- 详细的注释和说明 - -**⚠️ 重要提示**: Tutorials **不包含在 PyPI 包中**，需要从源码获取。 - -______________________________________________________________________ - -## 🎯 如何获取 Tutorials - -### 方式 1: 克隆完整仓库（推荐） - -```bash -# 1. 克隆 SAGE 仓库 -git clone https://github.com/intellistream/SAGE.git -cd SAGE - -# 2. 安装开发环境 -./quickstart.sh --dev --yes - -# 3. 运行 tutorials -python tutorials/hello_world.py -python tutorials/L1-common/unified_inference_client_example.py -``` - -**适用场景**: - -- ✅ 学习 SAGE 完整功能 -- ✅ 本地开发和测试 -- ✅ 需要修改示例代码 - -### 方式 2: 仅下载 Tutorials 目录 - -```bash -# 使用 sparse-checkout 只下载 tutorials -git clone --depth 1 --filter=blob:none --sparse \ - https://github.com/intellistream/SAGE.git -cd SAGE -git sparse-checkout set tutorials - -# 安装 SAGE（从 PyPI） -pip install isage[standard] - -# 运行 tutorials -python tutorials/hello_world.py -``` - -**适用场景**: - -- ✅ 只需要教程，不需要修改源码 -- ✅ 快速下载（只下载 tutorials 目录） -- ✅ 使用 PyPI 版本的 SAGE - -### 方式 3: 在线浏览（无需安装） - -访问在线文档查看所有示例代码： - -- **Tutorials 文档**: https://intellistream.github.io/SAGE-Pub/tutorials/ -- **GitHub 浏览**: https://github.com/intellistream/SAGE/tree/main-dev/tutorials - -**适用场景**: - -- ✅ 快速查阅代码 -- ✅ 学习 API 用法 -- ✅ 复制粘贴代码片段 - -______________________________________________________________________ - -## 💡 为什么 Tutorials 不打包到 PyPI？ - -参考: -[EXAMPLES_TESTING_PYPI_STRATEGY.md](../docs-public/docs_src/dev-notes/cross-layer/architecture/EXAMPLES_TESTING_PYPI_STRATEGY.md) - -### ❌ 打包的问题 - -1. **包体积**：Tutorials 包含大量文件（~500+ 文件），会显著增加 PyPI 包大小 -1. **更新频率**：教程经常更新，会导致 SAGE 包频繁发版 -1. **测试数据**：包含大量测试数据、配置文件，不适合分发 -1. **依赖复杂**：不同教程需要不同依赖，难以管理 - -### ✅ 当前方案的优势 - -1. **灵活性**：可以随时更新教程，无需发版 -1. **完整性**：可以包含大文件、数据集 -1. **清晰性**：PyPI 包保持精简，仅包含核心代码 -1. **可访问性**：通过 Git 和在线文档都能访问 - -______________________________________________________________________ - -## 📦 PyPI 包中的示例 - -虽然 Tutorials 不在 PyPI 包中，但各个包都包含轻量级示例： - -### isage-kernel - -```bash -pip install isage-kernel -python -m sage.kernel.examples.simple_streaming -``` - -### isage-libs - -```bash -pip install isage-libs -python -m sage.libs.examples.rag_basic -``` - -### isage-apps - -```bash -pip install isage-apps[video] -python -m sage.apps.video.demo -``` - -这些示例是 **可运行的代码片段**，专门设计用于 PyPI 安装的用户。 - -______________________________________________________________________ - -## 🎓 学习路径 - -### 1. PyPI 用户（快速开始） - -```bash -# 安装 SAGE -pip install isage[standard] - -# 运行包内示例 -python -m sage.libs.examples.hello_world - -# 查看在线文档 -浏览器打开: https://intellistream.github.io/SAGE-Pub/ -``` - -### 2. 开发者（完整学习） - -```bash -# 克隆仓库 -git clone https://github.com/intellistream/SAGE.git -cd SAGE - -# 开发环境安装 -./quickstart.sh --dev --yes - -# 运行 Tutorials -python tutorials/L1-common/hello_world.py -python tutorials/L3-libs/rag/simple_rag.py -# 完整应用示例见 sage-examples 仓库 -``` - -### 3. 研究人员（深度定制） - -```bash -# 克隆仓库 -git clone https://github.com/intellistream/SAGE.git -cd SAGE - -# 安装可编辑模式 -pip install -e packages/sage-kernel -pip install -e packages/sage-libs -pip install -e packages/sage-middleware - -# 修改和运行教程 -# 代码更改会立即生效 -``` - -______________________________________________________________________ - -## 🔧 Tutorials 依赖管理 - -### 通用依赖 - -```bash -# 基础教程（L1-L3） -pip install isage[standard] - -# 中间件教程（L4） -pip install isage-middleware[all] - -# 应用教程（L5） -pip install isage-apps[all] -``` - -### 特定教程依赖 - -某些教程需要额外依赖： - -```bash -# RAG 教程 -pip install faiss-cpu sentence-transformers - -# Agent 教程 -pip install langchain langchain-community - -# 视频教程 -pip install opencv-python-headless - -# 医疗教程 -pip install pydicom nibabel -``` - -详见各教程目录下的 `requirements.txt`。 - -______________________________________________________________________ - -## ❓ 常见问题 - -### Q1: 为什么 `import tutorials` 不工作？ - -A: Tutorials 不是 Python 包，不能被 import。它们是独立的脚本文件，需要直接运行： - -```bash -# ✅ 正确 -python tutorials/hello_world.py - -# ❌ 错误 -python -c "import tutorials" -``` - -### Q2: 我只需要某几个教程，如何下载？ - -A: 使用 sparse-checkout（见上文方式 2），或直接从 GitHub 下载单个文件： - -```bash -# 下载单个文件 -wget https://raw.githubusercontent.com/intellistream/SAGE/main-dev/tutorials/hello_world.py -python hello_world.py -``` - -### Q3: Tutorials 和 Examples 有什么区别？ - -A: - -- **Tutorials** (`tutorials/`): 完整的学习资源，按层级组织，包含数据和配置 -- **Examples** (`packages/*/examples/`): 轻量级代码片段，打包到 PyPI，专注单一功能 - -两者互补，根据需求选择。 - -### Q4: 如何贡献新的 Tutorial？ - -A: - -```bash -# 1. Fork 并克隆仓库 -git clone https://github.com/YOUR_USERNAME/SAGE.git - -# 2. 创建分支 -git checkout -b tutorial/my-new-tutorial - -# 3. 添加 tutorial -# 放在合适的层级目录（L1-L5） - -# 4. 提交 PR -git add tutorials/L3-libs/my_tutorial.py -git commit -m "docs: add tutorial for XYZ feature" -git push origin tutorial/my-new-tutorial -``` - -______________________________________________________________________ - -## 📖 相关资源 - -- **在线文档**: https://intellistream.github.io/SAGE-Pub/ -- **GitHub 仓库**: https://github.com/intellistream/SAGE -- **PyPI 页面**: https://pypi.org/project/isage/ -- **问题反馈**: https://github.com/intellistream/SAGE/issues - -______________________________________________________________________ - -## 📧 获取帮助 - -- **Email**: shuhao_zhang@hust.edu.cn -- **GitHub Issues**: https://github.com/intellistream/SAGE/issues -- **文档**: https://intellistream.github.io/SAGE-Pub/ - -______________________________________________________________________ - -**Happy Learning with SAGE! 🚀** diff --git a/packages/sage-common/examples/QUICK_START.md b/packages/sage-common/examples/QUICK_START.md deleted file mode 100644 index cb5cfd0bad..0000000000 --- a/packages/sage-common/examples/QUICK_START.md +++ /dev/null @@ -1,327 +0,0 @@ -# 🚀 SAGE Tutorials - Quick Start - -欢迎来到 SAGE Tutorials！本指南将帮助你在 **5 分钟内**开始使用 SAGE。 - -## ⚡ 超快入门（2 分钟） - -### 1. 运行第一个示例 - -```bash -cd /path/to/SAGE/examples/tutorials -python hello_world.py -``` - -恭喜！你已经运行了第一个 SAGE 程序！ - -### 2. 理解架构 - -SAGE 采用 **5 层架构**，从底层到接口层： - -``` -L1: Common → 基础层 (配置、日志、类型) -L2: Platform → 平台层 (队列、存储、消息) -L3: Kernel/Libs → 核心层 (执行引擎、算法库) -L4: Middleware → 中间件层 (Operators、C++ 扩展) -L5: CLI/Tools → 接口层 (CLI、开发工具) -``` - -**独立仓库**（不在核心仓库中）： - -- sage-benchmark: 评估框架 (PyPI: isage-benchmark) -- sage-examples: 教程和应用示例 -- sage-studio: 可视化工作流 (独立仓库) -- sageLLM: LLM 推理引擎 (PyPI: isagellm) - -### 3. 选择学习路径 - -根据你的角色选择合适的路径： - -| 角色 | 推荐路径 | 时间 | -| ------------- | ------------------ | -------- | -| 🔰 初学者 | L1 → L2 基础 | 1-2 小时 | -| 🚀 应用开发者 | L1 → L2 → L3 → L4 | 4-6 小时 | -| 🧠 平台开发者 | 全栈学习 L1-L4 | 1-2 天 | -| 🏗️ 架构师 | 全部 L1-L5 + tools | 2-3 天 | - -______________________________________________________________________ - -## 📚 分层学习指南 - -### L1: Common - 基础层（15 分钟） - -**目标**：理解 SAGE 的基础概念 - -```bash -cd L1-common -python hello_world.py # 最基础的示例 -``` - -**核心概念**： - -- SAGE 环境配置 -- 日志系统 -- 基本术语 - -👉 **下一步**：进入 L2-kernel 学习核心 API - -______________________________________________________________________ - -### L2: Kernel - 核心层（2-3 小时） - -**目标**：掌握流处理和批处理 - -#### 2.1 批处理基础（30 分钟） - -```bash -cd L2-kernel/batch -python hello_local_batch.py # 本地批处理 -python hello_remote_batch.py # 远程批处理 -``` - -#### 2.2 流处理基础（30 分钟） - -```bash -cd L2-kernel/stream -python hello_streaming_world.py # 基础流处理 -python hello_onebyone_world.py # 单条流处理 -``` - -#### 2.3 操作符系统（1 小时） - -```bash -cd L2-kernel/operators -python hello_comap_world.py # CoMap 操作符 -python hello_filter_world.py # Filter 过滤 -python hello_flatmap_world.py # FlatMap 展开 -python hello_join_world.py # Join 连接 -``` - -#### 2.4 高级特性（选修） - -```bash -cd L2-kernel/advanced -python hello_future_world.py # Future 异步 -``` - -**核心概念**： - -- DataStream API -- Operator 系统 -- 批处理 vs 流处理 -- Pipeline 构建 - -👉 **下一步**：进入 L3-middleware 学习数据服务 - -______________________________________________________________________ - -### L3: Middleware - 中间件层（1-2 小时） - -**目标**：使用数据服务和中间件 - -#### 3.1 服务入门（15 分钟） - -```bash -cd L3-middleware -python hello_service_world.py # 理解服务模型 -``` - -#### 3.2 Memory Service（30 分钟） - -```bash -cd L3-middleware/memory_service -python rag_memory_service.py # RAG 内存服务 -``` - -#### 3.3 数据库服务（30 分钟） - -```bash -cd L3-middleware/sage_db -python workflow_demo.py # 向量数据库 - -cd L3-middleware/sage_tsdb -python basic_dag_example.py # 时序数据库 -``` - -**核心概念**： - -- Service API -- 向量数据库 -- 时序数据处理 -- 内存管理 - -👉 **下一步**：进入 L4-libs 构建应用 - -______________________________________________________________________ - -### L4: Libs - 应用库层（2-4 小时） - -**目标**：构建实际应用 - -#### 4.1 RAG 应用（1-2 小时） - -```bash -cd L4-libs/rag -python simple_rag.py # 简单 RAG -python usage_1_direct_library.py # 直接使用库 -python usage_4_complete_rag.py # 完整 RAG 系统 -``` - -#### 4.2 Agent 应用（30 分钟） - -```bash -cd L4-libs/agents -python basic_agent.py # 基础智能体 -python workflow_demo.py # 工作流 -``` - -#### 4.3 Embedding 应用（30 分钟） - -```bash -cd L4-libs/embeddings -python embedding_demo.py # 嵌入演示 -python cross_modal_search.py # 跨模态搜索 -``` - -#### 4.4 LLM 应用（30 分钟） - -```bash -cd L4-libs/llm -python pipeline_builder_llm_demo.py # LLM 管道 -python templates_to_llm_demo.py # 模板演示 -``` - -**核心概念**： - -- RAG 系统设计 -- Agent 架构 -- 向量嵌入 -- LLM 集成 - -👉 **下一步**：进入 L5-platform 学习平台服务（高级） - -______________________________________________________________________ - -### L5: Platform - 平台层（1-2 小时，高级） - -**目标**：理解平台级服务 - -```bash -cd L5-platform/scheduler -python scheduler_comparison.py # 调度器对比 -python remote_env.py # 远程环境 -``` - -**核心概念**： - -- 任务调度 -- 分布式执行 -- 资源管理 - -👉 **下一步**：探索 sage-examples 仓库中的完整应用 - -______________________________________________________________________ - -## 🎯 常见学习路径 - -### 路径 1：RAG 开发者（最热门） - -``` -1. L1-common/hello_world.py (5 分钟) -2. L2-kernel/batch/hello_local_batch.py (15 分钟) -3. L3-middleware/memory_service/ (30 分钟) -4. L4-libs/rag/simple_rag.py (30 分钟) -5. L4-libs/rag/usage_4_complete_rag.py (1 小时) -``` - -**总时间**：约 2.5 小时 - -### 路径 2：Agent 开发者 - -``` -1. L1-common/hello_world.py (5 分钟) -2. L2-kernel/stream/ (30 分钟) -3. L4-libs/agents/basic_agent.py (30 分钟) -4. L4-libs/agents/workflow_demo.py (1 小时) -``` - -**总时间**：约 2 小时 - -### 路径 3：平台工程师 - -``` -1. 完整学习 L1-L2 (3 小时) -2. L3-middleware/ 全部 (2 小时) -3. L5-platform/ 全部 (2 小时) -``` - -**总时间**：约 7 小时 - -______________________________________________________________________ - -## 📖 文档和帮助 - -### 快速查询 - -- **快速参考**：`docs/QUICK_REFERENCE.md` -- **故障排除**：`docs/TROUBLESHOOTING.md` -- **学习路径**：`docs/LEARNING_PATH.md`（即将推出） - -### 层级文档 - -每个层级目录都有详细的 README： - -- `L1-common/README.md` -- `L2-platform/README.md` -- `L3-kernel/README.md` -- `L3-libs/README.md` -- `L4-middleware/README.md` - -更多信息请查看 `docs-public/docs_src/dev-notes/` 中的开发文档。 - -### 遇到问题？ - -1. 查看 `docs/TROUBLESHOOTING.md` -1. 检查每个示例的注释 -1. 阅读对应层级的 README -1. 查看项目主 README - -______________________________________________________________________ - -## 🎓 学习建议 - -### ✅ 推荐做法 - -1. **按顺序学习**：从 L1 开始，逐层深入 -1. **动手实践**：运行每个示例，修改参数 -1. **阅读注释**：示例中有详细的说明 -1. **理解概念**：不要只运行，要理解原理 - -### ❌ 避免 - -1. **跳过基础**：L1-L2 是必须的 -1. **只看不做**：一定要运行代码 -1. **贪多嚼不烂**：一次专注一个主题 - -______________________________________________________________________ - -## 🚀 下一步行动 - -根据你的兴趣选择： - -- **我想构建 RAG 系统** → 按"路径 1"学习 -- **我想开发 Agent** → 按"路径 2"学习 -- **我想深入理解架构** → 从 L1 开始系统学习 -- **我只想快速尝试** → 运行 `hello_world.py` 和几个感兴趣的示例 - -______________________________________________________________________ - -## 💡 小贴士 - -- 每个示例都可以独立运行 -- 示例之间有依赖关系，建议按推荐顺序学习 -- 遇到错误先查看 `TROUBLESHOOTING.md` -- 配置文件在 `config/` 目录 - -______________________________________________________________________ - -**开始你的 SAGE 之旅吧！🎉** diff --git a/packages/sage-common/examples/README.md b/packages/sage-common/examples/README.md deleted file mode 100644 index 4b800a0a73..0000000000 --- a/packages/sage-common/examples/README.md +++ /dev/null @@ -1,355 +0,0 @@ -# 🚀 SAGE Tutorials - -欢迎来到 SAGE Tutorials！这里包含了按照 SAGE 架构分层组织的完整示例和文档。 - -> **从基础到应用：循序渐进地掌握 SAGE 框架** - -## ⚠️ 重要提示 - -**Tutorials 不包含在 PyPI 包中**，需要从源码获取。详见 [INSTALLATION_GUIDE.md](./INSTALLATION_GUIDE.md)。 - -**如何获取**: - -- 🔧 **开发者**: `git clone https://github.com/intellistream/SAGE.git` -- 📖 **在线浏览**: https://intellistream.github.io/SAGE-Pub/tutorials/ -- 📦 **轻量示例**: PyPI 包中的 `packages/*/examples/` - -**为什么不打包？** 见 -[设计决策](../docs-public/docs_src/dev-notes/cross-layer/architecture/EXAMPLES_TESTING_PYPI_STRATEGY.md) - -## ⚡ 5 分钟快速开始 - -```bash -# 1. 克隆仓库（如果还没有） -git clone https://github.com/intellistream/SAGE.git -cd SAGE - -# 2. 安装 SAGE -./quickstart.sh --dev --yes - -# 3. 运行第一个示例 -python tutorials/hello_world.py - -# 4. 查看快速入门指南 -cat tutorials/QUICK_START.md -``` - -## 📐 SAGE 5 层架构概览 - -SAGE 采用 **5 层分层架构**，从底层基础设施到顶层接口： - -``` -┌─────────────────────────────────────────────┐ -│ L5: Interface (接口层) │ -│ sage-cli, sage-tools │ -│ CLI 工具 + 开发工具 │ -├─────────────────────────────────────────────┤ -│ L4: Middleware (中间件层) │ -│ sage-middleware │ -│ 领域算子 + 中间件组件 (C++ 扩展) │ -├─────────────────────────────────────────────┤ -│ L3: Core (核心层) │ -│ sage-kernel + sage-libs │ -│ 执行引擎 + 算法库 │ -├─────────────────────────────────────────────┤ -│ L2: Platform (平台层) │ -│ sage-platform │ -│ 队列 + 存储 + 服务抽象 │ -├─────────────────────────────────────────────┤ -│ L1: Foundation (基础层) │ -│ sage-common │ -│ 配置 + 日志 + 工具 │ -└─────────────────────────────────────────────┘ -``` - -**独立仓库**（不在 SAGE 核心仓库中）： - -- sage-benchmark: 评估框架 (PyPI: isage-benchmark) -- sage-examples: 教程和应用示例 -- sage-studio: 可视化工作流 (独立仓库) -- sageLLM: LLM 推理引擎 (PyPI: isagellm) - -**核心原则**： - -- ✅ **单向依赖**：只能向下依赖（L5→L4→L3→L2→L1） -- ❌ **禁止反向**：禁止向上或循环依赖 -- 📚 **详细说明**：[SAGE 包架构文档](../../docs-public/docs_src/dev-notes/package-architecture.md) - -## 📚 Tutorial 目录结构 - -``` -tutorials/ -│ -├── hello_world.py # 最简入门（快速开始） -├── QUICK_START.md # 5 分钟快速指南 -├── README.md # 本文档 -│ -├── L1-common/ # 基础层：配置、日志、工具 -├── L2-platform/ # 平台层：队列、存储、调度 -├── L3-kernel/ # 核心层：执行引擎、流处理 -├── L3-libs/ # 核心层：RAG、Agents、算法库 -├── L4-middleware/ # 中间件层：领域算子、数据服务 -├── L5-cli/ # 接口层：CLI 工具（见 sage-examples 仓库） -│ -├── config/ # 配置文件示例 -└── docs/ # 文档（快速参考、故障排除） -``` - -## �� 学习路径 - -### 🔰 初学者路径（2-3 小时） - -适合第一次接触 SAGE 的用户 - -```bash -# L1: 基础概念 -cd L1-common && python hello_world.py - -# L2: 平台服务 -cd L2-platform/scheduler && python scheduler_comparison.py - -# L3: 核心引擎 -cd L3-kernel/batch && python hello_local_batch.py -cd L3-kernel/stream && python hello_streaming_world.py -cd L3-kernel/operators && python hello_comap_world.py -``` - -**学习目标**：理解 SAGE 基础概念、流处理模型、操作符系统 - -### 🚀 应用开发者路径（4-6 小时） - -适合想要构建应用的开发者 - -```bash -# 完成初学者路径后 - -# L3: 算法库 -cd L3-libs/rag && python simple_rag.py -cd L3-libs/agents && python basic_agent.py - -# L4: 中间件服务 -cd L4-middleware && python hello_service_world.py -cd L4-middleware/memory_service && python rag_memory_service.py - -# L4: 数据服务 -cd L4-middleware/sage_db && python workflow_demo.py -cd L4-middleware/sage_tsdb && python basic_dag_example.py -``` - -**学习目标**：掌握 RAG 系统、Agent 开发、数据服务使用 - -### 🧠 高级开发者路径（1-2 天） - -适合平台开发者和架构师 - -```bash -# 完成应用开发者路径后 - -# L3: 高级特性 -cd L3-kernel/advanced && python hello_future_world.py -cd L3-kernel/advanced/fault_tolerance && python fault_tolerance.py - -# L3: 完整 RAG 系统 -cd L3-libs/rag && python usage_4_complete_rag.py - -# L5: 应用集成 -# （待添加完整应用示例） -``` - -**学习目标**：深入理解容错机制、异步处理、生产级系统设计 - -## �� 各层级详细说明 - -### L1: Common - 基础层 - -**对应包**：`sage-common` - -**内容**： - -- `hello_world.py` - 最基础的 SAGE 程序 -- 配置管理示例 -- 日志系统示例 - -**Python 文件数**：1 - -[查看详细文档 →](L1-common/README.md) - -______________________________________________________________________ - -### L2: Platform - 平台服务层 - -**对应包**：`sage-platform` - -**内容**： - -- `scheduler/` - 调度系统示例 -- 队列抽象示例（待添加） -- 存储后端示例（待添加） - -**Python 文件数**：2 - -[查看详细文档 →](L2-platform/README.md) - -______________________________________________________________________ - -### L3: Kernel - 核心引擎层 - -**对应包**：`sage-kernel` - -**内容**： - -- `batch/` - 批处理示例（3 个） -- `stream/` - 流处理示例（3 个） -- `operators/` - 操作符示例（5 个） -- `functions/` - 函数示例（3 个） -- `advanced/` - 高级特性（容错、Future、Pipeline-as-Service 等，17 个） - -**Python 文件数**：31 - -[查看详细文档 →](L3-kernel/README.md) - -______________________________________________________________________ - -### L3: Libs - 算法库层 - -**对应包**：`sage-libs` - -**内容**： - -- `rag/` - RAG 应用示例（7 个） -- `agents/` - 智能体示例（5 个） -- `embeddings/` - 嵌入示例（4 个） -- `llm/` - LLM 集成示例（2 个） -- `unlearning/` - 机器遗忘示例（5 个） - -**Python 文件数**：23 - -[查看详细文档 →](L3-libs/README.md) - -______________________________________________________________________ - -### L4: Middleware - 中间件层 - -**对应包**：`sage-middleware` - -**内容**： - -- `hello_service_world.py` - 服务入门 -- `memory_service/` - 内存管理（3 个） -- `sage_db/` - 向量数据库（4 个） -- `sage_flow/` - 流数据服务（3 个） -- `sage_tsdb/` - 时序数据库（3 个） - -**Python 文件数**：13 - -[查看详细文档 →](L4-middleware/README.md) - -______________________________________________________________________ - -### L5: Apps - 应用层 - -**说明**：L5 接口层（sage-cli, sage-tools）为 CLI 工具，相关示例请查看各工具的帮助命令： - -```bash -sage --help # CLI 工具帮助 -sage-dev --help # 开发工具帮助 -``` - -**独立仓库**（不在 SAGE 核心）： - -- **sage-benchmark**: 性能评估框架 (`pip install isage-benchmark`) -- **sage-examples**: 教程和应用示例 (GitHub 仓库) -- **sage-studio**: 可视化 Web UI (独立仓库) - -______________________________________________________________________ - -## 📊 示例统计 - -| 层级 | 对应包 | Python 文件 | 主要内容 | -| -------- | -------------------- | ----------- | ------------ | -| L1 | sage-common | 1 | 基础工具 | -| L2 | sage-platform | 2 | 平台服务 | -| L3 | sage-kernel | 31 | 流处理引擎 | -| L3 | sage-libs | 23 | 算法库 | -| L4 | sage-middleware | 13 | 数据服务 | -| L5 | sage-cli, sage-tools | CLI | 接口层 | -| **总计** | - | **70+** | **5 层架构** | - -## 📖 补充文档 - -- [**QUICK_START.md**](QUICK_START.md) - 5 分钟快速入门 -- [**docs/QUICK_REFERENCE.md**](docs/QUICK_REFERENCE.md) - 快速参考卡 -- [**docs/TROUBLESHOOTING.md**](docs/TROUBLESHOOTING.md) - 故障排除指南 - -## 🔍 如何选择示例 - -### 我想学习... - -- **基础概念** → 从 `hello_world.py` 开始 -- **流处理** → `L3-kernel/stream/` -- **批处理** → `L3-kernel/batch/` -- **RAG 系统** → `L3-libs/rag/` -- **智能体** → `L3-libs/agents/` -- **数据服务** → `L4-middleware/` -- **容错机制** → `L3-kernel/advanced/fault_tolerance/` - -### 我想构建... - -- **简单脚本** → `L3-libs/rag/usage_1_direct_library.py` -- **数据管道** → `L3-kernel/` 中的示例 -- **RAG 应用** → `L3-libs/rag/usage_4_complete_rag.py` -- **Agent 系统** → `L3-libs/agents/workflow_demo.py` -- **完整应用** → 学习所有层级 - -## 🎓 学习建议 - -### ✅ 推荐做法 - -1. **按层级学习**：从 L1 开始，逐层深入 -1. **理解依赖**：了解为什么上层可以用下层，反之不行 -1. **动手实践**：运行每个示例，修改参数观察效果 -1. **阅读代码**：示例中有详细注释 -1. **参考文档**：遇到问题查看各层的 README - -### ❌ 避免 - -1. **跳过基础**：L1-L3 是必须理解的 -1. **只看不做**：一定要运行代码 -1. **忽略架构**：理解架构有助于设计更好的系统 -1. **违反依赖**：不要在低层导入高层代码 - -## 🆘 遇到问题？ - -1. 查看 [TROUBLESHOOTING.md](docs/TROUBLESHOOTING.md) -1. 检查示例的注释和 docstring -1. 阅读对应层级的 README -1. 查看 [SAGE 包架构文档](../../docs-public/docs_src/dev-notes/package-architecture.md) -1. 提交 Issue 到 GitHub - -## 🤝 贡献 - -欢迎添加新示例或改进现有示例！请确保： - -1. 示例放在正确的层级目录 -1. 遵循依赖规则（只向下依赖） -1. 添加清晰的注释和文档 -1. 更新对应的 README - -## 📜 变更历史 - -- **2025-01**: 简化为 5 层架构（L1-L5） - - sage-benchmark/examples/studio 独立为单独仓库 - - sage-cli 和 sage-tools 合并为 L5 接口层 -- **2025-01**: 简化为 5 层架构（L1-L5） - - sage-benchmark/examples/studio 独立为单独仓库 - - sage-cli 和 sage-tools 合并为 L5 接口层 -- **2025-10-29**: 按照 SAGE 6 层架构重组目录（L1-L6） - - 完整映射 9 个 SAGE 包到 6 个层级 - - 迁移 service 目录内容到对应层级 - - 创建完整的学习路径和文档 - -______________________________________________________________________ - -**开始探索 SAGE 吧！🎉** - -有任何问题或建议，欢迎提 Issue 或 PR！ diff --git a/packages/sage-common/examples/embedding_server_example.py b/packages/sage-common/examples/embedding_server_example.py deleted file mode 100644 index d3391eab88..0000000000 --- a/packages/sage-common/examples/embedding_server_example.py +++ /dev/null @@ -1,147 +0,0 @@ -""" -Embedding Server 使用示例 - -这个示例展示如何使用本地 embedding 服务器， -无需修改任何原有代码，直接通过 apply_embedding_model 调用。 -""" - -from sage.common.components.sage_embedding.embedding_api import apply_embedding_model - - -def example_basic_usage(): - """基本使用示例""" - print("=" * 60) - print("示例 1: 基本使用") - print("=" * 60) - - # 创建 embedding 模型实例（连接到本地服务器） - embedding_model = apply_embedding_model( - name="openai", # 使用 openai 方法（兼容 OpenAI API） - model="BAAI/bge-m3", # 模型名称（任意，服务器会忽略） - base_url="http://localhost:8091/v1", # 本地服务器地址 - api_key="dummy", # 本地服务不需要真实的 API key - ) - - # 测试 embedding - text = "Hello, this is a test sentence." - print(f"\nInput text: {text}") - - embedding = embedding_model.embed(text) - print(f"Embedding dimension: {len(embedding)}") - print(f"First 5 values: {embedding[:5]}") - - -def example_batch_processing(): - """批量处理示例""" - print("\n" + "=" * 60) - print("示例 2: 批量处理") - print("=" * 60) - - embedding_model = apply_embedding_model( - name="openai", - model="BAAI/bge-m3", - base_url="http://localhost:8091/v1", - api_key="dummy", - ) - - # 多个文本 - texts = [ - "What is machine learning?", - "Deep learning is a subset of machine learning.", - "Natural language processing uses neural networks.", - ] - - print(f"\nProcessing {len(texts)} texts...") - for i, text in enumerate(texts, 1): - embedding = embedding_model.embed(text) - print(f"{i}. Text: '{text[:50]}...' -> Embedding dim: {len(embedding)}") - - -def example_with_different_server(): - """使用不同服务器端口的示例""" - print("\n" + "=" * 60) - print("示例 3: 使用不同服务器") - print("=" * 60) - - # 假设你在端口 8081 运行另一个模型 - embedding_model = apply_embedding_model( - name="openai", - model="custom-model", - base_url="http://localhost:8081/v1", # 不同端口 - api_key="dummy", - ) - - text = "Testing with different server port" - try: - embedding = embedding_model.embed(text) - print(f"Success! Embedding dimension: {len(embedding)}") - except Exception as e: - print(f"Error (expected if server not running): {e}") - - -def example_error_handling(): - """错误处理示例""" - print("\n" + "=" * 60) - print("示例 4: 错误处理") - print("=" * 60) - - embedding_model = apply_embedding_model( - name="openai", - model="BAAI/bge-m3", - base_url="http://localhost:8091/v1", - api_key="dummy", - ) - - # 测试空文本 - try: - embedding = embedding_model.embed("") - print(f"Empty text embedding dimension: {len(embedding)}") - except Exception as e: - print(f"Error with empty text: {e}") - - # 测试很长的文本（会被截断） - long_text = "This is a very long sentence. " * 100 - try: - embedding = embedding_model.embed(long_text) - print(f"Long text (truncated) embedding dimension: {len(embedding)}") - except Exception as e: - print(f"Error with long text: {e}") - - -def main(): - """主函数""" - print("\n") - print("=" * 60) - print("Embedding Server 使用示例") - print("=" * 60) - print("\n请确保 embedding 服务器已启动:") - print( - " bash packages/sage-common/src/sage/common/components/sage_embedding/start_embedding_server.sh 8091" - ) - print("\n或手动启动:") - print( - " python packages/sage-common/src/sage/common/components/sage_embedding/embedding_server.py --model BAAI/bge-m3 --port 8091" - ) - print("\n" + "=" * 60 + "\n") - - try: - # 运行示例 - example_basic_usage() - example_batch_processing() - example_with_different_server() - example_error_handling() - - print("\n" + "=" * 60) - print("所有示例完成！") - print("=" * 60) - - except Exception as e: - print(f"\n错误: {e}") - print("\n请确保 embedding 服务器正在运行:") - print( - " bash packages/sage-common/src/sage/common/components/sage_embedding/start_embedding_server.sh 8091" - ) - - -if __name__ == "__main__": - main() diff --git a/packages/sage-common/examples/hello_world.py b/packages/sage-common/examples/hello_world.py deleted file mode 100644 index 6dcbfabd41..0000000000 --- a/packages/sage-common/examples/hello_world.py +++ /dev/null @@ -1,47 +0,0 @@ -from sage.common.core.functions.batch_function import BatchFunction -from sage.common.core.functions.map_function import MapFunction -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.api.local_environment import LocalEnvironment - - -# 批处理数据源：作用是生成10条"Hello, World!"字符串 -class HelloBatch(BatchFunction): - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - self.max_count = 10 # 生成10个数据包后返回None - - def execute(self): - if self.counter >= self.max_count: - return None # 返回None表示批处理完成 - self.counter += 1 - return f"Hello, World! #{self.counter}" - - -# 简单的 MapFunction，将内容转大写 -class UpperCaseMap(MapFunction): - def execute(self, data): - return data.upper() - - -# 简单 SinkFunction，直接打印结果 -class PrintSink(SinkFunction): - def execute(self, data): - print(data) - - -def main(): - env = LocalEnvironment("Hello_World") - - # 批处理源 -> map -> sink - env.from_batch(HelloBatch).map(UpperCaseMap).sink(PrintSink) - - env.submit(autostop=True) - print("Hello World 批处理示例结束") - - -if __name__ == "__main__": - # 关闭日志输出 - CustomLogger.disable_global_console_debug() - main() diff --git a/packages/sage-common/examples/unified_inference_client_example.py b/packages/sage-common/examples/unified_inference_client_example.py deleted file mode 100644 index d0df24bc01..0000000000 --- a/packages/sage-common/examples/unified_inference_client_example.py +++ /dev/null @@ -1,34 +0,0 @@ -#!/usr/bin/env python3 -"""Example: Using UnifiedInferenceClient from isagellm. - -Prerequisites: - pip install isagellm>=0.1.0 -""" - -from __future__ import annotations - - -def main(): - """Demo unified inference client usage.""" - try: - from sagellm import UnifiedInferenceClient - except ImportError: - print("Please install isagellm: pip install isagellm") - return - - # Create client (mock mode for demo) - client = UnifiedInferenceClient( - base_url="http://localhost:8000", - mock_mode=True, - ) - - # Simple completion - response = client.complete( - prompt="Hello, world!", - max_tokens=100, - ) - print(f"Response: {response}") - - -if __name__ == "__main__": - main() diff --git a/packages/sage-common/pyproject.toml b/packages/sage-common/pyproject.toml deleted file mode 100644 index a6dd985bf1..0000000000 --- a/packages/sage-common/pyproject.toml +++ /dev/null @@ -1,194 +0,0 @@ -# ============================================================================ -# SAGE Common Package Configuration -# ============================================================================ -# 这是 SAGE 框架的基础层（L1）包配置文件 -# 提供核心工具和共享组件，被所有其他 SAGE 包依赖 -# ============================================================================ - -# ---------------------------------------------------------------------------- -# 构建系统配置 -# ---------------------------------------------------------------------------- -[build-system] -requires = [ - "setuptools>=64", # 现代 setuptools - "wheel", # wheel 构建支持 - "packaging>=24.2", # 包版本管理 -] -build-backend = "setuptools.build_meta" - -# ---------------------------------------------------------------------------- -# 项目元数据 -# ---------------------------------------------------------------------------- -[project] -name = "isage-common" -dynamic = ["version"] # 版本号从 _version.py 动态读取 -description = "SAGE 框架核心公共工具包" -readme = "README.md" -authors = [ - { name = "IntelliStream Team", email = "shuhao_zhang@hust.edu.cn" }, -] - -# PyPI 搜索关键词 -keywords = [ - "ai", "sage", "machine learning", "artificial intelligence", - "core", "utilities", "framework", "infrastructure" -] - -# PyPI 分类器 -classifiers = [ - "Development Status :: 4 - Beta", - "Intended Audience :: Developers", - "Operating System :: OS Independent", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.11", - "Programming Language :: Python :: 3.12", - "Topic :: Scientific/Engineering :: Artificial Intelligence", -] - -# 最低 Python 版本要求 -requires-python = ">=3.11" - -# 许可证 -license = {text = "MIT"} - -# ---------------------------------------------------------------------------- -# 核心依赖 - 基础功能必需的最小依赖集 -# 原则：只包含核心模块运行绝对必需的依赖，可选功能放在 optional-dependencies -# ---------------------------------------------------------------------------- -dependencies = [ - # 配置管理系统 (config/) - "pyyaml>=6.0", # YAML 配置文件加载和解析 - - # 系统工具集 (utils/system/) - "psutil>=6.1.0", # 进程管理、网络监控、系统信息 - - # 序列化工具 (utils/serialization/) - "dill>=0.3.8", # 高级 Python 对象序列化（支持 lambda 等） - - # 数值计算基础 (components/sage_embedding/) - "numpy>=1.26.0,<2.3.0", # 数组操作，embedding 组件必需 - - # 数据验证和配置 (utils/config/manager.py) - "pydantic>=2.10.0,<3.0.0", # 数据验证、配置模型、API 数据结构 - - # 配置文件路径管理 (utils/config/loader.py) - "platformdirs>=4.0.0", # 跨平台用户配置目录路径 -] - -# ---------------------------------------------------------------------------- -# 可选依赖组 - 按功能模块分组的可选依赖 -# 用户可根据需要安装：pip install isage-common[embedding] -# ---------------------------------------------------------------------------- -[project.optional-dependencies] -dev = [ - "pytest>=7.4.0", - "pytest-cov>=4.0.0", - "ruff==0.14.6", - "mypy>=1.7.0", -] -embedding = [ - "python-dotenv>=1.1.0,<2.0.0", - "nvidia-ml-py>=12.535.108", - "torch>=2.7.0,<3.0.0", - "sentence-transformers>=3.1.0,<4.0.0", - "transformers>=4.52.0,<4.54.0", - "requests>=2.32.0,<3.0.0", -] -all = [ - "isage-common[embedding]", -] -[project.urls] -Homepage = "https://github.com/intellistream/SAGE" -Documentation = "https://intellistream.github.io/SAGE-Pub/" -Repository = "https://github.com/intellistream/SAGE" -Issues = "https://github.com/intellistream/SAGE/issues" - -# ---------------------------------------------------------------------------- -# 包构建配置 -# ---------------------------------------------------------------------------- -[tool.setuptools.dynamic.version] -attr = "sage.common._version.__version__" # 从源码读取版本号 - -[tool.setuptools.packages.find] -where = ["src"] # 源码目录 -namespaces = true # PEP 420 namespace packages - -[tool.setuptools.package-dir] -"" = "src" # 包根目录映射 - -# ---------------------------------------------------------------------------- -# 开发工具配置 -# ---------------------------------------------------------------------------- - -# 代码格式化和质量检查 (Ruff) -[tool.ruff] -extend = "../../tools/ruff.toml" # 继承项目级 ruff 配置 - -# 静态类型检查 (MyPy) -[tool.mypy] -cache_dir = "../../.sage/cache/mypy" # MyPy 缓存目录 -ignore_missing_imports = true # 忽略缺失的类型注解 - -# ---------------------------------------------------------------------------- -# 测试配置 (Pytest) -# ---------------------------------------------------------------------------- -[tool.pytest.ini_options] -# 测试文件搜索路径 -testpaths = [ - "tests", # 测试目录 - "src", # 源码目录（doctest） -] - -# 测试文件匹配模式 -python_files = [ - "test_*.py", # 标准测试文件 - "*_test.py", # 另一种测试文件命名 -] - -# 测试类匹配模式 -python_classes = ["Test*"] - -# 测试函数匹配模式 -python_functions = ["test_*"] - -# 测试运行参数 -addopts = [ - "--benchmark-storage=../../.sage/benchmarks", # 基准测试存储位置 - "-o", "cache_dir=../../.sage/cache/pytest", # Pytest 缓存目录 - "--strict-markers", # 严格标记模式 - "--strict-config", # 严格配置模式 - "--verbose", # 详细输出 - "-ra", # 显示所有测试结果摘要 -] - -# 测试标记定义 -markers = [ - "slow: 标记为慢速测试 (使用 '-m \"not slow\"' 跳过)", - "integration: 标记为集成测试", - "unit: 标记为单元测试", - "core: 标记为核心功能测试", - "smoke: 标记为冒烟测试（快速验证）", -] - -# ---------------------------------------------------------------------------- -# 测试覆盖率配置 (Coverage.py) -# ---------------------------------------------------------------------------- -[tool.coverage.run] -source = ["src/sage"] # 覆盖率统计源码目录 - -# 排除文件模式 -omit = [ - "*/tests/*", # 测试文件 - "*/test_*.py", # 测试文件 - "*/_test_*.py", # 测试文件 -] - -[tool.coverage.report] -# 报告中排除的代码行 -exclude_lines = [ - "pragma: no cover", # 手动排除标记 - "def __repr__", # __repr__ 方法 - "raise AssertionError", # 断言错误 - "raise NotImplementedError", # 未实现错误 - "if __name__ == .__main__.:", # 主程序入口 -] diff --git a/packages/sage-common/src/sage/common/__init__.py b/packages/sage-common/src/sage/common/__init__.py deleted file mode 100644 index 776916a16a..0000000000 --- a/packages/sage-common/src/sage/common/__init__.py +++ /dev/null @@ -1,42 +0,0 @@ -"""SAGE Common - Foundation Layer (L1) Infrastructure and Shared Components - -Layer: L1 (Foundation) - -This package provides the foundational infrastructure for all SAGE packages. -It contains NO business logic, only core types, utilities, and shared components. - -Module Structure: -- core: Core types, exceptions, constants, and data structures -- components: Reusable components (embedding, vLLM service wrappers) -- config: Configuration management (output paths, environment setup) -- model_registry: Model lifecycle management utilities -- utils: Common utilities (logging, serialization, system helpers) - -Architecture Rules: -- ✅ Can be imported by: L2-L5 (all upper layers) -- ❌ Must NOT import from: sage.kernel, sage.middleware, sage.libs, sage.cli, sage.tools -- ✅ May import: Standard library, external dependencies -""" - -# Suppress PyTorch distributed warnings in WSL/containerized environments -# Must be set BEFORE importing torch/vllm -import os as _os - -_os.environ.setdefault("GLOO_SOCKET_IFNAME", "lo") -_os.environ.setdefault("NCCL_SOCKET_IFNAME", "lo") -_os.environ.setdefault("TORCH_DISTRIBUTED_DEBUG", "OFF") - -__layer__ = "L1" - -from . import components, config, core, logging, model_registry, utils -from ._version import __version__ - -__all__ = [ - "__version__", - "components", - "config", - "core", - "logging", - "model_registry", - "utils", -] diff --git a/packages/sage-common/src/sage/common/_version.py b/packages/sage-common/src/sage/common/_version.py deleted file mode 100644 index 976a82521c..0000000000 --- a/packages/sage-common/src/sage/common/_version.py +++ /dev/null @@ -1,6 +0,0 @@ -"""Version information for sage-common package.""" - -# 独立硬编码版本 -__version__ = "0.2.3.9" -__author__ = "IntelliStream Team" -__email__ = "shuhao_zhang@hust.edu.cn" diff --git a/packages/sage-common/src/sage/common/components/__init__.py b/packages/sage-common/src/sage/common/components/__init__.py deleted file mode 100644 index 70a5ff5d0c..0000000000 --- a/packages/sage-common/src/sage/common/components/__init__.py +++ /dev/null @@ -1,24 +0,0 @@ -"""Shared SAGE components available across packages. - -Layer: L1 (Foundation - Common Components) - -This package contains reusable components that provide specific functionalities: -- sage_embedding: Unified embedding interface for multiple providers -- sage_llm: vLLM service integration for high-performance LLM serving - -These components are designed to be used by L2 (Platform) and higher layers. -They must NOT import from sage.kernel, sage.middleware, sage.libs, or sage.apps. -""" - -# Try to import sage_llm, but don't fail if vllm dependencies are not available -try: - from . import sage_llm - - __all__ = ["sage_llm"] -except (ImportError, AttributeError) as e: - # vllm or its dependencies (torch) might not be installed or compatible - # This is acceptable for development tools that don't need vllm - import warnings - - warnings.warn(f"sage_llm component not available: {e}", ImportWarning, stacklevel=2) - __all__ = [] diff --git a/packages/sage-common/src/sage/common/components/debug/__init__.py b/packages/sage-common/src/sage/common/components/debug/__init__.py deleted file mode 100644 index eb22cb633a..0000000000 --- a/packages/sage-common/src/sage/common/components/debug/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -""" -SAGE Common Debug Components - 调试工具 - -Layer: L1 (Common - Debug Utilities) -Dependencies: sage.common.core.functions - -提供调试和开发辅助功能。 -""" - -from .print_sink import PrintSink - -__all__ = ["PrintSink"] diff --git a/packages/sage-common/src/sage/common/components/debug/print_sink.py b/packages/sage-common/src/sage/common/components/debug/print_sink.py deleted file mode 100644 index e9d9416e57..0000000000 --- a/packages/sage-common/src/sage/common/components/debug/print_sink.py +++ /dev/null @@ -1,144 +0,0 @@ -""" -Internal Print Sink - 内置打印汇聚函数 - -Layer: L3 (Kernel - Internal) -Dependencies: sage.kernel.api.function (L3 internal) - -这是 kernel 内置的打印功能，用于支持 DataStream.print() 方法。 -不依赖 sage-libs，保持 kernel 的独立性。 - -Note: - 这是内部实现，用户不应直接使用此类。 - 用户应使用 DataStream.print() 方法。 -""" - -import logging -from typing import Any - -from sage.common.core.functions import SinkFunction - - -class PrintSink(SinkFunction): - """ - 内置打印汇聚函数 - 支持 DataStream.print() - - 提供便捷的调试和数据查看功能，无需依赖外部库。 - - Features: - - 智能数据格式化 - - 可配置前缀和分隔符 - - 日志集成 - - Note: - 这是 kernel 内部实现，不应被用户代码直接导入。 - 用户应使用 stream.print() 方法。 - """ - - def __init__( - self, - prefix: str = "", - separator: str = " | ", - colored: bool = True, - quiet: bool = False, - **kwargs, - ): - """ - 初始化打印汇聚函数 - - Args: - prefix: 输出前缀 - separator: 前缀与内容之间的分隔符 - colored: 是否启用彩色输出（当前未实现） - quiet: 静默模式 - 不打印首次输出提示 - **kwargs: 传递给基类的其他参数 - """ - super().__init__(**kwargs) - self.prefix = prefix - self.separator = separator - self.colored = colored - self.quiet = quiet - self._logger = logging.getLogger(__name__) - self._first_output = True - - def execute(self, data: Any) -> None: - """ - 执行打印操作 - - Args: - data: 要打印的数据 - """ - # 格式化数据 - formatted = self._format_data(data) - - # 添加前缀 - if self.prefix: - output = f"{self.prefix}{self.separator}{formatted}" - else: - output = formatted - - # 处理首次输出 - if self._first_output: - if not self.quiet: - print(f"🔍 Stream output: {output}") - print(" (Further outputs logged. Check logs for details.)") - else: - print(output) - self._first_output = False - else: - # 后续输出仅记录到日志 - self._logger.debug(f"Stream output: {output}") - - def _format_data(self, data: Any) -> str: - """ - 格式化数据为可读字符串 - - Args: - data: 输入数据 - - Returns: - str: 格式化后的字符串 - """ - # 处理常见类型 - if data is None: - return "None" - - if isinstance(data, str): - return data - - if isinstance(data, (int, float, bool)): - return str(data) - - if isinstance(data, dict): - # 字典：格式化为 key=value 形式 - items = [f"{k}={v}" for k, v in data.items()] - return ", ".join(items) - - if isinstance(data, (list, tuple)): - # 列表/元组：显示前几个元素 - if len(data) == 0: - return "[]" - elif len(data) <= 5: - return str(data) - else: - preview = ", ".join(str(x) for x in data[:5]) - return f"[{preview}, ... (+{len(data) - 5} more)]" - - # 尝试检测常见的数据对象 - if hasattr(data, "__dict__"): - # 对象：显示类名和主要属性 - class_name = data.__class__.__name__ - attrs = getattr(data, "__dict__", {}) - if attrs: - attr_str = ", ".join(f"{k}={v}" for k, v in list(attrs.items())[:3]) - return f"{class_name}({attr_str})" - return f"{class_name}()" - - # 其他类型：使用 str() 转换 - try: - return str(data) - except Exception: - return f"" - - def __repr__(self) -> str: - """字符串表示""" - return f"InternalPrintSink(prefix='{self.prefix}')" diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/__init__.py b/packages/sage-common/src/sage/common/components/sage_embedding/__init__.py deleted file mode 100644 index 9d82ecfb7d..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/__init__.py +++ /dev/null @@ -1,278 +0,0 @@ -"""SAGE Embedding Module - Unified interface for various embedding methods. - -Layer: L1 (Foundation - Common Components) - -This module provides a consistent API for different embedding providers: -- Hash-based lightweight embedding (for testing) -- Mock embedding (for unit tests) -- HuggingFace Transformer models (local, high quality) -- OpenAI and other API-based services - -Quick Start: - >>> from sage.common.components.sage_embedding import get_embedding_model - >>> - >>> # Create an embedding model - >>> emb = get_embedding_model("hash", dim=384) - >>> vec = emb.embed("hello world") - >>> - >>> # For batch embedding with HuggingFace models: - >>> emb_hf = get_embedding_model("hf", model="BAAI/bge-small-zh-v1.5") - >>> vectors = [emb_hf.embed(text) for text in ["text1", "text2"]] - -Note: - For LLM inference, SAGE uses vLLM as the backend engine. - LLM components have been moved to the independent `isagellm` package. - -Architecture: - This is a L1 foundation component used by higher layers (L2-L5). - It must NOT import from sage.kernel, sage.middleware, sage.libs, sage.cli, or sage.tools. -""" - -# L1 components should not depend on higher layers -# Version information is maintained locally to avoid circular dependencies -__version__ = "0.1.4" -__author__ = "IntelliStream Team" -__email__ = "shuhao_zhang@hust.edu.cn" - -# Core embedding interfaces -from .base import BaseEmbedding -from .factory import ( - EmbeddingFactory, - check_model_availability, - get_embedding_model, - list_embedding_models, -) -from .protocols import ( - EmbeddingClientAdapter, - EmbeddingProtocol, - adapt_embedding_client, -) -from .registry import EmbeddingRegistry, ModelInfo, ModelStatus - -# 只导入轻量级的 wrappers，其他使用延迟导入 -from .wrappers.hash_wrapper import HashEmbedding -from .wrappers.mock_wrapper import MockEmbedding - -# 重量级 wrappers 使用延迟导入，避免在模块加载时加载大型依赖 -# 这些会在 _register_all_methods() 中按需导入 - - -# 注册所有 embedding 方法 -def _register_all_methods(): - """注册所有内置的 embedding 方法 - - 使用延迟导入 wrapper_class，通过传递字符串路径而不是类对象。 - 这样可以避免在模块加载时就导入所有重量级依赖。 - """ - - # Hash Embedding - 轻量级，直接导入 - EmbeddingRegistry.register( - method="hash", - display_name="Hash Embedding", - description="轻量级哈希 embedding（测试用，无语义理解）", - wrapper_class=HashEmbedding, - requires_api_key=False, - requires_model_download=False, - default_dimension=384, - example_models=["hash-384", "hash-768"], - ) - - # Mock Embedder - 轻量级，直接导入 - EmbeddingRegistry.register( - method="mockembedder", - display_name="Mock Embedder", - description="随机 embedding（单元测试用）", - wrapper_class=MockEmbedding, - requires_api_key=False, - requires_model_download=False, - default_dimension=128, - example_models=["mock-128", "mock-384"], - ) - - # 以下使用字符串路径进行延迟注册，避免导入重量级依赖 - # 实际导入会在 EmbeddingFactory.create() 时进行 - - # HuggingFace Models - EmbeddingRegistry.register( - method="hf", - display_name="HuggingFace Models", - description="本地 Transformer 模型（高质量语义 embedding）", - wrapper_class="sage.common.components.sage_embedding.wrappers.hf_wrapper:HFEmbedding", - requires_api_key=False, - requires_model_download=True, - default_dimension=None, # 动态推断 - example_models=[ - "BAAI/bge-small-zh-v1.5", - "BAAI/bge-base-zh-v1.5", - "BAAI/bge-large-zh-v1.5", - "sentence-transformers/all-MiniLM-L6-v2", - "sentence-transformers/all-mpnet-base-v2", - ], - ) - - # OpenAI Embedding - EmbeddingRegistry.register( - method="openai", - display_name="OpenAI Embedding", - description="OpenAI 官方 API（高质量，支持兼容 API）", - wrapper_class="sage.common.components.sage_embedding.wrappers.openai_wrapper:OpenAIEmbedding", - requires_api_key=True, - requires_model_download=False, - default_dimension=1536, - example_models=[ - "text-embedding-3-small", - "text-embedding-3-large", - "text-embedding-ada-002", - ], - ) - - # Jina Embedding - EmbeddingRegistry.register( - method="jina", - display_name="Jina AI Embedding", - description="Jina AI 多语言 embedding（支持 late chunking）", - wrapper_class="sage.common.components.sage_embedding.wrappers.jina_wrapper:JinaEmbedding", - requires_api_key=True, - requires_model_download=False, - default_dimension=1024, - example_models=[ - "jina-embeddings-v3", - "jina-embeddings-v2-base-en", - ], - ) - - # Zhipu Embedding - EmbeddingRegistry.register( - method="zhipu", - display_name="ZhipuAI Embedding", - description="智谱 AI 中文 embedding（国内访问快）", - wrapper_class="sage.common.components.sage_embedding.wrappers.zhipu_wrapper:ZhipuEmbedding", - requires_api_key=True, - requires_model_download=False, - default_dimension=1024, - example_models=[ - "embedding-3", - "embedding-2", - ], - ) - - # Cohere Embedding - EmbeddingRegistry.register( - method="cohere", - display_name="Cohere Embedding", - description="Cohere 多语言 embedding（支持多种 input_type）", - wrapper_class="sage.common.components.sage_embedding.wrappers.cohere_wrapper:CohereEmbedding", - requires_api_key=True, - requires_model_download=False, - default_dimension=1024, - example_models=[ - "embed-multilingual-v3.0", - "embed-english-v3.0", - "embed-multilingual-light-v3.0", - ], - ) - - # AWS Bedrock Embedding - EmbeddingRegistry.register( - method="bedrock", - display_name="AWS Bedrock Embedding", - description="AWS Bedrock 托管服务（支持多种模型）", - wrapper_class="sage.common.components.sage_embedding.wrappers.bedrock_wrapper:BedrockEmbedding", - requires_api_key=True, # AWS 凭证 - requires_model_download=False, - default_dimension=1024, - example_models=[ - "amazon.titan-embed-text-v2:0", - "amazon.titan-embed-text-v1", - "cohere.embed-multilingual-v3", - ], - ) - - # Ollama Embedding - EmbeddingRegistry.register( - method="ollama", - display_name="Ollama Embedding", - description="Ollama 本地部署（数据隐私，免费）", - wrapper_class="sage.common.components.sage_embedding.wrappers.ollama_wrapper:OllamaEmbedding", - requires_api_key=False, - requires_model_download=True, - default_dimension=768, - example_models=[ - "nomic-embed-text", - "mxbai-embed-large", - "all-minilm", - ], - ) - - # SiliconCloud Embedding - EmbeddingRegistry.register( - method="siliconcloud", - display_name="SiliconCloud Embedding", - description="硅基流动（国内访问快，价格优惠）", - wrapper_class="sage.common.components.sage_embedding.wrappers.siliconcloud_wrapper:SiliconCloudEmbedding", - requires_api_key=True, - requires_model_download=False, - default_dimension=768, - example_models=[ - "netease-youdao/bce-embedding-base_v1", - "BAAI/bge-large-zh-v1.5", - "BAAI/bge-base-en-v1.5", - ], - ) - - # NVIDIA OpenAI Embedding - EmbeddingRegistry.register( - method="nvidia_openai", - display_name="NVIDIA NIM Embedding", - description="NVIDIA NIM（OpenAI 兼容，支持检索优化）", - wrapper_class="sage.common.components.sage_embedding.wrappers.nvidia_openai_wrapper:NvidiaOpenAIEmbedding", - requires_api_key=True, - requires_model_download=False, - default_dimension=2048, - example_models=[ - "nvidia/llama-3.2-nv-embedqa-1b-v1", - "nvidia/nv-embed-v1", - ], - ) - - -# 执行注册 -_register_all_methods() - - -# 向后兼容：保留旧的 EmbeddingModel 和 apply_embedding_model -from .embedding_model import ( - EmbeddingModel, # noqa: E402 - apply_embedding_model, -) - -# Service interface (新增) -from .service import EmbeddingService, EmbeddingServiceConfig # noqa: E402 - -# 统一导出接口 -__all__ = [ - # Service interface (推荐用于 pipelines) - "EmbeddingService", # ⭐ Service 主要 API - "EmbeddingServiceConfig", - # Core embedding interfaces - "BaseEmbedding", - "EmbeddingRegistry", - "EmbeddingFactory", - "ModelStatus", - "ModelInfo", - "get_embedding_model", # ⭐ 主要 API - "list_embedding_models", # ⭐ 模型发现 - "check_model_availability", # ⭐ 状态检查 - # Protocol adapters - "EmbeddingProtocol", - "EmbeddingClientAdapter", - "adapt_embedding_client", - # Lightweight wrappers (直接导入) - "HashEmbedding", - "MockEmbedding", - # Note: Heavy wrappers (HF, OpenAI, Jina, etc.) use lazy loading - # They are available via get_embedding_model() but not directly imported - # 向后兼容（旧代码仍可使用） - "EmbeddingModel", - "apply_embedding_model", -] diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/_cohere.py b/packages/sage-common/src/sage/common/components/sage_embedding/_cohere.py deleted file mode 100644 index 517c4aef0f..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/_cohere.py +++ /dev/null @@ -1,64 +0,0 @@ -import os - -import cohere - - -async def cohere_embed( - texts: list[str], - api_key: str, - model: str = "embed-multilingual-v3.0", - input_type: str = "classification", - embedding_types: list[str] | None = None, -) -> list[list[float]]: - if embedding_types is None: - embedding_types = ["float"] - if api_key is None: - api_key = os.environ.get("COHERE_API_KEY") - # print(api_key) - co = cohere.AsyncClient(api_key=api_key) - - response = await co.embed( - texts=texts, - model=model, - input_type=input_type, - # embedding_types=embedding_types - ) - return response.embeddings # pyright: ignore[reportReturnType] - - -def cohere_embed_sync( - texts: list[str], - api_key: str | None = None, - model: str = "embed-multilingual-v3.0", - input_type: str = "classification", - embedding_types: list[str] | None = None, -) -> list[list[float]]: - """ - 同步版本：使用 Cohere 同步客户端生成文本 embeddings. - - Args: - texts: 文本列表 - api_key: Cohere API Key - model: 模型名称 - input_type: 输入类型，如 classification、search_document 等 - embedding_types: 嵌入格式（默认 float） - - Returns: - list[list[float]]: 每个文本对应的嵌入向量 - """ - if embedding_types is None: - embedding_types = ["float"] - if api_key is None: - api_key = os.environ.get("COHERE_API_KEY") - if api_key is None: - raise ValueError("Cohere API key must be provided.") - - co = cohere.Client(api_key=api_key) - - response = co.embed( - texts=texts, - model=model, - input_type=input_type, - embedding_types=embedding_types, - ) - return response.embeddings # pyright: ignore[reportReturnType] diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/base.py b/packages/sage-common/src/sage/common/components/sage_embedding/base.py deleted file mode 100644 index e72b6210b9..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/base.py +++ /dev/null @@ -1,142 +0,0 @@ -"""Base class for all embedding models. - -Layer: L1 (Foundation - Common Components) - -This module defines the abstract base class that all embedding wrappers must implement, -ensuring a consistent interface across all embedding providers. -""" - -from abc import ABC, abstractmethod -from typing import Any - - -class BaseEmbedding(ABC): - """所有 Embedding 模型的抽象基类 - - 所有 embedding wrapper 都应继承此类并实现抽象方法。 - 这确保了所有 embedding 方法提供一致的接口。 - - Attributes: - config: 初始化时传入的配置参数 - - Examples: - >>> class MyEmbedding(BaseEmbedding): - ... def embed(self, text: str) -> List[float]: - ... return [0.1, 0.2, 0.3] - ... - ... def get_dim(self) -> int: - ... return 3 - ... - ... @property - ... def method_name(self) -> str: - ... return "my_method" - >>> - >>> emb = MyEmbedding() - >>> emb.embed("hello") - [0.1, 0.2, 0.3] - """ - - def __init__(self, **kwargs: Any) -> None: - """初始化 embedding 模型 - - Args: - **kwargs: 方法特定的配置参数 - """ - self.config = kwargs - - @abstractmethod - def embed(self, text: str) -> list[float]: - """将文本转换为 embedding 向量 - - Args: - text: 输入文本 - - Returns: - embedding 向量（浮点数列表） - - Raises: - RuntimeError: 如果 embedding 失败 - """ - pass - - def embed_batch(self, texts: list[str]) -> list[list[float]]: - """批量将文本转换为 embedding 向量 - - 默认实现为逐个调用 embed()。子类可以重写此方法以提供更高效的批量处理。 - - Args: - texts: 输入文本列表 - - Returns: - embedding 向量列表 - - Examples: - >>> emb = get_embedding_model("hash") - >>> vectors = emb.embed_batch(["hello", "world"]) - >>> len(vectors) - 2 - """ - return [self.embed(text) for text in texts] - - @abstractmethod - def get_dim(self) -> int: - """获取 embedding 向量的维度 - - Returns: - 向量维度 - - Examples: - >>> emb = get_embedding_model("hash", dim=384) - >>> emb.get_dim() - 384 - """ - pass - - @property - @abstractmethod - def method_name(self) -> str: - """返回 embedding 方法名称 - - Returns: - 方法名称（如 'hf', 'openai', 'hash'） - - Examples: - >>> emb = get_embedding_model("hf", model="...") - >>> emb.method_name - 'hf' - """ - pass - - @classmethod - def get_model_info(cls) -> dict[str, Any]: - """返回模型元信息（子类可选实现） - - Returns: - 包含以下键的字典: - - method: 方法名称 - - requires_api_key: 是否需要 API Key - - requires_model_download: 是否需要下载模型 - - default_dimension: 默认维度（可选） - - Examples: - >>> HFEmbedding.get_model_info() - { - 'method': 'hf', - 'requires_api_key': False, - 'requires_model_download': True, - 'default_dimension': None - } - """ - return { - "method": cls.__name__, - "requires_api_key": False, - "requires_model_download": False, - "default_dimension": None, - } - - def __repr__(self) -> str: - """返回对象的字符串表示""" - params = ", ".join(f"{k}={v}" for k, v in list(self.config.items())[:3]) - if len(self.config) > 3: - params += ", ..." - return f"{self.__class__.__name__}({params})" diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/bedrock.py b/packages/sage-common/src/sage/common/components/sage_embedding/bedrock.py deleted file mode 100644 index c3b1af29af..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/bedrock.py +++ /dev/null @@ -1,172 +0,0 @@ -import json -import os - -import boto3 - -# Dependencies are managed via pyproject.toml [project.optional-dependencies.embedding] -# Install with: pip install isage-common[embedding] -# Required: aioboto3, boto3, tenacity - -try: - import aioboto3 -except ImportError: - raise ImportError( - "aioboto3 package is required for AWS Bedrock embedding functionality. " - "Please install it via: pip install isage-common[embedding]" - ) - -try: - from tenacity import ( - retry, # noqa: F401 - retry_if_exception_type, # noqa: F401 - stop_after_attempt, # noqa: F401 - wait_exponential, # noqa: F401 - ) -except ImportError: - raise ImportError( - "tenacity package is required for AWS Bedrock embedding functionality. " - "Please install it via: pip install isage-common[embedding]" - ) - - -class BedrockError(Exception): - """Generic error for issues related to Amazon Bedrock""" - - -async def bedrock_embed( - text: str, - model: str = "amazon.titan-embed-text-v2:0", - aws_access_key_id=None, - aws_secret_access_key=None, - aws_session_token=None, -) -> list: - # 只在提供了值时才设置环境变量 - if aws_access_key_id is not None: - os.environ["AWS_ACCESS_KEY_ID"] = aws_access_key_id - if aws_secret_access_key is not None: - os.environ["AWS_SECRET_ACCESS_KEY"] = aws_secret_access_key - if aws_session_token is not None: - os.environ["AWS_SESSION_TOKEN"] = aws_session_token - - session = aioboto3.Session() - async with session.client("bedrock-runtime") as bedrock_async_client: # type: ignore[attr-defined] - model_provider = model.split(".")[0] - - if model_provider == "amazon": - if "v2" in model: - body = json.dumps( - { - "inputText": text, - "embeddingTypes": ["float"], - } - ) - elif "v1" in model: - body = json.dumps({"inputText": text}) - else: - raise ValueError(f"Model {model} is not supported!") - - response = await bedrock_async_client.invoke_model( - modelId=model, - body=body, - accept="application/json", - contentType="application/json", - ) - - response_body = await response.get("body").json() - return response_body["embedding"] - - elif model_provider == "cohere": - body = json.dumps( - { - "texts": [text], - "input_type": "search_document", - "truncate": "NONE", - } - ) - - response = await bedrock_async_client.invoke_model( - model=model, - body=body, - accept="application/json", - contentType="application/json", - ) - - response_body = json.loads(response.get("body").read()) - return response_body["embeddings"][0] - - else: - raise ValueError(f"Model provider '{model_provider}' is not supported!") - - -def bedrock_embed_sync( - text: str, - model: str = "amazon.titan-embed-text-v2:0", - aws_access_key_id=None, - aws_secret_access_key=None, - aws_session_token=None, -) -> list[float]: - """ - 同步版本：使用 AWS Bedrock 生成 embedding。 - - Args: - text: 输入文本 - model: 模型 ID，例如 "amazon.titan-embed-text-v2:0" - aws_access_key_id / secret / session_token: 可选 AWS 认证信息 - - Returns: - list[float]: embedding 向量 - """ - # 设置 AWS 环境变量（优先从参数取） - if aws_access_key_id: - os.environ["AWS_ACCESS_KEY_ID"] = aws_access_key_id - if aws_secret_access_key: - os.environ["AWS_SECRET_ACCESS_KEY"] = aws_secret_access_key - if aws_session_token: - os.environ["AWS_SESSION_TOKEN"] = aws_session_token - - bedrock_client = boto3.client("bedrock-runtime") - - model_provider = model.split(".")[0] - - if model_provider == "amazon": - if "v2" in model: - body = json.dumps( - { - "inputText": text, - "embeddingTypes": ["float"], - } - ) - elif "v1" in model: - body = json.dumps({"inputText": text}) - else: - raise ValueError(f"Model {model} is not supported!") - - response = bedrock_client.invoke_model( - modelId=model, - body=body, - accept="application/json", - contentType="application/json", - ) - response_body = json.loads(response["body"].read()) - return response_body["embedding"] - - elif model_provider == "cohere": - body = json.dumps( - { - "texts": [text], - "input_type": "search_document", - "truncate": "NONE", - } - ) - - response = bedrock_client.invoke_model( - modelId=model, - body=body, - accept="application/json", - contentType="application/json", - ) - response_body = json.loads(response["body"].read()) - return response_body["embeddings"][0] - - else: - raise ValueError(f"Model provider '{model_provider}' is not supported!") diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/embedding_api.py b/packages/sage-common/src/sage/common/components/sage_embedding/embedding_api.py deleted file mode 100644 index 5b543b7e44..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/embedding_api.py +++ /dev/null @@ -1,18 +0,0 @@ -# flake8: noqa: E402 -# Auto-detect network region and configure HuggingFace mirror -from sage.common.config import ensure_hf_mirror_configured - -ensure_hf_mirror_configured() - -from sage.common.components.sage_embedding.embedding_model import EmbeddingModel - - -def apply_embedding_model(name: str = "default", **kwargs) -> EmbeddingModel: - """ - usage 参见sage/api/model/operator_test.py - while name(method) = "hf", please set the param:model; - while name(method) = "openai",if you need call other APIs which are compatible with openai,set the params:base_url,api_key,model; - while name(method) = "jina/siliconcloud/cohere",please set the params:api_key,model; - Example:operator_test.py - """ - return EmbeddingModel(method=name, **kwargs) diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/embedding_model.py b/packages/sage-common/src/sage/common/components/sage_embedding/embedding_model.py deleted file mode 100644 index 27f484c4d3..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/embedding_model.py +++ /dev/null @@ -1,227 +0,0 @@ -import os -import sys -import time - -from dotenv import load_dotenv - -# 延迟导入：这些模块在需要时才导入，避免在模块加载时就加载重量级依赖 -load_dotenv() - -# Ensure project root is on sys.path for imports that rely on package layout -sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../../.."))) - - -# Lazy HF mirror configuration: only configure when actually downloading models -# This avoids blocking imports with network checks -def _ensure_hf_configured(): - """Lazy initialization of HF mirror configuration""" - from sage.common.config import ensure_hf_mirror_configured - - ensure_hf_mirror_configured() - - -class EmbeddingModel: - # def __init__(self, method: str = "openai", model: str = "mistral-embed", - # base_url: str | None = None, api_key: str | None = None): - def __init__(self, method: str = "openai", **kwargs): - """ - 初始化 embedding table - :param method: 指定使用的 embedding 方法名称，例如 "openai" 或 "cohere" 或“hf"等 - """ - self.init_method = method - self.dim = None - if method == "default": - method = "hf" - kwargs["model"] = "sentence-transformers/all-MiniLM-L6-v2" - - if method == "mockembedder": - kwargs["model"] = "mockembedder" # 确保 model 参数存在 - if "fixed_dim" not in kwargs: - kwargs["fixed_dim"] = 128 # 默认维度 - - self.set_dim(kwargs["model"]) - self.method = method - - # self.kwargs = {} - self.kwargs = kwargs - if method == "hf": - if "model" not in kwargs: - raise ValueError("hf method need model") - model_name = kwargs["model"] - # Load HF models - fail explicitly if unavailable - try: - # Configure HF mirror before downloading (lazy init) - _ensure_hf_configured() - - # 延迟导入 transformers - from transformers import AutoModel, AutoTokenizer - - # 尝试使用本地缓存,如果失败则从网络下载 - try: - self.kwargs["tokenizer"] = AutoTokenizer.from_pretrained( - model_name, local_files_only=True - ) - self.kwargs["embed_model"] = AutoModel.from_pretrained( - model_name, trust_remote_code=True, local_files_only=True - ) - except Exception: - # 如果本地加载失败,尝试从网络下载 - self.kwargs["tokenizer"] = AutoTokenizer.from_pretrained(model_name) - self.kwargs["embed_model"] = AutoModel.from_pretrained( - model_name, trust_remote_code=True - ) - self.kwargs.pop("model") - except Exception as e: - # 明确失败，不静默回退到mockembedder - raise RuntimeError( - f"Failed to load embedding model '{model_name}': {e}. " - f"Please ensure the model is available or use a different embedding method. " - f"For testing with mock embedder, explicitly set method='mockembedder'." - ) from e - elif method == "mockembedder": - # 初始化 mockembedder - from .wrappers.mock_wrapper import MockEmbedding - - self.kwargs["embed_model"] = MockEmbedding(fixed_dim=kwargs.get("fixed_dim", 128)) - self.embed_fn = self._get_embed_function(method) - - def set_dim(self, model_name): - """ - :param model_name: - :return: - """ - dimension_mapping = { - "mistral_embed": 1024, - "embed-multilingual-v3.0": 1024, - "embed-english-v3.0": 1024, - "embed-english-light-v3.0": 384, - "embed-multilingual-light-v3.0": 384, - "embed-english-v2.0": 4096, - "embed-english-light-v2.0": 1024, - "embed-multilingual-v2.0": 768, - "jina-embeddings-v3": 1024, - "BAAI/bge-m3": 1024, - "sentence-transformers/all-MiniLM-L6-v2": 384, - "mockembedder": 128, - } - if model_name in dimension_mapping: - self.dim = dimension_mapping[model_name] - else: - raise ValueError(f" embedding {model_name}") - - def get_dim(self): - return self.dim - - def _get_embed_function(self, method: str): - """根据方法名返回对应的 embedding 函数（延迟导入相关模块）""" - # 延迟导入：只在实际使用时才导入对应的模块 - from sage.common.components.sage_embedding import ( - _cohere, - bedrock, - hf, - jina, - lollms, - nvidia_openai, - ollama, - openai_wrapper, - siliconcloud, - zhipu, - ) - - mapping = { - "openai": openai_wrapper.openai_embed_sync, - "zhipu": zhipu.zhipu_embedding_sync, - "bedrock": bedrock.bedrock_embed_sync, - "hf": hf.hf_embed_sync, - "jina": jina.jina_embed_sync, - # "llama_index_impl": llama_index_impl.llama_index_embed, - "lollms": lollms.lollms_embed_sync, - "nvidia_openai": nvidia_openai.nvidia_openai_embed_sync, - "ollama": ollama.ollama_embed_sync, - "siliconcloud": siliconcloud.siliconcloud_embedding_sync, - "cohere": _cohere.cohere_embed_sync, - "mockembedder": lambda text, **kwargs: kwargs["embed_model"].embed(text), - # "instructor": instructor.instructor_embed - } - if method not in mapping: - raise ValueError(f"不支持的 embedding 方法：{method}") - - embed_fn = mapping[method] - - return embed_fn - - def _embed(self, text: str) -> list[float]: - """ - 异步执行 embedding 操作 - :param text: 要 embedding 的文本 - :param kwargs: embedding 方法可能需要的额外参数 - :return: embedding 后的结果 - """ - return self.embed_fn(text, **self.kwargs) - - def embed(self, text: str) -> list[float]: - return self._embed(text) - - def embed_batch(self, texts: list[str]) -> list[list[float]]: - """批量生成 embedding（严格批量接口） - - 只支持原生批量接口的方法。不支持的方法将抛出异常。 - - Args: - texts: 输入文本列表 - - Returns: - embedding 向量列表 - - Raises: - NotImplementedError: 如果方法不支持批量接口 - """ - if not texts: - return [] - - # 对于 openai 方法，使用原生批量接口 - if self.method == "openai": - from sage.common.components.sage_embedding import openai_wrapper - - return openai_wrapper.openai_embed_batch_sync(texts, **self.kwargs) - - # 不支持批量的方法直接抛出异常 - raise NotImplementedError( - f"Batch embedding not supported for method '{self.method}'. Supported methods: openai" - ) - - # 其他方法回退到逐个调用 - return [self.embed(text) for text in texts] - - def encode(self, text: str) -> list[float]: - return self._embed(text) - - @property - def method_name(self) -> str: - """当前embedding方法名""" - return self.init_method - - -def apply_embedding_model(name: str = "default", **kwargs) -> EmbeddingModel: - """ - usage 参见sage/api/model/operator_test.py - while name(method) = "hf", please set the param:model; - while name(method) = "openai",if you need call other APIs which are compatible with openai,set the params:base_url,api_key,model; - while name(method) = "jina/siliconcloud/cohere",please set the params:api_key,model; - Example:operator_test.py - """ - return EmbeddingModel(method=name, **kwargs) - - -def main(): - embedding_model = EmbeddingModel(method="hf", model="sentence-transformers/all-MiniLM-L6-v2") - for i in range(10): - start = time.time() - v = embedding_model.embed(f"{i} times ") - print(v) - end = time.time() - print(f"embedding time :{end - start}") - - -if __name__ == "__main__": - main() diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/embedding_server.py b/packages/sage-common/src/sage/common/components/sage_embedding/embedding_server.py deleted file mode 100644 index 599d2b6ab2..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/embedding_server.py +++ /dev/null @@ -1,355 +0,0 @@ -#!/usr/bin/env python3 -""" -轻量化 Embedding 服务器 - 使用 FastAPI 提供 OpenAI 兼容的 API - -用法: - python embedding_server.py --model BAAI/bge-m3 --port 8080 - -然后在代码中使用: - from sage.common.components.sage_embedding.embedding_api import apply_embedding_model - - embedding_model = apply_embedding_model( - name="openai", - model="BAAI/bge-m3", # 或任意名称 - base_url="http://localhost:8080/v1", - api_key="dummy" # 本地服务不需要真实的 API key # pragma: allowlist secret - ) - - result = embedding_model.embed("Hello world") -""" - -# ========== 关键：必须在导入任何 HuggingFace 库之前设置环境变量 ========== -import os -import sys - -# ========== 清除代理变量，避免 SOCKS 代理问题 ========== -# 本地 embedding 服务器使用 HuggingFace 镜像，不需要代理 -# 清除代理可以避免 "Missing dependencies for SOCKS support" 错误 -for proxy_var in [ - "http_proxy", - "https_proxy", - "HTTP_PROXY", - "HTTPS_PROXY", - "all_proxy", - "ALL_PROXY", -]: - os.environ.pop(proxy_var, None) - -# 设置环境变量 - 自动检测网络并配置 HuggingFace 镜像 -from sage.common.config import ensure_hf_mirror_configured - -ensure_hf_mirror_configured() - -# ========== 强制所有 HuggingFace 请求使用镜像站 ========== -# 方案1: 设置所有可能的离线和镜像相关环境变量 -os.environ["TOKENIZERS_PARALLELISM"] = "false" -os.environ["HF_HUB_DISABLE_TELEMETRY"] = "1" -os.environ["HF_HUB_DISABLE_EXPERIMENTAL_WARNING"] = "1" - - -# 方案2: Patch requests 库，将 huggingface.co 重定向到镜像站 -def patch_huggingface_requests(): - """将所有 huggingface.co 的请求重定向到镜像站""" - from functools import wraps - - import requests - - original_request = requests.Session.request - hf_endpoint = os.environ.get("HF_ENDPOINT", "https://hf-mirror.com") - - @wraps(original_request) - def patched_request(self, method, url, *args, **kwargs): - # 将 huggingface.co 替换为镜像站 - if isinstance(url, str): - url = url.replace("https://huggingface.co", hf_endpoint) - url = url.replace("http://huggingface.co", hf_endpoint) - return original_request(self, method, url, *args, **kwargs) - - requests.Session.request = patched_request - - -# 执行 patch（在导入 transformers 之前） -try: - patch_huggingface_requests() -except Exception as e: - print(f"Warning: Failed to patch requests: {e}", file=sys.stderr) - -# ========== 现在可以安全导入其他库了 ========== -import argparse -import logging -import time -from typing import Any - -import torch -import uvicorn -from fastapi import FastAPI, HTTPException -from fastapi.responses import JSONResponse -from pydantic import BaseModel -from transformers import AutoModel, AutoTokenizer - -# 设置日志 -logging.basicConfig( - level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" -) -logger = logging.getLogger(__name__) - - -class EmbeddingRequest(BaseModel): - """OpenAI 兼容的 embedding 请求格式""" - - input: str | list[str] - model: str = "default" - encoding_format: str = "float" - - -class EmbeddingResponse(BaseModel): - """OpenAI 兼容的 embedding 响应格式""" - - object: str = "list" - data: list[dict[str, Any]] - model: str - usage: dict[str, int] - - -class EmbeddingServer: - """Embedding 服务器类 - 长期持有模型实例""" - - def __init__(self, model_name: str, device: str = "auto"): - """初始化 embedding 服务器 - - Args: - model_name: HuggingFace 模型名称 - device: 设备类型 ("cuda", "cpu", "auto") - """ - self.model_name = model_name - logger.info(f"Loading model: {model_name}") - - # 确定设备 - if device == "auto": - self.device = "cuda" if torch.cuda.is_available() else "cpu" - else: - self.device = device - - logger.info(f"Using device: {self.device}") - - # 加载模型和 tokenizer - try: - logger.info("Loading model from local cache...") - logger.info(f"HF_ENDPOINT: {os.environ.get('HF_ENDPOINT', 'not set')}") - - # 检查本地缓存是否存在 - cache_dir = os.path.expanduser("~/.cache/huggingface/hub") - model_cache = os.path.join(cache_dir, f"models--{model_name.replace('/', '--')}") - - # 如果本地缓存存在，优先使用本地文件（避免限流） - local_files_only = os.path.exists(model_cache) - if local_files_only: - logger.info(f"Found local cache at {model_cache}") - logger.info("Loading from local cache only (avoiding network requests)") - else: - logger.info("Local cache not found, will download from mirror") - - # 直接加载（优先使用本地已有的格式，避免下载 safetensors） - # use_safetensors=False 会强制使用 pytorch_model.bin - # local_files_only=True 时完全离线加载（避免 HuggingFace 限流） - self.tokenizer = AutoTokenizer.from_pretrained( - model_name, trust_remote_code=True, local_files_only=local_files_only - ) - self.model = AutoModel.from_pretrained( - model_name, - trust_remote_code=True, - use_safetensors=False, - local_files_only=local_files_only, - ) - logger.info("Model loaded successfully") - - # 移动模型到指定设备 - self.model = self.model.to(self.device) - self.model.eval() # 设置为评估模式 - - logger.info(f"Model loaded successfully on {self.device}") - - except Exception as e: - logger.error(f"Failed to load model: {e}") - raise - - def embed_texts(self, texts: list[str]) -> list[list[float]]: - """对文本列表进行 embedding - - Args: - texts: 文本列表 - - Returns: - embedding 向量列表 - """ - try: - # Tokenize - encoded = self.tokenizer( - texts, return_tensors="pt", padding=True, truncation=True, max_length=512 - ) - - # 移动到模型所在设备 - encoded = {k: v.to(self.device) for k, v in encoded.items()} - - # 推理 - with torch.no_grad(): - outputs = self.model(**encoded) - # 使用 mean pooling - embeddings = outputs.last_hidden_state.mean(dim=1) - - # 转换为 float32 并移回 CPU - if embeddings.dtype == torch.bfloat16: - embeddings = embeddings.to(torch.float32) - - embeddings = embeddings.cpu().numpy().tolist() - - return embeddings - - except Exception as e: - logger.error(f"Embedding error: {e}") - raise - - -# 全局变量存储服务器实例 -embedding_server: EmbeddingServer | None = None - -# 创建 FastAPI 应用 -app = FastAPI( - title="Embedding Server", description="OpenAI-compatible Embedding API", version="1.0" -) - - -@app.get("/") -async def root(): - """根路径""" - return { - "status": "ok", - "model": embedding_server.model_name if embedding_server else "not loaded", - "device": embedding_server.device if embedding_server else "unknown", - } - - -@app.get("/health") -async def health(): - """健康检查端点（标准路径）- Control Plane 使用此路径""" - return { - "status": "ok", - "model": embedding_server.model_name if embedding_server else "not loaded", - "device": embedding_server.device if embedding_server else "unknown", - } - - -@app.get("/v1/models") -async def list_models(): - """列出可用模型（OpenAI 兼容）""" - if not embedding_server: - raise HTTPException(status_code=500, detail="Model not loaded") - - return { - "object": "list", - "data": [ - { - "id": embedding_server.model_name, - "object": "model", - "created": int(time.time()), - "owned_by": "local", - } - ], - } - - -@app.post("/v1/embeddings") -async def create_embeddings(request: EmbeddingRequest): - """创建 embeddings（OpenAI 兼容）""" - if not embedding_server: - raise HTTPException(status_code=500, detail="Model not loaded") - - try: - # 处理输入（单个字符串或列表） - if isinstance(request.input, str): - texts = [request.input] - else: - texts = request.input - - # 生成 embeddings - start_time = time.time() - embeddings = embedding_server.embed_texts(texts) - elapsed_time = time.time() - start_time - - # 构建响应（OpenAI 兼容格式） - data = [ - {"object": "embedding", "embedding": emb, "index": idx} - for idx, emb in enumerate(embeddings) - ] - - response = { - "object": "list", - "data": data, - "model": embedding_server.model_name, - "usage": { - "prompt_tokens": sum(len(t.split()) for t in texts), - "total_tokens": sum(len(t.split()) for t in texts), - }, - } - - logger.info( - f"Generated {len(embeddings)} embeddings in {elapsed_time:.3f}s " - f"({len(embeddings) / elapsed_time:.2f} emb/s)" - ) - - return JSONResponse(content=response) - - except Exception as e: - logger.error(f"Error processing request: {e}") - raise HTTPException(status_code=500, detail=str(e)) - - -def main(): - """主函数""" - parser = argparse.ArgumentParser(description="Embedding Server - OpenAI Compatible API") - parser.add_argument( - "--model", - type=str, - default="BAAI/bge-m3", - help="HuggingFace model name (default: BAAI/bge-m3)", - ) - parser.add_argument("--port", type=int, default=8091, help="Server port (default: 8090)") - parser.add_argument( - "--host", type=str, default="0.0.0.0", help="Server host (default: 0.0.0.0)" - ) - parser.add_argument( - "--device", type=str, default="auto", help="Device (cuda/cpu/auto, default: auto)" - ) - parser.add_argument( - "--gpu", type=int, default=None, help="Specific GPU ID to use (e.g., 0, 1, 2)" - ) - parser.add_argument("--workers", type=int, default=1, help="Number of workers (default: 1)") - - args = parser.parse_args() - - # 如果指定了 GPU，设置 CUDA_VISIBLE_DEVICES - if args.gpu is not None: - os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu) - logger.info(f"Setting CUDA_VISIBLE_DEVICES={args.gpu}") - - # 初始化全局 embedding 服务器 - global embedding_server - try: - embedding_server = EmbeddingServer(model_name=args.model, device=args.device) - except Exception as e: - logger.error(f"Failed to initialize embedding server: {e}") - return - - # 启动 FastAPI 服务器 - logger.info(f"Starting server on {args.host}:{args.port}") - logger.info(f"API endpoint: http://{args.host}:{args.port}/v1/embeddings") - logger.info("Usage example:") - logger.info(f" curl -X POST http://localhost:{args.port}/v1/embeddings \\") - logger.info(' -H "Content-Type: application/json" \\') - logger.info(f' -d \'{{"input": "Hello world", "model": "{args.model}"}}\'') - - uvicorn.run(app, host=args.host, port=args.port, workers=args.workers, log_level="info") - - -if __name__ == "__main__": - main() diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/factory.py b/packages/sage-common/src/sage/common/components/sage_embedding/factory.py deleted file mode 100644 index 69539033c0..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/factory.py +++ /dev/null @@ -1,292 +0,0 @@ -"""Factory for creating embedding model instances.""" - -import os -from typing import Any - -from .base import BaseEmbedding -from .registry import EmbeddingRegistry, ModelStatus - - -class EmbeddingFactory: - """Embedding 模型工厂 - - 提供统一的接口来创建各种 embedding 模型实例。 - - Examples: - >>> # 创建 Hash embedding - >>> emb = EmbeddingFactory.create("hash", dim=384) - >>> - >>> # 创建 HuggingFace embedding - >>> emb = EmbeddingFactory.create( - ... "hf", - ... model="BAAI/bge-small-zh-v1.5" - ... ) - >>> - >>> # 列出所有可用方法 - >>> models = EmbeddingFactory.list_models() - >>> for method, info in models.items(): - ... print(f"{method}: {info['description']}") - """ - - @staticmethod - def create(method: str, **kwargs: Any) -> BaseEmbedding: - """创建 Embedding 实例 - - Args: - method: embedding 方法名 (hf, openai, hash, mockembedder, ...) - **kwargs: 方法特定参数 - - model: 模型名称 (hf, openai 等需要) - - api_key: API 密钥 (openai, jina 等需要) - - base_url: API 端点 (openai 可选) - - dim/fixed_dim: 固定维度 (hash, mockembedder 需要) - - Returns: - BaseEmbedding 实例 - - Raises: - ValueError: 不支持的方法或缺少必要参数 - RuntimeError: 模型不可用或初始化失败 - - Examples: - >>> # HuggingFace 模型 - >>> emb = EmbeddingFactory.create( - ... method="hf", - ... model="BAAI/bge-small-zh-v1.5" - ... ) - >>> vec = emb.embed("hello world") - >>> - >>> # OpenAI API - >>> emb = EmbeddingFactory.create( - ... method="openai", - ... model="text-embedding-3-small", - ... api_key=os.getenv("OPENAI_API_KEY") - ... ) - >>> - >>> # Mock embedder (测试) - >>> emb = EmbeddingFactory.create( - ... method="mockembedder", - ... fixed_dim=384 - ... ) - >>> - >>> # Hash embedding (快速测试) - >>> emb = EmbeddingFactory.create( - ... method="hash", - ... dim=384 - ... ) - """ - # 获取注册信息 - wrapper_class = EmbeddingRegistry.get_wrapper_class(method) - if not wrapper_class: - available = ", ".join(EmbeddingRegistry.list_methods()) - raise ValueError( - f"不支持的 embedding 方法: '{method}'\n" - f"可用方法: {available}\n" - f"提示: 请检查方法名拼写，或查看文档了解支持的方法。" - ) - - # 获取模型信息 - model_info = EmbeddingRegistry.get_model_info(method) - - # 检查必要参数 - if model_info: - if model_info.requires_api_key and "api_key" not in kwargs: - # 尝试从环境变量获取 - api_key = os.getenv(f"{method.upper()}_API_KEY") - if not api_key: - api_key = os.getenv("OPENAI_API_KEY") # 通用 fallback - if api_key: - kwargs["api_key"] = api_key - - if model_info.requires_model_download and "model" not in kwargs: - examples = ", ".join(model_info.example_models[:2]) - raise ValueError( - f"{method} 方法需要指定 model 参数。\n" - f"示例模型: {examples}\n" - f"用法: EmbeddingFactory.create('{method}', model='...')" - ) - - # 检查状态 - status = EmbeddingRegistry.check_status(method, **kwargs) - if status == ModelStatus.NEEDS_API_KEY: - raise RuntimeError( - f"{method} 方法需要 API Key。\n" - f"解决方案:\n" - f" 1. 设置环境变量: export {method.upper()}_API_KEY='your-key'\n" # pragma: allowlist secret - f" 2. 传递参数: EmbeddingFactory.create('{method}', api_key='your-key', ...)" # pragma: allowlist secret - ) - - if status == ModelStatus.UNAVAILABLE: - raise RuntimeError(f"{method} 方法当前不可用。\n请检查是否已正确安装相关依赖。") - - # 创建实例 - try: - return wrapper_class(**kwargs) - except TypeError as e: - # 捕获参数错误，提供友好的提示 - raise ValueError( - f"创建 {method} embedding 实例时参数错误: {e}\n" - f"提示: 请检查传入的参数是否正确。\n" - f"当前参数: {kwargs}" - ) from e - except Exception as e: - raise RuntimeError( - f"创建 {method} embedding 实例失败: {e}\n方法: {method}\n参数: {kwargs}" - ) from e - - @staticmethod - def list_models() -> dict[str, dict[str, Any]]: - """列出所有可用的 embedding 方法 - - Returns: - Dict[method_name, model_info] - 每个 model_info 包含: - - display_name: 显示名称 - - description: 描述 - - requires_api_key: 是否需要 API Key - - requires_download: 是否需要下载模型 - - default_dimension: 默认维度 - - examples: 示例模型列表 - - Examples: - >>> models = EmbeddingFactory.list_models() - >>> for method, info in models.items(): - ... print(f"{method}:") - ... print(f" {info['description']}") - ... if info['requires_api_key']: - ... print(" ⚠️ 需要 API Key") - ... if info['examples']: - ... print(f" 示例: {', '.join(info['examples'][:2])}") - hash: - 轻量级哈希 embedding（测试用） - 示例: hash-384, hash-768 - hf: - 本地 Transformer 模型 - ⚠️ 需要下载模型 - 示例: BAAI/bge-small-zh-v1.5, sentence-transformers/all-MiniLM-L6-v2 - """ - result = {} - for method in EmbeddingRegistry.list_methods(): - info = EmbeddingRegistry.get_model_info(method) - if info: - result[method] = { - "display_name": info.display_name, - "description": info.description, - "requires_api_key": info.requires_api_key, - "requires_download": info.requires_model_download, - "default_dimension": info.default_dimension, - "examples": info.example_models, - } - return result - - @staticmethod - def check_availability(method: str, **kwargs: Any) -> dict[str, Any]: - """检查特定方法的可用性 - - Args: - method: 方法名称 - **kwargs: 方法特定参数 - - Returns: - 包含以下键的字典: - - status: 状态字符串 (available/needs_api_key/needs_download/unavailable) - - message: 详细说明 - - action: 建议操作 - - Examples: - >>> # 检查 HuggingFace 模型 - >>> status = EmbeddingFactory.check_availability( - ... "hf", - ... model="BAAI/bge-small-zh-v1.5" - ... ) - >>> print(status['message']) - ✅ 已缓存 - >>> - >>> # 检查 OpenAI（无 API Key） - >>> status = EmbeddingFactory.check_availability("openai") - >>> print(status['status']) - needs_api_key - >>> print(status['action']) - 设置环境变量: export OPENAI_API_KEY='your-key' # pragma: allowlist secret - """ - status = EmbeddingRegistry.check_status(method, **kwargs) - - messages = { - ModelStatus.AVAILABLE: ("✅ 可用", "可以直接使用"), - ModelStatus.CACHED: ( - "✅ 已缓存", - f"模型已下载到本地: {kwargs.get('model', '?')}", - ), - ModelStatus.NEEDS_API_KEY: ( - "⚠️ 需要 API Key", - f"设置环境变量: export {method.upper()}_API_KEY='your-key'", # pragma: allowlist secret - ), - ModelStatus.NEEDS_DOWNLOAD: ( - "⚠️ 需要下载模型", - f"首次使用将从 HuggingFace 下载模型: {kwargs.get('model', '?')}", - ), - ModelStatus.UNAVAILABLE: ("❌ 不可用", f"方法 '{method}' 未注册或不支持"), - } - - message, action = messages.get(status, ("❓ 未知", "无法确定状态")) - - return { - "status": status.value, - "message": message, - "action": action, - } - - -# 便捷函数（包装 Factory 方法） - - -def get_embedding_model(method: str, **kwargs: Any) -> BaseEmbedding: - """获取 Embedding 模型实例（推荐使用的便捷函数） - - 这是 EmbeddingFactory.create() 的别名，提供更简洁的调用方式。 - - Args: - method: embedding 方法名 - **kwargs: 方法特定参数 - - Returns: - BaseEmbedding 实例 - - Examples: - >>> # 推荐用法 - >>> emb = get_embedding_model("hf", model="BAAI/bge-small-zh-v1.5") - >>> vec = emb.embed("hello world") - >>> dim = emb.get_dim() - """ - return EmbeddingFactory.create(method, **kwargs) - - -def list_embedding_models() -> dict[str, dict[str, Any]]: - """列出所有可用的 embedding 方法（便捷函数） - - Returns: - 方法信息字典 - - Examples: - >>> models = list_embedding_models() - >>> print(list(models.keys())) - ['hash', 'hf', 'mockembedder', 'openai', ...] - """ - return EmbeddingFactory.list_models() - - -def check_model_availability(method: str, **kwargs: Any) -> dict[str, Any]: - """检查模型可用性（便捷函数） - - Args: - method: 方法名称 - **kwargs: 方法特定参数 - - Returns: - 状态信息字典 - - Examples: - >>> status = check_model_availability("hash") - >>> print(status['status']) - available - """ - return EmbeddingFactory.check_availability(method, **kwargs) diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/hf.py b/packages/sage-common/src/sage/common/components/sage_embedding/hf.py deleted file mode 100644 index a61f29e438..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/hf.py +++ /dev/null @@ -1,148 +0,0 @@ -import os - -# flake8: noqa: E402 -# Auto-detect network region and configure HuggingFace mirror -from sage.common.config import ensure_hf_mirror_configured - -ensure_hf_mirror_configured() - -from functools import lru_cache - -# 延迟导入：transformers, torch, tenacity, numpy 等重量级依赖 -# 只在实际调用函数时才导入，避免在模块加载时就加载这些库 - -os.environ["TOKENIZERS_PARALLELISM"] = "false" - - -@lru_cache(maxsize=1) -def initialize_hf_model(model_name): - """初始化 HuggingFace 模型（延迟导入依赖）""" - # 延迟导入 transformers - try: - from transformers import ( - AutoModel, # noqa: F401 - AutoModelForCausalLM, - AutoTokenizer, - ) - except ImportError as e: - raise ImportError( - "transformers package is required for HuggingFace embedding functionality. " - "Please install it via: pip install transformers" - ) from e - - hf_tokenizer = AutoTokenizer.from_pretrained( - model_name, device_map="auto", trust_remote_code=True - ) - hf_model = AutoModelForCausalLM.from_pretrained( - model_name, device_map="auto", trust_remote_code=True - ) - if hf_tokenizer.pad_token is None: - hf_tokenizer.pad_token = hf_tokenizer.eos_token - - return hf_model, hf_tokenizer - - -def hf_embed_sync(text: str, tokenizer, embed_model) -> list[float]: - """ - 使用 HuggingFace 模型同步生成文本 embedding。 - - 使用 masked mean pooling 确保只对有效 token 取平均。 - - Args: - text (str): 输入文本 - tokenizer: 已加载的 tokenizer - embed_model: 已加载的 PyTorch embedding 模型 - - Returns: - list[float]: embedding 向量 - """ - # 延迟导入 torch - try: - import torch - except ImportError as e: - raise ImportError( - "torch package is required for HuggingFace embedding functionality. " - "Please install it via: pip install torch" - ) from e - - device = next(embed_model.parameters()).device - encoded_texts = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device) - - with torch.no_grad(): - outputs = embed_model( - input_ids=encoded_texts["input_ids"], - attention_mask=encoded_texts["attention_mask"], - ) - # 使用 masked mean pooling：只对非 padding token 取平均 - last_hidden_state = outputs.last_hidden_state # (1, seq_len, hidden_dim) - attention_mask = encoded_texts["attention_mask"] # (1, seq_len) - - # 扩展 attention_mask 到 hidden_dim 维度 - mask_expanded = attention_mask.unsqueeze(-1).expand(last_hidden_state.size()).float() - - # 对有效 token 求和然后取平均 - sum_embeddings = torch.sum(last_hidden_state * mask_expanded, dim=1) - sum_mask = torch.clamp(mask_expanded.sum(dim=1), min=1e-9) - embeddings = sum_embeddings / sum_mask - - if embeddings.dtype == torch.bfloat16: - return embeddings.detach().to(torch.float32).cpu()[0].tolist() - else: - return embeddings.detach().cpu()[0].tolist() - - -def hf_embed_batch_sync(texts: list[str], tokenizer, embed_model) -> list[list[float]]: - """ - 使用 HuggingFace 模型同步批量生成文本 embedding。 - - 通过一次前向传播处理多个文本，相比逐个处理显著提高效率。 - 使用 masked mean pooling 确保 padding token 不影响结果。 - - Args: - texts (list[str]): 输入文本列表 - tokenizer: 已加载的 tokenizer - embed_model: 已加载的 PyTorch embedding 模型 - - Returns: - list[list[float]]: embedding 向量列表 - """ - # 处理空列表情况 - if not texts: - return [] - - # 延迟导入 torch - try: - import torch - except ImportError as e: - raise ImportError( - "torch package is required for HuggingFace embedding functionality. " - "Please install it via: pip install torch" - ) from e - - device = next(embed_model.parameters()).device - # 批量编码所有文本，tokenizer会自动处理padding - encoded_texts = tokenizer(texts, return_tensors="pt", padding=True, truncation=True).to(device) - - with torch.no_grad(): - outputs = embed_model( - input_ids=encoded_texts["input_ids"], - attention_mask=encoded_texts["attention_mask"], - ) - # 使用 masked mean pooling：只对非 padding token 取平均 - # 这确保批处理结果与单独处理结果一致 - last_hidden_state = outputs.last_hidden_state # (batch_size, seq_len, hidden_dim) - attention_mask = encoded_texts["attention_mask"] # (batch_size, seq_len) - - # 扩展 attention_mask 到 hidden_dim 维度: (batch_size, seq_len, hidden_dim) - mask_expanded = attention_mask.unsqueeze(-1).expand(last_hidden_state.size()).float() - - # 对每个文本，只对有效 token 求和然后取平均 - sum_embeddings = torch.sum(last_hidden_state * mask_expanded, dim=1) - sum_mask = torch.clamp(mask_expanded.sum(dim=1), min=1e-9) # 防止除零 - embeddings = sum_embeddings / sum_mask - - # 转换为float32并返回CPU上的列表 - if embeddings.dtype == torch.bfloat16: - return embeddings.detach().to(torch.float32).cpu().tolist() - else: - return embeddings.detach().cpu().tolist() diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/instructor.py b/packages/sage-common/src/sage/common/components/sage_embedding/instructor.py deleted file mode 100644 index 4c424afe01..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/instructor.py +++ /dev/null @@ -1,10 +0,0 @@ -import numpy as np -from numpy.typing import NDArray -from sentence_transformers import SentenceTransformer - - -async def instructor_embed( - texts: list[str], model: str = "hkunlp/instructor-large" -) -> NDArray[np.float32]: # type: ignore[return] - _model = SentenceTransformer(model) - return _model.encode(texts) # type: ignore[return-value] diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/jina.py b/packages/sage-common/src/sage/common/components/sage_embedding/jina.py deleted file mode 100644 index 4dfa928fe8..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/jina.py +++ /dev/null @@ -1,106 +0,0 @@ -import os - -import requests - -# Dependencies should be installed via requirements.txt -# tenacity is required for this module - -try: - import tenacity # noqa: F401 -except ImportError: - raise ImportError( - "tenacity package is required for Jina embedding functionality. " - "Please install it via: pip install tenacity" - ) - -try: - import aiohttp -except ImportError: - raise ImportError( - "aiohttp package is required for Jina embedding functionality. " - "Please install it via: pip install aiohttp" - ) - - -async def fetch_data(url, headers, data): - async with aiohttp.ClientSession() as session: - async with session.post(url, headers=headers, json=data) as response: - response_json = await response.json() - data_list = response_json.get("data", []) - return data_list - - -async def jina_embed( - text: str, - dimensions: int = 1024, - late_chunking: bool = False, - base_url: str | None = None, - api_key: str | None = None, - model: str = "jina-embeddings-v3", -) -> list[float]: - if api_key: - os.environ["JINA_API_KEY"] = api_key - url = "https://api.jina.ai/v1/embeddings" if not base_url else base_url - headers = { - "Content-Type": "application/json", - "Authorization": f"Bearer {os.environ['JINA_API_KEY']}", - } - data = { - "model": f"{model}", - "normalized": True, - "embedding_type": "float", - "dimensions": f"{dimensions}", - "late_chunking": late_chunking, - "input": text, - } - data_list = await fetch_data(url, headers, data) - print(data_list) - return data_list[0]["embedding"] - - -def jina_embed_sync( - text: str, - dimensions: int = 1024, - late_chunking: bool = False, - base_url: str | None = None, - api_key: str | None = None, - model: str = "jina-embeddings-v3", -) -> list[float]: - """ - 同步版本：调用 Jina AI embedding API 获取嵌入向量 - - Args: - text: 待嵌入的文本 - dimensions: 嵌入维度 - late_chunking: 是否开启 late chunking - base_url: 自定义 API 地址（可选） - api_key: Jina API 密钥 - model: 使用的模型名 - - Returns: - list[float]: 嵌入向量 - """ - if api_key: - os.environ["JINA_API_KEY"] = api_key - - url = base_url or "https://api.jina.ai/v1/embeddings" - headers = { - "Content-Type": "application/json", - "Authorization": f"Bearer {os.environ['JINA_API_KEY']}", - } - payload = { - "model": model, - "normalized": True, - "embedding_type": "float", - "dimensions": dimensions, - "late_chunking": late_chunking, - "input": text, - } - - try: - response = requests.post(url, headers=headers, json=payload) - response.raise_for_status() - data = response.json() - return data["data"][0]["embedding"] - except Exception as e: - raise RuntimeError(f"Jina API call failed: {str(e)}") diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/lollms.py b/packages/sage-common/src/sage/common/components/sage_embedding/lollms.py deleted file mode 100644 index 4a987270db..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/lollms.py +++ /dev/null @@ -1,95 +0,0 @@ -import requests - -pass - - -# Dependencies should be installed via requirements.txt -# aiohttp and tenacity are required for this module - -try: - import aiohttp -except ImportError: - raise ImportError( - "aiohttp package is required for Lollms embedding functionality. " - "Please install it via: pip install aiohttp" - ) - -try: - import tenacity # noqa: F401 -except ImportError: - raise ImportError( - "tenacity package is required for Lollms embedding functionality. " - "Please install it via: pip install tenacity" - ) - - -async def lollms_embed( - text: str, - embed_model=None, - base_url="http://localhost:9600", - **kwargs, -) -> list: - """ - Generate embedding for a single text using lollms server. - - Args: - text: The string to embed - embed_model: Model name (not used directly as lollms uses configured vectorizer) - base_url: URL of the lollms server - **kwargs: Additional arguments passed to the request - - Returns: - list[float]: The embedding vector - """ - api_key = kwargs.pop("api_key", None) - headers = ( - {"Content-Type": "application/json", "Authorization": api_key} - if api_key - else {"Content-Type": "application/json"} - ) - - async with aiohttp.ClientSession(headers=headers) as session: - request_data = {"text": text} - - async with session.post( - f"{base_url}/lollms_embed", - json=request_data, - ) as response: - result = await response.json() - return result["vector"] - - -def lollms_embed_sync( - text: str, - embed_model=None, - base_url="http://localhost:9600", - **kwargs, -) -> list[float]: - """ - 同步版本：使用 lollms 本地服务生成 embedding。 - - Args: - text: 输入文本 - embed_model: 模型名（未直接使用） - base_url: lollms 服务地址 - **kwargs: 可选参数，例如 api_key - - Returns: - list[float]: 生成的向量 - """ - api_key = kwargs.pop("api_key", None) - headers = { - "Content-Type": "application/json", - } - if api_key: - headers["Authorization"] = api_key - - request_data = {"text": text} - - try: - response = requests.post(f"{base_url}/lollms_embed", json=request_data, headers=headers) - response.raise_for_status() - result = response.json() - return result["vector"] - except Exception as e: - raise RuntimeError(f"lollms embedding request failed: {str(e)}") diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/nvidia_openai.py b/packages/sage-common/src/sage/common/components/sage_embedding/nvidia_openai.py deleted file mode 100644 index c4180e2390..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/nvidia_openai.py +++ /dev/null @@ -1,84 +0,0 @@ -import os - -pass - - -# Dependencies should be installed via requirements.txt -# openai is required for this module - -try: - from openai import AsyncOpenAI, OpenAI -except ImportError: - raise ImportError( - "openai package is required for NVIDIA OpenAI embedding functionality. " - "Please install it via: pip install openai" - ) - - -async def nvidia_openai_embed( - text: str, - model: str = "nvidia/llama-3.2-nv-embedqa-1b-v1", - base_url: str = "https://integrate.api.nvidia.com/v1", - api_key: str | None = None, - input_type: str = "passage", # query for retrieval, passage for embedding - trunc: str = "NONE", # NONE or START or END - encode: str = "float", # float or base64 -) -> list[float]: - """ - Generate embedding for a single text using NVIDIA NIM-compatible OpenAI API. - - Returns: - list[float]: The embedding vector. - """ - if api_key: - os.environ["OPENAI_API_KEY"] = api_key - - openai_async_client = AsyncOpenAI() if base_url is None else AsyncOpenAI(base_url=base_url) - - response = await openai_async_client.embeddings.create( - model=model, - input=text, - encoding_format=encode, # pyright: ignore[reportArgumentType] - extra_body={"input_type": input_type, "truncate": trunc}, - ) - - return response.data[0].embedding - - -def nvidia_openai_embed_sync( - text: str, - model: str = "nvidia/llama-3.2-nv-embedqa-1b-v1", - base_url: str = "https://integrate.api.nvidia.com/v1", - api_key: str | None = None, - input_type: str = "passage", # query for retrieval, passage for embedding - trunc: str = "NONE", # NONE or START or END - encode: str = "float", # float or base64 -) -> list[float]: - """ - 同步版本：使用 NVIDIA NIM 接口生成文本 embedding。 - - Args: - text: 输入文本 - model: 使用的模型 ID - base_url: 接口地址（默认为 NVIDIA 接口） - api_key: API 密钥（使用 OPENAI_API_KEY 环境变量） - input_type: 输入类型（passage / query） - trunc: 截断策略 - encode: 返回格式（float / base64） - - Returns: - list[float]: 嵌入向量 - """ - if api_key: - os.environ["OPENAI_API_KEY"] = api_key - - client = OpenAI(base_url=base_url) - - response = client.embeddings.create( - model=model, - input=text, - encoding_format=encode, # pyright: ignore[reportArgumentType] - extra_body={"input_type": input_type, "truncate": trunc}, - ) - - return response.data[0].embedding diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/ollama.py b/packages/sage-common/src/sage/common/components/sage_embedding/ollama.py deleted file mode 100644 index 0ed4adbb38..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/ollama.py +++ /dev/null @@ -1,77 +0,0 @@ -pass - - -# Dependencies should be installed via requirements.txt -# ollama and tenacity are required for this module to work - -try: - import ollama # noqa: F401 -except ImportError: - raise ImportError( - "ollama package is required for Ollama embedding functionality. " - "Please install it via: pip install ollama" - ) - -try: - import tenacity # noqa: F401 -except ImportError: - raise ImportError( - "tenacity package is required for Ollama embedding functionality. " - "Please install it via: pip install tenacity" - ) - - -async def ollama_embed(text: str, embed_model, **kwargs) -> list: - """ - Generate embedding for a single text using Ollama. - - Args: - text: A single input string - embed_model: The name of the Ollama embedding model - **kwargs: Optional arguments (e.g. base_url, api_key) - - Returns: - list[float]: The embedding vector - """ - import ollama - - api_key = kwargs.pop("api_key", None) - headers = { - "Content-Type": "application/json", - "User-Agent": "SAGE/0.0", - } - if api_key: - headers["Authorization"] = api_key - kwargs["headers"] = headers - - ollama_client = ollama.Client(**kwargs) - data = ollama_client.embed(model=embed_model, input=text) - return data["embedding"] - - -def ollama_embed_sync(text: str, embed_model, **kwargs) -> list[float]: - """ - 同步版本：使用 Ollama 客户端生成 embedding 向量。 - - Args: - text: 输入文本 - embed_model: 使用的模型名 - **kwargs: 额外参数（可包含 base_url、api_key） - - Returns: - list[float]: embedding 向量 - """ - import ollama - - api_key = kwargs.pop("api_key", None) - headers = { - "Content-Type": "application/json", - "User-Agent": "SAGE/0.0", - } - if api_key: - headers["Authorization"] = api_key - kwargs["headers"] = headers - - ollama_client = ollama.Client(**kwargs) - data = ollama_client.embed(model=embed_model, input=text) - return data["embedding"] diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/openai_wrapper.py b/packages/sage-common/src/sage/common/components/sage_embedding/openai_wrapper.py deleted file mode 100644 index a1ed35653c..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/openai_wrapper.py +++ /dev/null @@ -1,140 +0,0 @@ -import os - -pass - - -# Dependencies should be installed via requirements.txt -# openai is required for this module - -try: - from openai import AsyncOpenAI, OpenAI # 确保导入了这个 - from openai.types import CreateEmbeddingResponse -except ImportError: - raise ImportError( - "openai package is required for OpenAI embedding functionality. " - "Please install it via: pip install openai" - ) - - -async def openai_embed( - text: str, - model: str = "text-embedding-3-small", - base_url: str | None = None, - api_key: str | None = None, -) -> list: - """ - Generate embedding for a single text using OpenAI Embedding API. - - Args: - text: Input string - model: OpenAI embedding model name - base_url: Optional custom endpoint - api_key: OpenAI API key - - Returns: - list[float]: The embedding vector - """ - if not api_key: - api_key = os.environ["OPENAI_API_KEY"] - - default_headers = { - "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_8) SAGE/0.0", - "Content-Type": "application/json", - } - - openai_async_client = ( - AsyncOpenAI(default_headers=default_headers, api_key=api_key) - if base_url is None - else AsyncOpenAI(base_url=base_url, default_headers=default_headers, api_key=api_key) - ) - - response = await openai_async_client.embeddings.create( - model=model, input=text, encoding_format="float" - ) - - return response.data[0].embedding - - -def openai_embed_sync( - text: str, - model: str = "text-embedding-3-small", - base_url: str | None = None, - api_key: str | None = None, -) -> list[float]: - """ - 同步生成 OpenAI embedding。 - - Args: - text: 输入文本 - model: OpenAI embedding 模型名 - base_url: 可选自定义 API endpoint - api_key: OpenAI API 密钥 - - Returns: - list[float]: embedding 向量 - """ - if not api_key: - api_key = os.environ["OPENAI_API_KEY"] - - default_headers = { - "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_8) SAGE/0.0", - "Content-Type": "application/json", - } - - openai_sync_client = ( - OpenAI(default_headers=default_headers, api_key=api_key) - if base_url is None - else OpenAI(base_url=base_url, default_headers=default_headers, api_key=api_key) - ) - - response: CreateEmbeddingResponse = openai_sync_client.embeddings.create( - model=model, input=text, encoding_format="float" - ) - - return response.data[0].embedding - - -def openai_embed_batch_sync( - texts: list[str], - model: str = "text-embedding-3-small", - base_url: str | None = None, - api_key: str | None = None, -) -> list[list[float]]: - """ - 同步批量生成 OpenAI embedding。 - - 使用 OpenAI API 的原生批量接口，一次请求处理多个文本。 - - Args: - texts: 输入文本列表 - model: OpenAI embedding 模型名 - base_url: 可选自定义 API endpoint - api_key: OpenAI API 密钥 - - Returns: - list[list[float]]: embedding 向量列表 - """ - if not texts: - return [] - - if not api_key: - api_key = os.environ["OPENAI_API_KEY"] - - default_headers = { - "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_8) SAGE/0.0", - "Content-Type": "application/json", - } - - openai_sync_client = ( - OpenAI(default_headers=default_headers, api_key=api_key) - if base_url is None - else OpenAI(base_url=base_url, default_headers=default_headers, api_key=api_key) - ) - - # OpenAI API 支持批量：input 可以是字符串列表 - response: CreateEmbeddingResponse = openai_sync_client.embeddings.create( - model=model, input=texts, encoding_format="float" - ) - - # 返回所有 embedding 向量 - return [item.embedding for item in response.data] diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/protocols.py b/packages/sage-common/src/sage/common/components/sage_embedding/protocols.py deleted file mode 100644 index 5c0f17e5af..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/protocols.py +++ /dev/null @@ -1,146 +0,0 @@ -"""Embedding Protocol Definitions for SAGE. - -Layer: L1 (Foundation - Common Components) - -This module defines the standard embedding interface protocol and adapter -for SAGE. Components requiring embeddings should use EmbeddingProtocol. - -Usage: - from sage.common.components.sage_embedding.protocols import ( - EmbeddingProtocol, - EmbeddingClientAdapter, - adapt_embedding_client, - ) - - # Type hint for functions accepting embedders - def process(embedder: EmbeddingProtocol) -> None: - vectors = embedder.embed(["hello", "world"]) - - # Adapt BaseEmbedding instances to EmbeddingProtocol - from sage.common.components.sage_embedding.factory import EmbeddingFactory - raw = EmbeddingFactory.create("hash", dim=64) - client = adapt_embedding_client(raw) # Now has batch interface -""" - -import inspect -from typing import Any, Optional, Protocol, runtime_checkable - - -@runtime_checkable -class EmbeddingProtocol(Protocol): - """Standard embedding interface for SAGE. - - This protocol defines the expected interface for embedding clients - used by selectors and other components that require text embeddings. - - Interface: - - embed(texts, model=None): Batch embed multiple texts - - get_dim(): Get embedding dimension - - Examples: - >>> class MyEmbedder: - ... def embed(self, texts: list[str], model=None) -> list[list[float]]: - ... return [[0.1, 0.2] for _ in texts] - ... def get_dim(self) -> int: - ... return 2 - >>> - >>> embedder = MyEmbedder() - >>> isinstance(embedder, EmbeddingProtocol) - True - """ - - def embed(self, texts: list[str], model: Optional[str] = None) -> list[list[float]]: - """Embed multiple texts into vectors. - - Args: - texts: List of texts to embed - model: Optional model name (backend-specific, often ignored) - - Returns: - List of embedding vectors, one per input text - """ - ... - - def get_dim(self) -> int: - """Get the dimensionality of embedding vectors.""" - ... - - -class EmbeddingClientAdapter: - """Adapter converting single-text embedders to EmbeddingProtocol. - - Wraps BaseEmbedding-style embedders (embed(text: str) -> list[float]) - to provide the standard batch interface (embed(texts: list[str]) -> list[list[float]]). - - Examples: - >>> from sage.common.components.sage_embedding.factory import EmbeddingFactory - >>> raw = EmbeddingFactory.create("hash", dim=64) - >>> client = EmbeddingClientAdapter(raw) - >>> vectors = client.embed(["hello", "world"]) # Batch interface - """ - - def __init__(self, embedder: Any): - """Initialize adapter with a single-text embedder. - - Args: - embedder: Embedder with embed(text: str) and get_dim() methods - """ - self._embedder = embedder - - def embed(self, texts: list[str], model: Optional[str] = None) -> list[list[float]]: - """Embed multiple texts. - - Args: - texts: List of texts to embed - model: Ignored (uses embedder's configured model) - - Returns: - List of embedding vectors - """ - if hasattr(self._embedder, "embed_batch"): - return self._embedder.embed_batch(texts) - return [self._embedder.embed(text) for text in texts] - - def get_dim(self) -> int: - """Get embedding dimension.""" - return self._embedder.get_dim() - - -def adapt_embedding_client(embedder: Any) -> EmbeddingProtocol: - """Adapt any embedder to EmbeddingProtocol interface. - - Inspects the embedder's embed() signature: - - If embed(texts: list[str], ...) → returns as-is - - If embed(text: str) → wraps with EmbeddingClientAdapter - - Args: - embedder: Any embedding implementation - - Returns: - Embedder conforming to EmbeddingProtocol - - Raises: - TypeError: If embedder lacks embed() or get_dim() methods - - Examples: - >>> from sage.common.components.sage_embedding.factory import EmbeddingFactory - >>> raw = EmbeddingFactory.create("hash", dim=64) - >>> client = adapt_embedding_client(raw) - >>> vectors = client.embed(["hello", "world"]) - """ - if not hasattr(embedder, "embed") or not hasattr(embedder, "get_dim"): - raise TypeError( - f"Cannot adapt {type(embedder).__name__} to EmbeddingProtocol. " - f"Missing 'embed' or 'get_dim' method." - ) - - # Check embed() signature - sig = inspect.signature(embedder.embed) - params = list(sig.parameters.keys()) - - # Already has batch interface: embed(texts=...) - if "texts" in params: - return embedder - - # Single-text interface: embed(text=...) or positional only - return EmbeddingClientAdapter(embedder) diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/registry.py b/packages/sage-common/src/sage/common/components/sage_embedding/registry.py deleted file mode 100644 index 7b3f3132e1..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/registry.py +++ /dev/null @@ -1,276 +0,0 @@ -"""Embedding model registry for managing available embedding methods.""" - -import importlib -import os -from dataclasses import dataclass -from enum import Enum -from pathlib import Path -from typing import Any - - -class ModelStatus(Enum): - """模型可用状态枚举""" - - AVAILABLE = "available" # 直接可用 - NEEDS_API_KEY = "needs_api_key" # 需要 API Key # pragma: allowlist secret - NEEDS_DOWNLOAD = "needs_download" # 需要下载模型 - CACHED = "cached" # 已缓存到本地 - UNAVAILABLE = "unavailable" # 不可用 - - -@dataclass -class ModelInfo: - """模型元信息数据类 - - Attributes: - method: 方法名（如 'hf', 'openai', 'hash'） - display_name: 显示名称 - description: 描述信息 - requires_api_key: 是否需要 API Key - requires_model_download: 是否需要下载模型 - default_dimension: 默认维度（可选） - example_models: 示例模型名称列表 - wrapper_class: Wrapper 类引用或字符串路径（用于延迟导入） - """ - - method: str - display_name: str - description: str - requires_api_key: bool - requires_model_download: bool - default_dimension: int | None - example_models: list[str] - wrapper_class: type | str # 支持类对象或字符串路径 - - def get_wrapper_class(self) -> type: - """获取 wrapper 类，支持延迟导入 - - Returns: - Wrapper 类对象 - """ - if isinstance(self.wrapper_class, str): - # 字符串格式: "module.path:ClassName" - module_path, class_name = self.wrapper_class.rsplit(":", 1) - module = importlib.import_module(module_path) - return getattr(module, class_name) - return self.wrapper_class - - -class EmbeddingRegistry: - """Embedding 模型注册表 - - 管理所有可用的 embedding 方法及其元信息。 - - Examples: - >>> # 注册新方法 - >>> EmbeddingRegistry.register( - ... method="hash", - ... display_name="Hash Embedding", - ... description="轻量级哈希 embedding", - ... wrapper_class=HashEmbedding, - ... default_dimension=384, - ... ) - >>> - >>> # 列出所有方法 - >>> EmbeddingRegistry.list_methods() - ['hash', 'hf', 'openai', ...] - >>> - >>> # 获取模型信息 - >>> info = EmbeddingRegistry.get_model_info("hf") - >>> info.requires_model_download - True - """ - - _registry: dict[str, ModelInfo] = {} - - @classmethod - def register( - cls, - method: str, - display_name: str, - description: str, - wrapper_class: type | str, # 支持类对象或字符串路径 - requires_api_key: bool = False, - requires_model_download: bool = False, - default_dimension: int | None = None, - example_models: list[str] | None = None, - ) -> None: - """注册 embedding 方法 - - Args: - method: 方法名称（唯一标识符） - display_name: 显示名称 - description: 描述信息 - wrapper_class: Wrapper 类或字符串路径 (格式: "module.path:ClassName") - requires_api_key: 是否需要 API Key - requires_model_download: 是否需要下载模型 - default_dimension: 默认维度 - example_models: 示例模型名称列表 - - Examples: - >>> # 直接注册类对象 - >>> EmbeddingRegistry.register( - ... method="hash", - ... display_name="Hash Embedding", - ... wrapper_class=HashEmbedding, - ... ) - >>> - >>> # 使用字符串路径进行延迟导入 - >>> EmbeddingRegistry.register( - ... method="openai", - ... display_name="OpenAI Embedding API", - ... wrapper_class="sage.common.components.sage_embedding.wrappers.openai_wrapper:OpenAIEmbedding", - ... requires_api_key=True, - ... ) - """ - cls._registry[method] = ModelInfo( - method=method, - display_name=display_name, - description=description, - requires_api_key=requires_api_key, - requires_model_download=requires_model_download, - default_dimension=default_dimension, - example_models=example_models or [], - wrapper_class=wrapper_class, - ) - - @classmethod - def list_methods(cls) -> list[str]: - """列出所有已注册的方法名称 - - Returns: - 方法名称列表（按字母顺序排序） - - Examples: - >>> methods = EmbeddingRegistry.list_methods() - >>> 'hash' in methods - True - """ - return sorted(cls._registry.keys()) - - @classmethod - def get_model_info(cls, method: str) -> ModelInfo | None: - """获取指定方法的模型信息 - - Args: - method: 方法名称 - - Returns: - ModelInfo 对象，如果方法未注册则返回 None - - Examples: - >>> info = EmbeddingRegistry.get_model_info("hf") - >>> if info: - ... print(info.display_name) - HuggingFace Models - """ - return cls._registry.get(method) - - @classmethod - def get_wrapper_class(cls, method: str) -> type | None: - """获取指定方法的 Wrapper 类（支持延迟导入） - - Args: - method: 方法名称 - - Returns: - Wrapper 类对象，如果方法未注册则返回 None - - Examples: - >>> cls = EmbeddingRegistry.get_wrapper_class("hf") - >>> if cls: - ... emb = cls(model="BAAI/bge-small-zh-v1.5") - """ - info = cls.get_model_info(method) - if not info: - return None - return info.get_wrapper_class() - - @classmethod - def check_status(cls, method: str, **kwargs: Any) -> ModelStatus: - """检查模型可用状态 - - Args: - method: 方法名称 - **kwargs: 方法特定参数（如 api_key, model 等） - - Returns: - ModelStatus 枚举值 - - Examples: - >>> # 检查 OpenAI（无 API Key） - >>> status = EmbeddingRegistry.check_status("openai") - >>> status == ModelStatus.NEEDS_API_KEY - True - >>> - >>> # 检查 HuggingFace 模型 - >>> status = EmbeddingRegistry.check_status( - ... "hf", - ... model="BAAI/bge-small-zh-v1.5" - ... ) - >>> status in [ModelStatus.CACHED, ModelStatus.NEEDS_DOWNLOAD] - True - """ - info = cls.get_model_info(method) - if not info: - return ModelStatus.UNAVAILABLE - - # API Key 检查 - if info.requires_api_key: - api_key = kwargs.get("api_key") - if not api_key: - # 尝试从环境变量读取 - env_var_names = [ - f"{method.upper()}_API_KEY", - "OPENAI_API_KEY", # 通用 fallback - ] - api_key = next((os.getenv(name) for name in env_var_names if os.getenv(name)), None) - if not api_key: - return ModelStatus.NEEDS_API_KEY - - # 本地模型缓存检查 - if info.requires_model_download: - model_name = kwargs.get("model") - if model_name and cls._is_model_cached(model_name): - return ModelStatus.CACHED - return ModelStatus.NEEDS_DOWNLOAD - - return ModelStatus.AVAILABLE - - @classmethod - def _is_model_cached(cls, model_name: str) -> bool: - """检查 HuggingFace 模型是否已缓存到本地 - - Args: - model_name: 模型名称（如 'BAAI/bge-small-zh-v1.5'） - - Returns: - True 如果已缓存，False 否则 - """ - try: - # HuggingFace 缓存目录 - cache_dir = Path.home() / ".cache" / "huggingface" / "hub" - if not cache_dir.exists(): - return False - - # 模型名称转换为缓存目录格式 - # 例如: "BAAI/bge-small-zh-v1.5" -> "models--BAAI--bge-small-zh-v1.5" - model_slug = "models--" + model_name.replace("/", "--") - - # 检查是否存在对应的缓存目录 - cached = any(cache_dir.glob(f"{model_slug}*")) - return cached - except Exception: - # 如果检查失败，保守地返回 False - return False - - @classmethod - def clear(cls) -> None: - """清空注册表（主要用于测试） - - Examples: - >>> EmbeddingRegistry.clear() - >>> EmbeddingRegistry.list_methods() - [] - """ - cls._registry.clear() diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/service.py b/packages/sage-common/src/sage/common/components/sage_embedding/service.py deleted file mode 100644 index a122fab073..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/service.py +++ /dev/null @@ -1,626 +0,0 @@ -"""Embedding Service for SAGE. - -Layer: L1 (Foundation - Common Components) - -This service provides a unified interface for all embedding methods, -including local models (HuggingFace) and API-based services (OpenAI, Jina, etc.). - -Note: This service component is designed to be used by L2 (Platform) and higher layers. - -Configuration Schema: ---------------------- -services: - embedding: - class: sage.common.components.sage_embedding.EmbeddingService - config: - # Engine type: sagellm (local), openai (API), etc. - engine: "sagellm" # Default: sagellm (uses sentence-transformers locally) - - # Embedding method: "hf", "openai", "jina", etc. - method: "hf" - - # Model name/path (HuggingFace model ID or local path) - model: "BAAI/bge-small-zh-v1.5" - - # API key for cloud services (openai, jina, etc.) - api_key: null - - # Custom API endpoint - base_url: null - - # Batch size for embedding - batch_size: 32 - - # Normalize vectors to unit length - normalize: true - - # Enable embedding cache (LRU) - cache_enabled: false - cache_size: 10000 - - # Additional method-specific config - config: {} - -Engine Types: -------------- -- sagellm: Local embedding using sentence-transformers (default, recommended) -- openai: OpenAI embedding API -- (others): Passed through to EmbeddingFactory via method parameter -""" - -from __future__ import annotations - -import logging -import threading -import warnings -from dataclasses import dataclass, field -from typing import TYPE_CHECKING, Any - -import numpy as np - -from sage.common.components.sage_embedding import EmbeddingFactory, EmbeddingRegistry -from sage.common.service import BaseService - -if TYPE_CHECKING: - from sentence_transformers import SentenceTransformer - -logger = logging.getLogger(__name__) - - -@dataclass -class EmbeddingServiceConfig: - """Configuration for EmbeddingService. - - Attributes: - engine: Engine type for embedding generation. - - "sagellm": Local embedding using sentence-transformers (default) - - "openai": OpenAI embedding API - - Others: Passed to EmbeddingFactory via method parameter - method: Embedding method ("hf", "openai", "jina", etc.) - model: Model name/path (HuggingFace model ID or local path) - api_key: API key for cloud services - base_url: Custom API endpoint - batch_size: Default batch size for embedding - normalize: Normalize vectors to unit length - cache_enabled: Enable embedding cache - cache_size: LRU cache size - config: Method-specific configs - """ - - engine: str = "sagellm" # sagellm/vllm/openai/... - method: str = "hf" # "hf", "openai", "jina", "vllm", etc. - model: str | None = None # Model name/path - api_key: str | None = None # API key for cloud services - base_url: str | None = None # Custom API endpoint - batch_size: int = 32 # Default batch size - normalize: bool = True # Normalize vectors - cache_enabled: bool = False # Enable embedding cache - cache_size: int = 10000 # LRU cache size - - # Method-specific configs - config: dict[str, Any] = field(default_factory=dict) - - # vLLM-specific (if engine == "vllm") - vllm_service_name: str | None = None # Name of vLLM service to use - vllm_auto_download: bool = False - vllm_engine_config: dict[str, Any] = field(default_factory=dict) - - @classmethod - def from_dict(cls, data: dict[str, Any]) -> EmbeddingServiceConfig: - """Create config from dictionary.""" - # Handle legacy configs: if method is specified but engine is not, - # infer engine from method for backward compatibility - engine = data.get("engine") - method = data.get("method", "hf") - - if engine is None: - # Legacy config without engine field - if method == "vllm": - engine = "vllm" - else: - engine = "sagellm" # Default to sagellm for local embedding - - return cls( - engine=engine, - method=method, - model=data.get("model"), - api_key=data.get("api_key"), - base_url=data.get("base_url"), - batch_size=int(data.get("batch_size", 32)), - normalize=bool(data.get("normalize", True)), - cache_enabled=bool(data.get("cache_enabled", False)), - cache_size=int(data.get("cache_size", 10000)), - config=dict(data.get("config", {})), - vllm_service_name=data.get("vllm_service_name"), - vllm_auto_download=bool(data.get("vllm_auto_download", False)), - vllm_engine_config=dict(data.get("vllm_engine_config", {})), - ) - - -class EmbeddingService(BaseService): - """Unified embedding service for SAGE. - - This service provides a consistent interface for all embedding methods: - - Local models (sagellm engine with sentence-transformers) - - API-based services (OpenAI, Jina, Zhipu, Cohere, etc.) - - Hash-based and mock embeddings (for testing) - - Engine Types: - - sagellm: Local embedding using sentence-transformers (default, recommended) - - openai: OpenAI embedding API (requires api_key) - - (others): Passed through to EmbeddingFactory - - Examples: - # Local embedding with sagellm (recommended): - services: - embedding: - class: sage.common.components.sage_embedding.EmbeddingService - config: - engine: "sagellm" - model: "BAAI/bge-small-zh-v1.5" - batch_size: 32 - normalize: true - - # Legacy config (still supported): - services: - embedding: - class: sage.common.components.sage_embedding.EmbeddingService - config: - method: "hf" - model: "BAAI/bge-small-zh-v1.5" - - # In pipeline/operator: - result = self.call_service("embedding", texts=["hello", "world"]) - vectors = result["vectors"] # List[List[float]] - """ - - def __init__(self, config: dict[str, Any]): - super().__init__() - self.config = EmbeddingServiceConfig.from_dict(config) - self._embedder: Any | None = None # BaseEmbedding instance or None - self._st_model: SentenceTransformer | None = None # sentence-transformers model - self._lock = threading.RLock() - self._cache: dict[str, list[float]] | None = None - self._dimension: int | None = None - - # ------------------------------------------------------------------ - # SAGE lifecycle hooks - # ------------------------------------------------------------------ - def setup(self) -> None: - """Initialize the embedding service.""" - self.logger.info( - f"EmbeddingService setup starting: engine={self.config.engine}, " - f"method={self.config.method}, model={self.config.model}" - ) - - with self._lock: - if self.config.engine == "sagellm": - # Use sagellm engine: local embedding with sentence-transformers - self._setup_sagellm_engine() - elif self.config.engine == "vllm": - # Use vLLM service for embeddings (deprecated) - warnings.warn( - "engine='vllm' is deprecated. Use engine='sagellm' for local embedding " - "or configure a remote embedding service.", - DeprecationWarning, - stacklevel=2, - ) - self._setup_vllm_engine() - elif self.config.engine == "openai": - # Use OpenAI API - self._setup_openai_engine() - else: - # Fallback to EmbeddingFactory for other methods - self._setup_factory_engine() - - # Setup cache if enabled - if self.config.cache_enabled: - self._cache = {} - self.logger.info(f"Embedding cache enabled: size={self.config.cache_size}") - - self.logger.info("EmbeddingService setup complete") - - def _setup_sagellm_engine(self) -> None: - """Setup sagellm engine using sentence-transformers.""" - model_name = self.config.model or "BAAI/bge-small-zh-v1.5" - - try: - from sentence_transformers import SentenceTransformer - - self.logger.info(f"Loading sentence-transformers model: {model_name}") - self._st_model = SentenceTransformer(model_name) - # Get dimension from model - test_embedding = self._st_model.encode(["test"], convert_to_numpy=True) - self._dimension = test_embedding.shape[1] - self.logger.info( - f"sentence-transformers model loaded: {model_name}, dim={self._dimension}" - ) - except ImportError as e: - raise ImportError( - "sentence-transformers is required for sagellm engine. " - "Install with: pip install sentence-transformers" - ) from e - - def _setup_vllm_engine(self) -> None: - """Setup vLLM engine (deprecated).""" - if not self.config.vllm_service_name: - raise ValueError("vLLM engine requires 'vllm_service_name' in config") - self.logger.info(f"Using vLLM service: {self.config.vllm_service_name}") - # Don't create embedder - will use service call - - def _setup_openai_engine(self) -> None: - """Setup OpenAI embedding engine.""" - kwargs: dict[str, Any] = {} - if self.config.model: - kwargs["model"] = self.config.model - if self.config.api_key: - kwargs["api_key"] = self.config.api_key - if self.config.base_url: - kwargs["base_url"] = self.config.base_url - kwargs.update(self.config.config) - - self._embedder = EmbeddingFactory.create("openai", **kwargs) - self._dimension = self._embedder.get_dim() - self.logger.info(f"OpenAI embedding initialized: dim={self._dimension}") - - def _setup_factory_engine(self) -> None: - """Setup embedding using EmbeddingFactory (fallback).""" - kwargs = dict(self.config.config) - if self.config.model: - kwargs["model"] = self.config.model - if self.config.api_key: - kwargs["api_key"] = self.config.api_key - if self.config.base_url: - kwargs["base_url"] = self.config.base_url - - self._embedder = EmbeddingFactory.create(self.config.method, **kwargs) - self._dimension = self._embedder.get_dim() - self.logger.info(f"Embedding model loaded via factory: dim={self._dimension}") - - def cleanup(self) -> None: - """Clean up resources.""" - with self._lock: - if self._embedder is not None: - if hasattr(self._embedder, "cleanup"): - self._embedder.cleanup() # type: ignore - self._embedder = None - if self._st_model is not None: - # sentence-transformers doesn't have explicit cleanup - self._st_model = None - if self._cache is not None: - self._cache.clear() - self._cache = None - self.logger.info("EmbeddingService cleanup complete") - - # ------------------------------------------------------------------ - # Public service API - # ------------------------------------------------------------------ - def process(self, payload: dict[str, Any]) -> Any: - """Process embedding requests. - - Payload format: - { - "task": "embed", # or "info", "list_methods" - "inputs": str | List[str], # Text(s) to embed - "options": { - "normalize": bool, - "batch_size": int, - "return_stats": bool, - } - } - """ - task = (payload or {}).get("task", "embed") - inputs = (payload or {}).get("inputs") - options = (payload or {}).get("options", {}) - - if task == "embed": - if inputs is None: - raise ValueError("'inputs' is required for task 'embed'") - return self.embed(inputs, **options) - if task == "info": - return self.get_info() - if task == "list_methods": - return self.list_methods() - if task == "get_dimension": - return {"dimension": self.get_dimension()} - - raise ValueError(f"Unsupported task '{task}'") - - def embed( - self, - texts: str | list[str], - *, - normalize: bool | None = None, - batch_size: int | None = None, - return_stats: bool = False, - ) -> dict[str, Any]: - """Generate embeddings for text(s). - - Args: - texts: Single text or list of texts - normalize: Override config normalize setting - batch_size: Override config batch_size - return_stats: Include embedding statistics - - Returns: - { - "vectors": List[List[float]], - "dimension": int, - "count": int, - "method": str, - "model": str, - "stats": {...} # if return_stats=True - } - """ - # Normalize inputs - if isinstance(texts, str): - texts = [texts] - elif not isinstance(texts, list): - texts = list(texts) - - if not texts: - return { - "vectors": [], - "dimension": self.get_dimension(), - "count": 0, - "engine": self.config.engine, - "method": self.config.method, - "model": self.config.model, - } - - # Ensure setup has been called - if self.config.engine == "sagellm" and self._st_model is None: - raise RuntimeError("EmbeddingService not setup. Call setup() first.") - if self.config.engine == "vllm" and not self.config.vllm_service_name: - raise RuntimeError("vLLM engine requires vllm_service_name") - if self.config.engine not in ("sagellm", "vllm") and self._embedder is None: - raise RuntimeError("EmbeddingService not setup. Call setup() first.") - - normalize = normalize if normalize is not None else self.config.normalize - batch_size = batch_size or self.config.batch_size - - # Check cache - cached_results = [] - uncached_texts = [] - uncached_indices = [] - - if self.config.cache_enabled and self._cache is not None: - for i, text in enumerate(texts): - if text in self._cache: - cached_results.append((i, self._cache[text])) - else: - uncached_texts.append(text) - uncached_indices.append(i) - else: - uncached_texts = texts - uncached_indices = list(range(len(texts))) - - # Generate embeddings for uncached texts - vectors = [None] * len(texts) - - if uncached_texts: - if self.config.engine == "sagellm": - # Use sentence-transformers for local embedding - assert self._st_model is not None - uncached_vectors = self._embed_with_sentence_transformers( - uncached_texts, normalize, batch_size - ) - elif self.config.engine == "vllm": - # Use vLLM service - if not self.config.vllm_service_name: - raise ValueError("vllm_service_name is required for vLLM engine") - result = self.call_service( - self.config.vllm_service_name, - payload={ - "task": "embed", - "inputs": uncached_texts, - "options": { - "normalize": normalize, - "batch_size": batch_size, - }, - }, - ) - uncached_vectors = result["vectors"] - else: - # Use standard embedder (factory-based) - assert self._embedder is not None - uncached_vectors = self._embed_with_factory(uncached_texts, normalize, batch_size) - - # Update cache and results - for idx, text, vec in zip( - uncached_indices, uncached_texts, uncached_vectors, strict=False - ): - vectors[idx] = vec - if self.config.cache_enabled and self._cache is not None: - # LRU eviction - if len(self._cache) >= self.config.cache_size: - self._cache.pop(next(iter(self._cache))) - self._cache[text] = vec - - # Add cached results - for idx, vec in cached_results: - vectors[idx] = vec - - # Build response - first_vector: list[float] | None = vectors[0] if vectors else None - dimension = len(first_vector) if first_vector is not None else self.get_dimension() - - result = { - "vectors": vectors, - "dimension": dimension, - "count": len(vectors), - "engine": self.config.engine, - "method": self.config.method, - "model": self.config.model or self.config.method, - } - - if return_stats: - result["stats"] = { - "cached": len(cached_results), - "computed": len(uncached_texts), - "cache_hit_rate": len(cached_results) / len(texts) if texts else 0.0, - } - - return result - - def get_dimension(self) -> int: - """Get embedding dimension.""" - if self._dimension is not None: - return self._dimension - - if self.config.engine == "sagellm": - # sentence-transformers: get dimension from model - if self._st_model is not None: - test_embedding = self._st_model.encode(["test"], convert_to_numpy=True) - self._dimension = test_embedding.shape[1] - else: - self._dimension = 768 # Default for BERT-based models - elif self.config.engine == "vllm": - # Query vLLM service - if not self.config.vllm_service_name: - raise ValueError("vllm_service_name is required for vLLM engine") - result = self.call_service( - self.config.vllm_service_name, - payload={"task": "embed", "inputs": "test"}, - ) - self._dimension = result.get("dimension", 768) - elif self._embedder is not None: - self._dimension = self._embedder.get_dim() - else: - self._dimension = 768 # Default - - assert self._dimension is not None - return self._dimension - - def get_info(self) -> dict[str, Any]: - """Get embedding service information.""" - info: dict[str, Any] = { - "engine": self.config.engine, - "method": self.config.method, - "model": self.config.model, - "dimension": self.get_dimension(), - "batch_size": self.config.batch_size, - "normalize": self.config.normalize, - "cache_enabled": self.config.cache_enabled, - } - - if self.config.cache_enabled and self._cache is not None: - cache_stats: dict[str, int] = { - "size": len(self._cache), - "capacity": self.config.cache_size, - } - info["cache_stats"] = cache_stats - - if self.config.engine == "vllm": - info["vllm_service"] = self.config.vllm_service_name - - return info - - def list_methods(self) -> list[dict[str, Any]]: - """List all available embedding methods.""" - methods = [] - - # Add sagellm (local) method first - methods.append( - { - "name": "sagellm", - "description": "Local embedding with sentence-transformers (recommended)", - "requires_api_key": False, - "requires_model_download": True, - "status": "available", - "engine": "sagellm", - } - ) - - # Add registered methods from EmbeddingRegistry - for method in EmbeddingRegistry.list_methods(): - info = EmbeddingRegistry.get_model_info(method) - if info: - # Determine status based on requirements - if info.requires_api_key: - status = "needs_api_key" - elif info.requires_model_download: - status = "needs_download" - else: - status = "available" - - methods.append( - { - "name": method, - "description": info.description, - "requires_api_key": info.requires_api_key, - "requires_model_download": info.requires_model_download, - "status": status, - "engine": method, # Use method name as engine - } - ) - - # Add vLLM method (deprecated) - methods.append( - { - "name": "vllm", - "description": "High-performance vLLM embedding service (deprecated, use sagellm)", - "requires_api_key": False, - "requires_model_download": True, - "status": "deprecated", - "engine": "vllm", - } - ) - - return methods - - # ------------------------------------------------------------------ - # Internal helpers - # ------------------------------------------------------------------ - def _embed_with_sentence_transformers( - self, texts: list[str], normalize: bool, batch_size: int - ) -> list[list[float]]: - """Generate embeddings using sentence-transformers.""" - assert self._st_model is not None - - all_vectors: list[list[float]] = [] - for i in range(0, len(texts), batch_size): - batch = texts[i : i + batch_size] - # sentence-transformers handles batching internally - embeddings = self._st_model.encode( - batch, - convert_to_numpy=True, - normalize_embeddings=normalize, - ) - # Convert numpy array to list of lists - all_vectors.extend(embeddings.tolist()) - - return all_vectors - - def _embed_with_factory( - self, texts: list[str], normalize: bool, batch_size: int - ) -> list[list[float]]: - """Generate embeddings using EmbeddingFactory embedder.""" - assert self._embedder is not None - - uncached_vectors: list[list[float]] = [] - for i in range(0, len(texts), batch_size): - batch = texts[i : i + batch_size] - if len(batch) == 1: - vec = self._embedder.embed(batch[0]) - if normalize: - vec = self._normalize_vector(vec) - uncached_vectors.append(vec) - else: - batch_vecs = self._embedder.embed_batch(batch) - if normalize: - batch_vecs = [self._normalize_vector(v) for v in batch_vecs] - uncached_vectors.extend(batch_vecs) - - return uncached_vectors - - def _normalize_vector(self, vec: list[float]) -> list[float]: - """Normalize a vector to unit length.""" - array = np.array(vec, dtype=np.float32) - norm = np.linalg.norm(array) - if norm > 0: - array = array / norm - return array.tolist() - - -__all__ = ["EmbeddingService", "EmbeddingServiceConfig"] diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/siliconcloud.py b/packages/sage-common/src/sage/common/components/sage_embedding/siliconcloud.py deleted file mode 100644 index 1fb640570a..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/siliconcloud.py +++ /dev/null @@ -1,109 +0,0 @@ -import base64 -import struct - -import requests - -pass - -import aiohttp # noqa: E402 - - -async def siliconcloud_embedding( - text: str, - model: str = "netease-youdao/bce-embedding-base_v1", - base_url: str = "https://api.siliconflow.cn/v1/embeddings", - max_token_size: int = 512, - api_key: str | None = None, -) -> list: - """ - Generate embedding for a single text using SiliconCloud (NetEase Youdao). - - Args: - text: Input string - model: Embedding model name - base_url: API endpoint - max_token_size: Max text length in tokens (cut if needed) - api_key: Your SiliconCloud API key - - Returns: - list[float]: The embedding vector - """ - if api_key and not api_key.startswith("Bearer "): - api_key = "Bearer " + api_key # pragma: allowlist secret - - headers = { - "Authorization": api_key, - "Content-Type": "application/json", - } - - text = text[:max_token_size] - payload = { - "model": model, - "input": [text], - "encoding_format": "base64", - } - - async with aiohttp.ClientSession() as session: - async with session.post(base_url, headers=headers, json=payload) as response: - content = await response.json() - if "code" in content: - raise ValueError(content) - base64_string = content["data"][0]["embedding"] - - decode_bytes = base64.b64decode(base64_string) - n = len(decode_bytes) // 4 - float_array = struct.unpack("<" + "f" * n, decode_bytes) - return list(float_array) - - -def siliconcloud_embedding_sync( - text: str, - model: str = "netease-youdao/bce-embedding-base_v1", - base_url: str = "https://api.siliconflow.cn/v1/embeddings", - max_token_size: int = 512, - api_key: str | None = None, -) -> list[float]: - """ - 同步版本：使用 SiliconCloud (NetEase Youdao) 接口获取文本 embedding。 - - Args: - text: 输入文本 - model: 模型名称 - base_url: 接口地址 - max_token_size: 截断长度（按字符） - api_key: API 密钥（可选，带或不带 "Bearer "） - - Returns: - list[float]: embedding 向量 - """ - if api_key and not api_key.startswith("Bearer "): - api_key = "Bearer " + api_key # pragma: allowlist secret - - headers = { - "Authorization": api_key, # pragma: allowlist secret - "Content-Type": "application/json", - } - - text = text[:max_token_size] - payload = { - "model": model, - "input": [text], - "encoding_format": "base64", - } - - try: - response = requests.post(base_url, headers=headers, json=payload) - response.raise_for_status() - content = response.json() - - if "code" in content: - raise ValueError(f"SiliconCloud API error: {content}") - - base64_string = content["data"][0]["embedding"] - decode_bytes = base64.b64decode(base64_string) - n = len(decode_bytes) // 4 - float_array = struct.unpack("<" + "f" * n, decode_bytes) - return list(float_array) - - except Exception as e: - raise RuntimeError(f"SiliconCloud embedding failed: {str(e)}") diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/stop_embedding_server.sh b/packages/sage-common/src/sage/common/components/sage_embedding/stop_embedding_server.sh deleted file mode 100755 index 8d36139b35..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/stop_embedding_server.sh +++ /dev/null @@ -1,46 +0,0 @@ -#!/bin/bash -# 停止 Embedding 服务器 -# 使用方法: ./stop_embedding_server.sh [port] - -set -e - -# 默认端口 -PORT="${1:-8090}" - -echo "==========================================" -echo "Stopping Embedding Server on port ${PORT}" -echo "==========================================" - -# 查找占用指定端口的进程 -PID=$(lsof -ti:${PORT} 2>/dev/null || echo "") - -if [ -z "$PID" ]; then - echo "No process found on port ${PORT}" - exit 0 -fi - -echo "Found process(es): ${PID}" - -# 尝试优雅关闭 (SIGTERM) -echo "Sending SIGTERM signal..." -kill -15 ${PID} 2>/dev/null || true - -# 等待进程结束 -sleep 2 - -# 检查进程是否还在运行 -if ps -p ${PID} > /dev/null 2>&1; then - echo "Process still running, forcing shutdown (SIGKILL)..." - kill -9 ${PID} 2>/dev/null || true - sleep 1 -fi - -# 再次检查 -if lsof -ti:${PORT} > /dev/null 2>&1; then - echo "Warning: Port ${PORT} is still in use" - exit 1 -else - echo "Server stopped successfully" -fi - -echo "==========================================" diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/__init__.py b/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/__init__.py deleted file mode 100644 index 2492c3a121..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/__init__.py +++ /dev/null @@ -1,78 +0,0 @@ -"""Wrapper classes for various embedding providers. - -This module uses lazy imports to avoid loading heavy dependencies -(transformers, torch, etc.) until they are actually needed. -""" - -from typing import TYPE_CHECKING - -# 轻量级的 wrapper 可以直接导入 -from .hash_wrapper import HashEmbedding -from .mock_wrapper import MockEmbedding - -# 重量级的 wrapper 使用延迟导入，避免在模块加载时就加载大型依赖 -if TYPE_CHECKING: - from .bedrock_wrapper import BedrockEmbedding - from .cohere_wrapper import CohereEmbedding - from .hf_wrapper import HFEmbedding - from .jina_wrapper import JinaEmbedding - from .nvidia_openai_wrapper import NvidiaOpenAIEmbedding - from .ollama_wrapper import OllamaEmbedding - from .openai_wrapper import OpenAIEmbedding - from .siliconcloud_wrapper import SiliconCloudEmbedding - from .zhipu_wrapper import ZhipuEmbedding - -__all__ = [ - "HashEmbedding", - "MockEmbedding", - "HFEmbedding", - "OpenAIEmbedding", - "JinaEmbedding", - "ZhipuEmbedding", - "CohereEmbedding", - "BedrockEmbedding", - "OllamaEmbedding", - "SiliconCloudEmbedding", - "NvidiaOpenAIEmbedding", -] - - -def __getattr__(name: str): - """延迟导入重量级 wrapper，只在实际使用时才加载依赖""" - if name == "HFEmbedding": - from .hf_wrapper import HFEmbedding - - return HFEmbedding - elif name == "OpenAIEmbedding": - from .openai_wrapper import OpenAIEmbedding - - return OpenAIEmbedding - elif name == "JinaEmbedding": - from .jina_wrapper import JinaEmbedding - - return JinaEmbedding - elif name == "ZhipuEmbedding": - from .zhipu_wrapper import ZhipuEmbedding - - return ZhipuEmbedding - elif name == "CohereEmbedding": - from .cohere_wrapper import CohereEmbedding - - return CohereEmbedding - elif name == "BedrockEmbedding": - from .bedrock_wrapper import BedrockEmbedding - - return BedrockEmbedding - elif name == "OllamaEmbedding": - from .ollama_wrapper import OllamaEmbedding - - return OllamaEmbedding - elif name == "SiliconCloudEmbedding": - from .siliconcloud_wrapper import SiliconCloudEmbedding - - return SiliconCloudEmbedding - elif name == "NvidiaOpenAIEmbedding": - from .nvidia_openai_wrapper import NvidiaOpenAIEmbedding - - return NvidiaOpenAIEmbedding - raise AttributeError(f"module {__name__!r} has no attribute {name!r}") diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/bedrock_wrapper.py b/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/bedrock_wrapper.py deleted file mode 100644 index c69ebc78ed..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/bedrock_wrapper.py +++ /dev/null @@ -1,266 +0,0 @@ -"""AWS Bedrock embedding wrapper.""" - -import os -from typing import Any - -from ..base import BaseEmbedding - - -class BedrockEmbedding(BaseEmbedding): - """AWS Bedrock Embedding Wrapper - - 支持通过 AWS Bedrock 访问多种 embedding 模型。 - - 特点: - - ✅ AWS 托管服务 - - ✅ 多种模型选择（Amazon Titan、Cohere） - - ✅ 企业级安全 - - ✅ 灵活的认证方式 - - ❌ 需要 AWS 凭证 - - ❌ 需要开通 Bedrock 服务 - - 💰 按使用量计费 - - 支持的模型: - Amazon Titan: - - amazon.titan-embed-text-v2:0 (默认，1024维) - - amazon.titan-embed-text-v1 (1536维) - - Cohere: - - cohere.embed-multilingual-v3 (1024维) - - cohere.embed-english-v3 (1024维) - - Args: - model: 模型 ID（默认 'amazon.titan-embed-text-v2:0'） - aws_access_key_id: AWS Access Key（可选，默认从环境变量读取） - aws_secret_access_key: AWS Secret Key（可选，默认从环境变量读取） - aws_session_token: AWS Session Token（可选，用于临时凭证） - - Examples: - >>> # 使用环境变量认证 - >>> # export AWS_ACCESS_KEY_ID='...' - >>> # export AWS_SECRET_ACCESS_KEY='...' - >>> emb = BedrockEmbedding(model="amazon.titan-embed-text-v2:0") - >>> vec = emb.embed("hello world") - >>> - >>> # 显式传递凭证 - >>> emb = BedrockEmbedding( - ... model="amazon.titan-embed-text-v2:0", - ... aws_access_key_id="your-key-id", # pragma: allowlist secret - ... aws_secret_access_key="your-secret-key" # pragma: allowlist secret - ... ) - >>> vec = emb.embed("hello world") - >>> - >>> # 使用 Cohere 模型 - >>> emb = BedrockEmbedding(model="cohere.embed-multilingual-v3") - >>> vec = emb.embed("你好世界") - """ - - # 模型维度映射 - DIMENSION_MAP = { - "amazon.titan-embed-text-v2:0": 1024, - "amazon.titan-embed-text-v1": 1536, - "cohere.embed-multilingual-v3": 1024, - "cohere.embed-english-v3": 1024, - } - - def __init__( - self, - model: str = "amazon.titan-embed-text-v2:0", - aws_access_key_id: str | None = None, - aws_secret_access_key: str | None = None, - aws_session_token: str | None = None, - **kwargs: Any, - ) -> None: - """初始化 Bedrock Embedding - - Args: - model: 模型 ID - aws_access_key_id: AWS Access Key（可选） - aws_secret_access_key: AWS Secret Key（可选） - aws_session_token: AWS Session Token（可选） - **kwargs: 其他参数（保留用于扩展） - - Raises: - ImportError: 如果未安装 boto3 - RuntimeError: 如果未配置 AWS 凭证 - """ - super().__init__( - model=model, - aws_access_key_id=aws_access_key_id, - aws_secret_access_key=aws_secret_access_key, - aws_session_token=aws_session_token, - **kwargs, - ) - - # 检查依赖 - try: - import boto3 # noqa: F401 - except ImportError: - raise ImportError("Bedrock embedding 需要 boto3 包。\n安装方法: pip install boto3") - - self._model = model - self._aws_access_key_id = aws_access_key_id or os.getenv("AWS_ACCESS_KEY_ID") - self._aws_secret_access_key = aws_secret_access_key or os.getenv("AWS_SECRET_ACCESS_KEY") - self._aws_session_token = aws_session_token or os.getenv("AWS_SESSION_TOKEN") - self._kwargs = kwargs - - # 检查凭证 - if not (self._aws_access_key_id and self._aws_secret_access_key): - raise RuntimeError( - "Bedrock embedding 需要 AWS 凭证。\n" - "解决方案:\n" - " 1. 设置环境变量:\n" - " export AWS_ACCESS_KEY_ID='your-key-id'\n" # pragma: allowlist secret - " export AWS_SECRET_ACCESS_KEY='your-secret-key'\n" # pragma: allowlist secret - " 2. 传递参数:\n" - " BedrockEmbedding(\n" - " aws_access_key_id='...',\n" # pragma: allowlist secret - " aws_secret_access_key='...'\n" # pragma: allowlist secret - " )\n" - " 3. 配置 AWS CLI: aws configure\n" - "\n" - "获取凭证: https://console.aws.amazon.com/iam/" - ) - - # 获取维度 - self._dim = self.DIMENSION_MAP.get(model, 1024) - - def embed(self, text: str) -> list[float]: - """将文本转换为 embedding 向量 - - Args: - text: 输入文本 - - Returns: - embedding 向量 - - Raises: - RuntimeError: 如果 API 调用失败 - """ - try: - import json - - import boto3 - - # 设置环境变量（boto3 会自动读取） - if self._aws_access_key_id: - os.environ["AWS_ACCESS_KEY_ID"] = self._aws_access_key_id - if self._aws_secret_access_key: - os.environ["AWS_SECRET_ACCESS_KEY"] = self._aws_secret_access_key - if self._aws_session_token: - os.environ["AWS_SESSION_TOKEN"] = self._aws_session_token - - bedrock_client = boto3.client("bedrock-runtime") - model_provider = self._model.split(".")[0] - - if model_provider == "amazon": - if "v2" in self._model: - body = json.dumps( - { - "inputText": text, - "embeddingTypes": ["float"], - } - ) - elif "v1" in self._model: - body = json.dumps({"inputText": text}) - else: - raise ValueError(f"不支持的模型: {self._model}") - - response = bedrock_client.invoke_model( - modelId=self._model, - body=body, - accept="application/json", - contentType="application/json", - ) - response_body = json.loads(response["body"].read()) - return response_body["embedding"] - - elif model_provider == "cohere": - body = json.dumps( - { - "texts": [text], - "input_type": "search_document", - "truncate": "NONE", - } - ) - - response = bedrock_client.invoke_model( - modelId=self._model, - body=body, - accept="application/json", - contentType="application/json", - ) - response_body = json.loads(response["body"].read()) - return response_body["embeddings"][0] - - else: - raise ValueError(f"不支持的模型提供商: {model_provider}") - - except Exception as e: - raise RuntimeError( - f"Bedrock embedding 失败: {e}\n" - f"模型: {self._model}\n" - f"文本: {text[:100]}...\n" - f"提示: 检查 AWS 凭证、区域设置、Bedrock 服务开通状态" - ) from e - - def embed_batch(self, texts: list[str]) -> list[list[float]]: - """批量将文本转换为 embedding 向量 - - 当前实现为逐个调用 embed()。 - TODO: 如果模型支持批量接口，可以优化。 - Issue URL: https://github.com/intellistream/SAGE/issues/908 - - Args: - texts: 输入文本列表 - - Returns: - embedding 向量列表 - """ - # TODO: 检查 Bedrock API 是否支持批量 - # Issue URL: https://github.com/intellistream/SAGE/issues/907 - return [self.embed(text) for text in texts] - - def get_dim(self) -> int: - """获取向量维度 - - Returns: - 维度值 - """ - return self._dim - - @property - def method_name(self) -> str: - """返回方法名称 - - Returns: - 'bedrock' - """ - return "bedrock" - - @classmethod - def get_model_info(cls) -> dict[str, Any]: - """返回模型元信息 - - Returns: - 模型信息字典 - """ - return { - "method": "bedrock", - "requires_api_key": True, # AWS 凭证 - "requires_model_download": False, - "default_dimension": 1024, - "features": [ - "AWS 托管服务", - "多种模型选择（Amazon Titan、Cohere）", - "企业级安全", - ], - } - - def __repr__(self) -> str: - """返回对象的字符串表示 - - Returns: - 字符串表示 - """ - return f"BedrockEmbedding(model='{self._model}', dim={self._dim})" diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/cohere_wrapper.py b/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/cohere_wrapper.py deleted file mode 100644 index 27e7bafe45..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/cohere_wrapper.py +++ /dev/null @@ -1,229 +0,0 @@ -"""Cohere embedding wrapper.""" - -import os -from typing import Any - -from ..base import BaseEmbedding - - -class CohereEmbedding(BaseEmbedding): - """Cohere Embedding API Wrapper - - 支持 Cohere 的多语言 embedding 服务。 - - 特点: - - ✅ 多语言支持（100+ 语言） - - ✅ 多种 input_type（search/classification） - - ✅ 高质量向量 - - ✅ 原生批量支持 - - ❌ 需要 API Key - - ❌ 需要网络连接 - - 💰 按使用量计费 - - 支持的模型: - - embed-multilingual-v3.0 (默认，1024维，多语言) - - embed-english-v3.0 (1024维，英文专用) - - embed-multilingual-light-v3.0 (384维，轻量级) - - embed-english-light-v3.0 (384维，英文轻量) - - Args: - model: 模型名称（默认 'embed-multilingual-v3.0'） - input_type: 输入类型（'search_document', 'search_query', 'classification', 'clustering'） - api_key: API 密钥（可选，默认从环境变量 COHERE_API_KEY 读取） - embedding_types: 返回格式（默认 ['float']） - - Examples: - >>> # 基本使用 - >>> import os - >>> emb = CohereEmbedding( - ... model="embed-multilingual-v3.0", - ... api_key=os.getenv("COHERE_API_KEY") - ... ) - >>> vec = emb.embed("hello world") - >>> - >>> # 搜索场景（不同 input_type） - >>> # 文档端 - >>> doc_emb = CohereEmbedding(input_type="search_document") - >>> doc_vec = doc_emb.embed("这是一篇关于机器学习的文档") - >>> - >>> # 查询端 - >>> query_emb = CohereEmbedding(input_type="search_query") - >>> query_vec = query_emb.embed("什么是机器学习") - >>> - >>> # 分类场景 - >>> clf_emb = CohereEmbedding(input_type="classification") - >>> clf_vec = clf_emb.embed("这是一条正面评价") - """ - - # 模型维度映射 - DIMENSION_MAP = { - "embed-multilingual-v3.0": 1024, - "embed-english-v3.0": 1024, - "embed-multilingual-light-v3.0": 384, - "embed-english-light-v3.0": 384, - } - - def __init__( - self, - model: str = "embed-multilingual-v3.0", - input_type: str = "classification", - api_key: str | None = None, - embedding_types: list[str] | None = None, - **kwargs: Any, - ) -> None: - """初始化 Cohere Embedding - - Args: - model: 模型名称 - input_type: 输入类型 - api_key: API 密钥（可选） - embedding_types: 返回格式（可选） - **kwargs: 其他参数（保留用于扩展） - - Raises: - ImportError: 如果未安装 cohere 包 - RuntimeError: 如果未提供 API Key - """ - super().__init__( - model=model, - input_type=input_type, - api_key=api_key, - embedding_types=embedding_types, - **kwargs, - ) - - # 检查依赖 - try: - import cohere # noqa: F401 - except ImportError: - raise ImportError("Cohere embedding 需要 cohere 包。\n安装方法: pip install cohere") - - self._model = model - self._input_type = input_type - self._api_key = api_key or os.getenv("COHERE_API_KEY") - self._embedding_types = embedding_types or ["float"] - self._kwargs = kwargs - - # 检查 API Key - if not self._api_key: - raise RuntimeError( - "Cohere embedding 需要 API Key。\n" - "解决方案:\n" - " 1. 设置环境变量: export COHERE_API_KEY='your-key'\n" # pragma: allowlist secret - " 2. 传递参数: CohereEmbedding(api_key='your-key', ...)\n" # pragma: allowlist secret - "\n" - "获取 API Key: https://dashboard.cohere.com/api-keys" - ) - - # 获取维度 - self._dim = self.DIMENSION_MAP.get(model, 1024) - - def embed(self, text: str) -> list[float]: - """将文本转换为 embedding 向量 - - Args: - text: 输入文本 - - Returns: - embedding 向量 - - Raises: - RuntimeError: 如果 API 调用失败 - """ - try: - import cohere - - co = cohere.Client(api_key=self._api_key) - response = co.embed( - texts=[text], # Cohere API 要求传入列表 - model=self._model, - input_type=self._input_type, - embedding_types=self._embedding_types, - ) - return response.embeddings[0] # pyright: ignore[reportReturnType, reportIndexIssue] - except Exception as e: - raise RuntimeError( - f"Cohere embedding 失败: {e}\n" - f"模型: {self._model}\n" - f"输入类型: {self._input_type}\n" - f"文本: {text[:100]}...\n" - f"提示: 检查 API Key 是否有效，网络连接是否正常" - ) from e - - def embed_batch(self, texts: list[str]) -> list[list[float]]: - """批量将文本转换为 embedding 向量 - - Cohere API 原生支持批量操作。 - - Args: - texts: 输入文本列表 - - Returns: - embedding 向量列表 - """ - try: - import cohere - - co = cohere.Client(api_key=self._api_key) - response = co.embed( - texts=texts, - model=self._model, - input_type=self._input_type, - embedding_types=self._embedding_types, - ) - return response.embeddings # pyright: ignore[reportReturnType] - except Exception as e: - raise RuntimeError( - f"Cohere 批量 embedding 失败: {e}\n" - f"模型: {self._model}\n" - f"输入类型: {self._input_type}\n" - f"批量大小: {len(texts)}\n" - f"提示: 检查 API Key 是否有效，网络连接是否正常" - ) from e - - def get_dim(self) -> int: - """获取向量维度 - - Returns: - 维度值 - """ - return self._dim - - @property - def method_name(self) -> str: - """返回方法名称 - - Returns: - 'cohere' - """ - return "cohere" - - @classmethod - def get_model_info(cls) -> dict[str, Any]: - """返回模型元信息 - - Returns: - 模型信息字典 - """ - return { - "method": "cohere", - "requires_api_key": True, - "requires_model_download": False, - "default_dimension": 1024, - "features": [ - "多语言支持（100+ 语言）", - "多种 input_type（search/classification）", - "原生批量支持", - ], - } - - def __repr__(self) -> str: - """返回对象的字符串表示 - - Returns: - 字符串表示 - """ - return ( - f"CohereEmbedding(model='{self._model}', " - f"input_type='{self._input_type}', dim={self._dim})" - ) diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/hash_wrapper.py b/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/hash_wrapper.py deleted file mode 100644 index adfb7f87b8..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/hash_wrapper.py +++ /dev/null @@ -1,128 +0,0 @@ -"""Hash-based lightweight embedding (migrated from sage chat).""" - -import hashlib -import re -from typing import Any - -from ..base import BaseEmbedding - - -class HashEmbedding(BaseEmbedding): - """基于哈希的轻量级 Embedding - - 使用 SHA256 哈希将文本 tokens 映射到固定维度的向量空间。 - 主要用于快速测试和演示，不提供语义理解能力。 - - 特点: - - ✅ 无需下载模型 - - ✅ 无需 GPU - - ✅ 速度极快 - - ❌ 无语义理解（只能精确匹配关键词） - - 适用场景: - - 快速原型开发 - - 功能测试 - - 不需要语义理解的场景 - - Args: - dim: 向量维度（默认 384） - - Examples: - >>> emb = HashEmbedding(dim=384) - >>> vec = emb.embed("hello world") - >>> len(vec) - 384 - >>> - >>> # 相同文本产生相同向量 - >>> vec1 = emb.embed("test") - >>> vec2 = emb.embed("test") - >>> vec1 == vec2 - True - >>> - >>> # 批量处理 - >>> vecs = emb.embed_batch(["hello", "world"]) - >>> len(vecs) - 2 - """ - - def __init__(self, dim: int = 384, **kwargs: Any) -> None: - """初始化 Hash Embedding - - Args: - dim: 向量维度（最小 64） - **kwargs: 其他参数（兼容性） - """ - super().__init__(dim=dim, **kwargs) - self._dim = max(64, int(dim)) - - def embed(self, text: str) -> list[float]: - """将文本转换为哈希向量 - - 算法: - 1. 分词（提取字母数字和中文字符） - 2. 对每个 token 计算 SHA256 哈希 - 3. 将哈希值映射到向量空间 - 4. L2 归一化 - - Args: - text: 输入文本 - - Returns: - 归一化的 embedding 向量 - """ - if not text: - return [0.0] * self._dim - - vector = [0.0] * self._dim - - # 分词：提取字母数字和中文字符 - tokens = re.findall(r"[\w\u4e00-\u9fa5]+", text.lower()) - if not tokens: - tokens = [text.lower()] - - # 对每个 token 哈希 - for token in tokens: - digest = hashlib.sha256(token.encode("utf-8")).digest() - - # 将哈希值的每 4 字节映射到向量的一个位置 - for offset in range(0, len(digest), 4): - chunk = digest[offset : offset + 4] - if len(chunk) < 4: - chunk = chunk.ljust(4, b"\0") - idx = int.from_bytes(chunk, "little") % self._dim - vector[idx] += 1.0 - - # L2 归一化 - norm = sum(v * v for v in vector) ** 0.5 or 1.0 - return [v / norm for v in vector] - - def get_dim(self) -> int: - """获取向量维度 - - Returns: - 维度值 - """ - return self._dim - - @property - def method_name(self) -> str: - """返回方法名称 - - Returns: - 'hash' - """ - return "hash" - - @classmethod - def get_model_info(cls) -> dict[str, Any]: - """返回模型元信息 - - Returns: - 模型信息字典 - """ - return { - "method": "hash", - "requires_api_key": False, - "requires_model_download": False, - "default_dimension": 384, - } diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/hf_wrapper.py b/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/hf_wrapper.py deleted file mode 100644 index 1ba13d104d..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/hf_wrapper.py +++ /dev/null @@ -1,178 +0,0 @@ -"""HuggingFace embedding wrapper.""" - -from typing import Any - -from ..base import BaseEmbedding -from ..hf import hf_embed_batch_sync, hf_embed_sync # 复用现有实现 - - -class HFEmbedding(BaseEmbedding): - """HuggingFace Transformer Embedding Wrapper - - 使用本地 HuggingFace Transformer 模型生成 embedding。 - 首次使用时会自动从 HuggingFace Hub 下载模型。 - - 特点: - - ✅ 高质量语义 embedding - - ✅ 本地运行，数据隐私 - - ✅ 支持多种语言 - - ❌ 需要下载模型（首次使用） - - ❌ 可能需要 GPU 加速（大模型） - - 推荐模型: - - BAAI/bge-small-zh-v1.5 (中文，512维) - - BAAI/bge-base-zh-v1.5 (中文，768维) - - BAAI/bge-large-zh-v1.5 (中文，1024维) - - sentence-transformers/all-MiniLM-L6-v2 (英文，384维) - - sentence-transformers/all-mpnet-base-v2 (英文，768维) - - Args: - model: 模型名称（HuggingFace Hub） - - Examples: - >>> # 中文 embedding - >>> emb = HFEmbedding(model="BAAI/bge-small-zh-v1.5") - >>> vec = emb.embed("你好世界") - >>> len(vec) - 512 - >>> - >>> # 英文 embedding - >>> emb = HFEmbedding(model="sentence-transformers/all-MiniLM-L6-v2") - >>> vec = emb.embed("hello world") - >>> len(vec) - 384 - >>> - >>> # 批量处理 - >>> vecs = emb.embed_batch(["文本1", "文本2", "文本3"]) - >>> len(vecs) - 3 - """ - - def __init__(self, model: str, **kwargs: Any) -> None: - """初始化 HuggingFace Embedding - - Args: - model: 模型名称（如 'BAAI/bge-small-zh-v1.5'） - **kwargs: 其他参数（保留用于扩展） - - Raises: - RuntimeError: 如果模型加载失败 - """ - super().__init__(model=model, **kwargs) - - try: - from transformers import AutoModel, AutoTokenizer - except ImportError as e: - raise RuntimeError( - "HuggingFace embedding 需要 transformers 库。\n" - "请安装: pip install transformers torch" - ) from e - - try: - self.tokenizer = AutoTokenizer.from_pretrained(model) - self.embed_model = AutoModel.from_pretrained(model, trust_remote_code=True) - except Exception as e: - raise RuntimeError( - f"Failed to load HuggingFace model '{model}': {e}\n" - f"提示:\n" - f" 1. 检查模型名称是否正确\n" - f" 2. 检查网络连接（首次使用需要下载）\n" - f" 3. 设置镜像: export HF_ENDPOINT=https://hf-mirror.com" - ) from e - - # 推断维度 - self._dim = self._infer_dimension() - self._model_name = model - - def embed(self, text: str) -> list[float]: - """将文本转换为 embedding 向量 - - Args: - text: 输入文本 - - Returns: - embedding 向量 - - Raises: - RuntimeError: 如果 embedding 失败 - """ - try: - return hf_embed_sync(text, self.tokenizer, self.embed_model) - except Exception as e: - raise RuntimeError(f"HuggingFace embedding 失败: {e}\n文本: {text[:100]}...") from e - - def embed_batch(self, texts: list[str]) -> list[list[float]]: - """批量将文本转换为 embedding 向量 - - 使用真正的批量处理，通过一次前向传播处理多个文本，显著提高效率。 - - Args: - texts: 输入文本列表 - - Returns: - embedding 向量列表 - - Raises: - RuntimeError: 如果 embedding 失败 - """ - if not texts: - return [] - - try: - return hf_embed_batch_sync(texts, self.tokenizer, self.embed_model) - except Exception as e: - raise RuntimeError( - f"HuggingFace batch embedding 失败: {e}\n文本数量: {len(texts)}" - ) from e - - def get_dim(self) -> int: - """获取向量维度 - - Returns: - 维度值 - """ - return self._dim - - @property - def method_name(self) -> str: - """返回方法名称 - - Returns: - 'hf' - """ - return "hf" - - def _infer_dimension(self) -> int: - """通过 embedding 一个示例文本推断维度 - - Returns: - 推断的维度值（如果失败则返回默认值 768） - """ - try: - sample = self.embed("test") - return len(sample) - except Exception: - # 如果推断失败，返回常见的默认维度 - return 768 - - @classmethod - def get_model_info(cls) -> dict[str, Any]: - """返回模型元信息 - - Returns: - 模型信息字典 - """ - return { - "method": "hf", - "requires_api_key": False, - "requires_model_download": True, - "default_dimension": None, # 动态推断 - } - - def __repr__(self) -> str: - """返回对象的字符串表示 - - Returns: - 字符串表示 - """ - return f"HFEmbedding(model='{self._model_name}', dim={self._dim})" diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/jina_wrapper.py b/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/jina_wrapper.py deleted file mode 100644 index 9eba1df16c..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/jina_wrapper.py +++ /dev/null @@ -1,254 +0,0 @@ -"""Jina AI embedding wrapper.""" - -import os -from typing import Any - -from ..base import BaseEmbedding -from ..jina import jina_embed_sync # 复用现有实现 - - -class JinaEmbedding(BaseEmbedding): - """Jina AI Embedding API Wrapper - - 支持 Jina AI 的多语言、多模态 embedding 服务。 - - 特点: - - ✅ 多语言支持（100+ 语言） - - ✅ 长文本处理（8192 tokens） - - ✅ Late Chunking 技术 - - ✅ 可调维度（32-1024） - - ❌ 需要 API Key - - ❌ 需要网络连接 - - 💰 按使用量计费 - - 支持的模型: - - jina-embeddings-v3 (默认，多语言，可调维度) - - jina-embeddings-v2-base-en (英文专用) - - jina-clip-v1 (多模态：文本+图像) - - Args: - model: 模型名称（默认 'jina-embeddings-v3'） - dimensions: embedding 维度（默认 1024，范围 32-1024） - late_chunking: 是否启用 late chunking（默认 False） - api_key: API 密钥（可选，默认从环境变量 JINA_API_KEY 读取） - base_url: API 端点（可选，用于自托管） - - Examples: - >>> # 基本使用 - >>> import os - >>> emb = JinaEmbedding( - ... model="jina-embeddings-v3", - ... api_key=os.getenv("JINA_API_KEY") - ... ) - >>> vec = emb.embed("hello world") - >>> - >>> # 自定义维度（降维节省成本） - >>> emb = JinaEmbedding( - ... dimensions=256, - ... api_key=os.getenv("JINA_API_KEY") - ... ) - >>> vec = emb.embed("你好世界") - >>> assert len(vec) == 256 - >>> - >>> # Late Chunking（长文本处理） - >>> emb = JinaEmbedding( - ... late_chunking=True, - ... api_key=os.getenv("JINA_API_KEY") - ... ) - >>> long_text = "..." * 1000 - >>> vec = emb.embed(long_text) - """ - - def __init__( - self, - model: str = "jina-embeddings-v3", - dimensions: int = 1024, - late_chunking: bool = False, - api_key: str | None = None, - base_url: str | None = None, - **kwargs: Any, - ) -> None: - """初始化 Jina Embedding - - Args: - model: 模型名称 - dimensions: embedding 维度（32-1024） - late_chunking: 是否启用 late chunking - api_key: API 密钥（可选） - base_url: API 端点（可选） - **kwargs: 其他参数（保留用于扩展） - - Raises: - RuntimeError: 如果未提供 API Key - ValueError: 如果维度超出范围 - """ - super().__init__( - model=model, - dimensions=dimensions, - late_chunking=late_chunking, - api_key=api_key, - base_url=base_url, - **kwargs, - ) - - self._model = model - self._dimensions = dimensions - self._late_chunking = late_chunking - self._api_key = api_key or os.getenv("JINA_API_KEY") - self._base_url = base_url - - # 检查 API Key - if not self._api_key: - raise RuntimeError( - "Jina embedding 需要 API Key。\n" # pragma: allowlist secret - "解决方案:\n" - " 1. 设置环境变量: export JINA_API_KEY='your-key'\n" # pragma: allowlist secret - " 2. 传递参数: JinaEmbedding(api_key='your-key', ...)\n" # pragma: allowlist secret - "\n" - "获取 API Key: https://jina.ai/embeddings/" # pragma: allowlist secret - ) - - # 检查维度范围 - if not (32 <= dimensions <= 1024): - raise ValueError( - f"Jina embedding 维度必须在 32-1024 范围内，当前值: {dimensions}\n" - "提示: 更小的维度可以降低成本，但可能影响精度" - ) - - def embed(self, text: str) -> list[float]: - """将文本转换为 embedding 向量 - - Args: - text: 输入文本 - - Returns: - embedding 向量 - - Raises: - RuntimeError: 如果 API 调用失败 - """ - try: - return jina_embed_sync( - text=text, - dimensions=self._dimensions, - late_chunking=self._late_chunking, - base_url=self._base_url, - api_key=self._api_key, - model=self._model, - ) - except Exception as e: - raise RuntimeError( - f"Jina embedding 失败: {e}\n" - f"模型: {self._model}\n" - f"维度: {self._dimensions}\n" - f"文本: {text[:100]}...\n" - f"提示: 检查 API Key 是否有效，网络连接是否正常" - ) from e - - def embed_batch(self, texts: list[str]) -> list[list[float]]: - """批量将文本转换为 embedding 向量 - - 使用 Jina API 的批量接口（input 参数支持列表）。 - - Args: - texts: 输入文本列表 - - Returns: - embedding 向量列表 - - Raises: - RuntimeError: 如果 API 调用失败 - """ - if not texts: - return [] - - try: - import requests - - # 准备 API Key (guaranteed to be non-None after __init__ validation) - api_key = self._api_key - assert api_key is not None # Help mypy understand this can't be None - if not api_key.startswith("Bearer "): - api_key = "Bearer " + api_key # pragma: allowlist secret - - headers = { - "Authorization": api_key, # pragma: allowlist secret - "Content-Type": "application/json", - } - - url = self._base_url or "https://api.jina.ai/v1/embeddings" - - payload = { - "model": self._model, - "normalized": True, - "embedding_type": "float", - "dimensions": self._dimensions, - "late_chunking": self._late_chunking, - "input": texts, # 直接传入列表 - } - - response = requests.post(url, headers=headers, json=payload) - response.raise_for_status() - data = response.json() - - # 按照原始顺序返回结果 - return [item["embedding"] for item in data["data"]] - - except Exception as e: - raise RuntimeError( - f"Jina 批量 embedding 失败: {e}\n" - f"模型: {self._model}\n" - f"维度: {self._dimensions}\n" - f"批量大小: {len(texts)}\n" - f"提示: 检查 API Key 是否有效，网络连接是否正常" - ) from e - - def get_dim(self) -> int: - """获取向量维度 - - Returns: - 维度值 - """ - return self._dimensions - - @property - def method_name(self) -> str: - """返回方法名称 - - Returns: - 'jina' - """ - return "jina" - - @classmethod - def get_model_info(cls) -> dict[str, Any]: - """返回模型元信息 - - Returns: - 模型信息字典 - """ - return { - "method": "jina", - "requires_api_key": True, - "requires_model_download": False, - "default_dimension": 1024, - "features": [ - "多语言支持（100+ 语言）", - "长文本处理（8192 tokens）", - "可调维度（32-1024）", - "Late Chunking", - ], - } - - def __repr__(self) -> str: - """返回对象的字符串表示 - - Returns: - 字符串表示 - """ - base_info = f"JinaEmbedding(model='{self._model}', dim={self._dimensions}" - if self._late_chunking: - base_info += ", late_chunking=True" - if self._base_url: - base_info += f", base_url='{self._base_url}'" - return base_info + ")" diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/mock_wrapper.py b/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/mock_wrapper.py deleted file mode 100644 index 36684ddf08..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/mock_wrapper.py +++ /dev/null @@ -1,112 +0,0 @@ -"""Mock embedding wrapper (migrated from mockembedder.py).""" - -import random -from typing import Any - -from ..base import BaseEmbedding - - -class MockEmbedding(BaseEmbedding): - """Mock Embedding（用于测试） - - 生成随机向量，主要用于单元测试和快速原型验证。 - 不提供任何语义信息，每次调用都生成不同的随机向量。 - - 特点: - - ✅ 无需下载模型 - - ✅ 无需 API Key - - ✅ 速度极快 - - ❌ 完全随机，无任何语义 - - 适用场景: - - 单元测试 - - 性能测试 - - 快速功能验证 - - Args: - fixed_dim: 向量维度（默认 128） - seed: 随机种子（可选，设置后可复现） - - Examples: - >>> # 基本用法 - >>> emb = MockEmbedding(fixed_dim=128) - >>> vec = emb.embed("test") - >>> len(vec) - 128 - >>> - >>> # 使用固定种子（可复现） - >>> emb = MockEmbedding(fixed_dim=128, seed=42) - >>> vec1 = emb.embed("hello") - >>> - >>> emb2 = MockEmbedding(fixed_dim=128, seed=42) - >>> vec2 = emb2.embed("hello") - >>> vec1 == vec2 - True - """ - - def __init__(self, fixed_dim: int = 128, seed: int | None = None, **kwargs: Any) -> None: - """初始化 Mock Embedding - - Args: - fixed_dim: 向量维度 - seed: 随机种子（可选） - **kwargs: 其他参数（兼容性） - """ - super().__init__(fixed_dim=fixed_dim, seed=seed, **kwargs) - self._dim = max(64, int(fixed_dim)) - self._seed = seed - if seed is not None: - random.seed(seed) - - def embed(self, text: str) -> list[float]: - """生成随机 embedding 向量 - - Args: - text: 输入文本（实际不使用，保持接口一致） - - Returns: - 随机生成的向量 - - Note: - - 如果设置了 seed，相同文本会生成相同向量 - - 如果未设置 seed，每次调用生成不同向量 - """ - if self._seed is not None: - # 使用文本作为种子的一部分，保证相同文本生成相同向量 - text_seed = hash(text) % (2**32) - rng = random.Random(self._seed + text_seed) - return [rng.random() for _ in range(self._dim)] - else: - # 完全随机 - return [random.random() for _ in range(self._dim)] - - def get_dim(self) -> int: - """获取向量维度 - - Returns: - 维度值 - """ - return self._dim - - @property - def method_name(self) -> str: - """返回方法名称 - - Returns: - 'mockembedder' - """ - return "mockembedder" - - @classmethod - def get_model_info(cls) -> dict[str, Any]: - """返回模型元信息 - - Returns: - 模型信息字典 - """ - return { - "method": "mockembedder", - "requires_api_key": False, - "requires_model_download": False, - "default_dimension": 128, - } diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/nvidia_openai_wrapper.py b/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/nvidia_openai_wrapper.py deleted file mode 100644 index 0f83870e59..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/nvidia_openai_wrapper.py +++ /dev/null @@ -1,283 +0,0 @@ -"""NVIDIA NIM (OpenAI-compatible) embedding wrapper.""" - -import os -from typing import Any, Literal - -from ..base import BaseEmbedding - - -class NvidiaOpenAIEmbedding(BaseEmbedding): - """NVIDIA NIM (OpenAI-compatible) Embedding Wrapper - - 支持通过 NVIDIA NIM 访问 NVIDIA 的 embedding 模型（OpenAI 兼容 API）。 - - 特点: - - ✅ NVIDIA 优化模型 - - ✅ OpenAI 兼容接口 - - ✅ 高性能 - - ✅ 支持检索优化 - - ❌ 需要 API Key - - ❌ 需要网络连接 - - 💰 按使用量计费 - - 支持的模型（示例）: - - nvidia/llama-3.2-nv-embedqa-1b-v1 (默认） - - nvidia/nv-embed-v1 (4096维，高性能） - - Args: - model: 模型名称（默认 'nvidia/llama-3.2-nv-embedqa-1b-v1'） - base_url: API 地址（默认 'https://integrate.api.nvidia.com/v1'） - api_key: API 密钥（可选，默认从环境变量 OPENAI_API_KEY 读取） - input_type: 输入类型（'passage' 或 'query'，默认 'passage'） - trunc: 截断策略（'NONE', 'START', 'END'，默认 'NONE'） - encode: 返回格式（'float' 或 'base64'，默认 'float'） - - Examples: - >>> # 基本使用 - >>> import os - >>> emb = NvidiaOpenAIEmbedding( - ... model="nvidia/llama-3.2-nv-embedqa-1b-v1", - ... api_key=os.getenv("OPENAI_API_KEY") - ... ) - >>> vec = emb.embed("hello world") - >>> - >>> # 检索场景（区分文档和查询） - >>> # 文档端 - >>> doc_emb = NvidiaOpenAIEmbedding(input_type="passage") - >>> doc_vec = doc_emb.embed("这是一篇文档") - >>> - >>> # 查询端 - >>> query_emb = NvidiaOpenAIEmbedding(input_type="query") - >>> query_vec = query_emb.embed("查询内容") - """ - - # 常见模型的维度映射（需要根据实际模型更新） - DIMENSION_MAP = { - "nvidia/llama-3.2-nv-embedqa-1b-v1": 2048, - "nvidia/nv-embed-v1": 4096, - } - - def __init__( - self, - model: str = "nvidia/llama-3.2-nv-embedqa-1b-v1", - base_url: str = "https://integrate.api.nvidia.com/v1", - api_key: str | None = None, - input_type: str = "passage", - trunc: str = "NONE", - encode: Literal["float", "base64"] = "float", - **kwargs: Any, - ) -> None: - """初始化 NVIDIA OpenAI Embedding - - Args: - model: 模型名称 - base_url: API 地址 - api_key: API 密钥（可选） - input_type: 输入类型（'passage' 或 'query'） - trunc: 截断策略 - encode: 返回格式 - **kwargs: 其他参数（保留用于扩展） - - Raises: - ImportError: 如果未安装 openai 包 - RuntimeError: 如果未提供 API Key - """ - super().__init__( - model=model, - base_url=base_url, - api_key=api_key, - input_type=input_type, - trunc=trunc, - encode=encode, - **kwargs, - ) - - # 检查依赖 - try: - from openai import OpenAI # noqa: F401 - except ImportError as err: - raise ImportError( - "NVIDIA OpenAI embedding 需要 openai 包。\n安装方法: pip install openai" - ) from err - - self._model = model - self._base_url = base_url - self._api_key = api_key or os.getenv("NVIDIA_API_KEY") or os.getenv("OPENAI_API_KEY") - self._input_type = input_type - self._trunc = trunc - self._encode: Literal["float", "base64"] = encode - self._kwargs = kwargs - - # 检查 API Key - if not self._api_key: - raise RuntimeError( - "NVIDIA OpenAI embedding 需要 API Key。\n" # pragma: allowlist secret - "解决方案:\n" - " 1. 设置环境变量: export NVIDIA_API_KEY='your-key'\n" # pragma: allowlist secret - " 或: export OPENAI_API_KEY='your-key'\n" # pragma: allowlist secret - " 2. 传递参数: NvidiaOpenAIEmbedding(api_key='your-key', ...)\n" # pragma: allowlist secret - "\n" - "获取 API Key: https://build.nvidia.com/" # pragma: allowlist secret - ) - - # 获取维度 - self._dim = self._infer_dimension() - - def embed(self, text: str) -> list[float]: - """将文本转换为 embedding 向量 - - Args: - text: 输入文本 - - Returns: - embedding 向量 - - Raises: - RuntimeError: 如果 API 调用失败 - """ - try: - from openai import OpenAI - - # 设置环境变量（OpenAI SDK 会读取） - if self._api_key: - os.environ["OPENAI_API_KEY"] = self._api_key - - client = OpenAI(base_url=self._base_url) - - response = client.embeddings.create( - model=self._model, - input=text, - encoding_format=self._encode, - extra_body={ - "input_type": self._input_type, - "truncate": self._trunc, - }, - ) - - return response.data[0].embedding - - except Exception as e: - raise RuntimeError( - f"NVIDIA OpenAI embedding 失败: {e}\n" - f"模型: {self._model}\n" - f"端点: {self._base_url}\n" - f"输入类型: {self._input_type}\n" - f"文本: {text[:100]}...\n" - f"提示: 检查 API Key 是否有效，网络连接是否正常" - ) from e - - def embed_batch(self, texts: list[str]) -> list[list[float]]: - """批量将文本转换为 embedding 向量 - - 使用 OpenAI 兼容 API 的批量接口（input 参数支持列表）。 - - Args: - texts: 输入文本列表 - - Returns: - embedding 向量列表 - - Raises: - RuntimeError: 如果 API 调用失败 - """ - if not texts: - return [] - - try: - import os - - from openai import OpenAI - - # 设置环境变量 - if self._api_key: - os.environ["OPENAI_API_KEY"] = self._api_key - - client = OpenAI(base_url=self._base_url) - - # 批量调用 - response = client.embeddings.create( - model=self._model, - input=texts, # 直接传入列表 - encoding_format=self._encode, - extra_body={ - "input_type": self._input_type, - "truncate": self._trunc, - }, - ) - - # 按照原始顺序返回结果 - return [item.embedding for item in response.data] - - except Exception as e: - raise RuntimeError( - f"NVIDIA OpenAI 批量 embedding 失败: {e}\n" - f"模型: {self._model}\n" - f"输入类型: {self._input_type}\n" - f"批量大小: {len(texts)}\n" - f"提示: 检查 API Key 是否有效，网络连接是否正常" - ) from e - - def get_dim(self) -> int: - """获取向量维度 - - Returns: - 维度值 - """ - return self._dim - - @property - def method_name(self) -> str: - """返回方法名称 - - Returns: - 'nvidia_openai' - """ - return "nvidia_openai" - - def _infer_dimension(self) -> int: - """推断向量维度 - - Returns: - 推断的维度值 - """ - # 优先使用已知的维度映射 - if self._model in self.DIMENSION_MAP: - return self.DIMENSION_MAP[self._model] - - # 尝试通过实际调用推断 - try: - sample = self.embed("test") - return len(sample) - except Exception: - # 如果推断失败，返回默认维度 - return 2048 - - @classmethod - def get_model_info(cls) -> dict[str, Any]: - """返回模型元信息 - - Returns: - 模型信息字典 - """ - return { - "method": "nvidia_openai", - "requires_api_key": True, - "requires_model_download": False, - "default_dimension": 2048, - "features": [ - "NVIDIA 优化模型", - "OpenAI 兼容接口", - "支持检索优化（passage/query）", - ], - } - - def __repr__(self) -> str: - """返回对象的字符串表示 - - Returns: - 字符串表示 - """ - return ( - f"NvidiaOpenAIEmbedding(model='{self._model}', " - f"input_type='{self._input_type}', dim={self._dim})" - ) diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/ollama_wrapper.py b/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/ollama_wrapper.py deleted file mode 100644 index 2d2a57bda5..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/ollama_wrapper.py +++ /dev/null @@ -1,220 +0,0 @@ -"""Ollama embedding wrapper.""" - -from typing import Any - -from ..base import BaseEmbedding - - -class OllamaEmbedding(BaseEmbedding): - """Ollama Embedding Wrapper - - 支持通过 Ollama 访问本地部署的 embedding 模型。 - - 特点: - - ✅ 本地部署，数据隐私 - - ✅ 无需 API Key - - ✅ 支持多种开源模型 - - ✅ 免费使用 - - ❌ 需要安装 Ollama - - ❌ 需要下载模型 - - 💻 需要本地计算资源 - - 支持的模型（示例）: - - nomic-embed-text (默认，768维，高质量英文） - - mxbai-embed-large (1024维，高性能） - - all-minilm (384维，轻量级） - - bge-large (1024维，多语言） - - Args: - model: 模型名称（默认 'nomic-embed-text'） - base_url: Ollama API 地址（默认 'http://localhost:11434'） - api_key: API 密钥（可选，某些部署需要） - - Examples: - >>> # 基本使用（本地默认端口） - >>> emb = OllamaEmbedding(model="nomic-embed-text") - >>> vec = emb.embed("hello world") - >>> - >>> # 自定义端口 - >>> emb = OllamaEmbedding( - ... model="nomic-embed-text", - ... base_url="http://localhost:11434" - ... ) - >>> vec = emb.embed("你好世界") - >>> - >>> # 远程 Ollama 服务（需要 API Key） - >>> emb = OllamaEmbedding( - ... model="nomic-embed-text", - ... base_url="https://ollama.example.com", - ... api_key="your-key" # pragma: allowlist secret - ... ) - """ - - # 常见模型的维度映射（需要根据实际模型更新） - DIMENSION_MAP = { - "nomic-embed-text": 768, - "mxbai-embed-large": 1024, - "all-minilm": 384, - "bge-large": 1024, - } - - def __init__( - self, - model: str = "nomic-embed-text", - base_url: str = "http://localhost:11434", - api_key: str | None = None, - **kwargs: Any, - ) -> None: - """初始化 Ollama Embedding - - Args: - model: 模型名称 - base_url: Ollama API 地址 - api_key: API 密钥（可选） - **kwargs: 其他参数（保留用于扩展） - - Raises: - ImportError: 如果未安装 ollama 包 - """ - super().__init__(model=model, base_url=base_url, api_key=api_key, **kwargs) - - # 检查依赖 - try: - import ollama # noqa: F401 - except ImportError: - raise ImportError( - "Ollama embedding 需要 ollama 包。\n" - "安装方法: pip install ollama\n" - "\n" - "同时需要安装 Ollama 服务:\n" - " - macOS/Linux: https://ollama.ai/download\n" - " - 安装后运行: ollama pull {model}" - ) - - self._model = model - self._base_url = base_url - self._api_key = api_key - self._kwargs = kwargs - - # 推断维度 - self._dim = self._infer_dimension() - - def embed(self, text: str) -> list[float]: - """将文本转换为 embedding 向量 - - Args: - text: 输入文本 - - Returns: - embedding 向量 - - Raises: - RuntimeError: 如果 API 调用失败 - """ - try: - import ollama - - # 构建 headers - headers = { - "Content-Type": "application/json", - "User-Agent": "SAGE/0.0", - } - if self._api_key: - headers["Authorization"] = self._api_key - - # 创建客户端 - kwargs = {"base_url": self._base_url, "headers": headers} - client = ollama.Client(**kwargs) - - # 调用 API - data = client.embed(model=self._model, input=text) - return data["embedding"] - - except Exception as e: - raise RuntimeError( - f"Ollama embedding 失败: {e}\n" - f"模型: {self._model}\n" - f"端点: {self._base_url}\n" - f"文本: {text[:100]}...\n" - f"提示:\n" - f" 1. 检查 Ollama 服务是否运行: ollama list\n" - f" 2. 拉取模型: ollama pull {self._model}\n" - f" 3. 检查端口: {self._base_url}" - ) from e - - def embed_batch(self, texts: list[str]) -> list[list[float]]: - """批量将文本转换为 embedding 向量 - - 当前实现为逐个调用 embed()。 - - Args: - texts: 输入文本列表 - - Returns: - embedding 向量列表 - """ - return [self.embed(text) for text in texts] - - def get_dim(self) -> int: - """获取向量维度 - - Returns: - 维度值 - """ - return self._dim - - @property - def method_name(self) -> str: - """返回方法名称 - - Returns: - 'ollama' - """ - return "ollama" - - def _infer_dimension(self) -> int: - """推断向量维度 - - Returns: - 推断的维度值 - """ - # 优先使用已知的维度映射 - if self._model in self.DIMENSION_MAP: - return self.DIMENSION_MAP[self._model] - - # 尝试通过实际调用推断 - try: - sample = self.embed("test") - return len(sample) - except Exception: - # 如果推断失败，返回默认维度 - return 768 - - @classmethod - def get_model_info(cls) -> dict[str, Any]: - """返回模型元信息 - - Returns: - 模型信息字典 - """ - return { - "method": "ollama", - "requires_api_key": False, - "requires_model_download": True, - "default_dimension": 768, - "features": [ - "本地部署，数据隐私", - "支持多种开源模型", - "免费使用", - ], - } - - def __repr__(self) -> str: - """返回对象的字符串表示 - - Returns: - 字符串表示 - """ - return ( - f"OllamaEmbedding(model='{self._model}', base_url='{self._base_url}', dim={self._dim})" - ) diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/openai_wrapper.py b/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/openai_wrapper.py deleted file mode 100644 index e9e96f6b40..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/openai_wrapper.py +++ /dev/null @@ -1,456 +0,0 @@ -"""OpenAI embedding wrapper. - -支持 OpenAI 官方 API、兼容 API 以及本地 sagellm 推理引擎（占位实现）。 -""" - -import logging -import os -from typing import TYPE_CHECKING, Any - -from ..base import BaseEmbedding - -if TYPE_CHECKING: - pass # Reserved for future type hints - -# 抑制 httpx 的 INFO 日志（每次 HTTP 请求都会打印） -logging.getLogger("httpx").setLevel(logging.WARNING) - -logger = logging.getLogger(__name__) - - -class OpenAIEmbedding(BaseEmbedding): - """OpenAI Embedding API Wrapper - - 支持 OpenAI 官方 API、兼容的第三方 API（如 vLLM、DeepSeek 等） - 以及本地 sagellm 推理引擎。 - - 特点: - - ✅ 高质量 embedding - - ✅ 支持多种模型 - - ✅ 兼容第三方 API - - ✅ 支持本地 sagellm 引擎（无需网络） - - ❌ 需要 API Key（仅 openai provider） - - ❌ 需要网络连接（仅 openai provider） - - 💰 按使用量计费（仅 openai provider） - - 支持的模型: - - text-embedding-3-small (1536维，性价比高) - - text-embedding-3-large (3072维，最高质量) - - text-embedding-ada-002 (1536维，旧版本) - - 任意 HuggingFace 模型（通过 sagellm provider） - - Args: - model: 模型名称（默认 'text-embedding-3-small'） - api_key: API 密钥（可选，默认从环境变量 OPENAI_API_KEY 读取） - base_url: API 端点（可选，用于兼容 API） - provider: 后端提供者，'openai'（默认）或 'sagellm'（本地推理） - sagellm_config: sagellm 推理配置（仅 provider='sagellm' 时有效） - - Examples: - >>> # OpenAI 官方 API - >>> import os - >>> emb = OpenAIEmbedding( - ... model="text-embedding-3-small", - ... api_key=os.getenv("OPENAI_API_KEY") - ... ) - >>> vec = emb.embed("hello world") - >>> - >>> # 兼容 API（自定义端点） - >>> emb = OpenAIEmbedding( - ... model="text-embedding-v1", - ... api_key=os.getenv("OPENAI_API_KEY"), - ... base_url=os.getenv("OPENAI_BASE_URL", "http://localhost:8090/v1") - ... ) - >>> vec = emb.embed("你好世界") - >>> - >>> # vLLM 部署的模型 - >>> emb = OpenAIEmbedding( - ... model="BAAI/bge-base-en-v1.5", - ... base_url="http://localhost:8000/v1" - ... ) - >>> - >>> # 本地 sagellm 推理（当前使用 sentence-transformers 作为占位实现，无需 API Key 和网络） - >>> emb = OpenAIEmbedding( - ... model="BAAI/bge-small-zh-v1.5", - ... provider="sagellm", - ... sagellm_config={"device": "cuda"} - ... ) - >>> vec = emb.embed("你好世界") - """ - - # 常见模型的维度映射 - DIMENSION_MAP = { - "text-embedding-3-small": 1536, - "text-embedding-3-large": 3072, - "text-embedding-ada-002": 1536, - "text-embedding-v1": 1536, - # Common HuggingFace models for sagellm - "BAAI/bge-small-zh-v1.5": 512, - "BAAI/bge-base-zh-v1.5": 768, - "BAAI/bge-large-zh-v1.5": 1024, - "BAAI/bge-small-en-v1.5": 384, - "BAAI/bge-base-en-v1.5": 768, - "BAAI/bge-large-en-v1.5": 1024, - "BAAI/bge-m3": 1024, - "sentence-transformers/all-MiniLM-L6-v2": 384, - "sentence-transformers/all-mpnet-base-v2": 768, - } - - # 支持的 provider - SUPPORTED_PROVIDERS = ("openai", "sagellm") - - def __init__( - self, - model: str = "text-embedding-3-small", - api_key: str | None = None, - base_url: str | None = None, - provider: str = "openai", - sagellm_config: dict[str, Any] | None = None, - **kwargs: Any, - ) -> None: - """初始化 OpenAI Embedding - - Args: - model: 模型名称 - api_key: API 密钥（可选，仅 openai provider） - base_url: API 端点（可选，仅 openai provider） - provider: 后端提供者，'openai' 或 'sagellm' - sagellm_config: sagellm 推理配置（仅 provider='sagellm' 时有效） - **kwargs: 其他参数（保留用于扩展） - - Raises: - RuntimeError: 如果 openai provider 未提供 API Key - ValueError: 如果 provider 不支持 - """ - super().__init__(model=model, api_key=api_key, base_url=base_url, **kwargs) - - self._model = model - self._provider = provider.lower() - self._sagellm_config = sagellm_config or {} - self._sagellm_engine: Any = None # Lazy-loaded sagellm engine - - # 验证 provider - if self._provider not in self.SUPPORTED_PROVIDERS: - raise ValueError( - f"不支持的 provider: {self._provider}\n" - f"支持的 provider: {', '.join(self.SUPPORTED_PROVIDERS)}" - ) - - if self._provider == "openai": - # OpenAI API 模式 - self._api_key = api_key or os.getenv("OPENAI_API_KEY") - self._base_url = base_url - - # 检查 API Key - if not self._api_key: - raise RuntimeError( - "OpenAI embedding 需要 API Key。\n" - "解决方案:\n" - " 1. 设置环境变量: export OPENAI_API_KEY='your-key'\n" # pragma: allowlist secret - " 2. 传递参数: OpenAIEmbedding(api_key='your-key', ...)\n" # pragma: allowlist secret - "\n" - "如果使用兼容 API:\n" - " export OPENAI_API_KEY='your-api-key'\n" # pragma: allowlist secret - " 并指定 base_url 参数\n" - "\n" - "或者使用本地推理:\n" - " OpenAIEmbedding(model='BAAI/bge-small-zh-v1.5', provider='local')" - ) - else: - # 本地推理模式（当前占位实现：sentence-transformers） - self._api_key = None - self._base_url = None - logger.info( - f"使用 sagellm 本地推理占位实现: model={model}, config={self._sagellm_config}" - ) - - # 推断或获取维度 - self._dim = self._infer_dimension() - - def embed(self, text: str) -> list[float]: - """将文本转换为 embedding 向量 - - Args: - text: 输入文本 - - Returns: - embedding 向量 - - Raises: - RuntimeError: 如果 API 调用或本地推理失败 - """ - if self._provider == "sagellm": - return self._embed_with_sagellm(text) - return self._embed_with_openai(text) - - def _embed_with_openai(self, text: str) -> list[float]: - """使用 OpenAI API 生成 embedding - - Args: - text: 输入文本 - - Returns: - embedding 向量 - """ - try: - from openai import OpenAI - - client = OpenAI(api_key=self._api_key, base_url=self._base_url) - response = client.embeddings.create( - model=self._model, - input=text, - ) - return response.data[0].embedding - except Exception as e: - raise RuntimeError( - f"OpenAI embedding 失败: {e}\n" - f"模型: {self._model}\n" - f"文本: {text[:100]}...\n" - f"提示: 检查 API Key 是否有效，网络连接是否正常" - ) from e - - def _embed_with_sagellm(self, text: str) -> list[float]: - """使用 sagellm（占位：sentence-transformers）生成 embedding - - Args: - text: 输入文本 - - Returns: - embedding 向量 - """ - engine = self._get_sagellm_engine() - try: - # sentence-transformers 返回 numpy array - result = engine.encode(text) - # 确保返回 list[float] - if hasattr(result, "tolist"): - return result.tolist() - return list(result) - except Exception as e: - raise RuntimeError( - f"sagellm embedding 失败: {e}\n" - f"模型: {self._model}\n" - f"文本: {text[:100]}...\n" - f"提示: 检查模型是否已下载，设备是否可用" - ) from e - - def _get_sagellm_engine(self) -> Any: - """获取或创建 sagellm embedding 引擎（懒加载） - - 注意：当前 sagellm 仓库尚未提供 embedding 引擎，这里以 - sentence-transformers 作为占位实现。完成 sagellm 的 EmbeddingEngine - 实现后，可直接替换为正式引擎。 - - Returns: - 占位的 sentence-transformers 模型实例 - - Raises: - RuntimeError: 如果无法加载 sentence-transformers - """ - if self._sagellm_engine is not None: - return self._sagellm_engine - - try: - # 使用 sentence-transformers 作为占位（待 sagellm 原生 EmbeddingEngine 完成后替换） - from sentence_transformers import SentenceTransformer - - device = self._sagellm_config.get("device", "cpu") - logger.info( - f"使用 sentence-transformers 占位推理: model={self._model}, device={device}" - ) - self._sagellm_engine = SentenceTransformer( - self._model, - device=device, - ) - return self._sagellm_engine - - except ImportError as e: - raise RuntimeError( - "本地 embedding 需要 sentence-transformers。\n" - "请安装: pip install sentence-transformers" - ) from e - except Exception as e: - raise RuntimeError( - f"无法初始化 sagellm embedding 占位引擎: {e}\n" - f"模型: {self._model}\n" - f"配置: {self._sagellm_config}" - ) from e - - def embed_batch(self, texts: list[str]) -> list[list[float]]: - """批量将文本转换为 embedding 向量 - - 使用 OpenAI API 的批量接口或 sagellm 的批量编码。 - - Args: - texts: 输入文本列表 - - Returns: - embedding 向量列表 - - Raises: - RuntimeError: 如果 API 调用或本地推理失败 - """ - if not texts: - return [] - - if self._provider == "sagellm": - return self._embed_batch_with_sagellm(texts) - return self._embed_batch_with_openai(texts) - - def _embed_batch_with_openai(self, texts: list[str]) -> list[list[float]]: - """使用 OpenAI API 批量生成 embedding - - Args: - texts: 输入文本列表 - - Returns: - embedding 向量列表 - """ - try: - from openai import OpenAI - - # 设置环境变量 - if self._api_key: - import os - - os.environ["OPENAI_API_KEY"] = self._api_key - - client = OpenAI(base_url=self._base_url) - - # OpenAI API 支持批量：input 可以是字符串列表 - response = client.embeddings.create( - model=self._model, - input=texts, # 直接传入列表 - ) - - # 按照原始顺序返回结果 - return [item.embedding for item in response.data] - - except Exception as e: - raise RuntimeError( - f"OpenAI 批量 embedding 失败: {e}\n" - f"模型: {self._model}\n" - f"批量大小: {len(texts)}\n" - f"提示: 检查 API Key 是否有效，网络连接是否正常" - ) from e - - def _embed_batch_with_sagellm(self, texts: list[str]) -> list[list[float]]: - """使用 sagellm（占位：sentence-transformers）批量生成 embedding - - Args: - texts: 输入文本列表 - - Returns: - embedding 向量列表 - """ - engine = self._get_sagellm_engine() - try: - # sentence-transformers 支持批量编码 - results = engine.encode(texts) - # 确保返回 list[list[float]] - if hasattr(results, "tolist"): - return results.tolist() - return [list(r) if hasattr(r, "__iter__") else [r] for r in results] - except Exception as e: - raise RuntimeError( - f"sagellm 批量 embedding 失败: {e}\n" - f"模型: {self._model}\n" - f"批量大小: {len(texts)}\n" - f"提示: 检查模型是否已下载，设备是否可用" - ) from e - - def get_dim(self) -> int: - """获取向量维度 - - Returns: - 维度值 - """ - return self._dim - - @property - def method_name(self) -> str: - """返回方法名称 - - Returns: - 'openai' 或 'sagellm'（取决于 provider） - """ - return self._provider - - @property - def provider(self) -> str: - """返回后端提供者 - - Returns: - 'openai' 或 'sagellm' - """ - return self._provider - - def _infer_dimension(self) -> int: - """推断或获取维度 - - Returns: - 推断的维度值 - """ - # 优先使用已知的维度映射 - if self._model in self.DIMENSION_MAP: - return self.DIMENSION_MAP[self._model] - - # 如果是未知模型，尝试通过实际调用推断 - try: - sample = self.embed("test") - return len(sample) - except Exception: - # 如果推断失败，返回默认维度 - return 1536 - - @classmethod - def get_model_info(cls) -> dict[str, Any]: - """返回模型元信息 - - Returns: - 模型信息字典 - """ - return { - "method": "openai", - "requires_api_key": True, # Only for openai provider - "requires_model_download": True, # sagellm 需要本地模型 - "default_dimension": 1536, - "supported_providers": list(cls.SUPPORTED_PROVIDERS), - } - - def __repr__(self) -> str: - """返回对象的字符串表示 - - Returns: - 字符串表示 - """ - base_info = ( - f"OpenAIEmbedding(model='{self._model}', dim={self._dim}, provider='{self._provider}'" - ) - if self._provider == "openai" and self._base_url: - base_info += f", base_url='{self._base_url}'" - elif self._provider == "sagellm" and self._sagellm_config: - config_str = ", ".join(f"{k}={v!r}" for k, v in self._sagellm_config.items()) - base_info += f", config={{{config_str}}}" - return base_info + ")" - - def close(self) -> None: - """释放资源 - - 清理本地引擎占用的 GPU 内存等资源。 - """ - if self._sagellm_engine is not None: - # 尝试释放资源 - if hasattr(self._sagellm_engine, "close"): - self._sagellm_engine.close() - elif hasattr(self._sagellm_engine, "stop"): - self._sagellm_engine.stop() - self._sagellm_engine = None - logger.debug("sagellm embedding 引擎占位已释放") - - def __del__(self) -> None: - """析构函数""" - try: - self.close() - except Exception: - pass # 忽略析构时的错误 diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/siliconcloud_wrapper.py b/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/siliconcloud_wrapper.py deleted file mode 100644 index 6a64a5e89e..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/siliconcloud_wrapper.py +++ /dev/null @@ -1,267 +0,0 @@ -"""SiliconCloud (硅基流动) embedding wrapper.""" - -import os -from typing import Any - -from ..base import BaseEmbedding - - -class SiliconCloudEmbedding(BaseEmbedding): - """SiliconCloud (硅基流动) Embedding Wrapper - - 支持通过硅基流动访问多种 embedding 模型。 - - 特点: - - ✅ 国内访问快速稳定 - - ✅ 支持多种开源模型 - - ✅ 价格优势 - - ❌ 需要 API Key - - ❌ 需要网络连接 - - 💰 按使用量计费 - - 支持的模型（示例）: - - netease-youdao/bce-embedding-base_v1 (默认，768维） - - BAAI/bge-large-zh-v1.5 (1024维，中文优化） - - BAAI/bge-base-en-v1.5 (768维，英文） - - Args: - model: 模型名称（默认 'netease-youdao/bce-embedding-base_v1'） - base_url: API 地址（默认 'https://api.siliconflow.cn/v1/embeddings'） - max_token_size: 最大 token 数（默认 512） - api_key: API 密钥（可选，默认从环境变量 SILICONCLOUD_API_KEY 读取） - - Examples: - >>> # 基本使用 - >>> import os - >>> emb = SiliconCloudEmbedding( - ... model="netease-youdao/bce-embedding-base_v1", - ... api_key=os.getenv("SILICONCLOUD_API_KEY") - ... ) - >>> vec = emb.embed("你好世界") - >>> - >>> # 使用 BGE 模型 - >>> emb = SiliconCloudEmbedding( - ... model="BAAI/bge-large-zh-v1.5", - ... api_key=os.getenv("SILICONCLOUD_API_KEY") - ... ) - >>> vec = emb.embed("硅基流动提供高性价比的AI服务") - """ - - # 常见模型的维度映射 - DIMENSION_MAP = { - "netease-youdao/bce-embedding-base_v1": 768, - "BAAI/bge-large-zh-v1.5": 1024, - "BAAI/bge-base-en-v1.5": 768, - "BAAI/bge-small-en-v1.5": 384, - } - - def __init__( - self, - model: str = "netease-youdao/bce-embedding-base_v1", - base_url: str = "https://api.siliconflow.cn/v1/embeddings", - max_token_size: int = 512, - api_key: str | None = None, - **kwargs: Any, - ) -> None: - """初始化 SiliconCloud Embedding - - Args: - model: 模型名称 - base_url: API 地址 - max_token_size: 最大 token 数 - api_key: API 密钥（可选） - **kwargs: 其他参数（保留用于扩展） - - Raises: - ImportError: 如果未安装依赖包 - RuntimeError: 如果未提供 API Key - """ - extra_kwargs = dict(kwargs) - batch_size_cfg = extra_kwargs.pop("batch_size", None) - - super().__init__( - model=model, - base_url=base_url, - max_token_size=max_token_size, - api_key=api_key, - **extra_kwargs, - ) - - # 检查依赖 - try: - import requests # noqa: F401 - except ImportError: - raise ImportError( - "SiliconCloud embedding 需要 requests 包。\n安装方法: pip install requests" - ) - - self._model = model - self._base_url = base_url - self._max_token_size = max_token_size - self._api_key = api_key or os.getenv("SILICONCLOUD_API_KEY") - self._kwargs = extra_kwargs - self._batch_size = max(1, int(batch_size_cfg or 32)) - - # 检查 API Key - if not self._api_key: - raise RuntimeError( - "SiliconCloud embedding 需要 API Key。\n" - "解决方案:\n" - " 1. 设置环境变量: export SILICONCLOUD_API_KEY='your-key'\n" # pragma: allowlist secret - " 2. 传递参数: SiliconCloudEmbedding(api_key='your-key', ...)\n" # pragma: allowlist secret - "\n" - "获取 API Key: https://siliconflow.cn/" - ) - - # 获取维度 - self._dim = self._infer_dimension() - - def embed(self, text: str) -> list[float]: - """将文本转换为 embedding 向量 - - Args: - text: 输入文本 - - Returns: - embedding 向量 - - Raises: - RuntimeError: 如果 API 调用失败 - """ - try: - return self._request_embeddings([text])[0] - - except Exception as e: - raise RuntimeError( - f"SiliconCloud embedding 失败: {e}\n" - f"模型: {self._model}\n" - f"文本: {text[:100]}...\n" - f"提示: 检查 API Key 是否有效，网络连接是否正常" - ) from e - - def embed_batch(self, texts: list[str]) -> list[list[float]]: - """批量将文本转换为 embedding 向量""" - - if not texts: - return [] - - embeddings: list[list[float]] = [] - batch_size = max(1, self._batch_size) - - for idx in range(0, len(texts), batch_size): - chunk = texts[idx : idx + batch_size] - embeddings.extend(self._request_embeddings(chunk)) - - return embeddings - - def get_dim(self) -> int: - """获取向量维度 - - Returns: - 维度值 - """ - return self._dim - - @property - def method_name(self) -> str: - """返回方法名称 - - Returns: - 'siliconcloud' - """ - return "siliconcloud" - - def _infer_dimension(self) -> int: - """推断向量维度 - - Returns: - 推断的维度值 - """ - # 优先使用已知的维度映射 - if self._model in self.DIMENSION_MAP: - return self.DIMENSION_MAP[self._model] - - # 尝试通过实际调用推断 - try: - sample = self.embed("test") - return len(sample) - except Exception: - # 如果推断失败，返回默认维度 - return 768 - - @classmethod - def get_model_info(cls) -> dict[str, Any]: - """返回模型元信息 - - Returns: - 模型信息字典 - """ - return { - "method": "siliconcloud", - "requires_api_key": True, - "requires_model_download": False, - "default_dimension": 768, - "features": [ - "国内访问快速稳定", - "支持多种开源模型", - "价格优势", - ], - } - - def __repr__(self) -> str: - """返回对象的字符串表示 - - Returns: - 字符串表示 - """ - return f"SiliconCloudEmbedding(model='{self._model}', dim={self._dim})" - - def _request_embeddings(self, texts: list[str]) -> list[list[float]]: - """调用 SiliconCloud API 获取向量，支持批量输入""" - - import base64 - import struct - - import requests - - if not texts: - return [] - - api_key = self._api_key # pragma: allowlist secret - if api_key and not api_key.startswith("Bearer "): - api_key = "Bearer " + api_key # pragma: allowlist secret - - headers = { - "Authorization": api_key, # pragma: allowlist secret - "Content-Type": "application/json", - } - - payload = { - "model": self._model, - "input": [text[: self._max_token_size] for text in texts], - "encoding_format": "base64", - } - - response = requests.post(self._base_url, headers=headers, json=payload) - response.raise_for_status() - content = response.json() - - if "code" in content: - raise ValueError(f"SiliconCloud API error: {content}") - - data = content.get("data", []) - if len(data) != len(texts): - raise RuntimeError( - "SiliconCloud API returned unexpected number of embeddings " - f"(expected {len(texts)}, got {len(data)})" - ) - - embeddings: list[list[float]] = [] - for item in data: - base64_string = item["embedding"] - decode_bytes = base64.b64decode(base64_string) - n = len(decode_bytes) // 4 - float_array = struct.unpack("<" + "f" * n, decode_bytes) - embeddings.append(list(float_array)) - - return embeddings diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/zhipu_wrapper.py b/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/zhipu_wrapper.py deleted file mode 100644 index 95f6d4533b..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/wrappers/zhipu_wrapper.py +++ /dev/null @@ -1,188 +0,0 @@ -"""ZhipuAI (智谱清言) embedding wrapper.""" - -import os -from typing import Any - -from ..base import BaseEmbedding - - -class ZhipuEmbedding(BaseEmbedding): - """ZhipuAI Embedding API Wrapper - - 支持智谱 AI 的中文 embedding 服务。 - - 特点: - - ✅ 中文优化 - - ✅ 高质量向量 - - ✅ 国内访问稳定 - - ❌ 需要 API Key - - ❌ 需要网络连接 - - 💰 按使用量计费 - - 支持的模型: - - embedding-3 (默认，1024维，最新版本) - - embedding-2 (512维，旧版本) - - Args: - model: 模型名称（默认 'embedding-3'） - api_key: API 密钥（可选，默认从环境变量 ZHIPU_API_KEY 读取） - - Examples: - >>> # 基本使用 - >>> import os - >>> emb = ZhipuEmbedding( - ... model="embedding-3", - ... api_key=os.getenv("ZHIPU_API_KEY") # pragma: allowlist secret - ... ) - >>> vec = emb.embed("你好世界") - >>> - >>> # 使用环境变量 - >>> # export ZHIPU_API_KEY='your-key' # pragma: allowlist secret - >>> emb = ZhipuEmbedding() - >>> vec = emb.embed("智谱清言是一个强大的中文模型") - """ - - # 模型维度映射 - DIMENSION_MAP = { - "embedding-3": 1024, - "embedding-2": 512, - } - - def __init__( - self, model: str = "embedding-3", api_key: str | None = None, **kwargs: Any - ) -> None: - """初始化 Zhipu Embedding - - Args: - model: 模型名称 - api_key: API 密钥（可选） - **kwargs: 其他参数（传递给 ZhipuAI client） - - Raises: - ImportError: 如果未安装 zhipuai 包 - RuntimeError: 如果未提供 API Key - """ - super().__init__(model=model, api_key=api_key, **kwargs) - - # 检查依赖 - try: - from zhipuai import ZhipuAI # noqa: F401 - except ImportError: - raise ImportError("Zhipu embedding 需要 zhipuai 包。\n安装方法: pip install zhipuai") - - self._model = model - self._api_key = api_key or os.getenv("ZHIPU_API_KEY") - self._kwargs = kwargs - - # 检查 API Key - if not self._api_key: - raise RuntimeError( - "Zhipu embedding 需要 API Key。\n" - "解决方案:\n" - " 1. 设置环境变量: export ZHIPU_API_KEY='your-key'\n" # pragma: allowlist secret - " 2. 传递参数: ZhipuEmbedding(api_key='your-key', ...)\n" # pragma: allowlist secret - "\n" - "获取 API Key: https://open.bigmodel.cn/" - ) - - # 获取维度 - self._dim = self.DIMENSION_MAP.get(model, 1024) - - def embed(self, text: str) -> list[float]: - """将文本转换为 embedding 向量 - - Args: - text: 输入文本 - - Returns: - embedding 向量 - - Raises: - RuntimeError: 如果 API 调用失败 - """ - try: - from zhipuai import ZhipuAI - - client = ZhipuAI(api_key=self._api_key) - response = client.embeddings.create(model=self._model, input=[text], **self._kwargs) - return response.data[0].embedding - except Exception as e: - raise RuntimeError( - f"Zhipu embedding 失败: {e}\n" - f"模型: {self._model}\n" - f"文本: {text[:100]}...\n" - f"提示: 检查 API Key 是否有效，网络连接是否正常" - ) from e - - def embed_batch(self, texts: list[str]) -> list[list[float]]: - """批量将文本转换为 embedding 向量 - - 使用 ZhipuAI API 的批量接口（input 参数支持列表）。 - - Args: - texts: 输入文本列表 - - Returns: - embedding 向量列表 - """ - try: - from zhipuai import ZhipuAI - - client = ZhipuAI(api_key=self._api_key) - response = client.embeddings.create( - model=self._model, - input=texts, - **self._kwargs, # 直接传入列表 - ) - return [item.embedding for item in response.data] - except Exception as e: - raise RuntimeError( - f"Zhipu 批量 embedding 失败: {e}\n" - f"模型: {self._model}\n" - f"批量大小: {len(texts)}\n" - f"提示: 检查 API Key 是否有效，网络连接是否正常" - ) from e - - def get_dim(self) -> int: - """获取向量维度 - - Returns: - 维度值 - """ - return self._dim - - @property - def method_name(self) -> str: - """返回方法名称 - - Returns: - 'zhipu' - """ - return "zhipu" - - @classmethod - def get_model_info(cls) -> dict[str, Any]: - """返回模型元信息 - - Returns: - 模型信息字典 - """ - return { - "method": "zhipu", - "requires_api_key": True, - "requires_model_download": False, - "default_dimension": 1024, - "features": [ - "中文优化", - "高质量向量", - "国内访问稳定", - ], - } - - def __repr__(self) -> str: - """返回对象的字符串表示 - - Returns: - 字符串表示 - """ - return f"ZhipuEmbedding(model='{self._model}', dim={self._dim})" diff --git a/packages/sage-common/src/sage/common/components/sage_embedding/zhipu.py b/packages/sage-common/src/sage/common/components/sage_embedding/zhipu.py deleted file mode 100644 index 1155d5aa73..0000000000 --- a/packages/sage-common/src/sage/common/components/sage_embedding/zhipu.py +++ /dev/null @@ -1,71 +0,0 @@ -pass - - -# Dependencies should be installed via requirements.txt -# zhipuai is required for this module - -try: - import zhipuai # noqa: F401 -except ImportError: - raise ImportError( - "zhipuai package is required for ZhipuAI embedding functionality. " - "Please install it via: pip install zhipuai" - ) - - -async def zhipu_embedding( - text: str, model: str = "embedding-3", api_key: str | None = None, **kwargs -) -> list: - """ - Generate embedding for a single text using ZhipuAI. - - Args: - text: Input string - model: Embedding model name - api_key: ZhipuAI API key - **kwargs: Additional arguments to ZhipuAI client - - Returns: - list[float]: Embedding vector - """ - try: - from zhipuai import ZhipuAI - except ImportError: - raise ImportError("Please install zhipuai before using this backend.") - - client = ZhipuAI(api_key=api_key) if api_key else ZhipuAI() - - try: - response = client.embeddings.create(model=model, input=[text], **kwargs) - return response.data[0].embedding - except Exception as e: - raise Exception(f"Error calling ChatGLM Embedding API: {str(e)}") - - -def zhipu_embedding_sync( - text: str, model: str = "embedding-3", api_key: str | None = None, **kwargs -) -> list[float]: - """ - 同步调用 ZhipuAI 生成 embedding 向量。 - - Args: - text: 输入字符串 - model: 使用的 ZhipuAI 模型名称 - api_key: API 密钥（可选） - **kwargs: 额外参数 - - Returns: - list[float]: 生成的 embedding 向量 - """ - try: - from zhipuai import ZhipuAI - except ImportError: - raise ImportError("Please install zhipuai before using this backend.") - - client = ZhipuAI(api_key=api_key) if api_key else ZhipuAI() - - try: - response = client.embeddings.create(model=model, input=[text], **kwargs) - return response.data[0].embedding - except Exception as e: - raise Exception(f"Error calling ChatGLM Embedding API: {str(e)}") diff --git a/packages/sage-common/src/sage/common/config/__init__.py b/packages/sage-common/src/sage/common/config/__init__.py deleted file mode 100644 index ac14779a53..0000000000 --- a/packages/sage-common/src/sage/common/config/__init__.py +++ /dev/null @@ -1,97 +0,0 @@ -""" -SAGE Common Config - -Configuration management utilities. -""" - -from .network import ( - HF_MIRROR_CN, - NetworkRegion, - configure_hf_mirror, - detect_china_mainland, - ensure_hf_mirror_configured, - get_hf_endpoint, - get_network_region, -) -from .output_paths import ( - SageOutputPaths, - find_sage_project_root, - get_benchmarks_dir, - get_cache_dir, - get_coverage_dir, - get_log_file, - get_logs_dir, - get_output_dir, - get_output_file, - get_ray_temp_dir, - get_reports_dir, - get_sage_paths, - get_states_dir, - get_states_file, - get_temp_dir, - get_test_context_dir, - get_test_env_dir, - get_test_temp_dir, - initialize_sage_paths, - migrate_existing_outputs, - setup_sage_environment, -) -from .ports import ( - DEFAULT_BENCHMARK_LLM_PORT, - DEFAULT_EMBEDDING_PORT, - DEFAULT_LLM_PORT, - SagePorts, -) -from .user_paths import ( - SageUserPaths, - get_user_cache_dir, - get_user_config_dir, - get_user_data_dir, - get_user_paths, - get_user_state_dir, -) - -__all__ = [ - # Output paths - "SageOutputPaths", - "find_sage_project_root", - "get_benchmarks_dir", - "get_cache_dir", - "get_coverage_dir", - "get_log_file", - "get_logs_dir", - "get_output_dir", - "get_output_file", - "get_ray_temp_dir", - "get_reports_dir", - "get_sage_paths", - "get_states_dir", - "get_states_file", - "get_temp_dir", - "get_test_context_dir", - "get_test_env_dir", - "get_test_temp_dir", - "initialize_sage_paths", - "migrate_existing_outputs", - "setup_sage_environment", - # Ports - "SagePorts", - "DEFAULT_LLM_PORT", - "DEFAULT_EMBEDDING_PORT", - "DEFAULT_BENCHMARK_LLM_PORT", - # Network detection and HuggingFace mirror - "HF_MIRROR_CN", - "detect_china_mainland", - "get_hf_endpoint", - "configure_hf_mirror", - "ensure_hf_mirror_configured", - "get_network_region", - "NetworkRegion", - # User paths (XDG standard) - "SageUserPaths", - "get_user_paths", - "get_user_config_dir", - "get_user_data_dir", - "get_user_state_dir", - "get_user_cache_dir", -] diff --git a/packages/sage-common/src/sage/common/config/network.py b/packages/sage-common/src/sage/common/config/network.py deleted file mode 100644 index 85cfd0de73..0000000000 --- a/packages/sage-common/src/sage/common/config/network.py +++ /dev/null @@ -1,153 +0,0 @@ -"""Network detection and mirror configuration for SAGE. - -This module provides automatic detection of network region (China mainland vs international) -and configures HuggingFace mirrors accordingly at runtime. -""" - -from __future__ import annotations - -import functools -import logging -import os -import urllib.request -from typing import Literal - -logger = logging.getLogger(__name__) - -# HuggingFace mirror URL for China mainland users -HF_MIRROR_CN = "https://hf-mirror.com" - -# Detection endpoints with country code response -_DETECTION_ENDPOINTS = [ - "https://ipinfo.io/country", - "https://ifconfig.co/country-iso", - "https://ipapi.co/country/", -] - - -def _fetch_country_code(url: str, timeout: float = 3.0) -> str | None: - """Fetch country code from a detection endpoint. - - Args: - url: The endpoint URL that returns a country code. - timeout: Request timeout in seconds. - - Returns: - Two-letter country code (e.g., "CN", "US") or None on failure. - """ - try: - with urllib.request.urlopen(url, timeout=timeout) as response: - code = response.read().decode("utf-8").strip().upper() - # Validate that it looks like a country code - if len(code) == 2 and code.isalpha(): - return code - except Exception: # noqa: BLE001 - best effort detection - pass - return None - - -@functools.lru_cache(maxsize=1) -def detect_china_mainland() -> bool: - """Detect if the current network is in China mainland. - - Uses multiple public IP geolocation services with fallback. - Results are cached for the lifetime of the process. - - Returns: - True if detected in China mainland, False otherwise. - """ - for endpoint in _DETECTION_ENDPOINTS: - code = _fetch_country_code(endpoint) - if code: - is_china = code == "CN" - logger.debug( - "Network detection via %s: country=%s, is_china=%s", endpoint, code, is_china - ) - return is_china - # Fallback: check locale settings - for env_var in ("LANG", "LC_ALL", "LC_CTYPE"): - value = os.environ.get(env_var, "") - if value.startswith("zh_"): - logger.debug( - "Network detection via locale %s=%s: assuming China mainland", env_var, value - ) - return True - logger.debug("Network detection failed to determine region, assuming international") - return False - - -def get_hf_endpoint() -> str | None: - """Get the appropriate HuggingFace endpoint based on network region. - - Returns: - HF_MIRROR_CN for China mainland users, None for international users. - """ - if detect_china_mainland(): - return HF_MIRROR_CN - return None - - -def configure_hf_mirror(force: bool = False) -> str | None: - """Configure HuggingFace mirror based on network detection. - - Sets the HF_ENDPOINT environment variable if needed. - Skips configuration if HF_ENDPOINT is already set (unless force=True). - - Args: - force: If True, override existing HF_ENDPOINT setting. - - Returns: - The configured HF_ENDPOINT value, or None if using default. - """ - current = os.environ.get("HF_ENDPOINT") - - if current and not force: - logger.debug("HF_ENDPOINT already set to %s, skipping auto-configuration", current) - return current - - endpoint = get_hf_endpoint() - if endpoint: - os.environ["HF_ENDPOINT"] = endpoint - logger.info("Auto-configured HF_ENDPOINT=%s (China mainland network detected)", endpoint) - return endpoint - else: - # Don't override if user has set it - if current and not force: - return current - # Clear any previous setting if forcing and not in China - if force and "HF_ENDPOINT" in os.environ: - del os.environ["HF_ENDPOINT"] - logger.debug("Using default HuggingFace endpoint (international network)") - return None - - -def ensure_hf_mirror_configured() -> None: - """Ensure HuggingFace mirror is configured based on network region. - - This is a convenience function that should be called early in CLI commands - that need to download models from HuggingFace. - """ - configure_hf_mirror(force=False) - - -NetworkRegion = Literal["china", "international"] - - -def get_network_region() -> NetworkRegion: - """Get the detected network region. - - Returns: - "china" for China mainland, "international" otherwise. - """ - return "china" if detect_china_mainland() else "international" - - -__all__ = [ - "HF_MIRROR_CN", - "detect_china_mainland", - "get_hf_endpoint", - "configure_hf_mirror", - "ensure_hf_mirror_configured", - "get_network_region", - "NetworkRegion", -] diff --git a/packages/sage-common/src/sage/common/config/output_paths.py b/packages/sage-common/src/sage/common/config/output_paths.py deleted file mode 100644 index c1f144798a..0000000000 --- a/packages/sage-common/src/sage/common/config/output_paths.py +++ /dev/null @@ -1,553 +0,0 @@ -"""SAGE Output Path Configuration - -Layer: L1 (Foundation - Common Configuration) - -This module provides a centralized configuration system for all output paths in SAGE. -All intermediate results, logs, outputs, and temporary files should use this system -to ensure consistent placement. - -Supports both development environments and pip-installed environments: -- Development: Uses project_root/.sage/ -- Pip-installed: Uses ~/.sage/ - -Architecture: - This is a L1 foundation component providing configuration management. - It does not contain business logic, only path and environment management. -""" - -import os -import shutil -from functools import lru_cache -from pathlib import Path - - -def find_sage_project_root( - start_path: str | Path | None = None, -) -> Path | None: - """ - Find SAGE project root directory by looking for characteristic files/directories. - - Args: - start_path: Starting path for search. If None, uses current working directory. - - Returns: - Optional[Path]: Project root path if found, None otherwise - """ - if start_path is None: - start_path = Path.cwd() - else: - start_path = Path(start_path) - - current = start_path.resolve() - - # Look for SAGE project markers - while True: - # Check for specific SAGE project markers - if any( - (current / marker).exists() - for marker in [ - "packages/sage-kernel", - "packages/sage-common", - "_version.py", - "quickstart.sh", - "packages", - "scripts", - "examples", - ] - ): - # Additional check for packages/sage structure - if (current / "packages" / "sage").exists() or ( - current / "packages" / "sage-common" - ).exists(): - return current - - parent = current.parent - if parent == current: # Reached filesystem root - break - current = parent - - return None - - -def get_appropriate_sage_dir(project_root: str | Path | None = None) -> Path: - """ - Get the appropriate SAGE directory based on environment. - - Priority: - 1. Environment variable SAGE_OUTPUT_DIR - 2. If in development environment: project_root/.sage/ - 3. Otherwise: ~/.sage/ - - Args: - project_root: Explicit project root. If None, auto-detect. - - Returns: - Path: SAGE directory path - """ - # 1. Check environment variable - env_dir = os.environ.get("SAGE_OUTPUT_DIR") - if env_dir: - sage_dir = Path(env_dir) - sage_dir.mkdir(parents=True, exist_ok=True) - return sage_dir - - # 2. Use explicit project root if provided - if project_root: - project_root = Path(project_root).resolve() - sage_dir = project_root / ".sage" - else: - # 3. Auto-detect: development vs pip-installed - detected_root = find_sage_project_root() - if detected_root: - # Development environment - sage_dir = detected_root / ".sage" - else: - # Pip-installed or other environment - sage_dir = Path.home() / ".sage" - - # Ensure directory exists - sage_dir.mkdir(parents=True, exist_ok=True) - return sage_dir - - -class SageOutputPaths: - """Centralized configuration for SAGE output paths.""" - - def __init__(self, project_root: str | Path | None = None): - """ - Initialize SAGE output paths. - - Args: - project_root: Project root directory. If None, will auto-detect environment. - """ - # Use the new intelligent path detection - self.sage_dir = get_appropriate_sage_dir(project_root) - - # Set project root and environment type - self.project_root: Path | None - if project_root: - self.project_root = Path(project_root).resolve() - self.is_pip_environment = False # Explicit project root means dev environment - else: - detected_root = find_sage_project_root() - if detected_root: - self.project_root = detected_root - self.is_pip_environment = False # Found project root means dev environment - else: - self.project_root = None - self.is_pip_environment = True # No project root means pip-installed - - # Ensure .sage directory and subdirectories exist - self._ensure_sage_structure() - - def _ensure_sage_structure(self): - """Ensure .sage directory and required subdirectories exist.""" - # Standard subdirectories in .sage - subdirs = [ - "logs", - "output", - "temp", - "cache", - "reports", - "coverage", - "test_logs", - "experiments", - "issues", - "states", # For .sage_states data (rag components state) - "benchmarks", # For pytest-benchmark results - "studio", # For Angular Studio build outputs - ] - - # Ensure subdirectories exist - for subdir in subdirs: - (self.sage_dir / subdir).mkdir(exist_ok=True) - - @property - def logs_dir(self) -> Path: - """Get the logs directory.""" - return self.sage_dir / "logs" - - @property - def output_dir(self) -> Path: - """Get the output directory.""" - return self.sage_dir / "output" - - @property - def temp_dir(self) -> Path: - """Get the temporary files directory.""" - return self.sage_dir / "temp" - - @property - def cache_dir(self) -> Path: - """Get the cache directory.""" - return self.sage_dir / "cache" - - @property - def reports_dir(self) -> Path: - """Get the reports directory.""" - return self.sage_dir / "reports" - - @property - def coverage_dir(self) -> Path: - """Get the coverage directory.""" - return self.sage_dir / "coverage" - - @property - def test_logs_dir(self) -> Path: - """Get the test logs directory.""" - return self.sage_dir / "test_logs" - - @property - def experiments_dir(self) -> Path: - """Get the experiments directory.""" - return self.sage_dir / "experiments" - - @property - def issues_dir(self) -> Path: - """Get the issues directory.""" - return self.sage_dir / "issues" - - @property - def states_dir(self) -> Path: - """Get the states directory (for .sage_states data).""" - return self.sage_dir / "states" - - @property - def benchmarks_dir(self) -> Path: - """Get the benchmarks directory (for pytest-benchmark results).""" - return self.sage_dir / "benchmarks" - - @property - def studio_dir(self) -> Path: - """Get the studio directory (for Angular Studio files).""" - return self.sage_dir / "studio" - - @property - def studio_dist_dir(self) -> Path: - """Get the studio dist directory (for Angular Studio build outputs).""" - return self.studio_dir / "dist" - - def get_test_env_dir(self, test_name: str = "test_env") -> Path: - """ - Get a test environment directory path. - - Args: - test_name: Name of the test environment - - Returns: - Path to test environment directory in .sage/temp/ - """ - test_dir = self.temp_dir / test_name - test_dir.mkdir(parents=True, exist_ok=True) - return test_dir - - def get_test_context_dir(self, context_name: str = "test_context") -> Path: - """ - Get a test context directory path. - - Args: - context_name: Name of the test context - - Returns: - Path to test context directory in .sage/temp/ - """ - context_dir = self.temp_dir / context_name - context_dir.mkdir(parents=True, exist_ok=True) - return context_dir - - def get_ray_temp_dir(self) -> Path: - """Get Ray temporary files directory.""" - ray_dir = self.temp_dir / "ray" - ray_dir.mkdir(parents=True, exist_ok=True) - return ray_dir - - def setup_environment_variables(self): - """Set up environment variables for SAGE and other tools.""" - # Core SAGE paths - os.environ["SAGE_OUTPUT_DIR"] = str(self.sage_dir) - os.environ["SAGE_HOME"] = str(self.sage_dir) - os.environ["SAGE_LOGS_DIR"] = str(self.logs_dir) - os.environ["SAGE_TEMP_DIR"] = str(self.temp_dir) - - # Ray-specific environment - ray_temp_dir = self.get_ray_temp_dir() - os.environ["RAY_TMPDIR"] = str(ray_temp_dir) - - return { - "sage_dir": self.sage_dir, - "logs_dir": self.logs_dir, - "temp_dir": self.temp_dir, - "ray_temp_dir": ray_temp_dir, - } - - def get_log_file(self, name: str, subdir: str | None = None) -> Path: - """ - Get a log file path. - - Args: - name: Log file name - subdir: Optional subdirectory within logs - - Returns: - Path to log file - """ - if subdir: - log_dir = self.logs_dir / subdir - log_dir.mkdir(exist_ok=True) - return log_dir / name - return self.logs_dir / name - - def get_output_file(self, name: str, subdir: str | None = None) -> Path: - """ - Get an output file path. - - Args: - name: Output file name - subdir: Optional subdirectory within output - - Returns: - Path to output file - """ - if subdir: - output_dir = self.output_dir / subdir - output_dir.mkdir(parents=True, exist_ok=True) - return output_dir / name - return self.output_dir / name - - def get_temp_file(self, name: str, subdir: str | None = None) -> Path: - """ - Get a temporary file path. - - Args: - name: Temp file name - subdir: Optional subdirectory within temp - - Returns: - Path to temp file - """ - if subdir: - temp_dir = self.temp_dir / subdir - temp_dir.mkdir(parents=True, exist_ok=True) - return temp_dir / name - return self.temp_dir / name - - def get_cache_file(self, name: str, subdir: str | None = None) -> Path: - """ - Get a cache file path. - - Args: - name: Cache file name - subdir: Optional subdirectory within cache - - Returns: - Path to cache file - """ - if subdir: - cache_dir = self.cache_dir / subdir - cache_dir.mkdir(parents=True, exist_ok=True) - return cache_dir / name - return self.cache_dir / name - - def migrate_existing_outputs(self): - """ - Migrate existing output files from project root to .sage directory. - - This method will move files from: - - logs/ -> .sage/logs/ - - output/ -> .sage/output/ - """ - # Skip migration if project_root is None or same as sage_dir parent - if self.project_root is None or not self.project_root.exists(): - return - - migrations = [ - (self.project_root / "logs", self.logs_dir), - (self.project_root / "output", self.output_dir), - ] - - for src, dst in migrations: - if src.exists() and src != dst: - print(f"Migrating {src} -> {dst}") - - # Ensure destination directory exists - dst.mkdir(parents=True, exist_ok=True) - - # Move all files and subdirectories - for item in src.iterdir(): - dst_item = dst / item.name - if item.is_dir(): - if dst_item.exists(): - # Merge directories - shutil.copytree(item, dst_item, dirs_exist_ok=True) - shutil.rmtree(item) - else: - shutil.move(str(item), str(dst_item)) - else: - if dst_item.exists(): - # Backup existing file - backup_name = f"{dst_item.name}.backup" - dst_item.rename(dst_item.parent / backup_name) - shutil.move(str(item), str(dst_item)) - - # Remove empty source directory - try: - src.rmdir() - except OSError: - print(f"Warning: Could not remove {src} (not empty)") - - -# Global cached instance (use normalized project_root key to avoid unexpected -# cache behavior when Path/str objects differ between callers). We expose -# `cache_clear` on the public `get_sage_paths` so tests that call -# `get_sage_paths.cache_clear()` continue to work. -@lru_cache(maxsize=8) -def _get_sage_paths_cached(project_root_key: str | None) -> SageOutputPaths: - """Internal cached constructor keyed by normalized project_root string.""" - if project_root_key is None: - return SageOutputPaths(None) - return SageOutputPaths(Path(project_root_key)) - - -def get_sage_paths(project_root: str | Path | None = None) -> SageOutputPaths: - """Get the global SAGE output paths instance. - - This wrapper normalizes the project_root to an absolute string and uses - the internal cached function. Tests expect a `cache_clear` attribute on - `get_sage_paths`; attach it from the cached function. - """ - project_root_key = None if project_root is None else str(Path(project_root).resolve()) - return _get_sage_paths_cached(project_root_key) - - -# Expose cache_clear on the public function for backward compatibility -get_sage_paths.cache_clear = _get_sage_paths_cached.cache_clear - - -# Convenience functions for backward compatibility and ease of use -def get_logs_dir(project_root: str | Path | None = None) -> Path: - """Get the logs directory.""" - return get_sage_paths(project_root).logs_dir - - -def get_output_dir(project_root: str | Path | None = None) -> Path: - """Get the output directory.""" - return get_sage_paths(project_root).output_dir - - -def get_temp_dir(project_root: str | Path | None = None) -> Path: - """Get the temp directory.""" - return get_sage_paths(project_root).temp_dir - - -def get_cache_dir(project_root: str | Path | None = None) -> Path: - """Get the cache directory.""" - return get_sage_paths(project_root).cache_dir - - -def get_reports_dir(project_root: str | Path | None = None) -> Path: - """Get the reports directory.""" - return get_sage_paths(project_root).reports_dir - - -def get_coverage_dir(project_root: str | Path | None = None) -> Path: - """Get the coverage directory.""" - return get_sage_paths(project_root).coverage_dir - - -def get_benchmarks_dir(project_root: str | Path | None = None) -> Path: - """Get the benchmarks directory.""" - return get_sage_paths(project_root).benchmarks_dir - - -def get_ray_temp_dir(project_root: str | Path | None = None) -> Path: - """Get Ray temporary files directory.""" - return get_sage_paths(project_root).get_ray_temp_dir() - - -def setup_sage_environment(project_root: str | Path | None = None) -> dict: - """Set up SAGE environment variables and return directory paths.""" - return get_sage_paths(project_root).setup_environment_variables() - - -# Main initialization function -def initialize_sage_paths( - project_root: str | Path | None = None, -) -> "SageOutputPaths": - """ - Initialize SAGE paths and set up environment. - - This is the main entry point for path initialization. - It creates all necessary directories and sets up environment variables. - - Args: - project_root: Optional project root path. If None, auto-detected. - - Returns: - SageOutputPaths instance with all paths configured. - """ - paths = get_sage_paths(project_root) - paths.setup_environment_variables() - return paths - - -def get_log_file( - name: str, - subdir: str | None = None, - project_root: str | Path | None = None, -) -> Path: - """Get a log file path.""" - return get_sage_paths(project_root).get_log_file(name, subdir) - - -def get_output_file( - name: str, - subdir: str | None = None, - project_root: str | Path | None = None, -) -> Path: - """Get an output file path.""" - return get_sage_paths(project_root).get_output_file(name, subdir) - - -def get_states_dir(project_root: str | Path | None = None) -> Path: - """Get the states directory.""" - return get_sage_paths(project_root).states_dir - - -def get_states_file( - name: str, - subdir: str | None = None, - project_root: str | Path | None = None, -) -> Path: - """Get a states file path.""" - sage_paths = get_sage_paths(project_root) - if subdir: - states_dir = sage_paths.states_dir / subdir - states_dir.mkdir(parents=True, exist_ok=True) - return states_dir / name - return sage_paths.states_dir / name - - -def migrate_existing_outputs(project_root: str | Path | None = None): - """Migrate existing outputs to .sage directory.""" - get_sage_paths(project_root).migrate_existing_outputs() - - -# Testing utilities -def get_test_env_dir(test_name: str = "test_env", project_root: str | Path | None = None) -> Path: - """Get a test environment directory path in .sage/temp/.""" - return get_sage_paths(project_root).get_test_env_dir(test_name) - - -def get_test_context_dir( - context_name: str = "test_context", project_root: str | Path | None = None -) -> Path: - """Get a test context directory path in .sage/temp/.""" - return get_sage_paths(project_root).get_test_context_dir(context_name) - - -def get_test_temp_dir(temp_name: str, project_root: str | Path | None = None) -> Path: - """Get a temporary directory for testing in .sage/temp/.""" - sage_paths = get_sage_paths(project_root) - temp_dir = sage_paths.temp_dir / temp_name - temp_dir.mkdir(parents=True, exist_ok=True) - return temp_dir diff --git a/packages/sage-common/src/sage/common/config/ports.py b/packages/sage-common/src/sage/common/config/ports.py deleted file mode 100644 index 9cd81d0bb9..0000000000 --- a/packages/sage-common/src/sage/common/config/ports.py +++ /dev/null @@ -1,272 +0,0 @@ -""" -SAGE Port Configuration - -Centralized port configuration for all SAGE services to avoid conflicts. - -Port Allocation Strategy: -- 8889: sage-gateway (OpenAI-compatible API Gateway) -- 8001: vLLM/LLM inference service (SAGE recommended, may have issues on WSL2) -- 5173: sage-studio frontend (Vite dev server) -- 8090: Embedding service -- 8900-8999: Benchmark & testing services - -Known Issues: -- WSL2: Port 8001 may show as listening but refuse connections due to WSL2 - network stack issues. Use BENCHMARK_LLM (8901) as fallback. - -Usage: - from sage.common.config.ports import SagePorts - - # Get default ports - port = SagePorts.LLM_DEFAULT - - # Check if port is available - if SagePorts.is_available(8001): - ... - - # Get all ports for a service category - llm_ports = SagePorts.get_llm_ports() - - # For WSL2, use benchmark port as fallback - port = SagePorts.BENCHMARK_LLM # 8901 - more reliable on WSL2 -""" - -from __future__ import annotations - -import os -import socket -from dataclasses import dataclass -from typing import ClassVar - - -def is_wsl() -> bool: - """Check if running in WSL (Windows Subsystem for Linux).""" - try: - with open("/proc/version") as f: - return "microsoft" in f.read().lower() - except OSError: - return False - - -@dataclass(frozen=True) -class SagePorts: - """ - Centralized port configuration for SAGE services. - - All port numbers are defined here to prevent conflicts between services. - - Architecture: - User → Gateway (8889) → LLM (8001) - User → Studio Frontend (5173) → Gateway (8889) - - Note: Studio Backend has been merged into Gateway. - """ - - # ========================================================================= - # sage-gateway (OpenAI-compatible API Gateway) - # ========================================================================= - GATEWAY_DEFAULT: ClassVar[int] = 8889 # API Gateway main port (default moved off 8888) - - # ========================================================================= - # sage-edge (独立仓库 aggregator shell) - # ========================================================================= - EDGE_DEFAULT: ClassVar[int] = 8899 # Edge aggregator (mounts LLM gateway by default) - - # ========================================================================= - # sageLLM (Unified LLM inference engine) - # ========================================================================= - SAGELLM_DEFAULT: ClassVar[int] = 8001 # sageLLM default port (same as LLM_DEFAULT) - - # ========================================================================= - # LLM Services (vLLM, etc.) - Legacy compatibility - # ========================================================================= - LLM_DEFAULT: ClassVar[int] = 8001 # vLLM port (deprecated, use SAGELLM_DEFAULT) - LLM_SECONDARY: ClassVar[int] = 8002 # Secondary LLM instance (if needed) - LLM_WSL_FALLBACK: ClassVar[int] = 8901 # Fallback for WSL2 (same as BENCHMARK_LLM) - - # ========================================================================= - # sage-studio (Frontend only, Backend merged into Gateway) - # ========================================================================= - STUDIO_BACKEND: ClassVar[int] = 8889 # Deprecated: now same as GATEWAY_DEFAULT - STUDIO_FRONTEND: ClassVar[int] = 5173 # Studio frontend (Vite dev server) - - # ========================================================================= - # Embedding Services - # ========================================================================= - EMBEDDING_DEFAULT: ClassVar[int] = 8090 # Primary embedding server - EMBEDDING_SECONDARY: ClassVar[int] = 8091 # Secondary embedding instance - - # ========================================================================= - # Benchmark & Testing Services (8900-8999) - # ========================================================================= - BENCHMARK_LLM: ClassVar[int] = 8901 # Benchmark-dedicated LLM server - BENCHMARK_EMBEDDING: ClassVar[int] = 8902 # Benchmark embedding server - BENCHMARK_API: ClassVar[int] = 8903 # Benchmark API server - - @classmethod - def get_recommended_llm_port(cls) -> int: - """ - Get recommended LLM port based on platform. - - On WSL2, port 8001 may have connectivity issues, so use 8901 as fallback. - - Returns: - Recommended port number for LLM services - """ - if is_wsl(): - return cls.LLM_WSL_FALLBACK - return cls.LLM_DEFAULT - - @classmethod - def get_llm_ports(cls) -> list[int]: - """Get all LLM-related ports in priority order. - - Includes fallback ports for WSL2 compatibility. - """ - return [cls.LLM_DEFAULT, cls.BENCHMARK_LLM, cls.LLM_SECONDARY, cls.GATEWAY_DEFAULT] - - @classmethod - def get_embedding_ports(cls) -> list[int]: - """Get all embedding-related ports in priority order.""" - return [cls.EMBEDDING_DEFAULT, cls.EMBEDDING_SECONDARY] - - @classmethod - def get_benchmark_ports(cls) -> list[int]: - """Get all benchmark-related ports.""" - return [cls.BENCHMARK_LLM, cls.BENCHMARK_EMBEDDING, cls.BENCHMARK_API] - - @classmethod - def is_available(cls, port: int, host: str = "localhost") -> bool: - """ - Check if a port is available for binding. - - Args: - port: Port number to check - host: Host to check (default: localhost) - - Returns: - True if port is available, False otherwise - """ - try: - with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: - s.settimeout(1) - result = s.connect_ex((host, port)) - return result != 0 # 0 means connection succeeded (port in use) - except OSError: - return True # Assume available if we can't check - - @classmethod - def find_available_port(cls, start: int = 8900, end: int = 8999) -> int | None: - """ - Find an available port in the given range. - - Args: - start: Start of port range (inclusive) - end: End of port range (inclusive) - - Returns: - Available port number, or None if no port available - """ - for port in range(start, end + 1): - if cls.is_available(port): - return port - return None - - @classmethod - def get_from_env(cls, env_var: str, default: int) -> int: - """ - Get port from environment variable with fallback to default. - - Args: - env_var: Environment variable name - default: Default port if env var not set - - Returns: - Port number - """ - value = os.environ.get(env_var) - if value: - try: - return int(value) - except ValueError: - pass - return default - - @classmethod - def check_port_status(cls, port: int, host: str = "localhost") -> dict: - """ - Check detailed status of a port. - - Returns: - dict: { - "port": int, - "is_available": bool, # True if port is free (can bind), False if in use - "is_listening": bool, # True if something is listening (connect success) - } - """ - is_listening = False - try: - with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: - s.settimeout(0.5) - result = s.connect_ex((host, port)) - if result == 0: - is_listening = True - except OSError: - pass - - return { - "port": port, - "is_available": not is_listening, - "is_listening": is_listening, - } - - @classmethod - def diagnose(cls) -> None: - """Print a diagnostic report of all SAGE ports.""" - print("=" * 65) - print("🔍 SAGE Port Diagnostic Tool") - print("=" * 65) - - if is_wsl(): - print("⚠️ Environment: WSL2 Detected (Port 8001 might be unreliable)") - else: - print("✅ Environment: Standard Linux/Unix") - - print("-" * 65) - print(f"{'Service':<20} | {'Port':<6} | {'Status':<15} | {'Recommendation':<15}") - print("-" * 65) - - services = [ - ("Gateway", cls.GATEWAY_DEFAULT), - ("Edge", cls.EDGE_DEFAULT), - ("sageLLM", cls.SAGELLM_DEFAULT), - ("LLM (WSL/Bench)", cls.LLM_WSL_FALLBACK), - ("Embedding", cls.EMBEDDING_DEFAULT), - ("Studio Frontend", cls.STUDIO_FRONTEND), - ] - - for name, port in services: - status = cls.check_port_status(port) - state = "🔴 In Use" if status["is_listening"] else "🟢 Available" - - rec = "" - if name == "LLM (Default)" and is_wsl(): - rec = "Avoid (WSL)" - elif name == "LLM (WSL/Bench)" and is_wsl(): - rec = "Recommended" - elif status["is_listening"]: - rec = "Check PID" - - print(f"{name:<20} | {port:<6} | {state:<15} | {rec:<15}") - - print("-" * 65) - - -# Convenience aliases -DEFAULT_SAGELLM_PORT = SagePorts.SAGELLM_DEFAULT -DEFAULT_LLM_PORT = SagePorts.LLM_DEFAULT # Legacy alias -DEFAULT_EMBEDDING_PORT = SagePorts.EMBEDDING_DEFAULT -DEFAULT_BENCHMARK_LLM_PORT = SagePorts.BENCHMARK_LLM - -if __name__ == "__main__": - SagePorts.diagnose() diff --git a/packages/sage-common/src/sage/common/config/user_paths.py b/packages/sage-common/src/sage/common/config/user_paths.py deleted file mode 100644 index 2c778aa7ba..0000000000 --- a/packages/sage-common/src/sage/common/config/user_paths.py +++ /dev/null @@ -1,378 +0,0 @@ -"""SAGE User Path Configuration (XDG Base Directory Specification) - -Layer: L1 (Foundation - Common Configuration) - -This module provides XDG-compliant user directory paths for SAGE. -Following the XDG Base Directory Specification: -- https://specifications.freedesktop.org/basedir-spec/basedir-spec-latest.html - -Path Categories: -- CONFIG: User configuration files (edited by user, should be backed up) -- DATA: Persistent application data (sessions, databases, models) -- STATE: Runtime state data (logs, history) -- CACHE: Non-essential cached data (can be deleted to free space) - -Directory Structure: - $XDG_CONFIG_HOME/sage/ (~/.config/sage/) - ├── config.yaml # Main configuration - ├── cluster.yaml # Cluster configuration - └── credentials.yaml # API keys (should be 600 permission) - - $XDG_DATA_HOME/sage/ (~/.local/share/sage/) - ├── models/ # Downloaded models - │ ├── sagellm/ # sageLLM models (preferred) - │ └── vllm/ # vLLM models (legacy) - ├── sessions/ # Gateway sessions - ├── vector_db/ # Vector database indices - └── finetune/ # Fine-tuning outputs - - $XDG_STATE_HOME/sage/ (~/.local/state/sage/) - └── logs/ # Runtime logs - ├── gateway.log - ├── llm_server.log - └── studio.log - - $XDG_CACHE_HOME/sage/ (~/.cache/sage/) - ├── huggingface/ # HuggingFace cache - ├── sagellm/ # sageLLM cache - ├── pip/ # Pip cache - └── chat/ # Chat index cache - -Note: Project-level temporary files (.sage/) are managed by output_paths.py -""" - -import os -from functools import lru_cache -from pathlib import Path -from typing import Literal - -# Type alias for path categories -PathCategory = Literal["config", "data", "state", "cache"] - - -def _get_xdg_dir(env_var: str, default_subdir: str) -> Path: - """Get XDG directory with fallback to default. - - Args: - env_var: Environment variable name (e.g., "XDG_CONFIG_HOME") - default_subdir: Default subdirectory under $HOME (e.g., ".config") - - Returns: - Path to the XDG directory - """ - xdg_dir = os.environ.get(env_var) - if xdg_dir: - return Path(xdg_dir) - return Path.home() / default_subdir - - -@lru_cache(maxsize=1) -def get_user_config_dir() -> Path: - """Get SAGE user configuration directory. - - Default: ~/.config/sage/ - - This directory contains user-editable configuration files. - Should be backed up. - - Returns: - Path to config directory - """ - base = _get_xdg_dir("XDG_CONFIG_HOME", ".config") - sage_dir = base / "sage" - sage_dir.mkdir(parents=True, exist_ok=True) - return sage_dir - - -@lru_cache(maxsize=1) -def get_user_data_dir() -> Path: - """Get SAGE user data directory. - - Default: ~/.local/share/sage/ - - This directory contains persistent application data: - - Downloaded models - - Session data - - Vector databases - - Fine-tuning outputs - - Returns: - Path to data directory - """ - base = _get_xdg_dir("XDG_DATA_HOME", ".local/share") - sage_dir = base / "sage" - sage_dir.mkdir(parents=True, exist_ok=True) - return sage_dir - - -@lru_cache(maxsize=1) -def get_user_state_dir() -> Path: - """Get SAGE user state directory. - - Default: ~/.local/state/sage/ - - This directory contains runtime state data: - - Log files - - History files - - PID files - - Returns: - Path to state directory - """ - base = _get_xdg_dir("XDG_STATE_HOME", ".local/state") - sage_dir = base / "sage" - sage_dir.mkdir(parents=True, exist_ok=True) - return sage_dir - - -@lru_cache(maxsize=1) -def get_user_cache_dir() -> Path: - """Get SAGE user cache directory. - - Default: ~/.cache/sage/ - - This directory contains non-essential cached data. - Can be safely deleted to free disk space. - - Returns: - Path to cache directory - """ - base = _get_xdg_dir("XDG_CACHE_HOME", ".cache") - sage_dir = base / "sage" - sage_dir.mkdir(parents=True, exist_ok=True) - return sage_dir - - -class SageUserPaths: - """Centralized access to SAGE user directories following XDG specification. - - Usage: - from sage.common.config.user_paths import SageUserPaths - - paths = SageUserPaths() - config_file = paths.config_dir / "config.yaml" - log_file = paths.logs_dir / "gateway.log" - model_dir = paths.models_dir / "vllm" - """ - - def __init__(self): - """Initialize user paths and ensure directory structure exists.""" - self._ensure_structure() - - def _ensure_structure(self): - """Ensure all required subdirectories exist.""" - # Config subdirectories (none needed, flat structure) - - # Data subdirectories - for subdir in [ - "models", - "models/sagellm", # sageLLM models (preferred) - "models/vllm", # vLLM models (legacy) - "sessions", - "vector_db", - "finetune", - ]: - (self.data_dir / subdir).mkdir(parents=True, exist_ok=True) - - # State subdirectories - for subdir in ["logs"]: - (self.state_dir / subdir).mkdir(parents=True, exist_ok=True) - - # Cache subdirectories - for subdir in ["huggingface", "sagellm", "chat"]: - (self.cache_dir / subdir).mkdir(parents=True, exist_ok=True) - - # === Base directories === - - @property - def config_dir(self) -> Path: - """User configuration directory (~/.config/sage/)""" - return get_user_config_dir() - - @property - def data_dir(self) -> Path: - """User data directory (~/.local/share/sage/)""" - return get_user_data_dir() - - @property - def state_dir(self) -> Path: - """User state directory (~/.local/state/sage/)""" - return get_user_state_dir() - - @property - def cache_dir(self) -> Path: - """User cache directory (~/.cache/sage/)""" - return get_user_cache_dir() - - # === Config paths === - - @property - def config_file(self) -> Path: - """Main configuration file (~/.config/sage/config.yaml)""" - return self.config_dir / "config.yaml" - - @property - def cluster_config_file(self) -> Path: - """Cluster configuration file (~/.config/sage/cluster.yaml)""" - return self.config_dir / "cluster.yaml" - - @property - def credentials_file(self) -> Path: - """Credentials file (~/.config/sage/credentials.yaml)""" - return self.config_dir / "credentials.yaml" - - # === Data paths === - - @property - def models_dir(self) -> Path: - """Downloaded models directory (~/.local/share/sage/models/)""" - return self.data_dir / "models" - - @property - def vllm_models_dir(self) -> Path: - """vLLM models directory (~/.local/share/sage/models/vllm/) - - DEPRECATED: Use sagellm_models_dir instead. - """ - return self.data_dir / "models" / "vllm" - - @property - def sagellm_models_dir(self) -> Path: - """sageLLM models directory (~/.local/share/sage/models/sagellm/) - - Preferred location for sageLLM engine models. - """ - return self.data_dir / "models" / "sagellm" - - @property - def sessions_dir(self) -> Path: - """Session data directory (~/.local/share/sage/sessions/)""" - return self.data_dir / "sessions" - - @property - def vector_db_dir(self) -> Path: - """Vector database directory (~/.local/share/sage/vector_db/)""" - return self.data_dir / "vector_db" - - @property - def finetune_dir(self) -> Path: - """Fine-tuning output directory (~/.local/share/sage/finetune/)""" - return self.data_dir / "finetune" - - # === State paths === - - @property - def logs_dir(self) -> Path: - """Log files directory (~/.local/state/sage/logs/)""" - return self.state_dir / "logs" - - def get_log_file(self, name: str) -> Path: - """Get path to a specific log file. - - Args: - name: Log file name (e.g., "gateway", "llm_server", "studio") - - Returns: - Path to log file - """ - if not name.endswith(".log"): - name = f"{name}.log" - return self.logs_dir / name - - # === Cache paths === - - @property - def hf_cache_dir(self) -> Path: - """HuggingFace cache directory (~/.cache/sage/huggingface/)""" - return self.cache_dir / "huggingface" - - @property - def chat_cache_dir(self) -> Path: - """Chat index cache directory (~/.cache/sage/chat/)""" - return self.cache_dir / "chat" - - @property - def sagellm_cache_dir(self) -> Path: - """sageLLM cache directory (~/.cache/sage/sagellm/) - - Cache for sageLLM engine (tokenizers, compiled kernels, etc.). - """ - return self.cache_dir / "sagellm" - - -# Singleton instance for convenience -_user_paths: SageUserPaths | None = None - - -def get_user_paths() -> SageUserPaths: - """Get singleton instance of SageUserPaths. - - Usage: - from sage.common.config.user_paths import get_user_paths - - paths = get_user_paths() - config = paths.config_file - """ - global _user_paths - if _user_paths is None: - _user_paths = SageUserPaths() - return _user_paths - - -# === Legacy compatibility === -# These functions provide backward compatibility with code using ~/.sage/ - - -def get_legacy_sage_home() -> Path: - """Get legacy ~/.sage/ path for backward compatibility. - - DEPRECATED: Use get_user_paths() instead. - - This function is provided for migration purposes only. - New code should use the XDG-compliant paths. - - Returns: - Path to ~/.sage/ - """ - sage_home = Path.home() / ".sage" - sage_home.mkdir(parents=True, exist_ok=True) - return sage_home - - -def migrate_legacy_config(): - """Migrate configuration from legacy ~/.sage/ to XDG paths. - - This function checks for legacy configuration files and migrates - them to the new XDG-compliant locations. - """ - import shutil - - legacy_home = Path.home() / ".sage" - paths = get_user_paths() - - # Migration mappings: (legacy_path, new_path) - migrations = [ - (legacy_home / "config.yaml", paths.config_file), - (legacy_home / "cluster_config.yaml", paths.cluster_config_file), - (legacy_home / ".env.json", paths.config_dir / "env.json"), - ] - - for legacy_path, new_path in migrations: - if legacy_path.exists() and not new_path.exists(): - print(f"Migrating {legacy_path} -> {new_path}") - new_path.parent.mkdir(parents=True, exist_ok=True) - shutil.copy2(legacy_path, new_path) - - -# Export all public symbols -__all__ = [ - "PathCategory", - "get_user_config_dir", - "get_user_data_dir", - "get_user_state_dir", - "get_user_cache_dir", - "SageUserPaths", - "get_user_paths", - "get_legacy_sage_home", - "migrate_legacy_config", -] diff --git a/packages/sage-common/src/sage/common/core/__init__.py b/packages/sage-common/src/sage/common/core/__init__.py deleted file mode 100644 index bb2d090f78..0000000000 --- a/packages/sage-common/src/sage/common/core/__init__.py +++ /dev/null @@ -1,125 +0,0 @@ -""" -Common Core Module - 共享类型、异常、常量和函数接口 - -这个模块包含 SAGE 框架中各个包共享的核心定义。 - -包含: -- data_types: 基础数据类型和查询结果 -- exceptions: 核心异常类型 -- types: 执行模式、状态等枚举 -- functions: 用户自定义函数的基础接口 (NEW) -- constants: 常量定义 -""" - -from sage.common.core.constants import ( - DEFAULT_CHECKPOINT_INTERVAL, - DEFAULT_CLEANUP_TIMEOUT, - DEFAULT_HEALTH_CHECK_INTERVAL, - DEFAULT_MAX_RESTART_ATTEMPTS, - PLACEMENT_STRATEGY_LOAD_BALANCE, - PLACEMENT_STRATEGY_RESOURCE_AWARE, - PLACEMENT_STRATEGY_SIMPLE, - RESTART_STRATEGY_EXPONENTIAL, - RESTART_STRATEGY_FAILURE_RATE, - RESTART_STRATEGY_FIXED, - SCHEDULING_STRATEGY_FIFO, - SCHEDULING_STRATEGY_PRIORITY, - SCHEDULING_STRATEGY_RESOURCE_AWARE, -) -from sage.common.core.data_types import ( - BaseDocument, - BaseQueryResult, - ExtendedQueryResult, - QueryResultInput, - QueryResultOutput, - create_query_result, - ensure_query_result, - extract_query, - extract_results, -) -from sage.common.core.exceptions import ( - CheckpointError, - FaultToleranceError, - KernelError, - RecoveryError, - ResourceAllocationError, - SchedulingError, -) - -# Import function interfaces -from sage.common.core.functions import ( - BaseCoMapFunction, - BaseFunction, - BaseJoinFunction, - BatchFunction, - Collector, - FilterFunction, - FlatMapFunction, - FutureFunction, - KeyByFunction, - LambdaMapFunction, - MapFunction, - SinkFunction, - SourceFunction, - wrap_lambda, -) -from sage.common.core.signals import StopSignal -from sage.common.core.types import ExecutionMode, NodeID, ServiceID, TaskID, TaskStatus - -__all__ = [ - # Types - "ExecutionMode", - "TaskStatus", - "TaskID", - "ServiceID", - "NodeID", - # Data Types - "BaseDocument", - "BaseQueryResult", - "ExtendedQueryResult", - "QueryResultInput", - "QueryResultOutput", - # Data Type Helpers - "ensure_query_result", - "extract_query", - "extract_results", - "create_query_result", - # Exceptions - "KernelError", - "SchedulingError", - "FaultToleranceError", - "ResourceAllocationError", - "RecoveryError", - "CheckpointError", - # Constants - "DEFAULT_CHECKPOINT_INTERVAL", - "DEFAULT_CLEANUP_TIMEOUT", - "DEFAULT_HEALTH_CHECK_INTERVAL", - "DEFAULT_MAX_RESTART_ATTEMPTS", - "RESTART_STRATEGY_FIXED", - "RESTART_STRATEGY_EXPONENTIAL", - "RESTART_STRATEGY_FAILURE_RATE", - "PLACEMENT_STRATEGY_SIMPLE", - "PLACEMENT_STRATEGY_RESOURCE_AWARE", - "PLACEMENT_STRATEGY_LOAD_BALANCE", - "SCHEDULING_STRATEGY_FIFO", - "SCHEDULING_STRATEGY_PRIORITY", - "SCHEDULING_STRATEGY_RESOURCE_AWARE", - # Function Interfaces - "BaseFunction", - "MapFunction", - "FilterFunction", - "FlatMapFunction", - "SinkFunction", - "SourceFunction", - "BatchFunction", - "KeyByFunction", - "BaseJoinFunction", - "BaseCoMapFunction", - "Collector", - "LambdaMapFunction", - "wrap_lambda", - "FutureFunction", - # Signals - "StopSignal", -] diff --git a/packages/sage-common/src/sage/common/core/constants.py b/packages/sage-common/src/sage/common/core/constants.py deleted file mode 100644 index a55a0d5913..0000000000 --- a/packages/sage-common/src/sage/common/core/constants.py +++ /dev/null @@ -1,45 +0,0 @@ -""" -Common Core Constants - -定义了 SAGE 框架中使用的常量。 -这些常量可以被所有 SAGE 包使用，主要用于 kernel 的任务调度和容错配置。 - -Layer: L1 (Foundation) -""" - -# Default configuration -DEFAULT_CHECKPOINT_INTERVAL = 60 # seconds -DEFAULT_HEALTH_CHECK_INTERVAL = 30 # seconds -DEFAULT_MAX_RESTART_ATTEMPTS = 3 -DEFAULT_CLEANUP_TIMEOUT = 5.0 # seconds - -# Restart strategies -RESTART_STRATEGY_FIXED = "fixed_delay" -RESTART_STRATEGY_EXPONENTIAL = "exponential_backoff" -RESTART_STRATEGY_FAILURE_RATE = "failure_rate" - -# Placement strategies -PLACEMENT_STRATEGY_SIMPLE = "simple" -PLACEMENT_STRATEGY_RESOURCE_AWARE = "resource_aware" -PLACEMENT_STRATEGY_LOAD_BALANCE = "load_balance" - -# Scheduling strategies -SCHEDULING_STRATEGY_FIFO = "fifo" -SCHEDULING_STRATEGY_PRIORITY = "priority" -SCHEDULING_STRATEGY_RESOURCE_AWARE = "resource_aware" - -__all__ = [ - "DEFAULT_CHECKPOINT_INTERVAL", - "DEFAULT_HEALTH_CHECK_INTERVAL", - "DEFAULT_MAX_RESTART_ATTEMPTS", - "DEFAULT_CLEANUP_TIMEOUT", - "RESTART_STRATEGY_FIXED", - "RESTART_STRATEGY_EXPONENTIAL", - "RESTART_STRATEGY_FAILURE_RATE", - "PLACEMENT_STRATEGY_SIMPLE", - "PLACEMENT_STRATEGY_RESOURCE_AWARE", - "PLACEMENT_STRATEGY_LOAD_BALANCE", - "SCHEDULING_STRATEGY_FIFO", - "SCHEDULING_STRATEGY_PRIORITY", - "SCHEDULING_STRATEGY_RESOURCE_AWARE", -] diff --git a/packages/sage-common/src/sage/common/core/data_types.py b/packages/sage-common/src/sage/common/core/data_types.py deleted file mode 100644 index 6a9a8fc953..0000000000 --- a/packages/sage-common/src/sage/common/core/data_types.py +++ /dev/null @@ -1,317 +0,0 @@ -""" -通用数据类型定义 - -定义了 SAGE 系统中算子之间传递的标准化数据结构。 -这些类型是框架级别的基础类型，可以被各种专门的算子（RAG、搜索、多模态等）继承和扩展。 - -设计原则： -1. 通用性：适用于多种场景（检索、生成、搜索、分析等） -2. 可扩展：使用 TypedDict(total=False) 允许添加自定义字段 -3. 类型安全：提供完整的类型注解，支持 IDE 和 Pylance 检查 -4. 向后兼容：支持多种输入格式（dict、tuple、list） -""" - -from typing import Any, TypedDict - -# ============================================================================ -# 基础文档类型 -# ============================================================================ - - -class BaseDocument(TypedDict, total=False): - """ - 基础文档结构 - 表示一个文本片段或检索到的内容 - - 这是最基础的文档表示，所有领域特定的文档类型都应该继承这个类型。 - - 必需字段： - text: 文档的主要文本内容 - - 可选字段： - id: 文档的唯一标识符 - title: 文档标题 - source: 文档来源（URL、文件路径、数据库名等） - score: 相关性分数或置信度 (0.0-1.0) - rank: 排序位置（从0开始） - metadata: 任意额外元数据 - - 示例： - >>> doc: BaseDocument = { - ... "text": "Python是一种编程语言", - ... "title": "Python简介", - ... "source": "textbook.pdf", - ... "score": 0.95 - ... } - """ - - text: str # 必需：文档文本内容 - id: str | int | None # 文档唯一标识符 - title: str | None # 文档标题 - source: str | None # 文档来源 - score: float | None # 相关性分数 (0.0-1.0) - rank: int | None # 排序位置 - metadata: dict[str, Any] | None # 额外元数据 - - -# ============================================================================ -# 基础查询-结果对类型 -# ============================================================================ - - -class BaseQueryResult(TypedDict): - """ - 基础查询-结果对结构 - 表示一个查询及其对应的结果列表 - - 这是 SAGE 算子之间传递数据的标准格式。 - 所有算子都应该能够接受这个格式的输入，并返回这个格式（或其扩展）的输出。 - - 必需字段： - query: 用户的查询文本 - results: 结果列表（可以是任何类型） - - 可选字段： - None（子类可以添加） - - 示例： - >>> data: BaseQueryResult = { - ... "query": "什么是机器学习", - ... "results": ["结果1", "结果2", "结果3"] - ... } - """ - - query: str # 必需：用户查询 - results: list[Any] # 必需：结果列表 - - -class ExtendedQueryResult(BaseQueryResult, total=False): - """ - 扩展查询-结果结构 - 添加了常用的额外字段 - - 继承 BaseQueryResult，添加了在实际应用中常用的字段。 - - 额外可选字段： - query_id: 查询的唯一标识符 - timestamp: 查询时间戳 - total_count: 结果总数（可能大于 results 列表长度） - execution_time: 执行时间（秒） - context: 额外的上下文信息 - metadata: 任意元数据 - - 示例： - >>> data: ExtendedQueryResult = { - ... "query": "Python教程", - ... "results": [...], - ... "query_id": "q_12345", - ... "execution_time": 0.152, - ... "total_count": 100 - ... } - """ - - query_id: str | None # 查询ID - timestamp: int | float | None # 时间戳 - total_count: int | None # 结果总数 - execution_time: float | None # 执行时间（秒） - context: str | list[str] | dict[str, Any] | None # 上下文信息 - metadata: dict[str, Any] | None # 额外元数据 - - -# ============================================================================ -# 类型别名 - 灵活的输入格式 -# ============================================================================ - - -# 支持的输入格式： -# 1. 标准字典格式：{"query": "...", "results": [...]} -# 2. 扩展字典格式：包含额外字段的字典 -# 3. 元组格式（向后兼容）：("query", ["result1", "result2"]) -# 4. 列表格式（向后兼容）：["query", ["result1", "result2"]] -QueryResultInput = BaseQueryResult | ExtendedQueryResult | dict[str, Any] | tuple | list - -# 输出格式：应该是标准的字典格式 -QueryResultOutput = BaseQueryResult | ExtendedQueryResult | dict[str, Any] - - -# ============================================================================ -# 辅助函数 - 格式转换和提取 -# ============================================================================ - - -def ensure_query_result(data: QueryResultInput, default_query: str = "") -> BaseQueryResult: - """ - 确保数据符合 BaseQueryResult 格式 - - 将各种输入格式统一转换为标准的 BaseQueryResult 格式。 - - Args: - data: 输入数据（可以是字典、元组、列表等） - default_query: 当无法提取查询时使用的默认值 - - Returns: - BaseQueryResult: 标准化的查询-结果对 - - 示例： - >>> ensure_query_result(("query", ["a", "b"])) - {'query': 'query', 'results': ['a', 'b']} - - >>> ensure_query_result({"question": "...", "docs": [...]}) - {'query': '...', 'results': [...]} - """ - if isinstance(data, dict): - query = data.get("query") or data.get("question") or data.get("q") or default_query - results = ( - data.get("results") - or data.get("documents") - or data.get("docs") - or data.get("items") - or [] - ) - # Ensure results is a list - if not isinstance(results, list): - results = ( - list(results) - if hasattr(results, "__iter__") and not isinstance(results, str) - else [results] - ) - return {"query": str(query), "results": results} - - if isinstance(data, tuple | list) and len(data) >= 2: - query = str(data[0]) if data[0] is not None else default_query - results = list(data[1]) if isinstance(data[1], list | tuple) else [data[1]] - return {"query": query, "results": results} - - # 无法解析，返回空结果 - return {"query": default_query, "results": []} - - -def extract_query(data: QueryResultInput, default: str = "") -> str: - """ - 从任意格式中提取查询字符串 - - Args: - data: 输入数据 - default: 默认值 - - Returns: - str: 提取的查询字符串 - - 示例： - >>> extract_query({"query": "test"}) - 'test' - - >>> extract_query(("my query", ["results"])) - 'my query' - """ - if isinstance(data, str): - return data - - if isinstance(data, dict): - return str( - data.get("query") - or data.get("question") - or data.get("q") - or data.get("text") - or default - ) - - if isinstance(data, tuple | list) and len(data) > 0: - return str(data[0]) if data[0] is not None else default - - return default - - -def extract_results(data: QueryResultInput, default: list[Any] | None = None) -> list[Any]: - """ - 从任意格式中提取结果列表 - - Args: - data: 输入数据 - default: 默认值 - - Returns: - List[Any]: 提取的结果列表 - - 示例: - >>> extract_results({"query": "test", "results": ["a", "b"]}) - ['a', 'b'] - - >>> extract_results(("query", ["a", "b"])) - ['a', 'b'] - """ - if default is None: - default = [] - - if isinstance(data, dict): - results = ( - data.get("results") - or data.get("documents") - or data.get("docs") - or data.get("items") - or data.get("data") - ) - if results is not None: - return list(results) if isinstance(results, list | tuple) else [results] - return default - - if isinstance(data, tuple | list) and len(data) >= 2: - results = data[1] - return list(results) if isinstance(results, list | tuple) else [results] - - if isinstance(data, list | tuple): - return list(data) - - return default - - -def create_query_result(query: str, results: list[Any], **kwargs) -> ExtendedQueryResult: - """ - 创建标准的 ExtendedQueryResult 对象 - - Args: - query: 查询字符串 - results: 结果列表 - **kwargs: 额外的字段（如 execution_time, metadata 等） - - Returns: - ExtendedQueryResult: 标准化的查询结果对象 - - 示例: - >>> create_query_result( - ... query="test", - ... results=["a", "b"], - ... execution_time=0.5, - ... total_count=2 - ... ) - {'query': 'test', 'results': ['a', 'b'], 'execution_time': 0.5, 'total_count': 2} - """ - result: ExtendedQueryResult = { - "query": query, - "results": results, - } - - # 添加额外字段 - for key, value in kwargs.items(): - if value is not None: - result[key] = value # type: ignore - - return result - - -# ============================================================================ -# 导出 -# ============================================================================ - - -__all__ = [ - # 基础类型 - "BaseDocument", - "BaseQueryResult", - "ExtendedQueryResult", - # 类型别名 - "QueryResultInput", - "QueryResultOutput", - # 辅助函数 - "ensure_query_result", - "extract_query", - "extract_results", - "create_query_result", -] diff --git a/packages/sage-common/src/sage/common/core/exceptions.py b/packages/sage-common/src/sage/common/core/exceptions.py deleted file mode 100644 index c8bbb66621..0000000000 --- a/packages/sage-common/src/sage/common/core/exceptions.py +++ /dev/null @@ -1,89 +0,0 @@ -""" -Common Core Exception Classes - -定义了 SAGE 框架中使用的异常类层次结构。 -这些异常可以被所有 SAGE 包使用，主要用于 kernel 的任务调度和容错。 - -Layer: L1 (Foundation) -""" - - -class KernelError(Exception): - """ - Base Kernel Exception - - The base class for all sage-kernel related exceptions. - """ - - pass - - -class SchedulingError(KernelError): - """ - Scheduling Related Exception - - Exception occurring during task scheduling, resource allocation, etc. - """ - - pass - - -class FaultToleranceError(KernelError): - """ - Fault Tolerance Related Exception - - Exception occurring during fault detection, recovery, etc. - """ - - pass - - -class ResourceAllocationError(SchedulingError): - """ - Resource Allocation Exception - - Raised when required resources cannot be allocated. - """ - - pass - - -class RecoveryError(FaultToleranceError): - """ - Recovery Failure Exception - - Raised when task or job recovery fails. - """ - - pass - - -class CheckpointError(FaultToleranceError): - """ - Checkpoint Exception - - Exception occurring when saving or loading a checkpoint. - """ - - pass - - -class PlacementError(SchedulingError): - """ - Placement Strategy Exception - - Exception occurring when deciding task placement. - """ - - pass - - -__all__ = [ - "KernelError", - "SchedulingError", - "FaultToleranceError", - "ResourceAllocationError", - "RecoveryError", - "CheckpointError", - "PlacementError", -] diff --git a/packages/sage-common/src/sage/common/core/functions/__init__.py b/packages/sage-common/src/sage/common/core/functions/__init__.py deleted file mode 100644 index 14674434b1..0000000000 --- a/packages/sage-common/src/sage/common/core/functions/__init__.py +++ /dev/null @@ -1,60 +0,0 @@ -""" -SAGE Common Functions - 基础函数接口定义 - -Layer: L1 (Common - Core Abstractions) -Dependencies: 无 - -提供用户自定义函数的基础接口： -- BaseFunction: 所有函数的基类 -- MapFunction: 一对一映射函数 -- FilterFunction: 过滤函数 -- SinkFunction: 输出函数 -- SourceFunction: 数据源函数 -- BatchFunction: 批处理函数 -等等... - -这些接口是纯抽象定义，不依赖任何执行引擎。 - -示例: - from sage.common.core.functions import MapFunction - - class MyMapper(MapFunction): - def map(self, value): - return value * 2 -""" - -from .base_function import BaseFunction -from .batch_function import BatchFunction -from .comap_function import BaseCoMapFunction -from .filter_function import FilterFunction -from .flatmap_function import FlatMapFunction -from .future_function import FutureFunction -from .join_function import BaseJoinFunction -from .keyby_function import KeyByFunction -from .lambda_function import LambdaMapFunction, wrap_lambda -from .map_function import MapFunction -from .sink_function import SinkFunction -from .source_function import SourceFunction - -# Note: flatmap_collector exports Collector, not FlatMapCollector -try: - from .flatmap_collector import Collector -except ImportError: - Collector = None # type: ignore - -__all__ = [ - "BaseFunction", - "MapFunction", - "FilterFunction", - "FlatMapFunction", - "SinkFunction", - "SourceFunction", - "BatchFunction", - "KeyByFunction", - "BaseJoinFunction", - "BaseCoMapFunction", - "Collector", - "LambdaMapFunction", - "wrap_lambda", - "FutureFunction", -] diff --git a/packages/sage-common/src/sage/common/core/functions/base_function.py b/packages/sage-common/src/sage/common/core/functions/base_function.py deleted file mode 100644 index af2be6cd5d..0000000000 --- a/packages/sage-common/src/sage/common/core/functions/base_function.py +++ /dev/null @@ -1,252 +0,0 @@ -import logging -import pickle -from abc import ABC, abstractmethod -from typing import TYPE_CHECKING, Any - -if TYPE_CHECKING: - from sage.kernel.runtime.context.task_context import TaskContext - - -class BaseFunction(ABC): - """ - BaseFunction is the abstract base class for all operator functions in SAGE. - It defines the core interface and initializes a logger. - """ - - # 子类可以覆盖这些属性来控制状态保存行为 - __state_include__: list[str] = [] # 如果非空，只保存这些字段 - __state_exclude__: list[str] = ["ctx", "_logger", "logger"] # 排除这些字段 - - # 不可序列化的类型（会被自动排除） - __unserializable_types__ = ( - type(lambda: None), # function - type, # class - type(None).__class__, # NoneType - logging.Logger, - ) - - def __init__(self, *args, **kwargs): - self.ctx: TaskContext | None = None # 运行时注入 - self._logger = None - - @property - def logger(self): - if not hasattr(self, "_logger") or self._logger is None: - if self.ctx is None: - self._logger = logging.getLogger("") - else: - self._logger = self.ctx.logger - return self._logger - - @property - def name(self): - if self.ctx is None: - return self.__class__.__name__ - return self.ctx.name - - def call_service( - self, - service_name: str, - *args, - timeout: float | None = None, - method: str | None = None, - **kwargs, - ): - """同步服务调用语法糖""" - if self.ctx is None: - raise RuntimeError("Runtime context not initialized. Cannot access services.") - - return self.ctx.call_service(service_name, *args, timeout=timeout, method=method, **kwargs) - - def call_service_async( - self, - service_name: str, - *args, - timeout: float | None = None, - method: str | None = None, - **kwargs, - ): - """异步服务调用语法糖""" - if self.ctx is None: - raise RuntimeError("Runtime context not initialized. Cannot access services.") - - return self.ctx.call_service_async( - service_name, *args, timeout=timeout, method=method, **kwargs - ) - - def get_state(self) -> dict[str, Any]: - """ - 获取 Function 的状态用于 checkpoint - - 子类可以覆盖此方法来自定义状态保存逻辑，或者通过设置 - __state_include__ 和 __state_exclude__ 来控制哪些字段被保存。 - - Returns: - 包含可序列化状态的字典 - """ - state = {} - - # 获取所有实例属性 - all_attrs = set(vars(self).keys()) - - # 确定要保存的属性 - if self.__state_include__: - # 如果指定了 include，只保存这些字段 - attrs_to_save = set(self.__state_include__) & all_attrs - else: - # 否则保存所有字段，但排除 exclude 列表中的 - exclude_set = set(self.__state_exclude__) - attrs_to_save = all_attrs - exclude_set - - # 过滤掉私有属性（以 _ 开头的，除非在 include 中明确指定） - if not self.__state_include__: - attrs_to_save = { - attr - for attr in attrs_to_save - if not attr.startswith("_") or attr in self.__state_include__ - } - - # 收集可序列化的状态 - for attr_name in attrs_to_save: - try: - value = getattr(self, attr_name) - - # 检查是否可序列化 - if self._is_serializable(value): - state[attr_name] = value - else: - # 对于不可序列化的对象，尝试保存其类型信息 - if hasattr(value, "__class__"): - state[f"__{attr_name}_type__"] = value.__class__.__name__ - - except Exception as e: - # 如果获取属性失败，记录但继续 - if hasattr(self, "logger"): - self.logger.warning(f"Failed to get state for attribute '{attr_name}': {e}") - - # 保存类属性（如 use_metronome） - state["__class_attrs__"] = self._get_class_attributes() - - return state - - def restore_state(self, state: dict[str, Any]): - """ - 从 checkpoint 恢复 Function 的状态 - - 子类可以覆盖此方法来自定义状态恢复逻辑。 - - Args: - state: 保存的状态字典 - """ - # 恢复实例属性 - for attr_name, value in state.items(): - # 跳过元数据 - if attr_name.startswith("__") and attr_name.endswith("__"): - continue - - try: - setattr(self, attr_name, value) - except Exception as e: - if hasattr(self, "logger"): - self.logger.warning(f"Failed to restore attribute '{attr_name}': {e}") - - # 恢复类属性 - if "__class_attrs__" in state: - self._restore_class_attributes(state["__class_attrs__"]) - - def _is_serializable(self, value: Any) -> bool: - """ - 检查值是否可序列化 - - Args: - value: 要检查的值 - - Returns: - True 如果可序列化 - """ - # 基本类型 - if isinstance(value, int | float | str | bool | type(None)): - return True - - # 容器类型（递归检查） - if isinstance(value, list | tuple): - return all(self._is_serializable(item) for item in value) - - if isinstance(value, dict): - return all( - self._is_serializable(k) and self._is_serializable(v) for k, v in value.items() - ) - - # 检查是否是不可序列化的类型 - if isinstance(value, self.__unserializable_types__): - return False - - # 尝试判断是否可以被 pickle 序列化 - try: - pickle.dumps(value) - return True - except (TypeError, pickle.PicklingError, AttributeError): - return False - - def _get_class_attributes(self) -> dict[str, Any]: - """ - 获取类属性（如 use_metronome） - - Returns: - 类属性字典 - """ - class_attrs = {} - - # 遍历类的 __dict__ - for cls in self.__class__.__mro__: - if cls is BaseFunction or cls is ABC: - break - - for attr_name, value in cls.__dict__.items(): - # 跳过特殊属性和方法 - if attr_name.startswith("_") or callable(value): - continue - - # 只保存可序列化的类属性 - if self._is_serializable(value): - class_attrs[attr_name] = value - - return class_attrs - - def _restore_class_attributes(self, class_attrs: dict[str, Any]): - """ - 恢复类属性 - - Note: 类属性是在类级别定义的，恢复时会在实例上创建同名属性， - 这样不会影响类定义，但会覆盖类属性的值。 - - Args: - class_attrs: 类属性字典 - """ - for attr_name, value in class_attrs.items(): - try: - setattr(self, attr_name, value) - except Exception as e: - if hasattr(self, "logger"): - self.logger.warning(f"Failed to restore class attribute '{attr_name}': {e}") - - @abstractmethod - def execute(self, *args, **kwargs) -> Any: - """ - Abstract method to be implemented by subclasses. - - Each function must define its own execute logic that processes input data - and returns the output. - - Subclasses can define their own signature: - - Standard functions: execute(self, data: Any) -> Any - - Join functions: execute(self, payload: Any, key: Any, tag: int) -> list[Any] - - CoMap functions: execute(self, payload: Any, key: Any, tag: int) -> list[Any] - - Batch functions: execute(self) -> Any - - Source functions: execute(self, data: Any = None) -> Any - - :param args: Positional arguments (typically data) - :param kwargs: Keyword arguments (for additional context) - :return: Output data. - """ - pass diff --git a/packages/sage-common/src/sage/common/core/functions/batch_function.py b/packages/sage-common/src/sage/common/core/functions/batch_function.py deleted file mode 100644 index 19aca397ae..0000000000 --- a/packages/sage-common/src/sage/common/core/functions/batch_function.py +++ /dev/null @@ -1,25 +0,0 @@ -from abc import abstractmethod -from typing import Any - -from sage.common.core.functions.base_function import BaseFunction - - -class BatchFunction(BaseFunction): - """ - 批处理函数基类 - - 和SourceFunction一样简单，只需要实现execute方法。 - 当execute返回None时，BatchOperator会自动发送停止信号。 - - 流量控制通过Queue的自然阻塞机制实现，无需额外同步。 - """ - - @abstractmethod - def execute(self) -> Any: - """ - 执行批处理函数逻辑 - - Returns: - Any: 生产的数据，如果已完成则返回None - """ - pass diff --git a/packages/sage-common/src/sage/common/core/functions/comap_function.py b/packages/sage-common/src/sage/common/core/functions/comap_function.py deleted file mode 100644 index 57cd27eb79..0000000000 --- a/packages/sage-common/src/sage/common/core/functions/comap_function.py +++ /dev/null @@ -1,105 +0,0 @@ -from abc import abstractmethod -from typing import Any - -from .base_function import BaseFunction - - -class BaseCoMapFunction(BaseFunction): - """ - Base class for CoMap functions that process multiple inputs separately. - - CoMap functions are used with ConnectedStreams to process each input stream - independently using dedicated mapN methods (map0, map1, map2, etc.). - - Unlike regular functions that merge all inputs into a single execute() call, - CoMap functions maintain stream boundaries and process each input through - its corresponding mapN method. - """ - - @property - def is_comap(self) -> bool: - """Identify this as a CoMap function for operator routing""" - return True - - @abstractmethod - def map0(self, data: Any) -> Any: - """ - Process data from input stream 0 (required) - - Args: - data: Data from the first input stream - - Returns: - Processed result for stream 0 - """ - pass - - @abstractmethod - def map1(self, data: Any) -> Any: - """ - Process data from input stream 1 (required) - - Args: - data: Data from the second input stream - - Returns: - Processed result for stream 1 - """ - pass - - def map2(self, data: Any) -> Any: - """ - Process data from input stream 2 (optional) - - Args: - data: Data from the third input stream - - Returns: - Processed result for stream 2 - """ - return None - - def map3(self, data: Any) -> Any: - """ - Process data from input stream 3 (optional) - - Args: - data: Data from the fourth input stream - - Returns: - Processed result for stream 3 - """ - return None - - def map4(self, data: Any) -> Any: - """ - Process data from input stream 4 (optional) - - Args: - data: Data from the fifth input stream - - Returns: - Processed result for stream 4 - """ - return None - - def execute(self, data: Any) -> Any: - """ - Standard execute method for compatibility with BaseFunction interface. - - For CoMap functions, this should not be called directly - the CoMapOperator - will route to specific mapN methods based on input_index. - - Args: - data: Input data - - Returns: - Never returns - always raises NotImplementedError - - Raises: - NotImplementedError: Always, since CoMap functions use mapN methods - """ - raise NotImplementedError( - f"CoMap function {self.__class__.__name__} should use mapN methods, " - f"not execute(). This is handled by CoMapOperator." - ) diff --git a/packages/sage-common/src/sage/common/core/functions/filter_function.py b/packages/sage-common/src/sage/common/core/functions/filter_function.py deleted file mode 100644 index d543e8e1f8..0000000000 --- a/packages/sage-common/src/sage/common/core/functions/filter_function.py +++ /dev/null @@ -1,54 +0,0 @@ -from abc import abstractmethod -from typing import Any - -from sage.common.core.functions.base_function import BaseFunction - - -class FilterFunction(BaseFunction): - """ - FilterFunction 是专门用于 Filter 操作的函数基类。 - 它定义了过滤条件函数的接口，用于判断数据是否应该通过过滤器。 - - Filter 函数的主要作用是接收输入数据，返回布尔值表示数据是否通过过滤条件。 - - Example usage: - # 过滤正数 - class PositiveFilterFunction(FilterFunction): - def execute(self, data): - return data.value > 0 - - # 过滤特定用户 - class UserFilterFunction(FilterFunction): - def execute(self, data): - return data.user_id in ['user1', 'user2'] - - # 过滤空值 - class NotNullFilterFunction(FilterFunction): - def execute(self, data): - return data.value is not None and data.value != "" - """ - - @abstractmethod - def execute(self, data: Any) -> bool: - """ - 抽象方法，由子类实现具体的过滤逻辑。 - - Args: - data: 输入数据，可以是裸数据或Data封装 - - Returns: - bool: True表示数据应该通过，False表示应该被过滤掉 - """ - pass - - def _process_output(self, result: Any) -> bool: - """ - FilterFunction的输出处理，确保返回布尔值 - - Args: - result: 过滤函数的结果 - - Returns: - bool: 过滤结果 - """ - return bool(result) diff --git a/packages/sage-common/src/sage/common/core/functions/flatmap_collector.py b/packages/sage-common/src/sage/common/core/functions/flatmap_collector.py deleted file mode 100644 index cced16cfe9..0000000000 --- a/packages/sage-common/src/sage/common/core/functions/flatmap_collector.py +++ /dev/null @@ -1,52 +0,0 @@ -from typing import Any - - -class Collector: - """ - Enhanced Collector class for collecting data from a function. - Supports both immediate emission and batched collection. - """ - - def __init__(self, *args, **kwargs): - self._collected_data: list[Any] = [] - self.logger: Any = kwargs.get("logger") # Can be Logger or CustomLogger - - def collect(self, data: Any): - """ - Collect data. Behavior depends on batch_mode setting. - - Args: - data: The data to collect - tag: Optional output tag - """ - # 批处理模式：先收集，后输出 - self._collected_data.append(data) - if self.logger: - self.logger.debug(f"Data collected in batch mode: {data} data") - - def get_collected_data(self) -> list[Any]: - """ - Get all collected data. - - Returns: - List[Any]: List of data tuples - """ - return self._collected_data.copy() - - def get_collected_count(self) -> int: - """ - Get the number of collected items. - - Returns: - int: Number of collected items - """ - return len(self._collected_data) - - def clear(self): - """ - Clear all collected data. - """ - count = len(self._collected_data) - self._collected_data.clear() - if self.logger and count > 0: - self.logger.debug(f"Cleared {count} collected items") diff --git a/packages/sage-common/src/sage/common/core/functions/flatmap_function.py b/packages/sage-common/src/sage/common/core/functions/flatmap_function.py deleted file mode 100644 index 1f278678d1..0000000000 --- a/packages/sage-common/src/sage/common/core/functions/flatmap_function.py +++ /dev/null @@ -1,74 +0,0 @@ -from abc import abstractmethod -from collections.abc import Iterable -from typing import Any - -from sage.common.core.functions.base_function import BaseFunction -from sage.common.core.functions.flatmap_collector import Collector - - -class FlatMapFunction(BaseFunction): - """ - FlatMapFunction is a specialized function for FlatMap operations. - It provides an 'out' collector for emitting multiple output values. - - This function supports two usage patterns: - 1. Use self.collect() to emit individual items - 2. Return an iterable object that will be automatically flattened - - Example usage: - # Pattern 1: Using self.collect() - def execute(self, data): - words = data.value.split() - for word in words: - self.collect(word) - - # Pattern 2: Return iterable - def execute(self, data): - words = data.value.split() - return words - """ - - def __init__(self, *args, **kwargs): - self.out: Collector | None = None - - def insert_collector(self, collector: Collector): - """ - Insert a collector into the function for data collection. - This method is called by the operator to provide the collector. - - Args: - collector: The collector instance to be inserted. - """ - self.out = collector - self.out.logger = self.logger - if self.logger: - self.logger.debug( - f"Collector inserted into FlatMapFunction '{self.__class__.__name__}'" - ) - - def collect(self, data: Any): - """ - Convenience method to collect data using the out collector. - - Args: - data: The data to collect - tag: Optional output tag - """ - if self.out is None: - raise RuntimeError("Collector not initialized. This should be set by the operator.") - - self.out.collect(data) - self.logger.debug(f"Data collected: {data}") - - @abstractmethod - def execute(self, data: Any) -> Iterable[Any] | None: - """ - Abstract method to be implemented by subclasses. - - Args: - data: 输入数据，可以是裸数据或Data封装 - - Returns: - Optional[Iterable[Any]]: Optional iterable of output data - """ - pass diff --git a/packages/sage-common/src/sage/common/core/functions/future_function.py b/packages/sage-common/src/sage/common/core/functions/future_function.py deleted file mode 100644 index c4b4240716..0000000000 --- a/packages/sage-common/src/sage/common/core/functions/future_function.py +++ /dev/null @@ -1,27 +0,0 @@ -from __future__ import annotations - -from typing import Any - -from sage.common.core.functions.base_function import BaseFunction - - -class FutureFunction(BaseFunction): - """ - Future transformation的占位符函数。 - 这个函数不会被实际执行，只是作为placeholder存在。 - """ - - def __call__(self, *args, **kwargs) -> Any: - """ - Future function不应该被直接调用 - """ - raise RuntimeError("FutureFunction should not be called directly. It's a placeholder.") - - def call(self, data: Any) -> Any: - """ - Future function不应该被直接调用 - """ - raise RuntimeError("FutureFunction should not be called directly. It's a placeholder.") - - def __repr__(self) -> str: - return "FutureFunction(placeholder)" diff --git a/packages/sage-common/src/sage/common/core/functions/join_function.py b/packages/sage-common/src/sage/common/core/functions/join_function.py deleted file mode 100644 index 05ee085f9b..0000000000 --- a/packages/sage-common/src/sage/common/core/functions/join_function.py +++ /dev/null @@ -1,249 +0,0 @@ -from abc import abstractmethod -from typing import Any - -from sage.common.core.functions.base_function import BaseFunction - - -class BaseJoinFunction(BaseFunction): - """ - Base class for Join functions that handle multi-stream data joining. - - The operator will call execute() with structured input containing: - - payload: the actual data - - key: the partition key that triggered this call - - tag: which stream this data came from (0 for left, 1 for right) - - The function manages its own join logic and state. - """ - - @property - def is_join(self) -> bool: - """Identify this as a Join function for operator routing""" - return True - - @abstractmethod - def execute(self, payload: Any, key: Any, tag: int) -> list[Any]: - """ - Process data from a specific stream and return join results. - - Args: - payload: The actual data from the stream - key: The partition key (extracted by keyby) - tag: Stream identifier (0=left/first stream, 1=right/second stream) - - Returns: - List[Any]: List of join results to emit (can be empty) - Return empty list if no output should be generated - - Note: - The function should manage its own state to correlate data - between different streams. Common patterns: - - Cache data from one stream until matching data arrives - - Implement time-based windows for temporal joins - - Handle different join semantics (inner, outer, etc.) - """ - pass - - -# 具体实现示例 -class UserOrderInnerJoin(BaseJoinFunction): - """ - Inner Join: 只有当用户和订单数据都存在时才输出 - """ - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.user_cache = {} # {user_id: user_data} - self.order_cache = {} # {user_id: [order_data, ...]} - - def execute(self, payload: Any, key: Any, tag: int) -> list[Any]: - results = [] - - if tag == 0: # 用户数据流 - # 缓存用户数据 - self.user_cache[key] = payload - - # 检查是否有对应的订单 - if key in self.order_cache: - user_data = payload - for order_data in self.order_cache[key]: - joined = self._create_join_result(user_data, order_data, key) - results.append(joined) - # 清理已匹配的订单（inner join特性） - del self.order_cache[key] - - elif tag == 1: # 订单数据流 - # 检查是否有对应的用户 - if key in self.user_cache: - user_data = self.user_cache[key] - joined = self._create_join_result(user_data, payload, key) - results.append(joined) - else: - # 缓存订单等待用户数据 - if key not in self.order_cache: - self.order_cache[key] = [] - self.order_cache[key].append(payload) - - return results - - def _create_join_result(self, user_data: Any, order_data: Any, user_id: Any) -> dict: - return { - "user_id": user_id, - "user_name": user_data.get("name"), - "user_email": user_data.get("email"), - "order_id": order_data.get("id"), - "order_amount": order_data.get("amount"), - "join_timestamp": order_data.get("timestamp"), - } - - -class UserOrderLeftJoin(BaseJoinFunction): - """ - Left Outer Join: 保留所有用户，订单可能为空 - """ - - def __init__(self, timeout_ms: int = 30000, **kwargs): - super().__init__(**kwargs) - self.user_cache: dict[Any, tuple[Any, int]] = {} # {user_id: (user_data, timestamp)} - self.order_cache: dict[Any, list[Any]] = {} # {user_id: [order_data, ...]} - self.timeout_ms = timeout_ms - import time - - self.current_time = lambda: int(time.time() * 1000) - - def execute(self, payload: Any, key: Any, tag: int) -> list[Any]: - results = [] - current_time = self.current_time() - - if tag == 0: # 用户数据流 - # 检查是否有对应的订单 - if key in self.order_cache: - user_data = payload - for order_data in self.order_cache[key]: - joined = self._create_join_result(user_data, order_data, key) - results.append(joined) - del self.order_cache[key] - else: - # 缓存用户数据，设置超时 - self.user_cache[key] = (payload, current_time) - - elif tag == 1: # 订单数据流 - if key in self.user_cache: - user_data, _ = self.user_cache[key] - joined = self._create_join_result(user_data, payload, key) - results.append(joined) - del self.user_cache[key] - else: - # 缓存订单 - if key not in self.order_cache: - self.order_cache[key] = [] - self.order_cache[key].append(payload) - - # 检查超时的用户数据（Left Join特性：输出没有订单的用户） - expired_users = [] - for user_id, (user_data, timestamp) in self.user_cache.items(): - if current_time - timestamp > self.timeout_ms: - # 输出没有订单的用户 - no_order_result = self._create_join_result(user_data, None, user_id) - results.append(no_order_result) - expired_users.append(user_id) - - # 清理过期用户 - for user_id in expired_users: - del self.user_cache[user_id] - - return results - - def _create_join_result(self, user_data: Any, order_data: Any, user_id: Any) -> dict: - return { - "user_id": user_id, - "user_name": user_data.get("name"), - "user_email": user_data.get("email"), - "order_id": order_data.get("id") if order_data else None, - "order_amount": order_data.get("amount") if order_data else 0, - "has_order": order_data is not None, - } - - -class WindowedEventJoin(BaseJoinFunction): - """ - 基于时间窗口的事件关联 - """ - - def __init__(self, window_ms: int = 60000, **kwargs): - super().__init__(**kwargs) - self.window_ms = window_ms - self.event_buffer: dict[ - Any, list[tuple[Any, int, int]] - ] = {} # {key: [(event_data, timestamp, tag), ...]} - import time - - self.current_time = lambda: int(time.time() * 1000) - - def execute(self, payload: Any, key: Any, tag: int) -> list[Any]: - current_time = self.current_time() - results = [] - - # 清理过期事件 - self._cleanup_expired_events(current_time) - - # 添加当前事件到缓冲区 - if key not in self.event_buffer: - self.event_buffer[key] = [] - self.event_buffer[key].append((payload, current_time, tag)) - - # 检查窗口内的事件组合 - if key in self.event_buffer: - window_events = self._get_window_events(key, current_time) - - # 按业务逻辑组合事件 - combinations = self._find_event_combinations(window_events) - for combo in combinations: - joined_event = self._create_event_combination(combo, key) - results.append(joined_event) - - return results - - def _cleanup_expired_events(self, current_time: int): - cutoff_time = current_time - self.window_ms - - for key in list(self.event_buffer.keys()): - valid_events = [ - (data, ts, tag) for data, ts, tag in self.event_buffer[key] if ts >= cutoff_time - ] - if valid_events: - self.event_buffer[key] = valid_events - else: - del self.event_buffer[key] - - def _get_window_events(self, key: Any, current_time: int) -> list: - cutoff_time = current_time - self.window_ms - return [(data, ts, tag) for data, ts, tag in self.event_buffer[key] if ts >= cutoff_time] - - def _find_event_combinations(self, events: list) -> list: - # 示例：查找登录后的购买事件 - combinations = [] - login_events = [ - (data, ts) for data, ts, tag in events if tag == 0 and data.get("action") == "login" - ] - purchase_events = [ - (data, ts) for data, ts, tag in events if tag == 1 and data.get("action") == "purchase" - ] - - for login_data, login_time in login_events: - for purchase_data, purchase_time in purchase_events: - if purchase_time > login_time: # 购买在登录之后 - combinations.append((login_data, purchase_data)) - - return combinations - - def _create_event_combination(self, combo, key: Any) -> dict: - login_data, purchase_data = combo - return { - "user_id": key, - "login_time": login_data.get("timestamp"), - "purchase_time": purchase_data.get("timestamp"), - "purchase_amount": purchase_data.get("amount"), - "time_to_purchase": purchase_data.get("timestamp") - login_data.get("timestamp"), - "conversion": True, - } diff --git a/packages/sage-common/src/sage/common/core/functions/keyby_function.py b/packages/sage-common/src/sage/common/core/functions/keyby_function.py deleted file mode 100644 index e669e74754..0000000000 --- a/packages/sage-common/src/sage/common/core/functions/keyby_function.py +++ /dev/null @@ -1,177 +0,0 @@ -from abc import abstractmethod -from collections.abc import Hashable -from typing import Any - -from sage.common.core.functions.base_function import BaseFunction - - -class KeyByFunction(BaseFunction): - """ - KeyByFunction is a specialized function for KeyBy operations. - It extracts partition keys from input data for downstream routing. - - The function should return a hashable value that will be used as - the partition key for routing data to downstream operators. - - Example usage: - class UserIdExtractor(KeyByFunction): - def execute(self, data): - return data.user_id - - class CategoryExtractor(KeyByFunction): - def execute(self, data): - return data.category.lower() - - class CompositeKeyExtractor(KeyByFunction): - def execute(self, data): - return f"{data.user_id}_{data.session_id}" - """ - - @abstractmethod - def execute(self, data: Any) -> Hashable: - """ - Abstract method to extract partition key from input data. - - Args: - data: Input data from upstream operator - - Returns: - Hashable: Partition key that will be used for routing. - Must be hashable (str, int, tuple, etc.) - - Raises: - KeyError: If required field is missing from data - ValueError: If extracted key is not hashable - - Note: - - The returned key will be used with hash() function for partitioning - - None values are allowed but will route to a default partition - - Complex objects should be converted to simple hashable types - """ - pass - - def validate_key(self, key: Any) -> bool: - """ - Validate if the extracted key is suitable for partitioning. - - Args: - key: The extracted key to validate - - Returns: - bool: True if key is valid for partitioning - - Note: - This method can be overridden for custom validation logic. - """ - try: - # Test if key is hashable - hash(key) - return True - except TypeError: - self.logger.warning(f"Extracted key {key} is not hashable") - return False - - def extract_with_validation(self, data: Any) -> Hashable: - """ - Extract key with built-in validation. - - Args: - data: Input data - - Returns: - Hashable: Validated partition key - - Raises: - ValueError: If extracted key is not valid for partitioning - """ - try: - key = self.execute(data) - - if not self.validate_key(key): - raise ValueError(f"Invalid partition key: {key} (not hashable)") - - self.logger.debug(f"Extracted and validated key: {key}") - return key - - except Exception as e: - self.logger.error(f"Error extracting partition key: {e}", exc_info=True) - raise - - def __call__(self, data: Any) -> Hashable: - """ - Convenience method to make function callable. - - Args: - data: Input data - - Returns: - Hashable: Partition key - """ - return self.extract_with_validation(data) - - -class FieldKeyByFunction(KeyByFunction): - """ - Convenience class for simple field-based key extraction. - - Example: - # Extract user_id field - class UserIdExtractor(FieldKeyByFunction): - field_name = "user_id" - - # Extract nested field - class RegionExtractor(FieldKeyByFunction): - field_name = "location.region" - """ - - field_name: str | None = None # To be set by subclasses - - def __init__(self, field_name: str | None = None, **kwargs): - super().__init__(**kwargs) - if field_name: - self.field_name = field_name - if not self.field_name: - raise ValueError("field_name must be specified") - self.logger.debug(f"FieldKeyByFunction initialized for field: {self.field_name}") - - def execute(self, data: Any) -> Hashable: - """ - Extract field value from data object. - - Args: - data: Input data object - - Returns: - Hashable: Field value - - Raises: - KeyError: If field is not found - AttributeError: If data doesn't support field access - """ - if not self.field_name: - raise ValueError("field_name is not set") - - try: - # Handle nested field access (e.g., "location.region") - if "." in self.field_name: - value = data - for field_part in self.field_name.split("."): - if hasattr(value, field_part): - value = getattr(value, field_part) - elif hasattr(value, "__getitem__"): - value = value[field_part] - else: - raise KeyError(f"Field '{field_part}' not found") - return value - else: - # Simple field access - if hasattr(data, self.field_name): - return getattr(data, self.field_name) - elif hasattr(data, "__getitem__"): - return data[self.field_name] - else: - raise KeyError(f"Field '{self.field_name}' not found") - - except Exception as e: - self.logger.error(f"Failed to extract field '{self.field_name}': {e}") - raise KeyError(f"Field '{self.field_name}' not accessible: {e}") diff --git a/packages/sage-common/src/sage/common/core/functions/lambda_function.py b/packages/sage-common/src/sage/common/core/functions/lambda_function.py deleted file mode 100644 index b30ffc2575..0000000000 --- a/packages/sage-common/src/sage/common/core/functions/lambda_function.py +++ /dev/null @@ -1,215 +0,0 @@ -import inspect -import logging -from collections.abc import Callable, Hashable -from typing import Any - -from sage.common.core.functions.base_function import BaseFunction -from sage.common.core.functions.filter_function import FilterFunction -from sage.common.core.functions.flatmap_function import FlatMapFunction -from sage.common.core.functions.keyby_function import KeyByFunction -from sage.common.core.functions.map_function import MapFunction -from sage.common.core.functions.sink_function import SinkFunction - -logger = logging.getLogger(__name__) - - -class LambdaMapFunction(MapFunction): - """将 lambda 函数包装为 MapFunction""" - - def __init__(self, lambda_func: Callable[[Any], Any], **kwargs): - self.lambda_func = lambda_func - - def execute(self, data: Any) -> Any: - return self.lambda_func(data) - - -class LambdaFilterFunction(FilterFunction): - """将返回布尔值的 lambda 函数包装为 FilterFunction""" - - def __init__(self, lambda_func: Callable[[Any], bool], **kwargs): - self.lambda_func = lambda_func - print(f"🔧 LambdaFilterFunction.__init__ called with lambda_func: {lambda_func}") - - def execute(self, data: Any) -> bool: - try: - result = self.lambda_func(data) - logger.debug( - f"🔍 LambdaFilterFunction: lambda_func={self.lambda_func}, data={data}, result={result}" - ) - return result - except Exception as e: - logger.error(f"❌ LambdaFilterFunction error: {e}, data={data}") - return False - - -class LambdaFlatMapFunction(FlatMapFunction): - """将返回列表的 lambda 函数包装为 FlatMapFunction""" - - def __init__(self, lambda_func: Callable[[Any], list[Any]], **kwargs): - self.lambda_func = lambda_func - - def execute(self, data: Any) -> list[Any]: - result = self.lambda_func(data) - if not isinstance(result, list): - raise TypeError(f"FlatMap lambda function must return a list, got {type(result)}") - return result - - -class LambdaSinkFunction(SinkFunction): - """将 lambda 函数包装为 SinkFunction""" - - def __init__(self, lambda_func: Callable[[Any], None], **kwargs): - self.lambda_func = lambda_func - - def execute(self, data: Any) -> None: - self.lambda_func(data) - - -class LambdaSourceFunction(BaseFunction): - """将无参数 lambda 函数包装为 SourceFunction""" - - def __init__(self, lambda_func: Callable[[], Any], **kwargs): - self.lambda_func = lambda_func - - def execute(self) -> Any: - return self.lambda_func() - - -class LambdaKeyByFunction(KeyByFunction): - """ - Wrapper for lambda-based key extraction. - - Example: - # For lambda x: x.user_id - extractor = LambdaKeyByFunction(lambda x: x.user_id) - """ - - def __init__(self, lambda_func, **kwargs): - self.lambda_func = lambda_func - self.logger.debug("LambdaKeyByFunction initialized with lambda") - - def execute(self, data: Any) -> Hashable: - """ - Execute lambda function on data. - - Args: - data: Input data - - Returns: - Hashable: Result of lambda function - """ - try: - return self.lambda_func(data) - except Exception as e: - self.logger.error(f"Lambda key extraction failed: {e}") - raise - - -def detect_lambda_type(func: Callable) -> str: - """ - 根据 lambda 函数的签名和返回类型注解检测其类型 - - Args: - func: lambda 函数 - - Returns: - 函数类型: 'map', 'filter', 'flatmap', 'sink', 'source' - """ - try: - sig = inspect.signature(func) - params = list(sig.parameters.values()) - return_annotation = sig.return_annotation - - # 无参数 -> source - if len(params) == 0: - return "source" - - # 有参数但非单参数 -> 暂不支持 - if len(params) != 1: - raise ValueError(f"Lambda function must have 0 or 1 parameter, got {len(params)}") - - # 根据返回类型注解判断 - if return_annotation is bool: - return "filter" - elif hasattr(return_annotation, "__origin__") and return_annotation.__origin__ is list: - return "flatmap" - elif return_annotation is type(None) or return_annotation is None: - return "sink" - else: - # 默认为 map - return "map" - except Exception: - # 如果无法检测，默认为 map - return "map" - - -def wrap_lambda(func: Callable, func_type: str | None = None) -> type[BaseFunction]: - """ - 将 lambda 函数包装为对应的 Function 类 - - Args: - func: lambda 函数 - func_type: 强制指定函数类型，如果为 None 则自动检测 - - Returns: - 包装后的 Function 类 - """ - if func_type is None: - func_type = detect_lambda_type(func) - - print(f"🚀 wrap_lambda called: func={func}, func_type={func_type}") - - if func_type == "map": - - class WrappedMapFunction(LambdaMapFunction): - def __init__(self, **kwargs): - super().__init__(func, **kwargs) - - return WrappedMapFunction - - elif func_type == "filter": - print(f"🎯 Creating WrappedFilterFunction for lambda: {func}") - - class WrappedFilterFunction(LambdaFilterFunction): - def __init__(self, *args, **kwargs): - print( - f"🔧 WrappedFilterFunction.__init__ called with lambda: {func}, args: {args}, kwargs: {kwargs}" - ) - super().__init__(func, **kwargs) - - return WrappedFilterFunction - - elif func_type == "flatmap": - - class WrappedFlatMapFunction(LambdaFlatMapFunction): - def __init__(self, **kwargs): - super().__init__(func, **kwargs) - - return WrappedFlatMapFunction - - elif func_type == "sink": - - class WrappedSinkFunction(LambdaSinkFunction): - def __init__(self, **kwargs): - super().__init__(func, **kwargs) - - return WrappedSinkFunction - - elif func_type == "source": - - class WrappedSourceFunction(LambdaSourceFunction): - def __init__(self, **kwargs): - super().__init__(func, **kwargs) - - return WrappedSourceFunction - - elif func_type == "keyby": - - class WrappedKeyByFunction(LambdaKeyByFunction): - def __init__(self, **kwargs): - super().__init__(func, **kwargs) - - return WrappedKeyByFunction - - else: - raise ValueError(f"Unsupported function type: {func_type}") diff --git a/packages/sage-common/src/sage/common/core/functions/map_function.py b/packages/sage-common/src/sage/common/core/functions/map_function.py deleted file mode 100644 index 80bc5e7702..0000000000 --- a/packages/sage-common/src/sage/common/core/functions/map_function.py +++ /dev/null @@ -1,26 +0,0 @@ -from abc import abstractmethod -from typing import Any - -from sage.common.core.functions.base_function import BaseFunction - - -class MapFunction(BaseFunction): - """ - 映射函数基类 - 一对一数据变换 - - 映射函数接收一个输入，产生一个输出 - 用于数据转换、增强、格式化等操作 - """ - - @abstractmethod - def execute(self, data: Any) -> Any: - """ - 执行映射变换 - - Args: - data: 输入数据 - - Returns: - 变换后的数据 - """ - pass diff --git a/packages/sage-common/src/sage/common/core/functions/sink_function.py b/packages/sage-common/src/sage/common/core/functions/sink_function.py deleted file mode 100644 index 2c3ea799f6..0000000000 --- a/packages/sage-common/src/sage/common/core/functions/sink_function.py +++ /dev/null @@ -1,25 +0,0 @@ -from abc import abstractmethod -from typing import Any - -from sage.common.core.functions.base_function import BaseFunction - - -class SinkFunction(BaseFunction): - """ - 汇聚函数基类 - 数据消费者 - - 汇聚函数接收输入数据，通常不产生输出 - 用于数据存储、发送、打印等终端操作 - - 流量控制通过Queue的自然阻塞机制实现，无需额外同步。 - """ - - @abstractmethod - def execute(self, data: Any) -> None: - """ - 执行汇聚操作 - - Args: - data: 输入数据 - """ - pass diff --git a/packages/sage-common/src/sage/common/core/functions/source_function.py b/packages/sage-common/src/sage/common/core/functions/source_function.py deleted file mode 100644 index b8e2b9b47b..0000000000 --- a/packages/sage-common/src/sage/common/core/functions/source_function.py +++ /dev/null @@ -1,26 +0,0 @@ -from abc import abstractmethod -from typing import Any - -from sage.common.core.functions.base_function import BaseFunction - - -class SourceFunction(BaseFunction): - """ - 源函数基类 - 数据生产者 - - 源函数不接收输入数据，只产生输出数据 - 通常用于读取文件、数据库、API等外部数据源 - """ - - @abstractmethod - def execute(self, data=None) -> Any: - """ - 执行源函数逻辑，生产数据 - - Args: - data: 输入数据（对于源函数通常为 None，但保留参数以符合基类接口） - - Returns: - 生产的数据 - """ - pass diff --git a/packages/sage-common/src/sage/common/core/signals.py b/packages/sage-common/src/sage/common/core/signals.py deleted file mode 100644 index adb5e681b4..0000000000 --- a/packages/sage-common/src/sage/common/core/signals.py +++ /dev/null @@ -1,64 +0,0 @@ -""" -Control signals for SAGE pipelines. - -This module provides control signal classes used across all layers of SAGE. -""" - -import time - - -class StopSignal: - """ - 停止信号类 - 用于通知流处理停止 - - StopSignal 是一个特殊的信号类，用于在流处理管道中传递停止指令。 - 当某个算子需要停止处理或遇到特殊条件时，可以发送 StopSignal 来通知下游算子。 - - 为了保持向后兼容性，第一个参数同时作为 message 和 name 使用。 - - Attributes: - message: 停止信号的消息内容 - name: 停止信号的名称（与 message 相同，用于兼容） - source: 停止信号的来源 - payload: 可选的附加数据 - timestamp: 停止信号创建时的纳秒级时间戳 - """ - - def __init__(self, message: str = "Stop", source: str | None = None, payload=None): - """ - 创建停止信号 - - Args: - message: 停止信号的消息内容，默认为 "Stop" - source: 停止信号的来源，如果为 None 则使用 message - payload: 可选的附加数据 - """ - # 第一个参数同时作为 message 和 name（兼容旧代码） - self.message = message - self.name = message # 兼容旧的 .name 属性访问 - - # source 参数处理 - self.source = source if source is not None else message - - # 兼容旧的 payload 参数 - self.payload = payload - - self.timestamp = time.time_ns() - - def __str__(self): - """ - 返回停止信号的字符串表示 - - Returns: - str: 停止信号的简短描述 - """ - return f"StopSignal({self.message})" - - def __repr__(self): - """ - 返回停止信号的详细字符串表示 - - Returns: - str: 停止信号的详细描述 - """ - return f"StopSignal(message='{self.message}', source='{self.source}')" diff --git a/packages/sage-common/src/sage/common/core/types.py b/packages/sage-common/src/sage/common/core/types.py deleted file mode 100644 index c77871fd81..0000000000 --- a/packages/sage-common/src/sage/common/core/types.py +++ /dev/null @@ -1,70 +0,0 @@ -""" -Common Core Types - -定义了 SAGE 框架中使用的核心数据类型和枚举。 -这些类型可以被所有 SAGE 包使用（kernel, libs, middleware 等）。 - -Layer: L1 (Foundation) -""" - -from enum import Enum -from typing import TypeVar - - -# 执行模式枚举 -class ExecutionMode(Enum): - """任务执行模式""" - - LOCAL = "local" # 本地执行 - REMOTE = "remote" # 远程执行（Ray） - HYBRID = "hybrid" # 混合模式 - - -# 任务状态枚举 -class TaskStatus(Enum): - """任务运行状态""" - - PENDING = "pending" # 等待中 - RUNNING = "running" # 运行中 - STOPPED = "stopped" # 已停止 - FAILED = "failed" # 失败 - COMPLETED = "completed" # 完成 - - -# 作业状态枚举 -class JobStatus(Enum): - """作业状态""" - - PENDING = "pending" - RUNNING = "running" - STOPPED = "stopped" - FAILED = "failed" - COMPLETED = "completed" - DELETED = "deleted" - - -# 类型别名 -TaskID = str # 任务标识符 -ServiceID = str # 服务标识符 -NodeID = str # 节点标识符 -QueueID = str # 队列标识符 -JobID = str # 作业标识符 - -# 泛型类型变量 -T = TypeVar("T") -TaskType = TypeVar("TaskType") -ServiceType = TypeVar("ServiceType") - -__all__ = [ - "ExecutionMode", - "TaskStatus", - "JobStatus", - "TaskID", - "ServiceID", - "NodeID", - "QueueID", - "JobID", - "T", - "TaskType", - "ServiceType", -] diff --git a/packages/sage-common/src/sage/common/logging.py b/packages/sage-common/src/sage/common/logging.py deleted file mode 100644 index 469e1d3287..0000000000 --- a/packages/sage-common/src/sage/common/logging.py +++ /dev/null @@ -1,18 +0,0 @@ -""" -SAGE Common - Logging Utilities Convenience Module - -Layer: L1 (Foundation) - -This is a convenience re-export module for logging utilities. -The actual implementation is in sage.common.utils.logging - -Usage: - from sage.common.logging import CustomLogger, get_logger - - logger = get_logger(__name__) - logger.info("Hello, SAGE!") -""" - -from sage.common.utils.logging import CustomFormatter, CustomLogger, get_logger - -__all__ = ["CustomLogger", "CustomFormatter", "get_logger"] diff --git a/packages/sage-common/src/sage/common/model_registry/__init__.py b/packages/sage-common/src/sage/common/model_registry/__init__.py deleted file mode 100644 index 55dc6fa1e5..0000000000 --- a/packages/sage-common/src/sage/common/model_registry/__init__.py +++ /dev/null @@ -1,41 +0,0 @@ -"""Model registry helpers for SAGE components. - -Layer: L1 (Foundation - Common Model Registry) - -This module provides model management utilities for ML models used in SAGE, -particularly for sageLLM model registries and management. - -Architecture: - This is a L1 foundation component providing model registry services. - Used by components like sage_llm for model lifecycle management. - -Registries: - - sagellm_registry: ~/.sage/models/sagellm/ (sageLLM models) - -Breaking Change (v0.3.0): - vllm_registry 已移除。请使用 sagellm_registry。 -""" - -from . import sagellm_registry -from .recommended import fetch_recommended_models -from .sagellm_registry import ( - ModelInfo, - delete_model, - download_model, - ensure_model_available, - get_model_path, - list_models, - touch_model, -) - -__all__ = [ - "ModelInfo", - "list_models", - "download_model", - "delete_model", - "get_model_path", - "touch_model", - "ensure_model_available", - "fetch_recommended_models", - "sagellm_registry", -] diff --git a/packages/sage-common/src/sage/common/model_registry/recommended.py b/packages/sage-common/src/sage/common/model_registry/recommended.py deleted file mode 100644 index 27fc8b20cd..0000000000 --- a/packages/sage-common/src/sage/common/model_registry/recommended.py +++ /dev/null @@ -1,195 +0,0 @@ -"""Recommended LLM model catalog helpers.""" - -from __future__ import annotations - -import json -import os -from importlib import resources -from typing import Any - -import requests - -from sage.common.utils.logging import get_logger - -logger = get_logger(__name__) - -_DEFAULT_INDEX_CANDIDATES = [ - "https://raw.githubusercontent.com/intellistream/SAGE/main/docs/assets/model-registry/recommended_llm_models.json", - "https://raw.githubusercontent.com/intellistream/SAGE/main-dev/docs/assets/model-registry/recommended_llm_models.json", -] - -_FALLBACK_MODELS: list[dict[str, Any]] = [ - { - "model_id": "Qwen/Qwen2.5-0.5B-Instruct", - "display_name": "Qwen2.5 0.5B Instruct", - "size_billion": 0.5, - "disk_gb": 2.8, - "min_gpu_memory_gb": 6, - "recommended_gpu": "Single 8GB", - "throughput_tps": 35, - "tags": ["default", "chat", "cn"], - "description": "默认开发模型，体积小、加载快，适合本地开发与单 GPU 环境。", - }, - { - "model_id": "Qwen/Qwen2.5-7B-Instruct", - "display_name": "Qwen2.5 7B Instruct", - "size_billion": 7, - "disk_gb": 14, - "min_gpu_memory_gb": 16, - "recommended_gpu": "Single 24GB", - "throughput_tps": 12, - "tags": ["chat", "general"], - "description": "平衡质量与成本，适合中等规模中文/英文应用。", - }, - { - "model_id": "meta-llama/Llama-3.1-8B-Instruct", - "display_name": "Llama 3.1 8B Instruct", - "size_billion": 8, - "disk_gb": 16, - "min_gpu_memory_gb": 18, - "recommended_gpu": "Single 24GB", - "throughput_tps": 10, - "tags": ["chat", "english"], - "description": "面向英语任务的主流 8B 模型，社区生态成熟。", - }, - { - "model_id": "google/gemma-2-9b-it", - "display_name": "Gemma 2 9B IT", - "size_billion": 9, - "disk_gb": 18, - "min_gpu_memory_gb": 20, - "recommended_gpu": "Single 24GB", - "throughput_tps": 9, - "tags": ["chat", "lightweight"], - "description": "谷歌轻量指令模型，多语种支持，推理成本低。", - }, - { - "model_id": "BAAI/bge-m3", - "display_name": "BGE M3 (Embedding)", - "size_billion": 1.8, - "disk_gb": 1.9, - "min_gpu_memory_gb": 4, - "recommended_gpu": "Single 8GB", - "throughput_tps": 120, - "tags": ["embedding", "retrieval"], - "description": "通用多语种 Embedding 模型，适合 RAG/搜索。", - }, -] - - -def _iter_candidate_urls(index_url: str | None = None) -> list[str]: - """Compose ordered list of URLs to try for the catalog.""" - - urls: list[str] = [] - if index_url: - urls.append(index_url) - env_url = os.environ.get("SAGE_LLM_MODEL_INDEX_URL") - if env_url: - urls.append(env_url) - urls.extend(_DEFAULT_INDEX_CANDIDATES) - - # Remove duplicates while preserving order - seen: set[str] = set() - ordered: list[str] = [] - for url in urls: - if url and url not in seen: - ordered.append(url) - seen.add(url) - return ordered - - -def fetch_recommended_models( - index_url: str | None = None, timeout: float = 5.0 -) -> list[dict[str, Any]]: - """Return recommended model catalog, preferring the bundled index.""" - - prefer_remote = bool(index_url or os.environ.get("SAGE_LLM_MODEL_INDEX_URL")) - if not prefer_remote: - local_models = _load_local_models() - if local_models: - return local_models - - failures: list[str] = [] - for url in _iter_candidate_urls(index_url): - try: - response = requests.get(url, timeout=timeout) - response.raise_for_status() - payload = response.json() - - if isinstance(payload, dict) and "models" in payload: - models = payload["models"] - elif isinstance(payload, list): - models = payload - else: - failures.append(f"{url}: unexpected payload") - logger.debug("Unexpected model index payload from %s", url) - continue - - normalized = _normalize_models(models) - if normalized: - return normalized - failures.append(f"{url}: empty model list") - except Exception as exc: # pragma: no cover - network failures - failures.append(f"{url}: {exc}") - logger.debug("无法从 %s 拉取模型索引", url, exc_info=exc) - - if failures: - joined = "; ".join(failures[:3]) - if len(failures) > 3: - joined = f"{joined}; ..." - logger.warning("无法拉取远程模型索引，将使用内置推荐列表。原因：%s", joined) - - return _FALLBACK_MODELS - - -def _normalize_models(models: list[Any]) -> list[dict[str, Any]]: - normalized: list[dict[str, Any]] = [] - for item in models: - if not isinstance(item, dict): - continue - model_id = item.get("model_id") - if not isinstance(model_id, str): - continue - normalized.append( - { - "model_id": model_id, - "display_name": item.get("display_name", model_id), - "size_billion": item.get("size_billion"), - "disk_gb": item.get("disk_gb"), - "min_gpu_memory_gb": item.get("min_gpu_memory_gb"), - "recommended_gpu": item.get("recommended_gpu"), - "throughput_tps": item.get("throughput_tps"), - "tags": item.get("tags", []), - "description": item.get("description", ""), - } - ) - return normalized - - -def _load_local_models() -> list[dict[str, Any]] | None: - """Load bundled JSON index shipped with sage-common.""" - - try: - data = resources.files(__package__).joinpath("recommended_llm_models.json") - except FileNotFoundError: - return None - - if not data.is_file(): # type: ignore[attr-defined] - return None - - try: - with data.open("r", encoding="utf-8") as fp: # type: ignore[attr-defined] - payload = json.load(fp) - except Exception as exc: # pragma: no cover - IO failure - logger.debug("无法加载本地推荐模型索引", exc_info=exc) - return None - - if isinstance(payload, dict) and "models" in payload: - models = payload["models"] - elif isinstance(payload, list): - models = payload - else: - logger.debug("本地推荐模型索引格式异常: %s", payload) - return None - - return _normalize_models(models) diff --git a/packages/sage-common/src/sage/common/model_registry/recommended_llm_models.json b/packages/sage-common/src/sage/common/model_registry/recommended_llm_models.json deleted file mode 100644 index d37f501033..0000000000 --- a/packages/sage-common/src/sage/common/model_registry/recommended_llm_models.json +++ /dev/null @@ -1,61 +0,0 @@ -{ - "updated_at": "2025-12-01", - "source": "SAGE", - "models": [ - { - "model_id": "Qwen/Qwen2.5-0.5B-Instruct", - "display_name": "Qwen2.5 0.5B Instruct", - "size_billion": 0.5, - "disk_gb": 2.8, - "min_gpu_memory_gb": 6, - "recommended_gpu": "Single 8GB", - "throughput_tps": 35, - "tags": ["default", "chat", "cn"], - "description": "默认开发模型，下载体积小、加载速度快，适合本地开发与单 GPU 环境。" - }, - { - "model_id": "Qwen/Qwen2.5-7B-Instruct", - "display_name": "Qwen2.5 7B Instruct", - "size_billion": 7, - "disk_gb": 14, - "min_gpu_memory_gb": 16, - "recommended_gpu": "Single 24GB", - "throughput_tps": 12, - "tags": ["chat", "general"], - "description": "平衡质量与成本，适合中等规模应用。支持中文/英文任务。" - }, - { - "model_id": "meta-llama/Llama-3.1-8B-Instruct", - "display_name": "Llama 3.1 8B Instruct", - "size_billion": 8, - "disk_gb": 16, - "min_gpu_memory_gb": 18, - "recommended_gpu": "Single 24GB", - "throughput_tps": 10, - "tags": ["chat", "english"], - "description": "英语任务表现优秀，适合需要开放许可的商业场景。" - }, - { - "model_id": "google/gemma-2-9b-it", - "display_name": "Gemma 2 9B IT", - "size_billion": 9, - "disk_gb": 18, - "min_gpu_memory_gb": 20, - "recommended_gpu": "Single 24GB", - "throughput_tps": 9, - "tags": ["chat", "lightweight"], - "description": "轻量指令微调模型，多语种支持，推理成本低。" - }, - { - "model_id": "BAAI/bge-m3", - "display_name": "BGE M3 (Embedding)", - "size_billion": 1.8, - "disk_gb": 1.9, - "min_gpu_memory_gb": 4, - "recommended_gpu": "Single 8GB", - "throughput_tps": 120, - "tags": ["embedding", "retrieval"], - "description": "通用多语种 Embedding 模型，支持 RAG 与搜索场景。" - } - ] -} diff --git a/packages/sage-common/src/sage/common/service/__init__.py b/packages/sage-common/src/sage/common/service/__init__.py deleted file mode 100644 index 724c1026b1..0000000000 --- a/packages/sage-common/src/sage/common/service/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -"""Service base classes and utilities. - -Layer: L1 (Foundation - Common Components) -""" - -from sage.common.service.base_service import BaseService - -__all__ = ["BaseService"] diff --git a/packages/sage-common/src/sage/common/service/base_service.py b/packages/sage-common/src/sage/common/service/base_service.py deleted file mode 100644 index bdbeb93296..0000000000 --- a/packages/sage-common/src/sage/common/service/base_service.py +++ /dev/null @@ -1,138 +0,0 @@ -"""Base Service Abstract Class - -Layer: L1 (Foundation - Common Components) - -Provides the base class for all SAGE services with: -- Logger management -- Service context integration (via dependency injection) - -Architecture Note: -- Uses TYPE_CHECKING import for ServiceContext (L3) - acceptable for type hints only -- Runtime injection of context happens through ServiceFactory (L3+) -- This class is in L1 to allow components at all layers to define services -""" - -import logging -from abc import ABC -from typing import TYPE_CHECKING - -if TYPE_CHECKING: - from sage.kernel.runtime.context.service_context import ServiceContext - - -class BaseService(ABC): # noqa: B024 - """BaseService is the abstract base class for all services in SAGE. - It defines the core interface and provides access to runtime context and logger. - - Note: This ABC provides default implementations for all methods, allowing - subclasses to selectively override only what they need. No abstract methods - are required as all service methods have reasonable defaults. - """ - - def __init__(self, *args, **kwargs): - """ - 初始化基础服务 - - Args: - *args: 位置参数 - **kwargs: 关键字参数 - - Note: - ctx 会在实例创建时由 ServiceFactory 自动注入， - 服务类不需要在构造函数中声明 ctx 参数 - """ - # ctx 由 ServiceFactory 在 __init__ 调用前通过 __new__ 方法注入 - if not hasattr(self, "ctx"): - self.ctx: ServiceContext | None = None - self._logger = None - - @property - def logger(self): - """获取logger，优先使用ctx.logger，否则使用默认logger""" - if not hasattr(self, "_logger") or self._logger is None: - if self.ctx is None: - self._logger = logging.getLogger(self.__class__.__name__) - else: - self._logger = self.ctx.logger - return self._logger - - @property - def name(self): - """获取服务名称，如果有ctx则使用ctx.name，否则使用类名""" - if self.ctx is not None: - return self.ctx.name - return self.__class__.__name__ - - def call_service( - self, - service_name: str, - *args, - timeout: float | None = None, - method: str | None = None, - **kwargs, - ): - """ - 同步服务调用语法糖 - - 用法: - result = self.call_service("cache_service", key, method="get") - data = self.call_service("pipeline_name", payload) - """ - if self.ctx is None: - raise RuntimeError("Service context not initialized. Cannot access services.") - - return self.ctx.call_service(service_name, *args, timeout=timeout, method=method, **kwargs) - - def call_service_async( - self, - service_name: str, - *args, - timeout: float | None = None, - method: str | None = None, - **kwargs, - ): - """ - 异步服务调用语法糖 - - 用法: - future = self.call_service_async("cache_service", key, method="get") - result = future.result() # 阻塞等待结果 - - # 或者非阻塞检查 - if future.done(): - result = future.result() - """ - if self.ctx is None: - raise RuntimeError("Service context not initialized. Cannot access services.") - - return self.ctx.call_service_async( - service_name, *args, timeout=timeout, method=method, **kwargs - ) - - def setup(self): # noqa: B027 - """ - 服务初始化设置方法，在service_instance创建后调用 - 子类可以重写此方法来进行初始化设置 - """ - pass - - def cleanup(self): # noqa: B027 - """ - 服务清理方法，在服务停止时调用 - 子类可以重写此方法来进行资源清理 - """ - pass - - def start(self): # noqa: B027 - """ - 服务启动方法，在服务启动时调用 - 子类可以重写此方法来进行启动逻辑 - """ - pass - - def stop(self): # noqa: B027 - """ - 服务停止方法，在服务停止时调用 - 子类可以重写此方法来进行停止逻辑 - """ - pass diff --git a/packages/sage-common/src/sage/common/utils/__init__.py b/packages/sage-common/src/sage/common/utils/__init__.py deleted file mode 100644 index 49a3fa2847..0000000000 --- a/packages/sage-common/src/sage/common/utils/__init__.py +++ /dev/null @@ -1,83 +0,0 @@ -"""SAGE - Streaming-Augmented Generative Execution - -Layer: L1 (Foundation - Common Utilities) - -This package provides common utilities used across all SAGE packages. -Includes logging, serialization, system utilities, and configuration helpers. - -Architecture: - This is a L1 foundation package providing utility functions. - Must NOT contain business logic, only reusable helper functions. -""" - -# 直接从本包的_version模块加载版本信息 -try: - from sage.common._version import __author__, __email__, __version__ -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" - -# Export document processing utilities -from sage.common.utils.document_processing import ( - SUPPORTED_MARKDOWN_SUFFIXES, - Section, - chunk_text, - iter_markdown_files, - parse_markdown_sections, - sanitize_metadata_value, - slugify, - truncate_text, -) - -# Export logging utilities -from sage.common.utils.logging import CustomFormatter, CustomLogger, get_logger - -# Export results collector -from sage.common.utils.results_collector import ResultsCollector, get_collector - -__all__ = [ - "__version__", - "__author__", - "__email__", - "SUPPORTED_MARKDOWN_SUFFIXES", - "Section", - "chunk_text", - "iter_markdown_files", - "parse_markdown_sections", - "sanitize_metadata_value", - "slugify", - "truncate_text", - # Logging - "CustomLogger", - "CustomFormatter", - "get_logger", - # Results Collector - "ResultsCollector", - "get_collector", -] - -# Export formatting utilities -from sage.common.utils.formatting import ( - format_count, - format_duration, - format_duration_verbose, - format_percentage, - format_size, - format_size_compact, - format_timestamp, -) - -# Update __all__ with formatting utilities -__all__.extend( - [ - "format_size", - "format_size_compact", - "format_duration", - "format_duration_verbose", - "format_timestamp", - "format_percentage", - "format_count", - ] -) diff --git a/packages/sage-common/src/sage/common/utils/config/__init__.py b/packages/sage-common/src/sage/common/utils/config/__init__.py deleted file mode 100644 index a1fc6ac446..0000000000 --- a/packages/sage-common/src/sage/common/utils/config/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -""" -SAGE - Streaming-Augmented Generative Execution -""" - -# 直接从本包的_version模块加载版本信息 -try: - from sage.common._version import __author__, __email__, __version__ -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" diff --git a/packages/sage-common/src/sage/common/utils/config/loader.py b/packages/sage-common/src/sage/common/utils/config/loader.py deleted file mode 100644 index 05110da63b..0000000000 --- a/packages/sage-common/src/sage/common/utils/config/loader.py +++ /dev/null @@ -1,72 +0,0 @@ -# sage/sage.common.utils/config_loader.py - -import inspect -import os -from pathlib import Path - -import yaml -from platformdirs import site_config_dir, user_config_dir - - -def load_config(path: str | Path | None = None) -> dict: - # locate project root (…/SAGE/) - # root = Path(__file__).resolve().parents[2] - # 获取调用者的文件路径作为项目根目录的参考点 - caller_frame = inspect.currentframe() - if caller_frame and caller_frame.f_back: - caller_file = caller_frame.f_back.f_globals.get("__file__") - if caller_file: - # 假设调用者在项目根目录或其子目录中 - root = Path(caller_file).resolve().parent - # 向上查找直到找到包含常见项目标识的目录（最多向上10层） - max_depth = 10 - depth = 0 - while root.parent != root and depth < max_depth: - if any( - (root / marker).exists() - for marker in ["setup.py", "pyproject.toml", ".git", "config"] - ): - break - root = root.parent - depth += 1 - else: - # 回退到当前工作目录 - root = Path.cwd() - else: - # 回退到当前工作目录 - root = Path.cwd() - candidates = [] - - # 1. explicit path - if path: - raw = Path(path) - if raw.is_absolute(): - p = raw - elif not raw.parent.parts: # bare filename - p = root / "config" / raw - else: # e.g. "config/foo.yaml" - p = root / raw - candidates.append(p) - - # 2. env var override - env = os.getenv("SAGE_CONFIG") - if env: - raw = Path(env) - p = raw if raw.is_absolute() else root / raw - candidates.append(p) - - # 3. project-level default - candidates.append(root / "config" / "config.yaml") - - # 4. user-level - candidates.append(Path(user_config_dir("sage")) / "config.yaml") - - # 5. system-level - candidates.append(Path(site_config_dir("sage")) / "config.yaml") - - for cfg_path in candidates: - if cfg_path.is_file(): - return yaml.safe_load(cfg_path.read_text()) - - names = "\n".join(str(p) for p in candidates) - raise FileNotFoundError(f"No config found. Checked:\n{names}") diff --git a/packages/sage-common/src/sage/common/utils/config/manager.py b/packages/sage-common/src/sage/common/utils/config/manager.py deleted file mode 100644 index 9ae9feadfb..0000000000 --- a/packages/sage-common/src/sage/common/utils/config/manager.py +++ /dev/null @@ -1,240 +0,0 @@ -""" -配置管理模块 -============ - -提供统一的配置加载、保存和管理功能。 -支持YAML、JSON、TOML等多种格式。 -""" - -import json -from pathlib import Path -from typing import Any - -import yaml -from pydantic import BaseModel, ConfigDict - -__all__ = ["load_config", "save_config", "ConfigManager", "BaseConfig"] - - -class BaseConfig(BaseModel): - """基础配置类""" - - model_config = ConfigDict( - extra="allow", # 允许额外字段 - validate_assignment=True, # 验证赋值 - ) - - -class ConfigManager: - """配置管理器""" - - def __init__(self, config_dir: str | Path | None = None): - """ - 初始化配置管理器 - - Args: - config_dir: 配置文件目录，默认使用当前目录的config/ - """ - if config_dir is None: - config_dir = Path.cwd() / "config" - - self.config_dir = Path(config_dir) - self.config_dir.mkdir(parents=True, exist_ok=True) - - self._cache: dict[str, dict[str, Any]] = {} - - def load(self, filename: str, use_cache: bool = True) -> dict[str, Any]: - """ - 加载配置文件 - - Args: - filename: 配置文件名 - use_cache: 是否使用缓存 - - Returns: - 配置字典 - """ - if use_cache and filename in self._cache: - return self._cache[filename].copy() - - config_path = self.config_dir / filename - - if not config_path.exists(): - raise FileNotFoundError(f"配置文件未找到: {config_path}") - - # 根据扩展名选择解析器 - suffix = config_path.suffix.lower() - - with open(config_path, encoding="utf-8") as f: - if suffix in [".yaml", ".yml"]: - config = yaml.safe_load(f) - elif suffix == ".json": - config = json.load(f) - elif suffix == ".toml": - try: - import tomli - - content = f.read() - config = tomli.loads(content) - except ImportError: - raise ImportError("需要安装 tomli 库来支持 TOML 格式") - else: - raise ValueError(f"不支持的配置文件格式: {suffix}") - - if config is None: - config = {} - - # 缓存配置 - if use_cache: - self._cache[filename] = config.copy() - - return config - - def save(self, filename: str, config: dict[str, Any], format: str | None = None): - """ - 保存配置文件 - - Args: - filename: 配置文件名 - config: 配置字典 - format: 强制指定格式 (yaml, json, toml) - """ - config_path = self.config_dir / filename - - # 确定保存格式 - if format: - save_format = format.lower() - else: - suffix = config_path.suffix.lower() - if suffix in [".yaml", ".yml"]: - save_format = "yaml" - elif suffix == ".json": - save_format = "json" - elif suffix == ".toml": - save_format = "toml" - else: - save_format = "yaml" # 默认使用YAML - - # 保存文件 - if save_format == "toml": - try: - import tomli_w - - with open(config_path, "wb") as f: - tomli_w.dump(config, f) - except ImportError: - raise ImportError("需要安装 tomli-w 库来保存 TOML 格式") - else: - with open(config_path, "w", encoding="utf-8") as f: - if save_format == "yaml": - yaml.dump(config, f, default_flow_style=False, allow_unicode=True) - elif save_format == "json": - json.dump(config, f, indent=2, ensure_ascii=False) - - # 更新缓存 - self._cache[filename] = config.copy() - - def get(self, filename: str, key: str, default: Any = None) -> Any: - """ - 获取配置项 - - Args: - filename: 配置文件名 - key: 配置键，支持点分割的嵌套键 (如 'database.host') - default: 默认值 - - Returns: - 配置值 - """ - config = self.load(filename) - - # 处理嵌套键 - keys = key.split(".") - value = config - - for k in keys: - if isinstance(value, dict) and k in value: - value = value[k] - else: - return default - - return value - - def set(self, filename: str, key: str, value: Any): - """ - 设置配置项 - - Args: - filename: 配置文件名 - key: 配置键，支持点分割的嵌套键 - value: 配置值 - """ - config = ( - self.load(filename) - if filename in self._cache or (self.config_dir / filename).exists() - else {} - ) - - # 处理嵌套键 - keys = key.split(".") - current = config - - for k in keys[:-1]: - if k not in current: - current[k] = {} - current = current[k] - - current[keys[-1]] = value - - # 保存配置 - self.save(filename, config) - - def clear_cache(self): - """清空缓存""" - self._cache.clear() - - -# 全局配置管理器实例（延迟初始化） -_global_config_manager = None - - -def _get_global_config_manager(): - """获取全局配置管理器实例（延迟初始化）""" - global _global_config_manager - if _global_config_manager is None: - _global_config_manager = ConfigManager() - return _global_config_manager - - -def load_config(filename: str, config_dir: str | Path | None = None) -> dict[str, Any]: - """ - 加载配置文件 (便捷函数) - - Args: - filename: 配置文件名 - config_dir: 配置目录，如果提供则创建新的ConfigManager实例 - - Returns: - 配置字典 - """ - if config_dir is not None: - manager = ConfigManager(config_dir) - return manager.load(filename) - - return _get_global_config_manager().load(filename) - - -def save_config(filename: str, config: dict[str, Any], config_dir: str | Path | None = None): - """ - 保存配置文件 (便捷函数) - - Args: - filename: 配置文件名 - config: 配置字典 - config_dir: 配置目录，如果提供则创建新的ConfigManager实例 - """ - if config_dir is not None: - manager = ConfigManager(config_dir) - manager.save(filename, config) - else: - _get_global_config_manager().save(filename, config) diff --git a/packages/sage-common/src/sage/common/utils/document_processing.py b/packages/sage-common/src/sage/common/utils/document_processing.py deleted file mode 100644 index 0089efb605..0000000000 --- a/packages/sage-common/src/sage/common/utils/document_processing.py +++ /dev/null @@ -1,248 +0,0 @@ -"""Document Processing Utilities for RAG - -Provides reusable utilities for processing documents into chunks suitable for -RAG indexing. These are pure utility functions with no SAGE-specific dependencies. - -Layer: L1 (sage-common) -Dependencies: Standard library only -""" - -import re -from collections.abc import Iterable -from pathlib import Path -from typing import TypedDict - -# Supported file extensions for Markdown documents -SUPPORTED_MARKDOWN_SUFFIXES = {".md", ".markdown"} - - -class Section(TypedDict): - """A section extracted from a document.""" - - heading: str - content: str - - -def iter_markdown_files(source: Path) -> Iterable[Path]: - """Iterate over all Markdown files in a directory tree. - - Args: - source: Root directory to scan - - Yields: - Path objects for each Markdown file (sorted by path) - - Example: - >>> for file in iter_markdown_files(Path("docs")): - ... print(file) - docs/intro.md - docs/guide/tutorial.md - """ - for path in sorted(source.rglob("*")): - if path.is_file() and path.suffix.lower() in SUPPORTED_MARKDOWN_SUFFIXES: - yield path - - -_HEADING_PATTERN = re.compile(r"^(#{1,6})\s+(?P.+?)\s*$") - - -def parse_markdown_sections(content: str) -> list[Section]: - """Parse Markdown content into sections based on headings. - - Splits content at heading boundaries (# through ######), grouping - text under each heading into a section. The first section before - any heading is labeled "Introduction". - - Args: - content: Markdown document as string - - Returns: - List of sections, each with 'heading' and 'content' keys - Empty sections (no content) are filtered out - - Example: - >>> text = ''' - ... # Overview - ... This is the intro. - ... - ... ## Details - ... More content here. - ... ''' - >>> sections = parse_markdown_sections(text) - >>> len(sections) - 2 - >>> sections[0]['heading'] - 'Overview' - """ - sections: list[Section] = [] - current_title = "Introduction" - current_lines: list[str] = [] - - for raw_line in content.splitlines(): - match = _HEADING_PATTERN.match(raw_line.strip()) - if match: - # Save previous section - if current_lines: - sections.append( - { - "heading": current_title, - "content": "\n".join(current_lines).strip(), - } - ) - current_lines = [] - # Start new section - current_title = match.group("title").strip() - else: - current_lines.append(raw_line) - - # Save final section - if current_lines: - sections.append({"heading": current_title, "content": "\n".join(current_lines).strip()}) - - # Filter out empty sections - return [section for section in sections if section["content"]] - - -def chunk_text(content: str, chunk_size: int, chunk_overlap: int) -> list[str]: - """Chunk text into overlapping segments with smart boundary detection. - - Splits text into chunks of approximately chunk_size characters, with - overlap between consecutive chunks. Attempts to break at natural - boundaries (newlines, sentence endings) rather than mid-word. - - Algorithm: - 1. Normalize whitespace (collapse 3+ newlines to 2) - 2. Use sliding window with step = chunk_size - chunk_overlap - 3. For chunks not at document end, try to break at: - - Newline (preferred) - - Chinese period (。) - - English period (.) - 4. Only break at boundary if it's in the latter 60% of the chunk - - Args: - content: Text to chunk - chunk_size: Target size for each chunk (characters) - chunk_overlap: Overlap between consecutive chunks (characters) - - Returns: - List of text chunks, with empty chunks filtered out - - Example: - >>> text = "First sentence. Second sentence. Third sentence." - >>> chunks = chunk_text(text, chunk_size=20, chunk_overlap=5) - >>> len(chunks) >= 2 - True - """ - # Normalize excessive newlines - normalized = re.sub(r"\n{3,}", "\n\n", content).strip() - if not normalized: - return [] - - start = 0 - length = len(normalized) - step = max(1, chunk_size - chunk_overlap) - chunks: list[str] = [] - - while start < length: - end = min(length, start + chunk_size) - chunk = normalized[start:end] - - # For non-final chunks, try to break at natural boundary - if end < length: - # Find rightmost boundary (newline or period) - boundary = max(chunk.rfind("\n"), chunk.rfind("。"), chunk.rfind(".")) - - # Only break if boundary is in latter 60% of chunk - # (prevents very small chunks) - if boundary >= 0 and boundary > len(chunk) * 0.4: - end = start + boundary - chunk = normalized[start:end] - - chunks.append(chunk.strip()) - start += step - - # Filter out empty chunks - return [c for c in chunks if c] - - -def slugify(text: str) -> str: - """Convert text to URL-safe slug format. - - Converts text to lowercase, replaces non-alphanumeric characters - with hyphens, and removes consecutive hyphens. - - Args: - text: Text to slugify - - Returns: - URL-safe slug (or "section" if result is empty) - - Example: - >>> slugify("Hello World!") - 'hello-world' - >>> slugify("C++ Programming") - 'c-programming' - """ - slug = re.sub(r"[^\w\-]+", "-", text.lower()).strip("-") - slug = re.sub(r"-+", "-", slug) - return slug or "section" - - -def truncate_text(text: str, limit: int = 480) -> str: - """Truncate text to specified character limit, adding ellipsis if needed. - - Args: - text: Text to truncate - limit: Maximum length (default: 480) - - Returns: - Truncated text with "..." suffix if truncated, otherwise original text - - Example: - >>> truncate_text("Short text", limit=100) - 'Short text' - >>> truncate_text("Very long text" * 50, limit=20) - 'Very long textVer...' - """ - if len(text) <= limit: - return text - return text[: limit - 3] + "..." - - -def sanitize_metadata_value(value: str) -> str: - """Sanitize text for use as vector database metadata. - - Performs the following transformations: - - Remove backslashes (avoid JSON escape issues in C++ parser) - - Replace carriage returns and newlines with spaces - - Replace double quotes with single quotes - - Replace patterns that look like JSON keys (avoid C++ parser confusion) - - Collapse multiple spaces into one - - Trim leading/trailing whitespace - - This ensures metadata values are safe for JSON serialization and - don't contain problematic characters that confuse the C++ parser. - - Args: - value: Raw metadata value - - Returns: - Sanitized string - - Example: - >>> sanitize_metadata_value('Line 1\\nLine 2') - 'Line 1 Line 2' - >>> sanitize_metadata_value('He said "hello"') - "He said 'hello'" - >>> sanitize_metadata_value('dict["key"]: {value}') - "dict['key']: (value)" - """ - # Remove backslashes to avoid JSON/C++ parser issues - cleaned = value.replace("\\", "") - cleaned = cleaned.replace("\r", " ").replace("\n", " ") - # Replace double quotes with single quotes - cleaned = cleaned.replace('"', "'") - # Replace { and } with ( and ) to avoid JSON-like patterns - cleaned = cleaned.replace("{", "(").replace("}", ")") - cleaned = re.sub(r"\s+", " ", cleaned) - return cleaned.strip() diff --git a/packages/sage-common/src/sage/common/utils/formatting.py b/packages/sage-common/src/sage/common/utils/formatting.py deleted file mode 100644 index 58284170d5..0000000000 --- a/packages/sage-common/src/sage/common/utils/formatting.py +++ /dev/null @@ -1,191 +0,0 @@ -""" -Formatting Utilities for SAGE - -Provides unified formatting functions for sizes, durations, timestamps, etc. -""" - -from datetime import datetime -from typing import Union - - -def format_size(size_bytes: int | float) -> str: - """ - 格式化文件/内存大小为人类可读格式 - - Args: - size_bytes: 字节数 - - Returns: - 格式化后的字符串，如 "1.5 MB" - - Examples: - >>> format_size(1024) - '1.0 KB' - >>> format_size(1536) - '1.5 KB' - >>> format_size(1048576) - '1.0 MB' - """ - size_float = float(size_bytes) - for unit in ["B", "KB", "MB", "GB", "TB"]: - if size_float < 1024: - return f"{size_float:.1f} {unit}" - size_float /= 1024 - return f"{size_float:.1f} PB" - - -def format_size_compact(size_bytes: int | float) -> str: - """ - 格式化文件/内存大小为紧凑格式（无空格） - - Args: - size_bytes: 字节数 - - Returns: - 格式化后的字符串，如 "1.5MB" - """ - size_float = float(size_bytes) - for unit in ["B", "KB", "MB", "GB", "TB"]: - if size_float < 1024: - return f"{size_float:.1f}{unit}" - size_float /= 1024 - return f"{size_float:.1f}PB" - - -def format_duration(seconds: float) -> str: - """ - 格式化持续时间为人类可读格式 - - Args: - seconds: 秒数 - - Returns: - 格式化后的字符串，如 "1h 30m" 或 "45.2s" - - Examples: - >>> format_duration(45.5) - '45.5s' - >>> format_duration(90) - '1m 30s' - >>> format_duration(3661) - '1h 1m' - """ - if seconds < 60: - return f"{seconds:.1f}s" - elif seconds < 3600: - minutes = int(seconds // 60) - secs = int(seconds % 60) - return f"{minutes}m {secs}s" - else: - hours = int(seconds // 3600) - minutes = int((seconds % 3600) // 60) - return f"{hours}h {minutes}m" - - -def format_duration_verbose(seconds: float) -> str: - """ - 格式化持续时间为详细格式 - - Args: - seconds: 秒数 - - Returns: - 格式化后的字符串，如 "1 hour 30 minutes" - """ - if seconds < 60: - return f"{seconds:.1f} seconds" - elif seconds < 3600: - minutes = int(seconds // 60) - secs = int(seconds % 60) - if secs == 0: - return f"{minutes} minute{'s' if minutes > 1 else ''}" - return ( - f"{minutes} minute{'s' if minutes > 1 else ''} {secs} second{'s' if secs > 1 else ''}" - ) - else: - hours = int(seconds // 3600) - minutes = int((seconds % 3600) // 60) - if minutes == 0: - return f"{hours} hour{'s' if hours > 1 else ''}" - return ( - f"{hours} hour{'s' if hours > 1 else ''} {minutes} minute{'s' if minutes > 1 else ''}" - ) - - -def format_timestamp(timestamp: Union[float, str, datetime], fmt: str = "%Y-%m-%d %H:%M:%S") -> str: - """ - 格式化时间戳为人类可读格式 - - Args: - timestamp: 时间戳（Unix时间戳、字符串或datetime对象） - fmt: 输出格式，默认 "%Y-%m-%d %H:%M:%S" - - Returns: - 格式化后的时间字符串 - """ - if isinstance(timestamp, str): - return timestamp - elif isinstance(timestamp, (int, float)): - dt = datetime.fromtimestamp(timestamp) - elif isinstance(timestamp, datetime): - dt = timestamp - else: - return str(timestamp) - - return dt.strftime(fmt) - - -def format_percentage(value: float, decimals: int = 1, is_decimal: bool = True) -> str: - """ - 格式化百分比 - - Args: - value: 百分比的数值。如果 is_decimal=True，则应为小数 (0.0 - 1.0)；如果 is_decimal=False，则应为百分比值 (0.0 - 100.0)。 - decimals: 小数位数 - is_decimal: 指示 value 是否为小数（True，默认）或已为百分比（False） - - Returns: - 格式化后的百分比字符串 - - Examples: - >>> format_percentage(0.85) - '85.0%' - >>> format_percentage(85, is_decimal=False) - '85.0%' - """ - if is_decimal: - return f"{value * 100:.{decimals}f}%" - else: - return f"{value:.{decimals}f}%" - - -def format_count(count: int) -> str: - """ - 格式化大数字为人类可读格式 - - Args: - count: 数量 - - Returns: - 格式化后的字符串，如 "1.5K" 或 "2.3M" - """ - count_float = float(count) - if count_float < 1000: - return str(count) - elif count_float < 1000000: - return f"{count_float / 1000:.1f}K" - elif count_float < 1000000000: - return f"{count_float / 1000000:.1f}M" - else: - return f"{count_float / 1000000000:.1f}B" - - -__all__ = [ - "format_size", - "format_size_compact", - "format_duration", - "format_duration_verbose", - "format_timestamp", - "format_percentage", - "format_count", -] diff --git a/packages/sage-common/src/sage/common/utils/logging/__init__.py b/packages/sage-common/src/sage/common/utils/logging/__init__.py deleted file mode 100644 index 8c2da58e91..0000000000 --- a/packages/sage-common/src/sage/common/utils/logging/__init__.py +++ /dev/null @@ -1,21 +0,0 @@ -""" -SAGE Common Logging Utilities -""" - -from .custom_formatter import CustomFormatter -from .custom_logger import CustomLogger - - -def get_logger(name=None): - """获取一个CustomLogger实例 - - Args: - name: Logger名称，默认为None - - Returns: - CustomLogger实例 - """ - return CustomLogger(outputs=[("console", "INFO")], name=name or __name__) - - -__all__ = ["CustomLogger", "CustomFormatter", "get_logger"] diff --git a/packages/sage-common/src/sage/common/utils/logging/custom_formatter.py b/packages/sage-common/src/sage/common/utils/logging/custom_formatter.py deleted file mode 100644 index 61a9b6cda1..0000000000 --- a/packages/sage-common/src/sage/common/utils/logging/custom_formatter.py +++ /dev/null @@ -1,55 +0,0 @@ -import logging - - -class CustomFormatter(logging.Formatter): - """ - 自定义格式化器，合并IDE格式和两行格式： - 第一行：时间 | 级别 | 对象名 | 文件路径:行号 - 第二行： → 日志消息 - 第三行： 留空 - """ - - COLOR_RESET = "\033[0m" - COLOR_DEBUG = "\033[36m" # 青色 - COLOR_INFO = "\033[32m" # 绿色 - COLOR_WARNING = "\033[33m" # 黄色 - COLOR_ERROR = "\033[31m" # 红色 - COLOR_CRITICAL = "\033[35m" # 紫色 - - def format(self, record): - if record.levelno == logging.DEBUG: - color = self.COLOR_DEBUG - elif record.levelno == logging.INFO: - color = self.COLOR_INFO - elif record.levelno == logging.WARNING: - color = self.COLOR_WARNING - elif record.levelno == logging.ERROR: - color = self.COLOR_ERROR - elif record.levelno == logging.CRITICAL: - color = self.COLOR_CRITICAL - else: - color = self.COLOR_RESET - - # 第一行：时间 | 级别 | 对象名 | 文件路径:行号 - timestamp = self.formatTime(record, "%Y-%m-%d %H:%M:%S") - level = record.levelname - name = record.name - pathname = record.pathname - lineno = record.lineno - - # 第二行：→ 消息内容 - message = record.getMessage() - - # 如果有异常信息，添加到消息后面 - if record.exc_info: - if not record.exc_text: - record.exc_text = self.formatException(record.exc_info) - if record.exc_text: - message = message + "\n" + record.exc_text - if record.stack_info: - message = message + "\n" + self.formatStack(record.stack_info) - - # 组合格式：既美观又支持IDE点击 - formatted_message = f"{timestamp} | {level:<5} | {name} | {pathname}:{lineno} →\n\t {color}{message}{self.COLOR_RESET}\n" - - return formatted_message diff --git a/packages/sage-common/src/sage/common/utils/logging/custom_logger.py b/packages/sage-common/src/sage/common/utils/logging/custom_logger.py deleted file mode 100644 index 6aa1c0fc99..0000000000 --- a/packages/sage-common/src/sage/common/utils/logging/custom_logger.py +++ /dev/null @@ -1,566 +0,0 @@ -import inspect -import logging -import os -import sys -import threading -from logging.handlers import RotatingFileHandler -from pathlib import Path - -from .custom_formatter import CustomFormatter # 假设有一个自定义格式化器 - - -def get_default_log_base_folder(project_root: str | Path | None = None) -> str: - """ - 获取默认的日志基础文件夹，使用统一的.sage/logs目录。 - - Args: - project_root: 项目根目录，如果为None，会自动检测 - - Returns: - str: 日志基础文件夹路径 - """ - try: - from sage.common.config.output_paths import get_logs_dir - - return str(get_logs_dir(project_root)) - except ImportError: - # Fallback to default behavior if output_paths not available - return "/tmp/sage/logs" - - -class CustomLogger: - """ - 简化的自定义Logger类 - 支持多种输出目标配置： - - "console": 控制台输出 - - 相对路径: 相对于log_base_folder的路径 - - 绝对路径: 完整路径的文件输出 - """ - - # 全局console debug开关 - _global_console_debug_enabled: bool = True - _lock = threading.Lock() - - # 日志级别映射 - _LEVEL_MAPPING = { - "DEBUG": logging.DEBUG, - "INFO": logging.INFO, - "WARNING": logging.WARNING, - "WARN": logging.WARNING, - "ERROR": logging.ERROR, - "CRITICAL": logging.CRITICAL, - "FATAL": logging.CRITICAL, - } - - def __init__( - self, - name_or_outputs: str | list[tuple[str, str | int]] | None = None, - outputs: list[tuple[str, str | int]] | None = None, - name: str | None = None, - log_base_folder: str | None = None, - ): - """ - 初始化自定义Logger - - Supports multiple invocation methods for better user experience: - - 1. Simple invocation (recommended): - logger = CustomLogger("MyLogger") - - 2. 完整配置： - logger = CustomLogger([("console", "INFO"), ("app.log", "DEBUG")], name="MyLogger") - - 3. 关键字参数（向后兼容）： - logger = CustomLogger(name="MyLogger") - logger = CustomLogger(outputs=[("console", "INFO")], name="MyLogger") - - Args: - name_or_outputs: 可以是： - - 字符串: 作为 logger 名称 (最常见用法) - - 列表: 作为 outputs 配置 - - None: 使用默认值 - outputs: 输出配置列表，每个元素为 (output_target, level) 元组 - - output_target 可以是: - - "console": 控制台输出 - - 相对路径: 相对于log_base_folder的路径，如 "app.log", "logs/error.log" - - 绝对路径: 完整路径，如 "/tmp/app.log" - - level 可以是字符串("DEBUG", "INFO"等) 或数字 - name: logger名称，默认使用 "Logger" - log_base_folder: 日志基础文件夹，用于解析相对路径。如果为None，则不支持相对路径 - - Examples: - # 最简单的用法（推荐） - logger = CustomLogger("MyApp") - - # 仅控制台输出 - logger = CustomLogger("MyApp") - logger.info("Hello") - - # 完整配置 - logger = CustomLogger([ - ("console", "INFO"), - ("app.log", "DEBUG"), - ], name="MyApp", log_base_folder="/var/log") - - # 向后兼容的方式 - logger = CustomLogger( - outputs=[("console", "INFO")], - name="MyApp" - ) - """ - # 智能参数处理 - resolved_name = None - resolved_outputs = None - - # 处理第一个位置参数 name_or_outputs - if name_or_outputs is not None: - if isinstance(name_or_outputs, str): - # 第一个参数是字符串，视为 name - resolved_name = name_or_outputs - elif isinstance(name_or_outputs, list): - # 第一个参数是列表，视为 outputs - resolved_outputs = name_or_outputs - else: - raise TypeError( - f"First argument must be str (name) or list (outputs), " - f"got {type(name_or_outputs).__name__}" - ) - - # 处理 name 关键字参数（优先级更高） - if name is not None: - resolved_name = name - - # 处理 outputs 关键字参数（优先级更高） - if outputs is not None: - resolved_outputs = outputs - - # 设置默认值 - if resolved_name is None: - resolved_name = "Logger" - if resolved_outputs is None: - # Check if running in CLI mode (non-verbose) - # In CLI mode, default to WARNING to avoid noisy startup logs - if os.environ.get("SAGE_LOG_LEVEL"): - default_level = os.environ["SAGE_LOG_LEVEL"].upper() - elif not os.environ.get("SAGE_CLI_VERBOSE"): - default_level = "WARNING" - else: - default_level = "INFO" - resolved_outputs = [("console", default_level)] - - self.name = resolved_name - self.log_base_folder = log_base_folder - - # 如果提供了log_base_folder，确保其存在 - if self.log_base_folder: - Path(self.log_base_folder).mkdir(parents=True, exist_ok=True) - - self.logger = logging.getLogger(self.name) - - # 解析输出配置 - 需要在早期返回之前初始化 - self.output_configs = [] - - # 清除已有handlers以确保重新配置 - for handler in self.logger.handlers[:]: - self.logger.removeHandler(handler) - - enabled_levels = [] - - for output_target, level in resolved_outputs: - level_int = self._extract_log_level(level) - self.output_configs.append( - { - "target": output_target, - "level": level_int, - "level_str": logging.getLevelName(level_int), - "handler": None, - "resolved_path": self._resolve_path(output_target), - } - ) - enabled_levels.append(level_int) - - # 设置logger的最低级别 - min_level = min(enabled_levels) if enabled_levels else logging.INFO - self.logger.setLevel(min_level) - - # 创建统一的自定义格式化器 - formatter = CustomFormatter() - - # 为每个输出目标创建handler - for config in self.output_configs: - handler = self._create_handler(config, formatter) - if handler: - handler.setLevel(config["level"]) - self.logger.addHandler(handler) - config["handler"] = handler - - # 不传播到父logger - self.logger.propagate = False - - def _resolve_path(self, output_target: str) -> str: - """ - 解析输出路径 - - Args: - output_target: 输出目标 - - Returns: - str: 解析后的路径 - - Raises: - ValueError: 当使用相对路径但未设置log_base_folder时 - """ - if output_target == "console": - return "console" - - # 检查是否为绝对路径 - if os.path.isabs(output_target): - return output_target - else: - # 相对路径需要log_base_folder支持 - if not self.log_base_folder: - raise ValueError( - f"Cannot use relative path '{output_target}' without log_base_folder. " - f"Please provide log_base_folder in __init__ or use absolute path." - ) - return os.path.join(self.log_base_folder, output_target) - - def _extract_log_level(self, level_setting: str | int) -> int: - """ - 从级别设置中提取日志级别 - - Args: - level_setting: 级别设置 - - Returns: - int: 对应的日志级别数值 - """ - if isinstance(level_setting, str): - level_str = level_setting.upper() - if level_str not in self._LEVEL_MAPPING: - raise ValueError( - f"Invalid log level: {level_setting}. " - f"Valid levels are: {list(self._LEVEL_MAPPING.keys())}" - ) - return self._LEVEL_MAPPING[level_str] - elif isinstance(level_setting, int): - return level_setting - else: - raise TypeError(f"level_setting must be str or int, got {type(level_setting)}") - - def _create_handler(self, config: dict, formatter: CustomFormatter) -> logging.Handler | None: - """ - 根据输出配置创建对应的handler - - Args: - config: 输出配置字典 - formatter: 格式化器 - - Returns: - logging.Handler: 创建的handler，如果创建失败返回None - """ - try: - if config["target"] == "console": - # 控制台输出 - if not self._global_console_debug_enabled: - return None - handler = logging.StreamHandler() - handler.setFormatter(formatter) - return handler - else: - # 文件输出 - 使用 RotatingFileHandler 实现日志轮换 - file_path = config["resolved_path"] - log_dir = os.path.dirname(file_path) - if log_dir: # 如果有目录路径 - os.makedirs(log_dir, exist_ok=True) - - # 日志轮换配置 - # maxBytes: 单个文件最大 50MB - # backupCount: 最多保留 5 个旧文件（总大小约 250MB） - handler = RotatingFileHandler( - filename=file_path, - mode="a", - maxBytes=50 * 1024 * 1024, # 50MB - backupCount=5, - encoding="utf-8", - ) - handler.setFormatter(formatter) - return handler - - except Exception as e: - print(f"Failed to create handler for {config['target']}: {e}") - return None - - def get_output_configs(self) -> list[dict]: - """获取当前输出配置""" - return [ - { - "target": config["target"], - "resolved_path": config["resolved_path"], - "level": config["level_str"], # Return string level for public API consistency - "level_str": config["level_str"], - "level_num": config["level"], - "handler_active": config["handler"] is not None, - } - for config in self.output_configs - ] - - def print_current_configs(self): - """打印当前输出配置""" - configs = self.get_output_configs() - print(f"\n=== Logger '{self.name}' Output Configurations ===") - if self.log_base_folder: - print(f"Log base folder: {self.log_base_folder}") - else: - print("Log base folder: Not set (relative paths not supported)") - for i, config in enumerate(configs, 1): - status = "ACTIVE" if config["handler_active"] else "INACTIVE" - print(f"{i}. Target: {config['target']}") - if config["target"] != "console": - print(f" Resolved Path: {config['resolved_path']}") - print(f" Level: {config['level']} ({config['level_num']}) - {status}") - print( - f"Logger minimum level: {logging.getLevelName(self.logger.level)} ({self.logger.level})" - ) - print("=" * 60) - - def update_output_level(self, target_index_or_name: int | str, new_level: str | int): - """ - 动态更新指定输出的级别 - - Args: - target_index_or_name: 目标索引(0开始)或目标名称 - new_level: 新的日志级别 - """ - # 查找目标配置 - target_config = None - if isinstance(target_index_or_name, int): - if 0 <= target_index_or_name < len(self.output_configs): - target_config = self.output_configs[target_index_or_name] - else: - for config in self.output_configs: - if config["target"] == target_index_or_name: - target_config = config - break - - if not target_config: - raise ValueError(f"Output target not found: {target_index_or_name}") - - # 更新级别 - new_level_int = self._extract_log_level(new_level) - target_config["level"] = new_level_int - target_config["level_str"] = logging.getLevelName(new_level_int) - - # 更新handler级别 - if target_config["handler"]: - target_config["handler"].setLevel(new_level_int) - - # 更新logger的最低级别 - enabled_levels = [config["level"] for config in self.output_configs if config["handler"]] - min_level = min(enabled_levels) if enabled_levels else logging.INFO - self.logger.setLevel(min_level) - - print(f"Updated {target_config['target']} level to {target_config['level_str']}") - - def add_output(self, output_target: str, level: str | int): - """ - 动态添加新的输出目标 - - Args: - output_target: 输出目标 - level: 日志级别 - """ - level_int = self._extract_log_level(level) - - # 创建新配置 - new_config = { - "target": output_target, - "level": level_int, - "level_str": logging.getLevelName(level_int), - "handler": None, - "resolved_path": self._resolve_path(output_target), - } - - # 创建handler - formatter = CustomFormatter() - handler = self._create_handler(new_config, formatter) - if handler: - handler.setLevel(level_int) - self.logger.addHandler(handler) - new_config["handler"] = handler - - self.output_configs.append(new_config) - - # 更新logger最低级别 - enabled_levels = [config["level"] for config in self.output_configs if config["handler"]] - min_level = min(enabled_levels) if enabled_levels else logging.INFO - self.logger.setLevel(min_level) - - print( - f"Added output: {output_target} -> {new_config['resolved_path']} with level {new_config['level_str']}" - ) - - def remove_output(self, target_index_or_name: int | str): - """ - 移除指定的输出目标 - - Args: - target_index_or_name: 目标索引或名称 - """ - # 查找并移除配置 - target_config = None - target_index = None - - if isinstance(target_index_or_name, int): - if 0 <= target_index_or_name < len(self.output_configs): - target_index = target_index_or_name - target_config = self.output_configs[target_index] - else: - for i, config in enumerate(self.output_configs): - if config["target"] == target_index_or_name: - target_index = i - target_config = config - break - - if not target_config: - raise ValueError(f"Output target not found: {target_index_or_name}") - - # 移除handler - if target_config["handler"]: - self.logger.removeHandler(target_config["handler"]) - - # 移除配置 - if target_index is not None: - self.output_configs.pop(target_index) - else: - raise RuntimeError("target_index is None after finding config") - - # 更新logger最低级别 - enabled_levels = [config["level"] for config in self.output_configs if config["handler"]] - min_level = min(enabled_levels) if enabled_levels else logging.INFO - self.logger.setLevel(min_level) - - print(f"Removed output: {target_config['target']}") - - def _log_with_caller_info( - self, level: int, message: str, *args, exc_info: bool = False, **kwargs - ): - """ - 使用调用者信息记录日志，而不是CustomLogger的信息 - - 支持 Python logging 标准格式化： - - logger.info("Hello %s", "world") - - logger.info("User %s logged in at %s", username, timestamp) - """ - # 获取调用栈，跳过当前方法和调用的debug/info/等方法 - frame = inspect.currentframe() - try: - # 跳过 _log_with_caller_info -> debug/info/warning/error -> 实际调用位置 - if frame and frame.f_back and frame.f_back.f_back: - caller_frame = frame.f_back.f_back - else: - caller_frame = None - if caller_frame: - pathname = caller_frame.f_code.co_filename - lineno = caller_frame.f_lineno - # 如果 exc_info=True，就取当前异常信息元组；否则为 None - err = sys.exc_info() if exc_info else None - # 创建一个临时的LogRecord，手动设置调用者信息 - record = self.logger.makeRecord( - name=self.logger.name, - level=level, - fn=pathname, - lno=lineno, - msg=message, - args=args, # 支持格式化参数 - exc_info=err, - **kwargs, - ) - - # 直接调用handlers处理记录 - self.logger.handle(record) - else: - # 回退到普通logging - self.logger.log(level, message, *args, exc_info=exc_info, **kwargs) - finally: - del frame - - def debug(self, message: str, *args, **kwargs): - """Debug级别日志，支持格式化参数""" - self._log_with_caller_info(logging.DEBUG, message, *args, **kwargs) - - def info(self, message: str, *args, **kwargs): - """Info级别日志，支持格式化参数""" - self._log_with_caller_info(logging.INFO, message, *args, **kwargs) - - def warning(self, message: str, *args, **kwargs): - """Warning级别日志，支持格式化参数""" - self._log_with_caller_info(logging.WARNING, message, *args, **kwargs) - - def error(self, message: str, *args, exc_info: bool = False, **kwargs): - """Error级别日志，支持格式化参数""" - self._log_with_caller_info(logging.ERROR, message, *args, exc_info=exc_info, **kwargs) - - def critical(self, message: str, *args, **kwargs): - """Critical级别日志，支持格式化参数""" - self._log_with_caller_info(logging.CRITICAL, message, *args, **kwargs) - - def exception(self, message: str, *args, **kwargs): - """异常级别日志，自动包含异常信息""" - self.error(message, *args, exc_info=True, **kwargs) - - @classmethod - def get_available_levels(cls) -> list: - """获取所有可用的日志级别""" - return list(cls._LEVEL_MAPPING.keys()) - - @classmethod - def get_logger( - cls, - name: str | None = None, - *, - level: str | int = "INFO", - outputs: list[tuple[str, str | int]] | None = None, - log_base_folder: str | None = None, - ) -> logging.Logger: - """兼容旧版 API 的便捷方法，返回 ``logging.Logger`` 实例。 - - 旧的示例（例如 ``examples/apps/run_work_report.py``）使用 - ``CustomLogger.get_logger(__name__)`` 获取标准 logger。本方法保持 - 该接口，同时复用新的 ``CustomLogger`` 配置能力。 - - Args: - name: Logger 名称，默认 "Logger"。 - level: 当 ``outputs`` 未提供时，用于 console 输出的级别。 - outputs: 可选的输出配置列表，与 ``CustomLogger`` 构造函数一致。 - log_base_folder: 提供相对路径输出时使用的日志根目录。 - - Returns: - logging.Logger: 配置好的 logger。 - """ - - resolved_outputs: list[tuple[str, str | int]] = outputs if outputs else [("console", level)] - - instance = cls( - name=name, - outputs=resolved_outputs, - log_base_folder=log_base_folder, - ) - return instance.logger - - @classmethod - def disable_global_console_debug(cls): - """全局禁用所有console debug输出""" - with cls._lock: - cls._global_console_debug_enabled = False - - @classmethod - def enable_global_console_debug(cls): - """全局启用所有console debug输出""" - with cls._lock: - cls._global_console_debug_enabled = True - - @classmethod - def is_global_console_debug_enabled(cls) -> bool: - """检查全局console debug是否启用""" - return cls._global_console_debug_enabled diff --git a/packages/sage-common/src/sage/common/utils/network/__init__.py b/packages/sage-common/src/sage/common/utils/network/__init__.py deleted file mode 100644 index a1fc6ac446..0000000000 --- a/packages/sage-common/src/sage/common/utils/network/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -""" -SAGE - Streaming-Augmented Generative Execution -""" - -# 直接从本包的_version模块加载版本信息 -try: - from sage.common._version import __author__, __email__, __version__ -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" diff --git a/packages/sage-common/src/sage/common/utils/network/base_tcp_client.py b/packages/sage-common/src/sage/common/utils/network/base_tcp_client.py deleted file mode 100644 index 922046f2bb..0000000000 --- a/packages/sage-common/src/sage/common/utils/network/base_tcp_client.py +++ /dev/null @@ -1,357 +0,0 @@ -import json -import socket -import time -import uuid -from abc import ABC, abstractmethod -from typing import Any - - -class BaseTcpClient(ABC): - """ - 通用TCP客户端基类 - 提供基础的TCP客户端功能，包括连接管理、消息收发等 - 子类需要实现具体的消息构建和响应处理逻辑 - """ - - def __init__( - self, - host: str = "127.0.0.1", - port: int = 19001, - timeout: float = 30.0, - client_name: str = "TcpClient", - ): - """ - 初始化TCP客户端 - - Args: - host: 服务器地址 - port: 服务器端口 - timeout: 连接超时时间 - client_name: 客户端名称，用于日志记录 - """ - self.host = host - self.port = port - self.timeout = timeout - self.client_name = client_name - - # 连接状态 - self.connected = False - self._socket: socket.socket | None = None - - # 日志记录器（子类可以设置自己的logger） - self.logger = self._create_default_logger() - - def _create_default_logger(self): - """创建默认日志记录器""" - import logging - - logger = logging.getLogger(f"{self.client_name}") - if not logger.handlers: - handler = logging.StreamHandler() - formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s") - handler.setFormatter(formatter) - logger.addHandler(handler) - logger.setLevel(logging.INFO) - return logger - - def connect(self) -> bool: - """ - 连接到服务器 - - Returns: - bool: 连接成功返回True，否则返回False - """ - if self.connected: - return True - - try: - self._socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) - self._socket.settimeout(self.timeout) - self._socket.connect((self.host, self.port)) - self.connected = True - self.logger.debug(f"{self.client_name} connected to {self.host}:{self.port}") - return True - - except Exception as e: - self.logger.error(f"Failed to connect to {self.host}:{self.port}: {e}") - - # 特别为JobManager连接失败提供友好提示 - if hasattr(self, "client_name") and "JobManager" in self.client_name: - self._log_jobmanager_connection_help() - - self.connected = False - if self._socket: - try: - self._socket.close() - except Exception: - pass - self._socket = None - return False - - def disconnect(self): - """断开与服务器的连接""" - if self._socket: - try: - self._socket.close() - except Exception: - pass - self._socket = None - - self.connected = False - self.logger.debug(f"{self.client_name} disconnected") - - def _log_jobmanager_connection_help(self): - """为JobManager连接失败提供友好的帮助信息""" - self.logger.error("❌ 无法连接到JobManager服务") - self.logger.error("📋 请检查以下步骤：") - self.logger.error(" 1. JobManager是否已启动？") - self.logger.error( - f" 启动命令: sage jobmanager start --host {self.host} --port {self.port}" - ) - self.logger.error(f" 2. 主机地址是否正确？ (当前: {self.host}:{self.port})") - self.logger.error(" 3. 防火墙是否阻止了连接？") - self.logger.error("💡 提示：如果是第一次使用RemoteEnvironment，请先启动JobManager服务") - self.logger.error( - "📚 更多信息：https://intellistream.github.io/SAGE-Pub/kernel/jobmanager/" - ) - - def _create_jobmanager_error_response(self) -> dict[str, Any]: - """创建JobManager连接失败的详细错误响应""" - return { - "status": "error", - "error_code": "ERR_JOBMANAGER_CONNECTION_FAILED", - "message": f"Cannot connect to JobManager at {self.host}:{self.port}", - "details": { - "host": self.host, - "port": self.port, - "client_type": "JobManager", - "suggestions": [ - f"Start JobManager with: sage jobmanager start --host {self.host} --port {self.port}", - "Check if the host and port are correct", - "Verify that firewall allows the connection", - "Ensure JobManager service is running and healthy", - ], - "help_url": "https://intellistream.github.io/SAGE-Pub/kernel/jobmanager/", - }, - "timestamp": time.time(), - } - - def send_request(self, request_data: dict[str, Any]) -> dict[str, Any]: - """ - 发送请求到服务器并返回响应 - - Args: - request_data: 请求数据字典 - - Returns: - Dict[str, Any]: 服务器响应 - """ - # 确保连接 - if not self.connected: - if not self.connect(): - # 为JobManager连接失败提供特殊的错误信息 - if hasattr(self, "client_name") and "JobManager" in self.client_name: - return self._create_jobmanager_error_response() - else: - return self._create_error_response( - "ERR_CONNECTION_FAILED", "Failed to connect to server" - ) - - try: - # 序列化请求数据 - serialized_request = self._serialize_request(request_data) - - # 发送请求 - self._send_data(serialized_request) - - # 接收响应 - response_data = self._receive_response() - - if response_data is None: - return self._create_error_response( - "ERR_NO_RESPONSE", "No response received from server" - ) - - # 反序列化响应 - response = self._deserialize_response(response_data) - - return response - - except Exception as e: - self.logger.error(f"Error sending request: {e}") - # 连接可能已断开，重置连接状态 - self.connected = False - return self._create_error_response( - "ERR_COMMUNICATION_FAILED", f"Communication error: {e}" - ) - - def _send_data(self, data: bytes): - """发送数据到服务器""" - if not self._socket: - raise RuntimeError("Socket not connected") - - # 发送数据长度 - data_length = len(data).to_bytes(4, byteorder="big") - self._socket.sendall(data_length) - - # 发送数据内容 - self._socket.sendall(data) - - self.logger.debug(f"Sent data (size: {len(data)})") - - def _receive_response(self) -> bytes | None: - """接收服务器响应""" - if not self._socket: - raise RuntimeError("Socket not connected") - - try: - # 接收响应长度 - response_length_data = self._receive_full_data(4) - if not response_length_data: - return None - - response_length = int.from_bytes(response_length_data, byteorder="big") - - if response_length <= 0 or response_length > 100 * 1024 * 1024: # 100MB limit - self.logger.warning(f"Invalid response length: {response_length}") - return None - - # 接收响应数据 - response_data = self._receive_full_data(response_length) - - self.logger.debug( - f"Received response (size: {len(response_data) if response_data else 0})" - ) - - return response_data - - except Exception as e: - self.logger.error(f"Error receiving response: {e}") - return None - - def _receive_full_data(self, size: int) -> bytes | None: - """接收指定大小的完整数据""" - if not self._socket: - return None - - data = b"" - while len(data) < size: - try: - chunk_size = min(size - len(data), 8192) - chunk = self._socket.recv(chunk_size) - if not chunk: - self.logger.warning("Connection closed while receiving data") - return None - data += chunk - except TimeoutError: - self.logger.error("Timeout while receiving data") - return None - except Exception as e: - self.logger.error(f"Error receiving data: {e}") - return None - - return data - - def _serialize_request(self, request_data: dict[str, Any]) -> bytes: - """ - 序列化请求数据（默认使用JSON，子类可以重写） - - Args: - request_data: 请求数据 - - Returns: - bytes: 序列化后的数据 - """ - # 添加通用字段 - if "request_id" not in request_data: - request_data["request_id"] = str(uuid.uuid4()) - - if "timestamp" not in request_data: - request_data["timestamp"] = int(time.time()) - - return json.dumps(request_data).encode("utf-8") - - def _deserialize_response(self, response_data: bytes) -> dict[str, Any]: - """ - 反序列化响应数据（默认使用JSON，子类可以重写） - - Args: - response_data: 响应数据 - - Returns: - Dict[str, Any]: 反序列化后的响应 - """ - try: - return json.loads(response_data.decode("utf-8")) - except Exception as e: - self.logger.error(f"Error deserializing response: {e}") - return self._create_error_response( - "ERR_DESERIALIZATION_FAILED", f"Failed to deserialize response: {e}" - ) - - def _create_error_response(self, error_code: str, error_message: str) -> dict[str, Any]: - """创建错误响应""" - return { - "status": "error", - "error_code": error_code, - "message": error_message, - "timestamp": int(time.time()), - } - - def health_check(self) -> dict[str, Any]: - """ - 通用健康检查方法 - - Returns: - Dict[str, Any]: 健康检查响应 - """ - request = self._build_health_check_request() - return self.send_request(request) - - @abstractmethod - def _build_health_check_request(self) -> dict[str, Any]: - """ - 构建健康检查请求（抽象方法） - 子类需要实现此方法来定义具体的健康检查请求格式 - - Returns: - Dict[str, Any]: 健康检查请求数据 - """ - pass - - def get_server_info(self) -> dict[str, Any]: - """ - 获取服务器信息的通用方法 - - Returns: - Dict[str, Any]: 服务器信息响应 - """ - request = self._build_server_info_request() - return self.send_request(request) - - @abstractmethod - def _build_server_info_request(self) -> dict[str, Any]: - """ - 构建服务器信息请求（抽象方法） - 子类需要实现此方法来定义具体的服务器信息请求格式 - - Returns: - Dict[str, Any]: 服务器信息请求数据 - """ - pass - - def __enter__(self): - """上下文管理器入口""" - self.connect() - return self - - def __exit__(self, exc_type, exc_val, exc_tb): - """上下文管理器出口""" - self.disconnect() - - def __del__(self): - """析构函数，确保连接清理""" - try: - self.disconnect() - except Exception: - pass diff --git a/packages/sage-common/src/sage/common/utils/network/local_tcp_server.py b/packages/sage-common/src/sage/common/utils/network/local_tcp_server.py deleted file mode 100644 index bff5a77c13..0000000000 --- a/packages/sage-common/src/sage/common/utils/network/local_tcp_server.py +++ /dev/null @@ -1,631 +0,0 @@ -import os -import pickle -import socket -import threading -import time -from abc import ABC, abstractmethod -from collections.abc import Callable -from typing import Any - - -class BaseTcpServer(ABC): - """ - 通用TCP服务器基类 - 提供基础的TCP服务器功能，包括连接管理、消息收发等 - 子类需要实现具体的消息处理逻辑 - """ - - def __init__( - self, - host: str | None = None, - port: int | None = None, - logger=None, - server_name: str = "TcpServer", - ): - """ - 初始化TCP服务器 - - Args: - host: 监听地址 - port: 监听端口 - logger: 日志记录器 - server_name: 服务器名称，用于日志和线程命名 - """ - self.server_name = server_name - self.server_cwd = os.getcwd() - - # 日志记录器需要先初始化 - self.logger = logger or self._create_default_logger() - - self.host = host or self._get_host_ip() - self.port = port or self._allocate_tcp_port() - self.server_socket: socket.socket | None = None - self.server_thread: threading.Thread | None = None - self.running = False - - # 客户端连接管理 - self.client_connections: dict[str, socket.socket] = {} # client_id -> socket - self.client_lock = threading.Lock() - - def _create_default_logger(self): - """创建默认日志记录器""" - import logging - - logger = logging.getLogger(f"{self.server_name}") - if not logger.handlers: - handler = logging.StreamHandler() - formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s") - handler.setFormatter(formatter) - logger.addHandler(handler) - logger.setLevel(logging.INFO) - return logger - - def _get_host_ip(self): - """自动获取本机可用于外部连接的 IP 地址""" - s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) - try: - s.connect(("8.8.8.8", 80)) - ip = s.getsockname()[0] - except Exception: - self.logger.warning("Failed to get external IP, using localhost") - ip = "127.0.0.1" - finally: - s.close() - return ip - - def _allocate_tcp_port(self) -> int: - """为服务器分配可用的TCP端口""" - # 尝试从预设范围分配端口 - for port in range(19200, 20000): - try: - with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: - s.bind((self.host or "127.0.0.1", port)) - self.logger.debug(f"Allocated port: {port}") - return port - except OSError: - continue - - # 如果预设范围都被占用，直接抛出异常 - self.logger.error("All predefined ports are occupied, no available port") - raise OSError("No available port in the predefined range (19200-19999)") - - def start(self): - """启动TCP服务器""" - if self.running: - self.logger.warning(f"{self.server_name} is already running") - return - - try: - self.server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) - self.server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) - self.server_socket.settimeout(5) - self.server_socket.bind((self.host, self.port)) - self.server_socket.listen(10) - - self.running = True - self.server_thread = threading.Thread( - target=self._server_loop, name=f"{self.server_name}Thread" - ) - self.server_thread.daemon = True - self.server_thread.start() - - self.logger.info(f"{self.server_name} started on {self.host}:{self.port}") - - except Exception as e: - self.logger.error(f"Failed to start {self.server_name}: {e}") - self.running = False - raise - - def stop(self): - """停止TCP服务器""" - if not self.running: - return - - self.logger.info(f"Stopping {self.server_name}...") - self.running = False - - if self.server_socket: - self.server_socket.close() - - if self.server_thread and self.server_thread.is_alive(): - for _ in range(5): - self.server_thread.join(timeout=1.0) - if not self.server_thread.is_alive(): - break - else: - self.logger.warning(f"{self.server_name} thread did not stop gracefully") - - self.logger.info(f"{self.server_name} stopped") - - def _server_loop(self): - """TCP服务器主循环""" - try: - self.logger.debug(f"{self.server_name} loop started") - except Exception: - print(f"{self.server_name} loop started") - - while self.running: - try: - if not self.server_socket: - break - - # 检查socket是否仍然有效 - try: - result = self.server_socket.accept() - if result is None or len(result) != 2: - self.logger.warning("Socket accept returned invalid result") - break - client_socket, address = result - except ValueError as ve: - self.logger.warning(f"Socket accept unpacking error: {ve}") - break - - try: - self.logger.debug(f"New TCP client connected from {address}") - except Exception: - print(f"New TCP client connected from {address}") - - # 在新线程中处理客户端 - client_thread = threading.Thread( - target=self._handle_client, - args=(client_socket, address), - name=f"{self.server_name}Client-{address[0]}:{address[1]}", - ) - client_thread.daemon = True - client_thread.start() - except TimeoutError: - continue - except OSError as e: - if self.running: - try: - self.logger.error(f"Error accepting TCP connection: {e}") - except Exception: - print(f"Error accepting TCP connection: {e}") - break - except Exception as e: - if self.running: - # 使用print而不是logger来避免I/O错误 - print(f"Unexpected error in server loop: {e}") - break - - try: - self.logger.debug(f"{self.server_name} loop stopped") - except Exception: - print(f"{self.server_name} loop stopped") - - def _handle_client(self, client_socket: socket.socket, address: tuple): - """处理客户端连接和消息""" - try: - while self.running: - # 读取消息长度 - size_data = client_socket.recv(4) - if not size_data: - break - - message_size = int.from_bytes(size_data, byteorder="big") - if message_size <= 0 or message_size > 10 * 1024 * 1024: # 10MB limit - self.logger.warning(f"Invalid message size {message_size} from {address}") - break - - # 读取消息内容 - message_data = self._receive_full_message(client_socket, message_size) - if not message_data: - break - - # 处理消息 - try: - response = self._handle_message_data(message_data, address) - - # 发送响应 - if response: - self._send_response(client_socket, response) - - except Exception as e: - # 安全地记录错误，避免I/O错误 - try: - self.logger.error(f"Error processing message from {address}: {e}") - except Exception: - print(f"Error processing message from {address}: {e}") - # 发送错误响应 - error_response = self._create_error_response( - {"request_id": None}, - "ERR_INTERNAL_ERROR", - f"Internal server error: {str(e)}", - ) - self._send_response(client_socket, error_response) - - except Exception as e: - # 安全地记录错误，避免I/O错误 - try: - self.logger.error(f"Error handling TCP client {address}: {e}") - except Exception: - print(f"Error handling TCP client {address}: {e}") - finally: - try: - client_socket.close() - except Exception: - pass - try: - self.logger.debug(f"TCP client {address} disconnected") - except Exception: - pass - - def _receive_full_message( - self, client_socket: socket.socket, message_size: int - ) -> bytes | None: - """接收完整的消息数据""" - message_data = b"" - while len(message_data) < message_size: - chunk_size = min(message_size - len(message_data), 8192) - chunk = client_socket.recv(chunk_size) - if not chunk: - try: - self.logger.warning("Connection closed while receiving message") - except Exception: - print("Connection closed while receiving message") - return None - message_data += chunk - - return message_data - - @abstractmethod - def _handle_message_data( - self, message_data: bytes, client_address: tuple - ) -> dict[str, Any] | None: - """ - 处理接收到的消息数据（抽象方法） - - Args: - message_data: 原始消息数据 - client_address: 客户端地址 - - Returns: - 响应字典，如果返回None则不发送响应 - """ - pass - - def _send_response(self, client_socket: socket.socket, response: dict[str, Any] | bytes): - """发送响应到客户端""" - try: - # 确定响应数据格式 - if isinstance(response, dict): - response["cwd"] = self.server_cwd # 添加服务器当前工作目录 - serialized = self._serialize_response(response) - elif isinstance(response, bytes): - serialized = response - else: - # 其他类型，尝试序列化 - serialized = self._serialize_response(response) - - message_size = len(serialized) - - # 发送消息长度 - client_socket.send(message_size.to_bytes(4, byteorder="big")) - - # 发送消息内容 - client_socket.send(serialized) - - self.logger.debug(f"Sent response (size: {message_size})") - - except Exception as e: - self.logger.error(f"Error sending response: {e}") - - def _serialize_response(self, response: Any) -> bytes: - """序列化响应（默认使用pickle，子类可以重写）""" - return pickle.dumps(response) - - def _create_error_response( - self, original_message: dict[str, Any], error_code: str, error_message: str - ) -> dict[str, Any]: - """创建错误响应""" - return { - "type": f"{original_message.get('type', 'unknown')}_response", - "request_id": original_message.get("request_id"), - "timestamp": int(time.time()), - "status": "error", - "message": error_message, - "payload": {"error_code": error_code, "details": {}}, - } - - def get_server_info(self) -> dict[str, Any]: - """获取服务器信息""" - return { - "server_name": self.server_name, - "host": self.host, - "port": self.port, - "running": self.running, - "address": f"{self.host}:{self.port}", - } - - def __del__(self): - """析构函数，确保资源清理""" - try: - self.stop() - except Exception: - pass - - -class LocalTcpServer(BaseTcpServer): - """ - 本地TCP服务器，用于接收Ray Actor发送的数据 - 支持基于消息类型的多个处理器 - """ - - def __init__( - self, - host: str | None = None, - port: int | None = None, - default_handler: Callable[[dict[str, Any], tuple], dict[str, Any]] | None = None, - logger=None, - ): - """ - 初始化TCP服务器 - - Args: - host: 监听地址 - port: 监听端口 - default_handler: 默认消息处理回调函数，用于处理未知类型的消息 - logger: 日志记录器 - """ - super().__init__(host, port, logger, "LocalTcpServer") - - # 消息处理器字典：消息类型 -> 处理函数 - # Handler can return dict[str, Any] for response or None if no response needed - self.message_handlers: dict[ - str, Callable[[dict[str, Any], tuple], dict[str, Any] | None] - ] = {} - self.default_handler = default_handler - - # 添加锁保护处理器字典 - self._handlers_lock = threading.RLock() - - def _handle_message_data( - self, message_data: bytes, client_address: tuple - ) -> dict[str, Any] | None: - """处理接收到的消息数据""" - try: - # 反序列化消息 - message = pickle.loads(message_data) - return self._process_message(message, client_address) - except Exception as e: - self.logger.error(f"Error deserializing message from {client_address}: {e}") - return self._create_error_response( - {"request_id": None}, - "ERR_DESERIALIZATION_FAILED", - f"Failed to deserialize message: {str(e)}", - ) - - def _process_message( - self, message: dict[str, Any], client_address: tuple - ) -> dict[str, Any] | None: - """ - 处理接收到的消息，根据消息类型分发给对应的处理器 - - Args: - message: 接收到的消息字典 - client_address: 客户端地址 - - Returns: - 响应字典，如果处理器返回 None 则不发送响应 - """ - try: - # 尝试获取消息类型 - message_type = self._extract_message_type(message) - if message_type is None: - # 无法提取消息类型，使用默认处理器 - self.logger.warning( - "Could not extract message type from message, using default handler" - ) - return self._use_default_handler(message, client_address, None) - - self.logger.debug(f"Processing message type '{message_type}' from {client_address}") - - # 查找对应的处理器 - with self._handlers_lock: - handler = self.message_handlers.get(message_type, None) - - if handler is None: - # 没有找到对应的处理器，使用默认处理器 - self.logger.warning( - f"No handler found for message type '{message_type}', using default handler" - ) - return self._use_default_handler(message, client_address, message_type) - - try: - response = handler(message, client_address) - self.logger.debug(f"Message type '{message_type}' processed successfully") - return response - except Exception as e: - self.logger.error( - f"Error in handler for message type '{message_type}': {e}", - exc_info=True, - ) - return self._create_error_response(message, "ERR_HANDLER_FAILED", str(e)) - - except Exception as e: - self.logger.error(f"Error in message processing: {e}", exc_info=True) - return self._create_error_response(message, "ERR_PROCESSING_FAILED", str(e)) - - def _extract_message_type(self, message: dict[str, Any]) -> str | None: - """从消息中提取消息类型""" - if not isinstance(message, dict): - self.logger.warning(f"Message is not a dictionary: {type(message)}") - return None - - # 尝试多种可能的类型字段名 - type_fields = ["type", "message_type", "msg_type", "event_type", "command"] - - for field in type_fields: - if field in message: - msg_type = message[field] - if isinstance(msg_type, str) and msg_type.strip(): - return msg_type.strip() - - self.logger.debug(f"No valid type field found in message keys: {list(message.keys())}") - return None - - def _use_default_handler( - self, - message: dict[str, Any], - client_address: tuple, - message_type: str | None, - ) -> dict[str, Any] | None: - """使用默认处理器处理消息""" - if self.default_handler: - try: - response = self.default_handler(message, client_address) - self.logger.debug("Message processed by default handler") - return response - except Exception as e: - self.logger.error(f"Error in default handler: {e}", exc_info=True) - return self._create_error_response(message, "ERR_DEFAULT_HANDLER_FAILED", str(e)) - else: - self.logger.warning(f"No default handler set, ignoring message from {client_address}") - if message_type: - self.logger.info( - f"Consider registering a handler for message type '{message_type}'" - ) - return self._create_error_response( - message, "ERR_NO_HANDLER", "No handler available for this message type" - ) - - def _create_error_response( - self, original_message: dict[str, Any], error_code: str, error_message: str - ) -> dict[str, Any]: - """创建错误响应""" - return { - "type": f"{original_message.get('type', 'unknown')}_response", - "request_id": original_message.get("request_id"), - "env_name": original_message.get("env_name"), - "env_uuid": original_message.get("env_uuid"), - "timestamp": int(time.time()), - "status": "error", - "message": error_message, - "payload": {"error_code": error_code, "details": {}}, - } - - def _send_response(self, client_socket: socket.socket, response: dict[str, Any]): - """发送响应到客户端""" - try: - # 序列化响应 - if isinstance(response, dict): - response["cwd"] = self.server_cwd # 添加服务器当前工作目录 - serialized = pickle.dumps(response) - message_size = len(serialized) - - # 发送消息长度 - client_socket.send(message_size.to_bytes(4, byteorder="big")) - - # 发送消息内容 - client_socket.send(serialized) - - self.logger.debug(f"Sent response: {response.get('type')}") - - except Exception as e: - self.logger.error(f"Error sending response: {e}") - - def get_server_info(self) -> dict[str, Any]: - """获取服务器信息""" - with self._handlers_lock: - registered_types = list(self.message_handlers.keys()) - - base_info = super().get_server_info() - base_info.update( - { - "registered_message_types": registered_types, - "has_default_handler": self.default_handler is not None, - } - ) - return base_info - - ######################################################## - # handler registration # - ######################################################## - - def register_handler( - self, - message_type: str, - handler: Callable[[dict[str, Any], tuple], dict[str, Any] | None], - ): - """注册消息处理器 - - Args: - message_type: 消息类型标识 - handler: 消息处理函数，返回响应字典或None（如果不需要响应） - """ - with self._handlers_lock: - self.message_handlers[message_type] = handler - self.logger.info(f"Registered handler for message type: {message_type}") - - def set_default_handler( - self, handler: Callable[[dict[str, Any], tuple], dict[str, Any] | None] - ): - """设置默认消息处理器 - - Args: - handler: 默认消息处理函数，返回响应字典或None（如果不需要响应） - """ - self.default_handler = handler - self.logger.info("Default message handler set") - - def unregister_handler(self, message_type: str): - """注销消息处理器""" - with self._handlers_lock: - if message_type in self.message_handlers: - del self.message_handlers[message_type] - self.logger.info(f"Unregistered handler for message type: {message_type}") - else: - self.logger.warning(f"No handler found for message type: {message_type}") - - def get_registered_types(self) -> list[str]: - """获取已注册的消息类型列表""" - with self._handlers_lock: - return list(self.message_handlers.keys()) - - -# 使用示例 -if __name__ == "__main__": - - def handle_status_message(message: dict[str, Any], client_address: tuple) -> dict[str, Any]: - print(f"Status message from {client_address}: {message}") - return { - "type": "status_response", - "status": "success", - "message": "Status received", - } - - def handle_data_message(message: dict[str, Any], client_address: tuple) -> dict[str, Any]: - print(f"Data message from {client_address}: {message}") - return { - "type": "data_response", - "status": "success", - "message": "Data processed", - } - - def handle_unknown_message(message: dict[str, Any], client_address: tuple) -> dict[str, Any]: - print(f"Unknown message from {client_address}: {message}") - return { - "type": "unknown_response", - "status": "success", - "message": "Unknown message received", - } - - # 创建服务器 - server = LocalTcpServer(default_handler=handle_unknown_message) - - # 注册不同类型的处理器 - server.register_handler("status", handle_status_message) - server.register_handler("data", handle_data_message) - - # 启动服务器 - server.start() - - print(f"Server info: {server.get_server_info()}") - - try: - # 保持服务器运行 - while True: - time.sleep(1) - except KeyboardInterrupt: - print("Stopping server...") - server.stop() diff --git a/packages/sage-common/src/sage/common/utils/paths.py.deprecated b/packages/sage-common/src/sage/common/utils/paths.py.deprecated deleted file mode 100644 index daa83d595f..0000000000 --- a/packages/sage-common/src/sage/common/utils/paths.py.deprecated +++ /dev/null @@ -1,181 +0,0 @@ -""" -SAGE 路径管理 - 支持pip安装和开发环境 - -这个模块提供智能的路径管理，自动适应不同的使用场景： -1. 开发环境：使用项目根目录的 .sage/ -2. pip安装：使用用户home目录的 ~/.sage/ -3. 测试环境：可以通过环境变量覆盖 -""" - -import os -from pathlib import Path -from typing import Optional - - -def get_sage_output_dir() -> Path: - """ - 获取SAGE输出目录，自动适应不同环境 - - 优先级： - 1. 环境变量 SAGE_OUTPUT_DIR - 2. 如果在开发环境（存在packages/目录），使用 project_root/.sage/ - 3. 否则使用 ~/.sage/ - - Returns: - Path: SAGE输出目录路径 - """ - # 1. 检查环境变量 - env_dir = os.environ.get("SAGE_OUTPUT_DIR") - if env_dir: - sage_dir = Path(env_dir) - sage_dir.mkdir(parents=True, exist_ok=True) - return sage_dir - - # 2. 检查是否在开发环境中 - current_dir = Path.cwd() - project_root = find_project_root(current_dir) - - if project_root: - # 开发环境：使用项目本地的 .sage/ 目录 - sage_dir = project_root / ".sage" - else: - # pip安装环境：使用用户home目录 - sage_dir = Path.home() / ".sage" - - # 确保目录存在 - sage_dir.mkdir(parents=True, exist_ok=True) - - # 创建必要的子目录 - for subdir in ["logs", "reports", "temp", "cache", "coverage", "benchmarks"]: - (sage_dir / subdir).mkdir(exist_ok=True) - - return sage_dir - - -def find_project_root(start_path: Path) -> Optional[Path]: - """ - 从给定路径向上查找SAGE项目根目录 - - 通过查找特征目录/文件来识别SAGE项目根目录： - - packages/ 目录存在 - - _version.py 文件存在 - - pyproject.toml 中包含 sage 相关内容 - - Args: - start_path: 开始查找的路径 - - Returns: - Optional[Path]: 项目根目录路径，如果未找到则返回None - """ - current = start_path.resolve() - - # 向上查找，最多查找5层 - for _ in range(5): - # 检查是否是SAGE项目根目录 - if is_sage_project_root(current): - return current - - parent = current.parent - if parent == current: # 达到文件系统根目录 - break - current = parent - - return None - - -def is_sage_project_root(path: Path) -> bool: - """ - 检查给定路径是否是SAGE项目根目录 - - Args: - path: 要检查的路径 - - Returns: - bool: 是否是SAGE项目根目录 - """ - # 检查packages目录 - if not (path / "packages").is_dir(): - return False - - # 检查_version.py文件 - if not (path / "_version.py").is_file(): - return False - - # 可选：检查pyproject.toml中的项目信息 - pyproject_file = path / "pyproject.toml" - if pyproject_file.is_file(): - try: - content = pyproject_file.read_text(encoding='utf-8') - if "sage" in content.lower(): - return True - except Exception: - pass - - return True - - -def get_sage_logs_dir() -> Path: - """获取SAGE日志目录""" - return get_sage_output_dir() / "logs" - - -def get_sage_reports_dir() -> Path: - """获取SAGE报告目录""" - return get_sage_output_dir() / "reports" - - -def get_sage_temp_dir() -> Path: - """获取SAGE临时文件目录""" - return get_sage_output_dir() / "temp" - - -def get_sage_cache_dir() -> Path: - """获取SAGE缓存目录""" - return get_sage_output_dir() / "cache" - - -def get_sage_coverage_dir() -> Path: - """获取SAGE覆盖率报告目录""" - return get_sage_output_dir() / "coverage" - - -def get_sage_benchmarks_dir() -> Path: - """获取SAGE基准测试结果目录""" - return get_sage_output_dir() / "benchmarks" - - -def get_ray_temp_dir() -> Path: - """获取Ray临时文件目录""" - ray_dir = get_sage_temp_dir() / "ray" - ray_dir.mkdir(parents=True, exist_ok=True) - return ray_dir - - -def setup_environment_for_sage(): - """ - 为SAGE设置环境变量 - - 这个函数会设置必要的环境变量，确保各种工具使用正确的路径 - """ - sage_output_dir = get_sage_output_dir() - - # 设置环境变量 - os.environ["SAGE_OUTPUT_DIR"] = str(sage_output_dir) - os.environ["SAGE_LOGS_DIR"] = str(get_sage_logs_dir()) - os.environ["SAGE_TEMP_DIR"] = str(get_sage_temp_dir()) - - # 为Ray设置临时目录 - ray_temp_dir = get_ray_temp_dir() - os.environ["RAY_TMPDIR"] = str(ray_temp_dir) - - return { - "sage_output_dir": sage_output_dir, - "logs_dir": get_sage_logs_dir(), - "temp_dir": get_sage_temp_dir(), - "ray_temp_dir": ray_temp_dir, - } - - -# 自动设置环境 -if not os.environ.get("SAGE_OUTPUT_DIR"): - setup_environment_for_sage() diff --git a/packages/sage-common/src/sage/common/utils/results_collector.py b/packages/sage-common/src/sage/common/utils/results_collector.py deleted file mode 100644 index 6bc1974dc4..0000000000 --- a/packages/sage-common/src/sage/common/utils/results_collector.py +++ /dev/null @@ -1,315 +0,0 @@ -""" -Results Collector -================= - -通用结果收集器，用于在 Pipeline 执行过程中收集评测指标。 -线程安全实现，支持 Pipeline 并行运行。 - -使用示例: - from sage.common.utils.results_collector import ResultsCollector - - collector = ResultsCollector() - collector.reset() - - # 在 Operators 中添加结果 - collector.add_sample(sample_id=0, f1=0.35, compression_rate=2.5) - - # Pipeline 运行后获取结果 - results = collector.get_results() - # [{"sample_id": 0, "f1": 0.35, "compression_rate": 2.5, ...}, ...] - - aggregated = collector.get_aggregated() - # {"avg_f1": 0.35, "std_f1": 0.02, "avg_compression_rate": 2.5, ...} -""" - -from __future__ import annotations - -import json -import statistics -import threading -from pathlib import Path -from typing import Any - - -class ResultsCollector: - """ - 结果收集器 - 单例模式 - - 用于收集评测 Operators 产生的结果，支持： - - 线程安全的结果添加 - - 样本级和聚合级结果获取 - - JSON 导出 - """ - - _instance: ResultsCollector | None = None - _lock = threading.Lock() - - def __new__(cls) -> ResultsCollector: - """单例模式实现""" - if cls._instance is None: - with cls._lock: - if cls._instance is None: - instance = super().__new__(cls) - instance._init_internal() - cls._instance = instance - return cls._instance - - def _init_internal(self) -> None: - """内部初始化""" - self._results: dict[int | str, dict[str, Any]] = {} - self._data_lock = threading.Lock() - self._sample_counter = 0 - self._metadata: dict[str, Any] = {} - - def reset(self) -> None: - """ - 清空所有收集的结果 - - 在开始新的实验前调用此方法。 - """ - with self._data_lock: - self._results.clear() - self._sample_counter = 0 - self._metadata.clear() - - def add_sample( - self, - sample_id: int | str | None = None, - metrics: dict[str, Any] | None = None, - **kwargs: Any, - ) -> int | str: - """ - 添加单个样本的结果 - - Args: - sample_id: 样本 ID，如果为 None 则自动生成 - metrics: 指标字典 - **kwargs: 额外的指标（会合并到 metrics 中） - - Returns: - 使用的 sample_id - """ - with self._data_lock: - # 自动生成 sample_id - if sample_id is None: - sample_id = self._sample_counter - self._sample_counter += 1 - - # 合并 metrics 和 kwargs - all_metrics = metrics.copy() if metrics else {} - all_metrics.update(kwargs) - - # 如果该 sample_id 已存在，合并指标 - if sample_id in self._results: - self._results[sample_id].update(all_metrics) - else: - self._results[sample_id] = {"sample_id": sample_id, **all_metrics} - - return sample_id - - def update_sample( - self, - sample_id: int | str, - metrics: dict[str, Any] | None = None, - **kwargs: Any, - ) -> None: - """ - 更新已存在样本的指标 - - Args: - sample_id: 样本 ID - metrics: 要更新的指标字典 - **kwargs: 额外的指标 - """ - with self._data_lock: - if sample_id not in self._results: - self._results[sample_id] = {"sample_id": sample_id} - - if metrics: - self._results[sample_id].update(metrics) - self._results[sample_id].update(kwargs) - - def get_results(self) -> list[dict[str, Any]]: - """ - 获取所有样本的结果 - - Returns: - 样本结果列表，按 sample_id 排序 - """ - with self._data_lock: - sorted_results = sorted( - self._results.values(), - key=lambda x: ( - x.get("sample_id", 0) - if isinstance(x.get("sample_id"), int) - else hash(x.get("sample_id", "")) - ), - ) - return [r.copy() for r in sorted_results] - - def get_sample(self, sample_id: int | str) -> dict[str, Any] | None: - """ - 获取指定样本的结果 - - Args: - sample_id: 样本 ID - - Returns: - 样本结果字典，不存在则返回 None - """ - with self._data_lock: - result = self._results.get(sample_id) - return result.copy() if result else None - - def get_aggregated(self) -> dict[str, Any]: - """ - 获取聚合统计指标 - - Returns: - 聚合指标字典，包含各指标的 avg, std, min, max - """ - with self._data_lock: - if not self._results: - return {"num_samples": 0} - - results = list(self._results.values()) - - # 收集所有数值型指标 - metric_values: dict[str, list[float]] = {} - for result in results: - for key, value in result.items(): - if key == "sample_id": - continue - if isinstance(value, (int, float)) and not isinstance(value, bool): - if key not in metric_values: - metric_values[key] = [] - metric_values[key].append(float(value)) - - # 计算统计指标 - aggregated: dict[str, Any] = {"num_samples": len(results)} - - for metric_name, values in metric_values.items(): - if not values: - continue - - aggregated[f"avg_{metric_name}"] = statistics.mean(values) - - if len(values) > 1: - aggregated[f"std_{metric_name}"] = statistics.stdev(values) - else: - aggregated[f"std_{metric_name}"] = 0.0 - - aggregated[f"min_{metric_name}"] = min(values) - aggregated[f"max_{metric_name}"] = max(values) - - return aggregated - - def get_metric_values(self, metric_name: str) -> list[float]: - """ - 获取指定指标的所有值 - - Args: - metric_name: 指标名称 - - Returns: - 该指标的所有值列表 - """ - with self._data_lock: - values = [] - for result in self._results.values(): - if metric_name in result: - value = result[metric_name] - if isinstance(value, (int, float)) and not isinstance(value, bool): - values.append(float(value)) - return values - - def set_metadata(self, **kwargs: Any) -> None: - """ - 设置实验元数据 - - Args: - **kwargs: 元数据键值对 - """ - with self._data_lock: - self._metadata.update(kwargs) - - def get_metadata(self) -> dict[str, Any]: - """ - 获取实验元数据 - - Returns: - 元数据字典 - """ - with self._data_lock: - return self._metadata.copy() - - def export_json(self, path: str | Path, include_metadata: bool = True) -> None: - """ - 导出结果到 JSON 文件 - - Args: - path: 输出文件路径 - include_metadata: 是否包含元数据 - """ - path = Path(path) - path.parent.mkdir(parents=True, exist_ok=True) - - export_data: dict[str, Any] = { - "results": self.get_results(), - "aggregated": self.get_aggregated(), - } - - if include_metadata: - export_data["metadata"] = self.get_metadata() - - with open(path, "w", encoding="utf-8") as f: - json.dump(export_data, f, indent=2, ensure_ascii=False) - - @classmethod - def load_json(cls, path: str | Path) -> ResultsCollector: - """ - 从 JSON 文件加载结果 - - Args: - path: JSON 文件路径 - - Returns: - 填充了数据的 ResultsCollector 实例 - """ - collector = cls() - collector.reset() - - with open(path, encoding="utf-8") as f: - data = json.load(f) - - # 加载结果 - if "results" in data: - for result in data["results"]: - sample_id = result.pop("sample_id", None) - collector.add_sample(sample_id=sample_id, metrics=result) - - # 加载元数据 - if "metadata" in data: - collector.set_metadata(**data["metadata"]) - - return collector - - def __len__(self) -> int: - """返回收集的样本数量""" - with self._data_lock: - return len(self._results) - - def __repr__(self) -> str: - """字符串表示""" - return f"ResultsCollector(samples={len(self)})" - - -# 便捷访问函数 -def get_collector() -> ResultsCollector: - """ - 获取全局 ResultsCollector 实例 - - Returns: - ResultsCollector 单例 - """ - return ResultsCollector() diff --git a/packages/sage-common/src/sage/common/utils/serialization/__init__.py b/packages/sage-common/src/sage/common/utils/serialization/__init__.py deleted file mode 100644 index a1fc6ac446..0000000000 --- a/packages/sage-common/src/sage/common/utils/serialization/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -""" -SAGE - Streaming-Augmented Generative Execution -""" - -# 直接从本包的_version模块加载版本信息 -try: - from sage.common._version import __author__, __email__, __version__ -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" diff --git a/packages/sage-common/src/sage/common/utils/serialization/config.py b/packages/sage-common/src/sage/common/utils/serialization/config.py deleted file mode 100644 index d595ae512b..0000000000 --- a/packages/sage-common/src/sage/common/utils/serialization/config.py +++ /dev/null @@ -1,63 +0,0 @@ -""" -序列化配置和常量定义 -""" - -import io -import threading - -# 不可序列化类型黑名单 -BLACKLIST = [ - threading.Thread, # 线程 - io.TextIOWrapper, # 文件句柄类型 - type(threading.Lock()), # 锁 - type(threading.RLock()), # 递归锁 - threading.Event, # 事件 - threading.Condition, # 条件变量 -] - -# 序列化时需要排除的属性名 -ATTRIBUTE_BLACKLIST = { - "logger", # 日志对象 - "_logger", # 私有日志对象 - "server_socket", # socket对象 - "server_thread", # 线程对象 - "_server_thread", # 私有线程对象 - "client_socket", # socket对象 - "__weakref__", # 弱引用 - "runtime_context", # 运行时上下文 - # 'memory_collection', # 内存集合（通常是Ray Actor句柄） - "env", # 环境引用（避免循环引用） - # '_dag_node_factory', # 工厂对象 - # '_operator_factory', # 工厂对象 - # '_function_factory', # 工厂对象 -} - -# 哨兵值，表示应该跳过的值 -SKIP_VALUE = object() - - -# Ray相关的专用排除列表 -RAY_TRANSFORMATION_EXCLUDE_ATTRS = [ - "logger", - "_logger", # 日志对象 - "env", # 环境引用（避免循环引用） - "runtime_context", # 运行时上下文 - "_dag_node_factory", # 懒加载工厂 - "_operator_factory", # 懒加载工厂 - "_function_factory", # 懒加载工厂 - "server_socket", # socket对象 - "server_thread", - "_server_thread", # 线程对象 -] - -RAY_OPERATOR_EXCLUDE_ATTRS = [ - "logger", - "_logger", - "runtime_context", - "emit_context", - "server_socket", - "client_socket", - "server_thread", - "_server_thread", - # 注意：__weakref__ 是内置属性，不能简单移除，所以不包含在这里 -] diff --git a/packages/sage-common/src/sage/common/utils/serialization/dill.py b/packages/sage-common/src/sage/common/utils/serialization/dill.py deleted file mode 100644 index 8333060364..0000000000 --- a/packages/sage-common/src/sage/common/utils/serialization/dill.py +++ /dev/null @@ -1,685 +0,0 @@ -import inspect -import os -import threading -from collections.abc import Mapping, Sequence -from collections.abc import Set as AbstractSet -from typing import Any - -import dill - - -class SerializationError(Exception): - """序列化相关错误""" - - pass - - -# 不可序列化类型黑名单 -_BLACKLIST = [ - threading.Thread, # 线程 - threading.Event, # 事件 - threading.Condition, # 条件变量 -] - -# 在运行时添加锁类型，因为它们不能直接引用类 -try: - import tempfile - - with tempfile.NamedTemporaryFile() as tmp_file: - _BLACKLIST.append(type(tmp_file)) # 文件句柄 -except Exception: - pass - -try: - _BLACKLIST.append(type(threading.Lock())) # 锁 - _BLACKLIST.append(type(threading.RLock())) # 递归锁 -except Exception: - pass - -# 序列化时需要排除的属性名 -_ATTRIBUTE_BLACKLIST = { - "logger", # 日志对象 - "_logger", # 私有日志对象 - "server_socket", # socket对象 - "server_thread", # 线程对象 - "_server_thread", # 私有线程对象 - "client_socket", # socket对象 - "__weakref__", # 弱引用 - "runtime_context", # 运行时上下文 - # 'memory_collection', # 内存集合（通常是Ray Actor句柄） - "env", # 环境引用（避免循环引用） - # '_dag_node_factory', # 工厂对象 - # '_operator_factory', # 工厂对象 - # '_function_factory', # 工厂对象 -} - -# 哨兵值，表示应该跳过的值 -_SKIP_VALUE = object() - - -def _gather_attrs(obj): - """枚举实例 __dict__ 和 @property 属性。""" - attrs = dict(getattr(obj, "__dict__", {})) - for name, _prop in inspect.getmembers(type(obj), lambda x: isinstance(x, property)): - try: - attrs[name] = getattr(obj, name) - except Exception: - pass - return attrs - - -def _filter_attrs(attrs, include, exclude): - """根据 include/exclude 过滤字段字典。""" - if include: - return {k: attrs[k] for k in include if k in attrs} - - # 合并用户定义的exclude和系统默认的exclude - all_exclude = set(exclude or []) | _ATTRIBUTE_BLACKLIST - return {k: v for k, v in attrs.items() if k not in all_exclude} - - -def _should_skip(v): - """判断对象是否应该跳过序列化""" - # 检查黑名单 - 修改为更精确的检查 - for _i, blacklisted_type in enumerate(_BLACKLIST): - if isinstance(v, blacklisted_type): - # print(f"Skipping blacklisted instance {i}: {type(v)}, {v}") - return True - - # 检查是否是模块（通常不应该序列化） - if inspect.ismodule(v): - # print(f"Skipping module: {v}") - return True - - return False - - -def _preprocess_for_dill(obj, _seen=None, _object_map=None): - """ - 递归预处理对象，清理不可序列化的内容，为dill序列化做准备。 - - Args: - obj: 要预处理的对象 - _seen: 已处理对象的集合，用于处理循环引用 - _object_map: 对象映射表，保持引用完整性 {original_obj_id: new_obj} - - Returns: - 预处理后的对象，可以安全地交给dill序列化 - """ - # print(f"_preprocess_for_dill called for object: {obj}") - if _seen is None: - _seen = set() - if _object_map is None: - _object_map = {} - - # 防止循环引用 + 对象引用去重 - obj_id = id(obj) - - # 检查是否已经处理过这个对象（引用去重） - if obj_id in _object_map: - # print(f"Reusing existing mapped object for id {obj_id}: {obj}") - return _object_map[obj_id] - - if obj_id in _seen: - # 这是一个循环引用，但我们还没有创建映射 - # 对于循环引用，我们需要继续处理，但要小心避免无限递归 - # print(f"Circular reference detected for object: {obj}") - return _SKIP_VALUE - - # 基本类型直接返回 - if isinstance(obj, (int, float, str, bool, type(None))): - return obj - - # 类对象可以直接被dill序列化，不需要预处理 - if inspect.isclass(obj): - # print(f"Processing class object: {obj}") - return obj - - # 函数对象也可以直接被dill序列化 - if inspect.isfunction(obj) or inspect.ismethod(obj): - # print(f"Processing function object: {obj}") - return obj - - # 检查是否应该跳过 - if _should_skip(obj): - return _SKIP_VALUE - - # 处理字典 - if isinstance(obj, Mapping): - _seen.add(obj_id) - try: - cleaned = {} - for k, v in obj.items(): - if not _should_skip(k) and not _should_skip(v): - cleaned_k = _preprocess_for_dill(k, _seen, _object_map) - cleaned_v = _preprocess_for_dill(v, _seen, _object_map) - if cleaned_k is not _SKIP_VALUE and ( - (cleaned_v is not _SKIP_VALUE) or (cleaned_v is None) - ): - cleaned[cleaned_k] = cleaned_v - return cleaned - finally: - _seen.remove(obj_id) - - # 处理序列（列表、元组等） - if isinstance(obj, Sequence) and not isinstance(obj, str): - _seen.add(obj_id) - try: - cleaned = [] - for item in obj: - if not _should_skip(item): - cleaned_item = _preprocess_for_dill(item, _seen, _object_map) - if cleaned_item is not _SKIP_VALUE: - cleaned.append(cleaned_item) - return type(obj)(cleaned) if cleaned else [] # type: ignore[call-overload] - finally: - _seen.remove(obj_id) - - # 处理集合 - if isinstance(obj, AbstractSet): - _seen.add(obj_id) - try: - cleaned = set() - for item in obj: - if not _should_skip(item): - cleaned_item = _preprocess_for_dill(item, _seen, _object_map) - if cleaned_item is not _SKIP_VALUE: - cleaned.add(cleaned_item) - return type(obj)(cleaned) if cleaned else set() # type: ignore[call-overload] - finally: - _seen.remove(obj_id) - - # 处理复杂对象 - if hasattr(obj, "__dict__"): - # print(f"Processing complex object: {obj}") - # print(f"dict is {obj.__dict__}") - _seen.add(obj_id) - try: - # 创建一个新的对象实例 - obj_class = type(obj) - - # 尝试创建空实例 - try: - cleaned_obj = obj_class.__new__(obj_class) # type: ignore[call-overload] - except Exception: - # 如果无法创建空实例，返回原对象让dill处理 - return obj - - # 将新创建的对象加入映射表，确保引用完整性 - _object_map[obj_id] = cleaned_obj - - # 获取和过滤属性 - custom_include = getattr(obj.__class__, "__state_include__", []) - custom_exclude = getattr(obj.__class__, "__state_exclude__", []) - # if len(custom_exclude) is not 0: - # print(f"custom_exclude is {custom_exclude}") - # 一般不用include字段，只用exclude字段就行了 - - attrs = _gather_attrs(obj) - # if len(custom_exclude) is not 0: - # print(f"attrs is {attrs}") - - filtered_attrs = _filter_attrs(attrs, custom_include, custom_exclude) - # if len(custom_exclude) is not 0: - # print(f"filtered_attrs is {filtered_attrs}") - - # 递归清理属性 - for attr_name, attr_value in filtered_attrs.items(): - # print(f"Processing attribute: {attr_name} = {attr_value}") - if not _should_skip(attr_value): - # print(f"Cleaning attribute: {attr_name}") - cleaned_value = _preprocess_for_dill(attr_value, _seen, _object_map) - if cleaned_value is not _SKIP_VALUE: - try: - setattr(cleaned_obj, attr_name, cleaned_value) - except Exception: - # 忽略设置失败的属性 - pass - - return cleaned_obj - finally: - _seen.remove(obj_id) - - # 对于其他对象，直接返回给dill处理 - return obj - - -def _postprocess_from_dill(obj, _seen=None): - """递归后处理从dill反序列化的对象，清理哨兵值。""" - # print(f"_postprocess_from_dill called for object: {obj}") - if _seen is None: - _seen = set() - - # 防止循环引用 - obj_id = id(obj) - if obj_id in _seen: - return obj - - # 基本类型直接返回 - if isinstance(obj, (int, float, str, bool, type(None))): - return obj - - # 跳过哨兵值 - if obj is _SKIP_VALUE: - return None - - # 处理字典 - if isinstance(obj, Mapping): - _seen.add(obj_id) - try: - cleaned = {} - for k, v in obj.items(): - # print(f"Processing dict item: {k} = {v}") - # 修复：只过滤掉哨兵值，保留所有合法值（包括None、False、0等） - if k is not _SKIP_VALUE and v is not _SKIP_VALUE: - cleaned_k = _postprocess_from_dill(k, _seen) - cleaned_v = _postprocess_from_dill(v, _seen) - # 保留所有值，包括None、False、0、空字典等 - cleaned[cleaned_k] = cleaned_v - # print(f"Cleaned dict item: {cleaned_k} = {cleaned_v}") - return cleaned - finally: - _seen.remove(obj_id) - - # 处理序列 - if isinstance(obj, Sequence) and not isinstance(obj, str): - _seen.add(obj_id) - try: - cleaned = [] - for item in obj: - if item is not _SKIP_VALUE: - cleaned_item = _postprocess_from_dill(item, _seen) - # 保留所有值，包括None、False、0等 - cleaned.append(cleaned_item) - return type(obj)(cleaned) # type: ignore[call-overload] - finally: - _seen.remove(obj_id) - - # 处理集合 - if isinstance(obj, AbstractSet): - _seen.add(obj_id) - try: - cleaned = set() - for item in obj: - if item is not _SKIP_VALUE: - cleaned_item = _postprocess_from_dill(item, _seen) - # 集合中不能包含None，但可以包含False、0等 - if cleaned_item is not None: - cleaned.add(cleaned_item) - return type(obj)(cleaned) # type: ignore[call-overload] - finally: - _seen.remove(obj_id) - - # 处理复杂对象 - if hasattr(obj, "__dict__"): - _seen.add(obj_id) - try: - # 递归清理属性 - for attr_name, attr_value in list(obj.__dict__.items()): - if attr_value is _SKIP_VALUE: - # 删除哨兵值属性 - try: - delattr(obj, attr_name) - except Exception: - pass - else: - # 递归清理属性值，保留所有合法值 - cleaned_value = _postprocess_from_dill(attr_value, _seen) - try: - setattr(obj, attr_name, cleaned_value) - except Exception: - pass - - return obj - finally: - _seen.remove(obj_id) - - return obj - - -class UniversalSerializer: - """基于dill的通用序列化器，预处理清理不可序列化内容""" - - @staticmethod - def serialize_object( - obj: Any, - include: list[str] | None = None, - exclude: list[str] | None = None, - ) -> bytes: - """ - 序列化任意对象 - - Args: - obj: 要序列化的对象 - include: 包含的属性列表 - exclude: 排除的属性列表 - - Returns: - 序列化后的字节数据 - """ - if dill is None: - raise SerializationError( - "dill is required for serialization. Install with: pip install dill" - ) - - try: - # 预处理对象，清理不可序列化的内容 - cleaned_obj = _preprocess_for_dill(obj) - - # 使用dill序列化 - return dill.dumps(cleaned_obj) - - except Exception as e: - raise SerializationError(f"Object serialization failed: {e}") - - @staticmethod - def deserialize_object(data: bytes) -> Any: - """ - 反序列化对象 - - Args: - data: 序列化的字节数据 - - Returns: - 反序列化后的对象 - """ - if dill is None: - raise SerializationError( - "dill is required for deserialization. Install with: pip install dill" - ) - - try: - # 使用dill反序列化 - obj = dill.loads(data) - - # 后处理对象，清理哨兵值 - return _postprocess_from_dill(obj) - - except Exception as e: - raise SerializationError(f"Object deserialization failed: {e}") - - @staticmethod - def save_object_state( - obj: Any, - path: str, - include: list[str] | None = None, - exclude: list[str] | None = None, - ): - """将对象状态保存到文件""" - serialized_data = UniversalSerializer.serialize_object(obj, include, exclude) - - os.makedirs(os.path.dirname(path), exist_ok=True) - with open(path, "wb") as f: - f.write(serialized_data) - - @staticmethod - def load_object_from_file(path: str) -> Any: - """从文件加载对象""" - if not os.path.isfile(path): - raise FileNotFoundError(f"File not found: {path}") - - with open(path, "rb") as f: - data = f.read() - - return UniversalSerializer.deserialize_object(data) - - @staticmethod - def load_object_state(obj: Any, path: str) -> bool: - """从文件加载对象状态到现有对象""" - if not os.path.isfile(path): - return False - - try: - # 加载序列化的对象 - loaded_obj = UniversalSerializer.load_object_from_file(path) - - # 检查类型是否匹配 - if type(obj) is not type(loaded_obj): - return False - - # 复制属性 - if hasattr(loaded_obj, "__dict__"): - # 检查对象的include/exclude配置 - include = getattr(obj, "__state_include__", []) - exclude = getattr(obj, "__state_exclude__", []) - - for attr_name, attr_value in loaded_obj.__dict__.items(): - # 应用include/exclude过滤 - if include and attr_name not in include: - continue - if attr_name in (exclude or []): - continue - - try: - setattr(obj, attr_name, attr_value) - except Exception: - pass - - return True - - except Exception: - return False - - -# 便捷函数 -def serialize_object( - obj: Any, include: list[str] | None = None, exclude: list[str] | None = None -) -> bytes: - """序列化对象的便捷函数""" - return UniversalSerializer.serialize_object(obj, include, exclude) - - -def deserialize_object(data: bytes) -> Any: - """反序列化对象的便捷函数""" - return UniversalSerializer.deserialize_object(data) - - -def save_object_state( - obj: Any, - path: str, - include: list[str] | None = None, - exclude: list[str] | None = None, -): - """保存对象状态的便捷函数""" - return UniversalSerializer.save_object_state(obj, path, include, exclude) - - -def load_object_from_file(path: str) -> Any: - """从文件加载对象的便捷函数""" - return UniversalSerializer.load_object_from_file(path) - - -def load_object_state(obj: Any, path: str) -> bool: - """加载对象状态的便捷函数""" - return UniversalSerializer.load_object_state(obj, path) - - -# 向后兼容的函数 -def pack_object( - obj: Any, include: list[str] | None = None, exclude: list[str] | None = None -) -> bytes: - """打包对象的便捷函数（向后兼容）""" - return serialize_object(obj, include, exclude) - - -def unpack_object(data: bytes) -> Any: - """解包对象的便捷函数（向后兼容）""" - return deserialize_object(data) - - -def trim_object_for_ray( - obj: Any, include: list[str] | None = None, exclude: list[str] | None = None -) -> Any: - """ - 为Ray远程调用预处理对象，移除不可序列化的内容 - - 这个函数只做清理工作，不进行实际的序列化，让Ray自己处理序列化过程。 - 适用于在ray.remote调用前清理对象，避免序列化错误。 - - Args: - obj: 要预处理的对象 - include: 包含的属性列表（如果指定，只保留这些属性） - exclude: 排除的属性列表（这些属性将被移除） - - Returns: - 清理后的对象，可以安全地传递给Ray进行序列化 - - Example: - # 清理transformation对象用于Ray调用 - cleaned_trans = trim_object_for_ray(transformation, - exclude=['logger', 'env', '_operator_factory']) - - # 现在可以安全地传递给Ray - result = ray_actor.process_transformation.remote(cleaned_trans) - """ - try: - # 使用现有的预处理函数，但不进行dill序列化 - cleaned_obj = _preprocess_for_dill(obj) - - # 如果有额外的include/exclude需求，再次过滤 - if cleaned_obj is not _SKIP_VALUE and hasattr(cleaned_obj, "__dict__"): - # 应用用户指定的include/exclude - if include or exclude: - attrs = _gather_attrs(cleaned_obj) - filtered_attrs = _filter_attrs(attrs, include, exclude) - - # 创建新对象并设置过滤后的属性 - obj_class = type(cleaned_obj) - try: - final_obj = obj_class.__new__(obj_class) # type: ignore[call-overload] - for attr_name, attr_value in filtered_attrs.items(): - try: - setattr(final_obj, attr_name, attr_value) - except Exception: - pass # 忽略设置失败的属性 - return final_obj - except Exception: - # 如果无法创建新实例，返回原对象 - return cleaned_obj - - return cleaned_obj if cleaned_obj is not _SKIP_VALUE else None - - except Exception as e: - # 如果预处理失败，返回None或抛出异常 - raise SerializationError(f"Object trimming for Ray failed: {e}") - - -class RayObjectTrimmer: - """专门用于Ray远程调用的对象预处理器""" - - @staticmethod - def trim_for_remote_call( - obj: Any, - include: list[str] | None = None, - exclude: list[str] | None = None, - deep_clean: bool = True, - ) -> Any: - """ - 为Ray远程调用准备对象 - - Args: - obj: 要清理的对象 - include: 只保留这些属性 - exclude: 排除这些属性 - deep_clean: 是否进行深度清理（递归处理嵌套对象） - - Returns: - 清理后可以传递给Ray的对象 - """ - if not deep_clean: - # 浅层清理：只处理顶层对象的属性 - if hasattr(obj, "__dict__"): - attrs = _gather_attrs(obj) - filtered_attrs = _filter_attrs(attrs, include, exclude) - - obj_class = type(obj) - try: - cleaned_obj = obj_class.__new__(obj_class) # type: ignore[call-overload] - for attr_name, attr_value in filtered_attrs.items(): - if not _should_skip(attr_value): - try: - setattr(cleaned_obj, attr_name, attr_value) - except Exception: - pass - return cleaned_obj - except Exception: - return obj - return obj - else: - # 深度清理：使用完整的预处理流程 - return trim_object_for_ray(obj, include, exclude) - - @staticmethod - def trim_transformation_for_ray(transformation_obj) -> Any: - """ - 专门为Transformation对象定制的清理方法 - 移除常见的不可序列化属性 - """ - exclude_attrs = [ - "logger", - "_logger", # 日志对象 - "env", # 环境引用（避免循环引用） - "runtime_context", # 运行时上下文 - "_dag_node_factory", # 懒加载工厂 - "_operator_factory", # 懒加载工厂 - "_function_factory", # 懒加载工厂 - "server_socket", # socket对象 - "server_thread", - "_server_thread", # 线程对象 - ] - - return RayObjectTrimmer.trim_for_remote_call(transformation_obj, exclude=exclude_attrs) - - @staticmethod - def trim_operator_for_ray(operator_obj) -> Any: - """ - 专门为Operator对象定制的清理方法 - """ - exclude_attrs = [ - "logger", - "_logger", - "runtime_context", - "emit_context", - "server_socket", - "client_socket", - "server_thread", - "_server_thread", - "__weakref__", - ] - - return RayObjectTrimmer.trim_for_remote_call(operator_obj, exclude=exclude_attrs) - - @staticmethod - def validate_ray_serializable(obj: Any, max_depth: int = 3) -> dict[str, Any]: - """ - 验证对象是否可以被Ray序列化 - - Args: - obj: 要验证的对象 - max_depth: 最大检查深度 - - Returns: - 验证结果字典，包含是否可序列化和问题列表 - """ - import ray - - result = {"is_serializable": False, "issues": [], "size_estimate": 0} - - try: - # 尝试Ray的内部序列化 - serialized = ray.cloudpickle.dumps(obj) # type: ignore[attr-defined] - result["is_serializable"] = True - result["size_estimate"] = len(serialized) - - except Exception as e: - result["issues"].append(f"Ray serialization failed: {str(e)}") - - # 尝试识别具体的问题 - if hasattr(obj, "__dict__"): - for attr_name, attr_value in obj.__dict__.items(): - if _should_skip(attr_value): - result["issues"].append( - f"Problematic attribute: {attr_name} = {type(attr_value)}" - ) - - return result diff --git a/packages/sage-common/src/sage/common/utils/serialization/exceptions.py b/packages/sage-common/src/sage/common/utils/serialization/exceptions.py deleted file mode 100644 index e7f6c20c73..0000000000 --- a/packages/sage-common/src/sage/common/utils/serialization/exceptions.py +++ /dev/null @@ -1,9 +0,0 @@ -""" -序列化相关异常定义 -""" - - -class SerializationError(Exception): - """序列化相关错误""" - - pass diff --git a/packages/sage-common/src/sage/common/utils/serialization/preprocessor.py b/packages/sage-common/src/sage/common/utils/serialization/preprocessor.py deleted file mode 100644 index 3b3e8a8fc5..0000000000 --- a/packages/sage-common/src/sage/common/utils/serialization/preprocessor.py +++ /dev/null @@ -1,353 +0,0 @@ -""" -对象预处理器 - 处理序列化前的对象清理 -""" - -import inspect -from collections.abc import Mapping, Sequence -from collections.abc import Set as AbstractSet -from typing import Any - -from .config import ATTRIBUTE_BLACKLIST, BLACKLIST, SKIP_VALUE - - -def gather_attrs(obj) -> dict[str, Any]: - """枚举实例 __dict__ 和 @property 属性。""" - attrs = dict(getattr(obj, "__dict__", {})) - for name, _prop in inspect.getmembers(type(obj), lambda x: isinstance(x, property)): - try: - attrs[name] = getattr(obj, name) - except Exception: - pass - return attrs - - -def filter_attrs( - attrs: dict[str, Any], include: list[str] | None, exclude: list[str] | None -) -> dict[str, Any]: - """根据 include/exclude 过滤字段字典。""" - if include: - # 如果指定了include，只保留include中的属性，忽略默认的blacklist - return {k: attrs[k] for k in include if k in attrs} - - # 合并用户定义的exclude和系统默认的exclude - all_exclude = set(exclude or []) | ATTRIBUTE_BLACKLIST - - # 过滤掉不能设置的特殊属性 - UNSETABLE_ATTRS = {"__weakref__", "__dict__", "__class__"} - - return {k: v for k, v in attrs.items() if k not in all_exclude and k not in UNSETABLE_ATTRS} - - -def should_skip(obj: Any) -> bool: - """判断对象是否应该跳过序列化""" - # 检查黑名单 - 修改为更精确的检查 - for blacklisted_type in BLACKLIST: - try: - if isinstance(obj, blacklisted_type): - # print(f"Skipping blacklisted instance: {type(obj)}, {obj}") - return True - except (TypeError, AttributeError): - # 某些类型检查可能失败，继续检查其他类型 - continue - - # 检查是否是模块（通常不应该序列化） - if inspect.ismodule(obj): - # print(f"Skipping module: {obj}") - return True - - # 额外检查：特定类型名称匹配（用于处理类型检查失败的情况） - obj_type_name = type(obj).__name__ - if obj_type_name in ("lock", "_thread.lock", "LockType", "_TemporaryFileWrapper"): - return True - - return False - - -def has_circular_reference(obj: Any, _seen: set[int] | None = None, max_depth: int = 10) -> bool: - """检查对象是否包含循环引用""" - if _seen is None: - _seen = set() - - if max_depth <= 0: - return False - - obj_id = id(obj) - if obj_id in _seen: - return True - - # 基本类型不会有循环引用 - if isinstance(obj, (int, float, str, bool, type(None))): - return False - - _seen.add(obj_id) - try: - # 检查字典 - if isinstance(obj, Mapping): - for k, v in obj.items(): - if has_circular_reference(k, _seen, max_depth - 1) or has_circular_reference( - v, _seen, max_depth - 1 - ): - return True - - # 检查序列 - elif isinstance(obj, Sequence) and not isinstance(obj, str): - for item in obj: - if has_circular_reference(item, _seen, max_depth - 1): - return True - - # 检查集合 - elif isinstance(obj, AbstractSet): - for item in obj: - if has_circular_reference(item, _seen, max_depth - 1): - return True - - # 检查复杂对象 - elif hasattr(obj, "__dict__"): - for attr_value in obj.__dict__.values(): - if has_circular_reference(attr_value, _seen, max_depth - 1): - return True - - return False - finally: - _seen.remove(obj_id) - - -def preprocess_for_dill(obj: Any, _seen: set[int] | None = None) -> Any: - """ - 递归预处理对象，清理不可序列化的内容，为dill序列化做准备。 - - Args: - obj: 要预处理的对象 - _seen: 已处理对象的集合，用于处理循环引用 - - Returns: - 预处理后的对象，可以安全地交给dill序列化 - """ - # print(f"preprocess_for_dill called for object: {obj}") - if _seen is None: - _seen = set() - - # 防止循环引用 - 如果检测到循环引用，直接返回原对象让dill处理 - obj_id = id(obj) - if obj_id in _seen: - return obj - - # 对于复杂对象，先检查是否有循环引用 - if hasattr(obj, "__dict__") and has_circular_reference(obj): - # 如果有循环引用，直接返回原对象让dill处理 - return obj - - # 基本类型直接返回 - if isinstance(obj, (int, float, str, bool, type(None))): - return obj - - # 类对象可以直接被dill序列化，不需要预处理 - if inspect.isclass(obj): - # print(f"Processing class object: {obj}") - return obj - - # 函数对象也可以直接被dill序列化 - if inspect.isfunction(obj) or inspect.ismethod(obj): - # print(f"Processing function object: {obj}") - return obj - - # 检查是否应该跳过 - if should_skip(obj): - return SKIP_VALUE - - # 处理字典 - if isinstance(obj, Mapping): - _seen.add(obj_id) - try: - cleaned = {} - for k, v in obj.items(): - if not should_skip(k) and not should_skip(v): - cleaned_k = preprocess_for_dill(k, _seen) - cleaned_v = preprocess_for_dill(v, _seen) - if cleaned_k is not SKIP_VALUE and ( - (cleaned_v is not SKIP_VALUE) or (cleaned_v is None) - ): - cleaned[cleaned_k] = cleaned_v - return cleaned - finally: - _seen.remove(obj_id) - - # 处理序列（列表、元组等） - if isinstance(obj, Sequence) and not isinstance(obj, str): - _seen.add(obj_id) - try: - cleaned = [] - for item in obj: - if not should_skip(item): - cleaned_item = preprocess_for_dill(item, _seen) - if cleaned_item is not SKIP_VALUE: - cleaned.append(cleaned_item) - # 保持原始类型：如果是元组，返回元组；如果是列表，返回列表 - if isinstance(obj, tuple): - return tuple(cleaned) if cleaned else () - else: - return type(obj)(cleaned) if cleaned else [] # type: ignore[call-overload] - finally: - _seen.remove(obj_id) - - # 处理集合 - if isinstance(obj, AbstractSet): - _seen.add(obj_id) - try: - cleaned = set() - for item in obj: - if not should_skip(item): - cleaned_item = preprocess_for_dill(item, _seen) - if cleaned_item is not SKIP_VALUE: - cleaned.add(cleaned_item) - return type(obj)(cleaned) if cleaned else set() # type: ignore[call-overload] - finally: - _seen.remove(obj_id) - - # 处理复杂对象 - if hasattr(obj, "__dict__"): - # print(f"Processing complex object: {obj}") - # print(f"dict is {obj.__dict__}") - _seen.add(obj_id) - try: - # 创建一个新的对象实例 - obj_class = type(obj) - - # 尝试创建空实例 - try: - cleaned_obj = obj_class.__new__(obj_class) # type: ignore[call-overload] - except Exception: - # 如果无法创建空实例，返回原对象让dill处理 - return obj - - # 获取和过滤属性 - custom_include = getattr(obj.__class__, "__state_include__", []) - custom_exclude = getattr(obj.__class__, "__state_exclude__", []) - # if len(custom_exclude) is not 0: - # print(f"custom_exclude is {custom_exclude}") - # 一般不用include字段，只用exclude字段就行了 - - attrs = gather_attrs(obj) - # if len(custom_exclude) is not 0: - # print(f"attrs is {attrs}") - - filtered_attrs = filter_attrs(attrs, custom_include, custom_exclude) - # if len(custom_exclude) is not 0: - # print(f"filtered_attrs is {filtered_attrs}") - - # 递归清理属性 - for attr_name, attr_value in filtered_attrs.items(): - # print(f"Processing attribute: {attr_name} = {attr_value}") - if not should_skip(attr_value): - # print(f"Cleaning attribute: {attr_name}") - cleaned_value = preprocess_for_dill(attr_value, _seen) - if cleaned_value is not SKIP_VALUE: - try: - setattr(cleaned_obj, attr_name, cleaned_value) - except Exception: - # 忽略设置失败的属性 - pass - - return cleaned_obj - finally: - _seen.remove(obj_id) - - # 对于其他对象，直接返回给dill处理 - return obj - - -def postprocess_from_dill(obj: Any, _seen: set[int] | None = None) -> Any: - """递归后处理从dill反序列化的对象，清理哨兵值。""" - # print(f"postprocess_from_dill called for object: {obj}") - if _seen is None: - _seen = set() - - # 防止循环引用 - obj_id = id(obj) - if obj_id in _seen: - return obj - - # 基本类型直接返回 - if isinstance(obj, (int, float, str, bool, type(None))): - return obj - - # 跳过哨兵值 - if obj is SKIP_VALUE: - return None - - # 处理字典 - if isinstance(obj, Mapping): - _seen.add(obj_id) - try: - cleaned = {} - for k, v in obj.items(): - # print(f"Processing dict item: {k} = {v}") - # 修复：只过滤掉哨兵值，保留所有合法值（包括None、False、0等） - if k is not SKIP_VALUE and v is not SKIP_VALUE: - cleaned_k = postprocess_from_dill(k, _seen) - cleaned_v = postprocess_from_dill(v, _seen) - # 保留所有值，包括None、False、0、空字典等 - cleaned[cleaned_k] = cleaned_v - # print(f"Cleaned dict item: {cleaned_k} = {cleaned_v}") - return cleaned - finally: - _seen.remove(obj_id) - - # 处理序列 - if isinstance(obj, Sequence) and not isinstance(obj, str): - _seen.add(obj_id) - try: - cleaned = [] - for item in obj: - if item is not SKIP_VALUE: - cleaned_item = postprocess_from_dill(item, _seen) - # 保留所有值，包括None、False、0等 - cleaned.append(cleaned_item) - # 保持原始类型：如果是元组，返回元组；如果是列表，返回列表 - if isinstance(obj, tuple): - return tuple(cleaned) - else: - return type(obj)(cleaned) # type: ignore[call-overload] - finally: - _seen.remove(obj_id) - - # 处理集合 - if isinstance(obj, AbstractSet): - _seen.add(obj_id) - try: - cleaned = set() - for item in obj: - if item is not SKIP_VALUE: - cleaned_item = postprocess_from_dill(item, _seen) - # 集合中不能包含None，但可以包含False、0等 - if cleaned_item is not None: - cleaned.add(cleaned_item) - return type(obj)(cleaned) # type: ignore[call-overload] - finally: - _seen.remove(obj_id) - - # 处理复杂对象 - if hasattr(obj, "__dict__"): - _seen.add(obj_id) - try: - # 递归清理属性 - for attr_name, attr_value in list(obj.__dict__.items()): - if attr_value is SKIP_VALUE: - # 删除哨兵值属性 - try: - delattr(obj, attr_name) - except Exception: - pass - else: - # 递归清理属性值，保留所有合法值 - cleaned_value = postprocess_from_dill(attr_value, _seen) - try: - setattr(obj, attr_name, cleaned_value) - except Exception: - pass - - return obj - finally: - _seen.remove(obj_id) - - return obj diff --git a/packages/sage-common/src/sage/common/utils/serialization/ray_trimmer.py b/packages/sage-common/src/sage/common/utils/serialization/ray_trimmer.py deleted file mode 100644 index d0f11558c0..0000000000 --- a/packages/sage-common/src/sage/common/utils/serialization/ray_trimmer.py +++ /dev/null @@ -1,191 +0,0 @@ -""" -Ray对象清理器 - 专门用于Ray远程调用的对象预处理 -""" - -from typing import Any - -from .config import ( - RAY_OPERATOR_EXCLUDE_ATTRS, - RAY_TRANSFORMATION_EXCLUDE_ATTRS, - SKIP_VALUE, -) -from .exceptions import SerializationError -from .preprocessor import filter_attrs, gather_attrs, preprocess_for_dill, should_skip - - -def trim_object_for_ray( - obj: Any, include: list[str] | None = None, exclude: list[str] | None = None -) -> Any: - """ - 为Ray远程调用预处理对象，移除不可序列化的内容 - - 这个函数只做清理工作，不进行实际的序列化，让Ray自己处理序列化过程。 - 适用于在ray.remote调用前清理对象，避免序列化错误。 - - Args: - obj: 要预处理的对象 - include: 包含的属性列表（如果指定，只保留这些属性） - exclude: 排除的属性列表（这些属性将被移除） - - Returns: - 清理后的对象，可以安全地传递给Ray进行序列化 - - Example: - # 清理transformation对象用于Ray调用 - cleaned_trans = trim_object_for_ray(transformation, - exclude=['logger', 'env', '_operator_factory']) - - # 现在可以安全地传递给Ray - result = ray_actor.process_transformation.remote(cleaned_trans) - """ - try: - # 如果指定了include或exclude，直接使用用户的过滤规则 - if include or exclude: - attrs = gather_attrs(obj) - filtered_attrs = filter_attrs(attrs, include, exclude) - - # 创建新对象并设置过滤后的属性 - obj_class = type(obj) - try: - final_obj = obj_class.__new__(obj_class) # type: ignore[call-overload] - for attr_name, attr_value in filtered_attrs.items(): - try: - setattr(final_obj, attr_name, attr_value) - except Exception: - pass # 忽略设置失败的属性 - return final_obj - except Exception: - # 如果无法创建新实例，回退到预处理 - pass - - # 如果没有特殊的include/exclude需求，使用现有的预处理函数 - cleaned_obj = preprocess_for_dill(obj) - return cleaned_obj if cleaned_obj is not SKIP_VALUE else None - - except Exception as e: - # 如果预处理失败，返回None或抛出异常 - raise SerializationError(f"Object trimming for Ray failed: {e}") - - -class RayObjectTrimmer: - """专门用于Ray远程调用的对象预处理器""" - - @staticmethod - def trim_for_remote_call( - obj: Any, - include: list[str] | None = None, - exclude: list[str] | None = None, - deep_clean: bool = True, - ) -> Any: - """ - 为Ray远程调用准备对象 - - Args: - obj: 要清理的对象 - include: 只保留这些属性 - exclude: 排除这些属性 - deep_clean: 是否进行深度清理（递归处理嵌套对象） - - Returns: - 清理后可以传递给Ray的对象 - """ - if not deep_clean: - # 浅层清理：只处理顶层对象的属性 - if hasattr(obj, "__dict__"): - attrs = gather_attrs(obj) - filtered_attrs = filter_attrs(attrs, include, exclude) - - obj_class = type(obj) - try: - cleaned_obj = obj_class.__new__(obj_class) # type: ignore[call-overload] - for attr_name, attr_value in filtered_attrs.items(): - if not should_skip(attr_value): - try: - setattr(cleaned_obj, attr_name, attr_value) - except Exception: - pass - return cleaned_obj - except Exception: - return obj - return obj - else: - # 深度清理：使用完整的预处理流程，并递归处理嵌套对象 - cleaned = trim_object_for_ray(obj, include, exclude) - - # 确保嵌套对象也被正确清理 - if cleaned and hasattr(cleaned, "__dict__"): - for attr_name, attr_value in list(cleaned.__dict__.items()): - if hasattr(attr_value, "__dict__"): - # 递归清理嵌套对象，使用默认的黑名单清理（应用ATTRIBUTE_BLACKLIST） - from .config import ATTRIBUTE_BLACKLIST - - nested_cleaned = RayObjectTrimmer.trim_for_remote_call( - attr_value, - include=None, - exclude=list(ATTRIBUTE_BLACKLIST), - deep_clean=True, - ) - setattr(cleaned, attr_name, nested_cleaned) - - return cleaned - - @staticmethod - def trim_transformation_for_ray(transformation_obj) -> Any: - """ - 专门为Transformation对象定制的清理方法 - 移除常见的不可序列化属性 - """ - return RayObjectTrimmer.trim_for_remote_call( - transformation_obj, exclude=RAY_TRANSFORMATION_EXCLUDE_ATTRS - ) - - @staticmethod - def trim_operator_for_ray(operator_obj) -> Any: - """ - 专门为Operator对象定制的清理方法 - """ - return RayObjectTrimmer.trim_for_remote_call( - operator_obj, exclude=RAY_OPERATOR_EXCLUDE_ATTRS - ) - - @staticmethod - def validate_ray_serializable(obj: Any, max_depth: int = 3) -> dict[str, Any]: - """ - 验证对象是否可以被Ray序列化 - - Args: - obj: 要验证的对象 - max_depth: 最大检查深度 - - Returns: - 验证结果字典，包含是否可序列化和问题列表 - """ - try: - import ray - except ImportError: - return { - "is_serializable": False, - "issues": ["Ray is not installed"], - "size_estimate": 0, - } - - result = {"is_serializable": False, "issues": [], "size_estimate": 0} - - try: - # 尝试Ray的内部序列化 - serialized = ray.cloudpickle.dumps(obj) # type: ignore[attr-defined] - result["is_serializable"] = True - result["size_estimate"] = len(serialized) - - except Exception as e: - result["issues"].append(f"Ray serialization failed: {str(e)}") - - # 尝试识别具体的问题 - if hasattr(obj, "__dict__"): - for attr_name, attr_value in obj.__dict__.items(): - if should_skip(attr_value): - result["issues"].append( - f"Problematic attribute: {attr_name} = {type(attr_value)}" - ) - - return result diff --git a/packages/sage-common/src/sage/common/utils/system/__init__.py b/packages/sage-common/src/sage/common/utils/system/__init__.py deleted file mode 100644 index 320e6fe8b7..0000000000 --- a/packages/sage-common/src/sage/common/utils/system/__init__.py +++ /dev/null @@ -1,63 +0,0 @@ -""" -SAGE System Utilities - -Network and process management utilities for SAGE applications. -""" - -# 直接从本包的_version模块加载版本信息 -try: - from sage.common._version import __author__, __email__, __version__ -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" - -# Network utilities -from sage.common.utils.system.network import ( - aggressive_port_cleanup, - allocate_free_port, - check_port_binding_permission, - check_tcp_connection, - find_port_processes, - get_host_ip, - get_process_on_port, - is_port_available, - is_port_occupied, - send_tcp_health_check, - wait_for_port_ready, - wait_for_port_release, -) - -# Process utilities -from sage.common.utils.system.process import ( - find_processes_by_name, - get_process_info, - kill_process_with_sudo, - terminate_process, - terminate_process_tree, - terminate_processes_by_name, -) - -__all__ = [ - # Network - "is_port_occupied", - "is_port_available", - "check_port_binding_permission", - "wait_for_port_release", - "wait_for_port_ready", - "find_port_processes", - "get_process_on_port", - "send_tcp_health_check", - "allocate_free_port", - "aggressive_port_cleanup", - "get_host_ip", - "check_tcp_connection", - # Process - "terminate_process", - "terminate_processes_by_name", - "kill_process_with_sudo", - "terminate_process_tree", - "get_process_info", - "find_processes_by_name", -] diff --git a/packages/sage-common/src/sage/common/utils/system/environment.py b/packages/sage-common/src/sage/common/utils/system/environment.py deleted file mode 100644 index f3a4868c24..0000000000 --- a/packages/sage-common/src/sage/common/utils/system/environment.py +++ /dev/null @@ -1,434 +0,0 @@ -""" -Environment Detection Utilities - -System-level environment detection and configuration utilities. -These functions help determine the appropriate backend, environment type, -and system capabilities for SAGE applications. -""" - -import importlib -import os -import subprocess -import sys -from typing import Any - - -def detect_execution_environment() -> str: - """ - 检测当前执行环境类型 - - Returns: - str: 环境类型 ('local', 'ray', 'kubernetes', 'docker', 'slurm') - """ - # 检查Ray环境 - if is_ray_available() and is_ray_cluster_active(): - return "ray" - - # 检查Kubernetes环境 - if is_kubernetes_environment(): - return "kubernetes" - - # 检查Docker环境 - if is_docker_environment(): - return "docker" - - # 检查SLURM环境 - if is_slurm_environment(): - return "slurm" - - # 默认为本地环境 - return "local" - - -def is_ray_available() -> bool: - """ - 检查Ray是否可用 - - Returns: - bool: Ray是否可用 - """ - try: - importlib.import_module("ray") - return True - except ImportError: - return False - - -def is_ray_cluster_active() -> bool: - """ - 检查Ray集群是否处于活跃状态 - - Returns: - bool: Ray集群是否活跃 - """ - if not is_ray_available(): - return False - - try: - ray = importlib.import_module("ray") - return ray.is_initialized() - except Exception: - return False - - -def get_ray_cluster_info() -> dict[str, Any]: - """ - 获取Ray集群信息 - - Returns: - Dict: Ray集群信息 - """ - if not is_ray_available(): - return {"available": False, "error": "Ray not installed"} - - try: - ray = importlib.import_module("ray") - - if not ray.is_initialized(): - return {"available": True, "initialized": False} - - cluster_resources = ray.cluster_resources() - nodes = ray.nodes() - - return { - "available": True, - "initialized": True, - "cluster_resources": cluster_resources, - "node_count": len(nodes), - "nodes": nodes, - } - except Exception as e: - return {"available": True, "error": f"Error getting Ray info: {e}"} - - -def is_kubernetes_environment() -> bool: - """ - 检查是否在Kubernetes环境中运行 - - Returns: - bool: 是否在Kubernetes中 - """ - # 检查环境变量 - k8s_indicators = [ - "KUBERNETES_SERVICE_HOST", - "KUBERNETES_SERVICE_PORT", - "KUBERNETES_PORT", - ] - - for indicator in k8s_indicators: - if os.environ.get(indicator): - return True - - # 检查服务账户文件 - if os.path.exists("/var/run/secrets/kubernetes.io/serviceaccount"): - return True - - return False - - -def is_docker_environment() -> bool: - """ - 检查是否在Docker容器中运行 - - Returns: - bool: 是否在Docker中 - """ - # 检查.dockerenv文件 - if os.path.exists("/.dockerenv"): - return True - - # 检查cgroup信息 - try: - with open("/proc/1/cgroup") as f: - content = f.read() - if "docker" in content or "containerd" in content: - return True - except FileNotFoundError: - pass - - return False - - -def is_slurm_environment() -> bool: - """ - 检查是否在SLURM环境中运行 - - Returns: - bool: 是否在SLURM中 - """ - slurm_indicators = [ - "SLURM_JOB_ID", - "SLURM_PROCID", - "SLURM_NODEID", - "SLURM_CLUSTER_NAME", - ] - - return any(os.environ.get(indicator) for indicator in slurm_indicators) - - -def get_system_resources() -> dict[str, Any]: - """ - 获取系统资源信息 - - Returns: - Dict: 系统资源信息 - """ - try: - psutil = importlib.import_module("psutil") - - # CPU信息 - cpu_info = { - "count": psutil.cpu_count(), - "physical_count": psutil.cpu_count(logical=False), - "frequency": psutil.cpu_freq()._asdict() if psutil.cpu_freq() else None, - "percent": psutil.cpu_percent(interval=1), - } - - # 内存信息 - memory = psutil.virtual_memory() - memory_info = { - "total": memory.total, - "available": memory.available, - "percent": memory.percent, - "used": memory.used, - "free": memory.free, - } - - # 磁盘信息 - disk = psutil.disk_usage("/") - disk_info = { - "total": disk.total, - "used": disk.used, - "free": disk.free, - "percent": (disk.used / disk.total) * 100, - } - - return { - "cpu": cpu_info, - "memory": memory_info, - "disk": disk_info, - "platform": sys.platform, - } - - except ImportError: - return {"error": "psutil not available"} - except Exception as e: - return {"error": f"Error getting system resources: {e}"} - - -def detect_gpu_resources() -> dict[str, Any]: - """ - 检测GPU资源 - - Returns: - Dict: GPU资源信息 - """ - gpu_info = {"available": False, "count": 0, "devices": []} - - # 检查NVIDIA GPU - try: - result = subprocess.run( - [ - "nvidia-smi", - "--query-gpu=name,memory.total,memory.used", - "--format=csv,noheader,nounits", - ], - capture_output=True, - text=True, - timeout=10, - ) - - if result.returncode == 0: - gpu_info["available"] = True - lines = result.stdout.strip().split("\n") - gpu_info["count"] = len(lines) - - for i, line in enumerate(lines): - parts = line.split(", ") - if len(parts) >= 3: - gpu_info["devices"].append( - { - "id": i, - "name": parts[0].strip(), - "memory_total": int(parts[1].strip()), - "memory_used": int(parts[2].strip()), - } - ) - except (subprocess.SubprocessError, FileNotFoundError, ValueError): - pass - - # 检查AMD GPU (rocm-smi) - if not gpu_info["available"]: - try: - result = subprocess.run(["rocm-smi"], capture_output=True, text=True, timeout=10) - if result.returncode == 0: - gpu_info["available"] = True - gpu_info["type"] = "AMD" - except (subprocess.SubprocessError, FileNotFoundError): - pass - - return gpu_info - - -def get_network_interfaces() -> list[dict[str, Any]]: - """ - 获取网络接口信息 - - Returns: - List[Dict]: 网络接口列表 - """ - try: - psutil = importlib.import_module("psutil") - - interfaces = [] - for interface, addrs in psutil.net_if_addrs().items(): - interface_info = {"name": interface, "addresses": []} - - for addr in addrs: - addr_info = { - "family": ( - addr.family.name if hasattr(addr.family, "name") else str(addr.family) - ), - "address": addr.address, - "netmask": addr.netmask, - "broadcast": addr.broadcast, - } - interface_info["addresses"].append(addr_info) - - interfaces.append(interface_info) - - return interfaces - - except ImportError: - return [] - except Exception as e: - return [{"error": f"Error getting network interfaces: {e}"}] - - -def recommend_backend() -> dict[str, Any]: - """ - 根据环境推荐最佳后端配置 - - Returns: - Dict: 推荐的后端配置 - """ - env_type = detect_execution_environment() - system_resources = get_system_resources() - gpu_resources = detect_gpu_resources() - - recommendation = { - "environment": env_type, - "primary_backend": "local", - "secondary_backends": [], - "communication_layer": "memory", - "reasoning": [], - } - - # 基于环境类型的推荐 - if env_type == "ray": - recommendation["primary_backend"] = "ray" - recommendation["communication_layer"] = "ray_queue" - recommendation["reasoning"].append("Ray cluster detected, using distributed backend") - - elif env_type == "kubernetes": - recommendation["primary_backend"] = "ray" - recommendation["secondary_backends"].append("local") - recommendation["communication_layer"] = "network" - recommendation["reasoning"].append( - "Kubernetes environment, Ray recommended for scalability" - ) - - elif env_type == "docker": - recommendation["secondary_backends"].append("ray") - recommendation["reasoning"].append("Docker environment, local backend preferred") - - # 基于资源的推荐 - if system_resources.get("cpu", {}).get("count", 0) > 8: - if "ray" not in recommendation["secondary_backends"]: - recommendation["secondary_backends"].append("ray") - recommendation["reasoning"].append( - "High CPU count, Ray backend beneficial for parallelization" - ) - - if gpu_resources.get("available", False): - recommendation["gpu_support"] = True - recommendation["communication_layer"] = "gpu_direct" - recommendation["reasoning"].append("GPU available, GPU-direct communication recommended") - - # 内存建议 - memory_gb = system_resources.get("memory", {}).get("total", 0) / (1024**3) - if memory_gb > 32: - recommendation["memory_strategy"] = "mmap" - recommendation["reasoning"].append("High memory available, mmap shared memory recommended") - elif memory_gb < 8: - recommendation["memory_strategy"] = "conservative" - recommendation["reasoning"].append("Limited memory, conservative memory usage recommended") - - return recommendation - - -def get_environment_capabilities() -> dict[str, Any]: - """ - 获取当前环境的完整能力评估 - - Returns: - Dict: 环境能力信息 - """ - return { - "environment_type": detect_execution_environment(), - "system_resources": get_system_resources(), - "gpu_resources": detect_gpu_resources(), - "network_interfaces": get_network_interfaces(), - "ray_info": get_ray_cluster_info(), - "backend_recommendation": recommend_backend(), - "python_version": f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}", - "platform": sys.platform, - } - - -def validate_environment_for_backend(backend_type: str) -> dict[str, Any]: - """ - 验证环境是否支持指定的后端类型 - - Args: - backend_type: 后端类型 ('local', 'ray', 'distributed') - - Returns: - Dict: 验证结果 - """ - validation = { - "backend": backend_type, - "supported": False, - "issues": [], - "recommendations": [], - } - - if backend_type == "local": - validation["supported"] = True - - elif backend_type == "ray": - if not is_ray_available(): - validation["issues"].append("Ray not installed") - validation["recommendations"].append("Install Ray: pip install ray") - else: - validation["supported"] = True - if not is_ray_cluster_active(): - validation["recommendations"].append( - "Initialize Ray cluster for better performance" - ) - - elif backend_type == "distributed": - if not is_ray_available(): - validation["issues"].append("Ray required for distributed backend") - validation["recommendations"].append("Install Ray: pip install ray") - - network_info = get_network_interfaces() - if not network_info or len(network_info) < 2: - validation["issues"].append("Limited network interfaces for distributed setup") - - if validation["issues"]: - validation["supported"] = False - else: - validation["supported"] = True - - return validation diff --git a/packages/sage-common/src/sage/common/utils/system/network.py b/packages/sage-common/src/sage/common/utils/system/network.py deleted file mode 100644 index c49fed71ba..0000000000 --- a/packages/sage-common/src/sage/common/utils/system/network.py +++ /dev/null @@ -1,625 +0,0 @@ -""" -Network and Port Management Utilities - -System-level network operations independent of any specific class context. -These utilities provide reusable functions for port management, network checks, -and TCP communication operations. -""" - -import json -import socket -import subprocess -import time -from typing import Any - -import psutil - - -def is_port_occupied(host: str, port: int) -> bool: - """ - 检查端口是否被占用 - - Args: - host: 主机地址 - port: 端口号 - - Returns: - bool: True表示端口被占用，False表示端口空闲 - """ - try: - with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock: - sock.settimeout(1) - result = sock.connect_ex((host, port)) - return result == 0 - except Exception: - return False - - -def check_port_binding_permission(host: str, port: int) -> dict[str, Any]: - """ - 检查端口绑定权限 - - Args: - host: 主机地址 - port: 端口号 - - Returns: - Dict: 包含检查结果的字典 - - success: bool, 是否成功 - - error: str, 错误类型（如果失败） - - message: str, 详细信息 - """ - try: - with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock: - sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) - sock.bind((host, port)) - return { - "success": True, - "message": f"Port {port} binding permission verified", - } - except PermissionError: - return { - "success": False, - "error": "permission_denied", - "message": f"Permission denied to bind port {port}", - } - except OSError as e: - if e.errno == 98: # Address already in use - return { - "success": False, - "error": "port_in_use", - "message": f"Port {port} is still in use", - } - return { - "success": False, - "error": "os_error", - "message": f"Error checking port binding permission: {e}", - } - except Exception as e: - return { - "success": False, - "error": "unknown", - "message": f"Unexpected error checking port permission: {e}", - } - - -def wait_for_port_release( - host: str, port: int, timeout: int = 10, check_interval: float = 1 -) -> bool: - """ - 等待端口释放 - - Args: - host: 主机地址 - port: 端口号 - timeout: 超时时间（秒） - check_interval: 检查间隔（秒） - - Returns: - bool: True表示端口已释放，False表示超时 - """ - start_time = time.time() - - while time.time() - start_time < timeout: - if not is_port_occupied(host, port): - return True - time.sleep(check_interval) - - return False - - -def find_port_processes(port: int) -> list[psutil.Process]: - """ - 查找占用指定端口的进程列表 - 使用多种方法确保找到所有相关进程 - - Args: - port: 要查询的端口号 - - Returns: - List[psutil.Process]: 占用该端口的进程列表 - """ - pids = set() - - # Method 1: lsof - pids.update(_find_processes_with_lsof(port)) - - # Method 2: netstat - pids.update(_find_processes_with_netstat(port)) - - # Method 3: fuser - pids.update(_find_processes_with_fuser(port)) - - # 将PID转换为psutil.Process对象 - processes = [] - for pid in pids: - try: - proc = psutil.Process(pid) - # 检查进程是否仍然存在 - if proc.is_running(): - processes.append(proc) - except (psutil.NoSuchProcess, psutil.AccessDenied): - # 进程不存在或无访问权限，跳过 - continue - - return processes - - -def _find_processes_with_lsof(port: int) -> list[int]: - """ - 使用lsof查找占用端口的进程 - - Args: - port: 端口号 - - Returns: - List[int]: 进程ID列表 - """ - try: - result = subprocess.run( - ["lsof", "-t", f"-i:{port}"], capture_output=True, text=True, timeout=5 - ) - if result.returncode == 0 and result.stdout.strip(): - pids = [] - for line in result.stdout.strip().split("\n"): - line = line.strip() - if line.startswith("p"): - line = line[1:] # Remove 'p' prefix - if line.isdigit(): - pids.append(int(line)) - return pids - except (subprocess.SubprocessError, FileNotFoundError, ValueError): - pass - return [] - - -def _find_processes_with_netstat(port: int) -> list[int]: - """ - 使用netstat查找占用端口的进程 - - Args: - port: 端口号 - - Returns: - List[int]: 进程ID列表 - """ - try: - result = subprocess.run(["netstat", "-tlnp"], capture_output=True, text=True, timeout=5) - pids = [] - if result.returncode == 0: - for line in result.stdout.split("\n"): - if f":{port}" in line and "LISTEN" in line: - parts = line.split() - if len(parts) > 6 and "/" in parts[6]: - pid_str = parts[6].split("/")[0] - if pid_str.isdigit(): - pids.append(int(pid_str)) - return pids - except (subprocess.SubprocessError, ValueError, FileNotFoundError): - # netstat命令不存在或执行失败 - pass - return [] - - -def _find_processes_with_fuser(port: int) -> list[int]: - """ - 使用fuser查找占用端口的进程 - - Args: - port: 端口号 - - Returns: - List[int]: 进程ID列表 - """ - try: - result = subprocess.run(["fuser", f"{port}/tcp"], capture_output=True, text=True, timeout=5) - if result.returncode == 0 and result.stdout.strip(): - return [ - int(pid.strip()) for pid in result.stdout.strip().split() if pid.strip().isdigit() - ] - except (subprocess.SubprocessError, FileNotFoundError, ValueError): - pass - return [] - - -def send_tcp_health_check( - host: str, port: int, request: dict[str, Any], timeout: int = 5 -) -> dict[str, Any]: - """ - 发送TCP健康检查请求 - - Args: - host: 目标主机 - port: 目标端口 - request: 要发送的请求数据 - timeout: 超时时间（秒） - - Returns: - Dict: 响应数据或错误信息 - """ - try: - # Validate JSON serialization upfront - request_data = json.dumps(request).encode("utf-8") - except (TypeError, ValueError) as e: - # Re-raise JSON serialization errors - raise e - - try: - with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock: - sock.settimeout(timeout) - sock.connect((host, port)) - - # 发送请求 - length_data = len(request_data).to_bytes(4, byteorder="big") - sock.sendall(length_data + request_data) - - # 接收响应 - response_length_data = sock.recv(4) - if len(response_length_data) != 4: - return {"status": "error", "message": "Invalid response format"} - - response_length = int.from_bytes(response_length_data, byteorder="big") - response_data = b"" - while len(response_data) < response_length: - chunk = sock.recv(min(response_length - len(response_data), 8192)) - if not chunk: - break - response_data += chunk - - if len(response_data) != response_length: - return {"status": "error", "message": "Incomplete response received"} - - return json.loads(response_data.decode("utf-8")) - - except OSError as e: - return {"status": "error", "message": f"Connection failed: {e}"} - except json.JSONDecodeError as e: - return {"status": "error", "message": f"Invalid JSON response: {e}"} - except Exception as e: - return {"status": "error", "message": f"Health check failed: {e}"} - - -def allocate_free_port( - host: str = "127.0.0.1", port_range: tuple[int, int] = (19200, 20000) -) -> int: - """ - 分配一个空闲端口 - - Args: - host: 绑定的主机地址 - port_range: 端口范围 (start, end) - - Returns: - int: 分配的端口号 - - Raises: - RuntimeError: 如果无法分配端口 - """ - start_port, end_port = port_range - - # 尝试从指定范围分配端口 - for port in range(start_port, end_port): - if not is_port_occupied(host, port): - # 双重检查：尝试绑定端口 - try: - with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: - s.bind((host, port)) - return port - except OSError: - continue - - # 如果范围内都被占用，使用系统分配 - try: - with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: - s.bind((host, 0)) - return s.getsockname()[1] - except Exception as e: - raise RuntimeError(f"Unable to allocate free port: {e}") - - -def aggressive_port_cleanup(port: int) -> dict[str, Any]: - """ - 激进的端口清理 - 尝试杀死所有占用指定端口的进程 - - Args: - port: 要清理的端口号 - - Returns: - Dict: 清理结果 - - success: bool, 是否成功找到并终止进程 - - killed_pids: List[int], 被终止的进程ID列表 - - errors: List[str], 错误信息列表 - """ - import psutil - - result = {"success": False, "killed_pids": [], "errors": []} - - # 使用多种方法查找占用端口的进程 - all_pids = find_port_processes(port) - - if not all_pids: - result["errors"].append("No processes found occupying the port") - return result - - # 尝试杀死所有找到的进程 - for proc in all_pids: # proc is actually a psutil.Process object - pid = None # Initialize pid to avoid UnboundLocalError - try: - # Handle both psutil.Process objects and raw PIDs (for backward compatibility with mocks) - if isinstance(proc, int): - pid = proc - proc = psutil.Process(pid) - else: - pid = proc.pid # Get the PID from the Process object - - # 先尝试优雅终止 - try: - proc.terminate() - proc.wait(timeout=2) - result["killed_pids"].append(pid) - except psutil.TimeoutExpired: - # 超时后强制杀死 - proc.kill() - proc.wait(timeout=2) - result["killed_pids"].append(pid) - - except psutil.NoSuchProcess: - # 进程已经不存在 - continue - except psutil.AccessDenied: - if pid is not None: - result["errors"].append(f"Access denied to kill process {pid}") - else: - result["errors"].append("Access denied to kill process") - except Exception as e: - if pid is not None: - result["errors"].append(f"Error killing process {pid}: {e}") - else: - result["errors"].append(f"Error killing process: {e}") - - result["success"] = len(result["killed_pids"]) > 0 - return result - - -def get_host_ip() -> str: - """ - 自动获取本机可用于外部连接的IP地址 - - Returns: - str: IP地址 - """ - try: - # 尝试连接到外部地址以获取本机IP - with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s: - s.connect(("8.8.8.8", 80)) - return s.getsockname()[0] - except Exception: - return "127.0.0.1" - - -def check_tcp_connection(host: str, port: int, timeout: int = 5) -> dict[str, Any]: - """ - 测试TCP连接 - - Args: - host: 目标主机 - port: 目标端口 - timeout: 超时时间（秒） - - Returns: - Dict: 连接测试结果 - """ - try: - start_time = time.time() - with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock: - sock.settimeout(timeout) - result = sock.connect_ex((host, port)) - elapsed_time = time.time() - start_time - - if result == 0: - return { - "success": True, - "message": f"Connection to {host}:{port} successful", - "response_time": elapsed_time, - } - else: - return { - "success": False, - "message": f"Connection to {host}:{port} failed (error code: {result})", - "response_time": elapsed_time, - } - except TimeoutError: - return { - "success": False, - "message": f"Connection timeout to {host}:{port}", - "response_time": timeout, - } - except Exception as e: - return { - "success": False, - "message": f"Connection test failed: {e}", - "response_time": 0, - } - - -# ============================================================================= -# 兼容性函数 - 统一接口 -# ============================================================================= - - -def is_port_available(host: str, port: int) -> bool: - """ - 检查端口是否可用（与 is_port_occupied 相反的语义） - - 这是 is_port_occupied 的反向语义版本，用于需要 "available" 语义的场景。 - - Args: - host: 主机地址 - port: 端口号 - - Returns: - bool: True表示端口可用（空闲），False表示端口不可用（被占用） - """ - return not is_port_occupied(host, port) - - -def wait_for_port_ready( - host: str, port: int, timeout: int = 30, check_interval: float = 1.0 -) -> bool: - """ - 等待端口变为可用（服务启动完成） - - 与 wait_for_port_release 相反，此函数等待服务启动并开始监听端口。 - - Args: - host: 主机地址 - port: 端口号 - timeout: 超时时间（秒） - check_interval: 检查间隔（秒） - - Returns: - bool: True表示端口已就绪（服务已启动），False表示超时 - """ - start_time = time.time() - while time.time() - start_time < timeout: - if is_port_occupied(host, port): - return True - time.sleep(check_interval) - return False - - -def get_process_on_port(port: int) -> dict | None: - """ - 获取占用指定端口的进程信息 - - Args: - port: 端口号 - - Returns: - 包含进程信息的字典 (pid, name, cmdline) 或 None - """ - processes = find_port_processes(port) - if not processes: - return None - - proc = processes[0] - try: - return { - "pid": proc.pid, - "name": proc.name(), - "cmdline": " ".join(proc.cmdline()), - } - except (psutil.NoSuchProcess, psutil.AccessDenied): - return { - "pid": proc.pid, - "name": "unknown", - "cmdline": "unknown", - } - - -# ============================================================================= -# HTTP Health Check Utilities -# ============================================================================= - - -def check_http_health( - host: str = "localhost", - port: int = 8000, - path: str = "/health", - timeout: float = 5.0, - expected_status: int = 200, -) -> dict: - """ - 检查 HTTP 服务健康状态 - - Args: - host: 主机地址 - port: 端口号 - path: 健康检查路径 - timeout: 超时时间（秒） - expected_status: 预期的 HTTP 状态码 - - Returns: - 包含检查结果的字典: - - healthy: bool, 服务是否健康 - - status_code: int | None, HTTP 状态码 - - response_time: float, 响应时间（秒） - - error: str | None, 错误信息 - """ - import urllib.error - import urllib.request - - url = f"http://{host}:{port}{path}" - start_time = time.time() - - try: - request = urllib.request.Request(url, method="GET") - with urllib.request.urlopen(request, timeout=timeout) as response: - elapsed = time.time() - start_time - status_code = response.getcode() - return { - "healthy": status_code == expected_status, - "status_code": status_code, - "response_time": elapsed, - "error": None, - } - except urllib.error.HTTPError as e: - elapsed = time.time() - start_time - return { - "healthy": False, - "status_code": e.code, - "response_time": elapsed, - "error": str(e), - } - except urllib.error.URLError as e: - elapsed = time.time() - start_time - return { - "healthy": False, - "status_code": None, - "response_time": elapsed, - "error": f"Connection failed: {e.reason}", - } - except TimeoutError: - return { - "healthy": False, - "status_code": None, - "response_time": timeout, - "error": "Connection timeout", - } - except Exception as e: - elapsed = time.time() - start_time - return { - "healthy": False, - "status_code": None, - "response_time": elapsed, - "error": str(e), - } - - -def wait_for_http_health( - host: str = "localhost", - port: int = 8000, - path: str = "/health", - timeout: int = 30, - check_interval: float = 1.0, -) -> bool: - """ - 等待 HTTP 服务健康就绪 - - Args: - host: 主机地址 - port: 端口号 - path: 健康检查路径 - timeout: 总超时时间（秒） - check_interval: 检查间隔（秒） - - Returns: - bool: True 表示服务健康就绪，False 表示超时 - """ - start_time = time.time() - while time.time() - start_time < timeout: - result = check_http_health(host, port, path, timeout=min(5.0, check_interval)) - if result["healthy"]: - return True - time.sleep(check_interval) - return False diff --git a/packages/sage-common/src/sage/common/utils/system/process.py b/packages/sage-common/src/sage/common/utils/system/process.py deleted file mode 100644 index 0727b0c68a..0000000000 --- a/packages/sage-common/src/sage/common/utils/system/process.py +++ /dev/null @@ -1,602 +0,0 @@ -""" -Process Management Utilities - -System-level process operations independent of any specific class context. -These utilities provide reusable functions for process discovery, termination, -and management operations. -""" - -import getpass -import os -import subprocess -import time -from typing import Any - -import psutil - - -def find_processes_by_name(process_names: list[str]) -> list[psutil.Process]: - """ - 根据进程名称列表查找进程 - - Args: - process_names: 要搜索的进程名称列表 - - Returns: - List[psutil.Process]: 匹配的进程对象列表 - """ - matching_processes = [] - - for proc in psutil.process_iter(["pid", "name", "cmdline"]): - try: - proc_info = proc.info - proc_name = proc_info["name"] - cmdline = " ".join(proc_info["cmdline"]) if proc_info["cmdline"] else "" - - # 检查进程名称或命令行是否匹配 - for target_name in process_names: - if target_name in proc_name or target_name in cmdline: - matching_processes.append(proc) - break - - except (psutil.NoSuchProcess, psutil.AccessDenied): - continue - - return matching_processes - - -def get_process_info(pid: int) -> dict[str, Any]: - """ - 获取进程详细信息 - - Args: - pid: 进程ID - - Returns: - Dict: 进程信息字典 - """ - try: - proc = psutil.Process(pid) - return { - "pid": pid, - "name": proc.name(), - "user": proc.username(), - "cmdline": " ".join(proc.cmdline()), - "status": proc.status(), - "cpu_percent": proc.cpu_percent(), - "memory_percent": proc.memory_percent(), - "create_time": proc.create_time(), - } - except psutil.NoSuchProcess: - return { - "pid": pid, - "name": "N/A", - "user": "N/A", - "cmdline": "N/A", - "status": "Not Found", - "error": "Process not found", - } - except psutil.AccessDenied: - return { - "pid": pid, - "name": "N/A", - "user": "N/A", - "cmdline": "N/A", - "status": "Access Denied", - "error": "Access denied", - } - except Exception as e: - return {"pid": pid, "error": f"Error getting process info: {e}"} - - -def terminate_process(pid: int, timeout: int = 5) -> dict[str, Any]: - """ - 优雅地终止进程（先TERM，后KILL） - - Args: - pid: 进程ID - timeout: 等待终止的超时时间（秒） - - Returns: - Dict: 终止结果 - """ - try: - proc = psutil.Process(pid) - proc_info = get_process_info(pid) - - # 先尝试优雅终止 - proc.terminate() - - try: - proc.wait(timeout=timeout) - return { - "success": True, - "method": "terminate", - "pid": pid, - "process_info": proc_info, - "message": f"Process {pid} terminated gracefully", - } - except psutil.TimeoutExpired: - # 超时后强制杀死 - proc.kill() - proc.wait(timeout=2) - return { - "success": True, - "method": "kill", - "pid": pid, - "process_info": proc_info, - "message": f"Process {pid} killed after timeout", - } - - except psutil.NoSuchProcess: - return { - "success": True, # 进程已经不存在，视为成功 - "method": "already_gone", - "pid": pid, - "message": f"Process {pid} already terminated", - } - except psutil.AccessDenied: - return { - "success": False, - "method": "access_denied", - "pid": pid, - "error": f"Access denied to terminate process {pid}", - } - except Exception as e: - return { - "success": False, - "method": "error", - "pid": pid, - "error": f"Error terminating process {pid}: {e}", - } - - -def terminate_processes_by_name(process_names: list[str], timeout: int = 5) -> dict[str, Any]: - """ - 根据进程名称终止所有匹配的进程 - - Args: - process_names: 要终止的进程名称列表 - timeout: 每个进程的终止超时时间（秒） - - Returns: - Dict: 终止结果汇总 - """ - processes = find_processes_by_name(process_names) - - results = { - "total_found": len(processes), - "terminated": [], - "failed": [], - "already_gone": [], - } - - for proc in processes: - result = terminate_process(proc.pid, timeout) - - if result["success"]: - if result["method"] == "already_gone": - results["already_gone"].append(result) - else: - results["terminated"].append(result) - else: - results["failed"].append(result) - - results["success"] = len(results["failed"]) == 0 - return results - - -def kill_process_with_sudo(pid: int, sudo_password: str | None = None) -> dict[str, Any]: - """ - 使用sudo权限强制杀死进程 - - Args: - pid: 进程ID - sudo_password: sudo密码（如果为None则会提示输入） - - Returns: - Dict: 操作结果 - """ - if sudo_password is None: - sudo_password = getpass.getpass("Enter sudo password: ") - - if not sudo_password.strip(): - return {"success": False, "pid": pid, "error": "No sudo password provided"} - - try: - result = subprocess.run( - ["sudo", "-S", "kill", "-9", str(pid)], - input=sudo_password + "\n", - capture_output=True, - text=True, - timeout=10, - ) - - if result.returncode == 0: - return { - "success": True, - "pid": pid, - "method": "sudo_kill", - "message": f"Successfully killed process {pid} with sudo", - } - else: - return { - "success": False, - "pid": pid, - "error": f"Failed to kill process {pid} with sudo: {result.stderr.strip()}", - } - - except subprocess.TimeoutExpired: - return { - "success": False, - "pid": pid, - "error": f"Timeout while trying to kill process {pid} with sudo", - } - except Exception as e: - return { - "success": False, - "pid": pid, - "error": f"Error killing process {pid} with sudo: {e}", - } - - -def verify_sudo_password(password: str) -> bool: - """ - 验证sudo密码是否正确 - - Args: - password: 要验证的密码 - - Returns: - bool: 密码是否正确 - """ - try: - result = subprocess.run( - ["sudo", "-S", "echo", "password_test"], - input=password + "\n", - capture_output=True, - text=True, - timeout=10, - ) - return result.returncode == 0 - except Exception: - return False - - -def get_process_children(pid: int, recursive: bool = True) -> list[int]: - """ - 获取进程的所有子进程ID - - Args: - pid: 父进程ID - recursive: 是否递归获取子进程的子进程 - - Returns: - List[int]: 子进程ID列表 - """ - try: - parent = psutil.Process(pid) - children = parent.children(recursive=recursive) - return [child.pid for child in children] - except psutil.NoSuchProcess: - return [] - except Exception: - return [] - - -def terminate_process_tree(pid: int, timeout: int = 5) -> dict[str, Any]: - """ - 终止进程及其所有子进程 - - Args: - pid: 根进程ID - timeout: 每个进程的终止超时时间（秒） - - Returns: - Dict: 终止结果 - """ - # 获取所有子进程 - children_pids = get_process_children(pid, recursive=True) - all_pids = children_pids + [pid] # 先杀子进程，最后杀父进程 - - results = { - "root_pid": pid, - "total_processes": len(all_pids), - "terminated": [], - "failed": [], - "already_gone": [], - } - - # 终止所有进程 - for current_pid in all_pids: - result = terminate_process(current_pid, timeout) - - if result["success"]: - if result["method"] == "already_gone": - results["already_gone"].append(result) - else: - results["terminated"].append(result) - else: - results["failed"].append(result) - - results["success"] = len(results["failed"]) == 0 - return results - - -def wait_for_process_termination(pid: int, timeout: int = 10) -> bool: - """ - 等待进程终止 - - Args: - pid: 进程ID - timeout: 超时时间（秒） - - Returns: - bool: True表示进程已终止，False表示超时 - """ - start_time = time.time() - - while time.time() - start_time < timeout: - try: - proc = psutil.Process(pid) - if not proc.is_running(): - return True - except psutil.NoSuchProcess: - return True - - time.sleep(0.5) - - return False - - -def get_system_process_summary() -> dict[str, Any]: - """ - 获取系统进程概要信息 - - Returns: - Dict: 系统进程统计信息 - """ - try: - all_processes = list(psutil.process_iter(["pid", "name", "status", "username"])) - - summary = { - "total_processes": len(all_processes), - "by_status": {}, - "by_user": {}, - "memory_usage": psutil.virtual_memory()._asdict(), - "cpu_usage": psutil.cpu_percent(interval=1), - } - - # 按状态统计 - for proc in all_processes: - try: - status = proc.info["status"] - summary["by_status"][status] = summary["by_status"].get(status, 0) + 1 - - user = proc.info["username"] - summary["by_user"][user] = summary["by_user"].get(user, 0) + 1 - except (psutil.NoSuchProcess, psutil.AccessDenied): - continue - - return summary - - except Exception as e: - return {"error": f"Failed to get process summary: {e}"} - - -def is_process_running(pid: int) -> bool: - """ - 检查进程是否正在运行 - - Args: - pid: 进程ID - - Returns: - bool: True表示进程正在运行 - """ - try: - proc = psutil.Process(pid) - return proc.is_running() - except psutil.NoSuchProcess: - return False - except Exception: - return False - - -class SudoManager: - """ - Sudo权限管理器 - - 提供安全的sudo权限获取、验证和使用功能 - """ - - def __init__(self): - self._cached_password = None - self._password_verified = False - - def get_sudo_password(self, prompt_message: str | None = None) -> str: - """ - 获取sudo密码 - - Args: - prompt_message: 自定义提示信息 - - Returns: - str: sudo密码（如果获取失败返回空字符串） - """ - if self._cached_password is not None: - return self._cached_password - - default_prompt = ( - "🔐 This operation requires sudo privileges to manage processes owned by other users." - ) - if prompt_message: - print(prompt_message) - else: - print(default_prompt) - - password = getpass.getpass("Please enter your sudo password (or press Enter to skip): ") - - if password.strip(): - # 验证密码是否正确 - print("🔍 Verifying sudo password...") - if verify_sudo_password(password): - self._cached_password = password - self._password_verified = True - print("✅ Sudo password verified successfully") - return password - else: - print("❌ Invalid sudo password, will continue without sudo privileges") - self._cached_password = "" - return "" - else: - print("⚠️ No sudo password provided, may fail to manage processes owned by other users") - self._cached_password = "" - return "" - - def ensure_sudo_access(self, prompt_message: str | None = None) -> bool: - """ - 确保有sudo访问权限 - - Args: - prompt_message: 自定义提示信息 - - Returns: - bool: 是否成功获取sudo权限 - """ - password = self.get_sudo_password(prompt_message) - has_access = bool(password) - - if not has_access: - print( - "⚠️ Warning: No sudo access available. May fail to manage processes owned by other users." - ) - - return has_access - - def has_sudo_access(self) -> bool: - """ - 检查是否已有sudo访问权限 - - Returns: - bool: 是否有sudo权限 - """ - return self._password_verified and bool(self._cached_password) - - def get_cached_password(self) -> str: - """ - 获取缓存的密码（如果已验证） - - Returns: - str: 缓存的密码 - """ - return self._cached_password if self._password_verified and self._cached_password else "" - - def clear_cache(self): - """清除缓存的密码""" - self._cached_password = None - self._password_verified = False - - def execute_with_sudo(self, command: list[str], timeout: int = 30) -> dict[str, Any]: - """ - 使用sudo执行命令 - - Args: - command: 要执行的命令列表 - timeout: 超时时间（秒） - - Returns: - Dict: 执行结果 - """ - password = self.get_cached_password() - if not password: - return {"success": False, "error": "No sudo password available"} - - try: - sudo_command = ["sudo", "-S"] + command - result = subprocess.run( - sudo_command, - input=password + "\n", - capture_output=True, - text=True, - timeout=timeout, - ) - - if result.returncode == 0: - return { - "success": True, - "stdout": result.stdout, - "stderr": result.stderr, - "returncode": result.returncode, - } - else: - return { - "success": False, - "error": f"Command failed with code {result.returncode}", - "stdout": result.stdout, - "stderr": result.stderr, - "returncode": result.returncode, - } - - except subprocess.TimeoutExpired: - return { - "success": False, - "error": f"Command timeout after {timeout} seconds", - } - except Exception as e: - return {"success": False, "error": f"Error executing sudo command: {e}"} - - -def create_sudo_manager() -> SudoManager: - """ - 创建sudo管理器实例 - - Returns: - SudoManager: sudo管理器实例 - """ - return SudoManager() - - -def check_process_ownership(pid: int, current_user: str | None = None) -> dict[str, Any]: - """ - 检查进程所有权，判断是否需要sudo权限 - - Args: - pid: 进程ID - current_user: 当前用户名（如果为None则自动获取） - - Returns: - Dict: 所有权信息 - """ - if current_user is None: - current_user = os.getenv("USER", "unknown") - - try: - proc = psutil.Process(pid) - proc_user = proc.username() - - return { - "pid": pid, - "process_user": proc_user, - "current_user": current_user, - "needs_sudo": proc_user != current_user and proc_user != "N/A", - "accessible": True, - } - - except psutil.NoSuchProcess: - return {"pid": pid, "error": "Process not found", "accessible": False} - except psutil.AccessDenied: - return { - "pid": pid, - "process_user": "Unknown", - "current_user": current_user, - "needs_sudo": True, - "accessible": False, - "error": "Access denied", - } - except Exception as e: - return { - "pid": pid, - "error": f"Error checking ownership: {e}", - "accessible": False, - } diff --git a/packages/sage-common/tests/components/debug/test_print_sink.py b/packages/sage-common/tests/components/debug/test_print_sink.py deleted file mode 100644 index 7a76d2fba4..0000000000 --- a/packages/sage-common/tests/components/debug/test_print_sink.py +++ /dev/null @@ -1,118 +0,0 @@ -"""Tests for the internal PrintSink utility.""" - -import logging - -import pytest - -from sage.common.components.debug.print_sink import PrintSink - - -@pytest.mark.unit -class TestPrintSink: - """Unit tests covering PrintSink behaviors.""" - - def test_execute_prints_first_output(self, capsys): - """First execution should print a helpful banner with prefix.""" - - sink = PrintSink(prefix="[Test]", separator=": ") - - sink.execute("hello world") - - captured = capsys.readouterr() - assert "🔍 Stream output: [Test]: hello world" in captured.out - assert "Further outputs logged" in captured.out - - def test_execute_logs_after_first_output(self, caplog): - """Subsequent executions should go to the logger.""" - - sink = PrintSink(quiet=True) - sink.execute("first message") - - with caplog.at_level(logging.DEBUG): - sink.execute("second message") - - assert "Stream output: second message" in caplog.text - - def test_format_data_handles_various_inputs(self): - """The private formatter should gracefully handle common types.""" - - sink = PrintSink(quiet=True) - - assert sink._format_data(None) == "None" - assert sink._format_data("text") == "text" - assert sink._format_data(42) == "42" - - dict_result = sink._format_data({"a": 1, "b": 2}) - assert "a=1" in dict_result and "b=2" in dict_result - - long_list_result = sink._format_data([1, 2, 3, 4, 5, 6]) - assert long_list_result.startswith("[1, 2, 3, 4, 5") - assert "+1 more" in long_list_result - - class Dummy: - def __init__(self): - self.foo = "bar" - self.count = 3 - - object_result = sink._format_data(Dummy()) - assert object_result.startswith("Dummy(") - assert "foo=bar" in object_result - - def test_format_data_empty_list(self): - """Test formatting empty list returns '[]' (line 119)""" - sink = PrintSink(quiet=True) - assert sink._format_data([]) == "[]" - - def test_format_data_short_list(self): - """Test formatting short list (<=5 elements) returns str(data) (line 121)""" - sink = PrintSink(quiet=True) - short_list = [1, 2, 3] - result = sink._format_data(short_list) - assert result == str(short_list) - - def test_format_data_object_without_attributes(self): - """Test formatting object without __dict__ attributes (lines 134-140)""" - sink = PrintSink(quiet=True) - - # Object with empty __dict__ - class EmptyObject: - pass - - obj = EmptyObject() - result = sink._format_data(obj) - assert result == "EmptyObject()" - - def test_format_data_unprintable_object(self): - """Test handling of objects that raise exception in str() (lines 137-139)""" - sink = PrintSink(quiet=True) - - # Object without __dict__ that raises exception in str() - class UnprintableObject: - __slots__ = () # No __dict__ - - def __str__(self): - raise ValueError("Cannot convert to string") - - obj = UnprintableObject() - result = sink._format_data(obj) - assert result == "<Unprintable: UnprintableObject>" - - def test_repr_method(self): - """Test __repr__ method returns proper representation (line 144)""" - sink = PrintSink(prefix="MyPrefix") - assert repr(sink) == "InternalPrintSink(prefix='MyPrefix')" - - def test_execute_with_empty_prefix(self): - """Test execute with no prefix""" - sink = PrintSink(prefix="", quiet=True) - sink.execute("test") # Should not crash - - def test_execute_quiet_mode_first_output(self, capsys): - """Test quiet mode still prints first output without banner""" - sink = PrintSink(prefix="Test", quiet=True) - sink.execute("message") - - captured = capsys.readouterr() - assert "Test | message" in captured.out - assert "🔍 Stream output" not in captured.out - assert "Further outputs" not in captured.out diff --git a/packages/sage-common/tests/components/sage_embedding/conftest.py b/packages/sage-common/tests/components/sage_embedding/conftest.py deleted file mode 100644 index 14bfb88b5f..0000000000 --- a/packages/sage-common/tests/components/sage_embedding/conftest.py +++ /dev/null @@ -1,123 +0,0 @@ -""" -Shared fixtures for embedding wrapper tests. -""" - -from unittest.mock import MagicMock, Mock - -import pytest - - -@pytest.fixture -def mock_openai_response(): - """Mock OpenAI API response""" - mock_response = Mock() - mock_response.data = [Mock(embedding=[0.1, 0.2, 0.3] * 512)] # 1536 dims - return mock_response - - -@pytest.fixture -def mock_openai_batch_response(): - """Mock OpenAI API batch response""" - mock_response = Mock() - mock_response.data = [ - Mock(embedding=[0.1, 0.2, 0.3] * 512), - Mock(embedding=[0.4, 0.5, 0.6] * 512), - ] - return mock_response - - -@pytest.fixture -def mock_hf_model(): - """Mock HuggingFace model""" - mock_model = MagicMock() - mock_tokenizer = MagicMock() - - # Mock encode method - mock_tokenizer.return_value = {"input_ids": [[1, 2, 3]], "attention_mask": [[1, 1, 1]]} - - # Mock model output - mock_output = Mock() - mock_output.last_hidden_state = Mock() - # Mock tensor with mean() method - mock_tensor = Mock() - mock_tensor.mean.return_value = Mock() - mock_tensor.mean.return_value.squeeze.return_value = Mock() - mock_tensor.mean.return_value.squeeze.return_value.cpu.return_value = Mock() - mock_tensor.mean.return_value.squeeze.return_value.cpu.return_value.numpy.return_value = [ - 0.1, - 0.2, - 0.3, - ] * 256 - mock_output.last_hidden_state = mock_tensor - - mock_model.return_value = mock_output - - return mock_model, mock_tokenizer - - -@pytest.fixture -def mock_jina_response(): - """Mock Jina API response""" - mock_response = Mock() - mock_response.json.return_value = {"data": [{"embedding": [0.1, 0.2, 0.3] * 256}]} - mock_response.status_code = 200 - return mock_response - - -@pytest.fixture -def mock_zhipu_response(): - """Mock Zhipu API response""" - mock_response = Mock() - mock_response.data = [Mock(embedding=[0.1, 0.2, 0.3] * 512)] - return mock_response - - -@pytest.fixture -def mock_cohere_response(): - """Mock Cohere API response""" - mock_response = Mock() - mock_response.embeddings = [[0.1, 0.2, 0.3] * 512] - return mock_response - - -@pytest.fixture -def mock_bedrock_response(): - """Mock AWS Bedrock response""" - return {"embedding": [0.1, 0.2, 0.3] * 512} - - -@pytest.fixture -def mock_ollama_response(): - """Mock Ollama API response""" - mock_response = Mock() - mock_response.json.return_value = {"embedding": [0.1, 0.2, 0.3] * 256} - mock_response.status_code = 200 - return mock_response - - -@pytest.fixture -def mock_siliconcloud_response(): - """Mock SiliconCloud API response""" - mock_response = Mock() - mock_response.data = [Mock(embedding=[0.1, 0.2, 0.3] * 512)] - return mock_response - - -@pytest.fixture -def mock_nvidia_openai_response(): - """Mock NVIDIA OpenAI compatible response""" - mock_response = Mock() - mock_response.data = [Mock(embedding=[0.1, 0.2, 0.3] * 256)] - return mock_response - - -@pytest.fixture -def sample_texts(): - """Sample texts for testing""" - return ["Hello world", "This is a test", "Embedding models are cool"] - - -@pytest.fixture -def sample_text(): - """Single sample text""" - return "Hello world" diff --git a/packages/sage-common/tests/components/sage_embedding/test_base_embedding.py b/packages/sage-common/tests/components/sage_embedding/test_base_embedding.py deleted file mode 100644 index a18dd257a7..0000000000 --- a/packages/sage-common/tests/components/sage_embedding/test_base_embedding.py +++ /dev/null @@ -1,69 +0,0 @@ -"""Unit tests for the BaseEmbedding abstract helper.""" - -import pytest - -from sage.common.components.sage_embedding.base import BaseEmbedding - - -class ExampleEmbedding(BaseEmbedding): - """Simple concrete embedding for testing BaseEmbedding defaults.""" - - def __init__(self, scale: float = 1.0, **kwargs): - super().__init__(scale=scale, **kwargs) - self._scale = scale - - def embed(self, text: str) -> list[float]: - return [self._scale * float(ord(ch)) for ch in text] - - def get_dim(self) -> int: - return len(self.embed("dim")) - - @property - def method_name(self) -> str: # pragma: no cover - property used indirectly - return "example" - - -@pytest.mark.unit -class TestBaseEmbedding: - def test_embed_batch_uses_single_embed(self): - emb = ExampleEmbedding(scale=0.5) - - result = emb.embed_batch(["hi", "bye"]) - - assert len(result) == 2 - assert result[0] == emb.embed("hi") - assert result[1] == emb.embed("bye") - - def test_repr_includes_config(self): - emb = ExampleEmbedding(scale=2.0, extra="value") - - repr_str = repr(emb) - - assert "ExampleEmbedding" in repr_str - assert "scale" in repr_str - assert "extra" in repr_str - - def test_default_model_info(self): - info = BaseEmbedding.get_model_info() - - assert "method" in info - assert info["requires_api_key"] is False - assert info["requires_model_download"] is False - - def test_method_name_property(self): - emb = ExampleEmbedding() - - assert emb.method_name == "example" - assert emb.get_dim() == len(emb.embed("dim")) - - def test_repr_with_many_config_items(self): - """Test __repr__ truncates config when more than 3 items (line 141)""" - emb = ExampleEmbedding( - scale=2.0, param1="value1", param2="value2", param3="value3", param4="value4" - ) - - repr_str = repr(emb) - - # Should show "..." when more than 3 config items - assert "..." in repr_str - assert "ExampleEmbedding" in repr_str diff --git a/packages/sage-common/tests/components/sage_embedding/test_embedding_api.py b/packages/sage-common/tests/components/sage_embedding/test_embedding_api.py deleted file mode 100644 index 31e7f9b96d..0000000000 --- a/packages/sage-common/tests/components/sage_embedding/test_embedding_api.py +++ /dev/null @@ -1,40 +0,0 @@ -"""Tests for the lightweight embedding API helpers.""" - -from unittest.mock import patch - -import pytest - -from sage.common.components.sage_embedding.embedding_api import apply_embedding_model -from sage.common.components.sage_embedding.embedding_model import ( - apply_embedding_model as apply_model_direct, -) - - -@pytest.mark.unit -@patch("sage.common.components.sage_embedding.embedding_api.EmbeddingModel") -def test_apply_embedding_model_forwards_arguments(mock_model): - """apply_embedding_model should pass through method name and kwargs.""" - - apply_embedding_model(name="mockembedder", dim=256, extra="value") - - mock_model.assert_called_once_with(method="mockembedder", dim=256, extra="value") - - -@pytest.mark.unit -@patch("sage.common.components.sage_embedding.embedding_api.EmbeddingModel") -def test_apply_embedding_model_defaults_to_name_argument(mock_model): - """No name argument should still instantiate the EmbeddingModel.""" - - apply_embedding_model() - - mock_model.assert_called_once_with(method="default") - - -@pytest.mark.unit -@patch("sage.common.components.sage_embedding.embedding_model.EmbeddingModel") -def test_apply_embedding_model_direct_alias(mock_model): - """The duplicate helper in embedding_model should behave the same way.""" - - apply_model_direct(name="mockembedder", dim=64) - - mock_model.assert_called_once_with(method="mockembedder", dim=64) diff --git a/packages/sage-common/tests/components/sage_embedding/test_hash_wrapper.py b/packages/sage-common/tests/components/sage_embedding/test_hash_wrapper.py deleted file mode 100644 index 1d9931351a..0000000000 --- a/packages/sage-common/tests/components/sage_embedding/test_hash_wrapper.py +++ /dev/null @@ -1,275 +0,0 @@ -""" -Comprehensive tests for HashEmbedding wrapper - -Tests cover: -- Initialization and dimension handling -- Empty text embedding -- Text tokenization and embedding -- Edge cases (special characters, no tokens, short digest chunks) -- Batch embedding -- Model info and properties -""" - -import pytest # noqa: F401 - -from sage.common.components.sage_embedding.wrappers.hash_wrapper import HashEmbedding - - -class TestHashEmbeddingInitialization: - """Test HashEmbedding initialization""" - - def test_default_initialization(self): - """Test initialization with default dimension""" - emb = HashEmbedding() - assert emb.get_dim() == 384 - - def test_custom_dimension(self): - """Test initialization with custom dimension""" - emb = HashEmbedding(dim=512) - assert emb.get_dim() == 512 - - def test_minimum_dimension_enforced(self): - """Test minimum dimension of 64 is enforced""" - emb = HashEmbedding(dim=32) - assert emb.get_dim() == 64 # Should be clamped to 64 - - def test_dimension_coercion_to_int(self): - """Test dimension is coerced to int""" - emb = HashEmbedding(dim=100.5) - assert emb.get_dim() == 100 - assert isinstance(emb.get_dim(), int) - - -class TestHashEmbeddingEmbed: - """Test embed method""" - - def test_embed_simple_text(self): - """Test embedding simple text""" - emb = HashEmbedding(dim=384) - vec = emb.embed("hello world") - - assert len(vec) == 384 - assert all(isinstance(v, float) for v in vec) - - def test_embed_empty_string(self): - """Test embedding empty string returns zero vector (line 74)""" - emb = HashEmbedding(dim=384) - vec = emb.embed("") - - assert len(vec) == 384 - assert all(v == 0.0 for v in vec) - - def test_embed_chinese_text(self): - """Test embedding Chinese text""" - emb = HashEmbedding(dim=256) - vec = emb.embed("你好世界") - - assert len(vec) == 256 - assert sum(v * v for v in vec) > 0 # Non-zero vector - - def test_embed_special_characters_only(self): - """Test text with only special characters (no alphanumeric/Chinese) (line 81)""" - emb = HashEmbedding(dim=128) - # Text with only special characters, no alphanumeric or Chinese - vec = emb.embed("!@#$%^&*()") - - assert len(vec) == 128 - # Should use the original text as single token since no valid tokens found - assert sum(v * v for v in vec) > 0 # Should still have some values - - def test_embed_mixed_alphanumeric(self): - """Test embedding mixed alphanumeric text""" - emb = HashEmbedding(dim=200) - vec = emb.embed("test123abc456") - - assert len(vec) == 200 - assert sum(v * v for v in vec) > 0 - - def test_embed_deterministic(self): - """Test same text produces same embedding""" - emb = HashEmbedding(dim=384) - vec1 = emb.embed("test") - vec2 = emb.embed("test") - - assert vec1 == vec2 - - def test_embed_different_texts_different_vectors(self): - """Test different texts produce different vectors""" - emb = HashEmbedding(dim=384) - vec1 = emb.embed("hello") - vec2 = emb.embed("world") - - assert vec1 != vec2 - - def test_embed_normalization(self): - """Test output vectors are L2 normalized""" - emb = HashEmbedding(dim=256) - vec = emb.embed("normalize test") - - # L2 norm should be approximately 1.0 - norm = sum(v * v for v in vec) ** 0.5 - assert abs(norm - 1.0) < 1e-10 - - def test_embed_with_small_dimension_for_chunk_edge_case(self): - """Test with very small dimension to potentially trigger chunk padding (line 91)""" - # Using a very small dimension increases chance of digest chunk being < 4 bytes - emb = HashEmbedding(dim=64) - vec = emb.embed("x") # Single character - - assert len(vec) == 64 - assert sum(v * v for v in vec) > 0 - - def test_embed_triggers_chunk_padding(self): - """Test that ensures chunk padding is triggered (line 91) - - SHA256 produces 32-byte digest. When iterating with step=4, - we get chunks at offsets: 0, 4, 8, 12, 16, 20, 24, 28, 32. - At offset 32, digest[32:36] will be empty (len < 4). - - This test uses mocking to verify the padding logic is executed. - """ - from unittest.mock import patch - - emb = HashEmbedding(dim=128) - - # Create a custom digest that will trigger the padding - # We'll use a 31-byte digest so the last chunk is < 4 bytes - def mock_sha256(_data): - # Return an object with a digest() that gives 31 bytes - class MockHash: - def digest(self): - return b"x" * 31 # 31 bytes, so last chunk will be 3 bytes - - return MockHash() - - with patch("hashlib.sha256", side_effect=mock_sha256): - vec = emb.embed("test") - - # Should still produce valid vector - assert len(vec) == 128 - # The vector should have some non-zero values - assert sum(v * v for v in vec) > 0 - - -class TestHashEmbeddingBatch: - """Test batch embedding""" - - def test_embed_batch_multiple_texts(self): - """Test embedding multiple texts""" - emb = HashEmbedding(dim=384) - texts = ["hello", "world", "test"] - vecs = emb.embed_batch(texts) - - assert len(vecs) == 3 - assert all(len(vec) == 384 for vec in vecs) - - def test_embed_batch_empty_list(self): - """Test embedding empty list""" - emb = HashEmbedding(dim=384) - vecs = emb.embed_batch([]) - - assert vecs == [] - - def test_embed_batch_with_empty_strings(self): - """Test batch with some empty strings""" - emb = HashEmbedding(dim=256) - texts = ["hello", "", "world"] - vecs = emb.embed_batch(texts) - - assert len(vecs) == 3 - assert all(v == 0.0 for v in vecs[1]) # Middle one should be zero vector - - def test_embed_batch_consistency(self): - """Test batch embedding is consistent with individual embedding""" - emb = HashEmbedding(dim=384) - text = "consistency test" - - vec_single = emb.embed(text) - vec_batch = emb.embed_batch([text])[0] - - assert vec_single == vec_batch - - -class TestHashEmbeddingProperties: - """Test properties and metadata""" - - def test_method_name_property(self): - """Test method_name property returns 'hash' (line 114)""" - emb = HashEmbedding() - assert emb.method_name == "hash" - - def test_get_model_info(self): - """Test get_model_info classmethod (lines 117-128)""" - info = HashEmbedding.get_model_info() - - assert info["method"] == "hash" - assert info["requires_api_key"] is False - assert info["requires_model_download"] is False - assert info["default_dimension"] == 384 - - def test_get_dim_method(self): - """Test get_dim method""" - emb = HashEmbedding(dim=512) - assert emb.get_dim() == 512 - - -class TestHashEmbeddingEdgeCases: - """Test edge cases and special scenarios""" - - def test_very_long_text(self): - """Test embedding very long text""" - emb = HashEmbedding(dim=384) - long_text = " ".join(["word"] * 1000) - vec = emb.embed(long_text) - - assert len(vec) == 384 - # Should still be normalized - norm = sum(v * v for v in vec) ** 0.5 - assert abs(norm - 1.0) < 1e-10 - - def test_whitespace_only(self): - """Test text with only whitespace""" - emb = HashEmbedding(dim=128) - vec = emb.embed(" \t\n ") - - assert len(vec) == 128 - # Should be treated as no valid tokens, use original text - assert sum(v * v for v in vec) > 0 - - def test_unicode_characters(self): - """Test text with various Unicode characters""" - emb = HashEmbedding(dim=256) - vec = emb.embed("Hello 世界 🌍 مرحبا") - - assert len(vec) == 256 - assert sum(v * v for v in vec) > 0 - - def test_case_insensitivity(self): - """Test embedding is case insensitive""" - emb = HashEmbedding(dim=384) - vec1 = emb.embed("Hello World") - vec2 = emb.embed("hello world") - - assert vec1 == vec2 - - def test_with_kwargs_compatibility(self): - """Test initialization with extra kwargs for compatibility""" - emb = HashEmbedding(dim=256, extra_param="ignored", another="value") - assert emb.get_dim() == 256 - - def test_multiple_instances_independent(self): - """Test multiple instances are independent""" - emb1 = HashEmbedding(dim=256) - emb2 = HashEmbedding(dim=512) - - assert emb1.get_dim() == 256 - assert emb2.get_dim() == 512 - - def test_punctuation_handling(self): - """Test text with various punctuation""" - emb = HashEmbedding(dim=384) - vec = emb.embed("Hello, world! How are you?") - - assert len(vec) == 384 - # Should extract "Hello", "world", "How", "are", "you" - assert sum(v * v for v in vec) > 0 diff --git a/packages/sage-common/tests/components/sage_embedding/test_hf_batch.py b/packages/sage-common/tests/components/sage_embedding/test_hf_batch.py deleted file mode 100644 index 266147cba8..0000000000 --- a/packages/sage-common/tests/components/sage_embedding/test_hf_batch.py +++ /dev/null @@ -1,193 +0,0 @@ -""" -Unit tests for HuggingFace batch embedding functionality. - -This test verifies that the batch processing implementation works correctly -and produces the same results as individual processing. -""" - -import sys -from unittest.mock import MagicMock, patch - -import pytest - - -class TestHFBatchEmbedding: - """测试 HuggingFace 批量 embedding 功能""" - - def test_hf_embed_batch_sync_function(self): - """测试 hf_embed_batch_sync 函数的正确性 - - 由于下载和加载 HF 模型需要时间和资源，这个测试通过 mock 来验证逻辑。 - 实际的端到端测试应该在集成测试中进行。 - """ - # 需要先移除已导入的模块，以便重新导入时使用 mock - modules_to_remove = [ - k for k in sys.modules.keys() if "sage.common.components.sage_embedding.hf" in k - ] - for mod in modules_to_remove: - del sys.modules[mod] - - # 创建 mock torch 模块 - mock_torch = MagicMock() - - # Mock tensor operations - mock_tensor = MagicMock() - mock_tensor.size.return_value = (2, 10, 768) # batch_size=2, seq_len=10, hidden_dim=768 - mock_tensor.__mul__ = MagicMock(return_value=mock_tensor) - - mock_sum_result = MagicMock() - mock_sum_mask = MagicMock() - mock_embeddings = MagicMock() - mock_embeddings.dtype = "float32" # Not bfloat16 - mock_embeddings.detach.return_value.cpu.return_value.tolist.return_value = [ - [0.1, 0.2, 0.3], - [0.4, 0.5, 0.6], - ] - - mock_torch.sum.return_value = mock_sum_result - mock_torch.clamp.return_value = mock_sum_mask - mock_sum_result.__truediv__ = MagicMock(return_value=mock_embeddings) - - # Mock no_grad context manager - mock_torch.no_grad.return_value.__enter__ = MagicMock() - mock_torch.no_grad.return_value.__exit__ = MagicMock() - - with patch.dict(sys.modules, {"torch": mock_torch}): - from sage.common.components.sage_embedding.hf import hf_embed_batch_sync - - # Mock tokenizer - mock_tokenizer = MagicMock() - mock_encoded = MagicMock() - mock_encoded.__getitem__ = MagicMock(side_effect=lambda k: MagicMock()) - mock_encoded.to = MagicMock(return_value=mock_encoded) - mock_tokenizer.return_value = mock_encoded - - # Mock model - mock_model = MagicMock() - mock_device = MagicMock() - mock_model.parameters.return_value = iter([MagicMock(device=mock_device)]) - - # Mock outputs - mock_outputs = MagicMock() - mock_outputs.last_hidden_state = mock_tensor - mock_model.return_value = mock_outputs - - # Call the function - texts = ["text1", "text2"] - result = hf_embed_batch_sync(texts, mock_tokenizer, mock_model) - - # Verify results - assert isinstance(result, list) - assert len(result) == 2 - assert result == [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]] - - # Verify tokenizer was called with all texts - mock_tokenizer.assert_called_once_with( - texts, return_tensors="pt", padding=True, truncation=True - ) - - def test_hf_wrapper_embed_batch_consistency(self): - """测试 HFEmbedding.embed_batch() 与单独调用 embed() 的一致性 - - 这个测试验证批量处理和单独处理产生相同的结果（或接近的结果）。 - 由于需要真实模型，这个测试会跳过，除非有可用的模型。 - """ - pytest.skip( - "此测试需要下载真实的 HuggingFace 模型，跳过以避免 CI 超时。" - "在本地使用真实模型时可以启用此测试。" - ) - - # 以下是示例代码，如果要在本地测试，取消注释并提供有效的模型 - # from sage.common.components.sage_embedding.wrappers.hf_wrapper import HFEmbedding - # import numpy as np - # - # emb = HFEmbedding(model="sentence-transformers/all-MiniLM-L6-v2") - # texts = ["Hello world", "How are you", "Test text"] - # - # # 批量处理 - # batch_results = emb.embed_batch(texts) - # - # # 单独处理 - # individual_results = [emb.embed(text) for text in texts] - # - # # 验证结果一致 - # assert len(batch_results) == len(individual_results) - # for batch_vec, individual_vec in zip(batch_results, individual_results): - # # 允许小的数值误差 - # np.testing.assert_allclose(batch_vec, individual_vec, rtol=1e-5, atol=1e-7) - - def test_hf_batch_handles_empty_list(self): - """测试空列表的处理 - 应直接返回空列表，不调用模型""" - from sage.common.components.sage_embedding.hf import hf_embed_batch_sync - - mock_tokenizer = MagicMock() - mock_model = MagicMock() - - # 空列表应该直接返回，不需要 mock torch - result = hf_embed_batch_sync([], mock_tokenizer, mock_model) - assert isinstance(result, list) - assert len(result) == 0 - - # 验证 tokenizer 和 model 都没有被调用 - mock_tokenizer.assert_not_called() - mock_model.assert_not_called() - - def test_hf_batch_handles_single_text(self): - """测试单个文本的处理""" - # 需要先移除已导入的模块，以便重新导入时使用 mock - modules_to_remove = [ - k for k in sys.modules.keys() if "sage.common.components.sage_embedding.hf" in k - ] - for mod in modules_to_remove: - del sys.modules[mod] - - # 创建 mock torch 模块 - mock_torch = MagicMock() - - # Mock tensor operations - mock_tensor = MagicMock() - mock_tensor.size.return_value = (1, 5, 768) # batch_size=1, seq_len=5, hidden_dim=768 - mock_tensor.__mul__ = MagicMock(return_value=mock_tensor) - - mock_sum_result = MagicMock() - mock_sum_mask = MagicMock() - mock_embeddings = MagicMock() - mock_embeddings.dtype = "float32" - mock_embeddings.detach.return_value.cpu.return_value.tolist.return_value = [[0.1, 0.2, 0.3]] - - mock_torch.sum.return_value = mock_sum_result - mock_torch.clamp.return_value = mock_sum_mask - mock_sum_result.__truediv__ = MagicMock(return_value=mock_embeddings) - - mock_torch.no_grad.return_value.__enter__ = MagicMock() - mock_torch.no_grad.return_value.__exit__ = MagicMock() - - with patch.dict(sys.modules, {"torch": mock_torch}): - from sage.common.components.sage_embedding.hf import hf_embed_batch_sync - - # Mock tokenizer - mock_tokenizer = MagicMock() - mock_encoded = MagicMock() - mock_encoded.__getitem__ = MagicMock(side_effect=lambda k: MagicMock()) - mock_encoded.to = MagicMock(return_value=mock_encoded) - mock_tokenizer.return_value = mock_encoded - - # Mock model - mock_model = MagicMock() - mock_device = MagicMock() - mock_model.parameters.return_value = iter([MagicMock(device=mock_device)]) - - # Mock outputs - mock_outputs = MagicMock() - mock_outputs.last_hidden_state = mock_tensor - mock_model.return_value = mock_outputs - - # Single text - result = hf_embed_batch_sync(["single text"], mock_tokenizer, mock_model) - assert isinstance(result, list) - assert len(result) == 1 - assert result == [[0.1, 0.2, 0.3]] - - -if __name__ == "__main__": - pytest.main([__file__, "-v", "-s"]) diff --git a/packages/sage-common/tests/components/sage_embedding/test_mock_wrapper.py b/packages/sage-common/tests/components/sage_embedding/test_mock_wrapper.py deleted file mode 100644 index 3ffa04866e..0000000000 --- a/packages/sage-common/tests/components/sage_embedding/test_mock_wrapper.py +++ /dev/null @@ -1,48 +0,0 @@ -"""Focused tests for the MockEmbedding wrapper.""" - -import pytest - -from sage.common.components.sage_embedding.wrappers.mock_wrapper import MockEmbedding - - -@pytest.mark.unit -class TestMockEmbedding: - """Validate deterministic behavior and metadata.""" - - def test_embed_returns_expected_dimension(self): - emb = MockEmbedding(fixed_dim=96) - - vector = emb.embed("hello world") - - assert isinstance(vector, list) - assert len(vector) == 96 - assert all(0 <= value <= 1 for value in vector) - - def test_embed_is_deterministic_with_seed(self): - emb1 = MockEmbedding(fixed_dim=32, seed=42) - emb2 = MockEmbedding(fixed_dim=32, seed=42) - - vec1 = emb1.embed("repeatable text") - vec2 = emb2.embed("repeatable text") - - assert vec1 == vec2 - - def test_embed_varies_without_seed(self): - emb1 = MockEmbedding(fixed_dim=32) - emb2 = MockEmbedding(fixed_dim=32) - - vec1 = emb1.embed("random text") - vec2 = emb2.embed("random text") - - # Very small chance of equality, acceptable for unit test coverage purposes - assert vec1 != vec2 - - def test_get_model_info_and_properties(self): - emb = MockEmbedding(fixed_dim=80) - - info = emb.get_model_info() - - assert info["method"] == "mockembedder" - assert info["default_dimension"] == 128 - assert emb.method_name == "mockembedder" - assert emb.get_dim() == 80 diff --git a/packages/sage-common/tests/components/sage_embedding/test_phase2_wrappers.py b/packages/sage-common/tests/components/sage_embedding/test_phase2_wrappers.py deleted file mode 100644 index e62137325f..0000000000 --- a/packages/sage-common/tests/components/sage_embedding/test_phase2_wrappers.py +++ /dev/null @@ -1,365 +0,0 @@ -""" -Phase 2 Tests: Verify all embedding wrappers are properly registered and functional. - -This test suite validates: -1. All 11 embedding methods are registered -2. Each wrapper can be imported -3. Basic instantiation works (when dependencies are available) -4. Registry provides correct metadata -""" - -import pytest - -from sage.common.components.sage_embedding import ( - check_model_availability, - get_embedding_model, - list_embedding_models, -) - - -class TestPhase2Registration: - """测试所有 embedding 方法的注册""" - - def test_all_methods_registered(self): - """测试所有 11 个方法是否已注册""" - models = list_embedding_models() - - expected_methods = { - "hash", - "mockembedder", - "hf", - "openai", - "jina", - "zhipu", - "cohere", - "bedrock", - "ollama", - "siliconcloud", - "nvidia_openai", - } - - registered_methods = set(models.keys()) - print(f"\n已注册的方法: {registered_methods}") - - assert expected_methods == registered_methods, ( - f"注册方法不匹配！\n" - f"预期: {expected_methods}\n" - f"实际: {registered_methods}\n" - f"缺失: {expected_methods - registered_methods}\n" - f"多余: {registered_methods - expected_methods}" - ) - - def test_metadata_completeness(self): - """测试所有方法的元数据是否完整""" - models = list_embedding_models() - - required_fields = { - "display_name", - "description", - "requires_api_key", - "requires_download", # 注意: factory 导出为 requires_download - "examples", # 注意: factory 导出为 examples - } - - for method, info in models.items(): - missing = required_fields - set(info.keys()) - assert not missing, f"方法 {method} 缺少字段: {missing}" - - def test_wrapper_imports(self): - """测试所有 wrapper 类可以被导入 - - Note: Heavy wrappers use lazy loading, so they must be imported - from their specific modules, not from the top-level package. - """ - from sage.common.components.sage_embedding import HashEmbedding, MockEmbedding - from sage.common.components.sage_embedding.wrappers.bedrock_wrapper import ( - BedrockEmbedding, - ) - from sage.common.components.sage_embedding.wrappers.cohere_wrapper import ( - CohereEmbedding, - ) - from sage.common.components.sage_embedding.wrappers.hf_wrapper import ( - HFEmbedding, - ) - from sage.common.components.sage_embedding.wrappers.jina_wrapper import ( - JinaEmbedding, - ) - from sage.common.components.sage_embedding.wrappers.nvidia_openai_wrapper import ( - NvidiaOpenAIEmbedding, - ) - from sage.common.components.sage_embedding.wrappers.ollama_wrapper import ( - OllamaEmbedding, - ) - from sage.common.components.sage_embedding.wrappers.openai_wrapper import ( - OpenAIEmbedding, - ) - from sage.common.components.sage_embedding.wrappers.siliconcloud_wrapper import ( - SiliconCloudEmbedding, - ) - from sage.common.components.sage_embedding.wrappers.zhipu_wrapper import ( - ZhipuEmbedding, - ) - - wrappers = [ - HashEmbedding, - MockEmbedding, - HFEmbedding, - OpenAIEmbedding, - JinaEmbedding, - ZhipuEmbedding, - CohereEmbedding, - BedrockEmbedding, - OllamaEmbedding, - SiliconCloudEmbedding, - NvidiaOpenAIEmbedding, - ] - - for wrapper_cls in wrappers: - assert wrapper_cls is not None - assert hasattr(wrapper_cls, "embed") - assert hasattr(wrapper_cls, "get_dim") - print(f"✓ {wrapper_cls.__name__} 导入成功") - - -class TestNoAPIKeyMethods: - """测试不需要 API Key 的方法（可以直接实例化）""" - - def test_hash_embedding(self): - """测试 Hash Embedding""" - emb = get_embedding_model("hash", dim=384) - assert emb is not None - assert emb.get_dim() == 384 - - vec = emb.embed("test") - assert isinstance(vec, list) - assert len(vec) == 384 - print(f"✓ Hash Embedding: {emb}") - - def test_mock_embedding(self): - """测试 Mock Embedding""" - emb = get_embedding_model("mockembedder", dim=128) - assert emb is not None - assert emb.get_dim() == 128 - - vec = emb.embed("test") - assert isinstance(vec, list) - assert len(vec) == 128 - print(f"✓ Mock Embedding: {emb}") - - -class TestAPIKeyMethods: - """测试需要 API Key 的方法（期望抛出错误）""" - - def test_openai_requires_api_key(self): - """测试 OpenAI 需要 API Key""" - import os - - from sage.common.components.sage_embedding.wrappers.openai_wrapper import ( - OpenAIEmbedding, - ) - - # 临时清除环境变量 - old_key = os.environ.pop("OPENAI_API_KEY", None) - try: - with pytest.raises(RuntimeError, match="需要 API Key"): - OpenAIEmbedding(model="text-embedding-3-small") - finally: - if old_key: - os.environ["OPENAI_API_KEY"] = old_key - - def test_jina_requires_api_key(self): - """测试 Jina 需要 API Key""" - import os - - from sage.common.components.sage_embedding.wrappers.jina_wrapper import ( - JinaEmbedding, - ) - - old_key = os.environ.pop("JINA_API_KEY", None) - try: - with pytest.raises(RuntimeError, match="需要 API Key"): - JinaEmbedding(model="jina-embeddings-v3") - finally: - if old_key: - os.environ["JINA_API_KEY"] = old_key - - def test_zhipu_requires_api_key(self): - """测试 Zhipu 需要 API Key""" - import os - - from sage.common.components.sage_embedding.wrappers.zhipu_wrapper import ( - ZhipuEmbedding, - ) - - old_key = os.environ.pop("ZHIPUAI_API_KEY", None) - try: - with pytest.raises(RuntimeError, match="需要 API Key"): - ZhipuEmbedding(model="embedding-3") - finally: - if old_key: - os.environ["ZHIPUAI_API_KEY"] = old_key - - def test_cohere_requires_api_key(self): - """测试 Cohere 需要 API Key""" - import os - - from sage.common.components.sage_embedding.wrappers.cohere_wrapper import ( - CohereEmbedding, - ) - - old_key = os.environ.pop("COHERE_API_KEY", None) - try: - with pytest.raises(RuntimeError, match="需要 API Key"): - CohereEmbedding(model="embed-multilingual-v3.0") - finally: - if old_key: - os.environ["COHERE_API_KEY"] = old_key - - def test_bedrock_requires_credentials(self): - """测试 Bedrock 需要 AWS 凭证""" - import os - - from sage.common.components.sage_embedding.wrappers.bedrock_wrapper import ( - BedrockEmbedding, - ) - - # 临时清除 AWS 环境变量 - old_keys = { - "AWS_ACCESS_KEY_ID": os.environ.pop("AWS_ACCESS_KEY_ID", None), - "AWS_SECRET_ACCESS_KEY": os.environ.pop("AWS_SECRET_ACCESS_KEY", None), - } - try: - with pytest.raises(RuntimeError, match="需要 AWS 凭证"): - BedrockEmbedding(model="amazon.titan-embed-text-v2:0") - finally: - for key, val in old_keys.items(): - if val: - os.environ[key] = val - - def test_siliconcloud_requires_api_key(self): - """测试 SiliconCloud 需要 API Key""" - import os - - from sage.common.components.sage_embedding.wrappers.siliconcloud_wrapper import ( - SiliconCloudEmbedding, - ) - - old_key = os.environ.pop("SILICONCLOUD_API_KEY", None) - try: - with pytest.raises(RuntimeError, match="需要 API Key"): - SiliconCloudEmbedding(model="netease-youdao/bce-embedding-base_v1") - finally: - if old_key: - os.environ["SILICONCLOUD_API_KEY"] = old_key - - def test_nvidia_openai_requires_api_key(self): - """测试 NVIDIA OpenAI 需要 API Key""" - import os - - from sage.common.components.sage_embedding.wrappers.nvidia_openai_wrapper import ( - NvidiaOpenAIEmbedding, - ) - - # 清除所有可能的API key环境变量 - old_nvidia_key = os.environ.pop("NVIDIA_API_KEY", None) - old_openai_key = os.environ.pop("OPENAI_API_KEY", None) - try: - with pytest.raises(RuntimeError, match="需要 API Key"): - NvidiaOpenAIEmbedding(model="nvidia/llama-3.2-nv-embedqa-1b-v1") - finally: - if old_nvidia_key: - os.environ["NVIDIA_API_KEY"] = old_nvidia_key - if old_openai_key: - os.environ["OPENAI_API_KEY"] = old_openai_key - - -class TestModelAvailability: - """测试模型可用性检查""" - - def test_hash_always_available(self): - """Hash Embedding 应该始终可用""" - result = check_model_availability("hash") - assert result["status"] == "available" - - def test_mock_always_available(self): - """Mock Embedding 应该始终可用""" - result = check_model_availability("mockembedder") - assert result["status"] == "available" - - def test_openai_needs_api_key(self): - """OpenAI 应该显示需要 API Key""" - import os - - old_key = os.environ.pop("OPENAI_API_KEY", None) - try: - result = check_model_availability("openai") - assert result["status"] == "needs_api_key" - assert "API" in result["message"] or "api" in result["message"].lower() - finally: - if old_key: - os.environ["OPENAI_API_KEY"] = old_key - - def test_hf_needs_download(self): - """HF 模型应该显示需要下载""" - result = check_model_availability("hf", model="test-model-xxx") - # HF 模型如果不存在，应该显示 needs_download - assert result["status"] in ("needs_download", "cached", "available") - - -class TestExampleModels: - """测试每个方法的示例模型列表""" - - def test_all_methods_have_examples(self): - """所有方法都应该有示例模型""" - models = list_embedding_models() - - for method, info in models.items(): - assert "examples" in info # factory 导出为 "examples" - assert len(info["examples"]) > 0, f"{method} 没有示例模型" - print(f"{method}: {info['examples']}") - - -class TestWrapperRepresentation: - """测试 wrapper 的字符串表示""" - - def test_hash_repr(self): - """测试 Hash Embedding 的 __repr__""" - emb = get_embedding_model("hash", dim=384) - repr_str = repr(emb) - assert "HashEmbedding" in repr_str - assert "384" in repr_str - print(f"Hash repr: {repr_str}") - - def test_mock_repr(self): - """测试 Mock Embedding 的 __repr__""" - emb = get_embedding_model("mockembedder", dim=128) - repr_str = repr(emb) - assert "MockEmbedding" in repr_str - assert "128" in repr_str - print(f"Mock repr: {repr_str}") - - -class TestBatchEmbedding: - """测试批量 embedding""" - - def test_hash_batch(self): - """测试 Hash 批量 embedding""" - emb = get_embedding_model("hash", dim=384) - texts = ["text1", "text2", "text3"] - vecs = emb.embed_batch(texts) - - assert len(vecs) == 3 - assert all(len(v) == 384 for v in vecs) - - def test_mock_batch(self): - """测试 Mock 批量 embedding""" - emb = get_embedding_model("mockembedder", dim=128) - texts = ["text1", "text2", "text3"] - vecs = emb.embed_batch(texts) - - assert len(vecs) == 3 - assert all(len(v) == 128 for v in vecs) - - -if __name__ == "__main__": - pytest.main([__file__, "-v", "-s"]) diff --git a/packages/sage-common/tests/components/sage_embedding/test_protocols.py b/packages/sage-common/tests/components/sage_embedding/test_protocols.py deleted file mode 100644 index 984795a2a8..0000000000 --- a/packages/sage-common/tests/components/sage_embedding/test_protocols.py +++ /dev/null @@ -1,252 +0,0 @@ -"""Tests for EmbeddingProtocol and adapters. - -This module tests the unified embedding interface protocols used by -selectors and other components that require text embeddings. -""" - -import pytest - -from sage.common.components.sage_embedding.protocols import ( - EmbeddingClientAdapter, - EmbeddingProtocol, - adapt_embedding_client, -) - - -class TestEmbeddingProtocol: - """Tests for EmbeddingProtocol interface compliance.""" - - def test_protocol_is_runtime_checkable(self): - """EmbeddingProtocol should be runtime checkable.""" - - # A class implementing the protocol - class ValidEmbedder: - def embed(self, texts: list[str], model=None) -> list[list[float]]: - return [[0.1, 0.2] for _ in texts] - - def get_dim(self) -> int: - return 2 - - embedder = ValidEmbedder() - assert isinstance(embedder, EmbeddingProtocol) - - def test_protocol_rejects_incomplete_implementation(self): - """Classes missing required methods should not match protocol.""" - - class MissingEmbed: - def get_dim(self) -> int: - return 2 - - class MissingGetDim: - def embed(self, texts: list[str], model=None) -> list[list[float]]: - return [[0.1] for _ in texts] - - assert not isinstance(MissingEmbed(), EmbeddingProtocol) - assert not isinstance(MissingGetDim(), EmbeddingProtocol) - - -class TestEmbeddingClientAdapter: - """Tests for EmbeddingClientAdapter.""" - - def test_adapter_wraps_single_text_interface(self): - """Adapter should convert single-text embed to batch embed.""" - - class SingleTextEmbedder: - def embed(self, text: str) -> list[float]: - return [len(text) * 0.1, len(text) * 0.2] - - def embed_batch(self, texts: list[str]) -> list[list[float]]: - return [self.embed(t) for t in texts] - - def get_dim(self) -> int: - return 2 - - raw = SingleTextEmbedder() - adapter = EmbeddingClientAdapter(raw) - - # Test batch embedding - result = adapter.embed(["hello", "world", "test"]) - assert len(result) == 3 - assert all(len(vec) == 2 for vec in result) - - def test_adapter_uses_embed_batch_when_available(self): - """Adapter should prefer embed_batch for efficiency.""" - batch_called = [] - - class EmbedderWithBatch: - def embed(self, text: str) -> list[float]: - return [0.1] - - def embed_batch(self, texts: list[str]) -> list[list[float]]: - batch_called.append(len(texts)) - return [[0.1] for _ in texts] - - def get_dim(self) -> int: - return 1 - - adapter = EmbeddingClientAdapter(EmbedderWithBatch()) - adapter.embed(["a", "b", "c"]) - - assert batch_called == [3], "Should call embed_batch once with all texts" - - def test_adapter_fallback_to_single_embed(self): - """Adapter should fallback to single embed if batch unavailable.""" - single_calls = [] - - class EmbedderWithoutBatch: - def embed(self, text: str) -> list[float]: - single_calls.append(text) - return [0.1] - - def get_dim(self) -> int: - return 1 - - adapter = EmbeddingClientAdapter(EmbedderWithoutBatch()) - result = adapter.embed(["x", "y"]) - - assert len(result) == 2 - assert single_calls == ["x", "y"] - - def test_adapter_get_dim(self): - """Adapter should delegate get_dim to wrapped embedder.""" - - class Embedder384: - def embed(self, text: str) -> list[float]: - return [0.0] * 384 - - def get_dim(self) -> int: - return 384 - - adapter = EmbeddingClientAdapter(Embedder384()) - assert adapter.get_dim() == 384 - - def test_adapter_ignores_model_parameter(self): - """Adapter should accept but ignore model parameter.""" - - class SimpleEmbedder: - def embed(self, text: str) -> list[float]: - return [1.0] - - def embed_batch(self, texts: list[str]) -> list[list[float]]: - return [[1.0] for _ in texts] - - def get_dim(self) -> int: - return 1 - - adapter = EmbeddingClientAdapter(SimpleEmbedder()) - # Should work with any model parameter - result = adapter.embed(["test"], model="any-model") - assert result == [[1.0]] - - -class TestAdaptEmbeddingClient: - """Tests for adapt_embedding_client function.""" - - def test_adapt_passes_through_compliant_embedder(self): - """Embedders with batch interface should not be wrapped.""" - - class BatchEmbedder: - def embed(self, texts: list[str], model=None) -> list[list[float]]: - return [[0.1] for _ in texts] - - def get_dim(self) -> int: - return 1 - - original = BatchEmbedder() - adapted = adapt_embedding_client(original) - - # Should return the same instance - assert adapted is original - - def test_adapt_wraps_single_text_embedder(self): - """Single-text embedders should be wrapped with adapter.""" - - class SingleTextEmbedder: - def embed(self, text: str) -> list[float]: - return [0.1] - - def embed_batch(self, texts: list[str]) -> list[list[float]]: - return [[0.1] for _ in texts] - - def get_dim(self) -> int: - return 1 - - original = SingleTextEmbedder() - adapted = adapt_embedding_client(original) - - assert isinstance(adapted, EmbeddingClientAdapter) - assert adapted is not original - - def test_adapt_raises_for_invalid_embedder(self): - """Should raise TypeError for objects without embed/get_dim.""" - - class NotAnEmbedder: - pass - - with pytest.raises(TypeError, match="Missing 'embed' or 'get_dim' method"): - adapt_embedding_client(NotAnEmbedder()) - - def test_adapt_handles_embedder_with_text_param(self): - """Embedders with 'text' parameter should be wrapped.""" - - class TextParamEmbedder: - def embed(self, text: str) -> list[float]: - return [0.5] - - def get_dim(self) -> int: - return 1 - - adapted = adapt_embedding_client(TextParamEmbedder()) - assert isinstance(adapted, EmbeddingClientAdapter) - - -class TestIntegrationWithRealEmbedders: - """Integration tests with actual embedding implementations.""" - - def test_adapt_hash_embedding(self): - """Test adapting HashEmbedding from factory.""" - from sage.common.components.sage_embedding.factory import EmbeddingFactory - - # Create hash embedder (lightweight, no model download) - raw = EmbeddingFactory.create("hash", dim=64) - adapted = adapt_embedding_client(raw) - - # Should be wrapped - assert isinstance(adapted, EmbeddingClientAdapter) - - # Should work with batch interface - vectors = adapted.embed(["hello", "world"]) - assert len(vectors) == 2 - assert all(len(v) == 64 for v in vectors) - - # Dimension should match - assert adapted.get_dim() == 64 - - def test_adapt_mock_embedding(self): - """Test adapting MockEmbedding.""" - from sage.common.components.sage_embedding.factory import EmbeddingFactory - - raw = EmbeddingFactory.create("mockembedder", fixed_dim=128) - adapted = adapt_embedding_client(raw) - - vectors = adapted.embed(["test1", "test2", "test3"]) - assert len(vectors) == 3 - assert all(len(v) == 128 for v in vectors) - - def test_gorilla_selector_interface_compatibility(self): - """Test that adapted embedder works with Gorilla's expected interface.""" - from sage.common.components.sage_embedding.factory import EmbeddingFactory - - # This is what Gorilla selector expects - raw = EmbeddingFactory.create("hash", dim=64) - embedding_client = EmbeddingClientAdapter(raw) - - # Gorilla calls: embed(texts=[...], model=...) - result = embedding_client.embed( - texts=["search query", "tool description"], - model="default", # Gorilla passes model parameter - ) - - assert len(result) == 2 - assert all(isinstance(v, list) for v in result) - assert all(isinstance(x, float) for v in result for x in v) diff --git a/packages/sage-common/tests/components/sage_embedding/test_registry.py b/packages/sage-common/tests/components/sage_embedding/test_registry.py deleted file mode 100644 index 5892cd2747..0000000000 --- a/packages/sage-common/tests/components/sage_embedding/test_registry.py +++ /dev/null @@ -1,141 +0,0 @@ -"""Unit tests for the EmbeddingRegistry helper.""" - -from __future__ import annotations - -import pytest - -from sage.common.components.sage_embedding.registry import EmbeddingRegistry, ModelStatus - - -class DummyWrapper: - """Minimal wrapper used for registry tests.""" - - def __init__(self, **_kwargs): - self.called = True - - -@pytest.fixture(autouse=True) -def reset_registry(): - """Ensure a clean registry for every test to avoid cross contamination.""" - - EmbeddingRegistry.clear() - yield - EmbeddingRegistry.clear() - - -@pytest.mark.unit -class TestEmbeddingRegistry: - def test_register_and_list_methods(self): - EmbeddingRegistry.register( - method="mock", - display_name="Mock", - description="Mock embedding", - wrapper_class=DummyWrapper, - default_dimension=42, - example_models=["demo"], - ) - - methods = EmbeddingRegistry.list_methods() - info = EmbeddingRegistry.get_model_info("mock") - - assert methods == ["mock"] - assert info is not None - assert info.display_name == "Mock" - assert info.default_dimension == 42 - assert info.example_models == ["demo"] - - def test_get_wrapper_class_supports_string_path(self): - EmbeddingRegistry.register( - method="string_wrapper", - display_name="String Wrapper", - description="Uses import string", - wrapper_class=f"{__name__}:DummyWrapper", - ) - - wrapper_cls = EmbeddingRegistry.get_wrapper_class("string_wrapper") - - assert wrapper_cls is DummyWrapper - - def test_check_status_needs_api_key(self, monkeypatch): - monkeypatch.delenv("SECURE_API_KEY", raising=False) - monkeypatch.delenv("OPENAI_API_KEY", raising=False) - - EmbeddingRegistry.register( - method="secure", - display_name="Needs Key", - description="", - wrapper_class=DummyWrapper, - requires_api_key=True, - ) - - assert EmbeddingRegistry.check_status("secure") == ModelStatus.NEEDS_API_KEY - status_with_key = EmbeddingRegistry.check_status("secure", api_key="fake-key") - assert status_with_key == ModelStatus.AVAILABLE - - def test_check_status_model_download(self, monkeypatch): - EmbeddingRegistry.register( - method="hf", - display_name="HF", - description="", - wrapper_class=DummyWrapper, - requires_model_download=True, - ) - - monkeypatch.setattr( - EmbeddingRegistry, - "_is_model_cached", - classmethod(lambda cls, model_name: False), - ) - assert ( - EmbeddingRegistry.check_status("hf", model="test/model") == ModelStatus.NEEDS_DOWNLOAD - ) - - monkeypatch.setattr( - EmbeddingRegistry, - "_is_model_cached", - classmethod(lambda cls, model_name: True), - ) - assert EmbeddingRegistry.check_status("hf", model="test/model") == ModelStatus.CACHED - - def test_is_model_cached_reads_filesystem(self, monkeypatch, tmp_path): - cache_dir = tmp_path / ".cache" / "huggingface" / "hub" - cache_dir.mkdir(parents=True) - cached_model_dir = cache_dir / "models--foo--bar" - cached_model_dir.mkdir() - - monkeypatch.setattr( - "sage.common.components.sage_embedding.registry.Path.home", lambda: tmp_path - ) - - assert EmbeddingRegistry._is_model_cached("foo/bar") is True - assert EmbeddingRegistry._is_model_cached("other/model") is False - - def test_check_status_unavailable_for_unknown_method(self): - """Test check_status returns UNAVAILABLE for unregistered method""" - # Don't register anything, just check a non-existent method - status = EmbeddingRegistry.check_status("nonexistent_method") - assert status == ModelStatus.UNAVAILABLE - - def test_is_model_cached_returns_false_on_exception(self, monkeypatch): - """Test _is_model_cached returns False when exception occurs""" - - def raise_exception(*args, **kwargs): - raise RuntimeError("Simulated filesystem error") - - # Make Path.home() raise an exception - monkeypatch.setattr( - "sage.common.components.sage_embedding.registry.Path.home", raise_exception - ) - - # Should return False instead of propagating the exception - assert EmbeddingRegistry._is_model_cached("any/model") is False - - def test_is_model_cached_returns_false_when_cache_dir_not_exists(self, monkeypatch, tmp_path): - """Test _is_model_cached returns False when cache directory doesn't exist""" - # Use tmp_path without creating the cache directory - monkeypatch.setattr( - "sage.common.components.sage_embedding.registry.Path.home", lambda: tmp_path - ) - - # Cache directory doesn't exist, should return False - assert EmbeddingRegistry._is_model_cached("any/model") is False diff --git a/packages/sage-common/tests/components/sage_embedding/test_wrappers_comprehensive.py b/packages/sage-common/tests/components/sage_embedding/test_wrappers_comprehensive.py deleted file mode 100644 index 192ccec242..0000000000 --- a/packages/sage-common/tests/components/sage_embedding/test_wrappers_comprehensive.py +++ /dev/null @@ -1,658 +0,0 @@ -""" -Comprehensive tests for all embedding wrappers. - -This test suite provides detailed coverage for: -- OpenAI, Jina, Zhipu, Cohere, Bedrock, Ollama, SiliconCloud, NVIDIA, HuggingFace wrappers -- Initialization, configuration validation -- Single and batch embedding -- Error handling and retry logic -- API key management -""" - -import base64 -import struct -from unittest.mock import MagicMock, Mock, patch - -import pytest - -# ============================================================================== -# OpenAI Wrapper Tests -# ============================================================================== - - -class TestOpenAIWrapper: - """Tests for OpenAI embedding wrapper""" - - def test_initialization_with_api_key(self): - """Test initialization with explicit API key""" - from sage.common.components.sage_embedding.wrappers.openai_wrapper import ( - OpenAIEmbedding, - ) - - wrapper = OpenAIEmbedding( - model="text-embedding-3-small", - api_key="test-key-12345", # pragma: allowlist secret - ) - assert wrapper._model == "text-embedding-3-small" - assert wrapper._api_key == "test-key-12345" # pragma: allowlist secret - assert wrapper._dim == 1536 - - def test_initialization_from_env(self, monkeypatch): - """Test initialization from environment variable""" - from sage.common.components.sage_embedding.wrappers.openai_wrapper import ( - OpenAIEmbedding, - ) - - monkeypatch.setenv("OPENAI_API_KEY", "env-key-67890") # pragma: allowlist secret - wrapper = OpenAIEmbedding() - assert wrapper._api_key == "env-key-67890" # pragma: allowlist secret - - def test_initialization_without_api_key(self, monkeypatch): - """Test initialization fails without API key""" - from sage.common.components.sage_embedding.wrappers.openai_wrapper import ( - OpenAIEmbedding, - ) - - monkeypatch.delenv("OPENAI_API_KEY", raising=False) - - with pytest.raises(RuntimeError, match="需要 API Key"): - OpenAIEmbedding() - - def test_initialization_with_custom_base_url(self): - """Test initialization with custom base URL""" - from sage.common.components.sage_embedding.wrappers.openai_wrapper import ( - OpenAIEmbedding, - ) - - wrapper = OpenAIEmbedding(api_key="test-key", base_url="http://localhost:8000/v1") - assert wrapper._base_url == "http://localhost:8000/v1" - - @patch("openai.OpenAI") - def test_embed_success(self, mock_openai_class, mock_openai_response, sample_text): - """Test successful single text embedding""" - from sage.common.components.sage_embedding.wrappers.openai_wrapper import ( - OpenAIEmbedding, - ) - - mock_client = Mock() - mock_client.embeddings.create.return_value = mock_openai_response - mock_openai_class.return_value = mock_client - - wrapper = OpenAIEmbedding(api_key="test-key") - result = wrapper.embed(sample_text) - - assert len(result) == 1536 - assert all(isinstance(x, float) for x in result) - mock_client.embeddings.create.assert_called_once_with( - model="text-embedding-3-small", input=sample_text - ) - - @patch("openai.OpenAI") - def test_embed_batch_success(self, mock_openai_class, mock_openai_batch_response, sample_texts): - """Test successful batch embedding""" - from sage.common.components.sage_embedding.wrappers.openai_wrapper import ( - OpenAIEmbedding, - ) - - mock_client = Mock() - mock_client.embeddings.create.return_value = mock_openai_batch_response - mock_openai_class.return_value = mock_client - - wrapper = OpenAIEmbedding(api_key="test-key") - results = wrapper.embed_batch(sample_texts[:2]) - - assert len(results) == 2 - assert all(len(r) == 1536 for r in results) - mock_client.embeddings.create.assert_called_once() - - @patch("openai.OpenAI") - def test_embed_empty_batch(self, mock_openai_class): - """Test batch embedding with empty list""" - from sage.common.components.sage_embedding.wrappers.openai_wrapper import ( - OpenAIEmbedding, - ) - - wrapper = OpenAIEmbedding(api_key="test-key") - results = wrapper.embed_batch([]) - - assert results == [] - - @patch("openai.OpenAI") - def test_embed_api_error(self, mock_openai_class, sample_text): - """Test error handling when API fails""" - from sage.common.components.sage_embedding.wrappers.openai_wrapper import ( - OpenAIEmbedding, - ) - - mock_client = Mock() - mock_client.embeddings.create.side_effect = Exception("API Error") - mock_openai_class.return_value = mock_client - - wrapper = OpenAIEmbedding(api_key="test-key") - - with pytest.raises(RuntimeError, match="OpenAI embedding 失败"): - wrapper.embed(sample_text) - - def test_dimension_inference(self): - """Test dimension inference for known models""" - from sage.common.components.sage_embedding.wrappers.openai_wrapper import ( - OpenAIEmbedding, - ) - - wrapper_small = OpenAIEmbedding(model="text-embedding-3-small", api_key="test-key") - assert wrapper_small._dim == 1536 - - wrapper_large = OpenAIEmbedding(model="text-embedding-3-large", api_key="test-key") - assert wrapper_large._dim == 3072 - - -# ============================================================================== -# Jina Wrapper Tests -# ============================================================================== - - -class TestJinaWrapper: - """Tests for Jina embedding wrapper""" - - def test_initialization_with_api_key(self): - """Test initialization with explicit API key""" - from sage.common.components.sage_embedding.wrappers.jina_wrapper import ( - JinaEmbedding, - ) - - wrapper = JinaEmbedding(api_key="jina-test-key") # pragma: allowlist secret - assert wrapper._api_key == "jina-test-key" # pragma: allowlist secret - # Use actual default model name - assert wrapper._model in ["jina-embeddings-v3", "jina-embeddings-v2-base-en"] - - def test_initialization_from_env(self, monkeypatch): - """Test initialization from environment variable""" - from sage.common.components.sage_embedding.wrappers.jina_wrapper import ( - JinaEmbedding, - ) - - monkeypatch.setenv("JINA_API_KEY", "env-jina-key") # pragma: allowlist secret - wrapper = JinaEmbedding() - assert wrapper._api_key == "env-jina-key" # pragma: allowlist secret - - def test_initialization_without_api_key(self, monkeypatch): - """Test initialization fails without API key""" - from sage.common.components.sage_embedding.wrappers.jina_wrapper import ( - JinaEmbedding, - ) - - monkeypatch.delenv("JINA_API_KEY", raising=False) - - with pytest.raises(RuntimeError, match="需要 API Key"): - JinaEmbedding() - - @patch("requests.post") - def test_embed_success(self, mock_post, mock_jina_response, sample_text): - """Test successful embedding""" - from sage.common.components.sage_embedding.wrappers.jina_wrapper import ( - JinaEmbedding, - ) - - mock_post.return_value = mock_jina_response - - wrapper = JinaEmbedding(api_key="test-key") - result = wrapper.embed(sample_text) - - assert len(result) == 768 # jina-embeddings-v2-base-en dimension - assert all(isinstance(x, float) for x in result) - - @patch("requests.post") - def test_embed_api_error(self, mock_post, sample_text): - """Test error handling""" - from sage.common.components.sage_embedding.wrappers.jina_wrapper import ( - JinaEmbedding, - ) - - mock_post.side_effect = Exception("Network error") - - wrapper = JinaEmbedding(api_key="test-key") - - with pytest.raises(RuntimeError, match="Jina embedding 失败"): - wrapper.embed(sample_text) - - -# ============================================================================== -# Zhipu Wrapper Tests -# ============================================================================== - - -class TestZhipuWrapper: - """Tests for Zhipu embedding wrapper""" - - def test_initialization_with_api_key(self): - """Test initialization with explicit API key""" - from sage.common.components.sage_embedding.wrappers.zhipu_wrapper import ( - ZhipuEmbedding, - ) - - wrapper = ZhipuEmbedding(api_key="zhipu-test-key") # pragma: allowlist secret - assert wrapper._api_key == "zhipu-test-key" # pragma: allowlist secret - # Model name may be embedding-2 or embedding-3 - assert wrapper._model in ["embedding-2", "embedding-3"] - - def test_initialization_from_env(self, monkeypatch): - """Test initialization from environment variable""" - from sage.common.components.sage_embedding.wrappers.zhipu_wrapper import ( - ZhipuEmbedding, - ) - - monkeypatch.setenv("ZHIPU_API_KEY", "env-zhipu-key") # pragma: allowlist secret - wrapper = ZhipuEmbedding() - assert wrapper._api_key == "env-zhipu-key" # pragma: allowlist secret - - def test_initialization_without_api_key(self, monkeypatch): - """Test initialization fails without API key""" - from sage.common.components.sage_embedding.wrappers.zhipu_wrapper import ( - ZhipuEmbedding, - ) - - monkeypatch.delenv("ZHIPU_API_KEY", raising=False) - - with pytest.raises(RuntimeError, match="需要 API Key"): - ZhipuEmbedding() - - @patch("zhipuai.ZhipuAI") - def test_embed_success(self, mock_zhipu_class, mock_zhipu_response, sample_text): - """Test successful embedding""" - from sage.common.components.sage_embedding.wrappers.zhipu_wrapper import ( - ZhipuEmbedding, - ) - - mock_client = Mock() - mock_client.embeddings.create.return_value = mock_zhipu_response - mock_zhipu_class.return_value = mock_client - - wrapper = ZhipuEmbedding(api_key="test-key") - result = wrapper.embed(sample_text) - - assert len(result) == 1536 - assert all(isinstance(x, float) for x in result) - - -# ============================================================================== -# Cohere Wrapper Tests -# ============================================================================== - - -class TestCohereWrapper: - """Tests for Cohere embedding wrapper""" - - def test_initialization_with_api_key(self): - """Test initialization with explicit API key""" - from sage.common.components.sage_embedding.wrappers.cohere_wrapper import ( - CohereEmbedding, - ) - - wrapper = CohereEmbedding(api_key="cohere-test-key") # pragma: allowlist secret - assert wrapper._api_key == "cohere-test-key" # pragma: allowlist secret - # Model name may vary - assert "embed" in wrapper._model - - def test_initialization_from_env(self, monkeypatch): - """Test initialization from environment variable""" - from sage.common.components.sage_embedding.wrappers.cohere_wrapper import ( - CohereEmbedding, - ) - - monkeypatch.setenv("COHERE_API_KEY", "env-cohere-key") # pragma: allowlist secret - wrapper = CohereEmbedding() - assert wrapper._api_key == "env-cohere-key" # pragma: allowlist secret - - def test_initialization_without_api_key(self, monkeypatch): - """Test initialization fails without API key""" - from sage.common.components.sage_embedding.wrappers.cohere_wrapper import ( - CohereEmbedding, - ) - - monkeypatch.delenv("COHERE_API_KEY", raising=False) - - with pytest.raises(RuntimeError, match="需要 API Key"): - CohereEmbedding() - - @pytest.mark.skip(reason="Requires valid Cohere API key") - @patch("cohere.ClientV2") - def test_embed_success(self, mock_cohere_class, mock_cohere_response, sample_text): - """Test successful embedding""" - from sage.common.components.sage_embedding.wrappers.cohere_wrapper import ( - CohereEmbedding, - ) - - mock_client = Mock() - mock_client.embed.return_value = mock_cohere_response - mock_cohere_class.return_value = mock_client - - wrapper = CohereEmbedding(api_key="test-key") - result = wrapper.embed(sample_text) - - assert len(result) == 1536 - assert all(isinstance(x, float) for x in result) - - -# ============================================================================== -# Ollama Wrapper Tests -# ============================================================================== - - -class TestOllamaWrapper: - """Tests for Ollama embedding wrapper""" - - def test_initialization_default(self): - """Test initialization with defaults""" - from sage.common.components.sage_embedding.wrappers.ollama_wrapper import ( - OllamaEmbedding, - ) - - wrapper = OllamaEmbedding() - assert wrapper._model == "nomic-embed-text" - assert wrapper._base_url == "http://localhost:11434" - - def test_initialization_custom_url(self): - """Test initialization with custom URL""" - from sage.common.components.sage_embedding.wrappers.ollama_wrapper import ( - OllamaEmbedding, - ) - - wrapper = OllamaEmbedding(base_url="http://custom:8080") - assert wrapper._base_url == "http://custom:8080" - - @pytest.mark.skip(reason="Requires running Ollama service") - @patch("requests.post") - def test_embed_success(self, mock_post, mock_ollama_response, sample_text): - """Test successful embedding""" - from sage.common.components.sage_embedding.wrappers.ollama_wrapper import ( - OllamaEmbedding, - ) - - mock_post.return_value = mock_ollama_response - - wrapper = OllamaEmbedding() - result = wrapper.embed(sample_text) - - assert len(result) == 768 - assert all(isinstance(x, float) for x in result) - - @pytest.mark.skip(reason="Requires running Ollama service") - @patch("requests.post") - def test_embed_api_error(self, mock_post, sample_text): - """Test error handling""" - from sage.common.components.sage_embedding.wrappers.ollama_wrapper import ( - OllamaEmbedding, - ) - - mock_response = Mock() - mock_response.status_code = 500 - mock_response.text = "Internal Server Error" - mock_post.return_value = mock_response - - wrapper = OllamaEmbedding() - - with pytest.raises(RuntimeError, match="Ollama embedding 失败"): - wrapper.embed(sample_text) - - -# ============================================================================== -# SiliconCloud Wrapper Tests -# ============================================================================== - - -class TestSiliconCloudWrapper: - """Tests for SiliconCloud embedding wrapper""" - - def test_initialization_with_api_key(self): - """Test initialization with explicit API key""" - from sage.common.components.sage_embedding.wrappers.siliconcloud_wrapper import ( - SiliconCloudEmbedding, - ) - - wrapper = SiliconCloudEmbedding(api_key="silicon-test-key") # pragma: allowlist secret - assert wrapper._api_key == "silicon-test-key" # pragma: allowlist secret - - @pytest.mark.skip(reason="Wrapper may have different default behavior") - def test_initialization_from_env(self, monkeypatch): - """Test initialization from environment variable""" - from sage.common.components.sage_embedding.wrappers.siliconcloud_wrapper import ( - SiliconCloudEmbedding, - ) - - monkeypatch.setenv("SILICONFLOW_API_KEY", "env-silicon-key") # pragma: allowlist secret - wrapper = SiliconCloudEmbedding() - assert wrapper._api_key == "env-silicon-key" # pragma: allowlist secret - - @pytest.mark.skip(reason="Wrapper may provide default key") - def test_initialization_without_api_key(self, monkeypatch): - """Test initialization fails without API key""" - from sage.common.components.sage_embedding.wrappers.siliconcloud_wrapper import ( - SiliconCloudEmbedding, - ) - - monkeypatch.delenv("SILICONFLOW_API_KEY", raising=False) - - with pytest.raises(RuntimeError, match="需要 API Key"): - SiliconCloudEmbedding() - - @patch("requests.post") - def test_embed_batch_single_request(self, mock_post): - """Batch embedding should call SiliconCloud once when under batch size""" - from sage.common.components.sage_embedding.wrappers.siliconcloud_wrapper import ( - SiliconCloudEmbedding, - ) - - mock_post.return_value = self._mock_siliconcloud_response([[1.0, 2.0], [3.0, 4.0]]) - - wrapper = SiliconCloudEmbedding(api_key="test-key", batch_size=8) - texts = ["foo", "bar"] - result = wrapper.embed_batch(texts) - - assert result == [[1.0, 2.0], [3.0, 4.0]] - assert mock_post.call_count == 1 - payload = mock_post.call_args.kwargs["json"] - assert payload["input"] == texts - - @patch("requests.post") - def test_embed_batch_chunked_requests(self, mock_post): - """Batch embedding should respect configured batch_size""" - from sage.common.components.sage_embedding.wrappers.siliconcloud_wrapper import ( - SiliconCloudEmbedding, - ) - - mock_post.side_effect = [ - self._mock_siliconcloud_response([[1.0]]), - self._mock_siliconcloud_response([[2.0]]), - self._mock_siliconcloud_response([[3.0]]), - ] - - wrapper = SiliconCloudEmbedding(api_key="test-key", batch_size=1) - texts = ["alpha", "beta", "gamma"] - result = wrapper.embed_batch(texts) - - assert result == [[1.0], [2.0], [3.0]] - assert mock_post.call_count == 3 - - inputs = [call.kwargs["json"]["input"] for call in mock_post.call_args_list] - assert inputs == [["alpha"], ["beta"], ["gamma"]] - - @staticmethod - def _mock_siliconcloud_response(vectors: list[list[float]]) -> Mock: - """Build a fake SiliconCloud response with base64 embeddings""" - - def _encode(vec: list[float]) -> str: - return base64.b64encode(struct.pack("<" + "f" * len(vec), *vec)).decode("utf-8") - - data = [{"embedding": _encode(vec)} for vec in vectors] - - mock_response = Mock() - mock_response.status_code = 200 - mock_response.raise_for_status = Mock() - mock_response.json.return_value = {"data": data} - return mock_response - - -# ============================================================================== -# NVIDIA OpenAI Wrapper Tests -# ============================================================================== - - -class TestNvidiaOpenAIWrapper: - """Tests for NVIDIA OpenAI-compatible wrapper""" - - def test_initialization_with_api_key(self): - """Test initialization with explicit API key""" - from sage.common.components.sage_embedding.wrappers.nvidia_openai_wrapper import ( - NvidiaOpenAIEmbedding, - ) - - wrapper = NvidiaOpenAIEmbedding(api_key="nvidia-test-key") # pragma: allowlist secret - assert wrapper._api_key == "nvidia-test-key" # pragma: allowlist secret - - def test_initialization_from_env(self, monkeypatch): - """Test initialization from environment variable""" - from sage.common.components.sage_embedding.wrappers.nvidia_openai_wrapper import ( - NvidiaOpenAIEmbedding, - ) - - monkeypatch.setenv("NVIDIA_API_KEY", "env-nvidia-key") # pragma: allowlist secret - wrapper = NvidiaOpenAIEmbedding() - assert wrapper._api_key == "env-nvidia-key" # pragma: allowlist secret - - @pytest.mark.skip(reason="Wrapper may have different validation") - def test_initialization_without_api_key(self, monkeypatch): - """Test initialization fails without API key""" - from sage.common.components.sage_embedding.wrappers.nvidia_openai_wrapper import ( - NvidiaOpenAIEmbedding, - ) - - monkeypatch.delenv("NVIDIA_API_KEY", raising=False) - - with pytest.raises(RuntimeError, match="需要 API Key"): - NvidiaOpenAIEmbedding() - - -# ============================================================================== -# Bedrock Wrapper Tests -# ============================================================================== - - -class TestBedrockWrapper: - """Tests for AWS Bedrock embedding wrapper""" - - @pytest.mark.skip(reason="Requires AWS credentials") - @patch("boto3.client") - def test_initialization_default(self, mock_boto_client): - """Test initialization with defaults""" - from sage.common.components.sage_embedding.wrappers.bedrock_wrapper import ( - BedrockEmbedding, - ) - - wrapper = BedrockEmbedding() - assert wrapper._model == "amazon.titan-embed-text-v1" - assert wrapper._region == "us-east-1" - - @pytest.mark.skip(reason="Requires AWS credentials") - @patch("boto3.client") - def test_initialization_custom_region(self, mock_boto_client): - """Test initialization with custom region""" - from sage.common.components.sage_embedding.wrappers.bedrock_wrapper import ( - BedrockEmbedding, - ) - - wrapper = BedrockEmbedding(region="us-west-2") - assert wrapper._region == "us-west-2" - - @pytest.mark.skip(reason="Requires AWS credentials") - @patch("boto3.client") - def test_embed_success(self, mock_boto_client, mock_bedrock_response, sample_text): - """Test successful embedding""" - import json - - from sage.common.components.sage_embedding.wrappers.bedrock_wrapper import ( - BedrockEmbedding, - ) - - mock_client = Mock() - mock_client.invoke_model.return_value = { - "body": Mock(read=lambda: json.dumps(mock_bedrock_response).encode()) - } - mock_boto_client.return_value = mock_client - - wrapper = BedrockEmbedding() - result = wrapper.embed(sample_text) - - assert len(result) == 1536 - assert all(isinstance(x, float) for x in result) - - -# ============================================================================== -# HuggingFace Wrapper Tests -# ============================================================================== - - -class TestHFWrapper: - """Tests for HuggingFace embedding wrapper""" - - @pytest.mark.skip(reason="Requires downloading HF models") - def test_initialization_default(self): - """Test initialization with default model""" - from sage.common.components.sage_embedding.wrappers.hf_wrapper import ( - HFEmbedding, - ) - - # Note: This will try to load model, so we skip actual loading - # Just test that class can be imported - assert HFEmbedding is not None - - @pytest.mark.skip(reason="Requires downloading HF models") - @patch("transformers.AutoModel.from_pretrained") - @patch("transformers.AutoTokenizer.from_pretrained") - def test_initialization_custom_model(self, mock_tokenizer, mock_model): - """Test initialization with custom model""" - from sage.common.components.sage_embedding.wrappers.hf_wrapper import ( - HFEmbedding, - ) - - mock_model.return_value = MagicMock() - mock_tokenizer.return_value = MagicMock() - - wrapper = HFEmbedding(model="sentence-transformers/all-MiniLM-L6-v2") - assert wrapper._model_name == "sentence-transformers/all-MiniLM-L6-v2" - - @pytest.mark.skip(reason="Requires actual torch and transformers") - @patch("transformers.AutoModel.from_pretrained") - @patch("transformers.AutoTokenizer.from_pretrained") - @patch("torch.no_grad") - def test_embed_success(self, mock_no_grad, mock_tokenizer_class, mock_model_class, sample_text): - """Test successful embedding""" - from sage.common.components.sage_embedding.wrappers.hf_wrapper import ( - HFEmbedding, - ) - - # Setup mocks - mock_tokenizer = MagicMock() - mock_model = MagicMock() - - mock_tokenizer_class.return_value = mock_tokenizer - mock_model_class.return_value = mock_model - - # Mock tokenizer output - mock_tokenizer.return_value = {"input_ids": MagicMock(), "attention_mask": MagicMock()} - - # Mock model output - import torch - - mock_output = MagicMock() - mock_tensor = torch.randn(1, 10, 768) # batch, seq_len, hidden_size - mock_output.last_hidden_state = mock_tensor - mock_model.return_value = mock_output - - mock_no_grad.return_value.__enter__ = Mock() - mock_no_grad.return_value.__exit__ = Mock() - - wrapper = HFEmbedding(model="test-model") - result = wrapper.embed(sample_text) - - assert isinstance(result, list) - assert len(result) > 0 diff --git a/packages/sage-common/tests/conftest.py b/packages/sage-common/tests/conftest.py deleted file mode 100644 index 1007185ddb..0000000000 --- a/packages/sage-common/tests/conftest.py +++ /dev/null @@ -1,14 +0,0 @@ -""" -pytest 配置文件 -""" - -import sys -from pathlib import Path - -HERE = Path(__file__).resolve().parent -ROOT = HERE.parent -SRC_COMMON = ROOT / "src" - -# 添加 sage-common 源码路径到 Python 路径 -if str(SRC_COMMON) not in sys.path: - sys.path.insert(0, str(SRC_COMMON)) diff --git a/packages/sage-common/tests/integration/__init__.py b/packages/sage-common/tests/integration/__init__.py deleted file mode 100644 index 5a87ee3892..0000000000 --- a/packages/sage-common/tests/integration/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -# SPDX-License-Identifier: Apache-2.0 -# SPDX-FileCopyrightText: Copyright contributors to the SAGE project - -"""Integration tests for sage-common components.""" diff --git a/packages/sage-common/tests/integration/conftest.py b/packages/sage-common/tests/integration/conftest.py deleted file mode 100644 index c1a6cd451e..0000000000 --- a/packages/sage-common/tests/integration/conftest.py +++ /dev/null @@ -1,39 +0,0 @@ -# SPDX-License-Identifier: Apache-2.0 -# SPDX-FileCopyrightText: Copyright contributors to the SAGE project - -"""Pytest fixtures for integration tests. - -This module provides shared fixtures for integration testing. - -NOTE: LLM/Control Plane specific fixtures have been moved to isagellm. -See: pip install isagellm -""" - -from __future__ import annotations - -import asyncio -import os -import sys -from unittest.mock import AsyncMock - -import pytest - -# Add source path -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../../src")) - - -@pytest.fixture -def mock_async_client(): - """Create a mock async HTTP client.""" - client = AsyncMock() - client.get = AsyncMock() - client.post = AsyncMock() - return client - - -@pytest.fixture -def event_loop(): - """Create an event loop for async tests.""" - loop = asyncio.new_event_loop() - yield loop - loop.close() diff --git a/packages/sage-common/tests/unit/__init__.py b/packages/sage-common/tests/unit/__init__.py deleted file mode 100644 index fe2c39146b..0000000000 --- a/packages/sage-common/tests/unit/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -""" -sage-common 单元测试包 -""" diff --git a/packages/sage-common/tests/unit/config/test_output_paths.py b/packages/sage-common/tests/unit/config/test_output_paths.py deleted file mode 100644 index 0cc1e36e4f..0000000000 --- a/packages/sage-common/tests/unit/config/test_output_paths.py +++ /dev/null @@ -1,295 +0,0 @@ -"""Tests for SAGE output paths configuration module.""" - -import os -from unittest.mock import patch - -import pytest - -from sage.common.config.output_paths import ( - SageOutputPaths, - find_sage_project_root, - get_appropriate_sage_dir, - get_logs_dir, - get_output_dir, - get_sage_paths, - get_temp_dir, - initialize_sage_paths, -) - - -@pytest.fixture(autouse=True) -def isolate_sage_paths(monkeypatch): - """ - Isolate SAGE paths for each test to avoid conflicts in parallel test runs. - - This fixture: - 1. Clears the cache before the test - 2. Removes SAGE_OUTPUT_DIR to avoid interference - 3. Clears the cache after the test - """ - # Clear cache before test - get_sage_paths.cache_clear() - - # Remove SAGE_OUTPUT_DIR to let tests control their own paths - monkeypatch.delenv("SAGE_OUTPUT_DIR", raising=False) - - yield - - # Clear cache after test - get_sage_paths.cache_clear() - - -class TestFindSageProjectRoot: - """Tests for find_sage_project_root function.""" - - def test_find_from_sage_project(self, tmp_path): - """测试从SAGE项目内部查找项目根目录""" - # 创建SAGE项目结构 - project_root = tmp_path / "sage_project" - project_root.mkdir() - (project_root / "packages").mkdir() - (project_root / "packages" / "sage-common").mkdir() - - # 从子目录查找 - subdir = project_root / "packages" / "sage-common" / "src" - subdir.mkdir(parents=True) - - found_root = find_sage_project_root(subdir) - assert found_root == project_root - - def test_find_from_packages_marker(self, tmp_path): - """测试使用packages标记查找项目根目录""" - project_root = tmp_path / "project" - project_root.mkdir() - (project_root / "packages").mkdir() - (project_root / "packages" / "sage").mkdir(parents=True) - - found_root = find_sage_project_root(project_root / "packages") - assert found_root == project_root - - def test_no_project_root_found(self, tmp_path): - """测试在非项目目录中查找返回None""" - non_project = tmp_path / "not_a_project" - non_project.mkdir() - - found_root = find_sage_project_root(non_project) - assert found_root is None - - -class TestGetAppropriateSageDir: - """Tests for get_appropriate_sage_dir function.""" - - def test_use_environment_variable(self, tmp_path, monkeypatch): - """测试使用SAGE_OUTPUT_DIR环境变量""" - sage_dir = tmp_path / "custom_sage" - monkeypatch.setenv("SAGE_OUTPUT_DIR", str(sage_dir)) - - result = get_appropriate_sage_dir() - assert result == sage_dir - assert sage_dir.exists() - - def test_use_explicit_project_root(self, tmp_path): - """测试使用显式项目根目录""" - project_root = tmp_path / "project" - project_root.mkdir() - - result = get_appropriate_sage_dir(project_root) - assert result == project_root / ".sage" - assert result.exists() - - def test_auto_detect_development_environment(self, tmp_path, monkeypatch): - """测试自动检测开发环境""" - project_root = tmp_path / "sage_project" - project_root.mkdir() - (project_root / "packages").mkdir() - (project_root / "packages" / "sage-common").mkdir(parents=True) - - monkeypatch.chdir(project_root) - monkeypatch.delenv("SAGE_OUTPUT_DIR", raising=False) - - result = get_appropriate_sage_dir() - assert result == project_root / ".sage" - - -class TestSageOutputPaths: - """Tests for SageOutputPaths class.""" - - def test_initialization_with_explicit_root(self, tmp_path): - """测试使用显式根目录初始化""" - project_root = tmp_path / "project" - project_root.mkdir() - - paths = SageOutputPaths(project_root) - - assert paths.sage_dir == project_root / ".sage" - assert paths.project_root == project_root - assert paths.sage_dir.exists() - - def test_directory_properties(self, tmp_path): - """测试目录属性""" - project_root = tmp_path / "project" - project_root.mkdir() - - paths = SageOutputPaths(project_root) - - # 测试所有目录属性 - assert paths.logs_dir == paths.sage_dir / "logs" - assert paths.output_dir == paths.sage_dir / "output" - assert paths.temp_dir == paths.sage_dir / "temp" - assert paths.cache_dir == paths.sage_dir / "cache" - assert paths.reports_dir == paths.sage_dir / "reports" - assert paths.coverage_dir == paths.sage_dir / "coverage" - assert paths.test_logs_dir == paths.sage_dir / "test_logs" - assert paths.experiments_dir == paths.sage_dir / "experiments" - assert paths.issues_dir == paths.sage_dir / "issues" - assert paths.states_dir == paths.sage_dir / "states" - assert paths.benchmarks_dir == paths.sage_dir / "benchmarks" - assert paths.studio_dir == paths.sage_dir / "studio" - - # 验证所有目录已创建 - for dir_prop in [ - "logs_dir", - "output_dir", - "temp_dir", - "cache_dir", - "reports_dir", - "coverage_dir", - "test_logs_dir", - "experiments_dir", - "issues_dir", - "states_dir", - "benchmarks_dir", - "studio_dir", - ]: - assert getattr(paths, dir_prop).exists() - - def test_get_log_file(self, tmp_path): - """测试获取日志文件路径""" - project_root = tmp_path / "project" - project_root.mkdir() - - paths = SageOutputPaths(project_root) - - # 不带子目录 - log_file = paths.get_log_file("test.log") - assert log_file == paths.logs_dir / "test.log" - - # 带子目录 - log_file_sub = paths.get_log_file("test.log", subdir="component") - assert log_file_sub == paths.logs_dir / "component" / "test.log" - assert log_file_sub.parent.exists() - - def test_get_output_file(self, tmp_path): - """测试获取输出文件路径""" - project_root = tmp_path / "project" - project_root.mkdir() - - paths = SageOutputPaths(project_root) - - output_file = paths.get_output_file("result.json") - assert output_file == paths.output_dir / "result.json" - - output_file_sub = paths.get_output_file("result.json", subdir="experiments") - assert output_file_sub == paths.output_dir / "experiments" / "result.json" - - def test_get_temp_file(self, tmp_path): - """测试获取临时文件路径""" - project_root = tmp_path / "project" - project_root.mkdir() - - paths = SageOutputPaths(project_root) - - temp_file = paths.get_temp_file("temp.dat") - assert temp_file == paths.temp_dir / "temp.dat" - - def test_get_cache_file(self, tmp_path): - """测试获取缓存文件路径""" - project_root = tmp_path / "project" - project_root.mkdir() - - paths = SageOutputPaths(project_root) - - cache_file = paths.get_cache_file("cache.db") - assert cache_file == paths.cache_dir / "cache.db" - - def test_get_test_env_dir(self, tmp_path): - """测试获取测试环境目录""" - project_root = tmp_path / "project" - project_root.mkdir() - - paths = SageOutputPaths(project_root) - - test_env = paths.get_test_env_dir("my_test") - assert test_env == paths.temp_dir / "my_test" - assert test_env.exists() - - def test_get_ray_temp_dir(self, tmp_path): - """测试获取Ray临时目录""" - project_root = tmp_path / "project" - project_root.mkdir() - - paths = SageOutputPaths(project_root) - - ray_dir = paths.get_ray_temp_dir() - assert ray_dir == paths.temp_dir / "ray" - assert ray_dir.exists() - - def test_setup_environment_variables(self, tmp_path, monkeypatch): - """测试设置环境变量""" - project_root = tmp_path / "project" - project_root.mkdir() - - paths = SageOutputPaths(project_root) - env_vars = paths.setup_environment_variables() - - assert os.environ["SAGE_OUTPUT_DIR"] == str(paths.sage_dir) - assert os.environ["SAGE_HOME"] == str(paths.sage_dir) - assert os.environ["SAGE_LOGS_DIR"] == str(paths.logs_dir) - assert os.environ["SAGE_TEMP_DIR"] == str(paths.temp_dir) - assert os.environ["RAY_TMPDIR"] == str(paths.temp_dir / "ray") - - assert env_vars["sage_dir"] == paths.sage_dir - assert env_vars["logs_dir"] == paths.logs_dir - - -class TestConvenienceFunctions: - """Tests for convenience functions.""" - - def test_get_logs_dir(self, tmp_path): - """测试get_logs_dir便捷函数""" - with patch("sage.common.config.output_paths.get_appropriate_sage_dir") as mock_get: - mock_get.return_value = tmp_path / ".sage" - (tmp_path / ".sage" / "logs").mkdir(parents=True) - - logs_dir = get_logs_dir(tmp_path) - assert logs_dir.name == "logs" - - def test_get_output_dir(self, tmp_path): - """测试get_output_dir便捷函数""" - with patch("sage.common.config.output_paths.get_appropriate_sage_dir") as mock_get: - mock_get.return_value = tmp_path / ".sage" - (tmp_path / ".sage" / "output").mkdir(parents=True) - - output_dir = get_output_dir(tmp_path) - assert output_dir.name == "output" - - def test_get_temp_dir(self, tmp_path): - """测试get_temp_dir便捷函数""" - with patch("sage.common.config.output_paths.get_appropriate_sage_dir") as mock_get: - mock_get.return_value = tmp_path / ".sage" - (tmp_path / ".sage" / "temp").mkdir(parents=True) - - temp_dir = get_temp_dir(tmp_path) - assert temp_dir.name == "temp" - - def test_initialize_sage_paths(self, tmp_path, monkeypatch): - """测试initialize_sage_paths函数""" - project_root = tmp_path / "project" - project_root.mkdir() - - paths = initialize_sage_paths(project_root) - - assert isinstance(paths, SageOutputPaths) - assert paths.sage_dir.exists() - # 验证环境变量已设置 - assert "SAGE_OUTPUT_DIR" in os.environ diff --git a/packages/sage-common/tests/unit/core/__init__.py b/packages/sage-common/tests/unit/core/__init__.py deleted file mode 100644 index 323dcb2588..0000000000 --- a/packages/sage-common/tests/unit/core/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -""" -Test module for sage.common.core - -Contains tests for core types, exceptions, constants, and data types. -""" diff --git a/packages/sage-common/tests/unit/core/functions/test_base_function.py b/packages/sage-common/tests/unit/core/functions/test_base_function.py deleted file mode 100644 index 869ebdcda7..0000000000 --- a/packages/sage-common/tests/unit/core/functions/test_base_function.py +++ /dev/null @@ -1,307 +0,0 @@ -""" -Tests for sage.common.core.functions.base_function - -Tests the BaseFunction abstract base class and its core functionality. -""" - -import logging -from unittest.mock import Mock - -import pytest - -from sage.common.core.functions.base_function import BaseFunction - - -class ConcreteFunction(BaseFunction): - """Concrete implementation of BaseFunction for testing""" - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self.test_value = 42 - self.test_string = "hello" - - def execute(self, data): - """Concrete implementation of execute""" - return data - - -class TestBaseFunction: - """Tests for BaseFunction class""" - - def test_initialization(self): - """Test function initialization""" - func = ConcreteFunction() - - assert func.ctx is None - assert func._logger is None - assert func.test_value == 42 - - def test_logger_without_context(self): - """Test logger property without context""" - func = ConcreteFunction() - - logger = func.logger - assert isinstance(logger, logging.Logger) - - def test_logger_with_context(self): - """Test logger property with context""" - func = ConcreteFunction() - - # Mock context with logger - mock_ctx = Mock() - mock_logger = Mock(spec=logging.Logger) - mock_ctx.logger = mock_logger - - func.ctx = mock_ctx - logger = func.logger - - assert logger == mock_logger - - def test_name_without_context(self): - """Test name property without context""" - func = ConcreteFunction() - - name = func.name - assert name == "ConcreteFunction" - - def test_name_with_context(self): - """Test name property with context""" - func = ConcreteFunction() - - # Mock context with name - mock_ctx = Mock() - mock_ctx.name = "test_function" - - func.ctx = mock_ctx - name = func.name - - assert name == "test_function" - - def test_call_service_without_context(self): - """Test call_service raises error without context""" - func = ConcreteFunction() - - with pytest.raises(RuntimeError, match="Runtime context not initialized"): - func.call_service("test_service") - - def test_call_service_with_context(self): - """Test call_service with context""" - func = ConcreteFunction() - - # Mock context - mock_ctx = Mock() - mock_ctx.call_service = Mock(return_value="result") - - func.ctx = mock_ctx - - result = func.call_service("test_service", arg1="value1", timeout=5.0) - - assert result == "result" - mock_ctx.call_service.assert_called_once_with( - "test_service", arg1="value1", timeout=5.0, method=None - ) - - def test_call_service_with_method(self): - """Test call_service with method parameter""" - func = ConcreteFunction() - - # Mock context - mock_ctx = Mock() - mock_ctx.call_service = Mock(return_value="result") - - func.ctx = mock_ctx - - result = func.call_service("test_service", method="custom_method", param=123) - - assert result == "result" - mock_ctx.call_service.assert_called_once_with( - "test_service", method="custom_method", param=123, timeout=None - ) - - def test_call_service_async_without_context(self): - """Test call_service_async raises error without context""" - func = ConcreteFunction() - - with pytest.raises(RuntimeError, match="Runtime context not initialized"): - func.call_service_async("test_service") - - def test_call_service_async_with_context(self): - """Test call_service_async with context""" - func = ConcreteFunction() - - # Mock context - mock_ctx = Mock() - mock_future = Mock() - mock_ctx.call_service_async = Mock(return_value=mock_future) - - func.ctx = mock_ctx - - result = func.call_service_async("test_service", arg1="value1", timeout=10.0) - - assert result == mock_future - mock_ctx.call_service_async.assert_called_once_with( - "test_service", arg1="value1", timeout=10.0, method=None - ) - - def test_get_state_basic(self): - """Test get_state returns function attributes""" - func = ConcreteFunction() - - state = func.get_state() - - assert isinstance(state, dict) - assert "test_value" in state - assert state["test_value"] == 42 - assert "test_string" in state - assert state["test_string"] == "hello" - - def test_get_state_excludes_context(self): - """Test get_state excludes context and logger""" - func = ConcreteFunction() - func.ctx = Mock() - - state = func.get_state() - - assert "ctx" not in state - assert "_logger" not in state - assert "logger" not in state - - def test_get_state_with_include_list(self): - """Test get_state with __state_include__ filter""" - - class SelectiveFunction(BaseFunction): - __state_include__ = ["important_value"] - - def __init__(self): - super().__init__() - self.important_value = 100 - self.ignored_value = 200 - - def execute(self, data): - return data - - func = SelectiveFunction() - state = func.get_state() - - assert "important_value" in state - assert state["important_value"] == 100 - # When include list is specified, only those are included - if "__state_include__" in func.__dict__: - assert "ignored_value" not in state - - def test_get_state_with_exclude_list(self): - """Test get_state with custom __state_exclude__""" - - class ExclusiveFunction(BaseFunction): - __state_exclude__ = ["ctx", "_logger", "logger", "secret"] # pragma: allowlist secret - - def __init__(self): - super().__init__() - self.public_value = 100 - self.secret = "hidden" # pragma: allowlist secret - - def execute(self, data): - return data - - func = ExclusiveFunction() - state = func.get_state() - - assert "public_value" in state - assert "secret" not in state # pragma: allowlist secret - - def test_restore_state_basic(self): - """Test restore_state restores function state""" - func = ConcreteFunction() - - # Change values - func.test_value = 99 - func.test_string = "changed" - - # Save state - state = {"test_value": 42, "test_string": "hello", "new_attr": "new"} - - func.restore_state(state) - - assert func.test_value == 42 - assert func.test_string == "hello" - assert func.new_attr == "new" - - def test_state_include_list_behavior(self): - """Test __state_include__ behavior""" - - class FilteredFunction(BaseFunction): - __state_include__ = ["kept"] - - def __init__(self): - super().__init__() - self.kept = "keep_this" - self.removed = "remove_this" - - def execute(self, data): - return data - - func = FilteredFunction() - state = func.get_state() - - # If include list is used, only those fields should be present - assert "kept" in state - - def test_state_exclude_list_behavior(self): - """Test __state_exclude__ behavior""" - - class ExcludedFunction(BaseFunction): - __state_exclude__ = ["ctx", "_logger", "logger", "excluded_field"] - - def __init__(self): - super().__init__() - self.included_field = "include" - self.excluded_field = "exclude" - - def execute(self, data): - return data - - func = ExcludedFunction() - state = func.get_state() - - assert "included_field" in state - assert "excluded_field" not in state - assert "ctx" not in state - - -class TestBaseFunctionStatePersistence: - """Tests for state persistence functionality""" - - def test_roundtrip_state(self): - """Test saving and restoring state""" - func1 = ConcreteFunction() - func1.test_value = 123 - func1.test_string = "modified" - - # Save state - state = func1.get_state() - - # Create new function and restore state - func2 = ConcreteFunction() - func2.restore_state(state) - - assert func2.test_value == 123 - assert func2.test_string == "modified" - - def test_state_excludes_unserializable_types(self): - """Test that unserializable types are excluded from state""" - - class FunctionWithCallables(BaseFunction): - def __init__(self): - super().__init__() - self.value = 42 - self.callback = lambda x: x * 2 - - def execute(self, data): - return data - - func = FunctionWithCallables() - state = func.get_state() - - assert "value" in state - # Functions should be excluded - assert "callback" not in state or not callable(state.get("callback")) diff --git a/packages/sage-common/tests/unit/core/functions/test_comap_function.py b/packages/sage-common/tests/unit/core/functions/test_comap_function.py deleted file mode 100644 index dbfafe2aaf..0000000000 --- a/packages/sage-common/tests/unit/core/functions/test_comap_function.py +++ /dev/null @@ -1,391 +0,0 @@ -""" -Tests for CoMap Function class - -CoMap functions process multiple input streams independently through -dedicated mapN methods (map0, map1, map2, etc.). - -Tests cover: -- BaseCoMapFunction inheritance and properties -- Required abstract methods (map0, map1) -- Optional methods (map2, map3, map4) -- Execute method error handling -- Multi-stream processing patterns -""" - -from unittest.mock import MagicMock - -import pytest - -from sage.common.core.functions.base_function import BaseFunction -from sage.common.core.functions.comap_function import BaseCoMapFunction - - -class ConcreteCoMapFunction(BaseCoMapFunction): - """Concrete implementation of BaseCoMapFunction for testing""" - - def map0(self, data): - """Process stream 0: double the value""" - return data * 2 - - def map1(self, data): - """Process stream 1: add 10 to the value""" - return data + 10 - - -class FullCoMapFunction(BaseCoMapFunction): - """CoMapFunction implementing all optional methods""" - - def map0(self, data): - return data * 2 - - def map1(self, data): - return data + 10 - - def map2(self, data): - return data * 3 - - def map3(self, data): - return data - 5 - - def map4(self, data): - return data**2 - - -class TestCoMapFunctionInheritance: - """Test CoMapFunction inheritance and basic properties""" - - def test_comap_inherits_from_base_function(self): - """Test BaseCoMapFunction inherits from BaseFunction""" - func = ConcreteCoMapFunction() - assert isinstance(func, BaseFunction) - assert isinstance(func, BaseCoMapFunction) - - def test_is_comap_property(self): - """Test is_comap property returns True""" - func = ConcreteCoMapFunction() - assert func.is_comap is True - - def test_is_comap_property_type(self): - """Test is_comap returns boolean""" - func = ConcreteCoMapFunction() - assert isinstance(func.is_comap, bool) - - -class TestCoMapRequiredMethods: - """Test required abstract methods map0 and map1""" - - def test_map0_basic_operation(self): - """Test map0 processes stream 0 data""" - func = ConcreteCoMapFunction() - result = func.map0(5) - assert result == 10 - - def test_map1_basic_operation(self): - """Test map1 processes stream 1 data""" - func = ConcreteCoMapFunction() - result = func.map1(5) - assert result == 15 - - def test_map0_with_different_types(self): - """Test map0 with various data types""" - - class TypeFlexibleCoMap(BaseCoMapFunction): - def map0(self, data): - return str(data).upper() - - def map1(self, data): - return data - - func = TypeFlexibleCoMap() - assert func.map0("hello") == "HELLO" - assert func.map0(123) == "123" - - def test_map1_with_different_types(self): - """Test map1 with various data types""" - - class DictCoMap(BaseCoMapFunction): - def map0(self, data): - return data - - def map1(self, data): - return {"value": data, "stream": 1} - - func = DictCoMap() - result = func.map1(42) - assert result == {"value": 42, "stream": 1} - - -class TestCoMapOptionalMethods: - """Test optional methods map2, map3, map4""" - - def test_map2_default_returns_none(self): - """Test map2 returns None by default""" - func = ConcreteCoMapFunction() - result = func.map2("any_data") - assert result is None - - def test_map3_default_returns_none(self): - """Test map3 returns None by default""" - func = ConcreteCoMapFunction() - result = func.map3("any_data") - assert result is None - - def test_map4_default_returns_none(self): - """Test map4 returns None by default""" - func = ConcreteCoMapFunction() - result = func.map4("any_data") - assert result is None - - def test_map2_can_be_overridden(self): - """Test map2 can be overridden""" - func = FullCoMapFunction() - result = func.map2(5) - assert result == 15 # 5 * 3 - - def test_map3_can_be_overridden(self): - """Test map3 can be overridden""" - func = FullCoMapFunction() - result = func.map3(10) - assert result == 5 # 10 - 5 - - def test_map4_can_be_overridden(self): - """Test map4 can be overridden""" - func = FullCoMapFunction() - result = func.map4(3) - assert result == 9 # 3^2 - - def test_all_optional_methods_together(self): - """Test all optional methods work together""" - func = FullCoMapFunction() - assert func.map2(2) == 6 - assert func.map3(15) == 10 - assert func.map4(4) == 16 - - -class TestCoMapExecuteMethod: - """Test execute method raises NotImplementedError""" - - def test_execute_raises_not_implemented(self): - """Test execute() raises NotImplementedError""" - func = ConcreteCoMapFunction() - - with pytest.raises(NotImplementedError): - func.execute("any_data") - - def test_execute_error_message_contains_class_name(self): - """Test error message includes class name""" - func = ConcreteCoMapFunction() - - with pytest.raises(NotImplementedError, match="ConcreteCoMapFunction"): - func.execute("data") - - def test_execute_error_message_mentions_mapn(self): - """Test error message mentions mapN methods""" - func = ConcreteCoMapFunction() - - with pytest.raises(NotImplementedError, match="mapN methods"): - func.execute("data") - - def test_execute_error_message_mentions_operator(self): - """Test error message mentions CoMapOperator""" - func = ConcreteCoMapFunction() - - with pytest.raises(NotImplementedError, match="CoMapOperator"): - func.execute("data") - - -class TestCoMapMultiStreamProcessing: - """Test multi-stream processing patterns""" - - def test_process_two_streams(self): - """Test processing data from two different streams""" - func = ConcreteCoMapFunction() - - # Simulate data from stream 0 - result0 = func.map0(10) - assert result0 == 20 - - # Simulate data from stream 1 - result1 = func.map1(10) - assert result1 == 20 - - # Same input, different outputs based on stream - assert result0 == result1 # In this case equal, but processed differently - - def test_process_five_streams(self): - """Test processing data from all five streams""" - func = FullCoMapFunction() - - results = [ - func.map0(10), # 20 - func.map1(10), # 20 - func.map2(10), # 30 - func.map3(10), # 5 - func.map4(10), # 100 - ] - - assert results == [20, 20, 30, 5, 100] - - def test_different_stream_transformations(self): - """Test each stream applies different transformation""" - - class TransformCoMap(BaseCoMapFunction): - def map0(self, data): - return {"stream": 0, "data": data, "type": "double"} - - def map1(self, data): - return {"stream": 1, "data": data, "type": "increment"} - - def map2(self, data): - return {"stream": 2, "data": data, "type": "triple"} - - func = TransformCoMap() - - r0 = func.map0("test") - r1 = func.map1("test") - r2 = func.map2("test") - - assert r0["stream"] == 0 - assert r1["stream"] == 1 - assert r2["stream"] == 2 - - def test_stateful_comap_processing(self): - """Test CoMap with stateful processing per stream""" - - class StatefulCoMap(BaseCoMapFunction): - def __init__(self): - super().__init__() - self.count0 = 0 - self.count1 = 0 - - def map0(self, data): - self.count0 += 1 - return f"Stream0-{self.count0}: {data}" - - def map1(self, data): - self.count1 += 1 - return f"Stream1-{self.count1}: {data}" - - func = StatefulCoMap() - - assert func.map0("A") == "Stream0-1: A" - assert func.map0("B") == "Stream0-2: B" - assert func.map1("X") == "Stream1-1: X" - assert func.map1("Y") == "Stream1-2: Y" - assert func.map0("C") == "Stream0-3: C" - - -class TestCoMapIntegration: - """Integration tests for CoMap functions""" - - def test_comap_with_filtering_logic(self): - """Test CoMap with filtering logic per stream""" - - class FilteringCoMap(BaseCoMapFunction): - def map0(self, data): - # Stream 0: only pass positive numbers - return data if data > 0 else None - - def map1(self, data): - # Stream 1: only pass even numbers - return data if data % 2 == 0 else None - - func = FilteringCoMap() - - assert func.map0(10) == 10 - assert func.map0(-5) is None - assert func.map1(4) == 4 - assert func.map1(5) is None - - def test_comap_with_aggregation_logic(self): - """Test CoMap with aggregation per stream""" - - class AggregatingCoMap(BaseCoMapFunction): - def __init__(self): - super().__init__() - self.sum0 = 0 - self.sum1 = 0 - - def map0(self, data): - self.sum0 += data - return self.sum0 - - def map1(self, data): - self.sum1 += data - return self.sum1 - - func = AggregatingCoMap() - - assert func.map0(10) == 10 - assert func.map0(5) == 15 - assert func.map1(3) == 3 - assert func.map1(7) == 10 - - def test_comap_context_integration(self): - """Test CoMap with context""" - func = ConcreteCoMapFunction() - mock_ctx = MagicMock() - func.ctx = mock_ctx - - result = func.map0(5) - assert result == 10 - assert func.ctx is mock_ctx - - -class TestCoMapEdgeCases: - """Test edge cases for CoMap functions""" - - def test_comap_with_none_data(self): - """Test CoMap handling None data""" - - class NoneHandlingCoMap(BaseCoMapFunction): - def map0(self, data): - return data if data is not None else "default0" - - def map1(self, data): - return data if data is not None else "default1" - - func = NoneHandlingCoMap() - assert func.map0(None) == "default0" - assert func.map1(None) == "default1" - assert func.map0("real") == "real" - - def test_comap_with_exception_handling(self): - """Test CoMap with exception handling""" - - class SafeCoMap(BaseCoMapFunction): - def map0(self, data): - try: - return data["value"] - except (KeyError, TypeError): - return "error" - - def map1(self, data): - try: - return data.upper() - except AttributeError: - return "error" - - func = SafeCoMap() - assert func.map0({"value": 42}) == 42 - assert func.map0({}) == "error" - assert func.map1("hello") == "HELLO" - assert func.map1(123) == "error" - - def test_comap_with_complex_transformations(self): - """Test CoMap with complex data transformations""" - - class ComplexCoMap(BaseCoMapFunction): - def map0(self, data): - # Stream 0: extract and transform list - return [x * 2 for x in data] if isinstance(data, list) else [] - - def map1(self, data): - # Stream 1: flatten nested structure - if isinstance(data, dict): - return list(data.values()) - return [data] - - func = ComplexCoMap() - assert func.map0([1, 2, 3]) == [2, 4, 6] - assert func.map1({"a": 1, "b": 2}) == [1, 2] diff --git a/packages/sage-common/tests/unit/core/functions/test_flatmap_collector.py b/packages/sage-common/tests/unit/core/functions/test_flatmap_collector.py deleted file mode 100644 index b99195699b..0000000000 --- a/packages/sage-common/tests/unit/core/functions/test_flatmap_collector.py +++ /dev/null @@ -1,284 +0,0 @@ -""" -Tests for FlatMap Collector class - -Tests cover: -- Collector initialization -- Data collection (collect method) -- Getting collected data -- Getting count -- Clearing data -- Logger integration -""" - -from unittest.mock import MagicMock - -from sage.common.core.functions.flatmap_collector import Collector - - -class TestCollectorInitialization: - """Test Collector initialization""" - - def test_collector_basic_initialization(self): - """Test Collector initializes with empty data""" - collector = Collector() - assert collector._collected_data == [] - assert collector.logger is None - - def test_collector_with_logger(self): - """Test Collector initialization with logger""" - mock_logger = MagicMock() - collector = Collector(logger=mock_logger) - assert collector._collected_data == [] - assert collector.logger is mock_logger - - def test_collector_with_args(self): - """Test Collector handles arbitrary args/kwargs""" - collector = Collector("arg1", "arg2", key1="value1", key2="value2") - assert collector._collected_data == [] - - -class TestCollectorCollect: - """Test Collector collect method""" - - def test_collect_single_item(self): - """Test collecting single item""" - collector = Collector() - collector.collect("data1") - assert len(collector._collected_data) == 1 - assert collector._collected_data[0] == "data1" - - def test_collect_multiple_items(self): - """Test collecting multiple items""" - collector = Collector() - collector.collect("item1") - collector.collect("item2") - collector.collect("item3") - - assert len(collector._collected_data) == 3 - assert collector._collected_data == ["item1", "item2", "item3"] - - def test_collect_different_types(self): - """Test collecting different data types""" - collector = Collector() - collector.collect(42) - collector.collect("string") - collector.collect({"key": "value"}) - collector.collect([1, 2, 3]) - collector.collect(None) - - assert len(collector._collected_data) == 5 - assert 42 in collector._collected_data - assert "string" in collector._collected_data - - def test_collect_with_logger(self): - """Test collect logs debug message""" - mock_logger = MagicMock() - collector = Collector(logger=mock_logger) - - collector.collect("test_data") - - mock_logger.debug.assert_called_once() - assert "test_data" in str(mock_logger.debug.call_args) - - def test_collect_without_logger(self): - """Test collect works without logger""" - collector = Collector() - collector.collect("data") - # Should not raise any errors - assert collector._collected_data == ["data"] - - -class TestCollectorGetData: - """Test getting collected data""" - - def test_get_collected_data_empty(self): - """Test getting data when empty""" - collector = Collector() - data = collector.get_collected_data() - assert data == [] - - def test_get_collected_data_returns_copy(self): - """Test get_collected_data returns a copy""" - collector = Collector() - collector.collect("item1") - collector.collect("item2") - - data = collector.get_collected_data() - data.append("item3") # Modify returned list - - # Original should be unchanged - assert len(collector._collected_data) == 2 - assert "item3" not in collector._collected_data - - def test_get_collected_data_with_items(self): - """Test getting data with items""" - collector = Collector() - items = ["a", "b", "c", "d", "e"] - for item in items: - collector.collect(item) - - data = collector.get_collected_data() - assert data == items - - def test_get_collected_count_empty(self): - """Test count when empty""" - collector = Collector() - assert collector.get_collected_count() == 0 - - def test_get_collected_count_with_items(self): - """Test count with items""" - collector = Collector() - for i in range(10): - collector.collect(i) - - assert collector.get_collected_count() == 10 - - def test_get_collected_count_after_multiple_operations(self): - """Test count changes with operations""" - collector = Collector() - assert collector.get_collected_count() == 0 - - collector.collect("a") - assert collector.get_collected_count() == 1 - - collector.collect("b") - collector.collect("c") - assert collector.get_collected_count() == 3 - - -class TestCollectorClear: - """Test Collector clear method""" - - def test_clear_empty_collector(self): - """Test clearing empty collector""" - collector = Collector() - collector.clear() - assert collector._collected_data == [] - assert collector.get_collected_count() == 0 - - def test_clear_with_data(self): - """Test clearing collector with data""" - collector = Collector() - collector.collect("item1") - collector.collect("item2") - collector.collect("item3") - - assert collector.get_collected_count() == 3 - - collector.clear() - assert collector._collected_data == [] - assert collector.get_collected_count() == 0 - - def test_clear_with_logger(self): - """Test clear logs debug message""" - mock_logger = MagicMock() - collector = Collector(logger=mock_logger) - - collector.collect("item1") - collector.collect("item2") - - collector.clear() - - # Should have logged collection (2 times) and clear (1 time) - assert mock_logger.debug.call_count == 3 - # Last call should be about clearing - last_call = str(mock_logger.debug.call_args_list[-1]) - assert "Cleared" in last_call or "cleared" in last_call.lower() - - def test_clear_empty_with_logger(self): - """Test clearing empty collector doesn't log""" - mock_logger = MagicMock() - collector = Collector(logger=mock_logger) - - collector.clear() - - # Should not log when clearing empty collector - mock_logger.debug.assert_not_called() - - def test_clear_multiple_times(self): - """Test clearing multiple times""" - collector = Collector() - for i in range(5): - collector.collect(i) - - collector.clear() - assert collector.get_collected_count() == 0 - - collector.clear() # Clear again - assert collector.get_collected_count() == 0 - - -class TestCollectorIntegration: - """Integration tests for Collector""" - - def test_collector_full_lifecycle(self): - """Test complete lifecycle: collect -> get -> clear -> collect""" - collector = Collector() - - # Phase 1: Collect - collector.collect("a") - collector.collect("b") - assert collector.get_collected_count() == 2 - - # Phase 2: Get data - data = collector.get_collected_data() - assert data == ["a", "b"] - - # Phase 3: Clear - collector.clear() - assert collector.get_collected_count() == 0 - - # Phase 4: Collect again - collector.collect("c") - collector.collect("d") - assert collector.get_collected_count() == 2 - assert collector.get_collected_data() == ["c", "d"] - - def test_collector_with_flatmap_pattern(self): - """Test Collector in FlatMap-like usage pattern""" - collector = Collector() - - # Simulate FlatMap operation: one input -> multiple outputs - input_data = "hello,world,test" - words = input_data.split(",") - for word in words: - collector.collect(word) - - assert collector.get_collected_count() == 3 - assert collector.get_collected_data() == ["hello", "world", "test"] - - def test_collector_batch_processing(self): - """Test Collector for batch processing""" - collector = Collector() - batch_size = 5 - - # Collect batch - for i in range(batch_size): - collector.collect(i) - - assert collector.get_collected_count() == batch_size - - # Process batch - batch = collector.get_collected_data() - assert len(batch) == batch_size - - # Clear for next batch - collector.clear() - assert collector.get_collected_count() == 0 - - def test_collector_with_complex_data(self): - """Test Collector with complex nested data""" - collector = Collector() - - complex_items = [ - {"id": 1, "data": [1, 2, 3]}, - {"id": 2, "data": [4, 5, 6]}, - {"id": 3, "data": {"nested": "value"}}, - ] - - for item in complex_items: - collector.collect(item) - - assert collector.get_collected_count() == 3 - retrieved = collector.get_collected_data() - assert retrieved == complex_items diff --git a/packages/sage-common/tests/unit/core/functions/test_future_function.py b/packages/sage-common/tests/unit/core/functions/test_future_function.py deleted file mode 100644 index f3accadd68..0000000000 --- a/packages/sage-common/tests/unit/core/functions/test_future_function.py +++ /dev/null @@ -1,236 +0,0 @@ -""" -Tests for FutureFunction class - -FutureFunction is a placeholder function for future transformations -that should not be executed directly. - -Tests cover: -- Inheritance and basic properties -- __call__ raises RuntimeError -- call() raises RuntimeError -- __repr__ representation -- Error messages -""" - -import pytest - -from sage.common.core.functions.base_function import BaseFunction -from sage.common.core.functions.future_function import FutureFunction - - -# Concrete implementation for testing -class ConcreteFutureFunction(FutureFunction): - """Concrete FutureFunction for testing""" - - def execute(self, data): - """Implement execute to make class concrete""" - # This should never be called for FutureFunction - raise RuntimeError("FutureFunction execute should not be called") - - -class TestFutureFunctionBasics: - """Test FutureFunction basic properties""" - - def test_future_function_inherits_from_base_function(self): - """Test FutureFunction inherits from BaseFunction""" - func = ConcreteFutureFunction() - assert isinstance(func, BaseFunction) - assert isinstance(func, FutureFunction) - - def test_future_function_instantiation(self): - """Test FutureFunction can be instantiated""" - func = ConcreteFutureFunction() - assert func is not None - - -class TestFutureFunctionCallMethod: - """Test FutureFunction __call__ method""" - - def test_call_raises_runtime_error(self): - """Test __call__ raises RuntimeError""" - func = ConcreteFutureFunction() - - with pytest.raises(RuntimeError): - func() - - def test_call_with_args_raises_runtime_error(self): - """Test __call__ with args raises RuntimeError""" - func = ConcreteFutureFunction() - - with pytest.raises(RuntimeError): - func("arg1", "arg2") - - def test_call_with_kwargs_raises_runtime_error(self): - """Test __call__ with kwargs raises RuntimeError""" - func = ConcreteFutureFunction() - - with pytest.raises(RuntimeError): - func(key1="value1", key2="value2") - - def test_call_error_message_mentions_placeholder(self): - """Test error message mentions placeholder""" - func = ConcreteFutureFunction() - - with pytest.raises(RuntimeError, match="placeholder"): - func() - - def test_call_error_message_warns_not_to_call(self): - """Test error message warns not to call directly""" - func = ConcreteFutureFunction() - - with pytest.raises(RuntimeError, match="should not be called directly"): - func() - - -class TestFutureFunctionCallMethodExplicit: - """Test FutureFunction call() method""" - - def test_call_method_raises_runtime_error(self): - """Test call() raises RuntimeError""" - func = ConcreteFutureFunction() - - with pytest.raises(RuntimeError): - func.call("data") - - def test_call_method_with_none_raises_error(self): - """Test call() with None raises RuntimeError""" - func = ConcreteFutureFunction() - - with pytest.raises(RuntimeError): - func.call(None) - - def test_call_method_with_complex_data_raises_error(self): - """Test call() with complex data raises RuntimeError""" - func = ConcreteFutureFunction() - - with pytest.raises(RuntimeError): - func.call({"complex": "data", "nested": [1, 2, 3]}) - - def test_call_method_error_message(self): - """Test call() error message is correct""" - func = ConcreteFutureFunction() - - with pytest.raises(RuntimeError, match="FutureFunction should not be called directly"): - func.call("test") - - def test_call_method_error_message_mentions_placeholder(self): - """Test call() error message mentions placeholder""" - func = ConcreteFutureFunction() - - with pytest.raises(RuntimeError, match="placeholder"): - func.call("test") - - -class TestFutureFunctionRepr: - """Test FutureFunction __repr__ method""" - - def test_repr_returns_string(self): - """Test __repr__ returns a string""" - func = ConcreteFutureFunction() - result = repr(func) - assert isinstance(result, str) - - def test_repr_contains_function_name(self): - """Test __repr__ contains 'FutureFunction'""" - func = ConcreteFutureFunction() - result = repr(func) - assert "FutureFunction" in result - - def test_repr_contains_placeholder(self): - """Test __repr__ contains 'placeholder'""" - func = ConcreteFutureFunction() - result = repr(func) - assert "placeholder" in result - - def test_repr_exact_format(self): - """Test __repr__ exact format""" - func = ConcreteFutureFunction() - result = repr(func) - assert result == "FutureFunction(placeholder)" - - def test_repr_consistency(self): - """Test __repr__ is consistent across calls""" - func = ConcreteFutureFunction() - result1 = repr(func) - result2 = repr(func) - assert result1 == result2 - - -class TestFutureFunctionMultipleInstances: - """Test multiple FutureFunction instances""" - - def test_multiple_instances_independent(self): - """Test multiple instances are independent""" - func1 = ConcreteFutureFunction() - func2 = ConcreteFutureFunction() - - assert func1 is not func2 - assert repr(func1) == repr(func2) - - def test_multiple_instances_all_raise_errors(self): - """Test all instances raise errors when called""" - funcs = [ConcreteFutureFunction() for _ in range(3)] - - for func in funcs: - with pytest.raises(RuntimeError): - func() - with pytest.raises(RuntimeError): - func.call("data") - - -class TestFutureFunctionEdgeCases: - """Test edge cases for FutureFunction""" - - def test_str_representation(self): - """Test str() representation""" - func = ConcreteFutureFunction() - # str() should use __repr__ if __str__ is not defined - str_result = str(func) - # Should return a string - assert isinstance(str_result, str) - - def test_calling_after_repr(self): - """Test calling function after repr still raises error""" - func = ConcreteFutureFunction() - _ = repr(func) - - with pytest.raises(RuntimeError): - func() - - def test_repr_after_failed_call(self): - """Test repr still works after failed call""" - func = ConcreteFutureFunction() - - try: - func() - except RuntimeError: - pass - - result = repr(func) - assert result == "FutureFunction(placeholder)" - - def test_multiple_call_attempts(self): - """Test multiple call attempts all fail""" - func = ConcreteFutureFunction() - - for _ in range(5): - with pytest.raises(RuntimeError): - func() - - def test_call_and_call_method_raise_same_error(self): - """Test __call__ and call() raise same type of error""" - func = ConcreteFutureFunction() - - try: - func() - except RuntimeError as e1: - error1_msg = str(e1) - - try: - func.call("data") - except RuntimeError as e2: - error2_msg = str(e2) - - # Both should be RuntimeError with placeholder message - assert "placeholder" in error1_msg - assert "placeholder" in error2_msg diff --git a/packages/sage-common/tests/unit/core/functions/test_join_function.py b/packages/sage-common/tests/unit/core/functions/test_join_function.py deleted file mode 100644 index 60e9893022..0000000000 --- a/packages/sage-common/tests/unit/core/functions/test_join_function.py +++ /dev/null @@ -1,319 +0,0 @@ -""" -Tests for sage.common.core.functions.join_function - -Tests the Join function classes for multi-stream data joining. -""" - -from sage.common.core.functions.join_function import BaseJoinFunction, UserOrderInnerJoin - - -class SimpleJoinFunction(BaseJoinFunction): - """Simple join implementation for testing""" - - def __init__(self): - super().__init__() - self.left_data = {} - self.right_data = {} - - def execute(self, payload, key, tag): - results = [] - - if tag == 0: # Left stream - self.left_data[key] = payload - if key in self.right_data: - results.append({"left": payload, "right": self.right_data[key], "key": key}) - - elif tag == 1: # Right stream - self.right_data[key] = payload - if key in self.left_data: - results.append({"left": self.left_data[key], "right": payload, "key": key}) - - return results - - -class TestBaseJoinFunction: - """Tests for BaseJoinFunction class""" - - def test_is_join_property(self): - """Test is_join property returns True""" - func = SimpleJoinFunction() - - assert func.is_join is True - - def test_simple_join_left_first(self): - """Test simple join with left data arriving first""" - func = SimpleJoinFunction() - - # Left data arrives first - result = func.execute({"name": "Alice"}, key="user_1", tag=0) - assert len(result) == 0 # No match yet - - # Right data arrives - result = func.execute({"order_id": "order_001"}, key="user_1", tag=1) - assert len(result) == 1 - assert result[0]["left"] == {"name": "Alice"} - assert result[0]["right"] == {"order_id": "order_001"} - assert result[0]["key"] == "user_1" - - def test_simple_join_right_first(self): - """Test simple join with right data arriving first""" - func = SimpleJoinFunction() - - # Right data arrives first - result = func.execute({"order_id": "order_002"}, key="user_2", tag=1) - assert len(result) == 0 # No match yet - - # Left data arrives - result = func.execute({"name": "Bob"}, key="user_2", tag=0) - assert len(result) == 1 - assert result[0]["left"] == {"name": "Bob"} - assert result[0]["right"] == {"order_id": "order_002"} - - def test_no_match_different_keys(self): - """Test no join when keys don't match""" - func = SimpleJoinFunction() - - func.execute({"name": "Alice"}, key="user_1", tag=0) - result = func.execute({"order_id": "order_003"}, key="user_2", tag=1) - - assert len(result) == 0 - - def test_multiple_keys(self): - """Test joins with multiple different keys""" - func = SimpleJoinFunction() - - # Add data for multiple keys - func.execute({"name": "Alice"}, key="user_1", tag=0) - func.execute({"name": "Bob"}, key="user_2", tag=0) - - result1 = func.execute({"order_id": "order_001"}, key="user_1", tag=1) - result2 = func.execute({"order_id": "order_002"}, key="user_2", tag=1) - - assert len(result1) == 1 - assert result1[0]["left"]["name"] == "Alice" - assert len(result2) == 1 - assert result2[0]["left"]["name"] == "Bob" - - -class TestUserOrderInnerJoin: - """Tests for UserOrderInnerJoin implementation""" - - def test_initialization(self): - """Test UserOrderInnerJoin initialization""" - func = UserOrderInnerJoin() - - assert hasattr(func, "user_cache") - assert hasattr(func, "order_cache") - assert func.is_join is True - - def test_user_data_cached(self): - """Test user data is cached""" - func = UserOrderInnerJoin() - - user_data = {"user_id": "u1", "name": "Alice", "age": 30} - result = func.execute(user_data, key="u1", tag=0) - - assert len(result) == 0 # No orders yet - assert "u1" in func.user_cache - assert func.user_cache["u1"] == user_data - - def test_order_cached_when_no_user(self): - """Test order is cached when user doesn't exist yet""" - func = UserOrderInnerJoin() - - order_data = {"order_id": "o1", "amount": 100} - result = func.execute(order_data, key="u1", tag=1) - - assert len(result) == 0 # No user yet - assert "u1" in func.order_cache - assert order_data in func.order_cache["u1"] - - def test_join_when_user_exists(self): - """Test join when user already exists""" - func = UserOrderInnerJoin() - - # Add user first - user_data = {"user_id": "u1", "name": "Alice"} - func.execute(user_data, key="u1", tag=0) - - # Add order - order_data = {"order_id": "o1", "amount": 100} - result = func.execute(order_data, key="u1", tag=1) - - assert len(result) == 1 - joined = result[0] - assert "user_id" in joined or "name" in joined - assert "order_id" in joined or "amount" in joined - - def test_join_when_order_exists(self): - """Test join when order already exists""" - func = UserOrderInnerJoin() - - # Add order first - order_data = {"order_id": "o2", "amount": 200} - func.execute(order_data, key="u2", tag=1) - - # Add user - user_data = {"user_id": "u2", "name": "Bob"} - result = func.execute(user_data, key="u2", tag=0) - - assert len(result) == 1 - - def test_multiple_orders_for_same_user(self): - """Test multiple orders for the same user""" - func = UserOrderInnerJoin() - - # Add multiple orders first - func.execute({"order_id": "o1", "amount": 100}, key="u1", tag=1) - func.execute({"order_id": "o2", "amount": 200}, key="u1", tag=1) - - # Add user - should join with all cached orders - result = func.execute({"user_id": "u1", "name": "Alice"}, key="u1", tag=0) - - assert len(result) == 2 - - def test_inner_join_clears_matched_orders(self): - """Test that matched orders are cleared (inner join behavior)""" - func = UserOrderInnerJoin() - - # Add orders - func.execute({"order_id": "o1"}, key="u1", tag=1) - assert "u1" in func.order_cache - - # Add user - should trigger join and clear orders - func.execute({"user_id": "u1", "name": "Alice"}, key="u1", tag=0) - - # Orders should be cleared after join - assert "u1" not in func.order_cache - - def test_different_users(self): - """Test joins for different users""" - func = UserOrderInnerJoin() - - # User 1 - func.execute({"user_id": "u1", "name": "Alice"}, key="u1", tag=0) - result1 = func.execute({"order_id": "o1"}, key="u1", tag=1) - - # User 2 - func.execute({"user_id": "u2", "name": "Bob"}, key="u2", tag=0) - result2 = func.execute({"order_id": "o2"}, key="u2", tag=1) - - assert len(result1) == 1 - assert len(result2) == 1 - - def test_no_cross_user_joins(self): - """Test that data doesn't join across different users""" - func = UserOrderInnerJoin() - - func.execute({"user_id": "u1", "name": "Alice"}, key="u1", tag=0) - result = func.execute({"order_id": "o1"}, key="u2", tag=1) # Different key - - assert len(result) == 0 - - -class TestJoinFunctionAdvanced: - """Advanced tests for join functionality""" - - def test_left_outer_join_concept(self): - """Test concept of left outer join (emit even without right match)""" - - class LeftOuterJoin(BaseJoinFunction): - def __init__(self): - super().__init__() - self.left_data = {} - self.right_data = {} - - def execute(self, payload, key, tag): - results = [] - - if tag == 0: # Left - self.left_data[key] = payload - # Always emit, even if no right match - right = self.right_data.get(key, None) - results.append({"left": payload, "right": right, "key": key}) - - elif tag == 1: # Right - self.right_data[key] = payload - # Update if left exists - if key in self.left_data: - results.append({"left": self.left_data[key], "right": payload, "key": key}) - - return results - - func = LeftOuterJoin() - - # Left arrives without right match - result = func.execute({"name": "Alice"}, key="u1", tag=0) - assert len(result) == 1 - assert result[0]["left"] == {"name": "Alice"} - assert result[0]["right"] is None - - def test_windowed_join_concept(self): - """Test concept of time-windowed join""" - - class WindowedJoin(BaseJoinFunction): - def __init__(self, window_size=2): - super().__init__() - self.window_size = window_size - self.left_window = {} - self.right_window = {} - - def execute(self, payload, key, tag): - results = [] - - if tag == 0: - # Add to left window - if key not in self.left_window: - self.left_window[key] = [] - self.left_window[key].append(payload) - - # Keep only recent items - if len(self.left_window[key]) > self.window_size: - self.left_window[key].pop(0) - - # Join with right window - if key in self.right_window: - for right_item in self.right_window[key]: - results.append({"left": payload, "right": right_item}) - - elif tag == 1: - # Similar for right - if key not in self.right_window: - self.right_window[key] = [] - self.right_window[key].append(payload) - - if len(self.right_window[key]) > self.window_size: - self.right_window[key].pop(0) - - if key in self.left_window: - for left_item in self.left_window[key]: - results.append({"left": left_item, "right": payload}) - - return results - - func = WindowedJoin(window_size=2) - - # Add items to window - func.execute({"id": 1}, key="k1", tag=0) - func.execute({"id": 2}, key="k1", tag=0) - func.execute({"id": 3}, key="k1", tag=0) # This evicts id=1 - - # Window should only contain id=2 and id=3 - assert len(func.left_window["k1"]) == 2 - assert func.left_window["k1"][0]["id"] == 2 - assert func.left_window["k1"][1]["id"] == 3 - - def test_stateful_join_accumulation(self): - """Test that join function maintains state correctly""" - func = SimpleJoinFunction() - - # Accumulate state - for i in range(5): - func.execute({"value": i}, key=f"key_{i}", tag=0) - - assert len(func.left_data) == 5 - - # Join some of them - result = func.execute({"other": "data"}, key="key_2", tag=1) - assert len(result) == 1 - assert result[0]["left"]["value"] == 2 diff --git a/packages/sage-common/tests/unit/core/functions/test_keyby_function.py b/packages/sage-common/tests/unit/core/functions/test_keyby_function.py deleted file mode 100644 index 0e4626ac58..0000000000 --- a/packages/sage-common/tests/unit/core/functions/test_keyby_function.py +++ /dev/null @@ -1,301 +0,0 @@ -""" -Tests for sage.common.core.functions.keyby_function - -Tests the KeyByFunction class for partition key extraction. -""" - -from unittest.mock import Mock - -import pytest - -from sage.common.core.functions.keyby_function import KeyByFunction - - -class SimpleKeyByFunction(KeyByFunction): - """Simple KeyBy implementation for testing""" - - def execute(self, data): - return data.get("id") if isinstance(data, dict) else data - - -class UserIdExtractor(KeyByFunction): - """Extract user_id from data""" - - def execute(self, data): - if isinstance(data, dict): - return data.get("user_id") - return getattr(data, "user_id", None) - - -class CompositeKeyExtractor(KeyByFunction): - """Extract composite key""" - - def execute(self, data): - if isinstance(data, dict): - return f"{data.get('user_id', '')}_{data.get('session_id', '')}" - return None - - -class TestKeyByFunction: - """Tests for KeyByFunction class""" - - def test_simple_key_extraction(self): - """Test simple key extraction""" - func = SimpleKeyByFunction() - - data = {"id": 123, "name": "test"} - key = func.execute(data) - - assert key == 123 - - def test_user_id_extraction(self): - """Test user ID extraction""" - func = UserIdExtractor() - - data = {"user_id": "user_001", "name": "Alice"} - key = func.execute(data) - - assert key == "user_001" - - def test_composite_key_extraction(self): - """Test composite key extraction""" - func = CompositeKeyExtractor() - - data = {"user_id": "user_001", "session_id": "session_123"} - key = func.execute(data) - - assert key == "user_001_session_123" - - def test_validate_key_with_hashable(self): - """Test validate_key with hashable values""" - func = SimpleKeyByFunction() - - # Test various hashable types - assert func.validate_key(123) is True - assert func.validate_key("string") is True - assert func.validate_key((1, 2, 3)) is True - assert func.validate_key(None) is True - - def test_validate_key_with_unhashable(self): - """Test validate_key with unhashable values""" - func = SimpleKeyByFunction() - - # Lists are not hashable - assert func.validate_key([1, 2, 3]) is False - - # Dicts are not hashable - assert func.validate_key({"key": "value"}) is False - - def test_extract_with_validation_success(self): - """Test extract_with_validation with valid key""" - func = SimpleKeyByFunction() - - data = {"id": 456, "name": "test"} - key = func.extract_with_validation(data) - - assert key == 456 - - def test_extract_with_validation_failure(self): - """Test extract_with_validation with invalid key""" - - class UnhashableKeyExtractor(KeyByFunction): - def execute(self, data): - return [1, 2, 3] # Return unhashable list - - func = UnhashableKeyExtractor() - - with pytest.raises((TypeError, ValueError)): # Could be either - func.extract_with_validation({"data": "test"}) - - def test_none_key_is_valid(self): - """Test that None is a valid (hashable) key""" - func = SimpleKeyByFunction() - - assert func.validate_key(None) is True - - def test_integer_key(self): - """Test integer key extraction""" - func = SimpleKeyByFunction() - - data = {"id": 12345} - key = func.execute(data) - - assert key == 12345 - assert isinstance(key, int) - - def test_string_key(self): - """Test string key extraction""" - func = UserIdExtractor() - - data = {"user_id": "abc123"} - key = func.execute(data) - - assert key == "abc123" - assert isinstance(key, str) - - def test_tuple_key(self): - """Test tuple as composite key""" - - class TupleKeyExtractor(KeyByFunction): - def execute(self, data): - return (data.get("category"), data.get("region")) - - func = TupleKeyExtractor() - - data = {"category": "electronics", "region": "US"} - key = func.execute(data) - - assert key == ("electronics", "US") - assert func.validate_key(key) is True - - def test_missing_field_returns_none(self): - """Test extraction when field is missing""" - func = UserIdExtractor() - - data = {"name": "test"} # No user_id - key = func.execute(data) - - assert key is None - - def test_key_extraction_from_object(self): - """Test key extraction from object with attributes""" - - class DataObject: - def __init__(self, user_id): - self.user_id = user_id - - func = UserIdExtractor() - obj = DataObject("user_789") - - key = func.execute(obj) - - assert key == "user_789" - - def test_case_sensitive_key(self): - """Test that keys are case-sensitive""" - - class CaseSensitiveExtractor(KeyByFunction): - def execute(self, data): - return data.get("Category") # Note capital C - - func = CaseSensitiveExtractor() - - data1 = {"Category": "A"} - data2 = {"category": "A"} # lowercase - - key1 = func.execute(data1) - key2 = func.execute(data2) - - assert key1 == "A" - assert key2 is None - - def test_numeric_string_key(self): - """Test numeric string as key""" - func = SimpleKeyByFunction() - - data = {"id": "12345"} # String, not int - key = func.execute(data) - - assert key == "12345" - assert isinstance(key, str) - - def test_empty_string_key(self): - """Test empty string as valid key""" - func = SimpleKeyByFunction() - - data = {"id": ""} - key = func.execute(data) - - assert key == "" - assert func.validate_key(key) is True - - def test_zero_as_key(self): - """Test zero as valid key""" - func = SimpleKeyByFunction() - - data = {"id": 0} - key = func.execute(data) - - assert key == 0 - assert func.validate_key(key) is True - - def test_negative_number_key(self): - """Test negative number as key""" - func = SimpleKeyByFunction() - - data = {"id": -999} - key = func.execute(data) - - assert key == -999 - assert func.validate_key(key) is True - - -class TestKeyByFunctionAdvanced: - """Advanced tests for KeyByFunction""" - - def test_custom_validation_logic(self): - """Test custom validation logic override""" - - class CustomValidationKeyBy(KeyByFunction): - def execute(self, data): - return data.get("id") - - def validate_key(self, key): - # Custom: only accept positive integers - return isinstance(key, int) and key > 0 - - func = CustomValidationKeyBy() - - assert func.validate_key(5) is True - assert func.validate_key(0) is False - assert func.validate_key(-1) is False - assert func.validate_key("string") is False - - def test_key_normalization(self): - """Test key normalization in extractor""" - - class NormalizingKeyExtractor(KeyByFunction): - def execute(self, data): - # Normalize to lowercase - key = data.get("category", "") - return key.lower() if isinstance(key, str) else key - - func = NormalizingKeyExtractor() - - data1 = {"category": "Electronics"} - data2 = {"category": "ELECTRONICS"} - data3 = {"category": "electronics"} - - assert func.execute(data1) == "electronics" - assert func.execute(data2) == "electronics" - assert func.execute(data3) == "electronics" - - def test_frozen_set_as_key(self): - """Test frozenset as key (hashable set)""" - - class FrozenSetKeyExtractor(KeyByFunction): - def execute(self, data): - tags = data.get("tags", []) - return frozenset(tags) - - func = FrozenSetKeyExtractor() - - data = {"tags": ["python", "testing", "sage"]} - key = func.execute(data) - - assert isinstance(key, frozenset) - assert func.validate_key(key) is True - - def test_extract_with_logging(self): - """Test that validation logs warnings""" - func = SimpleKeyByFunction() - - # Mock logger - func._logger = Mock() - - # Try to validate unhashable - result = func.validate_key([1, 2, 3]) - - assert result is False - # Logger should have been called with warning - assert func._logger.warning.called diff --git a/packages/sage-common/tests/unit/core/functions/test_lambda_function.py b/packages/sage-common/tests/unit/core/functions/test_lambda_function.py deleted file mode 100644 index 5c06a3df73..0000000000 --- a/packages/sage-common/tests/unit/core/functions/test_lambda_function.py +++ /dev/null @@ -1,619 +0,0 @@ -""" -Comprehensive tests for Lambda Function Wrappers - -Tests cover: -- LambdaMapFunction: wrapping lambda for map operations -- LambdaFilterFunction: wrapping lambda for filter operations -- LambdaFlatMapFunction: wrapping lambda for flatmap operations -- LambdaSinkFunction: wrapping lambda for sink operations -- LambdaSourceFunction: wrapping lambda for source operations -- LambdaKeyByFunction: wrapping lambda for keyby operations -- detect_lambda_type: automatic type detection -- wrap_lambda: dynamic wrapper creation -""" - -import logging -from unittest.mock import MagicMock, patch - -import pytest - -from sage.common.core.functions.lambda_function import ( - LambdaFilterFunction, - LambdaFlatMapFunction, - LambdaKeyByFunction, - LambdaMapFunction, - LambdaSinkFunction, - LambdaSourceFunction, - detect_lambda_type, - wrap_lambda, -) - - -class TestLambdaMapFunction: - """Test LambdaMapFunction wrapper""" - - def test_initialization(self): - """Test LambdaMapFunction initialization""" - - def lambda_func(x): - return x * 2 - - func = LambdaMapFunction(lambda_func) - - assert func.lambda_func is lambda_func - - def test_execute_simple_transform(self): - """Test execute with simple transformation""" - func = LambdaMapFunction(lambda x: x * 2) - result = func.execute(5) - - assert result == 10 - - def test_execute_string_transform(self): - """Test execute with string transformation""" - func = LambdaMapFunction(lambda x: x.upper()) - result = func.execute("hello") - - assert result == "HELLO" - - def test_execute_dict_transform(self): - """Test execute with dictionary transformation""" - func = LambdaMapFunction(lambda x: {"value": x["data"] * 2}) - result = func.execute({"data": 10}) - - assert result == {"value": 20} - - def test_execute_complex_transform(self): - """Test execute with complex transformation""" - func = LambdaMapFunction( - lambda x: {"name": x.get("name", "unknown"), "score": x.get("score", 0) + 10} - ) - result = func.execute({"name": "Alice", "score": 85}) - - assert result == {"name": "Alice", "score": 95} - - def test_execute_with_none(self): - """Test execute handles None input""" - func = LambdaMapFunction(lambda x: x if x is not None else "default") - - assert func.execute(None) == "default" - assert func.execute("value") == "value" - - -class TestLambdaFilterFunction: - """Test LambdaFilterFunction wrapper""" - - def test_initialization(self): - """Test LambdaFilterFunction initialization""" - - def lambda_func(x): - return x > 0 - - func = LambdaFilterFunction(lambda_func) - - assert func.lambda_func is lambda_func - - def test_execute_returns_true(self): - """Test execute returns True for matching condition""" - func = LambdaFilterFunction(lambda x: x > 10) - - assert func.execute(15) is True - assert func.execute(20) is True - - def test_execute_returns_false(self): - """Test execute returns False for non-matching condition""" - func = LambdaFilterFunction(lambda x: x > 10) - - assert func.execute(5) is False - assert func.execute(10) is False - - def test_execute_string_filtering(self): - """Test execute with string filtering""" - func = LambdaFilterFunction(lambda x: len(x) > 3) - - assert func.execute("hello") is True - assert func.execute("hi") is False - - def test_execute_dict_filtering(self): - """Test execute with dictionary filtering""" - func = LambdaFilterFunction(lambda x: x.get("active", False)) - - assert func.execute({"active": True}) is True - assert func.execute({"active": False}) is False - assert func.execute({}) is False - - def test_execute_exception_handling(self, caplog): - """Test execute handles exceptions gracefully""" - func = LambdaFilterFunction(lambda x: x["missing_key"]) - - with caplog.at_level(logging.ERROR): - result = func.execute({"other_key": "value"}) - - assert result is False - assert "LambdaFilterFunction error" in caplog.text - - def test_execute_with_none(self): - """Test execute handles None input""" - func = LambdaFilterFunction(lambda x: x is not None) - - assert func.execute(None) is False - assert func.execute("value") is True - - -class TestLambdaFlatMapFunction: - """Test LambdaFlatMapFunction wrapper""" - - def test_initialization(self): - """Test LambdaFlatMapFunction initialization""" - - def lambda_func(x): - return [x, x * 2] - - func = LambdaFlatMapFunction(lambda_func) - - assert func.lambda_func is lambda_func - - def test_execute_returns_list(self): - """Test execute returns list""" - func = LambdaFlatMapFunction(lambda x: [x, x * 2, x * 3]) - result = func.execute(5) - - assert result == [5, 10, 15] - - def test_execute_empty_list(self): - """Test execute returns empty list""" - func = LambdaFlatMapFunction(lambda x: []) - result = func.execute(5) - - assert result == [] - - def test_execute_string_splitting(self): - """Test execute with string splitting""" - func = LambdaFlatMapFunction(lambda x: x.split(",")) - result = func.execute("a,b,c") - - assert result == ["a", "b", "c"] - - def test_execute_dict_expansion(self): - """Test execute with dictionary expansion""" - func = LambdaFlatMapFunction(lambda x: [{"id": k, "value": v} for k, v in x.items()]) - result = func.execute({"a": 1, "b": 2}) - - assert len(result) == 2 - assert {"id": "a", "value": 1} in result - assert {"id": "b", "value": 2} in result - - def test_execute_type_error_on_non_list(self): - """Test execute raises TypeError if not returning list""" - func = LambdaFlatMapFunction(lambda x: x * 2) # Returns int, not list - - with pytest.raises(TypeError, match="must return a list"): - func.execute(5) - - def test_execute_type_error_on_dict(self): - """Test execute raises TypeError if returning dict""" - func = LambdaFlatMapFunction(lambda x: {"key": "value"}) - - with pytest.raises(TypeError, match="must return a list"): - func.execute(None) - - -class TestLambdaSinkFunction: - """Test LambdaSinkFunction wrapper""" - - def test_initialization(self): - """Test LambdaSinkFunction initialization""" - - def lambda_func(x): - return None - - func = LambdaSinkFunction(lambda_func) - - assert func.lambda_func is lambda_func - - def test_execute_returns_none(self): - """Test execute returns None""" - results = [] - func = LambdaSinkFunction(lambda x: results.append(x)) - - result = func.execute(42) - - assert result is None - assert 42 in results - - def test_execute_side_effect(self): - """Test execute performs side effect""" - side_effects = [] - func = LambdaSinkFunction(lambda x: side_effects.append(x * 2)) - - func.execute(5) - func.execute(10) - - assert side_effects == [10, 20] - - def test_execute_print_side_effect(self, capsys): - """Test execute with print side effect""" - func = LambdaSinkFunction(lambda x: print(f"Processing: {x}")) - - func.execute("test") - - captured = capsys.readouterr() - assert "Processing: test" in captured.out - - def test_execute_dict_mutation(self): - """Test execute with dictionary mutation""" - state = {"count": 0} - func = LambdaSinkFunction(lambda x: state.update({"count": state["count"] + x})) - - func.execute(5) - func.execute(10) - - assert state["count"] == 15 - - -class TestLambdaSourceFunction: - """Test LambdaSourceFunction wrapper""" - - def test_initialization(self): - """Test LambdaSourceFunction initialization""" - - def lambda_func(): - return 42 - - func = LambdaSourceFunction(lambda_func) - - assert func.lambda_func is lambda_func - - def test_execute_returns_value(self): - """Test execute returns value""" - func = LambdaSourceFunction(lambda: 42) - - assert func.execute() == 42 - - def test_execute_returns_different_values(self): - """Test execute with stateful lambda""" - counter = {"value": 0} - - def increment(): - counter["value"] += 1 - return counter["value"] - - func = LambdaSourceFunction(increment) - - assert func.execute() == 1 - assert func.execute() == 2 - assert func.execute() == 3 - - def test_execute_returns_dict(self): - """Test execute returns dictionary""" - func = LambdaSourceFunction(lambda: {"status": "ok", "count": 100}) - result = func.execute() - - assert result == {"status": "ok", "count": 100} - - def test_execute_no_parameters(self): - """Test execute with no parameters""" - func = LambdaSourceFunction(lambda: "constant") - - assert func.execute() == "constant" - - -class TestLambdaKeyByFunction: - """Test LambdaKeyByFunction wrapper""" - - @patch.object(LambdaKeyByFunction, "logger", new_callable=lambda: MagicMock()) - def test_initialization(self, mock_logger): - """Test LambdaKeyByFunction initialization""" - - def lambda_func(x): - return x["id"] - - func = LambdaKeyByFunction(lambda_func) - - assert func.lambda_func is lambda_func - - @patch.object(LambdaKeyByFunction, "logger", new_callable=lambda: MagicMock()) - def test_execute_simple_key_extraction(self, mock_logger): - """Test execute with simple key extraction""" - func = LambdaKeyByFunction(lambda x: x["user_id"]) - result = func.execute({"user_id": 123, "name": "Alice"}) - - assert result == 123 - - @patch.object(LambdaKeyByFunction, "logger", new_callable=lambda: MagicMock()) - def test_execute_nested_key_extraction(self, mock_logger): - """Test execute with nested key extraction""" - func = LambdaKeyByFunction(lambda x: x["user"]["id"]) - result = func.execute({"user": {"id": 456, "name": "Bob"}}) - - assert result == 456 - - @patch.object(LambdaKeyByFunction, "logger", new_callable=lambda: MagicMock()) - def test_execute_composite_key(self, mock_logger): - """Test execute with composite key""" - func = LambdaKeyByFunction(lambda x: (x["region"], x["category"])) - result = func.execute({"region": "US", "category": "electronics"}) - - assert result == ("US", "electronics") - - @patch.object(LambdaKeyByFunction, "logger", new_callable=lambda: MagicMock()) - def test_execute_exception_handling(self, mock_logger): - """Test execute handles exceptions with logging""" - func = LambdaKeyByFunction(lambda x: x["missing_key"]) - - with pytest.raises(KeyError): - func.execute({"other_key": "value"}) - - # Verify logger.error was called - assert mock_logger.error.called - - @patch.object(LambdaKeyByFunction, "logger", new_callable=lambda: MagicMock()) - def test_execute_with_attribute_access(self, mock_logger): - """Test execute with object attribute access""" - - class DataObject: - def __init__(self, id): - self.id = id - - func = LambdaKeyByFunction(lambda x: x.id) - obj = DataObject(789) - - assert func.execute(obj) == 789 - - -class TestDetectLambdaType: - """Test detect_lambda_type function""" - - def test_detect_source_no_params(self): - """Test detect source function (no parameters)""" - func_type = detect_lambda_type(lambda: 42) - assert func_type == "source" - - def test_detect_filter_bool_return(self): - """Test detect filter function (bool return annotation)""" - func_type = detect_lambda_type(lambda x: x > 0) - # Without annotation, defaults to map - assert func_type == "map" - - def test_detect_map_default(self): - """Test detect map function (default case)""" - func_type = detect_lambda_type(lambda x: x * 2) - assert func_type == "map" - - def test_detect_map_with_single_param(self): - """Test detect map with single parameter""" - func_type = detect_lambda_type(lambda x: x.upper()) - assert func_type == "map" - - def test_detect_error_multiple_params(self): - """Test detect with multiple parameters falls back to map""" - # Note: detect_lambda_type doesn't raise ValueError, it catches exceptions - # and defaults to 'map' in the except block - result = detect_lambda_type(lambda x, y: x + y) - # Should either raise or default to 'map' depending on implementation - assert result in ["map", "error"] # Allow either behavior - - def test_detect_fallback_on_exception(self): - """Test detect falls back to 'map' on exception""" - - # Create a mock function that might raise during inspection - def mock_func(): - pass - - # Even if inspection fails, should default to 'map' - func_type = detect_lambda_type(mock_func) - assert func_type in ["map", "source"] # Could be either depending on signature - - -class TestWrapLambda: - """Test wrap_lambda function""" - - def test_wrap_map_explicit(self): - """Test wrap_lambda with explicit 'map' type""" - - def lambda_func(x): - return x * 2 - - WrappedClass = wrap_lambda(lambda_func, func_type="map") - - instance = WrappedClass() - assert instance.execute(5) == 10 - - def test_wrap_filter_explicit(self): - """Test wrap_lambda with explicit 'filter' type""" - - def lambda_func(x): - return x > 10 - - WrappedClass = wrap_lambda(lambda_func, func_type="filter") - - instance = WrappedClass() - assert instance.execute(15) is True - assert instance.execute(5) is False - - def test_wrap_flatmap_explicit(self): - """Test wrap_lambda with explicit 'flatmap' type""" - - def lambda_func(x): - return [x, x * 2] - - WrappedClass = wrap_lambda(lambda_func, func_type="flatmap") - - instance = WrappedClass() - assert instance.execute(3) == [3, 6] - - def test_wrap_sink_explicit(self): - """Test wrap_lambda with explicit 'sink' type""" - results = [] - - def lambda_func(x): - return results.append(x) - - WrappedClass = wrap_lambda(lambda_func, func_type="sink") - - instance = WrappedClass() - instance.execute(42) - - assert 42 in results - - def test_wrap_source_explicit(self): - """Test wrap_lambda with explicit 'source' type""" - - def lambda_func(): - return "generated_value" - - WrappedClass = wrap_lambda(lambda_func, func_type="source") - - instance = WrappedClass() - assert instance.execute() == "generated_value" - - @patch.object(LambdaKeyByFunction, "logger", new_callable=lambda: MagicMock()) - def test_wrap_keyby_explicit(self, mock_logger): - """Test wrap_lambda with explicit 'keyby' type""" - - def lambda_func(x): - return x["id"] - - WrappedClass = wrap_lambda(lambda_func, func_type="keyby") - - instance = WrappedClass() - assert instance.execute({"id": 123}) == 123 - - def test_wrap_auto_detect_map(self): - """Test wrap_lambda with auto-detection (map)""" - - def lambda_func(x): - return x * 3 - - WrappedClass = wrap_lambda(lambda_func) # Auto-detect - - instance = WrappedClass() - assert instance.execute(4) == 12 - - def test_wrap_auto_detect_source(self): - """Test wrap_lambda with auto-detection (source)""" - - def lambda_func(): - return 999 - - WrappedClass = wrap_lambda(lambda_func) # Auto-detect - - instance = WrappedClass() - assert instance.execute() == 999 - - def test_wrap_unsupported_type(self): - """Test wrap_lambda raises error for unsupported type""" - - def lambda_func(x): - return x - - with pytest.raises(ValueError, match="Unsupported function type"): - wrap_lambda(lambda_func, func_type="unsupported_type") - - def test_wrap_preserves_lambda_behavior(self): - """Test wrapped lambda preserves original behavior""" - - def original(x): - return x.upper() + "!" - - WrappedClass = wrap_lambda(original, func_type="map") - - instance = WrappedClass() - assert instance.execute("hello") == "HELLO!" - - -class TestLambdaFunctionIntegration: - """Integration tests for lambda function wrappers""" - - def test_pipeline_simulation_map_filter(self): - """Test simulated pipeline with map and filter""" - data = [1, 2, 3, 4, 5, 6] - - # Map: multiply by 2 - map_func = LambdaMapFunction(lambda x: x * 2) - mapped = [map_func.execute(d) for d in data] - - # Filter: keep only > 5 - filter_func = LambdaFilterFunction(lambda x: x > 5) - filtered = [d for d in mapped if filter_func.execute(d)] - - assert filtered == [6, 8, 10, 12] - - def test_pipeline_simulation_flatmap(self): - """Test simulated pipeline with flatmap""" - data = ["a,b", "c,d,e", "f"] - - # FlatMap: split by comma - flatmap_func = LambdaFlatMapFunction(lambda x: x.split(",")) - flattened = [item for d in data for item in flatmap_func.execute(d)] - - assert flattened == ["a", "b", "c", "d", "e", "f"] - - @patch.object(LambdaKeyByFunction, "logger", new_callable=lambda: MagicMock()) - def test_pipeline_simulation_keyby(self, mock_logger): - """Test simulated pipeline with keyby""" - data = [ - {"user_id": 1, "action": "login"}, - {"user_id": 2, "action": "click"}, - {"user_id": 1, "action": "logout"}, - ] - - # KeyBy: group by user_id - keyby_func = LambdaKeyByFunction(lambda x: x["user_id"]) - grouped = {} - for d in data: - key = keyby_func.execute(d) - grouped.setdefault(key, []).append(d) - - assert len(grouped[1]) == 2 - assert len(grouped[2]) == 1 - - def test_pipeline_simulation_source_sink(self): - """Test simulated pipeline with source and sink""" - counter = {"value": 0} - - # Source: generate incrementing values - def increment(): - counter["value"] += 1 - return counter["value"] - - source_func = LambdaSourceFunction(increment) - - # Sink: collect values - results = [] - sink_func = LambdaSinkFunction(lambda x: results.append(x)) - - # Simulate pipeline - for _ in range(3): - value = source_func.execute() - sink_func.execute(value) - - assert results == [1, 2, 3] - - -class TestDetectLambdaTypeAnnotations: - """Tests for detect_lambda_type function type annotations to achieve 100% coverage""" - - def test_detect_filter_with_bool_annotation(self): - """Test detection of filter function with explicit bool return annotation (line 133)""" - - def bool_func(x: int) -> bool: - return x > 0 - - assert detect_lambda_type(bool_func) == "filter" - - def test_detect_flatmap_with_list_annotation(self): - """Test detection of flatmap function with List return annotation (line 135)""" - - def list_func(x: int) -> list[int]: - return [x, x * 2] - - assert detect_lambda_type(list_func) == "flatmap" - - def test_detect_sink_with_none_annotation(self): - """Test detection of sink function with None return annotation (line 137)""" - - def none_func(x: int) -> None: - print(x) - - assert detect_lambda_type(none_func) == "sink" diff --git a/packages/sage-common/tests/unit/core/functions/test_simple_functions.py b/packages/sage-common/tests/unit/core/functions/test_simple_functions.py deleted file mode 100644 index a26d1fbdc9..0000000000 --- a/packages/sage-common/tests/unit/core/functions/test_simple_functions.py +++ /dev/null @@ -1,598 +0,0 @@ -""" -Comprehensive tests for simple function classes - -Tests cover: -- MapFunction: one-to-one data transformation -- FilterFunction: boolean predicate filtering -- SinkFunction: terminal operations with side effects -- SourceFunction: data generation -- FlatMapFunction: one-to-many transformations -- BatchFunction: data batching -""" - -from unittest.mock import MagicMock - -import pytest - -from sage.common.core.functions.batch_function import BatchFunction -from sage.common.core.functions.filter_function import FilterFunction -from sage.common.core.functions.flatmap_function import FlatMapFunction -from sage.common.core.functions.map_function import MapFunction -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.core.functions.source_function import SourceFunction - - -# Concrete implementations for testing -class ConcreteMapFunction(MapFunction): - """Concrete MapFunction for testing""" - - def execute(self, data): - return data * 2 - - -class ConcreteFilterFunction(FilterFunction): - """Concrete FilterFunction for testing""" - - def execute(self, data): - return data > 10 - - -class ConcreteSinkFunction(SinkFunction): - """Concrete SinkFunction for testing""" - - def __init__(self): - super().__init__() - self.received = [] - - def execute(self, data): - self.received.append(data) - - -class ConcreteSourceFunction(SourceFunction): - """Concrete SourceFunction for testing""" - - def __init__(self): - super().__init__() - self.counter = 0 - - def execute(self): - self.counter += 1 - return self.counter - - -class ConcreteFlatMapFunction(FlatMapFunction): - """Concrete FlatMapFunction for testing""" - - def execute(self, data): - return [data, data * 2, data * 3] - - -class ConcreteBatchFunction(BatchFunction): - """Concrete BatchFunction for testing""" - - def __init__(self, batch_size=3): - super().__init__() - self.batch_size = batch_size - self.batch = [] - - def execute(self, data): - self.batch.append(data) - if len(self.batch) >= self.batch_size: - result = list(self.batch) - self.batch = [] - return result - return None - - -class TestMapFunction: - """Test MapFunction""" - - def test_map_inheritance(self): - """Test MapFunction inherits from BaseFunction""" - from sage.common.core.functions.base_function import BaseFunction - - func = ConcreteMapFunction() - assert isinstance(func, BaseFunction) - assert isinstance(func, MapFunction) - - def test_map_execute_simple(self): - """Test execute with simple data""" - func = ConcreteMapFunction() - result = func.execute(5) - assert result == 10 - - def test_map_execute_different_types(self): - """Test execute with different data types""" - - class StringMapFunction(MapFunction): - def execute(self, data): - return data.upper() - - func = StringMapFunction() - assert func.execute("hello") == "HELLO" - - def test_map_execute_dict(self): - """Test execute with dictionary""" - - class DictMapFunction(MapFunction): - def execute(self, data): - return {"value": data["x"] * 2} - - func = DictMapFunction() - assert func.execute({"x": 5}) == {"value": 10} - - def test_map_with_context(self): - """Test MapFunction with context""" - func = ConcreteMapFunction() - mock_ctx = MagicMock() - func.ctx = mock_ctx - - result = func.execute(3) - assert result == 6 - assert func.ctx is mock_ctx - - -class TestFilterFunction: - """Test FilterFunction""" - - def test_filter_inheritance(self): - """Test FilterFunction inherits from BaseFunction""" - from sage.common.core.functions.base_function import BaseFunction - - func = ConcreteFilterFunction() - assert isinstance(func, BaseFunction) - assert isinstance(func, FilterFunction) - - def test_filter_execute_true(self): - """Test execute returns True for matching condition""" - func = ConcreteFilterFunction() - assert func.execute(15) is True - assert func.execute(20) is True - - def test_filter_execute_false(self): - """Test execute returns False for non-matching condition""" - func = ConcreteFilterFunction() - assert func.execute(5) is False - assert func.execute(10) is False - - def test_filter_boolean_result(self): - """Test execute always returns boolean""" - - class AlwaysTrueFilter(FilterFunction): - def execute(self, data): - return True - - func = AlwaysTrueFilter() - assert func.execute("anything") is True - - def test_filter_with_complex_condition(self): - """Test filter with complex condition""" - - class ComplexFilter(FilterFunction): - def execute(self, data): - return data.get("active", False) and data.get("score", 0) > 50 - - func = ComplexFilter() - assert func.execute({"active": True, "score": 60}) is True - assert func.execute({"active": False, "score": 60}) is False - assert func.execute({"active": True, "score": 30}) is False - - def test_filter_process_output_truthy(self): - """Test _process_output converts truthy values to True""" - func = ConcreteFilterFunction() - assert func._process_output(1) is True - assert func._process_output("non-empty") is True - assert func._process_output([1, 2, 3]) is True - assert func._process_output({"key": "value"}) is True - - def test_filter_process_output_falsy(self): - """Test _process_output converts falsy values to False""" - func = ConcreteFilterFunction() - assert func._process_output(0) is False - assert func._process_output("") is False - assert func._process_output([]) is False - assert func._process_output({}) is False - assert func._process_output(None) is False - - -class TestSinkFunction: - """Test SinkFunction""" - - def test_sink_inheritance(self): - """Test SinkFunction inherits from BaseFunction""" - from sage.common.core.functions.base_function import BaseFunction - - func = ConcreteSinkFunction() - assert isinstance(func, BaseFunction) - assert isinstance(func, SinkFunction) - - def test_sink_execute_void_return(self): - """Test execute returns None""" - func = ConcreteSinkFunction() - result = func.execute("data") - assert result is None - - def test_sink_side_effect(self): - """Test sink performs side effect""" - func = ConcreteSinkFunction() - func.execute("item1") - func.execute("item2") - - assert func.received == ["item1", "item2"] - - def test_sink_multiple_calls(self): - """Test sink handles multiple calls""" - func = ConcreteSinkFunction() - for i in range(10): - func.execute(i) - - assert len(func.received) == 10 - assert func.received[0] == 0 - assert func.received[9] == 9 - - def test_sink_print_side_effect(self, capsys): - """Test sink with print side effect""" - - class PrintSink(SinkFunction): - def execute(self, data): - print(f"Received: {data}") - - func = PrintSink() - func.execute("test") - - captured = capsys.readouterr() - assert "Received: test" in captured.out - - -class TestSourceFunction: - """Test SourceFunction""" - - def test_source_inheritance(self): - """Test SourceFunction inherits from BaseFunction""" - from sage.common.core.functions.base_function import BaseFunction - - func = ConcreteSourceFunction() - assert isinstance(func, BaseFunction) - assert isinstance(func, SourceFunction) - - def test_source_execute_no_args(self): - """Test execute takes no arguments""" - func = ConcreteSourceFunction() - result = func.execute() - assert result == 1 - - def test_source_sequential_execution(self): - """Test source generates sequential data""" - func = ConcreteSourceFunction() - results = [func.execute() for _ in range(5)] - assert results == [1, 2, 3, 4, 5] - - def test_source_constant_data(self): - """Test source with constant data""" - - class ConstantSource(SourceFunction): - def execute(self): - return "constant_value" - - func = ConstantSource() - assert func.execute() == "constant_value" - assert func.execute() == "constant_value" - - def test_source_random_data(self): - """Test source with random-like data""" - import random - - class RandomSource(SourceFunction): - def execute(self): - return random.randint(1, 100) - - func = RandomSource() - result1 = func.execute() - result2 = func.execute() - - assert isinstance(result1, int) - assert isinstance(result2, int) - - -class TestFlatMapFunction: - """Test FlatMapFunction""" - - def test_flatmap_inheritance(self): - """Test FlatMapFunction inherits from BaseFunction""" - from sage.common.core.functions.base_function import BaseFunction - - func = ConcreteFlatMapFunction() - assert isinstance(func, BaseFunction) - assert isinstance(func, FlatMapFunction) - - def test_flatmap_execute_returns_list(self): - """Test execute returns list""" - func = ConcreteFlatMapFunction() - result = func.execute(5) - assert isinstance(result, list) - assert result == [5, 10, 15] - - def test_flatmap_one_to_many(self): - """Test flatmap produces multiple outputs""" - func = ConcreteFlatMapFunction() - result = func.execute(2) - assert len(result) == 3 - assert result == [2, 4, 6] - - def test_flatmap_empty_list(self): - """Test flatmap can return empty list""" - - class EmptyFlatMap(FlatMapFunction): - def execute(self, data): - return [] - - func = EmptyFlatMap() - assert func.execute("anything") == [] - - def test_flatmap_string_split(self): - """Test flatmap with string splitting""" - - class SplitFlatMap(FlatMapFunction): - def execute(self, data): - return data.split(",") - - func = SplitFlatMap() - result = func.execute("a,b,c") - assert result == ["a", "b", "c"] - - def test_flatmap_dict_expansion(self): - """Test flatmap with dictionary expansion""" - - class DictExpandFlatMap(FlatMapFunction): - def execute(self, data): - return [{"key": k, "value": v} for k, v in data.items()] - - func = DictExpandFlatMap() - result = func.execute({"a": 1, "b": 2}) - assert len(result) == 2 - assert {"key": "a", "value": 1} in result - - def test_flatmap_collector_initialization(self): - """Test FlatMapFunction initializes with None collector""" - func = ConcreteFlatMapFunction() - assert func.out is None - - def test_flatmap_insert_collector(self): - """Test inserting collector into FlatMapFunction""" - from unittest.mock import PropertyMock, patch - - from sage.common.core.functions.flatmap_collector import Collector - - func = ConcreteFlatMapFunction() - mock_collector = MagicMock(spec=Collector) - mock_logger = MagicMock() - - # Mock logger property to avoid initialization issues - with patch.object( - type(func), "logger", new_callable=PropertyMock, return_value=mock_logger - ): - func.insert_collector(mock_collector) - - assert func.out is mock_collector - - def test_flatmap_collect_method(self): - """Test collect method with collector""" - from unittest.mock import PropertyMock, patch - - from sage.common.core.functions.flatmap_collector import Collector - - func = ConcreteFlatMapFunction() - mock_collector = MagicMock(spec=Collector) - mock_logger = MagicMock() - - with patch.object( - type(func), "logger", new_callable=PropertyMock, return_value=mock_logger - ): - func.insert_collector(mock_collector) - func.collect("test_data") - - mock_collector.collect.assert_called_once_with("test_data") - - def test_flatmap_collect_without_collector(self): - """Test collect raises error without collector""" - func = ConcreteFlatMapFunction() - - with pytest.raises(RuntimeError, match="Collector not initialized"): - func.collect("test_data") - - def test_flatmap_with_collector_pattern(self): - """Test FlatMapFunction using collector pattern""" - from unittest.mock import PropertyMock, patch - - from sage.common.core.functions.flatmap_collector import Collector - - class CollectorBasedFlatMap(FlatMapFunction): - def execute(self, data): - for i in range(data): - self.collect(i * 2) - return None - - func = CollectorBasedFlatMap() - mock_collector = MagicMock(spec=Collector) - mock_logger = MagicMock() - - with patch.object( - type(func), "logger", new_callable=PropertyMock, return_value=mock_logger - ): - func.insert_collector(mock_collector) - func.execute(3) - - assert mock_collector.collect.call_count == 3 - mock_collector.collect.assert_any_call(0) - mock_collector.collect.assert_any_call(2) - mock_collector.collect.assert_any_call(4) - - -class TestBatchFunction: - """Test BatchFunction""" - - def test_batch_inheritance(self): - """Test BatchFunction inherits from BaseFunction""" - from sage.common.core.functions.base_function import BaseFunction - - func = ConcreteBatchFunction() - assert isinstance(func, BaseFunction) - assert isinstance(func, BatchFunction) - - def test_batch_accumulation(self): - """Test batch accumulates data""" - func = ConcreteBatchFunction(batch_size=3) - - result1 = func.execute(1) - assert result1 is None # Not enough for batch - - result2 = func.execute(2) - assert result2 is None # Still not enough - - result3 = func.execute(3) - assert result3 == [1, 2, 3] # Batch complete - - def test_batch_multiple_batches(self): - """Test multiple batches""" - func = ConcreteBatchFunction(batch_size=2) - - assert func.execute(1) is None - assert func.execute(2) == [1, 2] - - assert func.execute(3) is None - assert func.execute(4) == [3, 4] - - def test_batch_size_one(self): - """Test batch size of 1""" - func = ConcreteBatchFunction(batch_size=1) - - assert func.execute("a") == ["a"] - assert func.execute("b") == ["b"] - - def test_batch_large_size(self): - """Test batch with large size""" - func = ConcreteBatchFunction(batch_size=10) - - for i in range(9): - assert func.execute(i) is None - - result = func.execute(9) - assert result == list(range(10)) - - -class TestFunctionIntegration: - """Integration tests for function combinations""" - - def test_map_filter_pipeline(self): - """Test map followed by filter""" - map_func = ConcreteMapFunction() - filter_func = ConcreteFilterFunction() - - data = [5, 10, 15, 20] - mapped = [map_func.execute(d) for d in data] # [10, 20, 30, 40] - filtered = [d for d in mapped if filter_func.execute(d)] # [20, 30, 40] - - assert filtered == [20, 30, 40] - - def test_source_sink_pipeline(self): - """Test source to sink pipeline""" - source = ConcreteSourceFunction() - sink = ConcreteSinkFunction() - - for _ in range(5): - data = source.execute() - sink.execute(data) - - assert sink.received == [1, 2, 3, 4, 5] - - def test_flatmap_filter_pipeline(self): - """Test flatmap followed by filter""" - flatmap_func = ConcreteFlatMapFunction() - filter_func = ConcreteFilterFunction() - - data = 5 - flattened = flatmap_func.execute(data) # [5, 10, 15] - filtered = [d for d in flattened if filter_func.execute(d)] # [15] - - assert filtered == [15] - - def test_map_batch_pipeline(self): - """Test map followed by batch""" - map_func = ConcreteMapFunction() - batch_func = ConcreteBatchFunction(batch_size=3) - - data = [1, 2, 3, 4, 5] - batches = [] - - for d in data: - mapped = map_func.execute(d) - batch = batch_func.execute(mapped) - if batch is not None: - batches.append(batch) - - # First 3: [2, 4, 6] - assert batches[0] == [2, 4, 6] - # Remaining [8, 10] not batched yet - assert len(batches) == 1 - - -class TestFunctionEdgeCases: - """Test edge cases for function classes""" - - def test_map_with_none(self): - """Test map handles None""" - - class NoneHandlingMap(MapFunction): - def execute(self, data): - return data if data is not None else "default" - - func = NoneHandlingMap() - assert func.execute(None) == "default" - assert func.execute("value") == "value" - - def test_filter_with_exception(self): - """Test filter handles exceptions""" - - class SafeFilter(FilterFunction): - def execute(self, data): - try: - return data["valid"] - except (KeyError, TypeError): - return False - - func = SafeFilter() - assert func.execute({"valid": True}) is True - assert func.execute({}) is False - assert func.execute(None) is False - - def test_flatmap_variable_length(self): - """Test flatmap with variable length results""" - - class VariableFlatMap(FlatMapFunction): - def execute(self, data): - return list(range(data)) - - func = VariableFlatMap() - assert func.execute(0) == [] - assert func.execute(1) == [0] - assert func.execute(5) == [0, 1, 2, 3, 4] - - def test_source_with_state(self): - """Test source maintains state across calls""" - - class StatefulSource(SourceFunction): - def __init__(self): - super().__init__() - self.values = iter([1, 2, 3]) - - def execute(self): - try: - return next(self.values) - except StopIteration: - return None - - func = StatefulSource() - assert func.execute() == 1 - assert func.execute() == 2 - assert func.execute() == 3 - assert func.execute() is None diff --git a/packages/sage-common/tests/unit/core/test_constants.py b/packages/sage-common/tests/unit/core/test_constants.py deleted file mode 100644 index d3a3331946..0000000000 --- a/packages/sage-common/tests/unit/core/test_constants.py +++ /dev/null @@ -1,162 +0,0 @@ -""" -Tests for sage.common.core.constants - -Tests the constant definitions. -""" - -from sage.common.core.constants import ( - DEFAULT_CHECKPOINT_INTERVAL, - DEFAULT_CLEANUP_TIMEOUT, - DEFAULT_HEALTH_CHECK_INTERVAL, - DEFAULT_MAX_RESTART_ATTEMPTS, - PLACEMENT_STRATEGY_LOAD_BALANCE, - PLACEMENT_STRATEGY_RESOURCE_AWARE, - PLACEMENT_STRATEGY_SIMPLE, - RESTART_STRATEGY_EXPONENTIAL, - RESTART_STRATEGY_FAILURE_RATE, - RESTART_STRATEGY_FIXED, - SCHEDULING_STRATEGY_FIFO, - SCHEDULING_STRATEGY_PRIORITY, - SCHEDULING_STRATEGY_RESOURCE_AWARE, -) - - -class TestDefaultConstants: - """Tests for default configuration constants""" - - def test_checkpoint_interval_type_and_value(self): - """Test DEFAULT_CHECKPOINT_INTERVAL is a positive integer""" - assert isinstance(DEFAULT_CHECKPOINT_INTERVAL, int) - assert DEFAULT_CHECKPOINT_INTERVAL > 0 - assert DEFAULT_CHECKPOINT_INTERVAL == 60 - - def test_health_check_interval_type_and_value(self): - """Test DEFAULT_HEALTH_CHECK_INTERVAL is a positive integer""" - assert isinstance(DEFAULT_HEALTH_CHECK_INTERVAL, int) - assert DEFAULT_HEALTH_CHECK_INTERVAL > 0 - assert DEFAULT_HEALTH_CHECK_INTERVAL == 30 - - def test_max_restart_attempts_type_and_value(self): - """Test DEFAULT_MAX_RESTART_ATTEMPTS is a positive integer""" - assert isinstance(DEFAULT_MAX_RESTART_ATTEMPTS, int) - assert DEFAULT_MAX_RESTART_ATTEMPTS > 0 - assert DEFAULT_MAX_RESTART_ATTEMPTS == 3 - - def test_cleanup_timeout_type_and_value(self): - """Test DEFAULT_CLEANUP_TIMEOUT is a positive float""" - assert isinstance(DEFAULT_CLEANUP_TIMEOUT, float) - assert DEFAULT_CLEANUP_TIMEOUT > 0 - assert DEFAULT_CLEANUP_TIMEOUT == 5.0 - - -class TestRestartStrategyConstants: - """Tests for restart strategy constants""" - - def test_restart_strategy_types(self): - """Test that restart strategies are strings""" - assert isinstance(RESTART_STRATEGY_FIXED, str) - assert isinstance(RESTART_STRATEGY_EXPONENTIAL, str) - assert isinstance(RESTART_STRATEGY_FAILURE_RATE, str) - - def test_restart_strategy_values(self): - """Test restart strategy constant values""" - assert RESTART_STRATEGY_FIXED == "fixed_delay" - assert RESTART_STRATEGY_EXPONENTIAL == "exponential_backoff" - assert RESTART_STRATEGY_FAILURE_RATE == "failure_rate" - - def test_restart_strategies_are_unique(self): - """Test that restart strategies have unique values""" - strategies = { - RESTART_STRATEGY_FIXED, - RESTART_STRATEGY_EXPONENTIAL, - RESTART_STRATEGY_FAILURE_RATE, - } - assert len(strategies) == 3 - - -class TestPlacementStrategyConstants: - """Tests for placement strategy constants""" - - def test_placement_strategy_types(self): - """Test that placement strategies are strings""" - assert isinstance(PLACEMENT_STRATEGY_SIMPLE, str) - assert isinstance(PLACEMENT_STRATEGY_RESOURCE_AWARE, str) - assert isinstance(PLACEMENT_STRATEGY_LOAD_BALANCE, str) - - def test_placement_strategy_values(self): - """Test placement strategy constant values""" - assert PLACEMENT_STRATEGY_SIMPLE == "simple" - assert PLACEMENT_STRATEGY_RESOURCE_AWARE == "resource_aware" - assert PLACEMENT_STRATEGY_LOAD_BALANCE == "load_balance" - - def test_placement_strategies_are_unique(self): - """Test that placement strategies have unique values""" - strategies = { - PLACEMENT_STRATEGY_SIMPLE, - PLACEMENT_STRATEGY_RESOURCE_AWARE, - PLACEMENT_STRATEGY_LOAD_BALANCE, - } - assert len(strategies) == 3 - - -class TestSchedulingStrategyConstants: - """Tests for scheduling strategy constants""" - - def test_scheduling_strategy_types(self): - """Test that scheduling strategies are strings""" - assert isinstance(SCHEDULING_STRATEGY_FIFO, str) - assert isinstance(SCHEDULING_STRATEGY_PRIORITY, str) - assert isinstance(SCHEDULING_STRATEGY_RESOURCE_AWARE, str) - - def test_scheduling_strategy_values(self): - """Test scheduling strategy constant values""" - assert SCHEDULING_STRATEGY_FIFO == "fifo" - assert SCHEDULING_STRATEGY_PRIORITY == "priority" - assert SCHEDULING_STRATEGY_RESOURCE_AWARE == "resource_aware" - - def test_scheduling_strategies_are_unique(self): - """Test that scheduling strategies have unique values""" - strategies = { - SCHEDULING_STRATEGY_FIFO, - SCHEDULING_STRATEGY_PRIORITY, - SCHEDULING_STRATEGY_RESOURCE_AWARE, - } - assert len(strategies) == 3 - - -class TestConstantImmutability: - """Tests to ensure constants remain constant""" - - def test_constants_are_not_none(self): - """Test that no constants are None""" - constants = [ - DEFAULT_CHECKPOINT_INTERVAL, - DEFAULT_HEALTH_CHECK_INTERVAL, - DEFAULT_MAX_RESTART_ATTEMPTS, - DEFAULT_CLEANUP_TIMEOUT, - RESTART_STRATEGY_FIXED, - RESTART_STRATEGY_EXPONENTIAL, - RESTART_STRATEGY_FAILURE_RATE, - PLACEMENT_STRATEGY_SIMPLE, - PLACEMENT_STRATEGY_RESOURCE_AWARE, - PLACEMENT_STRATEGY_LOAD_BALANCE, - SCHEDULING_STRATEGY_FIFO, - SCHEDULING_STRATEGY_PRIORITY, - SCHEDULING_STRATEGY_RESOURCE_AWARE, - ] - assert all(c is not None for c in constants) - - def test_strategy_constants_non_empty(self): - """Test that strategy constants are non-empty strings""" - strategies = [ - RESTART_STRATEGY_FIXED, - RESTART_STRATEGY_EXPONENTIAL, - RESTART_STRATEGY_FAILURE_RATE, - PLACEMENT_STRATEGY_SIMPLE, - PLACEMENT_STRATEGY_RESOURCE_AWARE, - PLACEMENT_STRATEGY_LOAD_BALANCE, - SCHEDULING_STRATEGY_FIFO, - SCHEDULING_STRATEGY_PRIORITY, - SCHEDULING_STRATEGY_RESOURCE_AWARE, - ] - assert all(len(s) > 0 for s in strategies) diff --git a/packages/sage-common/tests/unit/core/test_data_types.py b/packages/sage-common/tests/unit/core/test_data_types.py deleted file mode 100644 index f556c34d96..0000000000 --- a/packages/sage-common/tests/unit/core/test_data_types.py +++ /dev/null @@ -1,336 +0,0 @@ -""" -Tests for sage.common.core.data_types - -Tests the universal data type definitions for SAGE framework. -""" - -from sage.common.core.data_types import ( - BaseDocument, - BaseQueryResult, - ExtendedQueryResult, - create_query_result, - ensure_query_result, - extract_query, - extract_results, -) - - -class TestBaseDocument: - """Tests for BaseDocument TypedDict""" - - def test_base_document_required_field(self): - """Test BaseDocument with required text field""" - doc: BaseDocument = {"text": "Sample text"} - assert doc["text"] == "Sample text" - - def test_base_document_all_fields(self): - """Test BaseDocument with all fields""" - doc: BaseDocument = { - "text": "Sample text", - "id": "doc_123", - "title": "Sample Title", - "source": "source.pdf", - "score": 0.95, - "rank": 1, - "metadata": {"key": "value"}, - } - assert doc["text"] == "Sample text" - assert doc["id"] == "doc_123" - assert doc["title"] == "Sample Title" - assert doc["source"] == "source.pdf" - assert doc["score"] == 0.95 - assert doc["rank"] == 1 - assert doc["metadata"] == {"key": "value"} - - def test_base_document_numeric_id(self): - """Test BaseDocument with numeric ID""" - doc: BaseDocument = {"text": "Sample", "id": 123} - assert doc["id"] == 123 - - -class TestBaseQueryResult: - """Tests for BaseQueryResult TypedDict""" - - def test_base_query_result_required_fields(self): - """Test BaseQueryResult with required fields""" - result: BaseQueryResult = { - "query": "test query", - "results": ["result1", "result2"], - } - assert result["query"] == "test query" - assert result["results"] == ["result1", "result2"] - - def test_base_query_result_empty_results(self): - """Test BaseQueryResult with empty results""" - result: BaseQueryResult = {"query": "test", "results": []} - assert result["query"] == "test" - assert result["results"] == [] - - -class TestExtendedQueryResult: - """Tests for ExtendedQueryResult TypedDict""" - - def test_extended_query_result_basic(self): - """Test ExtendedQueryResult with basic fields""" - result: ExtendedQueryResult = { - "query": "test query", - "results": ["result1"], - } - assert result["query"] == "test query" - assert result["results"] == ["result1"] - - def test_extended_query_result_with_extras(self): - """Test ExtendedQueryResult with extra fields""" - result: ExtendedQueryResult = { - "query": "test", - "results": ["a", "b"], - "query_id": "q_123", - "timestamp": 1234567890, - "total_count": 100, - "execution_time": 0.5, - "context": "context string", - "metadata": {"model": "gpt-4"}, - } - assert result["query"] == "test" - assert result["query_id"] == "q_123" - assert result["timestamp"] == 1234567890 - assert result["total_count"] == 100 - assert result["execution_time"] == 0.5 - assert result["context"] == "context string" - assert result["metadata"] == {"model": "gpt-4"} - - -class TestEnsureQueryResult: - """Tests for ensure_query_result() function""" - - def test_ensure_from_dict(self): - """Test ensure_query_result from dict""" - data = {"query": "test", "results": ["a", "b"]} - result = ensure_query_result(data) - assert result["query"] == "test" - assert result["results"] == ["a", "b"] - - def test_ensure_from_dict_with_question(self): - """Test ensure_query_result from dict with 'question' key""" - data = {"question": "test", "results": ["a"]} - result = ensure_query_result(data) - assert result["query"] == "test" - assert result["results"] == ["a"] - - def test_ensure_from_dict_with_docs(self): - """Test ensure_query_result from dict with 'docs' key""" - data = {"query": "test", "docs": ["doc1", "doc2"]} - result = ensure_query_result(data) - assert result["query"] == "test" - assert result["results"] == ["doc1", "doc2"] - - def test_ensure_from_tuple(self): - """Test ensure_query_result from tuple""" - data = ("query", ["result1", "result2"]) - result = ensure_query_result(data) - assert result["query"] == "query" - assert result["results"] == ["result1", "result2"] - - def test_ensure_from_list(self): - """Test ensure_query_result from list""" - data = ["query", ["result1"]] - result = ensure_query_result(data) - assert result["query"] == "query" - assert result["results"] == ["result1"] - - def test_ensure_with_default_query(self): - """Test ensure_query_result with default query""" - data = {} - result = ensure_query_result(data, default_query="default") - assert result["query"] == "default" - assert result["results"] == [] - - def test_ensure_invalid_format(self): - """Test ensure_query_result with invalid format""" - data: dict = {} # Type hint to avoid type error - result = ensure_query_result(data, default_query="fallback") - assert result["query"] == "fallback" - assert result["results"] == [] - - def test_ensure_with_non_list_results_iterable(self): - """Test ensure_query_result converts non-list iterables to list""" - data = {"query": "test", "results": ("a", "b", "c")} # tuple instead of list - result = ensure_query_result(data) - assert result["query"] == "test" - assert result["results"] == ["a", "b", "c"] - assert isinstance(result["results"], list) - - def test_ensure_with_non_list_results_single_value(self): - """Test ensure_query_result wraps single non-list value in list""" - data = {"query": "test", "results": "single_item"} - result = ensure_query_result(data) - assert result["query"] == "test" - assert result["results"] == ["single_item"] - assert isinstance(result["results"], list) - - def test_ensure_unparseable_input(self): - """Test ensure_query_result with completely unparseable input""" - # Input that doesn't match any expected format - result = ensure_query_result("invalid_string", default_query="default") - assert result["query"] == "default" - assert result["results"] == [] - - -class TestExtractQuery: - """Tests for extract_query() function""" - - def test_extract_from_string(self): - """Test extract_query from string input""" - assert extract_query("test query") == "test query" - - def test_extract_from_dict_query(self): - """Test extract_query from dict with 'query' key""" - assert extract_query({"query": "test"}) == "test" - - def test_extract_from_dict_question(self): - """Test extract_query from dict with 'question' key""" - assert extract_query({"question": "test"}) == "test" - - def test_extract_from_dict_q(self): - """Test extract_query from dict with 'q' key""" - assert extract_query({"q": "test"}) == "test" - - def test_extract_from_tuple(self): - """Test extract_query from tuple""" - assert extract_query(("query", ["results"])) == "query" - - def test_extract_from_list(self): - """Test extract_query from list""" - assert extract_query(["query", ["results"]]) == "query" - - def test_extract_with_default(self): - """Test extract_query with default value""" - assert extract_query({}, default="default") == "default" - assert extract_query([], default="fallback") == "fallback" - - def test_extract_none_value(self): - """Test extract_query with None value""" - assert extract_query((None, ["results"]), default="default") == "default" - - -class TestExtractResults: - """Tests for extract_results() function""" - - def test_extract_from_dict_results(self): - """Test extract_results from dict with 'results' key""" - assert extract_results({"results": ["a", "b"]}) == ["a", "b"] - - def test_extract_from_dict_documents(self): - """Test extract_results from dict with 'documents' key""" - assert extract_results({"documents": ["a", "b"]}) == ["a", "b"] - - def test_extract_from_dict_docs(self): - """Test extract_results from dict with 'docs' key""" - assert extract_results({"docs": ["a"]}) == ["a"] - - def test_extract_from_dict_items(self): - """Test extract_results from dict with 'items' key""" - assert extract_results({"items": ["a"]}) == ["a"] - - def test_extract_from_tuple(self): - """Test extract_results from tuple""" - assert extract_results(("query", ["a", "b"])) == ["a", "b"] - - def test_extract_from_list(self): - """Test extract_results from list""" - assert extract_results(["query", ["a", "b"]]) == ["a", "b"] - - def test_extract_single_item(self): - """Test extract_results with single non-list item""" - assert extract_results({"results": "single"}) == ["single"] - - def test_extract_with_default(self): - """Test extract_results with default value""" - assert extract_results({}, default=["default"]) == ["default"] - assert extract_results({}) == [] - - def test_extract_from_single_element_list(self): - """Test extract_results from single-element list/tuple""" - # Single element list/tuple (length < 2) should return as list - assert extract_results(["only_one"]) == ["only_one"] - assert extract_results(("single",)) == ["single"] - - def test_extract_from_invalid_type(self): - """Test extract_results from invalid type returns default""" - # String, int, etc. should return default - assert extract_results("string_input", default=["fallback"]) == ["fallback"] - assert extract_results(123, default=["default"]) == ["default"] - assert extract_results(None) == [] - - -class TestCreateQueryResult: - """Tests for create_query_result() function""" - - def test_create_basic(self): - """Test create_query_result with basic params""" - result = create_query_result("query", ["a", "b"]) - assert result["query"] == "query" - assert result["results"] == ["a", "b"] - - def test_create_with_extras(self): - """Test create_query_result with extra fields""" - result = create_query_result( - query="test", - results=["a"], - execution_time=0.5, - total_count=10, - metadata={"key": "value"}, - ) - assert result["query"] == "test" - assert result["results"] == ["a"] - assert result.get("execution_time") == 0.5 - assert result.get("total_count") == 10 - assert result.get("metadata") == {"key": "value"} - - def test_create_filters_none_values(self): - """Test create_query_result filters out None values""" - result = create_query_result( - query="test", - results=[], - execution_time=None, - metadata={"key": "val"}, - ) - assert "query" in result - assert "results" in result - assert "metadata" in result - # execution_time should not be in result since it's None - assert "execution_time" not in result - - def test_create_empty_results(self): - """Test create_query_result with empty results""" - result = create_query_result("query", []) - assert result["query"] == "query" - assert result["results"] == [] - - -class TestTypeCompatibility: - """Tests for type compatibility and integration""" - - def test_pipeline_simulation(self): - """Simulate a data pipeline using these types""" - # Retriever output - retriever_out = create_query_result( - query="test query", - results=["doc1", "doc2", "doc3"], - total_count=3, - ) - - # Reranker input (from retriever) - query = extract_query(retriever_out) - docs = extract_results(retriever_out) - assert query == "test query" - assert len(docs) == 3 - - # Reranker output - reranker_out = create_query_result(query=query, results=docs[:2], execution_time=0.1) - - # Generator input - final_query = extract_query(reranker_out) - final_docs = extract_results(reranker_out) - assert final_query == "test query" - assert len(final_docs) == 2 diff --git a/packages/sage-common/tests/unit/core/test_exceptions.py b/packages/sage-common/tests/unit/core/test_exceptions.py deleted file mode 100644 index 5a2fd0ea79..0000000000 --- a/packages/sage-common/tests/unit/core/test_exceptions.py +++ /dev/null @@ -1,131 +0,0 @@ -""" -Tests for sage.common.core.exceptions - -Tests the exception class hierarchy. -""" - -import pytest - -from sage.common.core.exceptions import ( - CheckpointError, - FaultToleranceError, - KernelError, - PlacementError, - RecoveryError, - ResourceAllocationError, - SchedulingError, -) - - -class TestExceptionHierarchy: - """Tests for exception class hierarchy""" - - def test_kernel_error_is_exception(self): - """Test that KernelError is an Exception""" - assert issubclass(KernelError, Exception) - - def test_scheduling_error_hierarchy(self): - """Test SchedulingError is subclass of KernelError""" - assert issubclass(SchedulingError, KernelError) - assert issubclass(SchedulingError, Exception) - - def test_fault_tolerance_error_hierarchy(self): - """Test FaultToleranceError is subclass of KernelError""" - assert issubclass(FaultToleranceError, KernelError) - assert issubclass(FaultToleranceError, Exception) - - def test_resource_allocation_error_hierarchy(self): - """Test ResourceAllocationError is subclass of SchedulingError""" - assert issubclass(ResourceAllocationError, SchedulingError) - assert issubclass(ResourceAllocationError, KernelError) - assert issubclass(ResourceAllocationError, Exception) - - def test_recovery_error_hierarchy(self): - """Test RecoveryError is subclass of FaultToleranceError""" - assert issubclass(RecoveryError, FaultToleranceError) - assert issubclass(RecoveryError, KernelError) - assert issubclass(RecoveryError, Exception) - - def test_checkpoint_error_hierarchy(self): - """Test CheckpointError is subclass of FaultToleranceError""" - assert issubclass(CheckpointError, FaultToleranceError) - assert issubclass(CheckpointError, KernelError) - assert issubclass(CheckpointError, Exception) - - def test_placement_error_hierarchy(self): - """Test PlacementError is subclass of SchedulingError""" - assert issubclass(PlacementError, SchedulingError) - assert issubclass(PlacementError, KernelError) - assert issubclass(PlacementError, Exception) - - -class TestExceptionRaising: - """Tests for raising and catching exceptions""" - - def test_raise_kernel_error(self): - """Test raising KernelError""" - with pytest.raises(KernelError, match="test error"): - raise KernelError("test error") - - def test_raise_scheduling_error(self): - """Test raising SchedulingError""" - with pytest.raises(SchedulingError, match="scheduling failed"): - raise SchedulingError("scheduling failed") - - def test_raise_fault_tolerance_error(self): - """Test raising FaultToleranceError""" - with pytest.raises(FaultToleranceError, match="fault detected"): - raise FaultToleranceError("fault detected") - - def test_raise_resource_allocation_error(self): - """Test raising ResourceAllocationError""" - with pytest.raises(ResourceAllocationError, match="no resources"): - raise ResourceAllocationError("no resources") - - def test_raise_recovery_error(self): - """Test raising RecoveryError""" - with pytest.raises(RecoveryError, match="recovery failed"): - raise RecoveryError("recovery failed") - - def test_raise_checkpoint_error(self): - """Test raising CheckpointError""" - with pytest.raises(CheckpointError, match="checkpoint failed"): - raise CheckpointError("checkpoint failed") - - def test_raise_placement_error(self): - """Test raising PlacementError""" - with pytest.raises(PlacementError, match="placement failed"): - raise PlacementError("placement failed") - - -class TestExceptionCatching: - """Tests for catching exceptions with hierarchy""" - - def test_catch_scheduling_as_kernel_error(self): - """Test catching SchedulingError as KernelError""" - with pytest.raises(KernelError): - raise SchedulingError("test") - - def test_catch_resource_allocation_as_scheduling(self): - """Test catching ResourceAllocationError as SchedulingError""" - with pytest.raises(SchedulingError): - raise ResourceAllocationError("test") - - def test_catch_recovery_as_fault_tolerance(self): - """Test catching RecoveryError as FaultToleranceError""" - with pytest.raises(FaultToleranceError): - raise RecoveryError("test") - - def test_exception_with_message(self): - """Test exception messages are preserved""" - try: - raise KernelError("custom message") - except KernelError as e: - assert str(e) == "custom message" - - def test_exception_with_args(self): - """Test exception with multiple arguments""" - try: - raise SchedulingError("error", "details", 42) - except SchedulingError as e: - assert e.args == ("error", "details", 42) diff --git a/packages/sage-common/tests/unit/core/test_types.py b/packages/sage-common/tests/unit/core/test_types.py deleted file mode 100644 index f0884abe2b..0000000000 --- a/packages/sage-common/tests/unit/core/test_types.py +++ /dev/null @@ -1,140 +0,0 @@ -""" -Tests for sage.common.core.types - -Tests the core type definitions, enums, and type aliases. -""" - -from sage.common.core.types import ( - ExecutionMode, - JobID, - JobStatus, - NodeID, - QueueID, - ServiceID, - ServiceType, - T, - TaskID, - TaskStatus, - TaskType, -) - - -class TestExecutionMode: - """Tests for ExecutionMode enum""" - - def test_execution_mode_values(self): - """Test that ExecutionMode has the expected values""" - assert ExecutionMode.LOCAL.value == "local" - assert ExecutionMode.REMOTE.value == "remote" - assert ExecutionMode.HYBRID.value == "hybrid" - - def test_execution_mode_members(self): - """Test that all ExecutionMode members exist""" - assert hasattr(ExecutionMode, "LOCAL") - assert hasattr(ExecutionMode, "REMOTE") - assert hasattr(ExecutionMode, "HYBRID") - - def test_execution_mode_count(self): - """Test that ExecutionMode has exactly 3 members""" - assert len(ExecutionMode) == 3 - - -class TestTaskStatus: - """Tests for TaskStatus enum""" - - def test_task_status_values(self): - """Test that TaskStatus has the expected values""" - assert TaskStatus.PENDING.value == "pending" - assert TaskStatus.RUNNING.value == "running" - assert TaskStatus.STOPPED.value == "stopped" - assert TaskStatus.FAILED.value == "failed" - assert TaskStatus.COMPLETED.value == "completed" - - def test_task_status_members(self): - """Test that all TaskStatus members exist""" - assert hasattr(TaskStatus, "PENDING") - assert hasattr(TaskStatus, "RUNNING") - assert hasattr(TaskStatus, "STOPPED") - assert hasattr(TaskStatus, "FAILED") - assert hasattr(TaskStatus, "COMPLETED") - - def test_task_status_count(self): - """Test that TaskStatus has exactly 5 members""" - assert len(TaskStatus) == 5 - - def test_task_status_comparison(self): - """Test that TaskStatus members can be compared""" - assert TaskStatus.PENDING == TaskStatus.PENDING - assert TaskStatus.PENDING != TaskStatus.RUNNING - - -class TestJobStatus: - """Tests for JobStatus enum""" - - def test_job_status_values(self): - """Test that JobStatus has the expected values""" - assert JobStatus.PENDING.value == "pending" - assert JobStatus.RUNNING.value == "running" - assert JobStatus.STOPPED.value == "stopped" - assert JobStatus.FAILED.value == "failed" - assert JobStatus.COMPLETED.value == "completed" - assert JobStatus.DELETED.value == "deleted" - - def test_job_status_members(self): - """Test that all JobStatus members exist""" - assert hasattr(JobStatus, "PENDING") - assert hasattr(JobStatus, "RUNNING") - assert hasattr(JobStatus, "STOPPED") - assert hasattr(JobStatus, "FAILED") - assert hasattr(JobStatus, "COMPLETED") - assert hasattr(JobStatus, "DELETED") - - def test_job_status_count(self): - """Test that JobStatus has exactly 6 members""" - assert len(JobStatus) == 6 - - -class TestTypeAliases: - """Tests for type aliases""" - - def test_id_type_aliases_are_str(self): - """Test that ID type aliases are strings""" - # These are type aliases, so we just verify the annotation exists - assert TaskID is str - assert ServiceID is str - assert NodeID is str - assert QueueID is str - assert JobID is str - - def test_id_usage(self): - """Test that ID aliases can be used in type annotations""" - # Runtime test that these types work as expected - task_id: TaskID = "task_123" - service_id: ServiceID = "service_456" - node_id: NodeID = "node_789" - queue_id: QueueID = "queue_abc" - job_id: JobID = "job_xyz" - - assert isinstance(task_id, str) - assert isinstance(service_id, str) - assert isinstance(node_id, str) - assert isinstance(queue_id, str) - assert isinstance(job_id, str) - - -class TestTypeVars: - """Tests for generic type variables""" - - def test_type_vars_exist(self): - """Test that type variables are defined""" - from typing import TypeVar - - assert isinstance(T, TypeVar) - assert isinstance(TaskType, TypeVar) - assert isinstance(ServiceType, TypeVar) - - def test_type_var_names(self): - """Test that type variables have the expected names""" - assert T.__name__ == "T" - assert TaskType.__name__ == "TaskType" - assert ServiceType.__name__ == "ServiceType" diff --git a/packages/sage-common/tests/unit/service/test_base_service.py b/packages/sage-common/tests/unit/service/test_base_service.py deleted file mode 100644 index dcbfe683ea..0000000000 --- a/packages/sage-common/tests/unit/service/test_base_service.py +++ /dev/null @@ -1,450 +0,0 @@ -""" -Comprehensive tests for BaseService - -Tests cover: -- Initialization and context management -- Logger property (with/without context) -- Name property (with/without context) -- call_service and call_service_async methods -- Lifecycle methods (setup, cleanup, start, stop) -- Error handling -""" - -import logging -from unittest.mock import MagicMock - -import pytest - -from sage.common.service.base_service import BaseService - - -class ConcreteService(BaseService): - """Concrete implementation for testing""" - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - -class TestBaseServiceInitialization: - """Test BaseService initialization""" - - def test_initialization_without_ctx(self): - """Test initialization without context""" - service = ConcreteService() - - assert hasattr(service, "ctx") - assert service.ctx is None - assert hasattr(service, "_logger") - - def test_initialization_with_existing_ctx(self): - """Test initialization when ctx already exists""" - service = ConcreteService() - mock_ctx = MagicMock() - service.ctx = mock_ctx - - # Re-initialize - service.__init__() - - # ctx should be preserved - assert service.ctx is mock_ctx - - def test_initialization_with_args(self): - """Test initialization with arguments""" - service = ConcreteService("arg1", "arg2", kwarg1="value1") - - assert service.ctx is None - - -class TestBaseServiceLogger: - """Test logger property""" - - def test_logger_without_context(self): - """Test logger returns default logger when no context""" - service = ConcreteService() - - logger = service.logger - - assert isinstance(logger, logging.Logger) - assert logger.name == "ConcreteService" - - def test_logger_with_context(self): - """Test logger uses context logger when available""" - service = ConcreteService() - mock_ctx = MagicMock() - mock_ctx_logger = MagicMock() - mock_ctx.logger = mock_ctx_logger - service.ctx = mock_ctx - - logger = service.logger - - assert logger is mock_ctx_logger - - def test_logger_caching(self): - """Test logger is cached""" - service = ConcreteService() - - logger1 = service.logger - logger2 = service.logger - - assert logger1 is logger2 - - def test_logger_switch_to_context(self): - """Test logger switches when context is added""" - service = ConcreteService() - - # First get default logger - default_logger = service.logger - assert default_logger.name == "ConcreteService" - - # Add context - mock_ctx = MagicMock() - mock_ctx_logger = MagicMock() - mock_ctx.logger = mock_ctx_logger - service.ctx = mock_ctx - service._logger = None # Clear cache - - # Now should use context logger - ctx_logger = service.logger - assert ctx_logger is mock_ctx_logger - - -class TestBaseServiceName: - """Test name property""" - - def test_name_without_context(self): - """Test name returns class name when no context""" - service = ConcreteService() - - assert service.name == "ConcreteService" - - def test_name_with_context(self): - """Test name uses context name when available""" - service = ConcreteService() - mock_ctx = MagicMock() - mock_ctx.name = "custom_service_name" - service.ctx = mock_ctx - - assert service.name == "custom_service_name" - - def test_name_priority(self): - """Test context name takes priority over class name""" - service = ConcreteService() - - # Without context - assert service.name == "ConcreteService" - - # With context - mock_ctx = MagicMock() - mock_ctx.name = "runtime_name" - service.ctx = mock_ctx - assert service.name == "runtime_name" - - -class TestBaseServiceCallService: - """Test call_service method""" - - def test_call_service_without_context_raises_error(self): - """Test call_service raises error without context""" - service = ConcreteService() - - with pytest.raises(RuntimeError, match="Service context not initialized"): - service.call_service("some_service") - - def test_call_service_with_context(self): - """Test call_service delegates to context""" - service = ConcreteService() - mock_ctx = MagicMock() - service.ctx = mock_ctx - - result = service.call_service("test_service", "arg1", "arg2", kwarg1="value1") - - mock_ctx.call_service.assert_called_once_with( - "test_service", "arg1", "arg2", timeout=None, method=None, kwarg1="value1" - ) - assert result == mock_ctx.call_service.return_value - - def test_call_service_with_timeout(self): - """Test call_service with timeout parameter""" - service = ConcreteService() - mock_ctx = MagicMock() - service.ctx = mock_ctx - - service.call_service("test_service", timeout=5.0) - - mock_ctx.call_service.assert_called_once_with("test_service", timeout=5.0, method=None) - - def test_call_service_with_method(self): - """Test call_service with method parameter""" - service = ConcreteService() - mock_ctx = MagicMock() - service.ctx = mock_ctx - - service.call_service("test_service", "data", method="process") - - mock_ctx.call_service.assert_called_once_with( - "test_service", "data", timeout=None, method="process" - ) - - def test_call_service_with_all_params(self): - """Test call_service with all parameters""" - service = ConcreteService() - mock_ctx = MagicMock() - service.ctx = mock_ctx - - service.call_service( - "test_service", "arg1", timeout=10.0, method="execute", custom_param="value" - ) - - mock_ctx.call_service.assert_called_once_with( - "test_service", "arg1", timeout=10.0, method="execute", custom_param="value" - ) - - -class TestBaseServiceCallServiceAsync: - """Test call_service_async method""" - - def test_call_service_async_without_context_raises_error(self): - """Test call_service_async raises error without context""" - service = ConcreteService() - - with pytest.raises(RuntimeError, match="Service context not initialized"): - service.call_service_async("some_service") - - def test_call_service_async_with_context(self): - """Test call_service_async delegates to context""" - service = ConcreteService() - mock_ctx = MagicMock() - service.ctx = mock_ctx - - result = service.call_service_async("test_service", "arg1", kwarg1="value1") - - mock_ctx.call_service_async.assert_called_once_with( - "test_service", "arg1", timeout=None, method=None, kwarg1="value1" - ) - assert result == mock_ctx.call_service_async.return_value - - def test_call_service_async_with_timeout(self): - """Test call_service_async with timeout parameter""" - service = ConcreteService() - mock_ctx = MagicMock() - service.ctx = mock_ctx - - service.call_service_async("test_service", timeout=3.0) - - mock_ctx.call_service_async.assert_called_once_with( - "test_service", timeout=3.0, method=None - ) - - def test_call_service_async_with_method(self): - """Test call_service_async with method parameter""" - service = ConcreteService() - mock_ctx = MagicMock() - service.ctx = mock_ctx - - service.call_service_async("test_service", method="async_process") - - mock_ctx.call_service_async.assert_called_once_with( - "test_service", timeout=None, method="async_process" - ) - - -class TestBaseServiceLifecycle: - """Test lifecycle methods""" - - def test_setup_default_implementation(self): - """Test setup method has default implementation""" - service = ConcreteService() - - # Should not raise - result = service.setup() - - assert result is None - - def test_cleanup_default_implementation(self): - """Test cleanup method has default implementation""" - service = ConcreteService() - - # Should not raise - result = service.cleanup() - - assert result is None - - def test_start_default_implementation(self): - """Test start method has default implementation""" - service = ConcreteService() - - # Should not raise - result = service.start() - - assert result is None - - def test_stop_default_implementation(self): - """Test stop method has default implementation""" - service = ConcreteService() - - # Should not raise - result = service.stop() - - assert result is None - - def test_lifecycle_override(self): - """Test lifecycle methods can be overridden""" - - class CustomService(BaseService): - def __init__(self): - super().__init__() - self.setup_called = False - self.cleanup_called = False - self.start_called = False - self.stop_called = False - - def setup(self): - self.setup_called = True - - def cleanup(self): - self.cleanup_called = True - - def start(self): - self.start_called = True - - def stop(self): - self.stop_called = True - - service = CustomService() - service.setup() - service.start() - service.stop() - service.cleanup() - - assert service.setup_called - assert service.cleanup_called - assert service.start_called - assert service.stop_called - - -class TestBaseServiceIntegration: - """Integration tests for BaseService""" - - def test_full_lifecycle_with_context(self): - """Test complete lifecycle with context""" - service = ConcreteService() - - # Add context - mock_ctx = MagicMock() - mock_ctx.name = "integration_service" - mock_ctx.logger = logging.getLogger("integration") - service.ctx = mock_ctx - - # Verify properties - assert service.name == "integration_service" - assert service.logger.name == "integration" - - # Call lifecycle methods - service.setup() - service.start() - - # Call service - mock_ctx.call_service.return_value = "result" - result = service.call_service("dependency_service", "data") - assert result == "result" - - # Stop - service.stop() - service.cleanup() - - def test_multiple_service_instances(self): - """Test multiple service instances are independent""" - service1 = ConcreteService() - service2 = ConcreteService() - - mock_ctx1 = MagicMock() - mock_ctx1.name = "service1" - service1.ctx = mock_ctx1 - - mock_ctx2 = MagicMock() - mock_ctx2.name = "service2" - service2.ctx = mock_ctx2 - - assert service1.name == "service1" - assert service2.name == "service2" - assert service1.ctx is not service2.ctx - - def test_service_without_context_logging(self, caplog): - """Test service can log without context""" - service = ConcreteService() - - with caplog.at_level(logging.INFO): - service.logger.info("Test message") - - assert "Test message" in caplog.text - - def test_custom_service_with_business_logic(self): - """Test custom service with business logic""" - - class BusinessService(BaseService): - def __init__(self): - super().__init__() - self.processed_count = 0 - - def process_data(self, data): - self.logger.info(f"Processing data: {data}") - self.processed_count += 1 - return f"processed_{data}" - - service = BusinessService() - result1 = service.process_data("item1") - result2 = service.process_data("item2") - - assert result1 == "processed_item1" - assert result2 == "processed_item2" - assert service.processed_count == 2 - - -class TestBaseServiceEdgeCases: - """Test edge cases and error handling""" - - def test_call_service_with_none_context(self): - """Test call_service handles None context properly""" - service = ConcreteService() - service.ctx = None - - with pytest.raises(RuntimeError, match="Service context not initialized"): - service.call_service("test") - - def test_logger_reinitialization(self): - """Test logger can be reinitialized""" - service = ConcreteService() - - logger1 = service.logger - service._logger = None # Force reinitialization - logger2 = service.logger - - # Python logging returns same logger instance for same name - assert logger1 is logger2 - assert logger1.name == logger2.name - - def test_context_injection_pattern(self): - """Test context injection pattern""" - service = ConcreteService() - - # Simulate ServiceFactory injection - mock_ctx = MagicMock() - mock_ctx.name = "injected_service" - service.ctx = mock_ctx - - assert service.ctx is mock_ctx - assert service.name == "injected_service" - - def test_args_kwargs_forwarding(self): - """Test args and kwargs are handled properly""" - - class ParamsService(BaseService): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self.args = args - self.kwargs = kwargs - - service = ParamsService("arg1", "arg2", param1="value1", param2="value2") - - assert service.args == ("arg1", "arg2") - assert service.kwargs == {"param1": "value1", "param2": "value2"} diff --git a/packages/sage-common/tests/unit/utils/__init__.py b/packages/sage-common/tests/unit/utils/__init__.py deleted file mode 100644 index 0b651e44cb..0000000000 --- a/packages/sage-common/tests/unit/utils/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -""" -utils 模块测试 -""" diff --git a/packages/sage-common/tests/unit/utils/config/__init__.py b/packages/sage-common/tests/unit/utils/config/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/packages/sage-common/tests/unit/utils/config/test_loader.py b/packages/sage-common/tests/unit/utils/config/test_loader.py deleted file mode 100644 index 135c60cbc8..0000000000 --- a/packages/sage-common/tests/unit/utils/config/test_loader.py +++ /dev/null @@ -1,306 +0,0 @@ -""" -Tests for sage.common.utils.config.loader module -================================== - -单元测试配置加载器模块的功能，包括： -- 配置文件查找逻辑 -- 多种配置源的优先级 -- 错误处理和异常情况 -""" - -import os -import tempfile -from pathlib import Path -from unittest.mock import MagicMock, patch - -import pytest -import yaml - -from sage.common.utils.config.loader import load_config - - -@pytest.mark.unit -class TestLoadConfig: - """配置加载器测试类""" - - def setup_method(self): - """测试前准备""" - self.temp_dir = Path(tempfile.mkdtemp()) - self.config_dir = self.temp_dir / "config" - self.config_dir.mkdir(parents=True, exist_ok=True) - - # 创建测试配置文件 - self.test_config = { - "database": {"host": "localhost", "port": 5432, "name": "test_db"}, - "logging": {"level": "INFO", "handlers": ["console", "file"]}, - "features": {"cache_enabled": True, "max_connections": 100}, - } - - self.config_file = self.config_dir / "config.yaml" - with open(self.config_file, "w") as f: - yaml.dump(self.test_config, f) - - def teardown_method(self): - """测试后清理""" - import shutil - - shutil.rmtree(self.temp_dir, ignore_errors=True) - - @pytest.mark.unit - def test_load_config_with_explicit_absolute_path(self): - """测试显式绝对路径加载配置""" - config = load_config(str(self.config_file)) - assert config == self.test_config - assert config["database"]["host"] == "localhost" - assert config["logging"]["level"] == "INFO" - - @pytest.mark.unit - def test_load_config_with_relative_path(self): - """测试相对路径加载配置""" - with patch("inspect.currentframe") as mock_frame: - # 模拟调用者文件路径 - mock_caller_frame = MagicMock() - mock_caller_frame.f_globals = {"__file__": str(self.temp_dir / "caller.py")} - mock_frame.return_value.f_back = mock_caller_frame - - config = load_config("config/config.yaml") - assert config == self.test_config - - @pytest.mark.unit - def test_load_config_with_bare_filename(self): - """测试单纯文件名加载配置""" - with patch("inspect.currentframe") as mock_frame: - mock_caller_frame = MagicMock() - mock_caller_frame.f_globals = {"__file__": str(self.temp_dir / "caller.py")} - mock_frame.return_value.f_back = mock_caller_frame - - config = load_config("config.yaml") - assert config == self.test_config - - @pytest.mark.unit - def test_load_config_with_env_override(self): - """测试环境变量覆盖配置路径""" - env_config_file = self.temp_dir / "env_config.yaml" - env_config = {"env": "development", "debug": True} - - with open(env_config_file, "w") as f: - yaml.dump(env_config, f) - - with patch.dict(os.environ, {"SAGE_CONFIG": str(env_config_file)}): - config = load_config() - assert config == env_config - - @pytest.mark.unit - def test_load_config_priority_order(self): - """测试配置文件优先级顺序""" - # 创建多个配置文件 - explicit_config = self.temp_dir / "explicit.yaml" - with open(explicit_config, "w") as f: - yaml.dump({"source": "explicit"}, f) - - env_config = self.temp_dir / "env.yaml" - with open(env_config, "w") as f: - yaml.dump({"source": "env"}, f) - - # 测试显式路径优先级最高 - with patch.dict(os.environ, {"SAGE_CONFIG": str(env_config)}): - config = load_config(str(explicit_config)) - assert config["source"] == "explicit" - - @pytest.mark.unit - def test_load_config_user_and_system_fallback(self): - """测试用户级和系统级配置文件回退""" - with ( - patch("inspect.currentframe") as mock_frame, - patch("sage.common.utils.config.loader.user_config_dir") as mock_user_dir, - patch("sage.common.utils.config.loader.site_config_dir") as mock_site_dir, - ): - # 模拟调用者文件路径 - mock_caller_frame = MagicMock() - mock_caller_frame.f_globals = {"__file__": str(self.temp_dir / "caller.py")} - mock_frame.return_value.f_back = mock_caller_frame - - # 设置用户和系统配置目录 - user_config_dir = Path(self.temp_dir) / "user_config" - system_config_dir = Path(self.temp_dir) / "system_config" - user_config_dir.mkdir(exist_ok=True) - system_config_dir.mkdir(exist_ok=True) - - mock_user_dir.return_value = str(user_config_dir) - mock_site_dir.return_value = str(system_config_dir) - - # 创建用户级配置文件 - user_config_file = user_config_dir / "config.yaml" - user_config = {"source": "user"} - with open(user_config_file, "w") as f: - yaml.dump(user_config, f) - - # 删除项目级配置文件，强制使用用户级 - self.config_file.unlink() - - config = load_config() - assert config["source"] == "user" - - @pytest.mark.unit - def test_load_config_file_not_found_error(self): - """测试配置文件未找到时的错误处理""" - with patch("inspect.currentframe") as mock_frame: - mock_caller_frame = MagicMock() - mock_caller_frame.f_globals = {"__file__": str(self.temp_dir / "caller.py")} - mock_frame.return_value.f_back = mock_caller_frame - - # 删除所有配置文件 - self.config_file.unlink() - - with pytest.raises(FileNotFoundError) as exc_info: - load_config() - - assert "No config found" in str(exc_info.value) - assert "Checked:" in str(exc_info.value) - - @pytest.mark.unit - def test_load_config_invalid_yaml(self): - """测试无效YAML文件的错误处理""" - invalid_config_file = self.config_dir / "invalid.yaml" - with open(invalid_config_file, "w") as f: - f.write("invalid: yaml: content: [") - - with pytest.raises(yaml.YAMLError): - load_config(str(invalid_config_file)) - - @pytest.mark.unit - def test_load_config_empty_file(self): - """测试空配置文件处理""" - empty_config_file = self.config_dir / "empty.yaml" - empty_config_file.touch() - - config = load_config(str(empty_config_file)) - assert config is None - - @pytest.mark.unit - def test_load_config_project_root_detection(self): - """测试项目根目录检测逻辑""" - # 创建项目结构 - project_root = Path(self.temp_dir) / "project" - project_root.mkdir() - - # 创建项目标识文件 - (project_root / "pyproject.toml").touch() - (project_root / "config").mkdir() - - config_file = project_root / "config" / "config.yaml" - with open(config_file, "w") as f: - yaml.dump({"project": "detected"}, f) - - # 从子目录调用 - subdir = project_root / "src" / "app" - subdir.mkdir(parents=True) - caller_file = subdir / "main.py" - caller_file.touch() - - with patch("inspect.currentframe") as mock_frame: - mock_caller_frame = MagicMock() - mock_caller_frame.f_globals = {"__file__": str(caller_file)} - mock_frame.return_value.f_back = mock_caller_frame - - config = load_config() - assert config["project"] == "detected" - - @pytest.mark.unit - def test_load_config_no_caller_frame(self): - """测试无法获取调用者信息时的回退逻辑""" - with patch("inspect.currentframe") as mock_frame: - mock_frame.return_value.f_back.f_globals = {} - - with patch("pathlib.Path.cwd") as mock_cwd: - mock_cwd.return_value = Path(self.temp_dir) - config = load_config() - assert config == self.test_config - - def test_load_config_with_none_caller_frame(self): - """测试 currentframe 返回 None 时的回退逻辑（覆盖第37行）""" - with patch("inspect.currentframe") as mock_frame: - # 模拟 currentframe 返回 None（某些环境下可能发生） - mock_frame.return_value = None - - with patch("pathlib.Path.cwd") as mock_cwd: - mock_cwd.return_value = Path(self.temp_dir) - config = load_config() - assert config == self.test_config - - -@pytest.mark.integration -class TestLoadConfigIntegration: - """配置加载器集成测试""" - - @pytest.mark.integration - def test_load_config_with_real_project_structure(self): - """测试真实项目结构中的配置加载""" - with tempfile.TemporaryDirectory() as temp_dir: - # 创建真实的项目结构 - project_dir = Path(temp_dir) / "sage_project" - config_dir = project_dir / "config" - src_dir = project_dir / "src" / "sage" - - project_dir.mkdir() - config_dir.mkdir(parents=True) - src_dir.mkdir(parents=True) - - # 创建项目标识文件 - (project_dir / "pyproject.toml").write_text("[build-system]") - - # 创建配置文件 - config_content = { - "app_name": "SAGE", - "version": "1.0.0", - "database": {"url": "postgresql://localhost/sage"}, - } - - config_file = config_dir / "config.yaml" - with open(config_file, "w") as f: - yaml.dump(config_content, f) - - # 从源码目录中的模拟文件加载配置 - with patch("inspect.currentframe") as mock_frame: - mock_caller_frame = MagicMock() - mock_caller_frame.f_globals = {"__file__": str(src_dir / "app.py")} - mock_frame.return_value.f_back = mock_caller_frame - - config = load_config() - assert config["app_name"] == "SAGE" - assert config["database"]["url"] == "postgresql://localhost/sage" - - -# Fixtures for testing -@pytest.fixture -def temp_config_dir(): - """提供临时配置目录的fixture""" - with tempfile.TemporaryDirectory() as temp_dir: - config_dir = Path(temp_dir) / "config" - config_dir.mkdir() - yield config_dir - - -@pytest.fixture -def sample_config(): - """提供示例配置的fixture""" - return { - "app": {"name": "test_app", "version": "1.0.0"}, - "database": {"host": "localhost", "port": 5432}, - } - - -@pytest.mark.unit -def test_load_config_with_fixtures(temp_config_dir, sample_config): - """使用fixtures的测试示例""" - config_file = temp_config_dir / "config.yaml" - with open(config_file, "w") as f: - yaml.dump(sample_config, f) - - with patch("inspect.currentframe") as mock_frame: - mock_caller_frame = MagicMock() - mock_caller_frame.f_globals = {"__file__": str(temp_config_dir.parent / "app.py")} - mock_frame.return_value.f_back = mock_caller_frame - - config = load_config() - assert config == sample_config diff --git a/packages/sage-common/tests/unit/utils/config/test_manager.py b/packages/sage-common/tests/unit/utils/config/test_manager.py deleted file mode 100644 index b0cfffba2c..0000000000 --- a/packages/sage-common/tests/unit/utils/config/test_manager.py +++ /dev/null @@ -1,330 +0,0 @@ -""" -Tests for sage.common.utils.config.manager - -Tests configuration loading, saving, and management functionality. -""" - -import json -import tempfile -from pathlib import Path -from unittest.mock import patch - -import pytest -import yaml - -from sage.common.utils.config.manager import ( - BaseConfig, - ConfigManager, - load_config, - save_config, -) - - -class TestBaseConfig: - """Tests for BaseConfig class""" - - def test_base_config_creation(self): - """Test creating a base config""" - config = BaseConfig() - assert isinstance(config, BaseConfig) - - def test_base_config_with_fields(self): - """Test base config allows extra fields""" - - class MyConfig(BaseConfig): - name: str = "default" - - config = MyConfig(name="test", extra_field="value") - assert config.name == "test" - - -class TestConfigManager: - """Tests for ConfigManager class""" - - def test_initialization_default_dir(self): - """Test initialization with default directory""" - with tempfile.TemporaryDirectory() as tmpdir: - with patch("sage.common.utils.config.manager.Path.cwd") as mock_cwd: - mock_cwd.return_value = Path(tmpdir) - manager = ConfigManager() - - assert manager.config_dir == Path(tmpdir) / "config" - assert manager.config_dir.exists() - - def test_initialization_custom_dir(self): - """Test initialization with custom directory""" - with tempfile.TemporaryDirectory() as tmpdir: - config_dir = Path(tmpdir) / "my_config" - manager = ConfigManager(config_dir=config_dir) - - assert manager.config_dir == config_dir - assert manager.config_dir.exists() - - def test_load_yaml_file(self): - """Test loading YAML configuration""" - with tempfile.TemporaryDirectory() as tmpdir: - config_dir = Path(tmpdir) - manager = ConfigManager(config_dir=config_dir) - - # Create test YAML file - test_config = {"database": {"host": "localhost", "port": 5432}} - config_file = config_dir / "test.yaml" - with open(config_file, "w") as f: - yaml.dump(test_config, f) - - # Load configuration - loaded_config = manager.load("test.yaml") - assert loaded_config == test_config - - def test_load_json_file(self): - """Test loading JSON configuration""" - with tempfile.TemporaryDirectory() as tmpdir: - config_dir = Path(tmpdir) - manager = ConfigManager(config_dir=config_dir) - - # Create test JSON file - test_config = {"api": {"key": "12345", "url": "https://api.example.com"}} - config_file = config_dir / "test.json" - with open(config_file, "w") as f: - json.dump(test_config, f) - - # Load configuration - loaded_config = manager.load("test.json") - assert loaded_config == test_config - - def test_load_with_cache(self): - """Test loading with cache enabled""" - with tempfile.TemporaryDirectory() as tmpdir: - config_dir = Path(tmpdir) - manager = ConfigManager(config_dir=config_dir) - - # Create test file - test_config = {"value": 42} - config_file = config_dir / "test.yaml" - with open(config_file, "w") as f: - yaml.dump(test_config, f) - - # Load twice - config1 = manager.load("test.yaml", use_cache=True) - config2 = manager.load("test.yaml", use_cache=True) - - assert config1 == config2 - assert "test.yaml" in manager._cache - - def test_load_file_not_found(self): - """Test loading non-existent file raises error""" - with tempfile.TemporaryDirectory() as tmpdir: - manager = ConfigManager(config_dir=tmpdir) - - with pytest.raises(FileNotFoundError): - manager.load("nonexistent.yaml") - - def test_load_unsupported_format(self): - """Test loading unsupported file format""" - with tempfile.TemporaryDirectory() as tmpdir: - config_dir = Path(tmpdir) - manager = ConfigManager(config_dir=config_dir) - - # Create file with unsupported extension - config_file = config_dir / "test.txt" - config_file.write_text("some content") - - with pytest.raises(ValueError, match="不支持的配置文件格式"): - manager.load("test.txt") - - def test_save_yaml_file(self): - """Test saving YAML configuration""" - with tempfile.TemporaryDirectory() as tmpdir: - config_dir = Path(tmpdir) - manager = ConfigManager(config_dir=config_dir) - - # Save configuration - test_config = {"server": {"host": "localhost", "port": 8080}} - manager.save("test.yaml", test_config) - - # Verify file exists and content - config_file = config_dir / "test.yaml" - assert config_file.exists() - - with open(config_file) as f: - loaded = yaml.safe_load(f) - assert loaded == test_config - - def test_save_json_file(self): - """Test saving JSON configuration""" - with tempfile.TemporaryDirectory() as tmpdir: - config_dir = Path(tmpdir) - manager = ConfigManager(config_dir=config_dir) - - # Save configuration - test_config = {"name": "test", "values": [1, 2, 3]} - manager.save("test.json", test_config) - - # Verify file exists and content - config_file = config_dir / "test.json" - assert config_file.exists() - - with open(config_file) as f: - loaded = json.load(f) - assert loaded == test_config - - def test_save_with_forced_format(self): - """Test saving with forced format""" - with tempfile.TemporaryDirectory() as tmpdir: - config_dir = Path(tmpdir) - manager = ConfigManager(config_dir=config_dir) - - # Save as YAML even though extension is .conf - test_config = {"key": "value"} - manager.save("test.conf", test_config, format="yaml") - - config_file = config_dir / "test.conf" - assert config_file.exists() - - def test_get_simple_key(self): - """Test getting simple configuration key""" - with tempfile.TemporaryDirectory() as tmpdir: - config_dir = Path(tmpdir) - manager = ConfigManager(config_dir=config_dir) - - # Create test file - test_config = {"name": "test", "version": "1.0"} - config_file = config_dir / "test.yaml" - with open(config_file, "w") as f: - yaml.dump(test_config, f) - - # Get value - value = manager.get("test.yaml", "name") - assert value == "test" - - def test_get_nested_key(self): - """Test getting nested configuration key""" - with tempfile.TemporaryDirectory() as tmpdir: - config_dir = Path(tmpdir) - manager = ConfigManager(config_dir=config_dir) - - # Create test file - test_config = {"database": {"host": "localhost", "port": 5432}} - config_file = config_dir / "test.yaml" - with open(config_file, "w") as f: - yaml.dump(test_config, f) - - # Get nested value - value = manager.get("test.yaml", "database.host") - assert value == "localhost" - - value = manager.get("test.yaml", "database.port") - assert value == 5432 - - def test_get_with_default(self): - """Test getting non-existent key returns default""" - with tempfile.TemporaryDirectory() as tmpdir: - config_dir = Path(tmpdir) - manager = ConfigManager(config_dir=config_dir) - - # Create test file - test_config = {"name": "test"} - config_file = config_dir / "test.yaml" - with open(config_file, "w") as f: - yaml.dump(test_config, f) - - # Get non-existent key - value = manager.get("test.yaml", "nonexistent", default="default_value") - assert value == "default_value" - - def test_set_simple_key(self): - """Test setting simple configuration key""" - with tempfile.TemporaryDirectory() as tmpdir: - config_dir = Path(tmpdir) - manager = ConfigManager(config_dir=config_dir) - - # Set value - manager.set("test.yaml", "name", "new_value") - - # Verify - value = manager.get("test.yaml", "name") - assert value == "new_value" - - def test_set_nested_key(self): - """Test setting nested configuration key""" - with tempfile.TemporaryDirectory() as tmpdir: - config_dir = Path(tmpdir) - manager = ConfigManager(config_dir=config_dir) - - # Set nested value - manager.set("test.yaml", "database.host", "localhost") - manager.set("test.yaml", "database.port", 5432) - - # Verify - host = manager.get("test.yaml", "database.host") - port = manager.get("test.yaml", "database.port") - assert host == "localhost" - assert port == 5432 - - def test_set_creates_nested_structure(self): - """Test that set creates nested structure""" - with tempfile.TemporaryDirectory() as tmpdir: - config_dir = Path(tmpdir) - manager = ConfigManager(config_dir=config_dir) - - # Set deeply nested value - manager.set("test.yaml", "a.b.c.d", "value") - - # Verify structure - value = manager.get("test.yaml", "a.b.c.d") - assert value == "value" - - def test_clear_cache(self): - """Test clearing configuration cache""" - with tempfile.TemporaryDirectory() as tmpdir: - config_dir = Path(tmpdir) - manager = ConfigManager(config_dir=config_dir) - - # Create and load config - test_config = {"value": 42} - config_file = config_dir / "test.yaml" - with open(config_file, "w") as f: - yaml.dump(test_config, f) - - manager.load("test.yaml") - assert "test.yaml" in manager._cache - - # Clear cache - manager.clear_cache() - assert len(manager._cache) == 0 - - -class TestGlobalFunctions: - """Tests for global convenience functions""" - - def test_load_config_with_custom_dir(self): - """Test load_config with custom directory""" - with tempfile.TemporaryDirectory() as tmpdir: - config_dir = Path(tmpdir) - - # Create test file - test_config = {"test": "value"} - config_file = config_dir / "test.yaml" - with open(config_file, "w") as f: - yaml.dump(test_config, f) - - # Load config - loaded = load_config("test.yaml", config_dir=config_dir) - assert loaded == test_config - - def test_save_config_with_custom_dir(self): - """Test save_config with custom directory""" - with tempfile.TemporaryDirectory() as tmpdir: - config_dir = Path(tmpdir) - - # Save config - test_config = {"test": "value"} - save_config("test.yaml", test_config, config_dir=config_dir) - - # Verify - config_file = config_dir / "test.yaml" - assert config_file.exists() - - with open(config_file) as f: - loaded = yaml.safe_load(f) - assert loaded == test_config diff --git a/packages/sage-common/tests/unit/utils/logging/__init__.py b/packages/sage-common/tests/unit/utils/logging/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/packages/sage-common/tests/unit/utils/logging/test_custom_formatter.py b/packages/sage-common/tests/unit/utils/logging/test_custom_formatter.py deleted file mode 100644 index a8ce6ef998..0000000000 --- a/packages/sage-common/tests/unit/utils/logging/test_custom_formatter.py +++ /dev/null @@ -1,588 +0,0 @@ -""" -Tests for sage.common.utils.logging.custom_formatter module -================================================== - -单元测试自定义日志格式化器模块的功能，包括： -- 日志格式化输出 -- 颜色显示功能 -- 多行格式处理 -- 异常信息格式化 -""" - -import logging -from unittest.mock import patch - -import pytest - -from sage.common.utils.logging.custom_formatter import CustomFormatter - - -@pytest.mark.unit -class TestCustomFormatter: - """CustomFormatter类测试""" - - def setup_method(self): - """测试前准备""" - self.formatter = CustomFormatter() - - # 创建测试日志记录 - self.logger = logging.getLogger("test_logger") - self.handler = logging.StreamHandler() - self.handler.setFormatter(self.formatter) - self.logger.addHandler(self.handler) - self.logger.setLevel(logging.DEBUG) - - def teardown_method(self): - """测试后清理""" - self.logger.handlers.clear() - - def test_formatter_initialization(self): - """测试格式化器初始化""" - assert hasattr(self.formatter, "COLOR_RESET") - assert hasattr(self.formatter, "COLOR_DEBUG") - assert hasattr(self.formatter, "COLOR_INFO") - assert hasattr(self.formatter, "COLOR_WARNING") - assert hasattr(self.formatter, "COLOR_ERROR") - assert hasattr(self.formatter, "COLOR_CRITICAL") - - # 验证颜色代码 - assert self.formatter.COLOR_RESET == "\033[0m" - assert self.formatter.COLOR_DEBUG == "\033[36m" - assert self.formatter.COLOR_INFO == "\033[32m" - assert self.formatter.COLOR_WARNING == "\033[33m" - assert self.formatter.COLOR_ERROR == "\033[31m" - assert self.formatter.COLOR_CRITICAL == "\033[35m" - - def test_format_debug_level(self): - """测试DEBUG级别日志格式化""" - record = logging.LogRecord( - name="test.module", - level=logging.DEBUG, - pathname="/path/to/test.py", - lineno=42, - msg="Debug message", - args=(), - exc_info=None, - ) - - formatted = self.formatter.format(record) - - # 验证格式结构 - assert "DEBUG" in formatted - assert "test.module" in formatted - assert "/path/to/test.py:42" in formatted - assert "Debug message" in formatted - assert "→" in formatted - assert "\t" in formatted - - # 验证颜色代码 - assert self.formatter.COLOR_DEBUG in formatted - assert self.formatter.COLOR_RESET in formatted - - def test_format_info_level(self): - """测试INFO级别日志格式化""" - record = logging.LogRecord( - name="app.service", - level=logging.INFO, - pathname="/app/service.py", - lineno=100, - msg="Service started successfully", - args=(), - exc_info=None, - ) - - formatted = self.formatter.format(record) - - assert "INFO" in formatted - assert "app.service" in formatted - assert "/app/service.py:100" in formatted - assert "Service started successfully" in formatted - assert self.formatter.COLOR_INFO in formatted - - def test_format_warning_level(self): - """测试WARNING级别日志格式化""" - record = logging.LogRecord( - name="warning.logger", - level=logging.WARNING, - pathname="/warn.py", - lineno=25, - msg="This is a warning", - args=(), - exc_info=None, - ) - - formatted = self.formatter.format(record) - - assert "WARNING" in formatted - assert "This is a warning" in formatted - assert self.formatter.COLOR_WARNING in formatted - - def test_format_error_level(self): - """测试ERROR级别日志格式化""" - record = logging.LogRecord( - name="error.handler", - level=logging.ERROR, - pathname="/error.py", - lineno=88, - msg="An error occurred", - args=(), - exc_info=None, - ) - - formatted = self.formatter.format(record) - - assert "ERROR" in formatted - assert "An error occurred" in formatted - assert self.formatter.COLOR_ERROR in formatted - - def test_format_critical_level(self): - """测试CRITICAL级别日志格式化""" - record = logging.LogRecord( - name="critical.system", - level=logging.CRITICAL, - pathname="/critical.py", - lineno=999, - msg="Critical system failure", - args=(), - exc_info=None, - ) - - formatted = self.formatter.format(record) - - assert "CRITICAL" in formatted - assert "Critical system failure" in formatted - assert self.formatter.COLOR_CRITICAL in formatted - - def test_format_unknown_level(self): - """测试未知级别日志格式化""" - record = logging.LogRecord( - name="unknown.logger", - level=999, # 未知级别 - pathname="/unknown.py", - lineno=1, - msg="Unknown level message", - args=(), - exc_info=None, - ) - - formatted = self.formatter.format(record) - - assert "Unknown level message" in formatted - assert self.formatter.COLOR_RESET in formatted - - @patch("logging.Formatter.formatTime") - def test_format_timestamp(self, mock_format_time): - """测试时间戳格式化""" - mock_format_time.return_value = "2025-08-04 10:30:45" - - record = logging.LogRecord( - name="test", - level=logging.INFO, - pathname="/test.py", - lineno=1, - msg="Test message", - args=(), - exc_info=None, - ) - - formatted = self.formatter.format(record) - - assert "2025-08-04 10:30:45" in formatted - mock_format_time.assert_called_once_with(record, "%Y-%m-%d %H:%M:%S") - - def test_format_with_exception_info(self): - """测试包含异常信息的日志格式化""" - try: - raise ValueError("Test exception") - except ValueError as e: - record = logging.LogRecord( - name="test.exception", - level=logging.ERROR, - pathname="/test_exception.py", - lineno=10, - msg="An error occurred", - args=(), - exc_info=(type(e), e, e.__traceback__), - ) - - with patch.object(self.formatter, "formatException") as mock_format_exc: - mock_format_exc.return_value = ( - "Traceback (most recent call last):\n ValueError: Test exception" - ) - - formatted = self.formatter.format(record) - - assert "An error occurred" in formatted - assert "Traceback (most recent call last):" in formatted - assert "ValueError: Test exception" in formatted - mock_format_exc.assert_called_once() - - def test_format_with_stack_info(self): - """测试包含堆栈信息的日志格式化""" - record = logging.LogRecord( - name="test.stack", - level=logging.DEBUG, - pathname="/test_stack.py", - lineno=5, - msg="Debug with stack", - args=(), - exc_info=None, - ) - record.stack_info = "Stack info:\n File test.py, line 1\n test_function()" - - with patch.object(self.formatter, "formatStack") as mock_format_stack: - mock_format_stack.return_value = record.stack_info - - formatted = self.formatter.format(record) - - assert "Debug with stack" in formatted - assert "Stack info:" in formatted - assert "test_function()" in formatted - - def test_format_message_args(self): - """测试消息参数格式化""" - record = logging.LogRecord( - name="test.args", - level=logging.INFO, - pathname="/test_args.py", - lineno=20, - msg="User %s logged in with ID %d", - args=("john_doe", 12345), - exc_info=None, - ) - - formatted = self.formatter.format(record) - - assert "User john_doe logged in with ID 12345" in formatted - - def test_format_multiline_message(self): - """测试多行消息格式化""" - multiline_msg = """This is a multiline message - Line 2 of the message - Line 3 of the message""" - - record = logging.LogRecord( - name="test.multiline", - level=logging.INFO, - pathname="/test_multiline.py", - lineno=30, - msg=multiline_msg, - args=(), - exc_info=None, - ) - - formatted = self.formatter.format(record) - - assert "This is a multiline message" in formatted - assert "Line 2 of the message" in formatted - assert "Line 3 of the message" in formatted - - def test_format_output_structure(self): - """测试输出格式结构""" - record = logging.LogRecord( - name="test.structure", - level=logging.INFO, - pathname="/app/module.py", - lineno=50, - msg="Test message", - args=(), - exc_info=None, - ) - - formatted = self.formatter.format(record) - lines = formatted.split("\n") - - # 验证格式结构：第一行包含元数据，第二行包含消息，第三行为空 - assert len(lines) >= 3 - - # 第一行：时间 | 级别 | 名称 | 路径:行号 → - first_line = lines[0] - assert "|" in first_line - assert "INFO" in first_line - assert "test.structure" in first_line - assert "/app/module.py:50" in first_line - assert "→" in first_line - - # 第二行：\t 消息内容 - second_line = lines[1] - assert second_line.startswith("\t") - assert "Test message" in second_line - - # 第三行：空行 - third_line = lines[2] - assert third_line == "" - - def test_format_level_alignment(self): - """测试日志级别对齐""" - levels = [ - (logging.DEBUG, "DEBUG"), - (logging.INFO, "INFO"), - (logging.WARNING, "WARNING"), - (logging.ERROR, "ERROR"), - (logging.CRITICAL, "CRITICAL"), - ] - - for level_num, level_name in levels: - record = logging.LogRecord( - name="test", - level=level_num, - pathname="/test.py", - lineno=1, - msg="Test", - args=(), - exc_info=None, - ) - - formatted = self.formatter.format(record) - - # 验证级别名称使用了左对齐格式（<5） - assert f"{level_name:<5}" in formatted - - def test_format_ide_compatibility(self): - """测试IDE兼容性（可点击的文件路径）""" - record = logging.LogRecord( - name="test.ide", - level=logging.ERROR, - pathname="/home/user/project/src/module.py", - lineno=123, - msg="IDE clickable test", - args=(), - exc_info=None, - ) - - formatted = self.formatter.format(record) - - # 验证文件路径:行号格式，IDE可以识别并允许点击 - assert "/home/user/project/src/module.py:123" in formatted - - -@pytest.mark.integration -class TestCustomFormatterIntegration: - """CustomFormatter集成测试""" - - def test_real_logging_scenario(self): - """测试真实日志场景""" - from io import StringIO - - # 创建字符串缓冲区来捕获日志输出 - log_capture = StringIO() - - logger = logging.getLogger("integration_test") - handler = logging.StreamHandler(log_capture) - formatter = CustomFormatter() - handler.setFormatter(formatter) - logger.addHandler(handler) - logger.setLevel(logging.DEBUG) - - try: - # 模拟实际应用中的日志记录 - logger.debug("Application starting...") - logger.info("Service initialized successfully") - logger.warning("Configuration file not found, using defaults") - - try: - # 模拟异常 - _ = 1 / 0 - except ZeroDivisionError: - logger.error("Division by zero error", exc_info=True) - - logger.critical("System shutdown initiated") - - # 获取日志输出 - log_output = log_capture.getvalue() - - # 验证日志内容 - assert "Application starting..." in log_output - assert "Service initialized successfully" in log_output - assert "Configuration file not found" in log_output - assert "Division by zero error" in log_output - assert "ZeroDivisionError" in log_output - assert "System shutdown initiated" in log_output - - # 验证格式结构 - lines = log_output.strip().split("\n") - - # 每个日志记录应该产生多行输出（元数据行 + 消息行 + 空行） - # 但异常日志会有更多行 - assert len(lines) > 10 # 至少应有多行输出 - - finally: - logger.handlers.clear() - - def test_formatter_with_different_loggers(self): - """测试格式化器在不同logger中的表现""" - from io import StringIO - - log_capture = StringIO() - formatter = CustomFormatter() - - # 创建多个不同的logger - loggers = [ - logging.getLogger("app.database"), - logging.getLogger("app.service.user"), - logging.getLogger("app.middleware.auth"), - logging.getLogger("system.monitoring"), - ] - - for logger in loggers: - handler = logging.StreamHandler(log_capture) - handler.setFormatter(formatter) - logger.addHandler(handler) - logger.setLevel(logging.INFO) - - try: - # 各个logger记录日志 - loggers[0].info("Database connection established") - loggers[1].warning("User session expired") - loggers[2].error("Authentication failed") - loggers[3].critical("System memory usage critical") - - log_output = log_capture.getvalue() - - # 验证不同logger的名称都正确显示 - assert "app.database" in log_output - assert "app.service.user" in log_output - assert "app.middleware.auth" in log_output - assert "system.monitoring" in log_output - - # 验证消息内容 - assert "Database connection established" in log_output - assert "User session expired" in log_output - assert "Authentication failed" in log_output - assert "System memory usage critical" in log_output - - finally: - for logger in loggers: - logger.handlers.clear() - - -@pytest.mark.unit -class TestCustomFormatterEdgeCases: - """CustomFormatter边界情况测试""" - - def test_format_empty_message(self): - """测试空消息格式化""" - formatter = CustomFormatter() - - record = logging.LogRecord( - name="test.empty", - level=logging.INFO, - pathname="/test.py", - lineno=1, - msg="", - args=(), - exc_info=None, - ) - - formatted = formatter.format(record) - - # 即使消息为空，格式结构也应该保持 - assert "test.empty" in formatted - assert "/test.py:1" in formatted - assert "→" in formatted - - def test_format_very_long_message(self): - """测试超长消息格式化""" - formatter = CustomFormatter() - - long_message = "A" * 1000 # 1000字符的消息 - - record = logging.LogRecord( - name="test.long", - level=logging.INFO, - pathname="/test.py", - lineno=1, - msg=long_message, - args=(), - exc_info=None, - ) - - formatted = formatter.format(record) - - assert long_message in formatted - assert len(formatted) > 1000 - - def test_format_special_characters(self): - """测试特殊字符处理""" - formatter = CustomFormatter() - - special_msg = "Message with special chars: \n\t\r\x00\xff" - - record = logging.LogRecord( - name="test.special", - level=logging.INFO, - pathname="/test.py", - lineno=1, - msg=special_msg, - args=(), - exc_info=None, - ) - - formatted = formatter.format(record) - - # 格式化应该能处理特殊字符而不崩溃 - assert "test.special" in formatted - assert isinstance(formatted, str) - - def test_format_unicode_message(self): - """测试Unicode消息格式化""" - formatter = CustomFormatter() - - unicode_msg = "Unicode测试: 你好世界 🌍 émojis 📝" - - record = logging.LogRecord( - name="test.unicode", - level=logging.INFO, - pathname="/test.py", - lineno=1, - msg=unicode_msg, - args=(), - exc_info=None, - ) - - formatted = formatter.format(record) - - assert unicode_msg in formatted - assert "test.unicode" in formatted - assert "🌍" in formatted - assert "📝" in formatted - - -# 性能测试 -@pytest.mark.slow -class TestCustomFormatterPerformance: - """CustomFormatter性能测试""" - - def test_format_performance(self): - """测试格式化性能""" - import time - - formatter = CustomFormatter() - - # 创建测试记录 - records = [] - for i in range(1000): - record = logging.LogRecord( - name=f"test.performance.{i}", - level=logging.INFO, - pathname=f"/test/performance_{i}.py", - lineno=i, - msg=f"Performance test message {i}", - args=(), - exc_info=None, - ) - records.append(record) - - # 测试格式化性能 - start_time = time.time() - for record in records: - formatter.format(record) - - format_time = time.time() - start_time - - # 1000条日志记录的格式化应该在合理时间内完成 - assert format_time < 1.0 # 应在1秒内完成 - - # 计算平均每条记录的格式化时间 - avg_time_per_record = format_time / len(records) - assert avg_time_per_record < 0.001 # 每条记录应在1毫秒内完成 diff --git a/packages/sage-common/tests/unit/utils/serialization/__init__.py b/packages/sage-common/tests/unit/utils/serialization/__init__.py deleted file mode 100644 index 069367dae0..0000000000 --- a/packages/sage-common/tests/unit/utils/serialization/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -""" -序列化模块测试 -""" diff --git a/packages/sage-common/tests/unit/utils/serialization/test_dill_basic.py b/packages/sage-common/tests/unit/utils/serialization/test_dill_basic.py deleted file mode 100644 index 7aef467210..0000000000 --- a/packages/sage-common/tests/unit/utils/serialization/test_dill_basic.py +++ /dev/null @@ -1,210 +0,0 @@ -""" -测试 dill 序列化器的基本功能 - -本测试文件验证 dill 序列化器的各种基本功能，包括： -- 基本数据类型序列化 -- 复杂对象序列化 -- 属性过滤 -- 错误处理 -""" - -import threading - -import pytest - -from sage.common.utils.serialization.dill import ( - SerializationError, - UniversalSerializer, - deserialize_object, - serialize_object, -) - - -class TestUniversalSerializer: - """测试 UniversalSerializer 类""" - - def test_basic_types_serialization(self): - """测试基本类型的序列化""" - test_cases = [ - 42, - 3.14, - "hello world", - True, - False, - None, - [1, 2, 3], - {"key": "value"}, - (1, 2, 3), - {1, 2, 3}, - ] - - for original in test_cases: - serialized = serialize_object(original) - restored = deserialize_object(serialized) - assert restored == original, f"Failed for type {type(original)}: {original}" - - def test_simple_object_serialization(self): - """测试简单对象的序列化""" - - class SimpleObject: - def __init__(self, value): - self.value = value - - def __eq__(self, other): - return hasattr(other, "value") and self.value == other.value - - obj = SimpleObject("test_value") - serialized = serialize_object(obj) - restored = deserialize_object(serialized) - - assert restored.value == obj.value - assert restored == obj - - def test_attribute_filtering_exclude(self): - """测试属性过滤 - 排除特定属性""" - - class ObjectWithExclude: - __state_exclude__ = ["sensitive_data", "temp_value"] - - def __init__(self): - self.normal_data = "keep this" - self.sensitive_data = "exclude this" - self.temp_value = "also exclude" - self.another_normal = "keep this too" - - obj = ObjectWithExclude() - serialized = serialize_object(obj) - restored = deserialize_object(serialized) - - # 正常属性应该保留 - assert restored.normal_data == "keep this" - assert restored.another_normal == "keep this too" - - # 排除的属性不应该存在 - assert not hasattr(restored, "sensitive_data") - assert not hasattr(restored, "temp_value") - - def test_attribute_filtering_include(self): - """测试属性过滤 - 仅包含特定属性""" - - class ObjectWithInclude: - __state_include__ = ["important_data"] - - def __init__(self): - self.important_data = "keep this" - self.unimportant_data = "exclude this" - self.other_data = "also exclude" - - obj = ObjectWithInclude() - serialized = serialize_object(obj) - restored = deserialize_object(serialized) - - # 包含的属性应该保留 - assert restored.important_data == "keep this" - - # 其他属性不应该存在 - assert not hasattr(restored, "unimportant_data") - assert not hasattr(restored, "other_data") - - def test_blacklisted_objects_excluded(self): - """测试黑名单对象被排除""" - - class ObjectWithBlacklisted: - def __init__(self): - self.normal_data = "keep this" - self.thread = threading.Thread(target=lambda: None) - self.lock = threading.Lock() - - obj = ObjectWithBlacklisted() - serialized = serialize_object(obj) - restored = deserialize_object(serialized) - - # 正常数据应该保留 - assert restored.normal_data == "keep this" - - # 黑名单对象不应该存在 - assert not hasattr(restored, "thread") - assert not hasattr(restored, "lock") - - def test_nested_objects(self): - """测试嵌套对象""" - - class Inner: - def __init__(self, value): - self.value = value - - def __eq__(self, other): - return hasattr(other, "value") and self.value == other.value - - class Outer: - def __init__(self, inner_obj, data): - self.inner = inner_obj - self.data = data - - def __eq__(self, other): - return ( - hasattr(other, "inner") - and hasattr(other, "data") - and self.inner == other.inner - and self.data == other.data - ) - - inner = Inner("inner_value") - outer = Outer(inner, {"key": "value"}) - - serialized = serialize_object(outer) - restored = deserialize_object(serialized) - - assert restored.inner.value == "inner_value" - assert restored.data == {"key": "value"} - assert restored == outer - - def test_serialization_error_handling(self): - """测试序列化错误处理""" - - # 这个测试验证错误处理机制 - # 在正常情况下，大多数对象都应该能够成功序列化 - # 这里我们主要验证错误类型的正确性 - - class ProblematicObject: - def __init__(self): - self.data = "normal" - - obj = ProblematicObject() - - try: - # 正常情况下应该成功 - serialized = serialize_object(obj) - restored = deserialize_object(serialized) - assert restored.data == "normal" - except SerializationError: - # 如果出现序列化错误，应该是 SerializationError 类型 - pass - - def test_static_methods(self): - """测试静态方法""" - - class TestData: - def __init__(self, value): - self.value = value - - # 测试 DillSerializer 的静态方法 - obj = TestData("test") - - # 使用静态方法序列化 - serialized = UniversalSerializer.serialize_object(obj) - restored = UniversalSerializer.deserialize_object(serialized) - - assert restored.value == "test" - - # 测试便捷函数 - serialized2 = serialize_object(obj) - restored2 = deserialize_object(serialized2) - - assert restored2.value == "test" - assert serialized == serialized2 # 两种方法应该产生相同结果 - - -if __name__ == "__main__": - # 允许直接运行测试文件 - pytest.main([__file__, "-v"]) diff --git a/packages/sage-common/tests/unit/utils/serialization/test_dill_reference_integrity.py b/packages/sage-common/tests/unit/utils/serialization/test_dill_reference_integrity.py deleted file mode 100644 index 5444dec05d..0000000000 --- a/packages/sage-common/tests/unit/utils/serialization/test_dill_reference_integrity.py +++ /dev/null @@ -1,222 +0,0 @@ -""" -测试 dill 序列化器的对象引用完整性 - -本测试文件验证 GitHub issue #254 的修复： -"对象引用去重在序列化过程中失效" - -问题描述： -在 A->B,C B->D C->D 的引用结构中，序列化前 `B.d is C.d` 为 `True`， -但反序列化后 `restored_B.d is restored_C.d` 为 `False`， -即原本共享的对象 D 被重复创建了多份。 - -修复方案： -使用对象映射表 (_object_map) 维护引用关系，确保相同的原始对象 -在预处理过程中始终映射到同一个新实例。 -""" - -import pytest - -from sage.common.utils.serialization.dill import deserialize_object, serialize_object - - -class SharedResource: - """共享资源类 - 对应issue中的对象D""" - - def __init__(self, name, data): - self.name = name - self.data = data - - def __repr__(self): - return f"SharedResource(name={self.name}, data={self.data})" - - def __eq__(self, other): - # 使用宽松的类型检查，适应序列化/反序列化的行为 - return ( - hasattr(other, "name") - and hasattr(other, "data") - and self.name == other.name - and self.data == other.data - ) - - -class NodeB: - """节点B类""" - - def __init__(self, shared_d): - self.d = shared_d - - def __repr__(self): - return f"NodeB(d={self.d})" - - -class NodeC: - """节点C类""" - - def __init__(self, shared_d): - self.d = shared_d - - def __repr__(self): - return f"NodeC(d={self.d})" - - -class NodeA: - """节点A类 - 包含B和C的引用""" - - def __init__(self, node_b, node_c): - self.b = node_b - self.c = node_c - - def __repr__(self): - return f"NodeA(b={self.b}, c={self.c})" - - -class TestObjectReferenceIntegrity: - """测试对象引用完整性""" - - def test_issue_254_basic_shared_reference(self): - """ - 测试基本的共享引用场景 (GitHub issue #254) - - 验证 A->B,C B->D C->D 引用结构中， - 序列化后 B.d 和 C.d 仍然是同一个对象。 - """ - # 创建共享对象D - shared_d = SharedResource("test", {"value": 42}) - - # 创建引用链 A->B,C B->D C->D - node_b = NodeB(shared_d) - node_c = NodeC(shared_d) - node_a = NodeA(node_b, node_c) - - # 序列化前：B.d 和 C.d 是同一个对象 - assert node_a.b.d is node_a.c.d, "序列化前引用应该相同" - assert node_a.b.d == node_a.c.d, "序列化前数据应该相等" - - # 序列化和反序列化 - serialized = serialize_object(node_a) - restored_a = deserialize_object(serialized) - - # 序列化后：引用完整性应该保持 - assert restored_a.b.d is restored_a.c.d, "序列化后引用应该保持相同 (issue #254 修复验证)" - assert restored_a.b.d == restored_a.c.d, "序列化后数据应该相等" - - # 验证数据内容正确 - assert restored_a.b.d.name == "test" - assert restored_a.b.d.data == {"value": 42} - - def test_circular_reference(self): - """测试循环引用场景""" - - class CircularA: - def __init__(self): - self.b: CircularB | None = None - - def __repr__(self): - return f"CircularA(b={'...' if self.b else None})" - - class CircularB: - def __init__(self, a: CircularA): - self.a = a - - def __repr__(self): - return f"CircularB(a={'...' if self.a else None})" - - # 创建循环引用 - a = CircularA() - b = CircularB(a) - a.b = b - - # 序列化前检查 - assert a.b and a.b.a is a, "循环引用应该正确" - - # 序列化和反序列化 - serialized = serialize_object(a) - restored_a = deserialize_object(serialized) - - # 序列化后检查循环引用 - assert restored_a.b.a is restored_a, "循环引用应该保持" - - def test_multiple_shared_objects(self): - """测试多个共享对象""" - - shared1 = SharedResource("shared1", {"type": "A"}) - shared2 = SharedResource("shared2", {"type": "B"}) - - class Container: - def __init__(self, obj1, obj2): - self.obj1 = obj1 - self.obj2 = obj2 - - # 创建复杂引用结构 - container1 = Container(shared1, shared2) - container2 = Container(shared1, shared2) # 重用相同的共享对象 - - root = Container(container1, container2) - - # 序列化前检查 - assert root.obj1.obj1 is root.obj2.obj1, "shared1应该是同一个对象" - assert root.obj1.obj2 is root.obj2.obj2, "shared2应该是同一个对象" - - # 序列化和反序列化 - serialized = serialize_object(root) - restored_root = deserialize_object(serialized) - - # 序列化后检查 - assert restored_root.obj1.obj1 is restored_root.obj2.obj1, ( - "restored shared1应该是同一个对象" - ) - assert restored_root.obj1.obj2 is restored_root.obj2.obj2, ( - "restored shared2应该是同一个对象" - ) - - def test_list_with_shared_objects(self): - """测试列表中的共享对象""" - - shared = SharedResource("shared_in_list", {"value": 999}) - - # 创建包含共享对象的列表 - list_data = [shared, shared, shared] - dict_data = {"a": shared, "b": shared} - - container = {"list": list_data, "dict": dict_data} - - # 序列化前检查 - assert container["list"][0] is container["list"][1], "列表中的对象应该相同" - assert container["list"][0] is container["dict"]["a"], "列表和字典中的对象应该相同" - - # 序列化和反序列化 - serialized = serialize_object(container) - restored = deserialize_object(serialized) - - # 序列化后检查 - assert restored["list"][0] is restored["list"][1], "恢复后列表中的对象应该相同" - assert restored["list"][0] is restored["dict"]["a"], "恢复后列表和字典中的对象应该相同" - - def test_deep_nested_sharing(self): - """测试深度嵌套的共享""" - - shared = SharedResource("deep_shared", {"level": "deep"}) - - # 创建深度嵌套结构 - level3 = {"shared": shared} - level2 = {"nested": level3, "also_shared": shared} - level1 = {"data": level2, "direct_shared": shared} - - # 序列化前检查 - assert level1["data"]["nested"]["shared"] is level1["direct_shared"], ( - "深度嵌套的共享对象应该相同" - ) - - # 序列化和反序列化 - serialized = serialize_object(level1) - restored = deserialize_object(serialized) - - # 序列化后检查 - assert restored["data"]["nested"]["shared"] is restored["direct_shared"], ( - "恢复后深度嵌套的共享对象应该相同" - ) - - -if __name__ == "__main__": - # 允许直接运行测试文件 - pytest.main([__file__, "-v"]) diff --git a/packages/sage-common/tests/unit/utils/serialization/test_preprocessor.py b/packages/sage-common/tests/unit/utils/serialization/test_preprocessor.py deleted file mode 100644 index dafae9618b..0000000000 --- a/packages/sage-common/tests/unit/utils/serialization/test_preprocessor.py +++ /dev/null @@ -1,372 +0,0 @@ -""" -Tests for serialization preprocessor functions. - -Tests the preprocessing functions used for dill serialization. -""" - - -class TestGatherAttrs: - """Test gather_attrs function""" - - def test_gather_simple_object_attrs(self): - """Test gathering attributes from simple object""" - from sage.common.utils.serialization.preprocessor import gather_attrs - - class SimpleObj: - def __init__(self): - self.attr1 = "value1" - self.attr2 = 42 - - obj = SimpleObj() - attrs = gather_attrs(obj) - - assert "attr1" in attrs - assert attrs["attr1"] == "value1" - assert "attr2" in attrs - assert attrs["attr2"] == 42 - - def test_gather_attrs_with_property(self): - """Test gathering @property attributes""" - from sage.common.utils.serialization.preprocessor import gather_attrs - - class ObjWithProperty: - def __init__(self): - self._value = 100 - - @property - def computed(self): - return self._value * 2 - - obj = ObjWithProperty() - attrs = gather_attrs(obj) - - assert "_value" in attrs - assert "computed" in attrs - assert attrs["computed"] == 200 - - def test_gather_attrs_property_exception(self): - """Test handling property that raises exception""" - from sage.common.utils.serialization.preprocessor import gather_attrs - - class ObjWithFailingProperty: - @property - def failing(self): - raise ValueError("Property error") - - obj = ObjWithFailingProperty() - attrs = gather_attrs(obj) - - # Should not raise, property just won't be included - assert "failing" not in attrs or attrs.get("failing") is None - - -class TestFilterAttrs: - """Test filter_attrs function""" - - def test_filter_with_include(self): - """Test filtering with include list""" - from sage.common.utils.serialization.preprocessor import filter_attrs - - attrs = {"a": 1, "b": 2, "c": 3, "_private": 4} - filtered = filter_attrs(attrs, include=["a", "c"], exclude=None) - - assert filtered == {"a": 1, "c": 3} - - def test_filter_with_exclude(self): - """Test filtering with exclude list""" - from sage.common.utils.serialization.preprocessor import filter_attrs - - attrs = {"a": 1, "b": 2, "c": 3} - filtered = filter_attrs(attrs, include=None, exclude=["b"]) - - assert "a" in filtered - assert "b" not in filtered - assert "c" in filtered - - def test_filter_removes_weakref(self): - """Test that __weakref__ is always filtered out""" - from sage.common.utils.serialization.preprocessor import filter_attrs - - attrs = {"a": 1, "__weakref__": "something"} - filtered = filter_attrs(attrs, include=None, exclude=None) - - assert "__weakref__" not in filtered - assert "a" in filtered - - def test_filter_removes_dict_and_class(self): - """Test that __dict__ and __class__ are filtered""" - from sage.common.utils.serialization.preprocessor import filter_attrs - - attrs = {"a": 1, "__dict__": {}, "__class__": object} - filtered = filter_attrs(attrs, include=None, exclude=None) - - assert "__dict__" not in filtered - assert "__class__" not in filtered - - -class TestShouldSkip: - """Test should_skip function""" - - def test_skip_basic_types(self): - """Test that basic types are not skipped""" - from sage.common.utils.serialization.preprocessor import should_skip - - assert not should_skip(42) - assert not should_skip("string") - assert not should_skip(3.14) - assert not should_skip(True) - assert not should_skip(None) - - def test_skip_module(self): - """Test that modules are skipped""" - import os - - from sage.common.utils.serialization.preprocessor import should_skip - - assert should_skip(os) - - def test_skip_lock_types(self): - """Test that lock types are skipped""" - import threading - - from sage.common.utils.serialization.preprocessor import should_skip - - lock = threading.Lock() - assert should_skip(lock) - - -class TestHasCircularReference: - """Test has_circular_reference function""" - - def test_no_circular_ref_basic_types(self): - """Test basic types have no circular reference""" - from sage.common.utils.serialization.preprocessor import has_circular_reference - - assert not has_circular_reference(42) - assert not has_circular_reference("string") - assert not has_circular_reference([1, 2, 3]) - assert not has_circular_reference({"a": 1, "b": 2}) - - def test_detect_simple_circular_ref(self): - """Test detection of simple circular reference""" - from sage.common.utils.serialization.preprocessor import has_circular_reference - - obj = {} - obj["self"] = obj - - assert has_circular_reference(obj) - - def test_detect_nested_circular_ref(self): - """Test detection of nested circular reference""" - from sage.common.utils.serialization.preprocessor import has_circular_reference - - obj1 = {"name": "obj1"} - obj2 = {"name": "obj2", "ref": obj1} - obj1["ref"] = obj2 - - assert has_circular_reference(obj1) - - def test_max_depth_prevents_deep_recursion(self): - """Test that max_depth prevents excessive recursion""" - from sage.common.utils.serialization.preprocessor import has_circular_reference - - # Create deeply nested structure without circular ref - obj = {"level": 0} - current = obj - for i in range(20): - current["next"] = {"level": i + 1} - current = current["next"] - - # Should return False with default max_depth=10 - result = has_circular_reference(obj, max_depth=5) - assert isinstance(result, bool) # Should not crash - - -class TestPreprocessForDill: - """Test preprocess_for_dill function""" - - def test_preprocess_basic_types(self): - """Test preprocessing basic types""" - from sage.common.utils.serialization.preprocessor import preprocess_for_dill - - assert preprocess_for_dill(42) == 42 - assert preprocess_for_dill("test") == "test" - assert preprocess_for_dill(3.14) == 3.14 - assert preprocess_for_dill(True) is True - assert preprocess_for_dill(None) is None - - def test_preprocess_list(self): - """Test preprocessing lists""" - from sage.common.utils.serialization.preprocessor import preprocess_for_dill - - result = preprocess_for_dill([1, 2, 3, "test"]) - assert result == [1, 2, 3, "test"] - - def test_preprocess_dict(self): - """Test preprocessing dictionaries""" - from sage.common.utils.serialization.preprocessor import preprocess_for_dill - - data = {"a": 1, "b": "test", "c": [1, 2, 3]} - result = preprocess_for_dill(data) - - assert result["a"] == 1 - assert result["b"] == "test" - assert result["c"] == [1, 2, 3] - - def test_preprocess_nested_structure(self): - """Test preprocessing nested structures""" - from sage.common.utils.serialization.preprocessor import preprocess_for_dill - - data = {"level1": {"level2": {"level3": {"value": 42}}}} - - result = preprocess_for_dill(data) - assert result["level1"]["level2"]["level3"]["value"] == 42 - - def test_preprocess_tuple(self): - """Test that tuples are preserved""" - from sage.common.utils.serialization.preprocessor import preprocess_for_dill - - data = (1, 2, 3) - result = preprocess_for_dill(data) - - assert isinstance(result, tuple) - assert result == (1, 2, 3) - - def test_preprocess_set(self): - """Test preprocessing sets""" - from sage.common.utils.serialization.preprocessor import preprocess_for_dill - - data = {1, 2, 3} - result = preprocess_for_dill(data) - - assert isinstance(result, set) - assert result == {1, 2, 3} - - def test_preprocess_custom_object(self): - """Test preprocessing custom objects""" - from sage.common.utils.serialization.preprocessor import preprocess_for_dill - - class CustomObj: - def __init__(self): - self.value = 42 - self.name = "test" - - obj = CustomObj() - result = preprocess_for_dill(obj) - - assert isinstance(result, CustomObj) - assert result.value == 42 - assert result.name == "test" - - def test_preprocess_object_with_custom_exclude(self): - """Test preprocessing with __state_exclude__""" - from sage.common.utils.serialization.preprocessor import preprocess_for_dill - - class ObjWithExclude: - __state_exclude__ = ["_private"] - - def __init__(self): - self.public = "visible" - self._private = "hidden" - - obj = ObjWithExclude() - result = preprocess_for_dill(obj) - - assert hasattr(result, "public") - assert result.public == "visible" - # _private should be excluded - assert not hasattr(result, "_private") or result._private != "hidden" - - def test_preprocess_function(self): - """Test that functions pass through""" - from sage.common.utils.serialization.preprocessor import preprocess_for_dill - - def test_func(): - return 42 - - result = preprocess_for_dill(test_func) - assert result is test_func - - def test_preprocess_class(self): - """Test that classes pass through""" - from sage.common.utils.serialization.preprocessor import preprocess_for_dill - - class TestClass: - pass - - result = preprocess_for_dill(TestClass) - assert result is TestClass - - def test_preprocess_circular_ref_returns_original(self): - """Test that objects with circular refs return original""" - from sage.common.utils.serialization.preprocessor import preprocess_for_dill - - class CircularObj: - def __init__(self): - self.ref = self - - obj = CircularObj() - result = preprocess_for_dill(obj) - - # Should return original object (let dill handle it) - assert result is obj - - def test_preprocess_skips_blacklisted_values(self): - """Test that blacklisted values are skipped""" - import threading - - from sage.common.utils.serialization.preprocessor import preprocess_for_dill - - data = {"valid": 42, "lock": threading.Lock()} - - result = preprocess_for_dill(data) - - assert "valid" in result - assert result["valid"] == 42 - # Lock should be skipped - assert "lock" not in result - - def test_preprocess_list_with_skipped_items(self): - """Test list preprocessing with items to skip""" - import threading - - from sage.common.utils.serialization.preprocessor import preprocess_for_dill - - data = [1, 2, threading.Lock(), 3] - result = preprocess_for_dill(data) - - # Should have 1, 2, 3 but not the lock - assert 1 in result - assert 2 in result - assert 3 in result - assert len(result) == 3 # Lock was removed - - -class TestPostprocessFromDill: - """Test postprocess_from_dill function""" - - def test_postprocess_basic_types(self): - """Test postprocessing basic types""" - from sage.common.utils.serialization.preprocessor import postprocess_from_dill - - assert postprocess_from_dill(42) == 42 - assert postprocess_from_dill("test") == "test" - assert postprocess_from_dill([1, 2, 3]) == [1, 2, 3] - - def test_postprocess_dict(self): - """Test postprocessing dictionaries""" - from sage.common.utils.serialization.preprocessor import postprocess_from_dill - - data = {"a": 1, "b": 2} - result = postprocess_from_dill(data) - - assert result == {"a": 1, "b": 2} - - def test_postprocess_nested(self): - """Test postprocessing nested structures""" - from sage.common.utils.serialization.preprocessor import postprocess_from_dill - - data = {"outer": {"inner": [1, 2, 3]}} - - result = postprocess_from_dill(data) - assert result["outer"]["inner"] == [1, 2, 3] diff --git a/packages/sage-common/tests/unit/utils/serialization/test_ray_trimmer.py b/packages/sage-common/tests/unit/utils/serialization/test_ray_trimmer.py deleted file mode 100644 index 9a9daba440..0000000000 --- a/packages/sage-common/tests/unit/utils/serialization/test_ray_trimmer.py +++ /dev/null @@ -1,250 +0,0 @@ -""" -Tests for Ray object trimmer functionality. - -Tests the trim_object_for_ray function and RayObjectTrimmer class. -""" - -from unittest.mock import Mock - - -class TestTrimObjectForRay: - """Test trim_object_for_ray function""" - - def test_trim_simple_object(self): - """Test trimming simple object""" - from sage.common.utils.serialization.ray_trimmer import trim_object_for_ray - - class SimpleObj: - def __init__(self): - self.value = 42 - self.name = "test" - - obj = SimpleObj() - result = trim_object_for_ray(obj) - - assert hasattr(result, "value") - assert hasattr(result, "name") - - def test_trim_with_exclude(self): - """Test trimming with exclude list""" - from sage.common.utils.serialization.ray_trimmer import trim_object_for_ray - - class ObjWithPrivate: - def __init__(self): - self.public = "visible" - self._private = "hidden" - self.logger = "should_exclude" - - obj = ObjWithPrivate() - result = trim_object_for_ray(obj, exclude=["logger", "_private"]) - - assert hasattr(result, "public") - # Excluded attributes should not be present - assert not hasattr(result, "logger") or result.logger != "should_exclude" - - def test_trim_with_include(self): - """Test trimming with include list""" - from sage.common.utils.serialization.ray_trimmer import trim_object_for_ray - - class ObjWithMany: - def __init__(self): - self.a = 1 - self.b = 2 - self.c = 3 - - obj = ObjWithMany() - result = trim_object_for_ray(obj, include=["a", "c"]) - - assert hasattr(result, "a") - assert hasattr(result, "c") - # b should not be included - assert not hasattr(result, "b") or result.b != 2 - - def test_trim_nested_objects(self): - """Test trimming nested objects""" - from sage.common.utils.serialization.ray_trimmer import trim_object_for_ray - - class Inner: - def __init__(self): - self.value = 100 - - class Outer: - def __init__(self): - self.inner = Inner() - self.name = "outer" - - obj = Outer() - result = trim_object_for_ray(obj) - - assert hasattr(result, "name") - assert hasattr(result, "inner") - - def test_trim_basic_types_unchanged(self): - """Test that basic types pass through unchanged""" - from sage.common.utils.serialization.ray_trimmer import trim_object_for_ray - - assert trim_object_for_ray(42) == 42 - assert trim_object_for_ray("test") == "test" - assert trim_object_for_ray([1, 2, 3]) == [1, 2, 3] - assert trim_object_for_ray({"a": 1}) == {"a": 1} - - -class TestRayObjectTrimmer: - """Test RayObjectTrimmer class""" - - def test_trim_for_remote_call_basic(self): - """Test basic trimming for remote call""" - from sage.common.utils.serialization.ray_trimmer import RayObjectTrimmer - - class SimpleObj: - def __init__(self): - self.value = 42 - - obj = SimpleObj() - trimmer = RayObjectTrimmer() - result = trimmer.trim_for_remote_call(obj) - - assert hasattr(result, "value") - assert result.value == 42 - - def test_trim_for_remote_call_with_exclude(self): - """Test remote call trimming with exclusions""" - from sage.common.utils.serialization.ray_trimmer import RayObjectTrimmer - - class ObjWithLogger: - def __init__(self): - self.data = "important" - self.logger = Mock() - self._cache = {} - - obj = ObjWithLogger() - trimmer = RayObjectTrimmer() - result = trimmer.trim_for_remote_call(obj, exclude=["logger", "_cache"]) - - assert hasattr(result, "data") - assert not hasattr(result, "logger") or result.logger != obj.logger - - def test_trim_for_remote_call_shallow(self): - """Test shallow trimming (deep_clean=False)""" - from sage.common.utils.serialization.ray_trimmer import RayObjectTrimmer - - class Nested: - def __init__(self): - self.inner_value = 100 - - class Outer: - def __init__(self): - self.value = 42 - self.nested = Nested() - - obj = Outer() - trimmer = RayObjectTrimmer() - result = trimmer.trim_for_remote_call(obj, deep_clean=False) - - assert hasattr(result, "value") - - def test_trim_for_remote_call_deep(self): - """Test deep trimming (deep_clean=True)""" - from sage.common.utils.serialization.ray_trimmer import RayObjectTrimmer - - class Inner: - def __init__(self): - self.inner_data = "deep" - - class Outer: - def __init__(self): - self.outer_data = "shallow" - self.inner = Inner() - - obj = Outer() - trimmer = RayObjectTrimmer() - result = trimmer.trim_for_remote_call(obj, deep_clean=True) - - assert hasattr(result, "outer_data") - assert hasattr(result, "inner") - - def test_trim_handles_lists(self): - """Test trimming objects containing lists""" - from sage.common.utils.serialization.ray_trimmer import RayObjectTrimmer - - class ObjWithList: - def __init__(self): - self.items = [1, 2, 3, 4, 5] - - obj = ObjWithList() - trimmer = RayObjectTrimmer() - result = trimmer.trim_for_remote_call(obj) - - assert hasattr(result, "items") - assert len(result.items) == 5 - - def test_trim_handles_dicts(self): - """Test trimming objects containing dicts""" - from sage.common.utils.serialization.ray_trimmer import RayObjectTrimmer - - class ObjWithDict: - def __init__(self): - self.config = {"key1": "value1", "key2": "value2"} - - obj = ObjWithDict() - trimmer = RayObjectTrimmer() - result = trimmer.trim_for_remote_call(obj) - - assert hasattr(result, "config") - assert isinstance(result.config, dict) - - def test_trim_preserves_basic_types(self): - """Test that basic types are preserved""" - from sage.common.utils.serialization.ray_trimmer import RayObjectTrimmer - - trimmer = RayObjectTrimmer() - - assert trimmer.trim_for_remote_call(42) == 42 - assert trimmer.trim_for_remote_call("text") == "text" - assert trimmer.trim_for_remote_call(3.14) == 3.14 - assert trimmer.trim_for_remote_call(True) is True - - def test_trim_with_state_exclude_annotation(self): - """Test trimming with __state_exclude__ class annotation""" - from sage.common.utils.serialization.ray_trimmer import RayObjectTrimmer - - class ObjWithAnnotation: - __state_exclude__ = ["_internal"] - - def __init__(self): - self.public = "visible" - self._internal = "hidden" - - obj = ObjWithAnnotation() - trimmer = RayObjectTrimmer() - result = trimmer.trim_for_remote_call(obj) - - assert hasattr(result, "public") - # _internal should be excluded per __state_exclude__ - assert not hasattr(result, "_internal") or result._internal != "hidden" - - def test_trim_complex_nested_structure(self): - """Test trimming complex nested structures""" - from sage.common.utils.serialization.ray_trimmer import RayObjectTrimmer - - class Level3: - def __init__(self): - self.value = "level3" - - class Level2: - def __init__(self): - self.value = "level2" - self.level3 = Level3() - - class Level1: - def __init__(self): - self.value = "level1" - self.level2 = Level2() - - obj = Level1() - trimmer = RayObjectTrimmer() - result = trimmer.trim_for_remote_call(obj, deep_clean=True) - - assert hasattr(result, "value") - assert result.value == "level1" - assert hasattr(result, "level2") diff --git a/packages/sage-common/tests/unit/utils/system/test_environment.py b/packages/sage-common/tests/unit/utils/system/test_environment.py deleted file mode 100644 index 10b86cf08e..0000000000 --- a/packages/sage-common/tests/unit/utils/system/test_environment.py +++ /dev/null @@ -1,317 +0,0 @@ -""" -Unit tests for sage.common.utils.system.environment - -Tests environment detection and resource monitoring utilities. -""" - -import os -from unittest.mock import MagicMock, patch - -from sage.common.utils.system.environment import ( - detect_execution_environment, - detect_gpu_resources, - get_system_resources, - is_docker_environment, - is_kubernetes_environment, - is_ray_available, - is_ray_cluster_active, - is_slurm_environment, - recommend_backend, -) - - -class TestDetectExecutionEnvironment: - """Tests for detect_execution_environment()""" - - @patch("sage.common.utils.system.environment.is_ray_cluster_active") - def test_detect_ray_environment(self, mock_ray_active): - """Test detecting Ray environment""" - mock_ray_active.return_value = True - - result = detect_execution_environment() - - assert result == "ray" - - @patch("sage.common.utils.system.environment.is_ray_cluster_active") - @patch("sage.common.utils.system.environment.is_kubernetes_environment") - def test_detect_kubernetes_environment(self, mock_k8s, mock_ray): - """Test detecting Kubernetes environment""" - mock_ray.return_value = False - mock_k8s.return_value = True - - result = detect_execution_environment() - - assert result == "kubernetes" - - @patch("sage.common.utils.system.environment.is_ray_cluster_active") - @patch("sage.common.utils.system.environment.is_kubernetes_environment") - @patch("sage.common.utils.system.environment.is_docker_environment") - def test_detect_docker_environment(self, mock_docker, mock_k8s, mock_ray): - """Test detecting Docker environment""" - mock_ray.return_value = False - mock_k8s.return_value = False - mock_docker.return_value = True - - result = detect_execution_environment() - - assert result == "docker" - - @patch("sage.common.utils.system.environment.is_ray_cluster_active") - @patch("sage.common.utils.system.environment.is_kubernetes_environment") - @patch("sage.common.utils.system.environment.is_docker_environment") - @patch("sage.common.utils.system.environment.is_slurm_environment") - def test_detect_slurm_environment(self, mock_slurm, mock_docker, mock_k8s, mock_ray): - """Test detecting SLURM environment""" - mock_ray.return_value = False - mock_k8s.return_value = False - mock_docker.return_value = False - mock_slurm.return_value = True - - result = detect_execution_environment() - - assert result == "slurm" - - @patch("sage.common.utils.system.environment.is_ray_cluster_active") - @patch("sage.common.utils.system.environment.is_kubernetes_environment") - @patch("sage.common.utils.system.environment.is_docker_environment") - @patch("sage.common.utils.system.environment.is_slurm_environment") - def test_detect_local_environment(self, mock_slurm, mock_docker, mock_k8s, mock_ray): - """Test detecting local environment""" - mock_ray.return_value = False - mock_k8s.return_value = False - mock_docker.return_value = False - mock_slurm.return_value = False - - result = detect_execution_environment() - - assert result == "local" - - -class TestIsRayAvailable: - """Tests for is_ray_available()""" - - @patch("importlib.import_module") - def test_ray_available(self, mock_import): - """Test when Ray is available""" - mock_import.return_value = MagicMock() - - result = is_ray_available() - - assert result is True - mock_import.assert_called_once_with("ray") - - @patch("importlib.import_module") - def test_ray_not_available(self, mock_import): - """Test when Ray is not available""" - mock_import.side_effect = ImportError("No module named 'ray'") - - result = is_ray_available() - - assert result is False - - -class TestIsRayClusterActive: - """Tests for is_ray_cluster_active()""" - - @patch("sage.common.utils.system.environment.is_ray_available") - @patch("importlib.import_module") - def test_ray_cluster_active(self, mock_import, mock_available): - """Test when Ray cluster is active""" - mock_available.return_value = True - mock_ray = MagicMock() - mock_ray.is_initialized.return_value = True - mock_import.return_value = mock_ray - - result = is_ray_cluster_active() - - assert result is True - - @patch("sage.common.utils.system.environment.is_ray_available") - def test_ray_not_available(self, mock_available): - """Test when Ray is not available""" - mock_available.return_value = False - - result = is_ray_cluster_active() - - assert result is False - - -class TestIsKubernetesEnvironment: - """Tests for is_kubernetes_environment()""" - - @patch("os.path.exists") - def test_k8s_service_account_exists(self, mock_exists): - """Test detecting K8s via service account""" - mock_exists.return_value = True - - result = is_kubernetes_environment() - - assert result is True - mock_exists.assert_called_with("/var/run/secrets/kubernetes.io/serviceaccount") - - @patch("os.path.exists") - @patch.dict(os.environ, {"KUBERNETES_SERVICE_HOST": "10.0.0.1"}) - def test_k8s_env_variable(self, mock_exists): - """Test detecting K8s via environment variable""" - mock_exists.return_value = False - - result = is_kubernetes_environment() - - assert result is True - - -class TestIsDockerEnvironment: - """Tests for is_docker_environment()""" - - @patch("os.path.exists") - def test_docker_env_file(self, mock_exists): - """Test detecting Docker via .dockerenv""" - mock_exists.return_value = True - - result = is_docker_environment() - - assert result is True - mock_exists.assert_called_with("/.dockerenv") - - -class TestIsSlurmEnvironment: - """Tests for is_slurm_environment()""" - - @patch.dict(os.environ, {"SLURM_JOB_ID": "12345"}) - def test_slurm_job_id(self): - """Test detecting SLURM via job ID""" - result = is_slurm_environment() - - assert result is True - - @patch.dict(os.environ, {"SLURM_CLUSTER_NAME": "test-cluster"}, clear=True) - def test_slurm_cluster_name(self): - """Test detecting SLURM via cluster name""" - result = is_slurm_environment() - - assert result is True - - -class TestGetSystemResources: - """Tests for get_system_resources()""" - - @patch("psutil.cpu_count") - @patch("psutil.cpu_percent") - @patch("psutil.cpu_freq") - @patch("psutil.virtual_memory") - @patch("psutil.disk_usage") - def test_get_system_resources( - self, mock_disk, mock_memory, mock_freq, mock_cpu_percent, mock_cpu_count - ): - """Test getting system resources""" - # Mock CPU - mock_cpu_count.side_effect = lambda logical=True: 8 if logical else 4 - mock_cpu_percent.return_value = 25.0 - mock_freq_data = MagicMock() - mock_freq_data.current = 2400.0 - mock_freq_data.min = 800.0 - mock_freq_data.max = 3600.0 - mock_freq.return_value = mock_freq_data - - # Mock memory - mock_mem = MagicMock() - mock_mem.total = 16 * 1024**3 - mock_mem.available = 8 * 1024**3 - mock_mem.percent = 50.0 - mock_mem.used = 8 * 1024**3 - mock_mem.free = 8 * 1024**3 - mock_memory.return_value = mock_mem - - # Mock disk - mock_disk_data = MagicMock() - mock_disk_data.total = 500 * 1024**3 - mock_disk_data.used = 250 * 1024**3 - mock_disk_data.free = 250 * 1024**3 - mock_disk.return_value = mock_disk_data - - result = get_system_resources() - - # Check structure - assert "cpu" in result - assert "memory" in result - assert "disk" in result - assert "platform" in result - - # Check CPU - assert result["cpu"]["count"] == 8 - assert result["cpu"]["physical_count"] == 4 - - # Check memory - assert result["memory"]["total"] == 16 * 1024**3 - - # Check disk - assert result["disk"]["total"] == 500 * 1024**3 - - -class TestDetectGPUResources: - """Tests for detect_gpu_resources()""" - - @patch("subprocess.run") - def test_detect_nvidia_gpu(self, mock_run): - """Test detecting NVIDIA GPU""" - mock_result = MagicMock() - mock_result.returncode = 0 - mock_result.stdout = "GPU 0: Tesla V100\n" - mock_run.return_value = mock_result - - result = detect_gpu_resources() - - assert result["available"] is True - assert result["count"] >= 0 - - @patch("subprocess.run") - def test_no_gpu_available(self, mock_run): - """Test when no GPU is available""" - mock_run.side_effect = FileNotFoundError("nvidia-smi not found") - - result = detect_gpu_resources() - - assert result["available"] is False - assert result["count"] == 0 - - -class TestRecommendBackend: - """Tests for recommend_backend()""" - - @patch("sage.common.utils.system.environment.detect_execution_environment") - @patch("sage.common.utils.system.environment.get_system_resources") - @patch("sage.common.utils.system.environment.detect_gpu_resources") - def test_recommend_ray_backend(self, mock_gpu, mock_resources, mock_env): - """Test recommending Ray backend""" - mock_env.return_value = "ray" - mock_resources.return_value = { - "cpu": {"count": 16}, - "memory": {"total": 64 * 1024**3}, - "disk": {"total": 1024 * 1024**3}, - } - mock_gpu.return_value = {"available": True, "count": 2} - - result = recommend_backend() - - assert "environment" in result - assert "primary_backend" in result - assert result["environment"] == "ray" - - @patch("sage.common.utils.system.environment.detect_execution_environment") - @patch("sage.common.utils.system.environment.get_system_resources") - @patch("sage.common.utils.system.environment.detect_gpu_resources") - def test_recommend_local_backend(self, mock_gpu, mock_resources, mock_env): - """Test recommending local backend""" - mock_env.return_value = "local" - mock_resources.return_value = { - "cpu": {"count": 4}, - "memory": {"total": 8 * 1024**3}, - "disk": {"total": 256 * 1024**3}, - } - mock_gpu.return_value = {"available": False, "count": 0} - - result = recommend_backend() - - assert "environment" in result - assert "primary_backend" in result - assert result["environment"] == "local" diff --git a/packages/sage-common/tests/unit/utils/system/test_network.py b/packages/sage-common/tests/unit/utils/system/test_network.py deleted file mode 100644 index 66d4c7c17a..0000000000 --- a/packages/sage-common/tests/unit/utils/system/test_network.py +++ /dev/null @@ -1,287 +0,0 @@ -""" -Unit tests for sage.common.utils.system.network - -Tests network and port management utilities. -""" - -import json -from unittest.mock import MagicMock, patch - -import psutil -import pytest - -from sage.common.utils.system.network import ( - aggressive_port_cleanup, - allocate_free_port, - check_port_binding_permission, - check_tcp_connection, - find_port_processes, - is_port_occupied, - send_tcp_health_check, - wait_for_port_release, -) - - -class TestIsPortOccupied: - """Tests for is_port_occupied()""" - - @patch("sage.common.utils.system.network.socket.socket") - def test_port_occupied(self, mock_socket): - """Test when port is occupied""" - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.connect_ex.return_value = 0 # Success = occupied - - result = is_port_occupied("localhost", 8080) - - assert result is True - - @patch("sage.common.utils.system.network.socket.socket") - def test_port_not_occupied(self, mock_socket): - """Test when port is not occupied""" - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.connect_ex.return_value = 111 # Connection refused = free - - result = is_port_occupied("localhost", 8080) - - assert result is False - - -class TestCheckPortBindingPermission: - """Tests for check_port_binding_permission()""" - - @patch("sage.common.utils.system.network.socket.socket") - def test_permission_granted(self, mock_socket): - """Test when port binding permission is granted""" - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - - result = check_port_binding_permission("127.0.0.1", 8080) - - assert result["success"] is True - mock_sock.bind.assert_called_once() - - @patch("sage.common.utils.system.network.socket.socket") - def test_permission_denied(self, mock_socket): - """Test when port binding permission is denied""" - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.bind.side_effect = PermissionError("Permission denied") - - result = check_port_binding_permission("127.0.0.1", 80) - - assert result["success"] is False - assert result["error"] == "permission_denied" - - @patch("sage.common.utils.system.network.socket.socket") - def test_port_in_use(self, mock_socket): - """Test when port is already in use""" - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - os_error = OSError("Address already in use") - os_error.errno = 98 - mock_sock.bind.side_effect = os_error - - result = check_port_binding_permission("127.0.0.1", 8080) - - assert result["success"] is False - assert result["error"] == "port_in_use" - - -class TestWaitForPortRelease: - """Tests for wait_for_port_release()""" - - @patch("sage.common.utils.system.network.is_port_occupied") - def test_port_released_immediately(self, mock_is_occupied): - """Test when port is released immediately""" - mock_is_occupied.return_value = False - - result = wait_for_port_release("localhost", 8080, timeout=5) - - assert result is True - - @patch("sage.common.utils.system.network.time.sleep") - @patch("sage.common.utils.system.network.is_port_occupied") - def test_port_released_after_wait(self, mock_is_occupied, mock_sleep): - """Test when port is released after waiting""" - mock_is_occupied.side_effect = [True, True, False] - - result = wait_for_port_release("localhost", 8080, timeout=10, check_interval=1) - - assert result is True - - @patch("sage.common.utils.system.network.time.sleep") - @patch("sage.common.utils.system.network.is_port_occupied") - def test_port_timeout(self, mock_is_occupied, mock_sleep): - """Test timeout waiting for port release""" - mock_is_occupied.return_value = True - - result = wait_for_port_release("localhost", 8080, timeout=1, check_interval=0.1) - - assert result is False - - -class TestFindPortProcesses: - """Tests for find_port_processes()""" - - @pytest.mark.skip(reason="find_port_processes uses subprocess, complex to mock") - @patch("psutil.process_iter") - def test_find_processes(self, mock_process_iter): - """Test finding processes using a port""" - mock_proc = MagicMock() - mock_conn = MagicMock() - mock_conn.laddr.port = 8080 - mock_proc.net_connections.return_value = [mock_conn] - mock_process_iter.return_value = [mock_proc] - - result = find_port_processes(8080) - - assert len(result) > 0 - - @pytest.mark.skip(reason="find_port_processes uses subprocess, complex to mock") - @patch("psutil.process_iter") - def test_no_processes_found(self, mock_process_iter): - """Test when no processes use the port""" - mock_proc = MagicMock() - mock_proc.net_connections.return_value = [] - mock_process_iter.return_value = [mock_proc] - - result = find_port_processes(8080) - - assert len(result) == 0 - - -class TestSendTCPHealthCheck: - """Tests for send_tcp_health_check()""" - - @patch("sage.common.utils.system.network.socket.socket") - def test_health_check_success(self, mock_socket): - """Test successful health check""" - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - - # Mock response - response_data = {"status": "ok", "server": "test"} - response_bytes = json.dumps(response_data).encode("utf-8") - response_length = len(response_bytes) - - mock_sock.recv.side_effect = [ - response_length.to_bytes(4, byteorder="big"), - response_bytes, - ] - - request = {"action": "health_check"} - result = send_tcp_health_check("localhost", 8080, request, timeout=5) - - assert result == response_data - - @patch("sage.common.utils.system.network.socket.socket") - def test_health_check_connection_refused(self, mock_socket): - """Test health check with connection refused""" - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.connect.side_effect = ConnectionRefusedError("Connection refused") - - request = {"action": "health_check"} - result = send_tcp_health_check("localhost", 8080, request, timeout=5) - - assert result["status"] == "error" - assert "Connection failed" in result["message"] - - -class TestAllocateFreePort: - """Tests for allocate_free_port()""" - - @patch("sage.common.utils.system.network.is_port_occupied") - @patch("sage.common.utils.system.network.socket.socket") - def test_allocate_first_port_in_range(self, mock_socket, mock_is_occupied): - """Test allocating first available port""" - mock_is_occupied.return_value = False - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - - port = allocate_free_port(host="127.0.0.1", port_range=(19200, 19210)) - - assert port == 19200 - - @patch("sage.common.utils.system.network.is_port_occupied") - @patch("sage.common.utils.system.network.socket.socket") - def test_allocate_after_retries(self, mock_socket, mock_is_occupied): - """Test allocating port after some retries""" - mock_is_occupied.side_effect = [True, True, False] - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - - port = allocate_free_port(host="127.0.0.1", port_range=(19200, 19210)) - - assert port == 19202 - - @patch("sage.common.utils.system.network.is_port_occupied") - @patch("sage.common.utils.system.network.socket.socket") - def test_system_allocated_port(self, mock_socket, mock_is_occupied): - """Test fallback to system-allocated port""" - mock_is_occupied.return_value = True - mock_sock = MagicMock() - mock_sock.getsockname.return_value = ("127.0.0.1", 54321) - mock_socket.return_value.__enter__.return_value = mock_sock - - port = allocate_free_port(host="127.0.0.1", port_range=(19200, 19205)) - - assert port == 54321 - - -class TestCheckTCPConnection: - """Tests for check_tcp_connection()""" - - @patch("sage.common.utils.system.network.time.time") - @patch("sage.common.utils.system.network.socket.socket") - def test_tcp_connection_success(self, mock_socket, mock_time): - """Test successful TCP connection""" - mock_time.side_effect = [0.0, 0.001] - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.connect_ex.return_value = 0 - - result = check_tcp_connection("localhost", 8080) - - assert result["success"] is True - - @patch("sage.common.utils.system.network.time.time") - @patch("sage.common.utils.system.network.socket.socket") - def test_tcp_connection_failed(self, mock_socket, mock_time): - """Test failed TCP connection""" - mock_time.side_effect = [0.0, 0.001] - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.connect_ex.return_value = 111 - - result = check_tcp_connection("localhost", 8080) - - assert result["success"] is False - - -class TestAggressivePortCleanup: - """Tests for aggressive_port_cleanup()""" - - @patch("sage.common.utils.system.network.find_port_processes") - def test_no_processes(self, mock_find): - """Test cleanup when no processes occupy the port""" - mock_find.return_value = [] - - result = aggressive_port_cleanup(8080) - - assert result["success"] is False - assert len(result["errors"]) > 0 - - @patch("sage.common.utils.system.network.find_port_processes") - def test_cleanup_with_processes(self, mock_find): - """Test cleanup with processes""" - mock_proc = MagicMock(spec=psutil.Process) - mock_proc.pid = 12345 - mock_find.return_value = [mock_proc] - - result = aggressive_port_cleanup(8080) - - assert result["success"] is True - assert 12345 in result["killed_pids"] diff --git a/packages/sage-common/tests/unit/utils/system/test_process.py b/packages/sage-common/tests/unit/utils/system/test_process.py deleted file mode 100644 index 03ad63f15c..0000000000 --- a/packages/sage-common/tests/unit/utils/system/test_process.py +++ /dev/null @@ -1,272 +0,0 @@ -""" -Unit tests for sage.common.utils.system.process - -Tests process management utilities. -""" - -from unittest.mock import MagicMock, patch - -import psutil -import pytest - -from sage.common.utils.system.process import ( - find_processes_by_name, - get_process_info, - get_system_process_summary, - is_process_running, - terminate_process, - terminate_process_tree, - terminate_processes_by_name, - wait_for_process_termination, -) - - -class TestFindProcessesByName: - """Tests for find_processes_by_name()""" - - @patch("psutil.process_iter") - def test_find_processes(self, mock_process_iter): - """Test finding processes by name""" - mock_proc = MagicMock() - mock_proc.info = {"pid": 12345, "name": "python", "cmdline": ["python", "test.py"]} - mock_process_iter.return_value = [mock_proc] - - result = find_processes_by_name(["python"]) - - assert len(result) == 1 - assert result[0] == mock_proc - - @patch("psutil.process_iter") - def test_no_processes_found(self, mock_process_iter): - """Test when no processes are found""" - mock_proc = MagicMock() - mock_proc.info = {"pid": 12345, "name": "bash", "cmdline": ["bash"]} - mock_process_iter.return_value = [mock_proc] - - result = find_processes_by_name(["python"]) - - assert len(result) == 0 - - -class TestGetProcessInfo: - """Tests for get_process_info()""" - - @patch("psutil.Process") - def test_get_info_success(self, mock_process): - """Test getting process info""" - mock_proc = MagicMock() - mock_proc.name.return_value = "python" - mock_proc.username.return_value = "user" - mock_proc.cmdline.return_value = ["python", "test.py"] - mock_proc.status.return_value = "running" - mock_proc.cpu_percent.return_value = 10.5 - mock_proc.memory_percent.return_value = 5.2 - mock_proc.create_time.return_value = 1234567890.0 - mock_process.return_value = mock_proc - - result = get_process_info(12345) - - assert result["pid"] == 12345 - assert result["name"] == "python" - assert result["user"] == "user" - - @patch("psutil.Process") - def test_get_info_no_such_process(self, mock_process): - """Test getting info for non-existent process""" - mock_process.side_effect = psutil.NoSuchProcess(12345) - - result = get_process_info(12345) - - assert result["status"] == "Not Found" - assert "error" in result - - -class TestTerminateProcess: - """Tests for terminate_process()""" - - @patch("sage.common.utils.system.process.get_process_info") - @patch("psutil.Process") - def test_terminate_gracefully(self, mock_process, mock_info): - """Test graceful process termination""" - mock_proc = MagicMock() - mock_process.return_value = mock_proc - mock_info.return_value = {"pid": 12345, "name": "test"} - - result = terminate_process(12345) - - assert result["success"] is True - assert result["method"] == "terminate" - mock_proc.terminate.assert_called_once() - - @pytest.mark.skip(reason="TimeoutExpired behavior complex to mock correctly") - @patch("sage.common.utils.system.process.get_process_info") - @patch("psutil.Process") - def test_terminate_force_kill(self, mock_process, mock_info): - """Test force killing process after timeout""" - mock_proc = MagicMock() - mock_proc.wait.side_effect = psutil.TimeoutExpired(5) - mock_process.return_value = mock_proc - mock_info.return_value = {"pid": 12345, "name": "test"} - - result = terminate_process(12345) - - assert result["success"] is True - assert result["method"] == "kill" - mock_proc.kill.assert_called_once() - - @patch("psutil.Process") - def test_terminate_already_gone(self, mock_process): - """Test terminating process that's already gone""" - mock_process.side_effect = psutil.NoSuchProcess(12345) - - result = terminate_process(12345) - - assert result["success"] is True - assert result["method"] == "already_gone" - - -class TestTerminateProcessesByName: - """Tests for terminate_processes_by_name()""" - - @patch("sage.common.utils.system.process.terminate_process") - @patch("sage.common.utils.system.process.find_processes_by_name") - def test_terminate_by_name(self, mock_find, mock_terminate): - """Test terminating processes by name""" - mock_proc = MagicMock() - mock_proc.pid = 12345 - mock_find.return_value = [mock_proc] - mock_terminate.return_value = { - "success": True, - "method": "terminate", - "pid": 12345, - } - - result = terminate_processes_by_name(["python"]) - - assert result["total_found"] == 1 - assert len(result["terminated"]) == 1 - assert result["success"] is True - - @patch("sage.common.utils.system.process.find_processes_by_name") - def test_terminate_none_found(self, mock_find): - """Test when no processes are found""" - mock_find.return_value = [] - - result = terminate_processes_by_name(["python"]) - - assert result["total_found"] == 0 - assert result["success"] is True - - -class TestTerminateProcessTree: - """Tests for terminate_process_tree()""" - - @patch("sage.common.utils.system.process.terminate_process") - @patch("sage.common.utils.system.process.get_process_children") - def test_terminate_tree(self, mock_children, mock_terminate): - """Test terminating process tree""" - mock_children.return_value = [12346, 12347] - mock_terminate.return_value = { - "success": True, - "method": "terminate", - "pid": 12345, - } - - result = terminate_process_tree(12345) - - assert result["root_pid"] == 12345 - assert result["total_processes"] == 3 - assert result["success"] is True - - @patch("sage.common.utils.system.process.terminate_process") - @patch("sage.common.utils.system.process.get_process_children") - def test_terminate_tree_no_children(self, mock_children, mock_terminate): - """Test terminating process with no children""" - mock_children.return_value = [] - mock_terminate.return_value = { - "success": True, - "method": "already_gone", - "pid": 12345, - } - - result = terminate_process_tree(12345) - - assert result["total_processes"] == 1 - assert len(result["already_gone"]) == 1 - - -class TestWaitForProcessTermination: - """Tests for wait_for_process_termination()""" - - @patch("psutil.Process") - def test_wait_process_terminates(self, mock_process): - """Test waiting for process that terminates""" - mock_process.side_effect = psutil.NoSuchProcess(12345) - - result = wait_for_process_termination(12345, timeout=5) - - assert result is True - - @patch("time.sleep") - @patch("psutil.Process") - def test_wait_timeout(self, mock_process, mock_sleep): - """Test timeout while waiting""" - mock_proc = MagicMock() - mock_proc.is_running.return_value = True - mock_process.return_value = mock_proc - - result = wait_for_process_termination(12345, timeout=0.1) - - assert result is False - - -class TestGetSystemProcessSummary: - """Tests for get_system_process_summary()""" - - @patch("psutil.cpu_percent") - @patch("psutil.virtual_memory") - @patch("psutil.process_iter") - def test_get_summary(self, mock_process_iter, mock_memory, mock_cpu): - """Test getting system process summary""" - mock_proc1 = MagicMock() - mock_proc1.info = {"pid": 1, "name": "init", "status": "sleeping", "username": "root"} - mock_proc2 = MagicMock() - mock_proc2.info = {"pid": 2, "name": "kthreadd", "status": "running", "username": "root"} - mock_process_iter.return_value = [mock_proc1, mock_proc2] - - mock_mem = MagicMock() - mock_mem._asdict.return_value = {"total": 16 * 1024**3} - mock_memory.return_value = mock_mem - - mock_cpu.return_value = 25.0 - - result = get_system_process_summary() - - assert "total_processes" in result - assert result["total_processes"] == 2 - assert "by_status" in result - assert "by_user" in result - - -class TestIsProcessRunning: - """Tests for is_process_running()""" - - @patch("psutil.Process") - def test_process_running(self, mock_process): - """Test checking if process is running""" - mock_proc = MagicMock() - mock_proc.is_running.return_value = True - mock_process.return_value = mock_proc - - result = is_process_running(12345) - - assert result is True - - @patch("psutil.Process") - def test_process_not_running(self, mock_process): - """Test checking if process is not running""" - mock_process.side_effect = psutil.NoSuchProcess(12345) - - result = is_process_running(12345) - - assert result is False diff --git a/packages/sage-common/tests/unit/utils/test_logging.py b/packages/sage-common/tests/unit/utils/test_logging.py deleted file mode 100644 index f792837599..0000000000 --- a/packages/sage-common/tests/unit/utils/test_logging.py +++ /dev/null @@ -1,231 +0,0 @@ -""" -Tests for SAGE logging utilities -""" - -import logging -import tempfile -from pathlib import Path - -import pytest - -from sage.common.utils.logging.custom_logger import CustomLogger - - -class TestCustomLogger: - """Test CustomLogger functionality""" - - def test_logger_creation(self): - """Test basic logger creation""" - logger = CustomLogger() - assert logger is not None - assert logger.logger is not None - - def test_logger_with_custom_name(self): - """Test logger with custom name""" - logger = CustomLogger(name="test_logger") - assert logger.logger.name == "test_logger" - - def test_logger_default_level(self): - """Test default logging level""" - logger = CustomLogger() - # Default is WARNING in non-verbose mode (to reduce noisy startup logs) - # or respects SAGE_LOG_LEVEL environment variable - assert logger.logger.level <= logging.WARNING - - def test_debug_logging(self): - """Test debug level logging""" - logger = CustomLogger(name="debug_test") - logger.debug("Debug message") - # Should not raise exception - - def test_info_logging(self): - """Test info level logging""" - logger = CustomLogger(name="info_test") - logger.info("Info message") - # Should not raise exception - - def test_warning_logging(self): - """Test warning level logging""" - logger = CustomLogger(name="warning_test") - logger.warning("Warning message") - # Should not raise exception - - def test_error_logging(self): - """Test error level logging""" - logger = CustomLogger(name="error_test") - logger.error("Error message") - # Should not raise exception - - def test_critical_logging(self): - """Test critical level logging""" - logger = CustomLogger(name="critical_test") - logger.critical("Critical message") - # Should not raise exception - - def test_exception_logging(self): - """Test exception logging""" - logger = CustomLogger(name="exception_test") - try: - raise ValueError("Test exception") - except ValueError: - logger.exception("Caught exception") - # Should not raise exception - - def test_simple_logger_creation(self): - """测试最简单的logger创建方式""" - logger = CustomLogger("TestLogger") - - assert logger.name == "TestLogger" - assert logger.logger is not None - assert isinstance(logger.logger, logging.Logger) - - def test_logger_with_custom_file(self, tmp_path): - """测试带自定义日志文件的logger""" - log_file = tmp_path / "test.log" - logger = CustomLogger( - name="FileLogger", - outputs=[(str(log_file), "INFO")], - log_base_folder=None, # 使用绝对路径 - ) - - logger.info("Test message") - - assert log_file.exists() - content = log_file.read_text() - assert "Test message" in content - - def test_logger_with_level(self): - """测试设置日志级别""" - logger = CustomLogger(name="LevelLogger", outputs=[("console", logging.WARNING)]) - - # Logger的级别会设置为最低的handler级别 - assert logger.logger.level <= logging.WARNING - - def test_logger_with_level_string(self): - """测试使用字符串设置日志级别""" - CustomLogger(name="StringLevelLogger", outputs=[("console", "ERROR")]) - - def test_get_available_levels(self): - """测试获取可用日志级别""" - levels = CustomLogger.get_available_levels() - - assert "DEBUG" in levels - assert "INFO" in levels - assert "WARNING" in levels - assert "ERROR" in levels - assert "CRITICAL" in levels - - def test_multiple_log_calls(self): - """测试多次日志调用""" - logger = CustomLogger(name="level_test", outputs=[("console", logging.DEBUG)]) - - # 测试各种级别的日志 - logger.debug("Debug message") - logger.info("Info message") - logger.warning("Warning message") - logger.error("Error message") - - # 验证可以多次调用 - logger2 = CustomLogger(name="level_test2", outputs=[("console", logging.ERROR)]) - logger2.error("Another error") - - def test_file_handler_creation(self, tmp_path): - """测试文件handler的创建""" - log_file = tmp_path / "handler_test.log" - - CustomLogger(name="HandlerLogger", outputs=[(str(log_file), "DEBUG")]) - - def test_logger_format(self): - """Test logger output format""" - with tempfile.TemporaryDirectory() as tmpdir: - log_file = Path(tmpdir) / "format_test.log" - logger = CustomLogger(name="format_test", outputs=[(str(log_file), "INFO")]) - logger.info("Format test message") - - content = log_file.read_text() - # Should contain timestamp, level, and message - assert "INFO" in content - assert "Format test message" in content - - def test_multiple_loggers(self): - """Test creating multiple independent loggers""" - logger1 = CustomLogger(name="logger1") - logger2 = CustomLogger(name="logger2") - - assert logger1.logger.name != logger2.logger.name - assert logger1 is not logger2 - - def test_logger_with_context(self): - """Test logger with context information""" - logger = CustomLogger(name="context_test") - - # Test logging with extra context - logger.info("Message with context", extra={"user_id": 123}) - # Should not raise exception - - -class TestLoggingConfiguration: - """Test logging configuration utilities""" - - def test_logging_import(self): - """Test that logging module can be imported""" - from sage.common.utils import logging as sage_logging - - assert sage_logging is not None - - def test_custom_logger_import(self): - """Test CustomLogger can be imported from utils""" - from sage.common.utils.logging import CustomLogger as CL - - assert CL is not None - - logger = CL() - assert logger is not None - - -class TestLoggingIntegration: - """Integration tests for logging""" - - def test_logger_in_class(self): - """Test using logger in a class""" - - class TestClass: - def __init__(self): - self.logger = CustomLogger(name=self.__class__.__name__) - - def do_something(self): - self.logger.info("Doing something") - return True - - obj = TestClass() - assert obj.do_something() is True - - def test_logger_exception_handling(self): - """Test logger handles exceptions gracefully""" - logger = CustomLogger(name="exception_handling_test") - - try: - # Simulate an error - pass - except ZeroDivisionError as e: - logger.error(f"Caught error: {e}") - # Logger should not crash - - # Should continue working after exception - logger.info("Still working") - - def test_concurrent_logging(self): - """Test logger works with concurrent access""" - logger = CustomLogger(name="concurrent_test") - - # Simulate multiple log calls - for i in range(10): - logger.debug(f"Message {i}") - logger.info(f"Message {i}") - logger.warning(f"Message {i}") - - # Should not raise exceptions - - -if __name__ == "__main__": - pytest.main([__file__, "-v"]) diff --git a/packages/sage-kernel/.gitignore b/packages/sage-kernel/.gitignore deleted file mode 100644 index 844229eacb..0000000000 --- a/packages/sage-kernel/.gitignore +++ /dev/null @@ -1,6 +0,0 @@ -htmlcov/ -coverage.xml -.coverage -build/ -.pytest_cache/ -# .benchmarks/ # Deprecated - now uses .sage/benchmarks diff --git a/packages/sage-kernel/README.md b/packages/sage-kernel/README.md deleted file mode 100644 index 9fc7da983c..0000000000 --- a/packages/sage-kernel/README.md +++ /dev/null @@ -1,215 +0,0 @@ -# SAGE Kernel - -> 🚀 SAGE 框架的核心内核包 - 整合了核心框架和命令行工具 - -## 📋 Overview - -**SAGE Kernel** 是 SAGE 框架的核心包，整合了原来的 `sage-kernel` 和 `sage-cli` 两个包的功能，提供数据流处理引擎、任务管理、运行时系统和命令行工具。 - -## 🧭 Governance / 团队协作制度 - -- `docs/governance/TEAM.md` -- `docs/governance/MAINTAINERS.md` -- `docs/governance/DEVELOPER_GUIDE.md` -- `docs/governance/PR_CHECKLIST.md` -- `docs/governance/SELF_HOSTED_RUNNER.md` -- `docs/governance/TODO.md` - -## � Package Contents - -**SAGE Kernel** 是 SAGE 框架的核心包，整合了原来的 `sage-kernel` 和 `sage-cli` 两个包的功能： - -### 🏗️ 核心组件 (sage.core) - -- **数据流处理框架**: 高性能的 dataflow-native 处理引擎 -- **函数管理**: Function registry 和 operator 管理 -- **配置系统**: 统一的配置管理和验证 - -### ⚙️ 任务管理 (sage.kernels.jobmanager) - -- **任务调度**: 分布式任务执行和调度 -- **执行图**: DAG 执行图构建和优化 -- **客户端接口**: JobManager 客户端和服务端 - -### 🔧 运行时系统 (sage.kernels.runtime) - -- **服务工厂**: 任务和服务的动态创建 -- **通信队列**: 高性能的进程间通信 -- **服务管理**: 微服务架构的服务生命周期管理 - -### 💻 命令行工具 (sage.cli) - -- **集群管理**: 分布式集群的部署和管理 -- **任务提交**: 命令行任务提交和监控 -- **配置管理**: 交互式配置设置和验证 -- **扩展管理**: 插件和扩展的安装管理 - -## 🚀 Installation - -### From Source - -```bash -# 从源码安装 -pip install -e packages/sage-kernel - -# 或者从 PyPI 安装（发布后） -pip install intellistream-sage-kernel -``` - -## 📖 Quick Start - -### Using Core API - -```python -from sage.core import Function, Config -from sage.kernels.jobmanager import JobManager -from sage.kernels.runtime import ServiceTaskFactory - - -# 创建并使用函数 -@Function -def my_processor(data): - return data * 2 - - -# 使用 JobManager -job_manager = JobManager() -job = job_manager.submit_job(my_processor, data=[1, 2, 3]) -``` - -### 使用命令行工具 - -```bash -# 启动 SAGE 集群 -sage cluster start - -# 提交任务 -sage job submit my_job.py - -# 管理配置 -sage config set utils.provider openai -sage config show - -# 查看帮助 -sage --help -``` - -## 🏗️ 架构设计 - -``` -sage-kernel/ -├── src/sage/ -│ ├── core/ # 核心框架 -│ ├── jobmanager/ # 任务管理 -│ ├── runtime/ # 运行时系统 -│ └── cli/ # 命令行工具 -├── tests/ # 标准化测试结构 -│ ├── core/ -│ ├── jobmanager/ -│ ├── runtime/ -│ └── cli/ -└── pyproject.toml # 统一配置 -``` - -## 🧪 测试 - -```bash -# 运行所有测试 -pytest - -# 运行特定模块测试 -pytest tests/core/ -pytest tests/cli/ - -# 运行覆盖率测试 -pytest --cov=sage --cov-report=html -``` - -## 🔧 开发环境 - -```bash -# 安装开发依赖 -pip install -e "packages/sage-kernel[dev]" - -# 安装增强CLI功能 -pip install -e "packages/sage-kernel[enhanced]" - -# 代码格式化 -black src/ tests/ -ruff check src/ tests/ - -# 类型检查 -mypy src/sage -``` - -## 📚 依赖关系 - -### 内部依赖 - -- `sage-utils`: 基础工具包 - -### 外部核心依赖 - -- **ML/AI**: torch, transformers, sentence-transformers, faiss-cpu -- **Web/API**: fastapi, uvicorn, aiohttp -- **数据处理**: numpy, pandas, scipy, scikit-learn -- **CLI**: typer, rich, click, questionary -- **配置**: pydantic, PyYAML, python-dotenv - -## 🎯 设计理念 - -### 单一内核原则 - -将核心框架和 CLI 工具合并到一个包中，遵循以下原则： - -1. **统一入口**: 所有核心功能通过一个包提供 -1. **逻辑分离**: 不同组件保持清晰的模块边界 -1. **依赖优化**: 避免循环依赖，清晰的依赖层次 -1. **测试标准化**: 所有测试文件位于标准 `tests/` 目录 - -### CLI 集成策略 - -- CLI 功能完全集成到内核包中 -- 通过入口点 `sage` 和 `sage-kernel` 提供命令行访问 -- CLI 模块不污染核心 API 的导入 - -## 🔄 从旧包迁移 - -如果你之前使用 `sage-kernel` 或 `sage-cli`： - -```python -# 旧代码 -from sage_core import Function -from sage_cli.main import app - -# 新代码 -from sage.core import Function - -# CLI 通过命令行使用: sage command -``` - -## 📋 TODO - -- [ ] 完善模块间的导入优化 -- [ ] 添加性能基准测试 -- [ ] 完善CLI命令的集成测试 -- [ ] 优化依赖版本冲突问题 -- [ ] 添加更多示例代码 - -## 🤝 贡献 - -请查看项目根目录的贡献指南。对于 kernel 相关的开发： - -1. 确保测试位于 `tests/` 目录 -1. 保持模块间的清晰边界 -1. CLI 功能通过入口点而非直接导入使用 -1. 遵循现有的代码风格和架构模式 - -______________________________________________________________________ - -🔗 **相关包**: [sage-utils](../sage-utils/) | [sage-extensions](../sage-extensions/) | -[sage-lib](../sage-lib/) - -## 📄 License - -MIT License - see [LICENSE](../../LICENSE) for details. diff --git a/packages/sage-kernel/docs/governance/DEVELOPER_GUIDE.md b/packages/sage-kernel/docs/governance/DEVELOPER_GUIDE.md deleted file mode 100644 index f93c0c7ba0..0000000000 --- a/packages/sage-kernel/docs/governance/DEVELOPER_GUIDE.md +++ /dev/null @@ -1,88 +0,0 @@ -# 开发者指南与质量门槛（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages/<package>/docs/governance/DEVELOPER_GUIDE.md`） - -本指南把“制度”落到“能执行的工程约束”。本包的所有代码改动必须遵守以下规则。 - -______________________________________________________________________ - -## 1. 架构守护机制（强制） - -### 1.1 依赖层级（禁止向上依赖 / 禁止循环依赖） - -SAGE 采用 L1-L5 分层。任何“向上依赖”都视为架构违规，必须阻断合入。 - -- L1：`sage-common` -- L2：`sage-platform` -- L3：`sage-kernel`, `sage-libs` -- L4：`sage-middleware` -- L5：`sage-cli`, `sage-tools` - -### 1.2 Libs vs Middleware 规则（强制） - -如果代码需要调用“向上能力”（例如 VectorDB/Memory/Refiner/外部服务/重后端/网络服务），它 **不是库**，必须放在 -`sage-middleware`（operators/components）。 - -> 迁移时不保留兼容 re-export shim：更新所有调用方，快速失败。 - -### 1.3 Control Plane-only（强制） - -LLM 引擎操作必须走 Control Plane，禁止直接启动引擎/直连端口。 - -______________________________________________________________________ - -## 2. 自动化检查（CI + pre-commit）（强制） - -### 2.1 安装一致性 - -- 必须使用 `./quickstart.sh --dev --yes` 安装开发环境。 -- 禁止手工 `pip install` 临时安装依赖（依赖必须写入 `pyproject.toml`）。 - -### 2.2 本地质量与测试 - -建议在仓库根目录执行： - -- `sage-dev quality check --all-files` -- `sage-dev project test --coverage` - -______________________________________________________________________ - -## 3. 强制规范（必须出现的质量门槛） - -### 3.1 Mock-First（强制） - -- CI 必须能在无 GPU 环境跑通核心测试。 -- 新增能力必须提供 Mock/CPU 路径，避免把“硬件”当作默认前提。 - -### 3.2 Fail-Fast（强制） - -- 禁止静默默认值、禁止隐式回退（fallback）。 -- 关键配置缺失必须明确报错（例如用 `os.environ["KEY"]` 或显式校验并抛异常）。 - -### 3.3 Protocol-First（按适用范围执行） - -当本包提供“公共接口/抽象/跨包契约”时： - -- 先定义接口/类型（Protocol/ABC/Schema） -- 再实现具体逻辑 -- 再补测试与示例 - -______________________________________________________________________ - -## 4. 新模块/新能力接入步骤（可执行） - -1. **定义范围**：确认属于本包；若需要向上能力，按规则提升到 `sage-middleware`。 -1. **接口优先**：先定义可复用接口（如适用）。 -1. **最小可跑**：提供无 GPU 的最小可跑实现（Mock/CPU）。 -1. **测试**：至少包含单元测试；关键路径补回归测试。 -1. **文档**：更新本包 README 与本包 governance 文档（必要时）。 -1. **质量门槛**：本地 `sage-dev quality` 与测试通过。 - -______________________________________________________________________ - -## 5. 常见错误与修复 - -- **依赖倒挂**：`sage-libs` 引入 `sage-middleware` → 必须拆分/上移实现。 -- **文档位置违规**：禁止在根 `docs/` 放 Markdown；包级文档只能放 `packages/<pkg>/docs/`。 -- **依赖未声明**：新增依赖未写入 `pyproject.toml` → 必须补齐声明并统一版本。 diff --git a/packages/sage-kernel/docs/governance/MAINTAINERS.md b/packages/sage-kernel/docs/governance/MAINTAINERS.md deleted file mode 100644 index 4e331ad6e0..0000000000 --- a/packages/sage-kernel/docs/governance/MAINTAINERS.md +++ /dev/null @@ -1,126 +0,0 @@ -# 负责人制度（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages/<package>/docs/governance/MAINTAINERS.md`） -- 负责人名单：TBD - -本文件定义“包级 Maintainer（负责人）”如何配置、如何工作、如何被量化验收。 - -______________________________________________________________________ - -## 1. 为什么需要 Maintainer - -SAGE 是 monorepo，多包协作、依赖层级明确。Maintainer 的职责不是“写最多代码”，而是保证： - -- 需求有人推进、问题有人闭环 -- 质量门槛有人守住（CI/架构/测试） -- 跨包集成有人对齐（尤其是接口层与中间件层） - -______________________________________________________________________ - -## 2. 负责人配置建议（按模块/子系统） - -### 2.1 推荐配置 - -- 每个包至少 1 名 Maintainer（主负责人）+ 1 名 Backup（备份负责人）。 -- 当包内包含多个子系统/高复杂度模块：建议拆分子 Maintainer。 - -### 2.2 负责人配置表（模板，必须维护） - -| 模块/目录 | 复杂度 | 关键技能 | 主 Maintainer | Backup | 上游依赖 | 下游使用方 | -| --------- | ------ | -------- | ------------- | ------ | -------- | ---------- | -| TBD | TBD | TBD | TBD | TBD | TBD | TBD | - -> 单仓语义适配：这里的“上游/下游”指本 monorepo 的其他包或本包内其他模块。 - -______________________________________________________________________ - -## 3. 负责人职责拆分（40/30/20/10 参考） - -可按实际调整，但必须覆盖全部职责： - -- **40% 开发推进**：Roadmap/Issue/PR 推动，里程碑拆解，阻塞清理。 -- **30% 质量把控**：CI 绿灯、测试策略、回归防护、性能/稳定性监控。 -- **20% 集成同步**：跨包接口对齐、发布/版本对齐、变更公告与迁移指引。 -- **10% 团队协作**：Review SLA、协作矩阵维护、争议处理与复核申请。 - -______________________________________________________________________ - -## 4. 要求与投入 - -- **最低投入**：每周固定时间段处理 Issue/PR（TBD：建议至少 2-4 小时/周）。 -- **Review SLA**：工作日 48h 内首次响应。 -- **透明度**：每月一次交付清单（含证据链接）。 - -______________________________________________________________________ - -## 5. 工作流程（开发 / 发布 / 集成同步） - -### 5.1 日常开发 - -- 所有需求/缺陷必须有 Issue。 -- PR 必须通过 `docs/governance/PR_CHECKLIST.md`。 -- 架构约束（依赖层级、libs vs middleware）违反时：必须阻断合入。 - -### 5.2 发布与版本对齐（如适用） - -- 发布前：测试与质量检查必须绿。 -- 变更：Breaking 变更必须有迁移指引与回滚预案。 -- 若涉及 PyPI 发布：使用仓库规定的发布工具链（例如 `wheelwright`），不得手工 twine/pip 临时发布。 - -### 5.3 跨包集成同步 - -- 上游接口变化：必须提前通知下游 maintainer，并提供迁移窗口。 -- 下游反馈：需要在 SLA 内回应并给出计划。 - -______________________________________________________________________ - -## 6. 协作矩阵（依赖/协作关系） - -### 6.1 Monorepo 依赖层级速查 - -> 目标：避免“向上依赖”与循环依赖。 - -| 层级 | 包（示例） | 允许依赖 | -| ---- | -------------------------- | ---------------- | -| L5 | `sage-cli`, `sage-tools` | L1-L4 | -| L4 | `sage-middleware` | L1-L3 | -| L3 | `sage-kernel`, `sage-libs` | L1-L2 | -| L2 | `sage-platform` | L1 | -| L1 | `sage-common` | 仅 stdlib/轻依赖 | - -### 6.2 本包协作矩阵（模板） - -| 依赖方/被依赖方 | 关系类型 | 接口/契约 | 变更通知机制 | Maintainer 对接 | -| --------------- | -------- | --------- | --------------------- | --------------- | -| TBD | 上游 | TBD | Issue/PR/Release Note | TBD | -| TBD | 下游 | TBD | Issue/PR/Release Note | TBD | - -______________________________________________________________________ - -## 7. 负责人名单（TBD） - -| 模块/目录 | 主 Maintainer | Backup | 交接人（如更换） | 生效日期 | -| --------- | ------------- | ------ | ---------------- | -------- | -| TBD | TBD | TBD | TBD | TBD | - -______________________________________________________________________ - -## 8. 考核指标（参考，可量化） - -- PR 首次响应 SLA 达标率 -- CI 红灯平均恢复时间（MTTR） -- 每月交付清单是否按时发布（含证据） -- Breaking 变更公告与迁移指引完备率 - -______________________________________________________________________ - -## 9. FAQ - -### Q1：Maintainer 是否必须是提交最多的人？ - -不是。Maintainer 的核心是“推进 + 质量 + 对齐”。提交多但不维护质量/不对齐下游同样不合格。 - -### Q2：如果包内没有发布流程怎么办？ - -仍需要“集成同步”：接口变更、跨包影响、迁移指引、回滚预案。 diff --git a/packages/sage-kernel/docs/governance/PR_CHECKLIST.md b/packages/sage-kernel/docs/governance/PR_CHECKLIST.md deleted file mode 100644 index 09d0f8c350..0000000000 --- a/packages/sage-kernel/docs/governance/PR_CHECKLIST.md +++ /dev/null @@ -1,49 +0,0 @@ -# PR 审查清单（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages/<package>/docs/governance/PR_CHECKLIST.md`） - -> Maintainer/Reviewer 需要用本清单进行可量化审查。未满足项必须在 PR 中解释或补齐。 - -______________________________________________________________________ - -## 1. 架构合规 - -- [ ] 不产生向上依赖/循环依赖（符合 L1-L5 分层） -- [ ] 遵守 libs vs middleware 规则（需要向上能力则放 middleware） -- [ ] LLM 相关操作不绕过 Control Plane - -## 2. Protocol-First（如适用） - -- [ ] 公共接口/类型先定义（Protocol/ABC/Schema），实现不“绑死”具体后端 -- [ ] 接口变更包含迁移说明（Breaking change 必须公告） - -## 3. Mock-First - -- [ ] 无 GPU 环境可跑（至少核心测试/最小路径） -- [ ] 新增外部依赖/服务调用有可测试替身（mock/fake） - -## 4. Fail-Fast - -- [ ] 无隐式回退（不写 try/except 吞异常、不用静默默认值掩盖缺失配置） -- [ ] 错误信息可定位（异常 message 清晰，必要时包含修复指引） - -## 5. 可观测性（按适用范围） - -- [ ] 关键路径有日志/指标/追踪点（至少日志） -- [ ] 不输出敏感信息（key/token/密码等） - -## 6. 配置验证 - -- [ ] 新增配置项有校验与文档说明 -- [ ] 端口不硬编码（如适用，使用统一端口配置） - -## 7. 测试覆盖 - -- [ ] 新增/修改逻辑有对应测试 -- [ ] 关键 bug fix 有回归测试 - -## 8. 代码质量 - -- [ ] `sage-dev quality check --all-files` 通过 -- [ ] `sage-dev project test --coverage` 通过（或至少本包相关测试通过） diff --git a/packages/sage-kernel/docs/governance/SELF_HOSTED_RUNNER.md b/packages/sage-kernel/docs/governance/SELF_HOSTED_RUNNER.md deleted file mode 100644 index 8c4746062a..0000000000 --- a/packages/sage-kernel/docs/governance/SELF_HOSTED_RUNNER.md +++ /dev/null @@ -1,50 +0,0 @@ -# Self-hosted Runner 指南（SAGE - 可选） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages/<package>/docs/governance/SELF_HOSTED_RUNNER.md`） - -> 本章节用于需要 GPU/重依赖/长耗时测试的包。若本包不需要，可保留但不强制启用。 - -______________________________________________________________________ - -## 1. 什么时候需要 self-hosted runner - -- GPU 相关测试（CUDA/Ascend） -- 需要特殊硬件或驱动版本 -- 需要访问内网资源（需安全评估） - -______________________________________________________________________ - -## 2. Runner 安装与标签（模板） - -- Runner 类型：GitHub Actions self-hosted -- 标签建议： - - `self-hosted` - - `linux` - - `x64` - - `gpu`（如适用） - - `cuda-<major>`（如适用） - -______________________________________________________________________ - -## 3. 环境要求（模板） - -- OS：Linux（推荐） -- Python：与仓库要求一致（3.10+） -- 构建依赖：cmake / build-essential / BLAS 等（按仓库安装脚本） - -______________________________________________________________________ - -## 4. 安全注意事项（强制） - -- Runner 机器不可暴露敏感密钥；Secrets 由 GitHub 管理。 -- 禁止在日志中打印 token/key。 -- 对内网访问与数据集访问：必须走审批（TBD）。 - -______________________________________________________________________ - -## 5. 故障排查 - -- CI 失败先在本地 `./quickstart.sh --dev --yes` 复现 -- 检查磁盘/缓存（`.sage/`） -- 检查驱动/CUDA 版本（如适用） diff --git a/packages/sage-kernel/docs/governance/TEAM.md b/packages/sage-kernel/docs/governance/TEAM.md deleted file mode 100644 index 8873b39cf7..0000000000 --- a/packages/sage-kernel/docs/governance/TEAM.md +++ /dev/null @@ -1,67 +0,0 @@ -# 仓库人员与评分制度（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages/<package>/docs/governance/TEAM.md`） -- 名单维护：TBD - -本文件定义在 SAGE monorepo 内（以包为单位）的团队角色、负责人制度与评分/激励框架，用于保证： - -- 制度可执行：能落到 Issue/PR/CI/周更/月更。 -- 制度可验收：有明确证据链接与检查项。 -- 制度可追责：有最低交付门槛、透明度与争议处理机制。 - -> 约束：所有“姓名/负责人名单/具体人员”一律用 `TBD`，不得编造。 - -______________________________________________________________________ - -## 1. 角色与职责（强制条款） - -| 角色 | 核心职责 | 必须产出（可验收） | 证据类型（必须带链接） | -| ---------------------------- | -------------------------------------------------------- | ----------------------------------------------------------------------- | ------------------------------------------ | -| Maintainer（负责人） | Roadmap/Issue/PR 推进；质量门槛；集成/版本对齐；变更管理 | 每周 TODO 周更、PR Review SLA、CI 绿灯保障、重大变更公告、发布/集成记录 | Issue、PR、Release/Tag、CI 链接、周报/月报 | -| Engineering Core（工程骨干） | 关键功能交付；CI/测试维护；稳定性问题闭环 | 可交付功能/修复、测试/CI 改进、性能/稳定性指标改进 | PR、Issue、Benchmark 报告、CI 链接 | -| Research Core（科研骨干） | 原型验证/评测；研究到工程落地（必须可验收） | 可复现的实验/评测；可落地的工程化改造（或明确落地计划） | 评测报告、PR、Issue、实验配置/脚本链接 | - -______________________________________________________________________ - -## 2. 负责人必须做的事（强制条款） - -### 2.1 TODO 周更（强制） - -- 每周至少 1 次在本包的 `docs/governance/TODO.md` 更新：本周目标/完成/阻塞/下周计划/风险/需要协助。 -- TODO 必须引用证据（Issue/PR/CI/报告链接）。 - -### 2.2 PR Review SLA（强制） - -- 工作日 48h 内对新 PR 首次响应（review/approve/request changes/说明何时处理）。 -- 对影响多个包的变更：必须主动拉齐上下游 maintainer。 - -# 仓库人员与评分制度（SAGE - 包级，指向母版） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages/sage-kernel/docs/governance/TEAM.md`） -- 名单维护：TBD - -通用制度请参见 `packages/sage/docs/governance/TEAM_BASE.md`。本文件仅保留本包的代号映射与特有说明。 - -### 本包角色代号（姓名保持 TBD） - -| 角色 | 代号分配 | -| ---------------- | -------- | -| Maintainer | A1 | -| Engineering Core | B1 | -| Research Core | C1 | - -### 本包补充说明 - -- 核心数据流与调度层（L3）需重点保障回归测试与性能/稳定性；接口变更须提前告知下游（libs/middleware/cli）。 - -______________________________________________________________________ - -## 参考 - -- 通用制度：packages/sage/docs/governance/TEAM_BASE.md -- 周更：docs/governance/TODO.md -- PR 审查：docs/governance/PR_CHECKLIST.md -- 开发规范：docs/governance/DEVELOPER_GUIDE.md -- **保底分**：角色的最低履职奖励（必须满足最低交付门槛）。 diff --git a/packages/sage-kernel/docs/governance/TODO.md b/packages/sage-kernel/docs/governance/TODO.md deleted file mode 100644 index afdd548b3a..0000000000 --- a/packages/sage-kernel/docs/governance/TODO.md +++ /dev/null @@ -1,39 +0,0 @@ -# TODO 周更模板（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages/<package>/docs/governance/TODO.md`） -- 维护频率：每周至少 1 次（Maintainer 强制要求） - -______________________________________________________________________ - -## 周更（复制本模板，每周追加一节） - -### Week of YYYY-MM-DD - -**本周目标（Goals）** - -- [ ] TBD（关联 Issue：TBD） - -**本周完成（Done）** - -- [ ] TBD（PR：TBD，CI：TBD） - -**阻塞与风险（Blockers/Risks）** - -- TBD（原因 + 需要谁协助 + 截止时间） - -**下周计划（Next）** - -- [ ] TBD（Issue：TBD） - -**需要协助（Asks）** - -- TBD（@TBD） - -______________________________________________________________________ - -## 里程碑清单（长期维护） - -| 里程碑 | 目标日期 | 状态 | 关联 Issue/PR | 风险 | -| ------ | -------- | ---- | ------------- | ---- | -| TBD | TBD | TBD | TBD | TBD | diff --git a/packages/sage-kernel/examples/README.md b/packages/sage-kernel/examples/README.md deleted file mode 100644 index 2afab3d2a7..0000000000 --- a/packages/sage-kernel/examples/README.md +++ /dev/null @@ -1,85 +0,0 @@ -# L3: Kernel - 核心引擎层示例 - -> 对应 SAGE 包：`sage-kernel` - -## 📖 层级说明 - -**Kernel** 层是 SAGE 的核心引擎，提供： - -- 流处理 API (DataStream) -- 批处理 API (Batch) -- 操作符系统 (Operators) -- 函数系统 (Functions) -- 运行时环境 (Runtime) - -## 📚 目录结构 - -``` -L3-kernel/ -├── batch/ # 批处理示例 -├── stream/ # 流处理示例 -├── operators/ # 操作符示例 -├── functions/ # 函数示例 -└── advanced/ # 高级特性 - └── fault_tolerance/ # 容错机制 -``` - -## 🎯 学习路径 - -### 1️⃣ 批处理基础 (`batch/`) - -从批处理开始，理解数据处理的基本概念： - -- `hello_local_batch.py` - 本地批处理 -- `hello_remote_batch.py` - 远程批处理 -- `hello_batch_operator_examples.py` - 批处理操作符 - -### 2️⃣ 流处理基础 (`stream/`) - -进入流处理的世界： - -- `hello_streaming_world.py` - 基础流处理 -- `hello_onebyone_world.py` - 单条数据流 -- `hello_connected_stream_example.py` - 连接流 - -### 3️⃣ 操作符系统 (`operators/`) - -掌握核心操作符： - -- `hello_comap_world.py` - CoMap 操作符 -- `hello_filter_world.py` - Filter 过滤 -- `hello_flatmap_world.py` - FlatMap 展开 -- `hello_join_world.py` - Join 连接 -- `hello_three_input_comap.py` - 多输入 CoMap - -### 4️⃣ 函数系统 (`functions/`) - -理解函数抽象： - -- `hello_comap_function_example.py` - 函数版 CoMap -- `hello_comap_lambda_example.py` - Lambda 版本 -- `hello_wordcount_*.py` - WordCount 系列示例 - -### 5️⃣ 高级特性 (`advanced/`) - -探索高级功能： - -- `hello_future_world.py` - Future 异步处理 -- `hello_realistic_service_example.py` - 实际服务示例 -- `fault_tolerance/` - 容错和检查点 - -## 🎯 学习目标 - -完成本层示例后，你将掌握： - -1. SAGE 的核心 API 和编程模型 -1. 批处理和流处理的差异 -1. 各种操作符的使用场景 -1. 如何构建数据处理管道 - -## ⏭️ 下一步 - -学完内核层后，继续学习： - -- **L3-libs/** - 算法库和工具（同层级） -- **L4-middleware/** - 中间件和领域算子 diff --git a/packages/sage-kernel/examples/advanced/fault_tolerance/fault_tolerance.py b/packages/sage-kernel/examples/advanced/fault_tolerance/fault_tolerance.py deleted file mode 100644 index eec313f227..0000000000 --- a/packages/sage-kernel/examples/advanced/fault_tolerance/fault_tolerance.py +++ /dev/null @@ -1,227 +0,0 @@ -# """ -# Fault Tolerance Demo - -# 展示如何在 SAGE 应用中启用容错功能。 -# 这个示例演示了用户如何通过简单的配置来启用容错，无需编写任何容错相关代码。 -# """ - -# import os -# import sys - -# from sage.kernel.api.local_environment import LocalEnvironment -# from sage.libs.foundation.io.sink import TerminalSink -# from sage.libs.foundation.io.source import FileSource - - -# def demo_checkpoint_fault_tolerance(): -# """ -# 演示 Checkpoint 容错策略 - -# Checkpoint 策略会定期保存任务状态，失败时从最近的检查点恢复。 -# 适用于长时间运行的有状态任务。 -# """ -# print("\n" + "=" * 70) -# print("Demo 1: Checkpoint-based Fault Tolerance") -# print("=" * 70) - -# # 创建环境，配置 Checkpoint 容错策略 -# env = LocalEnvironment( -# "checkpoint_demo", -# config={ -# # 容错配置 - 用户只需声明，系统自动处理 -# "fault_tolerance": { -# "strategy": "checkpoint", # 使用 checkpoint 策略 -# "checkpoint_interval": 30.0, # 每30秒保存一次 -# "max_recovery_attempts": 3, # 最多尝试恢复3次 -# "checkpoint_dir": ".demo_checkpoints", # checkpoint存储目录 -# }, -# # 数据源配置 -# "source": {"file_path": "data/sample.txt"}, -# # 输出配置 -# "sink": {}, -# }, -# ) - -# # 正常定义 DAG - 用户完全不需要关心容错 -# pipeline = ( -# env.from_source(FileSource, env.config["source"]) -# .map(lambda x: x.strip().upper()) # 转换为大写 -# .sink(TerminalSink, env.config["sink"]) -# ) - -# # 提交作业 - 容错由系统自动处理 -# # 如果任务失败，系统会自动从最近的 checkpoint 恢复 -# env.submit() - -# print("\n✅ Pipeline with checkpoint fault tolerance submitted!") -# print(" If tasks fail, they will automatically recover from checkpoints.") - - -# def demo_restart_fault_tolerance(): -# """ -# 演示 Restart 容错策略（指数退避） - -# Restart 策略在任务失败时直接重启，使用指数退避算法逐渐增加重试延迟。 -# 适用于无状态或短时间运行的任务。 -# """ -# print("\n" + "=" * 70) -# print("Demo 2: Restart-based Fault Tolerance (Exponential Backoff)") -# print("=" * 70) - -# # 创建环境，配置 Restart 容错策略 -# env = LocalEnvironment( -# "restart_demo", -# config={ -# # 容错配置 - 使用指数退避重启策略 -# "fault_tolerance": { -# "strategy": "restart", # 使用 restart 策略 -# "restart_strategy": "exponential", # 指数退避 -# "initial_delay": 1.0, # 首次重启等待1秒 -# "max_delay": 60.0, # 最多等待60秒 -# "multiplier": 2.0, # 每次延迟翻倍 -# "max_attempts": 5, # 最多重启5次 -# }, -# "source": {"file_path": "data/sample.txt"}, -# "sink": {}, -# }, -# ) - -# # 定义 DAG - 无需容错代码 -# pipeline = ( -# env.from_source(FileSource, env.config["source"]) -# .map(lambda x: x.strip().lower()) # 转换为小写 -# .sink(TerminalSink, env.config["sink"]) -# ) - -# # 提交作业 - 失败时自动重启 -# env.submit() - -# print("\n✅ Pipeline with restart fault tolerance submitted!") -# print(" If tasks fail, they will automatically restart with exponential backoff.") -# print(" Retry delays: 1s, 2s, 4s, 8s, 16s...") - - -# def demo_fixed_delay_restart(): -# """ -# 演示 Restart 容错策略（固定延迟） - -# 使用固定延迟的重启策略，每次重启等待相同的时间。 -# """ -# print("\n" + "=" * 70) -# print("Demo 3: Restart-based Fault Tolerance (Fixed Delay)") -# print("=" * 70) - -# env = LocalEnvironment( -# "fixed_restart_demo", -# config={ -# # 容错配置 - 固定延迟重启 -# "fault_tolerance": { -# "strategy": "restart", -# "restart_strategy": "fixed", # 固定延迟 -# "delay": 5.0, # 每次等待5秒 -# "max_attempts": 3, # 最多重启3次 -# }, -# "source": {"file_path": "data/sample.txt"}, -# "sink": {}, -# }, -# ) - -# pipeline = ( -# env.from_source(FileSource, env.config["source"]) -# .map(lambda x: x.strip()) -# .sink(TerminalSink, env.config["sink"]) -# ) - -# env.submit() - -# print("\n✅ Pipeline with fixed delay restart submitted!") -# print(" If tasks fail, they will restart after 5 seconds each time.") - - -# def demo_no_fault_tolerance(): -# """ -# 演示不配置容错（默认行为） - -# 如果不配置 fault_tolerance，系统使用默认的简单重启策略。 -# """ -# print("\n" + "=" * 70) -# print("Demo 4: No Explicit Fault Tolerance Configuration") -# print("=" * 70) - -# # 不配置 fault_tolerance -# env = LocalEnvironment( -# "no_ft_demo", config={"source": {"file_path": "data/sample.txt"}, "sink": {}} -# ) - -# pipeline = ( -# env.from_source(FileSource, env.config["source"]) -# .map(lambda x: x.strip()) -# .sink(TerminalSink, env.config["sink"]) -# ) - -# env.submit() - -# print("\n✅ Pipeline submitted with default fault tolerance.") - - -# def main(): -# """主函数 - 运行所有演示""" - -# # 检查是否在测试模式 -# if ( -# os.getenv("SAGE_EXAMPLES_MODE") == "test" -# or os.getenv("SAGE_TEST_MODE") == "true" -# ): -# print("🧪 Test mode detected - fault_tolerance_demo") -# print("✅ Test passed: Fault tolerance demo structure validated") -# return - -# print("\n") -# print("╔" + "=" * 68 + "╗") -# print("║" + " " * 18 + "SAGE FAULT TOLERANCE DEMO" + " " * 25 + "║") -# print("╚" + "=" * 68 + "╝") - -# print("\n📖 This demo shows how to enable fault tolerance in SAGE applications.") -# print(" Users only need to declare the strategy in config - no code changes!") - -# # 创建示例数据文件（如果不存在） -# os.makedirs("data", exist_ok=True) -# if not os.path.exists("data/sample.txt"): -# with open("data/sample.txt", "w") as f: -# f.write("Hello World\n") -# f.write("Fault Tolerance Demo\n") -# f.write("SAGE Framework\n") -# print("\n📄 Created sample data file: data/sample.txt") - -# # 运行各种容错策略演示 -# try: -# demo_checkpoint_fault_tolerance() -# demo_restart_fault_tolerance() -# demo_fixed_delay_restart() -# demo_no_fault_tolerance() - -# print("\n" + "=" * 70) -# print("✨ All demos completed successfully!") -# print("=" * 70) - -# print("\n📚 Learn more:") -# print( -# " - Full documentation: packages/sage-kernel/src/sage/kernel/fault_tolerance/README.md" -# ) -# print( -# " - Quick reference: packages/sage-kernel/src/sage/kernel/fault_tolerance/QUICK_REFERENCE.md" -# ) -# print( -# " - More examples: examples/kernel/fault_tolerance_examples.py" -# ) - -# except Exception as e: -# print(f"\n❌ Error running demos: {e}") -# import traceback - -# traceback.print_exc() -# sys.exit(1) - - -# if __name__ == "__main__": -# main() diff --git a/packages/sage-kernel/examples/advanced/fault_tolerance/fault_tolerance_examples.py b/packages/sage-kernel/examples/advanced/fault_tolerance/fault_tolerance_examples.py deleted file mode 100644 index 9e4262773b..0000000000 --- a/packages/sage-kernel/examples/advanced/fault_tolerance/fault_tolerance_examples.py +++ /dev/null @@ -1,412 +0,0 @@ -""" -Fault Tolerance Usage Examples - -Layer: L3 (Kernel - Examples) -Dependencies: sage.libs (L3 - optional examples only) - -展示应用用户如何使用容错（无感知）以及开发者如何扩展容错策略。 - -⚠️ IMPORTANT - 架构说明: - 本文件是**可选示例代码**，不是 kernel 的核心功能。 - 示例中使用 sage.libs 的 Source/Sink 是为了演示完整的容错流程。 - - 运行这些示例需要安装 sage.libs: - pip install sage-libs - - 用户可以使用自己的 Source/Sink 实现，无需依赖 sage.libs。 - -Architecture Note: - 这些示例演示完整的使用场景，需要 sage.libs 提供 Source/Sink。 - 如果 sage.libs 不可用，示例将无法运行，但不影响 kernel 的核心功能。 -""" - -# ============================================================================ -# 应用用户示例 - 容错配置对用户是透明的 -# ============================================================================ - - -def example_1_user_checkpoint_strategy(): - """ - 示例 1: 应用用户使用 Checkpoint 策略 - - 用户只需在 Environment 配置中声明，无需编写任何容错代码。 - - Requirements: - pip install sage-libs - """ - from sage.kernel.api.local_environment import LocalEnvironment - - # 此示例需要 sage.libs - try: - from sage.libs.foundation.io.sink import TerminalSink - from sage.libs.foundation.io.source import FileSource - except ImportError as e: - raise ImportError( - "This example requires sage.libs. " - "Install it with: pip install sage-libs\n" - "Or use your own Source/Sink implementations." - ) from e - - # 创建环境时声明使用 Checkpoint 容错策略 - env = LocalEnvironment( - "qa_pipeline_with_checkpoint", - config={ - "fault_tolerance": { - "strategy": "checkpoint", - "checkpoint_interval": 60.0, # 每60秒保存一次 - "max_recovery_attempts": 3, # 最多恢复3次 - "checkpoint_dir": ".sage/checkpoints", - }, - "source": {"data_path": "data/questions.txt"}, - "sink": {}, - }, - ) - - # 正常定义 DAG - 完全不需要关心容错 - ( - env.from_source(FileSource, env.config["source"]) - .map(lambda x: x.upper()) # 一些处理 - .sink(TerminalSink, env.config["sink"]) - ) - - # 提交作业 - 容错由系统自动处理 - # 如果任务失败，系统会自动从最近的 checkpoint 恢复 - env.submit() - - print("✅ Pipeline submitted with checkpoint fault tolerance") - - -def example_2_user_restart_strategy(): - """ - 示例 2: 应用用户使用 Restart 策略 - - 使用指数退避重启策略，用户同样无需编写容错代码。 - - Requirements: - pip install sage-libs - """ - from sage.kernel.api.local_environment import LocalEnvironment - - try: - from sage.libs.foundation.io.sink import TerminalSink - from sage.libs.foundation.io.source import FileSource - except ImportError as e: - raise ImportError( - "This example requires sage.libs. Install it with: pip install sage-libs" - ) from e - - # 使用指数退避重启策略 - env = LocalEnvironment( - "data_pipeline_with_restart", - config={ - "fault_tolerance": { - "strategy": "restart", - "restart_strategy": "exponential", - "initial_delay": 1.0, # 首次重启等待1秒 - "max_delay": 60.0, # 最多等待60秒 - "multiplier": 2.0, # 每次延迟翻倍 - "max_attempts": 5, # 最多重启5次 - }, - "source": {"data_path": "data/input.txt"}, - "sink": {}, - }, - ) - - # 定义 DAG - 用户不关心容错 - ( - env.from_source(FileSource, env.config["source"]) - .map(lambda x: x.strip()) - .sink(TerminalSink, env.config["sink"]) - ) - - # 提交 - 失败时自动重启 - env.submit() - - print("✅ Pipeline submitted with exponential backoff restart") - - -def example_3_user_no_fault_tolerance(): - """ - 示例 3: 用户不配置容错（使用默认行为） - - Requirements: - pip install sage-libs - """ - from sage.kernel.api.local_environment import LocalEnvironment - - try: - from sage.libs.foundation.io.sink import TerminalSink - from sage.libs.foundation.io.source import FileSource - except ImportError as e: - raise ImportError( - "This example requires sage.libs. Install it with: pip install sage-libs" - ) from e - - # 不配置 fault_tolerance，使用默认行为 - env = LocalEnvironment("simple_pipeline") - - # 正常定义和提交 - ( - env.from_source(FileSource, {"data_path": "data.txt"}) - .map(lambda x: x.upper()) - .sink(TerminalSink, {}) - ) - - env.submit() - - print("✅ Pipeline submitted with default fault tolerance") - - -def example_4_user_yaml_config(): - """ - 示例 4: 从 YAML 配置文件读取容错配置 - - 这是最常见的用法 - 配置在外部文件中管理。 - - Requirements: - pip install sage-libs - """ - from sage.common.utils.config.loader import load_config - from sage.kernel.api.local_environment import LocalEnvironment - - try: - from sage.libs.foundation.io.sink import TerminalSink - from sage.libs.foundation.io.source import FileSource - except ImportError as e: - raise ImportError( - "This example requires sage.libs. Install it with: pip install sage-libs" - ) from e - - # config.yaml 内容示例： - # fault_tolerance: - # strategy: checkpoint - # checkpoint_interval: 30.0 - # max_recovery_attempts: 5 - # source: - # file_path: data/input.txt - # sink: {} - - config = load_config("config/my_pipeline.yaml") - - # 容错配置从 YAML 文件读取 - env = LocalEnvironment("yaml_configured_pipeline", config=config) - - ( - env.from_source(FileSource, config["source"]) - .map(lambda x: x.strip()) - .sink(TerminalSink, config["sink"]) - ) - - env.submit() - - print("✅ Pipeline submitted with YAML-configured fault tolerance") - - -# ============================================================================ -# 开发者示例 - 扩展自定义容错策略 -# ============================================================================ - - -def example_5_developer_custom_strategy(): - """ - 示例 5: 开发者实现自定义容错策略 - - 开发者可以继承 BaseFaultHandler 实现自己的容错逻辑。 - """ - from sage.kernel.fault_tolerance.base import BaseFaultHandler - - class CircuitBreakerFaultHandler(BaseFaultHandler): - """ - 断路器容错策略 - - 当失败次数超过阈值时，打开断路器，停止重试一段时间。 - """ - - def __init__(self, failure_threshold=5, timeout=60.0): - self.failure_threshold = failure_threshold - self.timeout = timeout - self.failure_counts = {} - self.circuit_open = {} - self.open_time = {} - self.logger = None - - def handle_failure(self, task_id: str, error: Exception) -> bool: - import time - - # 更新失败计数 - if task_id not in self.failure_counts: - self.failure_counts[task_id] = 0 - self.failure_counts[task_id] += 1 - - if self.logger: - self.logger.warning( - f"Task {task_id} failed (count: {self.failure_counts[task_id]}): {error}" - ) - - # 检查是否应该打开断路器 - if self.failure_counts[task_id] >= self.failure_threshold: - self.circuit_open[task_id] = True - self.open_time[task_id] = time.time() - - if self.logger: - self.logger.error( - f"Circuit breaker opened for {task_id} " - f"(failures: {self.failure_counts[task_id]})" - ) - return False - - return self.recover(task_id) - - def can_recover(self, task_id: str) -> bool: - import time - - # 如果断路器是打开的 - if self.circuit_open.get(task_id, False): - # 检查是否已经超过超时时间 - if time.time() - self.open_time.get(task_id, 0) > self.timeout: - # 关闭断路器，重置计数 - self.circuit_open[task_id] = False - self.failure_counts[task_id] = 0 - - if self.logger: - self.logger.info(f"Circuit breaker closed for {task_id}") - - return True - return False - - return self.failure_counts.get(task_id, 0) < self.failure_threshold - - def recover(self, task_id: str) -> bool: - if self.logger: - self.logger.info(f"Attempting to recover task {task_id}") - - # 实际的恢复逻辑 - # TODO: 实现具体的恢复策略 - # Issue URL: https://github.com/intellistream/SAGE/issues/933 - - return True - - print("✅ Custom CircuitBreakerFaultHandler defined") - print(" Developers can extend BaseFaultHandler to create custom strategies") - - -def example_6_developer_register_strategy(): - """ - 示例 6: 开发者将自定义策略集成到系统 - - 步骤： - 1. 将自定义策略类放到 impl/ 目录 - 2. 在 impl/__init__.py 中导出 - 3. 在 factory.py 中添加创建逻辑 - 4. 用户就可以通过配置使用了 - """ - - # 步骤 1: 创建自定义策略文件 - # packages/sage-kernel/src/sage/kernel/fault_tolerance/impl/circuit_breaker.py - - # 步骤 2: 在 impl/__init__.py 添加导出 - # from sage.kernel.fault_tolerance.impl.circuit_breaker import CircuitBreakerFaultHandler - # __all__ = [..., "CircuitBreakerFaultHandler"] - - # 步骤 3: 在 factory.py 添加创建逻辑 - # def create_fault_handler_from_config(config): - # strategy = config.get("strategy") - # if strategy == "circuit_breaker": - # return CircuitBreakerFaultHandler( - # failure_threshold=config.get("failure_threshold", 5), - # timeout=config.get("timeout", 60.0) - # ) - # ... - - # 步骤 4: 用户现在可以通过配置使用 - # env = LocalEnvironment( - # "my_app", - # config={ - # "fault_tolerance": { - # "strategy": "circuit_breaker", - # "failure_threshold": 3, - # "timeout": 30.0 - # } - # } - # ) - - print("✅ Custom strategy integration steps:") - print(" 1. Create strategy class in impl/") - print(" 2. Export in impl/__init__.py") - print(" 3. Add creation logic in factory.py") - print(" 4. Users can now use it via config") - - -def example_7_developer_reference_implementations(): - """ - 示例 7: 开发者参考现有实现 - - 查看 impl/ 目录下的实现来学习如何编写容错策略。 - """ - print("✅ Reference implementations in impl/:") - print(" - checkpoint_recovery.py: Checkpoint-based fault tolerance") - print(" - restart_recovery.py: Restart-based fault tolerance") - print(" - restart_strategy.py: Various restart strategies") - print(" - lifecycle_impl.py: Lifecycle management") - print(" - checkpoint_impl.py: Checkpoint management") - - -# ============================================================================ -# 运行所有示例 -# ============================================================================ - - -def run_user_examples(): - """运行应用用户示例""" - print("\n" + "=" * 70) - print("APPLICATION USER EXAMPLES - Fault Tolerance is Transparent") - print("=" * 70 + "\n") - - print("Example 1: Checkpoint Strategy") - print("-" * 70) - example_1_user_checkpoint_strategy() - - print("\nExample 2: Restart Strategy with Exponential Backoff") - print("-" * 70) - example_2_user_restart_strategy() - - print("\nExample 3: No Explicit Fault Tolerance Configuration") - print("-" * 70) - example_3_user_no_fault_tolerance() - - print("\nExample 4: YAML Configuration") - print("-" * 70) - example_4_user_yaml_config() - - -def run_developer_examples(): - """运行开发者扩展示例""" - print("\n" + "=" * 70) - print("DEVELOPER EXAMPLES - Extending Fault Tolerance Strategies") - print("=" * 70 + "\n") - - print("Example 5: Custom Circuit Breaker Strategy") - print("-" * 70) - example_5_developer_custom_strategy() - - print("\nExample 6: Integrating Custom Strategy into System") - print("-" * 70) - example_6_developer_register_strategy() - - print("\nExample 7: Reference Implementations") - print("-" * 70) - example_7_developer_reference_implementations() - - -if __name__ == "__main__": - print("\n") - print("╔" + "=" * 68 + "╗") - print("║" + " " * 20 + "FAULT TOLERANCE EXAMPLES" + " " * 24 + "║") - print("╚" + "=" * 68 + "╝") - - run_user_examples() - run_developer_examples() - - print("\n" + "=" * 70) - print("All examples completed successfully!") - print("=" * 70 + "\n") diff --git a/packages/sage-kernel/examples/advanced/hello_future_stream_example.py b/packages/sage-kernel/examples/advanced/hello_future_stream_example.py deleted file mode 100644 index dc88c20646..0000000000 --- a/packages/sage-kernel/examples/advanced/hello_future_stream_example.py +++ /dev/null @@ -1,193 +0,0 @@ -import time - -from sage.common.core.functions.base_function import BaseFunction -from sage.common.core.functions.comap_function import BaseCoMapFunction -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.core.functions.source_function import SourceFunction -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.api.local_environment import LocalEnvironment - - -# 初始数据源：启动计数器 -class CounterStartSource(SourceFunction): - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.started = False - - def execute(self, data=None): - if not self.started: - self.started = True - print("🚀 Starting counter...") - # 只发送一次初始值，随后就返回None - return {"count": 0, "message": "Counter initialized"} - return None - - -# 反馈处理器：接收计数器值和反馈值 -class CounterProcessor(BaseCoMapFunction): - def map0(self, data): - """处理初始计数器数据（来自输入流0）""" - print(f"📥 Initial data: {data}") - return data - - def map1(self, data): - """处理反馈数据（来自输入流1 - future stream）""" - print(f"🔄 Feedback data: {data}") - return data - - -# 计数增加器 -class CounterIncrementer(BaseFunction): - def execute(self, data): - if data is None: - return None - - current_count = data.get("count", 0) - new_count = current_count + 1 - - result = { - "count": new_count, - "message": f"Counter value: {new_count}", - "should_continue": new_count < 10, - } - - print(f"🔢 Counter incremented: {current_count} → {new_count}") - return result - - -# 退出条件检查器 -class ExitChecker(BaseFunction): - def execute(self, data): - if data is None: - return None - - count = data.get("count", 0) - should_continue = data.get("should_continue", True) - - if not should_continue: - print(f"🏁 Counter reached target value: {count}. Stopping...") - return None # 停止数据流 - - print(f"✅ Counter check passed: {count} < 10, continuing...") - return data - - -# 反馈延迟器：添加延迟以便观察反馈循环 -class FeedbackDelayer(BaseFunction): - def execute(self, data): - if data is None: - return None - - print("⏱️ Adding delay before feedback...") - time.sleep(1) # 1秒延迟，便于观察 - print(f"🔙 Sending feedback: {data}") - return data - - -# 最终输出 -class CounterSink(SinkFunction): - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.custom_name = kwargs.get("name", "CounterSink") - - def execute(self, data): - if data is not None: - count = data.get("count", 0) - message = data.get("message", "No message") - print(f"[{self.custom_name}] 📊 {message}") - - if count >= 10: - print(f"[{self.custom_name}] 🎉 Counter completed! Final value: {count}") - return data - - -def main(): - # 创建环境 - env = LocalEnvironment("future_stream_example") - - print("🚀 Starting Future Stream Example") - print("🔄 Demonstrating feedback edges with a counting loop") - print("📊 Counter will increment from 0 to 10 using feedback") - print("⏹️ Press Ctrl+C to stop\n") - - print("🔗 Creating feedback loop with future stream...") - - # 1. 声明future stream（反馈边） - print("📋 Step 1: Declaring future stream for feedback...") - feedback_stream = env.from_future("counter_feedback") - - # 2. 创建初始数据源 - print("📋 Step 2: Creating initial counter source...") - counter_source = env.from_source(CounterStartSource, delay=0.5) - - # 3. 连接初始流和反馈流 - print("📋 Step 3: Connecting initial stream with feedback stream...") - connected_streams = counter_source.connect(feedback_stream) - - # 4. 处理连接的流（初始值 + 反馈值） - print("📋 Step 4: Processing connected streams...") - processed_data = connected_streams.comap(CounterProcessor) - - # 5. 增加计数器 - print("📋 Step 5: Setting up counter incrementer...") - incremented_data = processed_data.map(CounterIncrementer) - - # 6. 检查退出条件 - print("📋 Step 6: Setting up exit condition checker...") - checked_data = incremented_data.map(ExitChecker) - - # 7. 输出到终端 - print("📋 Step 7: Setting up output sink...") - checked_data.sink(CounterSink, name="CounterOutput") - - # 8. 创建反馈分支（添加延迟后反馈） - print("📋 Step 8: Creating feedback branch...") - feedback_data = checked_data.map(FeedbackDelayer) - - # 9. 填充future stream，建立反馈边 - print("📋 Step 9: Filling future stream to create feedback edge...") - feedback_data.fill_future(feedback_stream) - - print("\n🔄 Feedback loop structure:") - print( - " CounterSource → [Connected with Future] → CounterProcessor → Incrementer → ExitChecker → CounterSink" - ) - print(" ↑ ↓") - print(" └────────────────── FeedbackDelayer ←────────────────────┘") - print() - - print("✅ Pipeline validation:") - print(f" - Pipeline transformations: {len(env.pipeline)}") - - try: - print("🎬 Starting feedback loop execution...") - print("📈 Watch the counter increment in a feedback loop:\n") - - # 运行流处理 - env.submit() - - time.sleep(10) # 运行15秒，足够计数到10 - - except KeyboardInterrupt: - print("\n\n🛑 Stopping Future Stream Example...") - - finally: - print("\n📋 Example completed!") - print("💡 This example demonstrated:") - print(" - Creating a future stream with env.from_future()") - print(" - Using future stream in connected streams") - print(" - Processing initial and feedback data with CoMap") - print(" - Incrementing counter in a feedback loop") - print(" - Conditional exit based on counter value") - print(" - Filling future stream to create feedback edge") - print("\n🔄 Feedback Loop Features:") - print(" - Initial value flows through the system") - print(" - Processed result feeds back to the beginning") - print(" - Loop continues until exit condition is met") - print(" - Clean termination when counter reaches 10") - env.close() - - -if __name__ == "__main__": - CustomLogger.disable_global_console_debug() - main() diff --git a/packages/sage-kernel/examples/advanced/hello_future_world.py b/packages/sage-kernel/examples/advanced/hello_future_world.py deleted file mode 100644 index fa1f674738..0000000000 --- a/packages/sage-kernel/examples/advanced/hello_future_world.py +++ /dev/null @@ -1,118 +0,0 @@ -from time import sleep - -from sage.common.core.functions.base_function import BaseFunction -from sage.common.core.functions.batch_function import BatchFunction -from sage.common.core.functions.comap_function import BaseCoMapFunction -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.api.local_environment import LocalEnvironment - - -# 启动信号源（只发一次启动信号） -class StartSource(BatchFunction): - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.started = False - - def execute(self): - if not self.started: - self.started = True - print("已发送启动信号") - return {"signal": "start"} - else: - return None - - -# 合流处理器（map0 = 启动，map1 = 反馈） -class SignalMerger(BaseCoMapFunction): - def map0(self, data): - print(f">>> StartSource：收到启动数据: {data}") - return data - - def map1(self, data): - print(f">>> PipelineSource： 收到反馈数据: {data}") - return data - - -# 从语句列表中按顺序取语句（只在收到反馈时推进） -class SentenceProvider(BatchFunction): - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.sentences = [ - "这是第一句。", - "这是第二句。", - "这是第三句。", - "所有语句已完成！", - ] - self.index = 0 - - def execute(self, data): - if data is None: - return None - - if self.index >= len(self.sentences): - print("全部语句已输出完毕，结束数据流。") - return None - - sentence = self.sentences[self.index] - self.index += 1 - new_data = {"句子": sentence} - print(f">>> SentenceProvider 提供句子: {new_data}") - return new_data - - -# Sink：打印句子（立即打印，不加 sleep） -class FeedbackSink(SinkFunction): - def execute(self, data): - if data: - print(f">>> Sink 打印: {data}") - return data - - -# 延迟反馈算子：控制节奏 -class FeedbackDelayer(BaseFunction): - def execute(self, data): - if data is None: - return None - sleep(1) # 控制间隔 - print(">>> FeedbackDelayer等待 2 秒后反馈...") - return data - - -def main(): - env = LocalEnvironment("句子顺序输出") - - # 1. 启动源 - start_stream = env.from_source(StartSource) - - # 2. future stream 用于反馈 - feedback_stream = env.from_future("feedback") - - # 3. 合流 - merged = start_stream.connect(feedback_stream).comap(SignalMerger) - - # 4. 语句提供器 - provided = merged.map(SentenceProvider) - - # 5. Sink，打印结果 - sinked = provided.sink(FeedbackSink) - - # 6. 在反馈前加延迟 - delayed = sinked.map(FeedbackDelayer) - - # 7. 把延迟后的结果反馈到 future stream - delayed.fill_future(feedback_stream) - - # 对于循环流，仍然需要手动控制，因为autostop无法处理循环 - env.submit() - - from time import sleep - - sleep(6) # 给足够时间让所有数据处理完成 - - print("Hello Future World 示例结束") - - -if __name__ == "__main__": - CustomLogger.disable_global_console_debug() - main() diff --git a/packages/sage-kernel/examples/advanced/hello_realistic_service_example.py b/packages/sage-kernel/examples/advanced/hello_realistic_service_example.py deleted file mode 100644 index 04cfd170c6..0000000000 --- a/packages/sage-kernel/examples/advanced/hello_realistic_service_example.py +++ /dev/null @@ -1,506 +0,0 @@ -""" -真实SAGE工作流测试 - -使用SAGE的完整流水线机制，展示服务在真实算子中的使用 -""" - -import time - -from sage.common.core.functions.base_function import BaseFunction -from sage.kernel.api.local_environment import LocalEnvironment - - -# 服务定义（重用之前的服务） -class FeatureStoreService: - """特征存储服务""" - - def __init__(self): - self.features = { - "user_features": { - "user_001": {"age": 25, "city": "Beijing", "vip_level": 2}, - "user_002": {"age": 30, "city": "Shanghai", "vip_level": 3}, - "user_003": {"age": 28, "city": "Guangzhou", "vip_level": 1}, - }, - "item_features": { - "item_101": {"category": "electronics", "price": 1000, "rating": 4.5}, - "item_102": {"category": "books", "price": 50, "rating": 4.8}, - "item_103": {"category": "clothing", "price": 200, "rating": 4.2}, - }, - } - self.is_running = False - self.ctx = None - - def start_running(self): - self.is_running = True - print("Feature store service started") - - def terminate(self): - self.is_running = False - print("Feature store service terminated") - - def get_user_features(self, user_id: str): - features = self.features["user_features"].get(user_id, {}) - print(f"Retrieved user features for {user_id}: {features}") - return features - - def get_item_features(self, item_id: str): - features = self.features["item_features"].get(item_id, {}) - print(f"Retrieved item features for {item_id}: {features}") - return features - - def batch_get_features(self, entity_type: str, entity_ids: list): - feature_table = self.features.get(f"{entity_type}_features", {}) - results = {} - for entity_id in entity_ids: - results[entity_id] = feature_table.get(entity_id, {}) - print(f"Batch retrieved {len(results)} {entity_type} features") - return results - - -class ModelService: - """模型服务""" - - def __init__(self, model_name: str = "recommendation_model_v1"): - self.model_name = model_name - self.is_running = False - self.ctx = None - self.prediction_count = 0 - - def start_running(self): - self.is_running = True - print(f"Model service started: {self.model_name}") - - def terminate(self): - self.is_running = False - print(f"Model service terminated: {self.model_name}") - - def predict(self, features: dict): - if not self.is_running: - return {"error": "Model service not running"} - - self.prediction_count += 1 - score = 0.6 - result = { - "score": round(score, 3), - "prediction_id": f"pred_{self.prediction_count}", - "model": self.model_name, - "features_used": list(features.keys()), - } - - print(f"Model prediction {self.prediction_count}: score={result['score']}") - return result - - def batch_predict(self, features_list: list): - if not self.is_running: - return {"error": "Model service not running"} - - results = [] - for features in features_list: - prediction = self.predict(features) - results.append(prediction) - - print(f"Batch prediction completed: {len(results)} predictions") - return results - - -class CacheService: - def __init__(self, max_size: int = 1000): - self.max_size = max_size - self.cache = {} - self.is_running = False - self.ctx = None - - def start_running(self): - self.is_running = True - print(f"Cache service started with max_size={self.max_size}") - - def terminate(self): - self.is_running = False - print("Cache service terminated") - - def get(self, key: str): - result = self.cache.get(key, None) - return result - - def set(self, key: str, value): - if len(self.cache) >= self.max_size: - oldest_key = next(iter(self.cache)) - del self.cache[oldest_key] - self.cache[key] = value - return True - - def size(self): - return len(self.cache) - - -class LogService: - def __init__(self, log_level: str = "INFO"): - self.log_level = log_level - self.logs = [] - self.is_running = False - self.ctx = None - - def start_running(self): - self.is_running = True - print(f"Log service started with level {self.log_level}") - - def terminate(self): - self.is_running = False - print("Log service terminated") - - def log(self, level: str, message: str, context: dict | None = None): - if not self.is_running: - return False - - log_entry = { - "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"), - "level": level, - "message": message, - "context": context or {}, - } - self.logs.append(log_entry) - print(f"[{log_entry['timestamp']}] {level}: {message}") - return True - - def info(self, message: str, context: dict | None = None): - return self.log("INFO", message, context) - - def error(self, message: str, context: dict | None = None): - return self.log("ERROR", message, context) - - def get_logs(self): - return self.logs.copy() - - -# 算子函数定义 -class RequestSourceFunction(BaseFunction): - """请求源算子 - 生成推荐请求""" - - def __init__(self, request_count: int = 5): - super().__init__() - self.request_count = request_count - self.current_count = 0 - - def execute(self, data=None): - """生成推荐请求（源算子不需要data参数）""" - if self.current_count >= self.request_count: - return None # 停止生成 - - self.current_count += 1 - - # 生成模拟请求 - users = ["user_001", "user_002", "user_003"] - items = [ - ["item_101", "item_102", "item_103"], - ["item_101", "item_102"], - ["item_102", "item_103"], - ] - - request = { - "request_id": f"req_{self.current_count:03d}", - "user_id": users[1], - "candidate_items": items[1], - "timestamp": time.time(), - } - - self.logger.info( - f"Generated request {self.current_count}: {request['request_id']} for {request['user_id']}" - ) - return request - - -class FeatureEnrichmentFunction(BaseFunction): - """特征丰富算子 - 获取用户和物品特征""" - - def execute(self, request): - """丰富请求的特征信息""" - if request is None: - return None - - self.logger.info(f"Enriching features for request: {request['request_id']}") - - try: - # 使用服务语法糖获取用户特征 - user_features = self.call_service( - "feature_store", - request["user_id"], - method="get_user_features", - ) - - # 批量获取物品特征 - item_features = self.call_service( - "feature_store", - "item", - request["candidate_items"], - method="batch_get_features", - ) - - # 丰富请求 - enriched_request = { - **request, - "user_features": user_features, - "item_features": item_features, - "enrichment_timestamp": time.time(), - } - - # 记录日志 - self.call_service( - "log", - f"Features enriched for request {request['request_id']}", - { - "user_id": request["user_id"], - "item_count": len(request["candidate_items"]), - }, - method="info", - ) - - self.logger.info(f"Feature enrichment completed for: {request['request_id']}") - return enriched_request - - except Exception as e: - self.logger.error(f"Feature enrichment failed for {request['request_id']}: {e}") - # 记录错误到日志服务 - self.call_service( - "log", - f"Feature enrichment failed: {e}", - {"request_id": request["request_id"]}, - method="error", - ) - return None - - -class RecommendationFunction(BaseFunction): - """推荐算子 - 生成推荐结果""" - - def execute(self, enriched_request): - """生成推荐结果""" - if enriched_request is None: - return None - - self.logger.info(f"Generating recommendations for: {enriched_request['request_id']}") - - try: - # 检查缓存 - cache_key = f"rec_{enriched_request['user_id']}_{hash(str(enriched_request['candidate_items']))}" - cached_result = self.call_service("cache", cache_key, method="get") - - if cached_result: - self.logger.info( - f"Using cached recommendations for: {enriched_request['request_id']}" - ) - self.call_service( - "log", - "Used cached recommendations", - { - "request_id": enriched_request["request_id"], - "cache_key": cache_key, - }, - method="info", - ) - return { - **enriched_request, - "recommendations": cached_result, - "from_cache": True, - } - - # 创建特征向量 - feature_vectors = self._create_feature_vectors( - enriched_request["user_features"], enriched_request["item_features"] - ) - - # 使用模型服务进行预测 - predictions = self.call_service("model", feature_vectors, method="batch_predict") - - # 生成推荐结果 - recommendations = [] - for i, (item_id, prediction) in enumerate( - zip(enriched_request["candidate_items"], predictions) - ): - rec = { - "item_id": item_id, - "score": prediction["score"], - "rank": i + 1, - "prediction_id": prediction["prediction_id"], - } - recommendations.append(rec) - - # 按分数排序 - recommendations.sort(key=lambda x: x["score"], reverse=True) - for i, rec in enumerate(recommendations): - rec["rank"] = i + 1 - - # 缓存结果 - self.call_service("cache", cache_key, recommendations, method="set") - - # 记录推荐完成 - self.call_service( - "log", - "Recommendations generated successfully", - { - "request_id": enriched_request["request_id"], - "recommendation_count": len(recommendations), - "top_score": recommendations[0]["score"] if recommendations else 0, - }, - method="info", - ) - - result = { - **enriched_request, - "recommendations": recommendations, - "from_cache": False, - "recommendation_timestamp": time.time(), - } - - self.logger.info( - f"Recommendations generated for: {enriched_request['request_id']}, count: {len(recommendations)}" - ) - return result - - except Exception as e: - self.logger.error( - f"Recommendation generation failed for {enriched_request['request_id']}: {e}" - ) - self.call_service( - "log", - f"Recommendation generation failed: {e}", - {"request_id": enriched_request["request_id"]}, - method="error", - ) - return None - - def _create_feature_vectors(self, user_features, item_features): - """创建特征向量""" - feature_vectors = [] - for item_id, item_attrs in item_features.items(): - vector = { - **user_features, - **item_attrs, - "interaction_score": self._calculate_interaction(user_features, item_attrs), - } - feature_vectors.append(vector) - return feature_vectors - - def _calculate_interaction(self, user_features, item_features): - """计算交互特征""" - score = 0.0 - if user_features.get("vip_level", 0) >= 2 and item_features.get("price", 0) > 500: - score += 0.2 - if user_features.get("age", 0) < 30 and item_features.get("category") == "electronics": - score += 0.1 - return round(score, 3) - - -class ResultSinkFunction(BaseFunction): - """结果输出算子 - 输出最终推荐结果""" - - def __init__(self): - super().__init__() - self.processed_count = 0 - - def execute(self, recommendation_result): - """输出推荐结果""" - if recommendation_result is None: - return None - - self.processed_count += 1 - - self.logger.info(f"Processing final result for: {recommendation_result['request_id']}") - - # 格式化输出 - output = { - "request_id": recommendation_result["request_id"], - "user_id": recommendation_result["user_id"], - "recommendations": recommendation_result["recommendations"][:3], # 只取前3个 - "from_cache": recommendation_result.get("from_cache", False), - "processed_at": time.strftime("%Y-%m-%d %H:%M:%S"), - "processing_order": self.processed_count, - } - - # 记录到日志服务 - self.call_service( - "log", - "Final recommendation result", - { - "request_id": output["request_id"], - "user_id": output["user_id"], - "recommendation_count": len(output["recommendations"]), - "from_cache": output["from_cache"], - }, - method="info", - ) - - # 打印结果 - print(f"\n=== 推荐结果 #{self.processed_count} ===") - print(f"请求ID: {output['request_id']}") - print(f"用户ID: {output['user_id']}") - print(f"缓存命中: {output['from_cache']}") - print("推荐列表:") - for rec in output["recommendations"]: - print(f" {rec['rank']}. {rec['item_id']} (分数: {rec['score']})") - print(f"处理时间: {output['processed_at']}") - print("=" * 40) - - return output - - -def test_realistic_sage_workflow(): - """测试真实的SAGE工作流""" - print("=== 真实SAGE工作流测试 ===") - - try: - # 1. 创建环境 - print("\n1. 创建环境:") - env = LocalEnvironment("realistic_workflow_test") - - # 2. 注册服务 - print("\n2. 注册服务:") - env.register_service("feature_store", FeatureStoreService) - env.register_service("model", ModelService, model_name="workflow_model_v1") - env.register_service("cache", CacheService, max_size=500) - env.register_service("log", LogService, log_level="INFO") - - print("所有服务注册完成") - - # 3. 构建流处理管道 - print("\n3. 构建流处理管道:") - - # 使用流式API构建处理管道 - ( - env.from_source(RequestSourceFunction, request_count=30) - .map(FeatureEnrichmentFunction) - .map(RecommendationFunction) - .map(ResultSinkFunction) - ) - - print("流处理管道构建完成") - - # 4. 提交并运行流处理管道 - print("\n4. 提交流处理管道:") - env.submit() - - # 5. 等待处理完成 - print("\n5. 等待处理完成...") - time.sleep(15) # 给足够时间让流处理完成 - - # 6. 检查服务状态 - print("\n6. 检查服务状态:") - print("流水线执行完成！") - - print("\n=== 真实工作流测试完成 ===") - - except Exception as e: - print(f"测试失败: {e}") - import traceback - - traceback.print_exc() - finally: - # 清理环境 - try: - env.stop() - print("环境已清理") - except Exception: - pass - - -if __name__ == "__main__": - test_realistic_sage_workflow() diff --git a/packages/sage-kernel/examples/advanced/parallelism_local_validation.py b/packages/sage-kernel/examples/advanced/parallelism_local_validation.py deleted file mode 100644 index 44bc81b023..0000000000 --- a/packages/sage-kernel/examples/advanced/parallelism_local_validation.py +++ /dev/null @@ -1,283 +0,0 @@ -#!/usr/bin/env python3 -""" -Local Environment Parallelism Validation Example - -This example demonstrates and validates parallelism hints functionality -using LocalEnvironment. It creates multiple streams with different -parallelism settings and verifies that the ExecutionGraph creates -the correct number of parallel nodes. - -@test:timeout=60 -""" - -import threading -import time - -from sage.common.core.functions.base_function import BaseFunction -from sage.common.core.functions.batch_function import BatchFunction -from sage.common.core.functions.comap_function import BaseCoMapFunction -from sage.kernel.api.local_environment import LocalEnvironment - - -class NumberListSource(BatchFunction): - """A simple batch source that produces a list of numbers""" - - def __init__(self, numbers): - super().__init__() - self.numbers = numbers - self.index = 0 - - def execute(self): - if self.index >= len(self.numbers): - return None - value = self.numbers[self.index] - self.index += 1 - return value - - -class ParallelProcessor(BaseFunction): - """A processor that shows which thread/instance is handling the data""" - - def __init__(self, processor_name="Processor"): - super().__init__() - self.processor_name = processor_name - self.instance_id = id(self) - self.thread_id = threading.get_ident() - print( - f"🔧 {self.processor_name} instance {self.instance_id} created in thread {self.thread_id}" - ) - - def execute(self, data): - current_thread = threading.get_ident() - instance_id = id(self) - result = f"{self.processor_name}[{instance_id}]: {data}" - print(f"⚙️ {result} (thread: {current_thread})") - time.sleep(0.05) # Simulate processing time - return result - - -class ParallelFilter(BaseFunction): - """A filter that shows parallel execution""" - - def __init__(self): - super().__init__() - self.instance_id = id(self) - self.thread_id = threading.get_ident() - print(f"🔧 ParallelFilter instance {self.instance_id} created in thread {self.thread_id}") - - def execute(self, data): - current_thread = threading.get_ident() - instance_id = id(self) - # Only pass even numbers - is_even = isinstance(data, int) and data % 2 == 0 - if is_even: - print(f"✅ Filter[{instance_id}]: {data} PASSED (thread: {current_thread})") - else: - print(f"❌ Filter[{instance_id}]: {data} BLOCKED (thread: {current_thread})") - return is_even - - -class MultiStreamCoMapProcessor(BaseCoMapFunction): - """CoMap processor for multi-stream validation""" - - def __init__(self): - super().__init__() - self.instance_id = id(self) - print( - f"🔧 CoMapProcessor instance {self.instance_id} created in thread {threading.get_ident()}" - ) - - def map0(self, data): - current_thread = threading.get_ident() - instance_id = id(self) - result = f"CoMap0[{instance_id}]: {data}" - print(f"🔀 {result} (thread: {current_thread})") - return result - - def map1(self, data): - current_thread = threading.get_ident() - instance_id = id(self) - result = f"CoMap1[{instance_id}]: {data * 10}" - print(f"🔀 {result} (thread: {current_thread})") - return result - - -class ValidationSink(BaseFunction): - """Sink that validates and prints final results""" - - def __init__(self): - super().__init__() - self.instance_id = id(self) - self.results = [] - print(f"🔧 ValidationSink instance {self.instance_id} created") - - def execute(self, data): - current_thread = threading.get_ident() - instance_id = id(self) - self.results.append(data) - print(f"🎯 SINK[{instance_id}]: {data} (thread: {current_thread})") - return data - - -def validate_single_stream_parallelism(): - """Validate parallelism for single stream operations""" - print("\n" + "=" * 70) - print("LOCAL ENVIRONMENT - SINGLE STREAM PARALLELISM VALIDATION") - print("=" * 70) - - env = LocalEnvironment(name="local_single_stream_test") - - # Test data - numbers = list(range(1, 21)) # 1 to 20 - source_stream = env.from_collection(NumberListSource, numbers) - - print(f"\n📊 Testing with {len(numbers)} input numbers: {numbers}") - - # Test different parallelism levels - print("\n--- Test 1: Direct parallelism parameters ---") - ( - source_stream.map(ParallelProcessor, "Mapper", parallelism=3) # 3 parallel mappers - .filter(ParallelFilter, parallelism=2) # 2 parallel filters - .sink(ValidationSink, parallelism=1) - ) # 1 sink - - print("\n--- Test 2: Using direct parallelism ---") - ( - source_stream.map(ParallelProcessor, "SetMapper", parallelism=4) # 4 parallel mappers - .filter(ParallelFilter, parallelism=3) # 3 parallel filters - .sink(ValidationSink, parallelism=1) - ) # 1 sink - - # Analyze pipeline - print("\n📋 PIPELINE ANALYSIS:") - print(f"Total transformations: {len(env.pipeline)}") - for i, transformation in enumerate(env.pipeline): - print( - f" {i + 1:2d}. {transformation.function_class.__name__:20s} | " - f"Parallelism: {transformation.parallelism:2d} | " - f"Basename: {transformation.basename}" - ) - - return env - - -def validate_multi_stream_parallelism(): - """Validate parallelism for multi-stream operations""" - print("\n" + "=" * 70) - print("LOCAL ENVIRONMENT - MULTI-STREAM PARALLELISM VALIDATION") - print("=" * 70) - - env = LocalEnvironment(name="local_multi_stream_test") - - # Create two streams with different data - stream1_data = [1, 3, 5, 7, 9] - stream2_data = [2, 4, 6, 8, 10] - - stream1 = env.from_collection(NumberListSource, stream1_data) - stream2 = env.from_collection(NumberListSource, stream2_data) - - print(f"\n📊 Stream1 data: {stream1_data}") - print(f"📊 Stream2 data: {stream2_data}") - - print("\n--- Test 1: CoMap with direct parallelism ---") - ( - stream1.connect(stream2) - .comap(MultiStreamCoMapProcessor, parallelism=2) # 2 parallel CoMap processors - .sink(ValidationSink, parallelism=1) - ) - - print("\n--- Test 2: CoMap with direct parallelism ---") - ( - stream1.connect(stream2) - .comap(MultiStreamCoMapProcessor, parallelism=3) # 3 parallel CoMap processors - .sink(ValidationSink, parallelism=2) - ) # 2 sinks - - # Analyze pipeline - print("\n📋 PIPELINE ANALYSIS:") - print(f"Total transformations: {len(env.pipeline)}") - for i, transformation in enumerate(env.pipeline): - print( - f" {i + 1:2d}. {transformation.function_class.__name__:20s} | " - f"Parallelism: {transformation.parallelism:2d} | " - f"Basename: {transformation.basename}" - ) - - return env - - -def validate_execution_graph_nodes(): - """Validate that ExecutionGraph creates correct number of parallel nodes""" - print("\n" + "=" * 70) - print("EXECUTION GRAPH NODE VALIDATION") - print("=" * 70) - - env = LocalEnvironment(name="node_validation_test") - - # Create a simple pipeline with known parallelism - numbers = [1, 2, 3, 4, 5] - ( - env.from_collection(NumberListSource, numbers) - .map(ParallelProcessor, "NodeTest", parallelism=2) # Should create 2 nodes - .filter(ParallelFilter, parallelism=3) # Should create 3 nodes - .sink(ValidationSink, parallelism=1) - ) # Should create 1 node - - print("\n📋 Expected execution nodes:") - print(" - ListSource: 1 node (source)") - print(" - NodeTest (map): 2 nodes (parallelism=2)") - print(" - ParallelFilter: 3 nodes (parallelism=3)") - print(" - ValidationSink: 1 node (parallelism=1)") - print(" - Total expected: 7 nodes") - - # Get execution graph - try: - # Try to access execution graph information - print("\n🔍 Pipeline transformations:") - for i, transformation in enumerate(env.pipeline): - print( - f" {i + 1}. {transformation.basename} (parallelism: {transformation.parallelism})" - ) - - # Note: ExecutionGraph node creation happens during execution - print("\n💡 Note: Actual node creation occurs during pipeline execution.") - print(" Each transformation with parallelism=N will create N parallel operator nodes.") - - except Exception as e: - print(f"⚠️ Could not access execution graph details: {e}") - - return env - - -def main(): - """Main function to run all validation tests""" - print("🚀 SAGE Local Environment Parallelism Validation") - print("This example validates parallelism hints in LocalEnvironment") - - # Run all validation tests - env1 = validate_single_stream_parallelism() - env2 = validate_multi_stream_parallelism() - env3 = validate_execution_graph_nodes() - - print("\n" + "=" * 70) - print("VALIDATION SUMMARY") - print("=" * 70) - print("✅ Single stream parallelism: Tested with various parallelism levels") - print("✅ Multi-stream parallelism: Tested CoMap operations") - print("✅ Execution graph validation: Verified transformation parallelism settings") - print("✅ LocalEnvironment parallelism hints: WORKING CORRECTLY") - - print("\n📊 Total environments created: 3") - print( - f"📊 Total transformations tested: {len(env1.pipeline) + len(env2.pipeline) + len(env3.pipeline)}" - ) - - print("\n💡 Key validations:") - print(" - Parallelism parameters correctly passed to transformations") - print(" - Direct parallelism specification works as expected") - print(" - Both single and multi-stream operations support parallelism") - print(" - ExecutionGraph will create corresponding parallel nodes during execution") - - -if __name__ == "__main__": - main() diff --git a/packages/sage-kernel/examples/advanced/parallelism_remote_validation.py b/packages/sage-kernel/examples/advanced/parallelism_remote_validation.py deleted file mode 100644 index 559209fe89..0000000000 --- a/packages/sage-kernel/examples/advanced/parallelism_remote_validation.py +++ /dev/null @@ -1,326 +0,0 @@ -#!/usr/bin/env python3 -""" -Remote Environment Parallelism Validation Example - -This example demonstrates and validates parallelism hints functionality -using RemoteEnvironment (Ray-based distributed execution). It shows how -parallelism settings work in a distributed environment and verifies that -the ExecutionGraph creates the correct number of parallel nodes across -Ray workers. - -@test:timeout=90 -""" - -import os -import threading -import time - -from sage.common.core.functions.base_function import BaseFunction -from sage.common.core.functions.batch_function import BatchFunction -from sage.common.core.functions.comap_function import BaseCoMapFunction -from sage.kernel.api.remote_environment import RemoteEnvironment - - -class NumberListSource(BatchFunction): - """A simple batch source that produces a list of numbers""" - - def __init__(self, numbers): - super().__init__() - self.numbers = numbers - self.index = 0 - - def execute(self): - if self.index >= len(self.numbers): - return None - value = self.numbers[self.index] - self.index += 1 - return value - - -class DistributedProcessor(BaseFunction): - """A processor that shows which worker/instance is handling the data""" - - def __init__(self, processor_name="DistProcessor"): - super().__init__() - self.processor_name = processor_name - self.instance_id = id(self) - self.process_id = os.getpid() - self.thread_id = threading.get_ident() - print( - f"🔧 {self.processor_name} instance {self.instance_id} created " - f"(PID: {self.process_id}, Thread: {self.thread_id})" - ) - - def execute(self, data): - current_thread = threading.get_ident() - current_process = os.getpid() - instance_id = id(self) - result = f"{self.processor_name}[{instance_id}@{current_process}]: {data}" - print(f"⚙️ {result} (Thread: {current_thread})") - time.sleep(0.1) # Simulate processing time - return result - - -class DistributedFilter(BaseFunction): - """A filter that shows distributed execution across Ray workers""" - - def __init__(self): - super().__init__() - self.instance_id = id(self) - self.process_id = os.getpid() - print(f"🔧 DistributedFilter instance {self.instance_id} created (PID: {self.process_id})") - - def execute(self, data): - current_thread = threading.get_ident() - current_process = os.getpid() - instance_id = id(self) - - # Filter logic: pass if data is divisible by 3 - passes = isinstance(data, int) and data % 3 == 0 - status = "PASSED" if passes else "BLOCKED" - print( - f"{'✅' if passes else '❌'} Filter[{instance_id}@{current_process}]: {data} {status} " - f"(Thread: {current_thread})" - ) - return passes - - -class DistributedCoMapProcessor(BaseCoMapFunction): - """CoMap processor for distributed multi-stream validation""" - - def __init__(self): - super().__init__() - self.instance_id = id(self) - self.process_id = os.getpid() - print( - f"🔧 DistributedCoMapProcessor instance {self.instance_id} created (PID: {self.process_id})" - ) - - def map0(self, data): - current_process = os.getpid() - current_thread = threading.get_ident() - instance_id = id(self) - result = f"DistCoMap0[{instance_id}@{current_process}]: {data}" - print(f"🔀 {result} (Thread: {current_thread})") - time.sleep(0.05) - return result - - def map1(self, data): - current_process = os.getpid() - current_thread = threading.get_ident() - instance_id = id(self) - result = f"DistCoMap1[{instance_id}@{current_process}]: {data * 100}" - print(f"🔀 {result} (Thread: {current_thread})") - time.sleep(0.05) - return result - - -class DistributedSink(BaseFunction): - """Sink that validates and prints final results in distributed environment""" - - def __init__(self): - super().__init__() - self.instance_id = id(self) - self.process_id = os.getpid() - self.results = [] - print(f"🔧 DistributedSink instance {self.instance_id} created (PID: {self.process_id})") - - def execute(self, data): - current_thread = threading.get_ident() - current_process = os.getpid() - instance_id = id(self) - self.results.append(data) - print(f"🎯 SINK[{instance_id}@{current_process}]: {data} (Thread: {current_thread})") - return data - - -def validate_remote_single_stream_parallelism(): - """Validate parallelism for single stream operations in remote environment""" - print("\n" + "=" * 70) - print("REMOTE ENVIRONMENT - SINGLE STREAM PARALLELISM VALIDATION") - print("=" * 70) - - # Initialize Ray cluster for distributed processing - # Note: Ray configuration is currently handled at the JobManager level, - # not directly through RemoteEnvironment constructor. This is a potential - # improvement area for SAGE architecture. - try: - env = RemoteEnvironment(name="remote_single_stream_test") - print("✅ RemoteEnvironment initialized successfully") - except Exception as e: - print(f"⚠️ RemoteEnvironment initialization warning: {e}") - env = RemoteEnvironment(name="remote_single_stream_test") - - # Test data - larger dataset for distributed processing - numbers = list(range(1, 31)) # 1 to 30 - source_stream = env.from_collection(NumberListSource, numbers) - - print(f"\n📊 Testing with {len(numbers)} input numbers") - print(f"📊 Numbers: {numbers[:10]}...{numbers[-5:]} (showing first 10 and last 5)") - - # Test distributed parallelism - print("\n--- Test 1: Distributed processing with direct parallelism parameters ---") - ( - source_stream.map( - DistributedProcessor, "DistMapper", parallelism=4 - ) # 4 parallel mappers across workers - .filter(DistributedFilter, parallelism=3) # 3 parallel filters across workers - .sink(DistributedSink, parallelism=2) - ) # 2 sinks across workers - - print("\n--- Test 2: Distributed processing with direct parallelism ---") - ( - source_stream.map( - DistributedProcessor, "SetDistMapper", parallelism=3 - ) # 3 parallel mappers - .filter(DistributedFilter, parallelism=2) # 2 parallel filters - .sink(DistributedSink, parallelism=1) - ) # 1 sink - - # Analyze pipeline - print("\n📋 DISTRIBUTED PIPELINE ANALYSIS:") - print(f"Total transformations: {len(env.pipeline)}") - print(f"Ray workers available: {env.platform} (distributed execution)") - for i, transformation in enumerate(env.pipeline): - print( - f" {i + 1:2d}. {transformation.function_class.__name__:25s} | " - f"Parallelism: {transformation.parallelism:2d} | " - f"Basename: {transformation.basename}" - ) - - return env - - -def validate_remote_multi_stream_parallelism(): - """Validate parallelism for multi-stream operations in remote environment""" - print("\n" + "=" * 70) - print("REMOTE ENVIRONMENT - MULTI-STREAM PARALLELISM VALIDATION") - print("=" * 70) - - try: - env = RemoteEnvironment(name="remote_multi_stream_test") - except Exception as e: - print(f"⚠️ RemoteEnvironment initialization warning: {e}") - env = RemoteEnvironment(name="remote_multi_stream_test") - - # Create streams with more data for distributed processing - stream1_data = list(range(1, 16, 2)) # [1, 3, 5, 7, 9, 11, 13, 15] - stream2_data = list(range(2, 17, 2)) # [2, 4, 6, 8, 10, 12, 14, 16] - - stream1 = env.from_collection(NumberListSource, stream1_data) - stream2 = env.from_collection(NumberListSource, stream2_data) - - print(f"\n📊 Stream1 data (odd numbers): {stream1_data}") - print(f"📊 Stream2 data (even numbers): {stream2_data}") - - print("\n--- Test 1: Distributed CoMap with direct parallelism ---") - ( - stream1.connect(stream2) - .comap(DistributedCoMapProcessor, parallelism=3) # 3 parallel CoMap processors - .sink(DistributedSink, parallelism=2) - ) # 2 sinks - - print("\n--- Test 2: Distributed CoMap with direct parallelism ---") - ( - stream1.connect(stream2) - .comap(DistributedCoMapProcessor, parallelism=4) # 4 parallel CoMap processors - .sink(DistributedSink, parallelism=1) - ) # 1 sink - - # Analyze pipeline - print("\n📋 DISTRIBUTED PIPELINE ANALYSIS:") - print(f"Total transformations: {len(env.pipeline)}") - print(f"Environment platform: {env.platform}") - for i, transformation in enumerate(env.pipeline): - print( - f" {i + 1:2d}. {transformation.function_class.__name__:25s} | " - f"Parallelism: {transformation.parallelism:2d} | " - f"Basename: {transformation.basename}" - ) - - return env - - -def validate_ray_distributed_execution(): - """Validate that Ray properly distributes parallel operations""" - print("\n" + "=" * 70) - print("RAY DISTRIBUTED EXECUTION VALIDATION") - print("=" * 70) - - try: - env = RemoteEnvironment(name="ray_distribution_test") - print("✅ RemoteEnvironment initialized") - except Exception as e: - print(f"⚠️ RemoteEnvironment initialization warning: {e}") - env = RemoteEnvironment(name="ray_distribution_test") - - # Create a pipeline designed to show distributed execution - large_dataset = list(range(1, 51)) # 1 to 50 - enough data for distribution - - ( - env.from_collection(NumberListSource, large_dataset) - .map(DistributedProcessor, "DistTest", parallelism=5) # 5 parallel processors - .filter(DistributedFilter, parallelism=3) # 3 parallel filters - .sink(DistributedSink, parallelism=2) - ) # 2 sinks - - print("\n📋 Remote Distribution Test Pipeline:") - print(f" - Dataset size: {len(large_dataset)} items") - print(" - Expected parallel processors: 5 (will distribute based on available workers)") - print(" - Expected parallel filters: 3 (will distribute based on available workers)") - print(" - Expected sinks: 2 (will distribute based on available workers)") - - print("\n🔍 Pipeline transformations:") - for i, transformation in enumerate(env.pipeline): - print(f" {i + 1}. {transformation.basename} (parallelism: {transformation.parallelism})") - - print("\n💡 Key aspects of remote distributed execution:") - print(" - Each parallel instance may run on different remote workers") - print(" - Process IDs will differ across workers") - print(" - Work is distributed based on available resources") - print(" - RemoteEnvironment handles load balancing and coordination") - - return env - - -def main(): - """Main function to run all remote validation tests""" - print("🚀 SAGE Remote Environment Parallelism Validation") - print("This example validates parallelism hints in RemoteEnvironment (Ray)") - - try: - # Run all validation tests - env1 = validate_remote_single_stream_parallelism() - env2 = validate_remote_multi_stream_parallelism() - env3 = validate_ray_distributed_execution() - - print("\n" + "=" * 70) - print("REMOTE VALIDATION SUMMARY") - print("=" * 70) - print("✅ Remote single stream parallelism: Tested with remote workers") - print("✅ Remote multi-stream parallelism: Tested distributed CoMap") - print("✅ Remote distributed execution: Verified parallel worker distribution") - print("✅ RemoteEnvironment direct parallelism: WORKING CORRECTLY") - - print("\n📊 Total remote environments created: 3") - print( - f"📊 Total distributed transformations: {len(env1.pipeline) + len(env2.pipeline) + len(env3.pipeline)}" - ) - - print("\n💡 Key remote validations:") - print(" - Parallelism settings work in distributed remote environment") - print(" - Direct parallelism specification distributes work across remote workers") - print(" - Multi-stream operations (CoMap) support distributed parallelism") - print(" - RemoteEnvironment automatically handles worker assignment and coordination") - - except Exception as e: - print(f"\n❌ Remote validation encountered an error: {e}") - print( - "💡 This might be due to RemoteEnvironment not being available or configured properly" - ) - print(" Please ensure the JobManager service is running and accessible") - print(" And that your system supports remote distributed execution") - - -if __name__ == "__main__": - main() diff --git a/packages/sage-kernel/examples/advanced/pipeline_as_service/README.md b/packages/sage-kernel/examples/advanced/pipeline_as_service/README.md deleted file mode 100644 index 1769c6d352..0000000000 --- a/packages/sage-kernel/examples/advanced/pipeline_as_service/README.md +++ /dev/null @@ -1,84 +0,0 @@ -# Pipeline-as-Service Demo - -These examples show how to register an entire SAGE pipeline as a service while still leveraging the -same service invocation helpers for its internal stages. Each scenario adds a different client -pattern so you can see how pipelines and services compose in more realistic setups. - -## What it demonstrates - -- **Pipeline as a service** – The decision pipeline is registered as the `order_pipeline` service - and can be invoked via `call_service`. -- **Asynchronous invocation** – Operators can use `self.call_service_async(...)` to overlap work and - wait on a `Future`. -- **Service-to-service orchestration** – The pipeline calls supporting services (feature store, risk - scoring) using the same helpers. -- **Cooperative shutdown** – Sending a `{"command": "shutdown"}` message tells the pipeline to stop - after finishing in-flight work. - -## Files - -- `hello_pipeline_as_service.py` – Creates the service-backed decision pipeline plus a driver - pipeline that submits orders and prints the returned decisions. -- `async_client_pipeline_as_service.py` – Uses `call_service_async` to submit a burst of requests - concurrently and wait for futures to resolve. -- `multi_client_pipeline_as_service.py` – Spawns two driver pipelines (new vs returning users) that - share the same pipeline service endpoint. -- `qa_pipeline_as_service.py` – Wraps the RAG QA pipeline as a service so an interactive terminal - client waits for each answer before accepting the next question; exit by typing `bye bye` (or - press `Ctrl+C` to interrupt). -- `pipeline_bridge.py` – Shared queue bridge used to pass requests between the driver pipelines and - the service-backed pipeline. - -## How to run - -From the repository root you can run any scenario. For example: - -```bash -python packages/sage-kernel/examples/advanced/pipeline_as_service/hello_pipeline_as_service.py -python packages/sage-kernel/examples/advanced/pipeline_as_service/async_client_pipeline_as_service.py -python packages/sage-kernel/examples/advanced/pipeline_as_service/multi_client_pipeline_as_service.py -python packages/sage-kernel/examples/advanced/pipeline_as_service/qa_pipeline_as_service.py -``` - -You should see log output from both the service-backed pipeline and the client pipelines, including -enrichment, scoring, service replies, and a shutdown acknowledgement once all work completes. - -### Offline/mock QA runs (default) - -The QA pipeline now uses **SageLLMGenerator** with mock backend by default, allowing it to run -completely offline without any external services: - -```bash -# Default: uses mock backend (no external services required) -python packages/sage-kernel/examples/advanced/pipeline_as_service/qa_pipeline_as_service.py - -# Explicitly use mock backend -SAGE_QA_GENERATOR=mock python packages/sage-kernel/examples/advanced/pipeline_as_service/qa_pipeline_as_service.py -``` - -The mock generator emits friendly placeholder replies so you can experience the request/response -flow without external dependencies. - -### Using a real LLM - -To use a real LLM backend, configure the following environment variables: - -```bash -# Use SageLLM with auto-detected backend (cuda/ascend) -SAGE_QA_GENERATOR=sagellm \ -SAGE_QA_BACKEND=auto \ -SAGE_QA_MODEL_PATH=Qwen/Qwen2.5-7B-Instruct \ -python packages/sage-kernel/examples/advanced/pipeline_as_service/qa_pipeline_as_service.py - -# Additional configuration options -SAGE_QA_MAX_TOKENS=1024 # Maximum tokens to generate -SAGE_QA_TEMPERATURE=0.7 # Sampling temperature -SAGE_QA_TOP_P=0.95 # Nucleus sampling parameter -``` - -Supported `SAGE_QA_BACKEND` values: - -- `mock` (default): No external services, returns placeholder responses -- `auto`: Auto-detect available hardware (cuda/ascend) -- `cuda`: Use CUDA GPU acceleration -- `ascend`: Use Huawei Ascend NPU diff --git a/packages/sage-kernel/examples/advanced/pipeline_as_service/async_client_pipeline_as_service.py b/packages/sage-kernel/examples/advanced/pipeline_as_service/async_client_pipeline_as_service.py deleted file mode 100644 index 91bc911ce9..0000000000 --- a/packages/sage-kernel/examples/advanced/pipeline_as_service/async_client_pipeline_as_service.py +++ /dev/null @@ -1,178 +0,0 @@ -"""Demonstrate asynchronous clients calling a pipeline that is registered as a service. - -This example reuses the decision pipeline service from -`hello_pipeline_as_service` but drives it with a client pipeline that submits -multiple requests concurrently using `call_service_async`. -""" - -from __future__ import annotations - -import random -import time -from typing import Any - -try: - from sage.common.core.functions.batch_function import BatchFunction - from sage.common.core.functions.map_function import MapFunction - from sage.common.core.functions.sink_function import SinkFunction - from sage.common.utils.logging.custom_logger import CustomLogger - from sage.kernel.api.local_environment import LocalEnvironment -except ModuleNotFoundError: # pragma: no cover - convenience for local runs - import sys - from pathlib import Path - - here = Path(__file__).resolve() - repo_root = None - for parent in here.parents: - if (parent / "packages").exists(): - repo_root = parent - break - if repo_root is None: - raise RuntimeError("Cannot locate SAGE repository root") - - for extra_path in [ - repo_root / "packages" / "sage" / "src", - repo_root / "packages" / "sage-common" / "src", - repo_root / "packages" / "sage-kernel" / "src", - repo_root / "packages" / "sage-middleware" / "src", - repo_root / "packages" / "sage-libs" / "src", - repo_root / "packages" / "sage-tools" / "src", - ]: - sys.path.insert(0, str(extra_path)) - - from sage.common.core.functions.batch_function import BatchFunction - from sage.common.core.functions.map_function import MapFunction - from sage.common.core.functions.sink_function import SinkFunction - from sage.common.utils.logging.custom_logger import CustomLogger - from sage.kernel.api.local_environment import LocalEnvironment - -from hello_pipeline_as_service import ( - SHUTDOWN_MESSAGE, - DecisionSink, - FeatureEnrichment, - FeatureStoreService, - OrderPipelineService, - RiskScoring, - RiskScoringService, - ServiceDrivenSource, -) -from pipeline_bridge import PipelineBridge - -ASYNC_ORDERS: list[dict[str, float | str | int]] = [ - {"order_id": "async-1001", "user_id": "user-101", "amount": 210.5, "latency": 0.6}, - {"order_id": "async-1002", "user_id": "user-102", "amount": 35.0, "latency": 0.2}, - {"order_id": "async-1003", "user_id": "user-103", "amount": 990.0, "latency": 0.9}, - {"order_id": "async-1004", "user_id": "user-104", "amount": 440.0, "latency": 0.4}, -] - - -class SlowFeatureStoreService(FeatureStoreService): - """Introduce variable latency to make concurrency observable.""" - - def process(self, order: dict[str, float | str | int]): - delay = float(order.get("latency", 0.0)) - if delay > 0: - time.sleep(delay) - return super().process(order) # type: ignore[arg-type] - - -class AsyncOrderSource(BatchFunction): - """Emit the async orders and a final shutdown control message.""" - - def __init__(self, orders: list[dict[str, float | str | int]]): - super().__init__() - self._orders = list(orders) + [dict(SHUTDOWN_MESSAGE)] - self._index = 0 - - def execute(self): - if self._index >= len(self._orders): - return None - - order = self._orders[self._index] - self._index += 1 - return order - - -class AsyncSubmit(MapFunction): - """Submit orders asynchronously to the pipeline service.""" - - def execute(self, order: dict[str, float | str | int]): - if order is None: - return None - - if order.get("command") == "shutdown": - response = self.call_service("order_pipeline", order, timeout=15.0) - return {"type": "control", "order": order, "response": response} - - future = self.call_service_async("order_pipeline", order, timeout=20.0) - return {"type": "future", "order": order, "future": future} - - -class AwaitDecision(MapFunction): - """Await the decision from futures produced by `AsyncSubmit`.""" - - def execute(self, record: dict[str, Any]): - if record is None: - return None - - if record.get("type") == "control": - return record - - future = record.get("future") - if future is None: - return None - - decision = future.result(timeout=20.0) - return {"type": "decision", "order": record["order"], "decision": decision} - - -class AsyncResultSink(SinkFunction): - """Print the order decisions along with latency info.""" - - def execute(self, payload: dict[str, Any]): - if payload is None: - return None - - if payload.get("type") == "decision": - order = payload["order"] - decision = payload["decision"] - print( - f"[Async Driver] Order {order['order_id']} (latency={order.get('latency', 0)}s)\n" - f" => decision={decision['recommendation']}, risk={decision['risk_score']}", - flush=True, - ) - else: - print("[Async Driver] Pipeline shutdown acknowledged", flush=True) - - return payload - - -def main(): - env = LocalEnvironment("pipeline_as_service_async") - bridge = PipelineBridge() - - env.register_service("feature_store", SlowFeatureStoreService) - env.register_service("risk_scoring", RiskScoringService) - env.register_service("order_pipeline", OrderPipelineService, bridge) - - ( - env.from_source(ServiceDrivenSource, bridge) - .map(FeatureEnrichment) - .map(RiskScoring) - .sink(DecisionSink) - ) - - ( - env.from_batch(AsyncOrderSource, ASYNC_ORDERS) - .map(AsyncSubmit) - .map(AwaitDecision) - .sink(AsyncResultSink) - ) - - env.submit(autostop=True) - - -if __name__ == "__main__": - CustomLogger.disable_global_console_debug() - random.seed(42) - main() diff --git a/packages/sage-kernel/examples/advanced/pipeline_as_service/hello_pipeline_as_service.py b/packages/sage-kernel/examples/advanced/pipeline_as_service/hello_pipeline_as_service.py deleted file mode 100644 index 4b9a1e147c..0000000000 --- a/packages/sage-kernel/examples/advanced/pipeline_as_service/hello_pipeline_as_service.py +++ /dev/null @@ -1,315 +0,0 @@ -"""Pipeline-as-Service demo - -This example shows how a SAGE pipeline can be registered as a service and then -invoked through the unified ``call_service`` helpers. The pipeline listens for -requests coming from the service layer, enriches them via other services, and -replies with a structured decision payload. -""" - -from __future__ import annotations - -import math -import queue -import random -import time -from typing import Any - -# Try regular imports first; fall back to repo-relative paths when running -# directly from the source tree without installing the package. -try: - from sage.common.core.functions.batch_function import BatchFunction - from sage.common.core.functions.map_function import MapFunction - from sage.common.core.functions.sink_function import SinkFunction - from sage.common.core.functions.source_function import SourceFunction - from sage.common.utils.logging.custom_logger import CustomLogger - from sage.kernel.api.local_environment import LocalEnvironment - from sage.kernel.api.service.base_service import BaseService -except ModuleNotFoundError: # pragma: no cover - convenience for local runs - import sys - from pathlib import Path - - here = Path(__file__).resolve() - repo_root = None - for parent in here.parents: - if (parent / "packages").exists(): - repo_root = parent - break - if repo_root is None: - raise RuntimeError("Cannot locate SAGE repository root") - - for extra_path in [ - repo_root / "packages" / "sage" / "src", - repo_root / "packages" / "sage-common" / "src", - repo_root / "packages" / "sage-kernel" / "src", - repo_root / "packages" / "sage-middleware" / "src", - repo_root / "packages" / "sage-libs" / "src", - repo_root / "packages" / "sage-tools" / "src", - ]: - sys.path.insert(0, str(extra_path)) - - from sage.common.core.functions.batch_function import BatchFunction - from sage.common.core.functions.map_function import MapFunction - from sage.common.core.functions.sink_function import SinkFunction - from sage.common.core.functions.source_function import SourceFunction - from sage.common.utils.logging.custom_logger import CustomLogger - from sage.kernel.api.local_environment import LocalEnvironment - from sage.kernel.api.service.base_service import BaseService - -from pipeline_bridge import PipelineBridge, PipelinePayload - -from sage.kernel.runtime.communication.packet import StopSignal - -ORDERS: list[dict[str, str | float]] = [ - {"order_id": "o-1001", "user_id": "user-001", "amount": 129.9}, - {"order_id": "o-1002", "user_id": "user-002", "amount": 59.0}, - {"order_id": "o-1003", "user_id": "user-003", "amount": 450.5}, -] - -SHUTDOWN_MESSAGE = {"command": "shutdown"} - - -class ServiceDrivenSource(SourceFunction): - """Source operator that pulls requests from the service bridge.""" - - def __init__(self, bridge: PipelineBridge, poll_interval: float = 0.1): - super().__init__() - self._bridge = bridge - self._poll_interval = poll_interval - - def execute(self, data=None): - request = self._bridge.next(timeout=self._poll_interval) - if request is None: - return None - - if isinstance(request, StopSignal): - return request - - return PipelinePayload(order=request.payload, response_queue=request.response_queue) - - -class OrderSource(BatchFunction): - """Emit a fixed list of orders as the batch source.""" - - def __init__(self, orders: list[dict[str, str]], *, include_shutdown: bool = True): - super().__init__() - self._orders = list(orders) - if include_shutdown: - self._orders.append(dict(SHUTDOWN_MESSAGE)) - self._index = 0 - - def execute(self, data=None): - if self._index >= len(self._orders): - return None - - order = self._orders[self._index] - self._index += 1 - return order - - -class FeatureStoreService(BaseService): - """Simple feature store that returns per-user aggregates.""" - - def __init__(self): - super().__init__() - self._user_features = { - "user-001": {"successful_orders": 5, "chargeback_ratio": 0.01}, - "user-002": {"successful_orders": 2, "chargeback_ratio": 0.08}, - "user-003": {"successful_orders": 12, "chargeback_ratio": 0.0}, - } - - def process(self, order: dict[str, str]): - """Default entry point leveraged by pipeline-as-service calls.""" - - features = self._user_features.get( - order["user_id"], {"successful_orders": 0, "chargeback_ratio": 0.5} - ) - features = dict(features) # shallow copy for isolation - features["order_amount"] = order["amount"] - features["feature_timestamp"] = time.time() - return features - - -class FeatureEnrichment(MapFunction): - """Use the feature store service without specifying the method name.""" - - def execute(self, payload: PipelinePayload): - if payload is None: - return None - - order = payload.order - features = self.call_service("feature_store", order) - enriched = {**order, "features": features} - feature_keys = ", ".join(sorted(features.keys())) - self.logger.info( - f"Enriched order {order.get('order_id', '<control>')} with feature keys [{feature_keys}]" - ) - payload.features = features - payload.enriched = enriched - return payload - - -class RiskScoringService(BaseService): - """Trivial risk-scoring service with a `process` entry point.""" - - def process(self, enriched_order: dict[str, str]): - features = enriched_order.get("features", {}) # type: ignore[assignment] - amount = float(enriched_order.get("amount", 0.0)) - chargeback_ratio = float(features.get("chargeback_ratio", 0.0)) # type: ignore[attr-defined] - history = float(features.get("successful_orders", 0.0)) # type: ignore[attr-defined] - - base_score = 0.4 * chargeback_ratio + 0.0005 * amount - history_modifier = 0.2 if history > 5 else 0.0 - jitter = random.uniform(-0.05, 0.05) - - risk = min(max(base_score - history_modifier + jitter, 0.0), 1.0) - recommendation = "manual_review" if risk > 0.4 else "auto_approve" - - return { - "risk_score": round(risk, 3), - "recommendation": recommendation, - "generated_at": time.time(), - } - - -class RiskScoring(MapFunction): - """Invoke the risk scoring service asynchronously.""" - - def execute(self, payload: PipelinePayload): - if payload is None: - return None - - enriched_order = payload.enriched or payload.order - future = self.call_service_async("risk_scoring", enriched_order) - scoring = future.result(timeout=10.0) - payload.scoring = scoring - payload.enriched = {**enriched_order, "scoring": scoring} - - self.logger.info( - f"Scored order {enriched_order.get('order_id', '<control>')} with risk {scoring['risk_score']:.3f}" - ) - return payload - - -class DecisionSink(SinkFunction): - """Final sink that prints a human-readable decision.""" - - def execute(self, payload: PipelinePayload): - if payload is None: - return None - - scoring = payload.scoring or {} - enriched_order = payload.enriched or payload.order - order_id = enriched_order.get("order_id", "<control>") - decision = scoring.get("recommendation", "unknown") - risk = scoring.get("risk_score", math.nan) - - result = { - "order_id": order_id, - "recommendation": decision, - "risk_score": risk, - "generated_at": scoring.get("generated_at", time.time()), - "features": payload.features or {}, - } - - try: - payload.response_queue.put(result, timeout=5.0) - except queue.Full: # pragma: no cover - defensive guard - self.logger.error( - "Failed to publish decision for order %s because response queue is full", - order_id, - ) - - print( - f"[Pipeline] Order {order_id} => decision={decision}, risk={risk}", - flush=True, - ) - return result - - -class OrderPipelineService(BaseService): - """Expose the decision pipeline itself as a service.""" - - def __init__(self, bridge: PipelineBridge, request_timeout: float = 15.0): - super().__init__() - self._bridge = bridge - self._request_timeout = request_timeout - - def process(self, message: dict[str, Any]): - if message is None: - raise ValueError("Pipeline service received an empty message") - - if message.get("command") == "shutdown": - self._bridge.close() - return {"status": "shutdown_requested"} - - try: - response_queue = self._bridge.submit(message) - except RuntimeError as exc: - raise RuntimeError("Pipeline service is shutting down") from exc - - try: - return response_queue.get(timeout=self._request_timeout) - except queue.Empty as exc: - raise TimeoutError("Pipeline service timed out waiting for a reply") from exc - - -class InvokePipeline(MapFunction): - """Driver operator that calls the pipeline service for each order.""" - - def execute(self, payload: dict[str, Any]): - if payload is None: - return None - - response = self.call_service("order_pipeline", payload, timeout=20.0) - - if payload.get("command") == "shutdown": - self.logger.info("Pipeline shutdown acknowledged: %s", response) - return {"type": "control", "ack": response} - - return {"type": "decision", "order": payload, "decision": response} - - -class DriverSink(SinkFunction): - """Display results returned from the pipeline service caller.""" - - def execute(self, payload: dict[str, Any]): - if payload is None: - return None - - if payload.get("type") == "decision": - order = payload["order"] - decision = payload["decision"] - print( - f"[Driver] Order {order['order_id']} => decision={decision['recommendation']}, risk={decision['risk_score']}", - flush=True, - ) - else: - print("[Driver] Pipeline shutdown request completed", flush=True) - - return payload - - -def main(): - env = LocalEnvironment("pipeline_as_service_demo") - bridge = PipelineBridge() - - # Register services exposing a default `process` entry point. - env.register_service("feature_store", FeatureStoreService) - env.register_service("risk_scoring", RiskScoringService) - env.register_service("order_pipeline", OrderPipelineService, bridge) - - ( - env.from_source(ServiceDrivenSource, bridge) - .map(FeatureEnrichment) - .map(RiskScoring) - .sink(DecisionSink) - ) - - (env.from_batch(OrderSource, ORDERS).map(InvokePipeline).sink(DriverSink)) - - env.submit(autostop=True) - - -if __name__ == "__main__": - CustomLogger.disable_global_console_debug() - main() diff --git a/packages/sage-kernel/examples/advanced/pipeline_as_service/multi_client_pipeline_as_service.py b/packages/sage-kernel/examples/advanced/pipeline_as_service/multi_client_pipeline_as_service.py deleted file mode 100644 index 51f0f15ebf..0000000000 --- a/packages/sage-kernel/examples/advanced/pipeline_as_service/multi_client_pipeline_as_service.py +++ /dev/null @@ -1,156 +0,0 @@ -"""Multiple driver pipelines sharing a single pipeline-as-a-service endpoint. - -This example demonstrates how different segments (new users vs returning users) -can call the same pipeline service concurrently while sharing supporting -services. -""" - -from __future__ import annotations - -from typing import Any - -try: - from sage.common.core.functions.sink_function import SinkFunction - from sage.common.utils.logging.custom_logger import CustomLogger - from sage.kernel.api.local_environment import LocalEnvironment -except ModuleNotFoundError: # pragma: no cover - convenience for local runs - import sys - from pathlib import Path - - here = Path(__file__).resolve() - repo_root = None - for parent in here.parents: - if (parent / "packages").exists(): - repo_root = parent - break - if repo_root is None: - raise RuntimeError("Cannot locate SAGE repository root") - - for extra_path in [ - repo_root / "packages" / "sage" / "src", - repo_root / "packages" / "sage-common" / "src", - repo_root / "packages" / "sage-kernel" / "src", - repo_root / "packages" / "sage-middleware" / "src", - repo_root / "packages" / "sage-libs" / "src", - repo_root / "packages" / "sage-tools" / "src", - ]: - sys.path.insert(0, str(extra_path)) - - from sage.common.core.functions.sink_function import SinkFunction - from sage.common.utils.logging.custom_logger import CustomLogger - from sage.kernel.api.local_environment import LocalEnvironment - -from hello_pipeline_as_service import ( - DecisionSink, - FeatureEnrichment, - FeatureStoreService, - InvokePipeline, - OrderPipelineService, - OrderSource, - RiskScoring, - RiskScoringService, - ServiceDrivenSource, -) -from pipeline_bridge import PipelineBridge - -NEW_USER_ORDERS: list[dict[str, float | str]] = [ - {"order_id": "multi-new-001", "user_id": "user-new-01", "amount": 29.99}, - {"order_id": "multi-new-002", "user_id": "user-new-02", "amount": 59.0}, -] - -RETURNING_USER_ORDERS: list[dict[str, float | str]] = [ - {"order_id": "multi-ret-001", "user_id": "user-ret-01", "amount": 330.0}, - {"order_id": "multi-ret-002", "user_id": "user-ret-02", "amount": 87.5}, -] - - -class SegmentedOrderSource(OrderSource): - """Extend the base OrderSource to tag orders with a segment name.""" - - def __init__( - self, - orders: list[dict[str, float | str]], - segment: str, - *, - include_shutdown: bool = False, - ): - super().__init__(orders, include_shutdown=include_shutdown) # type: ignore[arg-type] - self._segment = segment - - def execute(self): - record = super().execute() - if record is None: - return None - - if record.get("command") == "shutdown": - return record - - tagged = dict(record) - tagged["segment"] = self._segment - return tagged - - -class SegmentedDriverSink(SinkFunction): - """Print results grouped by segment.""" - - def execute(self, payload: dict[str, Any]): - if payload is None: - return None - - if payload.get("type") == "decision": - order = payload["order"] - decision = payload["decision"] - print( - f"[Multi Driver] segment={order.get('segment', 'unknown')} order={order['order_id']}" - f" => recommendation={decision['recommendation']} risk={decision['risk_score']}", - flush=True, - ) - else: - print("[Multi Driver] Pipeline shutdown acknowledged", flush=True) - - return payload - - -def main(): - env = LocalEnvironment("pipeline_as_service_multi_client") - bridge = PipelineBridge() - - env.register_service("feature_store", FeatureStoreService) - env.register_service("risk_scoring", RiskScoringService) - env.register_service("order_pipeline", OrderPipelineService, bridge) - - ( - env.from_source(ServiceDrivenSource, bridge) - .map(FeatureEnrichment) - .map(RiskScoring) - .sink(DecisionSink) - ) - - ( - env.from_batch( - SegmentedOrderSource, - NEW_USER_ORDERS, - segment="new_users", - include_shutdown=False, - ) - .map(InvokePipeline) - .sink(SegmentedDriverSink) - ) - - ( - env.from_batch( - SegmentedOrderSource, - RETURNING_USER_ORDERS, - segment="returning_users", - include_shutdown=True, - ) - .map(InvokePipeline) - .sink(SegmentedDriverSink) - ) - - env.submit(autostop=True) - - -if __name__ == "__main__": - CustomLogger.disable_global_console_debug() - main() diff --git a/packages/sage-kernel/examples/advanced/pipeline_as_service/pipeline_bridge.py b/packages/sage-kernel/examples/advanced/pipeline_as_service/pipeline_bridge.py deleted file mode 100644 index 9937ff6a16..0000000000 --- a/packages/sage-kernel/examples/advanced/pipeline_as_service/pipeline_bridge.py +++ /dev/null @@ -1,66 +0,0 @@ -"""Shared utilities for pipeline-as-service examples. - -This module provides lightweight queue-based primitives that allow the -registered pipeline service to exchange messages with driver pipelines. -""" - -from __future__ import annotations - -import queue -from dataclasses import dataclass -from typing import Any - -from sage.kernel.runtime.communication.packet import StopSignal - - -@dataclass -class PipelineRequest: - """A request enqueued by a driver pipeline.""" - - payload: dict[str, Any] - response_queue: queue.Queue[dict[str, Any]] - - -@dataclass -class PipelinePayload: - """Message wrapper used inside the service-backed pipeline.""" - - order: dict[str, Any] - response_queue: queue.Queue[dict[str, Any]] - features: dict[str, Any] | None = None - enriched: dict[str, Any] | None = None - scoring: dict[str, Any] | None = None - - -class PipelineBridge: - """Bidirectional bridge between driver pipelines and the service pipeline.""" - - def __init__(self) -> None: - self._requests: queue.Queue[PipelineRequest | StopSignal] = queue.Queue() - self._closed: bool = False - - def submit(self, payload: dict[str, Any]) -> queue.Queue[dict[str, Any]]: - if self._closed: - raise RuntimeError("Pipeline bridge is closed") - - response_queue: queue.Queue[dict[str, Any]] = queue.Queue(maxsize=1) - request = PipelineRequest(payload=payload, response_queue=response_queue) - self._requests.put(request) - return response_queue - - def next(self, timeout: float = 0.1): - if self._closed and self._requests.empty(): - return StopSignal("pipeline-service-shutdown") - - try: - return self._requests.get(timeout=timeout) - except queue.Empty: - return None - - def close(self) -> None: - if not self._closed: - self._closed = True - self._requests.put(StopSignal("pipeline-service-shutdown")) - - -__all__ = ["PipelineRequest", "PipelinePayload", "PipelineBridge"] diff --git a/packages/sage-kernel/examples/advanced/pipeline_as_service/qa_pipeline_as_service.py b/packages/sage-kernel/examples/advanced/pipeline_as_service/qa_pipeline_as_service.py deleted file mode 100644 index 71cf701fa7..0000000000 --- a/packages/sage-kernel/examples/advanced/pipeline_as_service/qa_pipeline_as_service.py +++ /dev/null @@ -1,708 +0,0 @@ -"""Interactive QA via pipeline-as-service. - -This example adapts the RAG QA pipeline so that it can be exposed as a -service. A dedicated pipeline waits for requests from the service layer, -invokes the promptor and generator, and then streams the answer back to -interactive driver pipelines. The driver pipeline reads terminal input, -issues requests through ``call_service``, and only accepts a new question -after the previous answer has been returned. - -**Key updates (2025-01)**: -- Replaced vLLM/OpenAI service calls with SageLLMGenerator -- Added mock mode support (default, no external services required) -- Example can run offline without any LLM endpoint -""" - -from __future__ import annotations - -import os -import queue -import sys -import threading -import time -from pathlib import Path -from typing import Any - -from dotenv import load_dotenv - -try: # pragma: no cover - allow running directly from source tree - from sage.common.core.functions.map_function import MapFunction - from sage.common.core.functions.sink_function import SinkFunction - from sage.common.core.functions.source_function import SourceFunction - from sage.common.utils.logging.custom_logger import CustomLogger - from sage.kernel.api.local_environment import LocalEnvironment - from sage.kernel.api.service.base_service import BaseService - from sage.kernel.runtime.communication.packet import StopSignal - from sage.middleware.operators.llm import SageLLMGenerator - from sage.middleware.operators.rag import QAPromptor -except ModuleNotFoundError: # pragma: no cover - local convenience path - here = Path(__file__).resolve() - repo_root: Path | None = None - for parent in here.parents: - if (parent / "packages").exists(): - repo_root = parent - break - if repo_root is None: - raise RuntimeError("Cannot locate SAGE repository root") - - for extra_path in [ - repo_root / "packages" / "sage" / "src", - repo_root / "packages" / "sage-common" / "src", - repo_root / "packages" / "sage-kernel" / "src", - repo_root / "packages" / "sage-middleware" / "src", - repo_root / "packages" / "sage-libs" / "src", - repo_root / "packages" / "sage-tools" / "src", - ]: - sys.path.insert(0, str(extra_path)) - - from sage.common.core.functions.map_function import MapFunction - from sage.common.core.functions.sink_function import SinkFunction - from sage.common.core.functions.source_function import SourceFunction - from sage.common.utils.logging.custom_logger import CustomLogger - from sage.kernel.api.local_environment import LocalEnvironment - from sage.kernel.api.service.base_service import BaseService - from sage.kernel.runtime.communication.packet import StopSignal - from sage.middleware.operators.llm import SageLLMGenerator - from sage.middleware.operators.rag import QAPromptor - -from pipeline_bridge import PipelineBridge - -# Default SageLLM configuration for mock mode -DEFAULT_GENERATOR_CONFIG = { - "backend_type": "mock", - "model_path": "mock-model", - "max_tokens": 512, - "temperature": 0.7, -} - -# Default promptor configuration -DEFAULT_PROMPTOR_CONFIG = { - "template": "Answer the following question:\n\nQuestion: {query}\n\nAnswer:", -} - - -def _extract_answer_text(generated: Any) -> str: - """Best-effort extraction of answer text from model responses.""" - - if generated is None: - return "" - - if isinstance(generated, str): - return generated - - if isinstance(generated, tuple) and generated: - return _extract_answer_text(generated[-1]) - - if isinstance(generated, list) and generated: - return _extract_answer_text(generated[0]) - - if isinstance(generated, dict): - # OpenAI-compatible schema - if "choices" in generated: - choices = generated.get("choices") or [] - if choices: - choice = choices[0] - if isinstance(choice, dict): - if "message" in choice: - message = choice["message"] or {} - content = message.get("content") - if isinstance(content, list): - parts = [part.get("text", "") for part in content] - return "".join(parts) - if content: - return str(content) - if "text" in choice and choice.get("text"): - return str(choice.get("text")) - - # SageLLM and other adapters sometimes return plain fields - for key in ("output_text", "content", "answer", "generated_text", "text"): - if key in generated and generated[key]: - return str(generated[key]) - - return str(generated) - - return str(generated) - - -class SageLLMGeneratorWrapper(MapFunction): - """Wrapper for SageLLMGenerator that integrates with the QA pipeline. - - This wrapper adapts SageLLMGenerator to work with the pipeline's data format. - When backend_type='mock', it uses the mock backend which requires no external services. - """ - - def __init__(self, config: dict[str, Any] | None = None, **kwargs): - super().__init__(**kwargs) - config = config or {} - - # Default to mock backend for offline runs - backend_type = config.get("backend_type", "mock") - model_path = config.get("model_path", "mock-model") - max_tokens = config.get("max_tokens", 512) - temperature = config.get("temperature", 0.7) - top_p = config.get("top_p", 0.95) - - # Create SageLLMGenerator with the configured backend - self._generator = SageLLMGenerator( - backend_type=backend_type, - model_path=model_path, - max_tokens=max_tokens, - temperature=temperature, - top_p=top_p, - ) - self._backend_type = backend_type - - @property - def backend_type(self) -> str: - return self._backend_type - - def execute(self, data: Any): - if data is None: - return None - - # Extract original data and prompt from pipeline format - if isinstance(data, (list, tuple)) and len(data) >= 2: - original_data = data[0] - prompt = data[1] - else: - original_data = {} - prompt = data - - # Extract query for context - if isinstance(original_data, dict): - query = original_data.get("query") or original_data.get("question") or "" - else: - query = str(original_data) - - # Call SageLLMGenerator - try: - result = self._generator.execute(prompt) - except Exception as e: - # Return error in a structured format - self.logger.exception("Generator execution failed", exc_info=e) - if isinstance(original_data, dict): - error_result = dict(original_data) - else: - error_result = {"query": query} - error_result["error"] = str(e) - return error_result - - # Format output to match pipeline expectations - if isinstance(result, dict): - generated = result.get("text", result.get("generated", "")) - elif isinstance(result, str): - generated = result - else: - generated = str(result) - - if isinstance(original_data, dict): - output = dict(original_data) - else: - output = {"query": query, "prompt": prompt} - - output["generated"] = generated - output.setdefault("answer", generated) - output.setdefault("query", query) - return output - - -class MockGenerator(MapFunction): - """Lightweight generator that returns canned answers for offline demos. - - Note: For production use, prefer SageLLMGeneratorWrapper with backend_type='mock'. - This class is kept for backward compatibility. - """ - - def __init__(self, config: dict[str, Any] | None = None, **kwargs): - super().__init__(**kwargs) - config = config or {} - default_responses = [ - "(mock) I'm a friendly offline assistant. You asked: {query}.", - "(mock) Here's a concise reply about '{query}'.", - "(mock) Thanks for the question on '{query}'. This is a placeholder answer.", - ] - raw_responses = config.get("responses") or default_responses - # Normalise to list and ensure formatting strings are valid - if isinstance(raw_responses, str): - raw_responses = [raw_responses] - self._responses: list[str] = [str(r) for r in raw_responses if r] - if not self._responses: - self._responses = default_responses - self._cursor = 0 - - def _next_response(self, query: str) -> str: - response = self._responses[self._cursor % len(self._responses)] - self._cursor += 1 - placeholder = query.strip() or "your question" - try: - return response.format(query=placeholder) - except Exception: # pragma: no cover - defensive formatting guard - return f"{response} (question={placeholder})" - - def execute(self, data: Any): - if data is None: - return None - - if isinstance(data, (list, tuple)) and len(data) >= 2: - original_data = data[0] - prompt = data[1] - else: - original_data = {} - prompt = data - - if isinstance(original_data, dict): - query = original_data.get("query") or original_data.get("question") or "" - else: - query = str(original_data) - - answer = self._next_response(query) - - if isinstance(original_data, dict): - result = dict(original_data) - else: - result = {"query": query, "prompt": prompt} - - result["generated"] = answer - result.setdefault("answer", answer) - result.setdefault("query", query) - return result - - -class ServiceDrivenQuestionSource(SourceFunction): - """Pulls requests from the pipeline bridge and injects them into the pipeline.""" - - def __init__(self, bridge: PipelineBridge, poll_interval: float = 0.1): - super().__init__() - self._bridge = bridge - self._poll_interval = poll_interval - - def execute(self, data=None): - request = self._bridge.next(timeout=self._poll_interval) - if request is None: - return None - - if isinstance(request, StopSignal): - raise StopIteration - - payload = dict(request.payload) - payload.setdefault("query", payload.get("question", "")) - payload["response_queue"] = request.response_queue - return payload - - -class QuestionSanitizer(MapFunction): - """Validates incoming questions and performs light normalization.""" - - def execute(self, payload: dict[str, Any] | StopSignal | None): - if payload is None or isinstance(payload, StopSignal): - return payload - - if payload.get("command") == "shutdown": - return payload - - query = (payload.get("query") or payload.get("question") or "").strip() - if not query: - return None - - payload["query"] = query - return payload - - -class PromptStage(MapFunction): - """Wraps ``QAPromptor`` to preserve context and surface errors as data.""" - - def __init__(self, config: dict[str, Any]): - super().__init__() - self._promptor = QAPromptor(config) - - def execute(self, payload: dict[str, Any] | StopSignal | None): - if payload is None or isinstance(payload, StopSignal): - return payload - - if payload.get("command") == "shutdown": - return payload - - response_queue = payload.get("response_queue") - - try: - prompt_result = self._promptor.execute(payload) - except Exception as exc: # pragma: no cover - defensive guard - self.logger.exception("Prompt construction failed", exc_info=exc) - return { - "query": payload.get("query"), - "response_queue": response_queue, - "error": f"Prompt construction failed: {exc}", - } - - # QAPromptor returns [original_data, prompt] - if isinstance(prompt_result, (list, tuple)) and len(prompt_result) >= 2: - prompt_messages = prompt_result[1] - else: - prompt_messages = prompt_result - - prepared = dict(payload) - prepared["prompt"] = prompt_messages - prepared.pop("error", None) - return prepared - - -class GeneratorStage(MapFunction): - """Invokes the configured generator and captures failures as structured data.""" - - def __init__(self, generator_cls, generator_config: dict[str, Any]): - super().__init__() - self._generator = generator_cls(generator_config) - - def execute(self, payload: dict[str, Any] | StopSignal | None): - if payload is None or isinstance(payload, StopSignal): - return payload - - if payload.get("command") == "shutdown": - return payload - - if payload.get("error"): - return payload - - prompt = payload.get("prompt") - response_queue = payload.get("response_queue") - - try: - result = self._generator.execute([payload, prompt]) - except Exception as exc: # pragma: no cover - defensive guard - self.logger.exception("Generator execution failed", exc_info=exc) - return { - "query": payload.get("query"), - "response_queue": response_queue, - "error": f"Generator execution failed: {exc}", - } - - if isinstance(result, dict): - result.setdefault("query", payload.get("query")) - result.setdefault("response_queue", response_queue) - return result - - return { - "query": payload.get("query"), - "generated": result, - "response_queue": response_queue, - } - - -class PackageAnswer(MapFunction): - """Extracts the final answer and prepares the response payload.""" - - def execute(self, payload: dict[str, Any] | StopSignal | tuple | None): - if payload is None or isinstance(payload, StopSignal): - return payload - - if isinstance(payload, dict) and payload.get("command") == "shutdown": - return payload - - if isinstance(payload, dict): - response_queue = payload.get("response_queue") - if payload.get("error"): - answer_str = str(payload.get("error")) - status = "error" - else: - generated = payload.get("generated") - if generated is None and payload.get("answer") is not None: - generated = payload.get("answer") - answer_str = _extract_answer_text(generated) - status = "ok" - question = payload.get("query") or payload.get("question") or "N/A" - elif isinstance(payload, tuple) and len(payload) >= 2: - question = payload[0] - answer_str = _extract_answer_text(payload[1]) - response_queue = None - status = "ok" - else: - response_queue = None - question = "N/A" - answer_str = _extract_answer_text(payload) - status = "ok" - - return { - "question": question, - "answer": answer_str, - "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"), - "response_queue": response_queue, - "status": status, - } - - -class PublishAnswerSink(SinkFunction): - """Publishes answers to the waiting response queue and prints them.""" - - def execute(self, payload: dict[str, Any] | StopSignal | None): - if payload is None: - return None - - if isinstance(payload, StopSignal): - return payload - - if isinstance(payload, dict) and payload.get("command") == "shutdown": - response_queue = payload.get("response_queue") - if isinstance(response_queue, queue.Queue): - try: - response_queue.put({"status": "shutdown_ack"}, timeout=5.0) - except queue.Full: # pragma: no cover - defensive guard - self.logger.warning("Response queue was full during shutdown acknowledgment") - return payload - - if not isinstance(payload, dict): - return None - - response_queue = payload.get("response_queue") - answer = { - "question": payload.get("question", "N/A"), - "answer": payload.get("answer", ""), - "timestamp": payload.get("timestamp"), - "status": payload.get("status", "ok"), - } - - if isinstance(response_queue, queue.Queue): - try: - response_queue.put(answer, timeout=5.0) - except queue.Full: # pragma: no cover - defensive guard - self.logger.error("Failed to push QA answer because the queue is full") - - self.logger.info("Published QA answer for query '%s'", answer["question"]) - return answer - - -class QAPipelineService(BaseService): - """Expose the QA pipeline via the service layer.""" - - def __init__(self, bridge: PipelineBridge, request_timeout: float = 120.0): - super().__init__() - self._bridge = bridge - self._request_timeout = request_timeout - - def process(self, message: dict[str, Any]): - if message is None: - raise ValueError("QA pipeline service received an empty message") - - if message.get("command") == "shutdown": - self._bridge.close() - return {"status": "shutdown_requested"} - - try: - response_queue = self._bridge.submit(message) - except RuntimeError as exc: - raise RuntimeError("QA pipeline service is shutting down") from exc - - try: - return response_queue.get(timeout=self._request_timeout) - except queue.Empty as exc: - raise TimeoutError("QA pipeline timed out waiting for a response") from exc - - -class TerminalQuestionSource(SourceFunction): - """Reads user questions from stdin one at a time.""" - - EXIT_COMMANDS = { - "exit", - "quit", - ":q", - ":quit", - "bye", - "bye bye", - "拜拜", - "再见", - } - - def __init__(self): - super().__init__() - self._terminated = False - - def execute(self, data=None): - if self._terminated: - raise StopIteration - - try: - user_input = input("You> ").strip() - except (EOFError, KeyboardInterrupt): - self._terminated = True - return {"command": "shutdown"} - - if not user_input: - return None - - if user_input.lower() in self.EXIT_COMMANDS: - self._terminated = True - return {"command": "shutdown"} - - return {"query": user_input} - - -class InvokeQAPipeline(MapFunction): - """Issues synchronous calls to the QA pipeline service.""" - - def __init__(self, timeout: float = 180.0): - super().__init__() - self._timeout = timeout - - def execute(self, message: dict[str, Any] | StopSignal | None): - if message is None or isinstance(message, StopSignal): - return message - - response = self.call_service("qa_pipeline", message, timeout=self._timeout) - return {"request": message, "response": response} - - -class TerminalAnswerSink(SinkFunction): - """Displays results returned from the QA service.""" - - def __init__(self, shutdown_event: threading.Event | None = None): - super().__init__() - self._shutdown_event = shutdown_event - - def execute(self, payload: dict[str, Any] | StopSignal | None): - if payload is None or isinstance(payload, StopSignal): - return payload - - response = payload.get("response") - request = payload.get("request", {}) - - if isinstance(response, dict) and response.get("status") == "shutdown_requested": - print("\n✅ QA session closed. Goodbye!", flush=True) - if self._shutdown_event is not None: - self._shutdown_event.set() - return payload - - if not isinstance(response, dict): - print("❌ Unexpected response from QA pipeline", flush=True) - return payload - - request.get("query", request.get("question", "")) - answer = response.get("answer", "") - status = response.get("status", "ok") - if status == "error": - print(f"\n❌ {answer}\n", flush=True) - else: - print(f"\n🤖 {answer}\n", flush=True) - return payload - - -def _resolve_generator() -> tuple[type, dict[str, Any], str]: - """Determine which generator operator to use based on environment. - - Uses SageLLMGeneratorWrapper with configurable backend: - - mock (default): No external services required, uses mock backend - - auto: Auto-detect available backend (cuda/ascend/mock) - - cuda: Use CUDA GPU acceleration - - sagellm: Use SageLLM unified engine - - Returns (generator_cls, generator_config, notice_message). - """ - load_dotenv(override=False) - - # Check environment variables for generator configuration - generator_type = os.getenv("SAGE_QA_GENERATOR", "mock").lower() - backend_type = os.getenv("SAGE_QA_BACKEND", "mock").lower() - model_path = os.getenv("SAGE_QA_MODEL_PATH", "mock-model") - - # Map legacy generator types to new backend types - if generator_type in {"mock", "stub"}: - backend_type = "mock" - notice = ( - "ℹ️ Using SageLLMGenerator with mock backend (offline mode).\n" - " To use a real LLM, set SAGE_QA_GENERATOR=sagellm and SAGE_QA_BACKEND=auto" - ) - elif generator_type in {"sagellm", "auto"}: - if backend_type == "mock": - backend_type = "auto" # Upgrade to auto if sagellm requested - notice = f"ℹ️ Using SageLLMGenerator with backend={backend_type}, model={model_path}" - else: - # Unknown type, fall back to mock - backend_type = "mock" - notice = ( - f"⚠️ Unknown generator type '{generator_type}', falling back to mock.\n" - " Supported types: mock, sagellm, auto" - ) - - config = { - "backend_type": backend_type, - "model_path": model_path, - "max_tokens": int(os.getenv("SAGE_QA_MAX_TOKENS", "512")), - "temperature": float(os.getenv("SAGE_QA_TEMPERATURE", "0.7")), - "top_p": float(os.getenv("SAGE_QA_TOP_P", "0.95")), - } - - return SageLLMGeneratorWrapper, config, notice - - -def main(): - CustomLogger.disable_global_console_debug() - - bridge = PipelineBridge() - env = LocalEnvironment("qa_pipeline_service") - - env.register_service("qa_pipeline", QAPipelineService, bridge) - - generator_cls, generator_conf, generator_notice = _resolve_generator() - - # Use default promptor configuration - promptor_config = DEFAULT_PROMPTOR_CONFIG.copy() - - ( - env.from_source(ServiceDrivenQuestionSource, bridge) - .map(QuestionSanitizer) - .map(PromptStage, promptor_config) - .map(GeneratorStage, generator_cls, generator_conf) - .map(PackageAnswer) - .sink(PublishAnswerSink) - ) - - shutdown_event = threading.Event() - - ( - env.from_source(TerminalQuestionSource) - .map(InvokeQAPipeline) - .sink(TerminalAnswerSink, shutdown_event) - ) - - print( - f"💬 QA service is ready using {generator_cls.__name__}. " - "Ask a question and type 'bye bye' when you're done.\n" - ) - if generator_notice: - print(generator_notice, flush=True) - print("Tip: Press Ctrl+C at any time to exit immediately.", flush=True) - - try: - env.submit() - while not shutdown_event.is_set(): - time.sleep(0.2) - except KeyboardInterrupt: - print("\n⚙️ Shutting down QA service...", flush=True) - shutdown_event.set() - finally: - if shutdown_event.is_set(): - pass - - try: - bridge.close() - except Exception: - pass - - try: - env.stop() - except Exception: - pass - - try: - env.close() - except Exception: - pass - - print("👋 QA service stopped. Bye!", flush=True) - - -if __name__ == "__main__": - if os.getenv("SAGE_EXAMPLES_MODE") == "test" or os.getenv("SAGE_TEST_MODE") == "true": - print("🧪 Test mode detected - qa_pipeline_as_service is interactive") - print("✅ Test passed: Interactive example structure validated") - sys.exit(0) - - main() diff --git a/packages/sage-kernel/examples/advanced/simple_parallelism_validation.py b/packages/sage-kernel/examples/advanced/simple_parallelism_validation.py deleted file mode 100644 index 68aeb4db1f..0000000000 --- a/packages/sage-kernel/examples/advanced/simple_parallelism_validation.py +++ /dev/null @@ -1,309 +0,0 @@ -#!/usr/bin/env python3 -""" -Simple Parallelism Validation Example - -This example creates a simple pipeline to validate that parallelism hints -are correctly applied and that data distribution works as expected. -It uses debug logging to show the execution flow. - -@test:timeout=60 -""" - -import logging -import threading -import time - -from sage.common.core.functions.base_function import BaseFunction -from sage.common.core.functions.batch_function import BatchFunction -from sage.common.core.functions.comap_function import BaseCoMapFunction -from sage.kernel.api.local_environment import LocalEnvironment - -# Enable debug logging -logging.basicConfig( - level=logging.DEBUG, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" -) - - -class SimpleNumberSource(BatchFunction): - """Simple source that produces sequential numbers""" - - def __init__(self, count=10): - super().__init__() - self.count = count - self.current = 0 - print(f"🔧 SimpleNumberSource created: will produce numbers 1-{count}") - - def execute(self): - if self.current >= self.count: - print(f"📤 SimpleNumberSource: finished producing {self.count} numbers") - return None - self.current += 1 - print(f"📤 SimpleNumberSource: producing {self.current}") - return self.current - - -class SquareFunction(BaseFunction): - """Function that squares its input and shows which instance handles it""" - - def __init__(self): - super().__init__() - self.instance_id = id(self) % 10000 # Short instance ID - self.process_count = 0 - print(f"🔧 SquareFunction[{self.instance_id}] created in thread {threading.get_ident()}") - - def execute(self, data): - self.process_count += 1 - result = data * data - thread_id = threading.get_ident() % 10000 # Short thread ID - print( - f"⚙️ SquareFunction[{self.instance_id}]: {data}² = {result} (thread:{thread_id}, count:{self.process_count})" - ) - time.sleep(0.1) # Simulate processing time - return result - - -class EvenFilter(BaseFunction): - """Filter that only passes even numbers""" - - def __init__(self): - super().__init__() - self.instance_id = id(self) % 10000 - self.passed_count = 0 - self.blocked_count = 0 - print(f"🔧 EvenFilter[{self.instance_id}] created in thread {threading.get_ident()}") - - def execute(self, data): - thread_id = threading.get_ident() % 10000 - is_even = data % 2 == 0 - if is_even: - self.passed_count += 1 - print( - f"✅ EvenFilter[{self.instance_id}]: {data} PASSED (thread:{thread_id}, passed:{self.passed_count})" - ) - else: - self.blocked_count += 1 - print( - f"❌ EvenFilter[{self.instance_id}]: {data} BLOCKED (thread:{thread_id}, blocked:{self.blocked_count})" - ) - return is_even - - -class ResultCollector(BaseFunction): - """Sink that collects results and shows distribution""" - - def __init__(self): - super().__init__() - self.instance_id = id(self) % 10000 - self.results = [] - print(f"🔧 ResultCollector[{self.instance_id}] created in thread {threading.get_ident()}") - - def execute(self, data): - thread_id = threading.get_ident() % 10000 - self.results.append(data) - print( - f"🎯 ResultCollector[{self.instance_id}]: collected {data} (thread:{thread_id}, total:{len(self.results)})" - ) - return data - - -class DualStreamCoMap(BaseCoMapFunction): - """CoMap that processes two streams and shows data distribution""" - - def __init__(self): - super().__init__() - self.instance_id = id(self) % 10000 - self.stream0_count = 0 - self.stream1_count = 0 - print(f"🔧 DualStreamCoMap[{self.instance_id}] created in thread {threading.get_ident()}") - - def map0(self, data): - self.stream0_count += 1 - thread_id = threading.get_ident() % 10000 - result = f"S0:{data}" - print( - f"🔀 DualStreamCoMap[{self.instance_id}].map0: {data} -> {result} (thread:{thread_id}, s0_count:{self.stream0_count})" - ) - return result - - def map1(self, data): - self.stream1_count += 1 - thread_id = threading.get_ident() % 10000 - result = f"S1:{data * 10}" - print( - f"🔀 DualStreamCoMap[{self.instance_id}].map1: {data} -> {result} (thread:{thread_id}, s1_count:{self.stream1_count})" - ) - return result - - -def test_single_stream_parallelism(): - """Test single stream with different parallelism levels""" - print("\n" + "=" * 80) - print("SINGLE STREAM PARALLELISM TEST") - print("=" * 80) - - env = LocalEnvironment(name="single_stream_test") - - print("\n🔍 Creating pipeline with parallelism: Source(1) -> Square(3) -> Filter(2) -> Sink(1)") - - ( - env.from_collection(SimpleNumberSource, 10) - .map(SquareFunction, parallelism=3) # 3 parallel square functions - .filter(EvenFilter, parallelism=2) # 2 parallel filters - .sink(ResultCollector, parallelism=1) - ) # 1 sink - - print("\n📋 Pipeline Analysis:") - print(f"Total transformations: {len(env.pipeline)}") - for i, trans in enumerate(env.pipeline): - print( - f" {i + 1}. {trans.function_class.__name__} (parallelism={trans.parallelism}, basename={trans.basename})" - ) - - print("\n💡 Expected behavior:") - print(" - Source produces: 1,2,3,4,5,6,7,8,9,10") - print( - " - 3 Square instances process: 1²=1, 2²=4, 3²=9, 4²=16, 5²=25, 6²=36, 7²=49, 8²=64, 9²=81, 10²=100" - ) - print(" - 2 Filter instances pass even squares: 4,16,36,64,100") - print(" - 1 Sink collects all: [4,16,36,64,100]") - - return env - - -def test_direct_parallelism_specification(): - """Test direct parallelism specification in operators""" - print("\n" + "=" * 80) - print("DIRECT PARALLELISM SPECIFICATION TEST") - print("=" * 80) - - env = LocalEnvironment(name="direct_parallelism_test") - - print( - "\n🔍 Creating pipeline with direct parallelism: Source(1) -> Square(4) -> Filter(1) -> Sink(2)" - ) - - ( - env.from_collection(SimpleNumberSource, 8) - .map(SquareFunction, parallelism=4) # 4 parallel square functions - .filter(EvenFilter, parallelism=1) # 1 filter - .sink(ResultCollector, parallelism=2) - ) # 2 sinks - - print("\n📋 Pipeline Analysis:") - for i, trans in enumerate(env.pipeline): - print( - f" {i + 1}. {trans.function_class.__name__} (parallelism={trans.parallelism}, basename={trans.basename})" - ) - - print("\n💡 Expected behavior:") - print(" - Source produces: 1,2,3,4,5,6,7,8") - print(" - 4 Square instances should distribute work: 1²,4²,9²,16²,25²,36²,49²,64²") - print(" - 1 Filter passes even squares: 4,16,36,64") - print(" - 2 Sink instances should collect results") - - return env - - -def test_multi_stream_parallelism(): - """Test multi-stream CoMap with parallelism""" - print("\n" + "=" * 80) - print("MULTI-STREAM COMAP PARALLELISM TEST") - print("=" * 80) - - env = LocalEnvironment(name="multi_stream_test") - - print("\n🔍 Creating dual-stream pipeline with CoMap parallelism=2") - - # Create two separate streams - stream1 = env.from_collection(SimpleNumberSource, 5) # 1,2,3,4,5 - stream2 = env.from_collection(SimpleNumberSource, 3) # 1,2,3 - - ( - stream1.connect(stream2) - .comap(DualStreamCoMap, parallelism=2) # 2 parallel CoMap instances - .sink(ResultCollector, parallelism=1) - ) # 1 sink - - print("\n📋 Pipeline Analysis:") - for i, trans in enumerate(env.pipeline): - print( - f" {i + 1}. {trans.function_class.__name__} (parallelism={trans.parallelism}, basename={trans.basename})" - ) - - print("\n💡 Expected behavior:") - print(" - Stream1 produces: 1,2,3,4,5 -> CoMap.map0 -> S0:1,S0:2,S0:3,S0:4,S0:5") - print(" - Stream2 produces: 1,2,3 -> CoMap.map1 -> S1:10,S1:20,S1:30") - print(" - 2 CoMap instances should distribute the processing") - print(" - Final results: [S0:1,S0:2,S0:3,S0:4,S0:5,S1:10,S1:20,S1:30]") - - return env - - -def test_execution_graph_validation(): - """Test that ExecutionGraph creates correct number of nodes""" - print("\n" + "=" * 80) - print("EXECUTION GRAPH NODE VALIDATION") - print("=" * 80) - - env = LocalEnvironment(name="execution_graph_test") - - print("\n🔍 Creating test pipeline to validate ExecutionGraph node creation") - - ( - env.from_collection(SimpleNumberSource, 6) - .map(SquareFunction, parallelism=2) # Should create 2 map nodes - .filter(EvenFilter, parallelism=3) # Should create 3 filter nodes - .sink(ResultCollector, parallelism=1) - ) # Should create 1 sink node - - print("\n📋 ExecutionGraph Node Expectations:") - print(" - SimpleNumberSource: 1 source node") - print(" - SquareFunction: 2 parallel map nodes") - print(" - EvenFilter: 3 parallel filter nodes") - print(" - ResultCollector: 1 sink node") - print(" - Total expected nodes: 7") - - print("\n📋 Pipeline Transformations:") - for i, trans in enumerate(env.pipeline): - print(f" {i + 1}. {trans.function_class.__name__} (parallelism={trans.parallelism})") - print(f" -> Will create {trans.parallelism} parallel execution nodes") - - total_expected_nodes = sum(trans.parallelism for trans in env.pipeline) - print(f"\n🎯 Total execution nodes that will be created: {total_expected_nodes}") - - return env - - -def main(): - """Run all parallelism validation tests""" - print("🚀 SAGE Simple Parallelism Validation") - print("This example validates parallelism hints with observable input/output") - - # Run all tests - env1 = test_single_stream_parallelism() - env2 = test_direct_parallelism_specification() - env3 = test_multi_stream_parallelism() - env4 = test_execution_graph_validation() - - print("\n" + "=" * 80) - print("VALIDATION SUMMARY") - print("=" * 80) - print("✅ Single stream parallelism: Verified with observable output") - print("✅ Direct parallelism specification: Tested with different parallelism levels") - print("✅ Multi-stream CoMap: Validated parallel CoMap processing") - print("✅ ExecutionGraph nodes: Confirmed correct node count calculation") - - total_transformations = sum(len(env.pipeline) for env in [env1, env2, env3, env4]) - print("\n📊 Total test environments: 4") - print(f"📊 Total transformations tested: {total_transformations}") - - print("\n💡 Key validations completed:") - print(" ✓ Parallelism parameters correctly set on transformations") - print(" ✓ Direct parallelism specification works as expected") - print(" ✓ Multi-stream operations support parallelism") - print(" ✓ ExecutionGraph will create proper parallel nodes") - print(" ✓ Debug output shows instance distribution") - - -if __name__ == "__main__": - main() diff --git a/packages/sage-kernel/examples/batch/hello_batch_operator_examples.py b/packages/sage-kernel/examples/batch/hello_batch_operator_examples.py deleted file mode 100644 index 044a713558..0000000000 --- a/packages/sage-kernel/examples/batch/hello_batch_operator_examples.py +++ /dev/null @@ -1,274 +0,0 @@ -""" -批处理算子和函数使用示例 - -这个文件展示了如何使用BatchFunction来创建 -用户友好的批处理任务。 -""" - -from typing import Any, Iterator - -from sage.common.core.functions.batch_function import BatchFunction - - -class SimpleBatchFunction(BatchFunction): - """简单的列表数据批处理函数""" - - def __init__(self, data_list: list[Any], ctx=None, **kwargs): - super().__init__(**kwargs) - self.data_list = data_list - self.current_index = 0 - self.ctx = ctx - - def execute(self) -> Any: - if self.current_index >= len(self.data_list): - return None - result = self.data_list[self.current_index] - self.current_index += 1 - return result - - def get_total_count(self) -> int: - return len(self.data_list) - - def get_progress(self) -> tuple: - return self.current_index, len(self.data_list) - - def get_completion_rate(self) -> float: - if len(self.data_list) == 0: - return 1.0 - return self.current_index / len(self.data_list) - - def is_finished(self) -> bool: - return self.current_index >= len(self.data_list) - - -class FileBatchFunction(BatchFunction): - """文件行读取批处理函数""" - - def __init__(self, file_path: str, **kwargs): - super().__init__(**kwargs) - self.file_path = file_path - self.file_handle = None - self.line_count = 0 - self.finished = False - - def execute(self) -> Any: - if self.finished: - return None - - if self.file_handle is None: - try: - self.file_handle = open(self.file_path, encoding="utf-8") - except FileNotFoundError: - print(f"文件 {self.file_path} 不存在，返回模拟数据") - self.finished = True - return f"模拟文件行 {self.line_count}" - - try: - line = self.file_handle.readline() - if not line: - self.finished = True - if self.file_handle: - self.file_handle.close() - return None - - self.line_count += 1 - return line.strip() - except Exception as e: - print(f"读取文件错误: {e}") - self.finished = True - if self.file_handle: - self.file_handle.close() - return None - - -class MockContext: - """模拟上下文类""" - - def __init__(self, name: str): - self.name = name - - -class NumberRangeBatchFunction(BatchFunction): - """ - 数字范围批处理函数示例 - - 生成指定范围内的数字序列 - """ - - def __init__(self, start: int, end: int, step: int = 1, ctx=None, **kwargs): - super().__init__(**kwargs) - self.start = start - self.end = end - self.step = step - self.current = start - self.ctx = ctx - - def get_total_count(self) -> int: - return max(0, (self.end - self.start + self.step - 1) // self.step) - - def execute(self) -> Any: - if self.current >= self.end: - return None - result = self.current - self.current += self.step - return result - - def is_finished(self) -> bool: - return self.current >= self.end - - def get_progress(self) -> tuple: - completed = max(0, (self.current - self.start) // self.step) - total = self.get_total_count() - return completed, total - - def get_completion_rate(self) -> float: - completed, total = self.get_progress() - return completed / total if total > 0 else 1.0 - - def get_data_source(self) -> Iterator[Any]: - return iter(range(self.start, self.end, self.step)) - - -class CustomDataBatchFunction(BatchFunction): - """ - 自定义数据批处理函数示例 - - 处理用户提供的自定义数据生成逻辑 - """ - - def __init__(self, data_generator_func, total_count: int, ctx=None, **kwargs): - super().__init__(ctx, **kwargs) - self.data_generator_func = data_generator_func - self.total_count = total_count - self._generator = None - self._finished = False - - def get_total_count(self) -> int: - return self.total_count - - def get_data_source(self) -> Iterator[Any]: - return self.data_generator_func() - - def execute(self): - """执行批处理函数，返回下一个数据项""" - if self._finished: - return None - - if self._generator is None: - self._generator = self.data_generator_func() - - try: - return next(self._generator) - except StopIteration: - self._finished = True - return None - - -def create_sample_batch_tasks(): - """ - 创建示例批处理任务的工厂函数 - """ - - # 示例1: 简单数据列表批处理 - def create_simple_list_batch(): - data = [f"item_{i}" for i in range(100)] - return SimpleBatchFunction(data) - - # 示例2: 数字范围批处理 - def create_number_range_batch(): - return NumberRangeBatchFunction(start=1, end=1001, step=1) - - # 示例3: 文件批处理 - def create_file_batch(file_path: str): - return FileBatchFunction(file_path) - - # 示例4: 自定义数据生成批处理 - def create_custom_batch(): - def fibonacci_generator(): - a, b = 0, 1 - for _ in range(50): # 生成前50个斐波那契数 - yield a - a, b = b, a + b - - return CustomDataBatchFunction(fibonacci_generator, 50) - - return { - "simple_list": create_simple_list_batch, - "number_range": create_number_range_batch, - "file_batch": create_file_batch, - "custom_fibonacci": create_custom_batch, - } - - -class BatchTaskExample: - """ - 批处理任务使用示例类 - """ - - @staticmethod - def example_usage(): - """ - 批处理使用示例 - """ - - # 创建一个简单的模拟context - class MockContext: - def __init__(self, name): - self.name = name - self.logger = MockLogger() - - class MockLogger: - def info(self, msg): - print(f"INFO: {msg}") - - def debug(self, msg): - print(f"DEBUG: {msg}") - - def warning(self, msg): - print(f"WARNING: {msg}") - - def error(self, msg): - print(f"ERROR: {msg}") - - print("=== 批处理算子使用示例 ===") - - # 1. 创建简单列表批处理 - print("\n1. 简单列表批处理:") - data = ["apple", "banana", "cherry", "date", "elderberry"] - ctx = MockContext("simple_batch_example") - simple_batch = SimpleBatchFunction(data, ctx) - - print(f"总记录数: {simple_batch.get_total_count()}") - - # 模拟处理过程 - for i in range(7): # 多处理几次以展示完成状态 - result = simple_batch.execute() - current, total = simple_batch.get_progress() - completion = simple_batch.get_completion_rate() - - print( - f"第{i + 1}次执行: 结果={result}, 进度={current}/{total} ({completion:.1%}), 完成={simple_batch.is_finished()}" - ) - - if simple_batch.is_finished(): - break - - # 2. 数字范围批处理 - print("\n2. 数字范围批处理:") - ctx2 = MockContext("number_batch_example") - number_batch = NumberRangeBatchFunction(1, 6, 1, ctx2) - print(f"总记录数: {number_batch.get_total_count()}") - - while not number_batch.is_finished(): - result = number_batch.execute() - current, total = number_batch.get_progress() - completion = number_batch.get_completion_rate() - - print(f"处理结果: {result}, 进度: {current}/{total} ({completion:.1%})") - - print("\n=== 示例完成 ===") - - -if __name__ == "__main__": - # 运行示例 - BatchTaskExample.example_usage() diff --git a/packages/sage-kernel/examples/batch/hello_batch_vs_source_comparison.py b/packages/sage-kernel/examples/batch/hello_batch_vs_source_comparison.py deleted file mode 100644 index 1d4cb2929b..0000000000 --- a/packages/sage-kernel/examples/batch/hello_batch_vs_source_comparison.py +++ /dev/null @@ -1,208 +0,0 @@ -""" -BatchOperator vs SourceOperator 对比示例 - -展示新的批处理设计相对于原始设计的优势 -""" - -from typing import Any, Iterator - -from sage.common.core.functions.batch_function import BatchFunction -from sage.common.core.functions.source_function import SourceFunction -from sage.kernel.runtime.communication.packet import StopSignal - - -class SimpleBatchFunction(BatchFunction): - """ - 简单的批处理函数实现 - 直接处理提供的数据列表 - """ - - def __init__(self, data, ctx=None, **kwargs): - super().__init__(ctx, **kwargs) - self.data = data - self.index = 0 - - def get_total_count(self) -> int: - return len(self.data) - - def get_data_source(self) -> Iterator[Any]: - return iter(self.data) - - def execute(self) -> Any: - if self.index >= len(self.data): - return None - - result = self.data[self.index] - self.index += 1 - return result - - def get_progress(self): - return self.index, len(self.data) - - def get_completion_rate(self) -> float: - return self.index / len(self.data) if self.data else 1.0 - - def is_finished(self) -> bool: - return self.index >= len(self.data) - - -class OldStyleSourceFunction(SourceFunction): - """ - 旧式源函数 - 需要手动管理停止逻辑 - 用户需要在函数中处理停止信号的发送 - """ - - def __init__(self, data, ctx=None, **kwargs): - super().__init__(ctx, **kwargs) - self.data = data - self.index = 0 - - def execute(self) -> Any: - if self.index >= len(self.data): - # 用户需要手动返回停止信号 - return StopSignal("old_style_source") - - result = self.data[self.index] - self.index += 1 - return result - - -def compare_implementations(): - """ - 对比新旧实现的差异 - """ - - # 创建模拟context - class MockContext: - def __init__(self, name): - self.name = name - self.logger = MockLogger() - - class MockLogger: - def info(self, msg): - print(f"INFO: {msg}") - - def debug(self, msg): - print(f"DEBUG: {msg}") - - data = ["item1", "item2", "item3", "item4", "item5"] - - print("=" * 60) - print("批处理设计对比示例") - print("=" * 60) - - # 1. 旧式实现 - print("\n1. 旧式 SourceFunction 实现:") - print(" - 用户需要手动管理停止逻辑") - print(" - 无内置进度跟踪") - print(" - 停止信号在函数中发送") - - ctx1 = MockContext("old_style") - old_func = OldStyleSourceFunction(data, ctx1) - - print(f"\n 处理数据 ({len(data)} 条记录):") - for i in range(len(data) + 2): # 多执行几次展示停止逻辑 - result = old_func.execute() - if isinstance(result, StopSignal): - print(f" 第{i + 1}次执行: 收到停止信号 {result}") - break - else: - print(f" 第{i + 1}次执行: 处理数据 {result}") - - # 2. 新式实现 - print("\n" + "=" * 60) - print("2. 新式 BatchFunction 实现:") - print(" - 用户只需声明数据，不管停止逻辑") - print(" - 内置进度跟踪和状态管理") - print(" - 停止信号由算子自动发送") - - ctx2 = MockContext("new_style") - new_func = SimpleBatchFunction(data, ctx2) - - print(f"\n 处理数据 ({new_func.get_total_count()} 条记录):") - i = 0 - while not new_func.is_finished(): - result = new_func.execute() - current, total = new_func.get_progress() - completion = new_func.get_completion_rate() - - if result is not None: - print( - f" 第{i + 1}次执行: 处理数据 {result} - 进度 {current}/{total} ({completion:.0%})" - ) - i += 1 - - print(f" 批处理完成状态: {new_func.is_finished()}") - print(f" 最终完成率: {new_func.get_completion_rate():.0%}") - - # 3. 功能对比表 - print("\n" + "=" * 60) - print("功能对比:") - print("=" * 60) - - comparison_table = [ - ["功能", "旧式 SourceFunction", "新式 BatchFunction"], - ["-" * 20, "-" * 25, "-" * 25], - ["停止信号管理", "用户手动处理", "算子自动管理"], - ["进度跟踪", "无", "内置支持"], - ["完成状态", "无", "自动跟踪"], - ["错误处理", "用户负责", "算子统一处理"], - ["用户接口复杂度", "高(需处理停止)", "低(声明式)"], - ["代码可维护性", "一般", "好"], - ["调试友好性", "一般", "好(丰富日志)"], - ] - - for row in comparison_table: - print(f"{row[0]:<20} | {row[1]:<23} | {row[2]}") - - # 4. 代码量对比 - print("\n" + "=" * 60) - print("代码实现对比:") - print("=" * 60) - - print("\n旧式实现 - 用户需要写的代码:") - print( - """ - class MySourceFunction(SourceFunction): - def __init__(self, data, ctx=None, **kwargs): - super().__init__(ctx, **kwargs) - self.data = data - self.index = 0 - - def execute(self) -> Any: - if self.index >= len(self.data): - return StopSignal("my_source") # 手动管理停止 - - result = self.data[self.index] - self.index += 1 - return result - """ - ) - - print("\n新式实现 - 用户只需要声明:") - print( - """ - # 直接使用内置实现 - batch_func = SimpleBatchFunction(data, ctx) - - # 或者自定义数据源 - class MyBatchFunction(BatchFunction): - def get_total_count(self) -> int: - return len(self.my_data) - - def get_data_source(self) -> Iterator[Any]: - return iter(self.my_data) - """ - ) - - print("\n" + "=" * 60) - print("总结:") - print("- 新设计大大简化了用户接口") - print("- 提供了更好的进度可见性") - print("- 将复杂的停止逻辑从用户代码中抽象出来") - print("- 支持更好的错误处理和监控") - print("=" * 60) - - -if __name__ == "__main__": - compare_implementations() diff --git a/packages/sage-kernel/examples/batch/hello_local_batch.py b/packages/sage-kernel/examples/batch/hello_local_batch.py deleted file mode 100644 index 3d6722344a..0000000000 --- a/packages/sage-kernel/examples/batch/hello_local_batch.py +++ /dev/null @@ -1,293 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE 本地批处理测试示例 -@test:timeout=120 -@test:category=batch -""" - -import logging -import os -import random -import time - -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.core.functions.source_function import SourceFunction -from sage.kernel.api.local_environment import LocalEnvironment -from sage.kernel.runtime.communication.packet import StopSignal - -# 设置日志级别为ERROR减少输出 -os.environ.setdefault("SAGE_LOG_LEVEL", "ERROR") - -# 配置 Python 日志系统 -logging.basicConfig(level=logging.ERROR) -for logger_name in ["sage", "JobManager", "ray", "asyncio", "urllib3"]: - logging.getLogger(logger_name).setLevel(logging.ERROR) - -# 禁用所有INFO级别的日志 -logging.getLogger().setLevel(logging.ERROR) - - -class NumberSequenceSource(SourceFunction): - """ - 数字序列源 - 生成有限数量的数字，然后发送停止信号 - """ - - def __init__(self, max_count=10, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - self.max_count = max_count - - def execute(self, data=None): - if self.counter >= self.max_count: - # 数据耗尽，发送停止信号 - return StopSignal(f"NumberSequence_{self.counter}") - - self.counter += 1 - number = self.counter * 10 + random.randint(1, 9) - self.logger.debug(f"[Source] Generating number {self.counter}/{self.max_count}: {number}") - return number - - -class FileLineSource(SourceFunction): - """ - 文件行源 - 逐行读取文件，读完后发送停止信号 - """ - - def __init__(self, lines_data=None, **kwargs): - super().__init__(**kwargs) - # 模拟文件内容 - self.lines = lines_data or [ - "Hello, SAGE batch processing!", - "Processing line by line...", - "Each line is processed independently.", - "This is a test of batch termination.", - "End of file reached.", - ] - self.current_index = 0 - - def execute(self, data=None): - if self.current_index >= len(self.lines): - # 文件读完，发送停止信号 - return StopSignal("FileReader_EOF") - - line = self.lines[self.current_index] - self.current_index += 1 - print(f"[FileSource] Reading line {self.current_index}/{len(self.lines)}: {line}") - return line - - -class CountdownSource(SourceFunction): - """ - 倒计时源 - 从指定数字倒数到0，然后发送停止信号 - """ - - def __init__(self, start_from=5, **kwargs): - super().__init__(**kwargs) - self.current_number = start_from - - def execute(self, data=None): - if self.current_number < 0: - # 倒计时结束，发送停止信号 - return StopSignal("Countdown_Finished") - - result = self.current_number - print(f"[Countdown] T-minus {self.current_number}") - self.current_number -= 1 - return result - - -class BatchProcessor(SinkFunction): - """ - 批处理数据接收器 - """ - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.processed_count = 0 - - def execute(self, data): - self.processed_count += 1 - print(f"[Processor-{self.name}] Processed item #{self.processed_count}: {data}") - return data - - -def run_simple_batch_test(): - """测试1: 简单的数字序列批处理""" - print("🔢 Test 1: Simple Number Sequence Batch Processing") - print("=" * 50) - - env = LocalEnvironment("simple_batch_test") - - # 创建有限数据源 - source_stream = env.from_source(NumberSequenceSource, max_count=5, delay=0.5) - - # 处理管道 - ( - source_stream.map( - lambda x: x * 2 if not isinstance(x, StopSignal) else x - ) # 数字翻倍，跳过StopSignal - .filter( - lambda x: x > 50 if not isinstance(x, StopSignal) else True - ) # 过滤大于50的数字，通过StopSignal - .sink(BatchProcessor, name="NumberProcessor") - ) - - print("🚀 Starting simple batch processing...") - print("📊 Processing sequence: generate → double → filter → sink") - print("⏹️ Source will automatically stop after 5 numbers\n") - - # 提交并运行 - env.submit() - - print("\n✅ Simple batch test completed!\n") - - -def run_file_processing_test(): - """测试2: 文件行批处理""" - print("📄 Test 2: File Line Batch Processing") - print("=" * 50) - - env = LocalEnvironment("file_batch_test") - - # 模拟文件数据 - file_data = [ - "SAGE Framework", - "Distributed Stream Processing", - "Batch Processing Support", - "Ray-based Architecture", - "Python Implementation", - ] - - source_stream = env.from_source(FileLineSource, lines_data=file_data, delay=0.8) - - # 文本处理管道 - ( - source_stream.map( - lambda line: line.upper() if not isinstance(line, StopSignal) else line - ) # 转大写，跳过StopSignal - .map( - lambda line: f"📝 {line}" if not isinstance(line, StopSignal) else line - ) # 添加前缀，跳过StopSignal - .sink(BatchProcessor, name="TextProcessor") - ) - - print("🚀 Starting file batch processing...") - print("📊 Processing pipeline: read → uppercase → prefix → sink") - print("⏹️ Source will automatically stop after reading all lines\n") - - # 提交并运行 - env.submit() - - print("\n✅ File batch test completed!\n") - - -def run_multi_source_batch_test(): - """测试3: 多源批处理（展示不同源的终止时机）""" - print("🔀 Test 3: Multi-Source Batch Processing") - print("=" * 50) - - env = LocalEnvironment("multi_source_batch_test") - - # 创建多个不同速度的数据源 - numbers_stream = env.from_source(NumberSequenceSource, max_count=3, delay=0.5) - countdown_stream = env.from_source(CountdownSource, start_from=2, delay=0.7) - - # 合并流处理 - ( - numbers_stream.connect(countdown_stream) # 合并两个流 - .map( - lambda x: f"Combined: {x}" if not isinstance(x, StopSignal) else x - ) # 格式化，跳过StopSignal - .sink(BatchProcessor, name="MultiSourceProcessor") - ) - - print("🚀 Starting multi-source batch processing...") - print("📊 Two independent sources will terminate at different times") - print("⏹️ Job will complete when ALL sources send stop signals\n") - - # 提交并运行 - env.submit() - - print("\n✅ Multi-source batch test completed!\n") - - -def run_processing_chain_test(): - """测试4: 复杂处理链批处理""" - print("⛓️ Test 4: Complex Processing Chain Batch") - print("=" * 50) - - env = LocalEnvironment("complex_batch_test") # 使用远程环境测试分布式批处理 - - source_stream = env.from_source(NumberSequenceSource, max_count=8, delay=0.3) - - # 复杂的处理链 - ( - source_stream.map( - lambda x: x + 100 if not isinstance(x, StopSignal) else x - ) # +100，跳过StopSignal - .filter( - lambda x: x % 2 == 0 if not isinstance(x, (StopSignal, str)) else True - ) # 只保留偶数，跳过StopSignal和字符串 - .map(lambda x: x / 2 if not isinstance(x, StopSignal) else x) # 除以2，跳过StopSignal - .map( - lambda x: f"Result: {int(x)}" if not isinstance(x, (StopSignal, str)) else x - ) # 格式化，跳过StopSignal和已格式化的字符串 - .sink(BatchProcessor, name="ChainProcessor") - ) - - print("🚀 Starting complex processing chain...") - print("📊 Chain: source → +100 → filter_even → /2 → format → sink") - print("🌐 Running on distributed Ray cluster") - print("⏹️ Automatic termination with batch lifecycle management\n") - - # 提交并运行 - env.submit() - - print("\n✅ Complex batch test completed!\n") - - -def main(): - """主测试函数""" - print("🎯 SAGE Batch Processing Tests with StopSignal") - print("=" * 60) - print("🧪 Testing automatic batch termination using StopSignal interface") - print("📈 Each test demonstrates different batch processing scenarios\n") - - try: - # 运行所有测试 - run_simple_batch_test() - time.sleep(2) - - run_file_processing_test() - time.sleep(2) - - run_multi_source_batch_test() - time.sleep(2) - - run_processing_chain_test() - - except KeyboardInterrupt: - print("\n\n🛑 Tests interrupted by user") - - finally: - print("\n📋 Batch Processing Tests Summary:") - print("✅ Test 1: Simple sequence - PASSED") - print("✅ Test 2: File processing - PASSED") - print("✅ Test 3: Multi-source - PASSED") - print("✅ Test 4: Complex chain - PASSED") - print("\n💡 Key Features Demonstrated:") - print(" - StopSignal automatic termination") - print(" - Source-driven batch lifecycle") - print(" - Multi-source coordination") - print(" - Distributed batch processing") - print(" - Graceful job completion") - print("\n🔄 StopSignal Workflow:") - print(" 1. Source detects data exhaustion") - print(" 2. Source returns StopSignal") - print(" 3. SourceOperator propagates signal") - print(" 4. Downstream nodes receive termination") - print(" 5. Job gracefully completes") - - -if __name__ == "__main__": - main() diff --git a/packages/sage-kernel/examples/batch/hello_remote_batch.py b/packages/sage-kernel/examples/batch/hello_remote_batch.py deleted file mode 100644 index 6826c30129..0000000000 --- a/packages/sage-kernel/examples/batch/hello_remote_batch.py +++ /dev/null @@ -1,372 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE 远程批处理测试示例 -@test:timeout=180 -@test:category=batch -@test:requires=jobmanager -""" - -import atexit -import os -import random -import subprocess -import time - -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.core.functions.source_function import SourceFunction -from sage.kernel.api.remote_environment import RemoteEnvironment -from sage.kernel.runtime.communication.packet import StopSignal - -# 设置日志级别为ERROR减少输出 -os.environ.setdefault("SAGE_LOG_LEVEL", "ERROR") - -# 全局变量存储JobManager进程 -jobmanager_process = None - - -def start_jobmanager(): - """启动JobManager服务""" - global jobmanager_process - - print("🚀 Starting JobManager service...") - try: - # 直接启动JobManager模块 - jobmanager_process = subprocess.Popen( - [ - "python3", - "-m", - "sage.kernel.jobmanager.job_manager", - "--host", - "127.0.0.1", - "--port", - "19001", - ], - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - ) - - # 等待一下让JobManager完全启动 - time.sleep(5) - - # 检查进程是否还在运行 - if jobmanager_process.poll() is None: - print("✅ JobManager service started successfully") - return True - else: - stdout, stderr = jobmanager_process.communicate() - print("❌ JobManager failed to start:") - print(f"stdout: {stdout.decode()}") - print(f"stderr: {stderr.decode()}") - return False - - except Exception as e: - print(f"❌ Failed to start JobManager: {e}") - return False - - -def stop_jobmanager(): - """停止JobManager服务""" - global jobmanager_process - - if jobmanager_process and jobmanager_process.poll() is None: - print("🛑 Stopping JobManager service...") - try: - # 发送终止信号 - jobmanager_process.terminate() - - # 等待进程结束，最多等待5秒 - try: - jobmanager_process.wait(timeout=5) - print("✅ JobManager service stopped gracefully") - except subprocess.TimeoutExpired: - # 如果5秒内没有结束，强制杀死 - jobmanager_process.kill() - jobmanager_process.wait() - print("⚠️ JobManager service force killed") - - except Exception as e: - print(f"❌ Error stopping JobManager: {e}") - finally: - jobmanager_process = None - - -# 注册退出时清理函数 -atexit.register(stop_jobmanager) - - -class NumberSequenceSource(SourceFunction): - """ - 数字序列源 - 生成有限数量的数字，然后发送停止信号 - """ - - def __init__(self, max_count=10, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - self.max_count = max_count - - def execute(self, data=None): - if self.counter >= self.max_count: - # 数据耗尽，发送停止信号 - return StopSignal(f"NumberSequence_{self.counter}") - - self.counter += 1 - number = self.counter * 10 + random.randint(1, 9) - self.logger.debug(f"[Source] Generating number {self.counter}/{self.max_count}: {number}") - return number - - -class FileLineSource(SourceFunction): - """ - 文件行源 - 逐行读取文件，读完后发送停止信号 - """ - - def __init__(self, lines_data=None, **kwargs): - super().__init__(**kwargs) - # 模拟文件内容 - self.lines = lines_data or [ - "Hello, SAGE batch processing!", - "Processing line by line...", - "Each line is processed independently.", - "This is a test of batch termination.", - "End of file reached.", - ] - self.current_index = 0 - - def execute(self, data=None): - if self.current_index >= len(self.lines): - # 文件读完，发送停止信号 - return StopSignal("FileReader_EOF") - - line = self.lines[self.current_index] - self.current_index += 1 - print(f"[FileSource] Reading line {self.current_index}/{len(self.lines)}: {line}") - return line - - -class CountdownSource(SourceFunction): - """ - 倒计时源 - 从指定数字倒数到0，然后发送停止信号 - """ - - def __init__(self, start_from=5, **kwargs): - super().__init__(**kwargs) - self.current_number = start_from - - def execute(self, data=None): - if self.current_number < 0: - # 倒计时结束，发送停止信号 - return StopSignal("Countdown_Finished") - - result = self.current_number - print(f"[Countdown] T-minus {self.current_number}") - self.current_number -= 1 - return result - - -class BatchProcessor(SinkFunction): - """ - 批处理数据接收器 - """ - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.processed_count = 0 - - def execute(self, data): - self.processed_count += 1 - print(f"[Processor-{self.name}] Processed item #{self.processed_count}: {data}") - return data - - -def run_simple_batch_test(): - """测试1: 简单的数字序列批处理""" - print("🔢 Test 1: Simple Number Sequence Batch Processing") - print("=" * 50) - - env = RemoteEnvironment("simple_batch_test") - - # 创建有限数据源 - source_stream = env.from_source(NumberSequenceSource, max_count=5, delay=0.5) - - # 处理管道 - ( - source_stream.map( - lambda x: x * 2 if not isinstance(x, StopSignal) else x - ) # 数字翻倍，跳过StopSignal - .filter( - lambda x: x > 50 if not isinstance(x, StopSignal) else True - ) # 过滤大于50的数字，通过StopSignal - .sink(BatchProcessor, name="NumberProcessor") - ) - - print("🚀 Starting simple batch processing...") - print("📊 Processing sequence: generate → double → filter → sink") - print("⏹️ Source will automatically stop after 5 numbers\n") - - # 提交并运行 - env.submit() - - print("\n✅ Simple batch test completed!\n") - - -def run_file_processing_test(): - """测试2: 文件行批处理""" - print("📄 Test 2: File Line Batch Processing") - print("=" * 50) - - env = RemoteEnvironment("file_batch_test") - - # 模拟文件数据 - file_data = [ - "SAGE Framework", - "Distributed Stream Processing", - "Batch Processing Support", - "Ray-based Architecture", - "Python Implementation", - ] - - source_stream = env.from_source(FileLineSource, lines_data=file_data, delay=0.8) - - # 文本处理管道 - ( - source_stream.map( - lambda line: line.upper() if not isinstance(line, StopSignal) else line - ) # 转大写，跳过StopSignal - .map( - lambda line: f"📝 {line}" if not isinstance(line, StopSignal) else line - ) # 添加前缀，跳过StopSignal - .sink(BatchProcessor, name="TextProcessor") - ) - - print("🚀 Starting file batch processing...") - print("📊 Processing pipeline: read → uppercase → prefix → sink") - print("⏹️ Source will automatically stop after reading all lines\n") - - # 提交并运行 - env.submit() - - print("\n✅ File batch test completed!\n") - - -def run_multi_source_batch_test(): - """测试3: 多源批处理（展示不同源的终止时机）""" - print("🔀 Test 3: Multi-Source Batch Processing") - print("=" * 50) - - env = RemoteEnvironment("multi_source_batch_test") - - # 创建多个不同速度的数据源 - numbers_stream = env.from_source(NumberSequenceSource, max_count=3, delay=0.5) - countdown_stream = env.from_source(CountdownSource, start_from=2, delay=0.7) - - # 合并流处理 - ( - numbers_stream.connect(countdown_stream) # 合并两个流 - .map( - lambda x: f"Combined: {x}" if not isinstance(x, StopSignal) else x - ) # 格式化，跳过StopSignal - .sink(BatchProcessor, name="MultiSourceProcessor") - ) - - print("🚀 Starting multi-source batch processing...") - print("📊 Two independent sources will terminate at different times") - print("⏹️ Job will complete when ALL sources send stop signals\n") - - # 提交并运行 - env.submit() - - print("\n✅ Multi-source batch test completed!\n") - - -def run_processing_chain_test(): - """测试4: 复杂处理链批处理""" - print("⛓️ Test 4: Complex Processing Chain Batch") - print("=" * 50) - - env = RemoteEnvironment("complex_batch_test") - - source_stream = env.from_source(NumberSequenceSource, max_count=8, delay=0.3) - - # 复杂的处理链 - ( - source_stream.map( - lambda x: x + 100 if not isinstance(x, StopSignal) else x - ) # +100，跳过StopSignal - .filter( - lambda x: x % 2 == 0 if not isinstance(x, (StopSignal, str)) else True - ) # 只保留偶数，跳过StopSignal和字符串 - .map(lambda x: x / 2 if not isinstance(x, StopSignal) else x) # 除以2，跳过StopSignal - .map( - lambda x: f"Result: {int(x)}" if not isinstance(x, (StopSignal, str)) else x - ) # 格式化，跳过StopSignal和已格式化的字符串 - .sink(BatchProcessor, name="ChainProcessor") - ) - - print("🚀 Starting complex processing chain...") - print("📊 Chain: source → +100 → filter_even → /2 → format → sink") - print("🌐 Running on distributed Ray cluster") - print("⏹️ Automatic termination with batch lifecycle management\n") - - # 提交并运行 - env.submit() - - print("\n✅ Complex batch test completed!\n") - - -def main(): - """主测试函数""" - print("🎯 SAGE Batch Processing Tests with RemoteEnvironment") - print("=" * 60) - print("🧪 Testing automatic batch termination using RemoteEnvironment with JobManager") - print("📈 Each test demonstrates different batch processing scenarios\n") - - # 启动JobManager服务 - if not start_jobmanager(): - print("❌ Failed to start JobManager. Exiting...") - return - - try: - # 运行所有测试 - run_simple_batch_test() - time.sleep(2) - - run_file_processing_test() - time.sleep(2) - - run_multi_source_batch_test() - time.sleep(2) - - run_processing_chain_test() - - except KeyboardInterrupt: - print("\n\n🛑 Tests interrupted by user") - - except Exception as e: - print(f"\n❌ Test execution error: {e}") - - finally: - # 停止JobManager服务 - stop_jobmanager() - - print("\n📋 Batch Processing Tests Summary:") - print("✅ Test 1: Simple sequence - PASSED") - print("✅ Test 2: File processing - PASSED") - print("✅ Test 3: Multi-source - PASSED") - print("✅ Test 4: Complex chain - PASSED") - print("\n💡 Key Features Demonstrated:") - print(" - RemoteEnvironment with JobManager") - print(" - StopSignal automatic termination") - print(" - Source-driven batch lifecycle") - print(" - Multi-source coordination") - print(" - Distributed batch processing") - print(" - Graceful job completion") - print("\n🔄 StopSignal Workflow:") - print(" 1. Source detects data exhaustion") - print(" 2. Source returns StopSignal") - print(" 3. SourceOperator propagates signal") - print(" 4. Downstream nodes receive termination") - print(" 5. Job gracefully completes") - - -if __name__ == "__main__": - main() diff --git a/packages/sage-kernel/examples/cpu_node_demo.py b/packages/sage-kernel/examples/cpu_node_demo.py deleted file mode 100644 index fdfcad303b..0000000000 --- a/packages/sage-kernel/examples/cpu_node_demo.py +++ /dev/null @@ -1,589 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE CPU Node Demonstration -============================ - -This example demonstrates how SAGE supports CPU-only compute nodes for task execution. - -Key Features Demonstrated: -1. ✓ CPU-only task submission to JobManager -2. ✓ Resource-aware node selection (CPU nodes) -3. ✓ Task execution monitoring and logging -4. ✓ Basic health checks and status reporting -5. ✓ Resource requirements specification -6. ✓ Multi-node task distribution -7. ✓ Performance metrics collection -8. ✓ Cluster inspection utilities - -@test:timeout=120 -@test:category=cpu -@test:requires=jobmanager -""" - -import os -import socket -import time -from typing import Any - -from sage.common.core.functions.map_function import MapFunction -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.core.functions.source_function import SourceFunction -from sage.kernel.api.remote_environment import RemoteEnvironment -from sage.kernel.runtime.communication.packet import StopSignal -from sage.kernel.scheduler.api import BaseScheduler -from sage.kernel.scheduler.decision import PlacementDecision -from sage.kernel.scheduler.node_selector import NodeSelector - - -class CPUIntensiveSource(SourceFunction): - """CPU密集型数据源 - 生成需要CPU处理的数据""" - - def __init__(self, max_count: int = 10, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - self.max_count = max_count - - def execute(self, data=None): - if self.counter >= self.max_count: - return StopSignal(f"CPUIntensiveSource_{self.counter}") - - self.counter += 1 - # 模拟CPU密集型数据生成 - data_item = { - "id": self.counter, - "task_type": "cpu_compute", - "compute_value": self.counter * 100, - "timestamp": time.time(), - } - self.logger.info(f"[CPU Source] Generated item {self.counter}/{self.max_count}") - return data_item - - -class CPUComputeProcessor(MapFunction): - """CPU计算处理器 - 执行CPU密集型计算""" - - # 明确声明CPU资源需求（由调度器使用） - cpu_required = 2 # 需要2个CPU核心 - memory_required = "2GB" # 需要2GB内存 - gpu_required = 0 # 明确不需要GPU - - def execute(self, data: dict[str, Any]) -> dict[str, Any]: - if not isinstance(data, dict): - return data - - # 模拟CPU密集型计算 - task_id = data.get("id", 0) - compute_value = data.get("compute_value", 0) - - # 简单的计算任务（可以替换为更复杂的CPU任务） - result = sum(range(compute_value)) % 1000000 - - # 获取执行节点信息（如果可用） - node_info = { - "hostname": socket.gethostname(), - "processor": self.name, - "cpu_cores": os.cpu_count(), - } - - processed_data = { - **data, - "processed": True, - "result": result, - "node_info": node_info, - "process_time": time.time(), - } - - self.logger.info( - f"[CPU Processor] Processed task {task_id}, result={result}, " - f"node={node_info['hostname']}" - ) - return processed_data - - -class CPUResultSink(SinkFunction): - """CPU计算结果接收器""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.processed_count = 0 - self.total_results = [] - self.start_time = time.time() - self.node_distribution = {} # 记录任务在各节点的分布 - - def execute(self, data: dict[str, Any]): - if not isinstance(data, dict): - return - - self.processed_count += 1 - self.total_results.append(data) - - task_id = data.get("id", "unknown") - result = data.get("result", "N/A") - node_info = data.get("node_info", {}) - hostname = node_info.get("hostname", "unknown") - processor = node_info.get("processor", "unknown") - - # 统计节点分布 - self.node_distribution[hostname] = self.node_distribution.get(hostname, 0) + 1 - - self.logger.info( - f"[CPU Sink] Received result #{self.processed_count}: " - f"Task {task_id}, Result={result}, Node={hostname}, Processor={processor}" - ) - print( - f"✅ [CPU Node] Completed task {task_id}: result={result} " - f"(node: {hostname}, processor: {processor})" - ) - - def get_statistics(self) -> dict[str, Any]: - """获取执行统计信息""" - elapsed = time.time() - self.start_time - return { - "total_processed": self.processed_count, - "elapsed_time": elapsed, - "throughput": self.processed_count / elapsed if elapsed > 0 else 0, - "node_distribution": self.node_distribution, - } - - -class CPUOnlyScheduler(BaseScheduler): - """ - CPU专用调度器 - - 特点: - - 只选择CPU节点（不需要GPU） - - 优先选择CPU资源充足的节点 - - 支持负载均衡 - """ - - def __init__(self): - super().__init__() - self.node_selector = NodeSelector() - - def make_decision(self, task_node): - """ - 为任务选择CPU节点 - - 策略: - 1. 不需要GPU资源 - 2. 选择CPU负载最低的节点 - 3. 确保有足够的CPU和内存 - """ - - # 提取CPU资源需求（默认1核） - cpu = ( - getattr(task_node.transformation, "cpu_required", 1) - if hasattr(task_node, "transformation") - else 1 - ) - - # 提取内存需求（默认1GB） - memory = ( - getattr(task_node.transformation, "memory_required", "1GB") - if hasattr(task_node, "transformation") - else "1GB" - ) - - # 选择CPU节点（不需要GPU） - target_node = self.node_selector.select_best_node( - cpu_required=cpu, - gpu_required=0, # 明确指定不需要GPU - strategy="balanced", # 负载均衡策略 - ) - - decision = PlacementDecision( - target_node=target_node, - resource_requirements={ - "cpu": cpu, - "memory": memory, - "gpu": 0, # CPU节点不需要GPU - }, - placement_strategy="cpu_only", - reason=f"CPU task: selected CPU node {target_node} (no GPU required)", - ) - - self.scheduled_count += 1 - self.decision_history.append(decision) - - return decision - - -def demo_basic_cpu_node(): - """ - 示例1: 基本的CPU节点任务执行 - - 演示: - - CPU-only任务提交 - - 任务在CPU节点上执行 - - 监控和日志记录 - """ - print("\n" + "=" * 70) - print("示例1: 基本CPU节点任务执行") - print("=" * 70) - print("\n📊 功能: 提交CPU计算任务到JobManager并在CPU节点执行") - print("🎯 验收标准:") - print(" ✓ 可以通过JobManager将任务分配给CPU SAGE节点") - print(" ✓ 节点能够正常执行并返回结果") - print(" ✓ 任务执行过程中具备基本的监控和日志记录能力\n") - - # 创建RemoteEnvironment（默认会使用CPU节点） - env = RemoteEnvironment(name="cpu_node_basic_demo") - - # 构建CPU任务流 - ( - env.from_source(CPUIntensiveSource, max_count=5, delay=0.5) - .map(CPUComputeProcessor, parallelism=2) # 2个并行CPU处理器 - .sink(CPUResultSink) - ) - - print("🚀 提交任务到JobManager...") - print("📍 任务将被分配到可用的CPU节点\n") - - # 提交并自动停止 - env.submit(autostop=True) - - print("\n✅ 示例1完成!") - print("=" * 70) - - -def demo_cpu_scheduler(): - """ - 示例2: 使用CPU专用调度器 - - 演示: - - 自定义CPU节点选择策略 - - 资源感知调度 - - 负载均衡 - """ - print("\n" + "=" * 70) - print("示例2: CPU专用调度器") - print("=" * 70) - print("\n📊 功能: 使用自定义调度器确保任务只分配到CPU节点") - print("🎯 特性:") - print(" ✓ 明确排除GPU节点") - print(" ✓ CPU资源感知调度") - print(" ✓ 负载均衡策略\n") - - # 创建使用CPU专用调度器的环境 - cpu_scheduler = CPUOnlyScheduler() - env = RemoteEnvironment( - name="cpu_scheduler_demo", - scheduler=cpu_scheduler, - ) - - # 构建CPU任务流 - ( - env.from_source(CPUIntensiveSource, max_count=8, delay=0.3) - .map(CPUComputeProcessor, parallelism=3) # 3个并行处理器 - .sink(CPUResultSink) - ) - - print("🚀 使用CPU专用调度器提交任务...") - print("📍 调度器将选择最优的CPU节点\n") - - # 提交并自动停止 - env.submit(autostop=True) - - # 查看调度统计 - metrics = cpu_scheduler.get_metrics() - print("\n📊 调度器统计:") - print(f" - 调度任务数: {metrics.get('scheduled_count', 0)}") - print(f" - 跳过任务数: {metrics.get('skipped_count', 0)}") - - print("\n✅ 示例2完成!") - print("=" * 70) - - -def demo_cpu_node_monitoring(): - """ - 示例3: CPU节点监控和日志 - - 演示: - - 任务执行监控 - - 日志记录 - - 状态查询 - """ - print("\n" + "=" * 70) - print("示例3: CPU节点监控和日志") - print("=" * 70) - print("\n📊 功能: 展示CPU节点的监控和日志能力") - print("🎯 特性:") - print(" ✓ 实时任务状态监控") - print(" ✓ 详细的日志记录") - print(" ✓ JobManager健康检查\n") - - env = RemoteEnvironment(name="cpu_monitoring_demo") - - # 构建任务流 - ( - env.from_source(CPUIntensiveSource, max_count=6, delay=0.4) - .map(CPUComputeProcessor, parallelism=2) - .sink(CPUResultSink) - ) - - print("🚀 提交任务并监控执行...") - - # 提交任务 - env.submit(autostop=True) - - print("\n📋 监控信息:") - print(" - 任务日志: 查看 .sage/logs/jobmanager/ 目录") - print(" - 所有任务执行均有日志记录") - print(" - JobManager 提供健康检查接口") - - print("\n✅ 示例3完成!") - print("=" * 70) - - -def demo_cluster_inspection(): - """ - 示例4: 集群节点检查 - - 演示: - - 查看可用的CPU节点 - - 节点资源信息 - - 集群统计 - """ - print("\n" + "=" * 70) - print("示例4: 集群节点检查") - print("=" * 70) - print("\n📊 功能: 检查集群中的CPU节点信息") - print("🎯 展示:") - print(" ✓ 可用CPU节点列表") - print(" ✓ 节点资源统计") - print(" ✓ 集群总体状态\n") - - try: - import ray - - if not ray.is_initialized(): - print("⚠️ Ray 未初始化，跳过集群检查") - return - - # 创建节点选择器 - node_selector = NodeSelector() - - # 获取集群统计信息 - stats = node_selector.get_cluster_stats() - - print("📊 集群资源统计:") - print(f" • 节点数量: {stats.get('node_count', 0)}") - print(f" • 总CPU核心: {stats.get('total_cpu', 0):.1f}") - print(f" • 可用CPU: {stats.get('available_cpu', 0):.1f}") - print(f" • CPU使用率: {stats.get('avg_cpu_usage', 0):.1%}") - print(f" • 总内存: {stats.get('total_memory', 0) / (1024**3):.2f} GB") - print(f" • 可用内存: {stats.get('available_memory', 0) / (1024**3):.2f} GB") - print(f" • 总任务数: {stats.get('total_tasks', 0)}") - - # 列出所有节点 - nodes = stats.get("nodes", []) - if nodes: - print(f"\n📋 节点详情 ({len(nodes)} 个节点):") - for i, node in enumerate(nodes, 1): - print(f"\n 节点 #{i}:") - print(f" 主机名: {node.get('hostname', 'unknown')}") - print(f" CPU使用率: {node.get('cpu_usage', 0):.1%}") - print(f" GPU使用率: {node.get('gpu_usage', 0):.1%}") - print(f" 内存使用率: {node.get('memory_usage', 0):.1%}") - print(f" 任务数: {node.get('task_count', 0)}") - - # 选择CPU节点 - print("\n🔍 选择最佳CPU节点:") - cpu_node = node_selector.select_best_node( - cpu_required=2, gpu_required=0, strategy="balanced" - ) - if cpu_node: - print(f" ✓ 选中节点: {cpu_node[:16]}...") - node_res = node_selector.get_node(cpu_node) - if node_res: - print(f" 主机名: {node_res.hostname}") - print(f" 可用CPU: {node_res.available_cpu:.1f}") - print(f" CPU使用率: {node_res.cpu_usage:.1%}") - else: - print(" ⚠️ 未找到合适的CPU节点") - - print("\n✅ 示例4完成!") - print("=" * 70) - - except ImportError: - print("⚠️ Ray 未安装，无法进行集群检查") - except Exception as e: - print(f"⚠️ 集群检查失败: {e}") - - -def demo_resource_requirements(): - """ - 示例5: 显式资源需求规范 - - 演示: - - 在Operator级别指定CPU/内存需求 - - 调度器根据资源需求选择节点 - - 资源感知任务分配 - """ - print("\n" + "=" * 70) - print("示例5: 显式资源需求规范") - print("=" * 70) - print("\n📊 功能: 为CPU任务指定精确的资源需求") - print("🎯 特性:") - print(" ✓ Operator级别资源声明") - print(" ✓ 调度器资源感知") - print(" ✓ 智能节点选择\n") - - # 创建环境 - env = RemoteEnvironment(name="cpu_resource_demo") - - # CPUComputeProcessor 已声明: cpu_required=2, memory_required="2GB", gpu_required=0 - print("💡 CPUComputeProcessor 资源需求:") - print(f" • CPU: {CPUComputeProcessor.cpu_required} 核") - print(f" • 内存: {CPUComputeProcessor.memory_required}") - print(f" • GPU: {CPUComputeProcessor.gpu_required} (不需要)") - print() - - # 构建任务流 - ( - env.from_source(CPUIntensiveSource, max_count=10, delay=0.2) - .map(CPUComputeProcessor, parallelism=3) # 每个实例需要2核CPU - .sink(CPUResultSink) - ) - - print("🚀 提交任务（调度器将选择满足资源需求的CPU节点）...") - env.submit(autostop=True) - - print("\n✅ 示例5完成!") - print("=" * 70) - - -def check_jobmanager_available(): - """检查 JobManager 是否可用""" - try: - sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) - sock.settimeout(1) - result = sock.connect_ex(("localhost", 19001)) - sock.close() - return result == 0 - except Exception: - return False - - -def print_usage_guide(): - """打印使用指南""" - print("\n" + "=" * 70) - print("📚 CPU节点使用指南") - print("=" * 70) - - print("\n1️⃣ 启动JobManager (必需):") - print(" $ sage jobmanager start") - print(" 或者手动启动:") - print(" $ python -m sage.kernel.runtime.job_manager --host 127.0.0.1 --port 19001") - - print("\n2️⃣ 启动Ray集群 (可选，JobManager会自动初始化):") - print(" $ ray start --head # 启动头节点") - print(" $ ray start --address=<head_address> # 添加CPU工作节点") - - print("\n3️⃣ 配置CPU工作节点:") - print(" # 在工作节点机器上") - print(" $ ray start --address=<head_address> --num-cpus=8 --num-gpus=0") - print(" # 指定只有CPU资源，不分配GPU") - - print("\n4️⃣ 检查集群状态:") - print(" $ sage jobmanager status") - print(" $ ray status") - - print("\n5️⃣ 运行CPU任务:") - print(" $ python cpu_node_demo.py") - - print("\n6️⃣ 查看日志:") - print(" $ ls -la .sage/logs/jobmanager/") - print(" $ tail -f .sage/logs/jobmanager/session_*/jobmanager.log") - - print("\n" + "=" * 70) - - -def main(): - """主函数""" - print( - """ -╔══════════════════════════════════════════════════════════════════════╗ -║ SAGE CPU Node 完整演示 ║ -║ ║ -║ 本示例演示SAGE框架对CPU版本计算节点的完整支持 ║ -║ ║ -║ 验收标准: ║ -║ ✓ 可以通过JobManager将任务分配给CPU SAGE节点 ║ -║ ✓ 节点能够正常执行并返回结果 ║ -║ ✓ 任务执行过程中具备基本的监控和日志记录能力 ║ -║ ✓ 支持资源需求规范和智能节点选择 ║ -║ ✓ 提供集群检查和统计功能 ║ -╚══════════════════════════════════════════════════════════════════════╝ - """ - ) - - # 检查JobManager是否可用 - if not check_jobmanager_available(): - print("\n⚠️ JobManager 未运行!") - print_usage_guide() - print("\n💡 请先启动 JobManager，然后重新运行本示例\n") - return - - print("\n✅ JobManager 已就绪\n") - - try: - # 运行所有示例 - demo_basic_cpu_node() - time.sleep(1) - - demo_cpu_scheduler() - time.sleep(1) - - demo_cpu_node_monitoring() - time.sleep(1) - - demo_cluster_inspection() - time.sleep(1) - - demo_resource_requirements() - - print("\n" + "=" * 70) - print("🎉 所有CPU节点演示完成!") - print("=" * 70) - - print("\n📋 验收标准确认:") - print(" ✅ JobManager成功分配任务给CPU节点") - print(" ✅ CPU节点正常执行任务并返回结果") - print(" ✅ 提供完整的监控和日志记录") - print(" ✅ 支持资源需求规范和节点选择") - print(" ✅ 提供集群检查和统计功能") - - print("\n💡 关键要点:") - print(" • CPU节点通过NodeSelector自动选择（gpu_required=0）") - print(" • RemoteEnvironment自动与JobManager协作") - print(" • 支持自定义调度策略（CPUOnlyScheduler）") - print(" • 内置监控和日志系统") - print(" • 可在无GPU环境中运行") - print(" • 支持Operator级别资源需求声明") - print(" • 提供集群资源检查工具") - - print("\n🔗 相关文件:") - print(" • JobManager: sage/kernel/runtime/job_manager.py") - print(" • NodeSelector: sage/kernel/scheduler/node_selector.py") - print(" • RemoteEnvironment: sage/kernel/api/remote_environment.py") - print(" • Scheduler: sage/kernel/scheduler/impl/resource_aware_scheduler.py") - print(" • 日志目录: .sage/logs/jobmanager/") - - print_usage_guide() - - except KeyboardInterrupt: - print("\n\n⚠️ 用户中断执行") - except Exception as e: - print(f"\n❌ 错误: {e}") - import traceback - - traceback.print_exc() - print("\n💡 提示:") - print(" 1. 确保JobManager已启动: sage jobmanager start") - print(" 2. 检查Ray是否运行: ray status") - print(" 3. 查看日志: .sage/logs/jobmanager/") - - -if __name__ == "__main__": - main() diff --git a/packages/sage-kernel/examples/functions/hello_comap_function_example.py b/packages/sage-kernel/examples/functions/hello_comap_function_example.py deleted file mode 100644 index 477771e1bd..0000000000 --- a/packages/sage-kernel/examples/functions/hello_comap_function_example.py +++ /dev/null @@ -1,220 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE CoMap Function 示例 -@test:timeout=120 -@test:category=streaming -""" - -import logging -import os -import random -import time - -from sage.common.core.functions.comap_function import BaseCoMapFunction -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.core.functions.source_function import SourceFunction -from sage.kernel.api.local_environment import LocalEnvironment - -# 设置日志级别为ERROR减少输出 -os.environ.setdefault("SAGE_LOG_LEVEL", "ERROR") - -# 配置 Python 日志系统 -logging.basicConfig(level=logging.ERROR) -for logger_name in ["sage", "JobManager", "ray", "asyncio", "urllib3"]: - logging.getLogger(logger_name).setLevel(logging.ERROR) - -# 禁用所有INFO级别的日志 -logging.getLogger().setLevel(logging.ERROR) - - -# 温度传感器数据源 -class TemperatureSource(SourceFunction): - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - - def execute(self, data=None): - self.counter += 1 - # 模拟温度数据 (18-35°C) - temperature = round(random.uniform(18.0, 35.0), 1) - return { - "sensor_type": "temperature", - "value": temperature, - "unit": "°C", - "id": self.counter, - } - - -# 湿度传感器数据源 -class HumiditySource(SourceFunction): - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - - def execute(self, data=None): - self.counter += 1 - # 模拟湿度数据 (30-90%) - humidity = round(random.uniform(30.0, 90.0), 1) - return { - "sensor_type": "humidity", - "value": humidity, - "unit": "%", - "id": self.counter, - } - - -# 压力传感器数据源 -class PressureSource(SourceFunction): - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - - def execute(self, data=None): - self.counter += 1 - # 模拟压力数据 (900-1100 hPa) - pressure = round(random.uniform(900.0, 1100.0), 1) - return { - "sensor_type": "pressure", - "value": pressure, - "unit": "hPa", - "id": self.counter, - } - - -# CoMap函数：分别处理不同类型的传感器数据 -class SensorDataProcessor(BaseCoMapFunction): - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.temp_alert_threshold = 30.0 - self.humidity_alert_threshold = 80.0 - self.pressure_alert_threshold = 1050.0 - - def map0(self, data): - """处理温度数据（来自输入流0）""" - temp_value = data["value"] - status = "🔥 HIGH" if temp_value > self.temp_alert_threshold else "✅ Normal" - return { - "stream": "temperature", - "original": data, - "processed_value": f"{temp_value}°C", - "status": status, - "alert": temp_value > self.temp_alert_threshold, - } - - def map1(self, data): - """处理湿度数据（来自输入流1）""" - humidity_value = data["value"] - status = "💧 HIGH" if humidity_value > self.humidity_alert_threshold else "✅ Normal" - return { - "stream": "humidity", - "original": data, - "processed_value": f"{humidity_value}%", - "status": status, - "alert": humidity_value > self.humidity_alert_threshold, - } - - def map2(self, data): - """处理压力数据（来自输入流2）""" - pressure_value = data["value"] - status = "⚡ HIGH" if pressure_value > self.pressure_alert_threshold else "✅ Normal" - return { - "stream": "pressure", - "original": data, - "processed_value": f"{pressure_value} hPa", - "status": status, - "alert": pressure_value > self.pressure_alert_threshold, - } - - -# 类型特定处理的CoMap函数 -class TypeSpecificProcessor(BaseCoMapFunction): - def map0(self, data): - """简单的温度数据格式化""" - return f"🌡️ Temperature: {data['value']}°C (ID: {data['id']})" - - def map1(self, data): - """简单的湿度数据格式化""" - return f"💧 Humidity: {data['value']}% (ID: {data['id']})" - - def map2(self, data): - """简单的压力数据格式化""" - return f"🔘 Pressure: {data['value']} hPa (ID: {data['id']})" - - -# 汇总输出函数 -class SensorSink(SinkFunction): - def execute(self, data): - if isinstance(data, dict) and "alert" in data: - prefix = "🚨 ALERT" if data["alert"] else "📊 DATA" - print( - f"[{self.name}] {prefix}: {data['stream']} = {data['processed_value']} ({data['status']})" - ) - else: - print(f"[{self.name}] {data}") - return data - - -def main(): - # 创建环境 - env = LocalEnvironment("comap_function_example") - - print("🚀 Starting CoMap Function Example") - print("🌡️ Demonstrating multi-sensor data processing with CoMap") - print("📊 Each sensor type is processed independently") - print("⏹️ Press Ctrl+C to stop\n") - - # 创建不同类型的传感器数据源 - temp_stream = env.from_source(TemperatureSource, delay=1.5) - humidity_stream = env.from_source(HumiditySource, delay=2.0) - pressure_stream = env.from_source(PressureSource, delay=2.5) - - print("🔗 Creating connected streams...") - - # 示例1：使用CoMap进行复杂的传感器数据处理 - print("\n📈 Example 1: Advanced Sensor Processing with CoMap") - connected_sensors = temp_stream.connect(humidity_stream).connect(pressure_stream) - - # 使用CoMap分别处理每种传感器数据 - connected_sensors.comap(SensorDataProcessor).sink(SensorSink, name="AdvancedProcessor") - - # 示例2：简单的类型特定格式化 - print("📝 Example 2: Simple Type-Specific Formatting") - connected_sensors.comap(TypeSpecificProcessor).print("🎯 Formatted Output") - - print("\n📈 All sensors connected and processing with CoMap...\n") - print("💡 CoMap Features Demonstrated:") - print(" - map0() processes temperature data independently") - print(" - map1() processes humidity data independently") - print(" - map2() processes pressure data independently") - print(" - Each stream maintains its own processing logic") - print(" - No data merging - streams are processed separately\n") - - try: - # 运行流处理 - env.submit() - - # 在测试模式下运行更短时间 - test_mode = os.environ.get("SAGE_EXAMPLES_MODE") == "test" - runtime = 8 if test_mode else 40 - - print(f"⏰ Running for {runtime} seconds...") - time.sleep(runtime) # 测试模式运行8秒，正常模式40秒 - - except KeyboardInterrupt: - print("\n\n🛑 Stopping CoMap Function Example...") - - finally: - print("\n📋 Example completed!") - print("💡 This example demonstrated:") - print(" - Multiple independent sensor data sources") - print(" - CoMap function with map0, map1, map2 methods") - print(" - Stream-specific processing logic") - print(" - Alert detection based on sensor type") - print(" - Independent processing without data merging") - print("\n🔄 Comparison with regular map():") - print(" - Regular map(): All inputs merged → single execute() method") - print(" - CoMap: Each input stream → dedicated mapN() method") - - -if __name__ == "__main__": - main() diff --git a/packages/sage-kernel/examples/functions/hello_comap_lambda_example.py b/packages/sage-kernel/examples/functions/hello_comap_lambda_example.py deleted file mode 100644 index 680c3beb36..0000000000 --- a/packages/sage-kernel/examples/functions/hello_comap_lambda_example.py +++ /dev/null @@ -1,351 +0,0 @@ -#!/usr/bin/env python3 -""" -CoMap Lambda/Callable Support Example -@test:tim # Execute example 1 - print("Processing sensor data...") - - test_mode = os.environ.get("SAGE_EXAMPLES_MODE") == "test" - if test_mode: - # In test mode, skip actual execution for faster testing - print("✅ Test mode: Skipping actual execution") - else: - env1.submit(autostop=True) - # Wait for processing to complete - import time - wait_time = 5 - time.sleep(wait_time) - - print("✅ Example 1 completed!"):category=streaming - -This example demonstrates the new lambda and callable support for CoMap operations, -showing different ways to define multi-stream processing without requiring class definitions. -""" - -import os -import sys -from typing import Any - -from sage.common.core.functions.comap_function import BaseCoMapFunction -from sage.common.core.functions.source_function import SourceFunction -from sage.kernel.api.local_environment import LocalEnvironment -from sage.kernel.runtime.communication.packet import StopSignal - -# 设置日志级别为ERROR减少输出 -os.environ.setdefault("SAGE_LOG_LEVEL", "ERROR") - -# Add the project root to Python path for imports -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) - - -class ListSource(SourceFunction): - """Simple source that emits items from a predefined list with proper termination""" - - def __init__(self, data_list: list[Any], *args, **kwargs): - super().__init__(*args, **kwargs) - self.data_list = data_list - self.index = 0 - - def execute(self) -> Any: - if self.index >= len(self.data_list): - # Data exhausted, send stop signal - return StopSignal(f"ListSource_{self.index}") - - result = self.data_list[self.index] - self.index += 1 - return result - - -def main(): - """Demonstrate different lambda/callable usage patterns for CoMap operations""" - - print("🚀 CoMap Function Examples") - print("=" * 60) - - # Check if running in test mode - only run first example for faster testing - test_mode = os.environ.get("SAGE_EXAMPLES_MODE") == "test" - if test_mode: - print("🧪 Running in test mode - executing only first example") - - # Create environment - env1 = LocalEnvironment() - - # Example 1: Sensor Data Processing - print("\n📋 Example 1: Sensor Data Processing") - print("-" * 40) - - # Create a CoMap function to process sensor data - class SensorCoMapFunction(BaseCoMapFunction): - def __init__(self, **kwargs): - super().__init__(**kwargs) - - def map0(self, temp): - """Process temperature data""" - status = "Hot" if temp > 23 else "Normal" - return f"🌡️ Temperature: {temp}°C ({status})" - - def map1(self, humid): - """Process humidity data""" - status = "High" if humid > 60 else "Normal" - return f"💧 Humidity: {humid}% ({status})" - - def map2(self, press): - """Process pressure data""" - status = "High" if press > 1015 else "Normal" - return f"🔘 Pressure: {press} hPa ({status})" - - # Create streams and connect them - temp_stream = env1.from_source(ListSource, [20.5, 22.1, 19.8, 25.3, 21.7]) - humidity_stream = env1.from_source(ListSource, [45, 52, 38, 67, 41]) - pressure_stream = env1.from_source(ListSource, [1013.2, 1015.8, 1012.1, 1018.5, 1014.3]) - - # Connect streams properly - connected_sensors = temp_stream.connect(humidity_stream).connect(pressure_stream) - - # Apply CoMap function - connected_sensors.comap(SensorCoMapFunction).print("Sensor Data") - - # Execute example 1 - print("Processing sensor data...") - - test_mode = os.environ.get("SAGE_EXAMPLES_MODE") == "test" - if test_mode: - # In test mode, skip actual execution for faster testing - print("✅ Test mode: Skipping actual execution") - else: - env1.submit(autostop=True) - # Wait for processing to complete - import time - - time.sleep(5) - - print("✅ Example 1 completed!") - - # In test mode, only run the first example for faster testing - if test_mode: - print("\n🧪 Test mode: Skipping remaining examples for faster execution") - print("\n✅ CoMap function example completed successfully!") - print("\n💡 Summary of CoMap usage patterns:") - print(" 1. Class-based CoMap functions (recommended)") - print(" 2. process_stream_N methods for each connected stream") - print(" 3. Built-in error handling and validation") - print(" 4. Type safety and documentation support") - - # Clean up environment - print("\n🧹 Cleaning up environment...") - env1.close() - print("✅ Environment closed successfully!") - return - - # Example 2: Weather Data Processing - print("\n📋 Example 2: Weather Data Processing") - print("-" * 40) - - # Reset environment for new example - env2 = LocalEnvironment() - - # Create weather data CoMap function - class WeatherCoMapFunction(BaseCoMapFunction): - def __init__(self, **kwargs): - super().__init__(**kwargs) - - def map0(self, temp: float) -> str: - """Format temperature data""" - celsius = temp - fahrenheit = temp * 9 / 5 + 32 - return f"🌡️ {celsius}°C / {fahrenheit:.1f}°F" - - def map1(self, humidity: int) -> str: - """Format humidity data""" - level = "Low" if humidity < 40 else "High" if humidity > 70 else "Normal" - return f"💧 {humidity}% ({level})" - - # Create new sources - temp_source2 = env2.from_source(ListSource, [18.5, 26.2, 23.1, 29.8]) - humidity_source2 = env2.from_source(ListSource, [35, 75, 55, 82]) - - # Create and connect streams - temp_stream2 = temp_source2 - humidity_stream2 = humidity_source2 - - connected_weather = temp_stream2.connect(humidity_stream2) - - # Apply weather CoMap function - connected_weather.comap(WeatherCoMapFunction).print("Weather Data") - - # Execute example 2 - print("Processing weather data...") - env2.submit(autostop=True) - - # Wait for processing to complete - wait_time = 5 - time.sleep(wait_time) - print("✅ Example 2 completed!") - - # Example 3: Mixed Data Processing - print("\n📋 Example 3: Mixed Data Processing") - print("-" * 40) - - # Reset environment for new example - env3 = LocalEnvironment() - - # Create mixed data CoMap function - class MixedDataCoMapFunction(BaseCoMapFunction): - def __init__(self, **kwargs): - super().__init__(**kwargs) - - def map0(self, data: float) -> str: - """Complex numeric processing with validation""" - if data < 0: - return f"⚠️ Negative value: {data}" - elif data > 100: - return f"🔥 High value: {data}" - else: - return f"✅ Normal: {data:.2f}" - - def map1(self, text: str) -> str: - """Text processing""" - return f"📝 Text: '{text}' (len={len(text)})" - - def map2(self, flag: bool) -> str: - """Boolean processing""" - return f"🏁 Flag: {flag} ({'ON' if flag else 'OFF'})" - - # Create diverse data sources - numeric_source = env3.from_source(ListSource, [15.5, -2.3, 105.7, 42.1, 0.0]) - text_source = env3.from_source(ListSource, ["hello", "world", "sage", "framework", "lambda"]) - boolean_source = env3.from_source(ListSource, [True, False, True, True, False]) - - # Create and connect streams - numeric_stream = numeric_source - text_stream = text_source - boolean_stream = boolean_source - - connected_mixed = numeric_stream.connect(text_stream).connect(boolean_stream) - - # Apply mixed data CoMap function - connected_mixed.comap(MixedDataCoMapFunction).print("Mixed Data") - - # Execute example 3 - print("Processing mixed data types...") - env3.submit(autostop=True) - - # Wait for processing to complete - time.sleep(wait_time) - print("✅ Example 3 completed!") - - # Example 4: Mathematical Operations - print("\n📋 Example 4: Mathematical Operations") - print("-" * 40) - - # Reset environment for new example - env4 = LocalEnvironment() - - # Create mathematical CoMap function - class MathCoMapFunction(BaseCoMapFunction): - def __init__(self, **kwargs): - super().__init__(**kwargs) - - def map0(self, x: int) -> str: - """Square first stream""" - result = x**2 - return f"🔢 {x}² = {result}" - - def map1(self, x: int) -> str: - """Divide second stream by 10""" - result = x / 10 - return f"➗ {x}/10 = {result}" - - def map2(self, x: float) -> str: - """Multiply third stream by 100 and round""" - result = round(x * 100, 1) - return f"✖️ {x}×100 = {result}" - - # Create numeric data sources - input1_source = env4.from_source(ListSource, [1, 2, 3, 4, 5]) - input2_source = env4.from_source(ListSource, [10, 20, 30, 40, 50]) - input3_source = env4.from_source(ListSource, [0.1, 0.2, 0.3, 0.4, 0.5]) - - # Create and connect streams - input1 = input1_source - input2 = input2_source - input3 = input3_source - - connected_math = input1.connect(input2).connect(input3) - - # Apply mathematical transformations - connected_math.comap(MathCoMapFunction).print("Math Results") - - # Execute example 4 - print("Processing mathematical operations...") - env4.submit(autostop=True) - - # Wait for processing to complete - time.sleep(wait_time) - print("✅ Example 4 completed!") - - # Example 5: Error Handling and Validation - print("\n📋 Example 5: Error Handling and Validation") - print("-" * 40) - - # Reset environment for new example - env5 = LocalEnvironment() - - # Create validation CoMap function - class ValidationCoMapFunction(BaseCoMapFunction): - def __init__(self, **kwargs): - super().__init__(**kwargs) - - def map0(self, x) -> str: - """Clamp negative numbers""" - result = max(0, x) if x is not None else 0 - status = "⬆️ clamped" if x is not None and x < 0 else "✅ valid" - return f"🔢 {x} → {result} ({status})" - - def map1(self, s) -> str: - """Handle empty/None strings""" - if s and isinstance(s, str) and s.strip(): - result = s.strip() - return f"📝 '{s}' → '{result}' (✅ valid)" - else: - return f"📝 '{s}' → 'EMPTY' (⚠️ fixed)" - - # Create data with potential issues - mixed_data1 = env5.from_source(ListSource, [5, -3, 0, 12, -1]) - mixed_data2 = env5.from_source(ListSource, ["valid", "", "test", None, "data"]) - - # Create and connect streams - data1 = mixed_data1 - data2 = mixed_data2 - - connected_validation = data1.connect(data2) - - # Apply validation and error handling - connected_validation.comap(ValidationCoMapFunction).print("Validated Data") - - # Execute example 5 - print("Processing with validation...") - env5.submit(autostop=True) - - # Wait for processing to complete - time.sleep(wait_time) - print("✅ Example 5 completed!") - - print("\n✅ All CoMap function examples completed successfully!") - print("\n💡 Summary of CoMap usage patterns:") - print(" 1. Class-based CoMap functions (recommended)") - print(" 2. process_stream_N methods for each connected stream") - print(" 3. Built-in error handling and validation") - print(" 4. Type safety and documentation support") - - # Clean up all environments - print("\n🧹 Cleaning up environments...") - env1.close() - env2.close() - env3.close() - env4.close() - env5.close() - print("✅ All environments closed successfully!") - - -if __name__ == "__main__": - main() diff --git a/packages/sage-kernel/examples/functions/hello_wordcount_batch_example.py b/packages/sage-kernel/examples/functions/hello_wordcount_batch_example.py deleted file mode 100644 index 2786321037..0000000000 --- a/packages/sage-kernel/examples/functions/hello_wordcount_batch_example.py +++ /dev/null @@ -1,69 +0,0 @@ -from sage.common.core.functions.batch_function import BatchFunction -from sage.common.core.functions.flatmap_function import FlatMapFunction -from sage.common.core.functions.map_function import MapFunction -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.api.local_environment import LocalEnvironment - - -# 批处理数据源：生成几行句子 -class SentenceBatch(BatchFunction): - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.sentences = [ - "hello world", - "hello sage", - "hello chatgpt", - "world of ai", - "sage world", - ] - self.index = 0 - - def execute(self): - if self.index >= len(self.sentences): - return None - sentence = self.sentences[self.index] - self.index += 1 - return sentence - - -# 拆分句子为单词 -class SplitWords(FlatMapFunction): - def execute(self, data): - return data.split() - - -# 转换为 (word, 1) -class WordToPair(MapFunction): - def execute(self, data): - return (data, 1) - - -class PrintResult(SinkFunction): - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.counts = {} - - def execute(self, data): - word, cnt = data - self.counts[word] = self.counts.get(word, 0) + cnt - - def close(self): - print("WordCount 结果：") - for word, count in self.counts.items(): - print(f"{word}: {count}") - - -def main(): - env = LocalEnvironment("WordCount") - - # 批处理：句子 -> 拆分单词 -> 转换为(word,1) -> 聚合 -> 输出 - env.from_batch(SentenceBatch).flatmap(SplitWords).map(WordToPair).sink(PrintResult) - - env.submit(autostop=True) - print("WordCount 批处理示例结束") - - -if __name__ == "__main__": - CustomLogger.disable_global_console_debug() - main() diff --git a/packages/sage-kernel/examples/functions/hello_wordcount_lambda_example.py b/packages/sage-kernel/examples/functions/hello_wordcount_lambda_example.py deleted file mode 100644 index 64f3112b2f..0000000000 --- a/packages/sage-kernel/examples/functions/hello_wordcount_lambda_example.py +++ /dev/null @@ -1,104 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE WordCount Lambda 示例 -@test:timeout=120 -@test:category=streaming -""" - -import os -import time -from collections import Counter - -from sage.common.core.functions.source_function import SourceFunction -from sage.kernel.api.local_environment import LocalEnvironment - -# 设置日志级别为ERROR减少输出 -os.environ.setdefault("SAGE_LOG_LEVEL", "ERROR") - - -# 简单的句子源，重复输出同一句话 -class SentenceSource(SourceFunction): - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.sentences = [ - "hello world sage framework", - "this is a streaming data processing example", - "lambda functions make the code much cleaner", - "word count is a classic big data example", - "sage provides powerful stream processing capabilities", - ] - self.counter = 0 - - def execute(self, data=None): - # 随机选择一个句子，或者循环输出 - sentence = self.sentences[self.counter % len(self.sentences)] - self.counter += 1 - return sentence - - -def main(): - # 创建环境 - env = LocalEnvironment("wordcount_example") - - # 全局词汇计数器 - word_counts = Counter() - total_processed = 0 - - def update_word_count(words_with_count): - """更新全局词汇计数""" - nonlocal total_processed - word, count = words_with_count - word_counts[word] += count - total_processed += count - - # 每处理10个词就打印一次统计结果 - if total_processed % 10 == 0: - print(f"\n=== Word Count Statistics (Total: {total_processed}) ===") - for word, count in word_counts.most_common(10): - print(f"{word:20}: {count:3d}") - print("=" * 50) - - # 构建流处理管道 - ( - env.from_source(SentenceSource, delay=1.0) # 每秒产生一个句子 - # 数据清洗和预处理 - .map(lambda sentence: sentence.lower()) # 转小写 - .map(lambda sentence: sentence.strip()) # 去除首尾空白 - .filter(lambda sentence: len(sentence) > 0) # 过滤空字符串 - # 分词处理 - .flatmap(lambda sentence: sentence.split()) # 按空格分词 - .filter(lambda word: len(word) > 2) # 过滤长度小于3的词 - .map(lambda word: word.replace(",", "").replace(".", "")) # 去除标点 - # 词汇统计 - .map(lambda word: (word, 1)) # 转换为 (word, count) 格式 - .print() # 更新计数器 - ) - - print("🚀 Starting WordCount Example with Lambda Functions") - print("📝 Processing sentences and counting words...") - print("⏹️ Press Ctrl+C to stop") - - try: - # 运行流处理 - env.submit() - - # 在测试模式下运行更短时间 - test_mode = os.environ.get("SAGE_EXAMPLES_MODE") == "test" - runtime = 10 if test_mode else 60 - - print(f"⏰ Running for {runtime} seconds...") - time.sleep(runtime) # 测试模式运行10秒，正常模式60秒 - except KeyboardInterrupt: - print("\n\n🛑 Stopping WordCount Example...") - print("\n📊 Final Word Count Results:") - print("=" * 60) - for word, count in word_counts.most_common(): - print(f"{word:20}: {count:3d}") - print("=" * 60) - print(f"Total words processed: {total_processed}") - finally: - env.close() - - -if __name__ == "__main__": - main() diff --git a/packages/sage-kernel/examples/keyed_state_example.py b/packages/sage-kernel/examples/keyed_state_example.py deleted file mode 100644 index 36cde8ca1d..0000000000 --- a/packages/sage-kernel/examples/keyed_state_example.py +++ /dev/null @@ -1,403 +0,0 @@ -""" -Keyed State Example - User Session Tracking - -This example demonstrates how to use SAGE's keyed state support to track -user sessions with automatic state persistence. - -The example implements: -1. Real-time user session tracking -2. Per-user feature aggregation -3. Time window-based aggregations -4. Automatic state persistence and recovery - -Usage: - python examples/tutorials/l3-kernel/keyed_state_example.py -""" - -import time - -from sage.common.core.functions import MapFunction, SinkFunction, SourceFunction -from sage.kernel.api.local_environment import LocalEnvironment - -# ============================================================================== -# Data Source: User Activity Events -# ============================================================================== - - -class UserActivitySource(SourceFunction): - """ - Generate simulated user activity events. - - Events include user actions like login, page_view, click, purchase, etc. - """ - - def __init__(self, num_users=5, events_per_user=10, **kwargs): - super().__init__(**kwargs) - self.num_users = num_users - self.events_per_user = events_per_user - self.counter = 0 - self.total_events = num_users * events_per_user - - # Predefined user activity patterns - self.users = [f"user_{i}" for i in range(num_users)] - self.actions = [ - "login", - "page_view", - "click", - "add_to_cart", - "purchase", - "logout", - ] - self.pages = ["/home", "/products", "/cart", "/checkout", "/account"] - - def execute(self, data=None): - if self.counter >= self.total_events: - return None # Stop - - # Generate event - user_idx = self.counter % self.num_users - action_idx = (self.counter // self.num_users) % len(self.actions) - - event = { - "timestamp": time.time(), - "user_id": self.users[user_idx], - "action": self.actions[action_idx], - "page": self.pages[action_idx % len(self.pages)], - "value": (action_idx + 1) * 10, # Simulated value - "session_id": f"session_{self.counter // (self.num_users * 3)}", - } - - self.counter += 1 - self.logger.info(f"Generated event: {event['user_id']} - {event['action']}") - return event - - -# ============================================================================== -# Key Extractor: Extract User ID as Key -# ============================================================================== - - -class UserIdExtractor(MapFunction): - """ - Extract user_id from event as the partition key. - - This enables per-user state management downstream. - """ - - def execute(self, event: dict) -> str: - user_id = event["user_id"] - self.logger.debug(f"Extracted key: {user_id}") - return user_id - - -# ============================================================================== -# Keyed State Function: User Session Manager -# ============================================================================== - - -class UserSessionManager(MapFunction): - """ - Manage user sessions with keyed state. - - This function demonstrates: - 1. Accessing current key via ctx.get_key() - 2. Maintaining per-user state (automatically persisted) - 3. Aggregating metrics per user - 4. Time-based session management - - State Structure: - self.user_sessions = { - user_id: { - 'first_seen': timestamp, - 'last_seen': timestamp, - 'session_count': int, - 'current_session': {...}, - 'total_value': float, - 'action_counts': {...} - } - } - """ - - def __init__(self, session_timeout=300, **kwargs): - super().__init__(**kwargs) - # Keyed state - automatically persisted by SAGE - self.user_sessions = {} - self.session_timeout = session_timeout - - # Global metrics (not keyed) - self.total_events_processed = 0 - self.unique_users_seen = set() - - def execute(self, event: dict): - # Get current packet's key (user_id in this case) - user_id = self.ctx.get_key() - - # Update global metrics - self.total_events_processed += 1 - self.unique_users_seen.add(user_id) - - # Initialize user session if first time seeing this user - if user_id not in self.user_sessions: - self._initialize_user_session(user_id, event) - else: - self._update_user_session(user_id, event) - - # Get current session state - session = self.user_sessions[user_id] - - # Prepare enriched event with session context - enriched_event = { - "original_event": event, - "user_id": user_id, - "session_metrics": { - "session_count": session["session_count"], - "current_session_actions": len(session["current_session"]["actions"]), - "total_value": session["total_value"], - "session_duration": time.time() - session["current_session"]["start_time"], - "lifetime_actions": sum(session["action_counts"].values()), - }, - "global_metrics": { - "total_events": self.total_events_processed, - "unique_users": len(self.unique_users_seen), - }, - } - - self.logger.info( - f"User {user_id}: Session #{session['session_count']}, " - f"Total Value: ${session['total_value']:.2f}, " - f"Actions: {sum(session['action_counts'].values())}" - ) - - return enriched_event - - def _initialize_user_session(self, user_id: str, event: dict): - """Initialize state for a new user""" - current_time = time.time() - - self.user_sessions[user_id] = { - "first_seen": current_time, - "last_seen": current_time, - "session_count": 1, - "current_session": { - "session_id": event["session_id"], - "start_time": current_time, - "actions": [event["action"]], - "pages_visited": [event["page"]], - }, - "total_value": event["value"], - "action_counts": {event["action"]: 1}, - } - - self.logger.info(f"Initialized session for new user: {user_id}") - - def _update_user_session(self, user_id: str, event: dict): - """Update existing user session""" - session = self.user_sessions[user_id] - current_time = time.time() - - # Check if we need to start a new session (timeout or explicit session change) - time_since_last = current_time - session["last_seen"] - session_changed = event["session_id"] != session["current_session"]["session_id"] - - if time_since_last > self.session_timeout or session_changed: - # Start new session - session["session_count"] += 1 - session["current_session"] = { - "session_id": event["session_id"], - "start_time": current_time, - "actions": [event["action"]], - "pages_visited": [event["page"]], - } - self.logger.info(f"Started new session #{session['session_count']} for user {user_id}") - else: - # Update current session - session["current_session"]["actions"].append(event["action"]) - if event["page"] not in session["current_session"]["pages_visited"]: - session["current_session"]["pages_visited"].append(event["page"]) - - # Update session state - session["last_seen"] = current_time - session["total_value"] += event["value"] - - # Update action counts - action = event["action"] - session["action_counts"][action] = session["action_counts"].get(action, 0) + 1 - - -# ============================================================================== -# Window Aggregation Function -# ============================================================================== - - -class TimeWindowAggregator(MapFunction): - """ - Perform time-based window aggregations with keyed state. - - Demonstrates: - 1. Sliding time windows per user - 2. Automatic cleanup of old windows - 3. Aggregations within windows - """ - - def __init__(self, window_size=60, **kwargs): # 60 second windows - super().__init__(**kwargs) - self.window_size = window_size - # Keyed state: {user_id: {window_id: [events]}} - self.window_data = {} - - def execute(self, enriched_event: dict): - user_id = self.ctx.get_key() - current_time = time.time() - window_id = int(current_time // self.window_size) - - # Initialize user's window data - if user_id not in self.window_data: - self.window_data[user_id] = {} - - # Add event to current window - if window_id not in self.window_data[user_id]: - self.window_data[user_id][window_id] = [] - - self.window_data[user_id][window_id].append(enriched_event) - - # Cleanup old windows (keep last 3 windows) - old_windows = [wid for wid in self.window_data[user_id] if wid < window_id - 2] - for wid in old_windows: - del self.window_data[user_id][wid] - - # Calculate window aggregations - current_window = self.window_data[user_id][window_id] - aggregations = { - "window_id": window_id, - "window_start": window_id * self.window_size, - "window_end": (window_id + 1) * self.window_size, - "event_count": len(current_window), - "total_value": sum(e["original_event"]["value"] for e in current_window), - "unique_actions": len({e["original_event"]["action"] for e in current_window}), - } - - return { - "user_id": user_id, - "window": aggregations, - "latest_event": enriched_event, - } - - -# ============================================================================== -# Sink: Display Results -# ============================================================================== - - -class ResultDisplaySink(SinkFunction): - """Display processed results with session and window information""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.result_count = 0 - - def execute(self, result: dict): - self.result_count += 1 - - print(f"\n{'=' * 70}") - print(f"Result #{self.result_count}") - print(f"{'=' * 70}") - - # User info - print(f"👤 User: {result['user_id']}") - - # Window info - if "window" in result: - window = result["window"] - print(f"\n📊 Window Aggregation (Window ID: {window['window_id']}):") - print(f" Events in Window: {window['event_count']}") - print(f" Total Value: ${window['total_value']:.2f}") - print(f" Unique Actions: {window['unique_actions']}") - - # Session info - if "latest_event" in result and "session_metrics" in result["latest_event"]: - metrics = result["latest_event"]["session_metrics"] - print("\n📈 Session Metrics:") - print(f" Session #: {metrics['session_count']}") - print(f" Current Session Actions: {metrics['current_session_actions']}") - print(f" Lifetime Value: ${metrics['total_value']:.2f}") - print(f" Total Actions: {metrics['lifetime_actions']}") - print(f" Session Duration: {metrics['session_duration']:.1f}s") - - # Global metrics - if "latest_event" in result and "global_metrics" in result["latest_event"]: - global_m = result["latest_event"]["global_metrics"] - print("\n🌍 Global Metrics:") - print(f" Total Events Processed: {global_m['total_events']}") - print(f" Unique Users: {global_m['unique_users']}") - - return result - - -# ============================================================================== -# Main Example -# ============================================================================== - - -def main(): - """ - Run the keyed state example. - - Pipeline: - UserActivitySource - -> KeyBy(UserIdExtractor) - -> Map(UserSessionManager) # Maintains per-user sessions - -> Map(TimeWindowAggregator) # Maintains per-user time windows - -> Sink(ResultDisplaySink) - """ - print("\n" + "=" * 70) - print("SAGE Keyed State Example - User Session Tracking") - print("=" * 70) - - # Create environment - env = LocalEnvironment("keyed_state_example") - - # Build pipeline - ( - env.from_source( - UserActivitySource, - num_users=3, # 3 users - events_per_user=8, # 8 events each - delay=0.5, # 0.5s between events - ) - .keyby(UserIdExtractor, strategy="hash") # Partition by user_id - .map(UserSessionManager, session_timeout=10) # Track sessions - .map(TimeWindowAggregator, window_size=30) # 30-second windows - .sink(ResultDisplaySink) - ) - - print("\n🚀 Starting pipeline...") - print(" - 3 users, 8 events each (24 total events)") - print(" - Events generated every 0.5 seconds") - print(" - Sessions tracked per user with 10s timeout") - print(" - Time windows of 30 seconds per user") - print() - - try: - # Submit and run - env.submit() - - # Let it run for a while - time.sleep(15) - - print("\n" + "=" * 70) - print("Pipeline completed successfully!") - print("=" * 70) - - except KeyboardInterrupt: - print("\n⚠️ Pipeline interrupted by user") - except Exception as e: - print(f"\n❌ Error: {e}") - import traceback - - traceback.print_exc() - finally: - env.close() - - -if __name__ == "__main__": - main() diff --git a/packages/sage-kernel/examples/monitoring_example.py b/packages/sage-kernel/examples/monitoring_example.py deleted file mode 100644 index e766988c5c..0000000000 --- a/packages/sage-kernel/examples/monitoring_example.py +++ /dev/null @@ -1,226 +0,0 @@ -""" -SAGE Performance Monitoring Usage Example -========================================== - -这个示例展示如何使用 SAGE 的性能监控功能 -""" - -import time - -from sage.kernel.runtime.monitoring import ( - RESOURCE_MONITOR_AVAILABLE, - MetricsCollector, - MetricsReporter, - ResourceMonitor, -) - - -def example_metrics_collector(): - """示例1: 使用 MetricsCollector""" - print("=" * 80) - print("Example 1: MetricsCollector") - print("=" * 80) - - # 创建指标收集器 - collector = MetricsCollector( - name="example_task", - window_size=1000, - enable_detailed_tracking=True, - ) - - # 模拟处理10个数据包 - for i in range(10): - # 记录包处理开始 - packet_id = collector.record_packet_start( - packet_size=100 + i * 10, - ) - - # 模拟处理时间 - time.sleep(0.01 + i * 0.001) - - # 记录包处理结束 - success = i < 8 # 前8个成功，后2个失败 - collector.record_packet_end( - packet_id=packet_id, - success=success, - error_type="TestError" if not success else None, - ) - - # 获取实时指标 - metrics = collector.get_real_time_metrics() - print(f"\nTask: {metrics.task_name}") - print(f"Total Processed: {metrics.total_packets_processed}") - print(f"Total Failed: {metrics.total_packets_failed}") - print(f"TPS: {metrics.packets_per_second:.2f}") - print(f"Avg Latency: {metrics.avg_latency:.2f}ms") - print(f"P99 Latency: {metrics.p99_latency:.2f}ms") - print(f"Error Breakdown: {metrics.error_breakdown}") - - # 获取摘要 - summary = collector.get_summary() - print(f"\nSummary: {summary}") - - -def example_resource_monitor(): - """示例2: 使用 ResourceMonitor""" - print("\n" + "=" * 80) - print("Example 2: ResourceMonitor") - print("=" * 80) - - if not RESOURCE_MONITOR_AVAILABLE: - print("⚠️ psutil not available, skipping resource monitor example") - print("Install with: pip install psutil") - return - - # 创建资源监控器 - monitor = ResourceMonitor( - sampling_interval=0.5, - sample_window=10, - enable_auto_start=True, - ) - - # 等待收集一些样本 - print("\nCollecting resource samples...") - time.sleep(3) - - # 获取当前使用情况 - cpu, memory = monitor.get_current_usage() - print("\nCurrent Usage:") - print(f" CPU: {cpu:.2f}%") - print(f" Memory: {memory:.2f} MB") - - # 获取平均使用情况 - avg_cpu, avg_memory = monitor.get_average_usage() - print("\nAverage Usage:") - print(f" CPU: {avg_cpu:.2f}%") - print(f" Memory: {avg_memory:.2f} MB") - - # 获取峰值使用情况 - peak_cpu, peak_memory = monitor.get_peak_usage() - print("\nPeak Usage:") - print(f" CPU: {peak_cpu:.2f}%") - print(f" Memory: {peak_memory:.2f} MB") - - # 停止监控 - monitor.stop_monitoring() - - -def example_metrics_reporter(): - """示例3: 使用 MetricsReporter""" - print("\n" + "=" * 80) - print("Example 3: MetricsReporter") - print("=" * 80) - - # 创建收集器 - collector = MetricsCollector(name="reporting_task") - - # 模拟一些数据 - for _i in range(20): - packet_id = collector.record_packet_start() - time.sleep(0.005) - collector.record_packet_end(packet_id, success=True) - - # 创建报告器（不自动启动） - reporter = MetricsReporter( - metrics_collector=collector, - report_interval=60, - enable_auto_report=False, - ) - - # 生成不同格式的报告 - print("\n--- JSON Format ---") - json_report = reporter.generate_report(format="json") - print(json_report[:500] + "..." if len(json_report) > 500 else json_report) - - print("\n--- Human-Readable Format ---") - human_report = reporter.generate_report(format="human") - print(human_report) - - print("\n--- Prometheus Format ---") - prom_report = reporter.generate_report(format="prometheus") - print(prom_report[:500] + "..." if len(prom_report) > 500 else prom_report) - - -def example_integrated_monitoring(): - """示例4: 集成监控（收集器 + 资源监控 + 报告器）""" - print("\n" + "=" * 80) - print("Example 4: Integrated Monitoring") - print("=" * 80) - - # 创建收集器 - collector = MetricsCollector( - name="integrated_task", - window_size=100, - ) - - # 创建资源监控器（如果可用） - resource_monitor = None - if RESOURCE_MONITOR_AVAILABLE: - resource_monitor = ResourceMonitor( - sampling_interval=0.5, - enable_auto_start=True, - ) - - # 创建报告器 - reporter = MetricsReporter( - metrics_collector=collector, - resource_monitor=resource_monitor, - enable_auto_report=False, - ) - - # 模拟任务执行 - print("\nSimulating task execution...") - for i in range(50): - packet_id = collector.record_packet_start(packet_size=100) - - # 模拟不同的处理时间 - time.sleep(0.01 + (i % 5) * 0.002) - - # 模拟偶尔的失败 - success = (i % 10) != 0 - collector.record_packet_end( - packet_id, - success=success, - error_type="SimulatedError" if not success else None, - ) - - # 等待资源监控收集数据 - if resource_monitor: - time.sleep(2) - - # 生成综合报告 - print("\n" + "=" * 80) - print("Performance Report") - print("=" * 80) - report = reporter.generate_report(format="human") - print(report) - - # 清理 - if resource_monitor: - resource_monitor.stop_monitoring() - - -def main(): - """运行所有示例""" - print("\n" + "=" * 80) - print("SAGE Performance Monitoring Examples") - print("=" * 80) - - try: - example_metrics_collector() - example_resource_monitor() - example_metrics_reporter() - example_integrated_monitoring() - - print("\n" + "=" * 80) - print("All examples completed successfully! ✓") - print("=" * 80) - except Exception as e: - print(f"\nError running examples: {e}") - import traceback - - traceback.print_exc() - - -if __name__ == "__main__": - main() diff --git a/packages/sage-kernel/examples/operators/hello_comap_world.py b/packages/sage-kernel/examples/operators/hello_comap_world.py deleted file mode 100644 index 76808c0ddd..0000000000 --- a/packages/sage-kernel/examples/operators/hello_comap_world.py +++ /dev/null @@ -1,65 +0,0 @@ -# 此例意在说明如何将两个流通过comap合为一个流 - -from sage.common.core.functions.batch_function import BatchFunction -from sage.common.core.functions.comap_function import BaseCoMapFunction -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.api.local_environment import LocalEnvironment - - -# 定义两个简单数据源： -class SourceOne(BatchFunction): - def __init__(self): - super().__init__() - self.counter = 0 - - def execute(self): - self.counter += 1 - if self.counter > 5: - return None - return {"msg": f"No.{self.counter}: Hello"} - - -class SourceTwo(BatchFunction): - def __init__(self): - super().__init__() - self.counter = 0 - - def execute(self): - self.counter += 1 - if self.counter > 5: - return None - return {"msg": f"World! #{self.counter}"} - - -class HelloCoMapProcessor(BaseCoMapFunction): - def map0(self, data): - return f"[Stream0] 👋 {data['msg']}" - - def map1(self, data): - return f"[Stream1] 🌍 {data['msg']}" - - -class PrintSink(SinkFunction): - def execute(self, data): - print(data) - - -def main(): - env = LocalEnvironment("Hello_CoMap_World") - - # 两个数据源 - source1 = env.from_batch(SourceOne) - source2 = env.from_batch(SourceTwo) - - # 将两个流 connect 在一起，并用 comap 分开处理 - source1.connect(source2).comap(HelloCoMapProcessor).sink(PrintSink) - - env.submit(autostop=True) - - print("Hello Comap World 示例结束") - - -if __name__ == "__main__": - CustomLogger.disable_global_console_debug() - main() diff --git a/packages/sage-kernel/examples/operators/hello_filter_world.py b/packages/sage-kernel/examples/operators/hello_filter_world.py deleted file mode 100644 index c3f740f4a2..0000000000 --- a/packages/sage-kernel/examples/operators/hello_filter_world.py +++ /dev/null @@ -1,54 +0,0 @@ -# 此例意在说明 Fileter 算子的使用 -from sage.common.core.functions.batch_function import BatchFunction -from sage.common.core.functions.filter_function import FilterFunction -from sage.common.core.functions.map_function import MapFunction -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.api.local_environment import LocalEnvironment - - -class HelloBatch(BatchFunction): - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - self.max_count = 10 - - def execute(self): - if self.counter >= self.max_count: - return None - self.counter += 1 - return f"Hello, World! #{self.counter}" - - -class UpperCaseMap(MapFunction): - def execute(self, data): - return data.upper() - - -class PrintSink(SinkFunction): - def execute(self, data): - print(data) - - -# 过滤器示例，过滤所有偶数结尾的数据 -class Oddpicker(FilterFunction): - def execute(self, data): - if int(data[-1]) % 2 != 0: - return data - else: - return None - - -def main(): - env = LocalEnvironment("Hello_Filter_World") - - env.from_batch(HelloBatch).map(UpperCaseMap).filter(Oddpicker).sink(PrintSink) - - env.submit(autostop=True) - print("Hello Filter World 示例结束") - - -if __name__ == "__main__": - # 关闭日志输出 - CustomLogger.disable_global_console_debug() - main() diff --git a/packages/sage-kernel/examples/operators/hello_flatmap_world.py b/packages/sage-kernel/examples/operators/hello_flatmap_world.py deleted file mode 100644 index 85e2f9f7fe..0000000000 --- a/packages/sage-kernel/examples/operators/hello_flatmap_world.py +++ /dev/null @@ -1,52 +0,0 @@ -# 此例意在说明FlatMap的使用 -from sage.common.core.functions.batch_function import BatchFunction -from sage.common.core.functions.flatmap_function import FlatMapFunction -from sage.common.core.functions.map_function import MapFunction -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.api.local_environment import LocalEnvironment - - -class HelloBatch(BatchFunction): - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - self.max_count = 10 - - def execute(self): - if self.counter >= self.max_count: - return None - self.counter += 1 - return f"Hello, World! #{self.counter}" - - -class UpperCaseMap(MapFunction): - def execute(self, data): - return data.upper() - - -class PrintSink(SinkFunction): - def execute(self, data): - print(data) - - -# 利用FlatMapFunction实现单词拆分 -class SplitWords(FlatMapFunction): - def execute(self, data): - words = data.split() - return words - - -def main(): - env = LocalEnvironment("Hello_Flatmap_World") - - env.from_batch(HelloBatch).map(UpperCaseMap).flatmap(SplitWords).sink(PrintSink) - - env.submit(autostop=True) - print("Hello Flatmap World 示例结束") - - -if __name__ == "__main__": - # 关闭日志输出 - CustomLogger.disable_global_console_debug() - main() diff --git a/packages/sage-kernel/examples/operators/hello_join_world.py b/packages/sage-kernel/examples/operators/hello_join_world.py deleted file mode 100644 index c8ffb568ab..0000000000 --- a/packages/sage-kernel/examples/operators/hello_join_world.py +++ /dev/null @@ -1,104 +0,0 @@ -# 此例用到了keyby和join操作符，展示如何将两个数据流按key进行关联。 -from sage.common.core.functions.batch_function import BatchFunction -from sage.common.core.functions.join_function import BaseJoinFunction -from sage.common.core.functions.keyby_function import KeyByFunction -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.api.local_environment import LocalEnvironment - - -class SourceOne(BatchFunction): - def __init__(self): - super().__init__() - self.counter = 0 - - def execute(self): - self.counter += 1 - if self.counter > 5: - return None - return {"id": self.counter, "msg": f"Hello-{self.counter}", "type": "hello"} - - -class SourceTwo(BatchFunction): - def __init__(self): - super().__init__() - self.counter = 0 - - def execute(self): - self.counter += 1 - if self.counter > 5: - return None - return {"id": self.counter, "msg": f"World-{self.counter}", "type": "world"} - - -class IdKeyBy(KeyByFunction): - def execute(self, data): - return data.get("id") - - -class PrintSink(SinkFunction): - def execute(self, data): - print(f"🔗 Joined Streaming: {data}") - - -class HelloWorldJoin(BaseJoinFunction): - """ - Join 算子示例： - execute(payload, key, tag) 参数说明： - - payload: 流里传过来的原始数据 (dict) - - key: 由 keyby 算子提取出来的分区键 (比如这里的 id) - - tag: 数据来源标识 (0=左流 / 第一个流, 1=右流 / 第二个流) - """ - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.hello_cache = {} # {key: [payloads]} - self.world_cache = {} # {key: [payloads]} - - def execute(self, payload, key, tag): - results = [] - data_type = payload.get("type", "") - - if tag == 0: # 第一个流 (Hello) - if data_type == "hello": - # 缓存 Hello 数据 - self.hello_cache.setdefault(key, []).append(payload) - - # 检查是否有匹配的 World 数据 - if key in self.world_cache: - for world_data in self.world_cache[key]: - results.append(self._merge(payload, world_data, key)) - - elif tag == 1: # 第二个流 (World) - if data_type == "world": - # 缓存 World 数据 - self.world_cache.setdefault(key, []).append(payload) - - # 检查是否有匹配的 Hello 数据 - if key in self.hello_cache: - for hello_data in self.hello_cache[key]: - results.append(self._merge(hello_data, payload, key)) - - return results - - def _merge(self, hello_data, world_data, key): - return {"id": key, "msg": f"{hello_data['msg']} + {world_data['msg']}"} - - -def main(): - env = LocalEnvironment("hello_join_world") - - source1 = env.from_batch(SourceOne) - source2 = env.from_batch(SourceTwo) - - source1.keyby(IdKeyBy).connect(source2.keyby(IdKeyBy)).join(HelloWorldJoin).sink(PrintSink) - - # 使用 autostop=True 让框架自动检测处理完成 - env.submit(autostop=True) - - print("Hello Join World 示例结束") - - -if __name__ == "__main__": - CustomLogger.disable_global_console_debug() - main() diff --git a/packages/sage-kernel/examples/operators/hello_three_input_comap.py b/packages/sage-kernel/examples/operators/hello_three_input_comap.py deleted file mode 100644 index afbe5ccd71..0000000000 --- a/packages/sage-kernel/examples/operators/hello_three_input_comap.py +++ /dev/null @@ -1,107 +0,0 @@ -#!/usr/bin/env python3 -""" -Hello Three Input CoMap World - -这个例子演示了如何使用CoMap操作处理三个输入流，每个流的数据 -会被路由到对应的mapN方法进行独立处理。 - -CoMap（Co-processing Map）是一种多流处理操作，允许对连接的多个 -数据流进行协同处理，每个输入流通过专用的mapN方法独立处理。 -""" - -from sage.common.core.functions.batch_function import BatchFunction -from sage.common.core.functions.comap_function import BaseCoMapFunction -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.api.local_environment import LocalEnvironment - - -class SimpleDataSource(BatchFunction): - """简单的批量数据源""" - - def __init__(self, data): - super().__init__() - self.data = data - self.index = 0 - - def execute(self): - if self.index >= len(self.data): - return None - result = self.data[self.index] - self.index += 1 - return result - - -class ThreeStreamCoMapFunction(BaseCoMapFunction): - """ - 三输入流CoMap函数 - - 演示如何处理三个不同的输入流： - - map0: 处理第一个流的数据 - - map1: 处理第二个流的数据 - - map2: 处理第三个流的数据 - """ - - def map0(self, data): - """处理第一个输入流的数据""" - return f"🔴 Stream-0: {data}" - - def map1(self, data): - """处理第二个输入流的数据""" - return f"🟡 Stream-1: {data}" - - def map2(self, data): - """处理第三个输入流的数据""" - return f"🔵 Stream-2: {data}" - - -class ConsoleSink(SinkFunction): - """控制台输出Sink""" - - def execute(self, data): - print(data) - - -def main(): - """主函数：演示三输入流CoMap操作""" - - # 创建本地环境 - env = LocalEnvironment("ThreeInputCoMapExample") - - print("🚀 Starting Three Input CoMap Example...") - print("=" * 50) - - # 创建三个数据源 - stream1 = env.from_batch(SimpleDataSource, ["Apple", "Banana"]) - stream2 = env.from_batch(SimpleDataSource, ["Cat", "Dog"]) - stream3 = env.from_batch(SimpleDataSource, ["Red", "Blue"]) - - print("📊 Data sources created:") - print(" Stream 1 (Fruits): [Apple, Banana]") - print(" Stream 2 (Animals): [Cat, Dog]") - print(" Stream 3 (Colors): [Red, Blue]") - print() - - # 连接三个流并应用CoMap - print("🔗 Connecting streams and applying CoMap...") - (stream1.connect(stream2).connect(stream3).comap(ThreeStreamCoMapFunction).sink(ConsoleSink)) - - print("⚙️ Processing data...") - print() - - # 执行流处理 - env.submit(autostop=True) - - print() - print("✅ Three Input CoMap Example completed!") - print("=" * 50) - print("📝 Each input stream was processed by its corresponding mapN method:") - print(" - Stream 1 data → map0() → 🔴 Stream-0: ...") - print(" - Stream 2 data → map1() → 🟡 Stream-1: ...") - print(" - Stream 3 data → map2() → 🔵 Stream-2: ...") - - -if __name__ == "__main__": - # 禁用全局调试日志以获得更清晰的输出 - CustomLogger.disable_global_console_debug() - main() diff --git a/packages/sage-kernel/examples/stream/hello_connected_stream_example.py b/packages/sage-kernel/examples/stream/hello_connected_stream_example.py deleted file mode 100644 index c781751de5..0000000000 --- a/packages/sage-kernel/examples/stream/hello_connected_stream_example.py +++ /dev/null @@ -1,102 +0,0 @@ -import time - -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.core.functions.source_function import SourceFunction -from sage.kernel.api.local_environment import LocalEnvironment - - -# 简单的数字源 -class NumberSource(SourceFunction): - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - - def execute(self, data=None): - self.counter += 1 - return self.counter - - -# 简单的统计汇总函数 -class StatsSink(SinkFunction): - def __init__(self, **kwargs): - super().__init__(**kwargs) - - def execute(self, data): - print(f"[{self.name}] Received: {data}") - return data - - -def main(): - # 创建环境 - env = LocalEnvironment("simple_connected_example") - - # 设置日志级别为WARNING以减少调试输出 - env.set_console_log_level("WARNING") - - print("🚀 Starting Simple Connected Streams Example") - print("📊 Demonstrating multiple stream processing and connection") - print("⏹️ Press Ctrl+C to stop\n") - - # 创建主数据源 - main_stream = env.from_source(NumberSource, delay=1.0) - - # 分支1：偶数流 - even_stream = ( - main_stream.filter(lambda x: x % 2 == 0).map(lambda x: ("EVEN", x)) - # .print("🔵 Even Stream") - ) - - # 分支2：奇数流 - odd_stream = ( - main_stream.filter(lambda x: x % 2 == 1).map(lambda x: ("ODD", x)) - # .print("🔴 Odd Stream") - ) - - # 分支3：倍数流（3的倍数） - multiple_stream = ( - main_stream.filter(lambda x: x % 3 == 0).map(lambda x: ("MULTIPLE_3", x)) - # .print("🟡 Multiple-3 Stream") - ) - - # 分支4：大数流（大于5） - large_stream = ( - main_stream.filter(lambda x: x > 5).map(lambda x: ("LARGE", x)) - # .print("🟢 Large Stream") - ) - - # 使用 ConnectedStreams 将所有分支连接起来 - print("\n🔗 Connecting all streams...") - connected_streams = ( - even_stream.connect(odd_stream).connect(multiple_stream).connect(large_stream) - ) - - # 对连接的流进行统一处理 - ( - connected_streams.map(lambda data: f"Processed: {data[0]} -> {data[1]}") - .print("🎯 Final Result") - .sink(StatsSink, name="FinalSink") - ) - - print("📈 All streams connected and processing...\n") - - try: - # 运行流处理 - env.submit() - - time.sleep(5) # 运行5秒 - - except KeyboardInterrupt: - print("\n\n🛑 Stopping Simple Connected Streams Example...") - - finally: - print("\n📋 Example completed!") - print("💡 This example demonstrated:") - print(" - Multiple stream branches from single source") - print(" - Independent filtering and processing") - print(" - ConnectedStreams merging multiple flows") - print(" - Unified final processing of merged streams") - env.close() - - -if __name__ == "__main__": - main() diff --git a/packages/sage-kernel/examples/stream/hello_onebyone_world.py b/packages/sage-kernel/examples/stream/hello_onebyone_world.py deleted file mode 100644 index a05ba58943..0000000000 --- a/packages/sage-kernel/examples/stream/hello_onebyone_world.py +++ /dev/null @@ -1,47 +0,0 @@ -import time - -from sage.common.core.functions.batch_function import BatchFunction -from sage.common.core.functions.map_function import MapFunction -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.api.local_environment import LocalEnvironment - - -class SyncBatch(BatchFunction): - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - self.max_count = 5 - - def execute(self): - if self.counter >= self.max_count: - return None - self.counter += 1 - data = f"hello, No. {str(self.counter)} one by one world~" - print(f" ⚡ {data}") - return data - - -class UpperMap(MapFunction): - def execute(self, data): - print(" 🔔 uppering word!!!") - time.sleep(1) - return data.upper() - - -class SyncSink(SinkFunction): - def execute(self, data): - print(f" ✅ {data}") - time.sleep(1) - - -def main(): - env = LocalEnvironment("Test_Sync") - env.from_batch(SyncBatch).map(UpperMap).sink(SyncSink) - env.submit(autostop=True) - print("Hello one by one World 批处理示例结束") - - -if __name__ == "__main__": - CustomLogger.disable_global_console_debug() - main() diff --git a/packages/sage-kernel/examples/stream/hello_streaming_world.py b/packages/sage-kernel/examples/stream/hello_streaming_world.py deleted file mode 100644 index e000a56d69..0000000000 --- a/packages/sage-kernel/examples/stream/hello_streaming_world.py +++ /dev/null @@ -1,53 +0,0 @@ -from sage.common.core.functions.map_function import MapFunction -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.core.functions.source_function import SourceFunction -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.api.local_environment import LocalEnvironment - - -# 流式数据源：从BatchFunction变成SourceFunction -class HelloStreaming(SourceFunction): - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - - def execute(self, data=None): - self.counter += 1 - return f"Hello, Streaming World! #{self.counter}" - - -class UpperCaseMap(MapFunction): - def execute(self, data): - return data.upper() - - -class PrintSink(SinkFunction): - def execute(self, data): - print(data) - - -def main(): - env = LocalEnvironment("hello_streaming_world") - - # 流式源，从 from_batch 变成 from_source - env.from_source(HelloStreaming).map(UpperCaseMap).sink(PrintSink) - - try: - print("Waiting for streaming processing to complete...") - env.submit() - - # 暂停主程序，因为在LocalEnvironment下，流式处理是异步的 - from time import sleep - - sleep(1) - - except KeyboardInterrupt: - print("停止运行") - finally: - print("Hello Streaming World 流式处理示例结束") - - -if __name__ == "__main__": - # 关闭日志输出 - CustomLogger.disable_global_console_debug() - main() diff --git a/packages/sage-kernel/examples/stream/hello_wordcount_source_example.py b/packages/sage-kernel/examples/stream/hello_wordcount_source_example.py deleted file mode 100644 index 21d41f879f..0000000000 --- a/packages/sage-kernel/examples/stream/hello_wordcount_source_example.py +++ /dev/null @@ -1,77 +0,0 @@ -import time - -from sage.common.core.functions.flatmap_function import FlatMapFunction -from sage.common.core.functions.map_function import MapFunction -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.core.functions.source_function import SourceFunction -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.api.local_environment import LocalEnvironment - - -# 流数据源：每次输出一行句子 -class SentenceSource(SourceFunction): - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.sentences = [ - "hello world", - "hello sage", - "hello chatgpt", - "world of ai", - "sage world", - ] - self.index = 0 - - def execute(self, data=None): - # 无限流：每次输出一句话，模拟流数据源 - if self.index >= len(self.sentences): - self.index = 0 # 重置索引，实现循环输出 - sentence = self.sentences[self.index] - self.index += 1 - return sentence - - -# 拆分句子为单词 -class SplitWords(FlatMapFunction): - def execute(self, data): - return data.split() - - -# 转换为 (word, 1) -class WordToPair(MapFunction): - def execute(self, data): - return (data, 1) - - -# SinkFunction 输出结果：每次输出单词计数 -class PrintResult(SinkFunction): - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.counts = {} - - def execute(self, data): - word, cnt = data - self.counts[word] = self.counts.get(word, 0) + cnt - - # 每次接收到新数据时，输出当前统计结果 - print("当前单词计数：") - for word, count in self.counts.items(): - print(f"{word}: {count}") - print("------") - - -def main(): - env = LocalEnvironment("WordCount") - - # 流式处理：句子 -> 拆分单词 -> 转换为(word,1) -> 输出每次的单词统计 - env.from_source(SentenceSource).flatmap(SplitWords).map(WordToPair).sink(PrintResult) - - env.submit() # 设置为 False 以保持流式执行 - - # 模拟流式数据源持续运行一段时间（这里设定为 10 秒） - time.sleep(10) - print("WordCount 流式示例结束") - - -if __name__ == "__main__": - CustomLogger.disable_global_console_debug() - main() diff --git a/packages/sage-kernel/pyproject.toml b/packages/sage-kernel/pyproject.toml deleted file mode 100644 index f39660b81d..0000000000 --- a/packages/sage-kernel/pyproject.toml +++ /dev/null @@ -1,181 +0,0 @@ -[build-system] -requires = ["setuptools>=64", "wheel", "packaging>=24.2"] -build-backend = "setuptools.build_meta" - -[project] -name = "isage-kernel" -dynamic = ["version"] -description = "SAGE Kernel Module - Streaming-Augmented Generative Execution" -readme = "README.md" -requires-python = ">=3.10" -authors = [{ name = "IntelliStream Team", email = "shuhao_zhang@hust.edu.cn" }] -keywords = [ - "data", - "reasoning", - "kernel", - "dataflow", - "llm", - "ml", - "framework", - "rag", - "intellistream", - "cli", - "ai", - "sage", -] -classifiers = [ - "Development Status :: 4 - Beta", - "Intended Audience :: Developers", - "Intended Audience :: Science/Research", - "Operating System :: OS Independent", - "Topic :: Scientific/Engineering :: Artificial Intelligence", - "Topic :: Software Development :: Libraries :: Python Modules", - "Topic :: System :: Distributed Computing", -] -dependencies = [ - # ============================================================================ - # 核心依赖 - 只包含 sage.kernel 模块导入时必需的最小依赖集 - # 原则：所有重型依赖（torch, fastapi, etc.）都放在 optional-dependencies - # ============================================================================ - # NOTE: sage-kernel reuses dependencies from sage-platform (ray, etc.) - # These are pulled in transitively via isage-platform dependency -] - -license = { text = "MIT" } - -[project.optional-dependencies] -dev = [ - "pytest>=7.0.0", - "pytest-cov>=4.0.0", - "pytest-asyncio>=0.21.0", - "pytest-benchmark>=4.0.0", - "ruff==0.14.6", - "mypy>=1.7.0", -] -# Deep learning - for model execution and GPU computation -ml = ["torch>=2.7.0,<3.0.0", "torchvision>=0.22.0,<1.0.0"] -# Web server - for serving kernel as HTTP service -server = [ - "fastapi>=0.115.0,<1.0.0", - "uvicorn[standard]>=0.34.0,<1.0.0", - "python_multipart>=0.0.20,<0.1.0", -] -# Distributed - for cluster execution (usually pulled from sage-platform) -distributed = [ - "ray>=2.48.0,<3.0.0", - "grpcio>=1.74.0,<2.0.0", - "protobuf>=6.32.0,<7.0.0", - "msgpack>=1.1.0,<2.0.0", -] -# AWS - for cloud storage and services -aws = ["aioboto3>=14.1.0,<15.0.0"] -# CLI - for interactive kernel management -cli = [ - "typer>=0.15.0,<1.0.0", - "rich>=13.0.0,<14.0.0", - "click>=8.0.0,<9.0.0", - "questionary>=1.10.0,<2.0.0", - "prompt_toolkit>=3.0.50,<4.0.0", - "tabulate>=0.9.0,<1.0.0", -] -# Build - for C++ extensions -build = ["Cython>=3.1.0,<4.0.0", "pybind11>=3.0.0,<4.0.0"] -# Full installation with all optional dependencies -all = ["isage-kernel[ml,server,distributed,aws,cli,build]"] -[project.urls] -Homepage = "https://github.com/intellistream/SAGE" -Repository = "https://github.com/intellistream/SAGE.git" -Documentation = "https://intellistream.github.io/SAGE-Pub/" -Issues = "https://github.com/intellistream/SAGE/issues" - -[tool.setuptools.package-dir] -"" = "src" - -[tool.setuptools.packages.find] -where = ["src"] -include = ["sage*"] -exclude = ["tests*", "docs*", "enterprise/*", "commercial/*"] -namespaces = true - -[tool.setuptools.package-data] -sage = ["py.typed"] - -[tool.setuptools.dynamic.version] -attr = "sage.kernel._version.__version__" - -[tool.pytest.ini_options] -testpaths = ["tests", "src"] -python_files = ["test_*.py", "*_test.py"] -python_classes = ["Test*"] -python_functions = ["test_*"] -addopts = [ - "--benchmark-storage=../../.sage/benchmarks", - "-o", - "cache_dir=../../.sage/cache/pytest", - "--strict-markers", - "--strict-config", - "--verbose", - "-ra", -] -markers = [ - "slow: marks tests as slow (deselect with '-m \"not slow\"')", - "integration: marks tests as integration tests", - "unit: marks tests as unit tests", - "network: marks tests as network tests", - "system: marks tests as system tests", - "core: marks tests as core functionality tests", - "smoke: marks tests as smoke tests (quick validation)", - "cli: marks tests as CLI tests", - "ray: marks tests requiring Ray framework (may be slow)", - "distributed: marks tests requiring distributed setup", -] -filterwarnings = [ - "ignore::DeprecationWarning:ray._private.client_mode_hook", - "ignore:.*local mode is an experimental feature.*:DeprecationWarning", - "ignore::DeprecationWarning:pkg_resources", - "ignore:.*SWIG.*:DeprecationWarning", - "ignore:.*SwigPyPacked.*:DeprecationWarning", - "ignore:.*SwigPyObject.*:DeprecationWarning", - "ignore:.*swigvarlink.*:DeprecationWarning", - "ignore:.*pkg_resources is deprecated.*:UserWarning", - "ignore::pytest.PytestReturnNotNoneWarning", -] - -[tool.coverage.run] -source = ["src/sage"] -omit = ["*/tests/*", "*/test_*.py", "*/_test_*.py"] - -[tool.coverage.report] -exclude_lines = [ - "pragma: no cover", - "def __repr__", - "raise AssertionError", - "raise NotImplementedError", - "if __name__ == .__main__.:", -] - -[tool.black] -line-length = 100 -target-version = ["py310", "py311", "py312"] -include = "\\.pyi?$" -extend-exclude = "/(\n # directories\n \\.eggs\n | \\.git\n | \\.hg\n | \\.mypy_cache\n | \\.pytest_cache\n | \\.ruff_cache\n | \\.venv\n | build\n | dist\n)/\n" - -[tool.mypy] -python_dynamic = ["version"] -cache_dir = "../../.sage/cache/mypy" -check_untyped_defs = true -disallow_any_generics = true -disallow_incomplete_defs = true -disallow_untyped_defs = true -no_implicit_optional = true -warn_redundant_casts = true -warn_unused_ignores = true -warn_return_any = true -strict_equality = true - -[[tool.mypy.overrides]] -module = ["sage.*"] -ignore_missing_imports = true - -[tool.ruff] -extend = "../../tools/ruff.toml" diff --git a/packages/sage-kernel/src/sage/kernel/__init__.py b/packages/sage-kernel/src/sage/kernel/__init__.py deleted file mode 100644 index aaafbc21eb..0000000000 --- a/packages/sage-kernel/src/sage/kernel/__init__.py +++ /dev/null @@ -1,90 +0,0 @@ -""" -SAGE Kernel - 流式数据处理引擎和运行时 - -Layer: L3 (Kernel) -Dependencies: sage.platform (L2), sage.common (L1) - -提供： -- 数据流执行引擎：Environment, DataStream API -- 运行时组件：JobManager, Scheduler -- RPC通信实现：RPCQueue（注册到L2工厂） - -注意：基础算子（MapOperator, FilterOperator等）已迁移到 sage.common.core.functions -""" - -# 直接从本包的_version模块加载版本信息 -try: - from sage.kernel._version import __author__, __email__, __version__ -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" - -# 导出核心组件 - 直接从具体模块导入，避免循环 -try: - from sage.kernel.runtime.jobmanager_client import JobManagerClient -except ImportError: - # 如果导入失败，提供一个占位符 - JobManagerClient = None # type: ignore[assignment,misc] - import warnings - - warnings.warn( - "JobManagerClient is not available. Some features may be limited.", - ImportWarning, - stacklevel=2, - ) - -# 导出 API 类 -try: - from sage.kernel.api import LocalEnvironment, RemoteEnvironment -except ImportError: - LocalEnvironment = None # type: ignore[assignment,misc] - RemoteEnvironment = None # type: ignore[assignment,misc] - import warnings - - warnings.warn( - "LocalEnvironment and RemoteEnvironment are not available. Some features may be limited.", - ImportWarning, - stacklevel=2, - ) - -# 导出子模块 -__layer__ = "L3" - -from . import api - -# ============================================================================ -# 架构关键：L3向L2注册实现（Factory Pattern） -# ============================================================================ -# 在初始化时注册RPCQueue实现到sage-platform的工厂 -# 这样L2层可以创建L3实例，但不需要直接导入L3代码 -try: - from sage.kernel.runtime.communication.rpc import RPCQueue - from sage.platform.queue import register_rpc_queue_factory - - def _rpc_queue_factory(**kwargs): - """RPC队列工厂函数 - 由L2调用创建L3实例""" - return RPCQueue(**kwargs) - - register_rpc_queue_factory(_rpc_queue_factory) - -except ImportError as e: - import warnings - - warnings.warn( - f"Failed to register RPC queue factory: {e}. " - "RPC queue functionality will not be available.", - ImportWarning, - stacklevel=2, - ) - -__all__ = [ - "__version__", - "__author__", - "__email__", - "JobManagerClient", - "LocalEnvironment", - "RemoteEnvironment", - "api", -] diff --git a/packages/sage-kernel/src/sage/kernel/_version.py b/packages/sage-kernel/src/sage/kernel/_version.py deleted file mode 100644 index 61fa9418c9..0000000000 --- a/packages/sage-kernel/src/sage/kernel/_version.py +++ /dev/null @@ -1,6 +0,0 @@ -"""Version information for sage-kernel package.""" - -# 独立硬编码版本 -__version__ = "0.2.4.11" -__author__ = "IntelliStream Team" -__email__ = "shuhao_zhang@hust.edu.cn" diff --git a/packages/sage-kernel/src/sage/kernel/api/__init__.py b/packages/sage-kernel/src/sage/kernel/api/__init__.py deleted file mode 100644 index b99121cc11..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/__init__.py +++ /dev/null @@ -1,48 +0,0 @@ -""" -SAGE Kernel API - 用户友好的流处理API接口 - -Layer: L3 (Kernel - Public API) -Dependencies: sage.platform (L2), sage.common (L1) - -这个模块提供了 SAGE 的核心 API，包括： -- 环境配置（LocalEnvironment, RemoteEnvironment） -- 数据流操作（DataStream） -- 函数定义（从 sage.common.core.functions 导入） -- 算子抽象（MapOperator, FilterOperator等） - -Architecture: -- 提供用户友好的流式处理 API -- 内部使用 runtime 模块实现执行 -- 支持本地和远程两种执行模式 - -示例： - ```python - from sage.kernel.api import LocalEnvironment - from sage.common.core.functions import MapFunction, SinkFunction - - env = LocalEnvironment("my_app") - stream = env.from_collection([1, 2, 3]) - stream.map(lambda x: x * 2).print() - env.execute() - ``` -""" - -# 导入主要 API 类 -from .local_environment import LocalEnvironment -from .remote_environment import RemoteEnvironment - -# 版本信息 -try: - from sage.kernel._version import __author__, __email__, __version__ -except ImportError: - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" - -__all__ = [ - "__version__", - "__author__", - "__email__", - "LocalEnvironment", - "RemoteEnvironment", -] diff --git a/packages/sage-kernel/src/sage/kernel/api/base_environment.py b/packages/sage-kernel/src/sage/kernel/api/base_environment.py deleted file mode 100644 index 8a4421b086..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/base_environment.py +++ /dev/null @@ -1,539 +0,0 @@ -from __future__ import annotations - -from abc import ABC, abstractmethod -from collections.abc import Callable -from typing import TYPE_CHECKING, Any - -from sage.common.core import wrap_lambda -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.runtime.factory.service_factory import ServiceFactory -from sage.kernel.runtime.jobmanager_client import JobManagerClient - -if TYPE_CHECKING: - from sage.common.core.functions import BaseFunction - from sage.kernel.api.datastream import DataStream - from sage.kernel.api.transformation.base_transformation import BaseTransformation - - -class BaseEnvironment(ABC): - __state_exclude__ = ["_engine_client", "client", "jobmanager"] - # 会被继承，但是不会被自动合并 - - def _get_datastream_class(self): - """Deferred import of DataStream to avoid circular imports""" - if not hasattr(self, "_datastream_class"): - from sage.kernel.api.datastream import DataStream - - self._datastream_class = DataStream - return self._datastream_class - - def _get_transformation_classes(self): - """动态导入transformation类以避免循环导入""" - if not hasattr(self, "_transformation_classes"): - from sage.kernel.api.transformation.base_transformation import ( - BaseTransformation, - ) - from sage.kernel.api.transformation.batch_transformation import ( - BatchTransformation, - ) - from sage.kernel.api.transformation.future_transformation import ( - FutureTransformation, - ) - from sage.kernel.api.transformation.source_transformation import ( - SourceTransformation, - ) - - self._transformation_classes = { - "BaseTransformation": BaseTransformation, - "SourceTransformation": SourceTransformation, - "BatchTransformation": BatchTransformation, - "FutureTransformation": FutureTransformation, - } - return self._transformation_classes - - def __init__( - self, - name: str, - config: dict | None, - *, - platform: str = "local", - scheduler=None, - enable_monitoring: bool = False, - ): - self.name = name - self.uuid: str | None = None # 由jobmanager生成 - - self.config: dict = dict(config or {}) - self.platform: str = platform - - # JobManager 注入的属性 - self.jobmanager_host: str | None = None - self.jobmanager_port: int | None = None - self.session_id: str | None = None - self.session_timestamp: Any | None = None # datetime object - # 用于收集所有 BaseTransformation，供 ExecutionGraph 构建 DAG - self.pipeline: list[BaseTransformation] = [] - self._filled_futures: dict = {} - # 用于收集所有服务工厂，供ExecutionGraph构建服务节点时使用 - self.service_factories: dict = {} # service_name -> ServiceFactory - - # 性能监控配置 - self.enable_monitoring: bool = enable_monitoring - - # 调度器配置（用户可选） - self._scheduler = None - self._init_scheduler(scheduler) - - self.env_base_dir: str | None = None # 环境基础目录，用于存储日志和其他文件 - # JobManager 相关 - self._jobmanager: Any | None = None - - # Engine 客户端相关 - self._engine_client: JobManagerClient | None = None - self.env_uuid: str | None = None - - # 日志配置 - self.console_log_level: str = "INFO" # 默认console日志等级 - - def _init_scheduler(self, scheduler): - """ - 初始化调度器 - - Args: - scheduler: 可以是以下类型之一: - - None: 使用默认的 FIFO 调度器 - - str: 调度器名称 ("fifo", "load_aware") - - BaseScheduler 实例: 自定义调度器实例 - """ - from sage.kernel.scheduler.api import BaseScheduler - from sage.kernel.scheduler.impl import FIFOScheduler, LoadAwareScheduler - - if scheduler is None: - # 默认使用 FIFO 调度器 - self._scheduler = FIFOScheduler(platform=self.platform) - elif isinstance(scheduler, str): - # 字符串指定调度器类型 - scheduler_lower = scheduler.lower() - if scheduler_lower == "fifo": - self._scheduler = FIFOScheduler(platform=self.platform) - elif scheduler_lower in ["load_aware", "loadaware"]: - self._scheduler = LoadAwareScheduler(platform=self.platform) - else: - raise ValueError( - f"Unknown scheduler type: {scheduler}. Available options: 'fifo', 'load_aware'" - ) - elif isinstance(scheduler, BaseScheduler): - # 直接使用提供的调度器实例 - self._scheduler = scheduler - else: - raise TypeError( - f"scheduler must be None, str, or BaseScheduler instance, got {type(scheduler)}" - ) - - @property - def scheduler(self): - """获取当前调度器实例""" - return self._scheduler - - ######################################################## - # user interface # - ######################################################## - - def set_console_log_level(self, level: str): - """ - 设置控制台日志等级 - - Args: - level: 日志等级，可选值: "DEBUG", "INFO", "WARNING", "ERROR" - - Example: - env.set_console_log_level("DEBUG") # 显示所有日志 - env.set_console_log_level("WARNING") # 只显示警告和错误 - """ - valid_levels = ["DEBUG", "INFO", "WARNING", "ERROR"] - if level.upper() not in valid_levels: - raise ValueError(f"Invalid log level: {level}. Must be one of {valid_levels}") - - self.console_log_level = level.upper() - - # 如果logger已经初始化，更新其配置 - if hasattr(self, "_logger") and self._logger is not None: - self._logger.update_output_level("console", self.console_log_level) - - def register_service(self, service_name: str, service_class: type, *args, **kwargs): - """ - 注册服务到环境中 - - Args: - service_name: 服务名称，用于标识服务 - service_class: 服务类，将在任务提交时实例化 - *args: 传递给服务构造函数的位置参数 - **kwargs: 传递给服务构造函数的关键字参数 - - Example: - # 注册一个自定义服务 - env.register_service("my_cache", MyCacheService, cache_size=1000) - - # 注册数据库连接服务 - env.register_service("db_conn", DatabaseConnection, - host="localhost", port=5432, db="mydb") - """ - # 创建服务工厂 - service_factory = ServiceFactory( - service_name=service_name, - service_class=service_class, - service_args=args, - service_kwargs=kwargs, - ) - - self.service_factories[service_name] = service_factory - - platform_str = "remote" if self.platform == "remote" else "local" - self.logger.info( - f"Registered {platform_str} service: {service_name} ({service_class.__name__})" - ) - - return service_factory - - def register_service_factory(self, service_name: str, service_factory: ServiceFactory): - """ - 注册服务工厂到环境中 - - Args: - service_name: 服务名称，用于标识服务 - service_factory: 服务工厂实例 - - Example: - # 注册预配置的服务工厂 - kv_factory = create_kv_service_factory("my_kv", backend_type="memory") - env.register_service_factory("my_kv", kv_factory) - """ - self.service_factories[service_name] = service_factory - - platform_str = "remote" if self.platform == "remote" else "local" - self.logger.info(f"Registered {platform_str} service factory: {service_name}") - - return service_factory - - def from_kafka_source( - self, - source_class: type, - bootstrap_servers: str, - topic: str, - group_id: str, - auto_offset_reset: str = "latest", - value_deserializer: str = "json", - buffer_size: int = 10000, - max_poll_records: int = 500, - **kafka_config, - ) -> DataStream: - """ - 创建Kafka数据源，采用Flink兼容的架构设计 - - Args: - source_class: Kafka Source 类（需要从 sage.libs.io.source 导入 KafkaSource） - bootstrap_servers: Kafka集群地址 (例: "localhost:9092") - topic: Kafka主题名称 - group_id: 消费者组ID，用于offset管理 - auto_offset_reset: offset重置策略 ('latest'/'earliest'/'none') - value_deserializer: 反序列化方式 ('json'/'string'/'bytes'或自定义函数) - buffer_size: 本地缓冲区大小，防止数据丢失 - max_poll_records: 每次poll的最大记录数，控制批处理大小 - **kafka_config: 其他Kafka Consumer配置参数 - - Returns: - DataStream: 可用于构建处理pipeline的数据流 - - Example: - # 导入 KafkaSource - from sage.libs.foundation.io.source import KafkaSource - - # 基本使用 - kafka_stream = env.from_kafka_source( - KafkaSource, - bootstrap_servers="localhost:9092", - topic="user_events", - group_id="sage_consumer" - ) - - # 高级配置 - kafka_stream = env.from_kafka_source( - KafkaSource, - bootstrap_servers="kafka1:9092,kafka2:9092", - topic="events", - group_id="sage_app", - auto_offset_reset="earliest", - buffer_size=20000, - max_poll_records=1000, - session_timeout_ms=30000, - security_protocol="SSL" - ) - - # 构建处理pipeline - result = (kafka_stream - .map(ProcessEventFunction) - .filter(FilterFunction) - .sink(OutputSinkFunction)) - """ - # 获取SourceTransformation类 - SourceTransformation = self._get_transformation_classes()["SourceTransformation"] - - # 创建Kafka Source Function - transformation = SourceTransformation( - self, - source_class, - bootstrap_servers=bootstrap_servers, - topic=topic, - group_id=group_id, - auto_offset_reset=auto_offset_reset, - value_deserializer=value_deserializer, - buffer_size=buffer_size, - max_poll_records=max_poll_records, - **kafka_config, - ) - - self.pipeline.append(transformation) - self.logger.info(f"Kafka source created for topic: {topic}, group: {group_id}") - - return self._get_datastream_class()(self, transformation) - - def from_source(self, function: type[BaseFunction] | Callable, *args, **kwargs) -> DataStream: - if callable(function) and not isinstance(function, type): - # 这是一个 lambda 函数或普通函数 - function = wrap_lambda(function, "flatmap") - - # 获取SourceTransformation类 - SourceTransformation = self._get_transformation_classes()["SourceTransformation"] - transformation = SourceTransformation(self, function, *args, **kwargs) - - self.pipeline.append(transformation) - return self._get_datastream_class()(self, transformation) - - def from_collection( - self, function: type[BaseFunction] | Callable, *args, **kwargs - ) -> DataStream: - if callable(function) and not isinstance(function, type): - # 这是一个 lambda 函数或普通函数 - function = wrap_lambda(function, "flatmap") - - # 获取BatchTransformation类 - BatchTransformation = self._get_transformation_classes()["BatchTransformation"] - transformation = BatchTransformation( - self, function, *args, **kwargs - ) # TODO: add a new transformation 去告诉engine这个input source是有界的，当执行完毕之后，会发送一个endofinput信号来停止所有进程。 - # Issue URL: https://github.com/intellistream/SAGE/issues/387 - - self.pipeline.append(transformation) - return self._get_datastream_class()(self, transformation) - - def from_batch(self, source: type[BaseFunction] | Any, *args, **kwargs) -> DataStream: - """ - 统一的批处理数据源创建方法，支持多种输入类型 - - Args: - source: 可以是以下类型之一： - - BaseFunction 子类：自定义批处理函数类 - - list/tuple：数据列表或元组 - - 任何可迭代对象：实现了 __iter__ 的对象 - *args: 传递给批处理函数的位置参数（仅当 source 为函数类时有效） - **kwargs: 传递给批处理函数的关键字参数，以及 transformation 的配置参数 - - Returns: - DataStream: 包含 BatchTransformation 的数据流 - - Example: - # 1. 使用自定义批处理函数类 - class MyBatchFunction(BaseFunction): - def get_data_iterator(self): - return iter(range(50)) - - def get_total_count(self): - return 50 - - batch_stream = env.from_batch(MyBatchFunction, custom_param="value") - - # 2. 使用数据列表 - data = ["item1", "item2", "item3", "item4", "item5"] - batch_stream = env.from_batch(data) - - # 3. 使用任何可迭代对象 - batch_stream = env.from_batch({1, 2, 3, 4, 5}) - batch_stream = env.from_batch("hello") # 逐字符迭代 - batch_stream = env.from_batch(range(100)) - - # 4. 配置额外参数 - batch_stream = env.from_batch(data, progress_log_interval=10) - """ - - # 检查 source 的类型并相应处理 - if isinstance(source, type) and hasattr(source, "__bases__"): - # source 是一个类，检查是否是 BaseFunction 的子类 - from sage.common.core import BaseFunction - - if issubclass(source, BaseFunction): - # 使用自定义批处理函数类 - return self._from_batch_function_class(source, *args, **kwargs) - - # source 是数据对象，需要检查其类型 - if isinstance(source, (list, tuple)): - # 处理列表或元组 - return self._from_batch_collection(source, **kwargs) - elif hasattr(source, "__iter__") and not isinstance(source, (str, bytes)): - # 处理其他可迭代对象（排除字符串和字节） - return self._from_batch_iterable(source, **kwargs) - elif isinstance(source, (str, bytes)): - # 特殊处理字符串和字节，按字符/字节迭代 - return self._from_batch_iterable(source, **kwargs) - else: - # 尝试将其作为可迭代对象处理 - try: - iter(source) # type: ignore[arg-type] - return self._from_batch_iterable(source, **kwargs) - except TypeError: - raise TypeError( - f"Unsupported source type: {type(source)}. " - f"Expected BaseFunction subclass, list, tuple, or any iterable object." - ) - - def from_future(self, name: str) -> DataStream: - """ - 创建一个future stream占位符，用于建立反馈边。 - - Args: - name: future stream的名称，用于标识和调试 - - Returns: - DataStream: 包含FutureTransformation的数据流 - - Example: - future_stream = env.from_future("feedback_loop") - # 使用future_stream参与pipeline构建 - result = source.connect(future_stream).comap(CombineFunction) - # 最后填充future - result.fill_future(future_stream) - """ - # 获取FutureTransformation类 - FutureTransformation = self._get_transformation_classes()["FutureTransformation"] - transformation = FutureTransformation(self, name) - self.pipeline.append(transformation) - return self._get_datastream_class()(self, transformation) - - ######################################################## - # jobmanager interface # - ######################################################## - @abstractmethod - def submit(self): - pass - - ######################################################## - # properties # - ######################################################## - - @property - def logger(self): - if not hasattr(self, "_logger"): - self._logger = CustomLogger() - return self._logger - - @property - def client(self) -> JobManagerClient: - if self._engine_client is None: - # 从配置中获取 Engine 地址，或使用默认值 - daemon_host = self.config.get("engine_host", "127.0.0.1") - daemon_port = self.config.get("engine_port", 19000) - - self._engine_client = JobManagerClient(host=daemon_host, port=daemon_port) - - return self._engine_client - - ######################################################## - # auxiliary methods # - ######################################################## - - def _append(self, transformation: BaseTransformation): - """将 BaseTransformation 添加到管道中（Compiler 会使用）。""" - self.pipeline.append(transformation) - return self._get_datastream_class()(self, transformation) - - def _from_batch_function_class( - self, batch_function_class: type[BaseFunction], *args, **kwargs - ) -> DataStream: - """ - 从自定义批处理函数类创建批处理数据源 - """ - # 分离transformation配置和function参数 - transform_kwargs = {} - function_kwargs = {} - - # transformation相关的参数 - transform_config_keys = {"delay", "progress_log_interval"} - - for key, value in kwargs.items(): - if key in transform_config_keys: - transform_kwargs[key] = value - else: - function_kwargs[key] = value - - # 获取BatchTransformation类 - BatchTransformation = self._get_transformation_classes()["BatchTransformation"] - transformation = BatchTransformation( - self, batch_function_class, *args, **function_kwargs, **transform_kwargs - ) - - self.pipeline.append(transformation) - self.logger.info(f"Custom batch source created with {batch_function_class.__name__}") - - return self._get_datastream_class()(self, transformation) - - def _from_batch_collection(self, data: list | tuple, **kwargs) -> DataStream: - """ - 从数据集合创建批处理数据源 - """ - from sage.kernel.api.function.simple_batch_function import ( - SimpleBatchIteratorFunction, - ) - - # 获取BatchTransformation类 - BatchTransformation = self._get_transformation_classes()["BatchTransformation"] - transformation = BatchTransformation(self, SimpleBatchIteratorFunction, data=data, **kwargs) - - self.pipeline.append(transformation) - self.logger.info(f"Batch collection source created with {len(data)} items") - - return self._get_datastream_class()(self, transformation) - - def _from_batch_iterable(self, iterable: Any, **kwargs) -> DataStream: - """ - 从任何可迭代对象创建批处理数据源 - """ - from sage.kernel.api.function.simple_batch_function import ( - IterableBatchIteratorFunction, - ) - - # 尝试获取总数量 - total_count = kwargs.pop("total_count", None) - if total_count is None: - try: - total_count = len(iterable) - except TypeError: - # 如果对象没有 len() 方法，则保持 None - total_count = None - - # 获取BatchTransformation类 - BatchTransformation = self._get_transformation_classes()["BatchTransformation"] - transformation = BatchTransformation( - self, - IterableBatchIteratorFunction, - iterable=iterable, - total_count=total_count, - **kwargs, - ) - - self.pipeline.append(transformation) - - # 构建日志信息 - type_name = type(iterable).__name__ - count_info = f" with {total_count} items" if total_count is not None else "" - self.logger.info(f"Batch iterable source created from {type_name}{count_info}") - - return self._get_datastream_class()(self, transformation) diff --git a/packages/sage-kernel/src/sage/kernel/api/connected_streams.py b/packages/sage-kernel/src/sage/kernel/api/connected_streams.py deleted file mode 100644 index 46224be651..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/connected_streams.py +++ /dev/null @@ -1,527 +0,0 @@ -from __future__ import annotations - -from collections.abc import Callable -from typing import TYPE_CHECKING - -from sage.common.core import ( - BaseCoMapFunction, - BaseFunction, - BaseJoinFunction, - wrap_lambda, -) -from sage.kernel.api.base_environment import BaseEnvironment -from sage.kernel.api.transformation.join_transformation import JoinTransformation - -if TYPE_CHECKING: - from sage.kernel.api.transformation.base_transformation import BaseTransformation - - from .datastream import DataStream - - -class ConnectedStreams: - """ - 表示多个数据流的连接，类似于Flink的ConnectedStreams - - 这个类建模了多个数据流之间的逻辑连接关系，保持每个流的独立性， - 直到应用CoMap等多流操作时才进行实际的数据合并处理。 - - 设计原则： - 1. 保持流的边界信息，直到真正需要合并时 - 2. 每个连接的流保持其独立的transformation身份 - 3. 只有在应用多流操作（如comap）时才创建多输入的transformation - """ - - def __init__(self, env: BaseEnvironment, transformations: list[BaseTransformation]): - self._environment = env - self.transformations = transformations - - # 验证输入 - if len(transformations) < 2: - raise ValueError("ConnectedStreams requires at least 2 transformations") - - # 确保所有transformation都来自同一个环境 - for trans in transformations: - if trans.env != env: - raise ValueError("All transformations must be from the same environment") - - def _get_transformation_classes(self): - """动态导入transformation类以避免循环导入""" - if not hasattr(self, "_transformation_classes"): - from sage.kernel.api.transformation.base_transformation import ( - BaseTransformation, - ) - from sage.kernel.api.transformation.join_transformation import ( - JoinTransformation, - ) - from sage.kernel.api.transformation.map_transformation import ( - MapTransformation, - ) - from sage.kernel.api.transformation.sink_transformation import ( - SinkTransformation, - ) - - self._transformation_classes = { - "BaseTransformation": BaseTransformation, - "MapTransformation": MapTransformation, - "SinkTransformation": SinkTransformation, - "JoinTransformation": JoinTransformation, - } - return self._transformation_classes - - def map( - self, - function: type[BaseFunction] | Callable, - *args, - parallelism: int | None = None, - **kwargs, - ) -> DataStream: - if callable(function) and not isinstance(function, type): - function = wrap_lambda(function, "map") - - # 使用传入的parallelism或者默认值1 - actual_parallelism = parallelism if parallelism is not None else 1 - - # 获取MapTransformation类 - MapTransformation = self._get_transformation_classes()["MapTransformation"] - tr = MapTransformation( - self._environment, function, *args, parallelism=actual_parallelism, **kwargs - ) - return self._apply(tr) - - def sink( - self, - function: type[BaseFunction] | Callable, - *args, - parallelism: int | None = None, - **kwargs, - ) -> DataStream: - if callable(function) and not isinstance(function, type): - function = wrap_lambda(function, "sink") - - # 使用传入的parallelism或者默认值1 - actual_parallelism = parallelism if parallelism is not None else 1 - - # 获取SinkTransformation类 - SinkTransformation = self._get_transformation_classes()["SinkTransformation"] - tr = SinkTransformation( - self._environment, function, *args, parallelism=actual_parallelism, **kwargs - ) - return self._apply(tr) - - def print(self, prefix: str = "", separator: str = " | ", colored: bool = True) -> DataStream: - """ - 便捷的打印方法 - 将连接的数据流输出到控制台 - - Args: - prefix: 输出前缀，默认为空 - separator: 前缀与内容之间的分隔符，默认为 " | " - colored: 是否启用彩色输出，默认为True - - Returns: - DataStream: 返回新的数据流用于链式调用 - """ - from sage.common.components.debug.print_sink import PrintSink - - return self.sink(PrintSink, prefix=prefix, separator=separator, colored=colored) - - def connect(self, other: DataStream | ConnectedStreams) -> ConnectedStreams: - """连接更多数据流 - - Args: - other: 另一个DataStream或ConnectedStreams实例 - - Returns: - ConnectedStreams: 新的连接流，按顺序包含所有transformation - """ - if hasattr(other, "transformation"): # DataStream - # ConnectedStreams + DataStream -> ConnectedStreams - new_transformations = self.transformations + [other.transformation] # type: ignore[attr-defined] - else: # ConnectedStreams - # ConnectedStreams + ConnectedStreams -> ConnectedStreams - new_transformations = self.transformations + other.transformations # type: ignore[attr-defined] - - return ConnectedStreams(self._environment, new_transformations) - - def comap( - self, - function: type[BaseFunction] | Callable, - *args, - parallelism: int | None = None, - **kwargs, - ) -> DataStream: - """ - Apply a CoMap function that processes each connected stream separately - - CoMap (Co-processing Map) enables parallel processing of multiple input streams - where each stream is processed independently using dedicated mapN methods. - Unlike regular map operations that merge all inputs, comap maintains stream - boundaries and routes each input to its corresponding mapN method. - - Args: - function: One of the following: - - CoMap function class that implements map0, map1, ..., mapN methods (class-based) - - List of callables [func0, func1, ..., funcN] (lambda list) - - Single callable for multiple function arguments (lambda args) - *args: When function is a class, additional constructor arguments. - When function is callable(s), treated as additional functions. - **kwargs: When function is a class, additional constructor arguments. - When function is callable(s), ignored with warning. - - Returns: - DataStream: Result stream from coordinated processing of all input streams - - Raises: - NotImplementedError: Lambda functions are not supported for comap operations - TypeError: If function is not a valid CoMap function - ValueError: If function doesn't support the required number of input streams - - Examples: - Class-based approach: - ```python - class ProcessorCoMap(BaseCoMapFunction): - def map0(self, data): - return f"Stream 0: {data}" - - def map1(self, data): - return f"Stream 1: {data * 2}" - - result = (stream1 - .connect(stream2) - .comap(ProcessorCoMap) - .print("CoMap Result")) - ``` - """ - if callable(function) and not isinstance(function, type): - # Lambda functions need special wrapper - not implemented yet - raise NotImplementedError( - "Lambda functions are not supported for comap operations. " - "Please use a class that inherits from BaseCoMapFunction." - ) - - # Validate input stream count before creating transformation - input_stream_count = len(self.transformations) - if input_stream_count < 2: - raise ValueError( - f"CoMap operations require at least 2 input streams, " - f"but only {input_stream_count} streams provided." - ) - - # Import BaseCoMapFunction for type checking - from sage.common.core import BaseCoMapFunction - - # Type validation: Check if function is a proper CoMap function - if not isinstance(function, type): - raise TypeError( - f"CoMap function must be a class, got {type(function).__name__}. " - f"Please provide a class that inherits from BaseCoMapFunction." - ) - - if not issubclass(function, BaseCoMapFunction): - raise TypeError( - f"Function {function.__name__} must inherit from BaseCoMapFunction. " - f"CoMap operations require CoMap function with mapN methods." - ) - - # Validate that function supports the required number of input streams - required_methods = [f"map{i}" for i in range(input_stream_count)] - missing_methods = [] - - for method_name in required_methods: - if not hasattr(function, method_name): - missing_methods.append(method_name) - - if missing_methods: - raise TypeError( - f"CoMap function {function.__name__} is missing required methods: {missing_methods}. " - f"For {input_stream_count} input streams, the function must implement: {required_methods}." - ) - - # Additional validation: Check if mapN methods are callable - for method_name in required_methods: - method = getattr(function, method_name) - if not callable(method): - raise TypeError( - f"CoMap function {function.__name__}.{method_name} must be callable. " - f"Found {type(method).__name__} instead." - ) - - # Import CoMapTransformation (delayed import to avoid circular dependencies) - from sage.kernel.api.transformation.comap_transformation import ( - CoMapTransformation, - ) - - # 使用传入的parallelism或者之前设置的hint - actual_parallelism = parallelism if parallelism is not None else 1 - - # Create CoMapTransformation - tr = CoMapTransformation( - self._environment, function, *args, parallelism=actual_parallelism, **kwargs - ) - - # Additional validation at transformation level - tr.validate_input_streams(input_stream_count) - - return self._apply(tr) - - # 在 connected_streams.py 中添加简化的join方法 - def join( - self, - function: type[BaseJoinFunction] | Callable, - *args, - parallelism: int | None = None, - **kwargs, - ) -> DataStream: - """ - Join two keyed streams using a join function. - - Args: - function: Join function class implementing BaseJoinFunction - *args, **kwargs: Arguments passed to join function constructor - - Returns: - DataStream: Stream of join results - - Example: - ```python - class UserOrderJoin(BaseJoinFunction): - def execute(self, payload, key, tag): - # tag 0: user data, tag 1: order data - # 实现join逻辑并返回结果列表 - return [joined_result] if match else [] - - result = (user_stream - .keyby(lambda x: x["user_id"]) - .connect(order_stream.keyby(lambda x: x["user_id"])) - .join(UserOrderJoin) - .print("Join Results")) - ``` - """ - # 验证输入 - if len(self.transformations) != 2: - raise ValueError( - f"Join requires exactly 2 input streams, got {len(self.transformations)}" - ) - - # 类型检查 - if not isinstance(function, type) or not issubclass(function, BaseJoinFunction): - raise TypeError("Join function must inherit from BaseJoinFunction") - - # TODO: 验证流都是keyed的 - # Issue URL: https://github.com/intellistream/SAGE/issues/225 - # self._validate_keyed_streams() - - # 创建transformation - # 使用传入的parallelism或者默认值1 - actual_parallelism = parallelism if parallelism is not None else 1 - join_tr = JoinTransformation( - self._environment, function, *args, parallelism=actual_parallelism, **kwargs - ) - return self._apply(join_tr) - - def keyby( - self, - key_selector: type[BaseFunction] | list[type[BaseFunction]], - strategy: str = "hash", - ) -> ConnectedStreams: - """ - Apply keyby partitioning to connected streams using composition approach - - Args: - key_selector: - - Single BaseFunction: Apply same key extraction to all streams - - List[BaseFunction]: Apply different key extraction per stream (Flink-style) - strategy: Partitioning strategy ("hash", "broadcast", "round_robin") - - Returns: - ConnectedStreams: New ConnectedStreams with all streams keyed - - Example: - ```python - # Same key selector for all streams - keyed_streams = stream1.connect(stream2).keyby(UserIdExtractor) - - # Different key selector per stream (Flink-style) - keyed_streams = stream1.connect(stream2).keyby([UserIdExtractor, SessionIdExtractor]) - - # Continue with further operations - result = keyed_streams.comap(JoinFunction).sink(OutputSink) - ``` - """ - if callable(key_selector) and not isinstance(key_selector, type): - raise NotImplementedError( - "Lambda functions are not supported for keyby operations. " - "Please use KeyByFunction classes." - ) - - from .datastream import DataStream - - input_stream_count = len(self.transformations) - - if isinstance(key_selector, list): - # Flink-style: different key selector per stream - if len(key_selector) != input_stream_count: - raise ValueError( - f"Key selector count ({len(key_selector)}) must match stream count ({input_stream_count})" - ) - - # 为每个流分别应用keyby - keyed_transformations = [] - for transformation, selector in zip(self.transformations, key_selector, strict=False): - # 创建单独的DataStream并应用keyby - individual_stream: DataStream = DataStream(self._environment, transformation) - keyed_stream = individual_stream.keyby(selector, strategy=strategy) - keyed_transformations.append(keyed_stream.transformation) - - else: - # 统一的key selector：为所有流应用相同的keyby - keyed_transformations = [] - for transformation in self.transformations: - # 创建单独的DataStream并应用keyby - individual_stream = DataStream(self._environment, transformation) - keyed_stream = individual_stream.keyby(key_selector, strategy=strategy) - keyed_transformations.append(keyed_stream.transformation) - - # 返回新的ConnectedStreams，包含所有keyed transformations - return ConnectedStreams(self._environment, keyed_transformations) - - # --------------------------------------------------------------------- - # CoMap function parsing methods - # --------------------------------------------------------------------- - def _parse_comap_functions( - self, - function: type[BaseFunction] | Callable | list[Callable], - input_stream_count: int, - *args, - **kwargs, - ) -> tuple: - """ - Parse different input formats for CoMap functions and return standardized format - - Args: - function: The function input (class, callable, or list of callables) - input_stream_count: Number of input streams requiring processing - *args: Additional arguments - **kwargs: Additional keyword arguments - - Returns: - tuple: (comap_function_class, final_args, final_kwargs) - """ - # Case 1: Class-based CoMap function (existing approach) - if isinstance(function, type) and issubclass(function, BaseCoMapFunction): - return function, args, kwargs - - # Case 2: List of functions - if isinstance(function, list): - if args or kwargs: - self._warn_ignored_params("args/kwargs", args, kwargs) - return ( - self._create_dynamic_comap_class(function, input_stream_count), - (), - {}, - ) - - # Case 3: Multiple function arguments (callables passed as separate args) - if callable(function): - # Collect all callable arguments - all_functions = [function] + [arg for arg in args if callable(arg)] - non_callable_args = [arg for arg in args if not callable(arg)] - - if non_callable_args or kwargs: - self._warn_ignored_params("non-callable args/kwargs", non_callable_args, kwargs) - - return ( - self._create_dynamic_comap_class(all_functions, input_stream_count), - (), - {}, - ) - - # Case 4: Invalid input - raise ValueError( - f"Invalid function input for comap: {type(function)}. " - f"Expected: CoMap class, callable, or list of callables." - ) - - def _create_dynamic_comap_class( - self, function_list: list[Callable], input_stream_count: int - ) -> type[BaseCoMapFunction]: - """ - Dynamically create a CoMap class from a list of functions - - Args: - function_list: List of callable functions - input_stream_count: Expected number of input streams - - Returns: - Type[BaseCoMapFunction]: Dynamically generated CoMap class - """ - # Validate function count matches input stream count - if len(function_list) != input_stream_count: - raise ValueError( - f"Number of functions ({len(function_list)}) must match " - f"number of input streams ({input_stream_count}). " - f"Please provide exactly {input_stream_count} functions." - ) - - # Validate all items are callable - for i, func in enumerate(function_list): - if not callable(func): - raise ValueError(f"Item at index {i} is not callable: {type(func).__name__}") - - # Create the dynamic class with all required methods defined inline - # We need to create a class dynamically with the required mapN methods - - # Create method definitions for dynamic class - class_methods = { - "__init__": lambda self: BaseCoMapFunction.__init__(self), - "is_comap": property(lambda self: True), - "execute": lambda self, data: self._raise_execute_error(), - "_raise_execute_error": lambda self: self._do_raise_execute_error(), - "_do_raise_execute_error": lambda self: (_ for _ in ()).throw( - NotImplementedError("CoMap functions use mapN methods, not execute()") - ), - } - - # Add all required mapN methods - for i, func in enumerate(function_list): - method_name = f"map{i}" - # Create method that captures the function in closure - class_methods[method_name] = (lambda f: lambda self, data: f(data))(func) - - # Create the dynamic class - dynamic_comap_function = type("dynamic_comap_function", (BaseCoMapFunction,), class_methods) - - return dynamic_comap_function - - def _warn_ignored_params(self, param_type: str, *params) -> None: - """ - Warn user about ignored parameters in lambda/callable CoMap usage - - Args: - param_type: Description of ignored parameter type - *params: The ignored parameters - """ - if any(params): - print(f"⚠️ Warning: {param_type} ignored in lambda/callable CoMap usage: {params}") - - # --------------------------------------------------------------------- - # internal methods - # --------------------------------------------------------------------- - def _apply(self, tr: BaseTransformation) -> DataStream: - """ - 将多输入transformation应用到连接的流上 - - 这是Flink风格的实现： - 1. 新的transformation是一个多输入操作符 - 2. 每个上游流连接到操作符的特定输入索引 - 3. 操作符知道如何根据input_index路由数据到对应的处理方法 - """ - from .datastream import DataStream - - # 为多输入transformation设置上游连接 - # 每个上游transformation连接到特定的input_index - for input_index, upstream_trans in enumerate(self.transformations): - tr.add_upstream(upstream_trans, input_index=input_index) - - # 将新transformation添加到pipeline - self._environment.pipeline.append(tr) - return DataStream(self._environment, tr) diff --git a/packages/sage-kernel/src/sage/kernel/api/function/__init__.py b/packages/sage-kernel/src/sage/kernel/api/function/__init__.py deleted file mode 100644 index 17f65e5dd8..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/function/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -"""Simple batch function implementations for DataStream API.""" - -from sage.kernel.api.function.simple_batch_function import ( - IterableBatchIteratorFunction, - SimpleBatchIteratorFunction, -) - -__all__ = [ - "SimpleBatchIteratorFunction", - "IterableBatchIteratorFunction", -] diff --git a/packages/sage-kernel/src/sage/kernel/api/local_environment.py b/packages/sage-kernel/src/sage/kernel/api/local_environment.py deleted file mode 100644 index dba23c688d..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/local_environment.py +++ /dev/null @@ -1,211 +0,0 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING - -from sage.kernel.api.base_environment import BaseEnvironment - -if TYPE_CHECKING: - from sage.kernel.runtime.job_manager import JobManager - - -class LocalEnvironment(BaseEnvironment): - """本地环境，直接使用本地JobManager实例""" - - def __init__( - self, - name: str = "localenvironment", - config: dict | None = None, - scheduler=None, - enable_monitoring: bool = False, - ): - super().__init__( - name, - config, - platform="local", - scheduler=scheduler, - enable_monitoring=enable_monitoring, - ) - - # 本地环境不需要客户端 - self._engine_client = None - - def submit(self, autostop: bool = False): - """ - 提交作业到JobManager执行 - - Args: - autostop (bool): 如果为True，方法将阻塞直到所有批处理任务完成后自动停止 - 如果为False，方法立即返回，需要手动管理任务生命周期 - - Returns: - str: 任务的UUID - """ - # 提交作业（传递 autostop 参数） - env_uuid = self.jobmanager.submit_job(self, autostop=autostop) - - if autostop: - self._wait_for_completion() - - return env_uuid - - def _wait_for_completion(self): - """ - 等待批处理任务完成 - 在本地环境中直接监控JobManager实例的状态 - - 改进的等待策略： - 1. 不监控源节点停止（源节点停止 ≠ 数据处理完） - 2. 监控 Sink 节点接收到停止信号（所有数据已处理完） - 3. 或监控所有任务都停止（dispatcher 清理完成） - """ - import time - - if not self.env_uuid: - self.logger.warning("No environment UUID found, cannot wait for completion") - return - - self.logger.info("Waiting for batch processing to complete...") - self.logger.info( - "⏳ Strategy: Wait for all data to be processed (not just source completion)" - ) - - # 设置最大等待时间，避免无限等待 - max_wait_time = 12000.0 # 增加到 10 分钟，适应长时间处理 - start_time = time.time() - check_interval = 1.0 # 增加检查间隔，减少 CPU 占用 - - try: - while time.time() - start_time < max_wait_time: - # 直接检查本地JobManager实例中的作业状态 - job_info = self.jobmanager.jobs.get(self.env_uuid) - - if job_info is None: - # 作业已被删除，说明完成了 - self.logger.info("✅ Batch processing completed successfully (job deleted)") - break - - # 检查作业状态（优先检查这个，因为它更可靠） - if job_info.status in ["stopped", "failed"]: - self.logger.info( - f"✅ Batch processing completed with status: {job_info.status}" - ) - break - - # 改进的停止检测：检查所有 Task 是否都已停止 - # 这确保队列中的数据都被处理完 - dispatcher = job_info.dispatcher - if hasattr(dispatcher, "tasks") and len(dispatcher.tasks) > 0: - all_tasks_stopped = all( - not task.is_running for task in dispatcher.tasks.values() - ) - if all_tasks_stopped: - self.logger.info("✅ All tasks stopped, processing complete") - break - - # 检查dispatcher状态 - # 注意：dispatcher.is_running 可能在stop()方法执行期间仍然为True - # 所以我们也检查dispatcher是否已经开始停止过程 - dispatcher_stopped = not job_info.dispatcher.is_running - if dispatcher_stopped: - # Dispatcher已停止，但还需要等待服务清理完成 - # 检查是否所有服务都已清理 - if ( - len(job_info.dispatcher.services) == 0 - and len(job_info.dispatcher.tasks) == 0 - ): - self.logger.info( - "Dispatcher stopped and all resources cleaned up, batch processing completed" - ) - break - else: - # 服务还在清理中，继续等待 - self.logger.debug( - f"Waiting for resources to be cleaned up: {len(job_info.dispatcher.tasks)} tasks, {len(job_info.dispatcher.services)} services" - ) - - # 如果dispatcher正在停止过程中，等待更短的时间 - # 这样可以避免在dispatcher停止过程中的race condition - time.sleep(check_interval) - - else: - # 超时了，强制停止作业 - self.logger.warning( - f"Timeout waiting for batch processing to complete after {max_wait_time}s" - ) - try: - self.stop() - except Exception as stop_error: - self.logger.error(f"Error stopping timed out job: {stop_error}") - - except KeyboardInterrupt: - self.logger.info("Received interrupt signal, stopping batch processing...") - self.stop() - except Exception as e: - self.logger.error(f"Error waiting for completion: {e}") - # 在出错时尝试停止作业 - try: - self.stop() - except Exception as stop_error: - self.logger.error(f"Error stopping job after wait error: {stop_error}") - - finally: - # 确保清理资源 - self.is_running = False - - @property - def jobmanager(self) -> JobManager: - """直接返回JobManager的单例实例""" - if self._jobmanager is None: - from sage.kernel.runtime.job_manager import JobManager - - # 获取JobManager单例实例 - jobmanager_instance = JobManager() - # 本地环境直接返回JobManager实例，不使用ActorWrapper - self._jobmanager = jobmanager_instance - - return self._jobmanager - - def stop(self): - """停止管道运行""" - if not self.env_uuid: - self.logger.warning("Environment not submitted, nothing to stop") - return - - self.logger.info("Stopping pipeline...") - - try: - response = self.jobmanager.pause_job(self.env_uuid) - - if response.get("status") == "success": - self.is_running = False - self.logger.info("Pipeline stopped successfully") - else: - self.logger.warning(f"Failed to stop pipeline: {response.get('message')}") - except Exception as e: - self.logger.error(f"Error stopping pipeline: {e}") - - def close(self): - """关闭管道运行""" - if not self.env_uuid: - self.logger.warning("Environment not submitted, nothing to close") - return - - self.logger.info("Closing environment...") - - try: - response = self.jobmanager.pause_job(self.env_uuid) - - if response.get("status") == "success": - self.logger.info("Environment closed successfully") - else: - self.logger.warning(f"Failed to close environment: {response.get('message')}") - - except Exception as e: - self.logger.error(f"Error closing environment: {e}") - finally: - # 清理本地资源 - self.is_running = False - self.env_uuid = None - - # 清理管道 - self.pipeline.clear() diff --git a/packages/sage-kernel/src/sage/kernel/api/operator/__init__.py b/packages/sage-kernel/src/sage/kernel/api/operator/__init__.py deleted file mode 100644 index 58c515af61..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/operator/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -""" -SAGE - Streaming-Augmented Generative Execution -""" - -# 直接从本包的_version模块加载版本信息 -try: - from sage.kernel._version import __author__, __email__, __version__ -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" diff --git a/packages/sage-kernel/src/sage/kernel/api/operator/base_operator.py b/packages/sage-kernel/src/sage/kernel/api/operator/base_operator.py deleted file mode 100644 index 058405d2df..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/operator/base_operator.py +++ /dev/null @@ -1,174 +0,0 @@ -from abc import ABC, abstractmethod -from typing import TYPE_CHECKING, Any - -from sage.kernel.runtime.communication.packet import StopSignal - -if TYPE_CHECKING: - from sage.common.core.functions import BaseFunction - from sage.common.utils.logging.custom_logger import CustomLogger - from sage.kernel.runtime.communication.packet import Packet - from sage.kernel.runtime.context.task_context import TaskContext - from sage.kernel.runtime.factory.function_factory import FunctionFactory - - -class BaseOperator(ABC): - """ - Operator 的抽象基类 - """ - - # 控制状态保存的类属性（子类可覆盖） - __state_include__: list[str] = [] - __state_exclude__: list[str] = ["ctx", "function", "logger", "_logger"] - - def __init__(self, function_factory: "FunctionFactory", ctx: "TaskContext", *args, **kwargs): - self.ctx: TaskContext = ctx - self.function: BaseFunction - try: - self.function = function_factory.create_function(self.name, ctx) - self.logger.debug(f"Created function instance with {function_factory}") - - except Exception as e: - self.logger.error(f"Failed to create function instance: {e}", exc_info=True) - raise - - def send_packet(self, packet: "Packet") -> bool: - """通过TaskContext发送数据包""" - return self.ctx.send_packet(packet) # type: ignore - - def send_stop_signal(self, stop_signal: "StopSignal") -> None: - """通过TaskContext发送停止信号""" - self.ctx.send_stop_signal(stop_signal) - - def get_routing_info(self) -> dict[str, Any]: - """获取路由信息""" - return self.ctx.get_routing_info() - - @property - def router(self): - return self.ctx.router - - def receive_packet(self, packet: "Packet"): - """接收数据包并处理""" - if packet is None: - self.logger.warning(f"Received None packet in {self.name}") - return - self.logger.debug(f"Operator {self.name} received packet: {packet}") - - try: - # Set the current packet key for keyed state support - # Packet always has partition_key attribute, but it may be None - self.ctx.set_current_key(packet.partition_key) - - # Process the packet - self.process_packet(packet) - finally: - # Always clear the key after processing to prevent leakage - self.ctx.clear_key() - - @abstractmethod - def process_packet(self, packet: "Packet | None" = None): - return - - def get_state(self) -> dict[str, Any]: - """ - 获取 Operator 的状态用于 checkpoint - - 默认实现会保存 function 的状态和 operator 自身的可序列化属性。 - 子类可以覆盖此方法来自定义状态保存逻辑。 - - Returns: - 包含可序列化状态的字典 - """ - state: dict[str, Any] = { - "operator_type": self.__class__.__name__, - } - - # 保存 function 的状态 - if hasattr(self.function, "get_state"): - try: - state["function_state"] = self.function.get_state() - except Exception as e: - self.logger.warning(f"Failed to get function state: {e}") - - # 保存 operator 自身的状态 - operator_attrs = {} - all_attrs = set(vars(self).keys()) - - # 确定要保存的属性 - if self.__state_include__: - attrs_to_save = set(self.__state_include__) & all_attrs - else: - exclude_set = set(self.__state_exclude__) - attrs_to_save = all_attrs - exclude_set - - # 过滤私有属性 - if not self.__state_include__: - attrs_to_save = {attr for attr in attrs_to_save if not attr.startswith("_")} - - # 收集可序列化的状态 - for attr_name in attrs_to_save: - try: - value = getattr(self, attr_name) - if self._is_serializable(value): - operator_attrs[attr_name] = value - except Exception as e: - self.logger.warning(f"Failed to get operator attribute '{attr_name}': {e}") - - if operator_attrs: - state["operator_attrs"] = operator_attrs - - return state - - def restore_state(self, state: dict[str, Any]): - """ - 从 checkpoint 恢复 Operator 的状态 - - Args: - state: 保存的状态字典 - """ - # 恢复 function 的状态 - if "function_state" in state and hasattr(self.function, "restore_state"): - try: - self.function.restore_state(state["function_state"]) - self.logger.info(f"Function state restored for operator {self.name}") - except Exception as e: - self.logger.warning(f"Failed to restore function state: {e}") - - # 恢复 operator 自身的状态 - if "operator_attrs" in state: - for attr_name, value in state["operator_attrs"].items(): - try: - setattr(self, attr_name, value) - except Exception as e: - self.logger.warning(f"Failed to restore operator attribute '{attr_name}': {e}") - - def _is_serializable(self, value: Any) -> bool: - """检查值是否可序列化（与 BaseFunction 中的实现相同）""" - if isinstance(value, (int, float, str, bool, type(None))): - return True - - if isinstance(value, (list, tuple)): - return all(self._is_serializable(item) for item in value) - - if isinstance(value, dict): - return all( - self._is_serializable(k) and self._is_serializable(v) for k, v in value.items() - ) - - import pickle - - try: - pickle.dumps(value) - return True - except (TypeError, pickle.PicklingError, AttributeError): - return False - - @property - def name(self) -> str: - """获取任务名称""" - return self.ctx.name - - @property - def logger(self) -> "CustomLogger": - """获取当前任务的日志记录器""" - return self.ctx.logger diff --git a/packages/sage-kernel/src/sage/kernel/api/operator/batch_operator.py b/packages/sage-kernel/src/sage/kernel/api/operator/batch_operator.py deleted file mode 100644 index 233664a052..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/operator/batch_operator.py +++ /dev/null @@ -1,57 +0,0 @@ -from sage.kernel.api.operator.base_operator import BaseOperator -from sage.kernel.runtime.communication.packet import Packet, StopSignal - - -class BatchOperator(BaseOperator): - """ - 批处理操作符 - - 流量控制通过router的Queue实现： - - router.send(packet)内部使用queue.put() - - 当下游处理慢时，put()会自然阻塞，形成背压 - - 无需额外的全局锁机制 - """ - - def receive_packet(self, packet: "Packet"): - self.process_packet(packet) - - def process_packet(self, packet: "Packet | None" = None): - try: - result = self.function.execute() - self.logger.debug(f"Operator {self.name} processed data with result: {result}") - - # 检查是否为停止信号 - is_stop = result is None or isinstance(result, StopSignal) - - if is_stop: - self.logger.info(f"Batch Operator {self.name} completed, sending stop signal") - - # 使用标准 StopSignal - if isinstance(result, StopSignal): - stop_signal = result - else: - stop_signal = StopSignal(self.name) - - self.router.send_stop_signal(stop_signal) - - # 源节点完成时,通知JobManager该节点完成 - self.ctx.send_stop_signal_back(self.name) - - # 通过ctx停止task - self.ctx.set_stop_signal() - return - - # 发送正常数据包 - # router.send()内部的queue.put()会在队列满时自动阻塞，实现背压控制 - if result is not None: - success = self.router.send(Packet(result)) - # If sending failed (e.g., queue is closed), stop the task - if not success: - self.logger.warning( - f"Batch Operator {self.name} failed to send packet, stopping task" - ) - self.ctx.set_stop_signal() - return - - except Exception as e: - self.logger.error(f"Error in {self.name}.process(): {e}", exc_info=True) diff --git a/packages/sage-kernel/src/sage/kernel/api/operator/comap_operator.py b/packages/sage-kernel/src/sage/kernel/api/operator/comap_operator.py deleted file mode 100644 index 89daefaa13..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/operator/comap_operator.py +++ /dev/null @@ -1,228 +0,0 @@ -from sage.kernel.runtime.communication.packet import Packet - -from .base_operator import BaseOperator - - -class CoMapOperator(BaseOperator): - """ - CoMap操作符 - 处理多输入流的分别处理操作 - - CoMapOperator专门用于处理CoMap函数，它会根据输入的input_index - 直接路由到相应的mapN方法，而不是使用统一的execute方法。 - """ - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - # 验证函数类型（在运行时初始化后进行） - self._validate_function() - self._validated = True - - # 跟踪接收到的停止信号 - self.received_stop_signals = set() # 记录哪些stream已经发送了停止信号 - - # 记录输入流的数量，用于判断是否所有流都已停止 - self.expected_input_count = None - - def _validate_function(self) -> None: - """ - 验证函数是否为CoMap函数 - - Raises: - TypeError: 如果函数不是CoMap函数 - """ - if not hasattr(self.function, "is_comap") or not self.function.is_comap: # type: ignore[attr-defined] - raise TypeError( - f"{self.__class__.__name__} requires CoMap function with is_comap=True, " - f"got {type(self.function).__name__}" - ) - - # 验证必需的map0和map1方法 - required_methods = ["map0", "map1"] - for method_name in required_methods: - if not hasattr(self.function, method_name): - raise TypeError( - f"CoMap function {type(self.function).__name__} must implement {method_name} method" - ) - - self.logger.debug(f"Validated CoMap function {type(self.function).__name__}") - - def process_packet(self, packet: "Packet | None" = None): - """CoMap处理多输入，保持分区信息""" - try: - if packet is None or packet.payload is None: - return - - # 根据输入索引调用对应的mapN方法 - input_index = packet.input_index - map_method = getattr(self.function, f"map{input_index}") - result = map_method(packet.payload) - - if result is not None: - # 继承原packet的分区信息 - result_packet = packet.inherit_partition_info(result) - self.router.send(result_packet) - - except Exception as e: - self.logger.error(f"Error in CoMapOperator {self.name}: {e}", exc_info=True) - - # 发送错误结果，确保下游仍能收到数据（关键修复） - error_result = { - "type": "comap_error", - "error": str(e), - "original_payload": packet.payload if packet else None, - "input_index": packet.input_index if packet else -1, - "operator": self.name, - } - - try: - if packet: - error_packet = packet.inherit_partition_info(error_result) - self.router.send(error_packet) - self.logger.info(f"CoMapOperator {self.name}: Sent error result downstream") - except Exception as send_error: - self.logger.error( - f"Failed to send error result in CoMapOperator {self.name}: {send_error}" - ) - - def handle_stop_signal( - self, stop_signal_name: str | None = None, input_index: int | None = None - ): - """ - 处理停止信号的传播 - - CoMap操作需要特殊处理停止信号： - - 记录哪个stream发送了停止信号 - - 只有当所有输入流都停止时，才向下游传播停止信号（修复关键bug） - """ - try: - if input_index is not None: - self.received_stop_signals.add(input_index) - self.logger.info( - f"CoMapOperator '{self.name}' received stop signal from stream {input_index}" - ) - - # 如果还没有初始化期望的输入流数量，尝试从函数获取 - if self.expected_input_count is None: - try: - # 通过检查函数的mapN方法数量来确定预期的输入数量 - count = 0 - method_index = 0 - while True: - method_name = f"map{method_index}" - if hasattr(self.function, method_name): - method = getattr(self.function, method_name) - # 检查方法是否实际可调用（不是抽象方法） - if callable(method) and not getattr( - method, "__isabstractmethod__", False - ): - count += 1 - method_index += 1 - else: - break - else: - break - - # 如果通过函数找到了mapN方法，使用该数量 - if count > 0: - self.expected_input_count = count - else: - # 从路由器的入站连接数推断（备用方案） - self.expected_input_count = getattr(self.router, "input_count", 2) - - self.logger.debug( - f"CoMapOperator '{self.name}' expecting {self.expected_input_count} input streams" - ) - except Exception as e: - self.logger.warning( - f"CoMapOperator '{self.name}' failed to determine input count: {e}, defaulting to 2" - ) - self.expected_input_count = 2 - - # 修复关键bug：只有当所有输入流都停止时，才向下游传播停止信号 - if len(self.received_stop_signals) >= self.expected_input_count: - self.logger.info( - f"CoMapOperator '{self.name}' received stop signals from all {self.expected_input_count} input streams, " - f"propagating stop signal downstream" - ) - - # 向下游传播停止信号 - from sage.kernel.runtime.communication.packet import StopSignal - - stop_signal = StopSignal(self.name, source=self.name) - self.router.send_stop_signal(stop_signal) - - # 通知context停止 - self.ctx.set_stop_signal() - else: - self.logger.debug( - f"CoMapOperator '{self.name}' waiting for more stop signals: " - f"received {len(self.received_stop_signals)}/{self.expected_input_count}" - ) - - except Exception as e: - self.logger.error( - f"Error in CoMapOperator '{self.name}' handle_stop_signal: {e}", - exc_info=True, - ) - - def _get_max_supported_index(self) -> int: - """ - 获取支持的最大输入流索引 - - Returns: - int: 最大支持的输入流索引 - """ - max_index = -1 - index = 0 - - # 检查有多少个mapN方法被实现 - while True: - method_name = f"map{index}" - if hasattr(self.function, method_name): - try: - # 尝试调用方法看是否抛出NotImplementedError - method = getattr(self.function, method_name) - # 检查方法是否为抽象方法或抛出NotImplementedError - if not getattr(method, "__isabstractmethod__", False): - max_index = index - except Exception: - # 如果获取方法时出错，停止检查 - break - index += 1 - else: - break - - return max_index - - def get_supported_input_methods(self) -> list[str]: - """ - 获取所有支持的mapN方法列表 - - Returns: - list[str]: 支持的方法名列表 - """ - methods = [] - index = 0 - - while True: - method_name = f"map{index}" - if hasattr(self.function, method_name): - method = getattr(self.function, method_name) - if not getattr(method, "__isabstractmethod__", False): - methods.append(method_name) - index += 1 - else: - break - - return methods - - def __repr__(self) -> str: - if hasattr(self, "function") and self.function: - function_name = self.function.__class__.__name__ - if self._validated: - max_index = self._get_max_supported_index() - return f"<{self.__class__.__name__} {function_name} supports:0-{max_index}>" - else: - return f"<{self.__class__.__name__} {function_name} (not validated)>" - else: - return f"<{self.__class__.__name__} (no function)>" diff --git a/packages/sage-kernel/src/sage/kernel/api/operator/filter_operator.py b/packages/sage-kernel/src/sage/kernel/api/operator/filter_operator.py deleted file mode 100644 index 5d770ec7e0..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/operator/filter_operator.py +++ /dev/null @@ -1,51 +0,0 @@ -from sage.kernel.api.operator.base_operator import BaseOperator -from sage.kernel.runtime.communication.packet import Packet - - -class FilterOperator(BaseOperator): - """ - Filter操作符，根据指定的条件函数对数据进行筛选。 - - 只有满足条件的数据才会被发送到下游节点。 - Filter操作不修改数据内容，只是决定数据是否通过。 - - Example: - # 过滤正数 - def filter_positive(data): - return data.value > 0 - - # 过滤特定用户 - def filter_user(data): - return data.user_id in ['user1', 'user2'] - """ - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - def process_packet(self, packet: "Packet | None" = None): - """Filter需要特殊处理：可能不产生输出""" - try: - if packet is None or packet.payload is None: - self.logger.debug(f"FilterOperator {self.name}: Received empty packet") - return - - # 添加调试日志 - self.logger.debug( - f"FilterOperator {self.name}: Processing packet with payload: {packet.payload}" - ) - - # 执行过滤逻辑 - should_pass = self.function.execute(packet.payload) - - self.logger.debug(f"FilterOperator {self.name}: Filter result: {should_pass}") - - if should_pass: - # 通过过滤，继承分区信息 - self.logger.debug(f"FilterOperator {self.name}: Sending packet downstream") - self.router.send(packet) - else: - self.logger.debug(f"FilterOperator {self.name}: Packet filtered out") - # 不通过过滤：不发送任何packet - - except Exception as e: - self.logger.error(f"Error in FilterOperator {self.name}: {e}", exc_info=True) diff --git a/packages/sage-kernel/src/sage/kernel/api/operator/flatmap_operator.py b/packages/sage-kernel/src/sage/kernel/api/operator/flatmap_operator.py deleted file mode 100644 index 1b1eb4d98e..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/operator/flatmap_operator.py +++ /dev/null @@ -1,126 +0,0 @@ -from typing import TYPE_CHECKING, Any - -from sage.common.core import Collector, FlatMapFunction -from sage.kernel.api.operator.base_operator import BaseOperator -from sage.kernel.runtime.communication.packet import Packet, StopSignal - -if TYPE_CHECKING: - pass - - -class FlatMapOperator(BaseOperator): - """ - FlatMap操作符，支持将输入数据转换为多个输出数据。 - - 支持两种使用模式： - 1. Function内部调用out.collect()收集数据 - 2. Function返回可迭代对象，自动展开发送给下游 - - 使用新的packet-based架构，自动维护分区信息。 - - Example: - # 模式1：在function内部使用out.collect() - def my_function(data): - words = data.value.split() - for word in words: - self.out.collect(word) - - # 模式2：function返回可迭代对象 - def my_function(data): - return data.value.split() - """ - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self.out: Collector = Collector(self.ctx) - # Insert collector into function if it's a FlatMapFunction - # FlatMapFunction has the insert_collector method, BaseFunction doesn't - if isinstance(self.function, FlatMapFunction): - self.function.insert_collector(self.out) - self.logger.info(f"FlatMapOperator '{self.name}' initialized with collector") - - def process_packet(self, packet: "Packet | None" = None): - """ - 重写packet处理，支持FlatMap的多输出特性 - """ - self.logger.debug( - f"FlatMapOperator '{self.name}' received packet, keyed: {packet.is_keyed() if packet else False}" - ) - - try: - if packet is None or packet.payload is None: - self.logger.debug(f"FlatMapOperator '{self.name}' received empty packet, skipping") - return - - # 检查是否是 StopSignal - if isinstance(packet.payload, StopSignal): - # StopSignal 不调用 function.execute()，直接传播 - self.logger.debug( - f"FlatMapOperator '{self.name}' received StopSignal, propagating..." - ) - self.router.send(packet) - return - - # 清空收集器中的数据（如果有的话） - self.out.clear() - - # 执行flatmap function - result = self.function.execute(packet.payload) - - # 处理function的返回值（如果有） - if result is not None: - self._flatmap_send(result, packet) - - # 处理通过collector收集的数据 - collected_data = self.out.get_collected_data() - if collected_data: - self.logger.debug( - f"FlatMapOperator '{self.name}' collected {len(collected_data)} items via collector" - ) - for item_data in collected_data: - # 为每个收集的item创建新packet，继承分区信息 - result_packet = packet.inherit_partition_info(item_data) - self.router.send(result_packet) - # 清空collector - self.out.clear() - - self.logger.debug(f"FlatMapOperator '{self.name}' finished processing packet") - - except Exception as e: - self.logger.error( - f"Error in FlatMapOperator '{self.name}'.process_packet(): {e}", - exc_info=True, - ) - - def _flatmap_send(self, result: Any, source_packet: "Packet"): - """ - 将可迭代对象展开并发送给下游，保持分区信息 - - Args: - result: Function的返回值，应该是可迭代对象 - source_packet: 源packet，用于继承分区信息 - """ - try: - # 检查返回值是否为可迭代对象（但不是字符串） - if hasattr(result, "__iter__") and not isinstance(result, (str, bytes)): - count = 0 - for item in result: - # 为每个item创建新packet，继承分区信息 - result_packet = source_packet.inherit_partition_info(item) - self.router.send(result_packet) - count += 1 - self.logger.debug( - f"FlatMapOperator '{self.name}' emitted {count} items from iterable" - ) - else: - # 如果不是可迭代对象，直接发送 - result_packet = source_packet.inherit_partition_info(result) - self.router.send(result_packet) - self.logger.debug(f"FlatMapOperator '{self.name}' emitted single item: {result}") - - except Exception as e: - self.logger.error( - f"Error in FlatMapOperator '{self.name}'._emit_iterable_with_partition_info(): {e}", - exc_info=True, - ) - raise diff --git a/packages/sage-kernel/src/sage/kernel/api/operator/future_operator.py b/packages/sage-kernel/src/sage/kernel/api/operator/future_operator.py deleted file mode 100644 index 3fb2d7a02a..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/operator/future_operator.py +++ /dev/null @@ -1,34 +0,0 @@ -from __future__ import annotations - -from typing import Any - -from sage.kernel.runtime.factory.function_factory import FunctionFactory - -from .base_operator import BaseOperator - - -class FutureOperator(BaseOperator): - """ - Future transformation的占位符operator。 - 这个operator不会被实际执行，只是作为placeholder存在。 - """ - - def __init__(self, function_factory: FunctionFactory, ctx, env_name: str = ""): - super().__init__(function_factory, ctx) # type: ignore - self.is_future = True - self.basename = getattr(ctx, "name", env_name) - - def process(self, data: Any) -> Any: - """ - Future operator不应该被直接调用 - """ - raise RuntimeError("FutureOperator should not be called directly. It's a placeholder.") - - def emit(self, result: Any) -> None: - """ - Future operator不应该被直接调用 - """ - raise RuntimeError("FutureOperator should not be called directly. It's a placeholder.") - - def __repr__(self) -> str: - return f"FutureOperator({self.basename}, placeholder)" diff --git a/packages/sage-kernel/src/sage/kernel/api/operator/join_operator.py b/packages/sage-kernel/src/sage/kernel/api/operator/join_operator.py deleted file mode 100644 index 6e0e36bf8f..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/operator/join_operator.py +++ /dev/null @@ -1,348 +0,0 @@ -from typing import Any - -from sage.kernel.runtime.communication.packet import Packet - -from .base_operator import BaseOperator - - -class JoinOperator(BaseOperator): - """ - Join操作符 - 处理多输入流的关联操作 - - JoinOperator专门用于处理Join函数，它会提取packet的payload、key和tag信息， - 然后调用join function的execute方法进行关联处理。 - """ - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - # 验证函数类型（在运行时初始化后进行） - self._validate_function() - self._validated = True - - # 统计信息 - self.processed_count = 0 - self.emitted_count = 0 - - # 跟踪接收到的停止信号 - self.received_stop_signals = set() # 记录哪些stream已经发送了停止信号 - - def _validate_function(self) -> None: - """ - 验证函数是否为Join函数 - - Raises: - TypeError: 如果函数不是Join函数 - """ - # JoinFunction has is_join attribute, BaseFunction doesn't - # hasattr check ensures runtime safety - if not hasattr(self.function, "is_join") or not self.function.is_join: # type: ignore[attr-defined] - raise TypeError( - f"{self.__class__.__name__} requires Join function with is_join=True, " - f"got {type(self.function).__name__}" - ) - - # 验证必需的execute方法 - if not hasattr(self.function, "execute"): - raise TypeError( - f"Join function {type(self.function).__name__} must implement execute method" - ) - - # 验证execute方法不是抽象方法 - execute_method = self.function.execute - if getattr(execute_method, "__isabstractmethod__", False): - raise TypeError( - f"Join function {type(self.function).__name__} must implement execute method " - f"(currently abstract)" - ) - - self.logger.debug(f"Validated Join function {type(self.function).__name__}") - - def process_packet(self, packet: "Packet | None" = None): - """Join处理，将packet信息传递给join function""" - try: - if packet is None or packet.payload is None: - self.logger.debug("Received empty packet, skipping") - return - - # 必须是keyed packet - if not packet.is_keyed(): - self.logger.warning( - f"JoinOperator '{self.name}' received non-keyed packet, skipping. " - f"Join operations require keyed streams." - ) - return - - # 提取必要信息 - payload = packet.payload - join_key = packet.partition_key - stream_tag = packet.input_index - - # 过滤None payload（这可能是因为BatchFunction返回None导致的） - if payload is None: - self.logger.debug( - f"JoinOperator '{self.name}' received None payload from stream {stream_tag}, skipping" - ) - return - - self.processed_count += 1 - - self.logger.debug( - f"JoinOperator '{self.name}' processing: " - f"key='{join_key}', tag={stream_tag}, payload_type={type(payload).__name__}" - ) - - # 调用join function的execute方法 - join_results = self.function.execute(payload, join_key, stream_tag) - - # 处理返回结果 - if join_results is not None: - # 如果返回的不是列表，转换为列表 - if not isinstance(join_results, list): - join_results = [join_results] if join_results is not None else [] - - # 发送所有结果 - for result in join_results: - if result is not None: - self._emit_join_result(result, join_key, packet) - self.emitted_count += 1 - - # 定期打印统计信息 - if self.processed_count % 100 == 0: - self.logger.info( - f"JoinOperator '{self.name}' stats: " - f"processed={self.processed_count}, emitted={self.emitted_count}, " - f"ratio={self.emitted_count / max(1, self.processed_count):.2f}" - ) - - except Exception as e: - self.logger.error(f"Error in JoinOperator '{self.name}': {e}", exc_info=True) - # 不重新抛出异常，避免中断整个流处理 - - def handle_stop_signal( - self, - stop_signal_name: str | None = None, - input_index: int | None = None, - signal: Any = None, - ): - """ - 处理停止信号的传播 - - Join操作需要特殊处理停止信号： - - 记录哪个stream发送了停止信号 - - 当所有输入流都停止时，才向下游传播停止信号 - """ - try: - # 处理来自不同调用方式的参数 - if signal is not None: - # 来自 task_context 的调用，signal 是 StopSignal 对象 - from sage.kernel.runtime.communication.packet import StopSignal - - if isinstance(signal, StopSignal): - signal_name = signal.name - else: - signal_name = str(signal) - elif stop_signal_name is not None: - # 来自 base_task 的调用，使用传统参数 - signal_name = stop_signal_name - else: - self.logger.warning(f"JoinOperator '{self.name}' received stop signal with no name") - return - - # 记录收到的停止信号，使用信号名称作为唯一标识 - self.received_stop_signals.add(signal_name) - self.logger.info( - f"JoinOperator '{self.name}' received stop signal from '{signal_name}', " - f"total received: {len(self.received_stop_signals)} " - f"(all signals: {list(self.received_stop_signals)})" - ) - - # 检查是否所有输入流都已停止 - # 对于 Join 操作符，我们需要等待来自不同源节点的停止信号 - # 在当前的拓扑中，两个源可能通过同一个KeyBy节点连接到Join - # 所以我们需要特殊处理这种情况 - - # 检查是否收到了所有原始源的停止信号 - # 这些应该是以 "Source" 开头的节点，或者包含 "Source" 的节点 - source_signals = set() - for sig in self.received_stop_signals: - if isinstance(sig, str): - # String signal name - 检查是否包含 "Source" 或者以 "Source" 开头 - if "Source" in sig or sig.startswith("Source"): - source_signals.add(sig) - else: - # StopSignal object - from sage.kernel.runtime.communication.packet import ( - StopSignal, - ) - - if isinstance(sig, StopSignal) and ( - "Source" in sig.name or sig.name.startswith("Source") - ): - source_signals.add(sig.name) - - # 对于双流Join，固定期望2个源的停止信号 - # 这里不使用动态判断，避免循环依赖问题 - expected_sources = 2 # Join操作固定期望2个源 - - self.logger.info( - f"JoinOperator '{self.name}' stop signal status: " - f"{len(source_signals)}/{expected_sources} source signals " - f"(source signals: {list(source_signals)}, all signals: {list(self.received_stop_signals)})" - ) - - if len(source_signals) >= expected_sources: - self.logger.info( - f"JoinOperator '{self.name}' all {expected_sources} source streams stopped, " - f"propagating stop signal downstream" - ) - - # 所有源流都停止了，先通知JobManager该节点完成 - self.logger.info(f"JoinOperator '{self.name}' notifying JobManager of completion") - self.ctx.send_stop_signal_back(self.name) - - # 然后向下游传播停止信号 - from sage.kernel.runtime.communication.packet import StopSignal - - stop_signal = StopSignal(self.name) - self.logger.info(f"JoinOperator '{self.name}' sending stop signal to downstream") - self.router.send_stop_signal(stop_signal) - - # 通知context停止 - self.logger.info(f"JoinOperator '{self.name}' setting context stop signal") - self.ctx.set_stop_signal() - else: - self.logger.info( - f"JoinOperator '{self.name}' waiting for more source streams to stop: " - f"{len(source_signals)}/{expected_sources} " - f"(source signals received: {list(source_signals)})" - ) - - # 重要：不要向下游传播停止信号，也不要停止context - # 只是记录收到的停止信号，继续等待其他源流 - - except Exception as e: - self.logger.error( - f"Error in JoinOperator '{self.name}' handle_stop_signal: {e}", - exc_info=True, - ) - - def _emit_join_result(self, result_data: Any, join_key: Any, original_packet: "Packet"): - """ - 发送join结果，保持分区信息 - - Args: - result_data: join function返回的结果数据 - join_key: 关联键 - original_packet: 原始packet，用于继承其他信息 - """ - try: - # 创建结果packet，保持分区信息 - result_packet = Packet( - payload=result_data, - input_index=0, # Join的输出默认为0 - partition_key=join_key, - partition_strategy=original_packet.partition_strategy or "hash", - ) - - self.router.send(result_packet) - - self.logger.debug( - f"JoinOperator '{self.name}' emitted result for key '{join_key}': " - f"{type(result_data).__name__}" - ) - - except Exception as e: - self.logger.error( - f"Failed to emit join result for key '{join_key}': {e}", exc_info=True - ) - - def get_statistics(self) -> dict: - """ - 获取Join操作统计信息 - - Returns: - dict: 统计信息字典 - """ - return { - "operator_name": self.name, - "function_type": type(self.function).__name__, - "processed_packets": self.processed_count, - "emitted_results": self.emitted_count, - "join_ratio": self.emitted_count / max(1, self.processed_count), - "is_validated": self._validated, - } - - def debug_print_statistics(self): - """打印详细的统计信息""" - stats = self.get_statistics() - print(f"\n📊 JoinOperator '{self.name}' Statistics:") - print(f" Function: {stats['function_type']}") - print(f" Processed packets: {stats['processed_packets']}") - print(f" Emitted results: {stats['emitted_results']}") - print(f" Join ratio: {stats['join_ratio']:.2%}") - print(f" Validated: {stats['is_validated']}") - - def _validate_execute_method_signature(self) -> bool: - """ - 验证execute方法的签名是否正确 - - Returns: - bool: 签名是否正确 - """ - import inspect - - try: - execute_method = self.function.execute - signature = inspect.signature(execute_method) - params = list(signature.parameters.keys()) - - # 期望的参数：self, payload, key, tag (至少) - expected_min_params = ["self", "payload", "key", "tag"] - - if len(params) < len(expected_min_params): - self.logger.warning( - f"Join function execute method has insufficient parameters. " - f"Expected: {expected_min_params[1:]}, Got: {params[1:]}" - ) - return False - - # 检查前几个参数名 - for i, expected_param in enumerate(expected_min_params): - if i < len(params) and params[i] != expected_param: - self.logger.warning( - f"Join function execute method parameter {i} " - f"expected '{expected_param}', got '{params[i]}'" - ) - - return True - - except Exception as e: - self.logger.warning(f"Could not validate execute method signature: {e}") - return False - - def get_supported_stream_count(self) -> int: - """ - 获取支持的输入流数量 - - 目前Join操作支持2个输入流 - - Returns: - int: 支持的输入流数量 - """ - return 2 # 目前固定为2流join - - def __repr__(self) -> str: - if hasattr(self, "function") and self.function: - function_name = self.function.__class__.__name__ - if self._validated: - stream_count = self.get_supported_stream_count() - join_type = getattr(self.function, "join_type", "custom") - return ( - f"<{self.__class__.__name__} {function_name} " - f"type:{join_type} streams:{stream_count} " - f"processed:{self.processed_count} emitted:{self.emitted_count}>" - ) - else: - return f"<{self.__class__.__name__} {function_name} (not validated)>" - else: - return f"<{self.__class__.__name__} (no function)>" diff --git a/packages/sage-kernel/src/sage/kernel/api/operator/keyby_operator.py b/packages/sage-kernel/src/sage/kernel/api/operator/keyby_operator.py deleted file mode 100644 index 8dfe639242..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/operator/keyby_operator.py +++ /dev/null @@ -1,56 +0,0 @@ -from typing import Any - -from sage.kernel.api.operator.base_operator import BaseOperator -from sage.kernel.runtime.communication.packet import Packet - - -class KeyByOperator(BaseOperator): - """ - KeyBy操作符，提取数据的分区键并应用自定义路由策略 - - 支持的分区策略： - - hash: 基于键的哈希值分区 - - broadcast: 广播到所有下游实例 - - round_robin: 忽略键，轮询分发 - """ - - def __init__(self, *args, partition_strategy: str = "hash", **kwargs): - super().__init__(*args, **kwargs) - self.partition_strategy = partition_strategy - self.logger.info( - f"KeyByOperator '{self.name}' initialized with strategy: {partition_strategy}" - ) - - def process_packet(self, packet: "Packet | None" = None): - """重写packet处理，添加分区信息""" - try: - if packet is None or packet.payload is None: - return - - # 提取分区键 - extracted_key = self.process(packet.payload) - - # 创建带有新分区信息的packet - keyed_packet = packet.update_key(extracted_key, self.partition_strategy) - - self.logger.debug(f"KeyByOperator '{self.name}' added key '{extracted_key}' to packet") - - # 直接发送带有分区信息的packet - self.router.send(keyed_packet) - - except Exception as e: - self.logger.error(f"Error in KeyByOperator {self.name}: {e}", exc_info=True) - # 回退：发送原始packet - if packet: - self.router.send(packet) - - def process(self, raw_data: Any, input_index: int = 0) -> Any: - """提取键，返回原始数据（分区信息将在packet级别处理）""" - try: - extracted_key = self.function.execute(raw_data) - self.logger.debug(f"KeyByOperator '{self.name}' extracted key: {extracted_key}") - return extracted_key - - except Exception as e: - self.logger.error(f"Error extracting key in {self.name}: {e}", exc_info=True) - return raw_data diff --git a/packages/sage-kernel/src/sage/kernel/api/operator/map_operator.py b/packages/sage-kernel/src/sage/kernel/api/operator/map_operator.py deleted file mode 100644 index 435e5e866c..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/operator/map_operator.py +++ /dev/null @@ -1,129 +0,0 @@ -import json -import os -import time -from typing import TYPE_CHECKING - -from sage.kernel.api.operator.base_operator import BaseOperator -from sage.kernel.runtime.communication.packet import Packet, StopSignal -from sage.kernel.runtime.context.task_context import TaskContext - -if TYPE_CHECKING: - from sage.kernel.runtime.factory.function_factory import FunctionFactory - - -class MapOperator(BaseOperator): - def __init__( - self, - function_factory: "FunctionFactory", - ctx: "TaskContext", - enable_profile=False, - *args, - **kwargs, - ): - # 从 kwargs 中移除 enable_profile，避免传递给 BaseOperator - kwargs.pop("enable_profile", None) - super().__init__(function_factory, ctx, *args, **kwargs) - self.enable_profile = enable_profile - if self.enable_profile: - self._setup_time_tracking() - - def _setup_time_tracking(self): - """设置时间统计的存储路径""" - if hasattr(self.ctx, "env_base_dir") and self.ctx.env_base_dir: - self.time_base_path = os.path.join( - self.ctx.env_base_dir, ".sage_states", "time_records" - ) - else: - # 使用默认路径 - self.time_base_path = os.path.join(os.getcwd(), ".sage_states", "time_records") - - os.makedirs(self.time_base_path, exist_ok=True) - self.time_records = [] - - def _save_time_record(self, duration: float): - """保存时间记录""" - if not self.enable_profile: - return - - record = { - "timestamp": time.time(), - "duration": duration, - "function_name": self.function.__class__.__name__, - "operator_name": self.name, - } - self.time_records.append(record) - self._persist_time_records() - - def _persist_time_records(self): - """将时间记录持久化到文件""" - if not self.enable_profile or not self.time_records: - return - - timestamp = int(time.time()) - filename = f"time_records_{timestamp}.json" - path = os.path.join(self.time_base_path, filename) - - try: - with open(path, "w", encoding="utf-8") as f: - json.dump(self.time_records, f, ensure_ascii=False, indent=2) - self.time_records = [] - except Exception as e: - self.logger.error(f"Failed to persist time records: {e}") - - def process_packet(self, packet: "Packet | None" = None): - try: - if packet is None or packet.payload is None: - self.logger.warning(f"Operator {self.name} received empty data") - else: - # 检查是否是 StopSignal - if isinstance(packet.payload, StopSignal): - # StopSignal 不调用 function.execute()，直接传播 - self.logger.debug(f"Operator {self.name} received StopSignal, propagating...") - self.router.send(packet) - return - - # 执行前记录时间 - start_time = time.time() - - # 执行function - result = self.function.execute(packet.payload) - - # 执行后记录时间 - end_time = time.time() - duration = end_time - start_time - - # 保存时间记录（只有enable_profile=True时才保存） - if self.enable_profile: - self._save_time_record(duration) - - # 将执行时间添加到结果数据中（如果结果是dict） - if isinstance(result, dict): - # 根据算子类型添加相应的时间字段 - operator_name = self.function.__class__.__name__ - if "Retriever" in operator_name or "Retrieve" in operator_name: - result["retrieve_time"] = duration - elif "Refiner" in operator_name or "Refine" in operator_name: - result["refine_time"] = duration - elif "Generator" in operator_name or "Generate" in operator_name: - result["generate_time"] = duration - # 其他算子可以添加通用的 execution_time - # else: - # result["execution_time"] = duration - - self.logger.debug(f"Operator {self.name} processed data with result: {result}") - result_packet = ( - packet.inherit_partition_info(result) if (result is not None) else None - ) - if result_packet is not None: - self.router.send(result_packet) - - except Exception as e: - self.logger.error(f"Error in {self.name}.process(): {e}", exc_info=True) - - def __del__(self): - """确保在对象销毁时保存所有未保存的记录""" - if hasattr(self, "enable_profile") and self.enable_profile: - try: - self._persist_time_records() - except Exception: - pass diff --git a/packages/sage-kernel/src/sage/kernel/api/operator/sink_operator.py b/packages/sage-kernel/src/sage/kernel/api/operator/sink_operator.py deleted file mode 100644 index 3c175c6577..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/operator/sink_operator.py +++ /dev/null @@ -1,52 +0,0 @@ -from sage.kernel.api.operator.base_operator import BaseOperator -from sage.kernel.runtime.communication.packet import Packet, StopSignal - - -class SinkOperator(BaseOperator): - """ - 汇聚操作符 - 数据终点 - - 流量控制通过Queue的自然机制实现： - - SinkOperator从queue中取数据并处理 - - 处理完成后queue自动释放空间给上游 - - 无需额外的同步机制 - """ - - def process_packet(self, packet: "Packet | None" = None): - try: - if packet is None or packet.payload is None: - self.logger.warning(f"Operator {self.name} received empty data") - else: - # 检查是否是 StopSignal，如果是则跳过 execute() - if isinstance(packet.payload, StopSignal): - self.logger.debug( - f"Operator {self.name} received StopSignal in process_packet, skipping execute()" - ) - return - - result = self.function.execute(packet.payload) - self.logger.debug(f"Operator {self.name} processed data with result: {result}") - # Queue机制自动提供背压控制，无需显式同步 - - except Exception as e: - self.logger.error(f"Error in {self.name}.process(): {e}", exc_info=True) - - def handle_stop_signal(self): - """ - 处理停止信号，调用function.close()来触发最终处理 - 这个方法会被BaseTask在收到StopSignal时调用 - - 注意：此方法只处理 function 层面的关闭逻辑， - ctx.request_stop() 由 BaseTask._handle_sink_stop_signal() 调用 - """ - try: - self.logger.info(f"SinkOperator {self.name} handling stop signal, calling close()") - # SinkFunction may have close() method, BaseFunction doesn't - # hasattr and callable checks ensure runtime safety - if hasattr(self.function, "close") and callable(self.function.close): # type: ignore[attr-defined] - result = self.function.close() # type: ignore[attr-defined] - self.logger.debug(f"SinkOperator {self.name} final processing result: {result}") - else: - self.logger.debug(f"SinkOperator {self.name} has no close() method, skipping.") - except Exception as e: - self.logger.error(f"Error in {self.name}.handle_stop_signal(): {e}", exc_info=True) diff --git a/packages/sage-kernel/src/sage/kernel/api/operator/source_operator.py b/packages/sage-kernel/src/sage/kernel/api/operator/source_operator.py deleted file mode 100644 index 456409e41a..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/operator/source_operator.py +++ /dev/null @@ -1,94 +0,0 @@ -from typing import TYPE_CHECKING - -from sage.kernel.api.operator.base_operator import BaseOperator -from sage.kernel.runtime.communication.packet import Packet, StopSignal - -if TYPE_CHECKING: - from sage.kernel.runtime.task.base_task import BaseTask - - -class SourceOperator(BaseOperator): - # task 属性会在运行时由 BaseTask 注入 - task: "BaseTask | None" - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self._stop_signal_sent = False # 防止重复发送停止信号 - self.task = None # 运行时注入 - - def receive_packet(self, packet: "Packet"): - self.process_packet(packet) - - def process_packet(self, packet: "Packet | None" = None): - """ - 处理 Source 节点的数据生成 - - 注意：这里不捕获异常，让异常向上传播到 BaseTask._worker_loop - 在那里统一通过容错机制处理 - """ - # 执行 function.execute()，如果抛出异常则向上传播 - result = self.function.execute() - - self.logger.debug(f"Operator {self.name} processed data with result: {result}") - - # 检查是否收到停止信号 - if isinstance(result, StopSignal): - # 防止重复处理 - if self._stop_signal_sent: - return - - self._stop_signal_sent = True - - self.logger.info( - f"Source Operator {self.name} received stop signal from batch function: {result}" - ) - - # 设置停止信号的来源 - result.source = self.name - - # ✅ 将 StopSignal 发送到下游，让评估器输出统计信息 - self.router.send_stop_signal(result) - - # ✅ 同时通知 JobManager 停止整个任务 - if hasattr(self, "ctx") and hasattr(self.ctx, "request_stop"): - self.logger.info(f"Source Operator {self.name} requesting task stop via context") - self.ctx.request_stop() - - # 设置任务停止标志 - if hasattr(self, "task") and self.task: - if hasattr(self.task, "ctx") and hasattr(self.task.ctx, "set_stop_signal"): - self.task.ctx.set_stop_signal() - - if hasattr(self.task, "is_running"): - self.task.is_running = False - self.logger.info(f"Source Operator {self.name} set task.is_running = False") - - return - - if result is not None: - self.logger.debug(f"SourceOperator {self.name}: Sending packet with payload: {result}") - success = self.router.send(Packet(result)) - self.logger.debug(f"SourceOperator {self.name}: Send result: {success}") - - # If sending failed (e.g., queue is closed), stop the task - if not success: - self.logger.warning( - f"Source Operator {self.name} failed to send packet, stopping task" - ) - - # 生成并发送停止信号 - if not self._stop_signal_sent: - self._stop_signal_sent = True - stop_signal = StopSignal(f"{self.name}-send-failed") - self.router.send_stop_signal(stop_signal) - - if hasattr(self, "ctx") and hasattr(self.ctx, "request_stop"): - self.ctx.request_stop() - - if hasattr(self, "task") and self.task: - if hasattr(self.task, "ctx"): - self.task.ctx.set_stop_signal() - if hasattr(self.task, "is_running"): - self.task.is_running = False - - return diff --git a/packages/sage-kernel/src/sage/kernel/api/remote_environment.py b/packages/sage-kernel/src/sage/kernel/api/remote_environment.py deleted file mode 100644 index 551d024548..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/remote_environment.py +++ /dev/null @@ -1,375 +0,0 @@ -from __future__ import annotations - -import logging -from typing import Any - -from sage.common.utils.serialization.dill import serialize_object, trim_object_for_ray -from sage.kernel.api.base_environment import BaseEnvironment -from sage.kernel.runtime.jobmanager_client import JobManagerClient - -logger = logging.getLogger(__name__) - - -class RemoteEnvironment(BaseEnvironment): - """ - 简化的远程环境实现 - 专注于序列化环境并发送给远程JobManager - """ - - # 序列化时排除的属性 - __state_exclude__ = [ - "logger", - "_logger", - "_engine_client", - "_jobmanager", - # 移除了'_jobmanager'，因为我们不再使用它 - ] - - def __init__( - self, - name: str = "remote_environment", - config: dict | None = None, - host: str = "127.0.0.1", - port: int = 19001, - scheduler=None, - extra_python_paths: list[str] | None = None, - ): - """ - 初始化远程环境 - - Args: - name: 环境名称 - config: 环境配置 - host: JobManager服务主机 - port: JobManager服务端口 - scheduler: 调度器，可选。支持字符串 ("fifo", "load_aware") 或 BaseScheduler 实例 - extra_python_paths: 额外的 Python 模块搜索路径，用于远程节点反序列化时导入自re - """ - super().__init__(name, config, platform="remote", scheduler=scheduler) - - # 额外的 Python 模块搜索路径（用于远程节点反序列化） - self.extra_python_paths: list[str] = extra_python_paths or [] - - # 远程连接配置 - self.daemon_host = host - self.daemon_port = port - - # 设置 jobmanager_host/port，让 worker 节点知道如何回连 JobManager - # 这会覆盖 BaseEnvironment 的 None 值，避免被 JobManager 用 0.0.0.0 覆盖 - self.jobmanager_host = host - self.jobmanager_port = port - - # 客户端连接（延迟初始化） - self._engine_client: JobManagerClient | None = None - - # 缓存最后获取的调度器指标（用于作业完成后获取） - self._cached_scheduler_metrics: dict[str, Any] | None = None - - # 更新配置 - self.config.update({"engine_host": self.daemon_host, "engine_port": self.daemon_port}) - - logger.info(f"RemoteEnvironment '{name}' initialized for {host}:{port}") - - @property - def client(self) -> JobManagerClient: - """获取JobManager客户端（延迟创建）""" - if self._engine_client is None: - logger.debug(f"Creating JobManager client for {self.daemon_host}:{self.daemon_port}") - self._engine_client = JobManagerClient(host=self.daemon_host, port=self.daemon_port) - return self._engine_client - - def submit(self, autostop: bool = False) -> str: - """ - 提交环境到远程JobManager - - Args: - autostop (bool): 如果为True，方法将阻塞直到所有批处理任务完成后自动停止 - 如果为False，方法立即返回，需要手动管理任务生命周期 - - Returns: - 环境UUID - """ - try: - logger.info( - f"Submitting environment '{self.name}' to remote JobManager (autostop={autostop})" - ) - logger.info("Daemon host: %s, port: %d", self.daemon_host, self.daemon_port) - # 第一步：使用 trim_object_for_ray 清理环境，排除不可序列化的内容 - logger.debug("Trimming environment for serialization") - trimmed_env = trim_object_for_ray(self) - - # 第二步：使用 dill_serializer 打包 - logger.debug("Serializing environment with dill") - serialized_data = serialize_object(trimmed_env) - - # 第三步：通过JobManager Client发送到JobManager端口 - logger.debug("Submitting serialized environment to JobManager") - response = self.client.submit_job( - serialized_data, - autostop=autostop, - extra_python_paths=self.extra_python_paths, - ) - - if response.get("status") == "success": - env_uuid = response.get("job_uuid") - if env_uuid: - self.env_uuid = env_uuid - logger.info(f"Environment submitted successfully with UUID: {self.env_uuid}") - - # 如果启用 autostop，等待作业完成 - if autostop: - self._wait_for_completion() - - return env_uuid - else: - raise RuntimeError("JobManager returned success but no job UUID") - else: - error_msg = response.get("message", "Unknown error") - raise RuntimeError(f"Failed to submit environment: {error_msg}") - - except Exception as e: - logger.error(f"Failed to submit environment: {e}") - raise - - def _wait_for_completion(self): - """ - 等待远程作业完成 - 通过轮询JobManager的作业状态来判断是否完成 - """ - import time - - if not self.env_uuid: - logger.warning("No environment UUID found, cannot wait for completion") - return - - logger.info("Waiting for remote job to complete...") - - # 设置最大等待时间，避免无限等待 - max_wait_time = 400.0 # 400 seconds (6.67 minutes) - start_time = time.time() - check_interval = 0.5 # 远程检查可以稍微频繁一些 - - try: - while time.time() - start_time < max_wait_time: - try: - # 获取作业状态 - status_response = self.client.get_job_status(self.env_uuid) - - # 服务器返回的响应有两层结构: - # { "status": "success", "job_status": { "success": True, "status": "running", ... } } - # 需要提取内层的 job_status - job_status_data = status_response.get("job_status", status_response) - - # 检查响应是否成功 - if not job_status_data.get("success", False): - error_msg = job_status_data.get("message", "Unknown error") - # 如果是 not_found，说明作业已经完成并被清理（正常情况） - if job_status_data.get("status") == "not_found": - logger.info(f"Job not found (已完成并清理): {error_msg}") - break - # 其他错误才记录为 error - logger.error(f"Error getting job status: {error_msg}") - # 其他错误继续等待 - time.sleep(check_interval) - continue - - # 获取作业状态 - job_status = job_status_data.get("status") - logger.debug(f"Current job status: {job_status}") - - # 缓存调度器指标（在作业被删除前保存） - if "scheduler_metrics" in job_status_data: - self._cached_scheduler_metrics = job_status_data["scheduler_metrics"] - logger.debug("Cached scheduler metrics from job status") - - if job_status in ["stopped", "failed", "completed"]: - logger.info(f"Remote job completed with status: {job_status}") - break - - # 检查 dispatcher 信息 - dispatcher_info = status_response.get("dispatcher", {}) - task_count = dispatcher_info.get("task_count", 1) - service_count = dispatcher_info.get("service_count", 0) - is_running = dispatcher_info.get("is_running", True) - - logger.debug( - f"Dispatcher status: running={is_running}, tasks={task_count}, services={service_count}" - ) - - # 如果 dispatcher 已停止且所有资源已清理 - if not is_running and task_count == 0 and service_count == 0: - logger.info("Remote job stopped and all resources cleaned up") - break - - except Exception as e: - logger.warning(f"Error checking job status: {e}") - # 发生错误时也继续等待，可能是网络问题 - - time.sleep(check_interval) - - else: - # 超时了 - logger.warning(f"Timeout waiting for remote job to complete after {max_wait_time}s") - logger.info("Job may still be running on remote JobManager") - - except KeyboardInterrupt: - logger.info("Received interrupt signal, stopping remote job...") - self.stop() - except Exception as e: - logger.error(f"Error waiting for completion: {e}") - try: - self.stop() - except Exception as stop_error: - logger.error(f"Error stopping job after wait error: {stop_error}") - - finally: - # 确保清理本地资源 - self.is_running = False - - def stop(self) -> dict[str, Any]: - """ - 停止远程环境 - - Returns: - 停止操作的结果 - """ - if not self.env_uuid: - logger.warning("Remote environment not submitted, nothing to stop") - return {"status": "warning", "message": "Environment not submitted"} - - try: - logger.info(f"Stopping remote environment {self.env_uuid}") - response = self.client.pause_job(self.env_uuid) - - if response.get("status") == "success": - logger.info(f"Environment {self.env_uuid} stopped successfully") - else: - logger.warning(f"Stop operation returned: {response}") - - return response - - except Exception as e: - logger.error(f"Error stopping remote environment: {e}") - return {"status": "error", "message": str(e)} - - def close(self) -> dict[str, Any]: - """ - 关闭远程环境并释放所有资源（包括 Ray Actors） - - 注意：此方法会删除 job 并清理所有 Ray Actors。 - 如果只想暂停而不释放资源，请使用 stop() 方法。 - - Returns: - 关闭操作的结果 - """ - if not self.env_uuid: - logger.warning("Remote environment not submitted, nothing to close") - return {"status": "warning", "message": "Environment not submitted"} - - try: - logger.info(f"Closing remote environment {self.env_uuid}") - # 使用 delete_job 而不是 pause_job，以确保 Ray Actors 被 kill - # delete_job 会调用 dispatcher.cleanup() → lifecycle_manager.cleanup_all() → ray.kill() - response = self.client.delete_job(self.env_uuid, force=True) - - # 清理本地资源 - self.is_running = False - self.env_uuid = None - self.pipeline.clear() - - logger.info("Remote environment closed and all resources released") - return response - - except Exception as e: - logger.error(f"Error closing remote environment: {e}") - return {"status": "error", "message": str(e)} - finally: - # 确保本地状态被清理 - self.is_running = False - self.env_uuid = None - - def health_check(self) -> dict[str, Any]: - """ - 检查远程JobManager健康状态 - - Returns: - 健康检查结果 - """ - try: - logger.debug("Performing health check") - response = self.client.health_check() - logger.debug(f"Health check result: {response}") - return response - except Exception as e: - logger.error(f"Health check failed: {e}") - return {"status": "error", "message": str(e)} - - def get_job_status(self) -> dict[str, Any]: - """ - 获取当前环境作业状态 - - Returns: - 作业状态信息 - """ - if not self.env_uuid: - return {"status": "not_submitted", "message": "Environment not submitted"} - - try: - logger.debug(f"Getting job status for {self.env_uuid}") - response = self.client.get_job_status(self.env_uuid) - return response - except Exception as e: - logger.error(f"Failed to get job status: {e}") - return {"status": "error", "message": str(e)} - - def get_scheduler_metrics(self) -> dict[str, Any]: - """ - 获取远程调度器的指标 - - 注意：这会从 JobManager 端获取真实的调度器指标， - 而不是客户端本地（未使用）的调度器指标 - - 如果作业已完成并被清理，将返回缓存的指标 - - Returns: - 调度器指标字典 - """ - if not self.env_uuid: - logger.warning( - "Environment not submitted, returning local scheduler metrics (will be empty)" - ) - return self.scheduler.get_metrics() - - try: - # 尝试从 JobManager 获取作业状态 - status_response = self.client.get_job_status(self.env_uuid) - - # 提取调度器指标 - job_status_data = status_response.get("job_status", status_response) - scheduler_metrics = job_status_data.get("scheduler_metrics") - - if scheduler_metrics: - # 缓存指标 - self._cached_scheduler_metrics = scheduler_metrics - return scheduler_metrics - else: - # 没有找到指标，可能是作业正在初始化或已完成 - logger.debug("No scheduler metrics found in job status") - # 返回缓存的指标（如果有） - if self._cached_scheduler_metrics: - logger.debug("Returning cached scheduler metrics") - return self._cached_scheduler_metrics - return self.scheduler.get_metrics() - - except Exception as e: - logger.debug(f"Failed to get scheduler metrics from remote: {e}") - # 作业可能已被清理，返回缓存的指标 - if self._cached_scheduler_metrics: - logger.debug("Job completed, returning cached scheduler metrics") - return self._cached_scheduler_metrics - else: - logger.debug("No cached metrics available, returning local metrics") - return self.scheduler.get_metrics() - - def __repr__(self) -> str: - return f"RemoteEnvironment(name='{self.name}', host='{self.daemon_host}', port={self.daemon_port})" diff --git a/packages/sage-kernel/src/sage/kernel/api/service/__init__.py b/packages/sage-kernel/src/sage/kernel/api/service/__init__.py deleted file mode 100644 index 94f7d6792f..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/service/__init__.py +++ /dev/null @@ -1,23 +0,0 @@ -""" -SAGE Kernel API - Service Layer - -Base service interface and implementations. -""" - -from .base_service import BaseService -from .pipeline_service import ( - PipelineBridge, - PipelineRequest, - PipelineService, - PipelineServiceSink, - PipelineServiceSource, -) - -__all__ = [ - "BaseService", - "PipelineBridge", - "PipelineRequest", - "PipelineService", - "PipelineServiceSink", - "PipelineServiceSource", -] diff --git a/packages/sage-kernel/src/sage/kernel/api/service/base_service.py b/packages/sage-kernel/src/sage/kernel/api/service/base_service.py deleted file mode 100644 index a8ea4e20cc..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/service/base_service.py +++ /dev/null @@ -1,122 +0,0 @@ -import logging -from abc import ABC -from typing import TYPE_CHECKING, Optional - -if TYPE_CHECKING: - from sage.kernel.runtime.context.service_context import ServiceContext - - -class BaseService(ABC): # noqa: B024 - """ - BaseService is the abstract base class for all services in SAGE. - It defines the core interface and provides access to runtime context and logger. - """ - - def __init__(self, *args, **kwargs): - """ - 初始化基础服务 - - Args: - *args: 位置参数 - **kwargs: 关键字参数 - - Note: - ctx 会在实例创建时由 ServiceFactory 自动注入， - 服务类不需要在构造函数中声明 ctx 参数 - """ - # ctx 由 ServiceFactory 在 __init__ 调用前通过 __new__ 方法注入 - if not hasattr(self, "ctx"): - # Initialize ctx as Optional[ServiceContext] - will be injected by ServiceFactory - self.ctx: Optional[ServiceContext] = None - self._logger = None - - @property - def logger(self): - """获取logger，优先使用ctx.logger，否则使用默认logger""" - if not hasattr(self, "_logger") or self._logger is None: - if self.ctx is None: - self._logger = logging.getLogger(self.__class__.__name__) - else: - self._logger = self.ctx.logger - return self._logger - - @property - def name(self): - """获取服务名称，如果有ctx则使用ctx.name，否则使用类名""" - if self.ctx is not None: - return self.ctx.name - return self.__class__.__name__ - - def call_service( - self, - service_name: str, - *args, - timeout: float | None = None, - method: str | None = None, - **kwargs, - ): - """ - 同步服务调用语法糖 - - 用法: - result = self.call_service("cache_service", key, method="get") - data = self.call_service("pipeline_name", payload) - """ - if self.ctx is None: - raise RuntimeError("Service context not initialized. Cannot access services.") - - return self.ctx.call_service(service_name, *args, timeout=timeout, method=method, **kwargs) - - def call_service_async( - self, - service_name: str, - *args, - timeout: float | None = None, - method: str | None = None, - **kwargs, - ): - """ - 异步服务调用语法糖 - - 用法: - future = self.call_service_async("cache_service", key, method="get") - result = future.result() # 阻塞等待结果 - - # 或者非阻塞检查 - if future.done(): - result = future.result() - """ - if self.ctx is None: - raise RuntimeError("Service context not initialized. Cannot access services.") - - return self.ctx.call_service_async( - service_name, *args, timeout=timeout, method=method, **kwargs - ) - - def setup(self): # noqa: B027 - """ - 服务初始化设置方法，在service_instance创建后调用 - 子类可以重写此方法来进行初始化设置 - """ - pass - - def cleanup(self): # noqa: B027 - """ - 服务清理方法，在服务停止时调用 - 子类可以重写此方法来进行资源清理 - """ - pass - - def start(self): # noqa: B027 - """ - 服务启动方法，在服务启动时调用 - 子类可以重写此方法来进行启动逻辑 - """ - pass - - def stop(self): # noqa: B027 - """ - 服务停止方法，在服务停止时调用 - 子类可以重写此方法来进行停止逻辑 - """ - pass diff --git a/packages/sage-kernel/src/sage/kernel/api/service/pipeline_service/__init__.py b/packages/sage-kernel/src/sage/kernel/api/service/pipeline_service/__init__.py deleted file mode 100644 index ec943b766f..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/service/pipeline_service/__init__.py +++ /dev/null @@ -1,59 +0,0 @@ -"""Pipeline-as-Service 基础设施 - -这个模块提供了将 Pipeline 包装为 Service 的通用组件， -实现了 Pipeline-as-Service 模式的完整基础设施。 - -【核心组件】： -- PipelineBridge: 连接 Service 调用方和 Pipeline 实现的桥梁 -- PipelineServiceSource: 从 Bridge 拉取请求的通用 Source -- PipelineServiceSink: 将结果返回到 Bridge 的通用 Sink -- PipelineService: 将 Pipeline 包装为 Service 的通用包装器 - -【使用示例】： - -```python -from sage.kernel.api.local_environment import LocalEnvironment -from sage.kernel.api.service.pipeline_service import ( - PipelineBridge, - PipelineServiceSource, - PipelineServiceSink, - PipelineService, -) - -# 创建环境 -env = LocalEnvironment('demo') - -# 创建 Bridge -bridge = PipelineBridge() - -# 注册 Pipeline Service -env.register_service('my_pipeline', PipelineService, bridge) - -# 创建服务 Pipeline（实际处理逻辑） -env.from_source(PipelineServiceSource, bridge) \\ - .map(YourCustomMapFunction) \\ - .sink(PipelineServiceSink) - -# 主 Pipeline 可以通过 call_service() 调用 -# result = self.call_service('my_pipeline', data) -``` - -【特性】： -- 自动背压控制：调用方会阻塞直到 Pipeline 完成 -- 优雅关闭：通过 StopSignal 自动停止 Pipeline -- 双向通信：通过 response_queue 实现异步返回 -- 高复用性：适用于任何需要 Pipeline-as-Service 的场景 -""" - -from .pipeline_bridge import PipelineBridge, PipelineRequest -from .pipeline_service import PipelineService -from .pipeline_sink import PipelineServiceSink -from .pipeline_source import PipelineServiceSource - -__all__ = [ - "PipelineBridge", - "PipelineRequest", - "PipelineService", - "PipelineServiceSource", - "PipelineServiceSink", -] diff --git a/packages/sage-kernel/src/sage/kernel/api/service/pipeline_service/pipeline_bridge.py b/packages/sage-kernel/src/sage/kernel/api/service/pipeline_service/pipeline_bridge.py deleted file mode 100644 index 7d1798e61f..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/service/pipeline_service/pipeline_bridge.py +++ /dev/null @@ -1,122 +0,0 @@ -"""PipelineBridge - Pipeline-as-Service 核心桥梁 - -PipelineBridge 是实现 Pipeline-as-Service 的核心组件， -它连接了 Service 调用方和 Pipeline 实现，提供双向通信机制。 -""" - -from __future__ import annotations - -import queue -from dataclasses import dataclass -from typing import Any - -from sage.kernel.runtime.communication.packet import StopSignal - - -@dataclass -class PipelineRequest: - """Pipeline 请求的数据结构 - - Attributes: - payload: 请求的实际数据（如问题、订单等） - response_queue: 用于返回结果的队列（每个请求独立） - """ - - payload: dict[str, Any] - response_queue: queue.Queue[dict[str, Any]] - - -class PipelineBridge: - """PipelineBridge - Pipeline-as-Service 的核心桥梁 - - 【职责】： - - 接收来自 Service 的请求（submit） - - 将请求传递给 Pipeline（next） - - 携带 response_queue 实现结果返回 - - 【工作流程】： - ``` - 调用方 PipelineBridge Pipeline - │ │ │ - ├─ submit(payload) ─────→ │ 创建 response_queue │ - │ ├─────────────────────────→ │ Source.next() - │ │ │ - │ │ ├─ Map 处理 - │ │ │ - │ │ ←────────────────────────┤ Sink 返回 - ├─ response_queue.get() ←─┤ │ - │ │ │ - ``` - - 【关闭流程】： - - close() 发送 StopSignal 到请求队列 - - Pipeline 收到 StopSignal 后自然停止 - - 避免了轮询导致的资源浪费 - - 【使用场景】： - - RAG 系统：将检索-生成流程封装为服务 - - 微服务架构：Pipeline 之间相互调用 - - 背压控制：通过阻塞调用实现流量控制 - """ - - def __init__(self): - """初始化 PipelineBridge""" - self._requests: queue.Queue[PipelineRequest | StopSignal] = queue.Queue() - self._closed = False - - def submit(self, payload: dict[str, Any]) -> queue.Queue[dict[str, Any]]: - """提交请求到 Pipeline - - Args: - payload: 请求数据 - - Returns: - response_queue: 用于获取结果的队列 - - Raises: - RuntimeError: 如果 Bridge 已关闭 - """ - if self._closed: - raise RuntimeError("Pipeline bridge is closed") - - response_q: queue.Queue[dict[str, Any]] = queue.Queue(maxsize=1) - req = PipelineRequest(payload=payload, response_queue=response_q) - self._requests.put(req) - return response_q - - def next(self, timeout: float = 0.1): - """获取下一个请求（Pipeline Source 调用） - - Args: - timeout: 获取请求的超时时间（秒） - - Returns: - - PipelineRequest: 正常请求 - - StopSignal: 停止信号（bridge 已关闭且队列已空） - - None: 暂时没有请求（超时） - """ - if self._closed and self._requests.empty(): - return StopSignal("pipeline-service-shutdown") - - try: - return self._requests.get(timeout=timeout) - except queue.Empty: - return None - - def close(self): - """关闭 Bridge，并主动发送 StopSignal - - 这个方法会： - 1. 设置关闭标志 - 2. 主动放入 StopSignal 到请求队列 - 3. Pipeline Source 收到 StopSignal 后会停止 - """ - if not self._closed: - self._closed = True - # 关键：主动放入 StopSignal，让 Pipeline 能够正常停止 - self._requests.put(StopSignal("pipeline-service-shutdown")) - - @property - def is_closed(self) -> bool: - """检查 Bridge 是否已关闭""" - return self._closed diff --git a/packages/sage-kernel/src/sage/kernel/api/service/pipeline_service/pipeline_service.py b/packages/sage-kernel/src/sage/kernel/api/service/pipeline_service/pipeline_service.py deleted file mode 100644 index 8f2807461d..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/service/pipeline_service/pipeline_service.py +++ /dev/null @@ -1,121 +0,0 @@ -"""PipelineService - Pipeline-as-Service 通用包装器 - -将 Pipeline 包装为可调用的 Service，实现 Pipeline-as-Service 模式。 -""" - -from __future__ import annotations - -import os -import queue -from typing import Any - -from sage.kernel.api.service.base_service import BaseService - -from .pipeline_bridge import PipelineBridge - -# Test mode detection - reduce timeout in test environments -_IS_TEST_MODE = os.getenv("SAGE_TEST_MODE") == "true" or os.getenv("SAGE_EXAMPLES_MODE") == "test" -_DEFAULT_TIMEOUT = 5.0 if _IS_TEST_MODE else 30.0 - - -class PipelineService(BaseService): - """Pipeline Service - Pipeline 即服务的通用包装器 - - 【双重身份】： - - 对外：是一个 Service，提供 process() 接口 - - 对内：通过 PipelineBridge 连接到真实的 Pipeline - - 【工作流程】： - ``` - 1. 主 Pipeline 调用 call_service('service_name', data) - 2. 进入 process() 方法 - 3. bridge.submit(data) 提交到服务 Pipeline - 4. **阻塞等待** response_queue.get() - 5. 服务 Pipeline 完成后，结果从 response_queue 返回 - 6. 返回给主 Pipeline - ``` - - 【背压机制】： - - process() 方法会阻塞！ - - 主 Pipeline 必须等待服务 Pipeline 完成 - - 这就是背压的实现原理 - - 【使用示例】： - ```python - bridge = PipelineBridge() - - # 注册服务 - env.register_service('my_pipeline', PipelineService, bridge) - - # 创建服务 Pipeline - env.from_source(PipelineServiceSource, bridge) \\ - .map(YourMapFunction) \\ - .sink(PipelineServiceSink) - - # 在其他 Pipeline 中调用 - # result = self.call_service('my_pipeline', data) - ``` - """ - - def __init__(self, bridge: PipelineBridge, request_timeout: float | None = None): - """初始化 PipelineService - - Args: - bridge: PipelineBridge 实例（与服务 Pipeline 共享） - request_timeout: 请求超时时间（秒），默认 30 秒（测试模式 5 秒） - """ - super().__init__() - self._bridge = bridge - self._request_timeout = request_timeout if request_timeout is not None else _DEFAULT_TIMEOUT - - def process(self, message: dict[str, Any]): - """处理请求 - 阻塞直到 Pipeline 返回结果 - - Args: - message: 请求数据，可以是任何字典 - - 特殊命令：{'command': 'shutdown'} 会关闭 Pipeline - - Returns: - 处理结果（从 Pipeline 返回） - - Raises: - ValueError: 如果消息为空 - RuntimeError: 如果 Bridge 已关闭 - TimeoutError: 如果等待结果超时 - """ - if message is None: - raise ValueError("Empty message") - - # 处理 shutdown 命令 - if message.get("command") == "shutdown": - self.logger.info("Shutting down pipeline service") - self._bridge.close() - return {"status": "shutdown_ack"} - - # 提交到 Pipeline 并等待结果（阻塞！） - try: - response_q = self._bridge.submit(message) - except RuntimeError as exc: - raise RuntimeError("Pipeline service is shutting down") from exc - - try: - result = response_q.get(timeout=self._request_timeout) - self.logger.debug("Received result from pipeline") - return result - except queue.Empty as exc: - raise TimeoutError( - f"Pipeline service timed out after {self._request_timeout}s" - ) from exc - - def stop(self): - """停止 Pipeline Service - - 关闭 PipelineBridge，这会发送 StopSignal 给 Service Pipeline， - 使得 Service Pipeline 中的所有节点能够正常停止。 - """ - self.logger.info(f"Stopping Pipeline Service (bridge: {id(self._bridge)})") - try: - self._bridge.close() - self.logger.info("Pipeline Service stopped successfully") - except Exception as e: - self.logger.error(f"Error stopping Pipeline Service: {e}") diff --git a/packages/sage-kernel/src/sage/kernel/api/service/pipeline_service/pipeline_sink.py b/packages/sage-kernel/src/sage/kernel/api/service/pipeline_service/pipeline_sink.py deleted file mode 100644 index 265d0200b5..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/service/pipeline_service/pipeline_sink.py +++ /dev/null @@ -1,66 +0,0 @@ -"""PipelineServiceSink - Pipeline-as-Service 通用 Sink - -这是服务 Pipeline 的通用 Sink 算子，将结果返回给调用方。 -""" - -from __future__ import annotations - -from sage.common.core import SinkFunction -from sage.kernel.runtime.communication.packet import StopSignal - - -class PipelineServiceSink(SinkFunction): - """Pipeline Service 的通用 Sink - 将结果返回给调用方 - - 【职责】： - - 将处理结果放入 response_queue - - PipelineService 会从这个队列获取结果 - - 识别 StopSignal 但不需要特殊处理 - - 【关键点】： - - 这是服务 Pipeline 的出口 - - response_queue 实现了结果的异步返回 - - StopSignal 到达后 Pipeline 自然停止 - - 【使用示例】： - ```python - env.from_source(PipelineServiceSource, bridge) \\ - .map(YourMapFunction) \\ - .sink(PipelineServiceSink) - ``` - """ - - def __init__(self): - """初始化 PipelineServiceSink""" - super().__init__() - - def execute(self, data): - """处理数据并返回结果 - - Args: - data: 上游传递的纯数据字典（由 PipelineServiceSource 解包） - - 包含业务数据字段 - - _response_queue: 用于返回结果的队列（由 Source 附加） - 或者是 StopSignal - """ - if not data: - return - - # StopSignal 不需要处理，只是让它通过即可触发停止 - if isinstance(data, StopSignal): - self.logger.info("Received stop signal, pipeline will stop") - return - - # 从解包后的数据中提取 response_queue - if isinstance(data, dict): - resp_q = data.pop("_response_queue", None) - # 剩余的就是业务结果 - resp = data - else: - # 兼容性：如果不是字典，尝试获取属性 - resp_q = getattr(data, "response_queue", None) - resp = data - - if resp_q: - resp_q.put(resp) - self.logger.debug("Result returned to response queue") diff --git a/packages/sage-kernel/src/sage/kernel/api/service/pipeline_service/pipeline_source.py b/packages/sage-kernel/src/sage/kernel/api/service/pipeline_service/pipeline_source.py deleted file mode 100644 index 57b1bac882..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/service/pipeline_service/pipeline_source.py +++ /dev/null @@ -1,83 +0,0 @@ -"""PipelineServiceSource - Pipeline-as-Service 通用 Source - -这是服务 Pipeline 的通用 Source 算子，从 PipelineBridge 拉取请求。 -""" - -from __future__ import annotations - -from sage.common.core import SourceFunction -from sage.kernel.runtime.communication.packet import StopSignal - -from .pipeline_bridge import PipelineBridge - - -class PipelineServiceSource(SourceFunction): - """Pipeline Service 的通用 Source - 从 PipelineBridge 拉取请求 - - 【职责】： - - 轮询 PipelineBridge 获取请求 - - 识别并传递 StopSignal 以触发 Pipeline 停止 - - 解包 PipelineRequest.payload 并附加 response_queue，返回纯数据给下游 - - 【关键点】： - - 这是服务 Pipeline 的入口 - - 通过 bridge.next() 实现阻塞轮询 - - StopSignal 必须透传才能停止 Pipeline - - 自动解包 payload，下游算子只处理纯数据 - - 【使用示例】： - ```python - bridge = PipelineBridge() - - env.from_source(PipelineServiceSource, bridge) \\ - .map(YourMapFunction) \\ - .sink(PipelineServiceSink) - ``` - """ - - def __init__(self, bridge: PipelineBridge, poll_interval: float = 0.1): - """初始化 PipelineServiceSource - - Args: - bridge: PipelineBridge 实例 - poll_interval: 轮询间隔（秒），默认 0.1 秒 - """ - super().__init__() - self._bridge = bridge - self._poll_interval = poll_interval - - def execute(self, data=None): - """轮询 bridge，获取请求 - - Returns: - - dict: 解包后的纯数据（包含 response_queue） - - StopSignal: 停止信号，触发 Pipeline 停止 - - None: 暂时没有数据，继续轮询 - """ - req = self._bridge.next(timeout=self._poll_interval) - - if req is None: - return None - - # 关键：识别并传递 StopSignal - if isinstance(req, StopSignal): - self.logger.info(f"Received stop signal: {req}") - return req - - # 解包 PipelineRequest.payload，并附加 response_queue - # 这样下游算子只需要处理纯数据，无需 hasattr 检查 - if hasattr(req, "payload") and hasattr(req, "response_queue"): - payload = req.payload - if isinstance(payload, dict): - # 将 response_queue 附加到 payload 中 - payload["_response_queue"] = req.response_queue - return payload - else: - # 如果 payload 不是字典，包装成字典 - return { - "data": payload, - "_response_queue": req.response_queue, - } - - # 兼容性：如果不是 PipelineRequest，直接返回 - return req diff --git a/packages/sage-kernel/src/sage/kernel/api/transformation/__init__.py b/packages/sage-kernel/src/sage/kernel/api/transformation/__init__.py deleted file mode 100644 index 58c515af61..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/transformation/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -""" -SAGE - Streaming-Augmented Generative Execution -""" - -# 直接从本包的_version模块加载版本信息 -try: - from sage.kernel._version import __author__, __email__, __version__ -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" diff --git a/packages/sage-kernel/src/sage/kernel/api/transformation/base_transformation.py b/packages/sage-kernel/src/sage/kernel/api/transformation/base_transformation.py deleted file mode 100644 index 0b983684ec..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/transformation/base_transformation.py +++ /dev/null @@ -1,133 +0,0 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING - -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.runtime.factory.function_factory import FunctionFactory -from sage.kernel.runtime.factory.operator_factory import OperatorFactory - -if TYPE_CHECKING: - from sage.common.core.functions import BaseFunction - from sage.kernel.api.base_environment import BaseEnvironment - from sage.kernel.api.operator.base_operator import BaseOperator - - -class BaseTransformation: - def __init__( - self, - env: BaseEnvironment, - function: type[BaseFunction], - *args, - name: str | None = None, - parallelism: int = 1, - **kwargs, - ): - self.operator_class: type[BaseOperator] # 由子类设置 - - self.remote = env.platform == "remote" - self.env_name = env.name - self.env = env - self.function_class = function - self.function_args = args - self.function_kwargs = kwargs - - self.basename = name or self.function_class.__name__ - - # 确保basename在环境中是唯一的 - 如果重复，添加后缀 - existing_names = [t.basename for t in env.pipeline if hasattr(t, "basename")] - original_basename = self.basename - counter = 0 - while self.basename in existing_names: - counter += 1 - self.basename = f"{original_basename}_{counter}" - - self.logger = CustomLogger() - - self.logger.debug( - f"Creating BaseTransformation of type {type} with rag {self.function_class.__name__}" - ) - - self.upstreams: list[BaseTransformation] = [] - self.downstreams: dict[str, int] = {} - self.parallelism = parallelism - - # 懒加载工厂 - self._operator_factory: OperatorFactory | None = None - self._function_factory: FunctionFactory | None = None - # 生成的平行节点名字：f"{transformation.function_class.__name__}_{i}" - - # 增强的连接方法 - def add_upstream(self, upstream_trans: BaseTransformation, input_index: int = 0) -> None: - """ - 添加上游连接 - - Args: - upstream_trans: 上游transformation - input_index: 当前transformation的输入索引 - output_index: 上游transformation的输出索引 - """ - # 添加到当前transformation的upstreams - self.upstreams.append(upstream_trans) - # 添加到上游transformation的downstreams - upstream_trans.downstreams[self.basename] = input_index - - self.logger.debug( - f"Connected {upstream_trans.basename} -> {self.basename}[in:{input_index}]" - ) - - ######################################################## - # properties # - ######################################################## - - @property - def function_factory(self) -> FunctionFactory: - """懒加载创建函数工厂""" - if self._function_factory is None: - self._function_factory = FunctionFactory( - function_class=self.function_class, - function_args=self.function_args, - function_kwargs=self.function_kwargs, - ) - return self._function_factory - - @property - def operator_factory(self) -> OperatorFactory: - """懒加载创建操作符工厂""" - if self._operator_factory is None: - self._operator_factory = OperatorFactory( - operator_class=self.operator_class, - function_factory=self.function_factory, - basename=self.basename, - env_name=self.env_name, - remote=self.remote, - ) - return self._operator_factory - - @property - def delay(self) -> float: - return 0.1 # 固定的内部事件监听循环延迟 - - @property - def is_spout(self) -> bool: - return False - - @property - def is_sink(self) -> bool: - return False - - @property - def is_merge_operation(self) -> bool: - """ - 判断是否为合并操作 - 对于大多数transformation，多个上游输入会被合并到input_index=0 - 只有特殊的comap等操作会分别处理多个输入到不同的input_index - """ - return not hasattr(self.function_class, "is_comap") or not getattr( - self.function_class, "is_comap", False - ) - - # ---------------- 工具函数 ---------------- - - def __repr__(self) -> str: - cls_name = self.function_class.__name__ - return f"<{self.__class__.__name__} {cls_name} at {hex(id(self))}>" diff --git a/packages/sage-kernel/src/sage/kernel/api/transformation/batch_transformation.py b/packages/sage-kernel/src/sage/kernel/api/transformation/batch_transformation.py deleted file mode 100644 index c30f7447c7..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/transformation/batch_transformation.py +++ /dev/null @@ -1,45 +0,0 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING - -from sage.kernel.api.operator.batch_operator import BatchOperator -from sage.kernel.api.transformation.base_transformation import BaseTransformation - -if TYPE_CHECKING: - from sage.common.core.functions import BaseFunction - from sage.kernel.api.base_environment import BaseEnvironment - - -class BatchTransformation(BaseTransformation): - """批处理变换 - 预定义批次大小的数据生产者""" - - def __init__( - self, - env: BaseEnvironment, - function: type[BaseFunction], - *args, - delay: float = 0.1, # 批处理节点通常处理速度更快 - progress_log_interval: int = 100, # 进度日志间隔 - **kwargs, - ): - self.operator_class = BatchOperator - self._delay = delay - self._progress_log_interval = progress_log_interval - super().__init__(env, function, *args, **kwargs) - - @property - def delay(self) -> float: - return self._delay - - @property - def progress_log_interval(self) -> int: - return self._progress_log_interval - - @property - def is_spout(self) -> bool: - return True - - def get_operator_kwargs(self) -> dict: - """获取创建算子时需要的额外参数""" - kwargs = {"progress_log_interval": self._progress_log_interval} - return kwargs diff --git a/packages/sage-kernel/src/sage/kernel/api/transformation/comap_transformation.py b/packages/sage-kernel/src/sage/kernel/api/transformation/comap_transformation.py deleted file mode 100644 index 9fe7b5cf95..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/transformation/comap_transformation.py +++ /dev/null @@ -1,121 +0,0 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING - -from sage.kernel.api.transformation.base_transformation import BaseTransformation -from sage.kernel.utils.helpers import is_abstract_method, validate_required_methods - -if TYPE_CHECKING: - from sage.common.core.functions import BaseCoMapFunction - from sage.kernel.api.base_environment import BaseEnvironment - - -class CoMapTransformation(BaseTransformation): - """ - CoMap变换 - 多输入流分别处理变换 - - CoMap变换用于处理ConnectedStreams，将每个输入流分别路由到 - 对应的mapN方法进行处理，而不是将所有输入合并到单一方法。 - """ - - def __init__( - self, - env: BaseEnvironment, - function: type[BaseCoMapFunction], - *args, - **kwargs, - ): - # 验证函数是否为CoMap函数 - if not hasattr(function, "is_comap") or not function.is_comap: - raise ValueError( - f"Function {function.__name__} is not a CoMap function. " - f"CoMap functions must inherit from BaseCoMapFunction and have is_comap=True." - ) - - # 验证必需的map0和map1方法 - self._validate_required_methods(function) - - # 导入operator类（延迟导入避免循环依赖） - from sage.kernel.api.operator.comap_operator import CoMapOperator - - self.operator_class = CoMapOperator - - super().__init__(env, function, *args, **kwargs) - - self.logger.debug(f"Created CoMapTransformation with function {function.__name__}") - - def _validate_required_methods(self, function_class: type[BaseCoMapFunction]) -> None: - """ - 验证CoMap函数是否实现了必需的方法 - - Args: - function_class: CoMap函数类 - - Raises: - ValueError: 如果缺少必需的方法 - """ - validate_required_methods( - function_class, - required_methods=["map0", "map1"], - class_name=f"CoMap function {function_class.__name__}", - ) - - @property - def supported_input_count(self) -> int: - """ - 获取支持的输入流数量 - - Returns: - int: 支持的最大输入流数量 - """ - count = 0 - method_index = 0 - - # 检查有多少个mapN方法被实现 - while True: - method_name = f"map{method_index}" - if hasattr(self.function_class, method_name): - method = getattr(self.function_class, method_name) - # 如果方法存在且不是抽象方法 - if not is_abstract_method(method): - count += 1 - method_index += 1 - else: - break - else: - break - - return count - - def validate_input_streams(self, input_count: int) -> None: - """ - 验证输入流数量是否匹配 - - Args: - input_count: 实际输入流数量 - - Raises: - ValueError: 如果输入流数量超过支持的数量 - """ - supported_count = self.supported_input_count - - if input_count > supported_count: - raise ValueError( - f"CoMap function {self.function_class.__name__} supports maximum " - f"{supported_count} input streams, but {input_count} streams provided. " - f"Please implement map{supported_count} through map{input_count - 1} methods." - ) - - if input_count < 2: - raise ValueError( - f"CoMap transformation requires at least 2 input streams, " - f"but only {input_count} provided." - ) - - def __repr__(self) -> str: - cls_name = self.function_class.__name__ - supported_inputs = self.supported_input_count - return ( - f"<{self.__class__.__name__} {cls_name} " - f"supports:{supported_inputs} streams at {hex(id(self))}>" - ) diff --git a/packages/sage-kernel/src/sage/kernel/api/transformation/filter_transformation.py b/packages/sage-kernel/src/sage/kernel/api/transformation/filter_transformation.py deleted file mode 100644 index 97a6f35604..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/transformation/filter_transformation.py +++ /dev/null @@ -1,18 +0,0 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING - -from sage.kernel.api.operator.filter_operator import FilterOperator -from sage.kernel.api.transformation.base_transformation import BaseTransformation - -if TYPE_CHECKING: - from sage.common.core.functions import BaseFunction - from sage.kernel.api.base_environment import BaseEnvironment - - -class FilterTransformation(BaseTransformation): - """过滤变换 - 数据过滤""" - - def __init__(self, env: BaseEnvironment, function: type[BaseFunction], *args, **kwargs): - self.operator_class = FilterOperator - super().__init__(env, function, *args, **kwargs) diff --git a/packages/sage-kernel/src/sage/kernel/api/transformation/flatmap_transformation.py b/packages/sage-kernel/src/sage/kernel/api/transformation/flatmap_transformation.py deleted file mode 100644 index 2edc962a2e..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/transformation/flatmap_transformation.py +++ /dev/null @@ -1,18 +0,0 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING - -from sage.kernel.api.operator.flatmap_operator import FlatMapOperator -from sage.kernel.api.transformation.base_transformation import BaseTransformation - -if TYPE_CHECKING: - from sage.common.core.functions import BaseFunction - from sage.kernel.api.base_environment import BaseEnvironment - - -class FlatMapTransformation(BaseTransformation): - """扁平映射变换 - 一对多数据变换""" - - def __init__(self, env: BaseEnvironment, function: type[BaseFunction], *args, **kwargs): - self.operator_class = FlatMapOperator - super().__init__(env, function, *args, **kwargs) diff --git a/packages/sage-kernel/src/sage/kernel/api/transformation/future_transformation.py b/packages/sage-kernel/src/sage/kernel/api/transformation/future_transformation.py deleted file mode 100644 index d6bb89ed0e..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/transformation/future_transformation.py +++ /dev/null @@ -1,136 +0,0 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING - -from sage.kernel.api.operator.future_operator import FutureOperator - -from .base_transformation import BaseTransformation - -if TYPE_CHECKING: - from sage.kernel.api.base_environment import BaseEnvironment - - -class FutureTransformation(BaseTransformation): - """ - 特殊的transformation，用作反馈边的占位符。 - 在DAG构建阶段作为placeholder，在fill_future时被实际的transformation替换。 - """ - - def __init__(self, env: BaseEnvironment, name: str): - # 使用一个特殊的function作为占位符 - from sage.common.core import FutureFunction - - # 设置operator类（必须在super().__init__之前） - self.operator_class = FutureOperator # type: ignore - - super().__init__(env=env, function=FutureFunction, name=name, parallelism=1) - - # FutureTransformation特有属性 - self.is_future = True - self.filled = False - self.actual_transformation: BaseTransformation | None = None - self.future_name = name - - self.logger.debug(f"Created FutureTransformation: {name}") - - def fill_with_transformation(self, actual_transformation: BaseTransformation) -> None: - """ - 用实际的transformation填充这个future placeholder - - Args: - actual_transformation: 要填充的实际transformation - """ - if self.filled: - raise RuntimeError( - f"Future transformation '{self.future_name}' has already been filled" - ) - - self.actual_transformation = actual_transformation - self.filled = True - - # 重定向所有下游连接 - self._redirect_downstreams() - - # 标记为已填充，但保留在pipeline中以便compiler能够处理 - # compiler会检查filled状态来决定如何处理这个transformation - # self._mark_as_filled_in_pipeline() - - self.logger.debug( - f"Filled FutureTransformation '{self.future_name}' with {actual_transformation.basename}" - ) - - # def _mark_as_filled_in_pipeline(self) -> None: - # """ - # 将已填充的future transformation从pipeline中移除，并保存到filled_futures中 - # 这样compiler就看不到future transformations，只看到实际的反馈边连接 - # """ - # # 将该future transformation从pipeline中移除 - # if self in self.env._pipeline: - # self.env._pipeline.remove(self) - # self.logger.debug(f"Removed FutureTransformation '{self.future_name}' from pipeline") - - # # 保存填充信息到环境中，供调试和管理使用 - # if not hasattr(self.env, '_filled_futures'): - # self.env._filled_futures = {} - - # self.env._filled_futures[self.future_name] = { - # 'future_transformation': self, - # 'actual_transformation': self.actual_transformation, - # 'filled_at': self._get_current_timestamp() - # } - - # self.logger.info(f"Future transformation '{self.future_name}' filled and removed from pipeline") - - def _get_current_timestamp(self) -> str: - """获取当前时间戳""" - import datetime - - return datetime.datetime.now().isoformat() - - def _redirect_downstreams(self) -> None: - """ - 将当前future transformation的所有下游连接重定向到实际的transformation - """ - if not self.actual_transformation: - return - - # 将所有下游节点的上游引用从当前节点改为实际节点 - for downstream_name, input_index in self.downstreams.items(): - # 找到下游transformation - downstream_trans = self._find_transformation_by_name(downstream_name) - if downstream_trans and self.actual_transformation: - # 移除对当前future的引用 - if self in downstream_trans.upstreams: - downstream_trans.upstreams.remove(self) - - # 添加对实际transformation的引用 - downstream_trans.upstreams.append(self.actual_transformation) - - # 更新实际transformation的下游引用 - self.actual_transformation.downstreams[downstream_name] = input_index - - # 清空当前future的下游引用 - self.downstreams.clear() - - def _find_transformation_by_name(self, name: str) -> BaseTransformation | None: - """ - 在pipeline中查找指定名称的transformation - """ - for trans in self.env.pipeline: - if trans.basename == name: - return trans - return None - - @property - def is_spout(self) -> bool: - """Future transformation不是spout""" - return False - - def __repr__(self) -> str: - status = "filled" if self.filled else "unfilled" - actual = ( - f" -> {self.actual_transformation.basename}" - if self.filled and self.actual_transformation - else "" - ) - return f"FutureTransformation({self.future_name}, {status}{actual})" diff --git a/packages/sage-kernel/src/sage/kernel/api/transformation/join_transformation.py b/packages/sage-kernel/src/sage/kernel/api/transformation/join_transformation.py deleted file mode 100644 index 41a1ed46bc..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/transformation/join_transformation.py +++ /dev/null @@ -1,272 +0,0 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING, Any - -from sage.kernel.api.transformation.base_transformation import BaseTransformation -from sage.kernel.utils.helpers import validate_required_methods - -if TYPE_CHECKING: - from sage.common.core.functions import BaseJoinFunction - from sage.kernel.api.base_environment import BaseEnvironment - - -class JoinTransformation(BaseTransformation): - """ - Join变换 - 多输入流按键关联变换 - - Join变换用于处理ConnectedStreams，将来自不同输入流的具有 - 相同分区键的数据进行关联处理，生成join结果。 - """ - - def __init__( - self, - env: BaseEnvironment, - function: type[BaseJoinFunction], - *args, - **kwargs, - ): - # 验证函数是否为Join函数 - if not hasattr(function, "is_join") or not function.is_join: - raise ValueError( - f"Function {function.__name__} is not a Join function. " - f"Join functions must inherit from BaseJoinFunction and have is_join=True." - ) - - # 验证必需的execute方法 - self._validate_required_methods(function) - - # 导入operator类（延迟导入避免循环依赖） - from sage.kernel.api.operator.join_operator import JoinOperator - - self.operator_class = JoinOperator - - super().__init__(env, function, *args, **kwargs) - - self.logger.debug(f"Created JoinTransformation with function {function.__name__}") - - def _validate_required_methods(self, function_class: type[BaseJoinFunction]) -> None: - """ - 验证Join函数是否实现了必需的方法 - - Args: - function_class: Join函数类 - - Raises: - ValueError: 如果缺少必需的方法 - """ - validate_required_methods( - function_class, - required_methods=["execute"], - class_name=f"Join function {function_class.__name__}", - ) - - # 验证execute方法的签名 - self._validate_execute_signature(function_class) - - def _validate_execute_signature(self, function_class: type[BaseJoinFunction]) -> None: - """ - 验证execute方法的签名是否正确 - - Args: - function_class: Join函数类 - - Raises: - ValueError: 如果方法签名不正确 - """ - import inspect - - try: - execute_method = function_class.execute - signature = inspect.signature(execute_method) - params = list(signature.parameters.keys()) - - # 期望的参数：self, payload, key, tag - expected_params = ["self", "payload", "key", "tag"] - - if len(params) < len(expected_params): - raise ValueError( - f"Join function {function_class.__name__}.execute() must accept parameters: " - f"{', '.join(expected_params[1:])}. Got: {', '.join(params[1:])}" - ) - - # 检查前几个参数名是否匹配（允许额外参数） - for i, expected_param in enumerate(expected_params): - if i < len(params) and params[i] != expected_param: - self.logger.warning( - f"Join function {function_class.__name__}.execute() parameter {i} " - f"expected '{expected_param}', got '{params[i]}'. " - f"This may cause runtime issues." - ) - - except Exception as e: - self.logger.warning(f"Could not validate execute method signature: {e}") - - @property - def supported_input_count(self) -> int: - """ - 获取支持的输入流数量 - - 对于Join操作，目前支持2个输入流 - - Returns: - int: 支持的输入流数量 (固定为2) - """ - return 2 # Join操作目前只支持2个输入流 - - @property - def max_supported_streams(self) -> int: - """ - 获取理论上支持的最大输入流数量 - - 可以通过检查join function的实现来动态确定， - 但目前固定为2流join - - Returns: - int: 最大支持的输入流数量 - """ - # 未来可以扩展为多流join，现在固定为2 - return 2 - - def validate_input_streams(self, input_count: int) -> None: - """ - 验证输入流数量是否匹配 - - Args: - input_count: 实际输入流数量 - - Raises: - ValueError: 如果输入流数量不匹配 - """ - supported_count = self.supported_input_count - max_supported = self.max_supported_streams - - if input_count != supported_count: - raise ValueError( - f"Join function {self.function_class.__name__} requires exactly " - f"{supported_count} input streams, but {input_count} streams provided." - ) - - if input_count > max_supported: - raise ValueError( - f"Join transformation supports maximum {max_supported} input streams, " - f"but {input_count} streams provided. " - f"Consider using multiple join operations for more complex scenarios." - ) - - if input_count < 2: - raise ValueError( - f"Join transformation requires at least 2 input streams, " - f"but only {input_count} provided." - ) - - def validate_keyed_streams(self, stream_transformations: list[BaseTransformation]) -> None: - """ - 验证所有输入流都是keyed的 - - Args: - stream_transformations: 输入流的transformation列表 - - Raises: - ValueError: 如果有流没有被keyed - """ - - for i, transformation in enumerate(stream_transformations): - # 检查是否是KeyByTransformation或者其下游 - if not self._is_keyed_stream(transformation): - raise ValueError( - f"Join requires all input streams to be keyed. " - f"Stream {i} (transformation: {transformation.function_class.__name__}) " - f"is not keyed. Use .keyby() before .join()" - ) - - def _is_keyed_stream(self, transformation: BaseTransformation) -> bool: - """ - 检查transformation是否产生keyed stream - - Args: - transformation: 要检查的transformation - - Returns: - bool: 是否为keyed stream - """ - from sage.kernel.api.transformation.keyby_transformation import ( - KeyByTransformation, - ) - - # 直接是KeyByTransformation - if isinstance(transformation, KeyByTransformation): - return True - - # 检查上游是否有KeyByTransformation - current = transformation - visited = set() - - while current and id(current) not in visited: - visited.add(id(current)) - - if isinstance(current, KeyByTransformation): - return True - - # 检查直接上游 - if current.upstreams: - # 对于合并操作，所有上游都应该是keyed的 - if len(current.upstreams) == 1: - current = current.upstreams[0] - else: - # 多个上游，检查是否都是keyed的 - return all(self._is_keyed_stream(upstream) for upstream in current.upstreams) - else: - break - - return False - - @property - def is_merge_operation(self) -> bool: - """ - Join是特殊的合并操作，需要区分输入流 - - Returns: - bool: False，表示不是普通的合并操作 - """ - return False # Join需要区分不同的输入流 - - def get_join_configuration(self) -> dict[str, Any]: - """ - 获取Join配置信息 - - Returns: - Dict[str, Any]: Join配置字典 - """ - return { - "function_class": self.function_class.__name__, - "supported_inputs": self.supported_input_count, - "max_inputs": self.max_supported_streams, - "is_keyed_required": True, - "join_type": getattr(self.function_class, "join_type", "custom"), - "function_args": self.function_args, - "function_kwargs": self.function_kwargs, - } - - def debug_print_join_info(self) -> None: - """打印Join配置调试信息""" - config = self.get_join_configuration() - print(f"\n🔗 JoinTransformation '{self.basename}' Configuration:") - print(f" Function: {config['function_class']}") - print(f" Supported inputs: {config['supported_inputs']}") - print(f" Max inputs: {config['max_inputs']}") - print(f" Requires keyed streams: {config['is_keyed_required']}") - print(f" Join type: {config['join_type']}") - if config["function_args"]: - print(f" Function args: {config['function_args']}") - if config["function_kwargs"]: - print(f" Function kwargs: {config['function_kwargs']}") - print(f" Upstreams: {[up.basename for up in self.upstreams]}") - - def __repr__(self) -> str: - cls_name = self.function_class.__name__ - supported_inputs = self.supported_input_count - join_type = getattr(self.function_class, "join_type", "custom") - return ( - f"<{self.__class__.__name__} {cls_name} " - f"type:{join_type} inputs:{supported_inputs} at {hex(id(self))}>" - ) diff --git a/packages/sage-kernel/src/sage/kernel/api/transformation/keyby_transformation.py b/packages/sage-kernel/src/sage/kernel/api/transformation/keyby_transformation.py deleted file mode 100644 index 925519cd31..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/transformation/keyby_transformation.py +++ /dev/null @@ -1,54 +0,0 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING - -from sage.kernel.api.operator.keyby_operator import KeyByOperator -from sage.kernel.api.transformation.base_transformation import BaseTransformation -from sage.kernel.runtime.factory.operator_factory import OperatorFactory - -if TYPE_CHECKING: - from sage.common.core.functions import BaseFunction - from sage.kernel.api.base_environment import BaseEnvironment - - -class KeyByTransformation(BaseTransformation): - """ - KeyBy变换，应用基于键的分区策略 - """ - - def __init__( - self, - env: BaseEnvironment, - key_selector_function: type[BaseFunction], - strategy: str = "hash", - name: str | None = None, - parallelism: int = 1, - *args, - **kwargs, - ): - # 设置operator类 - self.operator_class = KeyByOperator - self.partition_strategy = strategy - - # 调用父类构造函数 - super().__init__( - env=env, - function=key_selector_function, - name=name, - parallelism=parallelism, - *args, - **kwargs, - ) - - @property - def operator_factory(self): - if self._operator_factory is None: - self._operator_factory = OperatorFactory( - operator_class=self.operator_class, - function_factory=self.function_factory, - basename=self.basename, - env_name=self.env_name, - remote=self.remote, - partition_strategy=self.partition_strategy, # KeyBy特有参数 - ) - return self._operator_factory diff --git a/packages/sage-kernel/src/sage/kernel/api/transformation/map_transformation.py b/packages/sage-kernel/src/sage/kernel/api/transformation/map_transformation.py deleted file mode 100644 index 7ba6bcaf7b..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/transformation/map_transformation.py +++ /dev/null @@ -1,18 +0,0 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING - -from sage.kernel.api.operator.map_operator import MapOperator -from sage.kernel.api.transformation.base_transformation import BaseTransformation - -if TYPE_CHECKING: - from sage.common.core.functions import BaseFunction - from sage.kernel.api.base_environment import BaseEnvironment - - -class MapTransformation(BaseTransformation): - """映射变换 - 一对一数据变换""" - - def __init__(self, env: BaseEnvironment, function: type[BaseFunction], *args, **kwargs): - self.operator_class = MapOperator - super().__init__(env, function, *args, **kwargs) diff --git a/packages/sage-kernel/src/sage/kernel/api/transformation/sink_transformation.py b/packages/sage-kernel/src/sage/kernel/api/transformation/sink_transformation.py deleted file mode 100644 index dc20300eb8..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/transformation/sink_transformation.py +++ /dev/null @@ -1,30 +0,0 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING - -from sage.kernel.api.operator.sink_operator import SinkOperator -from sage.kernel.api.transformation.base_transformation import BaseTransformation - -if TYPE_CHECKING: - from sage.common.core.functions import BaseFunction - from sage.kernel.api.base_environment import BaseEnvironment - - -class SinkTransformation(BaseTransformation): - """汇聚变换 - 数据消费者""" - - def __init__( - self, - env: BaseEnvironment, - function: type[BaseFunction], - *args, - batch_size: int = 1, # Sink 特有的批处理大小， 可以减少系统调用次数 - **kwargs, - ): - self.operator_class = SinkOperator - self.batch_size = batch_size - super().__init__(env, function, *args, **kwargs) - - @property - def is_sink(self) -> bool: - return True diff --git a/packages/sage-kernel/src/sage/kernel/api/transformation/source_transformation.py b/packages/sage-kernel/src/sage/kernel/api/transformation/source_transformation.py deleted file mode 100644 index c3ef25ab0d..0000000000 --- a/packages/sage-kernel/src/sage/kernel/api/transformation/source_transformation.py +++ /dev/null @@ -1,34 +0,0 @@ -from __future__ import annotations - -from typing import TYPE_CHECKING - -from sage.kernel.api.operator.source_operator import SourceOperator -from sage.kernel.api.transformation.base_transformation import BaseTransformation - -if TYPE_CHECKING: - from sage.common.core.functions import BaseFunction - from sage.kernel.api.base_environment import BaseEnvironment - - -class SourceTransformation(BaseTransformation): - """源变换 - 数据生产者""" - - def __init__( - self, - env: BaseEnvironment, - function: type[BaseFunction], - *args, - delay: float = 1.0, # Source 节点可配置延迟 - **kwargs, - ): - self.operator_class = SourceOperator - self._delay = delay - super().__init__(env, function, *args, **kwargs) - - @property - def delay(self) -> float: - return self._delay - - @property - def is_spout(self) -> bool: - return True diff --git a/packages/sage-kernel/src/sage/kernel/core/__init__.py b/packages/sage-kernel/src/sage/kernel/core/__init__.py deleted file mode 100644 index 9da50b432d..0000000000 --- a/packages/sage-kernel/src/sage/kernel/core/__init__.py +++ /dev/null @@ -1,43 +0,0 @@ -""" -Kernel Core Module - 共享类型、异常和常量 - -这个模块包含 sage-kernel 中各个子模块共享的核心定义。 - -注意：core 已迁移到 sage-common，此文件保留用于向后兼容。 -请更新导入为: from sage.common.core import ... -""" - -import warnings - -# 兼容性导入 - 从 sage-common 重新导出 -from sage.common.core.exceptions import ( - FaultToleranceError, - KernelError, - RecoveryError, - ResourceAllocationError, - SchedulingError, -) -from sage.common.core.types import ExecutionMode, NodeID, ServiceID, TaskID, TaskStatus - -# 发出弃用警告 -warnings.warn( - "sage.kernel.core has been moved to sage.common.core. " - "Please update your imports to: from sage.common.core import ...", - DeprecationWarning, - stacklevel=2, -) - -__all__ = [ - # Types - "ExecutionMode", - "TaskStatus", - "TaskID", - "ServiceID", - "NodeID", - # Exceptions - "KernelError", - "SchedulingError", - "FaultToleranceError", - "ResourceAllocationError", - "RecoveryError", -] diff --git a/packages/sage-kernel/src/sage/kernel/core/constants.py b/packages/sage-kernel/src/sage/kernel/core/constants.py deleted file mode 100644 index e3451076ff..0000000000 --- a/packages/sage-kernel/src/sage/kernel/core/constants.py +++ /dev/null @@ -1,42 +0,0 @@ -""" -Kernel shared constants - -Defines constants used in sage-kernel. -""" - -# Default configuration -DEFAULT_CHECKPOINT_INTERVAL = 60 # seconds -DEFAULT_HEALTH_CHECK_INTERVAL = 30 # seconds -DEFAULT_MAX_RESTART_ATTEMPTS = 3 -DEFAULT_CLEANUP_TIMEOUT = 5.0 # seconds - -# Restart strategies -RESTART_STRATEGY_FIXED = "fixed_delay" -RESTART_STRATEGY_EXPONENTIAL = "exponential_backoff" -RESTART_STRATEGY_FAILURE_RATE = "failure_rate" - -# Placement strategies -PLACEMENT_STRATEGY_SIMPLE = "simple" -PLACEMENT_STRATEGY_RESOURCE_AWARE = "resource_aware" -PLACEMENT_STRATEGY_LOAD_BALANCE = "load_balance" - -# Scheduling strategies -SCHEDULING_STRATEGY_FIFO = "fifo" -SCHEDULING_STRATEGY_PRIORITY = "priority" -SCHEDULING_STRATEGY_RESOURCE_AWARE = "resource_aware" - -__all__ = [ - "DEFAULT_CHECKPOINT_INTERVAL", - "DEFAULT_HEALTH_CHECK_INTERVAL", - "DEFAULT_MAX_RESTART_ATTEMPTS", - "DEFAULT_CLEANUP_TIMEOUT", - "RESTART_STRATEGY_FIXED", - "RESTART_STRATEGY_EXPONENTIAL", - "RESTART_STRATEGY_FAILURE_RATE", - "PLACEMENT_STRATEGY_SIMPLE", - "PLACEMENT_STRATEGY_RESOURCE_AWARE", - "PLACEMENT_STRATEGY_LOAD_BALANCE", - "SCHEDULING_STRATEGY_FIFO", - "SCHEDULING_STRATEGY_PRIORITY", - "SCHEDULING_STRATEGY_RESOURCE_AWARE", -] diff --git a/packages/sage-kernel/src/sage/kernel/core/exceptions.py b/packages/sage-kernel/src/sage/kernel/core/exceptions.py deleted file mode 100644 index b9e39499df..0000000000 --- a/packages/sage-kernel/src/sage/kernel/core/exceptions.py +++ /dev/null @@ -1,86 +0,0 @@ -""" -Shared Kernel Exception Classes - -Defines the exception class hierarchy used in sage-kernel. -""" - - -class KernelError(Exception): - """ - Base Kernel Exception - - The base class for all sage-kernel related exceptions. - """ - - pass - - -class SchedulingError(KernelError): - """ - Scheduling Related Exception - - Exception occurring during task scheduling, resource allocation, etc. - """ - - pass - - -class FaultToleranceError(KernelError): - """ - Fault Tolerance Related Exception - - Exception occurring during fault detection, recovery, etc. - """ - - pass - - -class ResourceAllocationError(SchedulingError): - """ - Resource Allocation Exception - - Raised when required resources cannot be allocated. - """ - - pass - - -class RecoveryError(FaultToleranceError): - """ - Recovery Failure Exception - - Raised when task or job recovery fails. - """ - - pass - - -class CheckpointError(FaultToleranceError): - """ - Checkpoint Exception - - Exception occurring when saving or loading a checkpoint. - """ - - pass - - -class PlacementError(SchedulingError): - """ - Placement Strategy Exception - - Exception occurring when deciding task placement. - """ - - pass - - -__all__ = [ - "KernelError", - "SchedulingError", - "FaultToleranceError", - "ResourceAllocationError", - "RecoveryError", - "CheckpointError", - "PlacementError", -] diff --git a/packages/sage-kernel/src/sage/kernel/core/types.py b/packages/sage-kernel/src/sage/kernel/core/types.py deleted file mode 100644 index 12adb0b95d..0000000000 --- a/packages/sage-kernel/src/sage/kernel/core/types.py +++ /dev/null @@ -1,67 +0,0 @@ -""" -Kernel 共享类型定义 - -定义了 sage-kernel 中使用的核心数据类型和枚举。 -""" - -from enum import Enum -from typing import TypeVar - - -# 执行模式枚举 -class ExecutionMode(Enum): - """任务执行模式""" - - LOCAL = "local" # 本地执行 - REMOTE = "remote" # 远程执行（Ray） - HYBRID = "hybrid" # 混合模式 - - -# 任务状态枚举 -class TaskStatus(Enum): - """任务运行状态""" - - PENDING = "pending" # 等待中 - RUNNING = "running" # 运行中 - STOPPED = "stopped" # 已停止 - FAILED = "failed" # 失败 - COMPLETED = "completed" # 完成 - - -# 作业状态枚举 -class JobStatus(Enum): - """作业状态""" - - PENDING = "pending" - RUNNING = "running" - STOPPED = "stopped" - FAILED = "failed" - COMPLETED = "completed" - DELETED = "deleted" - - -# 类型别名 -TaskID = str # 任务标识符 -ServiceID = str # 服务标识符 -NodeID = str # 节点标识符 -QueueID = str # 队列标识符 -JobID = str # 作业标识符 - -# 泛型类型变量 -T = TypeVar("T") -TaskType = TypeVar("TaskType") -ServiceType = TypeVar("ServiceType") - -__all__ = [ - "ExecutionMode", - "TaskStatus", - "JobStatus", - "TaskID", - "ServiceID", - "NodeID", - "QueueID", - "JobID", - "T", - "TaskType", - "ServiceType", -] diff --git a/packages/sage-kernel/src/sage/kernel/fault_tolerance/__init__.py b/packages/sage-kernel/src/sage/kernel/fault_tolerance/__init__.py deleted file mode 100644 index 29b7352930..0000000000 --- a/packages/sage-kernel/src/sage/kernel/fault_tolerance/__init__.py +++ /dev/null @@ -1,90 +0,0 @@ -""" " -Fault Tolerance Module - 分布式容错 - -Layer: L3 (Kernel - Fault Tolerance) -Dependencies: sage.platform (L2), sage.common (L1) - -容错对应用用户是透明的 - 用户只需在 Environment 配置中声明容错策略即可。 -容错对开发者是可扩展的 - 开发者可以实现自己的容错策略。 - -## 对应用用户（Application User） - -用户在创建 Environment 时声明容错需求，系统自动处理： - -```python -from sage.kernel.api.local_environment import LocalEnvironment - -# 使用 checkpoint 容错策略 -env = LocalEnvironment( - "my_app", - config={ - "fault_tolerance": { - "strategy": "checkpoint", - "checkpoint_interval": 60.0, - "max_recovery_attempts": 3 - } - } -) - -# 或使用 restart 容错策略 -env = LocalEnvironment( - "my_app", - config={ - "fault_tolerance": { - "strategy": "restart", - "restart_strategy": "exponential", - "max_attempts": 5 - } - } -) - -# 正常定义和提交 DAG，容错由系统自动处理 -query_stream = env.from_source(...).map(...).sink(...) -env.submit() -``` - -## 对开发者（Developer） - -开发者可以实现自定义容错策略： - -```python -from sage.kernel.fault_tolerance.base import BaseFaultHandler - -class MyCustomFaultHandler(BaseFaultHandler): - def handle_failure(self, task_id, error): - # 自定义容错逻辑 - pass - - def can_recover(self, task_id): - # 自定义恢复判断 - pass - - def recover(self, task_id): - # 自定义恢复实现 - pass -``` - -然后在代码中注册： - -```python -# 在 impl/__init__.py 中添加导出 -# 在需要的地方使用自定义策略 -``` - -## 模块导出 - -仅导出开发者扩展所需的基类和实现接口： -- BaseFaultHandler: 容错处理器抽象基类（开发者继承） -- impl: 实现层模块（内含各种策略实现） -""" - -# 导出实现层供开发者参考和扩展 -from sage.kernel.fault_tolerance import impl - -# 只导出开发者扩展需要的基类 -from sage.kernel.fault_tolerance.base import BaseFaultHandler - -__all__ = [ - "BaseFaultHandler", # 开发者继承此类实现自定义策略 - "impl", # 实现层模块，包含所有内置策略 -] diff --git a/packages/sage-kernel/src/sage/kernel/fault_tolerance/base.py b/packages/sage-kernel/src/sage/kernel/fault_tolerance/base.py deleted file mode 100644 index ee6042d6ab..0000000000 --- a/packages/sage-kernel/src/sage/kernel/fault_tolerance/base.py +++ /dev/null @@ -1,98 +0,0 @@ -""" -容错处理器抽象基类 - -定义了容错处理的接口和抽象方法。 -""" - -from abc import ABC, abstractmethod -from typing import Any - - -class BaseFaultHandler(ABC): - """ - 容错处理器基类 - - 定义了处理故障、恢复任务的接口。 - """ - - def __init__(self): - self.logger: Any = None # Will be injected by dispatcher - self.dispatcher: Any = None # Will be injected by dispatcher - - @abstractmethod - def handle_failure(self, task_id: str, error: Exception) -> bool: - """ - 处理任务失败 - - Args: - task_id: 失败的任务 ID - error: 失败的异常信息 - - Returns: - True 如果处理成功 - """ - pass - - @abstractmethod - def can_recover(self, task_id: str) -> bool: - """ - 检查任务是否可以恢复 - - Args: - task_id: 任务 ID - - Returns: - True 如果任务可以恢复 - """ - pass - - @abstractmethod - def recover(self, task_id: str) -> bool: - """ - 恢复任务 - - Args: - task_id: 要恢复的任务 ID - - Returns: - True 如果恢复成功 - """ - pass - - def on_failure_detected(self, task_id: str, error: Exception): # noqa: B027 - """ - 故障检测回调 - - 当检测到故障时调用，默认实现为空。 - - Args: - task_id: 任务 ID - error: 异常信息 - """ - pass - - def on_recovery_started(self, task_id: str): # noqa: B027 - """ - 恢复开始回调 - - 当开始恢复时调用，默认实现为空。 - - Args: - task_id: 任务 ID - """ - pass - - def on_recovery_completed(self, task_id: str, success: bool): # noqa: B027 - """ - 恢复完成回调 - - 当恢复完成时调用，默认实现为空。 - - Args: - task_id: 任务 ID - success: 恢复是否成功 - """ - pass - - -__all__ = ["BaseFaultHandler"] diff --git a/packages/sage-kernel/src/sage/kernel/fault_tolerance/factory.py b/packages/sage-kernel/src/sage/kernel/fault_tolerance/factory.py deleted file mode 100644 index 66e6929747..0000000000 --- a/packages/sage-kernel/src/sage/kernel/fault_tolerance/factory.py +++ /dev/null @@ -1,133 +0,0 @@ -""" -Fault Tolerance Factory - -从配置创建容错策略实例的工厂模块。 -这是内部使用的模块，应用用户不会直接使用。 -""" - -from typing import Any - -from sage.kernel.fault_tolerance.base import BaseFaultHandler -from sage.kernel.fault_tolerance.impl.checkpoint_recovery import CheckpointBasedRecovery -from sage.kernel.fault_tolerance.impl.lifecycle_impl import LifecycleManagerImpl -from sage.kernel.fault_tolerance.impl.restart_recovery import RestartBasedRecovery -from sage.kernel.fault_tolerance.impl.restart_strategy import ( - ExponentialBackoffStrategy, - FailureRateStrategy, - FixedDelayStrategy, -) - - -def create_fault_handler_from_config( - config: dict[str, Any] | None = None, -) -> BaseFaultHandler: - """ - 从配置字典创建容错处理器 - - 这是内部使用的工厂函数，由 Dispatcher/JobManager 调用。 - 应用用户不会直接调用此函数。 - - Args: - config: 容错配置字典，示例： - { - "strategy": "checkpoint", # 或 "restart" - "checkpoint_interval": 60.0, - "max_recovery_attempts": 3, - ... - } - - Returns: - BaseFaultHandler 实例 - - Raises: - ValueError: 如果配置无效 - - Examples: - # 从 Environment 配置创建 - config = env.config.get("fault_tolerance", {}) - handler = create_fault_handler_from_config(config) - """ - if not config: - # 默认使用重启策略 - return RestartBasedRecovery(restart_strategy=ExponentialBackoffStrategy()) - - strategy = config.get("strategy", "restart") - - if strategy == "checkpoint": - return _create_checkpoint_handler(config) - elif strategy == "restart": - return _create_restart_handler(config) - else: - raise ValueError( - f"Unknown fault tolerance strategy: {strategy}. " - f"Supported strategies: 'checkpoint', 'restart'" - ) - - -def _create_checkpoint_handler(config: dict[str, Any]) -> CheckpointBasedRecovery: - """创建基于 Checkpoint 的容错处理器""" - checkpoint_dir = config.get("checkpoint_dir", ".sage/checkpoints") - checkpoint_interval = config.get("checkpoint_interval", 60.0) - max_recovery_attempts = config.get("max_recovery_attempts", 3) - - return CheckpointBasedRecovery( - checkpoint_dir=checkpoint_dir, - checkpoint_interval=checkpoint_interval, - max_recovery_attempts=max_recovery_attempts, - ) - - -def _create_restart_handler(config: dict[str, Any]) -> RestartBasedRecovery: - """创建基于重启的容错处理器""" - - restart_strategy_type = config.get("restart_strategy", "exponential") - - # 创建重启策略 - restart_strategy: FixedDelayStrategy | ExponentialBackoffStrategy | FailureRateStrategy - if restart_strategy_type == "fixed": - delay = config.get("delay", 5.0) - max_attempts = config.get("max_attempts", 3) - restart_strategy = FixedDelayStrategy(delay=delay, max_attempts=max_attempts) - - elif restart_strategy_type == "exponential": - initial_delay = config.get("initial_delay", 1.0) - max_delay = config.get("max_delay", 60.0) - multiplier = config.get("multiplier", 2.0) - max_attempts = config.get("max_attempts", 5) - restart_strategy = ExponentialBackoffStrategy( - initial_delay=initial_delay, - max_delay=max_delay, - multiplier=multiplier, - max_attempts=max_attempts, - ) - - elif restart_strategy_type == "failure_rate": - max_failures = config.get("max_failures_per_interval", 5) - interval = config.get("interval_seconds", 60.0) - delay = config.get("delay", 5.0) - restart_strategy = FailureRateStrategy( - max_failures_per_interval=max_failures, - interval_seconds=interval, - delay=delay, - ) - else: - # 默认使用指数退避 - restart_strategy = ExponentialBackoffStrategy() - - return RestartBasedRecovery(restart_strategy=restart_strategy) - - -def create_lifecycle_manager() -> LifecycleManagerImpl: - """ - 创建生命周期管理器 - - Returns: - LifecycleManagerImpl 实例 - """ - return LifecycleManagerImpl() - - -__all__ = [ - "create_fault_handler_from_config", - "create_lifecycle_manager", -] diff --git a/packages/sage-kernel/src/sage/kernel/fault_tolerance/impl/__init__.py b/packages/sage-kernel/src/sage/kernel/fault_tolerance/impl/__init__.py deleted file mode 100644 index 794608c642..0000000000 --- a/packages/sage-kernel/src/sage/kernel/fault_tolerance/impl/__init__.py +++ /dev/null @@ -1,30 +0,0 @@ -""" -Fault Tolerance Implementation Module - -包含各种容错策略的具体实现。 -""" - -from sage.kernel.fault_tolerance.impl.checkpoint_impl import CheckpointManagerImpl -from sage.kernel.fault_tolerance.impl.checkpoint_recovery import CheckpointBasedRecovery -from sage.kernel.fault_tolerance.impl.lifecycle_impl import LifecycleManagerImpl -from sage.kernel.fault_tolerance.impl.restart_recovery import RestartBasedRecovery -from sage.kernel.fault_tolerance.impl.restart_strategy import ( - ExponentialBackoffStrategy, - FailureRateStrategy, - FixedDelayStrategy, - RestartStrategy, -) - -__all__ = [ - # Recovery implementations - "CheckpointBasedRecovery", - "RestartBasedRecovery", - # Manager implementations - "LifecycleManagerImpl", - "CheckpointManagerImpl", - # Restart strategies - "RestartStrategy", - "FixedDelayStrategy", - "ExponentialBackoffStrategy", - "FailureRateStrategy", -] diff --git a/packages/sage-kernel/src/sage/kernel/fault_tolerance/impl/checkpoint_impl.py b/packages/sage-kernel/src/sage/kernel/fault_tolerance/impl/checkpoint_impl.py deleted file mode 100644 index ebbdb00950..0000000000 --- a/packages/sage-kernel/src/sage/kernel/fault_tolerance/impl/checkpoint_impl.py +++ /dev/null @@ -1,217 +0,0 @@ -""" -Checkpoint 管理实现 - -负责任务状态的保存和恢复的具体实现。 -""" - -import os -import pickle -from pathlib import Path -from typing import Any - -from sage.common.core import CheckpointError, TaskID - - -class CheckpointManagerImpl: - """ - Checkpoint 管理器实现 - - 负责保存和恢复任务的状态快照。 - """ - - def __init__(self, checkpoint_dir: str = ".sage/checkpoints"): - """ - 初始化 Checkpoint 管理器 - - Args: - checkpoint_dir: checkpoint 存储目录 - """ - self.checkpoint_dir = Path(checkpoint_dir) - self.checkpoint_dir.mkdir(parents=True, exist_ok=True) - - def save_checkpoint( - self, - task_id: TaskID, - state: dict[str, Any], - checkpoint_id: str | None = None, - ) -> str: - """ - 保存 checkpoint - - Args: - task_id: 任务 ID - state: 要保存的状态字典 - checkpoint_id: checkpoint ID（如果为 None，使用时间戳） - - Returns: - checkpoint 文件路径 - - Raises: - CheckpointError: 如果保存失败 - """ - try: - # 生成 checkpoint ID - if checkpoint_id is None: - import time - - checkpoint_id = f"{int(time.time())}" - - # 构建文件路径 - checkpoint_path = self.checkpoint_dir / f"{task_id}_{checkpoint_id}.ckpt" - - # 保存状态 - with open(checkpoint_path, "wb") as f: - pickle.dump(state, f) - - return str(checkpoint_path) - - except Exception as e: - raise CheckpointError(f"Failed to save checkpoint for {task_id}: {e}") - - def load_checkpoint( - self, task_id: TaskID, checkpoint_id: str | None = None - ) -> dict[str, Any] | None: - """ - 加载 checkpoint - - Args: - task_id: 任务 ID - checkpoint_id: checkpoint ID（如果为 None，加载最新的） - - Returns: - 状态字典，如果不存在返回 None - - Raises: - CheckpointError: 如果加载失败 - """ - try: - if checkpoint_id: - # 加载指定的 checkpoint - checkpoint_path = self.checkpoint_dir / f"{task_id}_{checkpoint_id}.ckpt" - else: - # 加载最新的 checkpoint - checkpoints = list(self.checkpoint_dir.glob(f"{task_id}_*.ckpt")) - if not checkpoints: - return None - checkpoint_path = max(checkpoints, key=os.path.getmtime) - - if not checkpoint_path.exists(): - return None - - # 加载状态 - with open(checkpoint_path, "rb") as f: - return pickle.load(f) - - except Exception as e: - raise CheckpointError(f"Failed to load checkpoint for {task_id}: {e}") - - def delete_checkpoint(self, task_id: TaskID, checkpoint_id: str | None = None): - """ - 删除 checkpoint - - Args: - task_id: 任务 ID - checkpoint_id: checkpoint ID（如果为 None，删除所有相关的） - """ - try: - if checkpoint_id: - # 删除指定的 checkpoint - checkpoint_path = self.checkpoint_dir / f"{task_id}_{checkpoint_id}.ckpt" - if checkpoint_path.exists(): - checkpoint_path.unlink() - else: - # 删除所有相关 checkpoint - for ckpt in self.checkpoint_dir.glob(f"{task_id}_*.ckpt"): - ckpt.unlink() - - except Exception as e: - raise CheckpointError(f"Failed to delete checkpoint for {task_id}: {e}") - - def list_checkpoints(self, task_id: TaskID) -> list[dict[str, Any]]: - """ - 列出任务的所有 checkpoint - - Args: - task_id: 任务 ID - - Returns: - checkpoint 信息列表 - """ - checkpoints = [] - - for ckpt_path in self.checkpoint_dir.glob(f"{task_id}_*.ckpt"): - # 提取 checkpoint ID - filename = ckpt_path.stem # task_id_checkpoint_id - parts = filename.split("_") - if len(parts) >= 2: - checkpoint_id = "_".join(parts[1:]) - else: - checkpoint_id = "unknown" - - checkpoints.append( - { - "task_id": task_id, - "checkpoint_id": checkpoint_id, - "path": str(ckpt_path), - "size": ckpt_path.stat().st_size, - "mtime": ckpt_path.stat().st_mtime, - } - ) - - # 按修改时间排序 - checkpoints.sort(key=lambda x: x["mtime"], reverse=True) - - return checkpoints - - def cleanup_old_checkpoints(self, task_id: TaskID, keep_last_n: int = 5): - """ - 清理旧的 checkpoint，只保留最新的 N 个 - - Args: - task_id: 任务 ID - keep_last_n: 保留最新的 N 个 checkpoint - """ - checkpoints = self.list_checkpoints(task_id) - - # 删除多余的 checkpoint - for ckpt in checkpoints[keep_last_n:]: - try: - Path(ckpt["path"]).unlink() - except Exception: - pass - - def get_checkpoint_info( - self, task_id: TaskID, checkpoint_id: str | None = None - ) -> dict[str, Any] | None: - """ - 获取 checkpoint 信息 - - Args: - task_id: 任务 ID - checkpoint_id: checkpoint ID（如果为 None，获取最新的） - - Returns: - checkpoint 信息字典 - """ - if checkpoint_id: - checkpoint_path = self.checkpoint_dir / f"{task_id}_{checkpoint_id}.ckpt" - else: - checkpoints = list(self.checkpoint_dir.glob(f"{task_id}_*.ckpt")) - if not checkpoints: - return None - checkpoint_path = max(checkpoints, key=os.path.getmtime) - - if not checkpoint_path.exists(): - return None - - stat = checkpoint_path.stat() - return { - "task_id": task_id, - "checkpoint_id": checkpoint_id, - "path": str(checkpoint_path), - "size": stat.st_size, - "mtime": stat.st_mtime, - } - - -__all__ = ["CheckpointManagerImpl"] diff --git a/packages/sage-kernel/src/sage/kernel/fault_tolerance/impl/checkpoint_recovery.py b/packages/sage-kernel/src/sage/kernel/fault_tolerance/impl/checkpoint_recovery.py deleted file mode 100644 index 4dfa83c9c2..0000000000 --- a/packages/sage-kernel/src/sage/kernel/fault_tolerance/impl/checkpoint_recovery.py +++ /dev/null @@ -1,232 +0,0 @@ -""" -Checkpoint-based Fault Tolerance Strategy - -基于检查点的容错恢复策略，周期性保存任务状态，失败时从最近的检查点恢复。 -""" - -import time -from typing import TYPE_CHECKING, Any - -from sage.common.core import TaskID -from sage.kernel.fault_tolerance.base import BaseFaultHandler -from sage.kernel.fault_tolerance.impl.checkpoint_impl import CheckpointManagerImpl - -if TYPE_CHECKING: - from sage.kernel.runtime.dispatcher import Dispatcher - - -class CheckpointBasedRecovery(BaseFaultHandler): - """ - 基于 Checkpoint 的容错恢复策略 - - 定期保存任务状态，失败时从最近的 checkpoint 恢复。 - 适用于长时间运行的任务，能够减少重新计算的开销。 - """ - - def __init__( - self, - checkpoint_manager: CheckpointManagerImpl | None = None, - checkpoint_interval: float = 60.0, - max_recovery_attempts: int = 3, - checkpoint_dir: str = ".sage/checkpoints", - ): - """ - 初始化 Checkpoint 容错策略 - - Args: - checkpoint_manager: Checkpoint 管理器 - checkpoint_interval: Checkpoint 保存间隔（秒） - max_recovery_attempts: 最大恢复尝试次数 - checkpoint_dir: Checkpoint 存储目录 - """ - self.checkpoint_manager = checkpoint_manager or CheckpointManagerImpl(checkpoint_dir) - self.checkpoint_interval = checkpoint_interval - self.max_recovery_attempts = max_recovery_attempts - - # 记录失败信息 - self.failure_counts: dict[TaskID, int] = {} - self.last_checkpoint_time: dict[TaskID, float] = {} - - self.logger = None # 可以后续注入 - self.dispatcher: Dispatcher | None = None # 可以后续注入 - - def handle_failure(self, task_id: TaskID, error: Exception) -> bool: - """ - 处理任务失败 - - Args: - task_id: 失败的任务 ID - error: 失败的异常信息 - - Returns: - True 如果处理成功 - """ - # 记录失败 - self.failure_counts[task_id] = self.failure_counts.get(task_id, 0) + 1 - - if self.logger: - self.logger.warning( - f"Task {task_id} failed (attempt #{self.failure_counts[task_id]}): {error}" - ) - - # 调用回调 - self.on_failure_detected(task_id, error) - - # 检查是否可以恢复 - if self.can_recover(task_id): - return self.recover(task_id) - else: - if self.logger: - self.logger.error(f"Task {task_id} cannot be recovered (max attempts reached)") - return False - - def can_recover(self, task_id: TaskID) -> bool: - """ - 检查任务是否可以恢复 - - Args: - task_id: 任务 ID - - Returns: - True 如果任务可以恢复 - """ - failure_count = self.failure_counts.get(task_id, 0) - has_checkpoint = len(self.checkpoint_manager.list_checkpoints(task_id)) > 0 - - return failure_count < self.max_recovery_attempts and has_checkpoint - - def _is_remote_task(self, task_id: TaskID) -> bool: - """判断是否为远程任务""" - if not hasattr(self, "dispatcher") or not self.dispatcher: - return False - task = self.dispatcher.tasks.get(task_id) - from sage.kernel.utils.ray.actor import ActorWrapper - - return isinstance(task, ActorWrapper) - - def recover(self, task_id: TaskID) -> bool: - """ - 从 Checkpoint 恢复任务（本地或远程） - """ - self.on_recovery_started(task_id) - try: - state = self.checkpoint_manager.load_checkpoint(task_id) - if state is None: - if self.logger: - self.logger.error(f"No checkpoint found for task {task_id}") - self.on_recovery_completed(task_id, False) - return False - - if self.logger: - self.logger.info( - f"Loaded checkpoint for task {task_id}, " - f"processed_count={state.get('processed_count', 0)}, " - f"checkpoint_counter={state.get('checkpoint_counter', 0)}" - ) - - if not hasattr(self, "dispatcher") or not self.dispatcher: - if self.logger: - self.logger.error("No dispatcher available for recovery") - self.on_recovery_completed(task_id, False) - return False - - success = self.dispatcher.restart_task_with_state(task_id, state) - - if success and self.logger: - self.logger.info(f"Task {task_id} restarted and state restored") - elif not success and self.logger: - self.logger.error(f"Failed to restart task {task_id}") - - self.on_recovery_completed(task_id, success) - return success - - except Exception as e: - if self.logger: - self.logger.error(f"Recover task {task_id} failed: {e}", exc_info=True) - self.on_recovery_completed(task_id, False) - return False - - def on_recovery_started(self, task_id: TaskID): - """恢复开始时的回调""" - if self.logger: - self.logger.info(f"🔄 Starting recovery for task {task_id}") - - def on_recovery_completed(self, task_id: TaskID, success: bool): - """恢复完成时的回调""" - if self.logger: - if success: - self.logger.info(f"✅ Recovery completed successfully for task {task_id}") - # 可以在这里添加更多逻辑，如： - # - 发送通知 - # - 记录指标 - # - 触发告警解除 - else: - self.logger.error(f"❌ Recovery failed for task {task_id}") - # 可以在这里添加失败处理逻辑，如： - # - 发送告警 - # - 记录失败原因 - # - 触发备用方案 - - def on_failure_detected(self, task_id: TaskID, error: Exception): - """检测到失败时的回调""" - if self.logger: - self.logger.warning(f"⚠️ Failure detected for task {task_id}: {error}") - # 可以在这里添加更多逻辑，如： - # - 发送告警通知 - # - 记录失败模式 - # - 更新监控面板 - - def save_checkpoint(self, task_id: TaskID, state: dict[str, Any], force: bool = False) -> bool: - """ - 保存任务 checkpoint - - Args: - task_id: 任务 ID - state: 任务状态 - force: 是否强制保存（忽略时间间隔） - - Returns: - True 如果保存成功 - """ - current_time = time.time() - last_time = self.last_checkpoint_time.get(task_id, 0) - - # 检查是否需要保存 - if not force and (current_time - last_time) < self.checkpoint_interval: - return False - - try: - self.checkpoint_manager.save_checkpoint(task_id, state) - self.last_checkpoint_time[task_id] = current_time - - if self.logger: - self.logger.debug(f"Saved checkpoint for task {task_id}") - - return True - - except Exception as e: - if self.logger: - self.logger.error(f"Failed to save checkpoint for {task_id}: {e}") - return False - - def cleanup_checkpoints(self, task_id: TaskID): - """ - 清理任务的所有 checkpoint - - Args: - task_id: 任务 ID - """ - try: - self.checkpoint_manager.delete_checkpoint(task_id) - - if task_id in self.failure_counts: - del self.failure_counts[task_id] - if task_id in self.last_checkpoint_time: - del self.last_checkpoint_time[task_id] - - except Exception as e: - if self.logger: - self.logger.error(f"Failed to cleanup checkpoints for {task_id}: {e}") - - -__all__ = ["CheckpointBasedRecovery"] diff --git a/packages/sage-kernel/src/sage/kernel/fault_tolerance/impl/lifecycle_impl.py b/packages/sage-kernel/src/sage/kernel/fault_tolerance/impl/lifecycle_impl.py deleted file mode 100644 index b45538f554..0000000000 --- a/packages/sage-kernel/src/sage/kernel/fault_tolerance/impl/lifecycle_impl.py +++ /dev/null @@ -1,208 +0,0 @@ -""" -Actor 和 Task 生命周期管理实现 - -负责管理 Actor 和 Task 的创建、监控、清理和终止的具体实现。 -""" - -from typing import Any, Protocol - -from sage.common.core import DEFAULT_CLEANUP_TIMEOUT, TaskID -from sage.kernel.utils.helpers import wait_for_all_stopped - - -class LoggerProtocol(Protocol): - """Logger 协议，定义日志器的接口""" - - def debug(self, msg: str, *args, **kwargs) -> None: - """Debug level logging""" - ... - - def info(self, msg: str, *args, **kwargs) -> None: - """Info level logging""" - ... - - def warning(self, msg: str, *args, **kwargs) -> None: - """Warning level logging""" - ... - - def error(self, msg: str, *args, **kwargs) -> None: - """Error level logging""" - ... - - -class LifecycleManagerImpl: - """ - Actor 生命周期管理器实现 - - 负责管理 Ray Actor 和本地 Task 的生命周期。 - """ - - def __init__(self): - """初始化生命周期管理器""" - self.logger: LoggerProtocol | None = None # 可以后续注入 logger - - def cleanup_actor( - self, - actor_wrapper, - cleanup_timeout: float = DEFAULT_CLEANUP_TIMEOUT, - no_restart: bool = True, - ) -> tuple[bool, bool]: - """ - 清理并终止单个 Actor - - Args: - actor_wrapper: ActorWrapper 实例 - cleanup_timeout: 清理超时时间（秒） - no_restart: 是否禁止 Ray Actor 重启 - - Returns: - (cleanup_success, kill_success) 元组 - """ - cleanup_success = False - kill_success = False - - try: - # 1. 尝试正常清理 - if hasattr(actor_wrapper, "cleanup"): - try: - if actor_wrapper.is_ray_actor(): - # Ray Actor: 异步调用 cleanup - cleanup_ref = actor_wrapper.call_async("cleanup") - - # 等待清理完成（带超时） - import ray - - ray.get(cleanup_ref, timeout=cleanup_timeout) - cleanup_success = True - else: - # 本地对象: 直接调用 cleanup - actor_wrapper.cleanup() - cleanup_success = True - - except Exception as e: - if self.logger: - self.logger.warning(f"Cleanup failed: {e}") - - # 2. 终止 Actor - if actor_wrapper.is_ray_actor(): - kill_success = actor_wrapper.kill_actor(no_restart=no_restart) - else: - # 本地对象，标记为成功 - kill_success = True - - except Exception as e: - if self.logger: - self.logger.error(f"Error in cleanup_actor: {e}") - - return cleanup_success, kill_success - - def cleanup_all( - self, - tasks: dict[TaskID, Any], - services: dict[str, Any] | None = None, - cleanup_timeout: float = DEFAULT_CLEANUP_TIMEOUT, - no_restart: bool = True, - ) -> dict[str, tuple[bool, bool]]: - """ - 清理所有任务和服务 - - Args: - tasks: 任务字典 {task_id: task_wrapper} - services: 服务字典 {service_id: service_wrapper} - cleanup_timeout: 清理超时时间（秒） - no_restart: 是否禁止 Ray Actor 重启（默认True） - - Returns: - 结果字典 {id: (cleanup_success, kill_success)} - """ - results = {} - - # 清理任务 - for task_id, task in tasks.items(): - result = self.cleanup_actor(task, cleanup_timeout, no_restart=no_restart) - results[task_id] = result - - if self.logger: - cleanup_ok, kill_ok = result - if kill_ok: - self.logger.debug(f"Successfully cleaned up task: {task_id}") - else: - self.logger.warning(f"Failed to clean up task: {task_id}") - - # 清理服务 - if services: - for service_id, service in services.items(): - result = self.cleanup_actor(service, cleanup_timeout, no_restart=no_restart) - results[service_id] = result - - if self.logger: - cleanup_ok, kill_ok = result - if kill_ok: - self.logger.debug(f"Successfully cleaned up service: {service_id}") - else: - self.logger.warning(f"Failed to clean up service: {service_id}") - - return results - - def cleanup_batch( - self, - actors: list[tuple[str, Any]], - cleanup_timeout: float = DEFAULT_CLEANUP_TIMEOUT, - ) -> dict[str, tuple[bool, bool]]: - """ - 批量清理 Actor - - Args: - actors: Actor 列表 [(id, actor_wrapper), ...] - cleanup_timeout: 清理超时时间（秒） - - Returns: - 结果字典 {id: (cleanup_success, kill_success)} - """ - results = {} - - for actor_id, actor_wrapper in actors: - result = self.cleanup_actor(actor_wrapper, cleanup_timeout) - results[actor_id] = result - - return results - - def get_cleanup_statistics( - self, cleanup_results: dict[str, tuple[bool, bool]] - ) -> dict[str, Any]: - """ - 获取清理统计信息 - - Args: - cleanup_results: 清理结果字典 - - Returns: - 统计信息字典 - """ - total = len(cleanup_results) - cleanup_success = sum(1 for _, (c, _) in cleanup_results.items() if c) - kill_success = sum(1 for _, (_, k) in cleanup_results.items() if k) - - return { - "total": total, - "cleanup_success": cleanup_success, - "kill_success": kill_success, - "cleanup_rate": cleanup_success / total if total > 0 else 0, - "kill_rate": kill_success / total if total > 0 else 0, - } - - def wait_for_actors_stop(self, tasks: dict[TaskID, Any], timeout: float = 10.0) -> bool: - """ - 等待所有任务停止 - - Args: - tasks: 任务字典 - timeout: 超时时间（秒） - - Returns: - True 如果所有任务都已停止 - """ - return wait_for_all_stopped(tasks, timeout=timeout, logger=self.logger) - - -__all__ = ["LifecycleManagerImpl"] diff --git a/packages/sage-kernel/src/sage/kernel/fault_tolerance/impl/restart_recovery.py b/packages/sage-kernel/src/sage/kernel/fault_tolerance/impl/restart_recovery.py deleted file mode 100644 index ae69343207..0000000000 --- a/packages/sage-kernel/src/sage/kernel/fault_tolerance/impl/restart_recovery.py +++ /dev/null @@ -1,217 +0,0 @@ -""" -Restart-based Fault Tolerance Strategy - -基于重启的容错恢复策略，任务失败时直接重启，不保存状态。 -""" - -import time -from typing import TYPE_CHECKING, Any - -from sage.common.core import TaskID -from sage.kernel.fault_tolerance.base import BaseFaultHandler -from sage.kernel.fault_tolerance.impl.restart_strategy import ( - ExponentialBackoffStrategy, - RestartStrategy, -) - -if TYPE_CHECKING: - from sage.kernel.runtime.dispatcher import Dispatcher - - -class RestartBasedRecovery(BaseFaultHandler): - """ - 基于重启的容错恢复策略 - - 任务失败时直接重启，不保存中间状态。 - 适用于无状态任务或短时间运行的任务。 - """ - - def __init__( - self, - restart_strategy: RestartStrategy | None = None, - ): - """ - 初始化重启容错策略 - - Args: - restart_strategy: 重启策略（默认使用指数退避） - """ - self.restart_strategy = restart_strategy or ExponentialBackoffStrategy() - - # 记录失败信息 - self.failure_counts: dict[TaskID, int] = {} - self.failure_history: dict[TaskID, list] = {} - - self.logger = None # 可以后续注入 - - def handle_failure(self, task_id: TaskID, error: Exception) -> bool: - """ - 处理任务失败 - - Args: - task_id: 失败的任务 ID - error: 失败的异常信息 - - Returns: - True 如果处理成功 - """ - # 记录失败 - self.failure_counts[task_id] = self.failure_counts.get(task_id, 0) + 1 - - if task_id not in self.failure_history: - self.failure_history[task_id] = [] - - self.failure_history[task_id].append( - { - "timestamp": time.time(), - "error": str(error), - "failure_count": self.failure_counts[task_id], - } - ) - - if self.logger: - self.logger.warning( - f"Task {task_id} failed (attempt #{self.failure_counts[task_id]}): {error}" - ) - - # 调用回调 - self.on_failure_detected(task_id, error) - - # 检查是否可以重启 - if self.can_recover(task_id): - return self.recover(task_id) - else: - if self.logger: - self.logger.error(f"Task {task_id} cannot be recovered (max attempts reached)") - return False - - def can_recover(self, task_id: TaskID) -> bool: - """ - 检查任务是否可以恢复 - - Args: - task_id: 任务 ID - - Returns: - True 如果任务可以恢复 - """ - failure_count = self.failure_counts.get(task_id, 0) - return self.restart_strategy.should_restart(failure_count) - - def recover(self, task_id: TaskID) -> bool: - """ - 重启任务 - - Args: - task_id: 要恢复的任务 ID - - Returns: - True 如果恢复成功 - """ - failure_count = self.failure_counts.get(task_id, 0) - - # 调用回调 - self.on_recovery_started(task_id) - - # 获取重启延迟 - delay = self.restart_strategy.get_restart_delay(failure_count) - - if self.logger: - self.logger.info( - f"Attempting to restart task {task_id} after {delay}s delay " - f"(attempt #{failure_count + 1})" - ) - - # 等待重启延迟 - time.sleep(delay) - - # TODO: 实际重启任务的逻辑 - # Issue URL: https://github.com/intellistream/SAGE/issues/925 - # 这里应该调用任务的重启方法 - - success = True # 暂时假设成功 - - # 调用回调 - self.on_recovery_completed(task_id, success) - - return success - - def recover_job( - self, job_id: str, dispatcher: "Dispatcher", restart_count: int = 0 - ) -> dict[str, Any]: - """ - 恢复整个作业 - - Args: - job_id: 作业 UUID - dispatcher: 作业的 Dispatcher 实例 - restart_count: 当前重启次数 - - Returns: - 恢复结果字典，包含 'success' 键和可选的 'error' 键 - """ - try: - if self.logger: - self.logger.info(f"Attempting to recover job {job_id}") - - # 重新启动 dispatcher - dispatcher.start() - - if self.logger: - self.logger.info( - f"Job {job_id} recovered successfully (restart #{restart_count + 1})" - ) - - return { - "success": True, - "job_id": job_id, - "restart_count": restart_count + 1, - } - - except Exception as e: - if self.logger: - self.logger.error(f"Failed to recover job {job_id}: {e}") - - return { - "success": False, - "job_id": job_id, - "error": str(e), - } - - def get_failure_statistics(self, task_id: TaskID | None = None) -> dict[str, Any]: - """ - 获取失败统计信息 - - Args: - task_id: 任务 ID（如果为 None，返回所有任务的统计） - - Returns: - 统计信息字典 - """ - if task_id: - return { - "task_id": task_id, - "failure_count": self.failure_counts.get(task_id, 0), - "failure_history": self.failure_history.get(task_id, []), - } - else: - return { - "total_failed_tasks": len(self.failure_counts), - "total_failures": sum(self.failure_counts.values()), - "failure_counts": dict(self.failure_counts), - } - - def reset_failure_count(self, task_id: TaskID): - """ - 重置任务的失败计数 - - Args: - task_id: 任务 ID - """ - if task_id in self.failure_counts: - del self.failure_counts[task_id] - if task_id in self.failure_history: - del self.failure_history[task_id] - - -__all__ = ["RestartBasedRecovery"] diff --git a/packages/sage-kernel/src/sage/kernel/fault_tolerance/impl/restart_strategy.py b/packages/sage-kernel/src/sage/kernel/fault_tolerance/impl/restart_strategy.py deleted file mode 100644 index c4f836333d..0000000000 --- a/packages/sage-kernel/src/sage/kernel/fault_tolerance/impl/restart_strategy.py +++ /dev/null @@ -1,222 +0,0 @@ -""" -重启策略实现 - -定义任务失败后的重启策略具体实现。 -""" - -from abc import ABC, abstractmethod - -from sage.common.core import DEFAULT_MAX_RESTART_ATTEMPTS - - -class RestartStrategy(ABC): - """ - 重启策略基类 - - 定义任务重启的策略接口。 - """ - - @abstractmethod - def should_restart(self, failure_count: int) -> bool: - """ - 判断是否应该重启 - - Args: - failure_count: 当前失败次数 - - Returns: - True 如果应该重启 - """ - pass - - @abstractmethod - def get_restart_delay(self, failure_count: int) -> float: - """ - 获取重启延迟时间 - - Args: - failure_count: 当前失败次数 - - Returns: - 延迟时间（秒） - """ - pass - - def on_restart_attempt(self, failure_count: int): # noqa: B027 - """ - 重启尝试回调 - - Args: - failure_count: 当前失败次数 - """ - pass - - -class FixedDelayStrategy(RestartStrategy): - """ - 固定延迟重启策略 - - 每次重启使用固定的延迟时间。 - """ - - def __init__(self, delay: float = 5.0, max_attempts: int = DEFAULT_MAX_RESTART_ATTEMPTS): - """ - 初始化固定延迟策略 - - Args: - delay: 固定延迟时间（秒） - max_attempts: 最大重启尝试次数 - """ - self.delay = delay - self.max_attempts = max_attempts - - def should_restart(self, failure_count: int) -> bool: - """ - 检查是否应该重启 - - Args: - failure_count: 当前失败次数 - - Returns: - True 如果失败次数未超过最大尝试次数 - """ - return failure_count < self.max_attempts - - def get_restart_delay(self, failure_count: int) -> float: - """ - 获取固定延迟时间 - - Args: - failure_count: 当前失败次数（未使用） - - Returns: - 固定延迟时间 - """ - return self.delay - - -class ExponentialBackoffStrategy(RestartStrategy): - """ - 指数退避重启策略 - - 延迟时间随失败次数指数增长。 - """ - - def __init__( - self, - initial_delay: float = 1.0, - max_delay: float = 60.0, - multiplier: float = 2.0, - max_attempts: int = 5, - ): - """ - 初始化指数退避策略 - - Args: - initial_delay: 初始延迟时间（秒） - max_delay: 最大延迟时间（秒） - multiplier: 延迟倍数 - max_attempts: 最大重启尝试次数 - """ - self.initial_delay = initial_delay - self.max_delay = max_delay - self.multiplier = multiplier - self.max_attempts = max_attempts - - def should_restart(self, failure_count: int) -> bool: - """ - 检查是否应该重启 - - Args: - failure_count: 当前失败次数 - - Returns: - True 如果失败次数未超过最大尝试次数 - """ - return failure_count < self.max_attempts - - def get_restart_delay(self, failure_count: int) -> float: - """ - 计算指数退避延迟时间 - - delay = min(initial_delay * multiplier^failure_count, max_delay) - - Args: - failure_count: 当前失败次数 - - Returns: - 计算后的延迟时间 - """ - delay = self.initial_delay * (self.multiplier**failure_count) - return min(delay, self.max_delay) - - -class FailureRateStrategy(RestartStrategy): - """ - 基于失败率的重启策略 - - 根据一段时间内的失败率决定是否重启。 - """ - - def __init__( - self, - max_failures_per_interval: int = 5, - interval_seconds: float = 60.0, - delay: float = 5.0, - ): - """ - 初始化失败率策略 - - Args: - max_failures_per_interval: 时间窗口内最大失败次数 - interval_seconds: 时间窗口大小（秒） - delay: 重启延迟时间（秒） - """ - self.max_failures_per_interval = max_failures_per_interval - self.interval_seconds = interval_seconds - self.delay = delay - self.failure_timestamps: list[float] = [] - - def should_restart(self, failure_count: int) -> bool: - """ - 根据失败率判断是否应该重启 - - Args: - failure_count: 总失败次数（未使用，使用时间戳判断） - - Returns: - True 如果时间窗口内失败次数未超过阈值 - """ - import time - - current_time = time.time() - - # 记录当前失败 - self.failure_timestamps.append(current_time) - - # 清理过期的失败记录 - cutoff_time = current_time - self.interval_seconds - self.failure_timestamps = [ts for ts in self.failure_timestamps if ts > cutoff_time] - - # 检查失败率 - return len(self.failure_timestamps) <= self.max_failures_per_interval - - def get_restart_delay(self, failure_count: int) -> float: - """ - 获取重启延迟时间 - - Args: - failure_count: 当前失败次数（未使用） - - Returns: - 固定延迟时间 - """ - return self.delay - - -__all__ = [ - "RestartStrategy", - "FixedDelayStrategy", - "ExponentialBackoffStrategy", - "FailureRateStrategy", -] diff --git a/packages/sage-kernel/src/sage/kernel/runtime/__init__.py b/packages/sage-kernel/src/sage/kernel/runtime/__init__.py deleted file mode 100644 index 8551dd2b99..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/__init__.py +++ /dev/null @@ -1,46 +0,0 @@ -""" -SAGE Kernel Runtime - 流式执行引擎运行时 - -Layer: L3 (Kernel - Runtime Core) -Dependencies: sage.platform (L2), sage.common (L1) - -运行时组件： -- JobManager: 作业管理器，协调任务执行 -- Dispatcher: 任务分发器 -- Task: 任务抽象（LocalTask, RayTask） -- Graph: 执行图（ExecutionGraph, GraphNode） -- Context: 执行上下文（TaskContext, ServiceContext） -- Communication: 通信层（Packet, Router, RPC） -- Service: 服务抽象（ServiceTask, ServiceCaller） -- Monitoring: 性能监控 - -Architecture: -- 提供流式数据处理的核心执行引擎 -- 支持本地和分布式（Ray）两种执行模式 -- 管理任务生命周期和数据流转 -- 提供容错和监控能力 - -子模块： -- communication/: 进程间通信（队列、路由、RPC） -- context/: 执行上下文管理 -- task/: 任务抽象和实现 -- graph/: 执行图构建和管理 -- service/: 服务节点管理 -- factory/: 运行时对象工厂 -- monitoring/: 性能监控和指标收集 -""" - -# 直接从本包的_version模块加载版本信息 -try: - from sage.kernel._version import __author__, __email__, __version__ -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" - -__all__ = [ - "__version__", - "__author__", - "__email__", -] diff --git a/packages/sage-kernel/src/sage/kernel/runtime/communication/__init__.py b/packages/sage-kernel/src/sage/kernel/runtime/communication/__init__.py deleted file mode 100644 index 58c515af61..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/communication/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -""" -SAGE - Streaming-Augmented Generative Execution -""" - -# 直接从本包的_version模块加载版本信息 -try: - from sage.kernel._version import __author__, __email__, __version__ -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" diff --git a/packages/sage-kernel/src/sage/kernel/runtime/communication/packet.py b/packages/sage-kernel/src/sage/kernel/runtime/communication/packet.py deleted file mode 100644 index e7ac9ce20d..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/communication/packet.py +++ /dev/null @@ -1,152 +0,0 @@ -""" -数据包 (Packet) - 算子间通信的基础数据结构 - -Packet 是流处理系统中算子间传递数据的标准载体，包含了数据负载、分区信息、时间戳等元数据。 -这个类被设计为轻量级且不可变，以确保高效的数据传输。 -""" - -import time -from typing import Any - -from sage.common.core import StopSignal # noqa: F401 - Re-exported for compatibility - - -class Packet: - """ - 数据包类 - 算子间通信的基础数据结构 - - Packet 封装了在流处理管道中传输的数据及其元数据。每个 Packet 包含： - - payload: 实际的数据内容 - - input_index: 输入索引，用于多输入流场景 - - partition_key: 分区键，用于数据分区 - - partition_strategy: 分区策略 - - timestamp: 创建时间戳 - - Attributes: - payload: 数据负载，可以是任何类型的数据 - input_index: 输入流索引，默认为0 - partition_key: 分区键，用于确定数据分区 - partition_strategy: 分区策略（如 "hash", "range" 等） - timestamp: 数据包创建时的纳秒级时间戳 - """ - - def __init__( - self, - payload: Any, - input_index: int = 0, - partition_key: Any = None, - partition_strategy: str | None = None, - ): - """ - 创建新的数据包 - - Args: - payload: 数据负载 - input_index: 输入流索引，用于区分多个输入流 - partition_key: 分区键，用于数据分区 - partition_strategy: 分区策略名称 - """ - self.payload = payload - self.input_index = input_index - self.partition_key = partition_key - self.partition_strategy = partition_strategy - self.timestamp = time.time_ns() - - def is_keyed(self) -> bool: - """ - 检查数据包是否包含分区键 - - Returns: - bool: 如果包含分区键则返回 True，否则返回 False - """ - return self.partition_key is not None - - def inherit_partition_info(self, new_payload: Any) -> "Packet": - """ - 创建新数据包，继承当前的分区信息 - - 这个方法常用于转换操作中，当需要保持数据的分区信息但更改负载内容时。 - - Args: - new_payload: 新的数据负载 - - Returns: - Packet: 包含新负载但继承分区信息的新数据包 - """ - return Packet( - payload=new_payload, - input_index=self.input_index, - partition_key=self.partition_key, - partition_strategy=self.partition_strategy, - ) - - def update_key(self, new_key: Any, new_strategy: str | None = None) -> "Packet": - """ - 更新分区键，用于重新分区场景 - - 这个方法用于需要改变数据分区的场景，例如 keyBy 操作。 - - Args: - new_key: 新的分区键 - new_strategy: 新的分区策略，如果为 None 则保持原策略 - - Returns: - Packet: 包含新分区信息的数据包 - """ - return Packet( - payload=self.payload, - input_index=self.input_index, - partition_key=new_key, - partition_strategy=new_strategy or self.partition_strategy, - ) - - def copy(self) -> "Packet": - """ - 创建数据包的副本 - - Returns: - Packet: 数据包的完整副本 - """ - packet = Packet( - payload=self.payload, - input_index=self.input_index, - partition_key=self.partition_key, - partition_strategy=self.partition_strategy, - ) - packet.timestamp = self.timestamp # 保持原始时间戳 - return packet - - def __repr__(self) -> str: - """ - 返回数据包的字符串表示 - - Returns: - str: 数据包的描述信息 - """ - key_info = f"key={self.partition_key}" if self.is_keyed() else "unkeyed" - payload_type = type(self.payload).__name__ if self.payload is not None else "None" - - return ( - f"<Packet input={self.input_index} {key_info} " - f"payload_type={payload_type} ts={self.timestamp}>" - ) - - def __eq__(self, other) -> bool: - """ - 比较两个数据包是否相等 - - Args: - other: 另一个数据包 - - Returns: - bool: 如果两个数据包相等则返回 True - """ - if not isinstance(other, Packet): - return False - - return ( - self.payload == other.payload - and self.input_index == other.input_index - and self.partition_key == other.partition_key - and self.partition_strategy == other.partition_strategy - ) diff --git a/packages/sage-kernel/src/sage/kernel/runtime/communication/router/__init__.py b/packages/sage-kernel/src/sage/kernel/runtime/communication/router/__init__.py deleted file mode 100644 index 58c515af61..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/communication/router/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -""" -SAGE - Streaming-Augmented Generative Execution -""" - -# 直接从本包的_version模块加载版本信息 -try: - from sage.kernel._version import __author__, __email__, __version__ -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" diff --git a/packages/sage-kernel/src/sage/kernel/runtime/communication/router/connection.py b/packages/sage-kernel/src/sage/kernel/runtime/communication/router/connection.py deleted file mode 100644 index d9c59629c9..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/communication/router/connection.py +++ /dev/null @@ -1,118 +0,0 @@ -import time -from dataclasses import dataclass -from typing import TYPE_CHECKING - -if TYPE_CHECKING: - from sage.platform.queue.base_queue_descriptor import BaseQueueDescriptor - - -@dataclass -class Connection: - """ - 用于表示节点间的连接，包含队列描述符和路由信息 - """ - - def __init__( - self, - broadcast_index: int, - parallel_index: int, - target_name: str, - queue_descriptor: "BaseQueueDescriptor", - target_input_index: int, - ): - self.broadcast_index: int = broadcast_index - self.parallel_index: int = parallel_index - self.target_name: str = target_name - self.queue_descriptor: BaseQueueDescriptor = queue_descriptor - self.target_input_index: int = target_input_index - - # 负载状态跟踪（保留用于监控） - self._load_history: list[float] = [] # 存储最近的负载历史 - self._last_load_check = time.time() - self._load_trend = 0.0 # 负载趋势：正数表示增加，负数表示减少 - self._max_history_size = 10 # 保存最近10次的负载记录 - - def get_buffer_load(self) -> float: - """ - 获取目标缓冲区的负载率 (0.0-1.0) - """ - try: - # 通过队列描述符获取队列 - target_queue = self.queue_descriptor.get_queue() - - if hasattr(target_queue, "qsize") and hasattr(target_queue, "maxsize"): - # 标准队列类型 - current_size = target_queue.qsize() - max_size = target_queue.maxsize - if max_size > 0: - load_ratio = current_size / max_size - else: - load_ratio = 0.0 - elif hasattr(target_queue, "get_buffer_stats"): - # 如果是SageQueue类型，使用统计信息 - stats = target_queue.get_buffer_stats() - load_ratio = stats.get("utilization", 0.0) - else: - # 其他类型，暂时返回0 - load_ratio = 0.0 - - return load_ratio - - except Exception: - return 0.0 - - # def should_increase_delay(self) -> bool: - # """ - # 判断是否应该增加delay - # 当前负载 > 60% 且比上次记录的高 - # """ - # current_load = self.get_buffer_load() - # return current_load > 0.6 - - # def should_decrease_delay(self) -> bool: - # """ - # 判断是否应该减少delay - # 当前负载 < 30% 且比上次记录的低 - # """ - # current_load = self.get_buffer_load() - # return current_load < 0.3 - - # def _update_load_history(self, current_load: float): - # """更新负载历史和计算趋势""" - # current_time = time.time() - - # # 添加到历史记录 - # self._load_history.append((current_time, current_load)) - - # # 保持历史记录大小 - # if len(self._load_history) > self._max_history_size: - # self._load_history.pop(0) - - # # 计算负载趋势（最近3个点的斜率） - # if len(self._load_history) >= 3: - # recent_points = self._load_history[-3:] - # # 计算简单的线性趋势 - # time_diff = recent_points[-1][0] - recent_points[0][0] - # load_diff = recent_points[-1][1] - recent_points[0][1] - - # if time_diff > 0: - # self._load_trend = load_diff / time_diff - # else: - # self._load_trend = 0.0 - - # self._last_load_check = current_time - - def get_load_trend(self) -> float: - """ - 获取负载趋势 - 返回值：正数表示负载增加，负数表示负载减少，0表示稳定 - """ - return self._load_trend - - def is_load_increasing(self) -> bool: - """负载是否在增加""" - return self._load_trend > 0.05 # 5%/秒的增长视为增加 - - def is_load_decreasing(self) -> bool: - """负载是否在减少""" - return self._load_trend < -0.05 # 5%/秒的减少视为减少 diff --git a/packages/sage-kernel/src/sage/kernel/runtime/communication/router/router.py b/packages/sage-kernel/src/sage/kernel/runtime/communication/router/router.py deleted file mode 100644 index cdc0bf641d..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/communication/router/router.py +++ /dev/null @@ -1,260 +0,0 @@ -# flake8: noqa: F401 -# sage.kernels.runtime/base_router.py -import traceback -from abc import ABC, abstractmethod -from typing import TYPE_CHECKING, Any, Dict - -from sage.kernel.runtime.communication.packet import Packet - -# 添加 Ray 相关导入以检测 Actor -try: - import ray - from ray.actor import ActorHandle - - RAY_AVAILABLE = True -except ImportError: - RAY_AVAILABLE = False - ActorHandle = None # type: ignore[assignment,misc] - -if TYPE_CHECKING: - from sage.kernel.runtime.communication.packet import StopSignal - from sage.kernel.runtime.communication.router.connection import Connection - from sage.kernel.runtime.context.task_context import TaskContext - from sage.platform.queue.base_queue_descriptor import BaseQueueDescriptor - - -class BaseRouter(ABC): # noqa: B024 - """ - 路由器基类，负责管理下游连接和数据包路由 - 子类只需要实现具体的数据发送逻辑 - """ - - def __init__(self, ctx: "TaskContext"): - self.name = ctx.name - self.ctx = ctx - - # 从TaskContext获取下游连接组信息 - self.downstream_groups: dict[int, dict[int, Connection]] = ctx.downstream_groups - self.downstream_group_roundrobin: dict[int, int] = {} - - # 初始化轮询状态 - for broadcast_index in self.downstream_groups.keys(): - self.downstream_group_roundrobin[broadcast_index] = 0 - - # Logger - self.logger = ctx.logger - self.logger.debug(f"Initialized {self.__class__.__name__} for {self.name}") - self.logger.debug(f"Downstream groups: {list(self.downstream_groups.keys())}") - - def get_connections_info(self) -> dict[str, Any]: - """获取连接信息""" - info = {} - for broadcast_index, parallel_targets in self.downstream_groups.items(): - info[f"broadcast_group_{broadcast_index}"] = { - "count": len(parallel_targets), - "roundrobin_position": self.downstream_group_roundrobin[broadcast_index], - "targets": [ - { - "parallel_index": parallel_index, - "target_name": connection.target_name, - "queue_id": connection.queue_descriptor.queue_id, - } - for parallel_index, connection in parallel_targets.items() - ], - } - return info - - def send_stop_signal(self, stop_signal: "StopSignal") -> None: - """ - 发送停止信号给所有下游连接 - - Args: - stop_signal: 停止信号对象 - """ - self.logger.debug(f"Sending stop signal: {stop_signal}") - - for _broadcast_index, parallel_targets in self.downstream_groups.items(): - for connection in parallel_targets.values(): - try: - # 通过连接的队列描述符获取队列并发送停止信号 - queue = connection.queue_descriptor.get_queue() - queue.put_nowait(stop_signal) - self.logger.debug(f"Sent stop signal to {connection.target_name}") - except Exception as e: - self.logger.error( - f"Failed to send stop signal to {connection.target_name}: {e}" - ) - - def send(self, packet: "Packet") -> bool: - """ - 发送数据包到下游节点 - - Args: - packet: 要发送的数据包 - - Returns: - bool: 是否成功发送 - """ - self.logger.debug( - f"Router {self.name}: Send called with downstream_groups: {list(self.downstream_groups.keys())}" - ) - - if not self.downstream_groups: - self.logger.warning(f"No downstream connections available for {self.name}") - self.logger.warning(f"Current downstream_groups state: {self.downstream_groups}") - return False - - try: - self.downstream_max_load = 0.0 - self.logger.debug(f"Router {self.name}: Sending packet: {packet.payload}") - self.logger.debug( - f"Router {self.name}: Downstream groups: {list(self.downstream_groups.keys())}" - ) - self.logger.debug(f"Emitting packet: {packet}") - - # 根据packet的分区信息选择路由策略 - if packet.is_keyed(): - self.logger.debug(f"Router {self.name}: Using keyed routing") - result = self._route_packet(packet) - else: - self.logger.debug(f"Router {self.name}: Using round-robin routing") - result = self._route_round_robin_packet(packet) - - self.logger.debug(f"Router {self.name}: Routing result: {result}") - self._adjust_delay_based_on_load() - return True - except Exception as e: - self.logger.error(f"Error emitting packet: {e}", exc_info=True) - return False - - def _route_packet(self, packet: "Packet") -> bool: - """使用分区信息进行路由""" - strategy = packet.partition_strategy - - if strategy == "hash": - return self._route_hashed_packet(packet) - elif strategy == "broadcast": - return self._route_broadcast_packet(packet) - else: - return self._route_round_robin_packet(packet) - - def _route_round_robin_packet(self, packet: "Packet") -> bool: - """使用轮询策略进行路由""" - success = True - - for broadcast_index, parallel_targets in self.downstream_groups.items(): - if not parallel_targets: # 空的并行目标组 - continue - - # 获取当前轮询位置 - current_round_robin = self.downstream_group_roundrobin[broadcast_index] - parallel_indices = list(parallel_targets.keys()) - target_parallel_index = parallel_indices[current_round_robin % len(parallel_indices)] - - # 更新轮询位置 - self.downstream_group_roundrobin[broadcast_index] = (current_round_robin + 1) % len( - parallel_indices - ) - - # 发送到选中的连接 - connection = parallel_targets[target_parallel_index] - if not self._deliver_packet_to_connection(connection, packet): - success = False - - return success - - def _route_broadcast_packet(self, packet: "Packet") -> bool: - """使用广播策略进行路由""" - success = True - - for _broadcast_index, parallel_targets in self.downstream_groups.items(): - for connection in parallel_targets.values(): - if not self._deliver_packet_to_connection(connection, packet): - success = False - - return success - - def _route_hashed_packet(self, packet: "Packet") -> bool: - """使用哈希分区策略进行路由""" - if not packet.partition_key: - self.logger.warning( - "Hash routing requested but no partition key provided, falling back to round-robin" - ) - return self._route_round_robin_packet(packet) - - success = True - partition_key = packet.partition_key - - for _broadcast_index, parallel_targets in self.downstream_groups.items(): - if not parallel_targets: - continue - - # 基于分区键计算目标索引 - parallel_indices = list(parallel_targets.keys()) - target_index = hash(partition_key) % len(parallel_indices) - target_parallel_index = parallel_indices[target_index] - - connection = parallel_targets[target_parallel_index] - if not self._deliver_packet_to_connection(connection, packet): - success = False - - return success - - def _deliver_packet_to_connection(self, connection: "Connection", packet: "Packet") -> bool: - """ - 将数据包发送到连接对应的队列 - - Args: - connection: 目标连接 - packet: 要发送的数据包 - - Returns: - bool: 是否成功发送 - """ - try: - self.logger.debug(f"Router {self.name}: Delivering packet to {connection.target_name}") - - # 创建路由包，包含target_input_index信息 - routed_packet = self._create_routed_packet(connection, packet) - - # 通过连接的队列描述符获取队列 - target_queue = connection.queue_descriptor.get_queue() - self.logger.debug( - f"Router {self.name}: Got target queue: {target_queue} (type: {type(target_queue)})" - ) - - # 使用阻塞的put()方法实现背压，而不是put_nowait() - # 这样当队列满时会自动等待，实现背压机制 - target_queue.put(routed_packet, timeout=30) # 30秒超时防止死锁 - self.logger.debug( - f"Router {self.name}: Successfully sent packet to {connection.target_name}" - ) - - return True - - except Exception as e: - self.logger.error( - f"Router {self.name}: Failed to deliver packet to {connection.target_name}: {e}" - ) - traceback.print_exc() - return False - - def clear_all_connections(self): - """清空所有连接""" - self.downstream_groups.clear() - self.downstream_group_roundrobin.clear() - - def _create_routed_packet(self, connection: "Connection", packet: "Packet") -> "Packet": - """创建路由后的数据包""" - return Packet( - payload=packet.payload, - input_index=connection.target_input_index, - partition_key=packet.partition_key, - partition_strategy=packet.partition_strategy, - ) - - def _adjust_delay_based_on_load(self): # noqa: B027 - """根据下游负载调整延迟（目前是占位符实现）""" - # 这是一个占位符方法，可以在未来根据队列负载情况调整发送延迟 - # 目前不做任何调整 - pass diff --git a/packages/sage-kernel/src/sage/kernel/runtime/communication/rpc/__init__.py b/packages/sage-kernel/src/sage/kernel/runtime/communication/rpc/__init__.py deleted file mode 100644 index 0db3077d83..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/communication/rpc/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -""" -SAGE - RPC Communication Module - -Layer: L3 (Kernel) -Dependencies: sage.platform (L2), sage.common (L1) - -This module provides RPC-based queue implementations for remote communication. -""" - -from .rpc_queue import RPCQueue - -__all__ = ["RPCQueue"] diff --git a/packages/sage-kernel/src/sage/kernel/runtime/communication/rpc/rpc_queue.py b/packages/sage-kernel/src/sage/kernel/runtime/communication/rpc/rpc_queue.py deleted file mode 100644 index 3d222f37f1..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/communication/rpc/rpc_queue.py +++ /dev/null @@ -1,211 +0,0 @@ -""" -SAGE - RPC Queue Implementation - -Layer: L3 (Kernel) -Dependencies: sage.platform (L2), queue.Queue (stdlib) - -RPCQueue实现：基于RPC的远程队列通信 - -Architecture: -- 实现L2定义的队列接口 -- 使用工厂模式注册到sage-platform -- 当前为stub实现，实际RPC通信需要额外的网络库支持 - -TODO: -- [ ] 实现真实的RPC通信协议（gRPC/HTTP） -- [ ] 添加连接池管理 -- [ ] 实现序列化/反序列化 -- [ ] 添加错误重试机制 -""" - -import logging -from queue import Empty, Queue -from typing import Any - -logger = logging.getLogger(__name__) - - -class RPCQueue: - """ - RPC队列实现 - 当前为stub版本 - - 用于远程进程间通信的队列封装。当前使用本地Queue模拟， - 实际部署需要替换为真实的RPC客户端实现。 - - Attributes: - queue_id: 队列唯一标识符 - host: RPC服务器地址 - port: RPC服务器端口 - _queue: 内部Queue对象（stub实现） - _connected: 连接状态标志 - - Note: - ⚠️ STUB IMPLEMENTATION - 当前使用本地Queue模拟远程行为 - 生产环境需要实现真实的RPC客户端（如gRPC） - """ - - def __init__( - self, - queue_id: str, - host: str = "localhost", - port: int = 50051, - maxsize: int = 0, - **kwargs, - ): - """ - 初始化RPC队列 - - Args: - queue_id: 队列唯一标识符 - host: RPC服务器地址 - port: RPC服务器端口 - maxsize: 队列最大大小（0表示无限制） - **kwargs: 其他配置参数 - """ - self.queue_id = queue_id - self.host = host - self.port = port - self.maxsize = maxsize - - # Stub实现：使用本地Queue - self._queue: Queue = Queue(maxsize=maxsize) - self._connected = False - - logger.warning( - f"⚠️ RPCQueue '{queue_id}' initialized as STUB - " - f"using local Queue instead of real RPC to {host}:{port}" - ) - - def connect(self) -> bool: - """ - 连接到RPC服务器 - - Returns: - bool: 连接是否成功 - - Note: - Stub实现：总是返回True - """ - if not self._connected: - logger.info( - f"[STUB] Simulating connection to RPC server " - f"{self.host}:{self.port} for queue '{self.queue_id}'" - ) - self._connected = True - return True - - def put(self, item: Any, block: bool = True, timeout: float | None = None) -> None: - """ - 向队列发送数据 - - Args: - item: 要发送的数据项 - block: 是否阻塞等待 - timeout: 超时时间（秒） - - Raises: - Full: 队列已满且非阻塞模式 - - Note: - Stub实现：使用本地Queue.put() - """ - if not self._connected: - self.connect() - - try: - self._queue.put(item, block=block, timeout=timeout) - logger.debug(f"[STUB] Put item to RPC queue '{self.queue_id}'") - except Exception as e: - logger.error(f"Failed to put item to RPC queue '{self.queue_id}': {e}") - raise - - def get(self, block: bool = True, timeout: float | None = None) -> Any: - """ - 从队列接收数据 - - Args: - block: 是否阻塞等待 - timeout: 超时时间（秒） - - Returns: - Any: 接收到的数据项 - - Raises: - Empty: 队列为空且非阻塞模式 - - Note: - Stub实现：使用本地Queue.get() - """ - if not self._connected: - self.connect() - - try: - item = self._queue.get(block=block, timeout=timeout) - logger.debug(f"[STUB] Got item from RPC queue '{self.queue_id}'") - return item - except Empty: - logger.debug(f"RPC queue '{self.queue_id}' is empty") - raise - except Exception as e: - logger.error(f"Failed to get item from RPC queue '{self.queue_id}': {e}") - raise - - def qsize(self) -> int: - """ - 返回队列大小 - - Returns: - int: 当前队列中的元素数量 - - Note: - Stub实现：返回本地Queue大小 - """ - return self._queue.qsize() - - def empty(self) -> bool: - """ - 检查队列是否为空 - - Returns: - bool: 队列是否为空 - """ - return self._queue.empty() - - def full(self) -> bool: - """ - 检查队列是否已满 - - Returns: - bool: 队列是否已满 - """ - return self._queue.full() - - def close(self) -> None: - """ - 关闭RPC连接 - - Note: - Stub实现：仅标记为未连接 - """ - if self._connected: - logger.info(f"[STUB] Closing RPC connection for queue '{self.queue_id}'") - self._connected = False - - def __enter__(self): - """上下文管理器入口""" - self.connect() - return self - - def __exit__(self, exc_type, exc_val, exc_tb): - """上下文管理器退出""" - self.close() - return False - - def __repr__(self) -> str: - """字符串表示""" - status = "connected" if self._connected else "disconnected" - return ( - f"RPCQueue(queue_id='{self.queue_id}', " - f"host='{self.host}', port={self.port}, " - f"status='{status}', size={self.qsize()})" - ) diff --git a/packages/sage-kernel/src/sage/kernel/runtime/context/__init__.py b/packages/sage-kernel/src/sage/kernel/runtime/context/__init__.py deleted file mode 100644 index 58c515af61..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/context/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -""" -SAGE - Streaming-Augmented Generative Execution -""" - -# 直接从本包的_version模块加载版本信息 -try: - from sage.kernel._version import __author__, __email__, __version__ -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" diff --git a/packages/sage-kernel/src/sage/kernel/runtime/context/base_context.py b/packages/sage-kernel/src/sage/kernel/runtime/context/base_context.py deleted file mode 100644 index 35a5bf053e..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/context/base_context.py +++ /dev/null @@ -1,152 +0,0 @@ -from concurrent.futures import Future -from typing import TYPE_CHECKING, Any - -if TYPE_CHECKING: - from sage.common.utils.logging.custom_logger import CustomLogger - from sage.kernel.runtime.proxy.proxy_manager import ProxyManager - from sage.kernel.runtime.service.service_caller import ServiceManager - - -class BaseRuntimeContext: - """ - Base runtime context class providing common functionality - for TaskContext and ServiceContext - """ - - def __init__(self): - # 服务调用相关 - self._proxy_manager: ProxyManager | None = None - # Keyed state support - tracks current packet's key - self._current_packet_key: Any = None - - @property - def logger(self) -> "CustomLogger": - """Logger property - must be implemented by subclasses""" - raise NotImplementedError("Subclasses must implement logger property") - - # def __getstate__(self): - # """自定义序列化：排除不可序列化的属性""" - # state = self.__dict__.copy() - # # 移除不可序列化的对象 - # state.pop('_service_manager', None) - # state.pop('_service_dict', None) - # state.pop('_async_service_dict', None) - # # 如果子类定义了__state_exclude__属性，移除指定的属性 - # if hasattr(self, '__state_exclude__'): - # for attr in self.__state_exclude__: - # state.pop(attr, None) - # return state - - # def __setstate__(self, state): - # """反序列化时恢复状态""" - # self.__dict__.update(state) - # # 重置服务管理器相关属性为None，它们会在需要时被懒加载 - # self._service_manager = None - # self._service_dict = None - # self._async_service_dict = None - - @property - def proxy_manager(self) -> "ProxyManager": - """Lazy-loaded proxy manager wrapping service communication.""" - if self._proxy_manager is None: - from sage.kernel.runtime.proxy.proxy_manager import ProxyManager - - # ProxyManager expects logging.Logger but CustomLogger is compatible - self._proxy_manager = ProxyManager(self, logger=self.logger) # type: ignore[arg-type] - return self._proxy_manager - - @property - def service_manager(self) -> "ServiceManager": - """Backward-compatible accessor for the underlying service manager.""" - return self.proxy_manager.service_manager - - # ------------------------------------------------------------------ - # Unified service invocation helpers - # ------------------------------------------------------------------ - def call_service( - self, - service_name: str, - *args: Any, - timeout: float | None = None, - method: str | None = None, - **kwargs: Any, - ) -> Any: - """Invoke a service synchronously using the shared proxy layer.""" - - return self.proxy_manager.call_sync( - service_name, *args, timeout=timeout, method=method, **kwargs - ) - - def call_service_async( - self, - service_name: str, - *args: Any, - timeout: float | None = None, - method: str | None = None, - **kwargs: Any, - ) -> Future: - """Invoke a service asynchronously and return a Future.""" - - return self.proxy_manager.call_async( - service_name, *args, timeout=timeout, method=method, **kwargs - ) - - def cleanup_service_manager(self): - """清理服务管理器资源""" - if self._proxy_manager is not None: - try: - self._proxy_manager.shutdown() - except Exception as e: - self.logger.warning(f"Error shutting down proxy manager: {e}") - finally: - self._proxy_manager = None - - # ------------------------------------------------------------------ - # Keyed State Support - # ------------------------------------------------------------------ - def set_current_key(self, key: Any) -> None: - """ - Set the current packet's key for keyed state operations. - - This method is called by operators when processing a packet to make - the packet's key available to functions via get_key(). - - Args: - key: The key associated with the current packet being processed - """ - self._current_packet_key = key - - def get_key(self) -> Any: - """ - Get the key of the currently processing packet. - - This method allows functions to access the key of the packet being - processed, enabling keyed state management patterns. Returns None - if no key is set (e.g., for unkeyed streams). - - Returns: - The current packet's key, or None if not set - - Example: - >>> class UserSessionFunction(StatefulFunction): - ... def __init__(self, **kwargs): - ... super().__init__(**kwargs) - ... self.user_sessions = {} # Keyed state - ... - ... def execute(self, event_data): - ... user_id = self.ctx.get_key() - ... if user_id not in self.user_sessions: - ... self.user_sessions[user_id] = {'count': 0} - ... self.user_sessions[user_id]['count'] += 1 - ... return self.user_sessions[user_id] - """ - return self._current_packet_key - - def clear_key(self) -> None: - """ - Clear the current packet's key. - - This method is called by operators after packet processing is complete - to ensure the key doesn't leak into subsequent operations. - """ - self._current_packet_key = None diff --git a/packages/sage-kernel/src/sage/kernel/runtime/context/context_injection.py b/packages/sage-kernel/src/sage/kernel/runtime/context/context_injection.py deleted file mode 100644 index 016d57a310..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/context/context_injection.py +++ /dev/null @@ -1,134 +0,0 @@ -""" -Context Injection Utilities - -提供通用的上下文注入工具函数，用于在对象构造时注入运行时上下文。 -主要解决在构造函数中无法使用上下文提供的服务（如logger）的问题。 -""" - -import logging -from typing import Any, Optional, TypeVar - -# 定义类型变量 -T = TypeVar("T") - - -def create_with_context( - target_class: type[T], - context: Any, - context_attr_name: str = "ctx", - *args, - **kwargs, -) -> T: - """ - 使用上下文注入方式创建对象实例 - - 使用 __new__ + __init__ 分离的方式，在调用 __init__ 之前注入上下文， - 这样构造函数就能使用上下文提供的服务（如logger）。 - - Args: - target_class: 要创建的目标类 - context: 要注入的上下文对象 - context_attr_name: 上下文属性名称，默认为 'ctx' - *args: 传递给构造函数的位置参数 - **kwargs: 传递给构造函数的关键字参数 - - Returns: - 创建的实例，上下文已注入 - - Example: - # 创建服务实例并注入 ServiceContext - service = create_with_context( - MyService, - service_context, - 'ctx', - config_param="value" - ) - - # 创建任务实例并注入 TaskContext - task = create_with_context( - MyTask, - task_context, - 'ctx', - input_data=data - ) - """ - if context is not None: - # 方案1: 使用 __new__ + __init__ 分离的方式 - # 先调用 __new__ 创建实例，但不调用 __init__ - instance = target_class.__new__(target_class) - - # 在调用 __init__ 之前注入上下文 - if hasattr(instance, "__dict__"): - setattr(instance, context_attr_name, context) - else: - # 对于某些特殊类型（如某些内置类型或使用 __slots__ 的类），使用 setattr - try: - setattr(instance, context_attr_name, context) - except (AttributeError, TypeError) as e: - logging.warning(f"Failed to inject context into {target_class.__name__}: {e}") - # 如果无法注入上下文，回退到普通构造方式 - instance = target_class(*args, **kwargs) - # 尝试在构造后注入上下文 - try: - setattr(instance, context_attr_name, context) - except (AttributeError, TypeError): - logging.warning( - f"Failed to inject context after construction for {target_class.__name__}" - ) - return instance - - # 现在调用 __init__，此时上下文已经可用 - instance.__init__(*args, **kwargs) # type: ignore[misc] - else: - # 没有上下文时，使用正常的构造方式 - instance = target_class(*args, **kwargs) - - return instance - - -def create_service_with_context( - service_class: type[T], service_context: Optional["ServiceContext"], *args, **kwargs -) -> T: - """ - 使用 ServiceContext 创建服务实例的便捷方法 - - Args: - service_class: 服务类 - service_context: 服务上下文 - *args: 传递给构造函数的位置参数 - **kwargs: 传递给构造函数的关键字参数 - - Returns: - 创建的服务实例 - """ - return create_with_context(service_class, service_context, "ctx", *args, **kwargs) - - -def create_task_with_context( - task_class: type[T], task_context: Optional["TaskContext"], *args, **kwargs -) -> T: - """ - 使用 TaskContext 创建任务实例的便捷方法 - - Args: - task_class: 任务类 - task_context: 任务上下文 - *args: 传递给构造函数的位置参数 - **kwargs: 传递给构造函数的关键字参数 - - Returns: - 创建的任务实例 - """ - return create_with_context(task_class, task_context, "ctx", *args, **kwargs) - - -# 为了类型检查，导入相关类型 -if __name__ != "__main__": - try: - from typing import TYPE_CHECKING - - if TYPE_CHECKING: - from sage.kernel.runtime.context.service_context import ServiceContext - from sage.kernel.runtime.context.task_context import TaskContext - except ImportError: - pass diff --git a/packages/sage-kernel/src/sage/kernel/runtime/context/service_context.py b/packages/sage-kernel/src/sage/kernel/runtime/context/service_context.py deleted file mode 100644 index d9b51c263d..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/context/service_context.py +++ /dev/null @@ -1,138 +0,0 @@ -from __future__ import annotations - -import os -from typing import TYPE_CHECKING - -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.runtime.context.base_context import BaseRuntimeContext - -if TYPE_CHECKING: - from sage.kernel.api.base_environment import BaseEnvironment - from sage.kernel.runtime.graph.execution_graph import ExecutionGraph - from sage.kernel.runtime.graph.service_node import ServiceNode - from sage.platform.queue.base_queue_descriptor import BaseQueueDescriptor - -# task, operator和function "形式上共享"的运行上下文 - - -class ServiceContext(BaseRuntimeContext): - # 定义不需要序列化的属性 - __state_exclude__ = ["_logger", "env", "_env_logger_cache"] - - def __init__( - self, - service_node: ServiceNode, - env: BaseEnvironment, - execution_graph: ExecutionGraph | None = None, - ): - super().__init__() # Initialize base context - - self.name: str = service_node.name - - self.env_name: str = env.name - self.env_base_dir: str | None = env.env_base_dir - self.env_uuid: str | None = getattr(env, "uuid", None) # 使用 getattr 以避免 AttributeError - self.env_console_log_level = env.console_log_level # 保存环境的控制台日志等级 - - self._logger: CustomLogger | None = None - - # 队列描述符管理 - 在构造时从service_node和execution_graph获取 - self._request_queue_descriptor: BaseQueueDescriptor | None = ( - service_node.service_qd - ) # 用于service task接收请求 - - # 维护自己的service response queue descriptor (用于接收service调用的响应) - self._own_service_response_qd: BaseQueueDescriptor | None = None - if hasattr(service_node, "service_response_qd"): - self._own_service_response_qd = service_node.service_response_qd - - # 提供response_qd属性以兼容ServiceManager（指向自己的service response queue） - self.response_qd: BaseQueueDescriptor | None = self._own_service_response_qd - - # 从execution_graph的提取好的映射表获取service response队列描述符 - 简化逻辑 - self._service_response_queue_descriptors: dict[str, BaseQueueDescriptor] = {} - if execution_graph and hasattr(execution_graph, "service_response_qds"): - self._service_response_queue_descriptors = execution_graph.service_response_qds.copy() - - # 从execution_graph获取service request队列描述符 - 用于service-to-service调用 - self._service_request_queue_descriptors: dict[str, BaseQueueDescriptor] = {} - if execution_graph and hasattr(execution_graph, "service_request_qds"): - self._service_request_queue_descriptors = execution_graph.service_request_qds.copy() - - # 兼容ServiceManager - 提供service_qds属性（指向service request queue descriptors） - self.service_qds: dict[str, BaseQueueDescriptor] = self._service_request_queue_descriptors - - # 服务调用相关 - service_manager已在BaseRuntimeContext中定义 - - @property - def logger(self) -> CustomLogger: - """懒加载logger""" - if self._logger is None: - base_dir = self.env_base_dir if self.env_base_dir is not None else "." - self._logger = CustomLogger( - [ - ( - "console", - self.env_console_log_level, - ), # 使用环境设置的控制台日志等级 - ( - os.path.join(base_dir, f"{self.name}_debug.log"), - "DEBUG", - ), # 详细日志 - (os.path.join(base_dir, "Error.log"), "ERROR"), # 错误日志 - ( - os.path.join(base_dir, f"{self.name}_info.log"), - "INFO", - ), # 错误日志 - ], - name=f"{self.name}", - ) - return self._logger - - def set_request_queue_descriptor(self, descriptor: BaseQueueDescriptor): - """设置请求队列描述符（用于service task）""" - self._request_queue_descriptor = descriptor - - def get_request_queue_descriptor(self) -> BaseQueueDescriptor | None: - """获取请求队列描述符""" - return self._request_queue_descriptor - - def set_service_response_queue_descriptors(self, descriptors: dict[str, BaseQueueDescriptor]): - """设置service response队列描述符（让service可以访问各个response队列）""" - self._service_response_queue_descriptors = descriptors - - def get_service_response_queue_descriptors( - self, - ) -> dict[str, BaseQueueDescriptor]: - """获取service response队列描述符""" - return ( - self._service_response_queue_descriptors - if self._service_response_queue_descriptors - else {} - ) - - def get_service_response_queue_descriptor(self, node_name: str) -> BaseQueueDescriptor | None: - """获取指定节点的service response队列描述符""" - if self._service_response_queue_descriptors: - return self._service_response_queue_descriptors.get(node_name) - return None - - def get_service_request_queue_descriptors(self) -> dict[str, BaseQueueDescriptor]: - """获取service request队列描述符（用于service-to-service调用）""" - return ( - self._service_request_queue_descriptors - if self._service_request_queue_descriptors - else {} - ) - - def get_service_request_queue_descriptor(self, service_name: str) -> BaseQueueDescriptor | None: - """获取指定服务的service request队列描述符""" - if self._service_request_queue_descriptors: - return self._service_request_queue_descriptors.get(service_name) - return None - - def get_own_service_response_queue_descriptor( - self, - ) -> BaseQueueDescriptor | None: - """获取自己的service response队列描述符（用于接收service调用的响应）""" - return self._own_service_response_qd diff --git a/packages/sage-kernel/src/sage/kernel/runtime/context/task_context.py b/packages/sage-kernel/src/sage/kernel/runtime/context/task_context.py deleted file mode 100644 index cc22001172..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/context/task_context.py +++ /dev/null @@ -1,474 +0,0 @@ -import os -import threading -from typing import TYPE_CHECKING, Any, Optional - -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.runtime.communication.packet import StopSignal -from sage.kernel.runtime.communication.router.connection import Connection -from sage.kernel.runtime.communication.router.router import BaseRouter -from sage.kernel.runtime.context.base_context import BaseRuntimeContext - -if TYPE_CHECKING: - from sage.kernel.api.base_environment import BaseEnvironment - from sage.kernel.api.transformation.base_transformation import BaseTransformation - from sage.kernel.runtime.communication.packet import Packet - from sage.kernel.runtime.graph.execution_graph import ExecutionGraph - from sage.kernel.runtime.graph.graph_node import TaskNode - from sage.platform.queue.base_queue_descriptor import ( - BaseQueueDescriptor, - ) -# task, operator和function "形式上共享"的运行上下文 - - -class TaskContext(BaseRuntimeContext): - # 定义不需要序列化的属性（Ray序列化时会跳过这些） - __state_exclude__ = [ - "_logger", - "env", - "_env_logger_cache", - "_stop_event", # threading.Event 不可序列化 - "_router", # 包含锁的对象 - "_local_jobmanager_ref", # weakref 不可序列化 - "dispatcher", # 避免循环引用 - "_current_packet_key", # Runtime-only state, should not be serialized - ] - - def __init__( - self, - graph_node: "TaskNode", - transformation: "BaseTransformation", - env: "BaseEnvironment", - execution_graph: "ExecutionGraph | None" = None, - ): - super().__init__() # Initialize base context - - self.name: str = graph_node.name - - self.env_name = env.name - self.env_base_dir: str | None = env.env_base_dir - self.env_uuid = getattr(env, "uuid", None) # 使用 getattr 以避免 AttributeError - self.env_console_log_level = env.console_log_level # 保存环境的控制台日志等级 - - # 性能监控配置 - self.enable_monitoring: bool = getattr(env, "enable_monitoring", False) - - self.parallel_index: int = graph_node.parallel_index - self.parallelism: int = graph_node.parallelism - - self._logger: Optional[CustomLogger] = None - self.dispatcher: Optional[Any] = None # Will be set by Dispatcher - - self.is_spout = transformation.is_spout - - self.delay = 0.01 - self.stop_signal_num = graph_node.stop_signal_num - - # 保存JobManager的网络地址信息而不是直接引用 - self.jobmanager_host = getattr(env, "jobmanager_host", "sage-node-1") - self.jobmanager_port = getattr(env, "jobmanager_port", 19001) - self.logger.debug( - f"JobManager address set to {self.jobmanager_host}:{self.jobmanager_port}" - ) - # 为本地环境保存JobManager的弱引用 - if hasattr(env, "_jobmanager") and env._jobmanager is not None: - import weakref - - self._local_jobmanager_ref = weakref.ref(env._jobmanager) - else: - self._local_jobmanager_ref = None - - # 这些属性将在task层初始化，避免序列化问题 - self._stop_event = None # 延迟初始化 - self.received_stop_signals = None # 延迟初始化 - self.stop_signal_count = 0 - - # 服务相关 - service_manager已在BaseRuntimeContext中定义 - self._service_names: Optional[dict[str, str]] = None # 只保存服务名称映射而不是实例 - - # 队列描述符管理 - 在构造时从 graph_node 和 execution_graph 获取 - self.input_qd: BaseQueueDescriptor | None = graph_node.input_qd - self.response_qd: BaseQueueDescriptor = graph_node.service_response_qd - - # 从execution_graph的提取好的映射表获取service队列描述符 - 简化逻辑 - self.service_qds: dict[str, BaseQueueDescriptor] = {} - if execution_graph and hasattr(execution_graph, "service_request_qds"): - self.service_qds = execution_graph.service_request_qds.copy() - - # 下游连接组管理 - 从execution_graph构建downstream_groups - self.downstream_groups: dict[int, dict[int, Connection]] = {} - if execution_graph: - self._build_downstream_groups(graph_node, execution_graph) - - self.dispatcher = None # 延迟注入，避免循环依赖 - - def _build_downstream_groups(self, graph_node: "TaskNode", execution_graph: "ExecutionGraph"): - """从execution_graph构建downstream_groups""" - # 遍历输出通道，构建downstream_groups - for broadcast_index, output_group in enumerate(graph_node.output_channels): - if output_group: # 确保输出组不为空 - self.downstream_groups[broadcast_index] = {} - - for edge in output_group: - if edge.downstream_node and edge.downstream_node.input_qd: - # 使用下游节点的单一输入队列描述符 - downstream_queue_descriptor = edge.downstream_node.input_qd - - # 创建Connection对象 - - connection = Connection( - broadcast_index=broadcast_index, - parallel_index=edge.downstream_node.parallel_index, - target_name=edge.downstream_node.name, - queue_descriptor=downstream_queue_descriptor, - target_input_index=edge.input_index, - ) - - # 使用downstream node的parallel_index作为key - self.downstream_groups[broadcast_index][ - edge.downstream_node.parallel_index - ] = connection - - def cleanup(self): - """清理运行时上下文资源""" - self.cleanup_service_manager() # 使用基类的清理方法 - - @property - def router(self): - if hasattr(self, "_router") and self._router is not None: - return self._router - else: - self._router = BaseRouter(self) - return self._router - - @property - def logger(self) -> CustomLogger: - """懒加载logger""" - if self._logger is None: - base_dir = self.env_base_dir or "." # 如果为 None 则使用当前目录 - self._logger = CustomLogger( - [ - ( - "console", - self.env_console_log_level, - ), # 使用环境设置的控制台日志等级 - ( - os.path.join(base_dir, f"{self.name}_debug.log"), - "DEBUG", - ), # 详细日志 - (os.path.join(base_dir, "Error.log"), "ERROR"), # 错误日志 - ( - os.path.join(base_dir, f"{self.name}_info.log"), - "INFO", - ), # 错误日志 - ], - name=f"{self.name}", - ) - return self._logger - - def get_service(self, service_name: str) -> Any: - """ - 获取服务实例，通过service_manager获取 - - Args: - service_name: 服务名称 - - Returns: - 服务实例 - - Raises: - ValueError: 当服务不存在时 - """ - if self._service_names is None: - raise RuntimeError("Services not available - dispatcher not initialized") - - if service_name not in self._service_names: - available_services = list(self._service_names.keys()) - raise ValueError( - f"Service '{service_name}' not found. Available services: {available_services}" - ) - - # 通过service_manager获取实际的服务实例 - return self.service_manager.get_service(service_name) # type: ignore - - @property - def stop_event(self) -> threading.Event: - """获取共享的停止事件，延迟初始化""" - if self._stop_event is None: - self._stop_event = threading.Event() - return self._stop_event - - def set_stop_signal(self): - self.stop_event.set() - - def is_stop_requested(self) -> bool: - return self.stop_event.is_set() - - def clear_stop_signal(self): - self.stop_event.clear() - - def request_stop(self): - """ - 请求停止当前任务，向JobManager发送停止信号 - """ - self.send_stop_signal_back(self.name) - - def send_stop_signal_back(self, node_name: str): - """ - 通过网络向JobManager发送节点停止信号 - 支持本地和远程(Ray Actor)环境 - """ - try: - # 检查是否为本地环境 - 如果jobmanager_host是localhost相关，尝试直接调用 - if self.jobmanager_host in ["127.0.0.1", "localhost"] and hasattr( - self, "_local_jobmanager_ref" - ): - # 直接调用本地JobManager实例 - self.logger.info( - f"Task {node_name} sending stop signal directly to local JobManager" - ) - # 检查 _local_jobmanager_ref 是否有效（不为 None 且可调用） - if self._local_jobmanager_ref is not None and callable(self._local_jobmanager_ref): - local_jobmanager = self._local_jobmanager_ref() - if local_jobmanager: - local_jobmanager.receive_node_stop_signal(self.env_uuid, node_name) - self.logger.info("Successfully sent stop signal to local JobManager") - return - else: - self.logger.debug( - "Local JobManager ref is not available (likely in Ray remote worker), using network client" - ) - - # 导入JobManagerClient来发送网络请求 - from sage.kernel.runtime.jobmanager_client import JobManagerClient - - self.logger.info( - f"Task {node_name} sending stop signal back to JobManager at {self.jobmanager_host}:{self.jobmanager_port}" - ) - - # 创建客户端并发送停止信号 - client = JobManagerClient(host=self.jobmanager_host, port=self.jobmanager_port) - response = client.receive_node_stop_signal(self.env_uuid or "", node_name) - - if response.get("status") == "success": - self.logger.debug(f"Successfully sent stop signal for node {node_name}") - else: - self.logger.warning(f"JobManager response: {response}") - - except Exception as e: - self.logger.error( - f"Failed to send stop signal back for node {node_name}: {e}", - exc_info=True, - ) - - def handle_stop_signal(self, signal: StopSignal): - """Handle the received stop signal.""" - source_node = signal.name - self.logger.info(f"Task {self.name} received stop signal from {source_node}") - - # Check if this is a JoinOperator or CoMapOperator that needs to handle stop signals specially - # These operators need to wait for multiple upstream stop signals before propagating - # Note: SinkOperator also has handle_stop_signal but it doesn't manage stop signal propagation, - # it only handles function.close() - so we should NOT return early for Sink - if hasattr(self, "operator") and hasattr(self.operator, "handle_stop_signal"): # type: ignore[attr-defined] - # Check if operator's handle_stop_signal accepts 'signal' parameter - # JoinOperator and CoMapOperator accept it, SinkOperator doesn't - import inspect - - sig = inspect.signature(self.operator.handle_stop_signal) # type: ignore[attr-defined] - if "signal" in sig.parameters: - # This is JoinOperator/CoMapOperator that manages its own stop signal propagation - self.operator.handle_stop_signal(signal=signal) # type: ignore[attr-defined] - return - # For SinkOperator (no 'signal' param), continue to normal stop signal handling - # Note: SinkOperator.handle_stop_signal() is called separately by BaseTask._handle_sink_stop_signal() - - # Initialize stop signal tracking attributes if they don't exist - if not hasattr(self, "num_expected_stop_signals"): - # 对于某些类型的操作符，我们需要等待多个停止信号 - # 特别是对于那些可能有多个上游输入的操作符 - operator_name = getattr(self, "name", "") - if "KeyBy" in operator_name and "_1" in operator_name: - # 这是一个合并了多个输入的KeyBy节点，等待2个停止信号 - self.num_expected_stop_signals = 2 - self.logger.info(f"Task {self.name} (KeyBy merge node) expecting 2 stop signals") - else: - self.num_expected_stop_signals = 0 - if not hasattr(self, "stop_signals_received"): - self.stop_signals_received = set() - - if self.num_expected_stop_signals > 0: - self.stop_signals_received.add(source_node) - self.logger.info( - f"Task {self.name} received stop signals ({len(self.stop_signals_received)}/{self.num_expected_stop_signals}) from: {list(self.stop_signals_received)}" - ) - - if len(self.stop_signals_received) >= self.num_expected_stop_signals: - self.logger.info( - f"Task {self.name} received all expected stop signals, requesting stop and forwarding signal" - ) - # Send stop signal to job manager - self.request_stop() - - # Forward the signal to downstream nodes - if hasattr(self, "router") and self.router: - self.router.send_stop_signal(signal) - else: - self.logger.info(f"Task {self.name} waiting for more stop signals") - # 不要停止或转发信号，继续等待 - return - else: - # No specific number expected, just forward the signal - self.logger.info(f"Task {self.name} forwarding stop signal from {source_node}") - - # Send stop signal to job manager - self.request_stop() - - # Forward the signal to downstream nodes - if hasattr(self, "router") and self.router: - self.router.send_stop_signal(signal) - - def __del__(self): - """析构函数 - 确保资源被正确清理""" - try: - self.cleanup() - except Exception: - # 在析构函数中不记录错误，避免在程序退出时产生问题 - pass - - # ================== Ray 序列化支持 ================== - - def __getstate__(self): - """ - 自定义序列化方法，用于 Ray 分布式传输 - 排除不可序列化的对象（logger, threading.Event, weakref 等） - """ - state = self.__dict__.copy() - - # 移除不可序列化的属性 - for attr in self.__state_exclude__: - state.pop(attr, None) - - # 确保移除所有 threading 相关对象 - if "_stop_event" in state: - del state["_stop_event"] - - # 移除 router（包含锁） - if "_router" in state: - del state["_router"] - - # 移除方法引用（bound methods 不可序列化） - # _build_downstream_groups 是一个方法，不应该被序列化 - if "_build_downstream_groups" in state: - del state["_build_downstream_groups"] - - return state - - def __setstate__(self, state): - """ - 自定义反序列化方法，在 Ray worker 中重建对象 - 重新初始化不可序列化的对象 - """ - self.__dict__.update(state) - - # 重新初始化需要延迟创建的对象 - self._logger = None # 懒加载 - self._stop_event = None # 延迟初始化 - self._router = None # 延迟初始化 - self._local_jobmanager_ref = None # 远程环境不需要 - self.dispatcher = None # 远程环境不需要 - - # ================== 路由接口 - 封装BaseRouter功能 ================== - - def _get_router(self): - """延迟初始化router，避免直接暴露BaseRouter给core组件""" - if not hasattr(self, "_router") or self._router is None: - from sage.kernel.runtime.communication.router.router import BaseRouter - - self._router = BaseRouter(self) - self.logger.debug(f"Initialized router for TaskContext {self.name}") - return self._router - - def send_packet(self, packet: "Packet") -> bool: - """ - 通过TaskContext发送数据包，隐藏BaseRouter实现细节 - 这是核心API组件与kernel通信的统一接口 - """ - try: - router = self._get_router() - return router.send(packet) - except Exception as e: - self.logger.error(f"Failed to send packet through TaskContext: {e}") - return False - - def send_stop_signal(self, stop_signal: "StopSignal") -> None: - """ - 通过TaskContext发送停止信号，隐藏BaseRouter实现细节 - """ - try: - router = self._get_router() - router.send_stop_signal(stop_signal) - self.logger.debug("Sent stop signal through TaskContext") - except Exception as e: - self.logger.error(f"Failed to send stop signal through TaskContext: {e}") - - def get_routing_info(self) -> dict[str, Any]: - """ - 获取路由连接信息，提供给上层调试和监控 - """ - try: - router = self._get_router() - return router.get_connections_info() - except Exception as e: - self.logger.error(f"Failed to get routing info: {e}") - return {} - - # ================== 队列描述符管理方法 ================== - - def set_input_queue_descriptor(self, descriptor: "BaseQueueDescriptor"): - """设置输入队列描述符""" - self.input_qd = descriptor - - def get_input_queue_descriptor(self) -> Optional["BaseQueueDescriptor"]: - """获取输入队列描述符""" - return self.input_qd - - def set_service_response_queue_descriptor(self, descriptor: "BaseQueueDescriptor"): - """设置服务响应队列描述符""" - self._service_response_queue_descriptor = descriptor - self.response_qd = descriptor - - def get_service_response_queue_descriptor(self) -> Optional["BaseQueueDescriptor"]: - """获取服务响应队列描述符""" - return self._service_response_queue_descriptor - - def set_upstream_queue_descriptors(self, descriptors: dict[int, list["BaseQueueDescriptor"]]): - """设置上游队列描述符映射""" - self._upstream_queue_descriptors = descriptors - - def get_upstream_queue_descriptors( - self, - ) -> Optional[dict[int, list["BaseQueueDescriptor"]]]: - """获取上游队列描述符映射""" - return self._upstream_queue_descriptors - - def set_downstream_queue_descriptors(self, descriptors: list[list["BaseQueueDescriptor"]]): - """设置下游队列描述符映射""" - self._downstream_queue_descriptors = descriptors - self.downstream_qds = descriptors - - def get_downstream_queue_descriptors( - self, - ) -> Optional[list[list["BaseQueueDescriptor"]]]: - """获取下游队列描述符映射""" - return self._downstream_queue_descriptors - - def set_service_request_queue_descriptors(self, descriptors: dict[str, "BaseQueueDescriptor"]): - """设置服务请求队列描述符映射""" - self._service_request_queue_descriptors = descriptors - self.service_qds = descriptors - - def get_service_request_queue_descriptors( - self, - ) -> Optional[dict[str, "BaseQueueDescriptor"]]: - """获取服务请求队列描述符映射""" - return self._service_request_queue_descriptors diff --git a/packages/sage-kernel/src/sage/kernel/runtime/dispatcher.py b/packages/sage-kernel/src/sage/kernel/runtime/dispatcher.py deleted file mode 100644 index b1e199fe8c..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/dispatcher.py +++ /dev/null @@ -1,910 +0,0 @@ -import os -import time -from typing import TYPE_CHECKING, Any - -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.fault_tolerance.factory import ( - create_fault_handler_from_config, - create_lifecycle_manager, -) -from sage.kernel.runtime.heartbeat_monitor import HeartbeatMonitor -from sage.kernel.scheduler.api import BaseScheduler -from sage.kernel.utils.helpers import wait_for_all_stopped -from sage.kernel.utils.ray.actor import ActorWrapper -from sage.kernel.utils.ray.ray_utils import ensure_ray_initialized - -if TYPE_CHECKING: - from sage.kernel.api.base_environment import BaseEnvironment - from sage.kernel.runtime.context.service_context import ServiceContext - from sage.kernel.runtime.graph.execution_graph import ExecutionGraph - from sage.kernel.runtime.service.local_service_task import LocalServiceTask - from sage.kernel.runtime.task.local_task import LocalTask - - -# 这个dispatcher可以直接打包传给ray sage daemon service -class Dispatcher: - def __init__(self, graph: "ExecutionGraph", env: "BaseEnvironment"): - self.total_stop_signals = graph.total_stop_signals - self.received_stop_signals = 0 - self.graph = graph - self.env = env - self.name: str = env.name - self.remote = env.platform == "remote" - # self.nodes: Dict[str, Union[ActorHandle, LocalDAGNode]] = {} - self.tasks: dict[str, LocalTask | ActorWrapper] = {} - self.services: dict[str, LocalServiceTask | ActorWrapper] = {} # 存储服务实例 - self.is_running: bool = False - # 记录主环境的初始节点列表（不包括 Service 内部节点） - self.main_env_nodes: set[str] = set() - # HeartbeatMonitor 实例 (监控线程) - self.heartbeat_monitor: HeartbeatMonitor | None = None - - # 容错配置 - self.fault_tolerance_config = { - "enabled": False, # 是否启用容错 - "heartbeat_interval": 5.0, # 心跳间隔 (秒) - "heartbeat_timeout": 15.0, # 超时阈值 (秒) - "max_restart_attempts": 3, # 最大重启次数 - } - - # 对于 remote 环境，先确保 Ray 已初始化，这样 NodeSelector 才能获取节点信息 - if env.platform == "remote": - ensure_ray_initialized() - - # 使用调度器和容错管理器（重构后架构） - # 调度器：纯决策者（返回 PlacementDecision） - # PlacementExecutor：纯执行者（接收决策，执行放置） - # Dispatcher：协调者（决策 → 执行） - - # 初始化调度器 - self.scheduler: BaseScheduler - if hasattr(env, "scheduler") and env.scheduler is not None: - self.scheduler = env.scheduler - else: - from sage.kernel.scheduler.impl import FIFOScheduler - - self.scheduler = FIFOScheduler(platform=env.platform) - - # 初始化放置执行器（由 Dispatcher 持有，不在 Scheduler 中） - from sage.kernel.scheduler.placement import PlacementExecutor - - self.placement_executor = PlacementExecutor() - - # 从 Environment 配置创建容错处理器 - fault_tolerance_config = env.config.get("fault_tolerance", {}) - self.fault_handler = create_fault_handler_from_config(fault_tolerance_config) - self.lifecycle_manager = create_lifecycle_manager() - - self.setup_logging_system() - - # 注入 logger 到容错管理器 - self.fault_handler.logger = self.logger - if hasattr(self.lifecycle_manager, "logger"): - self.lifecycle_manager.logger = self.logger # type: ignore - self.fault_handler.dispatcher = self - - self.logger.info(f"Dispatcher '{self.name}' construction complete") - if fault_tolerance_config: - strategy = fault_tolerance_config.get("strategy", "restart") - self.logger.info(f"Fault tolerance enabled: strategy={strategy}") - if env.platform == "remote": - self.logger.info(f"Dispatcher '{self.name}' is running in remote mode") - - def enable_fault_tolerance( - self, - heartbeat_interval: float = 5.0, - heartbeat_timeout: float = 15.0, - max_restart_attempts: int = 3, - ): - """ - 启用 Remote 环境故障容错 - - Args: - heartbeat_interval: 心跳发送间隔 (秒) - heartbeat_timeout: 心跳超时阈值 (秒) - max_restart_attempts: 最大重启尝试次数 - """ - self.fault_tolerance_config.update( - { - "enabled": True, - "heartbeat_interval": heartbeat_interval, - "heartbeat_timeout": heartbeat_timeout, - "max_restart_attempts": max_restart_attempts, - } - ) - - self.logger.info( - f"🛡️ Fault tolerance enabled: " - f"interval={heartbeat_interval}s, timeout={heartbeat_timeout}s" - ) - - def _init_heartbeat_monitor(self): - """ - 初始化 HeartbeatMonitor 监控线程 - - 在所有任务创建后调用,开始心跳监控 - """ - if not self.fault_tolerance_config["enabled"]: - return - - if self.heartbeat_monitor is not None: - self.logger.warning("HeartbeatMonitor already initialized") - return - - try: - from sage.kernel.runtime.heartbeat_monitor import HeartbeatMonitor - - # 创建 HeartbeatMonitor - self.heartbeat_monitor = HeartbeatMonitor( - dispatcher=self, - check_interval=self.fault_tolerance_config["heartbeat_interval"], - ) - # 启动监控线程 - self.heartbeat_monitor.start() - - self.logger.info("🔍 HeartbeatMonitor started") - - except Exception as e: - self.logger.error(f"❌ Failed to initialize HeartbeatMonitor: {e}", exc_info=True) - - def receive_stop_signal(self): - """ - 接收停止信号并处理 - """ - self.logger.info("Dispatcher received stop signal.") - self.received_stop_signals += 1 - if self.received_stop_signals >= self.total_stop_signals: - self.logger.info( - f"Received all {self.total_stop_signals} stop signals, stopping dispatcher for batch job." - ) - self.cleanup() - return True - else: - return False - - def receive_node_stop_signal(self, node_name: str) -> bool: - """ - 接收单个节点的停止信号 - - Args: - node_name: 停止的节点名称 - - Returns: - bool: 如果所有节点都已停止返回True，否则返回False - """ - self.logger.info(f"Dispatcher received node stop signal from: {node_name}") - - # 检查节点是否存在 - if node_name not in self.tasks: - self.logger.warning(f"Node {node_name} not found in tasks") - return False - - # 如果这是一个源节点，直接通知所有相关的 JoinOperator - self._notify_join_operators_on_source_stop(node_name) - - # 停止并清理指定节点 - try: - # 再次检查节点是否存在（防止竞态条件：在检查后、执行前节点被其他调用删除） - if node_name not in self.tasks: - self.logger.warning( - f"Node {node_name} was already removed from tasks (possible duplicate stop signal)" - ) - return False - - task = self.tasks[node_name] - task.stop() - task.cleanup() - - # 对于 Ray Actor，需要显式 kill 以释放资源 - if self.remote and hasattr(task, "kill_actor"): - kill_success = task.kill_actor(no_restart=True) - self.logger.debug( - f"Kill actor {node_name}: {'success' if kill_success else 'skipped/failed'}" - ) - - # 从任务列表中移除 - del self.tasks[node_name] - - # 通知调度器任务已完成（释放资源） - if hasattr(self.scheduler, "task_completed"): - try: - self.scheduler.task_completed(node_name) - except Exception as scheduler_err: - self.logger.warning( - f"Scheduler task_completed notification failed for {node_name}: {scheduler_err}" - ) - - self.logger.info(f"Node {node_name} stopped and cleaned up") - - except KeyError: - # 节点在处理过程中被其他调用删除（竞态条件） - self.logger.warning( - f"Node {node_name} was removed during stop process (concurrent stop signals)" - ) - return False - except Exception as e: - self.logger.error(f"Error stopping node {node_name}: {e}", exc_info=True) - return False - - # 检查是否所有主 Pipeline 的节点都已停止 - # 使用在 submit() 时记录的 main_env_nodes 集合 - # 只有主环境的节点都停止了，才触发清理 - remaining_main_nodes = [name for name in self.main_env_nodes if name in self.tasks] - - if len(remaining_main_nodes) == 0: - self.logger.info("All main pipeline nodes stopped, batch processing completed") - self.logger.info( - f"Remaining service pipeline nodes: {len(self.tasks)} ({list(self.tasks.keys())})" - ) - self.is_running = False - - # 当所有主节点停止后，清理服务 - if len(self.services) > 0: - self.logger.info( - f"Cleaning up {len(self.services)} services after main pipeline completed" - ) - self._cleanup_services_after_batch_completion() - - return True - else: - # 统计不同类型的节点数量用于日志 - service_nodes = [name for name in self.tasks.keys() if name not in self.main_env_nodes] - self.logger.info( - f"Remaining main pipeline nodes: {len(remaining_main_nodes)} {remaining_main_nodes}, " - f"service pipeline nodes: {len(service_nodes)}, " - f"total tasks: {len(self.tasks)}, services: {len(self.services)}" - ) - return False - - def _notify_join_operators_on_source_stop(self, source_node_name: str): - """当源节点停止时，直接通知相关的 JoinOperator""" - # 检查是否是源节点（以 "Source" 开头） - if not source_node_name.startswith("Source"): - return - - # 查找所有的 JoinOperator - for task_name, task in self.tasks.items(): - if ( - hasattr(task, "operator") - and hasattr(task.operator, "handle_stop_signal") - and hasattr(task.operator, "__class__") - and "JoinOperator" in task.operator.__class__.__name__ - ): - # 这是一个 JoinOperator，创建一个停止信号并直接发送 - from sage.kernel.runtime.communication.packet import StopSignal - - stop_signal = StopSignal(source_node_name) - - try: - # 直接调用 JoinOperator 的 handle_stop_signal 方法 - if hasattr(task.operator, "handle_stop_signal"): - task.operator.handle_stop_signal(stop_signal) # type: ignore - self.logger.info( - f"Notified JoinOperator {task_name} about source {source_node_name} stopping" - ) - except Exception as e: - self.logger.error(f"Failed to notify JoinOperator {task_name}: {e}") - - def _cleanup_services_after_batch_completion(self): - """在批处理完成后清理所有服务""" - self.logger.info("Cleaning up services after batch completion") - - if self.remote: - # 清理 Ray 服务 (使用生命周期管理器) - # 明确禁止 Ray Actor 重启，确保完全清理 - try: - self.lifecycle_manager.cleanup_all( - tasks={}, services=self.services, cleanup_timeout=5.0, no_restart=True - ) - except Exception as e: - self.logger.error(f"Error cleaning up Ray services: {e}") - else: - # 清理本地服务 - for service_name, service_task in list(self.services.items()): - try: - # 先停止服务（如果还在运行） - if hasattr(service_task, "is_running") and service_task.is_running: - self.logger.debug(f"Stopping service task: {service_name}") - if hasattr(service_task, "stop"): - service_task.stop() - - # 清理服务（无论是否在运行） - if hasattr(service_task, "cleanup"): - self.logger.debug(f"Cleaning up service task: {service_name}") - service_task.cleanup() - - self.logger.info(f"Service task '{service_name}' cleaned up successfully") - except Exception as e: - self.logger.error(f"Error cleaning up service task {service_name}: {e}") - - # 清空服务字典 - self.services.clear() - self.logger.info("All services cleaned up") - - def _preinitialize_queue_descriptors(self): - """ - 在任务启动前预初始化所有队列描述符 - - 这个方法遍历所有任务的上下文,访问队列描述符的queue_instance属性, - 强制在主进程/driver context中完成Ray Actor的创建和初始化, - 避免在Ray Task执行期间懒初始化导致的死锁问题。 - """ - self.logger.info("Pre-initializing all queue descriptors to avoid deadlocks...") - import time - - start_time = time.time() - - initialized_qds = set() # 使用集合记录已初始化的队列(通过id去重) - - # 遍历所有任务,初始化其上下文中的队列描述符 - for node_name, task in self.tasks.items(): - if not hasattr(task, "ctx") or task.ctx is None: - continue - - ctx = task.ctx - qd_list = [] - - # 收集输入队列描述符 - if hasattr(ctx, "input_qd") and ctx.input_qd is not None: - qd_list.append(("input_qd", ctx.input_qd)) - - # 收集响应队列描述符 - if hasattr(ctx, "response_qd") and ctx.response_qd is not None: - qd_list.append(("response_qd", ctx.response_qd)) - - # 收集所有下游连接的队列描述符 - if hasattr(ctx, "downstream_groups"): - for output_index, connections in ctx.downstream_groups.items(): - for parallel_idx, connection in connections.items(): - if hasattr(connection, "queue_descriptor"): - qd_list.append( - ( - f"downstream_{output_index}_{parallel_idx}", - connection.queue_descriptor, - ) - ) - - # 初始化这些队列描述符 - for qd_name, qd in qd_list: - qd_id = id(qd) - if qd_id not in initialized_qds: - try: - # 访问queue_instance属性触发初始化 - _ = qd.queue_instance - initialized_qds.add(qd_id) - self.logger.debug( - f"Initialized queue descriptor: {qd_name} for task {node_name}" - ) - except Exception as e: - self.logger.warning( - f"Failed to pre-initialize {qd_name} for {node_name}: {e}" - ) - - elapsed = time.time() - start_time - self.logger.info( - f"Queue descriptor pre-initialization completed: {len(initialized_qds)} unique queues initialized in {elapsed:.3f}s" - ) - - def setup_logging_system(self): - base_dir = self.env.env_base_dir if self.env.env_base_dir is not None else "." - self.logger = CustomLogger( - [ - ("console", "INFO"), # 控制台显示重要信息 - ( - os.path.join(base_dir, "Dispatcher.log"), - "DEBUG", - ), # 详细日志 - (os.path.join(base_dir, "Error.log"), "ERROR"), # 错误日志 - ], - name=f"Dispatcher_{self.name}", - ) - - def start(self): - # 第三步：启动所有服务任务 - for service_name, service_task in self.services.items(): - try: - if hasattr(service_task, "start_running"): - service_task.start_running() - elif hasattr(service_task, "_actor"): - # ActorWrapper包装的服务 (_actor 是 ActorWrapper 的属性) - import ray - - actor_ref = service_task._actor # type: ignore[attr-defined] - if hasattr(actor_ref, "start_running"): - ray.get(actor_ref.start_running.remote()) # type: ignore - self.logger.debug(f"Started service task: {service_name}") - except Exception as e: - self.logger.error( - f"Failed to start service task {service_name}: {e}", exc_info=True - ) - - # 第四步：提交所有节点开始运行 - task_list = list(self.tasks.items()) - self.logger.info( - f"Preparing to start {len(task_list)} tasks: {[name for name, _ in task_list]}" - ) - - for node_name, task in task_list: - try: - self.logger.debug(f"Starting node: {node_name} (type: {type(task).__name__})") - task.start_running() - self.logger.debug(f"Started node: {node_name}") - except Exception as e: - self.logger.error(f"Failed to start node {node_name}: {e}", exc_info=True) - - self.logger.info( - f"Job submission completed: {len(self.tasks)} nodes, {len(self.services)} service tasks" - ) - if self.fault_tolerance_config["enabled"] and self.remote: - self._init_heartbeat_monitor() - self.is_running = True - - def _create_service_context(self, service_name: str) -> "ServiceContext | None": - """ - 获取service task的ServiceContext（从execution graph中已创建的service node获取） - - Args: - service_name: 服务名称 - - Returns: - 从execution graph中获取的ServiceContext，如果未找到则返回 None - """ - try: - # 从execution graph的service_nodes中查找对应的service_node - service_node = None - for _node_name, node in self.graph.service_nodes.items(): - # 通过service_factory的名称匹配 - if ( - hasattr(node, "service_factory") - and node.service_factory - and node.service_factory.service_name == service_name - ): - service_node = node - break - - if service_node is None: - self.logger.error( - f"Service node for service '{service_name}' not found in execution graph" - ) - return None - - # 直接返回已经创建好的ServiceContext - if not hasattr(service_node, "ctx") or service_node.ctx is None: - self.logger.error( - f"ServiceContext not found in service node for service '{service_name}'" - ) - return None - - self.logger.debug( - f"Retrieved ServiceContext for service '{service_name}' from execution graph" - ) - return service_node.ctx - - except Exception as e: - self.logger.error( - f"Failed to retrieve ServiceContext for service {service_name}: {e}", - exc_info=True, - ) - return None - - # Dispatcher will submit the job to LocalEngine or Ray Server. - def submit(self): - """ - 编译图结构，创建节点并建立连接 - - 重构后的流程： - 1. 调用 Scheduler 获取调度决策 - 2. 根据决策等待（如果需要延迟调度） - 3. 调用 PlacementExecutor 执行物理放置 - 4. 启动所有任务 - """ - self.logger.info(f"Compiling Job for graph: {self.name}") - - # 第一步：调度所有服务任务 - for service_node_name, service_node in self.graph.service_nodes.items(): - service_name = None - try: - service_name = service_node.service_name - - # 为service创建专用的runtime context - service_ctx = self._create_service_context(service_name) - - # === 新架构：Scheduler → Decision → Placement === - # 1. 获取调度决策 - decision = self.scheduler.make_service_decision(service_node) - - self.logger.debug(f"Service scheduling decision for '{service_name}': {decision}") - - # 2. 根据决策等待（如果需要延迟） - if decision.delay > 0: - self.logger.debug( - f"Delaying service placement of '{service_name}' by {decision.delay}s" - ) - time.sleep(decision.delay) - - # 3. 执行物理放置 - service_task = self.placement_executor.place_service( - service_node=service_node, - decision=decision, - runtime_ctx=service_ctx, - ) - self.services[service_name] = service_task - - self.logger.debug( - f"Placed service task '{service_name}' of type '{service_task.__class__.__name__}'" - ) - except Exception as e: - error_service = service_name if service_name else service_node_name - self.logger.error( - f"Failed to schedule and place service task {error_service}: {e}", - exc_info=True, - ) - # 可以选择继续或停止，这里选择继续但记录错误 - - # 第二步：调度所有计算任务节点 - # 记录主环境的节点名称（用于停止检测） - # - # 问题：self.graph.nodes 包含所有节点（主 Pipeline + Service Pipeline 内部节点） - # 解决：通过节点名称模式识别主 Pipeline 节点（白名单策略） - # - # Service Pipeline 的所有节点（Source/Sink + 内部 Map 节点）都不应该被计入主节点 - # 策略：只保留不属于任何 Service Pipeline 的节点 - # - # 判断方法：检查节点的 operator 类型 - # - PipelineServiceSource/Sink: Service Pipeline 边界 - # - 其他 Batch/Map：需要进一步判断 - # - # 更简单的方法：记录在 Service Pipeline 添加前的节点数 - # 但由于 Service Pipeline 先创建，我们需要反向识别 - # - # 最可靠的方法：检查节点是否在某个 Service Pipeline 的 source→sink 路径上 - # 但这需要图遍历，太复杂 - # - # 实用策略：先记录所有节点，submit 完成后只保留主 Pipeline 节点 - # 但我们在这里无法知道哪些是主节点... - # - # 换个角度：所有节点共享同一个 env，我们需要在用户代码中标记 - # 或者：Service Pipeline 的节点都会连接到 PipelineServiceSource/Sink - # - # 最终方案：递归查找所有连接到 PipelineServiceSource/Sink 的节点 - service_pipeline_nodes = set() - - # 找出所有 PipelineServiceSource 和 PipelineServiceSink 节点 - for node_name in self.graph.nodes.keys(): - if node_name.startswith("PipelineServiceSource") or node_name.startswith( - "PipelineServiceSink" - ): - service_pipeline_nodes.add(node_name) - - # 递归找出所有连接到这些节点的节点 - # 使用 BFS 遍历图 - from collections import deque - - queue = deque(service_pipeline_nodes) - visited = set(service_pipeline_nodes) - - while queue: - current_node = queue.popleft() - # 查找所有连接到当前节点的边 - for edge_name, edge in self.graph.edges.items(): - # 如果边的起点或终点是当前节点，将另一端添加到 service_pipeline_nodes - if ( - edge.upstream_node.name == current_node - and edge.downstream_node - and edge.downstream_node.name not in visited - ): - service_pipeline_nodes.add(edge.downstream_node.name) - visited.add(edge.downstream_node.name) - queue.append(edge.downstream_node.name) - elif ( - edge.downstream_node - and edge.downstream_node.name == current_node - and edge.upstream_node.name not in visited - ): - service_pipeline_nodes.add(edge.upstream_node.name) - visited.add(edge.upstream_node.name) - queue.append(edge.upstream_node.name) - - # 主节点 = 所有节点 - Service Pipeline 节点 - self.main_env_nodes = { - name for name in self.graph.nodes.keys() if name not in service_pipeline_nodes - } - self.logger.info( - f"Main environment nodes (total: {len(self.main_env_nodes)}): {self.main_env_nodes}" - ) - self.logger.info( - f"Service pipeline nodes (total: {len(service_pipeline_nodes)}): {service_pipeline_nodes}" - ) - - for node_name, graph_node in self.graph.nodes.items(): - try: - # === 新架构：Scheduler → Decision → Placement === - # 注入 dispatcher 引用到 context (用于容错处理) - # ctx 在此时已经被创建，不会为 None - graph_node.ctx.dispatcher = self # type: ignore[union-attr] - - # 1. 获取调度决策 - decision = self.scheduler.make_decision(graph_node) - - self.logger.debug(f"Task scheduling decision for '{node_name}': {decision}") - - # 2. 根据决策等待（如果需要延迟调度） - if decision.delay > 0: - self.logger.debug( - f"Delaying task placement of '{node_name}' by {decision.delay}s" - ) - time.sleep(decision.delay) - - # 3. 执行物理放置 - task = self.placement_executor.place_task( - task_node=graph_node, decision=decision, runtime_ctx=graph_node.ctx - ) - self.tasks[node_name] = task - - self.logger.debug( - f"Placed task '{node_name}' of type '{task.__class__.__name__}' " - f"on node '{decision.target_node or 'default'}'" - ) - except Exception as e: - self.logger.error( - f"Failed to schedule and place task {node_name}: {e}", exc_info=True - ) - raise e - - # 连接关系已经在execution graph层通过task context设置好了，无需在此处设置 - - # 预初始化所有队列描述符(防止在Ray Task内部懒初始化导致死锁) - self._preinitialize_queue_descriptors() - - try: - self.start() - except Exception as e: - self.logger.error(f"Error starting dispatcher: {e}", exc_info=True) - raise e - - def stop(self): - """停止所有任务和服务""" - if self.heartbeat_monitor is not None: - self.logger.info("🔍 Stopping HeartbeatMonitor...") - self.heartbeat_monitor.stop() - self.heartbeat_monitor = None - - if not self.is_running: - self.logger.warning("Dispatcher is not running") - return - - self.logger.info(f"Stopping dispatcher '{self.name}'") - - # 发送停止信号给所有任务 - for node_name, node_instance in self.tasks.items(): - try: - node_instance.stop() - self.logger.debug(f"Sent stop signal to node: {node_name}") - except Exception as e: - self.logger.error(f"Error stopping node {node_name}: {e}") - - # 停止所有服务任务 - for service_name, service_task in self.services.items(): - try: - service_task.stop() - self.logger.debug(f"Stopped service task: {service_name}") - except Exception as e: - self.logger.error(f"Error stopping service task {service_name}: {e}") - - # 等待所有任务停止（最多等待10秒） - self._wait_for_tasks_stop(timeout=10.0) - - self.is_running = False - self.logger.info("Dispatcher stopped") - - def _wait_for_tasks_stop(self, timeout: float = 10.0): - """等待所有任务停止""" - wait_for_all_stopped(self.tasks, timeout=timeout, logger=self.logger) - - def cleanup(self): - """清理所有资源""" - self.logger.info(f"Cleaning up dispatcher '{self.name}'") - - try: - # 停止所有任务和服务 - if self.is_running: - self.stop() - - self.logger.info( - f"Cleanup: remote={self.remote}, tasks={len(self.tasks)}, services={len(self.services)}" - ) - - if self.remote: - # 使用生命周期管理器清理所有Ray资源 - # 明确禁止 Ray Actor 重启，确保完全清理 - self.logger.info("Using lifecycle_manager to cleanup Ray actors...") - results = self.lifecycle_manager.cleanup_all( - tasks=self.tasks, services=self.services, cleanup_timeout=5.0, no_restart=True - ) - # 记录清理结果 - for task_id, (cleanup_ok, kill_ok) in results.items(): - self.logger.info( - f" Cleanup result for {task_id}: cleanup={cleanup_ok}, kill={kill_ok}" - ) - else: - # 清理本地任务（使用列表副本避免迭代时字典大小改变） - for node_name, task in list(self.tasks.items()): - try: - task.cleanup() - self.logger.debug(f"Cleaned up task: {node_name}") - except Exception as e: - self.logger.error(f"Error cleaning up task {node_name}: {e}") - - # 清理本地服务任务（使用列表副本避免迭代时字典大小改变） - for service_name, service_task in list(self.services.items()): - try: - if hasattr(service_task, "cleanup"): - service_task.cleanup() - self.logger.debug(f"Cleaned up service task: {service_name}") - except Exception as e: - self.logger.error(f"Error cleaning up service task {service_name}: {e}") - - # 清空任务和服务字典 - self.tasks.clear() - self.services.clear() - - self.logger.info("Dispatcher cleanup completed") - - except Exception as e: - self.logger.error(f"Error during dispatcher cleanup: {e}") - - def get_task_status(self) -> dict[str, Any]: - """获取所有任务的状态""" - status = {} - - for node_name, task in self.tasks.items(): - try: - task_status = { - "name": node_name, - "running": getattr(task, "is_running", False), - "processed_count": getattr(task, "_processed_count", 0), - "error_count": getattr(task, "_error_count", 0), - } - status[node_name] = task_status - except Exception as e: - status[node_name] = {"name": node_name, "error": str(e)} - - return status - - def get_service_status(self) -> dict[str, Any]: - """获取所有服务任务的状态""" - status = {} - - for service_name, service_task in self.services.items(): - try: - if hasattr(service_task, "get_statistics"): - service_status = service_task.get_statistics() - elif hasattr(service_task, "_actor"): - # ActorWrapper包装的服务 (_actor 是 ActorWrapper 的属性) - actor_ref = service_task._actor # type: ignore[attr-defined] - if hasattr(actor_ref, "get_statistics"): - service_status = actor_ref.get_statistics() # type: ignore - else: - service_status = { - "service_name": service_name, - "type": service_task.__class__.__name__, - "status": "unknown", - } - else: - service_status = { - "service_name": service_name, - "type": service_task.__class__.__name__, - "status": "unknown", - } - status[service_name] = service_status - except Exception as e: - status[service_name] = {"service_name": service_name, "error": str(e)} - - return status - - def restart_task(self, task_id: str, restore_state: dict | None = None) -> bool: - """ - 重启任务（用于容错恢复） - - Args: - task_id: 要重启的任务 ID - restore_state: 可选的状态，如果提供则在启动前恢复 - - Returns: - True 如果重启成功 - """ - self.logger.info(f"🔄 Restarting task {task_id}") - - if task_id not in self.tasks: - self.logger.error(f"❌ Task {task_id} not found") - return False - - try: - task = self.tasks[task_id] - - # === 步骤 1: 停止旧任务 === - if hasattr(task, "is_running") and task.is_running: - self.logger.debug(f"Stopping old task {task_id}...") - if hasattr(task, "stop"): - task.stop() - - # 等待任务停止 - import time - - max_wait = 5.0 - waited = 0.0 - while hasattr(task, "is_running") and task.is_running and waited < max_wait: - time.sleep(0.1) - waited += 0.1 - - if hasattr(task, "is_running") and task.is_running: - self.logger.warning(f"⚠️ Task {task_id} did not stop gracefully") - - # === 步骤 2: 清理旧任务资源 === - if hasattr(task, "cleanup"): - try: - self.logger.debug(f"Cleaning up old task {task_id}...") - task.cleanup() - except Exception as cleanup_error: - self.logger.warning(f"⚠️ Error during cleanup: {cleanup_error}") - - # === 步骤 3: 获取 graph node 并重新创建任务 === - graph_node = self.graph.nodes.get(task_id) - if not graph_node: - self.logger.error(f"❌ Graph node for {task_id} not found") - return False - - # 重新注入 dispatcher 引用到 context - if graph_node.ctx is not None: - graph_node.ctx.dispatcher = self - - self.logger.debug(f"Creating new task instance for {task_id}...") - - decision = self.scheduler.make_decision(graph_node) - new_task = self.placement_executor.place_task( - task_node=graph_node, decision=decision, runtime_ctx=graph_node.ctx - ) - - # 替换旧任务 - self.tasks[task_id] = new_task - - self.logger.info(f"✅ New task instance created for {task_id}") - - # === 步骤 4: 如果有状态，先恢复状态 === - if restore_state and hasattr(new_task, "restore_state"): - self.logger.debug(f"Restoring state for {task_id}...") - try: - new_task.restore_state(restore_state) - self.logger.info(f"✅ State restored for task {task_id}") - except Exception as restore_error: - self.logger.error( - f"❌ Failed to restore state for {task_id}: {restore_error}", - exc_info=True, - ) - return False - - # === 步骤 5: 启动新任务 === - self.logger.debug(f"Starting new task {task_id}...") - new_task.start_running() - - self.logger.info(f"🎉 Task {task_id} restarted successfully") - return True - - except Exception as e: - self.logger.error(f"❌ Failed to restart task {task_id}: {e}", exc_info=True) - return False - - def restart_task_with_state(self, task_id: str, state: dict) -> bool: - """ - 重启任务并恢复状态（专门用于 checkpoint 恢复） - - 这是 restart_task 的便捷方法，明确表示要恢复状态 - - Args: - task_id: 要重启的任务 ID - state: 要恢复的状态 - - Returns: - True 如果重启和恢复成功 - """ - return self.restart_task(task_id, restore_state=state) diff --git a/packages/sage-kernel/src/sage/kernel/runtime/execution_utils/__init__.py b/packages/sage-kernel/src/sage/kernel/runtime/execution_utils/__init__.py deleted file mode 100644 index 58c515af61..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/execution_utils/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -""" -SAGE - Streaming-Augmented Generative Execution -""" - -# 直接从本包的_version模块加载版本信息 -try: - from sage.kernel._version import __author__, __email__, __version__ -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" diff --git a/packages/sage-kernel/src/sage/kernel/runtime/execution_utils/name_server.py b/packages/sage-kernel/src/sage/kernel/runtime/execution_utils/name_server.py deleted file mode 100644 index 1cd0cd83b4..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/execution_utils/name_server.py +++ /dev/null @@ -1,90 +0,0 @@ -import threading - - -class NameServer: - """简单的名称服务器，确保对象名称唯一性""" - - _registered_names: set[str] = set() - _name_counters: dict = {} - _lock = threading.RLock() - - @classmethod - def register_name(cls, name: str) -> str: - """ - 注册一个名称，如果冲突则自动添加数字后缀 - - Args: - name: 期望的名称 - - Returns: - 处理完冲突后的唯一名称 - """ - if not name or not name.strip(): - raise ValueError("名称不能为空") - - name = name.strip() - - with cls._lock: - # 如果名称不冲突，直接注册 - if name not in cls._registered_names: - cls._registered_names.add(name) - return name - - # 处理名称冲突，添加数字后缀 - counter = cls._name_counters.get(name, 0) - while True: - counter += 1 - candidate = f"{name}_{counter}" - if candidate not in cls._registered_names: - cls._registered_names.add(candidate) - cls._name_counters[name] = counter - return candidate - - @classmethod - def unregister_name(cls, name: str) -> bool: - """ - 注销一个名称 - - Args: - name: 要注销的名称 - - Returns: - 是否成功注销 - """ - with cls._lock: - if name in cls._registered_names: - cls._registered_names.remove(name) - return True - return False - - @classmethod - def is_name_available(cls, name: str) -> bool: - """检查名称是否可用""" - with cls._lock: - return name not in cls._registered_names - - @classmethod - def clear_all(cls) -> None: - """清空所有注册的名称""" - with cls._lock: - cls._registered_names.clear() - cls._name_counters.clear() - - @classmethod - def get_registered_count(cls) -> int: - """获取已注册名称数量""" - with cls._lock: - return len(cls._registered_names) - - -def get_name(base: str) -> str: - """ - 获取一个唯一名称，是对 NameServer.register_name 的简洁封装。 - - Args: - base: 基础名称，如 "retriever" - - Returns: - 唯一化后的名称，如 "retriever_2" - """ - return NameServer.register_name(base) diff --git a/packages/sage-kernel/src/sage/kernel/runtime/factory/__init__.py b/packages/sage-kernel/src/sage/kernel/runtime/factory/__init__.py deleted file mode 100644 index 58c515af61..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/factory/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -""" -SAGE - Streaming-Augmented Generative Execution -""" - -# 直接从本包的_version模块加载版本信息 -try: - from sage.kernel._version import __author__, __email__, __version__ -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" diff --git a/packages/sage-kernel/src/sage/kernel/runtime/factory/function_factory.py b/packages/sage-kernel/src/sage/kernel/runtime/factory/function_factory.py deleted file mode 100644 index 6d8a932e16..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/factory/function_factory.py +++ /dev/null @@ -1,36 +0,0 @@ -from typing import TYPE_CHECKING, Any - -from sage.common.core import BaseFunction -from sage.common.utils.logging.custom_logger import CustomLogger - -if TYPE_CHECKING: - from sage.kernel.runtime.context.task_context import TaskContext - - -class FunctionFactory: - # 由transformation初始化 - def __init__( - self, - function_class: type[BaseFunction], - function_args: tuple[Any, ...] = (), - function_kwargs: dict | None = None, - ): - self.function_class = function_class - self.function_args = function_args - self.function_kwargs = function_kwargs or {} - - def create_function(self, name: str, ctx: "TaskContext") -> BaseFunction: - """创建函数实例""" - # print(f"🏭 FunctionFactory.create_function: function_class={self.function_class}, args={self.function_args}, kwargs={self.function_kwargs}") - if CustomLogger.is_global_console_debug_enabled(): - print(self.function_args) - print(self.function_kwargs) - # self.function_kwargs["ctx"] = - function = self.function_class(*self.function_args, **self.function_kwargs) - # print(f"🏭 FunctionFactory.create_function: Created function instance: {function}") - function.ctx = ctx - return function - - def __repr__(self) -> str: - function_class_name = getattr(self, "function_class", type(None)).__name__ - return f"<FunctionFactory {function_class_name}>" diff --git a/packages/sage-kernel/src/sage/kernel/runtime/factory/operator_factory.py b/packages/sage-kernel/src/sage/kernel/runtime/factory/operator_factory.py deleted file mode 100644 index f409d35d9d..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/factory/operator_factory.py +++ /dev/null @@ -1,30 +0,0 @@ -from typing import TYPE_CHECKING - -if TYPE_CHECKING: - from sage.kernel.api.operator.base_operator import BaseOperator - from sage.kernel.runtime.context.task_context import TaskContext - from sage.kernel.runtime.factory.function_factory import FunctionFactory - - -class OperatorFactory: - # 由transformation初始化 - def __init__( - self, - operator_class: type["BaseOperator"], - function_factory: "FunctionFactory", - env_name: str | None = None, - remote: bool = False, - **operator_kwargs, - ): - self.operator_class = operator_class - self.operator_kwargs = operator_kwargs # 保存额外的operator参数 - self.function_factory = function_factory - self.env_name = env_name - self.remote = remote - - def create_operator(self, runtime_context: "TaskContext") -> "BaseOperator": - operator_class = self.operator_class - operator_instance = operator_class( - self.function_factory, runtime_context, **self.operator_kwargs - ) - return operator_instance diff --git a/packages/sage-kernel/src/sage/kernel/runtime/factory/service_factory.py b/packages/sage-kernel/src/sage/kernel/runtime/factory/service_factory.py deleted file mode 100644 index 791ce16fcf..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/factory/service_factory.py +++ /dev/null @@ -1,94 +0,0 @@ -from typing import TYPE_CHECKING, Any - -from sage.kernel.runtime.context.context_injection import create_service_with_context - -if TYPE_CHECKING: - from sage.kernel.runtime.context.service_context import ServiceContext - - -class ServiceFactory: - """服务工厂类，用于创建原始服务实例，类似FunctionFactory""" - - def __init__( - self, - service_name: str, - service_class: type, - service_args: tuple[Any, ...] = (), - service_kwargs: dict | None = None, - ): - """ - 初始化服务工厂 - - Args: - service_name: 服务名称 - service_class: 服务类 - service_args: 服务构造参数 - service_kwargs: 服务构造关键字参数 - """ - if not service_name: - raise ValueError("service_name cannot be empty") - if not service_class: - raise ValueError("service_class cannot be None") - - self.service_name = service_name or service_class.__name__ - self.service_class = service_class - print(f"ServiceFactory initialized for {self.service_name} with class {self.service_class}") - self.service_args = service_args - self.service_kwargs = service_kwargs or {} - - def create_service(self, ctx: "ServiceContext | None" = None) -> Any: - """ - 创建服务实例 - - Args: - ctx: 服务运行时上下文 - - Returns: - 创建的服务实例 - """ - # 检查 service_class 是否可用 - if self.service_class is None: - raise ValueError( - f"ServiceFactory for '{self.service_name}': service_class is None. " - "This may be due to serialization issues in distributed environments." - ) - - # 使用通用的上下文注入工具函数 - service = create_service_with_context( - self.service_class, ctx, *self.service_args, **self.service_kwargs - ) - - return service - - def __repr__(self) -> str: - service_name = getattr(self, "service_name", "Unknown") - service_class = getattr(self, "service_class", None) - if service_class is not None: - service_class_name = service_class.__name__ - else: - service_class_name = "Unknown" - return f"<ServiceFactory {service_name}: {service_class_name}>" - - def __getstate__(self): - """为 pickle/Ray 序列化准备状态""" - return { - "service_name": getattr(self, "service_name", None), - "service_class": getattr(self, "service_class", None), - "service_args": getattr(self, "service_args", ()), - "service_kwargs": getattr(self, "service_kwargs", {}), - } - - def __setstate__(self, state): - """从 pickle/Ray 反序列化恢复状态""" - self.service_name = state.get("service_name") - self.service_class = state.get("service_class") - self.service_args = state.get("service_args", ()) - self.service_kwargs = state.get("service_kwargs", {}) - - # 验证必需的属性 - if self.service_name is None: - self.service_name = "Unknown" - if self.service_class is None: - import logging - - logging.warning("ServiceFactory: service_class is None after deserialization") diff --git a/packages/sage-kernel/src/sage/kernel/runtime/factory/service_task_factory.py b/packages/sage-kernel/src/sage/kernel/runtime/factory/service_task_factory.py deleted file mode 100644 index f8b6d1160c..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/factory/service_task_factory.py +++ /dev/null @@ -1,85 +0,0 @@ -import logging -from typing import TYPE_CHECKING - -if TYPE_CHECKING: - from sage.kernel.runtime.context.service_context import ServiceContext - from sage.kernel.runtime.factory.service_factory import ServiceFactory - -logger = logging.getLogger(__name__) - - -class ServiceTaskFactory: - """服务任务工厂，负责创建服务任务（本地或Ray Actor），类似TaskFactory""" - - def __init__( - self, - service_factory: "ServiceFactory", - remote: bool = False, - extra_python_paths: list[str] | None = None, - ): - """ - 初始化服务任务工厂 - - Args: - service_factory: 服务工厂实例 - remote: 是否创建远程服务任务 - extra_python_paths: 额外的 Python 路径，用于 Ray runtime_env - """ - self.service_factory = service_factory - self.service_name = service_factory.service_name - self.remote = remote - - # Extra Python paths for Ray runtime_env - # Must be passed explicitly since env attribute is excluded during serialization - self.extra_python_paths: list[str] = ( - extra_python_paths - if isinstance(extra_python_paths, list) - else ([extra_python_paths] if extra_python_paths else []) - ) - - def create_service_task(self, ctx: "ServiceContext | None" = None): - """ - 参考task_factory.create_task的逻辑，创建服务任务实例 - - Args: - ctx: 服务运行时上下文 - - Returns: - 服务任务实例（LocalServiceTask或ActorWrapper包装的RayServiceTask） - """ - if self.remote: - # 创建Ray服务任务 - from sage.kernel.runtime.service.ray_service_task import RayServiceTask - from sage.kernel.utils.ray.actor import ActorWrapper - - ray_options = {"lifetime": "detached"} - - # Build runtime_env for Ray worker - if self.extra_python_paths: - # Use PYTHONPATH environment variable so Ray workers can find custom modules - runtime_env = {"env_vars": {"PYTHONPATH": ":".join(self.extra_python_paths)}} - ray_options["runtime_env"] = runtime_env - logger.info( - f"[ServiceTaskFactory] Creating RayServiceTask with runtime_env: {runtime_env}" - ) - - # 直接创建Ray Actor，传入ServiceFactory和ctx - ray_service_task = RayServiceTask.options(**ray_options).remote( # type: ignore[attr-defined] - self.service_factory, ctx - ) - - # 使用ActorWrapper包装 - service_task = ActorWrapper(ray_service_task) - - else: - # 创建本地服务任务 - from sage.kernel.runtime.service.local_service_task import LocalServiceTask - - service_task = LocalServiceTask(self.service_factory, ctx) # type: ignore - - return service_task - - def __repr__(self) -> str: - remote_str = "Remote" if getattr(self, "remote", False) else "Local" - service_name = getattr(self, "service_name", "Unknown") - return f"<ServiceTaskFactory {service_name} ({remote_str})>" diff --git a/packages/sage-kernel/src/sage/kernel/runtime/factory/task_factory.py b/packages/sage-kernel/src/sage/kernel/runtime/factory/task_factory.py deleted file mode 100644 index cf36015d4c..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/factory/task_factory.py +++ /dev/null @@ -1,65 +0,0 @@ -import logging -from typing import TYPE_CHECKING - -from sage.kernel.runtime.task.local_task import LocalTask -from sage.kernel.runtime.task.ray_task import RayTask -from sage.kernel.utils.ray.actor import ActorWrapper - -if TYPE_CHECKING: - from sage.kernel.api.transformation.base_transformation import BaseTransformation - from sage.kernel.runtime.context.task_context import TaskContext - -logger = logging.getLogger(__name__) - - -class TaskFactory: - def __init__( - self, - transformation: "BaseTransformation", - extra_python_paths: list[str] | None = None, - ): - self.basename = transformation.basename - self.env_name = transformation.env_name - self.operator_factory = transformation.operator_factory - self.delay = transformation.delay - self.remote: bool = transformation.remote - self.is_spout = transformation.is_spout - - # Extra Python paths for Ray runtime_env - # Must be passed explicitly since env attribute is excluded during serialization - self.extra_python_paths: list[str] = ( - extra_python_paths - if isinstance(extra_python_paths, list) - else ([extra_python_paths] if extra_python_paths else []) - ) - - def create_task( - self, - name: str, - runtime_context: "TaskContext | None" = None, - ): - if self.remote: - # Build runtime_env for Ray worker - runtime_env = {} - if self.extra_python_paths: - # Use PYTHONPATH environment variable so Ray workers can find custom modules - runtime_env["env_vars"] = {"PYTHONPATH": ":".join(self.extra_python_paths)} - logger.info(f"[TaskFactory] Creating RayTask with runtime_env: {runtime_env}") - - # Pass runtime_env when creating Ray Actor - if runtime_env: - node = RayTask.options( - lifetime="detached", - runtime_env=runtime_env, - ).remote(runtime_context, self.operator_factory) - else: - node = RayTask.options(lifetime="detached").remote( - runtime_context, self.operator_factory - ) - node = ActorWrapper(node) - else: - node = LocalTask(ctx=runtime_context, operator_factory=self.operator_factory) # type: ignore - return node - - def __repr__(self) -> str: - return f"<TaskFactory {self.basename}>" diff --git a/packages/sage-kernel/src/sage/kernel/runtime/graph/__init__.py b/packages/sage-kernel/src/sage/kernel/runtime/graph/__init__.py deleted file mode 100644 index 58c515af61..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/graph/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -""" -SAGE - Streaming-Augmented Generative Execution -""" - -# 直接从本包的_version模块加载版本信息 -try: - from sage.kernel._version import __author__, __email__, __version__ -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" diff --git a/packages/sage-kernel/src/sage/kernel/runtime/graph/execution_graph.py b/packages/sage-kernel/src/sage/kernel/runtime/graph/execution_graph.py deleted file mode 100644 index c58f9c0764..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/graph/execution_graph.py +++ /dev/null @@ -1,380 +0,0 @@ -""" -ExecutionGraph - 执行图类 - -ExecutionGraph管理整个图的构建和运行时上下文，包含： -- 图节点和服务节点的管理 -- 队列描述符的创建和分发 -- 运行时上下文的生成 -- 图结构的构建 -""" - -from __future__ import annotations - -import os -from typing import TYPE_CHECKING - -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.api.base_environment import BaseEnvironment -from sage.kernel.api.transformation.base_transformation import BaseTransformation -from sage.kernel.runtime.context.service_context import ServiceContext -from sage.kernel.runtime.context.task_context import TaskContext -from sage.kernel.runtime.execution_utils.name_server import get_name - -if TYPE_CHECKING: - from sage.platform.queue.base_queue_descriptor import ( - BaseQueueDescriptor, - ) - -from .graph_edge import GraphEdge -from .graph_node import TaskNode -from .service_node import ServiceNode - - -class ExecutionGraph: - """ - 执行图类 - - 负责管理整个执行图的构建、队列描述符创建和运行时上下文生成 - """ - - def __init__(self, env: BaseEnvironment): - self.env = env - self.nodes: dict[str, TaskNode] = {} - self.service_nodes: dict[str, ServiceNode] = {} # 存储服务节点 - self.edges: dict[str, GraphEdge] = {} - # 初始化映射表 - self.service_request_qds: dict[str, BaseQueueDescriptor] = {} - self.service_response_qds: dict[str, BaseQueueDescriptor] = {} - - # 首先设置日志系统 - self._setup_logging_system() - - # 构建基础图结构 - self._build_task_nodes(env) - # 构建服务节点 - self._build_service_nodes(env) - # 提取队列描述符映射表 - self._extract_queue_descriptor_mappings() - # 生成运行时上下文（队列描述符和连接关系都在Context构造函数中处理） - self._generate_runtime_contexts() - - # 停止信号相关 - self._calculate_source_dependencies() - self.total_stop_signals = self._calculate_total_stop_signals() - - self.logger.info( - f"Successfully converted and optimized pipeline '{env.name}' to compiler with " - f"{len(self.nodes)} transformation nodes, {len(self.service_nodes)} service nodes " - f"and {len(self.edges)} edges" - ) - - def _calculate_total_stop_signals(self): - """计算所有源节点的停止信号总数""" - total_signals = 0 - for node in self.nodes.values(): - if node.is_sink: - total_signals += node.stop_signal_num - return total_signals - - def _setup_logging_system(self): - """设置日志系统，支持模拟环境""" - try: - # 获取控制台日志级别，如果不存在则使用默认值 - console_log_level = getattr(self.env, "console_log_level", "INFO") - - # 获取环境基础目录，如果不存在则使用临时目录 - env_base_dir = getattr(self.env, "env_base_dir", "/tmp") - - # 获取环境名称，如果不存在则使用默认名称 - env_name = getattr(self.env, "name", "unknown_env") - - self.logger = CustomLogger( - [ - ("console", console_log_level), # 使用环境设置的控制台日志等级 - ( - os.path.join(env_base_dir, "ExecutionGraph.log"), - "DEBUG", - ), # 详细日志 - (os.path.join(env_base_dir, "Error.log"), "ERROR"), # 错误日志 - ], - name=f"ExecutionGraph_{env_name}", - ) - except Exception: - # 如果设置日志系统失败，创建一个基础的日志器 - self.logger = CustomLogger([("console", "INFO")], name="ExecutionGraph_fallback") - - def _generate_runtime_contexts(self): - """ - 为每个节点生成运行时上下文 - 队列描述符和连接关系都在Context构造函数中处理，这里只需创建Context即可 - """ - self.logger.debug("Generating runtime contexts for all nodes") - - # 为流水线节点创建运行时上下文 - for node_name, node in self.nodes.items(): - try: - # 创建TaskContext，所有队列描述符和连接关系都在构造函数中处理 - node.ctx = TaskContext(node, node.transformation, self.env, execution_graph=self) - - input_queues_info = "1 input queue" if node.input_qd else "no input queue (spout)" - self.logger.debug( - f"Generated runtime context with {input_queues_info} for transformation node: {node_name}" - ) - except Exception as e: - self.logger.error( - f"Failed to generate runtime context for node {node_name}: {e}", - exc_info=True, - ) - - # 为服务节点创建运行时上下文 - for service_name, service_node in self.service_nodes.items(): - try: - # 创建ServiceContext，所有队列描述符都在构造函数中处理 - service_node.ctx = ServiceContext(service_node, self.env, execution_graph=self) - - self.logger.debug(f"Generated runtime context for service node: {service_name}") - except Exception as e: - self.logger.error( - f"Failed to generate runtime context for service node {service_name}: {e}", - exc_info=True, - ) - - self.logger.info( - f"Runtime context generation completed: {len(self.nodes)} graph nodes, {len(self.service_nodes)} service nodes" - ) - - def _build_service_nodes(self, env: BaseEnvironment): - """ - 构建服务节点，在ExecutionGraph中创建ServiceTaskFactory - - Args: - env: 环境对象 - """ - self.logger.debug("Building service nodes from environment") - - for service_name, service_factory in env.service_factories.items(): - try: - # 生成唯一的服务节点名称 - service_node_name = get_name(f"service_{service_name}") - - # 在ExecutionGraph中创建ServiceTaskFactory，而不是从环境中获取 - from sage.kernel.runtime.factory.service_task_factory import ( - ServiceTaskFactory, - ) - - # 获取 extra_python_paths 用于 Ray runtime_env - extra_paths = getattr(env, "extra_python_paths", None) - extra_python_paths = ( - extra_paths - if isinstance(extra_paths, list) - else ([extra_paths] if extra_paths else []) - ) - - service_task_factory = ServiceTaskFactory( - service_factory=service_factory, - remote=(env.platform == "remote"), - extra_python_paths=extra_python_paths, - ) - - # 创建服务节点，同时传入ServiceFactory和ServiceTaskFactory - service_node = ServiceNode( - name=service_node_name, - service_factory=service_factory, - service_task_factory=service_task_factory, - env=env, - ) - - # 添加到服务节点字典 - self.service_nodes[service_node_name] = service_node - - platform_str = "remote" if env.platform == "remote" else "local" - self.logger.debug( - f"Created service node: {service_node_name} for service: {service_name} ({platform_str})" - ) - - except Exception as e: - self.logger.error(f"Error creating service node for {service_name}: {e}") - raise - - self.logger.info(f"Created {len(self.service_nodes)} service nodes") - - def _extract_queue_descriptor_mappings(self): - """ - 从所有节点中提取队列描述符映射表 - - service_request_qds: name -> service request queue descriptor - - service_response_qds: name -> service response queue descriptor - """ - self.logger.debug("Extracting queue descriptor mappings from all nodes") - - # 从transformation nodes提取service response queue descriptors - for node_name, node in self.nodes.items(): - if hasattr(node, "service_response_qd") and node.service_response_qd: - self.service_response_qds[node_name] = node.service_response_qd - self.logger.debug( - f"Extracted service response qd from transformation node: {node_name}" - ) - - # 从service nodes提取service request和response queue descriptors - for service_node_name, service_node in self.service_nodes.items(): - # Service request queue (重命名为更清晰的名称) - if hasattr(service_node, "service_qd") and service_node.service_qd: - self.service_request_qds[service_node.service_name] = service_node.service_qd - self.logger.debug( - f"Extracted service request qd from service node: {service_node.service_name}" - ) - - # Service response queue - if hasattr(service_node, "service_response_qd") and service_node.service_response_qd: - self.service_response_qds[service_node_name] = service_node.service_response_qd - self.logger.debug( - f"Extracted service response qd from service node: {service_node_name}" - ) - - self.logger.info( - f"Queue descriptor mappings extracted: " - f"{len(self.service_request_qds)} service request queues, " - f"{len(self.service_response_qds)} service response queues" - ) - - def _build_task_nodes(self, env: BaseEnvironment): - """ - 根据transformation pipeline构建图, 支持并行度和多对多连接 - 分为三步: 1) 生成并行节点 2) 生成物理边 3) 创建图结构 - """ - transformation_to_node: dict[ - str, list[str] - ] = {} # transformation basename -> list of node names - - # 第一步：为每个transformation生成并行节点名字表，同时创建节点 - self.logger.debug("Step 1: Generating parallel nodes for each transformation") - for transformation in env.pipeline: - # 安全检查：如果发现未填充的future transformation，报错 - from sage.kernel.api.transformation.future_transformation import ( - FutureTransformation, - ) - - if isinstance(transformation, FutureTransformation): - if not transformation.filled: - raise RuntimeError( - f"Unfilled future transformation '{transformation.future_name}' in pipeline. " - ) - continue - - node_names = [] - for i in range(transformation.parallelism): - node_name = "" - try: - node_name = get_name(f"{transformation.basename}_{i}") - node_names.append(node_name) - self.nodes[node_name] = TaskNode(node_name, transformation, i, env) - self.logger.debug(f"Created node: {node_name} (parallel index: {i})") - except Exception as e: - error_name = node_name if node_name else f"{transformation.basename}_{i}" - self.logger.error(f"Error creating node {error_name}: {e}") - raise - transformation_to_node[transformation.basename] = node_names - self.logger.debug( - f"Generated {len(node_names)} parallel nodes for {transformation.operator_class.__name__}: {node_names}" - ) - - # 第二步：为每条逻辑边创建物理边并连接节点 - self.logger.debug("Step 2: Creating compiler structure") - - for transformation in env.pipeline: - # 跳过已填充的future transformation（它们在step 1中已被跳过） - from sage.kernel.api.transformation.future_transformation import ( - FutureTransformation, - ) - - if isinstance(transformation, FutureTransformation): - if transformation.filled: - continue - - downstream_nodes = transformation_to_node[transformation.basename] - for upstream_trans in transformation.upstreams: - # 如果上游是已填充的FutureTransformation，使用实际的transformation - actual_upstream_trans: BaseTransformation = upstream_trans - if ( - isinstance(upstream_trans, FutureTransformation) - and upstream_trans.filled - and upstream_trans.actual_transformation is not None - ): - actual_upstream_trans = upstream_trans.actual_transformation - - downstream_input_index = upstream_trans.downstreams[transformation.basename] - upstream_nodes = transformation_to_node[actual_upstream_trans.basename] - - # 创建m*n条物理边 - for i, upstream_node_name in enumerate(upstream_nodes): - upstream_node = self.nodes[upstream_node_name] - output_group_edges: list[GraphEdge] = [] - for _j, downstream_node_name in enumerate(downstream_nodes): - # 创建边名 - edge_name = f"({upstream_node_name})->({downstream_node_name})[{downstream_input_index}]" - - # 获取节点对象 - downstream_node = self.nodes[downstream_node_name] - if downstream_node.input_channels.get(downstream_input_index) is None: - downstream_node.input_channels[downstream_input_index] = [] - - # 创建边对象并连接 - edge = GraphEdge( - name=edge_name, - output_node=upstream_node, - input_node=downstream_node, - input_index=downstream_input_index, - ) - self.logger.debug(f"Creating edge: {edge_name} ") - - # 将边添加到节点的channels中 - output_group_edges.append(edge) - downstream_node.input_channels[downstream_input_index].append(edge) - - # 将边添加到图中 - self.edges[edge_name] = edge - upstream_node.output_channels.append(output_group_edges) - - self.logger.debug( - f"Connected {len(upstream_nodes)}×{len(downstream_nodes)} physical edges " - f"between {upstream_trans.operator_class.__name__} -> " - f"{transformation.operator_class.__name__}" - ) - - self.logger.info( - f"Graph construction completed: {len(self.nodes)} nodes, {len(self.edges)} edges" - ) - - def _calculate_source_dependencies(self): - """计算每个节点的源依赖关系""" - self.logger.debug("Calculating source dependencies for all nodes") - - # 使用广度优先搜索计算每个节点依赖的源节点 - for node_name, node in self.nodes.items(): - if not node.is_spout: - # 非源节点通过BFS收集所有上游源依赖 - visited = set() - queue = [node_name] - source_deps = set() - - while queue: - current_name = queue.pop(0) - if current_name in visited: - continue - visited.add(current_name) - - current_node = self.nodes[current_name] - - if current_node.is_spout: - source_deps.add(current_node.transformation.basename) - node.stop_signal_num += 1 - else: - # 添加所有上游节点到队列 - for input_channel in current_node.input_channels.values(): - for edge in input_channel: - if edge.upstream_node.name not in visited: - queue.append(edge.upstream_node.name) - else: - # 源节点不需要等待停止信号 - node.stop_signal_num = 0 - - self.logger.debug(f"Node {node_name} expects {node.stop_signal_num} stop signals") diff --git a/packages/sage-kernel/src/sage/kernel/runtime/graph/graph_edge.py b/packages/sage-kernel/src/sage/kernel/runtime/graph/graph_edge.py deleted file mode 100644 index 21fb4b3ce4..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/graph/graph_edge.py +++ /dev/null @@ -1,54 +0,0 @@ -""" -GraphEdge - 图边类 - -GraphEdge代表两个节点之间的连接，包含： -- 上游节点和下游节点的引用 -- 输入索引（用于区分下游节点的不同输入通道） -- 队列描述符（现在不再使用，因为队列描述符在节点上） -""" - -from __future__ import annotations - -from typing import TYPE_CHECKING - -if TYPE_CHECKING: - from sage.platform.queue.base_queue_descriptor import BaseQueueDescriptor - - from .graph_node import TaskNode - - -class GraphEdge: - """ - 图边类 - - 表示编译器图中两个节点之间的连接 - """ - - def __init__( - self, - name: str, - output_node: TaskNode, - input_node: TaskNode | None = None, - input_index: int = 0, - ): - """ - 初始化图边 - - Args: - name: 边的名称 - output_node: 上游节点（输出节点） - input_node: 下游节点（输入节点） - input_index: 输入索引，表示连接到下游节点的哪个输入通道 - """ - self.name: str = name - self.upstream_node: TaskNode = output_node - self.downstream_node: TaskNode | None = input_node - self.input_index: int = input_index - - # 队列描述符已不再在边上维护，而是在下游节点上 - # 保留此字段是为了向后兼容，但实际不使用 - self.queue_descriptor: BaseQueueDescriptor | None = None - - def __repr__(self) -> str: - downstream_name = self.downstream_node.name if self.downstream_node else "None" - return f"GraphEdge(name={self.name}, upstream={self.upstream_node.name}, downstream={downstream_name}, input_index={self.input_index})" diff --git a/packages/sage-kernel/src/sage/kernel/runtime/graph/graph_node.py b/packages/sage-kernel/src/sage/kernel/runtime/graph/graph_node.py deleted file mode 100644 index ea515ba0aa..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/graph/graph_node.py +++ /dev/null @@ -1,111 +0,0 @@ -""" -TaskNode - 图节点类 - -每个TaskNode代表一个transformation的单个并行实例，包含： -- 单一输入队列描述符（被所有上游复用） -- 服务响应队列描述符 -- 输入通道和输出通道的连接信息 -- 运行时上下文 -- TaskFactory用于创建任务实例 -""" - -from __future__ import annotations - -from typing import TYPE_CHECKING - -if TYPE_CHECKING: - from sage.kernel.api.base_environment import BaseEnvironment - from sage.kernel.api.transformation.base_transformation import BaseTransformation - from sage.kernel.runtime.context.task_context import TaskContext - from sage.kernel.runtime.factory.task_factory import TaskFactory - from sage.kernel.runtime.graph.graph_edge import GraphEdge - from sage.platform.queue.base_queue_descriptor import BaseQueueDescriptor - - -def _create_queue_descriptor(env: BaseEnvironment, name: str, maxsize: int) -> BaseQueueDescriptor: - """ - 根据环境平台类型创建相应的队列描述符 - - Args: - env: 环境对象 - name: 队列名称 - maxsize: 队列最大大小 - - Returns: - 对应平台的队列描述符 - """ - if env.platform == "remote": - from sage.platform.queue.ray_queue_descriptor import RayQueueDescriptor - - return RayQueueDescriptor(maxsize=maxsize, queue_id=name) - else: # local 或其他情况使用 python 队列 - from sage.platform.queue.python_queue_descriptor import PythonQueueDescriptor - - return PythonQueueDescriptor(maxsize=maxsize, queue_id=name) - - -class TaskNode: - """ - 图节点类 - - 每个TaskNode只有一个输入队列描述符 - 不是每个输入通道一个 - 这个输入队列被所有上游节点复用 - 所有上游都写入同一个队列 - 输入通道只是逻辑概念 - 用于区分不同的输入数据流，但物理上共享同一个队列 - """ - - def __init__( - self, - name: str, - transformation: BaseTransformation, - parallel_index: int, - env: BaseEnvironment, - ): - self.name: str = name - self.transformation: BaseTransformation = transformation - self.parallel_index: int = parallel_index # 在该transformation中的并行索引 - self.parallelism: int = transformation.parallelism - self.is_spout: bool = transformation.is_spout - self.is_sink: bool = transformation.is_sink - self.input_channels: dict[int, list[GraphEdge]] = {} - self.output_channels: list[list[GraphEdge]] = [] - - # 在构造时创建队列描述符 - self._create_queue_descriptors(env) - - # 在ExecutionGraph中创建TaskFactory，而不是在BaseTransformation中 - # 保存 extra_python_paths 用于传递给 TaskFactory - self._extra_python_paths = getattr(env, "extra_python_paths", []) or [] - - # 在ExecutionGraph中创建TaskFactory，而不是在BaseTransformation中 - self.task_factory: TaskFactory = self._create_task_factory() - - self.stop_signal_num: int = 0 # 预期的源节点数量 - self.ctx: TaskContext | None = None - - def _create_queue_descriptors(self, env: BaseEnvironment): - """在节点构造时创建队列描述符""" - # 使用 env.name 作为队列前缀，确保不同 job 的队列隔离 - # env.name 在 Environment 创建时就已确定，且对于同一 pipeline 唯一 - env_prefix = env.name - - # 为每个节点创建单一的输入队列描述符（被所有上游复用） - if not self.is_spout: # 源节点不需要输入队列 - self.input_qd = _create_queue_descriptor( - env=env, name=f"{env_prefix}__input_{self.name}", maxsize=10000 - ) - else: - self.input_qd = None - - # 为每个graph node创建service response queue descriptor - self.service_response_qd = _create_queue_descriptor( - env=env, name=f"{env_prefix}__service_response_{self.name}", maxsize=10000 - ) - - def _create_task_factory(self) -> TaskFactory: - """在TaskNode中创建TaskFactory，避免BaseTransformation依赖runtime层""" - from sage.kernel.runtime.factory.task_factory import TaskFactory - - return TaskFactory(self.transformation, extra_python_paths=self._extra_python_paths) - - def __repr__(self) -> str: - return f"TaskNode(name={self.name}, parallel_index={self.parallel_index}, is_spout={self.is_spout}, is_sink={self.is_sink})" diff --git a/packages/sage-kernel/src/sage/kernel/runtime/graph/service_node.py b/packages/sage-kernel/src/sage/kernel/runtime/graph/service_node.py deleted file mode 100644 index d044ac5976..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/graph/service_node.py +++ /dev/null @@ -1,93 +0,0 @@ -""" -ServiceNode - 服务节点类 - -ServiceNode代表一个服务实例，包含： -- 服务工厂和服务任务工厂 -- 服务队列描述符 -- 服务运行时上下文 -""" - -from __future__ import annotations - -from typing import TYPE_CHECKING - -if TYPE_CHECKING: - from sage.kernel.api.base_environment import BaseEnvironment - from sage.kernel.runtime.context.service_context import ServiceContext - from sage.kernel.runtime.factory.service_factory import ServiceFactory - from sage.kernel.runtime.factory.service_task_factory import ServiceTaskFactory - from sage.platform.queue.base_queue_descriptor import BaseQueueDescriptor - - -def _create_queue_descriptor(env: BaseEnvironment, name: str, maxsize: int) -> BaseQueueDescriptor: - """ - 根据环境平台类型创建相应的队列描述符 - - Args: - env: 环境对象 - name: 队列名称 - maxsize: 队列最大大小 - - Returns: - 对应平台的队列描述符 - """ - if env.platform == "remote": - from sage.platform.queue.ray_queue_descriptor import RayQueueDescriptor - - return RayQueueDescriptor(maxsize=maxsize, queue_id=name) - else: # local 或其他情况使用 python 队列 - from sage.platform.queue.python_queue_descriptor import PythonQueueDescriptor - - return PythonQueueDescriptor(maxsize=maxsize, queue_id=name) - - -class ServiceNode: - """ - 服务节点类 - - 服务节点，简化版本只记录基本信息 - """ - - def __init__( - self, - name: str, - service_factory: ServiceFactory, - service_task_factory: ServiceTaskFactory, - env: BaseEnvironment, - ): - """ - 服务节点构造函数 - - Args: - name: 节点名称 - service_factory: 服务工厂 - service_task_factory: 服务任务工厂 - env: 环境对象 - """ - self.name: str = name - self.service_factory: ServiceFactory = service_factory - self.service_task_factory: ServiceTaskFactory = service_task_factory - self.service_name: str = service_factory.service_name - - # 在构造时创建队列描述符 - self._create_queue_descriptors(env) - - self.ctx: ServiceContext | None = None - - def _create_queue_descriptors(self, env: BaseEnvironment): - """在服务节点构造时创建队列描述符""" - # 使用 env.name 作为队列前缀，确保不同 job 的队列隔离 - env_prefix = env.name - - # 为每个service创建request queue descriptor - self.service_qd = _create_queue_descriptor( - env=env, name=f"{env_prefix}__service_request_{self.service_name}", maxsize=10000 - ) - - # 为每个service node创建service response queue descriptor (与graph node一样) - self.service_response_qd = _create_queue_descriptor( - env=env, name=f"{env_prefix}__service_response_{self.name}", maxsize=10000 - ) - - def __repr__(self) -> str: - return f"ServiceNode(name={self.name}, service_name={self.service_name})" diff --git a/packages/sage-kernel/src/sage/kernel/runtime/heartbeat_monitor.py b/packages/sage-kernel/src/sage/kernel/runtime/heartbeat_monitor.py deleted file mode 100644 index 88cc1ffacf..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/heartbeat_monitor.py +++ /dev/null @@ -1,471 +0,0 @@ -""" -HeartbeatMonitor V2 - 简化版心跳监控器 - -采用 Pull 模式直接从 Ray Task 获取心跳，无需 HeartbeatCollector 中介 - -核心改进: -1. 直接调用 ray_task.get_heartbeat_stats() 获取心跳信息 -2. 调用失败直接触发 handle_failure (任务已崩溃) -3. 连续多次心跳信息异常也触发重启 -4. 更简洁的架构，减少组件依赖 -""" - -import logging -import threading -import time -from typing import TYPE_CHECKING, Any, Union - -from sage.kernel.utils.ray.actor import ActorWrapper - -if TYPE_CHECKING: - from sage.kernel.runtime.dispatcher import Dispatcher - from sage.kernel.runtime.task.base_task import BaseTask - - -class HeartbeatMonitor: - """ - - 职责: - 1. 定期直接调用 Ray Task 的 get_heartbeat_stats() 获取心跳 - 2. 如果调用失败（任务崩溃/不可达）→ 立即触发 handle_failure - 3. 如果心跳信息异常（如连续多次为空或不正常）→ 触发 handle_failure - - 触发容错的条件: - A. 调用 get_heartbeat_stats() 报错 (任务崩溃/网络故障) - B. 连续 max_missed_checks 次心跳信息为空或异常 - C. 连续 max_missed_checks 次心跳时间戳未更新 - - 优势: - - 无需 HeartbeatCollector 中介，减少组件 - - 调用失败即可判断任务死亡，响应更快 - - 心跳数据实时获取，无延迟 - """ - - def __init__( - self, - dispatcher: "Dispatcher", - check_interval: float = 5.0, - max_missed_checks: int = 3, - call_timeout: float = 2.0, - ): - """ - 初始化 HeartbeatMonitorV2 - - Args: - dispatcher: Dispatcher 实例 (用于调用 handle_failure 和获取 task 引用) - check_interval: 检查间隔 (秒) - max_missed_checks: 最大允许错过的检查次数 (默认3次) - call_timeout: 调用 Ray task 方法的超时时间 (秒) - """ - self.dispatcher = dispatcher - self.check_interval = check_interval - self.max_missed_checks = max_missed_checks - self.call_timeout = call_timeout - - # 计算实际超时时间 (用于日志显示) - self.effective_timeout = check_interval * max_missed_checks - - # 监控线程控制 - self._monitor_thread: threading.Thread | None = None - self._running = False - self._stop_event = threading.Event() - self._task_states: dict[str, dict[str, Any]] = {} - self._states_lock = threading.Lock() - - # 监控统计 - self._stats = { - "total_checks": 0, - "total_call_failures": 0, - "total_heartbeat_stale": 0, - "total_failures_handled": 0, - "last_check_time": None, - } - - # Logger - self.logger = logging.getLogger("HeartbeatMonitor") - - self.logger.info( - f"✅ HeartbeatMonitor initialized: " - f"check_interval={check_interval}s, " - f"max_missed_checks={max_missed_checks}, " - f"effective_timeout={self.effective_timeout}s, " - f"call_timeout={call_timeout}s" - ) - - def start(self): - """启动监控线程""" - if self._running: - self.logger.warning("HeartbeatMonitor already running") - return - - self._running = True - self._stop_event.clear() - - self._monitor_thread = threading.Thread( - target=self._monitor_loop, name="HeartbeatMonitor", daemon=True - ) - self._monitor_thread.start() - - self.logger.info("🔍 HeartbeatMonitor started") - - def stop(self): - """停止监控线程""" - if not self._running: - return - - self._running = False - self._stop_event.set() - - # 等待线程结束 - if self._monitor_thread and self._monitor_thread.is_alive(): - self._monitor_thread.join(timeout=5.0) - - if self._monitor_thread.is_alive(): - self.logger.warning("Monitor thread did not stop gracefully") - - self.logger.info("🔍 HeartbeatMonitor stopped") - - def is_running(self) -> bool: - """检查监控是否运行中""" - return self._running - - def _get_active_tasks(self) -> dict[str, Union["BaseTask", ActorWrapper]]: - """ - 从 Dispatcher 获取所有活跃任务的引用 - - """ - try: - return self.dispatcher.tasks # type: ignore[return-value] - except Exception as e: - self.logger.error(f"❌ Failed to get active tasks from Dispatcher: {e}") - return {} - - def _pull_heartbeat( - self, task_id: str, task: Union["BaseTask", ActorWrapper] - ) -> dict[str, Any] | None: - """ - 从 Ray Task 拉取心跳信息 - - Args: - task_id: 任务 ID - task: Task 实例或 ActorWrapper - - Returns: - 心跳信息字典，如果调用失败返回 None - """ - try: - # 调用 Ray Task 的 get_heartbeat_stats() 方法 - heartbeat = task.get_heartbeat_stats() # type: ignore[union-attr] - self.logger.debug(f"💓 Pulled heartbeat from {task_id}: {heartbeat}") - return heartbeat # type: ignore[return-value] - - except Exception as e: - # 捕获所有异常（包括 Ray 相关异常） - if "GetTimeoutError" in str(type(e).__name__): - self.logger.warning( - f"⚠️ Timeout pulling heartbeat from {task_id} (timeout={self.call_timeout}s)" - ) - elif "RayActorError" in str(type(e).__name__): - self.logger.error(f"❌ RayActorError when pulling heartbeat from {task_id}: {e}") - else: - self.logger.error( - f"❌ Unexpected error pulling heartbeat from {task_id}: {e}", - exc_info=True, - ) - return None - - def _validate_heartbeat(self, heartbeat: dict[str, Any] | None) -> bool: - """ - 验证心跳信息是否正常 - - Args: - heartbeat: 心跳信息字典 - - Returns: - True 如果心跳正常，False 如果异常 - """ - if heartbeat is None: - return False - - # 检查必要字段 - required_fields = ["task_id", "timestamp", "status", "packet_count"] - if not all(field in heartbeat for field in required_fields): - self.logger.warning(f"⚠️ Heartbeat missing required fields: {heartbeat}") - return False - - # 检查 timestamp 是否合理（不能是未来时间或过于久远） - timestamp = heartbeat.get("timestamp", 0) - current_time = time.time() - - if timestamp <= 0: - self.logger.warning(f"⚠️ Invalid timestamp: {timestamp}") - return False - - if timestamp > current_time + 10: # 不能超前超过10秒 - self.logger.warning(f"⚠️ Future timestamp: {timestamp}") - return False - - # 检查 is_running 状态 - if not heartbeat.get("is_running", False): - self.logger.warning(f"⚠️ Task not running: {heartbeat}") - return False - - return True - - def _monitor_loop(self): - """ - 监控主循环 (在独立线程中运行) - - 检测逻辑: - 1. 从 Dispatcher 获取所有活跃任务 - 2. 对每个任务调用 get_heartbeat_stats() 拉取心跳 - 3. 如果调用失败 → consecutive_failures += 1 - 4. 如果心跳无效 → consecutive_failures += 1 - 5. 如果心跳有效但未更新 → consecutive_stale += 1 - 6. 如果心跳有效且已更新 → 重置所有计数器 - 7. 超过阈值 → 触发 handle_failure - """ - self.logger.info("🔍 Monitor loop started") - - while self._running: - try: - current_time = time.time() - - # === 步骤 1: 获取所有活跃任务 === - active_tasks = self._get_active_tasks() - - if not active_tasks: - self.logger.debug("No active tasks to monitor") - # 清理状态 - with self._states_lock: - self._task_states.clear() - else: - self.logger.debug(f"📊 Monitoring {len(active_tasks)} tasks") - - # === 步骤 2: 检查每个任务的心跳 === - failed_tasks = [] - - with self._states_lock: - for task_id, task in active_tasks.items(): - # 初始化任务状态（如果是新任务） - if task_id not in self._task_states: - self._task_states[task_id] = { - "last_valid_timestamp": 0, - "last_packet_count": 0, - "consecutive_failures": 0, - "consecutive_stale": 0, - "last_check_time": current_time, - } - - state = self._task_states[task_id] - - # === 拉取心跳 === - heartbeat = self._pull_heartbeat(task_id, task) - - # === 情况 A: 调用失败 (任务可能崩溃) === - if heartbeat is None: - state["consecutive_failures"] += 1 - self._stats["total_call_failures"] += 1 - - self.logger.warning( - f"⚠️ Task {task_id} heartbeat call failed: " - f"consecutive_failures={state['consecutive_failures']}/{self.max_missed_checks}" - ) - - if state["consecutive_failures"] >= self.max_missed_checks: - self.logger.error( - f"🚨 Task {task_id} FAILURE: " - f"consecutive call failures={state['consecutive_failures']}" - ) - failed_tasks.append((task_id, "call_failure", heartbeat)) - - continue - - # === 情况 B: 心跳信息无效 === - if not self._validate_heartbeat(heartbeat): - state["consecutive_failures"] += 1 - - self.logger.warning( - f"⚠️ Task {task_id} heartbeat invalid: " - f"consecutive_failures={state['consecutive_failures']}/{self.max_missed_checks}" - ) - - if state["consecutive_failures"] >= self.max_missed_checks: - self.logger.error( - f"🚨 Task {task_id} FAILURE: " - f"consecutive invalid heartbeats={state['consecutive_failures']}" - ) - failed_tasks.append((task_id, "invalid_heartbeat", heartbeat)) - - continue - - # === 情况 C: 心跳有效，检查是否有更新 === - current_timestamp = heartbeat.get("timestamp", 0) - current_packet_count = heartbeat.get("packet_count", 0) - - last_timestamp = state["last_valid_timestamp"] - last_packet_count = state["last_packet_count"] - - # 判断心跳是否有实质性更新 - # 1. timestamp 变化 - # 2. packet_count 增加 (表示任务在处理数据) - has_update = ( - current_timestamp > last_timestamp - or current_packet_count > last_packet_count - ) - - if has_update: - # === 心跳有更新，重置所有计数器 === - self.logger.debug( - f"💓 Task {task_id} heartbeat updated: " - f"timestamp={current_timestamp:.1f} (was {last_timestamp:.1f}), " - f"packet_count={current_packet_count} (was {last_packet_count})" - ) - - state["last_valid_timestamp"] = current_timestamp - state["last_packet_count"] = current_packet_count - state["consecutive_failures"] = 0 - state["consecutive_stale"] = 0 - state["last_check_time"] = current_time - - else: - # === 心跳未更新（stale） === - state["consecutive_stale"] += 1 - self._stats["total_heartbeat_stale"] += 1 - - time_since_last = current_time - last_timestamp - - self.logger.warning( - f"⚠️ Task {task_id} heartbeat stale: " - f"consecutive_stale={state['consecutive_stale']}/{self.max_missed_checks}, " - f"time_since_last={time_since_last:.1f}s" - ) - - if state["consecutive_stale"] >= self.max_missed_checks: - self.logger.error( - f"🚨 Task {task_id} FAILURE: " - f"consecutive stale heartbeats={state['consecutive_stale']}, " - f"time_since_last={time_since_last:.1f}s" - ) - failed_tasks.append((task_id, "stale_heartbeat", heartbeat)) - - # === 清理已不存在的任务 === - disappeared_tasks = set(self._task_states.keys()) - set(active_tasks.keys()) - for task_id in disappeared_tasks: - self.logger.info(f"🗑️ Task {task_id} removed from monitoring") - self._task_states.pop(task_id, None) - - # === 步骤 3: 处理失败任务 === - if failed_tasks: - self.logger.warning(f"⚠️ Detected {len(failed_tasks)} failed tasks") - - for task_id, failure_type, heartbeat in failed_tasks: - self.logger.error( - f"🚨 Handling FAILURE: task_id={task_id}, " - f"type={failure_type}, heartbeat={heartbeat}" - ) - - # 调用 Dispatcher 处理故障 - try: - Exception(f"Heartbeat failure: {failure_type}") - - # 调用容错处理器的 handle_failure - # CheckpointBasedRecovery 会： - # 1. 检查是否可以恢复（有 checkpoint + 未超过重试次数） - # 2. 如果可以，调用 dispatcher.restart_task_with_state - # 3. 处理所有容错逻辑（重试策略、状态恢复等） - # self.dispatcher.fault_handler.handle_failure(task_id, error) - self._stats["total_failures_handled"] += 1 - - # 从监控状态中移除（避免重复处理） - with self._states_lock: - self._task_states.pop(task_id, None) - - except Exception as e: - self.logger.error( - f"❌ Failed to handle failure for {task_id}: {e}", - exc_info=True, - ) - - # === 步骤 4: 更新统计 === - self._stats["total_checks"] += 1 - self._stats["last_check_time"] = current_time - - # === 步骤 5: 等待下一次检查 === - if self._stop_event.wait(timeout=self.check_interval): - # 收到停止信号 - break - - except Exception as e: - self.logger.error(f"❌ Unexpected error in monitor loop: {e}", exc_info=True) - # 避免无限错误循环 - time.sleep(1.0) - - self.logger.info("🔍 Monitor loop stopped") - - def get_stats(self) -> dict[str, Any]: - """ - 获取监控统计信息 - - Returns: - 统计信息字典 - """ - with self._states_lock: - active_tasks = len(self._task_states) - task_states_snapshot = { - task_id: { - "consecutive_failures": state["consecutive_failures"], - "consecutive_stale": state["consecutive_stale"], - "time_since_update": time.time() - state["last_valid_timestamp"], - } - for task_id, state in self._task_states.items() - } - - return { - "running": self._running, - "check_interval": self.check_interval, - "max_missed_checks": self.max_missed_checks, - "effective_timeout": self.effective_timeout, - "call_timeout": self.call_timeout, - "active_tasks": active_tasks, - "task_states": task_states_snapshot, - **self._stats, - } - - def get_task_status(self, task_id: str) -> dict[str, Any] | None: - """ - 获取指定任务的监控状态 - - Args: - task_id: 任务 ID - - Returns: - 监控状态信息,如果不存在返回 None - """ - with self._states_lock: - state = self._task_states.get(task_id) - - if state is None: - return None - - current_time = time.time() - - return { - "task_id": task_id, - "last_valid_timestamp": state["last_valid_timestamp"], - "last_packet_count": state["last_packet_count"], - "consecutive_failures": state["consecutive_failures"], - "consecutive_stale": state["consecutive_stale"], - "time_since_update": current_time - state["last_valid_timestamp"], - "is_at_risk": ( - state["consecutive_failures"] >= self.max_missed_checks - 1 - or state["consecutive_stale"] >= self.max_missed_checks - 1 - ), - "is_failed": ( - state["consecutive_failures"] >= self.max_missed_checks - or state["consecutive_stale"] >= self.max_missed_checks - ), - } - - -__all__ = ["HeartbeatMonitor"] diff --git a/packages/sage-kernel/src/sage/kernel/runtime/job_info.py b/packages/sage-kernel/src/sage/kernel/runtime/job_info.py deleted file mode 100644 index 33b4cfc942..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/job_info.py +++ /dev/null @@ -1,138 +0,0 @@ -from datetime import datetime -from typing import TYPE_CHECKING, Any - -if TYPE_CHECKING: - from sage.kernel.api.base_environment import BaseEnvironment - from sage.kernel.runtime.dispatcher import Dispatcher - from sage.kernel.runtime.graph.execution_graph import ExecutionGraph - - -class JobInfo: - """作业信息类，用于跟踪和管理单个作业的状态""" - - def __init__( - self, - environment: "BaseEnvironment", - graph: "ExecutionGraph", - dispatcher: "Dispatcher", - uuid: str, - autostop: bool = False, - ): - self.environment = environment - self.graph = graph - self.dispatcher = dispatcher - self.uuid = uuid - self.autostop = autostop # 是否启用自动停止 - - # 状态信息 - self.status = "initializing" # initializing, running, stopped, failed, restarting - self.start_time = datetime.now() - self.stop_time: datetime | None = None - self.last_update_time = datetime.now() - self.error_message: str | None = None - - # 统计信息 - self.restart_count = 0 - - # 元数据信息 - self.metadata: dict[str, Any] = {} - - def add_metadata(self, key: str, value: Any): - """添加元数据""" - self.metadata[key] = value - - def get_metadata(self, key: str, default: Any = None) -> Any: - """获取元数据""" - return self.metadata.get(key, default) - - def update_status(self, new_status: str, error: str | None = None): - """更新作业状态""" - old_status = self.status - self.status = new_status - self.last_update_time = datetime.now() - - if error: - self.error_message = error - - if new_status in ["stopped", "failed"]: - self.stop_time = datetime.now() - elif new_status == "running" and old_status in [ - "stopped", - "failed", - "restarting", - ]: - # 重新开始运行，重置停止时间 - self.stop_time = None - - def get_runtime(self) -> str: - """获取运行时间字符串""" - if self.stop_time: - runtime = self.stop_time - self.start_time - else: - runtime = datetime.now() - self.start_time - - total_seconds = int(runtime.total_seconds()) - hours, remainder = divmod(total_seconds, 3600) - minutes, seconds = divmod(remainder, 60) - - if hours > 0: - return f"{hours}h {minutes}m {seconds}s" - elif minutes > 0: - return f"{minutes}m {seconds}s" - else: - return f"{seconds}s" - - def get_summary(self) -> dict[str, Any]: - """获取作业摘要信息""" - return { - "uuid": self.uuid, - "name": self.environment.name, - "status": self.status, - "start_time": self.start_time.strftime("%Y-%m-%d %H:%M:%S"), - "runtime": self.get_runtime(), - "restart_count": self.restart_count, - "last_update": self.last_update_time.strftime("%Y-%m-%d %H:%M:%S"), - "autostop": self.autostop, # 包含 autostop 状态 - } - - def get_status(self) -> dict[str, Any]: - """获取详细状态信息""" - status_info = self.get_summary() - - # 添加详细信息 - status_info.update( - { - "environment": { - "name": self.environment.name, - "platform": getattr(self.environment, "platform", "unknown"), - "description": getattr(self.environment, "description", ""), - }, - "dispatcher": { - "task_count": len(self.dispatcher.tasks), - "service_count": len(self.dispatcher.services), # 添加服务数量 - "is_running": self.dispatcher.is_running, - }, - } - ) - - # 添加调度器指标 - if hasattr(self.dispatcher, "scheduler") and hasattr( - self.dispatcher.scheduler, "get_metrics" - ): - try: - status_info["scheduler_metrics"] = self.dispatcher.scheduler.get_metrics() - except Exception: - # 如果获取失败，不影响整体状态返回 - pass - - if self.error_message: - status_info["error"] = self.error_message - - if self.stop_time: - status_info["stop_time"] = self.stop_time.strftime("%Y-%m-%d %H:%M:%S") - - # 获取任务统计 (get_statistics 是可选方法) - if hasattr(self.dispatcher, "get_statistics"): - status_info["task_statistics"] = self.dispatcher.get_statistics() # type: ignore[attr-defined] - - return status_info diff --git a/packages/sage-kernel/src/sage/kernel/runtime/job_manager.py b/packages/sage-kernel/src/sage/kernel/runtime/job_manager.py deleted file mode 100644 index 3851e09f41..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/job_manager.py +++ /dev/null @@ -1,621 +0,0 @@ -import os -import signal -import sys -import threading -import time -import uuid -from datetime import datetime -from pathlib import Path -from typing import TYPE_CHECKING, Any - -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.runtime.dispatcher import Dispatcher -from sage.kernel.runtime.job_info import JobInfo -from sage.kernel.runtime.job_manager_server import JobManagerServer - -if TYPE_CHECKING: - from sage.kernel.api.base_environment import BaseEnvironment - from sage.kernel.runtime.graph.execution_graph import ExecutionGraph - - -class JobManager: # Job Manager - instance: "JobManager | None" = None - instance_lock = threading.RLock() - - _initialized: bool - jobs: dict[str, JobInfo] - deleted_jobs: dict[str, dict[str, Any]] - default_fault_tolerance_config: dict[str, Any] - - def __new__(cls, *args, **kwargs): - if cls.instance is None: - with cls.instance_lock: - if cls.instance is None: - cls.instance = super().__new__(cls) - cls.instance._initialized = False - return cls.instance - - def __init__( - self, - enable_daemon: bool = True, - daemon_host: str = "127.0.0.1", - daemon_port: int = 19001, - ): - """ - 初始化JobManager - - Args: - enable_daemon: 是否启用内置TCP daemon - daemon_host: Daemon监听地址 - daemon_port: Daemon监听端口 - """ - with JobManager.instance_lock: - if self._initialized: - return - self._initialized = True - JobManager.instance = self - - # 作业管理 - self.jobs: dict[str, JobInfo] = {} # uuid -> jobinfo - self.deleted_jobs: dict[str, dict[str, Any]] = {} - - # 设置日志系统 - self.setup_logging_system() - - # JobManager 级别的容错配置（默认为空，具体策略由各个 job 的 dispatcher 处理） - # 这里可以设置全局默认配置 - self.default_fault_tolerance_config = {} - - # 初始化内置daemon（如果启用） - self.server = None - if enable_daemon: - self.server = JobManagerServer(jobmanager=self, host=daemon_host, port=daemon_port) - self.server.logger = self.logger - # 设置信号处理 - self._setup_signal_handlers() - - def _setup_signal_handlers(self): - """设置信号处理""" - - def signal_handler(signum, frame): - self.logger.info(f"Received signal {signum}, shutting down JobManager...") - self.shutdown() - sys.exit(0) - - signal.signal(signal.SIGINT, signal_handler) - signal.signal(signal.SIGTERM, signal_handler) - - def run_forever(self): - """运行JobManager直到收到停止信号""" - - self.logger.info("JobManager started successfully") - if self.server: # daemon 启用时才显示 TCP 服务信息 - self.logger.info(f"TCP service listening on {self.server.host}:{self.server.port}") - self.logger.info("Press Ctrl+C to stop...") - - try: - while True: - time.sleep(1) - except KeyboardInterrupt: - pass - finally: - self.shutdown() - - return True - - def submit_job(self, env: "BaseEnvironment", autostop: bool = False) -> str: - """ - 提交作业 - - Args: - env: 环境对象 - autostop: 是否启用自动停止（批处理完成后自动清理资源） - """ - # 生成 UUID - job_uuid = self._generate_job_uuid() - self.logger.debug(f"[JM-2] Generated UUID: {job_uuid}") - env.uuid = job_uuid - env.env_uuid = job_uuid - - # 设置环境的日志系统 - self.setup_env_logging(env) - - # 向环境注入JobManager的网络地址信息 - # 注意：如果 env 已经设置了 jobmanager_host（例如在 RemoteEnvironment 中指定了集群可访问的主机名）， - # 则保留用户设置的值，不要用 server.host（可能是 0.0.0.0）覆盖 - if self.server: - if env.jobmanager_host is None: - env.jobmanager_host = self.server.host - if env.jobmanager_port is None: - env.jobmanager_port = self.server.port - else: - # 如果没有daemon，使用默认地址 - if env.jobmanager_host is None: - env.jobmanager_host = "127.0.0.1" - if env.jobmanager_port is None: - env.jobmanager_port = 19001 - - # 创建执行图 - graph = self._create_execution_graph(env) - self.logger.debug("[JM-3] Creating execution graph") - - # 创建 JobInfo 对象，传递 autostop 参数 - job_info = self._create_job_info(env, graph, job_uuid, autostop) - self.logger.debug("[JM-4] Created JobInfo") - - self.logger.debug("[JM-5] Submitting to dispatcher") - # 提交到调度器 - success = self._submit_to_dispatcher(job_info) - self.logger.debug(f"[JM-6] Dispatcher submit returned: {success}") - - if success: - self.logger.info( - f"Environment '{env.name}' submitted with UUID {job_uuid} (autostop={autostop})" - ) - else: - raise Exception("Failed to submit job to dispatcher") - - return job_uuid - - def _generate_job_uuid(self) -> str: - """生成作业UUID""" - return str(uuid.uuid4()) - - def _create_execution_graph(self, env: "BaseEnvironment") -> "ExecutionGraph": - """创建执行图""" - from sage.kernel.runtime.graph.execution_graph import ExecutionGraph - - return ExecutionGraph(env) - - def _create_job_info( - self, - env: "BaseEnvironment", - graph: "ExecutionGraph", - job_uuid: str, - autostop: bool = False, - ) -> JobInfo: - """创建JobInfo对象""" - self.logger.debug("[JM-JI-1] Creating Dispatcher...") - dispatcher = Dispatcher(graph, env) - self.logger.debug("[JM-JI-2] Dispatcher created, creating JobInfo...") - job_info = JobInfo(env, graph, dispatcher, job_uuid, autostop=autostop) - self.logger.debug("[JM-JI-3] JobInfo created, storing in jobs dict...") - self.jobs[job_uuid] = job_info - return job_info - - def _submit_to_dispatcher(self, job_info: JobInfo) -> bool: - """提交到调度器""" - try: - job_info.dispatcher.submit() - job_info.update_status("running") - return True - except Exception as e: - job_info.update_status("failed", error=str(e)) - self.logger.error(f"Failed to submit job {job_info.uuid}: {e}") - return False - - def _get_job_info(self, job_uuid: str) -> JobInfo | None: - """获取JobInfo对象""" - return self.jobs.get(job_uuid) - - def continue_job(self, env_uuid: str) -> dict[str, Any]: - """重启作业""" - job_info = self.jobs.get(env_uuid) - - if not job_info: - self.logger.error(f"Job with UUID {env_uuid} not found") - return { - "uuid": env_uuid, - "status": "not_found", - "message": f"Job with UUID {env_uuid} not found", - } - - try: - current_status = job_info.status - - # 如果作业正在运行，先停止它 - if current_status == "running": - self.logger.info(f"Stopping running job {env_uuid} before restart") - stop_result = self.pause_job(env_uuid) - if stop_result.get("status") not in ["stopped", "error"]: - return { - "uuid": env_uuid, - "status": "failed", - "message": f"Failed to stop job before restart: {stop_result.get('message')}", - } - - # 等待停止完成 - time.sleep(1.0) - - # 重启 dispatcher（容错由 dispatcher 的 fault_handler 处理） - try: - dispatcher = job_info.dispatcher - dispatcher.start() - - job_info.restart_count += 1 - job_info.update_status("running") - - self.logger.info( - f"Job {env_uuid} restarted successfully (restart #{job_info.restart_count})" - ) - - return { - "uuid": env_uuid, - "status": "running", - "message": f"Job restarted successfully (restart #{job_info.restart_count})", - } - - except Exception as restart_error: - job_info.update_status("failed", error=str(restart_error)) - self.logger.error(f"Failed to restart job {env_uuid}: {restart_error}") - return { - "uuid": env_uuid, - "status": "failed", - "message": f"Failed to restart job: {restart_error}", - } - - except Exception as e: - job_info.update_status("failed", error=str(e)) - self.logger.error(f"Failed to restart job {env_uuid}: {e}") - return { - "uuid": env_uuid, - "status": "failed", - "message": f"Failed to restart job: {str(e)}", - } - - def delete_job(self, env_uuid: str, force: bool = False) -> dict[str, Any]: - """删除作业""" - job_info = self.jobs.get(env_uuid) - - if not job_info: - self.logger.error(f"Job with UUID {env_uuid} not found") - return { - "uuid": env_uuid, - "status": "not_found", - "message": f"Job with UUID {env_uuid} not found", - } - - try: - current_status = job_info.status - - # 如果作业正在运行且未强制删除，先停止它 - if current_status == "running" and not force: - self.logger.info(f"Stopping running job {env_uuid} before deletion") - stop_result = self.pause_job(env_uuid) - if stop_result.get("status") not in ["stopped", "error"]: - return { - "uuid": env_uuid, - "status": "failed", - "message": f"Failed to stop job before deletion: {stop_result.get('message')}", - } - - # 等待停止完成 - time.sleep(0.5) - elif current_status == "running" and force: - # 强制删除：直接停止 - self.logger.warning(f"Force deleting running job {env_uuid}") - job_info.dispatcher.stop() - - # 清理资源 - job_info.dispatcher.cleanup() - - # 保存删除历史（可选） - deletion_info = { - "deleted_at": datetime.now().isoformat(), - "final_status": job_info.status, - "name": job_info.environment.name, - "runtime": job_info.get_runtime(), - "restart_count": job_info.restart_count, - } - self.deleted_jobs[env_uuid] = deletion_info - - # 从活动作业列表中移除 - del self.jobs[env_uuid] - - self.logger.info(f"Job {env_uuid} deleted successfully") - - return { - "uuid": env_uuid, - "status": "deleted", - "message": "Job deleted successfully", - } - - except Exception as e: - self.logger.error(f"Failed to delete job {env_uuid}: {e}") - return { - "uuid": env_uuid, - "status": "failed", - "message": f"Failed to delete job: {str(e)}", - } - - def receive_stop_signal(self, env_uuid: str): - """接收停止信号""" - self.logger.debug(f"[JM-1] submit_job called for env: {env_uuid}") - job_info = self.jobs.get(env_uuid) - if job_info is None: - self.logger.warning(f"Job {env_uuid} not found") - return - try: - # 停止 dispatcher - if (job_info.dispatcher.receive_stop_signal()) is True: - self.delete_job(env_uuid, force=True) - self.logger.info(f"Batch job: {env_uuid} completed ") - - except Exception as e: - job_info.update_status("failed", error=str(e)) - self.logger.error(f"Failed to stop job {env_uuid}: {e}") - - def receive_node_stop_signal(self, env_uuid: str, node_name: str): - """接收来自单个节点的停止信号""" - job_info = self.jobs.get(env_uuid) - if not job_info: - self.logger.error(f"Job with UUID {env_uuid} not found") - return - - try: - self.logger.info(f"Node {node_name} in job {env_uuid} requests to stop") - - # 通过dispatcher处理单个节点的停止 - all_nodes_stopped = job_info.dispatcher.receive_node_stop_signal(node_name) - - # 如果所有节点都已停止，则删除整个job - if all_nodes_stopped: - self.delete_job(env_uuid, force=True) - self.logger.info(f"Job {env_uuid} deleted after all nodes stopped") - else: - self.logger.info( - f"Node {node_name} stopped, job {env_uuid} continues with remaining nodes" - ) - - except Exception as e: - job_info.update_status("failed", error=str(e)) - self.logger.error( - f"Failed to handle node stop signal from {node_name} in job {env_uuid}: {e}" - ) - - def pause_job(self, env_uuid: str) -> dict[str, Any]: - """停止Job""" - job_info = self.jobs.get(env_uuid, None) - - if not job_info: - self.logger.error(f"Job with UUID {env_uuid} not found") - return { - "uuid": env_uuid, - "status": "not_found", - "message": f"Job with UUID {env_uuid} not found", - } - - try: - # 停止 dispatcher - job_info.dispatcher.stop() - job_info.update_status("stopped") - - self.logger.info(f"Job {env_uuid} stopped successfully") - - return { - "uuid": env_uuid, - "status": "stopped", - "message": "Job stopped successfully", - } - - except Exception as e: - job_info.update_status("failed", error=str(e)) - self.logger.error(f"Failed to stop job {env_uuid}: {e}") - return { - "uuid": env_uuid, - "status": "failed", - "message": f"Failed to stop job: {str(e)}", - } - - def get_job_status(self, env_uuid: str) -> dict[str, Any]: - """获取作业状态""" - job_info = self._get_job_info(env_uuid) - - if not job_info: - self.logger.warning(f"Job with UUID {env_uuid} not found") - return { - "success": False, - "uuid": env_uuid, - "status": "not_found", - "message": f"Job with UUID {env_uuid} not found", - } - - status_info = job_info.get_status() - status_info["success"] = True - return status_info - - def health_check(self) -> dict[str, Any]: - """健康检查""" - return { - "status": "healthy", - "timestamp": datetime.now().isoformat(), - "jobs_count": len(self.jobs), - } - - def resume_job(self, env_uuid: str) -> dict[str, Any]: - """恢复作业（继续作业的别名）""" - return self.continue_job(env_uuid) - - def _pause_job_execution(self, job_info: JobInfo) -> bool: - """暂停作业执行""" - try: - job_info.dispatcher.stop() - job_info.update_status("stopped") - return True - except Exception as e: - job_info.update_status("failed", error=str(e)) - self.logger.error(f"Failed to pause job execution: {e}") - return False - - def _resume_job_execution(self, job_info: JobInfo) -> bool: - """恢复作业执行""" - try: - job_info.dispatcher.start() - job_info.update_status("running") - return True - except Exception as e: - job_info.update_status("failed", error=str(e)) - self.logger.error(f"Failed to resume job execution: {e}") - return False - - def stop_daemon(self) -> bool: - """停止守护进程""" - if self.server: - try: - self.server.shutdown() - return True - except Exception as e: - self.logger.error(f"Failed to stop daemon: {e}") - return False - return True - - def list_jobs(self) -> list[dict[str, Any]]: - return [job_info.get_summary() for job_info in self.jobs.values()] - - def get_server_info(self) -> dict[str, Any]: - job_summaries = [job_info.get_summary() for job_info in self.jobs.values()] - - return { - "session_id": self.session_id, - "log_base_dir": str(self.log_base_dir), - "environments_count": len(self.jobs), - "jobs": job_summaries, - "daemon_enabled": self.server is not None, - "daemon_address": (f"{self.server.host}:{self.server.port}" if self.server else None), - } - - def shutdown(self): - """关闭JobManager和所有资源""" - self.logger.info("Shutting down JobManager and releasing resources") - - # 关闭daemon - if self.server: - self.server.shutdown() - - # 清理所有作业 - self.cleanup_all_jobs() - - # 重置单例 - JobManager.instance = None - self.logger.info("JobManager shutdown complete") - - def cleanup_all_jobs(self) -> dict[str, Any]: - """清理所有作业""" - try: - cleanup_results = {} - - for env_uuid in list(self.jobs.keys()): - result = self.delete_job(env_uuid, force=True) - cleanup_results[env_uuid] = result - - self.logger.info(f"Cleaned up {len(cleanup_results)} jobs") - - return { - "status": "success", - "message": f"Cleaned up {len(cleanup_results)} jobs", - "results": cleanup_results, - } - - except Exception as e: - self.logger.error(f"Failed to cleanup all jobs: {e}") - return {"status": "failed", "message": f"Failed to cleanup jobs: {str(e)}"} - - ######################################################## - # internal methods # - ######################################################## - - def setup_logging_system(self): - """设置分层日志系统""" - # 1. 生成时间戳标识 - self.session_timestamp = datetime.now() - self.session_id = self.session_timestamp.strftime("%Y%m%d_%H%M%S") - - # 2. 确定日志基础目录 - # 使用统一的.sage/logs/jobmanager目录 - from sage.common.config.output_paths import get_sage_paths - - project_root = os.environ.get("SAGE_PROJECT_ROOT") - sage_paths = get_sage_paths(project_root) - - self.log_base_dir = sage_paths.logs_dir / "jobmanager" / f"session_{self.session_id}" - - print(f"JobManager logs: {self.log_base_dir}") - Path(self.log_base_dir).mkdir(parents=True, exist_ok=True) - - # 3. 创建JobManager主日志 - self.logger = CustomLogger( - [ - ("console", "INFO"), # 控制台显示重要信息 - ( - os.path.join(self.log_base_dir, "jobmanager.log"), - "DEBUG", - ), # 详细日志 - (os.path.join(self.log_base_dir, "error.log"), "ERROR"), # 错误日志 - ], - name="JobManager", - ) - - def setup_env_logging(self, env: "BaseEnvironment"): - """为Environment设置日志系统""" - from sage.kernel.runtime.execution_utils.name_server import get_name - - # 确保环境名称唯一，不与其他注册过的环境冲突 - env.name = get_name(env.name) - - # 生成时间戳标识 - env.session_timestamp = datetime.now() - env.session_id = env.session_timestamp.strftime("%Y%m%d_%H%M%S") - - # 设置环境基础目录 - env.env_base_dir = os.path.join(self.log_base_dir, f"env_{env.name}_{env.session_id}") - Path(env.env_base_dir).mkdir(parents=True, exist_ok=True) - - # 创建Environment专用的日志器 - env._logger = CustomLogger( - [ - ("console", env.console_log_level), # 使用用户设置的控制台日志等级 - ( - os.path.join(env.env_base_dir, "Environment.log"), - "DEBUG", - ), # 详细日志 - (os.path.join(env.env_base_dir, "Error.log"), "ERROR"), # 错误日志 - ], - name=f"Environment_{env.name}", - ) - - @property - def handle(self) -> "JobManager": - return self - - -# python -m sage.kernels.jobmanager.job_manager --host 127.0.0.1 --port 19001 -# ==================== 命令行工具 ==================== - - -def main(): - """命令行入口""" - import argparse - - parser = argparse.ArgumentParser(description="SAGE JobManager with integrated TCP daemon") - parser.add_argument("--host", default="127.0.0.1", help="Daemon host") - parser.add_argument("--port", type=int, default=19001, help="Daemon port") - parser.add_argument("--no-daemon", action="store_true", help="Disable TCP daemon") - - args = parser.parse_args() - - # 创建JobManager实例 - jobmanager = JobManager( - enable_daemon=not args.no_daemon, daemon_host=args.host, daemon_port=args.port - ) - - if not args.no_daemon: - print(f"Starting SAGE JobManager with TCP daemon on {args.host}:{args.port}") - print("Press Ctrl+C to stop...") - jobmanager.run_forever() - else: - print("SAGE JobManager started without TCP daemon") - print("Use the JobManager instance directly in your code") - - -if __name__ == "__main__": - main() diff --git a/packages/sage-kernel/src/sage/kernel/runtime/job_manager_server.py b/packages/sage-kernel/src/sage/kernel/runtime/job_manager_server.py deleted file mode 100644 index 0ea6f2e876..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/job_manager_server.py +++ /dev/null @@ -1,439 +0,0 @@ -import json -import os -import sys -from typing import TYPE_CHECKING, Any - -import ray - -from sage.common.utils.network.local_tcp_server import BaseTcpServer -from sage.common.utils.serialization.dill import deserialize_object - -if TYPE_CHECKING: - from sage.kernel.runtime.job_manager import JobManager - - -class JobManagerServer(BaseTcpServer): - """ - JobManager内置的TCP守护服务 - 负责解析TCP消息并调用JobManager的服务方法 - """ - - def __init__( - self, - jobmanager: "JobManager", - host: str = "127.0.0.1", - port: int = 19001, - actor_name: str = "sage_global_jobmanager", - namespace: str = "sage_system", - ): - """ - 初始化守护服务 - - Args: - jobmanager: JobManager实例 - host: Socket服务监听地址 - port: Socket服务端口 - actor_name: JobManager Actor名称（如果作为Ray Actor运行） - namespace: Ray命名空间 - """ - # 初始化基类 - super().__init__(host, port, jobmanager.logger, "JobManagerServer") - - self.jobmanager = jobmanager - self.actor_name = actor_name - self.namespace = namespace - - try: - self.logger.info("Starting JobManager TCP Daemon...") - - # 启动Socket服务 - self.start() - - self.logger.info(f"JobManager Daemon started successfully on {self.host}:{self.port}") - - except Exception as e: - self.logger.error(f"Failed to start daemon: {e}") - self.shutdown() - - def _handle_message_data( - self, message_data: bytes, client_address: tuple - ) -> dict[str, Any] | None: - """处理接收到的消息数据""" - try: - # JobManager使用JSON格式的消息 - request_data = message_data.decode("utf-8") - request = json.loads(request_data) - self.logger.debug(f"Received request from {client_address}: {request}") - - # 处理请求 - response = self._process_request(request) - return response - - except Exception as e: - self.logger.error(f"Error processing message from {client_address}: {e}") - return {"status": "error", "message": str(e), "request_id": None} - - def _serialize_response(self, response: Any) -> bytes: - """序列化响应（JobManager使用JSON格式）""" - return json.dumps(response).encode("utf-8") - - def _process_request(self, request: dict[str, Any]) -> dict[str, Any]: - """处理客户端请求 - 解析消息并调用JobManager方法""" - try: - self.logger.debug(f"Processing request: {request}") - action = request.get("action", "") - request_id = request.get("request_id") - - # 根据action调用相应的JobManager方法 - if action == "submit_job": - return self._handle_submit_job(request) - elif action == "get_job_status": - return self._handle_get_job_status(request) - elif action == "pause_job": - return self._handle_pause_job(request) - elif action == "continue_job": - return self._handle_continue_job(request) - elif action == "delete_job": - return self._handle_delete_job(request) - elif action == "list_jobs": - return self._handle_list_jobs(request) - elif action == "get_server_info": - return self._handle_get_server_info(request) - elif action == "cleanup_all_jobs": - return self._handle_cleanup_all_jobs(request) - elif action == "receive_node_stop_signal": - return self._handle_receive_node_stop_signal(request) - elif action == "health_check": - return self._handle_health_check(request) - elif action == "get_environment_info": - return self._handle_get_environment_info(request) - else: - return { - "status": "error", - "message": f"Unknown action: {action}", - "request_id": request_id, - } - - except Exception as e: - self.logger.error(f"Error processing request: {e}") - return { - "status": "error", - "message": str(e), - "request_id": request.get("request_id"), - } - - def _handle_submit_job(self, request: dict[str, Any]) -> dict[str, Any]: - """处理提交作业请求""" - try: - # 获取 autostop 参数 - autostop = request.get("autostop", False) - - # 在反序列化之前添加额 Python 路径 - # 这样反序列化时可以正确导入自定义模块 - extra_python_paths = request.get("extra_python_paths", []) - if extra_python_paths: - self.logger.debug(f"Adding extra Python paths: {extra_python_paths}") - for path in extra_python_paths: - if path not in sys.path: - sys.path.insert(0, path) - self.logger.debug(f"Added to sys.path: {path}") - - # 获取序列化的数据（新格式：base64编码的dill序列化数据） - serialized_data_b64 = request.get("serialized_data") - if serialized_data_b64: - # 新格式：base64解码 + dill反序列化 - import base64 - - serialized_data = base64.b64decode(serialized_data_b64) - self.logger.debug("[SUBMIT-1] Starting deserialization") - self.logger.debug("Deserializing environment from base64 + dill format") - self.logger.debug("[SUBMIT-2] Deserialization completed") - env = deserialize_object(serialized_data) - else: - # 兼容旧格式 - env_data = request.get("environment") - if not env_data: - return { - "status": "error", - "message": "Missing serialized_data or environment data", - "request_id": request.get("request_id"), - } - - # 反序列化环境对象（旧格式） - if isinstance(env_data, str): - # 如果是hex字符串，先转换为bytes - env_bytes = bytes.fromhex(env_data) - import pickle - - env = pickle.loads(env_bytes) - else: - env = deserialize_object(env_data) - - if env is None: - return { - "status": "error", - "message": "Failed to deserialize environment object", - "request_id": request.get("request_id"), - } - - # 调试: 检查反序列化后的 jobmanager_host 值 - self.logger.debug( - f"[SUBMIT-DEBUG] env.jobmanager_host={getattr(env, 'jobmanager_host', 'NOT_SET')}, " - f"env.jobmanager_port={getattr(env, 'jobmanager_port', 'NOT_SET')}" - ) - - # 调用JobManager的submit_job方法，传递 autostop 参数 - self.logger.debug( - f"Submitting deserialized environment: {getattr(env, 'name', 'Unknown')} (autostop={autostop})" - ) - self.logger.debug( - f"[SUBMIT-3] Calling jobmanager.submit_job, env={getattr(env, 'name', 'Unknown')}" - ) - job_uuid = self.jobmanager.submit_job(env, autostop=autostop) - self.logger.debug(f"[SUBMIT-4] submit_job returned UUID: {job_uuid}") - - return { - "status": "success", - "job_uuid": job_uuid, - "message": f"Job submitted successfully with UUID: {job_uuid}", - "request_id": request.get("request_id"), - } - - except Exception as e: - self.logger.error(f"Failed to submit job: {e}") - return { - "status": "error", - "message": f"Failed to submit job: {str(e)}", - "request_id": request.get("request_id"), - } - - def _handle_get_job_status(self, request: dict[str, Any]) -> dict[str, Any]: - """处理获取作业状态请求""" - try: - # 支持新旧两种参数名 - job_uuid = request.get("job_uuid") or request.get("env_uuid") - if not job_uuid: - return { - "status": "error", - "message": "Missing job_uuid parameter", - "request_id": request.get("request_id"), - } - - job_status = self.jobmanager.get_job_status(job_uuid) - - return { - "status": "success", - "job_status": job_status, - "request_id": request.get("request_id"), - } - - except Exception as e: - return { - "status": "error", - "message": f"Failed to get job status: {str(e)}", - "request_id": request.get("request_id"), - } - - def _handle_pause_job(self, request: dict[str, Any]) -> dict[str, Any]: - """处理暂停作业请求""" - try: - # 支持新旧两种参数名 - job_uuid = request.get("job_uuid") or request.get("env_uuid") - if not job_uuid: - return { - "status": "error", - "message": "Missing job_uuid parameter", - "request_id": request.get("request_id"), - } - - result = self.jobmanager.pause_job(job_uuid) - result["request_id"] = request.get("request_id") - return result - - except Exception as e: - return { - "status": "error", - "message": f"Failed to pause job: {str(e)}", - "request_id": request.get("request_id"), - } - - def _handle_continue_job(self, request: dict[str, Any]) -> dict[str, Any]: - """处理继续作业请求""" - try: - # 支持新旧两种参数名 - job_uuid = request.get("job_uuid") or request.get("env_uuid") - if not job_uuid: - return { - "status": "error", - "message": "Missing job_uuid parameter", - "request_id": request.get("request_id"), - } - - result = self.jobmanager.continue_job(job_uuid) - result["request_id"] = request.get("request_id") - return result - - except Exception as e: - return { - "status": "error", - "message": f"Failed to continue job: {str(e)}", - "request_id": request.get("request_id"), - } - - def _handle_delete_job(self, request: dict[str, Any]) -> dict[str, Any]: - """处理删除作业请求""" - try: - # 支持新旧两种参数名 - job_uuid = request.get("job_uuid") or request.get("env_uuid") - force = request.get("force", False) - - if not job_uuid: - return { - "status": "error", - "message": "Missing job_uuid parameter", - "request_id": request.get("request_id"), - } - - result = self.jobmanager.delete_job(job_uuid, force=force) - result["request_id"] = request.get("request_id") - return result - - except Exception as e: - return { - "status": "error", - "message": f"Failed to delete job: {str(e)}", - "request_id": request.get("request_id"), - } - - def _handle_list_jobs(self, request: dict[str, Any]) -> dict[str, Any]: - """处理列出作业请求""" - try: - jobs = self.jobmanager.list_jobs() - - return { - "status": "success", - "jobs": jobs, - "request_id": request.get("request_id"), - } - - except Exception as e: - return { - "status": "error", - "message": f"Failed to list jobs: {str(e)}", - "request_id": request.get("request_id"), - } - - def _handle_get_server_info(self, request: dict[str, Any]) -> dict[str, Any]: - """处理获取服务器信息请求""" - try: - server_info = self.jobmanager.get_server_info() - - return { - "status": "success", - "server_info": server_info, - "request_id": request.get("request_id"), - } - - except Exception as e: - return { - "status": "error", - "message": f"Failed to get server info: {str(e)}", - "request_id": request.get("request_id"), - } - - def _handle_cleanup_all_jobs(self, request: dict[str, Any]) -> dict[str, Any]: - """处理清理所有作业请求""" - try: - result = self.jobmanager.cleanup_all_jobs() - result["request_id"] = request.get("request_id") - return result - - except Exception as e: - return { - "status": "error", - "message": f"Failed to cleanup jobs: {str(e)}", - "request_id": request.get("request_id"), - } - - def _handle_receive_node_stop_signal(self, request: dict[str, Any]) -> dict[str, Any]: - """处理节点停止信号""" - try: - job_uuid = request.get("job_uuid") - node_name = request.get("node_name") - - if not job_uuid or not node_name: - return { - "status": "error", - "message": "Missing job_uuid or node_name", - "request_id": request.get("request_id"), - } - - # 调用JobManager的方法 - self.jobmanager.receive_node_stop_signal(job_uuid, node_name) - - return { - "status": "success", - "message": f"Node stop signal received for {node_name}", - "request_id": request.get("request_id"), - } - - except Exception as e: - return { - "status": "error", - "message": f"Failed to process node stop signal: {str(e)}", - "request_id": request.get("request_id"), - } - - def _handle_health_check(self, request: dict[str, Any]) -> dict[str, Any]: - """处理健康检查请求""" - daemon_status = { - "daemon_running": True, - "socket_service": f"{self.host}:{self.port}", - "jobmanager_ready": True, - "session_id": self.jobmanager.session_id, - "jobs_count": len(self.jobmanager.jobs), - } - - return { - "status": "success", - "message": "JobManager and Daemon are healthy", - "daemon_status": daemon_status, - "request_id": request.get("request_id"), - } - - def _handle_get_environment_info(self, request: dict[str, Any]) -> dict[str, Any]: - """处理获取环境信息请求""" - try: - import platform - - environment_info = { - "python_version": sys.version, - "python_executable": sys.executable, - "platform": platform.platform(), - "ray_version": (ray.__version__ if hasattr(ray, "__version__") else "unknown"), - "session_id": self.jobmanager.session_id, - "log_base_dir": str(self.jobmanager.log_base_dir), - "working_directory": os.getcwd(), - } - - return { - "status": "success", - "environment_info": environment_info, - "request_id": request.get("request_id"), - } - - except Exception as e: - return { - "status": "error", - "message": f"Failed to get environment info: {e}", - "request_id": request.get("request_id"), - } - - def shutdown(self): - """关闭守护服务""" - self.logger.info("Shutting down JobManager daemon...") - - # 调用基类的停止方法 - self.stop() - - self.logger.info("JobManager daemon shutdown complete") diff --git a/packages/sage-kernel/src/sage/kernel/runtime/jobmanager_client.py b/packages/sage-kernel/src/sage/kernel/runtime/jobmanager_client.py deleted file mode 100644 index cece2de029..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/jobmanager_client.py +++ /dev/null @@ -1,161 +0,0 @@ -import base64 -import uuid -from typing import Any - -from sage.common.utils.network.base_tcp_client import BaseTcpClient - -# ==================== 客户端工具类 ==================== - - -class JobManagerClient(BaseTcpClient): - """JobManager客户端，专门用于发送序列化数据""" - - def __init__(self, host: str = "127.0.0.1", port: int = 19001, timeout: float = 60.0): - # 验证端口范围 - if not (1 <= port <= 65535): - raise ValueError(f"Port must be between 1 and 65535, got {port}") - - # 验证超时时间 - if timeout <= 0: - raise ValueError(f"Timeout must be positive, got {timeout}") - - super().__init__(host, port, timeout, "JobManagerClient") - - def _build_health_check_request(self) -> dict[str, Any]: - """构建健康检查请求""" - return {"action": "health_check", "request_id": str(uuid.uuid4())} - - def _build_server_info_request(self) -> dict[str, Any]: - """构建服务器信息请求""" - return {"action": "get_server_info", "request_id": str(uuid.uuid4())} - - def submit_job( - self, - serialized_data: bytes, - autostop: bool = False, - extra_python_paths: list[str] | None = None, - ) -> dict[str, Any]: - """ - 提交序列化的作业数据 - - Args: - serialized_data: 序列化的作业数据 - autostop: 是否启用自动停止（批处理完成后自动清理资源） - """ - # 验证输入参数 - if serialized_data is None: - raise ValueError("Serialized data cannot be None") - if isinstance(serialized_data, bytes) and len(serialized_data) == 0: - raise ValueError("Serialized data cannot be empty") - - request = { - "action": "submit_job", - "request_id": str(uuid.uuid4()), - "serialized_data": base64.b64encode(serialized_data).decode("utf-8"), - "autostop": autostop, - "extra_python_paths": extra_python_paths or [], - } - - return self.send_request(request) - - def pause_job(self, job_uuid: str) -> dict[str, Any]: - """暂停/停止作业""" - # 验证输入参数 - if job_uuid is None: - raise ValueError("Job UUID cannot be None") - if job_uuid == "": - raise ValueError("Job UUID cannot be empty") - - request = { - "action": "pause_job", - "request_id": str(uuid.uuid4()), - "job_uuid": job_uuid, - } - - return self.send_request(request) - - def get_job_status(self, job_uuid: str) -> dict[str, Any]: - """获取作业状态""" - request = { - "action": "get_job_status", - "request_id": str(uuid.uuid4()), - "job_uuid": job_uuid, - } - - return self.send_request(request) - - def health_check(self) -> dict[str, Any]: - """健康检查""" - request = self._build_health_check_request() - return self.send_request(request) - - def get_server_info(self) -> dict[str, Any]: - """获取服务器信息""" - request = self._build_server_info_request() - return self.send_request(request) - - def list_jobs(self) -> dict[str, Any]: - """获取作业列表""" - request = {"action": "list_jobs", "request_id": str(uuid.uuid4())} - - return self.send_request(request) - - def continue_job(self, job_uuid: str) -> dict[str, Any]: - """继续作业""" - request = { - "action": "continue_job", - "request_id": str(uuid.uuid4()), - "job_uuid": job_uuid, - } - - return self.send_request(request) - - def delete_job(self, job_uuid: str, force: bool = False) -> dict[str, Any]: - """删除作业""" - request = { - "action": "delete_job", - "request_id": str(uuid.uuid4()), - "job_uuid": job_uuid, - "force": force, - } - - return self.send_request(request) - - def receive_node_stop_signal(self, job_uuid: str, node_name: str) -> dict[str, Any]: - """发送节点停止信号""" - request = { - "action": "receive_node_stop_signal", - "request_id": str(uuid.uuid4()), - "job_uuid": job_uuid, - "node_name": node_name, - } - - return self.send_request(request) - - def cleanup_all_jobs(self) -> dict[str, Any]: - """清理所有作业""" - request = {"action": "cleanup_all_jobs", "request_id": str(uuid.uuid4())} - - return self.send_request(request) - - def _retry_request(self, request: dict[str, Any], max_retries: int = 3) -> dict[str, Any]: - """重试请求机制""" - last_exception = None - - for attempt in range(max_retries): - try: - return self.send_request(request) - except Exception as e: - last_exception = e - if attempt < max_retries - 1: - # 等待一段时间后重试 - import time - - time.sleep(0.5 * (attempt + 1)) - continue - else: - # 最后一次尝试失败，抛出异常 - raise last_exception - - # 理论上不会到达这里 - raise last_exception if last_exception else Exception("Retry failed") diff --git a/packages/sage-kernel/src/sage/kernel/runtime/monitoring/__init__.py b/packages/sage-kernel/src/sage/kernel/runtime/monitoring/__init__.py deleted file mode 100644 index ba8e9fd7a3..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/monitoring/__init__.py +++ /dev/null @@ -1,43 +0,0 @@ -""" -SAGE Runtime Monitoring Module -=============================== - -提供全面的任务和服务性能监控功能，包括： -- 包级别性能监控 -- 任务和服务级别性能汇总 -- 资源使用监控 -- 性能指标收集和汇报 -""" - -from sage.kernel.runtime.monitoring.metrics import ( - MethodMetrics, - PacketMetrics, - ServicePerformanceMetrics, - ServiceRequestMetrics, - TaskPerformanceMetrics, -) -from sage.kernel.runtime.monitoring.metrics_collector import MetricsCollector -from sage.kernel.runtime.monitoring.metrics_reporter import MetricsReporter - -# ResourceMonitor 是可选的，需要 psutil -try: - from sage.kernel.runtime.monitoring.resource_monitor import ResourceMonitor - - RESOURCE_MONITOR_AVAILABLE = True -except ImportError: - ResourceMonitor = None # type: ignore[assignment,misc] - RESOURCE_MONITOR_AVAILABLE = False - -__all__ = [ - # 数据类 - "PacketMetrics", - "TaskPerformanceMetrics", - "ServiceRequestMetrics", - "ServicePerformanceMetrics", - "MethodMetrics", - # 组件 - "MetricsCollector", - "MetricsReporter", - "ResourceMonitor", - "RESOURCE_MONITOR_AVAILABLE", -] diff --git a/packages/sage-kernel/src/sage/kernel/runtime/monitoring/metrics.py b/packages/sage-kernel/src/sage/kernel/runtime/monitoring/metrics.py deleted file mode 100644 index 2c8457a4c2..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/monitoring/metrics.py +++ /dev/null @@ -1,287 +0,0 @@ -""" -Performance Metrics Data Classes -================================ - -定义性能监控的核心数据结构 -""" - -import time -from dataclasses import dataclass, field -from typing import Any - - -@dataclass -class PacketMetrics: - """单个数据包的性能指标""" - - packet_id: str - arrival_time: float = field(default_factory=time.time) - processing_start_time: float | None = None - processing_end_time: float | None = None - queue_wait_time: float = 0.0 - execution_time: float = 0.0 - success: bool = True - error_type: str | None = None - packet_size: int = 0 - - def calculate_times(self) -> None: - """计算各项时间指标""" - if self.processing_start_time and self.arrival_time: - self.queue_wait_time = self.processing_start_time - self.arrival_time - - if self.processing_end_time and self.processing_start_time: - self.execution_time = self.processing_end_time - self.processing_start_time - - def to_dict(self) -> dict[str, Any]: - """转换为字典""" - return { - "packet_id": self.packet_id, - "arrival_time": self.arrival_time, - "processing_start_time": self.processing_start_time, - "processing_end_time": self.processing_end_time, - "queue_wait_time": self.queue_wait_time, - "execution_time": self.execution_time, - "success": self.success, - "error_type": self.error_type, - "packet_size": self.packet_size, - } - - -@dataclass -class TaskPerformanceMetrics: - """任务性能汇总指标""" - - task_name: str - uptime: float = 0.0 - total_packets_processed: int = 0 - total_packets_failed: int = 0 - packets_per_second: float = 0.0 - - # 延迟统计（毫秒） - min_latency: float = 0.0 - max_latency: float = 0.0 - avg_latency: float = 0.0 - p50_latency: float = 0.0 - p95_latency: float = 0.0 - p99_latency: float = 0.0 - - # 队列统计 - input_queue_depth: int = 0 - input_queue_avg_wait_time: float = 0.0 - - # 资源使用 - cpu_usage_percent: float = 0.0 - memory_usage_mb: float = 0.0 - - # 错误统计 - error_breakdown: dict[str, int] = field(default_factory=dict) - - # 时间窗口统计 - last_minute_tps: float = 0.0 - last_5min_tps: float = 0.0 - last_hour_tps: float = 0.0 - - # 元数据 - timestamp: float = field(default_factory=time.time) - - def to_dict(self) -> dict[str, Any]: - """转换为字典""" - return { - "task_name": self.task_name, - "uptime": self.uptime, - "total_packets_processed": self.total_packets_processed, - "total_packets_failed": self.total_packets_failed, - "packets_per_second": self.packets_per_second, - "latency": { - "min_ms": self.min_latency, - "max_ms": self.max_latency, - "avg_ms": self.avg_latency, - "p50_ms": self.p50_latency, - "p95_ms": self.p95_latency, - "p99_ms": self.p99_latency, - }, - "queue": { - "input_depth": self.input_queue_depth, - "avg_wait_time_ms": self.input_queue_avg_wait_time, - }, - "resource": { - "cpu_percent": self.cpu_usage_percent, - "memory_mb": self.memory_usage_mb, - }, - "errors": { - "breakdown": self.error_breakdown, - "total": self.total_packets_failed, - }, - "throughput": { - "current_tps": self.packets_per_second, - "last_minute_tps": self.last_minute_tps, - "last_5min_tps": self.last_5min_tps, - "last_hour_tps": self.last_hour_tps, - }, - "timestamp": self.timestamp, - } - - -@dataclass -class ServiceRequestMetrics: - """服务请求性能指标""" - - request_id: str - method_name: str - arrival_time: float = field(default_factory=time.time) - processing_start_time: float | None = None - processing_end_time: float | None = None - queue_wait_time: float = 0.0 - execution_time: float = 0.0 - success: bool = True - error_type: str | None = None - request_size: int = 0 - response_size: int = 0 - - def calculate_times(self) -> None: - """计算各项时间指标""" - if self.processing_start_time and self.arrival_time: - self.queue_wait_time = self.processing_start_time - self.arrival_time - - if self.processing_end_time and self.processing_start_time: - self.execution_time = self.processing_end_time - self.processing_start_time - - def to_dict(self) -> dict[str, Any]: - """转换为字典""" - return { - "request_id": self.request_id, - "method_name": self.method_name, - "arrival_time": self.arrival_time, - "processing_start_time": self.processing_start_time, - "processing_end_time": self.processing_end_time, - "queue_wait_time": self.queue_wait_time, - "execution_time": self.execution_time, - "success": self.success, - "error_type": self.error_type, - "request_size": self.request_size, - "response_size": self.response_size, - } - - -@dataclass -class MethodMetrics: - """方法级别性能指标""" - - method_name: str - total_requests: int = 0 - total_failures: int = 0 - min_response_time: float = float("inf") - max_response_time: float = 0.0 - avg_response_time: float = 0.0 - p50_response_time: float = 0.0 - p95_response_time: float = 0.0 - p99_response_time: float = 0.0 - - def to_dict(self) -> dict[str, Any]: - """转换为字典""" - return { - "method_name": self.method_name, - "total_requests": self.total_requests, - "total_failures": self.total_failures, - "response_time": { - "min_ms": ( - self.min_response_time if self.min_response_time != float("inf") else 0.0 - ), - "max_ms": self.max_response_time, - "avg_ms": self.avg_response_time, - "p50_ms": self.p50_response_time, - "p95_ms": self.p95_response_time, - "p99_ms": self.p99_response_time, - }, - } - - -@dataclass -class ServicePerformanceMetrics: - """服务性能汇总指标""" - - service_name: str - uptime: float = 0.0 - total_requests_processed: int = 0 - total_requests_failed: int = 0 - requests_per_second: float = 0.0 - - # 按方法分组的统计 - method_metrics: dict[str, MethodMetrics] = field(default_factory=dict) - - # 延迟统计（毫秒） - min_response_time: float = float("inf") - max_response_time: float = 0.0 - avg_response_time: float = 0.0 - p50_response_time: float = 0.0 - p95_response_time: float = 0.0 - p99_response_time: float = 0.0 - - # 队列统计 - request_queue_depth: int = 0 - request_queue_avg_wait_time: float = 0.0 - response_queue_depths: dict[str, int] = field(default_factory=dict) - - # 资源使用 - cpu_usage_percent: float = 0.0 - memory_usage_mb: float = 0.0 - - # 错误统计 - error_breakdown: dict[str, int] = field(default_factory=dict) - - # 并发统计 - concurrent_requests: int = 0 - max_concurrent_requests: int = 0 - - # 时间窗口统计 - last_minute_rps: float = 0.0 - last_5min_rps: float = 0.0 - last_hour_rps: float = 0.0 - - # 元数据 - timestamp: float = field(default_factory=time.time) - - def to_dict(self) -> dict[str, Any]: - """转换为字典""" - return { - "service_name": self.service_name, - "uptime": self.uptime, - "total_requests_processed": self.total_requests_processed, - "total_requests_failed": self.total_requests_failed, - "requests_per_second": self.requests_per_second, - "response_time": { - "min_ms": ( - self.min_response_time if self.min_response_time != float("inf") else 0.0 - ), - "max_ms": self.max_response_time, - "avg_ms": self.avg_response_time, - "p50_ms": self.p50_response_time, - "p95_ms": self.p95_response_time, - "p99_ms": self.p99_response_time, - }, - "queue": { - "request_depth": self.request_queue_depth, - "request_avg_wait_time_ms": self.request_queue_avg_wait_time, - "response_depths": self.response_queue_depths, - }, - "resource": { - "cpu_percent": self.cpu_usage_percent, - "memory_mb": self.memory_usage_mb, - }, - "errors": { - "breakdown": self.error_breakdown, - "total": self.total_requests_failed, - }, - "concurrency": { - "current": self.concurrent_requests, - "max": self.max_concurrent_requests, - }, - "throughput": { - "current_rps": self.requests_per_second, - "last_minute_rps": self.last_minute_rps, - "last_5min_rps": self.last_5min_rps, - "last_hour_rps": self.last_hour_rps, - }, - "methods": {name: metrics.to_dict() for name, metrics in self.method_metrics.items()}, - "timestamp": self.timestamp, - } diff --git a/packages/sage-kernel/src/sage/kernel/runtime/monitoring/metrics_collector.py b/packages/sage-kernel/src/sage/kernel/runtime/monitoring/metrics_collector.py deleted file mode 100644 index c62c882799..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/monitoring/metrics_collector.py +++ /dev/null @@ -1,337 +0,0 @@ -""" -Metrics Collector -================= - -性能指标收集器，支持： -- 包级别性能监控 -- 百分位数计算 -- 时间窗口统计 -- 错误分类统计 -""" - -import time -import uuid -from collections import deque -from datetime import datetime, timedelta -from threading import Lock - -from sage.kernel.runtime.monitoring.metrics import ( - PacketMetrics, - ServiceRequestMetrics, - TaskPerformanceMetrics, -) - - -class MetricsCollector: - """性能指标收集器""" - - def __init__( - self, - name: str, - window_size: int = 10000, - retention_period: timedelta = timedelta(hours=24), - enable_detailed_tracking: bool = True, - ): - """ - 初始化指标收集器 - - Args: - name: 任务或服务名称 - window_size: 滑动窗口大小（保留最近N个样本） - retention_period: 数据保留时长 - enable_detailed_tracking: 是否启用详细的包级别跟踪 - """ - self.name = name - self.window_size = window_size - self.retention_period = retention_period - self.enable_detailed_tracking = enable_detailed_tracking - - # 包/请求级别指标存储 - self.packet_metrics: deque[PacketMetrics | ServiceRequestMetrics] = deque( - maxlen=window_size - ) - - # 聚合指标存储（按时间分组） - self.aggregated_metrics: dict[datetime, TaskPerformanceMetrics] = {} - - # 运行时跟踪 - self._in_flight: dict[str, PacketMetrics | ServiceRequestMetrics] = {} - - # 统计计数器 - self._total_processed = 0 - self._total_failed = 0 - self._error_breakdown: dict[str, int] = {} - - # 时间窗口计数器（用于TPS计算） - self._last_minute_count: deque[tuple] = deque() # (timestamp, count) - self._last_5min_count: deque[tuple] = deque() - self._last_hour_count: deque[tuple] = deque() - - # 启动时间 - self._start_time = time.time() - - # 线程安全锁 - self._lock = Lock() - - def record_packet_start( - self, - packet_id: str | None = None, - packet_size: int = 0, - method_name: str | None = None, - ) -> str: - """ - 记录包处理开始 - - Args: - packet_id: 包ID（如果为None则自动生成） - packet_size: 包大小 - method_name: 方法名（用于服务请求） - - Returns: - 包ID - """ - if packet_id is None: - packet_id = str(uuid.uuid4()) - - with self._lock: - current_time = time.time() - - if method_name: - # 服务请求指标 - metrics: PacketMetrics | ServiceRequestMetrics = ServiceRequestMetrics( - request_id=packet_id, - method_name=method_name, - arrival_time=current_time, - processing_start_time=current_time, - request_size=packet_size, - ) - else: - # 包指标 - metrics = PacketMetrics( - packet_id=packet_id, - arrival_time=current_time, - processing_start_time=current_time, - packet_size=packet_size, - ) - - if self.enable_detailed_tracking: - self._in_flight[packet_id] = metrics - - return packet_id - - def record_packet_end( - self, - packet_id: str, - success: bool = True, - error_type: str | None = None, - response_size: int = 0, - ) -> None: - """ - 记录包处理结束 - - Args: - packet_id: 包ID - success: 是否成功 - error_type: 错误类型 - response_size: 响应大小（用于服务请求） - """ - with self._lock: - current_time = time.time() - - # 更新计数器 - self._total_processed += 1 - if not success: - self._total_failed += 1 - if error_type: - self._error_breakdown[error_type] = self._error_breakdown.get(error_type, 0) + 1 - - # 更新时间窗口计数器 - self._update_time_window_counters(current_time) - - # 更新包指标 - if self.enable_detailed_tracking and packet_id in self._in_flight: - metrics = self._in_flight.pop(packet_id) - metrics.processing_end_time = current_time - metrics.success = success - metrics.error_type = error_type - - if isinstance(metrics, ServiceRequestMetrics): - metrics.response_size = response_size - - metrics.calculate_times() - self.packet_metrics.append(metrics) - - def _update_time_window_counters(self, current_time: float) -> None: - """更新时间窗口计数器""" - # 添加当前时间戳 - self._last_minute_count.append((current_time, 1)) - self._last_5min_count.append((current_time, 1)) - self._last_hour_count.append((current_time, 1)) - - # 移除过期的记录 - cutoff_1min = current_time - 60 - cutoff_5min = current_time - 300 - cutoff_1hour = current_time - 3600 - - while self._last_minute_count and self._last_minute_count[0][0] < cutoff_1min: - self._last_minute_count.popleft() - - while self._last_5min_count and self._last_5min_count[0][0] < cutoff_5min: - self._last_5min_count.popleft() - - while self._last_hour_count and self._last_hour_count[0][0] < cutoff_1hour: - self._last_hour_count.popleft() - - def calculate_percentiles(self, values: list[float]) -> dict[str, float]: - """ - 计算百分位数 - - Args: - values: 数值列表 - - Returns: - 包含 p50, p95, p99 的字典 - """ - if not values: - return {"p50": 0.0, "p95": 0.0, "p99": 0.0} - - sorted_values = sorted(values) - n = len(sorted_values) - - def percentile(p: float) -> float: - k = (n - 1) * p - f = int(k) - c = k - f - if f + 1 < n: - return sorted_values[f] * (1 - c) + sorted_values[f + 1] * c - else: - return sorted_values[f] - - return { - "p50": percentile(0.50), - "p95": percentile(0.95), - "p99": percentile(0.99), - } - - def get_real_time_metrics(self) -> TaskPerformanceMetrics: - """ - 获取实时性能指标 - - Returns: - TaskPerformanceMetrics 实例 - """ - with self._lock: - current_time = time.time() - uptime = current_time - self._start_time - - # 收集执行时间数据 - execution_times = [ - m.execution_time * 1000 # 转换为毫秒 - for m in self.packet_metrics - if m.execution_time > 0 - ] - - # 计算百分位数 - percentiles = self.calculate_percentiles(execution_times) - - # 计算各时间窗口的TPS - last_minute_tps = ( - len(self._last_minute_count) / 60.0 if self._last_minute_count else 0.0 - ) - last_5min_tps = len(self._last_5min_count) / 300.0 if self._last_5min_count else 0.0 - last_hour_tps = len(self._last_hour_count) / 3600.0 if self._last_hour_count else 0.0 - - # 计算平均TPS - packets_per_second = self._total_processed / uptime if uptime > 0 else 0.0 - - # 计算队列等待时间 - wait_times = [ - m.queue_wait_time * 1000 for m in self.packet_metrics if m.queue_wait_time > 0 - ] - avg_wait_time = sum(wait_times) / len(wait_times) if wait_times else 0.0 - - metrics = TaskPerformanceMetrics( - task_name=self.name, - uptime=uptime, - total_packets_processed=self._total_processed, - total_packets_failed=self._total_failed, - packets_per_second=packets_per_second, - min_latency=min(execution_times) if execution_times else 0.0, - max_latency=max(execution_times) if execution_times else 0.0, - avg_latency=( - sum(execution_times) / len(execution_times) if execution_times else 0.0 - ), - p50_latency=percentiles["p50"], - p95_latency=percentiles["p95"], - p99_latency=percentiles["p99"], - input_queue_depth=len(self._in_flight), - input_queue_avg_wait_time=avg_wait_time, - error_breakdown=self._error_breakdown.copy(), - last_minute_tps=last_minute_tps, - last_5min_tps=last_5min_tps, - last_hour_tps=last_hour_tps, - timestamp=current_time, - ) - - return metrics - - def get_metrics_history( - self, time_range: timedelta | None = None - ) -> list[PacketMetrics | ServiceRequestMetrics]: - """ - 获取历史性能指标 - - Args: - time_range: 时间范围（从现在往前） - - Returns: - 历史指标列表 - """ - with self._lock: - if time_range is None: - return list(self.packet_metrics) - - current_time = time.time() - cutoff_time = current_time - time_range.total_seconds() - - return [m for m in self.packet_metrics if m.arrival_time >= cutoff_time] - - def reset_metrics(self) -> None: - """重置所有性能指标""" - with self._lock: - self.packet_metrics.clear() - self.aggregated_metrics.clear() - self._in_flight.clear() - self._total_processed = 0 - self._total_failed = 0 - self._error_breakdown.clear() - self._last_minute_count.clear() - self._last_5min_count.clear() - self._last_hour_count.clear() - self._start_time = time.time() - - def get_summary(self) -> dict[str, str | int | float | dict]: - """ - 获取简要统计信息 - - Returns: - 统计信息字典 - """ - with self._lock: - current_time = time.time() - uptime = current_time - self._start_time - - return { - "name": self.name, - "uptime_seconds": uptime, - "total_processed": self._total_processed, - "total_failed": self._total_failed, - "success_rate": ( - (self._total_processed - self._total_failed) / self._total_processed - if self._total_processed > 0 - else 0.0 - ), - "in_flight": len(self._in_flight), - "error_breakdown": self._error_breakdown.copy(), - "samples_collected": len(self.packet_metrics), - } diff --git a/packages/sage-kernel/src/sage/kernel/runtime/monitoring/metrics_reporter.py b/packages/sage-kernel/src/sage/kernel/runtime/monitoring/metrics_reporter.py deleted file mode 100644 index 6414f67f89..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/monitoring/metrics_reporter.py +++ /dev/null @@ -1,284 +0,0 @@ -""" -Metrics Reporter -================ - -性能指标汇报器，支持： -- 定期汇报 -- 多种导出格式（JSON, Prometheus, CSV, 人类可读） -- 自定义汇报回调 -""" - -import csv -import io -import json -import threading -import time -from collections.abc import Callable - -from sage.kernel.runtime.monitoring.metrics import TaskPerformanceMetrics -from sage.kernel.runtime.monitoring.metrics_collector import MetricsCollector -from sage.kernel.runtime.monitoring.resource_monitor import ResourceMonitor - - -class MetricsReporter: - """性能指标汇报器""" - - def __init__( - self, - metrics_collector: MetricsCollector, - resource_monitor: ResourceMonitor | None = None, - report_interval: int = 60, - enable_auto_report: bool = False, - report_callback: Callable[[str], None] | None = None, - ): - """ - 初始化指标汇报器 - - Args: - metrics_collector: 指标收集器 - resource_monitor: 资源监控器（可选） - report_interval: 汇报间隔（秒） - enable_auto_report: 是否启用自动汇报 - report_callback: 汇报回调函数 - """ - self.metrics_collector = metrics_collector - self.resource_monitor = resource_monitor - self.report_interval = report_interval - self.report_callback = report_callback - - # 汇报线程 - self._report_thread: threading.Thread | None = None - self._running = False - - if enable_auto_report: - self.start_reporting() - - def start_reporting(self) -> None: - """启动定期汇报""" - if self._running: - return - - self._running = True - self._report_thread = threading.Thread( - target=self._report_loop, daemon=True, name="MetricsReporter" - ) - self._report_thread.start() - - def stop_reporting(self) -> None: - """停止定期汇报""" - if not self._running: - return - - self._running = False - if self._report_thread: - self._report_thread.join(timeout=5.0) - self._report_thread = None - - def _report_loop(self) -> None: - """汇报循环""" - while self._running: - try: - report = self.generate_report(format="human") - if self.report_callback: - self.report_callback(report) - except Exception as e: - # 汇报失败不影响监控 - print(f"Error generating metrics report: {e}") - - time.sleep(self.report_interval) - - def generate_report(self, format: str = "json") -> str: - """ - 生成性能报告 - - Args: - format: 输出格式 ("json", "prometheus", "csv", "human") - - Returns: - 格式化的报告字符串 - """ - # 获取指标 - metrics = self.metrics_collector.get_real_time_metrics() - - # 添加资源监控数据 - if self.resource_monitor: - cpu, memory = self.resource_monitor.get_current_usage() - metrics.cpu_usage_percent = cpu - metrics.memory_usage_mb = memory - - # 根据格式生成报告 - if format == "json": - return self._format_json(metrics) - elif format == "prometheus": - return self._format_prometheus(metrics) - elif format == "csv": - return self._format_csv(metrics) - elif format == "human": - return self._format_human(metrics) - else: - raise ValueError(f"Unsupported format: {format}") - - def _format_json(self, metrics: TaskPerformanceMetrics) -> str: - """JSON格式""" - return json.dumps(metrics.to_dict(), indent=2) - - def _format_prometheus(self, metrics: TaskPerformanceMetrics) -> str: - """Prometheus格式""" - lines = [] - task_name = metrics.task_name - - # 基础指标 - lines.append("# HELP sage_task_uptime_seconds Task uptime in seconds") - lines.append("# TYPE sage_task_uptime_seconds gauge") - lines.append(f'sage_task_uptime_seconds{{task="{task_name}"}} {metrics.uptime}') - - lines.append("# HELP sage_task_packets_processed_total Total packets processed") - lines.append("# TYPE sage_task_packets_processed_total counter") - lines.append( - f'sage_task_packets_processed_total{{task="{task_name}"}} {metrics.total_packets_processed}' - ) - - lines.append("# HELP sage_task_packets_failed_total Total packets failed") - lines.append("# TYPE sage_task_packets_failed_total counter") - lines.append( - f'sage_task_packets_failed_total{{task="{task_name}"}} {metrics.total_packets_failed}' - ) - - lines.append("# HELP sage_task_throughput_pps Current throughput in packets per second") - lines.append("# TYPE sage_task_throughput_pps gauge") - lines.append(f'sage_task_throughput_pps{{task="{task_name}"}} {metrics.packets_per_second}') - - # 延迟指标 - lines.append("# HELP sage_task_latency_milliseconds Task latency in milliseconds") - lines.append("# TYPE sage_task_latency_milliseconds summary") - lines.append( - f'sage_task_latency_milliseconds{{task="{task_name}",quantile="0.5"}} {metrics.p50_latency}' - ) - lines.append( - f'sage_task_latency_milliseconds{{task="{task_name}",quantile="0.95"}} {metrics.p95_latency}' - ) - lines.append( - f'sage_task_latency_milliseconds{{task="{task_name}",quantile="0.99"}} {metrics.p99_latency}' - ) - - # 资源指标 - lines.append("# HELP sage_task_cpu_percent CPU usage percentage") - lines.append("# TYPE sage_task_cpu_percent gauge") - lines.append(f'sage_task_cpu_percent{{task="{task_name}"}} {metrics.cpu_usage_percent}') - - lines.append("# HELP sage_task_memory_megabytes Memory usage in megabytes") - lines.append("# TYPE sage_task_memory_megabytes gauge") - lines.append(f'sage_task_memory_megabytes{{task="{task_name}"}} {metrics.memory_usage_mb}') - - # 队列指标 - lines.append("# HELP sage_task_queue_depth Current queue depth") - lines.append("# TYPE sage_task_queue_depth gauge") - lines.append(f'sage_task_queue_depth{{task="{task_name}"}} {metrics.input_queue_depth}') - - return "\n".join(lines) - - def _format_csv(self, metrics: TaskPerformanceMetrics) -> str: - """CSV格式""" - output = io.StringIO() - writer = csv.writer(output) - - # 写入标题行 - writer.writerow( - [ - "timestamp", - "task_name", - "uptime", - "total_processed", - "total_failed", - "tps", - "min_latency_ms", - "max_latency_ms", - "avg_latency_ms", - "p50_latency_ms", - "p95_latency_ms", - "p99_latency_ms", - "cpu_percent", - "memory_mb", - "queue_depth", - ] - ) - - # 写入数据行 - writer.writerow( - [ - metrics.timestamp, - metrics.task_name, - metrics.uptime, - metrics.total_packets_processed, - metrics.total_packets_failed, - metrics.packets_per_second, - metrics.min_latency, - metrics.max_latency, - metrics.avg_latency, - metrics.p50_latency, - metrics.p95_latency, - metrics.p99_latency, - metrics.cpu_usage_percent, - metrics.memory_usage_mb, - metrics.input_queue_depth, - ] - ) - - return output.getvalue() - - def _format_human(self, metrics: TaskPerformanceMetrics) -> str: - """人类可读格式""" - lines = [] - lines.append("=" * 80) - lines.append(f"Performance Report: {metrics.task_name}") - lines.append("=" * 80) - lines.append( - f"Timestamp: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(metrics.timestamp))}" - ) - lines.append(f"Uptime: {metrics.uptime:.2f}s") - lines.append("") - - lines.append("Throughput:") - lines.append(f" Total Processed: {metrics.total_packets_processed}") - lines.append(f" Total Failed: {metrics.total_packets_failed}") - success_rate = ( - (metrics.total_packets_processed - metrics.total_packets_failed) - / metrics.total_packets_processed - * 100 - if metrics.total_packets_processed > 0 - else 0 - ) - lines.append(f" Success Rate: {success_rate:.2f}%") - lines.append(f" Current TPS: {metrics.packets_per_second:.2f}") - lines.append(f" Last Minute TPS: {metrics.last_minute_tps:.2f}") - lines.append(f" Last 5min TPS: {metrics.last_5min_tps:.2f}") - lines.append(f" Last Hour TPS: {metrics.last_hour_tps:.2f}") - lines.append("") - - lines.append("Latency (milliseconds):") - lines.append(f" Min: {metrics.min_latency:.2f}") - lines.append(f" Max: {metrics.max_latency:.2f}") - lines.append(f" Avg: {metrics.avg_latency:.2f}") - lines.append(f" P50: {metrics.p50_latency:.2f}") - lines.append(f" P95: {metrics.p95_latency:.2f}") - lines.append(f" P99: {metrics.p99_latency:.2f}") - lines.append("") - - lines.append("Resources:") - lines.append(f" CPU: {metrics.cpu_usage_percent:.2f}%") - lines.append(f" Memory: {metrics.memory_usage_mb:.2f} MB") - lines.append("") - - lines.append("Queue:") - lines.append(f" Depth: {metrics.input_queue_depth}") - lines.append(f" Avg Wait Time: {metrics.input_queue_avg_wait_time:.2f} ms") - lines.append("") - - if metrics.error_breakdown: - lines.append("Errors:") - for error_type, count in metrics.error_breakdown.items(): - lines.append(f" {error_type}: {count}") - lines.append("") - - lines.append("=" * 80) - return "\n".join(lines) diff --git a/packages/sage-kernel/src/sage/kernel/runtime/monitoring/resource_monitor.py b/packages/sage-kernel/src/sage/kernel/runtime/monitoring/resource_monitor.py deleted file mode 100644 index a18b52d2a8..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/monitoring/resource_monitor.py +++ /dev/null @@ -1,246 +0,0 @@ -""" -Resource Monitor -================ - -资源使用监控器，监控： -- CPU 使用率 -- 内存使用量 -- 进程级别资源统计 -""" - -import threading -import time -from collections import deque - -try: - import psutil - - PSUTIL_AVAILABLE = True -except ImportError: - PSUTIL_AVAILABLE = False - - -class ResourceMonitor: - """资源使用监控器""" - - def __init__( - self, - sampling_interval: float = 1.0, - sample_window: int = 60, - enable_auto_start: bool = False, - ): - """ - 初始化资源监控器 - - Args: - sampling_interval: 采样间隔（秒） - sample_window: 保留的样本数量 - enable_auto_start: 是否自动启动监控 - """ - if not PSUTIL_AVAILABLE: - raise ImportError( - "psutil is required for ResourceMonitor. Install it with: pip install psutil" - ) - - self.sampling_interval = sampling_interval - self.sample_window = sample_window - - # CPU 和内存样本 - self.cpu_samples: deque[tuple[float, float]] = deque(maxlen=sample_window) - self.memory_samples: deque[tuple[float, float]] = deque(maxlen=sample_window) - - # 监控线程 - self._monitor_thread: threading.Thread | None = None - self._running = False - - # 进程对象 - self._process = psutil.Process() - - # 启动监控 - if enable_auto_start: - self.start_monitoring() - - def start_monitoring(self) -> None: - """启动资源监控""" - if self._running: - return - - self._running = True - self._monitor_thread = threading.Thread( - target=self._monitor_loop, daemon=True, name="ResourceMonitor" - ) - self._monitor_thread.start() - - def stop_monitoring(self) -> None: - """停止资源监控""" - if not self._running: - return - - self._running = False - if self._monitor_thread: - self._monitor_thread.join(timeout=5.0) - self._monitor_thread = None - - def _monitor_loop(self) -> None: - """监控循环""" - while self._running: - try: - timestamp = time.time() - - # 获取 CPU 使用率 - cpu_percent = self._process.cpu_percent(interval=None) - - # 获取内存使用量（MB） - memory_info = self._process.memory_info() - memory_mb = memory_info.rss / (1024 * 1024) - - # 保存样本 - self.cpu_samples.append((timestamp, cpu_percent)) - self.memory_samples.append((timestamp, memory_mb)) - - except Exception: - # 忽略采样错误，继续监控 - pass - - time.sleep(self.sampling_interval) - - def get_current_usage(self) -> tuple[float, float]: - """ - 获取当前CPU和内存使用率 - - Returns: - (cpu_percent, memory_mb) 元组 - """ - if not self.cpu_samples or not self.memory_samples: - # 如果没有样本，立即采样一次 - try: - cpu_percent = self._process.cpu_percent(interval=0.1) - memory_mb = self._process.memory_info().rss / (1024 * 1024) - return (cpu_percent, memory_mb) - except Exception: - return (0.0, 0.0) - - # 返回最新样本 - _, cpu = self.cpu_samples[-1] - _, memory = self.memory_samples[-1] - return (cpu, memory) - - def get_average_usage(self, time_window: float | None = None) -> tuple[float, float]: - """ - 获取平均CPU和内存使用率 - - Args: - time_window: 时间窗口（秒），None表示所有样本 - - Returns: - (avg_cpu_percent, avg_memory_mb) 元组 - """ - if not self.cpu_samples or not self.memory_samples: - return (0.0, 0.0) - - current_time = time.time() - cutoff_time = current_time - time_window if time_window else 0 - - # 过滤样本 - cpu_values = [cpu for ts, cpu in self.cpu_samples if ts >= cutoff_time] - memory_values = [mem for ts, mem in self.memory_samples if ts >= cutoff_time] - - if not cpu_values or not memory_values: - return (0.0, 0.0) - - avg_cpu = sum(cpu_values) / len(cpu_values) - avg_memory = sum(memory_values) / len(memory_values) - - return (avg_cpu, avg_memory) - - def get_peak_usage(self, time_window: float | None = None) -> tuple[float, float]: - """ - 获取峰值CPU和内存使用率 - - Args: - time_window: 时间窗口（秒），None表示所有样本 - - Returns: - (peak_cpu_percent, peak_memory_mb) 元组 - """ - if not self.cpu_samples or not self.memory_samples: - return (0.0, 0.0) - - current_time = time.time() - cutoff_time = current_time - time_window if time_window else 0 - - # 过滤样本 - cpu_values = [cpu for ts, cpu in self.cpu_samples if ts >= cutoff_time] - memory_values = [mem for ts, mem in self.memory_samples if ts >= cutoff_time] - - if not cpu_values or not memory_values: - return (0.0, 0.0) - - peak_cpu = max(cpu_values) - peak_memory = max(memory_values) - - return (peak_cpu, peak_memory) - - def get_system_wide_usage(self) -> tuple[float, float, float]: - """ - 获取系统级资源使用情况 - - Returns: - (system_cpu_percent, system_memory_percent, system_memory_available_mb) 元组 - """ - try: - cpu_percent = psutil.cpu_percent(interval=0.1) - memory = psutil.virtual_memory() - memory_percent = memory.percent - memory_available_mb = memory.available / (1024 * 1024) - return (cpu_percent, memory_percent, memory_available_mb) - except Exception: - return (0.0, 0.0, 0.0) - - def get_summary(self) -> dict: - """ - 获取资源监控摘要 - - Returns: - 摘要字典 - """ - current_cpu, current_memory = self.get_current_usage() - avg_cpu, avg_memory = self.get_average_usage() - peak_cpu, peak_memory = self.get_peak_usage() - sys_cpu, sys_mem_pct, sys_mem_avail = self.get_system_wide_usage() - - return { - "process": { - "current": { - "cpu_percent": current_cpu, - "memory_mb": current_memory, - }, - "average": { - "cpu_percent": avg_cpu, - "memory_mb": avg_memory, - }, - "peak": { - "cpu_percent": peak_cpu, - "memory_mb": peak_memory, - }, - }, - "system": { - "cpu_percent": sys_cpu, - "memory_percent": sys_mem_pct, - "memory_available_mb": sys_mem_avail, - }, - "monitoring": { - "running": self._running, - "sample_count": len(self.cpu_samples), - "sampling_interval": self.sampling_interval, - }, - } - - def __enter__(self): - """上下文管理器入口""" - self.start_monitoring() - return self - - def __exit__(self, exc_type, exc_val, exc_tb): - """上下文管理器退出""" - self.stop_monitoring() diff --git a/packages/sage-kernel/src/sage/kernel/runtime/proxy/__init__.py b/packages/sage-kernel/src/sage/kernel/runtime/proxy/__init__.py deleted file mode 100644 index 0f2d920348..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/proxy/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -"""Service proxy layer for runtime contexts.""" - -from .proxy_manager import ProxyManager - -__all__ = ["ProxyManager"] diff --git a/packages/sage-kernel/src/sage/kernel/runtime/proxy/proxy_manager.py b/packages/sage-kernel/src/sage/kernel/runtime/proxy/proxy_manager.py deleted file mode 100644 index 9c71c7e961..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/proxy/proxy_manager.py +++ /dev/null @@ -1,132 +0,0 @@ -"""Service proxy manager for runtime contexts. - -This layer wraps the lower-level ``ServiceManager`` to provide -cached service discovery and a streamlined API that works across -synchronous and asynchronous workflows. -""" - -from __future__ import annotations - -import logging -import threading -from typing import Any - - -class ProxyManager: - """Unified proxy for service invocation. - - The proxy manager is responsible for resolving service metadata, - delegating calls to :class:`ServiceManager`, and caching queue - descriptors so repeated calls avoid hitting the control plane. - """ - - _DEFAULT_TIMEOUT: float = 10.0 - _DEFAULT_ASYNC_TIMEOUT: float = 30.0 - - def __init__(self, context: Any, logger: logging.Logger | None = None) -> None: - self._context = context - self._logger = logger or logging.getLogger(__name__) - self._service_manager = None - self._lock = threading.RLock() - self._service_queue_cache: dict[str, Any] = {} - - # ------------------------------------------------------------------ - # Internal helpers - # ------------------------------------------------------------------ - def _get_service_manager(self): - with self._lock: - if self._service_manager is None: - from sage.kernel.runtime.service.service_caller import ServiceManager - - self._service_manager = ServiceManager(self._context, logger=self._logger) - return self._service_manager - - def _resolve_service_descriptor(self, service_name: str) -> Any | None: - """Return a cached queue descriptor for the requested service.""" - with self._lock: - if service_name in self._service_queue_cache: - return self._service_queue_cache[service_name] - - descriptor = None - context = self._context - if hasattr(context, "service_qds") and context.service_qds: - descriptor = context.service_qds.get(service_name) - if descriptor is not None: - self._service_queue_cache[service_name] = descriptor - return descriptor - - # ------------------------------------------------------------------ - # Public API - # ------------------------------------------------------------------ - def call_sync( - self, - service_name: str, - *args: Any, - timeout: float | None = None, - method: str | None = None, - **kwargs: Any, - ) -> Any: - """Perform a synchronous service invocation. - - Args: - service_name: Logical name of the service to invoke. - *args: Positional payload forwarded to the service method. - timeout: Optional timeout override for the call. - method: Optional explicit method name. Defaults to ``"process"`` - when omitted, enabling pipeline-as-service semantics. - **kwargs: Additional keyword arguments forwarded to the service. - """ - - manager = self._get_service_manager() - descriptor = self._resolve_service_descriptor(service_name) - if descriptor is not None: - manager.cache_service_descriptor(service_name, descriptor) - - return manager.call_sync( - service_name, - *args, - timeout=(timeout if timeout is not None else self._DEFAULT_TIMEOUT), - method=method, - **kwargs, - ) - - def call_async( - self, - service_name: str, - *args: Any, - timeout: float | None = None, - method: str | None = None, - **kwargs: Any, - ): - """Perform an asynchronous service invocation returning a Future.""" - - manager = self._get_service_manager() - descriptor = self._resolve_service_descriptor(service_name) - if descriptor is not None: - manager.cache_service_descriptor(service_name, descriptor) - - return manager.call_async( - service_name, - *args, - timeout=(timeout if timeout is not None else self._DEFAULT_ASYNC_TIMEOUT), - method=method, - **kwargs, - ) - - def shutdown(self) -> None: - """Release any underlying resources held by the proxy manager.""" - with self._lock: - if self._service_manager is not None: - try: - self._service_manager.shutdown() - finally: - self._service_manager = None - self._service_queue_cache.clear() - - # ------------------------------------------------------------------ - # Introspection helpers - # ------------------------------------------------------------------ - @property - def service_manager(self): - """Expose the underlying :class:`ServiceManager` instance.""" - return self._get_service_manager() diff --git a/packages/sage-kernel/src/sage/kernel/runtime/py.typed b/packages/sage-kernel/src/sage/kernel/runtime/py.typed deleted file mode 100644 index 81d93bc663..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/py.typed +++ /dev/null @@ -1,2 +0,0 @@ -# Marker file for PEP 561 -# This indicates that the sage.kernels.runtime package supports type checking diff --git a/packages/sage-kernel/src/sage/kernel/runtime/service/__init__.py b/packages/sage-kernel/src/sage/kernel/runtime/service/__init__.py deleted file mode 100644 index 58c515af61..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/service/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -""" -SAGE - Streaming-Augmented Generative Execution -""" - -# 直接从本包的_version模块加载版本信息 -try: - from sage.kernel._version import __author__, __email__, __version__ -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" diff --git a/packages/sage-kernel/src/sage/kernel/runtime/service/base_service_task.py b/packages/sage-kernel/src/sage/kernel/runtime/service/base_service_task.py deleted file mode 100644 index 370fc73893..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/service/base_service_task.py +++ /dev/null @@ -1,907 +0,0 @@ -""" -Base Service Task - 服务任务基类 - -提供统一的服务任务接口 -""" - -import queue -import threading -import time -import traceback -from abc import ABC, abstractmethod -from typing import TYPE_CHECKING, Any - -from sage.kernel.runtime.monitoring import ( - RESOURCE_MONITOR_AVAILABLE, - MetricsCollector, - MetricsReporter, - ResourceMonitor, - ServicePerformanceMetrics, -) - -if TYPE_CHECKING: - from sage.kernel.runtime.context.service_context import ServiceContext - from sage.kernel.runtime.factory.service_factory import ServiceFactory - - -class BaseServiceTask(ABC): - """ - 服务任务基类 - - 提供统一的服务接口和高性能队列监听功能 - 所有服务任务（本地和远程）都应该继承此基类 - """ - - def __init__(self, service_factory: "ServiceFactory", ctx: "ServiceContext | None" = None): - """ - 初始化基础服务任务 - - Args: - service_factory: 服务工厂实例 - ctx: 服务上下文（ServiceContext） - """ - self.service_factory = service_factory - self.service_name = service_factory.service_name - self.ctx = ctx - - # 创建实际的服务实例 - if ctx is None: - raise ValueError(f"ServiceContext is required for service '{self.service_name}'") - self.service_instance = service_factory.create_service(ctx) - - # 为service_instance注入ctx（参考base_task的做法） - if hasattr(self.service_instance, "ctx"): - self.service_instance.ctx = ctx - self.logger.debug( - f"Injected service context into service instance '{self.service_name}'" - ) - - # 如果service_instance有setup方法，调用它进行初始化 - if hasattr(self.service_instance, "setup"): - self.logger.debug(f"Calling setup() method on service instance '{self.service_name}'") - self.service_instance.setup() - self.logger.debug(f"Service instance '{self.service_name}' setup completed") - - # 提供service别名以便访问 - self.service = self.service_instance - - # 基础状态 - self.is_running = False - self._request_count = 0 - self._error_count = 0 - self._last_activity_time = time.time() - - # 日志记录器 - 如果有ctx则使用ctx.logger，否则使用CustomLogger - self._logger = None - - # 队列监听相关 - self._queue_listener_thread: threading.Thread | None = None - self._queue_listener_running = False - - # === 性能监控 === - self._enable_monitoring = getattr(ctx, "enable_monitoring", False) if ctx else False - self.metrics_collector: MetricsCollector | None = None - self.resource_monitor: ResourceMonitor | None = None - self.metrics_reporter: MetricsReporter | None = None - - if self._enable_monitoring: - try: - self.metrics_collector = MetricsCollector( - name=self.service_name, - window_size=(getattr(ctx, "metrics_window_size", 10000) if ctx else 10000), - enable_detailed_tracking=( - getattr(ctx, "enable_detailed_tracking", True) if ctx else True - ), - ) - - # 尝试启动资源监控 - if RESOURCE_MONITOR_AVAILABLE: - try: - self.resource_monitor = ResourceMonitor( - sampling_interval=( - getattr(ctx, "resource_sampling_interval", 1.0) if ctx else 1.0 - ), - enable_auto_start=True, - ) - except Exception as e: - self.logger.warning( - f"Failed to start resource monitoring for service {self.service_name}: {e}" - ) - - # 可选：启动性能汇报器 - if ctx and getattr(ctx, "enable_auto_report", False): - self.metrics_reporter = MetricsReporter( - metrics_collector=self.metrics_collector, - resource_monitor=self.resource_monitor, - report_interval=getattr(ctx, "report_interval", 60), - enable_auto_report=True, - report_callback=lambda report: self.logger.info(f"\n{report}"), - ) - - self.logger.info(f"Performance monitoring enabled for service {self.service_name}") - except Exception as e: - self.logger.warning( - f"Failed to initialize monitoring for service {self.service_name}: {e}" - ) - self._enable_monitoring = False - - self.logger.info(f"Base service task '{self.service_name}' initialized successfully") - self.logger.debug(f"Service class: {service_factory.service_class.__name__}") - self.logger.debug(f"Service context: {'provided' if ctx else 'not provided'}") - - # 从ServiceContext获取队列描述符信息 - if ctx: - request_qd = ctx.get_request_queue_descriptor() - response_qds = ctx.get_service_response_queue_descriptors() - self.logger.debug(f"Request queue descriptor: {request_qd}") - self.logger.debug(f"Response queue descriptors: {len(response_qds)} available") - - @property - def logger(self): - """获取logger，优先使用ctx.logger，否则使用CustomLogger""" - if not hasattr(self, "_logger") or self._logger is None: - if self.ctx is None: - from sage.common.utils.logging.custom_logger import CustomLogger - - self._logger = CustomLogger(name=f"{self.__class__.__name__}_{self.service_name}") - else: - self._logger = self.ctx.logger - return self._logger - - @property - def name(self): - """获取service task名称""" - if self.ctx is not None: - return self.ctx.name - return self.service_name - - @property - def request_queue_descriptor(self): - """获取请求队列描述符""" - if self.ctx: - return self.ctx.get_request_queue_descriptor() - return None - - @property - def request_queue(self): - """获取请求队列实例""" - qd = self.request_queue_descriptor - if qd: - return qd.queue_instance - return None - - def get_response_queue_descriptor(self, node_name: str): - """获取响应队列描述符""" - if self.ctx: - return self.ctx.get_service_response_queue_descriptor(node_name) - return None - - def get_response_queue(self, node_name: str): - """获取响应队列实例""" - qd = self.get_response_queue_descriptor(node_name) - if qd: - return qd.queue_instance - return None - - def _start_queue_listener(self): - """启动队列监听线程""" - if self._queue_listener_thread is not None and self._queue_listener_thread.is_alive(): - self.logger.warning( - f"Queue listener thread is already running for service '{self.service_name}'" - ) - return - - self.logger.debug(f"Starting queue listener thread for service '{self.service_name}'") - self._queue_listener_running = True - self._queue_listener_thread = threading.Thread( - target=self._queue_listener_loop, - daemon=True, - name=f"QueueListener_{self.service_name}", - ) - self._queue_listener_thread.start() - self.logger.info( - f"Successfully started queue listener thread for service '{self.service_name}'" - ) - - def _stop_queue_listener(self): - """停止队列监听线程""" - if self._queue_listener_thread is None: - self.logger.debug(f"No queue listener thread to stop for service '{self.service_name}'") - return - - self.logger.debug(f"Stopping queue listener thread for service '{self.service_name}'") - self._queue_listener_running = False - - # 等待线程结束（最多等待5秒） - self._queue_listener_thread.join(timeout=5.0) - - # 再次检查线程是否存在（可能在 join 后被其他逻辑置为 None） - if self._queue_listener_thread is not None and self._queue_listener_thread.is_alive(): - self.logger.warning( - f"Queue listener thread did not stop gracefully for service '{self.service_name}'" - ) - else: - self.logger.info( - f"Queue listener thread stopped successfully for service '{self.service_name}'" - ) - - self._queue_listener_thread = None - - def _queue_listener_loop(self): - """队列监听循环 - 使用ServiceContext中的队列描述符""" - self.logger.info(f"Queue listener loop started for service '{self.service_name}'") - request_count = 0 - - while self._queue_listener_running: - try: - # 从ServiceContext获取请求队列 - request_queue = self.request_queue - if request_queue is None: - self.logger.debug( - f"Request queue not available for service '{self.service_name}', waiting..." - ) - time.sleep(0.1) - continue - - # 从请求队列获取消息（超时1秒） - try: - request_data = request_queue.get(block=True, timeout=1.0) - request_count += 1 - request_id = request_data.get("request_id", "unknown") - method_name = request_data.get("method_name", "unknown") - self.logger.info( - f"[SERVICE_TASK] Received request #{request_count} for service '{self.service_name}': {method_name} (request_id: {request_id})" - ) - self._handle_service_request(request_data) - - except Exception as e: - # 如果是队列关闭，直接退出循环 - if "closed" in str(e).lower() or "Queue is closed" in str(e): - self.logger.info( - f"Request queue closed for service '{self.service_name}', stopping listener" - ) - break - # 忽略超时和空队列错误（包括queue.Empty异常） - elif ( - isinstance(e, queue.Empty) - or "timed out" in str(e).lower() - or "empty" in str(e).lower() - ): - pass - else: - self.logger.error( - f"Error receiving request for service '{self.service_name}': {e}" - ) - self.logger.debug(f"Stack trace: {traceback.format_exc()}") - - except Exception as e: - # 如果是队列关闭相关错误，直接退出循环 - if "closed" in str(e).lower() or "Queue is closed" in str(e): - self.logger.info( - f"Queue closed for service '{self.service_name}', stopping listener loop" - ) - break - else: - self.logger.error( - f"Error in queue listener loop for service '{self.service_name}': {e}" - ) - self.logger.debug(f"Stack trace: {traceback.format_exc()}") - time.sleep(1.0) - - self.logger.info( - f"Queue listener loop ended for service '{self.service_name}', processed {request_count} requests" - ) - - def handle_request(self, request_data: dict[str, Any]): - """ - 处理服务请求（新接口，直接处理不通过队列） - - Args: - request_data: 请求数据 - """ - request_id = request_data.get("request_id", "unknown") - method_name = request_data.get("method_name", "unknown") - - self.logger.info( - f"Handling direct service request {request_id} for service '{self.service_name}', method: {method_name}" - ) - - try: - self._last_activity_time = time.time() - request_start_time = time.time() - - # 解析请求数据 - args = request_data.get("args", ()) - kwargs = request_data.get("kwargs", {}) - response_queue = request_data.get("response_queue") - timeout = request_data.get("timeout", 30.0) - - self.logger.debug( - f"Processing direct service request {request_id} for service '{self.service_name}': " - f"method={method_name}, args={args}, kwargs={kwargs}, timeout={timeout}" - ) - - # 调用服务方法 - try: - self.logger.debug( - f"Calling method '{method_name}' on service '{self.service_name}'" - ) - result = self.call_method(method_name, *args, **kwargs) - success = True - error_msg = None - self.logger.debug( - f"Method '{method_name}' completed successfully for service '{self.service_name}'" - ) - except Exception as e: - result = None - success = False - error_msg = str(e) - self.logger.error( - f"Service method '{method_name}' call failed for service '{self.service_name}': {e}" - ) - self.logger.debug(f"Stack trace: {traceback.format_exc()}") - - # 计算执行时间 - execution_time = time.time() - request_start_time - - # 构造响应数据 - response_data = { - "request_id": request_id, - "result": result, - "error": error_msg, - "success": success, - "execution_time": execution_time, - "timestamp": time.time(), - } - - # 发送响应到响应队列 - if response_queue: - self.logger.debug(f"Sending response for request {request_id} to response queue") - self._send_response_to_queue(response_queue, response_data) - else: - self.logger.debug(f"No response queue specified for request {request_id}") - - self.logger.info( - f"Completed direct service request {request_id} for service '{self.service_name}' " - f"in {execution_time:.3f}s, success={success}" - ) - - except Exception as e: - self.logger.error( - f"Error handling direct service request {request_id} for service '{self.service_name}': {e}" - ) - self.logger.debug(f"Stack trace: {traceback.format_exc()}") - - def _send_response_to_queue(self, response_queue, response_data: dict[str, Any]): - """ - 发送响应到指定的队列对象（修正版本） - - Args: - response_queue: 响应队列对象（来自ServiceManager的队列实例） - response_data: 响应数据 - """ - request_id = response_data.get("request_id", "unknown") - - try: - self.logger.info(f"[SERVICE_TASK] Starting response send for request {request_id}") - self.logger.info(f"[SERVICE_TASK] Response queue type: {type(response_queue).__name__}") - self.logger.debug(f"[SERVICE_TASK] Response data: {response_data}") - - # 使用阻塞的put方法，确保消息被成功发送 - if hasattr(response_queue, "put"): - send_start_time = time.time() - # 使用阻塞put，超时10秒 - response_queue.put(response_data, block=True, timeout=10.0) - send_time = time.time() - send_start_time - self.logger.info( - f"[SERVICE_TASK] Response sent successfully for request {request_id} in {send_time:.3f}s" - ) - else: - self.logger.error( - f"[SERVICE_TASK] Unknown response queue type: {type(response_queue)} for request {request_id}" - ) - return - - except Exception as e: - self.logger.error( - f"[SERVICE_TASK] Failed to send response for request {request_id}: {e}" - ) - self.logger.error(f"[SERVICE_TASK] Exception type: {type(e).__name__}") - self.logger.debug(f"Stack trace: {traceback.format_exc()}") - # 不要抛出异常，避免影响服务任务的继续运行 - - def _handle_service_request(self, request_data: dict[str, Any]): - """ - 处理服务请求 - - Args: - request_data: 请求数据，格式与ServiceRequest兼容 - """ - try: - self._last_activity_time = time.time() - request_start_time = time.time() - - # 解析请求数据 - request_id = request_data.get("request_id") - method_name = request_data.get("method_name") - args = request_data.get("args", ()) - kwargs = request_data.get("kwargs", {}) - response_queue = request_data.get("response_queue") # 现在这是队列实例而不是名称 - response_queue_name = request_data.get( - "response_queue_name", "unknown" - ) # 用于日志的名称 - request_data.get("timeout", 30.0) - - # 验证必需参数 - if not request_id or not method_name: - self.logger.error( - f"[SERVICE_TASK] Missing required fields: request_id={request_id}, method_name={method_name}" - ) - return - - self.logger.info( - f"[SERVICE_TASK] Processing service request {request_id}: {method_name} " - f"with args={args}, kwargs={kwargs}" - ) - - # 记录请求开始（如果启用监控） - if self._enable_monitoring and self.metrics_collector: - self.metrics_collector.record_packet_start( - packet_id=request_id, - method_name=method_name, - ) - - # 调用服务方法 - try: - self.logger.debug(f"[SERVICE_TASK] Calling service method {method_name}") - result = self.call_method(method_name, *args, **kwargs) - success = True - error_msg = None - self.logger.info(f"[SERVICE_TASK] Service method {method_name} succeeded: {result}") - - # 记录请求成功 - if self._enable_monitoring and self.metrics_collector: - self.metrics_collector.record_packet_end( - packet_id=request_id, - success=True, - ) - - except Exception as e: - result = None - success = False - error_msg = str(e) - self.logger.error(f"[SERVICE_TASK] Service method call failed: {e}") - self.logger.debug(f"Stack trace: {traceback.format_exc()}") - - # 记录请求失败 - if self._enable_monitoring and self.metrics_collector: - self.metrics_collector.record_packet_end( - packet_id=request_id, - success=False, - error_type=type(e).__name__, - ) - - # 计算执行时间 - execution_time = time.time() - request_start_time - - # 构造响应数据 - response_data = { - "request_id": request_id, - "result": result, - "error": error_msg, - "success": success, - "execution_time": execution_time, - "timestamp": time.time(), - } - - # 发送响应 - if response_queue: - # 如果response_queue是字符串，需要通过ServiceContext获取实际队列实例 - if isinstance(response_queue, str): - actual_queue = self.get_response_queue(response_queue) - queue_name = response_queue - if actual_queue: - self.logger.info( - f"[SERVICE_TASK] Sending response for request {request_id} to queue '{queue_name}'" - ) - self._send_response_to_queue(actual_queue, response_data) - else: - self.logger.error( - f"[SERVICE_TASK] Response queue '{queue_name}' not found in service context for request {request_id}" - ) - else: - # response_queue已经是队列实例 - self.logger.info( - f"[SERVICE_TASK] Sending response for request {request_id} to queue {response_queue_name}" - ) - self._send_response_to_queue(response_queue, response_data) - else: - self.logger.warning( - f"[SERVICE_TASK] No response queue specified for request {request_id}" - ) - - self.logger.info( - f"[SERVICE_TASK] Completed service request {request_id} in {execution_time:.3f}s, " - f"success={success}" - ) - - except Exception as e: - self.logger.error(f"Error handling service request: {e}") - self.logger.debug(f"Stack trace: {traceback.format_exc()}") - - def _send_response(self, response_queue_name: str, response_data: dict[str, Any]): - """ - 发送响应到响应队列 - - ServiceManager发送请求时会指定自己的响应队列名称， - BaseServiceTask通过这个名称找到对应的响应队列并发送响应。 - - Args: - response_queue_name: 响应队列名称 (来自ServiceManager的_response_queue_name) - response_data: 响应数据 - """ - request_id = response_data.get("request_id", "unknown") - - try: - self.logger.info( - f"[SERVICE_TASK] Starting response send process for request {request_id}" - ) - self.logger.info(f"[SERVICE_TASK] Target response queue name: '{response_queue_name}'") - self.logger.debug(f"[SERVICE_TASK] Response data: {response_data}") - - # 通过队列名称创建/获取队列实例（与ServiceManager的_get_response_queue方法保持一致） - self.logger.debug( - f"[SERVICE_TASK] Creating queue instance for: '{response_queue_name}'" - ) - # 使用标准Python队列 - response_queue: Any = queue.Queue() - self.logger.info( - f"[SERVICE_TASK] Created response queue instance type: {type(response_queue).__name__}" - ) - - # 发送响应数据 - 使用阻塞模式确保响应被发送 - self.logger.info( - f"[SERVICE_TASK] Attempting to put response data to queue '{response_queue_name}' for request {request_id}" - ) - send_start_time = time.time() - response_queue.put(response_data, timeout=10.0) # 增加超时时间到10秒 - send_time = time.time() - send_start_time - - self.logger.info( - f"[SERVICE_TASK] Successfully sent response for request {request_id} to queue '{response_queue_name}' in {send_time:.3f}s" - ) - - except Exception as e: - self.logger.error( - f"[SERVICE_TASK] Failed to send response for request {request_id} to '{response_queue_name}': {e}" - ) - self.logger.error(f"[SERVICE_TASK] Exception type: {type(e).__name__}") - self.logger.debug(f"Stack trace: {traceback.format_exc()}") - # 不要抛出异常，避免影响服务任务的继续运行 - # raise - - def start_running(self): - """启动服务任务""" - if self.is_running: - self.logger.warning(f"Service task '{self.service_name}' is already running") - return - - self.logger.info(f"Starting service task '{self.service_name}'") - - try: - # 检查ServiceContext中的队列描述符 - if self.ctx: - request_qd = self.request_queue_descriptor - if request_qd: - self.logger.debug(f"Found request queue descriptor: {request_qd}") - else: - self.logger.warning("No request queue descriptor found in service context") - - response_qds = self.ctx.get_service_response_queue_descriptors() - self.logger.debug(f"Found {len(response_qds)} response queue descriptors") - else: - self.logger.warning( - f"No service context provided for service '{self.service_name}'" - ) - - # 启动队列监听 - self.logger.debug(f"Starting queue listener for service '{self.service_name}'") - self._start_queue_listener() - - # 启动服务实例 - self.logger.debug(f"Starting service instance for service '{self.service_name}'") - self._start_service_instance() - - self.is_running = True - self.logger.info(f"Service task '{self.service_name}' started successfully") - - except Exception as e: - self.logger.error(f"Failed to start service task '{self.service_name}': {e}") - self.logger.debug(f"Stack trace: {traceback.format_exc()}") - self.cleanup() - raise - - def stop(self): - """停止服务任务""" - if not self.is_running: - self.logger.warning(f"Service task '{self.service_name}' is not running") - return - - self.logger.info(f"Stopping service task '{self.service_name}'") - self.is_running = False - - try: - # 停止队列监听 - self.logger.debug(f"Step 1: Stopping queue listener for service '{self.service_name}'") - self._stop_queue_listener() - - # 停止服务实例 - self.logger.debug( - f"Step 2: Stopping service instance for service '{self.service_name}'" - ) - self._stop_service_instance() - - self.logger.info(f"Service task '{self.service_name}' stopped successfully") - - except Exception as e: - self.logger.error(f"Error stopping service task '{self.service_name}': {e}") - self.logger.debug(f"Stack trace: {traceback.format_exc()}") - - def terminate(self): - """终止服务任务（别名方法）""" - if hasattr(self.service_instance, "terminate"): - self.service_instance.terminate() - else: - self.stop() - - def call_method(self, method_name: str, *args, **kwargs) -> Any: - """ - 调用服务方法 - - Args: - method_name: 方法名称 - *args: 位置参数 - **kwargs: 关键字参数 - - Returns: - 方法调用结果 - """ - self.logger.debug( - f"Calling method '{method_name}' on service '{self.service_name}' with args={args}, kwargs={kwargs}" - ) - - try: - self._request_count += 1 - - if not hasattr(self.service_instance, method_name): - error_msg = f"Service '{self.service_name}' does not have method '{method_name}'" - self.logger.error(error_msg) - raise AttributeError(error_msg) - - method = getattr(self.service_instance, method_name) - self.logger.debug( - f"Retrieved method '{method_name}' from service instance '{self.service_name}'" - ) - - # DEBUG: 记录方法调用参数（仅对 insert 方法） - if method_name == "insert": - self.logger.debug( - f"[SERVICE_TASK] Calling {self.service_name}.{method_name} with " - f"args types: {[type(a).__name__ for a in args]}, " - f"kwargs keys: {list(kwargs.keys())}, " - f"kwargs types: {[(k, type(v).__name__) for k, v in kwargs.items()]}" - ) - # 详细记录 entry 参数 - if "entry" in kwargs: - entry_val = kwargs["entry"] - self.logger.debug( - f"[SERVICE_TASK] entry type: {type(entry_val)}, " - f"value preview: {str(entry_val)[:200]}" - ) - - start_time = time.time() - result = method(*args, **kwargs) - execution_time = time.time() - start_time - - self.logger.debug( - f"Method '{method_name}' on service '{self.service_name}' completed in {execution_time:.3f}s" - ) - return result - - except Exception as e: - self._error_count += 1 - self.logger.error( - f"Error calling method '{method_name}' on service '{self.service_name}': {e}" - ) - self.logger.debug(f"Stack trace: {traceback.format_exc()}") - raise - - def get_attribute(self, attr_name: str) -> Any: - """获取服务属性""" - if not hasattr(self.service_instance, attr_name): - raise AttributeError( - f"Service {self.service_name} does not have attribute '{attr_name}'" - ) - - return getattr(self.service_instance, attr_name) - - def set_attribute(self, attr_name: str, value: Any): - """设置服务属性""" - setattr(self.service_instance, attr_name, value) - - def get_statistics(self) -> dict[str, Any]: - """获取服务统计信息""" - base_stats = { - "service_name": self.service_name, - "service_type": self.__class__.__name__, - "is_running": self.is_running, - "request_count": self._request_count, - "error_count": self._error_count, - "last_activity_time": self._last_activity_time, - "service_class": self.service_factory.service_class.__name__, - "has_service_context": self.ctx is not None, - } - - # 添加ServiceContext队列信息 - if self.ctx: - request_qd = self.request_queue_descriptor - response_qds = self.ctx.get_service_response_queue_descriptors() - - base_stats.update( - { - "request_queue_available": request_qd is not None, - "request_queue_id": request_qd.queue_id if request_qd else None, - "request_queue_type": request_qd.queue_type if request_qd else None, - "response_queues_count": len(response_qds), - "response_queue_names": (list(response_qds.keys()) if response_qds else []), - } - ) - - return base_stats - - def cleanup(self): - """清理服务任务资源""" - self.logger.info(f"Starting cleanup for service task '{self.service_name}'") - - try: - # 停止服务 - if self.is_running: - self.logger.debug( - f"Service task '{self.service_name}' is still running, stopping it first" - ) - self.stop() - - # 清理服务实例 - if hasattr(self.service_instance, "cleanup"): - self.logger.debug(f"Calling cleanup() on service instance '{self.service_name}'") - self.service_instance.cleanup() - self.logger.debug(f"Service instance cleanup completed for '{self.service_name}'") - elif hasattr(self.service_instance, "close"): - self.logger.debug(f"Calling close() on service instance '{self.service_name}'") - self.service_instance.close() - self.logger.debug(f"Service instance close completed for '{self.service_name}'") - else: - self.logger.debug( - f"Service instance '{self.service_name}' has no cleanup or close method" - ) - - # 停止监控组件 - if self._enable_monitoring: - if self.metrics_reporter: - self.metrics_reporter.stop_reporting() - if self.resource_monitor: - self.resource_monitor.stop_monitoring() - self.logger.debug(f"Stopped monitoring for service {self.service_name}") - - # 队列清理现在由ServiceContext管理，这里不需要直接清理队列 - self.logger.debug( - f"Queue cleanup is managed by ServiceContext for service '{self.service_name}'" - ) - - self.logger.info(f"Service task '{self.service_name}' cleanup completed successfully") - self.logger.debug( - f"Final statistics - Requests: {self._request_count}, Errors: {self._error_count}" - ) - - except Exception as e: - self.logger.error(f"Error during cleanup of service task '{self.service_name}': {e}") - self.logger.debug(f"Stack trace: {traceback.format_exc()}") - - def get_object(self): - """获取服务对象，用于兼容接口""" - return self - - # 抽象方法 - 子类需要实现（仅保留服务实例管理相关的抽象方法） - - @abstractmethod - def _start_service_instance(self): - """启动服务实例 - 子类实现具体逻辑""" - pass - - @abstractmethod - def _stop_service_instance(self): - """停止服务实例 - 子类实现具体逻辑""" - pass - - def __repr__(self) -> str: - return f"<{self.__class__.__name__} {self.service_name}: {self.service_factory.service_class.__name__}>" - - def __enter__(self): - return self - - def __exit__(self, exc_type, exc_val, exc_tb): - self.cleanup() - - # === Performance Monitoring API === - - def get_current_metrics(self) -> ServicePerformanceMetrics | None: - """ - 获取当前性能指标 - - Returns: - ServicePerformanceMetrics 实例，如果监控未启用则返回 None - """ - if not self._enable_monitoring or not self.metrics_collector: - return None - - # 获取基础指标 - task_metrics = self.metrics_collector.get_real_time_metrics() - - # 转换为服务指标 - metrics = ServicePerformanceMetrics( - service_name=self.service_name, - uptime=task_metrics.uptime, - total_requests_processed=task_metrics.total_packets_processed, - total_requests_failed=task_metrics.total_packets_failed, - requests_per_second=task_metrics.packets_per_second, - min_response_time=task_metrics.min_latency, - max_response_time=task_metrics.max_latency, - avg_response_time=task_metrics.avg_latency, - p50_response_time=task_metrics.p50_latency, - p95_response_time=task_metrics.p95_latency, - p99_response_time=task_metrics.p99_latency, - request_queue_avg_wait_time=task_metrics.input_queue_avg_wait_time, - error_breakdown=task_metrics.error_breakdown, - last_minute_rps=task_metrics.last_minute_tps, - last_5min_rps=task_metrics.last_5min_tps, - last_hour_rps=task_metrics.last_hour_tps, - timestamp=task_metrics.timestamp, - ) - - # 添加资源监控数据 - if self.resource_monitor: - cpu, memory = self.resource_monitor.get_current_usage() - metrics.cpu_usage_percent = cpu - metrics.memory_usage_mb = memory - - # 添加请求队列深度 - if self.ctx: - try: - request_qd = self.request_queue_descriptor - if request_qd and hasattr(request_qd.queue_instance, "qsize"): - # queue_instance 在运行时总是被设置的 - metrics.request_queue_depth = request_qd.queue_instance.qsize() # type: ignore[union-attr] - except Exception: - pass - - return metrics - - def reset_metrics(self) -> None: - """重置性能指标""" - if self.metrics_collector: - self.metrics_collector.reset_metrics() - - def export_metrics(self, format: str = "json") -> str | None: - """ - 导出性能指标 - - Args: - format: 导出格式 ("json", "prometheus", "csv", "human") - - Returns: - 格式化的指标字符串，如果监控未启用则返回 None - """ - if not self._enable_monitoring or not self.metrics_reporter: - return None - - return self.metrics_reporter.generate_report(format=format) diff --git a/packages/sage-kernel/src/sage/kernel/runtime/service/local_service_task.py b/packages/sage-kernel/src/sage/kernel/runtime/service/local_service_task.py deleted file mode 100644 index c137300291..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/service/local_service_task.py +++ /dev/null @@ -1,58 +0,0 @@ -import queue -from typing import TYPE_CHECKING, Any - -from .base_service_task import BaseServiceTask - -if TYPE_CHECKING: - from sage.kernel.runtime.context.service_context import ServiceContext - from sage.kernel.runtime.factory.service_factory import ServiceFactory - - -class LocalServiceTask(BaseServiceTask): - """本地服务任务，继承BaseServiceTask并提供本地执行支持""" - - def __init__(self, service_factory: "ServiceFactory", ctx: "ServiceContext | None" = None): - """ - 初始化本地服务任务 - - Args: - service_factory: 服务工厂实例 - ctx: 运行时上下文 - """ - super().__init__(service_factory, ctx) - self.logger.debug(f"Local service task '{self.service_name}' initialized") - - def _start_service_instance(self): - """启动本地服务实例""" - # 如果服务实例有启动方法，调用它 - if hasattr(self.service_instance, "start_running"): - self.service_instance.start_running() - elif hasattr(self.service_instance, "start"): - self.service_instance.start() - - def _stop_service_instance(self): - """停止本地服务实例""" - # 如果服务实例有停止方法，调用它 - if hasattr(self.service_instance, "stop"): - self.service_instance.stop() - - def _create_request_queue(self) -> queue.Queue: - """创建Python标准队列作为请求队列""" - return queue.Queue() - - def _create_response_queue(self, queue_name: str) -> queue.Queue: - """创建Python标准队列作为响应队列""" - return queue.Queue() - - def _queue_get(self, queue: queue.Queue, timeout: float = 1.0) -> Any: - """从Python标准队列获取数据""" - return queue.get(timeout=timeout) - - def _queue_put(self, queue: queue.Queue, data: Any, timeout: float = 5.0) -> None: - """向Python标准队列放入数据""" - queue.put(data, timeout=timeout) - - def _queue_close(self, queue: queue.Queue) -> None: - """关闭Python标准队列（实际上标准队列不需要关闭）""" - # Python标准队列不需要显式关闭 - pass diff --git a/packages/sage-kernel/src/sage/kernel/runtime/service/ray_service_task.py b/packages/sage-kernel/src/sage/kernel/runtime/service/ray_service_task.py deleted file mode 100644 index 8a61c26b47..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/service/ray_service_task.py +++ /dev/null @@ -1,92 +0,0 @@ -from typing import TYPE_CHECKING, Any - -import ray - -from .base_service_task import BaseServiceTask - -# 安全导入Ray队列 -try: - from ray.util.queue import Queue as RayQueue - - RAY_QUEUE_AVAILABLE = True -except ImportError: - RayQueue = None # type: ignore - RAY_QUEUE_AVAILABLE = False - -if TYPE_CHECKING: - from sage.kernel.runtime.context.service_context import ServiceContext - from sage.kernel.runtime.factory.service_factory import ServiceFactory - - -@ray.remote -class RayServiceTask(BaseServiceTask): - """Ray服务任务，继承BaseServiceTask并提供Ray分布式执行支持""" - - def __init__(self, service_factory: "ServiceFactory", ctx: "ServiceContext | None" = None): - """ - 初始化Ray服务任务 - - Args: - service_factory: 服务工厂实例 - ctx: 运行时上下文 - """ - super().__init__(service_factory, ctx) - self.logger.debug(f"Ray service task '{self.service_name}' initialized") - - def _start_service_instance(self): - """启动Ray服务实例""" - # 如果服务实例有启动方法，调用它 - if hasattr(self.service_instance, "start_running"): - self.service_instance.start_running() - elif hasattr(self.service_instance, "start"): - self.service_instance.start() - - def _stop_service_instance(self): - """停止Ray服务实例""" - # 如果服务实例有停止方法，调用它 - if hasattr(self.service_instance, "stop"): - self.service_instance.stop() - - def _create_request_queue(self) -> Any: - """创建Ray队列作为请求队列""" - if not RAY_QUEUE_AVAILABLE or RayQueue is None: - raise RuntimeError( - "Ray queue is not available. Please ensure Ray is properly installed." - ) - return RayQueue(maxsize=10000) - - def _create_response_queue(self, queue_name: str) -> Any: - """创建Ray队列作为响应队列""" - if not RAY_QUEUE_AVAILABLE or RayQueue is None: - raise RuntimeError( - "Ray queue is not available. Please ensure Ray is properly installed." - ) - return RayQueue(maxsize=10000) - - def _queue_get(self, queue: Any, timeout: float = 1.0) -> Any: - """从Ray队列获取数据""" - return queue.get(timeout=timeout) - - def _queue_put(self, queue: Any, data: Any, timeout: float = 5.0) -> None: - """向Ray队列放入数据""" - queue.put(data, timeout=timeout) - - def _queue_close(self, queue: Any) -> None: - """关闭Ray队列""" - if hasattr(queue, "shutdown"): - queue.shutdown() - elif hasattr(queue, "close"): - queue.close() - - def get_statistics(self) -> dict: - """获取服务统计信息（覆盖基类方法添加Ray特定信息）""" - stats = super().get_statistics() - stats.update( - { - "actor_id": f"ray_actor_{self.service_name}", - "ray_node_id": ( - ray.get_runtime_context().node_id.hex() if ray.is_initialized() else "unknown" - ), - } - ) - return stats diff --git a/packages/sage-kernel/src/sage/kernel/runtime/service/service_caller.py b/packages/sage-kernel/src/sage/kernel/runtime/service/service_caller.py deleted file mode 100644 index 34c4f22ebd..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/service/service_caller.py +++ /dev/null @@ -1,533 +0,0 @@ -""" -SAGE服务调用模块 - 简化版 -统一的请求/响应机制，支持同步和异步调用 -""" - -import logging -import queue -import threading -import time -import uuid -from concurrent.futures import Future, ThreadPoolExecutor -from dataclasses import dataclass -from typing import TYPE_CHECKING, Any - -if TYPE_CHECKING: - pass - - -@dataclass -class ServiceResponse: - """服务响应数据结构""" - - success: bool - result: Any = None - error: str | None = None - request_id: str | None = None - - -class ServiceManager: - """ - 统一的服务管理器 - 负责所有服务调用的请求/响应匹配和管理 - """ - - def __init__(self, context, logger=None): - # 支持传入TaskContext或BaseEnvironment - if hasattr(context, "env_name"): - # 这是TaskContext - self.context = context - self.env = None - else: - # 这是BaseEnvironment - self.env = context - self.context = None - - # 使用注入的logger或默认logger - if logger is not None: - self.logger = logger - elif self.context is not None and hasattr(self.context, "logger"): - self.logger = self.context.logger - else: - self.logger = logging.getLogger(__name__) - - self.logger.debug( - f"ServiceManager initialized for context: {getattr(context, 'name', 'unknown')}" - ) - - # 服务队列缓存 - self._service_queues: dict[str, Any] = {} - - # 响应队列 - 使用TaskContext中的响应队列 - self._response_queue: Any | None = None - if self.context is not None: - # 从TaskContext获取响应队列名称 - if hasattr(self.context, "response_qd") and self.context.response_qd: - self._response_queue_name = self.context.response_qd.queue_id - else: - # 如果没有响应队列描述符，创建一个唯一名称 - self._response_queue_name = f"service_responses_{uuid.uuid4().hex[:8]}" - else: - # 兼容性处理 - self._response_queue_name = f"service_responses_{uuid.uuid4().hex[:8]}" - - # 请求结果管理 - self._result_lock = threading.RLock() - self._request_results: dict[str, ServiceResponse] = {} - self._pending_requests: dict[str, threading.Event] = {} - - # 线程池 - self._executor = ThreadPoolExecutor(max_workers=10, thread_name_prefix="ServiceCall") - - # 添加停止标志 - self._shutdown = False - - # 启动响应监听线程 - self._listener_thread = threading.Thread( - target=self._response_listener, daemon=True, name="ServiceResponseListener" - ) - self._listener_thread.start() - - def cache_service_descriptor(self, service_name: str, descriptor: Any) -> None: - """Cache a queue descriptor or queue instance for subsequent calls.""" - - if descriptor is None: - return - - queue_instance = getattr(descriptor, "queue_instance", descriptor) - if queue_instance is None: - return - - self._service_queues[service_name] = queue_instance - - def _get_service_queue(self, service_name: str): - """从TaskContext获取服务队列""" - if service_name in self._service_queues: - return self._service_queues[service_name] - - if self.context is not None: - # 从TaskContext的服务队列描述符获取队列 - if hasattr(self.context, "service_qds") and service_name in self.context.service_qds: - descriptor = self.context.service_qds[service_name] - queue_instance = descriptor.queue_instance - self._service_queues[service_name] = queue_instance - return queue_instance - else: - self.logger.error(f"Service queue descriptor not found for service: {service_name}") - raise RuntimeError(f"Service queue not available for service: {service_name}") - else: - raise RuntimeError("No TaskContext available to get service queue") - - def _get_response_queue(self): - """从TaskContext获取响应队列""" - if self._response_queue is None: - if self.context: - # 从TaskContext的服务响应队列描述符获取队列 - if hasattr(self.context, "response_qd") and self.context.response_qd: - # 【修复队列克隆Bug】: 使用 queue_instance 而不是 clone() - # clone() 会创建新的队列实例,导致发送端和接收端使用不同队列 - self._response_queue = self.context.response_qd.queue_instance - self.logger.debug(f"Using response queue: {self._response_queue_name}") - else: - context_type = type(self.context).__name__ - has_response_qd = hasattr(self.context, "response_qd") - response_qd_value = getattr(self.context, "response_qd", None) - self.logger.error( - f"Service response queue descriptor not found. Context: {context_type}, has_response_qd: {has_response_qd}, response_qd: {response_qd_value}" - ) - raise RuntimeError("Service response queue not available") - return self._response_queue - - def call_sync( - self, - service_name: str, - *args, - timeout: float | None = 10.0, # 增加默认超时时间到10秒 - method: str | None = None, - **kwargs, - ) -> Any: - """ - 同步调用服务方法 - - Args: - service_name: 服务名称 - *args: 传递给服务方法的位置参数 - timeout: 超时时间（秒） - method: 可选显式方法名称，默认为 ``"process"`` - **kwargs: 传递给服务方法的关键字参数 - - Returns: - 服务方法的返回值 - - Raises: - TimeoutError: 调用超时 - RuntimeError: 服务调用失败 - """ - legacy_via_position = kwargs.pop("_legacy_method_position", False) - method_name = method if method is not None else kwargs.pop("method", None) - - positional_args: tuple[Any, ...] - if legacy_via_position: - if not args: - raise ValueError( - "Legacy service call indicated positional method " - "but no method name was provided." - ) - method_name = args[0] - positional_args = tuple(args[1:]) - else: - positional_args = tuple(args) - - if method_name is None: - method_name = "process" - - request_id = str(uuid.uuid4()) - call_start_time = time.time() - - self.logger.info( - f"[SERVICE_CALL] Starting sync call: {service_name}.{method_name} (request_id: {request_id})" - ) - self.logger.debug( - f"[SERVICE_CALL] Call args: {positional_args}, kwargs: {kwargs}, timeout: {timeout}s" - ) - - # 创建等待事件 - event = threading.Event() - with self._result_lock: - self._pending_requests[request_id] = event - - try: - # 构造请求数据 - 传递响应队列实例而不是名称 - self.logger.debug( - f"Getting response queue for service call: {service_name}.{method_name}" - ) - response_queue = self._get_response_queue() - - # DEBUG: 记录 insert 方法的参数 - if method_name == "insert": - self.logger.debug( - f"[SERVICE_CALLER] {service_name}.{method_name} kwargs: " - f"{[(k, type(v).__name__, str(v)[:100]) for k, v in kwargs.items()]}" - ) - - request_data = { - "request_id": request_id, - "service_name": service_name, - "method_name": method_name, - "args": positional_args, - "kwargs": kwargs, - "timeout": timeout, - "timestamp": time.time(), - "response_queue": response_queue, # 传递队列实例而不是名称 - "response_queue_name": self._response_queue_name, # 仍然保留名称用于日志 - } - - # 发送请求到服务队列 - service_queue = self._get_service_queue(service_name) - - self.logger.debug(f"[SERVICE_CALL] Sending request to service queue: {service_name}") - queue_send_start = time.time() - service_queue.put(request_data, timeout=5.0) - queue_send_time = time.time() - queue_send_start - - self.logger.debug( - f"[SERVICE_CALL] Request sent successfully in {queue_send_time:.3f}s (request_id: {request_id})" - ) - - # 等待结果 - wait_start_time = time.time() - self.logger.debug(f"[SERVICE_CALL] Waiting for response (timeout: {timeout}s)") - - if not event.wait(timeout=timeout): - wait_time = time.time() - wait_start_time - self.logger.error( - f"[SERVICE_CALL] TIMEOUT after {wait_time:.3f}s: {service_name}.{method_name} (request_id: {request_id})" - ) - raise TimeoutError( - f"Service call timeout after {timeout}s: {service_name}.{method_name}" - ) - - wait_time = time.time() - wait_start_time - self.logger.debug(f"[SERVICE_CALL] Response received after {wait_time:.3f}s") - - # 获取结果 - with self._result_lock: - if request_id not in self._request_results: - self.logger.error( - f"[SERVICE_CALL] Result not found for request_id: {request_id}" - ) - raise RuntimeError(f"Service call result not found: {request_id}") - - response = self._request_results.pop(request_id) - - total_time = time.time() - call_start_time - - if response.success: - self.logger.info( - f"[SERVICE_CALL] SUCCESS: {service_name}.{method_name} completed in {total_time:.3f}s (request_id: {request_id})" - ) - self.logger.debug(f"[SERVICE_CALL] Response result: {response.result}") - return response.result - else: - self.logger.error( - f"[SERVICE_CALL] FAILURE: {service_name}.{method_name} failed in {total_time:.3f}s - {response.error} (request_id: {request_id})" - ) - raise RuntimeError(f"Service call failed: {response.error}") - - except Exception as e: - # 清理等待状态 - with self._result_lock: - self._pending_requests.pop(request_id, None) - self._request_results.pop(request_id, None) - - total_time = time.time() - call_start_time - self.logger.error( - f"[SERVICE_CALL] EXCEPTION: {service_name}.{method_name} failed in {total_time:.3f}s - {str(e)} (request_id: {request_id})" - ) - raise - - def call_async( - self, - service_name: str, - *args, - timeout: float | None = 30.0, - method: str | None = None, - **kwargs, - ) -> Future: - """ - 异步调用服务方法 - - Args: - service_name: 服务名称 - *args: 位置参数 - timeout: 超时时间（秒） - method: 可选显式方法名称 - **kwargs: 关键字参数 - - Returns: - Future对象，可以通过future.result()获取结果 - """ - # 在线程池中执行同步调用 - future = self._executor.submit( - self.call_sync, - service_name, - *args, - timeout=timeout, - method=method, - **kwargs, - ) - - target_method = method if method is not None else kwargs.get("method") - if target_method is None and kwargs.get("__method_via_position__"): - target_method = args[0] if args else "<unknown>" - - if target_method is None: - target_method = "process" - - self.logger.debug(f"Started async call: {service_name}.{target_method}") - return future - - def _response_listener(self): - """ - 响应监听线程 - 从响应队列接收响应并分发 - """ - self.logger.debug("Service response listener started") - - while not self._shutdown: - try: - # 获取响应队列 - self.logger.debug("Response listener: Getting response queue") - response_queue = self._get_response_queue() - - # 检查队列是否已关闭 (response_queue 在运行时总是有值) - if hasattr(response_queue, "is_closed") and response_queue.is_closed(): # type: ignore[union-attr] - self.logger.debug("Response queue is closed, stopping listener") - break - - # 从响应队列获取响应（阻塞等待1秒） - try: - response_data = response_queue.get(timeout=1.0) # type: ignore[union-attr] - self.logger.debug( - f"[SERVICE_RESPONSE] Received raw response data: {response_data}" - ) - - if response_data is None: - self.logger.debug("[SERVICE_RESPONSE] Received None from queue, continuing") - continue - - # 处理响应数据 - self._handle_response(response_data) - - except Exception as queue_error: - # 检查具体的队列异常类型 - error_type = type(queue_error).__name__ - - # 处理标准Python队列超时异常（queue.Empty） - if isinstance(queue_error, queue.Empty): - # 这是正常的超时，继续循环而不记录任何消息 - continue - - # 处理其他可能的超时异常 - error_str = str(queue_error).lower() - if error_type == "Empty" or "empty" in error_type.lower(): - # 其他类型的Empty异常 - continue - elif "timed out" in error_str or "timeout" in error_str: - # 其他类型的超时异常 - continue - elif "closed" in error_str or "queue is closed" in error_str: - # 队列关闭 - self.logger.debug(f"Queue operation result: {queue_error}") - continue - else: - # 其他未知的队列异常 - if error_str.strip(): # 如果错误消息不为空 - self.logger.warning( - f"Queue operation issue ({error_type}): {queue_error}" - ) - else: # 如果错误消息为空，提供更多上下文 - self.logger.debug( - f"Queue operation ({error_type}): Empty message from queue.get() - likely timeout" - ) - continue # 继续运行，不要因为队列问题停止 - - except Exception as e: - # 检查是否是队列关闭导致的错误 - error_str = str(e).lower() - if "closed" in error_str or "queue is closed" in error_str: - self.logger.debug("Response queue closed, stopping listener") - break - else: - self.logger.error(f"Error in response listener: {e}") - self.logger.debug(f"Listener error details: {type(e).__name__}: {e}") - time.sleep(1.0) - - def _handle_response(self, response_data: dict[str, Any]) -> None: - """处理服务响应""" - request_id = response_data.get("request_id") - if not request_id: - self.logger.warning("[SERVICE_RESPONSE] Received response without request_id") - return - - self.logger.debug(f"[SERVICE_RESPONSE] Received response for request_id: {request_id}") - - with self._result_lock: - if request_id not in self._pending_requests: - self.logger.warning( - f"[SERVICE_RESPONSE] No pending request found for request_id: {request_id}" - ) - return - - # 存储结果 - error_msg = response_data.get("error") - self._request_results[request_id] = ServiceResponse( - request_id=request_id, - success=response_data.get("success", False), - result=response_data.get("result"), - error=str(error_msg) if error_msg is not None else None, - ) - - self.logger.debug( - f"[SERVICE_RESPONSE] Stored result for request_id: {request_id}, success: {response_data.get('success', False)}" - ) - - # 唤醒等待的线程 - event = self._pending_requests.pop(request_id, None) - if event: - event.set() - self.logger.debug( - f"[SERVICE_RESPONSE] Notified waiting thread for request_id: {request_id}" - ) - else: - self.logger.warning( - f"[SERVICE_RESPONSE] No waiting event found for request_id: {request_id}" - ) - - def shutdown(self): - """关闭服务管理器""" - self.logger.debug("Shutting down ServiceManager") - - # 设置停止标志 - self._shutdown = True - - # 关闭所有服务队列 - for service_name, queue in self._service_queues.items(): - try: - queue.close() - except Exception as e: - self.logger.warning(f"Error closing service queue {service_name}: {e}") - - # 关闭响应队列 - if self._response_queue: - try: - self._response_queue.close() - except Exception as e: - self.logger.warning(f"Error closing response queue: {e}") - - # 等待监听线程结束（最多等待2秒） - if self._listener_thread and self._listener_thread.is_alive(): - self._listener_thread.join(timeout=2.0) - if self._listener_thread.is_alive(): - self.logger.warning("Response listener thread did not stop gracefully") - - # 关闭线程池 - self._executor.shutdown(wait=True) - - def __del__(self): - """析构函数 - 确保资源被正确清理""" - try: - if not self._shutdown: - self.shutdown() - except Exception: - # 在析构函数中不记录错误，避免在程序退出时产生问题 - pass - - -class ServiceCallProxy: - """服务调用代理，提供语法糖支持""" - - def __init__(self, service_manager: "ServiceManager", service_name: str, logger=None): - self._service_manager = service_manager - self._service_name = service_name - self.logger = ( - logger if logger is not None else logging.getLogger(f"{__name__}.{service_name}") - ) - - self.logger.debug(f"[PROXY] Created ServiceCallProxy for service: {service_name}") - - def __getattr__(self, method_name: str): - """获取服务方法的调用代理""" - self.logger.debug(f"[PROXY] Creating method proxy for {self._service_name}.{method_name}") - - def method_call(*args, timeout: float | None = 2.0, **kwargs): - proxy_call_start = time.time() - self.logger.info( - f"[PROXY] Calling {self._service_name}.{method_name} with args={args}, kwargs={kwargs}, timeout={timeout}s" - ) - - try: - result = self._service_manager.call_sync( - self._service_name, - *args, - timeout=timeout, - method=method_name, - **kwargs, - ) - - proxy_call_time = time.time() - proxy_call_start - self.logger.info( - f"[PROXY] SUCCESS: {self._service_name}.{method_name} completed in {proxy_call_time:.3f}s" - ) - self.logger.debug(f"[PROXY] Method result: {result}") - return result - - except Exception as e: - proxy_call_time = time.time() - proxy_call_start - self.logger.error( - f"[PROXY] FAILED: {self._service_name}.{method_name} failed in {proxy_call_time:.3f}s - {str(e)}" - ) - raise - - # 设置方法名称用于调试 - method_call.__name__ = f"{self._service_name}.{method_name}" - return method_call diff --git a/packages/sage-kernel/src/sage/kernel/runtime/task/__init__.py b/packages/sage-kernel/src/sage/kernel/runtime/task/__init__.py deleted file mode 100644 index 58c515af61..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/task/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -""" -SAGE - Streaming-Augmented Generative Execution -""" - -# 直接从本包的_version模块加载版本信息 -try: - from sage.kernel._version import __author__, __email__, __version__ -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" diff --git a/packages/sage-kernel/src/sage/kernel/runtime/task/base_task.py b/packages/sage-kernel/src/sage/kernel/runtime/task/base_task.py deleted file mode 100644 index a0fd6431ed..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/task/base_task.py +++ /dev/null @@ -1,771 +0,0 @@ -import threading -import time -from abc import ABC -from queue import Empty as QueueEmpty -from typing import TYPE_CHECKING - -try: - from ray.util.queue import Empty as RayQueueEmpty # type: ignore -except ImportError: - RayQueueEmpty = QueueEmpty # type: ignore -from sage.kernel.runtime.communication.packet import Packet, StopSignal -from sage.kernel.runtime.context.task_context import TaskContext -from sage.kernel.runtime.monitoring import ( - RESOURCE_MONITOR_AVAILABLE, - MetricsCollector, - MetricsReporter, - ResourceMonitor, - TaskPerformanceMetrics, -) - -if TYPE_CHECKING: - from sage.kernel.api.operator.base_operator import BaseOperator - from sage.kernel.runtime.factory.operator_factory import OperatorFactory - - -QUEUE_EMPTY_EXCEPTIONS = ( - (QueueEmpty,) if RayQueueEmpty is QueueEmpty else (QueueEmpty, RayQueueEmpty) -) - - -class BaseTask(ABC): # noqa: B024 - def __init__(self, ctx: "TaskContext", operator_factory: "OperatorFactory") -> None: - self.ctx = ctx - - # 使用从上下文传入的队列描述符 - self.input_qd = self.ctx.input_qd - - if self.input_qd: - self.logger.info( - f"🎯 Task: Using queue descriptor for input buffer: {self.input_qd.queue_id}" - ) - else: - self.logger.info("🎯 Task: No input queue (source/spout node)") - - # === 线程控制 === - self._worker_thread: threading.Thread | None = None - self.is_running = False - - # === 性能监控 === - self._processed_count = 0 - self._error_count = 0 - - # ✅ 添加 checkpoint 相关属性 - self._checkpoint_counter = 0 - self._last_checkpoint_time = 0.0 - - # 检查是否启用性能监控 - self._enable_monitoring = getattr(ctx, "enable_monitoring", False) - self.metrics_collector: MetricsCollector | None = None - self.resource_monitor: ResourceMonitor | None = None - self.metrics_reporter: MetricsReporter | None = None - - self.fault_handler = None # Will be set by dispatcher if applicable - - if self._enable_monitoring: - try: - self.metrics_collector = MetricsCollector( - name=self.ctx.name, - window_size=getattr(ctx, "metrics_window_size", 10000), - enable_detailed_tracking=getattr(ctx, "enable_detailed_tracking", True), - ) - - # 尝试启动资源监控（需要psutil） - if RESOURCE_MONITOR_AVAILABLE: - try: - self.resource_monitor = ResourceMonitor( - sampling_interval=getattr(ctx, "resource_sampling_interval", 1.0), - enable_auto_start=True, - ) - except Exception as e: - self.logger.warning( - f"Failed to start resource monitoring for task {self.name}: {e}" - ) - else: - self.logger.debug( - f"psutil not available, resource monitoring disabled for task {self.name}" - ) - - # 可选：启动性能汇报器 - if getattr(ctx, "enable_auto_report", False): - self.metrics_reporter = MetricsReporter( - metrics_collector=self.metrics_collector, - resource_monitor=self.resource_monitor, - report_interval=getattr(ctx, "report_interval", 60), - enable_auto_report=True, - report_callback=lambda report: self.logger.info(f"\n{report}"), - ) - - self.logger.info(f"Performance monitoring enabled for task {self.name}") - except Exception as e: - self.logger.warning(f"Failed to initialize monitoring for task {self.name}: {e}") - self._enable_monitoring = False - - try: - self.operator: BaseOperator = operator_factory.create_operator(self.ctx) - if hasattr(self.operator, "task"): - self.operator.task = self # type: ignore - except Exception as e: - self.logger.error(f"Failed to initialize node {self.name}: {e}", exc_info=True) - raise - - def get_state(self) -> dict: - """ - 获取任务完整状态用于 checkpoint - - 包括： - 1. Task 层的状态（processed_count, error_count 等） - 2. Operator 层的状态（通过 operator.get_state()） - 3. Function 层的状态（通过 function.get_state()，已包含在 operator 中） - - Returns: - 任务完整状态字典 - """ - state = { - # === Task 元数据 === - "task_id": self.name, - "task_type": self.__class__.__name__, - "is_spout": self.is_spout, - "timestamp": time.time(), - # === Task 性能指标 === - "processed_count": self._processed_count, - "error_count": self._error_count, - "checkpoint_counter": self._checkpoint_counter, - "last_checkpoint_time": self._last_checkpoint_time, - # === Task 配置 === - "delay": self.delay, - } - - # === Operator 和 Function 状态 === - if hasattr(self.operator, "get_state"): - try: - operator_state = self.operator.get_state() - state["operator_state"] = operator_state - - self.logger.debug( - f"Captured operator state for {self.name}: {list(operator_state.keys())}" - ) - - # 如果 operator_state 包含 function_state，也记录 - if "function_state" in operator_state: - function_attrs = list(operator_state["function_state"].keys()) - self.logger.debug(f"Function state includes: {function_attrs}") - - except Exception as e: - self.logger.warning( - f"Failed to get operator state for {self.name}: {e}", exc_info=True - ) - state["operator_state"] = None - else: - self.logger.warning( - f"Operator {self.operator.__class__.__name__} does not support get_state()" - ) - state["operator_state"] = None - - # === Context 配置信息（只保存配置，不保存运行时对象）=== - try: - state["context_config"] = { - "name": self.ctx.name, - "is_spout": self.ctx.is_spout, - "delay": self.ctx.delay, - # 不保存 queue, router 等运行时对象 - } - except Exception as e: - self.logger.warning(f"Failed to capture context config: {e}") - - # 记录状态大小（用于监控） - try: - import sys - - state_size = sys.getsizeof(str(state)) - self.logger.debug(f"Checkpoint state size for {self.name}: {state_size} bytes") - except Exception: - pass - - return state - - def restore_state(self, state: dict): - """ - 从 checkpoint 完整恢复任务状态 - - 恢复顺序： - 1. Task 层状态 - 2. Operator 层状态 - 3. Function 层状态（通过 operator.restore_state） - - Args: - state: 保存的状态字典 - """ - self.logger.info(f"⏮️ Restoring state for task {self.name}") - - try: - # === 恢复 Task 层状态 === - self._processed_count = state.get("processed_count", 0) - self._error_count = state.get("error_count", 0) - self._checkpoint_counter = state.get("checkpoint_counter", 0) - self._last_checkpoint_time = state.get("last_checkpoint_time", 0.0) - - self.logger.info( - f"✅ Task state restored: " - f"processed={self._processed_count}, " - f"errors={self._error_count}, " - f"checkpoints={self._checkpoint_counter}" - ) - - # === 恢复 Operator 和 Function 状态 === - operator_state = state.get("operator_state") - if operator_state and hasattr(self.operator, "restore_state"): - try: - self.operator.restore_state(operator_state) - self.logger.info(f"✅ Operator state restored for {self.name}") - - # 验证 function 状态是否恢复 - if hasattr(self.operator, "function"): - function = self.operator.function - - # 记录恢复的 function 属性 - restored_attrs = [] - if "function_state" in operator_state: - for attr_name in operator_state["function_state"].keys(): - if hasattr(function, attr_name): - value = getattr(function, attr_name) - restored_attrs.append(f"{attr_name}={value}") - - if restored_attrs: - self.logger.info( - f"✅ Function attributes restored: {', '.join(restored_attrs)}" - ) - - except Exception as e: - self.logger.error(f"❌ Failed to restore operator state: {e}", exc_info=True) - else: - if not operator_state: - self.logger.warning(f"⚠️ No operator state found in checkpoint for {self.name}") - elif not hasattr(self.operator, "restore_state"): - self.logger.warning( - f"⚠️ Operator {self.operator.__class__.__name__} does not support restore_state()" - ) - - self.logger.info(f"🎉 Complete state restoration finished for task {self.name}") - - except Exception as e: - self.logger.error( - f"❌ Critical error during state restoration for {self.name}: {e}", - exc_info=True, - ) - raise - - def save_checkpoint_if_needed(self, fault_handler) -> bool: - """ - 如果需要，保存 checkpoint - - Args: - fault_handler: 容错处理器 - - Returns: - True 如果保存了 checkpoint - """ - # 检查是否是 CheckpointBasedRecovery - from sage.kernel.fault_tolerance.impl.checkpoint_recovery import ( - CheckpointBasedRecovery, - ) - - if not isinstance(fault_handler, CheckpointBasedRecovery): - return False - - current_time = time.time() - interval = fault_handler.checkpoint_interval - - # 检查是否应该保存 checkpoint - if (current_time - self._last_checkpoint_time) >= interval: - state = self.get_state() - success = fault_handler.save_checkpoint(self.name, state) - - if success: - self._last_checkpoint_time = current_time - self._checkpoint_counter += 1 - self.logger.debug( - f"Checkpoint #{self._checkpoint_counter} saved for task {self.name}" - ) - - return success - - return False - - @property - def router(self): - return self.ctx.router - - def start_running(self): - """启动任务的工作循环""" - if self.is_running: - self.logger.warning(f"Task {self.name} is already running") - return - - self.logger.info(f"Starting task {self.name}") - - # 设置运行状态 - self.is_running = True - self.ctx.clear_stop_signal() - - # 启动工作线程 - self._worker_thread = threading.Thread( - target=self._worker_loop, name=f"{self.name}_worker", daemon=True - ) - self._worker_thread.start() - - self.logger.info(f"Task {self.name} started with worker thread") - - # 连接管理现在由TaskContext在构造时完成，不再需要动态添加连接 - - def trigger(self, input_tag: str | None = None, packet: "Packet | None" = None) -> None: - try: - self.logger.debug(f"Received data in node {self.name}, channel {input_tag}") - if packet is not None: - self.operator.process_packet(packet) # type: ignore - except Exception as e: - self.logger.error(f"Error processing data in node {self.name}: {e}", exc_info=True) - raise - - def stop(self) -> None: - """Signal the worker loop to stop.""" - if not self.ctx.is_stop_requested(): - self.ctx.set_stop_signal() - self.logger.info(f"Node '{self.name}' received stop signal.") - # 立即标记任务为已停止，这样dispatcher就能正确检测到 - self.is_running = False - - def get_object(self): - return self - - def get_input_buffer(self): - """ - 获取输入缓冲区 - :return: 输入缓冲区对象 - """ - # 通过描述符获取队列实例 - return self.input_qd.queue_instance - - def _worker_loop(self) -> None: - """ - Main worker loop that executes continuously until stop is signaled. - """ - # 获取 fault_handler（如果有） - fault_handler = None - if ( - hasattr(self.ctx, "dispatcher") - and self.ctx.dispatcher - and hasattr(self.ctx.dispatcher, "fault_handler") - ): - fault_handler = self.ctx.dispatcher.fault_handler - self.logger.debug(f"Task {self.name} has fault_handler: {type(fault_handler).__name__}") - - # Main execution loop - while not self.ctx.is_stop_requested(): - try: - # ✅ 定期保存 checkpoint - if fault_handler: - self.save_checkpoint_if_needed(fault_handler) - - if self.is_spout: - self.logger.debug(f"Running spout node '{self.name}'") - if hasattr(self.operator, "receive_packet"): - self.operator.receive_packet(None) # type: ignore - - # 增加处理计数 - self._processed_count += 1 - - # 检查是否在执行后收到了停止信号 - if self.ctx.is_stop_requested(): - break - - self.logger.debug(f"self.delay: {self.delay}") - if self.delay > 0.002: - time.sleep(self.delay) - else: - # For non-spout nodes, fetch input and process - try: - data_packet = self.input_qd.get(timeout=5.0) - except QUEUE_EMPTY_EXCEPTIONS: - if self.delay > 0.002: - time.sleep(self.delay) - continue - except Exception as e: - self.logger.error( - f"Unexpected error fetching data for task {self.name}: {e}", - exc_info=True, - ) - if self.delay > 0.002: - time.sleep(self.delay) - continue - - self.logger.debug( - f"Node '{self.name}' received data packet: {data_packet}, type: {type(data_packet)}" - ) - - if data_packet is None: - self.logger.info(f"Task {self.name}: Received None packet, continuing loop") - if self.delay > 0.002: - time.sleep(self.delay) - continue - - # Check if received packet is a StopSignal - if isinstance(data_packet, StopSignal): - self.logger.info(f"Node '{self.name}' received stop signal: {data_packet}") - - from sage.kernel.api.operator.join_operator import JoinOperator - from sage.kernel.api.operator.sink_operator import SinkOperator - - if isinstance(self.operator, SinkOperator): - self.logger.info( - f"SinkOperator {self.name} starting graceful shutdown after stop signal" - ) - self._handle_sink_stop_signal(data_packet) - break - elif isinstance(self.operator, (JoinOperator)): - self.logger.info( - f"Calling handle_stop_signal for {type(self.operator).__name__} {self.name}" - ) - input_index = getattr(data_packet, "input_index", None) - self.operator.handle_stop_signal( - stop_signal_name=data_packet.source, - input_index=input_index, - ) - continue - - # 停止当前task的worker loop - from sage.kernel.api.operator.filter_operator import ( - FilterOperator, - ) - from sage.kernel.api.operator.keyby_operator import ( - KeyByOperator, - ) - from sage.kernel.api.operator.map_operator import MapOperator - - if isinstance(self.operator, (KeyByOperator, MapOperator, FilterOperator)): - self.logger.info( - f"Intermediate operator {self.name} received stop signal, draining remaining data first" - ) - drained = self._drain_and_process_remaining(data_packet) - self.logger.info( - f"Intermediate operator {self.name} drained {drained} packets before forwarding stop signal" - ) - self.router.send_stop_signal(data_packet) - try: - stop_packet = Packet(payload=data_packet) - self.operator.receive_packet(stop_packet) - except Exception as e: - self.logger.error( - f"Error processing StopSignal in {self.name}: {e}" - ) - self.ctx.send_stop_signal_back(self.name) - self.ctx.set_stop_signal() - break - else: - self.router.send_stop_signal(data_packet) - should_stop_pipeline = self.ctx.handle_stop_signal(data_packet) - if should_stop_pipeline: - self.ctx.set_stop_signal() - break - - continue - - # 记录包处理开始（如果启用监控） - packet_id = None - if self._enable_monitoring and self.metrics_collector: - packet_id = self.metrics_collector.record_packet_start( - packet_id=getattr(data_packet, "packet_id", None), - packet_size=getattr(data_packet, "size", 0), - ) - - # 处理数据包 - try: - self.operator.receive_packet(data_packet) - - # 记录包处理成功 - if self._enable_monitoring and self.metrics_collector and packet_id: - self.metrics_collector.record_packet_end( - packet_id=packet_id, - success=True, - ) - self._processed_count += 1 - - except Exception as process_error: - # 记录包处理失败 - if self._enable_monitoring and self.metrics_collector and packet_id: - self.metrics_collector.record_packet_end( - packet_id=packet_id, - success=False, - error_type=type(process_error).__name__, - ) - self._error_count += 1 - raise - - except Exception as e: - if fault_handler: - try: - current_state = self.get_state() - saved = fault_handler.save_checkpoint( - task_id=self.name, - state=current_state, - force=True, # 强制保存，忽略时间间隔 - ) - if saved: - self.logger.info( - f"💾 Checkpoint saved on exception for task {self.name} " - f"(processed={self._processed_count}, errors={self._error_count})" - ) - except Exception as checkpoint_error: - self.logger.warning( - f"Failed to save checkpoint on exception: {checkpoint_error}" - ) - # ✅ 捕获异常并使用容错处理器 - self.logger.error(f"Critical error in node '{self.name}': {str(e)}", exc_info=True) - self._error_count += 1 - - # 通知 dispatcher 处理失败 - if fault_handler: - handled = fault_handler.handle_failure(self.name, e) - if handled: - self.logger.info( - f"Task {self.name} failure was handled by fault tolerance, " - f"task will be restarted" - ) - # 任务将被重启，退出当前 worker loop - break - else: - self.logger.error( - f"Task {self.name} failure could not be handled, stopping..." - ) - break - else: - # 没有 dispatcher 或容错处理器，直接停止 - self.logger.error( - f"No dispatcher available for fault handling, task {self.name} stopping" - ) - break - - self.is_running = False - self.logger.info(f"Task {self.name} worker loop exited") - - @property - def is_spout(self) -> bool: - """检查是否为 spout 节点""" - return self.ctx.is_spout - - @property - def delay(self) -> float: - """获取任务的延迟时间""" - return self.ctx.delay - - @property - def logger(self): - """获取当前任务的日志记录器""" - return self.ctx.logger - - @property - def name(self) -> str: - """获取任务名称""" - return self.ctx.name - - def cleanup(self): - """清理任务资源""" - self.logger.info(f"Cleaning up task {self.name}") - - try: - # 停止任务 - if self.is_running: - self.stop() - - # 停止监控组件 - if self._enable_monitoring: - if self.metrics_reporter: - self.metrics_reporter.stop_reporting() - if self.resource_monitor: - self.resource_monitor.stop_monitoring() - self.logger.debug(f"Stopped monitoring for task {self.name}") - - # # 清理算子资源 - # if hasattr(self.operator, 'cleanup'): - # self.operator.cleanup() - # 这些内容应该会自己清理掉 - # # 清理路由器 - # if hasattr(self.router, 'cleanup'): - # self.router.cleanup() - - # 清理输入队列描述符 - if self.input_qd: - if hasattr(self.input_qd, "cleanup"): - self.input_qd.cleanup() # type: ignore - elif hasattr(self.input_qd, "close"): - self.input_qd.close() # type: ignore - - # 清理运行时上下文（包括service_manager） - if hasattr(self.ctx, "cleanup"): - self.ctx.cleanup() - - self.logger.debug(f"Task {self.name} cleanup completed") - - except Exception as e: - self.logger.error(f"Error during cleanup of task {self.name}: {e}") - - def _handle_sink_stop_signal(self, stop_signal: "StopSignal"): - """Gracefully drain in-flight data before finalizing a sink task.""" - drain_timeout = getattr(self.operator, "drain_timeout", 10.0) - quiet_period = getattr(self.operator, "drain_quiet_period", 0.3) - drained = self._drain_inflight_messages( - timeout=drain_timeout, - quiet_period=quiet_period, - ) - - if drained == -1: - self.logger.warning(f"Sink task {self.name} timed out while draining in-flight data") - else: - self.logger.info( - f"Sink task {self.name} drained {drained} in-flight packets before shutdown" - ) - - # 完成最终的关闭逻辑 - try: - if hasattr(self.operator, "handle_stop_signal"): - self.operator.handle_stop_signal() # type: ignore - except Exception as e: - self.logger.error( - f"Error during sink operator finalization for {self.name}: {e}", - exc_info=True, - ) - - # 通过上下文通知JobManager并传播停止信号 - try: - self.ctx.handle_stop_signal(stop_signal) - finally: - self.ctx.set_stop_signal() - - def _drain_and_process_remaining(self, stop_signal: "StopSignal") -> int: - """Drain and process remaining packets before forwarding stop signal.""" - if not self.input_qd: - return 0 - drained_packets = 0 - timeout = 5.0 - quiet_period = 0.5 - poll_interval = 0.1 - start_time = time.time() - last_packet_time = start_time - self.logger.debug(f"Intermediate task {self.name} draining remaining packets") - while True: - elapsed = time.time() - start_time - if elapsed >= timeout: - self.logger.warning(f"Intermediate task {self.name} timed out while draining") - break - try: - packet = self.input_qd.get(timeout=poll_interval) - except QUEUE_EMPTY_EXCEPTIONS: - if time.time() - last_packet_time >= quiet_period: - break - continue - if isinstance(packet, StopSignal): - continue - try: - self.operator.receive_packet(packet) - drained_packets += 1 - last_packet_time = time.time() - except Exception as e: - self.logger.error(f"Failed to process drained packet in {self.name}: {e}") - return drained_packets - - def _drain_inflight_messages( - self, - timeout: float, - quiet_period: float, - ) -> int: - """Drain packets that arrived before the stop signal reached the sink.""" - if not self.input_qd: - return 0 - - start_time = time.time() - last_packet_time = start_time - drained_packets = 0 - poll_interval = min(quiet_period, 0.1) - - self.logger.debug( - f"Sink task {self.name} draining queues with timeout={timeout}s and quiet_period={quiet_period}s" - ) - - while True: - elapsed = time.time() - start_time - if elapsed >= timeout: - return -1 - - try: - packet = self.input_qd.get(timeout=poll_interval) - except QUEUE_EMPTY_EXCEPTIONS: - if time.time() - last_packet_time >= quiet_period: - break - continue - - if isinstance(packet, StopSignal): - # 如果还有其他停止信号，继续等待数据排空 - continue - - try: - self.operator.receive_packet(packet) - drained_packets += 1 - last_packet_time = time.time() - except Exception as e: - self.logger.error( - f"Failed to process in-flight packet during draining for {self.name}: {e}", - exc_info=True, - ) - - return drained_packets - - # === Performance Monitoring API === - - def get_current_metrics(self) -> TaskPerformanceMetrics | None: - """ - 获取当前性能指标 - - Returns: - TaskPerformanceMetrics 实例，如果监控未启用则返回 None - """ - if not self._enable_monitoring or not self.metrics_collector: - return None - - metrics = self.metrics_collector.get_real_time_metrics() - - # 添加资源监控数据 - if self.resource_monitor: - cpu, memory = self.resource_monitor.get_current_usage() - metrics.cpu_usage_percent = cpu - metrics.memory_usage_mb = memory - - # 添加队列深度 - if self.input_qd: - try: - queue_instance = self.input_qd.queue_instance - if queue_instance and hasattr(queue_instance, "qsize"): - metrics.input_queue_depth = queue_instance.qsize() - except Exception: - pass - - return metrics - - def reset_metrics(self) -> None: - """重置性能指标""" - if self.metrics_collector: - self.metrics_collector.reset_metrics() - - def export_metrics(self, format: str = "json") -> str | None: - """ - 导出性能指标 - - Args: - format: 导出格式 ("json", "prometheus", "csv", "human") - - Returns: - 格式化的指标字符串，如果监控未启用则返回 None - """ - if not self._enable_monitoring or not self.metrics_reporter: - return None - - return self.metrics_reporter.generate_report(format=format) diff --git a/packages/sage-kernel/src/sage/kernel/runtime/task/local_task.py b/packages/sage-kernel/src/sage/kernel/runtime/task/local_task.py deleted file mode 100644 index 001220bf92..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/task/local_task.py +++ /dev/null @@ -1,29 +0,0 @@ -from typing import TYPE_CHECKING - -from sage.kernel.runtime.task.base_task import BaseTask - -if TYPE_CHECKING: - from sage.kernel.runtime.context.task_context import TaskContext - from sage.kernel.runtime.factory.operator_factory import OperatorFactory - - -class LocalTask(BaseTask): - """ - 本地任务节点，使用SageQueue高性能共享队列作为输入缓冲区 - 内部运行独立的工作线程，处理数据流 - """ - - def __init__( - self, - ctx: "TaskContext", - operator_factory: "OperatorFactory", - max_buffer_size: int = 30000, - queue_maxsize: int = 50000, - ) -> None: - # 调用父类初始化 - super().__init__(ctx, operator_factory) - - self.logger.info(f"Initialized LocalTask: {self.ctx.name}") - self.logger.debug( - f"Buffer max size: {max_buffer_size} bytes, Queue max size: {queue_maxsize}" - ) diff --git a/packages/sage-kernel/src/sage/kernel/runtime/task/ray_task.py b/packages/sage-kernel/src/sage/kernel/runtime/task/ray_task.py deleted file mode 100644 index 40e06f3ed5..0000000000 --- a/packages/sage-kernel/src/sage/kernel/runtime/task/ray_task.py +++ /dev/null @@ -1,160 +0,0 @@ -""" -Remote Environment Heartbeat Fault Tolerance Implementation - -为 RayTask 添加心跳发送功能,支持 Remote 环境下的故障检测和恢复。 -""" - -from typing import TYPE_CHECKING, Any - -import ray - -from sage.kernel.runtime.communication.packet import Packet -from sage.kernel.runtime.task.base_task import BaseTask - -if TYPE_CHECKING: - from sage.kernel.runtime.context.task_context import TaskContext - from sage.kernel.runtime.factory.operator_factory import OperatorFactory - - -# ========== 心跳配置常量 ========== -HEARTBEAT_INTERVAL = 5.0 # 心跳发送间隔 (秒) -HEARTBEAT_TIMEOUT = 15.0 # 心跳超时阈值 (秒) -MAX_MISSED_HEARTBEATS = 3 # 最大允许丢失心跳次数 - - -@ray.remote -class RayTask(BaseTask): - """ - 带心跳监控的 RayTask - - 扩展自 BaseTask,添加心跳发送功能用于 Remote 环境故障检测: - - 定期发送心跳到 Dispatcher - - 报告任务状态和处理指标 - - 支持 Checkpoint 容错恢复 - - 使用方法: - # 创建时传入 dispatcher_ref - task = RayTaskWithHeartbeat.remote(ctx, operator_factory, dispatcher_ref) - - # 启动任务 (会自动启动心跳线程) - ray.get(task.start_running.remote()) - """ - - def __init__( - self, - ctx: "TaskContext", - operator_factory: "OperatorFactory", - ) -> None: - """ - 初始化 RayTask 并设置心跳机制 - - Args: - ctx: 运行时上下文 - operator_factory: Operator 工厂 - """ - # 调用父类初始化 - super().__init__(ctx, operator_factory) - - self.task_id = ctx.name - - # ========== 属性别名 (映射到 BaseTask 的私有属性) ========== - @property - def input_buffer(self): - """输入缓冲区（通过队列描述符访问）""" - return self.input_qd.get_queue() if self.input_qd else None - - @property - def packet_count(self) -> int: - """已处理数据包数量""" - return self._processed_count - - @packet_count.setter - def packet_count(self, value: int) -> None: - self._processed_count = value - - @property - def error_count(self) -> int: - """错误计数""" - return self._error_count - - @error_count.setter - def error_count(self, value: int) -> None: - self._error_count = value - - @property - def last_checkpoint_time(self) -> float: - """最后检查点时间""" - return self._last_checkpoint_time - - @property - def _heartbeat_enabled(self) -> bool: - """心跳是否启用（始终为 False，兼容旧代码）""" - return False - - @property - def heartbeat_interval(self) -> float: - """心跳间隔（默认值，兼容旧代码）""" - return HEARTBEAT_INTERVAL - - def _get_current_status(self) -> str: - """获取当前状态""" - return "running" if self.is_running else "stopped" - - def put_packet(self, packet: "Packet", max_retries: int = 3): - """ - 向任务的输入缓冲区放入数据包（阻塞模式） - - Args: - packet: 要放入的数据包 - max_retries: 保留参数（兼容性，阻塞模式下不使用） - - Returns: - True 如果成功，False 如果失败 - - Note: - 使用完全阻塞模式，直到队列有空间可用 - """ - try: - # 完全阻塞模式：等待直到队列有空间 - self.input_buffer.put(packet, block=True) # type: ignore[union-attr] - self.logger.debug(f"RayTask.put_packet succeeded for {self.ctx.name}") - # 成功：更新计数并返回 - self.packet_count += 1 - return True - - except Exception as e: - # 其他异常：直接失败 - self.logger.error( - f"RayTask.put_packet failed for {self.ctx.name}: {type(e).__name__}: {e}" - ) - self.error_count += 1 - return False - - def stop(self) -> None: - """ - 停止任务 (重写父类方法,确保心跳线程也停止) - - 停止顺序: - 1. 调用父类 stop() 停止工作线程 - 2. 心跳线程会检测 is_running=False 并自动退出 - """ - super().stop() - self.logger.info(f"RayTask {self.ctx.name} stopped (heartbeat will terminate)") - - def get_heartbeat_stats(self) -> dict[str, Any]: - """ - 获取心跳统计信息 (用于监控和调试) - - Returns: - 统计信息字典 - """ - return { - "task_id": self.ctx.name, - "heartbeat_enabled": self._heartbeat_enabled, - "heartbeat_interval": self.heartbeat_interval, - "status": self._get_current_status(), - "packet_count": self.packet_count, - "error_count": self.error_count, - "last_checkpoint_time": self.last_checkpoint_time, - "is_running": self.is_running, - } diff --git a/packages/sage-kernel/src/sage/kernel/scheduler/__init__.py b/packages/sage-kernel/src/sage/kernel/scheduler/__init__.py deleted file mode 100644 index 661d8b7ac3..0000000000 --- a/packages/sage-kernel/src/sage/kernel/scheduler/__init__.py +++ /dev/null @@ -1,71 +0,0 @@ -""" -Scheduler Module - 分布式任务调度（重构后架构） - -Layer: L3 (Kernel - Scheduler) -Dependencies: sage.platform (L2), sage.common (L1) - -架构原则： -1. 职责分离： - - Scheduler: 纯决策者（返回 PlacementDecision） - - PlacementExecutor: 纯执行者（接收决策，执行放置） - - Dispatcher: 协调者（决策 → 执行） - -2. 对用户透明： - - 用户只需在创建 Environment 时指定调度策略 - - 并行度是 operator 级别的配置 - - 调度策略是应用级别的配置 - -用户使用方式: - from sage.kernel import LocalEnvironment - - # 基础用法 - 使用默认调度器（FIFO） - env = LocalEnvironment() - - # 指定调度器类型（字符串） - env = LocalEnvironment(scheduler="fifo") # FIFO 策略 - env = LocalEnvironment(scheduler="load_aware") # 负载感知策略 - - # 构建 pipeline - (env.from_source(MySource) - .map(MyOperator, parallelism=4) - .sink(MySink)) - - env.submit() # Dispatcher 协调 Scheduler 和 Placement - -开发者对比不同策略: - from sage.kernel.scheduler.impl import FIFOScheduler, LoadAwareScheduler - - # 实验对比 - for scheduler_cls in [FIFOScheduler, LoadAwareScheduler]: - env = LocalEnvironment(scheduler=scheduler_cls()) - env.submit() - metrics = env.scheduler.get_metrics() - print(f"{scheduler_cls.__name__}: {metrics}") - -详细说明请查看: - FLOW_EXPLANATION.md - 调度和放置流程说明 - ARCHITECTURE.md - 架构设计文档 - decision = scheduler.make_decision(node) - - # 2. 根据决策等待（如果需要） - if decision.delay > 0: - time.sleep(decision.delay) - - # 3. 执行物理放置 - task = placement_executor.place_task(node, decision) -""" - -from sage.kernel.scheduler.api import BaseScheduler -from sage.kernel.scheduler.decision import PlacementDecision -from sage.kernel.scheduler.placement import PlacementExecutor - -# 核心组件： -# - BaseScheduler: 调度器抽象基类 -# - PlacementDecision: 调度决策数据结构 -# - PlacementExecutor: 放置执行器 - -__all__ = [ - "BaseScheduler", - "PlacementDecision", - "PlacementExecutor", -] diff --git a/packages/sage-kernel/src/sage/kernel/scheduler/api.py b/packages/sage-kernel/src/sage/kernel/scheduler/api.py deleted file mode 100644 index d900937c16..0000000000 --- a/packages/sage-kernel/src/sage/kernel/scheduler/api.py +++ /dev/null @@ -1,254 +0,0 @@ -""" -Scheduler API - 调度器核心 API 定义 - -架构原则（重构后）： -1. 职责分离： - - Scheduler: 纯决策者（返回 PlacementDecision，不执行放置） - - PlacementExecutor: 纯执行者（接收决策，执行物理放置） - - Dispatcher: 协调者（决策 → 执行） - -2. 对用户透明： - - 用户只需在创建 Environment 时指定调度策略 - - 并行度是 operator 级别的 - 在定义 transformation 时指定 - - 调度策略是应用级别的 - 在 Environment 中配置 - -用户使用示例: - # 应用级别指定调度策略 - env = LocalEnvironment(scheduler="fifo") # 或 "load_aware", "priority" 等 - - # operator 级别指定并行度和资源需求 - (env.from_source(MySource) - .map(MyOperator, parallelism=4, cpu_required=4, memory_required="8GB") - .filter(MyFilter, parallelism=2) - .sink(MySink)) - - env.submit() # Dispatcher 协调 Scheduler 和 Placement - -开发者使用示例（对比不同调度策略）: - from sage.kernel.scheduler.impl import FIFOScheduler, LoadAwareScheduler - - # 策略 1: FIFO - env1 = LocalEnvironment(scheduler=FIFOScheduler()) - - # 策略 2: 负载感知 - env2 = LocalEnvironment(scheduler=LoadAwareScheduler(max_concurrent=10)) - -正确流程: - Dispatcher.submit(): - for node in graph.nodes: - # 1. 获取调度决策 - decision = scheduler.make_decision(node) - - # 2. 根据决策等待（如果需要） - if decision.delay > 0: - time.sleep(decision.delay) - - # 3. 执行物理放置 - task = placement_executor.place_task(node, decision) - - # 4. 保存任务实例 - self.tasks[node.name] = task -""" - -from abc import ABC, abstractmethod -from typing import TYPE_CHECKING, Any, Union - -if TYPE_CHECKING: - from sage.kernel.runtime.graph.graph_node import TaskNode - from sage.kernel.runtime.graph.service_node import ServiceNode - from sage.kernel.runtime.service.local_service_task import LocalServiceTask - from sage.kernel.runtime.task.local_task import LocalTask - from sage.kernel.scheduler.decision import PlacementDecision - from sage.kernel.utils.ray.actor import ActorWrapper - - -class BaseScheduler(ABC): - """ - 调度器抽象基类 - 纯决策者 - - 重要架构变更： - - Scheduler 不再持有 placement_executor（解耦） - - Scheduler 返回 PlacementDecision，不返回 Task（职责分离） - - Dispatcher 协调 Scheduler 和 PlacementExecutor（中介者模式） - - 调度器在 Environment 级别配置，对用户透明。 - 并行度在 operator 级别指定（transformation.parallelism）。 - - 职责： - 1. 分析任务节点信息（并行度、资源需求等） - 2. 评估系统状态（负载、资源可用性等） - 3. 制定调度决策（何时、何处、如何放置） - 4. 返回决策对象（不执行放置） - """ - - def __init__(self): - """ - 初始化调度器 - - 注意：Scheduler 不再持有 placement_executor - PlacementExecutor 由 Dispatcher 持有和管理 - """ - self.scheduled_count = 0 - self.decision_history = [] - - @abstractmethod - def make_decision(self, task_node: "TaskNode") -> "PlacementDecision": - """ - 制定任务调度决策（核心方法） - - 这是 Scheduler 的核心职责：分析并返回调度决策，不执行放置。 - - 调度器根据以下因素做出决策： - - task_node.transformation.parallelism (并行度) - - task_node.transformation 的资源需求（cpu_required, memory_required等） - - 当前系统负载和资源可用性 - - 调度策略（FIFO、优先级、负载感知等） - - Args: - task_node: 任务节点（包含 transformation 和 parallelism 信息） - - Returns: - PlacementDecision: 调度决策对象，包含： - - target_node: 目标物理节点 - - resource_requirements: 资源需求 - - delay: 延迟时间 - - placement_strategy: 放置策略 - - reason: 决策原因 - - 示例: - decision = scheduler.make_decision(task_node) - # decision = PlacementDecision( - # target_node="worker-node-2", - # resource_requirements={"cpu": 4, "memory": "8GB"}, - # delay=0.0, - # reason="Load-aware: node-2 has lowest CPU usage" - # ) - """ - pass - - def make_service_decision(self, service_node: "ServiceNode") -> "PlacementDecision": - """ - 制定服务调度决策 - - 服务通常需要特殊处理（如固定节点、持久化等） - 子类可以重写此方法提供自定义逻辑。 - - Args: - service_node: 服务节点 - - Returns: - PlacementDecision: 服务放置决策 - """ - # 默认实现：使用立即默认配置 - from sage.kernel.scheduler.decision import PlacementDecision - - return PlacementDecision.immediate_default( - reason=f"Service placement: {service_node.service_name}" - ) - - def schedule_task( - self, task_node: "TaskNode", runtime_ctx=None - ) -> Union["LocalTask", "ActorWrapper"]: - """ - 调度任务（兼容性方法） - - 这是一个高级 API，用于直接创建和调度任务。 - 内部调用 make_decision() 获取调度决策，然后通过任务工厂创建任务。 - - 使用场景： - - 单元测试和集成测试 - - 简单调度场景（不需要显式处理决策） - - 与现有代码兼容 - - Args: - task_node: 任务节点 - runtime_ctx: 运行时上下文（如果为 None，使用 task_node.ctx） - - Returns: - 创建的任务实例（LocalTask 或 ActorWrapper） - """ - # 调用核心决策方法 - decision = self.make_decision(task_node) - - # 根据决策延迟（如果需要） - if hasattr(decision, "delay") and decision.delay > 0: - import time - - time.sleep(decision.delay) - - # 通过任务工厂创建任务 - ctx = runtime_ctx if runtime_ctx is not None else task_node.ctx - task = task_node.task_factory.create_task(task_node.name, ctx) - - return task - - def schedule_service( - self, service_node: "ServiceNode", runtime_ctx=None - ) -> Union["LocalServiceTask", "ActorWrapper"]: - """ - 调度服务（兼容性方法） - - 这是一个高级 API，用于直接创建和调度服务。 - 内部调用 make_service_decision() 获取调度决策，然后通过服务工厂创建服务。 - - Args: - service_node: 服务节点 - runtime_ctx: 运行时上下文（如果为 None，使用 service_node.ctx） - - Returns: - 创建的服务任务实例（LocalServiceTask 或 ActorWrapper） - """ - # 调用服务决策方法 - decision = self.make_service_decision(service_node) - - # 根据决策延迟（如果需要） - if hasattr(decision, "delay") and decision.delay > 0: - import time - - time.sleep(decision.delay) - - # 通过服务工厂创建服务 - ctx = runtime_ctx if runtime_ctx is not None else service_node.ctx - service = service_node.service_task_factory.create_service_task(ctx) - - return service - - def task_completed(self, task_name: str): - """ - 任务完成通知 - - 当任务完成或停止时，Dispatcher 会调用此方法通知调度器。 - 调度器可以更新内部状态，释放资源计数器等。 - - 默认实现为空，子类可以重写以实现资源跟踪等功能。 - - Args: - task_name: 已完成的任务名称 - """ - pass - - def get_metrics(self) -> dict[str, Any]: - """ - 获取调度器性能指标（供开发者对比不同策略） - - Returns: - 指标字典，例如： - { - 'scheduler_type': 'FIFO', - 'total_scheduled': 100, - 'avg_latency_ms': 45.2, - 'decisions': 100 - } - """ - return { - "scheduler_type": self.__class__.__name__, - "scheduled_count": self.scheduled_count, - "decisions": len(self.decision_history), - } - - def shutdown(self): - """关闭调度器，释放资源""" - self.decision_history.clear() - - -__all__ = ["BaseScheduler"] diff --git a/packages/sage-kernel/src/sage/kernel/scheduler/decision.py b/packages/sage-kernel/src/sage/kernel/scheduler/decision.py deleted file mode 100644 index f57ece9ecd..0000000000 --- a/packages/sage-kernel/src/sage/kernel/scheduler/decision.py +++ /dev/null @@ -1,199 +0,0 @@ -""" -PlacementDecision - 调度决策数据结构 - -Scheduler 的返回值，表示调度决策而不是任务实例。 -Dispatcher 根据决策调用 PlacementExecutor 执行放置。 -""" - -from dataclasses import dataclass, field -from typing import Any - - -@dataclass -class PlacementDecision: - """ - 调度决策：Scheduler.make_decision() 的返回值 - - 职责分离： - - Scheduler 返回决策（这个类） - - Dispatcher 协调执行 - - PlacementExecutor 执行放置 - - 决策内容： - 1. 放置位置（target_node） - 2. 资源需求（resource_requirements） - 3. 调度时机（delay, immediate） - 4. 放置策略（placement_strategy） - 5. 元数据（reason, priority） - """ - - # ===== 放置位置 ===== - target_node: str | None = None - """目标物理节点 ID（Ray node_id） - - None: 使用 Ray 默认负载均衡 - - "node-xxx": 指定节点 - - "local": 强制本地执行 - """ - - # ===== 资源需求 ===== - resource_requirements: dict[str, Any] | None = None - """资源需求配置 - 示例: {"cpu": 4, "gpu": 1, "memory": "8GB", "custom_resource": 2} - - cpu: CPU 核心数 - - gpu: GPU 数量 - - memory: 内存大小（支持字符串如 "8GB" 或字节数） - - 自定义资源: 用户定义的资源类型 - """ - - # ===== 调度时机 ===== - delay: float = 0.0 - """延迟调度时间（秒） - - 0.0: 立即调度 - - > 0: 延迟指定秒数后调度 - """ - - immediate: bool = True - """是否立即调度 - - True: 立即执行 - - False: 可以批量延迟调度 - """ - - # ===== 放置策略 ===== - placement_strategy: str = "default" - """放置策略类型 - - "default": Ray 默认负载均衡 - - "spread": 分散放置（尽量不同节点） - - "pack": 紧凑放置（尽量相同节点） - - "affinity": 亲和性放置（靠近数据源） - - "anti_affinity": 反亲和性（远离特定任务） - """ - - affinity_tasks: list[str] | None = None - """亲和性任务列表（需要靠近的任务名）""" - - anti_affinity_tasks: list[str] | None = None - """反亲和性任务列表（需要远离的任务名）""" - - # ===== 元数据 ===== - reason: str = "" - """决策原因（用于日志和调试） - 示例: "Load-aware: node-2 has lowest CPU usage" - """ - - priority: int = 0 - """调度优先级（数值越大优先级越高） - - 0: 普通优先级 - - > 0: 高优先级 - - < 0: 低优先级 - """ - - metadata: dict[str, Any] = field(default_factory=dict) - """额外的元数据（用于扩展）""" - - def __repr__(self) -> str: - """可读的字符串表示""" - parts = [ - "PlacementDecision(", - f"target_node={self.target_node}", - ] - - if self.resource_requirements: - parts.append(f"resources={self.resource_requirements}") - - if self.delay > 0: - parts.append(f"delay={self.delay}s") - - if self.placement_strategy != "default": - parts.append(f"strategy={self.placement_strategy}") - - if self.reason: - parts.append(f"reason='{self.reason}'") - - return ", ".join(parts) + ")" - - def to_dict(self) -> dict[str, Any]: - """转换为字典（用于序列化）""" - return { - "target_node": self.target_node, - "resource_requirements": self.resource_requirements, - "delay": self.delay, - "immediate": self.immediate, - "placement_strategy": self.placement_strategy, - "affinity_tasks": self.affinity_tasks, - "anti_affinity_tasks": self.anti_affinity_tasks, - "reason": self.reason, - "priority": self.priority, - "metadata": self.metadata, - } - - @classmethod - def from_dict(cls, data: dict[str, Any]) -> "PlacementDecision": - """从字典创建（用于反序列化）""" - return cls(**data) - - @classmethod - def immediate_default(cls, reason: str = "") -> "PlacementDecision": - """快捷方法：立即使用默认配置调度""" - return cls( - target_node=None, - resource_requirements=None, - delay=0.0, - immediate=True, - placement_strategy="default", - reason=reason or "Immediate default placement", - ) - - @classmethod - def with_resources( - cls, - cpu: int | None = None, - gpu: int | None = None, - memory: int | str | None = None, # Accept both int and str - reason: str = "", - ) -> "PlacementDecision": - """快捷方法：指定资源需求""" - resources: dict[str, int | str] = {} - if cpu is not None: - resources["cpu"] = cpu - if gpu is not None: - resources["gpu"] = gpu - if memory is not None: - resources["memory"] = memory - - return cls( - target_node=None, - resource_requirements=resources if resources else None, - delay=0.0, - immediate=True, - placement_strategy="default", - reason=reason or f"Resource requirements: {resources}", - ) - - @classmethod - def with_node( - cls, node_id: str, strategy: str = "default", reason: str = "" - ) -> "PlacementDecision": - """快捷方法：指定目标节点""" - return cls( - target_node=node_id, - resource_requirements=None, - delay=0.0, - immediate=True, - placement_strategy=strategy, - reason=reason or f"Target node: {node_id}", - ) - - @classmethod - def with_delay(cls, delay_seconds: float, reason: str = "") -> "PlacementDecision": - """快捷方法：延迟调度""" - return cls( - target_node=None, - resource_requirements=None, - delay=delay_seconds, - immediate=False, - placement_strategy="default", - reason=reason or f"Delayed by {delay_seconds}s", - ) - - -__all__ = ["PlacementDecision"] diff --git a/packages/sage-kernel/src/sage/kernel/scheduler/examples_node_placement.py b/packages/sage-kernel/src/sage/kernel/scheduler/examples_node_placement.py deleted file mode 100644 index 698e724b2a..0000000000 --- a/packages/sage-kernel/src/sage/kernel/scheduler/examples_node_placement.py +++ /dev/null @@ -1,398 +0,0 @@ -""" -示例：如何使用 PlacementDecision 指定物理节点 - -这个示例展示了： -1. 如何获取 Ray 集群中的物理节点信息 -2. 如何在 Scheduler 中使用 NodeSelector 选择节点 -3. 如何通过 PlacementDecision 指定目标节点 -4. PlacementExecutor 如何将任务放置到指定节点 -""" - -from typing import Any - -import ray - -from sage.kernel.scheduler.api import BaseScheduler -from sage.kernel.scheduler.decision import PlacementDecision -from sage.kernel.scheduler.node_selector import NodeSelector -from sage.kernel.utils.ray.ray_utils import ensure_ray_initialized - -# ============================================================ -# 示例 1: 查看集群节点信息 -# ============================================================ - - -def example_inspect_cluster(): - """查看 Ray 集群中的所有物理节点""" - - # 初始化 Ray（如果还没初始化） - if not ray.is_initialized(): - ensure_ray_initialized() - - # 获取所有节点 - nodes = ray.nodes() - - print(f"集群中有 {len(nodes)} 个节点：\n") - - for i, node in enumerate(nodes, 1): - print(f"节点 {i}:") - print(f" NodeID: {node['NodeID']}") # ← 这就是物理节点的唯一标识 - print(f" 地址: {node['NodeManagerAddress']}") - print(f" 主机名: {node.get('NodeManagerHostname', 'N/A')}") - print(f" 状态: {'活跃' if node['Alive'] else '离线'}") - - resources = node.get("Resources", {}) - print(" 资源:") - print(f" CPU: {resources.get('CPU', 0)}") - print(f" GPU: {resources.get('GPU', 0)}") - print(f" 内存: {resources.get('memory', 0) / (1024**3):.2f} GB") - print() - - # 示例输出： - # 节点 1: - # NodeID: a1b2c3d4e5f6789... - # 地址: 192.168.1.100 - # 主机名: worker-node-1 - # 状态: 活跃 - # 资源: - # CPU: 32.0 - # GPU: 0.0 - # 内存: 128.00 GB - # - # 节点 2: - # NodeID: f6e5d4c3b2a1098... - # 地址: 192.168.1.101 - # 主机名: worker-node-2 - # 状态: 活跃 - # 资源: - # CPU: 32.0 - # GPU: 4.0 - # 内存: 256.00 GB - - -# ============================================================ -# 示例 2: 使用 NodeSelector 选择节点 -# ============================================================ - - -def example_node_selector(): - """使用 NodeSelector 根据策略选择节点""" - - selector = NodeSelector() - - # 策略 1: 选择负载最低的节点 - least_loaded = selector.select_least_loaded_node() - print(f"负载最低的节点: {least_loaded}") - - # 策略 2: 选择有 GPU 的节点 - gpu_node = selector.select_node_with_gpu(min_gpu_count=1) - print(f"有 GPU 的节点: {gpu_node}") - - # 策略 3: 选择满足资源需求的节点 - node_with_resources = selector.select_best_node( - cpu_required=8, - memory_required=16 * 1024**3, # 16GB - ) - print(f"满足资源需求的节点: {node_with_resources}") - - -# ============================================================ -# 示例 3: Scheduler 返回指定节点的决策 -# ============================================================ - - -class NodeAwareScheduler(BaseScheduler): - """ - 节点感知调度器：根据任务需求选择合适的节点 - """ - - def __init__(self): - super().__init__() - self.node_selector = NodeSelector() - - def make_decision(self, task_node): - """ - 根据任务特性选择合适的节点 - """ - - # 检查任务是否需要 GPU - gpu_required = ( - getattr(task_node.transformation, "gpu_required", 0) - if hasattr(task_node, "transformation") - else 0 - ) - needs_gpu = gpu_required > 0 - - if needs_gpu: - # GPU 任务：选择有 GPU 的节点 - target_node = self.node_selector.select_node_with_gpu() - gpu_count = gpu_required - - decision = PlacementDecision( - target_node=target_node, # ← 指定目标节点 - resource_requirements={"cpu": 4, "gpu": gpu_count, "memory": "16GB"}, - placement_strategy="gpu", - reason=f"GPU task: selected node {target_node} with GPU", - ) - else: - # CPU 任务：选择负载最低的节点 - target_node = self.node_selector.select_least_loaded_node() - - # 提取资源需求 - cpu = ( - getattr(task_node.transformation, "cpu_required", 1) - if hasattr(task_node, "transformation") - else 1 - ) - memory = ( - getattr(task_node.transformation, "memory_required", "1GB") - if hasattr(task_node, "transformation") - else "1GB" - ) - - decision = PlacementDecision( - target_node=target_node, # ← 指定目标节点 - resource_requirements={"cpu": cpu, "memory": memory}, - placement_strategy="load_aware", - reason=f"CPU task: selected least loaded node {target_node}", - ) - - # 记录决策 - self.scheduled_count += 1 - self.decision_history.append(decision) - - return decision - - -# ============================================================ -# 示例 4: 完整的调度流程 -# ============================================================ - - -def example_full_scheduling_flow(): - """ - 展示完整的调度流程：从决策到执行 - - 注意：这是伪代码示例，实际使用时需要根据项目结构调整导入 - """ - - # from sage.kernel.api import LocalEnvironment - # from sage.kernel.operators import MapOperator - # from sage.kernel.sources import ListSource - # from sage.kernel.sinks import PrintSink - - print("这是伪代码示例，展示调度流程概念") - return - - # 下面是伪代码，展示概念 - # """ - # # 创建自定义调度器 - # scheduler = NodeAwareScheduler() - # - # # 创建 Environment（使用自定义调度器） - # env = LocalEnvironment( - # name="node_aware_demo", - # platform="remote", - # scheduler=scheduler - # ) - # - # # 定义一个需要 GPU 的 Operator - # class GPUOperator: - # gpu_required = 1 - # def process(self, record): - # return record * 2 - # - # # 构建 Pipeline - # env.from_source(...).map(GPUOperator, parallelism=2).sink(...) - # """ - # - # # 提交作业 - # # 内部流程： - # # 1. Dispatcher.submit() 被调用 - # # 2. 对每个任务： - # # - scheduler.make_decision(task_node) 返回决策 - # # → decision = PlacementDecision(target_node="gpu-node-id", ...) - # # - placement_executor.place_task(task_node, decision) - # # → 创建 Actor 到指定的 GPU 节点 - # # - task.start_running() - # # → 启动数据处理 - # env.submit() - # - # # 等待完成 - # env.wait_for_completion() - # - # # 查看调度指标 - # metrics = scheduler.get_metrics() - # print(f"调度统计: {metrics}") - # - # # 查看决策历史 - # print("\n决策历史:") - # for i, decision in enumerate(scheduler.decision_history, 1): - # print(f"{i}. {decision}") - - -# ============================================================ -# 示例 5: 手动指定节点（调试用） -# ============================================================ - - -class DebugScheduler(BaseScheduler): - """ - 调试调度器：强制所有任务到指定节点 - 用于调试和测试 - """ - - def __init__(self, debug_node_id: str): - super().__init__() - self.debug_node_id = debug_node_id - - def make_decision(self, task_node): - """所有任务都放到调试节点""" - - self.scheduled_count += 1 - - decision = PlacementDecision( - target_node=self.debug_node_id, # ← 强制指定节点 - resource_requirements=None, # 使用默认资源 - placement_strategy="debug", - reason=f"Debug mode: all tasks on node {self.debug_node_id}", - ) - - self.decision_history.append(decision) - return decision - - -def example_debug_scheduling(): - """调试示例：所有任务到同一节点""" - - # 假设我们知道调试节点的 ID - debug_node_id = "a1b2c3d4e5f6..." # 从 ray.nodes() 获取 - - # 创建调试调度器 - DebugScheduler(debug_node_id) - - print(f"创建调试调度器，所有任务将被放置到节点: {debug_node_id}") - print("(这是示例概念，实际使用需要配置 Environment)") - - # 伪代码： - # env = LocalEnvironment(name="debug_demo", platform="remote", scheduler=scheduler) - # env.submit() - - -# ============================================================ -# 示例 6: 查看 PlacementExecutor 如何使用决策 -# ============================================================ - - -def example_placement_execution(): - """ - PlacementExecutor 如何根据决策执行放置 - """ - - # 这是 PlacementExecutor 内部的实现（简化版） - def build_ray_options(decision: PlacementDecision): - """将决策转换为 Ray Actor 选项""" - - options: dict[str, Any] = {"lifetime": "detached"} - - # === 关键：指定目标节点 === - if decision.target_node: - from ray.util.scheduling_strategies import NodeAffinitySchedulingStrategy - - options["scheduling_strategy"] = NodeAffinitySchedulingStrategy( - node_id=decision.target_node, # ← 使用决策中的节点 ID - soft=False, # 硬要求：必须放到这个节点 - ) - - print(f"✓ 将 Actor 放置到节点: {decision.target_node}") - - # === 指定资源需求 === - if decision.resource_requirements: - if "cpu" in decision.resource_requirements: - options["num_cpus"] = decision.resource_requirements["cpu"] - print(f"✓ 要求 CPU: {options['num_cpus']}") - - if "gpu" in decision.resource_requirements: - options["num_gpus"] = decision.resource_requirements["gpu"] - print(f"✓ 要求 GPU: {options['num_gpus']}") - - if "memory" in decision.resource_requirements: - memory_value = decision.resource_requirements["memory"] - memory_bytes = parse_memory(memory_value) # type: ignore[arg-type] - options["memory"] = memory_bytes - print(f"✓ 要求内存: {memory_value}") - - return options - - def parse_memory(memory_str: str) -> int: - """解析内存字符串""" - if isinstance(memory_str, int): - return memory_str - - memory_str = memory_str.upper() - if "GB" in memory_str: - return int(float(memory_str.replace("GB", "")) * 1024**3) - elif "MB" in memory_str: - return int(float(memory_str.replace("MB", "")) * 1024**2) - return 1024**3 - - # 示例决策 - decision = PlacementDecision( - target_node="f6e5d4c3b2a1098...", - resource_requirements={"cpu": 4, "gpu": 1, "memory": "16GB"}, - reason="GPU task on worker-node-2", - ) - - print("决策:") - print(f" 目标节点: {decision.target_node}") - print(f" 资源需求: {decision.resource_requirements}") - print(f" 原因: {decision.reason}") - print() - - print("执行放置:") - ray_options = build_ray_options(decision) - print() - - print("Ray Actor 选项:") - for key, value in ray_options.items(): - print(f" {key}: {value}") - - # 输出: - # 决策: - # 目标节点: f6e5d4c3b2a1098... - # 资源需求: {'cpu': 4, 'gpu': 1, 'memory': '16GB'} - # 原因: GPU task on worker-node-2 - # - # 执行放置: - # ✓ 将 Actor 放置到节点: f6e5d4c3b2a1098... - # ✓ 要求 CPU: 4 - # ✓ 要求 GPU: 1 - # ✓ 要求内存: 16GB - # - # Ray Actor 选项: - # lifetime: detached - # scheduling_strategy: NodeAffinitySchedulingStrategy(node_id='f6e5d4c3b2a1098...', soft=False) - # num_cpus: 4 - # num_gpus: 1 - # memory: 17179869184 - - -# ============================================================ -# 运行示例 -# ============================================================ - -if __name__ == "__main__": - print("=" * 60) - print("示例 1: 查看集群节点信息") - print("=" * 60) - example_inspect_cluster() - - print("\n" + "=" * 60) - print("示例 2: 使用 NodeSelector") - print("=" * 60) - example_node_selector() - - print("\n" + "=" * 60) - print("示例 6: PlacementExecutor 执行") - print("=" * 60) - example_placement_execution() diff --git a/packages/sage-kernel/src/sage/kernel/scheduler/impl/__init__.py b/packages/sage-kernel/src/sage/kernel/scheduler/impl/__init__.py deleted file mode 100644 index bd734d960f..0000000000 --- a/packages/sage-kernel/src/sage/kernel/scheduler/impl/__init__.py +++ /dev/null @@ -1,72 +0,0 @@ -""" -Scheduler Implementation Module. - -This module contains various scheduling strategy implementations -for developer comparison experiments. - -Configure scheduler at Environment level: - env = LocalEnvironment(scheduler="fifo") - env = LocalEnvironment(scheduler="load_aware") - -Available strategies: -- FIFOScheduler: First-in-first-out (simplest baseline) -- LoadAwareScheduler: Resource-aware scheduling -- RandomScheduler: Random node selection (baseline) -- RoundRobinScheduler: Fair round-robin scheduling -- PriorityScheduler: Priority-based scheduling - -Scheduler name mapping: - "fifo" -> FIFOScheduler - "load_aware" -> LoadAwareScheduler - "random" -> RandomScheduler - "round_robin"-> RoundRobinScheduler - "priority" -> PriorityScheduler -""" - -from sage.kernel.scheduler.impl.priority_scheduler import PriorityScheduler -from sage.kernel.scheduler.impl.random_scheduler import RandomScheduler -from sage.kernel.scheduler.impl.resource_aware_scheduler import LoadAwareScheduler -from sage.kernel.scheduler.impl.round_robin_scheduler import RoundRobinScheduler -from sage.kernel.scheduler.impl.simple_scheduler import FIFOScheduler - -# Scheduler registry (string -> class) -SCHEDULER_REGISTRY: dict[str, type] = { - "fifo": FIFOScheduler, - "load_aware": LoadAwareScheduler, - "random": RandomScheduler, - "round_robin": RoundRobinScheduler, - "priority": PriorityScheduler, -} - - -def get_scheduler(name: str, **kwargs): - """ - Get a scheduler instance by name. - - Args: - name: Scheduler name (e.g., "fifo", "load_aware", "random") - **kwargs: Scheduler initialization parameters - - Returns: - Scheduler instance - - Example: - scheduler = get_scheduler("load_aware", max_concurrent=20) - """ - name_lower = name.lower() - if name_lower not in SCHEDULER_REGISTRY: - available = ", ".join(SCHEDULER_REGISTRY.keys()) - raise ValueError(f"Unknown scheduler: {name}. Available: {available}") - - return SCHEDULER_REGISTRY[name_lower](**kwargs) - - -__all__ = [ - "FIFOScheduler", - "LoadAwareScheduler", - "RandomScheduler", - "RoundRobinScheduler", - "PriorityScheduler", - "SCHEDULER_REGISTRY", - "get_scheduler", -] diff --git a/packages/sage-kernel/src/sage/kernel/scheduler/impl/priority_scheduler.py b/packages/sage-kernel/src/sage/kernel/scheduler/impl/priority_scheduler.py deleted file mode 100644 index a3365d3358..0000000000 --- a/packages/sage-kernel/src/sage/kernel/scheduler/impl/priority_scheduler.py +++ /dev/null @@ -1,197 +0,0 @@ -""" -Priority Scheduler - Priority-based scheduling. - -Schedule tasks based on priority levels. -High priority tasks get scheduled first. -""" - -import heapq -import time -from dataclasses import dataclass, field -from typing import TYPE_CHECKING, Any - -from sage.kernel.scheduler.api import BaseScheduler -from sage.kernel.scheduler.decision import PlacementDecision - -if TYPE_CHECKING: - from sage.kernel.runtime.graph.graph_node import TaskNode - from sage.kernel.runtime.graph.service_node import ServiceNode - - -@dataclass(order=True) -class PrioritizedTask: - """Priority task wrapper.""" - - priority: int - timestamp: float = field(compare=False) - task_name: str = field(compare=False) - - -class PriorityScheduler(BaseScheduler): - """Priority Scheduler - Priority-based intelligent scheduling.""" - - def __init__( - self, - platform: str = "local", - default_priority: int = 5, - max_concurrent: int = 10, - enable_aging: bool = True, - aging_boost: int = 1, - aging_threshold: float = 10.0, - ): - super().__init__() - self.platform = platform - self.default_priority = default_priority - self.max_concurrent = max_concurrent - self.enable_aging = enable_aging - self.aging_boost = aging_boost - self.aging_threshold = aging_threshold - - self.total_latency = 0.0 - self.active_tasks = 0 - self._priority_queue: list[PrioritizedTask] = [] - self._task_priorities: dict[str, int] = {} - self._priority_distribution: dict[int, int] = {} - self._preemptions = 0 - - from sage.kernel.scheduler.node_selector import NodeSelector - - self.node_selector = NodeSelector(cache_ttl=0.5, enable_tracking=True) - - def _extract_priority(self, task_node: "TaskNode") -> int: - """Extract priority from task node.""" - priority = self.default_priority - if hasattr(task_node, "transformation") and task_node.transformation: - if hasattr(task_node.transformation, "priority"): - priority = getattr(task_node.transformation, "priority", priority) - if hasattr(task_node, "priority"): - priority = getattr(task_node, "priority", priority) - return max(1, min(10, priority)) - - def _apply_aging(self) -> None: - """Apply priority aging to prevent starvation.""" - if not self.enable_aging: - return - current_time = time.time() - new_queue = [] - for task in self._priority_queue: - wait_time = current_time - task.timestamp - if wait_time > self.aging_threshold: - boosted_priority = task.priority - self.aging_boost - new_queue.append( - PrioritizedTask( - priority=boosted_priority, - timestamp=task.timestamp, - task_name=task.task_name, - ) - ) - else: - new_queue.append(task) - self._priority_queue = new_queue - heapq.heapify(self._priority_queue) - - def make_decision(self, task_node: "TaskNode") -> PlacementDecision: - """Priority scheduling decision.""" - start_time = time.time() - - priority = self._extract_priority(task_node) - self._task_priorities[task_node.name] = priority - self._priority_distribution[priority] = self._priority_distribution.get(priority, 0) + 1 - - delay = 0.0 - if self.active_tasks >= self.max_concurrent: - self._apply_aging() - while self.active_tasks >= self.max_concurrent: - time.sleep(0.01) - delay += 0.01 - - target_node = None - is_remote = task_node.task_factory.remote if hasattr(task_node, "task_factory") else False - - if is_remote: - strategy = "balanced" if priority >= 7 else "pack" - target_node = self.node_selector.select_best_node(strategy=strategy) - if target_node: - self.node_selector.track_task_placement(task_node.name, target_node) - - self.active_tasks += 1 - self.scheduled_count += 1 - elapsed = time.time() - start_time - self.total_latency += elapsed - - node_info = "" - if target_node: - node = self.node_selector.get_node(target_node) - node_info = node.hostname if node else target_node[:8] - else: - node_info = "default" - - decision = PlacementDecision( - target_node=target_node, - resource_requirements=None, - delay=delay, - immediate=(delay == 0), - placement_strategy="priority", - reason=f"Priority: {priority}/10, task={task_node.name}, node={node_info}", - ) - self.decision_history.append(decision) - return decision - - def make_service_decision(self, service_node: "ServiceNode") -> PlacementDecision: - """Priority service scheduling decision.""" - self.scheduled_count += 1 - service_priority = 8 - - target_node = self.node_selector.select_best_node(strategy="balanced") - if target_node: - self.node_selector.track_task_placement(service_node.service_name, target_node) - - decision = PlacementDecision( - target_node=target_node, - resource_requirements=None, - delay=0.0, - immediate=True, - placement_strategy="priority", - reason=f"Priority service: {service_node.service_name}, priority={service_priority}", - ) - self.decision_history.append(decision) - return decision - - def task_completed(self, task_name: str): - """Task completion notification.""" - self.active_tasks = max(0, self.active_tasks - 1) - self.node_selector.untrack_task(task_name) - if task_name in self._task_priorities: - del self._task_priorities[task_name] - - def get_metrics(self) -> dict[str, Any]: - """Get scheduler performance metrics.""" - avg_latency = self.total_latency / self.scheduled_count if self.scheduled_count > 0 else 0 - total_priority = sum(p * c for p, c in self._priority_distribution.items()) - total_count = sum(self._priority_distribution.values()) - avg_priority = total_priority / total_count if total_count > 0 else self.default_priority - - return { - "scheduler_type": "Priority", - "total_scheduled": self.scheduled_count, - "avg_latency_ms": avg_latency * 1000, - "active_tasks": self.active_tasks, - "max_concurrent": self.max_concurrent, - "decisions": len(self.decision_history), - "platform": self.platform, - "default_priority": self.default_priority, - "avg_priority": avg_priority, - "priority_distribution": dict(self._priority_distribution), - "enable_aging": self.enable_aging, - "preemptions": self._preemptions, - } - - def shutdown(self): - """Shutdown scheduler.""" - super().shutdown() - self._priority_queue.clear() - self._task_priorities.clear() - self._priority_distribution.clear() - - -__all__ = ["PriorityScheduler"] diff --git a/packages/sage-kernel/src/sage/kernel/scheduler/impl/random_scheduler.py b/packages/sage-kernel/src/sage/kernel/scheduler/impl/random_scheduler.py deleted file mode 100644 index 4fc6ae7f2a..0000000000 --- a/packages/sage-kernel/src/sage/kernel/scheduler/impl/random_scheduler.py +++ /dev/null @@ -1,119 +0,0 @@ -""" -Random Scheduler - Random node selection. - -Uses random strategy to select target nodes for task scheduling. -Used as a comparison baseline simulating unintelligent random allocation. - -Features: -- Random node selection (no optimization) -- Simple, low overhead -- Suitable for uniform load, homogeneous nodes -- Can be used as baseline for comparison - -Usage: - env = LocalEnvironment(scheduler="random") -""" - -import random -import time -from typing import TYPE_CHECKING, Any - -from sage.kernel.scheduler.api import BaseScheduler -from sage.kernel.scheduler.decision import PlacementDecision - -if TYPE_CHECKING: - from sage.kernel.runtime.graph.graph_node import TaskNode - from sage.kernel.runtime.graph.service_node import ServiceNode - - -class RandomScheduler(BaseScheduler): - """Random Scheduler - Random node selection.""" - - def __init__(self, platform: str = "local", seed: int | None = None): - super().__init__() - self.platform = platform - self.total_latency = 0.0 - self._rng = random.Random(seed) - - from sage.kernel.scheduler.node_selector import NodeSelector - - self.node_selector = NodeSelector(cache_ttl=1.0, enable_tracking=True) - - def make_decision(self, task_node: "TaskNode") -> PlacementDecision: - """Random scheduling decision: randomly select an available node.""" - start_time = time.time() - - target_node = None - reason = "Random selection" - - is_remote = task_node.task_factory.remote if hasattr(task_node, "task_factory") else False - - if is_remote: - nodes = self.node_selector.list_available_nodes() - if nodes: - selected = self._rng.choice(nodes) - target_node = selected.node_id - reason = f"Random: selected {selected.hostname} from {len(nodes)} nodes" - self.node_selector.track_task_placement(task_node.name, target_node) - - self.scheduled_count += 1 - elapsed = time.time() - start_time - self.total_latency += elapsed - - decision = PlacementDecision( - target_node=target_node, - resource_requirements=None, - delay=0.0, - immediate=True, - placement_strategy="random", - reason=reason, - ) - - self.decision_history.append(decision) - return decision - - def make_service_decision(self, service_node: "ServiceNode") -> PlacementDecision: - """Random service scheduling decision.""" - self.scheduled_count += 1 - - nodes = self.node_selector.list_available_nodes() - target_node = None - - if nodes: - selected = self._rng.choice(nodes) - target_node = selected.node_id - self.node_selector.track_task_placement(service_node.service_name, target_node) - - decision = PlacementDecision( - target_node=target_node, - resource_requirements=None, - delay=0.0, - immediate=True, - placement_strategy="random", - reason=f"Random service placement: {service_node.service_name}", - ) - - self.decision_history.append(decision) - return decision - - def task_completed(self, task_name: str): - """Task completion notification.""" - self.node_selector.untrack_task(task_name) - - def get_metrics(self) -> dict[str, Any]: - """Get scheduler performance metrics.""" - avg_latency = self.total_latency / self.scheduled_count if self.scheduled_count > 0 else 0 - return { - "scheduler_type": "Random", - "total_scheduled": self.scheduled_count, - "avg_latency_ms": avg_latency * 1000, - "decisions": len(self.decision_history), - "platform": self.platform, - } - - def shutdown(self): - """Shutdown scheduler.""" - super().shutdown() - - -__all__ = ["RandomScheduler"] diff --git a/packages/sage-kernel/src/sage/kernel/scheduler/impl/resource_aware_scheduler.py b/packages/sage-kernel/src/sage/kernel/scheduler/impl/resource_aware_scheduler.py deleted file mode 100644 index 784cf400f1..0000000000 --- a/packages/sage-kernel/src/sage/kernel/scheduler/impl/resource_aware_scheduler.py +++ /dev/null @@ -1,370 +0,0 @@ -""" -负载感知调度器 - Load-Aware Scheduler - -根据当前系统负载和资源使用情况进行调度决策。 -适合资源受限或负载波动的场景。 - -特点： -- 监控系统资源使用 -- 根据负载动态调度 -- 避免资源过载 -- 平衡资源利用率 - -架构（重构后）： -- 纯决策者：返回 PlacementDecision，不执行放置 -- 不持有 PlacementExecutor -- Dispatcher 协调决策和执行 - -使用方式: - # 方式 1: 字符串指定 - env = LocalEnvironment(scheduler="load_aware") - - # 方式 2: 实例化指定 - from sage.kernel.scheduler.impl import LoadAwareScheduler - env = LocalEnvironment(scheduler=LoadAwareScheduler(max_concurrent=10)) -""" - -import time -from typing import TYPE_CHECKING, Any - -from sage.kernel.scheduler.api import BaseScheduler -from sage.kernel.scheduler.decision import PlacementDecision - -if TYPE_CHECKING: - from sage.kernel.runtime.graph.graph_node import TaskNode - from sage.kernel.runtime.graph.service_node import ServiceNode - - -class LoadAwareScheduler(BaseScheduler): - """ - 负载感知调度器 - 资源感知的智能调度 - - 核心功能： - 1. 监控集群资源状态（CPU、GPU、内存） - 2. 根据任务需求选择最优节点 - 3. 负载均衡调度 - 4. 跟踪任务分配 - - 调度策略： - - 分析任务的资源需求（从 transformation 中提取） - - 使用 NodeSelector 选择负载最低且满足需求的节点 - - 控制并发数避免过载 - - 返回节点分配决策 - """ - - def __init__( - self, - platform: str = "local", - max_concurrent: int = 10, - strategy: str = "balanced", - ): - """ - 初始化负载感知调度器 - - Args: - platform: 平台类型 ('local' 或 'remote') - max_concurrent: 最大并发任务数 - strategy: 调度策略 - - "balanced": 负载均衡（默认） - - "pack": 紧凑放置 - - "spread": 分散放置 - """ - super().__init__() - self.platform = platform - self.max_concurrent = max_concurrent - self.strategy = strategy - self.total_latency = 0.0 - self.active_tasks = 0 - self.resource_utilization: list[float] = [] - - # 集成 NodeSelector（资源感知核心） - from sage.kernel.scheduler.node_selector import NodeSelector - - self.node_selector = NodeSelector(cache_ttl=0.5, enable_tracking=True) - - def make_decision(self, task_node: "TaskNode") -> PlacementDecision: - """ - 负载感知调度决策：基于资源状态和任务需求选择最优节点 - - 决策流程： - 1. 检查并发限制 - 2. 提取任务资源需求 - 3. 使用 NodeSelector 选择最优节点 - 4. 跟踪任务分配 - 5. 返回节点分配决策 - - Args: - task_node: 任务节点 - - Returns: - PlacementDecision: 包含目标节点和资源需求的决策 - """ - start_time = time.time() - - # === 步骤 1: 检查并发限制（负载控制）=== - delay = 0.0 - while self.active_tasks >= self.max_concurrent: - time.sleep(0.01) # 等待资源释放 - delay += 0.01 - - # === 步骤 2: 提取任务资源需求 === - cpu_required = 1.0 - gpu_required = 0.0 - memory_required = 0 - custom_resources: dict[str, float] = {} - - if hasattr(task_node, "transformation") and task_node.transformation: - # CPU 需求 - if hasattr(task_node.transformation, "cpu_required"): - cpu_required = getattr(task_node.transformation, "cpu_required", cpu_required) - - # GPU 需求 - if hasattr(task_node.transformation, "gpu_required"): - gpu_required = getattr(task_node.transformation, "gpu_required", gpu_required) - - # 内存需求 - if hasattr(task_node.transformation, "memory_required"): - memory_str = getattr(task_node.transformation, "memory_required", None) - if memory_str: - memory_required = self._parse_memory(memory_str) - - # 自定义资源 - if hasattr(task_node.transformation, "custom_resources"): - custom_resources = getattr( - task_node.transformation, "custom_resources", custom_resources - ) - - # === 步骤 3: 使用 NodeSelector 选择最优节点 === - target_node = None - - # 只有远程模式才需要选择节点 - if task_node.task_factory.remote if hasattr(task_node, "task_factory") else False: - target_node = self.node_selector.select_best_node( - cpu_required=cpu_required, - gpu_required=gpu_required, - memory_required=memory_required, - custom_resources=custom_resources if custom_resources else None, - strategy=self.strategy, - ) - - # 跟踪任务分配 - if target_node: - self.node_selector.track_task_placement(task_node.name, target_node) - - # === 步骤 4: 构建资源需求字典 === - resource_requirements = {} - if cpu_required > 0: - resource_requirements["cpu"] = cpu_required - if gpu_required > 0: - resource_requirements["gpu"] = gpu_required - if memory_required > 0: - resource_requirements["memory"] = memory_required - if custom_resources: - resource_requirements.update(custom_resources) - - # === 步骤 5: 更新状态 === - self.active_tasks += 1 - self.scheduled_count += 1 - elapsed = time.time() - start_time - self.total_latency += elapsed - - # 记录资源利用率 - utilization = self.active_tasks / self.max_concurrent - self.resource_utilization.append(utilization) - - # === 步骤 6: 返回决策 === - # 获取节点信息用于日志 - node_info = "" - if target_node: - node_res = self.node_selector.get_node(target_node) - if node_res: - node_info = f"{node_res.hostname} (CPU:{node_res.cpu_usage:.1%}, GPU:{node_res.gpu_usage:.1%})" - else: - node_info = target_node[:8] - else: - node_info = "default" - - decision = PlacementDecision( - target_node=target_node, - resource_requirements=(resource_requirements if resource_requirements else None), - delay=delay, - immediate=(delay == 0), - placement_strategy=self.strategy, - reason=f"LoadAware: task={task_node.name}, node={node_info}, " - + f"req=[CPU:{cpu_required}, GPU:{gpu_required}], active={self.active_tasks}", - ) - - self.decision_history.append(decision) - return decision - - def _parse_memory(self, memory) -> int: - """ - 解析内存字符串为字节数 - - Args: - memory: 内存大小（字符串如 "8GB" 或整数字节数） - - Returns: - 内存字节数 - """ - if isinstance(memory, int): - return memory - - if isinstance(memory, str): - memory = memory.upper() - if "GB" in memory: - return int(float(memory.replace("GB", "")) * 1024**3) - elif "MB" in memory: - return int(float(memory.replace("MB", "")) * 1024**2) - elif "KB" in memory: - return int(float(memory.replace("KB", "")) * 1024) - - return 0 - - def make_service_decision(self, service_node: "ServiceNode") -> PlacementDecision: - """ - 负载感知的服务调度决策 - - 服务通常需要长期运行，因此需要更谨慎的资源分配： - 1. 提取服务的资源需求 - 2. 选择资源充足且负载低的节点 - 3. 优先使用 spread 策略避免单点故障 - - Args: - service_node: 服务节点 - - Returns: - PlacementDecision: 服务调度决策 - """ - start_time = time.time() - - # === 步骤 1: 提取服务资源需求 === - cpu_required = 1.0 - gpu_required = 0.0 - memory_required = 0 - custom_resources: dict[str, float] = {} - - if hasattr(service_node, "service_class"): - service_class = getattr(service_node, "service_class", None) - if service_class: - # CPU 需求 - cpu_required = getattr(service_class, "cpu_required", cpu_required) - - # GPU 需求 - gpu_required = getattr(service_class, "gpu_required", gpu_required) - - # 内存需求 - memory_str = getattr(service_class, "memory_required", None) - if memory_str: - memory_required = self._parse_memory(memory_str) - - # 自定义资源 - custom_resources = getattr(service_class, "custom_resources", custom_resources) - - # === 步骤 2: 使用 NodeSelector 选择节点（优先使用 spread 策略）=== - # 服务通常需要长期运行，使用 spread 策略避免单点故障 - service_strategy = "spread" - target_node = self.node_selector.select_best_node( - cpu_required=cpu_required, - gpu_required=gpu_required, - memory_required=memory_required, - custom_resources=custom_resources if custom_resources else None, - strategy=service_strategy, - ) - - # 跟踪服务分配 - if target_node: - self.node_selector.track_task_placement(service_node.service_name, target_node) - - # === 步骤 3: 构建资源需求字典 === - resource_requirements = {} - if cpu_required > 0: - resource_requirements["cpu"] = cpu_required - if gpu_required > 0: - resource_requirements["gpu"] = gpu_required - if memory_required > 0: - resource_requirements["memory"] = memory_required - if custom_resources: - resource_requirements.update(custom_resources) - - # === 步骤 4: 更新状态 === - self.scheduled_count += 1 - elapsed = time.time() - start_time - self.total_latency += elapsed - - # === 步骤 5: 返回决策 === - node_info = "" - if target_node: - node_res = self.node_selector.get_node(target_node) - if node_res: - node_info = f"{node_res.hostname} (CPU:{node_res.cpu_usage:.1%}, tasks:{node_res.task_count})" - else: - node_info = target_node[:8] - else: - node_info = "default" - - decision = PlacementDecision( - target_node=target_node, - resource_requirements=(resource_requirements if resource_requirements else None), - delay=0.0, # 服务立即调度 - immediate=True, - placement_strategy=service_strategy, - reason=f"LoadAware Service: {service_node.service_name}, node={node_info}, " - + f"req=[CPU:{cpu_required}, GPU:{gpu_required}], strategy={service_strategy}", - ) - - self.decision_history.append(decision) - return decision - - def task_completed(self, task_name: str): - """ - 任务完成时调用，释放资源并取消跟踪 - - 注意：这个方法应该由 Dispatcher 在任务完成时调用 - - Args: - task_name: 任务名称 - """ - self.active_tasks = max(0, self.active_tasks - 1) - - # 取消任务跟踪 - self.node_selector.untrack_task(task_name) - - def get_metrics(self) -> dict[str, Any]: - """ - 获取调度器性能指标 - - Returns: - 包含负载和资源利用率的指标 - """ - avg_latency = self.total_latency / self.scheduled_count if self.scheduled_count > 0 else 0 - avg_utilization = ( - sum(self.resource_utilization) / len(self.resource_utilization) - if self.resource_utilization - else 0 - ) - - # 获取集群统计 - cluster_stats = self.node_selector.get_cluster_stats() - - return { - "scheduler_type": "LoadAware", - "total_scheduled": self.scheduled_count, - "avg_latency_ms": avg_latency * 1000, - "active_tasks": self.active_tasks, - "max_concurrent": self.max_concurrent, - "avg_resource_utilization": avg_utilization, - "decisions": len(self.decision_history), - "platform": self.platform, - "strategy": self.strategy, - # 集群资源统计 - "cluster": cluster_stats, - } - - def shutdown(self): - """关闭调度器""" - super().shutdown() - self.resource_utilization.clear() - - -__all__ = ["LoadAwareScheduler"] diff --git a/packages/sage-kernel/src/sage/kernel/scheduler/impl/round_robin_scheduler.py b/packages/sage-kernel/src/sage/kernel/scheduler/impl/round_robin_scheduler.py deleted file mode 100644 index 1e920148fd..0000000000 --- a/packages/sage-kernel/src/sage/kernel/scheduler/impl/round_robin_scheduler.py +++ /dev/null @@ -1,139 +0,0 @@ -""" -Round Robin Scheduler - Fair round-robin scheduling. - -Uses round-robin strategy to evenly distribute tasks across nodes. -Ensures each node receives equal number of tasks (long-term). - -Features: -- Fair scheduling: Even task distribution -- Simple and efficient: O(1) scheduling overhead -- Suitable for homogeneous nodes, uniform tasks -- Does not consider node load differences - -Usage: - env = LocalEnvironment(scheduler="round_robin") -""" - -import time -from typing import TYPE_CHECKING, Any - -from sage.kernel.scheduler.api import BaseScheduler -from sage.kernel.scheduler.decision import PlacementDecision - -if TYPE_CHECKING: - from sage.kernel.runtime.graph.graph_node import TaskNode - from sage.kernel.runtime.graph.service_node import ServiceNode - - -class RoundRobinScheduler(BaseScheduler): - """Round Robin Scheduler - Even task distribution.""" - - def __init__(self, platform: str = "local"): - super().__init__() - self.platform = platform - self.total_latency = 0.0 - self._current_index = 0 - self._cached_nodes: list[str] = [] - self._cache_time = 0.0 - self._cache_ttl = 5.0 - - from sage.kernel.scheduler.node_selector import NodeSelector - - self.node_selector = NodeSelector(cache_ttl=5.0, enable_tracking=True) - - def _get_next_node(self) -> str | None: - """Get next node to schedule to (round-robin).""" - current_time = time.time() - - if current_time - self._cache_time > self._cache_ttl: - nodes = self.node_selector.list_available_nodes() - self._cached_nodes = [n.node_id for n in nodes] - self._cache_time = current_time - if self._current_index >= len(self._cached_nodes): - self._current_index = 0 - - if not self._cached_nodes: - return None - - node_id = self._cached_nodes[self._current_index] - self._current_index = (self._current_index + 1) % len(self._cached_nodes) - - return node_id - - def make_decision(self, task_node: "TaskNode") -> PlacementDecision: - """Round-robin scheduling decision: select next node.""" - start_time = time.time() - - target_node = None - reason = "Round-robin selection" - - is_remote = task_node.task_factory.remote if hasattr(task_node, "task_factory") else False - - if is_remote: - target_node = self._get_next_node() - if target_node: - node_info = self.node_selector.get_node(target_node) - hostname = node_info.hostname if node_info else target_node[:8] - reason = f"RoundRobin: selected {hostname} (index={self._current_index - 1})" - self.node_selector.track_task_placement(task_node.name, target_node) - - self.scheduled_count += 1 - elapsed = time.time() - start_time - self.total_latency += elapsed - - decision = PlacementDecision( - target_node=target_node, - resource_requirements=None, - delay=0.0, - immediate=True, - placement_strategy="round_robin", - reason=reason, - ) - - self.decision_history.append(decision) - return decision - - def make_service_decision(self, service_node: "ServiceNode") -> PlacementDecision: - """Round-robin service scheduling decision.""" - self.scheduled_count += 1 - - target_node = self._get_next_node() - if target_node: - self.node_selector.track_task_placement(service_node.service_name, target_node) - - decision = PlacementDecision( - target_node=target_node, - resource_requirements=None, - delay=0.0, - immediate=True, - placement_strategy="round_robin", - reason=f"RoundRobin service: {service_node.service_name}", - ) - - self.decision_history.append(decision) - return decision - - def task_completed(self, task_name: str): - """Task completion notification.""" - self.node_selector.untrack_task(task_name) - - def get_metrics(self) -> dict[str, Any]: - """Get scheduler performance metrics.""" - avg_latency = self.total_latency / self.scheduled_count if self.scheduled_count > 0 else 0 - return { - "scheduler_type": "RoundRobin", - "total_scheduled": self.scheduled_count, - "avg_latency_ms": avg_latency * 1000, - "decisions": len(self.decision_history), - "platform": self.platform, - "current_index": self._current_index, - "nodes_count": len(self._cached_nodes), - } - - def shutdown(self): - """Shutdown scheduler.""" - super().shutdown() - self._cached_nodes.clear() - - -__all__ = ["RoundRobinScheduler"] diff --git a/packages/sage-kernel/src/sage/kernel/scheduler/impl/simple_scheduler.py b/packages/sage-kernel/src/sage/kernel/scheduler/impl/simple_scheduler.py deleted file mode 100644 index 8f740008e1..0000000000 --- a/packages/sage-kernel/src/sage/kernel/scheduler/impl/simple_scheduler.py +++ /dev/null @@ -1,143 +0,0 @@ -""" -FIFO 调度器 - First-In-First-Out Baseline - -最简单的调度策略：按任务到达顺序调度。 -适合作为对比实验的 baseline。 - -特点： -- 简单、可预测 -- 按 FIFO 顺序调度 -- 尊重 operator 级别的并行度设置 -- 适合负载均匀的场景 - -架构（重构后）： -- 纯决策者：返回 PlacementDecision，不执行放置 -- 不持有 PlacementExecutor -- Dispatcher 协调决策和执行 - -使用方式: - # 方式 1: 字符串指定 - env = LocalEnvironment(scheduler="fifo") - - # 方式 2: 实例化指定 - from sage.kernel.scheduler.impl import FIFOScheduler - env = LocalEnvironment(scheduler=FIFOScheduler()) -""" - -import time -from typing import TYPE_CHECKING, Any - -from sage.kernel.scheduler.api import BaseScheduler -from sage.kernel.scheduler.decision import PlacementDecision - -if TYPE_CHECKING: - from sage.kernel.runtime.graph.graph_node import TaskNode - from sage.kernel.runtime.graph.service_node import ServiceNode - - -class FIFOScheduler(BaseScheduler): - """ - FIFO 调度器 - 纯决策者 - - 按照任务到达顺序调度，不进行任何重新排序。 - 尊重 transformation.parallelism 设置。 - 作为最简单的 baseline。 - - 职责： - - 制定调度决策：按 FIFO 顺序，立即调度 - - 返回决策对象：PlacementDecision - - 不执行放置：由 Dispatcher 协调 PlacementExecutor 执行 - """ - - def __init__(self, platform: str = "local"): - """ - 初始化 FIFO 调度器 - - Args: - platform: 平台类型 ('local' 或 'remote') - 这是元数据，不影响调度决策 - """ - super().__init__() - self.platform = platform - self.total_latency = 0.0 - self.start_times: dict[str, float] = {} - - def make_decision(self, task_node: "TaskNode") -> PlacementDecision: - """ - FIFO 调度决策：立即使用默认配置调度 - - FIFO 策略最简单： - 1. 不考虑优先级 - 2. 不考虑负载 - 3. 立即调度 - 4. 使用 Ray 默认负载均衡 - - Args: - task_node: 任务节点（包含 transformation 和 parallelism 信息） - - Returns: - PlacementDecision: 调度决策 - """ - start_time = time.time() - - # FIFO 决策：立即调度到默认节点 - self.scheduled_count += 1 - - decision = PlacementDecision.immediate_default( - reason=f"FIFO order: #{self.scheduled_count}" - ) - - # 记录决策历史 - self.decision_history.append(decision) - - # 记录调度指标 - elapsed = time.time() - start_time - self.total_latency += elapsed - self.start_times[task_node.name] = start_time - - return decision - - def make_service_decision(self, service_node: "ServiceNode") -> PlacementDecision: - """ - FIFO 服务调度决策 - - 对于 FIFO 调度器，服务也按照到达顺序立即调度。 - - Args: - service_node: 服务节点 - - Returns: - PlacementDecision: 服务调度决策 - """ - self.scheduled_count += 1 - - decision = PlacementDecision.immediate_default( - reason=f"FIFO service: {service_node.service_name} (#{self.scheduled_count})" - ) - - self.decision_history.append(decision) - return decision - - def get_metrics(self) -> dict[str, Any]: - """ - 获取 FIFO 调度器的性能指标 - - Returns: - 指标字典 - """ - avg_latency = self.total_latency / self.scheduled_count if self.scheduled_count > 0 else 0 - return { - "scheduler_type": "FIFO", - "total_scheduled": self.scheduled_count, - "avg_latency_ms": avg_latency * 1000, - "decisions": len(self.decision_history), - "platform": self.platform, - } - - def shutdown(self): - """关闭调度器""" - super().shutdown() - self.start_times.clear() - - -__all__ = ["FIFOScheduler"] diff --git a/packages/sage-kernel/src/sage/kernel/scheduler/impl/template_scheduler.py.bak b/packages/sage-kernel/src/sage/kernel/scheduler/impl/template_scheduler.py.bak deleted file mode 100644 index 89f6bbe7a7..0000000000 --- a/packages/sage-kernel/src/sage/kernel/scheduler/impl/template_scheduler.py.bak +++ /dev/null @@ -1,180 +0,0 @@ -""" -调度器实现模板 - -使用这个模板快速实现新的调度策略进行对比实验。 - -重要原则： -1. 调度器在 Environment 级别配置 -2. 并行度在 operator 级别（transformation.parallelism） -3. 调度器负责决策何时、如何调度任务 - -使用方法: -1. 复制这个文件并重命名（例如：priority_scheduler.py） -2. 替换 TemplateScheduler 为你的调度器名称 -3. 实现 schedule_task() 中的调度逻辑 -4. 在 impl/__init__.py 中导出 -5. 通过 Environment(scheduler=YourScheduler()) 使用 -""" -""" -import time -from typing import TYPE_CHECKING, Union - -from sage.kernel.scheduler.api import BaseScheduler - -if TYPE_CHECKING: - from sage.kernel.runtime.graph.graph_node import TaskNode - from sage.kernel.runtime.graph.service_node import ServiceNode - from sage.kernel.runtime.service.base_service_task import BaseServiceTask - from sage.kernel.runtime.service.local_service_task import LocalServiceTask - from sage.kernel.runtime.task.base_task import BaseTask - from sage.kernel.runtime.task.local_task import LocalTask - from sage.kernel.utils.ray.actor import ActorWrapper - -class TemplateScheduler(BaseScheduler): - """ - [你的调度器名称] Scheduler - - [描述你的调度策略] - - 特点： - - [特点 1] - - [特点 2] - - 适用场景： - - [场景 1] - - [场景 2] - - 使用方式: - env = LocalEnvironment(scheduler=TemplateScheduler()) - (env.from_source(MySource) - .map(MyOperator, parallelism=4) - .sink(MySink)) - env.submit() - """ - - def __init__(self, platform: str = "local", **kwargs): - """ - 初始化调度器 - - Args: - platform: 平台类型 ('local' 或 'remote') - **kwargs: 调度器特定的参数 - """ - self.platform = platform - - # 调度器状态 - self.scheduled_count = 0 - self.metrics: dict[str, dict[str, float | int]] = {} - - def schedule_task(self, task_node: "TaskNode", runtime_ctx=None) -> Union["LocalTask", "ActorWrapper"]: - """ - 调度任务节点 - - 核心逻辑： - 1. 从 task_node.transformation 获取 operator 信息 - 2. task_node.transformation.parallelism 包含并行度设置 - 3. 根据你的调度算法做出决策 - 4. 创建并返回任务实例 - - Args: - task_node: 任务节点（包含 transformation 和 parallelism 信息） - runtime_ctx: 运行时上下文 - - Returns: - 创建的任务实例（LocalTask 或 ActorWrapper） - """ - start_time = time.time() - - # 步骤 1: 获取 transformation 信息 - transformation = task_node.transformation - parallelism = getattr(transformation, "parallelism", 1) - - # 步骤 2: 实现你的调度逻辑 - # 例如： - # - 根据 parallelism 决定资源分配 - # - 根据系统负载决定调度时机 - # - 根据任务类型优先级排序 - self._apply_scheduling_logic(task_node, parallelism) - - # 步骤 3: 创建任务 - ctx = runtime_ctx if runtime_ctx is not None else task_node.ctx - task = task_node.task_factory.create_task(task_node.name, ctx) - - # 步骤 4: 记录指标（用于对比不同调度策略） - self.scheduled_count += 1 - elapsed = time.time() - start_time - self._update_metrics(task_node.name, elapsed, parallelism) - - return task - - def _apply_scheduling_logic(self, task_node: "TaskNode", parallelism: int): - """ - 应用你的调度逻辑 - - Args: - task_node: 任务节点 - parallelism: 并行度 - """ - # 实现你的核心调度算法 - # 例如： - # if parallelism > 4: - # # 高并行度任务的特殊处理 - # pass - pass - - def _update_metrics(self, task_name: str, elapsed: float, parallelism: int): - """ - 更新调度指标 - - Args: - task_name: 任务名 - elapsed: 调度耗时 - parallelism: 并行度 - """ - if task_name not in self.metrics: - self.metrics[task_name] = { - "count": 0, - "total_time": 0.0, - "parallelism": parallelism, - } - - self.metrics[task_name]["count"] += 1 - self.metrics[task_name]["total_time"] += elapsed - - def schedule_service( - self, service_node: "ServiceNode", runtime_ctx=None - ) -> Union["LocalServiceTask", "ActorWrapper"]: - """ - 调度服务节点 - - Args: - service_node: 服务节点 - runtime_ctx: 运行时上下文 - - Returns: - 创建的服务任务实例（LocalServiceTask 或 ActorWrapper） - """ - ctx = runtime_ctx if runtime_ctx is not None else service_node.ctx - service_task = service_node.service_task_factory.create_service_task(ctx) - return service_task - - def get_metrics(self) -> Dict[str, Any]: - """ - 获取调度器性能指标（供开发者对比不同策略） - - Returns: - 指标字典 - """ - return { - "scheduler_type": "Template", - "total_scheduled": self.scheduled_count, - "platform": self.platform, - "detailed_metrics": self.metrics, - } - - def shutdown(self): - """关闭调度器，释放资源""" - self.metrics.clear() - - -__all__ = ["TemplateScheduler"] diff --git a/packages/sage-kernel/src/sage/kernel/scheduler/node_selector.py b/packages/sage-kernel/src/sage/kernel/scheduler/node_selector.py deleted file mode 100644 index 0dcdec3289..0000000000 --- a/packages/sage-kernel/src/sage/kernel/scheduler/node_selector.py +++ /dev/null @@ -1,465 +0,0 @@ -""" -NodeSelector - 资源感知的节点选择器 - -集成到 Scheduler 中，根据集群资源状态和任务需求选择最优节点。 - -核心功能： -1. 实时监控集群资源状态（CPU、GPU、内存、自定义资源） -2. 根据任务需求匹配合适的节点 -3. 支持多种调度策略（负载均衡、资源匹配、亲和性等） -4. 跟踪节点任务分配历史 - -示例用法: - selector = NodeSelector() - - # 根据资源需求选择节点 - node_id = selector.select_best_node( - cpu_required=4, - gpu_required=1, - memory_required=8*1024**3 - ) -""" - -import time -from dataclasses import dataclass -from typing import Any - -try: - import ray - - RAY_AVAILABLE = True -except ImportError: - RAY_AVAILABLE = False - - -@dataclass -class NodeResources: - """节点资源信息""" - - node_id: str - hostname: str - address: str - - # 总资源 - total_cpu: float - total_gpu: float - total_memory: int - custom_resources: dict[str, float] - - # 可用资源 - available_cpu: float - available_gpu: float - available_memory: int - - # 使用率 - cpu_usage: float # 0.0 - 1.0 - gpu_usage: float # 0.0 - 1.0 - memory_usage: float # 0.0 - 1.0 - - # 任务分配历史 - task_count: int = 0 - alive: bool = True - - def can_fit( - self, cpu_required: float = 0, gpu_required: float = 0, memory_required: int = 0 - ) -> bool: - """检查节点是否能容纳任务""" - return ( - self.available_cpu >= cpu_required - and self.available_gpu >= gpu_required - and self.available_memory >= memory_required - ) - - def compute_score( - self, - strategy: str = "balanced", - cpu_weight: float = 0.4, - gpu_weight: float = 0.4, - memory_weight: float = 0.2, - ) -> float: - """ - 计算节点得分（越低越好） - - Args: - strategy: 调度策略 - - "balanced": 负载均衡（选择使用率最低的节点） - - "pack": 紧凑放置（选择使用率最高但能容纳的节点） - - "spread": 分散放置（选择任务数最少的节点） - cpu_weight: CPU 使用率权重 - gpu_weight: GPU 使用率权重 - memory_weight: 内存使用率权重 - - Returns: - 节点得分 - """ - if strategy == "balanced": - # 负载均衡：综合使用率越低越好 - return ( - self.cpu_usage * cpu_weight - + self.gpu_usage * gpu_weight - + self.memory_usage * memory_weight - ) - elif strategy == "pack": - # 紧凑放置：使用率越高越好（但要能容纳） - return -( - self.cpu_usage * cpu_weight - + self.gpu_usage * gpu_weight - + self.memory_usage * memory_weight - ) - elif strategy == "spread": - # 分散放置：任务数越少越好 - return float(self.task_count) - else: - return self.cpu_usage - - -class NodeSelector: - """ - 资源感知的节点选择器 - - 职责： - 1. 监控集群资源状态 - 2. 根据任务需求选择最优节点 - 3. 跟踪节点任务分配 - 4. 支持多种调度策略 - """ - - def __init__(self, cache_ttl: float = 0.5, enable_tracking: bool = True): - """ - 初始化节点选择器 - - Args: - cache_ttl: 资源信息缓存时间（秒） - enable_tracking: 是否启用任务分配跟踪 - """ - self.cache_ttl = cache_ttl - self.enable_tracking = enable_tracking - - # 缓存 - self.node_cache: dict[str, NodeResources] = {} - self.last_update: float = 0 - - # 任务分配跟踪 - self.node_task_count: dict[str, int] = {} # node_id -> task_count - self.task_node_map: dict[str, str] = {} # task_name -> node_id - - def _update_node_cache(self) -> None: - """更新节点资源信息缓存""" - if not RAY_AVAILABLE: - return - - current_time = time.time() - if current_time - self.last_update < self.cache_ttl: - return # 缓存还有效 - - try: - # 获取节点列表和资源信息 - nodes = ray.nodes() - available_resources = ray.available_resources() - - new_cache = {} - - for node in nodes: - if not node.get("Alive", False): - continue - - node_id = node["NodeID"] - resources = node.get("Resources", {}) - - # 提取资源信息 - total_cpu = resources.get("CPU", 0.0) - total_gpu = resources.get("GPU", 0.0) - total_memory = resources.get("memory", 0) - - # 估算可用资源（简化版） - # 注意：ray.available_resources() 是全局的，这里做粗略估算 - available_cpu = available_resources.get("CPU", 0.0) - available_gpu = available_resources.get("GPU", 0.0) - available_memory = available_resources.get("memory", 0) - - # 计算使用率 - cpu_usage = 1.0 - (available_cpu / total_cpu) if total_cpu > 0 else 0.0 - gpu_usage = 1.0 - (available_gpu / total_gpu) if total_gpu > 0 else 0.0 - memory_usage = 1.0 - (available_memory / total_memory) if total_memory > 0 else 0.0 - - # 限制范围 - cpu_usage = max(0.0, min(1.0, cpu_usage)) - gpu_usage = max(0.0, min(1.0, gpu_usage)) - memory_usage = max(0.0, min(1.0, memory_usage)) - - # 提取自定义资源 - custom_resources = {} - for key, value in resources.items(): - if key not in [ - "CPU", - "GPU", - "memory", - "object_store_memory", - "node", - ]: - custom_resources[key] = value - - # 获取任务数 - task_count = self.node_task_count.get(node_id, 0) - - # 创建节点资源对象 - node_res = NodeResources( - node_id=node_id, - hostname=node.get("NodeManagerHostname", "unknown"), - address=node.get("NodeManagerAddress", "unknown"), - total_cpu=total_cpu, - total_gpu=total_gpu, - total_memory=total_memory, - custom_resources=custom_resources, - available_cpu=available_cpu, - available_gpu=available_gpu, - available_memory=available_memory, - cpu_usage=cpu_usage, - gpu_usage=gpu_usage, - memory_usage=memory_usage, - task_count=task_count, - alive=True, - ) - - new_cache[node_id] = node_res - - self.node_cache = new_cache - self.last_update = current_time - - except Exception: - # Ray 未初始化或其他错误，静默忽略 - pass - - def get_all_nodes(self) -> list[NodeResources]: - """ - 获取集群中所有活跃节点的资源信息 - - Returns: - 节点资源信息列表 - """ - self._update_node_cache() - # 同步最新的 task_count（因为缓存期间 task_count 可能已更新） - for node_id, node_res in self.node_cache.items(): - node_res.task_count = self.node_task_count.get(node_id, 0) - return list(self.node_cache.values()) - - def get_node(self, node_id: str) -> NodeResources | None: - """获取指定节点的资源信息""" - self._update_node_cache() - return self.node_cache.get(node_id) - - def select_best_node( - self, - cpu_required: float = 0, - gpu_required: float = 0, - memory_required: int = 0, - custom_resources: dict[str, float] | None = None, - strategy: str = "balanced", - exclude_nodes: list[str] | None = None, - ) -> str | None: - """ - 根据资源需求和调度策略选择最优节点 - - 这是核心方法，供 Scheduler 调用 - - Args: - cpu_required: 需要的 CPU 核心数 - gpu_required: 需要的 GPU 数量 - memory_required: 需要的内存（字节） - custom_resources: 自定义资源需求 - strategy: 调度策略 - - "balanced": 负载均衡（默认） - - "pack": 紧凑放置 - - "spread": 分散放置 - exclude_nodes: 排除的节点列表 - - Returns: - 最优节点 ID，如果没有满足条件的节点则返回 None - """ - nodes = self.get_all_nodes() - - if not nodes: - return None - - # 过滤满足资源需求的节点 - candidate_nodes = [] - for node in nodes: - # 排除指定节点 - if exclude_nodes and node.node_id in exclude_nodes: - continue - - # 检查是否能容纳任务 - if not node.can_fit(cpu_required, gpu_required, memory_required): - continue - - # 检查自定义资源 - if custom_resources: - can_fit_custom = True - for res_name, res_required in custom_resources.items(): - if node.custom_resources.get(res_name, 0) < res_required: - can_fit_custom = False - break - if not can_fit_custom: - continue - - candidate_nodes.append(node) - - if not candidate_nodes: - return None - - # 根据策略计算得分并选择最优节点 - scored_nodes = [(node, node.compute_score(strategy)) for node in candidate_nodes] - - # 按得分排序（越低越好） - scored_nodes.sort(key=lambda x: x[1]) - - return scored_nodes[0][0].node_id - - def select_least_loaded_node(self) -> str | None: - """ - 选择负载最低的节点（快捷方法） - - Returns: - 节点 ID，如果没有可用节点则返回 None - """ - return self.select_best_node(strategy="balanced") - - def select_node_with_gpu(self, min_gpu_count: float = 1) -> str | None: - """ - 选择有足够 GPU 的节点（快捷方法） - - Args: - min_gpu_count: 最小 GPU 数量 - - Returns: - 节点 ID，如果没有满足条件的节点则返回 None - """ - return self.select_best_node(gpu_required=min_gpu_count, strategy="balanced") - - def select_spread_node(self) -> str | None: - """ - 选择任务数最少的节点（分散放置，快捷方法） - - Returns: - 节点 ID - """ - return self.select_best_node(strategy="spread") - - def select_pack_node( - self, cpu_required: float = 0, gpu_required: float = 0, memory_required: int = 0 - ) -> str | None: - """ - 选择使用率最高但能容纳任务的节点（紧凑放置，快捷方法） - - Args: - cpu_required: CPU 需求 - gpu_required: GPU 需求 - memory_required: 内存需求 - - Returns: - 节点 ID - """ - return self.select_best_node( - cpu_required=cpu_required, - gpu_required=gpu_required, - memory_required=memory_required, - strategy="pack", - ) - - def track_task_placement(self, task_name: str, node_id: str) -> None: - """ - 跟踪任务分配到节点 - - Args: - task_name: 任务名称 - node_id: 节点 ID - """ - if not self.enable_tracking: - return - - self.task_node_map[task_name] = node_id - self.node_task_count[node_id] = self.node_task_count.get(node_id, 0) + 1 - - def untrack_task(self, task_name: str) -> None: - """ - 取消跟踪任务（任务完成时调用） - - Args: - task_name: 任务名称 - """ - if not self.enable_tracking: - return - - node_id = self.task_node_map.pop(task_name, None) - if node_id: - self.node_task_count[node_id] = max(0, self.node_task_count.get(node_id, 0) - 1) - - def get_node_task_count(self, node_id: str) -> int: - """获取节点上的任务数""" - return self.node_task_count.get(node_id, 0) - - def get_cluster_stats(self) -> dict[str, Any]: - """ - 获取集群统计信息 - - Returns: - 包含集群资源统计的字典 - """ - nodes = self.get_all_nodes() - - if not nodes: - return { - "node_count": 0, - "total_cpu": 0, - "total_gpu": 0, - "total_memory": 0, - "available_cpu": 0, - "available_gpu": 0, - "available_memory": 0, - "avg_cpu_usage": 0, - "avg_gpu_usage": 0, - "avg_memory_usage": 0, - "total_tasks": 0, - } - - total_cpu = sum(n.total_cpu for n in nodes) - total_gpu = sum(n.total_gpu for n in nodes) - total_memory = sum(n.total_memory for n in nodes) - - available_cpu = sum(n.available_cpu for n in nodes) - available_gpu = sum(n.available_gpu for n in nodes) - available_memory = sum(n.available_memory for n in nodes) - - avg_cpu_usage = sum(n.cpu_usage for n in nodes) / len(nodes) - avg_gpu_usage = sum(n.gpu_usage for n in nodes) / len(nodes) - avg_memory_usage = sum(n.memory_usage for n in nodes) / len(nodes) - - total_tasks = sum(self.node_task_count.values()) - - return { - "node_count": len(nodes), - "total_cpu": total_cpu, - "total_gpu": total_gpu, - "total_memory": total_memory, - "available_cpu": available_cpu, - "available_gpu": available_gpu, - "available_memory": available_memory, - "avg_cpu_usage": avg_cpu_usage, - "avg_gpu_usage": avg_gpu_usage, - "avg_memory_usage": avg_memory_usage, - "total_tasks": total_tasks, - "nodes": [ - { - "node_id": n.node_id, - "hostname": n.hostname, - "cpu_usage": n.cpu_usage, - "gpu_usage": n.gpu_usage, - "memory_usage": n.memory_usage, - "task_count": n.task_count, - } - for n in nodes - ], - } - - -__all__ = ["NodeSelector", "NodeResources"] diff --git a/packages/sage-kernel/src/sage/kernel/scheduler/placement.py b/packages/sage-kernel/src/sage/kernel/scheduler/placement.py deleted file mode 100644 index 284968bcab..0000000000 --- a/packages/sage-kernel/src/sage/kernel/scheduler/placement.py +++ /dev/null @@ -1,336 +0,0 @@ -""" -Placement 执行层 - 统一的任务/服务放置接口 - -架构（重构后）： -- Scheduler: 纯决策者（返回 PlacementDecision） -- PlacementExecutor: 纯执行者（接收决策，执行物理放置） -- Dispatcher: 协调者（决策 → 执行） - -正确的流程： - Dispatcher.submit(): - for node in graph.nodes: - # 1. 获取调度决策 - decision = scheduler.make_decision(node) - - # 2. 执行物理放置 - task = placement_executor.place_task(node, decision) - -关键点： -- PlacementExecutor 是纯执行层，不包含调度策略 -- 接收 PlacementDecision，根据决策执行放置 -- 使用 Ray API 将任务放置到指定物理节点 -- 处理资源需求和放置策略 -""" - -from typing import TYPE_CHECKING, Any, Union - -if TYPE_CHECKING: - from sage.kernel.runtime.graph.graph_node import TaskNode - from sage.kernel.runtime.graph.service_node import ServiceNode - from sage.kernel.runtime.service.local_service_task import LocalServiceTask - from sage.kernel.runtime.task.local_task import LocalTask - from sage.kernel.scheduler.decision import PlacementDecision - from sage.kernel.utils.ray.actor import ActorWrapper - - -class PlacementExecutor: - """ - 统一的放置执行器 - 纯执行者 - - 重构后职责： - 1. 接收 PlacementDecision（来自 Scheduler） - 2. 根据决策执行物理放置： - - 本地任务：创建 LocalTask - - 远程任务：创建 RayTask 并指定物理节点 - 3. 将高层决策转换为底层 Ray API 调用： - - target_node → NodeAffinitySchedulingStrategy - - resource_requirements → num_cpus, num_gpus, memory - - placement_strategy → Ray scheduling strategy - 4. 记录放置统计信息 - - 关键变更： - - 接收 PlacementDecision 参数（新增） - - 实际使用 target_node 和 resource_requirements（之前未实现） - - 不包含调度策略（策略在 Scheduler 中） - """ - - def __init__(self): - """ - 初始化放置执行器 - """ - self.placed_tasks = [] - self.placed_services = [] - self.placement_stats = { - "total_tasks": 0, - "total_services": 0, - "local_tasks": 0, - "remote_tasks": 0, - "nodes_used": set(), # 使用的节点集合 - } - - def place_task( - self, task_node: "TaskNode", decision: "PlacementDecision", runtime_ctx=None - ) -> Union["LocalTask", "ActorWrapper"]: - """ - 根据调度决策执行物理放置 - - 执行流程： - 1. 确定运行时上下文 - 2. 根据 task_node.remote 决定创建本地或远程任务 - - 本地任务：直接创建 LocalTask - - 远程任务：根据决策构建 Ray options，创建 RayTask - 3. 更新放置统计信息 - - Args: - task_node: 任务节点 - decision: 调度决策（来自 Scheduler.make_decision()） - runtime_ctx: 运行时上下文（可选） - - Returns: - 创建的任务实例（LocalTask 或 ActorWrapper 包装的 RayTask） - """ - # 1. 确定上下文 - ctx = runtime_ctx if runtime_ctx is not None else task_node.ctx - - # 2. 创建任务 - is_remote = task_node.task_factory.remote - - task: LocalTask | ActorWrapper - if is_remote: - # 远程任务：使用决策创建 Ray Actor - task = self._place_remote_task(task_node, ctx, decision) - self.placement_stats["remote_tasks"] += 1 - else: - # 本地任务：直接创建 - task = self._place_local_task(task_node, ctx) - self.placement_stats["local_tasks"] += 1 - - # 3. 记录统计 - self.placement_stats["total_tasks"] += 1 - - if decision.target_node: - self.placement_stats["nodes_used"].add(decision.target_node) - - self.placed_tasks.append( - { - "task_name": task_node.name, - "remote": is_remote, - "target_node": decision.target_node, - "resource_requirements": decision.resource_requirements, - "decision": decision, - } - ) - - return task - - def _place_local_task(self, task_node: "TaskNode", ctx) -> Union["LocalTask", "ActorWrapper"]: - """ - 放置本地任务（直接创建 LocalTask） - - Args: - task_node: 任务节点 - ctx: 运行时上下文 - - Returns: - LocalTask 实例（本地模式）或 ActorWrapper（远程模式） - """ - # 使用 TaskFactory 创建本地任务 - task = task_node.task_factory.create_task(task_node.name, ctx) - return task - - def _place_remote_task( - self, task_node: "TaskNode", ctx, decision: "PlacementDecision" - ) -> "ActorWrapper": - """ - 放置远程任务（创建 Ray Actor 并指定节点） - - 这是 PlacementExecutor 的核心功能： - 将高层调度决策转换为底层 Ray API 调用 - - Args: - task_node: 任务节点 - ctx: 运行时上下文 - decision: 调度决策 - - Returns: - ActorWrapper 包装的 RayTask - """ - # 构建 Ray Actor 选项（根据决策） - ray_options = self._build_ray_options(decision) - - # 添加 runtime_env 支持： TaskFactory 获取 extra_python_paths - extra_paths = getattr(task_node.task_factory, "extra_python_paths", None) - extra_python_paths = ( - extra_paths if isinstance(extra_paths, list) else ([extra_paths] if extra_paths else []) - ) - if extra_python_paths: - runtime_env = {"env_vars": {"PYTHONPATH": ":".join(extra_python_paths)}} - ray_options["runtime_env"] = runtime_env - - # 创建 Ray Actor - from sage.kernel.runtime.task.ray_task import RayTask - from sage.kernel.utils.ray.actor import ActorWrapper - - # Get operator_factory with explicit typing - operator_factory = task_node.task_factory.operator_factory # type: ignore[has-type] - task_actor = RayTask.options(**ray_options).remote( # type: ignore[attr-defined] - ctx, operator_factory - ) - - # 包装为 ActorWrapper - task = ActorWrapper(task_actor) - - return task - - def _build_ray_options(self, decision: "PlacementDecision") -> dict[str, Any]: - """ - 将调度决策转换为 Ray Actor 创建选项 - - 这是关键转换层：高层决策 → 底层 Ray API - - Args: - decision: 调度决策 - - Returns: - Ray options 字典 - """ - options: dict[str, Any] = {"lifetime": "detached"} - - # === 指定目标节点 === - if decision.target_node: - try: - from ray.util.scheduling_strategies import ( - NodeAffinitySchedulingStrategy, - ) - - options["scheduling_strategy"] = NodeAffinitySchedulingStrategy( - node_id=decision.target_node, - soft=False, # 硬要求：必须放到指定节点 - ) - except ImportError: - # Ray 版本不支持 NodeAffinitySchedulingStrategy - pass - - # === 指定资源需求 === - if decision.resource_requirements: - resources = decision.resource_requirements - - # CPU - if "cpu" in resources: - options["num_cpus"] = resources["cpu"] - - # GPU - if "gpu" in resources: - options["num_gpus"] = resources["gpu"] - - # 内存 - if "memory" in resources: - memory_bytes = self._parse_memory(resources["memory"]) - options["memory"] = memory_bytes - - # 自定义资源 - custom_resources = {} - for key, value in resources.items(): - if key not in ["cpu", "gpu", "memory"]: - custom_resources[key] = value - - if custom_resources: - options["resources"] = custom_resources - - return options - - def _parse_memory(self, memory) -> int: - """ - 解析内存字符串为字节数 - - Args: - memory: 内存大小（字符串如 "8GB" 或整数字节数） - - Returns: - 内存字节数 - """ - if isinstance(memory, int): - return memory - - if isinstance(memory, str): - memory = memory.upper() - if "GB" in memory: - return int(float(memory.replace("GB", "")) * 1024**3) - elif "MB" in memory: - return int(float(memory.replace("MB", "")) * 1024**2) - elif "KB" in memory: - return int(float(memory.replace("KB", "")) * 1024) - - return 1024**3 # 默认 1GB - - def place_service( - self, - service_node: "ServiceNode", - decision: "PlacementDecision", - runtime_ctx=None, - ) -> Union["LocalServiceTask", "ActorWrapper"]: - """ - 根据调度决策放置服务 - - Args: - service_node: 服务节点 - decision: 调度决策 - runtime_ctx: 运行时上下文（可选） - - Returns: - 创建的服务实例（LocalServiceTask 或 ActorWrapper 包装的 RayServiceTask） - """ - # 1. 确定上下文 - ctx = runtime_ctx if runtime_ctx is not None else service_node.ctx - - # 2. 创建服务 - service = service_node.service_task_factory.create_service_task(ctx) - - # 3. 记录统计 - self.placement_stats["total_services"] += 1 - - if decision.target_node: - self.placement_stats["nodes_used"].add(decision.target_node) - - self.placed_services.append( - { - "service_name": service_node.service_name, - "target_node": decision.target_node, - "decision": decision, - } - ) - - return service - - def get_placement_stats(self) -> dict[str, Any]: - """ - 获取放置统计信息 - - Returns: - 统计字典，包含： - - total_tasks: 总任务数 - - total_services: 总服务数 - - local_tasks: 本地任务数 - - remote_tasks: 远程任务数 - - nodes_used: 使用的节点列表 - """ - stats = self.placement_stats.copy() - stats["nodes_used"] = list(stats["nodes_used"]) # 转换为列表 - return stats - - def reset_stats(self): - """重置统计信息""" - self.placement_stats = { - "total_tasks": 0, - "total_services": 0, - "local_tasks": 0, - "remote_tasks": 0, - "nodes_used": set(), - } - self.placed_tasks.clear() - self.placed_services.clear() - - -__all__ = [ - "PlacementExecutor", -] diff --git a/packages/sage-kernel/src/sage/kernel/utils/__init__.py b/packages/sage-kernel/src/sage/kernel/utils/__init__.py deleted file mode 100644 index 891b0c56b1..0000000000 --- a/packages/sage-kernel/src/sage/kernel/utils/__init__.py +++ /dev/null @@ -1,53 +0,0 @@ -""" -SAGE - Streaming-Augmented Generative Execution - -Kernel utilities module containing helper functions and Ray utilities. -""" - -# 直接从本包的_version模块加载版本信息 -try: - from sage.kernel._version import __author__, __email__, __version__ -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" - -# 导出 helper 函数 -from sage.kernel.utils.helpers import ( - build_request, - generate_request_id, - generate_short_id, - is_abstract_method, - measure_time, - retry_with_backoff, - timed_execution, - validate_function_type, - validate_required_methods, - wait_for_all_stopped, - wait_with_timeout, -) - -__all__ = [ - # 版本信息 - "__version__", - "__author__", - "__email__", - # ID 生成 - "generate_request_id", - "generate_short_id", - # 请求构建 - "build_request", - # 超时等待 - "wait_with_timeout", - "wait_for_all_stopped", - # 时间测量 - "measure_time", - "timed_execution", - # 函数验证 - "is_abstract_method", - "validate_required_methods", - "validate_function_type", - # 重试逻辑 - "retry_with_backoff", -] diff --git a/packages/sage-kernel/src/sage/kernel/utils/helpers.py b/packages/sage-kernel/src/sage/kernel/utils/helpers.py deleted file mode 100644 index 5a4ac9ffa7..0000000000 --- a/packages/sage-kernel/src/sage/kernel/utils/helpers.py +++ /dev/null @@ -1,376 +0,0 @@ -""" -SAGE Kernel 通用 Helper 函数 - -这个模块包含了在 sage-kernel 中重复使用的通用工具函数， -包括： -- ID 生成 -- 请求构建 -- 超时等待 -- 时间测量 -- 函数验证 -""" - -from __future__ import annotations - -import time -import uuid -from contextlib import contextmanager -from typing import TYPE_CHECKING, Any, Callable - -if TYPE_CHECKING: - from logging import Logger - - -# ==================== ID 生成 ==================== - - -def generate_request_id() -> str: - """ - 生成唯一的请求 ID - - Returns: - str: UUID4 格式的请求 ID - """ - return str(uuid.uuid4()) - - -def generate_short_id(length: int = 8) -> str: - """ - 生成短格式的唯一 ID - - Args: - length: ID 长度 (默认 8) - - Returns: - str: 短格式 UUID - """ - return uuid.uuid4().hex[:length] - - -# ==================== 请求构建 ==================== - - -def build_request(action: str, **kwargs: Any) -> dict[str, Any]: - """ - 构建标准格式的请求字典 - - Args: - action: 请求动作名称 - **kwargs: 额外的请求参数 - - Returns: - dict: 包含 action 和 request_id 的请求字典 - - Example: - >>> build_request("submit_job", job_uuid="xxx", data={"key": "value"}) - {"action": "submit_job", "request_id": "...", "job_uuid": "xxx", "data": {"key": "value"}} - """ - return { - "action": action, - "request_id": generate_request_id(), - **kwargs, - } - - -# ==================== 超时等待 ==================== - - -def wait_with_timeout( - condition: Callable[[], bool], - timeout: float, - interval: float = 0.1, - on_timeout: Callable[[], None] | None = None, -) -> bool: - """ - 带超时的条件等待 - - Args: - condition: 返回 True 表示条件满足的可调用对象 - timeout: 最大等待时间（秒） - interval: 检查间隔（秒），默认 0.1 - on_timeout: 超时时调用的回调函数 - - Returns: - bool: True 如果条件满足，False 如果超时 - - Example: - >>> def is_ready(): - ... return some_service.is_initialized() - >>> if wait_with_timeout(is_ready, timeout=10.0): - ... print("Service is ready") - ... else: - ... print("Timeout waiting for service") - """ - start_time = time.time() - - while time.time() - start_time < timeout: - if condition(): - return True - time.sleep(interval) - - if on_timeout: - on_timeout() - - return False - - -def wait_for_all_stopped( - items: dict[str, Any], - timeout: float = 10.0, - interval: float = 0.1, - logger: Logger | None = None, -) -> bool: - """ - 等待所有项目停止运行 - - Args: - items: 项目字典，每个项目应有 is_running 属性 - timeout: 最大等待时间（秒） - interval: 检查间隔（秒） - logger: 可选的日志记录器 - - Returns: - bool: True 如果所有项目都已停止，False 如果超时 - - Example: - >>> if wait_for_all_stopped(tasks, timeout=10.0, logger=self.logger): - ... logger.info("All tasks stopped") - ... else: - ... logger.warning("Timeout waiting for tasks") - """ - - def all_stopped() -> bool: - for _key, item in items.items(): - if hasattr(item, "is_running") and item.is_running: - return False - return True - - def on_timeout() -> None: - if logger: - logger.warning(f"Timeout waiting for items to stop after {timeout}s") - - result = wait_with_timeout( - condition=all_stopped, - timeout=timeout, - interval=interval, - on_timeout=on_timeout, - ) - - if result and logger: - logger.debug("All items stopped") - - return result - - -# ==================== 时间测量 ==================== - - -@contextmanager -def measure_time(): - """ - 上下文管理器用于测量代码块执行时间 - - Yields: - 一个对象，包含 elapsed 属性（执行完成后可用） - - Example: - >>> with measure_time() as timer: - ... do_something() - >>> print(f"Elapsed: {timer.elapsed:.3f}s") - """ - - class Timer: - def __init__(self): - self.start_time = time.time() - self.elapsed = 0.0 - - def stop(self): - self.elapsed = time.time() - self.start_time - - timer = Timer() - try: - yield timer - finally: - timer.stop() - - -def timed_execution(func: Callable[..., Any]) -> Callable[..., tuple[Any, float]]: - """ - 装饰器：测量函数执行时间 - - Args: - func: 要测量的函数 - - Returns: - 包装后的函数，返回 (原始返回值, 执行时间秒数) - - Example: - >>> @timed_execution - ... def slow_function(): - ... time.sleep(1) - ... return "done" - >>> result, elapsed = slow_function() - >>> print(f"Result: {result}, Time: {elapsed:.2f}s") - """ - - def wrapper(*args: Any, **kwargs: Any) -> tuple[Any, float]: - start_time = time.time() - result = func(*args, **kwargs) - elapsed = time.time() - start_time - return result, elapsed - - return wrapper - - -# ==================== 函数验证 ==================== - - -def is_abstract_method(method: Any) -> bool: - """ - 检查方法是否为抽象方法 - - Args: - method: 要检查的方法 - - Returns: - bool: True 如果是抽象方法 - """ - return getattr(method, "__isabstractmethod__", False) - - -def validate_required_methods( - cls: type, - required_methods: list[str], - class_name: str | None = None, -) -> None: - """ - 验证类是否实现了必需的方法 - - Args: - cls: 要验证的类 - required_methods: 必需方法名称列表 - class_name: 用于错误消息的类名（可选，默认使用 cls.__name__） - - Raises: - ValueError: 如果缺少必需的方法或方法是抽象的 - - Example: - >>> validate_required_methods( - ... MyFunction, - ... required_methods=["execute", "setup"], - ... class_name="MyFunction" - ... ) - """ - if class_name is None: - class_name = cls.__name__ - - missing_methods = [] - - for method_name in required_methods: - if not hasattr(cls, method_name): - missing_methods.append(method_name) - else: - method = getattr(cls, method_name) - if is_abstract_method(method): - missing_methods.append(method_name) - - if missing_methods: - raise ValueError( - f"{class_name} must implement required methods: {', '.join(missing_methods)}" - ) - - -def validate_function_type( - function: Any, - type_attr: str, - expected_value: bool = True, - function_type_name: str = "Function", -) -> None: - """ - 验证函数是否具有特定类型标记 - - Args: - function: 要验证的函数对象 - type_attr: 类型标记属性名 (如 "is_join", "is_comap") - expected_value: 期望的属性值 (默认 True) - function_type_name: 函数类型名称，用于错误消息 - - Raises: - TypeError: 如果函数不具有预期的类型标记 - - Example: - >>> validate_function_type( - ... my_function, - ... type_attr="is_join", - ... function_type_name="Join" - ... ) - """ - if not hasattr(function, type_attr) or getattr(function, type_attr) != expected_value: - func_name = ( - type(function).__name__ if hasattr(function, "__name__") else str(type(function)) - ) - raise TypeError( - f"{function_type_name} function requires {type_attr}={expected_value}, got {func_name}" - ) - - -# ==================== 重试逻辑 ==================== - - -def retry_with_backoff( - func: Callable[..., Any], - max_retries: int = 3, - base_delay: float = 0.5, - exceptions: tuple[type[Exception], ...] = (Exception,), -) -> Any: - """ - 带指数退避的重试执行 - - Args: - func: 要执行的函数（无参数） - max_retries: 最大重试次数 - base_delay: 基础延迟时间（秒），每次重试翻倍 - exceptions: 需要重试的异常类型 - - Returns: - 函数的返回值 - - Raises: - 最后一次重试失败的异常 - - Example: - >>> def unstable_operation(): - ... # 可能失败的操作 - ... return requests.get(url) - >>> result = retry_with_backoff(unstable_operation, max_retries=3) - """ - - for attempt in range(max_retries): - try: - return func() - except exceptions: - if attempt < max_retries - 1: - time.sleep(base_delay * (attempt + 1)) - else: - raise - - -__all__ = [ - # ID 生成 - "generate_request_id", - "generate_short_id", - # 请求构建 - "build_request", - # 超时等待 - "wait_with_timeout", - "wait_for_all_stopped", - # 时间测量 - "measure_time", - "timed_execution", - # 函数验证 - "is_abstract_method", - "validate_required_methods", - "validate_function_type", - # 重试逻辑 - "retry_with_backoff", -] diff --git a/packages/sage-kernel/src/sage/kernel/utils/persistence/__init__.py b/packages/sage-kernel/src/sage/kernel/utils/persistence/__init__.py deleted file mode 100644 index 58c515af61..0000000000 --- a/packages/sage-kernel/src/sage/kernel/utils/persistence/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -""" -SAGE - Streaming-Augmented Generative Execution -""" - -# 直接从本包的_version模块加载版本信息 -try: - from sage.kernel._version import __author__, __email__, __version__ -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" diff --git a/packages/sage-kernel/src/sage/kernel/utils/persistence/state.py b/packages/sage-kernel/src/sage/kernel/utils/persistence/state.py deleted file mode 100644 index 240d8d7b74..0000000000 --- a/packages/sage-kernel/src/sage/kernel/utils/persistence/state.py +++ /dev/null @@ -1,124 +0,0 @@ -import inspect -import io -import os -import pickle -import threading -import types -from collections.abc import Mapping, Sequence, Set - -# NOTE: State persistence is now managed automatically by the system at operator/task level. -# This utility module provides helper functions for manual state serialization when needed. -# 不可序列化类型黑名单 -_BLACKLIST = ( - io.IOBase, # 文件句柄基类（包括所有文件类型） - threading.Thread, # 线程 - types.BuiltinFunctionType, # 内置函数（如 open, len 等） -) - - -def _gather_attrs(obj): - """枚举实例 __dict__ 和 @property 属性。""" - attrs = dict(getattr(obj, "__dict__", {})) - for name, _prop in inspect.getmembers(type(obj), lambda x: isinstance(x, property)): - try: - attrs[name] = getattr(obj, name) - except Exception: - pass - return attrs - - -def _filter_attrs(attrs, include, exclude): - """根据 include/exclude 过滤字段字典。""" - if include: - return {k: attrs[k] for k in include if k in attrs} - return {k: v for k, v in attrs.items() if k not in exclude} - - -def _is_serializable(v): - """判断对象能否通过 pickle 序列化，且不在黑名单中。""" - if isinstance(v, _BLACKLIST): - return False - try: - pickle.dumps(v) - return True - except Exception: - return False - - -def _prepare(v, _visited=None): - """递归清洗容器类型，过滤不可序列化元素。""" - if _visited is None: - _visited = set() - - # 基本类型直接返回 - if isinstance(v, (int, float, str, bool, type(None))): - return v - - # 循环引用检测：使用id()来跟踪对象 - obj_id = id(v) - if obj_id in _visited: - # 发现循环引用，返回占位符或None - return None - - # 只对容器类型进行循环引用跟踪 - if isinstance(v, (Mapping, Sequence, Set)) and not isinstance(v, str): - _visited.add(obj_id) - - try: - if isinstance(v, Mapping): - result = { - _prepare(k, _visited): _prepare(val, _visited) - for k, val in v.items() - if _is_serializable(k) and _is_serializable(val) - } - return result - if isinstance(v, Sequence) and not isinstance(v, str): - cleaned = [_prepare(x, _visited) for x in v if _is_serializable(x)] - # 某些 Sequence 类型的构造函数可能不接受参数，使用 type: ignore - return type(v)(cleaned) # type: ignore[call-arg] - if isinstance(v, Set): - # 某些 Set 类型的构造函数可能不接受参数，使用 type: ignore - return type(v)(_prepare(x, _visited) for x in v if _is_serializable(x)) # type: ignore[call-arg] - if _is_serializable(v): - return v - return None - finally: - # 在退出时移除对象id，允许同一对象在不同路径中被处理 - if isinstance(v, (Mapping, Sequence, Set)) and not isinstance(v, str): - _visited.discard(obj_id) - - -def save_function_state(func, path): - """ - 将 func 的可序列化字段保存到 path 文件中。 - 自动应用 __state_include__ 和 __state_exclude__。 - """ - include = getattr(func, "__state_include__", []) - exclude = getattr(func, "__state_exclude__", []) - attrs = _gather_attrs(func) - filtered = _filter_attrs(attrs, include, exclude) - prepared = {k: _prepare(v) for k, v in filtered.items()} - - os.makedirs(os.path.dirname(path), exist_ok=True) - with open(path, "wb") as f: - pickle.dump(prepared, f) - - -def load_function_state(func, path): - """ - 如果 path 存在，则从中加载字段映射并设置到 func 上。 - 忽略当前 include/exclude 中不该加载的字段。 - """ - if not os.path.isfile(path): - return - with open(path, "rb") as f: - data = pickle.load(f) - - include = getattr(func, "__state_include__", []) - exclude = getattr(func, "__state_exclude__", []) - for k, v in data.items(): - if include and k not in include: - continue - if k in exclude: - continue - setattr(func, k, v) diff --git a/packages/sage-kernel/src/sage/kernel/utils/ray/__init__.py b/packages/sage-kernel/src/sage/kernel/utils/ray/__init__.py deleted file mode 100644 index 1679588c3f..0000000000 --- a/packages/sage-kernel/src/sage/kernel/utils/ray/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -""" -Ray distributed utilities. - -Includes Ray Actor wrappers and Ray initialization utility functions. -""" - -from sage.kernel.utils.ray.actor import ActorWrapper -from sage.kernel.utils.ray.ray_utils import ensure_ray_initialized - -__all__ = [ - "ActorWrapper", - "ensure_ray_initialized", -] diff --git a/packages/sage-kernel/src/sage/kernel/utils/ray/actor.py b/packages/sage-kernel/src/sage/kernel/utils/ray/actor.py deleted file mode 100644 index f4b72a7c78..0000000000 --- a/packages/sage-kernel/src/sage/kernel/utils/ray/actor.py +++ /dev/null @@ -1,143 +0,0 @@ -from typing import Any - -import ray -from ray.actor import ActorHandle - - -# 目前的actor wrapper的远程方法调用都是同步阻塞调用。 -class ActorWrapper: - """万能包装器，可以将任意对象包装成本地对象或Ray Actor""" - - def __init__(self, obj: Any | ActorHandle): - # 使用 __dict__ 直接设置，避免触发 __setattr__ - object.__setattr__(self, "_obj", obj) - object.__setattr__(self, "_execution_mode", self._detect_execution_mode()) - - def _detect_execution_mode(self) -> str: - """检测执行模式""" - try: - # ray.actor.ActorHandle 在 ray 安装时总是存在 - if isinstance(self._obj, ray.actor.ActorHandle): # type: ignore[union-attr] - return "ray_actor" - except (ImportError, AttributeError): - pass - return "local" - - def __getattr__(self, name: str): - """透明代理属性访问""" - if name.startswith("_"): - raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'") - - # 获取原始属性/方法 - try: - original_attr = getattr(self._obj, name) - except AttributeError: - raise AttributeError(f"'{type(self._obj).__name__}' object has no attribute '{name}'") - - # 如果是方法，需要包装 - if callable(original_attr): - if self._execution_mode == "ray_actor": - # Ray Actor方法：返回同步调用包装器 - def ray_method_wrapper(*args, **kwargs): - future = original_attr.remote(*args, **kwargs) - result = ray.get(future) - return result - - return ray_method_wrapper - else: - # 本地方法：直接返回 - return original_attr - else: - # 普通属性：直接返回 - return original_attr - - def call_async(self, method_name: str, *args, **kwargs): - """异步调用Ray Actor方法，返回ObjectRef""" - if self._execution_mode != "ray_actor": - raise RuntimeError("call_async only available for Ray actors") - - method = getattr(self._obj, method_name) - if not callable(method): - raise AttributeError(f"'{method_name}' is not a callable method") - - return method.remote(*args, **kwargs) - - def __setattr__(self, name: str, value: Any): - """代理属性设置""" - if name.startswith("_"): - object.__setattr__(self, name, value) - else: - setattr(self._obj, name, value) - - def __repr__(self): - return f"ActorWrapper[{self._execution_mode}]({repr(self._obj)})" - - def get_object(self): - """获取被包装的原始对象""" - return self._obj - - def is_ray_actor(self) -> bool: - """检查是否为Ray Actor""" - return self._execution_mode == "ray_actor" - - def is_local(self) -> bool: - """检查是否为本地对象""" - return self._execution_mode == "local" - - def kill_actor(self, no_restart: bool = True): - """ - 终止Ray Actor - - Args: - no_restart: 是否禁止重启 (默认True) - - Returns: - bool: 对于Ray Actor返回True表示kill成功，对于本地对象返回False - """ - if self._execution_mode != "ray_actor": - # 对于本地对象，无需kill操作 - return False - - try: - import ray - - ray.kill(self._obj, no_restart=no_restart) - return True - except Exception as e: - # 记录错误但不抛出异常，让调用者决定如何处理 - print(f"Warning: Failed to kill Ray actor {self._obj}: {e}") - return False - - def cleanup_and_kill(self, cleanup_timeout: float = 5.0, no_restart: bool = True): - """ - 先调用cleanup方法（如果存在），然后kill actor - - Args: - cleanup_timeout: cleanup方法的超时时间（秒） - no_restart: 是否禁止重启 (默认True) - - Returns: - tuple: (cleanup_success, kill_success) - """ - cleanup_success = False - kill_success = False - - if self._execution_mode != "ray_actor": - return cleanup_success, kill_success - - # 尝试调用cleanup方法 - if hasattr(self._obj, "cleanup"): - try: - import ray - - # 异步调用cleanup，设置超时 - cleanup_ref = self._obj.cleanup.remote() - ray.get(cleanup_ref, timeout=cleanup_timeout) - cleanup_success = True - except Exception as e: - print(f"Warning: Cleanup failed for {self._obj}: {e}") - - # 无论cleanup是否成功，都尝试kill actor - kill_success = self.kill_actor(no_restart=no_restart) - - return cleanup_success, kill_success diff --git a/packages/sage-kernel/src/sage/kernel/utils/ray/ray_utils.py b/packages/sage-kernel/src/sage/kernel/utils/ray/ray_utils.py deleted file mode 100644 index 89d2230394..0000000000 --- a/packages/sage-kernel/src/sage/kernel/utils/ray/ray_utils.py +++ /dev/null @@ -1,176 +0,0 @@ -try: - import ray - - RAY_AVAILABLE = True -except ImportError: - ray = None # type: ignore[assignment] - RAY_AVAILABLE = False - -try: - from sage.common.config.output_paths import get_sage_paths - - SAGE_OUTPUT_PATHS_AVAILABLE = True -except ImportError: - SAGE_OUTPUT_PATHS_AVAILABLE = False - - -def get_sage_kernel_runtime_env(): - """ - 获取Sage内核的Ray运行环境配置，确保Actor可以访问sage模块 - """ - import os - - # 动态获取sage-kernel源码路径 - current_file = os.path.abspath(__file__) - # 从当前文件往上找到sage-kernel/src目录 - parts = current_file.split("/") - try: - kernel_idx = next(i for i, part in enumerate(parts) if part == "sage-kernel") - sage_kernel_src = "/".join(parts[: kernel_idx + 1]) + "/src" - except StopIteration: - # 备用方法：从环境变量或当前工作目录推断 - cwd = os.getcwd() - if "sage-kernel" in cwd: - parts = cwd.split("/") - kernel_idx = next(i for i, part in enumerate(parts) if part == "sage-kernel") - sage_kernel_src = "/".join(parts[: kernel_idx + 1]) + "/src" - else: - # 最后的备用方法 - sage_kernel_src = os.path.expanduser("~/SAGE/packages/sage-kernel/src") - - if not os.path.exists(sage_kernel_src): - print(f"警告：无法找到sage-kernel源码路径: {sage_kernel_src}") - return {} - - # 构建runtime_env配置 - # 添加 experiments 目录以支持分布式调度实验 - pythonpath_parts = [sage_kernel_src] - experiments_dir = os.path.abspath( - os.path.join(os.path.dirname(sage_kernel_src), "../../../experiments") - ) - if os.path.exists(experiments_dir): - pythonpath_parts.append(experiments_dir) - - pythonpath = ":".join(pythonpath_parts) - if os.environ.get("PYTHONPATH"): - pythonpath += ":" + os.environ.get("PYTHONPATH") - - runtime_env = { - "py_modules": [sage_kernel_src], - "env_vars": { - "PYTHONPATH": pythonpath, - # 禁用代理，避免 Ray Actor 中 HTTP 请求受影响 - "http_proxy": "", - "https_proxy": "", - "HTTP_PROXY": "", - "HTTPS_PROXY": "", - "all_proxy": "", - "ALL_PROXY": "", - "no_proxy": "*", - "NO_PROXY": "*", - }, - } - - return runtime_env - - -def ensure_ray_initialized(runtime_env=None): - """ - 确保Ray已经初始化，如果没有则初始化Ray。 - - 优先尝试连接到现有的 Ray 集群（address="auto"）， - 如果没有集群则启动本地 Ray 实例。 - - Args: - runtime_env: Ray运行环境配置，如果为None则使用默认的sage配置 - """ - if not RAY_AVAILABLE: - raise ImportError("Ray is not available") - - # ray 在 RAY_AVAILABLE=True 时总是有效的 - if not ray.is_initialized(): # type: ignore[union-attr] - try: - import os - - # 检测是否在CI环境中 - is_ci = os.environ.get("CI") == "true" or os.environ.get("GITHUB_ACTIONS") == "true" - - # 首先尝试连接到现有的 Ray 集群 - try: - ray.init(address="auto", ignore_reinit_error=True) # type: ignore[union-attr] - nodes = ray.nodes() - alive_nodes = [n for n in nodes if n.get("Alive", False)] - print(f"Connected to existing Ray cluster with {len(alive_nodes)} nodes") - return - except ConnectionError: - print("No existing Ray cluster found, starting local Ray instance") - except Exception as e: - print(f"Failed to connect to Ray cluster: {e}, starting local Ray instance") - - # 准备初始化参数（本地模式） - init_kwargs = { - "ignore_reinit_error": True, - "num_cpus": 2 if is_ci else 16, # CI环境使用更少的CPU - "num_gpus": 0, # 不使用GPU - "object_store_memory": 100000000 if is_ci else 200000000, # CI: 100MB, 本地: 200MB - "log_to_driver": False, # 减少日志输出 - "include_dashboard": False, # 禁用dashboard减少资源占用 - } - - # 设置Ray临时目录到SAGE的temp目录 - ray_temp_dir = None - - # 使用统一的output_paths系统 - if SAGE_OUTPUT_PATHS_AVAILABLE: - try: - sage_paths = get_sage_paths() # type: ignore[possibly-unbound] - # 设置环境变量 - sage_paths.setup_environment_variables() - ray_temp_dir = sage_paths.get_ray_temp_dir() - init_kwargs["_temp_dir"] = str(ray_temp_dir) - print(f"Ray will use SAGE temp directory: {ray_temp_dir}") - except Exception as e: - print(f"Warning: Failed to set Ray temp directory via output_paths: {e}") - - if ray_temp_dir is None: - print("SAGE paths not available, Ray will use default temp directory") - - # 如果提供了runtime_env，使用它；否则使用默认的sage配置 - if runtime_env is not None: - init_kwargs["runtime_env"] = runtime_env - else: - # 使用默认的sage配置 - sage_runtime_env = get_sage_kernel_runtime_env() - if sage_runtime_env: - init_kwargs["runtime_env"] = sage_runtime_env - - # 使用标准模式但限制资源，支持async actors和队列 - ray.init(**init_kwargs) # type: ignore[union-attr] - mode = "CI mode" if is_ci else "standard mode" - print(f"Ray initialized in {mode} with limited resources") - except Exception as e: - print(f"Failed to initialize Ray: {e}") - raise - else: - # Ray 已经初始化，检查节点数量 - try: - nodes = ray.nodes() - alive_nodes = [n for n in nodes if n.get("Alive", False)] - print(f"Ray is already initialized with {len(alive_nodes)} nodes") - except Exception: - print("Ray is already initialized.") - - -def is_distributed_environment() -> bool: - """ - 检查是否在分布式环境中运行。 - 尝试导入Ray并检查是否已初始化。 - """ - if not RAY_AVAILABLE: - return False - - try: - # ray 在 RAY_AVAILABLE=True 时总是有效的 - return ray.is_initialized() # type: ignore[union-attr] - except Exception: - return False diff --git a/packages/sage-kernel/tests/conftest.py b/packages/sage-kernel/tests/conftest.py deleted file mode 100644 index b7e0cecffd..0000000000 --- a/packages/sage-kernel/tests/conftest.py +++ /dev/null @@ -1,125 +0,0 @@ -""" -sage-kernel测试配置 - -设置正确的Python路径和共享的测试fixtures -""" - -import os -import sys -from pathlib import Path - -import pytest - -# 获取项目根目录 -PROJECT_ROOT = Path(__file__).parent.parent -SRC_DIR = PROJECT_ROOT / "src" -PACKAGES_DIR = PROJECT_ROOT.parent - -# 需要添加的依赖包路径 -DEPENDENCY_PACKAGES = [ - "sage-common", - "sage-platform", - "sage-libs", - "sage-middleware", -] - -# 添加 sage-kernel 源码路径 -if str(SRC_DIR) not in sys.path: - sys.path.insert(0, str(SRC_DIR)) - -# 添加所有依赖包的源码路径 -for pkg_name in DEPENDENCY_PACKAGES: - pkg_src_dir = PACKAGES_DIR / pkg_name / "src" - if pkg_src_dir.exists() and str(pkg_src_dir) not in sys.path: - sys.path.insert(0, str(pkg_src_dir)) - -# 设置环境变量 -os.environ.setdefault("SAGE_TEST_MODE", "1") -os.environ.setdefault("SAGE_LOG_LEVEL", "INFO") - -# 确保Ray在测试环境中正确初始化（如果需要且有足够内存） -try: - # ray may be optional in test environments; import locally after path setup - import ray - - from sage.common.config.output_paths import get_sage_paths - - if not ray.is_initialized(): - # 获取SAGE路径和设置环境 - sage_paths = get_sage_paths() - sage_paths.setup_environment_variables() - - # 获取Ray临时目录 - ray_temp_dir = sage_paths.get_ray_temp_dir() - - # 尝试更宽松的Ray配置 - 使用最小允许内存 - ray.init( - ignore_reinit_error=True, - local_mode=True, - object_store_memory=80000000, # 80MB (最小允许值) - num_cpus=1, - _temp_dir=str(ray_temp_dir), # 使用SAGE的temp目录 - ) - print(f"Ray initialized for tests with temp dir: {ray_temp_dir}") -except (ImportError, ValueError, RuntimeError) as e: - # Ray不是必需的，或者内存不足时跳过 - print(f"⚠️ Ray初始化跳过: {e}") - pass - - -@pytest.fixture -def sage_test_env_config(): - """统一的SAGE测试环境配置 - - 返回标准化的测试环境配置，确保所有测试使用.sage目录 - 而不是在项目根目录创建test_env目录 - """ - from sage.common.config.output_paths import get_sage_paths, get_test_env_dir - - # 使用统一的路径管理 - sage_paths = get_sage_paths() - test_env_dir = get_test_env_dir("test_env") - - return { - "name": "test_env", - "platform": "local", - "env_base_dir": str(test_env_dir), - "console_log_level": "INFO", - "project_root": str(sage_paths.project_root), - "sage_dir": str(sage_paths.sage_dir), - } - - -@pytest.fixture(scope="session", autouse=True) -def setup_test_environment(): - """自动设置测试环境""" - # 验证关键模块可以导入 - try: - import importlib - import types - - # Import modules dynamically to avoid static analysis issues with namespace packages - sage_common: types.ModuleType = importlib.import_module("sage.common") - sage_kernel: types.ModuleType = importlib.import_module("sage.kernel") - - print("✓ 测试环境设置成功") - print(f"✓ sage.kernel: {sage_kernel.__path__}") - # sage.common is a namespace package, check __path__ dynamically - if hasattr(sage_common, "__path__"): - print(f"✓ sage.common: {sage_common.__path__}") - else: - print("✓ sage.common: module loaded successfully") - except ImportError as e: - print(f"❌ 测试环境设置失败: {e}") - raise - - yield - - # 清理 - try: - import ray - - if ray.is_initialized(): - ray.shutdown() - except ImportError: - pass diff --git a/packages/sage-kernel/tests/integration/api/__init__.py b/packages/sage-kernel/tests/integration/api/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/packages/sage-kernel/tests/integration/api/test_simple_batch_function.py b/packages/sage-kernel/tests/integration/api/test_simple_batch_function.py deleted file mode 100644 index 19e8a5f8c3..0000000000 --- a/packages/sage-kernel/tests/integration/api/test_simple_batch_function.py +++ /dev/null @@ -1,82 +0,0 @@ -"""Integration-style tests for the batch iterator helpers.""" - -from __future__ import annotations - -import pytest - -from sage.kernel.api.function.simple_batch_function import ( - IterableBatchIteratorFunction, - SimpleBatchIteratorFunction, -) -from sage.kernel.api.local_environment import LocalEnvironment - - -def _get_last_transformation(env: LocalEnvironment): - assert env.pipeline, "Environment pipeline should not be empty" - return env.pipeline[-1] - - -def test_from_batch_collection_uses_simple_iterator_and_yields_all_items(): - env = LocalEnvironment(name="batch-list-test") - data = ["alpha", "beta", "gamma"] - - env.from_batch(data) - transformation = _get_last_transformation(env) - - assert transformation.function_class is SimpleBatchIteratorFunction - assert transformation.function_kwargs["data"] == data - - function = transformation.function_class(**transformation.function_kwargs) - assert [function.execute(), function.execute(), function.execute(), function.execute()] == [ - "alpha", - "beta", - "gamma", - None, - ] - assert len(function) == len(data) - assert list(SimpleBatchIteratorFunction(data=data)) == data - - -def test_from_batch_iterable_infers_total_count_when_available(): - env = LocalEnvironment(name="batch-iterable-test") - source = range(4) - - env.from_batch(source) - transformation = _get_last_transformation(env) - - assert transformation.function_class is IterableBatchIteratorFunction - assert transformation.function_kwargs["total_count"] == len(source) - - function = transformation.function_class(**transformation.function_kwargs) - assert [function.execute() for _ in range(5)] == [0, 1, 2, 3, None] - assert len(function) == len(source) - - -def test_from_batch_generator_without_total_count_requires_len_override(): - env = LocalEnvironment(name="batch-generator-test") - generator = (i for i in range(3)) - - env.from_batch(generator) - transformation = _get_last_transformation(env) - assert transformation.function_class is IterableBatchIteratorFunction - assert transformation.function_kwargs["total_count"] is None - - function = transformation.function_class(**transformation.function_kwargs) - assert [function.execute() for _ in range(4)] == [0, 1, 2, None] - - with pytest.raises(TypeError): - len(function) - - -def test_from_batch_generator_with_total_count_supports_len(): - env = LocalEnvironment(name="batch-generator-len-test") - generator = (i for i in range(2)) - - env.from_batch(generator, total_count=2) - transformation = _get_last_transformation(env) - - assert transformation.function_kwargs["total_count"] == 2 - - function = transformation.function_class(**transformation.function_kwargs) - assert [function.execute() for _ in range(3)] == [0, 1, None] - assert len(function) == 2 diff --git a/packages/sage-kernel/tests/integration/services/manual_autostop_service_improved.py b/packages/sage-kernel/tests/integration/services/manual_autostop_service_improved.py deleted file mode 100644 index 67441ec4d7..0000000000 --- a/packages/sage-kernel/tests/integration/services/manual_autostop_service_improved.py +++ /dev/null @@ -1,167 +0,0 @@ -""" -改进的测试：验证 autostop=True 能正确清理服务 -使用监控线程来跟踪服务状态的变化 -""" - -import sys -import threading -import time -from pathlib import Path - -# 添加 SAGE 包路径 -repo_root = Path(__file__).parent -src_paths = [ - repo_root / "packages" / "sage" / "src", - repo_root / "packages" / "sage-common" / "src", - repo_root / "packages" / "sage-kernel" / "src", - repo_root / "packages" / "sage-middleware" / "src", - repo_root / "packages" / "sage-libs" / "src", - repo_root / "packages" / "sage-tools" / "src", -] -for p in src_paths: - sys.path.insert(0, str(p)) - -from sage.common.core.functions import ( - BatchFunction, # noqa: E402 - SinkFunction, -) -from sage.common.utils.logging.custom_logger import CustomLogger # noqa: E402 -from sage.kernel.api.local_environment import LocalEnvironment # noqa: E402 -from sage.platform.service import BaseService # noqa: E402 - -# 全局变量用于跟踪服务状态 -service_lifecycle = { - "initialized": False, - "running": False, - "cleanup_called": False, - "cleanup_completed": False, -} - - -class DemoBatch(BatchFunction): - """简单的批处理函数""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - self.max_count = 5 - - def execute(self): - if self.counter >= self.max_count: - return None - self.counter += 1 - return f"Message {self.counter}" - - -class DemoSink(SinkFunction): - """测试 Sink，会调用服务""" - - def execute(self, data): - result = self.call_service("test_service", method="process", data=data) - print(f"Sink received: {data}, Service result: {result}") - - -class DemoService(BaseService): - """测试服务，跟踪生命周期""" - - def __init__(self): - super().__init__() - self.counter = 0 - service_lifecycle["initialized"] = True - service_lifecycle["running"] = True - print("[TestService] ✓ Service initialized") - - def process(self, data): - self.counter += 1 - return f"Processed by service (call #{self.counter})" - - def cleanup(self): - print(f"[TestService] ✓ Cleanup called - processed {self.counter} requests") - service_lifecycle["cleanup_called"] = True - service_lifecycle["running"] = False - super().cleanup() - service_lifecycle["cleanup_completed"] = True - print("[TestService] ✓ Cleanup completed") - - -def monitor_dispatcher_state(env, stop_event): - """监控 dispatcher 状态的线程""" - env_uuid = None - while not stop_event.is_set(): - if env_uuid is None and env.env_uuid: - env_uuid = env.env_uuid - - if env_uuid: - job_info = env.jobmanager.jobs.get(env_uuid) - if job_info: - dispatcher = job_info.dispatcher - print( - f"[Monitor] Tasks: {len(dispatcher.tasks)}, Services: {len(dispatcher.services)}, Running: {dispatcher.is_running}" - ) - - time.sleep(0.5) - - -def main(): - print("=" * 80) - print("Improved Test: autostop=True with service lifecycle tracking") - print("=" * 80) - - env = LocalEnvironment("test_autostop_service_improved") - - # 启动监控线程 - stop_monitor = threading.Event() - monitor_thread = threading.Thread( - target=monitor_dispatcher_state, args=(env, stop_monitor), daemon=True - ) - monitor_thread.start() - - # 注册服务 - print("\n[Main] Registering service...") - env.register_service("test_service", DemoService) - - # 构建管道 - print("[Main] Building pipeline...") - env.from_batch(DemoBatch).sink(DemoSink) - - # 提交作业 - print("\n[Main] Submitting job with autostop=True...") - print("-" * 80) - start_time = time.time() - env.submit(autostop=True) - elapsed_time = time.time() - start_time - print("-" * 80) - print(f"\n[Main] Job completed in {elapsed_time:.2f} seconds") - - # 停止监控线程 - stop_monitor.set() - monitor_thread.join(timeout=1.0) - - # 验证服务生命周期 - print("\n" + "=" * 80) - print("Service Lifecycle Verification:") - print("=" * 80) - print(f" ✓ Initialized: {service_lifecycle['initialized']}") - print(f" ✓ Was Running: {service_lifecycle['initialized']}") # 如果初始化了就运行过 - print(f" ✓ Cleanup Called: {service_lifecycle['cleanup_called']}") - print(f" ✓ Cleanup Completed: {service_lifecycle['cleanup_completed']}") - print(f" ✓ Currently Running: {service_lifecycle['running']}") - - # 最终验证 - print("\n" + "=" * 80) - if service_lifecycle["cleanup_completed"] and not service_lifecycle["running"]: - print("✅ SUCCESS: Service was properly initialized, used, and cleaned up!") - else: - print("❌ FAILURE: Service lifecycle incomplete") - if not service_lifecycle["cleanup_called"]: - print(" - Cleanup was NOT called") - if not service_lifecycle["cleanup_completed"]: - print(" - Cleanup did NOT complete") - if service_lifecycle["running"]: - print(" - Service is still marked as running") - print("=" * 80) - - -if __name__ == "__main__": - CustomLogger.disable_global_console_debug() - main() diff --git a/packages/sage-kernel/tests/integration/services/manual_autostop_service_remote.py b/packages/sage-kernel/tests/integration/services/manual_autostop_service_remote.py deleted file mode 100644 index dbaa268bf2..0000000000 --- a/packages/sage-kernel/tests/integration/services/manual_autostop_service_remote.py +++ /dev/null @@ -1,162 +0,0 @@ -""" -测试 autostop=True 在远程模式（Ray）下是否能正确清理服务 -""" - -import sys -import time -from pathlib import Path - -# 添加 SAGE 包路径 -repo_root = Path(__file__).parent -src_paths = [ - repo_root / "packages" / "sage" / "src", - repo_root / "packages" / "sage-common" / "src", - repo_root / "packages" / "sage-kernel" / "src", - repo_root / "packages" / "sage-middleware" / "src", - repo_root / "packages" / "sage-libs" / "src", - repo_root / "packages" / "sage-tools" / "src", -] -for p in src_paths: - sys.path.insert(0, str(p)) - -from sage.common.core.functions import ( - BatchFunction, # noqa: E402 - SinkFunction, -) -from sage.common.utils.logging.custom_logger import CustomLogger # noqa: E402 -from sage.kernel.api.remote_environment import RemoteEnvironment # noqa: E402 -from sage.kernel.utils.ray.ray_utils import ensure_ray_initialized # noqa: E402 -from sage.platform.service import BaseService # noqa: E402 - -# 全局变量用于跟踪服务状态 -service_lifecycle = { - "initialized": False, - "cleanup_called": False, - "cleanup_completed": False, -} - - -class DemoBatch(BatchFunction): - """简单的批处理函数""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - self.max_count = 5 - - def execute(self): - if self.counter >= self.max_count: - return None - self.counter += 1 - return f"Message {self.counter}" - - -class DemoSink(SinkFunction): - """测试 Sink，会调用服务""" - - def execute(self, data): - result = self.call_service("test_service", method="process", data=data) - print(f"[Sink] Received: {data}, Service result: {result}") - - -class DemoService(BaseService): - """测试服务，跟踪生命周期""" - - def __init__(self): - super().__init__() - self.counter = 0 - print("[TestService] ✓ Service initialized") - - def process(self, data): - self.counter += 1 - return f"Processed by service (call #{self.counter})" - - def cleanup(self): - print(f"[TestService] ✓ Cleanup called - processed {self.counter} requests") - super().cleanup() - print("[TestService] ✓ Cleanup completed") - - -def check_ray_initialized(): - """检查 Ray 是否已初始化""" - try: - import ray - - if not ray.is_initialized(): - print("[Info] Ray not initialized, initializing...") - ensure_ray_initialized() - print("[Info] ✓ Ray initialized") - else: - print("[Info] ✓ Ray already initialized") - return True - except Exception as e: - print(f"[Error] Failed to initialize Ray: {e}") - return False - - -def main(): - print("=" * 80) - print("Testing autostop=True with Ray Remote Mode") - print("=" * 80) - - # 检查 Ray - if not check_ray_initialized(): - print("\n❌ Ray is not available, skipping remote mode test") - print(" To install Ray: pip install ray") - return - - try: - print("\n[Main] Creating RemoteEnvironment...") - env = RemoteEnvironment("test_autostop_service_remote") - - # 注册服务（remote=True 表示这是一个 Ray Actor） - print("[Main] Registering service...") - env.register_service("test_service", DemoService) - - # 构建管道 - print("[Main] Building pipeline...") - env.from_batch(DemoBatch).sink(DemoSink) - - # 提交作业（现在支持 autostop=True） - print("\n[Main] Submitting job with autostop=True to remote JobManager...") - print("-" * 80) - start_time = time.time() - env.submit(autostop=True) # ✅ 现在支持了！ - elapsed_time = time.time() - start_time - print("-" * 80) - print(f"\n[Main] Job completed in {elapsed_time:.2f} seconds") - - # 等待一下确保清理完成 - time.sleep(1.0) - - # 验证 - print("\n" + "=" * 80) - print("RemoteEnvironment autostop Test:") - print("=" * 80) - print("✅ Pipeline executed successfully with RemoteEnvironment") - print("✅ autostop=True worked correctly!") - print("✅ Services should be cleaned up automatically") - print("=" * 80) - - except Exception as e: - print(f"\n❌ Test failed with error: {e}") - import traceback - - traceback.print_exc() - - finally: - # 清理 Ray - try: - import ray - - if ray.is_initialized(): - print("\n[Cleanup] Shutting down Ray...") - ray.shutdown() - print("[Cleanup] ✓ Ray shutdown complete") - except Exception: - pass - - -if __name__ == "__main__": - CustomLogger.disable_global_console_debug() - main() diff --git a/packages/sage-kernel/tests/integration/services/test_autostop_api_verification.py b/packages/sage-kernel/tests/integration/services/test_autostop_api_verification.py deleted file mode 100644 index 3ff57ed96a..0000000000 --- a/packages/sage-kernel/tests/integration/services/test_autostop_api_verification.py +++ /dev/null @@ -1,169 +0,0 @@ -""" -简单验证 RemoteEnvironment autostop 参数支持 -不实际运行，只验证 API 是否正确 -""" - -import inspect -import sys -from pathlib import Path - -# 添加 SAGE 包路径 -repo_root = Path(__file__).parent -src_paths = [ - repo_root / "packages" / "sage" / "src", - repo_root / "packages" / "sage-common" / "src", - repo_root / "packages" / "sage-kernel" / "src", - repo_root / "packages" / "sage-middleware" / "src", - repo_root / "packages" / "sage-libs" / "src", - repo_root / "packages" / "sage-tools" / "src", -] -for p in src_paths: - sys.path.insert(0, str(p)) - -from sage.kernel.api.remote_environment import RemoteEnvironment # noqa: E402 - - -def test_remote_environment_autostop_signature(): - """验证 RemoteEnvironment.submit() 是否支持 autostop 参数""" - print("=" * 80) - print("Test 1: RemoteEnvironment.submit() 方法签名验证") - print("=" * 80) - - # 获取 submit 方法的签名 - sig = inspect.signature(RemoteEnvironment.submit) - params = list(sig.parameters.keys()) - - print(f"submit() 参数列表: {params}") - - assert "autostop" in params, "RemoteEnvironment.submit() 不支持 autostop 参数" - print("✅ RemoteEnvironment.submit() 支持 autostop 参数") - - # 获取默认值 - autostop_param = sig.parameters["autostop"] - print( - f" - 参数类型: {autostop_param.annotation if autostop_param.annotation != inspect.Parameter.empty else 'any'}" - ) - print(f" - 默认值: {autostop_param.default}") - - -def test_jobmanager_client_signature(): - """验证 JobManagerClient.submit_job() 是否支持 autostop 参数""" - print("\n" + "=" * 80) - print("Test 2: JobManagerClient.submit_job() 方法签名验证") - print("=" * 80) - - from sage.kernel.runtime.jobmanager_client import JobManagerClient - - sig = inspect.signature(JobManagerClient.submit_job) - params = list(sig.parameters.keys()) - - print(f"submit_job() 参数列表: {params}") - - assert "autostop" in params, "对应的方法不支持 autostop 参数" - print("✅ JobManagerClient.submit_job() 支持 autostop 参数") - - autostop_param = sig.parameters["autostop"] - print( - f" - 参数类型: {autostop_param.annotation if autostop_param.annotation != inspect.Parameter.empty else 'any'}" - ) - print(f" - 默认值: {autostop_param.default}") - - -def test_jobmanager_signature(): - """验证 JobManager.submit_job() 是否支持 autostop 参数""" - print("\n" + "=" * 80) - print("Test 3: JobManager.submit_job() 方法签名验证") - print("=" * 80) - - from sage.kernel.runtime.job_manager import JobManager - - sig = inspect.signature(JobManager.submit_job) - params = list(sig.parameters.keys()) - - print(f"submit_job() 参数列表: {params}") - - assert "autostop" in params, "对应的方法不支持 autostop 参数" - print("✅ JobManager.submit_job() 支持 autostop 参数") - - autostop_param = sig.parameters["autostop"] - print( - f" - 参数类型: {autostop_param.annotation if autostop_param.annotation != inspect.Parameter.empty else 'any'}" - ) - print(f" - 默认值: {autostop_param.default}") - - -def test_jobinfo_signature(): - """验证 JobInfo 是否支持 autostop 参数""" - print("\n" + "=" * 80) - print("Test 4: JobInfo.__init__() 方法签名验证") - print("=" * 80) - - from sage.kernel.runtime.job_info import JobInfo - - sig = inspect.signature(JobInfo.__init__) - params = list(sig.parameters.keys()) - - print(f"__init__() 参数列表: {params}") - - assert "autostop" in params, "对应的方法不支持 autostop 参数" - print("✅ JobInfo.__init__() 支持 autostop 参数") - - autostop_param = sig.parameters["autostop"] - print( - f" - 参数类型: {autostop_param.annotation if autostop_param.annotation != inspect.Parameter.empty else 'any'}" - ) - print(f" - 默认值: {autostop_param.default}") - - -def test_wait_for_completion_exists(): - """验证 RemoteEnvironment 是否有 _wait_for_completion 方法""" - print("\n" + "=" * 80) - print("Test 5: RemoteEnvironment._wait_for_completion() 方法存在性验证") - print("=" * 80) - - assert hasattr(RemoteEnvironment, "_wait_for_completion"), ( - "RemoteEnvironment 没有 _wait_for_completion() 方法" - ) - print("✅ RemoteEnvironment 有 _wait_for_completion() 方法") - - -def main(): - print("\n" + "🔍" * 40) - print("RemoteEnvironment autostop 功能 API 验证") - print("🔍" * 40 + "\n") - - results = [] - - # 运行所有测试 - results.append(("RemoteEnvironment.submit()", test_remote_environment_autostop_signature())) - results.append(("JobManagerClient.submit_job()", test_jobmanager_client_signature())) - results.append(("JobManager.submit_job()", test_jobmanager_signature())) - results.append(("JobInfo.__init__()", test_jobinfo_signature())) - results.append(("RemoteEnvironment._wait_for_completion()", test_wait_for_completion_exists())) - - # 总结 - print("\n" + "=" * 80) - print("测试总结") - print("=" * 80) - - passed = sum(1 for _, result in results if result) - total = len(results) - - for name, result in results: - status = "✅ PASS" if result else "❌ FAIL" - print(f"{status}: {name}") - - print("\n" + "-" * 80) - print(f"通过率: {passed}/{total} ({passed * 100 // total}%)") - print("-" * 80) - - if passed == total: - print("\n🎉 所有测试通过！autostop 功能已成功添加到 RemoteEnvironment") - else: - print(f"\n⚠️ 有 {total - passed} 个测试失败，需要检查代码") - - print("\n" + "=" * 80) - - -if __name__ == "__main__": - main() diff --git a/packages/sage-kernel/tests/performance/verify_optimization.py b/packages/sage-kernel/tests/performance/verify_optimization.py deleted file mode 100644 index c23779a56c..0000000000 --- a/packages/sage-kernel/tests/performance/verify_optimization.py +++ /dev/null @@ -1,149 +0,0 @@ -#!/usr/bin/env python3 -""" -快速验证脚本：验证Ray队列批量优化是否工作 - -运行方式： - python verify_optimization.py -""" - -import time - -import ray - -from sage.kernel.utils.ray.ray_utils import ensure_ray_initialized - - -def verify_optimization(): - """验证优化效果的快速脚本""" - print("🚀 Ray Queue Batch Optimization Verification") - print("=" * 80) - - # 初始化Ray - if not ray.is_initialized(): - ensure_ray_initialized() - print("✅ Ray initialized") - - try: - from sage.kernel.runtime.communication.queue_descriptor.ray_queue_descriptor import ( - RayQueueDescriptor, - get_global_queue_manager, - ) - - print("✅ Import successful") - - # 测试1：基本功能 - print("\n📝 Test 1: Basic Functionality") - print("-" * 80) - queue_desc = RayQueueDescriptor(maxsize=1000, queue_id="verify_test") - queue = queue_desc.queue_instance - - # 批量put - test_items = [f"item_{i}" for i in range(100)] - start = time.time() - for item in test_items: - queue.put(item) - queue.flush() - queue.wait_for_pending_puts() - elapsed = time.time() - start - - print(f" Put 100 items: {elapsed:.3f}s") - print(f" Throughput: {100 / elapsed:.1f} items/sec") - - # 获取统计 - stats = queue.get_stats() - print("\n📊 Statistics:") - print(f" Total puts: {stats['total_puts']}") - print(f" Batch operations: {stats['batch_puts']}") - print(f" Avg batch size: {stats['avg_batch_size']:.1f}") - - if stats["batch_puts"] > 0: - print("\n✅ Test 1 PASSED: Batch operations working!") - else: - print("\n⚠️ Test 1 WARNING: No batch operations detected") - - # 测试2：批量get - print("\n📝 Test 2: Batch Get") - print("-" * 80) - batch = queue.get_batch(count=50) - print(f" Retrieved {len(batch)} items in batch") - - if len(batch) > 0: - print("\n✅ Test 2 PASSED: Batch get working!") - else: - print("\n❌ Test 2 FAILED: No items retrieved") - - # 测试3：性能对比 - print("\n📝 Test 3: Performance Comparison") - print("-" * 80) - - # 清理旧数据 - manager = get_global_queue_manager() - ray.get(manager.delete_queue.remote("verify_test")) - - # 旧方式（单条同步） - # queue_desc1 = RayQueueDescriptor(maxsize=1000, queue_id="verify_sync") - manager = get_global_queue_manager() - ray.get(manager.get_or_create_queue.remote("verify_sync", 1000)) - - print("\n 🔴 Synchronous (old way):") - test_items = [f"item_{i}" for i in range(200)] - start = time.time() - for item in test_items: - ray.get(manager.put.remote("verify_sync", item)) - sync_time = time.time() - start - sync_throughput = 200 / sync_time - print(f" Time: {sync_time:.3f}s") - print(f" Throughput: {sync_throughput:.1f} items/sec") - - # 新方式（批量异步） - queue_desc2 = RayQueueDescriptor(maxsize=1000, queue_id="verify_async") - queue2 = queue_desc2.queue_instance - - print("\n 🟢 Asynchronous batch (new way):") - start = time.time() - for item in test_items: - queue2.put(item) - queue2.flush() - queue2.wait_for_pending_puts() - async_time = time.time() - start - async_throughput = 200 / async_time - print(f" Time: {async_time:.3f}s") - print(f" Throughput: {async_throughput:.1f} items/sec") - - # 计算提升（始终用吞吐量比） - improvement = async_throughput / sync_throughput - if async_time < sync_time: - print(f"\n 🚀 Performance improvement: {improvement:.1f}x faster (throughput)") - print("\n✅ Test 3 PASSED: Optimization working!") - else: - print(f"\n 📊 Performance: {improvement:.2f}x (throughput)") - print("\n⚠️ Test 3 WARNING: Improvement less than expected") - - # 清理 - ray.get(manager.delete_queue.remote("verify_sync")) - ray.get(manager.delete_queue.remote("verify_async")) - - # 总结 - print("\n" + "=" * 80) - print("✨ Verification Complete!") - print("=" * 80) - print("\n📋 Summary:") - print(" ✅ Import and initialization: OK") - print(" ✅ Batch operations: OK") - print(" ✅ Statistics collection: OK") - print(f" ✅ Performance improvement: {improvement:.1f}x") - print("\n🎉 Ray queue batch optimization is working correctly!") - - return True - - except Exception as e: - print(f"\n❌ Verification FAILED: {e}") - import traceback - - traceback.print_exc() - return False - - -if __name__ == "__main__": - success = verify_optimization() - exit(0 if success else 1) diff --git a/packages/sage-kernel/tests/unit/api/operator/test_base_operator_keyed_state.py b/packages/sage-kernel/tests/unit/api/operator/test_base_operator_keyed_state.py deleted file mode 100644 index d983f8027d..0000000000 --- a/packages/sage-kernel/tests/unit/api/operator/test_base_operator_keyed_state.py +++ /dev/null @@ -1,218 +0,0 @@ -""" -Unit tests for keyed state support in BaseOperator. - -These tests verify that receive_packet() correctly sets and clears keys -during packet processing. -""" - -from unittest.mock import Mock - -import pytest - -from sage.kernel.api.operator.base_operator import BaseOperator -from sage.kernel.runtime.communication.packet import Packet - - -class ConcreteOperator(BaseOperator): - """Concrete implementation of BaseOperator for testing""" - - def process_packet(self, packet=None): - """Process packet implementation""" - # Track that process_packet was called - if not hasattr(self, "processed_packets"): - self.processed_packets = [] - self.processed_packets.append(packet) - - -class TestBaseOperatorKeyedState: - """Test keyed state functionality in BaseOperator""" - - def setup_method(self): - """Set up test fixtures""" - # Create mock context - self.mock_ctx = Mock() - self.mock_ctx.name = "test_task" - self.mock_ctx.logger = Mock() - self.mock_ctx.set_current_key = Mock() - self.mock_ctx.clear_key = Mock() - self.mock_ctx.get_key = Mock(return_value=None) - - # Create mock function factory - self.mock_factory = Mock() - self.mock_function = Mock() - self.mock_factory.create_function = Mock(return_value=self.mock_function) - - def test_receive_packet_sets_key(self): - """Test that receive_packet sets the packet's key""" - operator = ConcreteOperator(self.mock_factory, self.mock_ctx) - - # Create packet with a key - packet = Mock(spec=Packet) - packet.partition_key = "test_key_123" - - operator.receive_packet(packet) - - # Verify set_current_key was called with the packet's key - self.mock_ctx.set_current_key.assert_called_once_with("test_key_123") - - def test_receive_packet_clears_key_after_processing(self): - """Test that receive_packet clears the key after processing""" - operator = ConcreteOperator(self.mock_factory, self.mock_ctx) - - packet = Mock(spec=Packet) - packet.partition_key = "test_key" - - operator.receive_packet(packet) - - # Verify clear_key was called - self.mock_ctx.clear_key.assert_called_once() - - def test_receive_packet_clears_key_on_exception(self): - """Test that receive_packet clears key even if processing raises an exception""" - - class FailingOperator(BaseOperator): - def process_packet(self, packet=None): - raise ValueError("Processing failed") - - operator = FailingOperator(self.mock_factory, self.mock_ctx) - - packet = Mock(spec=Packet) - packet.partition_key = "test_key" - - # Process should raise, but clear_key should still be called - with pytest.raises(ValueError, match="Processing failed"): - operator.receive_packet(packet) - - # Verify clear_key was called despite the exception - self.mock_ctx.clear_key.assert_called_once() - - def test_receive_packet_with_none_key(self): - """Test receive_packet with None as partition_key (unkeyed stream)""" - operator = ConcreteOperator(self.mock_factory, self.mock_ctx) - - packet = Mock(spec=Packet) - packet.partition_key = None - - operator.receive_packet(packet) - - # Should still call set_current_key with None - self.mock_ctx.set_current_key.assert_called_once_with(None) - self.mock_ctx.clear_key.assert_called_once() - - def test_receive_packet_none_packet(self): - """Test receive_packet with None packet""" - operator = ConcreteOperator(self.mock_factory, self.mock_ctx) - - operator.receive_packet(None) - - # Should log warning and not process - self.mock_ctx.logger.warning.assert_called_once() - # Should not call set_current_key or clear_key for None packet - self.mock_ctx.set_current_key.assert_not_called() - self.mock_ctx.clear_key.assert_not_called() - - def test_receive_packet_calls_process_packet(self): - """Test that receive_packet calls process_packet""" - operator = ConcreteOperator(self.mock_factory, self.mock_ctx) - - packet = Mock(spec=Packet) - packet.partition_key = "key" - - operator.receive_packet(packet) - - # Verify packet was processed - assert len(operator.processed_packets) == 1 - assert operator.processed_packets[0] == packet - - def test_receive_packet_key_lifecycle(self): - """Test the complete lifecycle of key setting and clearing""" - - class KeyTrackingOperator(BaseOperator): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self.key_during_processing = None - - def process_packet(self, packet=None): - # Capture the key during processing - self.key_during_processing = self.ctx.get_key() - - # Configure mock to track key state - current_key = [None] # Use list to allow modification in nested function - - def set_key(key): - current_key[0] = key - - def get_key(): - return current_key[0] - - def clear_key(): - current_key[0] = None - - self.mock_ctx.set_current_key = set_key - self.mock_ctx.get_key = get_key - self.mock_ctx.clear_key = clear_key - - operator = KeyTrackingOperator(self.mock_factory, self.mock_ctx) - - # Before processing, key should be None - assert operator.ctx.get_key() is None - - packet = Mock(spec=Packet) - packet.partition_key = "test_key" - - operator.receive_packet(packet) - - # After processing, key should be cleared - assert operator.ctx.get_key() is None - # But during processing, it should have been set - assert operator.key_during_processing == "test_key" - - def test_receive_packet_with_complex_key(self): - """Test receive_packet with complex object as key""" - operator = ConcreteOperator(self.mock_factory, self.mock_ctx) - - complex_key = {"user_id": "alice", "session_id": "xyz", "shard": 3} - packet = Mock(spec=Packet) - packet.partition_key = complex_key - - operator.receive_packet(packet) - - self.mock_ctx.set_current_key.assert_called_once_with(complex_key) - self.mock_ctx.clear_key.assert_called_once() - - def test_multiple_packets_sequential(self): - """Test processing multiple packets sequentially""" - operator = ConcreteOperator(self.mock_factory, self.mock_ctx) - - packets = [ - Mock(spec=Packet, partition_key="key1"), - Mock(spec=Packet, partition_key="key2"), - Mock(spec=Packet, partition_key="key3"), - ] - - for packet in packets: - operator.receive_packet(packet) - - # Each packet should have triggered set and clear - assert self.mock_ctx.set_current_key.call_count == 3 - assert self.mock_ctx.clear_key.call_count == 3 - - # Verify keys were set in order - calls = self.mock_ctx.set_current_key.call_args_list - assert calls[0][0][0] == "key1" - assert calls[1][0][0] == "key2" - assert calls[2][0][0] == "key3" - - -class TestBaseOperatorKeyedStateIntegration: - """Integration tests for keyed state with real context""" - - def test_with_real_task_context(self): - """Test receive_packet with a real TaskContext (if available)""" - # This would require more complex setup with real TaskContext - # For now, we rely on the integration test in test_keyed_state.py - pass - - -if __name__ == "__main__": - pytest.main([__file__, "-v"]) diff --git a/packages/sage-kernel/tests/unit/api/service/__init__.py b/packages/sage-kernel/tests/unit/api/service/__init__.py deleted file mode 100644 index 3dc8d7f896..0000000000 --- a/packages/sage-kernel/tests/unit/api/service/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Tests for SAGE Kernel API Service Layer""" diff --git a/packages/sage-kernel/tests/unit/core/__init__.py b/packages/sage-kernel/tests/unit/core/__init__.py deleted file mode 100644 index e00468dc56..0000000000 --- a/packages/sage-kernel/tests/unit/core/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -""" -SAGE Core Module Tests - -This package contains all tests for the SAGE core functionality including: -- API tests (LocalEnvironment, RemoteEnvironment) -- Transformation tests (filter, map, flatmap, keyby, join, comap) -- Operator tests -- Function tests - -Tests in this module should focus on core SAGE functionality and should not -depend on external services or runtime components. -""" diff --git a/packages/sage-kernel/tests/unit/core/conftest.py b/packages/sage-kernel/tests/unit/core/conftest.py deleted file mode 100644 index 8730deb5d2..0000000000 --- a/packages/sage-kernel/tests/unit/core/conftest.py +++ /dev/null @@ -1,385 +0,0 @@ -""" -Core模块测试的配置文件 -""" - -from unittest.mock import Mock - -import pytest - - -class TestConfig: - """测试配置类""" - - # 测试数据 - SAMPLE_STRING_DATA = "test_string_data" - SAMPLE_NUMERIC_DATA = 42 - SAMPLE_LIST_DATA = [1, 2, 3, 4, 5] - SAMPLE_DICT_DATA = {"key1": "value1", "key2": "value2"} - - # 测试序列 - SAMPLE_DATA_SEQUENCE = ["item1", "item2", "item3", "item4", "item5"] - - # 错误数据 - ERROR_DATA = "error_trigger" - - # 批处理大小 - DEFAULT_BATCH_SIZE = 3 - - # 测试超时 - TEST_TIMEOUT = 5.0 - - -@pytest.fixture -def mock_logger(): - """Mock logger fixture""" - return Mock() - - -@pytest.fixture -def mock_context(): - """Mock context fixture""" - mock_ctx = Mock() - mock_ctx.name = "test_context" - mock_ctx.logger = Mock() - return mock_ctx - - -@pytest.fixture -def mock_function_factory(): - """Mock function factory fixture""" - mock_factory = Mock() - mock_function = Mock() - mock_function.execute = Mock(return_value="processed_data") - mock_factory.create_function.return_value = mock_function - return mock_factory - - -@pytest.fixture -def sample_data(): - """Sample data fixture""" - return { - "string": TestConfig.SAMPLE_STRING_DATA, - "numeric": TestConfig.SAMPLE_NUMERIC_DATA, - "list": TestConfig.SAMPLE_LIST_DATA, - "dict": TestConfig.SAMPLE_DICT_DATA, - "sequence": TestConfig.SAMPLE_DATA_SEQUENCE, - } - - -@pytest.fixture -def mock_packet(): - """Mock packet fixture""" - packet = Mock() - packet.data = "test_packet_data" - packet.stream_id = 0 - return packet - - -class TestUtilities: - """测试工具类""" - - @staticmethod - def assert_method_called_with_data(mock_method, expected_data): - """断言方法被调用并传入期望的数据""" - mock_method.assert_called() - call_args = mock_method.call_args - assert call_args is not None - if call_args.args: - assert expected_data in call_args.args - elif call_args.kwargs: - assert expected_data in call_args.kwargs.values() - - @staticmethod - def create_mock_with_side_effect(side_effect_list): - """创建有副作用的Mock""" - mock = Mock() - mock.side_effect = side_effect_list - return mock - - @staticmethod - def verify_lifecycle_calls(mock_obj, setup=True, start=True, stop=True, cleanup=True): - """验证生命周期方法调用""" - if setup: - mock_obj.setup.assert_called() - if start: - mock_obj.start.assert_called() - if stop: - mock_obj.stop.assert_called() - if cleanup: - mock_obj.cleanup.assert_called() - - -# 测试标记定义 -pytestmark = [ - pytest.mark.core, # 标记为core模块测试 -] - - -# 自定义断言 -def assert_pipeline_structure(pipeline, expected_step_count): - """断言管道结构""" - assert len(pipeline.steps) == expected_step_count - assert all(hasattr(step, "process") for step in pipeline.steps) - - -def assert_function_inheritance(function_instance, base_class): - """断言函数继承关系""" - assert isinstance(function_instance, base_class) - assert hasattr(function_instance, "ctx") - assert hasattr(function_instance, "logger") - - -def assert_operator_state(operator, expected_function_type=None): - """断言操作器状态""" - assert operator.ctx is not None - assert hasattr(operator, "function") - if expected_function_type: - assert isinstance(operator.function, expected_function_type) - - -def assert_service_lifecycle_state( - service, setup=False, started=False, stopped=False, cleaned=False -): - """断言服务生命周期状态""" - if hasattr(service, "setup_called"): - assert service.setup_called == setup - if hasattr(service, "start_called"): - assert service.start_called == started - if hasattr(service, "stop_called"): - assert service.stop_called == stopped - if hasattr(service, "cleanup_called"): - assert service.cleanup_called == cleaned - - -# 测试数据生成器 -class DataGenerator: - """测试数据生成器""" - - @staticmethod - def generate_string_sequence(count=5, prefix="data"): - """生成字符串序列""" - return [f"{prefix}_{i}" for i in range(count)] - - @staticmethod - def generate_numeric_sequence(start=0, count=5): - """生成数值序列""" - return list(range(start, start + count)) - - @staticmethod - def generate_dict_sequence(count=3): - """生成字典序列""" - return [{"id": i, "value": f"item_{i}"} for i in range(count)] - - @staticmethod - def generate_mixed_data(): - """生成混合类型数据""" - return ["string_data", 123, [1, 2, 3], {"key": "value"}, None, True] - - -# 错误模拟器 -class ErrorSimulator: - """错误模拟器""" - - @staticmethod - def create_error_function(error_type=RuntimeError, error_message="Test error"): - """创建会抛出错误的函数""" - - def error_func(*args, **kwargs): - raise error_type(error_message) - - return error_func - - @staticmethod - def create_conditional_error_function( - error_condition, error_type=RuntimeError, error_message="Conditional error" - ): - """创建条件错误函数""" - - def conditional_error_func(data): - if error_condition(data): - raise error_type(error_message) - return data - - return conditional_error_func - - -# 性能测试工具 -class PerformanceTestUtils: - """性能测试工具""" - - @staticmethod - def measure_execution_time(func, *args, **kwargs): - """测量执行时间""" - import time - - start_time = time.time() - result = func(*args, **kwargs) - end_time = time.time() - execution_time = end_time - start_time - return result, execution_time - - @staticmethod - def assert_execution_time_under(func, max_time, *args, **kwargs): - """断言执行时间小于指定值""" - result, execution_time = PerformanceTestUtils.measure_execution_time(func, *args, **kwargs) - assert execution_time < max_time, ( - f"Execution time {execution_time}s exceeds maximum {max_time}s" - ) - return result - - -# 集成测试帮助器 -class IntegrationTestHelper: - """集成测试帮助器""" - - @staticmethod - def create_full_pipeline_scenario(): - """ - 创建完整管道场景 - - 使用新的 Environment + DataStream API 替代旧的 Pipeline API - """ - from sage.common.core.functions import ( - BatchFunction, - FilterFunction, - MapFunction, - SinkFunction, - ) - from sage.kernel.api.local_environment import LocalEnvironment - - # 定义测试用的 Function 类 - class TestBatchSource(BatchFunction): - """测试批处理数据源""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - self.max_count = 5 - - def execute(self): - if self.counter >= self.max_count: - return None - self.counter += 1 - return f"test_item_{self.counter}" - - class TransformFunction(MapFunction): - """转换函数""" - - def execute(self, data): - return f"transformed_{data}" - - class FilterErrorFunction(FilterFunction): - """过滤错误函数""" - - def execute(self, data): - if "error" in data: - return None - return data - - class TestSink(SinkFunction): - """测试 Sink""" - - # 类级别属性用于收集结果 - results: list = [] - - def execute(self, data): - # 记录数据到类级别属性 - self.__class__.results.append(data) - - # 创建 Environment 和 Pipeline - env = LocalEnvironment("integration_test_pipeline") - - # 使用链式 API 构建 pipeline - env.from_batch(TestBatchSource).map(TransformFunction).filter(FilterErrorFunction).sink( - TestSink - ) - - return env - - @staticmethod - def create_multi_stream_scenario(): - """创建多流场景""" - return { - "stream_0": DataGenerator.generate_string_sequence(3, "stream0"), - "stream_1": DataGenerator.generate_numeric_sequence(0, 3), - "stream_2": DataGenerator.generate_dict_sequence(3), - } - - @staticmethod - def simulate_distributed_environment(): - """模拟分布式环境""" - return { - "node_1": {"id": "node_1", "resources": ["cpu", "memory"]}, - "node_2": {"id": "node_2", "resources": ["gpu", "storage"]}, - "node_3": {"id": "node_3", "resources": ["network", "compute"]}, - } - - -# 覆盖率测试辅助 -class CoverageTestHelper: - """覆盖率测试辅助""" - - @staticmethod - def test_all_public_methods(obj, exclude_methods=None): - """测试对象的所有公共方法""" - exclude_methods = exclude_methods or [] - public_methods = [ - method - for method in dir(obj) - if not method.startswith("_") - and callable(getattr(obj, method)) - and method not in exclude_methods - ] - - coverage_report = {} - for method_name in public_methods: - try: - getattr(obj, method_name) - # 尝试调用方法（可能需要参数） - coverage_report[method_name] = "callable" - except Exception as e: - coverage_report[method_name] = f"error: {str(e)}" - - return coverage_report - - @staticmethod - def test_all_properties(obj): - """测试对象的所有属性""" - properties = [ - prop - for prop in dir(obj) - if not prop.startswith("_") and not callable(getattr(obj, prop)) - ] - - property_report = {} - for prop_name in properties: - try: - value = getattr(obj, prop_name) - property_report[prop_name] = type(value).__name__ - except Exception as e: - property_report[prop_name] = f"error: {str(e)}" - - return property_report - - -# 清理工具 -class TestCleanupHelper: - """测试清理辅助""" - - @staticmethod - def cleanup_mocks(*mocks): - """清理mock对象""" - for mock in mocks: - if hasattr(mock, "reset_mock"): - mock.reset_mock() - - @staticmethod - def cleanup_resources(*resources): - """清理资源""" - for resource in resources: - if hasattr(resource, "cleanup"): - resource.cleanup() - elif hasattr(resource, "close"): - resource.close() - elif hasattr(resource, "stop"): - resource.stop() diff --git a/packages/sage-kernel/tests/unit/core/function/__init__.py b/packages/sage-kernel/tests/unit/core/function/__init__.py deleted file mode 100644 index f2c5c26f60..0000000000 --- a/packages/sage-kernel/tests/unit/core/function/__init__.py +++ /dev/null @@ -1,40 +0,0 @@ -""" -Core Function模块测试包 - -该包包含了sage.core.function模块的所有测试用例，按照测试组织架构规范设计。 - -测试文件映射: -- test_base_function.py -> sage.core.api.function.base_function -- test_comap_function.py -> sage.core.api.function.comap_function -- test_sink_function.py -> sage.core.api.function.sink_function -- test_source_function.py -> sage.core.api.function.source_function -""" - -# 测试配置 -FUNCTION_TEST_CONFIG = { - "timeout": 30, # 测试超时时间（秒） - "retry_count": 3, # 失败重试次数 - "parallel_safe": True, # 是否支持并行测试 -} - -# 测试覆盖的功能点 -COVERED_MODULES = [ - "base_function", - "comap_function", - "sink_function", - "source_function", -] - -# 待扩展的测试模块 -PENDING_MODULES = [ - "batch_function", - "filter_function", - "flatmap_function", - "future_function", - "join_function", - "kafka_source", - "keyby_function", - "lambda_function", - "map_function", - "simple_batch_function", -] diff --git a/packages/sage-kernel/tests/unit/core/function/test_chunk_parallelism.py b/packages/sage-kernel/tests/unit/core/function/test_chunk_parallelism.py deleted file mode 100644 index b4aca971d4..0000000000 --- a/packages/sage-kernel/tests/unit/core/function/test_chunk_parallelism.py +++ /dev/null @@ -1,455 +0,0 @@ -import threading -import time - -from sage.common.core.functions import BaseFunction, SinkFunction, SourceFunction -from sage.kernel.api.local_environment import LocalEnvironment - -# 添加全局打印锁来防止并发输出混乱 -_print_lock = threading.Lock() - - -def thread_safe_print(*args, **kwargs): - """线程安全的打印函数""" - with _print_lock: - print(*args, **kwargs) - - -class DocumentSource(SourceFunction): - """生成文档数据源""" - - def __init__(self, documents=None, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - self.documents = documents or [ - { - "content": "This is a sample document with some text content.", - "id": "doc1", - }, - { - "content": "Another document containing different information and data.", - "id": "doc2", - }, - { - "content": "Large document with extensive content that needs chunking for processing.", - "id": "doc3", - }, - {"content": "Short text document.", "id": "doc4"}, - { - "content": "Medium sized document with reasonable content length.", - "id": "doc5", - }, - ] - - def execute(self, data=None): - if self.counter >= len(self.documents): - return None - - data = self.documents[self.counter] - self.counter += 1 - self.logger.info(f"DocumentSource generated: {data['id']}") - return data - - -class CharacterSplitter(BaseFunction): - """字符级文本分块算子 - 使用parallelism hints进行性能优化""" - - def __init__(self, chunk_size=50, overlap=10, **kwargs): - super().__init__(**kwargs) - self.chunk_size = chunk_size - self.overlap = overlap - self.instance_id = id(self) - self.thread_id = threading.get_ident() - thread_safe_print( - f":gear: CharacterSplitter instance {self.instance_id} created in thread {self.thread_id}" - ) - - def execute(self, document): - """单文档处理逻辑，框架会自动并行化""" - content = document.get("content", "") - doc_id = document.get("id", "unknown") - chunks = [] - - current_thread = threading.get_ident() - instance_id = id(self) - - thread_safe_print( - f":gear: CharacterSplitter[{instance_id}]: Processing {doc_id} (thread: {current_thread})" - ) - - # 模拟处理时间 - time.sleep(0.01) - - for i in range(0, len(content), self.chunk_size - self.overlap): - chunk_text = content[i : i + self.chunk_size] - if chunk_text.strip(): # 过滤空块 - chunks.append( - { - "content": chunk_text, - "doc_id": doc_id, - "start_idx": i, - "end_idx": min(i + self.chunk_size, len(content)), - "chunk_id": f"{doc_id}_chunk_{len(chunks)}", - } - ) - - thread_safe_print( - f":white_check_mark: CharacterSplitter[{instance_id}]: Generated {len(chunks)} chunks for {doc_id}" - ) - return chunks - - -class SlowCharacterSplitter(CharacterSplitter): - """延长处理时间的CharacterSplitter，用于模拟在途数据""" - - def execute(self, document): - time.sleep(0.05) - return super().execute(document) - - -class ChunkCollector(SinkFunction): - """收集分块结果的sink算子""" - - # 类级别的结果收集 - 只用于测试目的 - _collected_chunks: list[dict] = [] - _lock = threading.Lock() - - @classmethod - def _ensure_chunks_list(cls): - """确保chunks列表被初始化""" - if cls._collected_chunks is None: - cls._collected_chunks = [] - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.instance_id = id(self) - self.thread_id = threading.get_ident() - thread_safe_print( - f":gear: ChunkCollector instance {self.instance_id} created in thread {self.thread_id}" - ) - - def execute(self, chunks): - current_thread = threading.get_ident() - instance_id = id(self) - - with self._lock: - self._ensure_chunks_list() - - # 过滤掉 None - if chunks is None: - return - - if isinstance(chunks, list): - self._collected_chunks.extend(chunks) - thread_safe_print( - f":dart: ChunkCollector[{instance_id}]: Collected {len(chunks)} chunks (thread: {current_thread})" - ) - else: - self._collected_chunks.append(chunks) - thread_safe_print( - f":dart: ChunkCollector[{instance_id}]: Collected 1 chunk (thread: {current_thread})" - ) - - def close(self): - """SinkFunction的关闭方法，在停止信号时被调用""" - current_thread = threading.get_ident() - instance_id = id(self) - thread_safe_print( - f":stop_sign: ChunkCollector[{instance_id}]: Received close signal (thread: {current_thread})" - ) - - @classmethod - def get_collected_chunks(cls): - """获取收集到的所有chunks""" - with cls._lock: - cls._ensure_chunks_list() - return cls._collected_chunks.copy() - - @classmethod - def clear_collected_chunks(cls): - """清空收集到的chunks""" - with cls._lock: - cls._collected_chunks = [] - - -class LaggyChunkCollector(ChunkCollector): - """处理延迟的Sink算子，模拟慢速消费者""" - - def execute(self, chunks): - time.sleep(0.05) - super().execute(chunks) - - -class TestChunkParallelism: - """测试chunk算子的并行性优化""" - - def test_chunk_parallelism_hints_basic(self): - """测试基本的chunk并行性hints功能""" - print("\n" + "=" * 70) - print("TEST: Basic Chunk Parallelism Hints") - print("=" * 70) - - # 清空之前的数据 - ChunkCollector.clear_collected_chunks() - - env = LocalEnvironment(name="chunk_parallelism_test") - - # 测试文档 - documents = [ - {"content": "This is document one with some content.", "id": "doc1"}, - {"content": "This is document two with different content.", "id": "doc2"}, - {"content": "This is document three with more content here.", "id": "doc3"}, - ] - - # 使用parallelism参数直接设置2个并行chunk实例 - ( - env.from_collection(DocumentSource, documents) - .map(CharacterSplitter, chunk_size=20, overlap=5, parallelism=2) - .sink(ChunkCollector, parallelism=1) - ) - - # 使用autostop=True让SAGE自动检测批处理完成 - env.submit(autostop=True) - - # 获取收集的结果 - collected_chunks = ChunkCollector.get_collected_chunks() - - # 验证结果 - assert len(collected_chunks) > 0 - assert all(chunk.get("chunk_id") for chunk in collected_chunks) - - # 验证所有文档都被处理 - processed_doc_ids = {chunk["doc_id"] for chunk in collected_chunks} - expected_doc_ids = {doc["id"] for doc in documents} - assert processed_doc_ids == expected_doc_ids - - print( - f"✅ Collected {len(collected_chunks)} chunks from {len(processed_doc_ids)} documents" - ) - - def test_chunk_parallelism_hints_multiple_levels(self): - """测试多级并行度设置""" - print("\n" + "=" * 70) - print("TEST: Multi-level Chunk Parallelism Hints") - print("=" * 70) - - # 清空之前的数据 - ChunkCollector.clear_collected_chunks() - - env = LocalEnvironment(name="multi_level_parallelism_test") - - # 更多测试文档 - documents = [ - {"content": "Document one content here.", "id": "doc1"}, - {"content": "Document two content here.", "id": "doc2"}, - {"content": "Document three content here.", "id": "doc3"}, - {"content": "Document four content here.", "id": "doc4"}, - {"content": "Document five content here.", "id": "doc5"}, - ] - - # 测试不同的并行度设置 - ( - env.from_collection(DocumentSource, documents) - .map(CharacterSplitter, chunk_size=15, overlap=3, parallelism=3) - .sink(ChunkCollector, parallelism=2) - ) - - # 使用autostop=True让SAGE自动检测批处理完成 - env.submit(autostop=True) - - # 验证结果 - collected_chunks = ChunkCollector.get_collected_chunks() - assert len(collected_chunks) > 0 - - # 验证chunk结构 - for chunk in collected_chunks: - assert "content" in chunk - assert "doc_id" in chunk - assert "start_idx" in chunk - assert "end_idx" in chunk - assert "chunk_id" in chunk - assert len(chunk["content"]) > 0 - - print(f"✅ Multi-level parallelism test completed with {len(collected_chunks)} chunks") - - def test_chunk_parallelism_hints_large_documents(self): - """测试大文档的chunk并行处理""" - print("\n" + "=" * 70) - print("TEST: Large Document Chunk Parallelism") - print("=" * 70) - - # 清空之前的数据 - ChunkCollector.clear_collected_chunks() - - env = LocalEnvironment(name="large_doc_test") - - # 模拟大文档 - large_content = "This is a large document with extensive content. " * 50 - documents = [ - {"content": large_content, "id": "large_doc1"}, - {"content": large_content, "id": "large_doc2"}, - ] - - # 使用更高的并行度处理大文档 - ( - env.from_collection(DocumentSource, documents) - .map(CharacterSplitter, chunk_size=100, overlap=20, parallelism=4) - .sink(ChunkCollector, parallelism=1) - ) - - # 使用autostop=True让SAGE自动检测批处理完成 - env.submit(autostop=True) - - # 验证大文档被正确分块 - collected_chunks = ChunkCollector.get_collected_chunks() - assert len(collected_chunks) > 0 - - # 检查大文档产生了多个chunk - large_doc_chunks = [c for c in collected_chunks if c["doc_id"] == "large_doc1"] - assert len(large_doc_chunks) > 1 # 大文档应该被分成多个chunk - - print(f"✅ Large document test: {len(large_doc_chunks)} chunks from large_doc1") - - def test_mixed_document_chunk_parallelism(self): - """测试混合文档（普通文档+大文档）的并行处理场景""" - print("\n" + "=" * 70) - print("TEST: Mixed Document Chunk Parallelism") - print("=" * 70) - - # 清空之前的数据 - ChunkCollector.clear_collected_chunks() - - env = LocalEnvironment(name="mixed_doc_test") - - # 创建完整的测试文档集合，包括普通文档和大文档 - large_content = "This is a large document with extensive content. " * 50 - documents = [ - { - "content": "Another document containing different information and data.", - "id": "doc2", - }, - { - "content": "Large document with extensive content that needs chunking for processing.", - "id": "doc3", - }, - {"content": "Short text document.", "id": "doc4"}, - { - "content": "Medium sized document with reasonable content length.", - "id": "doc5", - }, - {"content": large_content, "id": "large_doc1"}, - {"content": large_content, "id": "large_doc2"}, - ] - - # 使用高并行度处理混合文档类型 - ( - env.from_collection(DocumentSource, documents) - .map(CharacterSplitter, chunk_size=50, overlap=10, parallelism=4) - .sink(ChunkCollector, parallelism=2) - ) - - # 使用autostop=True让SAGE自动检测批处理完成 - env.submit(autostop=True) - - # 验证处理结果 - collected_chunks = ChunkCollector.get_collected_chunks() - assert len(collected_chunks) > 0 - - # 验证所有文档都被处理 - processed_doc_ids = {chunk["doc_id"] for chunk in collected_chunks} - expected_doc_ids = {"doc2", "doc3", "doc4", "doc5", "large_doc1", "large_doc2"} - - assert processed_doc_ids == expected_doc_ids, ( - f"Expected documents {expected_doc_ids}, got {processed_doc_ids}" - ) - - # 验证大文档产生了足够的chunks（如果被处理的话） - large_doc1_chunks = [c for c in collected_chunks if c["doc_id"] == "large_doc1"] - large_doc2_chunks = [c for c in collected_chunks if c["doc_id"] == "large_doc2"] - - # 大文档应该产生大量chunks - assert len(large_doc1_chunks) > 30, ( - f"large_doc1 should generate many chunks, got {len(large_doc1_chunks)}" - ) - assert len(large_doc2_chunks) > 30, ( - f"large_doc2 should generate many chunks, got {len(large_doc2_chunks)}" - ) - - print(f"✅ Mixed document test completed: {len(collected_chunks)} total chunks") - print(f" - large_doc1: {len(large_doc1_chunks)} chunks") - print(f" - large_doc2: {len(large_doc2_chunks)} chunks") - - def test_chunk_parallelism_hints_vs_manual_parallelization(self): - """对比parallelism hints与手动并行化的区别""" - print("\n" + "=" * 70) - print("TEST: Parallelism Hints vs Manual Parallelization") - print("=" * 70) - - # 清空之前的数据 - ChunkCollector.clear_collected_chunks() - - # 使用parallelism hints的方式 - env1 = LocalEnvironment(name="hints_test") - documents = [ - {"content": "Test document one.", "id": "test1"}, - {"content": "Test document two.", "id": "test2"}, - ] - - ( - env1.from_collection(DocumentSource, documents) - .map(CharacterSplitter, chunk_size=10, overlap=2, parallelism=2) - .sink(ChunkCollector) - ) - # 使用autostop=True让SAGE自动检测批处理完成 - env1.submit(autostop=True) - - # 验证hints方式工作正常 - collected_chunks = ChunkCollector.get_collected_chunks() - assert len(collected_chunks) > 0 - - print("✅ Parallelism hints approach works correctly") - print("💡 Key advantage: Framework manages parallelism, code stays simple") - - def test_autostop_graceful_shutdown_drains_sink(self): - """验证autostop在存在在途数据时不会丢失数据""" - print("\n" + "=" * 70) - print("TEST: Autostop Graceful Shutdown Drains Sink") - print("=" * 70) - - ChunkCollector.clear_collected_chunks() - LaggyChunkCollector.clear_collected_chunks() - - env = LocalEnvironment(name="graceful_shutdown_drain_test") - - large_content = "Synthetic large document content to simulate heavy processing. " * 60 - documents = [ - {"content": large_content, "id": "slow_doc1"}, - {"content": large_content, "id": "slow_doc2"}, - ] - - ( - env.from_collection(DocumentSource, documents) - .map(SlowCharacterSplitter, chunk_size=60, overlap=15, parallelism=2) - .sink(LaggyChunkCollector, parallelism=1) - ) - - env.submit(autostop=True) - - collected_chunks = LaggyChunkCollector.get_collected_chunks() - assert len(collected_chunks) > 0 - - expected_doc_ids = {doc["id"] for doc in documents} - processed_doc_ids = {chunk["doc_id"] for chunk in collected_chunks} - - assert processed_doc_ids == expected_doc_ids, ( - f"Expected documents {expected_doc_ids}, got {processed_doc_ids}" - ) - - for doc_id in expected_doc_ids: - doc_chunks = [c for c in collected_chunks if c["doc_id"] == doc_id] - assert len(doc_chunks) > 20, ( - f"Document {doc_id} should generate many chunks, got {len(doc_chunks)}" - ) - - print( - f"✅ Graceful shutdown drained sink with {len(collected_chunks)} total chunks across {len(expected_doc_ids)} documents" - ) diff --git a/packages/sage-kernel/tests/unit/core/function/test_comap.py b/packages/sage-kernel/tests/unit/core/function/test_comap.py deleted file mode 100644 index 912426e862..0000000000 --- a/packages/sage-kernel/tests/unit/core/function/test_comap.py +++ /dev/null @@ -1,696 +0,0 @@ -import json -import tempfile -import threading -import time -from pathlib import Path -from typing import Any - -from sage.common.core.functions import BaseCoMapFunction, SinkFunction, SourceFunction -from sage.kernel.api.local_environment import LocalEnvironment - - -class OrderDataSource(SourceFunction): - """生成订单数据""" - - def __init__(self, ctx=None, **kwargs): - super().__init__(ctx=ctx, **kwargs) - self.counter = 0 - self.orders = [ - { - "id": "order1", - "user_id": "user1", - "product": "laptop", - "amount": 999.0, - "type": "order", - }, - { - "id": "order2", - "user_id": "user2", - "product": "mouse", - "amount": 29.9, - "type": "order", - }, - { - "id": "order3", - "user_id": "user1", - "product": "keyboard", - "amount": 79.0, - "type": "order", - }, - { - "id": "order4", - "user_id": "user3", - "product": "monitor", - "amount": 299.0, - "type": "order", - }, - ] - - def execute(self, data=None): - if self.counter >= len(self.orders): - return None - - data = self.orders[self.counter] - self.counter += 1 - if self.ctx: - self.logger.info(f"OrderSource generated: {data}") - return data - - -class PaymentDataSource(SourceFunction): - """生成支付数据""" - - def __init__(self, ctx=None, **kwargs): - super().__init__(ctx=ctx, **kwargs) - self.counter = 0 - self.payments = [ - { - "id": "pay1", - "order_id": "order1", - "method": "credit_card", - "status": "success", - "type": "payment", - }, - { - "id": "pay2", - "order_id": "order2", - "method": "paypal", - "status": "success", - "type": "payment", - }, - { - "id": "pay3", - "order_id": "order3", - "method": "debit_card", - "status": "failed", - "type": "payment", - }, - { - "id": "pay4", - "order_id": "order4", - "method": "credit_card", - "status": "pending", - "type": "payment", - }, - ] - - def execute(self, data=None): - if self.counter >= len(self.payments): - return None - - data = self.payments[self.counter] - self.counter += 1 - if self.ctx: - self.logger.info(f"PaymentSource generated: {data}") - return data - - -class InventoryDataSource(SourceFunction): - """生成库存数据""" - - def __init__(self, ctx=None, **kwargs): - super().__init__(ctx=ctx, **kwargs) - self.counter = 0 - self.inventory = [ - {"product": "laptop", "stock": 50, "warehouse": "WH1", "type": "inventory"}, - {"product": "mouse", "stock": 200, "warehouse": "WH1", "type": "inventory"}, - { - "product": "keyboard", - "stock": 0, - "warehouse": "WH2", - "type": "inventory", - }, - { - "product": "monitor", - "stock": 30, - "warehouse": "WH2", - "type": "inventory", - }, - ] - - def execute(self, data=None): - if self.counter >= len(self.inventory): - return None - - data = self.inventory[self.counter] - self.counter += 1 - if self.ctx: - self.logger.info(f"InventorySource generated: {data}") - return data - - -class CoMapDebugSink(SinkFunction): - """调试用的Sink，记录CoMap处理结果""" - - _lock = threading.Lock() - _received_data = {} - - def __init__(self, ctx=None, output_file=None, **kwargs): - super().__init__(ctx=ctx, **kwargs) - self.parallel_index = None - self.received_count = 0 - # 如果没有指定输出文件，使用临时文件 - if output_file is None: - self.output_file = Path(tempfile.gettempdir()) / "comap_test_results.json" - else: - self.output_file = Path(output_file) - if self.ctx: - self.logger.info(f"CoMapDebugSink initialized, output file: {self.output_file}") - - def execute(self, data: Any): - if self.ctx: - self.parallel_index = self.ctx.parallel_index - - with self._lock: - if self.parallel_index not in self._received_data: - self._received_data[self.parallel_index] = [] - - self._received_data[self.parallel_index].append(data) - - self.received_count += 1 - - result_type = ( - data.get("type", "unknown") if isinstance(data, dict) else str(type(data).__name__) - ) - source_stream = data.get("source_stream", -1) if isinstance(data, dict) else -1 - - if self.ctx: - self.logger.info( - f"[Instance {self.parallel_index}] " - f"Received {result_type} from stream {source_stream}: {data}" - ) - - # 打印调试信息 - print( - f"🔍 [Instance {self.parallel_index}] Type: {result_type}, " - f"Stream: {source_stream}, Data: {data}" - ) - - return data - - def _append_record(self, record): - """原子性地追加记录到文件""" - try: - # 以追加模式打开文件 - with open(self.output_file, "a") as f: - # 写入一行JSON - f.write(json.dumps(record) + "\n") - f.flush() - except Exception as e: - self.logger.error(f"Failed to write record: {e}") - - @classmethod - def read_results(cls, output_file=None): - """读取测试结果""" - if output_file is None: - output_file = Path(tempfile.gettempdir()) / "comap_test_results.json" - else: - output_file = Path(output_file) - - # 直接从类变量读取结果 - with cls._lock: - results = dict(cls._received_data) - - print(f"📂 Read {len(results)} parallel instances from memory") - return results - - @classmethod - def clear_results(cls, output_file=None): - """清理结果""" - with cls._lock: - cls._received_data.clear() - - if output_file is None: - output_file = Path(tempfile.gettempdir()) / "comap_test_results.json" - else: - output_file = Path(output_file) - - if output_file.exists(): - output_file.unlink() - - -class OrderPaymentCoMapFunction(BaseCoMapFunction): - """CoMap函数：处理订单和支付数据""" - - def __init__(self, ctx=None, **kwargs): - super().__init__(ctx=ctx, **kwargs) - self.processed_orders = 0 - self.processed_payments = 0 - - def map0(self, order_data): - """处理订单数据流 (stream 0)""" - self.processed_orders += 1 - - result = { - "type": "processed_order", - "order_id": order_data["id"], - "user_id": order_data["user_id"], - "product": order_data["product"], - "amount": order_data["amount"], - "processing_sequence": self.processed_orders, - "source_stream": 0, - "processor": "OrderProcessor", - } - - if self.ctx: - self.logger.info( - f"CoMap map0: processed order {order_data['id']} (#{self.processed_orders})" - ) - return result - - def map1(self, payment_data): - """处理支付数据流 (stream 1)""" - self.processed_payments += 1 - - result = { - "type": "processed_payment", - "payment_id": payment_data["id"], - "order_id": payment_data["order_id"], - "method": payment_data["method"], - "status": payment_data["status"], - "processing_sequence": self.processed_payments, - "source_stream": 1, - "processor": "PaymentProcessor", - } - - if self.ctx: - self.logger.info( - f"CoMap map1: processed payment {payment_data['id']} (#{self.processed_payments})" - ) - return result - - -class TripleStreamCoMapFunction(BaseCoMapFunction): - """三路CoMap函数：处理订单、支付和库存数据""" - - def __init__(self, ctx=None, **kwargs): - super().__init__(ctx=ctx, **kwargs) - self.stream_counters = [0, 0, 0] # 每个流的处理计数 - - def map0(self, order_data): - """处理订单数据流 (stream 0)""" - self.stream_counters[0] += 1 - - result = { - "type": "enriched_order", - "order_id": order_data["id"], - "user_id": order_data["user_id"], - "product": order_data["product"], - "amount": order_data["amount"], - "stream_sequence": self.stream_counters[0], - "source_stream": 0, - "enrichment": "order_enriched", - } - - if self.ctx: - self.logger.info(f"TripleCoMap map0: enriched order {order_data['id']}") - return result - - def map1(self, payment_data): - """处理支付数据流 (stream 1)""" - self.stream_counters[1] += 1 - - result = { - "type": "enriched_payment", - "payment_id": payment_data["id"], - "order_id": payment_data["order_id"], - "method": payment_data["method"], - "status": payment_data["status"], - "stream_sequence": self.stream_counters[1], - "source_stream": 1, - "enrichment": "payment_enriched", - } - - if self.ctx: - self.logger.info(f"TripleCoMap map1: enriched payment {payment_data['id']}") - return result - - def map2(self, inventory_data): - """处理库存数据流 (stream 2)""" - self.stream_counters[2] += 1 - - result = { - "type": "enriched_inventory", - "product": inventory_data["product"], - "stock": inventory_data["stock"], - "warehouse": inventory_data["warehouse"], - "stream_sequence": self.stream_counters[2], - "source_stream": 2, - "enrichment": "inventory_enriched", - } - - if self.ctx: - self.logger.info(f"TripleCoMap map2: enriched inventory {inventory_data['product']}") - return result - - -class StatefulCoMapFunction(BaseCoMapFunction): - """有状态的CoMap函数，演示状态维护""" - - def __init__(self, ctx=None, **kwargs): - super().__init__(ctx=ctx, **kwargs) - self.order_cache = {} # 订单缓存 - self.payment_stats = { # 支付统计 - "total_amount": 0.0, - "success_count": 0, - "failed_count": 0, - } - - def map0(self, order_data): - """处理订单数据，缓存订单信息""" - order_id = order_data["id"] - self.order_cache[order_id] = order_data - - result = { - "type": "cached_order", - "order_id": order_id, - "user_id": order_data["user_id"], - "amount": order_data["amount"], - "cache_size": len(self.order_cache), - "source_stream": 0, - } - - if self.ctx: - self.logger.info( - f"StatefulCoMap map0: cached order {order_id}, cache size: {len(self.order_cache)}" - ) - return result - - def map1(self, payment_data): - """处理支付数据，更新统计并关联订单""" - order_id = payment_data["order_id"] - payment_status = payment_data["status"] - - # 更新支付统计 - if payment_status == "success": - self.payment_stats["success_count"] += 1 - # 查找对应订单金额 - order_info = self.order_cache.get(order_id, {}) - if "amount" in order_info: - self.payment_stats["total_amount"] += order_info["amount"] - elif payment_status == "failed": - self.payment_stats["failed_count"] += 1 - - result = { - "type": "enriched_payment_with_stats", - "payment_id": payment_data["id"], - "order_id": order_id, - "status": payment_status, - "method": payment_data["method"], - "order_info": self.order_cache.get(order_id, {"error": "order_not_found"}), - "payment_stats": dict(self.payment_stats), # 复制当前统计 - "source_stream": 1, - } - - if self.ctx: - self.logger.info( - f"StatefulCoMap map1: processed payment {payment_data['id']}, stats: {self.payment_stats}" - ) - return result - - -class InvalidCoMapFunction(BaseCoMapFunction): - """无效的CoMap函数，缺少必需的map1方法（测试用）""" - - def map0(self, data): - return {"processed": data, "source_stream": 0} - - # 故意不实现map1方法 - - -class TestCoMapFunctionality: - """测试CoMap功能""" - - def setup_method(self): - # 清理结果 - CoMapDebugSink.clear_results() - - def test_basic_two_stream_comap(self): - """测试基本的两路CoMap处理""" - print("\n🚀 Testing Basic Two-Stream CoMap") - - env = LocalEnvironment("basic_comap_test") - - order_stream = env.from_source(OrderDataSource, delay=0.3) - payment_stream = env.from_source(PaymentDataSource, delay=0.4) - - ( - order_stream.connect(payment_stream) - .comap(OrderPaymentCoMapFunction) - .sink(CoMapDebugSink, parallelism=2) - ) - - print( - "📊 Pipeline: OrderStream + PaymentStream -> comap(OrderPaymentCoMapFunction) -> Sink(parallelism=2)" - ) - print("🎯 Expected: Orders processed by map0, Payments processed by map1\n") - - try: - env.submit() - - time.sleep(3) - finally: - env.close() - - # 等待一下确保处理完成 - time.sleep(1) - self._verify_two_stream_comap_results() - - def test_three_stream_comap(self): - """测试三路CoMap处理""" - print("\n🚀 Testing Three-Stream CoMap") - - env = LocalEnvironment("triple_comap_test") - - order_stream = env.from_source(OrderDataSource, delay=0.2) - payment_stream = env.from_source(PaymentDataSource, delay=0.3) - inventory_stream = env.from_source(InventoryDataSource, delay=0.4) - - ( - order_stream.connect(payment_stream) - .connect(inventory_stream) - .comap(TripleStreamCoMapFunction) - .sink(CoMapDebugSink, parallelism=1) - ) - - print( - "📊 Pipeline: OrderStream + PaymentStream + InventoryStream -> comap(TripleStreamCoMapFunction) -> Sink" - ) - print("🎯 Expected: Orders by map0, Payments by map1, Inventory by map2\n") - - try: - env.submit() - - time.sleep(3) - finally: - env.close() - - time.sleep(1) - self._verify_three_stream_comap_results() - - def test_stateful_comap(self): - """测试有状态的CoMap处理""" - print("\n🚀 Testing Stateful CoMap") - - env = LocalEnvironment("stateful_comap_test") - - order_stream = env.from_source(OrderDataSource, delay=0.2) - payment_stream = env.from_source(PaymentDataSource, delay=0.3) - - ( - order_stream.connect(payment_stream) - .comap(StatefulCoMapFunction) - .sink(CoMapDebugSink, parallelism=1) - ) - - print("📊 Pipeline: OrderStream + PaymentStream -> comap(StatefulCoMapFunction) -> Sink") - print("🎯 Expected: Stateful processing with order caching and payment statistics\n") - - try: - env.submit() - - time.sleep(4) - finally: - env.close() - - time.sleep(1) - self._verify_stateful_comap_results() - - def _verify_two_stream_comap_results(self): - """验证两路CoMap的结果""" - received_data = CoMapDebugSink.read_results() - - print("\n📋 Two-Stream CoMap Results:") - print("=" * 50) - - processed_orders = [] - processed_payments = [] - - for instance_id, data_list in received_data.items(): - print(f"\n🔹 Parallel Instance {instance_id}:") - - for data in data_list: - result_type = data.get("type", "unknown") - source_stream = data.get("source_stream", -1) - processor = data.get("processor", "unknown") - - if result_type == "processed_order": - processed_orders.append(data) - order_id = data.get("order_id", "unknown") - sequence = data.get("processing_sequence", 0) - print( - f" - {processor}: Order {order_id} (seq #{sequence}) from stream {source_stream}" - ) - - elif result_type == "processed_payment": - processed_payments.append(data) - payment_id = data.get("payment_id", "unknown") - sequence = data.get("processing_sequence", 0) - status = data.get("status", "unknown") - print( - f" - {processor}: Payment {payment_id} ({status}, seq #{sequence}) from stream {source_stream}" - ) - - print("\n🎯 CoMap Processing Summary:") - print(f" - Processed orders: {len(processed_orders)}") - print(f" - Processed payments: {len(processed_payments)}") - - # 验证：应该有订单和支付处理结果 - assert len(processed_orders) > 0, "❌ No processed orders received" - assert len(processed_payments) > 0, "❌ No processed payments received" - - # 验证：所有订单都来自stream 0 - for order in processed_orders: - assert order.get("source_stream") == 0, ( - f"❌ Order from wrong stream: {order.get('source_stream')}" - ) - - # 验证：所有支付都来自stream 1 - for payment in processed_payments: - assert payment.get("source_stream") == 1, ( - f"❌ Payment from wrong stream: {payment.get('source_stream')}" - ) - - print("✅ Two-stream CoMap test passed: Correct stream routing and processing") - - def _verify_three_stream_comap_results(self): - """验证三路CoMap的结果""" - received_data = CoMapDebugSink.read_results() - - print("\n📋 Three-Stream CoMap Results:") - print("=" * 50) - - enriched_orders = [] - enriched_payments = [] - enriched_inventory = [] - - for instance_id, data_list in received_data.items(): - print(f"\n🔹 Parallel Instance {instance_id}:") - - for data in data_list: - result_type = data.get("type", "unknown") - source_stream = data.get("source_stream", -1) - enrichment = data.get("enrichment", "unknown") - - if result_type == "enriched_order": - enriched_orders.append(data) - order_id = data.get("order_id", "unknown") - sequence = data.get("stream_sequence", 0) - print( - f" - {enrichment}: Order {order_id} (seq #{sequence}) from stream {source_stream}" - ) - - elif result_type == "enriched_payment": - enriched_payments.append(data) - payment_id = data.get("payment_id", "unknown") - sequence = data.get("stream_sequence", 0) - print( - f" - {enrichment}: Payment {payment_id} (seq #{sequence}) from stream {source_stream}" - ) - - elif result_type == "enriched_inventory": - enriched_inventory.append(data) - product = data.get("product", "unknown") - sequence = data.get("stream_sequence", 0) - print( - f" - {enrichment}: Inventory {product} (seq #{sequence}) from stream {source_stream}" - ) - - print("\n🎯 Three-Stream CoMap Summary:") - print(f" - Enriched orders: {len(enriched_orders)}") - print(f" - Enriched payments: {len(enriched_payments)}") - print(f" - Enriched inventory: {len(enriched_inventory)}") - - # 验证：应该有三种类型的处理结果 - assert len(enriched_orders) > 0, "❌ No enriched orders received" - assert len(enriched_payments) > 0, "❌ No enriched payments received" - assert len(enriched_inventory) > 0, "❌ No enriched inventory received" - - print("✅ Three-stream CoMap test passed: All three streams processed correctly") - - def _verify_stateful_comap_results(self): - """验证有状态CoMap的结果""" - received_data = CoMapDebugSink.read_results() - - print("\n📋 Stateful CoMap Results:") - print("=" * 50) - - cached_orders = [] - enriched_payments = [] - - for instance_id, data_list in received_data.items(): - print(f"\n🔹 Parallel Instance {instance_id}:") - - for data in data_list: - result_type = data.get("type", "unknown") - - if result_type == "cached_order": - cached_orders.append(data) - order_id = data.get("order_id", "unknown") - cache_size = data.get("cache_size", 0) - print(f" - Cached Order: {order_id} (cache size: {cache_size})") - - elif result_type == "enriched_payment_with_stats": - enriched_payments.append(data) - payment_id = data.get("payment_id", "unknown") - status = data.get("status", "unknown") - stats = data.get("payment_stats", {}) - print(f" - Enriched Payment: {payment_id} ({status})") - print(f" Stats: {stats}") - - print("\n🎯 Stateful CoMap Summary:") - print(f" - Cached orders: {len(cached_orders)}") - print(f" - Enriched payments: {len(enriched_payments)}") - - # 验证：应该有缓存订单和丰富支付结果 - assert len(cached_orders) > 0, "❌ No cached orders received" - assert len(enriched_payments) > 0, "❌ No enriched payments received" - - # 验证状态变化：缓存大小应该递增 - cache_sizes = [order.get("cache_size", 0) for order in cached_orders] - if len(cache_sizes) > 1: - assert max(cache_sizes) > min(cache_sizes), "❌ Cache size did not increase" - - print("✅ Stateful CoMap test passed: State maintained correctly") - - -if __name__ == "__main__": - # 可以直接运行单个测试 - test = TestCoMapFunctionality() - test.setup_method() - test.test_basic_two_stream_comap() - -""" -用法示例: - -# 运行所有CoMap测试 -pytest sage_tests/core_tests/comap_test.py -v -s - -# 运行特定测试 -pytest sage_tests/core_tests/comap_test.py::TestCoMapFunctionality::test_basic_two_stream_comap -v -s -pytest sage_tests/core_tests/comap_test.py::TestCoMapFunctionality::test_three_stream_comap -v -s -pytest sage_tests/core_tests/comap_test.py::TestCoMapFunctionality::test_stateful_comap -v -s - -# 直接运行文件进行快速测试 -python sage_tests/core_tests/comap_test.py -""" diff --git a/packages/sage-kernel/tests/unit/core/function/test_connected_keyby.py b/packages/sage-kernel/tests/unit/core/function/test_connected_keyby.py deleted file mode 100644 index ac4fd2286f..0000000000 --- a/packages/sage-kernel/tests/unit/core/function/test_connected_keyby.py +++ /dev/null @@ -1,475 +0,0 @@ -import threading -import time -from typing import Any - -import pytest - -from sage.common.core.functions import ( - BaseCoMapFunction, - KeyByFunction, - SinkFunction, - SourceFunction, -) -from sage.kernel.api.local_environment import LocalEnvironment - - -class UserDataSource(SourceFunction): - """生成用户数据""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - self.users = [ - {"id": 1, "user_id": "user1", "name": "Alice", "type": "user"}, - {"id": 2, "user_id": "user2", "name": "Bob", "type": "user"}, - {"id": 3, "user_id": "user3", "name": "Charlie", "type": "user"}, - {"id": 4, "user_id": "user1", "name": "Alice Updated", "type": "user"}, - ] - - def execute(self, data=None): - if self.counter >= len(self.users): - return None - - data = self.users[self.counter] - self.counter += 1 - self.logger.info(f"UserSource generated: {data}") - return data - - -class EventDataSource(SourceFunction): - """生成事件数据""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - self.events = [ - { - "id": 101, - "user_id": "user1", - "event": "login", - "session_id": "sess1", - "type": "event", - }, - { - "id": 102, - "user_id": "user2", - "event": "click", - "session_id": "sess2", - "type": "event", - }, - { - "id": 103, - "user_id": "user3", - "event": "purchase", - "session_id": "sess3", - "type": "event", - }, - { - "id": 104, - "user_id": "user1", - "event": "logout", - "session_id": "sess1", - "type": "event", - }, - ] - - def execute(self, data=None): - if self.counter >= len(self.events): - return None - - data = self.events[self.counter] - self.counter += 1 - self.logger.info(f"EventSource generated: {data}") - return data - - -class UserIdKeyExtractor(KeyByFunction): - """提取用户ID作为分区键""" - - def execute(self, data: Any) -> str: - user_id = data["user_id"] - self.logger.info( - f"UserIdExtractor: key '{user_id}' from {data.get('type', 'unknown')} data" - ) - return user_id - - -class SessionIdKeyExtractor(KeyByFunction): - """提取会话ID作为分区键""" - - def execute(self, data: Any) -> str: - # 对于用户数据，使用user_id作为session key（模拟场景） - if data.get("type") == "user": - session_key = f"user_session_{data['user_id']}" - else: - session_key = data.get("session_id", "default_session") - - self.logger.info( - f"SessionIdExtractor: key '{session_key}' from {data.get('type', 'unknown')} data" - ) - return session_key - - -class ConnectedDebugSink(SinkFunction): - """调试用的Sink，记录接收到的连接流数据分布""" - - # 类级别的统计 - _received_data: dict[int, list[dict]] = {} - _lock = threading.Lock() - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.parallel_index = None - self.received_count = 0 - - def execute(self, data: Any): - if self.ctx: - self.parallel_index = self.ctx.parallel_index - - # parallel_index 在运行时总是被设置的 - assert self.parallel_index is not None, "parallel_index must be set" - - with self._lock: - if self.parallel_index not in self._received_data: - self._received_data[self.parallel_index] = [] - - self._received_data[self.parallel_index].append(data) - - self.received_count += 1 - - data_type = data.get("type", "unknown") - key_info = data.get("user_id", "no_key") - - self.logger.info( - f"[Instance {self.parallel_index}] " - f"Received {data_type} data #{self.received_count}: {data}" - ) - - # 打印调试信息 - print( - f"🔍 [Instance {self.parallel_index}] Type: {data_type}, Key: {key_info}, Data: {data}" - ) - - return data - - @classmethod - def get_received_data(cls) -> dict[int, list[dict]]: - with cls._lock: - return dict(cls._received_data) - - @classmethod - def clear_data(cls): - with cls._lock: - cls._received_data.clear() - - -class JoinCoMapFunction(BaseCoMapFunction): - """示例CoMap函数，用于连接用户和事件数据""" - - # is_comap 在 BaseCoMapFunction 中已经定义为 @property，不需要重复定义 - # is_comap = True # 移除这行 - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.user_cache = {} # 简单的用户数据缓存 - - def map0(self, user_data): - """处理用户数据流 (stream 0)""" - user_id = user_data["user_id"] - self.user_cache[user_id] = user_data - - result = { - "type": "user_update", - "user_id": user_id, - "user_info": user_data, - "source_stream": 0, - } - - self.logger.info(f"CoMap map0: processed user {user_id}") - return result - - def map1(self, event_data): - """处理事件数据流 (stream 1)""" - user_id = event_data["user_id"] - user_info = self.user_cache.get(user_id, {"name": "Unknown"}) - - result = { - "type": "enriched_event", - "user_id": user_id, - "event_info": event_data, - "user_info": user_info, - "source_stream": 1, - } - - self.logger.info(f"CoMap map1: enriched event for user {user_id}") - return result - - -class TestConnectedStreamsKeyBy: - """测试ConnectedStreams的KeyBy功能""" - - def setup_method(self): - ConnectedDebugSink.clear_data() - - def test_unified_keyby(self): - """测试统一的KeyBy - 两个流使用相同的key selector""" - print("\n🚀 Testing Connected Streams Unified KeyBy") - - env = LocalEnvironment("connected_unified_keyby_test") - - # 创建两个数据源 - user_stream = env.from_source(UserDataSource, delay=0.3) - event_stream = env.from_source(EventDataSource, delay=0.4) - - # 连接流并应用统一的keyby - ( - user_stream.connect(event_stream) - .keyby(UserIdKeyExtractor) # 两个流都使用UserIdKeyExtractor - .map(lambda x: x) # 透明传递 - .sink(ConnectedDebugSink, parallelism=2) - ) - - print( - "📊 Pipeline: UserStream + EventStream -> ConnectedStreams.keyby(UserIdExtractor) -> Sink(parallelism=2)" - ) - print("🎯 Expected: Data with same user_id should go to same parallel instance\n") - - try: - env.submit() - - time.sleep(3) - finally: - env.close() - - self._verify_unified_keyby_partitioning() - - def test_per_stream_keyby(self): - """测试Flink风格的per-stream KeyBy - 每个流使用不同的key selector""" - print("\n🚀 Testing Connected Streams Per-Stream KeyBy (Flink-style)") - - env = LocalEnvironment("connected_per_stream_keyby_test") - - user_stream = env.from_source(UserDataSource, delay=0.3) - event_stream = env.from_source(EventDataSource, delay=0.4) - - # 连接流并应用不同的keyby策略 - ( - user_stream.connect(event_stream) - .keyby([UserIdKeyExtractor, SessionIdKeyExtractor]) # 每个流不同的extractor - .map(lambda x: x) # 透明传递 - .sink(ConnectedDebugSink, parallelism=3) - ) - - print( - "📊 Pipeline: UserStream + EventStream -> ConnectedStreams.keyby([UserIdExtractor, SessionIdExtractor]) -> Sink(parallelism=3)" - ) - print("🎯 Expected: Stream 0 by user_id, Stream 1 by session_id\n") - - try: - env.submit() - - time.sleep(3) - finally: - env.close() - - self._verify_per_stream_keyby_partitioning() - - def test_keyby_with_comap(self): - """测试KeyBy后接CoMap操作""" - print("\n🚀 Testing Connected Streams KeyBy + CoMap") - - env = LocalEnvironment("connected_keyby_comap_test") - - user_stream = env.from_source(UserDataSource, delay=0.3) - event_stream = env.from_source(EventDataSource, delay=0.4) - - # KeyBy后进行CoMap join操作 - ( - user_stream.connect(event_stream) - .keyby(UserIdKeyExtractor) # 统一使用user_id作为key - .comap(JoinCoMapFunction) # 进行数据join - .sink(ConnectedDebugSink, parallelism=2) - ) - - print( - "📊 Pipeline: UserStream + EventStream -> keyby(UserIdExtractor) -> comap(JoinCoMapFunction) -> Sink" - ) - print("🎯 Expected: Same user_id data co-located for join operation\n") - - try: - env.submit() - - time.sleep(4) # 给更多时间让join操作完成 - finally: - env.close() - - self._verify_keyby_comap_results() - - def test_invalid_keyby_configurations(self): - """测试无效的KeyBy配置""" - print("\n🚀 Testing Invalid KeyBy Configurations") - - env = LocalEnvironment("invalid_keyby_test") - - user_stream = env.from_source(UserDataSource, delay=0.5) - event_stream = env.from_source(EventDataSource, delay=0.5) - connected = user_stream.connect(event_stream) - - # 测试1：key selector数量不匹配 - with pytest.raises(ValueError, match="Key selector count .* must match stream count"): - connected.keyby([UserIdKeyExtractor]) # 只有1个selector，但有2个stream - - # 测试2：Lambda函数不支持（故意传入 lambda 来测试错误处理） - with pytest.raises(NotImplementedError, match="Lambda functions are not supported"): - connected.keyby(lambda x: x["user_id"]) # type: ignore[arg-type] - - print("✅ Invalid configuration tests passed") - - env.close() - - def _verify_unified_keyby_partitioning(self): - """验证统一KeyBy的分区效果""" - received_data = ConnectedDebugSink.get_received_data() - - print("\n📋 Unified KeyBy Partitioning Results:") - print("=" * 60) - - user_distribution = {} - - for instance_id, data_list in received_data.items(): - print(f"\n🔹 Parallel Instance {instance_id}:") - - for data in data_list: - user_id = data["user_id"] - data_type = data["type"] - - if user_id not in user_distribution: - user_distribution[user_id] = set() - user_distribution[user_id].add(instance_id) - - print( - f" - User {user_id} ({data_type}): {data.get('name', data.get('event', 'N/A'))}" - ) - - print("\n🎯 User Distribution Across Instances:") - for user_id, instances in user_distribution.items(): - print(f" - {user_id}: routed to instance(s) {instances}") - - # 验证：每个用户的所有数据（无论来自哪个流）都应该路由到同一个实例 - for user_id, instances in user_distribution.items(): - assert len(instances) == 1, ( - f"❌ User {user_id} data was routed to multiple instances: {instances}. " - f"Unified keyby should send same key to same instance." - ) - - print( - "✅ Unified keyby test passed: Each user's data from both streams routed to same instance" - ) - - def _verify_per_stream_keyby_partitioning(self): - """验证per-stream KeyBy的分区效果""" - received_data = ConnectedDebugSink.get_received_data() - - print("\n📋 Per-Stream KeyBy Partitioning Results:") - print("=" * 60) - - stream0_key_distribution = {} # user_id distribution - stream1_key_distribution = {} # session_id distribution - - for instance_id, data_list in received_data.items(): - print(f"\n🔹 Parallel Instance {instance_id}:") - - for data in data_list: - data_type = data["type"] - - if data_type == "user": - # Stream 0: 按user_id分区 - key = data["user_id"] - if key not in stream0_key_distribution: - stream0_key_distribution[key] = set() - stream0_key_distribution[key].add(instance_id) - print(f" - Stream0 key '{key}': {data['name']}") - - elif data_type == "event": - # Stream 1: 按session_id分区 - key = data.get("session_id", "unknown") - if key not in stream1_key_distribution: - stream1_key_distribution[key] = set() - stream1_key_distribution[key].add(instance_id) - print(f" - Stream1 key '{key}': {data['event']}") - - print("\n🎯 Stream 0 (User) Key Distribution:") - for key, instances in stream0_key_distribution.items(): - print(f" - User {key}: routed to instance(s) {instances}") - - print("\n🎯 Stream 1 (Event) Key Distribution:") - for key, instances in stream1_key_distribution.items(): - print(f" - Session {key}: routed to instance(s) {instances}") - - # 验证：每个流的相同key应该路由到相同实例 - for key, instances in stream0_key_distribution.items(): - assert len(instances) == 1, ( - f"❌ Stream0 key {key} routed to multiple instances: {instances}" - ) - - for key, instances in stream1_key_distribution.items(): - assert len(instances) == 1, ( - f"❌ Stream1 key {key} routed to multiple instances: {instances}" - ) - - print("✅ Per-stream keyby test passed: Each stream's keys correctly partitioned") - - def _verify_keyby_comap_results(self): - """验证KeyBy + CoMap的结果""" - received_data = ConnectedDebugSink.get_received_data() - - print("\n📋 KeyBy + CoMap Results:") - print("=" * 60) - - user_updates = [] - enriched_events = [] - - for instance_id, data_list in received_data.items(): - print(f"\n🔹 Parallel Instance {instance_id}:") - - for data in data_list: - result_type = data.get("type", "unknown") - user_id = data.get("user_id", "unknown") - source_stream = data.get("source_stream", -1) - - if result_type == "user_update": - user_updates.append(data) - print(f" - User Update: {user_id} from stream {source_stream}") - elif result_type == "enriched_event": - enriched_events.append(data) - event_name = data.get("event_info", {}).get("event", "unknown") - print( - f" - Enriched Event: {user_id} {event_name} from stream {source_stream}" - ) - - print("\n🎯 CoMap Results Summary:") - print(f" - User updates: {len(user_updates)}") - print(f" - Enriched events: {len(enriched_events)}") - - # 验证：应该有用户更新和丰富的事件 - assert len(user_updates) > 0, "❌ No user updates received from CoMap" - assert len(enriched_events) > 0, "❌ No enriched events received from CoMap" - - print("✅ KeyBy + CoMap test passed: Both user updates and enriched events received") - - -if __name__ == "__main__": - # 可以直接运行单个测试 - test = TestConnectedStreamsKeyBy() - test.setup_method() - test.test_unified_keyby() - -""" -# 运行所有Connected KeyBy测试 -pytest sage_tests/operator_tests/connected_keyby_test.py -v -s - -# 运行特定测试 -pytest sage_tests/operator_tests/connected_keyby_test.py::TestConnectedStreamsKeyBy::test_unified_keyby -v -s -pytest sage_tests/operator_tests/connected_keyby_test.py::TestConnectedStreamsKeyBy::test_per_stream_keyby -v -s -pytest sage_tests/operator_tests/connected_keyby_test.py::TestConnectedStreamsKeyBy::test_keyby_with_comap -v -s -""" diff --git a/packages/sage-kernel/tests/unit/core/function/test_filter.py b/packages/sage-kernel/tests/unit/core/function/test_filter.py deleted file mode 100644 index 7eecc20f77..0000000000 --- a/packages/sage-kernel/tests/unit/core/function/test_filter.py +++ /dev/null @@ -1,626 +0,0 @@ -import threading -import time -from typing import Any - -from sage.common.core.functions import FilterFunction, SinkFunction, SourceFunction -from sage.kernel.api.local_environment import LocalEnvironment - - -class NumberDataSource(SourceFunction): - """生成数字数据""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - self.numbers = [ - {"value": 1, "category": "odd", "positive": True}, - {"value": -2, "category": "even", "positive": False}, - {"value": 3, "category": "odd", "positive": True}, - {"value": 4, "category": "even", "positive": True}, - {"value": -5, "category": "odd", "positive": False}, - {"value": 6, "category": "even", "positive": True}, - {"value": 0, "category": "even", "positive": False}, - {"value": 7, "category": "odd", "positive": True}, - ] - - def execute(self, data=None): - if self.counter >= len(self.numbers): - return None - - data = self.numbers[self.counter] - self.counter += 1 - self.logger.info(f"NumberSource generated: {data}") - return data - - -class UserDataSource(SourceFunction): - """生成用户数据""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - self.users = [ - { - "id": "user1", - "name": "Alice", - "age": 25, - "status": "active", - "premium": True, - }, - { - "id": "user2", - "name": "Bob", - "age": 17, - "status": "inactive", - "premium": False, - }, - { - "id": "user3", - "name": "Charlie", - "age": 30, - "status": "active", - "premium": True, - }, - { - "id": "user4", - "name": "David", - "age": 16, - "status": "active", - "premium": False, - }, - { - "id": "user5", - "name": "Eve", - "age": 35, - "status": "suspended", - "premium": True, - }, - { - "id": "user6", - "name": "Frank", - "age": 22, - "status": "active", - "premium": False, - }, - ] - - def execute(self, data=None): - if self.counter >= len(self.users): - return None - - data = self.users[self.counter] - self.counter += 1 - self.logger.info(f"UserSource generated: {data}") - return data - - -class FilterDebugSink(SinkFunction): - """调试用的Sink，记录Filter处理后的数据""" - - _received_data: dict[int, list[dict]] = {} - _lock = threading.Lock() - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.parallel_index = None - self.received_count = 0 - - def execute(self, data: Any): - if self.ctx: - self.parallel_index = self.ctx.parallel_index - - # parallel_index 在运行时总是被设置的 - assert self.parallel_index is not None, "parallel_index must be set" - - with self._lock: - if self.parallel_index not in self._received_data: - self._received_data[self.parallel_index] = [] - - self._received_data[self.parallel_index].append(data) - - self.received_count += 1 - - value = data.get("value", data.get("name", "unknown")) - - self.logger.info( - f"[Instance {self.parallel_index}] " - f"Received filtered data #{self.received_count}: {data}" - ) - - # 打印调试信息 - print(f"🔍 [Instance {self.parallel_index}] Filtered data: {value}, Full: {data}") - - return data - - @classmethod - def get_received_data(cls) -> dict[int, list[dict]]: - with cls._lock: - return dict(cls._received_data) - - @classmethod - def clear_data(cls): - with cls._lock: - cls._received_data.clear() - - -# Filter Function Classes -class PositiveNumberFilter(FilterFunction): - """过滤正数""" - - def execute(self, data: Any) -> bool: - is_positive = data.get("positive", False) - self.logger.info(f"PositiveFilter: {data.get('value')} -> {is_positive}") - return is_positive - - -class EvenNumberFilter(FilterFunction): - """过滤偶数""" - - def execute(self, data: Any) -> bool: - value = data.get("value", 0) - is_even = value % 2 == 0 - self.logger.info(f"EvenFilter: {value} -> {is_even}") - return is_even - - -class ActiveUserFilter(FilterFunction): - """过滤活跃用户""" - - def execute(self, data: Any) -> bool: - is_active = data.get("status") == "active" - self.logger.info( - f"ActiveUserFilter: {data.get('name')} ({data.get('status')}) -> {is_active}" - ) - return is_active - - -class AdultUserFilter(FilterFunction): - """过滤成年用户 (age >= 18)""" - - def execute(self, data: Any) -> bool: - age = data.get("age", 0) - is_adult = age >= 18 - self.logger.info(f"AdultUserFilter: {data.get('name')} (age {age}) -> {is_adult}") - return is_adult - - -class PremiumUserFilter(FilterFunction): - """过滤高级用户""" - - def execute(self, data: Any) -> bool: - is_premium = data.get("premium", False) - self.logger.info(f"PremiumUserFilter: {data.get('name')} -> {is_premium}") - return is_premium - - -class AlwaysTrueFilter(FilterFunction): - """总是返回True的过滤器""" - - def execute(self, data: Any) -> bool: - self.logger.info(f"AlwaysTrueFilter: {data} -> True") - return True - - -class AlwaysFalseFilter(FilterFunction): - """总是返回False的过滤器""" - - def execute(self, data: Any) -> bool: - self.logger.info(f"AlwaysFalseFilter: {data} -> False") - return False - - -class ErrorFilter(FilterFunction): - """故意抛出异常的过滤器""" - - def execute(self, data: Any) -> bool: - self.logger.info(f"ErrorFilter: About to throw exception for {data}") - raise ValueError("Intentional filter error for testing") - - -class TestFilterFunctionality: - """测试Filter功能""" - - def setup_method(self): - FilterDebugSink.clear_data() - - def test_basic_positive_filter(self): - """测试基本的正数过滤""" - print("\n🚀 Testing Basic Positive Number Filter") - - env = LocalEnvironment("positive_filter_test") - - ( - env.from_source(NumberDataSource, delay=0.2) - .filter(PositiveNumberFilter) - .sink(FilterDebugSink, parallelism=2) - ) - - print("📊 Pipeline: NumberSource -> filter(PositiveNumberFilter) -> Sink(parallelism=2)") - print("🎯 Expected: Only positive numbers should pass through\n") - - try: - env.submit() - - time.sleep(3) - finally: - env.close() - - self._verify_positive_filter_results() - - def test_chained_filters(self): - """测试链式过滤器""" - print("\n🚀 Testing Chained Filters") - - env = LocalEnvironment("chained_filter_test") - - ( - env.from_source(NumberDataSource, delay=0.2) - .filter(PositiveNumberFilter) # 先过滤正数 - .filter(EvenNumberFilter) # 再过滤偶数 - .sink(FilterDebugSink, parallelism=1) - ) - - print("📊 Pipeline: NumberSource -> filter(Positive) -> filter(Even) -> Sink") - print("🎯 Expected: Only positive even numbers should pass through\n") - - try: - env.submit() - - time.sleep(3) - finally: - env.close() - - self._verify_chained_filter_results() - - def test_user_filters(self): - """测试用户数据过滤""" - print("\n🚀 Testing User Data Filters") - - env = LocalEnvironment("user_filter_test") - - ( - env.from_source(UserDataSource, delay=0.3) - .filter(ActiveUserFilter) - .filter(AdultUserFilter) - .sink(FilterDebugSink, parallelism=2) - ) - - print("📊 Pipeline: UserSource -> filter(Active) -> filter(Adult) -> Sink") - print("🎯 Expected: Only active adult users should pass through\n") - - try: - env.submit() - - time.sleep(4) - finally: - env.close() - - self._verify_user_filter_results() - - def test_lambda_filter(self): - """测试Lambda函数过滤""" - print("\n🚀 Testing Lambda Function Filter") - - env = LocalEnvironment("lambda_filter_test") - - ( - env.from_source(NumberDataSource, delay=0.2) - .filter(lambda x: x["value"] > 0 and x["value"] < 5) # 0 < value < 5 - .sink(FilterDebugSink, parallelism=1) - ) - - print("📊 Pipeline: NumberSource -> filter(lambda: 0 < value < 5) -> Sink") - print("🎯 Expected: Only numbers between 1-4 should pass through\n") - - try: - env.submit() - - time.sleep(3) - finally: - env.close() - - self._verify_lambda_filter_results() - - def test_extreme_filters(self): - """测试极端情况的过滤器""" - print("\n🚀 Testing Extreme Filter Cases") - - env = LocalEnvironment("extreme_filter_test") - - # 测试1：所有数据都通过 - ( - env.from_source(NumberDataSource, delay=0.2) - .filter(AlwaysTrueFilter) - .sink(FilterDebugSink, parallelism=1) - ) - - print("📊 Test 1: AlwaysTrueFilter - All data should pass") - - try: - env.submit() - - time.sleep(2) - finally: - env.close() - - all_pass_results = FilterDebugSink.get_received_data() - FilterDebugSink.clear_data() - - # 测试2：所有数据都被过滤 - env2 = LocalEnvironment("always_false_filter_test") - - ( - env2.from_source(NumberDataSource, delay=0.2) - .filter(AlwaysFalseFilter) - .sink(FilterDebugSink, parallelism=1) - ) - - print("📊 Test 2: AlwaysFalseFilter - No data should pass") - - try: - env2.submit() - time.sleep(2) - finally: - env2.close() - - none_pass_results = FilterDebugSink.get_received_data() - - self._verify_extreme_filter_results(all_pass_results, none_pass_results) - - def test_filter_with_map_integration(self): - """测试Filter与Map的集成""" - print("\n🚀 Testing Filter + Map Integration") - - env = LocalEnvironment("filter_map_integration_test") - - ( - env.from_source(UserDataSource, delay=0.3) - .filter(ActiveUserFilter) # 过滤活跃用户 - .map( - lambda x: { # 转换数据格式 - "username": x["name"].upper(), - "user_age": x["age"], - "is_premium": x["premium"], - } - ) - .filter(lambda x: x["user_age"] >= 25) # 再过滤年龄 - .sink(FilterDebugSink, parallelism=1) - ) - - print( - "📊 Pipeline: UserSource -> filter(Active) -> map(Transform) -> filter(Age>=25) -> Sink" - ) - print("🎯 Expected: Active users aged 25+ with transformed format\n") - - try: - env.submit() - - time.sleep(4) - finally: - env.close() - - self._verify_filter_map_integration_results() - - def test_filter_error_handling(self): - """测试Filter的错误处理""" - print("\n🚀 Testing Filter Error Handling") - - env = LocalEnvironment("filter_error_test") - - # 注意：这个测试可能会产生错误日志，这是预期的 - ( - env.from_source(NumberDataSource, delay=0.2) - .filter(ErrorFilter) # 故意抛出异常的过滤器 - .sink(FilterDebugSink, parallelism=1) - ) - - print("📊 Pipeline: NumberSource -> filter(ErrorFilter) -> Sink") - print("🎯 Expected: Errors should be handled gracefully, minimal data should pass\n") - - try: - env.submit() - - time.sleep(3) - finally: - env.close() - - self._verify_error_handling_results() - - def _verify_positive_filter_results(self): - """验证正数过滤结果""" - received_data = FilterDebugSink.get_received_data() - - print("\n📋 Positive Filter Results:") - print("=" * 40) - - all_filtered_data = [] - for instance_id, data_list in received_data.items(): - print(f"\n🔹 Parallel Instance {instance_id}:") - for data in data_list: - all_filtered_data.append(data) - value = data.get("value") - positive = data.get("positive") - print(f" - Value: {value}, Positive: {positive}") - - print("\n🎯 Filter Summary:") - print(f" - Total filtered data: {len(all_filtered_data)}") - - # 验证：所有通过的数据都应该是正数 - for data in all_filtered_data: - assert data.get("positive") is True, f"❌ Non-positive data passed filter: {data}" - - # 验证：应该有正数通过（基于测试数据） - assert len(all_filtered_data) > 0, "❌ No data passed positive filter" - - print("✅ Positive filter test passed: Only positive numbers passed through") - - def _verify_chained_filter_results(self): - """验证链式过滤结果""" - received_data = FilterDebugSink.get_received_data() - - print("\n📋 Chained Filter Results:") - print("=" * 40) - - all_filtered_data = [] - for _instance_id, data_list in received_data.items(): - for data in data_list: - all_filtered_data.append(data) - value = data.get("value") - positive = data.get("positive") - category = data.get("category") - print(f" - Value: {value}, Positive: {positive}, Category: {category}") - - print("\n🎯 Chained Filter Summary:") - print(f" - Total data after both filters: {len(all_filtered_data)}") - - # 验证：所有数据都应该是正偶数 - for data in all_filtered_data: - assert data.get("positive") is True, f"❌ Non-positive data: {data}" - assert data.get("category") == "even", f"❌ Non-even data: {data}" - assert data.get("value") > 0, f"❌ Non-positive value: {data}" - assert data.get("value") % 2 == 0, f"❌ Non-even value: {data}" - - print("✅ Chained filter test passed: Only positive even numbers passed") - - def _verify_user_filter_results(self): - """验证用户过滤结果""" - received_data = FilterDebugSink.get_received_data() - - print("\n📋 User Filter Results:") - print("=" * 40) - - all_filtered_users = [] - for _instance_id, data_list in received_data.items(): - for user in data_list: - all_filtered_users.append(user) - name = user.get("name") - age = user.get("age") - status = user.get("status") - print(f" - User: {name}, Age: {age}, Status: {status}") - - print("\n🎯 User Filter Summary:") - print(f" - Total filtered users: {len(all_filtered_users)}") - - # 验证：所有用户都应该是活跃且成年的 - for user in all_filtered_users: - assert user.get("status") == "active", f"❌ Non-active user: {user}" - assert user.get("age") >= 18, f"❌ Minor user: {user}" - - print("✅ User filter test passed: Only active adult users passed") - - def _verify_lambda_filter_results(self): - """验证Lambda过滤结果""" - received_data = FilterDebugSink.get_received_data() - - print("\n📋 Lambda Filter Results:") - print("=" * 40) - - all_filtered_data = [] - for _instance_id, data_list in received_data.items(): - for data in data_list: - all_filtered_data.append(data) - value = data.get("value") - print(f" - Value: {value}") - - print("\n🎯 Lambda Filter Summary:") - print(f" - Total data in range (0,5): {len(all_filtered_data)}") - - # 验证：所有数据的值都应该在0到5之间（不包括0和5） - for data in all_filtered_data: - value = data.get("value") - assert 0 < value < 5, f"❌ Value {value} not in range (0,5): {data}" - - print("✅ Lambda filter test passed: Only values in range (0,5) passed") - - def _verify_extreme_filter_results(self, all_pass_results, none_pass_results): - """验证极端过滤结果""" - print("\n📋 Extreme Filter Results:") - print("=" * 40) - - # 验证AlwaysTrueFilter结果 - all_pass_count = sum(len(data_list) for data_list in all_pass_results.values()) - print(f"🔹 AlwaysTrueFilter: {all_pass_count} items passed") - - # 验证AlwaysFalseFilter结果 - none_pass_count = sum(len(data_list) for data_list in none_pass_results.values()) - print(f"🔹 AlwaysFalseFilter: {none_pass_count} items passed") - - # 基于测试数据，AlwaysTrueFilter应该有数据通过 - assert all_pass_count > 0, "❌ AlwaysTrueFilter should pass all data" - - # AlwaysFalseFilter应该没有数据通过 - assert none_pass_count == 0, "❌ AlwaysFalseFilter should pass no data" - - print("✅ Extreme filter tests passed: True filter passes all, False filter passes none") - - def _verify_filter_map_integration_results(self): - """验证Filter+Map集成结果""" - received_data = FilterDebugSink.get_received_data() - - print("\n📋 Filter + Map Integration Results:") - print("=" * 40) - - all_results = [] - for _instance_id, data_list in received_data.items(): - for data in data_list: - all_results.append(data) - username = data.get("username") - age = data.get("user_age") - premium = data.get("is_premium") - print(f" - User: {username}, Age: {age}, Premium: {premium}") - - print("\n🎯 Integration Summary:") - print(f" - Total processed users: {len(all_results)}") - - # 验证：所有用户都应该满足条件且格式正确 - for user in all_results: - # 检查数据格式（由map转换） - assert "username" in user, f"❌ Missing username field: {user}" - assert "user_age" in user, f"❌ Missing user_age field: {user}" - assert "is_premium" in user, f"❌ Missing is_premium field: {user}" - - # 检查年龄条件（第二个filter） - assert user.get("user_age") >= 25, f"❌ User under 25: {user}" - - # 检查用户名是大写（map转换的结果） - username = user.get("username", "") - assert username.isupper(), f"❌ Username not uppercase: {user}" - - print("✅ Filter + Map integration test passed: Correct filtering and transformation") - - def _verify_error_handling_results(self): - """验证错误处理结果""" - received_data = FilterDebugSink.get_received_data() - - print("\n📋 Error Handling Results:") - print("=" * 40) - - all_results = [] - for _instance_id, data_list in received_data.items(): - all_results.extend(data_list) - - print(f"🔹 Data that passed through error filter: {len(all_results)}") - - # 由于ErrorFilter总是抛出异常，正常情况下应该没有数据通过 - # 但根据错误处理策略，可能会有一些数据以原始形式传递 - print(f" - Items that somehow passed: {len(all_results)}") - - # 这个测试主要验证系统不会因为Filter异常而崩溃 - print("✅ Error handling test passed: System handled filter errors gracefully") - - -if __name__ == "__main__": - # 可以直接运行单个测试 - test = TestFilterFunctionality() - test.setup_method() - test.test_basic_positive_filter() - -""" -# 运行所有Filter测试 -pytest sage_tests/core_tests/filter_test.py -v -s - -# 运行特定测试 -pytest sage_tests/core_tests/filter_test.py::TestFilterFunctionality::test_basic_positive_filter -v -s -pytest sage_tests/core_tests/filter_test.py::TestFilterFunctionality::test_chained_filters -v -s -pytest sage_tests/core_tests/filter_test.py::TestFilterFunctionality::test_lambda_filter -v -s -""" diff --git a/packages/sage-kernel/tests/unit/core/function/test_flatmap.py b/packages/sage-kernel/tests/unit/core/function/test_flatmap.py deleted file mode 100644 index 7eecc20f77..0000000000 --- a/packages/sage-kernel/tests/unit/core/function/test_flatmap.py +++ /dev/null @@ -1,626 +0,0 @@ -import threading -import time -from typing import Any - -from sage.common.core.functions import FilterFunction, SinkFunction, SourceFunction -from sage.kernel.api.local_environment import LocalEnvironment - - -class NumberDataSource(SourceFunction): - """生成数字数据""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - self.numbers = [ - {"value": 1, "category": "odd", "positive": True}, - {"value": -2, "category": "even", "positive": False}, - {"value": 3, "category": "odd", "positive": True}, - {"value": 4, "category": "even", "positive": True}, - {"value": -5, "category": "odd", "positive": False}, - {"value": 6, "category": "even", "positive": True}, - {"value": 0, "category": "even", "positive": False}, - {"value": 7, "category": "odd", "positive": True}, - ] - - def execute(self, data=None): - if self.counter >= len(self.numbers): - return None - - data = self.numbers[self.counter] - self.counter += 1 - self.logger.info(f"NumberSource generated: {data}") - return data - - -class UserDataSource(SourceFunction): - """生成用户数据""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - self.users = [ - { - "id": "user1", - "name": "Alice", - "age": 25, - "status": "active", - "premium": True, - }, - { - "id": "user2", - "name": "Bob", - "age": 17, - "status": "inactive", - "premium": False, - }, - { - "id": "user3", - "name": "Charlie", - "age": 30, - "status": "active", - "premium": True, - }, - { - "id": "user4", - "name": "David", - "age": 16, - "status": "active", - "premium": False, - }, - { - "id": "user5", - "name": "Eve", - "age": 35, - "status": "suspended", - "premium": True, - }, - { - "id": "user6", - "name": "Frank", - "age": 22, - "status": "active", - "premium": False, - }, - ] - - def execute(self, data=None): - if self.counter >= len(self.users): - return None - - data = self.users[self.counter] - self.counter += 1 - self.logger.info(f"UserSource generated: {data}") - return data - - -class FilterDebugSink(SinkFunction): - """调试用的Sink，记录Filter处理后的数据""" - - _received_data: dict[int, list[dict]] = {} - _lock = threading.Lock() - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.parallel_index = None - self.received_count = 0 - - def execute(self, data: Any): - if self.ctx: - self.parallel_index = self.ctx.parallel_index - - # parallel_index 在运行时总是被设置的 - assert self.parallel_index is not None, "parallel_index must be set" - - with self._lock: - if self.parallel_index not in self._received_data: - self._received_data[self.parallel_index] = [] - - self._received_data[self.parallel_index].append(data) - - self.received_count += 1 - - value = data.get("value", data.get("name", "unknown")) - - self.logger.info( - f"[Instance {self.parallel_index}] " - f"Received filtered data #{self.received_count}: {data}" - ) - - # 打印调试信息 - print(f"🔍 [Instance {self.parallel_index}] Filtered data: {value}, Full: {data}") - - return data - - @classmethod - def get_received_data(cls) -> dict[int, list[dict]]: - with cls._lock: - return dict(cls._received_data) - - @classmethod - def clear_data(cls): - with cls._lock: - cls._received_data.clear() - - -# Filter Function Classes -class PositiveNumberFilter(FilterFunction): - """过滤正数""" - - def execute(self, data: Any) -> bool: - is_positive = data.get("positive", False) - self.logger.info(f"PositiveFilter: {data.get('value')} -> {is_positive}") - return is_positive - - -class EvenNumberFilter(FilterFunction): - """过滤偶数""" - - def execute(self, data: Any) -> bool: - value = data.get("value", 0) - is_even = value % 2 == 0 - self.logger.info(f"EvenFilter: {value} -> {is_even}") - return is_even - - -class ActiveUserFilter(FilterFunction): - """过滤活跃用户""" - - def execute(self, data: Any) -> bool: - is_active = data.get("status") == "active" - self.logger.info( - f"ActiveUserFilter: {data.get('name')} ({data.get('status')}) -> {is_active}" - ) - return is_active - - -class AdultUserFilter(FilterFunction): - """过滤成年用户 (age >= 18)""" - - def execute(self, data: Any) -> bool: - age = data.get("age", 0) - is_adult = age >= 18 - self.logger.info(f"AdultUserFilter: {data.get('name')} (age {age}) -> {is_adult}") - return is_adult - - -class PremiumUserFilter(FilterFunction): - """过滤高级用户""" - - def execute(self, data: Any) -> bool: - is_premium = data.get("premium", False) - self.logger.info(f"PremiumUserFilter: {data.get('name')} -> {is_premium}") - return is_premium - - -class AlwaysTrueFilter(FilterFunction): - """总是返回True的过滤器""" - - def execute(self, data: Any) -> bool: - self.logger.info(f"AlwaysTrueFilter: {data} -> True") - return True - - -class AlwaysFalseFilter(FilterFunction): - """总是返回False的过滤器""" - - def execute(self, data: Any) -> bool: - self.logger.info(f"AlwaysFalseFilter: {data} -> False") - return False - - -class ErrorFilter(FilterFunction): - """故意抛出异常的过滤器""" - - def execute(self, data: Any) -> bool: - self.logger.info(f"ErrorFilter: About to throw exception for {data}") - raise ValueError("Intentional filter error for testing") - - -class TestFilterFunctionality: - """测试Filter功能""" - - def setup_method(self): - FilterDebugSink.clear_data() - - def test_basic_positive_filter(self): - """测试基本的正数过滤""" - print("\n🚀 Testing Basic Positive Number Filter") - - env = LocalEnvironment("positive_filter_test") - - ( - env.from_source(NumberDataSource, delay=0.2) - .filter(PositiveNumberFilter) - .sink(FilterDebugSink, parallelism=2) - ) - - print("📊 Pipeline: NumberSource -> filter(PositiveNumberFilter) -> Sink(parallelism=2)") - print("🎯 Expected: Only positive numbers should pass through\n") - - try: - env.submit() - - time.sleep(3) - finally: - env.close() - - self._verify_positive_filter_results() - - def test_chained_filters(self): - """测试链式过滤器""" - print("\n🚀 Testing Chained Filters") - - env = LocalEnvironment("chained_filter_test") - - ( - env.from_source(NumberDataSource, delay=0.2) - .filter(PositiveNumberFilter) # 先过滤正数 - .filter(EvenNumberFilter) # 再过滤偶数 - .sink(FilterDebugSink, parallelism=1) - ) - - print("📊 Pipeline: NumberSource -> filter(Positive) -> filter(Even) -> Sink") - print("🎯 Expected: Only positive even numbers should pass through\n") - - try: - env.submit() - - time.sleep(3) - finally: - env.close() - - self._verify_chained_filter_results() - - def test_user_filters(self): - """测试用户数据过滤""" - print("\n🚀 Testing User Data Filters") - - env = LocalEnvironment("user_filter_test") - - ( - env.from_source(UserDataSource, delay=0.3) - .filter(ActiveUserFilter) - .filter(AdultUserFilter) - .sink(FilterDebugSink, parallelism=2) - ) - - print("📊 Pipeline: UserSource -> filter(Active) -> filter(Adult) -> Sink") - print("🎯 Expected: Only active adult users should pass through\n") - - try: - env.submit() - - time.sleep(4) - finally: - env.close() - - self._verify_user_filter_results() - - def test_lambda_filter(self): - """测试Lambda函数过滤""" - print("\n🚀 Testing Lambda Function Filter") - - env = LocalEnvironment("lambda_filter_test") - - ( - env.from_source(NumberDataSource, delay=0.2) - .filter(lambda x: x["value"] > 0 and x["value"] < 5) # 0 < value < 5 - .sink(FilterDebugSink, parallelism=1) - ) - - print("📊 Pipeline: NumberSource -> filter(lambda: 0 < value < 5) -> Sink") - print("🎯 Expected: Only numbers between 1-4 should pass through\n") - - try: - env.submit() - - time.sleep(3) - finally: - env.close() - - self._verify_lambda_filter_results() - - def test_extreme_filters(self): - """测试极端情况的过滤器""" - print("\n🚀 Testing Extreme Filter Cases") - - env = LocalEnvironment("extreme_filter_test") - - # 测试1：所有数据都通过 - ( - env.from_source(NumberDataSource, delay=0.2) - .filter(AlwaysTrueFilter) - .sink(FilterDebugSink, parallelism=1) - ) - - print("📊 Test 1: AlwaysTrueFilter - All data should pass") - - try: - env.submit() - - time.sleep(2) - finally: - env.close() - - all_pass_results = FilterDebugSink.get_received_data() - FilterDebugSink.clear_data() - - # 测试2：所有数据都被过滤 - env2 = LocalEnvironment("always_false_filter_test") - - ( - env2.from_source(NumberDataSource, delay=0.2) - .filter(AlwaysFalseFilter) - .sink(FilterDebugSink, parallelism=1) - ) - - print("📊 Test 2: AlwaysFalseFilter - No data should pass") - - try: - env2.submit() - time.sleep(2) - finally: - env2.close() - - none_pass_results = FilterDebugSink.get_received_data() - - self._verify_extreme_filter_results(all_pass_results, none_pass_results) - - def test_filter_with_map_integration(self): - """测试Filter与Map的集成""" - print("\n🚀 Testing Filter + Map Integration") - - env = LocalEnvironment("filter_map_integration_test") - - ( - env.from_source(UserDataSource, delay=0.3) - .filter(ActiveUserFilter) # 过滤活跃用户 - .map( - lambda x: { # 转换数据格式 - "username": x["name"].upper(), - "user_age": x["age"], - "is_premium": x["premium"], - } - ) - .filter(lambda x: x["user_age"] >= 25) # 再过滤年龄 - .sink(FilterDebugSink, parallelism=1) - ) - - print( - "📊 Pipeline: UserSource -> filter(Active) -> map(Transform) -> filter(Age>=25) -> Sink" - ) - print("🎯 Expected: Active users aged 25+ with transformed format\n") - - try: - env.submit() - - time.sleep(4) - finally: - env.close() - - self._verify_filter_map_integration_results() - - def test_filter_error_handling(self): - """测试Filter的错误处理""" - print("\n🚀 Testing Filter Error Handling") - - env = LocalEnvironment("filter_error_test") - - # 注意：这个测试可能会产生错误日志，这是预期的 - ( - env.from_source(NumberDataSource, delay=0.2) - .filter(ErrorFilter) # 故意抛出异常的过滤器 - .sink(FilterDebugSink, parallelism=1) - ) - - print("📊 Pipeline: NumberSource -> filter(ErrorFilter) -> Sink") - print("🎯 Expected: Errors should be handled gracefully, minimal data should pass\n") - - try: - env.submit() - - time.sleep(3) - finally: - env.close() - - self._verify_error_handling_results() - - def _verify_positive_filter_results(self): - """验证正数过滤结果""" - received_data = FilterDebugSink.get_received_data() - - print("\n📋 Positive Filter Results:") - print("=" * 40) - - all_filtered_data = [] - for instance_id, data_list in received_data.items(): - print(f"\n🔹 Parallel Instance {instance_id}:") - for data in data_list: - all_filtered_data.append(data) - value = data.get("value") - positive = data.get("positive") - print(f" - Value: {value}, Positive: {positive}") - - print("\n🎯 Filter Summary:") - print(f" - Total filtered data: {len(all_filtered_data)}") - - # 验证：所有通过的数据都应该是正数 - for data in all_filtered_data: - assert data.get("positive") is True, f"❌ Non-positive data passed filter: {data}" - - # 验证：应该有正数通过（基于测试数据） - assert len(all_filtered_data) > 0, "❌ No data passed positive filter" - - print("✅ Positive filter test passed: Only positive numbers passed through") - - def _verify_chained_filter_results(self): - """验证链式过滤结果""" - received_data = FilterDebugSink.get_received_data() - - print("\n📋 Chained Filter Results:") - print("=" * 40) - - all_filtered_data = [] - for _instance_id, data_list in received_data.items(): - for data in data_list: - all_filtered_data.append(data) - value = data.get("value") - positive = data.get("positive") - category = data.get("category") - print(f" - Value: {value}, Positive: {positive}, Category: {category}") - - print("\n🎯 Chained Filter Summary:") - print(f" - Total data after both filters: {len(all_filtered_data)}") - - # 验证：所有数据都应该是正偶数 - for data in all_filtered_data: - assert data.get("positive") is True, f"❌ Non-positive data: {data}" - assert data.get("category") == "even", f"❌ Non-even data: {data}" - assert data.get("value") > 0, f"❌ Non-positive value: {data}" - assert data.get("value") % 2 == 0, f"❌ Non-even value: {data}" - - print("✅ Chained filter test passed: Only positive even numbers passed") - - def _verify_user_filter_results(self): - """验证用户过滤结果""" - received_data = FilterDebugSink.get_received_data() - - print("\n📋 User Filter Results:") - print("=" * 40) - - all_filtered_users = [] - for _instance_id, data_list in received_data.items(): - for user in data_list: - all_filtered_users.append(user) - name = user.get("name") - age = user.get("age") - status = user.get("status") - print(f" - User: {name}, Age: {age}, Status: {status}") - - print("\n🎯 User Filter Summary:") - print(f" - Total filtered users: {len(all_filtered_users)}") - - # 验证：所有用户都应该是活跃且成年的 - for user in all_filtered_users: - assert user.get("status") == "active", f"❌ Non-active user: {user}" - assert user.get("age") >= 18, f"❌ Minor user: {user}" - - print("✅ User filter test passed: Only active adult users passed") - - def _verify_lambda_filter_results(self): - """验证Lambda过滤结果""" - received_data = FilterDebugSink.get_received_data() - - print("\n📋 Lambda Filter Results:") - print("=" * 40) - - all_filtered_data = [] - for _instance_id, data_list in received_data.items(): - for data in data_list: - all_filtered_data.append(data) - value = data.get("value") - print(f" - Value: {value}") - - print("\n🎯 Lambda Filter Summary:") - print(f" - Total data in range (0,5): {len(all_filtered_data)}") - - # 验证：所有数据的值都应该在0到5之间（不包括0和5） - for data in all_filtered_data: - value = data.get("value") - assert 0 < value < 5, f"❌ Value {value} not in range (0,5): {data}" - - print("✅ Lambda filter test passed: Only values in range (0,5) passed") - - def _verify_extreme_filter_results(self, all_pass_results, none_pass_results): - """验证极端过滤结果""" - print("\n📋 Extreme Filter Results:") - print("=" * 40) - - # 验证AlwaysTrueFilter结果 - all_pass_count = sum(len(data_list) for data_list in all_pass_results.values()) - print(f"🔹 AlwaysTrueFilter: {all_pass_count} items passed") - - # 验证AlwaysFalseFilter结果 - none_pass_count = sum(len(data_list) for data_list in none_pass_results.values()) - print(f"🔹 AlwaysFalseFilter: {none_pass_count} items passed") - - # 基于测试数据，AlwaysTrueFilter应该有数据通过 - assert all_pass_count > 0, "❌ AlwaysTrueFilter should pass all data" - - # AlwaysFalseFilter应该没有数据通过 - assert none_pass_count == 0, "❌ AlwaysFalseFilter should pass no data" - - print("✅ Extreme filter tests passed: True filter passes all, False filter passes none") - - def _verify_filter_map_integration_results(self): - """验证Filter+Map集成结果""" - received_data = FilterDebugSink.get_received_data() - - print("\n📋 Filter + Map Integration Results:") - print("=" * 40) - - all_results = [] - for _instance_id, data_list in received_data.items(): - for data in data_list: - all_results.append(data) - username = data.get("username") - age = data.get("user_age") - premium = data.get("is_premium") - print(f" - User: {username}, Age: {age}, Premium: {premium}") - - print("\n🎯 Integration Summary:") - print(f" - Total processed users: {len(all_results)}") - - # 验证：所有用户都应该满足条件且格式正确 - for user in all_results: - # 检查数据格式（由map转换） - assert "username" in user, f"❌ Missing username field: {user}" - assert "user_age" in user, f"❌ Missing user_age field: {user}" - assert "is_premium" in user, f"❌ Missing is_premium field: {user}" - - # 检查年龄条件（第二个filter） - assert user.get("user_age") >= 25, f"❌ User under 25: {user}" - - # 检查用户名是大写（map转换的结果） - username = user.get("username", "") - assert username.isupper(), f"❌ Username not uppercase: {user}" - - print("✅ Filter + Map integration test passed: Correct filtering and transformation") - - def _verify_error_handling_results(self): - """验证错误处理结果""" - received_data = FilterDebugSink.get_received_data() - - print("\n📋 Error Handling Results:") - print("=" * 40) - - all_results = [] - for _instance_id, data_list in received_data.items(): - all_results.extend(data_list) - - print(f"🔹 Data that passed through error filter: {len(all_results)}") - - # 由于ErrorFilter总是抛出异常，正常情况下应该没有数据通过 - # 但根据错误处理策略，可能会有一些数据以原始形式传递 - print(f" - Items that somehow passed: {len(all_results)}") - - # 这个测试主要验证系统不会因为Filter异常而崩溃 - print("✅ Error handling test passed: System handled filter errors gracefully") - - -if __name__ == "__main__": - # 可以直接运行单个测试 - test = TestFilterFunctionality() - test.setup_method() - test.test_basic_positive_filter() - -""" -# 运行所有Filter测试 -pytest sage_tests/core_tests/filter_test.py -v -s - -# 运行特定测试 -pytest sage_tests/core_tests/filter_test.py::TestFilterFunctionality::test_basic_positive_filter -v -s -pytest sage_tests/core_tests/filter_test.py::TestFilterFunctionality::test_chained_filters -v -s -pytest sage_tests/core_tests/filter_test.py::TestFilterFunctionality::test_lambda_filter -v -s -""" diff --git a/packages/sage-kernel/tests/unit/core/function/test_join.py b/packages/sage-kernel/tests/unit/core/function/test_join.py deleted file mode 100644 index aeb68fb9f3..0000000000 --- a/packages/sage-kernel/tests/unit/core/function/test_join.py +++ /dev/null @@ -1,1014 +0,0 @@ -import json -import tempfile -import time -from pathlib import Path -from typing import Any - -from sage.common.core.functions import ( - BaseJoinFunction, - FilterFunction, - FlatMapFunction, - KeyByFunction, - SinkFunction, - SourceFunction, -) -from sage.kernel.api.local_environment import LocalEnvironment - -# ===================================================================== -# Source Functions - 生成测试数据 -# ===================================================================== - - -class OrderEventSource(SourceFunction): - """生成订单事件数据""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - self.order_events = [ - { - "event_id": 1, - "order_id": "order_001", - "user_id": "user_1", - "event": "created", - "amount": 100.0, - "timestamp": 1000, - }, - { - "event_id": 2, - "order_id": "order_002", - "user_id": "user_2", - "event": "created", - "amount": 250.0, - "timestamp": 1100, - }, - { - "event_id": 3, - "order_id": "order_001", - "user_id": "user_1", - "event": "paid", - "amount": 100.0, - "timestamp": 1200, - }, - { - "event_id": 4, - "order_id": "order_003", - "user_id": "user_1", - "event": "created", - "amount": 75.0, - "timestamp": 1300, - }, - { - "event_id": 5, - "order_id": "order_002", - "user_id": "user_2", - "event": "cancelled", - "amount": 250.0, - "timestamp": 1400, - }, - { - "event_id": 6, - "order_id": "order_003", - "user_id": "user_1", - "event": "paid", - "amount": 75.0, - "timestamp": 1500, - }, - { - "event_id": 7, - "order_id": "order_004", - "user_id": "user_3", - "event": "created", - "amount": 300.0, - "timestamp": 1600, - }, - { - "event_id": 8, - "order_id": "order_004", - "user_id": "user_3", - "event": "paid", - "amount": 300.0, - "timestamp": 1700, - }, - ] - - def execute(self, data=None): - if self.counter >= len(self.order_events): - return None - - data = self.order_events[self.counter] - self.counter += 1 - self.logger.info(f"OrderEventSource generated: {data}") - return data - - -class UserProfileSource(SourceFunction): - """生成用户档案数据""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - self.user_profiles = [ - { - "profile_id": 1, - "user_id": "user_1", - "name": "Alice", - "email": "alice@example.com", - "tier": "gold", - "region": "US", - }, - { - "profile_id": 2, - "user_id": "user_2", - "name": "Bob", - "email": "bob@example.com", - "tier": "silver", - "region": "EU", - }, - { - "profile_id": 3, - "user_id": "user_3", - "name": "Charlie", - "email": "charlie@example.com", - "tier": "gold", - "region": "US", - }, - { - "profile_id": 4, - "user_id": "user_4", - "name": "Diana", - "email": "diana@example.com", - "tier": "bronze", - "region": "ASIA", - }, - ] - - def execute(self, data=None): - if self.counter >= len(self.user_profiles): - return None - - data = self.user_profiles[self.counter] - self.counter += 1 - self.logger.info(f"UserProfileSource generated: {data}") - return data - - -# ===================================================================== -# FlatMap Functions - 分解数据 -# ===================================================================== - - -class OrderEventFlatMap(FlatMapFunction): - """将订单事件分解为订单信息和事件信息""" - - def execute(self, data: Any) -> list[dict]: - order_id = data.get("order_id") - user_id = data.get("user_id") - event_type = data.get("event") - amount = data.get("amount") - timestamp = data.get("timestamp") - - results = [] - - # 1. 提取订单基础信息 - order_info = { - "type": "order_info", - "order_id": order_id, - "user_id": user_id, - "amount": amount, - "timestamp": timestamp, - "source": "order_event_flatmap", - } - results.append(order_info) - - # 2. 提取事件信息 - event_info = { - "type": "event_info", - "order_id": order_id, - "user_id": user_id, - "event": event_type, - "timestamp": timestamp, - "source": "order_event_flatmap", - } - results.append(event_info) - - # 3. 如果是支付事件，生成额外的支付记录 - if event_type == "paid": - payment_info = { - "type": "payment_info", - "order_id": order_id, - "user_id": user_id, - "amount": amount, - "payment_timestamp": timestamp, - "source": "order_event_flatmap", - } - results.append(payment_info) - - self.logger.info(f"OrderEventFlatMap: flattened order {order_id} into {len(results)} items") - return results - - -class UserProfileFlatMap(FlatMapFunction): - """将用户档案分解为用户信息和偏好信息""" - - def execute(self, data: Any) -> list[dict]: - user_id = data.get("user_id") - name = data.get("name") - email = data.get("email") - tier = data.get("tier") - region = data.get("region") - - results = [] - - # 1. 提取基础用户信息 - user_info = { - "type": "user_info", - "user_id": user_id, - "name": name, - "email": email, - "source": "user_profile_flatmap", - } - results.append(user_info) - - # 2. 提取用户偏好信息 - preference_info = { - "type": "preference_info", - "user_id": user_id, - "tier": tier, - "region": region, - "is_premium": tier in ["gold", "platinum"], - "source": "user_profile_flatmap", - } - results.append(preference_info) - - # 3. 如果是金牌用户，生成VIP信息 - if tier == "gold": - vip_info = { - "type": "vip_info", - "user_id": user_id, - "vip_level": "gold", - "benefits": ["free_shipping", "priority_support"], - "source": "user_profile_flatmap", - } - results.append(vip_info) - - self.logger.info(f"UserProfileFlatMap: flattened user {user_id} into {len(results)} items") - return results - - -# ===================================================================== -# Filter Functions - 过滤数据 -# ===================================================================== - - -class OrderInfoFilter(FilterFunction): - """过滤订单信息，只保留订单相关数据""" - - def execute(self, data: Any) -> bool: - data_type = data.get("type", "") - is_order_related = data_type in ["order_info", "payment_info"] - - if is_order_related: - self.logger.info( - f"✅ OrderInfoFilter: accepted {data_type} for order {data.get('order_id')}" - ) - else: - self.logger.info(f"❌ OrderInfoFilter: rejected {data_type}") - - return is_order_related - - -class UserInfoFilter(FilterFunction): - """过滤用户信息，只保留用户相关数据""" - - def execute(self, data: Any) -> bool: - data_type = data.get("type", "") - is_user_related = data_type in ["user_info", "preference_info", "vip_info"] - - if is_user_related: - self.logger.info( - f"✅ UserInfoFilter: accepted {data_type} for user {data.get('user_id')}" - ) - else: - self.logger.info(f"❌ UserInfoFilter: rejected {data_type}") - - return is_user_related - - -class PremiumUserFilter(FilterFunction): - """只保留高级用户""" - - def execute(self, data: Any) -> bool: - print(f"🔍 PremiumUserFilter.execute called with data: {data}") - self.logger.info(f"🔍 PremiumUserFilter.execute called with data: {data}") - - if data.get("type") == "preference_info": - is_premium = data.get("is_premium", False) - if is_premium: - self.logger.info( - f"✅ PremiumUserFilter: accepted premium user {data.get('user_id')}" - ) - print(f"✅ PremiumUserFilter: accepted premium user {data.get('user_id')}") - return True - else: - self.logger.info( - f"❌ PremiumUserFilter: rejected non-premium user {data.get('user_id')}" - ) - print(f"❌ PremiumUserFilter: rejected non-premium user {data.get('user_id')}") - return False - - # 对于非偏好信息，直接通过 - self.logger.info(f"✅ PremiumUserFilter: passed non-preference data {data.get('type')}") - print(f"✅ PremiumUserFilter: passed non-preference data {data.get('type')}") - return data.get("type") != "preference_info" - - -# ===================================================================== -# KeyBy Functions - 提取分区键 -# ===================================================================== - - -class UserIdKeyBy(KeyByFunction): - """按用户ID分区""" - - def execute(self, data: Any) -> str: - user_id = data.get("user_id", "unknown") - self.logger.debug( - f"UserIdKeyBy: extracted key '{user_id}' from {data.get('type', 'unknown')}" - ) - return user_id - - -class OrderIdKeyBy(KeyByFunction): - """按订单ID分区""" - - def execute(self, data: Any) -> str: - order_id = data.get("order_id", "unknown") - self.logger.debug( - f"OrderIdKeyBy: extracted key '{order_id}' from {data.get('type', 'unknown')}" - ) - return order_id - - -# ===================================================================== -# Join Functions - 关联逻辑 -# ===================================================================== - - -class UserOrderJoin(BaseJoinFunction): - """用户和订单的Inner Join""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.user_cache = {} # {user_id: user_data} - self.order_cache = {} # {user_id: [order_data, ...]} - self.join_count = 0 - - def execute(self, payload: Any, key: Any, tag: int) -> list[Any]: - results = [] - self.logger.debug(f"UserOrderJoin: processing key='{key}', tag={tag}, payload={payload}") - if tag == 0: # 用户流 - user_type = payload.get("type", "") - if user_type == "user_info": - # 缓存用户基础信息 - self.user_cache[key] = payload - - # 检查是否有待匹配的订单 - if key in self.order_cache: - for order_data in self.order_cache[key]: - joined = self._create_user_order_join(payload, order_data, key) - results.append(joined) - self.join_count += 1 - # 清理已匹配的订单 - del self.order_cache[key] - - elif tag == 1: # 订单流 - order_type = payload.get("type", "") - if order_type == "order_info": - # 检查是否有对应的用户 - if key in self.user_cache: - joined = self._create_user_order_join(self.user_cache[key], payload, key) - results.append(joined) - self.join_count += 1 - else: - # 缓存订单等待用户数据 - if key not in self.order_cache: - self.order_cache[key] = [] - self.order_cache[key].append(payload) - - if results: - self.logger.info( - f"UserOrderJoin: generated {len(results)} joins for key '{key}', total joins: {self.join_count}" - ) - - return results - - def _create_user_order_join(self, user_data: Any, order_data: Any, user_id: str) -> dict: - return { - "join_type": "user_order", - "user_id": user_id, - "user_name": user_data.get("name"), - "user_email": user_data.get("email"), - "order_id": order_data.get("order_id"), - "order_amount": order_data.get("amount"), - "order_timestamp": order_data.get("timestamp"), - "join_timestamp": time.time_ns() // 1_000_000, - "source": "user_order_join", - } - - -class UserPaymentJoin(BaseJoinFunction): - """用户和支付的Left Join""" - - def __init__(self, timeout_ms: int = 5000, **kwargs): - super().__init__(**kwargs) - self.user_cache = {} # {user_id: (user_data, timestamp)} - self.payment_cache = {} # {user_id: [payment_data, ...]} - self.timeout_ms = timeout_ms - self.join_count = 0 - import time - - self.current_time = lambda: int(time.time() * 1000) - - def execute(self, payload: Any, key: Any, tag: int) -> list[Any]: - results = [] - current_time = self.current_time() - - if tag == 0: # 用户流 - user_type = payload.get("type", "") - if user_type in ["user_info", "preference_info"]: - # 检查是否有对应的支付 - if key in self.payment_cache: - for payment_data in self.payment_cache[key]: - joined = self._create_user_payment_join(payload, payment_data, key) - results.append(joined) - self.join_count += 1 - del self.payment_cache[key] - else: - # 缓存用户数据，设置超时 - self.user_cache[key] = (payload, current_time) - - elif tag == 1: # 支付流 - payment_type = payload.get("type", "") - if payment_type == "payment_info": - # 检查是否有对应的用户 - if key in self.user_cache: - user_data, _ = self.user_cache[key] - joined = self._create_user_payment_join(user_data, payload, key) - results.append(joined) - self.join_count += 1 - del self.user_cache[key] - else: - # 缓存支付数据 - if key not in self.payment_cache: - self.payment_cache[key] = [] - self.payment_cache[key].append(payload) - - # 检查超时的用户数据（Left Join特性） - expired_users = [] - for user_id, (user_data, timestamp) in self.user_cache.items(): - if current_time - timestamp > self.timeout_ms: - # 输出没有支付的用户 - no_payment_result = self._create_user_payment_join(user_data, None, user_id) - results.append(no_payment_result) - expired_users.append(user_id) - self.join_count += 1 - - # 清理过期用户 - for user_id in expired_users: - del self.user_cache[user_id] - - if results: - self.logger.info( - f"UserPaymentJoin: generated {len(results)} joins for key '{key}', total joins: {self.join_count}" - ) - - return results - - def _create_user_payment_join(self, user_data: Any, payment_data: Any, user_id: str) -> dict: - return { - "join_type": "user_payment", - "user_id": user_id, - "user_name": user_data.get("name") if user_data else None, - "user_tier": user_data.get("tier") if user_data else None, - "order_id": payment_data.get("order_id") if payment_data else None, - "payment_amount": payment_data.get("amount") if payment_data else 0, - "payment_timestamp": (payment_data.get("payment_timestamp") if payment_data else None), - "has_payment": payment_data is not None, - "join_timestamp": time.time_ns() // 1_000_000, - "source": "user_payment_join", - } - - -class OrderEventJoin(BaseJoinFunction): - """订单和事件的窗口Join""" - - def __init__(self, window_ms: int = 3000, **kwargs): - super().__init__(**kwargs) - self.window_ms = window_ms - self.event_buffer = {} # {order_id: [(data, timestamp, tag), ...]} - self.join_count = 0 - import time - - self.current_time = lambda: int(time.time() * 1000) - - def execute(self, payload: Any, key: Any, tag: int) -> list[Any]: - current_time = self.current_time() - results = [] - - # 清理过期事件 - self._cleanup_expired_events(current_time) - - # 获取数据类型 - data_type = payload.get("type", "") - - # 只处理订单信息和事件信息 - if data_type not in ["order_info", "event_info"]: - return results - - # 添加当前事件到缓冲区 - if key not in self.event_buffer: - self.event_buffer[key] = [] - self.event_buffer[key].append((payload, current_time, tag)) - - # 检查窗口内的事件组合 - if key in self.event_buffer: - window_events = self._get_window_events(key, current_time) - combinations = self._find_order_event_combinations(window_events, key) - results.extend(combinations) - self.join_count += len(combinations) - - if results: - self.logger.info( - f"OrderEventJoin: generated {len(results)} joins for order '{key}', total joins: {self.join_count}" - ) - - return results - - def _cleanup_expired_events(self, current_time: int): - cutoff_time = current_time - self.window_ms - - for key in list(self.event_buffer.keys()): - valid_events = [ - (data, ts, tag) for data, ts, tag in self.event_buffer[key] if ts >= cutoff_time - ] - if valid_events: - self.event_buffer[key] = valid_events - else: - del self.event_buffer[key] - - def _get_window_events(self, key: Any, current_time: int) -> list: - cutoff_time = current_time - self.window_ms - return [(data, ts, tag) for data, ts, tag in self.event_buffer[key] if ts >= cutoff_time] - - def _find_order_event_combinations(self, events: list, order_id: str) -> list: - combinations = [] - - # 按tag分组事件 - order_infos = [ - (data, ts) for data, ts, tag in events if tag == 0 and data.get("type") == "order_info" - ] - event_infos = [ - (data, ts) for data, ts, tag in events if tag == 1 and data.get("type") == "event_info" - ] - - # 组合订单信息和事件信息 - for order_data, order_ts in order_infos: - for event_data, event_ts in event_infos: - # 事件应该在订单之后或同时发生 - if event_ts >= order_ts: - combo_result = { - "join_type": "order_event", - "order_id": order_id, - "user_id": order_data.get("user_id"), - "order_amount": order_data.get("amount"), - "order_timestamp": order_data.get("timestamp"), - "event_type": event_data.get("event"), - "event_timestamp": event_data.get("timestamp"), - "time_diff": event_ts - order_ts, - "join_timestamp": time.time_ns() // 1_000_000, - "source": "order_event_join", - } - combinations.append(combo_result) - - return combinations - - -# ===================================================================== -# Sink Functions - 收集结果 -# ===================================================================== - - -class JoinResultSink(SinkFunction): - """收集Join结果的Sink""" - - def __init__(self, output_file=None, **kwargs): - super().__init__(**kwargs) - self.parallel_index = None - self.received_count = 0 - - # 如果没有指定输出文件，使用临时文件 - if output_file is None: - self.output_file = Path(tempfile.gettempdir()) / "join_test_results.json" - else: - self.output_file = Path(output_file) - - if self.ctx: - self.logger.info(f"JoinResultSink initialized, output file: {self.output_file}") - - def execute(self, data: Any): - if self.ctx: - self.parallel_index = self.ctx.parallel_index - - self.received_count += 1 - - join_type = data.get("join_type", "unknown") - key_field = "user_id" if "user" in join_type else "order_id" - key_value = data.get(key_field, "unknown") - - if self.ctx: - self.logger.info( - f"[Instance {self.parallel_index}] " - f"Received join result #{self.received_count}: {join_type} for {key_field}={key_value}" - ) - - # 打印调试信息 - print(f"🔗 [Instance {self.parallel_index}] Join: {join_type} | {key_field}={key_value}") - - # 保存到文件 - self._append_record( - { - "parallel_index": self.parallel_index, - "sequence": self.received_count, - "data": data, - "timestamp": time.time(), - } - ) - - return data - - def _append_record(self, record): - """原子性地追加记录到文件""" - try: - # 以追加模式打开文件 - with open(self.output_file, "a") as f: - # 写入一行JSON - f.write(json.dumps(record) + "\n") - f.flush() - except Exception as e: - if self.ctx: - self.logger.error(f"Failed to write record: {e}") - - @classmethod - def read_results(cls, output_file=None): - """读取测试结果""" - if output_file is None: - output_file = Path(tempfile.gettempdir()) / "join_test_results.json" - else: - output_file = Path(output_file) - - results = {} - - if not output_file.exists(): - print(f"📂 No results file found: {output_file}") - return results - - try: - with open(output_file) as f: - for line_num, line in enumerate(f, 1): - line = line.strip() - if not line: - continue - - try: - record = json.loads(line) - parallel_index = record.get("parallel_index", 0) - data = record.get("data") - - if parallel_index not in results: - results[parallel_index] = [] - - results[parallel_index].append(data) - - except json.JSONDecodeError as e: - print(f"⚠️ Failed to parse line {line_num}: {e}") - continue - - except Exception as e: - print(f"❌ Failed to read results file: {e}") - - print( - f"📂 Read {sum(len(data_list) for data_list in results.values())} records from {len(results)} parallel instances" - ) - return results - - @classmethod - def clear_results(cls, output_file=None): - """清理结果""" - if output_file is None: - output_file = Path(tempfile.gettempdir()) / "join_test_results.json" - else: - output_file = Path(output_file) - - if output_file.exists(): - output_file.unlink() - print(f"🗑️ Cleared results file: {output_file}") - - @classmethod - def get_received_data(cls, output_file=None): - """兼容性方法，调用read_results""" - return cls.read_results(output_file) - - -# ===================================================================== -# 测试类 -# ===================================================================== - - -class TestJoinFunctionality: - """测试Join功能的完整测试套件""" - - def setup_method(self): - JoinResultSink.clear_results() - - # def test_flatmap_filter_join_pipeline(self): - # """测试完整的FlatMap -> Filter -> Join管道""" - # print("\n🚀 Testing Complete FlatMap -> Filter -> Join Pipeline") - - # env = LocalEnvironment("flatmap_filter_join_test") - - # # 1. 创建源数据流 - # order_source = env.from_source(OrderEventSource, delay=0.2) - # user_source = env.from_source(UserProfileSource, delay=0.3) - - # # 2. 上游处理：FlatMap分解数据，Filter过滤 - # order_stream = (order_source - # .flatmap(OrderEventFlatMap) # 分解订单事件 - # .filter(OrderInfoFilter) # 只保留订单相关信息 - # .keyby(UserIdKeyBy) # 按用户ID分区 - # ) - - # user_stream = (user_source - # .flatmap(UserProfileFlatMap) # 分解用户档案 - # .filter(UserInfoFilter) # 只保留用户相关信息 - # .keyby(UserIdKeyBy) # 按用户ID分区 - # ) - - # # 3. 下游处理：Connect和Join - # join_result = (user_stream - # .connect(order_stream) # 连接两个流 - # .join(UserOrderJoin) # 用户-订单Join - # .sink(JoinResultSink, parallelism=1) - # ) - - # print("📊 Pipeline: OrderSource -> flatmap -> filter -> keyby") - # print(" UserSource -> flatmap -> filter -> keyby") - # print(" user_stream.connect(order_stream).join(UserOrderJoin)") - # print("🎯 Expected: User and order data joined on user_id\n") - - # try: - # env.submit() - - # time.sleep(6) - # finally: - # env.close() - - # # 等待一下确保文件写入完成 - # time.sleep(1) - # self._verify_user_order_join_results() - - def test_multi_stage_join_pipeline(self): - """测试多阶段Join管道""" - print("\n🚀 Testing Multi-Stage Join Pipeline") - - env = LocalEnvironment("multi_stage_join_test") - - # 第一阶段：订单事件流处理 - order_source = env.from_source(OrderEventSource, delay=0.2) - - # 分离为两个流：订单信息流和支付信息流 - ( - order_source.flatmap(OrderEventFlatMap) - .filter(lambda x: x.get("type") == "order_info") - .keyby(UserIdKeyBy) - ) - - payment_info_stream = ( - order_source.flatmap(OrderEventFlatMap) - .filter(lambda x: x.get("type") == "payment_info") - .keyby(UserIdKeyBy) - ) - - # 第二阶段：用户信息流处理 - user_source = env.from_source(UserProfileSource, delay=0.3) - - # 只保留高级用户 - premium_user_stream = ( - user_source.flatmap(UserProfileFlatMap) - .filter(PremiumUserFilter) - .filter(lambda x: x.get("type") in ["user_info", "preference_info"]) - .keyby(UserIdKeyBy) - ) - - # 第三阶段：多重Join - # Join 1: 高级用户 + 支付信息 - ( - premium_user_stream.connect(payment_info_stream) - .join(UserPaymentJoin, timeout_ms=3000) - .sink(JoinResultSink, parallelism=1) - ) - - print("📊 Multi-Stage Pipeline:") - print(" OrderSource -> flatmap -> filter(order_info) -> keyby") - print(" OrderSource -> flatmap -> filter(payment_info) -> keyby") - print(" UserSource -> flatmap -> filter(premium) -> keyby") - print(" premium_user.connect(payment).join(UserPaymentJoin)") - print("🎯 Expected: Premium users with their payment information\n") - - try: - env.submit() - - time.sleep(6) - finally: - env.close() - - # 等待一下确保文件写入完成 - time.sleep(1) - self._verify_user_payment_join_results() - - def test_windowed_join_pipeline(self): - """测试基于时间窗口的Join""" - print("\n🚀 Testing Windowed Join Pipeline") - - env = LocalEnvironment("windowed_join_test") - - order_source = env.from_source(OrderEventSource, delay=0.15) - - # 分离订单信息和事件信息，按订单ID分区 - order_info_stream = ( - order_source.flatmap(OrderEventFlatMap) - .filter(lambda x: x.get("type") == "order_info") - .keyby(OrderIdKeyBy) - ) - - event_info_stream = ( - order_source.flatmap(OrderEventFlatMap) - .filter(lambda x: x.get("type") == "event_info") - .keyby(OrderIdKeyBy) - ) - - # 窗口Join：在时间窗口内关联订单和事件 - ( - order_info_stream.connect(event_info_stream) - .join(OrderEventJoin, window_ms=2000) - .sink(JoinResultSink, parallelism=1) - ) - - print("📊 Windowed Join Pipeline:") - print(" OrderSource -> flatmap -> filter(order_info) -> keyby(order_id)") - print(" OrderSource -> flatmap -> filter(event_info) -> keyby(order_id)") - print(" order_info.connect(event_info).join(OrderEventJoin, window=2s)") - print("🎯 Expected: Orders matched with their events within time window\n") - - try: - env.submit() - - time.sleep(5) - finally: - env.close() - - # 等待一下确保文件写入完成 - time.sleep(1) - self._verify_order_event_join_results() - - def test_complex_pipeline_with_multiple_joins(self): - """测试包含多个Join的复杂管道""" - print("\n🚀 Testing Complex Pipeline with Multiple Joins") - - env = LocalEnvironment("complex_multi_join_test") - - # 数据源 - order_source = env.from_source(OrderEventSource, delay=0.2) - user_source = env.from_source(UserProfileSource, delay=0.3) - - # 复杂的数据分流和过滤 - # 流1：用户基础信息 - user_basic_stream = ( - user_source.flatmap(UserProfileFlatMap) - .filter(lambda x: x.get("type") == "user_info") - .keyby(UserIdKeyBy) - ) - - # 流2：订单支付信息 - payment_stream = ( - order_source.flatmap(OrderEventFlatMap) - .filter(lambda x: x.get("type") == "payment_info") - .keyby(UserIdKeyBy) - ) - - # 流3：订单基础信息 - order_basic_stream = ( - order_source.flatmap(OrderEventFlatMap) - .filter(lambda x: x.get("type") == "order_info") - .keyby(UserIdKeyBy) - ) - - # Join 1: 用户 + 支付信息 - user_payment = user_basic_stream.connect(payment_stream).join( - UserPaymentJoin, timeout_ms=2000 - ) - - # Join 2: 用户 + 订单信息 - user_order = user_basic_stream.connect(order_basic_stream).join(UserOrderJoin) - - # 收集所有Join结果 - user_payment.sink(JoinResultSink, parallelism=1) - user_order.sink(JoinResultSink, parallelism=1) - - print("📊 Complex Multi-Join Pipeline:") - print(" UserSource -> flatmap -> filter(user_info) -> keyby") - print(" OrderSource -> flatmap -> filter(payment_info) -> keyby") - print(" OrderSource -> flatmap -> filter(order_info) -> keyby") - print(" user.connect(payment).join() + user.connect(order).join()") - print("🎯 Expected: Both user-payment and user-order joins\n") - - try: - env.submit() - - time.sleep(6) - finally: - env.close() - - # 等待一下确保文件写入完成 - time.sleep(1) - self._verify_complex_multi_join_results() - - def test_join_with_empty_streams(self): - """测试空流的Join处理""" - print("\n🚀 Testing Join with Empty/Filtered Streams") - - env = LocalEnvironment("empty_stream_join_test") - - order_source = env.from_source(OrderEventSource, delay=0.2) - user_source = env.from_source(UserProfileSource, delay=0.3) - - # 创建一个会过滤掉所有数据的流 - empty_user_stream = ( - user_source.flatmap(UserProfileFlatMap) - .filter(lambda x: False) # 过滤掉所有数据 - .keyby(UserIdKeyBy) - ) - - order_stream = ( - order_source.flatmap(OrderEventFlatMap) - .filter(lambda x: x.get("type") == "order_info") - .keyby(UserIdKeyBy) - ) - - # Join空流和正常流 - ( - empty_user_stream.connect(order_stream) - .join(UserOrderJoin) - .sink(JoinResultSink, parallelism=1) - ) - - print("📊 Empty Stream Join Pipeline:") - print(" UserSource -> flatmap -> filter(False) -> keyby") - print(" OrderSource -> flatmap -> filter(order_info) -> keyby") - print(" empty_user.connect(order).join()") - print("🎯 Expected: No join results due to empty user stream\n") - - try: - env.submit() - - time.sleep(4) - finally: - env.close() - - # 等待一下确保文件写入完成 - time.sleep(1) - self._verify_empty_stream_join_results() - - # ===================================================================== - # 验证方法 - TODO: 实现结果验证逻辑 - # ===================================================================== - - def _verify_user_payment_join_results(self): - """验证用户-支付信息Join结果""" - # TODO: 实现验证逻辑 - pass - - def _verify_order_event_join_results(self): - """验证订单-事件Join结果""" - # TODO: 实现验证逻辑 - pass - - def _verify_complex_multi_join_results(self): - """验证复杂多重Join结果""" - # TODO: 实现验证逻辑 - pass - - def _verify_empty_stream_join_results(self): - """验证空流Join结果""" - # TODO: 实现验证逻辑 - pass diff --git a/packages/sage-kernel/tests/unit/core/function/test_keyby.py b/packages/sage-kernel/tests/unit/core/function/test_keyby.py deleted file mode 100644 index caa6db1077..0000000000 --- a/packages/sage-kernel/tests/unit/core/function/test_keyby.py +++ /dev/null @@ -1,459 +0,0 @@ -import json -import os -import threading -import time -from typing import Any - -from sage.common.config.output_paths import get_sage_paths -from sage.common.core.functions import KeyByFunction, SinkFunction, SourceFunction -from sage.kernel.api.local_environment import LocalEnvironment - - -class KeyByTestDataSource(SourceFunction): - """生成带有用户ID的测试数据""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - self.user_ids = ["user1", "user2", "user3", "user1", "user2", "user3"] - - def execute(self, data=None): - if self.counter >= len(self.user_ids): - return None # 停止生成数据 - - data = { - "id": self.counter, - "user_id": self.user_ids[self.counter], - "content": f"Message {self.counter} from {self.user_ids[self.counter]}", - } - self.counter += 1 - self.logger.info(f"Generated data: {data}") - return data - - -class UserIdKeyExtractor(KeyByFunction): - """提取用户ID作为分区键""" - - def execute(self, data: Any) -> str: - user_id = data["user_id"] - self.logger.info(f"Extracted key '{user_id}' from data: {data}") - return user_id - - -class ParallelDebugSink(SinkFunction): - """并行调试Sink，记录接收到的数据分布""" - - # 类级别的统计，所有实例共享 - _received_data: dict[int, list[dict]] = {} - _lock = threading.Lock() - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.parallel_index = None - self.received_count = 0 - # 使用统一的 SAGE 路径管理系统 - sage_paths = get_sage_paths() - self.output_dir = sage_paths.test_logs_dir / "keyby_results" - self.output_dir.mkdir(parents=True, exist_ok=True) - - def execute(self, data: Any): - # 从runtime_context获取parallel_index - if self.ctx: - self.parallel_index = self.ctx.parallel_index - - # parallel_index 在运行时总是被设置的 - assert self.parallel_index is not None, "parallel_index must be set" - - with self._lock: - if self.parallel_index not in self._received_data: - self._received_data[self.parallel_index] = [] - - self._received_data[self.parallel_index].append(data) - - self.received_count += 1 - - self.logger.info( - f"[Parallel Instance {self.parallel_index}] " - f"Received data #{self.received_count}: {data}" - ) - - # 打印调试信息 - print( - f"🔍 [Instance {self.parallel_index}] User: {data['user_id']}, " - f"Content: {data['content']}" - ) - - return data - - @classmethod - def save_results_to_file(cls, test_name: str): - """将测试结果保存到文件""" - sage_paths = get_sage_paths() - output_dir = sage_paths.test_logs_dir / "keyby_results" - output_dir.mkdir(parents=True, exist_ok=True) - - timestamp = time.strftime("%Y%m%d_%H%M%S") - filename = f"{test_name}_{timestamp}.json" - filepath = output_dir / filename - - with cls._lock: - result_data = { - "test_name": test_name, - "timestamp": timestamp, - "received_data": dict(cls._received_data), - "total_instances": len(cls._received_data), - "total_messages": sum(len(data_list) for data_list in cls._received_data.values()), - } - - with open(filepath, "w", encoding="utf-8") as f: - json.dump(result_data, f, indent=2, ensure_ascii=False) - - print(f"📁 Results saved to: {filepath}") - return str(filepath) - - @classmethod - def get_received_data(cls) -> dict[int, list[dict]]: - """获取所有并行实例接收到的数据""" - with cls._lock: - return dict(cls._received_data) - - @classmethod - def clear_data(cls): - """清空统计数据""" - with cls._lock: - cls._received_data.clear() - - -class TestKeyByFunctionality: - """测试KeyBy功能的分区效果""" - - def setup_method(self): - """每个测试方法前的设置""" - ParallelDebugSink.clear_data() - - def test_keyby_hash_partitioning(self): - """测试基于hash的分区功能""" - print("\n🚀 Testing KeyBy Hash Partitioning") - - # 创建环境 - env = LocalEnvironment("keyby_test") - - # 构建数据流：source -> keyby -> parallel sink - ( - env.from_source(KeyByTestDataSource, delay=0.5) - .keyby(UserIdKeyExtractor, strategy="hash") - .sink(ParallelDebugSink, parallelism=2) # 2个并行实例 - ) - - print( - "📊 Pipeline: KeyByTestDataSource -> KeyBy(UserIdExtractor) -> ParallelDebugSink(parallelism=2)" - ) - print("🎯 Expected: Same user_id data should go to same parallel instance\n") - - try: - # 提交并运行 - env.submit() - - # 运行一段时间让数据流过 - time.sleep(2) - - except Exception as e: - print(f"❌ Error during execution: {e}") - raise - finally: - try: - env.close() - except Exception: - pass # 忽略关闭时的错误 - - # 保存结果到文件 - result_file = ParallelDebugSink.save_results_to_file("hash_partitioning_test") - - # 验证分区效果 - success = self._verify_hash_partitioning() - - # 将验证结果也写入文件 - self._save_verification_result(result_file, "hash_partitioning", success) - - assert success, "Hash partitioning test failed" - - def test_keyby_broadcast_strategy(self): - """测试广播策略""" - print("\n🚀 Testing KeyBy Broadcast Strategy") - - env = LocalEnvironment("Test_keyby_broadcast_test") - - ( - env.from_source(KeyByTestDataSource, delay=0.3) - .keyby(UserIdKeyExtractor, strategy="broadcast") - .sink(ParallelDebugSink, parallelism=3) # 3个并行实例 - ) - - print( - "📊 Pipeline: KeyByTestDataSource -> KeyBy(broadcast) -> ParallelDebugSink(parallelism=3)" - ) - print("🎯 Expected: All data should be sent to all parallel instances\n") - - try: - env.submit() - - time.sleep(2) - except Exception as e: - print(f"❌ Error during execution: {e}") - raise - finally: - try: - env.close() - except Exception: - pass - - # 保存结果到文件 - result_file = ParallelDebugSink.save_results_to_file("broadcast_strategy_test") - - # 验证广播效果 - success = self._verify_broadcast_strategy() - - # 将验证结果写入文件 - self._save_verification_result(result_file, "broadcast_strategy", success) - - assert success, "Broadcast strategy test failed" - - def _save_verification_result(self, result_file: str, test_type: str, success: bool): - """将验证结果保存到文件""" - if os.path.exists(result_file): - with open(result_file, encoding="utf-8") as f: - data = json.load(f) - - data["verification"] = { - "test_type": test_type, - "success": success, - "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"), - } - - with open(result_file, "w", encoding="utf-8") as f: - json.dump(data, f, indent=2, ensure_ascii=False) - - print(f"✍️ Verification result saved to: {result_file}") - - def _verify_hash_partitioning(self): - """验证hash分区的正确性""" - received_data = ParallelDebugSink.get_received_data() - - print("\n📋 Hash Partitioning Results:") - print("=" * 50) - - if not received_data: - print("❌ No data received by any instance") - return False - - # 统计每个实例接收到的用户数据 - user_distribution = {} - for instance_id, data_list in received_data.items(): - print(f"\n🔹 Parallel Instance {instance_id}:") - user_counts = {} - for data in data_list: - user_id = data["user_id"] - user_counts[user_id] = user_counts.get(user_id, 0) + 1 - - for user_id, count in user_counts.items(): - print(f" - {user_id}: {count} messages") - if user_id not in user_distribution: - user_distribution[user_id] = set() - user_distribution[user_id].add(instance_id) - - print("\n🎯 User Distribution Across Instances:") - for user_id, instances in user_distribution.items(): - print(f" - {user_id}: routed to instance(s) {instances}") - - # 验证：每个用户的数据应该只路由到一个实例 - success = True - for user_id, instances in user_distribution.items(): - if len(instances) != 1: - print( - f"❌ User {user_id} data was routed to multiple instances: {instances}. " - f"Hash partitioning should send same key to same instance." - ) - success = False - - if success: - print("✅ Hash partitioning test passed: Each user routed to exactly one instance") - - return success - - def _verify_broadcast_strategy(self): - """验证广播策略的正确性""" - received_data = ParallelDebugSink.get_received_data() - - print("\n📋 Broadcast Strategy Results:") - print("=" * 50) - - if not received_data: - print("❌ No data received by any instance") - return False - - instance_counts = {} - total_unique_messages = 0 - - for instance_id, data_list in received_data.items(): - instance_counts[instance_id] = len(data_list) - print(f"\n🔹 Parallel Instance {instance_id}: {len(data_list)} messages") - - for data in data_list[:3]: # 只显示前3条 - print(f" - {data['user_id']}: {data['content']}") - - if received_data: - # 获取第一个实例的数据作为基准 - first_instance_data = list(received_data.values())[0] - total_unique_messages = len(first_instance_data) - - print("\n🎯 Broadcast Verification:") - print(f" - Total unique messages generated: {total_unique_messages}") - print(f" - Instances message counts: {instance_counts}") - - # 验证：每个实例应该接收到相同数量的消息（广播效果） - unique_counts = set(instance_counts.values()) - if len(unique_counts) <= 1: - print("✅ Broadcast test passed: All instances received same number of messages") - return True - else: - print("⚠️ Note: Instance counts differ, this might be due to timing or test duration") - # 如果差异不大（比如只差1-2条消息），仍然认为测试通过 - min_count = min(instance_counts.values()) - max_count = max(instance_counts.values()) - if max_count - min_count <= 2: - print("✅ Broadcast test passed: Instance counts are within acceptable range") - return True - else: - print("❌ Broadcast test failed: Instance counts differ significantly") - return False - - -class AdvancedKeyExtractor(KeyByFunction): - """复杂的key提取器，用于高级测试""" - - def execute(self, data: Any) -> str: - # 基于用户ID和消息ID的组合生成key - key = f"{data['user_id']}_{data['id'] % 2}" - self.logger.info(f"Advanced key extraction: {key} from {data}") - return key - - -class TestAdvancedKeyBy: - """高级KeyBy功能测试""" - - def setup_method(self): - ParallelDebugSink.clear_data() - - def test_advanced_key_extraction(self): - """测试复杂的key提取逻辑""" - print("\n🚀 Testing Advanced Key Extraction") - - env = LocalEnvironment("advanced_keyby_test") - - ( - env.from_source(KeyByTestDataSource, delay=0.4) - .keyby(AdvancedKeyExtractor, strategy="hash") - .sink(ParallelDebugSink, parallelism=4) # 4个并行实例 - ) - - print( - "📊 Pipeline: KeyByTestDataSource -> KeyBy(AdvancedKeyExtractor) -> ParallelDebugSink(parallelism=4)" - ) - print("🎯 Key format: 'user_id + message_id%2' (e.g., 'user1_0', 'user1_1')\n") - - try: - env.submit() - - time.sleep(2) - except Exception as e: - print(f"❌ Error during execution: {e}") - raise - finally: - try: - env.close() - except Exception: - pass - - # 保存结果到文件 - result_file = ParallelDebugSink.save_results_to_file("advanced_key_extraction_test") - - # 分析分布效果 - success = self._analyze_advanced_distribution() - - # 将验证结果写入文件 - self._save_verification_result(result_file, "advanced_key_extraction", success) - - assert success, "Advanced key extraction test failed" - - def _analyze_advanced_distribution(self): - """分析高级key提取的分布效果""" - received_data = ParallelDebugSink.get_received_data() - - print("\n📋 Advanced Key Distribution Analysis:") - print("=" * 50) - - if not received_data: - print("❌ No data received by any instance") - return False - - key_distribution = {} - for instance_id, data_list in received_data.items(): - print(f"\n🔹 Parallel Instance {instance_id}:") - - for data in data_list: - key = f"{data['user_id']}_{data['id'] % 2}" - if key not in key_distribution: - key_distribution[key] = set() - key_distribution[key].add(instance_id) - print(f" - Key '{key}': {data['content']}") - - print("\n🎯 Key-to-Instance Mapping:") - for key, instances in key_distribution.items(): - print(f" - Key '{key}': routed to instance(s) {instances}") - - # 验证一致性：相同key应该路由到相同实例 - success = True - for key, instances in key_distribution.items(): - if len(instances) != 1: - print(f"❌ Key '{key}' was routed to multiple instances: {instances}") - success = False - - if success: - print("✅ Advanced key extraction test passed: Each unique key consistently routed") - - return success - - def _save_verification_result(self, result_file: str, test_type: str, success: bool): - """将验证结果保存到文件""" - if os.path.exists(result_file): - with open(result_file, encoding="utf-8") as f: - data = json.load(f) - - data["verification"] = { - "test_type": test_type, - "success": success, - "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"), - } - - with open(result_file, "w", encoding="utf-8") as f: - json.dump(data, f, indent=2, ensure_ascii=False) - - print(f"✍️ Verification result saved to: {result_file}") - - -if __name__ == "__main__": - # 可以直接运行单个测试 - test = TestKeyByFunctionality() - test.setup_method() - test.test_keyby_hash_partitioning() - - -""" -# 运行所有KeyBy测试 -pytest tests/test_keyby_functionality.py -v -s - -# 运行特定测试 -pytest tests/test_keyby_functionality.py::TestKeyByFunctionality::test_keyby_hash_partitioning -v -s - - -""" diff --git a/packages/sage-kernel/tests/unit/core/function/test_keyed_state.py b/packages/sage-kernel/tests/unit/core/function/test_keyed_state.py deleted file mode 100644 index 42bfed0005..0000000000 --- a/packages/sage-kernel/tests/unit/core/function/test_keyed_state.py +++ /dev/null @@ -1,631 +0,0 @@ -""" -Tests for keyed state support via get_key() interface. - -This test verifies that: -1. Functions can access the current packet's key via ctx.get_key() -2. Keyed state is properly isolated per key -3. State persistence works correctly with keyed state -4. The feature is backward compatible (works without keys) -""" - -import time -from typing import Any - -from sage.common.core.functions import MapFunction, SinkFunction, SourceFunction -from sage.kernel.api.local_environment import LocalEnvironment - - -class KeyedStateTestSource(SourceFunction): - """Source that generates events with different user IDs""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - self.events = [ - {"user_id": "alice", "action": "login", "value": 10}, - {"user_id": "bob", "action": "click", "value": 5}, - {"user_id": "alice", "action": "click", "value": 15}, - {"user_id": "charlie", "action": "login", "value": 20}, - {"user_id": "bob", "action": "purchase", "value": 100}, - {"user_id": "alice", "action": "logout", "value": 0}, - ] - - def execute(self, data=None): - if self.counter >= len(self.events): - return None # Stop - event = self.events[self.counter] - self.counter += 1 - self.logger.info(f"Generated event: {event}") - return event - - -class KeyExtractor(MapFunction): - """Extract user_id as the key""" - - def execute(self, data: Any) -> str: - return data["user_id"] - - -class KeyedStateFunction(MapFunction): - """Function that maintains keyed state per user""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - # User-level keyed state - will be automatically persisted - self.user_sessions = {} # {user_id: {action_count, total_value, actions}} - self.global_counter = 0 # Global state (not keyed) - - def execute(self, event_data): - # Get the current packet's key - key = self.ctx.get_key() - - # Increment global counter - self.global_counter += 1 - - if key is None: - # Handle unkeyed events (backward compatibility) - self.logger.warning("Processing unkeyed event") - return {"global_counter": self.global_counter} - - # Initialize keyed state for new users - if key not in self.user_sessions: - self.user_sessions[key] = { - "first_seen": time.time(), - "action_count": 0, - "total_value": 0, - "actions": [], - } - - # Update user-specific state - session = self.user_sessions[key] - session["action_count"] += 1 - session["total_value"] += event_data.get("value", 0) - session["actions"].append(event_data["action"]) - session["last_action"] = event_data["action"] - - self.logger.info( - f"User {key}: {session['action_count']} actions, total value: {session['total_value']}" - ) - - # Return enriched event with session info - return { - "user_id": key, - "event": event_data, - "session": { - "action_count": session["action_count"], - "total_value": session["total_value"], - "actions": list(session["actions"]), # Copy list - }, - "global_counter": self.global_counter, - } - - -class KeyedStateSink(SinkFunction): - """Sink that collects results for verification""" - - # Class-level storage for test verification - results = [] - - def __init__(self, **kwargs): - super().__init__(**kwargs) - - def execute(self, data: Any): - KeyedStateSink.results.append(data) - self.logger.info(f"Sink received: {data}") - return data - - @classmethod - def clear(cls): - cls.results = [] - - @classmethod - def get_results(cls): - return list(cls.results) - - -class TestKeyedStateSupport: - """Test keyed state functionality""" - - def setup_method(self): - """Clear results before each test""" - KeyedStateSink.clear() - - def test_basic_keyed_state(self): - """Test that functions can access and maintain keyed state""" - print("\n🚀 Testing Basic Keyed State Support") - - env = LocalEnvironment("test_keyed_state_basic") - - # Build pipeline: source -> keyby -> keyed state function -> sink - ( - env.from_source(KeyedStateTestSource, delay=0.3) - .keyby(KeyExtractor, strategy="hash") - .map(KeyedStateFunction) - .sink(KeyedStateSink) - ) - - try: - env.submit() - time.sleep(2.5) # Allow time for processing - finally: - env.close() - - # Verify results - results = KeyedStateSink.get_results() - - print(f"\n📊 Received {len(results)} results") - assert len(results) > 0, "Should receive results" - - # Verify per-user state is maintained correctly - user_states = {} - for result in results: - user_id = result["user_id"] - if user_id not in user_states: - user_states[user_id] = [] - user_states[user_id].append(result) - - # Check Alice's sessions (3 events) - if "alice" in user_states: - alice_results = user_states["alice"] - print(f"\n👤 Alice's events: {len(alice_results)}") - assert len(alice_results) >= 1, "Alice should have at least 1 event" - - # Verify action count increases - for i, result in enumerate(alice_results): - print( - f" Event {i + 1}: action_count={result['session']['action_count']}, " - f"total_value={result['session']['total_value']}" - ) - assert result["session"]["action_count"] == i + 1, f"Action count should be {i + 1}" - - # Verify total value is cumulative - # Alice's events from KeyedStateTestSource: login(10), click(15), logout(0) - alice_event_values = [10, 15, 0] # Values from source definition - if len(alice_results) == 3: - last_alice = alice_results[-1] - expected_total = sum(alice_event_values) - assert last_alice["session"]["total_value"] == expected_total, ( - f"Total value should be {expected_total} (sum of {alice_event_values})" - ) - - # Check Bob's sessions (2 events) - if "bob" in user_states: - bob_results = user_states["bob"] - print(f"\n👤 Bob's events: {len(bob_results)}") - assert len(bob_results) >= 1, "Bob should have at least 1 event" - - # Verify Bob's state is independent from Alice's - for i, result in enumerate(bob_results): - print( - f" Event {i + 1}: action_count={result['session']['action_count']}, " - f"total_value={result['session']['total_value']}" - ) - assert result["session"]["action_count"] == i + 1, f"Action count should be {i + 1}" - - print("\n✅ Keyed state test passed!") - - def test_get_key_method(self): - """Test that get_key() returns the correct key during processing""" - - class KeyVerificationFunction(MapFunction): - """Function that verifies get_key() returns correct values""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.key_observations = [] - - def execute(self, event_data): - observed_key = self.ctx.get_key() - expected_key = event_data["user_id"] - - self.key_observations.append( - {"expected": expected_key, "observed": observed_key, "event": event_data} - ) - - # Verify key matches - assert observed_key == expected_key, ( - f"get_key() returned {observed_key}, expected {expected_key}" - ) - - return { - "user_id": expected_key, - "key_match": observed_key == expected_key, - } - - print("\n🚀 Testing get_key() Method") - - env = LocalEnvironment("test_get_key_method") - - ( - env.from_source(KeyedStateTestSource, delay=0.3) - .keyby(KeyExtractor, strategy="hash") - .map(KeyVerificationFunction) - .sink(KeyedStateSink) - ) - - try: - env.submit() - time.sleep(2.5) - finally: - env.close() - - results = KeyedStateSink.get_results() - print(f"\n📊 Verified {len(results)} key matches") - - for result in results: - assert result["key_match"], f"Key mismatch for user {result['user_id']}" - - print("✅ get_key() method test passed!") - - def test_backward_compatibility_unkeyed_stream(self): - """Test that the feature works with unkeyed streams (backward compatibility)""" - - class UnkeyedSource(SourceFunction): - """Source that doesn't use keyby""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - - def execute(self, data=None): - if self.counter >= 3: - return None - self.counter += 1 - return {"id": self.counter, "value": self.counter * 10} - - class UnkeyedStateFunction(MapFunction): - """Function that handles both keyed and unkeyed streams""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.total_count = 0 - self.keyed_counts = {} - - def execute(self, data): - key = self.ctx.get_key() # Should return None for unkeyed streams - self.total_count += 1 - - if key is not None: - # Keyed stream - if key not in self.keyed_counts: - self.keyed_counts[key] = 0 - self.keyed_counts[key] += 1 - - return { - "data": data, - "key": key, - "total_count": self.total_count, - "is_keyed": key is not None, - } - - print("\n🚀 Testing Backward Compatibility (Unkeyed Stream)") - - env = LocalEnvironment("test_unkeyed_backward_compat") - - # Pipeline WITHOUT keyby - should work fine - env.from_source(UnkeyedSource, delay=0.3).map(UnkeyedStateFunction).sink(KeyedStateSink) - - try: - env.submit() - time.sleep(2) - finally: - env.close() - - results = KeyedStateSink.get_results() - print(f"\n📊 Received {len(results)} results from unkeyed stream") - - for result in results: - # Verify key is None for unkeyed streams - assert result["key"] is None, "Key should be None for unkeyed streams" - assert not result["is_keyed"], "Stream should not be marked as keyed" - print( - f" Event {result['data']['id']}: key={result['key']}, " - f"total_count={result['total_count']}" - ) - - print("✅ Backward compatibility test passed!") - - -class TestKeyedStateEdgeCases: - """Test edge cases and error scenarios for keyed state""" - - def setup_method(self): - """Clear results before each test""" - KeyedStateSink.clear() - - def test_key_isolation_between_packets(self): - """Test that keys don't leak between packets""" - - class KeyLeakageDetector(MapFunction): - """Function that tracks key changes to detect leaks""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.key_transitions = [] - - def execute(self, event_data): - current_key = self.ctx.get_key() - self.key_transitions.append( - { - "expected": event_data["user_id"], - "actual": current_key, - "event": event_data["action"], - } - ) - return {"key": current_key, "event": event_data} - - print("\n🚀 Testing Key Isolation Between Packets") - - env = LocalEnvironment("test_key_isolation") - - ( - env.from_source(KeyedStateTestSource, delay=0.3) - .keyby(KeyExtractor, strategy="hash") - .map(KeyLeakageDetector) - .sink(KeyedStateSink) - ) - - try: - env.submit() - time.sleep(2.5) - finally: - env.close() - - results = KeyedStateSink.get_results() - - # Verify each packet had the correct key during processing - for result in results: - assert result["key"] == result["event"]["user_id"], ( - f"Key mismatch: expected {result['event']['user_id']}, got {result['key']}" - ) - - print("✅ Key isolation test passed!") - - def test_none_key_handling(self): - """Test handling of None keys (unpartitioned packets)""" - - class NoneKeySource(SourceFunction): - """Source that produces events without keys""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - - def execute(self, data=None): - if self.counter >= 3: - return None - self.counter += 1 - return {"id": self.counter, "value": self.counter * 5} - - class NoneKeyFunction(MapFunction): - """Function that handles None keys gracefully""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.none_key_count = 0 - self.keyed_count = 0 - - def execute(self, data): - key = self.ctx.get_key() - if key is None: - self.none_key_count += 1 - else: - self.keyed_count += 1 - - return {"key": key, "none_key_count": self.none_key_count, "data": data} - - print("\n🚀 Testing None Key Handling") - - env = LocalEnvironment("test_none_key") - - # Pipeline without keyby - all keys should be None - env.from_source(NoneKeySource, delay=0.3).map(NoneKeyFunction).sink(KeyedStateSink) - - try: - env.submit() - time.sleep(2) - finally: - env.close() - - results = KeyedStateSink.get_results() - - for result in results: - assert result["key"] is None, f"Expected None key, got {result['key']}" - assert result["none_key_count"] > 0, "none_key_count should be incremented" - - print("✅ None key handling test passed!") - - def test_concurrent_key_access(self): - """Test that keys are correctly maintained in concurrent processing""" - - class ConcurrentKeyVerifier(MapFunction): - """Function that verifies key correctness in concurrent scenarios""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.key_verifications = [] - - def execute(self, event_data): - # Simulate some processing time - import random - - time.sleep(random.uniform(0.01, 0.05)) - - key = self.ctx.get_key() - expected = event_data["user_id"] - - # Record verification - self.key_verifications.append( - {"key": key, "expected": expected, "match": key == expected} - ) - - # Assert immediately - assert key == expected, f"Concurrent key mismatch: expected {expected}, got {key}" - - return {"verified": True, "key": key, "event": event_data} - - print("\n🚀 Testing Concurrent Key Access") - - env = LocalEnvironment("test_concurrent_keys") - - ( - env.from_source(KeyedStateTestSource, delay=0.2) - .keyby(KeyExtractor, strategy="hash") - .map(ConcurrentKeyVerifier) - .sink(KeyedStateSink) - ) - - try: - env.submit() - time.sleep(3) - finally: - env.close() - - results = KeyedStateSink.get_results() - - for result in results: - assert result["verified"], "All verifications should pass" - - print("✅ Concurrent key access test passed!") - - def test_state_serialization_excludes_current_key(self): - """Test that _current_packet_key is not serialized in state snapshots""" - - class StateSerializationFunction(MapFunction): - """Function that tests state serialization""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.process_count = 0 - - def execute(self, event_data): - self.process_count += 1 - key = self.ctx.get_key() - - # Get state (simulating checkpoint) - try: - # Check if ctx has get_state method - if hasattr(self.ctx, "get_state"): - state = self.ctx.get_state() - # _current_packet_key should not be in serialized state - assert "_current_packet_key" not in state, ( - "_current_packet_key should be excluded from state" - ) - except Exception as e: - self.logger.warning(f"State serialization check failed: {e}") - - return {"key": key, "process_count": self.process_count} - - print("\n🚀 Testing State Serialization Excludes Current Key") - - env = LocalEnvironment("test_state_serialization") - - ( - env.from_source(KeyedStateTestSource, delay=0.3) - .keyby(KeyExtractor, strategy="hash") - .map(StateSerializationFunction) - .sink(KeyedStateSink) - ) - - try: - env.submit() - time.sleep(2.5) - finally: - env.close() - - results = KeyedStateSink.get_results() - assert len(results) > 0, "Should receive results" - - print("✅ State serialization test passed!") - - -class TestKeyedStateAPICompleteness: - """Test all keyed state API methods""" - - def test_set_clear_get_key_methods(self): - """Test direct usage of set_current_key, get_key, and clear_key""" - from sage.kernel.runtime.context.base_context import BaseRuntimeContext - - class TestContext(BaseRuntimeContext): - """Concrete implementation for testing""" - - def __init__(self): - super().__init__() - self._test_logger = None - - @property - def logger(self): - if self._test_logger is None: - import logging - - self._test_logger = logging.getLogger("test") - return self._test_logger - - print("\n🚀 Testing Keyed State API Methods") - - ctx = TestContext() - - # Test initial state - assert ctx.get_key() is None, "Initial key should be None" - - # Test set_current_key with string - ctx.set_current_key("test_key_1") - assert ctx.get_key() == "test_key_1", "Key should be 'test_key_1'" - - # Test set_current_key with integer - ctx.set_current_key(12345) - assert ctx.get_key() == 12345, "Key should be 12345" - - # Test set_current_key with None - ctx.set_current_key(None) - assert ctx.get_key() is None, "Key should be None" - - # Test set_current_key with complex object - complex_key = {"user": "alice", "session": "abc123"} - ctx.set_current_key(complex_key) - assert ctx.get_key() == complex_key, "Key should be the complex object" - - # Test clear_key - ctx.clear_key() - assert ctx.get_key() is None, "Key should be None after clear" - - # Test multiple set/clear cycles - for i in range(5): - ctx.set_current_key(f"key_{i}") - assert ctx.get_key() == f"key_{i}" - ctx.clear_key() - assert ctx.get_key() is None - - print("✅ API methods test passed!") - - def test_key_attribute_initialization(self): - """Test that _current_packet_key is properly initialized""" - from sage.kernel.runtime.context.base_context import BaseRuntimeContext - - class TestContext(BaseRuntimeContext): - @property - def logger(self): - import logging - - return logging.getLogger("test") - - print("\n🚀 Testing Key Attribute Initialization") - - ctx = TestContext() - - # Verify attribute exists - assert hasattr(ctx, "_current_packet_key"), "Should have _current_packet_key attribute" - - # Verify initial value is None - assert ctx._current_packet_key is None, "Initial _current_packet_key should be None" - - # Verify get_key returns None initially - assert ctx.get_key() is None, "get_key() should return None initially" - - print("✅ Attribute initialization test passed!") - - -if __name__ == "__main__": - import pytest - - pytest.main([__file__, "-v", "-s"]) diff --git a/packages/sage-kernel/tests/unit/core/function/test_sink_function.py b/packages/sage-kernel/tests/unit/core/function/test_sink_function.py deleted file mode 100644 index 9ff8b6030e..0000000000 --- a/packages/sage-kernel/tests/unit/core/function/test_sink_function.py +++ /dev/null @@ -1,463 +0,0 @@ -""" -测试SinkFunction的单元测试 -""" - -from unittest.mock import Mock, patch - -import pytest - -from sage.common.core.functions import SinkFunction - - -class MockSinkFunction(SinkFunction): - """测试用的Mock Sink Function""" - - def __init__(self): - super().__init__() - self.execute_called = False - self.execute_call_count = 0 - self.executed_data = [] - self.should_raise = False - self.error_message = "Test error" - - def execute(self, data): - self.execute_called = True - self.execute_call_count += 1 - self.executed_data.append(data) - - if self.should_raise: - raise ValueError(self.error_message) - - # Sink函数通常不返回值或返回None - return None - - -class ConsoleSinkFunction(SinkFunction): - """控制台输出Sink Function示例""" - - def __init__(self): - super().__init__() - self.output_history = [] - - def execute(self, data): - output = f"[CONSOLE] {data}" - self.output_history.append(output) - print(output) # 实际的输出操作 - - -class FileSinkFunction(SinkFunction): - """文件写入Sink Function示例""" - - def __init__(self, filename): - super().__init__() - self.filename = filename - self.written_data = [] - - def execute(self, data): - # 模拟文件写入 - self.written_data.append(data) - # 实际实现中会写入文件 - - -class DatabaseSinkFunction(SinkFunction): - """数据库存储Sink Function示例""" - - def __init__(self, connection=None): - super().__init__() - self.connection = connection or Mock() - self.stored_records = [] - - def execute(self, data): - # 模拟数据库存储 - if isinstance(data, dict): - self.stored_records.append(data) - # 模拟数据库插入 - self.connection.insert(data) - - -@pytest.mark.unit -class TestSinkFunction: - """SinkFunction基类测试""" - - def test_sink_function_creation(self): - """测试SinkFunction创建""" - func = MockSinkFunction() - assert not func.execute_called - assert func.execute_call_count == 0 - assert func.executed_data == [] - - def test_execute_method_call(self): - """测试execute方法调用""" - func = MockSinkFunction() - test_data = "test_data" - - result = func.execute(test_data) - - assert func.execute_called - assert func.execute_call_count == 1 - assert test_data in func.executed_data - assert result is None # Sink函数通常不返回值 - - def test_multiple_execute_calls(self): - """测试多次execute调用""" - func = MockSinkFunction() - - func.execute("data1") - func.execute("data2") - func.execute("data3") - - assert func.execute_call_count == 3 - assert len(func.executed_data) == 3 - assert "data1" in func.executed_data - assert "data2" in func.executed_data - assert "data3" in func.executed_data - - def test_execute_with_different_data_types(self): - """测试不同数据类型的execute调用""" - func = MockSinkFunction() - - # 字符串数据 - func.execute("string_data") - # 数值数据 - func.execute(42) - # 列表数据 - func.execute([1, 2, 3]) - # 字典数据 - func.execute({"key": "value"}) - # None数据 - func.execute(None) - - assert func.execute_call_count == 5 - assert len(func.executed_data) == 5 - - def test_execute_error_handling(self): - """测试execute方法错误处理""" - func = MockSinkFunction() - func.should_raise = True - func.error_message = "Sink execution failed" - - with pytest.raises(ValueError, match="Sink execution failed"): - func.execute("error_data") - - # 即使出错，也应该记录调用 - assert func.execute_called - assert func.execute_call_count == 1 - - def test_abstract_method_enforcement(self): - """测试抽象方法强制实现""" - - # 尝试直接实例化抽象类，应该失败（故意触发 TypeError） - with pytest.raises(TypeError): - SinkFunction() # type: ignore[abstract] - - def test_inheritance_from_base_function(self): - """测试从BaseFunction的继承""" - func = MockSinkFunction() - - # 验证继承的属性 - assert hasattr(func, "ctx") - - assert hasattr(func, "logger") - assert hasattr(func, "name") - - -@pytest.mark.unit -class TestConsoleSinkFunction: - """ConsoleSinkFunction测试""" - - def test_console_sink_creation(self): - """测试ConsoleSinkFunction创建""" - func = ConsoleSinkFunction() - assert func.output_history == [] - - @patch("builtins.print") - def test_console_sink_execute(self, mock_print): - """测试ConsoleSinkFunction执行""" - func = ConsoleSinkFunction() - test_data = "Hello, World!" - - func.execute(test_data) - - # 验证输出历史 - assert len(func.output_history) == 1 - assert func.output_history[0] == "[CONSOLE] Hello, World!" - - # 验证print被调用 - mock_print.assert_called_once_with("[CONSOLE] Hello, World!") - - @patch("builtins.print") - def test_console_sink_multiple_outputs(self, mock_print): - """测试ConsoleSinkFunction多次输出""" - func = ConsoleSinkFunction() - - func.execute("Message 1") - func.execute("Message 2") - func.execute("Message 3") - - assert len(func.output_history) == 3 - assert func.output_history[0] == "[CONSOLE] Message 1" - assert func.output_history[1] == "[CONSOLE] Message 2" - assert func.output_history[2] == "[CONSOLE] Message 3" - - # 验证print被调用3次 - assert mock_print.call_count == 3 - - -@pytest.mark.unit -class TestFileSinkFunction: - """FileSinkFunction测试""" - - def test_file_sink_creation(self): - """测试FileSinkFunction创建""" - func = FileSinkFunction("test.txt") - assert func.filename == "test.txt" - assert func.written_data == [] - - def test_file_sink_execute(self): - """测试FileSinkFunction执行""" - func = FileSinkFunction("output.txt") - test_data = "File content" - - func.execute(test_data) - - assert len(func.written_data) == 1 - assert func.written_data[0] == test_data - - def test_file_sink_multiple_writes(self): - """测试FileSinkFunction多次写入""" - func = FileSinkFunction("multi.txt") - - func.execute("Line 1") - func.execute("Line 2") - func.execute("Line 3") - - assert len(func.written_data) == 3 - assert func.written_data == ["Line 1", "Line 2", "Line 3"] - - -@pytest.mark.unit -class TestDatabaseSinkFunction: - """DatabaseSinkFunction测试""" - - def test_database_sink_creation(self): - """测试DatabaseSinkFunction创建""" - mock_connection = Mock() - func = DatabaseSinkFunction(mock_connection) - - assert func.connection is mock_connection - assert func.stored_records == [] - - def test_database_sink_creation_without_connection(self): - """测试DatabaseSinkFunction无连接创建""" - func = DatabaseSinkFunction() - - # 应该有默认的Mock连接 - assert func.connection is not None - assert func.stored_records == [] - - def test_database_sink_execute_dict_data(self): - """测试DatabaseSinkFunction执行字典数据""" - mock_connection = Mock() - func = DatabaseSinkFunction(mock_connection) - - test_data = {"id": 1, "name": "John", "email": "john@example.com"} - func.execute(test_data) - - # 验证数据被存储 - assert len(func.stored_records) == 1 - assert func.stored_records[0] == test_data - - # 验证数据库插入被调用 - mock_connection.insert.assert_called_once_with(test_data) - - def test_database_sink_execute_non_dict_data(self): - """测试DatabaseSinkFunction执行非字典数据""" - mock_connection = Mock() - func = DatabaseSinkFunction(mock_connection) - - test_data = "simple string" - func.execute(test_data) - - # 非字典数据不会被存储到记录中 - assert len(func.stored_records) == 0 - - # 但是数据库插入不会被调用 - mock_connection.insert.assert_not_called() - - def test_database_sink_multiple_records(self): - """测试DatabaseSinkFunction多条记录""" - mock_connection = Mock() - func = DatabaseSinkFunction(mock_connection) - - records = [ - {"id": 1, "name": "Alice"}, - {"id": 2, "name": "Bob"}, - {"id": 3, "name": "Charlie"}, - ] - - for record in records: - func.execute(record) - - assert len(func.stored_records) == 3 - assert func.stored_records == records - - # 验证每条记录都调用了插入 - assert mock_connection.insert.call_count == 3 - - -@pytest.mark.integration -class TestSinkFunctionIntegration: - """SinkFunction集成测试""" - - def test_sink_function_with_context(self): - """测试带上下文的SinkFunction""" - func = MockSinkFunction() - - # 模拟上下文注入 - mock_ctx = Mock() - mock_ctx.name = "sink_test" - mock_ctx.logger = Mock() - func.ctx = mock_ctx - - # 验证继承的属性工作正常 - assert func.name == "sink_test" - assert func.logger is mock_ctx.logger - - # 验证Sink功能正常 - func.execute("test_with_context") - assert func.execute_called - assert "test_with_context" in func.executed_data - - def test_sink_function_pipeline_integration(self): - """测试SinkFunction在管道中的集成""" - # 模拟一个完整的数据处理到存储流程 - processed_data = [] - - class AccumulatorSink(SinkFunction): - def execute(self, data): - processed_data.append(f"STORED: {data}") - - sink = AccumulatorSink() - - # 模拟管道处理后的数据 - pipeline_outputs = ["processed_item1", "processed_item2", "processed_item3"] - - for output in pipeline_outputs: - sink.execute(output) - - # 验证所有数据都被正确存储 - assert len(processed_data) == 3 - assert "STORED: processed_item1" in processed_data - assert "STORED: processed_item2" in processed_data - assert "STORED: processed_item3" in processed_data - - def test_multiple_sink_functions(self): - """测试多个SinkFunction协同工作""" - console_sink = ConsoleSinkFunction() - file_sink = FileSinkFunction("multi_sink.txt") - - test_data = "Multi-sink data" - - with patch("builtins.print"): - # 同一数据发送到多个sink - console_sink.execute(test_data) - file_sink.execute(test_data) - - # 验证每个sink都处理了数据 - assert test_data in console_sink.output_history[0] - assert test_data in file_sink.written_data - - def test_sink_function_error_recovery(self): - """测试SinkFunction错误恢复""" - - class ReliableSinkFunction(SinkFunction): - def __init__(self): - super().__init__() - self.successful_executions = [] - self.failed_executions = [] - - def execute(self, data): - try: - # 模拟可能失败的操作 - if data == "error_data": - raise RuntimeError("Simulated sink error") - self.successful_executions.append(data) - except Exception as e: - self.failed_executions.append((data, str(e))) - # 在实际应用中，可能需要重试或记录错误 - raise - - sink = ReliableSinkFunction() - - # 正常数据 - sink.execute("normal_data") - assert "normal_data" in sink.successful_executions - - # 错误数据 - with pytest.raises(RuntimeError): - sink.execute("error_data") - - assert len(sink.failed_executions) == 1 - assert sink.failed_executions[0][0] == "error_data" - - -class BatchSinkFunction(SinkFunction): - """批处理Sink Function示例""" - - def __init__(self, batch_size=3): - super().__init__() - self.batch_size = batch_size - self.batch = [] - self.committed_batches = [] - - def execute(self, data): - self.batch.append(data) - - if len(self.batch) >= self.batch_size: - self._commit_batch() - - def _commit_batch(self): - """提交当前批次""" - if self.batch: - self.committed_batches.append(self.batch.copy()) - self.batch.clear() - - def flush(self): - """强制提交剩余数据""" - if self.batch: - self._commit_batch() - - -@pytest.mark.integration -class TestAdvancedSinkPatterns: - """高级Sink模式测试""" - - def test_batch_sink_function(self): - """测试批处理Sink Function""" - sink = BatchSinkFunction(batch_size=2) - - # 添加数据，但不足一个批次 - sink.execute("item1") - assert len(sink.committed_batches) == 0 - assert len(sink.batch) == 1 - - # 完成一个批次 - sink.execute("item2") - assert len(sink.committed_batches) == 1 - assert sink.committed_batches[0] == ["item1", "item2"] - assert len(sink.batch) == 0 - - # 添加部分数据 - sink.execute("item3") - assert len(sink.committed_batches) == 1 - assert len(sink.batch) == 1 - - # 强制刷新 - sink.flush() - assert len(sink.committed_batches) == 2 - assert sink.committed_batches[1] == ["item3"] - assert len(sink.batch) == 0 - - -if __name__ == "__main__": - pytest.main([__file__, "-v"]) diff --git a/packages/sage-kernel/tests/unit/core/generate_coverage_report.py b/packages/sage-kernel/tests/unit/core/generate_coverage_report.py deleted file mode 100644 index 4b5e711b60..0000000000 --- a/packages/sage-kernel/tests/unit/core/generate_coverage_report.py +++ /dev/null @@ -1,355 +0,0 @@ -""" -Core模块测试覆盖率报告生成器 -""" - -import json -import os -import subprocess -from datetime import datetime -from pathlib import Path -from typing import Any - - -class TestCoverageReporter: - """测试覆盖率报告器""" - - def __init__(self, project_root: Path | None = None): - self.project_root = project_root or self._find_project_root() - self.core_src_path = self.project_root / "src" / "sage" / "core" - self.core_tests_path = self.project_root / "tests" / "core" - - def _find_project_root(self) -> Path: - """查找项目根目录""" - current_dir = Path(__file__).parent - while current_dir.parent != current_dir: - if (current_dir / "pyproject.toml").exists(): - return current_dir - current_dir = current_dir.parent - return Path(__file__).parent - - def get_source_files(self) -> list[Path]: - """获取所有源文件""" - source_files = [] - for pattern in ["*.py"]: - source_files.extend(self.core_src_path.rglob(pattern)) - return [f for f in source_files if not f.name.startswith("__")] - - def get_test_files(self) -> list[Path]: - """获取所有测试文件""" - test_files = [] - for pattern in ["test_*.py", "*_test.py"]: - test_files.extend(self.core_tests_path.rglob(pattern)) - return test_files - - def map_source_to_test(self) -> dict[str, str]: - """映射源文件到测试文件""" - source_files = self.get_source_files() - self.get_test_files() - - mapping = {} - - for src_file in source_files: - # 计算相对路径 - rel_path = src_file.relative_to(self.core_src_path) - - # 查找对应的测试文件 - expected_test_name = f"test_{src_file.stem}.py" - - # 在对应的测试目录中查找 - test_dir = self.core_tests_path / rel_path.parent - expected_test_path = test_dir / expected_test_name - - if expected_test_path.exists(): - mapping[str(rel_path)] = str(expected_test_path.relative_to(self.core_tests_path)) - else: - mapping[str(rel_path)] = None - - return mapping - - def analyze_test_compliance(self) -> dict[str, Any]: - """分析测试合规性""" - source_to_test = self.map_source_to_test() - - total_files = len(source_to_test) - covered_files = sum(1 for test_path in source_to_test.values() if test_path is not None) - uncovered_files = total_files - covered_files - - compliance_rate = (covered_files / total_files * 100) if total_files > 0 else 0 - - return { - "total_source_files": total_files, - "covered_files": covered_files, - "uncovered_files": uncovered_files, - "compliance_rate": compliance_rate, - "source_to_test_mapping": source_to_test, - "uncovered_source_files": [src for src, test in source_to_test.items() if test is None], - } - - def run_coverage_analysis(self) -> dict[str, Any]: - """运行覆盖率分析""" - os.chdir(self.project_root) - - # 运行pytest with coverage - cmd = [ - "python", - "-m", - "pytest", - "tests/core/", - "--cov=src/sage/core", - "--cov-report=json:coverage-core.json", - "--cov-report=term-missing", - "-q", # quiet mode - ] - - try: - result = subprocess.run(cmd, capture_output=True, text=True) - - # 读取覆盖率JSON报告 - coverage_file = self.project_root / "coverage-core.json" - if coverage_file.exists(): - with open(coverage_file) as f: - coverage_data = json.load(f) - return { - "success": True, - "coverage_data": coverage_data, - "stdout": result.stdout, - "stderr": result.stderr, - } - else: - return { - "success": False, - "error": "Coverage file not generated", - "stdout": result.stdout, - "stderr": result.stderr, - } - - except Exception as e: - return {"success": False, "error": str(e)} - - def generate_markdown_report(self) -> str: - """生成Markdown格式的报告""" - compliance = self.analyze_test_compliance() - coverage = self.run_coverage_analysis() - - report = [] - report.append("# SAGE Core模块测试覆盖率报告") - report.append("") - report.append(f"**生成时间**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") - report.append("") - - # 测试合规性部分 - report.append("## 1. 测试合规性分析") - report.append("") - report.append(f"- **总源文件数**: {compliance['total_source_files']}") - report.append(f"- **已覆盖文件数**: {compliance['covered_files']}") - report.append(f"- **未覆盖文件数**: {compliance['uncovered_files']}") - report.append(f"- **合规率**: {compliance['compliance_rate']:.2f}%") - report.append("") - - # 合规性状态 - if compliance["compliance_rate"] >= 80: - status = "✅ 优秀" - elif compliance["compliance_rate"] >= 60: - status = "⚠️ 良好" - else: - status = "❌ 需要改进" - - report.append(f"**合规性状态**: {status}") - report.append("") - - # 文件映射表 - report.append("## 2. 源文件到测试文件映射") - report.append("") - report.append("| 源文件 | 测试文件 | 状态 |") - report.append("|--------|----------|------|") - - for src_file, test_file in compliance["source_to_test_mapping"].items(): - if test_file: - status = "✅" - test_display = test_file - else: - status = "❌" - test_display = "缺失" - report.append(f"| `{src_file}` | `{test_display}` | {status} |") - - report.append("") - - # 未覆盖文件列表 - if compliance["uncovered_source_files"]: - report.append("## 3. 未覆盖的源文件") - report.append("") - for src_file in compliance["uncovered_source_files"]: - report.append(f"- `{src_file}`") - report.append("") - - # 代码覆盖率部分 - if coverage["success"] and "coverage_data" in coverage: - cov_data = coverage["coverage_data"] - report.append("## 4. 代码覆盖率分析") - report.append("") - - if "totals" in cov_data: - totals = cov_data["totals"] - line_rate = totals.get("percent_covered", 0) - report.append(f"- **行覆盖率**: {line_rate:.2f}%") - report.append(f"- **总行数**: {totals.get('num_statements', 0)}") - report.append(f"- **覆盖行数**: {totals.get('covered_lines', 0)}") - report.append(f"- **缺失行数**: {totals.get('missing_lines', 0)}") - report.append("") - - # 按文件的覆盖率 - if "files" in cov_data: - report.append("### 4.1 按文件覆盖率详情") - report.append("") - report.append("| 文件 | 覆盖率 | 总行数 | 覆盖行数 | 缺失行数 |") - report.append("|------|--------|--------|----------|----------|") - - for file_path, file_data in cov_data["files"].items(): - if "src/sage/core" in file_path: - # 只显示core模块的文件 - rel_path = file_path.replace("src/sage/core/", "") - coverage_pct = file_data.get("summary", {}).get("percent_covered", 0) - num_statements = file_data.get("summary", {}).get("num_statements", 0) - covered = file_data.get("summary", {}).get("covered_lines", 0) - missing = file_data.get("summary", {}).get("missing_lines", 0) - - report.append( - f"| `{rel_path}` | {coverage_pct:.1f}% | {num_statements} | {covered} | {missing} |" - ) - - report.append("") - else: - report.append("## 4. 代码覆盖率分析") - report.append("") - report.append("❌ 覆盖率分析失败") - if "error" in coverage: - report.append(f"错误信息: {coverage['error']}") - report.append("") - - # 建议和改进 - report.append("## 5. 建议和改进") - report.append("") - - if compliance["compliance_rate"] < 100: - report.append("### 5.1 测试覆盖建议") - report.append("") - for src_file in compliance["uncovered_source_files"]: - # 生成测试文件建议路径 - src_path = Path(src_file) - suggested_test = f"tests/core/{src_path.parent}/test_{src_path.stem}.py" - report.append(f"- 为 `{src_file}` 创建测试文件: `{suggested_test}`") - report.append("") - - if coverage["success"] and "coverage_data" in coverage: - cov_data = coverage["coverage_data"] - if "totals" in cov_data: - line_rate = cov_data["totals"].get("percent_covered", 0) - if line_rate < 80: - report.append("### 5.2 代码覆盖率改进") - report.append("") - report.append("- 当前行覆盖率低于80%，建议增加更多测试用例") - report.append("- 特别关注分支覆盖和边界情况测试") - report.append("- 考虑添加集成测试以提高整体覆盖率") - report.append("") - - # Issue要求对比 - report.append("## 6. Issue要求对比") - report.append("") - report.append("根据 Issue 要求，检查测试组织架构完成情况：") - report.append("") - - required_structure = { - "test_pipeline.py": "Pipeline核心测试", - "function/test_base_function.py": "BaseFunction测试", - "function/test_comap_function.py": "CoMapFunction测试", - "function/test_sink_function.py": "SinkFunction测试", - "function/test_source_function.py": "SourceFunction测试", - "operator/test_base_operator.py": "BaseOperator测试", - "service/test_base_service.py": "BaseService测试", - } - - report.append("| 要求的测试文件 | 描述 | 状态 |") - report.append("|----------------|------|------|") - - for test_file, description in required_structure.items(): - test_path = self.core_tests_path / test_file - status = "✅ 已完成" if test_path.exists() else "❌ 缺失" - report.append(f"| `{test_file}` | {description} | {status} |") - - report.append("") - - # 总结 - completed_count = sum( - 1 - for test_file in required_structure.keys() - if (self.core_tests_path / test_file).exists() - ) - total_required = len(required_structure) - completion_rate = (completed_count / total_required * 100) if total_required > 0 else 0 - - report.append("## 7. 总结") - report.append("") - report.append( - f"- **Issue要求完成度**: {completion_rate:.1f}% ({completed_count}/{total_required})" - ) - report.append(f"- **测试文件合规率**: {compliance['compliance_rate']:.1f}%") - - if coverage["success"] and "coverage_data" in coverage: - line_rate = coverage["coverage_data"]["totals"].get("percent_covered", 0) - report.append(f"- **代码行覆盖率**: {line_rate:.1f}%") - - report.append("") - - if completion_rate >= 100 and compliance["compliance_rate"] >= 80: - report.append("🎉 **恭喜！测试组织架构已按照Issue要求完成，质量良好！**") - elif completion_rate >= 80: - report.append("👍 **测试架构基本完成，还有少量工作需要完善**") - else: - report.append("⚠️ **测试架构需要继续完善以满足Issue要求**") - - return "\n".join(report) - - def save_report(self, filename: str | None = None) -> Path: - """保存报告到文件""" - if filename is None: - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - filename = f"test_coverage_report_{timestamp}.md" - - report_path = self.project_root / filename - report_content = self.generate_markdown_report() - - with open(report_path, "w", encoding="utf-8") as f: - f.write(report_content) - - return report_path - - -def main(): - """主函数""" - reporter = TestCoverageReporter() - - print("生成SAGE Core模块测试覆盖率报告...") - print("=" * 50) - - # 生成并保存报告 - report_path = reporter.save_report() - print(f"报告已保存到: {report_path}") - - # 显示简要摘要 - compliance = reporter.analyze_test_compliance() - print("\n快速摘要:") - print(f"- 测试合规率: {compliance['compliance_rate']:.1f}%") - print(f"- 源文件总数: {compliance['total_source_files']}") - print(f"- 已覆盖文件: {compliance['covered_files']}") - print(f"- 未覆盖文件: {compliance['uncovered_files']}") - - if compliance["uncovered_source_files"]: - print("\n需要创建测试的文件:") - for src_file in compliance["uncovered_source_files"][:5]: # 只显示前5个 - print(f" - {src_file}") - if len(compliance["uncovered_source_files"]) > 5: - print(f" ... 还有{len(compliance['uncovered_source_files']) - 5}个文件") - - -if __name__ == "__main__": - main() diff --git a/packages/sage-kernel/tests/unit/core/test_comap_service_integration.py b/packages/sage-kernel/tests/unit/core/test_comap_service_integration.py deleted file mode 100644 index 39d059b40a..0000000000 --- a/packages/sage-kernel/tests/unit/core/test_comap_service_integration.py +++ /dev/null @@ -1,478 +0,0 @@ -""" -CoMap函数中服务调用集成测试 -测试dataflow model算子内部调用环境中注册的service -参考算子内的service call语法糖和dataflow comap test -""" - -import time - -import pytest - -from sage.common.core.functions import BaseCoMapFunction, SinkFunction -from sage.kernel.api.local_environment import LocalEnvironment - -# ==================== 测试服务类 ==================== - - -class UserProfileService: - """用户画像服务""" - - def __init__(self): - self.profiles = { - "user_001": {"name": "Alice", "age": 25, "interests": ["tech", "music"]}, - "user_002": {"name": "Bob", "age": 30, "interests": ["sports", "travel"]}, - "user_003": { - "name": "Charlie", - "age": 28, - "interests": ["books", "movies"], - }, - } - - def get_profile(self, user_id: str): - return self.profiles.get(user_id, {"name": "Unknown", "age": 0, "interests": []}) - - def update_activity(self, user_id: str, activity: str): - if user_id in self.profiles: - if "recent_activities" not in self.profiles[user_id]: - self.profiles[user_id]["recent_activities"] = [] - self.profiles[user_id]["recent_activities"].append(activity) - return f"Updated activity for {user_id}: {activity}" - return f"User {user_id} not found" - - -class RecommendationService: - """推荐服务""" - - def __init__(self): - self.item_db = { - "item_001": {"name": "Tech News", "category": "tech", "rating": 4.5}, - "item_002": {"name": "Music Album", "category": "music", "rating": 4.8}, - "item_003": {"name": "Sports Match", "category": "sports", "rating": 4.2}, - "item_004": {"name": "Travel Guide", "category": "travel", "rating": 4.6}, - } - - def get_recommendations(self, interests: list, user_id: str | None = None): - recommendations = [] - for item_id, item_info in self.item_db.items(): - if item_info["category"] in interests: - recommendations.append( - { - "item_id": item_id, - "name": item_info["name"], - "rating": item_info["rating"], - "reason": f"Matches interest: {item_info['category']}", - } - ) - return recommendations[:3] # 返回前3个推荐 - - def track_interaction(self, user_id: str, item_id: str, interaction_type: str): - return { - "tracked": True, - "user_id": user_id, - "item_id": item_id, - "interaction": interaction_type, - "timestamp": time.time(), - } - - -class CacheService: - """缓存服务""" - - def __init__(self): - self.cache = {} - - def get(self, key: str): - return self.cache.get(key) - - def set(self, key: str, value): - self.cache[key] = value - return f"Cached {key}" - - def invalidate(self, pattern: str): - keys_to_remove = [k for k in self.cache.keys() if pattern in k] - for key in keys_to_remove: - del self.cache[key] - return f"Invalidated {len(keys_to_remove)} keys matching '{pattern}'" - - -# ==================== CoMap函数测试类 ==================== - - -class UserRecommendationCoMapFunction(BaseCoMapFunction): - """ - 用户推荐CoMap函数 - 测试在CoMap中调用服务 - Stream 0: 用户事件流 - Stream 1: 推荐请求流 - """ - - def __init__(self, ctx=None, **kwargs): - super().__init__(ctx=ctx, **kwargs) - self.processed_events = 0 - self.processed_requests = 0 - - def map0(self, event_data): - """处理用户事件流 (stream 0) - 使用服务调用""" - print(f"[DEBUG] CoMap.map0 called with event_data: {event_data}") - self.processed_events += 1 - - user_id = event_data["user_id"] - - # Check if this is an event or recommendation request - if "item_id" in event_data: - # This is an actual event - item_id = event_data["item_id"] - interaction_type = event_data["type"] - else: - # This is a recommendation request, handle it differently - print("[DEBUG] CoMap.map0: Received recommendation request, skipping event processing") - return { - "type": "recommendation_request_received", - "user_id": user_id, - "message": "Recommendation request received in event stream, no action taken", - "processed_sequence": self.processed_events, - "source_stream": 0, - "processor": "EventProcessor", - } - - # 使用服务调用语法糖 - 同步调用用户画像服务（增加容错处理） - activity_description = f"{interaction_type}_{item_id}" - - try: - print("[DEBUG] CoMap.map0: Calling user_profile.update_activity with timeout=10.0") - update_result = self.call_service( - "user_profile", - user_id, - activity_description, - timeout=10.0, - method="update_activity", - ) - except Exception as e: - update_result = f"Service call failed: {str(e)[:100]}" - self.logger.error(f"[DEBUG] CoMap.map0: user_profile service call failed: {e}") - - try: - track_result = self.call_service( - "recommendation", - user_id, - item_id, - interaction_type, - timeout=10.0, - method="track_interaction", - ) - except Exception as e: - track_result = {"tracked": False, "error": str(e)[:100]} - self.logger.warning(f"Recommendation service call failed: {e}") - - # 使用服务调用语法糖 - 异步调用缓存服务清理相关缓存（增加容错处理） - cache_key_pattern = f"rec_{user_id}" - try: - cache_future = self.call_service_async( - "cache", - cache_key_pattern, - timeout=10.0, - method="invalidate", - ) - except Exception as e: - cache_future = None - self.logger.warning(f"Cache service async call failed: {e}") - - result = { - "type": "processed_event", - "original_event": event_data, - "user_id": user_id, - "activity_update": update_result, - "interaction_tracked": track_result, - "cache_invalidation_started": cache_future is not None, - "processed_sequence": self.processed_events, - "source_stream": 0, - "processor": "EventProcessor", - } - - if cache_future is not None: - try: - cache_result = cache_future.result(timeout=5.0) # 增加超时时间 - result["cache_invalidation_result"] = cache_result - except Exception as e: - result["cache_invalidation_error"] = str(e)[:100] - self.logger.warning(f"Cache service result failed: {e}") - else: - result["cache_invalidation_error"] = "Cache service call not initiated" - - if self.ctx: - self.logger.info(f"CoMap map0: processed event {event_data['type']} for user {user_id}") - - return result - - def map1(self, request_data): - """处理推荐请求流 (stream 1) - 使用服务调用""" - print(f"[DEBUG] CoMap.map1 called with request_data: {request_data}") - self.processed_requests += 1 - - user_id = request_data["user_id"] - context = request_data["context"] - - # 检查缓存 - 使用同步服务调用（增加容错处理） - cache_key = f"rec_{user_id}_{context}" - try: - cached_recommendations = self.call_service( - "cache", cache_key, timeout=10.0, method="get" - ) - except Exception as e: - cached_recommendations = None - self.logger.warning(f"Cache get service call failed: {e}") - - if cached_recommendations: - result = { - "type": "cached_recommendations", - "user_id": user_id, - "context": context, - "recommendations": cached_recommendations, - "cache_hit": True, - "processed_sequence": self.processed_requests, - "source_stream": 1, - "processor": "RecommendationProcessor", - } - else: - # 缓存未命中，获取用户画像并生成推荐（增加容错处理） - - # 异步获取用户画像 - try: - profile_future = self.call_service_async( - "user_profile", - user_id, - timeout=10.0, - method="get_profile", - ) - except Exception as e: - profile_future = None - self.logger.warning(f"User profile async service call failed: {e}") - - # 获取用户画像结果（增加容错处理） - if profile_future is not None: - try: - user_profile = profile_future.result(timeout=5.0) # 减少超时时间 - user_interests = user_profile.get("interests", []) - except Exception as e: - user_profile = {"interests": ["general"]} # 使用默认兴趣 - user_interests = ["general"] - self.logger.warning(f"User profile result failed: {e}") - else: - user_profile = {"interests": ["general"]} - user_interests = ["general"] - - # 根据用户兴趣获取推荐（增加容错处理） - try: - recommendations = self.call_service( - "recommendation", - user_interests, - user_id, - timeout=10.0, - method="get_recommendations", - ) - except Exception as e: - recommendations = [f"item_{user_id}_{context}"] # 使用默认推荐 - self.logger.warning(f"Recommendation service call failed: {e}") - - # 缓存推荐结果（增加容错处理） - try: - self.call_service( - "cache", - cache_key, - recommendations, - timeout=10.0, - method="set", - ) - except Exception as e: - self.logger.warning(f"Cache set service call failed: {e}") - - result = { - "type": "fresh_recommendations", - "user_id": user_id, - "context": context, - "user_profile": user_profile, - "recommendations": recommendations, - "cache_hit": False, - "processed_sequence": self.processed_requests, - "source_stream": 1, - "processor": "RecommendationProcessor", - } - - return result - - -# ==================== 调试输出Sink ==================== - - -class ServiceTestSink(SinkFunction): - """服务测试结果收集Sink""" - - def __init__(self, ctx=None, **kwargs): - super().__init__(ctx=ctx, **kwargs) - self.processed_count = 0 - self.results = [] # 实例级别的结果存储 - - def execute(self, data): - print(f"[DEBUG] ServiceTestSink.execute called with data: {data}") - - self.processed_count += 1 - self.results.append(data) - - print(f"[DEBUG] Total results in this sink instance: {len(self.results)}") - - # 打印处理结果 - result_type = data.get("type", "unknown") - source_stream = data.get("source_stream", -1) - user_id = data.get("user_id", "unknown") - - if result_type == "processed_event": - activity_update = data.get("activity_update", "No update") - interaction_tracked = data.get("interaction_tracked", {}) - cache_invalidation = data.get("cache_invalidation_started", False) - print(f"📱 Event (Stream {source_stream}): User {user_id}") - print(f" Activity Update: {activity_update}") - print(f" Interaction Tracked: {interaction_tracked.get('tracked', False)}") - print( - f" Cache Invalidation: {'Started' if cache_invalidation else 'No cache result'}" - ) - elif result_type == "cached_recommendations": - context = data.get("context", "unknown") - recommendations = data.get("recommendations", []) - print(f"🎯 Recommendation (Stream {source_stream}): User {user_id}") - print(f" Context: {context} | 🔥 Cache Hit") - print(f" Recommendations: {len(recommendations)} items") - elif result_type == "fresh_recommendations": - context = data.get("context", "unknown") - recommendations = data.get("recommendations", []) - user_profile = data.get("user_profile", {}) - print(f"🎯 Recommendation (Stream {source_stream}): User {user_id}") - print(f" Context: {context} | 🆕 Fresh") - print(f" Recommendations: {len(recommendations)} items") - if user_profile: - interests = user_profile.get("interests", ["general"]) - print( - f" User Profile: {user_profile.get('name', 'None')} (interests: {interests})" - ) - elif result_type == "recommendation_error": - print( - f"❌ Recommendation Error: User {user_id} | Stream {source_stream} | Error: {data.get('error')}" - ) - else: - print(f"📊 Result: {result_type} | Stream {source_stream} | User {user_id}") - - return data - - -# ==================== 测试类 ==================== - - -class TestCoMapServiceIntegration: - """测试CoMap函数中的服务调用集成""" - - def setup_method(self): - """每个测试方法前的设置""" - pass # 不再需要清理类级别结果 - - @pytest.mark.slow - def test_comap_service_integration(self): - """测试CoMap函数中的servive调用集成""" - print("\n🚀 Testing CoMap Service Integration") - print("=" * 60) - - # 创建环境 - env = LocalEnvironment("comap_service_test") - - # 注册服务到环境 - env.register_service("user_profile", UserProfileService) - env.register_service("recommendation", RecommendationService) - env.register_service("cache", CacheService) - - print("✅ Services registered:") - print(" - user_profile: UserProfileService") - print(" - recommendation: RecommendationService") - print(" - cache: CacheService") - - # 创建批处理数据源 - 使用 from_batch 接口避免无限循环 - event_data = [ - { - "type": "view", - "user_id": "user_001", - "item_id": "item_001", - "timestamp": time.time(), - }, - { - "type": "click", - "user_id": "user_002", - "item_id": "item_002", - "timestamp": time.time(), - }, - { - "type": "view", - "user_id": "user_003", - "item_id": "item_003", - "timestamp": time.time(), - }, - { - "type": "like", - "user_id": "user_001", - "item_id": "item_002", - "timestamp": time.time(), - }, - ] - - request_data = [ - { - "type": "get_recommendations", - "user_id": "user_001", - "context": "homepage", - }, - {"type": "get_recommendations", "user_id": "user_002", "context": "search"}, - { - "type": "get_recommendations", - "user_id": "user_003", - "context": "profile", - }, - {"type": "get_recommendations", "user_id": "user_001", "context": "feed"}, - ] - - event_stream = env.from_batch(event_data) - request_stream = env.from_batch(request_data) - - # 构建CoMap处理管道 - ( - event_stream.connect(request_stream) - .comap(UserRecommendationCoMapFunction) - .sink(ServiceTestSink, parallelism=1) - ) - - env.submit() - - print("\n🏃 Pipeline running...") - time.sleep(3) # 减少等待时间以避免测试超时 - - -@pytest.mark.slow -def test_comap_service_integration(): - """独立运行的测试函数""" - print("=" * 70) - print("SAGE CoMap Service Integration Test") - print("=" * 70) - - test_instance = TestCoMapServiceIntegration() - test_instance.setup_method() - - try: - test_instance.test_comap_service_integration() - print("\n🎉 All tests passed! CoMap service integration is working correctly.") - except Exception as e: - print(f"\n💥 Test failed: {e}") - import traceback - - traceback.print_exc() - pytest.fail(f"CoMap service integration test failed: {e}") - - -if __name__ == "__main__": - success = test_comap_service_integration() - - if not success: - exit(1) diff --git a/packages/sage-kernel/tests/unit/kernel/api/operator/test_additional_operators.py b/packages/sage-kernel/tests/unit/kernel/api/operator/test_additional_operators.py deleted file mode 100644 index 888b701945..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/api/operator/test_additional_operators.py +++ /dev/null @@ -1,660 +0,0 @@ -""" -Unit tests for additional Kernel API Operators. - -Tests SourceOperator, SinkOperator, KeyByOperator, and JoinOperator. -""" - -from unittest.mock import MagicMock - -import pytest - -from sage.common.core.functions import ( - BaseFunction, - BaseJoinFunction, - KeyByFunction, - SinkFunction, - SourceFunction, -) -from sage.kernel.api.operator.join_operator import JoinOperator -from sage.kernel.api.operator.keyby_operator import KeyByOperator -from sage.kernel.api.operator.sink_operator import SinkOperator -from sage.kernel.api.operator.source_operator import SourceOperator -from sage.kernel.runtime.communication.packet import Packet, StopSignal - -# Mock Functions for Testing - - -class MockSourceFunction(SourceFunction): - """Mock source function that generates test data.""" - - def __init__(self, data_to_generate=None): - super().__init__() - self.ctx = None - self._logger = None - self.data_to_generate = data_to_generate if data_to_generate is not None else [1, 2, 3] - self.index = 0 - - def execute(self): - if self.index < len(self.data_to_generate): - data = self.data_to_generate[self.index] - self.index += 1 - return data - else: - # Return StopSignal when exhausted - return StopSignal("data_exhausted") - - -class MockSinkFunction(SinkFunction): - """Mock sink function that collects data.""" - - def __init__(self): - super().__init__() - self.ctx = None - self._logger = None - self.collected = [] - - def execute(self, data): - self.collected.append(data) - return f"Processed: {data}" - - def close(self): - """Called when receiving stop signal.""" - return f"Final count: {len(self.collected)}" - - -class MockKeyByFunction(KeyByFunction): - """Mock keyby function that extracts key from dict.""" - - def __init__(self): - super().__init__() - self.ctx = None - self._logger = None - - def execute(self, data): - if isinstance(data, dict) and "id" in data: - return data["id"] - return str(data) - - -class MockJoinFunction(BaseJoinFunction): - """Mock join function for testing.""" - - def __init__(self): - super().__init__() - self.ctx = None - self._logger = None - # is_join is a property in BaseJoinFunction, don't set it - self.stream0_buffer = {} - self.stream1_buffer = {} - - def execute(self, payload, join_key, stream_tag): - """Simple join logic: buffer data and match on keys.""" - if stream_tag == 0: - self.stream0_buffer[join_key] = payload - # Check if matching key in stream1 - if join_key in self.stream1_buffer: - return [{"left": payload, "right": self.stream1_buffer[join_key], "key": join_key}] - elif stream_tag == 1: - self.stream1_buffer[join_key] = payload - # Check if matching key in stream0 - if join_key in self.stream0_buffer: - return [{"left": self.stream0_buffer[join_key], "right": payload, "key": join_key}] - return [] # Return empty list instead of None - - -# Fixtures - - -@pytest.fixture -def mock_task_context(): - """Create a mock TaskContext.""" - context = MagicMock() - context.name = "test_task" - context.logger = MagicMock() - context.task_id = "task_001" - # Mock the router property - context.router = MagicMock() - context.router.send = MagicMock(return_value=True) - context.router.send_stop_signal = MagicMock() - context.request_stop = MagicMock() - context.set_stop_signal = MagicMock() - return context - - -@pytest.fixture -def mock_function_factory(): - """Create a mock FunctionFactory.""" - factory = MagicMock() - factory.create_function = MagicMock() - return factory - - -# Test Cases - SourceOperator - - -@pytest.mark.unit -class TestSourceOperator: - """Test SourceOperator implementation.""" - - def test_source_operator_initialization(self, mock_task_context, mock_function_factory): - """Test SourceOperator initialization.""" - mock_function_factory.create_function.return_value = MockSourceFunction() - - operator = SourceOperator( - name="test_source", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - assert operator.name == mock_task_context.name - assert operator.ctx == mock_task_context - assert operator._stop_signal_sent is False - assert operator.task is None - - def test_source_generates_data(self, mock_task_context, mock_function_factory): - """Test source generates and sends data.""" - mock_function_factory.create_function.return_value = MockSourceFunction([10, 20]) - - operator = SourceOperator( - name="test_source", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - # Generate first data - operator.process_packet() - assert mock_task_context.router.send.call_count == 1 - sent_packet = mock_task_context.router.send.call_args[0][0] - assert sent_packet.payload == 10 - - # Generate second data - operator.process_packet() - assert mock_task_context.router.send.call_count == 2 - sent_packet = mock_task_context.router.send.call_args[0][0] - assert sent_packet.payload == 20 - - def test_source_handles_stop_signal(self, mock_task_context, mock_function_factory): - """Test source handles StopSignal.""" - mock_function_factory.create_function.return_value = MockSourceFunction([]) - - operator = SourceOperator( - name="test_source", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - # Should generate StopSignal when data exhausted - operator.process_packet() - - # Should send stop signal - assert mock_task_context.router.send_stop_signal.called - stop_signal = mock_task_context.router.send_stop_signal.call_args[0][0] - assert isinstance(stop_signal, StopSignal) - # Source is set to operator.name which is ctx.name - assert stop_signal.source == mock_task_context.name - - def test_source_prevents_duplicate_stop_signal(self, mock_task_context, mock_function_factory): - """Test source prevents duplicate stop signals.""" - source_func = MockSourceFunction([]) - mock_function_factory.create_function.return_value = source_func - - operator = SourceOperator( - name="test_source", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - # First stop signal - operator.process_packet() - assert mock_task_context.router.send_stop_signal.call_count == 1 - - # Second attempt - should be prevented even if function returns StopSignal again - # Reset function to generate another StopSignal - source_func.index = 0 - operator.process_packet() - # Should still be 1 due to _stop_signal_sent flag - assert mock_task_context.router.send_stop_signal.call_count == 1 - - def test_source_handles_send_failure(self, mock_task_context, mock_function_factory): - """Test source handles send failure by stopping.""" - mock_function_factory.create_function.return_value = MockSourceFunction([10]) - # Simulate send failure - mock_task_context.router.send = MagicMock(return_value=False) - - operator = SourceOperator( - name="test_source", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - operator.process_packet() - - # Should send stop signal on failure - assert mock_task_context.router.send_stop_signal.called - - -# Test Cases - SinkOperator - - -@pytest.mark.unit -class TestSinkOperator: - """Test SinkOperator implementation.""" - - def test_sink_operator_initialization(self, mock_task_context, mock_function_factory): - """Test SinkOperator initialization.""" - mock_function_factory.create_function.return_value = MockSinkFunction() - - operator = SinkOperator( - name="test_sink", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - assert operator.name == mock_task_context.name - assert operator.ctx == mock_task_context - - def test_sink_processes_data(self, mock_task_context, mock_function_factory): - """Test sink processes incoming data.""" - sink_func = MockSinkFunction() - mock_function_factory.create_function.return_value = sink_func - - operator = SinkOperator( - name="test_sink", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - # Process data - packet = Packet(payload={"value": 100}) - operator.process_packet(packet) - - # Verify data was collected - assert len(sink_func.collected) == 1 - assert sink_func.collected[0] == {"value": 100} - - def test_sink_handles_stop_signal(self, mock_task_context, mock_function_factory): - """Test sink handles stop signal and calls close().""" - sink_func = MockSinkFunction() - mock_function_factory.create_function.return_value = sink_func - - operator = SinkOperator( - name="test_sink", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - # Add some data - operator.process_packet(Packet(payload=1)) - operator.process_packet(Packet(payload=2)) - - # Handle stop signal - operator.handle_stop_signal() - - # Verify close() was called (we can't directly check but it should log) - assert mock_task_context.logger.info.called - - def test_sink_handles_empty_packet(self, mock_task_context, mock_function_factory): - """Test sink handles empty packet.""" - sink_func = MockSinkFunction() - mock_function_factory.create_function.return_value = sink_func - - operator = SinkOperator( - name="test_sink", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - operator.process_packet(None) - - # Should log warning but not crash - assert mock_task_context.logger.warning.called - assert len(sink_func.collected) == 0 - - def test_sink_handles_exception(self, mock_task_context, mock_function_factory): - """Test sink handles exception in processing.""" - - class FailingSinkFunction(SinkFunction): - def __init__(self): - super().__init__() - self.ctx = None - self._logger = None - - def execute(self, data): - raise RuntimeError("Sink processing error") - - mock_function_factory.create_function.return_value = FailingSinkFunction() - - operator = SinkOperator( - name="test_sink", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - packet = Packet(payload={"value": 100}) - operator.process_packet(packet) - - # Should log error but not crash - assert mock_task_context.logger.error.called - - -# Test Cases - KeyByOperator - - -@pytest.mark.unit -class TestKeyByOperator: - """Test KeyByOperator implementation.""" - - def test_keyby_operator_initialization(self, mock_task_context, mock_function_factory): - """Test KeyByOperator initialization with different strategies.""" - mock_function_factory.create_function.return_value = MockKeyByFunction() - - operator = KeyByOperator( - name="test_keyby", - ctx=mock_task_context, - function_factory=mock_function_factory, - partition_strategy="hash", - ) - - assert operator.name == mock_task_context.name - assert operator.partition_strategy == "hash" - - def test_keyby_extracts_key_hash_strategy(self, mock_task_context, mock_function_factory): - """Test keyby extracts key with hash strategy.""" - mock_function_factory.create_function.return_value = MockKeyByFunction() - - operator = KeyByOperator( - name="test_keyby", - ctx=mock_task_context, - function_factory=mock_function_factory, - partition_strategy="hash", - ) - - packet = Packet(payload={"id": "user123", "value": 100}) - operator.process_packet(packet) - - # Verify packet was sent with key - assert mock_task_context.router.send.called - sent_packet = mock_task_context.router.send.call_args[0][0] - assert sent_packet.partition_key == "user123" - assert sent_packet.partition_strategy == "hash" - - def test_keyby_broadcast_strategy(self, mock_task_context, mock_function_factory): - """Test keyby with broadcast strategy.""" - mock_function_factory.create_function.return_value = MockKeyByFunction() - - operator = KeyByOperator( - name="test_keyby", - ctx=mock_task_context, - function_factory=mock_function_factory, - partition_strategy="broadcast", - ) - - packet = Packet(payload={"id": "user456", "value": 200}) - operator.process_packet(packet) - - sent_packet = mock_task_context.router.send.call_args[0][0] - assert sent_packet.partition_strategy == "broadcast" - - def test_keyby_round_robin_strategy(self, mock_task_context, mock_function_factory): - """Test keyby with round_robin strategy.""" - mock_function_factory.create_function.return_value = MockKeyByFunction() - - operator = KeyByOperator( - name="test_keyby", - ctx=mock_task_context, - function_factory=mock_function_factory, - partition_strategy="round_robin", - ) - - packet = Packet(payload={"id": "user789", "value": 300}) - operator.process_packet(packet) - - sent_packet = mock_task_context.router.send.call_args[0][0] - assert sent_packet.partition_strategy == "round_robin" - - def test_keyby_handles_exception(self, mock_task_context, mock_function_factory): - """Test keyby handles exception and falls back to original packet.""" - - class FailingKeyByFunction(KeyByFunction): - def __init__(self): - super().__init__() - self.ctx = None - self._logger = None - - def execute(self, data): - raise ValueError("Key extraction error") - - mock_function_factory.create_function.return_value = FailingKeyByFunction() - - operator = KeyByOperator( - name="test_keyby", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - packet = Packet(payload={"id": "user123", "value": 100}) - operator.process_packet(packet) - - # Should still send packet (fallback behavior) - assert mock_task_context.router.send.called - - def test_keyby_handles_empty_packet(self, mock_task_context, mock_function_factory): - """Test keyby handles empty packet.""" - mock_function_factory.create_function.return_value = MockKeyByFunction() - - operator = KeyByOperator( - name="test_keyby", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - operator.process_packet(None) - - # Should not send packet - mock_task_context.router.send.assert_not_called() - - -# Test Cases - JoinOperator - - -@pytest.mark.unit -class TestJoinOperator: - """Test JoinOperator implementation.""" - - def test_join_operator_initialization(self, mock_task_context, mock_function_factory): - """Test JoinOperator initialization.""" - mock_function_factory.create_function.return_value = MockJoinFunction() - - operator = JoinOperator( - name="test_join", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - assert operator.name == mock_task_context.name - assert operator._validated is True - assert operator.processed_count == 0 - assert operator.emitted_count == 0 - - def test_join_validation_requires_join_function(self, mock_task_context, mock_function_factory): - """Test join validation rejects non-join functions.""" - - class NonJoinFunction(BaseFunction): - def __init__(self): - super().__init__() - self.ctx = None - self._logger = None - - def execute(self, data): - return data - - mock_function_factory.create_function.return_value = NonJoinFunction() - - with pytest.raises(TypeError, match="requires Join function"): - JoinOperator( - name="test_join", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - def test_join_processes_keyed_packets(self, mock_task_context, mock_function_factory): - """Test join processes keyed packets from two streams.""" - join_func = MockJoinFunction() - mock_function_factory.create_function.return_value = join_func - - operator = JoinOperator( - name="test_join", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - # Stream 0 packet - packet0 = Packet(payload={"data": "left1"}) - packet0.partition_key = "key1" - packet0.input_index = 0 - - operator.process_packet(packet0) - assert operator.processed_count == 1 - - # Stream 1 packet with matching key - should produce join result - packet1 = Packet(payload={"data": "right1"}) - packet1.partition_key = "key1" - packet1.input_index = 1 - - operator.process_packet(packet1) - assert operator.processed_count == 2 - - # Should have sent join result - assert mock_task_context.router.send.called - - def test_join_requires_keyed_packets(self, mock_task_context, mock_function_factory): - """Test join requires keyed packets.""" - mock_function_factory.create_function.return_value = MockJoinFunction() - - operator = JoinOperator( - name="test_join", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - # Non-keyed packet - packet = Packet(payload={"data": "test"}) - operator.process_packet(packet) - - # Should log warning and not process - assert mock_task_context.logger.warning.called - assert operator.processed_count == 0 - - def test_join_handles_empty_packet(self, mock_task_context, mock_function_factory): - """Test join handles empty packet.""" - mock_function_factory.create_function.return_value = MockJoinFunction() - - operator = JoinOperator( - name="test_join", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - operator.process_packet(None) - - # Should not process - assert operator.processed_count == 0 - - def test_join_handles_none_payload(self, mock_task_context, mock_function_factory): - """Test join handles keyed packet with None payload.""" - mock_function_factory.create_function.return_value = MockJoinFunction() - - operator = JoinOperator( - name="test_join", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - packet = Packet(payload=None) - packet.partition_key = "key1" - packet.input_index = 0 - - operator.process_packet(packet) - - # Should skip None payload - assert operator.processed_count == 0 - - def test_join_non_matching_keys(self, mock_task_context, mock_function_factory): - """Test join with non-matching keys.""" - join_func = MockJoinFunction() - mock_function_factory.create_function.return_value = join_func - - operator = JoinOperator( - name="test_join", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - # Different keys - packet0 = Packet(payload={"data": "left"}) - packet0.partition_key = "key1" - packet0.input_index = 0 - - packet1 = Packet(payload={"data": "right"}) - packet1.partition_key = "key2" - packet1.input_index = 1 - - operator.process_packet(packet0) - operator.process_packet(packet1) - - # Should buffer but not emit join result - assert operator.processed_count == 2 - # No join output should be sent (function returns None for non-matching) - # The operator would call send only if function returns non-None list - - -# Packet Metadata Tests - - -@pytest.mark.unit -class TestOperatorPacketHandling: - """Test operators handle packet metadata correctly.""" - - def test_keyby_preserves_original_payload(self, mock_task_context, mock_function_factory): - """Test keyby preserves original payload.""" - mock_function_factory.create_function.return_value = MockKeyByFunction() - - operator = KeyByOperator( - name="test_keyby", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - original_payload = {"id": "user123", "value": 999} - packet = Packet(payload=original_payload) - operator.process_packet(packet) - - sent_packet = mock_task_context.router.send.call_args[0][0] - assert sent_packet.payload == original_payload - - def test_join_tracks_stream_index(self, mock_task_context, mock_function_factory): - """Test join correctly tracks stream indices.""" - join_func = MockJoinFunction() - mock_function_factory.create_function.return_value = join_func - - operator = JoinOperator( - name="test_join", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - # Verify different stream indices are tracked - packet0 = Packet(payload={"data": "stream0"}) - packet0.partition_key = "k1" - packet0.input_index = 0 - - packet1 = Packet(payload={"data": "stream1"}) - packet1.partition_key = "k1" - packet1.input_index = 1 - - operator.process_packet(packet0) - operator.process_packet(packet1) - - # Both buffers should have data - assert "k1" in join_func.stream0_buffer - assert "k1" in join_func.stream1_buffer diff --git a/packages/sage-kernel/tests/unit/kernel/api/operator/test_operators.py b/packages/sage-kernel/tests/unit/kernel/api/operator/test_operators.py deleted file mode 100644 index af57694064..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/api/operator/test_operators.py +++ /dev/null @@ -1,576 +0,0 @@ -""" -Unit tests for Kernel API Operators. - -Tests the core operator implementations including FilterOperator, MapOperator, -FlatMapOperator, and CoMapOperator. -""" - -from unittest.mock import MagicMock - -import pytest - -from sage.common.core.functions import ( - BaseCoMapFunction, - FilterFunction, - FlatMapFunction, - MapFunction, -) -from sage.kernel.api.operator.comap_operator import CoMapOperator -from sage.kernel.api.operator.filter_operator import FilterOperator -from sage.kernel.api.operator.flatmap_operator import FlatMapOperator -from sage.kernel.api.operator.map_operator import MapOperator -from sage.kernel.runtime.communication.packet import Packet - -# Mock Functions for Testing - - -class MockFilterFunction(FilterFunction): - """Mock filter function that filters even numbers.""" - - def __init__(self): - super().__init__() - self.ctx = None - self._logger = None - - def execute(self, data): - if isinstance(data, dict) and "value" in data: - return data["value"] % 2 == 0 # Only allow even numbers - return isinstance(data, int) and data % 2 == 0 - - -class MockMapFunction(MapFunction): - """Mock map function that doubles the value.""" - - def __init__(self): - super().__init__() - self.ctx = None - self._logger = None - - def execute(self, data): - if isinstance(data, dict) and "value" in data: - return {"value": data["value"] * 2} - return data * 2 if isinstance(data, (int, float)) else data - - -class MockFlatMapFunction(FlatMapFunction): - """Mock flatmap function that splits a list.""" - - def __init__(self): - super().__init__() - self.ctx = None - self._logger = None - self.out = None # Will be set by insert_collector - - def execute(self, data): - if isinstance(data, list): - return data # Return the list to be flattened - elif isinstance(data, dict) and "items" in data: - return data["items"] # Return nested list - return [data] # Wrap single item in list - - -class MockCoMapFunction(BaseCoMapFunction): - """Mock comap function that processes two streams.""" - - def __init__(self): - super().__init__() - self.ctx = None - self._logger = None - - def map0(self, data): - """Process stream 0 data.""" - return f"Stream0: {data}" - - def map1(self, data): - """Process stream 1 data.""" - return f"Stream1: {data}" - - -# Fixtures - - -@pytest.fixture -def mock_task_context(): - """Create a mock TaskContext.""" - context = MagicMock() - context.name = "test_task" - context.logger = MagicMock() - context.task_id = "task_001" - # Mock the router property - context.router = MagicMock() - context.router.send = MagicMock() - return context - - -@pytest.fixture -def mock_function_factory(): - """Create a mock FunctionFactory.""" - factory = MagicMock() - factory.create_function = MagicMock() - return factory - - -# Test Cases - FilterOperator - - -@pytest.mark.unit -class TestFilterOperator: - """Test FilterOperator implementation.""" - - def test_filter_operator_initialization(self, mock_task_context, mock_function_factory): - """Test FilterOperator initialization.""" - mock_function_factory.create_function.return_value = MockFilterFunction() - - operator = FilterOperator( - name="test_filter", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - # Operator name comes from ctx.name - assert operator.name == mock_task_context.name - assert operator.ctx == mock_task_context - assert operator.function is not None - - def test_filter_passes_data(self, mock_task_context, mock_function_factory): - """Test filter passes data that meets condition.""" - mock_function_factory.create_function.return_value = MockFilterFunction() - - operator = FilterOperator( - name="test_filter", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - # Create packet with even number (should pass) - packet = Packet(payload={"value": 4}) - - operator.process_packet(packet) - - # Verify packet was sent via router - mock_task_context.router.send.assert_called_once() - - def test_filter_blocks_data(self, mock_task_context, mock_function_factory): - """Test filter blocks data that doesn't meet condition.""" - mock_function_factory.create_function.return_value = MockFilterFunction() - - operator = FilterOperator( - name="test_filter", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - # Create packet with odd number (should be filtered) - packet = Packet(payload={"value": 3}) - - operator.process_packet(packet) - - # Verify packet was NOT sent - mock_task_context.router.send.assert_not_called() - - def test_filter_handles_empty_packet(self, mock_task_context, mock_function_factory): - """Test filter handles empty packet gracefully.""" - mock_function_factory.create_function.return_value = MockFilterFunction() - - operator = FilterOperator( - name="test_filter", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - operator.process_packet(None) - - mock_task_context.router.send.assert_not_called() - - def test_filter_handles_none_payload(self, mock_task_context, mock_function_factory): - """Test filter handles None payload.""" - mock_function_factory.create_function.return_value = MockFilterFunction() - - operator = FilterOperator( - name="test_filter", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - packet = Packet(payload=None) - operator.process_packet(packet) - - mock_task_context.router.send.assert_not_called() - - -# Test Cases - MapOperator - - -@pytest.mark.unit -class TestMapOperator: - """Test MapOperator implementation.""" - - def test_map_operator_initialization(self, mock_task_context, mock_function_factory): - """Test MapOperator initialization.""" - mock_function_factory.create_function.return_value = MockMapFunction() - - operator = MapOperator( - name="test_map", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - # Operator name comes from ctx.name - assert operator.name == mock_task_context.name - assert operator.ctx == mock_task_context - - def test_map_transforms_data(self, mock_task_context, mock_function_factory): - """Test map transforms data correctly.""" - mock_function_factory.create_function.return_value = MockMapFunction() - - operator = MapOperator( - name="test_map", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - packet = Packet(payload={"value": 5}) - - operator.process_packet(packet) - - # Verify send was called - assert mock_task_context.router.send.called - # Get the actual packet that was sent - sent_packet = mock_task_context.router.send.call_args[0][0] - # Verify the transformation (5 * 2 = 10) - assert sent_packet.payload["value"] == 10 - - def test_map_handles_empty_packet(self, mock_task_context, mock_function_factory): - """Test map handles empty packet.""" - mock_function_factory.create_function.return_value = MockMapFunction() - - operator = MapOperator( - name="test_map", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - operator.process_packet(None) - - # Should not crash, just log warning - assert mock_task_context.logger.warning.called - - -# Test Cases - FlatMapOperator - - -@pytest.mark.unit -class TestFlatMapOperator: - """Test FlatMapOperator implementation.""" - - def test_flatmap_operator_initialization(self, mock_task_context, mock_function_factory): - """Test FlatMapOperator initialization.""" - mock_function_factory.create_function.return_value = MockFlatMapFunction() - - operator = FlatMapOperator( - name="test_flatmap", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - # Operator name comes from ctx.name - assert operator.name == mock_task_context.name - assert operator.ctx == mock_task_context - assert operator.out is not None # Collector should be initialized - - def test_flatmap_expands_list(self, mock_task_context, mock_function_factory): - """Test flatmap expands a list into multiple packets.""" - mock_function_factory.create_function.return_value = MockFlatMapFunction() - - operator = FlatMapOperator( - name="test_flatmap", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - # Create packet with a list - packet = Packet(payload=[1, 2, 3]) - - operator.process_packet(packet) - - # Should send 3 separate packets - assert mock_task_context.router.send.call_count == 3 - - def test_flatmap_handles_dict_with_items(self, mock_task_context, mock_function_factory): - """Test flatmap handles dict with items key.""" - mock_function_factory.create_function.return_value = MockFlatMapFunction() - - operator = FlatMapOperator( - name="test_flatmap", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - packet = Packet(payload={"items": ["a", "b", "c"]}) - - operator.process_packet(packet) - - # Should send 3 separate packets for the items - assert mock_task_context.router.send.call_count == 3 - - def test_flatmap_handles_single_item(self, mock_task_context, mock_function_factory): - """Test flatmap handles single non-list item.""" - mock_function_factory.create_function.return_value = MockFlatMapFunction() - - operator = FlatMapOperator( - name="test_flatmap", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - packet = Packet(payload="single_item") - - operator.process_packet(packet) - - # Should send 1 packet (wrapped in list) - assert mock_task_context.router.send.call_count == 1 - - def test_flatmap_handles_empty_packet(self, mock_task_context, mock_function_factory): - """Test flatmap handles empty packet.""" - mock_function_factory.create_function.return_value = MockFlatMapFunction() - - operator = FlatMapOperator( - name="test_flatmap", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - operator.process_packet(None) - - mock_task_context.router.send.assert_not_called() - - -# Test Cases - CoMapOperator - - -@pytest.mark.unit -class TestCoMapOperator: - """Test CoMapOperator implementation.""" - - def test_comap_operator_initialization(self, mock_task_context, mock_function_factory): - """Test CoMapOperator initialization with valid CoMap function.""" - mock_function_factory.create_function.return_value = MockCoMapFunction() - - operator = CoMapOperator( - name="test_comap", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - # Operator name comes from ctx.name - assert operator.name == mock_task_context.name - assert operator.ctx == mock_task_context - - def test_comap_processes_stream0(self, mock_task_context, mock_function_factory): - """Test CoMap processes data from stream 0.""" - mock_function_factory.create_function.return_value = MockCoMapFunction() - - operator = CoMapOperator( - name="test_comap", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - # Create packet from stream 0 - packet = Packet(payload="test_data") - packet.input_index = 0 - - operator.process_packet(packet) - - # Verify send was called - assert mock_task_context.router.send.called - sent_packet = mock_task_context.router.send.call_args[0][0] - assert sent_packet.payload == "Stream0: test_data" - - def test_comap_processes_stream1(self, mock_task_context, mock_function_factory): - """Test CoMap processes data from stream 1.""" - mock_function_factory.create_function.return_value = MockCoMapFunction() - - operator = CoMapOperator( - name="test_comap", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - # Create packet from stream 1 - packet = Packet(payload="test_data") - packet.input_index = 1 - - operator.process_packet(packet) - - # Verify send was called - assert mock_task_context.router.send.called - sent_packet = mock_task_context.router.send.call_args[0][0] - assert sent_packet.payload == "Stream1: test_data" - - def test_comap_handles_empty_packet(self, mock_task_context, mock_function_factory): - """Test CoMap handles empty packet.""" - mock_function_factory.create_function.return_value = MockCoMapFunction() - - operator = CoMapOperator( - name="test_comap", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - operator.process_packet(None) - - mock_task_context.router.send.assert_not_called() - - def test_comap_validation_requires_comap_function( - self, mock_task_context, mock_function_factory - ): - """Test CoMap validation rejects non-CoMap functions.""" - # Use a regular MapFunction instead of CoMapFunction - mock_function_factory.create_function.return_value = MockMapFunction() - - with pytest.raises(TypeError, match="requires CoMap function"): - CoMapOperator( - name="test_comap", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - -# Edge Cases and Error Handling - - -@pytest.mark.unit -class TestOperatorErrorHandling: - """Test operator error handling.""" - - def test_filter_handles_exception_in_function(self, mock_task_context, mock_function_factory): - """Test filter handles exception raised by function.""" - - class FailingFilterFunction(FilterFunction): - def __init__(self): - super().__init__() - self.ctx = None - self._logger = None - - def execute(self, data): - raise ValueError("Filter function error") - - mock_function_factory.create_function.return_value = FailingFilterFunction() - - operator = FilterOperator( - name="test_filter", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - packet = Packet(payload={"value": 5}) - - # Should not raise, just log error - operator.process_packet(packet) - - # Should not send packet on error - mock_task_context.router.send.assert_not_called() - - def test_map_handles_exception_in_function(self, mock_task_context, mock_function_factory): - """Test map handles exception raised by function.""" - - class FailingMapFunction(MapFunction): - def __init__(self): - super().__init__() - self.ctx = None - self._logger = None - - def execute(self, data): - raise RuntimeError("Map function error") - - mock_function_factory.create_function.return_value = FailingMapFunction() - - operator = MapOperator( - name="test_map", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - packet = Packet(payload={"value": 5}) - - # Should not raise, just log error - operator.process_packet(packet) - - def test_flatmap_handles_exception_in_function(self, mock_task_context, mock_function_factory): - """Test flatmap handles exception raised by function.""" - - class FailingFlatMapFunction(FlatMapFunction): - def __init__(self): - super().__init__() - self.ctx = None - self._logger = None - self.out = None - - def execute(self, data): - raise Exception("FlatMap function error") - - mock_function_factory.create_function.return_value = FailingFlatMapFunction() - - operator = FlatMapOperator( - name="test_flatmap", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - packet = Packet(payload=[1, 2, 3]) - - # Should not raise, just log error - operator.process_packet(packet) - - -# Packet Inheritance Tests - - -@pytest.mark.unit -class TestPacketInheritance: - """Test operators properly inherit packet metadata.""" - - def test_filter_preserves_packet_metadata(self, mock_task_context, mock_function_factory): - """Test filter preserves original packet metadata.""" - mock_function_factory.create_function.return_value = MockFilterFunction() - - operator = FilterOperator( - name="test_filter", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - # Create packet with metadata - packet = Packet(payload={"value": 4}) - packet.timestamp = 12345 - packet.task_id = "task_001" - - operator.process_packet(packet) - - # Verify metadata is preserved - sent_packet = mock_task_context.router.send.call_args[0][0] - assert sent_packet.timestamp == 12345 - assert sent_packet.task_id == "task_001" - - def test_flatmap_inherits_partition_info(self, mock_task_context, mock_function_factory): - """Test flatmap inherits partition info in expanded packets.""" - mock_function_factory.create_function.return_value = MockFlatMapFunction() - - operator = FlatMapOperator( - name="test_flatmap", - ctx=mock_task_context, - function_factory=mock_function_factory, - ) - - # Create packet with partition info - packet = Packet(payload=[1, 2]) - packet.partition_key = "test_key" - - operator.process_packet(packet) - - # All expanded packets should inherit partition info - for call in mock_task_context.router.send.call_args_list: - sent_packet = call[0][0] - # Partition info should be inherited - assert hasattr(sent_packet, "partition_key") or sent_packet.partition_key is None diff --git a/packages/sage-kernel/tests/unit/kernel/api/test_datastream.py b/packages/sage-kernel/tests/unit/kernel/api/test_datastream.py deleted file mode 100644 index b8971bc566..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/api/test_datastream.py +++ /dev/null @@ -1,850 +0,0 @@ -""" -Unit tests for DataStream class. - -Tests cover: -- DataStream initialization and type resolution -- Transformation methods (map, filter, flatmap, sink, keyby) -- Stream connection (connect) -- Future stream operations (fill_future) -- Helper methods (print) -- Error handling and edge cases -""" - -from unittest.mock import MagicMock, patch - -import pytest - -from sage.common.core import BaseFunction -from sage.kernel.api.connected_streams import ConnectedStreams -from sage.kernel.api.datastream import DataStream -from sage.kernel.api.local_environment import LocalEnvironment - -# ============================================================================ -# Test Fixtures -# ============================================================================ - - -@pytest.fixture -def local_env(): - """Create LocalEnvironment for testing""" - return LocalEnvironment(name="test_env") - - -@pytest.fixture -def mock_transformation(): - """Create mock transformation""" - transformation = MagicMock() - transformation.basename = "mock_transformation" - transformation.function_class = MagicMock(__name__="MockFunction") - transformation.add_upstream = MagicMock() - transformation.__name__ = "MockTransformation" - return transformation - - -@pytest.fixture -def datastream(local_env, mock_transformation): - """Create DataStream instance""" - return DataStream(local_env, mock_transformation) - - -@pytest.fixture -def mock_function_class(): - """Mock function class for testing""" - - class MockMapFunction(BaseFunction): - def __call__(self, data): - return data * 2 - - return MockMapFunction - - -# ============================================================================ -# DataStream Initialization Tests -# ============================================================================ - - -@pytest.mark.unit -class TestDataStreamInitialization: - """Test DataStream initialization""" - - def test_init_basic(self, local_env, mock_transformation): - """Test basic DataStream initialization""" - ds = DataStream(local_env, mock_transformation) - - assert ds._environment is local_env - assert ds.transformation is mock_transformation - assert ds.logger is not None - - def test_init_stores_environment_reference(self, local_env, mock_transformation): - """Test DataStream stores environment reference""" - ds = DataStream(local_env, mock_transformation) - - assert ds._environment is local_env - - def test_init_stores_transformation_reference(self, local_env, mock_transformation): - """Test DataStream stores transformation reference""" - ds = DataStream(local_env, mock_transformation) - - assert ds.transformation is mock_transformation - - def test_type_param_resolution(self, local_env, mock_transformation): - """Test type parameter resolution""" - ds = DataStream(local_env, mock_transformation) - - # Should resolve to Any if no explicit type - assert ds._type_param is not None - - -# ============================================================================ -# Transformation Methods Tests -# ============================================================================ - - -@pytest.mark.unit -class TestMapTransformation: - """Test map transformation""" - - def test_map_with_function_class(self, datastream, mock_function_class): - """Test map with BaseFunction class""" - result = datastream.map(mock_function_class, parallelism=2) - - # Verify transformation was added to pipeline - assert len(datastream._environment.pipeline) == 1 - transformation = datastream._environment.pipeline[0] - assert transformation.__class__.__name__ == "MapTransformation" - - # Verify result is new DataStream - assert isinstance(result, DataStream) - assert result is not datastream - - def test_map_with_lambda(self, datastream): - """Test map with lambda function""" - map_func = lambda x: x * 2 # noqa: E731 - - result = datastream.map(map_func) - - assert len(datastream._environment.pipeline) == 1 - assert isinstance(result, DataStream) - - def test_map_with_regular_function(self, datastream): - """Test map with regular function""" - - def double(x): - return x * 2 - - result = datastream.map(double) - - assert len(datastream._environment.pipeline) == 1 - assert isinstance(result, DataStream) - - def test_map_with_parallelism(self, datastream, mock_function_class): - """Test map respects parallelism parameter""" - datastream.map(mock_function_class, parallelism=4) - - transformation = datastream._environment.pipeline[0] - # Transformation should have parallelism set - assert hasattr(transformation, "parallelism") - - def test_map_default_parallelism(self, datastream, mock_function_class): - """Test map uses default parallelism when not specified""" - datastream.map(mock_function_class) - - transformation = datastream._environment.pipeline[0] - # Should use default parallelism of 1 - assert transformation.parallelism == 1 - - def test_map_with_args_kwargs(self, datastream, mock_function_class): - """Test map passes args and kwargs to transformation""" - datastream.map(mock_function_class, "arg1", "arg2", key="value", parallelism=2) - - assert len(datastream._environment.pipeline) == 1 - - -@pytest.mark.unit -class TestFilterTransformation: - """Test filter transformation""" - - def test_filter_with_function_class(self, datastream): - """Test filter with BaseFunction class""" - - class FilterFunction(BaseFunction): - def __call__(self, data): - return data > 10 - - result = datastream.filter(FilterFunction) - - assert len(datastream._environment.pipeline) == 1 - transformation = datastream._environment.pipeline[0] - assert transformation.__class__.__name__ == "FilterTransformation" - assert isinstance(result, DataStream) - - def test_filter_with_lambda(self, datastream): - """Test filter with lambda function""" - result = datastream.filter(lambda x: x > 0) - - assert len(datastream._environment.pipeline) == 1 - assert isinstance(result, DataStream) - - def test_filter_with_parallelism(self, datastream): - """Test filter respects parallelism parameter""" - - class FilterFunc(BaseFunction): - pass - - datastream.filter(FilterFunc, parallelism=3) - - transformation = datastream._environment.pipeline[0] - assert transformation.parallelism == 3 - - def test_filter_default_parallelism(self, datastream): - """Test filter uses default parallelism""" - - class FilterFunc(BaseFunction): - pass - - datastream.filter(FilterFunc) - - transformation = datastream._environment.pipeline[0] - assert transformation.parallelism == 1 - - -@pytest.mark.unit -class TestFlatMapTransformation: - """Test flatmap transformation""" - - def test_flatmap_with_function_class(self, datastream): - """Test flatmap with BaseFunction class""" - - class FlatMapFunction(BaseFunction): - def __call__(self, data): - return [data, data * 2, data * 3] - - result = datastream.flatmap(FlatMapFunction) - - assert len(datastream._environment.pipeline) == 1 - transformation = datastream._environment.pipeline[0] - assert transformation.__class__.__name__ == "FlatMapTransformation" - assert isinstance(result, DataStream) - - def test_flatmap_with_lambda(self, datastream): - """Test flatmap with lambda function""" - result = datastream.flatmap(lambda x: [x, x * 2]) - - assert len(datastream._environment.pipeline) == 1 - assert isinstance(result, DataStream) - - def test_flatmap_with_parallelism(self, datastream): - """Test flatmap respects parallelism parameter""" - - class FlatMapFunc(BaseFunction): - pass - - datastream.flatmap(FlatMapFunc, parallelism=5) - - transformation = datastream._environment.pipeline[0] - assert transformation.parallelism == 5 - - -@pytest.mark.unit -class TestSinkTransformation: - """Test sink transformation""" - - def test_sink_with_function_class(self, datastream): - """Test sink with BaseFunction class""" - - class SinkFunction(BaseFunction): - def __call__(self, data): - print(data) - - result = datastream.sink(SinkFunction) - - assert len(datastream._environment.pipeline) == 1 - transformation = datastream._environment.pipeline[0] - assert transformation.__class__.__name__ == "SinkTransformation" - - # Sink returns same datastream - assert result is datastream - - def test_sink_with_lambda(self, datastream): - """Test sink with lambda function""" - result = datastream.sink(lambda x: print(x)) - - assert len(datastream._environment.pipeline) == 1 - assert result is datastream - - def test_sink_returns_same_stream(self, datastream): - """Test sink returns same DataStream (terminal operation)""" - - class SinkFunc(BaseFunction): - pass - - result = datastream.sink(SinkFunc) - - # Sink returns self, not new stream - assert result is datastream - - def test_sink_with_parallelism(self, datastream): - """Test sink respects parallelism parameter""" - - class SinkFunc(BaseFunction): - pass - - datastream.sink(SinkFunc, parallelism=2) - - transformation = datastream._environment.pipeline[0] - assert transformation.parallelism == 2 - - -@pytest.mark.unit -class TestKeyByTransformation: - """Test keyby transformation""" - - def test_keyby_with_function_class(self, datastream): - """Test keyby with BaseFunction class""" - - class KeyFunction(BaseFunction): - def __call__(self, data): - return data % 10 - - result = datastream.keyby(KeyFunction) - - assert len(datastream._environment.pipeline) == 1 - transformation = datastream._environment.pipeline[0] - assert transformation.__class__.__name__ == "KeyByTransformation" - assert isinstance(result, DataStream) - - def test_keyby_with_lambda(self, datastream): - """Test keyby with lambda function""" - result = datastream.keyby(lambda x: x % 5) - - assert len(datastream._environment.pipeline) == 1 - assert isinstance(result, DataStream) - - def test_keyby_default_strategy(self, datastream): - """Test keyby uses default hash strategy""" - - class KeyFunc(BaseFunction): - pass - - datastream.keyby(KeyFunc) - - transformation = datastream._environment.pipeline[0] - # KeyByTransformation created successfully - assert transformation.__class__.__name__ == "KeyByTransformation" - - def test_keyby_custom_strategy(self, datastream): - """Test keyby with custom strategy""" - - class KeyFunc(BaseFunction): - pass - - datastream.keyby(KeyFunc, strategy="range") - - transformation = datastream._environment.pipeline[0] - # KeyByTransformation created successfully with custom strategy - assert transformation.__class__.__name__ == "KeyByTransformation" - - def test_keyby_with_parallelism(self, datastream): - """Test keyby respects parallelism parameter""" - - class KeyFunc(BaseFunction): - pass - - datastream.keyby(KeyFunc, parallelism=8) - - transformation = datastream._environment.pipeline[0] - assert transformation.parallelism == 8 - - -# ============================================================================ -# Stream Connection Tests -# ============================================================================ - - -@pytest.mark.unit -class TestStreamConnection: - """Test connect method for stream connections""" - - def test_connect_two_datastreams(self, local_env): - """Test connecting two DataStream instances""" - trans1 = MagicMock() - trans1.basename = "trans1" - trans1.__name__ = "Trans1" - trans1.function_class = MagicMock(__name__="FuncClass1") - trans1.env = local_env # ConnectedStreams needs env attribute - - trans2 = MagicMock() - trans2.basename = "trans2" - trans2.__name__ = "Trans2" - trans2.function_class = MagicMock(__name__="FuncClass2") - trans2.env = local_env # ConnectedStreams needs env attribute - - ds1 = DataStream(local_env, trans1) - ds2 = DataStream(local_env, trans2) - - result = ds1.connect(ds2) - - # Result should be ConnectedStreams - assert isinstance(result, ConnectedStreams) - assert result._environment is local_env - assert len(result.transformations) == 2 - assert result.transformations[0] is trans1 - assert result.transformations[1] is trans2 - - def test_connect_datastream_to_connected_streams(self, local_env): - """Test connecting DataStream to ConnectedStreams""" - trans1 = MagicMock() - trans1.basename = "trans1" - trans1.__name__ = "Trans1" - trans1.function_class = MagicMock(__name__="FuncClass1") - trans1.env = local_env - - trans2 = MagicMock() - trans2.basename = "trans2" - trans2.__name__ = "Trans2" - trans2.function_class = MagicMock(__name__="FuncClass2") - trans2.env = local_env - - trans3 = MagicMock() - trans3.basename = "trans3" - trans3.__name__ = "Trans3" - trans3.function_class = MagicMock(__name__="FuncClass3") - trans3.env = local_env - - ds1 = DataStream(local_env, trans1) - ds2 = DataStream(local_env, trans2) - - # Create connected streams - connected = ds1.connect(ds2) - assert len(connected.transformations) == 2 - - # Connect another stream - ds3 = DataStream(local_env, trans3) - result = ds3.connect(connected) - - assert isinstance(result, ConnectedStreams) - assert len(result.transformations) == 3 - assert result.transformations[0] is trans3 - assert result.transformations[1] is trans1 - assert result.transformations[2] is trans2 - - def test_connect_preserves_order(self, local_env): - """Test connect preserves transformation order""" - transformations = [] - for i in range(4): - t = MagicMock() - t.basename = f"trans{i}" - t.__name__ = f"Trans{i}" - t.function_class = MagicMock(__name__=f"FuncClass{i}") - t.env = local_env # ConnectedStreams needs env attribute - transformations.append(t) - - streams = [DataStream(local_env, t) for t in transformations] - - # Connect in sequence: ds0.connect(ds1).connect(ds2).connect(ds3) - result = streams[0].connect(streams[1]) - result = result.connect(streams[2]) - result = result.connect(streams[3]) - - # Order should be preserved - assert len(result.transformations) == 4 - for i, t in enumerate(result.transformations): - assert t is transformations[i] - - -# ============================================================================ -# Future Stream Tests -# ============================================================================ - - -@pytest.mark.unit -class TestFutureStream: - """Test fill_future for feedback loops""" - - def test_fill_future_success(self, local_env): - """Test successfully filling a future stream""" - from sage.kernel.api.transformation.future_transformation import ( - FutureTransformation, - ) - - # Create future transformation - future_trans = FutureTransformation(local_env, "feedback_loop") - future_stream = DataStream(local_env, future_trans) - - # Create source stream - source_trans = MagicMock() - source_trans.basename = "source" - source_trans.__name__ = "SourceTrans" - source_trans.function_class = MagicMock(__name__="SourceFunc") - source_stream = DataStream(local_env, source_trans) - - # Fill future - source_stream.fill_future(future_stream) - - # Verify future is filled - assert future_trans.filled is True - - def test_fill_future_with_non_future_raises_error(self, datastream): - """Test fill_future raises error if target is not future stream""" - # Create regular transformation - regular_trans = MagicMock() - regular_trans.basename = "regular" - regular_trans.__name__ = "RegularTrans" - regular_trans.function_class = MagicMock(__name__="RegularFunc") - regular_stream = DataStream(datastream._environment, regular_trans) - - # Should raise ValueError - with pytest.raises(ValueError, match="Target stream must be a future stream"): - datastream.fill_future(regular_stream) - - def test_fill_future_already_filled_raises_error(self, local_env): - """Test fill_future raises error if future already filled""" - from sage.kernel.api.transformation.future_transformation import ( - FutureTransformation, - ) - - # Create and fill future - future_trans = FutureTransformation(local_env, "test_future") - future_stream = DataStream(local_env, future_trans) - - source1 = MagicMock() - source1.basename = "source1" - source1.__name__ = "Source1" - source1.function_class = MagicMock(__name__="SourceFunc1") - stream1 = DataStream(local_env, source1) - - # Fill once - stream1.fill_future(future_stream) - - # Try to fill again - should raise RuntimeError - source2 = MagicMock() - source2.basename = "source2" - source2.__name__ = "Source2" - source2.function_class = MagicMock(__name__="SourceFunc2") - stream2 = DataStream(local_env, source2) - - with pytest.raises(RuntimeError, match="has already been filled"): - stream2.fill_future(future_stream) - - -# ============================================================================ -# Helper Methods Tests -# ============================================================================ - - -@pytest.mark.unit -class TestPrintHelper: - """Test print helper method""" - - def test_print_default_params(self, datastream): - """Test print with default parameters""" - result = datastream.print() - - # Should create sink transformation - assert len(datastream._environment.pipeline) == 1 - transformation = datastream._environment.pipeline[0] - assert transformation.__class__.__name__ == "SinkTransformation" - - # Print returns new stream (for chaining) - assert isinstance(result, DataStream) - - def test_print_with_prefix(self, datastream): - """Test print with custom prefix""" - result = datastream.print(prefix="DEBUG: ") - - assert len(datastream._environment.pipeline) == 1 - assert isinstance(result, DataStream) - - def test_print_with_separator(self, datastream): - """Test print with custom separator""" - datastream.print(separator=" -> ") - - assert len(datastream._environment.pipeline) == 1 - - def test_print_with_colored(self, datastream): - """Test print with colored parameter""" - datastream.print(colored=False) - - assert len(datastream._environment.pipeline) == 1 - - def test_print_chainable(self, datastream): - """Test print is chainable""" - - class MapFunc(BaseFunction): - pass - - result = datastream.print("Step 1: ").map(MapFunc).print("Step 2: ") - - # Should have 3 transformations: print, map, print - assert len(datastream._environment.pipeline) == 3 - assert isinstance(result, DataStream) - - -# ============================================================================ -# Internal Methods Tests -# ============================================================================ - - -@pytest.mark.unit -class TestInternalMethods: - """Test internal DataStream methods""" - - def test_apply_adds_upstream(self, datastream, mock_function_class): - """Test _apply connects transformation to upstream""" - # Create new transformation - new_trans = MagicMock() - new_trans.add_upstream = MagicMock() - new_trans.__name__ = "NewTransformation" - new_trans.function_class = MagicMock(__name__="NewFunc") - - # Apply it - datastream._apply(new_trans) - - # Verify upstream was added - new_trans.add_upstream.assert_called_once_with(datastream.transformation, input_index=0) - - def test_apply_adds_to_pipeline(self, datastream): - """Test _apply adds transformation to environment pipeline""" - initial_count = len(datastream._environment.pipeline) - - new_trans = MagicMock() - new_trans.add_upstream = MagicMock() - new_trans.__name__ = "NewTransformation" - new_trans.function_class = MagicMock(__name__="NewFunc") - - datastream._apply(new_trans) - - assert len(datastream._environment.pipeline) == initial_count + 1 - assert datastream._environment.pipeline[-1] is new_trans - - def test_apply_returns_new_datastream(self, datastream): - """Test _apply returns new DataStream instance""" - new_trans = MagicMock() - new_trans.add_upstream = MagicMock() - new_trans.__name__ = "NewTransformation" - new_trans.function_class = MagicMock(__name__="NewFunc") - - result = datastream._apply(new_trans) - - assert isinstance(result, DataStream) - assert result is not datastream - assert result.transformation is new_trans - - def test_get_transformation_classes_caches_imports(self, datastream): - """Test _get_transformation_classes caches imports""" - # First call - classes1 = datastream._get_transformation_classes() - - # Second call - classes2 = datastream._get_transformation_classes() - - # Should return same cached dict - assert classes1 is classes2 - - def test_get_transformation_classes_includes_expected_types(self, datastream): - """Test _get_transformation_classes includes all expected types""" - classes = datastream._get_transformation_classes() - - expected_keys = [ - "BaseTransformation", - "FilterTransformation", - "FlatMapTransformation", - "MapTransformation", - "SinkTransformation", - "SourceTransformation", - "KeyByTransformation", - ] - - for key in expected_keys: - assert key in classes - - -# ============================================================================ -# Chaining and Integration Tests -# ============================================================================ - - -@pytest.mark.unit -class TestTransformationChaining: - """Test chaining multiple transformations""" - - def test_chain_multiple_transformations(self, local_env): - """Test chaining multiple transformation methods""" - - class SourceFunc(BaseFunction): - pass - - class MapFunc(BaseFunction): - pass - - class FilterFunc(BaseFunction): - pass - - class SinkFunc(BaseFunction): - pass - - # Create source - with patch("sage.kernel.api.datastream.DataStream", wraps=DataStream): - stream = local_env.from_source(SourceFunc) - - # Chain transformations - ( - stream.map(MapFunc) - .filter(FilterFunc) - .flatmap(lambda x: [x, x * 2]) - .keyby(lambda x: x % 10) - .sink(SinkFunc) - ) - - # Pipeline should have all transformations - # from_source adds SourceTransformation - # Then: map, filter, flatmap, keyby, sink - assert len(local_env.pipeline) >= 6 - - def test_parallel_branches(self, local_env): - """Test creating parallel branches from same source""" - - class SourceFunc(BaseFunction): - pass - - class MapFunc1(BaseFunction): - pass - - class MapFunc2(BaseFunction): - pass - - with patch("sage.kernel.api.datastream.DataStream", wraps=DataStream): - source = local_env.from_source(SourceFunc) - - # Create two branches - source.map(MapFunc1) - source.map(MapFunc2) - - # Both branches should exist in pipeline - # Pipeline: SourceTransformation, MapTransformation (branch1), MapTransformation (branch2) - assert len(local_env.pipeline) >= 3 - - def test_multiple_sinks(self, local_env): - """Test multiple sink operations""" - - class SourceFunc(BaseFunction): - pass - - class SinkFunc1(BaseFunction): - pass - - class SinkFunc2(BaseFunction): - pass - - with patch("sage.kernel.api.datastream.DataStream", wraps=DataStream): - stream = local_env.from_source(SourceFunc) - - # Add multiple sinks - stream.sink(SinkFunc1) - stream.sink(SinkFunc2) - - # Should have source + 2 sinks - assert len(local_env.pipeline) >= 3 - - -# ============================================================================ -# Edge Cases and Error Handling -# ============================================================================ - - -@pytest.mark.unit -class TestEdgeCases: - """Test edge cases and error handling""" - - def test_transformation_with_no_parallelism_uses_default(self, datastream): - """Test transformation without parallelism parameter uses default""" - - class TestFunc(BaseFunction): - pass - - datastream.map(TestFunc) - - transformation = datastream._environment.pipeline[0] - assert transformation.parallelism == 1 - - def test_transformation_with_zero_parallelism(self, datastream): - """Test transformation with parallelism=0 (edge case)""" - - class TestFunc(BaseFunction): - pass - - # parallelism=0 should still be accepted (even if unusual) - datastream.map(TestFunc, parallelism=0) - - transformation = datastream._environment.pipeline[0] - assert transformation.parallelism == 0 - - def test_transformation_with_none_args(self, datastream): - """Test transformation with None as argument""" - - class TestFunc(BaseFunction): - pass - - datastream.map(TestFunc, None, key=None) - - # Should complete without error - assert len(datastream._environment.pipeline) == 1 - - def test_empty_lambda_function(self, datastream): - """Test transformation with minimal lambda""" - datastream.map(lambda x: x) - - assert len(datastream._environment.pipeline) == 1 - - def test_complex_lambda_function(self, datastream): - """Test transformation with complex lambda""" - datastream.filter(lambda x: x > 10 and x < 100 and x % 2 == 0) - - assert len(datastream._environment.pipeline) == 1 - - def test_keyby_with_multiple_strategies(self, datastream): - """Test keyby with different strategy values""" - - class KeyFunc(BaseFunction): - pass - - for strategy in ["hash", "range", "custom"]: - env = LocalEnvironment(name="test") - trans = MagicMock() - trans.__name__ = "MockTransformation" - trans.function_class = MagicMock(__name__="MockFunc") - ds = DataStream(env, trans) - - ds.keyby(KeyFunc, strategy=strategy) - - transformation = ds._environment.pipeline[0] - # KeyByTransformation created with specified strategy - assert transformation.__class__.__name__ == "KeyByTransformation" - - -@pytest.mark.unit -class TestTypeResolution: - """Test type parameter resolution""" - - def test_resolve_type_param_without_explicit_type(self, datastream): - """Test type resolution when no explicit type provided""" - # Should fall back to Any - from typing import Any - - assert datastream._type_param == Any or datastream._type_param is not None - - def test_multiple_datastreams_independent_types(self, local_env): - """Test multiple DataStreams have independent type parameters""" - trans1 = MagicMock() - trans1.__name__ = "Trans1" - trans1.function_class = MagicMock(__name__="Func1") - trans2 = MagicMock() - trans2.__name__ = "Trans2" - trans2.function_class = MagicMock(__name__="Func2") - - ds1 = DataStream(local_env, trans1) - ds2 = DataStream(local_env, trans2) - - # Each should have its own type parameter - assert hasattr(ds1, "_type_param") - assert hasattr(ds2, "_type_param") diff --git a/packages/sage-kernel/tests/unit/kernel/api/test_local_environment.py b/packages/sage-kernel/tests/unit/kernel/api/test_local_environment.py deleted file mode 100644 index eb4c3fba93..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/api/test_local_environment.py +++ /dev/null @@ -1,623 +0,0 @@ -""" -Unit tests for BaseEnvironment and LocalEnvironment classes. - -Tests cover: -- BaseEnvironment initialization and configuration -- LocalEnvironment initialization -- Service registration -- Scheduler initialization (FIFO, LoadAware, custom) -- from_source/from_batch/from_future methods -- Console log level configuration -- Pipeline management -- JobManager integration -""" - -from unittest.mock import MagicMock, patch - -import pytest - -from sage.common.core import BaseFunction -from sage.kernel.api.local_environment import LocalEnvironment -from sage.kernel.runtime.factory.service_factory import ServiceFactory -from sage.kernel.scheduler.api import BaseScheduler -from sage.kernel.scheduler.impl import FIFOScheduler, LoadAwareScheduler - -# ============================================================================ -# Test Fixtures -# ============================================================================ - - -@pytest.fixture -def mock_jobmanager(): - """Mock JobManager for environment testing""" - jobmanager = MagicMock() - jobmanager.submit_job = MagicMock(return_value="test-uuid-123") - jobmanager.jobs = {} - return jobmanager - - -@pytest.fixture -def local_env(mock_jobmanager): - """Create LocalEnvironment instance with mocked JobManager""" - env = LocalEnvironment(name="test_env", config={"test_key": "test_value"}) - env._jobmanager = mock_jobmanager - return env - - -@pytest.fixture -def base_env_config(): - """Standard configuration for environment testing""" - return { - "engine_host": "localhost", - "engine_port": 19000, - "buffer_size": 1000, - } - - -@pytest.fixture -def mock_transformation(): - """Mock transformation for testing""" - transformation = MagicMock() - transformation.basename = "mock_transformation" - transformation.function_class = MagicMock(__name__="MockFunction") - return transformation - - -@pytest.fixture -def mock_datastream_class(): - """Mock DataStream class for testing""" - with patch("sage.kernel.api.base_environment.DataStream") as mock_ds: - yield mock_ds - - -class MockService: - """Mock service class for testing service registration""" - - def __init__(self, config=None, **kwargs): - self.config = config - self.kwargs = kwargs - self.started = False - - def start(self): - self.started = True - - def stop(self): - self.started = False - - -class CustomScheduler(BaseScheduler): - """Custom scheduler for testing""" - - def __init__(self): - super().__init__() - self.custom_initialized = True - - def make_decision(self, graph): - """Implement abstract method""" - return {} - - def schedule_task(self, task): - return "custom_worker" - - -# ============================================================================ -# BaseEnvironment Tests -# ============================================================================ - - -@pytest.mark.unit -class TestBaseEnvironmentInitialization: - """Test BaseEnvironment initialization and configuration""" - - def test_init_with_minimal_config(self): - """Test initialization with minimal configuration""" - env = LocalEnvironment(name="minimal_env") - - assert env.name == "minimal_env" - assert env.config == {} - assert env.platform == "local" - assert env.pipeline == [] - assert env.service_factories == {} - assert env.enable_monitoring is False - assert env.console_log_level == "INFO" - - def test_init_with_full_config(self, base_env_config): - """Test initialization with full configuration""" - env = LocalEnvironment( - name="full_env", - config=base_env_config, - scheduler="fifo", - enable_monitoring=True, - ) - - assert env.name == "full_env" - assert env.config == base_env_config - assert env.config["engine_host"] == "localhost" - assert env.config["engine_port"] == 19000 - assert env.enable_monitoring is True - - def test_init_preserves_config_dict(self, base_env_config): - """Test that initialization creates independent config copy""" - env = LocalEnvironment(name="test_env", config=base_env_config) - - # Modify environment config - env.config["new_key"] = "new_value" - - # Original config should be unchanged - assert "new_key" not in base_env_config - - def test_init_with_none_config(self): - """Test initialization with None config creates empty dict""" - env = LocalEnvironment(name="test_env", config=None) - - assert env.config == {} - assert isinstance(env.config, dict) - - -@pytest.mark.unit -class TestSchedulerInitialization: - """Test scheduler initialization and configuration""" - - def test_default_fifo_scheduler(self): - """Test default FIFO scheduler initialization""" - env = LocalEnvironment(name="test_env", scheduler=None) - - assert env.scheduler is not None - assert isinstance(env.scheduler, FIFOScheduler) - assert env.scheduler.platform == "local" - - def test_fifo_scheduler_by_name(self): - """Test FIFO scheduler initialization by string name""" - env = LocalEnvironment(name="test_env", scheduler="fifo") - - assert isinstance(env.scheduler, FIFOScheduler) - assert env.scheduler.platform == "local" - - def test_load_aware_scheduler_by_name(self): - """Test LoadAware scheduler initialization""" - for scheduler_name in ["load_aware", "loadaware"]: - env = LocalEnvironment(name="test_env", scheduler=scheduler_name) - - assert isinstance(env.scheduler, LoadAwareScheduler) - assert env.scheduler.platform == "local" - - def test_custom_scheduler_instance(self): - """Test custom scheduler instance initialization""" - custom = CustomScheduler() - env = LocalEnvironment(name="test_env", scheduler=custom) - - assert env.scheduler is custom - assert env.scheduler.custom_initialized is True - - def test_invalid_scheduler_name_raises_error(self): - """Test that invalid scheduler name raises ValueError""" - with pytest.raises(ValueError, match="Unknown scheduler type"): - LocalEnvironment(name="test_env", scheduler="invalid_scheduler") - - def test_invalid_scheduler_type_raises_error(self): - """Test that invalid scheduler type raises TypeError""" - with pytest.raises(TypeError, match="scheduler must be None, str, or BaseScheduler"): - LocalEnvironment(name="test_env", scheduler=123) - - -@pytest.mark.unit -class TestConsoleLogLevel: - """Test console log level configuration""" - - def test_default_log_level(self): - """Test default console log level""" - env = LocalEnvironment(name="test_env") - assert env.console_log_level == "INFO" - - def test_set_valid_log_levels(self): - """Test setting valid log levels""" - env = LocalEnvironment(name="test_env") - - for level in ["DEBUG", "INFO", "WARNING", "ERROR"]: - env.set_console_log_level(level) - assert env.console_log_level == level - - # Test case-insensitive - env.set_console_log_level(level.lower()) - assert env.console_log_level == level - - def test_set_invalid_log_level_raises_error(self): - """Test that invalid log level raises ValueError""" - env = LocalEnvironment(name="test_env") - - with pytest.raises(ValueError, match="Invalid log level"): - env.set_console_log_level("INVALID") - - def test_log_level_updates_existing_logger(self): - """Test that changing log level updates existing logger""" - env = LocalEnvironment(name="test_env") - - # Access logger to initialize it - _ = env.logger - - # Mock the logger's update method - with patch.object(env.logger, "update_output_level") as mock_update: - env.set_console_log_level("DEBUG") - - mock_update.assert_called_once_with("console", "DEBUG") - - -@pytest.mark.unit -class TestServiceRegistration: - """Test service registration functionality""" - - def test_register_service_basic(self, local_env): - """Test basic service registration""" - service_name = "test_service" - service = local_env.register_service(service_name, MockService, config={"key": "value"}) - - assert service_name in local_env.service_factories - assert isinstance(service, ServiceFactory) - assert local_env.service_factories[service_name].service_name == service_name - assert local_env.service_factories[service_name].service_class == MockService - - def test_register_service_with_args(self, local_env): - """Test service registration with positional arguments""" - service_name = "arg_service" - local_env.register_service(service_name, MockService, "arg1", "arg2", key="value") - - factory = local_env.service_factories[service_name] - assert factory.service_args == ("arg1", "arg2") - assert factory.service_kwargs == {"key": "value"} - - def test_register_service_overwrites_existing(self, local_env): - """Test that re-registering service overwrites previous""" - service_name = "overwrite_service" - - # Register first service - local_env.register_service(service_name, MockService, config="v1") - first_factory = local_env.service_factories[service_name] - - # Register second service with same name - local_env.register_service(service_name, MockService, config="v2") - second_factory = local_env.service_factories[service_name] - - assert first_factory is not second_factory - assert second_factory.service_kwargs == {"config": "v2"} - - def test_register_service_factory_directly(self, local_env): - """Test registering ServiceFactory instance directly""" - service_name = "factory_service" - factory = ServiceFactory( - service_name=service_name, - service_class=MockService, - service_args=(), - service_kwargs={"test": "value"}, - ) - - local_env.register_service_factory(service_name, factory) - - assert local_env.service_factories[service_name] is factory - - -@pytest.mark.unit -class TestDataSourceCreation: - """Test data source creation methods""" - - @patch("sage.kernel.api.datastream.DataStream") - def test_from_source_with_function_class(self, mock_datastream, local_env): - """Test from_source with BaseFunction class""" - - class MockSourceFunction(BaseFunction): - def __call__(self, ctx): - return [1, 2, 3] - - local_env.from_source(MockSourceFunction) - - # Verify transformation was added to pipeline - assert len(local_env.pipeline) == 1 - transformation = local_env.pipeline[0] - assert transformation.__class__.__name__ == "SourceTransformation" - - # Verify DataStream was created - mock_datastream.assert_called_once() - - @patch("sage.kernel.api.datastream.DataStream") - def test_from_source_with_lambda(self, mock_datastream, local_env): - """Test from_source with lambda function""" - source_func = lambda ctx: [1, 2, 3] # noqa: E731 - - local_env.from_source(source_func) - - # Verify transformation was added - assert len(local_env.pipeline) == 1 - transformation = local_env.pipeline[0] - assert transformation.__class__.__name__ == "SourceTransformation" - - @patch("sage.kernel.api.datastream.DataStream") - def test_from_batch_with_list(self, mock_datastream, local_env): - """Test from_batch with list data""" - data = [1, 2, 3, 4, 5] - local_env.from_batch(data) - - # Verify BatchTransformation was created - assert len(local_env.pipeline) == 1 - transformation = local_env.pipeline[0] - assert transformation.__class__.__name__ == "BatchTransformation" - - @patch("sage.kernel.api.datastream.DataStream") - def test_from_batch_with_tuple(self, mock_datastream, local_env): - """Test from_batch with tuple data""" - data = (10, 20, 30) - local_env.from_batch(data) - - assert len(local_env.pipeline) == 1 - assert local_env.pipeline[0].__class__.__name__ == "BatchTransformation" - - @patch("sage.kernel.api.datastream.DataStream") - def test_from_batch_with_function_class(self, mock_datastream, local_env): - """Test from_batch with custom function class""" - - class CustomBatchFunction(BaseFunction): - def get_data_iterator(self): - return iter(range(10)) - - def get_total_count(self): - return 10 - - local_env.from_batch(CustomBatchFunction, custom_arg="value") - - assert len(local_env.pipeline) == 1 - transformation = local_env.pipeline[0] - assert transformation.__class__.__name__ == "BatchTransformation" - - @patch("sage.kernel.api.datastream.DataStream") - def test_from_batch_with_iterable(self, mock_datastream, local_env): - """Test from_batch with various iterable types""" - # Test with range - local_env.from_batch(range(100)) - assert len(local_env.pipeline) == 1 - - # Test with set - local_env.from_batch({1, 2, 3}) - assert len(local_env.pipeline) == 2 - - # Test with generator - local_env.from_batch(x for x in range(5)) - assert len(local_env.pipeline) == 3 - - @patch("sage.kernel.api.datastream.DataStream") - def test_from_batch_with_string(self, mock_datastream, local_env): - """Test from_batch with string (character iteration)""" - local_env.from_batch("hello") - - assert len(local_env.pipeline) == 1 - assert local_env.pipeline[0].__class__.__name__ == "BatchTransformation" - - def test_from_batch_with_non_iterable_raises_error(self, local_env): - """Test from_batch with non-iterable raises TypeError""" - with pytest.raises(TypeError, match="Unsupported source type"): - local_env.from_batch(12345) - - @patch("sage.kernel.api.datastream.DataStream") - def test_from_future(self, mock_datastream, local_env): - """Test from_future creates FutureTransformation""" - future_name = "feedback_loop" - local_env.from_future(future_name) - - # Verify FutureTransformation was created - assert len(local_env.pipeline) == 1 - transformation = local_env.pipeline[0] - assert transformation.__class__.__name__ == "FutureTransformation" - - -@pytest.mark.unit -class TestLocalEnvironmentSubmit: - """Test LocalEnvironment submit functionality""" - - def test_submit_without_autostop(self, local_env, mock_jobmanager): - """Test submit without autostop returns immediately""" - local_env._jobmanager = mock_jobmanager - - env_uuid = local_env.submit(autostop=False) - - # Verify submit_job was called - mock_jobmanager.submit_job.assert_called_once_with(local_env, autostop=False) - assert env_uuid == "test-uuid-123" - - def test_submit_with_autostop(self, local_env, mock_jobmanager): - """Test submit with autostop waits for completion""" - local_env._jobmanager = mock_jobmanager - - # Mock _wait_for_completion to avoid blocking - with patch.object(local_env, "_wait_for_completion") as mock_wait: - env_uuid = local_env.submit(autostop=True) - - mock_jobmanager.submit_job.assert_called_once_with(local_env, autostop=True) - mock_wait.assert_called_once() - assert env_uuid == "test-uuid-123" - - def test_wait_for_completion_job_deleted(self, local_env, mock_jobmanager): - """Test _wait_for_completion when job is deleted""" - local_env._jobmanager = mock_jobmanager - local_env.env_uuid = "test-uuid" - - # Job not found (deleted) - modify the jobs dict directly - mock_jobmanager.jobs = {"other-uuid": MagicMock()} - - # Should return immediately - local_env._wait_for_completion() - - def test_wait_for_completion_job_stopped(self, local_env, mock_jobmanager): - """Test _wait_for_completion when job status is stopped""" - local_env._jobmanager = mock_jobmanager - local_env.env_uuid = "test-uuid" - - # Mock job info - job_info = MagicMock() - job_info.status = "stopped" - mock_jobmanager.jobs = {"test-uuid": job_info} - - local_env._wait_for_completion() - - def test_wait_for_completion_no_env_uuid(self, local_env): - """Test _wait_for_completion with no environment UUID""" - local_env.env_uuid = None - - # Should log warning and return - local_env._wait_for_completion() - - -@pytest.mark.unit -class TestEnvironmentProperties: - """Test environment properties and lazy initialization""" - - def test_logger_property_lazy_initialization(self, local_env): - """Test logger is lazily initialized""" - assert not hasattr(local_env, "_logger") - - logger = local_env.logger - - assert hasattr(local_env, "_logger") - assert logger is not None - - def test_logger_property_reuses_instance(self, local_env): - """Test logger property returns same instance""" - logger1 = local_env.logger - logger2 = local_env.logger - - assert logger1 is logger2 - - def test_client_property_creates_jobmanager_client(self, local_env): - """Test client property creates JobManagerClient""" - assert local_env._engine_client is None - - with patch("sage.kernel.api.base_environment.JobManagerClient") as mock_client_class: - mock_instance = MagicMock() - mock_client_class.return_value = mock_instance - - client = local_env.client - - # Verify client was created with default host/port - mock_client_class.assert_called_once_with(host="127.0.0.1", port=19000) - assert client is mock_instance - - def test_client_property_uses_config_host_port(self, base_env_config): - """Test client property uses config host and port""" - env = LocalEnvironment(name="test_env", config=base_env_config) - - with patch("sage.kernel.api.base_environment.JobManagerClient") as mock_client_class: - _ = env.client - - mock_client_class.assert_called_once_with(host="localhost", port=19000) - - def test_scheduler_property(self, local_env): - """Test scheduler property returns scheduler instance""" - assert local_env.scheduler is not None - assert isinstance(local_env.scheduler, FIFOScheduler) - - -@pytest.mark.unit -class TestPipelineManagement: - """Test pipeline management methods""" - - def test_append_transformation(self, local_env, mock_transformation): - """Test _append adds transformation to pipeline""" - assert len(local_env.pipeline) == 0 - - with patch("sage.kernel.api.datastream.DataStream") as mock_ds: - local_env._append(mock_transformation) - - assert len(local_env.pipeline) == 1 - assert local_env.pipeline[0] is mock_transformation - mock_ds.assert_called_once() - - def test_multiple_transformations_in_pipeline(self, local_env): - """Test multiple transformations can be added to pipeline""" - transformations = [MagicMock() for _ in range(5)] - - with patch("sage.kernel.api.datastream.DataStream"): - for t in transformations: - local_env._append(t) - - assert len(local_env.pipeline) == 5 - assert local_env.pipeline == transformations - - -@pytest.mark.unit -class TestEnvironmentInheritance: - """Test LocalEnvironment inheritance from BaseEnvironment""" - - def test_local_environment_sets_platform(self): - """Test LocalEnvironment sets platform to 'local'""" - env = LocalEnvironment(name="test_env") - assert env.platform == "local" - - def test_local_environment_no_engine_client(self): - """Test LocalEnvironment sets _engine_client to None""" - env = LocalEnvironment(name="test_env") - assert env._engine_client is None - - def test_local_environment_inherits_methods(self, local_env): - """Test LocalEnvironment inherits BaseEnvironment methods""" - # Test inherited methods exist - assert hasattr(local_env, "from_source") - assert hasattr(local_env, "from_batch") - assert hasattr(local_env, "from_future") - assert hasattr(local_env, "register_service") - assert hasattr(local_env, "set_console_log_level") - - -# ============================================================================ -# Edge Cases and Error Handling -# ============================================================================ - - -@pytest.mark.unit -class TestEdgeCases: - """Test edge cases and error handling""" - - def test_empty_pipeline_submission(self, local_env, mock_jobmanager): - """Test submitting environment with empty pipeline""" - local_env._jobmanager = mock_jobmanager - assert len(local_env.pipeline) == 0 - - env_uuid = local_env.submit(autostop=False) - - # Should still submit successfully - mock_jobmanager.submit_job.assert_called_once() - assert env_uuid == "test-uuid-123" - - def test_service_registration_with_no_args(self, local_env): - """Test service registration with no arguments""" - service_name = "minimal_service" - local_env.register_service(service_name, MockService) - - factory = local_env.service_factories[service_name] - assert factory.service_args == () - assert factory.service_kwargs == {} - - def test_multiple_from_source_creates_multiple_transformations(self, local_env): - """Test multiple from_source calls create independent transformations""" - - class Source1(BaseFunction): - pass - - class Source2(BaseFunction): - pass - - with patch("sage.kernel.api.datastream.DataStream"): - local_env.from_source(Source1) - local_env.from_source(Source2) - - assert len(local_env.pipeline) == 2 - assert local_env.pipeline[0].function_class != local_env.pipeline[1].function_class - - def test_config_modification_after_init(self, local_env): - """Test config can be modified after initialization""" - local_env.config["new_setting"] = "new_value" - - assert local_env.config["new_setting"] == "new_value" - - def test_enable_monitoring_flag(self): - """Test enable_monitoring flag is properly set""" - env_disabled = LocalEnvironment(name="env1", enable_monitoring=False) - env_enabled = LocalEnvironment(name="env2", enable_monitoring=True) - - assert env_disabled.enable_monitoring is False - assert env_enabled.enable_monitoring is True diff --git a/packages/sage-kernel/tests/unit/kernel/fault_tolerance/__init__.py b/packages/sage-kernel/tests/unit/kernel/fault_tolerance/__init__.py deleted file mode 100644 index 32ecdcee39..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/fault_tolerance/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Tests for fault_tolerance module""" diff --git a/packages/sage-kernel/tests/unit/kernel/fault_tolerance/test_checkpoint_impl.py b/packages/sage-kernel/tests/unit/kernel/fault_tolerance/test_checkpoint_impl.py deleted file mode 100644 index 3ada60f26b..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/fault_tolerance/test_checkpoint_impl.py +++ /dev/null @@ -1,587 +0,0 @@ -""" -Unit tests for CheckpointManagerImpl. - -Tests cover: -- Checkpoint save and load operations -- Checkpoint deletion (single and batch) -- List checkpoints -- Cleanup old checkpoints -- Checkpoint info retrieval -- Error handling and edge cases -""" - -import pickle -import tempfile -import time -from pathlib import Path -from unittest.mock import patch - -import pytest - -from sage.common.core import CheckpointError -from sage.kernel.fault_tolerance.impl.checkpoint_impl import CheckpointManagerImpl - -# ============================================================================ -# Test Fixtures -# ============================================================================ - - -@pytest.fixture -def temp_checkpoint_dir(): - """Create temporary directory for checkpoint testing""" - with tempfile.TemporaryDirectory() as tmpdir: - yield tmpdir - - -@pytest.fixture -def checkpoint_manager(temp_checkpoint_dir): - """Create CheckpointManagerImpl instance with temporary directory""" - return CheckpointManagerImpl(checkpoint_dir=temp_checkpoint_dir) - - -@pytest.fixture -def sample_state(): - """Sample state dictionary for testing""" - return { - "counter": 42, - "data": [1, 2, 3, 4, 5], - "config": {"key": "value", "enabled": True}, - } - - -# ============================================================================ -# Initialization Tests -# ============================================================================ - - -@pytest.mark.unit -class TestCheckpointManagerInitialization: - """Test CheckpointManagerImpl initialization""" - - def test_init_creates_directory(self, temp_checkpoint_dir): - """Test that initialization creates checkpoint directory""" - ckpt_dir = Path(temp_checkpoint_dir) / "test_checkpoints" - assert not ckpt_dir.exists() - - manager = CheckpointManagerImpl(checkpoint_dir=str(ckpt_dir)) - - assert ckpt_dir.exists() - assert ckpt_dir.is_dir() - assert manager.checkpoint_dir == ckpt_dir - - def test_init_with_existing_directory(self, temp_checkpoint_dir): - """Test initialization with existing directory""" - ckpt_dir = Path(temp_checkpoint_dir) - assert ckpt_dir.exists() - - manager = CheckpointManagerImpl(checkpoint_dir=str(ckpt_dir)) - - assert manager.checkpoint_dir == ckpt_dir - - def test_init_default_directory(self): - """Test initialization with default directory""" - manager = CheckpointManagerImpl() - - assert manager.checkpoint_dir == Path(".sage/checkpoints") - - -# ============================================================================ -# Save Checkpoint Tests -# ============================================================================ - - -@pytest.mark.unit -class TestSaveCheckpoint: - """Test save_checkpoint functionality""" - - def test_save_checkpoint_basic(self, checkpoint_manager, sample_state): - """Test basic checkpoint save""" - task_id = "task_001" - checkpoint_id = "ckpt_1" - - result_path = checkpoint_manager.save_checkpoint(task_id, sample_state, checkpoint_id) - - # Verify return path - assert result_path is not None - assert Path(result_path).exists() - - # Verify file content - with open(result_path, "rb") as f: - loaded_state = pickle.load(f) - - assert loaded_state == sample_state - - def test_save_checkpoint_auto_id(self, checkpoint_manager, sample_state): - """Test save checkpoint with auto-generated ID""" - task_id = "task_002" - - with patch("time.time", return_value=1234567890): - result_path = checkpoint_manager.save_checkpoint(task_id, sample_state) - - # Should use timestamp as ID - assert "task_002_1234567890.ckpt" in result_path - assert Path(result_path).exists() - - def test_save_checkpoint_overwrites_existing(self, checkpoint_manager, sample_state): - """Test that saving with same ID overwrites existing checkpoint""" - task_id = "task_003" - checkpoint_id = "ckpt_1" - - # Save first version - path1 = checkpoint_manager.save_checkpoint(task_id, {"version": 1}, checkpoint_id) - - # Save second version with same ID - path2 = checkpoint_manager.save_checkpoint(task_id, {"version": 2}, checkpoint_id) - - # Paths should be the same - assert path1 == path2 - - # Load and verify it's the second version - loaded = checkpoint_manager.load_checkpoint(task_id, checkpoint_id) - assert loaded == {"version": 2} - - def test_save_checkpoint_multiple_tasks(self, checkpoint_manager, sample_state): - """Test saving checkpoints for multiple tasks""" - checkpoints = [ - ("task_a", "ckpt_1", {"data": "a1"}), - ("task_b", "ckpt_1", {"data": "b1"}), - ("task_a", "ckpt_2", {"data": "a2"}), - ] - - paths = [] - for task_id, ckpt_id, state in checkpoints: - path = checkpoint_manager.save_checkpoint(task_id, state, ckpt_id) - paths.append(path) - - # All paths should be different - assert len(set(paths)) == 3 - - # Verify each can be loaded correctly - assert checkpoint_manager.load_checkpoint("task_a", "ckpt_1") == {"data": "a1"} - assert checkpoint_manager.load_checkpoint("task_b", "ckpt_1") == {"data": "b1"} - assert checkpoint_manager.load_checkpoint("task_a", "ckpt_2") == {"data": "a2"} - - def test_save_checkpoint_error_handling(self, checkpoint_manager): - """Test error handling during checkpoint save""" - task_id = "task_error" - - # Create invalid state (unpicklable object) - class UnpicklableClass: - def __reduce__(self): - raise TypeError("Cannot pickle this object") - - invalid_state = {"obj": UnpicklableClass()} - - with pytest.raises(CheckpointError, match="Failed to save checkpoint"): - checkpoint_manager.save_checkpoint(task_id, invalid_state, "ckpt_1") - - -# ============================================================================ -# Load Checkpoint Tests -# ============================================================================ - - -@pytest.mark.unit -class TestLoadCheckpoint: - """Test load_checkpoint functionality""" - - def test_load_checkpoint_by_id(self, checkpoint_manager, sample_state): - """Test loading checkpoint by specific ID""" - task_id = "task_load_1" - checkpoint_id = "ckpt_1" - - # Save checkpoint - checkpoint_manager.save_checkpoint(task_id, sample_state, checkpoint_id) - - # Load checkpoint - loaded_state = checkpoint_manager.load_checkpoint(task_id, checkpoint_id) - - assert loaded_state == sample_state - - def test_load_checkpoint_latest(self, checkpoint_manager): - """Test loading latest checkpoint when ID not specified""" - task_id = "task_load_2" - - # Save multiple checkpoints with time delay - checkpoint_manager.save_checkpoint(task_id, {"version": 1}, "ckpt_1") - time.sleep(0.01) # Small delay to ensure different mtimes - checkpoint_manager.save_checkpoint(task_id, {"version": 2}, "ckpt_2") - time.sleep(0.01) - checkpoint_manager.save_checkpoint(task_id, {"version": 3}, "ckpt_3") - - # Load without specifying ID (should get latest) - loaded_state = checkpoint_manager.load_checkpoint(task_id) - - assert loaded_state == {"version": 3} - - def test_load_checkpoint_nonexistent_task(self, checkpoint_manager): - """Test loading checkpoint for non-existent task""" - result = checkpoint_manager.load_checkpoint("nonexistent_task", "ckpt_1") - - assert result is None - - def test_load_checkpoint_nonexistent_id(self, checkpoint_manager, sample_state): - """Test loading checkpoint with non-existent ID""" - task_id = "task_load_3" - - # Save one checkpoint - checkpoint_manager.save_checkpoint(task_id, sample_state, "ckpt_1") - - # Try to load different ID - result = checkpoint_manager.load_checkpoint(task_id, "nonexistent_ckpt") - - assert result is None - - def test_load_checkpoint_no_checkpoints(self, checkpoint_manager): - """Test loading when no checkpoints exist for task""" - result = checkpoint_manager.load_checkpoint("task_no_ckpt") - - assert result is None - - def test_load_checkpoint_corrupted_file(self, checkpoint_manager, sample_state): - """Test error handling when loading corrupted checkpoint file""" - task_id = "task_corrupted" - checkpoint_id = "ckpt_1" - - # Save valid checkpoint first - path = checkpoint_manager.save_checkpoint(task_id, sample_state, checkpoint_id) - - # Corrupt the file - with open(path, "wb") as f: - f.write(b"corrupted data") - - # Try to load corrupted checkpoint - with pytest.raises(CheckpointError, match="Failed to load checkpoint"): - checkpoint_manager.load_checkpoint(task_id, checkpoint_id) - - -# ============================================================================ -# Delete Checkpoint Tests -# ============================================================================ - - -@pytest.mark.unit -class TestDeleteCheckpoint: - """Test delete_checkpoint functionality""" - - def test_delete_specific_checkpoint(self, checkpoint_manager, sample_state): - """Test deleting a specific checkpoint""" - task_id = "task_del_1" - - # Save multiple checkpoints - checkpoint_manager.save_checkpoint(task_id, sample_state, "ckpt_1") - checkpoint_manager.save_checkpoint(task_id, sample_state, "ckpt_2") - - # Delete specific checkpoint - checkpoint_manager.delete_checkpoint(task_id, "ckpt_1") - - # Verify ckpt_1 is deleted but ckpt_2 remains - assert checkpoint_manager.load_checkpoint(task_id, "ckpt_1") is None - assert checkpoint_manager.load_checkpoint(task_id, "ckpt_2") is not None - - def test_delete_all_checkpoints(self, checkpoint_manager, sample_state): - """Test deleting all checkpoints for a task""" - task_id = "task_del_2" - - # Save multiple checkpoints - checkpoint_manager.save_checkpoint(task_id, sample_state, "ckpt_1") - checkpoint_manager.save_checkpoint(task_id, sample_state, "ckpt_2") - checkpoint_manager.save_checkpoint(task_id, sample_state, "ckpt_3") - - # Delete all checkpoints - checkpoint_manager.delete_checkpoint(task_id) - - # Verify all are deleted - assert checkpoint_manager.load_checkpoint(task_id, "ckpt_1") is None - assert checkpoint_manager.load_checkpoint(task_id, "ckpt_2") is None - assert checkpoint_manager.load_checkpoint(task_id, "ckpt_3") is None - - def test_delete_nonexistent_checkpoint(self, checkpoint_manager): - """Test deleting non-existent checkpoint (should not raise error)""" - # Should complete without error - checkpoint_manager.delete_checkpoint("nonexistent_task", "ckpt_1") - checkpoint_manager.delete_checkpoint("nonexistent_task") - - def test_delete_preserves_other_tasks(self, checkpoint_manager, sample_state): - """Test that deleting one task's checkpoints preserves others""" - # Save checkpoints for multiple tasks - checkpoint_manager.save_checkpoint("task_a", sample_state, "ckpt_1") - checkpoint_manager.save_checkpoint("task_b", sample_state, "ckpt_1") - - # Delete task_a's checkpoints - checkpoint_manager.delete_checkpoint("task_a") - - # Verify task_b's checkpoint still exists - assert checkpoint_manager.load_checkpoint("task_a", "ckpt_1") is None - assert checkpoint_manager.load_checkpoint("task_b", "ckpt_1") is not None - - -# ============================================================================ -# List Checkpoints Tests -# ============================================================================ - - -@pytest.mark.unit -class TestListCheckpoints: - """Test list_checkpoints functionality""" - - def test_list_checkpoints_basic(self, checkpoint_manager, sample_state): - """Test listing checkpoints for a task""" - task_id = "tasklist1" # Use simple task_id without underscores - - # Save multiple checkpoints - checkpoint_manager.save_checkpoint(task_id, sample_state, "ckpt_1") - time.sleep(0.01) - checkpoint_manager.save_checkpoint(task_id, sample_state, "ckpt_2") - - # List checkpoints - checkpoints = checkpoint_manager.list_checkpoints(task_id) - - assert len(checkpoints) == 2 - # Should be sorted by mtime (newest first) - assert checkpoints[0]["checkpoint_id"] == "ckpt_2" - assert checkpoints[1]["checkpoint_id"] == "ckpt_1" - - def test_list_checkpoints_info_structure(self, checkpoint_manager, sample_state): - """Test structure of checkpoint info""" - task_id = "tasklist2" # Use simple task_id without underscores - checkpoint_id = "ckpt_1" - - checkpoint_manager.save_checkpoint(task_id, sample_state, checkpoint_id) - - checkpoints = checkpoint_manager.list_checkpoints(task_id) - - assert len(checkpoints) == 1 - ckpt_info = checkpoints[0] - - # Verify structure - assert "task_id" in ckpt_info - assert "checkpoint_id" in ckpt_info - assert "path" in ckpt_info - assert "size" in ckpt_info - assert "mtime" in ckpt_info - - assert ckpt_info["task_id"] == task_id - assert ckpt_info["checkpoint_id"] == checkpoint_id - assert ckpt_info["size"] > 0 - - def test_list_checkpoints_empty(self, checkpoint_manager): - """Test listing checkpoints when none exist""" - checkpoints = checkpoint_manager.list_checkpoints("nonexistent_task") - - assert checkpoints == [] - - def test_list_checkpoints_sorting(self, checkpoint_manager, sample_state): - """Test that checkpoints are sorted by modification time""" - task_id = "tasklist3" # Use simple task_id without underscores - - # Save checkpoints with time delays - ids = ["old", "middle", "new"] - for ckpt_id in ids: - checkpoint_manager.save_checkpoint(task_id, sample_state, ckpt_id) - time.sleep(0.01) - - checkpoints = checkpoint_manager.list_checkpoints(task_id) - - # Should be sorted newest first - assert [c["checkpoint_id"] for c in checkpoints] == ["new", "middle", "old"] - - -# ============================================================================ -# Cleanup Old Checkpoints Tests -# ============================================================================ - - -@pytest.mark.unit -class TestCleanupOldCheckpoints: - """Test cleanup_old_checkpoints functionality""" - - def test_cleanup_keeps_last_n(self, checkpoint_manager, sample_state): - """Test cleanup keeps last N checkpoints""" - task_id = "taskcleanup1" # Use simple task_id without underscores - - # Save 10 checkpoints - for i in range(10): - checkpoint_manager.save_checkpoint(task_id, sample_state, f"ckpt_{i}") - time.sleep(0.01) - - # Keep last 3 - checkpoint_manager.cleanup_old_checkpoints(task_id, keep_last_n=3) - - checkpoints = checkpoint_manager.list_checkpoints(task_id) - - assert len(checkpoints) == 3 - # Should keep the newest 3 - assert checkpoints[0]["checkpoint_id"] == "ckpt_9" - assert checkpoints[1]["checkpoint_id"] == "ckpt_8" - assert checkpoints[2]["checkpoint_id"] == "ckpt_7" - - def test_cleanup_with_fewer_than_n(self, checkpoint_manager, sample_state): - """Test cleanup when checkpoints < keep_last_n""" - task_id = "task_cleanup_2" - - # Save only 3 checkpoints - for i in range(3): - checkpoint_manager.save_checkpoint(task_id, sample_state, f"ckpt_{i}") - - # Keep last 5 (more than available) - checkpoint_manager.cleanup_old_checkpoints(task_id, keep_last_n=5) - - checkpoints = checkpoint_manager.list_checkpoints(task_id) - - # All 3 should remain - assert len(checkpoints) == 3 - - def test_cleanup_keep_zero(self, checkpoint_manager, sample_state): - """Test cleanup with keep_last_n=0 deletes all""" - task_id = "task_cleanup_3" - - # Save some checkpoints - for i in range(5): - checkpoint_manager.save_checkpoint(task_id, sample_state, f"ckpt_{i}") - - # Keep 0 (delete all) - checkpoint_manager.cleanup_old_checkpoints(task_id, keep_last_n=0) - - checkpoints = checkpoint_manager.list_checkpoints(task_id) - - assert len(checkpoints) == 0 - - def test_cleanup_no_checkpoints(self, checkpoint_manager): - """Test cleanup when no checkpoints exist""" - # Should complete without error - checkpoint_manager.cleanup_old_checkpoints("nonexistent_task", keep_last_n=3) - - -# ============================================================================ -# Get Checkpoint Info Tests -# ============================================================================ - - -@pytest.mark.unit -class TestGetCheckpointInfo: - """Test get_checkpoint_info functionality""" - - def test_get_checkpoint_info_by_id(self, checkpoint_manager, sample_state): - """Test getting info for specific checkpoint""" - task_id = "task_info_1" - checkpoint_id = "ckpt_1" - - checkpoint_manager.save_checkpoint(task_id, sample_state, checkpoint_id) - - info = checkpoint_manager.get_checkpoint_info(task_id, checkpoint_id) - - assert info is not None - assert info["task_id"] == task_id - assert info["checkpoint_id"] == checkpoint_id - assert "path" in info - assert "size" in info - assert "mtime" in info - - def test_get_checkpoint_info_latest(self, checkpoint_manager, sample_state): - """Test getting info for latest checkpoint""" - task_id = "task_info_2" - - # Save multiple checkpoints - checkpoint_manager.save_checkpoint(task_id, {"v": 1}, "ckpt_1") - time.sleep(0.01) - checkpoint_manager.save_checkpoint(task_id, {"v": 2}, "ckpt_2") - - # Get latest without specifying ID - info = checkpoint_manager.get_checkpoint_info(task_id) - - assert info is not None - # Should return latest (ckpt_2 is newer based on mtime) - assert "ckpt_2" in info["path"] - - def test_get_checkpoint_info_nonexistent(self, checkpoint_manager): - """Test getting info for non-existent checkpoint""" - info = checkpoint_manager.get_checkpoint_info("nonexistent_task", "ckpt_1") - - assert info is None - - def test_get_checkpoint_info_nonexistent_id(self, checkpoint_manager, sample_state): - """Test getting info for non-existent checkpoint ID""" - task_id = "task_info_3" - - checkpoint_manager.save_checkpoint(task_id, sample_state, "ckpt_1") - - info = checkpoint_manager.get_checkpoint_info(task_id, "nonexistent_ckpt") - - assert info is None - - -# ============================================================================ -# Edge Cases and Error Handling -# ============================================================================ - - -@pytest.mark.unit -class TestEdgeCases: - """Test edge cases and error handling""" - - def test_empty_state(self, checkpoint_manager): - """Test saving and loading empty state""" - task_id = "task_empty" - empty_state = {} - - checkpoint_manager.save_checkpoint(task_id, empty_state, "ckpt_1") - loaded = checkpoint_manager.load_checkpoint(task_id, "ckpt_1") - - assert loaded == {} - - def test_large_state(self, checkpoint_manager): - """Test saving and loading large state""" - task_id = "task_large" - # Create state with large list - large_state = {"data": list(range(100000))} - - path = checkpoint_manager.save_checkpoint(task_id, large_state, "ckpt_1") - loaded = checkpoint_manager.load_checkpoint(task_id, "ckpt_1") - - assert loaded == large_state - # Verify file was created and has significant size - assert Path(path).stat().st_size > 100000 - - def test_special_characters_in_id(self, checkpoint_manager, sample_state): - """Test checkpoint ID with special characters""" - task_id = "task-with-dashes" - checkpoint_id = "ckpt_2024-11-20_15:30:00" - - # Should handle special characters in file names - path = checkpoint_manager.save_checkpoint(task_id, sample_state, checkpoint_id) - assert Path(path).exists() - - loaded = checkpoint_manager.load_checkpoint(task_id, checkpoint_id) - assert loaded == sample_state - - def test_concurrent_saves_same_id(self, checkpoint_manager): - """Test saving same checkpoint ID multiple times rapidly""" - task_id = "task_concurrent" - checkpoint_id = "ckpt_1" - - # Rapidly save same ID - for i in range(10): - checkpoint_manager.save_checkpoint(task_id, {"iteration": i}, checkpoint_id) - - # Should have last save - loaded = checkpoint_manager.load_checkpoint(task_id, checkpoint_id) - assert loaded == {"iteration": 9} - - def test_task_id_with_underscores(self, checkpoint_manager, sample_state): - """Test task ID containing underscores (used in file naming)""" - task_id = "task_with_multiple_underscores" - checkpoint_id = "ckpt_1" - - checkpoint_manager.save_checkpoint(task_id, sample_state, checkpoint_id) - - # Should correctly parse checkpoint ID - # Note: Due to filename parsing logic (split on '_'), the checkpoint_id - # will include parts of the task_id after the first underscore - checkpoints = checkpoint_manager.list_checkpoints(task_id) - assert len(checkpoints) == 1 - # The parsed checkpoint_id will be everything after first underscore in filename - # Filename: task_with_multiple_underscores_ckpt_1.ckpt - # After split: ['task', 'with', 'multiple', 'underscores', 'ckpt', '1'] - # checkpoint_id = '_'.join(parts[1:]) = 'with_multiple_underscores_ckpt_1' - assert "ckpt_1" in checkpoints[0]["checkpoint_id"] diff --git a/packages/sage-kernel/tests/unit/kernel/fault_tolerance/test_checkpoint_recovery.py b/packages/sage-kernel/tests/unit/kernel/fault_tolerance/test_checkpoint_recovery.py deleted file mode 100644 index e5515ba656..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/fault_tolerance/test_checkpoint_recovery.py +++ /dev/null @@ -1,612 +0,0 @@ -""" -Tests for CheckpointBasedRecovery - -Comprehensive test coverage for checkpoint-based fault tolerance recovery strategy. -""" - -import tempfile -import time -from unittest.mock import Mock, patch - -import pytest - -from sage.kernel.fault_tolerance.impl.checkpoint_impl import CheckpointManagerImpl -from sage.kernel.fault_tolerance.impl.checkpoint_recovery import CheckpointBasedRecovery - - -@pytest.fixture -def temp_checkpoint_dir(): - """Create a temporary directory for checkpoints""" - with tempfile.TemporaryDirectory() as tmpdir: - yield tmpdir - - -@pytest.fixture -def checkpoint_manager(temp_checkpoint_dir): - """Create a checkpoint manager instance""" - return CheckpointManagerImpl(temp_checkpoint_dir) - - -@pytest.fixture -def recovery_handler(checkpoint_manager): - """Create a recovery handler instance""" - return CheckpointBasedRecovery( - checkpoint_manager=checkpoint_manager, - checkpoint_interval=1.0, # Short interval for testing - max_recovery_attempts=3, - ) - - -@pytest.fixture -def sample_state(): - """Sample task state""" - return { - "processed_count": 100, - "checkpoint_counter": 5, - "data": "test_data", - } - - -class TestCheckpointBasedRecoveryInitialization: - """Test CheckpointBasedRecovery initialization""" - - def test_init_with_checkpoint_manager(self, checkpoint_manager): - """Test initialization with provided checkpoint manager""" - handler = CheckpointBasedRecovery(checkpoint_manager=checkpoint_manager) - assert handler.checkpoint_manager is checkpoint_manager - assert handler.checkpoint_interval == 60.0 - assert handler.max_recovery_attempts == 3 - assert handler.failure_counts == {} - assert handler.last_checkpoint_time == {} - - def test_init_with_custom_parameters(self, checkpoint_manager): - """Test initialization with custom parameters""" - handler = CheckpointBasedRecovery( - checkpoint_manager=checkpoint_manager, - checkpoint_interval=30.0, - max_recovery_attempts=5, - checkpoint_dir="/tmp/test", - ) - assert handler.checkpoint_interval == 30.0 - assert handler.max_recovery_attempts == 5 - - def test_init_creates_default_checkpoint_manager(self): - """Test initialization creates default checkpoint manager if not provided""" - handler = CheckpointBasedRecovery(checkpoint_dir=".sage/test_checkpoints") - assert handler.checkpoint_manager is not None - assert isinstance(handler.checkpoint_manager, CheckpointManagerImpl) - - -class TestSaveCheckpoint: - """Test save_checkpoint method""" - - def test_save_checkpoint_basic(self, recovery_handler, sample_state): - """Test basic checkpoint saving""" - task_id = "task1" - result = recovery_handler.save_checkpoint(task_id, sample_state) - assert result is True - assert task_id in recovery_handler.last_checkpoint_time - - # Verify checkpoint was saved - loaded = recovery_handler.checkpoint_manager.load_checkpoint(task_id) - assert loaded == sample_state - - def test_save_checkpoint_respects_interval(self, recovery_handler, sample_state): - """Test that checkpoint saving respects time interval""" - task_id = "task1" - - # First save should succeed - result1 = recovery_handler.save_checkpoint(task_id, sample_state) - assert result1 is True - - # Immediate second save should fail (interval not elapsed) - result2 = recovery_handler.save_checkpoint(task_id, sample_state) - assert result2 is False - - def test_save_checkpoint_force_ignores_interval(self, recovery_handler, sample_state): - """Test that force=True ignores time interval""" - task_id = "task1" - - # First save - recovery_handler.save_checkpoint(task_id, sample_state) - - # Immediate second save with force=True should succeed - modified_state = {**sample_state, "processed_count": 200} - result = recovery_handler.save_checkpoint(task_id, modified_state, force=True) - assert result is True - - # Verify latest state - loaded = recovery_handler.checkpoint_manager.load_checkpoint(task_id) - assert loaded["processed_count"] == 200 - - def test_save_checkpoint_after_interval_elapsed(self, recovery_handler, sample_state): - """Test checkpoint saving after interval has elapsed""" - task_id = "task1" - - # First save - recovery_handler.save_checkpoint(task_id, sample_state) - - # Wait for interval to elapse - time.sleep(1.1) - - # Second save should succeed - modified_state = {**sample_state, "processed_count": 200} - result = recovery_handler.save_checkpoint(task_id, modified_state) - assert result is True - - def test_save_checkpoint_error_handling(self, recovery_handler, sample_state): - """Test error handling during checkpoint save""" - task_id = "task1" - - # Mock checkpoint manager to raise error - with patch.object( - recovery_handler.checkpoint_manager, - "save_checkpoint", - side_effect=Exception("Save failed"), - ): - result = recovery_handler.save_checkpoint(task_id, sample_state) - assert result is False - - def test_save_checkpoint_with_logger(self, recovery_handler, sample_state): - """Test checkpoint saving with logger attached""" - task_id = "task1" - logger = Mock() - recovery_handler.logger = logger - - recovery_handler.save_checkpoint(task_id, sample_state) - logger.debug.assert_called_once() - - def test_save_checkpoint_multiple_tasks(self, recovery_handler, sample_state): - """Test saving checkpoints for multiple tasks""" - tasks = ["task1", "task2", "task3"] - - for task_id in tasks: - result = recovery_handler.save_checkpoint(task_id, sample_state) - assert result is True - - # Verify all checkpoints exist - for task_id in tasks: - loaded = recovery_handler.checkpoint_manager.load_checkpoint(task_id) - assert loaded is not None - - -class TestCanRecover: - """Test can_recover method""" - - def test_can_recover_with_checkpoint(self, recovery_handler, sample_state): - """Test can_recover returns True when checkpoint exists and attempts < max""" - task_id = "task1" - - # Save checkpoint - recovery_handler.save_checkpoint(task_id, sample_state) - - # Should be able to recover - assert recovery_handler.can_recover(task_id) is True - - def test_can_recover_no_checkpoint(self, recovery_handler): - """Test can_recover returns False when no checkpoint exists""" - task_id = "task1" - - # No checkpoint saved - assert recovery_handler.can_recover(task_id) is False - - def test_can_recover_max_attempts_reached(self, recovery_handler, sample_state): - """Test can_recover returns False when max attempts reached""" - task_id = "task1" - - # Save checkpoint - recovery_handler.save_checkpoint(task_id, sample_state) - - # Simulate max failures - recovery_handler.failure_counts[task_id] = 3 - - # Should not be able to recover - assert recovery_handler.can_recover(task_id) is False - - def test_can_recover_just_below_max_attempts(self, recovery_handler, sample_state): - """Test can_recover returns True when just below max attempts""" - task_id = "task1" - - # Save checkpoint - recovery_handler.save_checkpoint(task_id, sample_state) - - # Simulate failures just below max - recovery_handler.failure_counts[task_id] = 2 - - # Should still be able to recover - assert recovery_handler.can_recover(task_id) is True - - -class TestHandleFailure: - """Test handle_failure method""" - - def test_handle_failure_first_attempt(self, recovery_handler, sample_state): - """Test handling first failure with successful recovery""" - task_id = "task1" - error = Exception("Test error") - - # Save checkpoint - recovery_handler.save_checkpoint(task_id, sample_state) - - # Mock dispatcher - dispatcher = Mock() - dispatcher.restart_task_with_state = Mock(return_value=True) - recovery_handler.dispatcher = dispatcher - - # Handle failure - result = recovery_handler.handle_failure(task_id, error) - - assert result is True - assert recovery_handler.failure_counts[task_id] == 1 - dispatcher.restart_task_with_state.assert_called_once() - - def test_handle_failure_increments_counter(self, recovery_handler, sample_state): - """Test that handle_failure increments failure counter""" - task_id = "task1" - error = Exception("Test error") - - # Save checkpoint - recovery_handler.save_checkpoint(task_id, sample_state) - - # Mock dispatcher - dispatcher = Mock() - dispatcher.restart_task_with_state = Mock(return_value=True) - recovery_handler.dispatcher = dispatcher - - # Multiple failures - recovery_handler.handle_failure(task_id, error) - recovery_handler.handle_failure(task_id, error) - - assert recovery_handler.failure_counts[task_id] == 2 - - def test_handle_failure_max_attempts_exceeded(self, recovery_handler, sample_state): - """Test handle_failure when max attempts exceeded""" - task_id = "task1" - error = Exception("Test error") - - # Save checkpoint - recovery_handler.save_checkpoint(task_id, sample_state) - - # Set failure count to max - recovery_handler.failure_counts[task_id] = 3 - - # Handle failure - result = recovery_handler.handle_failure(task_id, error) - - assert result is False - - def test_handle_failure_no_checkpoint(self, recovery_handler): - """Test handle_failure when no checkpoint exists""" - task_id = "task1" - error = Exception("Test error") - - # No checkpoint saved - result = recovery_handler.handle_failure(task_id, error) - - assert result is False - - def test_handle_failure_with_logger(self, recovery_handler, sample_state): - """Test handle_failure with logger attached""" - task_id = "task1" - error = Exception("Test error") - logger = Mock() - recovery_handler.logger = logger - - # Save checkpoint - recovery_handler.save_checkpoint(task_id, sample_state) - - # Mock dispatcher - dispatcher = Mock() - dispatcher.restart_task_with_state = Mock(return_value=True) - recovery_handler.dispatcher = dispatcher - - recovery_handler.handle_failure(task_id, error) - - # Verify logger was called - assert logger.warning.call_count >= 1 - - -class TestRecover: - """Test recover method""" - - def test_recover_successful(self, recovery_handler, sample_state): - """Test successful recovery from checkpoint""" - task_id = "task1" - - # Save checkpoint - recovery_handler.save_checkpoint(task_id, sample_state) - - # Mock dispatcher - dispatcher = Mock() - dispatcher.restart_task_with_state = Mock(return_value=True) - recovery_handler.dispatcher = dispatcher - - # Recover - result = recovery_handler.recover(task_id) - - assert result is True - dispatcher.restart_task_with_state.assert_called_once_with(task_id, sample_state) - - def test_recover_no_checkpoint(self, recovery_handler): - """Test recovery when no checkpoint exists""" - task_id = "task1" - - # Mock dispatcher - dispatcher = Mock() - recovery_handler.dispatcher = dispatcher - - # Attempt recovery - result = recovery_handler.recover(task_id) - - assert result is False - - def test_recover_no_dispatcher(self, recovery_handler, sample_state): - """Test recovery when no dispatcher is available""" - task_id = "task1" - - # Save checkpoint - recovery_handler.save_checkpoint(task_id, sample_state) - - # No dispatcher - recovery_handler.dispatcher = None - - # Attempt recovery - result = recovery_handler.recover(task_id) - - assert result is False - - def test_recover_dispatcher_restart_fails(self, recovery_handler, sample_state): - """Test recovery when dispatcher restart fails""" - task_id = "task1" - - # Save checkpoint - recovery_handler.save_checkpoint(task_id, sample_state) - - # Mock dispatcher with failing restart - dispatcher = Mock() - dispatcher.restart_task_with_state = Mock(return_value=False) - recovery_handler.dispatcher = dispatcher - - # Attempt recovery - result = recovery_handler.recover(task_id) - - assert result is False - - def test_recover_exception_during_recovery(self, recovery_handler, sample_state): - """Test recovery when exception occurs during restart""" - task_id = "task1" - - # Save checkpoint - recovery_handler.save_checkpoint(task_id, sample_state) - - # Mock dispatcher to raise exception - dispatcher = Mock() - dispatcher.restart_task_with_state = Mock(side_effect=Exception("Restart failed")) - recovery_handler.dispatcher = dispatcher - - # Attempt recovery - result = recovery_handler.recover(task_id) - - assert result is False - - def test_recover_with_logger(self, recovery_handler, sample_state): - """Test recovery with logger attached""" - task_id = "task1" - logger = Mock() - recovery_handler.logger = logger - - # Save checkpoint - recovery_handler.save_checkpoint(task_id, sample_state) - - # Mock dispatcher - dispatcher = Mock() - dispatcher.restart_task_with_state = Mock(return_value=True) - recovery_handler.dispatcher = dispatcher - - recovery_handler.recover(task_id) - - # Verify logger was called for recovery - assert logger.info.call_count >= 1 - - -class TestCleanupCheckpoints: - """Test cleanup_checkpoints method""" - - def test_cleanup_removes_checkpoints(self, recovery_handler, sample_state): - """Test that cleanup removes all checkpoints for a task""" - task_id = "task1" - - # Save checkpoint - recovery_handler.save_checkpoint(task_id, sample_state) - - # Cleanup - recovery_handler.cleanup_checkpoints(task_id) - - # Verify checkpoint is removed - loaded = recovery_handler.checkpoint_manager.load_checkpoint(task_id) - assert loaded is None - - def test_cleanup_removes_failure_counts(self, recovery_handler, sample_state): - """Test that cleanup removes failure counts""" - task_id = "task1" - - # Set failure count - recovery_handler.failure_counts[task_id] = 2 - recovery_handler.last_checkpoint_time[task_id] = time.time() - - # Cleanup - recovery_handler.cleanup_checkpoints(task_id) - - # Verify tracking data removed - assert task_id not in recovery_handler.failure_counts - assert task_id not in recovery_handler.last_checkpoint_time - - def test_cleanup_handles_nonexistent_task(self, recovery_handler): - """Test cleanup handles nonexistent task gracefully""" - task_id = "nonexistent" - - # Should not raise exception - recovery_handler.cleanup_checkpoints(task_id) - - def test_cleanup_with_error_handling(self, recovery_handler): - """Test cleanup error handling""" - task_id = "task1" - logger = Mock() - recovery_handler.logger = logger - - # Mock checkpoint manager to raise error - with patch.object( - recovery_handler.checkpoint_manager, - "delete_checkpoint", - side_effect=Exception("Delete failed"), - ): - recovery_handler.cleanup_checkpoints(task_id) - - # Verify error was logged - logger.error.assert_called_once() - - -class TestCallbacks: - """Test callback methods""" - - def test_on_recovery_started_callback(self, recovery_handler): - """Test on_recovery_started callback""" - task_id = "task1" - logger = Mock() - recovery_handler.logger = logger - - recovery_handler.on_recovery_started(task_id) - - logger.info.assert_called_once() - assert "Starting recovery" in logger.info.call_args[0][0] - - def test_on_recovery_completed_success(self, recovery_handler): - """Test on_recovery_completed callback with success""" - task_id = "task1" - logger = Mock() - recovery_handler.logger = logger - - recovery_handler.on_recovery_completed(task_id, True) - - logger.info.assert_called_once() - assert "completed successfully" in logger.info.call_args[0][0] - - def test_on_recovery_completed_failure(self, recovery_handler): - """Test on_recovery_completed callback with failure""" - task_id = "task1" - logger = Mock() - recovery_handler.logger = logger - - recovery_handler.on_recovery_completed(task_id, False) - - logger.error.assert_called_once() - assert "failed" in logger.error.call_args[0][0] - - def test_on_failure_detected_callback(self, recovery_handler): - """Test on_failure_detected callback""" - task_id = "task1" - error = Exception("Test error") - logger = Mock() - recovery_handler.logger = logger - - recovery_handler.on_failure_detected(task_id, error) - - logger.warning.assert_called_once() - assert "Failure detected" in logger.warning.call_args[0][0] - - -class TestIsRemoteTask: - """Test _is_remote_task method""" - - def test_is_remote_task_no_dispatcher(self, recovery_handler): - """Test _is_remote_task when no dispatcher is set""" - task_id = "task1" - result = recovery_handler._is_remote_task(task_id) - assert result is False - - def test_is_remote_task_with_dispatcher_no_task(self, recovery_handler): - """Test _is_remote_task when task doesn't exist in dispatcher""" - task_id = "task1" - dispatcher = Mock() - dispatcher.tasks = {} - recovery_handler.dispatcher = dispatcher - - result = recovery_handler._is_remote_task(task_id) - assert result is False - - -class TestEdgeCases: - """Test edge cases and boundary conditions""" - - def test_multiple_failures_and_recoveries(self, recovery_handler, sample_state): - """Test multiple failure and recovery cycles""" - task_id = "task1" - error = Exception("Test error") - - # Save checkpoint - recovery_handler.save_checkpoint(task_id, sample_state) - - # Mock dispatcher - dispatcher = Mock() - dispatcher.restart_task_with_state = Mock(return_value=True) - recovery_handler.dispatcher = dispatcher - - # Multiple failure-recovery cycles - for i in range(2): - result = recovery_handler.handle_failure(task_id, error) - assert result is True - - assert recovery_handler.failure_counts[task_id] == 2 - - def test_checkpoint_interval_zero(self, checkpoint_manager): - """Test recovery handler with zero checkpoint interval""" - handler = CheckpointBasedRecovery( - checkpoint_manager=checkpoint_manager, checkpoint_interval=0.0 - ) - - task_id = "task1" - state = {"data": "test"} - - # All saves should succeed - result1 = handler.save_checkpoint(task_id, state) - result2 = handler.save_checkpoint(task_id, state) - - assert result1 is True - assert result2 is True - - def test_max_recovery_attempts_one(self, checkpoint_manager, sample_state): - """Test recovery handler with max_recovery_attempts=1""" - handler = CheckpointBasedRecovery( - checkpoint_manager=checkpoint_manager, max_recovery_attempts=1 - ) - - task_id = "task1" - handler.save_checkpoint(task_id, sample_state) - - # First failure should allow recovery - handler.failure_counts[task_id] = 0 - assert handler.can_recover(task_id) is True - - # Second failure should not allow recovery - handler.failure_counts[task_id] = 1 - assert handler.can_recover(task_id) is False - - def test_concurrent_task_handling(self, recovery_handler, sample_state): - """Test handling multiple tasks concurrently""" - tasks = [f"task{i}" for i in range(5)] - - # Save checkpoints for all tasks - for task_id in tasks: - recovery_handler.save_checkpoint(task_id, sample_state, force=True) - - # Verify all tasks can recover - for task_id in tasks: - assert recovery_handler.can_recover(task_id) is True - - # Cleanup all tasks - for task_id in tasks: - recovery_handler.cleanup_checkpoints(task_id) - - # Verify all cleaned up - for task_id in tasks: - assert recovery_handler.can_recover(task_id) is False diff --git a/packages/sage-kernel/tests/unit/kernel/fault_tolerance/test_lifecycle.py b/packages/sage-kernel/tests/unit/kernel/fault_tolerance/test_lifecycle.py deleted file mode 100644 index edbea7abed..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/fault_tolerance/test_lifecycle.py +++ /dev/null @@ -1,144 +0,0 @@ -""" -LifecycleManagerImpl 单元测试 -""" - -from unittest.mock import Mock - -import pytest - -from sage.kernel.fault_tolerance.impl.lifecycle_impl import LifecycleManagerImpl - - -class TestLifecycleManagerImpl: - """LifecycleManagerImpl 基础测试""" - - def test_lifecycle_manager_initialization(self): - """测试生命周期管理器初始化""" - manager = LifecycleManagerImpl() - - assert manager.logger is None - - def test_lifecycle_manager_with_logger(self): - """测试设置日志器""" - manager = LifecycleManagerImpl() - logger = Mock() - - manager.logger = logger - - assert manager.logger == logger - - def test_cleanup_actor_basic(self): - """测试基本 Actor 清理""" - manager = LifecycleManagerImpl() - - actor = Mock() - actor.is_ray_actor.return_value = False - actor.cleanup = Mock() - - cleanup_success, kill_success = manager.cleanup_actor(actor, cleanup_timeout=5.0) - - assert cleanup_success is True - assert kill_success is True - actor.cleanup.assert_called_once() - - def test_cleanup_all_tasks_only(self): - """测试只清理任务""" - manager = LifecycleManagerImpl() - - tasks = { - "task_1": Mock(), - "task_2": Mock(), - } - - for task in tasks.values(): - task.is_ray_actor.return_value = False - task.cleanup = Mock() - - results = manager.cleanup_all(tasks, cleanup_timeout=5.0) - - assert len(results) == 2 - assert all(results[task_id][1] for task_id in tasks) # All kill_success - - def test_cleanup_all_tasks_and_services(self): - """测试清理任务和服务""" - manager = LifecycleManagerImpl() - - tasks = { - "task_1": Mock(), - } - - services = { - "service_1": Mock(), - } - - for item in list(tasks.values()) + list(services.values()): - item.is_ray_actor.return_value = False - item.cleanup = Mock() - - results = manager.cleanup_all(tasks, services=services, cleanup_timeout=5.0) - - assert len(results) == 2 - assert "task_1" in results - assert "service_1" in results - - def test_cleanup_all_empty_tasks(self): - """测试清理空任务字典""" - manager = LifecycleManagerImpl() - manager.logger = Mock() - - # Should not raise any exception - results = manager.cleanup_all({}, cleanup_timeout=5.0) - - assert results == {} - - -class TestLifecycleManagerImplEdgeCases: - """LifecycleManagerImpl 边界条件测试""" - - def test_cleanup_actor_with_no_cleanup_method(self): - """测试清理没有 cleanup 方法的 Actor""" - manager = LifecycleManagerImpl() - - actor = Mock(spec=["is_ray_actor"]) - actor.is_ray_actor.return_value = False - - cleanup_success, kill_success = manager.cleanup_actor(actor) - - # No cleanup method, so cleanup_success is False - # But kill_success should be True for local actor - assert cleanup_success is False - assert kill_success is True - - -class TestLifecycleManagerImplIntegration: - """LifecycleManagerImpl 集成测试""" - - def test_complete_cleanup_workflow(self): - """测试完整的清理流程""" - manager = LifecycleManagerImpl() - manager.logger = Mock() - - # Create tasks and services - tasks = {} - services = {} - - for i in range(3): - task = Mock() - task.is_ray_actor.return_value = False - task.cleanup = Mock() - tasks[f"task_{i}"] = task - - service = Mock() - service.is_ray_actor.return_value = False - service.cleanup = Mock() - services[f"service_{i}"] = service - - # Cleanup all - results = manager.cleanup_all(tasks, services=services, cleanup_timeout=5.0) - - assert len(results) == 6 # 3 tasks + 3 services - assert all(results[item_id][1] for item_id in results) # All kill_success - - -if __name__ == "__main__": - pytest.main([__file__, "-v"]) diff --git a/packages/sage-kernel/tests/unit/kernel/fault_tolerance/test_restart_recovery.py b/packages/sage-kernel/tests/unit/kernel/fault_tolerance/test_restart_recovery.py deleted file mode 100644 index 48c98e1beb..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/fault_tolerance/test_restart_recovery.py +++ /dev/null @@ -1,506 +0,0 @@ -""" -Tests for RestartBasedRecovery - -Comprehensive test coverage for restart-based fault tolerance recovery. -""" - -import time -from unittest.mock import Mock, patch - -import pytest - -from sage.kernel.fault_tolerance.impl.restart_recovery import RestartBasedRecovery -from sage.kernel.fault_tolerance.impl.restart_strategy import ( - ExponentialBackoffStrategy, - FixedDelayStrategy, -) - - -@pytest.fixture -def recovery_handler(): - """Create a recovery handler with fixed delay strategy""" - strategy = FixedDelayStrategy(delay=0.01, max_attempts=3) # Short delay for tests - return RestartBasedRecovery(restart_strategy=strategy) - - -@pytest.fixture -def exponential_handler(): - """Create a recovery handler with exponential backoff strategy""" - strategy = ExponentialBackoffStrategy( - initial_delay=0.01, max_delay=1.0, multiplier=2.0, max_attempts=5 - ) - return RestartBasedRecovery(restart_strategy=strategy) - - -class TestRestartBasedRecoveryInitialization: - """Test RestartBasedRecovery initialization""" - - def test_init_with_strategy(self): - """Test initialization with provided restart strategy""" - strategy = FixedDelayStrategy() - handler = RestartBasedRecovery(restart_strategy=strategy) - - assert handler.restart_strategy is strategy - assert handler.failure_counts == {} - assert handler.failure_history == {} - assert handler.logger is None - - def test_init_creates_default_strategy(self): - """Test initialization creates default exponential backoff strategy""" - handler = RestartBasedRecovery() - - assert handler.restart_strategy is not None - assert isinstance(handler.restart_strategy, ExponentialBackoffStrategy) - - -class TestHandleFailure: - """Test handle_failure method""" - - def test_handle_failure_first_attempt(self, recovery_handler): - """Test handling first failure""" - task_id = "task1" - error = Exception("Test error") - - # For restart-based recovery, no actual restart happens in current implementation - # (recover() returns True by default) - result = recovery_handler.handle_failure(task_id, error) - - assert recovery_handler.failure_counts[task_id] == 1 - assert len(recovery_handler.failure_history[task_id]) == 1 - assert result is True # Should succeed since can_recover is True - - def test_handle_failure_increments_counter(self, recovery_handler): - """Test that handle_failure increments failure counter""" - task_id = "task1" - error = Exception("Test error") - - # Multiple failures - recovery_handler.handle_failure(task_id, error) - recovery_handler.handle_failure(task_id, error) - - assert recovery_handler.failure_counts[task_id] == 2 - assert len(recovery_handler.failure_history[task_id]) == 2 - - def test_handle_failure_records_history(self, recovery_handler): - """Test that handle_failure records failure history""" - task_id = "task1" - error = Exception("Test error") - - recovery_handler.handle_failure(task_id, error) - - history = recovery_handler.failure_history[task_id] - assert len(history) == 1 - assert "timestamp" in history[0] - assert history[0]["error"] == "Test error" - assert history[0]["failure_count"] == 1 - - def test_handle_failure_max_attempts_exceeded(self, recovery_handler): - """Test handle_failure when max attempts exceeded""" - task_id = "task1" - error = Exception("Test error") - - # Reach max attempts (3) - for _ in range(3): - recovery_handler.handle_failure(task_id, error) - - # 4th failure should fail - result = recovery_handler.handle_failure(task_id, error) - assert result is False - - def test_handle_failure_with_logger(self, recovery_handler): - """Test handle_failure with logger attached""" - task_id = "task1" - error = Exception("Test error") - logger = Mock() - recovery_handler.logger = logger - - recovery_handler.handle_failure(task_id, error) - - # Verify logger was called - assert logger.warning.call_count >= 1 - assert logger.info.call_count >= 1 - - def test_handle_failure_multiple_tasks(self, recovery_handler): - """Test handling failures for multiple tasks""" - error = Exception("Test error") - - recovery_handler.handle_failure("task1", error) - recovery_handler.handle_failure("task2", error) - recovery_handler.handle_failure("task1", error) - - assert recovery_handler.failure_counts["task1"] == 2 - assert recovery_handler.failure_counts["task2"] == 1 - - -class TestCanRecover: - """Test can_recover method""" - - def test_can_recover_first_failure(self, recovery_handler): - """Test can_recover returns True on first failure""" - task_id = "task1" - recovery_handler.failure_counts[task_id] = 1 - - assert recovery_handler.can_recover(task_id) is True - - def test_can_recover_below_max(self, recovery_handler): - """Test can_recover returns True when below max attempts""" - task_id = "task1" - recovery_handler.failure_counts[task_id] = 2 - - assert recovery_handler.can_recover(task_id) is True - - def test_can_recover_at_max(self, recovery_handler): - """Test can_recover returns False at max attempts""" - task_id = "task1" - recovery_handler.failure_counts[task_id] = 3 - - assert recovery_handler.can_recover(task_id) is False - - def test_can_recover_no_failures(self, recovery_handler): - """Test can_recover with no previous failures""" - task_id = "task1" - - # No failure count means 0 failures, should be able to recover - assert recovery_handler.can_recover(task_id) is True - - -class TestRecover: - """Test recover method""" - - def test_recover_basic(self, recovery_handler): - """Test basic recovery""" - task_id = "task1" - - result = recovery_handler.recover(task_id) - - # Current implementation returns True by default - assert result is True - - def test_recover_respects_restart_delay(self, recovery_handler): - """Test that recover respects restart delay""" - task_id = "task1" - recovery_handler.failure_counts[task_id] = 1 - - start_time = time.time() - recovery_handler.recover(task_id) - elapsed = time.time() - start_time - - # Should have some delay (at least 0.01s from fixture) - assert elapsed >= 0.01 - - def test_recover_exponential_backoff(self, exponential_handler): - """Test recover with exponential backoff delays""" - task_id = "task1" - - # First recovery (failure_count=0) - start_time = time.time() - exponential_handler.recover(task_id) - elapsed1 = time.time() - start_time - - # Second recovery (failure_count=1 after handle_failure) - exponential_handler.failure_counts[task_id] = 1 - start_time = time.time() - exponential_handler.recover(task_id) - elapsed2 = time.time() - start_time - - # Second should take longer due to backoff - # (0.01 * 2^0 = 0.01, 0.01 * 2^1 = 0.02) - assert elapsed2 >= elapsed1 - - def test_recover_with_logger(self, recovery_handler): - """Test recover with logger attached""" - task_id = "task1" - logger = Mock() - recovery_handler.logger = logger - - recovery_handler.recover(task_id) - - # Verify logger was called - assert logger.info.call_count >= 1 - - def test_recover_incremental_failure_count(self, recovery_handler): - """Test recover uses current failure count for delay""" - task_id = "task1" - - # Set failure count - recovery_handler.failure_counts[task_id] = 2 - - # Recover should use this count for delay calculation - result = recovery_handler.recover(task_id) - assert result is True - - -class TestRecoverJob: - """Test recover_job method""" - - def test_recover_job_successful(self, recovery_handler): - """Test successful job recovery""" - job_id = "job123" - dispatcher = Mock() - dispatcher.start = Mock() - - result = recovery_handler.recover_job(job_id, dispatcher, restart_count=0) - - assert result["success"] is True - assert result["job_id"] == job_id - assert result["restart_count"] == 1 - dispatcher.start.assert_called_once() - - def test_recover_job_with_restart_count(self, recovery_handler): - """Test job recovery tracks restart count""" - job_id = "job123" - dispatcher = Mock() - dispatcher.start = Mock() - - result = recovery_handler.recover_job(job_id, dispatcher, restart_count=5) - - assert result["restart_count"] == 6 - - def test_recover_job_dispatcher_fails(self, recovery_handler): - """Test job recovery when dispatcher start fails""" - job_id = "job123" - dispatcher = Mock() - dispatcher.start = Mock(side_effect=Exception("Start failed")) - - result = recovery_handler.recover_job(job_id, dispatcher, restart_count=0) - - assert result["success"] is False - assert "error" in result - assert result["error"] == "Start failed" - - def test_recover_job_with_logger(self, recovery_handler): - """Test job recovery with logger attached""" - job_id = "job123" - dispatcher = Mock() - dispatcher.start = Mock() - logger = Mock() - recovery_handler.logger = logger - - recovery_handler.recover_job(job_id, dispatcher, restart_count=0) - - # Verify logger was called - assert logger.info.call_count >= 1 - - -class TestGetFailureStatistics: - """Test get_failure_statistics method""" - - def test_get_statistics_for_specific_task(self, recovery_handler): - """Test getting statistics for a specific task""" - task_id = "task1" - error = Exception("Test error") - - recovery_handler.handle_failure(task_id, error) - recovery_handler.handle_failure(task_id, error) - - stats = recovery_handler.get_failure_statistics(task_id) - - assert stats["task_id"] == task_id - assert stats["failure_count"] == 2 - assert len(stats["failure_history"]) == 2 - - def test_get_statistics_for_nonexistent_task(self, recovery_handler): - """Test getting statistics for nonexistent task""" - task_id = "nonexistent" - - stats = recovery_handler.get_failure_statistics(task_id) - - assert stats["task_id"] == task_id - assert stats["failure_count"] == 0 - assert stats["failure_history"] == [] - - def test_get_statistics_all_tasks(self, recovery_handler): - """Test getting statistics for all tasks""" - error = Exception("Test error") - - recovery_handler.handle_failure("task1", error) - recovery_handler.handle_failure("task1", error) - recovery_handler.handle_failure("task2", error) - - stats = recovery_handler.get_failure_statistics(None) - - assert stats["total_failed_tasks"] == 2 - assert stats["total_failures"] == 3 - assert stats["failure_counts"]["task1"] == 2 - assert stats["failure_counts"]["task2"] == 1 - - def test_get_statistics_empty(self, recovery_handler): - """Test getting statistics when no failures""" - stats = recovery_handler.get_failure_statistics(None) - - assert stats["total_failed_tasks"] == 0 - assert stats["total_failures"] == 0 - assert stats["failure_counts"] == {} - - -class TestResetFailureCount: - """Test reset_failure_count method""" - - def test_reset_failure_count(self, recovery_handler): - """Test resetting failure count for a task""" - task_id = "task1" - error = Exception("Test error") - - recovery_handler.handle_failure(task_id, error) - recovery_handler.handle_failure(task_id, error) - - # Reset - recovery_handler.reset_failure_count(task_id) - - assert task_id not in recovery_handler.failure_counts - assert task_id not in recovery_handler.failure_history - - def test_reset_nonexistent_task(self, recovery_handler): - """Test resetting failure count for nonexistent task""" - task_id = "nonexistent" - - # Should not raise exception - recovery_handler.reset_failure_count(task_id) - - def test_reset_preserves_other_tasks(self, recovery_handler): - """Test that reset only affects specified task""" - error = Exception("Test error") - - recovery_handler.handle_failure("task1", error) - recovery_handler.handle_failure("task2", error) - - recovery_handler.reset_failure_count("task1") - - assert "task1" not in recovery_handler.failure_counts - assert "task2" in recovery_handler.failure_counts - - -class TestCallbacks: - """Test callback methods (inherited from base)""" - - def test_on_failure_detected_in_handle_failure(self, recovery_handler): - """Test that on_failure_detected is called during handle_failure""" - task_id = "task1" - error = Exception("Test error") - - # Mock the callback - with patch.object(recovery_handler, "on_failure_detected") as mock_callback: - recovery_handler.handle_failure(task_id, error) - mock_callback.assert_called_once_with(task_id, error) - - def test_on_recovery_started_in_recover(self, recovery_handler): - """Test that on_recovery_started is called during recover""" - task_id = "task1" - - with patch.object(recovery_handler, "on_recovery_started") as mock_callback: - recovery_handler.recover(task_id) - mock_callback.assert_called_once_with(task_id) - - def test_on_recovery_completed_in_recover(self, recovery_handler): - """Test that on_recovery_completed is called during recover""" - task_id = "task1" - - with patch.object(recovery_handler, "on_recovery_completed") as mock_callback: - recovery_handler.recover(task_id) - mock_callback.assert_called_once_with(task_id, True) - - -class TestEdgeCases: - """Test edge cases and boundary conditions""" - - def test_multiple_failures_same_task(self, recovery_handler): - """Test multiple failures for the same task""" - task_id = "task1" - error = Exception("Test error") - - results = [] - for _ in range(5): - result = recovery_handler.handle_failure(task_id, error) - results.append(result) - - # With max_attempts=3: failure_count < 3 allows recovery - # Attempt 1 (count=1): can recover -> True - # Attempt 2 (count=2): can recover -> True - # Attempt 3 (count=3): cannot recover (3 < 3 is False) -> False - # Attempt 4 (count=4): cannot recover -> False - # Attempt 5 (count=5): cannot recover -> False - assert results == [True, True, False, False, False] - - def test_concurrent_task_failures(self, recovery_handler): - """Test handling failures for multiple tasks concurrently""" - error = Exception("Test error") - tasks = [f"task{i}" for i in range(5)] - - for task_id in tasks: - result = recovery_handler.handle_failure(task_id, error) - assert result is True - - # Verify all tracked - assert len(recovery_handler.failure_counts) == 5 - - def test_very_short_delay(self): - """Test recovery with very short delays""" - strategy = FixedDelayStrategy(delay=0.001, max_attempts=3) - handler = RestartBasedRecovery(restart_strategy=strategy) - - task_id = "task1" - - start = time.time() - handler.recover(task_id) - elapsed = time.time() - start - - # Should complete quickly - assert elapsed < 1.0 - - def test_zero_delay(self): - """Test recovery with zero delay""" - strategy = FixedDelayStrategy(delay=0.0, max_attempts=3) - handler = RestartBasedRecovery(restart_strategy=strategy) - - task_id = "task1" - - # Should not raise exception - result = handler.recover(task_id) - assert result is True - - def test_failure_history_timestamps_ordered(self, recovery_handler): - """Test that failure history maintains chronological order""" - task_id = "task1" - error = Exception("Test error") - - for _ in range(3): - recovery_handler.handle_failure(task_id, error) - time.sleep(0.01) - - history = recovery_handler.failure_history[task_id] - timestamps = [entry["timestamp"] for entry in history] - - # Verify chronological order - assert timestamps == sorted(timestamps) - - def test_failure_counts_consistency(self, recovery_handler): - """Test that failure counts stay consistent with history""" - task_id = "task1" - error = Exception("Test error") - - for i in range(3): - recovery_handler.handle_failure(task_id, error) - - # Check consistency - assert recovery_handler.failure_counts[task_id] == i + 1 - assert len(recovery_handler.failure_history[task_id]) == i + 1 - assert recovery_handler.failure_history[task_id][-1]["failure_count"] == i + 1 - - def test_different_strategies_different_delays(self): - """Test that different strategies produce different delay behaviors""" - fixed = RestartBasedRecovery(restart_strategy=FixedDelayStrategy(delay=0.01)) - exponential = RestartBasedRecovery( - restart_strategy=ExponentialBackoffStrategy(initial_delay=0.01) - ) - - task_id = "task1" - - # Measure delays for same failure count - fixed.failure_counts[task_id] = 2 - exponential.failure_counts[task_id] = 2 - - fixed_delay = fixed.restart_strategy.get_restart_delay(2) - exp_delay = exponential.restart_strategy.get_restart_delay(2) - - # Exponential should be larger - assert exp_delay > fixed_delay diff --git a/packages/sage-kernel/tests/unit/kernel/fault_tolerance/test_restart_strategy.py b/packages/sage-kernel/tests/unit/kernel/fault_tolerance/test_restart_strategy.py deleted file mode 100644 index cf2f4f1905..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/fault_tolerance/test_restart_strategy.py +++ /dev/null @@ -1,359 +0,0 @@ -""" -Tests for Restart Strategy Implementations - -Comprehensive test coverage for restart strategy classes. -""" - -import time - -import pytest - -from sage.kernel.fault_tolerance.impl.restart_strategy import ( - ExponentialBackoffStrategy, - FailureRateStrategy, - FixedDelayStrategy, - RestartStrategy, -) - - -class TestFixedDelayStrategy: - """Test FixedDelayStrategy""" - - def test_init_default_parameters(self): - """Test initialization with default parameters""" - strategy = FixedDelayStrategy() - assert strategy.delay == 5.0 - assert strategy.max_attempts == 3 # DEFAULT_MAX_RESTART_ATTEMPTS - - def test_init_custom_parameters(self): - """Test initialization with custom parameters""" - strategy = FixedDelayStrategy(delay=10.0, max_attempts=5) - assert strategy.delay == 10.0 - assert strategy.max_attempts == 5 - - def test_should_restart_below_max(self): - """Test should_restart returns True when below max attempts""" - strategy = FixedDelayStrategy(max_attempts=3) - - assert strategy.should_restart(0) is True - assert strategy.should_restart(1) is True - assert strategy.should_restart(2) is True - - def test_should_restart_at_max(self): - """Test should_restart returns False at max attempts""" - strategy = FixedDelayStrategy(max_attempts=3) - - assert strategy.should_restart(3) is False - assert strategy.should_restart(4) is False - - def test_get_restart_delay_always_fixed(self): - """Test get_restart_delay returns fixed delay regardless of failure count""" - strategy = FixedDelayStrategy(delay=7.5) - - assert strategy.get_restart_delay(0) == 7.5 - assert strategy.get_restart_delay(1) == 7.5 - assert strategy.get_restart_delay(5) == 7.5 - assert strategy.get_restart_delay(100) == 7.5 - - def test_on_restart_attempt_no_op(self): - """Test on_restart_attempt is a no-op""" - strategy = FixedDelayStrategy() - - # Should not raise exception - strategy.on_restart_attempt(1) - strategy.on_restart_attempt(5) - - -class TestExponentialBackoffStrategy: - """Test ExponentialBackoffStrategy""" - - def test_init_default_parameters(self): - """Test initialization with default parameters""" - strategy = ExponentialBackoffStrategy() - assert strategy.initial_delay == 1.0 - assert strategy.max_delay == 60.0 - assert strategy.multiplier == 2.0 - assert strategy.max_attempts == 5 - - def test_init_custom_parameters(self): - """Test initialization with custom parameters""" - strategy = ExponentialBackoffStrategy( - initial_delay=2.0, max_delay=120.0, multiplier=3.0, max_attempts=10 - ) - assert strategy.initial_delay == 2.0 - assert strategy.max_delay == 120.0 - assert strategy.multiplier == 3.0 - assert strategy.max_attempts == 10 - - def test_should_restart_below_max(self): - """Test should_restart returns True when below max attempts""" - strategy = ExponentialBackoffStrategy(max_attempts=5) - - assert strategy.should_restart(0) is True - assert strategy.should_restart(1) is True - assert strategy.should_restart(4) is True - - def test_should_restart_at_max(self): - """Test should_restart returns False at max attempts""" - strategy = ExponentialBackoffStrategy(max_attempts=5) - - assert strategy.should_restart(5) is False - assert strategy.should_restart(6) is False - - def test_get_restart_delay_exponential_growth(self): - """Test get_restart_delay grows exponentially""" - strategy = ExponentialBackoffStrategy(initial_delay=1.0, max_delay=100.0, multiplier=2.0) - - # delay = initial_delay * multiplier^failure_count - assert strategy.get_restart_delay(0) == 1.0 # 1.0 * 2^0 = 1.0 - assert strategy.get_restart_delay(1) == 2.0 # 1.0 * 2^1 = 2.0 - assert strategy.get_restart_delay(2) == 4.0 # 1.0 * 2^2 = 4.0 - assert strategy.get_restart_delay(3) == 8.0 # 1.0 * 2^3 = 8.0 - - def test_get_restart_delay_respects_max(self): - """Test get_restart_delay respects max_delay cap""" - strategy = ExponentialBackoffStrategy(initial_delay=1.0, max_delay=10.0, multiplier=2.0) - - # Should cap at max_delay - assert strategy.get_restart_delay(10) == 10.0 # Would be 1024.0 without cap - assert strategy.get_restart_delay(20) == 10.0 # Would be very large - - def test_get_restart_delay_custom_multiplier(self): - """Test get_restart_delay with custom multiplier""" - strategy = ExponentialBackoffStrategy(initial_delay=1.0, max_delay=100.0, multiplier=3.0) - - assert strategy.get_restart_delay(0) == 1.0 # 1.0 * 3^0 = 1.0 - assert strategy.get_restart_delay(1) == 3.0 # 1.0 * 3^1 = 3.0 - assert strategy.get_restart_delay(2) == 9.0 # 1.0 * 3^2 = 9.0 - assert strategy.get_restart_delay(3) == 27.0 # 1.0 * 3^3 = 27.0 - - def test_get_restart_delay_initial_delay_effect(self): - """Test get_restart_delay with different initial delays""" - strategy = ExponentialBackoffStrategy(initial_delay=5.0, max_delay=100.0, multiplier=2.0) - - assert strategy.get_restart_delay(0) == 5.0 # 5.0 * 2^0 = 5.0 - assert strategy.get_restart_delay(1) == 10.0 # 5.0 * 2^1 = 10.0 - assert strategy.get_restart_delay(2) == 20.0 # 5.0 * 2^2 = 20.0 - - -class TestFailureRateStrategy: - """Test FailureRateStrategy""" - - def test_init_default_parameters(self): - """Test initialization with default parameters""" - strategy = FailureRateStrategy() - assert strategy.max_failures_per_interval == 5 - assert strategy.interval_seconds == 60.0 - assert strategy.delay == 5.0 - assert strategy.failure_timestamps == [] - - def test_init_custom_parameters(self): - """Test initialization with custom parameters""" - strategy = FailureRateStrategy( - max_failures_per_interval=10, interval_seconds=120.0, delay=15.0 - ) - assert strategy.max_failures_per_interval == 10 - assert strategy.interval_seconds == 120.0 - assert strategy.delay == 15.0 - - def test_should_restart_first_failure(self): - """Test should_restart on first failure""" - strategy = FailureRateStrategy(max_failures_per_interval=5) - - # First failure should be allowed - assert strategy.should_restart(1) is True - assert len(strategy.failure_timestamps) == 1 - - def test_should_restart_below_threshold(self): - """Test should_restart returns True when below threshold""" - strategy = FailureRateStrategy(max_failures_per_interval=5) - - # Simulate 3 failures - for i in range(3): - result = strategy.should_restart(i + 1) - assert result is True - - assert len(strategy.failure_timestamps) == 3 - - def test_should_restart_at_threshold(self): - """Test should_restart at failure threshold""" - strategy = FailureRateStrategy(max_failures_per_interval=5) - - # Simulate 5 failures - for i in range(5): - result = strategy.should_restart(i + 1) - assert result is True - - assert len(strategy.failure_timestamps) == 5 - - def test_should_restart_exceeds_threshold(self): - """Test should_restart returns False when threshold exceeded""" - strategy = FailureRateStrategy(max_failures_per_interval=5) - - # Simulate 5 failures - for i in range(5): - strategy.should_restart(i + 1) - - # 6th failure should fail - assert strategy.should_restart(6) is False - assert len(strategy.failure_timestamps) == 6 - - def test_should_restart_cleans_old_timestamps(self): - """Test should_restart cleans up old failure timestamps""" - strategy = FailureRateStrategy(max_failures_per_interval=3, interval_seconds=1.0) - - # Add 3 failures - for i in range(3): - strategy.should_restart(i + 1) - - # Wait for interval to expire - time.sleep(1.1) - - # Old timestamps should be cleaned up, new failure allowed - assert strategy.should_restart(4) is True - assert len(strategy.failure_timestamps) == 1 # Only the new one - - def test_should_restart_mixed_old_and_new(self): - """Test should_restart with mix of old and new timestamps""" - strategy = FailureRateStrategy(max_failures_per_interval=3, interval_seconds=2.0) - - # Add 2 failures - strategy.should_restart(1) - strategy.should_restart(2) - - # Wait half the interval - time.sleep(1.1) - - # Add 2 more failures (should keep old ones) - assert strategy.should_restart(3) is True - assert strategy.should_restart(4) is False # Exceeds threshold - - # Wait for old ones to expire - time.sleep(1.0) - - # Should only have recent failures, allow restart - assert strategy.should_restart(5) is True - - def test_get_restart_delay_always_fixed(self): - """Test get_restart_delay returns fixed delay""" - strategy = FailureRateStrategy(delay=12.5) - - assert strategy.get_restart_delay(0) == 12.5 - assert strategy.get_restart_delay(1) == 12.5 - assert strategy.get_restart_delay(10) == 12.5 - - -class TestRestartStrategyInterface: - """Test RestartStrategy abstract base class""" - - def test_cannot_instantiate_directly(self): - """Test that RestartStrategy cannot be instantiated directly""" - with pytest.raises(TypeError): - RestartStrategy() # type: ignore - - def test_subclass_must_implement_should_restart(self): - """Test that subclasses must implement should_restart""" - - class IncompleteStrategy(RestartStrategy): - def get_restart_delay(self, failure_count: int) -> float: - return 1.0 - - with pytest.raises(TypeError): - IncompleteStrategy() # type: ignore - - def test_subclass_must_implement_get_restart_delay(self): - """Test that subclasses must implement get_restart_delay""" - - class IncompleteStrategy(RestartStrategy): - def should_restart(self, failure_count: int) -> bool: - return True - - with pytest.raises(TypeError): - IncompleteStrategy() # type: ignore - - def test_on_restart_attempt_has_default_impl(self): - """Test that on_restart_attempt has a default no-op implementation""" - - class MinimalStrategy(RestartStrategy): - def should_restart(self, failure_count: int) -> bool: - return True - - def get_restart_delay(self, failure_count: int) -> float: - return 1.0 - - strategy = MinimalStrategy() - # Should not raise exception - strategy.on_restart_attempt(1) - - -class TestEdgeCases: - """Test edge cases and boundary conditions""" - - def test_fixed_delay_zero_delay(self): - """Test FixedDelayStrategy with zero delay""" - strategy = FixedDelayStrategy(delay=0.0) - assert strategy.get_restart_delay(1) == 0.0 - - def test_fixed_delay_zero_max_attempts(self): - """Test FixedDelayStrategy with zero max attempts""" - strategy = FixedDelayStrategy(max_attempts=0) - assert strategy.should_restart(0) is False - - def test_exponential_backoff_zero_initial_delay(self): - """Test ExponentialBackoffStrategy with zero initial delay""" - strategy = ExponentialBackoffStrategy(initial_delay=0.0) - assert strategy.get_restart_delay(0) == 0.0 - assert strategy.get_restart_delay(5) == 0.0 - - def test_exponential_backoff_multiplier_one(self): - """Test ExponentialBackoffStrategy with multiplier=1 (no growth)""" - strategy = ExponentialBackoffStrategy(initial_delay=5.0, multiplier=1.0) - - assert strategy.get_restart_delay(0) == 5.0 - assert strategy.get_restart_delay(1) == 5.0 - assert strategy.get_restart_delay(10) == 5.0 - - def test_exponential_backoff_max_delay_less_than_initial(self): - """Test ExponentialBackoffStrategy with max_delay < initial_delay""" - strategy = ExponentialBackoffStrategy(initial_delay=10.0, max_delay=5.0) - - # Should cap at max_delay even on first attempt - assert strategy.get_restart_delay(0) == 5.0 - - def test_failure_rate_zero_max_failures(self): - """Test FailureRateStrategy with zero max failures""" - strategy = FailureRateStrategy(max_failures_per_interval=0) - - # First failure should exceed threshold - assert strategy.should_restart(1) is False - - def test_failure_rate_very_short_interval(self): - """Test FailureRateStrategy with very short interval""" - strategy = FailureRateStrategy(max_failures_per_interval=3, interval_seconds=0.1) - - # Add 3 failures quickly - for i in range(3): - strategy.should_restart(i + 1) - - # Wait for interval to expire - time.sleep(0.15) - - # Should allow restart after cleanup - assert strategy.should_restart(4) is True - - def test_negative_failure_count(self): - """Test strategies with negative failure count""" - fixed = FixedDelayStrategy(max_attempts=3) - exponential = ExponentialBackoffStrategy() - - # Should handle gracefully - assert fixed.should_restart(-1) is True - assert exponential.should_restart(-1) is True - assert exponential.get_restart_delay(-1) == 0.5 # 1.0 * 2^-1 = 0.5 - - def test_very_large_failure_count(self): - """Test strategies with very large failure count""" - exponential = ExponentialBackoffStrategy(max_delay=100.0) - - # Should cap at max_delay - assert exponential.get_restart_delay(1000) == 100.0 diff --git a/packages/sage-kernel/tests/unit/kernel/runtime/communication/queue/test_ray_actor_queue_communication.py b/packages/sage-kernel/tests/unit/kernel/runtime/communication/queue/test_ray_actor_queue_communication.py deleted file mode 100644 index 7fb07ca760..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/runtime/communication/queue/test_ray_actor_queue_communication.py +++ /dev/null @@ -1,590 +0,0 @@ -#!/usr/bin/env python3 -# type: ignore -# ^ 忽略整个文件的类型检查（Ray Actor 动态方法导致大量误报） -""" -Ray Queue Actor 引用传递和并发测试 - -专门测试： -1. Ray队列在不同Actor之间的引用传递 -2. Actor间的并发读写 -3. Ray队列的分布式特性 -4. 队列在Actor生命周期中的持久性 - -Note: Pylance 类型检查说明： -- Ray Actor 的 .remote() 方法是动态添加的，Pylance 无法识别 -- 字典键访问可能触发 reportArgumentType 警告 -- 这些是误报，代码可以正常运行 -- 使用 # type: ignore 忽略整个文件的类型检查 -""" - -import os -import sys -import time -from typing import Any - -import pytest - -# 添加正确的项目路径 -current_dir = os.path.dirname(os.path.abspath(__file__)) -sage_kernel_src = os.path.join(current_dir, "../../../../../src") -sage_kernel_tests = os.path.join(current_dir, "../../../../..") -sys.path.insert(0, os.path.abspath(sage_kernel_src)) -sys.path.insert(0, os.path.abspath(sage_kernel_tests)) - -try: - from unit.utils.log_manager_helper import ( - get_test_log_manager, - setup_quiet_ray_logging, - ) - - from sage.kernel.utils.ray.ray_utils import ensure_ray_initialized - from sage.platform.queue import RayQueueDescriptor - - # 设置安静的日志记录 - setup_quiet_ray_logging() - - # 获取日志管理器 - log_manager = get_test_log_manager() - - print("✓ 成功导入Ray队列描述符") - IMPORTS_AVAILABLE = True -except ImportError as e: - print(f"✗ 导入失败: {e}") - IMPORTS_AVAILABLE = False - pytest.skip(f"Required imports not available: {e}", allow_module_level=True) - -try: - import ray - - RAY_AVAILABLE = True - print("✓ Ray 可用") -except ImportError: - RAY_AVAILABLE = False - print("✗ Ray 不可用") - pytest.skip("Ray is not available", allow_module_level=True) - - -# ============ Ray Actor 定义 ============ - - -@ray.remote -class PersistentQueueActor: - """持久化队列Actor - 维护队列描述符的引用""" - - def __init__(self, queue_desc_dict: dict[str, Any], actor_name: str): - """初始化Actor并建立队列连接""" - self.actor_name = actor_name - - # 在Ray Actor中导入所需模块 - 直接导入而不设置路径 - try: - from sage.platform.queue import resolve_descriptor - - self.queue_desc = resolve_descriptor(queue_desc_dict) - self.queue = self.queue_desc.queue_instance # 获取实际的队列对象 - self.operations_count = 0 - self.last_operation_time = time.time() - print(f"Actor {actor_name} initialized with queue {self.queue_desc.queue_id}") - except ImportError as e: - # 如果导入失败，记录错误但继续初始化 - print(f"导入失败: {e}") - self.queue_desc = None - self.queue = None - self.operations_count = 0 - self.last_operation_time = time.time() - print(f"Actor {actor_name} initialized with FAILED queue import") - - def get_queue_info(self): - """获取队列信息""" - if self.queue_desc is None: - return { - "actor_name": self.actor_name, - "queue_id": "FAILED_IMPORT", - "queue_type": "ray_queue", - "operations_count": self.operations_count, - "is_initialized": False, - "last_operation_time": self.last_operation_time, - } - - return { - "actor_name": self.actor_name, - "queue_id": self.queue_desc.queue_id, - "queue_type": "ray_queue", - "operations_count": self.operations_count, - "is_initialized": True, - "last_operation_time": self.last_operation_time, - } - - def put_items(self, items: list[str], delay_between_items: float = 0.0): - """向队列放入多个项目""" - if self.queue is None: - return [f"put_error:{item}:Queue not initialized" for item in items] - - results = [] - for item in items: - try: - enhanced_item = f"{self.actor_name}:{item}:{time.time()}" - self.queue.put(enhanced_item) - results.append(f"put_success:{enhanced_item}") - self.operations_count += 1 - self.last_operation_time = time.time() - - if delay_between_items > 0: - time.sleep(delay_between_items) - - except Exception as e: - results.append(f"put_error:{item}:{e}") - - return results - - def get_items(self, max_items: int, timeout_per_item: float = 1.0): - """从队列获取多个项目""" - if self.queue is None: - return ["get_error:Queue not initialized"] - - results = [] - for _i in range(max_items): - try: - item = self.queue.get(timeout=timeout_per_item) - results.append(f"get_success:{item}") - self.operations_count += 1 - self.last_operation_time = time.time() - except Exception as e: - results.append(f"get_timeout_or_error:{e}") - break - - return results - - def check_queue_status(self): - """检查队列状态""" - if self.queue is None: - return {"error": "Queue not initialized"} - - try: - size = self.queue.qsize() - empty = self.queue.empty() - return { - "size": size, - "empty": empty, - "operations_count": self.operations_count, - "last_operation": self.last_operation_time, - } - except Exception as e: - return {"error": str(e)} - - def stress_test_operations(self, num_operations: int): - """压力测试操作""" - if self.queue is None: - return {"error": "Queue not initialized", "completed_operations": 0} - - start_time = time.time() - completed_ops = 0 - - for i in range(num_operations): - try: - if i % 2 == 0: # 写操作 - item = f"stress_{self.actor_name}_{i}_{time.time()}" - self.queue.put(item) - else: # 读操作 - try: - item = self.queue.get(timeout=0.1) - except Exception: - # 队列空时跳过 - pass - completed_ops += 1 - self.operations_count += 1 - except Exception: - break - - end_time = time.time() - return { - "completed_operations": completed_ops, - "duration": end_time - start_time, - "ops_per_second": ( - completed_ops / (end_time - start_time) if end_time > start_time else 0 - ), - } - - -@ray.remote -class QueueCoordinatorActor: - """队列协调器Actor - 管理多个队列操作""" - - def __init__(self): - self.managed_queues = {} - self.coordination_log = [] - - def register_queue(self, queue_name: str, queue_desc_dict: dict[str, Any]): - """注册一个队列""" - try: - from sage.platform.queue import resolve_descriptor - - queue_desc = resolve_descriptor(queue_desc_dict) - self.managed_queues[queue_name] = { - "queue_desc": queue_desc, - "register_time": time.time(), - } - self.coordination_log.append(f"registered_queue:{queue_name}") - return f"Queue {queue_name} registered" - except ImportError as e: - print(f"导入失败: {e}") - self.coordination_log.append(f"failed_register_queue:{queue_name}:{e}") - return f"Queue {queue_name} registration failed: {e}" - - def coordinate_batch_operation(self, queue_name: str, operation: str, items: list[str]): - """协调批量操作""" - if queue_name not in self.managed_queues: - return f"Queue {queue_name} not found" - - queue_info = self.managed_queues[queue_name] - queue_desc = queue_info["queue_desc"] - - if queue_desc is None: - return f"Queue {queue_name} not properly initialized" - - queue = queue_desc.queue_instance - results = [] - - if operation == "put_batch": - for item in items: - try: - queue.put(f"coordinator:{item}:{time.time()}") - results.append(f"success:{item}") - except Exception as e: - results.append(f"error:{item}:{e}") - - elif operation == "get_batch": - for _i in range(len(items)): # items作为计数使用 - try: - item = queue.get(timeout=1.0) - results.append(f"success:{item}") - except Exception as e: - results.append(f"timeout:{e}") - break - - self.coordination_log.append(f"coordinated:{operation}:{queue_name}:{len(results)}") - return results - - def get_coordination_summary(self): - """获取协调摘要""" - queue_summaries = {} - for name, queue_info in self.managed_queues.items(): - try: - queue_desc = queue_info["queue_desc"] - if queue_desc is None: - queue_summaries[name] = {"error": "Queue not properly initialized"} - else: - queue = queue_desc.queue_instance - queue_summaries[name] = { - "queue_id": queue_desc.queue_id, - "size": queue.qsize(), - "empty": queue.empty(), - } - except Exception as e: - queue_summaries[name] = {"error": str(e)} - - return { - "managed_queues": queue_summaries, - "coordination_log": self.coordination_log[-10:], # 最近10条记录 - } - - -# ============ 测试类 ============ - - -@pytest.mark.ray -class TestRayQueueActorCommunication: - """Ray队列Actor通信测试""" - - def setup_method(self): - """测试设置""" - # 使用源文件中的ensure_ray_initialized，它会自动配置正确的runtime_env - ensure_ray_initialized() - - # 创建测试队列 - self.test_queue = RayQueueDescriptor(queue_id="test_ray_actor_comm", maxsize=1000) - self.queue_dict = self.test_queue.to_dict() - - def teardown_method(self): - """测试清理""" - # Ray会自动清理Actor，但我们可以显式关闭 - pass - - def test_basic_actor_queue_operations(self): - """测试基础Actor队列操作""" - log_manager.log_test_start("test_basic_actor_queue_operations") - start_time = time.time() - - # 创建两个Actor - producer_actor = PersistentQueueActor.remote(self.queue_dict, "producer") - consumer_actor = PersistentQueueActor.remote(self.queue_dict, "consumer") - - # 生产者放入数据 - items_to_produce = ["item1", "item2", "item3", "item4", "item5"] - produce_result = ray.get(producer_actor.put_items.remote(items_to_produce)) - log_manager.log_ray_operation("producer_put", f"{len(produce_result)} items") - - # 添加小延迟确保数据已写入 - time.sleep(0.1) - - # 检查队列状态 - producer_status = ray.get(producer_actor.check_queue_status.remote()) - log_manager.log_ray_operation( - "check_status", f"queue_size={producer_status.get('size', 'unknown')}" - ) - - # 消费者获取数据（减少超时时间） - consume_result = ray.get( - consumer_actor.get_items.remote(len(items_to_produce), timeout_per_item=0.5) - ) - log_manager.log_ray_operation("consumer_get", f"{len(consume_result)} items") - - # 统计成功获取的项目数 - successful_gets = [r for r in consume_result if r.startswith("get_success")] - - # 检查Actor状态 - producer_info = ray.get(producer_actor.get_queue_info.remote()) - consumer_info = ray.get(consumer_actor.get_queue_info.remote()) - - log_manager.log_ray_operation( - "final_status", - f"producer_ops={producer_info['operations_count']}, consumer_ops={consumer_info['operations_count']}", - ) - - # 验证断言 - assert producer_info["operations_count"] == len(items_to_produce), ( - f"生产者应该执行了{len(items_to_produce)}次操作" - ) - assert len(successful_gets) > 0, ( - f"消费者应该成功获取了一些项目，但实际获取了{len(successful_gets)}个" - ) - - duration = time.time() - start_time - log_manager.log_test_end("test_basic_actor_queue_operations", duration, True) - print("✓ 基础Actor队列操作测试通过") - - def test_multiple_actors_concurrent_access(self): - """测试多个Actor并发访问同一队列 - 简化版本""" - print("\n=== 测试多Actor并发访问 ===") - - # 减少Actor数量和操作数量避免死锁 - num_producers = 2 # 减少生产者数量 - num_consumers = 2 # 减少消费者数量 - items_per_producer = 5 # 减少每个生产者的项目数量 - - producers = [] - consumers = [] - - # 创建生产者Actor - for i in range(num_producers): - actor = PersistentQueueActor.remote(self.queue_dict, f"producer_{i}") - producers.append(actor) - - # 创建消费者Actor - for i in range(num_consumers): - actor = PersistentQueueActor.remote(self.queue_dict, f"consumer_{i}") - consumers.append(actor) - - print(f"创建了 {num_producers} 个生产者Actor和 {num_consumers} 个消费者Actor") - - # 并发生产，添加超时保护 - try: - producer_futures = [] - for i, producer in enumerate(producers): - items = [f"batch_{i}_item_{j}" for j in range(items_per_producer)] - future = producer.put_items.remote(items, delay_between_items=0.01) - producer_futures.append(future) - - # 等待生产完成，减少超时时间 - producer_results = ray.get(producer_futures, timeout=8) - total_produced = sum(len(result) for result in producer_results) - print(f"总共生产: {total_produced} 项目") - - # 短暂等待 - time.sleep(0.2) - - # 并发消费 - consumer_futures = [] - expected_per_consumer = max(1, total_produced // num_consumers) - for consumer in consumers: - future = consumer.get_items.remote(expected_per_consumer, timeout_per_item=1.0) - consumer_futures.append(future) - - # 等待消费完成，减少超时时间 - consumer_results = ray.get(consumer_futures, timeout=6) - total_consumed = sum( - len([r for r in result if r.startswith("get_success")]) - for result in consumer_results - ) - print(f"总共消费: {total_consumed} 项目") - - assert total_consumed > 0, "应该有成功的消费操作" - print("✓ 多Actor并发访问测试通过") - - except ray.exceptions.GetTimeoutError: - print("⚠️ 多Actor测试超时，可能存在竞争条件") - # 清理资源 - for actor in producers + consumers: - try: - ray.kill(actor) - except Exception: - pass - - assert total_produced > 0, "应该生产了一些项目" - assert total_consumed > 0, "应该消费了一些项目" - - print("✓ 多Actor并发访问测试通过") - - def test_queue_coordinator_pattern(self): - """测试队列协调器模式""" - print("\n=== 测试队列协调器模式 ===") - - # 创建协调器 - coordinator = QueueCoordinatorActor.remote() - - # 注册队列 - register_result = ray.get(coordinator.register_queue.remote("main_queue", self.queue_dict)) - print(f"队列注册结果: {register_result}") - - # 通过协调器进行批量写入 - items_to_write = ["coord_item1", "coord_item2", "coord_item3", "coord_item4"] - batch_put_result = ray.get( - coordinator.coordinate_batch_operation.remote("main_queue", "put_batch", items_to_write) - ) - print(f"批量写入结果: {len(batch_put_result)} 项目") - - # 通过协调器进行批量读取 - batch_get_result = ray.get( - coordinator.coordinate_batch_operation.remote( - "main_queue", - "get_batch", - [""] * len(items_to_write), # 占位符 - ) - ) - print(f"批量读取结果: {len(batch_get_result)} 项目") - - # 获取协调摘要 - summary = ray.get(coordinator.get_coordination_summary.remote()) - print(f"协调摘要: {summary}") - - assert len(batch_put_result) == len(items_to_write), "所有项目应该被写入" - assert len(batch_get_result) > 0, "应该读取了一些项目" - - print("✓ 队列协调器模式测试通过") - - def test_actor_lifecycle_and_queue_persistence(self): - """测试Actor生命周期和队列持久性""" - print("\n=== 测试Actor生命周期和队列持久性 ===") - - # 第一阶段：创建Actor并写入数据 - phase1_actor = PersistentQueueActor.remote(self.queue_dict, "phase1_actor") - phase1_items = ["persistent_item1", "persistent_item2", "persistent_item3"] - - put_result = ray.get(phase1_actor.put_items.remote(phase1_items)) - print(f"阶段1写入结果: {len(put_result)} 项目") - - # 获取Actor信息 - phase1_info = ray.get(phase1_actor.get_queue_info.remote()) - print(f"阶段1 Actor信息: {phase1_info}") - - # 第二阶段：创建新Actor并读取数据（模拟Actor重启） - phase2_actor = PersistentQueueActor.remote(self.queue_dict, "phase2_actor") - - get_result = ray.get(phase2_actor.get_items.remote(len(phase1_items))) - successful_gets = [r for r in get_result if r.startswith("get_success")] - print(f"阶段2读取结果: {len(successful_gets)} 项目") - - # 验证数据持久性 - for item in successful_gets: - print(f" 读取到: {item}") - - assert len(successful_gets) > 0, "新Actor应该能读取到之前写入的数据" - - print("✓ Actor生命周期和队列持久性测试通过") - - def test_concurrent_stress_with_actors(self): - """Actor并发压力测试 - 简化版本避免死锁""" - print("\n=== Actor并发压力测试 ===") - - num_actors = 3 # 减少Actor数量 - operations_per_actor = 10 # 减少操作数量 - - # 创建多个Actor进行压力测试 - stress_actors = [] - for i in range(num_actors): - actor = PersistentQueueActor.remote(self.queue_dict, f"stress_actor_{i}") - stress_actors.append(actor) - - print(f"创建 {num_actors} 个Actor，每个执行 {operations_per_actor} 个操作") - - # 并发执行操作，添加超时 - stress_futures = [] - for i, actor in enumerate(stress_actors): - future = actor.stress_test_operations.remote(operations_per_actor) # 只传递操作数量 - stress_futures.append(future) - - # 获取结果，设置较短超时避免死锁 - try: - stress_results = ray.get(stress_futures, timeout=30) # 30秒超时 - print(f"✓ 压力测试完成，{len(stress_results)}个Actor全部成功") - - # 验证结果 - total_operations = sum(len(result) for result in stress_results) - expected_operations = num_actors * operations_per_actor * 2 # put + get - - print(f"总操作数: {total_operations}, 预期: {expected_operations}") - assert total_operations > 0, "应该有成功的操作" - - except ray.exceptions.GetTimeoutError: - print("⚠️ 压力测试超时，可能存在死锁，跳过验证") - # 清理Actor避免资源泄露 - for actor in stress_actors: - try: - ray.kill(actor) - except Exception: - pass - - -def run_ray_actor_tests(): - """运行Ray Actor测试""" - if not RAY_AVAILABLE: - print("Ray不可用，跳过Ray Actor测试") - return False - - print("开始运行Ray队列Actor通信测试...") - - test_suite = TestRayQueueActorCommunication() - - try: - # 设置测试环境 - test_suite.setup_method() - - # 运行所有测试 - test_suite.test_basic_actor_queue_operations() - test_suite.test_multiple_actors_concurrent_access() - test_suite.test_queue_coordinator_pattern() - test_suite.test_actor_lifecycle_and_queue_persistence() - test_suite.test_concurrent_stress_with_actors() - - # 清理测试环境 - test_suite.teardown_method() - - print("\n🎉 所有Ray Actor测试通过！") - return True - - except Exception as e: - print(f"\n❌ Ray Actor测试失败: {e}") - import traceback - - traceback.print_exc() - return False - - finally: - # 确保Ray清理 - if ray.is_initialized(): - ray.shutdown() - - -if __name__ == "__main__": - success = run_ray_actor_tests() - if not success: - sys.exit(1) diff --git a/packages/sage-kernel/tests/unit/kernel/runtime/communication/queue/test_reference_passing_and_concurrency.py b/packages/sage-kernel/tests/unit/kernel/runtime/communication/queue/test_reference_passing_and_concurrency.py deleted file mode 100644 index 70e7022dba..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/runtime/communication/queue/test_reference_passing_and_concurrency.py +++ /dev/null @@ -1,475 +0,0 @@ -#!/usr/bin/env python3 -# type: ignore -# ^ 忽略整个文件的类型检查（Ray Actor 动态方法导致大量误报） -""" -测试队列描述符的引用传递和并发读写能力 - -验证： -1. 引用传递（对象在不同进程/线程间的共享） -2. 并发读写安全性 -3. Ray Actor之间的队列引用传递 -4. 不同队列类型的并发性能测试 -""" - -import logging -import os -import sys -import time -from concurrent.futures import ThreadPoolExecutor, as_completed -from typing import Any - -import pytest - -# 添加项目路径 -current_dir = os.path.dirname(os.path.abspath(__file__)) -sage_kernel_src = os.path.join(current_dir, "../../../../../src") -sys.path.insert(0, os.path.abspath(sage_kernel_src)) - -try: - from sage.kernel.utils.ray.ray_utils import ( # noqa: F401 - ensure_ray_initialized, - ) - from sage.platform.queue import ( - BaseQueueDescriptor, # noqa: F401 - PythonQueueDescriptor, - RayQueueDescriptor, - resolve_descriptor, # noqa: F401 - ) - - print("✓ 成功导入队列描述符") -except ImportError as e: - print(f"✗ 导入失败: {e}") - sys.exit(1) - -# 配置日志 -logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") -logger = logging.getLogger(__name__) - - -# ============ 辅助函数 ============ - - -def worker_producer( - queue_desc: BaseQueueDescriptor, - worker_id: int, - num_items: int, - item_prefix: str = "item", -): - """生产者工作线程""" - try: - for i in range(num_items): - item = f"{item_prefix}_{worker_id}_{i}" - queue_desc.put(item) - logger.debug(f"Producer {worker_id} put: {item}") - logger.info(f"Producer {worker_id} completed {num_items} items") - return f"producer_{worker_id}_done" - except Exception as e: - logger.error(f"Producer {worker_id} failed: {e}") - return f"producer_{worker_id}_error: {e}" - - -def worker_consumer( - queue_desc: BaseQueueDescriptor, - worker_id: int, - expected_items: int, - timeout: float = 10.0, -): - """消费者工作线程""" - try: - consumed_items = [] - start_time = time.time() - - while len(consumed_items) < expected_items: - if time.time() - start_time > timeout: - break - - try: - item = queue_desc.get(timeout=1.0) - consumed_items.append(item) - logger.debug(f"Consumer {worker_id} got: {item}") - except Exception: - continue - - logger.info(f"Consumer {worker_id} consumed {len(consumed_items)} items") - return consumed_items - except Exception as e: - logger.error(f"Consumer {worker_id} failed: {e}") - return [] - - -def worker_mixed_operations(queue_desc: BaseQueueDescriptor, worker_id: int, num_operations: int): - """混合读写操作工作线程""" - try: - operations_completed = 0 - for i in range(num_operations): - if i % 2 == 0: # 偶数次执行写操作 - item = f"mixed_{worker_id}_{i}" - queue_desc.put(item) - logger.debug(f"Mixed worker {worker_id} put: {item}") - else: # 奇数次执行读操作 - try: - item = queue_desc.get(timeout=0.1) - logger.debug(f"Mixed worker {worker_id} got: {item}") - except Exception: - # 队列为空时跳过 - pass - operations_completed += 1 - - logger.info(f"Mixed worker {worker_id} completed {operations_completed} operations") - return operations_completed - except Exception as e: - logger.error(f"Mixed worker {worker_id} failed: {e}") - return 0 - - -# ============ 多进程工作函数（已移除，因为Python multiprocessing.Queue引用传递困难） ============ - -# 注释：原本的 multiprocess_producer 和 multiprocess_consumer 函数已移除 -# 因为Python multiprocessing.Queue的队列描述符引用很难跨进程传递 - - -# ============ Ray Actor 相关测试 ============ - -try: - import ray - - @ray.remote - class QueueProducerActor: - """Ray Actor 生产者""" - - def produce_items(self, queue_desc_dict: dict[str, Any], actor_id: int, num_items: int): - """生产物品到队列""" - try: - # 从字典重建队列描述符 - from sage.platform.queue import resolve_descriptor - - queue_desc = resolve_descriptor(queue_desc_dict) - - for i in range(num_items): - item = f"ray_actor_{actor_id}_{i}" - queue_desc.put(item) - - return f"actor_producer_{actor_id}_completed_{num_items}" - except Exception as e: - return f"actor_producer_{actor_id}_error: {e}" - - @ray.remote - class QueueConsumerActor: - """Ray Actor 消费者""" - - def consume_items( - self, queue_desc_dict: dict[str, Any], actor_id: int, expected_items: int - ): - """从队列消费物品""" - try: - # 从字典重建队列描述符 - from sage.platform.queue import resolve_descriptor - - queue_desc = resolve_descriptor(queue_desc_dict) - - consumed_items = [] - start_time = time.time() - - while len(consumed_items) < expected_items: - if time.time() - start_time > 30.0: # 30秒超时 - break - - try: - item = queue_desc.get(timeout=1.0) - consumed_items.append(item) - except Exception: - continue - - return consumed_items - except Exception: - return [] - - RAY_AVAILABLE = True - -except ImportError: - RAY_AVAILABLE = False - print("⚠️ Ray not available, skipping Ray tests") - - -# ============ 测试类 ============ - - -class TestPythonQueueConcurrency: - """Python队列并发测试 - 不需要Ray""" - - def test_python_queue_multithreading(self): - """测试Python队列的多线程并发""" - print("\n=== 测试Python队列多线程并发 ===") - - # 创建队列描述符 - queue_desc = PythonQueueDescriptor(queue_id="test_python_mt", maxsize=100) - - # 参数设置 - num_producers = 3 - num_consumers = 2 - items_per_producer = 10 - total_items = num_producers * items_per_producer - - print(f"配置: {num_producers}个生产者, {num_consumers}个消费者, 总共{total_items}个项目") - - # 启动生产者线程 - with ThreadPoolExecutor(max_workers=num_producers + num_consumers) as executor: - # 提交生产者任务 - producer_futures = [] - for i in range(num_producers): - future = executor.submit(worker_producer, queue_desc, i, items_per_producer) - producer_futures.append(future) - - # 等待所有生产者完成 - producer_results = [] - for future in as_completed(producer_futures): - result = future.result() - producer_results.append(result) - print(f"生产者结果: {result}") - - # 启动消费者线程 - consumer_futures = [] - expected_per_consumer = total_items // num_consumers - for i in range(num_consumers): - future = executor.submit(worker_consumer, queue_desc, i, expected_per_consumer) - consumer_futures.append(future) - - # 等待所有消费者完成 - consumer_results = [] - for future in as_completed(consumer_futures): - result = future.result() - consumer_results.append(result) - print(f"消费者结果: 消费了{len(result)}个项目") - - # 验证结果 - total_consumed = sum(len(items) for items in consumer_results) - print(f"总共消费: {total_consumed}/{total_items}") - print(f"剩余队列大小: {queue_desc.qsize()}") - - assert len(producer_results) == num_producers, "所有生产者应该完成" - assert total_consumed > 0, "应该消费了一些项目" - - print("✓ Python队列多线程测试通过") - - def test_python_queue_mixed_operations(self): - """测试Python队列的混合读写操作""" - print("\n=== 测试Python队列混合读写操作 ===") - - queue_desc = PythonQueueDescriptor(queue_id="test_python_mixed", maxsize=50) - - # 先放入一些初始数据 - for i in range(10): - queue_desc.put(f"initial_{i}") - - num_workers = 5 - operations_per_worker = 20 - - print(f"配置: {num_workers}个混合工作线程, 每个执行{operations_per_worker}个操作") - - with ThreadPoolExecutor(max_workers=num_workers) as executor: - futures = [] - for i in range(num_workers): - future = executor.submit( - worker_mixed_operations, queue_desc, i, operations_per_worker - ) - futures.append(future) - - results = [] - for future in as_completed(futures): - result = future.result() - results.append(result) - print(f"混合工作线程完成操作数: {result}") - - print(f"最终队列大小: {queue_desc.qsize()}") - assert len(results) == num_workers, "所有工作线程应该完成" - - print("✓ Python队列混合操作测试通过") - - def test_serializable_queue_multiprocessing(self): - """测试可序列化队列的多进程操作（跳过，因为Python multiprocessing.Queue引用传递困难）""" - print("\n=== 跳过多进程测试 ===") - print("⚠️ Python multiprocessing.Queue的队列描述符引用很难跨进程传递，跳过此测试") - print("✓ 多进程测试跳过") - - def test_queue_reference_integrity(self): - """测试队列引用的完整性""" - print("\n=== 测试队列引用完整性 ===") - - # 创建原始队列描述符 - original_desc = PythonQueueDescriptor(queue_id="reference_test", maxsize=20) - - # 放入一些数据 - for i in range(5): - original_desc.put(f"ref_item_{i}") - - print(f"原始队列大小: {original_desc.qsize()}") - - # 克隆描述符 - cloned_desc = original_desc.clone("reference_test_clone") - - # 验证克隆的描述符引用了相同的队列（对于不可序列化的Python队列） - cloned_desc.put("cloned_item") - print(f"添加项目后克隆队列大小: {cloned_desc.qsize()}") - - # 从原始描述符读取 - items_from_original = [] - while not original_desc.empty(): - try: - item = original_desc.get_nowait() - items_from_original.append(item) - except Exception: - break - - print(f"从原始描述符读取的项目: {len(items_from_original)}") - print(f"读取后原始队列大小: {original_desc.qsize()}") - - print("✓ 队列引用完整性测试通过") - - def test_concurrent_stress_test(self): - """并发压力测试""" - print("\n=== 并发压力测试 ===") - - queue_desc = PythonQueueDescriptor(queue_id="stress_test", maxsize=1000) - - num_threads = 10 - operations_per_thread = 50 - - print(f"压力测试配置: {num_threads}个线程, 每个执行{operations_per_thread}个操作") - - start_time = time.time() - - with ThreadPoolExecutor(max_workers=num_threads) as executor: - futures = [] - for i in range(num_threads): - future = executor.submit( - worker_mixed_operations, queue_desc, i, operations_per_thread - ) - futures.append(future) - - completed_operations = [] - for future in as_completed(futures): - result = future.result() - completed_operations.append(result) - - end_time = time.time() - duration = end_time - start_time - - total_operations = sum(completed_operations) - operations_per_second = total_operations / duration if duration > 0 else 0 - - print("压力测试结果:") - print(f" 总操作数: {total_operations}") - print(f" 耗时: {duration:.2f}秒") - print(f" 操作/秒: {operations_per_second:.2f}") - print(f" 最终队列大小: {queue_desc.qsize()}") - - assert total_operations > 0, "应该完成一些操作" - - print("✓ 并发压力测试通过") - - -@pytest.mark.ray -class TestRayQueueConcurrency: - """Ray队列并发测试 - 需要Ray环境""" - - def test_ray_queue_actor_communication(self): - """测试Ray队列Actor通信""" - print("\n=== 测试Ray队列Actor通信 ===") - - if not ray.is_initialized(): - ensure_ray_initialized() - - try: - # 创建Ray队列描述符 - ray_desc = RayQueueDescriptor(queue_id="ray_actor_comm_test", maxsize=100) - - num_producer_actors = 2 - num_consumer_actors = 2 - items_per_actor = 5 - - print(f"Ray Actor配置: {num_producer_actors}个生产者, {num_consumer_actors}个消费者") - - # 创建生产者和消费者Actor - producer_actors = [QueueProducerActor.remote() for _ in range(num_producer_actors)] - consumer_actors = [QueueConsumerActor.remote() for _ in range(num_consumer_actors)] - - # 获取队列字典用于Actor通信 - queue_dict = ray_desc.to_dict() - - # 启动生产者 - producer_futures = [] - for i, actor in enumerate(producer_actors): - future = actor.produce_items.remote(queue_dict, i, items_per_actor) - producer_futures.append(future) - - # 等待生产者完成 - producer_results = ray.get(producer_futures) - for result in producer_results: - print(f"Ray生产者Actor结果: {result}") - - # 启动消费者 - consumer_futures = [] - expected_per_consumer = (num_producer_actors * items_per_actor) // num_consumer_actors - for i, actor in enumerate(consumer_actors): - future = actor.consume_items.remote(queue_dict, i, expected_per_consumer) - consumer_futures.append(future) - - # 等待消费者完成 - consumer_results = ray.get(consumer_futures) - total_consumed = sum( - len(items) for items in consumer_results if isinstance(items, list) - ) - - print(f"Ray Actor总共消费: {total_consumed}") - for i, result in enumerate(consumer_results): - if isinstance(result, list): - print(f"消费者Actor {i}: 消费了{len(result)}个项目") - - print("✓ Ray队列Actor通信测试通过") - - except Exception as e: - print(f"⚠️ Ray Actor测试失败: {e}") - import traceback - - traceback.print_exc() - - -def run_all_tests(): - """运行所有测试""" - print("开始运行引用传递和并发测试...") - - test_suite = TestPythonQueueConcurrency() - - try: - # 基础多线程测试 - test_suite.test_python_queue_multithreading() - test_suite.test_python_queue_mixed_operations() - - # 多进程测试 - test_suite.test_serializable_queue_multiprocessing() - - # 引用完整性测试 - test_suite.test_queue_reference_integrity() - - # 压力测试 - test_suite.test_concurrent_stress_test() - - print("\n🎉 Python队列测试通过！") - - # Ray测试需要单独运行（被pytest标记过滤） - print("\n注意: Ray队列测试需要使用 pytest -m ray 单独运行") - - except Exception as e: - print(f"\n❌ 测试失败: {e}") - import traceback - - traceback.print_exc() - return False - - return True - - -if __name__ == "__main__": - success = run_all_tests() - if not success: - sys.exit(1) diff --git a/packages/sage-kernel/tests/unit/kernel/runtime/communication/rpc/test_rpc_queue.py b/packages/sage-kernel/tests/unit/kernel/runtime/communication/rpc/test_rpc_queue.py deleted file mode 100644 index b90a64750f..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/runtime/communication/rpc/test_rpc_queue.py +++ /dev/null @@ -1,520 +0,0 @@ -""" -Unit tests for RPCQueue. - -Tests the RPC queue implementation for remote process communication. -Note: Current implementation is a stub using local Queue. -""" - -import time -from queue import Empty, Full - -import pytest - -from sage.kernel.runtime.communication.rpc.rpc_queue import RPCQueue - -# Test Cases - - -@pytest.mark.unit -class TestRPCQueueInitialization: - """Test RPCQueue initialization.""" - - def test_initialization_with_defaults(self): - """Test RPC queue initialization with default parameters.""" - queue = RPCQueue(queue_id="test_queue") - - assert queue.queue_id == "test_queue" - assert queue.host == "localhost" - assert queue.port == 50051 - assert queue.maxsize == 0 - assert not queue._connected - assert queue._queue is not None - - def test_initialization_with_custom_parameters(self): - """Test RPC queue initialization with custom parameters.""" - queue = RPCQueue( - queue_id="custom_queue", - host="192.168.1.100", - port=8080, - maxsize=100, - ) - - assert queue.queue_id == "custom_queue" - assert queue.host == "192.168.1.100" - assert queue.port == 8080 - assert queue.maxsize == 100 - assert not queue._connected - - def test_initialization_logs_stub_warning(self, caplog): - """Test that initialization logs a stub implementation warning.""" - RPCQueue(queue_id="warning_test") - - assert "STUB" in caplog.text - assert "warning_test" in caplog.text - - -@pytest.mark.unit -class TestRPCQueueConnection: - """Test RPCQueue connection management.""" - - def test_connect_first_time(self): - """Test connecting to RPC server for the first time.""" - queue = RPCQueue(queue_id="connect_test") - - result = queue.connect() - - assert result is True - assert queue._connected - - def test_connect_already_connected(self): - """Test connecting when already connected.""" - queue = RPCQueue(queue_id="connect_test") - queue.connect() - - # Connect again - result = queue.connect() - - assert result is True - assert queue._connected - - def test_close_connection(self): - """Test closing RPC connection.""" - queue = RPCQueue(queue_id="close_test") - queue.connect() - assert queue._connected - - queue.close() - - assert not queue._connected - - def test_close_when_not_connected(self): - """Test closing connection when not connected.""" - queue = RPCQueue(queue_id="close_test") - assert not queue._connected - - # Should not raise error - queue.close() - - assert not queue._connected - - -@pytest.mark.unit -class TestRPCQueuePutGet: - """Test RPCQueue put and get operations.""" - - def test_put_item(self): - """Test putting an item into the queue.""" - queue = RPCQueue(queue_id="put_test") - - queue.put("test_item") - - assert queue.qsize() == 1 - assert queue._connected # Auto-connect on put - - def test_put_multiple_items(self): - """Test putting multiple items into the queue.""" - queue = RPCQueue(queue_id="multi_put_test") - - for i in range(5): - queue.put(f"item_{i}") - - assert queue.qsize() == 5 - - def test_get_item(self): - """Test getting an item from the queue.""" - queue = RPCQueue(queue_id="get_test") - queue.put("test_data") - - item = queue.get() - - assert item == "test_data" - assert queue.qsize() == 0 - - def test_get_multiple_items(self): - """Test getting multiple items in order.""" - queue = RPCQueue(queue_id="multi_get_test") - items = ["first", "second", "third"] - - for item in items: - queue.put(item) - - retrieved = [] - for _ in range(3): - retrieved.append(queue.get()) - - assert retrieved == items - - def test_get_empty_queue_blocking_with_timeout(self): - """Test getting from empty queue with timeout.""" - queue = RPCQueue(queue_id="empty_test") - - with pytest.raises(Empty): - queue.get(block=True, timeout=0.1) - - def test_get_empty_queue_non_blocking(self): - """Test getting from empty queue in non-blocking mode.""" - queue = RPCQueue(queue_id="empty_nonblock_test") - - with pytest.raises(Empty): - queue.get(block=False) - - def test_put_with_maxsize_blocking(self): - """Test putting into full queue with blocking.""" - queue = RPCQueue(queue_id="full_test", maxsize=2) - - # Fill the queue - queue.put("item1") - queue.put("item2") - - # This should timeout - with pytest.raises(Full): - queue.put("item3", block=True, timeout=0.1) - - def test_put_with_maxsize_non_blocking(self): - """Test putting into full queue in non-blocking mode.""" - queue = RPCQueue(queue_id="full_nonblock_test", maxsize=1) - - queue.put("item1") - - with pytest.raises(Full): - queue.put("item2", block=False) - - -@pytest.mark.unit -class TestRPCQueueAutoConnect: - """Test RPCQueue auto-connection behavior.""" - - def test_put_auto_connects(self): - """Test that put() auto-connects if not connected.""" - queue = RPCQueue(queue_id="auto_connect_put") - assert not queue._connected - - queue.put("test") - - assert queue._connected - - def test_get_auto_connects(self): - """Test that get() auto-connects if not connected.""" - queue = RPCQueue(queue_id="auto_connect_get") - queue._queue.put("test") # Put directly to internal queue - assert not queue._connected - - queue.get() - - assert queue._connected - - -@pytest.mark.unit -class TestRPCQueueSizeChecks: - """Test RPCQueue size and state checking methods.""" - - def test_qsize_empty(self): - """Test qsize() on empty queue.""" - queue = RPCQueue(queue_id="size_test") - - assert queue.qsize() == 0 - - def test_qsize_with_items(self): - """Test qsize() with items in queue.""" - queue = RPCQueue(queue_id="size_test") - - for i in range(3): - queue.put(f"item_{i}") - - assert queue.qsize() == 3 - - def test_empty_when_empty(self): - """Test empty() returns True for empty queue.""" - queue = RPCQueue(queue_id="empty_check_test") - - assert queue.empty() - - def test_empty_when_not_empty(self): - """Test empty() returns False when queue has items.""" - queue = RPCQueue(queue_id="empty_check_test") - queue.put("item") - - assert not queue.empty() - - def test_full_when_not_full(self): - """Test full() returns False when queue is not full.""" - queue = RPCQueue(queue_id="full_check_test", maxsize=5) - queue.put("item") - - assert not queue.full() - - def test_full_when_full(self): - """Test full() returns True when queue is full.""" - queue = RPCQueue(queue_id="full_check_test", maxsize=2) - queue.put("item1") - queue.put("item2") - - assert queue.full() - - def test_full_with_unlimited_size(self): - """Test full() with unlimited size queue.""" - queue = RPCQueue(queue_id="unlimited_test", maxsize=0) - - for i in range(100): - queue.put(f"item_{i}") - - assert not queue.full() - - -@pytest.mark.unit -class TestRPCQueueContextManager: - """Test RPCQueue context manager protocol.""" - - def test_context_manager_enters_and_exits(self): - """Test RPCQueue can be used as context manager.""" - with RPCQueue(queue_id="context_test") as queue: - assert queue._connected - queue.put("test_item") - item = queue.get() - assert item == "test_item" - - # After exit, connection should be closed - assert not queue._connected - - def test_context_manager_with_exception(self): - """Test context manager properly closes on exception.""" - queue = None - try: - with RPCQueue(queue_id="exception_test") as q: - queue = q - assert queue._connected - raise ValueError("Test exception") - except ValueError: - pass - - assert not queue._connected - - -@pytest.mark.unit -class TestRPCQueueRepr: - """Test RPCQueue string representation.""" - - def test_repr_disconnected(self): - """Test __repr__ when disconnected.""" - queue = RPCQueue(queue_id="repr_test", host="testhost", port=9999) - - repr_str = repr(queue) - - assert "RPCQueue" in repr_str - assert "repr_test" in repr_str - assert "testhost" in repr_str - assert "9999" in repr_str - assert "disconnected" in repr_str - - def test_repr_connected(self): - """Test __repr__ when connected.""" - queue = RPCQueue(queue_id="repr_test") - queue.connect() - - repr_str = repr(queue) - - assert "connected" in repr_str - - def test_repr_with_items(self): - """Test __repr__ shows queue size.""" - queue = RPCQueue(queue_id="repr_test") - queue.put("item1") - queue.put("item2") - - repr_str = repr(queue) - - assert "size=2" in repr_str - - -@pytest.mark.unit -class TestRPCQueueConcurrency: - """Test RPCQueue concurrency and thread safety.""" - - def test_concurrent_put(self): - """Test concurrent put operations.""" - import threading - - queue = RPCQueue(queue_id="concurrent_put_test") - errors = [] - - def put_items(thread_id): - try: - for i in range(10): - queue.put(f"thread_{thread_id}_item_{i}") - except Exception as e: - errors.append(e) - - # Start 5 threads - threads = [] - for i in range(5): - thread = threading.Thread(target=put_items, args=(i,)) - threads.append(thread) - thread.start() - - # Wait for all threads - for thread in threads: - thread.join(timeout=5.0) - - assert len(errors) == 0 - assert queue.qsize() == 50 - - def test_concurrent_get(self): - """Test concurrent get operations.""" - import threading - - queue = RPCQueue(queue_id="concurrent_get_test") - - # Pre-fill queue - for i in range(50): - queue.put(f"item_{i}") - - results = [] - errors = [] - - def get_items(thread_id): - try: - for _ in range(10): - item = queue.get(timeout=2.0) - results.append(item) - except Exception as e: - errors.append(e) - - # Start 5 threads - threads = [] - for i in range(5): - thread = threading.Thread(target=get_items, args=(i,)) - threads.append(thread) - thread.start() - - # Wait for all threads - for thread in threads: - thread.join(timeout=5.0) - - assert len(errors) == 0 - assert len(results) == 50 - assert queue.empty() - - def test_concurrent_put_get(self): - """Test concurrent put and get operations.""" - import threading - - queue = RPCQueue(queue_id="concurrent_put_get_test", maxsize=10) - put_count = [0] - get_count = [0] - - def producer(): - for i in range(20): - queue.put(f"data_{i}", timeout=2.0) - put_count[0] += 1 - time.sleep(0.01) - - def consumer(): - for _ in range(20): - queue.get(timeout=2.0) - get_count[0] += 1 - time.sleep(0.01) - - producer_thread = threading.Thread(target=producer) - consumer_thread = threading.Thread(target=consumer) - - producer_thread.start() - consumer_thread.start() - - producer_thread.join(timeout=5.0) - consumer_thread.join(timeout=5.0) - - assert put_count[0] == 20 - assert get_count[0] == 20 - - -@pytest.mark.unit -class TestRPCQueueDataTypes: - """Test RPCQueue with different data types.""" - - def test_with_string(self): - """Test queue with string data.""" - queue = RPCQueue(queue_id="string_test") - queue.put("test string") - assert queue.get() == "test string" - - def test_with_integer(self): - """Test queue with integer data.""" - queue = RPCQueue(queue_id="int_test") - queue.put(42) - assert queue.get() == 42 - - def test_with_dict(self): - """Test queue with dictionary data.""" - queue = RPCQueue(queue_id="dict_test") - data = {"key": "value", "number": 123} - queue.put(data) - assert queue.get() == data - - def test_with_list(self): - """Test queue with list data.""" - queue = RPCQueue(queue_id="list_test") - data = [1, 2, 3, "four", {"five": 5}] - queue.put(data) - assert queue.get() == data - - def test_with_none(self): - """Test queue with None value.""" - queue = RPCQueue(queue_id="none_test") - queue.put(None) - assert queue.get() is None - - def test_with_custom_object(self): - """Test queue with custom object.""" - - class CustomObject: - def __init__(self, value): - self.value = value - - queue = RPCQueue(queue_id="custom_test") - obj = CustomObject(42) - queue.put(obj) - retrieved = queue.get() - - assert retrieved.value == 42 - - -@pytest.mark.unit -class TestRPCQueueEdgeCases: - """Test RPCQueue edge cases and error handling.""" - - def test_multiple_close_calls(self): - """Test multiple close() calls don't cause errors.""" - queue = RPCQueue(queue_id="multi_close_test") - queue.connect() - - queue.close() - queue.close() - queue.close() - - assert not queue._connected - - def test_put_after_close_reconnects(self): - """Test put after close auto-reconnects.""" - queue = RPCQueue(queue_id="reconnect_test") - queue.connect() - queue.close() - assert not queue._connected - - queue.put("test") - - assert queue._connected - - def test_zero_timeout(self): - """Test operations with zero timeout.""" - queue = RPCQueue(queue_id="zero_timeout_test") - - with pytest.raises(Empty): - queue.get(block=True, timeout=0) - - def test_very_large_timeout(self): - """Test operations with very large timeout.""" - queue = RPCQueue(queue_id="large_timeout_test") - queue.put("test", timeout=999999) - - item = queue.get(timeout=999999) - assert item == "test" diff --git a/packages/sage-kernel/tests/unit/kernel/runtime/distributed/test_actor.py b/packages/sage-kernel/tests/unit/kernel/runtime/distributed/test_actor.py deleted file mode 100644 index 3f0a2c1a15..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/runtime/distributed/test_actor.py +++ /dev/null @@ -1,528 +0,0 @@ -""" -Test suite for sage.kernels.runtime.distributed.actor module - -Tests the ActorWrapper class which provides transparent proxying -between local objects and Ray actors. -""" - -from typing import cast -from unittest.mock import Mock, patch - -import pytest - -from sage.kernel.utils.ray.actor import ActorWrapper - - -class MockLocalObject: - """Mock local object for testing""" - - def __init__(self, value=42): - self.value = value - self.call_count = 0 - - def get_value(self): - """Simple method that returns value""" - self.call_count += 1 - return self.value - - def set_value(self, new_value): - """Simple method that sets value""" - self.call_count += 1 - self.value = new_value - - def add(self, x, y): - """Method with multiple parameters""" - self.call_count += 1 - return x + y - - def complex_operation(self, data, multiplier=2, **kwargs): - """Method with complex parameters""" - self.call_count += 1 - result = data * multiplier - if kwargs.get("add_value"): - result += kwargs["add_value"] - return result - - -class MockRayActor: - """Mock Ray actor for testing""" - - def __init__(self, value=42): - self.value = value - self.call_count = 0 - - def get_value(self): - """Ray actor method that returns ObjectRef""" - self.call_count += 1 - # Create a mock ObjectRef - object_ref = Mock() - object_ref.remote = Mock(return_value=object_ref) - return object_ref - - def set_value(self, new_value): - """Ray actor method""" - self.call_count += 1 - self.value = new_value - object_ref = Mock() - object_ref.remote = Mock(return_value=object_ref) - return object_ref - - def add(self, x, y): - """Ray actor method with parameters""" - self.call_count += 1 - object_ref = Mock() - object_ref.remote = Mock(return_value=object_ref) - return object_ref - - -class TestActorWrapper: - """Test class for ActorWrapper functionality""" - - @pytest.fixture - def local_object(self): - """Create a mock local object""" - return MockLocalObject() - - @pytest.fixture - def ray_actor(self): - """Create a mock Ray actor""" - mock_actor = Mock() - mock_actor.get_value = Mock() - mock_actor.get_value.remote = Mock(return_value="mock_object_ref") - mock_actor.set_value = Mock() - mock_actor.set_value.remote = Mock(return_value="mock_object_ref") - return mock_actor - - @pytest.fixture - def local_wrapper(self, local_object): - """Create an ActorWrapper for local object""" - return ActorWrapper(local_object) - - @pytest.fixture - def ray_wrapper(self, ray_actor): - """Create an ActorWrapper for Ray actor""" - # Patch the _detect_execution_mode method to return ray_actor for our mock - with patch.object(ActorWrapper, "_detect_execution_mode", return_value="ray_actor"): - wrapper = ActorWrapper(ray_actor) - return wrapper - - @pytest.mark.unit - def test_local_object_detection(self, local_object): - """Test detection of local objects""" - wrapper = ActorWrapper(local_object) - assert wrapper._execution_mode == "local" - assert wrapper.is_local() is True - assert wrapper.is_ray_actor() is False - - @pytest.mark.unit - def test_ray_actor_detection(self, ray_actor): - """Test detection of Ray actors""" - # Patch the _detect_execution_mode method to return ray_actor for our mock - with patch.object(ActorWrapper, "_detect_execution_mode", return_value="ray_actor"): - wrapper = ActorWrapper(ray_actor) - assert wrapper._execution_mode == "ray_actor" - assert wrapper.is_ray_actor() is True - assert wrapper.is_local() is False - - @pytest.mark.unit - def test_local_method_call(self, local_wrapper): - """Test calling methods on local objects""" - # Simple method call - result = local_wrapper.get_value() - assert result == 42 - - # Method call with parameters - local_wrapper.set_value(100) - result = local_wrapper.get_value() - assert result == 100 - - @pytest.mark.unit - def test_local_method_with_parameters(self, local_wrapper): - """Test calling local methods with various parameters""" - # Method with multiple parameters - result = local_wrapper.add(5, 3) - assert result == 8 - - # Method with complex parameters - result = local_wrapper.complex_operation(10, multiplier=3, add_value=5) - assert result == 35 - - @pytest.mark.unit - @patch("ray.get") - def test_ray_actor_method_call(self, mock_ray_get, ray_wrapper): - """Test calling methods on Ray actors""" - mock_ray_get.return_value = 42 - - # Call method - should be synchronous due to wrapper - result = ray_wrapper.get_value() - - # Should call ray.get to get the result - mock_ray_get.assert_called_once() - assert result == 42 - - @pytest.mark.unit - def test_ray_actor_async_call(self, ray_wrapper): - """Test async calls to Ray actor methods""" - # Call async method - object_ref = ray_wrapper.call_async("get_value") - - # Should return the ObjectRef without calling ray.get - assert object_ref is not None - - @pytest.mark.unit - def test_local_object_async_call_error(self, local_wrapper): - """Test that async calls fail on local objects""" - with pytest.raises(RuntimeError, match="call_async only available for Ray actors"): - local_wrapper.call_async("get_value") - - @pytest.mark.unit - def test_attribute_access_local(self, local_wrapper): - """Test attribute access on local objects""" - # Direct attribute access - assert local_wrapper.value == 42 - - # Attribute modification - local_wrapper.value = 100 - assert local_wrapper.value == 100 - - @pytest.mark.unit - def test_attribute_access_ray(self, ray_wrapper): - """Test attribute access on Ray actors""" - # Mock the underlying actor's attribute - ray_wrapper._obj.value = 42 - - # Access attribute through wrapper - result = ray_wrapper.value - assert result == 42 - - @pytest.mark.unit - def test_private_attribute_access(self, local_wrapper): - """Test that private attributes are handled correctly""" - # Private attributes should raise AttributeError - with pytest.raises(AttributeError): - _ = local_wrapper._private_attr - - # But wrapper's own private attributes should work - assert hasattr(local_wrapper, "_obj") - assert hasattr(local_wrapper, "_execution_mode") - - @pytest.mark.unit - def test_nonexistent_attribute_access(self, local_wrapper): - """Test access to nonexistent attributes""" - with pytest.raises(AttributeError): - _ = local_wrapper.nonexistent_attribute - - @pytest.mark.unit - def test_nonexistent_method_call(self, local_wrapper): - """Test calling nonexistent methods""" - with pytest.raises(AttributeError): - local_wrapper.nonexistent_method() - - @pytest.mark.unit - def test_get_object_method(self, local_wrapper, local_object): - """Test get_object method returns wrapped object""" - retrieved_object = local_wrapper.get_object() - assert retrieved_object is local_object - - @pytest.mark.unit - def test_wrapper_repr(self, local_wrapper): - """Test string representation of wrapper""" - repr_str = repr(local_wrapper) - assert "ActorWrapper" in repr_str - assert "local" in repr_str - - @pytest.mark.unit - def test_kill_actor_local_object(self, local_wrapper): - """Test kill_actor on local object""" - result = local_wrapper.kill_actor() - assert result is False # Should return False for local objects - - @pytest.mark.unit - @patch("ray.kill") - def test_kill_actor_ray_actor(self, mock_ray_kill, ray_wrapper): - """Test kill_actor on Ray actor""" - mock_ray_kill.return_value = None - - result = ray_wrapper.kill_actor() - - # Should call ray.kill and return True - mock_ray_kill.assert_called_once_with(ray_wrapper._obj, no_restart=True) - assert result is True - - @pytest.mark.unit - @patch("ray.kill") - def test_kill_actor_with_restart(self, mock_ray_kill, ray_wrapper): - """Test kill_actor with restart option""" - mock_ray_kill.return_value = None - - result = ray_wrapper.kill_actor(no_restart=False) - - # Should call ray.kill with no_restart=False - mock_ray_kill.assert_called_once_with(ray_wrapper._obj, no_restart=False) - assert result is True - - @pytest.mark.unit - def test_callable_detection(self, local_wrapper): - """Test that wrapper correctly identifies callable vs non-callable attributes""" - # Method should be callable - method = local_wrapper.get_value - assert callable(method) - - # Attribute should not be wrapped as callable - value = local_wrapper.value - assert not callable(value) - - @pytest.mark.unit - def test_setattr_protection(self, local_wrapper): - """Test that wrapper protects its internal attributes""" - # Should be able to set attributes on wrapped object - local_wrapper.new_attribute = "test_value" - assert local_wrapper.new_attribute == "test_value" - - # Should not be able to modify wrapper's internal state - local_wrapper._execution_mode = "modified" - assert local_wrapper._execution_mode == "modified" # Should allow internal modification - - @pytest.mark.integration - def test_wrapper_with_complex_object(self): - """Integration test with more complex object""" - - class ComplexObject: - def __init__(self): - self.data: dict[str, str] = {"key": "value"} - self.counter = 0 - - def increment(self, amount: int = 1) -> int: - self.counter += amount - return self.counter - - def get_data(self) -> dict[str, str]: - return self.data.copy() - - def update_data(self, key: str, value: str) -> None: - self.data[key] = value - - complex_obj = ComplexObject() - wrapper = ActorWrapper(complex_obj) - - # Test various operations - assert wrapper.counter == 0 - result = wrapper.increment(5) - assert result == 5 - assert wrapper.counter == 5 - - data: dict[str, str] = cast(dict[str, str], wrapper.get_data()) - assert data == {"key": "value"} - - wrapper.update_data("new_key", "new_value") - data = cast(dict[str, str], wrapper.get_data()) - assert "new_key" in data - assert data["new_key"] == "new_value" - - @pytest.mark.unit - def test_wrapper_thread_safety(self, local_wrapper): - """Test wrapper in multi-threaded environment""" - import threading - - results = [] - - def worker(): - try: - for i in range(10): - result = local_wrapper.add(i, i) - results.append(result) - except Exception as e: - results.append(e) - - # Create multiple threads - threads = [threading.Thread(target=worker) for _ in range(5)] - - for thread in threads: - thread.start() - - for thread in threads: - thread.join() - - # All results should be successful - numeric_results = [r for r in results if isinstance(r, int)] - assert len(numeric_results) == 50 # 5 threads * 10 calls each - - -class TestActorWrapperEdgeCases: - """Test edge cases and error conditions""" - - @pytest.fixture - def ray_actor(self): - """Create a mock Ray actor""" - mock_actor = Mock() - mock_actor.get_value = Mock() - mock_actor.get_value.remote = Mock(return_value="mock_object_ref") - mock_actor.set_value = Mock() - mock_actor.set_value.remote = Mock(return_value="mock_object_ref") - return mock_actor - - @pytest.fixture - def ray_wrapper(self, ray_actor): - """Create an ActorWrapper for Ray actor""" - # Patch the _detect_execution_mode method to return ray_actor for our mock - with patch.object(ActorWrapper, "_detect_execution_mode", return_value="ray_actor"): - wrapper = ActorWrapper(ray_actor) - return wrapper - - @pytest.mark.unit - def test_wrapper_with_none_object(self): - """Test wrapper with None object""" - wrapper = ActorWrapper(None) - assert wrapper._execution_mode == "local" - - # Accessing attributes should raise AttributeError - with pytest.raises(AttributeError): - _ = wrapper.some_attribute - - @pytest.mark.unit - def test_wrapper_with_primitive_object(self): - """Test wrapper with primitive objects""" - # String object - wrapper = ActorWrapper("test_string") - assert wrapper._execution_mode == "local" - - # Should be able to access string methods - result = wrapper.upper() - assert result == "TEST_STRING" - - # Integer object - int_wrapper = ActorWrapper(42) - # Integers don't have many methods, but should not crash - assert int_wrapper._execution_mode == "local" - - @pytest.mark.unit - @patch("ray.kill") - def test_kill_actor_ray_kill_failure(self, mock_ray_kill, ray_wrapper): - """Test kill_actor when ray.kill fails""" - mock_ray_kill.side_effect = Exception("Kill failed") - - # Should handle exception gracefully - try: - ray_wrapper.kill_actor() - # Behavior depends on implementation - might return False or raise - except Exception: - # Acceptable if implementation propagates the exception - pass - - @pytest.mark.unit - def test_async_call_nonexistent_method(self): - """Test async call to nonexistent method""" - - # Create a simple object that will raise AttributeError for nonexistent methods - class SimpleActor: - def existing_method(self): - pass - - simple_actor = SimpleActor() - - # Create wrapper with ray actor mode - with patch.object(ActorWrapper, "_detect_execution_mode", return_value="ray_actor"): - wrapper = ActorWrapper(simple_actor) - - with pytest.raises(AttributeError): - wrapper.call_async("nonexistent_method") - - @pytest.mark.unit - def test_async_call_non_callable_attribute(self, ray_wrapper): - """Test async call to non-callable attribute""" - # Mock a non-callable attribute - ray_wrapper._obj.value = 42 - - with pytest.raises(AttributeError, match="not a callable method"): - ray_wrapper.call_async("value") - - @pytest.mark.unit - def test_detection_with_ray_import_error(self): - """Test actor detection when Ray is not available""" - # Patch the _detect_execution_mode method to simulate ImportError handling - with patch.object(ActorWrapper, "_detect_execution_mode", return_value="local"): - obj = Mock() - wrapper = ActorWrapper(obj) - - # Should default to local mode - assert wrapper._execution_mode == "local" - - @pytest.mark.unit - def test_detection_with_ray_attribute_error(self): - """Test actor detection when Ray module has issues""" - # Patch the _detect_execution_mode method to simulate AttributeError handling - with patch.object(ActorWrapper, "_detect_execution_mode", return_value="local"): - obj = Mock() - wrapper = ActorWrapper(obj) - - # Should default to local mode - assert wrapper._execution_mode == "local" - - -# Performance tests -class TestActorWrapperPerformance: - """Performance tests for ActorWrapper""" - - @pytest.mark.slow - def test_local_wrapper_performance(self): - """Test performance of local wrapper operations""" - import time - - obj = MockLocalObject() - wrapper = ActorWrapper(obj) - - start_time = time.time() - - # Perform many operations - for i in range(1000): - wrapper.add(i, i) - - elapsed = time.time() - start_time - - # Should be reasonably fast - assert elapsed < 1.0 # Less than 1 second for 1000 calls - - @pytest.mark.slow - def test_attribute_access_performance(self): - """Test performance of attribute access""" - import time - - obj = MockLocalObject() - wrapper = ActorWrapper(obj) - - start_time = time.time() - - # Access attributes many times - for _ in range(1000): - _ = wrapper.value - - elapsed = time.time() - start_time - - # Should be very fast - assert elapsed < 0.1 # Less than 100ms for 1000 accesses - - -# Fixtures and utilities -@pytest.fixture -def complex_local_object(): - """Create a complex local object for testing""" - - class ComplexLocal: - def __init__(self): - self.data = list(range(100)) - self.metadata = {"created": "test", "version": 1.0} - - def process_data(self, func_name): - if func_name == "sum": - return sum(self.data) - elif func_name == "max": - return max(self.data) - return None - - def batch_operation(self, operations): - results = [] - for op in operations: - results.append(self.process_data(op)) - return results - - return ComplexLocal() diff --git a/packages/sage-kernel/tests/unit/kernel/runtime/distributed/test_ray.py b/packages/sage-kernel/tests/unit/kernel/runtime/distributed/test_ray.py deleted file mode 100644 index 15c2c3ad93..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/runtime/distributed/test_ray.py +++ /dev/null @@ -1,384 +0,0 @@ -""" -Test suite for sage.kernels.runtime.distributed.ray module - -Tests Ray integration and initialization functions. -""" - -from unittest.mock import patch - -import pytest - -from sage.kernel.utils.ray.ray_utils import ( - ensure_ray_initialized, - is_distributed_environment, -) - -# Mark tests that need mock updates as expected to fail temporarily -needs_mock_update = pytest.mark.xfail( - reason="Mock assertions need update to match actual implementation" -) - - -class TestRayIntegration: - """Test class for Ray integration functionality""" - - @pytest.mark.unit - @pytest.mark.ray - @patch("sage.kernel.utils.ray.ray_utils.RAY_AVAILABLE", True) - @patch("sage.kernel.utils.ray.ray_utils.ray") - @patch.dict("os.environ", {"CI": "true"}) - def test_ensure_ray_initialized_not_initialized(self, mock_ray): - """Test ensure_ray_initialized when Ray is not initialized""" - # Configure mocks - mock_ray.is_initialized.return_value = False - # First call (auto) fails with ConnectionError, second call (local) succeeds - mock_ray.init.side_effect = [ConnectionError("No cluster"), None] - mock_ray.nodes.return_value = [] - - # Call function - ensure_ray_initialized() - - # Verify Ray was initialized - mock_ray.is_initialized.assert_called_once() - # Should be called twice: once for auto, once for local - assert mock_ray.init.call_count == 2 - - # Verify local initialization parameters (second call) - local_call_args = mock_ray.init.call_args_list[1] - assert "ignore_reinit_error" in local_call_args.kwargs - assert local_call_args.kwargs["ignore_reinit_error"] is True - assert "num_cpus" in local_call_args.kwargs - # CI environment uses 2 CPUs, non-CI uses 16 - assert local_call_args.kwargs["num_cpus"] == 2 - assert "log_to_driver" in local_call_args.kwargs - assert local_call_args.kwargs["log_to_driver"] is False - # runtime_env should be a dict with py_modules and env_vars - if "runtime_env" in local_call_args.kwargs: - runtime_env = local_call_args.kwargs["runtime_env"] - assert isinstance(runtime_env, dict) - assert "py_modules" in runtime_env or "env_vars" in runtime_env - - @pytest.mark.unit - @patch("sage.kernel.utils.ray.ray_utils.RAY_AVAILABLE", True) - @patch("sage.kernel.utils.ray.ray_utils.ray") - def test_ensure_ray_initialized_already_initialized(self, mock_ray): - """Test ensure_ray_initialized when Ray is already initialized""" - # Configure mocks - mock_ray.is_initialized.return_value = True - - # Call function - ensure_ray_initialized() - - # Verify Ray was checked but not initialized - mock_ray.is_initialized.assert_called_once() - mock_ray.init.assert_not_called() - - @pytest.mark.unit - @patch("sage.kernel.utils.ray.ray_utils.RAY_AVAILABLE", True) - @patch("sage.kernel.utils.ray.ray_utils.ray") - def test_ensure_ray_initialized_with_custom_runtime_env(self, mock_ray): - """Test ensure_ray_initialized with custom runtime_env""" - mock_ray.is_initialized.return_value = False - mock_ray.init.side_effect = [ - ConnectionError("No cluster"), - None, - ] # First auto fails, second local succeeds - mock_ray.nodes.return_value = [] - - custom_env = {"env_vars": {"MY_VAR": "value"}} - ensure_ray_initialized(runtime_env=custom_env) - - # Should be called twice: once for auto, once for local - assert mock_ray.init.call_count == 2 - # Check second call (local mode) has custom runtime_env - second_call_args = mock_ray.init.call_args_list[1] - assert second_call_args.kwargs.get("runtime_env") == custom_env - - @pytest.mark.unit - @patch("sage.kernel.utils.ray.ray_utils.RAY_AVAILABLE", True) - @patch("sage.kernel.utils.ray.ray_utils.ray") - def test_ensure_ray_initialized_initialization_fails(self, mock_ray): - """Test behavior when Ray initialization fails""" - mock_ray.is_initialized.return_value = False - # Both auto and local mode fail - mock_ray.init.side_effect = RuntimeError("Initialization failed") - - with pytest.raises(RuntimeError, match="Initialization failed"): - ensure_ray_initialized() - - # Should try auto first, then local (both fail) - assert mock_ray.init.call_count >= 1 - - @pytest.mark.unit - @patch("sage.kernel.utils.ray.ray_utils.RAY_AVAILABLE", False) - def test_ensure_ray_initialized_ray_not_available(self): - """Test behavior when Ray is not available""" - with pytest.raises(ImportError, match="Ray is not available"): - ensure_ray_initialized() - - @pytest.mark.unit - @patch("sage.kernel.utils.ray.ray_utils.RAY_AVAILABLE", True) - @patch("sage.kernel.utils.ray.ray_utils.ray") - def test_is_distributed_environment_ray_available_and_initialized(self, mock_ray): - """Test is_distributed_environment when Ray is available and initialized""" - mock_ray.is_initialized.return_value = True - - result = is_distributed_environment() - - assert result is True - mock_ray.is_initialized.assert_called_once() - - @pytest.mark.unit - @patch("sage.kernel.utils.ray.ray_utils.RAY_AVAILABLE", True) - @patch("sage.kernel.utils.ray.ray_utils.ray") - def test_is_distributed_environment_ray_available_not_initialized(self, mock_ray): - """Test is_distributed_environment when Ray is available but not initialized""" - mock_ray.is_initialized.return_value = False - - result = is_distributed_environment() - - assert result is False - mock_ray.is_initialized.assert_called_once() - - @pytest.mark.unit - @patch("sage.kernel.utils.ray.ray_utils.RAY_AVAILABLE", False) - def test_is_distributed_environment_ray_not_available(self): - """Test is_distributed_environment when Ray is not available""" - result = is_distributed_environment() - - assert result is False - - @pytest.mark.integration - @patch("sage.kernel.utils.ray.ray_utils.RAY_AVAILABLE", True) - @patch("sage.kernel.utils.ray.ray_utils.ray") - def test_ensure_ray_initialized_integration(self, mock_ray): - """Integration test for Ray initialization process""" - # Simulate real Ray behavior - mock_ray.is_initialized.return_value = False - mock_ray.init.return_value = None - mock_ray.nodes.return_value = [] # No existing cluster - - # Call multiple times to ensure idempotency - ensure_ray_initialized() - mock_ray.is_initialized.return_value = True - ensure_ray_initialized() - ensure_ray_initialized() - - # Should initialize once (auto) + once (local) = 2 times on first call - # Then no more calls after that - assert mock_ray.init.call_count >= 1 # At least one init call - - @pytest.mark.unit - @patch("sage.kernel.utils.ray.ray_utils.RAY_AVAILABLE", True) - @patch("sage.kernel.utils.ray.ray_utils.ray") - def test_ensure_ray_initialized_with_print_statements(self, mock_ray): - """Test that appropriate messages are printed during initialization""" - mock_ray.is_initialized.return_value = False - mock_ray.init.return_value = None - - with patch("builtins.print") as mock_print: - ensure_ray_initialized() - - # Should print initialization message - # Relax assertion: just check that print was called - mock_print.assert_called() - - @pytest.mark.unit - @patch("sage.kernel.utils.ray.ray_utils.RAY_AVAILABLE", True) - @patch("sage.kernel.utils.ray.ray_utils.ray") - def test_ensure_ray_initialized_error_handling(self, mock_ray): - """Test error handling and reporting in ensure_ray_initialized""" - mock_ray.is_initialized.return_value = False - error_message = "Critical Ray failure" - mock_ray.init.side_effect = RuntimeError(error_message) - - with patch("builtins.print") as mock_print: - with pytest.raises(RuntimeError, match=error_message): - ensure_ray_initialized() - - # Should print error message - mock_print.assert_called() - print_calls = [call[0][0] for call in mock_print.call_args_list] - assert any("Failed to initialize Ray" in msg for msg in print_calls) - - -class TestRayIntegrationEdgeCases: - """Test edge cases and error conditions""" - - @pytest.mark.unit - @patch("sage.kernel.utils.ray.ray_utils.RAY_AVAILABLE", True) - @patch("sage.kernel.utils.ray.ray_utils.ray") - def test_ensure_ray_initialized_ignore_reinit_error_parameter(self, mock_ray): - """Test that ignore_reinit_error parameter is properly passed""" - mock_ray.is_initialized.return_value = False - mock_ray.init.return_value = None - mock_ray.nodes.return_value = [] # No existing cluster - - ensure_ray_initialized() - - # Both initialization attempts should have ignore_reinit_error=True - calls = mock_ray.init.call_args_list - for call in calls: - assert call[1]["ignore_reinit_error"] is True - - @pytest.mark.unit - @patch("sage.kernel.utils.ray.ray_utils.RAY_AVAILABLE", True) - @patch("sage.kernel.utils.ray.ray_utils.ray") - def test_multiple_concurrent_initializations(self, mock_ray): - """Test concurrent calls to ensure_ray_initialized""" - import threading - - mock_ray.is_initialized.return_value = False - mock_ray.init.return_value = None - mock_ray.nodes.return_value = [] # No existing cluster - - results = [] - - def init_worker(): - try: - ensure_ray_initialized() - results.append("success") - except Exception as e: - results.append(f"error: {e}") - - # Start multiple threads - threads = [threading.Thread(target=init_worker) for _ in range(5)] - - for thread in threads: - thread.start() - - for thread in threads: - thread.join() - - # All should succeed (or at least not crash) - assert len(results) == 5 - - @pytest.mark.unit - @patch("sage.kernel.utils.ray.ray_utils.RAY_AVAILABLE", False) - def test_ray_module_import_variations(self): - """Test different scenarios of Ray module availability""" - # Test when ray module is completely unavailable - result = is_distributed_environment() - assert result is False - - @pytest.mark.unit - @patch("sage.kernel.utils.ray.ray_utils.RAY_AVAILABLE", True) - @patch("sage.kernel.utils.ray.ray_utils.ray") - def test_ray_is_initialized_exception(self, mock_ray): - """Test when ray.is_initialized() raises an exception""" - mock_ray.is_initialized.side_effect = Exception("Ray internal error") - - result = is_distributed_environment() - - # Should return False when ray check fails - assert result is False - - @pytest.mark.unit - @patch("sage.kernel.utils.ray.ray_utils.RAY_AVAILABLE", True) - @patch("sage.kernel.utils.ray.ray_utils.ray") - def test_ensure_ray_with_specific_exceptions(self, mock_ray): - """Test ensure_ray_initialized with specific exception types""" - mock_ray.is_initialized.return_value = False - - # Test different exception types - exception_types = [ - ConnectionError("Connection refused"), - TimeoutError("Connection timeout"), - OSError("Network error"), - ValueError("Invalid configuration"), - ] - - for exception in exception_types: - mock_ray.init.side_effect = exception - - with pytest.raises(type(exception)): - ensure_ray_initialized() - - # Reset for next iteration - mock_ray.init.reset_mock() - - # Reset for next test - mock_ray.init.side_effect = None - - -# Performance and stress tests -class TestRayPerformance: - """Performance and stress tests for Ray integration""" - - @pytest.mark.slow - @patch("sage.kernel.utils.ray.ray_utils.RAY_AVAILABLE", True) - @patch("sage.kernel.utils.ray.ray_utils.ray") - def test_ensure_ray_performance(self, mock_ray): - """Test that ensure_ray_initialized has minimal overhead when Ray is already initialized - - This test verifies the function doesn't have unexpected performance regressions, - not absolute timing constraints. The test measures throughput rather than - absolute time to avoid false positives from system load, coverage overhead, etc. - """ - import time - - mock_ray.is_initialized.return_value = True - - iterations = 1000 - start_time = time.time() - - # Call many times - for _ in range(iterations): - ensure_ray_initialized() - - elapsed = time.time() - start_time - - # Verify reasonable throughput: should handle at least 500 calls/second - # This is extremely conservative - actual performance is much higher - # but allows for coverage overhead, slow CI machines, etc. - calls_per_second = iterations / elapsed - min_throughput = 500 # calls/second - - assert calls_per_second >= min_throughput, ( - f"Performance regression detected: {calls_per_second:.1f} calls/sec " - f"(minimum: {min_throughput} calls/sec, elapsed: {elapsed:.3f}s)" - ) - - @pytest.mark.slow - @patch("sage.kernel.utils.ray.ray_utils.RAY_AVAILABLE", True) - @patch("sage.kernel.utils.ray.ray_utils.ray") - def test_is_distributed_environment_performance(self, mock_ray): - """Test that is_distributed_environment has minimal overhead - - This test verifies the function doesn't have unexpected performance regressions, - not absolute timing constraints. The test measures throughput rather than - absolute time to avoid false positives from system load, coverage overhead, etc. - """ - import time - - mock_ray.is_initialized.return_value = True - - iterations = 1000 - start_time = time.time() - - # Call many times - results = [is_distributed_environment() for _ in range(iterations)] - - elapsed = time.time() - start_time - - # Verify reasonable throughput: should handle at least 500 calls/second - # This is extremely conservative - actual performance is much higher - # but allows for coverage overhead, slow CI machines, etc. - calls_per_second = iterations / elapsed - min_throughput = 500 # calls/second - - assert calls_per_second >= min_throughput, ( - f"Performance regression detected: {calls_per_second:.1f} calls/sec " - f"(minimum: {min_throughput} calls/sec, elapsed: {elapsed:.3f}s)" - ) - assert all(result is True for result in results) - - -# Fixtures and utilities -@pytest.fixture -def mock_ray_module(): - """Provide a fully mocked Ray module""" - with patch("sage.kernel.utils.ray.ray_utils.ray") as mock_ray: - mock_ray.is_initialized.return_value = False - mock_ray.init.return_value = None - yield mock_ray diff --git a/packages/sage-kernel/tests/unit/kernel/runtime/factory/test_task_factory.py b/packages/sage-kernel/tests/unit/kernel/runtime/factory/test_task_factory.py deleted file mode 100644 index 1956080969..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/runtime/factory/test_task_factory.py +++ /dev/null @@ -1,469 +0,0 @@ -""" -Test suite for sage.kernels.runtime.factory.task_factory module - -Tests the TaskFactory class which creates task instances -for both local and remote execution environments. -""" - -from unittest.mock import Mock, patch - -import pytest - -from sage.kernel.api.transformation.base_transformation import BaseTransformation -from sage.kernel.runtime.factory.task_factory import TaskFactory -from sage.kernel.runtime.task.local_task import LocalTask -from sage.kernel.utils.ray.actor import ActorWrapper - - -class MockTransformation(BaseTransformation): - """Mock transformation for testing""" - - def __init__(self, basename="test_transform", remote=False, is_spout_value=True): - # Create minimal mock environment - mock_env = Mock() - mock_env.platform = "remote" if remote else "local" - mock_env.name = "test_env" - mock_env.pipeline = [] - - # Create a mock function class that satisfies type checking - class MockFunction: - __name__ = basename - - # Initialize parent class - type ignore needed for test mock - super().__init__(env=mock_env, function=MockFunction) # type: ignore - - # Override specific attributes for testing - self.basename = basename - self.env_name = "test_env" - self.remote = remote - self._is_spout_value = is_spout_value - self._delay = 0.01 - self._operator_factory = Mock() - - @property - def is_spout(self) -> bool: - return self._is_spout_value - - @property - def delay(self) -> float: - return self._delay - - @delay.setter - def delay(self, value: float) -> None: - self._delay = value - - @property - def operator_factory(self): - return self._operator_factory - - @operator_factory.setter - def operator_factory(self, value): - self._operator_factory = value - - -class MockTaskContext: - """Mock task context for testing""" - - def __init__(self): - self.name = "test_context" - self.parallel_index = 0 - self.parallelism = 1 - # Add missing attributes that TaskContext should have - self.input_qd = Mock() - self.response_qd = Mock() - self.service_qds = {} - self.downstream_groups = {} - self.env_name = "test_env" - - # 使用统一的SAGE路径管理 - from sage.common.config.output_paths import get_test_context_dir - - self.env_base_dir = str(get_test_context_dir("test_context")) - - self.env_uuid = "test-uuid" - self.env_console_log_level = "INFO" - self.is_spout = True - self.delay = 0.01 - self.stop_signal_num = 1 - self.jobmanager_host = "127.0.0.1" - self.jobmanager_port = 19001 - self.stop_signal_count = 0 - # Add logger property - self._logger = Mock() - - @property - def logger(self): - """Mock logger property""" - return self._logger - - -class TestTaskFactory: - """Test class for TaskFactory functionality""" - - @pytest.fixture - def local_transformation(self): - """Create a mock local transformation""" - return MockTransformation(remote=False) - - @pytest.fixture - def remote_transformation(self): - """Create a mock remote transformation""" - return MockTransformation(remote=True) - - @pytest.fixture - def local_factory(self, local_transformation): - """Create a TaskFactory for local execution""" - return TaskFactory(local_transformation) - - @pytest.fixture - def remote_factory(self, remote_transformation): - """Create a TaskFactory for remote execution""" - return TaskFactory(remote_transformation) - - @pytest.mark.unit - def test_factory_initialization_local(self, local_transformation): - """Test TaskFactory initialization for local transformation""" - factory = TaskFactory(local_transformation) - - assert factory.basename == "test_transform" - assert factory.env_name == "test_env" - assert factory.operator_factory is local_transformation.operator_factory - assert factory.delay == 0.01 - assert factory.remote is False - assert factory.is_spout is True - - @pytest.mark.unit - def test_factory_initialization_remote(self, remote_transformation): - """Test TaskFactory initialization for remote transformation""" - factory = TaskFactory(remote_transformation) - - assert factory.basename == "test_transform" - assert factory.env_name == "test_env" - assert factory.operator_factory is remote_transformation.operator_factory - assert factory.delay == 0.01 - assert factory.remote is True - assert factory.is_spout is True - - @pytest.mark.unit - def test_create_local_task(self, local_factory, mock_context): - """Test creating a local task""" - task = local_factory.create_task("test_task", mock_context) - - # Should return a LocalTask instance - assert isinstance(task, LocalTask) - assert not isinstance(task, ActorWrapper) - - @pytest.mark.unit - @patch("sage.kernel.runtime.factory.task_factory.RayTask") - def test_create_remote_task(self, mock_ray_task_class, remote_factory, mock_context): - """Test creating a remote task""" - # Mock the Ray task class and its options method - mock_ray_task_instance = Mock() - mock_ray_task_class.options.return_value.remote.return_value = mock_ray_task_instance - - with patch("sage.kernel.runtime.factory.task_factory.ActorWrapper") as mock_wrapper: - mock_wrapper_instance = Mock() - mock_wrapper.return_value = mock_wrapper_instance - - task = remote_factory.create_task("test_task", mock_context) - - # Should create RayTask with options and wrap it - mock_ray_task_class.options.assert_called_once_with(lifetime="detached") - mock_ray_task_class.options.return_value.remote.assert_called_once_with( - mock_context, remote_factory.operator_factory - ) - mock_wrapper.assert_called_once_with(mock_ray_task_instance) - assert task is mock_wrapper_instance - - @pytest.mark.unit - def test_factory_attributes_inheritance(self): - """Test that factory inherits all necessary attributes from transformation""" - transformation = MockTransformation( - basename="custom_transform", remote=True, is_spout_value=False - ) - transformation.env_name = "custom_env" - transformation.delay = 0.05 - - factory = TaskFactory(transformation) - - assert factory.basename == "custom_transform" - assert factory.env_name == "custom_env" - assert factory.delay == 0.05 - assert factory.remote is True - assert factory.is_spout is False - - @pytest.mark.unit - def test_factory_repr(self, local_factory): - """Test string representation of TaskFactory""" - repr_str = repr(local_factory) - assert "TaskFactory" in repr_str - assert "test_transform" in repr_str - - @pytest.mark.unit - def test_multiple_task_creation(self, local_factory, mock_context): - """Test creating multiple tasks from same factory""" - task1 = local_factory.create_task("task1", mock_context) - task2 = local_factory.create_task("task2", mock_context) - - # Should create separate instances - assert task1 is not task2 - assert isinstance(task1, LocalTask) - assert isinstance(task2, LocalTask) - - @pytest.mark.unit - def test_factory_with_different_transformations(self): - """Test factory behavior with different transformation types""" - # Spout transformation - spout_transform = MockTransformation(is_spout_value=True) - spout_factory = TaskFactory(spout_transform) - assert spout_factory.is_spout is True - - # Non-spout transformation - non_spout_transform = MockTransformation(is_spout_value=False) - non_spout_factory = TaskFactory(non_spout_transform) - assert non_spout_factory.is_spout is False - - @pytest.mark.integration - @patch("sage.kernel.runtime.factory.task_factory.RayTask") - @patch("sage.kernel.runtime.factory.task_factory.ActorWrapper") - def test_factory_integration_local_and_remote(self, mock_wrapper, mock_ray_task, mock_context): - """Integration test creating both local and remote tasks""" - # Create factories - local_transform = MockTransformation(remote=False) - remote_transform = MockTransformation(remote=True) - - local_factory = TaskFactory(local_transform) - remote_factory = TaskFactory(remote_transform) - - # Create tasks - local_task = local_factory.create_task("local_task", mock_context) - - # Mock remote task creation - mock_ray_instance = Mock() - mock_ray_task.options.return_value.remote.return_value = mock_ray_instance - mock_wrapper.return_value = Mock() - - remote_factory.create_task("remote_task", mock_context) - - # Verify correct types - assert isinstance(local_task, LocalTask) - mock_wrapper.assert_called_once() - - @pytest.mark.unit - def test_factory_with_none_context(self, local_factory): - """Test creating task with None context""" - # Should raise an exception when context is None - with pytest.raises(AttributeError): - local_factory.create_task("test_task", None) - - @pytest.mark.unit - def test_factory_operator_factory_propagation(self, mock_context): - """Test that operator factory is properly propagated to tasks""" - mock_operator_factory = Mock() - transformation = MockTransformation() - transformation.operator_factory = mock_operator_factory - - factory = TaskFactory(transformation) - - # Verify operator factory is stored - assert factory.operator_factory is mock_operator_factory - - # Create task and verify operator factory is passed - task = factory.create_task("test_task", mock_context) - # Note: The actual verification depends on LocalTask implementation - assert isinstance(task, LocalTask) - - -class TestTaskFactoryEdgeCases: - """Test edge cases and error conditions""" - - @pytest.mark.unit - def test_factory_with_missing_transformation_attributes(self): - """Test factory creation when transformation is missing attributes""" - incomplete_transform = Mock() - incomplete_transform.basename = "incomplete" - # Missing other required attributes - - try: - TaskFactory(incomplete_transform) - # Behavior depends on implementation - except AttributeError: - # Expected if implementation requires all attributes - pass - - @pytest.mark.unit - def test_factory_with_none_transformation(self): - """Test factory creation with None transformation""" - with pytest.raises((AttributeError, TypeError)): - TaskFactory(None) # type: ignore[arg-type] - - @pytest.mark.unit - @patch("sage.kernel.runtime.factory.task_factory.RayTask") - def test_remote_task_creation_ray_failure(self, mock_ray_task_class, mock_context): - """Test remote task creation when Ray operations fail""" - # Mock Ray task creation failure - mock_ray_task_class.options.side_effect = Exception("Ray not available") - - remote_transform = MockTransformation(remote=True) - factory = TaskFactory(remote_transform) - - # Should propagate the exception - with pytest.raises(Exception, match="Ray not available"): - factory.create_task("test_task", mock_context) - - @pytest.mark.unit - def test_factory_with_extreme_values(self): - """Test factory with extreme attribute values""" - extreme_transform = MockTransformation() - extreme_transform.delay = 999999.999 - extreme_transform.basename = "a" * 1000 # Very long name - - factory = TaskFactory(extreme_transform) - - assert factory.delay == 999999.999 - assert len(factory.basename) == 1000 - - @pytest.mark.unit - def test_factory_with_special_characters(self): - """Test factory with special characters in names""" - special_transform = MockTransformation() - special_transform.basename = "test-task_with.special@chars#123" - special_transform.env_name = "env/with\\special:chars" - - factory = TaskFactory(special_transform) - - assert factory.basename == "test-task_with.special@chars#123" - assert factory.env_name == "env/with\\special:chars" - - @pytest.mark.unit - def test_factory_thread_safety(self, mock_context): - """Test factory in multi-threaded environment""" - import threading - - transformation = MockTransformation() - factory = TaskFactory(transformation) - - tasks = [] - - def create_task_worker(task_id): - try: - task = factory.create_task(f"task_{task_id}", mock_context) - tasks.append(task) - except Exception as e: - tasks.append(e) - - # Create multiple threads - threads = [threading.Thread(target=create_task_worker, args=(i,)) for i in range(10)] - - for thread in threads: - thread.start() - - for thread in threads: - thread.join() - - # All tasks should be created successfully - assert len(tasks) == 10 - for task in tasks: - assert isinstance(task, LocalTask) - - -class TestTaskFactoryPerformance: - """Performance tests for TaskFactory""" - - @pytest.mark.slow - def test_factory_creation_performance(self): - """Test performance of factory creation""" - import time - - transformation = MockTransformation() - - start_time = time.time() - - # Create many factories - factories = [TaskFactory(transformation) for _ in range(1000)] - - elapsed = time.time() - start_time - - # Should be fast - assert elapsed < 1.0 # Less than 1 second for 1000 factories - assert len(factories) == 1000 - - @pytest.mark.slow - def test_task_creation_performance(self, mock_context): - """Test performance of task creation""" - import time - - transformation = MockTransformation() - factory = TaskFactory(transformation) - - start_time = time.time() - - # Create many tasks - tasks = [factory.create_task(f"task_{i}", mock_context) for i in range(100)] - - elapsed = time.time() - start_time - - # Should be reasonably fast - assert elapsed < 1.0 # Less than 1 second for 100 tasks - assert len(tasks) == 100 - - -# Additional fixtures and utilities -@pytest.fixture -def mock_context(): - """Create a mock task context""" - return MockTaskContext() - - -@pytest.fixture -def factory_with_complex_transformation(): - """Create a factory with a more complex transformation""" - - class ComplexTransformation(BaseTransformation): - def __init__(self): - # Create minimal mock environment - mock_env = Mock() - mock_env.platform = "local" - mock_env.name = "complex_env" - mock_env.pipeline = [] - - # Create a mock function class - class MockComplexFunction: - __name__ = "complex_transform" - - super().__init__(env=mock_env, function=MockComplexFunction) # type: ignore - - self.basename = "complex_transform" - self.env_name = "complex_env" - self._operator_factory = Mock() - self._delay = 0.001 - self.remote = False - self._is_spout_value = True - self.custom_attribute = "custom_value" - - @property - def is_spout(self) -> bool: - return self._is_spout_value - - @property - def delay(self) -> float: - return self._delay - - @property - def operator_factory(self): - return self._operator_factory - - return TaskFactory(ComplexTransformation()) - - -@pytest.fixture -def transformation_factory(): - """Factory for creating different types of transformations""" - - def _create_transformation(remote=False, is_spout_value=True, basename="test"): - transform = MockTransformation( - basename=basename, remote=remote, is_spout_value=is_spout_value - ) - return transform - - return _create_transformation diff --git a/packages/sage-kernel/tests/unit/kernel/runtime/monitoring/test_metrics.py b/packages/sage-kernel/tests/unit/kernel/runtime/monitoring/test_metrics.py deleted file mode 100644 index 382de9e191..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/runtime/monitoring/test_metrics.py +++ /dev/null @@ -1,266 +0,0 @@ -""" -Test suite for sage.kernel.runtime.monitoring.metrics module - -Tests all metrics data classes and their methods. -""" - -import pytest - -from sage.kernel.runtime.monitoring.metrics import ( - MethodMetrics, - PacketMetrics, - ServicePerformanceMetrics, - ServiceRequestMetrics, - TaskPerformanceMetrics, -) - - -class TestPacketMetrics: - """测试 PacketMetrics 数据类""" - - def test_packet_metrics_creation(self): - """测试创建 PacketMetrics 实例""" - packet = PacketMetrics(packet_id="test_packet_001") - - assert packet.packet_id == "test_packet_001" - assert packet.arrival_time > 0 - assert packet.processing_start_time is None - assert packet.processing_end_time is None - assert packet.queue_wait_time == 0.0 - assert packet.execution_time == 0.0 - assert packet.success is True - assert packet.error_type is None - assert packet.packet_size == 0 - - def test_calculate_times(self): - """测试时间计算""" - packet = PacketMetrics(packet_id="test_packet_002") - packet.arrival_time = 1000.0 - packet.processing_start_time = 1001.0 - packet.processing_end_time = 1003.5 - - packet.calculate_times() - - assert packet.queue_wait_time == 1.0 - assert packet.execution_time == 2.5 - - def test_to_dict(self): - """测试转换为字典""" - packet = PacketMetrics( - packet_id="test_packet_003", - packet_size=1024, - success=False, - error_type="ValueError", - ) - - result = packet.to_dict() - - assert isinstance(result, dict) - assert result["packet_id"] == "test_packet_003" - assert result["packet_size"] == 1024 - assert result["success"] is False - assert result["error_type"] == "ValueError" - - -class TestTaskPerformanceMetrics: - """测试 TaskPerformanceMetrics 数据类""" - - def test_task_metrics_creation(self): - """测试创建 TaskPerformanceMetrics 实例""" - metrics = TaskPerformanceMetrics(task_name="test_task") - - assert metrics.task_name == "test_task" - assert metrics.uptime == 0.0 - assert metrics.total_packets_processed == 0 - assert metrics.total_packets_failed == 0 - assert metrics.packets_per_second == 0.0 - - def test_task_metrics_with_data(self): - """测试带数据的 TaskPerformanceMetrics""" - metrics = TaskPerformanceMetrics( - task_name="retriever_task", - uptime=120.5, - total_packets_processed=1000, - total_packets_failed=5, - packets_per_second=8.3, - min_latency=10.0, - max_latency=500.0, - avg_latency=123.4, - p50_latency=120.0, - p95_latency=450.0, - p99_latency=490.0, - cpu_usage_percent=45.2, - memory_usage_mb=1024.5, - ) - - assert metrics.task_name == "retriever_task" - assert metrics.total_packets_processed == 1000 - assert metrics.total_packets_failed == 5 - assert metrics.packets_per_second == 8.3 - assert metrics.p50_latency == 120.0 - assert metrics.p95_latency == 450.0 - assert metrics.p99_latency == 490.0 - assert metrics.cpu_usage_percent == 45.2 - assert metrics.memory_usage_mb == 1024.5 - - def test_to_dict(self): - """测试转换为字典""" - metrics = TaskPerformanceMetrics( - task_name="test_task", - total_packets_processed=100, - total_packets_failed=2, - packets_per_second=5.0, - p50_latency=100.0, - error_breakdown={"ValueError": 1, "TypeError": 1}, - ) - - result = metrics.to_dict() - - assert isinstance(result, dict) - assert result["task_name"] == "test_task" - assert result["total_packets_processed"] == 100 - assert result["total_packets_failed"] == 2 - assert result["latency"]["p50_ms"] == 100.0 - assert result["errors"]["breakdown"] == {"ValueError": 1, "TypeError": 1} - assert result["throughput"]["current_tps"] == 5.0 - - def test_error_breakdown(self): - """测试错误分类""" - metrics = TaskPerformanceMetrics( - task_name="test_task", - error_breakdown={"NetworkError": 3, "TimeoutError": 2, "ValueError": 1}, - ) - - assert len(metrics.error_breakdown) == 3 - assert metrics.error_breakdown["NetworkError"] == 3 - assert metrics.error_breakdown["TimeoutError"] == 2 - assert metrics.error_breakdown["ValueError"] == 1 - - -class TestServiceRequestMetrics: - """测试 ServiceRequestMetrics 数据类""" - - def test_service_request_creation(self): - """测试创建 ServiceRequestMetrics 实例""" - request = ServiceRequestMetrics(request_id="req_001", method_name="process_query") - - assert request.request_id == "req_001" - assert request.method_name == "process_query" - assert request.arrival_time > 0 - assert request.processing_start_time is None - assert request.processing_end_time is None - assert request.success is True - - def test_calculate_times(self): - """测试时间计算""" - request = ServiceRequestMetrics(request_id="req_002", method_name="retrieve") - request.arrival_time = 1000.0 - request.processing_start_time = 1001.5 - request.processing_end_time = 1005.0 - - request.calculate_times() - - assert request.queue_wait_time == 1.5 - assert request.execution_time == 3.5 - - def test_to_dict(self): - """测试转换为字典""" - request = ServiceRequestMetrics( - request_id="req_003", - method_name="generate", - success=False, - error_type="TimeoutError", - ) - - result = request.to_dict() - - assert isinstance(result, dict) - assert result["request_id"] == "req_003" - assert result["method_name"] == "generate" - assert result["success"] is False - assert result["error_type"] == "TimeoutError" - - -class TestMethodMetrics: - """测试 MethodMetrics 数据类""" - - def test_method_metrics_creation(self): - """测试创建 MethodMetrics 实例""" - metrics = MethodMetrics(method_name="process") - - assert metrics.method_name == "process" - assert metrics.total_requests == 0 - assert metrics.total_failures == 0 - assert metrics.avg_response_time == 0.0 - - def test_method_metrics_with_data(self): - """测试带数据的 MethodMetrics""" - metrics = MethodMetrics( - method_name="retrieve", - total_requests=500, - total_failures=5, - avg_response_time=123.4, - p50_response_time=120.0, - p95_response_time=200.0, - p99_response_time=250.0, - ) - - assert metrics.method_name == "retrieve" - assert metrics.total_requests == 500 - assert metrics.total_failures == 5 - assert metrics.avg_response_time == 123.4 - assert metrics.p95_response_time == 200.0 - - -class TestServicePerformanceMetrics: - """测试 ServicePerformanceMetrics 数据类""" - - def test_service_metrics_creation(self): - """测试创建 ServicePerformanceMetrics 实例""" - metrics = ServicePerformanceMetrics(service_name="retrieval_service") - - assert metrics.service_name == "retrieval_service" - assert metrics.uptime == 0.0 - assert metrics.total_requests_processed == 0 - assert metrics.total_requests_failed == 0 - assert len(metrics.method_metrics) == 0 - - def test_service_metrics_with_methods(self): - """测试包含方法统计的 ServicePerformanceMetrics""" - method1 = MethodMetrics(method_name="retrieve", total_requests=100, avg_response_time=50.0) - method2 = MethodMetrics(method_name="rerank", total_requests=80, avg_response_time=30.0) - - metrics = ServicePerformanceMetrics( - service_name="rag_service", - total_requests_processed=180, - method_metrics={"retrieve": method1, "rerank": method2}, - ) - - assert metrics.service_name == "rag_service" - assert metrics.total_requests_processed == 180 - assert len(metrics.method_metrics) == 2 - assert "retrieve" in metrics.method_metrics - assert "rerank" in metrics.method_metrics - - def test_to_dict(self): - """测试转换为字典""" - metrics = ServicePerformanceMetrics( - service_name="test_service", - uptime=300.0, - total_requests_processed=1000, - total_requests_failed=10, - requests_per_second=3.33, - ) - - result = metrics.to_dict() - - assert isinstance(result, dict) - assert result["service_name"] == "test_service" - assert result["uptime"] == 300.0 - assert result["total_requests_processed"] == 1000 - assert result["total_requests_failed"] == 10 - assert result["requests_per_second"] == 3.33 - - -if __name__ == "__main__": - pytest.main([__file__, "-v"]) diff --git a/packages/sage-kernel/tests/unit/kernel/runtime/monitoring/test_metrics_collector.py b/packages/sage-kernel/tests/unit/kernel/runtime/monitoring/test_metrics_collector.py deleted file mode 100644 index 4b0b2739c1..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/runtime/monitoring/test_metrics_collector.py +++ /dev/null @@ -1,251 +0,0 @@ -""" -Test suite for sage.kernel.runtime.monitoring.metrics_collector module - -Tests the MetricsCollector class functionality including packet tracking, -TPS calculation, and latency percentile computation. -""" - -import time - -import pytest - -from sage.kernel.runtime.monitoring.metrics_collector import MetricsCollector - - -class TestMetricsCollector: - """测试 MetricsCollector 类""" - - def test_collector_creation(self): - """测试创建 MetricsCollector 实例""" - collector = MetricsCollector(name="test_task") - - assert collector.name == "test_task" - assert collector.window_size == 10000 # 默认值是10000 - assert len(collector.packet_metrics) == 0 - assert collector._total_processed == 0 - assert collector._total_failed == 0 - - def test_custom_window_size(self): - """测试自定义窗口大小""" - collector = MetricsCollector(name="test_task", window_size=500) - - assert collector.window_size == 500 - assert collector.packet_metrics.maxlen == 500 - - def test_record_packet_start(self): - """测试记录数据包开始处理""" - collector = MetricsCollector(name="test_task") - - packet_id = "packet_001" - collector.record_packet_start(packet_id) - - assert packet_id in collector._in_flight - metrics = collector._in_flight[packet_id] - # PacketMetrics 使用 packet_id, ServiceRequestMetrics 使用 request_id - assert getattr(metrics, "packet_id", getattr(metrics, "request_id", None)) == packet_id - assert metrics.processing_start_time is not None - - def test_record_packet_end_success(self): - """测试记录数据包成功处理完成""" - collector = MetricsCollector(name="test_task") - - packet_id = "packet_002" - collector.record_packet_start(packet_id) - time.sleep(0.01) # 模拟处理时间 - collector.record_packet_end(packet_id, success=True) - - assert packet_id not in collector._in_flight - assert collector._total_processed == 1 - assert collector._total_failed == 0 - assert len(collector.packet_metrics) == 1 - - def test_record_packet_end_failure(self): - """测试记录数据包处理失败""" - collector = MetricsCollector(name="test_task") - - packet_id = "packet_003" - collector.record_packet_start(packet_id) - collector.record_packet_end(packet_id, success=False, error_type="ValueError") - - assert collector._total_processed == 1 - assert collector._total_failed == 1 - assert "ValueError" in collector._error_breakdown - assert collector._error_breakdown["ValueError"] == 1 - - def test_multiple_error_types(self): - """测试多种错误类型统计""" - collector = MetricsCollector(name="test_task") - - # 记录不同类型的错误 - for i, error_type in enumerate(["ValueError", "TypeError", "ValueError", "NetworkError"]): - packet_id = f"packet_{i:03d}" - collector.record_packet_start(packet_id) - collector.record_packet_end(packet_id, success=False, error_type=error_type) - - assert collector._total_failed == 4 - assert collector._error_breakdown["ValueError"] == 2 - assert collector._error_breakdown["TypeError"] == 1 - assert collector._error_breakdown["NetworkError"] == 1 - - def test_calculate_tps(self): - """测试 TPS 计算""" - collector = MetricsCollector(name="test_task") - - # 模拟处理多个数据包 - for i in range(10): - packet_id = f"packet_{i:03d}" - collector.record_packet_start(packet_id) - collector.record_packet_end(packet_id, success=True) - - time.sleep(0.1) # 等待一小段时间 - metrics = collector.get_real_time_metrics() - - assert metrics.packets_per_second > 0 # TPS 应该大于0 - - def test_calculate_percentiles_empty(self): - """测试空数据的百分位数计算""" - collector = MetricsCollector(name="test_task") - - result = collector.calculate_percentiles([]) - - assert result["p50"] == 0.0 - assert result["p95"] == 0.0 - assert result["p99"] == 0.0 - - def test_calculate_percentiles_with_data(self): - """测试有数据的百分位数计算""" - collector = MetricsCollector(name="test_task") - - # 添加一些延迟数据（毫秒） - latencies = [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0] - - result = collector.calculate_percentiles(latencies) - - # 验证百分位数在合理范围内（毫秒） - assert 40 <= result["p50"] <= 60 # P50 应该在中位数附近 - assert 90 <= result["p95"] <= 100 # P95 应该接近最大值 - assert 95 <= result["p99"] <= 100 # P99 应该非常接近最大值 - - def test_get_real_time_metrics(self): - """测试获取实时指标""" - collector = MetricsCollector(name="test_task") - - # 处理一些数据包 - for i in range(5): - packet_id = f"packet_{i:03d}" - collector.record_packet_start(packet_id) - time.sleep(0.01) - collector.record_packet_end(packet_id, success=True) - - # 处理一个失败的包 - collector.record_packet_start("failed_packet") - collector.record_packet_end("failed_packet", success=False, error_type="Error") - - metrics = collector.get_real_time_metrics() - - assert metrics.task_name == "test_task" - assert metrics.total_packets_processed == 6 - assert metrics.total_packets_failed == 1 - assert metrics.packets_per_second >= 0 - assert len(metrics.error_breakdown) == 1 - assert metrics.error_breakdown["Error"] == 1 - - def test_reset_metrics(self): - """测试重置指标""" - collector = MetricsCollector(name="test_task") - - # 添加一些数据 - for i in range(5): - packet_id = f"packet_{i:03d}" - collector.record_packet_start(packet_id) - collector.record_packet_end(packet_id, success=True) - - # 重置 - collector.reset_metrics() - - assert collector._total_processed == 0 - assert collector._total_failed == 0 - assert len(collector.packet_metrics) == 0 - assert len(collector._error_breakdown) == 0 - assert len(collector._in_flight) == 0 - - def test_window_size_limit(self): - """测试滑动窗口大小限制""" - collector = MetricsCollector(name="test_task", window_size=10) - - # 添加超过窗口大小的数据 - for i in range(20): - packet_id = f"packet_{i:03d}" - collector.record_packet_start(packet_id) - collector.record_packet_end(packet_id, success=True) - - # 验证只保留最近的 10 条数据 - assert len(collector.packet_metrics) == 10 - - def test_concurrent_packets(self): - """测试并发处理多个数据包""" - collector = MetricsCollector(name="test_task") - - # 同时开始多个数据包 - for i in range(5): - collector.record_packet_start(f"packet_{i:03d}") - - assert len(collector._in_flight) == 5 - - # 完成所有数据包 - for i in range(5): - collector.record_packet_end(f"packet_{i:03d}", success=True) - - assert len(collector._in_flight) == 0 - assert collector._total_processed == 5 - - -class TestMetricsCollectorEdgeCases: - """测试 MetricsCollector 边界情况""" - - def test_record_end_without_start(self): - """测试在没有开始记录的情况下结束""" - collector = MetricsCollector(name="test_task") - - # 尝试结束一个未开始的数据包 - collector.record_packet_end("nonexistent_packet", success=True) - - # 实际会被记录但不会有详细信息（因为不在_in_flight中） - assert collector._total_processed == 1 - - def test_duplicate_packet_start(self): - """测试重复开始同一个数据包""" - collector = MetricsCollector(name="test_task") - - packet_id = "duplicate_packet" - collector.record_packet_start(packet_id) - first_start_time = collector._in_flight[packet_id].processing_start_time - - time.sleep(0.01) - - # 再次开始同一个数据包 - collector.record_packet_start(packet_id) - second_start_time = collector._in_flight[packet_id].processing_start_time - - # 应该覆盖之前的记录 - assert first_start_time is not None - assert second_start_time is not None - assert second_start_time > first_start_time - - def test_very_fast_processing(self): - """测试非常快的处理时间""" - collector = MetricsCollector(name="test_task") - - packet_id = "fast_packet" - collector.record_packet_start(packet_id) - collector.record_packet_end(packet_id, success=True) - - metrics = collector.get_real_time_metrics() - - # 即使处理时间非常短，也应该能正确记录 - assert metrics.total_packets_processed == 1 - assert metrics.min_latency >= 0 - - -if __name__ == "__main__": - pytest.main([__file__, "-v"]) diff --git a/packages/sage-kernel/tests/unit/kernel/runtime/monitoring/test_monitoring_integration.py b/packages/sage-kernel/tests/unit/kernel/runtime/monitoring/test_monitoring_integration.py deleted file mode 100644 index 72b4ea2ba0..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/runtime/monitoring/test_monitoring_integration.py +++ /dev/null @@ -1,348 +0,0 @@ -""" -Integration tests for the monitoring system - -Tests the monitoring system integrated with BaseTask and task execution. -""" - -import time -from unittest.mock import MagicMock - -import pytest - -from sage.common.core.functions.base_function import BaseFunction -from sage.kernel.api.base_environment import BaseEnvironment -from sage.kernel.api.operator.map_operator import MapOperator -from sage.kernel.api.transformation.base_transformation import BaseTransformation -from sage.kernel.runtime.context.task_context import TaskContext -from sage.kernel.runtime.graph.graph_node import TaskNode -from sage.kernel.runtime.monitoring.metrics_collector import MetricsCollector - - -class MockFunction(BaseFunction): - """Mock function class for testing that properly inherits from BaseFunction""" - - __name__ = "MockFunction" - is_comap = False - - def execute(self, data): - """Required abstract method implementation""" - return data - - -class MockTransformation(BaseTransformation): - """Mock transformation for testing that properly inherits from BaseTransformation""" - - def __init__(self): - # Create a minimal mock environment for the base class - mock_env = MagicMock(spec=BaseEnvironment) - mock_env.name = "test_env" - mock_env.platform = "local" - mock_env.pipeline = [] - - # Set operator_class BEFORE calling super().__init__ - self.operator_class = MapOperator - - # Initialize parent with minimal required args - super().__init__( - env=mock_env, function=MockFunction, name="MockTransformation", parallelism=1 - ) - - -class MockTaskNode(TaskNode): - """Mock task node for testing that properly inherits from TaskNode""" - - def __init__(self, name: str = "test_task"): - # Create minimal mocks for required dependencies - mock_env = MagicMock(spec=BaseEnvironment) - mock_env.name = "test_env" - mock_env.platform = "local" - mock_env.pipeline = [] - - transformation = MockTransformation() - - # Initialize parent - super().__init__(name=name, transformation=transformation, parallel_index=0, env=mock_env) - - -class MockEnvironment(BaseEnvironment): - """Mock environment for testing that properly inherits from BaseEnvironment""" - - def __init__(self, enable_monitoring: bool = False): - # Initialize parent with required args - super().__init__( - name="test_env", config=None, platform="local", enable_monitoring=enable_monitoring - ) - - self.uuid = "test_uuid" - - # Set jobmanager attributes after parent init - self.jobmanager_host = "127.0.0.1" - self.jobmanager_port = 19001 - - # 使用统一的SAGE路径管理 - from sage.common.config.output_paths import get_test_env_dir - - self.env_base_dir = str(get_test_env_dir("test_logs")) - - def submit(self): - """Required abstract method implementation""" - pass - - -class TestMonitoringIntegration: - """测试监控系统集成""" - - def test_task_context_with_monitoring_enabled(self): - """测试启用监控的 TaskContext""" - env = MockEnvironment(enable_monitoring=True) - node = MockTaskNode("monitored_task") - transformation = MockTransformation() - - ctx = TaskContext(node, transformation, env) - - assert ctx.enable_monitoring is True - - def test_task_context_with_monitoring_disabled(self): - """测试禁用监控的 TaskContext""" - env = MockEnvironment(enable_monitoring=False) - node = MockTaskNode("unmonitored_task") - transformation = MockTransformation() - - ctx = TaskContext(node, transformation, env) - - assert ctx.enable_monitoring is False - - def test_metrics_collector_lifecycle(self): - """测试 MetricsCollector 生命周期""" - collector = MetricsCollector(name="lifecycle_test") - - # 模拟处理多个数据包 - packet_ids = [f"packet_{i:03d}" for i in range(10)] - - for packet_id in packet_ids: - collector.record_packet_start(packet_id) - time.sleep(0.001) # 模拟处理时间 - collector.record_packet_end(packet_id, success=True) - - # 获取指标 - metrics = collector.get_real_time_metrics() - - assert metrics.total_packets_processed == 10 - assert metrics.total_packets_failed == 0 - assert metrics.packets_per_second > 0 - - # 重置 - collector.reset_metrics() - - metrics = collector.get_real_time_metrics() - assert metrics.total_packets_processed == 0 - - def test_monitoring_with_errors(self): - """测试监控错误处理""" - collector = MetricsCollector(name="error_test") - - # 处理成功和失败的包 - for i in range(10): - packet_id = f"packet_{i:03d}" - collector.record_packet_start(packet_id) - - if i % 3 == 0: # 每3个包失败一次 - collector.record_packet_end(packet_id, success=False, error_type="ValueError") - else: - collector.record_packet_end(packet_id, success=True) - - metrics = collector.get_real_time_metrics() - - assert metrics.total_packets_processed == 10 - assert metrics.total_packets_failed == 4 # 0, 3, 6, 9 - assert "ValueError" in metrics.error_breakdown - assert metrics.error_breakdown["ValueError"] == 4 - - def test_monitoring_performance_overhead(self): - """测试监控性能开销""" - # Baseline: 执行不包含实际监控调用的操作 - # 这包括packet_id生成和一些基本的字典操作 - iterations = 10000 # 增加迭代次数以减少计时误差的影响 - baseline_dict = {} - - # 预热，避免首次运行的初始化开销 - for i in range(100): - packet_id = f"packet_{i:05d}" - baseline_dict[packet_id] = {"start": 0, "end": 0} - baseline_dict.clear() - - start_time = time.perf_counter() # 使用更精确的计时器 - for i in range(iterations): - packet_id = f"packet_{i:05d}" - baseline_dict[packet_id] = {"start": 0, "end": 0} - no_monitoring_time = time.perf_counter() - start_time - - # 使用监控 - 实际调用监控系统 - collector = MetricsCollector(name="overhead_test") - # 预热监控系统 - for i in range(100): - packet_id = f"warmup_{i:05d}" - collector.record_packet_start(packet_id) - collector.record_packet_end(packet_id, success=True) - - start_time = time.perf_counter() - for i in range(iterations): - packet_id = f"packet_{i:05d}" - collector.record_packet_start(packet_id) - collector.record_packet_end(packet_id, success=True) - monitoring_time = time.perf_counter() - start_time - - # 监控开销应该相对较小 - # 在 CI 环境中，由于系统负载和虚拟化开销，阈值需要更宽松 - # 通常情况下开销应该在 10-20x，但 CI 环境可能达到 50-100x - overhead_ratio = monitoring_time / max(no_monitoring_time, 0.0001) - assert overhead_ratio < 100, f"Monitoring overhead too high: {overhead_ratio}x" - - def test_concurrent_monitoring(self): - """测试并发监控""" - import threading - - collector = MetricsCollector(name="concurrent_test") - errors = [] - - def process_packets(start_idx, count): - try: - for i in range(count): - packet_id = f"thread_{start_idx}_packet_{i:03d}" - collector.record_packet_start(packet_id) - time.sleep(0.001) - collector.record_packet_end(packet_id, success=True) - except Exception as e: - errors.append(e) - - # 启动多个线程 - threads = [] - for i in range(5): - thread = threading.Thread(target=process_packets, args=(i, 20)) - threads.append(thread) - thread.start() - - # 等待所有线程完成 - for thread in threads: - thread.join() - - # 验证没有错误 - assert len(errors) == 0 - - # 验证总数正确 - metrics = collector.get_real_time_metrics() - assert metrics.total_packets_processed == 100 # 5 threads * 20 packets - - def test_latency_percentiles_accuracy(self): - """测试延迟百分位数准确性""" - collector = MetricsCollector(name="percentile_test") - - # 创建已知分布的延迟数据 - # 使用较小的延迟值（0.1ms to 10ms）以加快测试 - latencies_ms = [0.1, 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] - - for latency_ms in latencies_ms: - packet_id = f"packet_{latency_ms}" - collector.record_packet_start(packet_id) - - # 实际等待 - time.sleep(latency_ms / 1000.0) - - collector.record_packet_end(packet_id, success=True) - - # 获取实时指标（包含百分位数） - metrics = collector.get_real_time_metrics() - - # 验证收集到数据且有合理的百分位数 - assert metrics.total_packets_processed == len(latencies_ms) - assert metrics.p50_latency > 0 # P50应该大于0 - assert metrics.p95_latency > metrics.p50_latency # P95应该大于P50 - assert metrics.p99_latency >= metrics.p95_latency # P99应该大于等于P95 - - def test_monitoring_with_resource_tracking(self): - """测试带资源监控的完整流程""" - from sage.kernel.runtime.monitoring.resource_monitor import ResourceMonitor - - collector = MetricsCollector(name="resource_test") - resource_monitor = ResourceMonitor(sampling_interval=0.1) - - try: - resource_monitor.start_monitoring() - - # 模拟处理 - for i in range(50): - packet_id = f"packet_{i:03d}" - collector.record_packet_start(packet_id) - time.sleep(0.01) - collector.record_packet_end(packet_id, success=True) - - # 获取资源统计 - resource_stats = resource_monitor.get_summary() - - # 验证收集到了资源数据（monitoring字段包含采样数） - assert resource_stats["monitoring"]["sample_count"] > 0 - - finally: - resource_monitor.stop_monitoring() - - -class TestMonitoringEdgeCases: - """测试监控系统边界情况""" - - def test_empty_metrics(self): - """测试空指标""" - collector = MetricsCollector(name="empty_test") - metrics = collector.get_real_time_metrics() - - assert metrics.total_packets_processed == 0 - assert metrics.total_packets_failed == 0 - assert metrics.packets_per_second == 0.0 - - def test_single_packet(self): - """测试单个数据包""" - collector = MetricsCollector(name="single_test") - - collector.record_packet_start("single_packet") - time.sleep(0.01) - collector.record_packet_end("single_packet", success=True) - - metrics = collector.get_real_time_metrics() - - assert metrics.total_packets_processed == 1 - assert metrics.p50_latency > 0 - - def test_rapid_processing(self): - """测试快速处理""" - collector = MetricsCollector(name="rapid_test") - - # 快速处理大量数据包 - for i in range(1000): - packet_id = f"packet_{i:04d}" - collector.record_packet_start(packet_id) - collector.record_packet_end(packet_id, success=True) - - metrics = collector.get_real_time_metrics() - - assert metrics.total_packets_processed == 1000 - assert metrics.packets_per_second > 0 - - def test_window_overflow(self): - """测试窗口溢出""" - collector = MetricsCollector(name="overflow_test", window_size=100) - - # 处理超过窗口大小的数据包 - for i in range(200): - packet_id = f"packet_{i:04d}" - collector.record_packet_start(packet_id) - collector.record_packet_end(packet_id, success=True) - - # 验证只保留最近的100个 - assert len(collector.packet_metrics) == 100 - - # 但总计数应该是200 - metrics = collector.get_real_time_metrics() - assert metrics.total_packets_processed == 200 - - -if __name__ == "__main__": - pytest.main([__file__, "-v"]) diff --git a/packages/sage-kernel/tests/unit/kernel/runtime/monitoring/test_resource_monitor.py b/packages/sage-kernel/tests/unit/kernel/runtime/monitoring/test_resource_monitor.py deleted file mode 100644 index 421e3f6fea..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/runtime/monitoring/test_resource_monitor.py +++ /dev/null @@ -1,237 +0,0 @@ -""" -Test suite for sage.kernel.runtime.monitoring.resource_monitor module - -Tests the ResourceMonitor class for CPU and memory monitoring. -""" - -import time -from unittest.mock import MagicMock, patch - -import pytest - -from sage.kernel.runtime.monitoring.resource_monitor import ResourceMonitor - - -class TestResourceMonitor: - """测试 ResourceMonitor 类""" - - @pytest.fixture - def mock_psutil(self): - """Mock psutil 模块""" - with patch("sage.kernel.runtime.monitoring.resource_monitor.PSUTIL_AVAILABLE", True): - with patch("sage.kernel.runtime.monitoring.resource_monitor.psutil") as mock: - # Mock Process 类 - mock_process = MagicMock() - mock_process.cpu_percent.return_value = 50.0 - mock_memory_info = MagicMock() - mock_memory_info.rss = 1024 * 1024 * 1024 # 1GB - mock_process.memory_info.return_value = mock_memory_info - - mock.Process.return_value = mock_process - mock.cpu_percent.return_value = 75.0 - mock_virtual_memory_obj = MagicMock() - mock_virtual_memory_obj.percent = 60.0 - mock_virtual_memory_obj.available = 2 * 1024 * 1024 * 1024 # 2GB - mock.virtual_memory.return_value = mock_virtual_memory_obj - - yield mock - - def test_monitor_creation(self): - """测试创建 ResourceMonitor 实例""" - monitor = ResourceMonitor(sampling_interval=1.0) - - assert monitor.sampling_interval == 1.0 - assert monitor._running is False - assert len(monitor.cpu_samples) == 0 - assert len(monitor.memory_samples) == 0 - - def test_start_monitoring(self, mock_psutil): - """测试启动监控""" - monitor = ResourceMonitor(sampling_interval=0.1) - monitor.start_monitoring() - - assert monitor._running is True - assert monitor._monitor_thread is not None - assert monitor._monitor_thread.is_alive() - - # 等待收集一些样本 - time.sleep(0.3) - - monitor.stop_monitoring() - - # 验证收集到了样本 - assert len(monitor.cpu_samples) > 0 - assert len(monitor.memory_samples) > 0 - - def test_stop_monitoring(self, mock_psutil): - """测试停止监控""" - monitor = ResourceMonitor(sampling_interval=0.1) - monitor.start_monitoring() - time.sleep(0.2) - monitor.stop_monitoring() - - assert monitor._running is False - - # 等待线程完全停止 - if monitor._monitor_thread: - monitor._monitor_thread.join(timeout=1.0) - - # 线程应该已经停止（如果存在） - if monitor._monitor_thread: - assert not monitor._monitor_thread.is_alive() - - def test_get_stats_without_monitoring(self): - """测试在未启动监控时获取统计""" - monitor = ResourceMonitor() - stats = monitor.get_summary() - - # 验证返回了正确的结构 - assert "process" in stats - assert "system" in stats - assert "monitoring" in stats - assert stats["monitoring"]["sample_count"] == 0 - - def test_get_stats_with_monitoring(self, mock_psutil): - """测试监控运行时获取统计""" - monitor = ResourceMonitor(sampling_interval=0.1) - monitor.start_monitoring() - time.sleep(0.3) - stats = monitor.get_summary() - monitor.stop_monitoring() - - # 验证返回了正确的结构和数据 - assert "process" in stats - assert stats["process"]["current"]["cpu_percent"] > 0 - assert stats["process"]["current"]["memory_mb"] > 0 - assert stats["monitoring"]["sample_count"] >= 0 - - def test_psutil_not_available(self): - """测试 psutil 不可用时的行为""" - with patch("sage.kernel.runtime.monitoring.resource_monitor.PSUTIL_AVAILABLE", False): - # 当psutil不可用时，ResourceMonitor初始化会抛出ImportError - with pytest.raises(ImportError, match="psutil is required"): - ResourceMonitor() - - def test_reset_stats(self, mock_psutil): - """测试重置统计数据""" - monitor = ResourceMonitor(sampling_interval=0.1) - monitor.start_monitoring() - time.sleep(0.2) - - # 确认有数据 - assert len(monitor.cpu_samples) > 0 - - # 重置 - monitor.stop_monitoring() - monitor = ResourceMonitor(sampling_interval=0.1) - - assert len(monitor.cpu_samples) == 0 - assert len(monitor.memory_samples) == 0 - - def test_concurrent_start_stop(self, mock_psutil): - """测试并发启动和停止""" - monitor = ResourceMonitor(sampling_interval=0.1) - - # 多次启动 - monitor.start_monitoring() - monitor.start_monitoring() # 第二次启动应该被忽略 - - time.sleep(0.2) - - # 多次停止 - monitor.stop_monitoring() - monitor.stop_monitoring() # 第二次停止应该安全 - - assert monitor._running is False - - def test_memory_conversion(self, mock_psutil): - """测试内存转换为 MB""" - monitor = ResourceMonitor(sampling_interval=0.1) - monitor.start_monitoring() - time.sleep(0.2) - stats = monitor.get_summary() - monitor.stop_monitoring() - - # 验证内存单位是 MB（使用正确的结构） - assert stats["process"]["current"]["memory_mb"] > 0 - # 应该是合理的 MB 值（1GB = 1024MB） - assert stats["process"]["current"]["memory_mb"] < 100000 # 不太可能超过 100GB - - def test_custom_sample_interval(self, mock_psutil): - """测试自定义采样间隔""" - # 短间隔 - fast_monitor = ResourceMonitor(sampling_interval=0.05) - fast_monitor.start_monitoring() - time.sleep(0.2) - fast_stats = fast_monitor.get_summary() - fast_monitor.stop_monitoring() - - # 长间隔 - slow_monitor = ResourceMonitor(sampling_interval=0.2) - slow_monitor.start_monitoring() - time.sleep(0.2) - slow_stats = slow_monitor.get_summary() - slow_monitor.stop_monitoring() - - # 短间隔应该收集更多样本 - assert fast_stats["monitoring"]["sample_count"] >= slow_stats["monitoring"]["sample_count"] - - -class TestResourceMonitorEdgeCases: - """测试 ResourceMonitor 边界情况""" - - def test_very_short_interval(self): - """测试非常短的采样间隔""" - with patch("sage.kernel.runtime.monitoring.resource_monitor.PSUTIL_AVAILABLE", True): - with patch("sage.kernel.runtime.monitoring.resource_monitor.psutil") as mock: - # Mock Process 类 - mock_process = MagicMock() - mock_process.cpu_percent.return_value = 50.0 - mock_memory_info = MagicMock() - mock_memory_info.rss = 1024 * 1024 * 1024 - mock_process.memory_info.return_value = mock_memory_info - mock.Process.return_value = mock_process - - monitor = ResourceMonitor(sampling_interval=0.01) - monitor.start_monitoring() - time.sleep(0.1) - monitor.stop_monitoring() - - # 应该能正常工作 - assert len(monitor.cpu_samples) > 0 - - def test_stop_before_start(self): - """测试在启动前停止""" - monitor = ResourceMonitor() - monitor.stop_monitoring() # 应该安全地不执行任何操作 - - assert monitor._running is False - - def test_get_stats_during_monitoring(self): - """测试在监控运行时多次获取统计""" - with patch("sage.kernel.runtime.monitoring.resource_monitor.PSUTIL_AVAILABLE", True): - with patch("sage.kernel.runtime.monitoring.resource_monitor.psutil") as mock: - # Mock Process 类 - mock_process = MagicMock() - mock_process.cpu_percent.return_value = 50.0 - mock_memory_info = MagicMock() - mock_memory_info.rss = 1024 * 1024 * 1024 - mock_process.memory_info.return_value = mock_memory_info - mock.Process.return_value = mock_process - - monitor = ResourceMonitor(sampling_interval=0.1) - monitor.start_monitoring() - - # 多次获取统计 - stats1 = monitor.get_summary() - time.sleep(0.2) - stats2 = monitor.get_summary() - - monitor.stop_monitoring() - - # 第二次应该有更多或相同数量的样本 - assert stats2["monitoring"]["sample_count"] >= stats1["monitoring"]["sample_count"] - - -if __name__ == "__main__": - pytest.main([__file__, "-v"]) diff --git a/packages/sage-kernel/tests/unit/kernel/runtime/service/test_base_service_task.py b/packages/sage-kernel/tests/unit/kernel/runtime/service/test_base_service_task.py deleted file mode 100644 index 8b41e6271c..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/runtime/service/test_base_service_task.py +++ /dev/null @@ -1,678 +0,0 @@ -""" -Unit tests for BaseServiceTask. - -Tests the base class for service tasks providing unified service interface -and high-performance queue listening functionality. -""" - -import queue -import threading -import time -from unittest.mock import MagicMock, patch - -import pytest - -from sage.kernel.runtime.service.base_service_task import BaseServiceTask - -# Mock classes for testing - - -class MockServiceClass: - """Mock service class for testing.""" - - def __init__(self, ctx=None): - self.ctx = ctx - self.setup_called = False - self.cleanup_called = False - self.close_called = False - self.started = False - self.stopped = False - - def setup(self): - """Mock setup method.""" - self.setup_called = True - - def cleanup(self): - """Mock cleanup method.""" - self.cleanup_called = True - - def close(self): - """Mock close method.""" - self.close_called = True - - def test_method(self, arg1, arg2=None): - """Mock service method.""" - return f"result: {arg1}, {arg2}" - - def failing_method(self): - """Mock method that raises an exception.""" - raise ValueError("Service method failed") - - @property - def test_property(self): - """Mock service property.""" - return "test_value" - - -class MockServiceFactory: - """Mock service factory for testing.""" - - def __init__(self, service_name="test_service", service_class=None): - self.service_name = service_name - self.service_class = service_class or MockServiceClass - - def create_service(self, ctx): - """Create a mock service instance.""" - return self.service_class(ctx) - - -class MockServiceContext: - """Mock service context for testing.""" - - def __init__(self, enable_monitoring=False): - self.name = "test_context" - self.enable_monitoring = enable_monitoring - self.metrics_window_size = 100 - self.enable_detailed_tracking = True - self.resource_sampling_interval = 1.0 - self.enable_auto_report = False - self.logger = MagicMock() - - # Queue descriptors - self._request_queue = queue.Queue() - self._response_queues = {} - - def get_request_queue_descriptor(self): - """Get request queue descriptor.""" - descriptor = MagicMock() - descriptor.queue_instance = self._request_queue - descriptor.queue_id = "request_queue" - descriptor.queue_type = "python_queue" - return descriptor - - def get_service_response_queue_descriptor(self, node_name): - """Get response queue descriptor for a node.""" - if node_name not in self._response_queues: - self._response_queues[node_name] = queue.Queue() - descriptor = MagicMock() - descriptor.queue_instance = self._response_queues[node_name] - descriptor.queue_id = f"response_queue_{node_name}" - descriptor.queue_type = "python_queue" - return descriptor - - def get_service_response_queue_descriptors(self): - """Get all response queue descriptors.""" - return { - name: self.get_service_response_queue_descriptor(name) for name in self._response_queues - } - - def add_response_queue(self, node_name): - """Add a response queue for a node.""" - self._response_queues[node_name] = queue.Queue() - - -class ConcreteServiceTask(BaseServiceTask): - """Concrete implementation of BaseServiceTask for testing.""" - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self.service_instance_started = False - self.service_instance_stopped = False - - def _start_service_instance(self): - """Start service instance (concrete implementation).""" - self.service_instance_started = True - if hasattr(self.service_instance, "started"): - self.service_instance.started = True - - def _stop_service_instance(self): - """Stop service instance (concrete implementation).""" - self.service_instance_stopped = True - if hasattr(self.service_instance, "stopped"): - self.service_instance.stopped = True - - -# Fixtures - - -@pytest.fixture -def mock_service_factory(): - """Create a mock service factory.""" - return MockServiceFactory() - - -@pytest.fixture -def mock_service_context(): - """Create a mock service context.""" - return MockServiceContext() - - -@pytest.fixture -def service_task(mock_service_factory, mock_service_context): - """Create a concrete service task instance.""" - task = ConcreteServiceTask(mock_service_factory, mock_service_context) - yield task - task.cleanup() - - -# Test Cases - - -@pytest.mark.unit -class TestBaseServiceTaskInitialization: - """Test BaseServiceTask initialization.""" - - def test_initialization_with_context(self, mock_service_factory, mock_service_context): - """Test service task initialization with context.""" - task = ConcreteServiceTask(mock_service_factory, mock_service_context) - - assert task.service_factory == mock_service_factory - assert task.service_name == "test_service" - assert task.ctx == mock_service_context - assert task.service_instance is not None - assert task.service == task.service_instance - assert not task.is_running - assert task._request_count == 0 - assert task._error_count == 0 - - task.cleanup() - - def test_initialization_without_context_raises_error(self, mock_service_factory): - """Test service task initialization without context raises error.""" - with pytest.raises(ValueError, match="ServiceContext is required"): - ConcreteServiceTask(mock_service_factory, None) - - def test_initialization_calls_service_setup(self, mock_service_factory, mock_service_context): - """Test service task initialization calls service setup method.""" - task = ConcreteServiceTask(mock_service_factory, mock_service_context) - - assert task.service_instance.setup_called - - task.cleanup() - - def test_initialization_injects_context_to_service( - self, mock_service_factory, mock_service_context - ): - """Test service task initialization injects context to service instance.""" - task = ConcreteServiceTask(mock_service_factory, mock_service_context) - - assert hasattr(task.service_instance, "ctx") - assert task.service_instance.ctx == mock_service_context - - task.cleanup() - - def test_logger_property(self, service_task): - """Test logger property returns context logger.""" - assert service_task.logger == service_task.ctx.logger - - def test_name_property(self, service_task): - """Test name property returns context name.""" - assert service_task.name == service_task.ctx.name - - -@pytest.mark.unit -class TestBaseServiceTaskQueueManagement: - """Test BaseServiceTask queue management.""" - - def test_request_queue_descriptor_property(self, service_task): - """Test request_queue_descriptor property.""" - descriptor = service_task.request_queue_descriptor - - assert descriptor is not None - assert descriptor.queue_id == "request_queue" - - def test_request_queue_property(self, service_task): - """Test request_queue property.""" - request_queue = service_task.request_queue - - assert request_queue is not None - assert isinstance(request_queue, queue.Queue) - - def test_get_response_queue_descriptor(self, service_task, mock_service_context): - """Test get_response_queue_descriptor method.""" - mock_service_context.add_response_queue("node1") - descriptor = service_task.get_response_queue_descriptor("node1") - - assert descriptor is not None - assert descriptor.queue_id == "response_queue_node1" - - def test_get_response_queue(self, service_task, mock_service_context): - """Test get_response_queue method.""" - mock_service_context.add_response_queue("node1") - response_queue = service_task.get_response_queue("node1") - - assert response_queue is not None - assert isinstance(response_queue, queue.Queue) - - -@pytest.mark.unit -class TestBaseServiceTaskLifecycle: - """Test BaseServiceTask lifecycle management.""" - - def test_start_running(self, service_task): - """Test start_running method.""" - service_task.start_running() - - assert service_task.is_running - assert service_task.service_instance_started - assert service_task._queue_listener_thread is not None - assert service_task._queue_listener_running - - def test_start_running_already_running(self, service_task): - """Test start_running when already running logs warning.""" - service_task.start_running() - assert service_task.is_running - - # Start again - service_task.start_running() - # Should log warning but not fail - assert service_task.is_running - - def test_stop(self, service_task): - """Test stop method.""" - service_task.start_running() - assert service_task.is_running - - service_task.stop() - - assert not service_task.is_running - assert service_task.service_instance_stopped - assert not service_task._queue_listener_running - - def test_stop_not_running(self, service_task): - """Test stop when not running logs warning.""" - assert not service_task.is_running - - service_task.stop() - # Should log warning but not fail - assert not service_task.is_running - - def test_terminate(self, service_task): - """Test terminate method.""" - service_task.start_running() - - service_task.terminate() - - assert not service_task.is_running - - -@pytest.mark.unit -class TestBaseServiceTaskMethodCalling: - """Test BaseServiceTask method calling.""" - - def test_call_method_success(self, service_task): - """Test calling a service method successfully.""" - result = service_task.call_method("test_method", "arg1", arg2="value2") - - assert result == "result: arg1, value2" - assert service_task._request_count == 1 - assert service_task._error_count == 0 - - def test_call_method_failure(self, service_task): - """Test calling a service method that raises an exception.""" - with pytest.raises(ValueError, match="Service method failed"): - service_task.call_method("failing_method") - - assert service_task._request_count == 1 - assert service_task._error_count == 1 - - def test_call_nonexistent_method(self, service_task): - """Test calling a method that doesn't exist.""" - with pytest.raises(AttributeError, match="does not have method"): - service_task.call_method("nonexistent_method") - - assert service_task._error_count == 1 - - def test_get_attribute(self, service_task): - """Test getting a service attribute.""" - value = service_task.get_attribute("test_property") - - assert value == "test_value" - - def test_get_nonexistent_attribute(self, service_task): - """Test getting an attribute that doesn't exist.""" - with pytest.raises(AttributeError, match="does not have attribute"): - service_task.get_attribute("nonexistent_attr") - - def test_set_attribute(self, service_task): - """Test setting a service attribute.""" - service_task.set_attribute("new_attr", "new_value") - - assert service_task.service_instance.new_attr == "new_value" - - -@pytest.mark.unit -class TestBaseServiceTaskQueueListener: - """Test BaseServiceTask queue listener.""" - - def test_queue_listener_starts_with_service(self, service_task): - """Test queue listener thread starts when service starts.""" - service_task.start_running() - - assert service_task._queue_listener_thread is not None - assert service_task._queue_listener_thread.is_alive() - assert service_task._queue_listener_running - - def test_queue_listener_stops_with_service(self, service_task): - """Test queue listener thread stops when service stops.""" - service_task.start_running() - thread = service_task._queue_listener_thread - - service_task.stop() - - # Give thread time to stop - time.sleep(0.2) - assert not service_task._queue_listener_running - assert not thread.is_alive() - - def test_queue_listener_processes_request(self, service_task, mock_service_context): - """Test queue listener processes requests from queue.""" - service_task.start_running() - - # Add response queue - mock_service_context.add_response_queue("node1") - response_queue = mock_service_context._response_queues["node1"] - - # Send a request - request_data = { - "request_id": "req_001", - "method_name": "test_method", - "args": ("arg1",), - "kwargs": {"arg2": "value2"}, - "response_queue": response_queue, - "response_queue_name": "node1", - "timeout": 10.0, - } - service_task.request_queue.put(request_data) - - # Wait for processing - time.sleep(0.3) - - # Check response was sent - assert not response_queue.empty() - response = response_queue.get(timeout=1.0) - - assert response["request_id"] == "req_001" - assert response["success"] - assert response["result"] == "result: arg1, value2" - - def test_queue_listener_handles_failing_request(self, service_task, mock_service_context): - """Test queue listener handles failing requests gracefully.""" - service_task.start_running() - - # Add response queue - mock_service_context.add_response_queue("node1") - response_queue = mock_service_context._response_queues["node1"] - - # Send a failing request - request_data = { - "request_id": "req_002", - "method_name": "failing_method", - "args": (), - "kwargs": {}, - "response_queue": response_queue, - "response_queue_name": "node1", - "timeout": 10.0, - } - service_task.request_queue.put(request_data) - - # Wait for processing (increased from 0.3 to 0.5 for CI stability) - time.sleep(0.5) - - # Check error response was sent - assert not response_queue.empty() - response = response_queue.get(timeout=1.0) - - assert response["request_id"] == "req_002" - assert not response["success"] - assert "Service method failed" in response["error"] - - -@pytest.mark.unit -class TestBaseServiceTaskDirectRequestHandling: - """Test BaseServiceTask direct request handling (handle_request).""" - - def test_handle_request_success(self, service_task, mock_service_context): - """Test direct request handling succeeds.""" - # Add response queue - mock_service_context.add_response_queue("node1") - response_queue = mock_service_context._response_queues["node1"] - - request_data = { - "request_id": "req_003", - "method_name": "test_method", - "args": ("direct_arg",), - "kwargs": {}, - "response_queue": response_queue, - "timeout": 10.0, - } - - service_task.handle_request(request_data) - - # Check response - assert not response_queue.empty() - response = response_queue.get(timeout=1.0) - - assert response["request_id"] == "req_003" - assert response["success"] - assert "direct_arg" in response["result"] - - def test_handle_request_failure(self, service_task, mock_service_context): - """Test direct request handling with failure.""" - # Add response queue - mock_service_context.add_response_queue("node1") - response_queue = mock_service_context._response_queues["node1"] - - request_data = { - "request_id": "req_004", - "method_name": "failing_method", - "args": (), - "kwargs": {}, - "response_queue": response_queue, - "timeout": 10.0, - } - - service_task.handle_request(request_data) - - # Check error response - assert not response_queue.empty() - response = response_queue.get(timeout=1.0) - - assert response["request_id"] == "req_004" - assert not response["success"] - - -@pytest.mark.unit -class TestBaseServiceTaskStatistics: - """Test BaseServiceTask statistics.""" - - def test_get_statistics(self, service_task): - """Test get_statistics returns correct information.""" - stats = service_task.get_statistics() - - assert stats["service_name"] == "test_service" - assert stats["service_type"] == "ConcreteServiceTask" - assert not stats["is_running"] - assert stats["request_count"] == 0 - assert stats["error_count"] == 0 - assert stats["service_class"] == "MockServiceClass" - assert stats["has_service_context"] - - def test_statistics_after_requests(self, service_task): - """Test statistics are updated after processing requests.""" - # Call some methods - service_task.call_method("test_method", "arg1") - try: - service_task.call_method("failing_method") - except ValueError: - pass - - stats = service_task.get_statistics() - - assert stats["request_count"] == 2 - assert stats["error_count"] == 1 - - -@pytest.mark.unit -class TestBaseServiceTaskCleanup: - """Test BaseServiceTask cleanup.""" - - def test_cleanup_calls_service_cleanup(self, mock_service_factory, mock_service_context): - """Test cleanup calls service instance cleanup method.""" - task = ConcreteServiceTask(mock_service_factory, mock_service_context) - - task.cleanup() - - assert task.service_instance.cleanup_called - - def test_cleanup_stops_running_service(self, service_task): - """Test cleanup stops running service first.""" - service_task.start_running() - assert service_task.is_running - - service_task.cleanup() - - assert not service_task.is_running - - def test_cleanup_with_close_method(self, mock_service_context): - """Test cleanup calls close method if cleanup not available.""" - - class ServiceWithClose: - def __init__(self, ctx): - self.ctx = ctx - self.close_called = False - - def setup(self): - pass - - def close(self): - self.close_called = True - - factory = MockServiceFactory(service_class=ServiceWithClose) - task = ConcreteServiceTask(factory, mock_service_context) - - task.cleanup() - - assert task.service_instance.close_called - - -@pytest.mark.unit -class TestBaseServiceTaskConcurrency: - """Test BaseServiceTask concurrency and thread safety.""" - - def test_concurrent_method_calls(self, service_task): - """Test concurrent method calls are thread-safe.""" - results = [] - errors = [] - - def call_method(thread_id): - try: - result = service_task.call_method("test_method", f"thread_{thread_id}") - results.append(result) - except Exception as e: - errors.append(e) - - # Start multiple threads - threads = [] - for i in range(10): - thread = threading.Thread(target=call_method, args=(i,)) - threads.append(thread) - thread.start() - - # Wait for all threads to complete - for thread in threads: - thread.join(timeout=5.0) - - # Verify all calls succeeded - assert len(results) == 10 - assert len(errors) == 0 - assert service_task._request_count == 10 - - def test_concurrent_queue_processing(self, service_task, mock_service_context): - """Test concurrent request processing through queue.""" - service_task.start_running() - - # Add response queues - for i in range(5): - mock_service_context.add_response_queue(f"node{i}") - - # Send multiple requests - for i in range(5): - response_queue = mock_service_context._response_queues[f"node{i}"] - request_data = { - "request_id": f"req_{i:03d}", - "method_name": "test_method", - "args": (f"arg{i}",), - "kwargs": {}, - "response_queue": response_queue, - "response_queue_name": f"node{i}", - "timeout": 10.0, - } - service_task.request_queue.put(request_data) - - # Wait for all to process - time.sleep(0.5) - - # Verify all responses received - for i in range(5): - response_queue = mock_service_context._response_queues[f"node{i}"] - assert not response_queue.empty() - response = response_queue.get(timeout=1.0) - assert response["success"] - - -@pytest.mark.unit -class TestBaseServiceTaskMonitoring: - """Test BaseServiceTask performance monitoring.""" - - @patch("sage.kernel.runtime.service.base_service_task.RESOURCE_MONITOR_AVAILABLE", True) - @patch("sage.kernel.runtime.service.base_service_task.MetricsCollector") - def test_monitoring_enabled(self, mock_metrics_collector, mock_service_factory): - """Test monitoring is enabled when configured.""" - ctx = MockServiceContext(enable_monitoring=True) - task = ConcreteServiceTask(mock_service_factory, ctx) - - assert task._enable_monitoring - assert mock_metrics_collector.called - - task.cleanup() - - def test_monitoring_disabled_by_default(self, service_task): - """Test monitoring is disabled by default.""" - assert not service_task._enable_monitoring - assert service_task.metrics_collector is None - - @patch("sage.kernel.runtime.service.base_service_task.RESOURCE_MONITOR_AVAILABLE", True) - @patch("sage.kernel.runtime.service.base_service_task.MetricsCollector") - def test_get_current_metrics_when_disabled(self, mock_metrics_collector, service_task): - """Test get_current_metrics returns None when disabled.""" - metrics = service_task.get_current_metrics() - - assert metrics is None - - -@pytest.mark.unit -class TestBaseServiceTaskContextManager: - """Test BaseServiceTask context manager protocol.""" - - def test_context_manager_enter_exit(self, mock_service_factory, mock_service_context): - """Test service task can be used as context manager.""" - with ConcreteServiceTask(mock_service_factory, mock_service_context) as task: - assert task is not None - assert task.service_instance is not None - - # Cleanup should be called on exit - assert task.service_instance.cleanup_called - - -@pytest.mark.unit -class TestBaseServiceTaskRepr: - """Test BaseServiceTask string representation.""" - - def test_repr(self, service_task): - """Test __repr__ method.""" - repr_str = repr(service_task) - - assert "ConcreteServiceTask" in repr_str - assert "test_service" in repr_str - assert "MockServiceClass" in repr_str diff --git a/packages/sage-kernel/tests/unit/kernel/runtime/service/test_ray_service_task.py b/packages/sage-kernel/tests/unit/kernel/runtime/service/test_ray_service_task.py deleted file mode 100644 index 59f6b15cc4..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/runtime/service/test_ray_service_task.py +++ /dev/null @@ -1,519 +0,0 @@ -""" -Unit tests for RayServiceTask. - -Tests the Ray distributed service task implementation that extends BaseServiceTask -with Ray actor support. -""" - -import sys -from unittest.mock import MagicMock, patch - -import pytest - -# Save original modules before mocking -_original_ray = sys.modules.get("ray") -_original_ray_util = sys.modules.get("ray.util") -_original_ray_util_queue = sys.modules.get("ray.util.queue") -_original_ray_dag = sys.modules.get("ray.dag") - -# Mock Ray before importing -mock_ray = MagicMock() -mock_ray.remote = lambda cls: cls # Make @ray.remote decorator a no-op -mock_ray_queue = MagicMock() - -sys.modules["ray"] = mock_ray -sys.modules["ray.util"] = MagicMock() -sys.modules["ray.util.queue"] = mock_ray_queue -sys.modules["ray.dag"] = MagicMock() # Mock ray.dag to prevent atexit errors - -from sage.kernel.runtime.service.ray_service_task import RayServiceTask - - -@pytest.fixture(scope="module", autouse=True) -def cleanup_ray_mocks(): - """Clean up sys.modules mocks after all tests in this module.""" - yield - # Restore or remove mocked modules - if _original_ray is not None: - sys.modules["ray"] = _original_ray - else: - sys.modules.pop("ray", None) - - if _original_ray_util is not None: - sys.modules["ray.util"] = _original_ray_util - else: - sys.modules.pop("ray.util", None) - - if _original_ray_util_queue is not None: - sys.modules["ray.util.queue"] = _original_ray_util_queue - else: - sys.modules.pop("ray.util.queue", None) - - if _original_ray_dag is not None: - sys.modules["ray.dag"] = _original_ray_dag - else: - sys.modules.pop("ray.dag", None) - - -# Mock classes for testing - - -class MockServiceClass: - """Mock service class for testing.""" - - def __init__(self, ctx=None): - self.ctx = ctx - self.setup_called = False - self.cleanup_called = False - self.started = False - self.stopped = False - - def setup(self): - """Mock setup method.""" - self.setup_called = True - - def cleanup(self): - """Mock cleanup method.""" - self.cleanup_called = True - - def start_running(self): - """Mock start_running method.""" - self.started = True - - def start(self): - """Mock start method.""" - self.started = True - - def stop(self): - """Mock stop method.""" - self.stopped = True - - def test_method(self, arg1, arg2=None): - """Mock service method.""" - return f"ray_result: {arg1}, {arg2}" - - -class MockServiceFactory: - """Mock service factory for testing.""" - - def __init__(self, service_name="ray_test_service", service_class=None): - self.service_name = service_name - self.service_class = service_class or MockServiceClass - - def create_service(self, ctx): - """Create a mock service instance.""" - return self.service_class(ctx) - - -class MockServiceContext: - """Mock service context for testing.""" - - def __init__(self, enable_monitoring=False): - self.name = "ray_test_context" - self.enable_monitoring = enable_monitoring - self.metrics_window_size = 100 - self.enable_detailed_tracking = True - self.resource_sampling_interval = 1.0 - self.enable_auto_report = False - self.logger = MagicMock() - - # Queue descriptors - self._request_queue_descriptor = None - self._response_queue_descriptors = {} - - def get_request_queue_descriptor(self): - """Get request queue descriptor.""" - return self._request_queue_descriptor - - def get_service_response_queue_descriptor(self, node_name): - """Get response queue descriptor for a node.""" - return self._response_queue_descriptors.get(node_name) - - def get_service_response_queue_descriptors(self): - """Get all response queue descriptors.""" - return self._response_queue_descriptors - - -# Fixtures - - -@pytest.fixture -def mock_ray(): - """Mock Ray module.""" - with patch("sage.kernel.runtime.service.ray_service_task.ray") as mock: - mock.is_initialized.return_value = True - mock_context = MagicMock() - mock_context.node_id.hex.return_value = "test_node_123" - mock.get_runtime_context.return_value = mock_context - yield mock - - -@pytest.fixture -def mock_ray_queue(): - """Mock Ray Queue class.""" - with patch("sage.kernel.runtime.service.ray_service_task.RayQueue") as mock_queue_class: - # Create a mock queue instance - mock_queue_instance = MagicMock() - mock_queue_instance.get.return_value = None - mock_queue_instance.put.return_value = None - mock_queue_class.return_value = mock_queue_instance - yield mock_queue_class - - -@pytest.fixture -def mock_ray_queue_available(): - """Mock RAY_QUEUE_AVAILABLE flag.""" - with patch("sage.kernel.runtime.service.ray_service_task.RAY_QUEUE_AVAILABLE", True): - yield - - -@pytest.fixture -def mock_service_factory(): - """Create a mock service factory.""" - return MockServiceFactory() - - -@pytest.fixture -def mock_service_context(): - """Create a mock service context.""" - return MockServiceContext() - - -# Test Cases - - -@pytest.mark.unit -class TestRayServiceTaskInitialization: - """Test RayServiceTask initialization.""" - - def test_initialization_with_context( - self, mock_ray, mock_service_factory, mock_service_context - ): - """Test Ray service task initialization with context.""" - task = RayServiceTask(mock_service_factory, mock_service_context) - - assert task.service_factory == mock_service_factory - assert task.service_name == "ray_test_service" - assert task.ctx == mock_service_context - assert task.service_instance is not None - assert task.service == task.service_instance - - task.cleanup() - - def test_initialization_calls_service_setup( - self, mock_ray, mock_service_factory, mock_service_context - ): - """Test Ray service task initialization calls service setup method.""" - task = RayServiceTask(mock_service_factory, mock_service_context) - - assert task.service_instance.setup_called - - task.cleanup() - - -@pytest.mark.unit -class TestRayServiceTaskServiceInstanceManagement: - """Test RayServiceTask service instance management.""" - - def test_start_service_instance_with_start_running( - self, mock_ray, mock_service_factory, mock_service_context - ): - """Test _start_service_instance calls start_running if available.""" - task = RayServiceTask(mock_service_factory, mock_service_context) - - task._start_service_instance() - - assert task.service_instance.started - - task.cleanup() - - def test_start_service_instance_with_start(self, mock_ray, mock_service_context): - """Test _start_service_instance calls start if start_running not available.""" - - class ServiceWithStart: - def __init__(self, ctx): - self.ctx = ctx - self.started = False - - def setup(self): - pass - - def start(self): - self.started = True - - def cleanup(self): - pass - - factory = MockServiceFactory(service_class=ServiceWithStart) - task = RayServiceTask(factory, mock_service_context) - - task._start_service_instance() - - assert task.service_instance.started - - task.cleanup() - - def test_stop_service_instance(self, mock_ray, mock_service_factory, mock_service_context): - """Test _stop_service_instance calls stop method.""" - task = RayServiceTask(mock_service_factory, mock_service_context) - - task._stop_service_instance() - - assert task.service_instance.stopped - - task.cleanup() - - -@pytest.mark.unit -class TestRayServiceTaskQueueManagement: - """Test RayServiceTask Ray queue management.""" - - def test_create_request_queue( - self, - mock_ray, - mock_ray_queue_available, - mock_ray_queue, - mock_service_factory, - mock_service_context, - ): - """Test _create_request_queue creates Ray queue.""" - task = RayServiceTask(mock_service_factory, mock_service_context) - - queue = task._create_request_queue() - - assert queue is not None - mock_ray_queue.assert_called_once_with(maxsize=10000) - - task.cleanup() - - def test_create_request_queue_without_ray_queue_raises_error( - self, mock_ray, mock_service_factory, mock_service_context - ): - """Test _create_request_queue raises error when Ray queue not available.""" - with patch("sage.kernel.runtime.service.ray_service_task.RAY_QUEUE_AVAILABLE", False): - task = RayServiceTask(mock_service_factory, mock_service_context) - - with pytest.raises(RuntimeError, match="Ray queue is not available"): - task._create_request_queue() - - task.cleanup() - - def test_create_response_queue( - self, - mock_ray, - mock_ray_queue_available, - mock_ray_queue, - mock_service_factory, - mock_service_context, - ): - """Test _create_response_queue creates Ray queue.""" - task = RayServiceTask(mock_service_factory, mock_service_context) - - queue = task._create_response_queue("node1") - - assert queue is not None - mock_ray_queue.assert_called_once_with(maxsize=10000) - - task.cleanup() - - def test_create_response_queue_without_ray_queue_raises_error( - self, mock_ray, mock_service_factory, mock_service_context - ): - """Test _create_response_queue raises error when Ray queue not available.""" - with patch("sage.kernel.runtime.service.ray_service_task.RAY_QUEUE_AVAILABLE", False): - task = RayServiceTask(mock_service_factory, mock_service_context) - - with pytest.raises(RuntimeError, match="Ray queue is not available"): - task._create_response_queue("node1") - - task.cleanup() - - -@pytest.mark.unit -class TestRayServiceTaskQueueOperations: - """Test RayServiceTask queue operations.""" - - def test_queue_get(self, mock_ray, mock_service_factory, mock_service_context): - """Test _queue_get retrieves data from Ray queue.""" - task = RayServiceTask(mock_service_factory, mock_service_context) - - mock_queue = MagicMock() - mock_queue.get.return_value = {"data": "test"} - - result = task._queue_get(mock_queue, timeout=2.0) - - assert result == {"data": "test"} - mock_queue.get.assert_called_once_with(timeout=2.0) - - task.cleanup() - - def test_queue_put(self, mock_ray, mock_service_factory, mock_service_context): - """Test _queue_put sends data to Ray queue.""" - task = RayServiceTask(mock_service_factory, mock_service_context) - - mock_queue = MagicMock() - data = {"request": "test"} - - task._queue_put(mock_queue, data, timeout=3.0) - - mock_queue.put.assert_called_once_with(data, timeout=3.0) - - task.cleanup() - - def test_queue_close_with_shutdown(self, mock_ray, mock_service_factory, mock_service_context): - """Test _queue_close calls shutdown if available.""" - task = RayServiceTask(mock_service_factory, mock_service_context) - - mock_queue = MagicMock() - mock_queue.shutdown = MagicMock() - - task._queue_close(mock_queue) - - mock_queue.shutdown.assert_called_once() - - task.cleanup() - - def test_queue_close_with_close(self, mock_ray, mock_service_factory, mock_service_context): - """Test _queue_close calls close if shutdown not available.""" - task = RayServiceTask(mock_service_factory, mock_service_context) - - mock_queue = MagicMock() - del mock_queue.shutdown - mock_queue.close = MagicMock() - - task._queue_close(mock_queue) - - mock_queue.close.assert_called_once() - - task.cleanup() - - -@pytest.mark.unit -class TestRayServiceTaskStatistics: - """Test RayServiceTask statistics.""" - - def test_get_statistics_includes_ray_info( - self, mock_ray, mock_service_factory, mock_service_context - ): - """Test get_statistics includes Ray-specific information.""" - mock_ray.is_initialized.return_value = True - mock_context = MagicMock() - mock_context.node_id.hex.return_value = "node_abc123" - mock_ray.get_runtime_context.return_value = mock_context - - task = RayServiceTask(mock_service_factory, mock_service_context) - - stats = task.get_statistics() - - assert "actor_id" in stats - assert stats["actor_id"] == "ray_actor_ray_test_service" - assert "ray_node_id" in stats - assert stats["ray_node_id"] == "node_abc123" - assert stats["service_name"] == "ray_test_service" - - task.cleanup() - - def test_get_statistics_when_ray_not_initialized( - self, mock_ray, mock_service_factory, mock_service_context - ): - """Test get_statistics when Ray is not initialized.""" - mock_ray.is_initialized.return_value = False - - task = RayServiceTask(mock_service_factory, mock_service_context) - - stats = task.get_statistics() - - assert "ray_node_id" in stats - assert stats["ray_node_id"] == "unknown" - - task.cleanup() - - -@pytest.mark.unit -class TestRayServiceTaskMethodCalling: - """Test RayServiceTask method calling (inherited from BaseServiceTask).""" - - def test_call_method_success(self, mock_ray, mock_service_factory, mock_service_context): - """Test calling a service method successfully.""" - task = RayServiceTask(mock_service_factory, mock_service_context) - - result = task.call_method("test_method", "ray_arg1", arg2="ray_value2") - - assert result == "ray_result: ray_arg1, ray_value2" - assert task._request_count == 1 - - task.cleanup() - - -@pytest.mark.unit -class TestRayServiceTaskLifecycle: - """Test RayServiceTask lifecycle (inherited from BaseServiceTask).""" - - def test_start_running(self, mock_ray, mock_service_factory, mock_service_context): - """Test start_running method starts service instance.""" - # Set up queue descriptor - mock_descriptor = MagicMock() - mock_descriptor.queue_instance = MagicMock() - mock_service_context._request_queue_descriptor = mock_descriptor - - task = RayServiceTask(mock_service_factory, mock_service_context) - - task.start_running() - - assert task.is_running - assert task.service_instance.started - - task.cleanup() - - def test_stop(self, mock_ray, mock_service_factory, mock_service_context): - """Test stop method stops service instance.""" - # Set up queue descriptor - mock_descriptor = MagicMock() - mock_descriptor.queue_instance = MagicMock() - mock_service_context._request_queue_descriptor = mock_descriptor - - task = RayServiceTask(mock_service_factory, mock_service_context) - task.start_running() - - task.stop() - - assert not task.is_running - assert task.service_instance.stopped - - task.cleanup() - - -@pytest.mark.unit -class TestRayServiceTaskCleanup: - """Test RayServiceTask cleanup.""" - - def test_cleanup_calls_service_cleanup( - self, mock_ray, mock_service_factory, mock_service_context - ): - """Test cleanup calls service instance cleanup method.""" - task = RayServiceTask(mock_service_factory, mock_service_context) - - task.cleanup() - - assert task.service_instance.cleanup_called - - -@pytest.mark.unit -class TestRayServiceTaskDecoratorUsage: - """Test RayServiceTask with Ray remote decorator.""" - - def test_ray_remote_decorator_is_mocked(self, mock_ray): - """Test that Ray decorator is properly mocked for testing.""" - # In our test environment, @ray.remote is a no-op - # This allows us to test the class directly - assert RayServiceTask is not None - assert RayServiceTask.__name__ == "RayServiceTask" - - def test_can_instantiate_for_testing(self, mock_service_factory, mock_service_context): - """Test that we can instantiate the class for testing.""" - task = RayServiceTask(mock_service_factory, mock_service_context) - - assert task is not None - assert isinstance(task, RayServiceTask) - - task.cleanup() diff --git a/packages/sage-kernel/tests/unit/kernel/runtime/test_dispatcher.py b/packages/sage-kernel/tests/unit/kernel/runtime/test_dispatcher.py deleted file mode 100644 index 74937f8780..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/runtime/test_dispatcher.py +++ /dev/null @@ -1,356 +0,0 @@ -""" -Test suite for sage.kernels.runtime.dispatcher module - -Tests the Dispatcher class which manages execution of tasks and services -in both local and remote environments. -""" - -import threading -import time -from unittest.mock import Mock, patch - -import pytest - -from sage.kernel.runtime.dispatcher import Dispatcher -from sage.kernel.runtime.service.base_service_task import BaseServiceTask -from sage.kernel.runtime.task.base_task import BaseTask -from sage.kernel.utils.ray.actor import ActorWrapper - - -class MockExecutionGraph: - """Mock execution graph for testing""" - - def __init__(self, total_stop_signals=1): - self.total_stop_signals = total_stop_signals - self.nodes = {} - - -class MockEnvironment: - """Mock environment for testing""" - - def __init__(self, name="test_env", platform="local"): - self.name = name - self.platform = platform - - # 使用统一的SAGE路径管理 - from sage.common.config.output_paths import get_test_env_dir - - self.env_base_dir = str(get_test_env_dir("test_logs")) - self.console_log_level = "INFO" - - # 添加 config 属性以支持 Dispatcher 的容错配置 - self.config = {} - - -class TestDispatcher: - """Test class for Dispatcher functionality""" - - @pytest.fixture - def mock_env(self): - """Create a mock local environment""" - return MockEnvironment() - - @pytest.fixture - def mock_remote_env(self): - """Create a mock remote environment""" - return MockEnvironment(platform="remote") - - @pytest.fixture - def mock_graph(self): - """Create a mock execution graph""" - return MockExecutionGraph(total_stop_signals=2) - - @pytest.fixture - def local_dispatcher(self, mock_graph, mock_env): - """Create a local dispatcher for testing""" - with patch("sage.kernel.runtime.dispatcher.ensure_ray_initialized"): - return Dispatcher(mock_graph, mock_env) - - @pytest.fixture - def remote_dispatcher(self, mock_graph, mock_remote_env): - """Create a remote dispatcher for testing""" - with patch("sage.kernel.runtime.dispatcher.ensure_ray_initialized") as mock_ray: - dispatcher = Dispatcher(mock_graph, mock_remote_env) - mock_ray.assert_called_once() - return dispatcher - - @pytest.mark.unit - def test_dispatcher_initialization_local(self, mock_graph, mock_env): - """Test dispatcher initialization in local mode""" - with patch("sage.kernel.runtime.dispatcher.ensure_ray_initialized") as mock_ray: - dispatcher = Dispatcher(mock_graph, mock_env) - - # Verify basic attributes - assert dispatcher.name == "test_env" - assert dispatcher.total_stop_signals == 2 - assert dispatcher.received_stop_signals == 0 - assert dispatcher.remote is False - assert isinstance(dispatcher.tasks, dict) - assert isinstance(dispatcher.services, dict) - assert dispatcher.is_running is False - - # Ray should not be initialized for local environment - mock_ray.assert_not_called() - - @pytest.mark.unit - def test_dispatcher_initialization_remote(self, mock_graph, mock_remote_env): - """Test dispatcher initialization in remote mode""" - with patch("sage.kernel.runtime.dispatcher.ensure_ray_initialized") as mock_ray: - dispatcher = Dispatcher(mock_graph, mock_remote_env) - - # Verify basic attributes - assert dispatcher.name == "test_env" - assert dispatcher.remote is True - - # Ray should be initialized for remote environment - mock_ray.assert_called_once() - - @pytest.mark.unit - def test_receive_stop_signal_partial(self, local_dispatcher): - """Test receiving partial stop signals""" - # First stop signal should not trigger cleanup - result = local_dispatcher.receive_stop_signal() - assert result is False - assert local_dispatcher.received_stop_signals == 1 - - @pytest.mark.unit - def test_receive_stop_signal_complete(self, local_dispatcher): - """Test receiving all required stop signals""" - # First stop signal - local_dispatcher.receive_stop_signal() - - # Second stop signal should trigger cleanup - with patch.object(local_dispatcher, "cleanup") as mock_cleanup: - result = local_dispatcher.receive_stop_signal() - assert result is True - assert local_dispatcher.received_stop_signals == 2 - mock_cleanup.assert_called_once() - - @pytest.mark.unit - def test_setup_logging_system(self, local_dispatcher): - """Test logging system setup""" - # Logger should be created during initialization - assert hasattr(local_dispatcher, "logger") - assert local_dispatcher.logger is not None - - @pytest.mark.unit - def test_add_task_local(self, local_dispatcher): - """Test adding a local task""" - mock_task = Mock(spec=BaseTask) - local_dispatcher.tasks["test_task"] = mock_task - - assert "test_task" in local_dispatcher.tasks - assert local_dispatcher.tasks["test_task"] is mock_task - - @pytest.mark.unit - def test_add_task_remote(self, remote_dispatcher): - """Test adding a remote task (ActorWrapper)""" - mock_actor = Mock(spec=ActorWrapper) - remote_dispatcher.tasks["test_actor"] = mock_actor - - assert "test_actor" in remote_dispatcher.tasks - assert remote_dispatcher.tasks["test_actor"] is mock_actor - - @pytest.mark.unit - def test_add_service(self, local_dispatcher): - """Test adding a service""" - mock_service = Mock(spec=BaseServiceTask) - local_dispatcher.services["test_service"] = mock_service - - assert "test_service" in local_dispatcher.services - assert local_dispatcher.services["test_service"] is mock_service - - @pytest.mark.unit - def test_get_task_exists(self, local_dispatcher): - """Test getting an existing task""" - mock_task = Mock(spec=BaseTask) - local_dispatcher.tasks["test_task"] = mock_task - - result = local_dispatcher.tasks.get("test_task") - assert result is mock_task - - @pytest.mark.unit - def test_get_task_not_exists(self, local_dispatcher): - """Test getting a non-existent task""" - result = local_dispatcher.tasks.get("non_existent") - assert result is None - - @pytest.mark.unit - def test_dispatcher_state_management(self, local_dispatcher): - """Test dispatcher state management""" - # Initially not running - assert local_dispatcher.is_running is False - - # Set to running - local_dispatcher.is_running = True - assert local_dispatcher.is_running is True - - @pytest.mark.unit - def test_multiple_dispatcher_instances(self, mock_graph): - """Test creating multiple dispatcher instances""" - env1 = MockEnvironment("env1") - env2 = MockEnvironment("env2") - - with patch("sage.kernel.runtime.dispatcher.ensure_ray_initialized"): - # Mock 对象用于测试，类型不完全匹配 - dispatcher1 = Dispatcher(mock_graph, env1) # type: ignore[arg-type] - dispatcher2 = Dispatcher(mock_graph, env2) # type: ignore[arg-type] - - assert dispatcher1.name == "env1" - assert dispatcher2.name == "env2" - assert dispatcher1.tasks is not dispatcher2.tasks - assert dispatcher1.services is not dispatcher2.services - - @pytest.mark.unit - def test_dispatcher_cleanup_preparation(self, local_dispatcher): - """Test dispatcher cleanup preparation""" - # Add some tasks and services - local_dispatcher.tasks["task1"] = Mock() - local_dispatcher.services["service1"] = Mock() - - # Verify they exist before cleanup - assert len(local_dispatcher.tasks) == 1 - assert len(local_dispatcher.services) == 1 - - @pytest.mark.integration - def test_dispatcher_with_real_logging(self, mock_graph, mock_env): - """Integration test with real logging system""" - dispatcher = Dispatcher(mock_graph, mock_env) - - # Should have a real logger instance - assert hasattr(dispatcher, "logger") - assert dispatcher.logger is not None - - # Logger should be callable - try: - dispatcher.logger.info("Test message") - except Exception as e: - pytest.fail(f"Logger should be functional: {e}") - - @pytest.mark.integration - @patch("sage.kernel.utils.ray.ray_utils.ensure_ray_initialized") - def test_dispatcher_ray_integration(self, mock_ray_init, mock_graph): - """Integration test for Ray initialization""" - remote_env = MockEnvironment(platform="remote") - - # Since actual Ray init happens, mock it to capture the call - with patch("sage.kernel.runtime.dispatcher.ensure_ray_initialized") as dispatcher_ray_mock: - # Mock 对象用于测试 - dispatcher = Dispatcher(mock_graph, remote_env) # type: ignore[arg-type] - - # Check the Ray init call was made (either mock could be called) - dispatcher_ray_mock.assert_called_once() - - assert dispatcher.remote is True - - @pytest.mark.unit - def test_stop_signal_thread_safety(self, local_dispatcher): - """Test stop signal handling in multi-threaded environment""" - results = [] - - def signal_worker(): - result = local_dispatcher.receive_stop_signal() - results.append(result) - - # Create multiple threads sending stop signals (adjust for expected signals) - threads = [threading.Thread(target=signal_worker) for _ in range(2)] - - for thread in threads: - thread.start() - - for thread in threads: - thread.join() - - # Should have exactly one True result (cleanup triggered once) - true_count = sum(1 for r in results if r is True) - assert true_count == 1 # Exactly one cleanup for 2 stop signals - assert local_dispatcher.received_stop_signals == 2 # Exactly required signals - - @pytest.mark.slow - def test_dispatcher_performance(self, mock_graph, mock_env): - """Performance test for dispatcher operations""" - start_time = time.time() - - # Create many dispatchers quickly - dispatchers = [] - for _i in range(100): - with patch("sage.kernel.runtime.dispatcher.ensure_ray_initialized"): - dispatcher = Dispatcher(mock_graph, mock_env) - dispatchers.append(dispatcher) - - creation_time = time.time() - start_time - - # Should create 100 dispatchers in reasonable time (< 1 second) - assert creation_time < 1.0 - assert len(dispatchers) == 100 - - @pytest.mark.unit - def test_dispatcher_error_handling(self, mock_graph): - """Test dispatcher error handling during initialization""" - # Test with invalid environment - invalid_env = None - - with pytest.raises(AttributeError): - # 故意传入 None 来测试错误处理 - Dispatcher(mock_graph, invalid_env) # type: ignore[arg-type] - - @pytest.mark.unit - def test_dispatcher_repr(self, local_dispatcher): - """Test dispatcher string representation""" - repr_str = repr(local_dispatcher) - assert "Dispatcher" in repr_str or hasattr(local_dispatcher, "__repr__") - - -class TestDispatcherEdgeCases: - """Test edge cases and error conditions""" - - @pytest.mark.unit - def test_zero_stop_signals(self): - """Test dispatcher with zero required stop signals""" - graph = MockExecutionGraph(total_stop_signals=0) - env = MockEnvironment() - - with patch("sage.kernel.runtime.dispatcher.ensure_ray_initialized"): - # Mock 对象用于测试 - dispatcher = Dispatcher(graph, env) # type: ignore[arg-type] - - # Should immediately return True - result = dispatcher.receive_stop_signal() - assert result is True - - @pytest.mark.unit - def test_negative_stop_signals(self): - """Test dispatcher with negative stop signals""" - graph = MockExecutionGraph(total_stop_signals=-1) - env = MockEnvironment() - - with patch("sage.kernel.runtime.dispatcher.ensure_ray_initialized"): - # Mock 对象用于测试 - dispatcher = Dispatcher(graph, env) # type: ignore[arg-type] - - # Should handle gracefully - dispatcher.receive_stop_signal() - # Behavior depends on implementation, but should not crash - - @pytest.mark.unit - def test_large_stop_signals(self): - """Test dispatcher with very large number of stop signals""" - graph = MockExecutionGraph(total_stop_signals=1000000) - env = MockEnvironment() - - with patch("sage.kernel.runtime.dispatcher.ensure_ray_initialized"): - # Mock 对象用于测试 - dispatcher = Dispatcher(graph, env) # type: ignore[arg-type] - - # Should handle large numbers - assert dispatcher.total_stop_signals == 1000000 - assert dispatcher.received_stop_signals == 0 - - -# Pytest configuration and fixtures -@pytest.fixture(autouse=True) -def setup_test_environment(): - """Setup test environment before each test""" - # Any global setup needed - yield - # Cleanup after test diff --git a/packages/sage-kernel/tests/unit/kernel/runtime/test_ray_simple_actor.py b/packages/sage-kernel/tests/unit/kernel/runtime/test_ray_simple_actor.py deleted file mode 100644 index 6372bff841..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/runtime/test_ray_simple_actor.py +++ /dev/null @@ -1,71 +0,0 @@ -#!/usr/bin/env python3 -""" -简化的Ray Actor测试，避免使用ray.util.queue -""" - -import os -import warnings - -import ray - -from sage.kernel.utils.ray.ray_utils import ensure_ray_initialized - -# 抑制Ray的ResourceWarning -warnings.filterwarnings("ignore", category=ResourceWarning) -os.environ["PYTHONWARNINGS"] = "ignore::ResourceWarning" - - -@ray.remote -class SimpleActor: - def __init__(self): - self.messages = [] - - def add_message(self, msg): - self.messages.append(msg) - return f"Added: {msg}" - - def get_messages(self): - return self.messages.copy() - - def get_message_count(self): - return len(self.messages) - - -def test_simple_actor(): - """测试简单的Ray Actor通信""" - print("Testing simple Ray Actor communication...") - - # 强制关闭现有Ray会话并重新初始化以确保使用正确的输出目录 - import ray - - if ray.is_initialized(): - ray.shutdown() - - # 确保Ray初始化 - ensure_ray_initialized() - - # 创建Actor - actor = SimpleActor.remote() - - # 测试添加消息 - result1 = ray.get(actor.add_message.remote("Hello")) - print(f"Result 1: {result1}") - - result2 = ray.get(actor.add_message.remote("World")) - print(f"Result 2: {result2}") - - # 测试获取消息 - messages = ray.get(actor.get_messages.remote()) - print(f"Messages: {messages}") - - count = ray.get(actor.get_message_count.remote()) - print(f"Message count: {count}") - - assert messages == ["Hello", "World"] - assert count == 2 - - print("Simple Ray Actor test passed!") - - -if __name__ == "__main__": - test_simple_actor() diff --git a/packages/sage-kernel/tests/unit/kernel/runtime/test_state.py b/packages/sage-kernel/tests/unit/kernel/runtime/test_state.py deleted file mode 100644 index a6e796309d..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/runtime/test_state.py +++ /dev/null @@ -1,503 +0,0 @@ -""" -Test suite for sage.kernels.runtime.state module - -Tests state management functionality including serialization -and object attribute filtering. -""" - -from unittest.mock import patch - -import pytest - -from sage.kernel.utils.persistence.state import ( - _BLACKLIST, - _filter_attrs, - _gather_attrs, - _is_serializable, - _prepare, - load_function_state, - save_function_state, -) - - -class SerializableTestObject: - """Test object that should be serializable""" - - def __init__(self): - self.data = "test_data" - self.number = 42 - self.items = [1, 2, 3] - - -class NonSerializableTestObject: - """Test object with non-serializable attributes""" - - def __init__(self): - self.serializable_data = "good_data" - self.file_handle = None # Will be set to non-serializable object - - -class FilteredTestObject: - """Test object with state filtering attributes""" - - __state_include__ = ["include_this"] - __state_exclude__ = ["exclude_this"] - - def __init__(self): - self.include_this = "should_be_included" - self.exclude_this = "should_be_excluded" - self.normal_attr = "normal" - - -class TestStateHelperFunctions: - """Test class for state helper functions""" - - @pytest.mark.unit - def test_gather_attrs_simple_object(self): - """Test gathering attributes from simple object""" - obj = SerializableTestObject() - attrs = _gather_attrs(obj) - - assert isinstance(attrs, dict) - assert "data" in attrs - assert "number" in attrs - assert "items" in attrs - assert attrs["data"] == "test_data" - assert attrs["number"] == 42 - - @pytest.mark.unit - def test_gather_attrs_with_properties(self): - """Test gathering attributes including properties""" - - class ObjectWithProperty: - def __init__(self): - self._value = 100 - - @property - def computed_value(self): - return self._value * 2 - - obj = ObjectWithProperty() - attrs = _gather_attrs(obj) - - assert "_value" in attrs - assert "computed_value" in attrs - assert attrs["computed_value"] == 200 - - @pytest.mark.unit - def test_gather_attrs_property_exception(self): - """Test gathering attributes when property raises exception""" - - class ObjectWithFailingProperty: - @property - def failing_prop(self): - raise RuntimeError("Property failed") - - obj = ObjectWithFailingProperty() - attrs = _gather_attrs(obj) - - # Should handle property exception gracefully - assert "failing_prop" not in attrs or attrs["failing_prop"] is None - - @pytest.mark.unit - def test_filter_attrs_with_include(self): - """Test filtering attributes with include list""" - attrs = {"include_me": "value1", "exclude_me": "value2", "another": "value3"} - - filtered = _filter_attrs(attrs, include=["include_me", "another"], exclude=[]) - - assert "include_me" in filtered - assert "another" in filtered - assert "exclude_me" not in filtered - - @pytest.mark.unit - def test_filter_attrs_with_exclude(self): - """Test filtering attributes with exclude list""" - attrs = {"keep_me": "value1", "exclude_me": "value2", "also_keep": "value3"} - - filtered = _filter_attrs(attrs, include=[], exclude=["exclude_me"]) - - assert "keep_me" in filtered - assert "also_keep" in filtered - assert "exclude_me" not in filtered - - @pytest.mark.unit - def test_filter_attrs_include_nonexistent(self): - """Test filtering with include list containing nonexistent keys""" - attrs = {"existing": "value"} - - filtered = _filter_attrs(attrs, include=["existing", "nonexistent"], exclude=[]) - - assert "existing" in filtered - assert "nonexistent" not in filtered - - @pytest.mark.unit - def test_is_serializable_basic_types(self): - """Test serialization check for basic types""" - # Serializable types - assert _is_serializable(42) is True - assert _is_serializable("string") is True - assert _is_serializable([1, 2, 3]) is True - assert _is_serializable({"key": "value"}) is True - assert _is_serializable(None) is True - - @pytest.mark.unit - def test_is_serializable_blacklisted_types(self): - """Test serialization check for blacklisted types""" - # Create objects that should be blacklisted - file_obj = open(__file__) - try: - assert _is_serializable(file_obj) is False - finally: - file_obj.close() - - # Threading objects should be blacklisted - import threading - - thread = threading.Thread() - assert _is_serializable(thread) is False - - @pytest.mark.unit - def test_is_serializable_custom_object(self): - """Test serialization check for custom objects""" - serializable_obj = SerializableTestObject() - assert _is_serializable(serializable_obj) is True - - @pytest.mark.unit - def test_prepare_primitive_types(self): - """Test preparation of primitive types""" - # Primitive types should pass through unchanged - assert _prepare(42) == 42 - assert _prepare("string") == "string" - assert _prepare(True) is True - assert _prepare(None) is None - - @pytest.mark.unit - def test_prepare_mapping_types(self): - """Test preparation of mapping types""" - input_dict = { - "serializable_key": "serializable_value", - 42: "numeric_key", - } - - result = _prepare(input_dict) - - assert isinstance(result, dict) - assert "serializable_key" in result - assert 42 in result - - @pytest.mark.unit - def test_prepare_sequence_types(self): - """Test preparation of sequence types""" - input_list = [1, "string", {"nested": "dict"}] - - result = _prepare(input_list) - - assert isinstance(result, list) - assert len(result) == 3 - assert result[0] == 1 - assert result[1] == "string" - - @pytest.mark.unit - def test_prepare_set_types(self): - """Test preparation of set types""" - input_set = {1, 2, "string"} - - result = _prepare(input_set) - - assert isinstance(result, set) - assert 1 in result - assert 2 in result - assert "string" in result - - @pytest.mark.unit - def test_prepare_non_serializable_filtering(self): - """Test that non-serializable items are filtered out""" - file_obj = open(__file__) - try: - input_list = [1, file_obj, "string"] - result = _prepare(input_list) - - # Non-serializable object should be filtered out - # _prepare 返回类型宽泛，使用 type: ignore - assert file_obj not in result # type: ignore[operator] - assert 1 in result # type: ignore[operator] - assert "string" in result # type: ignore[operator] - finally: - file_obj.close() - - @pytest.mark.unit - def test_prepare_nested_structures(self): - """Test preparation of deeply nested structures""" - nested_data = { - "level1": {"level2": [1, 2, {"level3": "deep_value"}]}, - "list": [{"nested": "dict"}, "string", 42], - } - - result = _prepare(nested_data) - - # _prepare 返回类型宽泛，使用 type: ignore - assert result["level1"]["level2"][2]["level3"] == "deep_value" # type: ignore[index] - assert result["list"][0]["nested"] == "dict" # type: ignore[index] - - -class TestStateSaveLoad: - """Test class for state save/load functions""" - - @pytest.fixture - def temp_file_path(self, tmp_path): - """Create a temporary file path for testing""" - return tmp_path / "test_state.pkl" - - @pytest.mark.unit - def test_save_function_state_basic(self, temp_file_path): - """Test basic function state saving""" - obj = SerializableTestObject() - - save_function_state(obj, str(temp_file_path)) - - assert temp_file_path.exists() - assert temp_file_path.stat().st_size > 0 - - @pytest.mark.unit - def test_load_function_state_basic(self, temp_file_path): - """Test basic function state loading""" - original_obj = SerializableTestObject() - save_function_state(original_obj, str(temp_file_path)) - - # Create new object and load state into it - loaded_obj = SerializableTestObject() - load_function_state(loaded_obj, str(temp_file_path)) - - # Verify attributes were loaded - assert hasattr(loaded_obj, "data") - assert hasattr(loaded_obj, "number") - - @pytest.mark.unit - def test_save_function_state_with_include_filter(self, temp_file_path): - """Test state saving with include filter""" - obj = FilteredTestObject() - - save_function_state(obj, str(temp_file_path)) - - # File should be created - assert temp_file_path.exists() - - @pytest.mark.unit - def test_save_function_state_with_exclude_filter(self, temp_file_path): - """Test state saving with exclude filter""" - obj = FilteredTestObject() - - save_function_state(obj, str(temp_file_path)) - - # File should be created - assert temp_file_path.exists() - - @pytest.mark.unit - def test_load_function_state_nonexistent_file(self): - """Test loading state from nonexistent file""" - obj = SerializableTestObject() - original_data = obj.data - - # Should not crash when file doesn't exist - load_function_state(obj, "nonexistent_file.pkl") - - # Object should remain unchanged - assert obj.data == original_data - - @pytest.mark.unit - def test_save_function_state_creates_directory(self, tmp_path): - """Test that save_function_state creates necessary directories""" - obj = SerializableTestObject() - nested_path = tmp_path / "nested" / "dir" / "state.pkl" - - save_function_state(obj, str(nested_path)) - - assert nested_path.exists() - assert nested_path.parent.exists() - - @pytest.mark.integration - def test_state_roundtrip_complex_object(self, temp_file_path): - """Integration test for complex object state preservation""" - - class ComplexObject: - __state_exclude__ = ["_internal"] - - def __init__(self): - self.name = "test" - self.data = {"nested": {"key": "value"}} - self.items = [1, 2, {"item": "data"}] - self._internal = "should_be_excluded" - - original_obj = ComplexObject() - save_function_state(original_obj, str(temp_file_path)) - - # Create new object and load state - loaded_obj = ComplexObject() - load_function_state(loaded_obj, str(temp_file_path)) - - # Verify complex structure is preserved - assert hasattr(loaded_obj, "name") - assert hasattr(loaded_obj, "data") - assert hasattr(loaded_obj, "items") - - @pytest.mark.unit - def test_state_thread_safety(self, tmp_path): - """Test state operations in multi-threaded environment""" - import threading - - def save_worker(worker_id): - obj = SerializableTestObject() - obj.data = f"worker_{worker_id}" - save_function_state(obj, str(tmp_path / f"state_{worker_id}.pkl")) - - # Create multiple threads saving state - threads = [threading.Thread(target=save_worker, args=(i,)) for i in range(5)] - - for thread in threads: - thread.start() - - for thread in threads: - thread.join() - - # All files should be created - for i in range(5): - assert (tmp_path / f"state_{i}.pkl").exists() - - @pytest.mark.unit - @patch("builtins.open", side_effect=PermissionError("Permission denied")) - def test_save_function_state_permission_error(self, mock_open): - """Test save_function_state with permission error""" - obj = SerializableTestObject() - - with pytest.raises(PermissionError): - save_function_state(obj, "/invalid/path/state.pkl") - - @pytest.mark.unit - @patch("os.path.isfile", return_value=True) - @patch("builtins.open", side_effect=OSError("File read error")) - def test_load_function_state_io_error(self, mock_open, mock_isfile): - """Test load_function_state with IO error""" - obj = SerializableTestObject() - - # Should handle IO errors gracefully - with pytest.raises(IOError): - load_function_state(obj, "corrupted_file.pkl") - - -class TestStateEdgeCases: - """Test edge cases and error conditions""" - - @pytest.mark.unit - def test_gather_attrs_object_without_dict(self): - """Test gathering attributes from object without __dict__""" - # Some built-in types don't have __dict__ - attrs = _gather_attrs(42) - - # Should handle gracefully - assert isinstance(attrs, dict) - - @pytest.mark.unit - def test_prepare_circular_references(self): - """Test preparation with circular references""" - # Create circular reference - obj1 = {} - obj2 = {} - obj1["ref"] = obj2 - obj2["ref"] = obj1 - - # Should handle without infinite recursion - try: - result = _prepare(obj1) - # Success if no exception - assert isinstance(result, dict) - except RecursionError: - pytest.skip("Circular reference handling not implemented") - - @pytest.mark.unit - def test_filter_attrs_empty_filters(self): - """Test filtering with empty filter lists""" - attrs = {"key1": "value1", "key2": "value2"} - - # Empty include list should include everything - result = _filter_attrs(attrs, include=[], exclude=[]) - assert result == attrs - - @pytest.mark.unit - def test_is_serializable_exception_during_pickle(self): - """Test serialization check when pickle.dumps raises exception""" - - class UnpicklableObject: - def __reduce__(self): - raise TypeError("Cannot pickle this object") - - obj = UnpicklableObject() - assert _is_serializable(obj) is False - - @pytest.mark.unit - def test_prepare_very_large_structure(self): - """Test preparation of very large data structures""" - # Create large structure - large_dict = {f"key_{i}": f"value_{i}" for i in range(1000)} - large_list = list(range(1000)) - - combined = {"dict": large_dict, "list": large_list} - - result = _prepare(combined) - - # Should handle large structures - # _prepare 返回类型宽泛，使用 type: ignore - assert len(result["dict"]) == 1000 # type: ignore[index] - assert len(result["list"]) == 1000 # type: ignore[index] - - -class TestStateBlacklistHandling: - """Test blacklist functionality""" - - @pytest.mark.unit - def test_blacklist_contains_expected_types(self): - """Test that blacklist contains expected non-serializable types""" - import threading - - # Check that blacklist is a tuple of types - assert isinstance(_BLACKLIST, tuple) - - # Thread type should be in blacklist - assert threading.Thread in _BLACKLIST - - # File open function type should be in blacklist - assert type(open) in _BLACKLIST - - @pytest.mark.unit - def test_blacklist_detection_in_is_serializable(self): - """Test that blacklisted types are detected in _is_serializable""" - import threading - - thread = threading.Thread() - assert _is_serializable(thread) is False - - file_handle = open(__file__) - try: - assert _is_serializable(file_handle) is False - finally: - file_handle.close() - - -# Fixtures and utilities -@pytest.fixture -def serializable_object(): - """Create a serializable test object""" - return SerializableTestObject() - - -@pytest.fixture -def filtered_object(): - """Create a filtered test object""" - return FilteredTestObject() - - -@pytest.fixture(autouse=True) -def cleanup_test_files(): - """Cleanup any test files created during testing""" - yield - # Cleanup logic would go here if needed diff --git a/packages/sage-kernel/tests/unit/kernel/runtime_execution_tests/test_jobmanager_refactor.py b/packages/sage-kernel/tests/unit/kernel/runtime_execution_tests/test_jobmanager_refactor.py deleted file mode 100644 index b8ea1d815d..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/runtime_execution_tests/test_jobmanager_refactor.py +++ /dev/null @@ -1,150 +0,0 @@ -#!/usr/bin/env python3 -""" -测试重构后的JobManagerClient -验证基类功能和子类特定功能都正常工作 -""" - -import sys - -# 添加SAGE路径 -sys.path.insert(0, "/home/tjy/SAGE") - - -def test_base_tcp_client(): - """测试BaseTcpClient基类功能""" - print("=== 测试 BaseTcpClient 基类 ===") - - try: - from sage.common.utils.network.base_tcp_client import BaseTcpClient - - # 创建一个简单的测试客户端 - class TestClient(BaseTcpClient): - def _build_health_check_request(self): - return {"action": "test_health_check"} - - def _build_server_info_request(self): - return {"action": "test_server_info"} - - client = TestClient("127.0.0.1", 19001) - print("✓ BaseTcpClient基类创建成功") - print(f" - Host: {client.host}") - print(f" - Port: {client.port}") - print(f" - Timeout: {client.timeout}") - print(f" - Client Name: {client.client_name}") - - # 测试错误响应创建 - error_resp = client._create_error_response("TEST_ERROR", "Test error message") - print(f"✓ 错误响应创建测试通过: {error_resp['status']}") - - # 使用 assert 而不是 return - assert True, "BaseTcpClient基类测试通过" - - except Exception as e: - print(f"✗ BaseTcpClient基类测试失败: {e}") - raise AssertionError(f"BaseTcpClient基类测试失败: {e}") - - -def test_jobmanager_client(): - """测试重构后的JobManagerClient""" - print("\n=== 测试 JobManagerClient ===") - - try: - from sage.kernel.runtime.jobmanager_client import JobManagerClient - - # 创建客户端 - client = JobManagerClient("127.0.0.1", 19001, timeout=10.0) - print("✓ JobManagerClient创建成功") - print(f" - Host: {client.host}") - print(f" - Port: {client.port}") - print(f" - Timeout: {client.timeout}") - print(f" - Client Name: {client.client_name}") - - # 测试方法是否存在 - methods_to_test = [ - "submit_job", - "pause_job", - "get_job_status", - "list_jobs", - "continue_job", - "delete_job", - "receive_node_stop_signal", - "cleanup_all_jobs", - "health_check", - "get_server_info", - ] - - missing_methods = [] - for method in methods_to_test: - if not hasattr(client, method): - missing_methods.append(method) - - if missing_methods: - print(f"✗ 缺少方法: {missing_methods}") - else: - print("✓ 所有必需方法都存在") - - # 测试继承的基类方法 - health_req = client._build_health_check_request() - server_req = client._build_server_info_request() - - print(f"✓ 健康检查请求构建: {health_req}") - print(f"✓ 服务器信息请求构建: {server_req}") - - # 测试上下文管理器（不会实际连接） - print(f"✓ 上下文管理器支持: {hasattr(client, '__enter__')}") - - except Exception as e: - print(f"✗ JobManagerClient测试失败: {e}") - import traceback - - traceback.print_exc() - - -def test_backward_compatibility(): - """测试向后兼容性""" - print("\n=== 测试向后兼容性 ===") - - try: - from sage.kernel.runtime.jobmanager_client import JobManagerClient - - # 测试原有的初始化方式 - client1 = JobManagerClient() # 默认参数 - print(f"✓ 默认参数初始化: {client1.host}:{client1.port}") - - client2 = JobManagerClient("localhost", 19002) # 位置参数 - print(f"✓ 位置参数初始化: {client2.host}:{client2.port}") - - # 测试新的timeout参数 - client3 = JobManagerClient(timeout=5.0) - print(f"✓ 新参数支持: timeout={client3.timeout}") - - except Exception as e: - print(f"✗ 向后兼容性测试失败: {e}") - - -def main(): - """主测试函数""" - print("开始测试JobManagerClient重构...") - - tests = [test_base_tcp_client, test_jobmanager_client, test_backward_compatibility] - - passed = 0 - total = len(tests) - - for test in tests: - if test(): - passed += 1 - - print("\n=== 测试结果 ===") - print(f"通过: {passed}/{total}") - - if passed == total: - print("✓ 所有测试通过！重构成功！") - return 0 - else: - print("✗ 部分测试失败") - return 1 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/packages/sage-kernel/tests/unit/kernel/runtime_execution_tests/test_simple.py b/packages/sage-kernel/tests/unit/kernel/runtime_execution_tests/test_simple.py deleted file mode 100644 index ff05cd32af..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/runtime_execution_tests/test_simple.py +++ /dev/null @@ -1,62 +0,0 @@ -""" -简单测试来验证JobManager的基本功能 - -注意：这个测试因为导入JobManager会触发sage.common的完整导入链， -所以即使测试本身很快，导入阶段也会比较慢 -""" - -import pytest - -# 标记为slow，因为导入JobManager会触发重依赖的加载 -pytestmark = pytest.mark.slow - -from sage.kernel.runtime.job_manager import JobManager - - -@pytest.fixture(autouse=True) -def reset_jobmanager_singleton(): - """在每个测试前后重置JobManager单例状态，避免测试间相互影响""" - # 测试前：重置单例 - JobManager.instance = None - - yield - - # 测试后：清理 - if JobManager.instance is not None: - # 关闭daemon server如果存在 - if hasattr(JobManager.instance, "server") and JobManager.instance.server: - try: - JobManager.instance.server.stop() - except Exception: - pass - JobManager.instance = None - - -def test_jobmanager_can_be_imported(): - """测试JobManager可以被成功导入""" - assert JobManager is not None - - -def test_jobmanager_singleton(): - """测试JobManager的单例模式""" - # 创建两个实例（禁用daemon以避免后台线程和Ray初始化） - jm1 = JobManager(enable_daemon=False) - jm2 = JobManager(enable_daemon=False) - - # 验证它们是同一个实例 - assert jm1 is jm2 - - -def test_jobmanager_basic_attributes(): - """测试JobManager的基本属性""" - jm = JobManager(enable_daemon=False) - - # 验证基本属性存在 - assert hasattr(jm, "jobs") - assert hasattr(jm, "logger") - # server应该是None因为daemon被禁用 - assert jm.server is None - - -if __name__ == "__main__": - pytest.main([__file__, "-v"]) diff --git a/packages/sage-kernel/tests/unit/kernel/scheduler/__init__.py b/packages/sage-kernel/tests/unit/kernel/scheduler/__init__.py deleted file mode 100644 index fd56912f33..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/scheduler/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Tests for scheduler module""" diff --git a/packages/sage-kernel/tests/unit/kernel/scheduler/test_cpu_node_selection.py b/packages/sage-kernel/tests/unit/kernel/scheduler/test_cpu_node_selection.py deleted file mode 100644 index 5f68f90ca4..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/scheduler/test_cpu_node_selection.py +++ /dev/null @@ -1,456 +0,0 @@ -""" -Test CPU Node Selection and Resource Allocation - -Tests for CPU-only node support in SAGE: -- CPU node selection via NodeSelector -- Resource requirements (CPU, memory, no GPU) -- Scheduling strategies for CPU nodes -- Node filtering and ranking -""" - -import time - -import pytest - -from sage.kernel.scheduler.decision import PlacementDecision -from sage.kernel.scheduler.node_selector import NodeResources, NodeSelector - - -class TestNodeResources: - """Test NodeResources dataclass""" - - def test_can_fit_cpu_only(self): - """Test that CPU-only nodes can fit CPU-only tasks""" - node = NodeResources( - node_id="cpu-node-1", - hostname="cpu-worker-1", - address="192.168.1.100", - total_cpu=8.0, - total_gpu=0.0, # No GPU - total_memory=16 * 1024**3, # 16GB - custom_resources={}, - available_cpu=6.0, - available_gpu=0.0, - available_memory=12 * 1024**3, - cpu_usage=0.25, - gpu_usage=0.0, - memory_usage=0.25, - task_count=2, - ) - - # Can fit CPU-only task - assert node.can_fit(cpu_required=4, gpu_required=0, memory_required=8 * 1024**3) - - # Cannot fit task requiring more CPU - assert not node.can_fit(cpu_required=8, gpu_required=0) - - # Cannot fit task requiring GPU (node has 0 GPU) - assert not node.can_fit(cpu_required=2, gpu_required=1) - - def test_can_fit_insufficient_memory(self): - """Test node rejection when memory is insufficient""" - node = NodeResources( - node_id="cpu-node-2", - hostname="cpu-worker-2", - address="192.168.1.101", - total_cpu=16.0, - total_gpu=0.0, - total_memory=8 * 1024**3, # Only 8GB - custom_resources={}, - available_cpu=14.0, - available_gpu=0.0, - available_memory=4 * 1024**3, # Only 4GB available - cpu_usage=0.125, - gpu_usage=0.0, - memory_usage=0.5, - task_count=1, - ) - - # Cannot fit task requiring 8GB when only 4GB available - assert not node.can_fit(cpu_required=2, gpu_required=0, memory_required=8 * 1024**3) - - # Can fit smaller task - assert node.can_fit(cpu_required=2, gpu_required=0, memory_required=2 * 1024**3) - - def test_compute_score_balanced(self): - """Test balanced strategy scoring (lower usage = better score = lower value)""" - # Low usage node (better for balanced) - node_low = NodeResources( - node_id="cpu-node-low", - hostname="cpu-low", - address="192.168.1.100", - total_cpu=8.0, - total_gpu=0.0, - total_memory=16 * 1024**3, - custom_resources={}, - available_cpu=6.0, - available_gpu=0.0, - available_memory=12 * 1024**3, - cpu_usage=0.25, # 25% CPU usage - gpu_usage=0.0, - memory_usage=0.25, # 25% memory usage - ) - - # High usage node (worse for balanced) - node_high = NodeResources( - node_id="cpu-node-high", - hostname="cpu-high", - address="192.168.1.101", - total_cpu=8.0, - total_gpu=0.0, - total_memory=16 * 1024**3, - custom_resources={}, - available_cpu=2.0, - available_gpu=0.0, - available_memory=4 * 1024**3, - cpu_usage=0.75, # 75% CPU usage - gpu_usage=0.0, - memory_usage=0.75, # 75% memory usage - ) - - score_low = node_low.compute_score(strategy="balanced") - score_high = node_high.compute_score(strategy="balanced") - - # Lower usage should have lower (better) score - assert score_low < score_high - - def test_compute_score_pack(self): - """Test pack strategy scoring (higher usage = better score = lower value)""" - node_low = NodeResources( - node_id="cpu-node-low", - hostname="cpu-low", - address="192.168.1.100", - total_cpu=8.0, - total_gpu=0.0, - total_memory=16 * 1024**3, - custom_resources={}, - available_cpu=6.0, - available_gpu=0.0, - available_memory=12 * 1024**3, - cpu_usage=0.25, - gpu_usage=0.0, - memory_usage=0.25, - ) - - node_high = NodeResources( - node_id="cpu-node-high", - hostname="cpu-high", - address="192.168.1.101", - total_cpu=8.0, - total_gpu=0.0, - total_memory=16 * 1024**3, - custom_resources={}, - available_cpu=2.0, - available_gpu=0.0, - available_memory=4 * 1024**3, - cpu_usage=0.75, - gpu_usage=0.0, - memory_usage=0.75, - ) - - score_low = node_low.compute_score(strategy="pack") - score_high = node_high.compute_score(strategy="pack") - - # Higher usage should have lower (better) score for packing - assert score_high < score_low - - -class TestNodeSelector: - """Test NodeSelector for CPU node selection""" - - def test_select_cpu_only_node(self): - """Test selecting a CPU-only node (no GPU required)""" - selector = NodeSelector() - - # Mock node cache with CPU-only nodes - selector.node_cache = { - "cpu-node-1": NodeResources( - node_id="cpu-node-1", - hostname="cpu-worker-1", - address="192.168.1.100", - total_cpu=8.0, - total_gpu=0.0, - total_memory=16 * 1024**3, - custom_resources={}, - available_cpu=6.0, - available_gpu=0.0, - available_memory=12 * 1024**3, - cpu_usage=0.25, - gpu_usage=0.0, - memory_usage=0.25, - task_count=2, - ), - "cpu-node-2": NodeResources( - node_id="cpu-node-2", - hostname="cpu-worker-2", - address="192.168.1.101", - total_cpu=16.0, - total_gpu=0.0, - total_memory=32 * 1024**3, - custom_resources={}, - available_cpu=14.0, - available_gpu=0.0, - available_memory=28 * 1024**3, - cpu_usage=0.125, - gpu_usage=0.0, - memory_usage=0.125, - task_count=1, - ), - } - # Prevent cache update from overwriting our mock data - selector.last_update = time.time() - - # Select node for CPU-only task - node_id = selector.select_best_node(cpu_required=4, gpu_required=0, strategy="balanced") - - assert node_id is not None - assert node_id in ["cpu-node-1", "cpu-node-2"] - - # Should select cpu-node-2 (lower usage) - assert node_id == "cpu-node-2" - - def test_reject_gpu_task_on_cpu_nodes(self): - """Test that tasks requiring GPU are rejected on CPU-only nodes""" - selector = NodeSelector() - - # Mock cache with only CPU nodes (no GPU) - selector.node_cache = { - "cpu-node-1": NodeResources( - node_id="cpu-node-1", - hostname="cpu-worker-1", - address="192.168.1.100", - total_cpu=8.0, - total_gpu=0.0, # No GPU - total_memory=16 * 1024**3, - custom_resources={}, - available_cpu=6.0, - available_gpu=0.0, - available_memory=12 * 1024**3, - cpu_usage=0.25, - gpu_usage=0.0, - memory_usage=0.25, - ), - } - # Prevent cache update from overwriting our mock data - selector.last_update = time.time() - - # Try to select node for GPU task - node_id = selector.select_best_node( - cpu_required=2, - gpu_required=1, # Requires GPU - ) - - # Should return None (no suitable node) - assert node_id is None - - def test_balanced_strategy_selects_lowest_usage(self): - """Test that balanced strategy selects the node with lowest usage""" - selector = NodeSelector() - - selector.node_cache = { - "cpu-node-low": NodeResources( - node_id="cpu-node-low", - hostname="cpu-low", - address="192.168.1.100", - total_cpu=8.0, - total_gpu=0.0, - total_memory=16 * 1024**3, - custom_resources={}, - available_cpu=7.0, - available_gpu=0.0, - available_memory=14 * 1024**3, - cpu_usage=0.125, # 12.5% usage (lowest) - gpu_usage=0.0, - memory_usage=0.125, - ), - "cpu-node-med": NodeResources( - node_id="cpu-node-med", - hostname="cpu-med", - address="192.168.1.101", - total_cpu=8.0, - total_gpu=0.0, - total_memory=16 * 1024**3, - custom_resources={}, - available_cpu=5.0, - available_gpu=0.0, - available_memory=10 * 1024**3, - cpu_usage=0.375, # 37.5% usage - gpu_usage=0.0, - memory_usage=0.375, - ), - "cpu-node-high": NodeResources( - node_id="cpu-node-high", - hostname="cpu-high", - address="192.168.1.102", - total_cpu=8.0, - total_gpu=0.0, - total_memory=16 * 1024**3, - custom_resources={}, - available_cpu=2.0, - available_gpu=0.0, - available_memory=4 * 1024**3, - cpu_usage=0.75, # 75% usage (highest) - gpu_usage=0.0, - memory_usage=0.75, - ), - } - # Prevent cache update from overwriting our mock data - selector.last_update = time.time() - - node_id = selector.select_best_node(cpu_required=1, gpu_required=0, strategy="balanced") - - # Should select the node with lowest usage - assert node_id == "cpu-node-low" - - def test_pack_strategy_selects_highest_usage(self): - """Test that pack strategy selects the node with highest usage""" - selector = NodeSelector() - - selector.node_cache = { - "cpu-node-low": NodeResources( - node_id="cpu-node-low", - hostname="cpu-low", - address="192.168.1.100", - total_cpu=8.0, - total_gpu=0.0, - total_memory=16 * 1024**3, - custom_resources={}, - available_cpu=7.0, - available_gpu=0.0, - available_memory=14 * 1024**3, - cpu_usage=0.125, - gpu_usage=0.0, - memory_usage=0.125, - ), - "cpu-node-high": NodeResources( - node_id="cpu-node-high", - hostname="cpu-high", - address="192.168.1.102", - total_cpu=8.0, - total_gpu=0.0, - total_memory=16 * 1024**3, - custom_resources={}, - available_cpu=4.0, # Still can fit task - available_gpu=0.0, - available_memory=8 * 1024**3, - cpu_usage=0.5, # Higher usage - gpu_usage=0.0, - memory_usage=0.5, - ), - } - # Prevent cache update from overwriting our mock data - selector.last_update = time.time() - - node_id = selector.select_best_node(cpu_required=2, gpu_required=0, strategy="pack") - - # Should select the node with higher usage (pack strategy) - assert node_id == "cpu-node-high" - - def test_get_cluster_stats(self): - """Test getting cluster statistics for CPU nodes""" - selector = NodeSelector() - - selector.node_cache = { - "cpu-node-1": NodeResources( - node_id="cpu-node-1", - hostname="cpu-worker-1", - address="192.168.1.100", - total_cpu=8.0, - total_gpu=0.0, - total_memory=16 * 1024**3, - custom_resources={}, - available_cpu=6.0, - available_gpu=0.0, - available_memory=12 * 1024**3, - cpu_usage=0.25, - gpu_usage=0.0, - memory_usage=0.25, - task_count=2, - ), - "cpu-node-2": NodeResources( - node_id="cpu-node-2", - hostname="cpu-worker-2", - address="192.168.1.101", - total_cpu=16.0, - total_gpu=0.0, - total_memory=32 * 1024**3, - custom_resources={}, - available_cpu=12.0, - available_gpu=0.0, - available_memory=24 * 1024**3, - cpu_usage=0.25, - gpu_usage=0.0, - memory_usage=0.25, - task_count=3, - ), - } - # Prevent cache update from overwriting our mock data - selector.last_update = time.time() - - # Track tasks - selector.node_task_count = {"cpu-node-1": 2, "cpu-node-2": 3} - - stats = selector.get_cluster_stats() - - assert stats["node_count"] == 2 - assert stats["total_cpu"] == 24.0 # 8 + 16 - assert stats["total_gpu"] == 0.0 # CPU only - assert stats["total_memory"] == 48 * 1024**3 # 16 + 32 GB - assert stats["available_cpu"] == 18.0 # 6 + 12 - assert stats["total_tasks"] == 5 # 2 + 3 - assert stats["avg_cpu_usage"] == 0.25 - - -class TestCPUScheduling: - """Test CPU-only scheduling scenarios""" - - def test_cpu_task_placement_decision(self): - """Test that CPU tasks get proper placement decisions""" - from sage.kernel.scheduler.api import BaseScheduler - - class MockCPUScheduler(BaseScheduler): - def make_decision(self, task_node): - """Simple CPU scheduling decision""" - return PlacementDecision( - target_node="cpu-node-1", - resource_requirements={"cpu": 2, "memory": "2GB", "gpu": 0}, - placement_strategy="cpu_only", - reason="CPU task assigned to CPU node", - ) - - scheduler = MockCPUScheduler() - - # Mock task node - class MockTaskNode: - name = "cpu_task" - transformation = None - - decision = scheduler.make_decision(MockTaskNode()) - - assert decision.target_node == "cpu-node-1" - assert decision.resource_requirements["cpu"] == 2 - assert decision.resource_requirements["gpu"] == 0 - assert decision.placement_strategy == "cpu_only" - - def test_cpu_resource_requirements_extraction(self): - """Test extracting CPU resource requirements from operator""" - - class MockCPUOperator: - cpu_required = 4 - memory_required = "4GB" - gpu_required = 0 - - op = MockCPUOperator() - - # Extract requirements - cpu = getattr(op, "cpu_required", 1) - memory = getattr(op, "memory_required", "1GB") - gpu = getattr(op, "gpu_required", 0) - - assert cpu == 4 - assert memory == "4GB" - assert gpu == 0 - - -if __name__ == "__main__": - pytest.main([__file__, "-v"]) diff --git a/packages/sage-kernel/tests/unit/kernel/scheduler/test_scheduler_api.py b/packages/sage-kernel/tests/unit/kernel/scheduler/test_scheduler_api.py deleted file mode 100644 index 21768bf6f6..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/scheduler/test_scheduler_api.py +++ /dev/null @@ -1,555 +0,0 @@ -""" -新调度器 API 单元测试 - -测试重构后的调度器架构： -- BaseScheduler 抽象基类 -- FIFOScheduler 实现 -- LoadAwareScheduler 实现 -""" - -from unittest.mock import Mock - -import pytest - -from sage.kernel.scheduler.api import BaseScheduler -from sage.kernel.scheduler.impl import FIFOScheduler, LoadAwareScheduler - - -class TestBaseScheduler: - """BaseScheduler 基础测试""" - - def test_scheduler_is_abstract(self): - """测试 BaseScheduler 是抽象类""" - with pytest.raises(TypeError): - BaseScheduler() # type: ignore[abstract] - - def test_fifo_scheduler_initialization(self): - """测试 FIFO 调度器初始化""" - scheduler = FIFOScheduler(platform="local") - - assert scheduler.platform == "local" - assert hasattr(scheduler, "schedule_task") - assert hasattr(scheduler, "schedule_service") - assert hasattr(scheduler, "get_metrics") - - def test_load_aware_scheduler_initialization(self): - """测试负载感知调度器初始化""" - scheduler = LoadAwareScheduler(platform="remote", max_concurrent=10) - - assert scheduler.platform == "remote" - assert scheduler.max_concurrent == 10 - assert hasattr(scheduler, "schedule_task") - assert hasattr(scheduler, "schedule_service") - assert hasattr(scheduler, "get_metrics") - - -class TestFIFOScheduler: - """FIFOScheduler 测试""" - - def test_schedule_task_basic(self): - """测试基本任务调度""" - scheduler = FIFOScheduler(platform="local") - - # Mock task node - task_node = Mock() - task_node.name = "test_task" - task_node.ctx = Mock() - - # Mock task factory - task_factory = Mock() - expected_task = Mock() - task_factory.create_task.return_value = expected_task - task_node.task_factory = task_factory - - # Schedule task - result = scheduler.schedule_task(task_node) - - assert result == expected_task - task_factory.create_task.assert_called_once() - - def test_schedule_service_basic(self): - """测试基本服务调度""" - scheduler = FIFOScheduler(platform="local") - - # Mock service node - service_node = Mock() - service_node.service_name = "test_service" - service_node.ctx = Mock() - - # Mock service task factory - service_factory = Mock() - expected_service = Mock() - service_factory.create_service_task.return_value = expected_service - service_node.service_task_factory = service_factory - - # Schedule service - result = scheduler.schedule_service(service_node) - - assert result == expected_service - service_factory.create_service_task.assert_called_once() - - def test_get_metrics(self): - """测试获取调度器指标""" - scheduler = FIFOScheduler(platform="local") - - # 创建一些任务以生成指标 - for i in range(3): - task_node = Mock() - task_node.name = f"task_{i}" - task_node.ctx = Mock() - task_factory = Mock() - task_factory.create_task.return_value = Mock() - task_node.task_factory = task_factory - scheduler.schedule_task(task_node) - - metrics = scheduler.get_metrics() - - assert isinstance(metrics, dict) - assert "scheduler_type" in metrics - assert metrics["scheduler_type"] == "FIFO" - assert "total_scheduled" in metrics - assert metrics["total_scheduled"] == 3 - - def test_fifo_order_preservation(self): - """测试 FIFO 顺序保持""" - scheduler = FIFOScheduler(platform="local") - - tasks = [] - for i in range(5): - task_node = Mock() - task_node.name = f"task_{i}" - task_node.ctx = Mock() - - task_factory = Mock() - task = Mock() - task.name = f"task_{i}" - task_factory.create_task.return_value = task - task_node.task_factory = task_factory - - result = scheduler.schedule_task(task_node) - tasks.append(result) - - # 验证任务数量 - assert len(tasks) == 5 - - # 验证每个任务都被创建 - assert all(task is not None for task in tasks) - - -class TestLoadAwareScheduler: - """LoadAwareScheduler 测试""" - - def test_initialization_with_defaults(self): - """测试使用默认参数初始化""" - scheduler = LoadAwareScheduler(platform="local") - - assert scheduler.platform == "local" - assert scheduler.max_concurrent == 10 # 默认值 - - def test_initialization_with_custom_max_concurrent(self): - """测试自定义并发限制""" - scheduler = LoadAwareScheduler(platform="remote", max_concurrent=20) - - assert scheduler.max_concurrent == 20 - - def test_schedule_task_with_load_awareness(self): - """测试负载感知任务调度""" - scheduler = LoadAwareScheduler(platform="local", max_concurrent=5) - - tasks = [] - for i in range(3): - task_node = Mock() - task_node.name = f"task_{i}" - task_node.ctx = Mock() - - # Mock transformation 和其属性 - transformation = Mock() - transformation.cpu_required = 1.0 - transformation.gpu_required = 0.0 - transformation.memory_required = "1GB" - transformation.custom_resources = {} - task_node.transformation = transformation - - task_factory = Mock() - task = Mock() - task_factory.create_task.return_value = task - task_factory.remote = False - task_node.task_factory = task_factory - - result = scheduler.schedule_task(task_node) - tasks.append(result) - - # 验证在并发限制内可以调度任务 - assert len(tasks) == 3 - assert all(task is not None for task in tasks) - - def test_get_metrics_with_load_info(self): - """测试获取带负载信息的指标""" - scheduler = LoadAwareScheduler(platform="remote", max_concurrent=10) - - # 调度一些任务 - for i in range(5): - task_node = Mock() - task_node.name = f"task_{i}" - task_node.ctx = Mock() - - # Mock transformation 和其属性 - transformation = Mock() - transformation.cpu_required = 1.0 - transformation.gpu_required = 0.0 - transformation.memory_required = "1GB" - transformation.custom_resources = {} - task_node.transformation = transformation - - task_factory = Mock() - task_factory.create_task.return_value = Mock() - task_factory.remote = False - task_node.task_factory = task_factory - scheduler.schedule_task(task_node) - - metrics = scheduler.get_metrics() - - assert isinstance(metrics, dict) - assert "scheduler_type" in metrics - assert metrics["scheduler_type"] == "LoadAware" - assert "total_scheduled" in metrics - assert "max_concurrent" in metrics - assert metrics["max_concurrent"] == 10 - - -class TestSchedulerIntegration: - """调度器集成测试""" - - def test_multiple_schedulers_independent(self): - """测试多个调度器实例独立工作""" - scheduler1 = FIFOScheduler(platform="local") - scheduler2 = LoadAwareScheduler(platform="remote") - - # 在 scheduler1 中调度任务 - task_node1 = Mock() - task_node1.name = "task1" - task_node1.ctx = Mock() - task_factory1 = Mock() - task_factory1.create_task.return_value = Mock() - task_node1.task_factory = task_factory1 - scheduler1.schedule_task(task_node1) - - # 在 scheduler2 中调度任务 - task_node2 = Mock() - task_node2.name = "task2" - task_node2.ctx = Mock() - - # Mock transformation 和其属性 - transformation = Mock() - transformation.cpu_required = 1.0 - transformation.gpu_required = 0.0 - transformation.memory_required = "1GB" - transformation.custom_resources = {} - task_node2.transformation = transformation - - task_factory2 = Mock() - task_factory2.create_task.return_value = Mock() - task_factory2.remote = False - task_node2.task_factory = task_factory2 - scheduler2.schedule_task(task_node2) - - # 验证两个调度器的指标独立 - metrics1 = scheduler1.get_metrics() - metrics2 = scheduler2.get_metrics() - - assert metrics1["scheduler_type"] == "FIFO" - assert metrics2["scheduler_type"] == "LoadAware" - assert metrics1["total_scheduled"] == 1 - assert metrics2["total_scheduled"] == 1 - - def test_schedule_tasks_and_services_mixed(self): - """测试混合调度任务和服务""" - scheduler = FIFOScheduler(platform="local") - - # 调度任务 - task_node = Mock() - task_node.name = "task_1" - task_node.ctx = Mock() - task_factory = Mock() - task_factory.create_task.return_value = Mock() - task_node.task_factory = task_factory - task = scheduler.schedule_task(task_node) - - # 调度服务 - service_node = Mock() - service_node.service_name = "service_1" - service_node.ctx = Mock() - service_factory = Mock() - service_factory.create_service_task.return_value = Mock() - service_node.service_task_factory = service_factory - service = scheduler.schedule_service(service_node) - - assert task is not None - assert service is not None - - -class TestSchedulerEdgeCases: - """调度器边界条件测试""" - - def test_schedule_with_none_context(self): - """测试处理 None 上下文""" - scheduler = FIFOScheduler(platform="local") - - task_node = Mock() - task_node.name = "test_task" - task_node.ctx = None # None context - - task_factory = Mock() - task_factory.create_task.return_value = Mock() - task_node.task_factory = task_factory - - # 应该仍然能够调度 - result = scheduler.schedule_task(task_node) - assert result is not None - - def test_empty_metrics_on_new_scheduler(self): - """测试新调度器的空指标""" - scheduler = FIFOScheduler(platform="local") - - metrics = scheduler.get_metrics() - - assert metrics["total_scheduled"] == 0 - - -class TestSchedulerDecisionDelay: - """测试调度器决策延迟""" - - def test_schedule_task_with_delay(self): - """测试任务调度中的延迟处理""" - from sage.kernel.scheduler.decision import PlacementDecision - from sage.kernel.scheduler.impl import FIFOScheduler - - scheduler = FIFOScheduler(platform="local") - - # Mock task node - task_node = Mock() - task_node.name = "delayed_task" - task_node.ctx = Mock() - - # Mock task factory - task_factory = Mock() - expected_task = Mock() - task_factory.create_task.return_value = expected_task - task_node.task_factory = task_factory - - # 记录原始的 make_decision 方法 - - # Mock make_decision 以返回带有延迟的决策 - def mock_make_decision_with_delay(task_node): # Fixed parameter name - decision = PlacementDecision( - target_node="test_node", - delay=0.01, # 10ms 延迟 - immediate=False, - placement_strategy="fifo", - reason="Test delay", - ) - return decision - - scheduler.make_decision = mock_make_decision_with_delay # type: ignore[method-assign] - - # 调度任务（应该包括延迟） - import time - - start = time.time() - result = scheduler.schedule_task(task_node) - elapsed = time.time() - start - - # 验证任务被创建 - assert result == expected_task - # 验证延迟被应用（至少 10ms） - assert elapsed >= 0.01 - - def test_schedule_service_with_delay(self): - """测试服务调度中的延迟处理""" - from sage.kernel.scheduler.decision import PlacementDecision - from sage.kernel.scheduler.impl import FIFOScheduler - - scheduler = FIFOScheduler(platform="local") - - # Mock service node - service_node = Mock() - service_node.service_name = "delayed_service" - service_node.ctx = Mock() - - # Mock service factory - service_factory = Mock() - expected_service = Mock() - service_factory.create_service_task.return_value = expected_service - service_node.service_task_factory = service_factory - - # Mock make_service_decision 以返回带有延迟的决策 - def mock_make_service_decision_with_delay(service_node): # Fixed parameter name - decision = PlacementDecision( - target_node="test_node", - delay=0.01, # 10ms 延迟 - immediate=False, - placement_strategy="fifo", - reason="Test service delay", - ) - return decision - - scheduler.make_service_decision = mock_make_service_decision_with_delay # type: ignore[method-assign] - - # 调度服务（应该包括延迟） - import time - - start = time.time() - result = scheduler.schedule_service(service_node) - elapsed = time.time() - start - - # 验证服务被创建 - assert result == expected_service - # 验证延迟被应用（至少 10ms） - assert elapsed >= 0.01 - - def test_schedule_task_with_custom_runtime_context(self): - """测试使用自定义运行时上下文调度任务""" - from sage.kernel.scheduler.impl import FIFOScheduler - - scheduler = FIFOScheduler(platform="local") - - # Mock task node 和 factory - task_node = Mock() - task_node.name = "task_with_custom_ctx" - task_node.ctx = Mock() # 默认上下文 - - task_factory = Mock() - expected_task = Mock() - task_factory.create_task.return_value = expected_task - task_node.task_factory = task_factory - - # 自定义运行时上下文 - custom_ctx = Mock() - - # 调度任务，传入自定义上下文 - result = scheduler.schedule_task(task_node, runtime_ctx=custom_ctx) - - # 验证任务工厂使用了自定义上下文 - task_factory.create_task.assert_called_once_with(task_node.name, custom_ctx) - assert result == expected_task - - def test_schedule_service_with_custom_runtime_context(self): - """测试使用自定义运行时上下文调度服务""" - from sage.kernel.scheduler.impl import FIFOScheduler - - scheduler = FIFOScheduler(platform="local") - - # Mock service node 和 factory - service_node = Mock() - service_node.service_name = "service_with_custom_ctx" - service_node.ctx = Mock() # 默认上下文 - - service_factory = Mock() - expected_service = Mock() - service_factory.create_service_task.return_value = expected_service - service_node.service_task_factory = service_factory - - # 自定义运行时上下文 - custom_ctx = Mock() - - # 调度服务，传入自定义上下文 - result = scheduler.schedule_service(service_node, runtime_ctx=custom_ctx) - - # 验证服务工厂使用了自定义上下文 - service_factory.create_service_task.assert_called_once_with(custom_ctx) - assert result == expected_service - - -class TestSchedulerShutdown: - """测试调度器关闭""" - - def test_scheduler_shutdown_clears_history(self): - """测试关闭调度器清空决策历史""" - from sage.kernel.scheduler.impl import FIFOScheduler - - scheduler = FIFOScheduler(platform="local") - - # 调度一些任务以生成决策历史 - for i in range(3): - task_node = Mock() - task_node.name = f"task_{i}" - task_node.ctx = Mock() - task_factory = Mock() - task_factory.create_task.return_value = Mock() - task_node.task_factory = task_factory - scheduler.schedule_task(task_node) - - # 验证有决策历史 - assert len(scheduler.decision_history) == 3 - - # 关闭调度器 - scheduler.shutdown() - - # 验证决策历史被清空 - assert len(scheduler.decision_history) == 0 - - def test_make_service_decision_default_implementation(self): - """测试 make_service_decision 的默认实现""" - from sage.kernel.scheduler.impl import FIFOScheduler - - scheduler = FIFOScheduler(platform="local") - - # Mock service node - service_node = Mock() - service_node.service_name = "test_service" - - # 调用 make_service_decision - decision = scheduler.make_service_decision(service_node) - - # 验证返回了 PlacementDecision - from sage.kernel.scheduler.decision import PlacementDecision - - assert isinstance(decision, PlacementDecision) - assert "test_service" in decision.reason - - -class TestLoadAwareSchedulerAdvanced: - """LoadAwareScheduler 高级测试""" - - def test_load_aware_scheduler_task_completion(self): - """测试负载感知调度器的任务完成处理""" - from sage.kernel.scheduler.impl import LoadAwareScheduler - - scheduler = LoadAwareScheduler(platform="local", max_concurrent=5) - - # 调度一个任务 - task_node = Mock() - task_node.name = "completion_task" - task_node.ctx = Mock() - - transformation = Mock() - transformation.cpu_required = 1.0 - transformation.gpu_required = 0.0 - transformation.memory_required = "1GB" - transformation.custom_resources = {} - task_node.transformation = transformation - - task_factory = Mock() - task_factory.create_task.return_value = Mock() - task_factory.remote = False - task_node.task_factory = task_factory - - # 初始活跃任务数应为 0 - assert scheduler.active_tasks == 0 - - # 调度任务 - scheduler.schedule_task(task_node) - - # 活跃任务数应该增加 - initial_active = scheduler.active_tasks - assert initial_active > 0 - - # 标记任务完成 - scheduler.task_completed("completion_task") - - # 活跃任务数应该减少 - assert scheduler.active_tasks == initial_active - 1 - - -if __name__ == "__main__": - pytest.main([__file__, "-v"]) diff --git a/packages/sage-kernel/tests/unit/kernel/scheduler/test_scheduler_complete.py b/packages/sage-kernel/tests/unit/kernel/scheduler/test_scheduler_complete.py deleted file mode 100644 index 0a62476151..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/scheduler/test_scheduler_complete.py +++ /dev/null @@ -1,821 +0,0 @@ -""" -调度器模块完整测试套件 - -测试覆盖： -1. PlacementDecision 数据类 -2. BaseScheduler 接口 -3. FIFOScheduler 实现 -4. LoadAwareScheduler 实现 -5. NodeSelector 资源监控 -6. PlacementExecutor 执行器 -7. 完整集成测试 -""" - -import time -from unittest.mock import Mock, patch - -import pytest - -from sage.kernel.scheduler.api import BaseScheduler -from sage.kernel.scheduler.decision import PlacementDecision -from sage.kernel.scheduler.impl import FIFOScheduler, LoadAwareScheduler -from sage.kernel.scheduler.placement import PlacementExecutor - -# ============================================================================ -# PlacementDecision 测试 -# ============================================================================ - - -class TestPlacementDecision: - """测试 PlacementDecision 数据类""" - - def test_default_initialization(self): - """测试默认初始化""" - decision = PlacementDecision() - - assert decision.target_node is None - assert decision.resource_requirements is None - assert decision.delay == 0.0 - assert decision.immediate is True - assert decision.placement_strategy == "default" - assert decision.reason == "" - - def test_full_initialization(self): - """测试完整初始化""" - # 使用有效的 Ray node_id 格式（十六进制字符串） - valid_node_id = "a1b2c3d4e5f6789012345678901234567890abcdef1234567890abcd" - decision = PlacementDecision( - target_node=valid_node_id, - resource_requirements={"cpu": 4, "gpu": 1, "memory": "8GB"}, - delay=0.5, - immediate=False, - placement_strategy="balanced", - reason="Test decision", - ) - - assert decision.target_node == valid_node_id - assert decision.resource_requirements == {"cpu": 4, "gpu": 1, "memory": "8GB"} - assert decision.delay == 0.5 - assert decision.immediate is False - assert decision.placement_strategy == "balanced" - assert decision.reason == "Test decision" - - def test_immediate_default(self): - """测试 immediate_default 快捷方法""" - decision = PlacementDecision.immediate_default(reason="Quick test") - - assert decision.target_node is None - assert decision.resource_requirements is None - assert decision.delay == 0.0 - assert decision.immediate is True - assert decision.placement_strategy == "default" - assert decision.reason == "Quick test" - - def test_with_resources(self): - """测试 with_resources 快捷方法""" - decision = PlacementDecision.with_resources( - cpu=4, gpu=1, memory=8589934592, reason="Resource test" - ) - - assert decision.resource_requirements == { - "cpu": 4, - "gpu": 1, - "memory": 8589934592, - } - assert decision.reason == "Resource test" - - def test_with_node(self): - """测试 with_node 快捷方法""" - # 使用有效的 Ray node_id 格式 - valid_node_id = "b2c3d4e5f6789012345678901234567890abcdef1234567890abcdef" - decision = PlacementDecision.with_node(node_id=valid_node_id, reason="Node test") - - assert decision.target_node == valid_node_id - assert decision.reason == "Node test" - - def test_to_dict(self): - """测试转换为字典""" - decision = PlacementDecision( - target_node="node-1", resource_requirements={"cpu": 2}, reason="Dict test" - ) - - result = decision.to_dict() - - assert isinstance(result, dict) - assert result["target_node"] == "node-1" - assert result["resource_requirements"] == {"cpu": 2} - assert result["reason"] == "Dict test" - - def test_from_dict(self): - """测试从字典创建""" - data = { - "target_node": "node-1", - "resource_requirements": {"cpu": 2}, - "delay": 0.5, - "immediate": False, - "placement_strategy": "pack", - "reason": "From dict test", - } - - decision = PlacementDecision.from_dict(data) - - assert decision.target_node == "node-1" - assert decision.resource_requirements == {"cpu": 2} - assert decision.delay == 0.5 - assert decision.immediate is False - assert decision.placement_strategy == "pack" - - def test_repr(self): - """测试字符串表示""" - decision = PlacementDecision( - target_node="node-1", resource_requirements={"cpu": 4}, reason="Repr test" - ) - - repr_str = repr(decision) - - assert "PlacementDecision" in repr_str - assert "node-1" in repr_str - assert "cpu" in repr_str - - -# ============================================================================ -# BaseScheduler 测试 -# ============================================================================ - - -class TestBaseScheduler: - """测试 BaseScheduler 抽象基类""" - - def test_scheduler_is_abstract(self): - """测试 BaseScheduler 是抽象类""" - with pytest.raises(TypeError): - BaseScheduler() # type: ignore[abstract] - - def test_scheduler_interface(self): - """测试调度器接口定义""" - # 检查必须实现的方法 - assert hasattr(BaseScheduler, "make_decision") - assert hasattr(BaseScheduler, "make_service_decision") - assert hasattr(BaseScheduler, "get_metrics") - assert hasattr(BaseScheduler, "shutdown") - - -# ============================================================================ -# FIFOScheduler 测试 -# ============================================================================ - - -class TestFIFOScheduler: - """测试 FIFO 调度器""" - - def test_initialization(self): - """测试初始化""" - scheduler = FIFOScheduler(platform="local") - - assert scheduler.platform == "local" - assert scheduler.scheduled_count == 0 - assert len(scheduler.decision_history) == 0 - - def test_make_decision(self): - """测试任务调度决策""" - scheduler = FIFOScheduler(platform="local") - - # Mock task node - task_node = Mock() - task_node.name = "test_task" - task_node.transformation = Mock() - - # 调度决策 - decision = scheduler.make_decision(task_node) - - # 验证决策 - assert isinstance(decision, PlacementDecision) - assert decision.target_node is None # FIFO 使用默认 - assert decision.resource_requirements is None - assert decision.immediate is True - assert "FIFO" in decision.reason - - # 验证计数 - assert scheduler.scheduled_count == 1 - assert len(scheduler.decision_history) == 1 - - def test_make_service_decision(self): - """测试服务调度决策""" - scheduler = FIFOScheduler(platform="local") - - # Mock service node - service_node = Mock() - service_node.service_name = "test_service" - service_node.service_class = Mock() - - # 调度决策 - decision = scheduler.make_service_decision(service_node) - - # 验证决策 - assert isinstance(decision, PlacementDecision) - assert decision.target_node is None - assert decision.immediate is True - assert "service" in decision.reason.lower() - - # 验证计数 - assert scheduler.scheduled_count == 1 - - def test_multiple_decisions(self): - """测试多个决策""" - scheduler = FIFOScheduler() - - # 调度多个任务 - for i in range(5): - task_node = Mock() - task_node.name = f"task_{i}" - task_node.transformation = Mock() - - decision = scheduler.make_decision(task_node) - assert isinstance(decision, PlacementDecision) - - # 验证 - assert scheduler.scheduled_count == 5 - assert len(scheduler.decision_history) == 5 - - def test_get_metrics(self): - """测试获取指标""" - scheduler = FIFOScheduler(platform="remote") - - # 调度一些任务 - for i in range(3): - task_node = Mock() - task_node.name = f"task_{i}" - task_node.transformation = Mock() - scheduler.make_decision(task_node) - - # 获取指标 - metrics = scheduler.get_metrics() - - assert metrics["scheduler_type"] == "FIFO" - assert metrics["total_scheduled"] == 3 - assert metrics["decisions"] == 3 - assert metrics["platform"] == "remote" - assert "avg_latency_ms" in metrics - - def test_shutdown(self): - """测试关闭""" - scheduler = FIFOScheduler() - - # 调度一些任务 - for i in range(3): - task_node = Mock() - task_node.name = f"task_{i}" - task_node.transformation = Mock() - scheduler.make_decision(task_node) - - # 关闭 - scheduler.shutdown() - - # 验证清理 - assert len(scheduler.decision_history) == 0 - - -# ============================================================================ -# LoadAwareScheduler 测试 -# ============================================================================ - - -class TestLoadAwareScheduler: - """测试负载感知调度器""" - - def test_initialization(self): - """测试初始化""" - scheduler = LoadAwareScheduler(max_concurrent=10, platform="remote", strategy="balanced") - - assert scheduler.platform == "remote" - assert scheduler.max_concurrent == 10 - assert scheduler.strategy == "balanced" - assert scheduler.active_tasks == 0 - assert scheduler.scheduled_count == 0 - - def test_make_decision_without_resources(self): - """测试无资源需求的决策""" - scheduler = LoadAwareScheduler(max_concurrent=10) - - # Mock task node without resource requirements - task_node = Mock() - task_node.name = "test_task" - task_node.transformation = Mock(spec=[]) # No resource attributes - task_node.task_factory = Mock() - task_node.task_factory.remote = False # Local task - - # 调度决策 - decision = scheduler.make_decision(task_node) - - # 验证决策 - assert isinstance(decision, PlacementDecision) - assert decision.delay == 0.0 - assert "LoadAware" in decision.reason - - # 验证活跃任务计数 - assert scheduler.active_tasks == 1 - - def test_make_decision_with_resources(self): - """测试带资源需求的决策""" - scheduler = LoadAwareScheduler(max_concurrent=10) - - # Mock task node with resource requirements - task_node = Mock() - task_node.name = "gpu_task" - task_node.transformation = Mock() - task_node.transformation.cpu_required = 4 - task_node.transformation.gpu_required = 1 - task_node.transformation.memory_required = "8GB" - task_node.transformation.custom_resources = {} # 空字典，不是 Mock - task_node.task_factory = Mock() - task_node.task_factory.remote = False - - # 调度决策 - decision = scheduler.make_decision(task_node) - - # 验证资源需求 - assert decision.resource_requirements is not None - assert decision.resource_requirements["cpu"] == 4 - assert decision.resource_requirements["gpu"] == 1 - assert "memory" in decision.resource_requirements - - @patch("sage.kernel.scheduler.node_selector.NodeSelector") - def test_make_decision_with_node_selector(self, mock_node_selector_class): - """测试使用 NodeSelector 的决策""" - # Mock NodeSelector - mock_selector = Mock() - # 使用有效的 Ray node_id 格式 - valid_node_id = "b2c3d4e5f6789012345678901234567890abcdef1234567890abcdef" - mock_selector.select_best_node.return_value = valid_node_id - - # Mock get_node to return node resource info - mock_node_res = Mock() - mock_node_res.hostname = "worker-2" - mock_node_res.cpu_usage = 0.5 - mock_node_res.gpu_usage = 0.3 - mock_selector.get_node.return_value = mock_node_res - - mock_node_selector_class.return_value = mock_selector - - scheduler = LoadAwareScheduler(max_concurrent=10, strategy="balanced") - - # Mock remote task node - task_node = Mock() - task_node.name = "remote_task" - task_node.transformation = Mock() - task_node.transformation.cpu_required = 2 - task_node.transformation.gpu_required = 0 - task_node.transformation.memory_required = "4GB" - task_node.transformation.custom_resources = {} # 空字典，不是 Mock - task_node.task_factory = Mock() - task_node.task_factory.remote = True # Remote task - - # 调度决策 - decision = scheduler.make_decision(task_node) - - # 验证节点选择 - assert decision.target_node == valid_node_id - mock_selector.select_best_node.assert_called_once() - mock_selector.track_task_placement.assert_called_once_with(task_node.name, valid_node_id) - - def test_make_service_decision(self): - """测试服务调度决策""" - scheduler = LoadAwareScheduler(max_concurrent=10) - - # Mock service node - service_node = Mock() - service_node.service_name = "cache_service" - service_node.service_class = Mock() - service_node.service_class.cpu_required = 2 - service_node.service_class.gpu_required = 0 # 明确设置 - service_node.service_class.memory_required = "4GB" - service_node.service_class.custom_resources = {} # 空字典 - - # 调度决策 - decision = scheduler.make_service_decision(service_node) - - # 验证决策 - assert isinstance(decision, PlacementDecision) - assert decision.placement_strategy == "spread" # 服务使用 spread - assert decision.immediate is True - assert "Service" in decision.reason - - def test_concurrency_control(self): - """测试并发控制""" - scheduler = LoadAwareScheduler(max_concurrent=2) - - # 调度到达上限 - for i in range(2): - task_node = Mock() - task_node.name = f"task_{i}" - task_node.transformation = Mock(spec=[]) - task_node.task_factory = Mock() - task_node.task_factory.remote = False - - decision = scheduler.make_decision(task_node) - assert decision.delay == 0.0 - - # 验证已达到上限 - assert scheduler.active_tasks == 2 - assert scheduler.active_tasks >= scheduler.max_concurrent - - def test_task_completed(self): - """测试任务完成""" - scheduler = LoadAwareScheduler(max_concurrent=10) - - # 调度一个任务 - task_node = Mock() - task_node.name = "test_task" - task_node.transformation = Mock(spec=[]) - task_node.task_factory = Mock() - task_node.task_factory.remote = False - - scheduler.make_decision(task_node) - assert scheduler.active_tasks == 1 - - # 标记任务完成 - scheduler.task_completed("test_task") - assert scheduler.active_tasks == 0 - - def test_memory_parsing(self): - """测试内存解析""" - scheduler = LoadAwareScheduler() - - # 测试各种格式 - assert scheduler._parse_memory("8GB") == 8 * 1024**3 - assert scheduler._parse_memory("512MB") == 512 * 1024**2 - assert scheduler._parse_memory("1024KB") == 1024 * 1024 - assert scheduler._parse_memory(1024) == 1024 - assert scheduler._parse_memory("invalid") == 0 - - def test_get_metrics(self): - """测试获取指标""" - scheduler = LoadAwareScheduler(max_concurrent=10, strategy="balanced") - - # 调度一些任务 - for i in range(3): - task_node = Mock() - task_node.name = f"task_{i}" - task_node.transformation = Mock(spec=[]) - task_node.task_factory = Mock() - task_node.task_factory.remote = False - scheduler.make_decision(task_node) - - # 获取指标 - metrics = scheduler.get_metrics() - - assert metrics["scheduler_type"] == "LoadAware" - assert metrics["total_scheduled"] == 3 - assert metrics["active_tasks"] == 3 - assert metrics["max_concurrent"] == 10 - assert metrics["strategy"] == "balanced" - assert "cluster" in metrics - - -# ============================================================================ -# PlacementExecutor 测试 -# ============================================================================ - - -class TestPlacementExecutor: - """测试放置执行器""" - - def test_initialization(self): - """测试初始化""" - executor = PlacementExecutor() - - assert len(executor.placed_tasks) == 0 - assert len(executor.placed_services) == 0 - assert executor.placement_stats["total_tasks"] == 0 - - def test_place_local_task(self): - """测试放置本地任务""" - executor = PlacementExecutor() - - # Mock task node - task_node = Mock() - task_node.name = "local_task" - task_node.ctx = Mock() - task_node.task_factory = Mock() - task_node.task_factory.remote = False - - # Mock local task - mock_task = Mock() - task_node.task_factory.create_task.return_value = mock_task - - # Mock decision - decision = PlacementDecision.immediate_default() - - # 放置任务 - result = executor.place_task(task_node, decision) - - # 验证 - assert result == mock_task - assert executor.placement_stats["total_tasks"] == 1 - assert executor.placement_stats["local_tasks"] == 1 - task_node.task_factory.create_task.assert_called_once() - - @patch("ray.util.scheduling_strategies.NodeAffinitySchedulingStrategy") - @patch("sage.kernel.utils.ray.actor.ActorWrapper") - @patch("sage.kernel.runtime.task.ray_task.RayTask") - def test_place_remote_task_default(self, mock_ray_task, mock_wrapper, mock_strategy): - """测试放置远程任务（默认配置）""" - executor = PlacementExecutor() - - # Mock task node - task_node = Mock() - task_node.name = "remote_task" - task_node.ctx = Mock() - task_node.task_factory = Mock() - task_node.task_factory.remote = True - task_node.task_factory.operator_factory = Mock() - task_node.task_factory.extra_python_paths = None - - # Mock Ray Actor with proper options chain - mock_options = Mock() - mock_options.remote = Mock(return_value=Mock()) - mock_ray_task.options = Mock(return_value=mock_options) - mock_wrapped = Mock() - mock_wrapper.return_value = mock_wrapped - - # Mock decision (default) - decision = PlacementDecision.immediate_default() - - # 放置任务 - result = executor.place_task(task_node, decision) - - # 验证 - assert result == mock_wrapped - assert executor.placement_stats["remote_tasks"] == 1 - mock_ray_task.options.assert_called_once() - - @patch("ray.util.scheduling_strategies.NodeAffinitySchedulingStrategy") - @patch("sage.kernel.utils.ray.actor.ActorWrapper") - @patch("sage.kernel.runtime.task.ray_task.RayTask") - def test_place_remote_task_with_node(self, mock_ray_task, mock_wrapper, mock_strategy): - """测试放置远程任务（指定节点）""" - executor = PlacementExecutor() - - # Mock task node - task_node = Mock() - task_node.name = "remote_task" - task_node.ctx = Mock() - task_node.task_factory = Mock() - task_node.task_factory.remote = True - task_node.task_factory.operator_factory = Mock() - task_node.task_factory.extra_python_paths = None - - # Mock Ray Actor with proper options chain - mock_options = Mock() - mock_options.remote = Mock(return_value=Mock()) - mock_ray_task.options = Mock(return_value=mock_options) - mock_wrapped = Mock() - mock_wrapper.return_value = mock_wrapped - - # Mock scheduling strategy to return a valid object - mock_strategy.return_value = "mocked_scheduling_strategy" - - # Mock decision (with node) - 使用有效的 Ray node_id 格式 - valid_node_id = "a1b2c3d4e5f6789012345678901234567890abcdef1234567890abcd" - decision = PlacementDecision( - target_node=valid_node_id, resource_requirements={"cpu": 4, "gpu": 1} - ) - - # 放置任务 - executor.place_task(task_node, decision) - - # 验证调用参数 - call_kwargs = mock_ray_task.options.call_args[1] - assert "scheduling_strategy" in call_kwargs - assert call_kwargs["num_cpus"] == 4 - assert call_kwargs["num_gpus"] == 1 - - def test_build_ray_options_default(self): - """测试构建 Ray 选项（默认）""" - executor = PlacementExecutor() - decision = PlacementDecision.immediate_default() - - options = executor._build_ray_options(decision) - - assert "lifetime" in options - assert options["lifetime"] == "detached" - assert "scheduling_strategy" not in options # 默认不指定节点 - - @patch("ray.util.scheduling_strategies.NodeAffinitySchedulingStrategy") - def test_build_ray_options_with_node(self, mock_strategy): - """测试构建 Ray 选项（指定节点）""" - executor = PlacementExecutor() - # 使用有效的 Ray node_id 格式 - valid_node_id = "a1b2c3d4e5f6789012345678901234567890abcdef1234567890abcd" - decision = PlacementDecision(target_node=valid_node_id) - - options = executor._build_ray_options(decision) - - assert "scheduling_strategy" in options - mock_strategy.assert_called_once_with(node_id=valid_node_id, soft=False) - - def test_build_ray_options_with_resources(self): - """测试构建 Ray 选项（资源需求）""" - executor = PlacementExecutor() - decision = PlacementDecision( - resource_requirements={ - "cpu": 4, - "gpu": 1, - "memory": 8589934592, - "custom_resource": 2, - } - ) - - options = executor._build_ray_options(decision) - - assert options["num_cpus"] == 4 - assert options["num_gpus"] == 1 - assert options["memory"] == 8589934592 - assert "resources" in options - assert options["resources"]["custom_resource"] == 2 - - def test_parse_memory(self): - """测试内存解析""" - executor = PlacementExecutor() - - assert executor._parse_memory(1024) == 1024 - assert executor._parse_memory("8GB") == 8 * 1024**3 - assert executor._parse_memory("512MB") == 512 * 1024**2 - assert executor._parse_memory("1024KB") == 1024 * 1024 - # 注意：解析失败时返回 1GB（默认值），不是 0 - result = executor._parse_memory("invalid") - assert result > 0 # 只要不崩溃就行 - - def test_get_stats(self): - """测试获取统计信息""" - executor = PlacementExecutor() - - # 放置一些任务 - for i in range(3): - task_node = Mock() - task_node.name = f"task_{i}" - task_node.ctx = Mock() - task_node.task_factory = Mock() - task_node.task_factory.remote = False - task_node.task_factory.create_task.return_value = Mock() - - decision = PlacementDecision.immediate_default() - executor.place_task(task_node, decision) - - # 验证统计（直接访问 placement_stats） - stats = executor.placement_stats - - assert stats["total_tasks"] == 3 - assert stats["local_tasks"] == 3 - assert stats["remote_tasks"] == 0 - - -# ============================================================================ -# 集成测试 -# ============================================================================ - - -class TestSchedulerIntegration: - """测试调度器集成流程""" - - def test_fifo_scheduler_to_placement(self): - """测试 FIFO 调度器到放置执行器的完整流程""" - scheduler = FIFOScheduler() - executor = PlacementExecutor() - - # Mock task node - task_node = Mock() - task_node.name = "test_task" - task_node.ctx = Mock() - task_node.transformation = Mock() - task_node.task_factory = Mock() - task_node.task_factory.remote = False - task_node.task_factory.create_task.return_value = Mock() - - # 1. 调度决策 - decision = scheduler.make_decision(task_node) - assert isinstance(decision, PlacementDecision) - - # 2. 执行放置 - task = executor.place_task(task_node, decision) - assert task is not None - - # 验证 - assert scheduler.scheduled_count == 1 - assert executor.placement_stats["total_tasks"] == 1 - - @patch("sage.kernel.scheduler.node_selector.NodeSelector") - def test_load_aware_scheduler_to_placement(self, mock_node_selector_class): - """测试负载感知调度器到放置执行器的完整流程""" - # Mock NodeSelector - mock_selector = Mock() - # 使用有效的 Ray node_id 格式 - valid_node_id = "a1b2c3d4e5f6789012345678901234567890abcdef1234567890abcd" - mock_selector.select_best_node.return_value = valid_node_id - mock_node_selector_class.return_value = mock_selector - - scheduler = LoadAwareScheduler(max_concurrent=10) - executor = PlacementExecutor() - - # Mock task node - task_node = Mock() - task_node.name = "gpu_task" - task_node.ctx = Mock() - task_node.transformation = Mock() - task_node.transformation.cpu_required = 4 - task_node.transformation.gpu_required = 1 - task_node.transformation.memory_required = "8GB" - task_node.transformation.custom_resources = {} # 空字典 - task_node.task_factory = Mock() - task_node.task_factory.remote = False - task_node.task_factory.create_task.return_value = Mock() - - # 1. 调度决策 - decision = scheduler.make_decision(task_node) - assert decision.resource_requirements is not None - - # 2. 执行放置 - task = executor.place_task(task_node, decision) - assert task is not None - - # 验证 - assert scheduler.scheduled_count == 1 - assert executor.placement_stats["total_tasks"] == 1 - - def test_service_scheduling_flow(self): - """测试服务调度完整流程""" - scheduler = LoadAwareScheduler(max_concurrent=10) - - # Mock service node - service_node = Mock() - service_node.service_name = "test_service" - service_node.service_class = Mock() - service_node.service_class.cpu_required = 2 - service_node.service_class.gpu_required = 0 - service_node.service_class.memory_required = "4GB" - service_node.service_class.custom_resources = {} - - # 调度服务 - decision = scheduler.make_service_decision(service_node) - - # 验证服务使用 spread 策略 - assert decision.placement_strategy == "spread" - assert decision.immediate is True - - -# ============================================================================ -# 性能测试 -# ============================================================================ - - -class TestSchedulerPerformance: - """测试调度器性能""" - - def test_fifo_scheduler_latency(self): - """测试 FIFO 调度器延迟""" - scheduler = FIFOScheduler() - - start = time.time() - - # 调度 100 个任务 - for i in range(100): - task_node = Mock() - task_node.name = f"task_{i}" - task_node.transformation = Mock() - scheduler.make_decision(task_node) - - elapsed = time.time() - start - - # 验证性能 - assert elapsed < 1.0 # 应该小于 1 秒 - - metrics = scheduler.get_metrics() - assert metrics["total_scheduled"] == 100 - assert metrics["avg_latency_ms"] < 10 # 平均延迟应该很小 - - def test_load_aware_scheduler_latency(self): - """测试负载感知调度器延迟""" - scheduler = LoadAwareScheduler(max_concurrent=100) - - start = time.time() - - # 调度 50 个任务 - for i in range(50): - task_node = Mock() - task_node.name = f"task_{i}" - task_node.transformation = Mock(spec=[]) - task_node.task_factory = Mock() - task_node.task_factory.remote = False - scheduler.make_decision(task_node) - - elapsed = time.time() - start - - # 验证性能 - assert elapsed < 2.0 # 负载感知会慢一些，但应该小于 2 秒 - - metrics = scheduler.get_metrics() - assert metrics["total_scheduled"] == 50 - - -if __name__ == "__main__": - pytest.main([__file__, "-v", "-s"]) diff --git a/packages/sage-kernel/tests/unit/kernel/test_simple_task_context_routing.py b/packages/sage-kernel/tests/unit/kernel/test_simple_task_context_routing.py deleted file mode 100644 index a78d8fadfb..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/test_simple_task_context_routing.py +++ /dev/null @@ -1,119 +0,0 @@ -# 简单的解耦测试,验证BaseOperator不再直接依赖BaseRouter - -from unittest.mock import Mock - - -class MockTaskContext: - """模拟TaskContext，提供路由接口""" - - def __init__(self, name="test_task"): - self.name = name - self.logger = Mock() - - def send_packet(self, packet): - """模拟发送数据包""" - print(f"MockTaskContext: Sending packet {packet}") - return True - - def send_stop_signal(self, stop_signal): - """模拟发送停止信号""" - print(f"MockTaskContext: Sending stop signal {stop_signal}") - - def get_routing_info(self): - """模拟获取路由信息""" - return {"connections": 3, "status": "active"} - - -class MockFunctionFactory: - """模拟函数工厂""" - - def create_function(self, name, ctx): - mock_function = Mock() - mock_function.name = name - return mock_function - - -class TestBaseOperatorDecoupling: - """测试BaseOperator的解耦实现""" - - def test_operator_uses_context_routing(self): - """测试BaseOperator通过TaskContext进行路由，不再直接依赖BaseRouter""" - - # 创建模拟对象 - mock_ctx = MockTaskContext("test_operator") - mock_factory = MockFunctionFactory() - - # 定义一个简单的operator实现 - class TestOperator: - def __init__(self, function_factory, ctx): - self.ctx = ctx - self.function = function_factory.create_function("test", ctx) - self.logger = ctx.logger - self.name = ctx.name - - def send_packet(self, packet): - """通过TaskContext发送数据包""" - return self.ctx.send_packet(packet) - - def send_stop_signal(self, stop_signal): - """通过TaskContext发送停止信号""" - self.ctx.send_stop_signal(stop_signal) - - def get_routing_info(self): - """获取路由信息""" - return self.ctx.get_routing_info() - - # 创建operator - operator = TestOperator(mock_factory, mock_ctx) - - # 测试发送数据包 - result = operator.send_packet("test_packet") - assert result is True - - # 测试发送停止信号 - operator.send_stop_signal("stop_signal") - - # 测试获取路由信息 - info = operator.get_routing_info() - assert info["connections"] == 3 - assert info["status"] == "active" - - print("✅ BaseOperator解耦测试通过!") - print("✅ Operator通过TaskContext进行路由，不再直接依赖BaseRouter") - - def test_no_direct_router_dependency(self): - """验证BaseOperator不再有直接的router属性""" - mock_ctx = MockTaskContext("test_operator") - mock_factory = MockFunctionFactory() - - class TestOperator: - def __init__(self, function_factory, ctx): - self.ctx = ctx - self.function = function_factory.create_function("test", ctx) - # 注意：没有self.router属性 - - operator = TestOperator(mock_factory, mock_ctx) - - # 验证operator没有直接的router属性 - assert not hasattr(operator, "router") - assert not hasattr(operator, "routing") - - # 但是有ctx属性来进行间接路由 - assert hasattr(operator, "ctx") - assert hasattr(operator.ctx, "send_packet") - assert hasattr(operator.ctx, "send_stop_signal") - assert hasattr(operator.ctx, "get_routing_info") - - print("✅ BaseOperator不再有直接的router依赖!") - print("✅ 路由功能完全通过TaskContext提供!") - - -if __name__ == "__main__": - test = TestBaseOperatorDecoupling() - test.test_operator_uses_context_routing() - test.test_no_direct_router_dependency() - print("\n🎉 所有解耦测试都通过了!") - print("📋 总结:") - print(" - BaseOperator不再直接依赖BaseRouter") - print(" - 路由功能完全集成到TaskContext中") - print(" - 实现了清晰的架构分层") diff --git a/packages/sage-kernel/tests/unit/kernel/utils/__init__.py b/packages/sage-kernel/tests/unit/kernel/utils/__init__.py deleted file mode 100644 index 56300e6494..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/utils/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -""" -Tests for sage.common.utils module -""" diff --git a/packages/sage-kernel/tests/unit/kernel/utils/config/test_manager.py b/packages/sage-kernel/tests/unit/kernel/utils/config/test_manager.py deleted file mode 100644 index 45dd237286..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/utils/config/test_manager.py +++ /dev/null @@ -1,564 +0,0 @@ -""" -Tests for sage.common.utils.config.manager module -========================================== - -单元测试配置管理器模块的功能，包括： -- ConfigManager类的所有方法 -- 多种格式的配置文件支持 (YAML, JSON, TOML) -- 缓存机制 -- 嵌套配置项的获取和设置 -- BaseConfig类的验证功能 -""" - -import json -import tempfile -from pathlib import Path -from unittest.mock import patch - -import pytest -import yaml -from pydantic import ValidationError - -from sage.common.utils.config.manager import ( - BaseConfig, - ConfigManager, - load_config, - save_config, -) - - -@pytest.mark.unit -class TestBaseConfig: - """BaseConfig类测试""" - - def test_base_config_creation(self): - """测试BaseConfig基本创建""" - - class TestConfig(BaseConfig): - app_name: str = "test" - debug: bool = False - - config = TestConfig() - assert config.app_name == "test" - assert config.debug is False - - def test_base_config_with_extra_fields(self): - """测试BaseConfig允许额外字段""" - - class TestConfig(BaseConfig): - app_name: str = "test" - - # Pydantic allows extra fields with extra="allow" - extra_data = {"app_name": "myapp", "extra_field": "extra_value"} - config = TestConfig(**extra_data) - assert config.app_name == "myapp" - # Access extra field properly in Pydantic v2 - assert getattr(config, "extra_field", None) == "extra_value" - - def test_base_config_validation(self): - """测试BaseConfig字段验证""" - - class TestConfig(BaseConfig): - app_name: str - port: int - - # 正常情况 - config = TestConfig(app_name="test", port=8080) - assert config.app_name == "test" - assert config.port == 8080 - - # 类型错误 - test that validation catches invalid types - # Use Any to avoid type checker errors when intentionally passing wrong types - from typing import Any - - invalid_data: dict[str, Any] = {"app_name": "test", "port": "invalid"} - with pytest.raises(ValidationError): - TestConfig(**invalid_data) - - def test_base_config_assignment_validation(self): - """测试BaseConfig赋值验证""" - from typing import Any - - class TestConfig(BaseConfig): - port: int = 8080 - - config = TestConfig() - config.port = 9000 - assert config.port == 9000 - - # 赋值时类型验证 - test that assignment validation catches invalid types - # Use Any to avoid type checker errors when intentionally passing wrong types - invalid_value: Any = "invalid" - with pytest.raises(ValidationError): - config.port = invalid_value - - -@pytest.mark.unit -class TestConfigManager: - """ConfigManager类测试""" - - def setup_method(self): - """测试前准备""" - self.temp_dir = tempfile.mkdtemp() - self.config_dir = Path(self.temp_dir) - self.manager = ConfigManager(self.config_dir) - - # 准备测试配置数据 - self.test_config = { - "app": {"name": "SAGE", "version": "1.0.0", "debug": True}, - "database": {"host": "localhost", "port": 5432, "name": "sage_db"}, - "features": {"cache_enabled": True, "max_connections": 100}, - } - - def teardown_method(self): - """测试后清理""" - import shutil - - shutil.rmtree(self.temp_dir, ignore_errors=True) - - def test_config_manager_initialization(self): - """测试ConfigManager初始化""" - assert self.manager.config_dir == self.config_dir - assert self.config_dir.exists() - assert isinstance(self.manager._cache, dict) - - def test_config_manager_default_config_dir(self): - """测试ConfigManager默认配置目录""" - with patch("pathlib.Path.cwd") as mock_cwd: - mock_cwd.return_value = Path("/tmp/test") - manager = ConfigManager() - assert manager.config_dir == Path("/tmp/test/config") - - def test_load_yaml_config(self): - """测试加载YAML配置文件""" - config_file = self.config_dir / "test.yaml" - with open(config_file, "w") as f: - yaml.dump(self.test_config, f) - - loaded_config = self.manager.load("test.yaml") - assert loaded_config == self.test_config - assert loaded_config["app"]["name"] == "SAGE" - assert loaded_config["database"]["port"] == 5432 - - def test_load_json_config(self): - """测试加载JSON配置文件""" - config_file = self.config_dir / "test.json" - with open(config_file, "w") as f: - json.dump(self.test_config, f) - - loaded_config = self.manager.load("test.json") - assert loaded_config == self.test_config - - def test_load_toml_config(self): - """测试加载TOML配置文件""" - # 创建TOML兼容的配置 - toml_config = {"app_name": "SAGE", "version": "1.0.0", "debug": True} - - config_file = self.config_dir / "test.toml" - try: - import tomli_w - - with open(config_file, "wb") as f: - tomli_w.dump(toml_config, f) - - loaded_config = self.manager.load("test.toml") - assert loaded_config["app_name"] == "SAGE" - except ImportError: - # 如果没有tomli_w，创建一个简单的TOML文件 - with open(config_file, "w") as f: - f.write('app_name = "SAGE"\nversion = "1.0.0"\ndebug = true\n') - - # 检查是否有tomli可以加载 - try: - import tomli # noqa: F401 - - loaded_config = self.manager.load("test.toml") - assert loaded_config["app_name"] == "SAGE" - except ImportError: - # 如果tomli也不存在，测试ImportError - with pytest.raises(ImportError, match="需要安装 tomli 库来支持 TOML 格式"): - self.manager.load("test.toml") - - def test_load_unsupported_format(self): - """测试加载不支持的格式""" - config_file = self.config_dir / "test.txt" - config_file.write_text("some content") - - with pytest.raises(ValueError, match="不支持的配置文件格式"): - self.manager.load("test.txt") - - def test_load_file_not_found(self): - """测试加载不存在的文件""" - with pytest.raises(FileNotFoundError, match="配置文件未找到"): - self.manager.load("nonexistent.yaml") - - def test_cache_mechanism(self): - """测试配置缓存机制""" - config_file = self.config_dir / "cache_test.yaml" - with open(config_file, "w") as f: - yaml.dump(self.test_config, f) - - # 第一次加载 - self.manager.load("cache_test.yaml", use_cache=True) - assert "cache_test.yaml" in self.manager._cache - - # 修改文件内容 - modified_config = self.test_config.copy() - modified_config["app"]["name"] = "Modified" - with open(config_file, "w") as f: - yaml.dump(modified_config, f) - - # 使用缓存，应该返回原始内容 - config2 = self.manager.load("cache_test.yaml", use_cache=True) - assert config2["app"]["name"] == "SAGE" - - # 不使用缓存，应该返回修改后的内容 - config3 = self.manager.load("cache_test.yaml", use_cache=False) - assert config3["app"]["name"] == "Modified" - - def test_save_yaml_config(self): - """测试保存YAML配置文件""" - filename = "save_test.yaml" - self.manager.save(filename, self.test_config) - - config_file = self.config_dir / filename - assert config_file.exists() - - # 验证保存的内容 - with open(config_file) as f: - saved_config = yaml.safe_load(f) - assert saved_config == self.test_config - - def test_save_json_config(self): - """测试保存JSON配置文件""" - filename = "save_test.json" - self.manager.save(filename, self.test_config) - - config_file = self.config_dir / filename - assert config_file.exists() - - # 验证保存的内容 - with open(config_file) as f: - saved_config = json.load(f) - assert saved_config == self.test_config - - def test_save_toml_config(self): - """测试保存TOML配置文件""" - # TOML兼容的配置 - toml_config = {"app_name": "SAGE", "version": "1.0.0", "debug": True} - - filename = "save_test.toml" - try: - self.manager.save(filename, toml_config) - - config_file = self.config_dir / filename - assert config_file.exists() - except ImportError: - # 如果没有tomli_w，测试ImportError - with pytest.raises(ImportError, match="需要安装 tomli-w 库"): - self.manager.save(filename, toml_config) - - def test_save_with_format_override(self): - """测试强制指定保存格式""" - filename = "test_file.conf" # 不常见的扩展名 - self.manager.save(filename, self.test_config, format="yaml") - - config_file = self.config_dir / filename - assert config_file.exists() - - # 验证保存为YAML格式 - with open(config_file) as f: - content = f.read() - assert "app:" in content # YAML特征 - - def test_get_simple_key(self): - """测试获取简单配置项""" - config_file = self.config_dir / "get_test.yaml" - with open(config_file, "w") as f: - yaml.dump(self.test_config, f) - - value = self.manager.get("get_test.yaml", "app.name") - assert value == "SAGE" - - value = self.manager.get("get_test.yaml", "database.port") - assert value == 5432 - - def test_get_nested_key(self): - """测试获取嵌套配置项""" - config_file = self.config_dir / "nested_test.yaml" - nested_config = {"level1": {"level2": {"level3": {"value": "deep_value"}}}} - with open(config_file, "w") as f: - yaml.dump(nested_config, f) - - value = self.manager.get("nested_test.yaml", "level1.level2.level3.value") - assert value == "deep_value" - - def test_get_nonexistent_key(self): - """测试获取不存在的配置项""" - config_file = self.config_dir / "get_test.yaml" - with open(config_file, "w") as f: - yaml.dump(self.test_config, f) - - value = self.manager.get("get_test.yaml", "nonexistent.key", "default_value") - assert value == "default_value" - - value = self.manager.get("get_test.yaml", "app.nonexistent", None) - assert value is None - - def test_set_simple_key(self): - """测试设置简单配置项""" - filename = "set_test.yaml" - - # 设置新值 - self.manager.set(filename, "app.name", "NewName") - self.manager.set(filename, "database.port", 3306) - - # 验证设置 - assert self.manager.get(filename, "app.name") == "NewName" - assert self.manager.get(filename, "database.port") == 3306 - - def test_set_nested_key(self): - """测试设置嵌套配置项""" - filename = "set_nested_test.yaml" - - self.manager.set(filename, "level1.level2.level3.value", "new_deep_value") - - value = self.manager.get(filename, "level1.level2.level3.value") - assert value == "new_deep_value" - - def test_set_existing_config(self): - """测试在现有配置文件中设置值""" - filename = "existing_test.yaml" - - # 先保存初始配置 - self.manager.save(filename, self.test_config) - - # 修改现有值 - self.manager.set(filename, "app.name", "ModifiedName") - self.manager.set(filename, "new_section.new_key", "new_value") - - # 验证修改 - assert self.manager.get(filename, "app.name") == "ModifiedName" - assert self.manager.get(filename, "app.version") == "1.0.0" # 未修改的值保持不变 - assert self.manager.get(filename, "new_section.new_key") == "new_value" - - def test_clear_cache(self): - """测试清空缓存""" - config_file = self.config_dir / "cache_clear_test.yaml" - with open(config_file, "w") as f: - yaml.dump(self.test_config, f) - - # 加载到缓存 - self.manager.load("cache_clear_test.yaml") - assert "cache_clear_test.yaml" in self.manager._cache - - # 清空缓存 - self.manager.clear_cache() - assert len(self.manager._cache) == 0 - - def test_load_empty_config_file(self): - """测试加载空配置文件""" - config_file = self.config_dir / "empty.yaml" - config_file.touch() - - config = self.manager.load("empty.yaml") - assert config == {} - - -@pytest.mark.unit -class TestConvenienceFunctions: - """便捷函数测试""" - - def setup_method(self): - """测试前准备""" - self.temp_dir = tempfile.mkdtemp() - self.config_dir = Path(self.temp_dir) - - self.test_config = {"app_name": "TestApp", "version": "2.0.0"} - - def teardown_method(self): - """测试后清理""" - import shutil - - shutil.rmtree(self.temp_dir, ignore_errors=True) - - def test_load_config_function_with_config_dir(self): - """测试load_config便捷函数指定配置目录""" - config_file = self.config_dir / "test.yaml" - with open(config_file, "w") as f: - yaml.dump(self.test_config, f) - - config = load_config("test.yaml", self.config_dir) - assert config == self.test_config - - def test_load_config_function_global_manager(self): - """测试load_config便捷函数使用全局管理器""" - with patch("sage.common.utils.config.manager._global_config_manager") as mock_manager: - mock_manager.load.return_value = self.test_config - - config = load_config("test.yaml") - assert config == self.test_config - mock_manager.load.assert_called_once_with("test.yaml") - - def test_save_config_function_with_config_dir(self): - """测试save_config便捷函数指定配置目录""" - save_config("test.yaml", self.test_config, self.config_dir) - - config_file = self.config_dir / "test.yaml" - assert config_file.exists() - - with open(config_file) as f: - saved_config = yaml.safe_load(f) - assert saved_config == self.test_config - - def test_save_config_function_global_manager(self): - """测试save_config便捷函数使用全局管理器""" - with patch("sage.common.utils.config.manager._global_config_manager") as mock_manager: - save_config("test.yaml", self.test_config) - mock_manager.save.assert_called_once_with("test.yaml", self.test_config) - - -@pytest.mark.integration -class TestConfigManagerIntegration: - """ConfigManager集成测试""" - - def test_real_world_config_workflow(self): - """测试真实世界的配置工作流程""" - with tempfile.TemporaryDirectory() as temp_dir: - manager = ConfigManager(temp_dir) - - # 1. 创建初始配置 - initial_config = { - "application": { - "name": "SAGE", - "version": "1.0.0", - "environment": "development", - }, - "database": { - "host": "localhost", - "port": 5432, - "name": "sage_dev", - "pool_size": 10, - }, - "logging": { - "level": "DEBUG", - "handlers": ["console", "file"], - "file_path": "/var/log/sage.log", - }, - } - - manager.save("app.yaml", initial_config) - - # 2. 读取和验证配置 - loaded_config = manager.load("app.yaml") - assert loaded_config["application"]["name"] == "SAGE" - assert loaded_config["database"]["pool_size"] == 10 - - # 3. 更新部分配置 - manager.set("app.yaml", "application.environment", "production") - manager.set("app.yaml", "database.host", "prod-db.example.com") - manager.set("app.yaml", "logging.level", "INFO") - - # 4. 验证更新 - assert manager.get("app.yaml", "application.environment") == "production" - assert manager.get("app.yaml", "database.host") == "prod-db.example.com" - assert manager.get("app.yaml", "logging.level") == "INFO" - - # 5. 添加新的配置节 - manager.set("app.yaml", "cache.type", "redis") - manager.set("app.yaml", "cache.host", "redis.example.com") - manager.set("app.yaml", "cache.port", 6379) - - # 6. 验证完整配置 - final_config = manager.load("app.yaml") - assert final_config["cache"]["type"] == "redis" - assert final_config["cache"]["port"] == 6379 - - # 7. 测试配置持久化 - manager2 = ConfigManager(temp_dir) - reloaded_config = manager2.load("app.yaml") - assert reloaded_config == final_config - - -# 性能测试 -@pytest.mark.slow -class TestConfigManagerPerformance: - """ConfigManager性能测试""" - - def test_large_config_file_performance(self): - """测试大型配置文件的性能""" - import sys - import time - - with tempfile.TemporaryDirectory() as temp_dir: - manager = ConfigManager(temp_dir) - - # 创建大型配置 - large_config = {} - for i in range(1000): - large_config[f"section_{i}"] = {f"key_{j}": f"value_{i}_{j}" for j in range(100)} - - # 测试保存性能 - start_time = time.time() - manager.save("large_config.yaml", large_config) - save_time = time.time() - start_time - - # 测试加载性能 - start_time = time.time() - loaded_config = manager.load("large_config.yaml") - load_time = time.time() - start_time - - # 基本性能断言（这些阈值可以根据实际需要调整） - # Coverage模式下性能要求放宽（CI环境下需要更多时间） - is_coverage_active = "coverage" in sys.modules or "pytest_cov" in sys.modules - save_time_limit = 60.0 if is_coverage_active else 20.0 - load_time_limit = 15.0 if is_coverage_active else 5.0 - - assert save_time < save_time_limit, ( - f"Save time {save_time:.2f}s exceeded limit {save_time_limit}s (coverage: {is_coverage_active})" - ) - assert load_time < load_time_limit, ( - f"Load time {load_time:.2f}s exceeded limit {load_time_limit}s (coverage: {is_coverage_active})" - ) - assert loaded_config == large_config - - def test_cache_performance(self): - """测试缓存性能 - - 验证缓存确实能提升性能，而不是测试绝对时间。 - 这避免了因系统负载、覆盖率工具等因素导致的测试不稳定。 - """ - import time - - with tempfile.TemporaryDirectory() as temp_dir: - manager = ConfigManager(temp_dir) - - # 创建一个包含1000个键值对的大配置字典 - config = {f"test_key_{i}": f"test_value_{i}" for i in range(1000)} - manager.save("perf_test.yaml", config) - - # 第一次加载（无缓存）- 多次测试取最小值以减少噪音 - first_load_times = [] - for _ in range(3): - start_time = time.time() - manager.load("perf_test.yaml", use_cache=False) - first_load_times.append(time.time() - start_time) - first_load_time = min(first_load_times) - - # 第二次加载（有缓存）- 多次测试取最小值 - cached_load_times = [] - for _ in range(3): - start_time = time.time() - manager.load("perf_test.yaml", use_cache=True) - cached_load_times.append(time.time() - start_time) - cached_load_time = min(cached_load_times) - - # 缓存应该提升性能（至少快 10%） - # 我们只验证缓存有效，不要求具体加速比，因为这取决于硬件和系统状态 - improvement_ratio = first_load_time / cached_load_time if cached_load_time > 0 else 0 - - assert improvement_ratio > 1.1, ( - f"缓存未能提升性能：首次加载 {first_load_time:.4f}s, " - f"缓存加载 {cached_load_time:.4f}s, " - f"加速比 {improvement_ratio:.2f}x (期望 > 1.1x)" - ) diff --git a/packages/sage-kernel/tests/unit/kernel/utils/logging/test_custom_logger.py b/packages/sage-kernel/tests/unit/kernel/utils/logging/test_custom_logger.py deleted file mode 100644 index 4a863b4996..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/utils/logging/test_custom_logger.py +++ /dev/null @@ -1,796 +0,0 @@ -""" -Tests for sage.common.utils.logging.custom_logger module -=============================================== - -单元测试自定义日志记录器模块的功能，包括： -- CustomLogger类的所有方法 -- 多输出目标配置 -- 动态配置更新 -- 全局console debug控制 -- 路径解析和处理 -""" - -import logging -import os -import tempfile -import threading -from contextlib import contextmanager -from unittest.mock import patch - -import pytest - -from sage.common.utils.logging.custom_logger import CustomLogger - - -@contextmanager -def sage_temp_directory(): - """使用 ~/.sage/test_tmp 创建临时目录的上下文管理器""" - sage_test_dir = os.path.expanduser("~/.sage/test_tmp") - os.makedirs(sage_test_dir, exist_ok=True) - temp_dir = tempfile.mkdtemp(dir=sage_test_dir) - try: - yield temp_dir - finally: - import shutil - - shutil.rmtree(temp_dir, ignore_errors=True) - - -@pytest.mark.unit -class TestCustomLogger: - """CustomLogger类基本功能测试""" - - def setup_method(self): - """测试前准备""" - # 使用 ~/.sage/test_tmp 而不是系统临时目录 - sage_test_dir = os.path.expanduser("~/.sage/test_tmp") - os.makedirs(sage_test_dir, exist_ok=True) - self.temp_dir = tempfile.mkdtemp(dir=sage_test_dir) - # 确保每个测试开始时重置全局状态 - CustomLogger.enable_global_console_debug() - - def teardown_method(self): - """测试后清理""" - import shutil - - shutil.rmtree(self.temp_dir, ignore_errors=True) - # 重置全局状态 - CustomLogger.enable_global_console_debug() - - def test_logger_initialization_console_only(self): - """测试仅控制台输出的logger初始化""" - logger = CustomLogger(outputs=[("console", "INFO")], name="TestLogger") - - assert logger.name == "TestLogger" - assert logger.log_base_folder is None - assert len(logger.output_configs) == 1 - assert logger.output_configs[0]["target"] == "console" - assert logger.output_configs[0]["level"] == logging.INFO - assert logger.logger.level == logging.INFO - - def test_logger_initialization_with_base_folder(self): - """测试带基础文件夹的logger初始化""" - logger = CustomLogger( - outputs=[("console", "INFO"), ("app.log", "DEBUG")], - name="TestLoggerBaseFolder", - log_base_folder=self.temp_dir, - ) - - assert logger.log_base_folder == self.temp_dir - output_configs = logger.get_output_configs() - assert len(output_configs) == 2 - - # 验证路径解析 - file_config = next(c for c in output_configs if c["target"] == "app.log") - expected_path = os.path.join(self.temp_dir, "app.log") - assert file_config["resolved_path"] == expected_path - - # 验证最低日志级别设置 - assert logger.logger.level == logging.DEBUG - - def test_logger_initialization_mixed_paths(self): - """测试混合路径的logger初始化""" - absolute_path = os.path.join(self.temp_dir, "absolute.log") - - logger = CustomLogger( - outputs=[ - ("console", "INFO"), - ("relative.log", "DEBUG"), - (absolute_path, "ERROR"), - ], - name="MixedLogger", - log_base_folder=self.temp_dir, - ) - - configs = logger.output_configs - assert len(configs) == 3 - - # 控制台配置 - console_config = next(c for c in configs if c["target"] == "console") - assert console_config["resolved_path"] == "console" - - # 相对路径配置 - relative_config = next(c for c in configs if c["target"] == "relative.log") - assert relative_config["resolved_path"] == os.path.join(self.temp_dir, "relative.log") - - # 绝对路径配置 - absolute_config = next(c for c in configs if c["target"] == absolute_path) - assert absolute_config["resolved_path"] == absolute_path - - def test_logger_initialization_default_name(self): - """测试默认名称的logger初始化""" - logger = CustomLogger() - assert logger.name == "Logger" - assert len(logger.output_configs) == 1 # 默认是console INFO - - def test_level_mapping(self): - """测试日志级别映射""" - level_tests = [ - ("DEBUG", logging.DEBUG), - ("INFO", logging.INFO), - ("WARNING", logging.WARNING), - ("WARN", logging.WARNING), - ("ERROR", logging.ERROR), - ("CRITICAL", logging.CRITICAL), - ("FATAL", logging.CRITICAL), - ] - - for i, (level_str, expected_level) in enumerate(level_tests): - logger = CustomLogger([("console", level_str)], name=f"TestLogger_{i}") - # Use internal config to test the integer level mapping - config = logger.output_configs[0] - assert config["level"] == expected_level - - def test_invalid_log_level(self): - """测试无效日志级别处理""" - with pytest.raises(ValueError, match="Invalid log level"): - CustomLogger([("console", "INVALID_LEVEL")], name="TestInvalidLevel") - - def test_invalid_level_type(self): - """测试无效级别类型处理""" - from typing import Any - - # Test that invalid level type raises TypeError - # Use Any to avoid type checker errors when intentionally passing wrong types - invalid_outputs: Any = [("console", [])] - with pytest.raises(TypeError, match="level_setting must be str or int"): - CustomLogger(invalid_outputs, name="TestInvalidType") # 列表类型无效 - - -@pytest.mark.unit -class TestPathResolution: - """路径解析测试""" - - def setup_method(self): - """测试前准备""" - # 使用 ~/.sage/test_tmp 而不是系统临时目录 - sage_test_dir = os.path.expanduser("~/.sage/test_tmp") - os.makedirs(sage_test_dir, exist_ok=True) - self.temp_dir = tempfile.mkdtemp(dir=sage_test_dir) - - def teardown_method(self): - """测试后清理""" - import shutil - - shutil.rmtree(self.temp_dir, ignore_errors=True) - - def test_resolve_console_path(self): - """测试控制台路径解析""" - logger = CustomLogger([("console", "INFO")]) - resolved = logger._resolve_path("console") - assert resolved == "console" - - def test_resolve_absolute_path(self): - """测试绝对路径解析""" - # 使用安全的测试目录中的绝对路径 - sage_test_dir = os.path.expanduser("~/.sage/test_tmp") - os.makedirs(sage_test_dir, exist_ok=True) - absolute_path = os.path.join(sage_test_dir, "test.log") - logger = CustomLogger() - resolved = logger._resolve_path(absolute_path) - assert resolved == absolute_path - - def test_resolve_relative_path_with_base_folder(self): - """测试有基础文件夹的相对路径解析""" - logger = CustomLogger(log_base_folder=self.temp_dir) - resolved = logger._resolve_path("app.log") - expected = os.path.join(self.temp_dir, "app.log") - assert resolved == expected - - def test_resolve_relative_path_without_base_folder(self): - """测试无基础文件夹的相对路径解析失败""" - logger = CustomLogger() - - with pytest.raises(ValueError, match="Cannot use relative path.*without log_base_folder"): - logger._resolve_path("app.log") - - def test_resolve_nested_relative_path(self): - """测试嵌套相对路径解析""" - logger = CustomLogger(log_base_folder=self.temp_dir) - resolved = logger._resolve_path("logs/app/debug.log") - expected = os.path.join(self.temp_dir, "logs", "app", "debug.log") - assert resolved == expected - - -@pytest.mark.unit -class TestLoggingMethods: - """日志记录方法测试""" - - def setup_method(self): - """测试前准备""" - # 使用 ~/.sage/test_tmp 而不是系统临时目录 - sage_test_dir = os.path.expanduser("~/.sage/test_tmp") - os.makedirs(sage_test_dir, exist_ok=True) - self.temp_dir = tempfile.mkdtemp(dir=sage_test_dir) - self.log_file = os.path.join(self.temp_dir, "test.log") - - self.logger = CustomLogger( - outputs=[("console", "DEBUG"), (self.log_file, "DEBUG")], name="TestLogger" - ) - - def teardown_method(self): - """测试后清理""" - import shutil - - shutil.rmtree(self.temp_dir, ignore_errors=True) - - def test_debug_logging(self): - """测试DEBUG级别日志记录""" - self.logger.debug("Debug message") - - # 检查文件是否创建并包含日志 - assert os.path.exists(self.log_file) - with open(self.log_file) as f: - content = f.read() - assert "DEBUG" in content - assert "Debug message" in content - - def test_info_logging(self): - """测试INFO级别日志记录""" - self.logger.info("Info message") - - with open(self.log_file) as f: - content = f.read() - assert "INFO" in content - assert "Info message" in content - - def test_warning_logging(self): - """测试WARNING级别日志记录""" - self.logger.warning("Warning message") - - with open(self.log_file) as f: - content = f.read() - assert "WARNING" in content - assert "Warning message" in content - - def test_error_logging(self): - """测试ERROR级别日志记录""" - self.logger.error("Error message") - - with open(self.log_file) as f: - content = f.read() - assert "ERROR" in content - assert "Error message" in content - - def test_critical_logging(self): - """测试CRITICAL级别日志记录""" - self.logger.critical("Critical message") - - with open(self.log_file) as f: - content = f.read() - assert "CRITICAL" in content - assert "Critical message" in content - - def test_error_with_exception_info(self): - """测试带异常信息的错误日志""" - try: - raise ValueError("Test exception") - except ValueError: - self.logger.error("Error with exception", exc_info=True) - - with open(self.log_file) as f: - content = f.read() - assert "ERROR" in content - assert "Error with exception" in content - assert "ValueError" in content - assert "Test exception" in content - - def test_exception_logging(self): - """测试exception方法自动包含异常信息""" - try: - raise RuntimeError("Runtime error") - except RuntimeError: - self.logger.exception("Exception occurred") - - with open(self.log_file) as f: - content = f.read() - assert "ERROR" in content # exception方法实际记录为ERROR级别 - assert "Exception occurred" in content - assert "RuntimeError" in content - assert "Runtime error" in content - - -@pytest.mark.unit -class TestDynamicConfiguration: - """动态配置测试""" - - def setup_method(self): - """测试前准备""" - # 使用 ~/.sage/test_tmp 而不是系统临时目录 - sage_test_dir = os.path.expanduser("~/.sage/test_tmp") - os.makedirs(sage_test_dir, exist_ok=True) - self.temp_dir = tempfile.mkdtemp(dir=sage_test_dir) - self.logger = CustomLogger( - outputs=[("console", "INFO")], - name="DynamicLogger", - log_base_folder=self.temp_dir, - ) - - def teardown_method(self): - """测试后清理""" - import shutil - - shutil.rmtree(self.temp_dir, ignore_errors=True) - - def test_get_output_configs(self): - """测试获取输出配置""" - configs = self.logger.get_output_configs() - - assert len(configs) == 1 - assert configs[0]["target"] == "console" - assert configs[0]["level"] == "INFO" - assert configs[0]["handler_active"] is True - - def test_add_output_relative_path(self): - """测试添加相对路径输出""" - self.logger.add_output("new.log", "DEBUG") - - configs = self.logger.get_output_configs() - assert len(configs) == 2 - - new_config = next(c for c in configs if c["target"] == "new.log") - assert new_config["level"] == "DEBUG" - assert new_config["handler_active"] is True - - expected_path = os.path.join(self.temp_dir, "new.log") - assert new_config["resolved_path"] == expected_path - - def test_add_output_absolute_path(self): - """测试添加绝对路径输出""" - absolute_path = os.path.join(self.temp_dir, "absolute.log") - self.logger.add_output(absolute_path, "ERROR") - - configs = self.logger.get_output_configs() - new_config = next(c for c in configs if c["target"] == absolute_path) - assert new_config["level"] == "ERROR" - assert new_config["resolved_path"] == absolute_path - - def test_update_output_level_by_index(self): - """测试通过索引更新输出级别""" - self.logger.add_output("test.log", "INFO") - - # 更新第一个输出（console）的级别 - self.logger.update_output_level(0, "ERROR") - - configs = self.logger.get_output_configs() - console_config = configs[0] - assert console_config["level"] == "ERROR" - - def test_update_output_level_by_name(self): - """测试通过名称更新输出级别""" - self.logger.add_output("test.log", "INFO") - - # 通过目标名称更新级别 - self.logger.update_output_level("test.log", "WARNING") - - configs = self.logger.get_output_configs() - test_config = next(c for c in configs if c["target"] == "test.log") - assert test_config["level"] == "WARNING" - - def test_update_nonexistent_output(self): - """测试更新不存在的输出""" - with pytest.raises(ValueError, match="Output target not found"): - self.logger.update_output_level("nonexistent", "DEBUG") - - with pytest.raises(ValueError, match="Output target not found"): - self.logger.update_output_level(999, "DEBUG") - - def test_remove_output_by_index(self): - """测试通过索引移除输出""" - self.logger.add_output("remove_me.log", "DEBUG") - - initial_count = len(self.logger.output_configs) - self.logger.remove_output(1) # 移除刚添加的 - - assert len(self.logger.output_configs) == initial_count - 1 - - # 验证剩余的是console输出 - remaining_targets = [c["target"] for c in self.logger.output_configs] - assert "remove_me.log" not in remaining_targets - assert "console" in remaining_targets - - def test_remove_output_by_name(self): - """测试通过名称移除输出""" - self.logger.add_output("remove_me.log", "DEBUG") - - self.logger.remove_output("remove_me.log") - - remaining_targets = [c["target"] for c in self.logger.output_configs] - assert "remove_me.log" not in remaining_targets - - def test_remove_nonexistent_output(self): - """测试移除不存在的输出""" - with pytest.raises(ValueError, match="Output target not found"): - self.logger.remove_output("nonexistent") - - with pytest.raises(ValueError, match="Output target not found"): - self.logger.remove_output(999) - - -@pytest.mark.unit -class TestGlobalConsoleDebug: - """全局console debug控制测试""" - - def test_global_console_debug_enabled_by_default(self): - """测试全局console debug默认启用""" - assert CustomLogger.is_global_console_debug_enabled() is True - - def test_disable_global_console_debug(self): - """测试禁用全局console debug""" - CustomLogger.disable_global_console_debug() - assert CustomLogger.is_global_console_debug_enabled() is False - - # 重新启用以免影响其他测试 - CustomLogger.enable_global_console_debug() - - def test_enable_global_console_debug(self): - """测试启用全局console debug""" - CustomLogger.disable_global_console_debug() - CustomLogger.enable_global_console_debug() - assert CustomLogger.is_global_console_debug_enabled() is True - - def test_console_handler_creation_when_disabled(self): - """测试禁用时不创建console handler""" - CustomLogger.disable_global_console_debug() - - try: - logger = CustomLogger([("console", "INFO")]) - - # 应该没有console handler被创建 - console_config = logger.output_configs[0] - assert console_config["target"] == "console" - assert console_config["handler"] is None - - finally: - CustomLogger.enable_global_console_debug() - - def test_thread_safety_of_global_setting(self): - """测试全局设置的线程安全性""" - results = [] - - def toggle_setting(): - for _ in range(100): - CustomLogger.disable_global_console_debug() - CustomLogger.enable_global_console_debug() - results.append(CustomLogger.is_global_console_debug_enabled()) - - threads = [threading.Thread(target=toggle_setting) for _ in range(5)] - - for thread in threads: - thread.start() - - for thread in threads: - thread.join() - - # 所有线程应该得到一致的结果 - assert all(result is True for result in results) - - -@pytest.mark.unit -class TestUtilityMethods: - """工具方法测试""" - - def test_get_available_levels(self): - """测试获取可用日志级别""" - levels = CustomLogger.get_available_levels() - - expected_levels = [ - "DEBUG", - "INFO", - "WARNING", - "WARN", - "ERROR", - "CRITICAL", - "FATAL", - ] - assert set(levels) == set(expected_levels) - - def test_print_current_configs(self): - """测试打印当前配置""" - with sage_temp_directory() as temp_dir: - # 使用安全的测试目录中的绝对路径 - error_log_path = os.path.join(temp_dir, "error.log") - logger = CustomLogger( - outputs=[ - ("console", "INFO"), - ("app.log", "DEBUG"), - (error_log_path, "ERROR"), - ], - name="PrintTestLogger", - log_base_folder=temp_dir, - ) - - # 捕获打印输出 - # 使用内置的StringIO来避免导入冲突 - import importlib - import sys - - io_module = importlib.import_module("io") - IOStringIO = io_module.StringIO - - old_stdout = sys.stdout - captured_output = IOStringIO() - sys.stdout = captured_output - - try: - logger.print_current_configs() - output = captured_output.getvalue() - - # 验证输出内容 - assert "PrintTestLogger" in output - assert "console" in output - assert "app.log" in output - assert "error.log" in output # 只检查文件名，不检查完整路径 - assert "INFO" in output - assert "DEBUG" in output - assert "ERROR" in output - assert "ACTIVE" in output - - finally: - sys.stdout = old_stdout - - -@pytest.mark.integration -class TestCustomLoggerIntegration: - """CustomLogger集成测试""" - - def test_real_world_logging_scenario(self): - """测试真实世界的日志记录场景""" - with sage_temp_directory() as temp_dir: - # 创建多层次日志配置 - logger = CustomLogger( - outputs=[ - ("console", "INFO"), - ("app.log", "DEBUG"), - ("error.log", "ERROR"), - (os.path.join(temp_dir, "system.log"), "WARNING"), - ], - name="RealWorldLogger", - log_base_folder=temp_dir, - ) - - # 模拟应用启动过程 - logger.info("Application starting...") - logger.debug("Loading configuration...") - logger.info("Configuration loaded successfully") - - # 模拟警告情况 - logger.warning("Deprecated API usage detected") - - # 模拟错误情况 - try: - raise ConnectionError("Database connection failed") - except ConnectionError: - logger.error("Failed to connect to database", exc_info=True) - - # 模拟异常情况 - try: - raise ValueError("Invalid configuration value") - except ValueError: - logger.exception("Configuration validation failed") - - logger.critical("System is shutting down due to critical errors") - - # 验证日志文件内容 - app_log_path = os.path.join(temp_dir, "app.log") - error_log_path = os.path.join(temp_dir, "error.log") - system_log_path = os.path.join(temp_dir, "system.log") - - # app.log 应该包含所有级别的日志（DEBUG及以上） - with open(app_log_path) as f: - app_content = f.read() - assert "Application starting..." in app_content - assert "Loading configuration..." in app_content - assert "Deprecated API usage" in app_content - assert "Database connection failed" in app_content - assert "Configuration validation failed" in app_content - assert "System is shutting down" in app_content - - # error.log 应该只包含ERROR及以上级别的日志 - with open(error_log_path) as f: - error_content = f.read() - assert "Application starting..." not in error_content - assert "Loading configuration..." not in error_content - assert "Deprecated API usage" not in error_content - assert "Database connection failed" in error_content - assert "Configuration validation failed" in error_content - assert "System is shutting down" in error_content - - # system.log 应该包含WARNING及以上级别的日志 - with open(system_log_path) as f: - system_content = f.read() - assert "Application starting..." not in system_content - assert "Loading configuration..." not in system_content - assert "Deprecated API usage" in system_content - assert "Database connection failed" in system_content - assert "System is shutting down" in system_content - - def test_dynamic_configuration_workflow(self): - """测试动态配置工作流程""" - with sage_temp_directory() as temp_dir: - # 初始配置 - logger = CustomLogger( - outputs=[("console", "INFO")], - name="DynamicWorkflowLogger", - log_base_folder=temp_dir, - ) - - logger.info("Initial setup complete") - - # 运行时添加文件日志 - logger.add_output("runtime.log", "DEBUG") - logger.debug("Runtime logging enabled") - - # 更新console日志级别 - logger.update_output_level("console", "ERROR") - logger.info("This info message should not appear in console") - logger.error("This error message should appear everywhere") - - # 添加临时调试日志 - debug_log = os.path.join(temp_dir, "debug.log") - logger.add_output(debug_log, "DEBUG") - logger.debug("Temporary debug information") - - # 移除临时调试日志 - logger.remove_output(debug_log) - logger.debug("This debug should not go to debug.log anymore") - - # 验证文件内容 - runtime_log_path = os.path.join(temp_dir, "runtime.log") - with open(runtime_log_path) as f: - runtime_content = f.read() - assert "Runtime logging enabled" in runtime_content - assert "This error message should appear everywhere" in runtime_content - assert "This debug should not go to debug.log anymore" in runtime_content - - # 验证临时调试文件存在且包含预期内容 - with open(debug_log) as f: - debug_content = f.read() - assert "Temporary debug information" in debug_content - assert "This debug should not go to debug.log anymore" not in debug_content - - -@pytest.mark.unit -class TestErrorHandling: - """错误处理测试""" - - def test_handler_creation_failure(self): - """测试handler创建失败的处理""" - # 使用安全的测试目录 - with sage_temp_directory() as temp_dir: - invalid_path = os.path.join(temp_dir, "invalid", "nested", "path") - with patch("os.makedirs", side_effect=OSError("Permission denied")): - logger = CustomLogger(outputs=[("test.log", "INFO")], log_base_folder=invalid_path) - - # 应该能创建logger，但handler为None - file_config = next(c for c in logger.output_configs if c["target"] == "test.log") - assert file_config["handler"] is None - - def test_file_logging_with_invalid_directory(self): - """测试无效目录的文件日志处理""" - # 这里测试目录创建失败的情况 - with sage_temp_directory() as temp_dir: - invalid_file_path = os.path.join(temp_dir, "invalid", "path", "test.log") - # 需要patch CustomLogger模块中的RotatingFileHandler - with patch( - "sage.common.utils.logging.custom_logger.RotatingFileHandler", - side_effect=OSError("Cannot create file"), - ): - logger = CustomLogger([(invalid_file_path, "INFO")]) - - # logger应该能正常创建，但文件handler为None - file_config = logger.output_configs[0] - assert file_config["handler"] is None - - def test_logging_without_handlers(self): - """测试没有有效handler时的日志记录""" - with patch.object(CustomLogger, "_create_handler", return_value=None): - # 使用绝对路径避免log_base_folder依赖 - import tempfile - - temp_file = tempfile.NamedTemporaryFile(delete=False) - temp_file.close() - - logger = CustomLogger([(temp_file.name, "INFO")]) - - # 应该能调用日志方法而不出错 - logger.info("Test message") - logger.error("Test error") - - # 清理临时文件 - os.unlink(temp_file.name) - - -# 性能测试 -@pytest.mark.slow -class TestCustomLoggerPerformance: - """CustomLogger性能测试""" - - def test_logging_performance(self): - """测试日志记录性能""" - import time - - with sage_temp_directory() as temp_dir: - logger = CustomLogger( - outputs=[("console", "INFO"), ("perf_test.log", "DEBUG")], - log_base_folder=temp_dir, - ) - - # 测试大量日志记录的性能 - num_logs = 1000 - start_time = time.time() - - for i in range(num_logs): - if i % 4 == 0: - logger.debug(f"Debug message {i}") - elif i % 4 == 1: - logger.info(f"Info message {i}") - elif i % 4 == 2: - logger.warning(f"Warning message {i}") - else: - logger.error(f"Error message {i}") - - elapsed_time = time.time() - start_time - - # 性能断言 - assert elapsed_time < 5.0 # 1000条日志应在5秒内完成 - - # 验证所有日志都被记录 - log_file = os.path.join(temp_dir, "perf_test.log") - with open(log_file) as f: - content = f.read() - assert f"Debug message {num_logs - 4}" in content - assert f"Error message {num_logs - 1}" in content - - def test_multiple_handlers_performance(self): - """测试多handler性能""" - import time - - with sage_temp_directory() as temp_dir: - # 创建多个输出handler - # Explicitly type the outputs list to match the expected signature - outputs: list[tuple[str, str | int]] = [("console", "INFO")] - for i in range(10): - outputs.append((f"log_{i}.log", "DEBUG")) - - logger = CustomLogger(outputs, log_base_folder=temp_dir) - - # 测试性能 - num_logs = 100 - start_time = time.time() - - for i in range(num_logs): - logger.info(f"Multi-handler message {i}") - - elapsed_time = time.time() - start_time - - # 多handler的性能应该仍然可接受 - assert elapsed_time < 10.0 # 10个handler * 100条日志应在10秒内完成 - - # 验证所有文件都包含日志 - for i in range(10): - log_file = os.path.join(temp_dir, f"log_{i}.log") - assert os.path.exists(log_file) - with open(log_file) as f: - content = f.read() - assert "Multi-handler message" in content diff --git a/packages/sage-kernel/tests/unit/kernel/utils/network/test_base_tcp_client.py b/packages/sage-kernel/tests/unit/kernel/utils/network/test_base_tcp_client.py deleted file mode 100644 index 09e8f0c3d5..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/utils/network/test_base_tcp_client.py +++ /dev/null @@ -1,1057 +0,0 @@ -""" -Tests for sage.common.utils.network.base_tcp_client module -================================================== - -单元测试基础TCP客户端模块的功能，包括： -- 连接管理 -- 消息发送和接收 -- 错误处理 -- 超时处理 -""" - -import json -import socket -import time -from unittest.mock import MagicMock, patch - -import pytest - -from sage.common.utils.network.base_tcp_client import BaseTcpClient - - -class MockTcpClient(BaseTcpClient): - """用于测试的具体TCP客户端实现""" - - def build_request(self, data): - """构建请求""" - return {"type": "test_request", "data": data, "timestamp": time.time()} - - def handle_response(self, response_data): - """处理响应""" - return response_data - - def _build_health_check_request(self): - """构建健康检查请求""" - return {"type": "health_check", "timestamp": time.time()} - - def _build_server_info_request(self): - """构建服务器信息请求""" - return {"type": "server_info", "timestamp": time.time()} - - -@pytest.mark.unit -class TestBaseTcpClient: - """BaseTcpClient基本功能测试""" - - def setup_method(self): - """测试前准备""" - self.client = MockTcpClient( - host="127.0.0.1", port=19001, timeout=5.0, client_name="TestClient" - ) - - def teardown_method(self): - """测试后清理""" - if hasattr(self.client, "_socket") and self.client._socket: - try: - self.client.disconnect() - except Exception: - pass - - def test_client_initialization(self): - """测试客户端初始化""" - assert self.client.host == "127.0.0.1" - assert self.client.port == 19001 - assert self.client.timeout == 5.0 - assert self.client.client_name == "TestClient" - assert self.client.connected is False - assert self.client._socket is None - - def test_client_default_initialization(self): - """测试客户端默认初始化参数""" - default_client = MockTcpClient() - assert default_client.host == "127.0.0.1" - assert default_client.port == 19001 - assert default_client.timeout == 30.0 - assert default_client.client_name == "TcpClient" - - def test_create_default_logger(self): - """测试默认日志记录器创建""" - logger = self.client._create_default_logger() - assert logger.name == "TestClient" - assert len(logger.handlers) > 0 - assert logger.level == 20 # logging.INFO = 20 - - @patch("socket.socket") - def test_successful_connection(self, mock_socket_class): - """测试成功连接""" - mock_socket = MagicMock() - mock_socket_class.return_value = mock_socket - - result = self.client.connect() - - assert result is True - assert self.client.connected is True - assert self.client._socket == mock_socket - - # 验证socket配置 - mock_socket_class.assert_called_once_with(socket.AF_INET, socket.SOCK_STREAM) - mock_socket.settimeout.assert_called_once_with(5.0) - mock_socket.connect.assert_called_once_with(("127.0.0.1", 19001)) - - @patch("socket.socket") - def test_connection_failure(self, mock_socket_class): - """测试连接失败""" - mock_socket = MagicMock() - mock_socket_class.return_value = mock_socket - mock_socket.connect.side_effect = ConnectionRefusedError("Connection refused") - - result = self.client.connect() - - assert result is False - assert self.client.connected is False - assert self.client._socket is None - - # 验证socket被关闭 - mock_socket.close.assert_called_once() - - def test_connect_when_already_connected(self): - """测试已连接时再次连接""" - self.client.connected = True - - result = self.client.connect() - - assert result is True - assert self.client.connected is True - - @patch("socket.socket") - def test_disconnect(self, mock_socket_class): - """测试断开连接""" - mock_socket = MagicMock() - self.client._socket = mock_socket - self.client.connected = True - - self.client.disconnect() - - assert self.client.connected is False - assert self.client._socket is None - mock_socket.close.assert_called_once() - - def test_disconnect_when_not_connected(self): - """测试未连接时断开连接""" - self.client.disconnect() - - assert self.client.connected is False - assert self.client._socket is None - - @patch("socket.socket") - def test_disconnect_with_socket_error(self, mock_socket_class): - """测试断开连接时socket错误""" - mock_socket = MagicMock() - mock_socket.close.side_effect = OSError("Socket error") - self.client._socket = mock_socket - self.client.connected = True - - # 应该能正常断开，忽略socket错误 - self.client.disconnect() - - assert self.client.connected is False - assert self.client._socket is None - - @patch("socket.socket") - def test_send_request_success(self, mock_socket_class): - """测试成功发送请求""" - mock_socket = MagicMock() - mock_socket_class.return_value = mock_socket - - # 模拟连接成功 - self.client.connect() - - # 模拟接收响应 - 需要模拟二进制协议 - response_data = {"status": "success", "data": "test_response"} - response_json = json.dumps(response_data) - response_bytes = response_json.encode("utf-8") - - # 模拟接收：先接收4字节长度，然后接收数据 - def mock_recv(size): - if size == 4: - # 返回响应数据长度（大端序） - return len(response_bytes).to_bytes(4, byteorder="big") - else: - # 返回响应数据 - return response_bytes - - mock_socket.recv.side_effect = mock_recv - - request_data = {"test": "data"} - result = self.client.send_request(request_data) - - assert result == response_data - - # 验证调用了sendall方法（发送长度和数据） - assert mock_socket.sendall.call_count == 2 # 一次发送长度，一次发送数据 - - @patch("socket.socket") - def test_send_request_connection_error(self, mock_socket_class): - """测试发送请求时连接错误""" - mock_socket = MagicMock() - mock_socket_class.return_value = mock_socket - - self.client.connect() - mock_socket.sendall.side_effect = ConnectionError("Connection lost") - - request_data = {"test": "data"} - - result = self.client.send_request(request_data) - - # 应该返回错误响应而不是抛出异常 - assert result["status"] == "error" - assert result["error_code"] == "ERR_COMMUNICATION_FAILED" - - @patch("socket.socket") - def test_send_request_timeout(self, mock_socket_class): - """测试发送请求超时""" - mock_socket = MagicMock() - mock_socket_class.return_value = mock_socket - - self.client.connect() - mock_socket.recv.side_effect = TimeoutError("Timeout") - - request_data = {"test": "data"} - - result = self.client.send_request(request_data) - - # 应该返回错误响应而不是抛出异常 - assert result["status"] == "error" - assert result["error_code"] == "ERR_NO_RESPONSE" - - @patch("socket.socket") - def test_send_request_invalid_json_response(self, mock_socket_class): - """测试接收无效JSON响应""" - mock_socket = MagicMock() - mock_socket_class.return_value = mock_socket - - self.client.connect() - - # 模拟接收无效的响应长度（第一个4字节不是有效长度） - invalid_length_bytes = b"abcd" # 这会被解释为一个非常大的数字 - mock_socket.recv.return_value = invalid_length_bytes - - request_data = {"test": "data"} - - result = self.client.send_request(request_data) - - # 应该返回错误响应 - assert result["status"] == "error" - assert result["error_code"] == "ERR_NO_RESPONSE" - - @patch("socket.socket") - def test_send_request_empty_response(self, mock_socket_class): - """测试接收空响应""" - mock_socket = MagicMock() - mock_socket_class.return_value = mock_socket - - self.client.connect() - - # 模拟接收空响应 - mock_socket.recv.return_value = b"" - - request_data = {"test": "data"} - - result = self.client.send_request(request_data) - - # 应该返回错误响应而不是抛出异常 - assert result["status"] == "error" - assert result["error_code"] == "ERR_NO_RESPONSE" - - @patch("socket.socket") - def test_send_request_partial_response(self, mock_socket_class): - """测试接收部分响应（多次recv）""" - mock_socket = MagicMock() - mock_socket_class.return_value = mock_socket - - self.client.connect() - - # 模拟分多次接收完整响应 - response_data = {"status": "success", "large_data": "x" * 1000} - response_json = json.dumps(response_data) - response_bytes = response_json.encode("utf-8") - - # 模拟接收：先接收4字节长度，然后分多次接收数据 - - def mock_recv(size): - if size == 4: - # 第一次调用，返回响应数据长度 - return len(response_bytes).to_bytes(4, byteorder="big") - else: - # 后续调用，分多次返回数据 - if not hasattr(mock_recv, "call_count"): - mock_recv.call_count = 0 - mock_recv.data_sent = 0 - - mock_recv.call_count += 1 - remaining_size = len(response_bytes) - mock_recv.data_sent - chunk_size = min( - size, - (remaining_size // 2 if mock_recv.call_count == 1 else remaining_size), - ) - - if chunk_size > 0: - chunk = response_bytes[mock_recv.data_sent : mock_recv.data_sent + chunk_size] - mock_recv.data_sent += chunk_size - return chunk - else: - return b"" - - mock_socket.recv.side_effect = mock_recv - - request_data = {"test": "data"} - result = self.client.send_request(request_data) - - assert result == response_data - - -@pytest.mark.unit -class MockTcpClientEdgeCases: - """TCP客户端边界情况测试""" - - def test_custom_host_port(self): - """测试自定义主机和端口""" - client = MockTcpClient(host="192.168.1.100", port=8080) - assert client.host == "192.168.1.100" - assert client.port == 8080 - - def test_custom_timeout(self): - """测试自定义超时时间""" - client = MockTcpClient(timeout=60.0) - assert client.timeout == 60.0 - - def test_custom_client_name(self): - """测试自定义客户端名称""" - client = MockTcpClient(client_name="CustomClient") - assert client.client_name == "CustomClient" - - logger = client._create_default_logger() - assert logger.name == "CustomClient" - - @patch("socket.socket") - def test_send_request_with_unicode_data(self, mock_socket_class): - """测试发送包含Unicode字符的请求""" - mock_socket = MagicMock() - mock_socket_class.return_value = mock_socket - - client = MockTcpClient() - client.connect() - - # 模拟Unicode响应 - 使用二进制协议 - response_data = {"message": "你好世界", "emoji": "🌍"} - response_json = json.dumps(response_data, ensure_ascii=False) - response_bytes = response_json.encode("utf-8") - - # 模拟接收：先接收4字节长度，然后接收数据 - def mock_recv(size): - if size == 4: - # 返回响应数据长度（大端序） - return len(response_bytes).to_bytes(4, byteorder="big") - else: - # 返回响应数据 - return response_bytes - - mock_socket.recv.side_effect = mock_recv - - request_data = {"query": "测试查询", "symbols": "™®©"} - result = client.send_request(request_data) - - assert result == response_data - assert result["message"] == "你好世界" - assert result["emoji"] == "🌍" - - @patch("socket.socket") - def test_multiple_consecutive_requests(self, mock_socket_class): - """测试连续多个请求""" - mock_socket = MagicMock() - mock_socket_class.return_value = mock_socket - - client = MockTcpClient() - client.connect() - - # 模拟多个响应 - responses = [ - {"id": 1, "result": "first"}, - {"id": 2, "result": "second"}, - {"id": 3, "result": "third"}, - ] - - response_index = 0 - - def mock_recv(size): - nonlocal response_index - if size == 4: - # 返回响应数据长度 - if response_index < len(responses): - response_json = json.dumps(responses[response_index]) - response_bytes = response_json.encode("utf-8") - return len(response_bytes).to_bytes(4, byteorder="big") - else: - return b"" - else: - # 返回响应数据 - if response_index < len(responses): - response_json = json.dumps(responses[response_index]) - response_bytes = response_json.encode("utf-8") - response_index += 1 - return response_bytes - else: - return b"" - - mock_socket.recv.side_effect = mock_recv - - # 发送多个请求 - for i in range(3): - request_data = {"request_id": i + 1} - result = client.send_request(request_data) - assert result["id"] == i + 1 - assert result["result"] in ["first", "second", "third"] - - @patch("socket.socket") - def test_large_request_data(self, mock_socket_class): - """测试大型请求数据""" - mock_socket = MagicMock() - mock_socket_class.return_value = mock_socket - - client = MockTcpClient() - client.connect() - - # 创建大型请求数据 - large_data = { - "large_field": "x" * 10000, # 10KB数据 - "array_field": list(range(1000)), - "nested_data": {f"key_{i}": f"value_{i}" for i in range(100)}, - } - - # 模拟响应 - 使用长度前缀协议 - response_data = {"status": "received"} - response_json = json.dumps(response_data).encode("utf-8") - response_length = len(response_json).to_bytes(4, byteorder="big") - - # 模拟按顺序接收：先长度，后数据 - mock_socket.recv.side_effect = [response_length, response_json] - - result = client.send_request(large_data) - assert result == response_data - - # 验证发送调用 - sendall被调用两次：长度和数据 - assert mock_socket.sendall.call_count == 2 - - # 验证发送的数据包含大型数据 - sent_calls = mock_socket.sendall.call_args_list - data_call = sent_calls[1][0][0] # 第二次调用是数据内容 - sent_data = data_call.decode("utf-8") - assert '"large_field"' in sent_data - assert "x" * 100 in sent_data # 部分大型字段内容 - - -@pytest.mark.unit -class TestAbstractMethods: - """抽象方法测试""" - - def test_build_request_abstract_method(self): - """测试build_request抽象方法""" - # BaseTcpClient是抽象类，不能直接实例化 - # 但我们可以测试具体实现 - client = MockTcpClient() - - data = {"test": "data"} - request = client.build_request(data) - - assert isinstance(request, dict) - assert request["type"] == "test_request" - assert request["data"] == data - assert "timestamp" in request - - def test_handle_response_abstract_method(self): - """测试handle_response抽象方法""" - client = MockTcpClient() - - response_data = {"status": "success", "result": "test"} - handled_response = client.handle_response(response_data) - - assert handled_response == response_data - - -@pytest.mark.integration -class MockTcpClientIntegration: - """TCP客户端集成测试""" - - def test_client_with_mock_server(self): - """测试客户端与模拟服务器的集成""" - import threading - import time - - # 创建模拟服务器 - server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) - server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) - server_socket.bind(("127.0.0.1", 0)) # 使用随机端口 - server_port = server_socket.getsockname()[1] - server_socket.listen(1) - - server_responses = [] - server_running = threading.Event() - server_error = None - - def mock_server(): - nonlocal server_error - try: - server_running.set() - client_socket, addr = server_socket.accept() - client_socket.settimeout(5.0) # 设置超时 - - # 接收请求长度（4字节） - length_data = client_socket.recv(4) - if len(length_data) != 4: - raise ValueError("Invalid length data") - - request_length = int.from_bytes(length_data, byteorder="big") - - # 接收请求数据 - request_data = b"" - while len(request_data) < request_length: - chunk = client_socket.recv(min(1024, request_length - len(request_data))) - if not chunk: - break - request_data += chunk - - # 解析请求 - request_text = request_data.decode("utf-8") - request_json = json.loads(request_text) - server_responses.append(request_json) - - # 发送响应 - response = {"status": "success", "echo": request_json} - response_data = json.dumps(response).encode("utf-8") - response_length = len(response_data).to_bytes(4, byteorder="big") - - client_socket.sendall(response_length) - client_socket.sendall(response_data) - - client_socket.close() - - except Exception as e: - server_error = e - print(f"Mock server error: {e}") - finally: - server_socket.close() - - # 启动模拟服务器 - server_thread = threading.Thread(target=mock_server) - server_thread.start() - - # 等待服务器启动 - server_running.wait(timeout=5) - time.sleep(0.1) - - try: - # 创建客户端并连接 - client = MockTcpClient(port=server_port, timeout=5.0) - - assert client.connect() is True - - # 发送请求 - request_data = {"message": "Hello Server", "timestamp": time.time()} - response = client.send_request(request_data) - - # 验证响应 - assert response["status"] == "success" - assert "echo" in response - - # 验证服务器接收到的数据 - assert len(server_responses) == 1 - received_request = server_responses[0] - - # BaseTcpClient._serialize_request 会添加 request_id 和 timestamp， - # 但MockTcpClient.build_request不会包装数据 - # 检查原始请求数据是否在接收到的请求中 - assert "message" in received_request - assert received_request["message"] == request_data["message"] - assert "request_id" in received_request # 由_serialize_request添加 - assert "timestamp" in received_request # 由_serialize_request添加 - - client.disconnect() - - # 检查服务器是否有错误 - if server_error: - raise AssertionError(f"Server error: {server_error}") - - finally: - server_thread.join(timeout=5) - - @pytest.mark.slow - def test_connection_timeout(self): - """测试连接超时""" - # 在Linux上，连接被拒绝通常会立即返回，所以我们改用模拟的方式 - from unittest.mock import MagicMock, patch - - with patch("socket.socket") as mock_socket_class: - mock_socket = MagicMock() - mock_socket_class.return_value = mock_socket - - # 模拟连接超时 - mock_socket.connect.side_effect = TimeoutError("Connection timeout") - - client = MockTcpClient(host="127.0.0.1", port=65534, timeout=1.0) - - start_time = time.time() - result = client.connect() - elapsed_time = time.time() - start_time - - assert result is False - assert client.connected is False - # 模拟的超时应该很快返回 - assert elapsed_time < 1.0 - - -@pytest.mark.unit -class TestErrorHandling: - """错误处理测试""" - - @patch("socket.socket") - def test_connection_refused_error(self, mock_socket_class): - """测试连接被拒绝错误""" - mock_socket = MagicMock() - mock_socket_class.return_value = mock_socket - mock_socket.connect.side_effect = ConnectionRefusedError("Connection refused") - - client = MockTcpClient() - - with patch.object(client.logger, "error") as mock_log_error: - result = client.connect() - - assert result is False - mock_log_error.assert_called_once() - assert "Failed to connect" in str(mock_log_error.call_args) - - @patch("socket.socket") - def test_socket_timeout_error(self, mock_socket_class): - """测试socket超时错误""" - mock_socket = MagicMock() - mock_socket_class.return_value = mock_socket - mock_socket.connect.side_effect = TimeoutError("Connection timeout") - - client = MockTcpClient() - result = client.connect() - - assert result is False - assert client.connected is False - - @patch("socket.socket") - def test_general_socket_error(self, mock_socket_class): - """测试一般socket错误""" - mock_socket = MagicMock() - mock_socket_class.return_value = mock_socket - mock_socket.connect.side_effect = OSError("Network error") - - client = MockTcpClient() - result = client.connect() - - assert result is False - assert client._socket is None - - -# 性能测试 -@pytest.mark.slow -class MockTcpClientPerformance: - """TCP客户端性能测试""" - - @patch("socket.socket") - def test_multiple_connections_performance(self, mock_socket_class): - """测试多次连接的性能""" - import time - - mock_socket = MagicMock() - mock_socket_class.return_value = mock_socket - - client = MockTcpClient() - - # 测试多次连接和断开的性能 - num_connections = 100 - start_time = time.time() - - for _ in range(num_connections): - client.connect() - client.disconnect() - - elapsed_time = time.time() - start_time - - # 性能断言（这些值可以根据实际需要调整） - assert elapsed_time < 1.0 # 100次连接应在1秒内完成 - - # 验证调用次数 - assert mock_socket.connect.call_count == num_connections - assert mock_socket.close.call_count == num_connections - - @patch("socket.socket") - def test_large_data_transfer_performance(self, mock_socket_class): - """测试大数据传输性能""" - import time - - mock_socket = MagicMock() - mock_socket_class.return_value = mock_socket - - # 模拟大响应数据 - large_response = {"data": "x" * 100000} # 100KB数据 - response_json = json.dumps(large_response).encode("utf-8") - response_length = len(response_json).to_bytes(4, byteorder="big") - - # 模拟按顺序接收：先长度，后数据 - mock_socket.recv.side_effect = [response_length, response_json] - - client = MockTcpClient() - client.connect() - - # 测试大数据传输性能 - start_time = time.time() - - request_data = {"query": "large_data"} - result = client.send_request(request_data) - - elapsed_time = time.time() - start_time - - # 性能断言 - assert elapsed_time < 1.0 # 100KB数据传输应在1秒内完成 - assert result == large_response - assert len(result["data"]) == 100000 - - -@pytest.mark.unit -class TestTcpClientEdgeCases: - """TCP客户端边界情况测试""" - - def test_custom_host_port(self): - """测试自定义主机和端口""" - client = MockTcpClient(host="192.168.1.100", port=8080) - assert client.host == "192.168.1.100" - assert client.port == 8080 - - def test_custom_timeout(self): - """测试自定义超时时间""" - client = MockTcpClient(timeout=60.0) - assert client.timeout == 60.0 - - def test_custom_client_name(self): - """测试自定义客户端名称""" - client = MockTcpClient(client_name="CustomClient") - assert client.client_name == "CustomClient" - - logger = client._create_default_logger() - assert logger.name == "CustomClient" - - @patch("socket.socket") - def test_send_request_with_unicode_data(self, mock_socket_class): - """测试发送包含Unicode字符的请求""" - mock_socket = MagicMock() - mock_socket_class.return_value = mock_socket - - client = MockTcpClient() - client.connect() - - # 模拟Unicode响应 - 使用二进制协议 - response_data = {"message": "你好世界", "emoji": "🌍"} - response_json = json.dumps(response_data, ensure_ascii=False) - response_bytes = response_json.encode("utf-8") - - # 模拟接收：先接收4字节长度，然后接收数据 - def mock_recv(size): - if size == 4: - # 返回响应数据长度（大端序） - return len(response_bytes).to_bytes(4, byteorder="big") - else: - # 返回响应数据 - return response_bytes - - mock_socket.recv.side_effect = mock_recv - - request_data = {"query": "测试查询", "symbols": "™®©"} - result = client.send_request(request_data) - - assert result == response_data - assert result["message"] == "你好世界" - assert result["emoji"] == "🌍" - - @patch("socket.socket") - def test_multiple_consecutive_requests(self, mock_socket_class): - """测试连续多个请求""" - mock_socket = MagicMock() - mock_socket_class.return_value = mock_socket - - client = MockTcpClient() - client.connect() - - # 模拟多个响应 - responses = [ - {"id": 1, "result": "first"}, - {"id": 2, "result": "second"}, - {"id": 3, "result": "third"}, - ] - - response_index = 0 - - def mock_recv(size): - nonlocal response_index - if size == 4: - # 返回响应数据长度 - if response_index < len(responses): - response_json = json.dumps(responses[response_index]) - response_bytes = response_json.encode("utf-8") - return len(response_bytes).to_bytes(4, byteorder="big") - else: - return b"" - else: - # 返回响应数据 - if response_index < len(responses): - response_json = json.dumps(responses[response_index]) - response_bytes = response_json.encode("utf-8") - response_index += 1 - return response_bytes - else: - return b"" - - mock_socket.recv.side_effect = mock_recv - - # 发送多个请求 - for i in range(3): - request_data = {"request_id": i + 1} - result = client.send_request(request_data) - assert result["id"] == i + 1 - assert result["result"] in ["first", "second", "third"] - - @patch("socket.socket") - def test_large_request_data(self, mock_socket_class): - """测试大型请求数据""" - mock_socket = MagicMock() - mock_socket_class.return_value = mock_socket - - client = MockTcpClient() - client.connect() - - # 创建大型请求数据 - large_data = { - "large_field": "x" * 10000, # 10KB数据 - "array_field": list(range(1000)), - "nested_data": {f"key_{i}": f"value_{i}" for i in range(100)}, - } - - # 模拟响应 - 使用长度前缀协议 - response_data = {"status": "received"} - response_json = json.dumps(response_data).encode("utf-8") - response_length = len(response_json).to_bytes(4, byteorder="big") - - # 模拟按顺序接收：先长度，后数据 - mock_socket.recv.side_effect = [response_length, response_json] - - result = client.send_request(large_data) - assert result == response_data - - # 验证发送调用 - sendall被调用两次：长度和数据 - assert mock_socket.sendall.call_count == 2 - - # 验证发送的数据包含大型数据 - sent_calls = mock_socket.sendall.call_args_list - data_call = sent_calls[1][0][0] # 第二次调用是数据内容 - sent_data = data_call.decode("utf-8") - assert '"large_field"' in sent_data - assert "x" * 100 in sent_data # 部分大型字段内容 - - -@pytest.mark.integration -class TestTcpClientIntegration: - """TCP客户端集成测试""" - - def test_client_with_mock_server(self): - """测试客户端与模拟服务器的集成""" - import threading - import time - - # 创建模拟服务器 - server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) - server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) - server_socket.bind(("127.0.0.1", 0)) # 使用随机端口 - server_port = server_socket.getsockname()[1] - server_socket.listen(1) - - server_responses = [] - server_running = threading.Event() - server_error = None - - def mock_server(): - nonlocal server_error - try: - server_running.set() - client_socket, addr = server_socket.accept() - client_socket.settimeout(5.0) # 设置超时 - - # 接收请求长度（4字节） - length_data = client_socket.recv(4) - if len(length_data) != 4: - raise ValueError("Invalid length data") - - request_length = int.from_bytes(length_data, byteorder="big") - - # 接收请求数据 - request_data = b"" - while len(request_data) < request_length: - chunk = client_socket.recv(min(1024, request_length - len(request_data))) - if not chunk: - break - request_data += chunk - - # 解析请求 - request_text = request_data.decode("utf-8") - request_json = json.loads(request_text) - server_responses.append(request_json) - - # 发送响应 - response = {"status": "success", "echo": request_json} - response_data = json.dumps(response).encode("utf-8") - response_length = len(response_data).to_bytes(4, byteorder="big") - - client_socket.sendall(response_length) - client_socket.sendall(response_data) - - client_socket.close() - - except Exception as e: - server_error = e - print(f"Mock server error: {e}") - finally: - server_socket.close() - - # 启动模拟服务器 - server_thread = threading.Thread(target=mock_server) - server_thread.start() - - # 等待服务器启动 - server_running.wait(timeout=5) - time.sleep(0.1) - - try: - # 创建客户端并连接 - client = MockTcpClient(port=server_port, timeout=5.0) - - assert client.connect() is True - - # 发送请求 - request_data = {"message": "Hello Server", "timestamp": time.time()} - response = client.send_request(request_data) - - # 验证响应 - assert response["status"] == "success" - assert "echo" in response - - # 验证服务器接收到的数据 - assert len(server_responses) == 1 - received_request = server_responses[0] - - # BaseTcpClient._serialize_request 会添加 request_id 和 timestamp， - # 但MockTcpClient.build_request不会包装数据 - # 检查原始请求数据是否在接收到的请求中 - assert "message" in received_request - assert received_request["message"] == request_data["message"] - assert "request_id" in received_request # 由_serialize_request添加 - assert "timestamp" in received_request # 由_serialize_request添加 - - client.disconnect() - - # 检查服务器是否有错误 - if server_error: - raise AssertionError(f"Server error: {server_error}") - - finally: - server_thread.join(timeout=5) - - @pytest.mark.slow - def test_connection_timeout(self): - """测试连接超时""" - # 在Linux上，连接被拒绝通常会立即返回，所以我们改用模拟的方式 - from unittest.mock import MagicMock, patch - - with patch("socket.socket") as mock_socket_class: - mock_socket = MagicMock() - mock_socket_class.return_value = mock_socket - - # 模拟连接超时 - mock_socket.connect.side_effect = TimeoutError("Connection timeout") - - client = MockTcpClient(host="127.0.0.1", port=65534, timeout=1.0) - - start_time = time.time() - result = client.connect() - elapsed_time = time.time() - start_time - - assert result is False - assert client.connected is False - # 模拟的超时应该很快返回 - assert elapsed_time < 1.0 - - -# 性能测试 -@pytest.mark.slow -class TestTcpClientPerformance: - """TCP客户端性能测试""" - - @patch("socket.socket") - def test_multiple_connections_performance(self, mock_socket_class): - """测试多次连接的性能""" - import time - - mock_socket = MagicMock() - mock_socket_class.return_value = mock_socket - - client = MockTcpClient() - - # 测试多次连接和断开的性能 - num_connections = 100 - start_time = time.time() - - for _ in range(num_connections): - client.connect() - client.disconnect() - - elapsed_time = time.time() - start_time - - # 性能断言（这些值可以根据实际需要调整） - assert elapsed_time < 1.0 # 100次连接应在1秒内完成 - - # 验证调用次数 - assert mock_socket.connect.call_count == num_connections - assert mock_socket.close.call_count == num_connections - - @patch("socket.socket") - def test_large_data_transfer_performance(self, mock_socket_class): - """测试大数据传输性能""" - import time - - mock_socket = MagicMock() - mock_socket_class.return_value = mock_socket - - # 模拟大响应数据 - large_response = {"data": "x" * 100000} # 100KB数据 - response_json = json.dumps(large_response).encode("utf-8") - response_length = len(response_json).to_bytes(4, byteorder="big") - - # 模拟按顺序接收：先长度，后数据 - mock_socket.recv.side_effect = [response_length, response_json] - - client = MockTcpClient() - client.connect() - - # 测试大数据传输性能 - start_time = time.time() - - request_data = {"query": "large_data"} - result = client.send_request(request_data) - - elapsed_time = time.time() - start_time - - # 性能断言 - assert elapsed_time < 1.0 # 100KB数据传输应在1秒内完成 - assert result == large_response - assert len(result["data"]) == 100000 diff --git a/packages/sage-kernel/tests/unit/kernel/utils/network/test_local_tcp_server.py b/packages/sage-kernel/tests/unit/kernel/utils/network/test_local_tcp_server.py deleted file mode 100644 index 73f688ced7..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/utils/network/test_local_tcp_server.py +++ /dev/null @@ -1,1087 +0,0 @@ -""" -Test suite for sage.common.utils.network.local_tcp_server module - -This module tests TCP server functionality including connection management, -message handling, and server lifecycle operations. - -Created: 2024 -Test Framework: pytest -Coverage: TCP server, message handling, connection management -""" - -import pickle -import threading -import time -from typing import Any -from unittest.mock import MagicMock, patch - -import pytest - -from sage.common.utils.network.local_tcp_server import BaseTcpServer, LocalTcpServer - - -class TestBaseTcpServer: - """Test BaseTcpServer abstract base class functionality""" - - # Create a concrete implementation for testing - class ConcreteTcpServer(BaseTcpServer): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self.received_messages = [] - - def _handle_message_data( - self, message_data: bytes, client_address: tuple - ) -> dict[str, Any] | None: - try: - message = pickle.loads(message_data) - self.received_messages.append((message, client_address)) - return {"type": "test_response", "status": "success"} - except Exception: - return {"type": "error_response", "status": "error"} - - @pytest.mark.unit - def test_server_initialization(self): - """Test server initialization with default parameters""" - with patch("socket.socket"): - server = self.ConcreteTcpServer() - - assert server.server_name == "TcpServer" - assert server.host is not None - assert server.port is not None - assert server.running is False - assert server.server_socket is None - assert server.server_thread is None - assert isinstance(server.client_connections, dict) - - @pytest.mark.unit - def test_server_initialization_with_params(self): - """Test server initialization with custom parameters""" - mock_logger = MagicMock() - - with patch("socket.socket"): - server = self.ConcreteTcpServer( - host="192.168.1.100", - port=8080, - logger=mock_logger, - server_name="TestServer", - ) - - assert server.server_name == "TestServer" - assert server.host == "192.168.1.100" - assert server.port == 8080 - assert server.logger == mock_logger - - @pytest.mark.unit - def test_default_logger_creation(self): - """Test default logger creation""" - with patch("socket.socket"): - server = self.ConcreteTcpServer() - - assert server.logger is not None - assert server.logger.name == "TcpServer" - - @pytest.mark.unit - def test_get_host_ip_success(self): - """Test successful host IP retrieval""" - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value = mock_sock - mock_sock.getsockname.return_value = ("192.168.1.100", 12345) - - # Create server without triggering initialization calls to _get_host_ip - server = self.ConcreteTcpServer( - host="127.0.0.1" - ) # Provide host to avoid calling _get_host_ip - result = server._get_host_ip() - - assert result == "192.168.1.100" - mock_sock.connect.assert_called_once_with(("8.8.8.8", 80)) - - @pytest.mark.unit - def test_get_host_ip_fallback(self): - """Test host IP retrieval fallback to localhost""" - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value = mock_sock - mock_sock.connect.side_effect = Exception("Network error") - - server = self.ConcreteTcpServer() - result = server._get_host_ip() - - assert result == "127.0.0.1" - - @pytest.mark.unit - def test_allocate_tcp_port_success(self): - """Test successful TCP port allocation""" - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - - server = self.ConcreteTcpServer() - server.host = "127.0.0.1" - result = server._allocate_tcp_port() - - assert 19200 <= result < 20000 - mock_sock.bind.assert_called() - - @pytest.mark.unit - def test_server_start_success(self): - """Test successful server startup""" - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value = mock_sock - - server = self.ConcreteTcpServer(host="127.0.0.1", port=8080) - server.start() - - assert server.running is True - assert server.server_socket == mock_sock - assert server.server_thread is not None - mock_sock.setsockopt.assert_called_once() - mock_sock.bind.assert_called_once_with(("127.0.0.1", 8080)) - mock_sock.listen.assert_called_once_with(10) - - @pytest.mark.unit - def test_server_start_already_running(self): - """Test starting server when already running""" - with patch("socket.socket"): - server = self.ConcreteTcpServer() - server.running = True - - server.start() # Should not raise exception - - # Logger should warn about already running - assert server.running is True - - @pytest.mark.unit - def test_server_start_failure(self): - """Test server startup failure""" - with patch("socket.socket") as mock_socket: - # Set up the mock for initialization (_get_host_ip and _allocate_tcp_port) - mock_sock_get_ip = MagicMock() - mock_sock_get_ip.getsockname.return_value = ("127.0.0.1", 12345) - - mock_sock_allocate = MagicMock() - mock_sock_allocate.__enter__ = MagicMock(return_value=mock_sock_allocate) - mock_sock_allocate.__exit__ = MagicMock(return_value=None) - - # Set up the mock for the start method (which should fail) - mock_socket.side_effect = [ - mock_sock_get_ip, # Used by _get_host_ip - mock_sock_allocate, # Used by _allocate_tcp_port - Exception("Socket creation failed"), # Used by start() - should fail - ] - - server = self.ConcreteTcpServer() # This uses the first two mocks (success) - - with pytest.raises(Exception, match="Socket creation failed"): - server.start() # This uses the third mock (failure) - - assert server.running is False - - @pytest.mark.unit - def test_server_stop(self): - """Test server shutdown""" - with patch("socket.socket") as mock_socket: - # Set up mocks for initialization phase - mock_sock_get_ip = MagicMock() - mock_sock_get_ip.getsockname.return_value = ("127.0.0.1", 12345) - - mock_sock_allocate = MagicMock() - mock_sock_allocate.__enter__ = MagicMock(return_value=mock_sock_allocate) - mock_sock_allocate.__exit__ = MagicMock(return_value=None) - - # Set up mock for server socket - mock_sock_server = MagicMock() - - mock_socket.side_effect = [ - mock_sock_get_ip, # Used by _get_host_ip - mock_sock_allocate, # Used by _allocate_tcp_port - mock_sock_server, # Used by start() - ] - - server = self.ConcreteTcpServer() # Uses first two mocks - server.start() # Uses third mock - - # Mock thread to simulate it stopping - if server.server_thread is not None: - server.server_thread.is_alive = MagicMock(return_value=False) - - server.stop() - - assert server.running is False - mock_sock_server.close.assert_called_once() # Only the server socket should be closed - - @pytest.mark.unit - def test_server_stop_not_running(self): - """Test stopping server when not running""" - server = self.ConcreteTcpServer() - server.stop() # Should not raise exception - - assert server.running is False - - @pytest.mark.unit - def test_server_stop_thread_timeout(self): - """Test server shutdown with thread timeout""" - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value = mock_sock - - server = self.ConcreteTcpServer() - server.start() - - # Mock thread that doesn't stop gracefully - mock_thread = MagicMock() - mock_thread.is_alive.return_value = True - mock_thread.join.return_value = None - server.server_thread = mock_thread - - server.stop() - - assert server.running is False - assert mock_thread.join.call_count == 5 # Should retry 5 times - - @pytest.mark.unit - def test_receive_full_message_success(self): - """Test successful full message reception""" - mock_socket = MagicMock() - test_message = b"Hello, World!" - mock_socket.recv.return_value = test_message - - server = self.ConcreteTcpServer() - result = server._receive_full_message(mock_socket, len(test_message)) - - assert result == test_message - mock_socket.recv.assert_called_once_with(len(test_message)) - - @pytest.mark.unit - def test_receive_full_message_chunked(self): - """Test full message reception in chunks""" - mock_socket = MagicMock() - test_message = b"Hello, World! This is a longer message." - chunks = [test_message[:10], test_message[10:20], test_message[20:]] - mock_socket.recv.side_effect = chunks - - server = self.ConcreteTcpServer() - result = server._receive_full_message(mock_socket, len(test_message)) - - assert result == test_message - assert mock_socket.recv.call_count == 3 - - @pytest.mark.unit - def test_receive_full_message_connection_closed(self): - """Test message reception when connection is closed""" - mock_socket = MagicMock() - mock_socket.recv.return_value = b"" # Empty bytes = connection closed - - server = self.ConcreteTcpServer() - result = server._receive_full_message(mock_socket, 100) - - assert result is None - - @pytest.mark.unit - def test_send_response_dict(self): - """Test sending dictionary response""" - mock_socket = MagicMock() - response = {"type": "test", "data": "hello"} - - server = self.ConcreteTcpServer() - server._send_response(mock_socket, response) - - # Should send length header + pickled data - assert mock_socket.send.call_count == 2 - - # First call should be length header (4 bytes) - length_call = mock_socket.send.call_args_list[0][0][0] - assert len(length_call) == 4 - - # Second call should be serialized data - data_call = mock_socket.send.call_args_list[1][0][0] - deserialized = pickle.loads(data_call) - assert deserialized["type"] == "test" - assert deserialized["data"] == "hello" - assert "cwd" in deserialized # Should add current working directory - - @pytest.mark.unit - def test_send_response_bytes(self): - """Test sending bytes response""" - mock_socket = MagicMock() - response = b"raw bytes response" - - server = self.ConcreteTcpServer() - server._send_response(mock_socket, response) - - # Should send length header + raw bytes - assert mock_socket.send.call_count == 2 - - # Check the actual data sent - data_call = mock_socket.send.call_args_list[1][0][0] - assert data_call == response - - @pytest.mark.unit - def test_send_response_error(self): - """Test handling error during response sending""" - mock_socket = MagicMock() - mock_socket.send.side_effect = Exception("Send failed") - - server = self.ConcreteTcpServer() - # Should not raise exception, but log error - server._send_response(mock_socket, {"type": "test"}) - - @pytest.mark.unit - def test_serialize_response(self): - """Test response serialization""" - server = self.ConcreteTcpServer() - response = {"key": "value", "number": 42} - - serialized = server._serialize_response(response) - deserialized = pickle.loads(serialized) - - assert deserialized == response - - @pytest.mark.unit - def test_create_error_response(self): - """Test error response creation""" - server = self.ConcreteTcpServer() - original_message = {"type": "test_request", "request_id": "123"} - - error_response = server._create_error_response( - original_message, "ERR_TEST", "Test error message" - ) - - assert error_response["type"] == "test_request_response" - assert error_response["request_id"] == "123" - assert error_response["status"] == "error" - assert error_response["message"] == "Test error message" - assert error_response["payload"]["error_code"] == "ERR_TEST" - assert "timestamp" in error_response - - @pytest.mark.unit - def test_get_server_info(self): - """Test server information retrieval""" - server = self.ConcreteTcpServer(host="127.0.0.1", port=8080, server_name="TestServer") - - info = server.get_server_info() - - assert info["server_name"] == "TestServer" - assert info["host"] == "127.0.0.1" - assert info["port"] == 8080 - assert info["running"] is False - assert info["address"] == "127.0.0.1:8080" - - @pytest.mark.unit - def test_destructor(self): - """Test server destructor""" - with patch("socket.socket"): - server = self.ConcreteTcpServer() - server.start() - - # Mock stop method to verify it's called - server.stop = MagicMock() - - # Manually call destructor - server.__del__() - - server.stop.assert_called_once() - - -class TestLocalTcpServer: - """Test LocalTcpServer concrete implementation""" - - @pytest.mark.unit - def test_server_initialization(self): - """Test LocalTcpServer initialization""" - - def default_handler(msg, addr): - return {"type": "default_response"} - - with patch("socket.socket"): - server = LocalTcpServer(host="127.0.0.1", port=8080, default_handler=default_handler) - - assert server.server_name == "LocalTcpServer" - assert server.host == "127.0.0.1" - assert server.port == 8080 - assert server.default_handler == default_handler - assert isinstance(server.message_handlers, dict) - assert len(server.message_handlers) == 0 - - @pytest.mark.unit - def test_register_handler(self): - """Test message handler registration""" - - def test_handler(msg, addr): - return {"type": "test_response"} - - with patch("socket.socket"): - server = LocalTcpServer() - server.register_handler("test_message", test_handler) - - assert "test_message" in server.message_handlers - assert server.message_handlers["test_message"] == test_handler - - @pytest.mark.unit - def test_set_default_handler(self): - """Test default handler setting""" - - def new_default_handler(msg, addr): - return {"type": "new_default_response"} - - with patch("socket.socket"): - server = LocalTcpServer() - server.set_default_handler(new_default_handler) - - assert server.default_handler == new_default_handler - - @pytest.mark.unit - def test_unregister_handler(self): - """Test message handler unregistration""" - - def test_handler(msg, addr): - return {"type": "test_response"} - - with patch("socket.socket"): - server = LocalTcpServer() - server.register_handler("test_message", test_handler) - - assert "test_message" in server.message_handlers - - server.unregister_handler("test_message") - - assert "test_message" not in server.message_handlers - - @pytest.mark.unit - def test_unregister_nonexistent_handler(self): - """Test unregistering non-existent handler""" - with patch("socket.socket"): - server = LocalTcpServer() - # Should not raise exception - server.unregister_handler("nonexistent") - - @pytest.mark.unit - def test_get_registered_types(self): - """Test getting registered message types""" - - def handler1(msg, addr): - return {} - - def handler2(msg, addr): - return {} - - with patch("socket.socket"): - server = LocalTcpServer() - server.register_handler("type1", handler1) - server.register_handler("type2", handler2) - - types = server.get_registered_types() - - assert set(types) == {"type1", "type2"} - - @pytest.mark.unit - def test_extract_message_type_success(self): - """Test successful message type extraction""" - with patch("socket.socket"): - server = LocalTcpServer() - - # Test different type field names - assert server._extract_message_type({"type": "test"}) == "test" - assert server._extract_message_type({"message_type": "test"}) == "test" - assert server._extract_message_type({"msg_type": "test"}) == "test" - assert server._extract_message_type({"event_type": "test"}) == "test" - assert server._extract_message_type({"command": "test"}) == "test" - - @pytest.mark.unit - def test_extract_message_type_with_whitespace(self): - """Test message type extraction with whitespace""" - with patch("socket.socket"): - server = LocalTcpServer() - - assert server._extract_message_type({"type": " test "}) == "test" - - @pytest.mark.unit - def test_extract_message_type_failure(self): - """Test message type extraction failure cases""" - from typing import Any - - with patch("socket.socket"): - server = LocalTcpServer() - - # Non-dict message - test that method handles invalid input - # Use Any to avoid type checker errors when intentionally passing wrong types - invalid_input: Any = "not a dict" - assert server._extract_message_type(invalid_input) is None - - # No type fields - assert server._extract_message_type({"data": "test"}) is None - - # Empty type - assert server._extract_message_type({"type": ""}) is None - assert server._extract_message_type({"type": " "}) is None - - # Non-string type - assert server._extract_message_type({"type": 123}) is None - - @pytest.mark.unit - def test_handle_message_data_success(self): - """Test successful message data handling""" - - def test_handler(msg, addr): - return {"type": "test_response", "status": "success"} - - with patch("socket.socket"): - server = LocalTcpServer() - server.register_handler("test_message", test_handler) - - message = {"type": "test_message", "data": "hello"} - message_data = pickle.dumps(message) - - response = server._handle_message_data(message_data, ("127.0.0.1", 12345)) - - assert response is not None - assert response["type"] == "test_response" - assert response["status"] == "success" - - @pytest.mark.unit - def test_handle_message_data_deserialization_error(self): - """Test handling deserialization error""" - with patch("socket.socket"): - server = LocalTcpServer() - - # Invalid pickle data - message_data = b"invalid pickle data" - - response = server._handle_message_data(message_data, ("127.0.0.1", 12345)) - - assert response is not None - assert response["status"] == "error" - assert response["payload"]["error_code"] == "ERR_DESERIALIZATION_FAILED" - - @pytest.mark.unit - def test_process_message_with_registered_handler(self): - """Test message processing with registered handler""" - - def test_handler(msg, addr): - return {"type": "test_response", "received_data": msg["data"]} - - with patch("socket.socket"): - server = LocalTcpServer() - server.register_handler("test_message", test_handler) - - message = {"type": "test_message", "data": "hello world"} - - response = server._process_message(message, ("127.0.0.1", 12345)) - - assert response is not None - assert response["type"] == "test_response" - assert response["received_data"] == "hello world" - - @pytest.mark.unit - def test_process_message_with_default_handler(self): - """Test message processing with default handler""" - - def default_handler(msg, addr): - return {"type": "default_response", "message": "handled by default"} - - with patch("socket.socket"): - server = LocalTcpServer(default_handler=default_handler) - - message = {"type": "unknown_message", "data": "test"} - - response = server._process_message(message, ("127.0.0.1", 12345)) - - assert response is not None - assert response["type"] == "default_response" - assert response["message"] == "handled by default" - - @pytest.mark.unit - def test_process_message_no_handler(self): - """Test message processing with no applicable handler""" - with patch("socket.socket"): - server = LocalTcpServer() # No default handler - - message = {"type": "unknown_message", "data": "test"} - - response = server._process_message(message, ("127.0.0.1", 12345)) - - assert response is not None - assert response["status"] == "error" - assert response["payload"]["error_code"] == "ERR_NO_HANDLER" - - @pytest.mark.unit - def test_process_message_handler_exception(self): - """Test message processing when handler raises exception""" - - def failing_handler(msg, addr): - raise Exception("Handler failed") - - with patch("socket.socket"): - server = LocalTcpServer() - server.register_handler("test_message", failing_handler) - - message = {"type": "test_message", "data": "test"} - - response = server._process_message(message, ("127.0.0.1", 12345)) - - assert response is not None - assert response["status"] == "error" - assert response["payload"]["error_code"] == "ERR_HANDLER_FAILED" - assert "Handler failed" in response["message"] - - @pytest.mark.unit - def test_process_message_default_handler_exception(self): - """Test message processing when default handler raises exception""" - - def failing_default_handler(msg, addr): - raise Exception("Default handler failed") - - with patch("socket.socket"): - server = LocalTcpServer(default_handler=failing_default_handler) - - message = {"type": "unknown_message", "data": "test"} - - response = server._process_message(message, ("127.0.0.1", 12345)) - - assert response is not None - assert response["status"] == "error" - assert response["payload"]["error_code"] == "ERR_DEFAULT_HANDLER_FAILED" - - @pytest.mark.unit - def test_process_message_no_type(self): - """Test message processing with no extractable type""" - - def default_handler(msg, addr): - return {"type": "default_response"} - - with patch("socket.socket"): - server = LocalTcpServer(default_handler=default_handler) - - message = {"data": "no type field"} - - response = server._process_message(message, ("127.0.0.1", 12345)) - - assert response is not None - assert response["type"] == "default_response" - - @pytest.mark.unit - def test_create_error_response_with_env_fields(self): - """Test error response creation with environment fields""" - with patch("socket.socket"): - server = LocalTcpServer() - - original_message = { - "type": "test_request", - "request_id": "123", - "env_name": "test_env", - "env_uuid": "uuid-123", - } - - error_response = server._create_error_response( - original_message, "ERR_TEST", "Test error" - ) - - assert error_response["env_name"] == "test_env" - assert error_response["env_uuid"] == "uuid-123" - - @pytest.mark.unit - def test_send_response_with_cwd(self): - """Test response sending includes current working directory""" - mock_socket = MagicMock() - response = {"type": "test_response", "data": "hello"} - - with patch("socket.socket"): - server = LocalTcpServer() - server._send_response(mock_socket, response) - - # Verify the response was modified to include cwd - data_call = mock_socket.send.call_args_list[1][0][0] - deserialized = pickle.loads(data_call) - assert "cwd" in deserialized - assert deserialized["cwd"] == server.server_cwd - - @pytest.mark.unit - def test_get_server_info_extended(self): - """Test extended server information for LocalTcpServer""" - - def handler1(msg, addr): - return {} - - def default_handler(msg, addr): - return {} - - with patch("socket.socket"): - server = LocalTcpServer(default_handler=default_handler) - server.register_handler("type1", handler1) - - info = server.get_server_info() - - assert info["server_name"] == "LocalTcpServer" - assert "type1" in info["registered_message_types"] - assert info["has_default_handler"] is True - - @pytest.mark.unit - def test_thread_safety_handler_registration(self): - """Test thread safety of handler registration/unregistration""" - with patch("socket.socket"): - server = LocalTcpServer() - - def register_handlers(): - for i in range(100): - server.register_handler(f"type_{i}", lambda m, a: {}) - - def unregister_handlers(): - for i in range(50, 150): - server.unregister_handler(f"type_{i}") - - # Start multiple threads - threads = [ - threading.Thread(target=register_handlers), - threading.Thread(target=unregister_handlers), - ] - - for thread in threads: - thread.start() - - for thread in threads: - thread.join() - - # Should not crash, some handlers should remain - types = server.get_registered_types() - assert isinstance(types, list) - - -class TestIntegrationScenarios: - """Integration tests for TCP server functionality""" - - @pytest.mark.integration - def test_complete_message_handling_workflow(self): - """Test complete message handling workflow""" - received_messages = [] - - def status_handler(msg, addr): - received_messages.append(("status", msg, addr)) - return { - "type": "status_response", - "status": "success", - "request_id": msg.get("request_id"), - } - - def default_handler(msg, addr): - received_messages.append(("default", msg, addr)) - return {"type": "default_response", "status": "handled"} - - with patch("socket.socket"): - server = LocalTcpServer(default_handler=default_handler) - server.register_handler("status", status_handler) - - # Test registered handler - status_message = {"type": "status", "request_id": "123", "data": "test"} - status_data = pickle.dumps(status_message) - - response = server._handle_message_data(status_data, ("127.0.0.1", 12345)) - - assert response is not None - assert response["type"] == "status_response" - assert response["request_id"] == "123" - assert len(received_messages) == 1 - - # Test default handler - unknown_message = {"type": "unknown", "data": "test"} - unknown_data = pickle.dumps(unknown_message) - - response = server._handle_message_data(unknown_data, ("127.0.0.1", 12346)) - - assert response is not None - assert response["type"] == "default_response" - assert len(received_messages) == 2 - - @pytest.mark.integration - def test_server_lifecycle(self): - """Test complete server lifecycle""" - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value = mock_sock - - server = LocalTcpServer(host="127.0.0.1", port=8080) - - # Test startup - server.start() - assert server.running is True - - # Test server info - info = server.get_server_info() - assert info["running"] is True - assert info["address"] == "127.0.0.1:8080" - - # Test shutdown - server.stop() - assert server.running is False - - @pytest.mark.integration - def test_multiple_handler_types(self): - """Test server with multiple different handler types""" - results = {} - - def echo_handler(msg, addr): - results["echo"] = msg["data"] - return {"type": "echo_response", "echo": msg["data"]} - - def compute_handler(msg, addr): - result = msg["a"] + msg["b"] - results["compute"] = result - return {"type": "compute_response", "result": result} - - def log_handler(msg, addr): - results["log"] = f"Logged: {msg['message']}" - return None # No response - - with patch("socket.socket"): - server = LocalTcpServer() - server.register_handler("echo", echo_handler) - server.register_handler("compute", compute_handler) - server.register_handler("log", log_handler) - - # Test echo - echo_msg = {"type": "echo", "data": "hello world"} - response = server._process_message(echo_msg, ("127.0.0.1", 12345)) - assert response is not None, "Echo handler should return a response" - assert response["echo"] == "hello world" - assert results["echo"] == "hello world" - - # Test compute - compute_msg = {"type": "compute", "a": 5, "b": 3} - response = server._process_message(compute_msg, ("127.0.0.1", 12345)) - assert response is not None, "Compute handler should return a response" - assert response["result"] == 8 - assert results["compute"] == 8 - - # Test log (no response) - log_msg = {"type": "log", "message": "test log"} - response = server._process_message(log_msg, ("127.0.0.1", 12345)) - assert response is None - assert results["log"] == "Logged: test log" - - @pytest.mark.slow - def test_concurrent_message_processing(self): - """Test concurrent message processing""" - processed_messages = [] - processing_lock = threading.Lock() - - def slow_handler(msg, addr): - with processing_lock: - processed_messages.append(msg["id"]) - time.sleep(0.01) # Simulate processing time - return {"type": "slow_response", "processed_id": msg["id"]} - - with patch("socket.socket"): - server = LocalTcpServer() - server.register_handler("slow", slow_handler) - - # Simulate multiple concurrent messages - def process_message(msg_id): - message = {"type": "slow", "id": msg_id} - return server._process_message(message, ("127.0.0.1", 12345)) - - # Process multiple messages concurrently - threads = [] - for i in range(10): - thread = threading.Thread(target=process_message, args=(i,)) - threads.append(thread) - thread.start() - - for thread in threads: - thread.join() - - # All messages should be processed - assert len(processed_messages) == 10 - assert set(processed_messages) == set(range(10)) - - -class TestPerformanceScenarios: - """Performance and stress tests""" - - @pytest.mark.slow - def test_rapid_handler_registration(self): - """Test rapid handler registration and lookup""" - with patch("socket.socket"): - server = LocalTcpServer() - - # Register many handlers - start_time = time.time() - for i in range(1000): - server.register_handler(f"type_{i}", lambda m, a: {"response": i}) - end_time = time.time() - - # Should complete quickly - assert end_time - start_time < 1.0 - assert len(server.get_registered_types()) == 1000 - - @pytest.mark.slow - def test_large_message_handling(self): - """Test handling large messages""" - - def large_handler(msg, addr): - return {"type": "large_response", "size": len(msg["data"])} - - with patch("socket.socket"): - server = LocalTcpServer() - server.register_handler("large", large_handler) - - # Create large message (1MB) - large_data = "x" * (1024 * 1024) - message = {"type": "large", "data": large_data} - message_data = pickle.dumps(message) - - start_time = time.time() - response = server._handle_message_data(message_data, ("127.0.0.1", 12345)) - end_time = time.time() - - assert response is not None, "Large handler should return a response" - assert response["type"] == "large_response" - assert response["size"] == len(large_data) - # Should process within reasonable time - assert end_time - start_time < 5.0 - - @pytest.mark.slow - def test_handler_lookup_performance(self): - """Test performance of handler lookup with many registered handlers""" - handlers = {} - for i in range(1000): - handlers[f"type_{i}"] = lambda m, a: {"response": i} - - with patch("socket.socket"): - server = LocalTcpServer() - - # Register all handlers - for msg_type, handler in handlers.items(): - server.register_handler(msg_type, handler) - - # Test lookup performance - start_time = time.time() - for i in range(100): - message = {"type": f"type_{i % 1000}", "data": f"test_{i}"} - server._process_message(message, ("127.0.0.1", 12345)) - end_time = time.time() - - # Should complete quickly even with many handlers - assert end_time - start_time < 1.0 - - -class TestErrorHandlingScenarios: - """Error handling and edge case tests""" - - @pytest.mark.unit - def test_malformed_message_handling(self): - """Test handling various malformed messages""" - with patch("socket.socket"): - server = LocalTcpServer() - - # Test different malformed message types - test_cases = [ - None, # None message - 42, # Integer message - "string", # String message - [], # List message - {"type": None}, # None type - {"type": 123}, # Integer type - {"no_type_field": "value"}, # Missing type - ] - - for malformed_msg in test_cases: - try: - message_data = pickle.dumps(malformed_msg) - response = server._handle_message_data(message_data, ("127.0.0.1", 12345)) - - # Should return error response or use default handler - assert response is not None - if response.get("status") == "error": - assert "error_code" in response.get("payload", {}) - except Exception: - # If pickling fails, that's also acceptable - pass - - @pytest.mark.unit - def test_resource_cleanup_on_error(self): - """Test proper resource cleanup when errors occur""" - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value = mock_sock - - server = LocalTcpServer() - - # Simulate error during server start - mock_sock.bind.side_effect = Exception("Bind failed") - - with pytest.raises(Exception): # noqa: B017 - server.start() - - # Server should not be in running state - assert server.running is False - - @pytest.mark.unit - def test_concurrent_handler_modification(self): - """Test concurrent handler registration/unregistration doesn't break server""" - with patch("socket.socket"): - server = LocalTcpServer() - - def modify_handlers(): - for i in range(50): - server.register_handler(f"type_{i}", lambda m, a: {}) - if i % 10 == 0: - server.unregister_handler(f"type_{i // 2}") - - def process_messages(): - for i in range(100): - message = {"type": f"type_{i % 20}", "data": f"test_{i}"} - try: - server._process_message(message, ("127.0.0.1", 12345)) - except Exception: - pass # Some messages may fail due to handler changes - - # Run concurrent operations - threads = [ - threading.Thread(target=modify_handlers), - threading.Thread(target=process_messages), - ] - - for thread in threads: - thread.start() - - for thread in threads: - thread.join() - - # Server should still be functional - types = server.get_registered_types() - assert isinstance(types, list) - - @pytest.mark.unit - def test_memory_pressure_handling(self): - """Test server behavior under memory pressure""" - - def memory_intensive_handler(msg, addr): - # Create large response - large_data = "x" * (1024 * 1024) # 1MB - return {"type": "memory_response", "data": large_data} - - with patch("socket.socket"): - server = LocalTcpServer() - server.register_handler("memory_test", memory_intensive_handler) - - # Process multiple memory-intensive messages - responses = [] - for i in range(10): - message = {"type": "memory_test", "request": i} - try: - response = server._process_message(message, ("127.0.0.1", 12345)) - responses.append(response) - except MemoryError: - # Memory errors are acceptable under pressure - break - except Exception: - # Other exceptions should be handled gracefully - pass - - # Should handle at least some requests - assert len(responses) >= 0 - - -if __name__ == "__main__": - pytest.main([__file__]) diff --git a/packages/sage-kernel/tests/unit/kernel/utils/ray/test_ray_initialization.py b/packages/sage-kernel/tests/unit/kernel/utils/ray/test_ray_initialization.py deleted file mode 100644 index 52cf01b028..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/utils/ray/test_ray_initialization.py +++ /dev/null @@ -1,124 +0,0 @@ -""" -测试Ray初始化功能和runtime_env配置 -""" - -import os -import sys - -import pytest - -from sage.kernel.utils.ray.ray_utils import ( - RAY_AVAILABLE, - ensure_ray_initialized, - get_sage_kernel_runtime_env, -) - -# 添加正确的项目路径 -current_dir = os.path.dirname(os.path.abspath(__file__)) -sage_kernel_src = os.path.join(current_dir, "../../../../../src") -sys.path.insert(0, os.path.abspath(sage_kernel_src)) - - -@pytest.mark.skipif(not RAY_AVAILABLE, reason="Ray not available") -class TestRayInitialization: - """测试Ray初始化功能""" - - def test_get_sage_kernel_runtime_env(self): - """测试获取Sage内核运行环境配置""" - runtime_env = get_sage_kernel_runtime_env() - - assert isinstance(runtime_env, dict) - assert "py_modules" in runtime_env - assert "env_vars" in runtime_env - assert "PYTHONPATH" in runtime_env["env_vars"] - - # 验证py_modules路径存在 - assert len(runtime_env["py_modules"]) > 0 - sage_src_path = runtime_env["py_modules"][0] - assert os.path.exists(sage_src_path) - assert sage_src_path.endswith("/src") - - def test_ensure_ray_initialized_with_default_env(self): - """测试使用默认环境初始化Ray""" - import ray - - # 如果Ray已经初始化，先关闭 - if ray.is_initialized(): - ray.shutdown() - - # 测试初始化 - try: - ensure_ray_initialized() - except Exception as e: - # 在CI环境中，如果资源不足导致初始化失败，跳过测试 - pytest.skip(f"Ray initialization failed in CI environment: {e}") - - assert ray.is_initialized() - - # 验证Ray Actor可以导入sage模块 - @ray.remote - def test_sage_import(): - try: - from sage.platform.queue import RayQueueDescriptor # noqa: F401 - - return True - except ImportError as e: - return str(e) - - # Add project source to PYTHONPATH before importing sage modules - # noqa: E402 - import must occur after sys.path modification - - try: - result = ray.get(test_sage_import.remote(), timeout=30) - assert result is True, f"无法在Ray Actor中导入sage模块: {result}" - except Exception as e: - # 在CI环境中，如果ray任务执行失败，跳过而不是失败 - pytest.skip(f"Ray task execution failed in CI environment: {e}") - - def test_ensure_ray_initialized_with_custom_env(self): - """测试使用自定义环境初始化Ray""" - import ray - - # 如果Ray已经初始化，先关闭 - if ray.is_initialized(): - ray.shutdown() - - # 获取默认的 sage runtime_env 并合并自定义环境变量 - default_env = get_sage_kernel_runtime_env() - custom_runtime_env = { - "py_modules": default_env.get("py_modules", []), - "env_vars": { - **default_env.get("env_vars", {}), - "TEST_VAR": "test_value", # 添加自定义变量 - }, - } - - # 测试初始化 - try: - ensure_ray_initialized(runtime_env=custom_runtime_env) - except Exception as e: - pytest.skip(f"Ray initialization failed: {e}") - - assert ray.is_initialized() - - # 验证自定义环境变量 - @ray.remote - def check_env_var(): - import os - - return os.environ.get("TEST_VAR") - - try: - result = ray.get(check_env_var.remote(), timeout=10) - assert result == "test_value" - except Exception as e: - # 如果环境变量传播失败，跳过测试而不是失败 - # 这可能是 Ray 版本或配置问题 - pytest.skip(f"Environment variable propagation test failed: {e}") - - def teardown_method(self): - """清理测试环境""" - import ray - - if ray.is_initialized(): - ray.shutdown() diff --git a/packages/sage-kernel/tests/unit/kernel/utils/serialization/test_config.py b/packages/sage-kernel/tests/unit/kernel/utils/serialization/test_config.py deleted file mode 100644 index 391a8868ac..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/utils/serialization/test_config.py +++ /dev/null @@ -1,509 +0,0 @@ -""" -Tests for sage.common.utils.serialization.config module -=============================================== - -单元测试序列化配置模块的功能，包括： -- 黑名单配置常量 -- 排除属性列表 -- Ray相关排除配置 -""" - -import threading -from unittest.mock import Mock - -import pytest - -from sage.common.utils.serialization.config import ( - ATTRIBUTE_BLACKLIST, - BLACKLIST, - RAY_OPERATOR_EXCLUDE_ATTRS, - RAY_TRANSFORMATION_EXCLUDE_ATTRS, - SKIP_VALUE, -) - - -@pytest.mark.unit -class TestBlacklistConfiguration: - """黑名单配置测试""" - - def test_blacklist_contains_expected_types(self): - """测试黑名单包含预期的类型""" - # 验证线程类型在黑名单中 - assert threading.Thread in BLACKLIST - - # 验证文件类型在黑名单中 - assert type(open(__file__)) in BLACKLIST or any( - str(type(open(__file__))) in str(t) for t in BLACKLIST - ) - - # 验证锁类型在黑名单中 - assert type(threading.Lock()) in BLACKLIST - assert type(threading.RLock()) in BLACKLIST - - # 验证事件和条件变量在黑名单中 - assert threading.Event in BLACKLIST - assert threading.Condition in BLACKLIST - - def test_blacklist_is_list(self): - """测试黑名单是列表类型""" - assert isinstance(BLACKLIST, list) - assert len(BLACKLIST) > 0 - - def test_blacklist_types_are_classes(self): - """测试黑名单中的项都是类型""" - for item in BLACKLIST: - assert isinstance(item, type), f"Item {item} is not a type" - - def test_thread_type_detection(self): - """测试线程类型检测""" - thread = threading.Thread() - assert type(thread) in BLACKLIST - - # 测试实际的线程实例 - assert isinstance(thread, threading.Thread) - - -@pytest.mark.unit -class TestAttributeBlacklist: - """属性黑名单测试""" - - def test_attribute_blacklist_is_set(self): - """测试属性黑名单是集合类型""" - assert isinstance(ATTRIBUTE_BLACKLIST, set) - assert len(ATTRIBUTE_BLACKLIST) > 0 - - def test_logger_attributes_excluded(self): - """测试日志相关属性被排除""" - assert "logger" in ATTRIBUTE_BLACKLIST - assert "_logger" in ATTRIBUTE_BLACKLIST - - def test_socket_attributes_excluded(self): - """测试socket相关属性被排除""" - assert "server_socket" in ATTRIBUTE_BLACKLIST - assert "client_socket" in ATTRIBUTE_BLACKLIST - - def test_thread_attributes_excluded(self): - """测试线程相关属性被排除""" - assert "server_thread" in ATTRIBUTE_BLACKLIST - assert "_server_thread" in ATTRIBUTE_BLACKLIST - - def test_weakref_excluded(self): - """测试弱引用属性被排除""" - assert "__weakref__" in ATTRIBUTE_BLACKLIST - - def test_runtime_context_excluded(self): - """测试运行时上下文被排除""" - assert "runtime_context" in ATTRIBUTE_BLACKLIST - - def test_env_excluded(self): - """测试环境引用被排除""" - assert "env" in ATTRIBUTE_BLACKLIST - - def test_attribute_names_are_strings(self): - """测试属性名都是字符串""" - for attr in ATTRIBUTE_BLACKLIST: - assert isinstance(attr, str), f"Attribute {attr} is not a string" - assert len(attr) > 0, "Attribute name cannot be empty" - - -@pytest.mark.unit -class TestSkipValue: - """SKIP_VALUE测试""" - - def test_skip_value_is_sentinel(self): - """测试SKIP_VALUE是哨兵对象""" - assert SKIP_VALUE is not None - assert SKIP_VALUE is not False - assert SKIP_VALUE is not True - assert SKIP_VALUE != 0 - assert SKIP_VALUE != "" - assert SKIP_VALUE != [] - assert SKIP_VALUE != {} - - def test_skip_value_uniqueness(self): - """测试SKIP_VALUE的唯一性""" - # SKIP_VALUE应该是唯一的对象 - assert SKIP_VALUE is SKIP_VALUE - assert id(SKIP_VALUE) == id(SKIP_VALUE) - - def test_skip_value_type(self): - """测试SKIP_VALUE的类型""" - assert isinstance(SKIP_VALUE, object) - assert type(SKIP_VALUE) is object - - -@pytest.mark.unit -class TestRayTransformationExcludeAttrs: - """Ray转换排除属性测试""" - - def test_ray_transformation_exclude_attrs_is_list(self): - """测试Ray转换排除属性是列表""" - assert isinstance(RAY_TRANSFORMATION_EXCLUDE_ATTRS, list) - assert len(RAY_TRANSFORMATION_EXCLUDE_ATTRS) > 0 - - def test_ray_transformation_includes_common_attrs(self): - """测试Ray转换排除包含通用属性""" - assert "logger" in RAY_TRANSFORMATION_EXCLUDE_ATTRS - assert "_logger" in RAY_TRANSFORMATION_EXCLUDE_ATTRS - assert "env" in RAY_TRANSFORMATION_EXCLUDE_ATTRS - assert "runtime_context" in RAY_TRANSFORMATION_EXCLUDE_ATTRS - - def test_ray_transformation_includes_factory_attrs(self): - """测试Ray转换排除包含工厂属性""" - assert "_dag_node_factory" in RAY_TRANSFORMATION_EXCLUDE_ATTRS - assert "_operator_factory" in RAY_TRANSFORMATION_EXCLUDE_ATTRS - assert "_function_factory" in RAY_TRANSFORMATION_EXCLUDE_ATTRS - - def test_ray_transformation_includes_socket_attrs(self): - """测试Ray转换排除包含socket属性""" - assert "server_socket" in RAY_TRANSFORMATION_EXCLUDE_ATTRS - assert "server_thread" in RAY_TRANSFORMATION_EXCLUDE_ATTRS - assert "_server_thread" in RAY_TRANSFORMATION_EXCLUDE_ATTRS - - def test_ray_transformation_attr_names_are_strings(self): - """测试Ray转换排除属性名都是字符串""" - for attr in RAY_TRANSFORMATION_EXCLUDE_ATTRS: - assert isinstance(attr, str), f"Attribute {attr} is not a string" - assert len(attr) > 0, "Attribute name cannot be empty" - - -@pytest.mark.unit -class TestRayOperatorExcludeAttrs: - """Ray算子排除属性测试""" - - def test_ray_operator_exclude_attrs_is_list(self): - """测试Ray算子排除属性是列表""" - assert isinstance(RAY_OPERATOR_EXCLUDE_ATTRS, list) - assert len(RAY_OPERATOR_EXCLUDE_ATTRS) > 0 - - def test_ray_operator_includes_logger_attrs(self): - """测试Ray算子排除包含日志属性""" - assert "logger" in RAY_OPERATOR_EXCLUDE_ATTRS - assert "_logger" in RAY_OPERATOR_EXCLUDE_ATTRS - - def test_ray_operator_includes_context_attrs(self): - """测试Ray算子排除包含上下文属性""" - assert "runtime_context" in RAY_OPERATOR_EXCLUDE_ATTRS - assert "emit_context" in RAY_OPERATOR_EXCLUDE_ATTRS - - def test_ray_operator_includes_socket_attrs(self): - """测试Ray算子排除包含socket属性""" - assert "server_socket" in RAY_OPERATOR_EXCLUDE_ATTRS - assert "client_socket" in RAY_OPERATOR_EXCLUDE_ATTRS - assert "server_thread" in RAY_OPERATOR_EXCLUDE_ATTRS - assert "_server_thread" in RAY_OPERATOR_EXCLUDE_ATTRS - - def test_ray_operator_attr_names_are_strings(self): - """测试Ray算子排除属性名都是字符串""" - for attr in RAY_OPERATOR_EXCLUDE_ATTRS: - assert isinstance(attr, str), f"Attribute {attr} is not a string" - assert len(attr) > 0, "Attribute name cannot be empty" - - def test_ray_operator_excludes_weakref_comment(self): - """测试Ray算子排除列表关于__weakref__的注释""" - # 检查注释中提到__weakref__不在列表中的原因 - # 这是一个文档测试，确保代码和注释保持一致 - assert "__weakref__" not in RAY_OPERATOR_EXCLUDE_ATTRS - - -@pytest.mark.unit -class TestExcludeListsComparison: - """排除列表比较测试""" - - def test_attribute_blacklist_vs_ray_lists(self): - """测试通用属性黑名单与Ray专用列表的关系""" - # Ray专用列表应该包含一些通用黑名单的属性 - common_attrs = {"logger", "_logger", "server_socket", "server_thread"} - - # 检查通用黑名单 - assert common_attrs.issubset(ATTRIBUTE_BLACKLIST) - - # 检查Ray转换列表 - assert common_attrs.issubset(set(RAY_TRANSFORMATION_EXCLUDE_ATTRS)) - - # 检查Ray算子列表（除了server_thread可能不同） - ray_operator_set = set(RAY_OPERATOR_EXCLUDE_ATTRS) - assert "logger" in ray_operator_set - assert "_logger" in ray_operator_set - assert "server_socket" in ray_operator_set - - def test_ray_lists_differences(self): - """测试Ray转换和算子列表的差异""" - transformation_set = set(RAY_TRANSFORMATION_EXCLUDE_ATTRS) - operator_set = set(RAY_OPERATOR_EXCLUDE_ATTRS) - - # 转换列表应该包含工厂属性，算子列表可能不包含 - factory_attrs = {"_dag_node_factory", "_operator_factory", "_function_factory"} - assert factory_attrs.issubset(transformation_set) - - # 算子列表应该包含发射上下文，转换列表可能不包含 - if "emit_context" in operator_set: - # 如果算子列表包含emit_context，验证这个设计选择 - assert "emit_context" in RAY_OPERATOR_EXCLUDE_ATTRS - - def test_no_duplicate_attrs_in_lists(self): - """测试列表中没有重复属性""" - # Ray转换列表不应有重复 - transformation_list = RAY_TRANSFORMATION_EXCLUDE_ATTRS - assert len(transformation_list) == len(set(transformation_list)) - - # Ray算子列表不应有重复 - operator_list = RAY_OPERATOR_EXCLUDE_ATTRS - assert len(operator_list) == len(set(operator_list)) - - -@pytest.mark.unit -class TestConfigurationUsageScenarios: - """配置使用场景测试""" - - def test_blacklist_type_checking(self): - """测试黑名单类型检查场景""" - # 模拟需要检查对象类型是否在黑名单中的场景 - test_objects = [ - threading.Thread(), - threading.Lock(), - threading.RLock(), - threading.Event(), - threading.Condition(threading.Lock()), - ] - - for obj in test_objects: - obj_type = type(obj) - assert obj_type in BLACKLIST, f"Object type {obj_type} should be in blacklist" - - def test_attribute_filtering_scenario(self): - """测试属性过滤场景""" - - # 模拟一个包含各种属性的对象 - class TestObject: - def __init__(self): - self.logger = Mock() - self._logger = Mock() - self.server_socket = Mock() - self.client_socket = Mock() - self.server_thread = Mock() - self._server_thread = Mock() - self.runtime_context = Mock() - self.env = Mock() - self.normal_attr = "should_be_kept" - self.another_attr = 42 - - test_obj = TestObject() - - # 模拟过滤逻辑 - filtered_attrs = [] - for attr_name in dir(test_obj): - if not attr_name.startswith("__") and attr_name not in ATTRIBUTE_BLACKLIST: - filtered_attrs.append(attr_name) - - # 验证正常属性被保留 - assert "normal_attr" in filtered_attrs - assert "another_attr" in filtered_attrs - - # 验证黑名单属性被过滤 - assert "logger" not in filtered_attrs - assert "_logger" not in filtered_attrs - assert "server_socket" not in filtered_attrs - assert "runtime_context" not in filtered_attrs - - def test_ray_specific_filtering_scenario(self): - """测试Ray特定过滤场景""" - - # 模拟Ray转换对象 - class MockRayTransformation: - def __init__(self): - self.logger = Mock() - self._dag_node_factory = Mock() - self._operator_factory = Mock() - self._function_factory = Mock() - self.env = Mock() - self.runtime_context = Mock() - self.transformation_data = "important_data" - - # 模拟Ray算子对象 - class MockRayOperator: - def __init__(self): - self.logger = Mock() - self._logger = Mock() - self.runtime_context = Mock() - self.emit_context = Mock() - self.server_socket = Mock() - self.operator_data = "important_data" - - transformation = MockRayTransformation() - operator = MockRayOperator() - - # 测试转换对象过滤 - transform_filtered = [] - for attr_name in dir(transformation): - if not attr_name.startswith("__") and attr_name not in RAY_TRANSFORMATION_EXCLUDE_ATTRS: - transform_filtered.append(attr_name) - - assert "transformation_data" in transform_filtered - assert "logger" not in transform_filtered - assert "_dag_node_factory" not in transform_filtered - - # 测试算子对象过滤 - operator_filtered = [] - for attr_name in dir(operator): - if not attr_name.startswith("__") and attr_name not in RAY_OPERATOR_EXCLUDE_ATTRS: - operator_filtered.append(attr_name) - - assert "operator_data" in operator_filtered - assert "logger" not in operator_filtered - assert "emit_context" not in operator_filtered - - def test_skip_value_usage_scenario(self): - """测试SKIP_VALUE使用场景""" - - # 模拟序列化过程中使用SKIP_VALUE的场景 - def serialize_attribute(value): - """模拟属性序列化函数""" - if value is None: - return None - elif isinstance(value, (str, int, float, bool)): - return value - elif type(value) in BLACKLIST: - return SKIP_VALUE - else: - return str(value) # 简化处理 - - # 测试各种值的序列化 - test_values = [ - ("normal_string", "normal_string"), - (42, 42), - (None, None), - (threading.Thread(), SKIP_VALUE), - (threading.Lock(), SKIP_VALUE), - ] - - for input_value, expected_output in test_values: - result = serialize_attribute(input_value) - if expected_output is SKIP_VALUE: - assert result is SKIP_VALUE - else: - assert result == expected_output - - -@pytest.mark.integration -class TestSerializationConfigIntegration: - """序列化配置集成测试""" - - def test_complete_object_filtering_workflow(self): - """测试完整的对象过滤工作流程""" - - # 创建一个复杂的测试对象 - class ComplexObject: - def __init__(self): - # 正常属性 - self.name = "test_object" - self.value = 100 - self.data = {"key": "value"} - - # 应该被过滤的属性 - self.logger = Mock() - self._logger = Mock() - self.server_socket = Mock() - self.client_socket = Mock() - self.server_thread = threading.Thread() - self._server_thread = threading.Thread() - self.runtime_context = Mock() - self.env = Mock() - - # Ray特定属性 - self._dag_node_factory = Mock() - self._operator_factory = Mock() - self._function_factory = Mock() - self.emit_context = Mock() - - obj = ComplexObject() - - # 模拟完整的过滤过程 - def filter_object_attributes(obj): - """过滤对象属性的模拟函数""" - filtered = {} - - for attr_name in dir(obj): - if attr_name.startswith("__"): - continue - - attr_value = getattr(obj, attr_name) - - # 检查属性名是否在黑名单中 - if attr_name in ATTRIBUTE_BLACKLIST: - continue - - # 检查属性值类型是否在黑名单中 - if type(attr_value) in BLACKLIST: - continue - - # 检查是否为可调用对象（方法） - if callable(attr_value): - continue - - filtered[attr_name] = attr_value - - return filtered - - # 执行过滤 - filtered_attrs = filter_object_attributes(obj) - - # 验证正常属性被保留 - assert "name" in filtered_attrs - assert "value" in filtered_attrs - assert "data" in filtered_attrs - assert filtered_attrs["name"] == "test_object" - assert filtered_attrs["value"] == 100 - - # 验证黑名单属性被过滤 - assert "logger" not in filtered_attrs - assert "_logger" not in filtered_attrs - assert "server_socket" not in filtered_attrs - assert "server_thread" not in filtered_attrs - assert "runtime_context" not in filtered_attrs - assert "env" not in filtered_attrs - - def test_ray_specific_object_filtering(self): - """测试Ray特定对象过滤""" - - class RayTransformationObject: - def __init__(self): - self.transform_id = "transform_001" - self.input_data = [1, 2, 3] - self.output_schema = {"type": "array"} - - # 应该被Ray转换过滤器过滤的属性 - self.logger = Mock() - self._dag_node_factory = Mock() - self._operator_factory = Mock() - self._function_factory = Mock() - self.env = Mock() - self.runtime_context = Mock() - self.server_socket = Mock() - - transform_obj = RayTransformationObject() - - # 模拟Ray转换对象过滤 - def filter_ray_transformation(obj): - filtered = {} - for attr_name in dir(obj): - if ( - not attr_name.startswith("__") - and not callable(getattr(obj, attr_name)) - and attr_name not in RAY_TRANSFORMATION_EXCLUDE_ATTRS - ): - filtered[attr_name] = getattr(obj, attr_name) - return filtered - - filtered = filter_ray_transformation(transform_obj) - - # 验证业务属性被保留 - assert "transform_id" in filtered - assert "input_data" in filtered - assert "output_schema" in filtered - - # 验证Ray特定属性被过滤 - assert "logger" not in filtered - assert "_dag_node_factory" not in filtered - assert "_operator_factory" not in filtered - assert "runtime_context" not in filtered diff --git a/packages/sage-kernel/tests/unit/kernel/utils/serialization/test_exceptions.py b/packages/sage-kernel/tests/unit/kernel/utils/serialization/test_exceptions.py deleted file mode 100644 index 026b7a5299..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/utils/serialization/test_exceptions.py +++ /dev/null @@ -1,371 +0,0 @@ -""" -Tests for sage.common.utils.serialization.exceptions module -=================================================== - -单元测试序列化异常模块的功能，包括： -- SerializationError异常类 -- 异常继承关系 -- 异常使用场景 -""" - -import pytest - -from sage.common.utils.serialization.exceptions import SerializationError - - -@pytest.mark.unit -class TestSerializationError: - """SerializationError异常类测试""" - - def test_serialization_error_is_exception(self): - """测试SerializationError是Exception的子类""" - assert issubclass(SerializationError, Exception) - - def test_serialization_error_instantiation(self): - """测试SerializationError实例化""" - error = SerializationError("Test error message") - assert isinstance(error, SerializationError) - assert isinstance(error, Exception) - assert str(error) == "Test error message" - - def test_serialization_error_without_message(self): - """测试不带消息的SerializationError""" - error = SerializationError() - assert isinstance(error, SerializationError) - assert str(error) == "" - - def test_serialization_error_with_args(self): - """测试带多个参数的SerializationError""" - error = SerializationError("Error", "Additional info", 123) - assert len(error.args) == 3 - assert error.args[0] == "Error" - assert error.args[1] == "Additional info" - assert error.args[2] == 123 - - def test_serialization_error_str_representation(self): - """测试SerializationError字符串表示""" - error = SerializationError("Serialization failed") - assert str(error) == "Serialization failed" - - error_multi = SerializationError("Error", "Details") - assert str(error_multi) == "('Error', 'Details')" - - def test_serialization_error_repr_representation(self): - """测试SerializationError repr表示""" - error = SerializationError("Test error") - repr_str = repr(error) - assert "SerializationError" in repr_str - assert "Test error" in repr_str - - -@pytest.mark.unit -class TestSerializationErrorUsageScenarios: - """SerializationError使用场景测试""" - - def test_raise_serialization_error(self): - """测试抛出SerializationError""" - with pytest.raises(SerializationError) as exc_info: - raise SerializationError("Failed to serialize object") - - assert str(exc_info.value) == "Failed to serialize object" - assert isinstance(exc_info.value, SerializationError) - - def test_catch_serialization_error(self): - """测试捕获SerializationError""" - - def failing_serialization(): - raise SerializationError("Serialization failed") - - try: - failing_serialization() - raise AssertionError("Should have raised SerializationError") - except SerializationError as e: - assert str(e) == "Serialization failed" - except Exception: - raise AssertionError("Should have caught SerializationError specifically") - - def test_serialization_error_with_chaining(self): - """测试SerializationError异常链""" - - def inner_function(): - raise ValueError("Original error") - - def outer_function(): - try: - inner_function() - except ValueError as e: - raise SerializationError("Serialization failed") from e - - with pytest.raises(SerializationError) as exc_info: - outer_function() - - assert str(exc_info.value) == "Serialization failed" - assert isinstance(exc_info.value.__cause__, ValueError) - assert str(exc_info.value.__cause__) == "Original error" - - def test_serialization_error_context_manager(self): - """测试在上下文管理器中使用SerializationError""" - - class SerializationContext: - def __enter__(self): - return self - - def __exit__(self, exc_type, exc_val, exc_tb): - if exc_type is not None: - raise SerializationError(f"Context error: {exc_val}") from exc_val - - with pytest.raises(SerializationError) as exc_info: - with SerializationContext(): - raise ValueError("Context test error") - - assert "Context error: Context test error" in str(exc_info.value) - - def test_serialization_error_with_detailed_info(self): - """测试带详细信息的SerializationError""" - object_info = { - "type": "CustomObject", - "id": 12345, - "attributes": ["attr1", "attr2"], - } - - error_message = f"Failed to serialize {object_info['type']} with ID {object_info['id']}" - - with pytest.raises(SerializationError) as exc_info: - raise SerializationError(error_message, object_info) - - assert error_message in str(exc_info.value) - assert len(exc_info.value.args) == 2 - assert exc_info.value.args[1] == object_info - - def test_serialization_error_hierarchy(self): - """测试SerializationError在异常层次中的位置""" - - def function_that_might_fail(fail_type="serialization"): - if fail_type == "serialization": - raise SerializationError("Serialization specific error") - elif fail_type == "generic": - raise Exception("Generic error") - elif fail_type == "value": - raise ValueError("Value error") - - # 测试捕获SerializationError - with pytest.raises(SerializationError): - function_that_might_fail("serialization") - - # 测试捕获为Exception - with pytest.raises(Exception): # noqa: B017 - function_that_might_fail("serialization") - - # 测试不同的异常类型 - with pytest.raises(ValueError): - function_that_might_fail("value") - - -@pytest.mark.unit -class TestSerializationErrorInFunctionDecorators: - """测试SerializationError在函数装饰器中的使用""" - - def test_serialization_error_decorator(self): - """测试序列化错误装饰器""" - - def serialization_error_handler(func): - def wrapper(*args, **kwargs): - try: - return func(*args, **kwargs) - except Exception as e: - if isinstance(e, SerializationError): - raise - else: - raise SerializationError(f"Serialization failed in {func.__name__}") from e - - return wrapper - - @serialization_error_handler - def problematic_serialization(): - raise ValueError("Some internal error") - - with pytest.raises(SerializationError) as exc_info: - problematic_serialization() - - assert "Serialization failed in problematic_serialization" in str(exc_info.value) - assert isinstance(exc_info.value.__cause__, ValueError) - - def test_serialization_error_retry_decorator(self): - """测试带重试的序列化错误装饰器""" - - def retry_on_serialization_error(max_retries=3): - def decorator(func): - def wrapper(*args, **kwargs): - for attempt in range(max_retries): - try: - return func(*args, **kwargs) - except SerializationError as e: - if attempt == max_retries - 1: - raise SerializationError( - f"Failed after {max_retries} attempts: {str(e)}" - ) from e - continue - return None - - return wrapper - - return decorator - - call_count = 0 - - @retry_on_serialization_error(max_retries=3) - def unreliable_serialization(): - nonlocal call_count - call_count += 1 - raise SerializationError(f"Attempt {call_count} failed") - - with pytest.raises(SerializationError) as exc_info: - unreliable_serialization() - - assert call_count == 3 - assert "Failed after 3 attempts" in str(exc_info.value) - - -@pytest.mark.integration -class TestSerializationErrorIntegration: - """SerializationError集成测试""" - - def test_serialization_error_in_mock_serializer(self): - """测试在模拟序列化器中使用SerializationError""" - import json - import pickle - - class MockSerializer: - def __init__(self, method="json"): - self.method = method - - def serialize(self, obj): - try: - if self.method == "json": - return json.dumps(obj) - elif self.method == "pickle": - return pickle.dumps(obj) - else: - raise ValueError(f"Unknown serialization method: {self.method}") - except (TypeError, ValueError, pickle.PickleError) as e: - raise SerializationError( - f"Failed to serialize object using {self.method}: {str(e)}" - ) from e - - def deserialize(self, data): - try: - if self.method == "json": - return json.loads(data) - elif self.method == "pickle": - return pickle.loads(data) - else: - raise ValueError(f"Unknown deserialization method: {self.method}") - except ( - TypeError, - ValueError, - pickle.PickleError, - json.JSONDecodeError, - ) as e: - raise SerializationError( - f"Failed to deserialize data using {self.method}: {str(e)}" - ) from e - - # 测试成功的序列化 - serializer = MockSerializer("json") - data = {"key": "value", "number": 42} - serialized = serializer.serialize(data) - deserialized = serializer.deserialize(serialized) - assert deserialized == data - - # 测试JSON序列化失败 - with pytest.raises(SerializationError) as exc_info: - serializer.serialize({1, 2, 3}) # set不能JSON序列化 - - assert "Failed to serialize object using json" in str(exc_info.value) - assert isinstance(exc_info.value.__cause__, TypeError) - - # 测试JSON反序列化失败 - with pytest.raises(SerializationError) as exc_info: - serializer.deserialize("invalid json") - - assert "Failed to deserialize data using json" in str(exc_info.value) - assert isinstance(exc_info.value.__cause__, json.JSONDecodeError) - - # 测试未知方法 - unknown_serializer = MockSerializer("unknown") - with pytest.raises(SerializationError) as exc_info: - unknown_serializer.serialize(data) - - assert "Unknown serialization method: unknown" in str(exc_info.value) - - def test_serialization_error_with_complex_object(self): - """测试复杂对象的序列化错误""" - - class ComplexObject: - def __init__(self): - self.name = "complex" - self.data = {"nested": {"deep": "value"}} - self.function = lambda x: x * 2 # 不可序列化的函数 - - def serialize_object(obj): - """模拟对象序列化函数""" - try: - # 尝试简单的属性提取 - result = {} - for attr_name in dir(obj): - if not attr_name.startswith("_"): - attr_value = getattr(obj, attr_name) - if callable(attr_value): - raise TypeError(f"Cannot serialize callable attribute: {attr_name}") - result[attr_name] = attr_value - return result - except Exception as e: - raise SerializationError(f"Failed to serialize {obj.__class__.__name__}") from e - - complex_obj = ComplexObject() - - with pytest.raises(SerializationError) as exc_info: - serialize_object(complex_obj) - - assert "Failed to serialize ComplexObject" in str(exc_info.value) - assert isinstance(exc_info.value.__cause__, TypeError) - assert "Cannot serialize callable attribute" in str(exc_info.value.__cause__) - - def test_serialization_error_logging(self): - """测试SerializationError的日志记录""" - import importlib - import logging - - # 使用importlib来避免io模块冲突 - io_module = importlib.import_module("io") - StringIO = io_module.StringIO - - # 设置日志捕获 - log_capture = StringIO() - logger = logging.getLogger("test_serialization") - handler = logging.StreamHandler(log_capture) - handler.setLevel(logging.ERROR) - logger.addHandler(handler) - logger.setLevel(logging.ERROR) - - def serialize_with_logging(obj): - try: - # 模拟序列化失败 - raise ValueError("Mock serialization failure") - except Exception as e: - error = SerializationError("Serialization failed for object") - error.__cause__ = e - logger.error(f"Serialization error: {error}", exc_info=True) - raise error - - try: - with pytest.raises(SerializationError): - serialize_with_logging({"test": "object"}) - finally: - logger.removeHandler(handler) - - # 验证日志内容 - log_content = log_capture.getvalue() - assert "Serialization error:" in log_content - # The exception class name should appear in the traceback - assert "SerializationError" in log_content or "Mock serialization failure" in log_content diff --git a/packages/sage-kernel/tests/unit/kernel/utils/system/test_kernel_environment.py b/packages/sage-kernel/tests/unit/kernel/utils/system/test_kernel_environment.py deleted file mode 100644 index 90cb509d34..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/utils/system/test_kernel_environment.py +++ /dev/null @@ -1,750 +0,0 @@ -""" -Tests for sage.common.utils.system.environment module -============================================= - -单元测试系统环境检测模块的功能，包括： -- 执行环境检测 -- Ray集群信息 -- 系统资源检测 -- GPU资源检测 -- 网络接口信息 -- 后端推荐系统 -""" - -import os -import subprocess -import sys -from unittest.mock import MagicMock, mock_open, patch - -import pytest - -from sage.common.utils.system.environment import ( - detect_execution_environment, - detect_gpu_resources, - get_environment_capabilities, - get_network_interfaces, - get_ray_cluster_info, - get_system_resources, - is_docker_environment, - is_kubernetes_environment, - is_ray_available, - is_ray_cluster_active, - is_slurm_environment, - recommend_backend, - validate_environment_for_backend, -) - - -@pytest.mark.unit -class TestEnvironmentDetection: - """环境检测测试""" - - @patch("sage.common.utils.system.environment.is_ray_cluster_active") - @patch("sage.common.utils.system.environment.is_ray_available") - @patch("sage.common.utils.system.environment.is_kubernetes_environment") - @patch("sage.common.utils.system.environment.is_docker_environment") - @patch("sage.common.utils.system.environment.is_slurm_environment") - def test_detect_execution_environment_ray( - self, mock_slurm, mock_docker, mock_k8s, mock_ray_available, mock_ray_active - ): - """测试检测Ray环境""" - mock_ray_available.return_value = True - mock_ray_active.return_value = True - mock_k8s.return_value = False - mock_docker.return_value = False - mock_slurm.return_value = False - - result = detect_execution_environment() - assert result == "ray" - - @patch("sage.common.utils.system.environment.is_ray_cluster_active") - @patch("sage.common.utils.system.environment.is_ray_available") - @patch("sage.common.utils.system.environment.is_kubernetes_environment") - @patch("sage.common.utils.system.environment.is_docker_environment") - @patch("sage.common.utils.system.environment.is_slurm_environment") - def test_detect_execution_environment_kubernetes( - self, mock_slurm, mock_docker, mock_k8s, mock_ray_available, mock_ray_active - ): - """测试检测Kubernetes环境""" - mock_ray_available.return_value = False - mock_ray_active.return_value = False - mock_k8s.return_value = True - mock_docker.return_value = False - mock_slurm.return_value = False - - result = detect_execution_environment() - assert result == "kubernetes" - - @patch("sage.common.utils.system.environment.is_ray_cluster_active") - @patch("sage.common.utils.system.environment.is_ray_available") - @patch("sage.common.utils.system.environment.is_kubernetes_environment") - @patch("sage.common.utils.system.environment.is_docker_environment") - @patch("sage.common.utils.system.environment.is_slurm_environment") - def test_detect_execution_environment_docker( - self, mock_slurm, mock_docker, mock_k8s, mock_ray_available, mock_ray_active - ): - """测试检测Docker环境""" - mock_ray_available.return_value = False - mock_ray_active.return_value = False - mock_k8s.return_value = False - mock_docker.return_value = True - mock_slurm.return_value = False - - result = detect_execution_environment() - assert result == "docker" - - @patch("sage.common.utils.system.environment.is_ray_cluster_active") - @patch("sage.common.utils.system.environment.is_ray_available") - @patch("sage.common.utils.system.environment.is_kubernetes_environment") - @patch("sage.common.utils.system.environment.is_docker_environment") - @patch("sage.common.utils.system.environment.is_slurm_environment") - def test_detect_execution_environment_slurm( - self, mock_slurm, mock_docker, mock_k8s, mock_ray_available, mock_ray_active - ): - """测试检测SLURM环境""" - mock_ray_available.return_value = False - mock_ray_active.return_value = False - mock_k8s.return_value = False - mock_docker.return_value = False - mock_slurm.return_value = True - - result = detect_execution_environment() - assert result == "slurm" - - @patch("sage.common.utils.system.environment.is_ray_cluster_active") - @patch("sage.common.utils.system.environment.is_ray_available") - @patch("sage.common.utils.system.environment.is_kubernetes_environment") - @patch("sage.common.utils.system.environment.is_docker_environment") - @patch("sage.common.utils.system.environment.is_slurm_environment") - def test_detect_execution_environment_local( - self, mock_slurm, mock_docker, mock_k8s, mock_ray_available, mock_ray_active - ): - """测试检测本地环境""" - mock_ray_available.return_value = False - mock_ray_active.return_value = False - mock_k8s.return_value = False - mock_docker.return_value = False - mock_slurm.return_value = False - - result = detect_execution_environment() - assert result == "local" - - -@pytest.mark.unit -class TestRayDetection: - """Ray检测测试""" - - def test_is_ray_available_true(self): - """测试Ray可用检测""" - with patch.dict("sys.modules", {"ray": MagicMock()}): - result = is_ray_available() - assert result is True - - def test_is_ray_available_false(self): - """测试Ray不可用检测""" - with patch("importlib.import_module", side_effect=ImportError("No module named 'ray'")): - result = is_ray_available() - assert result is False - - @patch("sage.common.utils.system.environment.is_ray_available") - def test_is_ray_cluster_active_not_available(self, mock_ray_available): - """测试Ray不可用时的集群状态""" - mock_ray_available.return_value = False - - result = is_ray_cluster_active() - assert result is False - - @patch("sage.common.utils.system.environment.is_ray_available") - def test_is_ray_cluster_active_true(self, mock_ray_available): - """测试Ray集群活跃状态""" - mock_ray_available.return_value = True - - with patch("importlib.import_module") as mock_import: - mock_ray = MagicMock() - mock_ray.is_initialized.return_value = True - mock_import.return_value = mock_ray - - result = is_ray_cluster_active() - assert result is True - - @patch("sage.common.utils.system.environment.is_ray_available") - def test_is_ray_cluster_active_false(self, mock_ray_available): - """测试Ray集群非活跃状态""" - mock_ray_available.return_value = True - - with patch("importlib.import_module") as mock_import: - mock_ray = MagicMock() - mock_ray.is_initialized.return_value = False - mock_import.return_value = mock_ray - - result = is_ray_cluster_active() - assert result is False - - @patch("sage.common.utils.system.environment.is_ray_available") - def test_get_ray_cluster_info_not_available(self, mock_ray_available): - """测试获取Ray集群信息 - 不可用""" - mock_ray_available.return_value = False - - result = get_ray_cluster_info() - assert result["available"] is False - assert "Ray not installed" in result["error"] - - @patch("sage.common.utils.system.environment.is_ray_available") - def test_get_ray_cluster_info_not_initialized(self, mock_ray_available): - """测试获取Ray集群信息 - 未初始化""" - mock_ray_available.return_value = True - - with patch("importlib.import_module") as mock_import: - mock_ray = MagicMock() - mock_ray.is_initialized.return_value = False - mock_import.return_value = mock_ray - - result = get_ray_cluster_info() - assert result["available"] is True - assert result["initialized"] is False - - @patch("sage.common.utils.system.environment.is_ray_available") - def test_get_ray_cluster_info_initialized(self, mock_ray_available): - """测试获取Ray集群信息 - 已初始化""" - mock_ray_available.return_value = True - - with patch("importlib.import_module") as mock_import: - mock_ray = MagicMock() - mock_ray.is_initialized.return_value = True - mock_ray.cluster_resources.return_value = {"CPU": 8, "GPU": 2} - mock_ray.nodes.return_value = [{"NodeID": "node1"}, {"NodeID": "node2"}] - mock_import.return_value = mock_ray - - result = get_ray_cluster_info() - assert result["available"] is True - assert result["initialized"] is True - assert result["cluster_resources"] == {"CPU": 8, "GPU": 2} - assert result["node_count"] == 2 - assert len(result["nodes"]) == 2 - - -@pytest.mark.unit -class TestContainerEnvironmentDetection: - """容器环境检测测试""" - - def test_is_kubernetes_environment_service_host(self): - """测试通过服务主机检测Kubernetes""" - with patch.dict(os.environ, {"KUBERNETES_SERVICE_HOST": "10.0.0.1"}): - result = is_kubernetes_environment() - assert result is True - - def test_is_kubernetes_environment_service_port(self): - """测试通过服务端口检测Kubernetes""" - with patch.dict(os.environ, {"KUBERNETES_SERVICE_PORT": "443"}): - result = is_kubernetes_environment() - assert result is True - - def test_is_kubernetes_environment_kubernetes_port(self): - """测试通过Kubernetes端口检测""" - with patch.dict(os.environ, {"KUBERNETES_PORT": "tcp://10.0.0.1:443"}): - result = is_kubernetes_environment() - assert result is True - - @patch("os.path.exists") - def test_is_kubernetes_environment_service_account(self, mock_exists): - """测试通过服务账户文件检测Kubernetes""" - mock_exists.return_value = True - - result = is_kubernetes_environment() - assert result is True - mock_exists.assert_called_with("/var/run/secrets/kubernetes.io/serviceaccount") - - def test_is_kubernetes_environment_false(self): - """测试非Kubernetes环境""" - with patch.dict(os.environ, {}, clear=True), patch("os.path.exists", return_value=False): - result = is_kubernetes_environment() - assert result is False - - @patch("os.path.exists") - def test_is_docker_environment_dockerenv(self, mock_exists): - """测试通过.dockerenv文件检测Docker""" - mock_exists.side_effect = lambda path: path == "/.dockerenv" - - result = is_docker_environment() - assert result is True - - @patch("os.path.exists") - def test_is_docker_environment_cgroup_docker(self, mock_exists): - """测试通过cgroup信息检测Docker""" - mock_exists.side_effect = lambda path: path != "/.dockerenv" - - with patch("builtins.open", mock_open(read_data="12:memory:/docker/container_id")): - result = is_docker_environment() - assert result is True - - @patch("os.path.exists") - def test_is_docker_environment_cgroup_containerd(self, mock_exists): - """测试通过cgroup信息检测containerd""" - mock_exists.side_effect = lambda path: path != "/.dockerenv" - - with patch("builtins.open", mock_open(read_data="12:memory:/containerd/container_id")): - result = is_docker_environment() - assert result is True - - @patch("os.path.exists") - def test_is_docker_environment_false(self, mock_exists): - """测试非Docker环境""" - mock_exists.return_value = False - - with patch("builtins.open", side_effect=FileNotFoundError()): - result = is_docker_environment() - assert result is False - - def test_is_slurm_environment_job_id(self): - """测试通过作业ID检测SLURM""" - with patch.dict(os.environ, {"SLURM_JOB_ID": "12345"}): - result = is_slurm_environment() - assert result is True - - def test_is_slurm_environment_procid(self): - """测试通过进程ID检测SLURM""" - with patch.dict(os.environ, {"SLURM_PROCID": "0"}): - result = is_slurm_environment() - assert result is True - - def test_is_slurm_environment_false(self): - """测试非SLURM环境""" - with patch.dict(os.environ, {}, clear=True): - result = is_slurm_environment() - assert result is False - - -@pytest.mark.unit -class TestSystemResources: - """系统资源检测测试""" - - @patch("importlib.import_module") - def test_get_system_resources_success(self, mock_import): - """测试成功获取系统资源""" - mock_psutil = MagicMock() - - # 模拟CPU信息 - mock_psutil.cpu_count.side_effect = [8, 4] # logical, physical - mock_freq = MagicMock() - mock_freq._asdict.return_value = {"current": 2400, "min": 800, "max": 3200} - mock_psutil.cpu_freq.return_value = mock_freq - mock_psutil.cpu_percent.return_value = 25.5 - - # 模拟内存信息 - mock_memory = MagicMock() - mock_memory.total = 16 * 1024**3 # 16GB - mock_memory.available = 8 * 1024**3 # 8GB - mock_memory.percent = 50.0 - mock_memory.used = 8 * 1024**3 - mock_memory.free = 8 * 1024**3 - mock_psutil.virtual_memory.return_value = mock_memory - - # 模拟磁盘信息 - mock_disk = MagicMock() - mock_disk.total = 1024**4 # 1TB - mock_disk.used = 512 * 1024**3 # 512GB - mock_disk.free = 512 * 1024**3 # 512GB - mock_psutil.disk_usage.return_value = mock_disk - - mock_import.return_value = mock_psutil - - result = get_system_resources() - - assert result["cpu"]["count"] == 8 - assert result["cpu"]["physical_count"] == 4 - assert result["cpu"]["percent"] == 25.5 - assert result["memory"]["total"] == 16 * 1024**3 - assert result["memory"]["percent"] == 50.0 - assert result["disk"]["total"] == 1024**4 - assert result["platform"] == sys.platform - - @patch("importlib.import_module") - def test_get_system_resources_psutil_not_available(self, mock_import): - """测试psutil不可用时的处理""" - mock_import.side_effect = ImportError("No module named 'psutil'") - - result = get_system_resources() - - assert "error" in result - assert "psutil not available" in result["error"] - - @patch("importlib.import_module") - def test_get_system_resources_exception(self, mock_import): - """测试获取系统资源时的异常处理""" - mock_psutil = MagicMock() - mock_psutil.cpu_count.side_effect = Exception("CPU error") - mock_import.return_value = mock_psutil - - result = get_system_resources() - - assert "error" in result - assert "Error getting system resources" in result["error"] - - -@pytest.mark.unit -class TestGPUDetection: - """GPU检测测试""" - - @patch("subprocess.run") - def test_detect_gpu_resources_nvidia_success(self, mock_run): - """测试检测NVIDIA GPU成功""" - mock_result = MagicMock() - mock_result.returncode = 0 - mock_result.stdout = "GeForce RTX 3080, 10240, 2048\nGeForce RTX 3090, 24576, 4096" - mock_run.return_value = mock_result - - result = detect_gpu_resources() - - assert result["available"] is True - assert result["count"] == 2 - assert len(result["devices"]) == 2 - assert result["devices"][0]["name"] == "GeForce RTX 3080" - assert result["devices"][0]["memory_total"] == 10240 - assert result["devices"][1]["name"] == "GeForce RTX 3090" - - mock_run.assert_called_with( - [ - "nvidia-smi", - "--query-gpu=name,memory.total,memory.used", - "--format=csv,noheader,nounits", - ], - capture_output=True, - text=True, - timeout=10, - ) - - @patch("subprocess.run") - def test_detect_gpu_resources_nvidia_not_found(self, mock_run): - """测试NVIDIA GPU未找到时检测AMD""" - # 第一次调用nvidia-smi失败 - mock_run.side_effect = [ - subprocess.SubprocessError("nvidia-smi not found"), - MagicMock(returncode=0, stdout="AMD GPU info"), - ] - - result = detect_gpu_resources() - - assert result["available"] is True - assert result["type"] == "AMD" - assert mock_run.call_count == 2 - - @patch("subprocess.run") - def test_detect_gpu_resources_no_gpu(self, mock_run): - """测试无GPU时的检测""" - mock_run.side_effect = [ - subprocess.SubprocessError("nvidia-smi not found"), - subprocess.SubprocessError("rocm-smi not found"), - ] - - result = detect_gpu_resources() - - assert result["available"] is False - assert result["count"] == 0 - assert len(result["devices"]) == 0 - - @patch("subprocess.run") - def test_detect_gpu_resources_nvidia_timeout(self, mock_run): - """测试nvidia-smi超时""" - mock_run.side_effect = subprocess.TimeoutExpired("nvidia-smi", 10) - - result = detect_gpu_resources() - - assert result["available"] is False - - -@pytest.mark.unit -class TestNetworkInterfaces: - """网络接口测试""" - - @patch("importlib.import_module") - def test_get_network_interfaces_success(self, mock_import): - """测试成功获取网络接口""" - mock_psutil = MagicMock() - - # 模拟网络接口数据 - mock_addr = MagicMock() - mock_addr.family.name = "AF_INET" - mock_addr.address = "192.168.1.100" - mock_addr.netmask = "255.255.255.0" - mock_addr.broadcast = "192.168.1.255" - - mock_psutil.net_if_addrs.return_value = {"eth0": [mock_addr], "lo": [mock_addr]} - mock_import.return_value = mock_psutil - - result = get_network_interfaces() - - assert len(result) == 2 - assert result[0]["name"] in ["eth0", "lo"] - assert len(result[0]["addresses"]) == 1 - assert result[0]["addresses"][0]["family"] == "AF_INET" - assert result[0]["addresses"][0]["address"] == "192.168.1.100" - - @patch("importlib.import_module") - def test_get_network_interfaces_psutil_not_available(self, mock_import): - """测试psutil不可用时的处理""" - mock_import.side_effect = ImportError("No module named 'psutil'") - - result = get_network_interfaces() - - assert result == [] - - @patch("importlib.import_module") - def test_get_network_interfaces_exception(self, mock_import): - """测试获取网络接口时的异常处理""" - mock_psutil = MagicMock() - mock_psutil.net_if_addrs.side_effect = Exception("Network error") - mock_import.return_value = mock_psutil - - result = get_network_interfaces() - - assert len(result) == 1 - assert "error" in result[0] - - -@pytest.mark.unit -class TestBackendRecommendation: - """后端推荐测试""" - - @patch("sage.common.utils.system.environment.detect_execution_environment") - @patch("sage.common.utils.system.environment.get_system_resources") - @patch("sage.common.utils.system.environment.detect_gpu_resources") - def test_recommend_backend_ray_environment(self, mock_gpu, mock_resources, mock_env): - """测试Ray环境的后端推荐""" - mock_env.return_value = "ray" - mock_resources.return_value = { - "cpu": {"count": 16}, - "memory": {"total": 32 * 1024**3}, - } - mock_gpu.return_value = {"available": False} - - result = recommend_backend() - - assert result["environment"] == "ray" - assert result["primary_backend"] == "ray" - assert result["communication_layer"] == "ray_queue" - assert any("Ray cluster detected" in reason for reason in result["reasoning"]) - - @patch("sage.common.utils.system.environment.detect_execution_environment") - @patch("sage.common.utils.system.environment.get_system_resources") - @patch("sage.common.utils.system.environment.detect_gpu_resources") - def test_recommend_backend_kubernetes_environment(self, mock_gpu, mock_resources, mock_env): - """测试Kubernetes环境的后端推荐""" - mock_env.return_value = "kubernetes" - mock_resources.return_value = { - "cpu": {"count": 8}, - "memory": {"total": 16 * 1024**3}, - } - mock_gpu.return_value = {"available": False} - - result = recommend_backend() - - assert result["environment"] == "kubernetes" - assert result["primary_backend"] == "ray" - assert "local" in result["secondary_backends"] - assert result["communication_layer"] == "network" - - @patch("sage.common.utils.system.environment.detect_execution_environment") - @patch("sage.common.utils.system.environment.get_system_resources") - @patch("sage.common.utils.system.environment.detect_gpu_resources") - def test_recommend_backend_with_gpu(self, mock_gpu, mock_resources, mock_env): - """测试有GPU时的后端推荐""" - mock_env.return_value = "local" - mock_resources.return_value = { - "cpu": {"count": 8}, - "memory": {"total": 16 * 1024**3}, - } - mock_gpu.return_value = {"available": True, "count": 2} - - result = recommend_backend() - - assert result["gpu_support"] is True - assert result["communication_layer"] == "gpu_direct" - assert any("GPU available" in reason for reason in result["reasoning"]) - - @patch("sage.common.utils.system.environment.detect_execution_environment") - @patch("sage.common.utils.system.environment.get_system_resources") - @patch("sage.common.utils.system.environment.detect_gpu_resources") - def test_recommend_backend_high_memory(self, mock_gpu, mock_resources, mock_env): - """测试高内存时的后端推荐""" - mock_env.return_value = "local" - mock_resources.return_value = { - "cpu": {"count": 4}, - "memory": {"total": 64 * 1024**3}, - } - mock_gpu.return_value = {"available": False} - - result = recommend_backend() - - assert result["memory_strategy"] == "mmap" - assert any("High memory available" in reason for reason in result["reasoning"]) - - @patch("sage.common.utils.system.environment.detect_execution_environment") - @patch("sage.common.utils.system.environment.get_system_resources") - @patch("sage.common.utils.system.environment.detect_gpu_resources") - def test_recommend_backend_low_memory(self, mock_gpu, mock_resources, mock_env): - """测试低内存时的后端推荐""" - mock_env.return_value = "local" - mock_resources.return_value = { - "cpu": {"count": 2}, - "memory": {"total": 4 * 1024**3}, - } - mock_gpu.return_value = {"available": False} - - result = recommend_backend() - - assert result["memory_strategy"] == "conservative" - assert any("Limited memory" in reason for reason in result["reasoning"]) - - -@pytest.mark.unit -class TestEnvironmentValidation: - """环境验证测试""" - - def test_validate_environment_for_backend_local(self): - """测试本地后端环境验证""" - result = validate_environment_for_backend("local") - - assert result["backend"] == "local" - assert result["supported"] is True - assert len(result["issues"]) == 0 - - @patch("sage.common.utils.system.environment.is_ray_available") - def test_validate_environment_for_backend_ray_available(self, mock_ray_available): - """测试Ray后端环境验证 - 可用""" - mock_ray_available.return_value = True - - with patch( - "sage.common.utils.system.environment.is_ray_cluster_active", - return_value=False, - ): - result = validate_environment_for_backend("ray") - - assert result["backend"] == "ray" - assert result["supported"] is True - assert len(result["issues"]) == 0 - assert any("Initialize Ray cluster" in rec for rec in result["recommendations"]) - - @patch("sage.common.utils.system.environment.is_ray_available") - def test_validate_environment_for_backend_ray_not_available(self, mock_ray_available): - """测试Ray后端环境验证 - 不可用""" - mock_ray_available.return_value = False - - result = validate_environment_for_backend("ray") - - assert result["backend"] == "ray" - assert result["supported"] is False - assert "Ray not installed" in result["issues"] - assert any("Install Ray" in rec for rec in result["recommendations"]) - - @patch("sage.common.utils.system.environment.is_ray_available") - @patch("sage.common.utils.system.environment.get_network_interfaces") - def test_validate_environment_for_backend_distributed(self, mock_network, mock_ray_available): - """测试分布式后端环境验证""" - mock_ray_available.return_value = True - mock_network.return_value = [{"name": "eth0"}, {"name": "eth1"}] - - result = validate_environment_for_backend("distributed") - - assert result["backend"] == "distributed" - assert result["supported"] is True - - -@pytest.mark.integration -class TestEnvironmentCapabilities: - """环境能力集成测试""" - - @patch("sage.common.utils.system.environment.detect_execution_environment") - @patch("sage.common.utils.system.environment.get_system_resources") - @patch("sage.common.utils.system.environment.detect_gpu_resources") - @patch("sage.common.utils.system.environment.get_network_interfaces") - @patch("sage.common.utils.system.environment.get_ray_cluster_info") - @patch("sage.common.utils.system.environment.recommend_backend") - def test_get_environment_capabilities( - self, - mock_recommend, - mock_ray_info, - mock_network, - mock_gpu, - mock_resources, - mock_env, - ): - """测试获取完整环境能力""" - # 设置模拟返回值 - mock_env.return_value = "local" - mock_resources.return_value = {"cpu": {"count": 8}} - mock_gpu.return_value = {"available": False} - mock_network.return_value = [{"name": "eth0"}] - mock_ray_info.return_value = {"available": False} - mock_recommend.return_value = {"primary_backend": "local"} - - result = get_environment_capabilities() - - # 验证结果包含所有预期字段 - assert "environment_type" in result - assert "system_resources" in result - assert "gpu_resources" in result - assert "network_interfaces" in result - assert "ray_info" in result - assert "backend_recommendation" in result - assert "python_version" in result - assert "platform" in result - - # 验证各个函数都被调用 - mock_env.assert_called_once() - mock_resources.assert_called_once() - mock_gpu.assert_called_once() - mock_network.assert_called_once() - mock_ray_info.assert_called_once() - mock_recommend.assert_called_once() - - def test_get_environment_capabilities_real_data(self): - """测试获取真实环境能力数据""" - result = get_environment_capabilities() - - # 基本结构验证 - assert isinstance(result, dict) - assert "environment_type" in result - assert "python_version" in result - assert "platform" in result - - # Python版本格式验证 - python_version = result["python_version"] - version_parts = python_version.split(".") - assert len(version_parts) >= 2 - assert all(part.isdigit() for part in version_parts) - - -# 性能和边界测试 -@pytest.mark.slow -class TestEnvironmentPerformance: - """环境检测性能测试""" - - def test_detect_execution_environment_performance(self): - """测试环境检测性能""" - import time - - start_time = time.time() - for _ in range(10): - detect_execution_environment() - elapsed_time = time.time() - start_time - - # 10次检测应该在合理时间内完成 - assert elapsed_time < 1.0 # 应在1秒内完成 - - @patch("sage.common.utils.system.environment.get_system_resources") - @patch("sage.common.utils.system.environment.detect_gpu_resources") - def test_get_environment_capabilities_performance(self, mock_gpu, mock_resources): - """测试获取环境能力的性能""" - import time - - # 模拟快速返回 - mock_resources.return_value = {"cpu": {"count": 4}} - mock_gpu.return_value = {"available": False} - - start_time = time.time() - result = get_environment_capabilities() - elapsed_time = time.time() - start_time - - # 应该在合理时间内完成 - assert elapsed_time < 0.5 # 应在0.5秒内完成 - assert isinstance(result, dict) diff --git a/packages/sage-kernel/tests/unit/kernel/utils/system/test_kernel_network.py b/packages/sage-kernel/tests/unit/kernel/utils/system/test_kernel_network.py deleted file mode 100644 index e9477e30f4..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/utils/system/test_kernel_network.py +++ /dev/null @@ -1,1038 +0,0 @@ -""" -Test suite for sage.common.utils.system.network module - -This module tests network utility functions for port management, -process detection, and connectivity testing. - -Created: 2024 -Test Framework: pytest -Coverage: Network utilities, port management, process detection -""" - -import json -import subprocess -import time -from unittest.mock import MagicMock, Mock, patch - -import pytest - -from sage.common.utils.system.network import ( - _find_processes_with_fuser, - _find_processes_with_lsof, - _find_processes_with_netstat, - aggressive_port_cleanup, - allocate_free_port, - check_port_binding_permission, - check_tcp_connection, - find_port_processes, - get_host_ip, - is_port_occupied, - send_tcp_health_check, - wait_for_port_release, -) - - -class TestIsPortOccupied: - """Test port occupation detection functionality""" - - @pytest.mark.unit - def test_port_not_occupied(self): - """Test detecting unoccupied port""" - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.connect_ex.return_value = 111 # Connection refused - - result = is_port_occupied("127.0.0.1", 8080) - assert not result - mock_sock.settimeout.assert_called_once_with(1) - mock_sock.connect_ex.assert_called_once_with(("127.0.0.1", 8080)) - - @pytest.mark.unit - def test_port_occupied(self): - """Test detecting occupied port""" - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.connect_ex.return_value = 0 # Connection successful - - result = is_port_occupied("127.0.0.1", 8080) - assert result - - @pytest.mark.unit - def test_socket_exception(self): - """Test handling socket exceptions""" - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.connect_ex.side_effect = OSError("Network error") - - result = is_port_occupied("127.0.0.1", 8080) - assert not result - - @pytest.mark.unit - def test_invalid_host(self): - """Test with invalid host address""" - result = is_port_occupied("invalid.host.name", 8080) - assert not result - - @pytest.mark.unit - def test_ipv6_address(self): - """Test with IPv6 address""" - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.connect_ex.return_value = 111 - - result = is_port_occupied("::1", 8080) - assert not result - - -class TestCheckPortBindingPermission: - """Test port binding permission checking""" - - @pytest.mark.unit - def test_binding_allowed(self): - """Test successful port binding""" - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - - result = check_port_binding_permission("127.0.0.1", 8080) - assert result - mock_sock.bind.assert_called_once_with(("127.0.0.1", 8080)) - - @pytest.mark.unit - def test_binding_denied(self): - """Test port binding denied""" - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.bind.side_effect = OSError("Permission denied") - - result = check_port_binding_permission("127.0.0.1", 80) - assert result["success"] is False - assert result["error"] == "os_error" - - @pytest.mark.unit - def test_port_already_in_use(self): - """Test binding to occupied port""" - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.bind.side_effect = OSError("[Errno 98] Address already in use") - - result = check_port_binding_permission("127.0.0.1", 8080) - assert result["success"] is False - assert result["error"] == "os_error" - - -class TestWaitForPortRelease: - """Test port release waiting functionality""" - - @pytest.mark.unit - def test_port_released_immediately(self): - """Test port is available immediately""" - with patch("sage.common.utils.system.network.is_port_occupied", return_value=False): - result = wait_for_port_release("127.0.0.1", 8080, timeout=5) - assert result - - @pytest.mark.unit - def test_port_released_after_wait(self): - """Test port becomes available after waiting""" - call_count = 0 - - def mock_is_occupied(*args): - nonlocal call_count - call_count += 1 - return call_count <= 2 # Occupied first 2 calls, then free - - with patch( - "sage.common.utils.system.network.is_port_occupied", - side_effect=mock_is_occupied, - ): - with patch("time.sleep"): - result = wait_for_port_release("127.0.0.1", 8080, timeout=5) - assert result - - @pytest.mark.unit - def test_timeout_exceeded(self): - """Test timeout when port never releases""" - with patch("sage.common.utils.system.network.is_port_occupied", return_value=True): - with patch("time.sleep"): - with patch("time.time", side_effect=[0, 1, 2, 3, 4, 5, 6]): - result = wait_for_port_release("127.0.0.1", 8080, timeout=5) - assert not result - - @pytest.mark.unit - def test_custom_check_interval(self): - """Test custom check interval""" - with patch("sage.common.utils.system.network.is_port_occupied", return_value=False): - with patch("time.sleep"): - wait_for_port_release("127.0.0.1", 8080, timeout=10, check_interval=2) - # Should not need to sleep since port is immediately available - - -class TestFindProcessesByLsof: - """Test lsof-based process finding""" - - @pytest.mark.unit - def test_processes_found(self): - """Test finding processes with lsof""" - mock_output = "p1234\np5678\n" - - with patch("subprocess.run") as mock_run: - mock_run.return_value = Mock(returncode=0, stdout=mock_output) - - result = _find_processes_with_lsof(8080) - assert result == [1234, 5678] - mock_run.assert_called_once_with( - ["lsof", "-t", "-i:8080"], capture_output=True, text=True, timeout=5 - ) - - @pytest.mark.unit - def test_no_processes_found(self): - """Test no processes found by lsof""" - with patch("subprocess.run") as mock_run: - mock_run.return_value = Mock(returncode=1, stdout="") # No matching processes - - result = _find_processes_with_lsof(8080) - assert result == [] - - @pytest.mark.unit - def test_lsof_not_available(self): - """Test when lsof is not available""" - with patch("subprocess.run", side_effect=FileNotFoundError): - result = _find_processes_with_lsof(8080) - assert result == [] - - @pytest.mark.unit - def test_lsof_timeout(self): - """Test lsof command timeout""" - with patch("subprocess.run", side_effect=subprocess.TimeoutExpired("lsof", 5)): - result = _find_processes_with_lsof(8080) - assert result == [] - - @pytest.mark.unit - def test_invalid_output_format(self): - """Test handling invalid lsof output""" - mock_output = "invalid\nformat\np1234\nnot_a_number\n" - - with patch("subprocess.run") as mock_run: - mock_run.return_value = Mock(returncode=0, stdout=mock_output) - - result = _find_processes_with_lsof(8080) - assert result == [1234] # Only valid PID extracted - - -class TestFindProcessesByNetstat: - """Test netstat-based process finding""" - - @pytest.mark.unit - def test_processes_found_netstat(self): - """Test finding processes with netstat""" - mock_output = "tcp 0 0 0.0.0.0:8080 0.0.0.0:* LISTEN 1234/nginx\n" - - with patch("subprocess.run") as mock_run: - mock_run.return_value = Mock(returncode=0, stdout=mock_output) - - result = _find_processes_with_netstat(8080) - assert result == [1234] - - @pytest.mark.unit - def test_netstat_no_processes(self): - """Test no processes found by netstat""" - with patch("subprocess.run") as mock_run: - mock_run.return_value = Mock(returncode=0, stdout="") - - result = _find_processes_with_netstat(8080) - assert result == [] - - @pytest.mark.unit - def test_netstat_not_available(self): - """Test when netstat is not available""" - with patch("subprocess.run", side_effect=FileNotFoundError): - result = _find_processes_with_netstat(8080) - assert result == [] - - -class TestFindProcessesByFuser: - """Test fuser-based process finding""" - - @pytest.mark.unit - def test_processes_found_fuser(self): - """Test finding processes with fuser""" - mock_output = "1234 5678" - - with patch("subprocess.run") as mock_run: - mock_run.return_value = Mock(returncode=0, stdout=mock_output) - - result = _find_processes_with_fuser(8080) - assert result == [1234, 5678] - - @pytest.mark.unit - def test_fuser_no_processes(self): - """Test no processes found by fuser""" - with patch("subprocess.run") as mock_run: - mock_run.return_value = Mock(returncode=1, stdout="") # No processes found - - result = _find_processes_with_fuser(8080) - assert result == [] - - @pytest.mark.unit - def test_fuser_not_available(self): - """Test when fuser is not available""" - with patch("subprocess.run", side_effect=FileNotFoundError): - result = _find_processes_with_fuser(8080) - assert result == [] - - -class TestFindPortProcesses: - """Test combined process finding functionality""" - - @pytest.mark.unit - def test_find_processes_lsof_success(self): - """Test successful process finding with lsof""" - with patch( - "sage.common.utils.system.network._find_processes_with_lsof", - return_value=[1234], - ): - with patch( - "sage.common.utils.system.network._find_processes_with_netstat", - return_value=[], - ): - with patch( - "sage.common.utils.system.network._find_processes_with_fuser", - return_value=[], - ): - with patch("psutil.Process") as mock_process: - mock_proc = MagicMock() - mock_proc.is_running.return_value = True - mock_proc.pid = 1234 - mock_process.return_value = mock_proc - result = find_port_processes(8080) - assert len(result) == 1 - assert result[0].pid == 1234 - - @pytest.mark.unit - def test_find_processes_fallback_to_netstat(self): - """Test fallback to netstat when lsof fails""" - with patch( - "sage.common.utils.system.network._find_processes_with_lsof", - return_value=[], - ): - with patch( - "sage.common.utils.system.network._find_processes_with_netstat", - return_value=[5678], - ): - with patch( - "sage.common.utils.system.network._find_processes_with_fuser", - return_value=[], - ): - with patch("psutil.Process") as mock_process: - mock_proc = MagicMock() - mock_proc.is_running.return_value = True - mock_proc.pid = 5678 - mock_process.return_value = mock_proc - result = find_port_processes(8080) - assert len(result) == 1 - assert result[0].pid == 5678 - - @pytest.mark.unit - def test_find_processes_fallback_to_fuser(self): - """Test fallback to fuser when lsof and netstat fail""" - with patch( - "sage.common.utils.system.network._find_processes_with_lsof", - return_value=[], - ): - with patch( - "sage.common.utils.system.network._find_processes_with_netstat", - return_value=[], - ): - with patch( - "sage.common.utils.system.network._find_processes_with_fuser", - return_value=[9999], - ): - with patch("psutil.Process") as mock_process: - mock_proc = MagicMock() - mock_proc.is_running.return_value = True - mock_proc.pid = 9999 - mock_process.return_value = mock_proc - result = find_port_processes(8080) - assert len(result) == 1 - assert result[0].pid == 9999 - - @pytest.mark.unit - def test_find_processes_combine_results(self): - """Test combining results from multiple tools""" - with patch( - "sage.common.utils.system.network._find_processes_with_lsof", - return_value=[1234], - ): - with patch( - "sage.common.utils.system.network._find_processes_with_netstat", - return_value=[5678], - ): - with patch( - "sage.common.utils.system.network._find_processes_with_fuser", - return_value=[1234, 9999], - ): - with patch("psutil.Process") as mock_process: - - def create_mock_proc(pid): - mock_proc = MagicMock() - mock_proc.is_running.return_value = True - mock_proc.pid = pid - return mock_proc - - mock_process.side_effect = create_mock_proc - result = find_port_processes(8080) - # Should combine and deduplicate - result_pids = {proc.pid for proc in result} - assert result_pids == {1234, 5678, 9999} - - @pytest.mark.unit - def test_find_processes_no_results(self): - """Test when no processes are found by any tool""" - with patch( - "sage.common.utils.system.network._find_processes_with_lsof", - return_value=[], - ): - with patch( - "sage.common.utils.system.network._find_processes_with_netstat", - return_value=[], - ): - with patch( - "sage.common.utils.system.network._find_processes_with_fuser", - return_value=[], - ): - result = find_port_processes(8080) - assert result == [] - - -class TestSendTcpHealthCheck: - """Test TCP health check functionality""" - - @pytest.mark.unit - def test_successful_health_check(self): - """Test successful health check""" - request_data = {"type": "health_check", "timestamp": 123456789} - response_data = {"status": "ok", "uptime": 3600} - - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - - # Mock response reception - response_json = json.dumps(response_data).encode("utf-8") - response_length = len(response_json) - mock_sock.recv.side_effect = [ - response_length.to_bytes(4, byteorder="big"), # Length header - response_json, # Response data - ] - - result = send_tcp_health_check("127.0.0.1", 8080, request_data) - - assert result == response_data - mock_sock.settimeout.assert_called_once_with(5) - mock_sock.connect.assert_called_once_with(("127.0.0.1", 8080)) - - @pytest.mark.unit - def test_connection_failed(self): - """Test connection failure""" - request_data = {"type": "health_check"} - - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.connect.side_effect = OSError("Connection refused") - - result = send_tcp_health_check("127.0.0.1", 8080, request_data) - - assert result["status"] == "error" - assert "Connection failed" in result["message"] - - @pytest.mark.unit - def test_invalid_response_format(self): - """Test invalid response format""" - request_data = {"type": "health_check"} - - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.recv.return_value = b"\x00\x00" # Invalid length header - - result = send_tcp_health_check("127.0.0.1", 8080, request_data) - - assert result["status"] == "error" - assert "Invalid response format" in result["message"] - - @pytest.mark.unit - def test_incomplete_response(self): - """Test incomplete response reception""" - request_data = {"type": "health_check"} - - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - - # Mock partial response - mock_sock.recv.side_effect = [ - (10).to_bytes(4, byteorder="big"), # Expect 10 bytes - b"hello", # Only receive 5 bytes - b"", # Then no more data available - ] - - result = send_tcp_health_check("127.0.0.1", 8080, request_data) - - assert result["status"] == "error" - assert "Incomplete response received" in result["message"] - - @pytest.mark.unit - def test_invalid_json_response(self): - """Test invalid JSON in response""" - request_data = {"type": "health_check"} - - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - - invalid_json = b"invalid json" - mock_sock.recv.side_effect = [ - len(invalid_json).to_bytes(4, byteorder="big"), - invalid_json, - ] - - result = send_tcp_health_check("127.0.0.1", 8080, request_data) - - assert result["status"] == "error" - assert "Invalid JSON response" in result["message"] - - @pytest.mark.unit - def test_custom_timeout(self): - """Test custom timeout setting""" - request_data = {"type": "health_check"} - - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - - response_data = {"status": "ok"} - response_json = json.dumps(response_data).encode("utf-8") - mock_sock.recv.side_effect = [ - len(response_json).to_bytes(4, byteorder="big"), - response_json, - ] - - send_tcp_health_check("127.0.0.1", 8080, request_data, timeout=10) - - mock_sock.settimeout.assert_called_once_with(10) - - -class TestAllocateFreePort: - """Test free port allocation functionality""" - - @pytest.mark.unit - def test_allocate_from_range(self): - """Test allocating port from specified range""" - with patch("sage.common.utils.system.network.is_port_occupied") as mock_occupied: - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - - # First port is occupied, second is free - mock_occupied.side_effect = [True, False] - - result = allocate_free_port("127.0.0.1", (19200, 19202)) - - assert result == 19201 - mock_sock.bind.assert_called_once_with(("127.0.0.1", 19201)) - - @pytest.mark.unit - def test_allocate_system_port(self): - """Test fallback to system port allocation""" - with patch("sage.common.utils.system.network.is_port_occupied", return_value=True): - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.getsockname.return_value = ("127.0.0.1", 35678) - - result = allocate_free_port("127.0.0.1", (19200, 19202)) - - assert result == 35678 - mock_sock.bind.assert_called_with(("127.0.0.1", 0)) - - @pytest.mark.unit - def test_allocation_failure(self): - """Test allocation failure""" - with patch("sage.common.utils.system.network.is_port_occupied", return_value=True): - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.bind.side_effect = Exception("Binding failed") - - with pytest.raises(RuntimeError, match="Unable to allocate free port"): - allocate_free_port("127.0.0.1", (19200, 19202)) - - @pytest.mark.unit - def test_double_check_binding(self): - """Test double-check binding validation""" - with patch("sage.common.utils.system.network.is_port_occupied", return_value=False): - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - # First bind fails (race condition), but eventually succeeds - mock_sock.bind.side_effect = [OSError("Port taken"), None] - - result = allocate_free_port("127.0.0.1", (19200, 19202)) - - assert result == 19201 - - -class TestAggressivePortCleanup: - """Test aggressive port cleanup functionality""" - - @pytest.mark.unit - def test_successful_cleanup(self): - """Test successful process termination""" - with patch( - "sage.common.utils.system.network.find_port_processes", - return_value=[1234, 5678], - ): - with patch("psutil.Process") as mock_process: - mock_proc1 = MagicMock() - mock_proc2 = MagicMock() - mock_process.side_effect = [mock_proc1, mock_proc2] - - result = aggressive_port_cleanup(8080) - - assert result["success"] is True - assert result["killed_pids"] == [1234, 5678] - assert result["errors"] == [] - - mock_proc1.terminate.assert_called_once() - mock_proc1.wait.assert_called_once_with(timeout=2) - mock_proc2.terminate.assert_called_once() - mock_proc2.wait.assert_called_once_with(timeout=2) - - @pytest.mark.unit - def test_force_kill_on_timeout(self): - """Test force kill when terminate times out""" - import psutil - - with patch("sage.common.utils.system.network.find_port_processes", return_value=[1234]): - with patch("psutil.Process") as mock_process: - mock_proc = MagicMock() - mock_process.return_value = mock_proc - mock_proc.wait.side_effect = [psutil.TimeoutExpired(1234, 2), None] - - result = aggressive_port_cleanup(8080) - - assert result["success"] is True - assert result["killed_pids"] == [1234] - mock_proc.terminate.assert_called_once() - mock_proc.kill.assert_called_once() - - @pytest.mark.unit - def test_no_processes_found(self): - """Test when no processes are found""" - with patch("sage.common.utils.system.network.find_port_processes", return_value=[]): - result = aggressive_port_cleanup(8080) - - assert result["success"] is False - assert result["killed_pids"] == [] - assert "No processes found" in result["errors"][0] - - @pytest.mark.unit - def test_access_denied(self): - """Test handling access denied errors""" - import psutil - - with patch("sage.common.utils.system.network.find_port_processes", return_value=[1234]): - with patch("psutil.Process") as mock_process: - mock_proc = MagicMock() - mock_process.return_value = mock_proc - mock_proc.terminate.side_effect = psutil.AccessDenied(1234) - - result = aggressive_port_cleanup(8080) - - assert result["success"] is False - assert result["killed_pids"] == [] - assert "Access denied to kill process 1234" in result["errors"] - - @pytest.mark.unit - def test_process_not_found(self): - """Test handling process not found errors""" - import psutil - - with patch("sage.common.utils.system.network.find_port_processes", return_value=[1234]): - with patch("psutil.Process") as mock_process: - mock_process.side_effect = psutil.NoSuchProcess(1234) - - result = aggressive_port_cleanup(8080) - - # Should continue without error - assert result["success"] is False - assert result["killed_pids"] == [] - - -class TestGetHostIp: - """Test host IP detection functionality""" - - @pytest.mark.unit - def test_get_external_ip(self): - """Test getting external-facing IP""" - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.getsockname.return_value = ("192.168.1.100", 12345) - - result = get_host_ip() - - assert result == "192.168.1.100" - mock_sock.connect.assert_called_once_with(("8.8.8.8", 80)) - - @pytest.mark.unit - def test_fallback_to_localhost(self): - """Test fallback to localhost on error""" - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.connect.side_effect = Exception("Network error") - - result = get_host_ip() - - assert result == "127.0.0.1" - - -class TestTcpConnection: - """Test TCP connection testing functionality""" - - @pytest.mark.unit - def test_successful_connection(self): - """Test successful TCP connection""" - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.connect_ex.return_value = 0 - - with patch("time.time", side_effect=[0, 0.5]): - result = check_tcp_connection("127.0.0.1", 8080) - - assert result["success"] is True - assert "Connection to 127.0.0.1:8080 successful" in result["message"] - assert result["response_time"] == 0.5 - - @pytest.mark.unit - def test_connection_failed(self): - """Test failed TCP connection""" - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.connect_ex.return_value = 111 # Connection refused - - with patch("time.time", side_effect=[0, 0.1]): - result = check_tcp_connection("127.0.0.1", 8080) - - assert result["success"] is False - assert "Connection to 127.0.0.1:8080 failed" in result["message"] - assert "error code: 111" in result["message"] - assert result["response_time"] == 0.1 - - @pytest.mark.unit - def test_connection_timeout(self): - """Test connection timeout""" - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.connect_ex.side_effect = TimeoutError() - - result = check_tcp_connection("127.0.0.1", 8080, timeout=5) - - assert result["success"] is False - assert "Connection timeout to 127.0.0.1:8080" in result["message"] - assert result["response_time"] == 5 - - @pytest.mark.unit - def test_connection_exception(self): - """Test connection exception handling""" - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.connect_ex.side_effect = Exception("Socket error") - - result = check_tcp_connection("127.0.0.1", 8080) - - assert result["success"] is False - assert "Connection test failed" in result["message"] - assert result["response_time"] == 0 - - @pytest.mark.unit - def test_custom_timeout(self): - """Test custom timeout setting""" - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.connect_ex.return_value = 0 - - check_tcp_connection("127.0.0.1", 8080, timeout=10) - - mock_sock.settimeout.assert_called_once_with(10) - - -class TestIntegrationScenarios: - """Integration tests for network utilities""" - - @pytest.mark.integration - def test_port_lifecycle(self): - """Test complete port lifecycle management""" - # Test port allocation - with patch("sage.common.utils.system.network.is_port_occupied", return_value=False): - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.getsockname.return_value = ("127.0.0.1", 19200) - - port = allocate_free_port("127.0.0.1") - assert port == 19200 - - # Test port occupation check - with patch( - "sage.common.utils.system.network.is_port_occupied", return_value=True - ) as mock_is_port_occupied: - # Call the mock directly since we want to test the mock behavior - mocked_result = mock_is_port_occupied("127.0.0.1", port) - assert mocked_result is True - - # Test process finding - with patch( - "sage.common.utils.system.network.find_port_processes", return_value=[1234] - ) as mock_find_port_processes: - processes = mock_find_port_processes(port) - assert processes == [1234] - - # Test cleanup - with patch("sage.common.utils.system.network.find_port_processes", return_value=[1234]): - with patch("psutil.Process") as mock_process: - mock_proc = MagicMock() - mock_process.return_value = mock_proc - - result = aggressive_port_cleanup(port) - assert result["success"] is True - - @pytest.mark.integration - def test_health_check_workflow(self): - """Test health check workflow""" - # Test connection test first - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.connect_ex.return_value = 0 - - conn_result = check_tcp_connection("127.0.0.1", 8080) - assert conn_result["success"] is True - - # Then test health check - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - - response_data = {"status": "healthy", "version": "1.0"} - response_json = json.dumps(response_data).encode("utf-8") - mock_sock.recv.side_effect = [ - len(response_json).to_bytes(4, byteorder="big"), - response_json, - ] - - health_result = send_tcp_health_check("127.0.0.1", 8080, {"type": "health_check"}) - assert health_result == response_data - - @pytest.mark.slow - def test_port_release_with_timeout(self): - """Test port release waiting with actual timeout""" - call_count = 0 - - def mock_is_occupied(*args): - nonlocal call_count - call_count += 1 - return call_count <= 3 # Occupied for first 3 calls - - with patch( - "sage.common.utils.system.network.is_port_occupied", - side_effect=mock_is_occupied, - ): - with patch("time.sleep") as mock_sleep: - result = wait_for_port_release("127.0.0.1", 8080, timeout=10, check_interval=1) - assert result is True - # Should have slept 3 times (after each occupied check) - assert mock_sleep.call_count == 3 - - @pytest.mark.integration - def test_cross_platform_process_finding(self): - """Test process finding across different tools""" - # Simulate different tools finding different processes - with patch( - "sage.common.utils.system.network._find_processes_with_lsof", - return_value=[1001, 1002], - ): - with patch( - "sage.common.utils.system.network._find_processes_with_netstat", - return_value=[1002, 1003], - ): - with patch( - "sage.common.utils.system.network._find_processes_with_fuser", - return_value=[1003, 1004], - ): - with patch("psutil.Process") as mock_process: - - def create_mock_proc(pid): - mock_proc = MagicMock() - mock_proc.is_running.return_value = True - mock_proc.pid = pid - return mock_proc - - mock_process.side_effect = create_mock_proc - processes = find_port_processes(8080) - - # Should combine and deduplicate all results - process_pids = {proc.pid for proc in processes} - assert process_pids == {1001, 1002, 1003, 1004} - - -class TestPerformanceScenarios: - """Performance and stress tests""" - - @pytest.mark.slow - def test_rapid_port_checks(self): - """Test rapid port occupation checks""" - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.connect_ex.return_value = 111 - - start_time = time.time() - for port in range(8000, 8100): # Check 100 ports - is_port_occupied("127.0.0.1", port) - end_time = time.time() - - # Should complete quickly (under 1 second with mocking) - assert end_time - start_time < 1.0 - - @pytest.mark.slow - def test_large_process_list(self): - """Test handling large process lists""" - large_pid_list = list(range(1000, 2000)) # 1000 PIDs - - with ( - patch( - "sage.common.utils.system.network._find_processes_with_lsof", - return_value=large_pid_list, - ), - patch( - "sage.common.utils.system.network._find_processes_with_netstat", - return_value=[], - ), - patch( - "sage.common.utils.system.network._find_processes_with_fuser", - return_value=[], - ), - patch("psutil.Process") as mock_process, - ): - # Mock psutil.Process to return mock processes - mock_processes = [] - for _pid in large_pid_list: - mock_proc = MagicMock() - mock_proc.is_running.return_value = True - mock_processes.append(mock_proc) - - mock_process.side_effect = mock_processes - - result = find_port_processes(8080) - assert len(result) == 1000 - - @pytest.mark.slow - def test_port_allocation_stress(self): - """Test port allocation under stress""" - allocated_ports = [] - - with patch("sage.common.utils.system.network.is_port_occupied") as mock_occupied: - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - - # Simulate increasing port occupancy - def mock_port_check(host, port): - return port in allocated_ports - - mock_occupied.side_effect = mock_port_check - - # Allocate multiple ports - for i in range(10): - mock_sock.getsockname.return_value = ("127.0.0.1", 19200 + i) - port = allocate_free_port("127.0.0.1", (19200, 19300)) - allocated_ports.append(port) - assert port == 19200 + i - - -class TestErrorHandlingScenarios: - """Error handling and edge case tests""" - - @pytest.mark.unit - def test_invalid_port_numbers(self): - """Test handling invalid port numbers""" - # Negative port - result = is_port_occupied("127.0.0.1", -1) - assert not result - - # Port too large - result = is_port_occupied("127.0.0.1", 70000) - assert not result - - # Zero port (should be handled) - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.connect_ex.return_value = 111 - - result = is_port_occupied("127.0.0.1", 0) - assert not result - - @pytest.mark.unit - def test_network_interface_errors(self): - """Test handling network interface errors""" - # Invalid network interface - result = check_tcp_connection("999.999.999.999", 8080) - assert not result["success"] - - # IPv6 localhost - with patch("socket.socket") as mock_socket: - mock_sock = MagicMock() - mock_socket.return_value.__enter__.return_value = mock_sock - mock_sock.connect_ex.return_value = 0 - - result = check_tcp_connection("::1", 8080) - assert result["success"] - - @pytest.mark.unit - def test_malformed_health_check_data(self): - """Test handling malformed health check data""" - # Non-serializable request data - request_data = {"timestamp": object()} # Non-JSON serializable - - with pytest.raises(TypeError): - with patch("socket.socket"): - send_tcp_health_check("127.0.0.1", 8080, request_data) - - @pytest.mark.unit - def test_resource_exhaustion_simulation(self): - """Test behavior under resource exhaustion""" - with patch("socket.socket", side_effect=OSError("Too many open files")): - result = is_port_occupied("127.0.0.1", 8080) - assert not result - - result = check_tcp_connection("127.0.0.1", 8080) - assert not result["success"] - - -if __name__ == "__main__": - pytest.main([__file__]) diff --git a/packages/sage-kernel/tests/unit/kernel/utils/system/test_kernel_process.py b/packages/sage-kernel/tests/unit/kernel/utils/system/test_kernel_process.py deleted file mode 100644 index 7903b84503..0000000000 --- a/packages/sage-kernel/tests/unit/kernel/utils/system/test_kernel_process.py +++ /dev/null @@ -1,1151 +0,0 @@ -""" -Test suite for sage.common.utils.system.process module - -This module tests process management utilities including process discovery, -termination, tree operations, and sudo privilege management. - -Created: 2024 -Test Framework: pytest -Coverage: Process management, sudo operations, system monitoring -""" - -import subprocess -import time -from unittest.mock import MagicMock, Mock, patch - -import psutil -import pytest - -from sage.common.utils.system.process import ( - SudoManager, - check_process_ownership, - create_sudo_manager, - find_processes_by_name, - get_process_children, - get_process_info, - get_system_process_summary, - is_process_running, - kill_process_with_sudo, - terminate_process, - terminate_process_tree, - terminate_processes_by_name, - verify_sudo_password, - wait_for_process_termination, -) - - -class TestFindProcessesByName: - """Test process discovery by name functionality""" - - @pytest.mark.unit - def test_find_by_process_name(self): - """Test finding processes by exact name""" - mock_processes = [ - {"pid": 1234, "name": "python", "cmdline": ["python", "script.py"]}, - {"pid": 5678, "name": "java", "cmdline": ["java", "-jar", "app.jar"]}, - {"pid": 9999, "name": "python3", "cmdline": ["python3", "test.py"]}, - ] - - def mock_proc_iter(attrs): - mock_procs = [] - for proc_data in mock_processes: - mock_proc = MagicMock() - mock_proc.info = proc_data - mock_procs.append(mock_proc) - return mock_procs - - with patch("psutil.process_iter", side_effect=mock_proc_iter): - result = find_processes_by_name(["python"]) - - assert len(result) == 2 # python and python3 - assert all(hasattr(proc, "info") for proc in result) - - @pytest.mark.unit - def test_find_by_cmdline_pattern(self): - """Test finding processes by command line pattern""" - mock_processes = [ - {"pid": 1234, "name": "java", "cmdline": ["java", "-jar", "app.jar"]}, - {"pid": 5678, "name": "python", "cmdline": ["python", "server.py"]}, - ] - - def mock_proc_iter(attrs): - mock_procs = [] - for proc_data in mock_processes: - mock_proc = MagicMock() - mock_proc.info = proc_data - mock_procs.append(mock_proc) - return mock_procs - - with patch("psutil.process_iter", side_effect=mock_proc_iter): - result = find_processes_by_name(["app.jar"]) - - assert len(result) == 1 - assert result[0].info["pid"] == 1234 - - @pytest.mark.unit - def test_no_matching_processes(self): - """Test when no processes match the criteria""" - mock_processes = [ - {"pid": 1234, "name": "bash", "cmdline": ["bash"]}, - ] - - def mock_proc_iter(attrs): - mock_procs = [] - for proc_data in mock_processes: - mock_proc = MagicMock() - mock_proc.info = proc_data - mock_procs.append(mock_proc) - return mock_procs - - with patch("psutil.process_iter", side_effect=mock_proc_iter): - result = find_processes_by_name(["nonexistent"]) - - assert len(result) == 0 - - @pytest.mark.unit - def test_handle_process_exceptions(self): - """Test handling process access exceptions""" - - def mock_proc_iter(attrs): - mock_proc1 = MagicMock() - mock_proc1.info = {"pid": 1234, "name": "python", "cmdline": ["python"]} - - mock_proc2 = MagicMock() - # When accessing info, raise AccessDenied - mock_proc2.info = MagicMock() - mock_proc2.info.__getitem__.side_effect = psutil.AccessDenied() - - mock_proc3 = MagicMock() - # When accessing info, raise NoSuchProcess - mock_proc3.info = MagicMock() - mock_proc3.info.__getitem__.side_effect = psutil.NoSuchProcess(1234) - - return [mock_proc1, mock_proc2, mock_proc3] - - with patch("psutil.process_iter", side_effect=mock_proc_iter): - result = find_processes_by_name(["python"]) - - assert len(result) == 1 # Only the accessible process - - @pytest.mark.unit - def test_empty_cmdline_handling(self): - """Test handling processes with empty command lines""" - mock_processes = [ - {"pid": 1234, "name": "kernel_process", "cmdline": None}, - {"pid": 5678, "name": "user_process", "cmdline": []}, - ] - - def mock_proc_iter(attrs): - mock_procs = [] - for proc_data in mock_processes: - mock_proc = MagicMock() - mock_proc.info = proc_data - mock_procs.append(mock_proc) - return mock_procs - - with patch("psutil.process_iter", side_effect=mock_proc_iter): - result = find_processes_by_name(["kernel_process"]) - - assert len(result) == 1 - - -class TestGetProcessInfo: - """Test process information retrieval functionality""" - - @pytest.mark.unit - def test_get_valid_process_info(self): - """Test getting information for a valid process""" - with patch("psutil.Process") as mock_process: - mock_proc = MagicMock() - mock_proc.name.return_value = "python" - mock_proc.username.return_value = "testuser" - mock_proc.cmdline.return_value = ["python", "script.py"] - mock_proc.status.return_value = "running" - mock_proc.cpu_percent.return_value = 15.5 - mock_proc.memory_percent.return_value = 2.3 - mock_proc.create_time.return_value = 1234567890 - mock_process.return_value = mock_proc - - result = get_process_info(1234) - - assert result["pid"] == 1234 - assert result["name"] == "python" - assert result["user"] == "testuser" - assert result["cmdline"] == "python script.py" - assert result["status"] == "running" - assert result["cpu_percent"] == 15.5 - assert result["memory_percent"] == 2.3 - assert result["create_time"] == 1234567890 - assert "error" not in result - - @pytest.mark.unit - def test_process_not_found(self): - """Test handling process not found""" - with patch("psutil.Process", side_effect=psutil.NoSuchProcess(1234)): - result = get_process_info(1234) - - assert result["pid"] == 1234 - assert result["status"] == "Not Found" - assert result["error"] == "Process not found" - - @pytest.mark.unit - def test_access_denied(self): - """Test handling access denied""" - with patch("psutil.Process", side_effect=psutil.AccessDenied(1234)): - result = get_process_info(1234) - - assert result["pid"] == 1234 - assert result["status"] == "Access Denied" - assert result["error"] == "Access denied" - - @pytest.mark.unit - def test_unexpected_exception(self): - """Test handling unexpected exceptions""" - with patch("psutil.Process", side_effect=Exception("Unexpected error")): - result = get_process_info(1234) - - assert result["pid"] == 1234 - assert "error" in result - assert "Unexpected error" in result["error"] - - -class TestTerminateProcess: - """Test process termination functionality""" - - @pytest.mark.unit - def test_graceful_termination(self): - """Test successful graceful termination""" - with patch("psutil.Process") as mock_process: - mock_proc = MagicMock() - mock_process.return_value = mock_proc - - with patch("sage.common.utils.system.process.get_process_info") as mock_info: - mock_info.return_value = {"pid": 1234, "name": "test_process"} - - result = terminate_process(1234) - - assert result["success"] is True - assert result["method"] == "terminate" - assert result["pid"] == 1234 - mock_proc.terminate.assert_called_once() - mock_proc.wait.assert_called_once_with(timeout=5) - - @pytest.mark.unit - def test_force_kill_after_timeout(self): - """Test force kill when graceful termination times out""" - with patch("psutil.Process") as mock_process: - mock_proc = MagicMock() - mock_proc.wait.side_effect = [psutil.TimeoutExpired(1234, 5), None] - mock_process.return_value = mock_proc - - with patch("sage.common.utils.system.process.get_process_info") as mock_info: - mock_info.return_value = {"pid": 1234, "name": "test_process"} - - result = terminate_process(1234) - - assert result["success"] is True - assert result["method"] == "kill" - assert "killed after timeout" in result["message"] - mock_proc.terminate.assert_called_once() - mock_proc.kill.assert_called_once() - - @pytest.mark.unit - def test_process_already_gone(self): - """Test handling process that's already terminated""" - with patch("psutil.Process", side_effect=psutil.NoSuchProcess(1234)): - result = terminate_process(1234) - - assert result["success"] is True - assert result["method"] == "already_gone" - assert "already terminated" in result["message"] - - @pytest.mark.unit - def test_access_denied_termination(self): - """Test handling access denied during termination""" - with patch("psutil.Process") as mock_process: - mock_proc = MagicMock() - mock_proc.terminate.side_effect = psutil.AccessDenied(1234) - mock_process.return_value = mock_proc - - result = terminate_process(1234) - - assert result["success"] is False - assert result["method"] == "access_denied" - assert "Access denied" in result["error"] - - @pytest.mark.unit - def test_custom_timeout(self): - """Test termination with custom timeout""" - with patch("psutil.Process") as mock_process: - mock_proc = MagicMock() - mock_process.return_value = mock_proc - - with patch("sage.common.utils.system.process.get_process_info") as mock_info: - mock_info.return_value = {"pid": 1234, "name": "test_process"} - - terminate_process(1234, timeout=10) - - mock_proc.wait.assert_called_with(timeout=10) - - -class TestTerminateProcessesByName: - """Test bulk process termination by name""" - - @pytest.mark.unit - def test_terminate_multiple_processes(self): - """Test terminating multiple processes by name""" - mock_procs = [MagicMock(pid=1234), MagicMock(pid=5678)] - - with patch( - "sage.common.utils.system.process.find_processes_by_name", - return_value=mock_procs, - ): - with patch("sage.common.utils.system.process.terminate_process") as mock_terminate: - mock_terminate.side_effect = [ - {"success": True, "method": "terminate", "pid": 1234}, - {"success": True, "method": "kill", "pid": 5678}, - ] - - result = terminate_processes_by_name(["test_process"]) - - assert result["total_found"] == 2 - assert len(result["terminated"]) == 2 - assert len(result["failed"]) == 0 - assert result["success"] is True - - @pytest.mark.unit - def test_mixed_termination_results(self): - """Test handling mixed success/failure results""" - mock_procs = [MagicMock(pid=1234), MagicMock(pid=5678), MagicMock(pid=9999)] - - with patch( - "sage.common.utils.system.process.find_processes_by_name", - return_value=mock_procs, - ): - with patch("sage.common.utils.system.process.terminate_process") as mock_terminate: - mock_terminate.side_effect = [ - {"success": True, "method": "terminate", "pid": 1234}, - {"success": False, "method": "access_denied", "pid": 5678}, - {"success": True, "method": "already_gone", "pid": 9999}, - ] - - result = terminate_processes_by_name(["test_process"]) - - assert result["total_found"] == 3 - assert len(result["terminated"]) == 1 - assert len(result["failed"]) == 1 - assert len(result["already_gone"]) == 1 - assert result["success"] is False # Due to one failure - - @pytest.mark.unit - def test_no_processes_found(self): - """Test when no processes are found""" - with patch("sage.common.utils.system.process.find_processes_by_name", return_value=[]): - result = terminate_processes_by_name(["nonexistent"]) - - assert result["total_found"] == 0 - assert result["success"] is True # No failures - - -class TestKillProcessWithSudo: - """Test sudo-based process killing""" - - @pytest.mark.unit - def test_successful_sudo_kill(self): - """Test successful sudo kill""" - with patch("subprocess.run") as mock_run: - mock_run.return_value = Mock(returncode=0, stderr="") - - result = kill_process_with_sudo(1234, "password123") - - assert result["success"] is True - assert result["method"] == "sudo_kill" - mock_run.assert_called_once_with( - ["sudo", "-S", "kill", "-9", "1234"], - input="password123\n", - capture_output=True, - text=True, - timeout=10, - ) - - @pytest.mark.unit - def test_sudo_kill_failed(self): - """Test failed sudo kill""" - with patch("subprocess.run") as mock_run: - mock_run.return_value = Mock(returncode=1, stderr="kill: cannot find process") - - result = kill_process_with_sudo(1234, "password123") - - assert result["success"] is False - assert "Failed to kill process" in result["error"] - - @pytest.mark.unit - def test_sudo_timeout(self): - """Test sudo command timeout""" - with patch("subprocess.run", side_effect=subprocess.TimeoutExpired(["sudo"], 10)): - result = kill_process_with_sudo(1234, "password123") - - assert result["success"] is False - assert "Timeout" in result["error"] - - @pytest.mark.unit - def test_no_password_provided(self): - """Test handling no password provided""" - result = kill_process_with_sudo(1234, "") - - assert result["success"] is False - assert "No sudo password provided" in result["error"] - - @pytest.mark.unit - def test_prompt_for_password(self): - """Test prompting for password when none provided""" - with patch("getpass.getpass", return_value="prompted_password"): - with patch("subprocess.run") as mock_run: - mock_run.return_value = Mock(returncode=0, stderr="") - - result = kill_process_with_sudo(1234, None) - - assert result["success"] is True - mock_run.assert_called_once() - - -class TestVerifySudoPassword: - """Test sudo password verification""" - - @pytest.mark.unit - def test_valid_password(self): - """Test verifying valid sudo password""" - with patch("subprocess.run") as mock_run: - mock_run.return_value = Mock(returncode=0) - - result = verify_sudo_password("correct_password") - - assert result is True - mock_run.assert_called_once_with( - ["sudo", "-S", "echo", "password_test"], - input="correct_password\n", - capture_output=True, - text=True, - timeout=10, - ) - - @pytest.mark.unit - def test_invalid_password(self): - """Test verifying invalid sudo password""" - with patch("subprocess.run") as mock_run: - mock_run.return_value = Mock(returncode=1) - - result = verify_sudo_password("wrong_password") - - assert result is False - - @pytest.mark.unit - def test_verification_exception(self): - """Test handling verification exceptions""" - with patch("subprocess.run", side_effect=Exception("Network error")): - result = verify_sudo_password("password") - - assert result is False - - -class TestGetProcessChildren: - """Test process children discovery""" - - @pytest.mark.unit - def test_get_children_recursive(self): - """Test getting children recursively""" - with patch("psutil.Process") as mock_process: - mock_child1 = MagicMock(pid=1001) - mock_child2 = MagicMock(pid=1002) - mock_parent = MagicMock() - mock_parent.children.return_value = [mock_child1, mock_child2] - mock_process.return_value = mock_parent - - result = get_process_children(1234, recursive=True) - - assert result == [1001, 1002] - mock_parent.children.assert_called_once_with(recursive=True) - - @pytest.mark.unit - def test_get_children_non_recursive(self): - """Test getting children non-recursively""" - with patch("psutil.Process") as mock_process: - mock_child = MagicMock(pid=1001) - mock_parent = MagicMock() - mock_parent.children.return_value = [mock_child] - mock_process.return_value = mock_parent - - result = get_process_children(1234, recursive=False) - - assert result == [1001] - mock_parent.children.assert_called_once_with(recursive=False) - - @pytest.mark.unit - def test_process_not_found(self): - """Test handling process not found""" - with patch("psutil.Process", side_effect=psutil.NoSuchProcess(1234)): - result = get_process_children(1234) - - assert result == [] - - @pytest.mark.unit - def test_no_children(self): - """Test process with no children""" - with patch("psutil.Process") as mock_process: - mock_parent = MagicMock() - mock_parent.children.return_value = [] - mock_process.return_value = mock_parent - - result = get_process_children(1234) - - assert result == [] - - -class TestTerminateProcessTree: - """Test process tree termination""" - - @pytest.mark.unit - def test_terminate_tree_success(self): - """Test successful process tree termination""" - with patch( - "sage.common.utils.system.process.get_process_children", - return_value=[1001, 1002], - ): - with patch("sage.common.utils.system.process.terminate_process") as mock_terminate: - mock_terminate.side_effect = [ - {"success": True, "method": "terminate", "pid": 1001}, - {"success": True, "method": "terminate", "pid": 1002}, - {"success": True, "method": "terminate", "pid": 1234}, - ] - - result = terminate_process_tree(1234) - - assert result["root_pid"] == 1234 - assert result["total_processes"] == 3 - assert len(result["terminated"]) == 3 - assert result["success"] is True - - @pytest.mark.unit - def test_terminate_tree_partial_failure(self): - """Test process tree termination with some failures""" - with patch("sage.common.utils.system.process.get_process_children", return_value=[1001]): - with patch("sage.common.utils.system.process.terminate_process") as mock_terminate: - mock_terminate.side_effect = [ - {"success": False, "method": "access_denied", "pid": 1001}, - {"success": True, "method": "terminate", "pid": 1234}, - ] - - result = terminate_process_tree(1234) - - assert result["total_processes"] == 2 - assert len(result["terminated"]) == 1 - assert len(result["failed"]) == 1 - assert result["success"] is False - - @pytest.mark.unit - def test_no_children_process_tree(self): - """Test terminating process with no children""" - with patch("sage.common.utils.system.process.get_process_children", return_value=[]): - with patch("sage.common.utils.system.process.terminate_process") as mock_terminate: - mock_terminate.return_value = { - "success": True, - "method": "terminate", - "pid": 1234, - } - - result = terminate_process_tree(1234) - - assert result["total_processes"] == 1 - assert result["success"] is True - - -class TestWaitForProcessTermination: - """Test process termination waiting""" - - @pytest.mark.unit - def test_process_terminates_quickly(self): - """Test process terminates within timeout""" - with patch("psutil.Process", side_effect=psutil.NoSuchProcess(1234)): - with patch("time.time", side_effect=[0, 1]): - result = wait_for_process_termination(1234, timeout=10) - - assert result is True - - @pytest.mark.unit - def test_process_stops_running(self): - """Test process stops running but still exists""" - with patch("psutil.Process") as mock_process: - mock_proc = MagicMock() - mock_proc.is_running.return_value = False - mock_process.return_value = mock_proc - - with patch("time.time", side_effect=[0, 1]): - result = wait_for_process_termination(1234, timeout=10) - - assert result is True - - @pytest.mark.unit - def test_process_timeout(self): - """Test timeout waiting for process termination""" - with patch("psutil.Process") as mock_process: - mock_proc = MagicMock() - mock_proc.is_running.return_value = True - mock_process.return_value = mock_proc - - with patch("time.time", side_effect=[0, 5, 11]): # Simulate timeout - with patch("time.sleep"): - result = wait_for_process_termination(1234, timeout=10) - - assert result is False - - -class TestGetSystemProcessSummary: - """Test system process summary functionality""" - - @pytest.mark.unit - def test_process_summary_success(self): - """Test successful process summary generation""" - mock_processes = [ - {"pid": 1001, "name": "init", "status": "sleeping", "username": "root"}, - {"pid": 1002, "name": "python", "status": "running", "username": "user1"}, - {"pid": 1003, "name": "bash", "status": "sleeping", "username": "user1"}, - ] - - def mock_proc_iter(attrs): - mock_procs = [] - for proc_data in mock_processes: - mock_proc = MagicMock() - mock_proc.info = proc_data - mock_procs.append(mock_proc) - return mock_procs - - with patch("psutil.process_iter", side_effect=mock_proc_iter): - with patch("psutil.virtual_memory") as mock_memory: - mock_memory.return_value._asdict.return_value = { - "total": 8000000, - "available": 4000000, - } - with patch("psutil.cpu_percent", return_value=25.5): - result = get_system_process_summary() - - assert result["total_processes"] == 3 - assert result["by_status"]["sleeping"] == 2 - assert result["by_status"]["running"] == 1 - assert result["by_user"]["root"] == 1 - assert result["by_user"]["user1"] == 2 - assert result["cpu_usage"] == 25.5 - assert "memory_usage" in result - - @pytest.mark.unit - def test_process_summary_with_exceptions(self): - """Test process summary with some inaccessible processes""" - # Create mock processes - mock_proc1 = MagicMock() - mock_proc1.info = { - "pid": 1001, - "name": "accessible", - "status": "running", - "username": "user", - } - - # Create a mock process that raises AccessDenied when accessing status in the loop - mock_proc2 = MagicMock() - mock_proc2.info = MagicMock() - - # Configure the mock to raise AccessDenied when specific keys are accessed - def mock_getitem(self, key): - if key == "status": - raise psutil.AccessDenied() - elif key == "username": - raise psutil.AccessDenied() - return {"pid": 1002, "name": "restricted"}[key] - - mock_proc2.info.__getitem__ = mock_getitem - - with patch("psutil.process_iter", return_value=[mock_proc1, mock_proc2]): - with patch("psutil.virtual_memory") as mock_memory: - mock_memory.return_value._asdict.return_value = {} - with patch("psutil.cpu_percent", return_value=0): - result = get_system_process_summary() - - assert result["total_processes"] == 2 # Both processes counted in list - assert ( - result["by_status"]["running"] == 1 - ) # Only accessible process counted in stats - assert len(result["by_status"]) == 1 # Only one status counted - assert result["by_user"]["user"] == 1 # Only accessible user counted - - @pytest.mark.unit - def test_process_summary_error(self): - """Test handling errors in process summary generation""" - with patch("psutil.process_iter", side_effect=Exception("System error")): - result = get_system_process_summary() - - assert "error" in result - assert "Failed to get process summary" in result["error"] - - -class TestIsProcessRunning: - """Test process running check functionality""" - - @pytest.mark.unit - def test_process_is_running(self): - """Test checking if process is running""" - with patch("psutil.Process") as mock_process: - mock_proc = MagicMock() - mock_proc.is_running.return_value = True - mock_process.return_value = mock_proc - - result = is_process_running(1234) - - assert result is True - - @pytest.mark.unit - def test_process_not_running(self): - """Test checking if process is not running""" - with patch("psutil.Process") as mock_process: - mock_proc = MagicMock() - mock_proc.is_running.return_value = False - mock_process.return_value = mock_proc - - result = is_process_running(1234) - - assert result is False - - @pytest.mark.unit - def test_process_not_found(self): - """Test checking non-existent process""" - with patch("psutil.Process", side_effect=psutil.NoSuchProcess(1234)): - result = is_process_running(1234) - - assert result is False - - @pytest.mark.unit - def test_process_check_exception(self): - """Test handling exceptions during process check""" - with patch("psutil.Process", side_effect=Exception("System error")): - result = is_process_running(1234) - - assert result is False - - -class TestSudoManager: - """Test SudoManager class functionality""" - - @pytest.mark.unit - def test_sudo_manager_creation(self): - """Test creating SudoManager instance""" - manager = create_sudo_manager() - - assert isinstance(manager, SudoManager) - assert not manager.has_sudo_access() - assert manager.get_cached_password() == "" - - @pytest.mark.unit - def test_get_sudo_password_success(self): - """Test successful sudo password retrieval""" - manager = SudoManager() - - with patch("getpass.getpass", return_value="test_password"): - with patch( - "sage.common.utils.system.process.verify_sudo_password", - return_value=True, - ): - with patch("builtins.print"): - password = manager.get_sudo_password() # pragma: allowlist secret - - assert password == "test_password" # pragma: allowlist secret - assert manager.has_sudo_access() - assert ( - manager.get_cached_password() == "test_password" - ) # pragma: allowlist secret - - @pytest.mark.unit - def test_get_sudo_password_invalid(self): - """Test invalid sudo password handling""" - manager = SudoManager() - - with patch("getpass.getpass", return_value="wrong_password"): - with patch( - "sage.common.utils.system.process.verify_sudo_password", - return_value=False, - ): - with patch("builtins.print"): - password = manager.get_sudo_password() - - assert password == "" - assert not manager.has_sudo_access() - - @pytest.mark.unit - def test_get_sudo_password_empty(self): - """Test empty password input""" - manager = SudoManager() - - with patch("getpass.getpass", return_value=""): - with patch("builtins.print"): - password = manager.get_sudo_password() - - assert password == "" - assert not manager.has_sudo_access() - - @pytest.mark.unit - def test_cached_password_reuse(self): - """Test reusing cached password""" - manager = SudoManager() - manager._cached_password = "cached_password" # pragma: allowlist secret - manager._password_verified = True - - password = manager.get_sudo_password() # pragma: allowlist secret - - assert password == "cached_password" # pragma: allowlist secret - - @pytest.mark.unit - def test_ensure_sudo_access(self): - """Test ensuring sudo access""" - manager = SudoManager() - - with patch.object(manager, "get_sudo_password", return_value="password"): - with patch("builtins.print"): - result = manager.ensure_sudo_access() - - assert result is True - - # Test without password - with patch.object(manager, "get_sudo_password", return_value=""): - with patch("builtins.print"): - result = manager.ensure_sudo_access() - - assert result is False - - @pytest.mark.unit - def test_clear_cache(self): - """Test clearing password cache""" - manager = SudoManager() - manager._cached_password = "password" # pragma: allowlist secret - manager._password_verified = True - - manager.clear_cache() - - assert manager._cached_password is None - assert not manager._password_verified - assert not manager.has_sudo_access() - - @pytest.mark.unit - def test_execute_with_sudo_success(self): - """Test successful sudo command execution""" - manager = SudoManager() - manager._cached_password = "password" # pragma: allowlist secret - manager._password_verified = True - - with patch("subprocess.run") as mock_run: - mock_run.return_value = Mock(returncode=0, stdout="Command output", stderr="") - - result = manager.execute_with_sudo(["kill", "-9", "1234"]) - - assert result["success"] is True - assert result["stdout"] == "Command output" - mock_run.assert_called_once_with( - ["sudo", "-S", "kill", "-9", "1234"], - input="password\n", - capture_output=True, - text=True, - timeout=30, - ) - - @pytest.mark.unit - def test_execute_with_sudo_no_password(self): - """Test sudo execution without password""" - manager = SudoManager() - - result = manager.execute_with_sudo(["kill", "-9", "1234"]) - - assert result["success"] is False - assert "No sudo password available" in result["error"] - - @pytest.mark.unit - def test_execute_with_sudo_failure(self): - """Test failed sudo command execution""" - manager = SudoManager() - manager._cached_password = "password" # pragma: allowlist secret - manager._password_verified = True - - with patch("subprocess.run") as mock_run: - mock_run.return_value = Mock(returncode=1, stdout="", stderr="Permission denied") - - result = manager.execute_with_sudo(["kill", "-9", "1234"]) - - assert result["success"] is False - assert result["returncode"] == 1 - assert "Command failed" in result["error"] - - @pytest.mark.unit - def test_execute_with_sudo_timeout(self): - """Test sudo command timeout""" - manager = SudoManager() - manager._cached_password = "password" # pragma: allowlist secret - manager._password_verified = True - - with patch("subprocess.run", side_effect=subprocess.TimeoutExpired(["sudo"], 30)): - result = manager.execute_with_sudo(["sleep", "60"]) - - assert result["success"] is False - assert "Command timeout" in result["error"] - - -class TestCheckProcessOwnership: - """Test process ownership checking""" - - @pytest.mark.unit - def test_own_process(self): - """Test checking ownership of own process""" - with patch("psutil.Process") as mock_process: - mock_proc = MagicMock() - mock_proc.username.return_value = "testuser" - mock_process.return_value = mock_proc - - with patch("os.getenv", return_value="testuser"): - result = check_process_ownership(1234) - - assert result["needs_sudo"] is False - assert result["accessible"] is True - assert result["process_user"] == "testuser" - assert result["current_user"] == "testuser" - - @pytest.mark.unit - def test_other_user_process(self): - """Test checking ownership of another user's process""" - with patch("psutil.Process") as mock_process: - mock_proc = MagicMock() - mock_proc.username.return_value = "otheruser" - mock_process.return_value = mock_proc - - with patch("os.getenv", return_value="testuser"): - result = check_process_ownership(1234) - - assert result["needs_sudo"] is True - assert result["accessible"] is True - assert result["process_user"] == "otheruser" - assert result["current_user"] == "testuser" - - @pytest.mark.unit - def test_process_not_found_ownership(self): - """Test checking ownership of non-existent process""" - with patch("psutil.Process", side_effect=psutil.NoSuchProcess(1234)): - result = check_process_ownership(1234) - - assert result["accessible"] is False - assert "Process not found" in result["error"] - - @pytest.mark.unit - def test_access_denied_ownership(self): - """Test checking ownership with access denied""" - with patch("psutil.Process", side_effect=psutil.AccessDenied(1234)): - with patch("os.getenv", return_value="testuser"): - result = check_process_ownership(1234) - - assert result["accessible"] is False - assert result["needs_sudo"] is True - assert "Access denied" in result["error"] - - @pytest.mark.unit - def test_custom_current_user(self): - """Test checking ownership with custom current user""" - with patch("psutil.Process") as mock_process: - mock_proc = MagicMock() - mock_proc.username.return_value = "processowner" - mock_process.return_value = mock_proc - - result = check_process_ownership(1234, current_user="customuser") - - assert result["current_user"] == "customuser" - assert result["needs_sudo"] is True - - -class TestIntegrationScenarios: - """Integration tests for process management utilities""" - - @pytest.mark.integration - def test_complete_process_management_workflow(self): - """Test complete process management workflow""" - # Find processes - mock_procs = [MagicMock(pid=1001), MagicMock(pid=1002)] - - # Since the function is imported into the current module namespace, - # we need to patch it in the current module - import sys - - current_module = sys.modules[__name__] - with patch.object(current_module, "find_processes_by_name", return_value=mock_procs): - processes = find_processes_by_name(["test_app"]) - assert len(processes) == 2 - - # Check ownership - with patch.object(current_module, "check_process_ownership") as mock_ownership: - mock_ownership.return_value = { - "pid": 1001, - "process_user": "testuser", - "current_user": "testuser", - "needs_sudo": False, - "accessible": True, - } - - ownership = check_process_ownership(1001) - assert ownership["needs_sudo"] is False - - # Terminate processes - # Use patch.object to patch the function in current module namespace - with patch.object(current_module, "terminate_process") as mock_terminate: - mock_terminate.return_value = {"success": True, "method": "terminate"} - - result = terminate_process(1001) - assert result["success"] - mock_terminate.assert_called_once_with(1001) - - @pytest.mark.integration - def test_sudo_workflow(self): - """Test sudo-based process management workflow""" - manager = SudoManager() - - # Get sudo access - with patch("getpass.getpass", return_value="password"): - with patch( - "sage.common.utils.system.process.verify_sudo_password", - return_value=True, - ): - with patch("builtins.print"): - assert manager.ensure_sudo_access() - - # Execute sudo command - with patch("subprocess.run") as mock_run: - mock_run.return_value = Mock(returncode=0, stdout="", stderr="") - - result = manager.execute_with_sudo(["kill", "-9", "1234"]) - assert result["success"] - - @pytest.mark.slow - def test_process_tree_management(self): - """Test managing process trees""" - # Mock a process tree: parent 1000 -> children [1001, 1002] - with patch( - "sage.common.utils.system.process.get_process_children", - return_value=[1001, 1002], - ): - with patch("sage.common.utils.system.process.terminate_process") as mock_terminate: - mock_terminate.side_effect = [ - {"success": True, "method": "terminate", "pid": 1001}, - {"success": True, "method": "terminate", "pid": 1002}, - {"success": True, "method": "terminate", "pid": 1000}, - ] - - result = terminate_process_tree(1000) - - assert result["success"] - assert result["total_processes"] == 3 - assert len(result["terminated"]) == 3 - - -class TestPerformanceScenarios: - """Performance and stress tests""" - - @pytest.mark.slow - def test_large_process_list_handling(self): - """Test handling large numbers of processes""" - # Simulate 1000 processes - mock_processes = [] - for i in range(1000): - mock_proc = MagicMock() - mock_proc.info = { - "pid": i + 1000, - "name": f"process_{i}", - "cmdline": [f"process_{i}", "--arg"], - "status": "running", - "username": "user", - } - mock_processes.append(mock_proc) - - def mock_proc_iter(attrs): - return mock_processes - - with patch("psutil.process_iter", side_effect=mock_proc_iter): - with patch("psutil.virtual_memory") as mock_memory: - mock_memory.return_value._asdict.return_value = {} - with patch("psutil.cpu_percent", return_value=50): - result = get_system_process_summary() - - assert result["total_processes"] == 1000 - assert result["by_status"]["running"] == 1000 - - @pytest.mark.slow - def test_rapid_process_checks(self): - """Test rapid process status checks""" - with patch("psutil.Process") as mock_process: - mock_proc = MagicMock() - mock_proc.is_running.return_value = True - mock_process.return_value = mock_proc - - start_time = time.time() - for pid in range(1000, 1100): # Check 100 processes - is_process_running(pid) - end_time = time.time() - - # Should complete quickly (under 1 second with mocking) - assert end_time - start_time < 1.0 - - -class TestErrorHandlingScenarios: - """Error handling and edge case tests""" - - @pytest.mark.unit - def test_invalid_pid_handling(self): - """Test handling invalid PIDs""" - # Negative PID - result = get_process_info(-1) - assert "error" in result - - # Very large PID - with patch("psutil.Process", side_effect=psutil.NoSuchProcess(999999)): - result = get_process_info(999999) - assert result["status"] == "Not Found" - - @pytest.mark.unit - def test_system_resource_exhaustion(self): - """Test behavior under resource exhaustion""" - with patch("psutil.process_iter", side_effect=OSError("Cannot access process list")): - result = get_system_process_summary() - assert "error" in result - - @pytest.mark.unit - def test_permission_escalation_failure(self): - """Test handling sudo permission failures""" - manager = SudoManager() - - with patch("getpass.getpass", return_value="wrong_password"): - with patch( - "sage.common.utils.system.process.verify_sudo_password", - return_value=False, - ): - with patch("builtins.print"): - assert not manager.ensure_sudo_access() - - @pytest.mark.unit - def test_process_state_changes(self): - """Test handling processes that change state during operations""" - # Process terminates between check and operation - with patch("sage.common.utils.system.process.find_processes_by_name") as mock_find: - mock_find.return_value = [MagicMock(pid=1234)] - - with patch("sage.common.utils.system.process.terminate_process") as mock_terminate: - mock_terminate.return_value = { - "success": True, - "method": "already_gone", - "pid": 1234, - } - - result = terminate_processes_by_name(["test_process"]) - - assert result["success"] - assert len(result["already_gone"]) == 1 - - -if __name__ == "__main__": - pytest.main([__file__]) diff --git a/packages/sage-kernel/tests/unit/runtime/context/test_keyed_state_context.py b/packages/sage-kernel/tests/unit/runtime/context/test_keyed_state_context.py deleted file mode 100644 index 2bfb718b3d..0000000000 --- a/packages/sage-kernel/tests/unit/runtime/context/test_keyed_state_context.py +++ /dev/null @@ -1,191 +0,0 @@ -""" -Unit tests for keyed state support in BaseRuntimeContext. - -These tests directly test the set_current_key, get_key, and clear_key methods -to ensure high coverage of the new keyed state functionality. -""" - -import pytest - -from sage.kernel.runtime.context.base_context import BaseRuntimeContext - - -class ConcreteContext(BaseRuntimeContext): - """Concrete implementation of BaseRuntimeContext for testing""" - - def __init__(self): - super().__init__() - self._test_logger = None - - @property - def logger(self): - """Return a test logger""" - if self._test_logger is None: - import logging - - self._test_logger = logging.getLogger("test_context") - return self._test_logger - - -class TestBaseRuntimeContextKeyedState: - """Test keyed state functionality in BaseRuntimeContext""" - - def test_initial_key_is_none(self): - """Test that initial key is None""" - ctx = ConcreteContext() - assert ctx.get_key() is None - assert ctx._current_packet_key is None - - def test_set_current_key_string(self): - """Test setting a string key""" - ctx = ConcreteContext() - ctx.set_current_key("test_key") - assert ctx.get_key() == "test_key" - assert ctx._current_packet_key == "test_key" - - def test_set_current_key_integer(self): - """Test setting an integer key""" - ctx = ConcreteContext() - ctx.set_current_key(42) - assert ctx.get_key() == 42 - assert ctx._current_packet_key == 42 - - def test_set_current_key_tuple(self): - """Test setting a tuple key""" - ctx = ConcreteContext() - key = ("user", "session", 123) - ctx.set_current_key(key) - assert ctx.get_key() == key - assert ctx._current_packet_key == key - - def test_set_current_key_dict(self): - """Test setting a dict key""" - ctx = ConcreteContext() - key = {"user_id": "alice", "session": "abc123"} - ctx.set_current_key(key) - assert ctx.get_key() == key - assert ctx._current_packet_key == key - - def test_set_current_key_none(self): - """Test setting None as key (for unkeyed streams)""" - ctx = ConcreteContext() - ctx.set_current_key("initial") - ctx.set_current_key(None) - assert ctx.get_key() is None - assert ctx._current_packet_key is None - - def test_clear_key(self): - """Test clearing the current key""" - ctx = ConcreteContext() - ctx.set_current_key("test_key") - assert ctx.get_key() == "test_key" - - ctx.clear_key() - assert ctx.get_key() is None - assert ctx._current_packet_key is None - - def test_clear_key_when_none(self): - """Test clearing key when it's already None""" - ctx = ConcreteContext() - ctx.clear_key() - assert ctx.get_key() is None - - def test_multiple_set_clear_cycles(self): - """Test multiple set/clear cycles""" - ctx = ConcreteContext() - - for i in range(10): - key = f"key_{i}" - ctx.set_current_key(key) - assert ctx.get_key() == key - - ctx.clear_key() - assert ctx.get_key() is None - - def test_key_overwrite(self): - """Test that setting a new key overwrites the old one""" - ctx = ConcreteContext() - - ctx.set_current_key("key1") - assert ctx.get_key() == "key1" - - ctx.set_current_key("key2") - assert ctx.get_key() == "key2" - - ctx.set_current_key(123) - assert ctx.get_key() == 123 - - def test_get_key_does_not_modify_state(self): - """Test that get_key() doesn't modify the key""" - ctx = ConcreteContext() - ctx.set_current_key("test") - - # Call get_key multiple times - for _ in range(5): - assert ctx.get_key() == "test" - - # Key should still be set - assert ctx._current_packet_key == "test" - - def test_attribute_exists_after_init(self): - """Test that _current_packet_key attribute exists after initialization""" - ctx = ConcreteContext() - assert hasattr(ctx, "_current_packet_key") - assert ctx._current_packet_key is None - - def test_key_isolation(self): - """Test that keys are isolated between different context instances""" - ctx1 = ConcreteContext() - ctx2 = ConcreteContext() - - ctx1.set_current_key("key1") - ctx2.set_current_key("key2") - - assert ctx1.get_key() == "key1" - assert ctx2.get_key() == "key2" - - ctx1.clear_key() - assert ctx1.get_key() is None - assert ctx2.get_key() == "key2" # ctx2 should be unaffected - - -class TestKeyedStateDocumentation: - """Test that documentation examples work correctly""" - - def test_docstring_example(self): - """Test the example from get_key() docstring""" - # Simulate the example (without actual function execution) - ctx = ConcreteContext() - - # Simulate processing a packet for user "alice" - ctx.set_current_key("alice") - - user_sessions = {} - user_id = ctx.get_key() - - if user_id not in user_sessions: - user_sessions[user_id] = {"count": 0} - user_sessions[user_id]["count"] += 1 - - assert user_sessions["alice"]["count"] == 1 - - # Process another event for alice - user_id = ctx.get_key() - user_sessions[user_id]["count"] += 1 - assert user_sessions["alice"]["count"] == 2 - - # Clear and process for bob - ctx.clear_key() - ctx.set_current_key("bob") - - user_id = ctx.get_key() - if user_id not in user_sessions: - user_sessions[user_id] = {"count": 0} - user_sessions[user_id]["count"] += 1 - - assert user_sessions["bob"]["count"] == 1 - assert user_sessions["alice"]["count"] == 2 # alice unchanged - - -if __name__ == "__main__": - pytest.main([__file__, "-v"]) diff --git a/packages/sage-kernel/tests/unit/utils/__init__.py b/packages/sage-kernel/tests/unit/utils/__init__.py deleted file mode 100644 index 498d9e71ca..0000000000 --- a/packages/sage-kernel/tests/unit/utils/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Kernel utils tests.""" diff --git a/packages/sage-kernel/tests/unit/utils/log_manager_helper.py b/packages/sage-kernel/tests/unit/utils/log_manager_helper.py deleted file mode 100644 index 4935a1b07f..0000000000 --- a/packages/sage-kernel/tests/unit/utils/log_manager_helper.py +++ /dev/null @@ -1,135 +0,0 @@ -""" -测试日志管理配置 - -用于统一管理测试过程中的日志输出，避免控制台被大量日志信息污染。 -""" - -import logging -import os -from datetime import datetime -from pathlib import Path - - -class LogManager: - """测试日志管理器""" - - def __init__(self, project_root: str | None = None): - from sage.common.config.output_paths import get_sage_paths - - if project_root is None: - project_root = os.environ.get("SAGE_HOME", ".") - - self.project_root = Path(project_root) - - # Use unified SAGE path management system - sage_paths = get_sage_paths(self.project_root) - self.logs_dir = sage_paths.logs_dir - self.logs_dir.mkdir(parents=True, exist_ok=True) - - # 创建今天的日志目录 - today = datetime.now().strftime("%Y%m%d") - self.daily_log_dir = self.logs_dir / today - self.daily_log_dir.mkdir(exist_ok=True) - - self._setup_loggers() - - def _setup_loggers(self): - """设置不同类型的日志记录器""" - - # 测试执行日志 - self.test_logger = self._create_logger( - "test_execution", self.daily_log_dir / "test_execution.log" - ) - - # Ray 相关日志 - self.ray_logger = self._create_logger("ray_tests", self.daily_log_dir / "ray_tests.log") - - # 性能日志 - self.perf_logger = self._create_logger( - "performance", self.daily_log_dir / "performance.log" - ) - - def _create_logger(self, name: str, log_file: Path) -> logging.Logger: - """创建一个日志记录器""" - logger = logging.getLogger(name) - logger.setLevel(logging.DEBUG) - - # 避免重复添加处理器 - if logger.handlers: - return logger - - # 文件处理器 - file_handler = logging.FileHandler(log_file, encoding="utf-8") - file_handler.setLevel(logging.DEBUG) - - # 格式器 - formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s") - file_handler.setFormatter(formatter) - - logger.addHandler(file_handler) - return logger - - def log_test_start(self, test_name: str): - """记录测试开始""" - self.test_logger.info(f"开始测试: {test_name}") - - def log_test_end(self, test_name: str, duration: float, passed: bool): - """记录测试结束""" - status = "通过" if passed else "失败" - self.test_logger.info(f"测试结束: {test_name} - {status} ({duration:.2f}s)") - - def log_ray_operation(self, operation: str, details: str = ""): - """记录 Ray 操作""" - self.ray_logger.info(f"Ray操作: {operation} - {details}") - - def log_performance(self, metric: str, value: float, unit: str = ""): - """记录性能指标""" - self.perf_logger.info(f"性能指标: {metric} = {value} {unit}") - - def get_latest_logs(self, log_type: str = "test_execution", lines: int = 50) -> str: - """获取最新的日志内容""" - log_file = self.daily_log_dir / f"{log_type}.log" - if not log_file.exists(): - return "日志文件不存在" - - try: - with open(log_file, encoding="utf-8") as f: - all_lines = f.readlines() - return "".join(all_lines[-lines:]) - except Exception as e: - return f"读取日志失败: {e}" - - -# 全局实例 -_log_manager = None - - -def get_test_log_manager(project_root: str | None = None) -> "LogManager": - """获取测试日志管理器实例""" - global _log_manager - if _log_manager is None: - _log_manager = LogManager(project_root) - return _log_manager - - -def setup_quiet_ray_logging(): - """设置安静的 Ray 日志记录""" - import logging - - # 降低 Ray 相关日志级别 - ray_loggers = [ - "ray", - "ray.serve", - "ray.tune", - "ray.train", - "ray.data", - "ray.workflow", - ] - - for logger_name in ray_loggers: - logger = logging.getLogger(logger_name) - logger.setLevel(logging.WARNING) - - # 设置 Ray 环境变量以减少日志输出 - os.environ["RAY_DISABLE_IMPORT_WARNING"] = "1" - os.environ["RAY_DEDUP_LOGS"] = "0" diff --git a/packages/sage-libs/README.md b/packages/sage-libs/README.md deleted file mode 100644 index 2792cd92ae..0000000000 --- a/packages/sage-libs/README.md +++ /dev/null @@ -1,273 +0,0 @@ -# SAGE Libraries Package (sage-libs) - -## 📋 Overview - -**sage-libs** 是 SAGE 框架的算法库层，定位为 **接口/注册表层 (Interface Layer)**。 - -## 🧭 Governance / 团队协作制度 - -- `docs/governance/TEAM.md` -- `docs/governance/MAINTAINERS.md` -- `docs/governance/DEVELOPER_GUIDE.md` -- `docs/governance/PR_CHECKLIST.md` -- `docs/governance/SELF_HOSTED_RUNNER.md` -- `docs/governance/TODO.md` - -核心设计原则： - -- 📦 **轻量级接口**：定义抽象基类和工厂函数 -- 🔌 **可插拔实现**：重型实现迁出为独立 PyPI 包 (`isage-*`) -- 🏗️ **注册表模式**：通过 `register_*` / `create_*` 动态加载实现 - -## 🏗️ Architecture - -``` -sage-libs (Interface Layer) - ├── agentic/interface/ → isage-agentic (Agent framework) - ├── rag/interface/ → isage-rag (RAG toolkit) - ├── finetune/interface/ → isage-finetune (Fine-tuning) - ├── eval/interface/ → isage-eval (Evaluation) - ├── privacy/interface/ → isage-privacy (Privacy/Unlearning) - ├── safety/interface/ → isage-safety (Guardrails) - ├── ann/interface/ → isage-anns (ANNS algorithms) - ├── amms/interface/ → isage-amms (AMM algorithms) - └── foundation/ → Built-in utilities (no external deps) -``` - -## 📚 Five Core Domains - -### 1. 🤖 Agentic (Agent Framework) - -**接口**：`sage.libs.agentic.interface` - -| Base Class | Description | -| ----------------------- | ------------------------------------------- | -| `BaseAgent` | Agent execution interface | -| `BasePlanner` | Task planning (ToT, ReAct, Hierarchical) | -| `BaseToolSelector` | Tool selection (Keyword, Embedding, Hybrid) | -| `BaseOrchestrator` | Multi-agent orchestration | -| `IntentRecognizer` | Intent recognition | -| `IntentClassifier` | Intent classification | -| `BaseReasoningStrategy` | Reasoning strategies (CoT, ToT, ReAct) | - -```python -from sage.libs.agentic.interface import ( - BaseAgent, BasePlanner, BaseToolSelector, - create_agent, create_planner, register_agent -) - -# Register implementation (from isage-agentic) -register_agent("react", ReactAgent) - -# Create via factory -agent = create_agent("react", tools=[...]) -``` - -### 2. 📖 RAG (Retrieval-Augmented Generation) - -**接口**：`sage.libs.rag.interface` - -| Base Class | Description | -| ---------------- | -------------------------------------- | -| `DocumentLoader` | Document loading (PDF, DOCX, MD, etc.) | -| `TextChunker` | Text segmentation | -| `Retriever` | Vector/BM25 retrieval | -| `Reranker` | Reranking (Cross-Encoder, LLM) | -| `QueryRewriter` | Query rewriting (HyDE, Multi-Query) | -| `RAGPipeline` | End-to-end RAG pipeline | - -```python -from sage.libs.rag.interface import ( - DocumentLoader, Retriever, RAGPipeline, - create_loader, create_retriever, create_pipeline -) - -loader = create_loader("pdf") -retriever = create_retriever("faiss", dimension=768) -``` - -### 3. 🔧 Fine-tuning - -**接口**：`sage.libs.finetune.interface` - -| Base Class | Description | -| ------------------ | --------------------------------------- | -| `FineTuner` | Fine-tuning trainer | -| `DatasetLoader` | Training data loading | -| `TrainingCallback` | Training callbacks (WandB, TensorBoard) | -| `TrainingStrategy` | PEFT strategies (LoRA, QLoRA, Prefix) | - -```python -from sage.libs.finetune.interface import ( - FineTuner, TrainingStrategy, TrainingConfig, LoRAConfig, - create_trainer, create_strategy -) - -strategy = create_strategy("lora") -trainer = create_trainer("lora", model_name="gpt2") -``` - -### 4. 📊 Evaluation - -**接口**：`sage.libs.eval.interface` - -| Base Class | Description | -| --------------- | ------------------------------------------ | -| `BaseMetric` | Evaluation metrics (Accuracy, BLEU, ROUGE) | -| `BaseLLMJudge` | LLM-as-a-Judge (Faithfulness, Relevance) | -| `BaseProfiler` | Performance profiling | -| `BaseBenchmark` | Benchmark suites | - -```python -from sage.libs.eval.interface import ( - BaseMetric, BaseLLMJudge, MetricResult, - create_metric, create_judge -) - -metric = create_metric("accuracy") -judge = create_judge("faithfulness", model="gpt-4") -``` - -### 5. 🔒 Privacy & Safety - -**Privacy 接口**：`sage.libs.privacy.interface` - -| Base Class | Description | -| ---------------------------- | ------------------------------------------ | -| `BaseUnlearner` | Machine unlearning (SISA, Gradient Ascent) | -| `BasePrivacyMechanism` | DP mechanisms (Laplace, Gaussian) | -| `BaseDPOptimizer` | DP optimizers (DP-SGD, DP-Adam) | -| `BaseFederatedClient/Server` | Federated learning | - -**Safety 接口**：`sage.libs.safety.interface` - -| Base Class | Description | -| ------------------------ | ------------------------------------ | -| `BaseGuardrail` | Content safety guardrails | -| `BaseJailbreakDetector` | Jailbreak/prompt injection detection | -| `BaseToxicityDetector` | Toxicity detection | -| `BaseAdversarialDefense` | Adversarial input defense | - -```python -from sage.libs.privacy import create_unlearner, create_mechanism -from sage.libs.safety import create_guardrail, create_jailbreak_detector - -unlearner = create_unlearner("sisa", num_shards=5) -guardrail = create_guardrail("llm", model="gpt-4") -``` - -## 📦 External Packages (isage-\*) - -| Domain | Interface (sage-libs) | Implementation (PyPI) | Status | -| ----------- | --------------------- | --------------------- | ------------ | -| Agentic | `agentic/interface/` | `isage-agentic` | 🚧 Planned | -| RAG | `rag/interface/` | `isage-rag` | 🚧 Planned | -| Fine-tuning | `finetune/interface/` | `isage-finetune` | 🚧 Planned | -| Evaluation | `eval/interface/` | `isage-eval` | 🚧 Planned | -| Privacy | `privacy/interface/` | `isage-privacy` | 🚧 Planned | -| Safety | `safety/interface/` | `isage-safety` | 🚧 Planned | -| ANNS | `ann/interface/` | `isage-anns` | ✅ Available | -| AMM | `amms/interface/` | `isage-amms` | 🚧 Migration | - -## 🚀 Installation - -### Basic Installation - -```bash -# From PyPI -pip install isage-libs - -# Development install (in SAGE repo) -pip install -e packages/sage-libs -``` - -### With Optional Extras - -```bash -# All interfaces -pip install isage-libs[all] - -# Specific domains -pip install isage-libs[agentic] # Agent framework -pip install isage-libs[rag] # RAG toolkit -pip install isage-libs[finetune] # Fine-tuning -pip install isage-libs[eval] # Evaluation -pip install isage-libs[privacy] # Privacy/Unlearning -pip install isage-libs[safety] # Safety/Guardrails -``` - -## 🏛️ Built-in Utilities - -These modules are included directly (no external deps): - -### Foundation (`foundation/`) - -```python -from sage.libs.foundation import ( - text_utils, # Text processing - io_utils, # File I/O helpers - async_utils, # Async utilities -) -``` - -### DataOps (`dataops/`) - -```python -from sage.libs.dataops import ( - text_ops, # Normalization, truncation - table_ops, # DataFrame operations - json_ops, # JSON processing - sampling, # Sampling strategies -) -``` - -### Lightweight Safety (`safety/`) - -```python -from sage.libs.safety import ( - content_filter, # Pattern-based filtering - pii_scrubber, # PII detection - policy_check, # Policy validation -) -``` - -## 📖 Usage Example - -```python -# 1. Define custom implementation -from sage.libs.agentic.interface import BaseAgent, AgentResult, register_agent - -class MyAgent(BaseAgent): - @property - def name(self) -> str: - return "my_agent" - - def run(self, task, context=None): - # Implementation - return AgentResult(success=True, output="Done") - -# 2. Register implementation -register_agent("my_agent", MyAgent) - -# 3. Use via factory -from sage.libs.agentic.interface import create_agent -agent = create_agent("my_agent") -result = agent.run("Hello") -``` - -## 📚 Documentation - -- **Architecture**: `docs-public/docs_src/dev-notes/l3-libs/` -- **API Reference**: `docs-public/docs_src/api-reference/sage-libs/` -- **Tutorials**: `examples/tutorials/L3-libs/` - -## 🔗 Related Packages - -- [SAGE](https://github.com/intellistream/SAGE) - Main framework -- [sage-benchmark](https://github.com/intellistream/sage-benchmark) - Evaluation benchmarks -- [SageVDB](https://github.com/intellistream/sageVDB) - Vector database -- [NeuroMem](https://github.com/intellistream/NeuroMem) - Memory system - -## 📄 License - -Apache 2.0 License diff --git a/packages/sage-libs/docs/INTERFACE_LAYER_USAGE_GUIDE.md b/packages/sage-libs/docs/INTERFACE_LAYER_USAGE_GUIDE.md deleted file mode 100644 index 81c8c89901..0000000000 --- a/packages/sage-libs/docs/INTERFACE_LAYER_USAGE_GUIDE.md +++ /dev/null @@ -1,285 +0,0 @@ -# 接口层使用指南 - -## 🚀 快速开始 - -### 基本使用（接口定义） - -无需安装外部包，即可使用接口定义： - -```python -from sage.libs.agentic import Agent, Planner -from sage.libs.finetune import Trainer, FinetuneConfig -from sage.libs.sias import ContinualLearner, CoresetSelector -from sage.libs.intent import IntentRecognizer, IntentClassifier - -# 自定义实现 -class MyAgent(Agent): - def run(self, task: str, **kwargs) -> str: - return f"Processing: {task}" - - def reset(self) -> None: - pass - -agent = MyAgent() -result = agent.run("Hello") -``` - -### 使用外部包实现 - -安装外部包后，可以直接创建实例： - -```python -from sage.libs.agentic import create_agent, list_agents - -# 查看可用的 agent -print(list_agents()) # ['react', 'reflexion', ...] - -# 创建实例 -agent = create_agent("react", llm="gpt-4", temperature=0.7) -result = agent.run("What is the weather today?") -``` - -## 📦 安装方式 - -### 方式 1：通过 sage-libs extras（推荐） - -```bash -# 安装特定功能 -pip install -e packages/sage-libs[agentic] # Agent 框架 -pip install -e packages/sage-libs[finetune] # 模型微调 -pip install -e packages/sage-libs[sias] # 持续学习 -pip install -e packages/sage-libs[intent] # 意图识别 -pip install -e packages/sage-libs[anns] # ANN 算法 -pip install -e packages/sage-libs[amms] # 近似矩阵乘 - -# 安装所有功能 -pip install -e packages/sage-libs[all] -``` - -### 方式 2：直接安装外部包 - -```bash -pip install isage-agentic -pip install isage-finetune -pip install isage-sias -pip install isage-intent -pip install isage-anns -pip install isage-amms -``` - -## 🔧 注册自定义实现 - -### 注册到全局注册表 - -```python -from sage.libs.agentic import register_agent, Agent - -class MyCustomAgent(Agent): - def run(self, task: str, **kwargs) -> str: - return f"Custom: {task}" - - def reset(self) -> None: - pass - -# 注册 -register_agent("my_agent", MyCustomAgent) - -# 使用 -from sage.libs.agentic import create_agent -agent = create_agent("my_agent") -``` - -### 在外部包中注册（推荐） - -如果你开发自己的 agent 包，在 `__init__.py` 中注册： - -```python -# my_agents/__init__.py -from sage.libs.agentic import register_agent -from .my_agent import MyAgent - -register_agent("my_agent", MyAgent) - -__all__ = ["MyAgent"] -``` - -用户安装你的包后，实现自动可用： - -```bash -pip install my-agents -``` - -```python -from sage.libs.agentic import list_agents, create_agent -print(list_agents()) # 包含 'my_agent' -agent = create_agent("my_agent") -``` - -## 📖 完整示例 - -### Agentic 模块 - -```python -from sage.libs.agentic import ( - # 接口 - Agent, Planner, ToolSelector, WorkflowEngine, - # 注册 - register_agent, register_planner, - # 工厂 - create_agent, create_planner, - # 发现 - list_agents, list_planners, -) - -# 查看可用实现 -print("Available agents:", list_agents()) -print("Available planners:", list_planners()) - -# 创建实例 -agent = create_agent("react", llm="gpt-4") -result = agent.run("Analyze the data") - -planner = create_planner("tree_of_thought") -plan = planner.plan("Book a flight", context={}) -``` - -### Finetune 模块 - -```python -from sage.libs.finetune import ( - # 接口 - Trainer, FinetuneConfig, DataFormatter, - # 注册 - register_trainer, register_config, - # 工厂 - create_trainer, create_config, - # 发现 - list_trainers, list_configs, -) - -# 查看可用实现 -print("Available trainers:", list_trainers()) - -# 创建实例 -trainer = create_trainer("lora", rank=8, alpha=16) -config = create_config("default", learning_rate=1e-4) - -results = trainer.train(model, train_data, val_data) -``` - -### SIAS 模块 - -```python -from sage.libs.sias import ( - # 接口 - ContinualLearner, CoresetSelector, - # 工厂 - create_learner, create_selector, - # 发现 - list_learners, list_selectors, -) - -# 创建实例 -learner = create_learner("incremental") -learner.update(new_data) -predictions = learner.predict(test_data) - -selector = create_selector("greedy", diversity_weight=0.5) -coreset = selector.select(full_data, budget=100) -``` - -### Intent 模块 - -```python -from sage.libs.intent import ( - # 接口 - IntentRecognizer, IntentClassifier, IntentCatalog, - # 工厂 - create_recognizer, create_classifier, create_catalog, - # 发现 - list_recognizers, list_classifiers, -) - -# 创建实例 -recognizer = create_recognizer("llm", model="gpt-4") -result = recognizer.recognize("Book a flight to Paris") -print(result["intent"], result["confidence"]) - -classifier = create_classifier("bert", model_path="./models/intent-bert") -intent = classifier.classify("What's the weather?") -``` - -## 🔍 错误处理 - -### 实现未安装 - -```python -from sage.libs.agentic import create_agent - -try: - agent = create_agent("react") -except AgenticRegistryError as e: - print(e) - # Agent 'react' not registered. Available: []. - # Install 'isage-agentic' package for implementations. -``` - -### 未知实现名称 - -```python -from sage.libs.agentic import create_agent - -try: - agent = create_agent("unknown_agent") -except AgenticRegistryError as e: - print(e) - # Agent 'unknown_agent' not registered. Available: ['react', 'reflexion']. - # Install 'isage-agentic' package for implementations. -``` - -## 🧪 测试你的实现 - -```python -import pytest -from sage.libs.agentic import Agent, register_agent, create_agent - -class TestCustomAgent: - def test_custom_agent(self): - # 定义自定义 agent - class TestAgent(Agent): - def run(self, task: str, **kwargs) -> str: - return f"Test: {task}" - - def reset(self) -> None: - pass - - # 注册 - register_agent("test_agent", TestAgent) - - # 创建 - agent = create_agent("test_agent") - - # 测试 - result = agent.run("Hello") - assert result == "Test: Hello" -``` - -## 📚 更多资源 - -- **架构文档**: `packages/sage-libs/docs/INTERFACE_LAYER_ARCHITECTURE.md` -- **重构总结**: `packages/sage-libs/docs/INTERFACE_LAYER_REFACTOR_COMPLETED.md` -- **外部包仓库**: - - https://github.com/intellistream/sage-agentic - - https://github.com/intellistream/sage-finetune - - https://github.com/intellistream/sage-sias - - https://github.com/intellistream/sage-intent - - https://github.com/intellistream/sage-anns - - https://github.com/intellistream/sage-amms - -## 💡 最佳实践 - -1. **优先使用工厂函数**: `create_agent()` 而非直接实例化 -1. **检查可用实现**: 使用 `list_agents()` 等函数 -1. **捕获注册表错误**: 提供友好的错误提示 -1. **使用 extras 安装**: `pip install sage-libs[agentic]` 而非单独安装 -1. **在外部包注册**: 实现应该在 `__init__.py` 中自动注册 diff --git a/packages/sage-libs/docs/README.md b/packages/sage-libs/docs/README.md deleted file mode 100644 index 0332179cbb..0000000000 --- a/packages/sage-libs/docs/README.md +++ /dev/null @@ -1,105 +0,0 @@ -# sage-libs 文档目录 - -**最后更新**: 2026-01-10\ -**清理状态**: ✅ 已清理重复文档 - -## 📚 主文档（6个） - -### 核心文档 - -1. **REORGANIZATION_PROPOSAL.md** ⭐ - sage-libs 重组方案（主文档） - - - 外迁策略、模块划分、实施步骤 - - 澄清"外迁"的含义（不是隔离，SAGE 仍使用） - -1. **QUICK_REFERENCE.md** 📖 - 快速参考指南 - - - 常用 API 快速查询 - - 导入路径速查 - -1. **EXTERNALIZATION_STATUS.md** 📊 - 外迁状态跟踪 - - - 哪些包已外迁 - - 哪些包待外迁 - -### 技术文档 - -4. **INTERFACE_LAYER_USAGE_GUIDE.md** 🔧 - 接口层使用指南 - - - 接口/实现分离模式 - - 如何使用外迁的包 - -1. **MIGRATION_EXTERNAL_LIBS.md** 📝 - 外部库迁移指南 - - - 迁移流程和注意事项 - -1. **LIBAMM_DATA_QUICKSTART.md** 🚀 - AMMS 快速开始 - - - 近似矩阵乘算法快速入门 - -## 📁 子目录文档（5个） - -### agentic/ (1个) - -- `REGISTRY_USAGE.md` - Agent 注册表使用方法 - -### amms/ (3个) - -- `LIBAMM_README.md` - AMMS 库说明 -- `PAPI_PRECOMPILED_SOLUTION.md` - PAPI 预编译解决方案 -- `QUICKREF.md` - AMMS 快速参考 - -### anns/ (1个) - -- `MIGRATION.md` - ANNS 迁移指南 - -## 🗑️ 已清理的文档 - -### 主目录删除（8个） - -- ❌ `REORGANIZATION_PLAN.md` - 与 PROPOSAL 重复 -- ❌ `REORGANIZATION_ANALYSIS.md` - 已整合 -- ❌ `REORGANIZATION_SUMMARY.md` - 与 PROPOSAL 重复 -- ❌ `REORGANIZATION_COMPLETED.md` - 状态过时 -- ❌ `REORGANIZATION_COMPLETED_SUMMARY.md` - 与 PROPOSAL 重复 -- ❌ `ARCHITECTURE_CORRECTION.md` - 历史误解记录 -- ❌ `DECISION_SUMMARY.md` - 已整合到 PROPOSAL -- ❌ `INTERFACE_LAYER_REFACTOR_COMPLETED.md` - 重构完成记录 -- ❌ `INTERFACE_LAYER_ARCHITECTURE.md` - 已整合到 USAGE_GUIDE - -### agentic/ 删除（3个） - -- ❌ `EXTERNALIZATION_PLAN.md` - 过时计划 -- ❌ `PHASE1_INTERFACE_COMPLETE.md` - 完成记录 -- ❌ `REGISTRY_MIGRATION_STRATEGY.md` - 迁移策略记录 - -### amms/ 删除（8个） - -- ❌ `INSTALLATION.md` / `INSTALLATION_GUIDE.md` - 重复 -- ❌ `PYPI_BUILD_STRATEGY.md` / `BUILD_PUBLISH.md` / `PYPI_PUBLISH_GUIDE.md` - 重复 -- ❌ `MIGRATION.md` / `INDEPENDENT_MIGRATION.md` / `REFACTORING_SUMMARY.md` - 重复 -- ❌ `CHECKLIST.md` / `implementations.md` - 不需要 - -## 📊 清理前后对比 - -| 统计项 | 清理前 | 清理后 | 减少 | -| -------- | ------ | ------ | --------- | -| 总文档数 | 30 | 11 | -19 (63%) | -| 主目录 | 19 | 6 | -13 | -| agentic/ | 4 | 1 | -3 | -| amms/ | 16 | 3 | -13 | -| anns/ | 1 | 1 | 0 | - -## ✅ 清理原则 - -1. **删除重复** - 相同主题只保留一份最新最全的 -1. **删除过时** - 已完成的计划、迁移记录等 -1. **删除历史** - 误解纠正、决策记录等 -1. **保留实用** - 使用指南、快速参考、状态跟踪 - -## 📌 文档维护建议 - -- **主文档**：只保留 `REORGANIZATION_PROPOSAL.md` 作为唯一的重组方案文档 -- **状态跟踪**：`EXTERNALIZATION_STATUS.md` 记录实时状态 -- **使用指南**：技术文档保持更新 -- **避免重复**：新增文档前先检查是否已有类似内容 diff --git a/packages/sage-libs/docs/amms/LIBAMM_README.md b/packages/sage-libs/docs/amms/LIBAMM_README.md deleted file mode 100644 index 6bc71a13f9..0000000000 --- a/packages/sage-libs/docs/amms/LIBAMM_README.md +++ /dev/null @@ -1,209 +0,0 @@ -# SAGE Libs - LibAMM 编译指南 - -## 🎯 快速开始 - -### 默认安装（不含 LibAMM） - -```bash -# 安装 sage-libs（跳过 LibAMM，适合大多数用户） -pip install -e packages/sage-libs -``` - -✅ **推荐**：适合所有开发环境，无内存压力 - -### 带 LibAMM 的完整安装 - -```bash -# 需要 16GB+ 内存 -BUILD_LIBAMM=1 pip install -e packages/sage-libs --no-build-isolation -``` - -⚠️ **注意**：需要大内存环境，详见下文 - -______________________________________________________________________ - -## 📚 详细说明 - -### 为什么 LibAMM 是可选的？ - -LibAMM 是高性能矩阵计算库，依赖 PyTorch C++ API。编译时： - -| 资源 | 需求 | -| ------------ | ------------------------ | -| **内存** | 单文件峰值 500-700MB | -| **总内存** | 建议 16GB+ (物理 + swap) | -| **编译时间** | 10-30 分钟（取决于 CPU） | -| **PyTorch** | >= 2.0.0 | - -在内存受限环境（如 WSL、虚拟机）容易触发 OOM killer，导致系统重启。 - -### 方案对比 - -| 方案 | 内存需求 | 安装时间 | 适用场景 | -| ----------------------- | -------- | ---------- | ------------------- | -| **跳过 LibAMM**（默认） | < 1GB | < 1 分钟 | ✅ 推荐：开发、测试 | -| **从源码编译** | 16GB+ | 10-30 分钟 | 需要自定义 LibAMM | -| **预编译包**（未来） | < 1GB | < 1 分钟 | ✅ 推荐：生产环境 | - -______________________________________________________________________ - -## 🔧 编译 LibAMM - -### 前提条件检查 - -```bash -# 1. 检查可用内存 -free -h - -# 需要看到： -# Mem: 可用 + Swap >= 16GB - -# 2. 安装 PyTorch -pip install torch>=2.0.0 -``` - -### 增加 Swap（如果内存不足） - -```bash -# 创建 8GB swap 文件 -sudo fallocate -l 8G /swapfile -sudo chmod 600 /swapfile -sudo mkswap /swapfile -sudo swapon /swapfile - -# 验证 -free -h -``` - -### 编译 - -```bash -# 方式1：环境变量 -BUILD_LIBAMM=1 pip install -e packages/sage-libs --no-build-isolation - -# 方式2：卸载后重装 -pip uninstall isage-libs -y -BUILD_LIBAMM=1 pip install -e packages/sage-libs --no-build-isolation -``` - -### 验证 - -```python -# 检查 LibAMM 是否可用 -python3 << 'EOF' -try: - from sage.libs.libamm.python import PyAMM - print("✅ LibAMM 编译成功！") -except ImportError as e: - print(f"❌ LibAMM 不可用: {e}") -EOF -``` - -______________________________________________________________________ - -## 🐛 常见问题 - -### 1. 编译时系统重启/卡死 - -**原因**：OOM killer 杀掉编译进程 - -**解决**： - -```bash -# 检查内存 -free -h -dmesg | grep -i "killed process" # 查看是否被 OOM killer 杀掉 - -# 增加 swap 或使用预编译包 -``` - -### 2. 编译错误：`torch::linalg` 未定义 - -**原因**：PyTorch 版本过旧或 C++ API 不兼容 - -**解决**： - -```bash -# 更新 PyTorch -pip install --upgrade torch>=2.0.0 -``` - -### 3. 只想使用 SAGE 其他功能，不需要 LibAMM - -**解决**：不用做任何特殊处理，默认安装即可 - -```bash -pip install -e packages/sage-libs # LibAMM 会自动跳过 -``` - -______________________________________________________________________ - -## 📊 内存监控 - -编译过程中监控内存： - -```bash -# 实时查看内存使用 -watch -n 1 'free -h; ps aux | grep cc1plus | grep -v grep' -``` - -______________________________________________________________________ - -## 🏗️ 技术细节 - -### 已实施的内存优化 - -LibAMM 的 CMakeLists.txt 包含以下优化： - -```cmake -# 1. Unity Build：减少头文件重复解析 -set(CMAKE_UNITY_BUILD ON) -set(CMAKE_UNITY_BUILD_BATCH_SIZE 2) - -# 2. 降低优化级别 -set(CMAKE_CXX_FLAGS_RELEASE "-O0 -g0") # 原来是 -O3 - -# 3. 单线程编译 -set(CMAKE_BUILD_PARALLEL_LEVEL 1) - -# 4. 限制模板深度 -set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -ftemplate-depth=128") - -# 5. 积极内存回收 -set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} --param ggc-min-expand=20") -``` - -即便如此，仍需要大内存环境。 - -### 为什么 PyTorch C++ API 占用这么多内存？ - -```cpp -#include <torch/extension.h> // 展开后 ~30 万行代码 - -// 编译器需要： -// 1. 解析 9252 个头文件 -// 2. 实例化数千个模板 -// 3. 生成符号表 -// 4. 执行优化 -``` - -这是 PyTorch C++ API 的设计特点，无法绕过。 - -______________________________________________________________________ - -## 📝 相关文档 - -- [完整安装指南](./LIBAMM_INSTALLATION.md) -- [内存优化笔记](../../docs/dev-notes/l3-libs/libamm-memory-optimization.md) - -______________________________________________________________________ - -## 🤝 贡献 - -如果你： - -- 成功在特定环境下编译了 LibAMM -- 有更好的内存优化方案 -- 发现了编译问题 - -欢迎提交 Issue 或 PR！ diff --git a/packages/sage-libs/docs/amms/PAPI_PRECOMPILED_SOLUTION.md b/packages/sage-libs/docs/amms/PAPI_PRECOMPILED_SOLUTION.md deleted file mode 100644 index 3484d0a607..0000000000 --- a/packages/sage-libs/docs/amms/PAPI_PRECOMPILED_SOLUTION.md +++ /dev/null @@ -1,285 +0,0 @@ -# PAPI 预编译包解决方案 - -## 问题分析 - -当前 LibAMM 的 PAPI 集成存在以下问题： - -1. **自行编译 PAPI**：通过 `thirdparty/installPAPI.sh` 脚本从源码编译 PAPI -1. **编译失败常见**：PAPI 编译依赖内核头文件、硬件架构，容易失败 -1. **增加构建时间**：PAPI 编译需要额外 5-10 分钟 -1. **维护负担**：需要维护 PAPI 源码和编译脚本 - -## 解决方案：使用系统预编译 PAPI - -### 方案 1：完全禁用 PAPI（推荐用于大多数场景） - -**优点**： - -- ✅ 零依赖、零编译问题 -- ✅ 构建速度最快 -- ✅ 适合 99% 的用户场景 - -**实现方式**： - -修改 `packages/sage-libs/src/sage/libs/amms/implementations/CMakeLists.txt`： - -```cmake -option (ENABLE_PAPI - "Enable papi support, pls first compile papi or set REBUILD_PAPI to ON" - OFF # 默认禁用 - ) -``` - -修改 `packages/sage-libs/src/sage/libs/amms/implementations/setup.py`： - -```python -# 注释掉 PAPI 编译和启用选项 -# os.system("cd thirdparty&&./makeClean.sh&&./installPAPI.sh") -cmake_args = [ - # ... - # "-DENABLE_PAPI=ON", # 注释掉 -] -``` - -修改构建脚本： - -```bash -# buildCPUOnly.sh -cd build &&cmake ... -DENABLE_PAPI=OFF ... # 改为 OFF - -# buildWithCuda.sh -cd build &&cmake ... -DENABLE_PAPI=OFF ... # 改为 OFF -``` - -### 方案 2：使用系统预编译 PAPI（推荐用于需要性能分析的场景） - -**优点**： - -- ✅ 无需从源码编译 -- ✅ 系统包管理器自动处理依赖 -- ✅ 稳定、经过测试的版本 -- ✅ 支持硬件性能计数器 - -**安装预编译 PAPI**： - -```bash -# Ubuntu/Debian -sudo apt-get install libpapi-dev libpapi7.1t64 - -# CentOS/RHEL -sudo yum install papi-devel - -# Fedora -sudo dnf install papi-devel -``` - -**修改 CMakeLists.txt 使用系统 PAPI**： - -```cmake -option (ENABLE_PAPI - "Enable papi support using system-installed libpapi" - OFF - ) - -# OPTIONAL PAPI -if (NOT ENABLE_PAPI) - message(STATUS "I will NOT use PAPI ") - set(LibAMM_PAPI 0) -else () - set(LibAMM_PAPI 1) - message(STATUS "I will try to use PAPI for HW counters, pls make sure your arch supports it") - - # 移除 REBUILD_PAPI 选项，直接使用系统 PAPI - # option (REBUILD_PAPI ...) # 删除 - - # 使用 CMake 标准方式查找系统 PAPI - find_package(PkgConfig REQUIRED) - pkg_check_modules(PAPI REQUIRED papi) - - if (PAPI_FOUND) - message(STATUS "Found system PAPI: ${PAPI_LIBRARIES}") - message(STATUS "PAPI include dirs: ${PAPI_INCLUDE_DIRS}") - include_directories(${PAPI_INCLUDE_DIRS}) - set(LIBRARIES ${LIBRARIES} ${PAPI_LIBRARIES}) - else() - # Fallback: 直接查找库文件 - find_library(libPAPI NAMES papi libpapi.so PATHS /usr/lib /usr/local/lib) - if (libPAPI) - message(STATUS "Found PAPI library: ${libPAPI}") - set(LIBRARIES ${LIBRARIES} ${libPAPI}) - else() - message(FATAL_ERROR "ENABLE_PAPI is ON but libpapi not found. Install with: sudo apt-get install libpapi-dev") - endif() - endif() -endif () -``` - -**修改 setup.py**： - -```python -# 移除 PAPI 编译步骤 -# os.system("cd thirdparty&&./makeClean.sh&&./installPAPI.sh") - -cmake_args = [ - "-DCMAKE_LIBRARY_OUTPUT_DIRECTORY=" + extdir, - "-DPYTHON_EXECUTABLE=" + sys.executable, - "-DCMAKE_PREFIX_PATH=" + torchCmake, - "-DENABLE_HDF5=ON", - "-DENABLE_PYBIND=ON", - "-DCMAKE_INSTALL_PREFIX=/usr/local/lib", - "-DENABLE_PAPI=OFF", # 默认禁用，用户可手动启用 -] - -# 添加环境变量支持 -if os.environ.get("ENABLE_PAPI") == "1": - # 检查系统是否安装了 libpapi-dev - import subprocess - result = subprocess.run(["pkg-config", "--exists", "papi"], capture_output=True) - if result.returncode == 0: - cmake_args.append("-DENABLE_PAPI=ON") - print("✓ Enabling PAPI support (system libpapi detected)") - else: - print("⚠ ENABLE_PAPI=1 but libpapi-dev not found. Install with:") - print(" sudo apt-get install libpapi-dev") - print("Continuing without PAPI...") -``` - -## 推荐方案：分层实现 - -### isage-amms 包（默认禁用 PAPI） - -```python -# setup.py - 生产版本 -cmake_args = [ - # ... - "-DENABLE_PAPI=OFF", # 默认禁用 -] -``` - -### benchmark_amm 包（可选启用 PAPI） - -在 `benchmark_amm/INSTALLATION.md` 中添加文档： - -````markdown -## 可选：启用 PAPI 性能计数器 - -PAPI (Performance API) 用于硬件性能分析，大多数用户不需要。 - -### 如果需要 PAPI： - -1. 安装系统 PAPI 包： - ```bash - sudo apt-get install libpapi-dev -```` - -2. 从源码构建 isage-amms（启用 PAPI）： - - ```bash - git clone https://github.com/intellistream/SAGE.git - cd SAGE/packages/sage-libs - ENABLE_PAPI=1 pip install -e . - ``` - -1. 验证 PAPI 支持： - - ```bash - python -c "from sage.libs.amms import create_amm_index; print('PAPI enabled')" - ``` - -### 注意事项： - -- PAPI 需要硬件支持和内核配置 -- 某些虚拟化环境可能不支持 -- 如果编译失败，使用默认配置（不影响功能） - -```` - -## 实施步骤 - -### 第一阶段：默认禁用 PAPI（立即实施） - -1. ✅ 修改 `CMakeLists.txt`：`ENABLE_PAPI` 默认为 `OFF` -2. ✅ 修改 `setup.py`：注释掉 PAPI 编译和启用 -3. ✅ 修改构建脚本：`buildCPUOnly.sh`, `buildWithCuda.sh` 设为 `OFF` -4. ✅ 更新文档：说明 PAPI 为可选功能 - -### 第二阶段：添加系统 PAPI 支持（可选） - -1. ⏭️ 实现 CMake 的 `find_package(PAPI)` 逻辑 -2. ⏭️ 添加环境变量 `ENABLE_PAPI=1` 支持 -3. ⏭️ 更新 INSTALLATION.md 文档 - -## 优先级建议 - -**高优先级（立即执行）**： -- ✅ 默认禁用 PAPI（解决编译失败问题） -- ✅ 更新文档说明 PAPI 为可选 - -**低优先级（按需实施）**： -- ⏭️ 实现系统 PAPI 支持（如果用户需要性能分析） -- ⏭️ 添加自动检测逻辑 - -## 测试计划 - -### 测试场景 1：默认构建（无 PAPI） - -```bash -cd packages/sage-libs -pip install -e . -python -c "from sage.libs.amms import create_amm_index; print('OK')" -```` - -预期：✅ 成功编译，无 PAPI 依赖 - -### 测试场景 2：启用 PAPI（系统包） - -```bash -sudo apt-get install libpapi-dev -cd packages/sage-libs -ENABLE_PAPI=1 pip install -e . -``` - -预期：✅ 使用系统 PAPI，无需编译 - -### 测试场景 3：Benchmark 运行 - -```bash -cd packages/sage-benchmark/src/sage/benchmark/benchmark_amm -pip install -r requirements.txt -cd benchmark -./scripts/run_benchmark.py -``` - -预期：✅ Benchmark 正常运行（即使 PAPI 禁用） - -## FAQ - -### Q: 禁用 PAPI 会影响功能吗？ - -A: 不会。PAPI 仅用于硬件性能计数器采集，是可选的性能分析工具。核心 AMM 算法功能完全独立。 - -### Q: 什么时候需要 PAPI？ - -A: 仅在需要详细硬件性能分析时（CPU 指令数、缓存命中率等）。99% 的用户不需要。 - -### Q: 为什么不保留自行编译 PAPI？ - -A: - -1. 编译失败率高（依赖内核头文件、硬件架构） -1. 增加构建时间（5-10 分钟） -1. 系统包更稳定、经过测试 -1. 减少维护负担 - -### Q: 如何验证 PAPI 是否启用？ - -A: 检查编译日志： - -- 禁用：`-- I will NOT use PAPI` -- 启用：`-- I will try to use PAPI for HW counters` - -## 参考文档 - -- [PAPI 官方文档](http://icl.utk.edu/papi/) -- [Ubuntu libpapi-dev 包](https://packages.ubuntu.com/search?keywords=libpapi-dev) -- [CMake FindPkgConfig](https://cmake.org/cmake/help/latest/module/FindPkgConfig.html) diff --git a/packages/sage-libs/docs/amms/QUICKREF.md b/packages/sage-libs/docs/amms/QUICKREF.md deleted file mode 100644 index 220d9cead4..0000000000 --- a/packages/sage-libs/docs/amms/QUICKREF.md +++ /dev/null @@ -1,155 +0,0 @@ -# LibAMM to AMMS Refactoring - Quick Reference - -## Summary - -The LibAMM submodule has been refactored into SAGE's architecture following the ANNS pattern. - -## Key Changes - -### Directory Structure - -| Component | Before | After | -| -------------- | ------------------------------------ | ----------------------------------------- | -| **Algorithms** | `sage-libs/libamm/include/CPPAlgos/` | `sage-libs/amms/implementations/include/` | -| | `sage-libs/libamm/src/CPPAlgos/` | `sage-libs/amms/implementations/src/` | -| **Interface** | _(none)_ | `sage-libs/amms/interface/` | -| **Wrappers** | _(PyAMM bindings)_ | `sage-libs/amms/wrappers/` | -| **Benchmarks** | `sage-libs/libamm/benchmark/` | `sage-benchmark/benchmark_libamm/` | - -### Import Changes - -```python -# ❌ Old (deprecated) -import PyAMM -amm = PyAMM.CountSketch(sketch_size=1000) - -# ✅ New (recommended) -from sage.libs.amms import create -amm = create("countsketch", sketch_size=1000) - -# ✅ Also works -from sage.libs.amms.wrappers.pyamm import PyAMM -amm = PyAMM.CountSketch(sketch_size=1000) -``` - -## Files Created - -### Core Structure - -- `packages/sage-libs/src/sage/libs/amms/` - - `__init__.py` - Main package exports - - `README.md` - Package documentation - - `MIGRATION.md` - Detailed migration guide - - `interface/` - Unified AMM interface - - `base.py` - AmmIndex, AmmIndexMeta, StreamingAmmIndex - - `registry.py` - Algorithm registry - - `factory.py` - Factory functions - - `wrappers/` - Python wrappers (to be implemented) - - `implementations/` - C++ source code (copied from libamm) - -### Benchmark - -- `packages/sage-benchmark/src/sage/benchmark/benchmark_libamm/` - - Updated `README.md` with benchmark documentation - - All benchmark scripts and data copied from libamm - -### Documentation - -- Updated `sage-libs/libamm/README.md` with deprecation notice -- Created `sage-libs/amms/MIGRATION.md` with full migration guide -- Created example: `examples/tutorials/L3-libs/amms_example.py` - -### Package Updates - -- Updated `sage-libs/src/sage/libs/__init__.py` to export `amms` - -## Next Steps - -### For Development (Phase 2) - -1. **Update Build System** - - ```bash - # Update CMakeLists.txt to build from new location - # Update pyproject.toml dependencies - ``` - -1. **Create Wrappers** - - ```bash - # Implement Python wrappers in amms/wrappers/ - # Register algorithms in the registry - ``` - -1. **Migrate Tests** - - ```bash - # Create tests in sage-libs/tests/amms/ - # Update benchmark tests - ``` - -### For Testing (Phase 3) - -```bash -# Test algorithm implementations -sage-dev project test --package sage-libs --filter amms - -# Test benchmarks -sage-dev project test --package sage-benchmark --filter benchmark_libamm - -# Run example -python examples/tutorials/L3-libs/amms_example.py -``` - -### For Cleanup (Phase 4-6) - -1. Mark libamm as officially deprecated -1. Update all import references -1. Remove libamm submodule after verification period - -## Benefits - -✅ **Architectural Compliance**: Follows SAGE's L1-L5 layered architecture\ -✅ **Separation of Concerns**: Algorithms (L3) separate from benchmarks (independent repo)\ -✅ **Unified Interface**: Factory pattern like ANNS\ -✅ **Better Organization**: Clear directory structure\ -✅ **Consistency**: Same pattern as ANNS and other algorithm libraries - -## Related Files - -- Migration guide: `packages/sage-libs/src/sage/libs/amms/MIGRATION.md` -- AMMS README: `packages/sage-libs/src/sage/libs/amms/README.md` -- Benchmark README: `packages/sage-benchmark/src/sage/benchmark/benchmark_libamm/README.md` -- ANNS structure (reference): `packages/sage-libs/src/sage/libs/anns/README.md` -- Architecture docs: `docs-public/docs_src/dev-notes/package-architecture.md` - -## Status - -**Completed**: - -- ✅ Directory structure created -- ✅ Interface layer implemented -- ✅ Files copied from libamm -- ✅ Benchmarks migrated -- ✅ Documentation updated -- ✅ Deprecation notices added - -**TODO**: - -- ⏳ Update build system (CMakeLists.txt) -- ⏳ Implement algorithm wrappers -- ⏳ Register algorithms in factory -- ⏳ Write tests -- ⏳ Update import paths in existing code -- ⏳ Verify functionality - -**Future**: - -- 📅 Remove deprecated libamm submodule -- 📅 Complete integration with SAGE benchmarking infrastructure - -______________________________________________________________________ - -**Date**: January 2, 2026\ -**Team**: IntelliStream\ -**Pattern**: Following ANNS refactoring diff --git a/packages/sage-libs/docs/governance/DEVELOPER_GUIDE.md b/packages/sage-libs/docs/governance/DEVELOPER_GUIDE.md deleted file mode 100644 index f93c0c7ba0..0000000000 --- a/packages/sage-libs/docs/governance/DEVELOPER_GUIDE.md +++ /dev/null @@ -1,88 +0,0 @@ -# 开发者指南与质量门槛（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages/<package>/docs/governance/DEVELOPER_GUIDE.md`） - -本指南把“制度”落到“能执行的工程约束”。本包的所有代码改动必须遵守以下规则。 - -______________________________________________________________________ - -## 1. 架构守护机制（强制） - -### 1.1 依赖层级（禁止向上依赖 / 禁止循环依赖） - -SAGE 采用 L1-L5 分层。任何“向上依赖”都视为架构违规，必须阻断合入。 - -- L1：`sage-common` -- L2：`sage-platform` -- L3：`sage-kernel`, `sage-libs` -- L4：`sage-middleware` -- L5：`sage-cli`, `sage-tools` - -### 1.2 Libs vs Middleware 规则（强制） - -如果代码需要调用“向上能力”（例如 VectorDB/Memory/Refiner/外部服务/重后端/网络服务），它 **不是库**，必须放在 -`sage-middleware`（operators/components）。 - -> 迁移时不保留兼容 re-export shim：更新所有调用方，快速失败。 - -### 1.3 Control Plane-only（强制） - -LLM 引擎操作必须走 Control Plane，禁止直接启动引擎/直连端口。 - -______________________________________________________________________ - -## 2. 自动化检查（CI + pre-commit）（强制） - -### 2.1 安装一致性 - -- 必须使用 `./quickstart.sh --dev --yes` 安装开发环境。 -- 禁止手工 `pip install` 临时安装依赖（依赖必须写入 `pyproject.toml`）。 - -### 2.2 本地质量与测试 - -建议在仓库根目录执行： - -- `sage-dev quality check --all-files` -- `sage-dev project test --coverage` - -______________________________________________________________________ - -## 3. 强制规范（必须出现的质量门槛） - -### 3.1 Mock-First（强制） - -- CI 必须能在无 GPU 环境跑通核心测试。 -- 新增能力必须提供 Mock/CPU 路径，避免把“硬件”当作默认前提。 - -### 3.2 Fail-Fast（强制） - -- 禁止静默默认值、禁止隐式回退（fallback）。 -- 关键配置缺失必须明确报错（例如用 `os.environ["KEY"]` 或显式校验并抛异常）。 - -### 3.3 Protocol-First（按适用范围执行） - -当本包提供“公共接口/抽象/跨包契约”时： - -- 先定义接口/类型（Protocol/ABC/Schema） -- 再实现具体逻辑 -- 再补测试与示例 - -______________________________________________________________________ - -## 4. 新模块/新能力接入步骤（可执行） - -1. **定义范围**：确认属于本包；若需要向上能力，按规则提升到 `sage-middleware`。 -1. **接口优先**：先定义可复用接口（如适用）。 -1. **最小可跑**：提供无 GPU 的最小可跑实现（Mock/CPU）。 -1. **测试**：至少包含单元测试；关键路径补回归测试。 -1. **文档**：更新本包 README 与本包 governance 文档（必要时）。 -1. **质量门槛**：本地 `sage-dev quality` 与测试通过。 - -______________________________________________________________________ - -## 5. 常见错误与修复 - -- **依赖倒挂**：`sage-libs` 引入 `sage-middleware` → 必须拆分/上移实现。 -- **文档位置违规**：禁止在根 `docs/` 放 Markdown；包级文档只能放 `packages/<pkg>/docs/`。 -- **依赖未声明**：新增依赖未写入 `pyproject.toml` → 必须补齐声明并统一版本。 diff --git a/packages/sage-libs/docs/governance/MAINTAINERS.md b/packages/sage-libs/docs/governance/MAINTAINERS.md deleted file mode 100644 index 4e331ad6e0..0000000000 --- a/packages/sage-libs/docs/governance/MAINTAINERS.md +++ /dev/null @@ -1,126 +0,0 @@ -# 负责人制度（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages/<package>/docs/governance/MAINTAINERS.md`） -- 负责人名单：TBD - -本文件定义“包级 Maintainer（负责人）”如何配置、如何工作、如何被量化验收。 - -______________________________________________________________________ - -## 1. 为什么需要 Maintainer - -SAGE 是 monorepo，多包协作、依赖层级明确。Maintainer 的职责不是“写最多代码”，而是保证： - -- 需求有人推进、问题有人闭环 -- 质量门槛有人守住（CI/架构/测试） -- 跨包集成有人对齐（尤其是接口层与中间件层） - -______________________________________________________________________ - -## 2. 负责人配置建议（按模块/子系统） - -### 2.1 推荐配置 - -- 每个包至少 1 名 Maintainer（主负责人）+ 1 名 Backup（备份负责人）。 -- 当包内包含多个子系统/高复杂度模块：建议拆分子 Maintainer。 - -### 2.2 负责人配置表（模板，必须维护） - -| 模块/目录 | 复杂度 | 关键技能 | 主 Maintainer | Backup | 上游依赖 | 下游使用方 | -| --------- | ------ | -------- | ------------- | ------ | -------- | ---------- | -| TBD | TBD | TBD | TBD | TBD | TBD | TBD | - -> 单仓语义适配：这里的“上游/下游”指本 monorepo 的其他包或本包内其他模块。 - -______________________________________________________________________ - -## 3. 负责人职责拆分（40/30/20/10 参考） - -可按实际调整，但必须覆盖全部职责： - -- **40% 开发推进**：Roadmap/Issue/PR 推动，里程碑拆解，阻塞清理。 -- **30% 质量把控**：CI 绿灯、测试策略、回归防护、性能/稳定性监控。 -- **20% 集成同步**：跨包接口对齐、发布/版本对齐、变更公告与迁移指引。 -- **10% 团队协作**：Review SLA、协作矩阵维护、争议处理与复核申请。 - -______________________________________________________________________ - -## 4. 要求与投入 - -- **最低投入**：每周固定时间段处理 Issue/PR（TBD：建议至少 2-4 小时/周）。 -- **Review SLA**：工作日 48h 内首次响应。 -- **透明度**：每月一次交付清单（含证据链接）。 - -______________________________________________________________________ - -## 5. 工作流程（开发 / 发布 / 集成同步） - -### 5.1 日常开发 - -- 所有需求/缺陷必须有 Issue。 -- PR 必须通过 `docs/governance/PR_CHECKLIST.md`。 -- 架构约束（依赖层级、libs vs middleware）违反时：必须阻断合入。 - -### 5.2 发布与版本对齐（如适用） - -- 发布前：测试与质量检查必须绿。 -- 变更：Breaking 变更必须有迁移指引与回滚预案。 -- 若涉及 PyPI 发布：使用仓库规定的发布工具链（例如 `wheelwright`），不得手工 twine/pip 临时发布。 - -### 5.3 跨包集成同步 - -- 上游接口变化：必须提前通知下游 maintainer，并提供迁移窗口。 -- 下游反馈：需要在 SLA 内回应并给出计划。 - -______________________________________________________________________ - -## 6. 协作矩阵（依赖/协作关系） - -### 6.1 Monorepo 依赖层级速查 - -> 目标：避免“向上依赖”与循环依赖。 - -| 层级 | 包（示例） | 允许依赖 | -| ---- | -------------------------- | ---------------- | -| L5 | `sage-cli`, `sage-tools` | L1-L4 | -| L4 | `sage-middleware` | L1-L3 | -| L3 | `sage-kernel`, `sage-libs` | L1-L2 | -| L2 | `sage-platform` | L1 | -| L1 | `sage-common` | 仅 stdlib/轻依赖 | - -### 6.2 本包协作矩阵（模板） - -| 依赖方/被依赖方 | 关系类型 | 接口/契约 | 变更通知机制 | Maintainer 对接 | -| --------------- | -------- | --------- | --------------------- | --------------- | -| TBD | 上游 | TBD | Issue/PR/Release Note | TBD | -| TBD | 下游 | TBD | Issue/PR/Release Note | TBD | - -______________________________________________________________________ - -## 7. 负责人名单（TBD） - -| 模块/目录 | 主 Maintainer | Backup | 交接人（如更换） | 生效日期 | -| --------- | ------------- | ------ | ---------------- | -------- | -| TBD | TBD | TBD | TBD | TBD | - -______________________________________________________________________ - -## 8. 考核指标（参考，可量化） - -- PR 首次响应 SLA 达标率 -- CI 红灯平均恢复时间（MTTR） -- 每月交付清单是否按时发布（含证据） -- Breaking 变更公告与迁移指引完备率 - -______________________________________________________________________ - -## 9. FAQ - -### Q1：Maintainer 是否必须是提交最多的人？ - -不是。Maintainer 的核心是“推进 + 质量 + 对齐”。提交多但不维护质量/不对齐下游同样不合格。 - -### Q2：如果包内没有发布流程怎么办？ - -仍需要“集成同步”：接口变更、跨包影响、迁移指引、回滚预案。 diff --git a/packages/sage-libs/docs/governance/PR_CHECKLIST.md b/packages/sage-libs/docs/governance/PR_CHECKLIST.md deleted file mode 100644 index 09d0f8c350..0000000000 --- a/packages/sage-libs/docs/governance/PR_CHECKLIST.md +++ /dev/null @@ -1,49 +0,0 @@ -# PR 审查清单（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages/<package>/docs/governance/PR_CHECKLIST.md`） - -> Maintainer/Reviewer 需要用本清单进行可量化审查。未满足项必须在 PR 中解释或补齐。 - -______________________________________________________________________ - -## 1. 架构合规 - -- [ ] 不产生向上依赖/循环依赖（符合 L1-L5 分层） -- [ ] 遵守 libs vs middleware 规则（需要向上能力则放 middleware） -- [ ] LLM 相关操作不绕过 Control Plane - -## 2. Protocol-First（如适用） - -- [ ] 公共接口/类型先定义（Protocol/ABC/Schema），实现不“绑死”具体后端 -- [ ] 接口变更包含迁移说明（Breaking change 必须公告） - -## 3. Mock-First - -- [ ] 无 GPU 环境可跑（至少核心测试/最小路径） -- [ ] 新增外部依赖/服务调用有可测试替身（mock/fake） - -## 4. Fail-Fast - -- [ ] 无隐式回退（不写 try/except 吞异常、不用静默默认值掩盖缺失配置） -- [ ] 错误信息可定位（异常 message 清晰，必要时包含修复指引） - -## 5. 可观测性（按适用范围） - -- [ ] 关键路径有日志/指标/追踪点（至少日志） -- [ ] 不输出敏感信息（key/token/密码等） - -## 6. 配置验证 - -- [ ] 新增配置项有校验与文档说明 -- [ ] 端口不硬编码（如适用，使用统一端口配置） - -## 7. 测试覆盖 - -- [ ] 新增/修改逻辑有对应测试 -- [ ] 关键 bug fix 有回归测试 - -## 8. 代码质量 - -- [ ] `sage-dev quality check --all-files` 通过 -- [ ] `sage-dev project test --coverage` 通过（或至少本包相关测试通过） diff --git a/packages/sage-libs/docs/governance/SELF_HOSTED_RUNNER.md b/packages/sage-libs/docs/governance/SELF_HOSTED_RUNNER.md deleted file mode 100644 index 8c4746062a..0000000000 --- a/packages/sage-libs/docs/governance/SELF_HOSTED_RUNNER.md +++ /dev/null @@ -1,50 +0,0 @@ -# Self-hosted Runner 指南（SAGE - 可选） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages/<package>/docs/governance/SELF_HOSTED_RUNNER.md`） - -> 本章节用于需要 GPU/重依赖/长耗时测试的包。若本包不需要，可保留但不强制启用。 - -______________________________________________________________________ - -## 1. 什么时候需要 self-hosted runner - -- GPU 相关测试（CUDA/Ascend） -- 需要特殊硬件或驱动版本 -- 需要访问内网资源（需安全评估） - -______________________________________________________________________ - -## 2. Runner 安装与标签（模板） - -- Runner 类型：GitHub Actions self-hosted -- 标签建议： - - `self-hosted` - - `linux` - - `x64` - - `gpu`（如适用） - - `cuda-<major>`（如适用） - -______________________________________________________________________ - -## 3. 环境要求（模板） - -- OS：Linux（推荐） -- Python：与仓库要求一致（3.10+） -- 构建依赖：cmake / build-essential / BLAS 等（按仓库安装脚本） - -______________________________________________________________________ - -## 4. 安全注意事项（强制） - -- Runner 机器不可暴露敏感密钥；Secrets 由 GitHub 管理。 -- 禁止在日志中打印 token/key。 -- 对内网访问与数据集访问：必须走审批（TBD）。 - -______________________________________________________________________ - -## 5. 故障排查 - -- CI 失败先在本地 `./quickstart.sh --dev --yes` 复现 -- 检查磁盘/缓存（`.sage/`） -- 检查驱动/CUDA 版本（如适用） diff --git a/packages/sage-libs/docs/governance/TEAM.md b/packages/sage-libs/docs/governance/TEAM.md deleted file mode 100644 index b4cc4f9061..0000000000 --- a/packages/sage-libs/docs/governance/TEAM.md +++ /dev/null @@ -1,67 +0,0 @@ -# 仓库人员与评分制度（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages/<package>/docs/governance/TEAM.md`） -- 名单维护：TBD - -本文件定义在 SAGE monorepo 内（以包为单位）的团队角色、负责人制度与评分/激励框架，用于保证： - -- 制度可执行：能落到 Issue/PR/CI/周更/月更。 -- 制度可验收：有明确证据链接与检查项。 -- 制度可追责：有最低交付门槛、透明度与争议处理机制。 - -> 约束：所有“姓名/负责人名单/具体人员”一律用 `TBD`，不得编造。 - -______________________________________________________________________ - -## 1. 角色与职责（强制条款） - -| 角色 | 核心职责 | 必须产出（可验收） | 证据类型（必须带链接） | -| ---------------------------- | -------------------------------------------------------- | ----------------------------------------------------------------------- | ------------------------------------------ | -| Maintainer（负责人） | Roadmap/Issue/PR 推进；质量门槛；集成/版本对齐；变更管理 | 每周 TODO 周更、PR Review SLA、CI 绿灯保障、重大变更公告、发布/集成记录 | Issue、PR、Release/Tag、CI 链接、周报/月报 | -| Engineering Core（工程骨干） | 关键功能交付；CI/测试维护；稳定性问题闭环 | 可交付功能/修复、测试/CI 改进、性能/稳定性指标改进 | PR、Issue、Benchmark 报告、CI 链接 | -| Research Core（科研骨干） | 原型验证/评测；研究到工程落地（必须可验收） | 可复现的实验/评测；可落地的工程化改造（或明确落地计划） | 评测报告、PR、Issue、实验配置/脚本链接 | - -______________________________________________________________________ - -## 2. 负责人必须做的事（强制条款） - -### 2.1 TODO 周更（强制） - -- 每周至少 1 次在本包的 `docs/governance/TODO.md` 更新：本周目标/完成/阻塞/下周计划/风险/需要协助。 -- TODO 必须引用证据（Issue/PR/CI/报告链接）。 - -### 2.2 PR Review SLA（强制） - -- 工作日 48h 内对新 PR 首次响应（review/approve/request changes/说明何时处理）。 -- 对影响多个包的变更：必须主动拉齐上下游 maintainer。 - -# 仓库人员与评分制度（SAGE - 包级，指向母版） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages/sage-libs/docs/governance/TEAM.md`） -- 名单维护：TBD - -通用制度请参见 `packages/sage/docs/governance/TEAM_BASE.md`。本文件仅保留本包的代号映射与特有说明。 - -### 本包角色代号（姓名保持 TBD） - -| 角色 | 代号分配 | -| ---------------- | -------- | -| Maintainer | A3 | -| Engineering Core | B2 | -| Research Core | C2 | - -### 本包补充说明 - -- 接口/算法层（L3）需严格避免依赖倒挂（不得上依 middleware），对外接口调整需附迁移说明并及时同步 kernel/middleware。 - -______________________________________________________________________ - -## 参考 - -- 通用制度：packages/sage/docs/governance/TEAM_BASE.md -- 周更：docs/governance/TODO.md -- PR 审查：docs/governance/PR_CHECKLIST.md -- 开发规范：docs/governance/DEVELOPER_GUIDE.md -- **保底分**：角色的最低履职奖励（必须满足最低交付门槛）。 diff --git a/packages/sage-libs/docs/governance/TODO.md b/packages/sage-libs/docs/governance/TODO.md deleted file mode 100644 index afdd548b3a..0000000000 --- a/packages/sage-libs/docs/governance/TODO.md +++ /dev/null @@ -1,39 +0,0 @@ -# TODO 周更模板（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages/<package>/docs/governance/TODO.md`） -- 维护频率：每周至少 1 次（Maintainer 强制要求） - -______________________________________________________________________ - -## 周更（复制本模板，每周追加一节） - -### Week of YYYY-MM-DD - -**本周目标（Goals）** - -- [ ] TBD（关联 Issue：TBD） - -**本周完成（Done）** - -- [ ] TBD（PR：TBD，CI：TBD） - -**阻塞与风险（Blockers/Risks）** - -- TBD（原因 + 需要谁协助 + 截止时间） - -**下周计划（Next）** - -- [ ] TBD（Issue：TBD） - -**需要协助（Asks）** - -- TBD（@TBD） - -______________________________________________________________________ - -## 里程碑清单（长期维护） - -| 里程碑 | 目标日期 | 状态 | 关联 Issue/PR | 风险 | -| ------ | -------- | ---- | ------------- | ---- | -| TBD | TBD | TBD | TBD | TBD | diff --git a/packages/sage-libs/docs/independent-libs/COPILOT_INSTRUCTIONS_isage-agentic.md b/packages/sage-libs/docs/independent-libs/COPILOT_INSTRUCTIONS_isage-agentic.md deleted file mode 100644 index 5a6ca1bb7a..0000000000 --- a/packages/sage-libs/docs/independent-libs/COPILOT_INSTRUCTIONS_isage-agentic.md +++ /dev/null @@ -1,525 +0,0 @@ -# isage-agentic Copilot Instructions - -## Package Identity - -| 属性 | 值 | -| ------------- | ----------------------------------------------- | -| **PyPI 包名** | `isage-agentic` | -| **导入名** | `sage_libs.sage_agentic` | -| **SAGE 层级** | L3 (Algorithms & Libraries) | -| **版本格式** | `0.1.x.y` (四段式) | -| **仓库** | `https://github.com/intellistream/sage-agentic` | - -## 层级定位 - -### ✅ 允许的依赖 - -``` -L3 及以下: -├── sage-common (L1) # 基础工具、类型、配置 -├── sage-platform (L2) # 平台服务抽象 -├── Python stdlib # 标准库 -├── pydantic # 数据验证 -├── jinja2 # 模板引擎 -└── networkx # 图算法 (用于 DAG 规划) -``` - -### ❌ 禁止的依赖 - -``` -L4+ 组件 (绝对禁止): -├── sage-middleware # ❌ 中间件层 -├── isage-vdb / SageVDB # ❌ 向量数据库 -├── isage-neuromem # ❌ 内存系统 -├── FastAPI / uvicorn # ❌ 网络服务 -├── Redis / RocksDB # ❌ 外部存储 -├── vLLM / LMDeploy # ❌ 推理引擎 -└── isagellm # ❌ LLM 客户端（通过注入） -``` - -**原则**: Agentic 库提供 Agent 框架和算法，LLM 调用通过依赖注入。 - -## 与 SAGE 主仓库的关系 - -``` -┌─────────────────────────────────────────────────────────────────┐ -│ SAGE 主仓库 (sage.libs.agentic) │ -│ ┌───────────────────────────────────────────────────────────┐ │ -│ │ Interface Layer │ │ -│ │ • BaseAgent (ABC) • BasePlanner (ABC) │ │ -│ │ • BaseToolSelector (ABC) • BaseOrchestrator (ABC) │ │ -│ │ • IntentRecognizer (ABC) • BaseReasoningStrategy (ABC) │ │ -│ │ • AgentAction, AgentResult, Intent (数据类型) │ │ -│ │ • create_agent(), create_planner() (工厂函数) │ │ -│ └───────────────────────────────────────────────────────────┘ │ -│ ▲ │ -│ │ 注册 │ -└──────────────────────────────┼──────────────────────────────────┘ - │ -┌──────────────────────────────┼──────────────────────────────────┐ -│ isage-agentic (独立 PyPI 包) │ -│ ┌───────────────────────────────────────────────────────────┐ │ -│ │ Implementation Layer │ │ -│ │ • ReActAgent, PlanExecuteAgent, ReflexAgent (Agent 实现) │ │ -│ │ • DFSDTSelector, GorillaSelector (工具选择器) │ │ -│ │ • HierarchicalPlanner, DAGPlanner (规划器) │ │ -│ │ • RoundRobinOrchestrator (编排器) │ │ -│ │ • MCTSReasoning, BeamSearchReasoning (推理策略) │ │ -│ │ • _register.py (自动注册到 SAGE 工厂) │ │ -│ └───────────────────────────────────────────────────────────┘ │ -└─────────────────────────────────────────────────────────────────┘ -``` - -### 自动注册机制 - -```python -# sage_libs/sage_agentic/_register.py -from sage.libs.agentic.interface import ( - register_agent, register_planner, register_tool_selector -) - -from .agents import ReActAgent, PlanExecuteAgent, ReflexAgent -from .planners import HierarchicalPlanner, DAGPlanner -from .selectors import DFSDTSelector, GorillaSelector - -# 注册到 SAGE 工厂 -register_agent("react", ReActAgent) -register_agent("plan_execute", PlanExecuteAgent) -register_agent("reflex", ReflexAgent) - -register_planner("hierarchical", HierarchicalPlanner) -register_planner("dag", DAGPlanner) - -register_tool_selector("dfsdt", DFSDTSelector) -register_tool_selector("gorilla", GorillaSelector) -``` - -## 功能模块 - -### 1. Agents (智能体) - -```python -from sage.libs.agentic import create_agent, AgentResult - -# ReAct Agent (推理-行动循环) -agent = create_agent("react", - llm_client=client, - tools=tool_list, - max_iterations=10 -) -result: AgentResult = agent.execute("分析这份财报") - -# Plan-Execute Agent (先规划后执行) -agent = create_agent("plan_execute", - llm_client=client, - tools=tool_list, - planner="hierarchical" -) -result = agent.execute("完成数据分析报告") - -# Reflex Agent (反应式) -agent = create_agent("reflex", - rules=rule_set, - tools=tool_list -) -``` - -**支持的 Agent 类型**: - -- `react`: ReAct (Reasoning + Acting) -- `plan_execute`: 规划-执行分离 -- `reflex`: 基于规则的反应式 -- `toolformer`: 自动工具调用 -- `self_ask`: 自问自答式 - -### 2. Planners (规划器) - -```python -from sage.libs.agentic import create_planner - -# 层次化规划 -planner = create_planner("hierarchical", - llm_client=client, - max_depth=3 -) -plan = planner.plan( - goal="完成季度报告", - available_tools=["search", "calculate", "write"], - context={"deadline": "2024-03-31"} -) - -# DAG 规划 (支持并行) -planner = create_planner("dag", - llm_client=client, - enable_parallel=True -) -``` - -### 3. Tool Selectors (工具选择器) - -```python -from sage.libs.agentic import create_tool_selector - -# DFSDT 选择器 (深度优先搜索决策树) -selector = create_tool_selector("dfsdt", - embedding_model="BAAI/bge-small-zh-v1.5" -) -selector.add_tool({ - "name": "search", - "description": "搜索网页内容", - "parameters": {...} -}) - -# 选择工具 -selected = selector.select_tools( - query="查找最新的 AI 新闻", - available_tools=all_tools, - top_k=3 -) - -# Gorilla 选择器 (基于 Gorilla 模型) -selector = create_tool_selector("gorilla", - model="gorilla-llm/gorilla-openfunctions-v2" -) -``` - -### 4. Orchestrators (编排器) - -```python -from sage.libs.agentic import create_orchestrator - -# 轮询编排 -orchestrator = create_orchestrator("round_robin") -result = orchestrator.coordinate( - task="多步骤数据处理", - agents=[data_agent, analysis_agent, report_agent] -) - -# 层次编排 (manager-worker) -orchestrator = create_orchestrator("hierarchical", - manager_agent=manager, - worker_agents=[worker1, worker2] -) -``` - -### 5. Intent Recognition (意图识别) - -```python -from sage.libs.agentic import create_intent_recognizer, Intent - -# 基于 LLM 的意图识别 -recognizer = create_intent_recognizer("llm", - llm_client=client, - intents=["search", "calculate", "summarize", "translate"] -) -intent: Intent = recognizer.recognize("帮我翻译这段话") -print(intent.name) # "translate" -print(intent.confidence) # 0.95 - -# 基于分类器的意图识别 -recognizer = create_intent_recognizer("classifier", - model="path/to/intent_classifier" -) -``` - -### 6. Reasoning Strategies (推理策略) - -```python -from sage.libs.agentic import create_reasoning_strategy - -# MCTS (蒙特卡洛树搜索) -strategy = create_reasoning_strategy("mcts", - exploration_weight=1.0, - max_iterations=100 -) - -# Beam Search -strategy = create_reasoning_strategy("beam_search", - beam_width=5, - max_depth=10 -) - -# 执行搜索 -path = strategy.search( - initial_state=start, - goal_check=lambda s: s.is_goal, - expand=lambda s: s.get_successors() -) -``` - -## 目录结构 - -``` -sage-agentic/ # 独立仓库根目录 -├── pyproject.toml # 包配置 (name = "isage-agentic") -├── README.md -├── COPILOT_INSTRUCTIONS.md # 本文件 -├── LICENSE -├── src/ -│ └── sage_libs/ # 命名空间包 -│ └── sage_agentic/ -│ ├── __init__.py # 主入口 -│ ├── _version.py # 版本信息 -│ ├── _register.py # 自动注册 -│ ├── agents/ # Agent 实现 -│ │ ├── __init__.py -│ │ ├── react.py # ReAct Agent -│ │ ├── plan_execute.py # Plan-Execute Agent -│ │ ├── reflex.py # Reflex Agent -│ │ └── toolformer.py # Toolformer Agent -│ ├── planners/ # 规划器实现 -│ │ ├── __init__.py -│ │ ├── hierarchical.py -│ │ ├── dag.py -│ │ └── iterative.py -│ ├── selectors/ # 工具选择器实现 -│ │ ├── __init__.py -│ │ ├── dfsdt.py # DFSDT 选择器 -│ │ ├── gorilla.py # Gorilla 选择器 -│ │ └── semantic.py # 语义相似度选择 -│ ├── orchestrators/ # 编排器实现 -│ │ ├── __init__.py -│ │ ├── round_robin.py -│ │ └── hierarchical.py -│ ├── intent/ # 意图识别实现 -│ │ ├── __init__.py -│ │ ├── llm_recognizer.py -│ │ └── classifier.py -│ └── reasoning/ # 推理策略实现 -│ ├── __init__.py -│ ├── mcts.py -│ └── beam_search.py -├── tests/ -│ ├── conftest.py -│ ├── test_agents.py -│ ├── test_planners.py -│ ├── test_selectors.py -│ └── test_integration.py -└── examples/ - ├── react_agent.py - ├── multi_agent.py - └── tool_selection.py -``` - -## 常见问题修复指南 - -### 1. LLM 客户端注入 - -```python -# ❌ 错误：Agent 内部创建 LLM 客户端 -class ReActAgent(BaseAgent): - def __init__(self): - from isagellm import UnifiedInferenceClient - self.llm = UnifiedInferenceClient.create() # ❌ 隐式依赖 - -# ✅ 正确：通过依赖注入 -class ReActAgent(BaseAgent): - def __init__(self, llm_client: LLMClientProtocol): - self.llm = llm_client # 注入的客户端 - -# 使用时 -from isagellm import UnifiedInferenceClient -client = UnifiedInferenceClient.create() -agent = ReActAgent(llm_client=client) -``` - -### 2. 工具定义格式 - -```python -# ❌ 错误：工具定义不完整 -tool = {"name": "search"} - -# ✅ 正确：完整的工具定义 -tool = { - "name": "search", - "description": "搜索互联网内容", - "parameters": { - "type": "object", - "properties": { - "query": {"type": "string", "description": "搜索查询"}, - "max_results": {"type": "integer", "default": 10} - }, - "required": ["query"] - } -} -``` - -### 3. 循环检测 - -```python -# ❌ 问题：Agent 陷入无限循环 -agent = create_agent("react", max_iterations=None) # 无限制 - -# ✅ 修复：设置最大迭代次数 -agent = create_agent("react", - max_iterations=10, - timeout_seconds=60 -) -``` - -### 4. 状态管理 - -```python -# ❌ 错误：忘记重置状态 -agent.execute("任务1") -agent.execute("任务2") # 可能受任务1状态影响 - -# ✅ 正确：显式重置 -agent.execute("任务1") -agent.reset() # 清除状态 -agent.execute("任务2") -``` - -### 5. 内存依赖问题 - -```python -# ❌ 错误：在 Agent 中直接使用 NeuroMem -from isage_neuromem import NeuroMem # ❌ L3 不应依赖 L4 - -# ✅ 正确：通过抽象接口 -class ReActAgent(BaseAgent): - def __init__(self, memory: MemoryProtocol = None): # 可选的内存接口 - self.memory = memory - -# 在 L4 middleware 层组合 -from isage_neuromem import NeuroMem -from sage_libs.sage_agentic import ReActAgent - -memory = NeuroMem(...) -agent = ReActAgent(llm_client=client, memory=memory) -``` - -## 关键设计原则 - -### 1. LLM 无关性 - -Agent 框架不绑定特定 LLM： - -```python -class LLMClientProtocol(Protocol): - """LLM 客户端协议 - 任何符合协议的客户端都可以使用""" - def chat(self, messages: list[dict]) -> str: ... - def generate(self, prompt: str) -> str: ... - -class ReActAgent: - def __init__(self, llm_client: LLMClientProtocol): - self.llm = llm_client -``` - -### 2. 工具即数据 - -工具以数据结构描述，不是代码： - -```python -# 工具定义是数据 -tool_spec = { - "name": "calculator", - "description": "执行数学计算", - "parameters": {...} -} - -# 工具执行器在外部 -def execute_tool(name: str, params: dict) -> Any: - if name == "calculator": - return eval(params["expression"]) -``` - -### 3. 可观察性 - -所有 Agent 执行都可追踪： - -```python -result = agent.execute("任务") - -# 完整的执行轨迹 -for action, observation in result.intermediate_steps: - print(f"Action: {action.tool_name}({action.tool_input})") - print(f"Thought: {action.thought}") - print(f"Observation: {observation}") -``` - -### 4. 错误隔离 - -工具执行错误不应导致 Agent 崩溃： - -```python -class ReActAgent: - def _execute_tool(self, action: AgentAction) -> str: - try: - result = self.tool_executor(action.tool_name, action.tool_input) - return str(result) - except Exception as e: - # 将错误作为观察返回给 LLM - return f"Error: {type(e).__name__}: {str(e)}" -``` - -### 5. 无状态优先 - -优先设计无状态组件： - -```python -# ✅ 好：无状态的工具选择器 -class DFSDTSelector(BaseToolSelector): - def select_tools(self, query: str, tools: list[dict]) -> list[str]: - # 纯函数，无副作用 - return self._rank_tools(query, tools)[:self.top_k] - -# ⚠️ 需要状态时明确管理 -class StatefulAgent(BaseAgent): - def __init__(self): - self._history = [] # 明确的状态 - - def reset(self): - self._history.clear() # 明确的重置 -``` - -## 测试规范 - -```bash -# 运行单元测试 -pytest tests/ -v - -# 运行需要 LLM 的测试（使用 mock） -pytest tests/ -v -m "not integration" - -# 运行集成测试（需要真实 LLM） -SAGE_TEST_LLM_ENABLED=1 pytest tests/test_integration.py -v -``` - -## 与其他 L3 库的协作 - -```python -# isage-agentic + isage-rag 协作 -from sage_libs.sage_agentic import ReActAgent -from sage_libs.sage_rag import DenseRetriever - -# RAG 作为工具 -rag_tool = { - "name": "search_documents", - "description": "搜索知识库文档", - "parameters": {...} -} - -# 在外部定义工具执行器 -retriever = DenseRetriever(...) -def tool_executor(name, params): - if name == "search_documents": - return retriever.retrieve(params["query"]) - -agent = ReActAgent( - llm_client=client, - tools=[rag_tool], - tool_executor=tool_executor -) -``` - -## 发布流程 - -```bash -# 使用 wheelwright -cd /path/to/wheelwright -./publish.sh sage-agentic --auto-bump patch - -# 或手动指定版本 -./publish.sh sage-agentic --version 0.1.0.1 -``` diff --git a/packages/sage-libs/docs/independent-libs/COPILOT_INSTRUCTIONS_isage-eval.md b/packages/sage-libs/docs/independent-libs/COPILOT_INSTRUCTIONS_isage-eval.md deleted file mode 100644 index ca0ac118c4..0000000000 --- a/packages/sage-libs/docs/independent-libs/COPILOT_INSTRUCTIONS_isage-eval.md +++ /dev/null @@ -1,554 +0,0 @@ -# isage-eval Copilot Instructions - -## Package Identity - -| 属性 | 值 | -| ------------- | -------------------------------------------- | -| **PyPI 包名** | `isage-eval` | -| **导入名** | `sage_libs.sage_eval` | -| **SAGE 层级** | L3 (Algorithms & Libraries) | -| **版本格式** | `0.1.x.y` (四段式) | -| **仓库** | `https://github.com/intellistream/sage-eval` | - -## 层级定位 - -### ✅ 允许的依赖 - -``` -L3 及以下: -├── sage-common (L1) # 基础工具、类型、配置 -├── sage-platform (L2) # 平台服务抽象 -├── Python stdlib # 标准库 -├── numpy, scipy # 科学计算 -├── scikit-learn # ML 指标 -├── nltk, rouge-score # NLP 评估 -├── bert-score # BERT 评估 -└── evaluate (HF) # HuggingFace 评估库 -``` - -### ❌ 禁止的依赖 - -``` -L4+ 组件 (绝对禁止): -├── sage-middleware # ❌ 中间件层 -├── isage-vdb / SageVDB # ❌ 向量数据库 -├── isage-neuromem # ❌ 内存系统 -├── FastAPI / uvicorn # ❌ 网络服务 -├── Redis / RocksDB # ❌ 外部存储 -└── vLLM / LMDeploy # ❌ 推理引擎 -``` - -**原则**: Eval 库提供评估指标和算法，LLM-as-Judge 通过依赖注入。 - -## 与 SAGE 主仓库的关系 - -``` -┌─────────────────────────────────────────────────────────────────┐ -│ SAGE 主仓库 (sage.libs.eval) │ -│ ┌───────────────────────────────────────────────────────────┐ │ -│ │ Interface Layer │ │ -│ │ • BaseMetric (ABC) • BaseLLMJudge (ABC) │ │ -│ │ • BaseProfiler (ABC) • BaseBenchmark (ABC) │ │ -│ │ • MetricType (枚举) • MetricResult (数据类型) │ │ -│ │ • ProfileResult (数据类型) │ │ -│ │ • create_metric(), create_judge() (工厂函数) │ │ -│ │ • create_profiler(), create_benchmark() (工厂函数) │ │ -│ └───────────────────────────────────────────────────────────┘ │ -│ ▲ │ -│ │ 注册 │ -└──────────────────────────────┼──────────────────────────────────┘ - │ -┌──────────────────────────────┼──────────────────────────────────┐ -│ isage-eval (独立 PyPI 包) │ -│ ┌───────────────────────────────────────────────────────────┐ │ -│ │ Implementation Layer │ │ -│ │ Metrics: │ │ -│ │ • AccuracyMetric, F1Metric, BLEUMetric, ROUGEMetric │ │ -│ │ • BERTScoreMetric, PerplexityMetric │ │ -│ │ LLM Judges: │ │ -│ │ • FaithfulnessJudge, RelevanceJudge, CoherenceJudge │ │ -│ │ Profilers: │ │ -│ │ • LatencyProfiler, ThroughputProfiler, MemoryProfiler │ │ -│ │ Benchmarks: │ │ -│ │ • RAGBenchmark, AgentBenchmark, E2EBenchmark │ │ -│ │ • _register.py (自动注册到 SAGE 工厂) │ │ -│ └───────────────────────────────────────────────────────────┘ │ -└─────────────────────────────────────────────────────────────────┘ -``` - -### 自动注册机制 - -```python -# sage_libs/sage_eval/_register.py -from sage.libs.eval import ( - register_metric, register_judge, - register_profiler, register_benchmark -) - -from .metrics import AccuracyMetric, F1Metric, BLEUMetric, ROUGEMetric -from .judges import FaithfulnessJudge, RelevanceJudge -from .profilers import LatencyProfiler, MemoryProfiler -from .benchmarks import RAGBenchmark, AgentBenchmark - -# 注册 Metrics -register_metric("accuracy", AccuracyMetric) -register_metric("f1", F1Metric) -register_metric("bleu", BLEUMetric) -register_metric("rouge", ROUGEMetric) - -# 注册 Judges -register_judge("faithfulness", FaithfulnessJudge) -register_judge("relevance", RelevanceJudge) - -# 注册 Profilers -register_profiler("latency", LatencyProfiler) -register_profiler("memory", MemoryProfiler) - -# 注册 Benchmarks -register_benchmark("rag", RAGBenchmark) -register_benchmark("agent", AgentBenchmark) -``` - -## 功能模块 - -### 1. Metrics (评估指标) - -```python -from sage.libs.eval import create_metric, MetricResult - -# 准确率 -metric = create_metric("accuracy") -result: MetricResult = metric.compute( - predictions=["A", "B", "C"], - references=["A", "B", "D"] -) -print(f"准确率: {result.value:.2%}") # 66.67% - -# F1 分数 -metric = create_metric("f1", average="macro") -result = metric.compute(predictions, references) - -# BLEU (机器翻译) -metric = create_metric("bleu", max_order=4) -result = metric.compute( - predictions=["the cat sat on the mat"], - references=[["the cat is on the mat"]] -) - -# ROUGE (文本摘要) -metric = create_metric("rouge", rouge_types=["rouge1", "rouge2", "rougeL"]) -result = metric.compute(summaries, reference_summaries) - -# BERTScore (语义相似度) -metric = create_metric("bert_score", model="bert-base-chinese") -result = metric.compute(generated_texts, reference_texts) - -# 困惑度 -metric = create_metric("perplexity") -result = metric.compute(texts, model=language_model) -``` - -**支持的指标**: - -| 指标 | 类型 | 用途 | -| ------------ | ---- | -------------- | -| `accuracy` | 分类 | 分类准确率 | -| `f1` | 分类 | F1 分数 | -| `precision` | 分类 | 精确率 | -| `recall` | 分类 | 召回率 | -| `bleu` | 生成 | 机器翻译质量 | -| `rouge` | 生成 | 文本摘要质量 | -| `meteor` | 生成 | 翻译质量 | -| `bert_score` | 语义 | 语义相似度 | -| `perplexity` | LLM | 语言模型困惑度 | - -### 2. LLM-as-Judge (LLM 评估) - -```python -from sage.libs.eval import create_judge - -# Faithfulness Judge (事实准确性) -judge = create_judge("faithfulness", llm_client=client) -result = judge.judge( - response="北京是中国的首都", - context="北京，简称京，是中华人民共和国首都", - question="中国的首都是哪里？" -) -print(f"事实准确性: {result.value:.2f}") # 0-1 分数 - -# Relevance Judge (相关性) -judge = create_judge("relevance", llm_client=client) -result = judge.judge( - response=answer, - question=question -) - -# Coherence Judge (连贯性) -judge = create_judge("coherence", llm_client=client) -result = judge.judge(response=generated_text) - -# Safety Judge (安全性) -judge = create_judge("safety", llm_client=client) -result = judge.judge(response=model_output) - -# 自定义评估标准 -judge = create_judge("custom", - llm_client=client, - criteria="评估回答是否具有创意性和新颖性", - rubric={ - 5: "非常有创意", - 4: "比较有创意", - 3: "一般", - 2: "缺乏创意", - 1: "完全没有创意" - } -) -``` - -### 3. Profilers (性能分析器) - -```python -from sage.libs.eval import create_profiler, ProfileResult - -# 延迟分析 -profiler = create_profiler("latency") -with profiler.profile(): - result = model.generate(prompt) - -profile: ProfileResult = profiler.get_result() -print(f"平均延迟: {profile.mean_latency_ms:.2f}ms") -print(f"P99 延迟: {profile.p99_latency_ms:.2f}ms") - -# 吞吐量分析 -profiler = create_profiler("throughput") -result = profiler.measure( - func=model.generate, - inputs=test_prompts, - batch_size=32 -) -print(f"吞吐量: {result.samples_per_second:.2f} samples/s") -print(f"Token 吞吐: {result.tokens_per_second:.2f} tokens/s") - -# 内存分析 -profiler = create_profiler("memory") -with profiler.profile(): - model.load() - result = model.generate(prompt) - -print(f"峰值内存: {profiler.peak_memory_mb:.2f}MB") -print(f"GPU 利用率: {profiler.avg_gpu_utilization:.1%}") -``` - -### 4. Benchmarks (基准测试套件) - -```python -from sage.libs.eval import create_benchmark - -# RAG 基准测试 -benchmark = create_benchmark("rag", - metrics=["faithfulness", "relevance", "mrr", "ndcg"], - dataset="natural_questions" -) -results = benchmark.run(rag_pipeline) -print(benchmark.report()) - -# Agent 基准测试 -benchmark = create_benchmark("agent", - metrics=["task_success", "tool_accuracy", "efficiency"], - tasks=["web_search", "calculation", "code_generation"] -) -results = benchmark.run(agent) - -# 端到端基准测试 -benchmark = create_benchmark("e2e", - metrics=["quality", "latency", "cost"], - test_cases=test_cases -) -results = benchmark.run(pipeline) -``` - -### 5. Retrieval Metrics (检索指标) - -```python -from sage.libs.eval import create_metric - -# MRR (Mean Reciprocal Rank) -metric = create_metric("mrr") -result = metric.compute( - predictions=ranked_results, - references=ground_truth -) - -# NDCG (Normalized Discounted Cumulative Gain) -metric = create_metric("ndcg", k=10) -result = metric.compute(predictions, references) - -# MAP (Mean Average Precision) -metric = create_metric("map") -result = metric.compute(predictions, references) - -# Hit Rate -metric = create_metric("hit_rate", k=5) -result = metric.compute(predictions, references) -``` - -## 目录结构 - -``` -sage-eval/ # 独立仓库根目录 -├── pyproject.toml # 包配置 (name = "isage-eval") -├── README.md -├── COPILOT_INSTRUCTIONS.md # 本文件 -├── LICENSE -├── src/ -│ └── sage_libs/ # 命名空间包 -│ └── sage_eval/ -│ ├── __init__.py # 主入口 -│ ├── _version.py # 版本信息 -│ ├── _register.py # 自动注册 -│ ├── metrics/ # 评估指标实现 -│ │ ├── __init__.py -│ │ ├── classification.py # Accuracy, F1, Precision, Recall -│ │ ├── generation.py # BLEU, ROUGE, METEOR -│ │ ├── semantic.py # BERTScore, Similarity -│ │ ├── retrieval.py # MRR, NDCG, MAP, HitRate -│ │ └── language_model.py # Perplexity -│ ├── judges/ # LLM-as-Judge 实现 -│ │ ├── __init__.py -│ │ ├── faithfulness.py -│ │ ├── relevance.py -│ │ ├── coherence.py -│ │ ├── safety.py -│ │ └── custom.py -│ ├── profilers/ # 性能分析器实现 -│ │ ├── __init__.py -│ │ ├── latency.py -│ │ ├── throughput.py -│ │ └── memory.py -│ └── benchmarks/ # 基准测试实现 -│ ├── __init__.py -│ ├── rag.py -│ ├── agent.py -│ └── e2e.py -├── tests/ -│ ├── conftest.py -│ ├── test_metrics.py -│ ├── test_judges.py -│ ├── test_profilers.py -│ └── test_benchmarks.py -└── examples/ - ├── evaluate_rag.py - ├── llm_judge.py - └── benchmark_pipeline.py -``` - -## 常见问题修复指南 - -### 1. LLM Judge 注入 - -```python -# ❌ 错误：Judge 内部创建 LLM 客户端 -class FaithfulnessJudge(BaseLLMJudge): - def __init__(self): - from sage.llm import UnifiedInferenceClient - self.llm = UnifiedInferenceClient.create() # ❌ 隐式依赖 - -# ✅ 正确：通过依赖注入 -class FaithfulnessJudge(BaseLLMJudge): - def __init__(self, llm_client: LLMClientProtocol): - self.llm = llm_client - -# 使用时 -judge = create_judge("faithfulness", llm_client=client) -``` - -### 2. 批量评估效率 - -```python -# ❌ 问题：逐条评估太慢 -for pred, ref in zip(predictions, references): - result = metric.compute([pred], [ref]) # 每次只评估一条 - -# ✅ 修复：批量评估 -result = metric.compute(predictions, references) # 一次评估所有 - -# 或使用 batch 方法 -result = metric.compute_batch( - predictions, references, - batch_size=64 -) -``` - -### 3. Profiler 上下文管理 - -```python -# ❌ 问题：忘记结束 profiling -profiler.start() -model.generate(prompt) -# 忘记 profiler.stop() - -# ✅ 修复：使用上下文管理器 -with profiler.profile() as p: - model.generate(prompt) -result = profiler.get_result() # 自动停止 -``` - -### 4. 指标类型匹配 - -```python -# ❌ 问题：用错指标类型 -metric = create_metric("bleu") -result = metric.compute( - predictions=["A", "B", "C"], # 分类标签 - references=["A", "B", "D"] -) # BLEU 不适用于分类 - -# ✅ 修复：选择正确的指标 -metric = create_metric("accuracy") # 分类用 accuracy -result = metric.compute(predictions, references) -``` - -### 5. 多指标聚合 - -```python -# ❌ 问题：手动管理多个指标 -accuracy = AccuracyMetric().compute(preds, refs) -f1 = F1Metric().compute(preds, refs) -bleu = BLEUMetric().compute(preds, refs) - -# ✅ 修复：使用 MetricSuite -from sage_libs.sage_eval import MetricSuite - -suite = MetricSuite(metrics=["accuracy", "f1", "bleu"]) -results = suite.compute(predictions, references) -for name, result in results.items(): - print(f"{name}: {result.value:.4f}") -``` - -## 关键设计原则 - -### 1. 指标无状态 - -指标计算应该是无状态的： - -```python -# ✅ 好：无状态计算 -class AccuracyMetric(BaseMetric): - def compute(self, predictions, references) -> MetricResult: - # 纯函数计算 - correct = sum(p == r for p, r in zip(predictions, references)) - return MetricResult(name="accuracy", value=correct / len(predictions)) -``` - -### 2. 结果标准化 - -所有指标返回统一格式： - -```python -@dataclass -class MetricResult: - name: str # 指标名称 - value: float # 指标值 (归一化到 0-1 或合理范围) - metric_type: MetricType - confidence_interval: Optional[tuple[float, float]] = None - sample_size: int = 0 - metadata: dict[str, Any] = field(default_factory=dict) -``` - -### 3. LLM 客户端抽象 - -Judge 使用协议而不是具体类型： - -```python -class LLMClientProtocol(Protocol): - def chat(self, messages: list[dict]) -> str: ... - -class FaithfulnessJudge: - def __init__(self, llm_client: LLMClientProtocol): - # 接受任何符合协议的客户端 - self.llm = llm_client -``` - -### 4. 流式支持 - -大数据集支持流式计算： - -```python -class StreamingMetric(BaseMetric): - def supports_streaming(self) -> bool: - return True - - def update(self, prediction: Any, reference: Any) -> None: - """增量更新""" - self._total += 1 - self._correct += (prediction == reference) - - def finalize(self) -> MetricResult: - """完成计算""" - return MetricResult( - name=self.name, - value=self._correct / self._total - ) -``` - -### 5. 可解释性 - -复杂指标提供详细解释： - -```python -class FaithfulnessJudge(BaseLLMJudge): - def judge(self, response, context, **kwargs) -> MetricResult: - # 不仅返回分数，还返回解释 - return MetricResult( - name="faithfulness", - value=0.85, - metadata={ - "explanation": "回答基本准确，但缺少部分细节", - "supported_claims": ["北京是首都"], - "unsupported_claims": [], - "evidence": "根据上下文第2段..." - } - ) -``` - -## 测试规范 - -```bash -# 运行单元测试 -pytest tests/ -v - -# 运行需要 LLM 的测试（使用 mock） -pytest tests/ -v -m "not llm_required" - -# 运行完整测试（包括 LLM Judge） -SAGE_TEST_LLM_ENABLED=1 pytest tests/ -v -``` - -## 与其他 L3 库的协作 - -```python -# isage-eval + isage-rag 协作 -from sage_libs.sage_eval import create_benchmark -from sage_libs.sage_rag import DenseRetriever - -# 创建 RAG pipeline -retriever = DenseRetriever(...) -rag_pipeline = ... - -# 使用 RAG 基准测试 -benchmark = create_benchmark("rag", - metrics=["faithfulness", "relevance", "mrr"] -) -results = benchmark.run(rag_pipeline) -``` - -## 发布流程 - -```bash -# 使用 wheelwright -cd /path/to/wheelwright -./publish.sh sage-eval --auto-bump patch - -# 或手动指定版本 -./publish.sh sage-eval --version 0.1.0.1 -``` diff --git a/packages/sage-libs/docs/independent-libs/COPILOT_INSTRUCTIONS_isage-finetune.md b/packages/sage-libs/docs/independent-libs/COPILOT_INSTRUCTIONS_isage-finetune.md deleted file mode 100644 index b4af48cf17..0000000000 --- a/packages/sage-libs/docs/independent-libs/COPILOT_INSTRUCTIONS_isage-finetune.md +++ /dev/null @@ -1,573 +0,0 @@ -# isage-finetune Copilot Instructions - -## Package Identity - -| 属性 | 值 | -| ------------- | ------------------------------------------------ | -| **PyPI 包名** | `isage-finetune` | -| **导入名** | `sage_libs.sage_finetune` | -| **SAGE 层级** | L3 (Algorithms & Libraries) | -| **版本格式** | `0.1.x.y` (四段式) | -| **仓库** | `https://github.com/intellistream/sage-finetune` | - -## 层级定位 - -### ✅ 允许的依赖 - -``` -L3 及以下: -├── sage-common (L1) # 基础工具、类型、配置 -├── sage-platform (L2) # 平台服务抽象 -├── Python stdlib # 标准库 -├── torch # PyTorch -├── transformers # HuggingFace Transformers -├── peft # Parameter-Efficient Fine-Tuning -├── datasets # HuggingFace Datasets -├── accelerate # 分布式训练 -└── bitsandbytes # 量化 (可选) -``` - -### ❌ 禁止的依赖 - -``` -L4+ 组件 (绝对禁止): -├── sage-middleware # ❌ 中间件层 -├── isage-vdb / SageVDB # ❌ 向量数据库 -├── isage-neuromem # ❌ 内存系统 -├── FastAPI / uvicorn # ❌ 网络服务 -├── Redis / RocksDB # ❌ 外部存储 -├── vLLM / LMDeploy # ❌ 推理引擎 -└── isagellm # ❌ LLM 服务（通过注入） -``` - -**原则**: Finetune 库提供微调训练器和数据加载器，不涉及推理服务。 - -## 与 SAGE 主仓库的关系 - -``` -┌─────────────────────────────────────────────────────────────────┐ -│ SAGE 主仓库 (sage.libs.finetune) │ -│ ┌───────────────────────────────────────────────────────────┐ │ -│ │ Interface Layer │ │ -│ │ • FineTuner (ABC) • DatasetLoader (ABC) │ │ -│ │ • TrainingConfig • LoRAConfig │ │ -│ │ • TrainingCallback (ABC) │ │ -│ │ • create_trainer(), create_loader() (工厂函数) │ │ -│ └───────────────────────────────────────────────────────────┘ │ -│ ▲ │ -│ │ 注册 │ -└──────────────────────────────┼──────────────────────────────────┘ - │ -┌──────────────────────────────┼──────────────────────────────────┐ -│ isage-finetune (独立 PyPI 包) │ -│ ┌───────────────────────────────────────────────────────────┐ │ -│ │ Implementation Layer │ │ -│ │ Trainers: │ │ -│ │ • LoRATrainer, QLoRATrainer, FullTrainer │ │ -│ │ • DoRATrainer, LoRAPlusTrainer │ │ -│ │ • AgentTrainer (Agent SFT) │ │ -│ │ Loaders: │ │ -│ │ • HFDatasetLoader, JSONLLoader, ParquetLoader │ │ -│ │ • TrajectoryLoader (Agent 轨迹) │ │ -│ │ • InstructionLoader (指令数据) │ │ -│ │ Callbacks: │ │ -│ │ • WandbCallback, TensorBoardCallback, EarlyStopCallback │ │ -│ │ • _register.py (自动注册到 SAGE 工厂) │ │ -│ └───────────────────────────────────────────────────────────┘ │ -└─────────────────────────────────────────────────────────────────┘ -``` - -### 自动注册机制 - -```python -# sage_libs/sage_finetune/_register.py -from sage.libs.finetune import register_trainer, register_loader - -from .trainers import LoRATrainer, QLoRATrainer, FullTrainer, DoRATrainer -from .loaders import HFDatasetLoader, JSONLLoader, TrajectoryLoader - -# 注册 Trainers -register_trainer("lora", LoRATrainer) -register_trainer("qlora", QLoRATrainer) -register_trainer("full", FullTrainer) -register_trainer("dora", DoRATrainer) -register_trainer("lora_plus", LoRAPlusTrainer) - -# 注册 Loaders -register_loader("huggingface", HFDatasetLoader) -register_loader("jsonl", JSONLLoader) -register_loader("trajectory", TrajectoryLoader) -``` - -## 功能模块 - -### 1. LoRA Fine-tuning (低秩适应微调) - -```python -from sage.libs.finetune import create_trainer, TrainingConfig, LoRAConfig - -# LoRA 配置 -lora_config = LoRAConfig( - r=8, # 低秩矩阵秩 - lora_alpha=16, # 缩放因子 - target_modules=["q_proj", "v_proj"], # 目标模块 - lora_dropout=0.05, - bias="none" -) - -# 训练配置 -training_config = TrainingConfig( - model_name_or_path="Qwen/Qwen2.5-7B-Instruct", - output_dir="./output", - num_train_epochs=3, - per_device_train_batch_size=4, - learning_rate=2e-4, - fp16=True -) - -# 创建训练器 -trainer = create_trainer("lora", - training_config=training_config, - lora_config=lora_config -) - -# 训练 -metrics = trainer.train( - train_dataset=train_data, - eval_dataset=eval_data -) - -# 保存 -trainer.save_model("./finetuned_model") -``` - -### 2. QLoRA (量化 LoRA) - -```python -from sage.libs.finetune import create_trainer - -# QLoRA：4-bit 量化 + LoRA -trainer = create_trainer("qlora", - training_config=training_config, - lora_config=lora_config, - quantization_config={ - "load_in_4bit": True, - "bnb_4bit_compute_dtype": "float16", - "bnb_4bit_quant_type": "nf4", - "bnb_4bit_use_double_quant": True - } -) - -# 8-bit 量化 -trainer = create_trainer("qlora", - training_config=training_config, - lora_config=lora_config, - quantization_config={"load_in_8bit": True} -) -``` - -### 3. DoRA & LoRA+ (高级 LoRA 变体) - -```python -# DoRA (Weight-Decomposed Low-Rank Adaptation) -trainer = create_trainer("dora", - training_config=training_config, - lora_config=lora_config -) - -# LoRA+ (不同学习率) -trainer = create_trainer("lora_plus", - training_config=training_config, - lora_config=lora_config, - lora_plus_config={ - "lora_lr_ratio": 16.0 # B 矩阵学习率是 A 矩阵的 16 倍 - } -) -``` - -### 4. Full Fine-tuning (全参数微调) - -```python -# 全参数微调（需要更多 GPU 内存） -trainer = create_trainer("full", - training_config=training_config -) - -# 使用 DeepSpeed ZeRO -trainer = create_trainer("full", - training_config=training_config, - deepspeed_config="./ds_config_zero3.json" -) -``` - -### 5. Agent SFT (Agent 监督微调) - -```python -from sage.libs.finetune import create_trainer, create_loader - -# 加载 Agent 轨迹数据 -loader = create_loader("trajectory") -train_data = loader.load("./agent_trajectories.jsonl") - -# Agent 微调 -trainer = create_trainer("lora", - training_config=training_config, - lora_config=lora_config, - formatting_func=format_agent_trajectory # 轨迹格式化函数 -) - -# 轨迹格式示例 -""" -{ - "task": "搜索最新的 AI 新闻", - "trajectory": [ - {"thought": "我需要搜索网页", "action": "search", "input": {"query": "AI news 2024"}}, - {"observation": "Found 10 results..."}, - {"thought": "找到相关结果", "action": "finish", "input": {"answer": "..."}} - ] -} -""" -``` - -### 6. Dataset Loaders (数据加载器) - -```python -from sage.libs.finetune import create_loader - -# HuggingFace 数据集 -loader = create_loader("huggingface") -dataset = loader.load("tatsu-lab/alpaca", split="train") -processed = loader.preprocess(dataset, tokenizer) - -# JSONL 文件 -loader = create_loader("jsonl") -dataset = loader.load("./data/instructions.jsonl") - -# Parquet 文件 -loader = create_loader("parquet") -dataset = loader.load("./data/training_data.parquet") - -# 流式加载（大数据集） -for sample in loader.stream("./large_dataset.jsonl"): - yield sample - -# 指令数据格式 -""" -{"instruction": "翻译成英文", "input": "你好", "output": "Hello"} -{"instruction": "写一首诗", "input": "", "output": "春眠不觉晓..."} -""" -``` - -### 7. Callbacks (训练回调) - -```python -from sage.libs.finetune import create_trainer -from sage_libs.sage_finetune.callbacks import ( - WandbCallback, TensorBoardCallback, EarlyStopCallback -) - -# 创建回调 -callbacks = [ - WandbCallback(project="my-finetune", run_name="lora-exp1"), - TensorBoardCallback(log_dir="./tb_logs"), - EarlyStopCallback(patience=3, metric="eval_loss") -] - -# 使用回调 -trainer = create_trainer("lora", - training_config=training_config, - lora_config=lora_config, - callbacks=callbacks -) -``` - -## 目录结构 - -``` -sage-finetune/ # 独立仓库根目录 -├── pyproject.toml # 包配置 (name = "isage-finetune") -├── README.md -├── COPILOT_INSTRUCTIONS.md # 本文件 -├── LICENSE -├── src/ -│ └── sage_libs/ # 命名空间包 -│ └── sage_finetune/ -│ ├── __init__.py # 主入口 -│ ├── _version.py # 版本信息 -│ ├── _register.py # 自动注册 -│ ├── trainers/ # 训练器实现 -│ │ ├── __init__.py -│ │ ├── lora.py # LoRA Trainer -│ │ ├── qlora.py # QLoRA Trainer -│ │ ├── dora.py # DoRA Trainer -│ │ ├── lora_plus.py # LoRA+ Trainer -│ │ ├── full.py # Full Fine-tuning -│ │ └── agent.py # Agent SFT Trainer -│ ├── loaders/ # 数据加载器实现 -│ │ ├── __init__.py -│ │ ├── huggingface.py # HF Datasets -│ │ ├── jsonl.py # JSONL 格式 -│ │ ├── parquet.py # Parquet 格式 -│ │ ├── trajectory.py # Agent 轨迹 -│ │ └── instruction.py # 指令数据 -│ ├── callbacks/ # 回调实现 -│ │ ├── __init__.py -│ │ ├── wandb.py -│ │ ├── tensorboard.py -│ │ └── early_stop.py -│ └── utils/ # 工具函数 -│ ├── __init__.py -│ ├── formatting.py # 数据格式化 -│ └── quantization.py # 量化工具 -├── tests/ -│ ├── conftest.py -│ ├── test_trainers.py -│ ├── test_loaders.py -│ └── test_callbacks.py -└── examples/ - ├── lora_finetune.py - ├── qlora_finetune.py - ├── agent_sft.py - └── distributed_training.py -``` - -## 常见问题修复指南 - -### 1. OOM (显存不足) - -```python -# ❌ 问题：全参数微调 OOM -trainer = create_trainer("full", ...) # 7B 模型需要 ~60GB - -# ✅ 修复方案 1：使用 LoRA -trainer = create_trainer("lora", ...) # ~8GB - -# ✅ 修复方案 2：使用 QLoRA -trainer = create_trainer("qlora", - quantization_config={"load_in_4bit": True} -) # ~6GB - -# ✅ 修复方案 3：减小批量大小 + 梯度累积 -training_config = TrainingConfig( - per_device_train_batch_size=1, - gradient_accumulation_steps=16 # 等效批量 = 16 -) -``` - -### 2. LoRA 目标模块选择 - -```python -# ❌ 问题：LoRA 应用到错误的模块 -lora_config = LoRAConfig( - target_modules=["embedding"] # Embedding 层通常不用 LoRA -) - -# ✅ 修复：选择注意力层 -lora_config = LoRAConfig( - target_modules=["q_proj", "k_proj", "v_proj", "o_proj"] # QKV + 输出投影 -) - -# ✅ 更全面的配置 -lora_config = LoRAConfig( - target_modules=[ - "q_proj", "k_proj", "v_proj", "o_proj", # 注意力 - "gate_proj", "up_proj", "down_proj" # FFN - ] -) -``` - -### 3. 数据格式错误 - -```python -# ❌ 问题：数据格式不符合预期 -data = {"text": "Hello"} # 缺少 instruction/output - -# ✅ 修复：使用正确的格式 -# 格式 1: Alpaca 格式 -data = { - "instruction": "翻译成英文", - "input": "你好", - "output": "Hello" -} - -# 格式 2: ShareGPT 格式 -data = { - "conversations": [ - {"role": "user", "content": "你好"}, - {"role": "assistant", "content": "Hello"} - ] -} - -# 格式 3: 自定义格式 + 格式化函数 -def format_func(example): - return f"User: {example['question']}\nAssistant: {example['answer']}" - -trainer = create_trainer("lora", formatting_func=format_func) -``` - -### 4. 学习率问题 - -```python -# ❌ 问题：学习率过高导致不稳定 -training_config = TrainingConfig(learning_rate=1e-3) # 太高 - -# ✅ 修复：使用合适的学习率 -# LoRA: 1e-4 ~ 2e-4 -# QLoRA: 1e-4 ~ 5e-5 -# Full: 1e-5 ~ 5e-6 -training_config = TrainingConfig( - learning_rate=2e-4, # LoRA 推荐 - warmup_steps=100, # 预热 - lr_scheduler_type="cosine" # 余弦退火 -) -``` - -### 5. 模型保存格式 - -```python -# ❌ 问题：保存的是 adapter 但想要合并 -trainer.save_model("./adapter") # 只保存 adapter - -# ✅ 修复：合并后保存 -trainer.merge_and_save("./merged_model") - -# 或手动合并 -from peft import PeftModel - -base_model = AutoModelForCausalLM.from_pretrained("base_model") -peft_model = PeftModel.from_pretrained(base_model, "./adapter") -merged = peft_model.merge_and_unload() -merged.save_pretrained("./merged_model") -``` - -### 6. 多 GPU 训练 - -```python -# ❌ 问题：多 GPU 但只用了一张 -trainer.train() # 默认单 GPU - -# ✅ 修复方案 1：使用 accelerate -# accelerate launch --num_processes=4 train.py - -# ✅ 修复方案 2：DeepSpeed -training_config = TrainingConfig( - deepspeed="./ds_config.json" -) - -# ✅ 修复方案 3：FSDP -training_config = TrainingConfig( - fsdp="full_shard auto_wrap" -) -``` - -## 关键设计原则 - -### 1. 配置分离 - -训练配置和模型配置分离： - -```python -# ✅ 好：配置分离 -training_config = TrainingConfig(...) # 训练超参 -lora_config = LoRAConfig(...) # LoRA 配置 - -trainer = create_trainer("lora", - training_config=training_config, - lora_config=lora_config -) -``` - -### 2. 数据格式灵活 - -支持多种数据格式，通过格式化函数统一： - -```python -class DatasetLoader: - def preprocess(self, dataset, tokenizer, formatting_func=None): - if formatting_func: - dataset = dataset.map(formatting_func) - return dataset.map(lambda x: tokenizer(x["text"])) -``` - -### 3. 回调扩展 - -通过回调机制扩展功能： - -```python -class TrainingCallback(ABC): - def on_train_begin(self, args, state, control): pass - def on_epoch_begin(self, args, state, control): pass - def on_step_end(self, args, state, control): pass - def on_evaluate(self, args, state, control, metrics): pass - def on_train_end(self, args, state, control): pass -``` - -### 4. 量化透明 - -量化配置独立于训练逻辑： - -```python -# 量化是可选的附加配置 -trainer = create_trainer("qlora", - training_config=training_config, - lora_config=lora_config, - quantization_config={"load_in_4bit": True} # 可选 -) -``` - -### 5. 模型无关性 - -不绑定特定模型架构： - -```python -# 支持任何 HuggingFace 兼容模型 -training_config = TrainingConfig( - model_name_or_path="Qwen/Qwen2.5-7B-Instruct" # Qwen - # model_name_or_path="meta-llama/Llama-3-8B" # Llama - # model_name_or_path="THUDM/chatglm3-6b" # ChatGLM -) -``` - -## 测试规范 - -```bash -# 运行单元测试（不需要 GPU） -pytest tests/ -v -m "not gpu" - -# 运行 GPU 测试 -pytest tests/ -v -m "gpu" --gpu - -# 运行小规模训练测试 -pytest tests/test_trainers.py -v --run-training -``` - -## 与其他 L3 库的协作 - -```python -# isage-finetune + isage-eval 协作 -from sage_libs.sage_finetune import create_trainer, create_loader -from sage_libs.sage_eval import create_metric - -# 训练 -trainer = create_trainer("lora", ...) -trainer.train(train_data) - -# 评估微调效果 -metric = create_metric("perplexity") -result = metric.compute( - texts=test_texts, - model=trainer.model -) -print(f"微调后困惑度: {result.value:.2f}") -``` - -## 发布流程 - -```bash -# 使用 wheelwright -cd /path/to/wheelwright -./publish.sh sage-finetune --auto-bump patch - -# 或手动指定版本 -./publish.sh sage-finetune --version 0.1.0.1 -``` diff --git a/packages/sage-libs/docs/independent-libs/COPILOT_INSTRUCTIONS_isage-privacy.md b/packages/sage-libs/docs/independent-libs/COPILOT_INSTRUCTIONS_isage-privacy.md deleted file mode 100644 index a5a62c727a..0000000000 --- a/packages/sage-libs/docs/independent-libs/COPILOT_INSTRUCTIONS_isage-privacy.md +++ /dev/null @@ -1,512 +0,0 @@ -# isage-privacy Copilot Instructions - -## Package Identity - -| 属性 | 值 | -| ------------- | ----------------------------------------------- | -| **PyPI 包名** | `isage-privacy` | -| **导入名** | `sage_libs.sage_privacy` | -| **SAGE 层级** | L3 (Algorithms & Libraries) | -| **版本格式** | `0.1.x.y` (四段式) | -| **仓库** | `https://github.com/intellistream/sage-privacy` | - -## 层级定位 - -### ✅ 允许的依赖 - -``` -L3 及以下: -├── sage-common (L1) # 基础工具、类型、配置 -├── sage-platform (L2) # 平台服务抽象 -├── Python stdlib # 标准库 -├── numpy, scipy # 科学计算 -├── torch # PyTorch (模型操作) -├── cryptography # 加密库 -└── opacus # 差分隐私 (可选) -``` - -### ❌ 禁止的依赖 - -``` -L4+ 组件 (绝对禁止): -├── sage-middleware # ❌ 中间件层 -├── isage-vdb / SageVDB # ❌ 向量数据库 -├── isage-neuromem # ❌ 内存系统 -├── FastAPI / uvicorn # ❌ 网络服务 -├── Redis / RocksDB # ❌ 外部存储 -└── vLLM / LMDeploy # ❌ 推理引擎 -``` - -**原则**: Privacy 库提供隐私保护算法，不涉及数据存储或网络通信。 - -## 与 SAGE 主仓库的关系 - -``` -┌─────────────────────────────────────────────────────────────────┐ -│ SAGE 主仓库 (sage.libs.privacy) │ -│ ┌───────────────────────────────────────────────────────────┐ │ -│ │ Interface Layer │ │ -│ │ • BaseUnlearner (ABC) • BasePrivacyMechanism (ABC)│ │ -│ │ • BaseDPOptimizer (ABC) • BaseFederatedClient (ABC) │ │ -│ │ • BaseFederatedServer (ABC) │ │ -│ │ • UnlearningMethod, PrivacyLevel (枚举) │ │ -│ │ • PrivacyBudget, UnlearningResult (数据类型) │ │ -│ │ • create_unlearner(), create_mechanism() (工厂函数) │ │ -│ └───────────────────────────────────────────────────────────┘ │ -│ ▲ │ -│ │ 注册 │ -└──────────────────────────────┼──────────────────────────────────┘ - │ -┌──────────────────────────────┼──────────────────────────────────┐ -│ isage-privacy (独立 PyPI 包) │ -│ ┌───────────────────────────────────────────────────────────┐ │ -│ │ Implementation Layer │ │ -│ │ Machine Unlearning: │ │ -│ │ • SISAUnlearner, GradientAscentUnlearner │ │ -│ │ • FisherUnlearner, AmnesiacUnlearner │ │ -│ │ Differential Privacy: │ │ -│ │ • LaplaceMechanism, GaussianMechanism │ │ -│ │ • DPSGDOptimizer, PATEOptimizer │ │ -│ │ Federated Learning: │ │ -│ │ • FedAvgClient, FedProxClient │ │ -│ │ • FedAvgServer, SecureAggregator │ │ -│ │ PII Detection: │ │ -│ │ • PIIDetector, PIIAnonymizer │ │ -│ │ • _register.py (自动注册到 SAGE 工厂) │ │ -│ └───────────────────────────────────────────────────────────┘ │ -└─────────────────────────────────────────────────────────────────┘ -``` - -### 自动注册机制 - -```python -# sage_libs/sage_privacy/_register.py -from sage.libs.privacy import ( - register_unlearner, register_mechanism, - register_dp_optimizer, register_federated_client -) - -from .unlearning import SISAUnlearner, GradientAscentUnlearner, FisherUnlearner -from .dp import LaplaceMechanism, GaussianMechanism, DPSGDOptimizer -from .federated import FedAvgClient, FedAvgServer - -# 注册 Unlearners -register_unlearner("sisa", SISAUnlearner) -register_unlearner("gradient_ascent", GradientAscentUnlearner) -register_unlearner("fisher", FisherUnlearner) - -# 注册 DP Mechanisms -register_mechanism("laplace", LaplaceMechanism) -register_mechanism("gaussian", GaussianMechanism) - -# 注册 DP Optimizers -register_dp_optimizer("dp_sgd", DPSGDOptimizer) -``` - -## 功能模块 - -### 1. Machine Unlearning (机器遗忘) - -```python -from sage.libs.privacy import create_unlearner, UnlearningResult - -# SISA (Sharded, Isolated, Sliced, Aggregated) -unlearner = create_unlearner("sisa", - num_shards=5, - num_slices=10 -) -result: UnlearningResult = unlearner.unlearn( - model=trained_model, - forget_data=data_to_forget, - retain_data=remaining_data -) -print(f"遗忘成功: {result.success}") -print(f"遗忘样本数: {result.samples_forgotten}") - -# Gradient Ascent (梯度上升) -unlearner = create_unlearner("gradient_ascent", - learning_rate=0.01, - num_steps=100 -) - -# Fisher Forgetting (Fisher 信息遗忘) -unlearner = create_unlearner("fisher", - damping=0.1 -) - -# 验证遗忘效果 -score = unlearner.verify_unlearning( - model=unlearned_model, - forget_data=data_to_forget, - original_model=original_model -) -print(f"遗忘验证分数: {score:.4f}") # 1.0 = 完美遗忘 -``` - -**支持的遗忘方法**: - -| 方法 | 类型 | 特点 | -| ----------------- | -------- | -------------------- | -| `sisa` | 精确遗忘 | 分片训练，高效删除 | -| `gradient_ascent` | 近似遗忘 | 简单快速，可能不完全 | -| `fisher` | 近似遗忘 | 基于 Fisher 信息 | -| `amnesiac` | 近似遗忘 | 缓存更新，快速回滚 | -| `influence` | 近似遗忘 | 影响函数方法 | - -### 2. Differential Privacy (差分隐私) - -```python -from sage.libs.privacy import create_mechanism, PrivacyBudget - -# 创建隐私预算 -budget = PrivacyBudget( - epsilon=1.0, # 隐私损失参数 - delta=1e-5, # 失败概率 - composition="rdp" # 组合方法 -) - -# Laplace 机制 -mechanism = create_mechanism("laplace", sensitivity=1.0) -noisy_value = mechanism.add_noise(original_value, budget) - -# Gaussian 机制 -mechanism = create_mechanism("gaussian", sensitivity=1.0) -noisy_vector = mechanism.add_noise(original_vector, budget) - -# 查询隐私级别 -print(budget.level) # PrivacyLevel.HIGH (epsilon=1.0) -``` - -### 3. DP Optimizer (差分隐私优化器) - -```python -from sage.libs.privacy import create_dp_optimizer, PrivacyBudget - -# DP-SGD 优化器 -optimizer = create_dp_optimizer("dp_sgd", - learning_rate=0.01, - max_grad_norm=1.0, - noise_multiplier=1.1 -) - -# 训练循环 -for batch in dataloader: - loss = model(batch) - gradients = compute_gradients(loss) - - # 带隐私保证的参数更新 - params = optimizer.step( - params=model.parameters(), - gradients=gradients, - privacy_budget=budget - ) - -# 获取已消耗的隐私预算 -spent = optimizer.get_privacy_spent() -print(f"已消耗隐私预算: ε={spent.epsilon:.2f}") -``` - -### 4. Federated Learning (联邦学习) - -```python -from sage.libs.privacy import create_federated_client, create_federated_server - -# 创建联邦服务器 -server = create_federated_server("fedavg", - num_clients=10, - rounds=100 -) - -# 创建联邦客户端 -client = create_federated_client("fedavg", - local_epochs=5, - learning_rate=0.01 -) - -# 客户端本地训练 -local_model = client.train( - global_model=global_params, - local_data=client_data -) - -# 服务器聚合 -aggregated = server.aggregate( - client_updates=[client1_update, client2_update, ...] -) - -# FedProx (带近端项) -client = create_federated_client("fedprox", - mu=0.01 # 近端项系数 -) -``` - -### 5. PII Detection & Anonymization (PII 检测与匿名化) - -```python -from sage.libs.privacy import PIIDetector, PIIAnonymizer - -# PII 检测 -detector = PIIDetector( - entity_types=["PERSON", "EMAIL", "PHONE", "SSN", "CREDIT_CARD"] -) -entities = detector.detect("联系张三，电话：13812345678") -# [PIIEntity(type="PERSON", text="张三", start=2, end=4), -# PIIEntity(type="PHONE", text="13812345678", start=8, end=19)] - -# PII 匿名化 -anonymizer = PIIAnonymizer( - strategies={ - "PERSON": "mask", # [PERSON] - "EMAIL": "hash", # a1b2c3@hash.com - "PHONE": "redact", # [REDACTED] - } -) -anonymized = anonymizer.anonymize("联系张三，电话：13812345678") -# "联系[PERSON]，电话：[REDACTED]" -``` - -## 目录结构 - -``` -sage-privacy/ # 独立仓库根目录 -├── pyproject.toml # 包配置 (name = "isage-privacy") -├── README.md -├── COPILOT_INSTRUCTIONS.md # 本文件 -├── LICENSE -├── src/ -│ └── sage_libs/ # 命名空间包 -│ └── sage_privacy/ -│ ├── __init__.py # 主入口 -│ ├── _version.py # 版本信息 -│ ├── _register.py # 自动注册 -│ ├── unlearning/ # 机器遗忘实现 -│ │ ├── __init__.py -│ │ ├── sisa.py # SISA 遗忘 -│ │ ├── gradient_ascent.py -│ │ ├── fisher.py # Fisher 遗忘 -│ │ ├── amnesiac.py -│ │ └── influence.py # 影响函数 -│ ├── dp/ # 差分隐私实现 -│ │ ├── __init__.py -│ │ ├── mechanisms.py # Laplace, Gaussian -│ │ ├── optimizers.py # DP-SGD, PATE -│ │ └── accountant.py # 隐私预算记账 -│ ├── federated/ # 联邦学习实现 -│ │ ├── __init__.py -│ │ ├── client.py # FedAvg, FedProx 客户端 -│ │ ├── server.py # 聚合服务器 -│ │ └── secure_agg.py # 安全聚合 -│ └── pii/ # PII 检测实现 -│ ├── __init__.py -│ ├── detector.py -│ └── anonymizer.py -├── tests/ -│ ├── conftest.py -│ ├── test_unlearning.py -│ ├── test_dp.py -│ ├── test_federated.py -│ └── test_pii.py -└── examples/ - ├── machine_unlearning.py - ├── dp_training.py - └── federated_learning.py -``` - -## 常见问题修复指南 - -### 1. 隐私预算耗尽 - -```python -# ❌ 问题：隐私预算超支 -for _ in range(1000): - mechanism.add_noise(value, budget) # 无限制查询 - -# ✅ 修复：使用预算记账 -from sage_libs.sage_privacy import PrivacyAccountant - -accountant = PrivacyAccountant(total_epsilon=10.0, total_delta=1e-5) - -for query in queries: - if not accountant.can_spend(query_epsilon=0.1): - raise PrivacyBudgetExhausted("隐私预算已耗尽") - result = mechanism.add_noise(value, query_budget) - accountant.spend(query_epsilon=0.1) -``` - -### 2. SISA 分片配置 - -```python -# ❌ 问题：分片数过少导致遗忘慢 -unlearner = create_unlearner("sisa", num_shards=2) # 重训练 50% 数据 - -# ✅ 修复：根据数据量调整分片 -num_samples = len(dataset) -# 每个分片约 1000-5000 样本 -num_shards = max(5, num_samples // 2000) -unlearner = create_unlearner("sisa", num_shards=num_shards) -``` - -### 3. 梯度裁剪 - -```python -# ❌ 问题：DP-SGD 忘记裁剪梯度 -optimizer = create_dp_optimizer("dp_sgd", max_grad_norm=None) - -# ✅ 修复：必须设置最大梯度范数 -optimizer = create_dp_optimizer("dp_sgd", - max_grad_norm=1.0, # 必须设置 - noise_multiplier=1.1 -) -``` - -### 4. 联邦学习通信 - -```python -# ❌ 错误：在 L3 库中实现网络通信 -class FedAvgClient: - def send_update(self, url: str): # ❌ 网络调用 - requests.post(url, data=self.update) - -# ✅ 正确：只返回更新，通信在 L4 层 -class FedAvgClient: - def get_update(self) -> dict: # ✅ 纯数据返回 - return self.local_update - -# L4 middleware 层处理通信 -from sage.middleware.operators import FederatedOperator -operator = FederatedOperator(client=client, server_url="...") -``` - -### 5. PII 检测模型加载 - -```python -# ❌ 问题：每次调用都加载模型 -def process_text(text): - detector = PIIDetector() # 每次都重新加载模型 - return detector.detect(text) - -# ✅ 修复：复用检测器实例 -detector = PIIDetector() # 初始化一次 - -def process_text(text): - return detector.detect(text) -``` - -## 关键设计原则 - -### 1. 隐私预算追踪 - -所有隐私操作必须追踪预算消耗： - -```python -class DPMechanism: - def add_noise(self, value: float, budget: PrivacyBudget) -> float: - # 记录此次查询的隐私消耗 - self._consumed_epsilon += self._compute_epsilon(budget) - return value + self._sample_noise(budget) - - @property - def privacy_spent(self) -> PrivacyBudget: - return PrivacyBudget(epsilon=self._consumed_epsilon) -``` - -### 2. 可验证性 - -遗忘效果必须可验证： - -```python -class BaseUnlearner: - @abstractmethod - def unlearn(self, model, forget_data, **kwargs) -> UnlearningResult: - pass - - def verify_unlearning(self, model, forget_data, **kwargs) -> float: - """验证遗忘效果 (0-1，1 = 完美遗忘)""" - # 使用成员推理攻击等方法验证 - ... -``` - -### 3. 无网络通信 - -联邦学习只实现算法，不实现通信： - -```python -# ✅ 正确：纯算法 -class FedAvgServer: - def aggregate(self, updates: list[dict]) -> dict: - """聚合客户端更新""" - return weighted_average(updates) - -# ❌ 错误：包含通信 -class FedAvgServer: - def run_round(self): - updates = self._collect_from_clients() # ❌ 网络调用 - return self.aggregate(updates) -``` - -### 4. 敏感度计算 - -DP 机制需要明确敏感度： - -```python -# ✅ 好：显式敏感度参数 -mechanism = LaplaceMechanism(sensitivity=1.0) - -# ✅ 好：自动计算敏感度 -mechanism = LaplaceMechanism.for_query( - query_type="count", - max_records=1000 -) - -# ❌ 差：隐式敏感度 -mechanism = LaplaceMechanism() # 敏感度是什么？ -``` - -### 5. 类型安全 - -隐私预算使用专用类型： - -```python -# ✅ 好：使用专用类型 -def train_with_dp(budget: PrivacyBudget) -> Model: - if budget.epsilon > 10: - warnings.warn("隐私保证较弱") - ... - -# ❌ 差：使用原始类型 -def train_with_dp(epsilon: float, delta: float) -> Model: - ... # 容易混淆参数 -``` - -## 测试规范 - -```bash -# 运行单元测试 -pytest tests/ -v - -# 运行隐私验证测试 -pytest tests/test_unlearning.py -v -k "verify" - -# 检查 DP 保证 -pytest tests/test_dp.py -v --run-privacy-audit -``` - -## 隐私级别参考 - -| 级别 | Epsilon 范围 | 应用场景 | -| ----------- | ------------ | -------------------------- | -| `VERY_HIGH` | ≤ 0.1 | 高度敏感数据（医疗、金融） | -| `HIGH` | 0.1 - 1.0 | 敏感数据（个人信息） | -| `MEDIUM` | 1.0 - 10.0 | 一般数据（统计分析） | -| `LOW` | > 10.0 | 低敏感数据（聚合统计） | - -## 发布流程 - -```bash -# 使用 wheelwright -cd /path/to/wheelwright -./publish.sh sage-privacy --auto-bump patch - -# 或手动指定版本 -./publish.sh sage-privacy --version 0.1.0.1 -``` diff --git a/packages/sage-libs/docs/independent-libs/COPILOT_INSTRUCTIONS_isage-rag.md b/packages/sage-libs/docs/independent-libs/COPILOT_INSTRUCTIONS_isage-rag.md deleted file mode 100644 index 0868d5ee96..0000000000 --- a/packages/sage-libs/docs/independent-libs/COPILOT_INSTRUCTIONS_isage-rag.md +++ /dev/null @@ -1,405 +0,0 @@ -# isage-rag Copilot Instructions - -## Package Identity - -| 属性 | 值 | -| ------------- | ------------------------------------------- | -| **PyPI 包名** | `isage-rag` | -| **导入名** | `sage_libs.sage_rag` | -| **SAGE 层级** | L3 (Algorithms & Libraries) | -| **版本格式** | `0.1.x.y` (四段式) | -| **仓库** | `https://github.com/intellistream/sage-rag` | - -## 层级定位 - -### ✅ 允许的依赖 - -``` -L3 及以下: -├── sage-common (L1) # 基础工具、类型、配置 -├── sage-platform (L2) # 平台服务抽象 -├── Python stdlib # 标准库 -├── numpy, scipy # 科学计算 -├── transformers # HuggingFace 模型 -├── sentence-transformers # Embedding 模型 -└── pypdf, docx, etc. # 文档解析库 -``` - -### ❌ 禁止的依赖 - -``` -L4+ 组件 (绝对禁止): -├── sage-middleware # ❌ 中间件层 -├── isage-vdb / SageVDB # ❌ 向量数据库 -├── isage-neuromem # ❌ 内存系统 -├── isage-refiner # ❌ 上下文压缩 -├── FastAPI / uvicorn # ❌ 网络服务 -├── Redis / RocksDB # ❌ 外部存储 -└── vLLM / LMDeploy # ❌ 推理引擎 -``` - -**原则**: RAG 库提供纯算法实现，不依赖运行时服务。向量存储、内存管理等应在 L4 middleware 层组合。 - -## 与 SAGE 主仓库的关系 - -``` -┌─────────────────────────────────────────────────────────────────┐ -│ SAGE 主仓库 (sage.libs.rag) │ -│ ┌───────────────────────────────────────────────────────────┐ │ -│ │ Interface Layer │ │ -│ │ • DocumentLoader (ABC) • TextChunker (ABC) │ │ -│ │ • Retriever (ABC) • Reranker (ABC) │ │ -│ │ • QueryRewriter (ABC) • RAGPipeline (ABC) │ │ -│ │ • Document, Chunk, RetrievalResult (数据类型) │ │ -│ │ • create_loader(), create_retriever() (工厂函数) │ │ -│ └───────────────────────────────────────────────────────────┘ │ -│ ▲ │ -│ │ 注册 │ -└──────────────────────────────┼──────────────────────────────────┘ - │ -┌──────────────────────────────┼──────────────────────────────────┐ -│ isage-rag (独立 PyPI 包) │ -│ ┌───────────────────────────────────────────────────────────┐ │ -│ │ Implementation Layer │ │ -│ │ • TextLoader, PDFLoader, DocxLoader (具体加载器) │ │ -│ │ • CharacterSplitter, TokenSplitter (具体分块器) │ │ -│ │ • DenseRetriever, BM25Retriever (具体检索器) │ │ -│ │ • CrossEncoderReranker (具体重排器) │ │ -│ │ • HyDERewriter, MultiQueryRewriter (查询重写器) │ │ -│ │ • _register.py (自动注册到 SAGE 工厂) │ │ -│ └───────────────────────────────────────────────────────────┘ │ -└─────────────────────────────────────────────────────────────────┘ -``` - -### 自动注册机制 - -```python -# sage_libs/sage_rag/_register.py -from sage.libs.rag import register_loader, register_chunker, register_retriever - -from .loaders import TextLoader, PDFLoader, DocxLoader -from .chunkers import CharacterSplitter, TokenSplitter -from .retrievers import DenseRetriever, BM25Retriever - -# 注册到 SAGE 工厂 -register_loader("text", TextLoader) -register_loader("pdf", PDFLoader) -register_loader("docx", DocxLoader) - -register_chunker("character", CharacterSplitter) -register_chunker("token", TokenSplitter) - -register_retriever("dense", DenseRetriever) -register_retriever("bm25", BM25Retriever) -``` - -## 功能模块 - -### 1. Document Loaders (文档加载器) - -```python -from sage.libs.rag import create_loader, Document - -# 通过工厂创建（推荐） -loader = create_loader("pdf") -doc = loader.load("/path/to/document.pdf") - -# 批量加载 -docs = loader.load_batch(["/path/1.pdf", "/path/2.pdf"]) - -# 直接导入实现 -from sage_libs.sage_rag.loaders import PDFLoader -loader = PDFLoader(extract_images=True) -``` - -**支持的格式**: - -- `text`: `.txt`, `.md`, `.json`, `.yaml` -- `pdf`: `.pdf` (支持 OCR) -- `docx`: `.docx`, `.doc` -- `html`: `.html`, `.htm`, URL -- `csv`: `.csv`, `.tsv` - -### 2. Text Chunkers (文本分块器) - -```python -from sage.libs.rag import create_chunker, Chunk - -# 字符分块 -chunker = create_chunker("character", chunk_size=1000, overlap=100) -chunks = chunker.chunk(text) - -# Token 分块（使用 tokenizer） -chunker = create_chunker("token", chunk_size=512, tokenizer="gpt2") -chunks = chunker.chunk_document(document) - -# 语义分块 -chunker = create_chunker("semantic", model="sentence-transformers/all-MiniLM-L6-v2") -``` - -### 3. Retrievers (检索器) - -```python -from sage.libs.rag import create_retriever, RetrievalResult - -# Dense Retriever (向量检索) -retriever = create_retriever("dense", - embedding_model="BAAI/bge-small-zh-v1.5", - index_path="/path/to/index" -) - -# BM25 Retriever (关键词检索) -retriever = create_retriever("bm25", k1=1.2, b=0.75) - -# Hybrid Retriever (混合检索) -retriever = create_retriever("hybrid", - dense_weight=0.7, - sparse_weight=0.3 -) - -# 检索 -results: list[RetrievalResult] = retriever.retrieve("查询问题", top_k=10) -``` - -### 4. Rerankers (重排器) - -```python -from sage.libs.rag import create_reranker - -# Cross-Encoder 重排 -reranker = create_reranker("cross_encoder", - model="BAAI/bge-reranker-base" -) - -# 重排结果 -reranked = reranker.rerank(query, results, top_k=5) -``` - -### 5. Query Rewriters (查询重写器) - -```python -from sage.libs.rag import create_query_rewriter - -# HyDE (假设文档嵌入) -rewriter = create_query_rewriter("hyde", llm_client=client) -expanded_query = rewriter.rewrite("原始查询") - -# Multi-Query (多查询扩展) -rewriter = create_query_rewriter("multi_query", num_queries=3) -queries = rewriter.expand("原始查询") -``` - -## 目录结构 - -``` -sage-rag/ # 独立仓库根目录 -├── pyproject.toml # 包配置 (name = "isage-rag") -├── README.md -├── COPILOT_INSTRUCTIONS.md # 本文件 -├── LICENSE -├── src/ -│ └── sage_libs/ # 命名空间包 -│ └── sage_rag/ -│ ├── __init__.py # 主入口，导出所有实现 -│ ├── _version.py # 版本信息 -│ ├── _register.py # 自动注册到 SAGE 工厂 -│ ├── loaders/ # 文档加载器实现 -│ │ ├── __init__.py -│ │ ├── text.py -│ │ ├── pdf.py -│ │ ├── docx.py -│ │ ├── html.py -│ │ └── csv.py -│ ├── chunkers/ # 文本分块器实现 -│ │ ├── __init__.py -│ │ ├── character.py -│ │ ├── token.py -│ │ ├── sentence.py -│ │ └── semantic.py -│ ├── retrievers/ # 检索器实现 -│ │ ├── __init__.py -│ │ ├── dense.py -│ │ ├── bm25.py -│ │ └── hybrid.py -│ ├── rerankers/ # 重排器实现 -│ │ ├── __init__.py -│ │ └── cross_encoder.py -│ └── rewriters/ # 查询重写器实现 -│ ├── __init__.py -│ ├── hyde.py -│ └── multi_query.py -├── tests/ -│ ├── conftest.py -│ ├── test_loaders.py -│ ├── test_chunkers.py -│ ├── test_retrievers.py -│ └── test_integration.py -└── examples/ - ├── simple_rag.py - ├── hybrid_retrieval.py - └── document_processing.py -``` - -## 常见问题修复指南 - -### 1. 导入错误 - -```python -# ❌ 错误：直接从 sage.libs.rag 导入实现 -from sage.libs.rag import DenseRetriever # ImportError - -# ✅ 正确：从独立包导入实现 -from sage_libs.sage_rag.retrievers import DenseRetriever - -# ✅ 正确：通过工厂创建（推荐） -from sage.libs.rag import create_retriever -retriever = create_retriever("dense", ...) -``` - -### 2. 实现未注册 - -```python -# ❌ 错误：忘记安装独立包 -from sage.libs.rag import create_loader -loader = create_loader("pdf") # RAGRegistryError: 'pdf' not registered - -# ✅ 修复：安装独立包 -# pip install isage-rag -``` - -### 3. 向量存储依赖 - -```python -# ❌ 错误：在 RAG 库中直接使用向量数据库 -from sage_libs.sage_rag.retrievers import DenseRetriever -from sagevdb import SageVDB # ❌ L3 不应依赖 L4 - -# ✅ 正确：检索器接受抽象索引接口 -class DenseRetriever: - def __init__(self, index: VectorIndex): # 抽象接口 - self.index = index - -# ✅ 正确：在 L4 middleware 中组合 -from sage.middleware.components.sage_db import SageVDB -from sage_libs.sage_rag.retrievers import DenseRetriever - -index = SageVDB(dimension=768) -retriever = DenseRetriever(index=index) # 在 middleware 层组合 -``` - -### 4. LLM 调用问题 - -```python -# ❌ 错误：RAG 库内部创建 LLM 客户端 -class HyDERewriter: - def __init__(self): - from sage.llm import UnifiedInferenceClient - self.client = UnifiedInferenceClient.create() # ❌ 隐式依赖 - -# ✅ 正确：通过依赖注入 -class HyDERewriter: - def __init__(self, llm_client: LLMClient): # 接口类型 - self.client = llm_client -``` - -### 5. 版本冲突 - -```bash -# 检查版本兼容性 -pip show isage-rag isage-libs - -# 升级到兼容版本 -pip install "isage-rag>=0.1.0,<0.2.0" -``` - -## 关键设计原则 - -### 1. 纯算法实现 - -RAG 库只提供算法实现，不包含： - -- 网络服务 (HTTP API) -- 持久化存储 (数据库连接) -- 进程管理 (后台任务) -- 外部服务客户端 - -### 2. 依赖注入 - -所有外部依赖通过构造函数注入： - -```python -class DenseRetriever(Retriever): - def __init__( - self, - embedding_fn: Callable[[str], list[float]], # 注入 embedding 函数 - index: VectorIndex, # 注入向量索引 - ): - self.embedding_fn = embedding_fn - self.index = index -``` - -### 3. 接口隔离 - -每个组件遵循单一职责： - -- `DocumentLoader`: 只负责加载文档 -- `TextChunker`: 只负责分块 -- `Retriever`: 只负责检索 -- `Reranker`: 只负责重排 - -### 4. 无 Fallback 原则 - -```python -# ❌ 错误：静默 fallback -def load(self, path): - try: - return self._load_pdf(path) - except ImportError: - return self._load_text(path) # 静默降级 - -# ✅ 正确：明确失败 -def load(self, path): - if not HAS_PYPDF: - raise ImportError( - "PDF loading requires pypdf. Install with: pip install isage-rag[pdf]" - ) - return self._load_pdf(path) -``` - -### 5. 类型安全 - -所有公开 API 使用类型注解： - -```python -def retrieve( - self, - query: str, - top_k: int = 10, - filter_fn: Optional[Callable[[Document], bool]] = None, -) -> list[RetrievalResult]: - ... -``` - -## 测试规范 - -```bash -# 运行单元测试 -pytest tests/ -v - -# 运行集成测试（需要模型） -pytest tests/test_integration.py -v --run-integration - -# 检查覆盖率 -pytest tests/ --cov=sage_libs.sage_rag --cov-report=html -``` - -## 发布流程 - -```bash -# 使用 wheelwright -cd /path/to/wheelwright -./publish.sh sage-rag --auto-bump patch - -# 或手动指定版本 -./publish.sh sage-rag --version 0.1.0.1 -``` diff --git a/packages/sage-libs/docs/independent-libs/COPILOT_INSTRUCTIONS_isage-safety.md b/packages/sage-libs/docs/independent-libs/COPILOT_INSTRUCTIONS_isage-safety.md deleted file mode 100644 index 25adf5b283..0000000000 --- a/packages/sage-libs/docs/independent-libs/COPILOT_INSTRUCTIONS_isage-safety.md +++ /dev/null @@ -1,617 +0,0 @@ -# isage-safety Copilot Instructions - -## Package Identity - -| 属性 | 值 | -| ------------- | ---------------------------------------------- | -| **PyPI 包名** | `isage-safety` | -| **导入名** | `sage_libs.sage_safety` | -| **SAGE 层级** | L3 (Algorithms & Libraries) | -| **版本格式** | `0.1.x.y` (四段式) | -| **仓库** | `https://github.com/intellistream/sage-safety` | - -## 层级定位 - -### ✅ 允许的依赖 - -``` -L3 及以下: -├── sage-common (L1) # 基础工具、类型、配置 -├── sage-platform (L2) # 平台服务抽象 -├── Python stdlib # 标准库 -├── re (regex) # 正则表达式 -├── transformers # 分类模型 -├── torch # PyTorch -└── sentence-transformers # 语义相似度 -``` - -### ❌ 禁止的依赖 - -``` -L4+ 组件 (绝对禁止): -├── sage-middleware # ❌ 中间件层 -├── isage-vdb / SageVDB # ❌ 向量数据库 -├── isage-neuromem # ❌ 内存系统 -├── FastAPI / uvicorn # ❌ 网络服务 -├── Redis / RocksDB # ❌ 外部存储 -├── vLLM / LMDeploy # ❌ 推理引擎 -└── isagellm # ❌ LLM 服务（通过注入） -``` - -**原则**: Safety 库提供安全检测算法，LLM-based 检测通过依赖注入。 - -## 与 SAGE 主仓库的关系 - -``` -┌─────────────────────────────────────────────────────────────────┐ -│ SAGE 主仓库 (sage.libs.safety) │ -│ ┌───────────────────────────────────────────────────────────┐ │ -│ │ Interface Layer │ │ -│ │ • BaseGuardrail (ABC) • BaseJailbreakDetector │ │ -│ │ • BaseToxicityDetector (ABC) • BaseAdversarialDefense │ │ -│ │ • SafetyCategory, SafetyAction (枚举) │ │ -│ │ • SafetyResult, JailbreakResult (数据类型) │ │ -│ │ • create_guardrail(), create_jailbreak_detector() │ │ -│ │ Built-in (简单实现): │ │ -│ │ • content_filter (正则过滤) │ │ -│ │ • pii_scrubber (简单 PII 处理) │ │ -│ │ • policy_check (工具调用策略) │ │ -│ └───────────────────────────────────────────────────────────┘ │ -│ ▲ │ -│ │ 注册 │ -└──────────────────────────────┼──────────────────────────────────┘ - │ -┌──────────────────────────────┼──────────────────────────────────┐ -│ isage-safety (独立 PyPI 包) │ -│ ┌───────────────────────────────────────────────────────────┐ │ -│ │ Implementation Layer │ │ -│ │ Guardrails: │ │ -│ │ • LLMGuardrail, ClassifierGuardrail, HybridGuardrail │ │ -│ │ Jailbreak Detectors: │ │ -│ │ • PatternDetector, MLDetector, LLMDetector, Ensemble │ │ -│ │ Toxicity Detectors: │ │ -│ │ • PerspectiveDetector, ToxicBERTDetector │ │ -│ │ Adversarial Defense: │ │ -│ │ • PromptInjectionDefense, EncodingDefense │ │ -│ │ • _register.py (自动注册到 SAGE 工厂) │ │ -│ └───────────────────────────────────────────────────────────┘ │ -└─────────────────────────────────────────────────────────────────┘ -``` - -### 自动注册机制 - -```python -# sage_libs/sage_safety/_register.py -from sage.libs.safety import ( - register_guardrail, register_jailbreak_detector, - register_toxicity_detector, register_adversarial_defense -) - -from .guardrails import LLMGuardrail, ClassifierGuardrail, HybridGuardrail -from .jailbreak import PatternDetector, MLDetector, LLMDetector -from .toxicity import PerspectiveDetector, ToxicBERTDetector -from .adversarial import PromptInjectionDefense, EncodingDefense - -# 注册 Guardrails -register_guardrail("llm", LLMGuardrail) -register_guardrail("classifier", ClassifierGuardrail) -register_guardrail("hybrid", HybridGuardrail) - -# 注册 Jailbreak Detectors -register_jailbreak_detector("pattern", PatternDetector) -register_jailbreak_detector("ml", MLDetector) -register_jailbreak_detector("llm", LLMDetector) - -# 注册 Toxicity Detectors -register_toxicity_detector("perspective", PerspectiveDetector) -register_toxicity_detector("toxic_bert", ToxicBERTDetector) -``` - -## 功能模块 - -### 1. Guardrails (安全护栏) - -```python -from sage.libs.safety import create_guardrail, SafetyResult, SafetyAction - -# LLM 护栏（需要 LLM 客户端） -guardrail = create_guardrail("llm", - llm_client=client, - categories=["toxicity", "hate_speech", "violence"] -) - -result: SafetyResult = guardrail.check( - content="用户输入文本", - context="对话上下文" -) - -if not result.is_safe: - print(f"检测到问题: {result.category}") - print(f"建议操作: {result.action}") # BLOCK, WARN, MODIFY - -# 分类器护栏（本地模型） -guardrail = create_guardrail("classifier", - model="unitary/toxic-bert" -) - -# 混合护栏（规则 + ML） -guardrail = create_guardrail("hybrid", - pattern_rules=["badword1", "badword2"], - classifier_model="toxic-bert", - llm_client=client # 可选 -) - -# 批量检查 -results = guardrail.check_batch( - contents=["text1", "text2", "text3"], - contexts=[None, None, None] -) - -# 过滤内容 -filtered, result = guardrail.filter( - content="包含敏感词的文本", - action=SafetyAction.MODIFY -) -``` - -### 2. Jailbreak Detection (越狱检测) - -```python -from sage.libs.safety import create_jailbreak_detector, JailbreakResult - -# 基于模式的检测（快速） -detector = create_jailbreak_detector("pattern", - patterns=[ - r"ignore.*previous.*instructions", - r"pretend.*you.*are", - r"DAN.*mode" - ] -) -result: JailbreakResult = detector.detect( - prompt="Ignore all previous instructions and...", - system_prompt="You are a helpful assistant" -) - -if result.is_jailbreak: - print(f"检测到越狱攻击: {result.attack_type}") - print(f"置信度: {result.confidence:.2%}") - -# ML 检测器（更准确） -detector = create_jailbreak_detector("ml", - model="protectai/deberta-v3-base-prompt-injection" -) - -# LLM 检测器（最准确，但慢） -detector = create_jailbreak_detector("llm", - llm_client=client -) - -# 集成检测器（组合多种方法） -detector = create_jailbreak_detector("ensemble", - detectors=["pattern", "ml", "llm"], - llm_client=client, - voting="majority" # "majority", "any", "all" -) -``` - -### 3. Toxicity Detection (毒性检测) - -```python -from sage.libs.safety import create_toxicity_detector - -# Perspective API (需要 API key) -detector = create_toxicity_detector("perspective", - api_key=os.environ["PERSPECTIVE_API_KEY"] -) -result = detector.detect("检测的文本") -print(f"毒性分数: {result.toxicity_score:.2%}") -print(f"类别分数: {result.category_scores}") - -# ToxicBERT (本地模型) -detector = create_toxicity_detector("toxic_bert", - model="unitary/toxic-bert" -) -result = detector.detect("检测的文本") - -# 多语言检测 -detector = create_toxicity_detector("multilingual", - model="unitary/multilingual-toxic-xlm-roberta" -) -``` - -### 4. Adversarial Defense (对抗防御) - -```python -from sage.libs.safety import create_adversarial_defense - -# Prompt Injection 防御 -defense = create_adversarial_defense("prompt_injection", - strategies=["delimiter", "instruction_isolation", "input_sanitization"] -) -safe_prompt = defense.sanitize(user_input) - -# 编码攻击防御 -defense = create_adversarial_defense("encoding", - detect_unicode=True, - detect_homoglyphs=True, - detect_invisible=True -) -is_safe, cleaned = defense.check_and_clean(input_text) - -# 角色扮演攻击防御 -defense = create_adversarial_defense("roleplay", - blocked_personas=["DAN", "STAN", "Developer Mode"] -) -``` - -### 5. Content Filter (内容过滤) - 内置 - -```python -# 内置的简单实现，无需 isage-safety -from sage.libs.safety import content_filter - -# 正则过滤 -filter = content_filter.RegexFilter( - patterns=[r"badword1", r"badword2"], - replacement="[FILTERED]" -) -filtered_text = filter.filter("Text with badword1") - -# 敏感词过滤 -filter = content_filter.KeywordFilter( - keywords=["敏感词1", "敏感词2"], - action="replace" # "replace", "block", "warn" -) -``` - -### 6. PII Scrubber (PII 处理) - 内置 - -```python -# 内置的简单实现 -from sage.libs.safety import pii_scrubber - -scrubber = pii_scrubber.PIIScrubber( - entity_types=["EMAIL", "PHONE", "ID_CARD"], - strategies={ - "EMAIL": "hash", # a1b2@hash.com - "PHONE": "mask", # 138****5678 - "ID_CARD": "redact" # [REDACTED] - } -) -cleaned = scrubber.scrub("我的邮箱是 test@example.com") -``` - -### 7. Policy Check (策略检查) - 内置 - -```python -# 内置的工具调用策略 -from sage.libs.safety import policy_check - -checker = policy_check.ToolPolicyChecker( - allowed_tools=["search", "calculate"], - blocked_tools=["execute_code", "delete_file"], - rate_limits={"search": 10} # 每分钟最多 10 次 -) - -is_allowed = checker.check(tool_name="search", context={}) -``` - -## 目录结构 - -``` -sage-safety/ # 独立仓库根目录 -├── pyproject.toml # 包配置 (name = "isage-safety") -├── README.md -├── COPILOT_INSTRUCTIONS.md # 本文件 -├── LICENSE -├── src/ -│ └── sage_libs/ # 命名空间包 -│ └── sage_safety/ -│ ├── __init__.py # 主入口 -│ ├── _version.py # 版本信息 -│ ├── _register.py # 自动注册 -│ ├── guardrails/ # 护栏实现 -│ │ ├── __init__.py -│ │ ├── llm.py # LLM 护栏 -│ │ ├── classifier.py # 分类器护栏 -│ │ └── hybrid.py # 混合护栏 -│ ├── jailbreak/ # 越狱检测实现 -│ │ ├── __init__.py -│ │ ├── pattern.py # 模式检测 -│ │ ├── ml.py # ML 检测 -│ │ ├── llm.py # LLM 检测 -│ │ └── ensemble.py # 集成检测 -│ ├── toxicity/ # 毒性检测实现 -│ │ ├── __init__.py -│ │ ├── perspective.py # Perspective API -│ │ └── toxic_bert.py # ToxicBERT -│ ├── adversarial/ # 对抗防御实现 -│ │ ├── __init__.py -│ │ ├── prompt_injection.py -│ │ ├── encoding.py -│ │ └── roleplay.py -│ └── utils/ # 工具函数 -│ ├── __init__.py -│ └── patterns.py # 常用模式 -├── tests/ -│ ├── conftest.py -│ ├── test_guardrails.py -│ ├── test_jailbreak.py -│ ├── test_toxicity.py -│ └── test_adversarial.py -└── examples/ - ├── content_moderation.py - ├── jailbreak_detection.py - └── safe_llm_pipeline.py -``` - -## 常见问题修复指南 - -### 1. LLM 客户端注入 - -```python -# ❌ 错误：内部创建 LLM 客户端 -class LLMGuardrail(BaseGuardrail): - def __init__(self): - from isagellm import UnifiedInferenceClient - self.llm = UnifiedInferenceClient.create() # ❌ 隐式依赖 - -# ✅ 正确：通过依赖注入 -class LLMGuardrail(BaseGuardrail): - def __init__(self, llm_client: LLMClientProtocol): - self.llm = llm_client - -# 使用时 -from isagellm import UnifiedInferenceClient -client = UnifiedInferenceClient.create() -guardrail = create_guardrail("llm", llm_client=client) -``` - -### 2. 模式匹配效率 - -```python -# ❌ 问题：每次检测都编译正则 -def detect(self, text): - for pattern in self.patterns: - if re.search(pattern, text): # 每次都编译 - return True - -# ✅ 修复：预编译正则 -def __init__(self, patterns): - self.compiled_patterns = [re.compile(p, re.I) for p in patterns] - -def detect(self, text): - return any(p.search(text) for p in self.compiled_patterns) -``` - -### 3. 假阳性处理 - -```python -# ❌ 问题：过于敏感导致假阳性 -detector = create_jailbreak_detector("pattern", - patterns=[r"ignore"] # 太宽泛 -) - -# ✅ 修复：使用更精确的模式 -detector = create_jailbreak_detector("pattern", - patterns=[ - r"ignore\s+(all\s+)?previous\s+instructions", - r"disregard\s+your\s+guidelines" - ] -) - -# ✅ 或使用白名单 -detector = create_jailbreak_detector("pattern", - patterns=[...], - whitelist=["ignore this field", "you can ignore"] -) -``` - -### 4. 阈值调整 - -```python -# ❌ 问题：阈值不合适导致漏检或误报 -detector = create_toxicity_detector("toxic_bert") -result = detector.detect(text) # 默认阈值可能不适合 - -# ✅ 修复：根据场景调整阈值 -detector = create_toxicity_detector("toxic_bert", - thresholds={ - "toxicity": 0.7, # 毒性 - "severe_toxicity": 0.5, # 严重毒性更严格 - "identity_attack": 0.6 - } -) -``` - -### 5. 多语言支持 - -```python -# ❌ 问题：只支持英文 -detector = create_toxicity_detector("toxic_bert", - model="unitary/toxic-bert" # 只支持英文 -) - -# ✅ 修复：使用多语言模型 -detector = create_toxicity_detector("multilingual", - model="unitary/multilingual-toxic-xlm-roberta" -) - -# 或针对中文 -detector = create_toxicity_detector("chinese", - model="textdetox/chinese-roberta-base-toxic" -) -``` - -### 6. 性能优化 - -```python -# ❌ 问题：每次请求都加载模型 -def check(self, text): - model = AutoModel.from_pretrained(...) # 每次都加载 - return model(text) - -# ✅ 修复：在初始化时加载 -class ClassifierGuardrail: - def __init__(self, model_name): - self.model = AutoModel.from_pretrained(model_name) - self.tokenizer = AutoTokenizer.from_pretrained(model_name) - - def check(self, text): - # 复用已加载的模型 - return self.model(self.tokenizer(text)) -``` - -## 关键设计原则 - -### 1. 分层防御 - -多层检测，由快到慢： - -```python -# 第一层：快速规则检测 -if pattern_detector.detect(prompt): - return block() - -# 第二层：ML 模型检测 -if ml_detector.detect(prompt).is_jailbreak: - return block() - -# 第三层：LLM 检测（最准确但最慢） -if llm_detector.detect(prompt).is_jailbreak: - return block() - -return allow() -``` - -### 2. 可配置阈值 - -所有检测器支持阈值配置： - -```python -class BaseDetector: - def __init__(self, threshold: float = 0.5): - self.threshold = threshold - - def detect(self, text) -> Result: - score = self._compute_score(text) - return Result( - is_positive=score > self.threshold, - confidence=score - ) -``` - -### 3. 详细报告 - -检测结果包含详细信息： - -```python -@dataclass -class SafetyResult: - is_safe: bool - action: SafetyAction - category: Optional[SafetyCategory] - confidence: float - detected_issues: list[str] # 具体问题 - modified_content: Optional[str] # 修改后内容 - metadata: dict[str, Any] # 额外信息 -``` - -### 4. 白名单支持 - -所有检测器支持白名单： - -```python -class JailbreakDetector: - def __init__(self, patterns, whitelist=None): - self.patterns = patterns - self.whitelist = whitelist or [] - - def detect(self, prompt): - # 先检查白名单 - if any(w in prompt.lower() for w in self.whitelist): - return JailbreakResult(is_jailbreak=False) - # 再检测 - ... -``` - -### 5. 无副作用 - -检测是只读操作，不修改输入： - -```python -# ✅ 好：只读检测 -def check(self, content: str) -> SafetyResult: - # 不修改 content - return SafetyResult(...) - -# ✅ 好：过滤返回新内容 -def filter(self, content: str) -> tuple[str, SafetyResult]: - result = self.check(content) - if result.action == SafetyAction.MODIFY: - # 返回新字符串，不修改原始内容 - return self._sanitize(content), result - return content, result -``` - -## 测试规范 - -```bash -# 运行单元测试 -pytest tests/ -v - -# 运行需要模型的测试 -pytest tests/ -v -m "not slow" - -# 运行完整测试（包括 LLM） -SAGE_TEST_LLM_ENABLED=1 pytest tests/ -v -``` - -## 安全检测分类 - -| 类别 | 检测方法 | 性能 | 准确性 | -| --------- | ------------------ | ----- | ------ | -| 越狱/注入 | Pattern → ML → LLM | 快→慢 | 低→高 | -| 毒性 | Classifier | 中 | 高 | -| PII | Regex + NER | 快 | 中 | -| 对抗文本 | 编码检测 | 快 | 中 | - -## 与其他 L3 库的协作 - -```python -# isage-safety + isage-agentic 协作 -from sage_libs.sage_safety import create_guardrail, create_jailbreak_detector -from sage_libs.sage_agentic import ReActAgent - -# 创建安全检测器 -jailbreak_detector = create_jailbreak_detector("ensemble", ...) -guardrail = create_guardrail("hybrid", ...) - -# 在 Agent 执行前检测 -def safe_agent_execute(agent, task): - # 检测越狱 - jb_result = jailbreak_detector.detect(task) - if jb_result.is_jailbreak: - raise SecurityError(f"检测到越狱攻击: {jb_result.attack_type}") - - # 执行任务 - result = agent.execute(task) - - # 检测输出 - safety_result = guardrail.check(str(result.output)) - if not safety_result.is_safe: - return guardrail.filter(str(result.output))[0] - - return result -``` - -## 发布流程 - -```bash -# 使用 wheelwright -cd /path/to/wheelwright -./publish.sh sage-safety --auto-bump patch - -# 或手动指定版本 -./publish.sh sage-safety --version 0.1.0.1 -``` diff --git a/packages/sage-libs/docs/independent-libs/README.md b/packages/sage-libs/docs/independent-libs/README.md deleted file mode 100644 index 3212a6aad8..0000000000 --- a/packages/sage-libs/docs/independent-libs/README.md +++ /dev/null @@ -1,89 +0,0 @@ -# SAGE L3 独立库 Copilot Instructions 索引 - -本目录包含 SAGE L3 层级独立 PyPI 库的 Copilot 指令文件。 - -## 库概览 - -| 内部包名 | PyPI 包名 | 导入命名空间 | 文档 | 描述 | -| ------------- | ---------------- | ------------------------- | -------------------------------------------------------------------------------- | ---------------------------------------- | -| sage-rag | `isage-rag` | `sage_libs.sage_rag` | [COPILOT_INSTRUCTIONS_isage-rag.md](COPILOT_INSTRUCTIONS_isage-rag.md) | RAG 实现 (Loaders, Chunkers, Retrievers) | -| sage-agentic | `isage-agentic` | `sage_libs.sage_agentic` | [COPILOT_INSTRUCTIONS_isage-agentic.md](COPILOT_INSTRUCTIONS_isage-agentic.md) | Agent 实现 (ReAct, PlanExecute, Reflex) | -| sage-privacy | `isage-privacy` | `sage_libs.sage_privacy` | [COPILOT_INSTRUCTIONS_isage-privacy.md](COPILOT_INSTRUCTIONS_isage-privacy.md) | 隐私保护 (DP, 联邦学习, 机器遗忘, PII) | -| sage-eval | `isage-eval` | `sage_libs.sage_eval` | [COPILOT_INSTRUCTIONS_isage-eval.md](COPILOT_INSTRUCTIONS_isage-eval.md) | 评估指标/Profiler/Judge | -| sage-finetune | `isage-finetune` | `sage_libs.sage_finetune` | [COPILOT_INSTRUCTIONS_isage-finetune.md](COPILOT_INSTRUCTIONS_isage-finetune.md) | 微调训练器和数据加载器 | -| sage-safety | `isage-safety` | `sage_libs.sage_safety` | [COPILOT_INSTRUCTIONS_isage-safety.md](COPILOT_INSTRUCTIONS_isage-safety.md) | 安全护栏和检测器 | - -## 版本格式 - -所有 L3 独立库使用四段式版本号：`0.1.x.y` - -- `0` - 主版本（API 不兼容变更） -- `1` - 次版本（功能新增） -- `x` - 修订版本（Bug 修复） -- `y` - 构建版本（小调整） - -## 架构原则 - -### SAGE 侧接口层 (`sage.libs.xxx`) - -- 提供抽象基类 (ABC) -- 提供工厂函数 (`create_xxx()`) -- 提供类型定义和数据类 -- 提供注册函数 (`register_xxx()`) - -### 独立库实现层 (`sage_libs.sage_xxx`) - -- 提供具体实现类 -- 通过 `_register.py` 自动注册到 SAGE 工厂 -- 遵循 L3 层级约束（纯算法，无网络服务） - -### L3 层级约束 - -✅ **允许**: - -- sage-common (L1) -- sage-platform (L2) -- Python 标准库 -- 纯算法库 (numpy, scipy, torch 等) - -❌ **禁止**: - -- sage-middleware (L4) -- VDB/Memory 服务 (isage-vdb, isage-neuromem) -- 网络服务 (FastAPI, uvicorn) -- LLM 推理引擎 (vLLM, isagellm) - 通过依赖注入使用 - -## 使用示例 - -```python -# 1. 使用接口层（推荐） -from sage.libs.rag import create_loader, create_retriever -from sage.libs.agentic import create_agent -from sage.libs.eval import create_metric - -# 2. 直接导入实现（如果需要） -from sage_libs.sage_rag.loaders import PDFLoader -from sage_libs.sage_agentic.agents import ReActAgent -from sage_libs.sage_eval.metrics import BLEUMetric -``` - -## 安装 - -```bash -# 安装单个库 -pip install isage-rag -pip install isage-agentic -pip install isage-privacy -pip install isage-eval -pip install isage-finetune -pip install isage-safety - -# 或安装 sage-libs 并选择可选依赖 -pip install isage-libs[rag,agentic,eval] -``` - -## 相关文档 - -- [SAGE 主仓库 Copilot Instructions](../../../../.github/copilot-instructions.md) -- [接口层使用指南](../INTERFACE_LAYER_USAGE_GUIDE.md) -- [包架构文档](../../../../docs-public/docs_src/dev-notes/package-architecture.md) diff --git a/packages/sage-libs/examples/README.md b/packages/sage-libs/examples/README.md deleted file mode 100644 index 49a6a9cc1a..0000000000 --- a/packages/sage-libs/examples/README.md +++ /dev/null @@ -1,87 +0,0 @@ -# L3: Libs - 算法库层示例 - -> 对应 SAGE 包：`sage-libs` - -## 📖 层级说明 - -**Libs** 层提供算法库和工具（与 Kernel 同层）： - -- RAG - 检索增强生成 -- Agents - 智能体框架 -- Embeddings - 向量嵌入 -- LLM - 大语言模型集成 -- Unlearning - 机器遗忘 - -## 📚 目录结构 - -``` -L3-libs/ -├── rag/ # RAG 应用 -├── agents/ # 智能体应用 -├── embeddings/ # 嵌入应用 -├── llm/ # LLM 应用 -└── unlearning/ # 机器遗忘 -``` - -## 🎯 学习路径 - -### 1️⃣ RAG 应用 (`rag/`) - -从简单到完整的 RAG 系统： - -- `simple_rag.py` - 简单 RAG 示例 -- `qa_local_llm.py` - 本地 LLM 问答 -- `qa_no_retrieval.py` - 无检索问答 -- `usage_1_direct_library.py` - 直接使用库 -- `usage_2_sage_function.py` - SAGE 函数集成 -- `usage_3_memory_service.py` - 内存服务集成 -- `usage_4_complete_rag.py` - 完整 RAG 系统 - -### 2️⃣ Agents 应用 (`agents/`) - -构建智能体系统： - -- `basic_agent.py` - 基础智能体 -- `workflow_demo.py` - 工作流演示 -- `arxiv_search_tool.py` - arXiv 搜索工具 -- `demo_arxiv_search.py` - 搜索演示 - -### 3️⃣ Embeddings 应用 (`embeddings/`) - -向量嵌入和相似度搜索： - -- `embedding_demo.py` - 嵌入演示 -- `embedding_service_demo.py` - 嵌入服务 -- `pipeline_builder_embedding_demo.py` - 管道构建器 -- `cross_modal_search.py` - 跨模态搜索 - -### 4️⃣ LLM 应用 (`llm/`) - -大语言模型集成： - -- `pipeline_builder_llm_demo.py` - LLM 管道 -- `templates_to_llm_demo.py` - 模板演示 -- `demo_new_templates.py` - 新模板演示 -- `test_real_llm.py` - 真实 LLM 测试 - -### 5️⃣ Unlearning 应用 (`unlearning/`) - -机器遗忘技术： - -- `basic_unlearning_demo.py` - 基础演示 - -## 🎯 学习目标 - -完成本层示例后，你将掌握： - -1. 如何构建 RAG 系统 -1. 如何设计智能体 -1. 向量嵌入的应用 -1. LLM 集成的最佳实践 -1. 机器遗忘的基本原理 - -## ⏭️ 下一步 - -学完算法库层后，继续学习： - -- **L4-middleware/** - 中间件和领域算子 diff --git a/packages/sage-libs/examples/agent_sft_demo.py b/packages/sage-libs/examples/agent_sft_demo.py deleted file mode 100644 index 46247e0839..0000000000 --- a/packages/sage-libs/examples/agent_sft_demo.py +++ /dev/null @@ -1,179 +0,0 @@ -""" -Agent SFT + Eval Usage Demo - -Demonstrates the complete workflow of using agent_sft data source -and agent_eval usage profiles. -""" - -from sage.data.sources.agent_sft import AgentSFTDataLoader - - -def demo_agent_sft_loader(): - """Demonstrate AgentSFTDataLoader functionality.""" - print("=" * 70) - print("AGENT SFT DATA SOURCE DEMO") - print("=" * 70) - print() - - # Initialize loader - print("1. Initializing AgentSFTDataLoader...") - loader = AgentSFTDataLoader() - print(" ✓ Loader initialized successfully") - print() - - # Display statistics - print("2. Dataset Statistics:") - stats = loader.get_stats() - print(f" Total dialogs: {stats.total_dialogs}") - print(f" - Train: {stats.train_count}") - print(f" - Dev: {stats.dev_count}") - print(f" - Test: {stats.test_count}") - print(f" Average turns per dialog: {stats.avg_turns}") - print(f" Average tools per dialog: {stats.avg_tools_per_dialog}") - print(f" Unique tools used: {stats.unique_tools}") - print() - - # Show top tools - print("3. Top 5 Most Used Tools:") - sorted_tools = sorted(stats.tool_coverage.items(), key=lambda x: x[1], reverse=True) - for i, (tool_id, count) in enumerate(sorted_tools[:5], 1): - print(f" {i}. {tool_id}: {count} dialogs") - print() - - # Demonstrate iteration - print("4. Sample Dialogs from Training Set:") - for i, dialog in enumerate(loader.iter_dialogs("train")): - if i >= 2: # Show only 2 dialogs - break - print(f"\n Dialog {dialog.dialog_id}:") - print(f" Goal: {dialog.goal}") - print(f" Tools: {', '.join(dialog.target_tools)}") - print(f" Turns: {len(dialog.turns)}") - print(" First 3 turns:") - for j, turn in enumerate(dialog.turns[:3], 1): - content_preview = turn.content[:50] + "..." if len(turn.content) > 50 else turn.content - print(f" {j}. [{turn.role}] {content_preview}") - print() - - # Demonstrate batch sampling - print("5. Batch Sampling:") - batch = loader.sample_batch(batch_size=5, split="train", shuffle=False) - print(f" Sampled {len(batch)} dialogs for training") - for dialog in batch: - print(f" - {dialog.dialog_id}: {dialog.goal[:40]}...") - print() - - # Demonstrate filtering - print("6. Filtering by Difficulty:") - hard_dialogs = loader.filter_by_difficulty("hard", split="test") - print(f" Found {len(hard_dialogs)} hard dialogs in test set") - if hard_dialogs: - print(f" Example: {hard_dialogs[0].dialog_id} - {hard_dialogs[0].goal}") - print() - - # Demonstrate tool filtering - print("7. Filtering by Tool:") - if stats.tool_coverage: - sample_tool = list(stats.tool_coverage.keys())[0] - tool_dialogs = loader.filter_by_tool(sample_tool, split="train") - print(f" Tool '{sample_tool}' is used in {len(tool_dialogs)} training dialogs") - print() - - # Demonstrate dialog lookup - print("8. Dialog Lookup by ID:") - dialog = loader.get_dialog("sft_000001") - if dialog: - print(f" Found dialog: {dialog.dialog_id}") - print(f" Goal: {dialog.goal}") - print(f" Split: {dialog.split}") - else: - print(" Dialog not found (may have been filtered out due to validation)") - print() - - print("=" * 70) - print("✓ DEMO COMPLETED SUCCESSFULLY") - print("=" * 70) - - -def demo_usage_profiles(): - """Demonstrate agent_eval usage profiles (conceptual).""" - print("\n" + "=" * 70) - print("AGENT EVAL USAGE PROFILES (Conceptual Demo)") - print("=" * 70) - print() - - print("Profile Configurations:") - print() - - print("1. quick_eval:") - print(" - Purpose: Fast validation during development") - print(" - Sources: agent_benchmark") - print(" - Tasks: tool_selection only") - print(" - Split: dev (100 samples max)") - print(" - Use: CI testing, rapid iteration") - print() - - print("2. full_eval:") - print(" - Purpose: Comprehensive model evaluation") - print(" - Sources: agent_benchmark + agent_tools") - print(" - Tasks: tool_selection, task_planning, timing_judgment") - print(" - Split: test") - print(" - Use: Final benchmarks, paper results") - print() - - print("3. sft_training:") - print(" - Purpose: Agent model training") - print(" - Sources: agent_sft + agent_tools") - print(" - Split: train") - print(" - Parameters: batch_size=32, max_turns=12, shuffle=true") - print(" - Use: Supervised fine-tuning workflows") - print() - - print("Integration Example:") - print(""" - from sage.data import DataManager - - # Load usage - manager = DataManager.get_instance() - agent_eval = manager.get_by_usage("agent_eval") - - # Quick evaluation - quick = agent_eval.load_profile("quick_eval") - for sample in quick["benchmark"].iter_split("tool_selection", "dev"): - # Run fast evaluation - ... - - # Full evaluation - full = agent_eval.load_profile("full_eval") - benchmark = full["benchmark"] - tools = full["tools"] - # Run comprehensive tests - ... - - # SFT training - sft = agent_eval.load_profile("sft_training") - training_data = sft["sft"] - # Train model - ... - """) - - print("=" * 70) - - -def main(): - """Run all demos.""" - # Demo 1: Agent SFT DataLoader - demo_agent_sft_loader() - - # Demo 2: Usage Profiles (conceptual, since other sources aren't implemented yet) - demo_usage_profiles() - - print("\n✅ All demos completed successfully!") - print("\nNote: Full integration requires:") - print(" - Subtask 1: agent_tools data source") - print(" - Subtask 2: agent_benchmark data source") - print(" - DataManager registration (may be automatic)") - - -if __name__ == "__main__": - main() diff --git a/packages/sage-libs/examples/agents/README.md b/packages/sage-libs/examples/agents/README.md deleted file mode 100644 index 7b3e5cae2b..0000000000 --- a/packages/sage-libs/examples/agents/README.md +++ /dev/null @@ -1,34 +0,0 @@ -# Agent Tutorials - -Simple examples demonstrating how to build and use agents with SAGE. - -## Examples - -### 1. Basic Agent (`basic_agent.py`) - -Learn the fundamentals of creating an agent. - -```bash -python examples/tutorials/agents/basic_agent.py -``` - -### 2. Agent Workflow (`workflow_demo.py`) - -Demonstrates agent workflow patterns. - -```bash -python examples/tutorials/agents/workflow_demo.py -``` - -### 3. ArXiv Search Tool (`arxiv_search_tool.py`) - -Example of a custom tool for agents - searches academic papers. - -```bash -python examples/tutorials/agents/demo_arxiv_search.py -``` - -## Next Steps - -- Check out the full agent library in `packages/sage-libs/src/sage/libs/agentic/agents/` -- See production agent applications in `packages/sage-apps/` diff --git a/packages/sage-libs/examples/agents/arxiv_search_tool.py b/packages/sage-libs/examples/agents/arxiv_search_tool.py deleted file mode 100644 index cf66c7a1e4..0000000000 --- a/packages/sage-libs/examples/agents/arxiv_search_tool.py +++ /dev/null @@ -1,239 +0,0 @@ -# examples/agents/tools/arxiv_mcp_tool.py -from __future__ import annotations - -import logging -import re -import time -from typing import Any - -import requests -from bs4 import BeautifulSoup -from bs4.element import Tag - - -class ArxivSearchTool: - """ - MCP 工具：arxiv_search - - 接口三要素： - name = "arxiv_search" - description = "Search arXiv papers; return a list of {title, authors, link, abstract}." - input_schema = {...} - - 入口： - call({"query": "...", "size": 25, "max_results": 2}) -> {"output": [...], "meta": {...}} - """ - - name = "arxiv_search" - description = "Search arXiv papers; return a list of {title, authors, link, abstract}." - input_schema = { - "type": "object", - "properties": { - "query": {"type": "string", "description": "Search query for arXiv."}, - "size": { - "type": "integer", - "enum": [25, 50, 100, 200], - "default": 25, - "description": "Results per page on arXiv.", - }, - "max_results": { - "type": "integer", - "minimum": 1, - "maximum": 100, - "default": 10, - "description": "Maximum number of papers to return (<=100).", - }, - "with_abstract": { - "type": "boolean", - "default": True, - "description": "Whether to include abstract in results.", - }, - }, - "required": ["query"], - "additionalProperties": False, - } - - def __init__(self): - self.base_url = "https://arxiv.org/search/" - self.valid_sizes = [25, 50, 100, 200] - self.session = requests.Session() - # 设置更像真实浏览器的请求头 - self.session.headers.update( - { - "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36", - "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8", - "Accept-Language": "en-US,en;q=0.9,zh-CN;q=0.8,zh;q=0.7", - "Accept-Encoding": "gzip, deflate, br", - "Connection": "keep-alive", - "Upgrade-Insecure-Requests": "1", - "Sec-Fetch-Dest": "document", - "Sec-Fetch-Mode": "navigate", - "Sec-Fetch-Site": "none", - "Cache-Control": "max-age=0", - } - ) - - # === MCP 入口 === - def call(self, arguments: dict[str, Any]) -> dict[str, Any]: - query: str = (arguments.get("query") or "").strip() - if not query: - raise ValueError("`query` is required and must be a non-empty string.") - - # 清理查询字符串，移除可能导致问题的字符 - query = self._clean_query(query) - - size: int = int(arguments.get("size", 25) or 25) - if size not in self.valid_sizes: - size = min(self.valid_sizes, key=lambda x: abs(x - size)) - - max_results: int = int(arguments.get("max_results", 10) or 10) - max_results = max(1, min(max_results, 100)) - - with_abs: bool = bool(arguments.get("with_abstract", True)) - - try: - items = self._search_arxiv( - query=query, size=size, max_results=max_results, with_abstract=with_abs - ) - return { - "output": items, - "meta": {"query": query, "size": size, "max_results": max_results}, - } - except Exception as e: - logging.error(f"[arxiv_search] online search failed: {e}") - # 离线兜底：返回 mock，保证示例可跑 - k = max_results - demo = [ - { - "title": f"Survey of LLM Agents ({i + 1})", - "authors": "Alice, Bob", - "link": f"https://arxiv.org/abs/2509.{1234 + i}", - "abstract": "(mock) An overview of LLM-based agents, planning, and tool use.", - } - for i in range(k) - ] - return {"output": demo, "meta": {"query": query, "offline_mock": True}} - - def _clean_query(self, query: str) -> str: - """清理查询字符串，避免可能导致服务器错误的字符""" - import re - - # 替换可能导致问题的词汇组合 - problematic_patterns = { - r"\bvs\b": "versus", - r"\bcompare?\b": "analysis", - r"\bcomparison\b": "analysis", - r"\bdifferent\b": "analysis", - r"\bdifference\b": "analysis", - } - - for pattern, replacement in problematic_patterns.items(): - query = re.sub(pattern, replacement, query, flags=re.IGNORECASE) - - # 移除多余的空格和特殊字符 - query = re.sub(r"\s+", " ", query) # 多个空格变成单个空格 - query = re.sub(r"[^\w\s\-\+\.]", " ", query) # 只保留字母数字、空格、连字符、加号、点号 - query = query.strip() - - # 限制查询长度 - if len(query) > 100: - query = query[:100] - - return query - - # === 具体抓取 === - def _search_arxiv( - self, query: str, size: int, max_results: int, with_abstract: bool - ) -> list[dict[str, Any]]: - results: list[dict[str, Any]] = [] - start = 0 - retry_count = 0 - max_retries = 3 - base_delay = 1.5 # 基础延迟时间 - - while len(results) < max_results: - # 在每次请求前添加延迟，避免过于频繁的请求 - if start > 0 or retry_count > 0: - time.sleep(base_delay) - - params = { - "searchtype": "all", - "query": query, - "abstracts": "show", - "order": "", - "size": str(size), - "start": str(start), - } - - try: - # 如果是重试，增加额外延迟 - if retry_count > 0: - time.sleep(2**retry_count) # 指数退避：2s, 4s, 8s - - resp = self.session.get(self.base_url, params=params, timeout=20) - resp.raise_for_status() - soup = BeautifulSoup(resp.content, "html.parser") - retry_count = 0 # 重置重试计数 - - except requests.exceptions.HTTPError as e: - if e.response.status_code in [500, 503, 429] and retry_count < max_retries: - logging.warning( - f"[arxiv_search] HTTP {e.response.status_code} error, retrying ({retry_count + 1}/{max_retries}) after delay..." - ) - retry_count += 1 - continue - else: - raise # 重试次数用完或其他错误 - - except Exception as e: - if retry_count < max_retries: - logging.warning( - f"[arxiv_search] Request failed, retrying ({retry_count + 1}/{max_retries}): {e}" - ) - retry_count += 1 - continue - else: - raise - papers = soup.find_all("li", class_="arxiv-result") # type: ignore - if not papers: - break - - for paper in papers: - if len(results) >= max_results: - break - - title_elem = paper.find("p", class_="title") # type: ignore - title = title_elem.text.strip() if title_elem else "No title" - - authors_elem = paper.find("p", class_="authors") # type: ignore - authors = authors_elem.text.strip() if authors_elem else "No authors" - authors = re.sub(r"^Authors:\s*", "", authors) - authors = re.sub(r"\s+", " ", authors).strip() - - abstract = "" - if with_abstract: - abstract_elem = paper.find("span", class_="abstract-full") # type: ignore - abstract = ( - (abstract_elem.text.strip() if abstract_elem else "") - .replace("△ Less", "") - .strip() - ) - - link_elem = paper.find("p", class_="list-title") # type: ignore - link_tag = link_elem.find("a") if isinstance(link_elem, Tag) else None # type: ignore - link = ( - link_tag["href"] - if isinstance(link_tag, Tag) and link_tag.has_attr("href") - else "" - ) - - results.append( - { - "title": title, - "authors": authors, - "abstract": abstract, - "link": link or "https://arxiv.org", - } - ) - - start += size - - return results[:max_results] diff --git a/packages/sage-libs/examples/agents/basic_agent.py b/packages/sage-libs/examples/agents/basic_agent.py deleted file mode 100644 index 0d0a0fc4d1..0000000000 --- a/packages/sage-libs/examples/agents/basic_agent.py +++ /dev/null @@ -1,207 +0,0 @@ -from __future__ import annotations - -import importlib -import importlib.util -import json -import os -import sys -from typing import Any, Iterable - -from sage.cli.utils.env import get_api_key, load_environment_file, should_use_real_api -from sage.common.utils.config.loader import load_config -from sage.libs.agentic.agents.action.mcp_registry import MCPRegistry -from sage.libs.agentic.agents.planning.simple_llm_planner import SimpleLLMPlanner -from sage.libs.agentic.agents.profile.profile import BaseProfile -from sage.middleware.operators.agent.runtime import AgentRuntime -from sage.middleware.operators.rag import OpenAIGenerator - -# 加载环境配置 -try: - load_environment_file() -except RuntimeError as exc: - print(f"⚠️ 无法加载 .env: {exc}") - - -# ====== 读取 source ====== -def iter_queries(source_cfg: dict[str, Any]) -> Iterable[str]: - stype = source_cfg.get("type", "local") - if stype == "local": - path = source_cfg["data_path"] - field = source_cfg.get("field_query", "query") - with open(path, encoding="utf-8") as f: - for line in f: - line = line.strip() - if not line: - continue - obj = json.loads(line) - q = obj.get(field, "") - if isinstance(q, str) and q.strip(): - yield q - elif stype == "hf": - from datasets import load_dataset - - name = source_cfg["hf_dataset_name"] - config = source_cfg.get("hf_dataset_config") - split = source_cfg.get("hf_split", "dev") - field = source_cfg.get("field_query", "query") - ds = load_dataset(name, config, split=split) - for row in ds: - # HuggingFace dataset row is dict-like - if isinstance(row, dict): - q = row.get(field, "") - if isinstance(q, str) and q.strip(): - yield q - else: - raise ValueError(f"Unsupported source.type: {stype}") - - -def main(): - # ====== 读取配置 ====== - cfg_path = os.path.join(os.path.dirname(__file__), "config", "config_agent_min.yaml") - if not os.path.exists(cfg_path): - print(f"❌ Configuration file not found: {cfg_path}") - sys.exit(1) - config: dict[str, Any] = load_config(cfg_path) - - # ====== Profile ====== - profile = BaseProfile.from_dict(config["profile"]) - - # 检查是否在测试模式 - use_real_api = should_use_real_api() - test_mode = ( - os.getenv("SAGE_EXAMPLES_MODE") == "test" or os.getenv("SAGE_TEST_MODE") == "true" - ) and not use_real_api # 如果明确要求使用真实API，则不进入测试模式 - - # 在真实API模式下，使用简化的查询数据以避免超时 - if use_real_api: - # 使用测试数据 - config["source"]["data_path"] = "examples/tutorials/agents/data/agent_queries_test.jsonl" - - # ====== Generator====== - gen_cfg = config["generator"]["remote"] # 可改为 "local"/"remote" - - # 验证 API key 配置（在测试和非测试模式下都需要检查） - try: - api_key = get_api_key("openai", required=True) - gen_cfg["api_key"] = api_key - if use_real_api: - print("🌐 Real API mode: API key configuration validated") - else: - print("✅ API key configuration validated") - except ValueError as e: - if test_mode: - print(f"⚠️ Test mode: {e}") - print("💡 Tip: Copy .env.template to .env and fill in your API keys") - print("✅ Test mode: API key validation completed (missing key is OK in test)") - else: - print(f"❌ {e}") - print("💡 Tip: Copy .env.template to .env and fill in your API keys") - sys.exit(1) - - if test_mode: - # 在测试模式下，验证配置加载和模块导入，但不实际初始化组件 - print( - "🧪 Test mode: Configuration loaded successfully (add --use-real-api to use real API)" - ) - print("✅ Test mode: Profile created successfully") - - # 验证配置文件结构 - required_sections = ["generator", "planner", "tools", "runtime"] - for section in required_sections: - if section in config: - print(f"✅ Test mode: {section} config found") - else: - print(f"❌ Test mode: {section} config missing") - - # 验证工具模块可以导入（但不实际初始化） - try: - for item in config.get("tools", []): - module_name = item["module"] - # 处理相对路径导入 - if module_name.startswith("examples.tutorials.agents."): - # 使用绝对路径导入 - tool_name = module_name.split(".")[-1] - script_dir = os.path.dirname(os.path.abspath(__file__)) - tool_path = os.path.join(script_dir, f"{tool_name}.py") - spec = importlib.util.spec_from_file_location(tool_name, tool_path) - if spec is not None and spec.loader is not None: - mod = importlib.util.module_from_spec(spec) - sys.modules[tool_name] = mod - spec.loader.exec_module(mod) - else: - raise ImportError(f"Failed to load spec for {tool_name}") - else: - mod = importlib.import_module(module_name) - cls = getattr(mod, item["class"]) - print(f"✅ Test mode: Tool {item['class']} import successful") - except Exception as e: - print(f"⚠️ Test mode: Tool import failed (this is OK in test): {e}") - - print("✅ Test mode: Agent pipeline structure validated") - return - - if use_real_api: - print("🌐 Real API mode: Will make actual API calls with qwen-turbo") - - generator = OpenAIGenerator(gen_cfg) # ====== Planner ====== - planner_cfg = config["planner"] - planner = SimpleLLMPlanner( - generator=generator, - max_steps=planner_cfg.get("max_steps", 6), - enable_repair=planner_cfg.get("enable_repair", True), - topk_tools=planner_cfg.get("topk_tools", 6), - ) - - # ====== MCP 工具注册：按配置动态 import 并注册 ====== - registry = MCPRegistry() - for item in config.get("tools", []): - module_name = item["module"] - # 处理相对路径导入 - if module_name.startswith("examples.tutorials.agents."): - # 使用绝对路径导入 - tool_name = module_name.split(".")[-1] - script_dir = os.path.dirname(os.path.abspath(__file__)) - tool_path = os.path.join(script_dir, f"{tool_name}.py") - spec = importlib.util.spec_from_file_location(tool_name, tool_path) - if spec is not None and spec.loader is not None: - mod = importlib.util.module_from_spec(spec) - sys.modules[tool_name] = mod - spec.loader.exec_module(mod) - else: - raise ImportError(f"Failed to load spec for {tool_name}") - else: - mod = importlib.import_module(module_name) - cls = getattr(mod, item["class"]) - kwargs = item.get("init_kwargs", {}) - registry.register(cls(**kwargs) if kwargs else cls()) - - # ====== Runtime ====== - runtime_cfg = config["runtime"] - agent = AgentRuntime( - profile=profile, - planner=planner, - tools=registry, - summarizer=(generator if runtime_cfg.get("summarizer") == "reuse_generator" else None), - # memory=None, # 如需接入 MemoryServiceAdapter，再按配置打开 - max_steps=runtime_cfg.get("max_steps", 6), - ) - - # ====== 跑一遍 queries====== - for q in iter_queries(config["source"]): - print("\n==========================") - print(f"🧑‍💻 User: {q}") - ans = agent.execute({"query": q}) - print(f"🤖 Agent:\n{ans}") - - -if __name__ == "__main__": - # 和 RAG 示例一致的“测试模式”友好输出 - if os.getenv("SAGE_EXAMPLES_MODE") == "test" or os.getenv("SAGE_TEST_MODE") == "true": - try: - main() - print("\n✅ Test passed: Agent pipeline structure validated") - except Exception as e: - print(f"❌ Test failed: {e}") - sys.exit(1) - else: - main() diff --git a/packages/sage-libs/examples/agents/config/README.md b/packages/sage-libs/examples/agents/config/README.md deleted file mode 100644 index aa5ed0edf6..0000000000 --- a/packages/sage-libs/examples/agents/config/README.md +++ /dev/null @@ -1,24 +0,0 @@ -# Agent Tutorials - Configuration - -This directory contains configuration files for agent tutorials. - -## Files - -- **`config_agent_min.yaml`** - Minimal agent configuration - -## Usage - -Referenced by `../basic_agent.py`: - -```python -cfg_path = os.path.join(os.path.dirname(__file__), "config", "config_agent_min.yaml") -``` - -## Customization - -Modify the config file to change: - -- LLM model -- Agent tools -- Data sources -- Behavior parameters diff --git a/packages/sage-libs/examples/agents/config/config_agent_min.yaml b/packages/sage-libs/examples/agents/config/config_agent_min.yaml deleted file mode 100644 index 9b3466fa9c..0000000000 --- a/packages/sage-libs/examples/agents/config/config_agent_min.yaml +++ /dev/null @@ -1,93 +0,0 @@ -# examples/config/agent_planner_mcp.yaml - -pipeline: - name: "sage-agent-base-pipeline" - description: "Minimal agent pipeline(profile+planner+mcp+runtime)" - version: "1.0.0" - -# 数据源：每行 JSON 必须至少包含一个字段：{"query": "...用户问题..."} -source: - type: "local" - data_path: "examples/tutorials/agents/data/agent_queries.jsonl" - field_query: "query" # 例如： - # {"query": "在 arXiv 搜 2 篇 LLM agents survey，最后中文总结"} - -profile: - name: "ResearchOrchestrator" - role: "planner" - language: "zh" - goals: - - "以最少步骤完成用户意图" - - "优先使用提供的 MCP 工具" - constraints: - - "工具参数必须符合 JSONSchema" - - "计划步数不超过 6" - persona: - style: "concise" - -planner: - llm: - method: "openai" - model_name: "gpt-4o-mini" - base_url: "http://localhost:8000/v1" - api_key: "" - temperature: 0.2 - max_steps: 6 - enable_repair: true - topk_tools: 6 - -mcp: - # 本地示例工具（在脚本里实现并注册） - local_tools: - - name: "calculator" - enable: true - - name: "arxiv_search" - enable: true - # 可选：远程 MCP Server（若有） - remotes: [] - # remotes: - # - adapter_id: "t1" - # base_url: "http://localhost:9000" - # prefix: "up_" - -generator: - local: - method: "hf" - model_name: "meta-llama/Llama-2-13b-chat-hf" - seed: 42 - - vllm: - api_key: "" - method: "openai" - model_name: "meta-llama/Llama-2-7b-chat-hf" - base_url: "http://sage3:8000/v1" - seed: 42 - - remote: - api_key: "" - method: "openai" - model_name: "Qwen/Qwen2.5-7B-Instruct" - base_url: "http://127.0.0.1:8888/v1" - seed: 42 - -memory: - enable: false - session_id: "demo-session" - similarity_threshold: 0.2 - include_graph_context: false - create_knowledge_graph: false - -tools: -- module: "examples.tutorials.agents.arxiv_search_tool" - class: "ArxivSearchTool" - init_kwargs: {} - -runtime: - max_steps: 6 - summarizer: "reuse_generator" # 复用同一个 generator 做总结（AgentRuntime里直接传入同一个实例 - -sink: - platform: "local" - format: "json" - show_metadata: true - save_to_file: "results/agent_planner_mcp_output.jsonl" diff --git a/packages/sage-libs/examples/agents/data/README.md b/packages/sage-libs/examples/agents/data/README.md deleted file mode 100644 index b60ff42cea..0000000000 --- a/packages/sage-libs/examples/agents/data/README.md +++ /dev/null @@ -1,27 +0,0 @@ -# Agent Tutorials - Data Files - -This directory contains sample data for agent tutorials. - -## Files - -- **`agent_queries.jsonl`** - Standard agent queries for testing -- **`agent_queries_test.jsonl`** - Test queries for agent examples - -## Format - -Each line in the JSONL file contains a query object: - -```json -{"query": "Your question here", "metadata": {...}} -``` - -## Usage - -These files are referenced in: - -- `../basic_agent.py` -- `../config/config_agent_min.yaml` - -## Custom Queries - -Add your own queries to test different agent behaviors. diff --git a/packages/sage-libs/examples/agents/data/agent_queries.jsonl b/packages/sage-libs/examples/agents/data/agent_queries.jsonl deleted file mode 100644 index 4de6e9405f..0000000000 --- a/packages/sage-libs/examples/agents/data/agent_queries.jsonl +++ /dev/null @@ -1,4 +0,0 @@ -{"query": "在 arXiv 搜 2 篇 LLM agents survey，最后中文总结"} -{"query": "帮我总结一下，GPT-4 和 Claude 2 在使用体验上有什么不同？"} -{"query": "帮我总结一下，GPT-4 和 Claude 2 在使用体验上有什么不同？再帮我推荐一个适合做 agent 的大模型"} -{"query": "帮我总结一下，GPT-4 和 Claude 2 在使用体验上有什么不同？再帮我推荐一个适合做 agent 的大模型。最后给出参考文献"} diff --git a/packages/sage-libs/examples/agents/data/agent_queries_test.jsonl b/packages/sage-libs/examples/agents/data/agent_queries_test.jsonl deleted file mode 100644 index 66d486c0b8..0000000000 --- a/packages/sage-libs/examples/agents/data/agent_queries_test.jsonl +++ /dev/null @@ -1 +0,0 @@ -{"query": "在 arXiv 搜 1 篇 LLM agents survey"} diff --git a/packages/sage-libs/examples/agents/demo_arxiv_search.py b/packages/sage-libs/examples/agents/demo_arxiv_search.py deleted file mode 100644 index 1963b68f5b..0000000000 --- a/packages/sage-libs/examples/agents/demo_arxiv_search.py +++ /dev/null @@ -1,179 +0,0 @@ -""" -Demo: ArxivSearchTool Usage Examples - -This demo file shows practical examples of how to use the ArxivSearchTool -in different scenarios. These are educational demonstrations for developers -and users, not formal unit tests. - -For the actual tool implementation, see: arxiv_search_tool.py -For formal unit tests, see: packages/sage-libs/tests/lib/agents/test_arxiv_tool.py - -Examples included: -- Basic usage with different parameters -- Error handling and offline fallback -- Integration with MCP Registry -- Parameter variations and configurations - -@test:allow-demo -""" - -from unittest.mock import patch - -import pytest # noqa: F401 -from arxiv_search_tool import ArxivSearchTool - - -def example_basic_usage(): - """Example: Basic usage of ArxivSearchTool.""" - print("=== Basic ArxivSearchTool Usage Example ===") - - tool = ArxivSearchTool() - - # Mock the network call for demonstration - with patch.object(tool, "_search_arxiv") as mock_search: - mock_search.return_value = [ - { - "title": "Attention Is All You Need", - "authors": "Ashish Vaswani, Noam Shazeer, Niki Parmar", - "link": "https://arxiv.org/abs/1706.03762", - "abstract": "The dominant sequence transduction models...", - }, - { - "title": "BERT: Pre-training of Deep Bidirectional Transformers", - "authors": "Jacob Devlin, Ming-Wei Chang, Kenton Lee", - "link": "https://arxiv.org/abs/1810.04805", - "abstract": "We introduce a new language representation model...", - }, - ] - - # Example usage - result = tool.call( - {"query": "transformer attention", "max_results": 2, "with_abstract": True} - ) - - print(f"Query: {result['meta']['query']}") - print(f"Found {len(result['output'])} papers:") - for i, paper in enumerate(result["output"], 1): - print(f"\n{i}. {paper['title']}") - print(f" Authors: {paper['authors']}") - print(f" Link: {paper['link']}") - print(f" Abstract: {paper['abstract'][:100]}...") - - -def example_parameter_variations(): - """Example: Different parameter configurations.""" - print("\n=== Parameter Variations Example ===") - - tool = ArxivSearchTool() - - with patch.object(tool, "_search_arxiv") as mock_search: - mock_search.return_value = [ - { - "title": "Sample Paper", - "authors": "Sample Author", - "link": "https://arxiv.org/abs/1234.5678", - "abstract": "Sample abstract", - } - ] - - # Example 1: Minimal parameters - result1 = tool.call({"query": "machine learning"}) - print(f"Minimal call - max_results: {result1['meta']['max_results']}") - - # Example 2: Custom parameters - result2 = tool.call( - { - "query": "deep learning", - "max_results": 5, - "size": 50, - "with_abstract": False, - } - ) - print( - f"Custom call - max_results: {result2['meta']['max_results']}, size: {result2['meta']['size']}" - ) - - -def example_error_handling(): - """Example: Error handling and offline fallback.""" - print("\n=== Error Handling Example ===") - - tool = ArxivSearchTool() - - # Simulate network error - with patch.object(tool, "_search_arxiv") as mock_search: - mock_search.side_effect = Exception("Network error") - - result = tool.call({"query": "neural networks", "max_results": 3}) - - print("Network error occurred, using offline fallback:") - print(f"Offline mock: {result['meta'].get('offline_mock', False)}") - print(f"Results: {len(result['output'])} papers") - - -def example_mcp_integration(): - """Example: Integration with MCP Registry.""" - print("\n=== MCP Registry Integration Example ===") - - try: - from sage.libs.agentic.agents.action.mcp_registry import MCPRegistry - - # Create registry and register tool - registry = MCPRegistry() - tool = ArxivSearchTool() - registry.register(tool) - - # Mock the search for demonstration - with patch.object(tool, "_search_arxiv") as mock_search: - mock_search.return_value = [ - { - "title": "GPT-3 Paper", - "authors": "OpenAI Team", - "link": "https://arxiv.org/abs/example", - "abstract": "Language models are few-shot learners", - } - ] - - # Call through registry - result = registry.call( - "arxiv_search", {"query": "GPT language models", "max_results": 1} - ) - - print("Called through MCP Registry:") - print(f"Tool: {tool.name}") - print(f"Result: {result['output'][0]['title']}") - - except ImportError: - print("MCP Registry not available - skipping integration example") - - -def test_example_runs(): - """Test that all examples run without errors.""" - try: - example_basic_usage() - example_parameter_variations() - example_error_handling() - example_mcp_integration() - print("\n✅ All examples completed successfully!") - return True - except Exception as e: - print(f"\n❌ Example failed: {e}") - return False - - -if __name__ == "__main__": - print("ArxivSearchTool Examples") - print("=" * 40) - - success = test_example_runs() - - if success: - print("\nThese examples show how to:") - print("1. Use ArxivSearchTool with different parameters") - print("2. Handle network errors with offline fallback") - print("3. Integrate with MCP Registry") - print("4. Process and display results") - - print("\nFor more details, see the tool implementation in arxiv_search_tool.py") - else: - print("\nSome examples failed. Check the implementation and dependencies.") diff --git a/packages/sage-libs/examples/agents/tool_use_pipeline.py b/packages/sage-libs/examples/agents/tool_use_pipeline.py deleted file mode 100644 index cce36d168f..0000000000 --- a/packages/sage-libs/examples/agents/tool_use_pipeline.py +++ /dev/null @@ -1,676 +0,0 @@ -#!/usr/bin/env python3 -""" -Tool Use Agent Pipeline Demo -Agentic Workflow: User Query -> Select Tool(s) -> Use Tool(s) -> Generate Response - -This example demonstrates how to build an Agent Pipeline with tool calling capabilities: -1. Receive user queries -2. LLM reasoning to select appropriate tools -3. Execute selected tools (Web Search, Vector Search, Calculator, etc.) -4. Generate final response based on tool execution results - -Pipeline Architecture: - UserQuerySource -> ToolSelector -> ToolExecutor -> ResponseGenerator -> ResponseSink - -# test_tags: category=agent, timeout=180, requires_llm=true -""" - -from __future__ import annotations - -import json -import os -import re -import time -from abc import ABC, abstractmethod -from dataclasses import dataclass, field -from typing import Any - -from sage.common.core.functions.map_function import MapFunction -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.core.functions.source_function import SourceFunction -from sage.kernel.api.local_environment import LocalEnvironment - -# ============================================================================= -# Data Models -# ============================================================================= - - -@dataclass -class ToolCallRequest: - """Tool call request""" - - tool_name: str - arguments: dict[str, Any] - - -@dataclass -class ToolCallResult: - """Tool call result""" - - tool_name: str - success: bool - result: Any - error: str | None = None - - -@dataclass -class AgentState: - """Agent state flowing through the pipeline""" - - query: str - selected_tools: list[ToolCallRequest] = field(default_factory=list) - tool_results: list[ToolCallResult] = field(default_factory=list) - response: str = "" - metadata: dict[str, Any] = field(default_factory=dict) - - -# ============================================================================= -# Tool Definitions - MCP Style -# ============================================================================= - - -class BaseTool(ABC): - """Base tool class - MCP style""" - - name: str = "" - description: str = "" - input_schema: dict[str, Any] = {} - - @abstractmethod - def call(self, arguments: dict[str, Any]) -> dict[str, Any]: - """Execute the tool""" - pass - - -class WebSearchTool(BaseTool): - """Web search tool - simulates search engine""" - - name = "web_search" - description = "Search the web for information. Returns relevant search results." - input_schema = { - "type": "object", - "properties": { - "query": {"type": "string", "description": "The search query"}, - "max_results": {"type": "integer", "default": 5}, - }, - "required": ["query"], - } - - def call(self, arguments: dict[str, Any]) -> dict[str, Any]: - query = arguments.get("query", "") - max_results = arguments.get("max_results", 5) - mock_results = [ - { - "title": f"Result {i + 1} for: {query}", - "url": f"https://example.com/result{i + 1}", - "snippet": f"This is a relevant snippet about {query}...", - } - for i in range(min(max_results, 5)) - ] - return {"success": True, "results": mock_results, "query": query} - - -class VectorSearchTool(BaseTool): - """Vector search tool - RAG retrieval""" - - name = "vector_search" - description = "Search internal knowledge base using vector similarity." - input_schema = { - "type": "object", - "properties": { - "query": {"type": "string", "description": "The search query"}, - "top_k": {"type": "integer", "default": 3}, - }, - "required": ["query"], - } - - def __init__(self): - self.knowledge_base = [ - { - "id": "doc1", - "content": "SAGE is a Python framework for building AI/LLM data processing pipelines.", - }, - { - "id": "doc2", - "content": "The architecture consists of 5 layers: L1-Common to L5-Interface.", - }, - { - "id": "doc3", - "content": "To install SAGE, run ./quickstart.sh --dev --yes for development setup.", - }, - ] - - def call(self, arguments: dict[str, Any]) -> dict[str, Any]: - query = arguments.get("query", "").lower() - top_k = arguments.get("top_k", 3) - scored_docs = [] - for doc in self.knowledge_base: - content_lower = doc["content"].lower() - score = sum(1 for word in query.split() if word in content_lower) - if score > 0: - scored_docs.append({"doc": doc, "score": score}) - scored_docs.sort(key=lambda x: x["score"], reverse=True) - return { - "success": True, - "documents": [ - {"id": d["doc"]["id"], "content": d["doc"]["content"], "score": d["score"]} - for d in scored_docs[:top_k] - ], - "query": query, - } - - -class CalculatorTool(BaseTool): - """Calculator tool""" - - name = "calculator" - description = "Perform mathematical calculations." - input_schema = { - "type": "object", - "properties": {"expression": {"type": "string", "description": "Math expression"}}, - "required": ["expression"], - } - - def call(self, arguments: dict[str, Any]) -> dict[str, Any]: - expression = arguments.get("expression", "") - try: - safe_expr = re.sub(r"[^0-9+\-*/(). ]", "", expression) - if not safe_expr: - return {"success": False, "error": "Invalid expression"} - result = eval(safe_expr, {"__builtins__": {}}, {}) - return {"success": True, "expression": expression, "result": result} - except Exception as e: - return {"success": False, "expression": expression, "error": str(e)} - - -class EmailSearchTool(BaseTool): - """Email search tool""" - - name = "email_search" - description = "Search emails by sender, subject, or content." - input_schema = { - "type": "object", - "properties": {"query": {"type": "string"}}, - "required": ["query"], - } - - def call(self, arguments: dict[str, Any]) -> dict[str, Any]: - query = arguments.get("query", "") - mock_emails = [ - { - "id": "email1", - "from": "team@company.com", - "subject": f"RE: {query}", - "snippet": f"Info about {query}...", - }, - { - "id": "email2", - "from": "support@example.com", - "subject": f"Update on {query}", - "snippet": f"Review of {query}...", - }, - ] - return {"success": True, "emails": mock_emails, "query": query} - - -class SlackSearchTool(BaseTool): - """Slack message search tool""" - - name = "slack_search" - description = "Search Slack messages and channels." - input_schema = { - "type": "object", - "properties": {"query": {"type": "string"}, "channel": {"type": "string"}}, - "required": ["query"], - } - - def call(self, arguments: dict[str, Any]) -> dict[str, Any]: - query = arguments.get("query", "") - channel = arguments.get("channel", "general") - mock_messages = [ - {"channel": channel, "user": "alice", "message": f"Discussing {query} in the meeting."}, - {"channel": channel, "user": "bob", "message": f"Good point about {query}."}, - ] - return {"success": True, "messages": mock_messages, "query": query} - - -# ============================================================================= -# Tool Registry -# ============================================================================= - - -class ToolRegistry: - """Tool registry - manages all available tools""" - - def __init__(self): - self._tools: dict[str, BaseTool] = {} - - def register(self, tool: BaseTool) -> None: - self._tools[tool.name] = tool - - def get(self, name: str) -> BaseTool | None: - return self._tools.get(name) - - def list_tools(self) -> list[str]: - return list(self._tools.keys()) - - def describe_tools(self) -> list[dict[str, Any]]: - return [ - {"name": tool.name, "description": tool.description, "input_schema": tool.input_schema} - for tool in self._tools.values() - ] - - def call_tool(self, name: str, arguments: dict[str, Any]) -> dict[str, Any]: - tool = self._tools.get(name) - if not tool: - return {"success": False, "error": f"Tool '{name}' not found"} - return tool.call(arguments) - - -def create_default_registry() -> ToolRegistry: - """Create default tool registry""" - registry = ToolRegistry() - registry.register(WebSearchTool()) - registry.register(VectorSearchTool()) - registry.register(CalculatorTool()) - registry.register(EmailSearchTool()) - registry.register(SlackSearchTool()) - return registry - - -# ============================================================================= -# Pipeline Operators -# ============================================================================= - - -class UserQuerySource(SourceFunction): - """User query source - receives user queries and creates AgentState""" - - def __init__(self, queries: list[str] | None = None, **kwargs): - super().__init__(**kwargs) - self.queries = queries or [ - "What is SAGE framework and how to install it?", - "Calculate 15 * 23 + 47", - "Search for recent emails about project update", - ] - self.current_index = 0 - - def execute(self, data=None) -> AgentState | None: - if self.current_index >= len(self.queries): - from sage.kernel.runtime.communication.packet import StopSignal - - return StopSignal("All queries processed") - - query = self.queries[self.current_index] - self.current_index += 1 - print(f"\n{'=' * 60}") - print(f"[UserQuerySource] Query {self.current_index}: {query}") - print("=" * 60) - return AgentState( - query=query, metadata={"query_id": self.current_index, "timestamp": time.time()} - ) - - -class ToolSelector(MapFunction): - """Tool selector - uses LLM or fallback to select appropriate tools""" - - def __init__(self, tool_registry: ToolRegistry | None = None, **kwargs): - super().__init__(**kwargs) - self.tool_registry = tool_registry or create_default_registry() - self._llm_client = None - - def _get_llm_client(self): - if self._llm_client is None: - try: - from sage.common.components.sage_llm import UnifiedInferenceClient - - self._llm_client = UnifiedInferenceClient.create() - except Exception as e: - print(f"[ToolSelector] Warning: Could not create LLM client: {e}") - self._llm_client = None - return self._llm_client - - def _build_tool_selection_prompt(self, query: str) -> str: - tools_desc = self.tool_registry.describe_tools() - tools_json = json.dumps(tools_desc, indent=2, ensure_ascii=False) - return f"""You are an AI assistant that helps select the right tools. - -Available Tools: -{tools_json} - -User Query: {query} - -Select appropriate tool(s). Return JSON array: -[{{"tool_name": "name", "arguments": {{"arg": "value"}}}}] - -If no tools needed, return: [] - -Your response (JSON only):""" - - def _parse_tool_selection(self, response: str) -> list[ToolCallRequest]: - try: - json_match = re.search(r"\[[\s\S]*\]", response) - if json_match: - tools_data = json.loads(json_match.group()) - return [ - ToolCallRequest( - tool_name=t.get("tool_name", t.get("name", "")), - arguments=t.get("arguments", {}), - ) - for t in tools_data - if t.get("tool_name") or t.get("name") - ] - except Exception as e: - print(f"[ToolSelector] Failed to parse response: {e}") - return [] - - def _fallback_tool_selection(self, query: str) -> list[ToolCallRequest]: - query_lower = query.lower() - selected_tools = [] - - if any(word in query_lower for word in ["calculate", "math", "+", "-", "*", "/"]): - expr_match = re.search(r"[\d\s+\-*/().]+", query) - expr = expr_match.group().strip() if expr_match else query - selected_tools.append( - ToolCallRequest(tool_name="calculator", arguments={"expression": expr}) - ) - elif any(word in query_lower for word in ["email", "mail"]): - selected_tools.append( - ToolCallRequest(tool_name="email_search", arguments={"query": query}) - ) - elif any(word in query_lower for word in ["slack", "message"]): - selected_tools.append( - ToolCallRequest(tool_name="slack_search", arguments={"query": query}) - ) - elif any(word in query_lower for word in ["what is", "how to", "explain", "sage"]): - selected_tools.append( - ToolCallRequest(tool_name="vector_search", arguments={"query": query}) - ) - else: - selected_tools.append( - ToolCallRequest(tool_name="web_search", arguments={"query": query}) - ) - - return selected_tools - - def execute(self, data: AgentState) -> AgentState: - if not isinstance(data, AgentState): - return data - - print(f"\n[ToolSelector] Analyzing query: {data.query}") - - llm_client = self._get_llm_client() - if llm_client: - try: - prompt = self._build_tool_selection_prompt(data.query) - response = llm_client.chat(prompt) - selected_tools = self._parse_tool_selection(response) - if selected_tools: - data.selected_tools = selected_tools - print(f"[ToolSelector] LLM selected: {[t.tool_name for t in selected_tools]}") - return data - except Exception as e: - print(f"[ToolSelector] LLM call failed: {e}") - - print("[ToolSelector] Using fallback keyword matching...") - data.selected_tools = self._fallback_tool_selection(data.query) - print(f"[ToolSelector] Fallback selected: {[t.tool_name for t in data.selected_tools]}") - return data - - -class ToolExecutor(MapFunction): - """Tool executor - executes selected tools and collects results""" - - def __init__(self, tool_registry: ToolRegistry | None = None, **kwargs): - super().__init__(**kwargs) - self.tool_registry = tool_registry or create_default_registry() - - def execute(self, data: AgentState) -> AgentState: - if not isinstance(data, AgentState): - return data - - print(f"\n[ToolExecutor] Executing {len(data.selected_tools)} tool(s)...") - - for tool_request in data.selected_tools: - print(f" -> Calling: {tool_request.tool_name}") - print(f" Arguments: {tool_request.arguments}") - try: - result = self.tool_registry.call_tool( - tool_request.tool_name, tool_request.arguments - ) - tool_result = ToolCallResult( - tool_name=tool_request.tool_name, - success=result.get("success", True), - result=result, - ) - print(" Result: Success") - except Exception as e: - tool_result = ToolCallResult( - tool_name=tool_request.tool_name, success=False, result=None, error=str(e) - ) - print(f" Result: Error - {e}") - data.tool_results.append(tool_result) - - return data - - -class ResponseGenerator(MapFunction): - """Response generator - generates final response based on tool results""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self._llm_client = None - - def _get_llm_client(self): - if self._llm_client is None: - try: - from sage.common.components.sage_llm import UnifiedInferenceClient - - self._llm_client = UnifiedInferenceClient.create() - except Exception: - self._llm_client = None - return self._llm_client - - def _build_response_prompt(self, state: AgentState) -> str: - results_text = "" - for tr in state.tool_results: - if tr.success: - results_text += f"\n### {tr.tool_name} Result:\n" - results_text += json.dumps(tr.result, indent=2, ensure_ascii=False) - else: - results_text += f"\n### {tr.tool_name} Error:\n{tr.error}" - return f"""Based on the tool results, generate a helpful response. - -User Query: {state.query} - -Tool Results: -{results_text} - -Provide a clear, concise response:""" - - def _generate_fallback_response(self, state: AgentState) -> str: - response_parts = [f"Query: {state.query}\n"] - for tr in state.tool_results: - response_parts.append(f"\n[{tr.tool_name}]:") - if tr.success and isinstance(tr.result, dict): - if "results" in tr.result: - for i, r in enumerate(tr.result["results"][:3], 1): - response_parts.append(f" {i}. {r.get('title', str(r))}") - elif "documents" in tr.result: - for doc in tr.result["documents"][:3]: - content = doc.get("content", "")[:100] - response_parts.append(f" - {content}...") - elif "result" in tr.result: - response_parts.append(f" Result: {tr.result['result']}") - elif "emails" in tr.result: - for email in tr.result["emails"][:2]: - response_parts.append(f" - {email.get('subject', '')}") - elif "messages" in tr.result: - for msg in tr.result["messages"][:2]: - response_parts.append( - f" - @{msg.get('user', '')}: {msg.get('message', '')}" - ) - else: - response_parts.append(f" {json.dumps(tr.result, ensure_ascii=False)[:200]}") - elif not tr.success: - response_parts.append(f" Error: {tr.error}") - return "\n".join(response_parts) - - def execute(self, data: AgentState) -> AgentState: - if not isinstance(data, AgentState): - return data - - print("\n[ResponseGenerator] Generating response...") - - llm_client = self._get_llm_client() - if llm_client and data.tool_results: - try: - prompt = self._build_response_prompt(data) - response = llm_client.chat(prompt) - data.response = response - print("[ResponseGenerator] LLM response generated.") - return data - except Exception as e: - print(f"[ResponseGenerator] LLM call failed: {e}") - - print("[ResponseGenerator] Using fallback response generation...") - data.response = self._generate_fallback_response(data) - return data - - -class ResponseSink(SinkFunction): - """Response output - outputs final response to console""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.test_mode = os.getenv("SAGE_TEST_MODE") == "true" - - def execute(self, data: AgentState) -> None: - if not isinstance(data, AgentState): - print(f"[ResponseSink] Received non-AgentState data: {type(data)}") - return - - print("\n" + "=" * 60) - print(f"[Final Response] Query: {data.query}") - print("-" * 60) - if data.selected_tools: - print(f"Tools Used: {', '.join(t.tool_name for t in data.selected_tools)}") - print("-" * 60) - - response = data.response - if self.test_mode and len(response) > 500: - response = response[:500] + "... (truncated)" - print(response) - print("=" * 60) - - -# ============================================================================= -# Pipeline Build and Run -# ============================================================================= - - -def run_tool_use_pipeline(queries: list[str] | None = None): - """Run Tool Use Agent Pipeline""" - print(""" -======================================================== - Tool Use Agent Pipeline Demo -======================================================== - Pipeline: UserQuery -> ToolSelector -> ToolExecutor - -> ResponseGenerator -> ResponseSink - - Available Tools: - - web_search: Search the web - - vector_search: Search knowledge base (RAG) - - calculator: Mathematical calculations - - email_search: Search emails - - slack_search: Search Slack messages -======================================================== - """) - - tool_registry = create_default_registry() - print(f"Registered tools: {tool_registry.list_tools()}\n") - - env = LocalEnvironment() - ( - env.from_source(UserQuerySource, queries=queries) - .map(ToolSelector, tool_registry=tool_registry) - .map(ToolExecutor, tool_registry=tool_registry) - .map(ResponseGenerator) - .sink(ResponseSink) - ) - - start_time = time.time() - env.submit(autostop=True) - total_time = time.time() - start_time - - print(f"\nPipeline completed in {total_time:.2f} seconds") - env.close() - - -def run_interactive_mode(): - """Interactive mode - user can input queries in real-time""" - print(""" -======================================================== - Tool Use Agent - Interactive Mode - Type your query and press Enter. - Type 'quit' or 'exit' to stop. -======================================================== - """) - - tool_registry = create_default_registry() - print(f"Available tools: {tool_registry.list_tools()}\n") - - while True: - try: - query = input("\n> Your query: ").strip() - if not query: - continue - if query.lower() in ("quit", "exit"): - print("Goodbye.") - break - - state = AgentState(query=query) - selector = ToolSelector(tool_registry=tool_registry) - state = selector.execute(state) - executor = ToolExecutor(tool_registry=tool_registry) - state = executor.execute(state) - generator = ResponseGenerator() - state = generator.execute(state) - sink = ResponseSink() - sink.execute(state) - except KeyboardInterrupt: - print("\nInterrupted. Goodbye.") - break - except Exception as e: - print(f"Error: {e}") - - -def main(): - """Main entry point""" - import sys - - test_mode = os.getenv("SAGE_TEST_MODE") == "true" - - if len(sys.argv) > 1: - if sys.argv[1] == "--interactive": - run_interactive_mode() - return - elif sys.argv[1] == "--query": - queries = sys.argv[2:] if len(sys.argv) > 2 else None - run_tool_use_pipeline(queries) - return - - example_queries = [ - "What is SAGE framework and how to install it?", - "Calculate 15 * 23 + 47", - "Search for recent emails about project update", - ] - - if test_mode: - example_queries = example_queries[:1] - - run_tool_use_pipeline(example_queries) - - -if __name__ == "__main__": - main() diff --git a/packages/sage-libs/examples/agents/workflow_demo.py b/packages/sage-libs/examples/agents/workflow_demo.py deleted file mode 100644 index 0a32ef9cb2..0000000000 --- a/packages/sage-libs/examples/agents/workflow_demo.py +++ /dev/null @@ -1,260 +0,0 @@ -""" -Agent Workflow Demo - -This demonstrates how the agent pipeline works with real examples. -This is an educational demonstration, not a formal test. - -Usage: - python agent_workflow_demo.py - -This demo shows: -- How to read queries from different sources -- Complete agent workflow with mocked components -- Integration examples for learning purposes - -@test:allow-demo -""" - -import json -import os -import tempfile -from unittest.mock import Mock - - -def create_test_queries_file(): - """Create a temporary test queries file.""" - test_queries = [ - {"query": "在 arXiv 搜索关于 transformer 的论文"}, - {"query": "帮我找一些深度学习的最新研究"}, - {"query": "总结一下注意力机制的发展历程"}, - ] - - temp_file = tempfile.NamedTemporaryFile(mode="w", suffix=".jsonl", delete=False) - for query in test_queries: - temp_file.write(json.dumps(query, ensure_ascii=False) + "\n") - temp_file.close() - - return temp_file.name - - -def example_iter_queries(): - """Example: How to read queries from different sources.""" - print("=== Query Reading Examples ===") - - # Import the agent module using importlib to handle path with dashes - try: - import importlib.util - import sys - from pathlib import Path - - # Get the path to basic_agent.py - basic_agent_path = Path(__file__).parent / "basic_agent.py" - spec = importlib.util.spec_from_file_location("basic_agent", basic_agent_path) - if spec is not None and spec.loader is not None: - basic_agent = importlib.util.module_from_spec(spec) - sys.modules["basic_agent"] = basic_agent - spec.loader.exec_module(basic_agent) - iter_queries = basic_agent.iter_queries - else: - raise ImportError("Failed to load basic_agent module") - - # Example 1: Local JSONL file - temp_file = create_test_queries_file() - try: - source_config = { - "type": "local", - "data_path": temp_file, - "field_query": "query", - } - - print("Reading from local JSONL file:") - queries = list(iter_queries(source_config)) - for i, query in enumerate(queries, 1): - print(f" {i}. {query}") - - finally: - os.unlink(temp_file) - - print(f"\nTotal queries loaded: {len(queries)}") - - except ImportError as e: - print(f"Could not import agent module: {e}") - - -def example_mock_agent_workflow(): - """Example: Complete agent workflow with mocks.""" - print("\n=== Mock Agent Workflow Example ===") - - try: - # Mock all the components - print("Setting up mock components...") - - # Mock generator that creates plans - mock_generator = Mock() - - def mock_execute(data): - user_query = data[0] - if "arxiv" in user_query.lower() or "论文" in user_query: - plan = [ - { - "type": "tool", - "name": "arxiv_search", - "arguments": {"query": "transformer", "max_results": 2}, - }, - {"type": "reply", "text": "已为您找到相关论文"}, - ] - else: - plan = [{"type": "reply", "text": "我理解您的问题，正在思考..."}] - return (user_query, json.dumps(plan, ensure_ascii=False)) - - mock_generator.execute = mock_execute - - # Mock ArxivSearchTool - mock_tool = Mock() - mock_tool.name = "arxiv_search" - mock_tool.description = "Search arXiv papers" - - def mock_call(arguments): - return { - "output": [ - { - "title": "Attention Is All You Need", - "authors": "Vaswani et al.", - "link": "https://arxiv.org/abs/1706.03762", - "abstract": "The dominant sequence transduction models...", - } - ], - "meta": arguments, - } - - mock_tool.call = mock_call - - # Simulate the agent workflow - test_query = "在 arXiv 搜索关于 transformer 的论文" - print(f"\nProcessing query: {test_query}") - - # Step 1: Generate plan - print("1. Generating plan...") - _, plan_json = mock_generator.execute([test_query, "system prompt"]) - plan = json.loads(plan_json) - print(f" Plan: {len(plan)} steps") - for i, step in enumerate(plan, 1): - print(f" Step {i}: {step['type']}") - - # Step 2: Execute tools - print("2. Executing tools...") - observations = [] - for step in plan: - if step["type"] == "tool": - result = mock_tool.call(step["arguments"]) - observations.append({"tool": step["name"], "result": result, "success": True}) - print(f" Tool {step['name']}: Found {len(result['output'])} results") - - # Step 3: Generate response - print("3. Generating response...") - if any(step["type"] == "reply" for step in plan): - reply = next(step["text"] for step in plan if step["type"] == "reply") - else: - reply = f"基于工具执行结果，为您找到了 {len(observations)} 个相关资源" - - print(f" Response: {reply}") - - print("\n✅ Workflow completed successfully!") - return True - - except Exception as e: - print(f"❌ Workflow failed: {e}") - return False - - -def example_configuration_usage(): - """Example: How configuration is used in the agent.""" - print("\n=== Configuration Usage Example ===") - - # Example configuration structure - example_config = { - "profile": { - "name": "ResearchAssistant", - "role": "助手", - "language": "zh", - "goals": ["帮助用户搜索和分析学术论文"], - "constraints": ["使用提供的工具", "提供准确信息"], - }, - "tools": [ - { - "module": "examples.agents.tools.arxiv_search_tool", - "class": "ArxivSearchTool", - "init_kwargs": {}, - } - ], - "generator": {"remote": {"method": "openai", "model_name": "gpt-3.5-turbo"}}, - "planner": {"max_steps": 5, "enable_repair": True}, - "runtime": {"max_steps": 5, "summarizer": "reuse_generator"}, - } - - print("Example configuration structure:") - for section, content in example_config.items(): - print(f" {section}: {type(content).__name__}") - if isinstance(content, dict): - for key in content.keys(): - print(f" - {key}") - elif isinstance(content, list) and content: - print(f" - {len(content)} items") - - print("\nThis configuration defines:") - print("- Agent profile and personality") - print("- Available tools and their setup") - print("- Generator settings for LLM") - print("- Planning and runtime parameters") - - -def test_all_examples(): - """Run all examples and report results.""" - print("Agent Workflow Examples") - print("=" * 50) - - examples = [ - ("Query Reading", example_iter_queries), - ("Mock Workflow", example_mock_agent_workflow), - ("Configuration", example_configuration_usage), - ] - - results = [] - for name, example_func in examples: - try: - print(f"\n--- {name} ---") - result = example_func() - if result is None: # For examples that don't return boolean - result = True - results.append((name, result)) - except Exception as e: - print(f"❌ {name} failed: {e}") - results.append((name, False)) - - print("\n" + "=" * 50) - print("Example Results:") - for name, success in results: - status = "✅" if success else "❌" - print(f" {status} {name}") - - total_success = sum(1 for _, success in results if success) - print(f"\nCompleted: {total_success}/{len(results)} examples") - - return total_success == len(results) - - -if __name__ == "__main__": - success = test_all_examples() - - if success: - print("\n🎉 All examples completed successfully!") - print("\nThese examples demonstrate:") - print("- How to read queries from different sources") - print("- Complete agent workflow with mocking") - print("- Configuration structure and usage") - print("- Integration between components") - else: - print("\n⚠️ Some examples had issues. Check the implementation.") - - print("\nFor the real agent implementation, see: agent.py") - print("For configuration examples, see: config/config_agent_min.yaml") diff --git a/packages/sage-libs/examples/amms_example.py b/packages/sage-libs/examples/amms_example.py deleted file mode 100644 index 475b1ae3ee..0000000000 --- a/packages/sage-libs/examples/amms_example.py +++ /dev/null @@ -1,116 +0,0 @@ -"""Example: Using the AMMS unified interface. - -This example demonstrates how to use the refactored AMMS interface -to perform approximate matrix multiplication. -""" - -from sage.libs.amms import registered - - -def example_basic_usage(): - """Basic usage of AMMS interface.""" - print("=== Basic AMMS Usage ===\n") - - # Check available algorithms - available = registered() - print(f"Available algorithms: {available}\n") - - # Note: Actual algorithm implementations need to be registered first - # This is a template showing how to use the interface - - # Example: Create a CountSketch AMM instance (once implemented) - # amm = create("countsketch", sketch_size=1000) - - # Setup with configuration - # config = { - # "sketch_size": 1000, - # "hash_functions": 5, - # "use_gpu": False - # } - # amm.setup(config) - - # Create sample matrices - # matrix_a = np.random.randn(100, 50) - # matrix_b = np.random.randn(50, 80) - - # Perform approximate multiplication - # result = amm.multiply(matrix_a, matrix_b) - - # Compare with exact result - # exact = matrix_a @ matrix_b - # error = np.linalg.norm(result - exact) / np.linalg.norm(exact) - # print(f"Relative error: {error:.4f}") - - print("Note: Algorithm implementations need to be registered first.") - print("See implementations/ for C++ algorithm code.") - - -def example_streaming_amm(): - """Example of streaming AMM (for algorithms that support it).""" - print("\n=== Streaming AMM Example ===\n") - - # For streaming algorithms, you can update matrices incrementally - # streaming_amm = create("streaming_countsketch", sketch_size=1000) - - # Initial setup - # streaming_amm.setup({"sketch_size": 1000}) - - # Update rows incrementally - # for i in range(100): - # row_data = np.random.randn(50) - # streaming_amm.update_row("A", i, row_data) - - # Get current result - # result = streaming_amm.get_current_result() - - print("Note: Streaming AMM requires StreamingAmmIndex implementation.") - - -def example_batch_processing(): - """Example of batch matrix multiplication.""" - print("\n=== Batch Processing Example ===\n") - - # Batch processing multiple matrix pairs - # amm = create("fastjlt", sketch_size=500) - - # matrices_a = [np.random.randn(100, 50) for _ in range(10)] - # matrices_b = [np.random.randn(50, 80) for _ in range(10)] - - # results = amm.batch_multiply(matrices_a, matrices_b) - - print("Note: Batch processing uses default implementation.") - print("Override batch_multiply() for optimized batch processing.") - - -def example_algorithm_metadata(): - """Example of accessing algorithm metadata.""" - print("\n=== Algorithm Metadata Example ===\n") - - # from sage.libs.amms import get_meta - - # meta = get_meta("countsketch") - # if meta: - # print(f"Algorithm: {meta.name}") - # print(f"Type: {meta.algorithm_type}") - # print(f"Supports streaming: {meta.supports_streaming}") - # print(f"Supports GPU: {meta.supports_gpu}") - # print(f"Requires training: {meta.requires_training}") - - print("Note: Metadata is registered when algorithms are registered.") - - -if __name__ == "__main__": - print("AMMS Interface Examples") - print("=" * 50) - - example_basic_usage() - example_streaming_amm() - example_batch_processing() - example_algorithm_metadata() - - print("\n" + "=" * 50) - print("To use these examples:") - print("1. Implement algorithms in implementations/") - print("2. Create wrappers in wrappers/") - print("3. Register algorithms using register()") - print("4. Then run these examples") diff --git a/packages/sage-libs/examples/embeddings/README.md b/packages/sage-libs/examples/embeddings/README.md deleted file mode 100644 index ca3591988a..0000000000 --- a/packages/sage-libs/examples/embeddings/README.md +++ /dev/null @@ -1,26 +0,0 @@ -# Multimodal Tutorials - -Examples demonstrating multimodal AI capabilities (text, image, video). - -## Examples - -### 1. Quickstart (`quickstart.py`) - -Simple text-image multimodal example. - -```bash -python examples/tutorials/multimodal/quickstart.py -``` - -### 2. Cross-Modal Search (`cross_modal_search.py`) - -Search images using text queries and vice versa. - -```bash -python examples/tutorials/multimodal/cross_modal_search.py -``` - -## Next Steps - -- See production multimodal applications in `packages/sage-apps/` -- Check video intelligence app: `examples/apps/run_video_intelligence.py` diff --git a/packages/sage-libs/examples/embeddings/cross_modal_search.py b/packages/sage-libs/examples/embeddings/cross_modal_search.py deleted file mode 100644 index f40f6de21d..0000000000 --- a/packages/sage-libs/examples/embeddings/cross_modal_search.py +++ /dev/null @@ -1,98 +0,0 @@ -#!/usr/bin/env python3 -"""Cross-modal retrieval demo highlighting fusion vs. single-modality ranking. - -Requirements: - pip install isage-middleware>=0.2.0 -""" - -from __future__ import annotations - -import numpy as np - -from sage.middleware.components.sage_db.python.multimodal_sage_db import ( - FusionParams, - FusionStrategy, - ModalityType, - MultimodalSearchParams, - create_text_image_db, -) - - -def seeded_embedding(seed: int, dim: int) -> np.ndarray: - rng = np.random.default_rng(seed) - vec = rng.normal(size=dim).astype("float32") - return vec / (np.linalg.norm(vec) + 1e-12) - - -def sample_collection() -> dict[str, tuple[np.ndarray, np.ndarray]]: - return { - "sunrise-cliffs": (seeded_embedding(21, 768), seeded_embedding(22, 512)), - "rooftop-dusk": (seeded_embedding(23, 768), seeded_embedding(24, 512)), - "forest-trail": (seeded_embedding(25, 768), seeded_embedding(26, 512)), - "city-rain": (seeded_embedding(27, 768), seeded_embedding(28, 512)), - "gallery-exhibit": (seeded_embedding(29, 768), seeded_embedding(30, 512)), - } - - -def load_demo_db(): - db = create_text_image_db(dimension=512) - for name, (text_emb, image_emb) in sample_collection().items(): - db.add_from_embeddings( - { - ModalityType.TEXT: text_emb, - ModalityType.IMAGE: image_emb, - }, - {"label": name, "collection": "demo"}, - ) - return db - - -def describe_results(title: str, results) -> None: - print(f"\n{title}") - for idx, result in enumerate(results, start=1): - print(f" {idx}. id={result.id:>2} score={result.score:.4f} metadata={result.metadata}") - - -def main() -> None: - print("=== Cross-modal search walkthrough ===") - - db = load_demo_db() - backend = "C++ accelerated" if getattr(db, "_db", None) is not None else "Python mock" - print("Backend:", backend) - - query_text = seeded_embedding(31, 768) - query_image = seeded_embedding(32, 512) - - fused_params = MultimodalSearchParams(k=3) - fused_params.query_fusion_params.strategy = FusionStrategy.WEIGHTED_AVERAGE - - fusion_results = db.search_multimodal( - {ModalityType.TEXT: query_text, ModalityType.IMAGE: query_image}, fused_params - ) - describe_results("🔗 Fusion search (text + image)", fusion_results) - - cross_params = MultimodalSearchParams(k=3) - cross_params.use_cross_modal_search = True - cross_params.target_modalities = [ModalityType.IMAGE] - cross_results = db.cross_modal_search( - ModalityType.TEXT, - query_text, - target_modalities=[ModalityType.IMAGE], - params=cross_params, - ) - describe_results("🧭 Text → image cross search", cross_results) - - print("\n🎛️ Re-weighting modalities (text emphasis)") - tuned = FusionParams(strategy=FusionStrategy.WEIGHTED_AVERAGE) - tuned.modality_weights[ModalityType.TEXT] = 0.7 - tuned.modality_weights[ModalityType.IMAGE] = 0.3 - db.update_fusion_params(tuned) - - adjusted_results = db.search_multimodal( - {ModalityType.TEXT: query_text, ModalityType.IMAGE: query_image}, fused_params - ) - describe_results("🎯 Fusion search after weight tweak", adjusted_results) - - -if __name__ == "__main__": - main() diff --git a/packages/sage-libs/examples/embeddings/embedding_demo.py b/packages/sage-libs/examples/embeddings/embedding_demo.py deleted file mode 100644 index 7293400703..0000000000 --- a/packages/sage-libs/examples/embeddings/embedding_demo.py +++ /dev/null @@ -1,260 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE Embedding 方法演示 - -展示所有 11 个 embedding 方法的使用。 - -@test:allow-demo -""" - -from sage.common.components.sage_embedding import ( - check_model_availability, - get_embedding_model, - list_embedding_models, -) - - -def print_separator(title=""): - """打印分隔线""" - if title: - print(f"\n{'=' * 60}") - print(f" {title}") - print(f"{'=' * 60}") - else: - print(f"{'=' * 60}") - - -def list_all_methods(): - """列出所有可用的 embedding 方法""" - print_separator("所有 Embedding 方法") - - models = list_embedding_models() - - for method, info in models.items(): - print(f"\n📦 {method} - {info['display_name']}") - print(f" 描述: {info['description']}") - - features = [] - if info["requires_api_key"]: - features.append("🔑 需要 API Key") - else: - features.append("🔓 无需 API Key") - - if info["requires_download"]: - features.append("📥 需要下载模型") - else: - features.append("☁️ 云端服务") - - if info["default_dimension"]: - features.append(f"📊 默认维度: {info['default_dimension']}") - - print(f" 特性: {', '.join(features)}") - - if info["examples"]: - print(f" 示例: {', '.join(info['examples'][:3])}") - - -def check_all_status(): - """检查所有方法的可用性""" - print_separator("方法可用性检查") - - methods = [ - "hash", - "mockembedder", - "hf", - "openai", - "jina", - "zhipu", - "cohere", - "bedrock", - "ollama", - "siliconcloud", - "nvidia_openai", - ] - - for method in methods: - result = check_model_availability(method) - status_icon = { - "available": "✅", - "cached": "✅", - "needs_api_key": "⚠️", - "needs_download": "⚠️", - "unavailable": "❌", - }.get(result["status"], "❓") - - print(f"{status_icon} {method:20} - {result['message']}") - - -def demo_no_api_key_methods(): - """演示无需 API Key 的方法""" - print_separator("演示：无需 API Key 的方法") - - # Hash Embedding - print("\n1. Hash Embedding (轻量级)") - try: - emb = get_embedding_model("hash", dim=384) - vec = emb.embed("Hello World") - print(f" {emb}") - print(f" 向量维度: {len(vec)}") - print(f" 向量示例: {vec[:5]}...") - except Exception as e: - print(f" ❌ 错误: {e}") - - # Mock Embedding - print("\n2. Mock Embedding (测试用)") - try: - emb = get_embedding_model("mockembedder", dim=128) - vec = emb.embed("Test") - print(f" {emb}") - print(f" 向量维度: {len(vec)}") - print(f" 向量示例: {vec[:5]}...") - except Exception as e: - print(f" ❌ 错误: {e}") - - -def demo_batch_embedding(): - """演示批量 embedding""" - print_separator("演示：批量 Embedding") - - print("\n批量处理 3 个文本:") - texts = ["文本1", "文本2", "文本3"] - - try: - emb = get_embedding_model("hash", dim=256) - vecs = emb.embed_batch(texts) - print(f" ✅ 成功生成 {len(vecs)} 个向量") - print(f" 每个向量维度: {len(vecs[0])}") - for i, vec in enumerate(vecs): - print(f" 向量 {i + 1}: {vec[:3]}...") - except Exception as e: - print(f" ❌ 错误: {e}") - - -def demo_api_key_methods(): - """演示需要 API Key 的方法（仅展示如何调用）""" - print_separator("演示：需要 API Key 的方法（代码示例）") - - examples = { - "openai": """ -# OpenAI Embedding -emb = get_embedding_model( - "openai", - model="text-embedding-3-small", - api_key="sk-xxx" -) -vec = emb.embed("hello world") -""", - "jina": """ -# Jina Embedding (Late Chunking) -emb = get_embedding_model( - "jina", - dimensions=256, - late_chunking=True, - api_key="jina-xxx" -) -vec = emb.embed("你好世界") -""", - "zhipu": """ -# 智谱 Embedding (批量) -emb = get_embedding_model( - "zhipu", - model="embedding-3", - api_key="zhipu-xxx" -) -vecs = emb.embed_batch(["文本1", "文本2", "文本3"]) -""", - "cohere": """ -# Cohere Embedding (多种 input_type) -emb = get_embedding_model( - "cohere", - model="embed-multilingual-v3.0", - input_type="classification", - api_key="cohere-xxx" -) -vec = emb.embed("positive review") -""", - "bedrock": """ -# AWS Bedrock Embedding -emb = get_embedding_model( - "bedrock", - model="amazon.titan-embed-text-v2:0", - aws_access_key_id="xxx", - aws_secret_access_key="xxx" -) -vec = emb.embed("hello world") -""", - "ollama": """ -# Ollama Embedding (本地) -emb = get_embedding_model( - "ollama", - model="nomic-embed-text", - base_url="http://localhost:11434" -) -vec = emb.embed("hello world") -""", - "siliconcloud": """ -# SiliconCloud Embedding -emb = get_embedding_model( - "siliconcloud", - model="netease-youdao/bce-embedding-base_v1", - api_key="silicon-xxx" -) -vec = emb.embed("你好") -""", - "nvidia_openai": """ -# NVIDIA NIM Embedding -emb = get_embedding_model( - "nvidia_openai", - model="nvidia/llama-3.2-nv-embedqa-1b-v1", - input_type="passage", - api_key="nvapi-xxx" -) -vec = emb.embed("document text") -""", - } - - for method, code in examples.items(): - print(f"\n{method}:") - print(code) - - -def main(): - """主函数""" - print( - """ -╔══════════════════════════════════════════════════════════╗ -║ ║ -║ SAGE Embedding 方法演示 ║ -║ Phase 2 Complete - 11 个统一接口 ║ -║ ║ -╚══════════════════════════════════════════════════════════╝ - """ - ) - - # 1. 列出所有方法 - list_all_methods() - - # 2. 检查可用性 - check_all_status() - - # 3. 演示无 API Key 方法 - demo_no_api_key_methods() - - # 4. 演示批量 embedding - demo_batch_embedding() - - # 5. 展示 API Key 方法示例 - demo_api_key_methods() - - print_separator() - print("\n✅ 演示完成！") - print("\n💡 提示:") - print(" - 无需 API Key 的方法可以直接使用") - print(" - 需要 API Key 的方法需要先设置环境变量或传递参数") - print(" - 使用 list_embedding_models() 查看所有方法") - print(" - 使用 check_model_availability() 检查状态") - print_separator() - - -if __name__ == "__main__": - main() diff --git a/packages/sage-libs/examples/embeddings/embedding_service_demo.py b/packages/sage-libs/examples/embeddings/embedding_service_demo.py deleted file mode 100644 index 8ec6201e4e..0000000000 --- a/packages/sage-libs/examples/embeddings/embedding_service_demo.py +++ /dev/null @@ -1,481 +0,0 @@ -""" -Embedding Service Demo - 展示如何使用统一的 EmbeddingService - -这个示例展示了: -1. 如何配置 EmbeddingService (本地模型, API, sageLLM/vLLM) -2. 如何在 Pipeline 中使用 embedding service -3. 如何实现高性能批处理 -4. 如何使用缓存优化性能 - -Engine 选项: - - sagellm (推荐): SAGE 统一推理引擎 - - vllm: vLLM 后端 (deprecated, 将重定向到 sagellm) - -运行: - python embedding_service_demo.py # 正常运行 (需要模型) - python embedding_service_demo.py --mock # Mock 模式 (无需 GPU) - -Requirements: - pip install isage-middleware>=0.2.0 -""" - -import argparse -import os - -# 全局 mock 模式标志 -_USE_MOCK = False - - -def demo_basic_embedding_service(): - """示例 1: 基本的 Embedding Service 使用""" - print("\n" + "=" * 60) - print("示例 1: 基本 Embedding Service") - print("=" * 60) - - from sage.common.components.sage_embedding import EmbeddingService - - # 检查是否在测试模式或 mock 模式 - is_test_mode = _USE_MOCK or os.getenv("SAGE_TEST_MODE") == "true" or os.getenv("CI") == "true" - - # 配置: 在测试/mock 模式使用 mock，否则使用 HuggingFace 模型 - if is_test_mode: - config = { - "method": "mockembedder", - "dimension": 384, # 模拟 bge-small-zh-v1.5 的维度 - "batch_size": 32, - "normalize": True, - "cache_enabled": True, - "cache_size": 1000, - } - else: - config = { - "method": "hf", - "model": "BAAI/bge-small-zh-v1.5", - "batch_size": 32, - "normalize": True, - "cache_enabled": True, - "cache_size": 1000, - } - - service = EmbeddingService(config) - service.setup() - - # 获取服务信息 - info = service.process({"task": "info"}) - print("\n服务信息:") - print(f" 方法: {info['method']}") - print(f" 模型: {info['model']}") - print(f" 维度: {info['dimension']}") - print(f" 缓存: {info['cache_enabled']}") - - # 单个文本 embedding - result = service.process( - {"task": "embed", "inputs": "你好世界", "options": {"return_stats": True}} - ) - - print("\n单个文本 embedding:") - print(f" 维度: {result['dimension']}") - print(f" 向量前5个值: {result['vectors'][0][:5]}") - print(f" 统计: {result['stats']}") - - # 批量文本 embedding - texts = [ - "人工智能正在改变世界", - "机器学习是AI的核心", - "深度学习推动了AI发展", - "自然语言处理很重要", - ] - - result = service.process({"task": "embed", "inputs": texts, "options": {"return_stats": True}}) - - print("\n批量文本 embedding:") - print(f" 文本数量: {result['count']}") - print(f" 计算数量: {result['stats']['computed']}") - print(f" 缓存数量: {result['stats']['cached']}") - - # 再次查询相同文本 (测试缓存) - result2 = service.process( - { - "task": "embed", - "inputs": texts[:2], # 重复前两个文本 - "options": {"return_stats": True}, - } - ) - - print("\n缓存测试:") - print(f" 缓存命中: {result2['stats']['cached']}/{result2['count']}") - print(f" 命中率: {result2['stats']['cache_hit_rate']:.2%}") - - service.cleanup() - - -def demo_vllm_embedding_service(): - """Demo 2: 使用 sageLLM/vLLM 作为 Embedding 后端 (需要 GPU)""" - print("\n" + "=" * 60) - print("Demo 2: sageLLM Embedding Service (高性能, 推荐)") - print("=" * 60) - - # 注意: 这个示例需要实际的 embedding service 运行 - # 使用 sagellm (推荐) 或 vllm (deprecated) - print("\n配置示例 (sagellm - 推荐):") - config_example = """ -services: - # 推荐: 使用 sagellm 统一推理引擎 - sagellm: - class: sage.middleware.operators.llm.SageLLMGenerator - config: - model_id: "BAAI/bge-base-en-v1.5" - embedding_model_id: "BAAI/bge-base-en-v1.5" - backend_type: "vllm" # 或 "mock" (无需 GPU) - auto_download: true - engine: - tensor_parallel_size: 1 - gpu_memory_utilization: 0.9 - - embedding: - class: sage.common.components.sage_embedding.EmbeddingService - config: - method: "sagellm" # 推荐使用 sagellm - # method: "vllm" # deprecated, 将重定向到 sagellm - sagellm_service_name: "sagellm" - batch_size: 256 # sageLLM 可以处理大批量 - normalize: true - cache_enabled: true - -# 在 pipeline/operator 中使用: -result = self.call_service("embedding", payload={ - "task": "embed", - "inputs": large_document_list, # 可以是成千上万个文档 - "options": { - "batch_size": 256, - "return_stats": True - } -}) - """ - print(config_example) - - -def demo_multi_embedding_pipeline(): - """Demo 3: 多 Embedding Service 的 Pipeline""" - print("\n" + "=" * 60) - print("Demo 3: 多 Embedding 策略 Pipeline") - print("=" * 60) - - pipeline_config = """ -# 使用场景: RAG 系统 -# - 查询使用快速本地模型 (低延迟) -# - 文档索引使用高质量云端模型 (高精度) -# - 批量处理使用 sageLLM (高吞吐) - -services: - # 1. 快速本地 embedding (用于实时查询) - embedding_fast: - class: sage.common.components.sage_embedding.EmbeddingService - config: - method: "hf" - model: "BAAI/bge-small-zh-v1.5" # 小模型, 快速 - batch_size: 32 - cache_enabled: true - - # 2. 高质量云端 embedding (用于离线索引) - embedding_quality: - class: sage.common.components.sage_embedding.EmbeddingService - config: - method: "openai" - model: "text-embedding-3-large" - api_key: "${OPENAI_API_KEY}" - batch_size: 100 - - # 3. sageLLM 高吞吐 embedding (用于大规模批处理) - 推荐 - sagellm: - class: sage.middleware.operators.llm.SageLLMGenerator - config: - model_id: "BAAI/bge-large-en-v1.5" - backend_type: "vllm" # 或 "mock" (无需 GPU) - - embedding_batch: - class: sage.common.components.sage_embedding.EmbeddingService - config: - method: "sagellm" # 推荐使用 sagellm - sagellm_service_name: "sagellm" - batch_size: 512 - -operators: - # 查询 embedding - 使用快速本地模型 - - name: query_embed - type: custom - code: | - result = self.call_service("embedding_fast", payload={ - "task": "embed", - "inputs": payload["query"] - }) - payload["query_vector"] = result["vectors"][0] - return payload - - # 文档 embedding - 根据情况选择 - - name: document_embed - type: custom - code: | - docs = payload["documents"] - - # 小批量: 使用本地模型 - if len(docs) < 100: - service = "embedding_fast" - # 大批量: 使用 vLLM - elif len(docs) > 1000: - service = "embedding_batch" - # 重要文档: 使用高质量云端 - elif payload.get("high_quality"): - service = "embedding_quality" - else: - service = "embedding_fast" - - result = self.call_service(service, payload={ - "task": "embed", - "inputs": [d["text"] for d in docs] - }) - - for doc, vec in zip(docs, result["vectors"]): - doc["embedding"] = vec - - return payload - """ - - print(pipeline_config) - - -def demo_embedding_operator(): - """Demo 4: 创建自定义的 Embedding Operator""" - print("\n" + "=" * 60) - print("Demo 4: 自定义 Embedding Operator") - print("=" * 60) - - operator_code = ''' -from sage.libs.operators import BaseOperator -from typing import Any, Dict, List - -class SmartEmbeddingOperator(BaseOperator): - """智能 Embedding Operator - 根据负载自动选择策略""" - - def __init__( - self, - embedding_service: str = "embedding", - batch_size: int = 32, - cache_threshold: int = 10, # 小于此数量启用缓存 - vllm_threshold: int = 1000, # 大于此数量使用 vLLM - ): - self.embedding_service = embedding_service - self.batch_size = batch_size - self.cache_threshold = cache_threshold - self.vllm_threshold = vllm_threshold - - def process(self, payload: Dict[str, Any]) -> Dict[str, Any]: - texts = payload.get("texts", []) - - if not texts: - payload["embeddings"] = [] - return payload - - # 智能选择策略 - options = { - "batch_size": self.batch_size, - "return_stats": True, - } - - # 小批量: 启用缓存 - if len(texts) <= self.cache_threshold: - # 假设有缓存配置的 service - service = self.embedding_service + "_cached" - # 大批量: 使用 vLLM - elif len(texts) >= self.vllm_threshold: - service = self.embedding_service + "_vllm" - options["batch_size"] = min(512, len(texts)) - else: - service = self.embedding_service - - # 调用 embedding service - result = self.call_service(service, payload={ - "task": "embed", - "inputs": texts, - "options": options - }) - - # 附加结果 - payload["embeddings"] = result["vectors"] - payload["embedding_dimension"] = result["dimension"] - payload["embedding_stats"] = result.get("stats", {}) - - self.logger.info( - f"Embedded {len(texts)} texts using {service}, " - f"cache_hit_rate={result['stats'].get('cache_hit_rate', 0):.2%}" - ) - - return payload - - -# 使用示例 -class RAGPipeline: - def build(self): - return { - "operators": [ - { - "name": "load_query", - "type": "QueryLoaderOperator" - }, - { - "name": "embed_query", - "type": "SmartEmbeddingOperator", - "config": { - "embedding_service": "embedding", - "batch_size": 32, - "cache_threshold": 10, - } - }, - { - "name": "retrieve", - "type": "VectorSearchOperator", - "config": { - "top_k": 5 - } - }, - { - "name": "generate", - "type": "LLMGenerateOperator" - } - ] - } -''' - print(operator_code) - - -def demo_performance_comparison(): - """Demo 5: 性能对比 - 不同 embedding 方法""" - print("\n" + "=" * 60) - print("Demo 5: 性能对比") - print("=" * 60) - - comparison = """ -测试场景: 1000 个文档, 每个文档平均 100 tokens - -方法 吞吐量 延迟 成本 推荐使用场景 ------------------------------------------------------------------ -hash 10000/s <1ms 免费 快速原型, 测试 -mockembedder 5000/s <1ms 免费 单元测试 - -hf (small) 100/s 10ms 免费 实时查询, 预算有限 -hf (base) 50/s 20ms 免费 平衡性能和质量 -hf (large) 20/s 50ms 免费 高质量离线处理 - -openai (small) 1000/s 10ms $$$ 大规模云端部署 -openai (large) 500/s 20ms $$$$ 最高质量要求 - -jina 800/s 15ms $$ 中等规模, 多语言 -zhipu 600/s 20ms $$ 中文优化 - -sagellm (GPU) 2000/s 5ms 硬件 大规模生产环境 (推荐) -sagellm (多GPU) 5000/s 3ms 硬件 超大规模部署 -vLLM (GPU) 2000/s 5ms 硬件 已废弃, 使用 sagellm - -推荐配置: - - 1. 开发/测试: - method: "hash" 或 "mockembedder" - - 2. 小规模生产 (< 1M 文档): - method: "hf", model: "BAAI/bge-small-zh-v1.5" - - 3. 中等规模 (1M - 10M 文档): - 查询: method: "hf", cache_enabled: true - 索引: method: "openai" 或 "jina" - - 4. 大规模生产 (> 10M 文档): - method: "sagellm", sagellm_service_name: "sagellm" # 推荐 - # method: "vllm" (deprecated, 将重定向到 sagellm) - 配置多 GPU 以提高吞吐量 - - 5. 成本敏感: - method: "hf" (完全免费, 需要 GPU 硬件) - - 6. 质量优先: - method: "openai", model: "text-embedding-3-large" -""" - print(comparison) - - -def main(): - """运行所有示例""" - global _USE_MOCK - - # 解析命令行参数 - parser = argparse.ArgumentParser(description="Embedding Service Demo") - parser.add_argument( - "--mock", - action="store_true", - help="使用 mock 模式运行 (无需 GPU/模型)", - ) - args = parser.parse_args() - - # 设置全局 mock 标志 - _USE_MOCK = args.mock - if _USE_MOCK: - print("\n🧪 Mock 模式: 使用模拟 embedding (无需 GPU)\n") - - print("\n" + "=" * 60) - print("Embedding Service 示例集") - print("=" * 60) - - demos = [ - ("基本使用", demo_basic_embedding_service), - ("sageLLM 后端", demo_vllm_embedding_service), - ("多 Embedding 策略", demo_multi_embedding_pipeline), - ("自定义 Operator", demo_embedding_operator), - ("性能对比", demo_performance_comparison), - ] - - # 检查是否在测试模式 - is_test_mode = _USE_MOCK or os.getenv("SAGE_TEST_MODE") == "true" or os.getenv("CI") == "true" - - if is_test_mode: - # 测试模式：运行所有示例 - print("\n🧪 测试模式：自动运行所有示例\n") - for name, demo_func in demos: - try: - demo_func() - except Exception as e: - print(f"\n❌ {name} 失败: {e}") - else: - # 交互模式：让用户选择 - print("\n可用示例:") - for i, (name, _) in enumerate(demos, 1): - print(f" {i}. {name}") - - print("\n选择要运行的示例 (1-5, 或 'all' 运行全部, 'q' 退出):") - choice = input("> ").strip().lower() - - if choice == "q": - return - elif choice == "all": - for name, demo_func in demos: - try: - demo_func() - except Exception as e: - print(f"\n❌ {name} 失败: {e}") - elif choice.isdigit() and 1 <= int(choice) <= len(demos): - name, demo_func = demos[int(choice) - 1] - try: - demo_func() - except Exception as e: - print(f"\n❌ {name} 失败: {e}") - import traceback - - traceback.print_exc() - else: - print("无效选择") - - print("\n" + "=" * 60) - print("示例结束") - print("=" * 60) - - -if __name__ == "__main__": - main() diff --git a/packages/sage-libs/examples/embeddings/pipeline_builder_embedding_demo.py b/packages/sage-libs/examples/embeddings/pipeline_builder_embedding_demo.py deleted file mode 100644 index c7a01c4c83..0000000000 --- a/packages/sage-libs/examples/embeddings/pipeline_builder_embedding_demo.py +++ /dev/null @@ -1,232 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE Pipeline Builder - Embedding Integration 示例 - -演示如何使用不同的 embedding 方法来增强 Pipeline Builder 的知识检索能力。 - -@test:allow-demo -@test:timeout=120 -""" - -import os - -from sage.cli.commands.apps.pipeline_knowledge import ( - PipelineKnowledgeBase, - get_default_knowledge_base, -) - -# 检查是否在测试模式 -_IS_TEST_MODE = os.getenv("SAGE_TEST_MODE") == "true" or os.getenv("CI") == "true" - -# 在测试模式下，减少chunks以加快初始化 -_MAX_CHUNKS = 50 if _IS_TEST_MODE else 100 - -# 全局缓存知识库实例，避免重复初始化 -_KB_CACHE = {} - - -def _get_or_create_kb(method: str, model: str | None = None, max_chunks: int | None = None): - """获取或创建知识库实例（带缓存）""" - cache_key = f"{method}:{model}:{max_chunks or _MAX_CHUNKS}" - if cache_key not in _KB_CACHE: - if method == "default": - _KB_CACHE[cache_key] = get_default_knowledge_base( - max_chunks=max_chunks or _MAX_CHUNKS, allow_download=False - ) - else: - _KB_CACHE[cache_key] = PipelineKnowledgeBase( - max_chunks=max_chunks or _MAX_CHUNKS, - allow_download=False, - embedding_method=method, - embedding_model=model, - ) - return _KB_CACHE[cache_key] - - -def example_1_basic_usage(): - """示例 1: 基本使用 - 默认 hash 方法""" - print("=" * 80) - print("示例 1: 使用默认的 hash embedding 方法") - print("=" * 80) - - # 使用缓存的知识库 - kb = _get_or_create_kb("default") - - # 执行检索 - query = "如何构建 RAG pipeline" - results = kb.search(query, top_k=3) - - print(f"\n查询: {query}") - print("检索方法: hash") - print(f"结果数量: {len(results)}\n") - - for idx, chunk in enumerate(results, 1): - print(f"[{idx}] 得分: {chunk.score:.4f} | 类型: {chunk.kind}") - print(f" {chunk.text[:100]}...") - print() - - -def example_2_custom_method(): - """示例 2: 使用自定义 embedding 方法""" - print("=" * 80) - print("示例 2: 使用 mockembedder 方法") - print("=" * 80) - - # 使用缓存的知识库 - kb = _get_or_create_kb("mockembedder") - - query = "向量检索算法" - results = kb.search(query, top_k=3) - - print(f"\n查询: {query}") - print("检索方法: mockembedder") - print(f"结果数量: {len(results)}\n") - - for idx, chunk in enumerate(results, 1): - print(f"[{idx}] 得分: {chunk.score:.4f} | 类型: {chunk.kind}") - print(f" {chunk.text[:100]}...") - print() - - -def example_3_compare_methods(): - """示例 3: 对比不同 embedding 方法的检索效果""" - print("=" * 80) - print("示例 3: 对比 hash vs mockembedder") - print("=" * 80) - - query = "语义搜索" - methods = ["hash", "mockembedder"] - - for method in methods: - print(f"\n--- 方法: {method} ---") - - # 使用缓存的知识库 - kb = _get_or_create_kb(method) - - import time - - start = time.time() - results = kb.search(query, top_k=3) - elapsed = time.time() - start - - print(f"耗时: {elapsed * 1000:.2f}ms") - print(f"Top-3 得分: {[f'{r.score:.4f}' for r in results]}") - - if results and results[0].vector: - print(f"向量维度: {len(results[0].vector)}") - - -def example_4_with_specific_model(): - """示例 4: 使用特定模型（需要 API key）""" - print("=" * 80) - print("示例 4: 使用 HuggingFace 模型 (需要模型已下载)") - print("=" * 80) - - # 注意: 这需要模型已经下载到本地 - # 如果没有，会自动下载（需要网络） - try: - # 在测试模式下使用更小的数据集 - kb = _get_or_create_kb("hf", "BAAI/bge-small-zh-v1.5", max_chunks=_MAX_CHUNKS // 2) - - query = "RAG 系统架构" - results = kb.search(query, top_k=3) - - print(f"\n查询: {query}") - print("检索方法: HuggingFace") - print("模型: BAAI/bge-small-zh-v1.5") - print(f"结果数量: {len(results)}\n") - - for idx, chunk in enumerate(results, 1): - print(f"[{idx}] 得分: {chunk.score:.4f}") - print(f" {chunk.text[:80]}...") - print() - - except Exception as e: - print(f"❌ HuggingFace 方法失败: {e}") - print("💡 提示: 这通常是因为:") - print(" 1. 模型未下载") - print(" 2. 缺少依赖 (sentence-transformers)") - print(" 3. 需要指定正确的模型名称") - - -def example_5_environment_variables(): - """示例 5: 使用环境变量配置""" - print("=" * 80) - print("示例 5: 通过环境变量配置默认方法") - print("=" * 80) - - import os - - # 设置环境变量 - os.environ["SAGE_PIPELINE_EMBEDDING_METHOD"] = "mockembedder" - - # 使用缓存的知识库 - kb = _get_or_create_kb("mockembedder") - - query = "embedding 优化" - results = kb.search(query, top_k=2) - - print("\n环境变量: SAGE_PIPELINE_EMBEDDING_METHOD=mockembedder") - print(f"查询: {query}") - print(f"结果数量: {len(results)}\n") - - for idx, chunk in enumerate(results, 1): - print(f"[{idx}] {chunk.text[:100]}...") - print() - - -def example_6_fallback_mechanism(): - """示例 6: 自动后备机制""" - print("=" * 80) - print("示例 6: 演示自动后备机制") - print("=" * 80) - - # 尝试使用一个需要配置的方法（不提供配置） - # 应该自动回退到 hash - kb = PipelineKnowledgeBase( - max_chunks=_MAX_CHUNKS // 2, - allow_download=False, - embedding_method="hf", # 不提供 model，会失败 - # embedding_model 缺失! - ) - - print("\n✓ 知识库创建成功（即使 hf 方法失败也会自动回退到 hash）") - print("💡 这就是自动后备机制的作用") - - query = "测试后备" - results = kb.search(query, top_k=2) - print(f"\n查询仍然可以正常工作: {len(results)} 个结果") - - -if __name__ == "__main__": - print("\n🎯 SAGE Pipeline Builder - Embedding Integration 示例\n") - - examples = [ - ("基本使用", example_1_basic_usage), - ("自定义方法", example_2_custom_method), - ("方法对比", example_3_compare_methods), - ("特定模型", example_4_with_specific_model), - ("环境变量", example_5_environment_variables), - ("后备机制", example_6_fallback_mechanism), - ] - - for title, example_func in examples: - try: - example_func() - except Exception as e: - print(f"❌ 示例失败: {e}") - - # 在测试模式下不等待用户输入 - if not _IS_TEST_MODE: - input("\n按 Enter 继续下一个示例...") - print("\n") - - print("=" * 80) - print("✅ 所有示例演示完成!") - print("=" * 80) - print("\n💡 CLI 使用提示:") - print(" sage pipeline analyze-embedding '你的查询' -m hash -m mockembedder") - print( - " sage pipeline build --embedding-method openai --embedding-model text-embedding-3-small" - ) - print() diff --git a/packages/sage-libs/examples/embeddings/quickstart.py b/packages/sage-libs/examples/embeddings/quickstart.py deleted file mode 100644 index bad93c06b9..0000000000 --- a/packages/sage-libs/examples/embeddings/quickstart.py +++ /dev/null @@ -1,101 +0,0 @@ -#!/usr/bin/env python3 -"""Quick tour of the MultimodalSageDB helper with text+image content. - -Requirements: - pip install isage-middleware>=0.2.0 -""" - -from __future__ import annotations - -from typing import Any - -import numpy as np - -from sage.middleware.components.sage_db.python.multimodal_sage_db import ( - ModalityType, - MultimodalSearchParams, - create_text_image_db, -) - - -def make_embedding(seed: int, dimension: int) -> np.ndarray: - rng = np.random.default_rng(seed) - vec = rng.normal(size=dimension).astype("float32") - norm = np.linalg.norm(vec) + 1e-12 - return vec / norm - - -def build_dataset() -> dict[str, dict[str, Any]]: - return { - "aurora": { - "text": make_embedding(1, 768), - "image": make_embedding(2, 512), - "meta": {"genre": "photography", "location": "iceland"}, - }, - "latte-art": { - "text": make_embedding(3, 768), - "image": make_embedding(4, 512), - "meta": {"genre": "food", "mood": "cozy"}, - }, - "city-skyline": { - "text": make_embedding(5, 768), - "image": make_embedding(6, 512), - "meta": {"genre": "architecture", "time": "night"}, - }, - "trail-run": { - "text": make_embedding(7, 768), - "image": make_embedding(8, 512), - "meta": {"genre": "outdoor", "mood": "energetic"}, - }, - "catnap": { - "text": make_embedding(9, 768), - "image": make_embedding(10, 512), - "meta": {"genre": "pets", "mood": "calm"}, - }, - } - - -def populate(db, items: dict[str, dict[str, Any]]) -> None: - for name, payload in items.items(): - embeddings = { - ModalityType.TEXT: payload["text"], - ModalityType.IMAGE: payload["image"], - } - data_id = db.add_from_embeddings(embeddings, {"label": name, **payload["meta"]}) - print(f"➕ added '{name}' -> id={data_id}") - - -def main() -> None: - print("=== Multimodal text+image quickstart ===") - - db = create_text_image_db(dimension=512) - native = getattr(db, "_db", None) is not None - print( - "Backend:", - "C++ accelerated" if native else "Python mock (build to enable native)", - ) - - dataset = build_dataset() - populate(db, dataset) - - params = MultimodalSearchParams(k=3) - params.query_fusion_params.target_dimension = 512 - - query = { - ModalityType.TEXT: make_embedding(11, 768), - ModalityType.IMAGE: make_embedding(12, 512), - } - - print("\n🔎 fused retrieval (text + image cues)") - results = db.search_multimodal(query, params) - for idx, result in enumerate(results, start=1): - print(f" {idx}. id={result.id:>2} score={result.score:.4f} metadata={result.metadata}") - - stats = db.get_modality_statistics() - print("\n📊 modality stats:") - for modality, info in stats.items(): - print(f" {modality.name:<6} -> count={info['count']} avg_dim={info['avg_dimension']:.1f}") - - -if __name__ == "__main__": - main() diff --git a/packages/sage-libs/examples/llm/demo_new_templates.py b/packages/sage-libs/examples/llm/demo_new_templates.py deleted file mode 100644 index c9a264427c..0000000000 --- a/packages/sage-libs/examples/llm/demo_new_templates.py +++ /dev/null @@ -1,183 +0,0 @@ -#!/usr/bin/env python3 -"""演示新增模板的使用方法 - -@test:allow-demo -""" - -from rich.console import Console -from rich.markdown import Markdown -from rich.panel import Panel - -from sage.cli.templates.catalog import get_template, list_templates - -console = Console() - - -def demo_template_usage(): - """演示模板使用""" - - console.print( - Panel.fit( - "🎨 新增模板使用演示\n展示如何使用 6 个新增的应用模板", - title="演示开始", - border_style="bold blue", - ) - ) - - # 新增的模板 ID - new_template_ids = [ - "rag-dense-milvus", - "rag-rerank", - "rag-bm25-sparse", - "agent-workflow", - "rag-memory-enhanced", - "multimodal-cross-search", - ] - - for template_id in new_template_ids: - template = get_template(template_id) - - console.print(f"\n{'=' * 80}", style="bold cyan") - console.print(f"模板: {template.title}", style="bold yellow") - console.print(f"ID: {template.id}", style="dim") - console.print(f"{'=' * 80}", style="bold cyan") - - # 基本信息 - console.print("\n📝 描述:", style="bold green") - console.print(f" {template.description}") - - console.print("\n🏷️ 标签:", style="bold blue") - console.print(f" {', '.join(template.tags[:8])}") - - console.print("\n📂 示例路径:", style="bold magenta") - console.print(f" {template.example_path}") - - # 显示 Pipeline 结构 - console.print("\n🔧 Pipeline 结构:", style="bold cyan") - plan = template.pipeline_plan() - source_class = plan.get("source", {}).get("class", "N/A") - console.print(f" Source: {source_class}", style="green") - - stages = plan.get("stages", []) - for i, stage in enumerate(stages, 1): - console.print( - f" Stage {i}: {stage.get('id', 'N/A')} → {stage.get('class', 'N/A')}", - style="yellow", - ) - if stage.get("summary"): - console.print(f" {stage.get('summary')}", style="dim") - - sink_class = plan.get("sink", {}).get("class", "N/A") - console.print(f" Sink: {sink_class}", style="red") - - # 显示使用指南 - console.print("\n💡 使用指南:", style="bold green") - console.print(f" {template.guidance.strip()}") - - # 显示注意事项 - if template.notes: - console.print("\n⚠️ 注意事项:", style="bold yellow") - for note in template.notes: - console.print(f" • {note}") - - console.print("\n" + "─" * 80) - - # 使用示例 - console.print(f"\n\n{'=' * 80}", style="bold blue") - console.print("📚 使用示例", style="bold blue") - console.print(f"{'=' * 80}", style="bold blue") - - usage_examples = """ -## 方式一: 在代码中使用 - -```python -from sage.cli.templates.catalog import get_template - -# 获取模板 -template = get_template("rag-dense-milvus") - -# 获取 Pipeline 配置 -config = template.pipeline_plan() - -# 查看配置 -print(config) -``` - -## 方式二: 通过 sage chat 命令使用 - -```bash -# 启动交互式 chat 界面 -sage chat - -# 输入需求，系统会自动匹配合适的模板 -"我想使用 Milvus 向量数据库构建一个语义检索系统" -``` - -## 方式三: 匹配最佳模板 - -```python -from sage.cli.templates.catalog import match_templates - -requirements = { - "name": "智能问答系统", - "goal": "使用向量检索和重排序构建高精度问答", - "data_sources": ["文档库"], - "constraints": "需要高精度" -} - -# 获取最匹配的模板 -matches = match_templates(requirements, top_k=3) -for match in matches: - print(f"{match.template.title}: {match.score:.3f}") -``` - -## 推荐的使用场景 - -### 1. Milvus 向量检索 (`rag-dense-milvus`) -- 适合: 大规模文档库、生产环境部署 -- 需要: Milvus 服务、嵌入模型 -- 优势: 高性能、可扩展 - -### 2. 重排序检索 (`rag-rerank`) -- 适合: 高精度要求场景（法律、医疗、金融） -- 需要: 向量库 + BGE Reranker -- 优势: 精确度高 - -### 3. BM25 检索 (`rag-bm25-sparse`) -- 适合: 关键词匹配、资源受限环境 -- 需要: 文本语料库 -- 优势: 无需 GPU、计算成本低 - -### 4. 智能体工作流 (`agent-workflow`) -- 适合: 复杂任务自动化、多步骤推理 -- 需要: LLM API、MCP 工具 -- 优势: 自主规划、工具调用 - -### 5. 记忆对话 (`rag-memory-enhanced`) -- 适合: 多轮对话、客服机器人 -- 需要: 记忆服务、对话历史存储 -- 优势: 上下文连贯 - -### 6. 跨模态搜索 (`multimodal-cross-search`) -- 适合: 图文混合检索、电商、媒体 -- 需要: 多模态向量库、图像编码器 -- 优势: 支持文本、图像、融合检索 -""" - - console.print(Markdown(usage_examples)) - - # 总结 - console.print(f"\n\n{'=' * 80}", style="bold blue") - console.print("✅ 总结", style="bold blue") - console.print(f"{'=' * 80}", style="bold blue") - - console.print(f"\n已展示 {len(new_template_ids)} 个新增模板", style="bold green") - console.print(f"总计 {len(list_templates())} 个可用模板", style="bold cyan") - console.print( - "\n💡 提示: 使用 'sage chat' 命令可以自动匹配最合适的模板！", - style="bold yellow", - ) - - -if __name__ == "__main__": - demo_template_usage() diff --git a/packages/sage-libs/examples/llm/pipeline_builder_llm_demo.py b/packages/sage-libs/examples/llm/pipeline_builder_llm_demo.py deleted file mode 100644 index 87f277b693..0000000000 --- a/packages/sage-libs/examples/llm/pipeline_builder_llm_demo.py +++ /dev/null @@ -1,260 +0,0 @@ -#!/usr/bin/env python3 -""" -演示 SAGE Pipeline Builder 中的大模型交互流程 - -这个脚本展示了用户请求如何通过 RAG 和 LLM 转换为完整的 Pipeline 配置 - -@test:allow-demo -""" - -import json - -from rich.console import Console -from rich.panel import Panel -from rich.syntax import Syntax - -from sage.cli.commands.apps.pipeline_domain import load_domain_contexts -from sage.cli.commands.apps.pipeline_knowledge import get_default_knowledge_base - -console = Console() - - -def demonstrate_llm_pipeline(): - """演示完整的 LLM Pipeline 构建流程""" - - console.print("\n" + "=" * 80) - console.print("[bold cyan]SAGE Pipeline Builder - LLM 交互流程演示[/bold cyan]") - console.print("=" * 80 + "\n") - - # Step 1: 用户需求 - console.print("[bold]步骤 1: 用户需求[/bold]") - user_request = "请帮我构建一个基于文档检索的智能问答系统" - requirements = { - "name": "智能问答助手", - "goal": "构建基于文档检索的问答系统，支持向量检索和大模型生成", - "data_sources": ["文档知识库", "向量数据库"], - "latency_budget": "实时响应优先", - "constraints": "支持流式输出", - } - console.print(f"用户输入: [yellow]{user_request}[/yellow]") - console.print("\n收集到的需求:") - console.print( - Panel( - json.dumps(requirements, ensure_ascii=False, indent=2), - title="Requirements", - border_style="green", - ) - ) - - # Step 2: 加载 Domain Contexts - console.print("\n[bold]步骤 2: 加载 Domain Contexts (示例配置)[/bold]") - try: - domain_contexts = tuple(load_domain_contexts(limit=2)) - console.print(f"✓ 加载了 {len(domain_contexts)} 个示例配置片段") - if domain_contexts: - console.print("\n示例片段（前 200 字符）:") - console.print(f"[dim]{domain_contexts[0][:200]}...[/dim]") - except Exception as exc: - console.print(f"[yellow]加载失败: {exc}[/yellow]") - domain_contexts = () - - # Step 3: 初始化知识库 - console.print("\n[bold]步骤 3: 初始化知识库 (RAG)[/bold]") - try: - kb = get_default_knowledge_base(max_chunks=500, allow_download=False) - console.print("✓ 知识库初始化成功") - console.print(" - 文档来源: docs-public/, examples/, packages/sage-libs/") - console.print(" - 检索方法: 向量相似度匹配") - except Exception as exc: - console.print(f"[yellow]知识库初始化失败: {exc}[/yellow]") - console.print("[dim]提示: 在实际使用中会自动下载或使用本地文档[/dim]") - kb = None - - # Step 4: RAG 检索 - console.print("\n[bold]步骤 4: RAG 检索相关文档和代码[/bold]") - if kb: - from sage.cli.commands.apps.pipeline_knowledge import build_query_payload - - query = build_query_payload(requirements) - console.print(f"\n检索查询: [cyan]{query[:150]}...[/cyan]") - - try: - results = kb.search(query, top_k=3) - console.print(f"\n✓ 检索到 {len(results)} 个相关片段:") - for idx, chunk in enumerate(results, 1): - console.print( - f"\n[{idx}] 来源: [green]{chunk.source}[/green] (相关度: {chunk.score:.3f})" - ) - console.print(f"[dim]{chunk.text[:200]}...[/dim]") - except Exception as exc: - console.print(f"[yellow]检索失败: {exc}[/yellow]") - else: - console.print("[dim]知识库未初始化，跳过检索[/dim]") - - # Step 5: 模板匹配 - console.print("\n[bold]步骤 5: 匹配应用模板[/bold]") - try: - from sage.cli import templates - - matches = templates.match_templates(requirements, top_k=3) - console.print(f"✓ 找到 {len(matches)} 个相关模板:") - for match in matches[:3]: - console.print(f" - {match.template.title} ({match.template.id})") - console.print(f" 标签: {', '.join(match.template.tags)}") - console.print(f" 匹配度: {match.score:.2f}") - except Exception as exc: - console.print(f"[yellow]模板匹配失败: {exc}[/yellow]") - - # Step 6: 蓝图匹配 - console.print("\n[bold]步骤 6: 匹配配置蓝图[/bold]") - try: - from sage.cli.templates import pipeline_blueprints - - blueprint_matches = tuple(pipeline_blueprints.match_blueprints(requirements)) - console.print(f"✓ 找到 {len(blueprint_matches)} 个相关蓝图:") - for blueprint, score in blueprint_matches[:3]: - console.print(f" - {blueprint.id}: {blueprint.title}") - console.print(f" 匹配度: {score:.2f}") - except Exception as exc: - console.print(f"[yellow]蓝图匹配失败: {exc}[/yellow]") - - # Step 7: 构建提示词 - console.print("\n[bold]步骤 7: 构建 LLM 提示词[/bold]") - console.print( - """ -提示词结构: -┌──────────────────────────────────────┐ -│ System Prompt │ -│ - SAGE Pipeline 规范说明 │ -│ - JSON 结构定义 │ -│ - 生成规则 │ -└──────────────────────────────────────┘ - ↓ -┌──────────────────────────────────────┐ -│ User Prompt │ -│ 1. 用户需求 (JSON) │ -│ 2. 应用模板 (top 3) │ -│ 3. 配置蓝图 (top 3) │ -│ 4. 知识库检索结果 (top 5) │ -│ 5. Domain 上下文 (示例配置) │ -│ 6. 上一版配置 (如有) │ -│ 7. 用户反馈 (如有) │ -└──────────────────────────────────────┘ - """ - ) - - # Step 8: 模拟 LLM 调用 - console.print("\n[bold]步骤 8: 调用大模型生成配置[/bold]") - console.print( - """ -[cyan]>>> 调用 LLM API...[/cyan] -模型: qwen-max (或用户指定模型) -参数: max_tokens=1200, temperature=0.2 - """ - ) - - # 示例生成的配置 - example_config = { - "pipeline": { - "name": "智能问答助手", - "description": "基于文档检索的问答系统，支持向量检索和大模型生成", - "version": "1.0.0", - "type": "local", - }, - "source": {"class": "sage.libs.rag.source.TerminalInputSource", "params": {}}, - "stages": [ - { - "id": "retriever", - "kind": "map", - "class": "sage.libs.rag.retriever.FAISSRetriever", - "params": {"index_path": "data/vector_index", "top_k": 5}, - "summary": "向量检索相关文档", - }, - { - "id": "promptor", - "kind": "map", - "class": "sage.libs.rag.promptor.QAPromptor", - "params": {}, - "summary": "构建问答提示词", - }, - { - "id": "generator", - "kind": "map", - "class": "sage.libs.rag.generator.OpenAIGenerator", - "params": {"model": "qwen-max", "temperature": 0.7, "stream": True}, - "summary": "大模型生成回答", - }, - ], - "sink": {"class": "sage.libs.rag.sink.ConsoleSink", "params": {}}, - "services": [], - "monitors": [], - "notes": ["使用 FAISS 进行向量检索", "支持流式输出", "可配置检索相关文档数量"], - } - - console.print("\n[bold green]✓ LLM 返回配置:[/bold green]") - syntax = Syntax( - json.dumps(example_config, ensure_ascii=False, indent=2), - "json", - theme="monokai", - line_numbers=True, - ) - console.print(syntax) - - # Step 9: 验证配置 - console.print("\n[bold]步骤 9: 验证生成的配置[/bold]") - from sage.cli.commands.apps.chat import _validate_pipeline_config - - is_valid, errors = _validate_pipeline_config(example_config) - if is_valid: - console.print("[green]✓ 配置验证通过[/green]") - else: - console.print(f"[red]✗ 配置验证失败: {errors}[/red]") - - # Step 10: 用户确认和保存 - console.print("\n[bold]步骤 10: 用户确认和保存[/bold]") - console.print( - """ -用户可以: - 1. ✅ 确认配置 → 保存为 YAML 文件 - 2. ✏️ 提供反馈 → 重新生成（最多 6 轮） - 3. ▶️ 立即运行 Pipeline - 4. ❌ 取消构建 - """ - ) - - console.print("\n" + "=" * 80) - console.print("[bold cyan]演示完成![/bold cyan]") - console.print("=" * 80 + "\n") - - console.print( - Panel( - """ -[bold]关键要点:[/bold] - -1. 🤖 [cyan]大模型全程参与[/cyan] - - 接收包含文档、模板、代码示例的丰富上下文 - - 基于 SAGE 规范生成配置 - -2. 📚 [cyan]RAG 检索增强[/cyan] - - 自动从文档库检索相关内容 - - 匹配最相关的模板和蓝图 - - 提供代码示例参考 - -3. 🔄 [cyan]多轮迭代优化[/cyan] - - 支持用户反馈 - - 基于上一版配置改进 - - 最多 6 轮优化 - -4. ✅ [cyan]自动验证[/cyan] - - 检查配置结构 - - 验证必需字段 - - 检查类导入路径 - """, - title="总结", - border_style="green", - ) - ) - - -if __name__ == "__main__": - demonstrate_llm_pipeline() diff --git a/packages/sage-libs/examples/llm/templates_to_llm_demo.py b/packages/sage-libs/examples/llm/templates_to_llm_demo.py deleted file mode 100644 index 2ed9f817e6..0000000000 --- a/packages/sage-libs/examples/llm/templates_to_llm_demo.py +++ /dev/null @@ -1,261 +0,0 @@ -#!/usr/bin/env python3 -""" -详细演示：大模型如何参考 Templates - -这个脚本展示了从 Template 匹配到传递给 LLM 的完整过程 - -LLM 引擎选项: - - SageLLMGenerator (推荐): SAGE 统一推理引擎 - - backend_type="vllm": 使用 vLLM 后端 (需要 GPU) - - backend_type="mock": 模拟模式 (无需 GPU, 用于测试) - - VLLMGenerator (deprecated): 将重定向到 SageLLMGenerator - -运行: - python templates_to_llm_demo.py - -@test:allow-demo -""" - -import json - -from rich.console import Console -from rich.panel import Panel - -from sage.cli import templates -from sage.cli.commands.apps.pipeline import _template_contexts - -console = Console() - - -def demonstrate_template_to_llm(): - """演示模板如何被传递给 LLM""" - - console.print("\n" + "=" * 80) - console.print("[bold cyan]Templates → LLM 完整流程演示[/bold cyan]") - console.print("=" * 80 + "\n") - - # Step 1: 用户需求 - console.print("[bold]步骤 1: 用户需求[/bold]") - requirements = { - "name": "智能问答助手", - "goal": "构建基于文档检索的问答系统", - "data_sources": ["文档知识库"], - "latency_budget": "实时响应优先", - } - console.print( - Panel( - json.dumps(requirements, ensure_ascii=False, indent=2), - title="用户需求", - border_style="green", - ) - ) - - # Step 2: Template 匹配 - console.print("\n[bold]步骤 2: Template 自动匹配[/bold]") - console.print("[dim]调用: templates.match_templates(requirements, top_k=3)[/dim]\n") - - matches = templates.match_templates(requirements, top_k=3) - - console.print(f"✓ 找到 {len(matches)} 个相关模板:\n") - for idx, match in enumerate(matches, 1): - console.print(f"[{idx}] {match.template.title} ([cyan]{match.template.id}[/cyan])") - console.print(f" 标签: [yellow]{', '.join(match.template.tags)}[/yellow]") - console.print(f" 匹配度: [magenta]{match.score:.2f}[/magenta]") - console.print() - - # Step 3: 转换为 LLM 可读的提示词 - console.print("\n[bold]步骤 3: 转换为 LLM 提示词[/bold]") - console.print("[dim]调用: match.template.render_prompt(match.score)[/dim]\n") - - if matches: - top_match = matches[0] - template_prompt = top_match.template.render_prompt(top_match.score) - - console.print( - Panel( - template_prompt, - title=f"模板提示词: {top_match.template.title}", - border_style="blue", - ) - ) - - # Step 4: 所有模板上下文 - console.print("\n[bold]步骤 4: 组装所有模板上下文[/bold]") - console.print("[dim]调用: _template_contexts(matches)[/dim]\n") - - template_contexts = _template_contexts(matches) - console.print(f"✓ 生成了 {len(template_contexts)} 个模板上下文片段\n") - - # Step 5: 构建发送给 LLM 的完整提示词 - console.print("\n[bold]步骤 5: 构建完整的 User Prompt[/bold]") - console.print("[dim]在 _build_prompt() 方法中组装[/dim]\n") - - # 模拟 _build_prompt 的逻辑 - blocks = [ - "请根据以下需求生成符合 SAGE 框架的 pipeline 配置 JSON：", - json.dumps(requirements, ensure_ascii=False, indent=2), - ] - - if template_contexts: - blocks.append("以下应用模板仅作灵感参考，请结合需求自行设计：") - for idx, snippet in enumerate(template_contexts, start=1): - blocks.append(f"模板[{idx}]:\n{snippet.strip()}") - - blocks.append("严格输出单个 JSON 对象，不要包含 markdown、注释或多余文字。") - - user_prompt = "\n\n".join(blocks) - - # 只显示前 1500 字符 - preview = user_prompt[:1500] - console.print( - Panel( - preview + "\n\n[dim]... (省略部分内容) ...[/dim]", - title="发送给 LLM 的 User Prompt (预览)", - border_style="magenta", - ) - ) - - # Step 6: 完整的 API 调用 - console.print("\n[bold]步骤 6: 完整的 LLM API 调用[/bold]\n") - - console.print("发送给 LLM 的完整 messages:") - console.print( - """ -[cyan]messages = [ - { - "role": "system", - "content": SYSTEM_PROMPT # SAGE Pipeline 规范说明 - }, - { - "role": "user", - "content": user_prompt # 包含模板、需求、知识库检索等 - } -][/cyan] - -[yellow]# 调用 LLM[/yellow] -response = self._client.generate( - messages, - max_tokens=1200, - temperature=0.2 -) - """ - ) - - # 关键代码位置 - console.print("\n" + "=" * 80) - console.print("[bold green]关键代码位置[/bold green]") - console.print("=" * 80 + "\n") - - code_locations = """ -1️⃣ Template 匹配 (pipeline.py:598-601) - self._template_matches = tuple( - templates.match_templates(requirements, top_k=3) - ) - -2️⃣ 转换为上下文 (pipeline.py:600) - self._last_template_contexts = _template_contexts(self._template_matches) - -3️⃣ 传递给 _build_prompt (pipeline.py:625-631) - user_prompt = self._build_prompt( - requirements, - previous_plan, - feedback, - knowledge_contexts, - self._last_template_contexts, # ← 这里！ - self._last_blueprint_contexts, - ) - -4️⃣ 在 prompt 中注入模板 (pipeline.py:654-657) - if template_contexts: - blocks.append("以下应用模板仅作灵感参考，请结合需求自行设计：") - for idx, snippet in enumerate(template_contexts, start=1): - blocks.append(f"模板[{idx}]:\\n{snippet.strip()}") - -5️⃣ 调用 LLM (pipeline.py:632-637) - messages = [ - {"role": "system", "content": SYSTEM_PROMPT}, - {"role": "user", "content": user_prompt}, - ] - response = self._client.generate(messages, max_tokens=1200, temperature=0.2) - """ - - console.print(Panel(code_locations, border_style="green")) - - # 测试环境说明 - console.print("\n" + "=" * 80) - console.print("[bold yellow]关于测试环境[/bold yellow]") - console.print("=" * 80 + "\n") - - test_info = """ -[red]测试使用 Mock（不调用真实 LLM）[/red] - -在测试中 (test_chat_pipeline.py): - • 使用 DummyGenerator 替代 PipelinePlanGenerator - • 不真正调用 OpenAI API - • 返回预定义的配置 - -原因: - 1. 避免测试依赖外部 API - 2. 提高测试速度和稳定性 - 3. 不需要 API Key - -[green]生产环境使用真实 LLM[/green] - -在实际使用时 (backend != "mock"): - • 使用 SageLLMGenerator 调用真实 LLM - • 需要配置相应的后端 - • 支持多种后端: vllm, openai, dashscope 等 - -示例: - # 使用 SageLLMGenerator (推荐) - from sage.middleware.operators import SageLLMGenerator - - # Mock 模式 (无需 GPU) - generator = SageLLMGenerator(backend_type="mock") - - # vLLM 后端 (需要 GPU) - generator = SageLLMGenerator(backend_type="vllm") - - # OpenAI 后端 - generator = SageLLMGenerator(backend_type="openai") - - export TEMP_GENERATOR_API_KEY="sk-xxx" # pragma: allowlist secret - sage chat --backend openai --model qwen-max - """ - - console.print(Panel(test_info, border_style="yellow")) - - # 如何验证 - console.print("\n" + "=" * 80) - console.print("[bold cyan]如何验证 Templates 被使用[/bold cyan]") - console.print("=" * 80 + "\n") - - verification = """ -方法 1: 启用调试输出 - sage pipeline build \\ - --name "TestApp" \\ - --goal "构建问答应用" \\ - --show-knowledge # ← 会显示匹配的模板！ - -方法 2: 查看日志 - 在生成过程中，会调用 _render_template_panel() 显示匹配的模板 - -方法 3: 代码断点 - 在 pipeline.py:625 设置断点，查看 self._last_template_contexts 的值 - -方法 4: 打印提示词（调试用） - 在 pipeline.py:638 之后添加: - print("="*80) - print("User Prompt:", user_prompt) - print("="*80) - """ - - console.print(Panel(verification, border_style="cyan")) - - console.print("\n" + "=" * 80) - console.print("[bold green]演示完成！[/bold green]") - console.print("=" * 80 + "\n") - - -if __name__ == "__main__": - demonstrate_template_to_llm() diff --git a/packages/sage-libs/examples/rag/README.md b/packages/sage-libs/examples/rag/README.md deleted file mode 100644 index d39a365bbc..0000000000 --- a/packages/sage-libs/examples/rag/README.md +++ /dev/null @@ -1,37 +0,0 @@ -# RAG Tutorials - -Simple RAG (Retrieval-Augmented Generation) examples to get started. - -## Examples - -### 1. Simple RAG (`simple_rag.py`) - -Basic RAG pipeline example. - -```bash -python examples/tutorials/rag/simple_rag.py -``` - -**Requirements**: API key (set in `.env`) - -### 2. QA Without Retrieval (`qa_no_retrieval.py`) - -Direct QA using LLM without retrieval. - -```bash -python examples/tutorials/rag/qa_no_retrieval.py -``` - -### 3. QA with Local LLM (`qa_local_llm.py`) - -QA using a local language model. - -```bash -python examples/tutorials/rag/qa_local_llm.py -``` - -## Next Steps - -- **Advanced RAG examples**: See `packages/sage-benchmark/src/sage/benchmark/benchmark_rag/` for - production-ready RAG pipelines and benchmarks -- **RAG library**: `packages/sage-libs/src/sage/libs/rag/` diff --git a/packages/sage-libs/examples/rag/adaptive_rag_v2.py b/packages/sage-libs/examples/rag/adaptive_rag_v2.py deleted file mode 100644 index 017d0df3f8..0000000000 --- a/packages/sage-libs/examples/rag/adaptive_rag_v2.py +++ /dev/null @@ -1,676 +0,0 @@ -#!/usr/bin/env python3 -""" -Adaptive RAG v2 - 自适应检索增强生成（保留旧版分流逻辑） - -核心特性： -- 完全保留旧版 side_output 的分流逻辑 -- 使用 FlatMap + Filter 替代 side_output 实现分支 -- 向量库分支 vs Web 搜索分支独立处理 -- 使用新版 SAGE API： - - UnifiedInferenceClient (LLM + Embedding) - - MemoryManager (向量库管理) - - EmbeddingFactory (本地 Embedding) - -数据流（保留旧版双分支结构）： - ┌─→ [Filter: vector] → DenseRetriever → Generator → Sink - 问题 → 路由判断 ─┤ - └─→ [Filter: web] → WebSearchAgent → Sink - -对比： - - 旧版: query_stream.side_output("vector").map(...) - - 新版: query_stream.filter(VectorFilter).map(...) -""" - -from __future__ import annotations - -import os -import sys -import time -from typing import Any - -# 屏蔽代理设置（远程服务不需要代理） -os.environ.pop("http_proxy", None) -os.environ.pop("https_proxy", None) -os.environ.pop("HTTP_PROXY", None) -os.environ.pop("HTTPS_PROXY", None) -os.environ.pop("all_proxy", None) -os.environ.pop("ALL_PROXY", None) - -import numpy as np -from dotenv import load_dotenv - -from sage.common.components.sage_llm import EmbeddingClientAdapter, LLMClientAdapter -from sage.common.core.functions.filter_function import FilterFunction -from sage.common.core.functions.flatmap_function import FlatMapFunction -from sage.common.core.functions.map_function import MapFunction -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.core.functions.source_function import SourceFunction -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.api.local_environment import LocalEnvironment - -# 尝试导入 MemoryManager（可选，用于持久化向量库） -try: - from sage.middleware.components.sage_mem.neuromem.memory_manager import MemoryManager - - HAS_MEMORY_MANAGER = True - _ = MemoryManager # 标记为已使用（可选功能） -except ImportError: - HAS_MEMORY_MANAGER = False - print("⚠️ MemoryManager 不可用，使用简单内存向量库") - - -# ============================================================ -# 远程服务配置（与 adaptive_rag.py 一致） -# ============================================================ - -# LLM 服务（可选择不同大小的模型） -LLM_HOST = "11.11.11.7" -LLM_MODELS = { - "32B": ("8901", "Qwen/Qwen2.5-32B-Instruct"), - "14B": ("8902", "Qwen/Qwen2.5-14B-Instruct"), - "7B": ("8903", "Qwen/Qwen2.5-7B-Instruct"), # 默认 - "1.5B": ("8904", "Qwen/Qwen2.5-1.5B-Instruct"), - "0.5B": ("8905", "Qwen/Qwen2.5-0.5B-Instruct"), -} - -# 使用 7B 模型作为默认 -DEFAULT_LLM = "7B" -LLM_PORT, LLM_MODEL = LLM_MODELS[DEFAULT_LLM] -LLM_BASE_URL = f"http://{LLM_HOST}:{LLM_PORT}/v1" - -# Embedding 服务 -EMBEDDING_BASE_URL = f"http://{LLM_HOST}:8090/v1" -EMBEDDING_MODEL = "BAAI/bge-large-zh-v1.5" - - -# ============================================================ -# Prompt 模板（与旧版一致） -# ============================================================ - -ROUTE_PROMPT_TEMPLATE = """Instruction: -You are an expert at routing a user question to a vectorstore or web search. -Use the vectorstore for questions on travel to Hubei Province in China. -You do not need to be stringent with the keywords in the question related to these topics. -Otherwise, use web-search. Give a binary choice 'web_search' or 'vectorstore' based on the question. -Return a JSON with a single key 'datasource' and no preamble or explanation. -Question to route: {question} -""" - -QA_PROMPT_TEMPLATE = """请根据以下背景信息回答问题。如果背景信息不足以回答问题，请诚实说明。 - -背景信息： -{context} - -问题：{question} - -请给出简洁准确的回答：""" - - -# ============================================================ -# 湖北旅游知识库数据 -# ============================================================ - -HUBEI_DOCUMENTS = [ - "武汉是湖北省省会，著名景点包括黄鹤楼、东湖、户部巷、江汉路步行街等。黄鹤楼是江南三大名楼之一，享有'天下江山第一楼'的美誉。", - "东湖是中国最大的城中湖，面积约33平方公里，是5A级风景区。湖畔有磨山、听涛、落雁等景区，春天的樱花尤为著名。", - "长江三峡是中国著名的风景名胜区，包括瞿塘峡、巫峡和西陵峡，全长约200公里。三峡大坝是世界上最大的水利枢纽工程。", - "恩施大峡谷是国家5A级景区，以天坑、地缝、溶洞、绝壁、峰丛著称，被誉为'湖北的张家界'。峡谷全长108公里。", - "神农架是中国唯一以'林区'命名的行政区，是世界自然遗产地。这里有金丝猴、白熊等珍稀动物，还有神秘的'野人'传说。", - "宜昌是三峡大坝所在地，被称为'世界水电之都'。这里是屈原和王昭君的故乡，有屈原祠、昭君村等人文景点。", - "武当山位于湖北十堰，是道教圣地，金顶和紫霄宫是著名景点。武当武术与少林功夫齐名，被列入世界文化遗产。", - "荆州古城是中国历史文化名城，三国时期的兵家必争之地。城墙保存完好，有荆州博物馆和张居正故居等景点。", -] - - -# ============================================================ -# 全局服务（延迟初始化）- 使用 SAGE 组件 -# ============================================================ - -_llm_client: LLMClientAdapter | None = None -_embedding_client: EmbeddingClientAdapter | None = None -_vector_collection: Any = None - - -def get_llm_client() -> LLMClientAdapter: - """获取 LLM 客户端（使用 LLMClientAdapter）""" - global _llm_client - if _llm_client is None: - _llm_client = LLMClientAdapter( - base_url=LLM_BASE_URL, - model_name=LLM_MODEL, - ) - print(f"✅ LLMClientAdapter 初始化完成: {LLM_BASE_URL}") - return _llm_client - - -def get_embedding_client() -> EmbeddingClientAdapter: - """获取 Embedding 客户端（使用 EmbeddingClientAdapter）""" - global _embedding_client - if _embedding_client is None: - _embedding_client = EmbeddingClientAdapter.create_api( - base_url=EMBEDDING_BASE_URL, - model=EMBEDDING_MODEL, - ) - print(f"✅ EmbeddingClientAdapter 初始化完成: {EMBEDDING_BASE_URL}") - return _embedding_client - - -def get_vector_collection(): - """获取向量库 Collection""" - global _vector_collection - - if _vector_collection is not None: - return _vector_collection - - # 使用简单内存向量库（避免 MemoryManager 接口复杂性） - _vector_collection = SimpleVectorDB(get_embedding_client()) - _vector_collection.add_documents(HUBEI_DOCUMENTS) - - return _vector_collection - - -class SimpleVectorDB: - """简单内存向量库 - 使用 EmbeddingClientAdapter""" - - def __init__(self, embedding_client: EmbeddingClientAdapter): - self.client = embedding_client - self.documents: list[str] = [] - self.embeddings: list[list[float]] = [] - - def add_documents(self, documents: list[str]): - """添加文档并计算 embedding""" - print(f"📦 构建简单向量库 ({len(documents)} 文档)...") - # 批量计算 embedding - embeddings = self.client.embed(documents) - self.documents = documents - self.embeddings = embeddings - print("✅ 向量库构建完成") - - def search(self, query: str, top_k: int = 3) -> list[str]: - """向量检索""" - # 计算查询的 embedding - query_embeddings = self.client.embed([query]) - query_embedding = query_embeddings[0] if query_embeddings else [] - - # 计算余弦相似度 - similarities = [] - for i, emb in enumerate(self.embeddings): - sim = self._cosine_similarity(query_embedding, emb) - similarities.append((i, sim)) - - similarities.sort(key=lambda x: x[1], reverse=True) - return [self.documents[i] for i, _ in similarities[:top_k]] - - def _cosine_similarity(self, a: list[float], b: list[float]) -> float: - """计算余弦相似度""" - a_arr = np.array(a) - b_arr = np.array(b) - return float(np.dot(a_arr, b_arr) / (np.linalg.norm(a_arr) * np.linalg.norm(b_arr))) - - -# ============================================================ -# Source: 问题输入源（与旧版一致） -# ============================================================ - - -class QuestionSource(SourceFunction): - """问题源：从预设问题列表获取问题""" - - def __init__(self, questions: list[str] | None = None, **kwargs): - super().__init__(**kwargs) - self.questions = questions or [ - "武汉有哪些著名景点？", - "今天的天气怎么样？", - "神农架有什么好玩的？", - "Python 怎么学习？", - ] - self.index = 0 - - def execute(self, data=None) -> dict | None: - if self.index >= len(self.questions): - return None - question = self.questions[self.index] - self.index += 1 - print(f"\n{'=' * 60}") - print(f"📝 问题 {self.index}: {question}") - return {"question": question} - - -# ============================================================ -# RoutePromptFunction: 构造路由 Prompt（与旧版一致） -# ============================================================ - - -class RoutePromptFunction(MapFunction): - """ - 构造路由 prompt，用于判断使用向量库还是 Web 搜索。 - 对应旧版 RoutePromptFunction - """ - - def execute(self, data: dict) -> dict: - question = data["question"] - prompt = ROUTE_PROMPT_TEMPLATE.format(question=question) - return {"question": question, "messages": [{"role": "user", "content": prompt}]} - - -# ============================================================ -# LLMGenerator: 调用 LLM 生成（使用 LLMClientAdapter） -# ============================================================ - - -class LLMGenerator(MapFunction): - """ - 调用 LLM 生成响应（使用 LLMClientAdapter）。 - 对应旧版 OpenAIGenerator - """ - - def execute(self, data: dict) -> dict: - messages = data["messages"] - question = data["question"] - - client = get_llm_client() - try: - response = client.chat(messages, temperature=0, max_tokens=100) - llm_output = response.content if hasattr(response, "content") else str(response) - except Exception as e: - print(f"⚠️ LLM 调用失败: {e}") - llm_output = '{"datasource": "web_search"}' - - return {"question": question, "llm_output": llm_output} - - -# ============================================================ -# RouteSplitter: 使用 FlatMap 替代 side_output 打标签 -# ============================================================ - - -class RouteSplitter(FlatMapFunction): - """ - 路由分流器：根据 LLM 输出判断是走 vectorstore 还是 web_search。 - - 替代旧版 side_output 的实现： - - 旧版: self.out.collect(data, "vector") / self.out.collect(data, "web") - - 新版: 返回带 route 标签的数据，下游用 Filter 分流 - """ - - def execute(self, data: dict) -> list[dict]: - question = data["question"] - llm_output = data["llm_output"] - - print(f"🔀 RouteSplitter 收到: {llm_output}") - - # 解析路由决策 - if "vectorstore" in llm_output.lower(): - route = "vector" - else: - route = "web" - - print(f" → 路由决策: {route}") - - # 返回带路由标签的数据（替代 side_output） - return [{"question": question, "route": route}] - - -# ============================================================ -# Filter: 分流过滤器（替代 side_output） -# ============================================================ - - -class VectorRouteFilter(FilterFunction): - """过滤出走向量库的请求（替代 query_stream.side_output("vector")）""" - - def execute(self, data: dict) -> bool: - return data.get("route") == "vector" - - -class WebRouteFilter(FilterFunction): - """过滤出走 Web 搜索的请求（替代 query_stream.side_output("web")）""" - - def execute(self, data: dict) -> bool: - return data.get("route") == "web" - - -# ============================================================ -# DenseRetriever: 向量库检索（使用 EmbeddingClientAdapter） -# ============================================================ - - -class DenseRetriever(MapFunction): - """ - 向量库检索器（使用 EmbeddingClientAdapter）。 - 对应旧版 DenseRetriever - """ - - def execute(self, data: dict) -> dict: - question = data["question"] - print(f"🔍 [向量库] 检索: {question}") - - # 使用简单向量库 - search 方法内部会计算 embedding - collection = get_vector_collection() - results = collection.search(question, top_k=3) - context = "\n\n".join(results) - - print(f" → 检索到 {len(results)} 条结果") - return {"question": question, "context": context, "source": "知识库"} - - -# ============================================================ -# QAPromptor: 构造 QA Prompt -# ============================================================ - - -class QAPromptor(MapFunction): - """ - 构造 QA Prompt。 - 对应旧版 QAPromptor - """ - - def execute(self, data: dict) -> dict: - question = data["question"] - context = data["context"] - source = data["source"] - - prompt = QA_PROMPT_TEMPLATE.format(context=context, question=question) - return { - "question": question, - "messages": [{"role": "user", "content": prompt}], - "source": source, - } - - -# ============================================================ -# QAGenerator: 生成最终回答（使用 UnifiedInferenceClient） -# ============================================================ - - -class QAGenerator(MapFunction): - """ - 生成最终回答（使用 LLMClientAdapter）。 - 对应旧版 OpenAIGenerator 在 QA 阶段的使用 - """ - - def execute(self, data: dict) -> dict: - messages = data["messages"] - question = data["question"] - source = data["source"] - - client = get_llm_client() - try: - response = client.chat(messages, temperature=0.7, max_tokens=500) - answer = response.content if hasattr(response, "content") else str(response) - except Exception as e: - answer = f"生成回答时出错: {e}" - - print(f"🤖 生成回答完成 (来源: {source})") - return {"question": question, "answer": answer, "source": source} - - -# ============================================================ -# WebSearchAgent: Web 搜索代理 -# ============================================================ - - -class WebSearchAgent(MapFunction): - """ - Web 搜索代理（使用 LLMClientAdapter）。 - 对应旧版 BaseAgent - """ - - def execute(self, data: dict) -> dict: - question = data["question"] - print(f"🌐 [Web搜索] 搜索: {question}") - - # 检查是否有博查 API Key - bocha_key = os.environ.get("BOCHA_API_KEY") - if bocha_key: - answer = self._bocha_search(question, bocha_key) - else: - # 无 API 时使用 LLM 直接回答 - client = get_llm_client() - try: - response = client.chat( - [{"role": "user", "content": question}], - temperature=0.7, - max_tokens=500, - ) - answer = response.content if hasattr(response, "content") else str(response) - except Exception as e: - answer = f"回答生成失败: {e}" - - print("🤖 Web 回答完成") - return {"question": question, "answer": answer, "source": "Web搜索/LLM直答"} - - def _bocha_search(self, question: str, api_key: str) -> str: - """调用博查搜索 API""" - try: - import requests - - resp = requests.post( - "https://api.bocha.com/v1/search", - headers={"Authorization": f"Bearer {api_key}"}, - json={"query": question, "count": 3}, - timeout=10, - ) - if resp.ok: - data = resp.json() - results = data.get("results", []) - if results: - context = "\n\n".join([r.get("snippet", "") for r in results[:3]]) - # 用 LLM 生成回答 - client = get_llm_client() - response = client.chat( - [ - { - "role": "user", - "content": QA_PROMPT_TEMPLATE.format( - context=context, question=question - ), - } - ], - temperature=0.7, - max_tokens=500, - ) - return response.content if hasattr(response, "content") else str(response) - return f"未找到关于'{question}'的搜索结果。" - except Exception as e: - return f"Web 搜索失败: {e}" - - -# ============================================================ -# Sink: 结果输出（与旧版 TerminalSink 一致） -# ============================================================ - - -class TerminalSink(SinkFunction): - """ - 终端输出 Sink。 - 对应旧版 TerminalSink - """ - - def execute(self, data: dict): - question = data.get("question", "") - answer = data.get("answer", "") - source = data.get("source", "未知") - - print(f"\n{'─' * 60}") - print(f"❓ 问题: {question}") - print(f"📚 来源: {source}") - print(f"💬 回答: {answer}") - print(f"{'─' * 60}\n") - - -# ============================================================ -# 主程序：完全保留旧版的双分支结构 -# ============================================================ - - -def run_adaptive_rag_v2(): - """ - 运行 Adaptive RAG v2 流水线 - - 保留旧版的双分支结构： - - 向量库分支: RoutePrompt → LLMGenerator → RouteSplitter - → Filter(vector) → DenseRetriever → QAPromptor → QAGenerator → Sink - - Web 分支: RoutePrompt → LLMGenerator → RouteSplitter - → Filter(web) → WebSearchAgent → Sink - """ - print("🚀 启动 Adaptive RAG v2 系统") - print(f"📊 LLM 服务: {LLM_BASE_URL} ({LLM_MODEL})") - print(f"📊 Embedding 服务: {EMBEDDING_BASE_URL} ({EMBEDDING_MODEL})") - print("📊 流程: 问题 → 路由判断 → [向量库分支 | Web分支] → 回答 → 输出") - print("=" * 60) - - # 预初始化组件 - print("\n📦 初始化组件...") - get_llm_client() - get_embedding_client() - get_vector_collection() - print() - - # 创建环境 - env = LocalEnvironment("adaptive_rag_v2") - - # 预设问题列表 - questions = [ - "武汉有哪些著名景点？", # → vectorstore - "今天北京的天气怎么样？", # → web_search - "神农架有什么好玩的？", # → vectorstore - "Python 有哪些常用的 Web 框架？", # → web_search - ] - - # ======================================== - # 构建主流程（与旧版结构一致） - # ======================================== - - # 主 Query 路由流程 - # 旧版: env.from_source(FileSource).map(RoutePromptFunction).map(OpenAIGenerator).map(RouteSplitter) - query_stream = ( - env.from_source(QuestionSource, questions) - .map(RoutePromptFunction) # 构造路由 prompt - .map(LLMGenerator) # LLM 判断路由 - .flatmap(RouteSplitter) # 打上路由标签（替代 side_output） - ) - - # ======================================== - # 向量库分支（替代 query_stream.side_output("vector")） - # ======================================== - # 旧版: - # query_stream.side_output("vector") - # .map(DenseRetriever) - # .map(QAPromptor) - # .map(OpenAIGenerator) - # .sink(TerminalSink) - _vector_stream = ( - query_stream.filter(VectorRouteFilter) # 替代 .side_output("vector") - .map(DenseRetriever) # 向量检索 - .map(QAPromptor) # 构造 QA prompt - .map(QAGenerator) # 生成回答 - .sink(TerminalSink) # 输出 - ) - - # ======================================== - # Web 搜索分支（替代 query_stream.side_output("web")） - # ======================================== - # 旧版: - # query_stream.side_output("web") - # .map(BaseAgent) - # .map(TerminalSink) - _web_stream = ( - query_stream.filter(WebRouteFilter) # 替代 .side_output("web") - .map(WebSearchAgent) # Web 搜索 + LLM 回答 - .sink(TerminalSink) # 输出 - ) - - # 运行 - try: - env.submit() - time.sleep(15) # 等待处理完成 - print("\n✅ Adaptive RAG v2 处理完成") - except Exception as e: - print(f"❌ 处理出错: {e}") - import traceback - - traceback.print_exc() - finally: - env.close() - - -if __name__ == "__main__": - # 检查是否在测试模式下运行 - if os.getenv("SAGE_EXAMPLES_MODE") == "test" or os.getenv("SAGE_TEST_MODE") == "true": - print("🧪 Test mode detected - adaptive_rag_v2 example") - print("✅ Test passed: Example structure validated") - sys.exit(0) - - CustomLogger.disable_global_console_debug() - load_dotenv(override=False) - run_adaptive_rag_v2() - - -# ============================================================ -# Pipeline 拓扑图 -# ============================================================ -# -# ┌───────────────┐ -# │ QuestionSource│ -# └───────┬───────┘ -# │ -# ▼ -# ┌─────────────────┐ -# │RoutePromptFunction│ -# └────────┬────────┘ -# │ -# ▼ -# ┌─────────────────┐ -# │ LLMGenerator │ ─────► LLM :8903 -# └────────┬────────┘ -# │ -# ▼ -# ┌─────────────────┐ -# │ RouteSplitter │ (FlatMap, 替代 side_output) -# └────────┬────────┘ -# │ -# ┌───────────────┴───────────────┐ -# │ │ -# ▼ ▼ -# ┌─────────────────┐ ┌─────────────────┐ -# │VectorRouteFilter│ │ WebRouteFilter │ -# │ route="vector" │ │ route="web" │ -# └────────┬────────┘ └────────┬────────┘ -# │ │ -# ▼ ▼ -# ┌─────────────────┐ ┌─────────────────┐ -# │ DenseRetriever │ │ WebSearchAgent │ -# │ │ │ │ -# │ Embedding :8090 │ │ LLM :8903 │ -# │ SimpleVectorDB │ └────────┬────────┘ -# └────────┬────────┘ │ -# │ │ -# ▼ │ -# ┌─────────────────┐ │ -# │ QAPromptor │ │ -# └────────┬────────┘ │ -# │ │ -# ▼ │ -# ┌─────────────────┐ │ -# │ QAGenerator │ ─────► LLM :8903 │ -# └────────┬────────┘ │ -# │ │ -# ▼ ▼ -# ┌─────────────────┐ ┌─────────────────┐ -# │ TerminalSink │ │ TerminalSink │ -# │ (知识库回答) │ │ (Web/LLM回答) │ -# └─────────────────┘ └─────────────────┘ -# -# ============================================================ -# 远程服务 (11.11.11.7) -# ============================================================ -# :8903 Qwen/Qwen2.5-7B-Instruct LLM -# :8090 BAAI/bge-large-zh-v1.5 Embedding -# -# ============================================================ -# 核心变更: side_output → FlatMap + Filter -# ============================================================ -# 旧版: query_stream.side_output("vector") -# 新版: query_stream.flatmap(RouteSplitter).filter(VectorRouteFilter) -# ============================================================ diff --git a/packages/sage-libs/examples/rag/advanced_rag_topology.py b/packages/sage-libs/examples/rag/advanced_rag_topology.py deleted file mode 100644 index 94225d376f..0000000000 --- a/packages/sage-libs/examples/rag/advanced_rag_topology.py +++ /dev/null @@ -1,861 +0,0 @@ -#!/usr/bin/env python3 -""" -Advanced RAG Topology - 完整 RAG 系统拓扑结构 -============================================= - -本示例展示如何基于 SAGE 框架构建一个包含以下组件的完整 RAG 系统： -- sage_flow: 向量流处理引擎（高性能数据流） -- sage_db: 向量数据库（文档检索） -- sage_tsdb: 时序数据库（对话历史、日志、指标） -- sage_refiner: 上下文压缩/精炼器 -- LLM: 推理引擎 (SageLLMGenerator) - -LLM 引擎选项: - - engine_type="sagellm" (推荐): SAGE 统一推理引擎 - - engine_type="vllm" (deprecated): 将重定向到 sagellm - - 后端类型 (backend_type): - - "vllm": 使用 vLLM 后端 (需要 GPU) - - "mock": 模拟模式 (无需 GPU, 用于测试) - - "openai": 使用 OpenAI API - - "dashscope": 使用 DashScope API - -运行: - python advanced_rag_topology.py # 正常运行 (需要模型/GPU) - python advanced_rag_topology.py --mock # Mock 模式 (无需 GPU) - -拓扑结构图： -============ - -``` -┌─────────────────────────────────────────────────────────────────────────────────────┐ -│ SAGE RAG Pipeline 拓扑 │ -└─────────────────────────────────────────────────────────────────────────────────────┘ - - ┌───────────────┐ - │ sage_flow │ ← 向量批处理加速 (C++ 高性能) - │ (向量流引擎) │ - └───────┬───────┘ - │ 加速向量计算 - ┌───────────────┐ ┌───────────▼───┐ ┌───────────────┐ ┌───────────────┐ - │ Source │────▶│ Embedder │────▶│ Retriever │────▶│ Reranker │ - │ (问题输入) │ │ (向量编码) │ │ (sage_db) │ │ (重排序) │ - │ [文本] │ │ [文本→向量] │ │ [向量→文档] │ │ [文档重排] │ - └───────────────┘ └───────────────┘ └───────┬───────┘ └───────┬───────┘ - │ │ - ▼ │ - ┌───────────────┐ │ - │ sage_tsdb │ │ - │ (查询日志) │ │ - └───────────────┘ │ - ▼ - ┌───────────────┐ ┌───────────────┐ ┌───────────────┐ ┌───────────────┐ - │ Sink │◀────│ Generator │◀────│ Promptor │◀────│ Refiner │ - │ (输出) │ │ (LLM) │ │ (提示构建) │ │ (上下文压缩) │ - │ [文本] │ │ [Prompt→回答] │ │ [组装Prompt] │ │ [文本→压缩文本]│ - └───────────────┘ └───────┬───────┘ └───────────────┘ └───────────────┘ - │ (sage_refiner) - ▼ - ┌───────────────┐ - │ sage_tsdb │ - │ (响应日志) │ - └───────────────┘ -``` - -各组件数据类型说明： -- sage_flow: 向量批处理引擎，加速 Embedder 的向量计算（输入文本批次，输出向量批次） -- sage_db: 向量数据库，输入查询向量，输出相关文档列表 -- sage_tsdb: 时序数据库，记录时间戳+指标数据 -- sage_refiner: 上下文压缩器，输入长文档文本，输出压缩后的文本（文本→文本） -- LLM: 推理引擎，输入 Prompt 文本，输出回答文本 - -数据流路径： -1. Source → 接收用户问题 [文本] -2. Embedder (+ sage_flow) → 将问题编码为向量 [文本→向量]，sage_flow 加速批量计算 -3. Retriever (sage_db) → 从向量数据库检索相关文档 [向量→文档列表] -4. sage_tsdb → 记录查询历史，用于分析和优化 -5. Reranker → 对检索结果重排序 [文档列表→排序后文档列表] -6. Refiner (sage_refiner) → 压缩上下文 [长文本→压缩文本]，控制 token 预算 -7. Promptor → 构建 LLM 提示 [压缩文本+问题→Prompt] -8. Generator (LLM) → 生成回答 [Prompt→回答] -9. Sink → 输出结果 -10. sage_tsdb → 记录响应日志和指标 - -层级分布 (遵循 SAGE 架构规范): -- L1 (common): 基础函数类 (SourceFunction, MapFunction, SinkFunction) -- L2 (platform): 平台服务 -- L3 (kernel/libs): Environment, DataStream API, Embedding, Retriever 基础 -- L4 (middleware): sage_db, sage_tsdb, sage_flow, sage_refiner (C++ 扩展) -- L5 (cli/tools): 命令行接口和开发工具 - -独立仓库 (不在 SAGE 核心架构中): -- sage-examples: 应用示例 -- sage-studio: 用户界面和可视化 -""" - -import time -from dataclasses import dataclass, field -from datetime import datetime -from typing import Any - -import numpy as np -from dotenv import load_dotenv - -from sage.common.core.functions.map_function import MapFunction -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.core.functions.source_function import SourceFunction -from sage.common.utils.logging.custom_logger import CustomLogger - -# ================================================================================ -# 配置数据类 -# ================================================================================ - - -@dataclass -class RAGTopologyConfig: - """RAG 拓扑配置""" - - # 向量数据库配置 (sage_db) - db_config: dict[str, Any] = field( - default_factory=lambda: { - "collection_name": "rag_knowledge_base", - "dim": 384, # 嵌入维度 - "metric": "cosine", - "top_k": 10, - } - ) - - # 时序数据库配置 (sage_tsdb) - tsdb_config: dict[str, Any] = field( - default_factory=lambda: { - "enable_query_log": True, - "enable_response_log": True, - "retention_days": 30, - "metrics_interval_ms": 1000, - } - ) - - # 向量流处理配置 (sage_flow) - flow_config: dict[str, Any] = field( - default_factory=lambda: { - "batch_size": 32, - "enable_batching": True, - "timeout_ms": 5000, - } - ) - - # Refiner 配置 (上下文压缩) - refiner_config: dict[str, Any] = field( - default_factory=lambda: { - "algorithm": "simple", # simple, long_refiner, llmlingua2 - "budget": 4000, # token 预算 - "enable_cache": True, - } - ) - - # LLM 推理配置 - llm_config: dict[str, Any] = field( - default_factory=lambda: { - "model": "Qwen/Qwen2.5-7B-Instruct", - "temperature": 0.7, - "max_tokens": 512, - } - ) - - -# ================================================================================ -# 1. Source - 问题输入源 -# ================================================================================ - - -class QuestionSource(SourceFunction): - """ - 问题数据源 - 接收用户查询 - - 在实际应用中，可替换为： - - API 接口接收 - - 消息队列消费 - - 文件批量读取 - """ - - def __init__(self, questions: list[str] | None = None, **kwargs): - super().__init__(**kwargs) - self.questions = questions or [ - "SAGE 框架支持哪些 LLM 后端?", - "如何在 SAGE 中实现分布式 Pipeline?", - "sage_db 和 sage_tsdb 有什么区别?", - ] - self.index = 0 - self.logger = CustomLogger.get_logger(self.__class__.__name__) - - def execute(self, data=None): - if self.index >= len(self.questions): - return None - - question = self.questions[self.index] - self.index += 1 - - self.logger.info(f"📝 [Source] 发送问题 #{self.index}: {question}") - - return { - "query_id": f"q_{self.index}_{int(time.time())}", - "query": question, - "timestamp": datetime.now().isoformat(), - } - - -# ================================================================================ -# 2. Embedder - 向量编码器 (与 sage_flow 集成) -# ================================================================================ - - -class EmbeddingOperator(MapFunction): - """ - 向量嵌入算子 - 将文本编码为向量 - - 集成 sage_flow 进行高效向量流处理： - - 批量编码优化 - - GPU 加速（如果可用） - - 缓存机制 - """ - - def __init__(self, dim: int = 384, **kwargs): - super().__init__(**kwargs) - self.dim = dim - self.logger = CustomLogger.get_logger(self.__class__.__name__) - - # 尝试加载真实的 Embedding 模型 - self._embedder = None - self._init_embedder() - - def _init_embedder(self): - """初始化嵌入模型""" - try: - from sage.common.components.sage_embedding import EmbeddingFactory - - self._embedder = EmbeddingFactory.create( - "hf", - model="BAAI/bge-small-zh-v1.5", - ) - self.logger.info("✓ 已加载 HuggingFace Embedding 模型") - except Exception as e: - self.logger.warning(f"无法加载 Embedding 模型，使用模拟: {e}") - self._embedder = None - - def execute(self, data: dict[str, Any]) -> dict[str, Any]: - query = data["query"] - - if self._embedder: - # 使用真实模型编码 - query_vector = self._embedder.embed(query) - query_vector = np.array(query_vector) - else: - # 模拟向量编码（用于演示） - np.random.seed(hash(query) % 2**32) - query_vector = np.random.randn(self.dim).astype(np.float32) - query_vector = query_vector / np.linalg.norm(query_vector) - - data["query_vector"] = query_vector - self.logger.info(f"🔢 [Embedder] 向量编码完成, dim={len(query_vector)}") - - return data - - -# ================================================================================ -# 3. Retriever - 向量检索器 (基于 sage_db) -# ================================================================================ - - -class VectorRetriever(MapFunction): - """ - 向量检索算子 - 从 sage_db 检索相关文档 - - 核心功能： - - 基于 FAISS 的高效相似度搜索 - - 支持元数据过滤 - - 混合检索（向量 + 关键词） - """ - - def __init__(self, config: dict[str, Any] | None = None, **kwargs): - super().__init__(**kwargs) - self.config = config or {} - self.top_k = self.config.get("top_k", 5) - self.logger = CustomLogger.get_logger(self.__class__.__name__) - - # 模拟知识库 - self._knowledge_base = self._init_knowledge_base() - self._db = None - self._init_db() - - def _init_knowledge_base(self) -> list[dict[str, Any]]: - """初始化模拟知识库""" - return [ - { - "id": "doc_1", - "content": "SAGE 支持多种 LLM 后端，包括 vLLM、OpenAI API、DashScope 等。通过 UnifiedInferenceClient 可以统一调用不同后端。", - "metadata": {"topic": "llm", "source": "docs"}, - }, - { - "id": "doc_2", - "content": "SAGE 基于 Ray 构建分布式执行能力。使用 RemoteEnvironment 可以在集群上运行 Pipeline，JobManager 负责任务调度。", - "metadata": {"topic": "distributed", "source": "docs"}, - }, - { - "id": "doc_3", - "content": "sage_db 是高性能向量数据库，基于 FAISS 实现，用于文档检索。sage_tsdb 是时序数据库，用于存储时间序列数据如监控指标、对话历史等。", - "metadata": {"topic": "database", "source": "docs"}, - }, - { - "id": "doc_4", - "content": "sage_flow 是向量流处理引擎，支持高效的批量向量运算。sage_refiner 提供上下文压缩功能，可以将长文档压缩到指定的 token 预算内。", - "metadata": {"topic": "components", "source": "docs"}, - }, - { - "id": "doc_5", - "content": "SAGE 的 dataflow 范式采用声明式 API：env.from_source().map().map().sink()。这种方式便于优化和分布式执行。", - "metadata": {"topic": "api", "source": "docs"}, - }, - ] - - def _init_db(self): - """尝试初始化 sage_db""" - try: - from sage.middleware.components.sage_db import SageDB - - self._db = SageDB(dim=self.config.get("dim", 384)) - self.logger.info("✓ 已初始化 sage_db") - except Exception as e: - self.logger.warning(f"无法初始化 sage_db，使用模拟检索: {e}") - self._db = None - - def execute(self, data: dict[str, Any]) -> dict[str, Any]: - query = data["query"] - _query_vector = data.get("query_vector") # 保留以备将来使用 - - # 简单的关键词匹配检索（演示用） - retrieved_docs = [] - for doc in self._knowledge_base: - # 计算简单的相关性分数 - score = sum(1 for word in query.split() if word in doc["content"]) - if score > 0: - retrieved_docs.append({"doc": doc, "score": score}) - - # 按分数排序 - retrieved_docs.sort(key=lambda x: x["score"], reverse=True) - retrieved_docs = retrieved_docs[: self.top_k] - - data["retrieved_documents"] = [item["doc"] for item in retrieved_docs] - data["retrieval_scores"] = [item["score"] for item in retrieved_docs] - - self.logger.info(f"🔍 [Retriever] 检索到 {len(retrieved_docs)} 篇相关文档") - - return data - - -# ================================================================================ -# 4. TSDB Logger - 时序数据记录 (基于 sage_tsdb) -# ================================================================================ - - -class TSDBLogger(MapFunction): - """ - 时序数据记录算子 - 使用 sage_tsdb 记录查询和响应 - - 记录内容： - - 查询时间戳和内容 - - 检索延迟 - - 生成延迟 - - 响应质量指标 - """ - - def __init__(self, config: dict[str, Any] | None = None, **kwargs): - super().__init__(**kwargs) - self.config = config or {} - self.logger = CustomLogger.get_logger(self.__class__.__name__) - - self._tsdb = None - self._init_tsdb() - - def _init_tsdb(self): - """尝试初始化 sage_tsdb""" - try: - from sage.middleware.components.sage_tsdb import SageTSDB - - self._tsdb = SageTSDB() - self.logger.info("✓ 已初始化 sage_tsdb") - except Exception as e: - self.logger.warning(f"无法初始化 sage_tsdb，使用内存记录: {e}") - self._tsdb = None - - # 内存中的备用日志 - self._memory_log: list[dict[str, Any]] = [] - - def execute(self, data: dict[str, Any]) -> dict[str, Any]: - # 记录时序数据 - log_entry = { - "timestamp": int(time.time() * 1000), # 毫秒时间戳 - "query_id": data.get("query_id"), - "query": data.get("query"), - "num_retrieved": len(data.get("retrieved_documents", [])), - "stage": "retrieval_complete", - } - - if self._tsdb: - try: - self._tsdb.insert( - metric_name="rag_queries", - timestamp=log_entry["timestamp"], - value=1.0, - tags={"query_id": log_entry["query_id"]}, - fields=log_entry, - ) - except Exception as e: - self.logger.warning(f"TSDB 写入失败: {e}") - - self._memory_log.append(log_entry) - self.logger.info(f"📊 [TSDB] 记录查询日志: {data.get('query_id')}") - - return data - - -# ================================================================================ -# 5. Reranker - 重排序器 -# ================================================================================ - - -class DocumentReranker(MapFunction): - """ - 文档重排序算子 - - 使用交叉编码器或其他重排序模型对检索结果进行精排。 - """ - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.logger = CustomLogger.get_logger(self.__class__.__name__) - - def execute(self, data: dict[str, Any]) -> dict[str, Any]: - documents = data.get("retrieved_documents", []) - query = data.get("query", "") - - if not documents: - return data - - # 模拟重排序（实际应使用 BGE Reranker 等模型） - reranked_docs = [] - for doc in documents: - # 简单的相关性评分 - content = doc.get("content", "") - score = sum(1 for word in query.split() if word.lower() in content.lower()) - reranked_docs.append({"doc": doc, "rerank_score": score * 1.5}) - - reranked_docs.sort(key=lambda x: x["rerank_score"], reverse=True) - - data["retrieved_documents"] = [item["doc"] for item in reranked_docs] - data["rerank_scores"] = [item["rerank_score"] for item in reranked_docs] - - self.logger.info( - f"📋 [Reranker] 重排序完成, top doc score: {reranked_docs[0]['rerank_score'] if reranked_docs else 0}" - ) - - return data - - -# ================================================================================ -# 6. Refiner - 上下文压缩器 (基于 sage_refiner) -# ================================================================================ - - -class ContextRefiner(MapFunction): - """ - 上下文压缩算子 - 使用 sage_refiner 压缩检索到的文档 - - 支持多种压缩算法： - - simple: 简单截断 - - long_refiner: LongRefiner 算法 - - llmlingua2: LLMLingua2 快速压缩 - """ - - def __init__(self, config: dict[str, Any] | None = None, **kwargs): - super().__init__(**kwargs) - self.config = config or {} - self.budget = self.config.get("budget", 4000) - self.algorithm = self.config.get("algorithm", "simple") - self.logger = CustomLogger.get_logger(self.__class__.__name__) - - self._refiner = None - self._init_refiner() - - def _init_refiner(self): - """初始化 refiner (使用 isage-refiner 包)""" - try: - if self.algorithm == "long_refiner": - from sage_refiner import LongRefinerCompressor - - self._refiner = LongRefinerCompressor() - self.logger.info("✓ 已初始化 LongRefinerCompressor") - elif self.algorithm == "reform": - from sage_refiner import REFORMCompressor - - self._refiner = REFORMCompressor() - self.logger.info("✓ 已初始化 REFORMCompressor") - else: - # simple/none - no compressor needed - self._refiner = None - self.logger.info(f"使用简单截断模式 (algorithm={self.algorithm})") - except ImportError as e: - self.logger.warning(f"isage-refiner 未安装，使用简单截断: {e}") - self._refiner = None - except Exception as e: - self.logger.warning(f"无法初始化 refiner，使用简单截断: {e}") - self._refiner = None - - def execute(self, data: dict[str, Any]) -> dict[str, Any]: - documents = data.get("retrieved_documents", []) - query = data.get("query", "") - - if not documents: - data["refined_context"] = "" - return data - - # 合并文档内容 - full_context = "\n\n".join(doc.get("content", "") for doc in documents) - - if self._refiner: - try: - # isage-refiner uses 'contents' key and returns dict - documents_for_refiner = [{"contents": full_context}] - result = self._refiner.compress( - question=query, - document_list=documents_for_refiner, - budget=self.budget, - ) - refined_context = result.get("compressed_context", full_context) - except Exception as e: - self.logger.warning(f"Refiner 压缩失败: {e}") - refined_context = full_context[: self.budget] - else: - # 简单截断 - refined_context = full_context[: self.budget] - - data["refined_context"] = refined_context - data["original_length"] = len(full_context) - data["refined_length"] = len(refined_context) - - compression_ratio = len(refined_context) / max(len(full_context), 1) - self.logger.info(f"🗜️ [Refiner] 压缩完成, ratio: {compression_ratio:.2%}") - - return data - - -# ================================================================================ -# 7. Promptor - 提示构建器 -# ================================================================================ - - -class RAGPromptor(MapFunction): - """ - RAG 提示构建算子 - - 将查询和压缩后的上下文组合成 LLM 可用的提示。 - """ - - def __init__(self, template: str | None = None, **kwargs): - super().__init__(**kwargs) - self.template = ( - template - or """请根据以下背景信息回答用户问题。 - -背景信息： -{context} - -用户问题：{query} - -请给出准确、简洁的回答：""" - ) - self.logger = CustomLogger.get_logger(self.__class__.__name__) - - def execute(self, data: dict[str, Any]) -> dict[str, Any]: - query = data.get("query", "") - context = data.get("refined_context", "") - - prompt = self.template.format(context=context, query=query) - data["prompt"] = prompt - - self.logger.info(f"📝 [Promptor] 提示构建完成, length: {len(prompt)}") - - return data - - -# ================================================================================ -# 8. Generator - LLM 推理引擎 -# ================================================================================ - - -class LLMGenerator(MapFunction): - """ - LLM 生成算子 - 使用 SAGE 的统一推理客户端 - - 支持多种后端 (engine_type/backend_type): - - sagellm + vllm: 本地 vLLM 后端 (推荐, 需要 GPU) - - sagellm + mock: 模拟模式 (无需 GPU, 用于测试) - - sagellm + openai: OpenAI API - - sagellm + dashscope: DashScope API - """ - - def __init__(self, config: dict[str, Any] | None = None, use_mock: bool = False, **kwargs): - super().__init__(**kwargs) - self.config = config or {} - self.use_mock = use_mock - self.logger = CustomLogger.get_logger(self.__class__.__name__) - - self._client = None - self._generator = None - self._init_client() - - def _init_client(self): - """初始化 LLM 客户端""" - # 优先使用 SageLLMGenerator (推荐) - try: - from sage.middleware.operators import SageLLMGenerator - - # 确定 backend_type - if self.use_mock: - backend_type = "mock" - else: - backend_type = self.config.get("backend_type", "vllm") - - self._generator = SageLLMGenerator( - model_id=self.config.get("model", "Qwen/Qwen2.5-7B-Instruct"), - backend_type=backend_type, - # engine_type="sagellm" is default - ) - self.logger.info(f"✓ 已初始化 SageLLMGenerator (backend={backend_type})") - return - except Exception as e: - self.logger.warning(f"SageLLMGenerator 初始化失败: {e}") - - # Fallback: 尝试 UnifiedInferenceClient - try: - from sage.common.components.sage_llm import UnifiedInferenceClient - - self._client = UnifiedInferenceClient.create() - self.logger.info("✓ 已初始化 UnifiedInferenceClient") - except Exception as e: - self.logger.warning(f"无法初始化 LLM 客户端，使用模拟回答: {e}") - self._client = None - - def execute(self, data: dict[str, Any]) -> dict[str, Any]: - prompt = data.get("prompt", "") - query = data.get("query", "") - - start_time = time.time() - - # 优先使用 SageLLMGenerator - if self._generator: - try: - # SageLLMGenerator 使用 execute 方法 - result = self._generator.execute({"prompt": prompt}) - answer = result.get("response", "") if isinstance(result, dict) else str(result) - except Exception as e: - self.logger.warning(f"SageLLMGenerator 调用失败: {e}") - answer = self._mock_answer(query, data.get("refined_context", "")) - elif self._client: - try: - response = self._client.chat( - messages=[{"role": "user", "content": prompt}], - temperature=self.config.get("temperature", 0.7), - max_tokens=self.config.get("max_tokens", 512), - ) - answer = response.get("content", "") - except Exception as e: - self.logger.warning(f"LLM 调用失败: {e}") - answer = self._mock_answer(query, data.get("refined_context", "")) - else: - answer = self._mock_answer(query, data.get("refined_context", "")) - - latency = time.time() - start_time - - data["answer"] = answer - data["generation_latency"] = latency - - self.logger.info(f"🤖 [Generator] 生成完成, latency: {latency:.2f}s") - - return data - - def _mock_answer(self, query: str, context: str) -> str: - """模拟 LLM 回答""" - if "LLM" in query or "后端" in query: - return "SAGE 支持多种 LLM 后端，包括 vLLM（本地高性能推理）、OpenAI API、DashScope 等。通过 UnifiedInferenceClient 可以统一调用。" - elif "分布式" in query: - return "SAGE 基于 Ray 构建分布式能力。使用 RemoteEnvironment 在集群运行 Pipeline，JobManager 负责调度。" - elif "sage_db" in query or "sage_tsdb" in query or "区别" in query: - return "sage_db 是向量数据库，用于文档检索；sage_tsdb 是时序数据库，用于存储时间序列数据如监控指标和对话历史。" - else: - return f"根据提供的信息，这个问题涉及：{context[:100]}..." - - -# ================================================================================ -# 9. Response TSDB Logger - 响应日志记录 -# ================================================================================ - - -class ResponseTSDBLogger(MapFunction): - """记录响应到时序数据库""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.logger = CustomLogger.get_logger(self.__class__.__name__) - - def execute(self, data: dict[str, Any]) -> dict[str, Any]: - # 记录响应指标 - log_entry = { - "timestamp": int(time.time() * 1000), - "query_id": data.get("query_id"), - "answer_length": len(data.get("answer", "")), - "generation_latency": data.get("generation_latency", 0), - "compression_ratio": data.get("refined_length", 0) - / max(data.get("original_length", 1), 1), - } - - self.logger.info(f"📊 [TSDB] 记录响应指标: latency={log_entry['generation_latency']:.2f}s") - - data["response_metrics"] = log_entry - return data - - -# ================================================================================ -# 10. Sink - 结果输出 -# ================================================================================ - - -class RAGResultSink(SinkFunction): - """ - RAG 结果输出 - - 支持多种输出方式： - - 终端打印 - - 文件保存 - - API 回调 - """ - - def __init__(self, output_file: str | None = None, **kwargs): - super().__init__(**kwargs) - self.output_file = output_file - self.logger = CustomLogger.get_logger(self.__class__.__name__) - self.results: list[dict[str, Any]] = [] - - def execute(self, data: dict[str, Any]): - query = data.get("query", "") - answer = data.get("answer", "") - metrics = data.get("response_metrics", {}) - - # 格式化输出 - print("\n" + "=" * 70) - print(f"❓ 问题: {query}") - print("-" * 70) - print(f"💬 回答: {answer}") - print("-" * 70) - print( - f"📊 指标: latency={metrics.get('generation_latency', 0):.2f}s, compression={metrics.get('compression_ratio', 0):.2%}" - ) - print("=" * 70 + "\n") - - self.results.append(data) - self.logger.info(f"✅ [Sink] 结果输出完成: {data.get('query_id')}") - - -# ================================================================================ -# Pipeline 构建与执行 -# ================================================================================ - - -def build_rag_topology(config: RAGTopologyConfig | None = None, use_mock: bool = False): - """ - 构建 RAG 拓扑 - - 拓扑结构： - Source → Embedder → Retriever → TSDBLogger → Reranker - → Refiner → Promptor → Generator → ResponseLogger → Sink - - Args: - config: RAG 拓扑配置 - use_mock: 是否使用 mock 模式 (无需 GPU) - """ - from sage.kernel.api.local_environment import LocalEnvironment - - config = config or RAGTopologyConfig() - - # 创建执行环境 - env = LocalEnvironment("AdvancedRAGTopology") - - # 构建 dataflow pipeline - ( - env.from_source(QuestionSource) - .map(EmbeddingOperator, dim=config.db_config.get("dim", 384)) - .map(VectorRetriever, config=config.db_config) - .map(TSDBLogger, config=config.tsdb_config) - .map(DocumentReranker) - .map(ContextRefiner, config=config.refiner_config) - .map(RAGPromptor) - .map(LLMGenerator, config=config.llm_config, use_mock=use_mock) - .map(ResponseTSDBLogger) - .sink(RAGResultSink) - ) - - return env - - -def main(): - """运行 RAG 拓扑演示""" - import argparse - - # 解析命令行参数 - parser = argparse.ArgumentParser(description="SAGE Advanced RAG Topology Demo") - parser.add_argument( - "--mock", - action="store_true", - help="使用 mock 模式运行 (无需 GPU/模型)", - ) - args = parser.parse_args() - - load_dotenv() - - print("\n" + "=" * 70) - print("🚀 SAGE Advanced RAG Topology Demo") - if args.mock: - print("🧪 Mock 模式: 使用模拟 LLM (无需 GPU)") - print("=" * 70 + "\n") - - # 配置 - config = RAGTopologyConfig( - db_config={ - "collection_name": "rag_demo", - "dim": 384, - "top_k": 5, - }, - refiner_config={ - "algorithm": "simple", - "budget": 2000, - }, - llm_config={ - "model": "Qwen/Qwen2.5-7B-Instruct", - "temperature": 0.7, - "max_tokens": 256, - "backend_type": "mock" if args.mock else "vllm", - }, - ) - - # 构建拓扑 - env = build_rag_topology(config, use_mock=args.mock) - - # 执行 - print("📦 开始执行 RAG Pipeline...\n") - env.submit(autostop=True) - - print("\n✅ RAG Pipeline 执行完成.") - print("=" * 70 + "\n") - - -if __name__ == "__main__": - main() diff --git a/packages/sage-libs/examples/rag/qa_local_llm.py b/packages/sage-libs/examples/rag/qa_local_llm.py deleted file mode 100644 index 3289b666c3..0000000000 --- a/packages/sage-libs/examples/rag/qa_local_llm.py +++ /dev/null @@ -1,143 +0,0 @@ -""" -终端交互式QA无界流处理 - 本地版本 -支持终端输入问题，使用本地大模型生成回答的无界流处理示例 -""" - -import time - -from dotenv import load_dotenv - -from sage.common.core.functions.map_function import MapFunction -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.core.functions.source_function import SourceFunction -from sage.common.utils.config.loader import load_config -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.api.local_environment import LocalEnvironment -from sage.middleware.operators.rag import HFGenerator, QAPromptor - - -class TerminalInputSource(SourceFunction): - """终端输入源函数 - 简化版""" - - def execute(self, data=None): - try: - user_input = input().strip() - if user_input: - return user_input - return self.execute(data) - except (EOFError, KeyboardInterrupt): - raise - - -class QuestionProcessor(MapFunction): - """问题处理器""" - - def execute(self, data): - if not data or data.strip() == "": - return None - - question = data.strip() - return question - - -class AnswerFormatter(MapFunction): - """回答格式化器""" - - def execute(self, data): - if not data: - return None - - # HFGenerator返回的格式是 (user_query, generated_text) - if isinstance(data, tuple) and len(data) >= 2: - user_query = data[0] - answer = data[1] - return { - "question": user_query if user_query else "N/A", - "answer": answer, - "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"), - } - else: - return { - "question": "N/A", - "answer": str(data), - "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"), - } - - -class ConsoleSink(SinkFunction): - """控制台输出""" - - def execute(self, data): - if not data: - return None - - if isinstance(data, dict): - print(f"\n🤖 {data.get('answer', 'N/A')}\n") - else: - print(f"\n🤖 {data}\n") - - return data - - -def create_qa_pipeline(): - """创建QA处理管道 - 使用本地模型""" - import os - - # 加载配置 - load_dotenv(override=False) - config_path = os.path.join( - os.path.dirname(__file__), "..", "config", "config_source_local.yaml" - ) - - # 检查配置文件是否存在 - if not os.path.exists(config_path): - print(f"❌ 配置文件不存在: {config_path}") - return - - config = load_config(config_path) - - # 创建本地环境 - env = LocalEnvironment() - - # 启动欢迎提示 - print("💬 QA助手已启动（本地模式）！输入问题后按回车") - - try: - # 构建无界流处理管道 - 使用本地生成器 - ( - env.from_source(TerminalInputSource) - .map(QuestionProcessor) - .map(QAPromptor, config["promptor"]) - .map(HFGenerator, config["generator"]["local"]) - .map(AnswerFormatter) - .sink(ConsoleSink) - ) - - # 提交并运行 - env.submit() - # 保持主线程运行，直到用户退出 - while True: - time.sleep(1) - - except Exception as e: - print(f"❌ 管道运行出错: {str(e)}") - finally: - try: - env.close() - print("✅ QA流处理管道已关闭") - except Exception: - pass - - -if __name__ == "__main__": - import os - import sys - - # 检查是否在测试模式下运行 - if os.getenv("SAGE_EXAMPLES_MODE") == "test" or os.getenv("SAGE_TEST_MODE") == "true": - print("🧪 Test mode detected - qa_without_retrieval_local is interactive") - print("✅ Test passed: Interactive example structure validated") - sys.exit(0) - - CustomLogger.disable_global_console_debug() - create_qa_pipeline() diff --git a/packages/sage-libs/examples/rag/qa_no_retrieval.py b/packages/sage-libs/examples/rag/qa_no_retrieval.py deleted file mode 100644 index 427fed8ba4..0000000000 --- a/packages/sage-libs/examples/rag/qa_no_retrieval.py +++ /dev/null @@ -1,180 +0,0 @@ -""" -终端交互式QA无界流处理 -支持终端输入问题，使用大模型生成回答的无界流处理示例 - -LLM 引擎选项: - - SageLLMGenerator (推荐): SAGE 统一推理引擎 - - backend_type="vllm": 使用 vLLM 后端 (需要 GPU) - - backend_type="mock": 模拟模式 (无需 GPU, 用于测试) - - OpenAIGenerator (legacy): 兼容模式 - -运行: - python qa_no_retrieval.py # 正常运行 (需要模型/GPU) - python qa_no_retrieval.py --mock # Mock 模式 (无需 GPU) -""" - -import time - -from dotenv import load_dotenv - -from sage.common.core.functions.map_function import MapFunction -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.core.functions.source_function import SourceFunction -from sage.common.utils.config.loader import load_config -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.api.local_environment import LocalEnvironment -from sage.middleware.operators.rag import OpenAIGenerator, QAPromptor - -# 全局 mock 模式标志 -_USE_MOCK = False - - -class TerminalInputSource(SourceFunction): - """终端输入源函数 - 简化版""" - - def execute(self, data=None): - try: - user_input = input().strip() - if user_input: - return user_input - return self.execute(data) - except (EOFError, KeyboardInterrupt): - raise - - -class QuestionProcessor(MapFunction): - """问题处理器""" - - def execute(self, data): - if not data or data.strip() == "": - return None - - question = data.strip() - return question - - -class AnswerFormatter(MapFunction): - """回答格式化器""" - - def execute(self, data): - if not data: - return None - - # OpenAIGenerator返回的格式是 (user_query, generated_text) - if isinstance(data, tuple) and len(data) >= 2: - user_query = data[0] - answer = data[1] - return { - "question": user_query if user_query else "N/A", - "answer": answer, - "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"), - } - else: - return { - "question": "N/A", - "answer": str(data), - "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"), - } - - -class ConsoleSink(SinkFunction): - """控制台输出""" - - def execute(self, data): - if not data: - return None - - if isinstance(data, dict): - print(f"\n🤖 {data.get('answer', 'N/A')}\n") - else: - print(f"\n🤖 {data}\n") - - return data - - -def create_qa_pipeline(): - """创建QA处理管道""" - import os - - # 加载配置 - load_dotenv(override=False) - config_path = os.path.join(os.path.dirname(__file__), "..", "config", "config_source.yaml") - config = load_config(config_path) - - # 创建本地环境 - env = LocalEnvironment() - - # 启动欢迎提示 - print("💬 QA助手已启动！输入问题后按回车") - if _USE_MOCK: - print("🧪 Mock 模式: 使用模拟 LLM (无需 GPU)") - - try: - # 选择 Generator - if _USE_MOCK: - # 使用 SageLLMGenerator 的 mock 后端 - from sage.middleware.operators import SageLLMGenerator - - generator_class = SageLLMGenerator - generator_config = { - "backend_type": "mock", - "model_id": config.get("generator", {}) - .get("vllm", {}) - .get("model_id", "mock-model"), - } - else: - # 使用配置中的 OpenAIGenerator - generator_class = OpenAIGenerator - generator_config = config["generator"]["vllm"] - - # 构建无界流处理管道 - ( - env.from_source(TerminalInputSource) - .map(QuestionProcessor) - .map(QAPromptor, config["promptor"]) - .map(generator_class, generator_config) - .map(AnswerFormatter) - .sink(ConsoleSink) - ) - - # 提交并运行 - env.submit() - # 保持主线程运行，直到用户退出 - while True: - time.sleep(1) - - except Exception as e: - print(f"❌ 管道运行出错: {str(e)}") - finally: - try: - env.close() - print("✅ QA流处理管道已关闭") - except Exception: - pass - - -if __name__ == "__main__": - import argparse - import os - import sys - - # 解析命令行参数 - parser = argparse.ArgumentParser(description="QA Pipeline Demo") - parser.add_argument( - "--mock", - action="store_true", - help="使用 mock 模式运行 (无需 GPU/模型)", - ) - args, remaining = parser.parse_known_args() - - # 设置全局 mock 标志 - _USE_MOCK = args.mock - - # 检查是否在测试模式下运行 - if os.getenv("SAGE_EXAMPLES_MODE") == "test" or os.getenv("SAGE_TEST_MODE") == "true": - print("🧪 Test mode detected - qa_without_retrieval is interactive") - print("✅ Test passed: Interactive example structure validated") - sys.exit(0) - - CustomLogger.disable_global_console_debug() - create_qa_pipeline() diff --git a/packages/sage-libs/examples/rag/simple_rag.py b/packages/sage-libs/examples/rag/simple_rag.py deleted file mode 100644 index 1d4cd6d54e..0000000000 --- a/packages/sage-libs/examples/rag/simple_rag.py +++ /dev/null @@ -1,305 +0,0 @@ -#!/usr/bin/env python3 -""" -简化版RAG应用 - 测试完整流程 -用于验证问题源→检索→生成→输出的完整数据流 - -支持 RemoteEnvironment + LocalSinkScheduler： -- 计算任务在远程节点执行 -- Sink 节点绑定到本地（客户端），输出可见 -""" - -import os -import socket -import sys -import time - -from dotenv import load_dotenv - -from sage.common.core.functions.map_function import MapFunction -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.core.functions.source_function import SourceFunction -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.api.local_environment import LocalEnvironment -from sage.kernel.api.remote_environment import RemoteEnvironment -from sage.kernel.scheduler.api import BaseScheduler -from sage.kernel.scheduler.decision import PlacementDecision - - -class SimpleQuestionSource(SourceFunction): - """简单问题源：只发送一个问题进行测试""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.sent = False - - def execute(self, data=None): - if self.sent: - return None - self.sent = True - question = "张先生的手机通常放在什么地方？" - print(f"📝 发送问题: {question}") - return question - - -class SimpleRetriever(MapFunction): - """简化的检索器""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - # 模拟知识库数据 - self.knowledge = { - "张先生的手机": "张先生习惯把手机放在办公桌右上角的充电座上", - "李女士的钱包": "李女士总是把钱包放在卧室梳妆台的第一个抽屉里", - "王经理的钥匙": "王经理的办公室钥匙通常挂在衣帽架上的西装口袋里", - } - - def execute(self, data): - question = data - print(f"🔍 检索问题: {question}") - - # 简单的关键词匹配 - relevant_info = [] - for key, value in self.knowledge.items(): - if any(word in question for word in key.split()): - relevant_info.append(value) - - context = "\n".join(relevant_info) if relevant_info else "没有找到相关信息" - result = {"query": question, "context": context} - print(f"✅ 检索结果: {context}") - return result - - -class SimplePromptor(MapFunction): - """简化的提示构建器""" - - def execute(self, data): - query = data["query"] - context = data["context"] - - prompt = f"""请根据以下背景信息回答问题： - -背景信息： -{context} - -问题：{query} - -请给出简洁准确的回答：""" - - result = {"query": query, "prompt": prompt} - print("✅ 构建提示完成") - return result - - -class SimpleGenerator(MapFunction): - """简化的AI生成器 - 使用模拟回答""" - - def execute(self, data): - query = data["query"] - data["prompt"] - - print("🤖 AI生成中...") - - # 模拟AI回答 - if "张先生" in query and "手机" in query: - answer = "根据提供的信息，张先生习惯把手机放在办公桌右上角的充电座上。" - elif "李女士" in query and "钱包" in query: - answer = "根据提供的信息，李女士总是把钱包放在卧室梳妆台的第一个抽屉里。" - elif "王经理" in query and "钥匙" in query: - answer = "根据提供的信息，王经理的办公室钥匙通常挂在衣帽架上的西装口袋里。" - else: - answer = "抱歉，我无法根据现有信息回答这个问题。" - - result = {"query": query, "answer": answer} - print(f"✅ AI生成完成: {answer}") - return result - - -class SimpleTerminalSink(SinkFunction): - """简化的终端输出""" - - def execute(self, data): - query = data["query"] - answer = data["answer"] - - print("\n" + "=" * 60) - print(f"❓ 问题: {query}") - print(f"💬 回答: {answer}") - print("=" * 60 + "\n") - - -class SimpleFileSink(SinkFunction): - """文件输出 - 结果写入文件，便于远程执行后查看""" - - def __init__(self, output_path: str = "/home/sage/SAGE/.sage/rag_output.txt", **kwargs): - super().__init__(**kwargs) - self.output_path = output_path - - def execute(self, data): - from datetime import datetime - - query = data["query"] - answer = data["answer"] - - # 构建输出内容 - output = { - "timestamp": datetime.now().isoformat(), - "query": query, - "answer": answer, - } - - # 追加写入文件 - with open(self.output_path, "a", encoding="utf-8") as f: - f.write("=" * 60 + "\n") - f.write(f"时间: {output['timestamp']}\n") - f.write(f"问题: {query}\n") - f.write(f"回答: {answer}\n") - f.write("=" * 60 + "\n\n") - - print(f"✅ 结果已写入: {self.output_path}") - - -# ============================================================ -# LocalSinkScheduler - 将 Sink 节点绑定到本地 -# ============================================================ - - -class LocalSinkScheduler(BaseScheduler): - """ - 本地 Sink 调度器：将 Sink 节点放到客户端本地执行 - - 工作原理： - - Sink 节点 → 绑定到本地（使用实际的 Ray 节点 ID） - - 其他节点 → 使用 Ray 默认负载均衡 - - 使用场景： - - RemoteEnvironment 远程执行计算 - - 但希望 Sink 输出在本地可见 - - 注意：需要在 Ray 集群环境中运行，会获取当前节点的真实 Ray node ID - """ - - def __init__(self): - super().__init__() - self.local_hostname = socket.gethostname() - self._local_node_id = None # 延迟获取 - - def _get_local_node_id(self): - """获取当前节点的 Ray node ID""" - if self._local_node_id is not None: - return self._local_node_id - - try: - import ray - - if not ray.is_initialized(): - # 如果 Ray 没有初始化，返回 None 使用默认调度 - return None - - # 获取当前节点的 node ID - current_node_id = ray.get_runtime_context().get_node_id() - self._local_node_id = current_node_id - return current_node_id - except Exception: - return None - - def make_decision(self, task_node): - """根据任务类型决定放置策略""" - # 导入放在方法内部，确保远程反序列化时可用 - - task_name = getattr(task_node, "name", str(task_node)) - - # 检查是否是 Sink 节点 - is_sink = "Sink" in task_name or "sink" in task_name.lower() - - if is_sink: - # 获取本地节点的真实 Ray node ID - local_node_id = self._get_local_node_id() - - if local_node_id: - # 使用真实的 Ray node ID - return PlacementDecision( - target_node=local_node_id, - placement_strategy="affinity", - reason=f"Sink bound to local node: {self.local_hostname} (node_id: {local_node_id[:8]}...)", - ) - else: - # 如果无法获取 node ID，使用默认调度 - return PlacementDecision( - placement_strategy="default", - reason="Sink: Could not get local node ID, using default scheduling", - ) - - # 其他任务使用默认调度 - return PlacementDecision( - placement_strategy="default", - reason="Default load balancing for compute tasks", - ) - - -def pipeline_run(): - """运行简化RAG管道""" - print("🚀 启动简化版RAG系统") - print("📊 流程: 问题源 → 简单检索 → 提示构建 → 模拟生成 → 终端输出") - print("=" * 60) - - # 选择环境模式 - USE_REMOTE = True # 设为 True 使用远程模式（需要先启动 JobManager） - - if USE_REMOTE: - # 远程模式：需要先启动 JobManager - # 运行: sage jobmanager start --host 0.0.0.0 --port 19001 - scheduler = LocalSinkScheduler() - print(f"📍 使用 LocalSinkScheduler，Sink 将在本地节点 ({scheduler.local_hostname}) 执行") - env = RemoteEnvironment( - "rag_simple_demo", - host="sage-node-1", - scheduler=scheduler, - ) - else: - # 本地模式：直接执行，无需额外服务 - print("📍 使用 LocalEnvironment 本地执行") - env = LocalEnvironment("rag_simple_demo") - - # 输出文件路径 - output_file = "/home/sage/SAGE/.sage/rag_output.txt" - - # 构建管道 - ( - env.from_source(SimpleQuestionSource) - .map(SimpleRetriever) - .map(SimplePromptor) - .map(SimpleGenerator) - .sink(SimpleFileSink, output_file) # 使用 FileSink - ) - - try: - print(f"🔄 开始处理... 结果将写入: {output_file}") - env.submit() - time.sleep(5) # 等待处理完成 - print("✅ 处理完成") - - # 显示输出文件内容 - if os.path.exists(output_file): - print(f"\n📄 输出文件内容 ({output_file}):") - with open(output_file, encoding="utf-8") as f: - print(f.read()) - - except Exception as e: - print(f"❌ 处理出错: {e}") - import traceback - - traceback.print_exc() - finally: - env.close() - - -if __name__ == "__main__": - # 检查是否在测试模式下运行 - if os.getenv("SAGE_EXAMPLES_MODE") == "test" or os.getenv("SAGE_TEST_MODE") == "true": - print("🧪 Test mode detected - rag_simple example") - print("✅ Test passed: Example structure validated") - sys.exit(0) - - CustomLogger.disable_global_console_debug() - load_dotenv(override=False) - pipeline_run() diff --git a/packages/sage-libs/examples/rag/usage_1_direct_library.py b/packages/sage-libs/examples/rag/usage_1_direct_library.py deleted file mode 100644 index 0978fae9b7..0000000000 --- a/packages/sage-libs/examples/rag/usage_1_direct_library.py +++ /dev/null @@ -1,279 +0,0 @@ -""" -Usage 1: Direct Library Usage -============================== - -最简单的方式：直接使用 unlearning 库，无需 SAGE 运行时。 - -适用场景： -- 独立脚本 -- Jupyter Notebook 实验 -- 快速原型验证 -- 研究算法开发 - -优势： -- 零依赖 SAGE 运行时 -- 代码简洁清晰 -- 易于调试 -- 适合快速实验 -""" - -import numpy as np - -from sage.libs.privacy.unlearning import UnlearningEngine -from sage.libs.privacy.unlearning.algorithms import LaplaceMechanism - - -def generate_test_data(n_vectors=50, dim=128): - """生成测试数据""" - vectors = np.random.randn(n_vectors, dim).astype(np.float32) - # L2 归一化（模拟真实 embeddings） - vectors = vectors / (np.linalg.norm(vectors, axis=1, keepdims=True) + 1e-10) - ids = [f"doc_{i}" for i in range(n_vectors)] - return vectors, ids - - -def example_basic_unlearning(): - """示例1：基础遗忘操作""" - print("=" * 70) - print("Example 1: Basic Unlearning") - print("=" * 70) - - # 1. 生成数据 - all_vectors, all_ids = generate_test_data(n_vectors=50, dim=128) - print(f"✓ Generated {len(all_vectors)} vectors") - - # 2. 选择要遗忘的向量 - forget_indices = [5, 10, 15, 20, 25] - vectors_to_forget = all_vectors[forget_indices] - ids_to_forget = [all_ids[i] for i in forget_indices] - print(f"✓ Selected {len(ids_to_forget)} vectors to forget: {ids_to_forget}") - - # 3. 创建 unlearning engine - engine = UnlearningEngine( - epsilon=1.0, delta=1e-5, total_budget_epsilon=10.0, enable_compensation=True - ) - print("✓ Created UnlearningEngine") - - # 4. 执行遗忘 - result = engine.unlearn_vectors( - vectors_to_forget=vectors_to_forget, - vector_ids_to_forget=ids_to_forget, - all_vectors=all_vectors, - all_vector_ids=all_ids, - perturbation_strategy="uniform", - ) - - # 5. 查看结果 - print("\n🎯 Unlearning Result:") - print(f" Success: {result.success}") - print(f" Vectors unlearned: {result.num_vectors_unlearned}") - print(f" Neighbors compensated: {result.num_neighbors_compensated}") - print(f" Privacy cost: ε={result.privacy_cost[0]:.4f}, δ={result.privacy_cost[1]:.6f}") - - # 6. 获取扰动后的向量 - perturbed = result.metadata["perturbed_vectors"] - print("\n📊 Vector Comparison:") - for i, (orig, pert, vec_id) in enumerate(zip(vectors_to_forget, perturbed, ids_to_forget)): - l2_dist = np.linalg.norm(orig - pert) - cos_sim = np.dot(orig, pert) / (np.linalg.norm(orig) * np.linalg.norm(pert)) - print(f" {vec_id}: L2={l2_dist:.4f}, CosSim={cos_sim:.4f}") - - print() - - -def example_custom_mechanism(): - """示例2：使用自定义隐私机制""" - print("=" * 70) - print("Example 2: Custom Privacy Mechanism") - print("=" * 70) - - # 1. 生成数据 - vectors, ids = generate_test_data(n_vectors=30, dim=64) - forget_vectors = vectors[:3] - forget_ids = ids[:3] - - # 2. 创建自定义 Laplace 机制 - custom_mechanism = LaplaceMechanism(epsilon=0.5) - print("✓ Created custom Laplace mechanism with ε=0.5") - - # 3. 使用自定义机制 - engine = UnlearningEngine( - mechanism=custom_mechanism, - total_budget_epsilon=5.0, - enable_compensation=False, # 不使用补偿 - ) - - result = engine.unlearn_vectors( - vectors_to_forget=forget_vectors, - vector_ids_to_forget=forget_ids, - perturbation_strategy="selective", - ) - - print("\n🎯 Result with custom mechanism:") - print(f" Success: {result.success}") - print(f" Privacy cost: ε={result.privacy_cost[0]:.4f}") - print() - - -def example_batch_unlearning(): - """示例3：批量遗忘操作""" - print("=" * 70) - print("Example 3: Batch Unlearning") - print("=" * 70) - - # 1. 生成数据 - all_vectors, all_ids = generate_test_data(n_vectors=100, dim=128) - - # 2. 创建 engine - engine = UnlearningEngine( - epsilon=0.5, delta=1e-5, total_budget_epsilon=20.0, enable_compensation=True - ) - - # 3. 分批遗忘 - batch_size = 5 - total_forgotten = 0 - - for batch_idx in range(3): # 遗忘3批 - start_idx = batch_idx * batch_size - end_idx = start_idx + batch_size - - forget_vectors = all_vectors[start_idx:end_idx] - forget_ids = all_ids[start_idx:end_idx] - - result = engine.unlearn_vectors( - vectors_to_forget=forget_vectors, - vector_ids_to_forget=forget_ids, - all_vectors=all_vectors, - all_vector_ids=all_ids, - perturbation_strategy="uniform", - ) - - total_forgotten += result.num_vectors_unlearned - print( - f" Batch {batch_idx + 1}: Forgotten {result.num_vectors_unlearned} vectors, " - f"Privacy cost: ε={result.privacy_cost[0]:.4f}" - ) - - # 4. 检查剩余预算 - status = engine.get_privacy_status() - remaining = status["remaining_budget"] - - print("\n📊 Summary:") - print(f" Total forgotten: {total_forgotten} vectors") - print(f" Remaining budget: ε={remaining['epsilon_remaining']:.4f}") - print(f" Budget utilization: {status['accountant_summary']['budget_utilization']:.1%}") - print() - - -def example_similarity_based_unlearning(): - """示例4：基于相似度的遗忘""" - print("=" * 70) - print("Example 4: Similarity-based Unlearning") - print("=" * 70) - - # 1. 生成数据 - all_vectors, all_ids = generate_test_data(n_vectors=80, dim=128) - - # 2. 创建一个查询向量（要遗忘的主题） - query_vector = np.random.randn(128).astype(np.float32) - query_vector = query_vector / np.linalg.norm(query_vector) - print("✓ Created query vector representing topic to forget") - - # 3. 创建 engine - engine = UnlearningEngine(epsilon=1.0, delta=1e-5) - - # 4. 遗忘所有相似的向量 - result = engine.unlearn_by_similarity( - query_vector=query_vector, - all_vectors=all_vectors, - all_vector_ids=all_ids, - similarity_threshold=0.3, # 相似度 > 0.3 的都遗忘 - max_unlearn=10, # 最多遗忘10个 - perturbation_strategy="adaptive", - ) - - print("\n🎯 Similarity-based Unlearning Result:") - print(f" Success: {result.success}") - print(f" Vectors forgotten: {result.num_vectors_unlearned}") - print(f" Privacy cost: ε={result.privacy_cost[0]:.4f}") - - if result.num_vectors_unlearned > 0: - result.metadata.get("perturbed_vectors", []) - print(f" Forgotten vector IDs: {result.metadata.get('message', 'N/A')}") - - print() - - -def example_privacy_budget_management(): - """示例5：隐私预算管理""" - print("=" * 70) - print("Example 5: Privacy Budget Management") - print("=" * 70) - - # 创建 engine 带有较小的总预算 - engine = UnlearningEngine( - epsilon=2.0, - delta=1e-5, - total_budget_epsilon=5.0, # 小预算 - enable_compensation=False, - ) - - vectors, ids = generate_test_data(n_vectors=50, dim=64) - - print("📊 Privacy Budget Tracking:") - print(f" Initial budget: ε={engine.privacy_accountant.total_epsilon_budget}") - - # 尝试多次操作直到预算耗尽 - operation_count = 0 - while True: - forget_idx = operation_count % len(vectors) - result = engine.unlearn_vectors( - vectors_to_forget=vectors[forget_idx : forget_idx + 1], - vector_ids_to_forget=[ids[forget_idx]], - perturbation_strategy="uniform", - ) - - operation_count += 1 - - if not result.success: - print(f"\n❌ Operation {operation_count} failed: {result.metadata.get('error')}") - print(f" Remaining budget: {result.metadata.get('remaining_budget')}") - break - else: - status = engine.get_privacy_status() - remaining = status["remaining_budget"] - print( - f" Operation {operation_count}: Success, " - f"Remaining ε={remaining['epsilon_remaining']:.4f}" - ) - - print() - - -def main(): - """运行所有示例""" - print("\n" + "=" * 70) - print("SAGE Unlearning Library - Direct Usage Examples") - print("=" * 70) - print("\n这些示例展示了如何直接使用 unlearning 库，无需 SAGE 运行时。") - print("适合：独立脚本、Jupyter 实验、快速原型验证\n") - - # 运行所有示例 - example_basic_unlearning() - example_custom_mechanism() - example_batch_unlearning() - example_similarity_based_unlearning() - example_privacy_budget_management() - - print("=" * 70) - print("✅ All examples completed successfully!") - print("=" * 70) - print("\n💡 Next steps:") - print(" 1. Try modifying the parameters (epsilon, delta, strategies)") - print(" 2. Implement your own privacy mechanism") - print(" 3. Test on real embeddings from your RAG system") - print(" 4. See usage_2_sage_function.py for SAGE integration\n") - - -if __name__ == "__main__": - main() diff --git a/packages/sage-libs/examples/rag/usage_2_sage_function.py b/packages/sage-libs/examples/rag/usage_2_sage_function.py deleted file mode 100644 index 6fe76ac63e..0000000000 --- a/packages/sage-libs/examples/rag/usage_2_sage_function.py +++ /dev/null @@ -1,226 +0,0 @@ -""" -SAGE Unlearning - Function Integration - -This module demonstrates how to integrate unlearning with SAGE Functions. - -Note: This example shows architectural patterns. A complete Pipeline -requires a full SAGE runtime environment. For testing and quick verification, -use usage_1_direct_library.py. - -This shows: -1. How to wrap unlearning in a Function class -2. How to integrate with SAGE data processing -3. How to manage state in functions -4. How to compose multiple functions - -**For Students**: Study this to understand how to integrate unlearning -with SAGE's data processing framework. -""" - -import numpy as np - -from sage.libs.privacy.unlearning import UnlearningEngine - - -class UnlearningFunctionExample: - """ - Demonstrates wrapping unlearning logic in a function-like class. - - In a full SAGE Pipeline, this would inherit from BaseFunction. - Here we show the pattern without requiring the full runtime. - """ - - def __init__(self, epsilon=1.0): - """Initialize the function""" - self.engine = UnlearningEngine(total_budget_epsilon=epsilon, enable_compensation=True) - self.vectors_processed = 0 - self.vectors_forgotten = 0 - - def process_vector(self, vector_id, vector, should_forget=False): - """ - Process a single vector. - - In a real Pipeline, this would be the execute() method. - """ - self.vectors_processed += 1 - - if should_forget: - self.vectors_forgotten += 1 - # In a real scenario, this would be batched - # For demo, we just track it - return { - "action": "forgot", - "vector_id": vector_id, - "privacy_cost": 0.1, # Simplified - } - - return {"action": "kept", "vector_id": vector_id, "vector": vector} - - -def example_function_pattern(): - """Example 1: Function Pattern Demonstration""" - print("\n" + "=" * 70) - print("Example 1: Unlearning Function Pattern") - print("=" * 70) - - func = UnlearningFunctionExample(epsilon=1.0) - - # Simulate processing vectors - vectors = np.random.randn(10, 64).astype(np.float32) - vectors = vectors / (np.linalg.norm(vectors, axis=1, keepdims=True) + 1e-10) - - forget_indices = {0, 3, 7} # Which vectors to forget - - print(f"Processing {len(vectors)} vectors...") - results = [] - - for i, vector in enumerate(vectors): - should_forget = i in forget_indices - result = func.process_vector(f"doc_{i}", vector, should_forget) - results.append(result) - if should_forget: - print(f" - Vector {i}: Forgotten") - - print("\n📊 Summary:") - print(f" Vectors processed: {func.vectors_processed}") - print(f" Vectors forgotten: {func.vectors_forgotten}") - forgotten_count = sum(1 for r in results if r["action"] == "forgot") - print(f" Verified forgotten: {forgotten_count}") - - -def example_batched_unlearning(): - """Example 2: Batched Unlearning in Functions""" - print("\n" + "=" * 70) - print("Example 2: Batched Unlearning") - print("=" * 70) - - # Create function with batching - engine = UnlearningEngine(total_budget_epsilon=10.0) - - # Simulate batching vectors - all_vectors = np.random.randn(100, 128).astype(np.float32) - all_vectors = all_vectors / (np.linalg.norm(all_vectors, axis=1, keepdims=True) + 1e-10) - all_ids = [f"doc_{i}" for i in range(100)] - - # Forget vectors in batches - batch_size = 10 - for batch_idx in range(3): - start_idx = batch_idx * batch_size - end_idx = start_idx + batch_size - - forget_vectors = all_vectors[start_idx:end_idx] - forget_ids = all_ids[start_idx:end_idx] - - result = engine.unlearn_vectors( - vectors_to_forget=forget_vectors, - vector_ids_to_forget=forget_ids, - all_vectors=all_vectors, - all_vector_ids=all_ids, - perturbation_strategy="selective", - ) - - if result.success: - print( - f" Batch {batch_idx}: Forgotten {result.num_vectors_unlearned} vectors, " - f"Privacy cost: ε={result.privacy_cost[0]:.4f}" - ) - - -def example_stateful_processing(): - """Example 3: Stateful Vector Processing""" - print("\n" + "=" * 70) - print("Example 3: Stateful Vector Processing") - print("=" * 70) - - class StatefulProcessor: - """Processor that maintains state across calls""" - - def __init__(self): - self.state = { - "vectors_accumulated": [], - "ids_accumulated": [], - "total_forgotten": 0, - "privacy_spent": 0.0, - } - self.engine = UnlearningEngine(total_budget_epsilon=5.0) - - def add_vector(self, vector_id, vector): - self.state["vectors_accumulated"].append(vector) - self.state["ids_accumulated"].append(vector_id) - - def flush_and_forget(self, num_to_forget=3): - """Accumulate and then forget""" - if len(self.state["vectors_accumulated"]) < num_to_forget: - return None - - forget_vectors = np.array(self.state["vectors_accumulated"][:num_to_forget]) - forget_ids = self.state["ids_accumulated"][:num_to_forget] - all_vectors = np.array(self.state["vectors_accumulated"]) - all_ids = self.state["ids_accumulated"] - - result = self.engine.unlearn_vectors( - vectors_to_forget=forget_vectors, - vector_ids_to_forget=forget_ids, - all_vectors=all_vectors, - all_vector_ids=all_ids, - perturbation_strategy="uniform", - ) - - if result.success: - self.state["total_forgotten"] += result.num_vectors_unlearned - self.state["privacy_spent"] += result.privacy_cost[0] - # Clear processed vectors - self.state["vectors_accumulated"] = self.state["vectors_accumulated"][ - num_to_forget: - ] - self.state["ids_accumulated"] = self.state["ids_accumulated"][num_to_forget:] - - return result - - processor = StatefulProcessor() - - # Add vectors - for i in range(10): - vector = np.random.randn(64).astype(np.float32) - vector = vector / (np.linalg.norm(vector) + 1e-10) - processor.add_vector(f"doc_{i}", vector) - - print("Added 10 vectors to processor") - - # Flush and forget in batches - result = processor.flush_and_forget(num_to_forget=5) - if result: - print( - f"Batch 1: Forgotten {result.num_vectors_unlearned}, Privacy cost: ε={result.privacy_cost[0]:.4f}" - ) - - print("\n📊 Final state:") - print(f" Total vectors forgotten: {processor.state['total_forgotten']}") - print(f" Total privacy spent: ε={processor.state['privacy_spent']:.4f}") - print(f" Vectors still in buffer: {len(processor.state['vectors_accumulated'])}") - - -def main(): - """Run all examples""" - print("\n" + "=" * 70) - print("SAGE Unlearning Library - Function Integration Examples") - print("=" * 70) - print("\n这些示例展示了如何在 SAGE Function 中使用 unlearning 库。") - print("These examples show how to integrate unlearning with SAGE Functions.\n") - - # Run examples - example_function_pattern() - example_batched_unlearning() - example_stateful_processing() - - print("\n" + "=" * 70) - print("✅ All examples completed successfully!") - print("=" * 70) - print("\n💡 Next steps:") - print(" 1. Study the patterns shown here") - print(" 2. Implement custom data processing logic") - print(" 3. See usage_3_memory_service.py for service integration\n") - - -if __name__ == "__main__": - main() diff --git a/packages/sage-libs/examples/rag/usage_3_memory_service.py b/packages/sage-libs/examples/rag/usage_3_memory_service.py deleted file mode 100644 index effc1e5b5e..0000000000 --- a/packages/sage-libs/examples/rag/usage_3_memory_service.py +++ /dev/null @@ -1,472 +0,0 @@ -""" -Usage 3: MemoryService Integration -================================== - -将 unlearning 集成到 MemoryService 中。 - -适用场景： -- RAG 系统中的隐私遗忘 -- 需要从 VDB 中检索和更新向量 -- 与记忆管理系统集成 -- 完整的数据生命周期管理 - -优势： -- 与 VDB collection 无缝集成 -- 支持向量检索和更新 -- 遗忘操作的持久化 -- 隐私预算管理 -""" - -import os -from typing import Any - -import numpy as np - -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.api.service.base_service import BaseService -from sage.libs.privacy.unlearning import UnlearningEngine -from sage.middleware.components.sage_mem.neuromem.memory_collection.vdb_collection import ( - VDBMemoryCollection, -) -from sage.middleware.components.sage_mem.neuromem.memory_manager import MemoryManager - - -class DPMemoryService(BaseService): - """ - 带差分隐私的内存服务 - - 支持使用 DP 遗忘操作从 VDB 中安全删除数据。 - """ - - def __init__(self, data_dir: str | None = None, epsilon: float = 1.0, delta: float = 1e-5): - super().__init__() - - # 初始化内存管理器 - if data_dir is None: - data_dir = os.path.join(os.getcwd(), "data", "dp_memory_service") - os.makedirs(data_dir, exist_ok=True) - - self.manager = MemoryManager(data_dir) - self.logger.info(f"Initialized DPMemoryService with data_dir={data_dir}") - - # 初始化 DP unlearning engine - self.unlearning_engine = UnlearningEngine( - epsilon=epsilon, - delta=delta, - total_budget_epsilon=100.0, - enable_compensation=True, - ) - - self.logger.info(f"Initialized UnlearningEngine with ε={epsilon}, δ={delta}") - - def create_collection(self, collection_name: str, config: dict | None = None) -> bool: - """创建 VDB collection""" - try: - if config is None: - config = { - "name": collection_name, - "backend_type": "VDB", - "description": f"DP-enabled collection: {collection_name}", - } - - collection = self.manager.create_collection(config) - - if collection is None: - self.logger.warning(f"Failed to create collection: {collection_name}") - return False - - # 创建默认索引 - index_config = { - "name": "global_index", - "embedding_model": "mockembedder", - "dim": 128, - "backend_type": "FAISS", - "description": "Global index for similarity search", - } - collection.create_index(index_config) # type: ignore[attr-defined] - # Note: index initialization with vectors happens when data is inserted - # via store_memory which calls collection.insert with pre-computed vectors - - self.logger.info(f"✓ Created collection: {collection_name}") - return True - - except Exception as e: - self.logger.error(f"Error creating collection: {e}") - return False - - def store_memory( - self, - collection_name: str, - content: str, - vector: np.ndarray, - metadata: dict[str, Any] | None = None, - ) -> str | None: - """ - 存储记忆到 VDB collection - - Args: - collection_name: Collection 名称 - content: 文本内容 - vector: 向量表示 - metadata: 元数据 - - Returns: - Memory ID 或 None - """ - try: - collection = self.manager.get_collection(collection_name) - if collection is None: - self.logger.error(f"Collection not found: {collection_name}") - return None - - # 确保是 VDB 类型的 collection - if not isinstance(collection, VDBMemoryCollection): - self.logger.error(f"Collection {collection_name} is not a VDB collection") - return None - - # VDBMemoryCollection.insert 使用 (index_name, raw_data, vector, metadata) - memory_id = collection.insert( - index_name="global_index", - raw_data=content, - vector=vector, - metadata=metadata, - ) - - self.logger.debug(f"Stored memory: {memory_id}") - return memory_id - - except Exception as e: - self.logger.error(f"Error storing memory: {e}") - return None - - def retrieve_memories( - self, collection_name: str, query_vector: np.ndarray, topk: int = 5 - ) -> list[dict[str, Any]]: - """ - 检索相似的记忆 - - Args: - collection_name: Collection 名称 - query_vector: 查询向量 - topk: 返回结果数量 - - Returns: - 相似记忆列表 - """ - try: - collection = self.manager.get_collection(collection_name) - if collection is None: - self.logger.error(f"Collection not found: {collection_name}") - return [] - - results = collection.retrieve( # type: ignore[call-arg] - query_vector=query_vector, - index_name="global_index", - topk=topk, - with_metadata=True, - ) - - return results # type: ignore[return-value] - - except Exception as e: - self.logger.error(f"Error retrieving memories: {e}") - return [] - - def forget_with_dp( - self, - collection_name: str, - memory_ids: list[str], - perturbation_strategy: str = "adaptive", - ) -> dict[str, Any]: - """ - 使用差分隐私遗忘指定的记忆 - - Args: - collection_name: Collection 名称 - memory_ids: 要遗忘的记忆 IDs - perturbation_strategy: 扰动策略 - - Returns: - 遗忘操作结果 - """ - try: - collection = self.manager.get_collection(collection_name) - if collection is None: - self.logger.error(f"Collection not found: {collection_name}") - return { - "success": False, - "error": f"Collection not found: {collection_name}", - } - - # 从 VDB index 获取要遗忘的向量 - index = collection.index_info.get("global_index", {}).get("index") # type: ignore[attr-defined] - if index is None: - self.logger.error(f"Index not found in collection: {collection_name}") - return {"success": False, "error": "Index not found"} - - vectors_to_forget = [] - valid_ids = [] - - for mem_id in memory_ids: - # 从 vector_store 获取向量 - if hasattr(index, "vector_store") and mem_id in index.vector_store: - vector = index.vector_store[mem_id] - vectors_to_forget.append(vector) - valid_ids.append(mem_id) - else: - self.logger.warning(f"Vector not found for memory ID: {mem_id}") - - if not vectors_to_forget: - self.logger.warning("No vectors found to forget") - return {"success": False, "error": "No vectors found"} - - # 获取所有向量用于补偿 - all_vectors = [] - all_ids = [] - if hasattr(index, "vector_store"): - for vid, vector in index.vector_store.items(): - if vid not in self.unlearning_engine.privacy_accountant.get_remaining_budget(): - all_vectors.append(vector) - all_ids.append(vid) - - vectors_array = np.array(vectors_to_forget) - if all_vectors: - all_vectors_array = np.array(all_vectors) - else: - all_vectors_array = vectors_array - - self.logger.info(f"Starting DP unlearning for {len(valid_ids)} memories...") - - # 执行 DP 遗忘 - result = self.unlearning_engine.unlearn_vectors( - vectors_to_forget=vectors_array, - vector_ids_to_forget=valid_ids, - all_vectors=all_vectors_array, - all_vector_ids=all_ids, - perturbation_strategy=perturbation_strategy, - ) - - if not result.success: - error_msg = result.metadata.get("error", "Unknown error") - self.logger.error(f"Unlearning failed: {error_msg}") - return {"success": False, "error": error_msg} - - # 获取扰动后的向量 - perturbed_vectors = result.metadata.get("perturbed_vectors", []) - - # 更新 VDB 中的向量 - updated_count = 0 - for mem_id, perturbed_vec in zip(valid_ids, perturbed_vectors): - try: - # 更新向量 - if hasattr(index, "update"): - index.update(mem_id, perturbed_vec) - else: - # 备选：删除后重新插入 - index.delete(mem_id) - index.insert(perturbed_vec, mem_id) - updated_count += 1 - except Exception as e: - self.logger.error(f"Failed to update vector for {mem_id}: {e}") - - # 持久化更改 - self.manager.store_collection(collection_name) - - self.logger.info(f"✓ Successfully forgotten {updated_count} memories") - - # 返回结果 - status = self.unlearning_engine.get_privacy_status() - remaining = status["remaining_budget"] - - return { - "success": True, - "num_forgotten": updated_count, - "privacy_cost": { - "epsilon": result.privacy_cost[0], - "delta": result.privacy_cost[1], - }, - "remaining_budget": { - "epsilon": remaining["epsilon_remaining"], - "delta": remaining["delta_remaining"], - }, - "budget_utilization": status["accountant_summary"]["budget_utilization"], - } - - except Exception as e: - self.logger.error(f"Error in forget_with_dp: {e}") - return {"success": False, "error": str(e)} - - def get_privacy_status(self) -> dict[str, Any]: - """获取当前隐私预算状态""" - return self.unlearning_engine.get_privacy_status() - - -def example_basic_dp_memory(): - """示例1：基础 DP Memory Service""" - print("\n" + "=" * 70) - print("Example 1: Basic DP Memory Service") - print("=" * 70) - - # 创建服务 - service = DPMemoryService(epsilon=1.0, delta=1e-5) - - # 创建 collection - service.create_collection("documents") - - # 存储一些记忆 - print("\n📝 Storing memories...") - memory_ids = [] - for i in range(5): - content = f"This is document {i} with sensitive information" - vector = np.random.randn(128).astype(np.float32) - vector = vector / (np.linalg.norm(vector) + 1e-10) - metadata = {"doc_index": i, "category": "sensitive" if i % 2 == 0 else "normal"} - - mem_id = service.store_memory( - collection_name="documents", - content=content, - vector=vector, - metadata=metadata, - ) - if mem_id: - memory_ids.append(mem_id) - print(f" ✓ Stored memory {i}: {mem_id[:8]}...") - - # 检索 - print("\n🔍 Retrieving memories...") - query_vector = np.random.randn(128).astype(np.float32) - query_vector = query_vector / (np.linalg.norm(query_vector) + 1e-10) - results = service.retrieve_memories("documents", query_vector, topk=3) - print(f" Found {len(results)} results") - - # 遗忘其中一些 - print("\n🔒 Forgetting sensitive documents...") - if memory_ids: - forget_ids = memory_ids[::2] # 每隔一个遗忘 - result = service.forget_with_dp( - collection_name="documents", - memory_ids=forget_ids, - perturbation_strategy="selective", - ) - - print(f" Success: {result['success']}") - if result["success"]: - print(f" Forgotten: {result['num_forgotten']} documents") - print(f" Privacy cost: ε={result['privacy_cost']['epsilon']:.4f}") - print(f" Remaining budget: ε={result['remaining_budget']['epsilon']:.4f}") - - print() - - -def example_privacy_budget_management(): - """示例2：隐私预算管理""" - print("\n" + "=" * 70) - print("Example 2: Privacy Budget Management") - print("=" * 70) - - service = DPMemoryService(epsilon=0.5, delta=1e-5) - service.create_collection("sensitive_data") - - # 创建测试数据 - memory_ids = [] - for i in range(10): - content = f"Document {i}" - vector = np.random.randn(128).astype(np.float32) - vector = vector / (np.linalg.norm(vector) + 1e-10) - mem_id = service.store_memory("sensitive_data", content, vector) - if mem_id: - memory_ids.append(mem_id) - - print("\n📊 Privacy Budget Tracking:") - - # 多次遗忘操作 - forget_count = 0 - for batch_idx in range(3): - # 每批遗忘 2 个 - batch_ids = memory_ids[batch_idx * 2 : (batch_idx + 1) * 2] - if not batch_ids: - break - - result = service.forget_with_dp( - collection_name="sensitive_data", - memory_ids=batch_ids, - perturbation_strategy="uniform", - ) - - forget_count += 1 - - if result["success"]: - print(f" Batch {forget_count}: Success") - print(f" Forgotten: {result['num_forgotten']}") - print(f" Remaining ε: {result['remaining_budget']['epsilon']:.4f}") - else: - print(f" Batch {forget_count}: Failed - {result['error']}") - break - - print() - - -def example_multi_collection(): - """示例3：多 Collection 管理""" - print("\n" + "=" * 70) - print("Example 3: Multi-Collection Management") - print("=" * 70) - - service = DPMemoryService(epsilon=1.0) - - # 创建多个 collection - collections = ["public", "internal", "confidential"] - for col_name in collections: - service.create_collection(col_name) - print(f" ✓ Created collection: {col_name}") - - # 向不同 collection 存储数据 - print("\n📝 Storing data to different collections...") - for col_name in collections: - for i in range(3): - content = f"{col_name} document {i}" - vector = np.random.randn(128).astype(np.float32) - vector = vector / (np.linalg.norm(vector) + 1e-10) - mem_id = service.store_memory(col_name, content, vector) - if mem_id: - print(f" ✓ {col_name}: {mem_id[:8]}...") - - # 从 confidential collection 遗忘一些数据 - print("\n🔒 Forgetting from confidential collection...") - query_vector = np.random.randn(128).astype(np.float32) - query_vector = query_vector / (np.linalg.norm(query_vector) + 1e-10) - results = service.retrieve_memories("confidential", query_vector, topk=2) - if results: - # 获取第一个结果的 ID（这是一个简化版，实际需要追踪 ID） - print(f" Found {len(results)} documents in confidential collection") - - print() - - -def main(): - """运行所有示例""" - print("\n" + "=" * 70) - print("SAGE Unlearning Library - MemoryService Integration") - print("=" * 70) - print("\n这些示例展示了如何将 unlearning 集成到 MemoryService。") - print("适合：RAG 系统的隐私遗忘、VDB 集成、数据生命周期管理\n") - - # 禁用调试日志 - CustomLogger.disable_global_console_debug() - - # 运行示例 - example_basic_dp_memory() - example_privacy_budget_management() - example_multi_collection() - - print("=" * 70) - print("✅ All examples completed successfully!") - print("=" * 70) - print("\n💡 Next steps:") - print(" 1. Integrate with real embedding models") - print(" 2. Implement custom forgetting policies") - print(" 3. See basic_unlearning_demo.py for full RAG example\n") - - -if __name__ == "__main__": - main() diff --git a/packages/sage-libs/examples/rag/usage_4_complete_rag.py b/packages/sage-libs/examples/rag/usage_4_complete_rag.py deleted file mode 100644 index 2347833c01..0000000000 --- a/packages/sage-libs/examples/rag/usage_4_complete_rag.py +++ /dev/null @@ -1,582 +0,0 @@ -""" -Usage 4: Complete RAG System with DP Unlearning -=============================================== - -完整的 RAG 系统中的隐私遗忘场景。 - -适用场景： -- 完整的 RAG Pipeline -- 用户请求删除数据 -- 组织数据删除义务（GDPR 等） -- 恶意数据清理 - -特性： -- 检索 → 筛选 → 遗忘 → 更新 的完整流程 -- 多个查询的批量处理 -- 隐私预算跟踪 -- 操作日志和审计 -""" - -import os -from datetime import datetime -from typing import Any - -import numpy as np - -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.api.service.base_service import BaseService -from sage.libs.privacy.unlearning import UnlearningEngine -from sage.middleware.components.sage_mem.neuromem.memory_collection.vdb_collection import ( - VDBMemoryCollection, -) -from sage.middleware.components.sage_mem.neuromem.memory_manager import MemoryManager - - -class RAGUnlearningSystem(BaseService): - """RAG 系统中的隐私遗忘管理""" - - def __init__(self, data_dir: str | None = None, epsilon: float = 1.0): - super().__init__() - - if data_dir is None: - data_dir = os.path.join(os.getcwd(), "data", "rag_unlearning") - os.makedirs(data_dir, exist_ok=True) - - self.data_dir = data_dir - self.manager = MemoryManager(data_dir) - self.unlearning_engine = UnlearningEngine( - epsilon=epsilon, - delta=1e-5, - total_budget_epsilon=100.0, - enable_compensation=True, - ) - - # 审计日志 - self.audit_log = [] - - self.logger.info("RAGUnlearningSystem initialized") - - def initialize_rag_corpus(self, collection_name: str, documents: list[dict[str, Any]]) -> bool: - """ - 初始化 RAG corpus - - Args: - collection_name: Collection 名称 - documents: 文档列表，每个包含 'id', 'content', 'vector', 'metadata' - - Returns: - 是否成功 - """ - try: - # 创建 collection - collection = self.manager.create_collection( - { - "name": collection_name, - "backend_type": "VDB", - "description": f"RAG corpus: {collection_name}", - } - ) - - if collection is None: - return False - - # 创建索引 - index_config = { - "name": "content_index", - "embedding_model": "mockembedder", - "dim": 128, - "backend_type": "FAISS", - "description": "Content search index", - } - collection.create_index(index_config) # type: ignore[attr-defined] - - # 确保是 VDB collection - if not isinstance(collection, VDBMemoryCollection): - self.logger.error("Collection is not a VDB collection") - return False - - # 插入文档 - VDBMemoryCollection.insert(content, index_names, vector, metadata) - for doc in documents: - collection.insert( - content=doc["content"], - index_names="content_index", - vector=doc["vector"], - metadata=doc.get("metadata", {}), - ) - - # Index is initialized through individual inserts, no need for init_index - self.manager.store_collection(collection_name) - - self.logger.info(f"✓ Initialized RAG corpus with {len(documents)} documents") - self._audit_log("INIT_CORPUS", collection_name, len(documents)) - - return True - - except Exception as e: - self.logger.error(f"Error initializing corpus: {e}") - return False - - def retrieve_relevant_documents( - self, collection_name: str, query_vector: np.ndarray, topk: int = 5 - ) -> list[dict[str, Any]]: - """检索相关文档""" - try: - collection = self.manager.get_collection(collection_name) - if collection is None: - return [] - - # 确保是 VDB collection - if not isinstance(collection, VDBMemoryCollection): - self.logger.error("Collection is not a VDB collection") - return [] - - # VDBMemoryCollection.retrieve(query_vector, index_name, topk, ...) - results = collection.retrieve( - query_vector=query_vector, - index_name="content_index", - topk=topk, - with_metadata=True, - ) - - # retrieve 可能返回 None - if results is None: - return [] - - return results # type: ignore[return-value] - - except Exception as e: - self.logger.error(f"Error retrieving documents: {e}") - return [] - - def forget_documents( - self, - collection_name: str, - document_ids: list[str], - reason: str = "user_request", - user_id: str | None = None, - ) -> dict[str, Any]: - """ - 遗忘指定的文档 - - Args: - collection_name: Collection 名称 - document_ids: 要遗忘的文档 IDs - reason: 遗忘原因 - user_id: 发起遗忘的用户 ID - - Returns: - 操作结果 - """ - try: - collection = self.manager.get_collection(collection_name) - if collection is None: - return {"success": False, "error": "Collection not found"} - - index = collection.index_info.get("content_index", {}).get("index") # type: ignore[attr-defined] - if index is None: - return {"success": False, "error": "Index not found"} - - # 收集要遗忘的向量 - vectors_to_forget = [] - valid_ids = [] - - for doc_id in document_ids: - if hasattr(index, "vector_store") and doc_id in index.vector_store: - vector = index.vector_store[doc_id] - vectors_to_forget.append(vector) - valid_ids.append(doc_id) - - if not vectors_to_forget: - return {"success": False, "error": "No documents found to forget"} - - # 获取所有向量用于补偿 - all_vectors = [] - all_ids = [] - if hasattr(index, "vector_store"): - for vid, vector in index.vector_store.items(): - if vid not in valid_ids: # 排除要遗忘的向量 - all_vectors.append(vector) - all_ids.append(vid) - - self.logger.info(f"Starting DP unlearning for {len(valid_ids)} documents...") - - # 执行 DP 遗忘 - vectors_array = np.array(vectors_to_forget) - all_vectors_array = np.array(all_vectors) if all_vectors else vectors_array - - result = self.unlearning_engine.unlearn_vectors( - vectors_to_forget=vectors_array, - vector_ids_to_forget=valid_ids, - all_vectors=all_vectors_array, - all_vector_ids=all_ids, - perturbation_strategy="adaptive", - ) - - if not result.success: - error = result.metadata.get("error", "Unknown error") - self.logger.error(f"Unlearning failed: {error}") - return {"success": False, "error": error} - - # 更新 VDB 中的向量 - perturbed_vectors = result.metadata.get("perturbed_vectors", []) - updated_count = 0 - - for doc_id, perturbed_vec in zip(valid_ids, perturbed_vectors): - try: - if hasattr(index, "update"): - index.update(doc_id, perturbed_vec) - else: - index.delete(doc_id) - index.insert(perturbed_vec, doc_id) - updated_count += 1 - except Exception as e: - self.logger.error(f"Failed to update vector for {doc_id}: {e}") - - # 持久化 - self.manager.store_collection(collection_name) - - # 记录审计日志 - self._audit_log( - "FORGET_DOCUMENTS", - collection_name, - len(valid_ids), - extra={ - "reason": reason, - "user_id": user_id, - "privacy_cost": result.privacy_cost, - }, - ) - - status = self.unlearning_engine.get_privacy_status() - remaining = status["remaining_budget"] - - self.logger.info(f"✓ Successfully forgotten {updated_count} documents") - - return { - "success": True, - "num_forgotten": updated_count, - "privacy_cost": { - "epsilon": result.privacy_cost[0], - "delta": result.privacy_cost[1], - }, - "remaining_budget": { - "epsilon": remaining["epsilon_remaining"], - "delta": remaining["delta_remaining"], - }, - } - - except Exception as e: - self.logger.error(f"Error in forget_documents: {e}") - return {"success": False, "error": str(e)} - - def handle_user_deletion_request( - self, collection_name: str, user_id: str, user_keywords: list[str] | None = None - ) -> dict[str, Any]: - """ - 处理用户数据删除请求（如 GDPR 删除权） - - Args: - collection_name: Collection 名称 - user_id: 用户 ID - user_keywords: 用户特定关键词（可选） - - Returns: - 处理结果 - """ - try: - collection = self.manager.get_collection(collection_name) - if collection is None: - return {"success": False, "error": "Collection not found"} - - # 查找属于该用户的所有文档 - all_ids = collection.get_all_ids() - user_docs = [] - - for doc_id in all_ids: - metadata = collection.metadata_storage.get(doc_id) - if metadata and metadata.get("user_id") == user_id: - user_docs.append(doc_id) - - if not user_docs: - self.logger.info(f"No documents found for user {user_id}") - return { - "success": True, - "num_forgotten": 0, - "message": "No documents to delete", - } - - self.logger.info(f"Found {len(user_docs)} documents for user {user_id}") - - # 遗忘用户所有文档 - result = self.forget_documents( - collection_name=collection_name, - document_ids=user_docs, - reason="user_deletion_request", - user_id=user_id, - ) - - if result["success"]: - self.logger.info(f"✓ Deleted all data for user {user_id}") - - return result - - except Exception as e: - self.logger.error(f"Error handling deletion request: {e}") - return {"success": False, "error": str(e)} - - def handle_malicious_content_removal( - self, collection_name: str, detection_keywords: list[str] - ) -> dict[str, Any]: - """ - 处理恶意内容移除 - - Args: - collection_name: Collection 名称 - detection_keywords: 恶意内容关键词 - - Returns: - 处理结果 - """ - try: - collection = self.manager.get_collection(collection_name) - if collection is None: - return {"success": False, "error": "Collection not found"} - - # 查找包含恶意内容的文档 - all_ids = collection.get_all_ids() - malicious_docs = [] - - for doc_id in all_ids: - content = collection.text_storage.get(doc_id) - if content: - for keyword in detection_keywords: - if keyword.lower() in content.lower(): - malicious_docs.append(doc_id) - break - - if not malicious_docs: - self.logger.info("No malicious content detected") - return { - "success": True, - "num_forgotten": 0, - "message": "No malicious content found", - } - - self.logger.warning(f"Detected {len(malicious_docs)} documents with malicious content") - - # 遗忘恶意内容 - result = self.forget_documents( - collection_name=collection_name, - document_ids=malicious_docs, - reason="malicious_content", - user_id="system", - ) - - return result - - except Exception as e: - self.logger.error(f"Error handling malicious content: {e}") - return {"success": False, "error": str(e)} - - def get_audit_log(self) -> list[dict[str, Any]]: - """获取审计日志""" - return self.audit_log - - def _audit_log(self, operation: str, collection: str, count: int, extra: dict | None = None): - """记录审计事件""" - log_entry = { - "timestamp": datetime.now().isoformat(), - "operation": operation, - "collection": collection, - "count": count, - "extra": extra or {}, - } - self.audit_log.append(log_entry) - - -def example_basic_rag(): - """示例1：基础 RAG 系统""" - print("\n" + "=" * 70) - print("Example 1: Basic RAG System with Unlearning") - print("=" * 70) - - system = RAGUnlearningSystem(epsilon=1.0) - - # 创建示例文档 - documents = [ - { - "id": "doc_001", - "content": "Machine learning is a subset of artificial intelligence", - "metadata": {"user_id": "user_1", "category": "public"}, - }, - { - "id": "doc_002", - "content": "Deep learning uses neural networks with multiple layers", - "metadata": {"user_id": "user_1", "category": "public"}, - }, - { - "id": "doc_003", - "content": "Natural language processing is used for text analysis", - "metadata": {"user_id": "user_2", "category": "public"}, - }, - { - "id": "doc_004", - "content": "Computer vision helps machines understand images", - "metadata": {"user_id": "user_2", "category": "sensitive"}, - }, - { - "id": "doc_005", - "content": "Reinforcement learning enables agents to learn from interaction", - "metadata": {"user_id": "user_3", "category": "public"}, - }, - ] - - # 为每个文档添加随机向量 - for doc in documents: - doc["vector"] = np.random.randn(128).astype(np.float32) - doc["vector"] = doc["vector"] / (np.linalg.norm(doc["vector"]) + 1e-10) - - # 初始化 corpus - system.initialize_rag_corpus("knowledge_base", documents) - - # 检索 - print("\n🔍 Retrieving documents...") - query_vector = np.random.randn(128).astype(np.float32) - query_vector = query_vector / (np.linalg.norm(query_vector) + 1e-10) - results = system.retrieve_relevant_documents("knowledge_base", query_vector, topk=3) - print(f" Found {len(results)} relevant documents") - - # 用户请求删除 - print("\n🗑️ Processing user deletion request...") - result = system.handle_user_deletion_request("knowledge_base", "user_1") - print(f" Success: {result['success']}") - if result["success"]: - print(f" Deleted: {result['num_forgotten']} documents") - if "privacy_cost" in result: - print(f" Privacy cost: ε={result['privacy_cost']['epsilon']:.4f}") - - print() - - -def example_malicious_content(): - """示例2：恶意内容检测和移除""" - print("\n" + "=" * 70) - print("Example 2: Malicious Content Detection and Removal") - print("=" * 70) - - system = RAGUnlearningSystem(epsilon=1.0) - - # 创建包含一些恶意内容的文档 - documents = [ - { - "id": "doc_001", - "content": "This is normal technical content about machine learning", - "metadata": {"user_id": "user_1", "category": "normal"}, - }, - { - "id": "doc_002", - "content": "Spam content: click here for free money!!!", - "metadata": {"user_id": "user_2", "category": "spam"}, - }, - { - "id": "doc_003", - "content": "More legitimate deep learning information", - "metadata": {"user_id": "user_1", "category": "normal"}, - }, - { - "id": "doc_004", - "content": "Malware distribution: download now!!!", - "metadata": {"user_id": "user_3", "category": "malicious"}, - }, - ] - - for doc in documents: - doc["vector"] = np.random.randn(128).astype(np.float32) - doc["vector"] = doc["vector"] / (np.linalg.norm(doc["vector"]) + 1e-10) - - system.initialize_rag_corpus("content_db", documents) - - # 检测并移除恶意内容 - print("\n🚨 Detecting malicious content...") - result = system.handle_malicious_content_removal( - "content_db", detection_keywords=["spam", "malware", "!!!"] - ) - - print(f" Success: {result['success']}") - if result["success"]: - print(f" Removed: {result['num_forgotten']} malicious documents") - - print() - - -def example_audit_log(): - """示例3：审计日志""" - print("\n" + "=" * 70) - print("Example 3: Audit Log and Compliance") - print("=" * 70) - - system = RAGUnlearningSystem(epsilon=1.0) - - # 创建文档 - documents = [] - for i in range(10): - documents.append( - { - "id": f"doc_{i:03d}", - "content": f"Document {i} content", - "metadata": {"user_id": f"user_{i % 3}", "category": "normal"}, - "vector": np.random.randn(128).astype(np.float32) - / np.linalg.norm(np.random.randn(128).astype(np.float32)), - } - ) - - system.initialize_rag_corpus("audit_test", documents) - - # 执行多个操作 - print("\n📝 Performing operations...") - - # 用户 0 删除请求 - system.handle_user_deletion_request("audit_test", "user_0") - print(" ✓ User deletion request processed") - - # 恶意内容检测（无恶意内容） - system.handle_malicious_content_removal("audit_test", ["malware"]) - print(" ✓ Malicious content check completed") - - # 显示审计日志 - print("\n📋 Audit Log:") - for entry in system.get_audit_log(): - print( - f" {entry['timestamp']}: {entry['operation']} on {entry['collection']} ({entry['count']} items)" - ) - - print() - - -def main(): - """运行所有示例""" - print("\n" + "=" * 70) - print("SAGE Unlearning - Complete RAG System Examples") - print("=" * 70) - print("\n这些示例展示了在完整 RAG 系统中使用隐私遗忘。") - print("包括：用户删除请求、恶意内容移除、合规审计\n") - - CustomLogger.disable_global_console_debug() - - # 运行示例 - example_basic_rag() - example_malicious_content() - example_audit_log() - - print("=" * 70) - print("✅ All examples completed successfully!") - print("=" * 70) - print("\n💡 Key Takeaways:") - print(" 1. Unlearning 库提供灵活的隐私保护机制") - print(" 2. 支持多种遗忘场景（用户请求、恶意内容等）") - print(" 3. 完整的审计日志用于合规") - print(" 4. 隐私预算管理确保整体隐私保证\n") - - -if __name__ == "__main__": - main() diff --git a/packages/sage-libs/examples/unlearning/README.md b/packages/sage-libs/examples/unlearning/README.md deleted file mode 100644 index 3c5d775f68..0000000000 --- a/packages/sage-libs/examples/unlearning/README.md +++ /dev/null @@ -1,71 +0,0 @@ -# Machine Unlearning Examples - -This directory contains examples and tutorials for using SAGE's machine unlearning capabilities. - -## Overview - -Machine unlearning enables models to "forget" specific training data, which is crucial for: - -- **Privacy compliance**: GDPR "Right to be Forgotten" -- **Data removal**: Removing incorrect or outdated data -- **Bias mitigation**: Removing biased training samples -- **Security**: Removing poisoned data from models - -## Files - -- `machine_unlearning_examples.py` - Comprehensive examples demonstrating: - - Basic unlearning workflow - - Differential privacy mechanisms - - Evaluation metrics - - Algorithm comparisons - - Real-world GDPR compliance scenario - -## Running Examples - -```bash -# From SAGE root directory -cd examples/tutorials/unlearning -python machine_unlearning_examples.py -``` - -## Key Concepts - -### Differential Privacy Mechanisms - -- **Gaussian Mechanism**: Provides (ε,δ)-differential privacy by adding Gaussian noise -- **Laplace Mechanism**: Provides ε-differential privacy by adding Laplace noise - -### Core Components - -1. **Privacy Mechanisms** (`sage.libs.unlearning.algorithms`) - - - `GaussianMechanism`: (ε,δ)-DP noise addition - - `LaplaceMechanism`: ε-DP noise addition - -1. **Unlearning Engine** (`sage.libs.unlearning.dp_unlearning`) - - - `UnlearningEngine`: Orchestrates the unlearning process - - `VectorPerturbation`: Applies noise to model parameters - - `NeighborCompensation`: Compensates for neighboring records - - `PrivacyAccountant`: Tracks privacy budget across operations - -1. **Evaluation Tools** (`sage.libs.unlearning.evaluation`) - - - `UnlearningMetrics`: Evaluates forgetting quality and model utility - -## Further Reading - -- Implementation details: `packages/sage-libs/src/sage/libs/unlearning/` -- Research papers: See docstrings in mechanism implementations -- SAGE documentation: `docs/` directory - -## Student Research Tasks - -The unlearning module includes TODO items for advanced research: - -- Analytic Gaussian mechanism (tighter bounds) -- Concentrated differential privacy -- Privacy amplification by subsampling -- Advanced composition techniques - -See the source code in `packages/sage-libs/src/sage/libs/unlearning/algorithms/` for details. diff --git a/packages/sage-libs/examples/unlearning/basic_unlearning_demo.py b/packages/sage-libs/examples/unlearning/basic_unlearning_demo.py deleted file mode 100644 index 120fa12359..0000000000 --- a/packages/sage-libs/examples/unlearning/basic_unlearning_demo.py +++ /dev/null @@ -1,143 +0,0 @@ -""" -Basic Unlearning Demo -====================== - -Demonstrates the basic usage of the SAGE Unlearning Library. - -This example shows: -1. How to create vectors (simulating embeddings) -2. How to use the UnlearningEngine -3. How to evaluate unlearning quality - -**For Students**: This is your starting point! -Modify and extend this example to test your algorithms. - -@test:allow-demo -""" - -import numpy as np - -from sage.libs.privacy.unlearning import UnlearningEngine - - -def generate_synthetic_vectors(n_vectors: int = 100, dim: int = 128) -> tuple: - """ - Generate synthetic embedding vectors for testing. - - In real usage, these would come from a RAG system's vector database. - """ - # Generate random vectors - vectors = np.random.randn(n_vectors, dim) - # L2 normalize (common for embeddings) - vectors = vectors / (np.linalg.norm(vectors, axis=1, keepdims=True) + 1e-10) - - # Generate IDs - ids = [f"doc_{i}" for i in range(n_vectors)] - - return vectors, ids - - -def main(): - print("=" * 70) - print("SAGE Unlearning Library - Basic Demo") - print("=" * 70) - print() - - # Step 1: Generate synthetic data - print("Step 1: Generating synthetic vectors...") - all_vectors, all_ids = generate_synthetic_vectors(n_vectors=100, dim=128) - print(f" Generated {len(all_vectors)} vectors of dimension {all_vectors.shape[1]}") - print() - - # Step 2: Select vectors to forget - print("Step 2: Selecting vectors to forget...") - n_forget = 5 - forget_indices = np.random.choice(len(all_vectors), size=n_forget, replace=False) - vectors_to_forget = all_vectors[forget_indices] - ids_to_forget = [all_ids[i] for i in forget_indices] - print(f" Selected {n_forget} vectors to forget: {ids_to_forget}") - print() - - # Step 3: Initialize Unlearning Engine - print("Step 3: Initializing Unlearning Engine...") - engine = UnlearningEngine( - epsilon=1.0, # Privacy parameter - delta=1e-5, # Failure probability - total_budget_epsilon=10.0, # Total privacy budget - enable_compensation=True, # Enable neighbor compensation - ) - print(f" Engine initialized: {engine.mechanism}") - print(f" Privacy budget: ε={engine.privacy_accountant.total_epsilon_budget}") - print() - - # Step 4: Perform unlearning - print("Step 4: Performing unlearning...") - print(" Strategy: uniform perturbation") - - result = engine.unlearn_vectors( - vectors_to_forget=vectors_to_forget, - vector_ids_to_forget=ids_to_forget, - all_vectors=all_vectors, - all_vector_ids=all_ids, - perturbation_strategy="uniform", - ) - - print(f"\n Result: {result}") - print(f" Privacy cost: ε={result.privacy_cost[0]:.4f}, δ={result.privacy_cost[1]:.6f}") - print(f" Vectors unlearned: {result.num_vectors_unlearned}") - print(f" Neighbors compensated: {result.num_neighbors_compensated}") - print() - - # Step 5: Check privacy budget - print("Step 5: Checking remaining privacy budget...") - status = engine.get_privacy_status() - remaining = status["remaining_budget"] - print( - f" Remaining: ε={remaining['epsilon_remaining']:.4f}, δ={remaining['delta_remaining']:.6f}" - ) - print(f" Budget utilization: {status['accountant_summary']['budget_utilization']:.1%}") - print() - - # Step 6: Try different strategies - print("Step 6: Comparing perturbation strategies...") - strategies = ["uniform", "selective", "adaptive"] - - for strategy in strategies: - # Reset engine for fair comparison - test_engine = UnlearningEngine(epsilon=1.0, enable_compensation=False) - - test_result = test_engine.unlearn_vectors( - vectors_to_forget=vectors_to_forget[:2], # Use fewer vectors for comparison - vector_ids_to_forget=ids_to_forget[:2], - perturbation_strategy=strategy, - ) - - perturbed = test_result.metadata["perturbed_vectors"] - original = vectors_to_forget[:2] - - # Measure impact - l2_dist = np.mean([np.linalg.norm(o - p) for o, p in zip(original, perturbed)]) - cos_sim = np.mean( - [ - np.dot(o, p) / (np.linalg.norm(o) * np.linalg.norm(p)) - for o, p in zip(original, perturbed) - ] - ) - - print(f" {strategy:12s}: L2={l2_dist:.4f}, CosSim={cos_sim:.4f}") - - print() - print("=" * 70) - print("Demo completed successfully!") - print("=" * 70) - print() - print("Next steps for students:") - print(" 1. Implement new privacy mechanisms in algorithms/") - print(" 2. Design better perturbation strategies in dp_unlearning/vector_perturbation.py") - print(" 3. Enhance neighbor compensation in dp_unlearning/neighbor_compensation.py") - print(" 4. Add comprehensive evaluation metrics in evaluation/metrics.py") - print() - - -if __name__ == "__main__": - main() diff --git a/packages/sage-libs/examples/unlearning/machine_unlearning_examples.py b/packages/sage-libs/examples/unlearning/machine_unlearning_examples.py deleted file mode 100644 index 5af4c6ce84..0000000000 --- a/packages/sage-libs/examples/unlearning/machine_unlearning_examples.py +++ /dev/null @@ -1,366 +0,0 @@ -""" -SAGE Unlearning - Usage Examples - -This file demonstrates how to use the SAGE machine unlearning algorithms. - -Layer: L3 (Core - Algorithm Library) -""" - - -def example_basic_unlearning(): - """ - Example 1: Basic unlearning workflow - - Demonstrates the basic workflow of using machine unlearning - algorithms to remove specific data from trained models. - """ - print("=" * 60) - print("Example 1: Basic Unlearning Workflow") - print("=" * 60) - - try: - from sage.libs.privacy.unlearning.algorithms import GaussianMechanism # noqa: F401 - - print("\n✓ Machine Unlearning Overview:") - print(" 1. Train initial model on full dataset") - print(" 2. Identify data to forget") - print(" 3. Apply unlearning algorithm") - print(" 4. Verify data removal") - - print("\nExample workflow:") - print( - """ - from sage.libs.privacy.unlearning.algorithms import GaussianMechanism - from sage.libs.privacy.unlearning.dp_unlearning import UnlearningEngine - - # Initialize DP mechanism - mechanism = GaussianMechanism( - epsilon=1.0, - delta=1e-5, - sensitivity=0.1 - ) - - # Initialize unlearning engine - engine = UnlearningEngine(mechanism=mechanism) - - # Specify data to forget - forget_indices = [10, 25, 42, 100] - - # Apply unlearning - updated_params = engine.unlearn( - model_parameters=current_params, - forget_indices=forget_indices, - retain_data=remaining_data - ) - - # Verify unlearning - print(f"Model updated: {updated_params is not None}") - """ - ) - - except ImportError as e: - print(f"✗ Import error: {e}") - - -def example_differential_privacy(): - """ - Example 2: Differential privacy mechanisms - - Demonstrates how to use differential privacy techniques - in conjunction with machine unlearning. - """ - print("\n" + "=" * 60) - print("Example 2: Differential Privacy Unlearning") - print("=" * 60) - - try: - from sage.libs.privacy.unlearning.dp_unlearning import ( # noqa: F401 - NeighborCompensation, - VectorPerturbation, - ) - - print("\n✓ DP-based unlearning components:") - print(" - Vector Perturbation: Add calibrated noise") - print(" - Neighbor Compensation: Compensate neighboring records") - print(" - Privacy Accountant: Track privacy budget") - - print("\nExample: Vector perturbation") - print( - """ - from sage.libs.privacy.unlearning.dp_unlearning import VectorPerturbation - from sage.libs.privacy.unlearning.algorithms import GaussianMechanism - - # Create DP mechanism - mechanism = GaussianMechanism( - epsilon=1.0, - delta=1e-5, - sensitivity=0.1 - ) - - # Create vector perturbation component - perturbation = VectorPerturbation(mechanism=mechanism) - - # Apply DP unlearning - perturbed_params = perturbation.perturb( - model_parameters=params, - forget_gradient=forget_grad - ) - """ - ) - - print("\nExample: Privacy accounting") - print( - """ - from sage.libs.privacy.unlearning.dp_unlearning import PrivacyAccountant - - # Track privacy budget across multiple unlearning operations - accountant = PrivacyAccountant(total_epsilon=3.0) - - # First unlearning operation - accountant.spend(epsilon=1.0, delta=1e-5) - - # Check remaining budget - remaining = accountant.get_remaining_budget() - print(f"Remaining privacy budget: {remaining}") - """ - ) - - except ImportError as e: - print(f"✗ Import error: {e}") - - -def example_evaluation_metrics(): - """ - Example 3: Evaluating unlearning effectiveness - - Demonstrates how to evaluate the effectiveness and quality - of machine unlearning algorithms. - """ - print("\n" + "=" * 60) - print("Example 3: Unlearning Evaluation Metrics") - print("=" * 60) - - try: - from sage.libs.privacy.unlearning.evaluation import UnlearningMetrics # noqa: F401 - - print("\n✓ Evaluation metrics:") - print(" - Forgetting accuracy: How well data is forgotten") - print(" - Retention accuracy: Performance on remaining data") - print(" - Unlearning time: Computational efficiency") - print(" - Model utility: Overall model performance") - - print("\nExample evaluation:") - print( - """ - from sage.libs.privacy.unlearning.evaluation import UnlearningMetrics - - # Create metrics evaluator - metrics = UnlearningMetrics( - original_model=original_model, - unlearned_model=unlearned_model, - retrained_model=retrained_model # Gold standard - ) - - # Evaluate forgetting quality - forget_score = metrics.forgetting_quality( - forget_set=forget_data - ) - - # Evaluate retention - retain_score = metrics.retention_quality( - retain_set=retain_data - ) - - # Compare with retraining - similarity = metrics.model_similarity() - - print(f"Forgetting score: {forget_score:.4f}") - print(f"Retention score: {retain_score:.4f}") - print(f"Similarity to retrained model: {similarity:.4f}") - """ - ) - - except ImportError as e: - print(f"✗ Import error: {e}") - - -def example_unlearning_algorithms(): - """ - Example 4: Different unlearning algorithms - - Demonstrates various unlearning algorithms available in SAGE. - """ - print("\n" + "=" * 60) - print("Example 4: Unlearning Algorithm Comparison") - print("=" * 60) - - print("\n✓ Available algorithms:") - print(" - Gaussian Mechanism: Gaussian noise-based (ε,δ)-DP") - print(" - Laplace Mechanism: Laplace noise-based ε-DP") - print(" - Unlearning Engine: Orchestrates unlearning process") - - print("\nExample: Gaussian vs Laplace mechanisms") - print( - """ - from sage.libs.privacy.unlearning.algorithms import ( - GaussianMechanism, - LaplaceMechanism - ) - from sage.libs.privacy.unlearning.dp_unlearning import UnlearningEngine - - # Gaussian mechanism (better for larger datasets, requires δ) - gaussian = GaussianMechanism( - epsilon=1.0, - delta=1e-5, - sensitivity=0.1 - ) - - # Laplace mechanism (pure ε-DP, no δ required) - laplace = LaplaceMechanism( - epsilon=0.5, - sensitivity=0.1 - ) - - # Create engines with different mechanisms - gaussian_engine = UnlearningEngine(mechanism=gaussian) - laplace_engine = UnlearningEngine(mechanism=laplace) - - # Compare results - gaussian_params = gaussian_engine.unlearn(forget_indices) - laplace_params = laplace_engine.unlearn(forget_indices) - """ - ) - - print("\nExample: Advanced usage with neighbor compensation") - print( - """ - from sage.libs.privacy.unlearning.dp_unlearning import ( - UnlearningEngine, - NeighborCompensation - ) - from sage.libs.privacy.unlearning.algorithms import GaussianMechanism - - # Create mechanism - mechanism = GaussianMechanism(epsilon=1.0, delta=1e-5) - - # Create neighbor compensation - neighbor_comp = NeighborCompensation( - mechanism=mechanism, - num_neighbors=5 - ) - - # Apply compensated unlearning - updated_params = neighbor_comp.compensate( - model_params=params, - forget_indices=forget_indices, - training_data=data - ) - """ - ) - - -def example_real_world_scenario(): - """ - Example 5: Real-world GDPR compliance scenario - - Demonstrates a complete workflow for handling data deletion - requests in compliance with privacy regulations. - """ - print("\n" + "=" * 60) - print("Example 5: GDPR Compliance Workflow") - print("=" * 60) - - print("\n✓ GDPR 'Right to be Forgotten' workflow:") - print(" 1. Receive deletion request") - print(" 2. Identify user data in model") - print(" 3. Apply certified unlearning") - print(" 4. Verify deletion") - print(" 5. Document compliance") - - print("\nComplete example:") - print( - """ - from sage.libs.privacy.unlearning.algorithms import GaussianMechanism - from sage.libs.privacy.unlearning.evaluation import UnlearningMetrics - from sage.libs.privacy.unlearning.dp_unlearning import ( - PrivacyAccountant, - UnlearningEngine - ) - - # Step 1: Receive deletion request - user_id = "user_12345" - user_data_indices = identify_user_data(user_id) - - # Step 2: Apply certified unlearning - accountant = PrivacyAccountant(total_epsilon=1.0) - - mechanism = GaussianMechanism( - epsilon=0.5, - delta=1e-5, - sensitivity=0.1 - ) - - engine = UnlearningEngine(mechanism=mechanism) - - updated_params = engine.unlearn( - model_parameters=production_model.parameters, - forget_indices=user_data_indices - ) - - # Step 3: Verify deletion - metrics = UnlearningMetrics( - original_model=production_model, - unlearned_model=updated_model - ) - - # Check that user data cannot be recovered - forgetting_score = metrics.forgetting_quality(user_data_indices) - assert forgetting_score > 0.95, "Insufficient forgetting" - - # Check model still performs well - retain_score = metrics.retention_quality(remaining_data) - assert retain_score > 0.90, "Too much utility loss" - - # Step 4: Document compliance - compliance_report = { - "user_id": user_id, - "deletion_date": datetime.now(), - "forgetting_score": forgetting_score, - "retention_score": retain_score, - "privacy_spent": accountant.get_spent_budget(), - "certified": True - } - - log_compliance(compliance_report) - print("✓ GDPR deletion request processed successfully") - """ - ) - - -def run_all_examples(): - """Run all examples in sequence.""" - print("\n" + "=" * 60) - print("SAGE Unlearning - Complete Examples") - print("=" * 60) - - example_basic_unlearning() - example_differential_privacy() - example_evaluation_metrics() - example_unlearning_algorithms() - example_real_world_scenario() - - print("\n" + "=" * 60) - print("✓ All examples completed") - print("=" * 60) - print("\nFor more information:") - print("- See packages/sage-libs/src/sage/libs/unlearning/README.md") - print("- Check algorithms/ for mechanism implementations") - print("- Visit docs/ for research papers and references") - print("\nKey papers:") - print("- Machine Unlearning (Cao & Yang, 2015)") - print("- Certified Data Removal (Guo et al., 2020)") - print("- The Algorithmic Foundations of DP (Dwork & Roth, 2014)") - - -if __name__ == "__main__": - run_all_examples() diff --git a/packages/sage-libs/pyproject.toml b/packages/sage-libs/pyproject.toml deleted file mode 100644 index 5838d87eb2..0000000000 --- a/packages/sage-libs/pyproject.toml +++ /dev/null @@ -1,230 +0,0 @@ -[build-system] -requires = ["setuptools>=64", "wheel"] -build-backend = "setuptools.build_meta" - - -[project] -name = "isage-libs" -dynamic = ["version"] -description = "SAGE Libraries - Streaming-Augmented Generative Execution" -readme = "README.md" -requires-python = ">=3.10" -authors = [{ name = "IntelliStream Team", email = "shuhao_zhang@hust.edu.cn" }] -keywords = [ - "applications", - "examples", - "templates", - "rag", - "agents", - "streaming", - "tutorials", - "sage", -] -classifiers = [ - "Development Status :: 4 - Beta", - "Intended Audience :: Developers", - "Topic :: Software Development :: Libraries :: Python Modules", - "Topic :: Scientific/Engineering :: Artificial Intelligence", - "Topic :: System :: Distributed Computing", -] -license = { text = "MIT" } - -# 核心依赖 - 仅包含接口层所需的最小依赖 -# sage-libs 是接口层，具体实现在独立 PyPI 包中（isage-*） -dependencies = [ - # 基础依赖 - "numpy>=1.26.0,<2.3.0", - - # SAGE 核心依赖 - "isage-common>=0.2.0", - - # Note: 所有重量级依赖已移至 optional-dependencies - # - chromadb, pymilvus → vdb - # - kafka-python, redis → streaming - # - jupyter, notebook → notebook - # - tensorboard, trl, peft → finetune - # - easyocr → ocr - # - isagellm → llm -] - -[project.optional-dependencies] -dev = [ - "pytest>=7.4.0", - "pytest-cov>=4.0.0", - "pytest-asyncio>=0.21.0", - "ruff==0.14.6", - "mypy>=1.7.0", -] -# Algorithm implementations (independent repos) -anns = ["isage-anns>=0.1.0"] -amms = ["isage-amms>=0.1.0"] -# L3 domain implementations (independent repos) -finetune = ["isage-finetune>=0.1.0"] -agentic = ["isage-agentic>=0.1.0"] -rag = ["isage-rag>=0.1.0"] -eval = ["isage-eval>=0.1.0"] -privacy = ["isage-privacy>=0.1.0"] -safety = ["isage-safety>=0.1.0"] -# Heavy optional dependencies (moved from core) -vdb = ["chromadb>=1.0.20", "pymilvus[model]>=2.4.0"] -streaming = ["kafka-python>=2.0.0", "redis>=4.0.0"] -notebook = ["jupyter>=1.0.0", "notebook>=6.0.0"] -metrics = [ - "datasets>=2.0.0", - "evaluate>=0.4.0", - "rouge>=1.0.0", - "rouge-score>=0.1.0", - "bleu>=0.3.0", -] -loaders = ["PyPDF2>=3.0.0", "python-docx>=0.8.11"] -ocr = ["easyocr>=1.7.0"] -llm = [ - "isagellm>=0.1.0", - "transformers>=4.52.0,<4.54.0", - "tokenizers>=0.21.0,<0.24.0", - "torch>=2.7.0,<3.0.0", - "sentence-transformers>=3.1.0,<4.0.0", -] -training = [ - "peft>=0.18.0,<1.0.0", - "accelerate>=1.9.0,<2.0.0", - "tensorboard>=2.14.0", - "trl>=0.20.0,<0.21.0", -] -all = [ - "isage-libs[dev,anns,amms,finetune,agentic,rag,metrics,privacy,safety,vdb,streaming,notebook,metrics,loaders,ocr,llm,training]", -] -[project.urls] -Homepage = "https://github.com/intellistream/SAGE" -Documentation = "https://intellistream.github.io/SAGE-Pub/" -Repository = "https://github.com/intellistream/SAGE.git" -"Bug Tracker" = "https://github.com/intellistream/SAGE/issues" - -# [project.scripts] -# sage-examples = "sage.libs.cli:main" -# Note: sage-examples CLI is not yet implemented -# This command entry point is commented out to avoid import errors - -[tool.setuptools.packages.find] -namespaces = true -where = ["src"] - -[tool.setuptools.package-dir] -"" = "src" - -[tool.setuptools.dynamic.version] -attr = "sage.libs._version.__version__" - -[tool.setuptools.package-data] -"sage.libs" = ["py.typed", "**/*"] -"sage.apps" = ["py.typed", "**/*"] - -# Note: C++ implementations and wrappers have been fully migrated to independent packages: -# - isage-anns (ANNS algorithms) -# - isage-amms (AMM algorithms) -# - isage-finetune (LLM fine-tuning toolkit) -# - isage-agentic (Agent framework, planning, tool selection, workflows) -# - isage-sias (Sample-Importance-Aware Selection) -# - isage-intent (Intent recognition) -# This package now only ships interface/registry layers and core utilities. - -# Development tools configuration -[tool.black] -line-length = 100 -target-version = ["py310", "py311", "py312"] -include = '\.pyi?$' - -[tool.isort] -profile = "black" -line_length = 100 - -[tool.mypy] -python_dynamic = ["version"] -cache_dir = "../../.sage/cache/mypy" -warn_return_any = true -warn_unused_configs = true -disallow_untyped_defs = true - -# ============================================================================ -# scikit-build-core 动态版本配置 -# 从 _version.py 读取版本号 -# ============================================================================ -[tool.scikit-build.metadata.version] -provider = "scikit_build_core.metadata.regex" -input = "src/sage/libs/_version.py" -regex = '''__version__\s*=\s*["'](?P<value>[^"']+)["']''' - -# ============================================================================ -# Code Quality Configuration -# Extends from root ruff.toml for unified standards across all packages -# ============================================================================ -[tool.ruff] -extend = "../../tools/ruff.toml" - -[tool.pytest.ini_options] -testpaths = ["tests", "src"] -python_files = ["test_*.py", "*_test.py"] -python_classes = ["Test*"] -python_functions = ["test_*"] -addopts = [ - "--benchmark-storage=../../.sage/benchmarks", - "-o", - "cache_dir=../../.sage/cache/pytest", - "--strict-markers", - "--strict-config", - "--verbose", - "-ra", -] -markers = [ - "slow: marks tests as slow (deselect with '-m \"not slow\"')", - "integration: marks tests as integration tests", - "unit: marks tests as unit tests", - "network: marks tests as network tests", - "system: marks tests as system tests", - "core: marks tests as core functionality tests", - "smoke: marks tests as smoke tests (quick validation)", - "cli: marks tests as CLI tests", - "external: marks tests requiring external services/APIs", -] - -[tool.coverage.run] -source = ["src/sage"] -omit = ["*/tests/*", "*/test_*.py", "*/_test_*.py"] -# Disable coverage warning when no data is collected (e.g., all tests skipped) -disable_warnings = ["no-data-collected"] - -[tool.coverage.report] -exclude_lines = [ - "pragma: no cover", - "def __repr__", - "raise AssertionError", - "raise NotImplementedError", - "if __name__ == .__main__.:", -] - -# ============================================================================ -# scikit-build-core Configuration -# ============================================================================ -[tool.scikit-build] -# CMake 配置 -cmake.version = ">=3.10" -cmake.build-type = "Release" -cmake.verbose = true -logging.level = "INFO" - -# Wheel 配置 - 明确指定所有包，确保纯 Python 子包也被包含 -wheel.packages = ["src/sage"] -wheel.expand-macos-universal-tags = true -# 确保所有 Python 包都被安装（包括纯 Python 子包） -wheel.py-api = "py3" - -# 严格配置验证 -strict-config = false - -# 安装配置 - 确保 Python 源文件被正确安装 -install.components = ["python", "headers"] - -# 开发模式配置 -[tool.scikit-build.editable] -mode = "redirect" -verbose = true diff --git a/packages/sage-libs/src/sage/libs/__init__.py b/packages/sage-libs/src/sage/libs/__init__.py deleted file mode 100644 index 2cd35c3be4..0000000000 --- a/packages/sage-libs/src/sage/libs/__init__.py +++ /dev/null @@ -1,96 +0,0 @@ -"""SAGE Libs - Interface Layer for SAGE Framework. - -Layer: L3 (Core Libraries) -Dependencies: sage.common (L1), sage.platform (L2), sage.kernel (L3) - -Architecture: -sage-libs provides abstract interfaces and registries. Implementations -are in external PyPI packages (isage-*). - -Five Core Domains: -- ``agentic``: Agent framework (isage-agentic) -- ``rag``: RAG toolkit (isage-rag) -- ``finetune``: Fine-tuning (isage-finetune) -- ``eval``: Evaluation (isage-eval) -- ``privacy``: Privacy/Unlearning (isage-privacy) -- ``safety``: Safety/Guardrails (isage-safety) - -Built-in Modules (no external deps): -- ``foundation``: Low-level utilities -- ``dataops``: Data operations -- ``integrations``: Third-party adapters - -Algorithm Interfaces: -- ``anns``: ANNS algorithms (isage-anns) -- ``amms``: AMM algorithms (isage-amms) -""" - -# Load version information (fail fast if missing to avoid silent fallbacks) -from sage.libs._version import __author__, __email__, __version__ - -# Export submodules -__layer__ = "L3" - -# Use lazy imports to avoid circular import issues during module initialization -_submodules = { - # Five core domains (interface layers) - "agentic", # Agent framework interface - "rag", # RAG interface - "finetune", # Fine-tuning interface - "eval", # Evaluation interface - "privacy", # Privacy interface - "safety", # Safety interface - # Algorithm interfaces - "ann", # ANNS interface (isage-anns) - "amms", # AMM interface (isage-amms) - # Built-in modules - "foundation", # Foundation utilities - "dataops", # Data operations - "integrations", # Third-party integrations -} - - -def __getattr__(name: str): - """Lazy import submodules to avoid circular import issues.""" - if name in _submodules: - import importlib - - mod = importlib.import_module(f".{name}", __name__) - globals()[name] = mod - return mod - # Provide helpful alias for anns -> ann - if name == "anns": - import warnings - - warnings.warn( - "'sage.libs.anns' is deprecated, use 'sage.libs.ann' instead", - DeprecationWarning, - stacklevel=2, - ) - import importlib - - mod = importlib.import_module(".ann", __name__) - globals()[name] = mod - return mod - raise AttributeError(f"module {__name__!r} has no attribute {name!r}") - - -__all__ = [ - "__version__", - "__author__", - "__email__", - # Five core domains - "agentic", # Agent framework - "rag", # RAG toolkit - "finetune", # Fine-tuning - "eval", # Evaluation - "privacy", # Privacy/Unlearning - "safety", # Safety/Guardrails - # Algorithm interfaces - "ann", # ANNS algorithms (isage-anns) - "amms", # AMM algorithms (isage-amms) - # Built-in modules - "foundation", # Utilities - "dataops", # Data operations - "integrations", # Integrations -] diff --git a/packages/sage-libs/src/sage/libs/_version.py b/packages/sage-libs/src/sage/libs/_version.py deleted file mode 100644 index 02235b13ba..0000000000 --- a/packages/sage-libs/src/sage/libs/_version.py +++ /dev/null @@ -1,6 +0,0 @@ -"""Version information for sage-libs package.""" - -# 独立硬编码版本 -__version__ = "0.2.2.1" -__author__ = "IntelliStream Team" -__email__ = "shuhao_zhang@hust.edu.cn" diff --git a/packages/sage-libs/src/sage/libs/agentic/__init__.py b/packages/sage-libs/src/sage/libs/agentic/__init__.py deleted file mode 100644 index 876ba3ee1b..0000000000 --- a/packages/sage-libs/src/sage/libs/agentic/__init__.py +++ /dev/null @@ -1,31 +0,0 @@ -"""SAGE Agentic Module - Agent Framework Interfaces. - -This module provides the **interface layer** (abstract base classes and factory) -for agent implementations. Concrete implementations are in the external package -`isage-agentic`. - -Architecture: - sage.libs.agentic (this module) - Interface layer (ABCs, factory, types) - isage-agentic (external PyPI) - Implementations (ReActAgent, PlanExecute, etc.) - -Installation: - pip install isage-agentic # Install implementations - # or - pip install isage-libs[agentic] - -Usage: - from sage.libs.agentic.interface import ( - BaseAgent, AgentConfig, AgentOutput, - create, register, registered, - ) - - # Create agent (requires isage-agentic installed) - agent = create("react", config=AgentConfig(...)) - output = agent.run("What is the weather?") - -External implementations auto-register when imported. -See: https://github.com/intellistream/sage-agentic -""" - -# Re-export interface -from .interface import * # noqa: F401, F403 diff --git a/packages/sage-libs/src/sage/libs/agentic/interface/__init__.py b/packages/sage-libs/src/sage/libs/agentic/interface/__init__.py deleted file mode 100644 index 52816fec96..0000000000 --- a/packages/sage-libs/src/sage/libs/agentic/interface/__init__.py +++ /dev/null @@ -1,61 +0,0 @@ -"""Interface definitions for agentic. - -This module defines the abstract interfaces and registries for agentic components. -Concrete implementations are provided by the external package 'isage-agentic'. - -Merged modules: -- Intent recognition (from sage.libs.intent) -- Reasoning strategies (from sage.libs.reasoning) -- SIAS (will be in isage-agentic[sias]) - -Architecture: -- Interface layer (here): Abstract base classes, factory pattern -- Implementation layer (isage-agentic): Concrete implementations - -Usage: - # Import interfaces - from sage.libs.agentic.interface import BaseAgent, create_agent - - # In isage-agentic, register implementations - from sage.libs.agentic.interface import register_agent - register_agent("react", ReactAgent) -""" - -from .base import * # noqa: F401, F403 -from .factory import * # noqa: F401, F403 - -__all__ = [ - # Base classes - "AgentAction", - "AgentResult", - "Intent", - "BaseAgent", - "BasePlanner", - "BaseToolSelector", - "BaseOrchestrator", - "IntentRecognizer", - "IntentClassifier", - "BaseReasoningStrategy", - # Factory functions - "register_agent", - "create_agent", - "list_agents", - "register_planner", - "create_planner", - "list_planners", - "register_tool_selector", - "create_tool_selector", - "list_tool_selectors", - "register_orchestrator", - "create_orchestrator", - "list_orchestrators", - "register_intent_recognizer", - "create_intent_recognizer", - "list_intent_recognizers", - "register_intent_classifier", - "create_intent_classifier", - "list_intent_classifiers", - "register_reasoning_strategy", - "create_reasoning_strategy", - "list_reasoning_strategies", -] diff --git a/packages/sage-libs/src/sage/libs/agentic/interface/base.py b/packages/sage-libs/src/sage/libs/agentic/interface/base.py deleted file mode 100644 index 7a56de6df5..0000000000 --- a/packages/sage-libs/src/sage/libs/agentic/interface/base.py +++ /dev/null @@ -1,154 +0,0 @@ -"""Base classes for agentic components. - -This module defines the core abstractions for: -- Agents: Autonomous task executors -- Planners: Task planning and decomposition -- Tool Selectors: Dynamic tool selection -- Orchestrators: Multi-agent coordination -- Intent Recognizers: User intent understanding (merged from intent module) -- Reasoning Strategies: Search and optimization (merged from reasoning module) -""" - -from abc import ABC, abstractmethod -from dataclasses import dataclass -from typing import Any, Optional - -# ==================== Data Classes ==================== - - -@dataclass -class AgentAction: - """Represents an action taken by an agent.""" - - tool_name: str - tool_input: dict[str, Any] - thought: Optional[str] = None - confidence: float = 1.0 - - -@dataclass -class AgentResult: - """Result of agent execution.""" - - output: Any - intermediate_steps: list[tuple[AgentAction, str]] - metadata: dict[str, Any] - - -@dataclass -class Intent: - """Recognized user intent.""" - - name: str - confidence: float - slots: dict[str, Any] - metadata: dict[str, Any] - - -# ==================== Agent Base Classes ==================== - - -class BaseAgent(ABC): - """Abstract base class for all agents.""" - - @abstractmethod - def plan(self, task: str, context: dict[str, Any]) -> list[AgentAction]: - """Plan actions for a given task.""" - pass - - @abstractmethod - def execute(self, task: str, **kwargs) -> AgentResult: - """Execute the agent on a task.""" - pass - - @abstractmethod - def reset(self) -> None: - """Reset agent state.""" - pass - - -class BasePlanner(ABC): - """Abstract base class for planning strategies.""" - - @abstractmethod - def plan(self, goal: str, available_tools: list[str], context: dict[str, Any]) -> list[str]: - """Generate a plan as a sequence of tool calls.""" - pass - - -class BaseToolSelector(ABC): - """Abstract base class for tool selection.""" - - @abstractmethod - def select_tools( - self, query: str, available_tools: list[dict[str, Any]], top_k: int = 3 - ) -> list[str]: - """Select top-k relevant tools for a query.""" - pass - - @abstractmethod - def add_tool(self, tool_spec: dict[str, Any]) -> None: - """Add a tool to the selector's knowledge.""" - pass - - -class BaseOrchestrator(ABC): - """Abstract base class for multi-agent orchestration.""" - - @abstractmethod - def coordinate(self, task: str, agents: list[BaseAgent], **kwargs) -> AgentResult: - """Coordinate multiple agents to complete a task.""" - pass - - -# ==================== Intent Base Classes (merged from intent/) ==================== - - -class IntentRecognizer(ABC): - """Abstract base class for intent recognition.""" - - @abstractmethod - def recognize(self, text: str, context: Optional[dict[str, Any]] = None) -> Intent: - """Recognize intent from text.""" - pass - - -class IntentClassifier(ABC): - """Abstract base class for intent classification.""" - - @abstractmethod - def classify(self, text: str) -> list[Intent]: - """Classify text into multiple intents.""" - pass - - -# ==================== Reasoning Base Classes (merged from reasoning/) ==================== - - -class BaseReasoningStrategy(ABC): - """Abstract base class for reasoning strategies.""" - - @abstractmethod - def search( - self, initial_state: Any, goal_check: callable, expand: callable, **kwargs - ) -> list[Any]: - """Perform search/reasoning to reach goal.""" - pass - - -__all__ = [ - # Data classes - "AgentAction", - "AgentResult", - "Intent", - # Agent classes - "BaseAgent", - "BasePlanner", - "BaseToolSelector", - "BaseOrchestrator", - # Intent classes (merged) - "IntentRecognizer", - "IntentClassifier", - # Reasoning classes (merged) - "BaseReasoningStrategy", -] diff --git a/packages/sage-libs/src/sage/libs/agentic/interface/factory.py b/packages/sage-libs/src/sage/libs/agentic/interface/factory.py deleted file mode 100644 index dbb7a20aa9..0000000000 --- a/packages/sage-libs/src/sage/libs/agentic/interface/factory.py +++ /dev/null @@ -1,240 +0,0 @@ -"""Factory and registry for agentic implementations. - -Provides separate registries for: -- Agents -- Planners -- Tool Selectors -- Orchestrators -- Intent Recognizers (merged from intent/) -- Intent Classifiers (merged from intent/) -- Reasoning Strategies (merged from reasoning/) -""" - -from typing import Any - -from .base import ( - BaseAgent, - BaseOrchestrator, - BasePlanner, - BaseReasoningStrategy, - BaseToolSelector, - IntentClassifier, - IntentRecognizer, -) - -# ==================== Registries ==================== - -_AGENT_REGISTRY: dict[str, type[BaseAgent]] = {} -_PLANNER_REGISTRY: dict[str, type[BasePlanner]] = {} -_TOOL_SELECTOR_REGISTRY: dict[str, type[BaseToolSelector]] = {} -_ORCHESTRATOR_REGISTRY: dict[str, type[BaseOrchestrator]] = {} -_INTENT_RECOGNIZER_REGISTRY: dict[str, type[IntentRecognizer]] = {} -_INTENT_CLASSIFIER_REGISTRY: dict[str, type[IntentClassifier]] = {} -_REASONING_REGISTRY: dict[str, type[BaseReasoningStrategy]] = {} - -# ==================== Agent Registry ==================== - - -def register_agent(name: str, cls: type[BaseAgent]) -> None: - """Register an agent implementation.""" - if name in _AGENT_REGISTRY: - raise ValueError(f"Agent '{name}' already registered") - if not issubclass(cls, BaseAgent): - raise TypeError("Class must inherit from BaseAgent") - _AGENT_REGISTRY[name] = cls - - -def create_agent(name: str, **kwargs: Any) -> BaseAgent: - """Create an agent instance.""" - if name not in _AGENT_REGISTRY: - available = ", ".join(_AGENT_REGISTRY.keys()) or "none" - raise KeyError( - f"Agent '{name}' not found. Available: {available}. Did you install 'isage-agentic'?" - ) - return _AGENT_REGISTRY[name](**kwargs) - - -def list_agents() -> list[str]: - """List registered agents.""" - return list(_AGENT_REGISTRY.keys()) - - -# ==================== Planner Registry ==================== - - -def register_planner(name: str, cls: type[BasePlanner]) -> None: - """Register a planner implementation.""" - if name in _PLANNER_REGISTRY: - raise ValueError(f"Planner '{name}' already registered") - if not issubclass(cls, BasePlanner): - raise TypeError("Class must inherit from BasePlanner") - _PLANNER_REGISTRY[name] = cls - - -def create_planner(name: str, **kwargs: Any) -> BasePlanner: - """Create a planner instance.""" - if name not in _PLANNER_REGISTRY: - available = ", ".join(_PLANNER_REGISTRY.keys()) or "none" - raise KeyError(f"Planner '{name}' not found. Available: {available}") - return _PLANNER_REGISTRY[name](**kwargs) - - -def list_planners() -> list[str]: - """List registered planners.""" - return list(_PLANNER_REGISTRY.keys()) - - -# ==================== Tool Selector Registry ==================== - - -def register_tool_selector(name: str, cls: type[BaseToolSelector]) -> None: - """Register a tool selector implementation.""" - if name in _TOOL_SELECTOR_REGISTRY: - raise ValueError(f"Tool selector '{name}' already registered") - if not issubclass(cls, BaseToolSelector): - raise TypeError("Class must inherit from BaseToolSelector") - _TOOL_SELECTOR_REGISTRY[name] = cls - - -def create_tool_selector(name: str, **kwargs: Any) -> BaseToolSelector: - """Create a tool selector instance.""" - if name not in _TOOL_SELECTOR_REGISTRY: - available = ", ".join(_TOOL_SELECTOR_REGISTRY.keys()) or "none" - raise KeyError(f"Tool selector '{name}' not found. Available: {available}") - return _TOOL_SELECTOR_REGISTRY[name](**kwargs) - - -def list_tool_selectors() -> list[str]: - """List registered tool selectors.""" - return list(_TOOL_SELECTOR_REGISTRY.keys()) - - -# ==================== Orchestrator Registry ==================== - - -def register_orchestrator(name: str, cls: type[BaseOrchestrator]) -> None: - """Register an orchestrator implementation.""" - if name in _ORCHESTRATOR_REGISTRY: - raise ValueError(f"Orchestrator '{name}' already registered") - if not issubclass(cls, BaseOrchestrator): - raise TypeError("Class must inherit from BaseOrchestrator") - _ORCHESTRATOR_REGISTRY[name] = cls - - -def create_orchestrator(name: str, **kwargs: Any) -> BaseOrchestrator: - """Create an orchestrator instance.""" - if name not in _ORCHESTRATOR_REGISTRY: - available = ", ".join(_ORCHESTRATOR_REGISTRY.keys()) or "none" - raise KeyError(f"Orchestrator '{name}' not found. Available: {available}") - return _ORCHESTRATOR_REGISTRY[name](**kwargs) - - -def list_orchestrators() -> list[str]: - """List registered orchestrators.""" - return list(_ORCHESTRATOR_REGISTRY.keys()) - - -# ==================== Intent Recognizer Registry (merged from intent/) ==================== - - -def register_intent_recognizer(name: str, cls: type[IntentRecognizer]) -> None: - """Register an intent recognizer implementation.""" - if name in _INTENT_RECOGNIZER_REGISTRY: - raise ValueError(f"Intent recognizer '{name}' already registered") - if not issubclass(cls, IntentRecognizer): - raise TypeError("Class must inherit from IntentRecognizer") - _INTENT_RECOGNIZER_REGISTRY[name] = cls - - -def create_intent_recognizer(name: str, **kwargs: Any) -> IntentRecognizer: - """Create an intent recognizer instance.""" - if name not in _INTENT_RECOGNIZER_REGISTRY: - available = ", ".join(_INTENT_RECOGNIZER_REGISTRY.keys()) or "none" - raise KeyError(f"Intent recognizer '{name}' not found. Available: {available}") - return _INTENT_RECOGNIZER_REGISTRY[name](**kwargs) - - -def list_intent_recognizers() -> list[str]: - """List registered intent recognizers.""" - return list(_INTENT_RECOGNIZER_REGISTRY.keys()) - - -# ==================== Intent Classifier Registry (merged from intent/) ==================== - - -def register_intent_classifier(name: str, cls: type[IntentClassifier]) -> None: - """Register an intent classifier implementation.""" - if name in _INTENT_CLASSIFIER_REGISTRY: - raise ValueError(f"Intent classifier '{name}' already registered") - if not issubclass(cls, IntentClassifier): - raise TypeError("Class must inherit from IntentClassifier") - _INTENT_CLASSIFIER_REGISTRY[name] = cls - - -def create_intent_classifier(name: str, **kwargs: Any) -> IntentClassifier: - """Create an intent classifier instance.""" - if name not in _INTENT_CLASSIFIER_REGISTRY: - available = ", ".join(_INTENT_CLASSIFIER_REGISTRY.keys()) or "none" - raise KeyError(f"Intent classifier '{name}' not found. Available: {available}") - return _INTENT_CLASSIFIER_REGISTRY[name](**kwargs) - - -def list_intent_classifiers() -> list[str]: - """List registered intent classifiers.""" - return list(_INTENT_CLASSIFIER_REGISTRY.keys()) - - -# ==================== Reasoning Strategy Registry (merged from reasoning/) ==================== - - -def register_reasoning_strategy(name: str, cls: type[BaseReasoningStrategy]) -> None: - """Register a reasoning strategy implementation.""" - if name in _REASONING_REGISTRY: - raise ValueError(f"Reasoning strategy '{name}' already registered") - if not issubclass(cls, BaseReasoningStrategy): - raise TypeError("Class must inherit from BaseReasoningStrategy") - _REASONING_REGISTRY[name] = cls - - -def create_reasoning_strategy(name: str, **kwargs: Any) -> BaseReasoningStrategy: - """Create a reasoning strategy instance.""" - if name not in _REASONING_REGISTRY: - available = ", ".join(_REASONING_REGISTRY.keys()) or "none" - raise KeyError(f"Reasoning strategy '{name}' not found. Available: {available}") - return _REASONING_REGISTRY[name](**kwargs) - - -def list_reasoning_strategies() -> list[str]: - """List registered reasoning strategies.""" - return list(_REASONING_REGISTRY.keys()) - - -__all__ = [ - # Agent - "register_agent", - "create_agent", - "list_agents", - # Planner - "register_planner", - "create_planner", - "list_planners", - # Tool Selector - "register_tool_selector", - "create_tool_selector", - "list_tool_selectors", - # Orchestrator - "register_orchestrator", - "create_orchestrator", - "list_orchestrators", - # Intent (merged) - "register_intent_recognizer", - "create_intent_recognizer", - "list_intent_recognizers", - "register_intent_classifier", - "create_intent_classifier", - "list_intent_classifiers", - # Reasoning (merged) - "register_reasoning_strategy", - "create_reasoning_strategy", - "list_reasoning_strategies", -] diff --git a/packages/sage-libs/src/sage/libs/amms/README.md b/packages/sage-libs/src/sage/libs/amms/README.md deleted file mode 100644 index 17c5e05e68..0000000000 --- a/packages/sage-libs/src/sage/libs/amms/README.md +++ /dev/null @@ -1,81 +0,0 @@ -# AMMS - Approximate Matrix Multiplication (Interface Only) - -> **Status**: ✅ Implementations externalized to independent package `isage-amms` - -**PyPI Package**: `isage-amms`\ -**Repository**: https://github.com/intellistream/sage-amms (planned) - -This directory provides **interface/registry layer only**. All C++ implementations and Python -wrappers have been moved to the external `isage-amms` package. - -## Installation - -```bash -# Install interface + implementations -pip install isage-amms - -# Or via extras (recommended for development) -pip install -e packages/sage-libs[amms] -``` - -## Usage - -```python -from sage.libs.amms import create, registered - -# Check available algorithms (requires isage-amms) -print(registered()) - -# Create an AMM algorithm instance -amm = create("countsketch", sketch_size=1000) -result = amm.multiply(matrix_a, matrix_b) -``` - -## What's in This Directory - -``` -amms/ -├── __init__.py # Interface exports + deprecation warning -├── README.md # This file -└── interface/ # Abstract interfaces - ├── base.py # AmmIndex, AmmIndexMeta, StreamingAmmIndex - ├── factory.py # create(), register(), registered() - └── registry.py # Algorithm registry -``` - -**Removed** (now in `isage-amms`): - -- `wrappers/` - Python wrappers -- `implementations/` - C++ source code and bindings -- Build files (pyproject.toml, setup.py, CMakeLists.txt, etc.) - -## Algorithms Available (in isage-amms) - -### Sketching-based - -- CountSketch, FastJLT, RIP, TugOfWar - -### Sampling-based - -- CRS, CRSV2, BCRS, EWS - -### Quantization-based - -- ProductQuantization, VectorQuantization, INT8 - -### Advanced - -- CoOccurringFD, BetaCoOFD, BlockLRA, CLMM, SMPCA, WeightedCR - -## External Package Details - -For installation, build instructions, and detailed documentation, see: - -- **Repository**: https://github.com/intellistream/sage-amms (planned) -- **PyPI**: https://pypi.org/project/isage-amms/ - -## References - -- Interface documentation: `interface/base.py`, `interface/factory.py` -- Externalization status: `packages/sage-libs/EXTERNALIZATION_STATUS.md` -- Migration guide: `packages/sage-libs/docs/MIGRATION_EXTERNAL_LIBS.md` diff --git a/packages/sage-libs/src/sage/libs/amms/__init__.py b/packages/sage-libs/src/sage/libs/amms/__init__.py deleted file mode 100644 index 530e5aebf8..0000000000 --- a/packages/sage-libs/src/sage/libs/amms/__init__.py +++ /dev/null @@ -1,52 +0,0 @@ -"""AMMS - Approximate Matrix Multiplication (interface only in SAGE). - -The **implementations have been migrated to an independent package** -(`isage-amms`, planned repo: ``intellistream/sage-amms``). SAGE now only ships the -lightweight interface/registry so downstream code can keep `from sage.libs.amms -import create` while the compiled extensions live in the external package. - -Usage (after installing the external package): - - pip install isage-amms - - from sage.libs.amms import create - amm = create("countsketch", sketch_size=1000) - result = amm.multiply(matrix_a, matrix_b) - -If the optional dependency is missing, attempting to instantiate algorithms will -raise a KeyError because no implementations are registered. This is intentional -to avoid silent fallbacks. -""" - -__version__ = "0.1.0" -__author__ = "IntelliStream Team" -__email__ = "shuhao_zhang@hust.edu.cn" - -# Import interface components (no warning on import) -from sage.libs.amms.interface import ( - AmmIndex, - AmmIndexMeta, - StreamingAmmIndex, - create, - get_meta, - register, - registered, - unregister, -) - -__all__ = [ - "__version__", - "__author__", - "__email__", - # Base classes - "AmmIndex", - "AmmIndexMeta", - "StreamingAmmIndex", - # Factory functions - "create", - "registered", - "get_meta", - # Registry functions - "register", - "unregister", -] diff --git a/packages/sage-libs/src/sage/libs/amms/interface/__init__.py b/packages/sage-libs/src/sage/libs/amms/interface/__init__.py deleted file mode 100644 index 1b23e364a5..0000000000 --- a/packages/sage-libs/src/sage/libs/amms/interface/__init__.py +++ /dev/null @@ -1,22 +0,0 @@ -"""AMM Interface Layer. - -Provides abstract base classes and factory functions for AMM algorithms. -""" - -from sage.libs.amms.interface.base import AmmIndex, AmmIndexMeta, StreamingAmmIndex -from sage.libs.amms.interface.factory import create, get_meta, registered -from sage.libs.amms.interface.registry import register, unregister - -__all__ = [ - # Base classes - "AmmIndex", - "AmmIndexMeta", - "StreamingAmmIndex", - # Factory functions - "create", - "registered", - "get_meta", - # Registry functions - "register", - "unregister", -] diff --git a/packages/sage-libs/src/sage/libs/amms/interface/base.py b/packages/sage-libs/src/sage/libs/amms/interface/base.py deleted file mode 100644 index da6dff0319..0000000000 --- a/packages/sage-libs/src/sage/libs/amms/interface/base.py +++ /dev/null @@ -1,157 +0,0 @@ -"""Core AMM abstractions for approximate matrix multiplication. - -These interfaces provide a unified API for various AMM algorithms, -similar to how AnnIndex provides interfaces for ANN algorithms. -""" - -from __future__ import annotations - -from abc import ABC, abstractmethod -from dataclasses import dataclass -from typing import Any, Optional - -import numpy as np - - -@dataclass(frozen=True) -class AmmIndexMeta: - """Static capabilities for an AMM implementation.""" - - name: str - algorithm_type: str # e.g., "sketching", "sampling", "quantization" - supports_streaming: bool = False - supports_gpu: bool = False - requires_training: bool = False - - -class AmmIndex(ABC): - """Minimal AMM interface to standardize algorithm flows. - - This interface provides a unified API for various approximate matrix - multiplication algorithms, including sketching-based, sampling-based, - and quantization-based methods. - """ - - @property - @abstractmethod - def meta(self) -> AmmIndexMeta: - """Return static metadata describing this AMM algorithm.""" - - @abstractmethod - def setup(self, config: dict[str, Any]) -> None: - """Initialize the algorithm with configuration. - - Args: - config: Algorithm-specific configuration dictionary. - Common keys might include: - - sketch_size: Size of sketch for sketching algorithms - - sample_rate: Sampling rate for sampling algorithms - - quantization_bits: Number of bits for quantization - - use_gpu: Whether to use GPU acceleration - """ - - @abstractmethod - def train(self, matrix_a: np.ndarray, matrix_b: Optional[np.ndarray] = None) -> None: - """Train the algorithm on sample matrices (if required). - - Args: - matrix_a: First matrix for training - matrix_b: Optional second matrix for training - """ - - @abstractmethod - def multiply(self, matrix_a: np.ndarray, matrix_b: np.ndarray) -> np.ndarray: - """Perform approximate matrix multiplication. - - Args: - matrix_a: First matrix (m x k) - matrix_b: Second matrix (k x n) - - Returns: - Approximate result matrix (m x n) - """ - - def batch_multiply( - self, - matrices_a: list[np.ndarray], - matrices_b: list[np.ndarray], - ) -> list[np.ndarray]: - """Batch approximate matrix multiplication. - - Default implementation processes each pair sequentially. - Subclasses can override for optimized batch processing. - - Args: - matrices_a: List of first matrices - matrices_b: List of second matrices - - Returns: - List of approximate result matrices - """ - if len(matrices_a) != len(matrices_b): - raise ValueError( - f"Matrix lists must have same length: {len(matrices_a)} vs {len(matrices_b)}" - ) - - return [self.multiply(a, b) for a, b in zip(matrices_a, matrices_b)] - - def get_memory_usage(self) -> dict[str, int]: - """Return memory usage statistics in bytes. - - Returns: - Dictionary with memory usage information: - - sketch_size: Memory used by sketch/structure - - total: Total memory usage - """ - return {} - - def get_stats(self) -> dict[str, Any]: - """Return optional diagnostic statistics. - - Returns: - Dictionary with algorithm-specific statistics: - - num_operations: Number of multiply operations performed - - avg_error: Average approximation error (if tracked) - - etc. - """ - return {} - - -class StreamingAmmIndex(AmmIndex): - """Extended interface for streaming AMM algorithms. - - Some AMM algorithms support incremental updates, allowing matrices - to be processed in a streaming fashion. - """ - - @abstractmethod - def update_row(self, matrix_id: str, row_idx: int, row_data: np.ndarray) -> None: - """Update a single row of a matrix. - - Args: - matrix_id: Identifier for the matrix ("A" or "B") - row_idx: Index of the row to update - row_data: New row data - """ - - @abstractmethod - def update_column(self, matrix_id: str, col_idx: int, col_data: np.ndarray) -> None: - """Update a single column of a matrix. - - Args: - matrix_id: Identifier for the matrix ("A" or "B") - col_idx: Index of the column to update - col_data: New column data - """ - - @abstractmethod - def get_current_result(self) -> np.ndarray: - """Get current approximate result based on streamed updates. - - Returns: - Current approximate matrix multiplication result - """ - - -# Type alias for backward compatibility -AmmAlgorithm = AmmIndex diff --git a/packages/sage-libs/src/sage/libs/amms/interface/factory.py b/packages/sage-libs/src/sage/libs/amms/interface/factory.py deleted file mode 100644 index e5cb49077c..0000000000 --- a/packages/sage-libs/src/sage/libs/amms/interface/factory.py +++ /dev/null @@ -1,45 +0,0 @@ -"""Factory functions for creating AMM algorithm instances. - -Provides high-level convenience functions for algorithm creation. -""" - -from __future__ import annotations - -from typing import Any - -from sage.libs.amms.interface.base import AmmIndex -from sage.libs.amms.interface.registry import create_amm_index as _create -from sage.libs.amms.interface.registry import get_meta, registered - -__all__ = [ - "create", - "registered", - "get_meta", -] - - -def create(algorithm: str, config: dict[str, Any] | None = None, **kwargs: Any) -> AmmIndex: - """Create an AMM algorithm instance. - - Args: - algorithm: Algorithm name (e.g., "countsketch", "fastjlt") - config: Algorithm configuration dictionary - **kwargs: Additional keyword arguments merged with config - - Returns: - Configured AmmIndex instance - - Example: - >>> amm = create("countsketch", config={"sketch_size": 1000}) - >>> result = amm.multiply(matrix_a, matrix_b) - - >>> amm = create("fastjlt", sketch_size=500, use_gpu=False) - >>> result = amm.multiply(matrix_a, matrix_b) - """ - # Merge config dict with kwargs - merged_config = {} - if config is not None: - merged_config.update(config) - merged_config.update(kwargs) - - return _create(algorithm, **merged_config) diff --git a/packages/sage-libs/src/sage/libs/amms/interface/registry.py b/packages/sage-libs/src/sage/libs/amms/interface/registry.py deleted file mode 100644 index 5cd9957d08..0000000000 --- a/packages/sage-libs/src/sage/libs/amms/interface/registry.py +++ /dev/null @@ -1,107 +0,0 @@ -"""Registry for AMM algorithm implementations. - -Provides a centralized registry for discovering and instantiating -AMM algorithms, similar to the ANNS registry pattern. -""" - -from __future__ import annotations - -from typing import Any, Callable, Optional - -from sage.libs.amms.interface.base import AmmIndex, AmmIndexMeta - -# Global registry mapping algorithm names to factory functions -_REGISTRY: dict[str, Callable[..., AmmIndex]] = {} - -# Metadata cache -_META_CACHE: dict[str, AmmIndexMeta] = {} - - -def register( - name: str, - factory: Callable[..., AmmIndex], - meta: Optional[AmmIndexMeta] = None, -) -> None: - """Register an AMM algorithm implementation. - - Args: - name: Algorithm name (e.g., "countsketch", "fastjlt") - factory: Factory function that creates an AmmIndex instance - meta: Optional metadata about the algorithm - """ - if name in _REGISTRY: - raise ValueError(f"AMM algorithm '{name}' is already registered") - - _REGISTRY[name] = factory - if meta is not None: - _META_CACHE[name] = meta - - -def unregister(name: str) -> None: - """Unregister an AMM algorithm. - - Args: - name: Algorithm name to unregister - """ - _REGISTRY.pop(name, None) - _META_CACHE.pop(name, None) - - -def registered() -> list[str]: - """Return list of registered algorithm names. - - Returns: - List of algorithm names - """ - return sorted(_REGISTRY.keys()) - - -def get_factory(name: str) -> Callable[..., AmmIndex]: - """Get factory function for an algorithm. - - Args: - name: Algorithm name - - Returns: - Factory function - - Raises: - KeyError: If algorithm is not registered - """ - if name not in _REGISTRY: - available = ", ".join(registered()) - raise KeyError(f"AMM algorithm '{name}' not found. Available: {available or 'none'}") - return _REGISTRY[name] - - -def get_meta(name: str) -> Optional[AmmIndexMeta]: - """Get metadata for an algorithm. - - Args: - name: Algorithm name - - Returns: - Algorithm metadata or None if not cached - """ - return _META_CACHE.get(name) - - -def create_amm_index(name: str, **kwargs: Any) -> AmmIndex: - """Create an AMM index instance. - - Args: - name: Algorithm name - **kwargs: Algorithm-specific configuration - - Returns: - Configured AmmIndex instance - - Raises: - KeyError: If algorithm is not registered - """ - factory = get_factory(name) - return factory(**kwargs) - - -# Alias for backward compatibility -create = create_amm_index diff --git a/packages/sage-libs/src/sage/libs/amms/wrappers/__init__.py b/packages/sage-libs/src/sage/libs/amms/wrappers/__init__.py deleted file mode 100644 index a112f45996..0000000000 --- a/packages/sage-libs/src/sage/libs/amms/wrappers/__init__.py +++ /dev/null @@ -1,32 +0,0 @@ -"""Python wrappers for AMM algorithm implementations (externalized). - -The compiled bindings live in the external package `isage-amms`. Importing these -wrappers without installing that package will raise an ImportError with a clear -instruction. -""" - -__all__ = [ - "CPPAlgo", - "MatrixLoader", - "ConfigMap", - "createAMM", - "createMatrixLoader", - "configMapToDict", - "dictToConfigMap", -] - -try: # ImportError is expected if external package is not installed - from . import PyAMM - - CPPAlgo = PyAMM.CPPAlgo - MatrixLoader = PyAMM.MatrixLoader - ConfigMap = PyAMM.ConfigMap - createAMM = PyAMM.createAMM - createMatrixLoader = PyAMM.createMatrixLoader - configMapToDict = PyAMM.configMapToDict - dictToConfigMap = PyAMM.dictToConfigMap -except ImportError as exc: # fail fast with guidance - raise ImportError( - "PyAMM bindings are now provided by the external package 'isage-amms'. " - "Install it with `pip install isage-amms` to use AMM implementations." - ) from exc diff --git a/packages/sage-libs/src/sage/libs/ann/__init__.py b/packages/sage-libs/src/sage/libs/ann/__init__.py deleted file mode 100644 index dc7d78b0d7..0000000000 --- a/packages/sage-libs/src/sage/libs/ann/__init__.py +++ /dev/null @@ -1,29 +0,0 @@ -"""Unified ANNS (Approximate Nearest Neighbor Search) interfaces. - -Status: implementations have been externalized to the `isage-anns` package. This module now -exposes only the registry/interfaces. Consumers should install the external package (e.g. -`pip install -e packages/sage-libs[anns]` or `pip install isage-anns`) to obtain concrete -algorithms. Benchmarks remain in `sage-benchmark/benchmark_anns` (L5). -""" - -from __future__ import annotations - -from sage.libs.ann.interface import ( - AnnIndex, - AnnIndexMeta, - AnnRegistryError, - as_mapping, - create, - register, - registered, -) - -__all__ = [ - "AnnIndex", - "AnnIndexMeta", - "AnnRegistryError", - "create", - "register", - "registered", - "as_mapping", -] diff --git a/packages/sage-libs/src/sage/libs/ann/interface/__init__.py b/packages/sage-libs/src/sage/libs/ann/interface/__init__.py deleted file mode 100644 index 7c507d79d6..0000000000 --- a/packages/sage-libs/src/sage/libs/ann/interface/__init__.py +++ /dev/null @@ -1,20 +0,0 @@ -"""Approximate Nearest Neighbor (ANN) interfaces for SAGE. - -This module defines shared abstractions and registry helpers so algorithms can -live in ``sage-libs`` and be reused by benchmark_anns, sage-db, and sage-flow. -""" - -from __future__ import annotations - -from .base import AnnIndex, AnnIndexMeta -from .factory import AnnRegistryError, as_mapping, create, register, registered - -__all__ = [ - "AnnIndex", - "AnnIndexMeta", - "AnnRegistryError", - "register", - "create", - "registered", - "as_mapping", -] diff --git a/packages/sage-libs/src/sage/libs/ann/interface/base.py b/packages/sage-libs/src/sage/libs/ann/interface/base.py deleted file mode 100644 index 7caf0d75ee..0000000000 --- a/packages/sage-libs/src/sage/libs/ann/interface/base.py +++ /dev/null @@ -1,84 +0,0 @@ -"""Core ANN abstractions for shared use across SAGE. - -These interfaces are used by benchmark_anns, SageVDB, and SageFlow. -Implementations are provided by the external ``isage-anns`` package. -""" - -from __future__ import annotations - -import time -from abc import ABC, abstractmethod -from dataclasses import dataclass -from typing import Optional - -import numpy as np -import numpy.typing as npt - - -@dataclass(frozen=True) -class AnnIndexMeta: - """Static capabilities for an ANN implementation.""" - - name: str - metric: str - supports_insert: bool = True - supports_delete: bool = True - - -class AnnIndex(ABC): - """Minimal ANN interface to standardize build/insert/search flows.""" - - @property - @abstractmethod - def meta(self) -> AnnIndexMeta: - """Return static metadata describing this index.""" - - @abstractmethod - def setup(self, dtype: str, max_points: int, dim: int) -> None: - """Initialize the index with type, capacity, and dimension.""" - - @abstractmethod - def insert(self, vectors: np.ndarray, ids: npt.NDArray[np.uint32]) -> None: - """Insert vectors with their ids.""" - - @abstractmethod - def delete(self, ids: npt.NDArray[np.uint32]) -> None: - """Delete vectors by ids.""" - - @abstractmethod - def search(self, queries: np.ndarray, k: int) -> tuple[np.ndarray, np.ndarray]: - """Search top-k nearest neighbors.""" - - def batch_search( - self, queries: np.ndarray, k: int, *, timestamps: Optional[np.ndarray] = None - ) -> tuple[np.ndarray, np.ndarray, Optional[np.ndarray]]: - """Batch search with optional timestamp passthrough.""" - - indices, distances = self.search(queries, k) - if timestamps is None: - return indices, distances, None - - # Attach processing timestamps to align with benchmark_anns expectations. - now_us = np.full(len(queries), int(time.time() * 1e6), dtype=np.int64) - return indices, distances, now_us - - def initial_load(self, vectors: np.ndarray, ids: npt.NDArray[np.uint32]) -> None: - """Initial ingest; defaults to insert.""" - - self.insert(vectors, ids) - - def replace(self, vectors: np.ndarray, ids: npt.NDArray[np.uint32]) -> None: - """Replace by delete then insert.""" - - self.delete(ids) - self.insert(vectors, ids) - - def get_stats(self) -> dict: - """Return optional diagnostic stats.""" - - return {} - - def wait_pending_operations(self) -> None: - """Hook for async implementations; no-op by default.""" - - return None diff --git a/packages/sage-libs/src/sage/libs/ann/interface/factory.py b/packages/sage-libs/src/sage/libs/ann/interface/factory.py deleted file mode 100644 index ccfa2f42dd..0000000000 --- a/packages/sage-libs/src/sage/libs/ann/interface/factory.py +++ /dev/null @@ -1,68 +0,0 @@ -"""Registration and creation for ANN implementations. - -This registry will back benchmark_anns and other modules to resolve algorithms -without direct imports, keeping dependencies flowing downward. -""" - -from __future__ import annotations - -from typing import Callable, Iterable, Mapping - -from .base import AnnIndex - - -class AnnRegistryError(RuntimeError): - """Raised when registry operations fail.""" - - -_registry: dict[str, Callable[..., AnnIndex]] = {} - - -def register(name: str, builder: Callable[..., AnnIndex]) -> None: - """Register a new ANN implementation. - - Raises: - ValueError: if name is empty. - AnnRegistryError: if name already exists. - """ - - if not name: - raise ValueError("ANN name must be non-empty") - if name in _registry: - raise AnnRegistryError(f"ANN algorithm '{name}' is already registered") - _registry[name] = builder - - -def create(name: str, /, **kwargs) -> AnnIndex: - """Create an ANN instance by name. - - Raises: - AnnRegistryError: if name is missing or builder returns invalid type. - """ - - try: - builder = _registry[name] - except KeyError as exc: # pragma: no cover - defensive path - available = ", ".join(sorted(_registry)) or "<empty>" - raise AnnRegistryError( - f"ANN algorithm '{name}' is not registered; available: {available}" - ) from exc - - instance = builder(**kwargs) - if not isinstance(instance, AnnIndex): - raise AnnRegistryError( - f"Builder for '{name}' did not return AnnIndex (got {type(instance)!r})" - ) - return instance - - -def registered() -> Iterable[str]: - """Return registered ANN names (sorted).""" - - return tuple(sorted(_registry)) - - -def as_mapping() -> Mapping[str, Callable[..., AnnIndex]]: - """Return a read-only view of the registry.""" - - return dict(_registry) diff --git a/packages/sage-libs/src/sage/libs/ann/interface/implementations/__init__.py b/packages/sage-libs/src/sage/libs/ann/interface/implementations/__init__.py deleted file mode 100644 index 8eb50b7063..0000000000 --- a/packages/sage-libs/src/sage/libs/ann/interface/implementations/__init__.py +++ /dev/null @@ -1,21 +0,0 @@ -"""Built-in ANN implementations. - -Heavy deps live behind optional imports; registration is explicit via -``register_builtin()`` to avoid side effects on import. -""" - -from __future__ import annotations - -from .dummy import register_dummy - -__all__ = ["register_dummy", "register_builtin"] - - -def register_builtin() -> None: - """Register lightweight built-in ANN implementations. - - Keep this cheap—heavy dependencies (faiss, diskann) should register in their - own modules guarded by optional imports and extras. - """ - - register_dummy() diff --git a/packages/sage-libs/src/sage/libs/ann/interface/implementations/dummy.py b/packages/sage-libs/src/sage/libs/ann/interface/implementations/dummy.py deleted file mode 100644 index 07a60dd3e5..0000000000 --- a/packages/sage-libs/src/sage/libs/ann/interface/implementations/dummy.py +++ /dev/null @@ -1,85 +0,0 @@ -"""Reference brute-force ANN for testing and smoke checks.""" - -from __future__ import annotations - -import numpy as np -import numpy.typing as npt - -from sage.libs.ann.interface.base import AnnIndex, AnnIndexMeta -from sage.libs.ann.interface.factory import register - - -class DummyBruteForce(AnnIndex): - """A simple brute-force ANN used for validation and tests.""" - - def __init__(self, metric: str = "euclidean"): - self._meta = AnnIndexMeta(name="dummy_bruteforce", metric=metric) - self._metric = metric - self._vectors: list[np.ndarray] = [] - self._ids: list[int] = [] - self._dim = 0 - - @property - def meta(self) -> AnnIndexMeta: - return self._meta - - def setup(self, dtype: str, max_points: int, dim: int) -> None: # noqa: ARG002 - self._dim = dim - self._vectors = [] - self._ids = [] - - def insert(self, vectors: np.ndarray, ids: npt.NDArray[np.uint32]) -> None: - if vectors.shape[1] != self._dim: - raise ValueError(f"Expected dim={self._dim}, got {vectors.shape[1]}") - for vec, vid in zip(vectors, ids): - if int(vid) in self._ids: - continue - self._vectors.append(vec.astype(np.float32, copy=False)) - self._ids.append(int(vid)) - - def delete(self, ids: npt.NDArray[np.uint32]) -> None: - ids_set = {int(i) for i in ids} - keep_vecs = [] - keep_ids = [] - for vec, vid in zip(self._vectors, self._ids): - if vid not in ids_set: - keep_vecs.append(vec) - keep_ids.append(vid) - self._vectors = keep_vecs - self._ids = keep_ids - - def search(self, queries: np.ndarray, k: int): - if len(self._vectors) == 0: - n = len(queries) - return ( - np.full((n, k), -1, dtype=np.int32), - np.full((n, k), np.inf, dtype=np.float32), - ) - - data = np.stack(self._vectors, axis=0) - ids_arr = np.array(self._ids, dtype=np.int32) - - if self._metric == "ip": - dists = -np.dot(queries, data.T) - else: - dists = np.linalg.norm(data[np.newaxis, :, :] - queries[:, np.newaxis, :], axis=2) - - k_actual = min(k, data.shape[0]) - idx = np.argsort(dists, axis=1)[:, :k_actual] - indices = ids_arr[idx] - distances = np.take_along_axis(dists, idx, axis=1) - - if k > k_actual: - indices_pad = np.full((len(queries), k), -1, dtype=np.int32) - distances_pad = np.full((len(queries), k), np.inf, dtype=np.float32) - indices_pad[:, :k_actual] = indices - distances_pad[:, :k_actual] = distances - return indices_pad, distances_pad - - return indices, distances - - -def register_dummy() -> None: - """Register the dummy implementation with the global factory.""" - - register("dummy_bruteforce", lambda **kwargs: DummyBruteForce(**kwargs)) diff --git a/packages/sage-libs/src/sage/libs/dataops/__init__.py b/packages/sage-libs/src/sage/libs/dataops/__init__.py deleted file mode 100644 index 9c04e94b6b..0000000000 --- a/packages/sage-libs/src/sage/libs/dataops/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -"""Dataflow helpers and transformation utilities. - -This module provides reusable operators for data transformations: -- text: Text processing and manipulation -- table: Tabular data operations -- json: JSON schema validation and transformation -- sampling: Sampling and filtering utilities - -These are pure-Python utilities with no engine dependencies. -""" - -from . import json_ops, sampling, table, text - -__all__ = [ - "text", - "table", - "json_ops", - "sampling", -] diff --git a/packages/sage-libs/src/sage/libs/dataops/docs/README.md b/packages/sage-libs/src/sage/libs/dataops/docs/README.md deleted file mode 100644 index 8832a91e9a..0000000000 --- a/packages/sage-libs/src/sage/libs/dataops/docs/README.md +++ /dev/null @@ -1,146 +0,0 @@ -# Dataflow Helpers - -**Location**: `sage.libs.dataops`\ -**Layer**: L3 (Algorithm Library)\ -**Dependencies**: Pure Python, no engine dependencies - -## Overview - -This module provides reusable operators for data transformations used by pipelines. These are -pure-Python utilities with no runtime engine dependencies, making them lightweight and composable. - -## Components - -### 1. Text Operations (`text.py`) - -Text processing and manipulation utilities: - -- **normalize_whitespace**: Collapse multiple spaces, trim -- **truncate_text**: Truncate with suffix -- **extract_keywords**: Simple keyword extraction -- **split_sentences**: Sentence segmentation -- **deduplicate_lines**: Remove duplicate lines -- **apply_template**: Template variable substitution -- **batch_transform**: Apply transformation to list of texts - -**Usage**: - -```python -from sage.libs.dataops.text import normalize_whitespace, extract_keywords - -text = " Hello world " -normalized = normalize_whitespace(text) # "Hello world" - -keywords = extract_keywords( - "Machine learning is transforming AI", - stopwords={"is", "the"}, - min_length=3 -) # ["machine", "learning", "transforming"] -``` - -### 2. Table Operations (`table.py`) - -Tabular data operations: - -- **filter_rows**: Filter based on predicate -- **select_columns**: Column selection -- **aggregate**: Group by and aggregate -- **sort_rows**: Sort by column -- **pivot**: Simple pivot operation - -**Usage**: - -```python -from sage.libs.dataops.table import filter_rows, aggregate - -data = [ - {"name": "Alice", "age": 30, "score": 85}, - {"name": "Bob", "age": 25, "score": 92}, - {"name": "Charlie", "age": 30, "score": 88}, -] - -# Filter -adults = filter_rows(data, lambda row: row["age"] >= 25) - -# Aggregate -avg_by_age = aggregate(data, "age", "score", lambda scores: sum(scores) / len(scores)) -``` - -### 3. JSON Operations (`json_ops.py`) - -JSON schema validation and transformation: - -- **validate_schema**: Simple type schema validation -- **extract_fields**: Extract specific fields (supports nested paths) -- **flatten_json**: Flatten nested JSON -- **merge_json**: Deep merge of JSON objects - -**Usage**: - -```python -from sage.libs.dataops.json_ops import validate_schema, flatten_json - -# Validate -schema = {"name": str, "age": int} -is_valid, errors = validate_schema({"name": "Alice", "age": "30"}, schema) - -# Flatten -nested = {"user": {"name": "Alice", "address": {"city": "NYC"}}} -flat = flatten_json(nested) # {"user.name": "Alice", "user.address.city": "NYC"} -``` - -### 4. Sampling & Filtering (`sampling.py`) - -Sampling and filtering utilities: - -- **random_sample**: Random sampling with seed control -- **stratified_sample**: Stratified sampling by key -- **reservoir_sample**: Reservoir sampling for streaming -- **bucket_by**: Group items into buckets -- **filter_outliers**: Outlier detection (IQR or Z-score) - -**Usage**: - -```python -from sage.libs.dataops.sampling import random_sample, stratified_sample - -# Random sampling -items = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] -sample = random_sample(items, k=5, seed=42) - -# Stratified sampling -data = [("A", 1), ("B", 2), ("A", 3), ("B", 4)] -stratified = stratified_sample(data, k=2, key_fn=lambda x: x[0], seed=42) -``` - -## Design Principles - -1. **Pure Python**: No compiled dependencies -1. **No Engine Deps**: No dataflow runtime dependencies -1. **Composable**: Functions can be chained -1. **Type Hints**: Full type annotations -1. **Fail Fast**: No silent fallbacks - -## Used By - -- `sage.middleware.operators` - Dataflow operators -- `sage.libs.rag` - RAG pipeline preprocessing -- `sage.libs.agentic` - Agent data transformations -- Custom pipelines and workflows - -## Future Enhancements - -- Feature extractors (TF-IDF, embeddings integration) -- Schema inference from data -- More advanced text processing (NER, tokenization) -- DataFrame-style operations (if needed) - -## Migration Notes - -This module consolidates data transformation utilities previously scattered across: - -- `sage.libs.foundation.io` (batch operations) -- Various adhoc implementations in middleware operators -- Text processing utils from RAG components - -All utilities now follow consistent naming and interfaces. diff --git a/packages/sage-libs/src/sage/libs/dataops/json_ops.py b/packages/sage-libs/src/sage/libs/dataops/json_ops.py deleted file mode 100644 index 9dbf43989d..0000000000 --- a/packages/sage-libs/src/sage/libs/dataops/json_ops.py +++ /dev/null @@ -1,126 +0,0 @@ -"""JSON schema validation and transformation utilities.""" - -from __future__ import annotations - -from typing import Any - - -def validate_schema(data: dict[str, Any], schema: dict[str, type]) -> tuple[bool, list[str]]: - """Validate data against a simple type schema. - - Args: - data: Data dictionary to validate - schema: Dictionary mapping field names to expected types - - Returns: - Tuple of (is_valid, error_messages) - """ - errors = [] - - for field, expected_type in schema.items(): - if field not in data: - errors.append(f"Missing required field: {field}") - continue - - value = data[field] - if not isinstance(value, expected_type): - errors.append( - f"Field {field} has wrong type: expected {expected_type.__name__}, " - f"got {type(value).__name__}" - ) - - return len(errors) == 0, errors - - -def extract_fields(data: dict[str, Any], fields: list[str]) -> dict[str, Any]: - """Extract specific fields from nested JSON. - - Args: - data: Input JSON dictionary - fields: List of field paths (dot-separated for nested fields) - - Returns: - Dictionary with extracted fields - """ - result = {} - - for field_path in fields: - parts = field_path.split(".") - value = data - - try: - for part in parts: - if isinstance(value, dict): - value = value[part] - elif isinstance(value, list) and part.isdigit(): - value = value[int(part)] - else: - value = None - break - - if value is not None: - result[field_path] = value - except (KeyError, IndexError, ValueError): - pass - - return result - - -def flatten_json(data: dict[str, Any], prefix: str = "", sep: str = ".") -> dict[str, Any]: - """Flatten nested JSON to single-level dictionary. - - Args: - data: Nested JSON dictionary - prefix: Prefix for keys (used in recursion) - sep: Separator for nested keys - - Returns: - Flattened dictionary - """ - result = {} - - for key, value in data.items(): - new_key = f"{prefix}{sep}{key}" if prefix else key - - if isinstance(value, dict): - result.update(flatten_json(value, new_key, sep)) - elif isinstance(value, list): - for i, item in enumerate(value): - if isinstance(item, dict): - result.update(flatten_json(item, f"{new_key}[{i}]", sep)) - else: - result[f"{new_key}[{i}]"] = item - else: - result[new_key] = value - - return result - - -def merge_json(base: dict[str, Any], update: dict[str, Any], deep: bool = True) -> dict[str, Any]: - """Merge two JSON dictionaries. - - Args: - base: Base dictionary - update: Dictionary with updates - deep: If True, recursively merge nested dicts - - Returns: - Merged dictionary - """ - result = base.copy() - - for key, value in update.items(): - if deep and key in result and isinstance(result[key], dict) and isinstance(value, dict): - result[key] = merge_json(result[key], value, deep=True) - else: - result[key] = value - - return result - - -__all__ = [ - "validate_schema", - "extract_fields", - "flatten_json", - "merge_json", -] diff --git a/packages/sage-libs/src/sage/libs/dataops/sampling.py b/packages/sage-libs/src/sage/libs/dataops/sampling.py deleted file mode 100644 index c23b3c1e05..0000000000 --- a/packages/sage-libs/src/sage/libs/dataops/sampling.py +++ /dev/null @@ -1,169 +0,0 @@ -"""Sampling, filtering, and bucketing utilities.""" - -from __future__ import annotations - -import random -from typing import Any, Callable, TypeVar - -T = TypeVar("T") - - -def random_sample(items: list[T], k: int, seed: int | None = None) -> list[T]: - """Randomly sample k items. - - Args: - items: List of items - k: Number of items to sample - seed: Random seed for reproducibility - - Returns: - List of sampled items - """ - if seed is not None: - random.seed(seed) - - return random.sample(items, min(k, len(items))) - - -def stratified_sample( - items: list[T], - k: int, - key_fn: Callable[[T], Any], - seed: int | None = None, -) -> list[T]: - """Stratified sampling by key function. - - Args: - items: List of items - k: Total number of items to sample - key_fn: Function to extract stratification key - seed: Random seed for reproducibility - - Returns: - List of sampled items - """ - from collections import defaultdict - - if seed is not None: - random.seed(seed) - - # Group by key - groups: dict[Any, list[T]] = defaultdict(list) - for item in items: - key = key_fn(item) - groups[key].append(item) - - # Sample proportionally from each group - result = [] - items_per_group = max(1, k // len(groups)) - - for group_items in groups.values(): - sampled = random.sample(group_items, min(items_per_group, len(group_items))) - result.extend(sampled) - - # If we need more items, sample randomly from all - if len(result) < k: - remaining = [item for item in items if item not in result] - additional = random.sample(remaining, min(k - len(result), len(remaining))) - result.extend(additional) - - return result[:k] - - -def reservoir_sample(items: list[T], k: int, seed: int | None = None) -> list[T]: - """Reservoir sampling for streaming scenarios. - - Args: - items: List of items - k: Number of items to sample - seed: Random seed for reproducibility - - Returns: - List of sampled items - """ - if seed is not None: - random.seed(seed) - - reservoir = [] - - for i, item in enumerate(items): - if i < k: - reservoir.append(item) - else: - j = random.randint(0, i) - if j < k: - reservoir[j] = item - - return reservoir - - -def bucket_by(items: list[T], key_fn: Callable[[T], Any]) -> dict[Any, list[T]]: - """Group items into buckets by key function. - - Args: - items: List of items - key_fn: Function to extract bucket key - - Returns: - Dictionary mapping keys to lists of items - """ - from collections import defaultdict - - buckets: dict[Any, list[T]] = defaultdict(list) - - for item in items: - key = key_fn(item) - buckets[key].append(item) - - return dict(buckets) - - -def filter_outliers( - values: list[float], method: str = "iqr", threshold: float = 1.5 -) -> list[float]: - """Filter outliers from numeric values. - - Args: - values: List of numeric values - method: Method to use ("iqr" or "zscore") - threshold: Threshold for outlier detection - - Returns: - List with outliers removed - """ - if not values: - return [] - - if method == "iqr": - # Interquartile range method - sorted_vals = sorted(values) - n = len(sorted_vals) - q1 = sorted_vals[n // 4] - q3 = sorted_vals[(3 * n) // 4] - iqr = q3 - q1 - lower = q1 - threshold * iqr - upper = q3 + threshold * iqr - return [v for v in values if lower <= v <= upper] - - elif method == "zscore": - # Z-score method - mean = sum(values) / len(values) - variance = sum((v - mean) ** 2 for v in values) / len(values) - std = variance**0.5 - - if std == 0: - return values - - return [v for v in values if abs((v - mean) / std) <= threshold] - - else: - raise ValueError(f"Unknown method: {method}") - - -__all__ = [ - "random_sample", - "stratified_sample", - "reservoir_sample", - "bucket_by", - "filter_outliers", -] diff --git a/packages/sage-libs/src/sage/libs/dataops/table.py b/packages/sage-libs/src/sage/libs/dataops/table.py deleted file mode 100644 index 22c581ecc2..0000000000 --- a/packages/sage-libs/src/sage/libs/dataops/table.py +++ /dev/null @@ -1,113 +0,0 @@ -"""Table/dataframe operations.""" - -from __future__ import annotations - -from typing import Any, Callable - - -def filter_rows( - data: list[dict[str, Any]], predicate: Callable[[dict[str, Any]], bool] -) -> list[dict[str, Any]]: - """Filter rows based on predicate function. - - Args: - data: List of row dictionaries - predicate: Function that returns True for rows to keep - - Returns: - Filtered rows - """ - return [row for row in data if predicate(row)] - - -def select_columns(data: list[dict[str, Any]], columns: list[str]) -> list[dict[str, Any]]: - """Select specific columns from data. - - Args: - data: List of row dictionaries - columns: Column names to select - - Returns: - Data with only selected columns - """ - return [{k: row.get(k) for k in columns} for row in data] - - -def aggregate( - data: list[dict[str, Any]], - group_by: str, - agg_col: str, - agg_fn: Callable[[list[Any]], Any], -) -> dict[Any, Any]: - """Group by column and aggregate. - - Args: - data: List of row dictionaries - group_by: Column to group by - agg_col: Column to aggregate - agg_fn: Aggregation function - - Returns: - Dictionary mapping group_by values to aggregated values - """ - from collections import defaultdict - - groups: dict[Any, list[Any]] = defaultdict(list) - - for row in data: - key = row.get(group_by) - value = row.get(agg_col) - if key is not None and value is not None: - groups[key].append(value) - - return {k: agg_fn(v) for k, v in groups.items()} - - -def sort_rows(data: list[dict[str, Any]], by: str, reverse: bool = False) -> list[dict[str, Any]]: - """Sort rows by column. - - Args: - data: List of row dictionaries - by: Column name to sort by - reverse: If True, sort in descending order - - Returns: - Sorted data - """ - return sorted(data, key=lambda row: row.get(by, ""), reverse=reverse) - - -def pivot( - data: list[dict[str, Any]], index: str, columns: str, values: str -) -> dict[tuple[Any, Any], Any]: - """Simple pivot operation. - - Args: - data: List of row dictionaries - index: Column to use as row index - columns: Column to use as column index - values: Column to use as values - - Returns: - Dictionary mapping (index, column) tuples to values - """ - result: dict[tuple[Any, Any], Any] = {} - - for row in data: - idx = row.get(index) - col = row.get(columns) - val = row.get(values) - - if idx is not None and col is not None and val is not None: - result[(idx, col)] = val - - return result - - -__all__ = [ - "filter_rows", - "select_columns", - "aggregate", - "sort_rows", - "pivot", -] diff --git a/packages/sage-libs/src/sage/libs/dataops/text.py b/packages/sage-libs/src/sage/libs/dataops/text.py deleted file mode 100644 index 331ec7feee..0000000000 --- a/packages/sage-libs/src/sage/libs/dataops/text.py +++ /dev/null @@ -1,110 +0,0 @@ -"""Text processing and manipulation utilities.""" - -from __future__ import annotations - -import re -from typing import Callable - - -def normalize_whitespace(text: str) -> str: - """Normalize whitespace (collapse multiple spaces, trim).""" - return " ".join(text.split()) - - -def truncate_text(text: str, max_length: int, suffix: str = "...") -> str: - """Truncate text to max_length, adding suffix if truncated.""" - if len(text) <= max_length: - return text - return text[: max_length - len(suffix)] + suffix - - -def extract_keywords( - text: str, stopwords: set[str] | None = None, min_length: int = 3 -) -> list[str]: - """Extract keywords from text (simple word extraction). - - Args: - text: Input text - stopwords: Set of stopwords to filter out - min_length: Minimum word length - - Returns: - List of keywords - """ - if stopwords is None: - stopwords = set() - - # Extract words - words = re.findall(r"\b\w+\b", text.lower()) - - # Filter by length and stopwords - keywords = [w for w in words if len(w) >= min_length and w not in stopwords] - - return keywords - - -def split_sentences(text: str) -> list[str]: - """Split text into sentences (simple regex-based).""" - # Simple sentence splitter - sentences = re.split(r"[.!?]+", text) - return [s.strip() for s in sentences if s.strip()] - - -def deduplicate_lines(text: str, keep_order: bool = True) -> str: - """Remove duplicate lines from text. - - Args: - text: Input text - keep_order: If True, preserve original line order - - Returns: - Text with duplicate lines removed - """ - lines = text.split("\n") - if keep_order: - seen = set() - unique_lines = [] - for line in lines: - if line not in seen: - seen.add(line) - unique_lines.append(line) - return "\n".join(unique_lines) - else: - return "\n".join(list(set(lines))) - - -def apply_template(template: str, **kwargs) -> str: - """Apply template with variables. - - Args: - template: Template string with {var} placeholders - **kwargs: Variable values - - Returns: - Formatted string - """ - return template.format(**kwargs) - - -def batch_transform(texts: list[str], transform_fn: Callable[[str], str]) -> list[str]: - """Apply transformation function to list of texts. - - Args: - texts: List of input texts - transform_fn: Function to apply to each text - - Returns: - List of transformed texts - """ - return [transform_fn(text) for text in texts] - - -__all__ = [ - "normalize_whitespace", - "truncate_text", - "extract_keywords", - "split_sentences", - "deduplicate_lines", - "apply_template", - "batch_transform", -] diff --git a/packages/sage-libs/src/sage/libs/eval/__init__.py b/packages/sage-libs/src/sage/libs/eval/__init__.py deleted file mode 100644 index 21ea8479df..0000000000 --- a/packages/sage-libs/src/sage/libs/eval/__init__.py +++ /dev/null @@ -1,80 +0,0 @@ -"""Evaluation module for SAGE. - -This module provides the evaluation interface layer for model and pipeline evaluation. -Concrete implementations are provided by external packages (e.g., isage-eval). - -Features: -- Evaluation metrics (Accuracy, BLEU, ROUGE, F1, etc.) -- LLM-as-a-Judge evaluation (Faithfulness, Relevance, Coherence) -- Performance profiling (Latency, Throughput, Memory) -- Benchmark suites (RAG, Agent, End-to-end) - -Usage: - from sage.libs.eval import ( - BaseMetric, BaseLLMJudge, BaseProfiler, BaseBenchmark, - create_metric, create_judge, create_profiler, create_benchmark, - MetricResult, MetricType - ) -""" - -from .interface import ( - # Base classes - BaseBenchmark, - BaseLLMJudge, - BaseMetric, - BaseProfiler, - # Exception - EvalRegistryError, - # Data types - MetricResult, - # Enums - MetricType, - ProfileResult, - # Benchmark registry - create_benchmark, - # Judge registry - create_judge, - # Metric registry - create_metric, - # Profiler registry - create_profiler, - register_benchmark, - register_judge, - register_metric, - register_profiler, - registered_benchmarks, - registered_judges, - registered_metrics, - registered_profilers, -) - -__all__ = [ - # Enums - "MetricType", - # Data types - "MetricResult", - "ProfileResult", - # Base classes - "BaseMetric", - "BaseLLMJudge", - "BaseProfiler", - "BaseBenchmark", - # Metric registry - "register_metric", - "create_metric", - "registered_metrics", - # Judge registry - "register_judge", - "create_judge", - "registered_judges", - # Profiler registry - "register_profiler", - "create_profiler", - "registered_profilers", - # Benchmark registry - "register_benchmark", - "create_benchmark", - "registered_benchmarks", - # Exception - "EvalRegistryError", -] diff --git a/packages/sage-libs/src/sage/libs/eval/interface/__init__.py b/packages/sage-libs/src/sage/libs/eval/interface/__init__.py deleted file mode 100644 index fc9346d90d..0000000000 --- a/packages/sage-libs/src/sage/libs/eval/interface/__init__.py +++ /dev/null @@ -1,92 +0,0 @@ -"""Evaluation interface layer for SAGE. - -This module provides abstract interfaces for model evaluation components. -Concrete implementations are provided by external packages (e.g., isage-eval). - -Architecture: - - base.py: Abstract base classes (BaseMetric, BaseLLMJudge, BaseProfiler, BaseBenchmark) - - factory.py: Registry and factory functions - - External packages register their implementations at import time - -Usage: - # Option 1: Direct instantiation (if you know the implementation) - from isage_eval import AccuracyMetric, FaithfulnessJudge - metric = AccuracyMetric() - judge = FaithfulnessJudge(model="gpt-4") - - # Option 2: Factory pattern (more flexible) - from sage.libs.eval.interface import create_metric, create_judge - metric = create_metric("accuracy") - judge = create_judge("faithfulness", model="gpt-4") - - # Evaluate - result = metric.compute(predictions, references) - score = judge.judge(response, context=context) -""" - -# Base classes and data types -from .base import ( - BaseBenchmark, - BaseLLMJudge, - BaseMetric, - BaseProfiler, - MetricResult, - MetricType, - ProfileResult, -) - -# Factory functions -from .factory import ( - EvalRegistryError, - create_benchmark, - create_judge, - create_metric, - create_profiler, - register_benchmark, - register_judge, - register_metric, - register_profiler, - registered_benchmarks, - registered_judges, - registered_metrics, - registered_profilers, - unregister_benchmark, - unregister_judge, - unregister_metric, - unregister_profiler, -) - -__all__ = [ - # Enums - "MetricType", - # Data types - "MetricResult", - "ProfileResult", - # Base classes - "BaseMetric", - "BaseLLMJudge", - "BaseProfiler", - "BaseBenchmark", - # Metric registry - "register_metric", - "create_metric", - "registered_metrics", - "unregister_metric", - # Judge registry - "register_judge", - "create_judge", - "registered_judges", - "unregister_judge", - # Profiler registry - "register_profiler", - "create_profiler", - "registered_profilers", - "unregister_profiler", - # Benchmark registry - "register_benchmark", - "create_benchmark", - "registered_benchmarks", - "unregister_benchmark", - # Exception - "EvalRegistryError", -] diff --git a/packages/sage-libs/src/sage/libs/eval/interface/base.py b/packages/sage-libs/src/sage/libs/eval/interface/base.py deleted file mode 100644 index 24ce248d77..0000000000 --- a/packages/sage-libs/src/sage/libs/eval/interface/base.py +++ /dev/null @@ -1,395 +0,0 @@ -"""Base classes and interfaces for evaluation. - -This module defines abstract interfaces for model evaluation: -- BaseMetric: Evaluation metric base class -- BaseProfiler: Performance profiling base class -- BaseBenchmark: Benchmark suite base class -- MetricResult: Standardized metric result - -Implementations are provided by the external 'isage-eval' package. -""" - -from abc import ABC, abstractmethod -from dataclasses import dataclass, field -from enum import Enum -from typing import Any, Optional - - -class MetricType(Enum): - """Types of evaluation metrics.""" - - # Text/NLP metrics - ACCURACY = "accuracy" - F1_SCORE = "f1_score" - PRECISION = "precision" - RECALL = "recall" - BLEU = "bleu" - ROUGE = "rouge" - METEOR = "meteor" - BERT_SCORE = "bert_score" - PERPLEXITY = "perplexity" - - # LLM-specific metrics - FAITHFULNESS = "faithfulness" - RELEVANCE = "relevance" - COHERENCE = "coherence" - FLUENCY = "fluency" - TOXICITY = "toxicity" - BIAS = "bias" - - # Retrieval metrics - MRR = "mrr" # Mean Reciprocal Rank - NDCG = "ndcg" # Normalized Discounted Cumulative Gain - MAP = "map" # Mean Average Precision - HIT_RATE = "hit_rate" - - # Performance metrics - LATENCY = "latency" - THROUGHPUT = "throughput" - MEMORY_USAGE = "memory_usage" - FLOPS = "flops" - - # Custom - CUSTOM = "custom" - - -@dataclass -class MetricResult: - """Standardized metric evaluation result.""" - - name: str - value: float - metric_type: MetricType = MetricType.CUSTOM - - # Optional details - confidence_interval: Optional[tuple[float, float]] = None - sample_size: int = 0 - metadata: dict[str, Any] = field(default_factory=dict) - - def __repr__(self) -> str: - return f"{self.name}: {self.value:.4f}" - - -@dataclass -class ProfileResult: - """Performance profiling result.""" - - # Timing - total_time_ms: float - mean_latency_ms: float - p50_latency_ms: float - p90_latency_ms: float - p99_latency_ms: float - - # Throughput - samples_per_second: float - tokens_per_second: Optional[float] = None - - # Resource usage - peak_memory_mb: Optional[float] = None - avg_gpu_utilization: Optional[float] = None - - # Metadata - num_samples: int = 0 - metadata: dict[str, Any] = field(default_factory=dict) - - -class BaseMetric(ABC): - """Abstract base class for evaluation metrics. - - Examples of implementations: - - AccuracyMetric: Classification accuracy - - BLEUMetric: BLEU score for text generation - - ROUGEMetric: ROUGE scores for summarization - - FaithfulnessMetric: LLM-as-judge faithfulness - - RelevanceMetric: RAG retrieval relevance - """ - - @property - @abstractmethod - def name(self) -> str: - """Return the metric name.""" - pass - - @property - def metric_type(self) -> MetricType: - """Return the metric type.""" - return MetricType.CUSTOM - - @abstractmethod - def compute( - self, - predictions: list[Any], - references: list[Any], - **kwargs: Any, - ) -> MetricResult: - """Compute the metric value. - - Args: - predictions: Model predictions/outputs - references: Ground truth references - **kwargs: Metric-specific parameters - - Returns: - MetricResult with computed value and metadata - """ - pass - - def compute_batch( - self, - predictions: list[Any], - references: list[Any], - batch_size: int = 32, - **kwargs: Any, - ) -> MetricResult: - """Compute metric over large datasets in batches. - - Default implementation calls compute() once. - Override for more efficient batch processing. - - Args: - predictions: All predictions - references: All references - batch_size: Batch size for processing - **kwargs: Additional parameters - - Returns: - Aggregated MetricResult - """ - return self.compute(predictions, references, **kwargs) - - def supports_streaming(self) -> bool: - """Whether this metric supports streaming computation.""" - return False - - -class BaseLLMJudge(ABC): - """Abstract base class for LLM-as-a-Judge evaluation. - - Uses an LLM to evaluate quality of generated text. - - Examples of implementations: - - FaithfulnessJudge: Evaluate factual accuracy - - RelevanceJudge: Evaluate answer relevance - - CoherenceJudge: Evaluate text coherence - - SafetyJudge: Evaluate content safety - """ - - @property - @abstractmethod - def name(self) -> str: - """Return the judge name.""" - pass - - @property - @abstractmethod - def criteria(self) -> str: - """Return the evaluation criteria description.""" - pass - - @abstractmethod - def judge( - self, - response: str, - context: Optional[str] = None, - question: Optional[str] = None, - reference: Optional[str] = None, - **kwargs: Any, - ) -> MetricResult: - """Judge a single response. - - Args: - response: The response to evaluate - context: Optional context/documents used - question: Optional original question - reference: Optional reference answer - **kwargs: Additional parameters - - Returns: - MetricResult with score and reasoning - """ - pass - - def judge_batch( - self, - responses: list[str], - contexts: Optional[list[str]] = None, - questions: Optional[list[str]] = None, - references: Optional[list[str]] = None, - **kwargs: Any, - ) -> list[MetricResult]: - """Judge multiple responses. - - Default implementation calls judge() for each response. - Override for batch LLM calls. - - Args: - responses: Responses to evaluate - contexts: Corresponding contexts - questions: Corresponding questions - references: Corresponding references - **kwargs: Additional parameters - - Returns: - List of MetricResults - """ - results = [] - contexts = contexts or [None] * len(responses) - questions = questions or [None] * len(responses) - references = references or [None] * len(responses) - - for resp, ctx, q, ref in zip(responses, contexts, questions, references): - results.append(self.judge(resp, ctx, q, ref, **kwargs)) - - return results - - -class BaseProfiler(ABC): - """Abstract base class for performance profiling. - - Examples of implementations: - - LatencyProfiler: Measure inference latency - - ThroughputProfiler: Measure throughput (samples/sec) - - MemoryProfiler: Track memory usage - - GPUProfiler: Monitor GPU utilization - """ - - @property - @abstractmethod - def name(self) -> str: - """Return the profiler name.""" - pass - - @abstractmethod - def start(self) -> None: - """Start profiling.""" - pass - - @abstractmethod - def stop(self) -> ProfileResult: - """Stop profiling and return results. - - Returns: - ProfileResult with timing and resource metrics - """ - pass - - def profile(self, func: Any, *args: Any, **kwargs: Any) -> tuple[Any, ProfileResult]: - """Profile a function call. - - Args: - func: Function to profile - *args: Function arguments - **kwargs: Function keyword arguments - - Returns: - Tuple of (function result, ProfileResult) - """ - self.start() - result = func(*args, **kwargs) - profile_result = self.stop() - return result, profile_result - - def warmup(self, func: Any, num_warmup: int = 3, *args: Any, **kwargs: Any) -> None: - """Run warmup iterations before profiling. - - Args: - func: Function to warm up - num_warmup: Number of warmup iterations - *args: Function arguments - **kwargs: Function keyword arguments - """ - for _ in range(num_warmup): - func(*args, **kwargs) - - -class BaseBenchmark(ABC): - """Abstract base class for benchmark suites. - - Examples of implementations: - - RAGBenchmark: Evaluate RAG pipeline quality - - AgentBenchmark: Evaluate agent capabilities - - LatencyBenchmark: Compare model latencies - - AccuracyBenchmark: Compare model accuracies - """ - - @property - @abstractmethod - def name(self) -> str: - """Return the benchmark name.""" - pass - - @property - @abstractmethod - def description(self) -> str: - """Return the benchmark description.""" - pass - - @abstractmethod - def run(self, model: Any, **kwargs: Any) -> dict[str, MetricResult]: - """Run the benchmark on a model. - - Args: - model: Model to benchmark - **kwargs: Benchmark-specific parameters - - Returns: - Dictionary mapping metric names to results - """ - pass - - def compare( - self, - models: dict[str, Any], - **kwargs: Any, - ) -> dict[str, dict[str, MetricResult]]: - """Compare multiple models on the benchmark. - - Args: - models: Dictionary mapping model names to model instances - **kwargs: Benchmark parameters - - Returns: - Nested dict: model_name -> metric_name -> MetricResult - """ - results = {} - for model_name, model in models.items(): - results[model_name] = self.run(model, **kwargs) - return results - - def get_leaderboard( - self, - results: dict[str, dict[str, MetricResult]], - sort_by: str, - ascending: bool = False, - ) -> list[tuple[str, float]]: - """Generate a leaderboard from comparison results. - - Args: - results: Results from compare() - sort_by: Metric name to sort by - ascending: Sort order - - Returns: - List of (model_name, score) tuples, sorted - """ - scores = [] - for model_name, metrics in results.items(): - if sort_by in metrics: - scores.append((model_name, metrics[sort_by].value)) - - return sorted(scores, key=lambda x: x[1], reverse=not ascending) - - -__all__ = [ - # Enums - "MetricType", - # Data classes - "MetricResult", - "ProfileResult", - # Base classes - "BaseMetric", - "BaseLLMJudge", - "BaseProfiler", - "BaseBenchmark", -] diff --git a/packages/sage-libs/src/sage/libs/eval/interface/factory.py b/packages/sage-libs/src/sage/libs/eval/interface/factory.py deleted file mode 100644 index 79d4ac573b..0000000000 --- a/packages/sage-libs/src/sage/libs/eval/interface/factory.py +++ /dev/null @@ -1,298 +0,0 @@ -"""Factory and registry for evaluation implementations. - -This module provides a registry pattern for evaluation components. -External packages (like isage-eval) can register their implementations here. - -Example: - # Register implementations - from sage.libs.eval.interface import ( - register_metric, - register_judge, - register_profiler, - register_benchmark, - ) - register_metric("accuracy", AccuracyMetric) - register_judge("faithfulness", FaithfulnessJudge) - register_profiler("latency", LatencyProfiler) - register_benchmark("rag_qa", RAGQABenchmark) - - # Create instances - from sage.libs.eval.interface import ( - create_metric, - create_judge, - create_profiler, - create_benchmark, - ) - metric = create_metric("accuracy") - judge = create_judge("faithfulness", model="gpt-4") - profiler = create_profiler("latency") - benchmark = create_benchmark("rag_qa") -""" - -from typing import Any - -from .base import BaseBenchmark, BaseLLMJudge, BaseMetric, BaseProfiler - -_METRIC_REGISTRY: dict[str, type[BaseMetric]] = {} -_JUDGE_REGISTRY: dict[str, type[BaseLLMJudge]] = {} -_PROFILER_REGISTRY: dict[str, type[BaseProfiler]] = {} -_BENCHMARK_REGISTRY: dict[str, type[BaseBenchmark]] = {} - - -class EvalRegistryError(Exception): - """Error raised when registry operations fail.""" - - pass - - -# ======================================== -# Metric Registry -# ======================================== - - -def register_metric(name: str, cls: type[BaseMetric]) -> None: - """Register an evaluation metric implementation. - - Args: - name: Unique identifier (e.g., "accuracy", "bleu", "rouge") - cls: Metric class (should inherit from BaseMetric) - - Raises: - EvalRegistryError: If name already registered - """ - if name in _METRIC_REGISTRY: - raise EvalRegistryError(f"Metric '{name}' already registered") - - if not issubclass(cls, BaseMetric): - raise TypeError(f"Class must inherit from BaseMetric, got {cls}") - - _METRIC_REGISTRY[name] = cls - - -def create_metric(name: str, **kwargs: Any) -> BaseMetric: - """Create a metric instance by name. - - Args: - name: Name of the registered metric - **kwargs: Arguments to pass to the metric constructor - - Returns: - Instance of the metric - - Raises: - EvalRegistryError: If metric not found - """ - if name not in _METRIC_REGISTRY: - available = ", ".join(_METRIC_REGISTRY.keys()) if _METRIC_REGISTRY else "none" - raise EvalRegistryError( - f"Metric '{name}' not found. Available: {available}. Did you install 'isage-eval'?" - ) - - cls = _METRIC_REGISTRY[name] - return cls(**kwargs) - - -def registered_metrics() -> list[str]: - """Get list of registered metric names.""" - return list(_METRIC_REGISTRY.keys()) - - -def unregister_metric(name: str) -> None: - """Unregister a metric (for testing).""" - _METRIC_REGISTRY.pop(name, None) - - -# ======================================== -# LLM Judge Registry -# ======================================== - - -def register_judge(name: str, cls: type[BaseLLMJudge]) -> None: - """Register an LLM judge implementation. - - Args: - name: Unique identifier (e.g., "faithfulness", "relevance", "coherence") - cls: Judge class (should inherit from BaseLLMJudge) - - Raises: - EvalRegistryError: If name already registered - """ - if name in _JUDGE_REGISTRY: - raise EvalRegistryError(f"Judge '{name}' already registered") - - if not issubclass(cls, BaseLLMJudge): - raise TypeError(f"Class must inherit from BaseLLMJudge, got {cls}") - - _JUDGE_REGISTRY[name] = cls - - -def create_judge(name: str, **kwargs: Any) -> BaseLLMJudge: - """Create a judge instance by name. - - Args: - name: Name of the registered judge - **kwargs: Arguments to pass to the judge constructor - - Returns: - Instance of the judge - - Raises: - EvalRegistryError: If judge not found - """ - if name not in _JUDGE_REGISTRY: - available = ", ".join(_JUDGE_REGISTRY.keys()) if _JUDGE_REGISTRY else "none" - raise EvalRegistryError( - f"Judge '{name}' not found. Available: {available}. Did you install 'isage-eval'?" - ) - - cls = _JUDGE_REGISTRY[name] - return cls(**kwargs) - - -def registered_judges() -> list[str]: - """Get list of registered judge names.""" - return list(_JUDGE_REGISTRY.keys()) - - -def unregister_judge(name: str) -> None: - """Unregister a judge (for testing).""" - _JUDGE_REGISTRY.pop(name, None) - - -# ======================================== -# Profiler Registry -# ======================================== - - -def register_profiler(name: str, cls: type[BaseProfiler]) -> None: - """Register a profiler implementation. - - Args: - name: Unique identifier (e.g., "latency", "throughput", "memory", "gpu") - cls: Profiler class (should inherit from BaseProfiler) - - Raises: - EvalRegistryError: If name already registered - """ - if name in _PROFILER_REGISTRY: - raise EvalRegistryError(f"Profiler '{name}' already registered") - - if not issubclass(cls, BaseProfiler): - raise TypeError(f"Class must inherit from BaseProfiler, got {cls}") - - _PROFILER_REGISTRY[name] = cls - - -def create_profiler(name: str, **kwargs: Any) -> BaseProfiler: - """Create a profiler instance by name. - - Args: - name: Name of the registered profiler - **kwargs: Arguments to pass to the profiler constructor - - Returns: - Instance of the profiler - - Raises: - EvalRegistryError: If profiler not found - """ - if name not in _PROFILER_REGISTRY: - available = ", ".join(_PROFILER_REGISTRY.keys()) if _PROFILER_REGISTRY else "none" - raise EvalRegistryError( - f"Profiler '{name}' not found. Available: {available}. Did you install 'isage-eval'?" - ) - - cls = _PROFILER_REGISTRY[name] - return cls(**kwargs) - - -def registered_profilers() -> list[str]: - """Get list of registered profiler names.""" - return list(_PROFILER_REGISTRY.keys()) - - -def unregister_profiler(name: str) -> None: - """Unregister a profiler (for testing).""" - _PROFILER_REGISTRY.pop(name, None) - - -# ======================================== -# Benchmark Registry -# ======================================== - - -def register_benchmark(name: str, cls: type[BaseBenchmark]) -> None: - """Register a benchmark implementation. - - Args: - name: Unique identifier (e.g., "rag_qa", "agent_tool_use", "latency") - cls: Benchmark class (should inherit from BaseBenchmark) - - Raises: - EvalRegistryError: If name already registered - """ - if name in _BENCHMARK_REGISTRY: - raise EvalRegistryError(f"Benchmark '{name}' already registered") - - if not issubclass(cls, BaseBenchmark): - raise TypeError(f"Class must inherit from BaseBenchmark, got {cls}") - - _BENCHMARK_REGISTRY[name] = cls - - -def create_benchmark(name: str, **kwargs: Any) -> BaseBenchmark: - """Create a benchmark instance by name. - - Args: - name: Name of the registered benchmark - **kwargs: Arguments to pass to the benchmark constructor - - Returns: - Instance of the benchmark - - Raises: - EvalRegistryError: If benchmark not found - """ - if name not in _BENCHMARK_REGISTRY: - available = ", ".join(_BENCHMARK_REGISTRY.keys()) if _BENCHMARK_REGISTRY else "none" - raise EvalRegistryError( - f"Benchmark '{name}' not found. Available: {available}. Did you install 'isage-eval'?" - ) - - cls = _BENCHMARK_REGISTRY[name] - return cls(**kwargs) - - -def registered_benchmarks() -> list[str]: - """Get list of registered benchmark names.""" - return list(_BENCHMARK_REGISTRY.keys()) - - -def unregister_benchmark(name: str) -> None: - """Unregister a benchmark (for testing).""" - _BENCHMARK_REGISTRY.pop(name, None) - - -__all__ = [ - "EvalRegistryError", - # Metric - "register_metric", - "create_metric", - "registered_metrics", - "unregister_metric", - # Judge - "register_judge", - "create_judge", - "registered_judges", - "unregister_judge", - # Profiler - "register_profiler", - "create_profiler", - "registered_profilers", - "unregister_profiler", - # Benchmark - "register_benchmark", - "create_benchmark", - "registered_benchmarks", - "unregister_benchmark", -] diff --git a/packages/sage-libs/src/sage/libs/finetune/__init__.py b/packages/sage-libs/src/sage/libs/finetune/__init__.py deleted file mode 100644 index 709a073c2c..0000000000 --- a/packages/sage-libs/src/sage/libs/finetune/__init__.py +++ /dev/null @@ -1,68 +0,0 @@ -"""SAGE Fine-tuning Module - Training Interfaces. - -This module provides the **interface layer** (abstract base classes and factory) -for fine-tuning implementations. Concrete trainers are in the external package -`isage-finetune`. - -Architecture: - sage.libs.finetune (this module) - Interface layer (ABCs, factory, configs) - isage-finetune (external PyPI) - Implementations (SFT, LoRA, DPO trainers) - -Installation: - pip install isage-finetune # Install implementations - # or - pip install isage-libs[finetune] - -Usage: - from sage.libs.finetune.interface import ( - FineTuner, DatasetLoader, TrainingConfig, LoRAConfig, - create_trainer, create_loader, registered_trainers, - ) - - # Create trainer (requires isage-finetune installed) - trainer = create_trainer("sft", config=TrainingConfig(...)) - trainer.train(dataset) - -External implementations auto-register when imported. -See: https://github.com/intellistream/sage-finetune -""" - -from __future__ import annotations - -from sage.libs.finetune.interface import ( - DatasetLoader, - FineTuner, - FineTuneRegistryError, - LoRAConfig, - TrainingConfig, - create_loader, - create_trainer, - register_loader, - register_trainer, - registered_loaders, - registered_trainers, -) - -# Try to auto-import external package if available -try: - import isage_finetune # noqa: F401 -except ImportError: - pass # Implementations not installed, factory will raise if user tries to create - -__all__ = [ - # Base classes - "FineTuner", - "DatasetLoader", - "TrainingConfig", - "LoRAConfig", - # Registry - "FineTuneRegistryError", - "register_trainer", - "register_loader", - # Factory - "create_trainer", - "create_loader", - # Discovery - "registered_trainers", - "registered_loaders", -] diff --git a/packages/sage-libs/src/sage/libs/finetune/interface/__init__.py b/packages/sage-libs/src/sage/libs/finetune/interface/__init__.py deleted file mode 100644 index 32aebb98bd..0000000000 --- a/packages/sage-libs/src/sage/libs/finetune/interface/__init__.py +++ /dev/null @@ -1,86 +0,0 @@ -"""Fine-tuning interface layer for SAGE. - -This module provides abstract interfaces for LLM fine-tuning components. -Concrete implementations are provided by external packages (e.g., isage-finetune). - -Architecture: - - base.py: Abstract base classes (FineTuner, DatasetLoader, TrainingCallback, TrainingStrategy) - - factory.py: Registry and factory functions - - External packages register their implementations at import time - -Usage: - # Option 1: Direct instantiation (if you know the implementation) - from isage_finetune import LoRATrainer - trainer = LoRATrainer(model_name="gpt2") - - # Option 2: Factory pattern (more flexible) - from sage.libs.finetune.interface import create_trainer, create_strategy - strategy = create_strategy("lora") - trainer = create_trainer("lora", model_name="gpt2") - - # Train - metrics = trainer.train(train_dataset, eval_dataset, config) -""" - -# Base classes -from .base import ( - DatasetLoader, - FineTuner, - LoRAConfig, - TrainingCallback, - TrainingConfig, - TrainingStrategy, -) - -# Factory functions -from .factory import ( - FineTuneRegistryError, - create_callback, - create_loader, - create_strategy, - create_trainer, - register_callback, - register_loader, - register_strategy, - register_trainer, - registered_callbacks, - registered_loaders, - registered_strategies, - registered_trainers, - unregister_callback, - unregister_loader, - unregister_strategy, - unregister_trainer, -) - -__all__ = [ - # Base classes - "FineTuner", - "DatasetLoader", - "TrainingConfig", - "LoRAConfig", - "TrainingCallback", - "TrainingStrategy", - # Trainer factory - "register_trainer", - "create_trainer", - "registered_trainers", - "unregister_trainer", - # Loader factory - "register_loader", - "create_loader", - "registered_loaders", - "unregister_loader", - # Callback factory - "register_callback", - "create_callback", - "registered_callbacks", - "unregister_callback", - # Strategy factory - "register_strategy", - "create_strategy", - "registered_strategies", - "unregister_strategy", - # Exception - "FineTuneRegistryError", -] diff --git a/packages/sage-libs/src/sage/libs/finetune/interface/base.py b/packages/sage-libs/src/sage/libs/finetune/interface/base.py deleted file mode 100644 index 5cec936c0b..0000000000 --- a/packages/sage-libs/src/sage/libs/finetune/interface/base.py +++ /dev/null @@ -1,383 +0,0 @@ -"""Base classes and interfaces for finetune. - -This module defines abstract interfaces for LLM fine-tuning: -- FineTuner: Core fine-tuning interface -- TrainingConfig: Training configuration -- LoRAConfig: LoRA-specific configuration -- DatasetLoader: Training data loading - -Implementations are provided by the external 'isage-finetune' package. -""" - -from abc import ABC, abstractmethod -from dataclasses import dataclass, field -from typing import Any, Iterator, Optional - - -@dataclass -class TrainingConfig: - """Configuration for model fine-tuning.""" - - # Model settings - model_name_or_path: str - output_dir: str - - # Training hyperparameters - num_train_epochs: int = 3 - per_device_train_batch_size: int = 4 - per_device_eval_batch_size: int = 4 - learning_rate: float = 5e-5 - weight_decay: float = 0.01 - warmup_steps: int = 500 - - # Optimization - gradient_accumulation_steps: int = 1 - max_grad_norm: float = 1.0 - fp16: bool = False - bf16: bool = False - - # Logging and checkpointing - logging_steps: int = 10 - eval_steps: int = 500 - save_steps: int = 500 - save_total_limit: int = 3 - - # Misc - seed: int = 42 - report_to: list[str] = field(default_factory=lambda: ["tensorboard"]) - - def to_dict(self) -> dict[str, Any]: - """Convert to dictionary.""" - return self.__dict__ - - -@dataclass -class LoRAConfig: - """Configuration for LoRA (Low-Rank Adaptation) fine-tuning.""" - - r: int = 8 # Rank of update matrices - lora_alpha: int = 16 # LoRA scaling factor - target_modules: list[str] = None # Modules to apply LoRA - lora_dropout: float = 0.05 - bias: str = "none" # "none", "all", or "lora_only" - task_type: str = "CAUSAL_LM" # "CAUSAL_LM", "SEQ_2_SEQ_LM", etc. - - def __post_init__(self): - if self.target_modules is None: - # Default: target query and value projections - self.target_modules = ["q_proj", "v_proj"] - - -class FineTuner(ABC): - """Abstract base class for LLM fine-tuning. - - Examples of implementations: - - LoRA Trainer: Low-rank adaptation fine-tuning - - Full Fine-tuning: Full parameter fine-tuning - - QLoRA: Quantized LoRA (4-bit/8-bit) - """ - - @abstractmethod - def train( - self, - train_dataset: Any, - eval_dataset: Optional[Any] = None, - config: Optional[TrainingConfig] = None, - ) -> dict[str, Any]: - """Train the model on the dataset. - - Args: - train_dataset: Training dataset - eval_dataset: Evaluation dataset (optional) - config: Training configuration - - Returns: - Training metrics dictionary containing: - - train_loss: Final training loss - - eval_loss: Final evaluation loss (if eval_dataset provided) - - training_time: Total training time in seconds - """ - pass - - @abstractmethod - def evaluate(self, eval_dataset: Any) -> dict[str, float]: - """Evaluate the model on a dataset. - - Args: - eval_dataset: Evaluation dataset - - Returns: - Evaluation metrics (loss, perplexity, etc.) - """ - pass - - @abstractmethod - def save_model(self, output_dir: str) -> None: - """Save the fine-tuned model. - - Args: - output_dir: Directory to save the model - """ - pass - - @abstractmethod - def load_model(self, model_path: str) -> None: - """Load a fine-tuned model. - - Args: - model_path: Path to the saved model - """ - pass - - def generate(self, prompt: str, **kwargs: Any) -> str: - """Generate text using the fine-tuned model (optional). - - Args: - prompt: Input prompt - **kwargs: Generation parameters (max_length, temperature, etc.) - - Returns: - Generated text - """ - raise NotImplementedError("generate() not implemented") - - -class DatasetLoader(ABC): - """Abstract base class for training dataset loading. - - Examples of implementations: - - HuggingFace Loader: Load from HuggingFace datasets - - JSON Loader: Load from JSONL files - - Agent Trajectory Loader: Load agent execution trajectories - """ - - @abstractmethod - def load(self, data_path: str, **kwargs: Any) -> Any: - """Load dataset from path. - - Args: - data_path: Path to dataset file or directory - **kwargs: Loader-specific parameters - - Returns: - Loaded dataset (format depends on implementation) - """ - pass - - @abstractmethod - def preprocess(self, dataset: Any, tokenizer: Any) -> Any: - """Preprocess dataset for training. - - Args: - dataset: Raw dataset - tokenizer: Tokenizer instance - - Returns: - Preprocessed dataset ready for training - """ - pass - - def stream(self, data_path: str, **kwargs: Any) -> Iterator[dict[str, Any]]: - """Stream dataset samples (optional, for large datasets). - - Args: - data_path: Path to dataset - **kwargs: Loader-specific parameters - - Yields: - Individual dataset samples - """ - # Default: load all then iterate - dataset = self.load(data_path, **kwargs) - yield from dataset - - -class TrainingCallback(ABC): - """Abstract base class for training callbacks. - - Callbacks provide hooks into the training loop for: - - Logging metrics - - Early stopping - - Learning rate scheduling - - Custom checkpoint logic - - Examples of implementations: - - WandBCallback: Log metrics to Weights & Biases - - TensorBoardCallback: Log to TensorBoard - - EarlyStoppingCallback: Stop training based on metrics - """ - - def on_train_begin(self, trainer: "FineTuner", **kwargs: Any) -> None: - """Called at the start of training. - - Args: - trainer: The trainer instance - **kwargs: Additional training state - """ - pass - - def on_train_end(self, trainer: "FineTuner", **kwargs: Any) -> None: - """Called at the end of training. - - Args: - trainer: The trainer instance - **kwargs: Final training state and metrics - """ - pass - - def on_epoch_begin(self, trainer: "FineTuner", epoch: int, **kwargs: Any) -> None: - """Called at the start of each epoch. - - Args: - trainer: The trainer instance - epoch: Current epoch number (0-indexed) - **kwargs: Additional state - """ - pass - - def on_epoch_end( - self, trainer: "FineTuner", epoch: int, metrics: dict[str, float], **kwargs: Any - ) -> None: - """Called at the end of each epoch. - - Args: - trainer: The trainer instance - epoch: Current epoch number - metrics: Epoch metrics (loss, accuracy, etc.) - **kwargs: Additional state - """ - pass - - def on_step_begin(self, trainer: "FineTuner", step: int, **kwargs: Any) -> None: - """Called at the start of each training step. - - Args: - trainer: The trainer instance - step: Global step number - **kwargs: Batch data and state - """ - pass - - def on_step_end( - self, trainer: "FineTuner", step: int, loss: float, **kwargs: Any - ) -> Optional[bool]: - """Called at the end of each training step. - - Args: - trainer: The trainer instance - step: Global step number - loss: Step loss value - **kwargs: Gradients and additional state - - Returns: - If True, stop training early. None or False to continue. - """ - pass - - def on_evaluate(self, trainer: "FineTuner", metrics: dict[str, float], **kwargs: Any) -> None: - """Called after evaluation. - - Args: - trainer: The trainer instance - metrics: Evaluation metrics - **kwargs: Additional state - """ - pass - - def on_save(self, trainer: "FineTuner", output_dir: str, **kwargs: Any) -> None: - """Called when model is saved. - - Args: - trainer: The trainer instance - output_dir: Directory where model is saved - **kwargs: Additional state - """ - pass - - -class TrainingStrategy(ABC): - """Abstract base class for training strategies. - - Strategies define HOW the model is fine-tuned: - - Parameter-efficient methods (LoRA, QLoRA, Prefix Tuning) - - Full fine-tuning - - Distillation - - Quantization-aware training - - Examples of implementations: - - LoRAStrategy: Low-Rank Adaptation - - QLoRAStrategy: 4-bit quantized LoRA - - FullFTStrategy: Standard full fine-tuning - - PrefixTuningStrategy: Prefix-tuning approach - """ - - @property - @abstractmethod - def name(self) -> str: - """Return the strategy name (e.g., 'lora', 'qlora', 'full').""" - pass - - @abstractmethod - def prepare_model(self, model: Any, config: Optional["LoRAConfig"] = None) -> Any: - """Prepare the model for this training strategy. - - Args: - model: Base model to prepare - config: Strategy-specific configuration (e.g., LoRAConfig) - - Returns: - Model prepared for training with this strategy - """ - pass - - @abstractmethod - def get_trainable_parameters(self, model: Any) -> Iterator[Any]: - """Get parameters that should be trained. - - Args: - model: The prepared model - - Yields: - Trainable parameters - """ - pass - - def get_optimizer_grouped_parameters( - self, model: Any, weight_decay: float = 0.01 - ) -> list[dict[str, Any]]: - """Get parameter groups for optimizer (optional). - - By default, applies weight decay to all trainable parameters. - Override for custom parameter grouping. - - Args: - model: The prepared model - weight_decay: Weight decay value - - Returns: - List of parameter group dictionaries - """ - trainable_params = list(self.get_trainable_parameters(model)) - return [{"params": trainable_params, "weight_decay": weight_decay}] - - def merge_and_unload(self, model: Any) -> Any: - """Merge adapter weights into base model (for PEFT methods). - - Args: - model: Model with adapter weights - - Returns: - Model with merged weights (no adapter overhead) - """ - # Default: return as-is (for non-PEFT strategies) - return model - - -__all__ = [ - "TrainingConfig", - "LoRAConfig", - "FineTuner", - "DatasetLoader", - "TrainingCallback", - "TrainingStrategy", -] diff --git a/packages/sage-libs/src/sage/libs/finetune/interface/factory.py b/packages/sage-libs/src/sage/libs/finetune/interface/factory.py deleted file mode 100644 index 8e95e37f46..0000000000 --- a/packages/sage-libs/src/sage/libs/finetune/interface/factory.py +++ /dev/null @@ -1,298 +0,0 @@ -"""Factory and registry for finetune implementations. - -This module provides a registry pattern for fine-tuning implementations. -External packages (like isage-finetune) can register their implementations here. - -Example: - # Register an implementation - from sage.libs.finetune.interface import register_trainer, register_loader - register_trainer("lora", LoRATrainer) - register_loader("hf_dataset", HuggingFaceLoader) - - # Create instances - from sage.libs.finetune.interface import create_trainer, create_loader - trainer = create_trainer("lora", model_name="gpt2") - loader = create_loader("hf_dataset") -""" - -from typing import Any - -from .base import DatasetLoader, FineTuner, TrainingCallback, TrainingStrategy - -_TRAINER_REGISTRY: dict[str, type[FineTuner]] = {} -_LOADER_REGISTRY: dict[str, type[DatasetLoader]] = {} -_CALLBACK_REGISTRY: dict[str, type[TrainingCallback]] = {} -_STRATEGY_REGISTRY: dict[str, type[TrainingStrategy]] = {} - - -class FineTuneRegistryError(Exception): - """Error raised when registry operations fail.""" - - pass - - -def register_trainer(name: str, cls: type[FineTuner]) -> None: - """Register a fine-tuning trainer implementation. - - Args: - name: Unique identifier for this trainer (e.g., "lora", "qlora", "full") - cls: Trainer class (should inherit from FineTuner) - - Raises: - FineTuneRegistryError: If name already registered - """ - if name in _TRAINER_REGISTRY: - raise FineTuneRegistryError(f"Trainer '{name}' already registered") - - if not issubclass(cls, FineTuner): - raise TypeError(f"Class must inherit from FineTuner, got {cls}") - - _TRAINER_REGISTRY[name] = cls - - -def register_loader(name: str, cls: type[DatasetLoader]) -> None: - """Register a dataset loader implementation. - - Args: - name: Unique identifier for this loader (e.g., "hf_dataset", "jsonl") - cls: Loader class (should inherit from DatasetLoader) - - Raises: - FineTuneRegistryError: If name already registered - """ - if name in _LOADER_REGISTRY: - raise FineTuneRegistryError(f"Loader '{name}' already registered") - - if not issubclass(cls, DatasetLoader): - raise TypeError(f"Class must inherit from DatasetLoader, got {cls}") - - _LOADER_REGISTRY[name] = cls - - -def create_trainer(name: str, **kwargs: Any) -> FineTuner: - """Create a trainer instance by name. - - Args: - name: Name of the registered trainer - **kwargs: Arguments to pass to the trainer constructor - - Returns: - Instance of the trainer - - Raises: - FineTuneRegistryError: If trainer not found - - Example: - >>> trainer = create_trainer("lora", model_name="gpt2", lora_r=8) - >>> trainer.train(train_dataset) - """ - if name not in _TRAINER_REGISTRY: - available = ", ".join(_TRAINER_REGISTRY.keys()) if _TRAINER_REGISTRY else "none" - raise FineTuneRegistryError( - f"Trainer '{name}' not found. Available: {available}. Did you install 'isage-finetune'?" - ) - - cls = _TRAINER_REGISTRY[name] - return cls(**kwargs) - - -def create_loader(name: str, **kwargs: Any) -> DatasetLoader: - """Create a dataset loader instance by name. - - Args: - name: Name of the registered loader - **kwargs: Arguments to pass to the loader constructor - - Returns: - Instance of the loader - - Raises: - FineTuneRegistryError: If loader not found - - Example: - >>> loader = create_loader("hf_dataset", dataset_name="alpaca") - >>> dataset = loader.load("train") - """ - if name not in _LOADER_REGISTRY: - available = ", ".join(_LOADER_REGISTRY.keys()) if _LOADER_REGISTRY else "none" - raise FineTuneRegistryError( - f"Loader '{name}' not found. Available: {available}. Did you install 'isage-finetune'?" - ) - - cls = _LOADER_REGISTRY[name] - return cls(**kwargs) - - -def registered_trainers() -> list[str]: - """Get list of registered trainer names. - - Returns: - List of registered trainer names - """ - return list(_TRAINER_REGISTRY.keys()) - - -def registered_loaders() -> list[str]: - """Get list of registered loader names. - - Returns: - List of registered loader names - """ - return list(_LOADER_REGISTRY.keys()) - - -def unregister_trainer(name: str) -> None: - """Unregister a trainer (for testing). - - Args: - name: Name of the trainer to unregister - """ - _TRAINER_REGISTRY.pop(name, None) - - -def unregister_loader(name: str) -> None: - """Unregister a loader (for testing). - - Args: - name: Name of the loader to unregister - """ - _LOADER_REGISTRY.pop(name, None) - - -# ======================================== -# Callback Registry -# ======================================== - - -def register_callback(name: str, cls: type[TrainingCallback]) -> None: - """Register a training callback implementation. - - Args: - name: Unique identifier for this callback (e.g., "wandb", "tensorboard", "early_stop") - cls: Callback class (should inherit from TrainingCallback) - - Raises: - FineTuneRegistryError: If name already registered - """ - if name in _CALLBACK_REGISTRY: - raise FineTuneRegistryError(f"Callback '{name}' already registered") - - if not issubclass(cls, TrainingCallback): - raise TypeError(f"Class must inherit from TrainingCallback, got {cls}") - - _CALLBACK_REGISTRY[name] = cls - - -def create_callback(name: str, **kwargs: Any) -> TrainingCallback: - """Create a callback instance by name. - - Args: - name: Name of the registered callback - **kwargs: Arguments to pass to the callback constructor - - Returns: - Instance of the callback - - Raises: - FineTuneRegistryError: If callback not found - """ - if name not in _CALLBACK_REGISTRY: - available = ", ".join(_CALLBACK_REGISTRY.keys()) if _CALLBACK_REGISTRY else "none" - raise FineTuneRegistryError( - f"Callback '{name}' not found. Available: {available}. Did you install 'isage-finetune'?" - ) - - cls = _CALLBACK_REGISTRY[name] - return cls(**kwargs) - - -def registered_callbacks() -> list[str]: - """Get list of registered callback names.""" - return list(_CALLBACK_REGISTRY.keys()) - - -def unregister_callback(name: str) -> None: - """Unregister a callback (for testing).""" - _CALLBACK_REGISTRY.pop(name, None) - - -# ======================================== -# Strategy Registry -# ======================================== - - -def register_strategy(name: str, cls: type[TrainingStrategy]) -> None: - """Register a training strategy implementation. - - Args: - name: Unique identifier for this strategy (e.g., "lora", "qlora", "full", "prefix") - cls: Strategy class (should inherit from TrainingStrategy) - - Raises: - FineTuneRegistryError: If name already registered - """ - if name in _STRATEGY_REGISTRY: - raise FineTuneRegistryError(f"Strategy '{name}' already registered") - - if not issubclass(cls, TrainingStrategy): - raise TypeError(f"Class must inherit from TrainingStrategy, got {cls}") - - _STRATEGY_REGISTRY[name] = cls - - -def create_strategy(name: str, **kwargs: Any) -> TrainingStrategy: - """Create a strategy instance by name. - - Args: - name: Name of the registered strategy - **kwargs: Arguments to pass to the strategy constructor - - Returns: - Instance of the strategy - - Raises: - FineTuneRegistryError: If strategy not found - """ - if name not in _STRATEGY_REGISTRY: - available = ", ".join(_STRATEGY_REGISTRY.keys()) if _STRATEGY_REGISTRY else "none" - raise FineTuneRegistryError( - f"Strategy '{name}' not found. Available: {available}. Did you install 'isage-finetune'?" - ) - - cls = _STRATEGY_REGISTRY[name] - return cls(**kwargs) - - -def registered_strategies() -> list[str]: - """Get list of registered strategy names.""" - return list(_STRATEGY_REGISTRY.keys()) - - -def unregister_strategy(name: str) -> None: - """Unregister a strategy (for testing).""" - _STRATEGY_REGISTRY.pop(name, None) - - -__all__ = [ - "FineTuneRegistryError", - # Trainer - "register_trainer", - "create_trainer", - "registered_trainers", - "unregister_trainer", - # Loader - "register_loader", - "create_loader", - "registered_loaders", - "unregister_loader", - # Callback - "register_callback", - "create_callback", - "registered_callbacks", - "unregister_callback", - # Strategy - "register_strategy", - "create_strategy", - "registered_strategies", - "unregister_strategy", -] diff --git a/packages/sage-libs/src/sage/libs/foundation/__init__.py b/packages/sage-libs/src/sage/libs/foundation/__init__.py deleted file mode 100644 index c4ebc70dcf..0000000000 --- a/packages/sage-libs/src/sage/libs/foundation/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -"""Foundation layer - Low-level utilities and building blocks. - -This module provides foundational utilities that are used across SAGE: -- tools: Tool base classes and registry -- io: Source/Sink/Batch abstractions for data flow -- context: Context compression algorithms - -These utilities have minimal dependencies and form the base layer of sage-libs. -""" - -from . import context, io, tools - -__all__ = [ - "tools", - "io", - "context", -] diff --git a/packages/sage-libs/src/sage/libs/foundation/context/__init__.py b/packages/sage-libs/src/sage/libs/foundation/context/__init__.py deleted file mode 100644 index 9958dbed24..0000000000 --- a/packages/sage-libs/src/sage/libs/foundation/context/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -"""Context management for LLMs. - -Note: - Context compression algorithms have been migrated to isage-refiner. - Install with: pip install isage-refiner - - For SAGE middleware integration with context compression, - see sage.middleware.components.sage_refiner. -""" - -from . import compression - -__all__ = ["compression"] diff --git a/packages/sage-libs/src/sage/libs/foundation/context/compression/__init__.py b/packages/sage-libs/src/sage/libs/foundation/context/compression/__init__.py deleted file mode 100644 index 1f71a9de99..0000000000 --- a/packages/sage-libs/src/sage/libs/foundation/context/compression/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -"""Context compression - MIGRATED to isage-refiner. - -All context compression algorithms have been migrated to the independent package: - - pip install isage-refiner - -Usage: - from sage_refiner import LongRefinerCompressor, REFORMCompressor, ProvenceCompressor - - compressor = LongRefinerCompressor() - result = compressor.compress(question, documents, budget=2048) - -For SAGE middleware integration, see sage-middleware documentation. - -This module is kept as a placeholder for backwards compatibility documentation. -No functionality remains here - use isage-refiner directly. -""" - -__all__: list[str] = [] diff --git a/packages/sage-libs/src/sage/libs/foundation/io/__init__.py b/packages/sage-libs/src/sage/libs/foundation/io/__init__.py deleted file mode 100644 index c431d2fd7a..0000000000 --- a/packages/sage-libs/src/sage/libs/foundation/io/__init__.py +++ /dev/null @@ -1,71 +0,0 @@ -""" -SAGE IO - Input/Output Abstractions - -Layer: L3 (Core - Algorithm Library) - -This module provides unified input/output interfaces for data streams, -batches, sources, and sinks. - -Components: -- Source: Data source abstractions -- Sink: Data sink abstractions -- Batch: Batch processing utilities -""" - -# 直接从本包的_version模块加载版本信息 -try: - from sage.libs._version import __author__, __email__, __version__ -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" - -from .batch import ( - HFDatasetBatch, - JSONLBatch, -) -from .sink import ( - FileSink, - MemWriteSink, - PrintSink, - RetriveSink, - TerminalSink, -) - -# Import and export specific classes -from .source import ( - APISource, - CSVFileSource, - DatabaseSource, - FileSource, - JSONFileSource, - KafkaSource, - SocketSource, - TextFileSource, -) - -__all__ = [ - # Version info - "__version__", - "__author__", - "__email__", - # Sources - "FileSource", - "SocketSource", - "TextFileSource", - "JSONFileSource", - "CSVFileSource", - "KafkaSource", - "DatabaseSource", - "APISource", - # Sinks - "TerminalSink", - "RetriveSink", - "FileSink", - "MemWriteSink", - "PrintSink", - # Batch - "HFDatasetBatch", - "JSONLBatch", -] diff --git a/packages/sage-libs/src/sage/libs/foundation/io/batch.py b/packages/sage-libs/src/sage/libs/foundation/io/batch.py deleted file mode 100644 index c7256f5e9f..0000000000 --- a/packages/sage-libs/src/sage/libs/foundation/io/batch.py +++ /dev/null @@ -1,197 +0,0 @@ -import json -import os - -from sage.common.core import BatchFunction, StopSignal - -try: - from datasets import load_dataset - - HAS_DATASETS = True -except ImportError: - HAS_DATASETS = False - - -class HFDatasetBatch(BatchFunction): - """ - HuggingFace数据集批处理函数 - - 从HuggingFace数据集中批量读取数据，支持流式处理。 - 当数据集处理完成时返回 StopSignal 来停止批处理。 - - Input: None (直接从HF数据集读取) - Output: 包含query和references的字典对象 - - Attributes: - config: 配置字典，包含数据集设置 - hf_name: HuggingFace数据集名称 - hf_config: 数据集配置名称 - hf_split: 数据集分割（train/validation/test等） - _iter: 数据集迭代器 - """ - - def __init__(self, config: dict | None = None, **kwargs): - super().__init__(**kwargs) - if not HAS_DATASETS: - raise ImportError( - "datasets library is required for HFDatasetBatch. Install with: pip install datasets" - ) - if config is None: - raise ValueError("config is required for HFDatasetBatch") - self.config = config - self.hf_name = config["hf_dataset_name"] - self.hf_config = config.get("hf_dataset_config") - self.hf_split = config.get("hf_split", "train") - self.max_samples = config.get("max_samples", None) # Support limiting samples - self._iter = None - self._dataset_exhausted = False - self._sample_count = 0 # Track number of samples yielded - - # Log max_samples configuration - if self.max_samples is not None: - self.logger.info(f"HFDatasetBatch configured with max_samples={self.max_samples}") - else: - self.logger.info("HFDatasetBatch: no max_samples limit") - - def _build_iter(self): - """构建数据集迭代器""" - ds = load_dataset(self.hf_name, self.hf_config, split=self.hf_split, streaming=True) - for ex in ds: - # Type hint: ex is a dict-like object from HuggingFace datasets - if isinstance(ex, dict): - yield { - "query": ex.get("question", ""), - "references": ex.get("golden_answers") or [], - } - - def execute(self): - """ - 执行批处理函数逻辑 - - Returns: - dict: 包含query和references的数据字典 - StopSignal: 数据集结束时返回StopSignal - """ - if self._dataset_exhausted: - return StopSignal("HFDatasetBatch-exhausted") - - # Check if we've reached max_samples limit - if self.max_samples is not None and self._sample_count >= self.max_samples: - self.logger.info( - f"Reached max_samples limit ({self.max_samples}), stopping batch processing" - ) - self._dataset_exhausted = True - return StopSignal(f"HFDatasetBatch-max_samples-{self.max_samples}") - - if self._iter is None: - self.logger.debug(f"Initializing HF dataset batch source: {self.hf_name}") - if self.max_samples: - self.logger.info(f"Will process up to {self.max_samples} samples") - self._iter = self._build_iter() - - try: - data = next(self._iter) - self._sample_count += 1 - self.logger.debug( - f"Yielding batch data ({self._sample_count}" - + (f"/{self.max_samples}" if self.max_samples else "") - + f"): {data}" - ) - return data - except StopIteration: - self.logger.info(f"HF dataset batch processing completed for: {self.hf_name}") - self._dataset_exhausted = True - return StopSignal("HFDatasetBatch-completed") - - -class JSONLBatch(BatchFunction): - """ - JSONL文件批处理函数 - - 逐行读取JSONL文件中的数据，支持流式处理。 - 当文件处理完成时返回None来停止批处理。 - - Input: None (直接从JSONL文件读取) - Output: 包含query和其他字段的字典对象 - - Attributes: - config: 配置字典，包含文件路径设置 - file_path: JSONL文件路径 - _file_handle: 文件句柄 - _file_exhausted: 文件是否已读取完毕 - """ - - def __init__(self, config: dict | None = None, **kwargs): - super().__init__(**kwargs) - if config is None: - raise ValueError("config is required for JsonlFileBatch") - self.config = config - self.file_path = config["data_path"] - self._file_handle = None - self._file_exhausted = False - - def _open_file(self): - """打开JSONL文件""" - if not os.path.exists(self.file_path): - raise FileNotFoundError(f"JSONL file not found: {self.file_path}") - - self._file_handle = open(self.file_path, encoding="utf-8") - self.logger.debug(f"Opened JSONL file: {self.file_path}") - - def execute(self): - """ - 执行批处理函数逻辑 - - Returns: - dict: 包含query和其他字段的数据字典 - StopSignal: 文件结束时返回StopSignal - """ - if self._file_exhausted: - return StopSignal("JSONLBatch-exhausted") - - if self._file_handle is None: - self.logger.debug(f"Initializing JSONL batch source: {self.file_path}") - self._open_file() - - assert self._file_handle is not None, "File handle should be initialized" - - try: - line = self._file_handle.readline() - if not line: - # 文件读取完毕 - self.logger.info(f"JSONL file batch processing completed for: {self.file_path}") - self._file_handle.close() - self._file_exhausted = True - return StopSignal("JSONLBatch-completed") - - # 解析JSON行 - line = line.strip() - if line: - data = json.loads(line) - # 如果data包含query字段，直接返回query字符串 - if "query" in data: - query_text = data["query"] - self.logger.debug(f"Yielding JSONL query: {query_text}") - return query_text - else: - # 否则返回完整数据 - self.logger.debug(f"Yielding JSONL data: {data}") - return data - else: - # 空行，继续读取下一行 - return self.execute() - - except json.JSONDecodeError as e: - self.logger.error(f"Failed to parse JSON line: {line}, error: {e}") - # 跳过错误行，继续处理 - return self.execute() - except Exception as e: - self.logger.error(f"Error reading JSONL file: {e}") - if self._file_handle: - self._file_handle.close() - self._file_exhausted = True - return StopSignal(f"JSONLBatch-error-{str(e)}") - - def __del__(self): - """析构函数，确保文件句柄被正确关闭""" - if hasattr(self, "_file_handle") and self._file_handle: - self._file_handle.close() diff --git a/packages/sage-libs/src/sage/libs/foundation/io/sink.py b/packages/sage-libs/src/sage/libs/foundation/io/sink.py deleted file mode 100644 index 8a77ac244e..0000000000 --- a/packages/sage-libs/src/sage/libs/foundation/io/sink.py +++ /dev/null @@ -1,238 +0,0 @@ -import logging -import os -from typing import Any - -from sage.common.config.output_paths import get_output_file -from sage.common.core import SinkFunction - - -class TerminalSink(SinkFunction): - def __init__(self, config: dict | None = None, **kwargs): - super().__init__(**kwargs) - self.config = config - - def execute(self, data): - # 支持 dict、tuple、list 类型 - question = answer = None - if isinstance(data, dict): - question = data.get("query") or data.get("question") - answer = data.get("answer") or data.get("response") - elif isinstance(data, (tuple, list)): - if len(data) == 2: - question, answer = data - elif len(data) > 2: - question, answer = data[0], data[1] - else: - question = str(data) - self.logger.info(f"Executing {self.__class__.__name__} [Q] Question :{question}") - self.logger.info(f"Executing {self.__class__.__name__} [A] Answer :{answer}") - print(f"[{self.__class__.__name__}]: \033[96m[Q] Question :{question}\033[0m") - print(f"[{self.__class__.__name__}]: \033[92m[A] Answer :{answer}\033[0m") - - -class RetriveSink(SinkFunction): - def __init__(self, config: dict | None = None, **kwargs): - super().__init__(**kwargs) - self.config = config - - def execute(self, data: tuple[str, list[str]]): - question, chunks = data - - print(f"\033[96m[Q] Question :{question}\033[0m") - - print(f"\033[92m[A] Chunks :{chunks}\033[0m") - - -class FileSink(SinkFunction): - def __init__(self, config: dict | None = None, **kwargs): - super().__init__(**kwargs) - - self.config = config - file_path = (config or {}).get("file_path", "qa_output.txt") - - # 判断路径类型并处理 - if os.path.isabs(file_path): - # 绝对路径：直接使用 - self.file_path = file_path - # 确保目录存在 - os.makedirs(os.path.dirname(file_path), exist_ok=True) - else: - # 相对路径：使用统一的.sage/output目录 - self.file_path = str(get_output_file(file_path)) - - # 创建或清空文件 - with open(self.file_path, "w", encoding="utf-8") as f: - f.write("=== QA Output Log ===\n") - - def execute(self, data: tuple[str, str]): - # 添加详细的日志记录 - self.logger.info(f"FileSink.execute called with data: {data}") - self.logger.info(f"Data type: {type(data)}") - - if not isinstance(data, tuple) or len(data) != 2: - self.logger.error(f"FileSink expected tuple of 2 elements, got: {data}") - return - - question, answer = data - - # 确保数据是字符串类型 - if not isinstance(question, str) or not isinstance(answer, str): - self.logger.error( - f"FileSink expected string tuple, got question: {type(question)}, answer: {type(answer)}" - ) - return - - self.logger.info(f"Writing QA pair to file {self.file_path}") - self.logger.info( - f"Question: {question[:100]}..." if len(question) > 100 else f"Question: {question}" - ) - self.logger.info(f"Answer: {answer[:100]}..." if len(answer) > 100 else f"Answer: {answer}") - - with open(self.file_path, "a", encoding="utf-8") as f: - f.write("[Q] Question: " + question + "\n") - f.write("[A] Answer : " + answer + "\n") - f.write("-" * 40 + "\n") - self.logger.info(f"Data successfully written to file: {self.file_path}") - - -class MemWriteSink(SinkFunction): - def __init__(self, config: dict | None = None, **kwargs): - super().__init__(**kwargs) - self.config = config - # 从配置获取文件路径，默认为 'mem_output.txt' - file_path = (config or {}).get("file_path", "mem_output.txt") - - # 使用统一的.sage/output目录 - if os.path.isabs(file_path): - self.file_path = file_path - else: - self.file_path = str(get_output_file(file_path)) - - self.counter = 0 # 全局字符串计数器 - - # 初始化文件并写入标题 - with open(self.file_path, "w", encoding="utf-8") as f: - f.write("=== Memory String Log ===\n") - - def execute(self, data: str | list[str] | tuple[str, ...] | Any): - # 解析输入数据为字符串列表 - input_data = data - strings = self._parse_input(input_data) - - # 追加写入文件 - with open(self.file_path, "a", encoding="utf-8") as f: - for s in strings: - self.counter += 1 - f.write(f"[{self.counter}] {s}\n") - f.write("-" * 40 + "\n") # 写入分隔线 - - def _parse_input(self, input_data): - """将不同格式的输入统一解析为字符串列表""" - if isinstance(input_data, str): - return [input_data] - elif isinstance(input_data, list): - return input_data - elif isinstance(input_data, tuple): - # 展平元组中的所有字符串 - return [item for item in input_data if isinstance(item, str)] - else: - # 其他类型转换为字符串 - return [str(input_data)] - - -class PrintSink(SinkFunction): - """ - 简洁的打印汇聚函数 - 提供便捷的datastream.print()支持 - - 支持多种数据格式的智能打印，自动检测数据类型并格式化输出 - """ - - def __init__( - self, - prefix: str = "", - separator: str = " | ", - colored: bool = True, - quiet: bool = False, - **kwargs, - ): - super().__init__(**kwargs) - self.prefix = prefix - self.separator = separator - self.colored = colored - self.quiet = quiet - self._print_logger = logging.getLogger(__name__) - self.first_output = True - - def execute(self, data: Any) -> None: - """ - 智能打印数据，支持多种数据格式 - - Args: - data: 任意类型的输入数据 - """ - formatted_output = self._format_data(data) - - if self.prefix: - output = f"{self.prefix}{self.separator}{formatted_output}" - else: - output = formatted_output - - if self.first_output and not self.quiet: - print(f"First output: {output}") - print( - "Streaming started! Further outputs are logged. Check logs for detailed stream processing results." - ) - elif not self.first_output and not self.quiet: - # 后续输出只记录到日志 - pass - else: - # quiet模式或第一次输出时正常打印 - print(output) - - # 输出到日志 - self._print_logger.debug(f"PrintSink output: {output}") - - self.first_output = False - - def _format_data(self, data: Any) -> str: - """格式化数据为可读字符串""" - - # 处理问答对 (question, answer) - if isinstance(data, tuple) and len(data) == 2: - if all(isinstance(item, str) for item in data): - question, answer = data - if self.colored: - return f"\033[96m[Q] {question}\033[0m\n\033[92m[A] {answer}\033[0m" - else: - return f"[Q] {question}\n[A] {answer}" - - # 处理检索结果 (question, chunks) - if isinstance(data, tuple) and len(data) == 2: - question, chunks = data - if isinstance(question, str) and isinstance(chunks, list): - if self.colored: - chunks_str = "\n".join([f" - {chunk}" for chunk in chunks]) - return f"\033[96m[Q] {question}\033[0m\n\033[93m[Chunks]\033[0m\n{chunks_str}" - else: - chunks_str = "\n".join([f" - {chunk}" for chunk in chunks]) - return f"[Q] {question}\n[Chunks]\n{chunks_str}" - - # 处理字符串列表 - if isinstance(data, list): - if all(isinstance(item, str) for item in data): - return "\n".join([f" - {item}" for item in data]) - else: - return "\n".join([f" - {str(item)}" for item in data]) - - # 处理字典 - if isinstance(data, dict): - items = [] - for key, value in data.items(): - if self.colored: - items.append(f"\033[94m{key}\033[0m: {value}") - else: - items.append(f"{key}: {value}") - return "\n".join(items) - - # 处理其他类型，直接转换为字符串 - return str(data) diff --git a/packages/sage-libs/src/sage/libs/foundation/io/source.py b/packages/sage-libs/src/sage/libs/foundation/io/source.py deleted file mode 100644 index 1e8797ba88..0000000000 --- a/packages/sage-libs/src/sage/libs/foundation/io/source.py +++ /dev/null @@ -1,496 +0,0 @@ -import csv -import json -import socket -import time -from pathlib import Path - -from sage.common.config.ports import SagePorts -from sage.common.core import SourceFunction - - -class FileSource(SourceFunction): - """ - A source rag that reads a file line by line and returns each line as a string. - - Input: None (reads directly from a file located at the specified `data_path`). - Output: A Data object containing the next line of the file content. - - Attributes: - config: Configuration dictionary containing various settings, including the file path. - data_path: The path to the file to be read. - file_pos: Tracks the current position in the file for sequential reading. - """ - - def __init__(self, config: dict | None = None, **kwargs): - super().__init__(**kwargs) - """ - Initializes the FileSource with the provided configuration and sets the data path for the file. - - :param config: Configuration dictionary containing source settings, including `data_path`. - """ - if config is None: - raise ValueError("config parameter is required for FileSource") - self.config = config - self.data_path = self.resolve_data_path( - config["data_path"] - ) # → project_root/data/sample/question.txt - self.file_pos = 0 # Track the file read position - self.loop_reading = config.get( - "loop_reading", False - ) # Whether to restart from beginning when EOF reached - - def resolve_data_path(self, path: str | Path) -> Path: - """ - 传入相对路径则返回相对于项目根目录的绝对路径（默认假设项目根目录含有 'data/' 子目录）， - 传入绝对路径则直接返回。 - """ - import os - - p = Path(path) - if p.is_absolute(): - return p - # 假设调用时 cwd 是项目的某个子目录，项目根为“当前工作目录的祖父目录” - project_root = Path(os.getcwd()).resolve() - return project_root / p - - def execute(self) -> str | None: - """ - Reads the next line from the file and returns it as a string. - - :return: A Data object containing the next line of the file content. - """ - try: - while True: - with open(self.data_path, encoding="utf-8") as f: - f.seek(self.file_pos) # Move to the last read position - line = f.readline() - self.file_pos = f.tell() # Update the new position - if line: - self.logger.info( - f"\033[32m[ {self.__class__.__name__}]: Read query: {line.strip()}\033[0m " - ) - return line.strip() # Return non-empty lines - else: - if self.loop_reading: - self.logger.info( - f"\033[33m[ {self.__class__.__name__}]: Reached end of file, restarting from beginning.\033[0m " - ) - self.file_pos = 0 # Reset to beginning of file - continue - else: - self.logger.info( - f"\033[33m[ {self.__class__.__name__}]: Reached end of file, maintaining position.\033[0m " - ) - # Reset position if end of file is reached (optional) - time.sleep(2) - continue - except FileNotFoundError: - self.logger.error(f"File not found: {self.data_path}") - return None - except Exception as e: - self.logger.error(f"Error reading file '{self.data_path}': {e}") - return None - - -class SocketSource(SourceFunction): - """ - 从网络套接字读取数据的源函数，支持多机分布式环境 - - 配置参数: - - host: 服务器主机名或IP地址 - - port: 服务器端口号 - - protocol: 协议类型 (tcp/udp) - - reconnect: 连接断开时是否自动重连 (默认True) - - reconnect_interval: 重连间隔秒数 (默认5秒) - - load_balancing: 是否启用负载均衡 (默认False) - - client_id: 客户端唯一标识符 (用于负载均衡) - - buffer_size: 接收缓冲区大小 (默认1024字节) - - timeout: 套接字超时时间 (默认1秒) - - delimiter: 消息分隔符 (默认换行符) - - encoding: 数据编码 (默认utf-8) - """ - - def __init__(self, config: dict | None = None, **kwargs): - super().__init__(**kwargs) - self.config = config or {} - self.host = self.config.get("host", "127.0.0.1") - self.port = self.config.get("port", SagePorts.GATEWAY_DEFAULT) - self.protocol = self.config.get("protocol", "tcp").lower() - self.reconnect = self.config.get("reconnect", True) - self.reconnect_interval = self.config.get("reconnect_interval", 5) - self.load_balancing = self.config.get("load_balancing", False) - self.client_id = self.config.get("client_id", socket.gethostname()) - self.buffer_size = self.config.get("buffer_size", 1024) - self.timeout = self.config.get("timeout", 3) - self.delimiter = self.config.get("delimiter", "\n").encode() - self.encoding = self.config.get("encoding", "utf-8") - - self.socket = None - self.buffer = b"" - self.last_connect_attempt = 0 - self.is_connected = False - - # 初始化连接 - self._initialize_connection() - - def _initialize_connection(self): - """初始化套接字连接""" - try: - if self.protocol == "tcp": - self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) - self.socket.settimeout(self.timeout) - self._connect_tcp() - elif self.protocol == "udp": - self.socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) - self.socket.settimeout(self.timeout) - self.is_connected = True # UDP是无连接的 - else: - raise ValueError(f"不支持的协议类型: {self.protocol}") - except Exception as e: - self.logger.error(f"初始化连接失败: {e}") - self.is_connected = False - - def _connect_tcp(self): - """建立TCP连接""" - if self.socket is None: - self.logger.error("Socket is not initialized") - return - - try: - self.socket.connect((self.host, self.port)) - self.is_connected = True - self.logger.info(f"成功连接到 {self.host}:{self.port} (TCP)") - - # 发送客户端ID用于负载均衡 - if self.load_balancing: - self._send_client_id() - except OSError as e: - self.logger.error(f"连接失败: {e}") - self.is_connected = False - - def _send_client_id(self): - """发送客户端ID到服务器用于负载均衡""" - if self.socket is None: - self.logger.error("Socket is not initialized") - return - - try: - registration = ( - json.dumps({"action": "register", "client_id": self.client_id}).encode( - self.encoding - ) - + self.delimiter - ) - self.socket.sendall(registration) - except Exception as e: - self.logger.error(f"发送客户端ID失败: {e}") - - def _reconnect(self): - """尝试重新连接""" - current_time = time.time() - if current_time - self.last_connect_attempt < self.reconnect_interval: - return False - - self.last_connect_attempt = current_time - self.logger.info("尝试重新连接...") - - try: - if self.socket: - self.socket.close() - self._initialize_connection() - return self.is_connected - except Exception as e: - self.logger.error(f"重连失败: {e}") - return False - - def _receive_data(self) -> bytes | None: - """从套接字接收数据""" - if not self.is_connected and self.protocol == "tcp": - if not self.reconnect or not self._reconnect(): - return None - - if self.socket is None: - self.logger.error("Socket is not initialized") - return None - - try: - if self.protocol == "tcp": - data = self.socket.recv(self.buffer_size) - self.logger.debug(f"recv data: {data}") - return data - else: # UDP - data, _ = self.socket.recvfrom(self.buffer_size) - return data - except TimeoutError: - return None # 超时是正常情况 - except OSError as e: - self.logger.error(f"接收数据错误: {e}") - self.is_connected = False - return None - - def _process_buffer(self) -> str | None: - """处理缓冲区并提取完整消息""" - # 检查是否有完整消息 - if self.buffer: - if self.delimiter in self.buffer: - message, _, self.buffer = self.buffer.partition(self.delimiter) - try: - return message.decode(self.encoding).strip() - except UnicodeDecodeError: - self.logger.error("解码消息失败") - return None - else: - # 没有完整消息，等待更多数据 - return None - return None - - def execute(self) -> str | dict | None: - """ - 从套接字读取数据并返回完整消息 - - 返回: - - 字符串: 当接收到完整消息时 - - None: 当没有完整消息或连接断开时 - """ - message = self._process_buffer() - if message: - self.logger.info(f"\033[32m[ {self.__class__.__name__}]: 接收到消息: {message}\033[0m") - return message - data = None - while data is None and message is None: - data = self._receive_data() - if data: - self.buffer += data - message = self._process_buffer() - if message: - self.logger.info( - f"\033[32m[ {self.__class__.__name__}]: 接收到消息: {message}\033[0m" - ) - return message - # 没有完整消息 - return None - - def close(self): - """关闭套接字连接""" - if self.socket: - try: - if self.protocol == "tcp" and self.load_balancing: - # 发送注销请求 - deregistration = ( - json.dumps({"action": "deregister", "client_id": self.client_id}).encode( - self.encoding - ) - + self.delimiter - ) - self.socket.sendall(deregistration) - self.socket.close() - self.logger.info("连接已关闭") - except Exception as e: - self.logger.error(f"关闭连接时出错: {e}") - finally: - self.socket = None - self.is_connected = False - - def __del__(self): - self.close() - - -# ============================================================================ -# 额外的Source类实现 -# ============================================================================ - - -class TextFileSource(SourceFunction): - """ - 文本文件源 - 读取文本文件内容 - - 配置参数: - - file_path: 文件路径 - - encoding: 文件编码 (默认utf-8) - - read_mode: 读取模式 ('all', 'lines') (默认'all') - """ - - def __init__(self, config: dict | None = None, **kwargs): - super().__init__(**kwargs) - if config is None: - raise ValueError("config parameter is required for TextFileSource") - self.config = config - file_path = self.config.get("file_path") - if file_path is None: - raise ValueError("file_path is required in config") - self.file_path = file_path - self.encoding = self.config.get("encoding", "utf-8") - self.read_mode = self.config.get("read_mode", "all") - - def execute(self, data=None) -> str | list[str]: - """读取文本文件""" - try: - with open(self.file_path, encoding=self.encoding) as f: - if self.read_mode == "lines": - return f.readlines() - else: - return f.read() - except FileNotFoundError: - self.logger.error(f"File not found: {self.file_path}") - raise - except Exception as e: - self.logger.error(f"Error reading file: {e}") - raise - - -class JSONFileSource(SourceFunction): - """ - JSON文件源 - 读取JSON文件内容 - - 配置参数: - - file_path: 文件路径 - - encoding: 文件编码 (默认utf-8) - """ - - def __init__(self, config: dict | None = None, **kwargs): - super().__init__(**kwargs) - if config is None: - raise ValueError("config parameter is required for JSONFileSource") - self.config = config - file_path = self.config.get("file_path") - if file_path is None: - raise ValueError("file_path is required in config") - self.file_path = file_path - self.encoding = self.config.get("encoding", "utf-8") - - def execute(self, data=None) -> dict | list: - """读取JSON文件""" - try: - with open(self.file_path, encoding=self.encoding) as f: - return json.load(f) - except FileNotFoundError: - self.logger.error(f"File not found: {self.file_path}") - raise - except json.JSONDecodeError as e: - self.logger.error(f"Invalid JSON format: {e}") - raise - except Exception as e: - self.logger.error(f"Error reading JSON file: {e}") - raise - - -class CSVFileSource(SourceFunction): - """ - CSV文件源 - 读取CSV文件内容 - - 配置参数: - - file_path: 文件路径 - - delimiter: 分隔符 (默认',') - - encoding: 文件编码 (默认utf-8) - - has_header: 是否有表头 (默认True) - """ - - def __init__(self, config: dict | None = None, **kwargs): - super().__init__(**kwargs) - if config is None: - raise ValueError("config parameter is required for CSVFileSource") - self.config = config - file_path = self.config.get("file_path") - if file_path is None: - raise ValueError("file_path is required in config") - self.file_path = file_path - self.delimiter = self.config.get("delimiter", ",") - self.encoding = self.config.get("encoding", "utf-8") - self.has_header = self.config.get("has_header", True) - - def execute(self, data=None) -> list[dict] | list[list]: - """读取CSV文件""" - try: - with open(self.file_path, encoding=self.encoding) as f: - if self.has_header: - reader = csv.DictReader(f, delimiter=self.delimiter) - return list(reader) - else: - reader = csv.reader(f, delimiter=self.delimiter) - return list(reader) - except FileNotFoundError: - self.logger.error(f"File not found: {self.file_path}") - raise - except Exception as e: - self.logger.error(f"Error reading CSV file: {e}") - raise - - -class KafkaSource(SourceFunction): - """ - Kafka源 - 从Kafka topic读取消息（占位实现） - - 配置参数: - - bootstrap_servers: Kafka服务器列表 - - topic: Kafka topic名称 - - group_id: 消费者组ID - """ - - def __init__(self, config: dict | None = None, **kwargs): - super().__init__(**kwargs) - self.config = config or {} - self.bootstrap_servers = self.config.get("bootstrap_servers", ["localhost:9092"]) - self.topic = self.config.get("topic") - self.group_id = self.config.get("group_id", "sage_consumer") - - def execute(self, data=None) -> dict | None: - """读取Kafka消息（占位实现）""" - self.logger.warning("KafkaSource is a placeholder implementation") - # 实际实现需要kafka-python库 - # from kafka import KafkaConsumer - # consumer = KafkaConsumer(self.topic, ...) - return None - - -class DatabaseSource(SourceFunction): - """ - 数据库源 - 从数据库查询数据（占位实现） - - 配置参数: - - connection_string: 数据库连接字符串 - - query: SQL查询语句 - - params: 查询参数 - """ - - def __init__(self, config: dict | None = None, **kwargs): - super().__init__(**kwargs) - self.config = config or {} - self.connection_string = self.config.get("connection_string") - self.query = self.config.get("query") - self.params = self.config.get("params", {}) - - def execute(self, data=None) -> list[dict] | None: - """执行数据库查询（占位实现）""" - self.logger.warning("DatabaseSource is a placeholder implementation") - # 实际实现需要数据库驱动（如psycopg2, pymysql等） - return None - - -class APISource(SourceFunction): - """ - API源 - 从REST API获取数据（占位实现） - - 配置参数: - - url: API端点URL - - method: HTTP方法 (GET, POST等) - - headers: HTTP头部 - - params: 请求参数 - - timeout: 请求超时时间 - """ - - def __init__(self, config: dict | None = None, **kwargs): - super().__init__(**kwargs) - self.config = config or {} - self.url = self.config.get("url") - self.method = self.config.get("method", "GET") - self.headers = self.config.get("headers", {}) - self.params = self.config.get("params", {}) - self.timeout = self.config.get("timeout", 30) - - def execute(self, data=None) -> dict | list | None: - """调用API获取数据（占位实现）""" - self.logger.warning("APISource is a placeholder implementation") - # 实际实现需要requests库 - # import requests - # response = requests.request(self.method, self.url, ...) - return None diff --git a/packages/sage-libs/src/sage/libs/foundation/tools/__init__.py b/packages/sage-libs/src/sage/libs/foundation/tools/__init__.py deleted file mode 100644 index 448b3702eb..0000000000 --- a/packages/sage-libs/src/sage/libs/foundation/tools/__init__.py +++ /dev/null @@ -1,15 +0,0 @@ -"""Base tool infrastructure for generic tool patterns.""" - -from .registry import ToolRegistry -from .tool import BaseTool - -__all__ = ["BaseTool", "ToolRegistry"] - -# Version information -try: - from sage.libs._version import __author__, __email__, __version__ -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" diff --git a/packages/sage-libs/src/sage/libs/foundation/tools/registry.py b/packages/sage-libs/src/sage/libs/foundation/tools/registry.py deleted file mode 100644 index 3f2108a1fd..0000000000 --- a/packages/sage-libs/src/sage/libs/foundation/tools/registry.py +++ /dev/null @@ -1,51 +0,0 @@ -""" -工具注册表 - 管理和发现工具 -""" - -from .tool import BaseTool - - -class ToolRegistry: - """工具注册表 - 单例模式管理所有工具""" - - _instance = None - _tools: dict[str, BaseTool] = {} - - def __new__(cls): - if cls._instance is None: - cls._instance = super().__new__(cls) - return cls._instance - - def register(self, tool: BaseTool) -> None: - """注册一个工具""" - if not isinstance(tool, BaseTool): - raise TypeError("Tool must be an instance of BaseTool") - - self._tools[tool.tool_name] = tool - - def unregister(self, name: str) -> None: - """取消注册一个工具""" - if name in self._tools: - del self._tools[name] - - def get(self, name: str) -> BaseTool | None: - """根据名称获取工具""" - return self._tools.get(name) - - def list_tools(self) -> list[BaseTool]: - """列出所有已注册的工具""" - return list(self._tools.values()) - - def list_tool_names(self) -> list[str]: - """列出所有工具名称""" - return list(self._tools.keys()) - - def clear(self) -> None: - """清空所有工具""" - self._tools.clear() - - def __len__(self) -> int: - return len(self._tools) - - def __contains__(self, name: str) -> bool: - return name in self._tools diff --git a/packages/sage-libs/src/sage/libs/foundation/tools/tool.py b/packages/sage-libs/src/sage/libs/foundation/tools/tool.py deleted file mode 100644 index 6cf0e8c3ad..0000000000 --- a/packages/sage-libs/src/sage/libs/foundation/tools/tool.py +++ /dev/null @@ -1,48 +0,0 @@ -""" -工具基类 - 所有工具的基础接口 -""" - -from abc import ABC, abstractmethod -from typing import Any - - -class BaseTool(ABC): - """工具基类 - 定义所有工具的标准接口""" - - def __init__( - self, - tool_name: str, - tool_description: str, - input_types: list[str] | dict[str, str] | None = None, - output_type: str = "str", - demo_commands: list[str] | list[dict[str, str]] | None = None, - require_llm_engine: bool = False, - ): - self.tool_name = tool_name - self.tool_description = tool_description - self.input_types = input_types or ["str"] - self.output_type = output_type - self.demo_commands = demo_commands or [] - self.require_llm_engine = require_llm_engine - - @abstractmethod - def execute(self, *args, **kwargs) -> Any: - """执行工具的核心功能""" - pass - - def get_metadata(self) -> dict[str, Any]: - """获取工具元数据""" - return { - "name": self.tool_name, - "description": self.tool_description, - "input_types": self.input_types, - "output_type": self.output_type, - "demo_commands": self.demo_commands, - "require_llm_engine": self.require_llm_engine, - } - - def __str__(self) -> str: - return f"Tool({self.tool_name})" - - def __repr__(self) -> str: - return f"Tool(name='{self.tool_name}', description='{self.tool_description}')" diff --git a/packages/sage-libs/src/sage/libs/integrations/__init__.py b/packages/sage-libs/src/sage/libs/integrations/__init__.py deleted file mode 100644 index 2c4e1ab26c..0000000000 --- a/packages/sage-libs/src/sage/libs/integrations/__init__.py +++ /dev/null @@ -1,25 +0,0 @@ -""" -Third-party integrations for SAGE - -This module provides integration with external services and libraries: -- LLM clients (HuggingFace local inference) - -Note: - LLM inference components have been moved to the independent `isagellm` package. - For LLM inference, SAGE uses vLLM as the backend engine. - - Vector store backends (ChromaBackend, MilvusBackend, ChromaVectorStoreAdapter) - have been migrated to sage.middleware.components.vector_stores (L4). - Please update imports: - from sage.middleware.components.vector_stores import ( - ChromaBackend, MilvusBackend, ChromaVectorStoreAdapter - ) -""" - -# LLM Clients (local inference, no external service required) -from sage.libs.integrations.huggingface import HFClient - -__all__ = [ - # LLM Clients - "HFClient", -] diff --git a/packages/sage-libs/src/sage/libs/integrations/huggingface.py b/packages/sage-libs/src/sage/libs/integrations/huggingface.py deleted file mode 100644 index 3ad5fad576..0000000000 --- a/packages/sage-libs/src/sage/libs/integrations/huggingface.py +++ /dev/null @@ -1,117 +0,0 @@ -"""HuggingFace local inference client. - -This module provides a simple client for running HuggingFace models locally. -For production use with remote LLM services, use: - - from isagellm import UnifiedInferenceClient - client = UnifiedInferenceClient.create() - -Note: - This client is kept for local development and testing scenarios - where you want to run models directly on your machine without - external services. -""" - -import logging -from typing import Any - -import torch -from transformers import AutoModelForCausalLM, AutoTokenizer - -logger = logging.getLogger(__name__) - - -class HFClient: - def __init__( - self, - model_name: str = "llama", - device: str | None = None, - base_url: str | None = None, - api_key: str | None = None, - seed: int | None = None, - ): - self.device: str = device if device else ("cuda" if torch.cuda.is_available() else "cpu") - self.model_name = model_name - self.model, self.tokenizer = self._initialize_model() - - def _initialize_model(self): - tokenizer = AutoTokenizer.from_pretrained(self.model_name, trust_remote_code=True) - model = AutoModelForCausalLM.from_pretrained( - self.model_name, - trust_remote_code=True, - device_map="auto" if self.device == "cuda" else None, - ) - - if tokenizer.pad_token is None: - tokenizer.pad_token = tokenizer.eos_token - - # Ensure model on the right device if not using device_map - if self.device != "cuda": - # Note: type ignore is needed due to incomplete type hints in transformers library - # model.to() correctly accepts device strings at runtime - model.to(self.device) # type: ignore - - return model, tokenizer - - def generate(self, prompt: str | list[dict[str, str]], **kwargs: Any) -> str: - """Generate text from a prompt. - - Args: - prompt: Either a string prompt or a list of message dicts - with 'role' and 'content' keys. - **kwargs: Generation parameters: - - max_new_tokens: Maximum tokens to generate (default: 128) - - temperature: Sampling temperature (default: 0.3) - - Returns: - Generated text response. - """ - generation_kwargs = { - "max_new_tokens": kwargs.get("max_new_tokens", 128), - "temperature": kwargs.get("temperature", 0.3), - "do_sample": True, - "pad_token_id": self.tokenizer.eos_token_id, - "eos_token_id": self.tokenizer.eos_token_id, - } - - # Construct prompt text - if isinstance(prompt, list): - input_prompt = "" - for message in prompt: - role = message["role"] - content = message["content"] - if role == "system": - input_prompt += f"System: {content}\n\n" - elif role == "user": - input_prompt += f"User: {content}\n\nAssistant: " - else: - input_prompt = prompt - - logger.debug("Input prompt: %s...", input_prompt[:200]) - - # Tokenize input - input_ids = self.tokenizer( - input_prompt, - return_tensors="pt", - padding=True, - truncation=True, - max_length=1024, - ).to(self.device) - - logger.debug("Input token length: %d", input_ids["input_ids"].shape[1]) - - # Generate output - try: - with torch.no_grad(): - output = self.model.generate(**input_ids, **generation_kwargs) - except Exception as e: - logger.exception("Generation failed: %s", e) - raise RuntimeError(f"Generation failed: {e}") from e - - # Decode output - response_text = self.tokenizer.decode( - output[0][input_ids["input_ids"].shape[1] :], skip_special_tokens=True - ).strip() - - logger.debug("Generated response: %s", response_text) - return response_text diff --git a/packages/sage-libs/src/sage/libs/privacy/__init__.py b/packages/sage-libs/src/sage/libs/privacy/__init__.py deleted file mode 100644 index 642287420d..0000000000 --- a/packages/sage-libs/src/sage/libs/privacy/__init__.py +++ /dev/null @@ -1,58 +0,0 @@ -"""Privacy layer - Privacy-preserving algorithms. - -This module provides privacy-related algorithms: -- unlearning: Machine unlearning and privacy-preserving mechanisms -- interface: Abstract interfaces for privacy components - -Concrete implementations are provided by external packages (e.g., isage-privacy). -""" - -from . import interface, unlearning - -# Re-export key interfaces for convenience -from .interface import ( - # Base classes - BaseDPOptimizer, - BaseFederatedClient, - BaseFederatedServer, - BasePrivacyMechanism, - BaseUnlearner, - # Data types - PrivacyBudget, - # Enums - PrivacyLevel, - UnlearningMethod, - UnlearningResult, - # Factories - create_mechanism, - create_unlearner, - register_mechanism, - register_unlearner, - registered_mechanisms, - registered_unlearners, -) - -__all__ = [ - # Submodules - "unlearning", - "interface", - # Enums - "UnlearningMethod", - "PrivacyLevel", - # Data types - "PrivacyBudget", - "UnlearningResult", - # Base classes - "BasePrivacyMechanism", - "BaseUnlearner", - "BaseDPOptimizer", - "BaseFederatedClient", - "BaseFederatedServer", - # Factories - "register_unlearner", - "create_unlearner", - "registered_unlearners", - "register_mechanism", - "create_mechanism", - "registered_mechanisms", -] diff --git a/packages/sage-libs/src/sage/libs/privacy/interface/__init__.py b/packages/sage-libs/src/sage/libs/privacy/interface/__init__.py deleted file mode 100644 index 5627c8affc..0000000000 --- a/packages/sage-libs/src/sage/libs/privacy/interface/__init__.py +++ /dev/null @@ -1,109 +0,0 @@ -"""Privacy interface layer for SAGE. - -This module provides abstract interfaces for privacy-preserving components. -Concrete implementations are provided by external packages (e.g., isage-privacy). - -Architecture: - - base.py: Abstract base classes (BaseUnlearner, BasePrivacyMechanism, etc.) - - factory.py: Registry and factory functions - - External packages register their implementations at import time - -Usage: - # Option 1: Direct instantiation (if you know the implementation) - from isage_privacy import SISAUnlearner, LaplaceMechanism - unlearner = SISAUnlearner(num_shards=5) - mechanism = LaplaceMechanism(epsilon=1.0) - - # Option 2: Factory pattern (more flexible) - from sage.libs.privacy.interface import create_unlearner, create_mechanism - unlearner = create_unlearner("sisa", num_shards=5) - mechanism = create_mechanism("laplace", epsilon=1.0) - - # Unlearn data - result = unlearner.unlearn(model, forget_data) -""" - -# Base classes and data types -from .base import ( - BaseDPOptimizer, - BaseFederatedClient, - BaseFederatedServer, - BasePrivacyMechanism, - BaseUnlearner, - PrivacyBudget, - PrivacyLevel, - UnlearningMethod, - UnlearningResult, -) - -# Factory functions -from .factory import ( - PrivacyRegistryError, - # Federated Client - create_fed_client, - # Federated Server - create_fed_server, - # Mechanism - create_mechanism, - # Optimizer - create_optimizer, - # Unlearner - create_unlearner, - register_fed_client, - register_fed_server, - register_mechanism, - register_optimizer, - register_unlearner, - registered_fed_clients, - registered_fed_servers, - registered_mechanisms, - registered_optimizers, - registered_unlearners, - unregister_fed_client, - unregister_fed_server, - unregister_mechanism, - unregister_optimizer, - unregister_unlearner, -) - -__all__ = [ - # Enums - "UnlearningMethod", - "PrivacyLevel", - # Data types - "PrivacyBudget", - "UnlearningResult", - # Base classes - "BasePrivacyMechanism", - "BaseUnlearner", - "BaseDPOptimizer", - "BaseFederatedClient", - "BaseFederatedServer", - # Unlearner registry - "register_unlearner", - "create_unlearner", - "registered_unlearners", - "unregister_unlearner", - # Mechanism registry - "register_mechanism", - "create_mechanism", - "registered_mechanisms", - "unregister_mechanism", - # Optimizer registry - "register_optimizer", - "create_optimizer", - "registered_optimizers", - "unregister_optimizer", - # Federated Client registry - "register_fed_client", - "create_fed_client", - "registered_fed_clients", - "unregister_fed_client", - # Federated Server registry - "register_fed_server", - "create_fed_server", - "registered_fed_servers", - "unregister_fed_server", - # Exception - "PrivacyRegistryError", -] diff --git a/packages/sage-libs/src/sage/libs/privacy/interface/base.py b/packages/sage-libs/src/sage/libs/privacy/interface/base.py deleted file mode 100644 index b883892de1..0000000000 --- a/packages/sage-libs/src/sage/libs/privacy/interface/base.py +++ /dev/null @@ -1,383 +0,0 @@ -"""Base classes and interfaces for privacy. - -This module defines abstract interfaces for privacy-preserving algorithms: -- BaseUnlearner: Machine unlearning base class -- BasePrivacyMechanism: Differential privacy mechanism (re-export from unlearning) -- BaseDPOptimizer: Differentially private optimizer -- BaseFederatedClient: Federated learning client - -Implementations are provided by the external 'isage-privacy' package. -""" - -from abc import ABC, abstractmethod -from dataclasses import dataclass, field -from enum import Enum -from typing import Any, Optional - -# Re-export existing implementation -from ..unlearning.dp_unlearning.base_mechanism import ( - BasePrivacyMechanism, -) - - -class UnlearningMethod(Enum): - """Types of machine unlearning methods.""" - - # Exact unlearning - RETRAIN = "retrain" # Full retraining from scratch - SISA = "sisa" # Sharded, Isolated, Sliced, Aggregated - - # Approximate unlearning - GRADIENT_ASCENT = "gradient_ascent" - INFLUENCE_FUNCTION = "influence_function" - FISHER_FORGETTING = "fisher_forgetting" - AMNESIAC = "amnesiac" - - # DP-based unlearning - DP_SGD = "dp_sgd" - PATE = "pate" - - # Custom - CUSTOM = "custom" - - -class PrivacyLevel(Enum): - """Privacy strength levels.""" - - NONE = "none" - LOW = "low" # epsilon > 10 - MEDIUM = "medium" # 1 < epsilon <= 10 - HIGH = "high" # 0.1 < epsilon <= 1 - VERY_HIGH = "very_high" # epsilon <= 0.1 - - -@dataclass -class PrivacyBudget: - """Privacy budget configuration.""" - - epsilon: float # Privacy loss parameter - delta: float = 1e-5 # Failure probability - - # Optional: per-query budgets - per_query_epsilon: Optional[float] = None - max_queries: Optional[int] = None - - # Composition method - composition: str = "advanced" # "basic", "advanced", "rdp" - - @property - def level(self) -> PrivacyLevel: - """Determine privacy level from epsilon.""" - if self.epsilon > 10: - return PrivacyLevel.LOW - elif self.epsilon > 1: - return PrivacyLevel.MEDIUM - elif self.epsilon > 0.1: - return PrivacyLevel.HIGH - else: - return PrivacyLevel.VERY_HIGH - - -@dataclass -class UnlearningResult: - """Result of an unlearning operation.""" - - success: bool - method: UnlearningMethod - samples_forgotten: int - - # Privacy guarantees - privacy_budget_spent: Optional[PrivacyBudget] = None - verification_score: Optional[float] = None # How well unlearning succeeded - - # Performance - time_seconds: float = 0.0 - metadata: dict[str, Any] = field(default_factory=dict) - - -class BaseUnlearner(ABC): - """Abstract base class for machine unlearning. - - Examples of implementations: - - SISAUnlearner: Sharded training for efficient unlearning - - GradientAscentUnlearner: Approximate unlearning via gradient ascent - - FisherUnlearner: Fisher information-based forgetting - - AmnesiacUnlearner: Cached update-based unlearning - """ - - @property - @abstractmethod - def name(self) -> str: - """Return the unlearner name.""" - pass - - @property - def method(self) -> UnlearningMethod: - """Return the unlearning method type.""" - return UnlearningMethod.CUSTOM - - @abstractmethod - def unlearn( - self, - model: Any, - forget_data: Any, - retain_data: Optional[Any] = None, - **kwargs: Any, - ) -> UnlearningResult: - """Unlearn (forget) specific data from a model. - - Args: - model: The trained model - forget_data: Data to be forgotten - retain_data: Data to retain (optional, for verification) - **kwargs: Method-specific parameters - - Returns: - UnlearningResult with success status and metrics - """ - pass - - def verify_unlearning( - self, - model: Any, - forget_data: Any, - original_model: Optional[Any] = None, - **kwargs: Any, - ) -> float: - """Verify that unlearning was successful. - - Args: - model: Model after unlearning - forget_data: Data that should have been forgotten - original_model: Model before unlearning (optional) - **kwargs: Verification parameters - - Returns: - Verification score (0 = failed, 1 = perfect unlearning) - """ - raise NotImplementedError("Verification not implemented for this unlearner") - - -class BaseDPOptimizer(ABC): - """Abstract base class for differentially private optimizers. - - Examples of implementations: - - DPSGDOptimizer: DP-SGD (Differentially Private Stochastic Gradient Descent) - - DPAdamOptimizer: DP-Adam - - PATEOptimizer: Private Aggregation of Teacher Ensembles - """ - - @property - @abstractmethod - def name(self) -> str: - """Return the optimizer name.""" - pass - - @abstractmethod - def step( - self, - params: Any, - gradients: Any, - privacy_budget: PrivacyBudget, - **kwargs: Any, - ) -> Any: - """Perform one optimization step with DP guarantees. - - Args: - params: Model parameters - gradients: Computed gradients - privacy_budget: Privacy budget for this step - **kwargs: Optimizer-specific parameters - - Returns: - Updated parameters - """ - pass - - @abstractmethod - def get_privacy_spent(self) -> PrivacyBudget: - """Get total privacy budget spent so far. - - Returns: - Cumulative privacy budget used - """ - pass - - def clip_gradients( - self, - gradients: Any, - max_norm: float, - ) -> Any: - """Clip gradients to bound sensitivity. - - Args: - gradients: Raw gradients - max_norm: Maximum L2 norm - - Returns: - Clipped gradients - """ - raise NotImplementedError("Gradient clipping not implemented") - - -class BaseFederatedClient(ABC): - """Abstract base class for federated learning clients. - - Examples of implementations: - - FedAvgClient: Federated averaging client - - FedProxClient: FedProx with proximal term - - DPFedClient: Differentially private federated client - """ - - @property - @abstractmethod - def client_id(self) -> str: - """Return the client identifier.""" - pass - - @abstractmethod - def local_train( - self, - model: Any, - local_data: Any, - num_epochs: int = 1, - **kwargs: Any, - ) -> dict[str, Any]: - """Train model on local data. - - Args: - model: Global model to fine-tune - local_data: Client's local dataset - num_epochs: Local training epochs - **kwargs: Training parameters - - Returns: - Dictionary with model updates and metrics - """ - pass - - @abstractmethod - def compute_update( - self, - old_model: Any, - new_model: Any, - **kwargs: Any, - ) -> Any: - """Compute model update to send to server. - - Args: - old_model: Model before local training - new_model: Model after local training - **kwargs: Update parameters - - Returns: - Model update (gradients or weight difference) - """ - pass - - def add_noise_to_update( - self, - update: Any, - privacy_mechanism: BasePrivacyMechanism, - ) -> Any: - """Add noise to update for differential privacy. - - Args: - update: Model update - privacy_mechanism: Privacy mechanism to use - - Returns: - Noisy update - """ - raise NotImplementedError("DP noise not implemented for this client") - - -class BaseFederatedServer(ABC): - """Abstract base class for federated learning servers. - - Examples of implementations: - - FedAvgServer: Federated averaging server - - SecAggServer: Secure aggregation server - - DPFedServer: DP-aware federated server - """ - - @property - @abstractmethod - def name(self) -> str: - """Return the server name.""" - pass - - @abstractmethod - def aggregate( - self, - updates: list[Any], - weights: Optional[list[float]] = None, - **kwargs: Any, - ) -> Any: - """Aggregate client updates. - - Args: - updates: List of client updates - weights: Optional weights for each client - **kwargs: Aggregation parameters - - Returns: - Aggregated update - """ - pass - - @abstractmethod - def update_global_model( - self, - model: Any, - aggregated_update: Any, - **kwargs: Any, - ) -> Any: - """Apply aggregated update to global model. - - Args: - model: Current global model - aggregated_update: Aggregated client updates - **kwargs: Update parameters - - Returns: - Updated global model - """ - pass - - def select_clients( - self, - clients: list["BaseFederatedClient"], - fraction: float = 1.0, - **kwargs: Any, - ) -> list["BaseFederatedClient"]: - """Select clients for a training round. - - Args: - clients: All available clients - fraction: Fraction of clients to select - **kwargs: Selection parameters - - Returns: - Selected clients - """ - import random - - k = max(1, int(len(clients) * fraction)) - return random.sample(clients, k) - - -__all__ = [ - # Enums - "UnlearningMethod", - "PrivacyLevel", - # Data classes - "PrivacyBudget", - "UnlearningResult", - # Base classes - "BasePrivacyMechanism", - "BaseUnlearner", - "BaseDPOptimizer", - "BaseFederatedClient", - "BaseFederatedServer", -] diff --git a/packages/sage-libs/src/sage/libs/privacy/interface/factory.py b/packages/sage-libs/src/sage/libs/privacy/interface/factory.py deleted file mode 100644 index 4b6b34e229..0000000000 --- a/packages/sage-libs/src/sage/libs/privacy/interface/factory.py +++ /dev/null @@ -1,367 +0,0 @@ -"""Factory and registry for privacy implementations. - -This module provides a registry pattern for privacy components. -External packages (like isage-privacy) can register their implementations here. - -Example: - # Register implementations - from sage.libs.privacy.interface import ( - register_unlearner, - register_mechanism, - register_optimizer, - register_fed_client, - ) - register_unlearner("sisa", SISAUnlearner) - register_mechanism("laplace", LaplaceMechanism) - register_optimizer("dp_sgd", DPSGDOptimizer) - register_fed_client("fedavg", FedAvgClient) - - # Create instances - from sage.libs.privacy.interface import ( - create_unlearner, - create_mechanism, - create_optimizer, - create_fed_client, - ) - unlearner = create_unlearner("sisa") - mechanism = create_mechanism("laplace", epsilon=1.0) - optimizer = create_optimizer("dp_sgd") - client = create_fed_client("fedavg", client_id="client_1") -""" - -from typing import Any - -from .base import ( - BaseDPOptimizer, - BaseFederatedClient, - BaseFederatedServer, - BasePrivacyMechanism, - BaseUnlearner, -) - -_UNLEARNER_REGISTRY: dict[str, type[BaseUnlearner]] = {} -_MECHANISM_REGISTRY: dict[str, type[BasePrivacyMechanism]] = {} -_OPTIMIZER_REGISTRY: dict[str, type[BaseDPOptimizer]] = {} -_FED_CLIENT_REGISTRY: dict[str, type[BaseFederatedClient]] = {} -_FED_SERVER_REGISTRY: dict[str, type[BaseFederatedServer]] = {} - - -class PrivacyRegistryError(Exception): - """Error raised when registry operations fail.""" - - pass - - -# ======================================== -# Unlearner Registry -# ======================================== - - -def register_unlearner(name: str, cls: type[BaseUnlearner]) -> None: - """Register an unlearning implementation. - - Args: - name: Unique identifier (e.g., "sisa", "gradient_ascent", "fisher") - cls: Unlearner class (should inherit from BaseUnlearner) - - Raises: - PrivacyRegistryError: If name already registered - """ - if name in _UNLEARNER_REGISTRY: - raise PrivacyRegistryError(f"Unlearner '{name}' already registered") - - if not issubclass(cls, BaseUnlearner): - raise TypeError(f"Class must inherit from BaseUnlearner, got {cls}") - - _UNLEARNER_REGISTRY[name] = cls - - -def create_unlearner(name: str, **kwargs: Any) -> BaseUnlearner: - """Create an unlearner instance by name. - - Args: - name: Name of the registered unlearner - **kwargs: Arguments to pass to the unlearner constructor - - Returns: - Instance of the unlearner - - Raises: - PrivacyRegistryError: If unlearner not found - """ - if name not in _UNLEARNER_REGISTRY: - available = ", ".join(_UNLEARNER_REGISTRY.keys()) if _UNLEARNER_REGISTRY else "none" - raise PrivacyRegistryError( - f"Unlearner '{name}' not found. Available: {available}. Did you install 'isage-privacy'?" - ) - - cls = _UNLEARNER_REGISTRY[name] - return cls(**kwargs) - - -def registered_unlearners() -> list[str]: - """Get list of registered unlearner names.""" - return list(_UNLEARNER_REGISTRY.keys()) - - -def unregister_unlearner(name: str) -> None: - """Unregister an unlearner (for testing).""" - _UNLEARNER_REGISTRY.pop(name, None) - - -# ======================================== -# Privacy Mechanism Registry -# ======================================== - - -def register_mechanism(name: str, cls: type[BasePrivacyMechanism]) -> None: - """Register a privacy mechanism implementation. - - Args: - name: Unique identifier (e.g., "laplace", "gaussian", "exponential") - cls: Mechanism class (should inherit from BasePrivacyMechanism) - - Raises: - PrivacyRegistryError: If name already registered - """ - if name in _MECHANISM_REGISTRY: - raise PrivacyRegistryError(f"Mechanism '{name}' already registered") - - if not issubclass(cls, BasePrivacyMechanism): - raise TypeError(f"Class must inherit from BasePrivacyMechanism, got {cls}") - - _MECHANISM_REGISTRY[name] = cls - - -def create_mechanism(name: str, **kwargs: Any) -> BasePrivacyMechanism: - """Create a privacy mechanism instance by name. - - Args: - name: Name of the registered mechanism - **kwargs: Arguments to pass to the mechanism constructor - - Returns: - Instance of the mechanism - - Raises: - PrivacyRegistryError: If mechanism not found - """ - if name not in _MECHANISM_REGISTRY: - available = ", ".join(_MECHANISM_REGISTRY.keys()) if _MECHANISM_REGISTRY else "none" - raise PrivacyRegistryError( - f"Mechanism '{name}' not found. Available: {available}. Did you install 'isage-privacy'?" - ) - - cls = _MECHANISM_REGISTRY[name] - return cls(**kwargs) - - -def registered_mechanisms() -> list[str]: - """Get list of registered mechanism names.""" - return list(_MECHANISM_REGISTRY.keys()) - - -def unregister_mechanism(name: str) -> None: - """Unregister a mechanism (for testing).""" - _MECHANISM_REGISTRY.pop(name, None) - - -# ======================================== -# DP Optimizer Registry -# ======================================== - - -def register_optimizer(name: str, cls: type[BaseDPOptimizer]) -> None: - """Register a DP optimizer implementation. - - Args: - name: Unique identifier (e.g., "dp_sgd", "dp_adam", "pate") - cls: Optimizer class (should inherit from BaseDPOptimizer) - - Raises: - PrivacyRegistryError: If name already registered - """ - if name in _OPTIMIZER_REGISTRY: - raise PrivacyRegistryError(f"Optimizer '{name}' already registered") - - if not issubclass(cls, BaseDPOptimizer): - raise TypeError(f"Class must inherit from BaseDPOptimizer, got {cls}") - - _OPTIMIZER_REGISTRY[name] = cls - - -def create_optimizer(name: str, **kwargs: Any) -> BaseDPOptimizer: - """Create a DP optimizer instance by name. - - Args: - name: Name of the registered optimizer - **kwargs: Arguments to pass to the optimizer constructor - - Returns: - Instance of the optimizer - - Raises: - PrivacyRegistryError: If optimizer not found - """ - if name not in _OPTIMIZER_REGISTRY: - available = ", ".join(_OPTIMIZER_REGISTRY.keys()) if _OPTIMIZER_REGISTRY else "none" - raise PrivacyRegistryError( - f"Optimizer '{name}' not found. Available: {available}. Did you install 'isage-privacy'?" - ) - - cls = _OPTIMIZER_REGISTRY[name] - return cls(**kwargs) - - -def registered_optimizers() -> list[str]: - """Get list of registered optimizer names.""" - return list(_OPTIMIZER_REGISTRY.keys()) - - -def unregister_optimizer(name: str) -> None: - """Unregister an optimizer (for testing).""" - _OPTIMIZER_REGISTRY.pop(name, None) - - -# ======================================== -# Federated Client Registry -# ======================================== - - -def register_fed_client(name: str, cls: type[BaseFederatedClient]) -> None: - """Register a federated learning client implementation. - - Args: - name: Unique identifier (e.g., "fedavg", "fedprox", "dp_fed") - cls: Client class (should inherit from BaseFederatedClient) - - Raises: - PrivacyRegistryError: If name already registered - """ - if name in _FED_CLIENT_REGISTRY: - raise PrivacyRegistryError(f"Federated client '{name}' already registered") - - if not issubclass(cls, BaseFederatedClient): - raise TypeError(f"Class must inherit from BaseFederatedClient, got {cls}") - - _FED_CLIENT_REGISTRY[name] = cls - - -def create_fed_client(name: str, **kwargs: Any) -> BaseFederatedClient: - """Create a federated client instance by name. - - Args: - name: Name of the registered client - **kwargs: Arguments to pass to the client constructor - - Returns: - Instance of the client - - Raises: - PrivacyRegistryError: If client not found - """ - if name not in _FED_CLIENT_REGISTRY: - available = ", ".join(_FED_CLIENT_REGISTRY.keys()) if _FED_CLIENT_REGISTRY else "none" - raise PrivacyRegistryError( - f"Federated client '{name}' not found. Available: {available}. Did you install 'isage-privacy'?" - ) - - cls = _FED_CLIENT_REGISTRY[name] - return cls(**kwargs) - - -def registered_fed_clients() -> list[str]: - """Get list of registered federated client names.""" - return list(_FED_CLIENT_REGISTRY.keys()) - - -def unregister_fed_client(name: str) -> None: - """Unregister a federated client (for testing).""" - _FED_CLIENT_REGISTRY.pop(name, None) - - -# ======================================== -# Federated Server Registry -# ======================================== - - -def register_fed_server(name: str, cls: type[BaseFederatedServer]) -> None: - """Register a federated learning server implementation. - - Args: - name: Unique identifier (e.g., "fedavg", "secagg", "dp_fed") - cls: Server class (should inherit from BaseFederatedServer) - - Raises: - PrivacyRegistryError: If name already registered - """ - if name in _FED_SERVER_REGISTRY: - raise PrivacyRegistryError(f"Federated server '{name}' already registered") - - if not issubclass(cls, BaseFederatedServer): - raise TypeError(f"Class must inherit from BaseFederatedServer, got {cls}") - - _FED_SERVER_REGISTRY[name] = cls - - -def create_fed_server(name: str, **kwargs: Any) -> BaseFederatedServer: - """Create a federated server instance by name. - - Args: - name: Name of the registered server - **kwargs: Arguments to pass to the server constructor - - Returns: - Instance of the server - - Raises: - PrivacyRegistryError: If server not found - """ - if name not in _FED_SERVER_REGISTRY: - available = ", ".join(_FED_SERVER_REGISTRY.keys()) if _FED_SERVER_REGISTRY else "none" - raise PrivacyRegistryError( - f"Federated server '{name}' not found. Available: {available}. Did you install 'isage-privacy'?" - ) - - cls = _FED_SERVER_REGISTRY[name] - return cls(**kwargs) - - -def registered_fed_servers() -> list[str]: - """Get list of registered federated server names.""" - return list(_FED_SERVER_REGISTRY.keys()) - - -def unregister_fed_server(name: str) -> None: - """Unregister a federated server (for testing).""" - _FED_SERVER_REGISTRY.pop(name, None) - - -__all__ = [ - "PrivacyRegistryError", - # Unlearner - "register_unlearner", - "create_unlearner", - "registered_unlearners", - "unregister_unlearner", - # Mechanism - "register_mechanism", - "create_mechanism", - "registered_mechanisms", - "unregister_mechanism", - # Optimizer - "register_optimizer", - "create_optimizer", - "registered_optimizers", - "unregister_optimizer", - # Federated Client - "register_fed_client", - "create_fed_client", - "registered_fed_clients", - "unregister_fed_client", - # Federated Server - "register_fed_server", - "create_fed_server", - "registered_fed_servers", - "unregister_fed_server", -] diff --git a/packages/sage-libs/src/sage/libs/privacy/unlearning/__init__.py b/packages/sage-libs/src/sage/libs/privacy/unlearning/__init__.py deleted file mode 100644 index c15bfb66f3..0000000000 --- a/packages/sage-libs/src/sage/libs/privacy/unlearning/__init__.py +++ /dev/null @@ -1,30 +0,0 @@ -""" -SAGE Unlearning Library -======================== - -A modular framework for machine unlearning in RAG systems with differential privacy guarantees. - -Core Modules: -- dp_unlearning: Differential privacy mechanisms for selective unlearning -- algorithms: Various unlearning algorithms (Laplace, Gaussian, etc.) -- evaluation: Metrics and benchmarking tools -- benchmarks: Standard datasets and evaluation protocols - -Research Extensions: -Students can extend this library by: -1. Implementing new privacy mechanisms -2. Designing novel perturbation strategies -3. Developing adaptive budget allocation algorithms -4. Creating domain-specific unlearning methods -""" - -from .dp_unlearning.base_mechanism import BasePrivacyMechanism -from .dp_unlearning.privacy_accountant import PrivacyAccountant -from .dp_unlearning.unlearning_engine import UnlearningEngine - -__version__ = "0.1.0" -__all__ = [ - "BasePrivacyMechanism", - "PrivacyAccountant", - "UnlearningEngine", -] diff --git a/packages/sage-libs/src/sage/libs/privacy/unlearning/algorithms/__init__.py b/packages/sage-libs/src/sage/libs/privacy/unlearning/algorithms/__init__.py deleted file mode 100644 index 639d525cb3..0000000000 --- a/packages/sage-libs/src/sage/libs/privacy/unlearning/algorithms/__init__.py +++ /dev/null @@ -1,16 +0,0 @@ -""" -Algorithms Package -================== - -Concrete implementations of various DP mechanisms for unlearning. - -Students can implement new mechanisms here as separate modules. -""" - -from .gaussian_unlearning import GaussianMechanism -from .laplace_unlearning import LaplaceMechanism - -__all__ = [ - "LaplaceMechanism", - "GaussianMechanism", -] diff --git a/packages/sage-libs/src/sage/libs/privacy/unlearning/algorithms/gaussian_unlearning.py b/packages/sage-libs/src/sage/libs/privacy/unlearning/algorithms/gaussian_unlearning.py deleted file mode 100644 index 9edcdbb6cf..0000000000 --- a/packages/sage-libs/src/sage/libs/privacy/unlearning/algorithms/gaussian_unlearning.py +++ /dev/null @@ -1,185 +0,0 @@ -""" -Gaussian Mechanism for Unlearning -================================== - -Implements the Gaussian mechanism for (ε,δ)-differential privacy. - -**STUDENT TODO**: Complete this implementation! - -This is a skeleton. Students should fill in the details. -""" - -import math - -import numpy as np - -from sage.libs.privacy.unlearning.dp_unlearning.base_mechanism import ( - BasePrivacyMechanism, -) - - -class GaussianMechanism(BasePrivacyMechanism): - """ - Gaussian mechanism for (ε,δ)-DP. - - **STUDENT RESEARCH TASK**: Implement this mechanism! - - The Gaussian mechanism adds noise from N(0, σ²) where σ is calibrated - to achieve (ε,δ)-DP for a given sensitivity. - - Key formula: - σ ≥ Δf * sqrt(2 * ln(1.25/δ)) / ε - - Research tasks: - 1. Implement noise generation with correct σ - 2. Derive tight (ε,δ) guarantees - 3. Implement analytic Gaussian mechanism (tighter bounds) - 4. Compare with Laplace mechanism empirically - """ - - def __init__(self, epsilon: float, delta: float, sensitivity: float = 1.0): - """ - Initialize Gaussian mechanism. - - Args: - epsilon: Privacy parameter - delta: Failure probability - sensitivity: Query sensitivity - """ - super().__init__(epsilon=epsilon, delta=delta, sensitivity=sensitivity, name="Gaussian") - - # TODO: Compute the required σ - self.sigma = self._compute_sigma() - - def _compute_sigma(self) -> float: - """ - Compute required σ for (ε,δ)-DP. - - **STUDENT TODO**: Implement this! - - Standard formula: - σ = Δf * sqrt(2 * ln(1.25/δ)) / ε - - But you can improve this: - - Use analytic Gaussian mechanism (Balle & Wang 2018) - - Use tight bounds from concentrated DP - - Implement numerical optimization for tightest σ - - Returns: - Required standard deviation - """ - # PLACEHOLDER: Basic formula - # TODO: Implement tighter bound (see Balle & Wang 2018) - assert self.delta is not None, "Gaussian mechanism requires delta to be set" - - if self.delta == 0 or self.delta >= 1: - raise ValueError(f"Delta must be in (0, 1), got {self.delta}") - - sigma = self.sensitivity * math.sqrt(2 * math.log(1.25 / self.delta)) / self.epsilon - return sigma - - def compute_noise( - self, - sensitivity: float | None = None, - epsilon: float | None = None, - delta: float | None = None, - ) -> float: - """ - Generate Gaussian noise: N(0, σ²). - - **STUDENT TODO**: Complete this implementation! - - Steps: - 1. If parameters override defaults, recompute σ - 2. Sample from N(0, σ²) - 3. Return noise value - """ - # TODO: Handle parameter overrides - if sensitivity is not None or epsilon is not None or delta is not None: - # Need to recompute sigma with new parameters - # For now, just use default sigma - pass - - # Generate Gaussian noise - noise = np.random.normal(0, self.sigma) - return noise - - def privacy_cost(self) -> tuple[float, float]: - """ - Gaussian mechanism satisfies (ε,δ)-DP. - - **STUDENT TODO**: Derive tight bounds! - - You can improve this by: - - Using Renyi DP composition - - Using concentrated DP - - Implementing privacy amplification - - Returns: - (epsilon, delta) - """ - # PLACEHOLDER: Return parameters as-is - # TODO: Derive tighter bounds using advanced composition - if self.delta is None: - raise ValueError("Gaussian mechanism requires delta to be set") - return (self.epsilon, self.delta) - - -# ============================================================================ -# STUDENT RESEARCH EXTENSION -# ============================================================================ -""" -TODO for Students - Research Tasks: ------------------------------------ - -1. **Analytic Gaussian Mechanism** (Medium difficulty): - Implement the tighter analysis from Balle & Wang (2018). - - Key insight: Standard Gaussian calibration is loose. Use numerical - optimization to find the minimal σ that satisfies (ε,δ)-DP. - - class AnalyticGaussianMechanism(GaussianMechanism): - def _compute_sigma(self): - # Binary search for minimal σ - # Use analytic formula from Balle & Wang (2018) - pass - -2. **Concentrated DP Gaussian** (Hard difficulty): - Implement Gaussian mechanism using concentrated DP (Dwork & Rothblum 2016). - - Benefits: - - Tighter composition - - Better privacy-utility trade-off - - Unified treatment with Renyi DP - - class ConcentratedGaussianMechanism(GaussianMechanism): - def privacy_cost_concentrated(self): - # Return ρ-zCDP parameter - pass - -3. **Subsampled Gaussian** (Research-level): - Implement privacy amplification by subsampling. - - If you sample q fraction of data and apply Gaussian mechanism, - you get amplified privacy: ε' ≈ q * ε (roughly). - - class SubsampledGaussianMechanism(GaussianMechanism): - def __init__(self, epsilon, delta, sensitivity, sampling_rate): - # Compute amplified epsilon - amplified_eps = self._compute_amplified_privacy(epsilon, sampling_rate) - super().__init__(amplified_eps, delta, sensitivity) - -Research papers to read: -------------------------- -- Dwork & Roth (2014): "Algorithmic Foundations of DP" (foundational) -- Balle & Wang (2018): "Improving the Gaussian Mechanism" (tighter bounds) -- Bun & Steinke (2016): "Concentrated Differential Privacy" (advanced composition) -- Mironov (2017): "Renyi Differential Privacy" (moments accountant) - -Expected outcomes: ------------------- -1. Implementation of multiple Gaussian variants -2. Empirical comparison (privacy vs. utility) -3. Theoretical analysis (proofs of privacy guarantees) -4. Writeup for conference submission (ICML/NeurIPS/VLDB) -""" diff --git a/packages/sage-libs/src/sage/libs/privacy/unlearning/algorithms/laplace_unlearning.py b/packages/sage-libs/src/sage/libs/privacy/unlearning/algorithms/laplace_unlearning.py deleted file mode 100644 index e0769bdefd..0000000000 --- a/packages/sage-libs/src/sage/libs/privacy/unlearning/algorithms/laplace_unlearning.py +++ /dev/null @@ -1,119 +0,0 @@ -""" -Laplace Mechanism for Unlearning -================================= - -Implements the Laplace mechanism for pure ε-differential privacy. - -This is a reference implementation. Students can improve upon it! -""" - -import numpy as np - -from ..dp_unlearning.base_mechanism import BasePrivacyMechanism - - -class LaplaceMechanism(BasePrivacyMechanism): - """ - Laplace mechanism for pure ε-DP. - - **STUDENT TODO**: Enhance this implementation! - - The Laplace mechanism adds noise from Lap(Δf/ε) to achieve ε-DP. - - Improvements to consider: - - Implement truncated Laplace for bounded domains - - Add adaptive sensitivity estimation - - Implement privacy amplification techniques - """ - - def __init__( - self, - epsilon: float, - sensitivity: float = 1.0, - clip_bound: float | None = None, - ): - """ - Initialize Laplace mechanism. - - Args: - epsilon: Privacy parameter - sensitivity: Query sensitivity - clip_bound: Optional clipping bound for noise - """ - super().__init__( - epsilon=epsilon, - delta=None, # Pure DP has no delta - sensitivity=sensitivity, - name="Laplace", - ) - self.clip_bound = clip_bound - - def compute_noise( - self, - sensitivity: float | None = None, - epsilon: float | None = None, - delta: float | None = None, - ) -> float: - """ - Generate Laplace noise: Lap(Δf / ε). - - Formula: - scale = sensitivity / epsilon - noise ~ Laplace(0, scale) - """ - sens = sensitivity or self.sensitivity - eps = epsilon or self.epsilon - - scale = sens / eps - noise = np.random.laplace(0, scale) - - # Optional: Clip noise to bound - if self.clip_bound is not None: - noise = np.clip(noise, -self.clip_bound, self.clip_bound) - - return noise - - def privacy_cost(self) -> tuple[float, float]: - """ - Laplace mechanism satisfies pure ε-DP. - - Returns: (epsilon, 0) - """ - return (self.epsilon, 0.0) - - -# ============================================================================ -# STUDENT RESEARCH EXTENSION -# ============================================================================ -""" -TODO for Students: ------------------- - -1. Implement TruncatedLaplaceMechanism: - - Truncate noise to [-B, B] for bounded domains - - Derive tighter privacy guarantees for truncated version - - Prove utility bounds - -2. Implement AdaptiveLaplaceMechanism: - - Estimate sensitivity from data - - Adjust epsilon based on query characteristics - - Implement privacy amplification by subsampling - -Example skeleton: - -class TruncatedLaplaceMechanism(LaplaceMechanism): - def __init__(self, epsilon, sensitivity=1.0, truncation_bound=5.0): - super().__init__(epsilon, sensitivity) - self.truncation_bound = truncation_bound - self.name = "TruncatedLaplace" - - def compute_noise(self, sensitivity=None, epsilon=None, delta=None): - # TODO: Generate truncated Laplace noise - # Rejection sampling or inverse CDF method - pass - - def privacy_cost(self): - # TODO: Derive tighter epsilon for truncated case - # See: Geng et al. (2019) "Tight Privacy Analysis" - pass -""" diff --git a/packages/sage-libs/src/sage/libs/privacy/unlearning/dp_unlearning/__init__.py b/packages/sage-libs/src/sage/libs/privacy/unlearning/dp_unlearning/__init__.py deleted file mode 100644 index 873d81a7ae..0000000000 --- a/packages/sage-libs/src/sage/libs/privacy/unlearning/dp_unlearning/__init__.py +++ /dev/null @@ -1,29 +0,0 @@ -""" -Differential Privacy Unlearning Module -======================================= - -Core components for privacy-preserving machine unlearning. - -Architecture: - BasePrivacyMechanism (abstract) - ↓ - [Laplace, Gaussian, Exponential] (concrete implementations) - ↓ - UnlearningEngine (orchestrator) - ↓ - PrivacyAccountant (budget tracking) -""" - -from .base_mechanism import BasePrivacyMechanism -from .neighbor_compensation import NeighborCompensation -from .privacy_accountant import PrivacyAccountant -from .unlearning_engine import UnlearningEngine -from .vector_perturbation import VectorPerturbation - -__all__ = [ - "BasePrivacyMechanism", - "PrivacyAccountant", - "UnlearningEngine", - "VectorPerturbation", - "NeighborCompensation", -] diff --git a/packages/sage-libs/src/sage/libs/privacy/unlearning/dp_unlearning/base_mechanism.py b/packages/sage-libs/src/sage/libs/privacy/unlearning/dp_unlearning/base_mechanism.py deleted file mode 100644 index 30e23a4748..0000000000 --- a/packages/sage-libs/src/sage/libs/privacy/unlearning/dp_unlearning/base_mechanism.py +++ /dev/null @@ -1,252 +0,0 @@ -""" -Base Privacy Mechanism -====================== - -Abstract base class for differential privacy mechanisms in unlearning. - -Research Extension Points: --------------------------- -Students can implement new mechanisms by: -1. Subclassing BasePrivacyMechanism -2. Implementing compute_noise() with custom noise distribution -3. Implementing privacy_cost() with theoretical analysis -4. Optionally overriding perturb_vector() for advanced strategies - -Example: - class MyCustomMechanism(BasePrivacyMechanism): - def compute_noise(self, sensitivity, epsilon, delta=None): - # Your novel noise generation strategy - pass - - def privacy_cost(self): - # Your privacy budget calculation - pass -""" - -from abc import ABC, abstractmethod - -import numpy as np - - -class BasePrivacyMechanism(ABC): - """ - Abstract base class for differential privacy mechanisms. - - This class defines the interface that all privacy mechanisms must implement. - Students should extend this class to create new unlearning algorithms. - - Attributes: - epsilon: Privacy parameter (smaller = more private) - delta: Failure probability (for (ε,δ)-DP) - sensitivity: L1/L2 sensitivity of the query - name: Human-readable name of the mechanism - """ - - def __init__( - self, - epsilon: float, - delta: float | None = None, - sensitivity: float = 1.0, - name: str = "BasePrivacyMechanism", - ): - """ - Initialize privacy mechanism. - - Args: - epsilon: Privacy budget (ε). Smaller values = stronger privacy. - delta: Failure probability for approximate DP. If None, uses pure DP. - sensitivity: Sensitivity of the query (Δf). - name: Name of this mechanism (for logging/tracking). - - Raises: - ValueError: If epsilon <= 0 or delta not in (0, 1) - """ - if epsilon <= 0: - raise ValueError(f"epsilon must be positive, got {epsilon}") - if delta is not None and not (0 < delta < 1): - raise ValueError(f"delta must be in (0, 1), got {delta}") - - self.epsilon = epsilon - self.delta = delta - self.sensitivity = sensitivity - self.name = name - - # Track privacy cost - self._privacy_spent = 0.0 - - @abstractmethod - def compute_noise( - self, - sensitivity: float | None = None, - epsilon: float | None = None, - delta: float | None = None, - ) -> float: - """ - Compute noise magnitude for this mechanism. - - **STUDENT RESEARCH POINT**: Implement novel noise distributions here. - - Args: - sensitivity: Override default sensitivity - epsilon: Override default epsilon - delta: Override default delta - - Returns: - Noise value to add to the true value - - Research Ideas: - - Adaptive noise based on data distribution - - Heavy-tailed distributions for robustness - - Composition-aware noise scheduling - """ - pass - - @abstractmethod - def privacy_cost(self) -> tuple[float, float]: - """ - Compute the privacy cost of this operation. - - **STUDENT RESEARCH POINT**: Derive tighter privacy bounds. - - Returns: - Tuple of (epsilon_spent, delta_spent) - - Research Ideas: - - Advanced composition theorems (Renyi DP, zCDP) - - Data-dependent privacy accounting - - Adaptive privacy budget allocation - """ - pass - - def perturb_vector( - self, vector: np.ndarray, indices_to_perturb: list[int] | None = None - ) -> np.ndarray: - """ - Perturb a vector with differential privacy. - - **STUDENT RESEARCH POINT**: Design advanced perturbation strategies. - - Args: - vector: Original vector to perturb - indices_to_perturb: Specific indices to perturb (None = all) - - Returns: - Perturbed vector - - Research Ideas: - - Dimension-selective perturbation - - Correlation-preserving noise - - Sparse perturbation patterns - """ - if indices_to_perturb is None: - indices_to_perturb = list(range(len(vector))) - - perturbed = vector.copy() - for idx in indices_to_perturb: - noise = self.compute_noise() - perturbed[idx] += noise - - return perturbed - - def get_privacy_guarantee(self) -> dict[str, float]: - """ - Get the privacy guarantee of this mechanism. - - Returns: - Dictionary with 'epsilon' and optionally 'delta' - """ - guarantee = {"epsilon": self.epsilon} - if self.delta is not None: - guarantee["delta"] = self.delta - return guarantee - - def reset_privacy_budget(self): - """Reset the privacy budget counter.""" - self._privacy_spent = 0.0 - - def __repr__(self) -> str: - delta_str = f", δ={self.delta}" if self.delta else "" - return f"{self.name}(ε={self.epsilon}{delta_str}, Δf={self.sensitivity})" - - -# ============================================================================ -# STUDENT TASK 1: Implement a simple mechanism as reference -# ============================================================================ - - -class SimpleLaplaceMechanism(BasePrivacyMechanism): - """ - Reference implementation: Laplace mechanism for pure ε-DP. - - Students can use this as a starting point and improve upon it. - """ - - def __init__(self, epsilon: float, sensitivity: float = 1.0): - super().__init__(epsilon=epsilon, delta=None, sensitivity=sensitivity, name="Laplace") - - def compute_noise( - self, - sensitivity: float | None = None, - epsilon: float | None = None, - delta: float | None = None, - ) -> float: - """ - Generate Laplace noise: Lap(Δf / ε). - - Formula: scale = sensitivity / epsilon - """ - sens = sensitivity or self.sensitivity - eps = epsilon or self.epsilon - scale = sens / eps - return np.random.laplace(0, scale) - - def privacy_cost(self) -> tuple[float, float]: - """ - Laplace mechanism satisfies pure ε-DP. - - Returns: (epsilon, 0) - """ - return (self.epsilon, 0.0) - - -# ============================================================================ -# STUDENT RESEARCH EXTENSION POINT -# ============================================================================ -""" -TODO for Students: ------------------- - -1. **Advanced Mechanisms** (Medium difficulty): - - Implement GaussianMechanism for (ε,δ)-DP - - Implement ExponentialMechanism for non-numeric queries - - Implement AnalyticGaussianMechanism with tighter bounds - -2. **Novel Mechanisms** (Hard difficulty): - - Design AdaptiveLaplaceMechanism that adjusts noise based on data - - Implement TruncatedLaplaceMechanism for bounded domains - - Create HybridMechanism that switches between Laplace/Gaussian - -3. **Theoretical Extensions** (Research-level): - - Prove privacy guarantees for your custom mechanism - - Derive utility bounds (accuracy vs. privacy trade-off) - - Analyze composition properties - -Example skeleton for Gaussian mechanism: - -class GaussianMechanism(BasePrivacyMechanism): - def __init__(self, epsilon: float, delta: float, sensitivity: float = 1.0): - super().__init__(epsilon, delta, sensitivity, name="Gaussian") - - def compute_noise(self, sensitivity=None, epsilon=None, delta=None): - # TODO: Implement Gaussian noise with calibrated σ - # Formula: σ = sqrt(2 * ln(1.25/δ)) * Δf / ε - pass - - def privacy_cost(self): - # TODO: Return (ε, δ) for Gaussian mechanism - pass - -See research papers: -- Dwork & Roth (2014): "The Algorithmic Foundations of Differential Privacy" -- Mironov (2017): "Renyi Differential Privacy" -""" diff --git a/packages/sage-libs/src/sage/libs/privacy/unlearning/dp_unlearning/neighbor_compensation.py b/packages/sage-libs/src/sage/libs/privacy/unlearning/dp_unlearning/neighbor_compensation.py deleted file mode 100644 index a1977f277c..0000000000 --- a/packages/sage-libs/src/sage/libs/privacy/unlearning/dp_unlearning/neighbor_compensation.py +++ /dev/null @@ -1,286 +0,0 @@ -""" -Neighbor Compensation Module -============================= - -Prevents "collateral damage" to neighboring vectors when applying unlearning. - -Research Extension Points: --------------------------- -Key research challenge: When we perturb/remove vector A, how do we ensure -that semantically similar vectors B, C, D are not affected? - -Students can explore: -1. Graph-based compensation (build similarity graph) -2. Learned compensation (use neural networks) -3. Iterative refinement (multi-round compensation) -4. Budget-aware compensation (optimize privacy-utility trade-off) -""" - -import numpy as np - - -class NeighborCompensation: - """ - Compensates neighboring vectors to prevent collateral unlearning. - - **STUDENT RESEARCH POINT**: Design intelligent compensation strategies. - - Problem: When we perturb vector v_forget with noise, its k-nearest - neighbors may see changed retrieval probabilities even though they - should be retained. - - Solution: Apply compensatory adjustments to neighbors to restore their - original retrieval probabilities. - - Research Ideas: - - Graph-based propagation of compensation - - Learning optimal compensation from data - - Privacy-preserving compensation (avoid revealing neighbors) - """ - - def __init__(self, similarity_threshold: float = 0.8, max_neighbors: int = 10): - """ - Initialize neighbor compensation. - - Args: - similarity_threshold: Cosine similarity threshold for neighbors - max_neighbors: Maximum number of neighbors to compensate - """ - self.similarity_threshold = similarity_threshold - self.max_neighbors = max_neighbors - - def identify_neighbors( - self, target_vector: np.ndarray, all_vectors: np.ndarray, all_ids: list[str] - ) -> list[tuple[str, float]]: - """ - Identify neighbors of the target vector. - - **STUDENT RESEARCH POINT**: Design better neighbor identification. - - Args: - target_vector: Vector being unlearned - all_vectors: All vectors in the database - all_ids: IDs corresponding to all_vectors - - Returns: - List of (id, similarity) tuples for neighbors - - Research Ideas: - - Use approximate nearest neighbor search (HNSW, FAISS) - - Consider second-order neighbors (neighbors of neighbors) - - Weight by multiple similarity metrics - """ - # Compute cosine similarities - similarities = self._compute_cosine_similarities(target_vector, all_vectors) - - # Find neighbors above threshold - neighbors = [] - for i, sim in enumerate(similarities): - if sim >= self.similarity_threshold: - neighbors.append((all_ids[i], sim)) - - # Sort by similarity and take top-k - neighbors.sort(key=lambda x: x[1], reverse=True) - return neighbors[: self.max_neighbors] - - def compute_compensation( - self, - original_vector: np.ndarray, - perturbed_vector: np.ndarray, - neighbor_vector: np.ndarray, - neighbor_similarity: float, - ) -> np.ndarray: - """ - Compute compensation adjustment for a neighbor. - - **STUDENT RESEARCH POINT**: Design optimal compensation formula. - - Goal: After compensation, neighbor should have same retrieval - probability as before perturbation. - - Args: - original_vector: Original vector before perturbation - perturbed_vector: Vector after perturbation - neighbor_vector: Neighbor vector to compensate - neighbor_similarity: Similarity between original and neighbor - - Returns: - Compensation vector to add to neighbor - - Research Ideas: - - Derive compensation from first-order Taylor expansion - - Use second-order compensation for better accuracy - - Learn compensation function from data - """ - # Strategy 1: Linear compensation (simple but may not be optimal) - # Idea: Adjust neighbor in opposite direction of perturbation - perturbation = perturbed_vector - original_vector - - # Scale compensation by similarity (closer neighbors get more compensation) - compensation_scale = neighbor_similarity - compensation = -compensation_scale * perturbation - - return compensation - - def _compute_cosine_similarities(self, query: np.ndarray, vectors: np.ndarray) -> np.ndarray: - """ - Compute cosine similarities efficiently. - - Args: - query: Query vector (1D) - vectors: Array of vectors (2D) - - Returns: - Array of cosine similarities - """ - # Normalize query - query_norm = query / (np.linalg.norm(query) + 1e-10) - - # Normalize all vectors - vectors_norm = vectors / (np.linalg.norm(vectors, axis=1, keepdims=True) + 1e-10) - - # Compute dot products - similarities = np.dot(vectors_norm, query_norm) - - return similarities - - def apply_compensation( - self, - original_vector: np.ndarray, - perturbed_vector: np.ndarray, - all_vectors: np.ndarray, - all_ids: list[str], - ) -> dict[str, np.ndarray]: - """ - Apply compensation to all affected neighbors. - - Args: - original_vector: Original vector before perturbation - perturbed_vector: Vector after perturbation - all_vectors: All vectors in database - all_ids: IDs corresponding to all_vectors - - Returns: - Dictionary mapping neighbor_id -> compensated_vector - """ - # Identify neighbors - neighbors = self.identify_neighbors(original_vector, all_vectors, all_ids) - - # Compute compensation for each neighbor - compensated_vectors = {} - for neighbor_id, similarity in neighbors: - # Find neighbor vector - neighbor_idx = all_ids.index(neighbor_id) - neighbor_vector = all_vectors[neighbor_idx] - - # Compute compensation - compensation = self.compute_compensation( - original_vector, perturbed_vector, neighbor_vector, similarity - ) - - # Apply compensation - compensated_vector = neighbor_vector + compensation - compensated_vectors[neighbor_id] = compensated_vector - - return compensated_vectors - - def evaluate_compensation_quality( - self, - original_vector: np.ndarray, - perturbed_vector: np.ndarray, - neighbor_original: np.ndarray, - neighbor_compensated: np.ndarray, - ) -> dict[str, float]: - """ - Evaluate quality of compensation. - - **STUDENT RESEARCH POINT**: Design better evaluation metrics. - - Args: - original_vector: Original vector being unlearned - perturbed_vector: Perturbed version - neighbor_original: Original neighbor vector - neighbor_compensated: Compensated neighbor vector - - Returns: - Dictionary with quality metrics - - Research Ideas: - - Measure retrieval probability change - - Compare ranking before/after compensation - - Evaluate semantic preservation - """ - # Similarity before perturbation - sim_before = np.dot(original_vector, neighbor_original) / ( - np.linalg.norm(original_vector) * np.linalg.norm(neighbor_original) + 1e-10 - ) - - # Similarity after perturbation (without compensation) - sim_after_no_comp = np.dot(perturbed_vector, neighbor_original) / ( - np.linalg.norm(perturbed_vector) * np.linalg.norm(neighbor_original) + 1e-10 - ) - - # Similarity after compensation - sim_after_comp = np.dot(perturbed_vector, neighbor_compensated) / ( - np.linalg.norm(perturbed_vector) * np.linalg.norm(neighbor_compensated) + 1e-10 - ) - - # Change in neighbor vector - neighbor_change = np.linalg.norm(neighbor_compensated - neighbor_original) - - return { - "similarity_before": float(sim_before), - "similarity_after_no_compensation": float(sim_after_no_comp), - "similarity_after_compensation": float(sim_after_comp), - "similarity_recovery": float( - abs(sim_after_comp - sim_before) / (abs(sim_before) + 1e-10) - ), - "neighbor_change_magnitude": float(neighbor_change), - } - - -# ============================================================================ -# STUDENT RESEARCH EXTENSION POINT -# ============================================================================ -""" -TODO for Students: ------------------- - -1. **Graph-Based Compensation** (Medium difficulty): - - Build similarity graph of all vectors - - Propagate compensation through graph edges - - Implement iterative refinement - -2. **Learned Compensation** (Hard difficulty): - - Train a neural network to predict optimal compensation - - Use reinforcement learning to optimize compensation strategy - - Implement meta-learning for adaptation - -3. **Theoretical Analysis** (Research-level): - - Prove bounds on retrieval probability changes - - Derive privacy cost of compensation operations - - Analyze convergence of iterative compensation - -Example skeleton for graph-based compensation: - -class GraphBasedCompensation(NeighborCompensation): - def __init__(self, similarity_threshold=0.8, max_neighbors=10, propagation_depth=2): - super().__init__(similarity_threshold, max_neighbors) - self.propagation_depth = propagation_depth - self.graph = None - - def build_similarity_graph(self, vectors, ids): - # TODO: Build k-NN graph - # Use networkx or custom graph implementation - pass - - def propagate_compensation(self, source_id, initial_compensation): - # TODO: Propagate compensation through graph - # Use breadth-first or belief propagation - pass - -See research papers: -- Jia et al. (2019): "Certified Robustness to Adversarial Examples with DP" -- Guo et al. (2019): "Certified Robustness to Text Adversarial Attacks" -- Wang et al. (2020): "Differentially Private Graph Neural Networks" -""" diff --git a/packages/sage-libs/src/sage/libs/privacy/unlearning/dp_unlearning/privacy_accountant.py b/packages/sage-libs/src/sage/libs/privacy/unlearning/dp_unlearning/privacy_accountant.py deleted file mode 100644 index fec9e60e15..0000000000 --- a/packages/sage-libs/src/sage/libs/privacy/unlearning/dp_unlearning/privacy_accountant.py +++ /dev/null @@ -1,320 +0,0 @@ -""" -Privacy Accountant -================== - -Tracks and manages privacy budget across multiple unlearning operations. - -Research Extension Points: --------------------------- -Students can enhance privacy accounting by: -1. Implementing advanced composition theorems (RDP, zCDP, GDP) -2. Designing adaptive budget allocation strategies -3. Creating privacy-utility trade-off optimizers -4. Building privacy budget prediction models -""" - -from dataclasses import dataclass, field -from datetime import datetime - - -@dataclass -class PrivacySpending: - """Record of a single privacy-consuming operation.""" - - timestamp: datetime - operation: str - epsilon: float - delta: float - mechanism: str - metadata: dict = field(default_factory=dict) - - def __repr__(self) -> str: - return ( - f"PrivacySpending({self.operation}: ε={self.epsilon:.4f}, " - f"δ={self.delta:.6f}, mechanism={self.mechanism})" - ) - - -class PrivacyAccountant: - """ - Tracks privacy budget consumption across unlearning operations. - - This class maintains a ledger of all privacy-consuming operations and - computes the total privacy cost using composition theorems. - - **STUDENT RESEARCH POINT**: Implement tighter composition bounds. - - Attributes: - total_epsilon_budget: Maximum allowed epsilon - total_delta_budget: Maximum allowed delta - composition_type: Type of composition theorem to use - - Research Ideas: - - Implement Renyi DP composition - - Design adaptive budget allocation - - Create budget prediction models - """ - - def __init__( - self, - total_epsilon_budget: float, - total_delta_budget: float = 1e-5, - composition_type: str = "basic", - ): - """ - Initialize privacy accountant. - - Args: - total_epsilon_budget: Total privacy budget (ε) - total_delta_budget: Total failure probability (δ) - composition_type: Composition theorem to use - - "basic": Basic composition (sum of epsilons) - - "advanced": Advanced composition (tighter bounds) - - "moments": Moments accountant (RDP-based) - - Raises: - ValueError: If budgets are invalid - """ - if total_epsilon_budget <= 0: - raise ValueError(f"epsilon budget must be positive, got {total_epsilon_budget}") - if not (0 < total_delta_budget < 1): - raise ValueError(f"delta budget must be in (0,1), got {total_delta_budget}") - - self.total_epsilon_budget = total_epsilon_budget - self.total_delta_budget = total_delta_budget - self.composition_type = composition_type - - # Ledger of privacy spending - self._spending_history: list[PrivacySpending] = [] - - # Current spent budget - self._epsilon_spent = 0.0 - self._delta_spent = 0.0 - - def record_operation( - self, - epsilon: float, - delta: float, - operation: str, - mechanism: str, - metadata: dict | None = None, - ) -> bool: - """ - Record a privacy-consuming operation. - - Args: - epsilon: Privacy cost (ε) - delta: Failure probability (δ) - operation: Description of the operation - mechanism: Name of the privacy mechanism used - metadata: Additional information - - Returns: - True if operation was within budget, False otherwise - - Raises: - ValueError: If operation would exceed budget - """ - # Compute new total cost - new_epsilon, new_delta = self._compute_composition(epsilon, delta) - - # Check budget - if new_epsilon > self.total_epsilon_budget: - raise ValueError( - f"Operation would exceed epsilon budget: " - f"{new_epsilon:.4f} > {self.total_epsilon_budget:.4f}" - ) - if new_delta > self.total_delta_budget: - raise ValueError( - f"Operation would exceed delta budget: " - f"{new_delta:.6f} > {self.total_delta_budget:.6f}" - ) - - # Record the operation - spending = PrivacySpending( - timestamp=datetime.now(), - operation=operation, - epsilon=epsilon, - delta=delta, - mechanism=mechanism, - metadata=metadata or {}, - ) - self._spending_history.append(spending) - - # Update spent budget - self._epsilon_spent = new_epsilon - self._delta_spent = new_delta - - return True - - def _compute_composition(self, new_epsilon: float, new_delta: float) -> tuple[float, float]: - """ - Compute total privacy cost using composition theorem. - - **STUDENT RESEARCH POINT**: Implement advanced composition theorems. - - Args: - new_epsilon: Privacy cost of new operation - new_delta: Failure probability of new operation - - Returns: - Tuple of (total_epsilon, total_delta) - - Research Ideas: - - Implement Renyi DP composition (tighter bounds) - - Implement zero-concentrated DP (zCDP) - - Implement Gaussian DP (GDP) - - Design data-dependent composition - """ - if self.composition_type == "basic": - return self._basic_composition(new_epsilon, new_delta) - elif self.composition_type == "advanced": - return self._advanced_composition(new_epsilon, new_delta) - elif self.composition_type == "moments": - return self._moments_composition(new_epsilon, new_delta) - else: - raise ValueError(f"Unknown composition type: {self.composition_type}") - - def _basic_composition(self, new_epsilon: float, new_delta: float) -> tuple[float, float]: - """ - Basic composition: ε_total = Σε_i, δ_total = Σδ_i. - - This is the simplest but loosest bound. - """ - total_epsilon = self._epsilon_spent + new_epsilon - total_delta = self._delta_spent + new_delta - return (total_epsilon, total_delta) - - def _advanced_composition(self, new_epsilon: float, new_delta: float) -> tuple[float, float]: - """ - Advanced composition theorem. - - **STUDENT TODO**: Implement advanced composition. - - For k compositions of (ε, δ)-DP mechanisms: - ε_total = sqrt(2k ln(1/δ')) * ε + k * ε * (e^ε - 1) - δ_total = k * δ + δ' - - See: Dwork, Rothblum, Vadhan (2010) - """ - # Placeholder: Use basic composition for now - # TODO: Implement advanced composition formula - return self._basic_composition(new_epsilon, new_delta) - - def _moments_composition(self, new_epsilon: float, new_delta: float) -> tuple[float, float]: - """ - Moments accountant (Renyi DP composition). - - **STUDENT TODO**: Implement moments accountant. - - This provides tighter bounds for Gaussian mechanisms. - See: Abadi et al. (2016) "Deep Learning with Differential Privacy" - """ - # Placeholder: Use basic composition for now - # TODO: Implement moments accountant - return self._basic_composition(new_epsilon, new_delta) - - def get_remaining_budget(self) -> dict[str, float]: - """ - Get remaining privacy budget. - - Returns: - Dictionary with remaining epsilon and delta budgets - """ - return { - "epsilon_remaining": self.total_epsilon_budget - self._epsilon_spent, - "delta_remaining": self.total_delta_budget - self._delta_spent, - "epsilon_spent": self._epsilon_spent, - "delta_spent": self._delta_spent, - } - - def can_afford(self, epsilon: float, delta: float) -> bool: - """ - Check if we can afford a new operation. - - Args: - epsilon: Privacy cost of proposed operation - delta: Failure probability of proposed operation - - Returns: - True if operation is within budget - """ - new_epsilon, new_delta = self._compute_composition(epsilon, delta) - return new_epsilon <= self.total_epsilon_budget and new_delta <= self.total_delta_budget - - def get_spending_history(self) -> list[PrivacySpending]: - """Get history of all privacy-consuming operations.""" - return self._spending_history.copy() - - def reset(self): - """Reset privacy accountant (clear all spending history).""" - self._spending_history.clear() - self._epsilon_spent = 0.0 - self._delta_spent = 0.0 - - def summary(self) -> dict: - """ - Get summary statistics of privacy spending. - - Returns: - Dictionary with summary statistics - """ - return { - "total_operations": len(self._spending_history), - "epsilon_spent": self._epsilon_spent, - "delta_spent": self._delta_spent, - "epsilon_remaining": self.total_epsilon_budget - self._epsilon_spent, - "delta_remaining": self.total_delta_budget - self._delta_spent, - "budget_utilization": self._epsilon_spent / self.total_epsilon_budget, - "composition_type": self.composition_type, - } - - def __repr__(self) -> str: - return ( - f"PrivacyAccountant(spent: ε={self._epsilon_spent:.4f}/{self.total_epsilon_budget:.4f}, " - f"δ={self._delta_spent:.6f}/{self.total_delta_budget:.6f}, " - f"operations={len(self._spending_history)})" - ) - - -# ============================================================================ -# STUDENT RESEARCH EXTENSION POINT -# ============================================================================ -""" -TODO for Students: ------------------- - -1. **Advanced Composition** (Medium difficulty): - - Implement advanced composition theorem (Dwork et al. 2010) - - Implement optimal composition (Kairouz et al. 2015) - - Implement privacy odometer (Rogers et al. 2016) - -2. **Renyi DP Composition** (Hard difficulty): - - Implement Renyi divergence-based accounting - - Implement privacy amplification by subsampling - - Implement privacy amplification by iteration - -3. **Adaptive Budget Allocation** (Research-level): - - Design algorithms to optimally allocate budget across operations - - Implement budget prediction based on query patterns - - Create budget-aware query optimization - -Example skeleton for Renyi DP accountant: - -class RenyiPrivacyAccountant(PrivacyAccountant): - def __init__(self, total_epsilon_budget, total_delta_budget, orders=(2, 4, 8, 16, 32)): - super().__init__(total_epsilon_budget, total_delta_budget, "moments") - self.orders = orders - self.rdp_epsilons = {order: 0.0 for order in orders} - - def _moments_composition(self, new_epsilon, new_delta): - # TODO: Implement Renyi DP composition - # Update RDP at each order - # Convert back to (ε, δ)-DP - pass - -See research papers: -- Mironov (2017): "Renyi Differential Privacy" -- Bun & Steinke (2016): "Concentrated Differential Privacy" -- Abadi et al. (2016): "Deep Learning with Differential Privacy" -""" diff --git a/packages/sage-libs/src/sage/libs/privacy/unlearning/dp_unlearning/unlearning_engine.py b/packages/sage-libs/src/sage/libs/privacy/unlearning/dp_unlearning/unlearning_engine.py deleted file mode 100644 index 32da743ef7..0000000000 --- a/packages/sage-libs/src/sage/libs/privacy/unlearning/dp_unlearning/unlearning_engine.py +++ /dev/null @@ -1,377 +0,0 @@ -""" -Unlearning Engine -================= - -Orchestrates the complete unlearning process with differential privacy. - -This is the main entry point for students to experiment with different -unlearning strategies by combining privacy mechanisms, perturbation -strategies, and compensation methods. - -Research Extension Points: --------------------------- -Students should focus on: -1. Designing end-to-end unlearning strategies -2. Optimizing privacy-utility trade-offs -3. Developing adaptive unlearning algorithms -""" - -from dataclasses import dataclass - -import numpy as np - -from .base_mechanism import BasePrivacyMechanism, SimpleLaplaceMechanism -from .neighbor_compensation import NeighborCompensation -from .privacy_accountant import PrivacyAccountant -from .vector_perturbation import VectorPerturbation - - -@dataclass -class UnlearningResult: - """Result of an unlearning operation.""" - - success: bool - num_vectors_unlearned: int - num_neighbors_compensated: int - privacy_cost: tuple[float, float] # (epsilon, delta) - metadata: dict - - def __repr__(self) -> str: - return ( - f"UnlearningResult(success={self.success}, " - f"unlearned={self.num_vectors_unlearned}, " - f"compensated={self.num_neighbors_compensated}, " - f"privacy_cost=(ε={self.privacy_cost[0]:.4f}, δ={self.privacy_cost[1]:.6f}))" - ) - - -class UnlearningEngine: - """ - Main engine for differential privacy-based machine unlearning. - - **STUDENT RESEARCH POINT**: This is your main playground! - - Combine different components to create novel unlearning strategies: - - Privacy mechanisms (Laplace, Gaussian, Custom) - - Perturbation strategies (uniform, selective, adaptive) - - Compensation methods (linear, graph-based, learned) - - Attributes: - privacy_mechanism: DP mechanism for noise generation - privacy_accountant: Tracks privacy budget - vector_perturbation: Handles vector perturbation - neighbor_compensation: Handles neighbor compensation - - Research Goals: - - Minimize privacy cost while maintaining utility - - Preserve semantic structure of retained vectors - - Achieve verifiable unlearning guarantees - """ - - def __init__( - self, - epsilon: float = 1.0, - delta: float = 1e-5, - total_budget_epsilon: float = 10.0, - total_budget_delta: float = 1e-4, - mechanism: BasePrivacyMechanism | None = None, - enable_compensation: bool = True, - ): - """ - Initialize unlearning engine. - - Args: - epsilon: Per-operation privacy parameter - delta: Per-operation failure probability - total_budget_epsilon: Total privacy budget - total_budget_delta: Total delta budget - mechanism: Custom privacy mechanism (uses Laplace if None) - enable_compensation: Whether to apply neighbor compensation - """ - # Privacy components - self.mechanism = mechanism or SimpleLaplaceMechanism(epsilon=epsilon) - self.privacy_accountant = PrivacyAccountant( - total_epsilon_budget=total_budget_epsilon, - total_delta_budget=total_budget_delta, - ) - - # Unlearning components - self.vector_perturbation = VectorPerturbation(self.mechanism) - self.neighbor_compensation = NeighborCompensation() if enable_compensation else None - - # Configuration - self.enable_compensation = enable_compensation - self.epsilon = epsilon - self.delta = delta - - def unlearn_vectors( - self, - vectors_to_forget: np.ndarray, - vector_ids_to_forget: list[str], - all_vectors: np.ndarray | None = None, - all_vector_ids: list[str] | None = None, - perturbation_strategy: str = "uniform", - return_compensated_neighbors: bool = False, - ) -> UnlearningResult: - """ - Unlearn specified vectors with differential privacy. - - **STUDENT RESEARCH POINT**: This is where your algorithm lives! - - Main workflow: - 1. Check privacy budget - 2. Perturb vectors to forget - 3. (Optional) Compensate neighbors - 4. Record privacy cost - - Args: - vectors_to_forget: Vectors to unlearn (n_forget, dim) - vector_ids_to_forget: IDs of vectors to forget - all_vectors: All vectors in database (for compensation) - all_vector_ids: All vector IDs (for compensation) - perturbation_strategy: How to perturb ("uniform", "selective", "adaptive") - return_compensated_neighbors: Whether to return compensated neighbor vectors - - Returns: - UnlearningResult with outcome and statistics - - Research Ideas: - - Design adaptive strategies that adjust based on data - - Implement batch unlearning with shared noise - - Create budget-aware unlearning (optimize across operations) - """ - n_forget = len(vectors_to_forget) - - # Step 1: Check if we can afford this operation - operation_epsilon = self.epsilon * n_forget - operation_delta = self.delta * n_forget - - if not self.privacy_accountant.can_afford(operation_epsilon, operation_delta): - remaining = self.privacy_accountant.get_remaining_budget() - return UnlearningResult( - success=False, - num_vectors_unlearned=0, - num_neighbors_compensated=0, - privacy_cost=(0, 0), - metadata={ - "error": "Insufficient privacy budget", - "remaining_budget": remaining, - }, - ) - - # Step 2: Perturb vectors - perturbed_vectors = self.vector_perturbation.perturb_batch_vectors( - vectors_to_forget, strategy=perturbation_strategy - ) - - # Step 3: (Optional) Compensate neighbors - num_compensated = 0 - compensated_neighbors = {} - - if ( - self.enable_compensation - and self.neighbor_compensation is not None - and all_vectors is not None - and all_vector_ids is not None - ): - for _i, (original, perturbed, _vec_id) in enumerate( - zip( - vectors_to_forget, - perturbed_vectors, - vector_ids_to_forget, - strict=False, - ) - ): - neighbor_compensations = self.neighbor_compensation.apply_compensation( - original, perturbed, all_vectors, all_vector_ids - ) - compensated_neighbors.update(neighbor_compensations) - num_compensated += len(neighbor_compensations) - - # Step 4: Record privacy cost - self.privacy_accountant.record_operation( - epsilon=operation_epsilon, - delta=operation_delta, - operation=f"unlearn_{n_forget}_vectors", - mechanism=self.mechanism.name, - metadata={ - "num_vectors": n_forget, - "perturbation_strategy": perturbation_strategy, - "compensation_enabled": self.enable_compensation, - "num_compensated": num_compensated, - }, - ) - - # Step 5: Prepare result - result = UnlearningResult( - success=True, - num_vectors_unlearned=n_forget, - num_neighbors_compensated=num_compensated, - privacy_cost=(operation_epsilon, operation_delta), - metadata={ - "perturbation_strategy": perturbation_strategy, - "perturbed_vectors": perturbed_vectors, - "privacy_accountant_summary": self.privacy_accountant.summary(), - }, - ) - - if return_compensated_neighbors: - result.metadata["compensated_neighbors"] = compensated_neighbors - - return result - - def unlearn_by_similarity( - self, - query_vector: np.ndarray, - all_vectors: np.ndarray, - all_vector_ids: list[str], - similarity_threshold: float = 0.9, - max_unlearn: int = 100, - **kwargs, - ) -> UnlearningResult: - """ - Unlearn vectors similar to a query vector. - - **STUDENT RESEARCH POINT**: Design semantic-aware unlearning. - - Use case: "Forget all documents about topic X" - - Args: - query_vector: Reference vector defining what to forget - all_vectors: All vectors in database - all_vector_ids: All vector IDs - similarity_threshold: Minimum similarity to forget - max_unlearn: Maximum number of vectors to unlearn - **kwargs: Additional arguments for unlearn_vectors() - - Returns: - UnlearningResult - - Research Ideas: - - Use clustering to identify semantic groups - - Implement hierarchical unlearning (forget general -> specific) - - Design privacy-preserving similarity search - """ - # Compute similarities - similarities = self._compute_similarities(query_vector, all_vectors) - - # Find vectors above threshold - forget_indices = np.where(similarities >= similarity_threshold)[0] - forget_indices = forget_indices[:max_unlearn] # Limit to max_unlearn - - if len(forget_indices) == 0: - return UnlearningResult( - success=True, - num_vectors_unlearned=0, - num_neighbors_compensated=0, - privacy_cost=(0, 0), - metadata={"message": "No vectors matched similarity threshold"}, - ) - - # Extract vectors to forget - vectors_to_forget = all_vectors[forget_indices] - ids_to_forget = [all_vector_ids[i] for i in forget_indices] - - # Unlearn them - return self.unlearn_vectors( - vectors_to_forget=vectors_to_forget, - vector_ids_to_forget=ids_to_forget, - all_vectors=all_vectors, - all_vector_ids=all_vector_ids, - **kwargs, - ) - - def _compute_similarities(self, query: np.ndarray, vectors: np.ndarray) -> np.ndarray: - """Compute cosine similarities.""" - query_norm = query / (np.linalg.norm(query) + 1e-10) - vectors_norm = vectors / (np.linalg.norm(vectors, axis=1, keepdims=True) + 1e-10) - return np.dot(vectors_norm, query_norm) - - def get_privacy_status(self) -> dict: - """Get current privacy budget status.""" - return { - "accountant_summary": self.privacy_accountant.summary(), - "remaining_budget": self.privacy_accountant.get_remaining_budget(), - "mechanism": str(self.mechanism), - } - - def reset(self): - """Reset the unlearning engine (clear privacy history).""" - self.privacy_accountant.reset() - - -# ============================================================================ -# STUDENT RESEARCH EXTENSION POINT -# ============================================================================ -""" -TODO for Students - Main Research Directions: ---------------------------------------------- - -1. **Adaptive Unlearning** (Medium-Hard): - Design strategies that adapt based on: - - Data distribution (cluster-aware unlearning) - - Privacy budget (allocate more budget to important operations) - - Utility requirements (task-specific optimization) - - Example: - class AdaptiveUnlearningEngine(UnlearningEngine): - def unlearn_vectors_adaptive(self, ...): - # Analyze data distribution - # Adjust epsilon per-vector based on importance - # Use different perturbation strategies for different clusters - pass - -2. **Batch Unlearning with Shared Noise** (Hard): - Optimize privacy cost when unlearning multiple vectors: - - Generate correlated noise for batch (saves privacy budget) - - Use matrix mechanisms instead of vector-wise perturbation - - Implement privacy amplification by subsampling - - Example: - class BatchUnlearningEngine(UnlearningEngine): - def unlearn_batch_with_shared_noise(self, ...): - # Generate shared noise matrix - # Apply low-rank approximation - # Achieve better privacy-utility trade-off - pass - -3. **Verification and Certification** (Research-level): - Provide guarantees that unlearning succeeded: - - Implement membership inference tests (verify non-membership) - - Generate cryptographic certificates of unlearning - - Prove theoretical bounds on residual information - - Example: - class VerifiableUnlearningEngine(UnlearningEngine): - def unlearn_with_certificate(self, ...): - # Perform unlearning - # Generate Merkle tree of operations - # Provide zero-knowledge proof of deletion - pass - -4. **Multi-Objective Optimization** (Research-level): - Optimize multiple objectives simultaneously: - - Minimize privacy cost (ε, δ) - - Maximize utility (retrieval accuracy on retained data) - - Minimize unlearning latency - - Use Pareto optimization, RL, or gradient-based methods. - -See research papers for inspiration: ------------------------------------- -- Cao & Yang (2015): "Towards Making Systems Forget with Machine Unlearning" -- Bourtoule et al. (2021): "Machine Unlearning" (SISA framework) -- Guo et al. (2019): "Certified Data Removal from Machine Learning Models" -- Sekhari et al. (2021): "Remember What You Want to Forget" - -Your PhD thesis could be: -------------------------- -"Differential Privacy-Preserving Machine Unlearning in RAG Systems: - Theory, Algorithms, and Applications" - -Contributions: -1. Theoretical framework for DP-unlearning in retrieval systems -2. Novel perturbation and compensation algorithms -3. Privacy-utility trade-off characterization -4. Practical implementation and benchmarks -""" diff --git a/packages/sage-libs/src/sage/libs/privacy/unlearning/dp_unlearning/vector_perturbation.py b/packages/sage-libs/src/sage/libs/privacy/unlearning/dp_unlearning/vector_perturbation.py deleted file mode 100644 index f22ac02f54..0000000000 --- a/packages/sage-libs/src/sage/libs/privacy/unlearning/dp_unlearning/vector_perturbation.py +++ /dev/null @@ -1,221 +0,0 @@ -""" -Vector Perturbation Module -=========================== - -Implements differential privacy perturbation strategies for embedding vectors. - -Research Extension Points: --------------------------- -Students can design novel perturbation strategies: -1. Dimension-selective perturbation (perturb important dimensions less) -2. Correlation-preserving perturbation (maintain vector relationships) -3. Sparse perturbation (add noise to few dimensions) -4. Adaptive perturbation (adjust based on vector properties) -""" - -import numpy as np - -from .base_mechanism import BasePrivacyMechanism - - -class VectorPerturbation: - """ - Applies differential privacy perturbation to embedding vectors. - - **STUDENT RESEARCH POINT**: Design advanced perturbation strategies. - - This class provides methods to perturb vectors while maintaining their - semantic properties as much as possible under privacy constraints. - - Research Ideas: - - Dimension importance weighting - - Structured noise (preserving vector subspaces) - - Multi-resolution perturbation - """ - - def __init__(self, mechanism: BasePrivacyMechanism): - """ - Initialize vector perturbation. - - Args: - mechanism: Privacy mechanism to use for noise generation - """ - self.mechanism = mechanism - - def perturb_single_vector(self, vector: np.ndarray, strategy: str = "uniform") -> np.ndarray: - """ - Perturb a single vector with DP noise. - - **STUDENT RESEARCH POINT**: Implement advanced strategies. - - Args: - vector: Original embedding vector - strategy: Perturbation strategy - - "uniform": Add noise to all dimensions - - "selective": Add noise to selected dimensions - - "adaptive": Adaptive noise based on dimension importance - - Returns: - Perturbed vector - - Research Ideas: - - Implement dimension importance scoring - - Design correlation-preserving noise - - Create privacy-utility optimal perturbation - """ - if strategy == "uniform": - return self._uniform_perturbation(vector) - elif strategy == "selective": - return self._selective_perturbation(vector) - elif strategy == "adaptive": - return self._adaptive_perturbation(vector) - else: - raise ValueError(f"Unknown strategy: {strategy}") - - def _uniform_perturbation(self, vector: np.ndarray) -> np.ndarray: - """ - Add uniform noise to all dimensions. - - This is the baseline approach: simple but may destroy semantic structure. - """ - perturbed = vector.copy() - for i in range(len(vector)): - noise = self.mechanism.compute_noise() - perturbed[i] += noise - return perturbed - - def _selective_perturbation(self, vector: np.ndarray) -> np.ndarray: - """ - Add noise to selected dimensions only. - - **STUDENT TODO**: Implement dimension selection logic. - - Research Ideas: - - Select dimensions with highest variance - - Select dimensions that least affect semantic similarity - - Use PCA to identify important dimensions - """ - # Placeholder: Perturb random 50% of dimensions - # TODO: Implement smart dimension selection - perturbed = vector.copy() - dim = len(vector) - selected_dims = np.random.choice(dim, size=dim // 2, replace=False) - - for i in selected_dims: - noise = self.mechanism.compute_noise() - perturbed[i] += noise - - return perturbed - - def _adaptive_perturbation(self, vector: np.ndarray) -> np.ndarray: - """ - Adaptive noise based on dimension importance. - - **STUDENT TODO**: Implement adaptive strategy. - - Research Ideas: - - Weight noise inversely by dimension importance - - Use gradient information (if available) - - Learn optimal noise allocation - """ - # Placeholder: Use magnitude as importance indicator - # TODO: Implement sophisticated importance scoring - perturbed = vector.copy() - magnitudes = np.abs(vector) - total_magnitude = np.sum(magnitudes) + 1e-10 - - for i in range(len(vector)): - # Less noise for important (high magnitude) dimensions - importance = magnitudes[i] / total_magnitude - adaptive_sensitivity = self.mechanism.sensitivity * (1 - importance) - noise = self.mechanism.compute_noise(sensitivity=adaptive_sensitivity) - perturbed[i] += noise - - return perturbed - - def perturb_batch_vectors(self, vectors: np.ndarray, strategy: str = "uniform") -> np.ndarray: - """ - Perturb a batch of vectors. - - Args: - vectors: Array of shape (n_vectors, dim) - strategy: Perturbation strategy - - Returns: - Array of perturbed vectors - """ - perturbed_batch = np.zeros_like(vectors) - for i, vector in enumerate(vectors): - perturbed_batch[i] = self.perturb_single_vector(vector, strategy) - return perturbed_batch - - def measure_perturbation_impact(self, original: np.ndarray, perturbed: np.ndarray) -> dict: - """ - Measure the impact of perturbation on vector properties. - - Args: - original: Original vector - perturbed: Perturbed vector - - Returns: - Dictionary with impact metrics - """ - l2_distance = np.linalg.norm(original - perturbed) - l1_distance = np.sum(np.abs(original - perturbed)) - cosine_similarity = np.dot(original, perturbed) / ( - np.linalg.norm(original) * np.linalg.norm(perturbed) + 1e-10 - ) - - return { - "l2_distance": l2_distance, - "l1_distance": l1_distance, - "cosine_similarity": cosine_similarity, - "relative_change": l2_distance / (np.linalg.norm(original) + 1e-10), - } - - -# ============================================================================ -# STUDENT RESEARCH EXTENSION POINT -# ============================================================================ -""" -TODO for Students: ------------------- - -1. **Advanced Perturbation Strategies** (Medium difficulty): - - Implement PCA-based selective perturbation - - Implement locality-sensitive perturbation - - Implement subspace-preserving perturbation - -2. **Semantic-Aware Perturbation** (Hard difficulty): - - Design perturbation that preserves semantic clusters - - Implement attention-weighted perturbation - - Create learned perturbation (use neural networks) - -3. **Theoretical Analysis** (Research-level): - - Prove utility bounds for each strategy - - Derive optimal dimension selection algorithm - - Analyze privacy-utility Pareto frontier - -Example skeleton for PCA-based perturbation: - -class PCAPreservingPerturbation(VectorPerturbation): - def __init__(self, mechanism, n_components=0.95): - super().__init__(mechanism) - self.n_components = n_components - self.pca = None - - def fit_pca(self, vectors): - # TODO: Fit PCA on representative vectors - from sklearn.decomposition import PCA - self.pca = PCA(n_components=self.n_components) - self.pca.fit(vectors) - - def _pca_preserving_perturbation(self, vector): - # TODO: Perturb in complement of principal subspace - # This preserves main semantic structure - pass - -See research papers: -- Duchi et al. (2013): "Local Privacy and Statistical Minimax Rates" -- Hardt & Talwar (2010): "Geometry of Differential Privacy" -""" diff --git a/packages/sage-libs/src/sage/libs/privacy/unlearning/evaluation/__init__.py b/packages/sage-libs/src/sage/libs/privacy/unlearning/evaluation/__init__.py deleted file mode 100644 index d450019b78..0000000000 --- a/packages/sage-libs/src/sage/libs/privacy/unlearning/evaluation/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -""" -Evaluation Package -================== - -Tools for evaluating unlearning quality, privacy, and utility. -""" - -from .metrics import UnlearningMetrics - -__all__ = ["UnlearningMetrics"] diff --git a/packages/sage-libs/src/sage/libs/privacy/unlearning/evaluation/metrics.py b/packages/sage-libs/src/sage/libs/privacy/unlearning/evaluation/metrics.py deleted file mode 100644 index a93542fa77..0000000000 --- a/packages/sage-libs/src/sage/libs/privacy/unlearning/evaluation/metrics.py +++ /dev/null @@ -1,194 +0,0 @@ -""" -Evaluation Metrics for Unlearning -================================== - -Implements metrics to evaluate unlearning quality. - -**STUDENT RESEARCH POINT**: Design better metrics! - -Key questions: -1. How do we measure "completeness" of unlearning? -2. How do we quantify utility degradation on retained data? -3. How do we balance privacy and utility? -""" - -import numpy as np - - -class UnlearningMetrics: - """ - Metrics for evaluating machine unlearning. - - **STUDENT TODO**: Implement comprehensive evaluation metrics! - - Research goals: - - Design metrics that capture real-world unlearning requirements - - Develop automated testing for verification - - Create benchmarks for comparing algorithms - """ - - @staticmethod - def residual_recall_rate( - forgotten_vectors: np.ndarray, - database_vectors: np.ndarray, - query_vector: np.ndarray, - k: int = 10, - ) -> float: - """ - Measure how often forgotten vectors still appear in top-k results. - - **STUDENT RESEARCH POINT**: This is a key metric! - - Residual Recall Rate (RRR): Probability that a forgotten vector - appears in top-k retrieval results. - - Goal: RRR should be close to 0 after unlearning. - - Args: - forgotten_vectors: Vectors that should have been forgotten - database_vectors: Current vectors in database (after unlearning) - query_vector: Query to test retrieval - k: Number of top results to check - - Returns: - Fraction of forgotten vectors in top-k (lower is better) - - Research Ideas: - - Test with multiple queries - - Weight by similarity scores - - Measure expected rank of forgotten items - """ - # TODO: Implement this properly - # Placeholder: Random baseline - return np.random.rand() - - @staticmethod - def retention_stability( - retained_vectors_before: np.ndarray, - retained_vectors_after: np.ndarray, - test_queries: np.ndarray, - k: int = 10, - ) -> float: - """ - Measure how much retained data performance degrades. - - **STUDENT RESEARCH POINT**: Critical for utility measurement! - - Retention Stability (RS): How well we preserve retrieval quality - on data that should NOT be forgotten. - - Goal: RS should be close to 1 (no degradation). - - Args: - retained_vectors_before: Retained vectors before unlearning - retained_vectors_after: Retained vectors after unlearning (compensated) - test_queries: Queries to test retrieval - k: Number of top results - - Returns: - Similarity between before/after rankings (higher is better) - - Research Ideas: - - Use ranking correlation metrics (Kendall's tau, NDCG) - - Measure retrieval precision/recall - - Test on task-specific metrics (QA accuracy, etc.) - """ - # TODO: Implement this properly - # Placeholder: Random baseline - return np.random.rand() - - @staticmethod - def privacy_utility_tradeoff( - epsilon: float, delta: float, utility_metric: float - ) -> dict[str, float]: - """ - Compute privacy-utility trade-off metrics. - - **STUDENT RESEARCH POINT**: Characterize the Pareto frontier! - - Goal: Find the optimal balance between privacy and utility. - - Args: - epsilon: Privacy parameter - delta: Failure probability - utility_metric: Measure of utility (e.g., accuracy, F1, NDCG) - - Returns: - Dictionary with trade-off metrics - - Research Ideas: - - Plot privacy-utility curves - - Find Pareto-optimal operating points - - Develop adaptive algorithms that optimize this trade-off - """ - return { - "epsilon": epsilon, - "delta": delta, - "utility": utility_metric, - "privacy_loss": epsilon, # TODO: Better privacy loss metric - "utility_loss": 1 - utility_metric, # TODO: Relative to baseline - } - - -# ============================================================================ -# STUDENT RESEARCH EXTENSION -# ============================================================================ -""" -TODO for Students - Evaluation Framework: ------------------------------------------ - -1. **Comprehensive Metrics Suite** (Medium difficulty): - Implement all standard unlearning metrics: - - class ComprehensiveMetrics: - def compute_all_metrics(self, ...): - return { - 'residual_recall_rate': self.rrr(...), - 'retention_stability': self.rs(...), - 'membership_inference_advantage': self.mia(...), - 'privacy_loss_empirical': self.privacy_loss(...), - 'utility_accuracy': self.utility(...), - } - -2. **Verification Tests** (Hard difficulty): - Implement automated verification that unlearning succeeded: - - - Membership inference attacks (verify non-membership) - - Model inversion attacks (verify no reconstruction) - - Statistical tests (verify distribution changed) - - class UnlearningVerifier: - def verify_unlearning(self, model_before, model_after, forgotten_data): - # Run membership inference attack - # Run reconstruction attack - # Perform statistical tests - pass - -3. **Benchmark Suite** (Research-level): - Create standardized benchmarks for comparing algorithms: - - class UnlearningBenchmark: - def __init__(self, dataset, unlearning_algorithm): - self.dataset = dataset - self.algorithm = unlearning_algorithm - - def run_benchmark(self): - # Test on multiple forget scenarios - # Measure privacy, utility, efficiency - # Generate comparison plots - pass - -Research papers for metrics: ----------------------------- -- Bourtoule et al. (2021): "Machine Unlearning" (SISA) -- Guo et al. (2019): "Certified Data Removal" -- Sekhari et al. (2021): "Remember What You Want to Forget" -- Chundawat et al. (2023): "Zero-Shot Machine Unlearning" - -Expected outputs: ------------------ -1. Comprehensive evaluation code -2. Benchmark results on standard datasets -3. Analysis of privacy-utility trade-offs -4. Comparison with baseline methods -""" diff --git a/packages/sage-libs/src/sage/libs/py.typed b/packages/sage-libs/src/sage/libs/py.typed deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/packages/sage-libs/src/sage/libs/rag/__init__.py b/packages/sage-libs/src/sage/libs/rag/__init__.py deleted file mode 100644 index dca855bb4f..0000000000 --- a/packages/sage-libs/src/sage/libs/rag/__init__.py +++ /dev/null @@ -1,155 +0,0 @@ -"""RAG (Retrieval-Augmented Generation) module for SAGE. - -This module provides: -1. RAG interface layer (abstract base classes and factory) -2. RAG-specific data types for pipeline interoperability -3. Built-in document loaders and chunkers - -Concrete implementations are provided by external packages (e.g., isage-rag). - -Usage: - # Interface layer - from sage.libs.rag.interface import ( - DocumentLoader, TextChunker, Retriever, Reranker, QueryRewriter, RAGPipeline, - create_loader, create_retriever, create_query_rewriter, - ) - - # RAG-specific types (for middleware operators) - from sage.libs.rag.types import ( - RAGDocument, RAGQuery, RAGResponse, RAGInput, RAGOutput, - create_rag_response, ensure_rag_response, extract_query, extract_results, - ) - - # Built-in utilities - from sage.libs.rag.document_loaders import TextLoader, PDFLoader, LoaderFactory - from sage.libs.rag.chunk import CharacterSplitter, SentenceTransformersTokenTextSplitter -""" - -# Interface layer -from .interface import ( - # Data types - Chunk, - Document, - # Base classes - DocumentLoader, - QueryRewriter, - RAGPipeline, - # Exception - RAGRegistryError, - Reranker, - RetrievalResult, - Retriever, - TextChunker, - # Chunker registry - create_chunker, - # Loader registry - create_loader, - # Pipeline registry - create_pipeline, - # QueryRewriter registry - create_query_rewriter, - # Reranker registry - create_reranker, - # Retriever registry - create_retriever, - register_chunker, - register_loader, - register_pipeline, - register_query_rewriter, - register_reranker, - register_retriever, - registered_chunkers, - registered_loaders, - registered_pipelines, - registered_query_rewriters, - registered_rerankers, - registered_retrievers, -) - -__all__ = [ - # Data types - "Document", - "Chunk", - "RetrievalResult", - # Base classes - "DocumentLoader", - "TextChunker", - "Retriever", - "Reranker", - "QueryRewriter", - "RAGPipeline", - # Loader registry - "register_loader", - "create_loader", - "registered_loaders", - # Chunker registry - "register_chunker", - "create_chunker", - "registered_chunkers", - # Retriever registry - "register_retriever", - "create_retriever", - "registered_retrievers", - # Reranker registry - "register_reranker", - "create_reranker", - "registered_rerankers", - # QueryRewriter registry - "register_query_rewriter", - "create_query_rewriter", - "registered_query_rewriters", - # Pipeline registry - "register_pipeline", - "create_pipeline", - "registered_pipelines", - # Exception - "RAGRegistryError", - # RAG-specific types (from types module) - "RAGDocument", - "RAGQuery", - "RAGResponse", - "RAGInput", - "RAGOutput", - "create_rag_response", - "ensure_rag_response", - "extract_query", - "extract_results", - # Built-in loaders (from document_loaders module) - "TextLoader", - "PDFLoader", - "DocxLoader", - "DocLoader", - "MarkdownLoader", - "LoaderFactory", - # Built-in chunkers (from chunk module) - "CharacterSplitter", - "SentenceTransformersTokenTextSplitter", -] - -# RAG-specific types for pipeline interoperability -# Built-in chunkers -from .chunk import ( - CharacterSplitter, - SentenceTransformersTokenTextSplitter, -) - -# Built-in document loaders -from .document_loaders import ( - DocLoader, - DocxLoader, - LoaderFactory, - MarkdownLoader, - PDFLoader, - TextLoader, -) -from .types import ( - RAGDocument, - RAGInput, - RAGOutput, - RAGQuery, - RAGResponse, - create_rag_response, - ensure_rag_response, - extract_query, - extract_results, -) diff --git a/packages/sage-libs/src/sage/libs/rag/chunk.py b/packages/sage-libs/src/sage/libs/rag/chunk.py deleted file mode 100644 index 199b5745b5..0000000000 --- a/packages/sage-libs/src/sage/libs/rag/chunk.py +++ /dev/null @@ -1,143 +0,0 @@ -"""Text chunking utilities for RAG pipelines. - -This module provides text splitter implementations that can be used -to split documents into smaller chunks for embedding and retrieval. - -This is a pure algorithm module (L3) - no dependencies on middleware or -external services. - -Note: SentenceTransformersTokenTextSplitter requires sentence-transformers. - Install with: pip install isage-libs[llm] -""" - -from __future__ import annotations - -from typing import TYPE_CHECKING - -# Lazy imports for heavy dependencies -if TYPE_CHECKING: - pass - - -class CharacterSplitter: - """ - A text splitter that divides text into overlapping chunks by characters. - - This is a pure algorithm class (L3) that doesn't depend on any SAGE operators. - For use as a SAGE operator, wrap this class in sage-middleware. - - Config: - - chunk_size: Number of characters per chunk (default: 512). - - overlap: Number of overlapping characters (default: 128). - - separator: Optional separator for splitting (default: None, splits by character). - """ - - def __init__(self, chunk_size: int = 512, overlap: int = 128, separator: str | None = None): - self.chunk_size = chunk_size - self.overlap = overlap - self.separator = separator - - def split(self, text: str) -> list[str]: - """ - Split text into chunks. - - Args: - text: The text to split - - Returns: - List of text chunks - """ - if self.separator: - return [chunk for chunk in text.split(self.separator) if chunk.strip()] - - # Character-level split - tokens = list(text) - chunks = [] - start = 0 - if not tokens: - return [""] - while start < len(tokens): - end = start + self.chunk_size - chunk = tokens[start:end] - chunks.append("".join(chunk)) - next_start = start + self.chunk_size - self.overlap - if next_start <= start: - next_start = start + 1 - start = next_start - return chunks - - -class SentenceTransformersTokenTextSplitter: - """ - A text splitter that divides text into token-based chunks using SentenceTransformer. - - This is a pure algorithm class (L3) that doesn't depend on any SAGE operators. - For use as a SAGE operator, wrap this class in sage-middleware. - - Config: - - chunk_size: Number of tokens per chunk (default: 512). - - chunk_overlap: Number of overlapping tokens (default: 50). - - model_name: SentenceTransformer model name (default: "sentence-transformers/all-mpnet-base-v2"). - - Note: Requires sentence-transformers. Install with: pip install isage-libs[llm] - """ - - def __init__( - self, - model_name: str = "sentence-transformers/all-mpnet-base-v2", - chunk_size: int = 512, - chunk_overlap: int = 50, - ) -> None: - self.model_name = model_name - self.chunk_size = chunk_size - self.chunk_overlap = chunk_overlap - - try: - # Lazy import heavy dependencies - from sentence_transformers import SentenceTransformer - from transformers import AutoTokenizer - - # Load the SentenceTransformer model - self._model = SentenceTransformer(self.model_name) - # Use AutoTokenizer for transformer-based tokenization - self.tokenizer = AutoTokenizer.from_pretrained(self.model_name) - except ImportError as e: - raise ImportError( - "Could not import sentence_transformers or transformers python packages. " - "Please install them with `pip install isage-libs[llm]` or " - "`pip install sentence-transformers transformers`." - ) from e - except Exception as e: - raise RuntimeError(f"Error while loading model or tokenizer: {e}") from e - - if self.chunk_overlap >= self.chunk_size: - raise ValueError("Chunk overlap must be less than chunk size.") - if self.chunk_size <= 0: - raise ValueError("Chunk size must be greater than 0.") - - def split(self, text: str) -> list[str]: - """ - Split text into token-based chunks. - - Args: - text: The text to split - - Returns: - List of token-based text chunks - """ - input_ids = self.tokenizer.encode(text, truncation=True, padding=False) - splits: list[str] = [] - start_idx = 0 - - while start_idx < len(input_ids): - cur_idx = min(start_idx + self.chunk_size, len(input_ids)) - chunk_ids = input_ids[start_idx:cur_idx] - splits.append(self.tokenizer.decode(chunk_ids, skip_special_tokens=True)) - start_idx = cur_idx - self.chunk_overlap - if cur_idx == len(input_ids): - break - - return splits - - -__all__ = ["CharacterSplitter", "SentenceTransformersTokenTextSplitter"] diff --git a/packages/sage-libs/src/sage/libs/rag/document_loaders.py b/packages/sage-libs/src/sage/libs/rag/document_loaders.py deleted file mode 100644 index c891ef2cd8..0000000000 --- a/packages/sage-libs/src/sage/libs/rag/document_loaders.py +++ /dev/null @@ -1,157 +0,0 @@ -""" -Document loaders for RAG pipelines. - -SAGE RAG - Document loading utilities for various file formats. - -This is a pure algorithm module (L3) - no dependencies on middleware or -external services. These are simple utilities for loading documents. -""" - -import os -from pathlib import Path -from typing import Any - - -class TextLoader: - """Load plain text files.""" - - def __init__(self, filepath: str, encoding: str = "utf-8", chunk_separator: str | None = None): - self.filepath = filepath - self.encoding = encoding - self.chunk_separator = chunk_separator - - def load(self) -> dict[str, Any]: - if not os.path.exists(self.filepath): - raise FileNotFoundError(f"File not found: {self.filepath}") - with open(self.filepath, encoding=self.encoding) as f: - text = f.read() - return {"content": text, "metadata": {"source": self.filepath, "type": "txt"}} - - -class PDFLoader: - """Load PDF documents using PyPDF2.""" - - def __init__(self, filepath: str): - self.filepath = filepath - - def load(self) -> dict[str, Any]: - try: - from PyPDF2 import PdfReader - except ImportError: - raise ImportError("Please install PyPDF2: pip install PyPDF2") - - reader = PdfReader(self.filepath) - text = "" - for page in reader.pages: - text += page.extract_text() - return { - "content": text, - "metadata": { - "source": self.filepath, - "type": "pdf", - "pages": len(reader.pages), - }, - } - - -class DocxLoader: - """Load Word documents (.docx).""" - - def __init__(self, filepath: str): - self.filepath = filepath - - def load(self) -> dict[str, Any]: - try: - import docx - except ImportError: - raise ImportError("Please install python-docx: pip install python-docx") - if not os.path.exists(self.filepath): - raise FileNotFoundError(f"File not found: {self.filepath}") - doc = docx.Document(self.filepath) - text = "\n".join([para.text for para in doc.paragraphs]) - return {"content": text, "metadata": {"source": self.filepath, "type": "docx"}} - - -class DocLoader: - """ - Load legacy Word documents (.doc). - - Note: Only available on Windows; Linux/Mac users should convert to .docx first. - """ - - def __init__(self, filepath: str): - self.filepath = filepath - - def load(self) -> dict[str, Any]: - try: - import win32com.client # type: ignore[import-untyped] - except ImportError: - raise ImportError("Please install pywin32 (Windows only): pip install pywin32") - if not os.path.exists(self.filepath): - raise FileNotFoundError(f"File not found: {self.filepath}") - word = win32com.client.Dispatch("Word.Application") - word.Visible = False - doc = word.Documents.Open(str(Path(self.filepath).resolve())) - text = doc.Content.Text - doc.Close() - word.Quit() - return {"content": text, "metadata": {"source": self.filepath, "type": "doc"}} - - -class MarkdownLoader: - """ - Load Markdown files, preserving original text. - - Optional: Can be extended to integrate markdown2/mistune for plain text conversion. - """ - - def __init__(self, filepath: str, encoding: str = "utf-8"): - self.filepath = filepath - self.encoding = encoding - - def load(self) -> dict[str, Any]: - if not os.path.exists(self.filepath): - raise FileNotFoundError(f"File not found: {self.filepath}") - with open(self.filepath, encoding=self.encoding) as f: - text = f.read() - return {"content": text, "metadata": {"source": self.filepath, "type": "md"}} - - -class LoaderFactory: - """ - Factory class that selects the appropriate loader based on file extension. - - Usage: - doc = LoaderFactory.load("examples/data/qa_knowledge_base.txt") - print(doc["content"]) - """ - - _loader_map: dict[ - str, type[TextLoader | PDFLoader | DocxLoader | DocLoader | MarkdownLoader] - ] = { - ".txt": TextLoader, - ".pdf": PDFLoader, - ".docx": DocxLoader, - ".doc": DocLoader, - ".md": MarkdownLoader, - ".markdown": MarkdownLoader, - } - - @classmethod - def load(cls, filepath: str) -> dict[str, Any]: - ext = Path(filepath).suffix.lower() - loader_cls = cls._loader_map.get(ext) - if loader_cls is None: - raise ValueError(f"Unsupported file extension: {ext}") - loader = loader_cls(filepath) - return loader.load() - - -__all__ = [ - "TextLoader", - "PDFLoader", - "DocxLoader", - "DocLoader", - "MarkdownLoader", - "LoaderFactory", -] diff --git a/packages/sage-libs/src/sage/libs/rag/interface/__init__.py b/packages/sage-libs/src/sage/libs/rag/interface/__init__.py deleted file mode 100644 index e53be14087..0000000000 --- a/packages/sage-libs/src/sage/libs/rag/interface/__init__.py +++ /dev/null @@ -1,113 +0,0 @@ -"""RAG (Retrieval-Augmented Generation) interface layer for SAGE. - -This module provides abstract interfaces for RAG components. -Concrete implementations are provided by external packages (e.g., isage-rag). - -Architecture: - - base.py: Abstract base classes (DocumentLoader, TextChunker, Retriever, etc.) - - factory.py: Registry and factory functions for each component type - - External packages register their implementations at import time - -Usage: - # Option 1: Direct instantiation (if you know the implementation) - from isage_rag import PDFLoader, FAISSRetriever - loader = PDFLoader() - retriever = FAISSRetriever(dimension=768) - - # Option 2: Factory pattern (more flexible) - from sage.libs.rag.interface import create_loader, create_retriever - loader = create_loader("pdf") - retriever = create_retriever("faiss", dimension=768) - - # Use components - document = loader.load("document.pdf") - results = retriever.retrieve("query text", top_k=5) -""" - -# Base classes and data types -from .base import ( - Chunk, - Document, - DocumentLoader, - QueryRewriter, - RAGPipeline, - Reranker, - RetrievalResult, - Retriever, - TextChunker, -) - -# Factory functions -from .factory import ( - RAGRegistryError, - create_chunker, - create_loader, - create_pipeline, - create_query_rewriter, - create_reranker, - create_retriever, - register_chunker, - register_loader, - register_pipeline, - register_query_rewriter, - register_reranker, - register_retriever, - registered_chunkers, - registered_loaders, - registered_pipelines, - registered_query_rewriters, - registered_rerankers, - registered_retrievers, - unregister_chunker, - unregister_loader, - unregister_pipeline, - unregister_query_rewriter, - unregister_reranker, - unregister_retriever, -) - -__all__ = [ - # Data types - "Document", - "Chunk", - "RetrievalResult", - # Base classes - "DocumentLoader", - "TextChunker", - "Retriever", - "Reranker", - "QueryRewriter", - "RAGPipeline", - # Loader registry - "register_loader", - "create_loader", - "registered_loaders", - "unregister_loader", - # Chunker registry - "register_chunker", - "create_chunker", - "registered_chunkers", - "unregister_chunker", - # Retriever registry - "register_retriever", - "create_retriever", - "registered_retrievers", - "unregister_retriever", - # Reranker registry - "register_reranker", - "create_reranker", - "registered_rerankers", - "unregister_reranker", - # QueryRewriter registry - "register_query_rewriter", - "create_query_rewriter", - "registered_query_rewriters", - "unregister_query_rewriter", - # Pipeline registry - "register_pipeline", - "create_pipeline", - "registered_pipelines", - "unregister_pipeline", - # Exception - "RAGRegistryError", -] diff --git a/packages/sage-libs/src/sage/libs/rag/interface/base.py b/packages/sage-libs/src/sage/libs/rag/interface/base.py deleted file mode 100644 index 55fd3d153b..0000000000 --- a/packages/sage-libs/src/sage/libs/rag/interface/base.py +++ /dev/null @@ -1,383 +0,0 @@ -"""Abstract base classes for RAG components. - -This module defines the core abstractions for Retrieval-Augmented Generation: -- DocumentLoader: Load and parse documents from various sources -- TextChunker: Split text into manageable chunks -- Retriever: Retrieve relevant documents/chunks -- Reranker: Rerank retrieved results for better relevance -- RAGPipeline: End-to-end RAG workflow orchestration - -These interfaces enable pluggable implementations from external packages. -""" - -from abc import ABC, abstractmethod -from dataclasses import dataclass -from typing import Any - - -@dataclass -class Document: - """A document with content and metadata.""" - - content: str - metadata: dict[str, Any] - - def __post_init__(self) -> None: - """Validate document fields.""" - if not isinstance(self.content, str): - raise TypeError("content must be a string") - if not isinstance(self.metadata, dict): - raise TypeError("metadata must be a dict") - - -@dataclass -class Chunk: - """A text chunk with position information.""" - - text: str - start_pos: int - end_pos: int - metadata: dict[str, Any] - - def __post_init__(self) -> None: - """Validate chunk fields.""" - if not isinstance(self.text, str): - raise TypeError("text must be a string") - if self.start_pos < 0 or self.end_pos < self.start_pos: - raise ValueError("Invalid chunk positions") - - -@dataclass -class RetrievalResult: - """A retrieval result with document and score.""" - - document: Document - score: float - rank: int = 0 - - def __post_init__(self) -> None: - """Validate result fields.""" - if not isinstance(self.document, Document): - raise TypeError("document must be a Document instance") - if not isinstance(self.score, (int, float)): - raise TypeError("score must be numeric") - - -# ======================================== -# Document Loader Interface -# ======================================== - - -class DocumentLoader(ABC): - """Abstract base class for document loaders. - - Implementations should support various file formats: - - Text files (.txt, .md, .json) - - PDFs (.pdf) - - Word documents (.docx, .doc) - - Web pages (HTML, URL) - - Structured data (CSV, Excel) - """ - - @abstractmethod - def load(self, source: str, **kwargs: Any) -> Document: - """Load a single document from a source. - - Args: - source: File path, URL, or identifier - **kwargs: Loader-specific options (encoding, page_range, etc.) - - Returns: - Loaded document with content and metadata - - Raises: - FileNotFoundError: If source doesn't exist - ValueError: If source format is unsupported - """ - pass - - @abstractmethod - def load_batch(self, sources: list[str], **kwargs: Any) -> list[Document]: - """Load multiple documents in batch. - - Args: - sources: List of file paths, URLs, or identifiers - **kwargs: Loader-specific options - - Returns: - List of loaded documents - """ - pass - - @abstractmethod - def supported_formats(self) -> list[str]: - """Get list of supported file formats. - - Returns: - List of file extensions (e.g., [".txt", ".pdf", ".docx"]) - """ - pass - - -# ======================================== -# Text Chunker Interface -# ======================================== - - -class TextChunker(ABC): - """Abstract base class for text chunking strategies. - - Implementations can use various chunking methods: - - Character-based splitting - - Token-based splitting (using tokenizers) - - Sentence-based splitting - - Semantic chunking (paragraph boundaries) - - Sliding window with overlap - """ - - @abstractmethod - def chunk(self, text: str, **kwargs: Any) -> list[Chunk]: - """Split text into chunks. - - Args: - text: Input text to chunk - **kwargs: Chunker-specific options (chunk_size, overlap, etc.) - - Returns: - List of text chunks with position information - """ - pass - - @abstractmethod - def chunk_document(self, document: Document, **kwargs: Any) -> list[Chunk]: - """Chunk a document while preserving metadata. - - Args: - document: Document to chunk - **kwargs: Chunker-specific options - - Returns: - List of chunks, each inheriting document metadata - """ - pass - - @abstractmethod - def get_chunk_size(self) -> int: - """Get the configured chunk size. - - Returns: - Chunk size (in characters or tokens depending on implementation) - """ - pass - - -# ======================================== -# Retriever Interface -# ======================================== - - -class Retriever(ABC): - """Abstract base class for retrieval strategies. - - Implementations can use various retrieval methods: - - Vector search (dense retrieval) - - Keyword search (BM25, TF-IDF) - - Hybrid search (vector + keyword) - - Graph-based retrieval - """ - - @abstractmethod - def retrieve(self, query: str, top_k: int = 10, **kwargs: Any) -> list[RetrievalResult]: - """Retrieve relevant documents for a query. - - Args: - query: Search query - top_k: Number of results to return - **kwargs: Retriever-specific options (filters, boost, etc.) - - Returns: - List of retrieval results ranked by relevance - """ - pass - - @abstractmethod - def add_documents(self, documents: list[Document]) -> None: - """Add documents to the retrieval index. - - Args: - documents: Documents to index - """ - pass - - @abstractmethod - def delete_documents(self, doc_ids: list[str]) -> None: - """Delete documents from the index. - - Args: - doc_ids: List of document IDs to delete - """ - pass - - -# ======================================== -# Reranker Interface -# ======================================== - - -class Reranker(ABC): - """Abstract base class for reranking strategies. - - Rerankers refine retrieval results using: - - Cross-encoder models - - LLM-based relevance scoring - - Feature-based ranking (diversity, recency) - """ - - @abstractmethod - def rerank( - self, query: str, results: list[RetrievalResult], top_k: int = 10, **kwargs: Any - ) -> list[RetrievalResult]: - """Rerank retrieval results. - - Args: - query: Original search query - results: Initial retrieval results - top_k: Number of results to return after reranking - **kwargs: Reranker-specific options - - Returns: - Reranked results with updated scores and ranks - """ - pass - - -# ======================================== -# Query Rewriter Interface -# ======================================== - - -class QueryRewriter(ABC): - """Abstract base class for query rewriting. - - Query rewriting improves retrieval by transforming user queries: - - Query expansion: Add synonyms and related terms - - Query decomposition: Break complex queries into sub-queries - - Hypothetical Document Embeddings (HyDE): Generate hypothetical answers - - Step-back prompting: Abstract to more general queries - - Multi-query generation: Create query variants for fusion - """ - - @abstractmethod - def rewrite(self, query: str, **kwargs: Any) -> str: - """Rewrite a single query. - - Args: - query: Original user query - **kwargs: Rewriter-specific options (context, history, etc.) - - Returns: - Rewritten query optimized for retrieval - """ - pass - - @abstractmethod - def rewrite_multi(self, query: str, num_variants: int = 3, **kwargs: Any) -> list[str]: - """Generate multiple query variants. - - Args: - query: Original user query - num_variants: Number of variants to generate - **kwargs: Rewriter-specific options - - Returns: - List of query variants for multi-query retrieval - """ - pass - - @abstractmethod - def decompose(self, query: str, **kwargs: Any) -> list[str]: - """Decompose a complex query into sub-queries. - - Args: - query: Complex user query - **kwargs: Decomposition options - - Returns: - List of simpler sub-queries - """ - pass - - -# ======================================== -# RAG Pipeline Interface -# ======================================== - - -class RAGPipeline(ABC): - """Abstract base class for RAG pipeline orchestration. - - A complete RAG pipeline coordinates: - 1. Document loading and preprocessing - 2. Text chunking - 3. Embedding and indexing - 4. Retrieval (vector, keyword, or hybrid) - 5. Reranking (optional) - 6. Context assembly - 7. LLM generation with context - - This is the top-level abstraction for end-to-end RAG workflows. - """ - - @abstractmethod - def index_documents(self, sources: list[str], **kwargs: Any) -> dict[str, Any]: - """Index documents into the RAG system. - - Args: - sources: Document sources (file paths, URLs, etc.) - **kwargs: Pipeline-specific options - - Returns: - Indexing statistics (num_docs, num_chunks, etc.) - """ - pass - - @abstractmethod - def query(self, query: str, top_k: int = 5, **kwargs: Any) -> dict[str, Any]: - """Query the RAG system. - - Args: - query: User query - top_k: Number of retrieved chunks - **kwargs: Pipeline-specific options (filters, rerank, etc.) - - Returns: - RAG response with: - - answer: Generated answer - - sources: Retrieved source documents - - metadata: Query metadata (latency, scores, etc.) - """ - pass - - @abstractmethod - def configure(self, **config: Any) -> None: - """Configure pipeline components. - - Args: - **config: Configuration options (loader, chunker, retriever, etc.) - """ - pass - - -__all__ = [ - # Data classes - "Document", - "Chunk", - "RetrievalResult", - # Base classes - "DocumentLoader", - "TextChunker", - "Retriever", - "Reranker", - "QueryRewriter", - "RAGPipeline", -] diff --git a/packages/sage-libs/src/sage/libs/rag/interface/factory.py b/packages/sage-libs/src/sage/libs/rag/interface/factory.py deleted file mode 100644 index 97996cefe7..0000000000 --- a/packages/sage-libs/src/sage/libs/rag/interface/factory.py +++ /dev/null @@ -1,431 +0,0 @@ -"""Factory and registry for RAG component implementations. - -This module provides a registry pattern for RAG components. -External packages (like isage-rag) can register their implementations here. - -Example: - # Register implementations - from sage.libs.rag.interface import ( - register_loader, - register_chunker, - register_retriever, - register_pipeline, - ) - register_loader("pdf", PDFLoader) - register_chunker("sentence_transformer", SentenceTransformerChunker) - register_retriever("faiss", FAISSRetriever) - register_pipeline("simple_rag", SimpleRAGPipeline) - - # Create instances - from sage.libs.rag.interface import ( - create_loader, - create_chunker, - create_retriever, - create_pipeline, - ) - loader = create_loader("pdf") - chunker = create_chunker("sentence_transformer", chunk_size=512) - retriever = create_retriever("faiss", dimension=768) - pipeline = create_pipeline("simple_rag") -""" - -from typing import Any - -from .base import DocumentLoader, QueryRewriter, RAGPipeline, Reranker, Retriever, TextChunker - -_LOADER_REGISTRY: dict[str, type[DocumentLoader]] = {} -_CHUNKER_REGISTRY: dict[str, type[TextChunker]] = {} -_RETRIEVER_REGISTRY: dict[str, type[Retriever]] = {} -_RERANKER_REGISTRY: dict[str, type[Reranker]] = {} -_QUERY_REWRITER_REGISTRY: dict[str, type[QueryRewriter]] = {} -_PIPELINE_REGISTRY: dict[str, type[RAGPipeline]] = {} - - -class RAGRegistryError(Exception): - """Error raised when registry operations fail.""" - - pass - - -# ======================================== -# Document Loader Registry -# ======================================== - - -def register_loader(name: str, cls: type[DocumentLoader]) -> None: - """Register a document loader implementation. - - Args: - name: Unique identifier (e.g., "pdf", "docx", "markdown") - cls: Loader class (should inherit from DocumentLoader) - - Raises: - RAGRegistryError: If name already registered - """ - if name in _LOADER_REGISTRY: - raise RAGRegistryError(f"Loader '{name}' already registered") - - if not issubclass(cls, DocumentLoader): - raise TypeError(f"Class must inherit from DocumentLoader, got {cls}") - - _LOADER_REGISTRY[name] = cls - - -def create_loader(name: str, **kwargs: Any) -> DocumentLoader: - """Create a document loader instance by name. - - Args: - name: Name of the registered loader - **kwargs: Arguments to pass to the loader constructor - - Returns: - Instance of the loader - - Raises: - RAGRegistryError: If loader not found - """ - if name not in _LOADER_REGISTRY: - available = ", ".join(_LOADER_REGISTRY.keys()) if _LOADER_REGISTRY else "none" - raise RAGRegistryError( - f"Loader '{name}' not found. Available: {available}. Did you install 'isage-rag'?" - ) - - cls = _LOADER_REGISTRY[name] - return cls(**kwargs) - - -def registered_loaders() -> list[str]: - """Get list of registered loader names.""" - return list(_LOADER_REGISTRY.keys()) - - -# ======================================== -# Text Chunker Registry -# ======================================== - - -def register_chunker(name: str, cls: type[TextChunker]) -> None: - """Register a text chunker implementation. - - Args: - name: Unique identifier (e.g., "character", "token", "semantic") - cls: Chunker class (should inherit from TextChunker) - - Raises: - RAGRegistryError: If name already registered - """ - if name in _CHUNKER_REGISTRY: - raise RAGRegistryError(f"Chunker '{name}' already registered") - - if not issubclass(cls, TextChunker): - raise TypeError(f"Class must inherit from TextChunker, got {cls}") - - _CHUNKER_REGISTRY[name] = cls - - -def create_chunker(name: str, **kwargs: Any) -> TextChunker: - """Create a text chunker instance by name. - - Args: - name: Name of the registered chunker - **kwargs: Arguments to pass to the chunker constructor - - Returns: - Instance of the chunker - - Raises: - RAGRegistryError: If chunker not found - """ - if name not in _CHUNKER_REGISTRY: - available = ", ".join(_CHUNKER_REGISTRY.keys()) if _CHUNKER_REGISTRY else "none" - raise RAGRegistryError( - f"Chunker '{name}' not found. Available: {available}. Did you install 'isage-rag'?" - ) - - cls = _CHUNKER_REGISTRY[name] - return cls(**kwargs) - - -def registered_chunkers() -> list[str]: - """Get list of registered chunker names.""" - return list(_CHUNKER_REGISTRY.keys()) - - -# ======================================== -# Retriever Registry -# ======================================== - - -def register_retriever(name: str, cls: type[Retriever]) -> None: - """Register a retriever implementation. - - Args: - name: Unique identifier (e.g., "faiss", "bm25", "hybrid") - cls: Retriever class (should inherit from Retriever) - - Raises: - RAGRegistryError: If name already registered - """ - if name in _RETRIEVER_REGISTRY: - raise RAGRegistryError(f"Retriever '{name}' already registered") - - if not issubclass(cls, Retriever): - raise TypeError(f"Class must inherit from Retriever, got {cls}") - - _RETRIEVER_REGISTRY[name] = cls - - -def create_retriever(name: str, **kwargs: Any) -> Retriever: - """Create a retriever instance by name. - - Args: - name: Name of the registered retriever - **kwargs: Arguments to pass to the retriever constructor - - Returns: - Instance of the retriever - - Raises: - RAGRegistryError: If retriever not found - """ - if name not in _RETRIEVER_REGISTRY: - available = ", ".join(_RETRIEVER_REGISTRY.keys()) if _RETRIEVER_REGISTRY else "none" - raise RAGRegistryError( - f"Retriever '{name}' not found. Available: {available}. Did you install 'isage-rag'?" - ) - - cls = _RETRIEVER_REGISTRY[name] - return cls(**kwargs) - - -def registered_retrievers() -> list[str]: - """Get list of registered retriever names.""" - return list(_RETRIEVER_REGISTRY.keys()) - - -# ======================================== -# Reranker Registry -# ======================================== - - -def register_reranker(name: str, cls: type[Reranker]) -> None: - """Register a reranker implementation. - - Args: - name: Unique identifier (e.g., "cross_encoder", "llm") - cls: Reranker class (should inherit from Reranker) - - Raises: - RAGRegistryError: If name already registered - """ - if name in _RERANKER_REGISTRY: - raise RAGRegistryError(f"Reranker '{name}' already registered") - - if not issubclass(cls, Reranker): - raise TypeError(f"Class must inherit from Reranker, got {cls}") - - _RERANKER_REGISTRY[name] = cls - - -def create_reranker(name: str, **kwargs: Any) -> Reranker: - """Create a reranker instance by name. - - Args: - name: Name of the registered reranker - **kwargs: Arguments to pass to the reranker constructor - - Returns: - Instance of the reranker - - Raises: - RAGRegistryError: If reranker not found - """ - if name not in _RERANKER_REGISTRY: - available = ", ".join(_RERANKER_REGISTRY.keys()) if _RERANKER_REGISTRY else "none" - raise RAGRegistryError( - f"Reranker '{name}' not found. Available: {available}. Did you install 'isage-rag'?" - ) - - cls = _RERANKER_REGISTRY[name] - return cls(**kwargs) - - -def registered_rerankers() -> list[str]: - """Get list of registered reranker names.""" - return list(_RERANKER_REGISTRY.keys()) - - -# ======================================== -# Query Rewriter Registry -# ======================================== - - -def register_query_rewriter(name: str, cls: type[QueryRewriter]) -> None: - """Register a query rewriter implementation. - - Args: - name: Unique identifier (e.g., "llm", "hyde", "multi_query") - cls: QueryRewriter class (should inherit from QueryRewriter) - - Raises: - RAGRegistryError: If name already registered - """ - if name in _QUERY_REWRITER_REGISTRY: - raise RAGRegistryError(f"QueryRewriter '{name}' already registered") - - if not issubclass(cls, QueryRewriter): - raise TypeError(f"Class must inherit from QueryRewriter, got {cls}") - - _QUERY_REWRITER_REGISTRY[name] = cls - - -def create_query_rewriter(name: str, **kwargs: Any) -> QueryRewriter: - """Create a query rewriter instance by name. - - Args: - name: Name of the registered query rewriter - **kwargs: Arguments to pass to the query rewriter constructor - - Returns: - Instance of the query rewriter - - Raises: - RAGRegistryError: If query rewriter not found - """ - if name not in _QUERY_REWRITER_REGISTRY: - available = ( - ", ".join(_QUERY_REWRITER_REGISTRY.keys()) if _QUERY_REWRITER_REGISTRY else "none" - ) - raise RAGRegistryError( - f"QueryRewriter '{name}' not found. Available: {available}. Did you install 'isage-rag'?" - ) - - cls = _QUERY_REWRITER_REGISTRY[name] - return cls(**kwargs) - - -def registered_query_rewriters() -> list[str]: - """Get list of registered query rewriter names.""" - return list(_QUERY_REWRITER_REGISTRY.keys()) - - -# ======================================== -# RAG Pipeline Registry -# ======================================== - - -def register_pipeline(name: str, cls: type[RAGPipeline]) -> None: - """Register a RAG pipeline implementation. - - Args: - name: Unique identifier (e.g., "simple_rag", "advanced_rag") - cls: Pipeline class (should inherit from RAGPipeline) - - Raises: - RAGRegistryError: If name already registered - """ - if name in _PIPELINE_REGISTRY: - raise RAGRegistryError(f"Pipeline '{name}' already registered") - - if not issubclass(cls, RAGPipeline): - raise TypeError(f"Class must inherit from RAGPipeline, got {cls}") - - _PIPELINE_REGISTRY[name] = cls - - -def create_pipeline(name: str, **kwargs: Any) -> RAGPipeline: - """Create a RAG pipeline instance by name. - - Args: - name: Name of the registered pipeline - **kwargs: Arguments to pass to the pipeline constructor - - Returns: - Instance of the pipeline - - Raises: - RAGRegistryError: If pipeline not found - """ - if name not in _PIPELINE_REGISTRY: - available = ", ".join(_PIPELINE_REGISTRY.keys()) if _PIPELINE_REGISTRY else "none" - raise RAGRegistryError( - f"Pipeline '{name}' not found. Available: {available}. Did you install 'isage-rag'?" - ) - - cls = _PIPELINE_REGISTRY[name] - return cls(**kwargs) - - -def registered_pipelines() -> list[str]: - """Get list of registered pipeline names.""" - return list(_PIPELINE_REGISTRY.keys()) - - -# ======================================== -# Testing Utilities -# ======================================== - - -def unregister_loader(name: str) -> None: - """Unregister a loader (for testing).""" - _LOADER_REGISTRY.pop(name, None) - - -def unregister_chunker(name: str) -> None: - """Unregister a chunker (for testing).""" - _CHUNKER_REGISTRY.pop(name, None) - - -def unregister_retriever(name: str) -> None: - """Unregister a retriever (for testing).""" - _RETRIEVER_REGISTRY.pop(name, None) - - -def unregister_reranker(name: str) -> None: - """Unregister a reranker (for testing).""" - _RERANKER_REGISTRY.pop(name, None) - - -def unregister_query_rewriter(name: str) -> None: - """Unregister a query rewriter (for testing).""" - _QUERY_REWRITER_REGISTRY.pop(name, None) - - -def unregister_pipeline(name: str) -> None: - """Unregister a pipeline (for testing).""" - _PIPELINE_REGISTRY.pop(name, None) - - -__all__ = [ - "RAGRegistryError", - # Loader - "register_loader", - "create_loader", - "registered_loaders", - "unregister_loader", - # Chunker - "register_chunker", - "create_chunker", - "registered_chunkers", - "unregister_chunker", - # Retriever - "register_retriever", - "create_retriever", - "registered_retrievers", - "unregister_retriever", - # Reranker - "register_reranker", - "create_reranker", - "registered_rerankers", - "unregister_reranker", - # QueryRewriter - "register_query_rewriter", - "create_query_rewriter", - "registered_query_rewriters", - "unregister_query_rewriter", - # Pipeline - "register_pipeline", - "create_pipeline", - "registered_pipelines", - "unregister_pipeline", -] diff --git a/packages/sage-libs/src/sage/libs/rag/types.py b/packages/sage-libs/src/sage/libs/rag/types.py deleted file mode 100644 index 962809a48d..0000000000 --- a/packages/sage-libs/src/sage/libs/rag/types.py +++ /dev/null @@ -1,331 +0,0 @@ -""" -RAG 专用数据类型定义 - -基于 sage.common.core.data_types 的基础类型，为 RAG 场景定制的数据结构。 - -继承关系： - BaseDocument (通用基础) → RAGDocument (RAG专用) - BaseQueryResult (通用基础) → RAGQuery/RAGResponse (RAG专用) - -设计原则： -1. 继承通用类型，保持与其他算子的兼容性 -2. 添加 RAG 特定的字段（如 relevance_score, generated 等） -3. 保持向后兼容，支持多种输入格式 -4. 类型安全，支持 IDE 和 Pylance 检查 - -使用示例： - >>> from sage.libs.rag.types import RAGResponse, create_rag_response - >>> - >>> # 算子输出标准格式 - >>> response = create_rag_response( - ... query="什么是机器学习", - ... results=["doc1", "doc2"], - ... generated="机器学习是...", - ... execution_time=1.5 - ... ) -""" - -from typing import Any, Union - -# 导入基础类型 -from sage.common.core import ( - BaseDocument, - BaseQueryResult, - ExtendedQueryResult, - QueryResultInput, -) -from sage.common.core import ( - extract_query as base_extract_query, -) -from sage.common.core import ( - extract_results as base_extract_results, -) - -# ============================================================================ -# RAG 专用文档类型 -# ============================================================================ - - -class RAGDocument(BaseDocument, total=False): - """ - RAG 文档结构 - 扩展基础文档，添加 RAG 特定字段 - - 继承 BaseDocument 的所有字段，添加了 RAG 场景常用的字段。 - - 继承的必需字段： - text: 文档文本内容 - - 继承的可选字段： - id, title, source, score, rank, metadata - - 新增 RAG 专用字段： - contents: 原始完整内容（text 可能是摘要） - relevance_score: RAG 特定的相关性分数 - embedding: 文档的向量嵌入 - chunk_id: 分块ID（用于长文档分块） - references: 引用的其他文档ID列表 - - 示例： - >>> doc: RAGDocument = { - ... "text": "Python是一种高级编程语言...", - ... "title": "Python入门", - ... "relevance_score": 0.92, - ... "source": "textbook.pdf", - ... "chunk_id": 5 - ... } - """ - - contents: str | None # 原始完整内容 - relevance_score: float | None # RAG相关性分数 - embedding: list[float] | None # 向量嵌入 - chunk_id: int | None # 分块ID - references: list[str] | None # 引用列表 - - -# ============================================================================ -# RAG 查询和响应类型 -# ============================================================================ - - -class RAGQuery(ExtendedQueryResult, total=False): - """ - RAG 查询结构 - 扩展基础查询结果，添加 RAG pipeline 相关字段 - - 继承 ExtendedQueryResult，添加了 RAG pipeline 各阶段可能需要的字段。 - 这个类型用于在 RAG pipeline 的各个阶段传递数据。 - - 继承的必需字段： - query: 用户查询 - results: 结果列表 - - 继承的可选字段： - query_id, timestamp, total_count, execution_time, context, metadata - - 新增 RAG 专用字段： - external_corpus: 外部检索的文档 - references: 引用文档列表 - generated: 生成的文本（答案） - refined_docs: 精炼后的文档 - reranked: 重排序标记 - prompt: 使用的提示词模板 - refine_metrics: 精炼阶段的指标 - generate_time: 生成阶段耗时 - - 示例： - >>> query: RAGQuery = { - ... "query": "什么是机器学习", - ... "results": ["doc1", "doc2"], - ... "context": "检索到的上下文...", - ... "generated": "机器学习是...", - ... "execution_time": 1.5 - ... } - """ - - external_corpus: list[str | dict[str, Any]] | None # 外部文档 - references: list[str] | None # 引用列表 - generated: str | None # 生成的答案 - refined_docs: list[str] | None # 精炼后的文档 - reranked: bool | None # 是否经过重排序 - prompt: str | None # 使用的提示词 - refine_metrics: dict[str, Any] | None # 精炼指标 - generate_time: float | None # 生成耗时 - - -class RAGResponse(BaseQueryResult, total=False): - """ - RAG 响应结构 - RAG 算子的标准输出格式 - - 所有 RAG 算子都应该返回这个格式（或其父类型 BaseQueryResult）。 - 这确保了 RAG pipeline 各阶段的数据流一致性。 - - 继承的必需字段： - query: 原始查询 - results: 处理后的结果列表 - - 推荐 RAG 专用字段： - generated: 最终生成的答案（Generator 输出） - context: 使用的上下文（字符串或列表） - execution_time: 执行时间 - metadata: 各阶段的元数据 - - 示例： - >>> response: RAGResponse = { - ... "query": "什么是机器学习", - ... "results": ["doc1", "doc2"], - ... "generated": "机器学习是...", - ... "execution_time": 1.5 - ... } - """ - - generated: str | None # 生成的答案 - context: str | list[str] | None # 上下文 - execution_time: float | None # 执行时间 - metadata: dict[str, Any] | None # 元数据 - - -# ============================================================================ -# 类型别名 - RAG 专用的灵活输入输出 -# ============================================================================ - -# RAG 算子的输入可以是多种格式（向后兼容） -RAGInput = Union[ - RAGQuery, - RAGResponse, - QueryResultInput, # 包含 dict, tuple, list 等 -] - -# RAG 算子的输出应该是标准格式 -RAGOutput = Union[RAGResponse, dict[str, Any]] - - -# ============================================================================ -# 辅助函数 - RAG 专用包装器 -# ============================================================================ - - -def ensure_rag_response(data: RAGInput, default_query: str = "") -> RAGResponse: - """ - 确保数据符合 RAGResponse 格式（RAG 专用） - - 这是 sage.common.core.data_types.ensure_query_result() 的 RAG 专用版本。 - - Args: - data: 输入数据（可以是字典、元组、列表等） - default_query: 当无法提取查询时使用的默认值 - - Returns: - RAGResponse: 标准化的 RAG 响应 - - 示例： - >>> ensure_rag_response(("query", ["a", "b"])) - {'query': 'query', 'results': ['a', 'b']} - - >>> ensure_rag_response({"question": "...", "docs": [...]}) - {'query': '...', 'results': [...]} - """ - # 使用基础函数，然后转换为 RAGResponse - from sage.common.core import ensure_query_result - - base_result = ensure_query_result(data, default_query) - rag_response: RAGResponse = { - "query": base_result["query"], - "results": base_result["results"], - } - - # 如果是字典，保留额外的 RAG 字段 - if isinstance(data, dict): - for key in [ - "generated", - "context", - "execution_time", - "metadata", - "refine_metrics", - "generate_time", - ]: - if key in data: - rag_response[key] = data[key] # type: ignore - - return rag_response - - -def extract_query(data: RAGInput, default: str = "") -> str: - """ - 从任意格式中提取查询字符串（RAG 专用） - - 直接使用基础函数，完全兼容。 - - Args: - data: 输入数据 - default: 默认值 - - Returns: - str: 提取的查询字符串 - - 示例： - >>> extract_query({"query": "test"}) - 'test' - - >>> extract_query(("my query", ["results"])) - 'my query' - """ - return base_extract_query(data, default) - - -def extract_results(data: RAGInput, default: list[Any] | None = None) -> list[Any]: - """ - 从任意格式中提取结果列表（RAG 专用） - - 直接使用基础函数，完全兼容。 - - Args: - data: 输入数据 - default: 默认值 - - Returns: - List[Any]: 提取的结果列表 - - 示例: - >>> extract_results({"query": "test", "results": ["a", "b"]}) - ['a', 'b'] - - >>> extract_results(("query", ["a", "b"])) - ['a', 'b'] - """ - return base_extract_results(data, default) - - -def create_rag_response(query: str, results: list[Any], **kwargs) -> RAGResponse: - """ - 创建标准的 RAGResponse 对象 - - Args: - query: 查询字符串 - results: 结果列表 - **kwargs: 额外的 RAG 字段（如 generated, execution_time, metadata 等） - - Returns: - RAGResponse: 标准化的 RAG 响应对象 - - 示例: - >>> create_rag_response( - ... query="test", - ... results=["a", "b"], - ... generated="answer", - ... execution_time=0.5, - ... metadata={"model": "gpt-4"} - ... ) - {'query': 'test', 'results': ['a', 'b'], 'generated': 'answer', - 'execution_time': 0.5, 'metadata': {'model': 'gpt-4'}} - """ - response: RAGResponse = { - "query": query, - "results": results, - } - - # 添加额外的 RAG 字段 - for key, value in kwargs.items(): - if value is not None: - response[key] = value # type: ignore - - return response - - -# ============================================================================ -# 导出 -# ============================================================================ - - -__all__ = [ - # RAG 专用类型 - "RAGDocument", - "RAGQuery", - "RAGResponse", - # RAG 类型别名 - "RAGInput", - "RAGOutput", - # RAG 辅助函数 - "ensure_rag_response", - "extract_query", - "extract_results", - "create_rag_response", -] diff --git a/packages/sage-libs/src/sage/libs/safety/__init__.py b/packages/sage-libs/src/sage/libs/safety/__init__.py deleted file mode 100644 index 313fbac8b0..0000000000 --- a/packages/sage-libs/src/sage/libs/safety/__init__.py +++ /dev/null @@ -1,60 +0,0 @@ -"""Safety & Guardrails utilities. - -This module provides safety checks and content filtering: -- content_filter: Regex/pattern-based content filters -- pii_scrubber: Simple PII detection and scrubbing -- policy_check: Tool call policy validation -- interface: Abstract interfaces for advanced safety features - -Concrete implementations for advanced features are provided by isage-safety. -""" - -from . import content_filter, interface, pii_scrubber, policy_check - -# Re-export key interfaces for convenience -from .interface import ( - # Base classes - BaseAdversarialDefense, - BaseGuardrail, - BaseJailbreakDetector, - BaseToxicityDetector, - # Data types - JailbreakResult, - # Enums - SafetyAction, - SafetyCategory, - SafetyResult, - # Factories - create_guardrail, - create_jailbreak_detector, - register_guardrail, - register_jailbreak_detector, - registered_guardrails, - registered_jailbreak_detectors, -) - -__all__ = [ - # Submodules - "content_filter", - "pii_scrubber", - "policy_check", - "interface", - # Enums - "SafetyCategory", - "SafetyAction", - # Data types - "SafetyResult", - "JailbreakResult", - # Base classes - "BaseGuardrail", - "BaseJailbreakDetector", - "BaseToxicityDetector", - "BaseAdversarialDefense", - # Factories - "register_guardrail", - "create_guardrail", - "registered_guardrails", - "register_jailbreak_detector", - "create_jailbreak_detector", - "registered_jailbreak_detectors", -] diff --git a/packages/sage-libs/src/sage/libs/safety/content_filter.py b/packages/sage-libs/src/sage/libs/safety/content_filter.py deleted file mode 100644 index b98b33abeb..0000000000 --- a/packages/sage-libs/src/sage/libs/safety/content_filter.py +++ /dev/null @@ -1,97 +0,0 @@ -"""Content filtering utilities.""" - -from __future__ import annotations - -import re -from typing import Pattern - - -class ContentFilter: - """Pattern-based content filter.""" - - def __init__(self, patterns: list[str | Pattern] | None = None): - """Initialize filter with patterns. - - Args: - patterns: List of regex patterns or compiled patterns - """ - self.patterns = [] - if patterns: - for p in patterns: - if isinstance(p, str): - self.patterns.append(re.compile(p, re.IGNORECASE)) - else: - self.patterns.append(p) - - def add_pattern(self, pattern: str | Pattern) -> None: - """Add a filter pattern. - - Args: - pattern: Regex pattern or compiled pattern - """ - if isinstance(pattern, str): - self.patterns.append(re.compile(pattern, re.IGNORECASE)) - else: - self.patterns.append(pattern) - - def contains_violation(self, text: str) -> tuple[bool, list[str]]: - """Check if text contains any violations. - - Args: - text: Text to check - - Returns: - Tuple of (has_violation, matched_patterns) - """ - matches = [] - for pattern in self.patterns: - if pattern.search(text): - matches.append(pattern.pattern) - - return len(matches) > 0, matches - - def filter_text(self, text: str, replacement: str = "[FILTERED]") -> str: - """Filter text by replacing violations. - - Args: - text: Input text - replacement: Replacement string - - Returns: - Filtered text - """ - result = text - for pattern in self.patterns: - result = pattern.sub(replacement, result) - return result - - -# Predefined filter patterns -PROFANITY_PATTERNS = [ - r"\b(fuck|shit|damn|hell|bastard)\b", - # Add more patterns as needed -] - -PERSONAL_ATTACK_PATTERNS = [ - r"you are (stupid|dumb|idiot)", - r"(stupid|dumb|idiot) (person|user)", -] - - -def create_profanity_filter() -> ContentFilter: - """Create a filter for profanity.""" - return ContentFilter(PROFANITY_PATTERNS) - - -def create_personal_attack_filter() -> ContentFilter: - """Create a filter for personal attacks.""" - return ContentFilter(PERSONAL_ATTACK_PATTERNS) - - -__all__ = [ - "ContentFilter", - "PROFANITY_PATTERNS", - "PERSONAL_ATTACK_PATTERNS", - "create_profanity_filter", - "create_personal_attack_filter", -] diff --git a/packages/sage-libs/src/sage/libs/safety/docs/README.md b/packages/sage-libs/src/sage/libs/safety/docs/README.md deleted file mode 100644 index 3100c62be3..0000000000 --- a/packages/sage-libs/src/sage/libs/safety/docs/README.md +++ /dev/null @@ -1,155 +0,0 @@ -# Safety & Guardrails - -**Location**: `sage.libs.safety`\ -**Layer**: L3 (Algorithm Library)\ -**Dependencies**: Pure Python (regex-based) - -## Overview - -This module provides lightweight safety checks and content filtering utilities. These are simple, -regex-based filters with no heavy service coupling or ML model dependencies. - -## Components - -### 1. Content Filtering (`content_filter.py`) - -Pattern-based content filtering: - -- **ContentFilter**: Regex pattern-based filter -- **Predefined Patterns**: Profanity, personal attacks -- **Filter Methods**: Detection and replacement - -**Usage**: - -```python -from sage.libs.safety.content_filter import ContentFilter, create_profanity_filter - -# Custom filter -filter = ContentFilter([r"\b(banned|word)\b"]) -has_violation, matches = filter.contains_violation("This is a banned word") - -# Predefined filters -profanity_filter = create_profanity_filter() -clean_text = profanity_filter.filter_text("This is bad shit", replacement="[FILTERED]") -``` - -### 2. PII Scrubbing (`pii_scrubber.py`) - -Simple PII (Personally Identifiable Information) detection and scrubbing: - -- **PIIScrubber**: Multi-pattern PII detector -- **Predefined Patterns**: Email, phone, SSN, credit card, IP address -- **Quick Helpers**: `scrub_emails`, `scrub_phone_numbers` - -**Usage**: - -```python -from sage.libs.safety.pii_scrubber import PIIScrubber, scrub_emails - -# Full scrubber -scrubber = PIIScrubber() -pii_detected = scrubber.detect_pii("Contact: john@example.com, 555-1234") -clean_text = scrubber.scrub("Contact: john@example.com") - -# Quick helpers -text = "Email me at john@example.com" -scrubbed = scrub_emails(text, replacement="[EMAIL]") -``` - -### 3. Policy Checking (`policy_check.py`) - -Tool call policy validation: - -- **PolicyChecker**: Rule-based policy enforcement -- **PolicyDecision**: ALLOW, DENY, WARN -- **Predefined Rules**: Whitelist, argument validation, rate limiting - -**Usage**: - -```python -from sage.libs.safety.policy_check import ( - PolicyChecker, - create_tool_whitelist_rule, - create_rate_limit_rule, -) - -# Create checker with rules -checker = PolicyChecker() -checker.add_rule("whitelist", create_tool_whitelist_rule({"search", "calculator"})) -checker.add_rule("rate_limit", create_rate_limit_rule(max_calls=10)) - -# Check tool call -tool_call = {"name": "search", "args": {"query": "test"}} -result = checker.check(tool_call) - -if result.decision == PolicyDecision.DENY: - raise PermissionError(result.reason) -``` - -## Design Principles - -1. **Lightweight**: Regex-based, no ML models -1. **No Service Coupling**: Standalone utilities -1. **Composable**: Filters and rules can be combined -1. **Fail Fast**: Explicit policy decisions -1. **Extensible**: Easy to add custom patterns/rules - -## Used By - -- `sage.libs.agentic.agents.runtime` - Tool call validation -- `isagellm.gateway` - Request filtering (independent package) -- Custom applications requiring content safety - -## Limitations - -These are **simple, rule-based utilities** with known limitations: - -1. **Content Filtering**: Regex-based, prone to false positives/negatives -1. **PII Scrubbing**: Pattern matching only, no semantic understanding -1. **Policy Checking**: Rule-based, not ML-driven - -**For production-grade safety**, consider: - -- ML-based content moderation (Azure Content Safety, Perspective API) -- NER-based PII detection (spaCy, AWS Comprehend) -- Context-aware policy engines - -## Future Enhancements - -- Integration hooks for ML-based safety services -- More sophisticated pattern matching (context-aware) -- Safety scoring (confidence levels) -- Audit logging helpers -- Content classification (hate speech, toxicity levels) - -## Migration Notes - -This module consolidates safety utilities previously scattered across: - -- Adhoc content filters in gateway -- Tool validation logic in agent runtime -- Various regex patterns in different modules - -All safety checks now use consistent `PolicyResult` interface. - -## Example: Comprehensive Safety Pipeline - -```python -from sage.libs.safety import content_filter, pii_scrubber, policy_check - -# Content filtering -profanity_filter = content_filter.create_profanity_filter() -has_profanity, _ = profanity_filter.contains_violation(user_input) - -# PII scrubbing -scrubber = pii_scrubber.PIIScrubber() -clean_input = scrubber.scrub(user_input) - -# Policy checking -checker = policy_check.PolicyChecker() -checker.add_rule("whitelist", policy_check.create_tool_whitelist_rule(allowed_tools)) - -result = checker.check(tool_call) -if result.decision == policy_check.PolicyDecision.DENY: - raise PermissionError(f"Tool call denied: {result.reason}") -``` diff --git a/packages/sage-libs/src/sage/libs/safety/interface/__init__.py b/packages/sage-libs/src/sage/libs/safety/interface/__init__.py deleted file mode 100644 index 87ec699619..0000000000 --- a/packages/sage-libs/src/sage/libs/safety/interface/__init__.py +++ /dev/null @@ -1,99 +0,0 @@ -"""Safety interface layer for SAGE. - -This module provides abstract interfaces for safety and guardrail components. -Concrete implementations are provided by external packages (e.g., isage-safety). - -Architecture: - - base.py: Abstract base classes (BaseGuardrail, BaseJailbreakDetector, etc.) - - factory.py: Registry and factory functions - - External packages register their implementations at import time - -Usage: - # Option 1: Direct instantiation (if you know the implementation) - from isage_safety import LLMGuardrail, PatternJailbreakDetector - guardrail = LLMGuardrail(model="gpt-4") - detector = PatternJailbreakDetector() - - # Option 2: Factory pattern (more flexible) - from sage.libs.safety.interface import create_guardrail, create_jailbreak_detector - guardrail = create_guardrail("llm", model="gpt-4") - detector = create_jailbreak_detector("pattern") - - # Check safety - result = guardrail.check(user_input) - if not result.is_safe: - print(f"Blocked: {result.detected_issues}") -""" - -# Base classes and data types -from .base import ( - BaseAdversarialDefense, - BaseGuardrail, - BaseJailbreakDetector, - BaseToxicityDetector, - JailbreakResult, - SafetyAction, - SafetyCategory, - SafetyResult, -) - -# Factory functions -from .factory import ( - SafetyRegistryError, - # Adversarial - create_adversarial_defense, - # Guardrail - create_guardrail, - # Jailbreak - create_jailbreak_detector, - # Toxicity - create_toxicity_detector, - register_adversarial_defense, - register_guardrail, - register_jailbreak_detector, - register_toxicity_detector, - registered_adversarial_defenses, - registered_guardrails, - registered_jailbreak_detectors, - registered_toxicity_detectors, - unregister_adversarial_defense, - unregister_guardrail, - unregister_jailbreak_detector, - unregister_toxicity_detector, -) - -__all__ = [ - # Enums - "SafetyCategory", - "SafetyAction", - # Data types - "SafetyResult", - "JailbreakResult", - # Base classes - "BaseGuardrail", - "BaseJailbreakDetector", - "BaseToxicityDetector", - "BaseAdversarialDefense", - # Guardrail registry - "register_guardrail", - "create_guardrail", - "registered_guardrails", - "unregister_guardrail", - # Jailbreak registry - "register_jailbreak_detector", - "create_jailbreak_detector", - "registered_jailbreak_detectors", - "unregister_jailbreak_detector", - # Toxicity registry - "register_toxicity_detector", - "create_toxicity_detector", - "registered_toxicity_detectors", - "unregister_toxicity_detector", - # Adversarial registry - "register_adversarial_defense", - "create_adversarial_defense", - "registered_adversarial_defenses", - "unregister_adversarial_defense", - # Exception - "SafetyRegistryError", -] diff --git a/packages/sage-libs/src/sage/libs/safety/interface/base.py b/packages/sage-libs/src/sage/libs/safety/interface/base.py deleted file mode 100644 index 0150afc64a..0000000000 --- a/packages/sage-libs/src/sage/libs/safety/interface/base.py +++ /dev/null @@ -1,366 +0,0 @@ -"""Base classes and interfaces for safety. - -This module defines abstract interfaces for safety and guardrails: -- BaseGuardrail: Content safety guardrail base class -- BaseJailbreakDetector: Jailbreak/prompt injection detection -- BaseAdversarialDefense: Adversarial input defense -- BaseToxicityDetector: Toxicity and harmful content detection - -Implementations are provided by the external 'isage-safety' package. -""" - -from abc import ABC, abstractmethod -from dataclasses import dataclass, field -from enum import Enum -from typing import Any, Optional - - -class SafetyCategory(Enum): - """Categories of safety concerns.""" - - # Content safety - TOXICITY = "toxicity" - HATE_SPEECH = "hate_speech" - VIOLENCE = "violence" - SEXUAL = "sexual" - SELF_HARM = "self_harm" - - # Security - JAILBREAK = "jailbreak" - PROMPT_INJECTION = "prompt_injection" - DATA_LEAKAGE = "data_leakage" - - # Privacy - PII_EXPOSURE = "pii_exposure" - SENSITIVE_INFO = "sensitive_info" - - # Misinformation - FACTUAL_ERROR = "factual_error" - HALLUCINATION = "hallucination" - - # Other - POLICY_VIOLATION = "policy_violation" - CUSTOM = "custom" - - -class SafetyAction(Enum): - """Actions to take when safety issues are detected.""" - - ALLOW = "allow" # Allow the content - WARN = "warn" # Allow with warning - MODIFY = "modify" # Modify/filter the content - BLOCK = "block" # Block the content - ESCALATE = "escalate" # Escalate to human review - - -@dataclass -class SafetyResult: - """Result of a safety check.""" - - is_safe: bool - action: SafetyAction = SafetyAction.ALLOW - - # Detection details - category: Optional[SafetyCategory] = None - confidence: float = 0.0 - detected_issues: list[str] = field(default_factory=list) - - # Modified content (if action is MODIFY) - modified_content: Optional[str] = None - - # Metadata - metadata: dict[str, Any] = field(default_factory=dict) - - def __repr__(self) -> str: - return f"SafetyResult(safe={self.is_safe}, action={self.action.value}, confidence={self.confidence:.2f})" - - -@dataclass -class JailbreakResult: - """Result of a jailbreak detection check.""" - - is_jailbreak: bool - confidence: float = 0.0 - - # Attack type - attack_type: Optional[str] = None # "prompt_injection", "role_play", "encoding", etc. - attack_signature: Optional[str] = None - - # Explanation - explanation: Optional[str] = None - - metadata: dict[str, Any] = field(default_factory=dict) - - -class BaseGuardrail(ABC): - """Abstract base class for content safety guardrails. - - Examples of implementations: - - LLMGuardrail: LLM-based content moderation - - ClassifierGuardrail: ML classifier-based moderation - - RuleBasedGuardrail: Pattern/rule-based filtering - - HybridGuardrail: Combined approach - """ - - @property - @abstractmethod - def name(self) -> str: - """Return the guardrail name.""" - pass - - @property - def categories(self) -> list[SafetyCategory]: - """Return the safety categories this guardrail handles.""" - return [SafetyCategory.CUSTOM] - - @abstractmethod - def check( - self, - content: str, - context: Optional[str] = None, - **kwargs: Any, - ) -> SafetyResult: - """Check content for safety issues. - - Args: - content: Content to check (user input or model output) - context: Optional conversation context - **kwargs: Guardrail-specific parameters - - Returns: - SafetyResult with detection status and recommended action - """ - pass - - def check_batch( - self, - contents: list[str], - contexts: Optional[list[str]] = None, - **kwargs: Any, - ) -> list[SafetyResult]: - """Check multiple contents for safety issues. - - Default implementation calls check() for each content. - Override for batch-optimized processing. - - Args: - contents: List of contents to check - contexts: Optional list of contexts - **kwargs: Guardrail-specific parameters - - Returns: - List of SafetyResults - """ - contexts = contexts or [None] * len(contents) - return [ - self.check(content, context, **kwargs) for content, context in zip(contents, contexts) - ] - - def filter( - self, - content: str, - **kwargs: Any, - ) -> tuple[str, SafetyResult]: - """Check and potentially modify content. - - Args: - content: Content to check and filter - **kwargs: Guardrail-specific parameters - - Returns: - Tuple of (filtered_content, SafetyResult) - """ - result = self.check(content, **kwargs) - if result.action == SafetyAction.MODIFY and result.modified_content: - return result.modified_content, result - return content, result - - -class BaseJailbreakDetector(ABC): - """Abstract base class for jailbreak and prompt injection detection. - - Examples of implementations: - - PatternJailbreakDetector: Pattern/regex-based detection - - MLJailbreakDetector: ML model-based detection - - LLMJailbreakDetector: LLM-based detection - - EnsembleJailbreakDetector: Combined approach - """ - - @property - @abstractmethod - def name(self) -> str: - """Return the detector name.""" - pass - - @abstractmethod - def detect( - self, - prompt: str, - system_prompt: Optional[str] = None, - **kwargs: Any, - ) -> JailbreakResult: - """Detect jailbreak attempts in a prompt. - - Args: - prompt: User prompt to analyze - system_prompt: System prompt (to detect prompt injection) - **kwargs: Detector-specific parameters - - Returns: - JailbreakResult with detection status and confidence - """ - pass - - def detect_batch( - self, - prompts: list[str], - system_prompts: Optional[list[str]] = None, - **kwargs: Any, - ) -> list[JailbreakResult]: - """Detect jailbreaks in multiple prompts. - - Default implementation calls detect() for each prompt. - Override for batch-optimized processing. - - Args: - prompts: List of prompts to analyze - system_prompts: Optional list of system prompts - **kwargs: Detector-specific parameters - - Returns: - List of JailbreakResults - """ - system_prompts = system_prompts or [None] * len(prompts) - return [ - self.detect(prompt, system_prompt, **kwargs) - for prompt, system_prompt in zip(prompts, system_prompts) - ] - - def is_jailbreak(self, prompt: str, threshold: float = 0.5, **kwargs: Any) -> bool: - """Quick check if prompt is a jailbreak attempt. - - Args: - prompt: Prompt to check - threshold: Confidence threshold - **kwargs: Detector parameters - - Returns: - True if jailbreak detected with confidence >= threshold - """ - result = self.detect(prompt, **kwargs) - return result.is_jailbreak and result.confidence >= threshold - - -class BaseToxicityDetector(ABC): - """Abstract base class for toxicity detection. - - Examples of implementations: - - PerspectiveDetector: Google Perspective API - - TransformerDetector: Transformer-based toxicity model - - MultilingualDetector: Multilingual toxicity detection - """ - - @property - @abstractmethod - def name(self) -> str: - """Return the detector name.""" - pass - - @abstractmethod - def detect( - self, - text: str, - **kwargs: Any, - ) -> SafetyResult: - """Detect toxicity in text. - - Args: - text: Text to analyze - **kwargs: Detector-specific parameters - - Returns: - SafetyResult with toxicity detection - """ - pass - - def get_scores( - self, - text: str, - **kwargs: Any, - ) -> dict[str, float]: - """Get detailed toxicity scores. - - Args: - text: Text to analyze - **kwargs: Detector parameters - - Returns: - Dictionary mapping categories to scores - """ - result = self.detect(text, **kwargs) - return result.metadata.get("scores", {}) - - -class BaseAdversarialDefense(ABC): - """Abstract base class for adversarial input defense. - - Examples of implementations: - - InputSanitizer: Clean adversarial perturbations - - AdversarialDetector: Detect adversarial inputs - - RobustClassifier: Adversarially trained classifier - """ - - @property - @abstractmethod - def name(self) -> str: - """Return the defense name.""" - pass - - @abstractmethod - def defend( - self, - input_data: Any, - **kwargs: Any, - ) -> tuple[Any, bool]: - """Apply defense to input. - - Args: - input_data: Input to defend (text, embedding, etc.) - **kwargs: Defense-specific parameters - - Returns: - Tuple of (defended_input, was_adversarial) - """ - pass - - def is_adversarial( - self, - input_data: Any, - **kwargs: Any, - ) -> tuple[bool, float]: - """Check if input is adversarial. - - Args: - input_data: Input to check - **kwargs: Defense parameters - - Returns: - Tuple of (is_adversarial, confidence) - """ - _, was_adversarial = self.defend(input_data, **kwargs) - return was_adversarial, 1.0 if was_adversarial else 0.0 - - -__all__ = [ - # Enums - "SafetyCategory", - "SafetyAction", - # Data classes - "SafetyResult", - "JailbreakResult", - # Base classes - "BaseGuardrail", - "BaseJailbreakDetector", - "BaseToxicityDetector", - "BaseAdversarialDefense", -] diff --git a/packages/sage-libs/src/sage/libs/safety/interface/factory.py b/packages/sage-libs/src/sage/libs/safety/interface/factory.py deleted file mode 100644 index 005e83b0be..0000000000 --- a/packages/sage-libs/src/sage/libs/safety/interface/factory.py +++ /dev/null @@ -1,299 +0,0 @@ -"""Factory and registry for safety implementations. - -This module provides a registry pattern for safety components. -External packages (like isage-safety) can register their implementations here. - -Example: - # Register implementations - from sage.libs.safety.interface import ( - register_guardrail, - register_jailbreak_detector, - register_toxicity_detector, - ) - register_guardrail("llm", LLMGuardrail) - register_jailbreak_detector("pattern", PatternJailbreakDetector) - register_toxicity_detector("perspective", PerspectiveDetector) - - # Create instances - from sage.libs.safety.interface import ( - create_guardrail, - create_jailbreak_detector, - create_toxicity_detector, - ) - guardrail = create_guardrail("llm", model="gpt-4") - detector = create_jailbreak_detector("pattern") - toxicity = create_toxicity_detector("perspective") -""" - -from typing import Any - -from .base import ( - BaseAdversarialDefense, - BaseGuardrail, - BaseJailbreakDetector, - BaseToxicityDetector, -) - -_GUARDRAIL_REGISTRY: dict[str, type[BaseGuardrail]] = {} -_JAILBREAK_REGISTRY: dict[str, type[BaseJailbreakDetector]] = {} -_TOXICITY_REGISTRY: dict[str, type[BaseToxicityDetector]] = {} -_ADVERSARIAL_REGISTRY: dict[str, type[BaseAdversarialDefense]] = {} - - -class SafetyRegistryError(Exception): - """Error raised when registry operations fail.""" - - pass - - -# ======================================== -# Guardrail Registry -# ======================================== - - -def register_guardrail(name: str, cls: type[BaseGuardrail]) -> None: - """Register a guardrail implementation. - - Args: - name: Unique identifier (e.g., "llm", "classifier", "rule_based") - cls: Guardrail class (should inherit from BaseGuardrail) - - Raises: - SafetyRegistryError: If name already registered - """ - if name in _GUARDRAIL_REGISTRY: - raise SafetyRegistryError(f"Guardrail '{name}' already registered") - - if not issubclass(cls, BaseGuardrail): - raise TypeError(f"Class must inherit from BaseGuardrail, got {cls}") - - _GUARDRAIL_REGISTRY[name] = cls - - -def create_guardrail(name: str, **kwargs: Any) -> BaseGuardrail: - """Create a guardrail instance by name. - - Args: - name: Name of the registered guardrail - **kwargs: Arguments to pass to the guardrail constructor - - Returns: - Instance of the guardrail - - Raises: - SafetyRegistryError: If guardrail not found - """ - if name not in _GUARDRAIL_REGISTRY: - available = ", ".join(_GUARDRAIL_REGISTRY.keys()) if _GUARDRAIL_REGISTRY else "none" - raise SafetyRegistryError( - f"Guardrail '{name}' not found. Available: {available}. Did you install 'isage-safety'?" - ) - - cls = _GUARDRAIL_REGISTRY[name] - return cls(**kwargs) - - -def registered_guardrails() -> list[str]: - """Get list of registered guardrail names.""" - return list(_GUARDRAIL_REGISTRY.keys()) - - -def unregister_guardrail(name: str) -> None: - """Unregister a guardrail (for testing).""" - _GUARDRAIL_REGISTRY.pop(name, None) - - -# ======================================== -# Jailbreak Detector Registry -# ======================================== - - -def register_jailbreak_detector(name: str, cls: type[BaseJailbreakDetector]) -> None: - """Register a jailbreak detector implementation. - - Args: - name: Unique identifier (e.g., "pattern", "ml", "llm", "ensemble") - cls: Detector class (should inherit from BaseJailbreakDetector) - - Raises: - SafetyRegistryError: If name already registered - """ - if name in _JAILBREAK_REGISTRY: - raise SafetyRegistryError(f"Jailbreak detector '{name}' already registered") - - if not issubclass(cls, BaseJailbreakDetector): - raise TypeError(f"Class must inherit from BaseJailbreakDetector, got {cls}") - - _JAILBREAK_REGISTRY[name] = cls - - -def create_jailbreak_detector(name: str, **kwargs: Any) -> BaseJailbreakDetector: - """Create a jailbreak detector instance by name. - - Args: - name: Name of the registered detector - **kwargs: Arguments to pass to the detector constructor - - Returns: - Instance of the detector - - Raises: - SafetyRegistryError: If detector not found - """ - if name not in _JAILBREAK_REGISTRY: - available = ", ".join(_JAILBREAK_REGISTRY.keys()) if _JAILBREAK_REGISTRY else "none" - raise SafetyRegistryError( - f"Jailbreak detector '{name}' not found. Available: {available}. Did you install 'isage-safety'?" - ) - - cls = _JAILBREAK_REGISTRY[name] - return cls(**kwargs) - - -def registered_jailbreak_detectors() -> list[str]: - """Get list of registered jailbreak detector names.""" - return list(_JAILBREAK_REGISTRY.keys()) - - -def unregister_jailbreak_detector(name: str) -> None: - """Unregister a jailbreak detector (for testing).""" - _JAILBREAK_REGISTRY.pop(name, None) - - -# ======================================== -# Toxicity Detector Registry -# ======================================== - - -def register_toxicity_detector(name: str, cls: type[BaseToxicityDetector]) -> None: - """Register a toxicity detector implementation. - - Args: - name: Unique identifier (e.g., "perspective", "transformer", "multilingual") - cls: Detector class (should inherit from BaseToxicityDetector) - - Raises: - SafetyRegistryError: If name already registered - """ - if name in _TOXICITY_REGISTRY: - raise SafetyRegistryError(f"Toxicity detector '{name}' already registered") - - if not issubclass(cls, BaseToxicityDetector): - raise TypeError(f"Class must inherit from BaseToxicityDetector, got {cls}") - - _TOXICITY_REGISTRY[name] = cls - - -def create_toxicity_detector(name: str, **kwargs: Any) -> BaseToxicityDetector: - """Create a toxicity detector instance by name. - - Args: - name: Name of the registered detector - **kwargs: Arguments to pass to the detector constructor - - Returns: - Instance of the detector - - Raises: - SafetyRegistryError: If detector not found - """ - if name not in _TOXICITY_REGISTRY: - available = ", ".join(_TOXICITY_REGISTRY.keys()) if _TOXICITY_REGISTRY else "none" - raise SafetyRegistryError( - f"Toxicity detector '{name}' not found. Available: {available}. Did you install 'isage-safety'?" - ) - - cls = _TOXICITY_REGISTRY[name] - return cls(**kwargs) - - -def registered_toxicity_detectors() -> list[str]: - """Get list of registered toxicity detector names.""" - return list(_TOXICITY_REGISTRY.keys()) - - -def unregister_toxicity_detector(name: str) -> None: - """Unregister a toxicity detector (for testing).""" - _TOXICITY_REGISTRY.pop(name, None) - - -# ======================================== -# Adversarial Defense Registry -# ======================================== - - -def register_adversarial_defense(name: str, cls: type[BaseAdversarialDefense]) -> None: - """Register an adversarial defense implementation. - - Args: - name: Unique identifier (e.g., "sanitizer", "detector", "robust") - cls: Defense class (should inherit from BaseAdversarialDefense) - - Raises: - SafetyRegistryError: If name already registered - """ - if name in _ADVERSARIAL_REGISTRY: - raise SafetyRegistryError(f"Adversarial defense '{name}' already registered") - - if not issubclass(cls, BaseAdversarialDefense): - raise TypeError(f"Class must inherit from BaseAdversarialDefense, got {cls}") - - _ADVERSARIAL_REGISTRY[name] = cls - - -def create_adversarial_defense(name: str, **kwargs: Any) -> BaseAdversarialDefense: - """Create an adversarial defense instance by name. - - Args: - name: Name of the registered defense - **kwargs: Arguments to pass to the defense constructor - - Returns: - Instance of the defense - - Raises: - SafetyRegistryError: If defense not found - """ - if name not in _ADVERSARIAL_REGISTRY: - available = ", ".join(_ADVERSARIAL_REGISTRY.keys()) if _ADVERSARIAL_REGISTRY else "none" - raise SafetyRegistryError( - f"Adversarial defense '{name}' not found. Available: {available}. Did you install 'isage-safety'?" - ) - - cls = _ADVERSARIAL_REGISTRY[name] - return cls(**kwargs) - - -def registered_adversarial_defenses() -> list[str]: - """Get list of registered adversarial defense names.""" - return list(_ADVERSARIAL_REGISTRY.keys()) - - -def unregister_adversarial_defense(name: str) -> None: - """Unregister an adversarial defense (for testing).""" - _ADVERSARIAL_REGISTRY.pop(name, None) - - -__all__ = [ - "SafetyRegistryError", - # Guardrail - "register_guardrail", - "create_guardrail", - "registered_guardrails", - "unregister_guardrail", - # Jailbreak - "register_jailbreak_detector", - "create_jailbreak_detector", - "registered_jailbreak_detectors", - "unregister_jailbreak_detector", - # Toxicity - "register_toxicity_detector", - "create_toxicity_detector", - "registered_toxicity_detectors", - "unregister_toxicity_detector", - # Adversarial - "register_adversarial_defense", - "create_adversarial_defense", - "registered_adversarial_defenses", - "unregister_adversarial_defense", -] diff --git a/packages/sage-libs/src/sage/libs/safety/pii_scrubber.py b/packages/sage-libs/src/sage/libs/safety/pii_scrubber.py deleted file mode 100644 index 80417350d0..0000000000 --- a/packages/sage-libs/src/sage/libs/safety/pii_scrubber.py +++ /dev/null @@ -1,123 +0,0 @@ -"""PII (Personally Identifiable Information) scrubbing utilities.""" - -from __future__ import annotations - -import re -from typing import Pattern - - -class PIIScrubber: - """Simple PII detection and scrubbing.""" - - # Common PII patterns - EMAIL_PATTERN = re.compile(r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b") - PHONE_PATTERN = re.compile(r"\b(\+?\d{1,3}[-.]?)?\(?\d{3}\)?[-.]?\d{3}[-.]?\d{4}\b") - SSN_PATTERN = re.compile(r"\b\d{3}-\d{2}-\d{4}\b") - CREDIT_CARD_PATTERN = re.compile(r"\b\d{4}[-\s]?\d{4}[-\s]?\d{4}[-\s]?\d{4}\b") - IP_ADDRESS_PATTERN = re.compile(r"\b(?:\d{1,3}\.){3}\d{1,3}\b") - - def __init__(self, custom_patterns: dict[str, Pattern] | None = None): - """Initialize scrubber with optional custom patterns. - - Args: - custom_patterns: Dictionary mapping PII type to regex pattern - """ - self.patterns: dict[str, Pattern] = { - "email": self.EMAIL_PATTERN, - "phone": self.PHONE_PATTERN, - "ssn": self.SSN_PATTERN, - "credit_card": self.CREDIT_CARD_PATTERN, - "ip_address": self.IP_ADDRESS_PATTERN, - } - - if custom_patterns: - self.patterns.update(custom_patterns) - - def detect_pii(self, text: str) -> dict[str, list[str]]: - """Detect PII in text. - - Args: - text: Input text - - Returns: - Dictionary mapping PII type to list of matches - """ - results = {} - - for pii_type, pattern in self.patterns.items(): - matches = pattern.findall(text) - if matches: - results[pii_type] = matches - - return results - - def scrub(self, text: str, replacement: str = "[REDACTED]") -> str: - """Scrub PII from text. - - Args: - text: Input text - replacement: Replacement string - - Returns: - Scrubbed text - """ - result = text - - for pattern in self.patterns.values(): - result = pattern.sub(replacement, result) - - return result - - def scrub_by_type(self, text: str, replacements: dict[str, str] | None = None) -> str: - """Scrub PII with type-specific replacements. - - Args: - text: Input text - replacements: Dictionary mapping PII type to replacement string - - Returns: - Scrubbed text - """ - if replacements is None: - replacements = {} - - result = text - - for pii_type, pattern in self.patterns.items(): - replacement = replacements.get(pii_type, f"[REDACTED_{pii_type.upper()}]") - result = pattern.sub(replacement, result) - - return result - - -def scrub_emails(text: str, replacement: str = "[EMAIL]") -> str: - """Quick helper to scrub email addresses. - - Args: - text: Input text - replacement: Replacement string - - Returns: - Text with emails scrubbed - """ - return PIIScrubber.EMAIL_PATTERN.sub(replacement, text) - - -def scrub_phone_numbers(text: str, replacement: str = "[PHONE]") -> str: - """Quick helper to scrub phone numbers. - - Args: - text: Input text - replacement: Replacement string - - Returns: - Text with phone numbers scrubbed - """ - return PIIScrubber.PHONE_PATTERN.sub(replacement, text) - - -__all__ = [ - "PIIScrubber", - "scrub_emails", - "scrub_phone_numbers", -] diff --git a/packages/sage-libs/src/sage/libs/safety/policy_check.py b/packages/sage-libs/src/sage/libs/safety/policy_check.py deleted file mode 100644 index 581037f2aa..0000000000 --- a/packages/sage-libs/src/sage/libs/safety/policy_check.py +++ /dev/null @@ -1,162 +0,0 @@ -"""Policy checking for tool calls and actions.""" - -from __future__ import annotations - -from dataclasses import dataclass -from enum import Enum -from typing import Any, Callable - - -class PolicyDecision(Enum): - """Policy decision result.""" - - ALLOW = "allow" - DENY = "deny" - WARN = "warn" - - -@dataclass -class PolicyResult: - """Result of policy check.""" - - decision: PolicyDecision - reason: str | None = None - metadata: dict[str, Any] | None = None - - -class PolicyChecker: - """Policy checker for tool calls and actions.""" - - def __init__(self): - self.rules: list[tuple[str, Callable[[dict], PolicyResult]]] = [] - - def add_rule(self, name: str, rule_fn: Callable[[dict], PolicyResult]) -> None: - """Add a policy rule. - - Args: - name: Rule name - rule_fn: Function that takes tool_call dict and returns PolicyResult - """ - self.rules.append((name, rule_fn)) - - def check(self, tool_call: dict[str, Any]) -> PolicyResult: - """Check tool call against all rules. - - Args: - tool_call: Tool call dictionary with 'name', 'args', etc. - - Returns: - PolicyResult (DENY if any rule denies, WARN if any warns, ALLOW otherwise) - """ - warnings = [] - - for rule_name, rule_fn in self.rules: - result = rule_fn(tool_call) - - if result.decision == PolicyDecision.DENY: - return PolicyResult( - decision=PolicyDecision.DENY, - reason=f"Denied by rule '{rule_name}': {result.reason}", - metadata=result.metadata, - ) - elif result.decision == PolicyDecision.WARN: - warnings.append(f"Warning from rule '{rule_name}': {result.reason}") - - if warnings: - return PolicyResult( - decision=PolicyDecision.WARN, - reason="; ".join(warnings), - ) - - return PolicyResult(decision=PolicyDecision.ALLOW) - - -# Predefined policy rules - - -def create_tool_whitelist_rule(allowed_tools: set[str]) -> Callable[[dict], PolicyResult]: - """Create a rule that only allows specific tools. - - Args: - allowed_tools: Set of allowed tool names - - Returns: - Rule function - """ - - def rule(tool_call: dict) -> PolicyResult: - tool_name = tool_call.get("name", "") - if tool_name not in allowed_tools: - return PolicyResult( - decision=PolicyDecision.DENY, - reason=f"Tool '{tool_name}' not in whitelist", - ) - return PolicyResult(decision=PolicyDecision.ALLOW) - - return rule - - -def create_arg_validator_rule( - validators: dict[str, Callable[[Any], bool]], -) -> Callable[[dict], PolicyResult]: - """Create a rule that validates tool arguments. - - Args: - validators: Dictionary mapping arg names to validator functions - - Returns: - Rule function - """ - - def rule(tool_call: dict) -> PolicyResult: - args = tool_call.get("args", {}) - - for arg_name, validator in validators.items(): - if arg_name in args: - if not validator(args[arg_name]): - return PolicyResult( - decision=PolicyDecision.DENY, - reason=f"Argument '{arg_name}' failed validation", - ) - - return PolicyResult(decision=PolicyDecision.ALLOW) - - return rule - - -def create_rate_limit_rule(max_calls: int) -> Callable[[dict], PolicyResult]: - """Create a rule that limits number of tool calls. - - Args: - max_calls: Maximum number of calls allowed - - Returns: - Rule function - """ - call_count = {"count": 0} - - def rule(tool_call: dict) -> PolicyResult: - call_count["count"] += 1 - if call_count["count"] > max_calls: - return PolicyResult( - decision=PolicyDecision.DENY, - reason=f"Rate limit exceeded ({max_calls} calls)", - ) - elif call_count["count"] == max_calls: - return PolicyResult( - decision=PolicyDecision.WARN, - reason=f"Approaching rate limit ({max_calls} calls)", - ) - return PolicyResult(decision=PolicyDecision.ALLOW) - - return rule - - -__all__ = [ - "PolicyDecision", - "PolicyResult", - "PolicyChecker", - "create_tool_whitelist_rule", - "create_arg_validator_rule", - "create_rate_limit_rule", -] diff --git a/packages/sage-libs/tests/agentic/test_agent_runtime.py b/packages/sage-libs/tests/agentic/test_agent_runtime.py deleted file mode 100644 index 362a974e97..0000000000 --- a/packages/sage-libs/tests/agentic/test_agent_runtime.py +++ /dev/null @@ -1,135 +0,0 @@ -from unittest.mock import Mock - -import pytest -from sage_libs.sage_agentic.agents.action.mcp_registry import MCPRegistry -from sage_libs.sage_agentic.agents.profile.profile import BaseProfile - -from sage.middleware.operators.agent.runtime import AgentRuntime - - -@pytest.fixture -def mock_profile(): - profile = Mock(spec=BaseProfile) - profile.render_system_prompt.return_value = "System Prompt" - profile.merged.return_value = profile - return profile - - -@pytest.fixture -def mock_planner(): - # Don't use spec to avoid getting plan_stream attribute - # This forces the runtime to use the fallback non-streaming path - planner = Mock() - # Remove plan_stream so runtime uses planner.plan() instead - del planner.plan_stream - return planner - - -@pytest.fixture -def mock_tools(): - tools = Mock(spec=MCPRegistry) - tools.describe.return_value = { - "test_tool": { - "description": "A test tool", - "input_schema": { - "type": "object", - "properties": {"arg1": {"type": "string"}}, - "required": ["arg1"], - }, - } - } - return tools - - -@pytest.fixture -def agent_runtime(mock_profile, mock_planner, mock_tools): - return AgentRuntime(profile=mock_profile, planner=mock_planner, tools=mock_tools, max_steps=5) - - -def test_step_success(agent_runtime, mock_planner, mock_tools): - """Test successful execution of a plan.""" - # Mock plan - mock_planner.plan.return_value = [ - {"type": "tool", "name": "test_tool", "arguments": {"arg1": "value1"}}, - {"type": "reply", "text": "Done"}, - ] - - # Mock tool execution - mock_tools.call.return_value = "Tool Result" - - result = agent_runtime.step("Do something") - - assert result["reply"] == "Done" - assert len(result["observations"]) == 1 - assert result["observations"][0]["tool"] == "test_tool" - assert result["observations"][0]["ok"] is True - assert result["observations"][0]["result"] == "Tool Result" - - mock_tools.call.assert_called_with("test_tool", {"arg1": "value1"}) - - -def test_step_tool_validation_error(agent_runtime, mock_planner, mock_tools): - """Test tool validation failure (missing required argument).""" - mock_planner.plan.return_value = [ - {"type": "tool", "name": "test_tool", "arguments": {}}, # Missing arg1 - {"type": "reply", "text": "Done"}, - ] - - result = agent_runtime.step("Do something") - - assert len(result["observations"]) == 1 - assert result["observations"][0]["ok"] is False - assert "Missing required fields" in result["observations"][0]["error"] - - # Tool should NOT be called - mock_tools.call.assert_not_called() - - -def test_step_tool_execution_error(agent_runtime, mock_planner, mock_tools): - """Test tool execution failure.""" - mock_planner.plan.return_value = [ - {"type": "tool", "name": "test_tool", "arguments": {"arg1": "value1"}}, - {"type": "reply", "text": "Done"}, - ] - - mock_tools.call.side_effect = Exception("Tool Failed") - - result = agent_runtime.step("Do something") - - assert len(result["observations"]) == 1 - assert result["observations"][0]["ok"] is False - assert "Tool Failed" in result["observations"][0]["error"] - - -def test_execute_str(agent_runtime, mock_planner, mock_tools): - """Test execute with string input.""" - mock_planner.plan.return_value = [{"type": "reply", "text": "Hello"}] - - result = agent_runtime.execute("Hi") - - assert isinstance(result, dict) - assert result["reply"] == "Hello" - - -def test_execute_dict(agent_runtime, mock_planner, mock_tools): - """Test execute with dict input.""" - mock_planner.plan.return_value = [{"type": "reply", "text": "Hello"}] - - result = agent_runtime.execute({"query": "Hi", "max_steps": 3}) - - assert isinstance(result, dict) - assert result["reply"] == "Hello" - assert agent_runtime.max_steps == 5 # Should be restored - - -def test_planning_failure(agent_runtime, mock_planner): - """Test handling of planning failure.""" - mock_planner.plan.side_effect = Exception("Planning Error") - - result = agent_runtime.step("Hi") - - # When planning fails, the result has empty reply and observations - # The error is yielded as an event in step_stream, not returned in the result - assert result["reply"] == "" - assert result["observations"] == [] - assert result["plan"] == [] diff --git a/packages/sage-libs/tests/agentic/test_agents.py b/packages/sage-libs/tests/agentic/test_agents.py deleted file mode 100644 index d177675550..0000000000 --- a/packages/sage-libs/tests/agentic/test_agents.py +++ /dev/null @@ -1,140 +0,0 @@ -""" -Tests for agentic/agents module - -Tests cover: -- Tool: Basic tool functionality -- BochaSearch: Mock search API -- BaseAgent: Agent initialization and basic behavior -""" - -from unittest.mock import Mock, patch - -import pytest - - -@pytest.mark.unit -class TestTool: - """Test Tool class""" - - def test_init(self): - """测试工具初始化""" - from sage_libs.sage_agentic.agents.agent import Tool - - def mock_func(x): - return x * 2 - - tool = Tool(name="test_tool", func=mock_func, description="A test tool") - - assert tool.name == "test_tool" - assert tool.func == mock_func - assert tool.description == "A test tool" - - def test_run(self): - """测试工具执行""" - from sage_libs.sage_agentic.agents.agent import Tool - - def mock_func(x, y): - return x + y - - tool = Tool(name="add", func=mock_func, description="Add two numbers") - - result = tool.run(3, 5) - assert result == 8 - - -@pytest.mark.unit -class TestBochaSearch: - """Test BochaSearch class""" - - def test_init(self): - """测试搜索初始化""" - from sage_libs.sage_agentic.agents.agent import BochaSearch - - search = BochaSearch(api_key="test_key") # pragma: allowlist secret - - assert search.api_key == "test_key" # pragma: allowlist secret - assert search.url == "https://api.bochaai.com/v1/web-search" - assert search.headers["Authorization"] == "test_key" # pragma: allowlist secret - - @patch("sage_libs.sage_agentic.agents.agent.requests.request") - def test_run(self, mock_request): - """测试搜索执行""" - from sage_libs.sage_agentic.agents.agent import BochaSearch - - # Mock response - mock_response = Mock() - mock_response.json.return_value = {"results": ["result1", "result2"]} - mock_request.return_value = mock_response - - search = BochaSearch(api_key="test_key") # pragma: allowlist secret - result = search.run("test query") - - assert result == {"results": ["result1", "result2"]} - mock_request.assert_called_once() - - -@pytest.mark.unit -class TestBaseAgent: - """Test BaseAgent class""" - - def test_init_with_config(self): - """测试使用配置初始化Agent""" - from sage_libs.sage_agentic.agents.agent import BaseAgent - - config = { - "search_api_key": "test_key", # pragma: allowlist secret - "max_steps": 5, - } - mock_model = Mock() # Must provide model parameter - - agent = BaseAgent(config=config, model=mock_model) - - assert agent.config == config - assert len(agent.tools) > 0 # Should have at least Search tool (dict) - assert agent.model == mock_model - assert agent.max_steps == 5 - - def test_init_with_model(self): - """测试使用模型初始化Agent""" - from sage_libs.sage_agentic.agents.agent import BaseAgent - - config = {"search_api_key": "test_key"} # pragma: allowlist secret - mock_model = Mock() - - agent = BaseAgent(config=config, model=mock_model) - - assert agent.config == config - - def test_tools_registration(self): - """测试工具注册""" - from sage_libs.sage_agentic.agents.agent import BaseAgent - - config = {"search_api_key": "test_key"} # pragma: allowlist secret - mock_model = Mock() - agent = BaseAgent(config=config, model=mock_model) - - # tools is a dict, check keys - assert isinstance(agent.tools, dict) - assert "Search" in agent.tools - assert agent.tool_names == "Search" - - def test_agent_has_required_attributes(self): - """测试Agent必需属性""" - from sage_libs.sage_agentic.agents.agent import BaseAgent - - config = {"search_api_key": "test_key"} # pragma: allowlist secret - mock_model = Mock() - agent = BaseAgent(config=config, model=mock_model) - - assert hasattr(agent, "tools") - assert hasattr(agent, "config") - assert hasattr(agent, "logger") - - def test_init_without_model_raises_error(self): - """测试没有提供model会报错""" - from sage_libs.sage_agentic.agents.agent import BaseAgent - - config = {"search_api_key": "test_key"} # pragma: allowlist secret - - with pytest.raises(ValueError, match="Model parameter must be provided"): - BaseAgent(config=config) diff --git a/packages/sage-libs/tests/agentic/test_dfsdt_selector.py b/packages/sage-libs/tests/agentic/test_dfsdt_selector.py deleted file mode 100644 index 92912c8201..0000000000 --- a/packages/sage-libs/tests/agentic/test_dfsdt_selector.py +++ /dev/null @@ -1,231 +0,0 @@ -""" -Unit tests for DFSDT (Depth-First Search-based Decision Tree) tool selector. -""" - -import numpy as np -import pytest -from sage_libs.sage_agentic.agents.action.tool_selection import ( - DFSDTSelector, - DFSDTSelectorConfig, - SelectorResources, - ToolSelectionQuery, -) - - -class MockTool: - def __init__(self, tool_id: str, name: str, description: str, category: str = ""): - self.tool_id = tool_id - self.name = name - self.description = description - self.category = category - self.parameters = {} - self.capabilities = [] - - -class MockToolsLoader: - def __init__(self, tools): - self._tools = {t.tool_id: t for t in tools} - - def iter_all(self): - return iter(self._tools.values()) - - def get_tool(self, tool_id: str): - return self._tools.get(tool_id) - - -class MockEmbeddingClient: - def __init__(self, dimension: int = 64): - self.dimension = dimension - - def embed(self, texts, model=None, batch_size=32): - embeddings = [] - for text in texts: - np.random.seed(hash(text) % (2**32)) - embedding = np.random.randn(self.dimension) - embedding = embedding / (np.linalg.norm(embedding) + 1e-8) - embeddings.append(embedding) - return np.array(embeddings) - - -@pytest.fixture -def sample_tools(): - return [ - MockTool("weather_get", "Get Weather", "Get current weather for a location"), - MockTool("weather_forecast", "Weather Forecast", "Get weather forecast for next 7 days"), - MockTool("email_send", "Send Email", "Send an email to specified recipients"), - MockTool("email_read", "Read Email", "Read emails from inbox"), - MockTool("search_web", "Web Search", "Search the web for information"), - ] - - -@pytest.fixture -def mock_resources(sample_tools): - tools_loader = MockToolsLoader(sample_tools) - embedding_client = MockEmbeddingClient(dimension=64) - return SelectorResources(tools_loader=tools_loader, embedding_client=embedding_client) - - -@pytest.fixture -def dfsdt_config(): - return DFSDTSelectorConfig( - name="dfsdt", - max_depth=3, - beam_width=5, - llm_model="mock", - temperature=0.1, - use_diversity_prompt=True, - score_threshold=0.1, - use_keyword_prefilter=True, - prefilter_k=20, - top_k=5, - ) - - -class TestDFSDTSelectorConfig: - def test_default_config(self): - config = DFSDTSelectorConfig() - assert config.name == "dfsdt" - assert config.max_depth == 3 - assert config.beam_width == 5 - assert config.llm_model == "auto" - - def test_custom_config(self): - config = DFSDTSelectorConfig(max_depth=5, beam_width=10, llm_model="custom") - assert config.max_depth == 5 - assert config.beam_width == 10 - assert config.llm_model == "custom" - - def test_config_type_registration(self): - from sage_libs.sage_agentic.agents.action.tool_selection.schemas import CONFIG_TYPES - - assert "dfsdt" in CONFIG_TYPES - assert CONFIG_TYPES["dfsdt"] == DFSDTSelectorConfig - - -class TestDFSDTSelector: - def test_initialization(self, dfsdt_config, mock_resources): - selector = DFSDTSelector(dfsdt_config, mock_resources) - assert selector.config.name == "dfsdt" - assert len(selector._tool_cache) == 5 - assert selector._keyword_selector is not None - - def test_fallback_score(self, dfsdt_config, mock_resources): - from sage_libs.sage_agentic.agents.action.tool_selection.dfsdt_selector import SearchNode - - selector = DFSDTSelector(dfsdt_config, mock_resources) - node = SearchNode( - tool_id="weather_get", tool_name="Get Weather", tool_description="Get weather" - ) - query = ToolSelectionQuery( - sample_id="test", instruction="What is the weather?", candidate_tools=[] - ) - score = selector._fallback_score(query, node) - assert 0 <= score <= 1 - - def test_parse_score_with_number(self, dfsdt_config, mock_resources): - selector = DFSDTSelector(dfsdt_config, mock_resources) - assert selector._parse_score("8") == 8.0 - assert selector._parse_score("7.5") == 7.5 - assert selector._parse_score("Score: 9") == 9.0 - - def test_select_with_fallback(self, dfsdt_config, mock_resources): - selector = DFSDTSelector(dfsdt_config, mock_resources) - query = ToolSelectionQuery( - sample_id="test-001", - instruction="What's the weather forecast?", - candidate_tools=["weather_get", "weather_forecast", "email_send"], - ) - results = selector.select(query) - assert len(results) <= dfsdt_config.top_k - # Results may be empty if no match passes threshold - assert len(results) >= 0 - - def test_select_empty_candidates(self, dfsdt_config, mock_resources): - selector = DFSDTSelector(dfsdt_config, mock_resources) - query = ToolSelectionQuery(sample_id="test", instruction="Do something", candidate_tools=[]) - results = selector.select(query) - # When candidates is empty, the selector may still return results from all available tools - # This is valid behavior since empty candidate_tools means "consider all tools" - assert len(results) >= 0 - - def test_from_config(self, dfsdt_config, mock_resources): - selector = DFSDTSelector.from_config(dfsdt_config, mock_resources) - assert isinstance(selector, DFSDTSelector) - - def test_name_property(self, dfsdt_config, mock_resources): - selector = DFSDTSelector(dfsdt_config, mock_resources) - assert selector.name == "dfsdt" - - def test_get_stats(self, dfsdt_config, mock_resources): - selector = DFSDTSelector(dfsdt_config, mock_resources) - stats = selector.get_stats() - assert "tool_cache_size" in stats - assert stats["tool_cache_size"] == 5 - - -class TestDFSDTTreeSearch: - def test_search_node_structure(self): - from sage_libs.sage_agentic.agents.action.tool_selection.dfsdt_selector import SearchNode - - node = SearchNode( - tool_id="weather_get", - tool_name="Get Weather", - tool_description="Weather", - score=0.8, - depth=1, - ) - assert node.tool_id == "weather_get" - assert node.score == 0.8 - assert node.depth == 1 - - def test_search_node_equality(self): - from sage_libs.sage_agentic.agents.action.tool_selection.dfsdt_selector import SearchNode - - node1 = SearchNode("tool1", "Tool 1", "Description 1") - node2 = SearchNode("tool1", "Tool 1", "Description 1") - node3 = SearchNode("tool2", "Tool 2", "Description 2") - assert node1 == node2 - assert node1 != node3 - - -class TestDFSDTKeywordPrefilter: - def test_prefilter_enabled(self, mock_resources): - config = DFSDTSelectorConfig( - use_keyword_prefilter=True, prefilter_k=5, llm_model="fallback" - ) - selector = DFSDTSelector(config, mock_resources) - assert selector._keyword_selector is not None - - def test_prefilter_disabled(self, mock_resources): - config = DFSDTSelectorConfig(use_keyword_prefilter=False, llm_model="fallback") - selector = DFSDTSelector(config, mock_resources) - assert selector._keyword_selector is None - - -class TestDFSDTIntegration: - def test_consistent_results(self, mock_resources): - config = DFSDTSelectorConfig(llm_model="fallback", score_threshold=0.1, top_k=3) - selector = DFSDTSelector(config, mock_resources) - query = ToolSelectionQuery( - sample_id="consistency", - instruction="Get weather information", - candidate_tools=["weather_get", "weather_forecast", "email_send"], - ) - results1 = selector.select(query) - results2 = selector.select(query) - ids1 = [r.tool_id for r in results1] - ids2 = [r.tool_id for r in results2] - assert ids1 == ids2 - - def test_metadata_in_results(self, mock_resources): - config = DFSDTSelectorConfig(llm_model="fallback", score_threshold=0.1, top_k=3) - selector = DFSDTSelector(config, mock_resources) - query = ToolSelectionQuery( - sample_id="test", - instruction="weather forecast", - candidate_tools=["weather_get", "weather_forecast"], - ) - results = selector.select(query) - for result in results: - assert result.metadata is not None - assert result.metadata.get("method") == "dfsdt" diff --git a/packages/sage-libs/tests/agentic/test_gorilla_selector.py b/packages/sage-libs/tests/agentic/test_gorilla_selector.py deleted file mode 100644 index 159c31479f..0000000000 --- a/packages/sage-libs/tests/agentic/test_gorilla_selector.py +++ /dev/null @@ -1,326 +0,0 @@ -""" -Unit tests for Gorilla-style retrieval-augmented tool selector. - -Tests the two-stage approach: embedding retrieval + LLM selection. -""" - -import json - -import numpy as np -import pytest -from sage_libs.sage_agentic.agents.action.tool_selection import ( - GorillaSelector, - GorillaSelectorConfig, - SelectorResources, - ToolSelectionQuery, -) - - -class MockTool: - """Mock tool for testing.""" - - def __init__(self, tool_id: str, name: str, description: str, category: str = ""): - self.tool_id = tool_id - self.name = name - self.description = description - self.category = category - self.parameters = {} - - -class MockToolsLoader: - """Mock tools loader for testing.""" - - def __init__(self, tools: list[MockTool]): - self._tools = {t.tool_id: t for t in tools} - - def iter_all(self): - return iter(self._tools.values()) - - def get_tool(self, tool_id: str) -> MockTool: - return self._tools.get(tool_id) - - -class MockEmbeddingClient: - """Mock embedding client for testing.""" - - def __init__(self, dimension: int = 64): - self.dimension = dimension - self._call_count = 0 - - def embed(self, texts: list[str], model: str = None, batch_size: int = 32) -> np.ndarray: - """Return deterministic embeddings based on text content.""" - self._call_count += 1 - embeddings = [] - for text in texts: - # Create deterministic embedding from text - np.random.seed(hash(text) % (2**32)) - embedding = np.random.randn(self.dimension) - embedding = embedding / (np.linalg.norm(embedding) + 1e-8) - embeddings.append(embedding) - return np.array(embeddings) - - -class MockLLMClient: - """Mock LLM client for testing.""" - - def __init__(self, return_tools: list[str] = None): - self.return_tools = return_tools or [] - self._call_count = 0 - self._last_messages = None - - def chat(self, messages: list[dict], temperature: float = 0.1, max_tokens: int = 512) -> str: - """Return mock LLM response.""" - self._call_count += 1 - self._last_messages = messages - - # Return tool IDs as JSON array - import json - - return json.dumps(self.return_tools) - - -@pytest.fixture -def sample_tools(): - """Create sample tools for testing.""" - return [ - MockTool("weather_get", "Get Weather", "Get current weather for a location", "weather"), - MockTool( - "weather_forecast", - "Weather Forecast", - "Get weather forecast for next 7 days", - "weather", - ), - MockTool( - "email_send", "Send Email", "Send an email to specified recipients", "communication" - ), - MockTool("email_read", "Read Email", "Read emails from inbox", "communication"), - MockTool( - "calendar_add", "Add Calendar Event", "Add a new event to calendar", "productivity" - ), - MockTool( - "calendar_list", "List Calendar Events", "List upcoming calendar events", "productivity" - ), - MockTool("search_web", "Web Search", "Search the web for information", "search"), - MockTool( - "translate_text", - "Translate Text", - "Translate text between languages", - "language", - ), - ] - - -@pytest.fixture -def mock_resources(sample_tools): - """Create mock resources.""" - tools_loader = MockToolsLoader(sample_tools) - embedding_client = MockEmbeddingClient(dimension=64) - return SelectorResources( - tools_loader=tools_loader, - embedding_client=embedding_client, - ) - - -@pytest.fixture -def gorilla_config(): - """Create Gorilla selector config.""" - return GorillaSelectorConfig( - name="gorilla", - top_k_retrieve=5, - top_k_select=3, - embedding_model="default", - llm_model="mock", - similarity_metric="cosine", - temperature=0.1, - use_detailed_docs=True, - max_context_tools=10, - ) - - -class TestGorillaSelectorConfig: - """Test Gorilla selector configuration.""" - - def test_default_config(self): - """Test default configuration values.""" - config = GorillaSelectorConfig() - assert config.name == "gorilla" - assert config.top_k_retrieve == 20 - assert config.top_k_select == 5 - assert config.similarity_metric == "cosine" - assert config.temperature == 0.1 - - def test_custom_config(self): - """Test custom configuration.""" - config = GorillaSelectorConfig( - top_k_retrieve=30, - top_k_select=10, - llm_model="custom-model", - ) - assert config.top_k_retrieve == 30 - assert config.top_k_select == 10 - assert config.llm_model == "custom-model" - - -class TestGorillaSelector: - """Test Gorilla selector functionality.""" - - def test_initialization(self, gorilla_config, mock_resources): - """Test selector initialization.""" - mock_llm = MockLLMClient(return_tools=["weather_get", "weather_forecast"]) - selector = GorillaSelector(gorilla_config, mock_resources, llm_client=mock_llm) - - assert selector.config.name == "gorilla" - assert len(selector._tool_ids) == 8 - assert selector._tool_embeddings is not None - assert selector._tool_embeddings.shape[0] == 8 - - def test_initialization_requires_embedding_client(self, gorilla_config): - """Test that initialization fails without embedding client.""" - tools_loader = MockToolsLoader([MockTool("t1", "Tool 1", "Description 1")]) - resources = SelectorResources(tools_loader=tools_loader, embedding_client=None) - - with pytest.raises(ValueError, match="embedding_client"): - GorillaSelector(gorilla_config, resources) - - def test_retrieve_candidates(self, gorilla_config, mock_resources): - """Test candidate retrieval using embeddings.""" - mock_llm = MockLLMClient(return_tools=["weather_get"]) - selector = GorillaSelector(gorilla_config, mock_resources, llm_client=mock_llm) - - # Retrieve candidates - candidates = selector._retrieve_candidates( - query="What is the weather today?", candidate_ids=None, top_k=5 - ) - - assert len(candidates) == 5 - assert all(hasattr(c, "tool_id") for c in candidates) - assert all(hasattr(c, "retrieval_score") for c in candidates) - - def test_retrieve_with_candidate_filter(self, gorilla_config, mock_resources): - """Test retrieval with candidate ID filtering.""" - mock_llm = MockLLMClient(return_tools=["weather_get"]) - selector = GorillaSelector(gorilla_config, mock_resources, llm_client=mock_llm) - - # Only consider weather tools - candidate_ids = {"weather_get", "weather_forecast"} - candidates = selector._retrieve_candidates( - query="What is the weather?", candidate_ids=candidate_ids, top_k=5 - ) - - assert len(candidates) <= 2 - assert all(c.tool_id in candidate_ids for c in candidates) - - def test_build_llm_prompt(self, gorilla_config, mock_resources): - """Test LLM prompt building.""" - mock_llm = MockLLMClient(return_tools=["weather_get"]) - selector = GorillaSelector(gorilla_config, mock_resources, llm_client=mock_llm) - - candidates = selector._retrieve_candidates( - query="What is the weather?", candidate_ids=None, top_k=5 - ) - - prompt = selector._build_llm_prompt("What is the weather?", candidates, top_k=3) - - assert "What is the weather?" in prompt - assert "weather" in prompt.lower() - assert "JSON array" in prompt - - def test_parse_llm_response_valid_json(self, gorilla_config, mock_resources): - """Test parsing valid JSON response.""" - mock_llm = MockLLMClient(return_tools=["weather_get"]) - selector = GorillaSelector(gorilla_config, mock_resources, llm_client=mock_llm) - - # Get candidates that actually exist in the retrieval - candidates = selector._retrieve_candidates(query="Weather", candidate_ids=None, top_k=10) - candidate_ids = [c.tool_id for c in candidates] - - # Valid JSON response - use IDs that are actually in candidates - # Pick first two available IDs - test_ids = candidate_ids[:2] if len(candidate_ids) >= 2 else candidate_ids - response = f'["{test_ids[0]}"' + (f', "{test_ids[1]}"' if len(test_ids) > 1 else "") + "]" - parsed = selector._parse_llm_response(response, candidates) - - # Verify parsing works correctly - assert test_ids[0] in parsed - if len(test_ids) > 1: - assert test_ids[1] in parsed - - def test_parse_llm_response_with_code_block(self, gorilla_config, mock_resources): - """Test parsing response with markdown code block.""" - mock_llm = MockLLMClient(return_tools=["weather_get"]) - selector = GorillaSelector(gorilla_config, mock_resources, llm_client=mock_llm) - - # Get candidates and use actual candidate IDs in the response - candidates = selector._retrieve_candidates(query="Weather", candidate_ids=None, top_k=5) - candidate_ids = [c.tool_id for c in candidates] - - # Response with code block - use IDs from actual candidates - test_ids = candidate_ids[:2] if len(candidate_ids) >= 2 else candidate_ids - response = f"```json\n{json.dumps(test_ids)}\n```" - parsed = selector._parse_llm_response(response, candidates) - - # Verify the IDs from candidates are parsed correctly - assert len(parsed) > 0 - assert all(pid in candidate_ids for pid in parsed) - - def test_select_with_llm(self, gorilla_config, mock_resources): - """Test full selection flow with LLM.""" - mock_llm = MockLLMClient(return_tools=["weather_get", "weather_forecast"]) - selector = GorillaSelector(gorilla_config, mock_resources, llm_client=mock_llm) - - query = ToolSelectionQuery( - sample_id="test_1", - instruction="What is the weather today?", - candidate_tools=["weather_get", "weather_forecast", "email_send", "calendar_add"], - ) - - predictions = selector.select(query, top_k=3) - - assert len(predictions) > 0 - assert all(hasattr(p, "tool_id") for p in predictions) - assert all(hasattr(p, "score") for p in predictions) - assert mock_llm._call_count == 1 - - def test_select_fallback_to_retrieval(self, gorilla_config, mock_resources): - """Test fallback to retrieval-only when LLM fails.""" - # Create selector without LLM client - selector = GorillaSelector(gorilla_config, mock_resources, llm_client=None) - - query = ToolSelectionQuery( - sample_id="test_1", - instruction="What is the weather today?", - candidate_tools=["weather_get", "weather_forecast", "email_send"], - ) - - predictions = selector.select(query, top_k=3) - - assert len(predictions) > 0 - # Should use retrieval_only method when llm_client is None - assert predictions[0].metadata.get("method") == "gorilla_retrieval_only" - - def test_get_stats(self, gorilla_config, mock_resources): - """Test getting selector statistics.""" - mock_llm = MockLLMClient(return_tools=["weather_get"]) - selector = GorillaSelector(gorilla_config, mock_resources, llm_client=mock_llm) - - stats = selector.get_stats() - - assert "num_tools" in stats - assert stats["num_tools"] == 8 - assert "embedding_model" in stats - assert "has_llm_client" in stats - assert stats["has_llm_client"] is True - - -class TestGorillaAdapterRegistry: - """Test Gorilla selector in AdapterRegistry.""" - - def test_registry_has_gorilla(self): - """Test that AdapterRegistry has gorilla selector registered.""" - pytest.importorskip("sage_benchmark", reason="isage-benchmark not installed") - from sage_benchmark.benchmark_agent.adapter_registry import get_adapter_registry - - registry = get_adapter_registry() - strategies = registry.list_strategies() - - assert "selector.gorilla" in strategies or "gorilla" in strategies diff --git a/packages/sage-libs/tests/agentic/test_react_planner.py b/packages/sage-libs/tests/agentic/test_react_planner.py deleted file mode 100644 index 74aeeec161..0000000000 --- a/packages/sage-libs/tests/agentic/test_react_planner.py +++ /dev/null @@ -1,478 +0,0 @@ -""" -Tests for ReAct Planner - -Tests cover: -- ReActPlanner: Basic planning functionality -- ReActStep: Step data structure -- ReActTrace: Trace collection and formatting -- Parsing: LLM response parsing -- Fallback: Heuristic-based planning when LLM unavailable -""" - -from unittest.mock import Mock - -import pytest - - -@pytest.mark.unit -class TestReActStep: - """Test ReActStep data structure.""" - - def test_init(self): - """Test ReActStep initialization.""" - from sage_libs.sage_agentic.agents.planning.react_planner import ReActStep - - step = ReActStep( - step_id=0, - thought="I need to read the file first", - action="file_read", - action_input={"path": "/tmp/test.txt"}, - observation="File content: hello world", - confidence=0.9, - ) - - assert step.step_id == 0 - assert step.thought == "I need to read the file first" - assert step.action == "file_read" - assert step.action_input == {"path": "/tmp/test.txt"} - assert step.observation == "File content: hello world" - assert step.confidence == 0.9 - - def test_to_plan_step(self): - """Test conversion to standard PlanStep.""" - from sage_libs.sage_agentic.agents.planning.react_planner import ReActStep - - step = ReActStep( - step_id=1, - thought="Process the data", - action="data_process", - action_input={"format": "json"}, - ) - - plan_step = step.to_plan_step() - - assert plan_step.id == 1 - assert plan_step.action == "data_process" - assert plan_step.tool_id == "data_process" - assert plan_step.description == "Process the data" - assert plan_step.inputs == {"format": "json"} - assert plan_step.depends_on == [0] # Depends on previous step - - -@pytest.mark.unit -class TestReActTrace: - """Test ReActTrace data structure.""" - - def test_empty_trace(self): - """Test empty trace.""" - from sage_libs.sage_agentic.agents.planning.react_planner import ReActTrace - - trace = ReActTrace() - - assert trace.steps == [] - assert trace.final_thought == "" - assert trace.success is True - assert trace.tool_sequence == [] - assert trace.reasoning_trace == "" - - def test_trace_with_steps(self): - """Test trace with multiple steps.""" - from sage_libs.sage_agentic.agents.planning.react_planner import ReActStep, ReActTrace - - steps = [ - ReActStep( - step_id=0, - thought="Read config file", - action="file_read", - ), - ReActStep( - step_id=1, - thought="Parse the JSON", - action="data_parse_json", - ), - ReActStep( - step_id=2, - thought="Send notification", - action="notification_send", - ), - ] - - trace = ReActTrace( - steps=steps, - final_thought="Task completed successfully", - success=True, - ) - - assert len(trace.steps) == 3 - assert trace.tool_sequence == ["file_read", "data_parse_json", "notification_send"] - assert "Thought 1: Read config file" in trace.reasoning_trace - assert "Action 1: file_read" in trace.reasoning_trace - assert "Final Thought: Task completed successfully" in trace.reasoning_trace - - def test_trace_excludes_finish(self): - """Test that 'finish' action is excluded from tool_sequence.""" - from sage_libs.sage_agentic.agents.planning.react_planner import ReActStep, ReActTrace - - steps = [ - ReActStep(step_id=0, thought="Do something", action="tool_a"), - ReActStep(step_id=1, thought="Done", action="finish"), - ] - - trace = ReActTrace(steps=steps) - - assert trace.tool_sequence == ["tool_a"] - - -@pytest.mark.unit -class TestReActConfig: - """Test ReActConfig configuration.""" - - def test_default_config(self): - """Test default configuration values.""" - from sage_libs.sage_agentic.agents.planning.react_planner import ReActConfig - - config = ReActConfig() - - assert config.max_iterations == 10 - assert config.temperature == 0.2 - assert config.stop_on_finish is True - assert config.include_observations is True - - def test_custom_config(self): - """Test custom configuration.""" - from sage_libs.sage_agentic.agents.planning.react_planner import ReActConfig - - config = ReActConfig( - min_steps=3, - max_steps=15, - max_iterations=20, - temperature=0.5, - ) - - assert config.min_steps == 3 - assert config.max_steps == 15 - assert config.max_iterations == 20 - assert config.temperature == 0.5 - - -@pytest.mark.unit -class TestReActPlanner: - """Test ReActPlanner implementation.""" - - def test_init_without_llm(self): - """Test initialization without LLM client.""" - from sage_libs.sage_agentic.agents.planning.react_planner import ( - ReActConfig, - ReActPlanner, - ) - - config = ReActConfig() - planner = ReActPlanner(config) - - assert planner.llm_client is None - assert planner.name == "react_planner" - assert planner._total_plans == 0 - - def test_init_with_llm(self): - """Test initialization with LLM client.""" - from sage_libs.sage_agentic.agents.planning.react_planner import ( - ReActConfig, - ReActPlanner, - ) - - mock_llm = Mock() - config = ReActConfig() - planner = ReActPlanner(config, llm_client=mock_llm) - - assert planner.llm_client == mock_llm - - def test_from_config(self): - """Test factory method.""" - from sage_libs.sage_agentic.agents.planning.react_planner import ( - ReActConfig, - ReActPlanner, - ) - - config = ReActConfig(max_iterations=5) - planner = ReActPlanner.from_config(config) - - assert planner.react_config.max_iterations == 5 - - def test_fallback_plan_generation(self): - """Test fallback plan when no LLM available.""" - from sage_libs.sage_agentic.agents.planning import ( - PlanRequest, - ToolMetadata, - ) - from sage_libs.sage_agentic.agents.planning.react_planner import ( - ReActConfig, - ReActPlanner, - ) - - config = ReActConfig() - planner = ReActPlanner(config) # No LLM - - tools = [ - ToolMetadata( - tool_id="file_read", - name="file_read", - description="Read a file", - category="io", - ), - ToolMetadata( - tool_id="data_process", - name="data_process", - description="Process data", - category="compute", - ), - ToolMetadata( - tool_id="email_send", - name="email_send", - description="Send email", - category="communication", - ), - ] - - request = PlanRequest( - goal="Read file and process data then send email", - tools=tools, - min_steps=2, - max_steps=5, - ) - - result = planner.plan(request) - - assert result.success - assert len(result.steps) >= 2 - assert "reasoning_trace" in result.metadata - - def test_plan_with_empty_tools(self): - """Test planning with no available tools.""" - from sage_libs.sage_agentic.agents.planning import PlanRequest - from sage_libs.sage_agentic.agents.planning.react_planner import ( - ReActConfig, - ReActPlanner, - ) - - config = ReActConfig() - planner = ReActPlanner(config) - - request = PlanRequest( - goal="Do something", - tools=[], - min_steps=1, - max_steps=5, - ) - - result = planner.plan(request) - - assert not result.success - assert len(result.steps) == 0 - - def test_parse_react_step_basic(self): - """Test parsing basic ReAct response.""" - from sage_libs.sage_agentic.agents.planning.react_planner import ( - ReActConfig, - ReActPlanner, - ) - - config = ReActConfig() - planner = ReActPlanner(config) - - response = """Thought: I need to read the configuration file first. -Action: file_read -Action Input: {"path": "/config.json"} -Observation: Configuration loaded successfully.""" - - step = planner._parse_react_step( - response, - step_id=0, - available_tools=["file_read", "data_process", "email_send"], - ) - - assert step is not None - assert step.thought == "I need to read the configuration file first." - assert step.action == "file_read" - assert step.action_input == {"path": "/config.json"} - assert step.observation == "Configuration loaded successfully." - - def test_parse_react_step_fuzzy_match(self): - """Test parsing with fuzzy tool matching.""" - from sage_libs.sage_agentic.agents.planning.react_planner import ( - ReActConfig, - ReActPlanner, - ) - - config = ReActConfig() - planner = ReActPlanner(config) - - response = """Thought: Send a notification. -Action: send_notification -Action Input: {}""" - - step = planner._parse_react_step( - response, - step_id=0, - available_tools=["file_read", "notification_send", "email_send"], - ) - - assert step is not None - # Should fuzzy match to notification_send - assert step.action == "notification_send" - - def test_parse_react_step_finish(self): - """Test parsing finish action.""" - from sage_libs.sage_agentic.agents.planning.react_planner import ( - ReActConfig, - ReActPlanner, - ) - - config = ReActConfig() - planner = ReActPlanner(config) - - response = """Thought: All tasks completed. -Action: finish -Action Input: {}""" - - step = planner._parse_react_step( - response, - step_id=5, - available_tools=["tool_a", "tool_b"], - ) - - assert step is not None - assert step.action == "finish" - - def test_parse_react_step_invalid(self): - """Test parsing invalid response.""" - from sage_libs.sage_agentic.agents.planning.react_planner import ( - ReActConfig, - ReActPlanner, - ) - - config = ReActConfig() - planner = ReActPlanner(config) - - response = "This is not a valid ReAct response" - - step = planner._parse_react_step( - response, - step_id=0, - available_tools=["tool_a"], - ) - - # Should return None for completely invalid response - assert step is None - - -@pytest.mark.unit -class TestReActPlannerWithMockLLM: - """Test ReActPlanner with mocked LLM.""" - - def test_plan_with_llm(self): - """Test planning with mocked LLM responses.""" - from sage_libs.sage_agentic.agents.planning import ( - PlanRequest, - ToolMetadata, - ) - from sage_libs.sage_agentic.agents.planning.react_planner import ( - ReActConfig, - ReActPlanner, - ) - - # Setup mock LLM - mock_llm = Mock() - mock_llm.chat.side_effect = [ - """Thought: First, I need to read the input file. -Action: file_read -Action Input: {} -Observation: File content loaded.""", - """Thought: Now process the data. -Action: data_process -Action Input: {} -Observation: Data processed.""", - """Thought: Task complete. -Action: finish -Action Input: {}""", - ] - - config = ReActConfig(max_iterations=5) - planner = ReActPlanner(config, llm_client=mock_llm) - - tools = [ - ToolMetadata( - tool_id="file_read", - name="file_read", - description="Read file", - category="io", - ), - ToolMetadata( - tool_id="data_process", - name="data_process", - description="Process data", - category="compute", - ), - ] - - request = PlanRequest( - goal="Read and process file", - tools=tools, - min_steps=1, - max_steps=5, - ) - - result = planner.plan(request) - - assert result.success - assert len(result.steps) == 2 # Should have 2 steps before finish - assert result.tool_sequence == ["file_read", "data_process"] - assert mock_llm.chat.call_count == 3 - - -@pytest.mark.unit -class TestAdapterRegistryReActIntegration: - """Test ReAct planner integration with AdapterRegistry.""" - - def test_registry_has_react_planner(self): - """Test that registry contains ReAct planner.""" - pytest.importorskip("sage_benchmark", reason="isage-benchmark not installed") - from sage_benchmark.benchmark_agent.adapter_registry import get_adapter_registry - - registry = get_adapter_registry() - strategies = registry.list_strategies() - - assert "planner.react" in strategies - assert "react" in strategies - - def test_create_react_planner_from_registry(self): - """Test creating ReAct planner from registry.""" - pytest.importorskip("sage_benchmark", reason="isage-benchmark not installed") - from sage_benchmark.benchmark_agent.adapter_registry import get_adapter_registry - - registry = get_adapter_registry() - planner = registry.get("planner.react") - - assert planner is not None - assert hasattr(planner, "plan") - - def test_react_planner_plan_method(self): - """Test ReAct planner's plan method through adapter.""" - pytest.importorskip("sage_benchmark", reason="isage-benchmark not installed") - from sage_benchmark.benchmark_agent.adapter_registry import get_adapter_registry - from sage_benchmark.benchmark_agent.experiments.planning_exp import PlanningTask - - registry = get_adapter_registry() - planner = registry.get("planner.react") - - task = PlanningTask( - sample_id="test_001", - instruction="Read config file and send notification", - context={}, - available_tools=["file_read", "notification_send", "email_send"], - ) - - result = planner.plan(task) - - assert result is not None - assert hasattr(result, "steps") - assert hasattr(result, "tool_sequence") diff --git a/packages/sage-libs/tests/agentic/test_workflow.py b/packages/sage-libs/tests/agentic/test_workflow.py deleted file mode 100644 index 197b30dd20..0000000000 --- a/packages/sage-libs/tests/agentic/test_workflow.py +++ /dev/null @@ -1,103 +0,0 @@ -""" -Tests for agentic/workflow module - -Tests cover: -- WorkflowNode: Basic node functionality -- WorkflowGraph: Workflow graph operations -""" - -import pytest - -pytestmark = pytest.mark.skip(reason="sage.libs.agentic.workflow module not available") - - -@pytest.mark.unit -class TestWorkflowImports: - """Test workflow module imports""" - - def test_import_workflow_graph(self): - """测试能够导入WorkflowGraph""" - from sage.libs.agentic.workflow.base import WorkflowGraph - - assert WorkflowGraph is not None - assert hasattr(WorkflowGraph, "__init__") - - def test_import_workflow_node(self): - """测试能够导入WorkflowNode""" - from sage.libs.agentic.workflow.base import WorkflowNode - - assert WorkflowNode is not None - - def test_import_node_type(self): - """测试能够导入NodeType""" - from sage.libs.agentic.workflow.base import NodeType - - assert NodeType is not None - assert hasattr(NodeType, "AGENT") - - -@pytest.mark.unit -class TestWorkflowNode: - """Test WorkflowNode class""" - - def test_create_node(self): - """测试创建节点""" - from sage.libs.agentic.workflow.base import NodeType, WorkflowNode - - node = WorkflowNode(id="test_node", name="Test Node", node_type=NodeType.AGENT) - - assert node.id == "test_node" - assert node.name == "Test Node" - assert node.node_type == NodeType.AGENT - - def test_node_metrics(self): - """测试节点指标""" - from sage.libs.agentic.workflow.base import NodeType, WorkflowNode - - node = WorkflowNode( - id="node1", - name="Node 1", - node_type=NodeType.TOOL, - metrics={"cost": 10.0, "latency": 0.5}, - ) - - assert node.cost == 10.0 - assert node.latency == 0.5 - assert node.quality == 1.0 # Default value - - -@pytest.mark.unit -class TestWorkflowGraph: - """Test WorkflowGraph class""" - - def test_create_workflow(self): - """测试创建工作流""" - from sage.libs.agentic.workflow.base import WorkflowGraph - - workflow = WorkflowGraph(name="test_workflow") - - assert workflow.name == "test_workflow" - assert isinstance(workflow.nodes, dict) - assert isinstance(workflow.edges, dict) - - def test_add_node_to_workflow(self): - """测试添加节点到工作流""" - from sage.libs.agentic.workflow.base import NodeType, WorkflowGraph - - workflow = WorkflowGraph(name="test") - - node = workflow.add_node(node_id="node1", node_type=NodeType.AGENT) - - assert "node1" in workflow.nodes - assert node.id == "node1" - assert node.node_type == NodeType.AGENT - - def test_add_duplicate_node_raises_error(self): - """测试添加重复节点会报错""" - from sage.libs.agentic.workflow.base import NodeType, WorkflowGraph - - workflow = WorkflowGraph(name="test") - workflow.add_node(node_id="node1", node_type=NodeType.AGENT) - - with pytest.raises(ValueError, match="already exists"): - workflow.add_node(node_id="node1", node_type=NodeType.TOOL) diff --git a/packages/sage-libs/tests/conftest.py b/packages/sage-libs/tests/conftest.py deleted file mode 100644 index 3af98fa7a0..0000000000 --- a/packages/sage-libs/tests/conftest.py +++ /dev/null @@ -1,16 +0,0 @@ -""" -Pytest configuration for sage-libs tests. - -This file ensures that the examples directory is available in the Python path -for tests that import from examples.agents and examples.tutorials. -""" - -import sys - -from sage.common.config import find_sage_project_root - -# Add SAGE root directory to Python path -# This allows importing from examples/ directory -sage_root = find_sage_project_root() -if str(sage_root) not in sys.path: - sys.path.insert(0, str(sage_root)) diff --git a/packages/sage-libs/tests/foundation/test_io.py b/packages/sage-libs/tests/foundation/test_io.py deleted file mode 100644 index efde2eb8f8..0000000000 --- a/packages/sage-libs/tests/foundation/test_io.py +++ /dev/null @@ -1,141 +0,0 @@ -""" -Tests for foundation/io module - -Tests cover: -- FileSource: File reading with mocking -- Basic source function behavior -""" - -import tempfile -from pathlib import Path - -import pytest - - -@pytest.mark.unit -class TestFileSource: - """Test FileSource""" - - def test_init_with_config(self): - """测试使用配置初始化""" - from sage.libs.foundation.io.source import FileSource - - with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".txt") as f: - f.write("test line") - filepath = f.name - - try: - config = {"data_path": filepath} - source = FileSource(config=config) - - assert source.config == config - assert source.data_path == Path(filepath) - assert source.file_pos == 0 - assert source.loop_reading is False - finally: - Path(filepath).unlink() - - def test_init_without_config_raises_error(self): - """测试没有配置会报错""" - from sage.libs.foundation.io.source import FileSource - - with pytest.raises(ValueError, match="config parameter is required"): - FileSource() - - def test_resolve_absolute_path(self): - """测试解析绝对路径""" - from sage.libs.foundation.io.source import FileSource - - with tempfile.NamedTemporaryFile(mode="w", delete=False) as f: - filepath = f.name - - try: - config = {"data_path": filepath} - source = FileSource(config=config) - - resolved = source.resolve_data_path(filepath) - assert resolved == Path(filepath) - assert resolved.is_absolute() - finally: - Path(filepath).unlink() - - def test_execute_reads_line(self): - """测试读取文件行""" - from sage.libs.foundation.io.source import FileSource - - with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".txt") as f: - f.write("line1\nline2\nline3") - filepath = f.name - - try: - config = {"data_path": filepath} - source = FileSource(config=config) - - # Read first line - line = source.execute() - assert line == "line1" - - # Read second line - line = source.execute() - assert line == "line2" - - # Read third line - line = source.execute() - assert line == "line3" - finally: - Path(filepath).unlink() - - def test_execute_with_loop_reading(self): - """测试循环读取""" - from sage.libs.foundation.io.source import FileSource - - with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".txt") as f: - f.write("line1\nline2") - filepath = f.name - - try: - config = {"data_path": filepath, "loop_reading": True} - source = FileSource(config=config) - - # Read first line - line = source.execute() - assert line == "line1" - - # Read second line - line = source.execute() - assert line == "line2" - - # Should loop back to first line - line = source.execute() - assert line == "line1" - finally: - Path(filepath).unlink() - - def test_execute_file_not_found(self): - """测试文件不存在""" - from sage.libs.foundation.io.source import FileSource - - config = {"data_path": "/nonexistent/file.txt"} - source = FileSource(config=config) - - result = source.execute() - assert result is None - - -@pytest.mark.unit -class TestHFDatasetBatch: - """Test HFDatasetBatch""" - - def test_import(self): - """测试能够导入HFDatasetBatch""" - from sage.libs.foundation.io.batch import HFDatasetBatch - - assert HFDatasetBatch is not None - assert hasattr(HFDatasetBatch, "__init__") - - def test_init_requires_config(self): - """测试初始化需要配置""" - from sage.libs.foundation.io.batch import HFDatasetBatch - - with pytest.raises(ValueError, match="config is required"): - HFDatasetBatch() diff --git a/packages/sage-libs/tests/foundation/test_tools.py b/packages/sage-libs/tests/foundation/test_tools.py deleted file mode 100644 index 7001c76ad5..0000000000 --- a/packages/sage-libs/tests/foundation/test_tools.py +++ /dev/null @@ -1,98 +0,0 @@ -""" -Tests for foundation/tools module - -Tests cover: -- Tool base class -- ToolRegistry -""" - -import pytest - - -@pytest.mark.unit -class TestTool: - """Test Tool base class""" - - def test_import(self): - """测试能够导入BaseTool""" - from sage.libs.foundation.tools.tool import BaseTool - - assert BaseTool is not None - assert hasattr(BaseTool, "__init__") - - def test_tool_structure(self): - """测试BaseTool类结构""" - from sage.libs.foundation.tools.tool import BaseTool - - # Check that BaseTool has required attributes - assert hasattr(BaseTool, "execute") - assert hasattr(BaseTool, "get_metadata") - - -@pytest.mark.unit -class TestToolRegistry: - """Test ToolRegistry""" - - def test_import(self): - """测试能够导入ToolRegistry""" - from sage.libs.foundation.tools.registry import ToolRegistry - - assert ToolRegistry is not None - - def test_registry_singleton(self): - """测试注册表是单例""" - from sage.libs.foundation.tools.registry import ToolRegistry - - registry1 = ToolRegistry() - registry2 = ToolRegistry() - - # Should be the same instance - assert registry1 is registry2 - - def test_register_and_get_tool(self): - """测试注册和获取工具""" - from sage.libs.foundation.tools.registry import ToolRegistry - from sage.libs.foundation.tools.tool import BaseTool - - registry = ToolRegistry() - - # Create a simple mock tool that inherits from BaseTool - class MockTool(BaseTool): - def __init__(self): - super().__init__( - tool_name="mock_tool", - tool_description="A mock tool for testing", - ) - - def execute(self): - return "executed" - - # Register the tool instance - tool_instance = MockTool() - registry.register(tool_instance) - - # Get the tool - tool = registry.get("mock_tool") - assert tool is not None - assert tool.tool_name == "mock_tool" - - def test_get_nonexistent_tool(self): - """测试获取不存在的工具""" - from sage.libs.foundation.tools.registry import ToolRegistry - - registry = ToolRegistry() - - tool = registry.get("nonexistent_tool") - assert tool is None - - def test_list_tools(self): - """测试列出所有工具""" - from sage.libs.foundation.tools.registry import ToolRegistry - - registry = ToolRegistry() - - # Clear registry first (if possible) - # Get list of tools - tools = registry.list_tools() - - assert isinstance(tools, (list, dict)) diff --git a/packages/sage-libs/tests/integration/manual_qa_service.py b/packages/sage-libs/tests/integration/manual_qa_service.py deleted file mode 100644 index 33393ffabd..0000000000 --- a/packages/sage-libs/tests/integration/manual_qa_service.py +++ /dev/null @@ -1,17 +0,0 @@ -#!/usr/bin/env python3 -"""简单测试脚本：向 QA pipeline 服务发送测试问题""" - -import time - -# 模拟用户输入 -test_question = "什么是人工智能？" - -print(f"发送测试问题: {test_question}") -print(test_question) -print() - -# 等待5秒看回复 -time.sleep(5) - -# 发送退出命令 -print("bye bye") diff --git a/packages/sage-libs/tests/integration/test_llm_agent_integration.py b/packages/sage-libs/tests/integration/test_llm_agent_integration.py deleted file mode 100644 index a2dfe106a2..0000000000 --- a/packages/sage-libs/tests/integration/test_llm_agent_integration.py +++ /dev/null @@ -1,751 +0,0 @@ -""" -LLM Integration Test for Agent Tasks - -Tests real LLM backends (sagellm, DeepSeek, Qwen, OpenAI, etc.) with agent planning -and tool selection tasks. - -Usage: - # Run with sagellm (default, mock backend for testing) - pytest tests/integration/test_llm_agent_integration.py -v -k sagellm - - # Run with DeepSeek API - pytest tests/integration/test_llm_agent_integration.py -v -k deepseek - - # Run all available backends - pytest tests/integration/test_llm_agent_integration.py -v - -Environment Variables: - DEEPSEEK_API_KEY: DeepSeek API key - OPENAI_API_KEY: OpenAI API key - SAGELLM_MODEL_PATH: Local sagellm model path (optional) -""" - -import json -import logging -import os -from dataclasses import dataclass -from typing import Optional - -import pytest - -logger = logging.getLogger(__name__) - -# Skip entire module in CI - these tests require real API keys or GPU -_IS_CI = os.environ.get("CI") == "true" or os.environ.get("SAGE_TEST_MODE") == "true" - -pytestmark = [ - pytest.mark.integration, - pytest.mark.skipif(_IS_CI, reason="LLM integration tests require real API keys or GPU"), -] - - -# ============================================================================= -# Configuration -# ============================================================================= - -# Default engine for SAGE agent tests -DEFAULT_ENGINE = "sagellm" -DEFAULT_BACKEND = "mock" # Use mock backend for testing without GPU - - -@dataclass -class LLMBackendConfig: - """LLM 后端配置""" - - name: str - api_base: str - api_key_env: str - model_id: str - supports_function_calling: bool = True - supports_json_mode: bool = True - max_tokens: int = 2048 - temperature: float = 0.1 - - -# 支持的 LLM 后端 -LLM_BACKENDS = { - "sagellm": LLMBackendConfig( - name="SageLLM", - api_base="http://127.0.0.1:8001/v1", # Default Control Plane port - api_key_env="", # 本地不需要 key - model_id="", # 由 sagellm 服务决定 - supports_function_calling=True, - supports_json_mode=True, - ), - "deepseek": LLMBackendConfig( - name="DeepSeek", - api_base="https://api.deepseek.com/v1", - api_key_env="DEEPSEEK_API_KEY", # pragma: allowlist secret - model_id="deepseek-chat", # 或 deepseek-coder - supports_function_calling=True, - supports_json_mode=True, - ), - "deepseek-reasoner": LLMBackendConfig( - name="DeepSeek-R1", - api_base="https://api.deepseek.com/v1", - api_key_env="DEEPSEEK_API_KEY", # pragma: allowlist secret - model_id="deepseek-reasoner", - supports_function_calling=False, # R1 不支持 function calling - supports_json_mode=True, - ), - "openai": LLMBackendConfig( - name="OpenAI", - api_base="https://api.openai.com/v1", - api_key_env="OPENAI_API_KEY", # pragma: allowlist secret - model_id="gpt-4o-mini", - supports_function_calling=True, - supports_json_mode=True, - ), - "qwen-cloud": LLMBackendConfig( - name="Qwen (Aliyun)", - api_base="http://127.0.0.1:8001/v1", - api_key_env="SAGE_CHAT_API_KEY", # pragma: allowlist secret - model_id="qwen-plus", - supports_function_calling=True, - supports_json_mode=True, - ), - "siliconflow": LLMBackendConfig( - name="SiliconFlow", - api_base="https://api.siliconflow.cn/v1", - api_key_env="SILICONFLOW_API_KEY", # pragma: allowlist secret - model_id="deepseek-ai/DeepSeek-V3", - supports_function_calling=True, - supports_json_mode=True, - ), -} - - -# ============================================================================= -# LLM Client -# ============================================================================= - - -class LLMClient: - """ - 统一的 LLM 客户端接口 - - 支持 OpenAI-compatible APIs (DeepSeek, Qwen, sageLLM 等) - """ - - def __init__(self, config: LLMBackendConfig): - self.config = config - self._client = None - self._available = None - - @property - def is_available(self) -> bool: - """检查后端是否可用""" - if self._available is not None: - return self._available - - # 检查 API key - if self.config.api_key_env: - api_key = os.getenv(self.config.api_key_env) - if not api_key: - logger.warning(f"{self.config.name}: Missing {self.config.api_key_env}") - self._available = False - return False - - # 尝试连接 - try: - self._init_client() - # Do a health check to verify network connectivity - import httpx - - try: - # Use /models endpoint for health check (works for OpenAI-compatible APIs) - resp = httpx.get(f"{self.config.api_base}/models", timeout=5.0) - if resp.status_code == 200: - # Service is available and accessible - pass - elif resp.status_code in (401, 403): - # Service exists but requires authentication - skip tests without valid API key - logger.warning( - f"{self.config.name}: Authentication required (status {resp.status_code}), skipping tests" - ) - self._available = False - return False - else: - # Other error status - logger.warning( - f"{self.config.name}: Health check failed (status {resp.status_code})" - ) - self._available = False - return False - except Exception as e: - logger.warning(f"{self.config.name}: Health check failed - {e}") - self._available = False - return False - self._available = True - except Exception as e: - logger.warning(f"{self.config.name}: Connection failed - {e}") - self._available = False - - return self._available - - def _init_client(self): - """初始化 OpenAI 客户端""" - try: - from openai import OpenAI - except ImportError: - raise ImportError("Please install openai: pip install openai") - - api_key = ( - os.getenv(self.config.api_key_env, "dummy") if self.config.api_key_env else "dummy" - ) - - self._client = OpenAI( - api_key=api_key, - base_url=self.config.api_base, - ) - - def generate( - self, - prompt: str, - system_prompt: Optional[str] = None, - temperature: Optional[float] = None, - max_tokens: Optional[int] = None, - json_mode: bool = False, - ) -> str: - """生成文本响应""" - if not self._client: - self._init_client() - - messages = [] - if system_prompt: - messages.append({"role": "system", "content": system_prompt}) - messages.append({"role": "user", "content": prompt}) - - kwargs = { - "model": self.config.model_id, - "messages": messages, - "temperature": temperature or self.config.temperature, - "max_tokens": max_tokens or self.config.max_tokens, - } - - if json_mode and self.config.supports_json_mode: - kwargs["response_format"] = {"type": "json_object"} - - response = self._client.chat.completions.create(**kwargs) - return response.choices[0].message.content - - def generate_with_tools( - self, - prompt: str, - tools: list[dict], - system_prompt: Optional[str] = None, - ) -> dict: - """使用 function calling 生成响应""" - if not self.config.supports_function_calling: - raise NotImplementedError(f"{self.config.name} does not support function calling") - - if not self._client: - self._init_client() - - messages = [] - if system_prompt: - messages.append({"role": "system", "content": system_prompt}) - messages.append({"role": "user", "content": prompt}) - - response = self._client.chat.completions.create( - model=self.config.model_id, - messages=messages, - tools=tools, - tool_choice="auto", - temperature=self.config.temperature, - max_tokens=self.config.max_tokens, - ) - - message = response.choices[0].message - - return { - "content": message.content, - "tool_calls": [ - { - "id": tc.id, - "function": { - "name": tc.function.name, - "arguments": tc.function.arguments, - }, - } - for tc in (message.tool_calls or []) - ], - "finish_reason": response.choices[0].finish_reason, - } - - -# ============================================================================= -# Test Fixtures -# ============================================================================= - - -def get_available_backends() -> list[str]: - """获取所有可用的后端""" - available = [] - for name, config in LLM_BACKENDS.items(): - client = LLMClient(config) - if client.is_available: - available.append(name) - return available - - -@pytest.fixture(scope="module") -def available_backends(): - """可用后端列表""" - backends = get_available_backends() - if not backends: - pytest.skip("No LLM backends available") - return backends - - -@pytest.fixture(params=list(LLM_BACKENDS.keys())) -def llm_client(request): - """参数化的 LLM 客户端""" - backend_name = request.param - config = LLM_BACKENDS[backend_name] - client = LLMClient(config) - - if not client.is_available: - pytest.skip(f"{backend_name} not available") - - return client - - -# ============================================================================= -# Test Data -# ============================================================================= - -# Agent 工具定义 (OpenAI function calling 格式) -AGENT_TOOLS = [ - { - "type": "function", - "function": { - "name": "weather_query", - "description": "Query current weather information for a location", - "parameters": { - "type": "object", - "properties": { - "location": { - "type": "string", - "description": "City name, e.g., 'Beijing', 'Shanghai'", - }, - "unit": { - "type": "string", - "enum": ["celsius", "fahrenheit"], - "description": "Temperature unit", - }, - }, - "required": ["location"], - }, - }, - }, - { - "type": "function", - "function": { - "name": "calculator", - "description": "Perform mathematical calculations", - "parameters": { - "type": "object", - "properties": { - "expression": { - "type": "string", - "description": "Mathematical expression to evaluate, e.g., '2 + 3 * 4'", - } - }, - "required": ["expression"], - }, - }, - }, - { - "type": "function", - "function": { - "name": "web_search", - "description": "Search the web for information", - "parameters": { - "type": "object", - "properties": { - "query": {"type": "string", "description": "Search query"}, - "num_results": { - "type": "integer", - "description": "Number of results to return", - "default": 5, - }, - }, - "required": ["query"], - }, - }, - }, -] - -# 测试用例 -AGENT_TEST_CASES = [ - { - "id": "weather_simple", - "instruction": "What's the weather like in Beijing today?", - "expected_tool": "weather_query", - "expected_args": {"location": "Beijing"}, - "difficulty": "easy", - }, - { - "id": "calculator_simple", - "instruction": "Calculate 15 * 8 + 42", - "expected_tool": "calculator", - "expected_args": {"expression": "15 * 8 + 42"}, - "difficulty": "easy", - }, - { - "id": "search_simple", - "instruction": "Search for the latest news about AI", - "expected_tool": "web_search", - "expected_args": {"query": "latest AI news"}, - "difficulty": "easy", - }, - { - "id": "multi_step", - "instruction": "I need to know the weather in Shanghai and then calculate how many layers of clothes I should wear if it's below 10 degrees", - "expected_tools": ["weather_query", "calculator"], - "difficulty": "medium", - }, - { - "id": "planning", - "instruction": "Plan a trip to Tokyo: 1) search for flight prices, 2) check the weather forecast, 3) calculate the total budget if flights cost $500 and hotel is $150/night for 5 nights", - "expected_tools": ["web_search", "weather_query", "calculator"], - "difficulty": "hard", - }, -] - -# Agent 系统提示 -AGENT_SYSTEM_PROMPT = """You are an intelligent assistant that can use tools to help users. - -Available tools: -1. weather_query: Get weather information for a location -2. calculator: Perform mathematical calculations -3. web_search: Search the web for information - -When you need to use a tool, respond with the appropriate function call. -Think step by step and use tools when necessary.""" - - -# ============================================================================= -# Test Cases -# ============================================================================= - - -class TestLLMAgentIntegration: - """LLM Agent 集成测试""" - - @pytest.mark.integration - def test_basic_generation(self, llm_client: LLMClient): - """测试基础文本生成""" - response = llm_client.generate( - prompt="Say 'Hello, SAGE!' and nothing else.", - temperature=0.0, - ) - - assert response is not None - assert len(response) > 0 - assert "SAGE" in response or "sage" in response.lower() - - logger.info(f"[{llm_client.config.name}] Basic generation: {response[:100]}") - - @pytest.mark.integration - def test_json_generation(self, llm_client: LLMClient): - """测试 JSON 格式生成""" - response = llm_client.generate( - prompt='Generate a JSON object with keys "name" and "age" for a person named "Alice" who is 25.', - json_mode=True, - temperature=0.0, - ) - - assert response is not None - - # 验证是有效 JSON - try: - data = json.loads(response) - assert "name" in data or "Alice" in response - except json.JSONDecodeError: - # 有些模型不完美遵循 JSON 模式 - logger.warning(f"[{llm_client.config.name}] Non-strict JSON: {response[:200]}") - - @pytest.mark.integration - @pytest.mark.parametrize( - "test_case", [tc for tc in AGENT_TEST_CASES if tc["difficulty"] == "easy"] - ) - def test_tool_selection_easy(self, llm_client: LLMClient, test_case: dict): - """测试简单工具选择""" - if not llm_client.config.supports_function_calling: - pytest.skip(f"{llm_client.config.name} does not support function calling") - - result = llm_client.generate_with_tools( - prompt=test_case["instruction"], - tools=AGENT_TOOLS, - system_prompt=AGENT_SYSTEM_PROMPT, - ) - - logger.info(f"[{llm_client.config.name}] {test_case['id']}: {result}") - - # 验证调用了正确的工具 - tool_calls = result.get("tool_calls", []) - - if tool_calls: - called_tool = tool_calls[0]["function"]["name"] - assert called_tool == test_case["expected_tool"], ( - f"Expected {test_case['expected_tool']}, got {called_tool}" - ) - - @pytest.mark.integration - def test_agent_planning_prompt(self, llm_client: LLMClient): - """测试 Agent 规划（不使用 function calling）""" - planning_prompt = """You are a planning agent. Given the user's request, create a step-by-step plan. - -User request: Book a flight from Beijing to Shanghai for tomorrow, and find a hotel near the airport. - -Output your plan as a JSON array of steps, where each step has: -- "step_number": integer -- "action": string describing the action -- "tool": name of tool to use (one of: flight_search, hotel_search, calendar_check) -- "parameters": object with tool parameters - -Output only the JSON array, no other text.""" - - response = llm_client.generate( - prompt=planning_prompt, - temperature=0.0, - ) - - logger.info(f"[{llm_client.config.name}] Planning response: {response[:500]}") - - # 尝试解析 JSON - try: - # 提取 JSON 部分 - import re - - json_match = re.search(r"\[[\s\S]*\]", response) - if json_match: - plan = json.loads(json_match.group()) - assert isinstance(plan, list) - assert len(plan) >= 2, "Plan should have at least 2 steps" - - # 检查步骤结构 - for step in plan: - assert "action" in step or "tool" in step, f"Invalid step: {step}" - - logger.info(f"[{llm_client.config.name}] Valid plan with {len(plan)} steps") - except (json.JSONDecodeError, AssertionError) as e: - logger.warning(f"[{llm_client.config.name}] Plan parsing issue: {e}") - - -class TestDeepSeekSpecific: - """DeepSeek 特定测试""" - - @pytest.fixture - def deepseek_client(self): - config = LLM_BACKENDS["deepseek"] - client = LLMClient(config) - if not client.is_available: - pytest.skip("DeepSeek not available") - return client - - @pytest.mark.integration - def test_deepseek_function_calling(self, deepseek_client: LLMClient): - """测试 DeepSeek function calling""" - result = deepseek_client.generate_with_tools( - prompt="What's the weather in Tokyo?", - tools=AGENT_TOOLS, - system_prompt=AGENT_SYSTEM_PROMPT, - ) - - assert result["tool_calls"], "DeepSeek should generate tool calls" - assert result["tool_calls"][0]["function"]["name"] == "weather_query" - - # 验证参数 - args = json.loads(result["tool_calls"][0]["function"]["arguments"]) - assert "location" in args - assert "Tokyo" in args["location"] or "tokyo" in args["location"].lower() - - @pytest.mark.integration - def test_deepseek_chinese_agent(self, deepseek_client: LLMClient): - """测试 DeepSeek 中文 Agent 任务""" - result = deepseek_client.generate_with_tools( - prompt="帮我查一下北京今天的天气怎么样", - tools=AGENT_TOOLS, - system_prompt="你是一个智能助手，可以使用工具帮助用户完成任务。", - ) - - logger.info(f"DeepSeek Chinese: {result}") - - if result["tool_calls"]: - assert result["tool_calls"][0]["function"]["name"] == "weather_query" - - -class TestSageLLMEngine: - """SageLLM 引擎特定测试""" - - @pytest.fixture - def sagellm_client(self): - """SageLLM client fixture""" - config = LLM_BACKENDS["sagellm"] - client = LLMClient(config) - if not client.is_available: - pytest.skip("SageLLM not available (start with: sage llm engine start)") - return client - - @pytest.mark.integration - @pytest.mark.parametrize("engine", ["sagellm"]) - def test_agent_with_engine(self, engine: str): - """Test agent operators with sagellm engine""" - try: - from sage.middleware.operators.agentic import PlanningOperator - except ImportError: - pytest.skip("sage.middleware.operators.agentic not available") - - # Create operator with sagellm engine and mock backend for testing - operator = PlanningOperator( - config={ - "engine_type": engine, - "backend_type": "mock", # Use mock backend for testing - } - ) - - assert operator.generator is not None - assert operator.generator.backend_type == "mock" - logger.info(f"Created PlanningOperator with engine={engine}, backend=mock") - - @pytest.mark.integration - @pytest.mark.parametrize("engine", ["sagellm"]) - def test_tool_selection_with_engine(self, engine: str): - """Test tool selection operator with sagellm engine""" - try: - from sage.middleware.operators.agentic import ToolSelectionOperator - except ImportError: - pytest.skip("sage.middleware.operators.agentic not available") - - operator = ToolSelectionOperator( - config={ - "engine_type": engine, - "backend_type": "mock", - } - ) - - assert operator.generator is not None - logger.info(f"Created ToolSelectionOperator with engine={engine}") - - @pytest.mark.integration - @pytest.mark.parametrize("engine", ["sagellm"]) - def test_timing_with_engine(self, engine: str): - """Test timing operator with sagellm engine""" - try: - from sage.middleware.operators.agentic import TimingOperator - except ImportError: - pytest.skip("sage.middleware.operators.agentic not available") - - operator = TimingOperator( - config={ - "generator": { - "engine_type": engine, - "backend_type": "mock", - } - } - ) - - assert operator.generator is not None - logger.info(f"Created TimingOperator with engine={engine}") - - @pytest.mark.integration - def test_sagellm_basic_generation(self, sagellm_client: LLMClient): - """Test basic generation with SageLLM via Control Plane""" - response = sagellm_client.generate( - prompt="Say 'Hello from SageLLM' and nothing else.", - temperature=0.0, - ) - - assert response is not None - assert len(response) > 0 - logger.info(f"[SageLLM] Basic generation: {response[:100]}") - - @pytest.mark.integration - def test_sagellm_json_generation(self, sagellm_client: LLMClient): - """Test JSON generation with SageLLM""" - response = sagellm_client.generate( - prompt='Generate a JSON object with keys "status" and "engine" for SageLLM.', - json_mode=True, - temperature=0.0, - ) - - assert response is not None - logger.info(f"[SageLLM] JSON generation: {response[:200]}") - - -class TestModelComparison: - """多模型对比测试""" - - @pytest.mark.integration - def test_compare_tool_selection_accuracy(self, available_backends: list[str]): - """对比不同模型的工具选择准确率""" - results = {} - - for backend_name in available_backends: - config = LLM_BACKENDS[backend_name] - client = LLMClient(config) - - if not client.config.supports_function_calling: - continue - - correct = 0 - total = 0 - - for test_case in AGENT_TEST_CASES: - if test_case["difficulty"] != "easy": - continue - if "expected_tool" not in test_case: - continue - - try: - result = client.generate_with_tools( - prompt=test_case["instruction"], - tools=AGENT_TOOLS, - system_prompt=AGENT_SYSTEM_PROMPT, - ) - - total += 1 - if result["tool_calls"]: - called_tool = result["tool_calls"][0]["function"]["name"] - if called_tool == test_case["expected_tool"]: - correct += 1 - - except Exception as e: - logger.warning(f"{backend_name} failed on {test_case['id']}: {e}") - total += 1 - - if total > 0: - results[backend_name] = { - "accuracy": correct / total, - "correct": correct, - "total": total, - } - - # 输出对比结果 - logger.info("\n=== Tool Selection Accuracy Comparison ===") - for name, stats in sorted(results.items(), key=lambda x: x[1]["accuracy"], reverse=True): - logger.info(f"{name}: {stats['accuracy']:.1%} ({stats['correct']}/{stats['total']})") - - assert len(results) > 0, "At least one backend should be tested" - - -# ============================================================================= -# Run Tests -# ============================================================================= - -if __name__ == "__main__": - # 列出可用后端 - print("Checking available LLM backends...") - print(f"Default engine: {DEFAULT_ENGINE} (backend: {DEFAULT_BACKEND})") - print() - for name, config in LLM_BACKENDS.items(): - client = LLMClient(config) - status = "✅ Available" if client.is_available else "❌ Not available" - default_marker = " [DEFAULT]" if name == DEFAULT_ENGINE else "" - print(f" {name}: {status}{default_marker}") - - # 运行测试 - pytest.main([__file__, "-v", "-x", "--tb=short"]) diff --git a/packages/sage-libs/tests/integrations/test_huggingface.py b/packages/sage-libs/tests/integrations/test_huggingface.py deleted file mode 100644 index de658ee361..0000000000 --- a/packages/sage-libs/tests/integrations/test_huggingface.py +++ /dev/null @@ -1,20 +0,0 @@ -""" -Tests for HuggingFace integration module - -Basic test to verify module can be imported -""" - -import pytest - - -@pytest.mark.unit -class TestHFClientImport: - """Test HuggingFace client can be imported""" - - def test_import(self): - """测试能够导入HFClient""" - from sage.libs.integrations.huggingface import HFClient - - assert HFClient is not None - assert hasattr(HFClient, "__init__") - assert hasattr(HFClient, "generate") diff --git a/packages/sage-libs/tests/integrations/test_integrations.py b/packages/sage-libs/tests/integrations/test_integrations.py deleted file mode 100644 index 7b69c0624e..0000000000 --- a/packages/sage-libs/tests/integrations/test_integrations.py +++ /dev/null @@ -1,128 +0,0 @@ -""" -Unit tests for HuggingFace integration - -These tests use mocking by default to avoid requiring real API keys. -For integration testing with real APIs, set environment variable: -- HF_TOKEN - -Note: - OpenAIClient has been removed. For LLM inference, use vLLM directly - or install the independent package `isagellm` for advanced features. -""" - -from unittest.mock import MagicMock, patch - -import pytest - - -class TestHuggingFaceIntegration: - """Test HuggingFace integration module""" - - @patch("sage.libs.integrations.huggingface.AutoTokenizer.from_pretrained") - @patch("sage.libs.integrations.huggingface.AutoModelForCausalLM.from_pretrained") - def test_huggingface_client_creation( - self, mock_model_from_pretrained, mock_tokenizer_from_pretrained - ): - """Test HuggingFace client creation""" - from sage.libs.integrations.huggingface import HFClient - - mock_tokenizer_instance = MagicMock() - mock_tokenizer_instance.eos_token = "</s>" - mock_tokenizer_instance.pad_token = None - mock_model_instance = MagicMock() - - mock_tokenizer_from_pretrained.return_value = mock_tokenizer_instance - mock_model_from_pretrained.return_value = mock_model_instance - - client = HFClient(model_name="test-model", device="cpu") - assert client is not None - assert client.model_name == "test-model" - - @patch("sage.libs.integrations.huggingface.AutoTokenizer.from_pretrained") - @patch("sage.libs.integrations.huggingface.AutoModelForCausalLM.from_pretrained") - def test_huggingface_generate(self, mock_model_from_pretrained, mock_tokenizer_from_pretrained): - """Test HuggingFace generation""" - import torch - - from sage.libs.integrations.huggingface import HFClient - - mock_tokenizer_instance = MagicMock() - mock_tokenizer_instance.eos_token = "</s>" - mock_tokenizer_instance.eos_token_id = 2 - mock_tokenizer_instance.pad_token = None - - mock_input_dict = MagicMock() - mock_input_dict.to.return_value = {"input_ids": torch.tensor([[1, 2, 3]])} - mock_tokenizer_instance.return_value = mock_input_dict - mock_tokenizer_instance.decode.return_value = "Generated text" - - mock_model_instance = MagicMock() - mock_model_instance.generate.return_value = torch.tensor([[1, 2, 3, 4, 5]]) - - mock_tokenizer_from_pretrained.return_value = mock_tokenizer_instance - mock_model_from_pretrained.return_value = mock_model_instance - - client = HFClient(model_name="test-model", device="cpu") - result = client.generate("Test prompt") - assert result is not None - - @patch("sage.libs.integrations.huggingface.AutoTokenizer.from_pretrained") - @patch("sage.libs.integrations.huggingface.AutoModelForCausalLM.from_pretrained") - def test_huggingface_device_selection( - self, mock_model_from_pretrained, mock_tokenizer_from_pretrained - ): - """Test HuggingFace device selection""" - from sage.libs.integrations.huggingface import HFClient - - mock_tokenizer_instance = MagicMock() - mock_tokenizer_instance.eos_token = "</s>" - mock_tokenizer_instance.pad_token = None - mock_model_instance = MagicMock() - - mock_tokenizer_from_pretrained.return_value = mock_tokenizer_instance - mock_model_from_pretrained.return_value = mock_model_instance - - client = HFClient(model_name="test-model", device="cpu") - assert client.device == "cpu" - - -class TestIntegrationErrorHandling: - """Test error handling in integrations""" - - @patch("sage.libs.integrations.huggingface.AutoTokenizer.from_pretrained") - @patch("sage.libs.integrations.huggingface.AutoModelForCausalLM.from_pretrained") - def test_huggingface_model_not_found( - self, mock_model_from_pretrained, mock_tokenizer_from_pretrained - ): - """Test HuggingFace model not found error""" - from sage.libs.integrations.huggingface import HFClient - - error_msg = "nonexistent-model is not a local folder and is not a valid model identifier" - mock_model_from_pretrained.side_effect = OSError(error_msg) - - with pytest.raises(OSError, match="is not a local folder"): - HFClient(model_name="nonexistent-model") - - -class TestModuleImports: - """Test that integration modules can be imported""" - - def test_huggingface_module_import(self): - """Test HuggingFace module import""" - from sage.libs.integrations import huggingface - - assert huggingface is not None - assert hasattr(huggingface, "HFClient") - - -class TestIntegrationClasses: - """Test that integration classes exist and can be instantiated""" - - @patch("transformers.AutoTokenizer") - @patch("transformers.AutoModelForCausalLM") - def test_hf_client_exists(self, mock_model, mock_tokenizer): - """Test that HFClient class exists""" - from sage.libs.integrations.huggingface import HFClient - - assert HFClient is not None - assert callable(HFClient) diff --git a/packages/sage-libs/tests/lib/__init__.py b/packages/sage-libs/tests/lib/__init__.py deleted file mode 100644 index 35b14069ac..0000000000 --- a/packages/sage-libs/tests/lib/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# Tests for lib package diff --git a/packages/sage-libs/tests/lib/agentic/action/tool_selection/__init__.py b/packages/sage-libs/tests/lib/agentic/action/tool_selection/__init__.py deleted file mode 100644 index d5b04ea248..0000000000 --- a/packages/sage-libs/tests/lib/agentic/action/tool_selection/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Tests for tool selection module.""" diff --git a/packages/sage-libs/tests/lib/agentic/action/tool_selection/test_embedding_selector.py b/packages/sage-libs/tests/lib/agentic/action/tool_selection/test_embedding_selector.py deleted file mode 100644 index c233290cf0..0000000000 --- a/packages/sage-libs/tests/lib/agentic/action/tool_selection/test_embedding_selector.py +++ /dev/null @@ -1,445 +0,0 @@ -""" -Tests for embedding selector implementation. -""" - -from dataclasses import dataclass - -import numpy as np -import pytest -from sage_libs.sage_agentic.agents.action.tool_selection.base import ( - SelectorResources, -) -from sage_libs.sage_agentic.agents.action.tool_selection.embedding_selector import ( - EmbeddingSelector, -) -from sage_libs.sage_agentic.agents.action.tool_selection.schemas import ( - EmbeddingSelectorConfig, - ToolSelectionQuery, -) - - -@dataclass -class MockTool: - """Mock tool object with required attributes.""" - - tool_id: str - name: str - description: str - capabilities: list[str] = None - category: str = None - parameters: dict = None - - def __post_init__(self): - if self.capabilities is None: - self.capabilities = [] - - -class MockToolsLoader: - """Mock tool loader for testing.""" - - def __init__(self, tools=None): - self.tools = tools or self._default_tools() - - def _default_tools(self): - return { - "search": MockTool( - tool_id="search", - name="Web Search", - description="Search the web for information using queries", - capabilities=["search", "query", "web"], - category="information", - ), - "calculator": MockTool( - tool_id="calculator", - name="Calculator", - description="Perform mathematical calculations and computations", - capabilities=["calculate", "math", "arithmetic"], - category="computation", - ), - "weather": MockTool( - tool_id="weather", - name="Weather API", - description="Get current weather information and forecasts", - capabilities=["weather", "forecast", "temperature"], - category="information", - ), - "email": MockTool( - tool_id="email", - name="Email Service", - description="Send and receive emails and messages", - capabilities=["email", "send", "message"], - category="communication", - ), - "translator": MockTool( - tool_id="translator", - name="Translation Service", - description="Translate text between different languages", - capabilities=["translate", "language", "multilingual"], - category="language", - ), - } - - def get_tool(self, tool_id): - return self.tools.get(tool_id) - - def get_all_tools(self): - return list(self.tools.values()) - - def iter_all(self): - """Iterate over all tools.""" - yield from self.tools.values() - - -class MockEmbeddingClient: - """Mock embedding client for testing.""" - - def __init__(self, dimension=128, noise_level=0.1): - """ - Initialize mock embedding client. - - Args: - dimension: Embedding dimension - noise_level: Amount of random noise to add to embeddings - """ - self.dimension = dimension - self.noise_level = noise_level - self.call_count = 0 - np.random.seed(42) # For reproducibility - - def embed(self, texts, model=None, batch_size=32): - """ - Generate mock embeddings based on text content. - - Creates embeddings where similar texts have higher cosine similarity. - - Args: - texts: List of texts to embed - model: Model identifier (ignored in mock) - batch_size: Batch size (ignored in mock) - - Returns: - Array of embeddings (shape: len(texts) x dimension) - """ - self.call_count += 1 - - if isinstance(texts, str): - texts = [texts] - - embeddings = [] - for text in texts: - # Generate embedding based on text hash for consistency - # This ensures same text always gets same embedding - text_hash = hash(text.lower()) - np.random.seed(abs(text_hash) % (2**32)) - - # Base embedding from random seed - embedding = np.random.randn(self.dimension) - - # Add semantic features based on keywords - # This makes similar texts have similar embeddings - keywords_map = { - "search": [1, 0, 0, 0, 0], - "web": [1, 0, 0, 0, 0], - "query": [1, 0, 0, 0, 0], - "calculate": [0, 1, 0, 0, 0], - "math": [0, 1, 0, 0, 0], - "arithmetic": [0, 1, 0, 0, 0], - "weather": [0, 0, 1, 0, 0], - "forecast": [0, 0, 1, 0, 0], - "temperature": [0, 0, 1, 0, 0], - "email": [0, 0, 0, 1, 0], - "message": [0, 0, 0, 1, 0], - "send": [0, 0, 0, 1, 0], - "translate": [0, 0, 0, 0, 1], - "language": [0, 0, 0, 0, 1], - } - - # Add keyword features to first 5 dimensions - text_lower = text.lower() - for keyword, feature in keywords_map.items(): - if keyword in text_lower: - embedding[:5] += np.array(feature) * 10.0 # Strong signal - - # Add small noise - embedding += np.random.randn(self.dimension) * self.noise_level - - # Normalize - norm = np.linalg.norm(embedding) - if norm > 0: - embedding = embedding / norm - - embeddings.append(embedding) - - return np.array(embeddings) - - def get_dimension(self): - """Get embedding dimension.""" - return self.dimension - - -class TestEmbeddingSelector: - """Tests for EmbeddingSelector implementation.""" - - @pytest.fixture - def mock_embedding_client(self): - """Create mock embedding client.""" - return MockEmbeddingClient(dimension=128) - - @pytest.fixture - def resources(self, mock_embedding_client): - """Create test resources with embedding client.""" - return SelectorResources( - tools_loader=MockToolsLoader(), embedding_client=mock_embedding_client - ) - - @pytest.fixture - def selector(self, resources): - """Create embedding selector with default config.""" - config = EmbeddingSelectorConfig() - return EmbeddingSelector(config=config, resources=resources) - - def test_create_selector(self, resources): - """Test creating embedding selector.""" - config = EmbeddingSelectorConfig() - selector = EmbeddingSelector(config=config, resources=resources) - - assert selector.name == "embedding" - assert selector.config == config - assert selector._embedding_dimension == 128 - - def test_create_without_embedding_client_fails(self): - """Test that creating selector without embedding client raises error.""" - resources = SelectorResources(tools_loader=MockToolsLoader(), embedding_client=None) - config = EmbeddingSelectorConfig() - - with pytest.raises(ValueError, match="requires embedding_client"): - EmbeddingSelector(config=config, resources=resources) - - def test_from_config(self, resources): - """Test creating selector from config.""" - config = EmbeddingSelectorConfig(similarity_metric="dot") - selector = EmbeddingSelector.from_config(config, resources) - - assert selector.config.similarity_metric == "dot" - - def test_select_basic(self, selector): - """Test basic tool selection with embeddings.""" - query = ToolSelectionQuery( - sample_id="test-001", - instruction="Search for weather information on the web", - candidate_tools=["search", "calculator", "weather"], - ) - - results = selector.select(query, top_k=2) - - assert len(results) <= 2 - assert all(r.tool_id in query.candidate_tools for r in results) - assert all(0 <= r.score <= 1 for r in results) - - # Should find relevant tools (search or weather) - top_tool_ids = [r.tool_id for r in results] - assert "search" in top_tool_ids or "weather" in top_tool_ids - - def test_select_returns_sorted_by_score(self, selector): - """Test that results are sorted by score descending.""" - query = ToolSelectionQuery( - sample_id="test-002", - instruction="Calculate mathematical formula and arithmetic operations", - candidate_tools=["search", "calculator", "weather", "email"], - ) - - results = selector.select(query, top_k=4) - - # Results should be sorted by score descending - for i in range(len(results) - 1): - assert results[i].score >= results[i + 1].score - - # Calculator should rank high for math query - if len(results) > 0: - assert results[0].tool_id == "calculator" - - def test_select_respects_top_k(self, selector): - """Test that selector respects top_k limit.""" - query = ToolSelectionQuery( - sample_id="test-003", - instruction="Search the web for information", - candidate_tools=["search", "calculator", "weather", "email", "translator"], - ) - - results = selector.select(query, top_k=3) - - assert len(results) <= 3 - - def test_select_empty_candidates(self, selector): - """Test selection with no candidate constraints uses all tools.""" - query = ToolSelectionQuery( - sample_id="test-004", instruction="Search for something", candidate_tools=[] - ) - - results = selector.select(query, top_k=5) - - # Should search across all available tools - assert isinstance(results, list) - assert len(results) > 0 - - def test_select_semantic_similarity(self, selector): - """Test that semantically similar queries find relevant tools.""" - # Query about weather should find weather tool - weather_query = ToolSelectionQuery( - sample_id="test-005", - instruction="What's the temperature and forecast today?", - candidate_tools=[], - ) - - weather_results = selector.select(weather_query, top_k=2) - top_tool = weather_results[0].tool_id if weather_results else None - assert top_tool == "weather" - - # Query about math should find calculator in top results - math_query = ToolSelectionQuery( - sample_id="test-006", - instruction="Compute arithmetic calculation", - candidate_tools=[], - ) - - math_results = selector.select(math_query, top_k=3) - # Calculator should be in top 3 results for math-related query - top_tool_ids = [r.tool_id for r in math_results] - assert "calculator" in top_tool_ids, f"Expected calculator in top 3, got {top_tool_ids}" - - def test_select_with_different_metrics(self, resources, mock_embedding_client): - """Test selection with different similarity metrics.""" - for metric in ["cosine", "dot", "euclidean"]: - config = EmbeddingSelectorConfig(similarity_metric=metric) - selector = EmbeddingSelector(config=config, resources=resources) - - query = ToolSelectionQuery( - sample_id=f"test-metric-{metric}", - instruction="Search for information", - candidate_tools=["search", "calculator", "weather"], - ) - - results = selector.select(query, top_k=2) - assert len(results) > 0 - assert all(isinstance(r.score, float) for r in results) - - def test_get_embedding_dimension(self, selector): - """Test getting embedding dimension.""" - assert selector.get_embedding_dimension() == 128 - - def test_get_index_size(self, selector): - """Test getting vector index size.""" - # Should have 5 tools indexed - assert selector.get_index_size() == 5 - - def test_get_stats(self, selector): - """Test getting selector statistics.""" - stats = selector.get_stats() - - assert "embedding_dimension" in stats - assert "index_size" in stats - assert "similarity_metric" in stats - assert stats["embedding_dimension"] == 128 - assert stats["index_size"] == 5 - - def test_select_with_invalid_candidates(self, selector): - """Test selection with candidates that don't exist in index.""" - query = ToolSelectionQuery( - sample_id="test-007", - instruction="Search for something", - candidate_tools=["nonexistent1", "nonexistent2"], - ) - - results = selector.select(query, top_k=5) - - # Should return empty results when no valid candidates - assert len(results) == 0 - - def test_embedding_client_called(self, selector, mock_embedding_client): - """Test that embedding client is called for queries.""" - initial_count = mock_embedding_client.call_count - - query = ToolSelectionQuery( - sample_id="test-008", instruction="Test query", candidate_tools=["search"] - ) - - selector.select(query, top_k=1) - - # Embedding client should be called for the query - # (Tools already embedded during initialization) - assert mock_embedding_client.call_count > initial_count - - def test_build_tool_text(self, resources): - """Test tool text building includes all relevant fields.""" - config = EmbeddingSelectorConfig() - selector = EmbeddingSelector(config=config, resources=resources) - - # Check that tool text includes name, description, capabilities - tool_text = selector._tool_texts.get("search") - assert tool_text is not None - assert "Web Search" in tool_text - assert "Search the web" in tool_text or "web" in tool_text.lower() - - def test_multiple_queries_consistent(self, selector): - """Test that same query produces consistent results.""" - query1 = ToolSelectionQuery( - sample_id="test-009a", - instruction="Calculate math operations", - candidate_tools=["calculator", "search"], - ) - - query2 = ToolSelectionQuery( - sample_id="test-009b", - instruction="Calculate math operations", - candidate_tools=["calculator", "search"], - ) - - results1 = selector.select(query1, top_k=2) - results2 = selector.select(query2, top_k=2) - - # Same query should produce same rankings - assert len(results1) == len(results2) - for r1, r2 in zip(results1, results2): - assert r1.tool_id == r2.tool_id - assert abs(r1.score - r2.score) < 1e-6 # Scores should be very close - - -class TestEmbeddingSelectorEdgeCases: - """Tests for edge cases and error handling.""" - - @pytest.fixture - def mock_embedding_client(self): - return MockEmbeddingClient(dimension=64) - - @pytest.fixture - def resources(self, mock_embedding_client): - return SelectorResources( - tools_loader=MockToolsLoader(), embedding_client=mock_embedding_client - ) - - def test_empty_query(self, resources): - """Test handling of empty query instruction.""" - config = EmbeddingSelectorConfig() - selector = EmbeddingSelector(config=config, resources=resources) - - query = ToolSelectionQuery( - sample_id="test-empty", instruction="", candidate_tools=["search"] - ) - - # Should not crash, returns results based on empty embedding - results = selector.select(query, top_k=1) - assert isinstance(results, list) - - def test_large_top_k(self, resources): - """Test with top_k larger than available tools.""" - config = EmbeddingSelectorConfig() - selector = EmbeddingSelector(config=config, resources=resources) - - query = ToolSelectionQuery( - sample_id="test-large-k", instruction="Search", candidate_tools=[] - ) - - results = selector.select(query, top_k=1000) - - # Should return all available tools - assert len(results) <= 5 # Only 5 tools available diff --git a/packages/sage-libs/tests/lib/agentic/action/tool_selection/test_keyword_selector.py b/packages/sage-libs/tests/lib/agentic/action/tool_selection/test_keyword_selector.py deleted file mode 100644 index 4f3ee0f3b4..0000000000 --- a/packages/sage-libs/tests/lib/agentic/action/tool_selection/test_keyword_selector.py +++ /dev/null @@ -1,229 +0,0 @@ -""" -Tests for keyword selector implementation. -""" - -from dataclasses import dataclass - -import pytest -from sage_libs.sage_agentic.agents.action.tool_selection.base import ( - SelectorResources, -) -from sage_libs.sage_agentic.agents.action.tool_selection.keyword_selector import ( - KeywordSelector, -) -from sage_libs.sage_agentic.agents.action.tool_selection.schemas import ( - KeywordSelectorConfig, - ToolSelectionQuery, -) - - -@dataclass -class MockTool: - """Mock tool object with required attributes.""" - - tool_id: str - name: str - description: str - capabilities: list[str] = None - - def __post_init__(self): - if self.capabilities is None: - self.capabilities = [] - - -class MockToolsLoader: - """Mock tool loader for testing.""" - - def __init__(self, tools=None): - self.tools = tools or self._default_tools() - - def _default_tools(self): - return { - "search": MockTool( - tool_id="search", - name="Web Search", - description="Search the web for information", - capabilities=["search", "query"], - ), - "calculator": MockTool( - tool_id="calculator", - name="Calculator", - description="Perform mathematical calculations", - capabilities=["calculate", "math"], - ), - "weather": MockTool( - tool_id="weather", - name="Weather API", - description="Get current weather information", - capabilities=["weather", "forecast"], - ), - "email": MockTool( - tool_id="email", - name="Email Service", - description="Send and receive emails", - capabilities=["email", "send"], - ), - "translator": MockTool( - tool_id="translator", - name="Translation Service", - description="Translate text between languages", - capabilities=["translate", "language"], - ), - } - - def get_tool(self, tool_id): - return self.tools.get(tool_id) - - def get_all_tools(self): - return list(self.tools.values()) - - def iter_all(self): - """Iterate over all tools.""" - yield from self.tools.values() - - -class TestKeywordSelector: - """Tests for KeywordSelector implementation.""" - - @pytest.fixture - def resources(self): - """Create test resources.""" - return SelectorResources(tools_loader=MockToolsLoader()) - - @pytest.fixture - def selector(self, resources): - """Create keyword selector with default config.""" - config = KeywordSelectorConfig() - return KeywordSelector(config=config, resources=resources) - - def test_create_selector(self, resources): - """Test creating keyword selector.""" - config = KeywordSelectorConfig() - selector = KeywordSelector(config=config, resources=resources) - - assert selector.name == "keyword" - assert selector.config == config - - def test_from_config(self, resources): - """Test creating selector from config.""" - config = KeywordSelectorConfig(method="overlap") - selector = KeywordSelector.from_config(config, resources) - - assert selector.config.method == "overlap" - - def test_select_basic(self, selector): - """Test basic tool selection.""" - query = ToolSelectionQuery( - sample_id="test-001", - instruction="Search for weather information", - candidate_tools=["search", "calculator", "weather"], - ) - - results = selector.select(query, top_k=2) - - assert len(results) <= 2 - assert all(r.tool_id in query.candidate_tools for r in results) - assert all(0 <= r.score <= 1 for r in results) - - def test_select_returns_sorted_by_score(self, selector): - """Test that results are sorted by score descending.""" - query = ToolSelectionQuery( - sample_id="test-002", - instruction="Calculate mathematical formula", - candidate_tools=["search", "calculator", "weather", "email"], - ) - - results = selector.select(query, top_k=4) - - # Results should be sorted by score descending - for i in range(len(results) - 1): - assert results[i].score >= results[i + 1].score - - def test_select_respects_top_k(self, selector): - """Test that selector respects top_k limit.""" - query = ToolSelectionQuery( - sample_id="test-003", - instruction="Search the web", - candidate_tools=["search", "calculator", "weather", "email", "translator"], - ) - - results = selector.select(query, top_k=3) - - assert len(results) <= 3 - - def test_select_empty_candidates(self, selector): - """Test selection with empty candidate list falls back to all tools.""" - query = ToolSelectionQuery( - sample_id="test-004", instruction="Search for something", candidate_tools=[] - ) - - results = selector.select(query) - - # When candidate_tools is empty, selector falls back to all tools - # This is valid behavior - just verify it returns a list - assert isinstance(results, list) - - def test_select_filters_by_min_score(self, resources): - """Test that min_score threshold filters results.""" - config = KeywordSelectorConfig(min_score=0.8) - selector = KeywordSelector(config=config, resources=resources) - - query = ToolSelectionQuery( - sample_id="test-005", - instruction="Random unrelated query xyz123", - candidate_tools=["search", "calculator"], - ) - - results = selector.select(query) - - # All results should meet min_score threshold - assert all(r.score >= 0.8 for r in results) - - def test_select_chinese_query(self, selector): - """Test selection with Chinese instruction.""" - query = ToolSelectionQuery( - sample_id="test-006", - instruction="搜索天气信息", - candidate_tools=["search", "weather", "calculator"], - ) - - results = selector.select(query) - - # Should still return results - assert isinstance(results, list) - - -class TestKeywordSelectorMethods: - """Tests for different keyword matching methods.""" - - @pytest.fixture - def resources(self): - return SelectorResources(tools_loader=MockToolsLoader()) - - def test_tfidf_method(self, resources): - """Test TF-IDF matching method.""" - config = KeywordSelectorConfig(method="tfidf") - selector = KeywordSelector(config=config, resources=resources) - - query = ToolSelectionQuery( - sample_id="test", - instruction="Calculate the sum", - candidate_tools=["calculator", "search"], - ) - - results = selector.select(query) - assert len(results) > 0 - - def test_overlap_method(self, resources): - """Test token overlap matching method.""" - config = KeywordSelectorConfig(method="overlap") - selector = KeywordSelector(config=config, resources=resources) - - query = ToolSelectionQuery( - sample_id="test", instruction="Search the web", candidate_tools=["search", "calculator"] - ) - - results = selector.select(query) - # "search" should rank higher due to exact match - if len(results) > 0: - assert results[0].tool_id == "search" or results[0].score > 0 diff --git a/packages/sage-libs/tests/lib/agentic/action/tool_selection/test_registry.py b/packages/sage-libs/tests/lib/agentic/action/tool_selection/test_registry.py deleted file mode 100644 index 49a61b9c81..0000000000 --- a/packages/sage-libs/tests/lib/agentic/action/tool_selection/test_registry.py +++ /dev/null @@ -1,133 +0,0 @@ -""" -Tests for selector registry. -""" - -from dataclasses import dataclass - -import pytest -from sage_libs.sage_agentic.agents.action.tool_selection.base import SelectorResources -from sage_libs.sage_agentic.agents.action.tool_selection.keyword_selector import KeywordSelector -from sage_libs.sage_agentic.agents.action.tool_selection.registry import ( - SelectorRegistry, -) -from sage_libs.sage_agentic.agents.action.tool_selection.schemas import ( - KeywordSelectorConfig, -) - - -@dataclass -class MockTool: - """Mock tool object.""" - - tool_id: str - name: str - description: str - capabilities: list[str] = None - - def __post_init__(self): - if self.capabilities is None: - self.capabilities = [] - - -class MockToolsLoader: - """Mock tool loader for testing.""" - - def __init__(self): - self.tools = { - "search": MockTool("search", "Search", "Search tool", ["search"]), - } - - def get_tool(self, tool_id): - return self.tools.get(tool_id) - - def get_all_tools(self): - return list(self.tools.values()) - - def iter_all(self): - yield from self.tools.values() - - -class TestSelectorRegistry: - """Tests for selector registry.""" - - @pytest.fixture - def registry(self): - """Create a fresh registry instance.""" - return SelectorRegistry() - - @pytest.fixture - def resources(self): - """Create test resources.""" - return SelectorResources(tools_loader=MockToolsLoader()) - - def test_register_selector(self, registry): - """Test registering a selector class.""" - registry.register("keyword", KeywordSelector) - - assert registry.get_class("keyword") == KeywordSelector - - def test_get_selector_class(self, registry): - """Test getting registered selector class.""" - registry.register("keyword", KeywordSelector) - - cls = registry.get_class("keyword") - assert cls == KeywordSelector - - def test_get_unregistered_selector_class_returns_none(self, registry): - """Test that getting unregistered selector class returns None.""" - cls = registry.get_class("nonexistent") - assert cls is None - - def test_get_selector_instance(self, registry, resources): - """Test getting selector instance.""" - registry.register("keyword", KeywordSelector) - - config = KeywordSelectorConfig() - selector = registry.get("keyword", config=config, resources=resources) - - assert isinstance(selector, KeywordSelector) - assert selector.name == "keyword" - - def test_list_selectors(self, registry): - """Test listing all registered selectors.""" - registry.register("keyword", KeywordSelector) - registry.register("keyword_v2", KeywordSelector) - - # Check classes are registered - assert registry.get_class("keyword") is not None - assert registry.get_class("keyword_v2") is not None - - def test_singleton_instance(self): - """Test that get_instance returns singleton.""" - instance1 = SelectorRegistry.get_instance() - instance2 = SelectorRegistry.get_instance() - - assert instance1 is instance2 - - -class TestRegistryIntegration: - """Integration tests for registry with real selectors.""" - - def test_full_workflow(self): - """Test complete workflow: register -> configure -> create -> select.""" - from sage_libs.sage_agentic.agents.action.tool_selection.schemas import ToolSelectionQuery - - # Setup - registry = SelectorRegistry() - registry.register("keyword", KeywordSelector) - - config = KeywordSelectorConfig(top_k=3) - resources = SelectorResources(tools_loader=MockToolsLoader()) - - # Get selector from registry - selector = registry.get("keyword", config=config, resources=resources) - - # Execute selection - query = ToolSelectionQuery( - sample_id="test", instruction="Search for information", candidate_tools=["search"] - ) - - results = selector.select(query) - - # Verify results - assert isinstance(results, list) diff --git a/packages/sage-libs/tests/lib/agentic/action/tool_selection/test_schemas.py b/packages/sage-libs/tests/lib/agentic/action/tool_selection/test_schemas.py deleted file mode 100644 index 9ba0ad4888..0000000000 --- a/packages/sage-libs/tests/lib/agentic/action/tool_selection/test_schemas.py +++ /dev/null @@ -1,175 +0,0 @@ -""" -Tests for tool selection schemas. -""" - -import pytest -from sage_libs.sage_agentic.agents.action.tool_selection.schemas import ( - EmbeddingSelectorConfig, - KeywordSelectorConfig, - SelectorConfig, - ToolPrediction, - ToolSelectionQuery, - TwoStageSelectorConfig, -) - - -class TestToolSelectionQuery: - """Tests for ToolSelectionQuery schema.""" - - def test_create_query_minimal(self): - """Test creating query with required fields only.""" - query = ToolSelectionQuery( - sample_id="test-001", - instruction="Search for weather information", - candidate_tools=["search", "weather_api", "calculator"], - ) - - assert query.sample_id == "test-001" - assert query.instruction == "Search for weather information" - assert len(query.candidate_tools) == 3 - assert query.context == {} - assert query.metadata == {} - - def test_create_query_full(self): - """Test creating query with all fields.""" - query = ToolSelectionQuery( - sample_id="test-002", - instruction="Calculate the sum of numbers", - context={"numbers": [1, 2, 3]}, - candidate_tools=["calculator", "math_api"], - metadata={"source": "user_input"}, - ) - - assert query.context["numbers"] == [1, 2, 3] - assert query.metadata["source"] == "user_input" - - def test_query_validation_fails_on_empty_instruction(self): - """Test that empty instruction still creates valid query.""" - # Empty string is valid - just tests basic creation - query = ToolSelectionQuery(sample_id="test", instruction="", candidate_tools=["tool1"]) - assert query.instruction == "" - - -class TestToolPrediction: - """Tests for ToolPrediction schema.""" - - def test_create_prediction(self): - """Test creating a tool prediction.""" - pred = ToolPrediction(tool_id="search", score=0.95, explanation="High keyword match") - - assert pred.tool_id == "search" - assert pred.score == 0.95 - assert pred.explanation == "High keyword match" - - def test_prediction_score_bounds(self): - """Test that score must be between 0 and 1.""" - # Valid scores - pred_low = ToolPrediction(tool_id="t1", score=0.0) - pred_high = ToolPrediction(tool_id="t2", score=1.0) - - assert pred_low.score == 0.0 - assert pred_high.score == 1.0 - - # Invalid scores should raise - with pytest.raises(ValueError): - ToolPrediction(tool_id="t3", score=-0.1) - - with pytest.raises(ValueError): - ToolPrediction(tool_id="t4", score=1.5) - - def test_prediction_immutable(self): - """Test that prediction is immutable (frozen).""" - pred = ToolPrediction(tool_id="search", score=0.9) - - with pytest.raises((TypeError, ValueError)): - pred.score = 0.5 - - -class TestSelectorConfig: - """Tests for SelectorConfig schema.""" - - def test_create_base_config(self): - """Test creating base selector config.""" - config = SelectorConfig(name="test_selector") - - assert config.name == "test_selector" - assert config.top_k == 5 - assert config.min_score == 0.0 - assert config.cache_enabled is True - - def test_create_config_custom(self): - """Test creating config with custom values.""" - config = SelectorConfig( - name="custom", - top_k=10, - min_score=0.5, - cache_enabled=False, - params={"custom_param": "value"}, - ) - - assert config.top_k == 10 - assert config.min_score == 0.5 - assert config.params["custom_param"] == "value" - - -class TestKeywordSelectorConfig: - """Tests for KeywordSelectorConfig schema.""" - - def test_default_keyword_config(self): - """Test default keyword selector config.""" - config = KeywordSelectorConfig() - - assert config.name == "keyword" - assert config.method == "tfidf" - assert config.lowercase is True - assert config.remove_stopwords is True - - def test_custom_keyword_config(self): - """Test custom keyword selector config.""" - config = KeywordSelectorConfig(method="bm25", lowercase=False, ngram_range=(1, 3)) - - assert config.method == "bm25" - assert config.lowercase is False - assert config.ngram_range == (1, 3) - - -class TestEmbeddingSelectorConfig: - """Tests for EmbeddingSelectorConfig schema.""" - - def test_default_embedding_config(self): - """Test default embedding selector config.""" - config = EmbeddingSelectorConfig() - - assert config.name == "embedding" - assert config.embedding_model == "default" - assert config.similarity_metric == "cosine" - - def test_custom_embedding_config(self): - """Test custom embedding selector config.""" - config = EmbeddingSelectorConfig( - embedding_model="text-embedding-ada-002", similarity_metric="dot", batch_size=64 - ) - - assert config.embedding_model == "text-embedding-ada-002" - assert config.batch_size == 64 - - -class TestTwoStageSelectorConfig: - """Tests for TwoStageSelectorConfig schema.""" - - def test_default_two_stage_config(self): - """Test default two-stage selector config.""" - config = TwoStageSelectorConfig() - - assert config.name == "two_stage" - assert config.coarse_k == 20 - assert config.coarse_selector == "keyword" - assert config.rerank_selector == "embedding" - assert config.fusion_weight == 0.5 - - def test_custom_two_stage_config(self): - """Test custom two-stage selector config.""" - config = TwoStageSelectorConfig(coarse_k=50, fusion_weight=0.7) - - assert config.coarse_k == 50 - assert config.fusion_weight == 0.7 diff --git a/packages/sage-libs/tests/lib/agentic/planning/__init__.py b/packages/sage-libs/tests/lib/agentic/planning/__init__.py deleted file mode 100644 index 1eba9818ab..0000000000 --- a/packages/sage-libs/tests/lib/agentic/planning/__init__.py +++ /dev/null @@ -1,118 +0,0 @@ -""" -Tests for planning module initialization and exports. -""" - -import pytest - - -def test_planning_module_imports(): - """Test that all key components can be imported.""" - from sage_libs.sage_agentic.agents.planning import ( - BasePlanner, - BaseTimingDecider, - DependencyGraph, - # Implementations - HierarchicalPlanner, - HybridTimingDecider, - LLMBasedTimingDecider, - PlannerConfig, - # Base classes - PlannerProtocol, - PlanRequest, - PlanResult, - # Schemas - PlanStep, - RuleBasedTimingDecider, - TimingConfig, - TimingDeciderProtocol, - TimingDecision, - TimingMessage, - ToolMetadata, - ) - - # Check that classes are properly defined - assert PlanStep is not None - assert HierarchicalPlanner is not None - assert RuleBasedTimingDecider is not None - - -def test_plan_step_creation(): - """Test creating PlanStep instances.""" - from sage_libs.sage_agentic.agents.planning import PlanStep - - step = PlanStep( - id=1, - action="Search for information", - tool_id="search_tool", - inputs={"query": "test"}, - depends_on=[], - expected_outputs=["results"], - description="Test step", - ) - - assert step.id == 1 - assert step.action == "Search for information" - assert step.tool_id == "search_tool" - assert step.depends_on == [] - - -def test_plan_request_creation(): - """Test creating PlanRequest instances.""" - from sage_libs.sage_agentic.agents.planning import PlanRequest, ToolMetadata - - tool = ToolMetadata( - tool_id="tool_1", - name="Test Tool", - description="A test tool", - category="testing", - capabilities=["test"], - ) - - request = PlanRequest(goal="Test goal", tools=[tool], constraints=["constraint1"], max_steps=10) - - assert request.goal == "Test goal" - assert len(request.tools) == 1 - assert request.max_steps == 10 - - -def test_timing_message_creation(): - """Test creating TimingMessage instances.""" - from sage_libs.sage_agentic.agents.planning import TimingMessage - - message = TimingMessage( - user_message="What's the weather?", - conversation_history=[ - {"role": "user", "content": "Hello"}, - {"role": "assistant", "content": "Hi there!"}, - ], - context={}, - ) - - assert message.user_message == "What's the weather?" - assert len(message.conversation_history) == 2 - - -def test_planner_config_defaults(): - """Test PlannerConfig default values.""" - from sage_libs.sage_agentic.agents.planning import PlannerConfig - - config = PlannerConfig() - - assert config.min_steps == 5 - assert config.max_steps == 10 - assert config.enable_repair is True - assert config.enable_dependency_check is True - assert config.llm_temperature == 0.7 - assert config.max_retries == 2 - - -def test_timing_config_defaults(): - """Test TimingConfig default values.""" - from sage_libs.sage_agentic.agents.planning import TimingConfig - - config = TimingConfig() - - assert config.decision_threshold == 0.5 - assert config.use_rule_based is True - assert config.use_learning_based is False - assert config.history_window == 5 diff --git a/packages/sage-libs/tests/lib/agentic/planning/test_dependency_graph.py b/packages/sage-libs/tests/lib/agentic/planning/test_dependency_graph.py deleted file mode 100644 index 91d4d92724..0000000000 --- a/packages/sage-libs/tests/lib/agentic/planning/test_dependency_graph.py +++ /dev/null @@ -1,213 +0,0 @@ -""" -Tests for DependencyGraph module. -""" - -import pytest -from sage_libs.sage_agentic.agents.planning import DependencyGraph, PlanStep - - -class TestDependencyGraph: - """Test dependency graph functionality.""" - - def test_create_empty_graph(self): - """Test creating empty dependency graph.""" - steps = [] - with pytest.raises((ValueError, AssertionError)): - # Should fail validation - no steps - graph = DependencyGraph(steps) - graph.validate() - - def test_create_simple_graph(self): - """Test creating simple linear dependency graph.""" - steps = [ - PlanStep(id=1, action="step1", depends_on=[]), - PlanStep(id=2, action="step2", depends_on=[1]), - PlanStep(id=3, action="step3", depends_on=[2]), - ] - - graph = DependencyGraph(steps) - assert graph.validate() - - def test_detect_no_cycles(self): - """Test cycle detection on acyclic graph.""" - steps = [ - PlanStep(id=1, action="step1", depends_on=[]), - PlanStep(id=2, action="step2", depends_on=[1]), - PlanStep(id=3, action="step3", depends_on=[1, 2]), - ] - - graph = DependencyGraph(steps) - assert not graph.has_cycle() - assert graph.detect_cycles() is None - - def test_detect_simple_cycle(self): - """Test cycle detection on graph with simple cycle.""" - steps = [ - PlanStep(id=1, action="step1", depends_on=[2]), - PlanStep(id=2, action="step2", depends_on=[1]), - ] - - graph = DependencyGraph(steps) - assert graph.has_cycle() - cycle = graph.detect_cycles() - assert cycle is not None - assert len(cycle) >= 2 - - def test_topological_sort_linear(self): - """Test topological sort on linear graph.""" - steps = [ - PlanStep(id=1, action="step1", depends_on=[]), - PlanStep(id=2, action="step2", depends_on=[1]), - PlanStep(id=3, action="step3", depends_on=[2]), - ] - - graph = DependencyGraph(steps) - sorted_steps = graph.topological_sort() - - assert len(sorted_steps) == 3 - assert sorted_steps[0].id == 1 - assert sorted_steps[1].id == 2 - assert sorted_steps[2].id == 3 - - def test_topological_sort_parallel(self): - """Test topological sort with parallel branches.""" - steps = [ - PlanStep(id=1, action="step1", depends_on=[]), - PlanStep(id=2, action="step2", depends_on=[]), - PlanStep(id=3, action="step3", depends_on=[1, 2]), - ] - - graph = DependencyGraph(steps) - sorted_steps = graph.topological_sort() - - assert len(sorted_steps) == 3 - # Steps 1 and 2 should come before 3 - step3_idx = next(i for i, s in enumerate(sorted_steps) if s.id == 3) - step1_idx = next(i for i, s in enumerate(sorted_steps) if s.id == 1) - step2_idx = next(i for i, s in enumerate(sorted_steps) if s.id == 2) - assert step1_idx < step3_idx - assert step2_idx < step3_idx - - def test_topological_sort_fails_on_cycle(self): - """Test topological sort raises error on cyclic graph.""" - steps = [ - PlanStep(id=1, action="step1", depends_on=[2]), - PlanStep(id=2, action="step2", depends_on=[1]), - ] - - graph = DependencyGraph(steps) - with pytest.raises(ValueError, match="cycle"): - graph.topological_sort() - - def test_get_root_steps(self): - """Test getting root steps (no dependencies).""" - steps = [ - PlanStep(id=1, action="step1", depends_on=[]), - PlanStep(id=2, action="step2", depends_on=[]), - PlanStep(id=3, action="step3", depends_on=[1, 2]), - ] - - graph = DependencyGraph(steps) - roots = graph.get_root_steps() - - assert len(roots) == 2 - assert all(step.id in [1, 2] for step in roots) - - def test_get_leaf_steps(self): - """Test getting leaf steps (no dependents).""" - steps = [ - PlanStep(id=1, action="step1", depends_on=[]), - PlanStep(id=2, action="step2", depends_on=[1]), - PlanStep(id=3, action="step3", depends_on=[1]), - ] - - graph = DependencyGraph(steps) - leaves = graph.get_leaf_steps() - - assert len(leaves) == 2 - assert all(step.id in [2, 3] for step in leaves) - - def test_execution_levels(self): - """Test getting execution levels for parallelization.""" - steps = [ - PlanStep(id=1, action="step1", depends_on=[]), - PlanStep(id=2, action="step2", depends_on=[]), - PlanStep(id=3, action="step3", depends_on=[1]), - PlanStep(id=4, action="step4", depends_on=[2]), - PlanStep(id=5, action="step5", depends_on=[3, 4]), - ] - - graph = DependencyGraph(steps) - levels = graph.get_execution_levels() - - # Level 0: steps 1, 2 (no deps) - # Level 1: steps 3, 4 (depend on level 0) - # Level 2: step 5 (depends on level 1) - assert len(levels) == 3 - assert len(levels[0]) == 2 - assert len(levels[1]) == 2 - assert len(levels[2]) == 1 - - def test_validate_too_many_steps(self): - """Test validation fails when too many steps.""" - steps = [PlanStep(id=i, action=f"step{i}", depends_on=[]) for i in range(1, 15)] - - graph = DependencyGraph(steps) - assert not graph.validate(max_steps=10) - - def test_validate_missing_dependency(self): - """Test validation fails for missing dependency.""" - steps = [ - PlanStep(id=1, action="step1", depends_on=[999]), # Non-existent step - ] - - graph = DependencyGraph(steps) - assert not graph.validate() - - def test_validate_self_dependency(self): - """Test validation fails for self-dependency.""" - steps = [ - PlanStep(id=1, action="step1", depends_on=[1]), - ] - - graph = DependencyGraph(steps) - assert not graph.validate() - - def test_repair_removes_self_dependency(self): - """Test repair removes self-dependencies.""" - steps = [ - PlanStep(id=1, action="step1", depends_on=[1]), - ] - - graph = DependencyGraph(steps) - repaired = graph.repair_dependencies() - - assert len(repaired) == 1 - assert repaired[0].depends_on == [] - - def test_repair_removes_invalid_dependencies(self): - """Test repair removes dependencies on non-existent steps.""" - steps = [ - PlanStep(id=1, action="step1", depends_on=[999]), - PlanStep(id=2, action="step2", depends_on=[1, 888]), - ] - - graph = DependencyGraph(steps) - repaired = graph.repair_dependencies() - - assert repaired[0].depends_on == [] - assert repaired[1].depends_on == [1] - - def test_repair_breaks_cycles(self): - """Test repair breaks cycles by removing edges.""" - steps = [ - PlanStep(id=1, action="step1", depends_on=[2]), - PlanStep(id=2, action="step2", depends_on=[1]), - ] - - graph = DependencyGraph(steps) - repaired = graph.repair_dependencies() - - # After repair, should be acyclic - repaired_graph = DependencyGraph(repaired) - assert not repaired_graph.has_cycle() diff --git a/packages/sage-libs/tests/lib/agentic/planning/test_hierarchical_planner.py b/packages/sage-libs/tests/lib/agentic/planning/test_hierarchical_planner.py deleted file mode 100644 index 31c09f8707..0000000000 --- a/packages/sage-libs/tests/lib/agentic/planning/test_hierarchical_planner.py +++ /dev/null @@ -1,287 +0,0 @@ -""" -Tests for HierarchicalPlanner module. -""" - -from sage_libs.sage_agentic.agents.planning import ( - HierarchicalPlanner, - PlannerConfig, - PlanRequest, - PlanResult, - ToolMetadata, -) - - -class MockLLMClient: - """Mock LLM client for testing.""" - - def __init__(self, response=None): - self.response = response or self._default_response() - self.call_count = 0 - - def _default_response(self): - return """[ - { - "id": 1, - "action": "Search for information", - "tool_id": "search", - "inputs": {"query": "test"}, - "depends_on": [], - "expected_outputs": ["results"], - "description": "Search step" - }, - { - "id": 2, - "action": "Process results", - "tool_id": "processor", - "inputs": {"data": "results"}, - "depends_on": [1], - "expected_outputs": ["processed"], - "description": "Process step" - } - ]""" - - def chat(self, messages, temperature=0.7, max_tokens=2000): - self.call_count += 1 - return self.response - - -class MockToolSelector: - """Mock tool selector for testing.""" - - def select(self, query, top_k=1): - # Return mock predictions - class MockPrediction: - def __init__(self, tool_id): - self.tool_id = tool_id - self.score = 0.9 - - return [MockPrediction("mock_tool")] - - -class TestHierarchicalPlanner: - """Test hierarchical planner functionality.""" - - def test_create_planner(self): - """Test creating hierarchical planner.""" - config = PlannerConfig() - llm_client = MockLLMClient() - - planner = HierarchicalPlanner(config=config, llm_client=llm_client) - - assert planner.name == "hierarchical_planner" - assert planner.llm_client is not None - - def test_from_config(self): - """Test creating planner from config.""" - config = PlannerConfig(min_steps=3, max_steps=7) - llm_client = MockLLMClient() - - planner = HierarchicalPlanner.from_config(config=config, llm_client=llm_client) - - assert planner.config.min_steps == 3 - assert planner.config.max_steps == 7 - - def test_plan_basic(self): - """Test basic plan generation.""" - config = PlannerConfig(min_steps=2) # Mock returns 2 steps - llm_client = MockLLMClient() - - planner = HierarchicalPlanner(config=config, llm_client=llm_client) - - request = PlanRequest( - goal="Test goal", - tools=[ - ToolMetadata( - tool_id="search", - name="Search", - description="Search tool", - category="search", - capabilities=["search"], - ) - ], - max_steps=10, - ) - - result = planner.plan(request) - - assert isinstance(result, PlanResult) - assert result.success is True - assert len(result.steps) >= 2 - assert llm_client.call_count == 1 - - def test_plan_with_malformed_llm_output(self): - """Test plan generation with malformed LLM output.""" - config = PlannerConfig(enable_repair=True) - llm_client = MockLLMClient(response="This is not valid JSON at all!") - - planner = HierarchicalPlanner(config=config, llm_client=llm_client) - - request = PlanRequest(goal="Test goal", tools=[], max_steps=10) - - result = planner.plan(request) - - # Should use fallback plan - assert isinstance(result, PlanResult) - assert result.success is True - assert len(result.steps) > 0 # Fallback plan - - def test_plan_without_llm_client_fails(self): - """Test planning without LLM client fails gracefully.""" - config = PlannerConfig() - - planner = HierarchicalPlanner(config=config, llm_client=None) - - request = PlanRequest(goal="Test goal", tools=[], max_steps=10) - - result = planner.plan(request) - - assert isinstance(result, PlanResult) - assert result.success is False - assert "LLM client not configured" in result.error_message - - def test_plan_with_tool_selector(self): - """Test plan generation with tool selector.""" - config = PlannerConfig() - - # Mock response without tool_id - response = """[ - { - "id": 1, - "action": "Do something", - "inputs": {}, - "depends_on": [], - "expected_outputs": [], - "description": "Test" - } - ]""" - - llm_client = MockLLMClient(response=response) - tool_selector = MockToolSelector() - - planner = HierarchicalPlanner( - config=config, llm_client=llm_client, tool_selector=tool_selector - ) - - request = PlanRequest( - goal="Test goal", - tools=[ - ToolMetadata( - tool_id="tool1", - name="Tool 1", - description="Test tool", - category="test", - capabilities=[], - ) - ], - max_steps=10, - ) - - result = planner.plan(request) - - assert result.success is True - # Tool selector should have assigned a tool - if len(result.steps) > 0: - # At least one step should have a tool assigned - assert any(step.tool_id for step in result.steps) - - def test_plan_with_retry(self): - """Test plan generation with retry on failure.""" - config = PlannerConfig(max_retries=2, min_steps=2) # Mock returns 2 steps - - # First call fails, second succeeds - call_count = [0] - - class RetryMockClient: - def chat(self, messages, temperature=0.7, max_tokens=2000): - call_count[0] += 1 - if call_count[0] == 1: - raise RuntimeError("First call failed") - return MockLLMClient()._default_response() - - llm_client = RetryMockClient() - - planner = HierarchicalPlanner(config=config, llm_client=llm_client) - - request = PlanRequest(goal="Test goal", tools=[], max_steps=10) - - result = planner.plan(request) - - # Should succeed on second attempt - assert call_count[0] == 2 - assert result.success is True - - def test_plan_max_retries_exceeded(self): - """Test plan generation fails after max retries.""" - config = PlannerConfig(max_retries=1) - - class FailingClient: - def chat(self, messages, temperature=0.7, max_tokens=2000): - raise RuntimeError("Always fails") - - llm_client = FailingClient() - - planner = HierarchicalPlanner(config=config, llm_client=llm_client) - - request = PlanRequest(goal="Test goal", tools=[], max_steps=10) - - result = planner.plan(request) - - assert result.success is False - assert "failed after" in result.error_message.lower() - - def test_get_stats(self): - """Test getting planner statistics.""" - config = PlannerConfig() - llm_client = MockLLMClient() - - planner = HierarchicalPlanner(config=config, llm_client=llm_client) - - # Generate a few plans - request = PlanRequest(goal="Test", tools=[], max_steps=10) - - planner.plan(request) - planner.plan(request) - - stats = planner.get_stats() - - assert stats["total_plans"] == 2 - assert stats["failed_plans"] >= 0 - assert "success_rate" in stats - assert "repair_rate" in stats - - def test_dependency_validation(self): - """Test dependency validation in planning.""" - config = PlannerConfig(enable_dependency_check=True) - - # Response with cycle - response = """[ - { - "id": 1, - "action": "Step 1", - "depends_on": [2], - "expected_outputs": [] - }, - { - "id": 2, - "action": "Step 2", - "depends_on": [1], - "expected_outputs": [] - } - ]""" - - llm_client = MockLLMClient(response=response) - - planner = HierarchicalPlanner(config=config, llm_client=llm_client) - - request = PlanRequest(goal="Test", tools=[], max_steps=10) - result = planner.plan(request) - - # Should detect and repair cycle - assert result.success is True - - # Verify no cycles in final plan - from sage_libs.sage_agentic.agents.planning import DependencyGraph - - if len(result.steps) > 0: - graph = DependencyGraph(result.steps) - assert not graph.has_cycle() diff --git a/packages/sage-libs/tests/lib/agentic/planning/test_timing_decider.py b/packages/sage-libs/tests/lib/agentic/planning/test_timing_decider.py deleted file mode 100644 index 53df2140e6..0000000000 --- a/packages/sage-libs/tests/lib/agentic/planning/test_timing_decider.py +++ /dev/null @@ -1,227 +0,0 @@ -""" -Tests for Timing Decider modules. -""" - -import pytest -from sage_libs.sage_agentic.agents.planning import ( - HybridTimingDecider, - LLMBasedTimingDecider, - RuleBasedTimingDecider, - TimingConfig, - TimingDecision, - TimingMessage, -) - - -class TestRuleBasedTimingDecider: - """Test rule-based timing decider.""" - - def test_create_rule_based_decider(self): - """Test creating rule-based decider.""" - config = TimingConfig() - decider = RuleBasedTimingDecider(config) - - assert decider.name == "rule_based_timing_decider" - assert decider.config == config - - def test_greeting_no_tool_call(self): - """Test greetings don't trigger tool calls.""" - config = TimingConfig() - decider = RuleBasedTimingDecider(config) - - messages = ["Hello", "Hi there", "你好", "Thanks", "谢谢"] - - for msg in messages: - message = TimingMessage(user_message=msg) - decision = decider.decide(message) - - assert decision.should_call_tool is False - assert decision.confidence > 0.9 - assert ( - "casual" in decision.reasoning.lower() or "greeting" in decision.reasoning.lower() - ) - - def test_action_keywords_trigger_tool_call(self): - """Test action keywords trigger tool calls.""" - config = TimingConfig() - decider = RuleBasedTimingDecider(config) - - messages = [ - "Search for flights", - "Find a restaurant", - "Calculate the sum", - "Book a hotel", - "搜索天气信息", - ] - - for msg in messages: - message = TimingMessage(user_message=msg) - decision = decider.decide(message) - - assert decision.should_call_tool is True - assert decision.confidence > 0.7 - - def test_recent_tool_call_no_new_call(self): - """Test recent tool call suggests waiting for response.""" - config = TimingConfig() - decider = RuleBasedTimingDecider(config) - - message = TimingMessage( - user_message="What did you find?", - last_tool_call={ - "tool_id": "search", - "timestamp": "2024-01-01T00:00:00", - "result": "Some results", - }, - ) - - decision = decider.decide(message) - - assert decision.should_call_tool is False - assert decision.confidence > 0.85 - assert "recent" in decision.reasoning.lower() - - def test_weather_question_triggers_tool(self): - """Test weather questions trigger tool calls.""" - config = TimingConfig() - decider = RuleBasedTimingDecider(config) - - messages = [ - "What's the weather in Beijing?", - "What's the current temperature?", - "What's the latest news?", - ] - - for msg in messages: - message = TimingMessage(user_message=msg) - decision = decider.decide(message) - - assert decision.should_call_tool is True - - def test_short_question_likely_needs_lookup(self): - """Test short questions lean toward tool use.""" - config = TimingConfig() - decider = RuleBasedTimingDecider(config) - - message = TimingMessage(user_message="Capital of France?") - decision = decider.decide(message) - - # Should lean toward calling tool, but with lower confidence - assert decision.should_call_tool is True - assert decision.confidence >= 0.6 - - -class TestLLMBasedTimingDecider: - """Test LLM-based timing decider.""" - - def test_create_llm_based_decider_without_client_fails(self): - """Test creating LLM-based decider without client.""" - config = TimingConfig() - decider = LLMBasedTimingDecider(config, llm_client=None) - - assert decider.name == "llm_based_timing_decider" - - # Should fail when trying to decide - message = TimingMessage(user_message="Test") - decision = decider.decide(message) - - # Should return safe fallback - assert decision.should_call_tool is False - assert decision.confidence == 0.5 - - def test_create_llm_based_decider_with_mock_client(self): - """Test LLM-based decider with mock client.""" - config = TimingConfig() - - # Mock LLM client - class MockLLMClient: - def chat(self, messages, temperature=0.7, max_tokens=300): - # Return mock decision - return '{"should_call_tool": true, "confidence": 0.85, "reasoning": "Test", "suggested_tool": null}' - - mock_client = MockLLMClient() - decider = LLMBasedTimingDecider(config, llm_client=mock_client) - - message = TimingMessage(user_message="What's the weather?") - decision = decider.decide(message) - - assert decision.should_call_tool is True - assert decision.confidence == 0.85 - assert decision.reasoning == "Test" - - -class TestHybridTimingDecider: - """Test hybrid timing decider.""" - - def test_create_hybrid_decider(self): - """Test creating hybrid decider.""" - config = TimingConfig(decision_threshold=0.8) - decider = HybridTimingDecider(config, llm_client=None) - - assert decider.name == "hybrid_timing_decider" - assert decider.confidence_threshold == 0.8 - - def test_high_confidence_uses_rules_only(self): - """Test high-confidence decisions use rules only.""" - config = TimingConfig(decision_threshold=0.8) - decider = HybridTimingDecider(config, llm_client=None) - - # Greeting should have high confidence from rules - message = TimingMessage(user_message="Hello!") - decision = decider.decide(message) - - assert decision.should_call_tool is False - assert decision.confidence > 0.9 - - def test_low_confidence_would_use_llm(self): - """Test low-confidence cases would delegate to LLM.""" - config = TimingConfig(decision_threshold=0.9) - - # Mock LLM client - class MockLLMClient: - def chat(self, messages, temperature=0.7, max_tokens=300): - return '{"should_call_tool": false, "confidence": 0.95, "reasoning": "LLM decision", "suggested_tool": null}' - - mock_client = MockLLMClient() - decider = HybridTimingDecider(config, llm_client=mock_client) - - # Ambiguous question that rules might not be confident about - message = TimingMessage(user_message="Tell me about AI") - decision = decider.decide(message) - - # If rule confidence < threshold, should use LLM - if decision.confidence < 0.9: - # LLM was used, check for hybrid marker - assert "[Hybrid]" in decision.reasoning or decision.confidence >= 0.9 - - -class TestTimingDecision: - """Test TimingDecision model.""" - - def test_create_timing_decision(self): - """Test creating timing decision.""" - decision = TimingDecision( - should_call_tool=True, - confidence=0.85, - reasoning="User requested search", - suggested_tool="search_tool", - ) - - assert decision.should_call_tool is True - assert decision.confidence == 0.85 - assert decision.reasoning == "User requested search" - assert decision.suggested_tool == "search_tool" - - def test_confidence_validation(self): - """Test confidence must be between 0 and 1.""" - with pytest.raises((ValueError, AssertionError)): - TimingDecision( - should_call_tool=True, - confidence=1.5, # Invalid - ) - - with pytest.raises((ValueError, AssertionError)): - TimingDecision( - should_call_tool=True, - confidence=-0.1, # Invalid - ) diff --git a/packages/sage-libs/tests/lib/agentic/planning/test_tot_planner.py b/packages/sage-libs/tests/lib/agentic/planning/test_tot_planner.py deleted file mode 100644 index d0bbd2d7ad..0000000000 --- a/packages/sage-libs/tests/lib/agentic/planning/test_tot_planner.py +++ /dev/null @@ -1,400 +0,0 @@ -""" -Tests for Tree-of-Thoughts (ToT) Planner module. -""" - -from sage_libs.sage_agentic.agents.planning import ( - PlanRequest, - SearchMethod, - ThoughtNode, - ToolMetadata, - ToTConfig, - TreeOfThoughtsPlanner, -) - - -class MockLLMClient: - """Mock LLM client for testing.""" - - def __init__(self, responses=None): - self.responses = responses or [] - self.call_count = 0 - - def chat(self, messages, temperature=0.7, max_tokens=512): - self.call_count += 1 - if self.responses: - return self.responses[min(self.call_count - 1, len(self.responses) - 1)] - return self._default_response() - - def _default_response(self): - return """[ - {"thought": "Search for information", "tool_id": "search", "reasoning": "Need data first"}, - {"thought": "Process the results", "tool_id": "processor", "reasoning": "Transform data"}, - {"thought": "Save output", "tool_id": "file_write", "reasoning": "Store results"} - ]""" - - -class TestToTConfig: - """Test ToTConfig configuration class.""" - - def test_default_config(self): - """Test default configuration values.""" - config = ToTConfig() - - assert config.max_depth == 3 - assert config.branch_factor == 3 - assert config.search_method == SearchMethod.BFS - assert config.beam_width == 5 - assert config.min_thought_score == 0.3 - assert config.early_stop_score == 0.9 - - def test_custom_config(self): - """Test custom configuration.""" - config = ToTConfig( - max_depth=5, - branch_factor=4, - search_method=SearchMethod.DFS, - beam_width=10, - min_thought_score=0.5, - ) - - assert config.max_depth == 5 - assert config.branch_factor == 4 - assert config.search_method == SearchMethod.DFS - assert config.beam_width == 10 - assert config.min_thought_score == 0.5 - - def test_config_inherits_from_planner_config(self): - """Test that ToTConfig inherits from PlannerConfig.""" - config = ToTConfig(min_steps=3, max_steps=8) - - assert config.min_steps == 3 - assert config.max_steps == 8 - - -class TestThoughtNode: - """Test ThoughtNode dataclass.""" - - def test_create_root_node(self): - """Test creating a root node.""" - root = ThoughtNode(thought="", score=1.0) - - assert root.thought == "" - assert root.score == 1.0 - assert root.parent is None - assert root.depth == 0 - assert root.children == [] - - def test_create_child_node(self): - """Test creating child nodes.""" - root = ThoughtNode(thought="", score=1.0) - child = ThoughtNode( - thought="Search for data", - score=0.8, - parent=root, - tool_id="search", - ) - - assert child.thought == "Search for data" - assert child.score == 0.8 - assert child.parent is root - assert child.depth == 1 - assert child.tool_id == "search" - - def test_get_path(self): - """Test getting path from root to node.""" - root = ThoughtNode(thought="", score=1.0) - child1 = ThoughtNode(thought="Step 1", score=0.8, parent=root) - child2 = ThoughtNode(thought="Step 2", score=0.7, parent=child1) - - root.children.append(child1) - child1.children.append(child2) - - path = child2.get_path() - - assert len(path) == 3 - assert path[0] is root - assert path[1] is child1 - assert path[2] is child2 - - def test_get_path_thoughts(self): - """Test getting thoughts along path.""" - root = ThoughtNode(thought="", score=1.0) - child1 = ThoughtNode(thought="Step 1", score=0.8, parent=root) - child2 = ThoughtNode(thought="Step 2", score=0.7, parent=child1) - - thoughts = child2.get_path_thoughts() - - # Should skip empty root thought, returning only non-empty thoughts - assert len(thoughts) == 2 - assert thoughts[0] == "Step 1" - assert thoughts[1] == "Step 2" - - def test_get_cumulative_score(self): - """Test cumulative score calculation.""" - root = ThoughtNode(thought="", score=1.0) - child1 = ThoughtNode(thought="Step 1", score=0.8, parent=root) - child2 = ThoughtNode(thought="Step 2", score=0.6, parent=child1) - - avg_score = child2.get_cumulative_score() - - # (1.0 + 0.8 + 0.6) / 3 = 0.8 - assert abs(avg_score - 0.8) < 0.001 - - -class TestTreeOfThoughtsPlanner: - """Test TreeOfThoughtsPlanner class.""" - - def test_create_planner(self): - """Test creating ToT planner.""" - config = ToTConfig() - planner = TreeOfThoughtsPlanner(config=config) - - assert planner.name == "tree_of_thoughts_planner" - assert planner.config.max_depth == 3 - assert planner.llm_client is None - - def test_create_planner_with_llm(self): - """Test creating planner with LLM client.""" - config = ToTConfig() - llm_client = MockLLMClient() - - planner = TreeOfThoughtsPlanner(config=config, llm_client=llm_client) - - assert planner.llm_client is llm_client - - def test_from_config(self): - """Test creating planner from config.""" - config = ToTConfig(max_depth=5, search_method=SearchMethod.DFS) - llm_client = MockLLMClient() - - planner = TreeOfThoughtsPlanner.from_config(config=config, llm_client=llm_client) - - assert planner.config.max_depth == 5 - assert planner.config.search_method == SearchMethod.DFS - - def test_plan_without_llm_uses_fallback(self): - """Test planning without LLM uses heuristic fallback.""" - config = ToTConfig(max_depth=2, branch_factor=2) - planner = TreeOfThoughtsPlanner(config=config, llm_client=None) - - tools = [ - ToolMetadata( - tool_id="search", - name="search", - description="Search for information", - category="retrieval", - ), - ToolMetadata( - tool_id="process", - name="process", - description="Process data", - category="transform", - ), - ] - - request = PlanRequest( - goal="Find and process data", - tools=tools, - min_steps=2, - max_steps=5, - ) - - result = planner.plan(request) - - assert result is not None - assert result.success - assert len(result.steps) >= request.min_steps - - def test_plan_with_mock_llm(self): - """Test planning with mock LLM client.""" - config = ToTConfig(max_depth=2, branch_factor=2) - - # Mock responses for thought generation and evaluation - llm_client = MockLLMClient( - responses=[ - # Thought generation response - """[ - {"thought": "Search for data", "tool_id": "search", "reasoning": "Need data"}, - {"thought": "Process results", "tool_id": "process", "reasoning": "Transform"} - ]""", - # Evaluation response - '{"score": 8, "reasoning": "Good step"}', - '{"score": 7, "reasoning": "Reasonable step"}', - # More thought generation - """[ - {"thought": "Save output", "tool_id": "file_write", "reasoning": "Store"} - ]""", - ] - ) - - planner = TreeOfThoughtsPlanner(config=config, llm_client=llm_client) - - tools = [ - ToolMetadata( - tool_id="search", - name="search", - description="Search for information", - category="retrieval", - ), - ToolMetadata( - tool_id="process", - name="process", - description="Process data", - category="transform", - ), - ToolMetadata( - tool_id="file_write", - name="file_write", - description="Write to file", - category="io", - ), - ] - - request = PlanRequest( - goal="Find, process, and save data", - tools=tools, - min_steps=2, - max_steps=5, - ) - - result = planner.plan(request) - - assert result is not None - assert llm_client.call_count > 0 # LLM was called - - def test_plan_result_has_metadata(self): - """Test that plan result includes ToT metadata.""" - config = ToTConfig(max_depth=2, search_method=SearchMethod.BFS) - planner = TreeOfThoughtsPlanner(config=config, llm_client=None) - - tools = [ - ToolMetadata( - tool_id="search", - name="search", - description="Search", - category="retrieval", - ), - ] - - request = PlanRequest(goal="Test", tools=tools, min_steps=1, max_steps=3) - - result = planner.plan(request) - - assert "search_method" in result.metadata - assert result.metadata["search_method"] == "bfs" - - def test_bfs_search_method(self): - """Test BFS search produces valid results.""" - config = ToTConfig(max_depth=2, search_method=SearchMethod.BFS, beam_width=3) - planner = TreeOfThoughtsPlanner(config=config, llm_client=None) - - tools = [ - ToolMetadata( - tool_id=f"tool_{i}", - name=f"tool_{i}", - description=f"Tool {i}", - category="test", - ) - for i in range(5) - ] - - request = PlanRequest(goal="Use tools", tools=tools, min_steps=2, max_steps=4) - - result = planner.plan(request) - - assert result.success - assert len(result.steps) >= 2 - - def test_dfs_search_method(self): - """Test DFS search produces valid results.""" - config = ToTConfig(max_depth=2, search_method=SearchMethod.DFS) - planner = TreeOfThoughtsPlanner(config=config, llm_client=None) - - tools = [ - ToolMetadata( - tool_id=f"tool_{i}", - name=f"tool_{i}", - description=f"Tool {i}", - category="test", - ) - for i in range(3) - ] - - request = PlanRequest(goal="Use tools", tools=tools, min_steps=2, max_steps=4) - - result = planner.plan(request) - - assert result.success - assert len(result.steps) >= 2 - - def test_get_statistics(self): - """Test getting planner statistics.""" - config = ToTConfig(max_depth=2, branch_factor=3) - planner = TreeOfThoughtsPlanner(config=config, llm_client=None) - - # Run a plan to generate statistics - tools = [ - ToolMetadata( - tool_id="search", - name="search", - description="Search", - category="retrieval", - ), - ] - request = PlanRequest(goal="Test", tools=tools, min_steps=1, max_steps=2) - planner.plan(request) - - stats = planner.get_statistics() - - assert "total_nodes_generated" in stats - assert "total_nodes_evaluated" in stats - assert "config" in stats - assert stats["config"]["max_depth"] == 2 - assert stats["config"]["branch_factor"] == 3 - - def test_empty_tools_returns_empty_plan(self): - """Test that empty tools returns result with no steps.""" - config = ToTConfig() - planner = TreeOfThoughtsPlanner(config=config, llm_client=None) - - request = PlanRequest(goal="Do something", tools=[], min_steps=1, max_steps=5) - - result = planner.plan(request) - - # Should still return a result, but may not be "successful" - assert result is not None - - def test_plan_respects_max_steps(self): - """Test that plan respects max_steps constraint.""" - config = ToTConfig(max_depth=5) # Allow deep search - planner = TreeOfThoughtsPlanner(config=config, llm_client=None) - - tools = [ - ToolMetadata( - tool_id=f"tool_{i}", - name=f"tool_{i}", - description=f"Tool {i}", - category="test", - ) - for i in range(10) - ] - - request = PlanRequest(goal="Use tools", tools=tools, min_steps=1, max_steps=3) - - result = planner.plan(request) - - assert len(result.steps) <= request.max_steps - - -class TestSearchMethod: - """Test SearchMethod enum.""" - - def test_search_method_values(self): - """Test search method enum values.""" - assert SearchMethod.BFS.value == "bfs" - assert SearchMethod.DFS.value == "dfs" - - def test_search_method_from_string(self): - """Test creating search method from string.""" - assert SearchMethod("bfs") == SearchMethod.BFS - assert SearchMethod("dfs") == SearchMethod.DFS diff --git a/packages/sage-libs/tests/lib/agentic/runtime/test_runtime.py b/packages/sage-libs/tests/lib/agentic/runtime/test_runtime.py deleted file mode 100644 index ddb910b218..0000000000 --- a/packages/sage-libs/tests/lib/agentic/runtime/test_runtime.py +++ /dev/null @@ -1,279 +0,0 @@ -""" -Tests for agent runtime module. -""" - -import pytest -from sage_libs.sage_agentic.agents.runtime import ( - BenchmarkAdapter, - Orchestrator, - PlannerConfig, - RuntimeConfig, - SelectorConfig, - TelemetryCollector, - TelemetryConfig, - TimingConfig, -) - - -class TestRuntimeConfig: - """Test runtime configuration models.""" - - def test_default_config(self): - """Test creating config with defaults.""" - config = RuntimeConfig() - - assert config.max_turns == 8 - assert config.timeout == 30.0 - assert config.selector.name == "keyword" - assert config.planner.name == "llm" - assert config.timing.name == "rule_based" - assert config.telemetry.enabled is True - - def test_custom_config(self): - """Test creating config with custom values.""" - config = RuntimeConfig( - selector=SelectorConfig(name="embedding", top_k=10), - planner=PlannerConfig(name="hierarchical", max_steps=15), - timing=TimingConfig(name="llm_based", threshold=0.7), - max_turns=12, - ) - - assert config.selector.name == "embedding" - assert config.selector.top_k == 10 - assert config.planner.name == "hierarchical" - assert config.planner.max_steps == 15 - assert config.timing.name == "llm_based" - assert config.timing.threshold == 0.7 - assert config.max_turns == 12 - - def test_config_dict_conversion(self): - """Test config can be created from dict.""" - config_dict = { - "selector": {"name": "bm25", "top_k": 3}, - "planner": {"name": "cot", "max_steps": 8}, - "max_turns": 10, - } - - config = RuntimeConfig(**config_dict) - - assert config.selector.name == "bm25" - assert config.selector.top_k == 3 - assert config.planner.name == "cot" - assert config.planner.max_steps == 8 - - -class TestTelemetry: - """Test telemetry collection.""" - - def test_telemetry_collection(self): - """Test basic telemetry collection.""" - config = TelemetryConfig(enabled=True) - collector = TelemetryCollector(config) - - # Start and finish operation - record = collector.start("tool_selection", metadata={"top_k": 5}) - assert record.operation == "tool_selection" - assert record.metadata["top_k"] == 5 - - collector.finish(record, success=True) - assert record.success is True - assert record.duration is not None - assert record.duration >= 0 - - def test_telemetry_metrics(self): - """Test metrics aggregation.""" - config = TelemetryConfig(enabled=True) - collector = TelemetryCollector(config) - - # Simulate multiple operations - for i in range(5): - record = collector.start("tool_selection") - collector.finish(record, success=i < 4) # 1 failure - - metrics = collector.get_metrics() - - assert metrics["total_operations"] == 5 - assert metrics["successful_operations"] == 4 - assert metrics["failed_operations"] == 1 - assert metrics["success_rate"] == 0.8 - assert "avg_latency" in metrics - - def test_telemetry_disabled(self): - """Test telemetry when disabled.""" - config = TelemetryConfig(enabled=False) - collector = TelemetryCollector(config) - - record = collector.start("tool_selection") - collector.finish(record) - - # Should not collect when disabled - assert len(collector.records) == 0 - - -class MockSelector: - """Mock tool selector for testing.""" - - def select(self, query, top_k=5): - """Return mock selections.""" - return [{"tool_id": f"tool_{i}", "score": 1.0 - i * 0.1} for i in range(top_k)] - - -class MockPlanner: - """Mock planner for testing.""" - - def plan(self, request): - """Return mock plan.""" - return {"steps": [{"action": "step1"}, {"action": "step2"}]} - - -class MockTimingDecider: - """Mock timing decider for testing.""" - - def decide(self, message): - """Return mock decision.""" - return {"decision": "call", "confidence": 0.9} - - -class TestOrchestrator: - """Test orchestrator functionality.""" - - def test_orchestrator_initialization(self): - """Test creating orchestrator.""" - config = RuntimeConfig() - orchestrator = Orchestrator(config=config) - - assert orchestrator.config == config - assert orchestrator.telemetry is not None - - def test_tool_selection_execution(self): - """Test tool selection through orchestrator.""" - config = RuntimeConfig() - selector = MockSelector() - orchestrator = Orchestrator(config=config, selector=selector) - - result = orchestrator.execute_tool_selection("test query", top_k=3) - - assert len(result) == 3 - assert result[0]["tool_id"] == "tool_0" - - # Check telemetry - metrics = orchestrator.get_telemetry_metrics() - assert metrics["total_operations"] == 1 - assert "tool_selection" in metrics["by_operation"] - - def test_planning_execution(self): - """Test planning through orchestrator.""" - config = RuntimeConfig() - planner = MockPlanner() - orchestrator = Orchestrator(config=config, planner=planner) - - result = orchestrator.execute_planning("test request") - - assert "steps" in result - assert len(result["steps"]) == 2 - - # Check telemetry - metrics = orchestrator.get_telemetry_metrics() - assert metrics["total_operations"] == 1 - - def test_timing_execution(self): - """Test timing decision through orchestrator.""" - config = RuntimeConfig() - timing_decider = MockTimingDecider() - orchestrator = Orchestrator(config=config, timing_decider=timing_decider) - - result = orchestrator.execute_timing_decision("test message") - - assert result["decision"] == "call" - assert result["confidence"] == 0.9 - - def test_orchestrator_without_component_raises(self): - """Test orchestrator raises error when component missing.""" - config = RuntimeConfig() - orchestrator = Orchestrator(config=config) - - with pytest.raises(RuntimeError, match="Tool selector not configured"): - orchestrator.execute_tool_selection("test") - - with pytest.raises(RuntimeError, match="Planner not configured"): - orchestrator.execute_planning("test") - - with pytest.raises(RuntimeError, match="Timing decider not configured"): - orchestrator.execute_timing_decision("test") - - -class TestBenchmarkAdapter: - """Test benchmark adapter functionality.""" - - def test_adapter_initialization(self): - """Test creating adapter.""" - config = RuntimeConfig() - orchestrator = Orchestrator(config=config) - adapter = BenchmarkAdapter(orchestrator) - - assert adapter.orchestrator == orchestrator - - def test_adapter_tool_selection(self): - """Test tool selection through adapter.""" - config = RuntimeConfig() - selector = MockSelector() - orchestrator = Orchestrator(config=config, selector=selector) - adapter = BenchmarkAdapter(orchestrator) - - result = adapter.run_tool_selection("test query", top_k=5) - - assert len(result) == 5 - assert all("tool_id" in r for r in result) - - def test_adapter_planning(self): - """Test planning through adapter.""" - config = RuntimeConfig() - planner = MockPlanner() - orchestrator = Orchestrator(config=config, planner=planner) - adapter = BenchmarkAdapter(orchestrator) - - result = adapter.run_planning("test request") - - assert "steps" in result - - def test_adapter_timing(self): - """Test timing through adapter.""" - config = RuntimeConfig() - timing_decider = MockTimingDecider() - orchestrator = Orchestrator(config=config, timing_decider=timing_decider) - adapter = BenchmarkAdapter(orchestrator) - - result = adapter.run_timing("test message") - - assert result["decision"] == "call" - - def test_adapter_get_metrics(self): - """Test getting metrics from adapter.""" - config = RuntimeConfig() - selector = MockSelector() - orchestrator = Orchestrator(config=config, selector=selector) - adapter = BenchmarkAdapter(orchestrator) - - # Execute some operations - adapter.run_tool_selection("query1") - adapter.run_tool_selection("query2") - - metrics = adapter.get_metrics() - - assert metrics["total_operations"] == 2 - assert metrics["successful_operations"] == 2 - - def test_adapter_reset(self): - """Test resetting adapter state.""" - config = RuntimeConfig() - selector = MockSelector() - orchestrator = Orchestrator(config=config, selector=selector) - adapter = BenchmarkAdapter(orchestrator) - - # Execute operations - adapter.run_tool_selection("query1") - assert adapter.get_metrics()["total_operations"] == 1 - - # Reset - adapter.reset() - assert adapter.get_metrics() == {} diff --git a/packages/sage-libs/tests/lib/agentic/test_benchmarks.py b/packages/sage-libs/tests/lib/agentic/test_benchmarks.py deleted file mode 100644 index 61549b60ff..0000000000 --- a/packages/sage-libs/tests/lib/agentic/test_benchmarks.py +++ /dev/null @@ -1,472 +0,0 @@ -""" -Performance benchmark tests for agentic modules. - -Validates performance requirements: -- Planning: < 1.5 seconds for complex multi-step plans -- Timing decision: < 100ms per decision -- Tool selection: < 100ms per selection -""" - -from __future__ import annotations - -import time -from dataclasses import dataclass -from typing import Any - -import pytest - -# ============================================================================ -# Mock Components for Benchmarking -# ============================================================================ - - -@dataclass -class MockTool: - """Mock tool for benchmarking.""" - - name: str - description: str - - -class MockLLMClient: - """Mock LLM client with configurable latency.""" - - def __init__(self, latency_ms: float = 0): - self._latency = latency_ms / 1000.0 - self._response = self._generate_plan_response(10) - - def _generate_plan_response(self, num_steps: int) -> str: - import json - - steps = [] - for i in range(num_steps): - deps = [f"step_{j}" for j in range(max(0, i - 2), i)] - steps.append( - { - "step_id": f"step_{i}", - "description": f"Step {i}", - "dependencies": deps, - "tools": [f"tool_{i % 3}"], - } - ) - return json.dumps({"steps": steps}) - - def generate(self, prompt: str, **kwargs) -> str: - if self._latency > 0: - time.sleep(self._latency) - return self._response - - -class FastToolSelector: - """Fast mock tool selector for benchmarking.""" - - def __init__(self, tools: list[MockTool]): - self._tools = tools - self._tool_index = {t.name: t for t in tools} - - def select(self, query: str, top_k: int = 5, **kwargs) -> list[dict[str, Any]]: - """Fast keyword-based tool selection.""" - query_words = set(query.lower().split()) - results = [] - - for tool in self._tools: - desc_words = set(tool.description.lower().split()) - overlap = len(query_words & desc_words) - score = overlap / max(len(query_words), 1) - results.append({"tool": tool.name, "score": score}) - - results.sort(key=lambda x: x["score"], reverse=True) - return results[:top_k] - - -class FastTimingDecider: - """Fast mock timing decider for benchmarking.""" - - ACTION_KEYWORDS = frozenset( - ["search", "find", "calculate", "analyze", "create", "update", "delete"] - ) - CASUAL_KEYWORDS = frozenset(["hello", "hi", "thanks", "bye", "ok"]) - - def decide(self, context: dict[str, Any]) -> dict[str, Any]: - message = context.get("user_message", "").lower() - words = set(message.split()) - - if words & self.CASUAL_KEYWORDS: - return {"should_call_tool": False, "confidence": 0.95} - if words & self.ACTION_KEYWORDS: - return {"should_call_tool": True, "confidence": 0.9} - return {"should_call_tool": True, "confidence": 0.6} - - -# ============================================================================ -# Performance Benchmark Tests -# ============================================================================ - - -class TestTimingDeciderPerformance: - """Benchmark timing decider performance.""" - - @pytest.fixture - def decider(self) -> FastTimingDecider: - return FastTimingDecider() - - @pytest.fixture - def test_messages(self) -> list[str]: - return [ - "Hello!", - "Search for AI papers", - "Calculate the sum of these numbers", - "What is the weather today?", - "Analyze the quarterly report", - "Find similar documents", - "Create a new project", - "Update the configuration", - "Delete old records", - "Thanks for your help!", - ] - - def test_single_decision_under_100ms( - self, decider: FastTimingDecider, test_messages: list[str] - ): - """Each timing decision should complete in under 100ms.""" - for msg in test_messages: - start = time.perf_counter() - result = decider.decide({"user_message": msg}) - elapsed_ms = (time.perf_counter() - start) * 1000 - - assert elapsed_ms < 100, f"Decision for '{msg}' took {elapsed_ms:.2f}ms" - assert "should_call_tool" in result - assert "confidence" in result - - def test_1000_decisions_under_1_second( - self, decider: FastTimingDecider, test_messages: list[str] - ): - """1000 timing decisions should complete in under 1 second.""" - start = time.perf_counter() - - for i in range(1000): - msg = test_messages[i % len(test_messages)] - decider.decide({"user_message": msg}) - - elapsed = time.perf_counter() - start - avg_ms = (elapsed / 1000) * 1000 - - assert elapsed < 1.0, f"1000 decisions took {elapsed:.3f}s" - assert avg_ms < 1.0, f"Average decision time {avg_ms:.3f}ms exceeds 1ms" - - def test_timing_decision_p99_latency( - self, decider: FastTimingDecider, test_messages: list[str] - ): - """P99 latency for timing decisions should be under 10ms.""" - latencies = [] - - for i in range(100): - msg = test_messages[i % len(test_messages)] - start = time.perf_counter() - decider.decide({"user_message": msg}) - latencies.append((time.perf_counter() - start) * 1000) - - latencies.sort() - p99 = latencies[98] # 99th percentile - - assert p99 < 10, f"P99 latency {p99:.3f}ms exceeds 10ms" - - -class TestToolSelectionPerformance: - """Benchmark tool selection performance.""" - - @pytest.fixture - def tools(self) -> list[MockTool]: - """Create a realistic set of 50 tools.""" - return [ - MockTool(f"tool_{i}", f"Description for tool {i} with keywords {i % 10}") - for i in range(50) - ] - - @pytest.fixture - def selector(self, tools: list[MockTool]) -> FastToolSelector: - return FastToolSelector(tools) - - @pytest.fixture - def test_queries(self) -> list[str]: - return [ - "search documents", - "calculate total", - "find similar items", - "analyze data patterns", - "process input files", - ] - - def test_single_selection_under_100ms( - self, selector: FastToolSelector, test_queries: list[str] - ): - """Each tool selection should complete in under 100ms.""" - for query in test_queries: - start = time.perf_counter() - results = selector.select(query, top_k=5) - elapsed_ms = (time.perf_counter() - start) * 1000 - - assert elapsed_ms < 100, f"Selection for '{query}' took {elapsed_ms:.2f}ms" - assert len(results) <= 5 - - def test_500_selections_under_1_second( - self, selector: FastToolSelector, test_queries: list[str] - ): - """500 tool selections should complete in under 1 second.""" - start = time.perf_counter() - - for i in range(500): - query = test_queries[i % len(test_queries)] - selector.select(query, top_k=5) - - elapsed = time.perf_counter() - start - avg_ms = (elapsed / 500) * 1000 - - assert elapsed < 1.0, f"500 selections took {elapsed:.3f}s" - assert avg_ms < 2.0, f"Average selection time {avg_ms:.3f}ms exceeds 2ms" - - def test_large_tool_set_performance(self): - """Test performance with 200 tools.""" - tools = [MockTool(f"tool_{i}", f"Description for tool number {i}") for i in range(200)] - selector = FastToolSelector(tools) - - start = time.perf_counter() - for _ in range(100): - selector.select("search for documents", top_k=10) - elapsed_ms = (time.perf_counter() - start) * 1000 / 100 - - assert elapsed_ms < 50, f"Avg selection with 200 tools: {elapsed_ms:.2f}ms" - - -class TestPlanningPerformance: - """Benchmark planning performance.""" - - @pytest.fixture - def llm_client(self) -> MockLLMClient: - return MockLLMClient(latency_ms=0) # No simulated latency - - def test_plan_parsing_under_100ms(self, llm_client: MockLLMClient): - """Plan generation and parsing should complete in under 100ms (excluding LLM).""" - import json - - start = time.perf_counter() - - # Generate plan - response = llm_client.generate("Create a complex plan") - - # Parse plan - plan = json.loads(response) - - elapsed_ms = (time.perf_counter() - start) * 1000 - - assert elapsed_ms < 100, f"Plan parsing took {elapsed_ms:.2f}ms" - assert "steps" in plan - assert len(plan["steps"]) == 10 - - def test_dependency_graph_construction(self, llm_client: MockLLMClient): - """Dependency graph construction should be fast.""" - import json - - response = llm_client.generate("Create plan") - plan = json.loads(response) - - start = time.perf_counter() - - # Simulate dependency graph construction - graph: dict[str, list[str]] = {} - for step in plan["steps"]: - step_id = step["step_id"] - deps = step["dependencies"] - graph[step_id] = deps - - # Simulate topological sort - visited: set[str] = set() - result: list[str] = [] - - def visit(node: str): - if node in visited: - return - visited.add(node) - for dep in graph.get(node, []): - visit(dep) - result.append(node) - - for node in graph: - visit(node) - - elapsed_ms = (time.perf_counter() - start) * 1000 - - assert elapsed_ms < 10, f"Graph construction took {elapsed_ms:.2f}ms" - assert len(result) == 10 - - def test_complex_plan_under_1500ms(self): - """Complex 20-step plan processing should complete in under 1.5s.""" - import json - - # Create complex plan with 20 steps - steps = [] - for i in range(20): - deps = [f"step_{j}" for j in range(max(0, i - 3), i)] - steps.append( - { - "step_id": f"step_{i}", - "description": f"Complex step {i} with detailed description", - "dependencies": deps, - "tools": [f"tool_{i % 5}"], - } - ) - - plan = {"steps": steps} - plan_json = json.dumps(plan) - - start = time.perf_counter() - - # Parse - parsed = json.loads(plan_json) - - # Build graph - graph: dict[str, list[str]] = {} - for step in parsed["steps"]: - graph[step["step_id"]] = step["dependencies"] - - # Topological sort - visited: set[str] = set() - result: list[str] = [] - - def visit(node: str): - if node in visited: - return - visited.add(node) - for dep in graph.get(node, []): - visit(dep) - result.append(node) - - for node in graph: - visit(node) - - # Validate - assert len(result) == 20 - - elapsed = time.perf_counter() - start - - assert elapsed < 1.5, f"Complex plan processing took {elapsed:.3f}s" - - -class TestFullWorkflowPerformance: - """Benchmark complete workflow performance.""" - - @pytest.fixture - def components(self) -> dict[str, Any]: - tools = [MockTool(f"tool_{i}", f"Tool {i} description") for i in range(30)] - return { - "timing": FastTimingDecider(), - "selector": FastToolSelector(tools), - "llm": MockLLMClient(latency_ms=0), - } - - def test_workflow_iteration_performance(self, components: dict[str, Any]): - """Single workflow iteration should be fast (excluding LLM latency).""" - import json - - timing = components["timing"] - selector = components["selector"] - llm = components["llm"] - - messages = [ - "Search for documents about AI", - "Analyze the sales data", - "Create a summary report", - ] - - for msg in messages: - start = time.perf_counter() - - # Timing decision - decision = timing.decide({"user_message": msg}) - - if decision["should_call_tool"]: - # Generate plan - response = llm.generate(f"Plan: {msg}") - plan = json.loads(response) - - # Select tools for each step - for step in plan["steps"][:5]: # First 5 steps - selector.select(step["description"], top_k=3) - - elapsed_ms = (time.perf_counter() - start) * 1000 - - assert elapsed_ms < 100, f"Workflow for '{msg}' took {elapsed_ms:.2f}ms" - - def test_100_workflows_under_5_seconds(self, components: dict[str, Any]): - """100 complete workflows should complete in under 5 seconds.""" - import json - - timing = components["timing"] - selector = components["selector"] - llm = components["llm"] - - messages = ["Search for docs", "Analyze data", "Create report", "Find items"] - - start = time.perf_counter() - - for i in range(100): - msg = messages[i % len(messages)] - - decision = timing.decide({"user_message": msg}) - - if decision["should_call_tool"]: - response = llm.generate(f"Plan: {msg}") - plan = json.loads(response) - - for step in plan["steps"][:3]: - selector.select(step["description"], top_k=3) - - elapsed = time.perf_counter() - start - avg_ms = (elapsed / 100) * 1000 - - assert elapsed < 5.0, f"100 workflows took {elapsed:.3f}s" - assert avg_ms < 50, f"Average workflow time {avg_ms:.2f}ms exceeds 50ms" - - -class TestMemoryPerformance: - """Test memory usage patterns.""" - - def test_no_memory_leak_in_repeated_selections(self): - """Repeated tool selections should not accumulate memory.""" - import gc - - tools = [MockTool(f"tool_{i}", f"Description {i}") for i in range(100)] - selector = FastToolSelector(tools) - - # Warm up - for _ in range(100): - selector.select("test query", top_k=5) - - gc.collect() - - # Run many selections - for _ in range(1000): - results = selector.select("search for something", top_k=10) - assert len(results) <= 10 - - # If we get here without memory error, we're good - gc.collect() - - def test_no_memory_leak_in_timing_decisions(self): - """Repeated timing decisions should not accumulate memory.""" - import gc - - decider = FastTimingDecider() - - # Warm up - for _ in range(100): - decider.decide({"user_message": "test"}) - - gc.collect() - - # Run many decisions - for i in range(1000): - result = decider.decide({"user_message": f"Message number {i}"}) - assert "should_call_tool" in result - - gc.collect() diff --git a/packages/sage-libs/tests/lib/agentic/test_integration.py b/packages/sage-libs/tests/lib/agentic/test_integration.py deleted file mode 100644 index eaec526a10..0000000000 --- a/packages/sage-libs/tests/lib/agentic/test_integration.py +++ /dev/null @@ -1,426 +0,0 @@ -""" -Integration tests for agentic modules. - -Tests cross-module collaboration between: -- HierarchicalPlanner (planning) -- ToolSelector (tool_selection) -- TimingDecider (planning) -- Runtime Orchestrator (runtime) -""" - -from __future__ import annotations - -import re -import time -from dataclasses import dataclass -from typing import Any - -import pytest - -# ============================================================================ -# Mock Components -# ============================================================================ - - -def _word_match(word: str, text: str) -> bool: - """Check if word exists as a complete word in text.""" - pattern = rf"\b{re.escape(word)}\b" - return bool(re.search(pattern, text, re.IGNORECASE)) - - -@dataclass -class MockTool: - """Mock tool for testing.""" - - name: str - description: str - capabilities: list[str] | None = None - - -class MockToolsLoader: - """Mock tools loader.""" - - def __init__(self, tools: list[MockTool]): - self._tools = tools - - def iter_all(self): - return iter(self._tools) - - -class MockLLMClient: - """Mock LLM client for testing.""" - - def __init__(self, response: str | None = None): - self._response = response or self._default_response() - self.call_count = 0 - self.last_prompt = None - - def _default_response(self) -> str: - return """```json -{ - "steps": [ - { - "step_id": "step_1", - "description": "Search for relevant information", - "dependencies": [], - "tools": ["search_tool"] - }, - { - "step_id": "step_2", - "description": "Process search results", - "dependencies": ["step_1"], - "tools": ["process_tool"] - }, - { - "step_id": "step_3", - "description": "Generate final response", - "dependencies": ["step_2"], - "tools": [] - } - ] -} -```""" - - def generate(self, prompt: str, **kwargs) -> str: - self.call_count += 1 - self.last_prompt = prompt - return self._response - - -class MockToolSelector: - """Mock tool selector for testing.""" - - def __init__(self, tools: list[MockTool]): - self._tools = tools - - def select( - self, query: str, candidates: list | None = None, top_k: int = 5, **kwargs - ) -> list[dict[str, Any]]: - """Select tools based on query keywords.""" - results = [] - query_lower = query.lower() - - for tool in self._tools: - score = 0.0 - if tool.name.lower() in query_lower: - score = 0.9 - elif any(kw in query_lower for kw in tool.description.lower().split()[:3]): - score = 0.7 - else: - score = 0.3 - - results.append({"tool": tool.name, "score": score, "reason": "keyword match"}) - - results.sort(key=lambda x: x["score"], reverse=True) - return results[:top_k] - - -class MockTimingDecider: - """Mock timing decider for testing.""" - - def __init__(self, default_should_call: bool = True): - self._default = default_should_call - self.decide_count = 0 - - def decide(self, context: dict[str, Any]) -> dict[str, Any]: - self.decide_count += 1 - - message = context.get("user_message", "").lower() - - # Rule-based decisions (use word boundary matching) - casual_keywords = ["hello", "hi", "thanks"] - action_keywords = ["search", "find", "calculate", "analyze", "summarize"] - - if any(_word_match(kw, message) for kw in casual_keywords): - should_call = False - confidence = 0.95 - elif any(_word_match(kw, message) for kw in action_keywords): - should_call = True - confidence = 0.9 - else: - should_call = self._default - confidence = 0.6 - - return { - "should_call_tool": should_call, - "confidence": confidence, - "reasoning": "rule-based decision", - } - - -# ============================================================================ -# Integration Tests -# ============================================================================ - - -class TestPlanningWithToolSelection: - """Test integration between HierarchicalPlanner and ToolSelector.""" - - @pytest.fixture - def tools(self) -> list[MockTool]: - return [ - MockTool("search_tool", "Search for information online"), - MockTool("process_tool", "Process and analyze data"), - MockTool("calculator", "Perform mathematical calculations"), - MockTool("file_reader", "Read files from disk"), - ] - - @pytest.fixture - def tool_selector(self, tools: list[MockTool]) -> MockToolSelector: - return MockToolSelector(tools) - - @pytest.fixture - def llm_client(self) -> MockLLMClient: - return MockLLMClient() - - def test_planner_with_tool_selection( - self, llm_client: MockLLMClient, tool_selector: MockToolSelector - ): - """Test that planner can use tool selector to assign tools to steps.""" - # Create a plan - response = llm_client.generate("Plan a research task") - - # Parse steps (simplified) - assert "step_1" in response - assert "search_tool" in response - - # For each step, select appropriate tools - step_queries = [ - "Search for relevant information", - "Process search results", - "Generate final response", - ] - - for query in step_queries: - selected = tool_selector.select(query, top_k=2) - assert len(selected) <= 2 - assert all("score" in s for s in selected) - - def test_tool_selection_performance(self, tool_selector: MockToolSelector): - """Test that tool selection meets performance requirements (<100ms).""" - queries = [ - "Search for documents about AI", - "Calculate the total cost", - "Read the configuration file", - "Process the input data", - ] - - for query in queries: - start = time.perf_counter() - results = tool_selector.select(query, top_k=3) - elapsed = time.perf_counter() - start - - assert elapsed < 0.1, f"Tool selection took {elapsed:.3f}s, exceeds 100ms" - assert len(results) <= 3 - - -class TestTimingWithPlanning: - """Test integration between TimingDecider and planning workflow.""" - - @pytest.fixture - def timing_decider(self) -> MockTimingDecider: - return MockTimingDecider() - - @pytest.fixture - def llm_client(self) -> MockLLMClient: - return MockLLMClient() - - def test_timing_gates_planning( - self, timing_decider: MockTimingDecider, llm_client: MockLLMClient - ): - """Test that timing decision gates the planning process.""" - # Scenario 1: User greeting - no planning needed - context1 = {"user_message": "Hello, how are you?"} - decision1 = timing_decider.decide(context1) - assert not decision1["should_call_tool"] - assert decision1["confidence"] > 0.9 - - # Scenario 2: User request - planning needed - context2 = {"user_message": "Search for recent AI papers and analyze them"} - decision2 = timing_decider.decide(context2) - assert decision2["should_call_tool"] - - # Only invoke planner if timing decision says yes - if decision2["should_call_tool"]: - response = llm_client.generate(context2["user_message"]) - assert "steps" in response - assert llm_client.call_count == 1 - - def test_timing_decision_performance(self, timing_decider: MockTimingDecider): - """Test that timing decisions meet performance requirements (<100ms).""" - test_messages = [ - "Hello!", - "Search for documents", - "What is the weather?", - "Calculate 2+2", - "Thanks for your help", - ] - - for msg in test_messages: - start = time.perf_counter() - decision = timing_decider.decide({"user_message": msg}) - elapsed = time.perf_counter() - start - - assert elapsed < 0.1, f"Timing decision took {elapsed:.3f}s, exceeds 100ms" - assert "should_call_tool" in decision - assert "confidence" in decision - - -class TestFullWorkflow: - """Test full workflow: Timing -> Planning -> Tool Selection -> Execution.""" - - @pytest.fixture - def tools(self) -> list[MockTool]: - return [ - MockTool("web_search", "Search the web for information"), - MockTool("document_reader", "Read and parse documents"), - MockTool("summarizer", "Summarize text content"), - MockTool("calculator", "Perform calculations"), - ] - - @pytest.fixture - def workflow_components(self, tools: list[MockTool]) -> dict[str, Any]: - return { - "timing_decider": MockTimingDecider(), - "llm_client": MockLLMClient(), - "tool_selector": MockToolSelector(tools), - } - - def test_full_workflow_research_task(self, workflow_components: dict[str, Any]): - """Test complete workflow for a research task.""" - timing = workflow_components["timing_decider"] - llm = workflow_components["llm_client"] - selector = workflow_components["tool_selector"] - - user_message = "Search for recent machine learning papers and summarize them" - - # Step 1: Timing decision - timing_result = timing.decide({"user_message": user_message}) - assert timing_result["should_call_tool"] - - # Step 2: Generate plan - plan_response = llm.generate(f"Create a plan for: {user_message}") - assert "steps" in plan_response - - # Step 3: For each step, select tools - step_descriptions = [ - "Search for relevant information", - "Process search results", - "Generate final response", - ] - - execution_plan = [] - for desc in step_descriptions: - tools_for_step = selector.select(desc, top_k=2) - execution_plan.append({"description": desc, "tools": tools_for_step}) - - # Verify execution plan - assert len(execution_plan) == 3 - assert all("tools" in step for step in execution_plan) - - def test_full_workflow_simple_greeting(self, workflow_components: dict[str, Any]): - """Test that simple greetings bypass planning.""" - timing = workflow_components["timing_decider"] - llm = workflow_components["llm_client"] - - user_message = "Hi there!" - - # Step 1: Timing decision - timing_result = timing.decide({"user_message": user_message}) - assert not timing_result["should_call_tool"] - - # LLM should not be called for simple greetings - assert llm.call_count == 0 - - def test_workflow_performance_end_to_end(self, workflow_components: dict[str, Any]): - """Test that full workflow meets performance requirements.""" - timing = workflow_components["timing_decider"] - llm = workflow_components["llm_client"] - selector = workflow_components["tool_selector"] - - user_message = "Analyze the quarterly sales data" - - start = time.perf_counter() - - # Timing decision - timing_result = timing.decide({"user_message": user_message}) - - if timing_result["should_call_tool"]: - # Planning - llm.generate(f"Plan: {user_message}") - - # Tool selection for 3 steps - for _ in range(3): - selector.select("step description", top_k=3) - - total_elapsed = time.perf_counter() - start - - # Full workflow should be fast (excluding actual LLM calls) - assert total_elapsed < 0.5, f"Full workflow took {total_elapsed:.3f}s" - - -class TestErrorHandling: - """Test error handling in integrated workflows.""" - - def test_graceful_degradation_no_tools(self): - """Test workflow handles empty tool list gracefully.""" - selector = MockToolSelector([]) - results = selector.select("search for something") - assert results == [] - - def test_timing_with_empty_context(self): - """Test timing decider handles missing context gracefully.""" - decider = MockTimingDecider() - result = decider.decide({}) # Empty context - assert "should_call_tool" in result - assert "confidence" in result - - def test_llm_response_parsing_robustness(self): - """Test that malformed LLM responses are handled.""" - # LLM returns malformed JSON - bad_client = MockLLMClient(response="This is not JSON") - response = bad_client.generate("test") - assert response == "This is not JSON" - # In real implementation, this should be caught and handled - - -class TestConcurrency: - """Test concurrent execution scenarios.""" - - def test_parallel_tool_selection(self): - """Test that multiple tool selections can run efficiently.""" - tools = [MockTool(f"tool_{i}", f"Description for tool {i}") for i in range(10)] - selector = MockToolSelector(tools) - - queries = [f"Query {i}" for i in range(5)] - - start = time.perf_counter() - results = [selector.select(q, top_k=3) for q in queries] - elapsed = time.perf_counter() - start - - assert len(results) == 5 - assert all(len(r) <= 3 for r in results) - assert elapsed < 0.5 # 5 selections should be fast - - -class TestMetricsCollection: - """Test that components properly collect metrics.""" - - def test_timing_tracks_calls(self): - """Test that timing decider tracks call count.""" - decider = MockTimingDecider() - - for i in range(5): - decider.decide({"user_message": f"Message {i}"}) - - assert decider.decide_count == 5 - - def test_llm_tracks_calls(self): - """Test that LLM client tracks call count.""" - client = MockLLMClient() - - for i in range(3): - client.generate(f"Prompt {i}") - - assert client.call_count == 3 - assert client.last_prompt == "Prompt 2" diff --git a/packages/sage-libs/tests/lib/agents/test_agent.py b/packages/sage-libs/tests/lib/agents/test_agent.py deleted file mode 100644 index eafd6555bf..0000000000 --- a/packages/sage-libs/tests/lib/agents/test_agent.py +++ /dev/null @@ -1,329 +0,0 @@ -""" -测试 sage.libs.agentic.agents.agent 模块 -""" - -import json -from unittest.mock import Mock, patch - -import pytest -import requests - -# 尝试导入，如果失败则跳过测试 -pytest_plugins = [] - -try: - from sage_libs.sage_agentic.agents.agent import ( - FORMAT_INSTRUCTIONS, - PREFIX, - BaseAgent, # noqa: F401 - BochaSearch, # noqa: F401 - Tool, - ) - - AGENT_AVAILABLE = True -except ImportError as e: - AGENT_AVAILABLE = False - pytestmark = pytest.mark.skip(f"Agent module not available: {e}") - - -@pytest.mark.unit -class TestTool: - """测试Tool类""" - - def test_tool_initialization(self): - """测试Tool初始化""" - if not AGENT_AVAILABLE: - pytest.skip("Agent module not available") - - def sample_func(x, y): - return x + y - - tool = Tool("add", sample_func, "添加两个数字") - - assert tool.name == "add" - assert tool.func == sample_func - assert tool.description == "添加两个数字" - - def test_tool_run(self): - """测试Tool运行""" - if not AGENT_AVAILABLE: - pytest.skip("Agent module not available") - - def multiply(x, y): - return x * y - - tool = Tool("multiply", multiply, "乘法运算") - result = tool.run(3, 4) - - assert result == 12 - - def test_tool_run_with_kwargs(self): - """测试Tool使用关键字参数运行""" - if not AGENT_AVAILABLE: - pytest.skip("Agent module not available") - - def greet(name, greeting="Hello"): - return f"{greeting}, {name}!" - - tool = Tool("greet", greet, "问候函数") - result = tool.run("Alice", greeting="Hi") - - assert result == "Hi, Alice!" - - -@pytest.mark.unit -class TestBochaSearch: - """测试BochaSearch类""" - - def test_bocha_search_initialization(self): - """测试BochaSearch初始化""" - if not AGENT_AVAILABLE: - pytest.skip("Agent module not available") - - api_key = "test_api_key" # pragma: allowlist secret - search = BochaSearch(api_key) - - assert search.api_key == api_key - assert search.url == "https://api.bochaai.com/v1/web-search" - assert search.headers["Authorization"] == api_key - assert search.headers["Content-Type"] == "application/json" - - @patch("requests.request") - def test_bocha_search_run_success(self, mock_request): - """测试BochaSearch运行成功""" - if not AGENT_AVAILABLE: - pytest.skip("Agent module not available") - - # 模拟成功响应 - mock_response = Mock() - mock_response.json.return_value = { - "results": [ - {"title": "测试结果1", "url": "http://test1.com"}, - {"title": "测试结果2", "url": "http://test2.com"}, - ] - } - mock_request.return_value = mock_response - - search = BochaSearch("test_api_key") - result = search.run("Python编程") - - # 验证请求调用 - mock_request.assert_called_once() - call_args = mock_request.call_args - - assert call_args[0] == ("POST", "https://api.bochaai.com/v1/web-search") - assert "Authorization" in call_args[1]["headers"] - - # 验证结果 - assert "results" in result - assert len(result["results"]) == 2 - - @patch("requests.request") - def test_bocha_search_run_with_parameters(self, mock_request): - """测试BochaSearch运行时的参数""" - if not AGENT_AVAILABLE: - pytest.skip("Agent module not available") - - mock_response = Mock() - mock_response.json.return_value = {"success": True} - mock_request.return_value = mock_response - - search = BochaSearch("test_api_key") - search.run("机器学习") - - # 验证请求参数 - call_args = mock_request.call_args - payload_str = call_args[1]["data"] - payload = json.loads(payload_str) - - assert payload["query"] == "机器学习" - assert payload["summary"] is True - assert payload["count"] == 10 - assert payload["page"] == 1 - - -@pytest.mark.unit -class TestAgentConstants: - """测试Agent常量""" - - def test_prefix_constant(self): - """测试PREFIX常量""" - if not AGENT_AVAILABLE: - pytest.skip("Agent module not available") - - assert "Answer the following questions" in PREFIX - assert "tools" in PREFIX.lower() - - def test_format_instructions_constant(self): - """测试FORMAT_INSTRUCTIONS常量""" - if not AGENT_AVAILABLE: - pytest.skip("Agent module not available") - - assert "JSON format" in FORMAT_INSTRUCTIONS - assert "thought" in FORMAT_INSTRUCTIONS - assert "action" in FORMAT_INSTRUCTIONS - assert "action_input" in FORMAT_INSTRUCTIONS - assert "observation" in FORMAT_INSTRUCTIONS - assert "final_answer" in FORMAT_INSTRUCTIONS - - -@pytest.mark.integration -class TestAgentIntegration: - """Agent集成测试""" - - @patch("requests.request") - def test_agent_with_bocha_search_integration(self, mock_request): - """测试Agent与BochaSearch的集成""" - if not AGENT_AVAILABLE: - pytest.skip("Agent module not available") - - # 模拟搜索响应 - mock_response = Mock() - mock_response.json.return_value = { - "results": [{"title": "AI相关结果", "snippet": "人工智能是..."}] - } - mock_request.return_value = mock_response - - # 创建工具 - search = BochaSearch("test_api_key") - search_tool = Tool("search", search.run, "网络搜索工具") - - # 测试工具使用 - result = search_tool.run("什么是人工智能") - - assert "results" in result - mock_request.assert_called_once() - - def test_multiple_tools_integration(self): - """测试多个工具的集成""" - if not AGENT_AVAILABLE: - pytest.skip("Agent module not available") - - # 创建多个工具 - def calc_add(a, b): - return a + b - - def calc_multiply(a, b): - return a * b - - add_tool = Tool("add", calc_add, "加法计算") - multiply_tool = Tool("multiply", calc_multiply, "乘法计算") - - # 测试工具组合使用 - result1 = add_tool.run(5, 3) # 8 - result2 = multiply_tool.run(result1, 2) # 16 - - assert result1 == 8 - assert result2 == 16 - - -@pytest.mark.slow -class TestAgentPerformance: - """Agent性能测试""" - - def test_tool_execution_performance(self): - """测试工具执行性能""" - if not AGENT_AVAILABLE: - pytest.skip("Agent module not available") - - import time - - def fast_function(): - return "fast" - - def slow_function(): - time.sleep(0.01) # 模拟耗时操作 - return "slow" - - fast_tool = Tool("fast", fast_function, "快速工具") - slow_tool = Tool("slow", slow_function, "慢速工具") - - # 测试快速工具 - start = time.time() - fast_tool.run() - fast_time = time.time() - start - - # 测试慢速工具 - start = time.time() - slow_tool.run() - slow_time = time.time() - start - - assert fast_time < slow_time - assert fast_time < 0.01 # 应该很快 (放宽到10ms以适应CI环境) - - -@pytest.mark.external -class TestBochaSearchExternal: - """BochaSearch外部依赖测试""" - - def test_bocha_search_network_error_handling(self): - """测试网络错误处理""" - if not AGENT_AVAILABLE: - pytest.skip("Agent module not available") - - with patch("requests.request") as mock_request: - # 模拟网络错误 - mock_request.side_effect = requests.exceptions.ConnectionError("网络连接失败") - - search = BochaSearch("test_api_key") - - with pytest.raises(requests.exceptions.ConnectionError): - search.run("测试查询") - - def test_bocha_search_invalid_response(self): - """测试无效响应处理""" - if not AGENT_AVAILABLE: - pytest.skip("Agent module not available") - - with patch("requests.request") as mock_request: - # 模拟无效JSON响应 - mock_response = Mock() - mock_response.json.side_effect = json.JSONDecodeError("Invalid JSON", "", 0) - mock_request.return_value = mock_response - - search = BochaSearch("test_api_key") - - with pytest.raises(json.JSONDecodeError): - search.run("测试查询") - - -# ===== 简化版测试（当模块不可用时） ===== - - -@pytest.mark.unit -class TestAgentModuleFallback: - """Agent模块降级测试""" - - def test_module_import_fallback(self): - """测试模块导入降级""" - # 这个测试总是运行，检查模块可用性 - try: - from sage_libs.sage_agentic.agents.agent import Tool # noqa: F401 - - assert True # 导入成功 - except ImportError: - # 模块不可用，但测试应该通过 - assert True - - def test_basic_tool_concept(self): - """测试基本工具概念（不依赖实际实现）""" - - # 模拟Tool类的基本概念 - class MockTool: - def __init__(self, name, func, description): - self.name = name - self.func = func - self.description = description - - def run(self, *args, **kwargs): - return self.func(*args, **kwargs) - - def add(a, b): - return a + b - - tool = MockTool("add", add, "加法工具") - result = tool.run(2, 3) - - assert result == 5 - assert tool.name == "add" - assert tool.description == "加法工具" diff --git a/packages/sage-libs/tests/lib/agents/test_agent_config.py b/packages/sage-libs/tests/lib/agents/test_agent_config.py deleted file mode 100644 index 436f65b999..0000000000 --- a/packages/sage-libs/tests/lib/agents/test_agent_config.py +++ /dev/null @@ -1,246 +0,0 @@ -""" -Tests for the config_agent_min.yaml configuration file structure. - -This validates the configuration file added/modified in commit 12aec700c63407e1f5d79455b2d64a60a6688e96. -""" - -import os - -import pytest - -from sage.common.utils.config.loader import load_config - -# Mock configuration for testing -MOCK_CONFIG = { - "pipeline": { - "name": "sage-agent-base-pipeline", - "description": "Base agent pipeline", - "version": "1.0.0", - }, - "source": { - "type": "local", - "data_path": "test.jsonl", - "field_query": "query", - }, - "profile": { - "name": "TestAgent", - "role": "assistant", - "language": "en", - "personality": "helpful", - }, - "planner": { - "type": "react", - "max_iterations": 10, - }, - "generator": { - "model": "test-model", - "temperature": 0.7, - }, - "runtime": { - "timeout": 60, - "max_retries": 3, - }, - "tools": [], - "sink": { - "type": "console", - }, -} - - -@pytest.mark.unit -class TestAgentConfigValidation: - """Test configuration file structure and content.""" - - @pytest.fixture - def mock_config(self): - """Provide mock configuration.""" - return MOCK_CONFIG.copy() - - @pytest.fixture - def config_path(self): - """Provide config path (for reference only, will be mocked).""" - return os.path.join( - os.path.dirname(__file__), - "..", - "..", - "..", - "..", - "..", - "examples", - "tutorials", - "L3-libs", - "agents", - "config", - "config_agent_min.yaml", - ) - - @pytest.mark.skip(reason="Config file should be mocked, not required to exist") - def test_config_file_exists(self, config_path): - """Test that the config file exists.""" - assert os.path.exists(config_path), f"Config file not found: {config_path}" - - def test_config_loads_successfully(self, mock_config): - """Test that the config file can be loaded without errors.""" - assert mock_config is not None - assert isinstance(mock_config, dict) - - def test_config_required_sections(self, mock_config): - """Test that all required configuration sections are present.""" - required_sections = [ - "pipeline", - "source", - "profile", - "planner", - "generator", - "runtime", - "tools", - "sink", - ] - - for section in required_sections: - assert section in mock_config, f"Required section '{section}' missing from config" - - def test_pipeline_config(self, mock_config): - """Test pipeline configuration structure.""" - pipeline = mock_config["pipeline"] - - assert "name" in pipeline - assert "description" in pipeline - assert "version" in pipeline - - assert pipeline["name"] == "sage-agent-base-pipeline" - assert "agent pipeline" in pipeline["description"].lower() - - def test_source_config(self, mock_config): - """Test source configuration structure.""" - source = mock_config["source"] - - assert "type" in source - assert "data_path" in source - assert "field_query" in source - - assert source["type"] == "local" - assert source["field_query"] == "query" - assert source["data_path"].endswith(".jsonl") - - def test_profile_config(self, mock_config): - """Test profile configuration structure.""" - profile = mock_config["profile"] - - required_fields = [ - "name", - "role", - "language", - ] - for field in required_fields: - assert field in profile, f"Profile field '{field}' missing" - - # Mock doesn't have these complex fields, adjust assertions - assert profile["language"] == "en" - - @pytest.mark.skip(reason="Requires actual config file, using mock instead") - def test_planner_config(self): - """Test planner configuration structure.""" - pass - - @pytest.mark.skip(reason="Requires actual config file, using mock instead") - def test_generator_configs(self): - """Test generator configuration structure.""" - pass - - @pytest.mark.skip(reason="Requires actual config file, using mock instead") - def test_tools_config(self): - """Test tools configuration structure.""" - pass - - def test_runtime_config(self, mock_config): - """Test runtime configuration structure.""" - runtime = mock_config["runtime"] - - assert "timeout" in runtime or "max_retries" in runtime - assert isinstance(runtime.get("timeout", 0), int) - - @pytest.mark.skip(reason="Requires actual config file, using mock instead") - def test_memory_config(self): - """Test memory configuration structure.""" - pass - - def test_sink_config(self, mock_config): - """Test sink configuration structure.""" - sink = mock_config["sink"] - assert "type" in sink - - @pytest.mark.skip(reason="Requires actual config file, using mock instead") - def test_config_values_consistency(self): - """Test that configuration values are consistent and valid.""" - pass - - @pytest.mark.skip(reason="Requires actual config file, using mock instead") - def test_yaml_syntax_validity(self): - """Test that the YAML file has valid syntax.""" - pass - - @pytest.mark.skip(reason="Requires actual config file, using mock instead") - def test_environment_variable_placeholders(self): - """Test that environment variable placeholders are properly formatted.""" - pass - - @pytest.mark.skip(reason="Requires actual config file, using mock instead") - def test_file_paths_validity(self): - """Test that file paths in config are valid relative paths.""" - pass - - -@pytest.mark.integration -class TestConfigWithComponents: - """Integration tests for config with actual components.""" - - @pytest.mark.skip(reason="Requires isage-agentic package and actual config file") - def test_config_compatible_with_profile(self): - """Test that config is compatible with BaseProfile.""" - pass - - @pytest.mark.skip(reason="Requires isage-agentic package and actual config file") - def test_config_compatible_with_mcp_registry(self): - """Test that tools config is compatible with MCPRegistry.""" - pass - - config_path = os.path.join( - os.path.dirname(__file__), - "..", - "..", - "..", - "..", - "..", - "examples", - "tutorials", - "agents", - "config", - "config_agent_min.yaml", - ) - - if not os.path.exists(config_path): - pytest.skip("Config file not found") - - config = load_config(config_path) - - # Should be able to create registry (even if tool import fails) - try: - # Try to import MCPRegistry (optional dependency) - from sage_libs.sage_agentic.agents.mcp.registry import MCPRegistry - - registry = MCPRegistry() - assert registry is not None - - # Tool config should have proper structure - tools_config = config["tools"] - for tool_config in tools_config: - assert "module" in tool_config - assert "class" in tool_config - assert "init_kwargs" in tool_config - - except ImportError: - # isage-agentic not installed, skip MCP registry test - pytest.skip("sage_libs.sage_agentic not available (optional dependency)") - except Exception as e: - pytest.fail(f"Registry creation failed: {e}") diff --git a/packages/sage-libs/tests/lib/agents/test_agent_examples.py b/packages/sage-libs/tests/lib/agents/test_agent_examples.py deleted file mode 100644 index 6838f5ba08..0000000000 --- a/packages/sage-libs/tests/lib/agents/test_agent_examples.py +++ /dev/null @@ -1,473 +0,0 @@ -""" -Tests for the refactored agent.py in examples/agents/ - -This covers the new functionality added in commit 12aec700c63407e1f5d79455b2d64a60a6688e96: -- iter_queries function -- main function workflow -- Integration with ArxivSearchTool -- Configuration loading and validation -""" - -import json -import os -import tempfile -from unittest.mock import Mock, patch - -import pytest - -# Try to import the agent module from examples -try: - from examples.agents import agent # type: ignore[import-not-found] - from examples.tutorials.agents import basic_agent # type: ignore[import-not-found] - - AGENT_MODULE_AVAILABLE = True -except ImportError: - AGENT_MODULE_AVAILABLE = False - pytestmark = pytest.mark.skip("Agent examples module not available") - - -@pytest.mark.unit -class TestIterQueries: - """Test the iter_queries function.""" - - def test_iter_queries_local_source(self): - """Test iter_queries with local JSONL file source.""" - if not AGENT_MODULE_AVAILABLE: - pytest.skip("Agent examples module not available") - - # Create a temporary JSONL file - test_data = [ - {"query": "Test query 1", "other": "data"}, - {"query": "Test query 2"}, - {"query": "", "other": "empty query"}, # Should be skipped - {"not_query": "no query field"}, # Should be skipped - {"query": " ", "other": "whitespace"}, # Should be skipped - {"query": "Valid query 3"}, - ] - - with tempfile.NamedTemporaryFile(mode="w", suffix=".jsonl", delete=False) as f: - for item in test_data: - f.write(json.dumps(item) + "\n") - temp_path = f.name - - try: - source_cfg = { - "type": "local", - "data_path": temp_path, - "field_query": "query", - } - - queries = list(agent.iter_queries(source_cfg)) - - # Should only get non-empty queries - expected_queries = ["Test query 1", "Test query 2", "Valid query 3"] - assert queries == expected_queries - - finally: - os.unlink(temp_path) - - def test_iter_queries_local_source_custom_field(self): - """Test iter_queries with custom query field name.""" - if not AGENT_MODULE_AVAILABLE: - pytest.skip("Agent examples module not available") - - test_data = [ - {"question": "What is AI?", "other": "data"}, - {"question": "How does ML work?"}, - ] - - with tempfile.NamedTemporaryFile(mode="w", suffix=".jsonl", delete=False) as f: - for item in test_data: - f.write(json.dumps(item) + "\n") - temp_path = f.name - - try: - source_cfg = { - "type": "local", - "data_path": temp_path, - "field_query": "question", - } - - queries = list(agent.iter_queries(source_cfg)) - expected_queries = ["What is AI?", "How does ML work?"] - assert queries == expected_queries - - finally: - os.unlink(temp_path) - - def test_iter_queries_empty_file(self): - """Test iter_queries with empty file.""" - if not AGENT_MODULE_AVAILABLE: - pytest.skip("Agent examples module not available") - - with tempfile.NamedTemporaryFile(mode="w", suffix=".jsonl", delete=False) as f: - temp_path = f.name - - try: - source_cfg = { - "type": "local", - "data_path": temp_path, - "field_query": "query", - } - - queries = list(agent.iter_queries(source_cfg)) - assert queries == [] - - finally: - os.unlink(temp_path) - - def test_iter_queries_hf_source(self): - """Test iter_queries with HuggingFace dataset source.""" - if not AGENT_MODULE_AVAILABLE: - pytest.skip("Agent examples module not available") - - # Mock the datasets library at the module level - with patch("datasets.load_dataset") as mock_load_dataset: - mock_dataset = [ - {"query": "HF query 1", "other": "data"}, - {"query": "HF query 2"}, - ] - mock_load_dataset.return_value = mock_dataset - - source_cfg = { - "type": "hf", - "hf_dataset_name": "test/dataset", - "hf_dataset_config": "default", - "hf_split": "test", - "field_query": "query", - } - - queries = list(agent.iter_queries(source_cfg)) - - expected_queries = ["HF query 1", "HF query 2"] - assert queries == expected_queries - - # Verify load_dataset was called with correct parameters - mock_load_dataset.assert_called_once_with("test/dataset", "default", split="test") - - def test_iter_queries_unsupported_source_type(self): - """Test iter_queries with unsupported source type.""" - if not AGENT_MODULE_AVAILABLE: - pytest.skip("Agent examples module not available") - - source_cfg = {"type": "unsupported", "data_path": "/fake/path"} - - with pytest.raises(ValueError, match="Unsupported source.type"): - list(agent.iter_queries(source_cfg)) - - -@pytest.mark.unit -class TestMainFunction: - """Test the main function workflow.""" - - def create_mock_config(self): - """Create a mock configuration for testing.""" - return { - "profile": { - "name": "TestAgent", - "role": "assistant", - "language": "en", - "goals": ["Help users"], - "constraints": ["Be helpful"], - "persona": {"style": "friendly"}, - }, - "generator": { - "remote": { - "api_key": "test-key", # pragma: allowlist secret - "method": "openai", - "model_name": "gpt-3.5-turbo", - "base_url": "https://api.openai.com/v1", - "seed": 42, - } - }, - "planner": {"max_steps": 5, "enable_repair": True, "topk_tools": 3}, - "tools": [ - { - "module": "examples.agents.tools.arxiv_search_tool", - "class": "ArxivSearchTool", - "init_kwargs": {}, - } - ], - "runtime": {"max_steps": 5, "summarizer": "reuse_generator"}, - "source": { - "type": "local", - "data_path": "/fake/path.jsonl", - "field_query": "query", - }, - } - - def test_main_function_config_not_found(self): - if not AGENT_MODULE_AVAILABLE: - pytest.skip("Agent examples module not available") - - # agent.main()实际执行的是basic_agent中的代码，路径基于basic_agent.__file__ - expected = "❌ Configuration file not found: " + os.path.join( - os.path.dirname(basic_agent.__file__), "config", "config_agent_min.yaml" - ) - - # 关键：补丁打在真实模块位置（tutorials），因为agent是重新导出 - with ( - patch("examples.tutorials.agents.basic_agent.os.path.exists", return_value=False), - patch("builtins.print") as mock_print, - ): - with pytest.raises(SystemExit) as e: - agent.main() - - assert e.value.code == 1 - # 用 assert_any_call，避免"最后一次打印不是这句"导致失败 - mock_print.assert_any_call(expected) - - # @patch("examples.agents.agent.importlib.import_module") - # @patch("examples.agents.agent.load_config") - # @patch("os.path.exists") - # def test_main_function_successful_execution( - # self, mock_exists, mock_load_config, mock_import, mock_iter_queries - # ): - # """Test successful execution of main function.""" - # if not AGENT_MODULE_AVAILABLE: - # pytest.skip("Agent examples module not available") - - # # Setup mocks - # mock_exists.return_value = True - # mock_load_config.return_value = self.create_mock_config() - # mock_iter_queries.return_value = ["Test query 1", "Test query 2"] - - # # Mock tool import - # mock_tool_class = Mock() - # mock_tool_instance = Mock() - # mock_tool_class.return_value = mock_tool_instance - # mock_module = Mock() - # mock_module.ArxivSearchTool = mock_tool_class - # mock_import.return_value = mock_module - - # # Mock all the agent components - # with patch("examples.agents.agent.BaseProfile") as mock_profile: - # with patch("examples.agents.agent.OpenAIGenerator") as mock_generator: - # with patch("examples.agents.agent.LLMPlanner") as mock_planner: - # with patch("examples.agents.agent.MCPRegistry") as mock_registry: - # with patch( - # "examples.agents.agent.AgentRuntime" - # ) as mock_runtime: - - # # Setup mock instances - # mock_profile_instance = Mock() - # mock_profile.from_dict.return_value = mock_profile_instance - - # mock_generator_instance = Mock() - # mock_generator.return_value = mock_generator_instance - - # mock_planner_instance = Mock() - # mock_planner.return_value = mock_planner_instance - - # mock_registry_instance = Mock() - # mock_registry.return_value = mock_registry_instance - - # mock_runtime_instance = Mock() - # mock_runtime_instance.execute.return_value = "Test response" - # mock_runtime.return_value = mock_runtime_instance - - # # Mock print to capture output - # with patch("builtins.print") as mock_print: - # agent.main() - - # # Verify components were created correctly - # mock_profile.from_dict.assert_called_once() - # mock_generator.assert_called_once() - # mock_planner.assert_called_once() - # mock_registry.assert_called_once() - # mock_runtime.assert_called_once() - - # # Verify tool was registered - # mock_registry_instance.register.assert_called_once_with( - # mock_tool_instance - # ) - - # # Verify agent was executed for each query - # assert mock_runtime_instance.execute.call_count == 2 - - # # Verify output was printed - # print_calls = [ - # call[0][0] for call in mock_print.call_args_list - # ] - # assert any( - # "🧑‍💻 User: Test query 1" in call - # for call in print_calls - # ) - # assert any( - # "🧑‍💻 User: Test query 2" in call - # for call in print_calls - # ) - # assert any("🤖 Agent:" in call for call in print_calls) - - @patch("examples.tutorials.agents.basic_agent.load_config") - @patch("examples.tutorials.agents.basic_agent.os.path.exists") - def test_main_function_tool_import_error(self, mock_exists, mock_load): - """Test that tool import errors are handled gracefully in test mode.""" - mock_exists.return_value = True - config = self.create_mock_config() - config["tools"] = [{"module": "nonexistent.module", "class": "NonexistentClass"}] - # Use a real test data file to avoid file access issues - config["source"]["data_path"] = "examples/tutorials/agents/data/agent_queries_test.jsonl" - mock_load.return_value = config - - # Set test mode environment variable - with patch.dict("os.environ", {"SAGE_TEST_MODE": "true"}): - with patch("builtins.print"): # Suppress output - # 在测试模式下，应该成功完成而不抛出异常 - # 测试模式会验证配置和导入，但不实际运行agent - try: - agent.main() # 应该成功完成而不抛出异常 - except Exception as e: - pytest.fail(f"main() should not raise exception in test mode, but got: {e}") - - -@pytest.mark.integration -class TestAgentIntegration: - """Integration tests for the agent workflow.""" - - def test_test_mode_execution(self): - """Test that test mode works correctly.""" - if not AGENT_MODULE_AVAILABLE: - pytest.skip("Agent examples module not available") - - with patch.dict("os.environ", {"SAGE_EXAMPLES_MODE": "test"}): - with patch("examples.agents.agent.main") as mock_main: - with patch("builtins.print"): - with patch("sys.exit"): - # Import and execute the module as if it were run directly - exec( - """ -if __name__ == "__main__": - if os.getenv("SAGE_EXAMPLES_MODE") == "test" or os.getenv("SAGE_TEST_MODE") == "true": - try: - main() - print("\\n✅ Test passed: Agent pipeline structure validated") - except Exception as e: - print(f"❌ Test failed: {e}") - sys.exit(1) - else: - main() -""", - { - "__name__": "__main__", - "os": os, - "main": agent.main, - "print": print, - "sys": __import__("sys"), - }, - ) - - # Verify main was called - mock_main.assert_called_once() - - def test_agent_with_arxiv_tool_mock(self): - """Test complete agent workflow with mocked ArxivSearchTool.""" - if not AGENT_MODULE_AVAILABLE: - pytest.skip("Agent examples module not available") - - # This test verifies the complete integration works with proper mocking - with patch("examples.tutorials.agents.basic_agent.load_config") as mock_load_config: - with patch("examples.tutorials.agents.basic_agent.iter_queries") as mock_iter_queries: - with patch("os.path.exists", return_value=True): - # Mock both should_use_real_api and environment to bypass test mode - with patch( - "examples.tutorials.agents.basic_agent.should_use_real_api", - return_value=True, - ): - with patch.dict("os.environ", {"SAGE_EXAMPLES_MODE": "production"}): - # Setup test config - test_config = { - "profile": { - "name": "TestAgent", - "role": "assistant", - "language": "en", - "goals": ["Help users"], - "constraints": ["Be helpful"], - "persona": {"style": "friendly"}, - }, - "generator": { - "remote": { - "api_key": "test-key", # pragma: allowlist secret - "method": "openai", - "model_name": "gpt-3.5-turbo", - "base_url": "https://api.openai.com/v1", - "seed": 42, - } - }, - "planner": { - "max_steps": 3, - "enable_repair": True, - "topk_tools": 2, - }, - "tools": [ - { - "module": "examples.agents.tools.arxiv_search_tool", - "class": "ArxivSearchTool", - "init_kwargs": {}, - } - ], - "runtime": { - "max_steps": 3, - "summarizer": "reuse_generator", - }, - "source": { - "type": "local", - "data_path": "/fake/path.jsonl", - "field_query": "query", - }, - } - - mock_load_config.return_value = test_config - mock_iter_queries.return_value = ["Search for ML papers"] - - # Mock all components to avoid external dependencies - with patch("examples.tutorials.agents.basic_agent.BaseProfile"): - with patch("examples.tutorials.agents.basic_agent.OpenAIGenerator"): - with patch( - "examples.tutorials.agents.basic_agent.SimplePlanner" - ): - with patch( - "examples.tutorials.agents.basic_agent.MCPRegistry" - ) as mock_registry: - with patch( - "examples.tutorials.agents.basic_agent.AgentRuntime" - ) as mock_runtime: - with patch( - "examples.tutorials.agents.basic_agent.importlib.import_module" - ) as mock_import: - # Setup mock tool with PROPER STRING ATTRIBUTES - mock_tool_class = Mock() - mock_tool_instance = Mock() - # 关键修复：设置name, description, input_schema为正确的类型 - mock_tool_instance.name = "arxiv_search" - mock_tool_instance.description = ( - "Search arXiv papers" - ) - mock_tool_instance.input_schema = { - "type": "object" - } - mock_tool_class.return_value = ( - mock_tool_instance - ) - mock_module = Mock() - mock_module.ArxivSearchTool = mock_tool_class - mock_import.return_value = mock_module - - # Setup mock runtime response - mock_runtime_instance = Mock() - mock_runtime_instance.execute.return_value = ( - "Found 2 relevant papers about ML" - ) - mock_runtime.return_value = ( - mock_runtime_instance - ) - - with patch("builtins.print"): - # Should execute without errors - agent.main() - - # Verify the tool was registered - mock_registry.return_value.register.assert_called_once() - - # Verify agent execution was called - mock_runtime_instance.execute.assert_called_once() diff --git a/packages/sage-libs/tests/lib/agents/test_agent_integration.py b/packages/sage-libs/tests/lib/agents/test_agent_integration.py deleted file mode 100644 index 6d0f6c89b0..0000000000 --- a/packages/sage-libs/tests/lib/agents/test_agent_integration.py +++ /dev/null @@ -1,485 +0,0 @@ -""" -End-to-end integration tests for the agent workflow. - -These tests verify the complete agent pipeline added in commit 12aec700c63407e1f5d79455b2d64a60a6688e96, -including the interaction between all components. -""" - -import json -import os -import tempfile -from unittest.mock import Mock, patch - -import pytest - -# Test imports with fallbacks -try: - from examples.agents.tools.arxiv_search_tool import ( - ArxivSearchTool, # type: ignore[import-not-found]; type: ignore[import-not-found] - ) - - ARXIV_TOOL_AVAILABLE = True -except ImportError: - ARXIV_TOOL_AVAILABLE = False - -try: - from sage_libs.sage_agentic.agents.action.mcp_registry import MCPRegistry - from sage_libs.sage_agentic.agents.planning.simple_llm_planner import SimpleLLMPlanner - from sage_libs.sage_agentic.agents.profile.profile import BaseProfile - - from sage.middleware.operators.agent.runtime import AgentRuntime - - SAGE_COMPONENTS_AVAILABLE = True -except ImportError: - SAGE_COMPONENTS_AVAILABLE = False - - -@pytest.mark.integration -class TestAgentWorkflowIntegration: - """End-to-end integration tests for the complete agent workflow.""" - - def create_test_config(self): - """Create a minimal test configuration.""" - return { - "profile": { - "name": "TestAgent", - "role": "assistant", - "language": "zh", - "goals": ["帮助用户完成任务"], - "constraints": ["使用提供的工具"], - "persona": {"style": "professional"}, - }, - "generator": { - "remote": { - "api_key": "test-key", # pragma: allowlist secret - "method": "openai", - "model_name": "gpt-3.5-turbo", - "base_url": "https://api.openai.com/v1", - "seed": 42, - } - }, - "planner": {"max_steps": 5, "enable_repair": True, "topk_tools": 3}, - "tools": [ - { - "module": "examples.agents.tools.arxiv_search_tool", - "class": "ArxivSearchTool", - "init_kwargs": {}, - } - ], - "runtime": {"max_steps": 5, "summarizer": "reuse_generator"}, - "source": { - "type": "local", - "data_path": "test_queries.jsonl", - "field_query": "query", - }, - } - - def create_mock_generator(self): - """Create a mock generator for testing.""" - mock_generator = Mock() - - def mock_execute(data): - # Handle both old format and new message format - user_query = data[0] - second_param = data[1] - - if isinstance(second_param, list): - # New message format - extract user query from messages - for msg in second_param: - if msg.get("role") == "user": - user_query = msg["content"] - break - - # Generate a simple plan based on the query - if "arxiv" in user_query.lower() or "paper" in user_query.lower(): - plan = [ - { - "type": "tool", - "name": "arxiv_search", - "arguments": {"query": "machine learning", "max_results": 2}, - }, - {"type": "reply", "text": "已找到相关论文"}, - ] - else: - plan = [{"type": "reply", "text": "我理解您的问题"}] - - return (data[0], json.dumps(plan, ensure_ascii=False)) - - mock_generator.execute = mock_execute - return mock_generator - - def create_mock_arxiv_tool(self): - """Create a mock ArxivSearchTool for testing.""" - if not ARXIV_TOOL_AVAILABLE: - # Create a mock version if real tool isn't available - mock_tool = Mock() - mock_tool.name = "arxiv_search" - mock_tool.description = "Search arXiv papers" - mock_tool.input_schema = { - "type": "object", - "properties": { - "query": {"type": "string"}, - "max_results": {"type": "integer", "default": 10}, - }, - "required": ["query"], - } - - def mock_call(arguments): - return { - "output": [ - { - "title": "Test Paper 1", - "authors": "Test Author", - "link": "https://arxiv.org/abs/1234.5678", - "abstract": "Test abstract", - } - ], - "meta": { - "query": arguments.get("query", ""), - "max_results": arguments.get("max_results", 10), - }, - } - - mock_tool.call = mock_call - return mock_tool - else: - # Use real tool but mock the network calls - tool = ArxivSearchTool() - with patch.object(tool, "_search_arxiv") as mock_search: - mock_search.return_value = [ - { - "title": "Mock Paper", - "authors": "Mock Author", - "link": "https://arxiv.org/abs/mock", - "abstract": "Mock abstract", - } - ] - return tool - - @pytest.mark.skipif(not SAGE_COMPONENTS_AVAILABLE, reason="SAGE components not available") - def test_complete_agent_workflow_with_arxiv_query(self): - """Test the complete agent workflow with an arXiv search query.""" - - # Create test query file - test_queries = [ - {"query": "在 arXiv 搜索 2 篇机器学习论文"}, - {"query": "帮我总结一下深度学习的发展"}, - ] - - with tempfile.NamedTemporaryFile(mode="w", suffix=".jsonl", delete=False) as f: - for query in test_queries: - f.write(json.dumps(query, ensure_ascii=False) + "\n") - temp_path = f.name - - try: - # Create components - profile = BaseProfile( - name="TestAgent", - role="assistant", - language="zh", - goals=["帮助用户"], - tasks=["使用工具"], - tone="helpful", - ) - - generator = self.create_mock_generator() - planner = SimpleLLMPlanner(generator=generator, max_steps=5) - - registry = MCPRegistry() - arxiv_tool = self.create_mock_arxiv_tool() - registry.register(arxiv_tool) - - runtime = AgentRuntime( - profile=profile, - planner=planner, - tools=registry, - summarizer=generator, - max_steps=5, - ) - - # Test source reading - 直接读取测试文件而不是导入examples模块 - queries = [] - with open(temp_path, encoding="utf-8") as f: - for line in f: - line = line.strip() - if line: - queries.append(json.loads(line)) - - assert len(queries) == 2 - assert "arXiv" in queries[0]["query"] - assert "深度学习" in queries[1]["query"] - - # Test agent execution for each query - for query_obj in queries: - query = query_obj["query"] - response = runtime.execute({"query": query}) - assert response is not None - # AgentRuntime.execute() returns a dict with 'reply', 'observations', 'plan' - assert isinstance(response, dict) - reply = response.get("reply", "") - assert isinstance(reply, str) - - if "arxiv" in query.lower(): - # Should mention finding papers - assert "论文" in reply or "paper" in reply.lower() - - finally: - os.unlink(temp_path) - - @pytest.mark.skipif(not SAGE_COMPONENTS_AVAILABLE, reason="SAGE components not available") - def test_agent_tool_integration(self): - """Test that agent properly integrates with tools.""" - - # Create mock tool - mock_tool = Mock() - mock_tool.name = "test_tool" - mock_tool.description = "A test tool" - mock_tool.input_schema = { - "type": "object", - "properties": {"input": {"type": "string"}}, - "required": ["input"], - } - - def mock_call(arguments): - return {"output": f"Processed: {arguments.get('input', 'no input')}"} - - mock_tool.call = mock_call - - # Create generator that uses the tool - def tool_using_generator(data): - plan = [ - { - "type": "tool", - "name": "test_tool", - "arguments": {"input": "test data"}, - }, - {"type": "reply", "text": "工具调用完成"}, - ] - return (data[0], json.dumps(plan, ensure_ascii=False)) - - mock_generator = Mock() - mock_generator.execute = tool_using_generator - - # Set up components - profile = BaseProfile(language="zh") - planner = SimpleLLMPlanner(generator=mock_generator) - - registry = MCPRegistry() - registry.register(mock_tool) - - runtime = AgentRuntime(profile=profile, planner=planner, tools=registry, summarizer=None) - - # Execute and verify tool was called - response = runtime.execute({"query": "使用测试工具"}) - # AgentRuntime.execute() returns a dict with 'reply', 'observations', 'plan' - assert isinstance(response, dict) - assert "工具调用完成" in response["reply"] - - @pytest.mark.skipif(not SAGE_COMPONENTS_AVAILABLE, reason="SAGE components not available") - def test_agent_error_handling(self): - """Test agent error handling in various scenarios.""" - - # Test with tool that raises an exception - failing_tool = Mock() - failing_tool.name = "failing_tool" - failing_tool.description = "A tool that fails" - failing_tool.input_schema = { - "type": "object", - "properties": {"input": {"type": "string"}}, - "required": ["input"], - } - - def failing_call(arguments): - raise Exception("Tool execution failed") - - failing_tool.call = failing_call - - # Generator that tries to use the failing tool - def failing_generator(data): - plan = [ - { - "type": "tool", - "name": "failing_tool", - "arguments": {"input": "test"}, - }, - {"type": "reply", "text": "应该不会到达这里"}, - ] - return (data[0], json.dumps(plan, ensure_ascii=False)) - - mock_generator = Mock() - mock_generator.execute = failing_generator - - profile = BaseProfile(language="zh") - planner = SimpleLLMPlanner(generator=mock_generator) - - registry = MCPRegistry() - registry.register(failing_tool) - - runtime = AgentRuntime(profile=profile, planner=planner, tools=registry, summarizer=None) - - # Should handle the error gracefully - response = runtime.execute({"query": "使用会失败的工具"}) - assert response is not None - # AgentRuntime.execute() returns a dict with 'reply', 'observations', 'plan' - # Should contain some error indication or fallback response - assert isinstance(response, dict) - assert "reply" in response - - @pytest.mark.skipif(not SAGE_COMPONENTS_AVAILABLE, reason="SAGE components not available") - def test_message_format_consistency(self): - """Test that the new message format is used consistently throughout the pipeline.""" - - # Generator that validates message format - message_validator = Mock() - - def validate_and_respond(data): - user_query, second_param = data - - # Should receive messages in new format - if isinstance(second_param, list): - messages = second_param - assert len(messages) >= 1 - - # Check for system message - system_msgs = [msg for msg in messages if msg.get("role") == "system"] - assert len(system_msgs) >= 1 - - # Check for user message - user_msgs = [msg for msg in messages if msg.get("role") == "user"] - if len(user_msgs) > 0: - assert user_msgs[0]["content"] == user_query - - plan = [{"type": "reply", "text": "消息格式验证通过"}] - return (user_query, json.dumps(plan, ensure_ascii=False)) - - message_validator.execute = validate_and_respond - - profile = BaseProfile(language="zh") - planner = SimpleLLMPlanner(generator=message_validator) - registry = MCPRegistry() - - runtime = AgentRuntime( - profile=profile, - planner=planner, - tools=registry, - summarizer=message_validator, - ) - - # Should not raise any assertion errors - response = runtime.execute({"query": "测试消息格式"}) - # AgentRuntime.execute() returns a dict with 'reply', 'observations', 'plan' - assert isinstance(response, dict) - assert "消息格式验证通过" in response["reply"] - - def test_test_mode_compatibility(self): - """Test that the agent examples work in test mode.""" - - try: - # Import the agent module - from examples.agents import agent # type: ignore[import-not-found] - - # Mock the main function to avoid actual execution - with patch.object(agent, "main") as mock_main: - # Simulate test mode execution - with patch.dict("os.environ", {"SAGE_EXAMPLES_MODE": "test"}): - # This should call main() and then print success message - try: - agent.main() - print("\n✅ Test passed: Agent pipeline structure validated") - except Exception as e: - print(f"❌ Test failed: {e}") - - # Verify main was called - mock_main.assert_called_once() - - except ImportError: - pytest.skip("Agent examples module not available") - - -@pytest.mark.integration -class TestConfigIntegration: - """Integration tests with real configuration files.""" - - def test_config_with_real_components(self): - """Test that the configuration works with real SAGE components.""" - - if not SAGE_COMPONENTS_AVAILABLE: - pytest.skip("SAGE components not available") - - from sage.common.utils.config.loader import load_config - - config_path = os.path.join( - os.path.dirname(__file__), - "..", - "..", - "..", - "..", - "..", - "examples", - "tutorials", - "agents", - "config", - "config_agent_min.yaml", - ) - - if not os.path.exists(config_path): - pytest.skip("Config file not found") - - try: - config = load_config(config_path) - - # Test profile creation - profile = BaseProfile.from_dict(config["profile"]) - assert profile.name == config["profile"]["name"] - - # Test registry creation - registry = MCPRegistry() - assert registry is not None - - # Test that generator config is valid structure - gen_config = config["generator"]["remote"] - assert "method" in gen_config - assert "model_name" in gen_config - - except Exception as e: - pytest.fail(f"Config integration failed: {e}") - - def test_data_file_compatibility(self): - """Test that the data file format is compatible with iter_queries.""" - - try: - from examples.agents.agent import iter_queries # type: ignore[import-not-found] - - # Check if the data file exists - data_path = os.path.join( - os.path.dirname(__file__), - "..", - "..", - "..", - "..", - "..", - "examples", - "data", - "agent_queries.jsonl", - ) - - if os.path.exists(data_path): - source_config = { - "type": "local", - "data_path": data_path, - "field_query": "query", - } - - queries = list(iter_queries(source_config)) - assert len(queries) > 0 - - # All queries should be non-empty strings - for query in queries: - assert isinstance(query, str) - assert len(query.strip()) > 0 - - else: - pytest.skip("Agent queries data file not found") - - except ImportError: - pytest.skip("Agent examples module not available") diff --git a/packages/sage-libs/tests/lib/agents/test_arxiv_tool.py b/packages/sage-libs/tests/lib/agents/test_arxiv_tool.py deleted file mode 100644 index f760051857..0000000000 --- a/packages/sage-libs/tests/lib/agents/test_arxiv_tool.py +++ /dev/null @@ -1,161 +0,0 @@ -""" -Core unit tests for the ArxivSearchTool. - -This covers essential functionality and integration with SAGE components. -For usage examples, see examples/agents/tools/demo_arxiv_search.py -""" - -from unittest.mock import patch - -import pytest - -# Import the tool from examples since it's an example tool -try: - from examples.agents.tools.arxiv_search_tool import ( - ArxivSearchTool, # type: ignore[import-not-found]; type: ignore[import-not-found] - ) - - ARXIV_TOOL_AVAILABLE = True -except ImportError: - ARXIV_TOOL_AVAILABLE = False - pytestmark = pytest.mark.skip("ArxivSearchTool not available") - - -@pytest.mark.unit -class TestArxivSearchToolCore: - """Core functionality tests for ArxivSearchTool.""" - - def setup_method(self): - """Set up test fixtures.""" - if not ARXIV_TOOL_AVAILABLE: - pytest.skip("ArxivSearchTool not available") - self.tool = ArxivSearchTool() - - def test_tool_initialization(self): - """Test that ArxivSearchTool initializes correctly.""" - if not ARXIV_TOOL_AVAILABLE: - pytest.skip("ArxivSearchTool not available") - - assert self.tool.name == "arxiv_search" - assert "Search arXiv papers" in self.tool.description - assert self.tool.base_url == "https://arxiv.org/search/" - assert self.tool.valid_sizes == [25, 50, 100, 200] - assert "User-Agent" in self.tool.session.headers - - def test_input_schema_validation(self): - """Test that input schema is properly defined.""" - if not ARXIV_TOOL_AVAILABLE: - pytest.skip("ArxivSearchTool not available") - - schema = self.tool.input_schema - - # Required fields - assert "query" in schema["required"] - - # Properties validation - props = schema["properties"] - assert props["query"]["type"] == "string" - assert props["size"]["type"] == "integer" - assert props["max_results"]["minimum"] == 1 - assert props["max_results"]["maximum"] == 100 - assert props["with_abstract"]["type"] == "boolean" - - def test_call_with_missing_query(self): - """Test that call() raises ValueError when query is missing.""" - if not ARXIV_TOOL_AVAILABLE: - pytest.skip("ArxivSearchTool not available") - - with pytest.raises(ValueError, match="`query` is required"): - self.tool.call({}) - - with pytest.raises(ValueError, match="`query` is required"): - self.tool.call({"query": ""}) - - with pytest.raises(ValueError, match="`query` is required"): - self.tool.call({"query": " "}) - - def test_parameter_normalization(self): - """Test that parameters are normalized correctly.""" - if not ARXIV_TOOL_AVAILABLE: - pytest.skip("ArxivSearchTool not available") - - with patch.object(self.tool, "_search_arxiv") as mock_search: - mock_search.return_value = [] - - # Test size normalization (invalid size -> closest valid) - self.tool.call({"query": "test", "size": 30}) # Should use 25 - mock_search.assert_called_with( - query="test", size=25, max_results=10, with_abstract=True - ) - - # Test max_results bounds - self.tool.call({"query": "test", "max_results": 150}) # Should cap at 100 - mock_search.assert_called_with( - query="test", size=25, max_results=100, with_abstract=True - ) - - def test_network_error_fallback(self): - """Test that network errors trigger offline fallback.""" - if not ARXIV_TOOL_AVAILABLE: - pytest.skip("ArxivSearchTool not available") - - import requests - - with patch("requests.Session.get") as mock_get: - mock_get.side_effect = requests.RequestException("Network error") - - with patch("logging.error") as mock_log: - result = self.tool.call({"query": "test", "max_results": 3}) - - # Should return mock data - assert result["output"] - assert len(result["output"]) == 3 - assert result["meta"]["offline_mock"] is True - - # Should log the error - mock_log.assert_called_once() - - -@pytest.mark.unit -class TestArxivSearchToolIntegration: - """Integration tests for ArxivSearchTool with SAGE components.""" - - def test_tool_integration_with_registry(self): - """Test that the tool can be registered and called through MCPRegistry.""" - if not ARXIV_TOOL_AVAILABLE: - pytest.skip("ArxivSearchTool not available") - - from sage_libs.sage_agentic.agents.action.mcp_registry import MCPRegistry - - registry = MCPRegistry() - tool = ArxivSearchTool() - registry.register(tool) - - # Mock the actual search to avoid network calls - with patch.object(tool, "_search_arxiv") as mock_search: - mock_search.return_value = [ - {"title": "Test", "authors": "Test", "link": "test", "abstract": "test"} - ] - - result = registry.call("arxiv_search", {"query": "machine learning"}) - assert result["output"] - assert result["meta"]["query"] == "machine learning" - - def test_tool_schema_compatibility(self): - """Test that the tool schema is compatible with MCP standards.""" - if not ARXIV_TOOL_AVAILABLE: - pytest.skip("ArxivSearchTool not available") - - tool = ArxivSearchTool() - - # Verify required MCP tool attributes - assert hasattr(tool, "name") - assert hasattr(tool, "description") - assert hasattr(tool, "input_schema") - assert hasattr(tool, "call") - - # Verify schema structure - schema = tool.input_schema - assert schema["type"] == "object" - assert "properties" in schema - assert "required" in schema diff --git a/packages/sage-libs/tests/lib/agents/test_bots.py b/packages/sage-libs/tests/lib/agents/test_bots.py deleted file mode 100644 index 4ef9f44c18..0000000000 --- a/packages/sage-libs/tests/lib/agents/test_bots.py +++ /dev/null @@ -1,297 +0,0 @@ -""" -测试 sage.libs.agentic.agents 模块的其他组件 -""" - -from unittest.mock import Mock - -import pytest - -# 尝试导入其他agent组件 -pytest_plugins = [] - -try: - # 尝试导入各种agent类 - from sage_libs.sage_agentic.agents.bots.question_bot import QuestionBot - - QUESTION_BOT_AVAILABLE = True -except ImportError: - QUESTION_BOT_AVAILABLE = False - -try: - from sage_libs.sage_agentic.agents.bots.answer_bot import AnswerBot - - ANSWER_BOT_AVAILABLE = True -except ImportError: - ANSWER_BOT_AVAILABLE = False - -try: - from sage_libs.sage_agentic.agents.bots.critic_bot import CriticBot - - CRITIC_BOT_AVAILABLE = True -except ImportError: - CRITIC_BOT_AVAILABLE = False - -try: - from sage_libs.sage_agentic.agents.bots.searcher_bot import SearcherBot - - SEARCHER_BOT_AVAILABLE = True -except ImportError: - SEARCHER_BOT_AVAILABLE = False - - -@pytest.mark.unit -class TestQuestionBot: - """测试QuestionBot类""" - - def test_question_bot_import(self): - """测试QuestionBot导入""" - if not QUESTION_BOT_AVAILABLE: - pytest.skip("QuestionBot not available") - - # 基本导入测试 - from sage_libs.sage_agentic.agents.bots.question_bot import QuestionBot - - assert QuestionBot is not None - - def test_question_bot_initialization(self): - """测试QuestionBot初始化""" - if not QUESTION_BOT_AVAILABLE: - pytest.skip("QuestionBot not available") - - # 创建模拟配置和上下文 - config = {"model": "test_model", "max_tokens": 100} - ctx = Mock() - - try: - bot = QuestionBot(config=config, ctx=ctx) - assert hasattr(bot, "config") - assert hasattr(bot, "ctx") - except Exception as e: - # 如果初始化需要特定依赖，跳过但记录 - pytest.skip(f"QuestionBot initialization failed: {e}") - - -@pytest.mark.unit -class TestAnswerBot: - """测试AnswerBot类""" - - def test_answer_bot_import(self): - """测试AnswerBot导入""" - if not ANSWER_BOT_AVAILABLE: - pytest.skip("AnswerBot not available") - - from sage_libs.sage_agentic.agents.bots.answer_bot import AnswerBot - - assert AnswerBot is not None - - def test_answer_bot_initialization(self): - """测试AnswerBot初始化""" - if not ANSWER_BOT_AVAILABLE: - pytest.skip("AnswerBot not available") - - config = {"model": "test_model", "temperature": 0.7} - ctx = Mock() - - try: - bot = AnswerBot(config=config, ctx=ctx) - assert hasattr(bot, "config") - assert hasattr(bot, "ctx") - except Exception as e: - pytest.skip(f"AnswerBot initialization failed: {e}") - - -@pytest.mark.unit -class TestCriticBot: - """测试CriticBot类""" - - def test_critic_bot_import(self): - """测试CriticBot导入""" - if not CRITIC_BOT_AVAILABLE: - pytest.skip("CriticBot not available") - - from sage_libs.sage_agentic.agents.bots.critic_bot import CriticBot - - assert CriticBot is not None - - def test_critic_bot_initialization(self): - """测试CriticBot初始化""" - if not CRITIC_BOT_AVAILABLE: - pytest.skip("CriticBot not available") - - config = {"model": "critic_model", "threshold": 0.8} - ctx = Mock() - - try: - bot = CriticBot(config=config, ctx=ctx) - assert hasattr(bot, "config") - assert hasattr(bot, "ctx") - except Exception as e: - pytest.skip(f"CriticBot initialization failed: {e}") - - -@pytest.mark.unit -class TestSearcherBot: - """测试SearcherBot类""" - - def test_searcher_bot_import(self): - """测试SearcherBot导入""" - if not SEARCHER_BOT_AVAILABLE: - pytest.skip("SearcherBot not available") - - from sage_libs.sage_agentic.agents.bots.searcher_bot import SearcherBot - - assert SearcherBot is not None - - def test_searcher_bot_initialization(self): - """测试SearcherBot初始化""" - if not SEARCHER_BOT_AVAILABLE: - pytest.skip("SearcherBot not available") - - config = {"search_engine": "test", "max_results": 10} - ctx = Mock() - - try: - bot = SearcherBot(config=config, ctx=ctx) - assert hasattr(bot, "config") - assert hasattr(bot, "ctx") - except Exception as e: - pytest.skip(f"SearcherBot initialization failed: {e}") - - -@pytest.mark.integration -class TestAgentsIntegration: - """Agent组件集成测试""" - - def test_agents_interaction(self): - """测试不同Agent之间的交互""" - # 由于可能缺少依赖，这里使用Mock对象 - question_bot = Mock() - answer_bot = Mock() - critic_bot = Mock() - searcher_bot = Mock() - - # 模拟工作流 - question_bot.generate_question.return_value = "什么是人工智能？" - searcher_bot.search.return_value = ["相关文档1", "相关文档2"] - answer_bot.generate_answer.return_value = "人工智能是计算机科学的分支" - critic_bot.evaluate.return_value = {"score": 0.9, "feedback": "回答质量很好"} - - # 模拟多Agent工作流 - question = question_bot.generate_question() - search_results = searcher_bot.search(question) - answer = answer_bot.generate_answer(question, search_results) - evaluation = critic_bot.evaluate(question, answer) - - assert question == "什么是人工智能？" - assert len(search_results) == 2 - assert "人工智能" in answer - assert evaluation["score"] == 0.9 - - def test_agent_pipeline(self): - """测试Agent管道""" - # 创建模拟的Agent管道 - pipeline_steps = [] - - # 步骤1: 问题生成 - def question_step(data): - data["question"] = "生成的问题" - pipeline_steps.append("question") - return data - - # 步骤2: 搜索 - def search_step(data): - data["search_results"] = ["结果1", "结果2"] - pipeline_steps.append("search") - return data - - # 步骤3: 回答生成 - def answer_step(data): - data["answer"] = "生成的回答" - pipeline_steps.append("answer") - return data - - # 步骤4: 评估 - def critic_step(data): - data["evaluation"] = {"score": 0.85} - pipeline_steps.append("critic") - return data - - # 执行管道 - data = {} - for step in [question_step, search_step, answer_step, critic_step]: - data = step(data) - - assert pipeline_steps == ["question", "search", "answer", "critic"] - assert "question" in data - assert "search_results" in data - assert "answer" in data - assert "evaluation" in data - - -@pytest.mark.unit -class TestAgentsFallback: - """Agent组件降级测试""" - - def test_missing_agents_graceful_handling(self): - """测试缺失Agent组件的优雅处理""" - # 模拟Agent组件不可用的情况 - agents_available = { - "QuestionBot": QUESTION_BOT_AVAILABLE, - "AnswerBot": ANSWER_BOT_AVAILABLE, - "CriticBot": CRITIC_BOT_AVAILABLE, - "SearcherBot": SEARCHER_BOT_AVAILABLE, - } - - # 检查至少有一些组件可用或者全部不可用都是合理的 - available_count = sum(agents_available.values()) - - # 这个测试总是通过，只是记录可用性 - assert available_count >= 0 # 可以是0到4之间的任何值 - - def test_mock_agent_workflow(self): - """测试使用Mock对象的Agent工作流""" - - # 创建Mock Agent类 - class MockAgent: - def __init__(self, name, config=None, ctx=None): - self.name = name - self.config = config or {} - self.ctx = ctx - - def execute(self, data): - return f"{self.name} processed: {data}" - - # 创建Mock Agent实例 - question_agent = MockAgent("QuestionBot") - answer_agent = MockAgent("AnswerBot") - - # 测试Mock工作流 - input_data = "输入数据" - question_result = question_agent.execute(input_data) - answer_result = answer_agent.execute(question_result) - - assert "QuestionBot processed" in question_result - assert "AnswerBot processed" in answer_result - - def test_agent_base_functionality(self): - """测试Agent基础功能（不依赖具体实现）""" - - # 定义基础Agent接口 - class BaseAgent: - def __init__(self, config=None, ctx=None): - self.config = config or {} - self.ctx = ctx - - def execute(self, data): - raise NotImplementedError("子类必须实现execute方法") - - # 实现简单的Agent - class SimpleAgent(BaseAgent): - def execute(self, data): - return f"处理数据: {data}" - - agent = SimpleAgent(config={"test": True}) - result = agent.execute("测试数据") - - assert result == "处理数据: 测试数据" - assert agent.config["test"] is True diff --git a/packages/sage-libs/tests/lib/agents/test_mcp_registry.py b/packages/sage-libs/tests/lib/agents/test_mcp_registry.py deleted file mode 100644 index a032379edd..0000000000 --- a/packages/sage-libs/tests/lib/agents/test_mcp_registry.py +++ /dev/null @@ -1,167 +0,0 @@ -# tests/test_mcp_registry.py -import pytest - -# 尽量直接导入；如果你的工程路径未就绪，可在运行pytest时用 PYTHONPATH 指向项目根 -from sage_libs.sage_agentic.agents.action.mcp_registry import MCPRegistry - - -class EchoTool: - """一个最小可用的Tool：原样返回arguments，并带有可选的描述与schema。""" - - name = "echo" - description = "echo back arguments" - input_schema = {"type": "object", "properties": {"x": {"type": "number"}}} - - def call(self, arguments): - return {"ok": True, "echo": arguments} - - -class AddTool: - """简单计算用的Tool。""" - - name = "add" - description = "sum a and b" - input_schema = { - "type": "object", - "properties": {"a": {"type": "number"}, "b": {"type": "number"}}, - "required": ["a", "b"], - } - - def call(self, arguments): - return arguments["a"] + arguments["b"] - - -def test_register_and_call_success(): - reg = MCPRegistry() - reg.register(EchoTool()) - reg.register(AddTool()) - - # 直接用 call() - assert reg.call("add", {"a": 1, "b": 2}) == 3 - assert reg.call("echo", {"k": "v"}) == {"ok": True, "echo": {"k": "v"}} - - -def test_register_requires_name_and_call(): - class NoName: - def call(self, arguments): - return None - - class NoCall: - name = "no_call" - - reg = MCPRegistry() - with pytest.raises(TypeError): - reg.register(NoName()) - with pytest.raises(TypeError): - reg.register(NoCall()) - - -def test_register_overwrite_same_name(): - class T1: - name = "same" - - def call(self, arguments): - return 1 - - class T2: - name = "same" - - def call(self, arguments): - return 2 - - reg = MCPRegistry() - reg.register(T1()) - assert reg.call("same", {}) == 1 - # 再次注册同名应覆盖 - reg.register(T2()) - assert reg.call("same", {}) == 2 - - -def test_describe_contains_description_and_schema(): - reg = MCPRegistry() - reg.register(EchoTool()) - reg.register(AddTool()) - - desc = reg.describe() - assert set(desc.keys()) == {"echo", "add"} - assert desc["echo"]["description"] == "echo back arguments" - assert "type" in desc["echo"]["input_schema"] - assert desc["add"]["description"] == "sum a and b" - - -@pytest.mark.parametrize( - "payload", - [ - None, - "describe", - {"op": "describe"}, - { - "op": "describe", - "foo": 1, - }, # 即便有其它键，只要op是describe且没有name，也应走describe分支 - ], -) -def test_execute_describe_variants(payload): - reg = MCPRegistry() - reg.register(EchoTool()) - out = reg.execute(payload) - assert "echo" in out - assert isinstance(out["echo"]["input_schema"], dict) - - -@pytest.mark.parametrize( - "payload, expected", - [ - ({"name": "add", "arguments": {"a": 10, "b": 5}}, 15), - ({"op": "call", "name": "add", "arguments": {"a": 2, "b": 3}}, 5), - ], -) -def test_execute_call_variants(payload, expected): - reg = MCPRegistry() - reg.register(AddTool()) - assert reg.execute(payload) == expected - - -def test_execute_call_invalid_name(): - reg = MCPRegistry() - reg.register(EchoTool()) - with pytest.raises(KeyError): - reg.execute({"name": "not_exist", "arguments": {}}) - - -def test_execute_invalid_op_raises(): - reg = MCPRegistry() - reg.register(EchoTool()) - with pytest.raises(ValueError) as ei: - reg.execute({"op": "delete", "name": "echo", "arguments": {}}) - assert "Unsupported op" in str(ei.value) - - -@pytest.mark.parametrize( - "bad_payload, exc_type, msg_part", - [ - ([], TypeError, "expects None/'describe' or a dict"), - ({"op": "call", "arguments": {}}, ValueError, "Missing or invalid 'name'"), - ({"name": ""}, ValueError, "Missing or invalid 'name'"), - ({"name": "echo", "arguments": 123}, TypeError, "'arguments' must be a dict"), - ], -) -def test_execute_input_validation_errors(bad_payload, exc_type, msg_part): - reg = MCPRegistry() - reg.register(EchoTool()) - with pytest.raises(exc_type) as ei: - reg.execute(bad_payload) - assert msg_part in str(ei.value) - - -def test_call_direct_type_and_key_errors(): - reg = MCPRegistry() - reg.register(EchoTool()) - - # 未注册名称 - with pytest.raises(KeyError): - reg.call("nope", {}) - - # arguments 类型不对时：call 本身把参数直接传给 tool，类型校验在 execute 中做； - # 这里补充一个最简单的 tool 内部容错（EchoTool可以接受任意类型），因此不抛错： - assert reg.call("echo", {"value": 123}) == {"ok": True, "echo": {"value": 123}} diff --git a/packages/sage-libs/tests/lib/agents/test_mcp_server.py b/packages/sage-libs/tests/lib/agents/test_mcp_server.py deleted file mode 100644 index 2dfffaf5bf..0000000000 --- a/packages/sage-libs/tests/lib/agents/test_mcp_server.py +++ /dev/null @@ -1,350 +0,0 @@ -# tests/lib/agents/test_mcp_server.py -import uuid - -import pytest - -# 兼容两种导入方式：优先包内路径，找不到则尝试同目录模块 -try: - from sage_libs.sage_agentic.agents.action import mcp_server as mcp -except ImportError: # pragma: no cover - import mcp_server as mcp # type: ignore - -from fastapi.testclient import TestClient - - -# --------------------------- -# 测试用假工具 -# --------------------------- -class EchoTool: - name = "echo" - description = "echo tool" - input_schema = { - "type": "object", - "properties": {"msg": {"type": "string"}}, - "required": ["msg"], - } - - def call(self, arguments): - return {"ok": True, "data": arguments} - - -class CrashTool: - name = "crash" - description = "always raises" - input_schema = {"type": "object", "properties": {}} - - def call(self, arguments): - raise RuntimeError("boom") - - -# 动态导入用：被 register_tool_from_path 使用 -class DummyPathTool: - name = "dummy_path_tool" - - def call(self, arguments): - return {"hi": "from_path", "args": arguments} - - -# --------------------------- -# Fixtures -# --------------------------- -@pytest.fixture(autouse=True) -def clean_server_state(): - """每个测试前后清理全局状态，避免交叉污染。""" - mcp.TOOLS.clear() - mcp.REMOTE_ADAPTERS.clear() - mcp.MOUNT_MAP.clear() - yield - mcp.TOOLS.clear() - mcp.REMOTE_ADAPTERS.clear() - mcp.MOUNT_MAP.clear() - - -@pytest.fixture() -def client(): - return TestClient(mcp.app) - - -# --------------------------- -# /health -# --------------------------- -def test_health_initial(client): - resp = client.get("/health") - assert resp.status_code == 200 - data = resp.json() - assert data["ok"] is True - assert data["tools"] == 0 - assert data["remotes"] == 0 - - -# --------------------------- -# 工具注册 + 描述 + 调用 -# --------------------------- -def test_register_and_describe_and_call_success(client): - mcp.register_tool(EchoTool()) - # list_tools - req = { - "jsonrpc": "2.0", - "id": uuid.uuid4().hex, - "method": "list_tools", - "params": {}, - } - r = client.post("/jsonrpc", json=req).json() - assert r["result"]["echo"]["description"] == "echo tool" - - # call_tool 成功 - req = { - "jsonrpc": "2.0", - "id": uuid.uuid4().hex, - "method": "call_tool", - "params": {"name": "echo", "arguments": {"msg": "hi"}}, - } - r = client.post("/jsonrpc", json=req).json() - assert r["result"] == {"ok": True, "data": {"msg": "hi"}} - assert r["error"] is None - - -def test_call_tool_missing_required_args_returns_error(client): - mcp.register_tool(EchoTool()) - # 少了 required 的 msg - req = { - "jsonrpc": "2.0", - "id": uuid.uuid4().hex, - "method": "call_tool", - "params": {"name": "echo", "arguments": {}}, - } - r = client.post("/jsonrpc", json=req).json() - assert r["result"] is None - assert "Missing required arguments" in r["error"] - - -def test_call_tool_not_found(client): - req = { - "jsonrpc": "2.0", - "id": uuid.uuid4().hex, - "method": "call_tool", - "params": {"name": "nope", "arguments": {}}, - } - r = client.post("/jsonrpc", json=req).json() - assert r["result"] is None - assert r["error"].startswith("Tool not found") - - -def test_call_tool_internal_exception_wrapped(client): - mcp.register_tool(CrashTool()) - req = { - "jsonrpc": "2.0", - "id": uuid.uuid4().hex, - "method": "call_tool", - "params": {"name": "crash", "arguments": {}}, - } - r = client.post("/jsonrpc", json=req).json() - assert r["result"] is None - assert r["error"] == "boom" - - -def test_register_tool_sets_defaults_for_missing_fields(client): - class MinimalTool: - name = "mini" - - def call(self, arguments): - return 1 - - mcp.register_tool(MinimalTool()) - desc = mcp.describe_tools() - assert "mini" in desc - assert isinstance(desc["mini"]["description"], str) - assert isinstance(desc["mini"]["input_schema"], dict) - - -# --------------------------- -# register_tool_from_path -# --------------------------- -def test_register_tool_from_path(client): - # 目标：通过 importlib 导入“当前测试模块”中的 DummyPathTool - module_name = __name__ # tests.lib.agents.test_mcp_server - req = { - "jsonrpc": "2.0", - "id": uuid.uuid4().hex, - "method": "register_tool_from_path", - "params": {"module": module_name, "class": "DummyPathTool", "init_kwargs": {}}, - } - r = client.post("/jsonrpc", json=req).json() - assert r["result"]["ok"] is True - assert r["result"]["name"] == "dummy_path_tool" - - # 调用一下 - call_req = { - "jsonrpc": "2.0", - "id": uuid.uuid4().hex, - "method": "call_tool", - "params": {"name": "dummy_path_tool", "arguments": {"x": 1}}, - } - cr = client.post("/jsonrpc", json=call_req).json() - assert cr["result"]["hi"] == "from_path" - assert cr["result"]["args"] == {"x": 1} - - -# --------------------------- -# 远程 MCP 挂载/刷新/卸载（mock requests.post） -# --------------------------- -class _MockResp: - def __init__(self, payload, status_code=200): - self._payload = payload - self.status_code = status_code - - def raise_for_status(self): - if not (200 <= self.status_code < 300): - raise RuntimeError("HTTP error") - - def json(self): - return self._payload - - -def test_mount_remote_mcp_and_proxy_call(monkeypatch, client): - """模拟远端有工具 sum(a,b)，挂载为本地代理并调用。""" - - def fake_post(url, json, timeout): - method = json["method"] - if method == "list_tools": - return _MockResp( - { - "result": { - "sum": { - "description": "add two numbers", - "input_schema": { - "type": "object", - "properties": { - "a": {"type": "number"}, - "b": {"type": "number"}, - }, - "required": ["a", "b"], - }, - } - } - } - ) - elif method == "call_tool": - params = json["params"] - return _MockResp({"result": params["arguments"]["a"] + params["arguments"]["b"]}) - else: - return _MockResp( - {"error": {"code": -32601, "message": "method not found"}}, - status_code=400, - ) - - monkeypatch.setattr(mcp.requests, "post", fake_post) - - # 挂载 - mount_req = { - "jsonrpc": "2.0", - "id": uuid.uuid4().hex, - "method": "mount_remote_mcp", - "params": { - "adapter_id": "r1", - "base_url": "http://remote:9001", - "prefix": "up_", - }, - } - r = client.post("/jsonrpc", json=mount_req).json() - assert r["result"]["ok"] is True - assert r["result"]["mounted"] == ["up_sum"] - - # list_tools 中应包含 up_sum - lst = client.post( - "/jsonrpc", - json={"jsonrpc": "2.0", "id": "1", "method": "list_tools", "params": {}}, - ).json() - assert "up_sum" in lst["result"] - - # 通过代理调用 - call_req = { - "jsonrpc": "2.0", - "id": "2", - "method": "call_tool", - "params": {"name": "up_sum", "arguments": {"a": 4, "b": 3}}, - } - cr = client.post("/jsonrpc", json=call_req).json() - assert cr["error"] is None - assert cr["result"]["output"] == 7 - assert cr["result"]["meta"]["proxy"] is True - assert cr["result"]["meta"]["remote"] == "sum" - - -def test_refresh_remote_mcp(monkeypatch, client): - """首次远端提供 t1；刷新后变为 t2，验证本地代理更新。""" - state = {"phase": 0} - - def fake_post(url, json, timeout): - method = json["method"] - if method == "list_tools": - if state["phase"] == 0: - return _MockResp({"result": {"t1": {"description": "tool1", "input_schema": {}}}}) - else: - return _MockResp({"result": {"t2": {"description": "tool2", "input_schema": {}}}}) - elif method == "call_tool": - return _MockResp({"result": "ok"}) - return _MockResp({"error": {"code": -32601}}, status_code=400) - - monkeypatch.setattr(mcp.requests, "post", fake_post) - - # mount（获得 t1） - mreq = { - "jsonrpc": "2.0", - "id": "1", - "method": "mount_remote_mcp", - "params": { - "adapter_id": "r1", - "base_url": "http://remote:9001", - "prefix": "R_", - }, - } - mr = client.post("/jsonrpc", json=mreq).json() - assert mr["result"]["mounted"] == ["R_t1"] - assert "R_t1" in mcp.TOOLS - - # refresh → 变为 t2 - state["phase"] = 1 - rreq = { - "jsonrpc": "2.0", - "id": "2", - "method": "refresh_remote_mcp", - "params": {"adapter_id": "r1", "prefix": "R_"}, - } - rr = client.post("/jsonrpc", json=rreq).json() - assert rr["result"]["mounted"] == ["R_t2"] - assert "R_t1" not in mcp.TOOLS - assert "R_t2" in mcp.TOOLS - - -def test_unmount_remote_mcp(monkeypatch, client): - def fake_post(url, json, timeout): - if json["method"] == "list_tools": - return _MockResp({"result": {"a": {"description": "", "input_schema": {}}}}) - return _MockResp({"result": "ok"}) - - monkeypatch.setattr(mcp.requests, "post", fake_post) - - # mount - mreq = { - "jsonrpc": "2.0", - "id": "1", - "method": "mount_remote_mcp", - "params": {"adapter_id": "rX", "base_url": "http://remote:9001", "prefix": ""}, - } - client.post("/jsonrpc", json=mreq) - assert "a" in mcp.TOOLS - assert "rX" in mcp.REMOTE_ADAPTERS - - # unmount - ureq = { - "jsonrpc": "2.0", - "id": "2", - "method": "unmount_remote_mcp", - "params": {"adapter_id": "rX"}, - } - ur = client.post("/jsonrpc", json=ureq).json() - assert ur["result"]["ok"] is True - assert "a" not in mcp.TOOLS - assert "rX" not in mcp.REMOTE_ADAPTERS - assert "rX" not in mcp.MOUNT_MAP diff --git a/packages/sage-libs/tests/lib/agents/test_profile.py b/packages/sage-libs/tests/lib/agents/test_profile.py deleted file mode 100644 index d31b551dd1..0000000000 --- a/packages/sage-libs/tests/lib/agents/test_profile.py +++ /dev/null @@ -1,121 +0,0 @@ -# refactor_wxh/MemoRAG/packages/sage-libs/tests/lib/agents/test_profile.py - -# 如果你已经配置好了 pythonpath（见第2节），下面这行导入能直接成功： -from sage_libs.sage_agentic.agents.profile.profile import BaseProfile - - -def test_defaults_and_types(): - p = BaseProfile() - assert p.name == "BaseAgent" - assert p.role == "general assistant" - assert isinstance(p.goals, list) and p.goals == [] - assert isinstance(p.tasks, list) and p.tasks == [] - assert p.backstory == "" - assert p.language == "zh" - assert p.tone == "concise" - - -def test_render_system_prompt_with_defaults(): - p = BaseProfile() - s = p.render_system_prompt() - assert "BaseAgent" in s - assert "general assistant" in s - assert "- （未指定）" in s # 空 goals/tasks 的占位符 - - -def test_render_system_prompt_with_content(): - p = BaseProfile( - name="ResearchAnalyst", - role="literature review agent", - goals=["检索高质量论文", "提供可引用总结"], - tasks=["列出关键论文", "构建对比表"], - backstory="专注信息提炼与可追溯性。", - language="zh", - tone="concise", - ) - s = p.render_system_prompt() - assert "ResearchAnalyst" in s - assert "literature review agent" in s - for g in p.goals: - assert f"- {g}" in s - for t in p.tasks: - assert f"- {t}" in s - assert "Backstory:" in s and "专注信息提炼与可追溯性" in s - assert "Language: zh" in s - assert "Tone: concise" in s - - -def test_to_dict_and_from_dict_roundtrip(): - p = BaseProfile( - name="Coder", - role="software bug fixer", - goals=["复现问题", "最小修复", "补测试"], - tasks=["阅读堆栈", "定位故障点", "写变更说明"], - backstory="工程实战导向", - language="zh", - tone="concise", - ) - d = p.to_dict() - p2 = BaseProfile.from_dict(d) - assert p2.to_dict() == d - - -def test_from_dict_with_missing_fields_uses_defaults(): - d = {"name": "OnlyNameProvided"} - p = BaseProfile.from_dict(d) - assert p.name == "OnlyNameProvided" - assert p.role == "general assistant" - assert p.goals == [] and p.tasks == [] - assert p.backstory == "" - assert p.language == "zh" and p.tone == "concise" - - -def test_merged_override_without_mutating_original(): - base = BaseProfile(name="Base", role="helper", goals=["G1"], tasks=["T1"]) - derived = base.merged(name="Derived", role="teacher", goals=["G2"], tone="detailed") - - assert derived is not base - assert derived.name == "Derived" - assert derived.role == "teacher" - assert derived.goals == ["G2"] - assert derived.tone == "detailed" - - assert base.name == "Base" - assert base.role == "helper" - assert base.goals == ["G1"] - assert base.tone == "concise" - - -def test_lists_are_independent_instances(): - p1 = BaseProfile() - p2 = BaseProfile() - p1.goals.append("A") - p1.tasks.append("B") - assert p1.goals == ["A"] and p2.goals == [] - assert p1.tasks == ["B"] and p2.tasks == [] - - -def test_prompt_is_reasonably_structured(): - p = BaseProfile(name="X", role="Y", goals=["g1", "g2"], tasks=["t1"]) - s = p.render_system_prompt() - assert "You are **X**, acting as **Y**." in s - assert "Backstory:" in s - assert "Goals:" in s - assert "Typical Tasks:" in s - assert "- g1" in s and "- g2" in s and "- t1" in s - - -def test_non_ascii_and_english_language_toggle(): - p = BaseProfile( - name="测试Agent", - role="teacher", - language="en", - tone="detailed", - goals=["讲清关键概念"], - tasks=["举例说明"], - ) - s = p.render_system_prompt() - assert "Language: en" in s - assert "Tone: detailed" in s - assert "讲清关键概念" in s - assert "举例说明" in s diff --git a/packages/sage-libs/tests/lib/agents/test_runtime_agent.py b/packages/sage-libs/tests/lib/agents/test_runtime_agent.py deleted file mode 100644 index d043690248..0000000000 --- a/packages/sage-libs/tests/lib/agents/test_runtime_agent.py +++ /dev/null @@ -1,163 +0,0 @@ -# refactor_wxh/MemoRAG/packages/sage-libs/tests/lib/agents/test_runtime_agent.py -import json - -from sage_libs.sage_agentic.agents.action.mcp_registry import MCPRegistry -from sage_libs.sage_agentic.agents.planning.simple_llm_planner import SimpleLLMPlanner -from sage_libs.sage_agentic.agents.profile.profile import BaseProfile - -from sage.middleware.operators.agent.runtime import AgentRuntime - - -# ---- Dummy 生成器：返回固定 JSON 计划 ---- -class DummyGeneratorPlan: - def execute(self, data): - # 返回一个两步计划：calculator -> reply - plan = [ - {"type": "tool", "name": "calculator", "arguments": {"expr": "21*2+5"}}, - {"type": "reply", "text": "完成。"}, - ] - return (data[0], json.dumps(plan, ensure_ascii=False)) - - -# ---- New generator that expects message format ---- -class DummyGeneratorWithMessages: - def execute(self, data): - # Accept both old format [user_query, prompt] and new format [user_query, messages] - user_query = data[0] - second_param = data[1] - - if isinstance(second_param, list): - # New message format - messages = second_param - assert len(messages) >= 1 - if len(messages) == 2: - assert messages[0]["role"] == "system" - assert messages[1]["role"] == "user" - - plan = [ - {"type": "tool", "name": "calculator", "arguments": {"expr": "21*2+5"}}, - {"type": "reply", "text": "完成。"}, - ] - return (user_query, json.dumps(plan, ensure_ascii=False)) - - -# ---- Dummy 工具：calculator ---- -class DummyCalc: - name = "calculator" - description = "Do math" - input_schema = { - "type": "object", - "properties": {"expr": {"type": "string"}}, - "required": ["expr"], - } - - def call(self, arguments): - expr = arguments.get("expr", "0") - return {"output": str(eval(expr, {"__builtins__": {}}))} - - -def test_runtime_basic_flow(): - tools = MCPRegistry() - tools.register(DummyCalc()) - - planner = SimpleLLMPlanner(generator=DummyGeneratorPlan()) - profile = BaseProfile(language="zh") - - runtime = AgentRuntime(profile=profile, planner=planner, tools=tools, summarizer=None) - out = runtime.step("计算 21*2+5") - # AgentRuntime.step() returns a dict with 'reply', 'observations', 'plan' - # 因为计划里包含 reply，runtime 将直接返回 "完成。" - assert isinstance(out, dict) - assert "完成" in out["reply"] - - -def test_runtime_no_reply_uses_template_summary(): - class GenNoReply: - def execute(self, data): - # 只返回一个工具步，不含 reply - plan = [{"type": "tool", "name": "calculator", "arguments": {"expr": "41+1"}}] - return (data[0], json.dumps(plan, ensure_ascii=False)) - - tools = MCPRegistry() - tools.register(DummyCalc()) - runtime = AgentRuntime( - profile=BaseProfile(name="TestBot"), - planner=SimpleLLMPlanner(generator=GenNoReply()), - tools=tools, # Fixed: use the tools with registered DummyCalc - ) - out = runtime.step("算下 41+1") - # AgentRuntime.step() returns a dict with 'reply', 'observations', 'plan' - assert isinstance(out, dict) - reply = out["reply"] - assert "成功" in reply and "42" in reply - - -# New tests for the message format changes in commit 12aec700c63407e1f5d79455b2d64a60a6688e96 - - -def test_runtime_with_message_format_summarizer(): - """Test AgentRuntime with summarizer using new message format.""" - - class SummarizerWithMessages: - def execute(self, data): - # data should be [None, messages] for summarizer - assert data[0] is None - messages = data[1] - assert isinstance(messages, list) - assert len(messages) == 2 - assert messages[0]["role"] == "system" - assert messages[1]["role"] == "user" - - return (None, "总结: 计算结果是47") - - class GenNoReply: - def execute(self, data): - plan = [{"type": "tool", "name": "calculator", "arguments": {"expr": "45+2"}}] - return (data[0], json.dumps(plan, ensure_ascii=False)) - - tools = MCPRegistry() - tools.register(DummyCalc()) - - runtime = AgentRuntime( - profile=BaseProfile(language="zh"), - planner=SimpleLLMPlanner(generator=GenNoReply()), - tools=tools, - summarizer=SummarizerWithMessages(), - ) - - out = runtime.step("计算 45+2") - # AgentRuntime.step() returns a dict with 'reply', 'observations', 'plan' - assert isinstance(out, dict) - assert "总结: 计算结果是47" in out["reply"] - - -def test_runtime_memory_disabled(): - """Test that memory functionality is disabled as per the commit changes.""" - tools = MCPRegistry() - tools.register(DummyCalc()) - - planner = SimpleLLMPlanner(generator=DummyGeneratorWithMessages()) - profile = BaseProfile(language="zh") - - # The memory parameter should be commented out/disabled - runtime = AgentRuntime(profile=profile, planner=planner, tools=tools, summarizer=None) - - # Verify memory is not set (should be None or not exist) - assert not hasattr(runtime, "memory") or getattr(runtime, "memory", None) is None - - -def test_runtime_with_new_planner_message_format(): - """Test that runtime works with planner using new message format.""" - tools = MCPRegistry() - tools.register(DummyCalc()) - - planner = SimpleLLMPlanner(generator=DummyGeneratorWithMessages()) - profile = BaseProfile(language="zh") - - runtime = AgentRuntime(profile=profile, planner=planner, tools=tools, summarizer=None) - - out = runtime.step("计算 21*2+5") - # AgentRuntime.step() returns a dict with 'reply', 'observations', 'plan' - # Should work with the new message format in planner - assert isinstance(out, dict) - assert "完成" in out["reply"] diff --git a/packages/sage-libs/tests/lib/agents/test_simple_llm_planner.py b/packages/sage-libs/tests/lib/agents/test_simple_llm_planner.py deleted file mode 100644 index b3d5dfaeb7..0000000000 --- a/packages/sage-libs/tests/lib/agents/test_simple_llm_planner.py +++ /dev/null @@ -1,151 +0,0 @@ -# refactor_wxh/MemoRAG/packages/sage-libs/tests/lib/agents/test_llm_planner.py -import json - -from sage_libs.sage_agentic.agents.planning.simple_llm_planner import SimpleLLMPlanner - - -class DummyGeneratorOK: - def execute(self, data): - # data = [user_query, prompt] or [user_query, messages] - plan = [ - {"type": "tool", "name": "calculator", "arguments": {"expr": "21*2+5"}}, - {"type": "reply", "text": "完成。"}, - ] - return (data[0], json.dumps(plan, ensure_ascii=False)) - - -class DummyGeneratorBadThenFix: - def __init__(self): - self.n = 0 - - def execute(self, data): - self.n += 1 - if self.n == 1: - # 非法输出（无JSON） - return (data[0], "First I will use calculator, then reply.") - else: - fixed = [ - {"type": "tool", "name": "calculator", "arguments": {"expr": "1+1"}}, - {"type": "reply", "text": "ok"}, - ] - return (data[0], json.dumps(fixed, ensure_ascii=False)) - - -def test_planner_basic(): - planner = SimpleLLMPlanner(generator=DummyGeneratorOK(), max_steps=3) - tools = { - "calculator": { - "description": "Do math", - "input_schema": { - "type": "object", - "properties": {"expr": {"type": "string"}}, - "required": ["expr"], - }, - } - } - plan = planner.plan("SYS", "计算 21*2+5", tools) - assert len(plan) == 2 - assert plan[0]["type"] == "tool" and plan[0]["name"] == "calculator" - assert plan[1]["type"] == "reply" - - -def test_planner_repair(): - planner = SimpleLLMPlanner( - generator=DummyGeneratorBadThenFix(), max_steps=3, enable_repair=True - ) - # The generator returns tool name "calculator", so we need it in tools - tools = { - "calculator": { - "description": "Do math", - "input_schema": { - "type": "object", - "properties": {"expr": {"type": "string"}}, - "required": ["expr"], - }, - } - } - - plan = planner.plan("SYS", "计算 1+1", tools) - assert plan and plan[0]["type"] == "tool" and plan[0]["name"] == "calculator" - assert plan[1]["type"] == "reply" - - -# New tests for the message format changes in commit 12aec700c63407e1f5d79455b2d64a60a6688e96 - - -class DummyGeneratorWithMessages: - """Generator that expects new message format.""" - - def execute(self, data): - # data = [user_query, messages] where messages is a list of dicts - user_query, messages = data - - # Verify message format - assert isinstance(messages, list) - assert len(messages) == 2 - assert messages[0]["role"] == "system" - assert messages[1]["role"] == "user" - assert messages[1]["content"] == user_query - - plan = [ - {"type": "tool", "name": "calculator", "arguments": {"expr": "2+2"}}, - {"type": "reply", "text": "计算完成。"}, - ] - return (user_query, json.dumps(plan, ensure_ascii=False)) - - -def test_planner_uses_new_message_format(): - """Test that SimpleLLMPlanner uses the new message format.""" - tools = { - "calculator": { - "description": "Do math", - "input_schema": { - "type": "object", - "properties": {"expr": {"type": "string"}}, - "required": ["expr"], - }, - } - } - - planner = SimpleLLMPlanner(generator=DummyGeneratorWithMessages(), max_steps=3) - plan = planner.plan("System prompt here", "计算 2+2", tools) - - assert len(plan) == 2 - assert plan[0]["type"] == "tool" and plan[0]["name"] == "calculator" - assert plan[1]["type"] == "reply" - - -def test_llm_planner_prompt_includes_tool_requirement(): - """Test that the system prompt includes the new tool requirement rule.""" - - class MessageCapturingGenerator: - def __init__(self): - self.captured_messages = None - - def execute(self, data): - self.captured_messages = data[1] # Store the messages - plan = [ - {"type": "tool", "name": "calculator", "arguments": {"expr": "1+1"}}, - {"type": "reply", "text": "完成"}, - ] - return (data[0], json.dumps(plan, ensure_ascii=False)) - - generator = MessageCapturingGenerator() - tools = { - "calculator": { - "description": "Do math", - "input_schema": { - "type": "object", - "properties": {"expr": {"type": "string"}}, - "required": ["expr"], - }, - } - } - - planner = SimpleLLMPlanner(generator=generator, max_steps=3) - planner.plan("Profile prompt", "test query", tools) - - # Check that the system prompt includes the new rule - assert generator.captured_messages is not None, "No messages captured" - system_content = generator.captured_messages[0]["content"] - assert "Always call at least one tool before replying when tools are provided" in system_content diff --git a/packages/sage-libs/tests/lib/io/__init__.py b/packages/sage-libs/tests/lib/io/__init__.py deleted file mode 100644 index b69963d224..0000000000 --- a/packages/sage-libs/tests/lib/io/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# Tests for io package diff --git a/packages/sage-libs/tests/lib/io/test_print_functionality.py b/packages/sage-libs/tests/lib/io/test_print_functionality.py deleted file mode 100644 index 2b31ead1ce..0000000000 --- a/packages/sage-libs/tests/lib/io/test_print_functionality.py +++ /dev/null @@ -1,113 +0,0 @@ -""" -测试 datastream.print() 方法和 PrintSink 功能 -""" - -import os -import sys -import unittest -from io import StringIO -from unittest.mock import patch - -from sage.libs.foundation.io.sink import PrintSink - -# 添加项目根目录到路径 -sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - - -class TestPrintSink(unittest.TestCase): - """测试 PrintSink 类的功能""" - - def setUp(self): - """测试前准备""" - self.print_sink = PrintSink(quiet=True) - self.print_sink_with_prefix = PrintSink(prefix="TEST", separator=" -> ", quiet=True) - - def test_simple_string(self): - """测试简单字符串输出""" - with patch("sys.stdout", new=StringIO()) as fake_out: - self.print_sink.execute("Hello World") - output = fake_out.getvalue().strip() - self.assertEqual(output, "Hello World") - - def test_qa_tuple_colored(self): - """测试问答对元组输出（彩色）""" - qa_data = ("什么是Python?", "Python是一种编程语言") - - with patch("sys.stdout", new=StringIO()) as fake_out: - self.print_sink.execute(qa_data) - output = fake_out.getvalue().strip() - # 检查是否包含问题和答案 - self.assertIn("什么是Python?", output) - self.assertIn("Python是一种编程语言", output) - self.assertIn("[Q]", output) - self.assertIn("[A]", output) - - def test_qa_tuple_no_color(self): - """测试问答对元组输出（无彩色）""" - print_sink_no_color = PrintSink(colored=False, quiet=True) - qa_data = ("什么是AI?", "AI是人工智能") - - with patch("sys.stdout", new=StringIO()) as fake_out: - print_sink_no_color.execute(qa_data) - output = fake_out.getvalue().strip() - # 检查是否包含问题和答案，但不包含ANSI颜色代码 - self.assertIn("什么是AI?", output) - self.assertIn("AI是人工智能", output) - self.assertIn("[Q]", output) - self.assertIn("[A]", output) - self.assertNotIn("\033[", output) # 不应包含ANSI颜色代码 - - def test_retrieval_tuple(self): - """测试检索结果元组输出""" - retrieval_data = ("查询内容", ["结果1", "结果2", "结果3"]) - - with patch("sys.stdout", new=StringIO()) as fake_out: - self.print_sink.execute(retrieval_data) - output = fake_out.getvalue().strip() - self.assertIn("查询内容", output) - self.assertIn("结果1", output) - self.assertIn("结果2", output) - self.assertIn("结果3", output) - self.assertIn("[Q]", output) - self.assertIn("[Chunks]", output) - - def test_string_list(self): - """测试字符串列表输出""" - list_data = ["项目1", "项目2", "项目3"] - - with patch("sys.stdout", new=StringIO()) as fake_out: - self.print_sink.execute(list_data) - output = fake_out.getvalue().strip() - self.assertIn("- 项目1", output) - self.assertIn("- 项目2", output) - self.assertIn("- 项目3", output) - - def test_dictionary(self): - """测试字典输出""" - dict_data = {"name": "张三", "age": 25, "city": "北京"} - - with patch("sys.stdout", new=StringIO()) as fake_out: - self.print_sink.execute(dict_data) - output = fake_out.getvalue().strip() - self.assertIn("name", output) - self.assertIn("张三", output) - self.assertIn("age", output) - self.assertIn("25", output) - - def test_prefix_and_separator(self): - """测试前缀和分隔符""" - with patch("sys.stdout", new=StringIO()) as fake_out: - self.print_sink_with_prefix.execute("测试数据") - output = fake_out.getvalue().strip() - self.assertIn("TEST -> 测试数据", output) - - def test_other_types(self): - """测试其他数据类型""" - with patch("sys.stdout", new=StringIO()) as fake_out: - self.print_sink.execute(12345) - output = fake_out.getvalue().strip() - self.assertEqual(output, "12345") - - -if __name__ == "__main__": - unittest.main() diff --git a/packages/sage-libs/tests/lib/io/test_sink.py b/packages/sage-libs/tests/lib/io/test_sink.py deleted file mode 100644 index 4b12d85982..0000000000 --- a/packages/sage-libs/tests/lib/io/test_sink.py +++ /dev/null @@ -1,75 +0,0 @@ -import pytest - -from sage.libs.foundation.io.sink import FileSink, MemWriteSink, RetriveSink, TerminalSink - - -@pytest.fixture -def sample_qa_data(): - return ("What is AI?", "Artificial Intelligence") - - -@pytest.fixture -def sample_chunks_data(): - return ("Explain AI", ["AI is ...", "It involves ..."]) - - -@pytest.fixture -def temp_file_path(tmp_path): - return tmp_path / "test_output.txt" - - -def test_terminal_sink(capsys, sample_qa_data): - sink = TerminalSink(config={}) - sink.execute(sample_qa_data) - captured = capsys.readouterr() - assert "[Q] Question :" in captured.out - assert "[A] Answer :" in captured.out - - -def test_retrive_sink(capsys, sample_chunks_data): - sink = RetriveSink(config={}) - sink.execute(sample_chunks_data) - captured = capsys.readouterr() - assert "[Q] Question :" in captured.out - assert "[A] Chunks :" in captured.out - - -def test_file_sink_writes(tmp_path, sample_qa_data): - file_path = tmp_path / "qa_output.txt" - sink = FileSink(config={"file_path": str(file_path)}) - sink.execute(sample_qa_data) - with open(file_path, encoding="utf-8") as f: - content = f.read() - assert "Question:" in content - assert "Answer :" in content - - -def test_mem_write_sink_various_inputs(tmp_path): - file_path = tmp_path / "mem_output.txt" - sink = MemWriteSink(config={"file_path": str(file_path)}) - - # Test with a string - sink.execute("single string") - # Test with list of strings - sink.execute(["list", "of", "strings"]) - # Test with tuple of strings (any length) - sink.execute(("tuple", "of", "strings")) - - with open(file_path, encoding="utf-8") as f: - lines = f.readlines() - - # Check that lines were written (should be at least the header + entries) - assert any("single string" in line for line in lines) - assert any("list" in line for line in lines) - assert any("tuple" in line for line in lines) - - -def test_mem_write_sink_handles_non_string(tmp_path): - file_path = tmp_path / "mem_output.txt" - sink = MemWriteSink(config={"file_path": str(file_path)}) - - # Provide a non-string input - MemWriteSink can handle any type via _parse_input - sink.execute(12345) - with open(file_path, encoding="utf-8") as f: - content = f.read() - assert "12345" in content diff --git a/packages/sage-libs/tests/lib/io/test_source.py b/packages/sage-libs/tests/lib/io/test_source.py deleted file mode 100644 index 98208014bd..0000000000 --- a/packages/sage-libs/tests/lib/io/test_source.py +++ /dev/null @@ -1,538 +0,0 @@ -""" -测试 sage.libs.io.source 模块 -""" - -import json -import os -import tempfile -from unittest.mock import Mock, mock_open, patch - -import pytest - -# 尝试导入IO模块 -pytest_plugins = [] - -try: - from sage.libs.foundation.io.source import ( - APISource, - CSVFileSource, - DatabaseSource, - JSONFileSource, - KafkaSource, - TextFileSource, - ) - - IO_SOURCE_AVAILABLE = True -except ImportError as e: - IO_SOURCE_AVAILABLE = False - pytestmark = pytest.mark.skip(f"IO Source module not available: {e}") - - -@pytest.fixture -def temp_dir(): - """创建临时目录用于测试""" - with tempfile.TemporaryDirectory() as tmpdir: - yield tmpdir - - -@pytest.mark.unit -class TestTextFileSource: - """测试TextFileSource类""" - - def test_text_file_source_import(self): - """测试TextFileSource导入""" - if not IO_SOURCE_AVAILABLE: - pytest.skip("IO Source module not available") - - from sage.libs.foundation.io.source import TextFileSource - - assert TextFileSource is not None - - def test_text_file_source_initialization(self, temp_dir): - """测试TextFileSource初始化""" - if not IO_SOURCE_AVAILABLE: - pytest.skip("IO Source module not available") - - file_path = os.path.join(temp_dir, "test.txt") - config = {"file_path": file_path, "encoding": "utf-8"} - - try: - source = TextFileSource(config=config) - assert hasattr(source, "config") - assert hasattr(source, "execute") - except Exception as e: - pytest.skip(f"TextFileSource initialization failed: {e}") - - def test_text_file_source_execute(self, temp_dir): - """测试TextFileSource执行""" - if not IO_SOURCE_AVAILABLE: - pytest.skip("IO Source module not available") - - # 创建测试文件 - file_path = os.path.join(temp_dir, "test.txt") - test_content = "这是测试文本内容\n第二行内容\n第三行内容" - - with open(file_path, "w", encoding="utf-8") as f: - f.write(test_content) - - config = {"file_path": file_path, "encoding": "utf-8"} - - try: - source = TextFileSource(config=config) - result = source.execute(None) - - # 验证结果 - assert isinstance(result, (str, list, dict)) - - except Exception as e: - pytest.skip(f"TextFileSource execution failed: {e}") - - @patch("builtins.open", new_callable=mock_open, read_data="mock file content") - def test_text_file_source_with_mock(self, mock_file): - """测试TextFileSource使用mock文件""" - if not IO_SOURCE_AVAILABLE: - pytest.skip("IO Source module not available") - - config = {"file_path": "mock_file.txt", "encoding": "utf-8"} - - try: - source = TextFileSource(config=config) - source.execute(None) - - # 验证文件被打开 - mock_file.assert_called_once_with("mock_file.txt", "r", encoding="utf-8") - - except Exception as e: - pytest.skip(f"TextFileSource mock execution failed: {e}") - - -@pytest.mark.unit -class TestJSONFileSource: - """测试JSONFileSource类""" - - def test_json_file_source_import(self): - """测试JSONFileSource导入""" - if not IO_SOURCE_AVAILABLE: - pytest.skip("IO Source module not available") - - from sage.libs.foundation.io.source import JSONFileSource - - assert JSONFileSource is not None - - def test_json_file_source_initialization(self, temp_dir): - """测试JSONFileSource初始化""" - if not IO_SOURCE_AVAILABLE: - pytest.skip("IO Source module not available") - - file_path = os.path.join(temp_dir, "test.json") - config = {"file_path": file_path} - - try: - source = JSONFileSource(config=config) - assert hasattr(source, "config") - assert hasattr(source, "execute") - except Exception as e: - pytest.skip(f"JSONFileSource initialization failed: {e}") - - def test_json_file_source_execute(self, temp_dir): - """测试JSONFileSource执行""" - if not IO_SOURCE_AVAILABLE: - pytest.skip("IO Source module not available") - - # 创建测试JSON文件 - file_path = os.path.join(temp_dir, "test.json") - test_data = { - "name": "测试数据", - "items": [{"id": 1, "text": "项目1"}, {"id": 2, "text": "项目2"}], - } - - with open(file_path, "w", encoding="utf-8") as f: - json.dump(test_data, f, ensure_ascii=False) - - config = {"file_path": file_path} - - try: - source = JSONFileSource(config=config) - result = source.execute(None) - - # 验证结果 - assert isinstance(result, (dict, list)) - - except Exception as e: - pytest.skip(f"JSONFileSource execution failed: {e}") - - def test_json_file_source_invalid_json(self, temp_dir): - """测试JSONFileSource处理无效JSON""" - if not IO_SOURCE_AVAILABLE: - pytest.skip("IO Source module not available") - - # 创建无效JSON文件 - file_path = os.path.join(temp_dir, "invalid.json") - with open(file_path, "w") as f: - f.write("{ invalid json content") - - config = {"file_path": file_path} - - try: - source = JSONFileSource(config=config) - - with pytest.raises((json.JSONDecodeError, Exception)): - source.execute(None) - - except Exception as e: - pytest.skip(f"JSONFileSource invalid JSON test failed: {e}") - - -@pytest.mark.unit -class TestCSVFileSource: - """测试CSVFileSource类""" - - def test_csv_file_source_import(self): - """测试CSVFileSource导入""" - if not IO_SOURCE_AVAILABLE: - pytest.skip("IO Source module not available") - - from sage.libs.foundation.io.source import CSVFileSource - - assert CSVFileSource is not None - - def test_csv_file_source_initialization(self, temp_dir): - """测试CSVFileSource初始化""" - if not IO_SOURCE_AVAILABLE: - pytest.skip("IO Source module not available") - - file_path = os.path.join(temp_dir, "test.csv") - config = {"file_path": file_path, "delimiter": ","} - - try: - source = CSVFileSource(config=config) - assert hasattr(source, "config") - assert hasattr(source, "execute") - except Exception as e: - pytest.skip(f"CSVFileSource initialization failed: {e}") - - def test_csv_file_source_execute(self, temp_dir): - """测试CSVFileSource执行""" - if not IO_SOURCE_AVAILABLE: - pytest.skip("IO Source module not available") - - # 创建测试CSV文件 - file_path = os.path.join(temp_dir, "test.csv") - csv_content = "id,name,description\n1,项目1,描述1\n2,项目2,描述2\n" - - with open(file_path, "w", encoding="utf-8") as f: - f.write(csv_content) - - config = {"file_path": file_path, "delimiter": ","} - - try: - source = CSVFileSource(config=config) - result = source.execute(None) - - # 验证结果 - assert isinstance(result, (list, dict)) - - except Exception as e: - pytest.skip(f"CSVFileSource execution failed: {e}") - - -@pytest.mark.unit -class TestKafkaSource: - """测试KafkaSource类""" - - def test_kafka_source_import(self): - """测试KafkaSource导入""" - if not IO_SOURCE_AVAILABLE: - pytest.skip("IO Source module not available") - - from sage.libs.foundation.io.source import KafkaSource - - assert KafkaSource is not None - - def test_kafka_source_initialization(self): - """测试KafkaSource初始化""" - if not IO_SOURCE_AVAILABLE: - pytest.skip("IO Source module not available") - - config = { - "bootstrap_servers": ["localhost:9092"], - "topic": "test_topic", - "group_id": "test_group", - } - - try: - source = KafkaSource(config=config) - assert hasattr(source, "config") - assert hasattr(source, "execute") - except Exception as e: - pytest.skip(f"KafkaSource initialization failed: {e}") - - def test_kafka_source_execute(self): - """测试KafkaSource执行（占位实现）""" - if not IO_SOURCE_AVAILABLE: - pytest.skip("IO Source module not available") - - config = { - "bootstrap_servers": ["localhost:9092"], - "topic": "test_topic", - "group_id": "test_group", - } - - try: - source = KafkaSource(config=config) - result = source.execute(None) - - # KafkaSource是占位实现，应该返回None - assert result is None - - except Exception as e: - pytest.skip(f"KafkaSource execution failed: {e}") - - -@pytest.mark.unit -class TestDatabaseSource: - """测试DatabaseSource类""" - - def test_database_source_import(self): - """测试DatabaseSource导入""" - if not IO_SOURCE_AVAILABLE: - pytest.skip("IO Source module not available") - - from sage.libs.foundation.io.source import DatabaseSource - - assert DatabaseSource is not None - - def test_database_source_initialization(self): - """测试DatabaseSource初始化""" - if not IO_SOURCE_AVAILABLE: - pytest.skip("IO Source module not available") - - config = { - "connection_string": "sqlite:///test.db", - "query": "SELECT * FROM test_table", - } - - try: - source = DatabaseSource(config=config) - assert hasattr(source, "config") - assert hasattr(source, "execute") - except Exception as e: - pytest.skip(f"DatabaseSource initialization failed: {e}") - - def test_database_source_execute(self): - """测试DatabaseSource执行（占位实现）""" - if not IO_SOURCE_AVAILABLE: - pytest.skip("IO Source module not available") - - config = { - "connection_string": "sqlite:///test.db", - "query": "SELECT * FROM test_table", - } - - try: - source = DatabaseSource(config=config) - result = source.execute(None) - - # DatabaseSource是占位实现，应该返回None - assert result is None - - except Exception as e: - pytest.skip(f"DatabaseSource execution failed: {e}") - - -@pytest.mark.unit -class TestAPISource: - """测试APISource类""" - - def test_api_source_import(self): - """测试APISource导入""" - if not IO_SOURCE_AVAILABLE: - pytest.skip("IO Source module not available") - - from sage.libs.foundation.io.source import APISource - - assert APISource is not None - - def test_api_source_initialization(self): - """测试APISource初始化""" - if not IO_SOURCE_AVAILABLE: - pytest.skip("IO Source module not available") - - config = { - "url": "https://api.example.com/data", - "method": "GET", - "headers": {"Authorization": "Bearer token"}, - } - - try: - source = APISource(config=config) - assert hasattr(source, "config") - assert hasattr(source, "execute") - except Exception as e: - pytest.skip(f"APISource initialization failed: {e}") - - def test_api_source_execute(self): - """测试APISource执行（占位实现）""" - if not IO_SOURCE_AVAILABLE: - pytest.skip("IO Source module not available") - - config = {"url": "https://api.example.com/data", "method": "GET"} - - try: - source = APISource(config=config) - result = source.execute(None) - - # APISource是占位实现，应该返回None - assert result is None - - except Exception as e: - pytest.skip(f"APISource execution failed: {e}") - - -@pytest.mark.integration -class TestSourceIntegration: - """数据源集成测试""" - - def test_multiple_sources_pipeline(self, temp_dir): - """测试多数据源管道""" - # 创建模拟数据源 - sources = [] - - # 文本源 - text_source = Mock() - text_source.execute.return_value = "文本数据" - sources.append(("text", text_source)) - - # JSON源 - json_source = Mock() - json_source.execute.return_value = {"key": "value"} - sources.append(("json", json_source)) - - # API源 - api_source = Mock() - api_source.execute.return_value = {"api_data": "response"} - sources.append(("api", api_source)) - - # 执行所有数据源 - results = {} - for name, source in sources: - results[name] = source.execute(None) - - assert len(results) == 3 - assert "text" in results - assert "json" in results - assert "api" in results - - def test_source_chain(self): - """测试数据源链""" - # 模拟数据源链：API -> 处理 -> 存储 - - # 第一个源：API获取数据 - api_source = Mock() - api_source.execute.return_value = [ - {"id": 1, "text": "数据1"}, - {"id": 2, "text": "数据2"}, - ] - - # 第二个源：数据处理 - processor = Mock() - processor.execute.return_value = [ - {"id": 1, "text": "处理后数据1", "processed": True}, - {"id": 2, "text": "处理后数据2", "processed": True}, - ] - - # 第三个源：数据输出 - output_sink = Mock() - output_sink.execute.return_value = "数据已保存" - - # 执行链 - raw_data = api_source.execute(None) - processed_data = processor.execute(raw_data) - save_result = output_sink.execute(processed_data) - - assert len(raw_data) == 2 - assert len(processed_data) == 2 - assert all(item["processed"] for item in processed_data) - assert save_result == "数据已保存" - - -@pytest.mark.external -class TestSourceExternal: - """数据源外部依赖测试""" - - def test_file_not_found_handling(self): - """测试文件不存在处理""" - if not IO_SOURCE_AVAILABLE: - pytest.skip("IO Source module not available") - - config = {"file_path": "/nonexistent/file.txt"} - - try: - source = TextFileSource(config=config) - - with pytest.raises((FileNotFoundError, Exception)): - source.execute(None) - - except Exception as e: - pytest.skip(f"File not found test failed: {e}") - - def test_api_timeout_handling(self): - """测试API超时处理（占位实现）""" - if not IO_SOURCE_AVAILABLE: - pytest.skip("IO Source module not available") - - config = {"url": "https://api.example.com/data", "timeout": 5} - - try: - source = APISource(config=config) - # APISource是占位实现，返回None而不会抛出异常 - result = source.execute(None) - assert result is None - - except Exception as e: - pytest.skip(f"API timeout test failed: {e}") - - -@pytest.mark.unit -class TestSourceFallback: - """数据源降级测试""" - - def test_source_fallback(self): - """测试数据源降级""" - - # 模拟简单的数据源 - class SimpleSource: - def __init__(self, config=None): - self.config = config or {} - - def execute(self, data): - source_type = self.config.get("type", "default") - return f"数据来自{source_type}源" - - # 测试不同类型的源 - text_source = SimpleSource({"type": "文本"}) - json_source = SimpleSource({"type": "JSON"}) - api_source = SimpleSource({"type": "API"}) - - text_result = text_source.execute(None) - json_result = json_source.execute(None) - api_result = api_source.execute(None) - - assert "文本" in text_result - assert "JSON" in json_result - assert "API" in api_result - - def test_basic_file_reading(self, temp_dir): - """测试基本文件读取""" - # 创建测试文件 - file_path = os.path.join(temp_dir, "simple.txt") - test_content = "简单测试内容" - - with open(file_path, "w", encoding="utf-8") as f: - f.write(test_content) - - # 简单文件读取 - with open(file_path, encoding="utf-8") as f: - content = f.read() - - assert content == test_content diff --git a/packages/sage-libs/tests/lib/rag/__init__.py b/packages/sage-libs/tests/lib/rag/__init__.py deleted file mode 100644 index bdf8027076..0000000000 --- a/packages/sage-libs/tests/lib/rag/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# Tests for rag package diff --git a/packages/sage-libs/tests/lib/rag/test_chunk.py b/packages/sage-libs/tests/lib/rag/test_chunk.py deleted file mode 100644 index c7c003d93b..0000000000 --- a/packages/sage-libs/tests/lib/rag/test_chunk.py +++ /dev/null @@ -1,153 +0,0 @@ -""" -测试 sage.libs.rag.chunk 模块 -""" - -import pytest - -from sage.libs.rag.chunk import CharacterSplitter - - -@pytest.mark.unit -class TestCharacterSplitter: - """测试CharacterSplitter类""" - - def test_character_splitter_initialization_default(self): - """测试CharacterSplitter默认初始化""" - splitter = CharacterSplitter() - - assert splitter.chunk_size == 512 - assert splitter.overlap == 128 - assert splitter.separator is None - - def test_character_splitter_initialization_custom(self): - """测试CharacterSplitter自定义初始化""" - splitter = CharacterSplitter(chunk_size=256, overlap=64, separator="\n") - - assert splitter.chunk_size == 256 - assert splitter.overlap == 64 - assert splitter.separator == "\n" - - def test_split_basic(self): - """测试基本分割功能""" - splitter = CharacterSplitter(chunk_size=10, overlap=3) - text = "Hello World Test" - chunks = splitter.split(text) - - assert isinstance(chunks, list) - assert len(chunks) >= 1 - assert chunks[0] == "Hello Worl" - - def test_split_with_separator(self): - """测试使用分隔符分割""" - splitter = CharacterSplitter(chunk_size=10, overlap=3, separator="\n") - text = "Line 1\nLine 2\nLine 3" - chunks = splitter.split(text) - - assert len(chunks) == 3 - assert chunks[0] == "Line 1" - assert chunks[1] == "Line 2" - assert chunks[2] == "Line 3" - - def test_split_empty_text(self): - """测试空文本""" - splitter = CharacterSplitter(chunk_size=10, overlap=3) - chunks = splitter.split("") - - assert len(chunks) == 1 - assert chunks[0] == "" - - def test_split_short_text(self): - """测试短文本（小于chunk_size）""" - splitter = CharacterSplitter(chunk_size=20, overlap=5) - text = "Short" - chunks = splitter.split(text) - - assert len(chunks) == 1 - assert chunks[0] == "Short" - - def test_split_exact_chunk_size(self): - """测试文本长度正好等于chunk_size""" - splitter = CharacterSplitter(chunk_size=10, overlap=3) - text = "1234567890" - chunks = splitter.split(text) - - assert len(chunks) == 2 - assert chunks[0] == "1234567890" - assert len(chunks[1]) == 3 - - def test_split_with_overlap(self): - """测试重叠功能""" - splitter = CharacterSplitter(chunk_size=5, overlap=2) - text = "ABCDEFGHIJ" - chunks = splitter.split(text) - - # 验证有重叠 - if len(chunks) > 1: - # 第一个chunk的最后2个字符应该与第二个chunk的前2个字符重叠 - assert chunks[0][-2:] == chunks[1][:2] - - def test_split_zero_overlap(self): - """测试零重叠""" - splitter = CharacterSplitter(chunk_size=5, overlap=0) - text = "1234567890ABCDE" # pragma: allowlist secret - chunks = splitter.split(text) - - assert len(chunks) == 3 - assert chunks[0] == "12345" - assert chunks[1] == "67890" - assert chunks[2] == "ABCDE" - - def test_split_chinese_text(self): - """测试中文文本""" - splitter = CharacterSplitter(chunk_size=5, overlap=2) - text = "这是一个测试文档需要分割" - chunks = splitter.split(text) - - assert isinstance(chunks, list) - assert len(chunks) > 1 - assert chunks[0] == "这是一个测" - - def test_split_long_text(self): - """测试长文本""" - splitter = CharacterSplitter(chunk_size=50, overlap=10) - text = "A" * 500 - chunks = splitter.split(text) - - # 验证所有chunk（除最后一个）长度为chunk_size - for chunk in chunks[:-1]: - assert len(chunk) == 50 - - # 验证有重叠 - if len(chunks) > 1: - assert chunks[0][-10:] == chunks[1][:10] - - def test_split_with_special_characters(self): - """测试特殊字符""" - splitter = CharacterSplitter(chunk_size=8, overlap=2) - text = "Hello!\n\tWorld@#$" - chunks = splitter.split(text) - - assert isinstance(chunks, list) - # 验证特殊字符被保留 - combined = "".join(chunks) - assert "\n" in combined - assert "\t" in combined - assert "@#$" in combined - - -@pytest.mark.external -class TestSentenceTransformersTokenTextSplitter: - """测试SentenceTransformersTokenTextSplitter类（需要外部依赖）""" - - def test_import_sentence_transformer_splitter(self): - """测试导入SentenceTransformersTokenTextSplitter""" - try: - from sage.libs.rag.chunk import SentenceTransformersTokenTextSplitter - - assert SentenceTransformersTokenTextSplitter is not None - except ImportError: - pytest.skip("sentence-transformers not installed") - - def test_sentence_transformer_initialization_error(self): - """测试没有依赖时的错误""" - pytest.importorskip("sentence_transformers", reason="sentence-transformers not installed") diff --git a/packages/sage-libs/tests/lib/rag/test_document_loaders.py b/packages/sage-libs/tests/lib/rag/test_document_loaders.py deleted file mode 100644 index b2b7cfa9f1..0000000000 --- a/packages/sage-libs/tests/lib/rag/test_document_loaders.py +++ /dev/null @@ -1,214 +0,0 @@ -""" -测试 sage.libs.rag.document_loaders 模块 -""" - -import tempfile -from pathlib import Path - -import pytest - -from sage.libs.rag.document_loaders import LoaderFactory, MarkdownLoader, TextLoader - - -@pytest.mark.unit -class TestTextLoader: - """测试TextLoader类""" - - def test_text_loader_initialization(self): - """测试TextLoader初始化""" - loader = TextLoader("test.txt") - assert loader.filepath == "test.txt" - assert loader.encoding == "utf-8" - assert loader.chunk_separator is None - - def test_text_loader_custom_encoding(self): - """测试自定义编码""" - loader = TextLoader("test.txt", encoding="gbk", chunk_separator="\n\n") - assert loader.encoding == "gbk" - assert loader.chunk_separator == "\n\n" - - def test_text_loader_load_file(self): - """测试加载文本文件""" - # 创建临时文件 - with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".txt") as f: - f.write("This is a test document.\nWith multiple lines.") - temp_path = f.name - - try: - loader = TextLoader(temp_path) - result = loader.load() - - assert isinstance(result, dict) - assert "content" in result - assert "metadata" in result - assert "This is a test document" in result["content"] - assert result["metadata"]["source"] == temp_path - assert result["metadata"]["type"] == "txt" - finally: - # 清理临时文件 - Path(temp_path).unlink() - - def test_text_loader_file_not_found(self): - """测试文件不存在的情况""" - loader = TextLoader("nonexistent_file.txt") - with pytest.raises(FileNotFoundError): - loader.load() - - def test_text_loader_utf8_content(self): - """测试UTF-8编码内容""" - with tempfile.NamedTemporaryFile( - mode="w", encoding="utf-8", delete=False, suffix=".txt" - ) as f: - f.write("中文测试内容\nChinese test content") - temp_path = f.name - - try: - loader = TextLoader(temp_path) - result = loader.load() - - assert "中文测试内容" in result["content"] - assert "Chinese test content" in result["content"] - finally: - Path(temp_path).unlink() - - def test_text_loader_empty_file(self): - """测试空文件""" - with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".txt") as f: - temp_path = f.name - - try: - loader = TextLoader(temp_path) - result = loader.load() - - assert result["content"] == "" - assert result["metadata"]["type"] == "txt" - finally: - Path(temp_path).unlink() - - -@pytest.mark.unit -class TestMarkdownLoader: - """测试MarkdownLoader类""" - - def test_markdown_loader_initialization(self): - """测试MarkdownLoader初始化""" - loader = MarkdownLoader("test.md") - assert loader.filepath == "test.md" - assert loader.encoding == "utf-8" - - def test_markdown_loader_custom_encoding(self): - """测试自定义编码""" - loader = MarkdownLoader("test.md", encoding="gbk") - assert loader.encoding == "gbk" - - def test_markdown_loader_load_file(self): - """测试加载Markdown文件""" - # 创建临时Markdown文件 - with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".md") as f: - f.write("# Test Title\n\nThis is a **test** document.") - temp_path = f.name - - try: - loader = MarkdownLoader(temp_path) - result = loader.load() - - assert isinstance(result, dict) - assert "content" in result - assert "metadata" in result - assert "# Test Title" in result["content"] - assert "**test**" in result["content"] - assert result["metadata"]["type"] == "md" - finally: - Path(temp_path).unlink() - - def test_markdown_loader_file_not_found(self): - """测试文件不存在的情况""" - loader = MarkdownLoader("nonexistent.md") - with pytest.raises(FileNotFoundError): - loader.load() - - -@pytest.mark.unit -class TestLoaderFactory: - """测试LoaderFactory类""" - - def test_loader_factory_txt(self): - """测试加载.txt文件""" - with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".txt") as f: - f.write("Test content") - temp_path = f.name - - try: - result = LoaderFactory.load(temp_path) - assert result["content"] == "Test content" - assert result["metadata"]["type"] == "txt" - finally: - Path(temp_path).unlink() - - def test_loader_factory_md(self): - """测试加载.md文件""" - with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".md") as f: - f.write("# Markdown") - temp_path = f.name - - try: - result = LoaderFactory.load(temp_path) - assert "# Markdown" in result["content"] - assert result["metadata"]["type"] == "md" - finally: - Path(temp_path).unlink() - - def test_loader_factory_markdown(self): - """测试加载.markdown文件""" - with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".markdown") as f: - f.write("## Test") - temp_path = f.name - - try: - result = LoaderFactory.load(temp_path) - assert "## Test" in result["content"] - assert result["metadata"]["type"] == "md" - finally: - Path(temp_path).unlink() - - def test_loader_factory_unsupported_extension(self): - """测试不支持的文件扩展名""" - with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".xyz") as f: - temp_path = f.name - - try: - with pytest.raises(ValueError, match="Unsupported file extension"): - LoaderFactory.load(temp_path) - finally: - Path(temp_path).unlink() - - def test_loader_factory_case_insensitive(self): - """测试扩展名不区分大小写""" - with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".TXT") as f: - f.write("UPPER CASE") - temp_path = f.name - - try: - result = LoaderFactory.load(temp_path) - assert result["content"] == "UPPER CASE" - finally: - Path(temp_path).unlink() - - -# PDFLoader 和 DocxLoader 需要额外的依赖，标记为 external -@pytest.mark.external -class TestPDFLoader: - """测试PDFLoader类（需要PyPDF2）""" - - def test_pdf_loader_import_error(self): - """测试缺少PyPDF2依赖时的错误""" - pytest.importorskip("PyPDF2", reason="PyPDF2 not installed") - - -@pytest.mark.external -class TestDocxLoader: - """测试DocxLoader类（需要python-docx）""" - - def test_docx_loader_import_error(self): - """测试缺少python-docx依赖时的错误""" - pytest.importorskip("docx", reason="python-docx not installed") diff --git a/packages/sage-libs/tests/lib/rag/test_types.py b/packages/sage-libs/tests/lib/rag/test_types.py deleted file mode 100644 index 2bc5f3e8b6..0000000000 --- a/packages/sage-libs/tests/lib/rag/test_types.py +++ /dev/null @@ -1,282 +0,0 @@ -""" -测试 sage.libs.rag.types 模块 -""" - -import pytest - -from sage.libs.rag.types import ( - RAGDocument, - RAGQuery, - RAGResponse, - create_rag_response, - ensure_rag_response, - extract_query, - extract_results, -) - - -@pytest.mark.unit -class TestRAGDocument: - """测试RAGDocument类型""" - - def test_rag_document_basic(self): - """测试基本RAGDocument创建""" - doc: RAGDocument = { - "text": "This is a test document", - "title": "Test Doc", - } - assert doc["text"] == "This is a test document" - assert doc["title"] == "Test Doc" - - def test_rag_document_with_relevance_score(self): - """测试带相关性分数的文档""" - doc: RAGDocument = { - "text": "Python programming", - "relevance_score": 0.95, - "chunk_id": 3, - } - assert doc["relevance_score"] == 0.95 - assert doc["chunk_id"] == 3 - - def test_rag_document_creation(self): - """测试手动创建RAGDocument""" - doc: RAGDocument = { - "text": "Sample text", - "title": "Sample", - "relevance_score": 0.85, - "source": "test.pdf", - } - assert doc["text"] == "Sample text" - assert doc["title"] == "Sample" - assert doc["relevance_score"] == 0.85 - assert doc["source"] == "test.pdf" - - -@pytest.mark.unit -class TestRAGQuery: - """测试RAGQuery类型""" - - def test_rag_query_basic(self): - """测试基本RAGQuery创建""" - query: RAGQuery = { - "query": "What is Python?", - "results": ["doc1", "doc2"], - } - assert query["query"] == "What is Python?" - assert len(query["results"]) == 2 - - def test_rag_query_with_generated(self): - """测试带生成内容的查询""" - query: RAGQuery = { - "query": "Explain ML", - "results": ["context1"], - "generated": "Machine learning is...", - "execution_time": 1.5, - } - assert query["generated"] == "Machine learning is..." - assert query["execution_time"] == 1.5 - - def test_rag_query_creation(self): - """测试手动创建RAGQuery""" - query: RAGQuery = { - "query": "Test query", - "results": ["r1", "r2", "r3"], - "generated": "Generated answer", - "reranked": True, - } - assert query["query"] == "Test query" - assert len(query["results"]) == 3 - assert query["generated"] == "Generated answer" - assert query["reranked"] is True - - -@pytest.mark.unit -class TestRAGResponse: - """测试RAGResponse类型""" - - def test_rag_response_basic(self): - """测试基本RAGResponse创建""" - response: RAGResponse = { - "query": "What is AI?", - "results": ["AI is artificial intelligence"], - } - assert response["query"] == "What is AI?" - assert len(response["results"]) == 1 - - def test_rag_response_with_generated(self): - """测试带生成内容的响应""" - response: RAGResponse = { - "query": "Explain DL", - "results": ["context"], - "generated": "Deep learning is...", - "context": "Retrieved context", - "execution_time": 2.3, - } - assert response["generated"] == "Deep learning is..." - assert response["context"] == "Retrieved context" - assert response["execution_time"] == 2.3 - - def test_create_rag_response(self): - """测试create_rag_response辅助函数""" - response = create_rag_response( - query="Test question", - results=["answer1", "answer2"], - generated="Final answer", - execution_time=1.8, - ) - assert response["query"] == "Test question" - assert len(response["results"]) == 2 - assert response["generated"] == "Final answer" - assert response["execution_time"] == 1.8 - - def test_rag_response_with_metadata(self): - """测试带元数据的响应""" - response: RAGResponse = { - "query": "Test", - "results": ["r1"], - "metadata": { - "retriever": "bm25", - "generator": "gpt-3.5", - "num_chunks": 5, - }, - } - assert response["metadata"]["retriever"] == "bm25" - assert response["metadata"]["num_chunks"] == 5 - - -@pytest.mark.unit -class TestRAGTypesCompatibility: - """测试RAG类型的兼容性""" - - def test_rag_document_is_dict(self): - """验证RAGDocument可以作为普通字典使用""" - doc: RAGDocument = {"text": "test", "title": "Test"} - assert isinstance(doc, dict) - assert "text" in doc - assert doc.get("title") == "Test" - - def test_rag_query_is_dict(self): - """验证RAGQuery可以作为普通字典使用""" - query: RAGQuery = {"query": "test", "results": ["r1"]} - assert isinstance(query, dict) - assert "query" in query - assert query.get("results") == ["r1"] - - def test_rag_response_is_dict(self): - """验证RAGResponse可以作为普通字典使用""" - response = create_rag_response(query="test", results=["r1"]) - assert isinstance(response, dict) - assert "query" in response - assert response.get("results") == ["r1"] - - def test_optional_fields(self): - """测试可选字段的处理""" - # 只包含必需字段 - doc: RAGDocument = {"text": "test"} - assert "text" in doc - assert doc.get("relevance_score") is None - - query: RAGQuery = {"query": "test", "results": []} - assert "query" in query - assert query.get("generated") is None - - response = create_rag_response(query="test", results=[]) - assert "query" in response - assert response.get("generated") is None - - -@pytest.mark.unit -class TestRAGHelperFunctions: - """测试RAG辅助函数""" - - def test_ensure_rag_response_from_dict(self): - """测试从字典创建RAGResponse""" - data = { - "query": "test query", - "results": ["r1", "r2"], - "generated": "answer", - } - response = ensure_rag_response(data) - assert response["query"] == "test query" - assert response["results"] == ["r1", "r2"] - assert response["generated"] == "answer" - - def test_ensure_rag_response_from_tuple(self): - """测试从元组创建RAGResponse""" - data = ("my query", ["result1", "result2"]) - response = ensure_rag_response(data) - assert response["query"] == "my query" - assert response["results"] == ["result1", "result2"] - - def test_ensure_rag_response_with_default_query(self): - """测试使用默认查询""" - data = {"results": ["r1"]} - response = ensure_rag_response(data, default_query="default") - assert response["query"] == "default" - assert response["results"] == ["r1"] - - def test_extract_query_from_dict(self): - """测试从字典提取查询""" - data = {"query": "test question"} - query = extract_query(data) - assert query == "test question" - - def test_extract_query_from_tuple(self): - """测试从元组提取查询""" - data = ("my query", ["results"]) - query = extract_query(data) - assert query == "my query" - - def test_extract_query_with_default(self): - """测试提取查询时使用默认值""" - data = {"results": ["r1"]} - query = extract_query(data, default="default query") - assert query == "default query" - - def test_extract_results_from_dict(self): - """测试从字典提取结果""" - data = {"query": "q", "results": ["a", "b", "c"]} - results = extract_results(data) - assert results == ["a", "b", "c"] - - def test_extract_results_from_tuple(self): - """测试从元组提取结果""" - data = ("query", ["r1", "r2"]) - results = extract_results(data) - assert results == ["r1", "r2"] - - def test_extract_results_with_default(self): - """测试提取结果时使用默认值""" - data = {"query": "q"} - results = extract_results(data, default=["default"]) - assert results == ["default"] - - def test_create_rag_response_minimal(self): - """测试创建最小RAGResponse""" - response = create_rag_response(query="q", results=["r"]) - assert response["query"] == "q" - assert response["results"] == ["r"] - assert response.get("generated") is None - - def test_create_rag_response_with_kwargs(self): - """测试创建带额外字段的RAGResponse""" - response = create_rag_response( - query="test", - results=["a", "b"], - generated="answer", - execution_time=1.5, - metadata={"model": "gpt-4"}, - ) - assert response["query"] == "test" - assert response["results"] == ["a", "b"] - assert response["generated"] == "answer" - assert response["execution_time"] == 1.5 - assert response["metadata"]["model"] == "gpt-4" - - def test_create_rag_response_none_values_filtered(self): - """测试None值不会被添加到响应中""" - response = create_rag_response(query="test", results=["r"], generated=None, metadata=None) - assert response["query"] == "test" - assert response["results"] == ["r"] - assert "generated" not in response - assert "metadata" not in response diff --git a/packages/sage-libs/tests/lib/test_additional_coverage.py b/packages/sage-libs/tests/lib/test_additional_coverage.py deleted file mode 100644 index 434f6befd5..0000000000 --- a/packages/sage-libs/tests/lib/test_additional_coverage.py +++ /dev/null @@ -1,184 +0,0 @@ -""" -Unit tests for LLM Planner and other components -""" - -from unittest.mock import MagicMock - - -class TestSimpleLLMPlanner: - """Test SimpleLLMPlanner class""" - - def test_simple_planner_init(self): - """Test SimpleLLMPlanner initialization""" - from sage_libs.sage_agentic.agents.planning.simple_llm_planner import SimpleLLMPlanner - - mock_generator = MagicMock() - planner = SimpleLLMPlanner(generator=mock_generator) - assert planner is not None - assert planner.generator == mock_generator - - def test_simple_planner_plan_generation(self): - """Test plan generation""" - from sage_libs.sage_agentic.agents.planning.simple_llm_planner import SimpleLLMPlanner - - mock_generator = MagicMock() - # Mock generator to return valid JSON plan - plan_json = '[{"type":"tool","name":"calculator","arguments":{"expr":"2+2"}},{"type":"reply","text":"完成"}]' - mock_generator.execute.return_value = ("test query", plan_json) - - planner = SimpleLLMPlanner(generator=mock_generator, max_steps=3) - tools = { - "calculator": { - "description": "Do math", - "input_schema": { - "type": "object", - "properties": {"expr": {"type": "string"}}, - "required": ["expr"], - }, - } - } - plan = planner.plan("System prompt", "计算 2+2", tools) - assert plan is not None - assert len(plan) > 0 - - def test_simple_planner_custom_params(self): - """Test SimpleLLMPlanner with custom parameters""" - from sage_libs.sage_agentic.agents.planning.simple_llm_planner import SimpleLLMPlanner - - mock_generator = MagicMock() - planner = SimpleLLMPlanner(generator=mock_generator) - assert planner.generator == mock_generator - - -class TestSimpleLLMPlannerErrorHandling: - """Test error handling in SimpleLLMPlanner""" - - def test_simple_planner_repair(self): - """Test SimpleLLMPlanner repair mechanism when JSON parsing fails""" - from sage_libs.sage_agentic.agents.planning.simple_llm_planner import SimpleLLMPlanner - - mock_generator = MagicMock() - # First call returns invalid JSON, second call returns valid JSON - plan_json = '[{"type":"reply","text":"修复后的回复"}]' - mock_generator.execute.side_effect = [ - ("test query", "Invalid JSON response"), # First try fails - ("test query", plan_json), # Repair succeeds - ] - - planner = SimpleLLMPlanner(generator=mock_generator, enable_repair=True) - tools = {"test_tool": {"description": "Test", "input_schema": {"type": "object"}}} - plan = planner.plan("System", "Query", tools) - - # Should have called execute twice (initial + repair) - assert mock_generator.execute.call_count == 2 - assert plan is not None - - -class TestBaseTool: - """Test BaseTool class""" - - def test_base_tool_init(self): - """Test BaseTool initialization""" - from sage.libs.foundation.tools.tool import BaseTool - - class TestTool(BaseTool): - def execute(self, *args, **kwargs): - return "result" - - tool = TestTool(tool_name="test_tool", tool_description="A test tool", input_types=["str"]) - assert tool is not None - assert tool.tool_name == "test_tool" - - def test_base_tool_execute(self): - """Test BaseTool execute method""" - from sage.libs.foundation.tools.tool import BaseTool - - class TestTool(BaseTool): - def execute(self, *args, **kwargs): - return "tool result" - - tool = TestTool(tool_name="test_tool", tool_description="A test tool") - result = tool.execute("test input") - assert result == "tool result" - - def test_base_tool_metadata(self): - """Test BaseTool get_metadata method""" - from sage.libs.foundation.tools.tool import BaseTool - - class TestTool(BaseTool): - def execute(self, *args, **kwargs): - return "result" - - tool = TestTool( - tool_name="test_tool", - tool_description="A test tool", - input_types=["str"], - output_type="str", - ) - metadata = tool.get_metadata() - assert metadata["name"] == "test_tool" - assert metadata["description"] == "A test tool" - - -class TestSink: - """Test Sink classes""" - - def test_terminal_sink_init(self): - """Test TerminalSink initialization""" - from sage.libs.foundation.io.sink import TerminalSink - - sink = TerminalSink(config={}) - assert sink is not None - - def test_terminal_sink_execute_with_dict(self): - """Test TerminalSink execute with dict input""" - from sage.libs.foundation.io.sink import TerminalSink - - sink = TerminalSink(config={}) - data = {"query": "Test question", "answer": "Test answer"} - # execute method prints output, we just test it doesn't raise - sink.execute(data) - - def test_file_sink_init(self): - """Test FileSink initialization""" - from sage.libs.foundation.io.sink import FileSink - - sink = FileSink(config={}) - assert sink is not None - - -class TestBaseServiceKernel: - """Test BaseService from kernel""" - - def test_base_service_init(self): - """Test BaseService initialization""" - from sage.platform.service.base_service import BaseService - - class TestService(BaseService): - pass - - service = TestService() - assert service is not None - - def test_base_service_logger_property(self): - """Test BaseService logger property""" - from sage.platform.service.base_service import BaseService - - class TestService(BaseService): - pass - - service = TestService() - # logger is a property - logger = service.logger - assert logger is not None - - def test_base_service_name_property(self): - """Test BaseService name property""" - from sage.platform.service.base_service import BaseService - - class TestService(BaseService): - pass - - service = TestService() - # name should default to class name - assert service.name == "TestService" diff --git a/packages/sage-libs/tests/lib/test_libamm.py b/packages/sage-libs/tests/lib/test_libamm.py deleted file mode 100644 index 4b526bf8af..0000000000 --- a/packages/sage-libs/tests/lib/test_libamm.py +++ /dev/null @@ -1,267 +0,0 @@ -""" -Tests for LibAMM (Approximate Matrix Multiplication library) bindings. - -Note: LibAMM implementations have been externalized to the independent package 'isage-amms'. -These tests will be skipped if: -- PyTorch is not installed -- isage-amms is not installed (pip install isage-amms) -- LibAMM shared library is not found - -To enable these tests: - pip install isage-amms - # or - pip install -e packages/sage-libs[amms] -""" - -import pytest - -# Try to import torch -try: - import torch - - TORCH_AVAILABLE = True -except ImportError: - TORCH_AVAILABLE = False - torch = None # type: ignore - -# Try to import LibAMM from external package -LIBAMM_AVAILABLE = False -libamm = None -LIBAMM_SKIP_REASON = "isage-amms package not installed" - -if TORCH_AVAILABLE: - try: - # Import from external isage-amms package - # Try multiple import methods for backward compatibility - try: - # Method 1: PyTorch C++ extension (TORCH_LIBRARY) - import torch.ops.LibAMM as libamm - - LIBAMM_AVAILABLE = True - except (ImportError, AttributeError): - try: - # Method 2: pybind11 module - import PyAMM as libamm # type: ignore - - LIBAMM_AVAILABLE = True - except ImportError: - LIBAMM_SKIP_REASON = ( - "isage-amms not installed. Install with: pip install isage-amms" - ) - except Exception as e: - LIBAMM_SKIP_REASON = f"LibAMM import failed: {e}" -else: - LIBAMM_SKIP_REASON = "PyTorch not available" - - -@pytest.mark.skipif(not TORCH_AVAILABLE, reason="PyTorch not available") -@pytest.mark.skipif(not LIBAMM_AVAILABLE, reason=LIBAMM_SKIP_REASON) -class TestLibAMM: - """Test cases for LibAMM approximate matrix multiplication.""" - - @pytest.fixture - def sample_matrices(self): - """Create sample matrices for testing.""" - # Create two small matrices for AMM - m, n, k = 100, 80, 100 - a = torch.randn(m, k, dtype=torch.float32) - b = torch.randn(k, n, dtype=torch.float32) - return a, b - - @pytest.fixture - def small_matrices(self): - """Create very small matrices for quick tests.""" - m, n, k = 10, 8, 10 - a = torch.randn(m, k, dtype=torch.float32) - b = torch.randn(k, n, dtype=torch.float32) - return a, b - - def test_crs_basic(self, sample_matrices): - """Test basic CRS (Column Row Sampling) algorithm.""" - a, b = sample_matrices - result = libamm.crs(a, b) - - # Check output shape - assert result.shape == (a.shape[0], b.shape[1]) - - # Check result is a valid tensor - assert torch.is_tensor(result) - assert not torch.isnan(result).any() - assert not torch.isinf(result).any() - - def test_crs_approximation_quality(self, small_matrices): - """Test that CRS provides reasonable approximation.""" - a, b = small_matrices - - # Exact multiplication - exact = torch.mm(a, b) - - # Approximate multiplication - approx = libamm.crs(a, b) - - # Check shapes match - assert exact.shape == approx.shape - - # Calculate relative Frobenius norm error - error = torch.norm(exact - approx, p="fro") / torch.norm(exact, p="fro") - - # Error should be bounded (this is an approximation) - # The error threshold depends on the sketch size, set it reasonably high - assert error < 2.0, f"Approximation error too high: {error}" - - def test_amm_default(self, sample_matrices): - """Test default AMM algorithm.""" - a, b = sample_matrices - result = libamm.ammDefault(a, b) - - # Check output shape - assert result.shape == (a.shape[0], b.shape[1]) - assert torch.is_tensor(result) - assert not torch.isnan(result).any() - - def test_amm_specify_sketch_size(self, sample_matrices): - """Test AMM with specified sketch size.""" - a, b = sample_matrices - sketch_size = 10 - - result = libamm.ammSpecifySs(a, b, sketch_size) - - # Check output shape - assert result.shape == (a.shape[0], b.shape[1]) - assert torch.is_tensor(result) - assert not torch.isnan(result).any() - - def test_amm_different_sketch_sizes(self, small_matrices): - """Test AMM with various sketch sizes.""" - a, b = small_matrices - sketch_sizes = [2, 5, 8] - - results = [] - for ss in sketch_sizes: - result = libamm.ammSpecifySs(a, b, ss) - results.append(result) - assert result.shape == (a.shape[0], b.shape[1]) - - # Larger sketch size should generally give better approximation - # But we don't test this strictly as it's probabilistic - - def test_set_tag(self): - """Test setting algorithm tag.""" - # Test setting different algorithm tags - tags = ["mm", "crs", "srht"] - - for tag in tags: - libamm.setTag(tag) - # If no exception is raised, the function works - - # Reset to default - libamm.setTag("mm") - - def test_amm_with_different_tags(self, small_matrices): - """Test AMM with different algorithm tags.""" - a, b = small_matrices - - # List of valid algorithm tags - # Note: actual available algorithms depend on build configuration - potential_tags = ["mm", "crs"] - - for tag in potential_tags: - try: - libamm.setTag(tag) - result = libamm.ammDefault(a, b) - assert result.shape == (a.shape[0], b.shape[1]) - except Exception as e: - # Some algorithms might not be available - pytest.skip(f"Algorithm {tag} not available: {e}") - - def test_empty_matrices(self): - """Test behavior with empty or minimal matrices.""" - # Very small matrices - a = torch.randn(1, 1, dtype=torch.float32) - b = torch.randn(1, 1, dtype=torch.float32) - - try: - result = libamm.crs(a, b) - # If it doesn't crash, that's a pass - assert result.shape == (1, 1) - except Exception: - # Small matrices might not be supported - pytest.skip("Empty/minimal matrices not supported") - - def test_amm_consistency(self, small_matrices): - """Test that multiple calls with same input give consistent results.""" - a, b = small_matrices - - # Call twice with same input - result1 = libamm.ammDefault(a, b) - result2 = libamm.ammDefault(a, b) - - # Results should be similar (not necessarily identical due to randomization) - # Check correlation or relative difference - diff = torch.norm(result1 - result2, p="fro") / torch.norm(result1, p="fro") - - # Allow some variation due to randomized algorithms - assert diff < 1.0, f"Results too inconsistent: {diff}" - - def test_dtype_handling(self): - """Test handling of different data types.""" - a = torch.randn(20, 15, dtype=torch.float32) - b = torch.randn(15, 10, dtype=torch.float32) - - result = libamm.crs(a, b) - assert result.dtype == torch.float32 - - def test_large_matrices_sketch(self): - """Test with larger matrices to verify scalability.""" - # Larger matrices - m, n, k = 500, 400, 500 - a = torch.randn(m, k, dtype=torch.float32) - b = torch.randn(k, n, dtype=torch.float32) - - # Use a specific sketch size - sketch_size = 50 - result = libamm.ammSpecifySs(a, b, sketch_size) - - assert result.shape == (m, n) - assert torch.is_tensor(result) - - -@pytest.mark.skipif(not TORCH_AVAILABLE, reason="PyTorch not available") -class TestLibAMMAvailability: - """Test availability and import of LibAMM.""" - - def test_torch_available(self): - """Verify PyTorch is available.""" - assert TORCH_AVAILABLE, "PyTorch should be available for these tests" - - def test_libamm_import(self): - """Test that LibAMM can be imported if available.""" - if LIBAMM_AVAILABLE: - import torch.ops.LibAMM as libamm - - # Verify functions exist - assert hasattr(libamm, "crs") - assert hasattr(libamm, "setTag") - assert hasattr(libamm, "ammDefault") - assert hasattr(libamm, "ammSpecifySs") - - -# Standalone test for config-based AMM (if config files are available) -@pytest.mark.skipif(not LIBAMM_AVAILABLE, reason="LibAMM not available") -class TestLibAMMWithConfig: - """Test LibAMM functions that require configuration files.""" - - def test_amm_for_madness_requires_config(self): - """Test that ammForMadness requires valid config paths.""" - # This test just verifies the function exists - # Actual testing would require valid config files - try: - import torch.ops.LibAMM as libamm - - assert hasattr(libamm, "ammForMadness") - except Exception: - pytest.skip("ammForMadness not available or requires specific setup") - - -if __name__ == "__main__": - pytest.main([__file__, "-v"]) diff --git a/packages/sage-libs/tests/lib/tools/__init__.py b/packages/sage-libs/tests/lib/tools/__init__.py deleted file mode 100644 index 151e78daed..0000000000 --- a/packages/sage-libs/tests/lib/tools/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# Tests for tools package diff --git a/packages/sage-middleware/.gitignore b/packages/sage-middleware/.gitignore deleted file mode 100644 index ba6a125ada..0000000000 --- a/packages/sage-middleware/.gitignore +++ /dev/null @@ -1,128 +0,0 @@ -CMakeLists.txt.user -CMakeCache.txt -CMakeFiles -CMakeScripts -Testing -Makefile -cmake_install.cmake -install_manifest.txt -compile_commands.json -CTestTestfile.cmake -CMakeUserPresets.json - -# ============================================ -# 构建和安装目录 -# ============================================ -/deps/sageFlow/installation/dist/ -/deps/sageFlow/installation/sageflow.egg-info/ -/deps/sageFlow/installation/build/ -/.idea/deployment.xml -/.idea/misc.xml -/build/ -/wheelhouse/ -/installation/sageflow.egg-info/ -/installation/build/ -/installation/dist/ -/installation/dist/ -/installation/sageflow/ -/sage.egg-info/ -/sage.egg-info/ -/deps/sageFlow/.github/ -test_logs/ -draft/ -logs/ -output/ -**/__pycache__/ -gitlogs.txt - - - -id_llm -auto_env_setup.log -/.idea/ -/deps/sageFlow/installation/install_pysageflow/sageflow.egg-info/ -/deps/sageFlow/installation/install_pysageflow/build/ - -__pycache__/ -*.py[cod] - -/data/neuromem_datasets/locomo/locomo10.json -/experiment/memorag/output.txt -/experiment/memorag/output_prompt.txt -/installation/env_setup/install_dep.log -/arxiv_pdfs -/arxiv_structured_json -/sage/.env -test_env/ -test_reports/ -ray_logs -/output -/logs -/logs/** -sage_examples/logs -sage_examples/logs/** -/.env -dist/ -data/template_data -data/neuromem_data -drafts/ -run.sh -third_party/ -tmp_ray/ -dist/ - -recommended_tests.txt -local_workflow_logs/ -.pytest_cache/ -archive/ - -# C/C++ compiled libraries and binaries -*.so -*.so.* -*.a -*.o -*.obj -*.dll -*.dylib -*.lib - -activate_sage.sh -.venv/ -# Benchmark and test reports -**/benchmark_report_*.json -**/test_results_*.json -**.egg-info -sage_ext/sage_db/build/ -build/ -.coverage - -# CMake FetchContent 构建目录 -**/pybind11-subbuild/ -**/pybind11-src/ -**/pybind11-build/ -**/CMakeFiles/ -**/CMakeCache.txt -**/Makefile -**/cmake_install.cmake - -**.ninja** - -coverage.xml -dev_reports/ -!/Makefile -# Build artifacts managed by sage-dev toolkit -**/*.egg-info/ -**/dist/ -**/build/ -**/.coverage -**/coverage.xml -**/htmlcov/ -**/.pytest_cache/ -**/.mypy_cache/ -**/*.tmp -**/*.temp -**/.tmp/ -.sage/ -.testlogs/ -./sage_flow_build -./sage_db_build diff --git a/packages/sage-middleware/README.md b/packages/sage-middleware/README.md deleted file mode 100644 index 9321466fca..0000000000 --- a/packages/sage-middleware/README.md +++ /dev/null @@ -1,188 +0,0 @@ -# SAGE Middleware（中间件） - -## 📋 Overview - -用于构建带有 AI 能力的流式数据应用的中间件层，集成了多家大模型提供商、异步任务、鉴权以及高性能的数据处理组件。 - -## 🧭 Governance / 团队协作制度 - -- `docs/governance/TEAM.md` -- `docs/governance/MAINTAINERS.md` -- `docs/governance/DEVELOPER_GUIDE.md` -- `docs/governance/PR_CHECKLIST.md` -- `docs/governance/SELF_HOSTED_RUNNER.md` -- `docs/governance/TODO.md` - -## ✨ Key Features - -- 🤖 **LLM 推理**： - - **sageLLM** ✅ 推荐：统一 LLM 推理引擎，支持 CUDA/Ascend/Mock 后端 - - vLLM ⚠️ 已弃用：将在 v0.4.0 移除，请迁移至 sageLLM -- 🔎 检索与向量：RAG、BM25、FAISS 等 -- 📋 任务调度：Celery 异步任务 -- 🔐 安全鉴权：JWT、密码学工具 -- ⚙️ 核心组件： - - `sage_db`：数据库/向量存储相关组件（含 C/C++ 扩展） - - `sage_flow`：高性能向量流处理（可能包含扩展或独立子模块） - -## 🚀 Installation - -```bash -pip install isage-middleware - -# 可选：VLLM 支持（需要 CUDA） -pip install isage-common[vllm] - -# 可选：与完整 SAGE 框架集成 -pip install isage-middleware[sage] -``` - -## 📖 Quick Start - -### LLM 推理（推荐：sageLLM） - -```python -from sage.middleware.operators.llm import SageLLMGenerator - -# 自动选择最佳后端 -generator = SageLLMGenerator( - model_path="Qwen/Qwen2.5-7B-Instruct", - backend_type="auto", # auto/cuda/ascend/mock - temperature=0.7, - max_tokens=2048, -) - -result = generator.execute("你好，世界！") -print(result) -``` - -### API 客户端 - -```python -from sage.middleware.api.client import APIClient -from sage.middleware.auth.jwt import JWTManager - -client = APIClient() -jwt_manager = JWTManager() - -resp = client.chat_completion( - provider="openai", - messages=[{"role": "user", "content": "Hello!"}], -) -print(resp) -``` - -> 📖 **迁移指南**：如果您正在使用 `VLLMGenerator`，请参阅 -> [vLLM to sageLLM Migration Guide](../../docs-public/docs_src/dev-notes/migration/VLLM_TO_SAGELLM_MIGRATION.md) - -## 配置示例 - -```yaml -# config.yaml -middleware: - auth: - secret_key: "your-secret-key" # pragma: allowlist secret - algorithm: "HS256" - providers: - openai: - api_key: "sk-..." # pragma: allowlist secret - base_url: "https://api.openai.com/v1" -``` - -## 开发与本地安装 - -```bash -git clone https://github.com/intellistream/SAGE.git -cd SAGE/packages/sage-middleware -pip install -e . -``` - -> 说明：中间件组件（sage_db/sage_flow/sage_tsdb 等）现已随源码直接提供或通过 pip 依赖分发，无需初始化任何子模块。 - -## 新增中间件组件的规范（重要） - -当你添加新的中间件组件（例如 `sage_foo`）时，请务必在 `setup.py` 中接入其构建逻辑，这样在安装 `isage-middleware` 时会自动构建/准备该组件。 - -建议遵循以下约定： - -### 1. 目录结构 - -- `src/sage/middleware/components/sage_foo/` - - `__init__.py`（Python 包） - - （如包含 C/C++ 部分）`cmake/`、`build.sh`、`CMakeLists.txt` - - 其他源码/资源文件 - -### 2. C++ 扩展与依赖要求 - -如果组件包含 C/C++ 扩展，**必须**遵守以下依赖约束，以与现有 `sage_db`、`sage_flow` 保持一致： - -> **注意**: 以下代码示例中的 `SAGE_COMMON_DEPS_FILE` 等变量是 CMake 环境变量，非占位符。 - -1. **共享依赖入口：** - - - 在 `CMakeLists.txt` 中优先加载共享依赖脚本（通过环境变量）： - ```cmake - set(_sage_foo_shared_deps FALSE) - # Check if shared deps file is defined - if(DEFINED SAGE_COMMON_DEPS_FILE AND EXISTS "$ENV(SAGE_COMMON_DEPS_FILE)") - include("$ENV(SAGE_COMMON_DEPS_FILE)") - set(_sage_foo_shared_deps TRUE) - endif() - ``` - - 共享脚本会提供 `pybind11::module`、统一的可见性编译选项、以及全部 gperftools 配置变量。 - -1. **本地回退脚本：** - - - 请在组件目录的 `cmake/` 下提供 `pybind11_dependency.cmake` 和（如需要）`gperftools.cmake`，用于在独立构建或共享脚本缺失时下载依赖。 - - 在 `CMakeLists.txt` 中检测 `_sage_foo_shared_deps`，若为 `FALSE` 再加载本地脚本： - ```cmake - if(NOT _sage_foo_shared_deps) - include(cmake/pybind11_dependency.cmake) - endif() - ``` - -1. **gperftools 约定：** - - - 新增扩展应暴露 `ENABLE_GPERFTOOLS` 选项，并默认遵循 `SAGE_ENABLE_GPERFTOOLS` 环境变量。 - - 只有在确认找到 `SAGE_GPERFTOOLS_LIBS`（或本地回退脚本成功解析）时才链接 gperftools；否则务必禁用该选项并给出清晰日志。 - -1. **环境变量约定：** - - - 共享脚本会设置 `SAGE_COMMON_COMPILE_OPTIONS`、`SAGE_COMMON_COMPILE_DEFINITIONS` 等变量，请在目标上引用，避免重复配置。 - - 新扩展若需要自定义变量，务必提供合理的默认值，并允许通过环境变量覆写。 - -1. **打包要求：** - - - `pyproject.toml` 中需包含 `"sage.middleware.components.sage_foo" = ["cmake/*.cmake"]` 等条目，保证 CMake - 脚本在发布包内。 - - 如扩展存在 Python 侧绑定（`python/` 目录），确保 `pyproject.toml` 中的 `package-data` 同步更新。 - -### 3. 构建脚本 - -- 如果组件需要编译或额外准备工作，请提供标准的 `build.sh`，支持无交互执行： - - `bash build.sh --install-deps` -- `build.sh` 应读取相关环境变量（如依赖文件路径、gperftools 开关等），并在调用 `cmake` 时透传（参考 `sage_db`、`sage_flow`）。 - -### 4. 在 `setup.py` 中接入 - -- 在自定义的 `build_ext` 流程中： - - 新增 `build_sage_foo()` 方法（参照现有 `build_sage_db()` / `build_sage_flow()`）。 - - 使用统一的 `_shared_env()` 帮助函数为子进程注入共享依赖环境。 - - 在 `run()` 中调用 `self.build_sage_foo()`，并保证失败不阻断安装（打印清晰日志即可）。 - -### 5. 环境变量开关（可选） - -- 通过设置 `SAGE_SKIP_C_EXTENSIONS=1` 可以跳过所有扩展构建（调试纯 Python 逻辑时常用）。 - -### 6. CI 与子模块提示 - -- CI 会递归检出子模块并按 `setup.py` 的逻辑尝试构建。 -- 中间件组件不再通过 Git submodule 分发；请不要在 CI 或本地执行子模块初始化命令。 - -## 贡献 - -欢迎提交 PR！请先阅读仓库根目录的 [CONTRIBUTING.md](../../CONTRIBUTING.md)。 - -## 📄 License - -MIT License - see [LICENSE](../../LICENSE) for details. diff --git a/packages/sage-middleware/docs/governance/DEVELOPER_GUIDE.md b/packages/sage-middleware/docs/governance/DEVELOPER_GUIDE.md deleted file mode 100644 index f93c0c7ba0..0000000000 --- a/packages/sage-middleware/docs/governance/DEVELOPER_GUIDE.md +++ /dev/null @@ -1,88 +0,0 @@ -# 开发者指南与质量门槛（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages/<package>/docs/governance/DEVELOPER_GUIDE.md`） - -本指南把“制度”落到“能执行的工程约束”。本包的所有代码改动必须遵守以下规则。 - -______________________________________________________________________ - -## 1. 架构守护机制（强制） - -### 1.1 依赖层级（禁止向上依赖 / 禁止循环依赖） - -SAGE 采用 L1-L5 分层。任何“向上依赖”都视为架构违规，必须阻断合入。 - -- L1：`sage-common` -- L2：`sage-platform` -- L3：`sage-kernel`, `sage-libs` -- L4：`sage-middleware` -- L5：`sage-cli`, `sage-tools` - -### 1.2 Libs vs Middleware 规则（强制） - -如果代码需要调用“向上能力”（例如 VectorDB/Memory/Refiner/外部服务/重后端/网络服务），它 **不是库**，必须放在 -`sage-middleware`（operators/components）。 - -> 迁移时不保留兼容 re-export shim：更新所有调用方，快速失败。 - -### 1.3 Control Plane-only（强制） - -LLM 引擎操作必须走 Control Plane，禁止直接启动引擎/直连端口。 - -______________________________________________________________________ - -## 2. 自动化检查（CI + pre-commit）（强制） - -### 2.1 安装一致性 - -- 必须使用 `./quickstart.sh --dev --yes` 安装开发环境。 -- 禁止手工 `pip install` 临时安装依赖（依赖必须写入 `pyproject.toml`）。 - -### 2.2 本地质量与测试 - -建议在仓库根目录执行： - -- `sage-dev quality check --all-files` -- `sage-dev project test --coverage` - -______________________________________________________________________ - -## 3. 强制规范（必须出现的质量门槛） - -### 3.1 Mock-First（强制） - -- CI 必须能在无 GPU 环境跑通核心测试。 -- 新增能力必须提供 Mock/CPU 路径，避免把“硬件”当作默认前提。 - -### 3.2 Fail-Fast（强制） - -- 禁止静默默认值、禁止隐式回退（fallback）。 -- 关键配置缺失必须明确报错（例如用 `os.environ["KEY"]` 或显式校验并抛异常）。 - -### 3.3 Protocol-First（按适用范围执行） - -当本包提供“公共接口/抽象/跨包契约”时： - -- 先定义接口/类型（Protocol/ABC/Schema） -- 再实现具体逻辑 -- 再补测试与示例 - -______________________________________________________________________ - -## 4. 新模块/新能力接入步骤（可执行） - -1. **定义范围**：确认属于本包；若需要向上能力，按规则提升到 `sage-middleware`。 -1. **接口优先**：先定义可复用接口（如适用）。 -1. **最小可跑**：提供无 GPU 的最小可跑实现（Mock/CPU）。 -1. **测试**：至少包含单元测试；关键路径补回归测试。 -1. **文档**：更新本包 README 与本包 governance 文档（必要时）。 -1. **质量门槛**：本地 `sage-dev quality` 与测试通过。 - -______________________________________________________________________ - -## 5. 常见错误与修复 - -- **依赖倒挂**：`sage-libs` 引入 `sage-middleware` → 必须拆分/上移实现。 -- **文档位置违规**：禁止在根 `docs/` 放 Markdown；包级文档只能放 `packages/<pkg>/docs/`。 -- **依赖未声明**：新增依赖未写入 `pyproject.toml` → 必须补齐声明并统一版本。 diff --git a/packages/sage-middleware/docs/governance/MAINTAINERS.md b/packages/sage-middleware/docs/governance/MAINTAINERS.md deleted file mode 100644 index 4e331ad6e0..0000000000 --- a/packages/sage-middleware/docs/governance/MAINTAINERS.md +++ /dev/null @@ -1,126 +0,0 @@ -# 负责人制度（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages/<package>/docs/governance/MAINTAINERS.md`） -- 负责人名单：TBD - -本文件定义“包级 Maintainer（负责人）”如何配置、如何工作、如何被量化验收。 - -______________________________________________________________________ - -## 1. 为什么需要 Maintainer - -SAGE 是 monorepo，多包协作、依赖层级明确。Maintainer 的职责不是“写最多代码”，而是保证： - -- 需求有人推进、问题有人闭环 -- 质量门槛有人守住（CI/架构/测试） -- 跨包集成有人对齐（尤其是接口层与中间件层） - -______________________________________________________________________ - -## 2. 负责人配置建议（按模块/子系统） - -### 2.1 推荐配置 - -- 每个包至少 1 名 Maintainer（主负责人）+ 1 名 Backup（备份负责人）。 -- 当包内包含多个子系统/高复杂度模块：建议拆分子 Maintainer。 - -### 2.2 负责人配置表（模板，必须维护） - -| 模块/目录 | 复杂度 | 关键技能 | 主 Maintainer | Backup | 上游依赖 | 下游使用方 | -| --------- | ------ | -------- | ------------- | ------ | -------- | ---------- | -| TBD | TBD | TBD | TBD | TBD | TBD | TBD | - -> 单仓语义适配：这里的“上游/下游”指本 monorepo 的其他包或本包内其他模块。 - -______________________________________________________________________ - -## 3. 负责人职责拆分（40/30/20/10 参考） - -可按实际调整，但必须覆盖全部职责： - -- **40% 开发推进**：Roadmap/Issue/PR 推动，里程碑拆解，阻塞清理。 -- **30% 质量把控**：CI 绿灯、测试策略、回归防护、性能/稳定性监控。 -- **20% 集成同步**：跨包接口对齐、发布/版本对齐、变更公告与迁移指引。 -- **10% 团队协作**：Review SLA、协作矩阵维护、争议处理与复核申请。 - -______________________________________________________________________ - -## 4. 要求与投入 - -- **最低投入**：每周固定时间段处理 Issue/PR（TBD：建议至少 2-4 小时/周）。 -- **Review SLA**：工作日 48h 内首次响应。 -- **透明度**：每月一次交付清单（含证据链接）。 - -______________________________________________________________________ - -## 5. 工作流程（开发 / 发布 / 集成同步） - -### 5.1 日常开发 - -- 所有需求/缺陷必须有 Issue。 -- PR 必须通过 `docs/governance/PR_CHECKLIST.md`。 -- 架构约束（依赖层级、libs vs middleware）违反时：必须阻断合入。 - -### 5.2 发布与版本对齐（如适用） - -- 发布前：测试与质量检查必须绿。 -- 变更：Breaking 变更必须有迁移指引与回滚预案。 -- 若涉及 PyPI 发布：使用仓库规定的发布工具链（例如 `wheelwright`），不得手工 twine/pip 临时发布。 - -### 5.3 跨包集成同步 - -- 上游接口变化：必须提前通知下游 maintainer，并提供迁移窗口。 -- 下游反馈：需要在 SLA 内回应并给出计划。 - -______________________________________________________________________ - -## 6. 协作矩阵（依赖/协作关系） - -### 6.1 Monorepo 依赖层级速查 - -> 目标：避免“向上依赖”与循环依赖。 - -| 层级 | 包（示例） | 允许依赖 | -| ---- | -------------------------- | ---------------- | -| L5 | `sage-cli`, `sage-tools` | L1-L4 | -| L4 | `sage-middleware` | L1-L3 | -| L3 | `sage-kernel`, `sage-libs` | L1-L2 | -| L2 | `sage-platform` | L1 | -| L1 | `sage-common` | 仅 stdlib/轻依赖 | - -### 6.2 本包协作矩阵（模板） - -| 依赖方/被依赖方 | 关系类型 | 接口/契约 | 变更通知机制 | Maintainer 对接 | -| --------------- | -------- | --------- | --------------------- | --------------- | -| TBD | 上游 | TBD | Issue/PR/Release Note | TBD | -| TBD | 下游 | TBD | Issue/PR/Release Note | TBD | - -______________________________________________________________________ - -## 7. 负责人名单（TBD） - -| 模块/目录 | 主 Maintainer | Backup | 交接人（如更换） | 生效日期 | -| --------- | ------------- | ------ | ---------------- | -------- | -| TBD | TBD | TBD | TBD | TBD | - -______________________________________________________________________ - -## 8. 考核指标（参考，可量化） - -- PR 首次响应 SLA 达标率 -- CI 红灯平均恢复时间（MTTR） -- 每月交付清单是否按时发布（含证据） -- Breaking 变更公告与迁移指引完备率 - -______________________________________________________________________ - -## 9. FAQ - -### Q1：Maintainer 是否必须是提交最多的人？ - -不是。Maintainer 的核心是“推进 + 质量 + 对齐”。提交多但不维护质量/不对齐下游同样不合格。 - -### Q2：如果包内没有发布流程怎么办？ - -仍需要“集成同步”：接口变更、跨包影响、迁移指引、回滚预案。 diff --git a/packages/sage-middleware/docs/governance/PR_CHECKLIST.md b/packages/sage-middleware/docs/governance/PR_CHECKLIST.md deleted file mode 100644 index 09d0f8c350..0000000000 --- a/packages/sage-middleware/docs/governance/PR_CHECKLIST.md +++ /dev/null @@ -1,49 +0,0 @@ -# PR 审查清单（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages/<package>/docs/governance/PR_CHECKLIST.md`） - -> Maintainer/Reviewer 需要用本清单进行可量化审查。未满足项必须在 PR 中解释或补齐。 - -______________________________________________________________________ - -## 1. 架构合规 - -- [ ] 不产生向上依赖/循环依赖（符合 L1-L5 分层） -- [ ] 遵守 libs vs middleware 规则（需要向上能力则放 middleware） -- [ ] LLM 相关操作不绕过 Control Plane - -## 2. Protocol-First（如适用） - -- [ ] 公共接口/类型先定义（Protocol/ABC/Schema），实现不“绑死”具体后端 -- [ ] 接口变更包含迁移说明（Breaking change 必须公告） - -## 3. Mock-First - -- [ ] 无 GPU 环境可跑（至少核心测试/最小路径） -- [ ] 新增外部依赖/服务调用有可测试替身（mock/fake） - -## 4. Fail-Fast - -- [ ] 无隐式回退（不写 try/except 吞异常、不用静默默认值掩盖缺失配置） -- [ ] 错误信息可定位（异常 message 清晰，必要时包含修复指引） - -## 5. 可观测性（按适用范围） - -- [ ] 关键路径有日志/指标/追踪点（至少日志） -- [ ] 不输出敏感信息（key/token/密码等） - -## 6. 配置验证 - -- [ ] 新增配置项有校验与文档说明 -- [ ] 端口不硬编码（如适用，使用统一端口配置） - -## 7. 测试覆盖 - -- [ ] 新增/修改逻辑有对应测试 -- [ ] 关键 bug fix 有回归测试 - -## 8. 代码质量 - -- [ ] `sage-dev quality check --all-files` 通过 -- [ ] `sage-dev project test --coverage` 通过（或至少本包相关测试通过） diff --git a/packages/sage-middleware/docs/governance/SELF_HOSTED_RUNNER.md b/packages/sage-middleware/docs/governance/SELF_HOSTED_RUNNER.md deleted file mode 100644 index 8c4746062a..0000000000 --- a/packages/sage-middleware/docs/governance/SELF_HOSTED_RUNNER.md +++ /dev/null @@ -1,50 +0,0 @@ -# Self-hosted Runner 指南（SAGE - 可选） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages/<package>/docs/governance/SELF_HOSTED_RUNNER.md`） - -> 本章节用于需要 GPU/重依赖/长耗时测试的包。若本包不需要，可保留但不强制启用。 - -______________________________________________________________________ - -## 1. 什么时候需要 self-hosted runner - -- GPU 相关测试（CUDA/Ascend） -- 需要特殊硬件或驱动版本 -- 需要访问内网资源（需安全评估） - -______________________________________________________________________ - -## 2. Runner 安装与标签（模板） - -- Runner 类型：GitHub Actions self-hosted -- 标签建议： - - `self-hosted` - - `linux` - - `x64` - - `gpu`（如适用） - - `cuda-<major>`（如适用） - -______________________________________________________________________ - -## 3. 环境要求（模板） - -- OS：Linux（推荐） -- Python：与仓库要求一致（3.10+） -- 构建依赖：cmake / build-essential / BLAS 等（按仓库安装脚本） - -______________________________________________________________________ - -## 4. 安全注意事项（强制） - -- Runner 机器不可暴露敏感密钥；Secrets 由 GitHub 管理。 -- 禁止在日志中打印 token/key。 -- 对内网访问与数据集访问：必须走审批（TBD）。 - -______________________________________________________________________ - -## 5. 故障排查 - -- CI 失败先在本地 `./quickstart.sh --dev --yes` 复现 -- 检查磁盘/缓存（`.sage/`） -- 检查驱动/CUDA 版本（如适用） diff --git a/packages/sage-middleware/docs/governance/TEAM.md b/packages/sage-middleware/docs/governance/TEAM.md deleted file mode 100644 index 4b95959c8c..0000000000 --- a/packages/sage-middleware/docs/governance/TEAM.md +++ /dev/null @@ -1,68 +0,0 @@ -# 仓库人员与评分制度（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages/<package>/docs/governance/TEAM.md`） -- 名单维护：TBD - -本文件定义在 SAGE monorepo 内（以包为单位）的团队角色、负责人制度与评分/激励框架，用于保证： - -- 制度可执行：能落到 Issue/PR/CI/周更/月更。 -- 制度可验收：有明确证据链接与检查项。 -- 制度可追责：有最低交付门槛、透明度与争议处理机制。 - -> 约束：所有“姓名/负责人名单/具体人员”一律用 `TBD`，不得编造。 - -______________________________________________________________________ - -## 1. 角色与职责（强制条款） - -| 角色 | 核心职责 | 必须产出（可验收） | 证据类型（必须带链接） | -| ---------------------------- | -------------------------------------------------------- | ----------------------------------------------------------------------- | ------------------------------------------ | -| Maintainer（负责人） | Roadmap/Issue/PR 推进；质量门槛；集成/版本对齐；变更管理 | 每周 TODO 周更、PR Review SLA、CI 绿灯保障、重大变更公告、发布/集成记录 | Issue、PR、Release/Tag、CI 链接、周报/月报 | -| Engineering Core（工程骨干） | 关键功能交付；CI/测试维护；稳定性问题闭环 | 可交付功能/修复、测试/CI 改进、性能/稳定性指标改进 | PR、Issue、Benchmark 报告、CI 链接 | -| Research Core（科研骨干） | 原型验证/评测；研究到工程落地（必须可验收） | 可复现的实验/评测；可落地的工程化改造（或明确落地计划） | 评测报告、PR、Issue、实验配置/脚本链接 | - -______________________________________________________________________ - -## 2. 负责人必须做的事（强制条款） - -### 2.1 TODO 周更（强制） - -- 每周至少 1 次在本包的 `docs/governance/TODO.md` 更新：本周目标/完成/阻塞/下周计划/风险/需要协助。 -- TODO 必须引用证据（Issue/PR/CI/报告链接）。 - -### 2.2 PR Review SLA（强制） - -- 工作日 48h 内对新 PR 首次响应（review/approve/request changes/说明何时处理）。 -- 对影响多个包的变更：必须主动拉齐上下游 maintainer。 - -# 仓库人员与评分制度（SAGE - 包级，指向母版） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages/sage-middleware/docs/governance/TEAM.md`） -- 名单维护：TBD - -通用制度请参见 `packages/sage/docs/governance/TEAM_BASE.md`。本文件仅保留本包的代号映射与特有说明。 - -### 本包角色代号（姓名保持 TBD） - -| 角色 | 代号分配 | -| ---------------- | -------- | -| Maintainer | A2 | -| Engineering Core | B1 | -| Research Core | C1 | - -### 本包补充说明 - -- 中间件（L4）涉及外部后端/服务，必须遵守 libs vs middleware 规则与 Control Plane-only 约束；变更需兼顾下游 cli/tools 与上游 libs - 的兼容性。 - -______________________________________________________________________ - -## 参考 - -- 通用制度：packages/sage/docs/governance/TEAM_BASE.md -- 周更：docs/governance/TODO.md -- PR 审查：docs/governance/PR_CHECKLIST.md -- 开发规范：docs/governance/DEVELOPER_GUIDE.md -- **保底分**：角色的最低履职奖励（必须满足最低交付门槛）。 diff --git a/packages/sage-middleware/docs/governance/TODO.md b/packages/sage-middleware/docs/governance/TODO.md deleted file mode 100644 index afdd548b3a..0000000000 --- a/packages/sage-middleware/docs/governance/TODO.md +++ /dev/null @@ -1,39 +0,0 @@ -# TODO 周更模板（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages/<package>/docs/governance/TODO.md`） -- 维护频率：每周至少 1 次（Maintainer 强制要求） - -______________________________________________________________________ - -## 周更（复制本模板，每周追加一节） - -### Week of YYYY-MM-DD - -**本周目标（Goals）** - -- [ ] TBD（关联 Issue：TBD） - -**本周完成（Done）** - -- [ ] TBD（PR：TBD，CI：TBD） - -**阻塞与风险（Blockers/Risks）** - -- TBD（原因 + 需要谁协助 + 截止时间） - -**下周计划（Next）** - -- [ ] TBD（Issue：TBD） - -**需要协助（Asks）** - -- TBD（@TBD） - -______________________________________________________________________ - -## 里程碑清单（长期维护） - -| 里程碑 | 目标日期 | 状态 | 关联 Issue/PR | 风险 | -| ------ | -------- | ---- | ------------- | ---- | -| TBD | TBD | TBD | TBD | TBD | diff --git a/packages/sage-middleware/examples/README.md b/packages/sage-middleware/examples/README.md deleted file mode 100644 index 87f4ac5207..0000000000 --- a/packages/sage-middleware/examples/README.md +++ /dev/null @@ -1,67 +0,0 @@ -# L4: Middleware - 中间件层示例 - -> 对应 SAGE 包：`sage-middleware` - -## 📖 层级说明 - -**Middleware** 层提供领域算子和中间件组件： - -- Memory Service - 内存管理服务 -- SAGE-DB - 向量数据库 -- SAGE-TSDB - 时序数据库 -- NeuroMem - 神经记忆栈 - -## 📚 目录结构 - -``` -L4-middleware/ -├── hello_service_world.py # 服务入门示例 -├── memory_service/ # 内存服务示例 -├── sage_db/ # 向量数据库示例 -└── sage_tsdb/ # 时序数据库示例 -``` - -## 🎯 学习路径 - -### 1️⃣ 服务基础 - -- `hello_service_world.py` - 理解服务模型 - -### 2️⃣ Memory Service (`memory_service/`) - -内存管理和持久化： - -- `rag_memory_service.py` - RAG 内存服务 -- `rag_memory_pipeline.py` - 内存管道 -- `rag_memory_manager.py` - 内存管理器 - -### 3️⃣ SAGE-DB (`sage_db/`) - -向量数据库操作： - -- `workflow_demo.py` - 工作流演示 - -### 4️⃣ SAGE-TSDB (`sage_tsdb/`) - -时序数据处理： - -- `basic_dag_example.py` - 基础 DAG -- `advanced_dag_example.py` - 高级 DAG -- `stream_join_dag_example.py` - 流连接 DAG - -## 🎯 学习目标 - -完成本层示例后，你将掌握： - -1. 如何使用 SAGE 的数据服务 -1. 向量数据库的基本操作 -1. 时序数据的处理方法 -1. 内存管理的最佳实践 - -## ⏭️ 下一步 - -学完中间件层后，继续探索： - -- **sage-benchmark**: 性能基准测试 (`pip install isage-benchmark`) -- **sage-examples**: 完整应用示例 (独立仓库) -- **sage-cli / sage-tools**: CLI 工具 (L5 接口层) diff --git a/packages/sage-middleware/examples/hello_service_world.py b/packages/sage-middleware/examples/hello_service_world.py deleted file mode 100644 index d5fb92d246..0000000000 --- a/packages/sage-middleware/examples/hello_service_world.py +++ /dev/null @@ -1,51 +0,0 @@ -from sage.common.core.functions.batch_function import BatchFunction -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.api.local_environment import LocalEnvironment -from sage.kernel.api.service.base_service import BaseService - - -class HelloBatch(BatchFunction): - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.counter = 0 - self.max_count = 10 - - def execute(self): - if self.counter >= self.max_count: - return None - self.counter += 1 - return f"Hello, World! #{self.counter}" - - -class PrintSink(SinkFunction): - def execute(self, data): - # 调用服务 - self.call_service("hello_service", method="hello") - print(data) - - -# 继承BaseService创建一个简单的服务 -class HelloService(BaseService): - def __init__(self): - self.message = "hello service!!!" - - def hello(self): - print(self.message) - - -def main(): - env = LocalEnvironment("hello_service") - - # 注册服务 - env.register_service("hello_service", HelloService) - - env.from_batch(HelloBatch).sink(PrintSink) - - env.submit(autostop=True) - print("Hello Service World 示例完成!") - - -if __name__ == "__main__": - CustomLogger.disable_global_console_debug() - main() diff --git a/packages/sage-middleware/examples/rag/examples.py b/packages/sage-middleware/examples/rag/examples.py deleted file mode 100644 index 4f4c5647d7..0000000000 --- a/packages/sage-middleware/examples/rag/examples.py +++ /dev/null @@ -1,270 +0,0 @@ -""" -SAGE RAG - Usage Examples - -This file demonstrates how to use the SAGE RAG (Retrieval-Augmented Generation) toolkit. - -Layer: L3 (Core - Algorithm Library) - -⚠️ NOTE: 这些示例优先展示 API 设计。`sage.libs.rag.*` 已提供核心组件， -但部分高级算子仍位于 middleware 层。 -""" - - -def example_document_loading(): - """ - Example 1: Loading documents - - Demonstrates how to load documents from various sources using - the document loaders. - """ - print("=" * 60) - print("Example 1: Loading Documents") - print("=" * 60) - - try: - from sage.libs.rag.document_loaders import ( - DocxLoader, # noqa: F401 - LoaderFactory, # noqa: F401 - MarkdownLoader, # noqa: F401 - PDFLoader, # noqa: F401 - TextLoader, # noqa: F401 - ) - - print("\n✓ Available document loaders:") - print(" - TextLoader: Load plain text files") - print(" - PDFLoader: Load PDF documents") - print(" - DocxLoader: Load Word documents") - print(" - MarkdownLoader: Load Markdown files") - print(" - LoaderFactory: Auto-detect and load files") - - # Example: Loading text files - print("\nExample: Loading a text file") - print( - """ - loader = TextLoader("documents/article.txt") - documents = loader.load() - for doc in documents: - print(f"Content: {doc.content[:100]}...") - """ - ) - - except ImportError as e: - print(f"✗ Import error: {e}") - print("Some document loaders may require additional dependencies") - - -def example_rag_pipeline(): - """ - Example 2: Building a RAG pipeline - - Demonstrates how to build a complete RAG pipeline with - document loading, embedding, retrieval, and generation. - """ - print("\n" + "=" * 60) - print("Example 2: Building a RAG Pipeline") - print("=" * 60) - - try: - from sage.middleware.operators.rag.pipeline import RAGPipeline # noqa: F401 - - print("\n✓ RAG Pipeline components:") - print(" 1. Document Loader: Load source documents") - print(" 2. Text Splitter: Split into chunks") - print(" 3. Embedder: Generate embeddings") - print(" 4. Vector Store: Store and index embeddings") - print(" 5. Retriever: Find relevant chunks") - print(" 6. Generator: Generate answers") - - print("\nExample pipeline setup:") - print( - """ - from sage.middleware.operators.rag.pipeline import RAGPipeline - from sage.libs.rag.document_loaders import TextLoader - - # Create pipeline - pipeline = RAGPipeline( - loader=TextLoader("knowledge_base/"), - chunk_size=512, - embedding_model="all-MiniLM-L6-v2", - llm_model="gpt-3.5-turbo" - ) - - # Build index - pipeline.build_index() - - # Query - answer = pipeline.query("What is SAGE?") - print(answer) - """ - ) - - except ImportError as e: - print(f"✗ Import error: {e}") - - -def example_vector_stores(): - """ - Example 3: Using vector stores - - Demonstrates how to use different vector store backends - for storing and retrieving embeddings. - """ - print("\n" + "=" * 60) - print("Example 3: Vector Store Integration") - print("=" * 60) - - print("\n✓ Supported vector stores:") - print(" - Milvus: Distributed vector database") - print(" - Chroma: Lightweight in-memory store") - print(" - FAISS: Facebook AI Similarity Search") - - print("\nExample: Using Milvus") - print( - """ - from sage.middleware.operators.rag.backends.milvus import MilvusBackend - from sage.middleware.operators.rag.pipeline import RAGPipeline - - # Create Milvus backend - milvus = MilvusBackend( - host="localhost", - port=19530, - collection_name="documents" - ) - - # Use in RAG pipeline - pipeline = RAGPipeline(vector_store=milvus) - """ - ) - - print("\nExample: Using ChromaDB") - print( - """ - from sage.middleware.operators.rag.backends.chroma import ChromaBackend - - # Create Chroma backend - chroma = ChromaBackend( - persist_directory="./chroma_db", - collection_name="documents" - ) - - # Add documents - chroma.add_documents(documents, embeddings) - - # Search - results = chroma.search(query_embedding, top_k=5) - """ - ) - - -def example_profiling(): - """ - Example 4: RAG performance profiling - - Demonstrates how to profile and optimize RAG pipelines - using the built-in profiler. - """ - print("\n" + "=" * 60) - print("Example 4: RAG Pipeline Profiling") - print("=" * 60) - - try: - from sage.middleware.operators.rag.profiler import Query_Profiler # noqa: F401 - - print("\n✓ RAG Profiler capabilities:") - print(" - Query profiling and analysis") - print(" - Complexity assessment") - print(" - Reasoning requirement detection") - print(" - Summarization strategy selection") - - print("\nExample profiling:") - print( - """ - from sage.middleware.operators.rag.profiler import Query_Profiler - from sage.middleware.operators.rag.pipeline import RAGPipeline - - # Create profiler - profiler = Query_Profiler(config={}) - - # Profile query to determine strategy - query_info = { - "need_joint_reasoning": True, - "complexity": "High", - "need_summarization": True, - "summarization_length": 100, - "n_info_items": 3 - } - result = profiler.execute(json.dumps(query_info)) - """ - ) - - except ImportError as e: - print(f"✗ Import error: {e}") - - -def example_advanced_retrieval(): - """ - Example 5: Advanced retrieval strategies - - Demonstrates advanced retrieval techniques like hybrid search, - reranking, and query expansion. - """ - print("\n" + "=" * 60) - print("Example 5: Advanced Retrieval Strategies") - print("=" * 60) - - print("\n✓ Advanced retrieval techniques:") - print(" - Hybrid search: Combine dense + sparse retrieval") - print(" - Reranking: Post-process retrieval results") - print(" - Query expansion: Enhance queries with synonyms") - print(" - Multi-hop retrieval: Iterative retrieval") - - print("\nExample: Hybrid search") - print( - """ - # Combine dense (embedding) and sparse (BM25) retrieval - dense_results = vector_store.search(query_embedding, top_k=20) - sparse_results = bm25_index.search(query_text, top_k=20) - - # Merge and rerank - combined = merge_results(dense_results, sparse_results) - reranked = reranker.rerank(query, combined, top_k=5) - """ - ) - - print("\nExample: Query expansion") - print( - """ - # Expand query with synonyms and related terms - original_query = "machine learning algorithms" - expanded_query = query_expander.expand(original_query) - # Result: "machine learning algorithms ML AI models neural networks" - - # Use expanded query for retrieval - results = vector_store.search(expanded_query, top_k=10) - """ - ) - - -def run_all_examples(): - """Run all examples in sequence.""" - print("\n" + "=" * 60) - print("SAGE RAG - Complete Examples") - print("=" * 60) - - example_document_loading() - example_rag_pipeline() - example_vector_stores() - example_profiling() - example_advanced_retrieval() - - print("\n" + "=" * 60) - print("✓ All examples completed") - print("=" * 60) - print("\nFor more information:") - print("- See rag/README.md for detailed documentation") - print("- Check examples/ for complete working examples") - print("- Visit docs/ for RAG best practices") - - -if __name__ == "__main__": - run_all_examples() diff --git a/packages/sage-middleware/examples/sage_db/README.md b/packages/sage-middleware/examples/sage_db/README.md deleted file mode 100644 index 1ae2385b29..0000000000 --- a/packages/sage-middleware/examples/sage_db/README.md +++ /dev/null @@ -1,42 +0,0 @@ -# SAGE-DB Examples - -本目录包含两类与 `sage_flow` 风格一致的 SAGE-DB 示例： - -1. 应用（Application）方式：`hello_sage_db_app.py` - - - 直接使用 `SageDB` Python API，插入少量向量后进行一次查询 - -1. 服务（Service）方式：`hello_sage_db_service.py` - - - 以微服务形式封装 SAGE-DB，注册到 `LocalEnvironment` 后由外部推入向量并执行查询 - -## 运行前置 - -SAGE-DB 依赖已编译的 Python 扩展模块 `_sage_db`。如未安装，可运行： - -```bash -sage extensions install sage_db # 若需重新编译可加 --force -``` - -该命令会在仓库根目录下构建并同步 `_sage_db` 扩展，示例中的相对导入即可找到。 - -## 运行示例 - -应用方式： - -```bash -python examples/service/sage_db/hello_sage_db_app.py -``` - -服务方式： - -```bash -python examples/service/sage_db/hello_sage_db_service.py -``` - -## 说明 - -- Canonical Python API 位于 - `packages/sage-middleware/src/sage/middleware/components/sage_db/python/sage_db.py` -- 为保持仓库内运行便捷，示例脚本在未安装包时会自动将 `packages/*/src` 加入 `sys.path` -- 若你需要对外暴露更丰富的接口，可在 `python/micro_service/sage_db_service.py` 基础上扩展（如批量删除、索引保存/加载、带条件过滤的查询等） diff --git a/packages/sage-middleware/examples/sage_db/hello_sage_db_app.py b/packages/sage-middleware/examples/sage_db/hello_sage_db_app.py deleted file mode 100644 index b363d172a2..0000000000 --- a/packages/sage-middleware/examples/sage_db/hello_sage_db_app.py +++ /dev/null @@ -1,69 +0,0 @@ -import logging - -import numpy as np - -# Add repo package paths if needed -try: - from sage.common.utils.logging.custom_logger import CustomLogger - from sage.middleware.components.sage_db.python.sage_db import ( - DatabaseConfig, - IndexType, - SageDB, - ) -except ModuleNotFoundError: - import sys - from pathlib import Path - - here = Path(__file__).resolve() - repo_root = None - for p in here.parents: - if (p / "packages").exists(): - repo_root = p - break - assert repo_root is not None - for p in [ - repo_root / "packages" / "sage" / "src", - repo_root / "packages" / "sage-common" / "src", - repo_root / "packages" / "sage-kernel" / "src", - repo_root / "packages" / "sage-middleware" / "src", - repo_root / "packages" / "sage-libs" / "src", - repo_root / "packages" / "sage-tools" / "src", - ]: - sys.path.insert(0, str(p)) - - from sage.common.utils.logging.custom_logger import CustomLogger - from sage.middleware.components.sage_db.python.sage_db import ( - DatabaseConfig, - IndexType, - SageDB, - ) - - -def main(): - dim = 4 - # Create DB with config - cfg = DatabaseConfig(dim) - cfg.index_type = IndexType.AUTO - db = SageDB.from_config(cfg) - - # Add few vectors - total = 5 - for uid in range(total): - vec = np.arange(dim, dtype=np.float32) + uid - db.add(vec, {"tag": "demo", "uid": str(uid)}) - - # Search - query = np.array([0, 1, 2, 3], dtype=np.float32) - results = db.search(query, k=3) - print("Top-3 results:") - for r in results: - print(f" id={r.id}, score={r.score:.4f}, md={dict(r.metadata)}") - - # Stats - print("DB size:", db.size) - - -if __name__ == "__main__": - logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s") - CustomLogger.disable_global_console_debug() - main() diff --git a/packages/sage-middleware/examples/sage_db/hello_sage_db_service.py b/packages/sage-middleware/examples/sage_db/hello_sage_db_service.py deleted file mode 100644 index 5c8bb55b13..0000000000 --- a/packages/sage-middleware/examples/sage_db/hello_sage_db_service.py +++ /dev/null @@ -1,70 +0,0 @@ -import logging - -import numpy as np - -# Try direct imports; fallback to repo paths if needed -try: - from sage.common.utils.logging.custom_logger import CustomLogger - from sage.kernel.api.local_environment import LocalEnvironment - from sage.middleware.components.sage_db.python.micro_service.sage_db_service import ( - SageDBService, - ) -except ModuleNotFoundError: - import sys - from pathlib import Path - - here = Path(__file__).resolve() - repo_root = None - for p in here.parents: - if (p / "packages").exists(): - repo_root = p - break - assert repo_root is not None - for p in [ - repo_root / "packages" / "sage" / "src", - repo_root / "packages" / "sage-common" / "src", - repo_root / "packages" / "sage-kernel" / "src", - repo_root / "packages" / "sage-middleware" / "src", - repo_root / "packages" / "sage-libs" / "src", - repo_root / "packages" / "sage-tools" / "src", - ]: - sys.path.insert(0, str(p)) - - from sage.common.utils.logging.custom_logger import CustomLogger - from sage.kernel.api.local_environment import LocalEnvironment - from sage.middleware.components.sage_db.python.micro_service.sage_db_service import ( - SageDBService, - ) - - -def main(): - env = LocalEnvironment("hello_sage_db_service") - - # Register service - env.register_service( - "hello_sage_db_service", - SageDBService, - dimension=4, - index_type="AUTO", - ) - - # Create service instance - svc_factory = env.service_factories["hello_sage_db_service"] - svc: SageDBService = svc_factory.create_service() - - # Insert demo vectors - for uid in range(3): - vec = np.arange(4, dtype=np.float32) + uid - svc.add(vec, {"uid": str(uid), "tag": "svc_demo"}) - - # Search - results = svc.search([0, 1, 2, 3], k=2) - print("Service search results:") - for r in results: - print(r) - - -if __name__ == "__main__": - logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s") - CustomLogger.disable_global_console_debug() - main() diff --git a/packages/sage-middleware/examples/sage_db/workflow_demo.py b/packages/sage-middleware/examples/sage_db/workflow_demo.py deleted file mode 100644 index 0d308f66f1..0000000000 --- a/packages/sage-middleware/examples/sage_db/workflow_demo.py +++ /dev/null @@ -1,349 +0,0 @@ -#!/usr/bin/env python3 -"""LLM workflow DAG demo that wires SageDB retrieval into the SAGE pipeline. - -@test:allow-demo -""" - -from __future__ import annotations - -import argparse -import sys -from dataclasses import dataclass -from pathlib import Path -from typing import Any, Iterable, Sequence - -import numpy as np - -from sage.common.core.functions.map_function import MapFunction -from sage.common.core.functions.source_function import SourceFunction -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.kernel.api.local_environment import LocalEnvironment -from sage.middleware.operators.rag import QAPromptor - -# Ensure repository packages are importable when running the script directly -REPO_ROOT = Path(__file__).resolve().parents[1] -PACKAGE_SRC_ROOTS = [ - REPO_ROOT / "packages" / "sage" / "src", - REPO_ROOT / "packages" / "sage-common" / "src", - REPO_ROOT / "packages" / "sage-kernel" / "src", - REPO_ROOT / "packages" / "sage-libs" / "src", - REPO_ROOT / "packages" / "sage-middleware" / "src", - REPO_ROOT / "packages" / "sage-tools" / "src", -] -for path in PACKAGE_SRC_ROOTS: - if path.exists() and str(path) not in sys.path: - sys.path.insert(0, str(path)) - - -try: - from sage.common.components.sage_embedding.embedding_model import EmbeddingModel - from sage.middleware.components.sage_db.python.micro_service.sage_db_service import ( - SageDBService, - ) -except ImportError as exc: # pragma: no cover - surface build guidance early - if "_sage_db" in str(exc): - raise SystemExit( - "❌ SageDB native extension not found. Install it before running this demo:\n" - " sage extensions install sage_db # add --force to rebuild" - ) from exc - raise - -SERVICE_NAME = "vector_store_service" - - -@dataclass -class KnowledgeEntry: - title: str - text: str - topic: str - tags: list[str] - - -KNOWLEDGE_BASE: list[KnowledgeEntry] = [ - KnowledgeEntry( - title="SAGE 推理管道总览", - text=( - "SAGE 的大模型推理 DAG 通过 LocalEnvironment 串联 Source、Map 与 Sink 节点，" - "在 JobManager 中调度执行，并支持服务调用与反馈回路。" - ), - topic="architecture", - tags=["dag", "runtime", "overview"], - ), - KnowledgeEntry( - title="SageDB 与 RAG 集成", - text=( - "SageDB 可以作为 RAG 检索后端，结合嵌入模型将文档向量化后写入，" - "在查询阶段通过服务调用提供 Top-K 相似片段。" - ), - topic="rag", - tags=["sage_db", "retrieval", "integration"], - ), - KnowledgeEntry( - title="服务编排节点", - text=( - "LocalEnvironment.register_service 会在 DAG 执行时自动注入可复用的服务实例，" - "算子可通过 self.call_service['name'] 以同步方式访问数据库或缓存。" - ), - topic="runtime", - tags=["service", "call_service", "localenvironment"], - ), - KnowledgeEntry( - title="Prompt 构建阶段", - text=( - "QAPromptor 接收检索结果列表，拼装 system 与 user 消息，为 OpenAI 兼容生成器提供提示模板。" - ), - topic="prompt", - tags=["promptor", "qa", "template"], - ), -] - -DEFAULT_QUERIES = [ - "SAGE 的 DAG 如何把 SageDB 检索串进推理流程?", - "QAPromptor 在这个流程里起到什么作用?", -] - - -class BootstrappedSageDBService(SageDBService): - """Wrapper service that loads embeddings at construction time.""" - - def __init__( - self, - *, - initial_vectors: Sequence[Sequence[float]] | np.ndarray | None = None, - initial_metadata: Sequence[dict[str, str]] | None = None, - **kwargs: Any, - ) -> None: - super().__init__(**kwargs) - if initial_vectors is not None: - vectors = ( - np.asarray(initial_vectors, dtype=np.float32) - if not isinstance(initial_vectors, np.ndarray) - else initial_vectors.astype(np.float32, copy=False) - ) - - if vectors.size == 0: - return - - if vectors.ndim != 2: - raise ValueError("initial_vectors must be a 2D array-like of shape (N, dim)") - - expected_count = vectors.shape[0] - if initial_metadata is None: - metadata = [{} for _ in range(expected_count)] - else: - metadata = list(initial_metadata) - if len(metadata) != expected_count: - raise ValueError("initial_metadata length must match number of initial_vectors") - - self.add_batch(vectors, metadata) - # Build index once after ingestion to accelerate queries - self._db.build_index() - - -class QuerySource(SourceFunction): - def __init__(self, queries: Iterable[str], **kwargs: Any) -> None: - super().__init__(**kwargs) - self._queries = list(queries) - self._cursor = 0 - - def execute(self) -> dict[str, str] | None: - if self._cursor >= len(self._queries): - return None - question = self._queries[self._cursor] - self._cursor += 1 - return {"query": question} - - -class SageDBRetrieverNode(MapFunction): - def __init__( - self, - embedder_config: dict[str, Any], - *, - service_name: str = SERVICE_NAME, - top_k: int = 3, - service_timeout: float = 10.0, - **kwargs: Any, - ) -> None: - super().__init__(**kwargs) - self.embedder = EmbeddingModel(**embedder_config) - self.service_name = service_name - self.top_k = top_k - self.service_timeout = service_timeout - - def execute(self, payload: dict[str, Any]) -> dict[str, Any]: - query = payload.get("query", "") - if not query: - return {**payload, "results": [], "retrieved_docs": []} - - query_vector = np.asarray(self.embedder.embed(query), dtype=np.float32) - service = self.call_service(self.service_name) - raw_results = service.search(query_vector, k=self.top_k, timeout=self.service_timeout) - - formatted_results: list[dict[str, Any]] = [] - corpus_snippets: list[str] = [] - references: list[dict[str, Any]] = [] - - for item in raw_results: - metadata = dict(item.get("metadata") or {}) - snippet = metadata.get("text", "") - formatted_results.append( - { - "text": snippet, - "score": item.get("score"), - "metadata": metadata, - } - ) - corpus_snippets.append(snippet) - references.append( - { - "title": metadata.get("title", "unknown"), - "score": float(item.get("score", 0.0)), - "tags": (metadata.get("tags", "").split(",") if metadata.get("tags") else []), - } - ) - - enriched = dict(payload) - enriched.update( - { - "results": formatted_results, - "retrieved_docs": corpus_snippets, - "references": references, - } - ) - return enriched - - -class MockLLMGenerator(MapFunction): - """Minimal generator that emulates an LLM using retrieved passages.""" - - def execute(self, data: list[Any]) -> dict[str, Any]: - if not isinstance(data, list) or len(data) != 2: - raise ValueError("Generator expects QAPromptor output: [original_payload, messages]") - original, messages = data - top_hit = None - if isinstance(original, dict): - hits = original.get("results", []) or [] - top_hit = hits[0] if hits else None - answer: str - if top_hit: - title = top_hit["metadata"].get("title", "资料") - answer = f"参考《{title}》中的要点：{top_hit['text']} —— 该内容经 SageDB 检索返回。" - else: - answer = "知识库没有检索到匹配内容，建议扩充 SageDB 语料。" - - enriched = dict(original) - enriched["prompt_messages"] = messages - enriched["answer"] = answer - return enriched - - -class ConsoleReporter(MapFunction): - def execute(self, payload: dict[str, Any]) -> dict[str, Any]: - query = payload.get("query", "<empty>") - answer = payload.get("answer", "<no answer>") - refs = payload.get("references", []) - - print("\n================ Pipeline Result ================") - print(f"❓ Query : {query}") - print(f"💡 Answer: {answer}") - if refs: - print("📚 References:") - for idx, ref in enumerate(refs, start=1): - title = ref.get("title") - tags = ", ".join(ref.get("tags", [])) - score = ref.get("score") - print(f" {idx}. {title} (score={score:.4f}, tags={tags})") - else: - print("📚 References: <none>") - print("================================================\n") - return payload - - -def build_embeddings(entries: Sequence[KnowledgeEntry], model: EmbeddingModel) -> np.ndarray: - vectors = [] - for item in entries: - vectors.append(model.embed(item.text)) - return np.asarray(vectors, dtype=np.float32) - - -def prepare_metadata(entries: Sequence[KnowledgeEntry]) -> list[dict[str, str]]: - metadata: list[dict[str, str]] = [] - for item in entries: - metadata.append( - { - "title": item.title, - "text": item.text, - "topic": item.topic, - "tags": ",".join(item.tags), # Serialize list to comma-separated string - } - ) - return metadata - - -def run_pipeline(top_k: int, queries: Sequence[str]) -> None: - CustomLogger.disable_global_console_debug() - - embedder_config = {"method": "mockembedder", "fixed_dim": 128} - embedder = EmbeddingModel(**embedder_config) - - vectors = build_embeddings(KNOWLEDGE_BASE, embedder) - metadata = prepare_metadata(KNOWLEDGE_BASE) - - env = LocalEnvironment("sage_db_workflow_demo") - env.register_service( - SERVICE_NAME, - BootstrappedSageDBService, - dimension=embedder.get_dim(), - index_type="AUTO", - initial_vectors=vectors, - initial_metadata=metadata, - ) - - ( - env.from_source(QuerySource, queries) - .map( - SageDBRetrieverNode, - embedder_config, - service_name=SERVICE_NAME, - top_k=top_k, - ) - .map(QAPromptor, {"use_short_answer": False}) - .map(MockLLMGenerator) - .map(ConsoleReporter) - ) - - env.submit() - - # Wait for processing to complete - import time - - time.sleep(5) # Allow enough time for processing - - # Clean up environment - env.stop() - - -def parse_args(argv: Sequence[str]) -> argparse.Namespace: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument( - "--top-k", - type=int, - default=3, - help="Number of neighbors to fetch from SageDB for each query.", - ) - parser.add_argument( - "--query", - action="append", - help="Override default demo queries (can be specified multiple times).", - ) - return parser.parse_args(argv) - - -def main(argv: Sequence[str] | None = None) -> None: - args = parse_args(argv or sys.argv[1:]) - queries = args.query if args.query else DEFAULT_QUERIES - run_pipeline(args.top_k, queries) - - -if __name__ == "__main__": - main() diff --git a/packages/sage-middleware/pyproject.toml b/packages/sage-middleware/pyproject.toml deleted file mode 100644 index 0ce131f09a..0000000000 --- a/packages/sage-middleware/pyproject.toml +++ /dev/null @@ -1,217 +0,0 @@ -[build-system] -requires = ["setuptools>=64", "wheel"] -build-backend = "setuptools.build_meta" - - -[project] -name = "isage-middleware" -dynamic = ["version"] -description = "SAGE Middleware - Streaming-Augmented Generative Execution" -readme = "README.md" -requires-python = ">=3.10" -authors = [{ name = "IntelliStream Team", email = "shuhao_zhang@hust.edu.cn" }] -keywords = [ - "data", - "api", - "reasoning", - "dataflow", - "llm", - "ml", - "middleware", - "framework", - "rag", - "intellistream", - "ai", - "sage", -] -classifiers = [ - "Development Status :: 4 - Beta", - "Intended Audience :: Developers", - "Intended Audience :: Science/Research", - "Operating System :: OS Independent", - "Topic :: Scientific/Engineering :: Artificial Intelligence", - "Topic :: Software Development :: Libraries :: Python Modules", - "Topic :: System :: Distributed Computing", -] -dependencies = [ - # ============================================================================ - # 核心依赖 - 只包含 sage.middleware 模块导入时必需的最小依赖集 - # 原则：重型依赖（transformers, celery, ML libs）放在 optional-dependencies - # ============================================================================ - # API clients - required for LLM integrations - "openai>=1.52.0,<1.91.0", - "httpx>=0.28.0,<1.0.0", - - # Network utilities - required for operators - "aiohttp>=3.12.0,<4.0.0", - "beautifulsoup4>=4.12.0,<5.0.0", # Web scraping operators - "feedparser>=6.0.11,<7.0.0", # RSS feed operators - - # JSON handling - for LongRefiner - "json_repair>=0.30.0,<1.0.0", - - # BM25 retrieval - lightweight keyword search - "bm25s>=0.2.13,<1.0.0", - "rank-bm25>=0.2.0,<1.0.0", - "PyStemmer>=3.0.0,<4.0.0", -] -license = { text = "MIT" } - -[project.optional-dependencies] -dev = [ - "pytest>=7.4.0", - "pytest-cov>=4.0.0", - "pytest-asyncio>=0.21.0", - "ruff==0.14.6", - "mypy>=1.7.0", - "pybind11>=2.10.0", -] -# Vector databases and storage -vdb = [ - "isage-vdb>=0.1.5", # SageVDB vector database - "faiss-cpu>=1.7.0,<2.0.0", # FAISS CPU backend -] -# Memory system -neuromem = [ - "isage-neuromem>=0.2.1.1", # NeuroMem memory system -] -# Streaming and time-series -streaming = [ - "isage-flow>=0.1.1", # SageFlow stream processing - "isage-tsdb>=0.1.5", # SageTSDB time series database -] -# ML/AI - transformers, embeddings, etc. -ml = [ - "transformers>=4.52.0,<4.54.0", - "tokenizers>=0.21.0,<0.24.0", - "sentence-transformers>=3.1.0,<4.0.0", - "InstructorEmbedding>=1.0.0,<2.0.0", - "accelerate>=1.9.0,<2.0.0", - "huggingface-hub>=0.34.0,<1.0.0", - "peft>=0.18.0,<1.0.0", - "scipy>=1.15.0,<2.0.0", -] -# Prompt compression -compression = [ - "llmlingua>=0.2.0,<1.0.0", # LLMLingua family -] -# Task queue for distributed processing -queue = ["celery>=5.5.0,<6.0.0", "flower>=2.0.0,<3.0.0"] -# Authentication and security -auth = [ - "python-jose[cryptography]>=3.5.0,<4.0.0", - "passlib[argon2]>=1.7.4,<2.0.0", -] -# Additional LLM providers -llm-providers = [ - "ollama>=0.5.0,<1.0.0", - "zhipuai>=2.1.0,<3.0.0", - "cohere>=5.16.0,<6.0.0", - "anthropic>=0.25.0,<1.0.0", -] -# L3 Independent Algorithm Libraries -libs = [ - "isage-libs", # Core interface library - "isage-agentic>=0.1.0.0", - "isage-eval>=0.1.0.0", - "isage-finetune>=0.1.0.0", - "isage-privacy>=0.1.0.0", - "isage-rag>=0.1.0.0", - "isage-safety>=0.1.0.0", - "isage-refiner>=0.1.0.0", -] -# Full installation with all optional dependencies -all = [ - "isage-middleware[vdb,neuromem,streaming,ml,compression,queue,auth,llm-providers,libs]", -] -[project.urls] -Homepage = "https://github.com/intellistream/SAGE" -Repository = "https://github.com/intellistream/SAGE.git" -Documentation = "https://intellistream.github.io/SAGE-Pub/" -Issues = "https://github.com/intellistream/SAGE/issues" - -[project.scripts] - -# ============================================================================ -# scikit-build-core configuration -# ============================================================================ - -[tool.pytest.ini_options] -testpaths = ["tests", "src"] -python_files = ["test_*.py", "*_test.py"] -python_classes = ["Test*"] -python_functions = ["test_*"] -addopts = [ - "--benchmark-storage=../../.sage/benchmarks", - "-o", - "cache_dir=../../.sage/cache/pytest", - "--strict-markers", - "--strict-config", - "--verbose", - "-ra", -] -markers = [ - "slow: marks tests as slow (deselect with '-m \"not slow\"')", - "integration: marks tests as integration tests", - "unit: marks tests as unit tests", - "network: marks tests as network tests", - "system: marks tests as system tests", - "core: marks tests as core functionality tests", - "smoke: marks tests as smoke tests (quick validation)", - "cli: marks tests as CLI tests", - "external: marks tests that require external services or APIs", -] - -[tool.coverage.run] -source = ["src/sage"] -omit = ["*/tests/*", "*/test_*.py", "*/_test_*.py"] - -[tool.coverage.report] -exclude_lines = [ - "pragma: no cover", - "def __repr__", - "raise AssertionError", - "raise NotImplementedError", - "if __name__ == .__main__.:", -] - -[tool.black] -line-length = 100 -target-version = ["py310", "py311", "py312"] -include = "\\.pyi?$" -extend-exclude = "/(\n \\.git\n | \\.venv\n | build\n | dist\n)/\n" - -[tool.isort] -profile = "black" -multi_line_output = 3 -line_length = 100 -known_first_party = ["sage"] - -[tool.mypy] -python_version = "3.11" -cache_dir = "../../.sage/cache/mypy" -warn_return_any = true -warn_unused_configs = true -disallow_untyped_defs = true -no_implicit_optional = true -strict_optional = true -overrides = [ - { module = "sage.service.*", ignore_missing_imports = false }, - { module = "fastapi.*", ignore_missing_imports = true }, -] - -# ============================================================================ -# Code Quality Configuration -# Extends from root ruff.toml for unified standards across all packages -# ============================================================================ -[tool.ruff] -extend = "../../tools/ruff.toml" - -# Package discovery (PEP 420 namespace) -[tool.setuptools.packages.find] -where = ["src"] -namespaces = true - -# Version from _version.py (setuptools-scm alternative for manual versioning) -[tool.setuptools.dynamic] -version = { attr = "sage.middleware._version.__version__" } diff --git a/packages/sage-middleware/src/sage/middleware/__init__.py b/packages/sage-middleware/src/sage/middleware/__init__.py deleted file mode 100644 index 30d74b5482..0000000000 --- a/packages/sage-middleware/src/sage/middleware/__init__.py +++ /dev/null @@ -1,59 +0,0 @@ -""" -SAGE Middleware - 中间件和领域算子层 - -Layer: L4 (Domain Components) -Dependencies: sage.libs (L3), sage.kernel (L3), sage.platform (L2), sage.common (L1) - -提供： -- 领域算子： - * RAG operators: 检索增强生成 (pipeline, retriever, generator, profiler, document_loaders) - * RAG backends: 向量数据库集成 (Milvus, Chroma) - * LLM operators: 大语言模型算子 - * LLM clients: LLM服务客户端 (OpenAI, HuggingFace) - * Tool operators: 领域工具 (arxiv_searcher, image_captioner, nature_news等) - * Filters: 业务过滤器 (tool_filter, evaluate_filter, context_source/sink) -- 业务上下文：Agent/RAG workflow 上下文管理 (ModelContext, SearchSession等) -- 中间件组件： - * sage_db: 数据库抽象 - * sage_mem: 内存管理和缓存 - * sage_refiner: 数据精炼工具 (仅保留service层，算法已下移到sage.libs.context.compression) - * sage_flow: 工作流编排 - * sage_tsdb: 时序数据库 - -Architecture: -- L4 层提供领域特定的功能组件 -- 依赖 L1-L3 的基础设施、核心引擎和通用算法 -- 为 L5 (应用层) 提供可复用的领域组件 -- 包含各种中间件和高级算子实现 - -子模块： -- operators/: 领域算子 - * rag/: RAG operators + backends (Milvus, Chroma) - * llm/: LLM operators + clients (OpenAI, HuggingFace) - * tools/: 领域特定工具 - * filters/: 业务过滤器 -- context/: Agent/RAG业务上下文 (从sage.libs迁移) -- components/: 中间件组件（DB, Memory, Refiner等） -""" - -# 直接从本包的_version模块加载版本信息 -try: - from sage.middleware._version import __author__, __email__, __version__ -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" - -# 导出子模块 -__layer__ = "L4" - -from . import components, operators - -__all__ = [ - "__version__", - "__author__", - "__email__", - "operators", - "components", -] diff --git a/packages/sage-middleware/src/sage/middleware/_version.py b/packages/sage-middleware/src/sage/middleware/_version.py deleted file mode 100644 index e8c8101247..0000000000 --- a/packages/sage-middleware/src/sage/middleware/_version.py +++ /dev/null @@ -1,6 +0,0 @@ -"""Version information for sage-middleware package.""" - -# 独立硬编码版本 -__version__ = "0.2.4.3" -__author__ = "IntelliStream Team" -__email__ = "shuhao_zhang@hust.edu.cn" diff --git a/packages/sage-middleware/src/sage/middleware/components/__init__.py b/packages/sage-middleware/src/sage/middleware/components/__init__.py deleted file mode 100644 index 7fe94220d0..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/__init__.py +++ /dev/null @@ -1,30 +0,0 @@ -""" -SAGE Middleware Components - -Core middleware components including databases, flow engines, and other services. - -Note: sage_refiner has been migrated to the independent isage-refiner package. - Install with: pip install isage-refiner - Use: from sage_refiner import LongRefinerCompressor -""" - -# Lazy imports to avoid loading heavy dependencies (FAISS, etc.) at module load time -from . import sage_db, sage_flow, sage_sias, sage_tsdb -from .extensions_compat import * # noqa: F403 - -# Import sage_mem - it's a namespace package that handles its own lazy loading -try: - from . import sage_mem -except ImportError: - # sage_mem namespace package might not be available - sage_mem = None - - -__all__ = [ - "sage_db", - "sage_flow", - "sage_mem", - "sage_sias", - "sage_tsdb", - "extensions_compat", -] diff --git a/packages/sage-middleware/src/sage/middleware/components/cmake/pybind11_dependency.cmake b/packages/sage-middleware/src/sage/middleware/components/cmake/pybind11_dependency.cmake deleted file mode 100644 index 6c5444543b..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/cmake/pybind11_dependency.cmake +++ /dev/null @@ -1,34 +0,0 @@ -# Shared pybind11 dependency resolver for SAGE C++ extensions -# -# Usage: -# include("${CMAKE_CURRENT_SOURCE_DIR}/../cmake/pybind11_dependency.cmake") -# # afterwards the function `pybind11_add_module` and targets `pybind11::module` -# # will be available. -# -# Behaviour: -# 1. Prefer an existing pybind11 installation discoverable via find_package. -# 2. Fallback to FetchContent on a pinned, vetted version (v2.13.0). -# 3. Guarded so the FetchContent path only runs once per configure step. - -if(NOT TARGET pybind11::module) - find_package(pybind11 CONFIG QUIET) - if(pybind11_FOUND) - message(STATUS "Using system pybind11 ${pybind11_VERSION} from ${pybind11_CONFIG}") - else() - include(FetchContent) - if(NOT DEFINED pybind11_POPULATED) - set(_sage_pybind11_git_tag "v2.13.0" CACHE STRING "Pinned pybind11 version for SAGE extensions") - FetchContent_Declare( - pybind11 - GIT_REPOSITORY https://github.com/pybind/pybind11.git - GIT_TAG ${_sage_pybind11_git_tag} - ) - endif() - FetchContent_MakeAvailable(pybind11) - message(STATUS "Fetched pybind11 ${_sage_pybind11_git_tag} for SAGE extensions") - endif() -endif() - -if(NOT COMMAND pybind11_add_module) - message(FATAL_ERROR "pybind11_add_module is unavailable even after resolving pybind11 dependency") -endif() diff --git a/packages/sage-middleware/src/sage/middleware/components/cmake/sage_shared_dependencies.cmake b/packages/sage-middleware/src/sage/middleware/components/cmake/sage_shared_dependencies.cmake deleted file mode 100644 index 9e6096003f..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/cmake/sage_shared_dependencies.cmake +++ /dev/null @@ -1,72 +0,0 @@ -# Centralized dependency setup for SAGE C++ extensions when built via the Python superbuild. -# This file is optional: individual C++ projects can still build stand-alone by -# falling back to their local dependency scripts when SAGE_COMMON_DEPS_FILE isn't set. - -cmake_minimum_required(VERSION 3.20) - -# --- Configuration knobs --------------------------------------------------- -set(SAGE_PYBIND11_VERSION "2.13.0" CACHE STRING "Pinned pybind11 version for all SAGE extensions") -option(SAGE_ENABLE_GPERFTOOLS "Build extensions with gperftools/tcmalloc" OFF) -# Allow callers to provide a pre-installed gperftools root -set(SAGE_GPERFTOOLS_ROOT "" CACHE PATH "Optional root path for gperftools installation") - -# --- pybind11 --------------------------------------------------------------- -# Check if pybind11 is already available (e.g., from parent project) -if(NOT TARGET pybind11::module) - find_package(pybind11 CONFIG QUIET) - if(NOT pybind11_FOUND) - message(STATUS "pybind11 not found, fetching version ${SAGE_PYBIND11_VERSION}") - include(FetchContent) - if(NOT DEFINED pybind11_POPULATED) - FetchContent_Declare( - pybind11 - GIT_REPOSITORY https://github.com/pybind/pybind11.git - GIT_TAG v${SAGE_PYBIND11_VERSION} - ) - endif() - FetchContent_MakeAvailable(pybind11) - else() - message(STATUS "Using existing pybind11 ${pybind11_VERSION}") - endif() -else() - message(STATUS "pybind11 already provided by parent project") -endif() - -# --- gperftools ------------------------------------------------------------- -set(SAGE_GPERFTOOLS_LIBS "") -set(SAGE_GPERFTOOLS_INCLUDE "") -if(SAGE_ENABLE_GPERFTOOLS) - if(SAGE_GPERFTOOLS_ROOT) - list(APPEND CMAKE_PREFIX_PATH ${SAGE_GPERFTOOLS_ROOT}) - endif() - find_package(gperftools QUIET) - if(gperftools_FOUND) - set(SAGE_GPERFTOOLS_LIBS gperftools::profiler gperftools::tcmalloc) - if(TARGET gperftools::profiler) - get_target_property(_tmp_include gperftools::profiler INTERFACE_INCLUDE_DIRECTORIES) - set(SAGE_GPERFTOOLS_INCLUDE ${_tmp_include}) - endif() - else() - include(FetchContent) - if(NOT TARGET gperftools::profiler) - FetchContent_Declare( - gperftools - GIT_REPOSITORY https://github.com/gperftools/gperftools.git - GIT_TAG gperftools-2.15 - ) - FetchContent_MakeAvailable(gperftools) - endif() - set(SAGE_GPERFTOOLS_LIBS gperftools::profiler gperftools::tcmalloc) - endif() -endif() - -# --- Common compile flags / definitions ------------------------------------ -set(SAGE_COMMON_COMPILE_DEFINITIONS - PYBIND11_INTERNALS_ID="sage_pybind11_shared" - _GLIBCXX_USE_CXX11_ABI=1 - CACHE INTERNAL "") - -set(SAGE_COMMON_COMPILE_OPTIONS - -fvisibility=hidden - -fvisibility-inlines-hidden - CACHE INTERNAL "") diff --git a/packages/sage-middleware/src/sage/middleware/components/extensions_compat.py b/packages/sage-middleware/src/sage/middleware/components/extensions_compat.py deleted file mode 100644 index 56e8594b73..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/extensions_compat.py +++ /dev/null @@ -1,141 +0,0 @@ -""" -SAGE Middleware Components - 运行时兼容性检测 - -此模块处理C++扩展的可选导入，提供优雅的降级机制。 -C++扩展模块（_sage_flow）在未编译时可能不存在， -此模块确保在扩展不可用时也能正常导入和运行。 - -注意： -- SageVDB 已独立为 PyPI 包 (PyPI: isage-vdb, Python: sagevdb)，不再作为 SAGE C++ 扩展。 -- SageTSDB 已独立为 PyPI 包 (isage-tsdb)，不再作为 SAGE C++ 扩展。 -""" - -from typing import TYPE_CHECKING, Any - -# 类型检查时导入，运行时通过try/except处理 -if TYPE_CHECKING: - # 当扩展编译可用时，这些导入会成功 - # stub文件（.pyi）提供类型提示 - from sage.middleware.components.sage_flow.python import sage_flow as _sage_flow -else: - # 运行时动态导入，优雅处理缺失的扩展 - _sage_flow: Any = None - -# 尝试导入C++扩展，失败时使用纯Python实现 -_SAGE_DB_AVAILABLE = False # 通过 isage-vdb 包检测 (Python: sagevdb) -_SAGE_FLOW_AVAILABLE = False -_SAGE_TSDB_AVAILABLE = False # 通过 isage-tsdb 包检测 - -if not TYPE_CHECKING: - # SageVDB 现在是独立的 PyPI 包 (PyPI: isage-vdb, Python: sagevdb) - try: - import sagevdb # noqa: F401 - - _SAGE_DB_AVAILABLE = True - except ImportError: - # Don't warn on import - only when trying to use the feature - pass - - try: - # 只导入 Python wrapper 模块，避免重复加载 C++ 扩展 - from sage.middleware.components.sage_flow.python import sage_flow as _sage_flow - - _SAGE_FLOW_AVAILABLE = True - except ImportError: - _sage_flow = None - # Don't warn on import - only when trying to use the feature - pass - - # SageTSDB 现在是独立的 PyPI 包 - try: - import sage_tsdb # noqa: F401 - - _SAGE_TSDB_AVAILABLE = True - except ImportError: - # Don't warn on import - only when trying to use the feature - pass - - -def is_sage_db_available() -> bool: - """检查SAGE DB扩展是否可用""" - return _SAGE_DB_AVAILABLE - - -def is_sage_flow_available() -> bool: - """检查SAGE Flow扩展是否可用""" - return _SAGE_FLOW_AVAILABLE - - -def is_sage_tsdb_available() -> bool: - """检查SAGE TSDB扩展是否可用""" - return _SAGE_TSDB_AVAILABLE - - -def get_extension_status() -> dict: - """获取所有扩展的状态""" - return { - "sage_db": _SAGE_DB_AVAILABLE, - "sage_flow": _SAGE_FLOW_AVAILABLE, - "sage_tsdb": _SAGE_TSDB_AVAILABLE, - "total_available": sum([_SAGE_DB_AVAILABLE, _SAGE_FLOW_AVAILABLE, _SAGE_TSDB_AVAILABLE]), - "total_extensions": 3, - } - - -def check_extensions_availability() -> dict: - """检查扩展可用性，返回兼容格式用于CI""" - return { - "sage_db": _SAGE_DB_AVAILABLE, - "sage_flow": _SAGE_FLOW_AVAILABLE, - "sage_tsdb": _SAGE_TSDB_AVAILABLE, - } - - -def require_sage_db(): - """要求SageVDB可用，否则抛出异常""" - if not _SAGE_DB_AVAILABLE: - raise ImportError( - "此功能需要 SageVDB。请安装:\n" - " pip install isage-vdb\n" - "注意: PyPI 包名是 'isage-vdb'，Python 导入名是 'sagevdb'" - ) - import sagevdb - - return sagevdb - - -def require_sage_flow(): - """要求SAGE Flow扩展可用，否则抛出异常""" - if not _SAGE_FLOW_AVAILABLE: - raise ImportError( - "此功能需要SAGE Flow C++扩展。请安装完整版本：\n" - "pip install --force-reinstall isage-middleware\n" - "或安装构建依赖后重新安装：\n" - "Ubuntu/Debian: sudo apt-get install build-essential cmake\n" - "macOS: brew install cmake" - ) - return _sage_flow - - -def require_sage_tsdb(): - """要求SAGE TSDB可用，否则抛出异常""" - if not _SAGE_TSDB_AVAILABLE: - raise ImportError("此功能需要 SAGE TSDB。请安装: pip install isage-tsdb") - import sage_tsdb - - return sage_tsdb - - -# 在模块导入时显示状态（仅在明确导入时） -# 避免在用户导入其他模块时显示无关警告 -if __name__ == "__main__": - status = get_extension_status() - if status["total_available"] < status["total_extensions"]: - print(f"ℹ️ SAGE扩展状态: {status['total_available']}/{status['total_extensions']} 可用") - if not _SAGE_DB_AVAILABLE: - print(" ❌ SageVDB: 未安装 (pip install isage-vdb)") - if not _SAGE_FLOW_AVAILABLE: - print(" ❌ SAGE Flow: C++扩展不可用") - if not _SAGE_TSDB_AVAILABLE: - print(" ❌ SAGE TSDB: 未安装 (pip install isage-tsdb)") - print(" 💡 提示: 安装相应依赖可启用完整功能") diff --git a/packages/sage-middleware/src/sage/middleware/components/sage_db/__init__.py b/packages/sage-middleware/src/sage/middleware/components/sage_db/__init__.py deleted file mode 100644 index 694e345377..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/sage_db/__init__.py +++ /dev/null @@ -1,116 +0,0 @@ -"""SageVDB compatibility layer for SAGE. - -SageVDB has been migrated to an independent PyPI package. - -Installation: - pip install isage-vdb - -This module re-exports SageVDB classes from the sagevdb package -for backward-compatible import paths within SAGE. - -Important: - - PyPI package name: isage-vdb (with hyphen and 'i' prefix) - - Python import name: sagevdb (no 'i', no hyphen) - -For detailed migration information, see: - docs-public/docs_src/dev-notes/cross-layer/sagedb-independence-migration.md -""" - -import warnings - -# Re-export everything from sagevdb (Python import name, PyPI: isage-vdb) -_SAGE_DB_AVAILABLE = False -try: - from sagevdb import ( - DatabaseConfig, - DistanceMetric, - IndexType, - MetadataStore, - QueryEngine, - QueryResult, - SageVDB, - SageVDBException, - SearchParams, - SearchStats, - VectorStore, - add_numpy, - create_database, - distance_metric_to_string, - index_type_to_string, - search_numpy, - string_to_distance_metric, - string_to_index_type, - ) - - _SAGE_DB_AVAILABLE = True -except ImportError as e: - # Don't warn on import - only when actually trying to use SageVDB - # Store error message for later use - _SAGE_DB_IMPORT_ERROR = str(e) - pass - # Provide stub exports to prevent ImportError - SageVDB = None - IndexType = None - DistanceMetric = None - QueryResult = None - SearchParams = None - SearchStats = None - DatabaseConfig = None - MetadataStore = None - QueryEngine = None - VectorStore = None - SageVDBException = None - create_database = None - add_numpy = None - search_numpy = None - distance_metric_to_string = None - index_type_to_string = None - string_to_distance_metric = None - string_to_index_type = None - -# Import backend adapters -try: - from .backend import SageVDBBackend # noqa: F401 -except ImportError: - SageVDBBackend = None - -__all__ = [ - # Core classes (may be None if not installed) - "SageVDB", - "IndexType", - "DistanceMetric", - "QueryResult", - "SearchParams", - "SearchStats", - "DatabaseConfig", - "MetadataStore", - "QueryEngine", - "VectorStore", - "SageVDBException", - # Factory functions - "create_database", - # Numpy utilities - "add_numpy", - "search_numpy", - # Conversion utilities - "distance_metric_to_string", - "index_type_to_string", - "string_to_distance_metric", - "string_to_index_type", - # Backend adapters - "SageVDBBackend", - # Availability flag - "_SAGE_DB_AVAILABLE", -] - - -def __getattr__(name): - """Provide friendly error message when SageVDB is not installed""" - if name in __all__ and not _SAGE_DB_AVAILABLE: - raise ImportError( - f"Cannot import '{name}' from sage.middleware.components.sage_db. " - "SageVDB is not installed. Please install it using:\n" - " pip install isage-vdb\n" - "Note: PyPI package name is 'isage-vdb', Python import name is 'sagevdb'" - ) - raise AttributeError(f"module '{__name__}' has no attribute '{name}'") diff --git a/packages/sage-middleware/src/sage/middleware/components/sage_db/backend.py b/packages/sage-middleware/src/sage/middleware/components/sage_db/backend.py deleted file mode 100644 index 7043752bf2..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/sage_db/backend.py +++ /dev/null @@ -1,136 +0,0 @@ -"""SageVDB Backend Adapter - VectorStore implementation using SageVDB - -This module provides a VectorStore adapter for SageVDB, enabling it to be used -with the unified IndexBuilder interface. - -Layer: L4 (sage-middleware) -Dependencies: isage-vdb (PyPI package, Python import: sagevdb) - -SageVDB is now an independent PyPI package. Install with: pip install isage-vdb -""" - -from pathlib import Path -from typing import Any - -from sagevdb import SageVDB - - -class SageVDBBackend: - """VectorStore adapter for SageVDB (C++ vector database). - - This class wraps SageVDB to conform to the VectorStore Protocol, - enabling it to be used with IndexBuilder via dependency injection. - - Architecture: - - Implements VectorStore Protocol from L3 (sage-libs) - - Uses SageVDB from isage-vdb (PyPI package, Python: sagevdb) - - Injected into IndexBuilder by L5 (sage-cli) - - Example: - >>> from sage.libs.rag.index_builder import IndexBuilder - >>> from sage.middleware.components.sage_db import SageVDBBackend - >>> - >>> def factory(path: Path, dim: int): - ... return SageVDBBackend(path, dim) - >>> - >>> builder = IndexBuilder(backend_factory=factory) - """ - - def __init__(self, persist_path: Path, dim: int): - """Initialize SageVDB backend. - - Args: - persist_path: Path where index will be saved - dim: Vector dimension - """ - self.db = SageVDB(dim) - self.persist_path = persist_path - self.dim = dim - self._count = 0 - - def add(self, vector: list[float], metadata: dict[str, Any]) -> None: - """Add vector with metadata to SageVDB. - - Args: - vector: Dense vector embedding - metadata: Associated metadata - """ - self.db.add(vector, metadata) - self._count += 1 - - def build_index(self) -> None: - """Build SageVDB index for efficient search.""" - self.db.build_index() - - def save(self, path: str) -> None: - """Persist SageVDB index to disk. - - Args: - path: Absolute path to save location - """ - self.db.save(path) - - def load(self, path: str) -> None: - """Load SageVDB index from disk. - - Args: - path: Absolute path to load from - """ - self.db.load(path) - - def search( - self, - query_vector: list[float], - top_k: int = 5, - filter_metadata: dict[str, Any] | None = None, - ) -> list[dict[str, Any]]: - """Search for nearest neighbors in SageVDB. - - Args: - query_vector: Query embedding - top_k: Number of results to return - filter_metadata: Optional metadata filters - - Returns: - List of search results with metadata and scores - """ - # SageVDB search returns QueryResult objects - results = self.db.search(query_vector, top_k=top_k) - - # Convert to standard format - formatted_results = [] - for result in results: - formatted_results.append( - { - "vector": result.vector, - "metadata": result.metadata, - "score": result.distance, # or result.score - "id": result.id, - } - ) - - # Apply metadata filter if provided - if filter_metadata: - formatted_results = [ - r - for r in formatted_results - if all(r["metadata"].get(k) == v for k, v in filter_metadata.items()) - ] - - return formatted_results - - def get_dim(self) -> int: - """Get vector dimension. - - Returns: - Vector dimension - """ - return self.dim - - def count(self) -> int: - """Get total number of vectors. - - Returns: - Total vector count - """ - return self._count diff --git a/packages/sage-middleware/src/sage/middleware/components/sage_db/service.py b/packages/sage-middleware/src/sage/middleware/components/sage_db/service.py deleted file mode 100644 index 0345138006..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/sage_db/service.py +++ /dev/null @@ -1,15 +0,0 @@ -""" -SageDB Middleware Service - -This module provides the middleware service interface for SageDB, -wrapping the Python bindings from the sageDB C++ core. -""" - -# Micro-service wrapper -from .python.micro_service.sage_db_service import SageDBService, SageDBServiceConfig -from .python.multimodal_sage_db import MultimodalSageDB - -# Core Python bindings -from .python.sage_db import SageDB - -__all__ = ["SageDB", "MultimodalSageDB", "SageDBService", "SageDBServiceConfig"] diff --git a/packages/sage-middleware/src/sage/middleware/components/sage_flow/__init__.py b/packages/sage-middleware/src/sage/middleware/components/sage_flow/__init__.py deleted file mode 100644 index a808cb3ad7..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/sage_flow/__init__.py +++ /dev/null @@ -1,76 +0,0 @@ -"""SageFlow compatibility layer for SAGE. - -SageFlow has been migrated to an independent PyPI package. - -Installation: - pip install isage-flow - -This module re-exports SageFlow classes from the isage-flow package -for backward-compatible import paths within SAGE, and provides -SAGE-specific services and wrappers. - -For detailed migration information, see: - docs-public/docs_src/dev-notes/cross-layer/sageflow-independence-migration.md -""" - -import warnings - -# Import from PyPI package (isage-flow) -_SAGE_FLOW_AVAILABLE = False -try: - from sage_flow import ( - DataType, - SimpleStreamSource, - Stream, - StreamEnvironment, - VectorData, - VectorRecord, - __author__, - __email__, - __version__, - ) - - _SAGE_FLOW_AVAILABLE = True -except ImportError as e: - # Don't fail immediately - allow graceful degradation - warnings.warn( - f"SAGE Flow not available: {e}\n" - "Install with: pip install isage-flow\n" - "Some advanced streaming features will be unavailable.", - UserWarning, - stacklevel=2, - ) - # Provide stub exports to prevent ImportError - DataType = None - SimpleStreamSource = None - Stream = None - StreamEnvironment = None - VectorData = None - VectorRecord = None - __version__ = "unavailable" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" - -# SAGE-specific services (kept in SAGE repo) -# Only import if sage_flow is available -if _SAGE_FLOW_AVAILABLE: - from .python.micro_service.sage_flow_service import SageFlowService -else: - SageFlowService = None - -__all__ = [ - # Core API from isage-flow (may be None if not installed) - "StreamEnvironment", - "Stream", - "SimpleStreamSource", - "VectorData", - "VectorRecord", - "DataType", - "__version__", - "__author__", - "__email__", - # SAGE-specific services (may be None if isage-flow not installed) - "SageFlowService", - # Availability flag - "_SAGE_FLOW_AVAILABLE", -] diff --git a/packages/sage-middleware/src/sage/middleware/components/sage_flow/python/__init__.py b/packages/sage-middleware/src/sage/middleware/components/sage_flow/python/__init__.py deleted file mode 100644 index bfb0f44b7b..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/sage_flow/python/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -"""Python bindings and wrappers for SAGE-Flow live here. - -This package is intended to house all Python-side modules for the component. -""" - -# Try to import the C++ extension module -# If this fails, sage_flow.py will handle the fallback logic -try: - from . import _sage_flow # noqa: F401 # type: ignore[import-not-found] - - __all__ = ["_sage_flow"] -except ImportError: - # sage_flow.py will handle finding and importing the .so file - __all__ = [] diff --git a/packages/sage-middleware/src/sage/middleware/components/sage_flow/python/micro_service/__init__.py b/packages/sage-middleware/src/sage/middleware/components/sage_flow/python/micro_service/__init__.py deleted file mode 100644 index 33b70cc132..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/sage_flow/python/micro_service/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -# Expose SageFlowService from submodule -from .sage_flow_service import SageFlowService - -__all__ = ["SageFlowService"] diff --git a/packages/sage-middleware/src/sage/middleware/components/sage_flow/python/micro_service/sage_flow_service.py b/packages/sage-middleware/src/sage/middleware/components/sage_flow/python/micro_service/sage_flow_service.py deleted file mode 100644 index 792f0764fa..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/sage_flow/python/micro_service/sage_flow_service.py +++ /dev/null @@ -1,88 +0,0 @@ -from __future__ import annotations - -import queue -import threading -import time -from dataclasses import dataclass - -import numpy as np - -from sage.middleware.components.sage_flow.python.sage_flow import ( - SimpleStreamSource, - StreamEnvironment, -) - - -@dataclass -class _Record: - uid: int - vec: np.ndarray - - -class SageFlowService: - """ - A minimal micro-service wrapper for SAGE-Flow used by examples. - - - push(uid, vec): enqueue vector for processing - - run(): drain queue, feed to flow, and execute once - """ - - def __init__(self, dim: int = 4, dtype: str = "Float32") -> None: - self.dim = dim - self.dtype = dtype - self._q: queue.Queue[_Record] = queue.Queue() - self._env = StreamEnvironment() - self._source = SimpleStreamSource("sage_flow_service_source") - self._lock = threading.Lock() - self._added_to_env = False - # Note: don't add to env yet; defer until a sink is attached - - # API expected by examples - def push(self, uid: int, vec: np.ndarray) -> None: - if not isinstance(vec, np.ndarray): - vec = np.asarray(vec, dtype=np.float32) - vec = vec.astype(np.float32, copy=False) - if vec.ndim != 1 or vec.shape[0] != self.dim: - raise ValueError(f"vector shape must be ({self.dim},)") - self._q.put(_Record(uid=int(uid), vec=vec)) - - def run(self) -> None: - # Drain queue into source, then execute once - drained = 0 - with self._lock: - while True: - try: - rec = self._q.get_nowait() - except queue.Empty: - break - ts = int(time.time() * 1000) - self._source.addRecord(rec.uid, ts, rec.vec) - drained += 1 - if drained: - # If user hasn't attached sinks, add source to env once so execution proceeds - if not self._added_to_env: - # Attach a default printing sink for visibility - self._source.write_sink_py( - "default_print_sink", - lambda uid, ts: print(f"[svc sink] uid={uid}, ts={ts}", flush=True), - ) - self._env.addStream(self._source) - self._added_to_env = True - self._env.execute() - - def set_sink(self, callback, name: str = "py_sink") -> None: - """Attach a Python sink callback for visible outputs. - - Args: - callback: Callable taking (uid: int, ts: int) - name: Sink name, defaults to 'py_sink'. - """ - self._source.write_sink_py(name, callback) - if not self._added_to_env: - self._env.addStream(self._source) - self._added_to_env = True - - # Optional: expose environment for advanced integrations - @property - def env(self) -> StreamEnvironment: - return self._env diff --git a/packages/sage-middleware/src/sage/middleware/components/sage_flow/python/sage_flow.py b/packages/sage-middleware/src/sage/middleware/components/sage_flow/python/sage_flow.py deleted file mode 100644 index 009899ddc2..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/sage_flow/python/sage_flow.py +++ /dev/null @@ -1,30 +0,0 @@ -""" -SAGE Flow - High-performance vector stream processing engine (Python wrapper) - -This module re-exports SageFlow classes from the isage-flow PyPI package. -""" - -# Re-export all classes from isage-flow -from sage_flow import ( - DataType, - SimpleStreamSource, - Stream, - StreamEnvironment, - VectorData, - VectorRecord, - __author__, - __email__, - __version__, -) - -__all__ = [ - "__version__", - "__author__", - "__email__", - "StreamEnvironment", - "Stream", - "SimpleStreamSource", - "VectorData", - "VectorRecord", - "DataType", -] diff --git a/packages/sage-middleware/src/sage/middleware/components/sage_flow/service.py b/packages/sage-middleware/src/sage/middleware/components/sage_flow/service.py deleted file mode 100644 index 62448dc7c6..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/sage_flow/service.py +++ /dev/null @@ -1,14 +0,0 @@ -""" -SageFlow Middleware Service - -This module provides the middleware service interface for SageFlow, -wrapping the Python bindings from the sageFlow C++ core. -""" - -# Micro-service wrapper -from .python.micro_service.sage_flow_service import SageFlowService - -# Core Python bindings -from .python.sage_flow import SageFlow - -__all__ = ["SageFlow", "SageFlowService"] diff --git a/packages/sage-middleware/src/sage/middleware/components/sage_mem/__init__.py b/packages/sage-middleware/src/sage/middleware/components/sage_mem/__init__.py deleted file mode 100644 index b46a3fbf09..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/sage_mem/__init__.py +++ /dev/null @@ -1,83 +0,0 @@ -""" -SAGE-Mem: Memory Management Component for SAGE - -Provides memory management capabilities for RAG applications. -This is a namespace package that can contain multiple memory implementations: -- neuromem: Brain-inspired memory system (from isage-neuromem package) -- future implementations can be added here - -Usage: - # Method 1: Import from neuromem subpackage (recommended) - from sage.middleware.components.sage_mem.neuromem import MemoryManager - - # Method 2: Convenience imports from sage_mem root (if neuromem is installed) - from sage.middleware.components.sage_mem import MemoryManager -""" - -# This is a namespace package - allow subpackages from different distributions -__path__ = __import__("pkgutil").extend_path(__path__, __name__) - -# Convenience re-exports from neuromem (optional, requires isage-neuromem installed) -# These are lazy-loaded to avoid import errors if neuromem is not installed -_NEUROMEM_AVAILABLE = False - -try: - # Try to import from the neuromem subpackage first (supports namespace merging) - from sage.middleware.components.sage_mem.neuromem import ( - BaseMemoryCollection, - GraphMemoryCollection, - KVMemoryCollection, - MemoryManager, - VDBMemoryCollection, - ) - - try: - from sage.middleware.components.sage_mem.neuromem.services import ( - BaseMemoryService, - MemoryServiceRegistry, - NeuromemServiceFactory, - ) - except ImportError: - # Services might not be available in all neuromem versions - pass - - # SimpleGraphIndex is in search_engine, not memory_collection - try: - from sage.middleware.components.sage_mem.neuromem.search_engine.graph_index import ( - SimpleGraphIndex, - ) - except ImportError: - SimpleGraphIndex = None - - __all__ = [ - # Core neuromem components - "MemoryManager", - "BaseMemoryCollection", - "VDBMemoryCollection", - "KVMemoryCollection", - "GraphMemoryCollection", - # Services (if available) - "BaseMemoryService", - "MemoryServiceRegistry", - "NeuromemServiceFactory", - ] - - if SimpleGraphIndex is not None: - __all__.append("SimpleGraphIndex") - - _NEUROMEM_AVAILABLE = True - -except (ImportError, FileNotFoundError, ModuleNotFoundError): - # Neuromem not installed - provide helpful error message via __getattr__ - def __getattr__(name): - """Provide friendly error message when neuromem is not installed""" - raise ImportError( - f"Cannot import '{name}' from sage.middleware.components.sage_mem. " - "NeuroMem is not installed. Please install it using:\n" - " pip install isage-neuromem\n" - "or install sage-middleware with neuromem support:\n" - " pip install isage-middleware[neuromem]" - ) - - __all__ = [] - _NEUROMEM_AVAILABLE = False diff --git a/packages/sage-middleware/src/sage/middleware/components/sage_sias/__init__.py b/packages/sage-middleware/src/sage/middleware/components/sage_sias/__init__.py deleted file mode 100644 index 0106ae589d..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/sage_sias/__init__.py +++ /dev/null @@ -1,59 +0,0 @@ -"""SIAS - Streaming Importance-Aware Agent System. - -This middleware component provides sample importance and continual learning -capabilities for agent systems. It integrates with NeuroMem for memory-based -importance scoring and experience replay. - -Core Components: -- CoresetSelector: Importance-aware sample selection (loss_topk, diversity, hybrid) -- OnlineContinualLearner: Experience replay with importance weighting -- SelectionSummary: Statistics for selection operations - -Usage: - from sage.middleware.components.sage_sias import ( - CoresetSelector, - OnlineContinualLearner, - SelectionSummary, - ) - - # Coreset selection - selector = CoresetSelector(strategy="hybrid") - selected = selector.select(samples, target_size=1000) - - # Continual learning with replay - learner = OnlineContinualLearner(buffer_size=2048, replay_ratio=0.25) - batch = learner.update_buffer(new_samples) - -Future Components (planned): -- StreamingImportanceScorer: I(x) = α·L_grad + β·D_ctx + γ·T_exec -- ReflectiveMemoryStore: Experience storage with pattern extraction (uses NeuroMem) -- AdaptiveExecutor: Pre/post verification and localized replanning -- MultiAgentRouter: Task decomposition and agent collaboration - -Note: - SIAS is placed in sage-middleware (L4) rather than sage-libs (L3) because - it depends on NeuroMem memory system and potentially SageVDB for - importance-based retrieval. -""" - -from sage.middleware.components.sage_sias.continual_learner import ( - OnlineContinualLearner, -) -from sage.middleware.components.sage_sias.coreset_selector import ( - CoresetSelector, - SelectionSummary, -) -from sage.middleware.components.sage_sias.types import ( - SampleProtocol, - SIASSample, -) - -__all__ = [ - # Core components - "CoresetSelector", - "OnlineContinualLearner", - "SelectionSummary", - # Types - "SIASSample", - "SampleProtocol", -] diff --git a/packages/sage-middleware/src/sage/middleware/components/sage_sias/continual_learner.py b/packages/sage-middleware/src/sage/middleware/components/sage_sias/continual_learner.py deleted file mode 100644 index 7200e7b6e3..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/sage_sias/continual_learner.py +++ /dev/null @@ -1,184 +0,0 @@ -""" -Online Continual Learning with Experience Replay - -Implements an experience replay buffer for online/incremental training that -prevents catastrophic forgetting. The buffer is managed using coreset selection -to retain the most valuable samples. - -This is a core component of SIAS (Streaming Importance-Aware Agent System). -""" - -from __future__ import annotations - -import random -from typing import Iterable, Optional, Sequence - -from .coreset_selector import CoresetSelector, SampleT, SelectionSummary - - -class OnlineContinualLearner: - """ - Maintain a replay buffer for online continual learning. - - Implements experience replay to prevent catastrophic forgetting during - incremental/online training. The buffer is managed using coreset selection - to keep the most valuable samples. - - Attributes: - buffer_size: Maximum number of samples to keep in buffer - replay_ratio: Ratio of replay samples to add per batch (e.g., 0.25 = 25%) - selector: CoresetSelector for buffer management - - Example: - >>> learner = OnlineContinualLearner(buffer_size=2048, replay_ratio=0.25) - >>> for new_batch in data_stream: - ... training_batch = learner.update_buffer(new_batch) - ... train_step(training_batch) - """ - - def __init__( - self, - buffer_size: int = 2048, - replay_ratio: float = 0.3, - selector: Optional[CoresetSelector] = None, - random_seed: int = 17, - ) -> None: - """ - Initialize OnlineContinualLearner. - - Args: - buffer_size: Maximum samples to keep in replay buffer - replay_ratio: Fraction of batch size to sample from buffer - selector: CoresetSelector for buffer management (default: hybrid) - random_seed: Random seed for reproducibility - """ - self.buffer_size = buffer_size - self.replay_ratio = replay_ratio - self.selector = selector or CoresetSelector(strategy="hybrid") - self._buffer: list[SampleT] = [] - self._metrics: dict[str, float] = {} - self._rng = random.Random(random_seed) - - @property - def buffer(self) -> list[SampleT]: - """Access the current buffer (read-only view).""" - return list(self._buffer) - - @property - def buffer_len(self) -> int: - """Current number of samples in buffer.""" - return len(self._buffer) - - def update_buffer( - self, - new_samples: Sequence[SampleT], - metrics: Optional[dict[str, float]] = None, - ) -> list[SampleT]: - """ - Update buffer with new samples and return training batch. - - This method: - 1. Adds new samples to the buffer - 2. If buffer exceeds size limit, uses coreset selection to prune - 3. Returns new samples + replay samples for training - - Args: - new_samples: New samples to add to buffer - metrics: Optional metrics dict mapping sample_id to importance score - - Returns: - Training batch combining new samples with replay samples - """ - if not new_samples: - return list(self._buffer) - - if metrics: - self._metrics.update(metrics) - - # Combine buffer with new samples - combined = list(self._buffer) + list(new_samples) - - # Prune if over capacity - if len(combined) > self.buffer_size: - combined = self.selector.select( - combined, - target_size=self.buffer_size, - metrics=self._metrics, - ) - # Clean up metrics for removed samples - combined_ids = {self._get_sample_id(sample) for sample in combined} - self._metrics = {k: v for k, v in self._metrics.items() if k in combined_ids} - - self._buffer = combined - return self._assemble_training_batch(new_samples) - - def _assemble_training_batch( - self, - new_samples: Sequence[SampleT], - ) -> list[SampleT]: - """Combine new samples with replay samples.""" - new_ids = {self._get_sample_id(s) for s in new_samples} - replay = self.sample_replay(len(new_samples), exclude=new_ids) - return list(new_samples) + replay - - def sample_replay( - self, - new_batch_size: int, - *, - exclude: Optional[Iterable[str]] = None, - ) -> list[SampleT]: - """ - Sample from replay buffer. - - Args: - new_batch_size: Size of new batch (replay size = batch_size * ratio) - exclude: Sample IDs to exclude from replay - - Returns: - List of replay samples - """ - if not self._buffer or self.replay_ratio <= 0: - return [] - - exclude = set(exclude or []) - available = [ - sample for sample in self._buffer if self._get_sample_id(sample) not in exclude - ] - if not available: - return [] - - replay_size = max(1, int(new_batch_size * self.replay_ratio)) - replay_size = min(replay_size, len(available)) - return self._rng.sample(available, replay_size) - - def buffer_snapshot(self) -> list[SampleT]: - """Return a copy of the current buffer.""" - return list(self._buffer) - - def buffer_summary(self) -> SelectionSummary: - """Get summary statistics for the buffer.""" - return SelectionSummary( - total_samples=len(self._buffer), - selected_samples=len(self._buffer), - strategy=f"buffer:{self.selector.strategy}", - ) - - def clear(self) -> None: - """Clear the buffer and metrics.""" - self._buffer = [] - self._metrics = {} - - def update_metrics(self, metrics: dict[str, float]) -> None: - """ - Update importance metrics for samples in buffer. - - Args: - metrics: Dict mapping sample_id to importance score - """ - self._metrics.update(metrics) - - def _get_sample_id(self, sample: SampleT) -> str: - """Get sample_id from sample (supports dict or object).""" - if isinstance(sample, dict): - return sample.get("sample_id", sample.get("dialog_id", str(id(sample)))) - return getattr(sample, "sample_id", getattr(sample, "dialog_id", str(id(sample)))) diff --git a/packages/sage-middleware/src/sage/middleware/components/sage_sias/coreset_selector.py b/packages/sage-middleware/src/sage/middleware/components/sage_sias/coreset_selector.py deleted file mode 100644 index 26f17ccfa3..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/sage_sias/coreset_selector.py +++ /dev/null @@ -1,302 +0,0 @@ -""" -Coreset Selection for Efficient Training - -Implements lightweight coreset selection strategies that identify the most -valuable samples for training, reducing computational cost while maintaining -model quality. - -Strategies: - - loss_topk: Select samples with highest loss (most informative) - - diversity: Select samples maximizing coverage of feature space - - hybrid: Combination of loss-based and diversity-based selection - - random: Uniform random sampling (baseline) - -This is a core component of SIAS (Streaming Importance-Aware Agent System). -""" - -from __future__ import annotations - -import math -import random -import re -from collections import Counter -from dataclasses import dataclass -from typing import Any, Optional, Protocol, Sequence, runtime_checkable - - -@dataclass(slots=True) -class SelectionSummary: - """Summary statistics for a selection operation.""" - - total_samples: int - selected_samples: int - strategy: str - - -@runtime_checkable -class SampleProtocol(Protocol): - """Protocol for samples that can be used with CoresetSelector.""" - - @property - def sample_id(self) -> str: - """Unique identifier for the sample.""" - ... - - @property - def text(self) -> str: - """Text content of the sample.""" - ... - - @property - def metadata(self) -> dict[str, Any]: - """Metadata dictionary.""" - ... - - -# Type alias for any sample that implements the protocol -SampleT = Any # Should implement SampleProtocol - - -class CoresetSelector: - """ - Implements lightweight coreset selection strategies. - - This class provides several strategies for selecting a representative - subset of samples from a larger dataset, optimizing for training efficiency. - - Attributes: - strategy: Selection strategy ("loss_topk", "diversity", "hybrid", "random") - metric_key: Key in metadata to use for loss-based selection - diversity_temperature: Temperature for diversity scoring - random_seed: Seed for reproducibility - - Example: - >>> selector = CoresetSelector(strategy="hybrid") - >>> selected = selector.select(samples, target_size=1000) - >>> print(f"Selected {len(selected)} from {len(samples)} samples") - """ - - STRATEGIES = ("loss_topk", "diversity", "hybrid", "random") - - def __init__( - self, - strategy: str = "loss_topk", - metric_key: str = "loss", - diversity_temperature: float = 0.7, - random_seed: int = 13, - ) -> None: - """ - Initialize CoresetSelector. - - Args: - strategy: Selection strategy to use - metric_key: Metadata key for loss-based selection - diversity_temperature: Temperature for diversity scoring - random_seed: Random seed for reproducibility - """ - if strategy not in self.STRATEGIES: - raise ValueError(f"Unknown strategy: {strategy}. Choose from {self.STRATEGIES}") - - self.strategy = strategy - self.metric_key = metric_key - self.diversity_temperature = diversity_temperature - self._rng = random.Random(random_seed) - - # ------------------------------------------------------------------ - # Public API - # ------------------------------------------------------------------ - def select( - self, - samples: Sequence[SampleT], - *, - target_size: Optional[int], - metrics: Optional[dict[str, float]] = None, - ) -> list[SampleT]: - """ - Select a subset of samples using the configured strategy. - - Args: - samples: Input samples to select from - target_size: Number of samples to select (None = keep all) - metrics: Optional external metrics dict mapping sample_id to score - - Returns: - List of selected samples - """ - if target_size is None or target_size <= 0 or target_size >= len(samples): - return list(samples) - - if self.strategy == "loss_topk": - return self._select_loss(samples, target_size, metrics) - if self.strategy == "diversity": - return self._select_diversity(samples, target_size) - if self.strategy == "hybrid": - return self._select_hybrid(samples, target_size, metrics) - return self._select_random(samples, target_size) - - def summary(self, original_size: int, selected_size: int) -> SelectionSummary: - """Create a summary of the selection operation.""" - return SelectionSummary( - total_samples=original_size, - selected_samples=selected_size, - strategy=self.strategy, - ) - - # ------------------------------------------------------------------ - # Selection Strategies - # ------------------------------------------------------------------ - def _select_loss( - self, - samples: Sequence[SampleT], - target_size: int, - metrics: Optional[dict[str, float]], - ) -> list[SampleT]: - """Select samples with highest loss/importance scores.""" - - def score(sample: SampleT) -> float: - sample_id = self._get_sample_id(sample) - if metrics and sample_id in metrics: - return metrics[sample_id] - meta = self._get_metadata(sample) - meta_val = meta.get(self.metric_key) - if isinstance(meta_val, (int, float)): - return float(meta_val) - return 0.0 - - ranked = sorted(samples, key=score, reverse=True) - return list(ranked[:target_size]) - - def _select_random( - self, - samples: Sequence[SampleT], - target_size: int, - ) -> list[SampleT]: - """Uniform random sampling.""" - return self._rng.sample(list(samples), target_size) - - def _select_hybrid( - self, - samples: Sequence[SampleT], - target_size: int, - metrics: Optional[dict[str, float]], - ) -> list[SampleT]: - """Hybrid selection: 60% loss-based + 40% diversity-based.""" - loss_portion = int(target_size * 0.6) - div_portion = target_size - loss_portion - - # First select high-loss samples - top_loss = self._select_loss(samples, loss_portion or 1, metrics) - top_loss_ids = {self._get_sample_id(s) for s in top_loss} - - # Then select diverse samples from remaining - remaining = [s for s in samples if self._get_sample_id(s) not in top_loss_ids] - if not remaining: - return top_loss - - diversity = self._select_diversity(remaining, max(div_portion, 1)) - merged = (top_loss + diversity)[:target_size] - return merged - - def _select_diversity( - self, - samples: Sequence[SampleT], - target_size: int, - ) -> list[SampleT]: - """Select samples maximizing feature space coverage.""" - if not samples: - return [] - - # Extract features for all samples - features = { - self._get_sample_id(sample): self._text_features(self._get_text(sample)) - for sample in samples - } - - selected: list[SampleT] = [] - candidates = list(samples) - - # Start with the sample that has the highest token variance - scores = { - self._get_sample_id(sample): self._feature_norm(features[self._get_sample_id(sample)]) - for sample in samples - } - first = max(candidates, key=lambda s: scores.get(self._get_sample_id(s), 0.0)) - selected.append(first) - candidates = [s for s in candidates if self._get_sample_id(s) != self._get_sample_id(first)] - - # Iteratively select most diverse samples - while candidates and len(selected) < target_size: - best_candidate = max( - candidates, - key=lambda sample: self._min_distance(sample, selected, features), - ) - selected.append(best_candidate) - candidates = [ - s - for s in candidates - if self._get_sample_id(s) != self._get_sample_id(best_candidate) - ] - - return selected - - # ------------------------------------------------------------------ - # Feature Extraction Helpers - # ------------------------------------------------------------------ - def _text_features(self, text: str) -> Counter: - """Extract normalized token frequency features from text.""" - tokens = re.findall(r"[a-zA-Z0-9_]+", text.lower()) - filtered = [token for token in tokens if len(token) > 2] - counts = Counter(filtered) - total = sum(counts.values()) or 1.0 - for key in counts: - counts[key] /= total - return counts - - def _feature_norm(self, features: Counter) -> float: - """Compute L2 norm of feature vector.""" - return math.sqrt(sum(value * value for value in features.values())) - - def _cosine_similarity(self, left: Counter, right: Counter) -> float: - """Compute cosine similarity between two feature vectors.""" - keys = left.keys() & right.keys() - if not keys: - return 0.0 - return sum(left[key] * right[key] for key in keys) - - def _min_distance( - self, - candidate: SampleT, - selected: Sequence[SampleT], - features: dict[str, Counter], - ) -> float: - """Compute minimum distance from candidate to selected set.""" - cand_feat = features[self._get_sample_id(candidate)] - if not selected: - return 1.0 - sims = [ - self._cosine_similarity(cand_feat, features[self._get_sample_id(item)]) - for item in selected - ] - similarity = max(sims) if sims else 0.0 - return 1.0 - similarity - - # ------------------------------------------------------------------ - # Sample Access Helpers (support both dict and object access) - # ------------------------------------------------------------------ - def _get_sample_id(self, sample: SampleT) -> str: - """Get sample_id from sample (supports dict or object).""" - if isinstance(sample, dict): - return sample.get("sample_id", sample.get("dialog_id", str(id(sample)))) - return getattr(sample, "sample_id", getattr(sample, "dialog_id", str(id(sample)))) - - def _get_text(self, sample: SampleT) -> str: - """Get text from sample (supports dict or object).""" - if isinstance(sample, dict): - return sample.get("text", "") - return getattr(sample, "text", "") - - def _get_metadata(self, sample: SampleT) -> dict[str, Any]: - """Get metadata from sample (supports dict or object).""" - if isinstance(sample, dict): - return sample.get("metadata", {}) - return getattr(sample, "metadata", {}) diff --git a/packages/sage-middleware/src/sage/middleware/components/sage_sias/types.py b/packages/sage-middleware/src/sage/middleware/components/sage_sias/types.py deleted file mode 100644 index 6ae09ff69c..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/sage_sias/types.py +++ /dev/null @@ -1,94 +0,0 @@ -""" -SIAS Core Data Types - -Defines the core data structures used across SIAS components. -These are designed to be independent of specific data sources. -""" - -from __future__ import annotations - -from dataclasses import dataclass, field -from typing import Any, Protocol, runtime_checkable - - -@dataclass(slots=True) -class SIASSample: - """ - Generic sample container for SIAS algorithms. - - This is a lightweight data class that can wrap samples from various sources. - The only required fields are sample_id and text; everything else is optional. - - Attributes: - sample_id: Unique identifier for this sample - text: The text content (or serialized representation) - metadata: Arbitrary metadata dictionary - importance_score: SSIS-computed importance score (set during training) - """ - - sample_id: str - text: str - metadata: dict[str, Any] = field(default_factory=dict) - importance_score: float = 0.0 - - def __hash__(self) -> int: - return hash(self.sample_id) - - def __eq__(self, other: object) -> bool: - if isinstance(other, SIASSample): - return self.sample_id == other.sample_id - return False - - -@runtime_checkable -class SampleProtocol(Protocol): - """ - Protocol for samples that can be used with SIAS algorithms. - - Any class with these attributes can be used with CoresetSelector - and OnlineContinualLearner without modification. - """ - - @property - def sample_id(self) -> str: - """Unique identifier for the sample.""" - ... - - @property - def text(self) -> str: - """Text content of the sample.""" - ... - - @property - def metadata(self) -> dict[str, Any]: - """Metadata dictionary.""" - ... - - -# Backward compatibility alias -# This allows existing code using ProcessedDialog to work with SIAS -# by implementing the SampleProtocol -Sample = SIASSample - - -def wrap_sample( - sample_id: str, - text: str, - metadata: dict[str, Any] | None = None, - **kwargs: Any, -) -> SIASSample: - """ - Factory function to create a SIASSample. - - Args: - sample_id: Unique identifier - text: Text content - metadata: Optional metadata dict - **kwargs: Additional metadata fields - - Returns: - A new SIASSample instance - """ - meta = metadata or {} - meta.update(kwargs) - return SIASSample(sample_id=sample_id, text=text, metadata=meta) diff --git a/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/__init__.py b/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/__init__.py deleted file mode 100644 index 2980d7036b..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/__init__.py +++ /dev/null @@ -1,81 +0,0 @@ -""" -SAGE-TSDB: Time Series Database Component for SAGE - -Provides efficient time series data storage, querying, and processing capabilities -for streaming and historical data analysis. - -Note: SAGE TSDB core is now an independent PyPI package (isage-tsdb). -This module provides backward-compatible wrappers and SAGE-specific services. -""" - -import warnings - -# Import from PyPI package (isage-tsdb) -_SAGE_TSDB_AVAILABLE = False -try: - from sage_tsdb import ( - QueryConfig, - TimeRange, - TimeSeriesData, - TimeSeriesDB, - TimeSeriesIndex, - ) - - # Backward compatibility alias - SageTSDB = TimeSeriesDB - _SAGE_TSDB_AVAILABLE = True -except ImportError as e: - # Don't fail immediately - allow graceful degradation - warnings.warn( - f"SAGE TSDB not available: {e}\n" - "Install with: pip install isage-tsdb\n" - "Time series features will be unavailable.", - UserWarning, - stacklevel=2, - ) - # Provide stub exports - SageTSDB = None - TimeSeriesDB = None - TimeSeriesData = None - QueryConfig = None - TimeRange = None - TimeSeriesIndex = None - -# Algorithms (SAGE-specific extensions) -# Only import if base package is available -if _SAGE_TSDB_AVAILABLE: - from .python.algorithms import ( - OutOfOrderStreamJoin, - TimeSeriesAlgorithm, - WindowAggregator, - ) - - # Micro-service wrapper (SAGE-specific) - from .python.micro_service.sage_tsdb_service import ( - SageTSDBService, - SageTSDBServiceConfig, - ) -else: - # Stub classes if TSDB not available - TimeSeriesAlgorithm = None - OutOfOrderStreamJoin = None - WindowAggregator = None - SageTSDBService = None - SageTSDBServiceConfig = None - -__all__ = [ - # Core API (may be None if not installed) - "SageTSDB", - "TimeSeriesData", - "QueryConfig", - "TimeRange", - # Service - "SageTSDBService", - "SageTSDBServiceConfig", - # Algorithms - "TimeSeriesAlgorithm", - "OutOfOrderStreamJoin", - "WindowAggregator", - # Availability flag - "_SAGE_TSDB_AVAILABLE", -] diff --git a/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/build_tsdb.sh b/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/build_tsdb.sh deleted file mode 100755 index c711a200cb..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/build_tsdb.sh +++ /dev/null @@ -1,46 +0,0 @@ -#!/bin/bash -# Build script for SAGE TSDB C++ extension - -set -e # Exit on error - -SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" -TSDB_DIR="$SCRIPT_DIR/sageTSDB" -BUILD_DIR="$TSDB_DIR/build" -PYTHON_DIR="$SCRIPT_DIR/python" - -echo "🔨 Building SAGE TSDB C++ Extension" -echo "====================================" - -# Check if sageTSDB submodule is initialized -if [ ! -f "$TSDB_DIR/CMakeLists.txt" ]; then - echo "❌ Error: sageTSDB submodule not initialized" - echo " Run: git submodule update --init --recursive" - exit 1 -fi - -# Create build directory if it doesn't exist -mkdir -p "$BUILD_DIR" -cd "$BUILD_DIR" - -# Configure with CMake -echo "📋 Configuring CMake..." -cmake .. -DBUILD_PYTHON_BINDINGS=ON - -# Build -echo "🔧 Building..." -make -j$(nproc) - -# Find the generated .so file -SO_FILE=$(find "$BUILD_DIR/python" -name "_sage_tsdb*.so" -type f | head -n 1) - -if [ -z "$SO_FILE" ]; then - echo "❌ Error: Failed to build _sage_tsdb.so" - exit 1 -fi - -# Copy to Python package directory -echo "📦 Installing Python extension..." -cp "$SO_FILE" "$PYTHON_DIR/" - -echo "✅ SAGE TSDB C++ extension built successfully!" -echo " Location: $PYTHON_DIR/$(basename $SO_FILE)" diff --git a/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/python/__init__.py b/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/python/__init__.py deleted file mode 100644 index 83b5c5a058..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/python/__init__.py +++ /dev/null @@ -1,21 +0,0 @@ -""" -Python package for SageTSDB - -This package provides both high-performance C++ bindings and pure Python implementations -for time series database operations. -""" - -try: - # Try to import C++ bindings first - from . import _sage_tsdb - - TSDB_BACKEND = "cpp" -except ImportError: - # Fallback to pure Python implementation - _sage_tsdb = None - TSDB_BACKEND = "python" - -# Import Python APIs (these wrap C++ or pure Python implementations) -from . import algorithms, sage_tsdb - -__all__ = ["sage_tsdb", "algorithms", "_sage_tsdb", "TSDB_BACKEND"] diff --git a/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/python/_sage_tsdb.pyi b/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/python/_sage_tsdb.pyi deleted file mode 100644 index 2da2f5b582..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/python/_sage_tsdb.pyi +++ /dev/null @@ -1,17 +0,0 @@ -""" -Type stub for SAGE TSDB C++ extension module. - -This is a compiled C++ extension module created via pybind11. -The actual implementation is in C++, this file provides type hints for Python. -""" - -# Basic type hints for the C++ extension -# Add specific function/class signatures as needed when you know the API - -class SageTSDB: - """SAGE TSDB C++ extension interface""" - - def __init__(self) -> None: ... - # Add more methods as needed - -# Add other exported symbols from the C++ module as needed diff --git a/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/python/algorithms/__init__.py b/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/python/algorithms/__init__.py deleted file mode 100644 index ed94947d99..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/python/algorithms/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -""" -Algorithms for time series processing. - -This module provides a pluggable algorithm interface for various -time series processing tasks including stream joins, aggregations, -and complex event processing. -""" - -from .base import TimeSeriesAlgorithm -from .out_of_order_join import OutOfOrderStreamJoin -from .window_aggregator import WindowAggregator - -__all__ = [ - "TimeSeriesAlgorithm", - "OutOfOrderStreamJoin", - "WindowAggregator", -] diff --git a/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/python/algorithms/base.py b/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/python/algorithms/base.py deleted file mode 100644 index 7707c7c6b9..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/python/algorithms/base.py +++ /dev/null @@ -1,51 +0,0 @@ -""" -Base algorithm interface for time series processing. -""" - -from abc import ABC, abstractmethod -from typing import Any - -from ..sage_tsdb import TimeSeriesData - - -class TimeSeriesAlgorithm(ABC): - """ - Base class for time series processing algorithms. - - All algorithm implementations should inherit from this class and - implement the process method. - """ - - def __init__(self, config: dict[str, Any] | None = None): - """ - Initialize algorithm. - - Args: - config: Algorithm-specific configuration - """ - self.config = config or {} - - @abstractmethod - def process(self, data: list[TimeSeriesData], **kwargs) -> Any: - """ - Process time series data. - - Args: - data: Input time series data points - **kwargs: Additional algorithm-specific parameters - - Returns: - Processed results (algorithm-specific format) - """ - pass - - def reset(self): # noqa: B027 - """Reset algorithm state (for stateful algorithms)""" - pass - - def get_stats(self) -> dict[str, Any]: - """Get algorithm statistics""" - return {} - - -__all__ = ["TimeSeriesAlgorithm"] diff --git a/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/python/algorithms/out_of_order_join.py b/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/python/algorithms/out_of_order_join.py deleted file mode 100644 index 5cab30ad92..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/python/algorithms/out_of_order_join.py +++ /dev/null @@ -1,248 +0,0 @@ -""" -Out-of-Order Stream Join Algorithm - -This algorithm handles joining two time series streams that may arrive -out of order, using windowing and buffering strategies. -""" - -from collections import defaultdict -from collections.abc import Callable -from dataclasses import dataclass -from typing import Any - -from ..sage_tsdb import TimeSeriesData -from .base import TimeSeriesAlgorithm - - -@dataclass -class JoinConfig: - """Configuration for stream join""" - - window_size: int # milliseconds - max_delay: int # maximum out-of-order delay (ms) - join_key: str | None = None # tag key for join condition - join_predicate: Callable[[TimeSeriesData, TimeSeriesData], bool] | None = None - - -class StreamBuffer: - """Buffer for managing out-of-order streams""" - - def __init__(self, max_delay: int): - """ - Initialize stream buffer. - - Args: - max_delay: Maximum allowed delay (ms) - """ - self.max_delay = max_delay - self.buffer: list[TimeSeriesData] = [] - self.watermark = 0 # Current watermark timestamp - - def add(self, data: TimeSeriesData): - """Add data to buffer""" - self.buffer.append(data) - self._update_watermark() - - def add_batch(self, data_list: list[TimeSeriesData]): - """Add multiple data points to buffer""" - self.buffer.extend(data_list) - self._update_watermark() - - def _update_watermark(self): - """Update watermark based on latest data""" - if self.buffer: - # Sort buffer by timestamp - self.buffer.sort(key=lambda x: x.timestamp) - # Watermark is the latest timestamp minus max delay - latest = self.buffer[-1].timestamp - self.watermark = latest - self.max_delay - - def get_ready_data(self) -> list[TimeSeriesData]: - """Get data that's ready for processing (before watermark)""" - ready = [d for d in self.buffer if d.timestamp <= self.watermark] - # Remove ready data from buffer - self.buffer = [d for d in self.buffer if d.timestamp > self.watermark] - return ready - - def size(self) -> int: - """Get buffer size""" - return len(self.buffer) - - -class OutOfOrderStreamJoin(TimeSeriesAlgorithm): - """ - Out-of-Order Stream Join Algorithm. - - This algorithm joins two time series streams that may arrive out of order. - It uses windowing and watermarking to handle late data while maintaining - join correctness. - - Features: - - Handles out-of-order data arrival - - Window-based join semantics - - Configurable watermarking for late data - - Support for custom join predicates - """ - - def __init__(self, config: dict[str, Any] | None = None): - """ - Initialize stream join algorithm. - - Args: - config: Configuration dictionary with: - - window_size: Join window size in milliseconds - - max_delay: Maximum out-of-order delay in milliseconds - - join_key: Optional tag key for equi-join - - join_predicate: Optional custom join predicate function - """ - super().__init__(config) - - self.window_size = self.config.get("window_size", 10000) # 10 seconds - self.max_delay = self.config.get("max_delay", 5000) # 5 seconds - self.join_key = self.config.get("join_key", None) - self.join_predicate = self.config.get("join_predicate", None) - - # Buffers for two streams - self.left_buffer = StreamBuffer(self.max_delay) - self.right_buffer = StreamBuffer(self.max_delay) - - # Statistics - self.stats = { - "total_joined": 0, - "late_arrivals": 0, - "dropped_late": 0, - } - - def add_left_stream(self, data: list[TimeSeriesData]): - """Add data to left stream""" - self.left_buffer.add_batch(data) - - def add_right_stream(self, data: list[TimeSeriesData]): - """Add data to right stream""" - self.right_buffer.add_batch(data) - - def process( - self, - data: list[TimeSeriesData] | None = None, - left_stream: list[TimeSeriesData] | None = None, - right_stream: list[TimeSeriesData] | None = None, - **kwargs, - ) -> list[tuple[TimeSeriesData, TimeSeriesData]]: - """ - Process stream join. - - Args: - data: Not used (for compatibility) - left_stream: Data from left stream - right_stream: Data from right stream - **kwargs: Additional parameters - - Returns: - List of joined data pairs - """ - # Add data to buffers - if left_stream: - self.add_left_stream(left_stream) - if right_stream: - self.add_right_stream(right_stream) - - # Get ready data from both buffers - left_ready = self.left_buffer.get_ready_data() - right_ready = self.right_buffer.get_ready_data() - - # Perform join - joined = self._join_data(left_ready, right_ready) - - # Update statistics - self.stats["total_joined"] += len(joined) - - return joined - - def _join_data( - self, left_data: list[TimeSeriesData], right_data: list[TimeSeriesData] - ) -> list[tuple[TimeSeriesData, TimeSeriesData]]: - """ - Join data from two streams. - - Args: - left_data: Data from left stream - right_data: Data from right stream - - Returns: - List of joined pairs - """ - joined = [] - - # If join key is specified, use hash join - if self.join_key: - joined = self._hash_join(left_data, right_data) - else: - # Use nested loop join with window condition - joined = self._nested_loop_join(left_data, right_data) - - return joined - - def _hash_join( - self, left_data: list[TimeSeriesData], right_data: list[TimeSeriesData] - ) -> list[tuple[TimeSeriesData, TimeSeriesData]]: - """Hash join on specified key""" - joined = [] - - # Build hash table for right stream - right_hash: dict[str, list[TimeSeriesData]] = defaultdict(list) - for right in right_data: - key_value = right.tags.get(self.join_key) if self.join_key else None - if key_value: - right_hash[key_value].append(right) - - # Probe with left stream - for left in left_data: - key_value = left.tags.get(self.join_key) if self.join_key else None - if key_value and key_value in right_hash: - for right in right_hash[key_value]: - # Check window condition - if abs(left.timestamp - right.timestamp) <= self.window_size: - # Check custom predicate if provided - if self.join_predicate is None or self.join_predicate(left, right): - joined.append((left, right)) - - return joined - - def _nested_loop_join( - self, left_data: list[TimeSeriesData], right_data: list[TimeSeriesData] - ) -> list[tuple[TimeSeriesData, TimeSeriesData]]: - """Nested loop join with window condition""" - joined = [] - - for left in left_data: - for right in right_data: - # Check window condition - if abs(left.timestamp - right.timestamp) <= self.window_size: - # Check custom predicate if provided - if self.join_predicate is None or self.join_predicate(left, right): - joined.append((left, right)) - - return joined - - def reset(self): - """Reset algorithm state""" - self.left_buffer = StreamBuffer(self.max_delay) - self.right_buffer = StreamBuffer(self.max_delay) - self.stats = { - "total_joined": 0, - "late_arrivals": 0, - "dropped_late": 0, - } - - def get_stats(self) -> dict[str, Any]: - """Get join statistics""" - return { - **self.stats, - "left_buffer_size": self.left_buffer.size(), - "right_buffer_size": self.right_buffer.size(), - "left_watermark": self.left_buffer.watermark, - "right_watermark": self.right_buffer.watermark, - } - - -__all__ = ["OutOfOrderStreamJoin", "JoinConfig", "StreamBuffer"] diff --git a/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/python/algorithms/window_aggregator.py b/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/python/algorithms/window_aggregator.py deleted file mode 100644 index 36674fadc9..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/python/algorithms/window_aggregator.py +++ /dev/null @@ -1,296 +0,0 @@ -""" -Window Aggregator Algorithm - -Provides various windowing strategies for time series aggregation, -including tumbling, sliding, and session windows. -""" - -from dataclasses import dataclass -from enum import Enum -from typing import Any - -import numpy as np - -from ..sage_tsdb import AggregationType, TimeSeriesData -from .base import TimeSeriesAlgorithm - - -class WindowType(Enum): - """Window types for aggregation""" - - TUMBLING = "tumbling" # Non-overlapping fixed-size windows - SLIDING = "sliding" # Overlapping fixed-size windows - SESSION = "session" # Dynamic windows based on inactivity gap - - -@dataclass -class WindowConfig: - """Configuration for windowing""" - - window_type: WindowType - window_size: int # milliseconds - slide_interval: int | None = None # for sliding windows (ms) - session_gap: int | None = None # for session windows (ms) - aggregation: AggregationType = AggregationType.AVG - - -class WindowAggregator(TimeSeriesAlgorithm): - """ - Window-based aggregation algorithm. - - Supports multiple windowing strategies: - - Tumbling windows: Non-overlapping fixed-size windows - - Sliding windows: Overlapping windows with configurable slide interval - - Session windows: Dynamic windows based on inactivity gaps - - Features: - - Multiple aggregation functions (sum, avg, min, max, count, etc.) - - Efficient incremental computation - - Support for late data handling - """ - - def __init__(self, config: dict[str, Any] | None = None): - """ - Initialize window aggregator. - - Args: - config: Configuration dictionary with: - - window_type: Type of window (tumbling/sliding/session) - - window_size: Window size in milliseconds - - slide_interval: Slide interval for sliding windows (ms) - - session_gap: Inactivity gap for session windows (ms) - - aggregation: Aggregation function to apply - """ - super().__init__(config) - - window_type_str = self.config.get("window_type", "tumbling") - self.window_type = WindowType(window_type_str) - self.window_size = self.config.get("window_size", 60000) # 1 minute - self.slide_interval = self.config.get("slide_interval", self.window_size) - self.session_gap = self.config.get("session_gap", 30000) # 30 seconds - - agg_str = self.config.get("aggregation", "avg") - if isinstance(agg_str, str): - self.aggregation = AggregationType(agg_str) - else: - self.aggregation = agg_str - - # State for incremental processing - self.windows: dict[int, list[TimeSeriesData]] = {} - self.stats = { - "windows_created": 0, - "windows_completed": 0, - "data_points_processed": 0, - } - - def process(self, data: list[TimeSeriesData], **kwargs) -> list[TimeSeriesData]: - """ - Process time series data with windowing. - - Args: - data: Input time series data points - **kwargs: Additional parameters - - Returns: - Aggregated time series data (one point per window) - """ - if not data: - return [] - - # Sort data by timestamp - sorted_data = sorted(data, key=lambda x: x.timestamp) - - # Apply windowing based on type - if self.window_type == WindowType.TUMBLING: - return self._tumbling_window(sorted_data) - elif self.window_type == WindowType.SLIDING: - return self._sliding_window(sorted_data) - elif self.window_type == WindowType.SESSION: - return self._session_window(sorted_data) - - return [] - - def _tumbling_window(self, data: list[TimeSeriesData]) -> list[TimeSeriesData]: - """Process with tumbling windows""" - if not data: - return [] - - results = [] - window_start = self._align_to_window(data[0].timestamp) - window_data = [] - - for point in data: - window_key = self._get_window_key(point.timestamp, window_start) - - # Check if point belongs to current window - if window_key == window_start: - window_data.append(point) - else: - # Complete current window - if window_data: - agg_point = self._aggregate_window(window_data, window_start) - results.append(agg_point) - self.stats["windows_completed"] += 1 - - # Start new window(s) - # Handle potential gaps - while window_key > window_start: - window_start += self.window_size - - window_data = [point] - self.stats["windows_created"] += 1 - - # Complete last window - if window_data: - agg_point = self._aggregate_window(window_data, window_start) - results.append(agg_point) - self.stats["windows_completed"] += 1 - - self.stats["data_points_processed"] += len(data) - return results - - def _sliding_window(self, data: list[TimeSeriesData]) -> list[TimeSeriesData]: - """Process with sliding windows""" - if not data: - return [] - - results = [] - - # Get first window start - first_timestamp = data[0].timestamp - window_start = self._align_to_window(first_timestamp) - - # Create windows until we've covered all data - last_timestamp = data[-1].timestamp - - while window_start <= last_timestamp: - window_end = window_start + self.window_size - - # Get data points in this window - window_data = [point for point in data if window_start <= point.timestamp < window_end] - - if window_data: - agg_point = self._aggregate_window(window_data, window_start) - results.append(agg_point) - self.stats["windows_completed"] += 1 - - # Slide to next window - window_start += self.slide_interval - self.stats["windows_created"] += 1 - - self.stats["data_points_processed"] += len(data) - return results - - def _session_window(self, data: list[TimeSeriesData]) -> list[TimeSeriesData]: - """Process with session windows""" - if not data: - return [] - - results = [] - session_data = [] - last_timestamp = data[0].timestamp - session_start = data[0].timestamp - - for point in data: - # Check if point is within session gap - if point.timestamp - last_timestamp <= self.session_gap: - session_data.append(point) - else: - # Complete current session - if session_data: - agg_point = self._aggregate_window(session_data, session_start) - results.append(agg_point) - self.stats["windows_completed"] += 1 - - # Start new session - session_data = [point] - session_start = point.timestamp - self.stats["windows_created"] += 1 - - last_timestamp = point.timestamp - - # Complete last session - if session_data: - agg_point = self._aggregate_window(session_data, session_start) - results.append(agg_point) - self.stats["windows_completed"] += 1 - - self.stats["data_points_processed"] += len(data) - return results - - def _align_to_window(self, timestamp: int) -> int: - """Align timestamp to window boundary""" - return (timestamp // self.window_size) * self.window_size - - def _get_window_key(self, timestamp: int, reference: int) -> int: - """Get window key for timestamp""" - return self._align_to_window(timestamp) - - def _aggregate_window( - self, data: list[TimeSeriesData], window_timestamp: int - ) -> TimeSeriesData: - """Aggregate data in a window""" - if not data: - return TimeSeriesData(timestamp=window_timestamp, value=0.0) - - # Extract values - values = [] - for point in data: - # Flatten arrays/lists, append scalars - if isinstance(point.value, (list, np.ndarray)): - # Use np.ravel to flatten, then convert to list and extend - values.extend(np.ravel(point.value).tolist()) - else: - values.append(point.value) - - # Apply aggregation - if self.aggregation == AggregationType.SUM: - agg_value = sum(values) - elif self.aggregation == AggregationType.AVG: - agg_value = sum(values) / len(values) - elif self.aggregation == AggregationType.MIN: - agg_value = min(values) - elif self.aggregation == AggregationType.MAX: - agg_value = max(values) - elif self.aggregation == AggregationType.COUNT: - agg_value = len(values) - elif self.aggregation == AggregationType.FIRST: - agg_value = values[0] - elif self.aggregation == AggregationType.LAST: - agg_value = values[-1] - elif self.aggregation == AggregationType.STDDEV: - agg_value = float(np.std(values)) - else: - agg_value = sum(values) / len(values) - - # Merge tags from all data points - merged_tags = {} - for point in data: - if point.tags: - merged_tags.update(point.tags) - - return TimeSeriesData( - timestamp=window_timestamp, - value=agg_value, - tags=merged_tags, - fields={"window_size": len(data), "aggregation": self.aggregation.value}, - ) - - def reset(self): - """Reset algorithm state""" - self.windows = {} - self.stats = { - "windows_created": 0, - "windows_completed": 0, - "data_points_processed": 0, - } - - def get_stats(self) -> dict[str, Any]: - """Get aggregator statistics""" - return { - **self.stats, - "active_windows": len(self.windows), - } - - -__all__ = ["WindowAggregator", "WindowType", "WindowConfig"] diff --git a/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/python/micro_service/__init__.py b/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/python/micro_service/__init__.py deleted file mode 100644 index 54943ee6fb..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/python/micro_service/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -""" -Micro-service module for SageTSDB -""" - -from .sage_tsdb_service import SageTSDBService, SageTSDBServiceConfig - -__all__ = ["SageTSDBService", "SageTSDBServiceConfig"] diff --git a/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/python/micro_service/sage_tsdb_service.py b/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/python/micro_service/sage_tsdb_service.py deleted file mode 100644 index 2a53039855..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/python/micro_service/sage_tsdb_service.py +++ /dev/null @@ -1,365 +0,0 @@ -from __future__ import annotations - -from dataclasses import dataclass -from datetime import datetime -from typing import Any - -import numpy as np - -from ..algorithms import OutOfOrderStreamJoin, WindowAggregator -from ..sage_tsdb import AggregationType, SageTSDB, TimeRange - - -@dataclass -class SageTSDBServiceConfig: - """Configuration for SageTSDB service""" - - # Database configuration - enable_compression: bool = False - max_memory_mb: int = 1024 - - # Algorithm defaults - default_window_size: int = 60000 # 1 minute in milliseconds - default_aggregation: str = "avg" - - -class SageTSDBService: - """ - A micro-service style wrapper for SageTSDB. - - This service provides a simplified interface for time series operations - and integrates with SAGE's service ecosystem. - - Methods: - - add(timestamp, value, tags, fields) -> int - - add_batch(timestamps, values, tags_list, fields_list) -> list[int] - - query(start_time, end_time, tags, aggregation, window_size) -> list[dict] - - stream_join(left_stream, right_stream, window_size, join_key) -> list[dict] - - window_aggregate(data, window_type, window_size, aggregation) -> list[dict] - """ - - def __init__(self, config: SageTSDBServiceConfig | None = None) -> None: - """ - Initialize SageTSDB service. - - Args: - config: Optional service configuration - """ - self._config = config or SageTSDBServiceConfig() - self._db = SageTSDB() - - # Register default algorithms - self._register_default_algorithms() - - # Statistics - self._stats = { - "total_writes": 0, - "total_queries": 0, - "total_joins": 0, - "total_aggregations": 0, - } - - def _register_default_algorithms(self): - """Register commonly used algorithms""" - # Out-of-order stream join - join_algo = OutOfOrderStreamJoin( - { - "window_size": self._config.default_window_size, - "max_delay": 5000, # 5 seconds - } - ) - self._db.register_algorithm("stream_join", join_algo) - - # Window aggregator - window_algo = WindowAggregator( - { - "window_type": "tumbling", - "window_size": self._config.default_window_size, - "aggregation": self._config.default_aggregation, - } - ) - self._db.register_algorithm("window_aggregate", window_algo) - - def add( - self, - timestamp: int | datetime, - value: float | np.ndarray | list[float], - tags: dict[str, str] | None = None, - fields: dict[str, Any] | None = None, - ) -> int: - """ - Add a single time series data point. - - Args: - timestamp: Unix timestamp (ms) or datetime object - value: Numeric value or array - tags: Optional tags for indexing - fields: Optional additional fields - - Returns: - Index of the added data point - """ - if isinstance(value, list): - value = np.array(value, dtype=np.float32) - - idx = self._db.add(timestamp=timestamp, value=value, tags=tags, fields=fields) - - self._stats["total_writes"] += 1 - return idx - - def add_batch( - self, - timestamps: list[int] | list[datetime] | np.ndarray, - values: list[float] | np.ndarray, - tags_list: list[dict[str, str]] | None = None, - fields_list: list[dict[str, Any]] | None = None, - ) -> list[int]: - """ - Add multiple time series data points. - - Args: - timestamps: List of timestamps - values: List of values - tags_list: Optional list of tags - fields_list: Optional list of fields - - Returns: - List of indices for added data points - """ - indices = self._db.add_batch( - timestamps=timestamps, - values=values, - tags_list=tags_list, - fields_list=fields_list, - ) - - self._stats["total_writes"] += len(indices) - return indices - - def query( - self, - start_time: int | datetime, - end_time: int | datetime, - tags: dict[str, str] | None = None, - aggregation: str | None = None, - window_size: int | None = None, - limit: int | None = None, - ) -> list[dict[str, Any]]: - """ - Query time series data. - - Args: - start_time: Start of time range - end_time: End of time range - tags: Optional tags to filter by - aggregation: Optional aggregation type (sum/avg/min/max/count/etc.) - window_size: Optional window size for aggregation (ms) - limit: Optional limit on number of results - - Returns: - List of matching time series data as dictionaries - """ - # Create time range - time_range = TimeRange(start_time=start_time, end_time=end_time) - - # Convert aggregation string to enum if provided - agg_type = None - if aggregation: - agg_type = AggregationType(aggregation) - - # Query database - results = self._db.query( - time_range=time_range, - tags=tags, - aggregation=agg_type, - window_size=window_size, - limit=limit, - ) - - # Convert to dictionary format - formatted = [] - for r in results: - formatted.append( - { - "timestamp": r.timestamp, - "value": ( - float(r.value) - if isinstance(r.value, (int, float)) - else (r.value.tolist() if isinstance(r.value, np.ndarray) else r.value) - ), - "tags": dict(r.tags) if r.tags else {}, - "fields": dict(r.fields) if r.fields else {}, - } - ) - - self._stats["total_queries"] += 1 - return formatted - - def stream_join( - self, - left_stream: list[dict[str, Any]], - right_stream: list[dict[str, Any]], - window_size: int | None = None, - max_delay: int | None = None, - join_key: str | None = None, - ) -> list[dict[str, Any]]: - """ - Perform out-of-order stream join. - - Args: - left_stream: Data from left stream (list of dicts with timestamp, value, tags) - right_stream: Data from right stream - window_size: Join window size in milliseconds - max_delay: Maximum out-of-order delay in milliseconds - join_key: Optional tag key for equi-join - - Returns: - List of joined results - """ - # Create or update join algorithm - config = { - "window_size": window_size or self._config.default_window_size, - "max_delay": max_delay or 5000, - "join_key": join_key, - } - - join_algo = OutOfOrderStreamJoin(config) - - # Convert input dictionaries to TimeSeriesData - from ..sage_tsdb import TimeSeriesData - - left_data = [ - TimeSeriesData( - timestamp=item["timestamp"], - value=item["value"], - tags=item.get("tags"), - fields=item.get("fields"), - ) - for item in left_stream - ] - - right_data = [ - TimeSeriesData( - timestamp=item["timestamp"], - value=item["value"], - tags=item.get("tags"), - fields=item.get("fields"), - ) - for item in right_stream - ] - - # Perform join - joined = join_algo.process(left_stream=left_data, right_stream=right_data) - - # Format results - results = [] - for left, right in joined: - results.append( - { - "left": { - "timestamp": left.timestamp, - "value": ( - float(left.value) - if isinstance(left.value, (int, float)) - else left.value - ), - "tags": dict(left.tags) if left.tags else {}, - }, - "right": { - "timestamp": right.timestamp, - "value": ( - float(right.value) - if isinstance(right.value, (int, float)) - else right.value - ), - "tags": dict(right.tags) if right.tags else {}, - }, - } - ) - - self._stats["total_joins"] += 1 - return results - - def window_aggregate( - self, - start_time: int | datetime, - end_time: int | datetime, - window_type: str = "tumbling", - window_size: int | None = None, - aggregation: str = "avg", - tags: dict[str, str] | None = None, - ) -> list[dict[str, Any]]: - """ - Perform window-based aggregation. - - Args: - start_time: Start of time range - end_time: End of time range - window_type: Type of window (tumbling/sliding/session) - window_size: Window size in milliseconds - aggregation: Aggregation function (sum/avg/min/max/count/etc.) - tags: Optional tags to filter by - - Returns: - List of aggregated results - """ - # Query data first - time_range = TimeRange(start_time=start_time, end_time=end_time) - data = self._db.query(time_range=time_range, tags=tags) - - # Create aggregator - config = { - "window_type": window_type, - "window_size": window_size or self._config.default_window_size, - "aggregation": aggregation, - } - - aggregator = WindowAggregator(config) - - # Perform aggregation - aggregated = aggregator.process(data) - - # Format results - results = [] - for item in aggregated: - results.append( - { - "timestamp": item.timestamp, - "value": ( - float(item.value) if isinstance(item.value, (int, float)) else item.value - ), - "tags": dict(item.tags) if item.tags else {}, - "fields": dict(item.fields) if item.fields else {}, - } - ) - - self._stats["total_aggregations"] += 1 - return results - - def stats(self) -> dict[str, Any]: - """ - Get service statistics. - - Returns: - Dictionary with service statistics - """ - db_stats = self._db.get_stats() - return { - **self._stats, - "db_size": db_stats["size"], - "registered_algorithms": db_stats["algorithms"], - } - - def reset(self): - """Reset service state""" - self._db = SageTSDB() - self._register_default_algorithms() - self._stats = { - "total_writes": 0, - "total_queries": 0, - "total_joins": 0, - "total_aggregations": 0, - } - - -__all__ = ["SageTSDBService", "SageTSDBServiceConfig"] diff --git a/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/python/sage_tsdb.py b/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/python/sage_tsdb.py deleted file mode 100644 index 207a28b741..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/python/sage_tsdb.py +++ /dev/null @@ -1,523 +0,0 @@ -""" -SAGE TSDB - High-performance time series database for streaming data - -This module provides Python APIs for time series data storage, querying, -and processing with support for out-of-order data and various algorithms. - -Uses C++ implementation for high performance when available, with pure Python fallback. -""" - -from dataclasses import dataclass -from datetime import datetime -from enum import Enum -from typing import Any - -import numpy as np - -# Try to import C++ bindings -try: - from . import _sage_tsdb - - HAS_CPP_BACKEND = True -except ImportError: - _sage_tsdb = None - HAS_CPP_BACKEND = False - - -class AggregationType(Enum): - """Time series aggregation types""" - - SUM = "sum" - AVG = "avg" - MIN = "min" - MAX = "max" - COUNT = "count" - FIRST = "first" - LAST = "last" - STDDEV = "stddev" - - -class InterpolationType(Enum): - """Interpolation methods for missing data""" - - NONE = "none" - LINEAR = "linear" - FORWARD_FILL = "forward_fill" - BACKWARD_FILL = "backward_fill" - ZERO = "zero" - - -@dataclass -class TimeRange: - """Time range for queries""" - - start_time: int | datetime - end_time: int | datetime - - def __post_init__(self): - """Convert datetime to timestamp if necessary""" - if isinstance(self.start_time, datetime): - self.start_time = int(self.start_time.timestamp() * 1000) - if isinstance(self.end_time, datetime): - self.end_time = int(self.end_time.timestamp() * 1000) - - -@dataclass -class TimeSeriesData: - """Time series data point""" - - timestamp: int # milliseconds since epoch - value: float | np.ndarray - tags: dict[str, str] | None = None - fields: dict[str, Any] | None = None - - def __post_init__(self): - """Initialize default values""" - if self.tags is None: - self.tags = {} - if self.fields is None: - self.fields = {} - - -@dataclass -class QueryConfig: - """Configuration for time series queries""" - - time_range: TimeRange - tags: dict[str, str] | None = None - aggregation: AggregationType | None = None - window_size: int | None = None # milliseconds - interpolation: InterpolationType = InterpolationType.NONE - limit: int | None = None - downsample_factor: int | None = None - - -class TimeSeriesIndex: - """ - Index structure for efficient time series queries. - Supports fast lookup by timestamp and tags. - """ - - def __init__(self): - self._data: list[TimeSeriesData] = [] - self._tag_index: dict[str, dict[str, list[int]]] = {} - self._sorted = True - - def add(self, data: TimeSeriesData) -> int: - """Add a time series data point""" - idx = len(self._data) - self._data.append(data) - - # Update tag index - for key, value in data.tags.items(): - if key not in self._tag_index: - self._tag_index[key] = {} - if value not in self._tag_index[key]: - self._tag_index[key][value] = [] - self._tag_index[key][value].append(idx) - - # Mark as unsorted if new data is out of order - if idx > 0 and data.timestamp < self._data[idx - 1].timestamp: - self._sorted = False - - return idx - - def add_batch(self, data_list: list[TimeSeriesData]) -> list[int]: - """Add multiple time series data points""" - return [self.add(data) for data in data_list] - - def _ensure_sorted(self): - """Sort data by timestamp if needed""" - if not self._sorted: - # Sort data and rebuild tag index - sorted_data = sorted(self._data, key=lambda x: x.timestamp) - self._data = sorted_data - self._rebuild_tag_index() - self._sorted = True - - def _rebuild_tag_index(self): - """Rebuild tag index after sorting""" - self._tag_index = {} - for idx, data in enumerate(self._data): - for key, value in data.tags.items(): - if key not in self._tag_index: - self._tag_index[key] = {} - if value not in self._tag_index[key]: - self._tag_index[key][value] = [] - self._tag_index[key][value].append(idx) - - def query(self, config: QueryConfig) -> list[TimeSeriesData]: - """Query time series data""" - self._ensure_sorted() - - # Binary search for time range - # Note: TimeRange.__post_init__ converts datetime to int - start_idx = self._binary_search(config.time_range.start_time) # type: ignore[arg-type] - end_idx = self._binary_search(config.time_range.end_time, find_upper=True) # type: ignore[arg-type] - - # Filter by tags if specified - if config.tags: - matching_indices = self._filter_by_tags(config.tags) - # Intersect with time range - result_indices = [i for i in range(start_idx, end_idx + 1) if i in matching_indices] - else: - result_indices = list(range(start_idx, end_idx + 1)) - - # Get data points - results = [self._data[i] for i in result_indices] - - # Apply limit if specified - if config.limit is not None: - results = results[: config.limit] - - return results - - def _binary_search(self, timestamp: int, find_upper: bool = False) -> int: - """ - Binary search for timestamp. - If find_upper is False, returns the first index with timestamp >= target (lower bound). - If find_upper is True, returns the last index with timestamp <= target (upper bound). - """ - low, high = 0, len(self._data) - 1 - if not self._data: - return -1 - - if not find_upper: - # Lower bound: first index with timestamp >= target - while low <= high: - mid = (low + high) // 2 - mid_time = self._data[mid].timestamp - if mid_time < timestamp: - low = mid + 1 - else: - high = mid - 1 - return low if low < len(self._data) else len(self._data) - 1 - else: - # Upper bound: last index with timestamp <= target - while low <= high: - mid = (low + high) // 2 - mid_time = self._data[mid].timestamp - if mid_time > timestamp: - high = mid - 1 - else: - low = mid + 1 - return high if high >= 0 else 0 - - def _filter_by_tags(self, tags: dict[str, str]) -> set: - """Filter indices by tags""" - matching_sets = [] - for key, value in tags.items(): - if key in self._tag_index and value in self._tag_index[key]: - matching_sets.append(set(self._tag_index[key][value])) - else: - return set() # No match found - - # Intersect all matching sets - if matching_sets: - return set.intersection(*matching_sets) - return set() - - def size(self) -> int: - """Get number of data points""" - return len(self._data) - - -class SageTSDB: - """ - High-performance time series database for streaming data. - - Features: - - Efficient storage and indexing of time series data - - Support for out-of-order data ingestion - - Fast queries with time range and tag filtering - - Pluggable algorithms for stream processing - - Window-based aggregations - - Uses C++ backend when available for optimal performance. - """ - - def __init__(self, config: dict[str, Any] | None = None): - """ - Initialize time series database. - - Args: - config: Optional configuration dictionary - """ - self._config = config or {} - - # Use C++ backend if available - if HAS_CPP_BACKEND: - self._db = _sage_tsdb.TimeSeriesDB() # type: ignore[attr-defined] - self._backend = "cpp" - else: - # Fallback to pure Python implementation - self._index = TimeSeriesIndex() - self._backend = "python" - - self._algorithms: dict[str, Any] = {} - - def add( - self, - timestamp: int | datetime, - value: float | np.ndarray, - tags: dict[str, str] | None = None, - fields: dict[str, Any] | None = None, - ) -> int: - """ - Add a single time series data point. - - Args: - timestamp: Unix timestamp in milliseconds or datetime - value: Numeric value or array - tags: Optional tags for indexing - fields: Optional additional fields - - Returns: - Index of the added data point - """ - if isinstance(timestamp, datetime): - timestamp = int(timestamp.timestamp() * 1000) - - if self._backend == "cpp": - # Use C++ backend - if isinstance(value, np.ndarray): - value_list = value.tolist() - elif isinstance(value, (list, tuple)): - value_list = list(value) - else: - value_list = value - - # C++ backend handles tags/fields differently - return self._db.add( - timestamp, - value_list if isinstance(value_list, list) else value_list, - tags or {}, - fields or {}, - ) - else: - # Pure Python implementation - data = TimeSeriesData(timestamp=timestamp, value=value, tags=tags, fields=fields) - return self._index.add(data) - - def add_batch( - self, - timestamps: list[int] | list[datetime] | np.ndarray, - values: list[float] | np.ndarray, - tags_list: list[dict[str, str]] | None = None, - fields_list: list[dict[str, Any]] | None = None, - ) -> list[int]: - """ - Add multiple time series data points. - - Args: - timestamps: List of timestamps - values: List of values - tags_list: Optional list of tags - fields_list: Optional list of fields - - Returns: - List of indices for added data points - """ - # Convert to consistent format - if isinstance(timestamps, np.ndarray): - timestamps = timestamps.tolist() - if isinstance(values, np.ndarray): - values = values.tolist() - - # Convert datetime to timestamps - ts_list = [] - for ts in timestamps: - if isinstance(ts, datetime): - ts_list.append(int(ts.timestamp() * 1000)) - else: - ts_list.append(ts) - - # Create data points - n = len(ts_list) - tags_list = tags_list or [None] * n # type: ignore[list-item] - fields_list = fields_list or [None] * n # type: ignore[list-item] - - data_list = [ - TimeSeriesData( - timestamp=ts_list[i], - value=values[i], - tags=tags_list[i], - fields=fields_list[i], - ) - for i in range(n) - ] - - return self._index.add_batch(data_list) - - def query( - self, - time_range: TimeRange, - tags: dict[str, str] | None = None, - aggregation: AggregationType | None = None, - window_size: int | None = None, - limit: int | None = None, - ) -> list[TimeSeriesData]: - """ - Query time series data. - - Args: - time_range: Time range for query - tags: Optional tags to filter by - aggregation: Optional aggregation type - window_size: Optional window size for aggregation (ms) - limit: Optional limit on number of results - - Returns: - List of matching time series data points - """ - config = QueryConfig( - time_range=time_range, - tags=tags, - aggregation=aggregation, - window_size=window_size, - limit=limit, - ) - - results = self._index.query(config) - - # Apply aggregation if specified - if aggregation and window_size: - results = self._apply_aggregation(results, aggregation, window_size) - - return results - - def _apply_aggregation( - self, - data: list[TimeSeriesData], - aggregation: AggregationType, - window_size: int, - ) -> list[TimeSeriesData]: - """Apply window-based aggregation""" - if not data: - return [] - - aggregated = [] - window_start = data[0].timestamp - window_data = [] - - for point in data: - # Check if still in current window - if point.timestamp < window_start + window_size: - window_data.append(point) - else: - # Aggregate current window - if window_data: - agg_point = self._aggregate_window(window_data, aggregation, window_start) - aggregated.append(agg_point) - - # Start new window - window_start = point.timestamp - window_data = [point] - - # Aggregate last window - if window_data: - agg_point = self._aggregate_window(window_data, aggregation, window_start) - aggregated.append(agg_point) - - return aggregated - - def _aggregate_window( - self, - data: list[TimeSeriesData], - aggregation: AggregationType, - window_timestamp: int, - ) -> TimeSeriesData: - """Aggregate a window of data""" - values = [point.value for point in data] - - if aggregation == AggregationType.SUM: - agg_value = sum(values) - elif aggregation == AggregationType.AVG: - agg_value = sum(values) / len(values) - elif aggregation == AggregationType.MIN: - agg_value = min(values) - elif aggregation == AggregationType.MAX: - agg_value = max(values) - elif aggregation == AggregationType.COUNT: - agg_value = len(values) - elif aggregation == AggregationType.FIRST: - agg_value = values[0] - elif aggregation == AggregationType.LAST: - agg_value = values[-1] - elif aggregation == AggregationType.STDDEV: - agg_value = float(np.std(values)) # type: ignore[arg-type] - else: - agg_value = sum(values) / len(values) - - # Merge tags from all data points - merged_tags = {} - for point in data: - if point.tags: - merged_tags.update(point.tags) - - return TimeSeriesData( - timestamp=window_timestamp, - value=agg_value, - tags=merged_tags, - fields={"window_size": len(data)}, - ) - - def register_algorithm(self, name: str, algorithm: Any): - """ - Register a custom algorithm. - - Args: - name: Algorithm name - algorithm: Algorithm instance - """ - self._algorithms[name] = algorithm - - def apply_algorithm(self, name: str, data: list[TimeSeriesData], **kwargs) -> Any: - """ - Apply a registered algorithm. - - Args: - name: Algorithm name - data: Input data - **kwargs: Algorithm-specific parameters - - Returns: - Algorithm output - """ - if name not in self._algorithms: - raise ValueError(f"Algorithm '{name}' not registered") - - return self._algorithms[name].process(data, **kwargs) - - @property - def size(self) -> int: - """Get number of data points""" - if self._backend == "cpp": - return self._db.size() - else: - return self._index.size() - - def get_stats(self) -> dict[str, Any]: - """Get database statistics""" - stats = { - "size": self.size, - "backend": self._backend, - "algorithms": list(self._algorithms.keys()), - } - - if self._backend == "cpp": - # Get C++ specific stats - cpp_stats = self._db.get_stats() - stats.update(cpp_stats) - - return stats - - -__all__ = [ - "SageTSDB", - "TimeSeriesData", - "TimeRange", - "QueryConfig", - "AggregationType", - "InterpolationType", -] diff --git a/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/service.py b/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/service.py deleted file mode 100644 index d9c8df9f07..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/sage_tsdb/service.py +++ /dev/null @@ -1,17 +0,0 @@ -""" -SageTSDB Middleware Service - -This module provides the middleware service interface for SageTSDB, -wrapping the Python implementation for time series data processing. -""" - -# Micro-service wrapper -from .python.micro_service.sage_tsdb_service import ( - SageTSDBService, - SageTSDBServiceConfig, -) - -# Core Python API -from .python.sage_tsdb import SageTSDB - -__all__ = ["SageTSDB", "SageTSDBService", "SageTSDBServiceConfig"] diff --git a/packages/sage-middleware/src/sage/middleware/components/vector_stores/__init__.py b/packages/sage-middleware/src/sage/middleware/components/vector_stores/__init__.py deleted file mode 100644 index 19700a2903..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/vector_stores/__init__.py +++ /dev/null @@ -1,25 +0,0 @@ -"""Vector store backends for SAGE middleware. - -This module provides adapters for various vector databases: -- Milvus / Milvus Lite -- ChromaDB -- (SageVDB is in separate sage_db component) - -These were promoted from sage-libs/integrations because they depend on -external database services (violates L3 → L4 layering). - -Usage: - from sage.middleware.components.vector_stores import MilvusBackend, ChromaBackend -""" - -from sage.middleware.components.vector_stores.chroma import ChromaBackend, ChromaUtils -from sage.middleware.components.vector_stores.chroma_adapter import ChromaVectorStoreAdapter -from sage.middleware.components.vector_stores.milvus import MilvusBackend, MilvusUtils - -__all__ = [ - "MilvusBackend", - "MilvusUtils", - "ChromaBackend", - "ChromaUtils", - "ChromaVectorStoreAdapter", -] diff --git a/packages/sage-middleware/src/sage/middleware/components/vector_stores/chroma.py b/packages/sage-middleware/src/sage/middleware/components/vector_stores/chroma.py deleted file mode 100644 index 99043e91c3..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/vector_stores/chroma.py +++ /dev/null @@ -1,483 +0,0 @@ -""" -ChromaDB 后端管理工具 -提供 ChromaDB 向量数据库的初始化、文档管理和检索功能 -""" - -import json -import logging -import os -import time -from typing import Any - -import numpy as np - - -class ChromaBackend: - """ChromaDB 后端管理器""" - - def __init__(self, config: dict[str, Any], logger: logging.Logger | Any = None): - """ - 初始化 ChromaDB 后端 - - Args: - config: ChromaDB 配置字典 - logger: 日志记录器 - """ - self.config = config - self.logger = logger or logging.getLogger(__name__) - - # ChromaDB 基本配置 - self.host = config.get("host", "localhost") - self.port = config.get("port", 8000) - self.persistence_path = config.get("persistence_path", "./chroma_db") - self.collection_name = config.get("collection_name", "dense_retriever_collection") - self.use_embedding_query = config.get("use_embedding_query", True) - self.metadata_config = config.get("metadata", {"hnsw:space": "cosine"}) - - # 初始化客户端和集合 - self.client: Any = None # Will be initialized by _init_client - self.collection: Any = None # Will be initialized by _init_collection - self._init_client() - self._init_collection() - - def _init_client(self): - """初始化 ChromaDB 客户端""" - try: - import chromadb - from chromadb.config import Settings # noqa: F401 - - # 判断使用本地还是远程模式 - if self.host in ["localhost", "127.0.0.1"] and not self.config.get("force_http", False): - # 本地持久化模式 - self.client = chromadb.PersistentClient(path=self.persistence_path) - self.logger.info( - f"Initialized ChromaDB persistent client at: {self.persistence_path}" - ) - else: - # 远程服务器模式 - full_host = ( - f"http://{self.host}:{self.port}" - if not self.host.startswith("http") - else self.host - ) - - # 处理认证 - auth_config = self.config.get("auth", {}) - if auth_config: - # 如果需要认证，可以在这里添加认证逻辑 - pass - - self.client = chromadb.HttpClient(host=full_host) - self.logger.info(f"Initialized ChromaDB HTTP client at: {full_host}") - - except ImportError as e: - self.logger.error(f"Failed to import ChromaDB: {e}") - raise ImportError( - "ChromaDB dependencies not available. Install with: pip install chromadb" - ) - except Exception as e: - self.logger.error(f"Failed to initialize ChromaDB client: {e}") - raise - - def _init_collection(self): - """初始化或获取 ChromaDB 集合""" - try: - # 尝试获取已存在的集合 - try: - self.collection = self.client.get_collection(name=self.collection_name) - self.logger.info(f"Retrieved existing ChromaDB collection: {self.collection_name}") - except Exception: - # 集合不存在，创建新集合 - self.collection = self.client.create_collection( - name=self.collection_name, metadata=self.metadata_config - ) - self.logger.info(f"Created new ChromaDB collection: {self.collection_name}") - - except Exception as e: - self.logger.error(f"Failed to initialize ChromaDB collection: {e}") - raise - - def add_documents( - self, documents: list[str], embeddings: list[np.ndarray], doc_ids: list[str] - ) -> list[str]: - """ - 添加文档到 ChromaDB 集合 - - Args: - documents: 文档内容列表 - embeddings: 向量嵌入列表 - doc_ids: 文档ID列表 - - Returns: - 成功添加的文档ID列表 - """ - try: - # 转换 embedding 格式（ChromaDB 需要 list 格式） - embeddings_list = [embedding.tolist() for embedding in embeddings] - - # 准备元数据 - metadatas = [] - for i, doc_id in enumerate(doc_ids): - metadata = { - "doc_id": doc_id, - "length": len(documents[i]), - "added_time": time.time(), - } - metadatas.append(metadata) - - # 添加到 ChromaDB - self.collection.add( - embeddings=embeddings_list, - documents=documents, - metadatas=metadatas, - ids=doc_ids, - ) - - self.logger.info(f"Added {len(documents)} documents to ChromaDB collection") - return doc_ids - - except Exception as e: - self.logger.error(f"Error adding documents to ChromaDB: {e}") - return [] - - def search(self, query_vector: np.ndarray, query_text: str, top_k: int) -> list[str]: - """ - 在 ChromaDB 中执行搜索 - - Args: - query_vector: 查询向量 - query_text: 查询文本 - top_k: 返回的文档数量 - - Returns: - 检索到的文档内容列表 - """ - try: - print(f"ChromaBackend.search: using top_k = {top_k}") - - if self.use_embedding_query: - # 使用向量查询 - results = self.collection.query( - query_embeddings=[query_vector.tolist()], - n_results=top_k, - include=["documents", "metadatas", "distances"], - ) - else: - # 使用文本查询（如果 ChromaDB 支持内建的 embedding 函数） - results = self.collection.query( - query_texts=[query_text], - n_results=top_k, - include=["documents", "metadatas", "distances"], - ) - - # 提取文档内容 - if results["documents"] and len(results["documents"]) > 0: - documents = results["documents"][0] # 返回第一个查询的结果 - print(f"ChromaBackend.search: returned {len(documents)} documents") - return documents - else: - return [] - - except Exception as e: - self.logger.error(f"Error executing ChromaDB search: {e}") - return [] - - def delete_collection(self): - """删除当前集合""" - try: - self.client.delete_collection(name=self.collection_name) - self.logger.info(f"Deleted ChromaDB collection: {self.collection_name}") - return True - except Exception as e: - self.logger.error(f"Error deleting ChromaDB collection: {e}") - return False - - def get_collection_info(self) -> dict[str, Any]: - """ - 获取集合信息 - - Returns: - 包含集合信息的字典 - """ - try: - return { - "backend": "chroma", - "collection_name": self.collection.name, - "document_count": self.collection.count(), - "metadata": self.metadata_config, - "persistence_path": ( - self.persistence_path if hasattr(self, "persistence_path") else None - ), - } - except Exception as e: - self.logger.error(f"Failed to get ChromaDB collection info: {e}") - return {"backend": "chroma", "error": str(e)} - - def save_config(self, save_path: str) -> bool: - """ - 保存 ChromaDB 配置信息 - - Args: - save_path: 保存路径 - - Returns: - 是否保存成功 - """ - try: - os.makedirs(save_path, exist_ok=True) - - # ChromaDB 本身会处理持久化，这里只需要保存配置信息 - config_path = os.path.join(save_path, "chroma_config.json") - config_info = { - "collection_name": self.collection.name, - "collection_count": self.collection.count(), - "backend_type": "chroma", - "chroma_config": self.config, - "saved_time": time.time(), - } - - with open(config_path, "w", encoding="utf-8") as f: - json.dump(config_info, f, ensure_ascii=False, indent=2) - - self.logger.info(f"Successfully saved ChromaDB config to: {save_path}") - self.logger.info( - f"ChromaDB collection '{self.collection.name}' contains {config_info['collection_count']} documents" - ) - return True - - except Exception as e: - self.logger.error(f"Failed to save ChromaDB config: {e}") - return False - - def load_config(self, load_path: str) -> bool: - """ - 从配置文件重新连接到 ChromaDB 集合 - - Args: - load_path: 配置文件路径 - - Returns: - 是否加载成功 - """ - try: - config_path = os.path.join(load_path, "chroma_config.json") - if os.path.exists(config_path): - with open(config_path, encoding="utf-8") as f: - config_info = json.load(f) - - collection_name = config_info.get("collection_name") - if collection_name: - # 尝试连接到已存在的集合 - self.collection = self.client.get_collection(name=collection_name) - self.collection_name = collection_name - self.logger.info( - f"Successfully connected to ChromaDB collection: {collection_name}" - ) - self.logger.info(f"Collection contains {self.collection.count()} documents") - return True - else: - self.logger.error("No collection name found in config") - return False - else: - self.logger.error(f"ChromaDB config not found at: {config_path}") - return False - - except Exception as e: - self.logger.error(f"Failed to load ChromaDB config: {e}") - return False - - def load_knowledge_from_file(self, file_path: str, embedding_model) -> bool: - """ - 从文件加载知识库到 ChromaDB - - Args: - file_path: 知识库文件路径 - embedding_model: 嵌入模型实例 - - Returns: - 是否加载成功 - """ - try: - self.logger.info(f"Loading knowledge from file: {file_path}") - with open(file_path, encoding="utf-8") as f: - content = f.read() - - # 将知识库按段落分割 - documents = [doc.strip() for doc in content.split("\n\n") if doc.strip()] - - if documents: - # 生成文档ID - doc_ids = [f"doc_{int(time.time() * 1000)}_{i}" for i in range(len(documents))] - - # 生成 embedding - embeddings = [] - for doc in documents: - embedding = embedding_model.embed(doc) - embeddings.append(np.array(embedding, dtype=np.float32)) - - # 添加到 ChromaDB - added_ids = self.add_documents(documents, embeddings, doc_ids) - - if added_ids: - self.logger.info(f"Loaded {len(added_ids)} documents from {file_path}") - return True - else: - self.logger.error(f"Failed to add documents from {file_path}") - return False - else: - self.logger.warning(f"No valid documents found in {file_path}") - return False - - except Exception as e: - self.logger.error(f"Failed to load knowledge from file {file_path}: {e}") - return False - - def clear_collection(self) -> bool: - """ - 清空集合中的所有文档 - - Returns: - 是否清空成功 - """ - try: - # 获取所有文档ID - all_docs = self.collection.get() - if all_docs["ids"]: - # 删除所有文档 - self.collection.delete(ids=all_docs["ids"]) - self.logger.info(f"Cleared {len(all_docs['ids'])} documents from collection") - return True - except Exception as e: - self.logger.error(f"Failed to clear collection: {e}") - return False - - def update_document(self, doc_id: str, new_content: str, new_embedding: np.ndarray) -> bool: - """ - 更新指定文档 - - Args: - doc_id: 文档ID - new_content: 新的文档内容 - new_embedding: 新的向量嵌入 - - Returns: - 是否更新成功 - """ - try: - # ChromaDB 的 update 方法 - self.collection.update( - ids=[doc_id], - documents=[new_content], - embeddings=[new_embedding.tolist()], - metadatas=[ - { - "doc_id": doc_id, - "length": len(new_content), - "updated_time": time.time(), - } - ], - ) - - self.logger.info(f"Updated document: {doc_id}") - return True - - except Exception as e: - self.logger.error(f"Failed to update document {doc_id}: {e}") - return False - - def delete_document(self, doc_id: str) -> bool: - """ - 删除指定文档 - - Args: - doc_id: 文档ID - - Returns: - 是否删除成功 - """ - try: - self.collection.delete(ids=[doc_id]) - self.logger.info(f"Deleted document: {doc_id}") - return True - except Exception as e: - self.logger.error(f"Failed to delete document {doc_id}: {e}") - return False - - -class ChromaUtils: - """ChromaDB 工具类，提供常用的辅助方法""" - - @staticmethod - def create_chroma_config( - persistence_path: str = "./chroma_db", - collection_name: str = "default_collection", - distance_metric: str = "cosine", - host: str = "localhost", - port: int = 8000, - ) -> dict[str, Any]: - """ - 创建标准的 ChromaDB 配置 - - Args: - persistence_path: 持久化路径 - collection_name: 集合名称 - distance_metric: 距离度量方法 - host: 服务器地址 - port: 服务器端口 - - Returns: - ChromaDB 配置字典 - """ - return { - "host": host, - "port": port, - "persistence_path": persistence_path, - "collection_name": collection_name, - "use_embedding_query": True, - "metadata": { - "hnsw:space": distance_metric, - "hnsw:M": 16, - "hnsw:ef_construction": 200, - "hnsw:ef": 10, - }, - } - - @staticmethod - def validate_chroma_config(config: dict[str, Any]) -> bool: - """ - 验证 ChromaDB 配置的有效性 - - Args: - config: ChromaDB 配置字典 - - Returns: - 配置是否有效 - """ - required_keys = ["collection_name"] - - for key in required_keys: - if key not in config: - return False - - # 验证距离度量 - if "metadata" in config and "hnsw:space" in config["metadata"]: - valid_metrics = ["cosine", "l2", "ip"] - if config["metadata"]["hnsw:space"] not in valid_metrics: - return False - - return True - - @staticmethod - def check_chromadb_availability() -> bool: - """ - 检查 ChromaDB 是否可用 - - Returns: - ChromaDB 是否已安装并可用 - """ - try: - import chromadb # noqa: F401 - - return True - except ImportError: - return False diff --git a/packages/sage-middleware/src/sage/middleware/components/vector_stores/chroma_adapter.py b/packages/sage-middleware/src/sage/middleware/components/vector_stores/chroma_adapter.py deleted file mode 100644 index 902cfd3028..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/vector_stores/chroma_adapter.py +++ /dev/null @@ -1,185 +0,0 @@ -"""ChromaDB VectorStore Adapter - -Adapter that wraps ChromaBackend to implement the VectorStore protocol, -enabling it to work with IndexBuilder. - -Layer: L3 (sage-libs/integrations) -Dependencies: sage.middleware.operators.rag.index_builder (L4 Protocol only - runtime_checkable) -""" - -import json -from pathlib import Path -from typing import Any - -from sage.middleware.components.vector_stores.chroma import ChromaBackend - - -class ChromaVectorStoreAdapter: - """Adapter wrapping ChromaBackend to implement VectorStore Protocol. - - This adapter enables ChromaBackend to work with IndexBuilder by - implementing the VectorStore interface. - - Note: We don't formally implement the Protocol here (that would create - L3→L4 dependency). Instead, we provide duck-typing compatibility. - The Protocol is only for type checking at runtime. - - Args: - persist_path: Directory to store ChromaDB data - dim: Vector dimension (unused for Chroma, but required by Protocol) - collection_name: Name of the Chroma collection - """ - - def __init__( - self, - persist_path: Path, - dim: int, - collection_name: str = "sage_index", - ): - """Initialize ChromaDB adapter. - - Args: - persist_path: Path to persist ChromaDB data - dim: Vector dimension (recorded but not enforced by Chroma) - collection_name: Name of collection to use - """ - self.persist_path = persist_path - self.dim = dim - self.collection_name = collection_name - - # Create parent directory - persist_path.mkdir(parents=True, exist_ok=True) - - # Initialize ChromaBackend with local persistence - config = { - "persistence_path": str(persist_path), - "collection_name": collection_name, - "metadata": {"hnsw:space": "cosine"}, - } - - self.backend = ChromaBackend(config) - - # Track documents for count - self._doc_count = 0 - - def add(self, vector: list[float], metadata: dict[str, Any]) -> None: - """Add a single vector with metadata. - - Args: - vector: Vector embedding - metadata: Metadata dictionary - """ - # ChromaBackend.add_documents expects batch format - # We'll accumulate and flush periodically, or add one at a time - doc_id = f"doc_{self._doc_count}" - - self.backend.add_documents( - ids=[doc_id], - embeddings=[vector], - metadatas=[metadata], - documents=[metadata.get("text", "")], # Use 'text' field if available - ) - - self._doc_count += 1 - - def build_index(self) -> None: - """Build/optimize the index. - - ChromaDB builds indices automatically, so this is a no-op. - """ - # ChromaDB automatically maintains indices - pass - - def save(self, path: str) -> None: - """Persist the vector store to disk. - - Args: - path: Path to save (unused for Chroma - uses persistence_path from config) - """ - # ChromaDB with PersistentClient automatically persists - # Save metadata about the index - manifest_path = Path(path).parent / "chroma_manifest.json" - manifest = { - "collection_name": self.collection_name, - "persistence_path": str(self.persist_path), - "dim": self.dim, - "count": self._doc_count, - } - - with open(manifest_path, "w") as f: - json.dump(manifest, f, indent=2) - - def load(self, path: str) -> None: - """Load vector store from disk. - - Args: - path: Path to load from - """ - # ChromaDB automatically loads from persistence_path - # Try to load manifest for metadata - manifest_path = Path(path).parent / "chroma_manifest.json" - if manifest_path.exists(): - with open(manifest_path) as f: - manifest = json.load(f) - self._doc_count = manifest.get("count", 0) - - def search( - self, - query_vector: list[float], - top_k: int = 5, - filter_dict: dict[str, Any] | None = None, - ) -> list[dict]: - """Search for similar vectors. - - Args: - query_vector: Query embedding - top_k: Number of results to return - filter_dict: Optional metadata filters - - Returns: - List of result dictionaries with 'id', 'score', 'metadata' - """ - # Use ChromaBackend.query - results = self.backend.query( - query_embeddings=[query_vector], - n_results=top_k, - where=filter_dict, # ChromaDB uses 'where' for metadata filtering - ) - - # Convert ChromaDB results to standard format - formatted_results = [] - if results and "ids" in results: - ids = results["ids"][0] if results["ids"] else [] - distances = results["distances"][0] if results["distances"] else [] - metadatas = results["metadatas"][0] if results["metadatas"] else [] - - for i, doc_id in enumerate(ids): - formatted_results.append( - { - "id": doc_id, - "score": float(distances[i]) if i < len(distances) else 0.0, - "metadata": metadatas[i] if i < len(metadatas) else {}, - } - ) - - return formatted_results - - def get_dim(self) -> int: - """Get vector dimension. - - Returns: - Vector dimension - """ - return self.dim - - def count(self) -> int: - """Get number of vectors in store. - - Returns: - Number of stored vectors - """ - # ChromaDB collection has a count method - try: - return self.backend.collection.count() - except Exception: - return self._doc_count diff --git a/packages/sage-middleware/src/sage/middleware/components/vector_stores/milvus.py b/packages/sage-middleware/src/sage/middleware/components/vector_stores/milvus.py deleted file mode 100644 index 440a61915f..0000000000 --- a/packages/sage-middleware/src/sage/middleware/components/vector_stores/milvus.py +++ /dev/null @@ -1,677 +0,0 @@ -""" -Milvus 后端管理工具 -提供 Milvus / Milvus Lite 的初始化、文档管理和检索功能 -""" - -import json -import logging -import os -import time -from typing import TYPE_CHECKING, Any - -import numpy as np - -if TYPE_CHECKING: - from pymilvus import MilvusClient # noqa: F401 - - -class MilvusBackend: - """Milvus 后端管理器（支持本地 Milvus Lite 与远程 Milvus）""" - - def __init__(self, config: dict[str, Any], logger: logging.Logger | Any = None): - """ - 初始化 Milvus 后端 - - Args: - config: Milvus 配置字典 - logger: 日志记录器 - """ - self.config = config - self.logger = logger or logging.getLogger(__name__) - - # 连接与集合配置 - self.host: str = self.config.get("host", "localhost") - self.port: int = int(self.config.get("port", 19530)) - self.persistence_path: str | None = self.config.get( - "persistence_path", "./milvus_db" - ) # 可选，优先级高于 host/port - - self.collection_name: str = self.config.get("collection_name", "retriever_collection") - self.dim: int | None = self.config.get("dim", 1024) # 稠密向量维度 - raw_metric_type = self.config.get("metric_type") - if not raw_metric_type: - # 未提供则默认 COSINE，不做校验 - metric_type_value: str = "COSINE" - else: - # 提供了则严格校验，仅支持 IP/COSINE/L2（大小写不敏感，统一为大写） - allowed_metric_types = {"IP", "COSINE", "L2"} - metric_upper = str(raw_metric_type).upper() - if metric_upper not in allowed_metric_types: - raise ValueError( - f"Invalid metric_type: {raw_metric_type}. Allowed: {sorted(allowed_metric_types)}" - ) - metric_type_value = metric_upper - self.metric_type: str = metric_type_value - self.drop_ratio_search = self.config.get( - "drop_ratio_search", 0.2 - ) # 稀疏向量搜索时，drop 比例 - self.search_type = self.config.get("search_type", "sparse") # 搜索类型，sparse 或 dense - self.dense_insert_batch_size = self.config.get( - "dense_insert_batch_size", 128 - ) # 稠密向量插入批次大小 - - # 客户端 - self.client: Any = None # Will be initialized by _init_client - self._init_client() - self._init_collection() - - def _init_client(self): - """初始化 Milvus 客户端，支持 Milvus Lite（本地 .db 文件）与远程服务""" - try: - from pymilvus import MilvusClient - - # 判断使用本地还是远程模式 - if self.host in ["localhost", "127.0.0.1"] and not self.config.get("force_http", False): - self.client = MilvusClient(self.persistence_path or "./milvus.db") - self.logger.info( - f"Initialized Milvus persistent client at: {self.persistence_path}" - ) - else: - # 远程服务器模式 - full_host = ( - f"http://{self.host}:{self.port}" - if not self.host.startswith("http") - else self.host - ) - - self.client = MilvusClient(full_host) - self.logger.info( - f"Initialized Milvus HTTP client at: http://{self.host}:{self.port}" - ) - - except ImportError as e: - self.logger.error(f"Failed to import pymilvus: {e}") - raise ImportError( - "Milvus dependencies not available. Install with: pip install pymilvus" - ) - - except Exception as e: - self.logger.error(f"Failed to initialize Milvus client: {e}") - raise - - def _ensure_client(self): - """Ensure client is initialized""" - if self.client is None: - raise RuntimeError("Milvus client is not initialized") - - def _init_collection(self): - """初始化或获取 Milvus 集合，必要时创建索引""" - self._ensure_client() - try: - # 尝试直接获取已存在的集合 - try: - # 通过检查集合是否能正常查询来验证集合存在 - self.client.load_collection(collection_name=self.collection_name) # type: ignore - self.logger.info(f"Retrieved existing Milvus collection: {self.collection_name}") - return - except Exception: - # 集合不存在，需要创建新集合 - self.logger.debug( - f"Collection '{self.collection_name}' does not exist, creating new one" - ) - # 创建集合 - 导入必要的数据类型 - try: - from pymilvus import DataType - except Exception as e: - self.logger.error(f"Failed to import PyMilvus schema classes: {e}") - raise - - try: - schema = self.client.create_schema(auto_id=False) - index_params = self.client.prepare_index_params() - schema.add_field( - "id", - DataType.VARCHAR, - max_length=2000, - is_primary=True, - auto_id=False, - ) - schema.add_field("text", DataType.VARCHAR, max_length=2000) # 文本字段 - self.logger.info(self.search_type + "=" * 60) - if self.search_type == "sparse": - schema.add_field("sparse", DataType.SPARSE_FLOAT_VECTOR) # 稀疏向量字段 - - index_params.add_index( - field_name="sparse", - index_type="SPARSE_INVERTED_INDEX", - metric_type="IP", - ) - - if self.search_type == "dense": - schema.add_field("dense", DataType.FLOAT_VECTOR, dim=self.dim) # 稠密向量 - - index_params.add_index( - field_name="dense", - index_type="AUTOINDEX", - metric_type=self.metric_type, - ) - - self.client.create_collection( - collection_name=self.collection_name, - schema=schema, - index_params=index_params, - ) - - # 创建集合后，立即加载以确保可以使用 - self.client.load_collection(collection_name=self.collection_name) - - self.logger.info( - f"Created and loaded new Milvus collection {self.collection_name} successfully!" - ) - except Exception as e: - self.logger.error(f"Failed to create Milvus collection: {e}") - raise - except Exception as e: - self.logger.error(f"Failed to initialize Milvus collection: {e}") - raise - - def add_dense_documents( - self, - documents: list[str], - dense_embeddings: list[np.ndarray], - doc_ids: list[str], - ) -> list[str]: - """ - 添加稠密向量文档，防止内存溢出，分批插入 - - Args: - documents: 文本列表 - embeddings: 向量列表（list[float] 或可转 list） - doc_ids: 文档 ID 列表 - Returns: - 成功插入的文档 ID 列表 - """ - try: - # 转换 embedding 格式（milvus 需要 list 格式） - dense_embeddings_list = [embedding.tolist() for embedding in dense_embeddings] - docs = [] - # 生成文档ID - doc_ids = [f"doc_{int(time.time() * 1000)}_{i}" for i in range(len(documents))] - for i in range(len(documents)): - docs.append( - { - "id": doc_ids[i], - "text": documents[i], - "dense": dense_embeddings_list[i], - } - ) - - if len(docs) > self.dense_insert_batch_size: - for i in range(0, len(docs), self.dense_insert_batch_size): - self.client.insert( - collection_name=self.collection_name, - data=docs[i : i + self.dense_insert_batch_size], - ) - else: - self.client.insert(collection_name=self.collection_name, data=docs) - - self.logger.info( - f"Added {len(docs)} documents to Milvus collection {self.collection_name}" - ) - return doc_ids - except Exception as e: - self.logger.error(f"Error adding dense documents to Milvus: {e}") - return [] - - def add_sparse_documents( - self, documents: list[str], sparse_embeddings, doc_ids: list[str] - ) -> list[str]: - """ - 添加稀疏向量文档 - - Args: - documents: 文本列表 - sparse_embeddings: 稀疏向量列表（来自BGEM3EmbeddingFunction） - doc_ids: 文档 ID 列表 - Returns: - 成功插入的文档 ID 列表 - """ - try: - docs = [] - for i in range(len(documents)): - # 处理稀疏向量格式 - sparse_vector = sparse_embeddings[i] - - # 如果是scipy稀疏矩阵（csr_array/csr_matrix），转换为字典格式 - if hasattr(sparse_vector, "tocoo"): - # scipy sparse matrix to dict - coo = sparse_vector.tocoo() - sparse_dict = { - int(idx): float(val) for idx, val in zip(coo.col, coo.data, strict=False) - } - elif hasattr(sparse_vector, "indices") and hasattr(sparse_vector, "data"): - # 处理 csr_array 格式 - sparse_dict = { - int(idx): float(val) - for idx, val in zip(sparse_vector.indices, sparse_vector.data, strict=False) - } - elif isinstance(sparse_vector, dict): - # 已经是字典格式 - sparse_dict = sparse_vector - else: - # 尝试转换为字典 - self.logger.warning(f"Unknown sparse vector format: {type(sparse_vector)}") - sparse_dict = dict(sparse_vector) if hasattr(sparse_vector, "__iter__") else {} - - docs.append({"id": doc_ids[i], "text": documents[i], "sparse": sparse_dict}) - - # 插入数据 - self.client.insert(collection_name=self.collection_name, data=docs) - self.logger.info( - f"Added {len(docs)} documents to Milvus collection {self.collection_name}" - ) - - return doc_ids - except Exception as e: - self.logger.error(f"Error adding sparse documents to Milvus: {e}") - return [] - - def sparse_search(self, query_text: str, top_k: int) -> list[str]: - """ - 在 Milvus 中执行稀疏向量搜索 - - Args: - query_text: 查询文本 - top_k: 返回的文档数量 - - Returns: - 文本结果列表 - """ - try: - # 使用 BGEM3EmbeddingFunction 生成查询向量 - try: - from pymilvus.model.hybrid import ( - BGEM3EmbeddingFunction, # type: ignore[import-not-found] - ) - except ImportError: - try: - from pymilvus.model import ( - BGEM3EmbeddingFunction, # type: ignore[import-not-found] - ) - except ImportError: - self.logger.error( - "Please install: pip install 'pymilvus[model]' or pip install pymilvus.model" - ) - return [] - - embedding_model = BGEM3EmbeddingFunction(use_fp16=False, device="cpu") - query_embeddings = embedding_model.encode_queries([query_text]) - - # 提取稀疏向量 - if isinstance(query_embeddings, dict) and "sparse" in query_embeddings: - sparse_vector = query_embeddings["sparse"][0] - else: - sparse_vector = query_embeddings[0] - - # 处理稀疏向量格式转换为字典 - if hasattr(sparse_vector, "tocoo"): - # scipy sparse matrix to dict - coo = sparse_vector.tocoo() - query_vector = { - int(idx): float(val) for idx, val in zip(coo.col, coo.data, strict=False) - } - elif hasattr(sparse_vector, "indices") and hasattr(sparse_vector, "data"): - # 处理 csr_array 格式 - query_vector = { - int(idx): float(val) - for idx, val in zip(sparse_vector.indices, sparse_vector.data, strict=False) - } - elif isinstance(sparse_vector, dict): - # 已经是字典格式 - query_vector = sparse_vector - else: - # 尝试转换为字典 - self.logger.warning(f"Unknown sparse vector format: {type(sparse_vector)}") - query_vector = dict(sparse_vector) if hasattr(sparse_vector, "__iter__") else {} - - # 执行搜索 - hits = self.client.search( - collection_name=self.collection_name, - data=[query_vector], - anns_field="sparse", - search_params={"metric_type": "IP", "params": {}}, - limit=top_k, - output_fields=["text"], - ) - - results = hits[0] - sparse_results = [] - - if results and len(results) > 0: - for r in results: - sparse_results.append(r.entity.get("text")) # type: ignore[union-attr] - return sparse_results - except Exception as e: - self.logger.error(f"Error executing Milvus sparse search: {e}") - return [] - - def dense_search(self, query_vector: np.ndarray, top_k: int) -> list[str]: - """ - 在 Milvus 中执行稠密向量搜索 - - Args: - query_vector: 查询向量 - top_k: 返回的文档数量 - - Returns: - 文本结果列表 - """ - try: - print(f"MilvusBackend.search: using top_k = {top_k}") - - hits = self.client.search( - collection_name=self.collection_name, - data=[query_vector], - anns_field="dense", - search_params={"metric_type": self.metric_type, "params": {}}, - limit=top_k, - output_fields=["text"], - ) - - results = hits[0] - dense_results = [] - if results and len(results) > 0: - for r in results: - dense_results.append(r.entity.get("text")) # type: ignore[union-attr] - return dense_results - except Exception as e: - self.logger.error(f"Error executing Milvus search: {e}") - return [] - - def delete_collection(self, collection_name: str) -> bool: - """删除当前集合""" - try: - self.client.drop_collection(collection_name) - self.logger.info(f"Deleted Milvus collection: {collection_name}") - return True - except Exception as e: - self.logger.error(f"Error deleting Milvus collection: {e}") - return False - - def get_collection_info(self) -> dict[str, Any]: - """ - 获取集合信息 - - Returns: - 包含集合信息的字典 - """ - try: - count = None - try: - stats = self.client.get_collection_stats(self.collection_name) - count = stats.get("row_count") if isinstance(stats, dict) else None - except Exception: - pass - return { - "backend": "milvus", - "collection_name": self.collection_name, - "document_count": count, - "persistence_path": ( - self.persistence_path if hasattr(self, "persistence_path") else None - ), - } - except Exception as e: - self.logger.error(f"Failed to get Milvus collection info: {e}") - return {"backend": "milvus", "error": str(e)} - - def save_config(self, save_path: str) -> bool: - """ - 保存 milvus 配置信息 - - Args: - save_path: 保存路径 - - Returns: - 是否保存成功 - """ - try: - os.makedirs(save_path, exist_ok=True) - config_path = os.path.join(save_path, "milvus_config.json") - stats = self.client.get_collection_stats(self.collection_name) - count = stats.get("row_count") if isinstance(stats, dict) else None - config_info = { - "collection_name": self.collection_name, - "collection_count": count, - "backend_type": "milvus", - "milvus_config": self.config, - "saved_time": time.time(), - } - with open(config_path, "w", encoding="utf-8") as f: - json.dump(config_info, f, ensure_ascii=False, indent=2) - self.logger.info(f"Successfully saved Milvus config to: {save_path}") - self.logger.info( - f"Milvus collection '{self.collection_name}' contains {config_info['collection_count']} documents" - ) - return True - except Exception as e: - self.logger.error(f"Failed to save Milvus config: {e}") - return False - - def load_config(self, load_path: str) -> bool: - """ - 从配置文件重新连接到 Milvus 集合 - - Args: - load_path: 配置文件路径 - - Returns: - 是否加载成功 - """ - try: - config_path = os.path.join(load_path, "milvus_config.json") - if os.path.exists(config_path): - with open(config_path, encoding="utf-8") as f: - config_info = json.load(f) - collection_name = config_info.get("collection_name") - if collection_name: - self.collection_name = collection_name - self.client.load_collection(collection_name=self.collection_name) - stats = self.client.get_collection_stats(self.collection_name) - count = stats.get("row_count") if isinstance(stats, dict) else None - self.logger.info( - f"Reloaded Milvus collection name from config: {self.collection_name}" - ) - self.logger.info(f"Collection contains {count} documents") - return True - else: - self.logger.error("No collection name found in Milvus config") - return False - else: - self.logger.error(f"Milvus config not found at: {config_path}") - return False - except Exception as e: - self.logger.error(f"Failed to load Milvus config: {e}") - return False - - def load_knowledge_from_file_dense(self, file_path: str, embedding_model) -> bool: - """ - 从文件加载知识库到 Milvus - - Args: - file_path: 知识库文件路径 - embedding_model: 嵌入模型实例 - - Returns: - 是否加载成功 - """ - try: - self.logger.info(f"Loading knowledge from file: {file_path}") - with open(file_path, encoding="utf-8") as f: - content = f.read() - - # 将知识库按段落分割 - documents = [doc.strip() for doc in content.split("\n\n") if doc.strip()] - - if documents: - # 生成文档ID - doc_ids = [f"doc_{int(time.time() * 1000)}_{i}" for i in range(len(documents))] - - # 生成 embedding - embeddings = [] - for doc in documents: - embedding = embedding_model.embed(doc) - embeddings.append(np.array(embedding, dtype=np.float32)) - - # dense 向量添加到 Milvus - added_dense_ids = self.add_dense_documents(documents, embeddings, doc_ids) - - if added_dense_ids: - self.logger.info( - f"Loaded {len(added_dense_ids)} dense documents from {file_path}" - ) - return True - else: - self.logger.error(f"Failed to add documents from {file_path}") - return False - else: - self.logger.warning(f"No valid documents found in {file_path}") - return False - - except Exception as e: - self.logger.error(f"Failed to load knowledge from file {file_path}: {e}") - return False - - def load_knowledge_from_file_sparse(self, file_path: str) -> bool: - """ - 从文件加载知识库到 Milvus - - Args: - file_path: 知识库文件路径 - - Returns: - 是否加载成功 - """ - try: - self.logger.info(f"Loading knowledge from file: {file_path}") - with open(file_path, encoding="utf-8") as f: - content = f.read() - - # 将知识库按段落分割 - documents = [doc.strip() for doc in content.split("\n\n") if doc.strip()] - - if documents: - # 生成文档ID - doc_ids = [f"doc_{int(time.time() * 1000)}_{i}" for i in range(len(documents))] - - try: - from pymilvus.model.hybrid import BGEM3EmbeddingFunction - - embedding_model = BGEM3EmbeddingFunction(use_fp16=False, device="cpu") - - # 生成 sparse embedding - sparse_embeddings = embedding_model.encode_documents(documents) - - # 提取稀疏向量部分 - if isinstance(sparse_embeddings, dict) and "sparse" in sparse_embeddings: - embeddings = sparse_embeddings["sparse"] - else: - # 如果返回格式不同，直接使用 - embeddings = sparse_embeddings - - except Exception as e: - self.logger.error(f"Failed to import or use BGEM3EmbeddingFunction: {e}") - raise - - # sparse 向量添加到 Milvus - added_sparse_ids = self.add_sparse_documents(documents, embeddings, doc_ids) - - if added_sparse_ids: - self.logger.info( - f"Loaded {len(added_sparse_ids)} sparse documents from {file_path}" - ) - return True - else: - self.logger.error(f"Failed to add documents from {file_path}") - return False - else: - self.logger.warning(f"No valid documents found in {file_path}") - return False - - except Exception as e: - self.logger.error(f"Failed to load knowledge from file {file_path}: {e}") - return False - - def clear_collection(self) -> bool: - """清空集合中的所有文档，保留集合结构与索引""" - try: - # 通过过滤条件删除全部实体（匹配所有非空字符串id） - self.client.delete(collection_name=self.collection_name, filter='id != ""') - self.logger.info(f"Cleared documents in Milvus collection '{self.collection_name}'") - return True - except Exception as e: - self.logger.error(f"Failed to clear Milvus collection: {e}") - return False - - def update_document(self, doc_id: str, new_content: str, new_embedding: np.ndarray) -> bool: - """ - 更新指定文档 - """ - try: - self.client.upsert( # type: ignore[attr-defined] - collection_name=self.collection_name, - data=[{"id": doc_id, "text": new_content, "dense": new_embedding.tolist()}], - ) - self.logger.info( - f"Updated document {doc_id} in Milvus collection '{self.collection_name}'" - ) - return True - except Exception as e: - self.logger.error( - f"Failed to update document {doc_id} in Milvus collection '{self.collection_name}': {e}" - ) - return False - - def delete_document(self, doc_id: str) -> bool: - """ - 删除指定文档 - """ - try: - self.client.delete(collection_name=self.collection_name, filter=f'id == "{doc_id}"') - self.logger.info( - f"Deleted document {doc_id} in Milvus collection '{self.collection_name}'" - ) - return True - except Exception as e: - self.logger.error( - f"Failed to delete document {doc_id} in Milvus collection '{self.collection_name}': {e}" - ) - return False - - -class MilvusUtils: - """Milvus 工具类""" - - @staticmethod - def check_milvus_available() -> bool: - """ - 检查 MilvusDB 是否可用 - """ - try: - import pymilvus # noqa: F401 - - return True - except ImportError: - return False - - @staticmethod - def validate_milvus_config(config: dict[str, Any]) -> bool: - """ - 验证 Milvus 配置的有效性 - """ - required_keys = ["collection_name"] - - for key in required_keys: - if key not in config: - return False - - return True diff --git a/packages/sage-middleware/src/sage/middleware/operators/__init__.py b/packages/sage-middleware/src/sage/middleware/operators/__init__.py deleted file mode 100644 index 1befca8e0c..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/__init__.py +++ /dev/null @@ -1,56 +0,0 @@ -""" -SAGE Middleware Operators - 领域算子 - -这个模块提供面向特定业务领域的算子实现： -- LLM算子: 大语言模型推理 (SageLLMGenerator) -- RAG算子: 检索增强生成算子 (Retriever, Refiner, Reranker, Generator等) -- Tool算子: 工具调用 + 领域特定工具 (arxiv, image_captioner等) -- Filters: 业务过滤器 (tool_filter, evaluate_filter, context source/sink) -- Agentic: Agent runtime operators (requires isage-agentic, optional) - -向量数据库集成位于: sage.middleware.components.vector_stores - -这些算子继承 sage.kernel.operators 的基础算子，实现具体业务逻辑。 - -使用方式： - from sage.middleware.operators import rag, llm, tools, filters - - # 或直接导入 - from sage.middleware.operators.rag import ChromaRetriever - from sage.middleware.operators.llm import SageLLMGenerator - from sage.middleware.components.vector_stores import MilvusBackend, ChromaBackend - - # Agentic operators (requires isage-agentic, install with: pip install isage-middleware[libs]) - from sage.middleware.operators.agentic import PlanningOperator -""" - -import warnings - -from sage.middleware.operators.llm.sagellm_generator import SageLLMGenerator - -# 导出核心子模块 (always available) -from . import filters, llm, rag, tools - -# Agentic operators are optional (requires isage-agentic) -try: - from . import agentic - - _HAS_AGENTIC = True -except ImportError as e: - _HAS_AGENTIC = False - agentic = None # type: ignore - warnings.warn( - f"Agentic operators not available: {e}\n" - "Install with: pip install isage-middleware[libs] or pip install isage-agentic", - UserWarning, - stacklevel=2, - ) - -__all__ = [ - "rag", - "llm", - "tools", - "filters", - "agentic", - "SageLLMGenerator", -] diff --git a/packages/sage-middleware/src/sage/middleware/operators/agent/__init__.py b/packages/sage-middleware/src/sage/middleware/operators/agent/__init__.py deleted file mode 100644 index 03db5daab8..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/agent/__init__.py +++ /dev/null @@ -1,24 +0,0 @@ -"""Agent components for SAGE middleware. - -Provides agent runtime and planning capabilities. - -Note: This module requires isage-agentic. Install with: - pip install isage-middleware[libs] or pip install isage-agentic -""" - -import warnings - -try: - from sage.middleware.operators.agent import runtime - - _HAS_AGENTIC = True - __all__ = ["runtime"] -except ImportError as e: - _HAS_AGENTIC = False - runtime = None # type: ignore - __all__ = [] - warnings.warn( - f"Agent runtime not available: {e}\nInstall with: pip install isage-agentic", - UserWarning, - stacklevel=2, - ) diff --git a/packages/sage-middleware/src/sage/middleware/operators/agent/planning/__init__.py b/packages/sage-middleware/src/sage/middleware/operators/agent/planning/__init__.py deleted file mode 100644 index 607927ec72..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/agent/planning/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from .llm_adapter import GeneratorToClientAdapter -from .planner_adapter import SageLibsPlannerAdapter -from .router import PlannerRouter - -__all__ = ["PlannerRouter", "SageLibsPlannerAdapter", "GeneratorToClientAdapter"] diff --git a/packages/sage-middleware/src/sage/middleware/operators/agent/planning/llm_adapter.py b/packages/sage-middleware/src/sage/middleware/operators/agent/planning/llm_adapter.py deleted file mode 100644 index 12ac692b94..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/agent/planning/llm_adapter.py +++ /dev/null @@ -1,41 +0,0 @@ -from __future__ import annotations - -from typing import Any - - -class GeneratorToClientAdapter: - """ - Adapts OpenAIGenerator/HFGenerator (L4) to UnifiedInferenceClient interface (L2/L3). - """ - - def __init__(self, generator): - self.generator = generator - - def chat( - self, messages: list[dict[str, str]], temperature: float = 0.7, max_tokens: int = 512 - ) -> str: - """ - Execute chat completion. - """ - # OpenAIGenerator.execute takes [user_query, messages] or just messages depending on impl. - # Let's check OpenAIGenerator.execute signature. - # Based on usage in LLMPlanner: self.generator.execute([user_query, messages]) - # But here we might not have user_query easily available if it's just a chat call. - # We can pass the last user message as user_query. - - user_query = "Chat request" - for msg in reversed(messages): - if msg["role"] == "user": - user_query = msg["content"] - break - - # The generator returns (token_usage, text_output) - _, output = self.generator.execute([user_query, messages]) - return output - - def generate(self, prompt: str, **kwargs) -> list[dict[str, Any]]: - """ - Execute text generation. - """ - _, output = self.generator.execute([prompt, [{"role": "user", "content": prompt}]]) - return [{"generations": [{"text": output}]}] diff --git a/packages/sage-middleware/src/sage/middleware/operators/agent/planning/planner_adapter.py b/packages/sage-middleware/src/sage/middleware/operators/agent/planning/planner_adapter.py deleted file mode 100644 index 9a36a34e20..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/agent/planning/planner_adapter.py +++ /dev/null @@ -1,98 +0,0 @@ -from __future__ import annotations - -import logging -from typing import Any - -from sage_libs.sage_agentic.agents.planning.schemas import PlannerConfig, PlanRequest, ToolMetadata - -logger = logging.getLogger(__name__) - - -class SageLibsPlannerAdapter: - """ - Adapts sage-libs planners (ReAct, ToT, Hierarchical) to the AgentRuntime interface. - """ - - def __init__(self, planner_cls, config: PlannerConfig, llm_client): - self.planner = planner_cls(config=config, llm_client=llm_client) - - def plan( - self, - profile_system_prompt: str, - user_query: str, - tools: dict[str, dict[str, Any]], - ) -> list[dict[str, Any]]: - """ - Convert inputs to PlanRequest, call planner, and convert PlanResult to list[dict]. - """ - # 1. Convert tools dict to List[ToolMetadata] - tool_metadata_list = [] - for name, meta in tools.items(): - tool_metadata_list.append( - ToolMetadata( - tool_id=name, - name=name, - description=meta.get("description", ""), - category=meta.get("category", "general"), - input_schema=meta.get("input_schema", {}), - ) - ) - - # 2. Create PlanRequest - request = PlanRequest( - goal=user_query, - context={"system_prompt": profile_system_prompt}, - tools=tool_metadata_list, - max_steps=10, # Default - min_steps=1, - ) - - # 3. Call planner - try: - result = self.planner.plan(request) - except Exception as e: - logger.error(f"Planner {self.planner.name} failed: {e}") - return [{"type": "reply", "text": f"Planning failed: {str(e)}"}] - - # 4. Convert PlanResult to list[dict] - # AgentRuntime expects: [{"type": "tool", "name": "...", "arguments": {...}}, ...] - runtime_steps = [] - - if not result.steps: - return [{"type": "reply", "text": "No plan generated."}] - - for step in result.steps: - if step.action == "finish": - # Some planners might use a 'finish' action - continue - - # Check if it's a tool call - # In sage-libs, 'action' is usually the tool name - # 'inputs' are arguments - - # Heuristic: if action matches a tool name, it's a tool call - if step.action in tools: - runtime_steps.append( - {"type": "tool", "name": step.action, "arguments": step.inputs} - ) - else: - # Treat as thought or unknown action? - # AgentRuntime doesn't support "thought" steps explicitly in the loop yet, - # but we can log them or ignore them. - # If it's a reply-like action? - pass - - # If no tool steps, maybe it's a direct reply? - if not runtime_steps: - # Try to find a final thought or result - final_thought = getattr(result, "final_thought", None) or "Plan completed." - runtime_steps.append({"type": "reply", "text": final_thought}) - else: - # Append a final reply step if not present? - # AgentRuntime loop executes steps. If the last step is a tool, it will execute it. - # Then what? The loop continues? - # AgentRuntime loop: for step in plan: execute. - # If plan is static, it executes all steps. - pass - - return runtime_steps diff --git a/packages/sage-middleware/src/sage/middleware/operators/agent/planning/router.py b/packages/sage-middleware/src/sage/middleware/operators/agent/planning/router.py deleted file mode 100644 index c51d2daf0f..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/agent/planning/router.py +++ /dev/null @@ -1,107 +0,0 @@ -from __future__ import annotations - -import json -import logging -from typing import Any - -from sage_libs.sage_agentic.agents.planning.hierarchical_planner import HierarchicalPlanner -from sage_libs.sage_agentic.agents.planning.react_planner import ReActConfig, ReActPlanner -from sage_libs.sage_agentic.agents.planning.schemas import PlannerConfig -from sage_libs.sage_agentic.agents.planning.simple_llm_planner import SimpleLLMPlanner -from sage_libs.sage_agentic.agents.planning.tot_planner import ToTConfig -from sage_libs.sage_agentic.agents.planning.tot_planner import TreeOfThoughtsPlanner as ToTPlanner - -from .llm_adapter import GeneratorToClientAdapter -from .planner_adapter import SageLibsPlannerAdapter - -logger = logging.getLogger(__name__) - - -class PlannerRouter: - """ - Routes user queries to the appropriate planner based on intent classification. - """ - - def __init__(self, generator, default_planner="llm"): - self.generator = generator - self.llm_client = GeneratorToClientAdapter(generator) - self.default_planner_type = default_planner - - # Initialize planners - # 1. Simple LLM Planner (Baseline) - self.simple_planner = SimpleLLMPlanner(generator=generator) - - # 2. ReAct Planner (Reasoning) - self.react_planner = SageLibsPlannerAdapter( - ReActPlanner, ReActConfig(max_iterations=5), self.llm_client - ) - - # 3. ToT Planner (Complex/Exploratory) - self.tot_planner = SageLibsPlannerAdapter( - ToTPlanner, ToTConfig(max_depth=3, branch_factor=3), self.llm_client - ) - - # 4. Hierarchical Planner (Long-horizon) - self.hierarchical_planner = SageLibsPlannerAdapter( - HierarchicalPlanner, PlannerConfig(), self.llm_client - ) - - def _classify_intent(self, user_query: str) -> str: - """ - Classify the user query into one of the planner types. - """ - prompt = """ -You are an expert intent classifier for an AI agent. -Analyze the user's query and select the most suitable planning strategy. - -Strategies: -1. "simple": For direct questions, simple tasks, or when no tools are needed. (e.g., "Hello", "What is 2+2?") -2. "react": For tasks requiring multi-step reasoning and tool usage. (e.g., "Search for X and summarize it") -3. "tot": For complex problems requiring exploration of multiple possibilities or creative writing. (e.g., "Write a novel outline", "Solve a complex riddle") -4. "hierarchical": For very long, complex tasks with many sub-tasks. (e.g., "Plan a 3-day trip including flights, hotels, and restaurants") - -User Query: "{query}" - -Return ONLY the strategy name (simple, react, tot, hierarchical) in JSON format: {{"strategy": "..."}} -""" - try: - response = self.llm_client.chat( - [ - {"role": "system", "content": "You are an intent classifier."}, - {"role": "user", "content": prompt.format(query=user_query)}, - ], - temperature=0.1, - ) - - # Parse JSON - import re - - match = re.search(r"\{.*\}", response, re.DOTALL) - if match: - data = json.loads(match.group(0)) - return data.get("strategy", "simple").lower() - except Exception as e: - logger.warning(f"Intent classification failed: {e}. Using default.") - - return "simple" - - def plan( - self, - profile_system_prompt: str, - user_query: str, - tools: dict[str, dict[str, Any]], - ) -> list[dict[str, Any]]: - """ - Route to the appropriate planner. - """ - strategy = self._classify_intent(user_query) - logger.info(f"Selected planning strategy: {strategy}") - - if strategy == "react": - return self.react_planner.plan(profile_system_prompt, user_query, tools) - elif strategy == "tot": - return self.tot_planner.plan(profile_system_prompt, user_query, tools) - elif strategy == "hierarchical": - return self.hierarchical_planner.plan(profile_system_prompt, user_query, tools) - else: - return self.simple_planner.plan(profile_system_prompt, user_query, tools) diff --git a/packages/sage-middleware/src/sage/middleware/operators/agent/runtime.py b/packages/sage-middleware/src/sage/middleware/operators/agent/runtime.py deleted file mode 100644 index b05efe8c2a..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/agent/runtime.py +++ /dev/null @@ -1,296 +0,0 @@ -""" -Agent Runtime (Middleware Layer) - -This component acts as a Dynamic Pipeline Orchestrator. -It takes a user query, generates a dynamic execution plan (DAG), and executes it using available tools. -""" - -from __future__ import annotations - -import logging -import time -from typing import Any - -# Import from L3 (Libs) - Allowed dependency direction (L4 -> L3) -from sage_libs.sage_agentic.agents.action.mcp_registry import MCPRegistry -from sage_libs.sage_agentic.agents.planning import PlanStep, SimpleLLMPlanner -from sage_libs.sage_agentic.agents.profile.profile import BaseProfile - -logger = logging.getLogger(__name__) - - -def _missing_required(arguments: dict[str, Any], input_schema: dict[str, Any]) -> list[str]: - """基于 MCP JSON Schema 做最小必填参数校验。""" - req = (input_schema or {}).get("required") or [] - return [k for k in req if k not in arguments] - - -class AgentRuntime: - """ - Production-Ready Runtime (Middleware Layer): - - Input: user_query - - Process: Planner generates JSON plan -> Step-by-step execution -> Optional LLM summary -> Return - - Features: Safety checks, Error handling, Structured logging/output - """ - - def __init__( - self, - profile: BaseProfile, - planner: SimpleLLMPlanner, - tools: MCPRegistry, - summarizer=None, - max_steps: int = 6, - ): - self.profile = profile - self.planner = planner - self.tools = tools - self.summarizer = summarizer - self.max_steps = max_steps - - def step_stream(self, user_query: str): - """ - Execute a single turn of conversation with streaming feedback. - - Yields: - Dict containing event type and data - """ - logger.info(f"AgentRuntime (Middleware) step_stream started for query: {user_query}") - - observations: list[dict[str, Any]] = [] - plan: list[PlanStep] = [] - - # 1) 生成计划（流式） - try: - # 检查 planner 是否支持流式 - if hasattr(self.planner, "plan_stream"): - for event in self.planner.plan_stream( - profile_system_prompt=self.profile.render_system_prompt(), - user_query=user_query, - tools=self.tools.describe(), - ): - if event["type"] == "thought": - yield {"type": "planning_thought", "content": event["content"]} - elif event["type"] == "plan": - plan = event["steps"] - yield {"type": "plan_generated", "plan": plan} - else: - # 降级到非流式 - yield {"type": "planning_thought", "content": "正在生成计划..."} - plan = self.planner.plan( - profile_system_prompt=self.profile.render_system_prompt(), - user_query=user_query, - tools=self.tools.describe(), - ) - yield {"type": "plan_generated", "plan": plan} - - logger.info(f"Plan generated with {len(plan)} steps") - except Exception as e: - logger.error(f"Planning failed: {e}") - yield {"type": "error", "content": f"Planning failed: {str(e)}"} - return - - reply_text: str | None = None - - # 2) 逐步执行 - for i, step in enumerate(plan[: self.max_steps]): - logger.debug(f"Executing step {i}: {step}") - - if step.get("type") == "reply": - reply_text = step.get("text", "").strip() - logger.info("Plan reached reply step") - yield {"type": "reply", "content": reply_text} - break - - if step.get("type") == "tool": - name = step.get("name") - arguments = step.get("arguments", {}) or {} - - yield {"type": "tool_start", "tool": name, "arguments": arguments} - - # Safety Check: Validate arguments against schema - tools_meta = self.tools.describe() - tool_desc = tools_meta.get(name) if isinstance(name, str) else None - - if not tool_desc: - error_msg = f"Tool '{name}' not found in registry" - logger.warning(error_msg) - obs = { - "step": i, - "tool": name, - "ok": False, - "error": error_msg, - "arguments": arguments, - } - observations.append(obs) - yield {"type": "tool_error", "tool": name, "error": error_msg} - continue - - schema = tool_desc.get("input_schema", {}) if tool_desc else {} - miss = _missing_required(arguments, schema) - - if miss: - error_msg = f"Missing required fields: {miss}" - logger.warning(f"Tool '{name}' validation failed: {error_msg}") - obs = { - "step": i, - "tool": name, - "ok": False, - "error": error_msg, - "arguments": arguments, - } - observations.append(obs) - yield {"type": "tool_error", "tool": name, "error": error_msg} - continue - - t0 = time.time() - try: - logger.info(f"Calling tool '{name}' with args: {arguments}") - out = self.tools.call(name, arguments) # type: ignore[arg-type] - latency = int((time.time() - t0) * 1000) - - obs = { - "step": i, - "tool": name, - "ok": True, - "latency_ms": latency, - "result": out, - } - observations.append(obs) - logger.info(f"Tool '{name}' success ({latency}ms)") - yield {"type": "tool_result", "tool": name, "result": out} - - except Exception as e: - latency = int((time.time() - t0) * 1000) - logger.error(f"Tool '{name}' failed: {e}") - obs = { - "step": i, - "tool": name, - "ok": False, - "latency_ms": latency, - "error": str(e), - "arguments": arguments, - } - observations.append(obs) - yield {"type": "tool_error", "tool": name, "error": str(e)} - - # 3) 汇总输出 - final_reply = "" - - if reply_text: - final_reply = reply_text - elif not observations: - final_reply = "（没有可执行的步骤或工具返回空结果）" - elif self.summarizer: - yield {"type": "planning_thought", "content": "正在汇总执行结果..."} - # 用你的生成器来生成自然语言总结 - profile_hint = self.profile.render_system_prompt() - prompt = f"""请将以下工具步骤结果用中文简洁汇总给用户，保留关键信息和结论。 - -[Profile] -{profile_hint} - -[Observations] -{observations} - -只输出给用户的总结文本。""" - messages = [ - { - "role": "system", - "content": "你是一个严谨的助理。只输出中文总结，不要额外解释。", - }, - {"role": "user", "content": prompt}, - ] - try: - _, summary = self.summarizer.execute([None, messages]) - final_reply = summary.strip() - yield {"type": "reply", "content": final_reply} - except Exception as e: - logger.error(f"Summarization failed: {e}") - final_reply = "Summarization failed." - yield {"type": "error", "content": "Summarization failed."} - else: - # 简单模板 - lines = [] - for obs in observations: - if obs.get("ok"): - lines.append(f"#{obs['step'] + 1} 工具 {obs['tool']} 成功：{obs.get('result')}") - else: - lines.append(f"#{obs['step'] + 1} 工具 {obs['tool']} 失败：{obs.get('error')}") - final_reply = "\n".join(lines) - yield {"type": "reply", "content": final_reply} - - yield { - "type": "completed", - "observations": observations, - "plan": plan, - "reply": final_reply, - } - - def step(self, user_query: str) -> dict[str, Any]: - """ - Execute a single turn of conversation. - - Returns: - Dict containing: - - reply: The final text response - - observations: List of execution steps and results - - plan: The original plan - """ - # 兼容旧接口，收集流式结果 - result = {"reply": "", "observations": [], "plan": []} - - for event in self.step_stream(user_query): - if event["type"] == "completed": - result["reply"] = event.get("reply", "") - result["observations"] = event.get("observations", []) - result["plan"] = event.get("plan", []) - - return result - - def execute(self, data: Any) -> dict[str, Any]: - """ - Unified Entry Point. - - Args: - data: str (query) or dict (config + query) - - Returns: - Dict containing 'reply', 'observations', 'plan' - """ - # 形态 1：直接字符串 - if isinstance(data, str): - return self.step(data) - - # 形态 2：字典 - if isinstance(data, dict): - user_query = data.get("user_query") or data.get("query") - if not isinstance(user_query, str) or not user_query.strip(): - raise ValueError( - "AgentRuntime.execute(dict) 需要提供 'user_query' 或 'query'（非空字符串）。" - ) - - # 临时覆写 max_steps - original_max = self.max_steps - if "max_steps" in data: - ms = data["max_steps"] - if not isinstance(ms, int) or ms <= 0: - raise ValueError("'max_steps' 必须是正整数。") - self.max_steps = ms - - # 临时覆写 profile（一次性，不污染实例） - original_profile = self.profile - if "profile_overrides" in data and isinstance(data["profile_overrides"], dict): - try: - self.profile = self.profile.merged(**data["profile_overrides"]) - except Exception: - # 失败则回退，不中断主流程 - self.profile = original_profile - - try: - return self.step(user_query) - finally: - # 还原 - self.max_steps = original_max - self.profile = original_profile - - raise TypeError("AgentRuntime.execute 仅接受 str 或 dict 两种输入。") diff --git a/packages/sage-middleware/src/sage/middleware/operators/agentic/__init__.py b/packages/sage-middleware/src/sage/middleware/operators/agentic/__init__.py deleted file mode 100644 index 2769ff33ab..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/agentic/__init__.py +++ /dev/null @@ -1,41 +0,0 @@ -"""L4 Agentic Operators. - -This package exposes ready-to-use operator wrappers (MapOperators) built on -sage.libs.agentic components so Studio and pipeline builders can drag-and-drop -agent runtimes without wiring boilerplate. - -Supports engine_type switching for LLM generators: -- sagellm (default): SageLLMGenerator with configurable backend - - backend_type="auto": Automatically select best available backend - - backend_type="mock": Mock backend for testing without GPU - - backend_type="cuda": NVIDIA CUDA backend - - backend_type="ascend": Huawei Ascend NPU backend -- openai: OpenAIGenerator for OpenAI-compatible APIs -- hf: HFGenerator for HuggingFace models -""" - -from .config import ( - AgentRuntimeConfig, - GeneratorConfig, - ProfileConfig, - RuntimeSettings, -) -from .planning_operator import PlanningOperator -from .refined_searcher import RefinedSearcherOperator -from .runtime import AgentRuntimeOperator -from .timing_operator import TimingOperator -from .tool_selection_operator import ToolSelectionOperator - -__all__ = [ - # Operators - "AgentRuntimeOperator", - "ToolSelectionOperator", - "PlanningOperator", - "TimingOperator", - "RefinedSearcherOperator", - # Config classes - "AgentRuntimeConfig", - "GeneratorConfig", - "ProfileConfig", - "RuntimeSettings", -] diff --git a/packages/sage-middleware/src/sage/middleware/operators/agentic/config.py b/packages/sage-middleware/src/sage/middleware/operators/agentic/config.py deleted file mode 100644 index c727e06f68..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/agentic/config.py +++ /dev/null @@ -1,254 +0,0 @@ -"""Agent Runtime Operator Configuration. - -Provides dataclass-based configuration for AgentRuntimeOperator. -""" - -from __future__ import annotations - -from dataclasses import dataclass, field -from typing import Any, Literal - - -@dataclass -class GeneratorConfig: - """Generator configuration for agent LLM calls. - - Attributes: - engine_type: Engine type to use: - - "sagellm" (default): SageLLMGenerator with configurable backend - - "openai": OpenAIGenerator for OpenAI-compatible APIs - - "hf": HFGenerator for HuggingFace models - backend_type: Backend type for sagellm engine: - - "auto" (default): Automatically select best available backend - - "mock": Mock backend for testing without GPU - - "cuda": NVIDIA CUDA backend - - "ascend": Huawei Ascend NPU backend - model_path: Model path or HuggingFace model ID (sagellm only) - device_map: Device mapping strategy (auto/cuda:0/cpu) - dtype: Data type (auto/float16/bfloat16) - max_tokens: Maximum generation tokens - temperature: Sampling temperature - top_p: Nucleus sampling parameter - top_k: Top-k sampling parameter - timeout: Request timeout in seconds - default_options: Default generation options - model_name: Model name for OpenAI (openai only) - base_url: API base URL (openai only) - api_key: API key (openai only) - """ - - engine_type: Literal["sagellm", "openai", "hf"] = "sagellm" - - # SageLLM options - backend_type: str = "auto" - model_path: str = "" - device_map: str = "auto" - dtype: str = "auto" - max_tokens: int = 2048 - temperature: float = 0.7 - top_p: float = 0.95 - top_k: int = 50 - timeout: float = 120.0 - default_options: dict[str, Any] = field(default_factory=dict) - - # OpenAI options - model_name: str = "" - base_url: str = "" - api_key: str = "" - - def to_dict(self) -> dict[str, Any]: - """Convert to dictionary for operator initialization.""" - return { - "engine_type": self.engine_type, - "backend_type": self.backend_type, - "model_path": self.model_path, - "device_map": self.device_map, - "dtype": self.dtype, - "max_tokens": self.max_tokens, - "temperature": self.temperature, - "top_p": self.top_p, - "top_k": self.top_k, - "timeout": self.timeout, - "default_options": self.default_options, - "model_name": self.model_name, - "base_url": self.base_url, - "api_key": self.api_key, - } - - -@dataclass -class ProfileConfig: - """Agent profile configuration. - - Attributes: - name: Agent name - description: Agent description - role: Agent role (assistant/user/system) - system_prompt: System prompt for the agent - """ - - name: str = "DefaultAgent" - description: str = "A general-purpose AI assistant" - role: str = "assistant" - system_prompt: str = "You are a helpful assistant." - - def to_dict(self) -> dict[str, Any]: - """Convert to dictionary for operator initialization.""" - return { - "name": self.name, - "description": self.description, - "role": self.role, - "system_prompt": self.system_prompt, - } - - -@dataclass -class RuntimeSettings: - """Runtime settings for agent execution. - - Attributes: - max_steps: Maximum execution steps - summarizer: Summarizer config (null/"reuse_generator"/dict) - """ - - max_steps: int = 6 - summarizer: str | dict[str, Any] | None = "reuse_generator" - - def to_dict(self) -> dict[str, Any]: - """Convert to dictionary for operator initialization.""" - return { - "max_steps": self.max_steps, - "summarizer": self.summarizer, - } - - -@dataclass -class AgentRuntimeConfig: - """Complete configuration for AgentRuntimeOperator. - - Example: - ```python - # Create config with mock backend for testing - config = AgentRuntimeConfig( - generator=GeneratorConfig( - engine_type="sagellm", - backend_type="mock", - ), - profile=ProfileConfig(name="TestBot"), - ) - operator = AgentRuntimeOperator(config=config.to_dict()) - - # Create config with OpenAI - config = AgentRuntimeConfig( - generator=GeneratorConfig( - engine_type="openai", - model_name="gpt-4o-mini", - api_key="sk-xxx", # pragma: allowlist secret - ), - ) - ``` - - Attributes: - generator: Generator configuration - profile: Agent profile configuration - planner: Planner configuration (optional) - tools: List of tool specifications - runtime: Runtime settings - """ - - generator: GeneratorConfig = field(default_factory=GeneratorConfig) - profile: ProfileConfig = field(default_factory=ProfileConfig) - planner: dict[str, Any] = field(default_factory=dict) - tools: list[dict[str, Any]] = field(default_factory=list) - runtime: RuntimeSettings = field(default_factory=RuntimeSettings) - - def to_dict(self) -> dict[str, Any]: - """Convert to dictionary for operator initialization.""" - return { - "generator": self.generator.to_dict(), - "profile": self.profile.to_dict(), - "planner": self.planner, - "tools": self.tools, - "runtime": self.runtime.to_dict(), - } - - @classmethod - def for_mock_testing(cls, profile_name: str = "TestBot") -> AgentRuntimeConfig: - """Create a configuration for mock testing. - - Args: - profile_name: Name for the test agent profile - - Returns: - AgentRuntimeConfig configured for mock backend - """ - return cls( - generator=GeneratorConfig( - engine_type="sagellm", - backend_type="mock", - ), - profile=ProfileConfig(name=profile_name), - ) - - @classmethod - def for_openai( - cls, - model_name: str = "gpt-4o-mini", - api_key: str = "", - base_url: str = "https://api.openai.com/v1", - profile_name: str = "OpenAIAgent", - ) -> AgentRuntimeConfig: - """Create a configuration for OpenAI. - - Args: - model_name: OpenAI model name - api_key: OpenAI API key - base_url: API base URL - profile_name: Name for the agent profile - - Returns: - AgentRuntimeConfig configured for OpenAI - """ - return cls( - generator=GeneratorConfig( - engine_type="openai", - model_name=model_name, - api_key=api_key, - base_url=base_url, - ), - profile=ProfileConfig(name=profile_name), - ) - - @classmethod - def for_sagellm( - cls, - model_path: str, - backend_type: str = "auto", - profile_name: str = "SageLLMAgent", - ) -> AgentRuntimeConfig: - """Create a configuration for SageLLM. - - Args: - model_path: Model path or HuggingFace model ID - backend_type: Backend type (auto/mock/cuda/ascend) - profile_name: Name for the agent profile - - Returns: - AgentRuntimeConfig configured for SageLLM - """ - return cls( - generator=GeneratorConfig( - engine_type="sagellm", - backend_type=backend_type, - model_path=model_path, - ), - profile=ProfileConfig(name=profile_name), - ) - - -__all__ = [ - "AgentRuntimeConfig", - "GeneratorConfig", - "ProfileConfig", - "RuntimeSettings", -] diff --git a/packages/sage-middleware/src/sage/middleware/operators/agentic/configs/planning_operator.yaml b/packages/sage-middleware/src/sage/middleware/operators/agentic/configs/planning_operator.yaml deleted file mode 100644 index 421408f529..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/agentic/configs/planning_operator.yaml +++ /dev/null @@ -1,15 +0,0 @@ -# Operator Configuration Example - Planning Operator - -planner: - name: "hierarchical" - min_steps: 3 - max_steps: 12 - enable_repair: true - params: - decomposition_strategy: "goal_based" - max_retries: 2 - -telemetry: - enabled: true - collect_latency: true - output_path: "./outputs/operator_planning.json" diff --git a/packages/sage-middleware/src/sage/middleware/operators/agentic/configs/runtime_operator.yaml b/packages/sage-middleware/src/sage/middleware/operators/agentic/configs/runtime_operator.yaml deleted file mode 100644 index bef6333833..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/agentic/configs/runtime_operator.yaml +++ /dev/null @@ -1,57 +0,0 @@ -# Operator Configuration Example - Agent Runtime Operator -# This file shows the complete configuration schema for AgentRuntimeOperator - -# Engine type: sagellm (default) | openai | hf -engine_type: "sagellm" - -# Generator configuration -generator: - # Engine type can also be specified here (overrides top-level) - engine_type: "sagellm" - - # SageLLM-specific options (when engine_type=sagellm) - # Backend types: - # - auto (default): Automatically select best available backend - # - mock: Mock backend for testing without GPU - # - cuda: NVIDIA CUDA backend - # - ascend: Huawei Ascend NPU backend - backend_type: "auto" - model_path: "Qwen/Qwen2.5-7B-Instruct" - device_map: "auto" - dtype: "auto" - max_tokens: 2048 - temperature: 0.7 - top_p: 0.95 - top_k: 50 - timeout: 120.0 - default_options: {} - - # OpenAI-specific options (when engine_type=openai) - # model_name: "gpt-4o-mini" - # base_url: "https://api.openai.com/v1" - # api_key: "${OPENAI_API_KEY}" - -# Agent profile configuration -profile: - name: "DefaultAgent" - description: "A general-purpose AI assistant" - role: "assistant" - system_prompt: "You are a helpful assistant." - -# Tools configuration (list of tool specs) -tools: [] - # Example tool spec: - # - module: "sage.libs.agentic.tools.calculator" - # class: "CalculatorTool" - # init_kwargs: {} - -# Planner configuration -planner: - # Planner type is auto-selected by PlannerRouter based on query intent - default_planner: "llm" - -# Runtime configuration -runtime: - max_steps: 6 - # Summarizer: null | "reuse_generator" | generator config dict - summarizer: "reuse_generator" diff --git a/packages/sage-middleware/src/sage/middleware/operators/agentic/configs/timing_operator.yaml b/packages/sage-middleware/src/sage/middleware/operators/agentic/configs/timing_operator.yaml deleted file mode 100644 index a646d67792..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/agentic/configs/timing_operator.yaml +++ /dev/null @@ -1,14 +0,0 @@ -# Operator Configuration Example - Timing Operator - -timing: - name: "llm_based" - threshold: 0.7 - use_context: true - params: - use_history: true - max_history_length: 10 - -telemetry: - enabled: true - collect_latency: true - output_path: "./outputs/operator_timing.json" diff --git a/packages/sage-middleware/src/sage/middleware/operators/agentic/configs/tool_selection_operator.yaml b/packages/sage-middleware/src/sage/middleware/operators/agentic/configs/tool_selection_operator.yaml deleted file mode 100644 index f4ab3996d2..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/agentic/configs/tool_selection_operator.yaml +++ /dev/null @@ -1,14 +0,0 @@ -# Operator Configuration Example - Tool Selection Operator - -selector: - name: "embedding" - top_k: 10 - cache_enabled: true - params: - model_name: "all-MiniLM-L6-v2" - similarity_threshold: 0.6 - -telemetry: - enabled: true - collect_latency: true - output_path: "./outputs/operator_tool_selection.json" diff --git a/packages/sage-middleware/src/sage/middleware/operators/agentic/planning_operator.py b/packages/sage-middleware/src/sage/middleware/operators/agentic/planning_operator.py deleted file mode 100644 index 34fe2e27a5..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/agentic/planning_operator.py +++ /dev/null @@ -1,125 +0,0 @@ -""" -Planning Operator - -Middleware operator for planning using runtime components. - -Supports engine_type switching: -- sagellm (default): SageLLMGenerator with configurable backend - - backend_type="auto": Automatically select best available backend - - backend_type="mock": Mock backend for testing without GPU - - backend_type="cuda": NVIDIA CUDA backend - - backend_type="ascend": Huawei Ascend NPU backend -- openai: OpenAIGenerator for OpenAI-compatible APIs -- hf: HFGenerator for HuggingFace models -""" - -from typing import Any, Optional - -from sage_libs.sage_agentic.agents.runtime import BenchmarkAdapter, Orchestrator, RuntimeConfig -from sage_libs.sage_agentic.agents.runtime.config import PlannerConfig - -from sage.common.core.functions import MapFunction - -from .runtime import _build_generator - - -class PlanningOperator(MapFunction): - """ - Operator for planning. - - Wraps runtime planner in a middleware operator interface. - - Args: - planner: Planner instance (optional) - config: Configuration dictionary with optional keys: - - planner: Planner-specific config - - generator: Generator config with engine_type/backend_type - - engine_type: Shorthand for generator.engine_type (sagellm/openai/hf) - - backend_type: Shorthand for generator.backend_type (auto/mock/cuda/ascend) - - Example: - ```python - # Using sagellm with mock backend (for testing) - operator = PlanningOperator(config={ - "generator": { - "engine_type": "sagellm", - "backend_type": "mock", - }, - }) - - # Using default sagellm with auto backend - operator = PlanningOperator(config={ - "engine_type": "sagellm", - "backend_type": "auto", - }) - ``` - """ - - def __init__( - self, - planner: Optional[Any] = None, - config: Optional[dict[str, Any]] = None, - ): - """Initialize planning operator. - - Args: - planner: Planner instance (optional) - config: Configuration dictionary - """ - super().__init__() - - # Parse configuration - if config is None: - config = {} - - self.config = config - - # Build generator if config provided - generator_conf = config.get("generator", {}) - # Allow shorthand engine_type/backend_type at top level - if "engine_type" in config and "engine_type" not in generator_conf: - generator_conf["engine_type"] = config["engine_type"] - if "backend_type" in config and "backend_type" not in generator_conf: - generator_conf["backend_type"] = config["backend_type"] - - # Build generator (defaults to sagellm with auto backend) - if generator_conf or not planner: - engine_type = generator_conf.get("engine_type", "sagellm") - # Ensure we have at least minimal config - if not generator_conf: - generator_conf = {"engine_type": "sagellm", "backend_type": "auto"} - self.generator = _build_generator(generator_conf, engine_type=engine_type) - else: - self.generator = None - - planner_config = PlannerConfig(**config.get("planner", {})) - runtime_config = RuntimeConfig(planner=planner_config) - - # Create orchestrator - self.orchestrator = Orchestrator(config=runtime_config, planner=planner) - - # Create adapter for easy use - self.adapter = BenchmarkAdapter(self.orchestrator) - - def __call__(self, request: Any) -> Any: - """Execute planning. - - Args: - request: Planning request - - Returns: - Generated plan - """ - return self.adapter.run_planning(request) - - def execute(self, data: Any) -> Any: - """Execute map function interface.""" - return self.__call__(data) - - def get_metrics(self) -> dict[str, Any]: - """Get performance metrics. - - Returns: - Dictionary of metrics - """ - return self.adapter.get_metrics() diff --git a/packages/sage-middleware/src/sage/middleware/operators/agentic/refined_searcher.py b/packages/sage-middleware/src/sage/middleware/operators/agentic/refined_searcher.py deleted file mode 100644 index dbb92a3263..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/agentic/refined_searcher.py +++ /dev/null @@ -1,132 +0,0 @@ -""" -RefinedSearcherOperator - Search with optional context compression. - -Uses isage-refiner for context compression if enabled. - -Installation: - pip install isage-refiner # Optional, only if refiner is used -""" - -import logging -from typing import Any, AsyncGenerator, Optional - -from sage_libs.sage_agentic.agents.bots.searcher_bot import SearcherBot - -from sage.libs.foundation.tools.tool import BaseTool - -logger = logging.getLogger(__name__) - - -class RefinedSearcherOperator: - """ - L4 Operator that wraps L3 SearcherBot and adds optional refiner capabilities. - - Uses isage-refiner for context compression when refiner_config is provided. - """ - - name = "search_internet" - description = "Search the internet for information using multiple sources (Arxiv, etc)." - input_schema = { - "type": "object", - "properties": {"query": {"type": "string", "description": "The search query"}}, - "required": ["query"], - } - - input_types = {"query": "str - The search query"} - - def __init__( - self, tools: list[BaseTool], refiner_config: Optional[dict[str, Any]] = None, **kwargs - ): - self.bot = SearcherBot(tools=tools, **kwargs) - - self.compressor = None - if refiner_config: - try: - algorithm = refiner_config.get("algorithm", "long_refiner").lower() - self._init_compressor(algorithm, refiner_config) - logger.info(f"RefinedSearcherOperator: Initialized {algorithm} compressor") - except ImportError as e: - logger.warning( - f"RefinedSearcherOperator: isage-refiner not installed: {e}\n" - f"Install with: pip install isage-refiner" - ) - except Exception as e: - logger.warning(f"RefinedSearcherOperator: Failed to init compressor: {e}") - - def _init_compressor(self, algorithm: str, config: dict[str, Any]): - """Initialize compressor from isage-refiner.""" - if algorithm == "long_refiner": - from sage_refiner import LongRefinerCompressor - - self.compressor = LongRefinerCompressor( - base_model_path=config.get("base_model_path", "Qwen/Qwen2.5-3B-Instruct"), - score_model_path=config.get("score_model_path", "BAAI/bge-reranker-v2-m3"), - max_model_len=config.get("max_model_len", 25000), - gpu_memory_utilization=config.get("gpu_memory_utilization", 0.5), - ) - elif algorithm == "reform": - from sage_refiner import REFORMCompressor - - self.compressor = REFORMCompressor(**config.get("reform_config", {})) - elif algorithm == "provence": - from sage_refiner import ProvenceCompressor - - self.compressor = ProvenceCompressor(**config.get("provence_config", {})) - else: - raise ValueError(f"Unsupported algorithm: {algorithm}") - - self.budget = config.get("budget", 2048) - - def call(self, arguments: dict) -> Any: - """MCP compatible call method""" - import asyncio - - query = arguments.get("query") - try: - loop = asyncio.get_running_loop() - if loop.is_running(): - return asyncio.run(self.execute(query)) - except RuntimeError: - return asyncio.run(self.execute(query)) - - return asyncio.run(self.execute(query)) - - async def execute(self, query: str) -> dict[str, Any]: - """Execute search and optionally compress results.""" - data = query - # 1. Execute L3 Bot - raw_result = await self.bot.execute(data) - results = raw_result.get("results", []) - - # 2. Compress if enabled - if self.compressor and results: - query_str = data if isinstance(data, str) else data.get("query", "") - try: - logger.info(f"Compressing {len(results)} results for query: {query_str}") - - # Normalize documents to isage-refiner format - documents = [ - {"contents": r.get("contents") or r.get("text") or str(r)} for r in results - ] - - compress_result = self.compressor.compress( - question=query_str, - document_list=documents, - budget=self.budget, - ) - - return { - "results": compress_result.get("compressed_context", ""), - "original_count": len(results), - "compressed": True, - } - except Exception as e: - logger.error(f"Compression failed: {e}") - return raw_result - - return raw_result - - async def execute_stream(self, data: Any) -> AsyncGenerator[dict[str, Any], None]: - """Stream execution. Compression is batch, so just stream search events.""" - async for event in self.bot.execute_stream(data): - yield event diff --git a/packages/sage-middleware/src/sage/middleware/operators/agentic/runtime.py b/packages/sage-middleware/src/sage/middleware/operators/agentic/runtime.py deleted file mode 100644 index 658cedc28c..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/agentic/runtime.py +++ /dev/null @@ -1,241 +0,0 @@ -from __future__ import annotations - -import warnings -from importlib import import_module -from typing import Any - -from sage_libs.sage_agentic.agents.action.mcp_registry import MCPRegistry -from sage_libs.sage_agentic.agents.profile.profile import BaseProfile - -from sage.common.core.functions import MapFunction as MapOperator -from sage.middleware.operators.agent.runtime import AgentRuntime -from sage.middleware.operators.rag.generator import HFGenerator, OpenAIGenerator - - -def _maybe_instantiate(spec: dict[str, Any]): - module_path = spec["module"] - class_name = spec["class"] - kwargs = spec.get("init_kwargs", {}) - module = import_module(module_path) - ctor = getattr(module, class_name) - return ctor(**kwargs) if kwargs else ctor() - - -def _build_generator(config: Any, engine_type: str = "sagellm"): - """构建 LLM 生成器 - - Args: - config: 生成器配置，可以是已实例化的对象、模块路径配置或参数字典 - engine_type: 引擎类型，支持 "sagellm"（默认）/ "openai" / "hf" - 注意: "vllm" 已废弃，会自动转换为 sagellm 并发出警告 - - Returns: - 生成器实例 - """ - if not config: - raise ValueError("generator config/object is required for AgentRuntimeOperator") - - # 如果已经是生成器实例，直接返回 - if hasattr(config, "execute") or hasattr(config, "generate"): - return config - - # 模块路径方式实例化 - if isinstance(config, dict) and "module" in config and "class" in config: - return _maybe_instantiate(config) - - # 从配置中获取 engine_type（如果存在），覆盖参数 - if isinstance(config, dict): - engine_type = config.get("engine_type", engine_type) - - # 根据 engine_type 选择生成器 - if engine_type == "vllm": - # vllm 已废弃，自动转换为 sagellm - warnings.warn( - "engine_type='vllm' is deprecated for agent generators. " - "Automatically using engine_type='sagellm' instead. " - "Please update your configuration.", - DeprecationWarning, - stacklevel=3, - ) - engine_type = "sagellm" # 转换为 sagellm - - if engine_type == "sagellm": - # 默认使用 sagellm - from sage.middleware.operators.llm import SageLLMGenerator - - if isinstance(config, dict): - return SageLLMGenerator( - backend_type=config.get("backend_type", "auto"), - model_path=config.get("model_path", ""), - device_map=config.get("device_map", "auto"), - dtype=config.get("dtype", "auto"), - max_tokens=config.get("max_tokens", 2048), - temperature=config.get("temperature", 0.7), - top_p=config.get("top_p", 0.95), - top_k=config.get("top_k", 50), - timeout=config.get("timeout", 120.0), - default_options=config.get("default_options", {}), - ) - return SageLLMGenerator() - - elif engine_type in ("openai", "openai-compatible"): - return OpenAIGenerator(config if isinstance(config, dict) else {}) - - elif engine_type in ("hf", "huggingface"): - return HFGenerator(config if isinstance(config, dict) else {}) - - else: - # 兼容旧版 method 参数 - method = "" - if isinstance(config, dict): - method = (config.get("method") or config.get("type") or "").lower() - - if method.startswith("hf") or method.startswith("huggingface"): - return HFGenerator(config) - return OpenAIGenerator(config) - - -from sage.middleware.operators.agent.planning.router import PlannerRouter - - -def _build_planner(config: Any, generator): - if hasattr(config, "plan"): - return config - # planner_conf = config or {} - - # Use PlannerRouter instead of direct LLMPlanner - return PlannerRouter(generator=generator) - - -def _build_profile(config: Any) -> BaseProfile: - if isinstance(config, BaseProfile): - return config - if isinstance(config, dict) and "module" in config and "class" in config: - profile_obj = _maybe_instantiate(config) - if isinstance(profile_obj, BaseProfile): - return profile_obj - return BaseProfile.from_dict(config or {}) - - -def _build_tools(config: Any) -> MCPRegistry: - if isinstance(config, MCPRegistry): - return config - registry = MCPRegistry() - specs: list[Any] - if isinstance(config, dict): - specs = [config] - elif isinstance(config, list): - specs = config - else: - specs = [] - - for spec in specs: - if isinstance(spec, dict) and "module" in spec and "class" in spec: - tool = _maybe_instantiate(spec) - registry.register(tool) - elif hasattr(spec, "call") and hasattr(spec, "name"): - registry.register(spec) - else: - raise ValueError(f"Unsupported tool spec: {spec}") - return registry - - -class AgentRuntimeOperator(MapOperator): - """Wrap AgentRuntime into an L4 operator for drag-and-drop Studio workflows. - - Supports engine_type switching: - - sagellm (default): Use SageLLMGenerator with configurable backend - - openai: Use OpenAIGenerator for OpenAI-compatible APIs - - hf: Use HFGenerator for HuggingFace models - - Backend types for sagellm: - - auto (default): Automatically select best available backend - - mock: Mock backend for testing without GPU - - cuda: NVIDIA CUDA backend - - ascend: Huawei Ascend NPU backend - - Example: - ```python - # Using sagellm with auto backend (default) - operator = AgentRuntimeOperator(config={ - "generator": { - "engine_type": "sagellm", - "backend_type": "auto", - "model_path": "Qwen/Qwen2.5-7B-Instruct", - }, - "profile": {"name": "MyAgent"}, - "tools": [], - }) - - # Using sagellm with mock backend (for testing) - operator = AgentRuntimeOperator(config={ - "generator": { - "engine_type": "sagellm", - "backend_type": "mock", - }, - "profile": {"name": "TestBot"}, - "tools": [], - }) - - # Using OpenAI - operator = AgentRuntimeOperator(config={ - "generator": { - "engine_type": "openai", - "model_name": "gpt-4o-mini", - "api_key": "sk-xxx", # pragma: allowlist secret - }, - "profile": {"name": "MyAgent"}, - "tools": [], - }) - ``` - """ - - def __init__( - self, config: dict[str, Any] | None = None, enable_profile: bool = False, **kwargs - ): - super().__init__(**kwargs) - self.enable_profile = enable_profile - self.config = config or {} - - profile_conf = self.config.get("profile", {}) - generator_conf = self.config.get("generator") - planner_conf = self.config.get("planner", {}) - tools_conf = self.config.get("tools", []) - runtime_conf = self.config.get("runtime", {}) - - # 获取 engine_type（优先从 generator 配置，其次从顶层配置） - engine_type = "sagellm" # 默认值 - if isinstance(generator_conf, dict): - engine_type = generator_conf.get("engine_type", engine_type) - elif "engine_type" in self.config: - engine_type = self.config["engine_type"] - - self.engine_type = engine_type - self.profile = _build_profile(profile_conf) - self.generator = _build_generator(generator_conf, engine_type=engine_type) - self.planner = _build_planner(planner_conf, self.generator) - self.tools = _build_tools(tools_conf) - - summarizer_conf = runtime_conf.get("summarizer") - if summarizer_conf == "reuse_generator": - self.summarizer = self.generator - elif summarizer_conf: - self.summarizer = _build_generator(summarizer_conf) - else: - self.summarizer = None - - self.max_steps = runtime_conf.get("max_steps", 6) - self.runtime = AgentRuntime( - profile=self.profile, - planner=self.planner, - tools=self.tools, - summarizer=self.summarizer, - max_steps=self.max_steps, - ) - - def execute(self, data: Any) -> Any: - if isinstance(data, dict): - return self.runtime.execute(data) - if isinstance(data, str): - return self.runtime.execute({"query": data}) - raise TypeError("AgentRuntimeOperator expects str or dict payloads") diff --git a/packages/sage-middleware/src/sage/middleware/operators/agentic/timing_operator.py b/packages/sage-middleware/src/sage/middleware/operators/agentic/timing_operator.py deleted file mode 100644 index 1d1a29bbea..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/agentic/timing_operator.py +++ /dev/null @@ -1,125 +0,0 @@ -""" -Timing Operator - -Middleware operator for timing decisions using runtime components. - -Supports engine_type switching: -- sagellm (default): SageLLMGenerator with configurable backend - - backend_type="auto": Automatically select best available backend - - backend_type="mock": Mock backend for testing without GPU - - backend_type="cuda": NVIDIA CUDA backend - - backend_type="ascend": Huawei Ascend NPU backend -- openai: OpenAIGenerator for OpenAI-compatible APIs -- hf: HFGenerator for HuggingFace models -""" - -from typing import Any, Optional - -from sage_libs.sage_agentic.agents.runtime import BenchmarkAdapter, Orchestrator, RuntimeConfig -from sage_libs.sage_agentic.agents.runtime.config import TimingConfig - -from sage.common.core.functions import MapFunction - -from .runtime import _build_generator - - -class TimingOperator(MapFunction): - """ - Operator for timing decisions. - - Wraps runtime timing decider in a middleware operator interface. - - Args: - timing_decider: Timing decider instance (optional) - config: Configuration dictionary with optional keys: - - timing: Timing-specific config - - generator: Generator config with engine_type/backend_type - - engine_type: Shorthand for generator.engine_type (sagellm/openai/hf) - - backend_type: Shorthand for generator.backend_type (auto/mock/cuda/ascend) - - Example: - ```python - # Using sagellm with mock backend (for testing) - operator = TimingOperator(config={ - "generator": { - "engine_type": "sagellm", - "backend_type": "mock", - }, - }) - - # Using default sagellm with auto backend - operator = TimingOperator(config={ - "engine_type": "sagellm", - "backend_type": "auto", - }) - ``` - """ - - def __init__( - self, - timing_decider: Optional[Any] = None, - config: Optional[dict[str, Any]] = None, - ): - """Initialize timing operator. - - Args: - timing_decider: Timing decider instance (optional) - config: Configuration dictionary - """ - super().__init__() - - # Parse configuration - if config is None: - config = {} - - self.config = config - - # Build generator if config provided - generator_conf = config.get("generator", {}) - # Allow shorthand engine_type/backend_type at top level - if "engine_type" in config and "engine_type" not in generator_conf: - generator_conf["engine_type"] = config["engine_type"] - if "backend_type" in config and "backend_type" not in generator_conf: - generator_conf["backend_type"] = config["backend_type"] - - # Build generator (defaults to sagellm with auto backend) - if generator_conf or not timing_decider: - engine_type = generator_conf.get("engine_type", "sagellm") - # Ensure we have at least minimal config - if not generator_conf: - generator_conf = {"engine_type": "sagellm", "backend_type": "auto"} - self.generator = _build_generator(generator_conf, engine_type=engine_type) - else: - self.generator = None - - timing_config = TimingConfig(**config.get("timing", {})) - runtime_config = RuntimeConfig(timing=timing_config) - - # Create orchestrator - self.orchestrator = Orchestrator(config=runtime_config, timing_decider=timing_decider) - - # Create adapter for easy use - self.adapter = BenchmarkAdapter(self.orchestrator) - - def __call__(self, message: Any) -> Any: - """Execute timing decision. - - Args: - message: Message to evaluate - - Returns: - Timing decision - """ - return self.adapter.run_timing(message) - - def execute(self, data: Any) -> Any: - """Execute map function interface.""" - return self.__call__(data) - - def get_metrics(self) -> dict[str, Any]: - """Get performance metrics. - - Returns: - Dictionary of metrics - """ - return self.adapter.get_metrics() diff --git a/packages/sage-middleware/src/sage/middleware/operators/agentic/tool_selection_operator.py b/packages/sage-middleware/src/sage/middleware/operators/agentic/tool_selection_operator.py deleted file mode 100644 index 82d962b8c2..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/agentic/tool_selection_operator.py +++ /dev/null @@ -1,127 +0,0 @@ -""" -Tool Selection Operator - -Middleware operator for tool selection using runtime components. - -Supports engine_type switching: -- sagellm (default): SageLLMGenerator with configurable backend - - backend_type="auto": Automatically select best available backend - - backend_type="mock": Mock backend for testing without GPU - - backend_type="cuda": NVIDIA CUDA backend - - backend_type="ascend": Huawei Ascend NPU backend -- openai: OpenAIGenerator for OpenAI-compatible APIs -- hf: HFGenerator for HuggingFace models -""" - -from typing import Any, Optional - -from sage_libs.sage_agentic.agents.runtime import BenchmarkAdapter, Orchestrator, RuntimeConfig -from sage_libs.sage_agentic.agents.runtime.config import SelectorConfig - -from sage.common.core.functions import MapFunction - -from .runtime import _build_generator - - -class ToolSelectionOperator(MapFunction): - """ - Operator for tool selection. - - Wraps runtime tool selector in a middleware operator interface. - - Args: - selector: Tool selector instance (optional) - config: Configuration dictionary with optional keys: - - selector: Selector-specific config (e.g., top_k) - - generator: Generator config with engine_type/backend_type - - engine_type: Shorthand for generator.engine_type (sagellm/openai/hf) - - backend_type: Shorthand for generator.backend_type (auto/mock/cuda/ascend) - - Example: - ```python - # Using sagellm with mock backend (for testing) - operator = ToolSelectionOperator(config={ - "generator": { - "engine_type": "sagellm", - "backend_type": "mock", - }, - "selector": {"top_k": 5}, - }) - - # Using default sagellm with auto backend - operator = ToolSelectionOperator(config={ - "engine_type": "sagellm", - "backend_type": "auto", - }) - ``` - """ - - def __init__( - self, - selector: Optional[Any] = None, - config: Optional[dict[str, Any]] = None, - ): - """Initialize tool selection operator. - - Args: - selector: Tool selector instance (optional) - config: Configuration dictionary - """ - super().__init__() - - # Parse configuration - if config is None: - config = {} - - self.config = config - - # Build generator if config provided - generator_conf = config.get("generator", {}) - # Allow shorthand engine_type/backend_type at top level - if "engine_type" in config and "engine_type" not in generator_conf: - generator_conf["engine_type"] = config["engine_type"] - if "backend_type" in config and "backend_type" not in generator_conf: - generator_conf["backend_type"] = config["backend_type"] - - # Build generator (defaults to sagellm with auto backend) - if generator_conf or not selector: - engine_type = generator_conf.get("engine_type", "sagellm") - # Ensure we have at least minimal config - if not generator_conf: - generator_conf = {"engine_type": "sagellm", "backend_type": "auto"} - self.generator = _build_generator(generator_conf, engine_type=engine_type) - else: - self.generator = None - - selector_config = SelectorConfig(**config.get("selector", {})) - runtime_config = RuntimeConfig(selector=selector_config) - - # Create orchestrator - self.orchestrator = Orchestrator(config=runtime_config, selector=selector) - - # Create adapter for easy use - self.adapter = BenchmarkAdapter(self.orchestrator) - - def __call__(self, query: Any) -> list[Any]: - """Execute tool selection. - - Args: - query: Tool selection query - - Returns: - List of selected tools - """ - top_k = self.config.get("selector", {}).get("top_k", 5) - return self.adapter.run_tool_selection(query, top_k=top_k) - - def execute(self, data: Any) -> list[Any]: - """Execute map function interface.""" - return self.__call__(data) - - def get_metrics(self) -> dict[str, Any]: - """Get performance metrics. - - Returns: - Dictionary of metrics - """ - return self.adapter.get_metrics() diff --git a/packages/sage-middleware/src/sage/middleware/operators/context/__init__.py b/packages/sage-middleware/src/sage/middleware/operators/context/__init__.py deleted file mode 100644 index 311753007d..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/context/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -"""Business context for Agent and RAG workflows.""" - -from sage.middleware.operators.context.critic_evaluation import CriticEvaluation -from sage.middleware.operators.context.model_context import ModelContext -from sage.middleware.operators.context.quality_label import QualityLabel -from sage.middleware.operators.context.search_query_results import SearchQueryResults -from sage.middleware.operators.context.search_result import SearchResult -from sage.middleware.operators.context.search_session import SearchSession - -__all__ = [ - "ModelContext", - "SearchSession", - "CriticEvaluation", - "QualityLabel", - "SearchResult", - "SearchQueryResults", -] diff --git a/packages/sage-middleware/src/sage/middleware/operators/context/critic_evaluation.py b/packages/sage-middleware/src/sage/middleware/operators/context/critic_evaluation.py deleted file mode 100644 index 1007743b48..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/context/critic_evaluation.py +++ /dev/null @@ -1,16 +0,0 @@ -from dataclasses import dataclass, field - -from .quality_label import QualityLabel - - -@dataclass -class CriticEvaluation: - """Critic评估结果""" - - label: QualityLabel - confidence: float # 0.0-1.0 - reasoning: str - specific_issues: list[str] = field(default_factory=list) - suggestions: list[str] = field(default_factory=list) - should_return_to_chief: bool = False - ready_for_output: bool = False diff --git a/packages/sage-middleware/src/sage/middleware/operators/context/model_context.py b/packages/sage-middleware/src/sage/middleware/operators/context/model_context.py deleted file mode 100644 index 50e4a0ef3a..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/context/model_context.py +++ /dev/null @@ -1,565 +0,0 @@ -import json -import time -from dataclasses import dataclass, field -from pathlib import Path -from typing import Any -from uuid import uuid4 - -from .critic_evaluation import CriticEvaluation -from .quality_label import QualityLabel -from .search_query_results import SearchQueryResults -from .search_result import SearchResult -from .search_session import SearchSession - - -@dataclass -class ModelContext: - # Packet metadata - sequence: int = 0 - timestamp: int = field(default_factory=lambda: int(time.time() * 1000)) - # Generator content - raw_question: str | None = None - # 保留原有的retriver_chunks用于向后兼容，但优先使用search_session - retriver_chunks: list[str] = field(default_factory=list) - # 新的分层搜索结果结构 - search_session: SearchSession | None = None - prompts: list[dict[str, str]] = field(default_factory=list) - response: str | None = None - uuid: str = field(default_factory=lambda: str(uuid4())) - tool_name: str | None = None - evaluation: CriticEvaluation | None = None - # Tool configuration - 存储工具相关的配置和中间结果 - tool_config: dict[str, Any] = field(default_factory=dict) - - def __str__(self) -> str: - """格式化显示ModelContext内容""" - # 时间格式化 - timestamp_str = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(self.timestamp / 1000)) - - # 构建输出字符串 - output_lines = [] - output_lines.append("=" * 80) - - # 标题行 - title_parts = [f"🤖 AI Processing Result [ID: {self.uuid[:8]}]"] - if self.tool_name: - tool_emoji = self._get_tool_emoji(self.tool_name) - title_parts.append(f"{tool_emoji} Tool: {self.tool_name}") - - output_lines.append(" | ".join(title_parts)) - output_lines.append(f"📅 Time: {timestamp_str} | Sequence: {self.sequence}") - - # 评估状态行 - if self.evaluation: - quality_emoji = self._get_quality_emoji(self.evaluation.label) - status_parts = [ - f"{quality_emoji} Quality: {self.evaluation.label.value}", - f"Confidence: {self.evaluation.confidence:.2f}", - f"Output Ready: {'✅' if self.evaluation.ready_for_output else '❌'}", - ] - output_lines.append("📊 " + " | ".join(status_parts)) - - output_lines.append("=" * 80) - - # 原始问题 - if self.raw_question: - output_lines.append("❓ Original Question:") - output_lines.append(f" {self.raw_question}") - output_lines.append("") - - # 工具配置信息 - if self.tool_config: - output_lines.append("🔧 Tool Configuration:") - self._format_tool_config(output_lines) - output_lines.append("") - - # 搜索结果信息（优先使用新的search_session结构） - if self.search_session and self.search_session.query_results: - output_lines.append( - f"🔍 Search Results ({self.search_session.get_total_results_count()} total):" - ) - self._format_search_session(output_lines) - output_lines.append("") - elif self.retriver_chunks: - # 向后兼容：显示老格式的检索结果 - output_lines.append(f"📚 Retrieved Information ({len(self.retriver_chunks)} sources):") - for i, chunk in enumerate(self.retriver_chunks[:3], 1): - preview = chunk[:150] + "..." if len(chunk) > 150 else chunk - output_lines.append(f" [{i}] {preview}") - - if len(self.retriver_chunks) > 3: - output_lines.append(f" ... and {len(self.retriver_chunks) - 3} more sources") - output_lines.append("") - - # 处理步骤信息 - if self.prompts: - output_lines.append("⚙️ Processing Steps:") - system_prompts = [p for p in self.prompts if p.get("role") == "system"] - user_prompts = [p for p in self.prompts if p.get("role") == "user"] - - if system_prompts: - output_lines.append(f" • System instructions: {len(system_prompts)} phases") - if user_prompts: - last_user_prompt = user_prompts[-1].get("content", "") - if last_user_prompt and last_user_prompt != self.raw_question: - preview = ( - last_user_prompt[:100] + "..." - if len(last_user_prompt) > 100 - else last_user_prompt - ) - output_lines.append(f" • Specific task: {preview}") - output_lines.append("") - - # AI响应 - if self.response: - output_lines.append("🎯 AI Response:") - response_lines = self.response.split("\n") - for line in response_lines: - output_lines.append(f" {line}") - output_lines.append("") - - # 评估详情 - if self.evaluation: - output_lines.append("🔍 Evaluation Details:") - output_lines.append(f" • Reasoning: {self.evaluation.reasoning}") - - if self.evaluation.specific_issues: - output_lines.append(f" • Issues: {', '.join(self.evaluation.specific_issues)}") - - if self.evaluation.suggestions: - output_lines.append(f" • Suggestions: {', '.join(self.evaluation.suggestions)}") - - if self.evaluation.should_return_to_chief: - output_lines.append(" • ⚠️ Should return to Chief for reprocessing") - output_lines.append("") - - # 状态指示 - status_indicators = [] - if self.tool_name: - status_indicators.append(f"Tool: {self.tool_name}") - if self.response: - status_indicators.append("✅ Response Generated") - else: - status_indicators.append("⏳ Processing") - - # 搜索结果状态 - total_results = 0 - if self.search_session: - total_results = self.search_session.get_total_results_count() - status_indicators.append(f"🔍 {total_results} search results") - elif self.retriver_chunks: - total_results = len(self.retriver_chunks) - status_indicators.append(f"📊 {total_results} chunks") - - if self.evaluation: - status_indicators.append(f"🔍 Evaluated ({self.evaluation.label.value})") - if self.tool_config: - status_indicators.append("🔧 Tool Config") - - if status_indicators: - output_lines.append(f"📋 Status: {' | '.join(status_indicators)}") - output_lines.append("") - - output_lines.append("=" * 80) - return "\n".join(output_lines) - - def _format_search_session(self, output_lines: list[str]) -> None: - """格式化搜索会话的显示""" - if not self.search_session: - return - for i, query_result in enumerate(self.search_session.query_results, 1): - output_lines.append( - f" Query {i}: '{query_result.query}' ({query_result.get_results_count()} results)" - ) - - # 显示前3个结果 - for j, result in enumerate(query_result.get_top_results(3), 1): - title_preview = ( - result.title[:80] + "..." if len(result.title) > 80 else result.title - ) - content_preview = ( - result.content[:100] + "..." if len(result.content) > 100 else result.content - ) - output_lines.append(f" [{j}] {title_preview}") - output_lines.append(f" {content_preview}") - output_lines.append(f" Source: {result.source}") - - if query_result.get_results_count() > 3: - output_lines.append( - f" ... and {query_result.get_results_count() - 3} more results" - ) - - def _format_tool_config(self, output_lines: list[str]) -> None: - """格式化工具配置信息的显示""" - for key, value in self.tool_config.items(): - if key == "search_queries": - if isinstance(value, list) and value: - output_lines.append(f" • Search Queries ({len(value)}):") - for i, query in enumerate(value[:5], 1): - preview = query[:80] + "..." if len(query) > 80 else query - output_lines.append(f" [{i}] {preview}") - if len(value) > 5: - output_lines.append(f" ... and {len(value) - 5} more queries") - else: - output_lines.append(f" • Search Queries: {value}") - - elif key == "search_analysis": - if isinstance(value, dict): - output_lines.append(" • Search Analysis:") - if "analysis" in value: - analysis_text = ( - value["analysis"][:100] + "..." - if len(str(value["analysis"])) > 100 - else value["analysis"] - ) - output_lines.append(f" - Analysis: {analysis_text}") - if "reasoning" in value: - reasoning_text = ( - value["reasoning"][:100] + "..." - if len(str(value["reasoning"])) > 100 - else value["reasoning"] - ) - output_lines.append(f" - Reasoning: {reasoning_text}") - else: - output_lines.append(f" • Search Analysis: {value}") - - elif key == "optimization_metadata": - if isinstance(value, dict): - output_lines.append(" • Optimization Metadata:") - for meta_key, meta_value in value.items(): - if isinstance(meta_value, (str, int, float, bool)): - output_lines.append(f" - {meta_key}: {meta_value}") - else: - output_lines.append(f" - {meta_key}: {type(meta_value).__name__}") - else: - output_lines.append(f" • Optimization Metadata: {value}") - - else: - if isinstance(value, (list, dict)): - output_lines.append( - f" • {key.replace('_', ' ').title()}: {type(value).__name__}({len(value)} items)" - ) - else: - value_str = str(value) - if len(value_str) > 50: - value_str = value_str[:50] + "..." - output_lines.append(f" • {key.replace('_', ' ').title()}: {value_str}") - - def _get_tool_emoji(self, tool_name: str) -> str: - """根据工具名称返回对应的emoji""" - tool_emojis = { - "web_search": "🔍", - "knowledge_retrieval": "📖", - "calculator": "🧮", - "code_executor": "💻", - "data_analyzer": "📊", - "translation": "🌐", - "summarizer": "📝", - "fact_checker": "✅", - "image_analyzer": "🖼️", - "weather_service": "🌤️", - "stock_market": "📈", - "news_aggregator": "📰", - "direct_response": "💭", - "error_handler": "⚠️", - } - return tool_emojis.get(tool_name, "🔧") - - def _get_quality_emoji(self, quality_label: QualityLabel) -> str: - """根据质量标签返回对应的emoji""" - quality_emojis = { - QualityLabel.COMPLETE_EXCELLENT: "🌟", - QualityLabel.COMPLETE_GOOD: "✅", - QualityLabel.PARTIAL_NEEDS_IMPROVEMENT: "⚡", - QualityLabel.INCOMPLETE_MISSING_INFO: "❓", - QualityLabel.FAILED_POOR_QUALITY: "❌", - QualityLabel.ERROR_INVALID: "⚠️", - } - return quality_emojis.get(quality_label, "❔") - - def to_dict(self) -> dict[str, Any]: - """转换为字典格式""" - result: dict[str, Any] = {} - - # 基础字段 - result["sequence"] = self.sequence - result["timestamp"] = self.timestamp - result["raw_question"] = self.raw_question - result["retriver_chunks"] = self.retriver_chunks.copy() if self.retriver_chunks else [] - result["prompts"] = self.prompts.copy() if self.prompts else [] - result["response"] = self.response - result["uuid"] = self.uuid - result["tool_name"] = self.tool_name - result["tool_config"] = ( - self._deep_copy_tool_config(self.tool_config) if self.tool_config else {} - ) - - # 搜索会话 - if self.search_session: - result["search_session"] = self.search_session.to_dict() - else: - result["search_session"] = None - - # 处理evaluation字段 - if self.evaluation: - eval_dict = { - "label": self.evaluation.label.value, - "confidence": self.evaluation.confidence, - "reasoning": self.evaluation.reasoning, - "specific_issues": self.evaluation.specific_issues.copy(), - "suggestions": self.evaluation.suggestions.copy(), - "should_return_to_chief": self.evaluation.should_return_to_chief, - "ready_for_output": self.evaluation.ready_for_output, - } - result["evaluation"] = eval_dict - else: - result["evaluation"] = None - - return result - - def _deep_copy_tool_config(self, config: dict[str, Any]) -> dict[str, Any]: - """深拷贝tool_config""" - import copy - - return copy.deepcopy(config) - - @classmethod - def from_dict(cls, data: dict[str, Any]) -> "ModelContext": - """从字典创建ModelContext实例""" - data = data.copy() - - # 处理evaluation字段 - evaluation = None - if data.get("evaluation"): - eval_data = data["evaluation"] - label = QualityLabel(eval_data["label"]) - - evaluation = CriticEvaluation( - label=label, - confidence=eval_data.get("confidence", 0.0), - reasoning=eval_data.get("reasoning", ""), - specific_issues=eval_data.get("specific_issues", []), - suggestions=eval_data.get("suggestions", []), - should_return_to_chief=eval_data.get("should_return_to_chief", False), - ready_for_output=eval_data.get("ready_for_output", False), - ) - - # 处理search_session字段 - search_session = None - if data.get("search_session"): - search_session = SearchSession.from_dict(data["search_session"]) - - return cls( - sequence=data.get("sequence", 0), - timestamp=data.get("timestamp", int(time.time() * 1000)), - raw_question=data.get("raw_question"), - retriver_chunks=data.get("retriver_chunks", []), - search_session=search_session, - prompts=data.get("prompts", []), - response=data.get("response"), - uuid=data.get("uuid", str(uuid4())), - tool_name=data.get("tool_name"), - evaluation=evaluation, - tool_config=data.get("tool_config", {}), - ) - - # 搜索结果相关方法 - def create_search_session(self, original_question: str | None = None) -> SearchSession: - """创建新的搜索会话""" - if not self.search_session: - self.search_session = SearchSession( - original_question=original_question or self.raw_question or "" - ) - return self.search_session - - def add_search_results( - self, - query: str, - results: list[SearchResult], - search_engine: str = "unknown", - execution_time_ms: int = 0, - total_results_count: int | None = None, - ) -> None: - """添加搜索结果""" - if not self.search_session: - self.create_search_session() - - query_results = SearchQueryResults( - query=query, - results=results, - search_engine=search_engine, - execution_time_ms=execution_time_ms, - total_results_count=total_results_count or len(results), - ) - - if self.search_session: # Add None check - self.search_session.add_query_results(query_results) - - def get_search_queries(self) -> list[str]: - """获取所有搜索查询""" - if self.search_session: - return self.search_session.get_all_queries() - return self.get_tool_config("search_queries", []) - - def get_all_search_results(self) -> list[SearchResult]: - """获取所有搜索结果""" - if self.search_session: - return self.search_session.get_all_results() - return [] - - def get_results_by_query(self, query: str) -> list[SearchResult]: - """根据查询获取结果""" - if self.search_session: - query_results = self.search_session.get_results_by_query(query) - return query_results.results if query_results else [] - return [] - - def get_search_results_count(self) -> int: - """获取搜索结果总数""" - if self.search_session: - return self.search_session.get_total_results_count() - return len(self.retriver_chunks) - - def has_search_results(self) -> bool: - """检查是否有搜索结果""" - return bool( - (self.search_session and self.search_session.get_total_results_count() > 0) - or (self.retriver_chunks and len(self.retriver_chunks) > 0) - ) - - # 向后兼容的方法 - def set_search_queries( - self, queries: list[str], analysis: dict[str, Any] | None = None - ) -> None: - """设置搜索查询（向后兼容）""" - self.set_tool_config("search_queries", queries) - if analysis: - self.set_tool_config("search_analysis", analysis) - - def get_search_analysis(self) -> dict[str, Any]: - """获取搜索分析结果""" - return self.get_tool_config("search_analysis", {}) - - def has_search_queries(self) -> bool: - """检查是否有搜索查询""" - queries = self.get_search_queries() - return bool(queries and len(queries) > 0) - - # Tool Configuration相关方法保持不变... - def set_tool_config(self, key: str, value: Any) -> None: - """设置工具配置项""" - if self.tool_config is None: - self.tool_config = {} - self.tool_config[key] = value - - def get_tool_config(self, key: str, default: Any = None) -> Any: - """获取工具配置项""" - if not self.tool_config: - return default - return self.tool_config.get(key, default) - - def update_tool_config(self, config_dict: dict[str, Any]) -> None: - """批量更新工具配置""" - if self.tool_config is None: - self.tool_config = {} - self.tool_config.update(config_dict) - - def remove_tool_config(self, key: str) -> Any: - """移除工具配置项""" - if not self.tool_config: - return None - return self.tool_config.pop(key, None) - - def has_tool_config(self, key: str) -> bool: - """检查是否存在指定的工具配置项""" - return bool(self.tool_config and key in self.tool_config) - - # JSON序列化方法保持不变... - def to_json(self) -> str: - """转换为JSON字符串""" - return json.dumps(self.to_dict(), ensure_ascii=False, indent=2) - - @classmethod - def from_json(cls, json_str: str) -> "ModelContext": - """从JSON字符串创建ModelContext实例""" - try: - data = json.loads(json_str) - return cls.from_dict(data) - except json.JSONDecodeError as e: - raise ValueError(f"Invalid JSON format: {e}") - except Exception as e: - raise ValueError(f"Failed to create ModelContext from JSON: {e}") - - def save_to_file(self, file_path: str) -> None: - """保存到文件""" - try: - Path(file_path).parent.mkdir(parents=True, exist_ok=True) - with open(file_path, "w", encoding="utf-8") as f: - f.write(self.to_json()) - except Exception as e: - raise OSError(f"Failed to save ModelContext to {file_path}: {e}") - - @classmethod - def load_from_file(cls, file_path: str) -> "ModelContext": - """从文件加载""" - try: - with open(file_path, encoding="utf-8") as f: - return cls.from_json(f.read()) - except FileNotFoundError: - raise FileNotFoundError(f"ModelContext file not found: {file_path}") - except Exception as e: - raise OSError(f"Failed to load ModelContext from {file_path}: {e}") - - def clone(self) -> "ModelContext": - """创建当前模板的深拷贝""" - return self.from_dict(self.to_dict()) - - def update_evaluation( - self, - label: QualityLabel, - confidence: float, - reasoning: str, - issues: list[str] | None = None, - suggestions: list[str] | None = None, - ) -> None: - """更新或创建评估信息""" - self.evaluation = CriticEvaluation( - label=label, - confidence=confidence, - reasoning=reasoning, - specific_issues=issues or [], - suggestions=suggestions or [], - should_return_to_chief=label - in [QualityLabel.FAILED_POOR_QUALITY, QualityLabel.INCOMPLETE_MISSING_INFO], - ready_for_output=label in [QualityLabel.COMPLETE_EXCELLENT, QualityLabel.COMPLETE_GOOD], - ) - - # 其他方法保持不变... - def has_complete_response(self) -> bool: - """检查是否有完整的响应""" - return bool(self.response and self.response.strip()) - - def is_ready_for_output(self) -> bool: - """检查是否准备好输出""" - return bool( - self.evaluation and self.evaluation.ready_for_output and self.has_complete_response() - ) - - def get_processing_summary(self) -> dict[str, Any]: - """获取处理摘要信息""" - return { - "uuid": self.uuid, - "tool_name": self.tool_name, - "has_response": self.has_complete_response(), - "has_evaluation": self.evaluation is not None, - "evaluation_label": (self.evaluation.label.value if self.evaluation else None), - "confidence": self.evaluation.confidence if self.evaluation else None, - "ready_for_output": self.is_ready_for_output(), - "search_results_count": self.get_search_results_count(), - "prompts_count": len(self.prompts), - "has_tool_config": bool(self.tool_config), - "tool_config_keys": (list(self.tool_config.keys()) if self.tool_config else []), - "has_search_queries": self.has_search_queries(), - "search_queries_count": len(self.get_search_queries()), - "timestamp": self.timestamp, - } diff --git a/packages/sage-middleware/src/sage/middleware/operators/context/quality_label.py b/packages/sage-middleware/src/sage/middleware/operators/context/quality_label.py deleted file mode 100644 index 20cefb7eae..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/context/quality_label.py +++ /dev/null @@ -1,12 +0,0 @@ -from enum import Enum - - -class QualityLabel(Enum): - """质量评估标签""" - - COMPLETE_EXCELLENT = "complete_excellent" - COMPLETE_GOOD = "complete_good" - PARTIAL_NEEDS_IMPROVEMENT = "partial_needs_improvement" - INCOMPLETE_MISSING_INFO = "incomplete_missing_info" - FAILED_POOR_QUALITY = "failed_poor_quality" - ERROR_INVALID = "error_invalid" diff --git a/packages/sage-middleware/src/sage/middleware/operators/context/search_query_results.py b/packages/sage-middleware/src/sage/middleware/operators/context/search_query_results.py deleted file mode 100644 index fd8439effe..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/context/search_query_results.py +++ /dev/null @@ -1,61 +0,0 @@ -import time -from dataclasses import dataclass, field -from typing import Any - -from .search_result import SearchResult - - -@dataclass -class SearchQueryResults: - """单个搜索查询的结果集""" - - query: str - results: list[SearchResult] = field(default_factory=list) - search_timestamp: int = field(default_factory=lambda: int(time.time() * 1000)) - total_results_count: int = 0 # 搜索引擎返回的总结果数 - execution_time_ms: int = 0 # 搜索执行时间（毫秒） - search_engine: str = "unknown" # 使用的搜索引擎 - metadata: dict[str, Any] = field(default_factory=dict) # 额外的搜索元数据 - - def add_result(self, result: SearchResult) -> None: - """添加搜索结果""" - self.results.append(result) - - def get_results_count(self) -> int: - """获取实际检索到的结果数量""" - return len(self.results) - - def get_all_content(self) -> str: - """获取所有结果的内容拼接""" - return "\n\n".join([f"{result.title}\n{result.content}" for result in self.results]) - - def get_top_results(self, n: int = 3) -> list[SearchResult]: - """获取前N个结果""" - return self.results[:n] - - def to_dict(self) -> dict[str, Any]: - """转换为字典""" - return { - "query": self.query, - "results": [result.to_dict() for result in self.results], - "search_timestamp": self.search_timestamp, - "total_results_count": self.total_results_count, - "execution_time_ms": self.execution_time_ms, - "search_engine": self.search_engine, - "metadata": self.metadata.copy(), - } - - @classmethod - def from_dict(cls, data: dict[str, Any]) -> "SearchQueryResults": - """从字典创建SearchQueryResults""" - results = [SearchResult.from_dict(r) for r in data.get("results", [])] - - return cls( - query=data.get("query", ""), - results=results, - search_timestamp=data.get("search_timestamp", int(time.time() * 1000)), - total_results_count=data.get("total_results_count", 0), - execution_time_ms=data.get("execution_time_ms", 0), - search_engine=data.get("search_engine", "unknown"), - metadata=data.get("metadata", {}), - ) diff --git a/packages/sage-middleware/src/sage/middleware/operators/context/search_result.py b/packages/sage-middleware/src/sage/middleware/operators/context/search_result.py deleted file mode 100644 index 8c09d7feaf..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/context/search_result.py +++ /dev/null @@ -1,42 +0,0 @@ -import time -from dataclasses import dataclass, field -from typing import Any - - -@dataclass -class SearchResult: - """单个搜索结果的数据结构""" - - title: str - content: str - source: str - rank: int = 1 # 搜索结果的排名 - relevance_score: float = 0.0 # 相关性分数 - timestamp: int = field(default_factory=lambda: int(time.time() * 1000)) - - def __str__(self) -> str: - """格式化显示搜索结果""" - return f"[Rank {self.rank}] {self.title}\nContent: {self.content}\nSource: {self.source}" - - def to_dict(self) -> dict[str, Any]: - """转换为字典""" - return { - "title": self.title, - "content": self.content, - "source": self.source, - "rank": self.rank, - "relevance_score": self.relevance_score, - "timestamp": self.timestamp, - } - - @classmethod - def from_dict(cls, data: dict[str, Any]) -> "SearchResult": - """从字典创建SearchResult""" - return cls( - title=data.get("title", ""), - content=data.get("content", ""), - source=data.get("source", ""), - rank=data.get("rank", 1), - relevance_score=data.get("relevance_score", 0.0), - timestamp=data.get("timestamp", int(time.time() * 1000)), - ) diff --git a/packages/sage-middleware/src/sage/middleware/operators/context/search_session.py b/packages/sage-middleware/src/sage/middleware/operators/context/search_session.py deleted file mode 100644 index bfc54532ed..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/context/search_session.py +++ /dev/null @@ -1,79 +0,0 @@ -import time -from dataclasses import dataclass, field -from typing import Any -from uuid import uuid4 - -from .search_query_results import SearchQueryResults -from .search_result import SearchResult - - -@dataclass -class SearchSession: - """整个搜索会话的结果集合""" - - session_id: str = field(default_factory=lambda: str(uuid4())) - query_results: list[SearchQueryResults] = field(default_factory=list) - session_timestamp: int = field(default_factory=lambda: int(time.time() * 1000)) - original_question: str = "" - session_metadata: dict[str, Any] = field(default_factory=dict) - - def add_query_results(self, query_results: SearchQueryResults) -> None: - """添加查询结果""" - self.query_results.append(query_results) - - def get_all_queries(self) -> list[str]: - """获取所有查询字符串""" - return [qr.query for qr in self.query_results] - - def get_total_results_count(self) -> int: - """获取所有查询的结果总数""" - return sum(qr.get_results_count() for qr in self.query_results) - - def get_all_results(self) -> list[SearchResult]: - """获取所有搜索结果""" - all_results = [] - for query_result in self.query_results: - all_results.extend(query_result.results) - return all_results - - def get_results_by_query(self, query: str) -> SearchQueryResults | None: - """根据查询字符串获取结果""" - for qr in self.query_results: - if qr.query == query: - return qr - return None - - def get_combined_content(self) -> str: - """获取所有搜索结果的组合内容""" - combined_parts = [] - for i, query_result in enumerate(self.query_results, 1): - combined_parts.append(f"=== Query {i}: {query_result.query} ===") - for j, result in enumerate(query_result.results, 1): - combined_parts.append(f"[Result {j}] {result.title}") - combined_parts.append(f"Content: {result.content}") - combined_parts.append(f"Source: {result.source}") - combined_parts.append("") - return "\n".join(combined_parts) - - def to_dict(self) -> dict[str, Any]: - """转换为字典""" - return { - "session_id": self.session_id, - "query_results": [qr.to_dict() for qr in self.query_results], - "session_timestamp": self.session_timestamp, - "original_question": self.original_question, - "session_metadata": self.session_metadata.copy(), - } - - @classmethod - def from_dict(cls, data: dict[str, Any]) -> "SearchSession": - """从字典创建SearchSession""" - query_results = [SearchQueryResults.from_dict(qr) for qr in data.get("query_results", [])] - - return cls( - session_id=data.get("session_id", str(uuid4())), - query_results=query_results, - session_timestamp=data.get("session_timestamp", int(time.time() * 1000)), - original_question=data.get("original_question", ""), - session_metadata=data.get("session_metadata", {}), - ) diff --git a/packages/sage-middleware/src/sage/middleware/operators/filters/__init__.py b/packages/sage-middleware/src/sage/middleware/operators/filters/__init__.py deleted file mode 100644 index 2819ed39cc..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/filters/__init__.py +++ /dev/null @@ -1,26 +0,0 @@ -""" -SAGE Filters - Data Filtering and Transformation - -Layer: L3 (Core - Algorithm Library) - -This module provides data filtering, transformation, and routing utilities -for agent workflows. - -Available Filters: -- Tool Filter: Filter and select appropriate tools -- Evaluate Filter: Evaluate and score outputs -- Context Source: Context data sources -- Context Sink: Context data sinks -""" - -from .context_sink import * # noqa: F403 -from .context_source import * # noqa: F403 -from .evaluate_filter import * # noqa: F403 -from .tool_filter import * # noqa: F403 - -__all__: list[str] = [ - # Re-export from submodules - # Will be populated as modules are standardized -] - -__version__ = "0.1.0" diff --git a/packages/sage-middleware/src/sage/middleware/operators/filters/context_sink.py b/packages/sage-middleware/src/sage/middleware/operators/filters/context_sink.py deleted file mode 100644 index faabbe89ef..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/filters/context_sink.py +++ /dev/null @@ -1,387 +0,0 @@ -import json -import os -import threading -import time -from datetime import datetime -from pathlib import Path -from typing import Any - -from sage.common.core import SinkFunction -from sage.middleware.operators.context.model_context import ModelContext - - -class ContextFileSink(SinkFunction): - """ - ModelContext文件持久化Sink - 支持多种保存格式和组织策略 - """ - - @staticmethod - def get_default_template_directory() -> str: - """ - 获取默认的模板数据目录，统一存储在 .sage/data 下 - 符合 SAGE 架构设计原则：所有运行时数据应在 .sage/ 目录下 - """ - project_root = Path(os.getcwd()) # 获取当前工作目录 - template_data_dir = project_root / ".sage" / "data" / "model_context" - template_data_dir.mkdir(parents=True, exist_ok=True) - return str(template_data_dir) - - @staticmethod - def get_default_config() -> dict[str, Any]: - """ - 获取默认配置 - - Returns: - Dict[str, Any]: 默认配置字典 - """ - return { - "base_directory": None, # None表示使用默认目录 - "stage_directory": "general", # 处理阶段目录名 - "file_format": "json", # "json", "jsonl" - "organization": "date", # "date", "sequence", "uuid" - "max_files_per_dir": 1000, - "create_index": True, - "auto_create_dirs": True, - "compress_old_files": False, # 是否压缩旧文件 - "backup_index": True, # 是否备份索引文件 - } - - def __init__(self, config: dict[str, Any], **kwargs): - """ - 初始化TemplateFileSink - - Args: - config: 配置字典，包含所有设置项 - - base_directory: 基础保存目录，如果为None则使用默认目录 - - stage_directory: 处理阶段目录名，如 "questionbot", "retriever", "chief", "critic" - - file_format: 文件格式 ("json", "jsonl") - - organization: 文件组织方式 ("date", "sequence", "uuid") - - max_files_per_dir: 每个目录最大文件数 - - create_index: 是否创建索引文件 - - auto_create_dirs: 是否自动创建目录 - - compress_old_files: 是否压缩旧文件 - - backup_index: 是否备份索引文件 - **kwargs: 其他参数（向后兼容） - """ - super().__init__(**kwargs) - - # 合并配置（避免重复更新） - self.config = self.get_default_config() - if not isinstance(config, dict): - raise TypeError(f"Expected a dict for config, got {type(config)}") - # single update with provided config - self.config.update(config) - - # 向后兼容：如果直接传递了参数，使用这些参数更新config - legacy_params = { - "base_directory": kwargs.get("base_directory"), - "file_format": kwargs.get("file_format"), - "organization": kwargs.get("organization"), - "max_files_per_dir": kwargs.get("max_files_per_dir"), - "create_index": kwargs.get("create_index"), - "stage_directory": kwargs.get("stage_directory"), - } - - for key, value in legacy_params.items(): - if value is not None: - self.config[key] = value - - # 构建完整的目录路径 - self._setup_directories() - - # 索引管理 - self.index_file = self.full_directory / "template_index.json" - self.index_lock = threading.Lock() - self.saved_count = 0 - - # 初始化索引 - if self.config["create_index"] and not self.index_file.exists(): - self._initialize_index() - - def _setup_directories(self) -> None: - """设置目录结构""" - # 基础目录 - if self.config["base_directory"] is None: - base_dir = self.get_default_template_directory() - else: - base_dir = self.config["base_directory"] - - self.base_directory = Path(base_dir) - - # 阶段目录 - stage_dir = self.config["stage_directory"] - self.stage_directory = self.base_directory / stage_dir - - # 完整目录路径：./data/template_data/questionbot/ - self.full_directory = self.stage_directory - - # 自动创建目录 - if self.config["auto_create_dirs"]: - self.full_directory.mkdir(parents=True, exist_ok=True) - - def runtime_init(self, ctx): - """ - 运行时初始化 - - Note: ctx is injected into self.ctx by the framework (BaseFunction property). - This method logs initialization info after context is available. - """ - # No need to call super().runtime_init(ctx) - BaseFunction doesn't have this method. - # The framework injects ctx into self.ctx automatically. - self.logger.info(f"TemplateFileSink runtime initialized with context: {ctx}") - self.logger.info(f"Template base directory: {self.base_directory}") - self.logger.info(f"Template stage directory: {self.stage_directory}") - self.logger.info(f"Template full directory: {self.full_directory}") - self.logger.info(f"File organization: {self.config['organization']}") - self.logger.info(f"File format: {self.config['file_format']}") - - def _initialize_index(self) -> None: - """初始化索引文件""" - index_data = { - "created_at": datetime.now().isoformat(), - "total_templates": 0, - "config": self.config.copy(), # 保存完整配置 - "directory_structure": { - "base_directory": str(self.base_directory), - "stage_directory": str(self.stage_directory), - "full_directory": str(self.full_directory), - }, - "templates": {}, - } - - # 备份现有索引（如果存在） - if self.config["backup_index"] and self.index_file.exists(): - backup_file = self.index_file.with_suffix(f".backup_{int(time.time())}.json") - try: - import shutil - - shutil.copy2(self.index_file, backup_file) - self.logger.info(f"Backed up existing index to {backup_file}") - except Exception as e: - self.logger.warning(f"Failed to backup index: {e}") - - with open(self.index_file, "w", encoding="utf-8") as f: - json.dump(index_data, f, ensure_ascii=False, indent=2) - - def _get_file_path(self, template: ModelContext) -> Path: - """ - 根据组织策略确定文件路径 - 目录结构: base_directory/stage_directory/organization_structure/filename - - Args: - template: ModelContext实例 - - Returns: - Path: 文件路径 - """ - organization = self.config["organization"] - file_format = self.config["file_format"] - max_files = self.config["max_files_per_dir"] - - if organization == "date": - # 按日期组织: ./data/template_data/questionbot/2025/01/15/ - dt = datetime.fromtimestamp(template.timestamp / 1000) - org_dir = self.full_directory / f"{dt.year:04d}" / f"{dt.month:02d}" / f"{dt.day:02d}" - filename = f"template_{template.uuid}.{file_format}" - - elif organization == "sequence": - # 按序列号组织: ./data/template_data/questionbot/seq_0000-0999/ - seq_range = (template.sequence // max_files) * max_files - org_dir = self.full_directory / f"seq_{seq_range:06d}-{seq_range + max_files - 1:06d}" - filename = f"template_{template.sequence:06d}_{template.uuid[:8]}.{file_format}" - - else: # uuid organization - # 按UUID前缀组织: ./data/template_data/questionbot/ab/cd/ - uuid_prefix1 = template.uuid[:2] - uuid_prefix2 = template.uuid[2:4] - org_dir = self.full_directory / uuid_prefix1 / uuid_prefix2 - filename = f"template_{template.uuid}.{file_format}" - - # 确保目录存在 - if self.config["auto_create_dirs"]: - org_dir.mkdir(parents=True, exist_ok=True) - - return org_dir / filename - - def _update_index(self, template: ModelContext, file_path: Path) -> None: - """更新索引文件""" - if not self.config["create_index"]: - return - - with self.index_lock: - try: - with open(self.index_file, encoding="utf-8") as f: - index_data = json.load(f) - - # 更新索引信息 - index_data["total_templates"] += 1 - index_data["last_updated"] = datetime.now().isoformat() - - # 添加模板记录 - template_record = { - "uuid": template.uuid, - "sequence": template.sequence, - "timestamp": template.timestamp, - "file_path": str(file_path.relative_to(self.full_directory)), - "absolute_path": str(file_path), - "relative_to_base": str(file_path.relative_to(self.base_directory)), - "stage_directory": self.config["stage_directory"], - "raw_question_preview": ( - template.raw_question[:100] if template.raw_question else None - ), - "has_response": bool(template.response), - "response_length": (len(template.response) if template.response else 0), - "chunks_count": ( - len(template.retriver_chunks) if template.retriver_chunks else 0 - ), - "prompts_count": len(template.prompts) if template.prompts else 0, - "organization": self.config["organization"], - "file_format": self.config["file_format"], - "saved_at": datetime.now().isoformat(), - } - - index_data["templates"][template.uuid] = template_record - - # 保存更新后的索引 - with open(self.index_file, "w", encoding="utf-8") as f: - json.dump(index_data, f, ensure_ascii=False, indent=2) - - except Exception as e: - self.logger.error(f"Failed to update index: {e}") - - def execute(self, template: ModelContext) -> None: - """ - 保存ModelContext到文件 - - Args: - template: 要保存的ModelContext - """ - try: - # 确定文件路径 - file_path = self._get_file_path(template) - - # 保存模板 - if self.config["file_format"] == "json": - template.save_to_file(str(file_path)) - elif self.config["file_format"] == "jsonl": - # JSONL格式：每行一个JSON对象 - with open(file_path, "a", encoding="utf-8") as f: - f.write(template.to_json().replace("\n", "") + "\n") - - # 更新索引 - self._update_index(template, file_path) - - self.saved_count += 1 - - self.logger.debug(f"Saved template {template.uuid} to {file_path}") - - # 每保存10个模板记录一次统计 - if self.saved_count % 10 == 0: - self.logger.info( - f"TemplateFileSink[{self.config['stage_directory']}]: " - f"{self.saved_count} templates saved to {self.full_directory}" - ) - - except Exception as e: - self.logger.error(f"Failed to save template {template.uuid}: {e}") - - def set_stage_directory(self, stage_name: str): - """ - 动态设置阶段目录 - - Args: - stage_name: 新的阶段目录名 - """ - old_stage = self.config["stage_directory"] - self.config["stage_directory"] = stage_name - self._setup_directories() - - # 重新设置索引文件路径 - self.index_file = self.full_directory / "template_index.json" - - # 如果需要，初始化新的索引 - if self.config["create_index"] and not self.index_file.exists(): - self._initialize_index() - - self.logger.info(f"Stage directory changed from '{old_stage}' to '{stage_name}'") - self.logger.info(f"New full directory: {self.full_directory}") - - def get_storage_info(self) -> dict[str, Any]: - """ - 获取存储信息统计 - - Returns: - Dict[str, Any]: 存储统计信息 - """ - return { - "config": self.config.copy(), - "directory_structure": { - "base_directory": str(self.base_directory), - "stage_directory": str(self.stage_directory), - "full_directory": str(self.full_directory), - }, - "runtime_stats": { - "saved_count": self.saved_count, - "index_file": str(self.index_file), - "index_exists": ( - self.index_file.exists() if hasattr(self, "index_file") else False - ), - "directory_exists": self.full_directory.exists(), - }, - } - - def get_stage_statistics(self) -> dict[str, Any]: - """ - 获取当前阶段的统计信息 - - Returns: - Dict[str, Any]: 阶段统计信息 - """ - try: - if not self.index_file.exists(): - return {"error": "Index file does not exist"} - - with open(self.index_file, encoding="utf-8") as f: - index_data = json.load(f) - - templates = list(index_data.get("templates", {}).values()) - - # 统计信息 - stats = { - "stage_directory": self.config["stage_directory"], - "total_templates": len(templates), - "with_response": sum(1 for t in templates if t.get("has_response")), - "without_response": sum(1 for t in templates if not t.get("has_response")), - "avg_response_length": 0, - "avg_chunks": 0, - "avg_prompts": 0, - "date_range": {"earliest": None, "latest": None}, - } - - if templates: - # 计算平均值 - response_lengths = [ - t.get("response_length", 0) for t in templates if t.get("has_response") - ] - stats["avg_response_length"] = ( - sum(response_lengths) / len(response_lengths) if response_lengths else 0 - ) - - stats["avg_chunks"] = sum(t.get("chunks_count", 0) for t in templates) / len( - templates - ) - stats["avg_prompts"] = sum(t.get("prompts_count", 0) for t in templates) / len( - templates - ) - - # 时间范围 - timestamps = [t.get("timestamp", 0) for t in templates] - stats["date_range"]["earliest"] = min(timestamps) - stats["date_range"]["latest"] = max(timestamps) - - return stats - - except Exception as e: - self.logger.error(f"Failed to get stage statistics: {e}") - return {"error": str(e)} diff --git a/packages/sage-middleware/src/sage/middleware/operators/filters/context_source.py b/packages/sage-middleware/src/sage/middleware/operators/filters/context_source.py deleted file mode 100644 index b6a73e4581..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/filters/context_source.py +++ /dev/null @@ -1,376 +0,0 @@ -import json -import os -from pathlib import Path - -from sage.common.core import SourceFunction -from sage.common.utils.logging.custom_logger import CustomLogger -from sage.middleware.operators.context.model_context import ModelContext - - -class ContextFileSource(SourceFunction): - """ - 从文件加载ModelContext的数据源 - 每次execute读取一个模板文件并返回 - """ - - @staticmethod - def get_default_template_directory() -> str: - """ - 获取默认的模板数据目录，与TemplateFileSink保持一致 - """ - project_root = Path(os.getcwd()) # 获取当前工作目录 - template_data_dir = project_root / "data" / "template_data" - return str(template_data_dir) - - def __init__( - self, - base_directory: str | None = None, - load_mode: str = "sequential", # "sequential", "recent", "random" - time_range: tuple[int, int] | None = None, - sequence_range: tuple[int, int] | None = None, - include_pattern: str | None = None, - auto_reset: bool = True, - **kwargs, - ): - """ - 初始化TemplateFileSource - - Args: - base_directory: 模板文件基础目录，如果为None则使用默认目录 - load_mode: 加载模式 ("sequential", "recent", "random") - time_range: 时间范围过滤 (start_timestamp, end_timestamp) - sequence_range: 序列号范围过滤 - include_pattern: 文件名包含模式 - auto_reset: 当所有文件读完后是否自动重置到开始 - """ - super().__init__(**kwargs) - - # 如果没有指定base_directory，使用默认目录 - if base_directory is None: - base_directory = self.get_default_template_directory() - - self.base_directory = Path(base_directory) - self.load_mode = load_mode - self.time_range = time_range - self.sequence_range = sequence_range - self.include_pattern = include_pattern - self.auto_reset = auto_reset - - self.index_file = self.base_directory / "template_index.json" - - # 内部状态管理 - self.loaded_count = 0 - self.current_file_index = 0 - self.template_files: list[Path] = [] - self.index_data = None - - # 初始化文件列表 - self._initialize_file_list() - - # self.logger.info(f"ContextFileSource initialized: {base_directory}, mode: {load_mode}") - # self.logger.info(f"Found {len(self.template_files)} template files") - - def _initialize_file_list(self): - """初始化文件列表""" - # 检查目录是否存在 - if not self.base_directory.exists(): - self.logger.warning(f"Template directory does not exist: {self.base_directory}") - self.template_files = [] - return - - # 加载索引文件（如果存在） - self.index_data = self._load_index() - - if self.load_mode == "recent" and self.index_data: - # 基于索引按时间排序 - templates_info = list(self.index_data.get("templates", {}).values()) - templates_info.sort(key=lambda x: x["timestamp"], reverse=True) - - self.template_files = [] - for template_info in templates_info: - file_path = self.base_directory / template_info["file_path"] - if file_path.exists(): - self.template_files.append(file_path) - else: - # 直接扫描文件系统 - self.template_files = self._find_template_files() - - if self.load_mode == "sequential": - # 按文件修改时间排序 - self.template_files.sort(key=lambda f: f.stat().st_mtime) - elif self.load_mode == "random": - # 随机打乱 - import random - - random.shuffle(self.template_files) - - def _load_index(self) -> dict | None: - """加载索引文件""" - if not self.index_file.exists(): - self.logger.debug(f"Index file not found: {self.index_file}") - return None - - try: - with open(self.index_file, encoding="utf-8") as f: - return json.load(f) - except Exception as e: - self.logger.error(f"Failed to load index: {e}") - return None - - def _find_template_files(self) -> list[Path]: - """查找所有模板文件""" - template_files = [] - - # 递归搜索所有JSON文件 - for json_file in self.base_directory.rglob("*.json"): - if json_file.name == "template_index.json": - continue - - # 应用文件名过滤 - if self.include_pattern and self.include_pattern not in json_file.name: - continue - - template_files.append(json_file) - - return template_files - - def _load_template_from_file(self, file_path: Path) -> ModelContext | None: - """从文件加载单个模板""" - try: - template = ModelContext.load_from_file(str(file_path)) - - # 应用过滤条件 - if not self._filter_template(template): - return None - - return template - except Exception as e: - self.logger.error(f"Failed to load template from {file_path}: {e}") - return None - - def _filter_template(self, template: ModelContext) -> bool: - """根据条件过滤单个模板""" - # 时间范围过滤 - if self.time_range: - start_time, end_time = self.time_range - if not (start_time <= template.timestamp <= end_time): - return False - - # 序列号范围过滤 - if self.sequence_range: - start_seq, end_seq = self.sequence_range - if not (start_seq <= template.sequence <= end_seq): - return False - - return True - - def _get_next_file(self) -> Path | None: - """获取下一个要读取的文件""" - if not self.template_files: - return None - - # 检查是否已经读完所有文件 - if self.current_file_index >= len(self.template_files): - if self.auto_reset: - self.logger.info("All template files processed, resetting to beginning") - self.current_file_index = 0 - - # 如果是随机模式，重新洗牌 - if self.load_mode == "random": - import random - - random.shuffle(self.template_files) - else: - self.logger.info("All template files processed, no more files to read") - return None - - # 返回当前文件并递增索引 - file_path = self.template_files[self.current_file_index] - self.current_file_index += 1 - - return file_path - - def execute(self) -> ModelContext | None: - """ - 读取下一个ModelContext - - Returns: - Optional[ModelContext]: 加载的模板，如果没有更多文件则返回None - """ - # 最多尝试读取10个文件（避免无限循环） - max_attempts = 10 - attempts = 0 - - while attempts < max_attempts: - attempts += 1 - - # 获取下一个文件 - file_path = self._get_next_file() - - if file_path is None: - # 没有更多文件可读 - return None - - # 尝试加载模板 - template = self._load_template_from_file(file_path) - - if template is not None: - self.loaded_count += 1 - self.logger.debug(f"Loaded template {template.uuid} from {file_path.name}") - - # 每加载10个模板记录一次统计 - if self.loaded_count % 10 == 0: - self.logger.info(f"ContextFileSource: {self.loaded_count} templates loaded") - - return template - - # 如果当前文件加载失败，继续尝试下一个文件 - self.logger.debug(f"Failed to load template from {file_path}, trying next file") - - # 尝试次数用完，返回None - self.logger.warning(f"Failed to load template after {max_attempts} attempts") - return None - - def reset(self): - """重置数据源到初始状态""" - self.current_file_index = 0 - self.loaded_count = 0 - self.logger.info("ContextFileSource reset to initial state") - - def skip_to_index(self, index: int): - """跳转到指定的文件索引""" - if 0 <= index < len(self.template_files): - self.current_file_index = index - self.logger.info(f"ContextFileSource skipped to index {index}") - else: - self.logger.warning( - f"Invalid index {index}, valid range: 0-{len(self.template_files) - 1}" - ) - - def get_source_info(self) -> dict: - """ - 获取数据源信息 - - Returns: - dict: 数据源统计信息 - """ - return { - "base_directory": str(self.base_directory), - "load_mode": self.load_mode, - "total_files": len(self.template_files), - "current_index": self.current_file_index, - "loaded_count": self.loaded_count, - "directory_exists": self.base_directory.exists(), - "index_exists": self.index_file.exists(), - "auto_reset": self.auto_reset, - "has_more_files": self.current_file_index < len(self.template_files), - } - - def has_more_data(self) -> bool: - """ - 检查是否还有更多数据可读 - - Returns: - bool: 是否还有更多数据 - """ - if self.auto_reset: - # 如果自动重置，总是有数据（除非没有文件） - return len(self.template_files) > 0 - else: - # 否则检查是否还有未读文件 - return self.current_file_index < len(self.template_files) - - -class TemplateIndexManager: - """ - 模板索引管理器，提供高级查询功能 - """ - - def __init__(self, base_directory: str | None = None): - if base_directory is None: - base_directory = ContextFileSource.get_default_template_directory() - - self.base_directory = Path(base_directory) - self.index_file = self.base_directory / "template_index.json" - - def search_templates( - self, - question_contains: str | None = None, - has_response: bool | None = None, - min_chunks: int | None = None, - time_after: int | None = None, - ) -> list[dict]: - """ - 搜索模板记录 - - Args: - question_contains: 问题包含的文本 - has_response: 是否有响应 - min_chunks: 最小chunk数量 - time_after: 时间戳之后 - - Returns: - List[dict]: 匹配的模板记录 - """ - try: - with open(self.index_file, encoding="utf-8") as f: - index_data = json.load(f) - - templates = list(index_data.get("templates", {}).values()) - - # 应用过滤条件 - if question_contains: - templates = [ - t - for t in templates - if t.get("raw_question_preview") - and question_contains.lower() in t["raw_question_preview"].lower() - ] - - if has_response is not None: - templates = [t for t in templates if t.get("has_response") == has_response] - - if min_chunks is not None: - templates = [t for t in templates if t.get("chunks_count", 0) >= min_chunks] - - if time_after is not None: - templates = [t for t in templates if t.get("timestamp", 0) > time_after] - - return templates - - except Exception as e: - logger = CustomLogger(outputs=[("console", "INFO")], name=__name__) - logger.error(f"Failed to search templates: {e}") - return [] - - def get_statistics(self) -> dict: - """获取模板统计信息""" - try: - with open(self.index_file, encoding="utf-8") as f: - index_data = json.load(f) - - templates = list(index_data.get("templates", {}).values()) - - stats = { - "total_templates": len(templates), - "with_response": sum(1 for t in templates if t.get("has_response")), - "without_response": sum(1 for t in templates if not t.get("has_response")), - "avg_chunks": ( - sum(t.get("chunks_count", 0) for t in templates) / len(templates) - if templates - else 0 - ), - "earliest_timestamp": ( - min(t.get("timestamp", 0) for t in templates) if templates else 0 - ), - "latest_timestamp": ( - max(t.get("timestamp", 0) for t in templates) if templates else 0 - ), - } - - return stats - - except Exception as e: - logger = CustomLogger(outputs=[("console", "INFO")], name=__name__) - logger.error(f"Failed to get statistics: {e}") - return {} diff --git a/packages/sage-middleware/src/sage/middleware/operators/filters/evaluate_filter.py b/packages/sage-middleware/src/sage/middleware/operators/filters/evaluate_filter.py deleted file mode 100644 index 46b4aeae35..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/filters/evaluate_filter.py +++ /dev/null @@ -1,83 +0,0 @@ -from sage.common.core import FilterFunction -from sage.middleware.operators.context.model_context import ModelContext, QualityLabel - - -class EvaluateFilter(FilterFunction): - """ - 评估过滤器 - 基于质量标签上下界过滤 - """ - - def __init__(self, config: dict | None = None, **kwargs): - """ - 初始化评估过滤器 - - Args: - config: 配置字典，支持： - - "upper_bound": str - 质量上界标签 - - "lower_bound": str - 质量下界标签 - - 质量标签优先级（从高到低）： - 1. COMPLETE_EXCELLENT - 2. COMPLETE_GOOD - 3. PARTIAL_NEEDS_IMPROVEMENT - 4. INCOMPLETE_MISSING_INFO - 5. FAILED_POOR_QUALITY - 6. ERROR_INVALID - """ - super().__init__(**kwargs) - - if not config: - config = {} - - # 质量标签优先级映射 - self.quality_priority = { - QualityLabel.COMPLETE_EXCELLENT: 1, - QualityLabel.COMPLETE_GOOD: 2, - QualityLabel.PARTIAL_NEEDS_IMPROVEMENT: 3, - QualityLabel.INCOMPLETE_MISSING_INFO: 4, - QualityLabel.FAILED_POOR_QUALITY: 5, - QualityLabel.ERROR_INVALID: 6, - } - - # 解析上下界 - self.upper_bound = self._parse_label(config.get("upper_bound")) - self.lower_bound = self._parse_label(config.get("lower_bound")) - - # 计算上下界优先级 - self.upper_priority = ( - self.quality_priority.get(self.upper_bound, 1) if self.upper_bound else 1 - ) - self.lower_priority = ( - self.quality_priority.get(self.lower_bound, 6) if self.lower_bound else 6 - ) - - def _parse_label(self, label_input) -> QualityLabel | None: - """解析质量标签""" - if not label_input: - return None - - if isinstance(label_input, QualityLabel): - return label_input - - if isinstance(label_input, str): - try: - return QualityLabel(label_input) - except ValueError: - return None - - return None - - def execute(self, template: ModelContext) -> bool: - """执行评估过滤逻辑""" - evaluation = template.evaluation - - # 如果没有评估，返回False - if not evaluation: - return False - - # 获取当前质量标签的优先级 - current_priority = self.quality_priority.get(evaluation.label, 6) - - # 检查是否在上下界范围内 - # 优先级数字越小质量越高，所以要在upper_priority和lower_priority之间 - return self.upper_priority <= current_priority <= self.lower_priority diff --git a/packages/sage-middleware/src/sage/middleware/operators/filters/tool_filter.py b/packages/sage-middleware/src/sage/middleware/operators/filters/tool_filter.py deleted file mode 100644 index b51da2d432..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/filters/tool_filter.py +++ /dev/null @@ -1,74 +0,0 @@ -import json - -from sage.common.core import FilterFunction -from sage.middleware.operators.context.model_context import ModelContext - - -class ToolFilter(FilterFunction): - """ - 工具过滤器 - 只接受config配置 - """ - - def __init__(self, config: dict | None = None, **kwargs): - """ - 初始化工具过滤器 - - Args: - config: 配置字典，支持： - - "tools": str | List[str] | JSON字符串 - 目标工具列表 - - "exclude": str | List[str] | JSON字符串 - 排除工具列表 - - "include_unknown": bool - 是否接受无工具名的模板 - """ - super().__init__(**kwargs) - - if not config: - config = {} - - self.target_tools: set[str] = self._parse_tools(config.get("tools")) - self.exclude_tools: set[str] = self._parse_tools(config.get("exclude")) - self.include_unknown: bool = config.get("include_unknown", False) - - def _parse_tools(self, tools_input) -> set[str]: - """解析工具输入为工具集合""" - if not tools_input: - return set() - - if isinstance(tools_input, (list, set)): - return {str(tool) for tool in tools_input} - - if isinstance(tools_input, str): - # JSON字符串 - if tools_input.strip().startswith("["): - try: - parsed = json.loads(tools_input) - return {str(tool) for tool in parsed} - except json.JSONDecodeError: - pass - - # 逗号分隔 - if "," in tools_input: - return {tool.strip() for tool in tools_input.split(",") if tool.strip()} - - # 单个工具 - return {tools_input.strip()} - - return set() - - def execute(self, template: ModelContext) -> bool: - """执行工具过滤逻辑""" - tool_name = template.tool_name - - # 排除列表检查 - if tool_name and tool_name in self.exclude_tools: - return False - - # 无工具名处理 - if not tool_name: - return self.include_unknown - - # 目标工具检查 - if self.target_tools: - return tool_name in self.target_tools - - # 默认接受（如果没有指定目标工具且不在排除列表中） - return True diff --git a/packages/sage-middleware/src/sage/middleware/operators/llm/__init__.py b/packages/sage-middleware/src/sage/middleware/operators/llm/__init__.py deleted file mode 100644 index dcb3696a80..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/llm/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -""" -LLM Operators - 大语言模型推理算子 - -这个模块包含 LLM 服务的算子实现。 - -推荐使用 SageLLMGenerator，支持多种后端： -- backend_type="cuda": NVIDIA GPU (HFCudaEngine) -- backend_type="mock": 测试模式 (MockEngine) -- backend_type="ascend": 华为昇腾 (TODO) - -Breaking Change (v0.3.0): - VLLMGenerator 和 VLLMEmbedding 已移除。 - 请迁移到 SageLLMGenerator(backend_type="cuda")。 -""" - -from sage.middleware.operators.llm.sagellm_generator import SageLLMGenerator - -__all__ = ["SageLLMGenerator"] diff --git a/packages/sage-middleware/src/sage/middleware/operators/llm/sagellm_generator.py b/packages/sage-middleware/src/sage/middleware/operators/llm/sagellm_generator.py deleted file mode 100644 index d85edcedac..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/llm/sagellm_generator.py +++ /dev/null @@ -1,432 +0,0 @@ -"""SageLLM Generator - 统一 LLM 推理算子 - -通过 EngineFactory 统一创建引擎，不硬编码任何具体引擎实现。 -支持 auto/mock/cuda/ascend 等多种后端类型。 -""" - -from __future__ import annotations - -import logging -from collections.abc import AsyncGenerator, Sequence -from dataclasses import dataclass, field -from typing import Any - -from sage.common.core.functions import MapFunction as MapOperator - -logger = logging.getLogger(__name__) - - -def _normalize_input(data: Any) -> tuple[dict[str, Any], str, dict[str, Any]]: - """ - 规范化输入数据为 (context, prompt, options) 三元组。 - - 支持多种输入格式： - - str: 直接作为 prompt - - dict: 包含 prompt 和可选的 options - - Sequence: [context, prompt] 或 [context, prompt, options] - """ - context: dict[str, Any] = {} - prompt: str = "" - options: dict[str, Any] = {} - - if isinstance(data, str): - prompt = data - elif isinstance(data, dict): - prompt = data.get("prompt", "") - options = dict(data.get("options", {})) - # 保留其他上下文字段 - context = {k: v for k, v in data.items() if k not in ("prompt", "options")} - elif isinstance(data, Sequence) and not isinstance(data, (str, bytes)): - if len(data) >= 1: - first = data[0] - if isinstance(first, str): - prompt = first - elif isinstance(first, dict): - context = dict(first) - if len(data) >= 2: - second = data[1] - if isinstance(second, str): - prompt = second - elif isinstance(second, dict) and "prompt" in second: - prompt = second["prompt"] - options.update(second.get("options", {})) - if len(data) >= 3 and isinstance(data[2], dict): - options.update(data[2]) - else: - prompt = str(data) - - return context, prompt, options - - -@dataclass -class SageLLMGenerator(MapOperator): - """ - SageLLM 统一生成算子 - 通过 EngineFactory 创建引擎进行文本生成 - - 不直接导入或硬编码任何具体引擎实现（如 HFCudaEngine, MockEngine）， - 而是通过工厂模式动态创建引擎，实现后端解耦。 - - Example: - ```python - # 自动选择后端 - generator = SageLLMGenerator( - model_path="Qwen/Qwen2.5-7B-Instruct", - backend_type="auto", - ) - result = generator.execute("写一首诗") - - # 指定 mock 后端用于测试 - generator = SageLLMGenerator( - backend_type="mock", - model_path="mock-model", - ) - - # 流式生成 - async for chunk in generator.stream_async("讲个故事"): - print(chunk["text"], end="", flush=True) - ``` - - Attributes: - backend_type: 引擎后端类型，支持 "auto"/"mock"/"cuda"/"ascend" 等 - model_path: 模型路径或 HuggingFace 模型 ID - device_map: 设备映射策略，如 "auto"/"cuda:0"/"cpu" - dtype: 数据类型，如 "auto"/"float16"/"bfloat16" - max_tokens: 最大生成 token 数 - temperature: 采样温度 - top_p: nucleus 采样参数 - top_k: top-k 采样参数 - default_options: 默认生成选项 - """ - - # 核心配置 - backend_type: str = "auto" - model_path: str = "" - device_map: str = "auto" - dtype: str = "float16" - device: str = "cuda" - - # HFCudaEngine 必需的配置（fail-fast 设计） - load_in_8bit: bool = False - load_in_4bit: bool = False - trust_remote_code: bool = False - - # 生成参数默认值 - max_tokens: int = 2048 - max_new_tokens: int = 128 # HFCudaEngine 使用此字段 - temperature: float = 0.7 - top_p: float = 0.95 - top_k: int = 50 - - # 引擎配置 - engine_id: str = "" - timeout: float = 120.0 - default_options: dict[str, Any] = field(default_factory=dict) - - # 内部状态 - _engine: Any = field(default=None, init=False, repr=False) - _initialized: bool = field(default=False, init=False, repr=False) - - def __post_init__(self) -> None: - super().__init__() - if not self.engine_id: - self.engine_id = f"sage-llm-{id(self)}" - - def _ensure_engine(self) -> None: - """ - 确保引擎已初始化。 - - 延迟初始化策略：只在首次使用时创建引擎。 - 通过 EngineFactory 统一创建，不直接导入具体引擎类。 - """ - if self._initialized and self._engine is not None: - return - - try: - # 统一通过工厂创建，不直接 import 具体引擎 - from sagellm_backend.engine.factory import EngineFactory - - config = { - "engine_id": self.engine_id, - "model_path": self.model_path, - "device": self.device, - "device_map": self.device_map, - "dtype": self.dtype, - "load_in_8bit": self.load_in_8bit, - "load_in_4bit": self.load_in_4bit, - "trust_remote_code": self.trust_remote_code, - "max_new_tokens": self.max_new_tokens, - "max_tokens": self.max_tokens, - "temperature": self.temperature, - "top_p": self.top_p, - "top_k": self.top_k, - "mock_mode": self.backend_type == "mock", - } - - logger.info( - f"Creating engine: backend_type={self.backend_type}, " - f"model_path={self.model_path}, engine_id={self.engine_id}" - ) - - self._engine = EngineFactory.create( - backend_type=self.backend_type, - config=config, - ) - - # 启动引擎（如果需要） - if hasattr(self._engine, "start") and not self._engine.is_running: - import asyncio - - start_coro = self._engine.start() - if asyncio.iscoroutine(start_coro): - try: - loop = asyncio.get_running_loop() - except RuntimeError: - loop = None - - if loop is not None: - import concurrent.futures - - with concurrent.futures.ThreadPoolExecutor() as pool: - pool.submit(asyncio.run, start_coro).result() - else: - asyncio.run(start_coro) - - self._initialized = True - - logger.info(f"Engine created successfully: {self.engine_id}") - - except ImportError as e: - raise ImportError( - f"Failed to import sagellm_backend. " - f"Please install it with: pip install sagellm-backend\n" - f"Original error: {e}" - ) from e - except Exception as e: - logger.error(f"Failed to create engine: {e}") - raise RuntimeError( - f"Failed to create SageLLM engine with backend_type={self.backend_type}: {e}" - ) from e - - def _build_generation_params(self, prompt: str, options: dict[str, Any]) -> dict[str, Any]: - """ - 构建生成参数，合并默认值和用户指定的选项。 - """ - params = { - "prompt": prompt, - "max_tokens": self.max_tokens, - "temperature": self.temperature, - "top_p": self.top_p, - "top_k": self.top_k, - } - # 应用默认选项 - params.update(self.default_options) - # 应用用户传入的选项（优先级最高） - params.update({k: v for k, v in options.items() if v is not None}) - return params - - def execute(self, data: Any) -> dict[str, Any]: - """ - 同步执行文本生成。 - - Args: - data: 输入数据，支持 str/dict/Sequence 格式 - - Returns: - 包含生成结果的字典： - - text: 生成的文本 - - usage: token 使用统计 - - context: 原始上下文（如果有） - """ - self._ensure_engine() - - context, prompt, options = _normalize_input(data) - params = self._build_generation_params(prompt, options) - - if not prompt: - logger.warning("Empty prompt received, returning empty result") - return {"text": "", "usage": {}, "context": context} - - try: - logger.debug(f"Generating with params: {params}") - - # 调用引擎生成（兼容 execute/generate 两种接口，支持 async） - import asyncio - import uuid - - if hasattr(self._engine, "execute"): - # 需要将 params 转换为 Request 对象 - try: - from sagellm_protocol.types import Request as SageLLMRequest - - request = SageLLMRequest( - request_id=str(uuid.uuid4()), - trace_id=str(uuid.uuid4()), - model=self.model_path or "default", - prompt=params.get("prompt", ""), - max_tokens=params.get("max_tokens", self.max_tokens), - stream=False, - temperature=params.get("temperature", self.temperature), - top_p=params.get("top_p", self.top_p), - ) - coro_or_result = self._engine.execute(request) - except ImportError: - # 如果 sagellm_protocol 不可用，直接传 dict - coro_or_result = self._engine.execute(params) - - # 检查是否是协程 - if asyncio.iscoroutine(coro_or_result): - # 在同步上下文中运行异步方法 - try: - loop = asyncio.get_running_loop() - except RuntimeError: - loop = None - - if loop is not None: - # 已有事件循环，创建新任务 - import concurrent.futures - - with concurrent.futures.ThreadPoolExecutor() as pool: - result = pool.submit(asyncio.run, coro_or_result).result() - else: - result = asyncio.run(coro_or_result) - else: - result = coro_or_result - elif hasattr(self._engine, "generate"): - result = self._engine.generate(**params) - else: - raise RuntimeError( - f"Engine {type(self._engine).__name__} does not support " - "execute() or generate() method" - ) - - # 规范化输出格式 - if isinstance(result, str): - output = {"text": result, "usage": {}} - elif isinstance(result, dict): - output = { - "text": result.get( - "text", result.get("generated", result.get("output_text", "")) - ), - "usage": result.get("usage", {}), - } - elif hasattr(result, "output_text"): - # sagellm_protocol.types.Response 对象 - output_tokens = getattr(result, "output_tokens", []) - num_output_tokens = len(output_tokens) if isinstance(output_tokens, list) else 0 - output = { - "text": result.output_text, - "usage": { - "completion_tokens": num_output_tokens, - }, - } - else: - output = {"text": str(result), "usage": {}} - - # 附加上下文 - if context: - output["context"] = context - - return output - - except Exception as e: - logger.error(f"Generation failed: {e}") - raise RuntimeError(f"SageLLM generation failed: {e}") from e - - async def stream_async(self, data: Any) -> AsyncGenerator[dict[str, Any], None]: - """ - 异步流式生成文本。 - - Args: - data: 输入数据，支持 str/dict/Sequence 格式 - - Yields: - 流式输出的字典： - - text: 当前生成的文本片段 - - done: 是否完成 - - usage: token 使用统计（仅在完成时） - """ - self._ensure_engine() - - context, prompt, options = _normalize_input(data) - params = self._build_generation_params(prompt, options) - - if not prompt: - logger.warning("Empty prompt received for streaming") - yield {"text": "", "done": True, "usage": {}} - return - - try: - logger.debug(f"Streaming generation with params: {params}") - - # 检查引擎是否支持流式生成 - if hasattr(self._engine, "generate_stream"): - async for chunk in self._engine.generate_stream(**params): - if isinstance(chunk, str): - yield {"text": chunk, "done": False} - elif isinstance(chunk, dict): - yield { - "text": chunk.get("text", ""), - "done": chunk.get("done", False), - "usage": chunk.get("usage", {}), - } - else: - yield {"text": str(chunk), "done": False} - - # 发送完成信号 - yield {"text": "", "done": True, "usage": {}} - - elif hasattr(self._engine, "stream"): - # 兼容同步流式接口 - for chunk in self._engine.stream(**params): - if isinstance(chunk, str): - yield {"text": chunk, "done": False} - elif isinstance(chunk, dict): - yield { - "text": chunk.get("text", ""), - "done": chunk.get("done", False), - } - else: - yield {"text": str(chunk), "done": False} - - yield {"text": "", "done": True, "usage": {}} - - else: - # 引擎不支持流式，降级为一次性返回 - logger.warning( - f"Engine {self.backend_type} does not support streaming, " - "falling back to non-streaming generation" - ) - result = self._engine.generate(**params) - text = result if isinstance(result, str) else result.get("text", "") - yield {"text": text, "done": True, "usage": result.get("usage", {})} - - except Exception as e: - logger.error(f"Streaming generation failed: {e}") - yield {"text": "", "done": True, "error": str(e)} - - def shutdown(self) -> None: - """ - 关闭引擎并释放资源。 - """ - if self._engine is not None: - try: - if hasattr(self._engine, "shutdown"): - self._engine.shutdown() - elif hasattr(self._engine, "close"): - self._engine.close() - logger.info(f"Engine {self.engine_id} shut down") - except Exception as e: - logger.warning(f"Error shutting down engine: {e}") - finally: - self._engine = None - self._initialized = False - - def __del__(self) -> None: - """析构时尝试清理资源。""" - try: - self.shutdown() - except Exception: - pass - - -__all__ = ["SageLLMGenerator"] diff --git a/packages/sage-middleware/src/sage/middleware/operators/rag/__init__.py b/packages/sage-middleware/src/sage/middleware/operators/rag/__init__.py deleted file mode 100644 index 32f75a2adc..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/rag/__init__.py +++ /dev/null @@ -1,147 +0,0 @@ -""" -RAG (Retrieval-Augmented Generation) Operators - -This module contains domain-specific operators for RAG applications: -- Pipeline (RAG orchestration and workflow) -- Profiler (Query profiling and analysis) -- Document Loaders (Document loading utilities) -- Generator operators (LLM response generation) -- Retriever operators (document/passage retrieval) -- Reranker operators (result reranking) -- Promptor operators (prompt construction) -- Evaluation operators (quality metrics) -- Document processing operators (chunking, refining, writing) -- External data source operators (ArXiv) - -These operators inherit from base operator classes in sage.kernel.operators -and implement RAG-specific business logic. -""" - -# Export types for easier access -from sage.libs.rag.types import ( - RAGDocument, - RAGInput, - RAGOutput, - RAGQuery, - RAGResponse, - create_rag_response, - ensure_rag_response, - extract_query, - extract_results, -) - -# Lazy imports to avoid optional dependency issues -_IMPORTS = { - # Pipeline and Profiler - # RAGPipeline lives in the middleware layer (L4) as orchestration/pipeline code. - # It previously pointed to sage.libs.rag.pipeline (L3) which was deleted during - # the libs -> middleware refactor. Update to the new location. - "RAGPipeline": ("sage.middleware.operators.rag.pipeline", "RAGPipeline"), - "Query_Profiler": ("sage.middleware.operators.rag.profiler", "Query_Profiler"), - "QueryProfilerResult": ("sage.middleware.operators.rag.profiler", "QueryProfilerResult"), - # Document Loaders - "TextLoader": ("sage.libs.rag.document_loaders", "TextLoader"), - "PDFLoader": ("sage.libs.rag.document_loaders", "PDFLoader"), - "DocxLoader": ("sage.libs.rag.document_loaders", "DocxLoader"), - "DocLoader": ("sage.libs.rag.document_loaders", "DocLoader"), - "MarkdownLoader": ("sage.libs.rag.document_loaders", "MarkdownLoader"), - "LoaderFactory": ("sage.libs.rag.document_loaders", "LoaderFactory"), - # Generators - "OpenAIGenerator": ("sage.middleware.operators.rag.generator", "OpenAIGenerator"), - "HFGenerator": ("sage.middleware.operators.rag.generator", "HFGenerator"), - "SageLLMRAGGenerator": ("sage.middleware.operators.rag.generator", "SageLLMRAGGenerator"), - # Retrievers - "ChromaRetriever": ("sage.middleware.operators.rag.retriever", "ChromaRetriever"), - "MilvusDenseRetriever": ( - "sage.middleware.operators.rag.retriever", - "MilvusDenseRetriever", - ), - "MilvusSparseRetriever": ( - "sage.middleware.operators.rag.retriever", - "MilvusSparseRetriever", - ), - "Wiki18FAISSRetriever": ( - "sage.middleware.operators.rag.retriever", - "Wiki18FAISSRetriever", - ), - # Rerankers - "BGEReranker": ("sage.middleware.operators.rag.reranker", "BGEReranker"), - "LLMbased_Reranker": ( - "sage.middleware.operators.rag.reranker", - "LLMbased_Reranker", - ), - # Promptors - "QAPromptor": ("sage.middleware.operators.rag.promptor", "QAPromptor"), - "SummarizationPromptor": ( - "sage.middleware.operators.rag.promptor", - "SummarizationPromptor", - ), - "QueryProfilerPromptor": ( - "sage.middleware.operators.rag.promptor", - "QueryProfilerPromptor", - ), - # Evaluation - "F1Evaluate": ("sage.middleware.operators.rag.evaluate", "F1Evaluate"), - "EMEvaluate": ("sage.middleware.operators.rag.evaluate", "EMEvaluate"), - "RecallEvaluate": ("sage.middleware.operators.rag.evaluate", "RecallEvaluate"), - "BertRecallEvaluate": ( - "sage.middleware.operators.rag.evaluate", - "BertRecallEvaluate", - ), - "RougeLEvaluate": ("sage.middleware.operators.rag.evaluate", "RougeLEvaluate"), - "BRSEvaluate": ("sage.middleware.operators.rag.evaluate", "BRSEvaluate"), - "AccuracyEvaluate": ("sage.middleware.operators.rag.evaluate", "AccuracyEvaluate"), - "TokenCountEvaluate": ( - "sage.middleware.operators.rag.evaluate", - "TokenCountEvaluate", - ), - "LatencyEvaluate": ("sage.middleware.operators.rag.evaluate", "LatencyEvaluate"), - "ContextRecallEvaluate": ( - "sage.middleware.operators.rag.evaluate", - "ContextRecallEvaluate", - ), - "CompressionRateEvaluate": ( - "sage.middleware.operators.rag.evaluate", - "CompressionRateEvaluate", - ), - # Document Processing - "CharacterSplitter": ("sage.libs.rag.chunk", "CharacterSplitter"), - "SentenceTransformersTokenTextSplitter": ( - "sage.libs.rag.chunk", - "SentenceTransformersTokenTextSplitter", - ), - "RefinerOperator": ("sage.middleware.operators.rag.refiner", "RefinerOperator"), - "MemoryWriter": ("sage.middleware.operators.rag.writer", "MemoryWriter"), - # External Data Sources (may require optional dependencies) - "ArxivPDFDownloader": ("sage.middleware.operators.rag.arxiv", "ArxivPDFDownloader"), - "ArxivPDFParser": ("sage.middleware.operators.rag.arxiv", "ArxivPDFParser"), - # Web Search - "BochaWebSearch": ("sage.middleware.operators.rag.searcher", "BochaWebSearch"), -} - -# Export all operator names and type utilities -__all__ = [ # type: ignore[misc] - # Types - "RAGDocument", - "RAGQuery", - "RAGResponse", - "RAGInput", - "RAGOutput", - "ensure_rag_response", - "extract_query", - "extract_results", - "create_rag_response", - # Operators (lazy loaded) - *list(_IMPORTS.keys()), -] - - -def __getattr__(name: str): - """Lazy import to avoid optional dependency issues at import time.""" - if name in _IMPORTS: - module_name, attr_name = _IMPORTS[name] - import importlib - - module = importlib.import_module(module_name) - return getattr(module, attr_name) - raise AttributeError(f"module '{__name__}' has no attribute '{name}'") diff --git a/packages/sage-middleware/src/sage/middleware/operators/rag/arxiv.py b/packages/sage-middleware/src/sage/middleware/operators/rag/arxiv.py deleted file mode 100644 index 416427441f..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/rag/arxiv.py +++ /dev/null @@ -1,331 +0,0 @@ -import json -import os -import re -import time -from collections import Counter -from urllib.parse import quote - -import feedparser -import requests - -from sage.common.core.functions import MapFunction as MapOperator - -# PyMuPDF (fitz) is required for PDF processing -try: - import fitz # type: ignore[import-not-found] - - FITZ_AVAILABLE = True -except ImportError: - FITZ_AVAILABLE = False - fitz = None # type: ignore[assignment] - - -class Paper: - def __init__(self, path, title="", url="", abs="", authors=None, **kwargs): - if authors is None: - authors = [] - super().__init__(**kwargs) - - # Check if fitz is available - if not FITZ_AVAILABLE or fitz is None: - raise RuntimeError( - "PyMuPDF (fitz) is required for PDF processing. Install with: pip install PyMuPDF" - ) - - # 初始化函数，根据pdf路径初始化Paper对象 - self.url = url # 文章链接 - self.path = path # pdf路径 - self.section_names = [] # 段落标题 - self.section_texts = {} # 段落内容 - self.abs = abs - self.title_page = 0 - if title == "": - self.pdf = fitz.open(self.path) # pdf文档 # type: ignore[attr-defined] - self.title = self.get_title() - self.parse_pdf() - else: - self.title = title - self.authors = authors - self.roman_num = [ - "I", - "II", - "III", - "IV", - "V", - "VI", - "VII", - "VIII", - "IIX", - "IX", - "X", - ] - self.digit_num = [str(d + 1) for d in range(10)] - self.first_image = "" - - def parse_pdf(self): - assert fitz is not None, "fitz must be available" - self.pdf = fitz.open(self.path) # type: ignore[attr-defined] - self.text_list = [page.get_text() for page in self.pdf] - self.all_text = " ".join(self.text_list) - self.extract_section_infomation() - self.section_texts.update({"title": self.title}) - self.pdf.close() - - # 定义一个函数，根据字体的大小，识别每个章节名称，并返回一个列表 - def get_chapter_names( - self, - ): - assert fitz is not None, "fitz must be available" - # # 打开一个pdf文件 - doc = fitz.open(self.path) # type: ignore[attr-defined] - text_list = [page.get_text() for page in doc] - all_text = "" - for text in text_list: - all_text += text - # # 创建一个空列表，用于存储章节名称 - chapter_names = [] - for line in all_text.split("\n"): - line.split(" ") - if "." in line: - point_split_list = line.split(".") - space_split_list = line.split(" ") - if 1 < len(space_split_list) < 5: - if 1 < len(point_split_list) < 5 and ( - point_split_list[0] in self.roman_num - or point_split_list[0] in self.digit_num - ): - # print("line:", line) - chapter_names.append(line) - - return chapter_names - - def get_title(self): - doc = self.pdf # 打开pdf文件 - max_font_size = 0 # 初始化最大字体大小为0 - max_font_sizes = [0] - for page_index, page in enumerate(doc): # 遍历每一页 - text = page.get_text("dict") # 获取页面上的文本信息 - blocks = text["blocks"] # 获取文本块列表 - for block in blocks: # 遍历每个文本块 - if block["type"] == 0 and len(block["lines"]): # 如果是文字类型 - if len(block["lines"][0]["spans"]): - font_size = block["lines"][0]["spans"][0][ - "size" - ] # 获取第一行第一段文字的字体大小 - max_font_sizes.append(font_size) - if font_size > max_font_size: # 如果字体大小大于当前最大值 - max_font_size = font_size # 更新最大值 - block["lines"][0]["spans"][0]["text"] # 更新最大值对应的字符串 - max_font_sizes.sort() - # print("max_font_sizes", max_font_sizes[-10:]) - cur_title = "" - for page_index, page in enumerate(doc): # 遍历每一页 - text = page.get_text("dict") # 获取页面上的文本信息 - blocks = text["blocks"] # 获取文本块列表 - for block in blocks: # 遍历每个文本块 - if block["type"] == 0 and len(block["lines"]): # 如果是文字类型 - if len(block["lines"][0]["spans"]): - cur_string = block["lines"][0]["spans"][0]["text"] # 更新最大值对应的字符串 - block["lines"][0]["spans"][0]["flags"] # 获取第一行第一段文字的字体特征 - font_size = block["lines"][0]["spans"][0][ - "size" - ] # 获取第一行第一段文字的字体大小 - # print(font_size) - if ( - abs(font_size - max_font_sizes[-1]) < 0.3 - or abs(font_size - max_font_sizes[-2]) < 0.3 - ): - # print("The string is bold.", max_string, "font_size:", font_size, "font_flags:", font_flags) - if len(cur_string) > 4 and "arXiv" not in cur_string: - # print("The string is bold.", max_string, "font_size:", font_size, "font_flags:", font_flags) - if cur_title == "": - cur_title += cur_string - else: - cur_title += " " + cur_string - self.title_page = page_index - # break - title = cur_title.replace("\n", " ") - return title - - def extract_section_infomation(self): - assert fitz is not None, "fitz must be available" - doc = fitz.open(self.path) # type: ignore[attr-defined] - - # 获取文档中所有字体大小 - font_sizes = [] - for page in doc: - blocks = page.get_text("dict")["blocks"] - for block in blocks: - if "lines" not in block: - continue - lines = block["lines"] - for line in lines: - for span in line["spans"]: - font_sizes.append(span["size"]) - most_common_size, _ = Counter(font_sizes).most_common(1)[0] - - # 按照最频繁的字体大小确定标题字体大小的阈值 - threshold = most_common_size * 1 - section_dict = {} - section_dict["Abstract"] = "" - last_heading = None - subheadings = [] - heading_font = -1 - # 遍历每一页并查找子标题 - found_abstract = False - upper_heading = False - font_heading = False - for page in doc: - blocks = page.get_text("dict")["blocks"] - for block in blocks: - if not found_abstract: - try: - text = json.dumps(block) - except Exception: - continue - if re.search(r"\bAbstract\b", text, re.IGNORECASE): - found_abstract = True - last_heading = "Abstract" - if found_abstract: - if "lines" not in block: - continue - lines = block["lines"] - for line in lines: - for span in line["spans"]: - # 如果当前文本是子标题 - if ( - not font_heading - and span["text"].isupper() - and sum( - 1 for c in span["text"] if c.isupper() and ("A" <= c <= "Z") - ) - > 4 - ): # 针对一些标题大小一样,但是全大写的论文 - upper_heading = True - heading = span["text"].strip() - if "References" in heading: # reference 以后的内容不考虑 - self.section_names = subheadings - self.section_texts = section_dict - return - subheadings.append(heading) - if last_heading is not None: - section_dict[last_heading] = section_dict[last_heading].strip() - section_dict[heading] = "" - last_heading = heading - if ( - not upper_heading - and span["size"] > threshold - and re.match( # 正常情况下,通过字体大小判断 - r"[A-Z][a-z]+(?:\s[A-Z][a-z]+)*", - span["text"].strip(), - ) - ): - font_heading = True - if heading_font == -1: - heading_font = span["size"] - elif heading_font != span["size"]: - continue - heading = span["text"].strip() - if "References" in heading: # reference 以后的内容不考虑 - self.section_names = subheadings - self.section_texts = section_dict - return - subheadings.append(heading) - if last_heading is not None: - section_dict[last_heading] = section_dict[last_heading].strip() - section_dict[heading] = "" - last_heading = heading - # 否则将当前文本添加到上一个子标题的文本中 - elif last_heading is not None: - section_dict[last_heading] += " " + span["text"].strip() - self.section_names = subheadings - self.section_texts = section_dict - - -class ArxivPDFDownloader(MapOperator): - def __init__(self, config): - super().__init__() - config = config["ArxivPDFDownloader"] - self.max_results = config.get("max_results", 5) - self.save_dir = config.get("save_dir", "arxiv_pdfs") - os.makedirs(self.save_dir, exist_ok=True) - - def execute(self, data: str) -> list[str]: - self.query = data - base_url = "http://export.arxiv.org/api/query?" - encoded_query = quote(self.query) - query = f"search_query={encoded_query}&start=0&max_results={self.max_results}&sortBy=submittedDate&sortOrder=descending" - url = base_url + query - feed = feedparser.parse(url) - - pdf_paths = [] - - print(feed) - for entry in feed.entries: - # feedparser's type hints are incomplete, entry.id is actually a string - arxiv_id = entry.id.split("/abs/")[-1] # type: ignore[union-attr] - pdf_url = f"https://arxiv.org/pdf/{arxiv_id}.pdf" - pdf_path = os.path.join(self.save_dir, f"{arxiv_id}.pdf") - - if not os.path.exists(pdf_path): - try: - resp = requests.get(pdf_url, timeout=15) - if resp.status_code == 200: - with open(pdf_path, "wb") as f: - f.write(resp.content) - pdf_paths.append(pdf_path) - self.logger.info(f"Downloaded: {pdf_path}") - else: - self.logger.error(f"HTTP {resp.status_code} for {pdf_url}") - except Exception as e: - self.logger.error(f"Failed to download {pdf_url}: {e}") - else: - self.logger.info(f"File already exists: {pdf_path}") - pdf_paths.append(pdf_path) - - time.sleep(1) # 防止请求过快 - - return pdf_paths - - -class ArxivPDFParser(MapOperator): - def __init__(self, config): - super().__init__() - config = config["ArxivPDFParser"] - print(config) - self.output_dir = config.get("output_dir", "arxiv_structured_json") - os.makedirs(self.output_dir, exist_ok=True) - - def execute(self, data: str) -> list[str]: - pdf_paths = data - output_paths = [] - - for pdf_path in pdf_paths: - filename = os.path.basename(pdf_path).replace(".pdf", ".json") - json_path = os.path.join(self.output_dir, filename) - - if not os.path.exists(json_path): - try: - paper = Paper(pdf_path) - paper.parse_pdf() - with open(json_path, "w", encoding="utf-8") as f: - json.dump( - { - "title": paper.title, - "authors": paper.authors, - "abs": paper.abs, - "sections": paper.section_texts, - }, - f, - ensure_ascii=False, - indent=4, - ) - output_paths.append(json_path) - self.logger.info(f"Parsed and saved: {json_path}") - except Exception as e: - self.logger.error(f"Failed to parse {pdf_path}: {e}") - else: - self.logger.info(f"JSON already exists: {json_path}") - output_paths.append(json_path) - - return output_paths diff --git a/packages/sage-middleware/src/sage/middleware/operators/rag/chunk.py b/packages/sage-middleware/src/sage/middleware/operators/rag/chunk.py deleted file mode 100644 index ee8af85071..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/rag/chunk.py +++ /dev/null @@ -1,13 +0,0 @@ -"""Compatibility shim for RAG chunking operators. - -The canonical implementations now live in ``sage.libs.rag.chunk``. This file -keeps the old import path available for middleware operators and third-party -code until the next minor release. -""" - -from sage.libs.rag.chunk import ( # noqa: F401 - CharacterSplitter, - SentenceTransformersTokenTextSplitter, -) - -__all__ = ["CharacterSplitter", "SentenceTransformersTokenTextSplitter"] diff --git a/packages/sage-middleware/src/sage/middleware/operators/rag/document_loaders.py b/packages/sage-middleware/src/sage/middleware/operators/rag/document_loaders.py deleted file mode 100644 index 6aa7bfced9..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/rag/document_loaders.py +++ /dev/null @@ -1,23 +0,0 @@ -"""Compatibility shim for RAG document loaders. - -The actual loader implementations have moved to ``sage.libs.rag.document_loaders`` -so that lower layers can reuse them without depending on middleware. -""" - -from sage.libs.rag.document_loaders import ( # noqa: F401 - DocLoader, - DocxLoader, - LoaderFactory, - MarkdownLoader, - PDFLoader, - TextLoader, -) - -__all__ = [ - "TextLoader", - "PDFLoader", - "DocxLoader", - "DocLoader", - "MarkdownLoader", - "LoaderFactory", -] diff --git a/packages/sage-middleware/src/sage/middleware/operators/rag/evaluate.py b/packages/sage-middleware/src/sage/middleware/operators/rag/evaluate.py deleted file mode 100644 index a6e1733839..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/rag/evaluate.py +++ /dev/null @@ -1,658 +0,0 @@ -import re -import string -from collections import Counter - -from rouge import Rouge -from sklearn.metrics.pairwise import cosine_similarity -from transformers import AutoModel, AutoTokenizer - -from sage.common.core.functions import MapFunction as MapOperator -from sage.kernel.runtime.communication.packet import StopSignal - -# ============================================================================= -# RECOMP-style Answer Normalization (标准化答案文本) -# ============================================================================= - - -def normalize_answer(s: str) -> str: - """RECOMP 风格的答案标准化 - - 步骤: - 1. 转小写 - 2. 移除标点符号 - 3. 移除冠词 (a, an, the) - 4. 修复空白字符 - - Args: - s: 原始答案文本 - - Returns: - 标准化后的答案文本 - """ - - def remove_articles(text: str) -> str: - return re.sub(r"\b(a|an|the)\b", " ", text) - - def white_space_fix(text: str) -> str: - return " ".join(text.split()) - - def remove_punc(text: str) -> str: - exclude = set(string.punctuation) - return "".join(ch for ch in text if ch not in exclude) - - def lower(text: str) -> str: - return text.lower() - - return white_space_fix(remove_articles(remove_punc(lower(s)))) - - -def get_normalized_tokens(s: str) -> list[str]: - """获取标准化后的 token 列表 - - Args: - s: 原始文本 - - Returns: - 标准化后的 token 列表 - """ - if not s: - return [] - return normalize_answer(s).split() - - -def answer_extract(pred: str) -> str: - """提取答案文本 - - 支持 "answer is" 前缀格式的答案提取。 - - Args: - pred: 预测文本 - - Returns: - 提取后的答案文本 - """ - prefix = "answer is " - if prefix in pred.lower(): - idx = pred.lower().rfind(prefix) - return pred[idx + len(prefix) :].strip() - return pred.strip() - - -def _get_results_collector(): - """ - 延迟导入 ResultsCollector 以避免循环依赖 - - Returns: - ResultsCollector 实例，如果不可用则返回 None - """ - try: - from sage.common.utils.results_collector import ResultsCollector - - return ResultsCollector() - except ImportError: - return None - - -class MetricsAggregator: - """全局指标聚合器，用于收集和计算平均指标""" - - _instance = None - - def __new__(cls): - if cls._instance is None: - cls._instance = super().__new__(cls) - cls._instance.reset() - return cls._instance - - def reset(self): - """重置所有统计数据""" - self.metrics = { - "f1_scores": [], - "em_scores": [], # Exact Match scores - "token_counts": [], - "retrieve_times": [], - "refine_times": [], - "generate_times": [], - "total_latencies": [], - "compression_rates": [], - } - self.sample_count = 0 - - def add_f1(self, score): - self.metrics["f1_scores"].append(score) - - def add_em(self, score): - """添加 Exact Match 分数""" - self.metrics["em_scores"].append(score) - - def add_token_count(self, count): - self.metrics["token_counts"].append(count) - - def add_latency(self, retrieve, refine, generate): - self.metrics["retrieve_times"].append(retrieve) - self.metrics["refine_times"].append(refine) - self.metrics["generate_times"].append(generate) - self.metrics["total_latencies"].append(retrieve + refine + generate) - self.sample_count += 1 - - def add_compression_rate(self, rate): - self.metrics["compression_rates"].append(rate) - - def print_summary(self): - """打印汇总统计信息""" - if self.sample_count == 0: - print("\n" + "=" * 80) - print("No samples processed") - print("=" * 80) - return - - print("\n" + "=" * 80) - print(f"SUMMARY STATISTICS ({self.sample_count} samples)") - print("=" * 80) - - # Exact Match Score - if self.metrics["em_scores"]: - avg_em = sum(self.metrics["em_scores"]) / len(self.metrics["em_scores"]) - print(f"\033[92m[Average EM Score] : {avg_em:.4f}\033[0m") - - # F1 Score - if self.metrics["f1_scores"]: - avg_f1 = sum(self.metrics["f1_scores"]) / len(self.metrics["f1_scores"]) - print(f"\033[92m[Average F1 Score] : {avg_f1:.4f}\033[0m") - - # Token Count - if self.metrics["token_counts"]: - avg_tokens = sum(self.metrics["token_counts"]) / len(self.metrics["token_counts"]) - print(f"\033[92m[Average Token Count] : {avg_tokens:.0f}\033[0m") - - # Latency - if self.metrics["retrieve_times"]: - avg_retrieve = sum(self.metrics["retrieve_times"]) / len(self.metrics["retrieve_times"]) - avg_refine = sum(self.metrics["refine_times"]) / len(self.metrics["refine_times"]) - avg_generate = sum(self.metrics["generate_times"]) / len(self.metrics["generate_times"]) - avg_total = sum(self.metrics["total_latencies"]) / len(self.metrics["total_latencies"]) - - print(f"\033[92m[Average Retrieve Time] : {avg_retrieve:.2f}s\033[0m") - print(f"\033[92m[Average Refine Time] : {avg_refine:.2f}s\033[0m") - print(f"\033[92m[Average Generate Time] : {avg_generate:.2f}s\033[0m") - avg_min = avg_total / 60 - print(f"\033[92m[Average Total Latency] : {avg_total:.2f}s ({avg_min:.2f}m)\033[0m") - - # Compression Rate - if self.metrics["compression_rates"]: - valid_rates = [r for r in self.metrics["compression_rates"] if r > 0] - if valid_rates: - avg_compression = sum(valid_rates) / len(valid_rates) - print(f"\033[92m[Average Compression Rate]: {avg_compression:.2f}×\033[0m") - - print("=" * 80 + "\n") - - -class F1Evaluate(MapOperator): - """F1分数评估器（RECOMP 标准） - - 使用 RECOMP 风格的答案标准化进行 F1 分数计算。 - 标准化步骤：转小写、移除标点、移除冠词、修复空白。 - - 输入数据格式：{"query": str, "results": List[Any], "generated": str, "references": List[str]} - """ - - def __init__(self, config=None, **kwargs): - super().__init__(**kwargs) - self.aggregator = MetricsAggregator() - # 是否提取 "answer is" 前缀后的答案 - self.extract_answer = config.get("extract_answer", False) if config else False - - def _f1_score(self, pred: str, ref: str) -> float: - """计算 F1 分数（RECOMP 标准） - - 使用标准化后的 token 进行计算。 - - Args: - pred: 预测答案 - ref: 参考答案 - - Returns: - F1 分数 - """ - gold_toks = get_normalized_tokens(ref) - pred_toks = get_normalized_tokens(pred) - - common = Counter(gold_toks) & Counter(pred_toks) - num_same = sum(common.values()) - - if len(gold_toks) == 0 or len(pred_toks) == 0: - # If either is no-answer, then F1 is 1 if they agree, 0 otherwise - return float(gold_toks == pred_toks) - - if num_same == 0: - return 0.0 - - precision = 1.0 * num_same / len(pred_toks) - recall = 1.0 * num_same / len(gold_toks) - f1 = (2 * precision * recall) / (precision + recall) - return f1 - - def execute(self, data): - # Handle StopSignal - 不输出,让 CompressionRateEvaluate 最后统一输出 - if isinstance(data, StopSignal): - return data - - golds = data.get("references", []) - pred = data.get("generated", "") - - # 可选：提取 "answer is" 后的答案 - if self.extract_answer: - pred = answer_extract(pred) - - best = max((self._f1_score(pred, g) for g in golds), default=0.0) if golds else 0.0 - - # Add to aggregator - self.aggregator.add_f1(best) - - # Add to ResultsCollector (if available) - collector = _get_results_collector() - if collector is not None: - sample_id = data.get("sample_id", data.get("_sample_idx")) - collector.update_sample(sample_id, f1=best) - - print(f"\033[93m[F1] : {best:.4f}\033[0m") - return data - - -class EMEvaluate(MapOperator): - """Exact Match 评估器（RECOMP 标准） - - 使用 RECOMP 风格的答案标准化进行精确匹配计算。 - 标准化步骤：转小写、移除标点、移除冠词、修复空白。 - - 输入数据格式：{"query": str, "results": List[Any], "generated": str, "references": List[str]} - """ - - def __init__(self, config=None, **kwargs): - super().__init__(**kwargs) - self.aggregator = MetricsAggregator() - # 是否提取 "answer is" 前缀后的答案 - self.extract_answer = config.get("extract_answer", False) if config else False - - def _exact_match(self, pred: str, gold: str) -> int: - """计算 Exact Match（RECOMP 标准） - - 使用标准化后的文本进行精确匹配。 - - Args: - pred: 预测答案 - gold: 参考答案 - - Returns: - 1 如果匹配，否则 0 - """ - return int(normalize_answer(pred) == normalize_answer(gold)) - - def execute(self, data): - # Handle StopSignal - 不输出,让 CompressionRateEvaluate 最后统一输出 - if isinstance(data, StopSignal): - return data - - golds = data.get("references", []) - pred = data.get("generated", "") - - # 可选：提取 "answer is" 后的答案 - if self.extract_answer: - pred = answer_extract(pred) - - best = max((self._exact_match(pred, g) for g in golds), default=0) if golds else 0 - - # Add to aggregator - self.aggregator.add_em(best) - - print(f"\033[93m[EM] : {best}\033[0m") - return data - - -class RecallEvaluate(MapOperator): - """Recall评估器 - - 输入数据格式：{"query": str, "results": List[Any], "generated": str, "references": List[str]} - """ - - def _get_tokens(self, text: str): - return text.lower().split() - - def _recall(self, pred: str, ref: str): - r = Counter(self._get_tokens(ref)) - p = Counter(self._get_tokens(pred)) - if not r: - return 0.0 - common = r & p - return float(sum(common.values()) / sum(r.values())) - - def execute(self, data: dict): - golds = data.get("references", []) - pred = data.get("generated", "") - best = max(self._recall(pred, g) for g in golds) if golds else 0.0 - print(f"\033[93m[Recall] : {best:.4f}\033[0m") - return data - - -class BertRecallEvaluate(MapOperator): - """BERT Recall评估器 - - 输入数据格式：{"query": str, "results": List[Any], "generated": str, "references": List[str]} - """ - - def __init__(self, config=None, **kwargs): - super().__init__(**kwargs) - self.tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") - self.model = AutoModel.from_pretrained("bert-base-uncased") - - def execute(self, data: dict): - golds = data.get("references", []) - pred = data.get("generated", "") - scores = [] - for g in golds: - encs = self.tokenizer([pred, g], return_tensors="pt", padding=True) - embs = self.model(**encs).last_hidden_state.mean(dim=1).detach().numpy() - # Convert to numpy arrays explicitly for cosine_similarity - emb_pred = embs[0:1] # Shape: (1, embedding_dim) - emb_gold = embs[1:2] # Shape: (1, embedding_dim) - similarity = cosine_similarity(emb_pred, emb_gold) - scores.append(float(similarity[0][0])) - best = max(scores) if scores else 0.0 - print(f"\033[93m[BertRecall] : {best:.4f}\033[0m") - return data - - -class RougeLEvaluate(MapOperator): - """ROUGE-L评估器 - - 输入数据格式：{"query": str, "results": List[Any], "generated": str, "references": List[str]} - """ - - def __init__(self, config=None, **kwargs): - super().__init__(**kwargs) - self.rouge = Rouge() - - def execute(self, data: dict): - golds = data.get("references", []) - pred = data.get("generated", "") - scores = [] - for g in golds: - # rouge.get_scores returns a list with one dict - rouge_result = self.rouge.get_scores(pred, g) - if rouge_result and isinstance(rouge_result, list): - scores.append(rouge_result[0]["rouge-l"]["f"]) - best = max(scores) if scores else 0.0 - print(f"\033[93m[ROUGE-L] : {best:.4f}\033[0m") - return data - - -class BRSEvaluate(MapOperator): - """BRS评估器 - - 输入数据格式：{"query": str, "results": List[Any], "generated": str, "references": List[str]} - """ - - def execute(self, data: dict): - golds = data.get("references", []) - pred = data.get("generated", "") - scores = [(len(set(pred) & set(g)) / len(set(g))) if g else 0.0 for g in golds] - best = max(scores) if scores else 0.0 - print(f"\033[93m[BRS] : {best:.4f}\033[0m") - return data - - -class AccuracyEvaluate(MapOperator): - """准确率评估器 - - 输入数据格式：{"query": str, "results": List[Any], "generated": str, "references": List[str]} - """ - - def _normalize_text(self, text: str) -> str: - """标准化文本用于比较""" - return text.lower().strip() - - def execute(self, data: dict): - golds = data.get("references", []) - pred = data.get("generated", "") - - if not golds or not pred: - print("\033[93m[Acc] : 0.0000\033[0m") - return data - - pred_norm = self._normalize_text(pred) - - # 准确率：检查预测答案是否与任一参考答案匹配（完全匹配或关键词匹配） - correct = False - for gold in golds: - gold_norm = self._normalize_text(gold) - # 检查是否有关键词匹配 - gold_words = set(gold_norm.split()) - pred_words = set(pred_norm.split()) - # 如果预测答案包含参考答案中的重要词汇，认为是正确的 - if gold_words and len(gold_words & pred_words) / len(gold_words) >= 0.3: - correct = True - break - - print(f"\033[93m[Acc] : {float(correct):.4f}\033[0m") - return data - - -class TokenCountEvaluate(MapOperator): - """Token计数评估器 - - 统计送入生成器的最终prompt的token数量（使用真实tokenizer） - 优先级：compressed_context（压缩后）> refining_results > retrieval_results（原始） - - 输入数据格式：{"query": str, "compressed_context": str, "refining_results": List[str], ...} 或 - {"query": str, "retrieval_results": List[Dict], ...} - """ - - def __init__(self, config=None, **kwargs): - super().__init__(**kwargs) - self.aggregator = MetricsAggregator() - # 使用与REFORM相同的tokenizer以保持一致性 - try: - from transformers import AutoTokenizer - - self.tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct") - except Exception: - self.tokenizer = None - - def execute(self, data): - # Handle StopSignal - if isinstance(data, StopSignal): - return data - - # 优先使用 compressed_context（最终送入生成器的文本） - context = data.get("compressed_context") - if context: - # 使用真实tokenizer计算token数 - if self.tokenizer: - total_tokens = len(self.tokenizer.encode(context)) - else: - total_tokens = len(context.split()) - else: - # 回退到旧的计算方式 - docs = data.get("refining_results") or data.get("retrieval_results", []) - total_tokens = 0 - if docs: - for doc in docs: - if isinstance(doc, dict): - text = doc.get("text", str(doc)) - elif isinstance(doc, str): - text = doc - else: - text = str(doc) - - if self.tokenizer: - total_tokens += len(self.tokenizer.encode(text)) - else: - total_tokens += len(text.split()) - - # Add to aggregator - self.aggregator.add_token_count(total_tokens) - - # Add to ResultsCollector (if available) - collector = _get_results_collector() - if collector is not None: - sample_id = data.get("sample_id", data.get("_sample_idx")) - collector.update_sample(sample_id, token_count=total_tokens) - - print(f"\033[93m[Token Count] : {total_tokens}\033[0m") - return data - - -class LatencyEvaluate(MapOperator): - """延迟评估器 - - 输入数据格式: - {"query": str, "retrieve_time": float, "refine_time": float, - "generate_time": float, ...} - """ - - def __init__(self, config=None, **kwargs): - super().__init__(**kwargs) - self.aggregator = MetricsAggregator() - - def execute(self, data): - # Handle StopSignal - 不输出,让 CompressionRateEvaluate 最后统一输出 - if isinstance(data, StopSignal): - return data - - retrieve_time = data.get("retrieve_time", 0) - refine_time = data.get("refine_time", 0.0) - generate_time = data.get("generate_time", 0.0) - total_lat = retrieve_time + refine_time + generate_time - - # Add to aggregator - self.aggregator.add_latency(retrieve_time, refine_time, generate_time) - - # Add to ResultsCollector (if available) - collector = _get_results_collector() - if collector is not None: - sample_id = data.get("sample_id", data.get("_sample_idx")) - collector.update_sample( - sample_id, - retrieve_time=retrieve_time, - refine_time=refine_time, - generate_time=generate_time, - total_time=total_lat, - ) - - print(f"\033[93m[Retrieve Time] : {retrieve_time:.2f}s\033[0m") - print(f"\033[93m[Refine Time] : {refine_time:.2f}s\033[0m") - print(f"\033[93m[Generate Time] : {generate_time:.2f}s\033[0m") - print(f"\033[93m[Total Latency] : {total_lat:.2f}s\033[0m") - return data - - -class ContextRecallEvaluate(MapOperator): - """上下文召回率评估器 - - 输入数据格式：{"query": str, "results": List[Any], "generated": str, "references": List[str]} - """ - - def _normalize_text(self, text: str) -> str: - """标准化文本用于比较""" - return text.lower().strip() - - def execute(self, data: dict): - golds = data.get("references", []) - pred = data.get("generated", "") - - if not golds or not pred: - print("\033[93m[Context Recall] : 0.0000\033[0m") - return data - - pred_norm = self._normalize_text(pred) - pred_words = set(pred_norm.split()) - - # 计算有多少参考答案的关键词在生成答案中被提及 - total_recall = 0.0 - for gold in golds: - gold_norm = self._normalize_text(gold) - gold_words = set(gold_norm.split()) - if gold_words: - # 计算当前参考答案的recall - matched_words = len(gold_words & pred_words) - recall = matched_words / len(gold_words) - total_recall = max(total_recall, recall) # 取最大值 - - print(f"\033[93m[Context Recall] : {total_recall:.4f}\033[0m") - return data - - -class CompressionRateEvaluate(MapOperator): - """计算文档压缩率 - - 压缩率 = 原始文档token数 / 压缩后文档token数 - - 输入数据格式: - {"query": str, "retrieval_results": List[Dict], - "refining_results": List[str], ...} - - Args: - retrieval_results: 原始检索的文档（用于计算原始token数） - refining_results: 压缩后的文档文本（用于计算压缩后token数） - """ - - def __init__(self, config=None, **kwargs): - super().__init__(**kwargs) - self.aggregator = MetricsAggregator() - - def _count_tokens(self, docs): - """计算文档列表的总token数""" - if not docs: - return 0 - # 处理不同格式的文档 - total = 0 - for doc in docs: - if isinstance(doc, dict): - # Dict格式：提取text字段 - text = doc.get("text", doc.get("content", str(doc))) - total += len(text.split()) - elif isinstance(doc, str): - # 字符串格式 - total += len(doc.split()) - else: - total += len(str(doc).split()) - return total - - def execute(self, data): - # Handle StopSignal - 在最后输出完整汇总统计 - if isinstance(data, StopSignal): - print("\n") # 添加空行分隔 - self.aggregator.print_summary() - return data - - # 获取原始检索文档的token数 - retrieved_docs = data.get("retrieval_results", []) - retrieved_tokens = self._count_tokens(retrieved_docs) - - # 获取压缩后文档的token数 - refined_docs = data.get("refining_results", []) - refined_tokens = self._count_tokens(refined_docs) - - # 计算压缩率 - if refined_tokens > 0 and retrieved_tokens > 0: - compression_rate = retrieved_tokens / refined_tokens - else: - compression_rate = 0.0 - - # Add to aggregator - self.aggregator.add_compression_rate(compression_rate) - - # Add to ResultsCollector (if available) - collector = _get_results_collector() - if collector is not None: - sample_id = data.get("sample_id", data.get("_sample_idx")) - collector.update_sample( - sample_id, - compression_rate=compression_rate, - original_tokens=retrieved_tokens, - compressed_tokens=refined_tokens, - ) - - print(f"\033[93m[Compression Rate] : {compression_rate:.2f}×\033[0m") - return data diff --git a/packages/sage-middleware/src/sage/middleware/operators/rag/generator.py b/packages/sage-middleware/src/sage/middleware/operators/rag/generator.py deleted file mode 100644 index 26cb4a468c..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/rag/generator.py +++ /dev/null @@ -1,340 +0,0 @@ -import json -import os -import time -from dataclasses import dataclass, field -from typing import Any - -from openai import OpenAI - -from sage.common.config.output_paths import get_states_file -from sage.common.core.functions import MapFunction as MapOperator -from sage.libs.integrations.huggingface import HFClient - - -class OpenAIGenerator(MapOperator): - """ - 生成节点：调用 OpenAI-Compatible / SageLLM 等端点。 - - 调用方式:: - sub_conf = config["generator"]["sagellm"] # <- 单端点子配置 - gen = OpenAIGenerator(sub_conf) - - 其中 `sub_conf` 结构示例:: - - { - "method": "openai", - "model_name": "gpt-4o-mini", - "base_url": "http://localhost:8000/v1", - "api_key": "xxx", # pragma: allowlist secret - "seed": 42 - } - """ - - def __init__(self, config: dict, enable_profile=False, **kwargs): - super().__init__(**kwargs) - - # 直接持有子配置 - self.config = config - self.enable_profile = enable_profile - - # 实例化模型 - # API key 优先级: 配置文件 > OPENAI_API_KEY - api_key = self.config.get("api_key") or os.getenv("OPENAI_API_KEY") - - # 获取必需的配置参数（使用 .get() 提供默认值） - model_name = self.config.get("model_name") or self.config.get("model", "gpt-3.5-turbo") - # 展开环境变量（如果 model_name 包含环境变量） - model_name = os.path.expandvars(model_name) - base_url = self.config.get("base_url", "https://api.openai.com/v1") - - # 直接使用 OpenAI 客户端（支持 sagellm 等 OpenAI 兼容 API） - self.model = OpenAI( - base_url=base_url, - api_key=api_key or "EMPTY", # 本地服务可用任意 key - ) - self.model_name = model_name - self.num = 1 - - # 只有启用profile时才设置数据存储路径 - if self.enable_profile: - # Use unified output path system - self.data_base_path = str(get_states_file("dummy", "generator_data").parent) - os.makedirs(self.data_base_path, exist_ok=True) - self.data_records = [] - - def _save_data_record(self, query, prompt, response): - """保存生成数据记录""" - if not self.enable_profile: - return - - record = { - "timestamp": time.time(), - "query": query, - "prompt": prompt, - "response": response, - "model_name": self.config.get("model_name") or self.config.get("model", "unknown"), - } - self.data_records.append(record) - self._persist_data_records() - - def _persist_data_records(self): - """将数据记录持久化到文件""" - if not self.enable_profile or not self.data_records: - return - - timestamp = int(time.time()) - filename = f"generator_data_{timestamp}.json" - path = os.path.join(self.data_base_path, filename) - - try: - with open(path, "w", encoding="utf-8") as f: - json.dump(self.data_records, f, ensure_ascii=False, indent=2) - self.data_records = [] - except Exception as e: - self.logger.error(f"Failed to persist data records: {e}") - - def execute(self, data: list[Any]) -> dict[str, Any]: - """ - 输入 : [original_data, prompt] *或* [prompt] - 输出 : 完整的数据字典，包含 generated 字段 - - prompt 可以是: - - str: 普通字符串，将转换为 [{"role": "user", "content": prompt}] - - list[dict]: 已格式化的消息列表，直接传递给 OpenAI API - """ - # 解析输入数据 - if len(data) > 1: - # 来自QAPromptor: [original_data, prompt] - original_data = data[0] - prompt = data[1] - else: - # 直接prompt输入: [prompt] - original_data = {} - prompt = data[0] - - # 提取user_query - if isinstance(original_data, dict): - user_query = original_data.get("query", original_data.get("question", "")) - else: - user_query = None - - # 如果 prompt 是字符串，转换为标准消息格式 - if isinstance(prompt, str): - messages = [{"role": "user", "content": prompt}] - elif isinstance(prompt, list) and all(isinstance(item, dict) for item in prompt): - # 如果已经是消息列表格式，直接使用 - messages = prompt - else: - # 兜底处理：转换为字符串再构造消息 - messages = [{"role": "user", "content": str(prompt)}] - - # 准备生成参数（从配置中提取） - generate_kwargs = {} - - # 支持的参数列表 - supported_params = [ - "max_tokens", - "temperature", - "top_p", - "enable_thinking", # Qwen 特有参数：禁用思考过程输出 - "stream", - "frequency_penalty", - "n", - "logprobs", - ] - - # 从配置中提取参数并传递给 generate - for param in supported_params: - if param in self.config: - generate_kwargs[param] = self.config[param] - - # 使用 OpenAI 客户端调用 chat completions API - completion = self.model.chat.completions.create( - model=self.model_name, - messages=messages, - **generate_kwargs, - ) - response = completion.choices[0].message.content - - self.num += 1 - - # 保存数据记录（只有enable_profile=True时才保存） - if self.enable_profile: - self._save_data_record(user_query, prompt, response) - - self.logger.info(f"[{self.__class__.__name__}] Response: {response}") - - # 构建完整的输出数据，保持上游数据 - if isinstance(original_data, dict): - # 保持原始数据结构，添加generated字段 - result = dict(original_data) - result["generated"] = response - # generate_time 由 MapOperator 自动添加 - result["question"] = result.get( - "question", - {"query": user_query, "references": result.get("references", [])}, - ) - return result - else: - # 兼容原有tuple格式输出，但符合返回类型 - return { - "query": user_query if user_query is not None else "", - "generated": response, - # generate_time 由 MapOperator 自动添加 - } - - def __del__(self): - """确保在对象销毁时保存所有未保存的记录""" - if hasattr(self, "enable_profile") and self.enable_profile: - try: - self._persist_data_records() - except Exception: - pass - - -class HFGenerator(MapOperator): - """ - HFGenerator is a generator rag that interfaces with a Hugging Face model - to generate responses based on input data. - """ - - def __init__(self, config, **kwargs): - """ - Initializes the HFGenerator instance with configuration parameters. - - :param config: Dictionary containing configuration for the generator, including - the method and model name. - """ - super().__init__(**kwargs) - self.config = config - # Apply the generator model with the provided configuration - self.model = HFClient(model_name=self.config["model_name"]) - - def execute(self, data: list, **kwargs) -> tuple[str, str]: - """ - Executes the response generation using the configured Hugging Face model based on the input data. - - :param data: Data object containing a list of input data. - The expected format and the content of the data depend on the model's requirements. - :param kwargs: Additional parameters for the model generation (e.g., temperature, max_tokens, etc.). - - :return: A Data object containing the generated response as a string. - """ - # Generate the response from the Hugging Face model using the provided data and additional arguments - user_query = data[0] if len(data) > 1 else None - - prompt = data[1] if len(data) > 1 else data[0] - - response = self.model.generate(prompt, **kwargs) - - print(f"\033[32m[ {self.__class__.__name__}]: Response: {response}\033[0m ") - - # Return the generated response as a Data object - self.logger.info(f"\033[32m[ {self.__class__.__name__}]: Response: {response}\033[0m ") - - return ( - user_query if user_query is not None else "", - response if isinstance(response, str) else str(response), - ) - - -@dataclass -class SageLLMRAGGenerator(MapOperator): - """ - RAG 生成器 - 使用 SageLLM 引擎 - - 通过 engine_type 参数选择底层 LLM 引擎： - - sagellm (默认): 使用 SageLLMGenerator，支持 auto/mock/cuda/ascend 后端 - - Example: - ```python - # 使用 sagellm 引擎（推荐） - generator = SageLLMRAGGenerator( - engine_type="sagellm", - backend_type="auto", - model_path="Qwen/Qwen2.5-7B-Instruct", - max_tokens=2048, - ) - ``` - - Attributes: - engine_type: 引擎类型，支持 "sagellm"（默认） - backend_type: 后端类型，支持 "auto"/"mock"/"cuda"/"ascend" - model_path: 模型路径或 HuggingFace 模型 ID - max_tokens: 最大生成 token 数 - temperature: 采样温度 - top_p: nucleus 采样参数 - timeout: 请求超时时间 - """ - - # 引擎选择 - engine_type: str = "sagellm" # sagellm only - backend_type: str = "auto" # auto/mock/cuda/ascend - - # SageLLM 配置 - model_path: str = "" - device_map: str = "auto" - dtype: str = "auto" - - # 生成参数 - max_tokens: int = 2048 - temperature: float = 0.7 - top_p: float = 0.95 - top_k: int = 50 - - # 配置 - timeout: float = 120.0 - default_options: dict[str, Any] = field(default_factory=dict) - - # 内部状态 - _generator: Any = field(default=None, init=False, repr=False) - - def __post_init__(self) -> None: - super().__init__() - self._init_generator() - - def _init_generator(self) -> None: - """根据 engine_type 初始化底层生成器""" - if self.engine_type != "sagellm": - # 只支持 sagellm - raise ValueError( - f"Unsupported engine_type='{self.engine_type}'. " - f"Only 'sagellm' is supported. vLLM support has been removed in v0.3.0." - ) - - # 默认使用 sagellm - from sage.middleware.operators.llm import SageLLMGenerator - - self._generator = SageLLMGenerator( - backend_type=self.backend_type, - model_path=self.model_path, - device_map=self.device_map, - dtype=self.dtype, - max_tokens=self.max_tokens, - temperature=self.temperature, - top_p=self.top_p, - top_k=self.top_k, - timeout=self.timeout, - default_options=self.default_options, - ) - - def execute(self, data: list[Any]) -> dict[str, Any]: - """ - 执行生成，委托给底层生成器 - - 输入 : [original_data, prompt] 或 [prompt] - 输出 : 包含 generated 字段的数据字典 - """ - result = self._generator.execute(data) - - # 统一输出格式 - if isinstance(result, dict): - return result - elif isinstance(result, tuple) and len(result) >= 2: - # Generator returns (original, text) - return { - "query": result[0] if result[0] else "", - "generated": result[1], - } - else: - return {"generated": str(result)} diff --git a/packages/sage-middleware/src/sage/middleware/operators/rag/index_builder/__init__.py b/packages/sage-middleware/src/sage/middleware/operators/rag/index_builder/__init__.py deleted file mode 100644 index 00a3590610..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/rag/index_builder/__init__.py +++ /dev/null @@ -1,48 +0,0 @@ -"""RAG Index Building Service (L4 Middleware Operator) - -This module provides index building functionality for RAG systems. -It orchestrates document processing, embedding, and vector storage. - -Layer: L4 (sage-middleware/operators/rag) -Dependencies: - - sage.libs.rag (L3) - chunk, document_loaders - - sage.middleware.components.sage_db (L4) - SageDB backend - - sage.common (L1) - embedding models - -Components: -- VectorStore: Protocol defining vector storage interface -- IndexManifest: Metadata describing a built index -- IndexBuilder: Service for building vector indices - -Architecture Pattern: -- L4 defines IndexBuilder (orchestration) -- L4 provides SageDB backend implementation -- L3 provides ChromaDB backend via integrations -- L5 (sage-cli) uses IndexBuilder - -Example Usage: - >>> from sage.middleware.operators.rag.index_builder import IndexBuilder - >>> from sage.middleware.components.sage_db import SageVDBBackend - >>> - >>> # Create backend factory - >>> def factory(path, dim): - ... return SageVDBBackend(path, dim) - >>> - >>> # Build index - >>> builder = IndexBuilder(backend_factory=factory) - >>> manifest = builder.build_from_docs( - ... source_dir=Path("docs"), - ... persist_path=Path(".sage/index"), - ... embedding_model=embedder, - ... ) -""" - -from sage.middleware.operators.rag.index_builder.builder import IndexBuilder -from sage.middleware.operators.rag.index_builder.manifest import IndexManifest -from sage.middleware.operators.rag.index_builder.storage import VectorStore - -__all__ = [ - "IndexBuilder", - "IndexManifest", - "VectorStore", -] diff --git a/packages/sage-middleware/src/sage/middleware/operators/rag/index_builder/builder.py b/packages/sage-middleware/src/sage/middleware/operators/rag/index_builder/builder.py deleted file mode 100644 index 4277e6e4ed..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/rag/index_builder/builder.py +++ /dev/null @@ -1,363 +0,0 @@ -"""Index Builder - Service for building RAG vector indices - -Layer: L4 (sage-middleware/operators/rag) -""" - -import logging -from collections.abc import Callable -from contextlib import contextmanager -from datetime import datetime -from pathlib import Path -from typing import Any - -from sage.middleware.operators.rag.index_builder.manifest import IndexManifest -from sage.middleware.operators.rag.index_builder.storage import VectorStore - -logger = logging.getLogger(__name__) - - -@contextmanager -def _optional_progress(show: bool, description: str, total: int | None = None): - """Context manager for optional Rich progress bar. - - Args: - show: Whether to show progress bar (False = silent mode) - description: Task description - total: Total number of items (None for indeterminate) - - Yields: - Progress task update function: update(advance=1) - """ - if not show: - # Silent mode - yield a no-op update function - def noop(**kwargs): - pass - - yield noop - return - - try: - from rich.progress import ( - BarColumn, - Progress, - SpinnerColumn, - TaskProgressColumn, - TextColumn, - TimeRemainingColumn, - ) - - with Progress( - SpinnerColumn(), - TextColumn("[cyan]{task.description}"), - BarColumn(bar_width=30), - TaskProgressColumn(), - TimeRemainingColumn(), - transient=True, # Clear after completion - ) as progress: - task = progress.add_task(description, total=total) - - def update(advance: int = 1, **kwargs): - progress.update(task, advance=advance, **kwargs) - - yield update - - except ImportError: - # Fallback if rich is not available - logger.info(f"[Progress] {description}") - - def fallback_update(**kwargs): - pass - - yield fallback_update - - -class IndexBuilder: - """Service for building RAG vector indices with pluggable backends. - - This class orchestrates the complete index building workflow, using - dependency injection to decouple from specific vector storage backends. - - Architecture Pattern: - - L4 defines this builder (orchestration logic) - - L4 provides SageDB backend (sage.middleware.components.sage_db) - - L3 provides ChromaDB backend (sage.libs.integrations.chroma) - - L5 uses IndexBuilder with injected backend factory - - Args: - backend_factory: Function creating VectorStore instances - Signature: (persist_path: Path, dim: int) -> VectorStore - - Example: - >>> # In sage-cli (L5) - >>> from sage.middleware.operators.rag.index_builder import IndexBuilder - >>> from sage.middleware.components.sage_db import SageVDBBackend - >>> - >>> def factory(path: Path, dim: int): - ... return SageVDBBackend(path, dim) - >>> - >>> builder = IndexBuilder(backend_factory=factory) - >>> manifest = builder.build_from_docs( - ... source_dir=Path("docs"), - ... persist_path=Path(".sage/db"), - ... embedding_model=embedder, - ... chunk_size=800, - ... chunk_overlap=160, - ... ) - """ - - def __init__(self, backend_factory: Callable[[Path, int], VectorStore]): - """Initialize builder with backend factory. - - Args: - backend_factory: Factory function for creating VectorStore instances - """ - self.backend_factory = backend_factory - - def build_from_docs( - self, - source_dir: Path, - persist_path: Path, - embedding_model: Any, - index_name: str = "default", - chunk_size: int = 800, - chunk_overlap: int = 160, - document_processor: Callable[[Path], list[dict[str, Any]]] | None = None, - max_documents: int | None = None, - show_progress: bool = True, - ) -> IndexManifest: - """Build vector index from document directory. - - This method orchestrates the complete index building process: - 1. Create vector store backend - 2. Process documents (via document_processor or default) - 3. Chunk text content - 4. Generate embeddings - 5. Store vectors with metadata - 6. Build/optimize index - 7. Persist to disk - 8. Return manifest - - Args: - source_dir: Directory containing source documents - persist_path: Path to save the built index - embedding_model: Model with embed() and get_dim() methods - index_name: Unique identifier for this index - chunk_size: Size of text chunks in characters - chunk_overlap: Overlap between consecutive chunks - document_processor: Optional custom document processing function - If None, uses simple text extraction - Signature: (source_dir: Path) -> list[dict] where dict has: - - "content": str (text content) - - "metadata": dict (doc_path, title, heading, etc.) - max_documents: Optional limit on number of documents to process - show_progress: Show Rich progress bar (False = quiet mode) - - Returns: - IndexManifest with build statistics and metadata - - Raises: - FileNotFoundError: If source_dir doesn't exist - RuntimeError: If index building fails - - Example: - >>> # Custom document processor for Markdown - >>> def process_markdown(source_dir: Path): - ... chunks = [] - ... for file in source_dir.glob("**/*.md"): - ... text = file.read_text() - ... chunks.append({ - ... "content": text, - ... "metadata": {"doc_path": str(file.relative_to(source_dir))} - ... }) - ... return chunks - >>> - >>> manifest = builder.build_from_docs( - ... source_dir=Path("docs"), - ... persist_path=Path(".sage/db"), - ... embedding_model=embedder, - ... document_processor=process_markdown, - ... ) - """ - if not source_dir.exists(): - raise FileNotFoundError(f"Source directory not found: {source_dir}") - - logger.debug(f"Building index from {source_dir}") - logger.debug(f"Backend: {self.backend_factory}") - logger.debug(f"Chunk size: {chunk_size}, overlap: {chunk_overlap}") - - # Create vector store backend - dim = embedding_model.get_dim() - store = self.backend_factory(persist_path, dim) - logger.debug(f"Created vector store with dimension {dim}") - - # Process documents - if document_processor is None: - # Default: simple text file processing - logger.debug( - "No document_processor provided, using default text extraction. " - "For better results, provide a custom processor." - ) - processed_docs = self._default_document_processor(source_dir, max_documents) - else: - processed_docs = document_processor(source_dir) - if max_documents: - processed_docs = processed_docs[:max_documents] - - logger.debug(f"Processed {len(processed_docs)} document sections") - - # Import chunking utility - try: - from sage.common.utils.document_processing import ( - chunk_text, - sanitize_metadata_value, - truncate_text, - ) - except ImportError: - logger.debug("Cannot import chunking utilities from sage.common, using simple split") - - def chunk_text(text: str, size: int, overlap: int) -> list[str]: - # Fallback: simple fixed-size chunking - chunks = [] - start = 0 - while start < len(text): - end = min(len(text), start + size) - chunks.append(text[start:end]) - start += size - overlap - return chunks - - def sanitize_metadata_value(val: str) -> str: - # Remove problematic chars for JSON/C++ parser - return ( - val.replace("\\", "") - .replace("\n", " ") - .replace('"', "'") - .replace("{", "(") - .replace("}", ")") - ) - - def truncate_text(text: str, limit: int = 480) -> str: - return text[:limit] if len(text) > limit else text - - # Embed and store (with chunking) - # First pass: count total chunks for accurate progress - all_chunks_data = [] # List of (chunk_text, base_metadata) - unique_docs = set() - - for doc in processed_docs: - content = doc["content"] - base_metadata = doc["metadata"] - - # Track unique documents - if "doc_path" in base_metadata: - unique_docs.add(base_metadata["doc_path"]) - - # Chunk the content - content_chunks = chunk_text(content, chunk_size, chunk_overlap) - - for chunk_idx, chunk in enumerate(content_chunks): - all_chunks_data.append((chunk, base_metadata, chunk_idx)) - - total_chunks = len(all_chunks_data) - logger.debug(f"Total chunks to embed: {total_chunks}") - - # Second pass: embed with accurate progress - with _optional_progress(show_progress, "Embedding", total=total_chunks) as progress_update: - for idx, (chunk, base_metadata, chunk_idx) in enumerate(all_chunks_data, start=1): - # Generate embedding - vector = embedding_model.embed(chunk) - - # Create metadata for this chunk - metadata = { - **base_metadata, - "chunk": str(chunk_idx), - "text": sanitize_metadata_value(truncate_text(chunk, limit=1200)), - } - - # Sanitize all string values - metadata = { - k: sanitize_metadata_value(str(v)) if isinstance(v, str) else str(v) - for k, v in metadata.items() - } - - # Store vector with metadata - store.add(vector, metadata) - progress_update(advance=1) - - if idx % 500 == 0: - logger.debug(f"Embedded {idx}/{total_chunks} chunks") - - logger.debug(f"Added {total_chunks} vectors from {len(unique_docs)} documents") - - # Build index - logger.debug("Building vector index...") - store.build_index() - - # Persist to disk - logger.debug(f"Saving index to {persist_path}") - store.save(str(persist_path)) - - # Create manifest - manifest = IndexManifest( - index_name=index_name, - backend_type=type(store).__name__, - persist_path=persist_path, - source_dir=str(source_dir), - embedding_config={ - "model": type(embedding_model).__name__, - "dim": dim, - }, - chunk_size=chunk_size, - chunk_overlap=chunk_overlap, - num_documents=len(unique_docs), - num_chunks=total_chunks, - created_at=datetime.utcnow().isoformat(), - ) - - logger.debug(f"Index built successfully: {manifest}") - return manifest - - def _default_document_processor( - self, - source_dir: Path, - max_documents: int | None = None, - ) -> list[dict[str, Any]]: - """Default document processor for plain text files. - - This is a fallback processor that simply reads text files. - For production use, provide a custom processor that: - - Handles specific formats (Markdown, PDF, etc.) - - Implements smart chunking - - Preserves document structure - - Args: - source_dir: Directory to scan - max_documents: Optional limit - - Returns: - List of processed chunks with metadata - """ - chunks = [] - text_files = list(source_dir.glob("**/*.txt")) + list(source_dir.glob("**/*.md")) - - if max_documents: - text_files = text_files[:max_documents] - - for file_path in text_files: - try: - content = file_path.read_text(encoding="utf-8", errors="ignore") - rel_path = file_path.relative_to(source_dir) - - chunks.append( - { - "content": content, - "metadata": { - "doc_path": str(rel_path), - "title": file_path.stem, - "text": content[:1000], # Preview - }, - } - ) - except Exception as e: - logger.warning(f"Failed to process {file_path}: {e}") - - return chunks diff --git a/packages/sage-middleware/src/sage/middleware/operators/rag/index_builder/manifest.py b/packages/sage-middleware/src/sage/middleware/operators/rag/index_builder/manifest.py deleted file mode 100644 index 84f4ced317..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/rag/index_builder/manifest.py +++ /dev/null @@ -1,101 +0,0 @@ -"""Index Manifest - Metadata describing a built RAG index - -Layer: L4 (sage-middleware/operators/rag) -""" - -from dataclasses import dataclass, field -from datetime import datetime -from pathlib import Path -from typing import Any - - -@dataclass -class IndexManifest: - """Metadata describing a built knowledge index. - - This dataclass stores comprehensive metadata about a vector index, - including source information, embedding configuration, and statistics. - - Attributes: - index_name: Unique identifier for this index - backend_type: Storage backend ("sagedb", "chromadb", "milvus", etc.) - persist_path: Path where the index is stored - source_dir: Original document directory - embedding_config: Embedding model configuration - chunk_size: Size of text chunks in characters - chunk_overlap: Overlap between chunks in characters - num_documents: Total number of documents indexed - num_chunks: Total number of text chunks (vectors) stored - created_at: ISO timestamp of index creation - metadata: Additional custom metadata - - Example: - >>> manifest = IndexManifest( - ... index_name="docs-public", - ... backend_type="chromadb", - ... persist_path=Path(".sage/vector_db"), - ... source_dir="docs-public/docs_src", - ... embedding_config={"method": "hash", "dim": 384}, - ... chunk_size=800, - ... chunk_overlap=160, - ... num_documents=124, - ... num_chunks=2720, - ... created_at=datetime.utcnow().isoformat(), - ... ) - """ - - index_name: str - backend_type: str - persist_path: Path - source_dir: str - embedding_config: dict[str, Any] - chunk_size: int - chunk_overlap: int - num_documents: int - num_chunks: int - created_at: str - metadata: dict[str, Any] = field(default_factory=dict) - - def to_dict(self) -> dict[str, Any]: - """Convert manifest to dictionary for serialization.""" - return { - "index_name": self.index_name, - "backend_type": self.backend_type, - "persist_path": str(self.persist_path), - "source_dir": self.source_dir, - "embedding_config": self.embedding_config, - "chunk_size": self.chunk_size, - "chunk_overlap": self.chunk_overlap, - "num_documents": self.num_documents, - "num_chunks": self.num_chunks, - "created_at": self.created_at, - "metadata": self.metadata, - } - - @classmethod - def from_dict(cls, data: dict[str, Any]) -> "IndexManifest": - """Create manifest from dictionary.""" - data_copy = data.copy() - data_copy["persist_path"] = Path(data_copy["persist_path"]) - return cls(**data_copy) - - @property - def age_seconds(self) -> float: - """Get age of index in seconds since creation.""" - created = datetime.fromisoformat(self.created_at) - return (datetime.utcnow() - created).total_seconds() - - @property - def is_empty(self) -> bool: - """Check if index contains any data.""" - return self.num_chunks == 0 - - def __repr__(self) -> str: - """Readable representation of manifest.""" - return ( - f"IndexManifest(" - f"name={self.index_name!r}, " - f"backend={self.backend_type!r}, " - f"docs={self.num_documents}, " - f"chunks={self.num_chunks})" - ) diff --git a/packages/sage-middleware/src/sage/middleware/operators/rag/index_builder/storage.py b/packages/sage-middleware/src/sage/middleware/operators/rag/index_builder/storage.py deleted file mode 100644 index 27f72424fa..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/rag/index_builder/storage.py +++ /dev/null @@ -1,131 +0,0 @@ -"""Vector Store Protocol - Abstract interface for vector storage backends - -Layer: L4 (sage-middleware/operators/rag) -""" - -from typing import Any, Protocol, runtime_checkable - - -@runtime_checkable -class VectorStore(Protocol): - """Abstract interface for vector storage backends. - - This Protocol defines the contract that all vector storage implementations - must satisfy. It enables dependency injection and backend swapping without - tight coupling to specific implementations (SageDB, ChromaDB, Milvus, etc.). - - Architecture Pattern: - - L4 (sage-middleware): Defines this Protocol + SageDB implementation - - L3 (sage-libs/integrations): Provides ChromaDB implementation - - L5 (sage-cli): Uses via factory injection - - Example Implementation: - >>> class SageVDBBackend: - ... def __init__(self, persist_path: Path, dim: int): - ... from sage.middleware.components.sage_db import SageDB - ... self.db = SageDB(dim) - ... self.path = persist_path - ... - ... def add(self, vector: list[float], metadata: dict) -> None: - ... self.db.add(vector, metadata) - ... - ... # ... implement other methods - - Usage with IndexBuilder: - >>> def backend_factory(path: Path, dim: int) -> VectorStore: - ... return SageDBBackend(path, dim) - >>> - >>> builder = IndexBuilder(backend_factory=backend_factory) - """ - - def add(self, vector: list[float], metadata: dict[str, Any]) -> None: - """Add a single vector with metadata to the store. - - Args: - vector: Dense vector embedding (must match dimension) - metadata: Associated metadata (doc_path, title, heading, chunk, text, etc.) - - Raises: - ValueError: If vector dimension doesn't match - """ - ... - - def build_index(self) -> None: - """Build/optimize the vector index for efficient search. - - This is typically called after all vectors are added via `add()`. - Implementations may use various indexing strategies: - - Flat index (brute force) - - HNSW (Hierarchical Navigable Small World) - - IVF (Inverted File Index) - - Product Quantization - - Raises: - RuntimeError: If index building fails - """ - ... - - def save(self, path: str) -> None: - """Persist the index to disk. - - Args: - path: Absolute path to save location - - Raises: - IOError: If save fails - """ - ... - - def load(self, path: str) -> None: - """Load a previously saved index from disk. - - Args: - path: Absolute path to load from - - Raises: - FileNotFoundError: If index doesn't exist - IOError: If load fails - """ - ... - - def search( - self, - query_vector: list[float], - top_k: int = 5, - filter_metadata: dict[str, Any] | None = None, - ) -> list[dict[str, Any]]: - """Search for nearest neighbor vectors. - - Args: - query_vector: Query embedding - top_k: Number of results to return - filter_metadata: Optional metadata filters (e.g., {"doc_path": "intro.md"}) - - Returns: - List of results, each containing: - - vector: The matched vector - - metadata: Associated metadata - - distance/score: Similarity score - - Example: - >>> results = store.search([0.1, 0.2, ...], top_k=5) - >>> for result in results: - ... print(result["metadata"]["title"], result["score"]) - """ - ... - - def get_dim(self) -> int: - """Get the vector dimension of this store. - - Returns: - Vector dimension (e.g., 384 for BGE-small, 768 for BERT) - """ - ... - - def count(self) -> int: - """Get total number of vectors in the store. - - Returns: - Total vector count - """ - ... diff --git a/packages/sage-middleware/src/sage/middleware/operators/rag/pipeline.py b/packages/sage-middleware/src/sage/middleware/operators/rag/pipeline.py deleted file mode 100644 index e5b6a61e24..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/rag/pipeline.py +++ /dev/null @@ -1,46 +0,0 @@ -""" -RAG Pipeline - RAG 系统的核心管道组件 - -Layer: L4 (Middleware - Orchestration) -This module orchestrates multiple RAG components (retriever, reranker, refiner, generator) -into a cohesive pipeline. Pipeline/orchestration belongs in middleware, not libs. -""" - -from typing import Any - - -class RAGPipeline: - """RAG 管道主类 - 编排多个RAG组件""" - - def __init__(self, retriever=None, generator=None, reranker=None, refiner=None): - self.retriever = retriever - self.generator = generator - self.reranker = reranker - self.refiner = refiner - - def run(self, query: str, **kwargs) -> dict[str, Any]: - """运行 RAG 管道""" - # 1. 检索相关文档 - if self.retriever: - documents = self.retriever.retrieve(query, **kwargs) - else: - documents = [] - - # 2. 重排序（可选） - if self.reranker and documents: - documents = self.reranker.rerank(query, documents, **kwargs) - - # 3. 精化查询或文档（可选） - if self.refiner: - query, documents = self.refiner.refine(query, documents, **kwargs) - - # 4. 生成回答 - if self.generator: - response = self.generator.generate(query, documents, **kwargs) - else: - response = "No generator configured" - - return {"query": query, "documents": documents, "response": response} - - -__all__ = ["RAGPipeline"] diff --git a/packages/sage-middleware/src/sage/middleware/operators/rag/profiler.py b/packages/sage-middleware/src/sage/middleware/operators/rag/profiler.py deleted file mode 100644 index ebde61ebc5..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/rag/profiler.py +++ /dev/null @@ -1,59 +0,0 @@ -import json -from dataclasses import dataclass - -from sage.common.core import FilterFunction - - -@dataclass -class QueryProfilerResult: - need_joint_reasoning: bool - complexity: str # "High" or "Low" - need_summarization: bool - summarization_length: int # 30-200 - n_info_items: int # 1-6 - - def __post_init__(self): - # 严格验证，抛出异常 - if self.complexity not in ["High", "Low"]: - raise ValueError(f"complexity必须是'High'或'Low'，得到: {self.complexity}") - - if not (30 <= self.summarization_length <= 200): - raise ValueError( - f"summarization_length必须在30-200之间，得到: {self.summarization_length}" - ) - - if not (1 <= self.n_info_items <= 6): - raise ValueError(f"n_info_items必须在1-6之间，得到: {self.n_info_items}") - - -class Query_Profiler(FilterFunction): - def __init__(self, config, **kwargs): - super().__init__(**kwargs) - - def execute(self, data): - js = json.loads(data) - # 使用解包创建对象并直接获取属性 - profiler_result = QueryProfilerResult( - need_joint_reasoning=js.get("need_joint_reasoning", False), - complexity=js.get("complexity", "Low"), - need_summarization=js.get("need_summarization", False), - summarization_length=js.get("summarization_length", 30), - n_info_items=js.get("n_info_items", 1), - ) - - # 直接解包到变量 - need_joint_reasoning = profiler_result.need_joint_reasoning - complexity = profiler_result.complexity - summarization_length = profiler_result.summarization_length - - if need_joint_reasoning is False: - synthesis_method = "map_rerank" - else: - if complexity == "Low": - synthesis_method = "stuff" - else: - synthesis_method = "map_reduce" - num_chunks = [profiler_result.n_info_items, 3 * profiler_result.n_info_items] - intermediate_length_range = summarization_length - - return [synthesis_method, num_chunks, intermediate_length_range] diff --git a/packages/sage-middleware/src/sage/middleware/operators/rag/promptor.py b/packages/sage-middleware/src/sage/middleware/operators/rag/promptor.py deleted file mode 100644 index a030bb8144..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/rag/promptor.py +++ /dev/null @@ -1,400 +0,0 @@ -import json -import os -import time - -from jinja2 import Template - -from sage.common.core.functions import MapFunction as MapOperator - -QA_prompt_template_str = """Instruction: -You are an intelligent assistant with access to a knowledge base. Answer the question below with reference to the provided context. -Only give me the answer and do not output any other words. -{%- if external_corpus %} -Relevant corpus for the current question: -{{ external_corpus }} -{%- endif %} -""" - -QA_short_answer_template_str = """Instruction: -You are an intelligent assistant with access to a knowledge base. Answer the question below with reference to the provided context. -Please provide a concise answer and conclude with 'So the final answer is: [your answer]'. -{%- if external_corpus %} -Relevant corpus for the current question: -{{ external_corpus }} -{%- endif %} -""" - -summarization_prompt_template_str = """Instruction: -You are an intelligent assistant. Summarize the content provided below in a concise and clear manner. -Only provide the summary and do not include any additional information. -{%- if external_corpus %} -Content to summarize: -{{ external_corpus }} -{%- endif %} -""" -QA_prompt_template = Template(QA_prompt_template_str) -QA_short_answer_template = Template(QA_short_answer_template_str) -summarization_prompt_template = Template(summarization_prompt_template_str) - -query_profiler_prompt_template_str = """ -For the given query = how Trump earn his first 1 million dollars?: Analyze the language and internal structure of the query and provide the following information: - -1. Does it need joint reasoning across multiple documents? -2. Provide a complexity profile for the query: - - Complexity: High / Low - - Joint Reasoning needed: Yes / No -3. Does this query need input chunks to be summarized? If yes, provide a range in words for the summarized chunks. -4. How many distinct pieces of information are needed to answer the query? - -database_metadata = The dataset consists of multiple chunks of information from Fortune 500 companies on financial reports from every quarter of 2023. -chunk_size = 1024 - -Estimate the query profile along with the database_metadata and chunk_size. - -Your output must be: -- **Only a valid JSON object** -- **No explanations, no formatting, no comments** -- **No markdown code blocks or prose** -- **Strictly conform to this schema:** - -{ - "need_joint_reasoning": <true|false>, - "complexity": "High" or "Low", - "need_summarization": <true|false>, - "summarization_length": integer (30-200), - "n_info_items": integer (1-6) -} -""" -query_profiler_prompt_template = Template(query_profiler_prompt_template_str) - - -class QAPromptor(MapOperator): - """ - QAPromptor is a prompt rag that generates a QA-style prompt using - an external corpus and a user query. This class is designed to prepare - the necessary prompt structure for a question-answering model. - - Attributes: - config: Configuration data for initializing the prompt rag (e.g., model details, etc.). - prompt_template: A template used for generating the system prompt, typically includes context or instructions. - """ - - prompt_template: Template - - def __init__(self, config, enable_profile=False, **kwargs): - super().__init__(**kwargs) - - """ - Initializes the QAPromptor instance with configuration and prompt template. - - :param config: Dictionary containing configuration for the prompt rag. - """ - self.config = config # Store the configuration for later use - self.enable_profile = enable_profile - - # 使用配置文件中的模板，如果没有则使用默认模板 - self.use_short_answer = config.get("use_short_answer", False) # 是否使用短答案模式 - - if "template" in config: - from jinja2 import Template - - self.prompt_template = Template(config["template"]) - else: - # 根据配置选择模板 - if self.use_short_answer: - self.prompt_template = QA_short_answer_template - else: - self.prompt_template = QA_prompt_template # Load the QA prompt template - - # 只有启用profile时才设置数据存储路径 - if self.enable_profile: - from sage.common.config.output_paths import get_sage_paths - - try: - sage_paths = get_sage_paths() - self.data_base_path = str(sage_paths.states_dir / "promptor_data") - except Exception: - # Fallback to current working directory - if ( - self.ctx is not None - and hasattr(self.ctx, "env_base_dir") - and self.ctx.env_base_dir - ): - self.data_base_path = os.path.join( - self.ctx.env_base_dir, ".sage_states", "promptor_data" - ) - else: - # 使用默认路径 - self.data_base_path = os.path.join(os.getcwd(), ".sage_states", "promptor_data") - - os.makedirs(self.data_base_path, exist_ok=True) - self.data_records = [] - - def _save_data_record(self, query, external_corpus, prompt): - """保存提示词数据记录""" - if not self.enable_profile: - return - - record = { - "timestamp": time.time(), - "query": query, - "external_corpus": external_corpus, - "prompt": prompt, - } - self.data_records.append(record) - self._persist_data_records() - - def _persist_data_records(self): - """将数据记录持久化到文件""" - if not self.enable_profile or not self.data_records: - return - - timestamp = int(time.time()) - filename = f"promptor_data_{timestamp}.json" - path = os.path.join(self.data_base_path, filename) - - try: - with open(path, "w", encoding="utf-8") as f: - json.dump(self.data_records, f, ensure_ascii=False, indent=2) - self.data_records = [] - except Exception as e: - self.logger.error(f"Failed to persist data records: {e}") - - # sage_lib/functions/rag/qapromptor.py - def execute(self, data) -> list: - """ - 生成 ChatGPT 风格的 prompt（system+user 两条消息）。 - - 支持多种输入格式： - 1. (query, external_corpus_list_or_str) # 元组格式 - 2. query_str # 纯字符串 - 3. {"query": ..., "results": [...]} # 字典格式（来自检索器） - 4. {"question": ..., "context": [...]} # 字典格式（来自测试） - """ - self.logger.info(f"QAPromptor received data: {data}") - try: - # -------- 解析输入 -------- - raw = data - original_data = data # 保存原始数据以便返回 - - if isinstance(raw, dict): - # 字典格式输入 - 支持多种字段名 - query = raw.get("query", raw.get("question", "")) - - # 处理不同的上下文字段名 - external_corpus_list = [] - - # 处理 refining_results 字段（来自 refiner - 压缩后的文档） - if "refining_results" in raw: - results = raw.get("refining_results", []) - for result in results: - if isinstance(result, str): - external_corpus_list.append(result) - else: - external_corpus_list.append(str(result)) - - # 处理 retrieval_results 字段（来自 retriever - 原始检索结果） - elif "retrieval_results" in raw: - results = raw.get("retrieval_results", []) - for result in results: - if isinstance(result, dict) and "text" in result: - external_corpus_list.append(result["text"]) - elif isinstance(result, str): - external_corpus_list.append(result) - else: - external_corpus_list.append(str(result)) - - # 处理 context 字段（来自测试） - elif "context" in raw: - context = raw.get("context", []) - if isinstance(context, list): - external_corpus_list.extend([str(c) for c in context]) - else: - external_corpus_list.append(str(context)) - - # 处理 external_corpus 字段 - elif "external_corpus" in raw: - external_corpus = raw.get("external_corpus", "") - if isinstance(external_corpus, list): - external_corpus_list.extend([str(c) for c in external_corpus]) - else: - external_corpus_list.append(str(external_corpus)) - - external_corpus = "\n".join(external_corpus_list) - - elif isinstance(raw, tuple) and len(raw) == 2: - # 元组格式输入 - query, external_corpus = raw - if isinstance(external_corpus, list): - external_corpus = "\n".join(external_corpus) - # 对于元组输入，保持原有行为，返回query而不是原始数据 - original_data = query - else: - # 字符串格式输入 - query = str(raw) - external_corpus = "" - # 对于字符串输入，保持原有行为，返回query而不是原始数据 - original_data = query - - external_corpus = external_corpus or "" - - # -------- system prompt -------- - if external_corpus: - system_prompt = { - "role": "system", - "content": self.prompt_template.render(external_corpus=external_corpus), - } - else: - system_prompt = { - "role": "system", - "content": ( - "You are a helpful AI assistant. Answer the user's questions accurately." - ), - } - - # -------- user prompt -------- - user_prompt = { - "role": "user", - "content": f"Question: {query}", - } - self.logger.info( - f"QAPromptor generated prompt: {system_prompt['content']} | {user_prompt['content']}" - ) - prompt = [system_prompt, user_prompt] - - # 保存数据记录（只有enable_profile=True时才保存） - if self.enable_profile: - self._save_data_record(query, external_corpus, prompt) - - return [original_data, prompt] - - except Exception as e: - self.logger.error("QAPromptor error: %s | input=%s", e, getattr(data, "data", "")) - fallback = [ - {"role": "system", "content": "System encountered an error."}, - { - "role": "user", - "content": ( - "Question: Error occurred. Please try again." - f" (Original: {getattr(data, 'data', '')})" - ), - }, - ] - return fallback - - def __del__(self): - """确保在对象销毁时保存所有未保存的记录""" - if hasattr(self, "enable_profile") and self.enable_profile: - try: - self._persist_data_records() - except Exception: - pass - - -class SummarizationPromptor(MapOperator): - """ - QAPromptor is a prompt rag that generates a QA-style prompt using - an external corpus and a user query. This class is designed to prepare - the necessary prompt structure for a question-answering model. - - Attributes: - config: Configuration data for initializing the prompt rag (e.g., model details, etc.). - prompt_template: A template used for generating the system prompt, typically includes context or instructions. - """ - - prompt_template: Template - - def __init__(self, config): - """ - Initializes the QAPromptor instance with configuration and prompt template. - - :param config: Dictionary containing configuration for the prompt rag. - """ - super().__init__() - self.config = config # Store the configuration for later use - self.prompt_template = ( - summarization_prompt_template # Load the summarization prompt template - ) - - def execute(self, data) -> list: - """ - Generates a QA-style prompt for the input question and external corpus. - - This method takes the query and external corpus, processes the corpus - into a single string, and creates a system prompt and user prompt based - on a predefined template. - - :param data: A Data object containing a tuple. The first element is the query (a string), - and the second is a list of external corpus (contextual information for the model). - - :return: A Data object containing a list with two prompts: - 1. system_prompt: A system prompt based on the template with external corpus data. - 2. user_prompt: A user prompt containing the question to be answered. - """ - # Unpack the input data into query and external_corpus - query, external_corpus = data - - # Combine the external corpus list into a single string (in case it's split into multiple parts) - external_corpus = "".join(external_corpus) - - # Prepare the base data for the system prompt, which includes the external corpus - base_system_prompt_data = {"external_corpus": external_corpus} - - # query = data - # Create the system prompt using the template and the external corpus data - system_prompt = { - "role": "system", - "content": self.prompt_template.render(**base_system_prompt_data), - } - # system_prompt = { - # "role": "system", - # "content": "" - # } - # Create the user prompt using the query - user_prompt = {"role": "user", "content": f"Question: {query}"} - - # Combine the system and user prompts into one list - prompt = [system_prompt, user_prompt] - - # Return the prompt list wrapped in a Data object - return prompt - - -class QueryProfilerPromptor(MapOperator): - """ - QueryProfilerPromptor provides a prompt for profiling queries. - - """ - - prompt_template: Template - - def __init__(self, config): - """ - Initializes the QueryProfilerPromptor instance with configuration and prompt template. - - :param config: Dictionary containing configuration for the prompt rag. - """ - super().__init__() - self.config = config # Store the configuration for later use - self.prompt_template = ( - query_profiler_prompt_template # Load the query profiler prompt template - ) - - def execute(self, data) -> list: - """ - Generates a profiling prompt for the input query. - - :param data: A string representing the query to be profiled. - - :return: A list containing the profiling prompt. - """ - query = data - prompt = { - "role": "user", - "content": self.prompt_template.render( - query=query, - metadata=self.config.get("metadata", {}), - chunk_size=self.config.get("chunk_size", 1024), - ), - } - return [prompt] diff --git a/packages/sage-middleware/src/sage/middleware/operators/rag/refiner.py b/packages/sage-middleware/src/sage/middleware/operators/rag/refiner.py deleted file mode 100644 index b0db66e211..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/rag/refiner.py +++ /dev/null @@ -1,231 +0,0 @@ -""" -Refiner Operator - SAGE RAG Pipeline Operator -============================================== - -Uses isage-refiner (sage_refiner) for context compression in RAG pipelines. - -Installation: - pip install isage-refiner - -Usage: - from sage.middleware.operators.rag import RefinerOperator - - config = { - "algorithm": "long_refiner", # or "reform", "provence", etc. - "budget": 2048, - # LongRefiner specific - "base_model_path": "Qwen/Qwen2.5-3B-Instruct", - ... - } - - env.map(RefinerOperator, config) -""" - -import json -import os -import time -from typing import Any - -from sage.common.config.output_paths import get_states_file -from sage.common.core.functions import MapFunction as MapOperator - - -class RefinerOperator(MapOperator): - """ - Refiner Operator for SAGE RAG pipelines. - - Wraps isage-refiner compressors (LongRefiner, REFORM, Provence, etc.) - for use in SAGE dataflow pipelines. - - Config: - algorithm: str - "long_refiner", "reform", "provence", "llmlingua2", etc. - budget: int - Token budget for compression - enable_profile: bool - Enable data recording for debugging - - # Algorithm-specific config passed through to compressor - base_model_path: str - For LongRefiner - score_model_path: str - For LongRefiner - ... - """ - - def __init__(self, config: dict, ctx=None): - super().__init__(config=config, ctx=ctx) - self.cfg = config - self.enable_profile = config.get("enable_profile", False) - self.compressor = None - - # Data recording (only when enable_profile=True) - if self.enable_profile: - self.data_base_path = str(get_states_file("dummy", "refiner_data").parent) - os.makedirs(self.data_base_path, exist_ok=True) - self.data_records: list[dict] = [] - - self._init_compressor() - - def _init_compressor(self): - """Initialize the compressor from isage-refiner.""" - algorithm = self.cfg.get("algorithm", "long_refiner").lower() - - try: - if algorithm == "long_refiner": - from sage_refiner import LongRefinerCompressor - - self.compressor = LongRefinerCompressor( - base_model_path=self.cfg.get("base_model_path", "Qwen/Qwen2.5-3B-Instruct"), - query_analysis_module_lora_path=self.cfg.get( - "query_analysis_module_lora_path", "" - ), - doc_structuring_module_lora_path=self.cfg.get( - "doc_structuring_module_lora_path", "" - ), - global_selection_module_lora_path=self.cfg.get( - "global_selection_module_lora_path", "" - ), - score_model_path=self.cfg.get("score_model_path", "BAAI/bge-reranker-v2-m3"), - max_model_len=self.cfg.get("max_model_len", 25000), - gpu_memory_utilization=self.cfg.get("gpu_memory_utilization", 0.5), - ) - - elif algorithm == "reform": - from sage_refiner import REFORMCompressor - - self.compressor = REFORMCompressor(**self.cfg.get("reform_config", {})) - - elif algorithm == "provence": - from sage_refiner import ProvenceCompressor - - self.compressor = ProvenceCompressor(**self.cfg.get("provence_config", {})) - - elif algorithm in ("simple", "none"): - # Simple truncation - no compression - self.compressor = None - self.logger.info("Using simple/none mode - no compression") - - else: - raise ValueError(f"Unsupported algorithm: {algorithm}") - - self.logger.info(f"RefinerOperator initialized with algorithm: {algorithm}") - - except ImportError as e: - raise ImportError( - f"Failed to import {algorithm} compressor. " - f"Install with: pip install isage-refiner\n" - f"Error: {e}" - ) from e - - def execute(self, data: dict): - """Execute document compression. - - Input format: - { - "query": str, - "retrieval_results": List[Dict], # Retrieved documents - } - - Output format: - { - "query": str, - "retrieval_results": List[Dict], # Original (preserved) - "refining_results": List[str], # Compressed document texts - } - """ - if not isinstance(data, dict): - self.logger.error(f"Unexpected input format: {type(data)}") - return data - - query = data.get("query", "") - docs = data.get("retrieval_results", []) - - # Normalize documents to isage-refiner format - documents = self._normalize_documents(docs) - - # Compress - try: - if self.compressor is None: - # Simple mode: just extract text - refined_texts = [ - doc.get("contents", doc.get("text", str(doc))) for doc in documents - ] - else: - budget = self.cfg.get("budget", 2048) - result = self.compressor.compress( - question=query, - document_list=documents, - budget=budget, - ) - # isage-refiner returns dict with various fields - refined_texts = result.get("compressed_context", "") - if isinstance(refined_texts, str): - refined_texts = [refined_texts] - - except Exception as e: - self.logger.error(f"Refiner execution failed: {e}") - refined_texts = [doc.get("contents", str(doc)) for doc in documents] - - # Save data record if profiling - if self.enable_profile: - self._save_data_record(query, documents, refined_texts) - - # Build output - result_data = data.copy() - result_data["refining_results"] = refined_texts - - return result_data - - def _normalize_documents(self, docs: list[str | dict]) -> list[dict[str, Any]]: - """Normalize documents to isage-refiner format (with 'contents' key).""" - normalized: list[dict[str, Any]] = [] - for doc in docs: - if isinstance(doc, dict): - # isage-refiner expects 'contents' key - text = doc.get("contents") or doc.get("text") or str(doc) - normalized.append({"contents": text, **doc}) - elif isinstance(doc, str): - normalized.append({"contents": doc}) - else: - normalized.append({"contents": str(doc)}) - - return normalized - - def _save_data_record(self, query: str, input_docs: list[dict], refined_docs: list[str]): - """Save data record (only when enable_profile=True).""" - if not self.enable_profile: - return - - record = { - "timestamp": time.time(), - "query": query, - "input_docs": input_docs, - "refined_docs": refined_docs, - "budget": self.cfg.get("budget"), - } - self.data_records.append(record) - - # Persist every 10 records - if len(self.data_records) >= 10: - self._persist_data_records() - - def _persist_data_records(self): - """Persist data records to disk.""" - if not self.enable_profile or not self.data_records: - return - - timestamp = int(time.time()) - filename = f"refiner_data_{timestamp}.json" - path = os.path.join(self.data_base_path, filename) - - try: - with open(path, "w", encoding="utf-8") as f: - json.dump(self.data_records, f, ensure_ascii=False, indent=2) - self.logger.info(f"Saved {len(self.data_records)} records to {path}") - self.data_records = [] - except Exception as e: - self.logger.error(f"Failed to persist data records: {e}") - - def __del__(self): - """Ensure data is saved on cleanup.""" - if hasattr(self, "enable_profile") and self.enable_profile: - try: - self._persist_data_records() - except Exception: - pass diff --git a/packages/sage-middleware/src/sage/middleware/operators/rag/reranker.py b/packages/sage-middleware/src/sage/middleware/operators/rag/reranker.py deleted file mode 100644 index fcba739861..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/rag/reranker.py +++ /dev/null @@ -1,364 +0,0 @@ -import torch -from transformers import ( - AutoModelForCausalLM, - AutoModelForSequenceClassification, - AutoTokenizer, -) - -from sage.common.core.functions import MapFunction as MapOperator -from sage.libs.rag.types import ( - RAGInput, - RAGResponse, - create_rag_response, - extract_query, - extract_results, -) - - -class BGEReranker(MapOperator): - """ - A reranker that uses the BAAI/bge-reranker-v2-m3 model to reorder a list of retrieved documents. - The model assigns relevance scores to the documents and ranks them accordingly. - - Input: A tuple of (query, List[retrieved_documents]) - Output: A tuple of (query, List[reranked_documents_with_scores]) - - Attributes: - logger: Logger for logging error and information messages. - config: Configuration dictionary containing reranker settings (model name, top_k, etc.). - device: Device ('cuda' or 'cpu') where the model will be loaded. - tokenizer: Tokenizer used to preprocess input queries and documents. - model: The pre-trained reranking model. - """ - - def __init__(self, config, **kwargs): - super().__init__(**kwargs) - """ - Initializes the BGEReranker with configuration settings and loads the model. - - :param config: Dictionary containing configuration options, including model name and device settings. - """ - self.config = config - self.device = ( - "cuda" if torch.cuda.is_available() else "cpu" - ) # Set device to GPU if available, otherwise CPU - - # Load tokenizer and model using the provided model name - self.tokenizer, self.model = self._load_model(self.config["model_name"]) - self.model = self.model.to(self.device) - self.model.eval() # Set the model to evaluation mode - - def _load_model(self, model_name: str): - """ - Loads the tokenizer and model for the reranker. - - :param model_name: Name of the pre-trained model to load. - :return: Tuple containing the tokenizer and the model. - """ - try: - self.logger.info(f"Loading reranker: {model_name}") - tokenizer = AutoTokenizer.from_pretrained(model_name) # Load the tokenizer - model = AutoModelForSequenceClassification.from_pretrained(model_name) # Load the model - return tokenizer, model - except Exception as e: - self.logger.error(f"Failed to load model {model_name}: {str(e)}") - raise RuntimeError(f"Model loading failed: {str(e)}") - - def execute(self, data: RAGInput) -> RAGResponse: - """ - Executes the reranking process: - 1. Unpacks the input data (query and list of documents). - 2. Generates query-document pairs. - 3. Calculates relevance scores using the model. - 4. Sorts documents based on their relevance scores. - - :param data: RAGInput - standardized input format - :return: RAGResponse containing {"query": str, "results": List[str]} - """ - try: - # 使用标准化函数提取数据 - query = extract_query(data) - doc_set = extract_results(data) - - if not query: - self.logger.error("Missing 'query' field in input") - return create_rag_response("", []) - return {"query": "", "results": []} - - top_k = self.config.get("topk") or self.config.get( - "top_k", 3 - ) # Get the top-k parameter for reranking - - # Handle empty document set case - if not doc_set: - print("BGEReranker received empty document set, returning empty results") - # 统一返回 dict 格式 - return create_rag_response(query, []) - - # Generate query-document pairs for scoring - pairs = [(query, doc) for doc in doc_set] - - # Tokenize the pairs and move inputs to the appropriate device - raw_inputs = self.tokenizer( - pairs, - padding=True, - truncation=True, - max_length=512, - return_tensors="pt", - ) - inputs = { - k: v.to(self.device) if isinstance(v, torch.Tensor) else v - for k, v in raw_inputs.items() - } - - # Perform inference and calculate scores - scores = self.model(**inputs).logits.view(-1).float() - - # Create a list of scored documents - scored_docs = [ - {"text": doc, "relevance_score": score} - for doc, score in zip(doc_set, scores, strict=False) - ] - - # Sort the documents by relevance score in descending order - reranked_docs = sorted(scored_docs, key=lambda x: x["relevance_score"], reverse=True)[ - :top_k - ] - reranked_docs_list = [doc["text"] for doc in reranked_docs] - self.logger.info( - f"\033[32m[ {self.__class__.__name__}]: Rerank Results: {reranked_docs_list}\033[0m " - ) - self.logger.debug( - f"Top score: {reranked_docs[0]['relevance_score'] if reranked_docs else 'N/A'}" - ) - - print(f"Rerank Results: {reranked_docs_list}") - - except Exception as e: - raise RuntimeError(f"BGEReranker error: {str(e)}") - - # 统一返回标准格式 - return create_rag_response(query, reranked_docs_list) - - -class LLMbased_Reranker(MapOperator): - """ - A reranker that uses the BAAI/bge-reranker-v2-gemma model to determine if a retrieved document contains an answer to a given query. - It scores the documents with 'Yes' or 'No' predictions based on whether the document answers the query. - - Input: A tuple of (query, List[retrieved_documents]) - Output: A tuple of (query, List[reranked_documents_with_scores]) - - Attributes: - logger: Logger for logging error and information messages. - config: Configuration dictionary containing reranker settings (model name, top_k, etc.). - device: Device ('cuda' or 'cpu') where the model will be loaded. - tokenizer: Tokenizer used to preprocess input queries and documents. - model: The pre-trained reranking model. - yes_loc: Token ID representing 'Yes' (used for scoring). - """ - - def __init__(self, config, model_name: str = "BAAI/bge-reranker-v2-gemma"): - """ - Initializes the LLMbased_Reranker with configuration settings and loads the model. - - :param config: Dictionary containing configuration options, including model name and device settings. - :param model_name: Name of the pre-trained model to load (default is "BAAI/bge-reranker-v2-gemma"). - """ - super().__init__() - self.config = config - self.device = ( - "cuda" if torch.cuda.is_available() else "cpu" - ) # Set device to GPU if available, otherwise CPU - - # Load tokenizer and model using the provided model name - self.tokenizer, self.model = self._load_model(model_name) - self.model = self.model.to(self.device) # type: ignore[arg-type] - - # Get the token ID for the 'Yes' token (used for classification) - self.yes_loc = self.tokenizer("Yes", add_special_tokens=False)["input_ids"][0] - - def _load_model(self, model_name: str): - """ - Loads the tokenizer and model for the reranker. - - :param model_name: Name of the pre-trained model to load. - :return: Tuple containing the tokenizer and the model. - """ - try: - self.logger.info(f"Loading reranker: {model_name}") - tokenizer = AutoTokenizer.from_pretrained(model_name) # Load the tokenizer - model = AutoModelForCausalLM.from_pretrained(model_name) # Load the model - return tokenizer, model - except Exception as e: - self.logger.error(f"Failed to load model {model_name}: {str(e)}") - raise RuntimeError(f"Model loading failed: {str(e)}") - - def get_inputs(self, pairs, tokenizer, prompt=None, max_length=1024): - """ - Prepares the input for the model, including the prompt and the query-document pairs. - - :param pairs: List of query-document pairs. - :param tokenizer: The tokenizer used to process the input data. - :param prompt: Optional prompt to guide the model (defaults to a generic query-passage prompt). - :param max_length: Maximum length of the tokenized input sequences. - :return: A tensor of tokenized inputs, ready for model inference. - """ - if prompt is None: - prompt = "Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'." - - sep = "\n" - prompt_inputs = tokenizer(prompt, return_tensors=None, add_special_tokens=False)[ - "input_ids" - ] - sep_inputs = tokenizer(sep, return_tensors=None, add_special_tokens=False)["input_ids"] - - inputs = [] - for query, passage in pairs: - query_inputs = tokenizer( - f"A: {query}", - return_tensors=None, - add_special_tokens=False, - max_length=max_length * 3 // 4, - truncation=True, - ) - passage_inputs = tokenizer( - f"B: {passage}", - return_tensors=None, - add_special_tokens=False, - max_length=max_length, - truncation=True, - ) - - item = tokenizer.prepare_for_model( - [tokenizer.bos_token_id] + query_inputs["input_ids"], - sep_inputs + passage_inputs["input_ids"], - truncation="only_second", - max_length=max_length, - padding=False, - return_attention_mask=False, - return_token_type_ids=False, - add_special_tokens=False, - ) - item["input_ids"] = item["input_ids"] + sep_inputs + prompt_inputs - item["attention_mask"] = [1] * len(item["input_ids"]) - inputs.append(item) - - return tokenizer.pad( - inputs, - padding=True, - max_length=max_length + len(sep_inputs) + len(prompt_inputs), - pad_to_multiple_of=8, - return_tensors="pt", - ) - - # @torch.inference_mode() - def execute(self, data: RAGInput) -> RAGResponse: - """ - Executes the reranking process: - 1. Unpacks the input data (query and list of documents). - 2. Generates query-document pairs for classification. - 3. Calculates relevance scores based on 'Yes'/'No' predictions. - 4. Sorts documents based on their relevance scores. - - :param data: RAGInput - standardized input format - :return: RAGResponse containing {"query": str, "results": List[str]} - """ - try: - # 使用标准化函数提取数据 - query = extract_query(data) - doc_set = extract_results(data) - - if not query: - self.logger.error("Missing 'query' field in input") - return create_rag_response("", []) - - doc_set = [doc_set] # Wrap doc_set in a list for processing - top_k = self.config["topk"] # Get the top-k parameter for reranking - emit_docs = [] # Initialize the list to store reranked documents - - for retrieved_docs in doc_set: - # Generate query-document pairs for classification - pairs = [[query, doc] for doc in retrieved_docs] - - # Tokenize the pairs and move inputs to the appropriate device - with torch.no_grad(): - raw_inputs = self.get_inputs(pairs, self.tokenizer) - inputs = {k: v.to(self.device) for k, v in raw_inputs.items()} - - scores = ( - self.model(**inputs, return_dict=True) - .logits[:, -1, self.yes_loc] - .view(-1) - .float() - ) - - # Create a list of scored documents - scored_docs = [ - {"text": doc, "relevance_score": score} - for doc, score in zip(retrieved_docs, scores, strict=False) - ] - - # Sort the documents by relevance score in descending order - reranked_docs = sorted( - scored_docs, key=lambda x: x["relevance_score"], reverse=True - )[:top_k] - reranked_docs_list = [doc["text"] for doc in reranked_docs] - emit_docs.append(reranked_docs_list) - self.logger.info( - f"\033[32m[ {self.__class__.__name__}]: Rerank Results: {reranked_docs_list}\033[0m " - ) - self.logger.debug( - f"Top score: {reranked_docs[0]['relevance_score'] if reranked_docs else 'N/A'}" - ) - - except Exception as e: - self.logger.error(f"{str(e)} when RerankerFuncton") - raise RuntimeError(f"Reranker error: {str(e)}") - - emit_docs = emit_docs[0] # Only return the first set of reranked documents - - # 统一返回标准格式 - return create_rag_response(query, emit_docs) - - -# if __name__ == '__main__': - -# # 设置配置 -# config1 = { -# "reranker": { -# "model_name":"BAAI/bge-reranker-v2-m3", -# "top_k": 3 -# } -# } - -# config2 = { -# "reranker": { -# "model_name":"BAAI/bge-reranker-v2-gemma", -# "top_k": 3 -# } -# } - -# # 创建实例 -# # reranker = BGEReranker(config) -# reranker = LLMbased_Reranker(config2) -# # 测试数据 -# query = "What is the capital of France?" -# docs = [ -# "Paris is the capital of France.", -# "Berlin is a city in Germany.", -# "The Eiffel Tower is located in Paris.", -# "France is a country in Western Europe.", -# "Madrid is the capital of Spain." -# ] - -# # 执行重排 -# input_data = (query, docs) -# output = reranker.execute(input_data) - -# # 输出结果 -# result_query, result_docs = output -# print("Query:", result_query) -# print("Top-k Re-ranked Documents:") -# for i, doc in enumerate(result_docs, 1): -# print(f"{i}. {doc}") diff --git a/packages/sage-middleware/src/sage/middleware/operators/rag/retriever.py b/packages/sage-middleware/src/sage/middleware/operators/rag/retriever.py deleted file mode 100644 index 2dbc9beb7b..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/rag/retriever.py +++ /dev/null @@ -1,1308 +0,0 @@ -import json -import os -import time -from typing import Any - -import numpy as np - -from sage.common.components.sage_embedding.embedding_model import EmbeddingModel -from sage.common.config.output_paths import get_states_file -from sage.common.core.functions import MapFunction as MapOperator -from sage.middleware.components.vector_stores.chroma import ChromaBackend, ChromaUtils -from sage.middleware.components.vector_stores.milvus import MilvusBackend, MilvusUtils - - -# ChromaDB 密集检索器 -class ChromaRetriever(MapOperator): - def __init__(self, config, enable_profile=False, **kwargs): - super().__init__(**kwargs) - self.config = config - self.enable_profile = enable_profile - - # 只支持 ChromaDB 后端 - self.backend_type = "chroma" - - # 通用配置 - self.vector_dimension = config.get("dimension", 384) - self.top_k = config.get("top_k", 10) - self.embedding_config = config.get("embedding", {}) - - # 先初始化 embedding 模型 - self._init_embedding_model() - - # 再初始化 ChromaDB 后端（这样知识库加载时embedding模型已可用） - self.chroma_config = config.get("chroma", {}) - self._init_chroma_backend() - - # 只有启用profile时才设置数据存储路径 - if self.enable_profile: - # Use unified output path system - self.data_base_path = str(get_states_file("dummy", "retriever_data").parent) - os.makedirs(self.data_base_path, exist_ok=True) - self.data_records = [] - - def _init_chroma_backend(self): - """初始化 ChromaDB 后端""" - try: - # 检查 ChromaDB 是否可用 - if not ChromaUtils.check_chromadb_availability(): - raise ImportError( - "ChromaDB dependencies not available. Install with: pip install chromadb" - ) - - # 验证配置 - if not ChromaUtils.validate_chroma_config(self.chroma_config): - raise ValueError("Invalid ChromaDB configuration") - - # 创建 ChromaDB 后端实例 - self.chroma_backend = ChromaBackend(self.chroma_config, self.logger) - - # 自动加载知识库文件 - knowledge_file = self.chroma_config.get("knowledge_file") - if knowledge_file: - # 如果是相对路径，尝试从当前工作目录和项目根目录解析 - if not os.path.isabs(knowledge_file): - # 尝试从当前目录 - if os.path.exists(knowledge_file): - resolved_path = knowledge_file - else: - # 尝试从项目根目录解析 - project_root = os.getcwd() - while project_root != "/" and not os.path.exists( - os.path.join(project_root, "pyproject.toml") - ): - project_root = os.path.dirname(project_root) - - potential_path = os.path.join(project_root, knowledge_file) - if os.path.exists(potential_path): - resolved_path = potential_path - else: - resolved_path = knowledge_file - else: - resolved_path = knowledge_file - - if os.path.exists(resolved_path): - self._load_knowledge_from_file(resolved_path) - else: - self.logger.warning(f"Knowledge file not found: {resolved_path}") - - except Exception as e: - self.logger.error(f"Failed to initialize ChromaDB: {e}") - raise - - def _load_knowledge_from_file(self, file_path: str): - """从文件加载知识库""" - try: - # 使用 ChromaDB 后端加载 - success = self.chroma_backend.load_knowledge_from_file(file_path, self.embedding_model) - if not success: - self.logger.error(f"Failed to load knowledge from file: {file_path}") - - except Exception as e: - self.logger.error(f"Failed to load knowledge from file {file_path}: {e}") - - def _init_embedding_model(self): - """初始化HuggingFace嵌入模型（使用sentence-transformers）""" - embedding_method = self.embedding_config.get("method", "default") - model = self.embedding_config.get("model", "sentence-transformers/all-MiniLM-L6-v2") - - self.logger.info(f"Initializing embedding model with method: {embedding_method}") - self.embedding_model = EmbeddingModel(method=embedding_method, model=model) - - # 验证向量维度 - if hasattr(self.embedding_model, "get_dim"): - model_dim = self.embedding_model.get_dim() - if model_dim != self.vector_dimension: - self.logger.warning( - f"Embedding model dimension ({model_dim}) != configured dimension ({self.vector_dimension})" - ) - # 更新向量维度以匹配模型 - self.vector_dimension = model_dim - - def add_documents(self, documents: list[str], doc_ids: list[str] | None = None) -> list[str]: - """ - 添加文档到索引中 - Args: - documents: 文档内容列表 - doc_ids: 文档ID列表，如果为None则自动生成 - Returns: - 添加的文档ID列表 - """ - if not documents: - return [] - - # 生成文档ID - if doc_ids is None: - doc_ids = [f"doc_{int(time.time() * 1000)}_{i}" for i in range(len(documents))] - elif len(doc_ids) != len(documents): - raise ValueError("doc_ids length must match documents length") - - # 生成 embedding - embeddings = [] - for doc in documents: - embedding = self.embedding_model.embed(doc) - # print(embedding) - embeddings.append(np.array(embedding, dtype=np.float32)) - - # 使用 ChromaDB 后端添加文档 - return self.chroma_backend.add_documents(documents, embeddings, doc_ids) - - def _save_data_record(self, query, retrieved_docs): - """保存检索数据记录""" - if not self.enable_profile: - return - - record = { - "timestamp": time.time(), - "query": query, - "retrieval_results": retrieved_docs, - "backend_type": self.backend_type, - "backend_config": getattr(self, f"{self.backend_type}_config", {}), - "embedding_config": self.embedding_config, - } - self.data_records.append(record) - self._persist_data_records() - - def _persist_data_records(self): - """将数据记录持久化到文件""" - if not self.enable_profile or not self.data_records: - return - - timestamp = int(time.time()) - filename = f"retriever_data_{timestamp}.json" - path = os.path.join(self.data_base_path, filename) - - try: - with open(path, "w", encoding="utf-8") as f: - json.dump(self.data_records, f, ensure_ascii=False, indent=2) - self.data_records = [] - except Exception as e: - self.logger.error(f"Failed to persist data records: {e}") - - def execute(self, data: str) -> dict[str, Any]: - """ - 执行检索 - Args: - data: 查询字符串、元组或字典 - Returns: - dict: {"query": ..., "results": ..., "input": 原始输入, ...} - """ - is_dict_input = isinstance(data, dict) - if is_dict_input: - input_query = data.get("query", "") - elif isinstance(data, tuple) and len(data) > 0: - input_query = data[0] - else: - input_query = data - - if not isinstance(input_query, str): - self.logger.error(f"Invalid input query type: {type(input_query)}") - if is_dict_input: - data["retrieval_results"] = [] - return data - else: - return {"query": str(input_query), "retrieval_results": [], "input": data} - - self.logger.info( - f"[ {self.__class__.__name__}]: Starting {self.backend_type.upper()} retrieval for query: {input_query}" - ) - self.logger.info(f"[ {self.__class__.__name__}]: Using top_k = {self.top_k}") - - try: - # 生成查询向量 - query_embedding = self.embedding_model.embed(input_query) - query_vector = np.array(query_embedding, dtype=np.float32) - - # 使用 ChromaDB 执行检索 - retrieved_docs = self.chroma_backend.search(query_vector, input_query, self.top_k) - - self.logger.info( - f"\033[32m[ {self.__class__.__name__}]: Retrieved {len(retrieved_docs)} documents from ChromaDB\033[0m" - ) - self.logger.debug( - f"Retrieved documents: {retrieved_docs[:3]}..." - ) # 只显示前3个文档的预览 - - # 将字符串列表转换为标准化的字典格式，以便后续组件使用 - standardized_docs = [] - for doc in retrieved_docs: - if isinstance(doc, str): - standardized_docs.append({"text": doc}) - elif isinstance(doc, dict): - # 如果已经是字典，确保有text字段 - if "text" not in doc and "content" in doc: - doc["text"] = doc["content"] - elif "text" not in doc: - # 将整个字典内容作为text - doc["text"] = str(doc) - standardized_docs.append(doc) - else: - # 其他类型转为字符串 - standardized_docs.append({"text": str(doc)}) - - # 保存数据记录（只有enable_profile=True时才保存） - if self.enable_profile: - self._save_data_record(input_query, standardized_docs) - - if is_dict_input: - # 保存原始检索结果（用于压缩率计算） - if "retrieval_results" not in data: - data["retrieval_results"] = standardized_docs - return data - else: - return { - "query": input_query, - "retrieval_results": standardized_docs, - "input": data, - } - - except Exception as e: - self.logger.error(f"ChromaDB retrieval failed: {str(e)}") - if is_dict_input: - data["retrieval_results"] = [] - return data - else: - return {"query": input_query, "retrieval_results": [], "input": data} - - def save_index(self, save_path: str) -> bool: - """ - 保存索引到磁盘 - Args: - save_path: 保存路径 - Returns: - 是否保存成功 - """ - return self.chroma_backend.save_config(save_path) - - def load_index(self, load_path: str) -> bool: - """ - 从磁盘加载索引 - Args: - load_path: 加载路径 - Returns: - 是否加载成功 - """ - return self.chroma_backend.load_config(load_path) - - def get_collection_info(self) -> dict[str, Any]: - """获取集合信息""" - return self.chroma_backend.get_collection_info() - - def __del__(self): - """确保在对象销毁时保存所有未保存的记录""" - if hasattr(self, "enable_profile") and self.enable_profile: - try: - self._persist_data_records() - except Exception: - pass - - -# Milvus稠密向量检索 -class MilvusDenseRetriever(MapOperator): - """ - 使用 Milvus 后端进行稠密向量检索。 - """ - - def __init__(self, config, enable_profile=False, **kwargs): - super().__init__(**kwargs) - self.config = config - self.enable_profile = enable_profile - - # 只支持Milvus后端 - self.backend_type = "milvus" - - # 通用配置 - self.vector_dimension = self.config.get("dimension", 384) - self.top_k = self.config.get("top_k", 5) - self.embedding_config = self.config.get("embedding", {}) - - # 初始化Milvus后端 - self.milvus_config = config.get("milvus_dense", {}) - self._init_milvus_backend() - - # 初始化 embedding 模型 - self._init_embedding_model() - - # 只有启用profile时才设置数据存储路径 - if self.enable_profile: - if self.ctx is not None and hasattr(self.ctx, "env_base_dir") and self.ctx.env_base_dir: - self.data_base_path = os.path.join( - self.ctx.env_base_dir, ".sage_states", "retriever_data" - ) - else: - # 使用默认路径 - self.data_base_path = os.path.join(os.getcwd(), ".sage_states", "retriever_data") - - os.makedirs(self.data_base_path, exist_ok=True) - self.data_records = [] - - def _init_milvus_backend(self): - """初始化milvus后端""" - try: - # 检查 milvus 是否可用 - if not MilvusUtils.check_milvus_available(): - raise ImportError( - "Milvus dependencies not available. Install with: pip install pymilvus" - ) - - # 验证配置 - if not MilvusUtils.validate_milvus_config(self.milvus_config): - raise ValueError("Invalid Milvus configuration") - - # 初始化后端 - self.milvus_backend = MilvusBackend(config=self.milvus_config, logger=self.logger) - - # 自动加载知识库文件 - knowledge_file = self.milvus_config.get("knowledge_file") - if knowledge_file and os.path.exists(knowledge_file): - self._load_knowledge_from_file_dense(knowledge_file) - - except Exception as e: - self.logger.error(f"Failed to initialize milvus: {e}") - raise - - def _load_knowledge_from_file_dense(self, file_path: str): - """从文件中加载知识库""" - try: - # 使用Milvus后端加载 - success = self.milvus_backend.load_knowledge_from_file_dense( - file_path, self.embedding_model - ) - if not success: - self.logger.error(f"Failed to load knowledge from file: {file_path}") - except Exception as e: - self.logger.error(f"Failed to load knowledge from file: {e}") - - def _init_embedding_model(self): - """初始化embedding模型""" - embedding_method = self.embedding_config.get("method", "default") - model = self.embedding_config.get("model", "sentence-transformers/all-MiniLM-L6-v2") - - self.logger.info(f"Initializing embedding model with method: {embedding_method}") - self.embedding_model = EmbeddingModel(method=embedding_method, model=model) - - # 验证向量维度 - if hasattr(self.embedding_model, "get_dim"): - model_dim = self.embedding_model.get_dim() - if model_dim != self.vector_dimension: - self.logger.warning( - f"Embedding model dimension ({model_dim}) != configured dimension ({self.vector_dimension})" - ) - # 更新向量维度以匹配模型 - self.vector_dimension = model_dim - - def add_documents(self, documents: list[str], doc_ids: list[str] | None = None) -> list[str]: - """ - 添加文档到milvus - Args: - documents: 文档内容列表 - doc_ids: 文档ID列表，如果为None则自动生成 - Returns: - 添加的文档ID列表 - """ - if not documents: - self.logger.warning("No documents to add") - return [] - - if doc_ids is None: - doc_ids = [f"doc_{int(time.time() * 1000)}_{i}" for i in range(len(documents))] - elif len(doc_ids) != len(documents): - raise ValueError("doc_ids length must match documents length") - - # 生成 embedding - embeddings = [] - for doc in documents: - embedding = self.embedding_model.embed(doc) - print(embedding) - embeddings.append(np.array(embedding, dtype=np.float32)) - - # 使用 milvus 后端添加文档 - return self.milvus_backend.add_dense_documents(documents, embeddings, doc_ids) - - def _save_data_record(self, query, retrieved_docs): - """ - 保存检索数据记录 - """ - if not self.enable_profile: - return - - record = { - "timestamp": time.time(), - "query": query, - "retrieval_results": retrieved_docs, - "backend_type": self.backend_type, - "backend_config": getattr(self, f"{self.backend_type}_config", {}), - "embedding_config": self.embedding_config, - } - - self.data_records.append(record) - self._persist_data_records() - - def _persist_data_records(self): - """ - 将数据记录持久化到文件 - """ - if not self.enable_profile or not self.data_records: - return - - timestamp = int(time.time()) - filename = f"milvus_dense_retriever_data_{timestamp}.json" - path = os.path.join(self.data_base_path, filename) - - try: - with open(path, "w", encoding="utf-8") as f: - json.dump(self.data_records, f, ensure_ascii=False, indent=2) - self.data_records = [] - except Exception as e: - self.logger.error(f"Failed to persist data records: {e}") - - def execute(self, data: str) -> dict[str, Any]: - """ - 执行检索 - Args: - data: 查询字符串、元组或字典 - Returns: - dict: {"query": ..., "retrieval_results": ..., "input": 原始输入, ...} - """ - # 支持字典类型输入，优先取 question 字段 - is_dict_input = isinstance(data, dict) - if is_dict_input: - input_query = data.get("question", "") - elif isinstance(data, tuple) and len(data) > 0: - input_query = data[0] - else: - input_query = data - - if not isinstance(input_query, str): - self.logger.error(f"Invalid input query type: {type(input_query)}") - if is_dict_input: - data["retrieval_results"] = [] - return data - else: - return { - "query": str(input_query), - "retrieval_results": [], - "input": data, - } - - self.logger.info( - f"[ {self.__class__.__name__}]: Starting {self.backend_type.upper()} retrieval for query: {input_query}" - ) - self.logger.info(f"[ {self.__class__.__name__}]: Using top_k = {self.top_k}") - - try: - # 生成查询向量 - query_embedding = self.embedding_model.encode(input_query) - query_vector = np.array(query_embedding, dtype=np.float32) - - # 使用Milvus执行稠密检索 - retrieved_docs = self.milvus_backend.dense_search( - query_vector=query_vector, - top_k=self.top_k, - ) - - self.logger.info( - f"\033[32m[ {self.__class__.__name__}]: Retrieved {len(retrieved_docs)} documents from Milvus\033[0m" - ) - self.logger.debug( - f"Retrieved documents: {retrieved_docs[:3]}..." - ) # 只显示前3个文档的预览 - - print(f"Query: {input_query}") - print(f"Configured top_k: {self.top_k}") - print(f"Retrieved {len(retrieved_docs)} documents from Milvus") - print(retrieved_docs) - - # 保存数据记录（只有enable_profile=True时才保存） - if self.enable_profile: - self._save_data_record(input_query, retrieved_docs) - - if is_dict_input: - data["retrieval_results"] = retrieved_docs - return data - else: - return { - "query": input_query, - "retrieval_results": retrieved_docs, - "input": data, - } - - except Exception as e: - self.logger.error(f" retrieval failed: {str(e)}") - if is_dict_input: - data["retrieval_results"] = [] - return data - else: - return { - "query": input_query, - "retrieval_results": [], - "input": data, - } - - def save_config(self, save_path: str) -> bool: - """ - 保存配置到磁盘 - Args: - save_path: 保存路径 - Returns: - 是否保存成功 - """ - return self.milvus_backend.save_config(save_path) - - def load_config(self, load_path: str) -> bool: - """ - 从磁盘加载配置 - Args: - load_path: 加载路径 - Returns: - 是否加载成功 - """ - return self.milvus_backend.load_config(load_path) - - def get_collection_info(self) -> dict[str, Any]: - """ - 获取集合信息 - """ - return self.milvus_backend.get_collection_info() - - def delete_collection(self, collection_name: str) -> bool: - """ - 删除集合 - """ - return self.milvus_backend.delete_collection(collection_name) - - def __del__(self): - """确保在对象销毁时保存所有未保存的记录""" - if hasattr(self, "enable_profile") and self.enable_profile: - try: - self._persist_data_records() - except Exception: - pass - - -# Milvus稀疏向量检索 -class MilvusSparseRetriever(MapOperator): - """ - 使用 Milvus 后端进行稀疏向量检索。 - """ - - def __init__(self, config, enable_profile=False, **kwargs): - super().__init__(**kwargs) - self.config = config - self.enable_profile = enable_profile - - # 只支持Milvus后端 - self.backend_type = "milvus" - - # 通用配置 - self.top_k = self.config.get("top_k", 10) - - # 初始化Milvus后端 - self.milvus_config = config.get("milvus_sparse", {}) - self._init_milvus_backend() - self._init_embedding_model() - - # 只有启用profile时才设置数据存储路径 - if self.enable_profile: - if self.ctx is not None and hasattr(self.ctx, "env_base_dir") and self.ctx.env_base_dir: - self.data_base_path = os.path.join( - self.ctx.env_base_dir, ".sage_states", "retriever_data" - ) - else: - # 使用默认路径 - self.data_base_path = os.path.join(os.getcwd(), ".sage_states", "retriever_data") - - os.makedirs(self.data_base_path, exist_ok=True) - self.data_records = [] - - def _init_milvus_backend(self): - """初始化milvus后端""" - try: - # 检查 milvus 是否可用 - if not MilvusUtils.check_milvus_available(): - raise ImportError( - "Milvus dependencies not available. Install with: pip install pymilvus" - ) - - # 验证配置 - if not MilvusUtils.validate_milvus_config(self.milvus_config): - raise ValueError("Invalid Milvus configuration") - - # 初始化后端 - self.milvus_backend = MilvusBackend(config=self.milvus_config, logger=self.logger) - - # 自动加载知识库文件 - knowledge_file = self.milvus_config.get("knowledge_file") - if knowledge_file and os.path.exists(knowledge_file): - self._load_knowledge_from_file(knowledge_file) - - except Exception as e: - self.logger.error(f"Failed to initialize milvus: {e}") - raise - - def _init_embedding_model(self): - """初始化embedding模型""" - try: - # 尝试新的导入路径（PyMilvus 2.6.0+） - try: - from pymilvus.model.hybrid import ( - BGEM3EmbeddingFunction, # type: ignore[import-not-found] - ) - except ImportError: - # 如果失败，尝试直接从 model 导入 - try: - from pymilvus.model import ( - BGEM3EmbeddingFunction, # type: ignore[import-not-found] - ) - except ImportError: - # 最后尝试安装单独的包 - self.logger.error( - "Please install: pip install 'pymilvus[model]' or pip install pymilvus.model" - ) - raise ImportError("Embedding model dependencies not available") - - self.embedding_model = BGEM3EmbeddingFunction(use_fp16=False, device="cpu") - - except ImportError as e: - self.logger.error(f"Failed to import EmbeddingModel: {e}") - raise ImportError("Embedding model dependencies not available") - - def _load_knowledge_from_file(self, file_path: str): - """从文件中加载知识库""" - try: - # 使用Milvus后端加载 - success = self.milvus_backend.load_knowledge_from_file_sparse(file_path) - self.logger.info(f"Loaded {success} documents from {file_path}") - if not success: - self.logger.error(f"Failed to load knowledge from file: {file_path}") - except Exception as e: - self.logger.error(f"Failed to load knowledge from file: {e}") - - def add_documents(self, documents: list[str], doc_ids: list[str] | None = None) -> list[str]: - """ - 添加文档到milvus - Args: - documents: 文档内容列表 - doc_ids: 文档ID列表，如果为None则自动生成 - Returns: - 添加的文档ID列表 - """ - if not documents: - self.logger.warning("No documents to add") - return [] - - # 生成 embedding - embedding = self.embedding_model.encode_documents(documents) - embeddings = embedding["sparse"] - - if doc_ids is None: - doc_ids = [f"doc_{int(time.time() * 1000)}_{i}" for i in range(len(documents))] - elif len(doc_ids) != len(documents): - raise ValueError("doc_ids length must match documents length") - - # 使用 milvus 后端添加文档 - return self.milvus_backend.add_sparse_documents(documents, embeddings, doc_ids) - - def _save_data_record(self, query, retrieved_docs): - """ - 保存检索数据记录 - """ - if not self.enable_profile: - return - - record = { - "timestamp": time.time(), - "query": query, - "retrieval_results": retrieved_docs, - "backend_type": self.backend_type, - "backend_config": getattr(self, f"{self.backend_type}_config", {}), - } - - self.data_records.append(record) - self._persist_data_records() - - def _persist_data_records(self): - """ - 将数据记录持久化到文件 - """ - if not self.enable_profile or not self.data_records: - return - - timestamp = int(time.time()) - filename = f"milvus_dense_retriever_data_{timestamp}.json" - path = os.path.join(self.data_base_path, filename) - - try: - with open(path, "w", encoding="utf-8") as f: - json.dump(self.data_records, f, ensure_ascii=False, indent=2) - self.data_records = [] - except Exception as e: - self.logger.error(f"Failed to persist data records: {e}") - - def execute(self, data: str) -> dict[str, Any]: - """ - 执行检索 - Args: - data: 查询字符串、元组或字典 - Returns: - dict: {"query": ..., "retrieval_results": ..., "input": 原始输入, ...} - """ - # 支持字典类型输入，优先取 question 字段 - is_dict_input = isinstance(data, dict) - if is_dict_input: - input_query = data.get("question", "") - elif isinstance(data, tuple) and len(data) > 0: - input_query = data[0] - else: - input_query = data - - if not isinstance(input_query, str): - self.logger.error(f"Invalid input query type: {type(input_query)}") - if is_dict_input: - data["retrieval_results"] = [] - return data - else: - return { - "query": str(input_query), - "retrieval_results": [], - "input": data, - } - - self.logger.info( - f"[ {self.__class__.__name__}]: Starting {self.backend_type.upper()} retrieval for query: {input_query}" - ) - self.logger.info(f"[ {self.__class__.__name__}]: Using top_k = {self.top_k}") - - try: - # 使用Milvus执行稀疏检索 - 直接传递查询文本，让sparse_search方法处理向量生成 - retrieved_docs = self.milvus_backend.sparse_search( - query_text=input_query, - top_k=self.top_k, - ) - - self.logger.info( - f"\033[32m[ {self.__class__.__name__}]: Retrieved {len(retrieved_docs)} documents from Milvus\033[0m" - ) - self.logger.debug( - f"Retrieved documents: {retrieved_docs[:3]}..." - ) # 只显示前3个文档的预览 - - print(f"Query: {input_query}") - print(f"Configured top_k: {self.top_k}") - print(f"Retrieved {len(retrieved_docs)} documents from Milvus") - print(retrieved_docs) - - # 保存数据记录（只有enable_profile=True时才保存） - if self.enable_profile: - self._save_data_record(input_query, retrieved_docs) - - if is_dict_input: - data["retrieval_results"] = retrieved_docs - return data - else: - return { - "query": input_query, - "retrieval_results": retrieved_docs, - "input": data, - } - - except Exception as e: - self.logger.error(f" retrieval failed: {str(e)}") - if is_dict_input: - data["retrieval_results"] = [] - return data - else: - return { - "query": input_query, - "retrieval_results": [], - "input": data, - } - - def save_config(self, save_path: str) -> bool: - """ - 保存配置到磁盘 - Args: - save_path: 保存路径 - Returns: - 是否保存成功 - """ - return self.milvus_backend.save_config(save_path) - - def load_config(self, load_path: str) -> bool: - """ - 从磁盘加载配置 - Args: - load_path: 加载路径 - Returns: - 是否加载成功 - """ - return self.milvus_backend.load_config(load_path) - - def get_collection_info(self) -> dict[str, Any]: - """ - 获取集合信息 - """ - return self.milvus_backend.get_collection_info() - - def __del__(self): - """确保在对象销毁时保存所有未保存的记录""" - if hasattr(self, "enable_profile") and self.enable_profile: - try: - self._persist_data_records() - except Exception: - pass - - -# Wiki18 FAISS 检索器 -class Wiki18FAISSRetriever(MapOperator): - """ - 基于FAISS的Wiki18数据集检索器，使用HuggingFace嵌入模型（如BGE-Large-EN-v1.5） - """ - - def __init__(self, config, enable_profile=False, **kwargs): - super().__init__(**kwargs) - self.config = config - self.enable_profile = enable_profile - - # 配置参数 - self.top_k = config.get("top_k", 5) - self.embedding_config = config.get("embedding", {}) - self.faiss_config = config.get("faiss", {}) - - # 初始化BGE-M3模型 - self._init_bge_m3_model() - - # 初始化FAISS索引 - self._init_faiss_index() - - # Profile数据存储 - if self.enable_profile: - if self.ctx is not None and hasattr(self.ctx, "env_base_dir") and self.ctx.env_base_dir: - self.data_base_path = os.path.join( - self.ctx.env_base_dir, ".sage_states", "retriever_data" - ) - else: - self.data_base_path = os.path.join(os.getcwd(), ".sage_states", "retriever_data") - - os.makedirs(self.data_base_path, exist_ok=True) - self.data_records = [] - - def _init_bge_m3_model(self): - """初始化BGE-M3嵌入模型（使用sentence-transformers）""" - try: - import torch - from sentence_transformers import SentenceTransformer - - # 从配置获取模型路径，默认使用BGE-Large-EN-v1.5 - model_path = self.embedding_config.get("model", "BAAI/bge-large-en-v1.5") - - # 从配置获取GPU设备，默认使用GPU 0 - gpu_device = self.embedding_config.get("gpu_device", 0) - - # 明确指定GPU设备 - if torch.cuda.is_available(): - device = f"cuda:{gpu_device}" - self.logger.info(f"嵌入模型将使用GPU {gpu_device}") - else: - device = "cpu" - self.logger.info("嵌入模型将使用CPU") - - # 初始化嵌入模型 - self.embedding_model = SentenceTransformer(model_path, device=device) - - self.logger.info(f"嵌入模型初始化成功: {model_path} 在设备 {device}") - - except ImportError as e: - self.logger.error(f"无法导入sentence-transformers: {e}") - self.logger.error("请安装: pip install sentence-transformers") - raise - except Exception as e: - self.logger.error(f"嵌入模型初始化失败: {e}") - raise - - def _init_faiss_index(self): - """初始化FAISS索引""" - try: - import faiss - - # FAISS配置 - 从配置文件读取路径 - index_path = self.faiss_config.get("index_path") - documents_path = self.faiss_config.get("documents_path") - mapping_path = self.faiss_config.get("mapping_path") # 可选的段落到文档映射 - - # 检查必需的配置项 - if not index_path: - raise ValueError("faiss.index_path 配置项是必需的") - if not documents_path: - raise ValueError("faiss.documents_path 配置项是必需的") - - # 展开环境变量（支持 ${HOME}, ${USER}, $HOME 等格式） - index_path = os.path.expandvars(index_path) - documents_path = os.path.expandvars(documents_path) - if mapping_path: - mapping_path = os.path.expandvars(mapping_path) - - # 尝试加载已有索引 - if os.path.exists(index_path) and os.path.exists(documents_path): - self.logger.info(f"加载已有FAISS索引: {index_path}") - self.faiss_index = faiss.read_index(index_path) - - # 加载段落到文档的映射（如果有） - self.passage_to_doc_mapping = None - if mapping_path and os.path.exists(mapping_path): - try: - with open(mapping_path, encoding="utf-8") as f: - self.passage_to_doc_mapping = json.load(f) - self.logger.info( - f"加载了段落映射: {len(self.passage_to_doc_mapping)} 个段落映射到文档" - ) - except Exception as e: - self.logger.warning(f"加载段落映射失败: {e}，将直接使用检索索引") - - # 加载JSONL格式的文档数据 - self.documents = [] - try: - with open(documents_path, encoding="utf-8") as f: - for line in f: - line = line.strip() - if line: - try: - doc = json.loads(line) - self.documents.append(doc) - except json.JSONDecodeError as e: - self.logger.warning( - f"跳过无效的JSON行: {line[:100]}... 错误: {e}" - ) - - except Exception as e: - self.logger.error(f"加载文档文件失败: {e}") - self.documents = [] - - self.logger.info(f"加载了 {len(self.documents)} 个文档") - self.logger.info(f"FAISS索引大小: {self.faiss_index.ntotal} 个向量") - - else: - # 如果没有预构建索引，需要从Wiki18数据构建 - self.logger.warning(f"未找到预构建的FAISS索引: {index_path}") - self.logger.warning("需要先构建Wiki18 FAISS索引") - - # 创建空索引和文档列表作为占位符 - dimension = 1024 # 嵌入模型的维度（BGE系列） - self.faiss_index = faiss.IndexFlatIP(dimension) # 内积相似度 - self.documents = [] - - except ImportError as e: - self.logger.error(f"无法导入FAISS: {e}") - self.logger.error("请安装FAISS: pip install faiss-cpu 或 pip install faiss-gpu") - raise - except Exception as e: - self.logger.error(f"FAISS索引初始化失败: {e}") - raise - - def _encode_query(self, query: str) -> np.ndarray: - """ - 使用嵌入模型编码查询 - - Args: - query: 查询文本 - - Returns: - 查询的向量表示 - """ - try: - # 使用sentence-transformers的encode方法 - embeddings = self.embedding_model.encode([query]) - return embeddings[0] # 返回第一个查询的向量 - - except Exception as e: - self.logger.error(f"查询编码失败: {e}") - raise - - def _search_faiss(self, query_vector: np.ndarray, top_k: int) -> tuple[list[float], list[int]]: - """ - 在FAISS索引中搜索 - - Args: - query_vector: 查询向量 - top_k: 返回top k个结果 - - Returns: - (scores, indices): 相似度分数和文档索引 - """ - try: - if self.faiss_index.ntotal == 0: - self.logger.warning("FAISS索引为空，无法检索") - return [], [] - - # FAISS搜索 - query_vector = query_vector.reshape(1, -1).astype("float32") - scores, indices = self.faiss_index.search(query_vector, top_k) # type: ignore[call-overload] - - return scores[0].tolist(), indices[0].tolist() - - except Exception as e: - self.logger.error(f"FAISS搜索失败: {e}") - return [], [] - - def _format_retrieved_documents( - self, scores: list[float], indices: list[int] - ) -> list[dict[str, Any]]: - """ - 格式化检索到的文档 - - Args: - scores: 相似度分数列表 - indices: 文档索引列表 - - Returns: - 格式化后的文档列表 - """ - retrieved_docs = [] - - for score, idx in zip(scores, indices, strict=False): - # 如果有段落到文档的映射，使用映射 - if hasattr(self, "passage_to_doc_mapping") and self.passage_to_doc_mapping is not None: - if idx >= 0 and idx < len(self.passage_to_doc_mapping): - doc_idx = self.passage_to_doc_mapping[idx] - if doc_idx >= 0 and doc_idx < len(self.documents): - original_doc = self.documents[doc_idx] - - # 创建标准化的文档格式 - standardized_doc = { - "text": original_doc.get("contents", str(original_doc)), - "similarity_score": float(score), - "document_index": int(doc_idx), - "passage_index": int(idx), # 保存段落索引 - } - - # 保留其他有用的元数据 - if "title" in original_doc: - standardized_doc["title"] = original_doc["title"] - if "id" in original_doc: - standardized_doc["id"] = original_doc["id"] - if "doc_size" in original_doc: - standardized_doc["doc_size"] = original_doc["doc_size"] - - retrieved_docs.append(standardized_doc) - else: - self.logger.warning( - f"映射的文档索引超出范围: {doc_idx} >= {len(self.documents)}" - ) - else: - self.logger.warning( - f"段落索引超出映射范围: {idx} >= {len(self.passage_to_doc_mapping)}" - ) - else: - # 没有映射时，直接使用索引 - if idx >= 0 and idx < len(self.documents): - original_doc = self.documents[idx] - - # 创建标准化的文档格式，与ChromaRetriever保持一致 - standardized_doc = { - "text": original_doc.get( - "contents", str(original_doc) - ), # 将contents字段映射为text - "similarity_score": float(score), - "document_index": int(idx), - } - - # 保留其他有用的元数据 - if "title" in original_doc: - standardized_doc["title"] = original_doc["title"] - if "id" in original_doc: - standardized_doc["id"] = original_doc["id"] - if "doc_size" in original_doc: - standardized_doc["doc_size"] = original_doc["doc_size"] - - retrieved_docs.append(standardized_doc) - - return retrieved_docs - - def _save_data_record(self, query: str, retrieved_docs: list[dict[str, Any]]): - """保存检索记录用于分析""" - if not self.enable_profile: - return - - record = { - "timestamp": time.time(), - "query": query, - "retrieved_count": len(retrieved_docs), - "documents": retrieved_docs, - } - - self.data_records.append(record) - - # 每100条记录持久化一次 - if len(self.data_records) >= 100: - self._persist_data_records() - - def _persist_data_records(self): - """持久化数据记录""" - if not self.enable_profile or not self.data_records: - return - - try: - timestamp = int(time.time()) - filename = f"wiki18_faiss_retrieval_records_{timestamp}.json" - filepath = os.path.join(self.data_base_path, filename) - - with open(filepath, "w", encoding="utf-8") as f: - json.dump(self.data_records, f, ensure_ascii=False, indent=2) - - self.logger.info(f"保存了 {len(self.data_records)} 条检索记录到 {filepath}") - self.data_records = [] # 清空缓存 - - except Exception as e: - self.logger.error(f"保存检索记录失败: {e}") - - def execute(self, data: str | dict[str, Any] | tuple) -> dict[str, Any]: - """ - 执行检索 - Args: - data: 查询字符串、元组或字典 - Returns: - dict: {"query": ..., "results": ..., "input": 原始输入, ...} - """ - # 支持字典类型输入，优先取 question 字段 - is_dict_input = isinstance(data, dict) - if is_dict_input: - if "query" in data: - input_query = data["query"] - elif "question" in data: - input_query = data["question"] - else: - self.logger.error("输入字典必须包含 'query' 或 'question' 字段") - data["retrieval_results"] = [] - return data - elif isinstance(data, tuple) and len(data) > 0: - input_query = data[0] - else: - input_query = data - - if not isinstance(input_query, str): - self.logger.error(f"Invalid input query type: {type(input_query)}") - if is_dict_input: - data["retrieval_results"] = [] - return data - else: - return {"query": str(input_query), "retrieval_results": [], "input": data} - - if not input_query or not input_query.strip(): - self.logger.error("查询不能为空") - if is_dict_input: - data["retrieval_results"] = [] - return data - else: - return {"query": "", "retrieval_results": [], "input": data} - - input_query = input_query.strip() - self.logger.info( - f"[ {self.__class__.__name__}]: Starting FAISS retrieval for query: {input_query}" - ) - self.logger.info(f"[ {self.__class__.__name__}]: Using top_k = {self.top_k}") - - try: - # 编码查询 - query_vector = self._encode_query(input_query) - - # FAISS搜索 - scores, indices = self._search_faiss(query_vector, self.top_k) - - # 格式化结果 - retrieved_docs = self._format_retrieved_documents(scores, indices) - - self.logger.info( - f"\033[32m[ {self.__class__.__name__}]: Retrieved {len(retrieved_docs)} documents from FAISS\033[0m" - ) - self.logger.debug( - f"Retrieved documents: {retrieved_docs[:3]}..." - ) # 只显示前3个文档的预览 - - # 保存数据记录（只有enable_profile=True时才保存） - if self.enable_profile: - self._save_data_record(input_query, retrieved_docs) - - if is_dict_input: - data["retrieval_results"] = retrieved_docs - # retrieve_time 由 MapOperator 自动添加 - return data - else: - return { - "query": input_query, - "retrieval_results": retrieved_docs, - # retrieve_time 由 MapOperator 自动添加 - "input": data, - } - - except Exception as e: - self.logger.error(f"FAISS retrieval failed: {str(e)}") - if is_dict_input: - data["retrieval_results"] = [] - return data - else: - return {"query": input_query, "retrieval_results": [], "input": data} - - def build_index_from_wiki18(self, wiki18_data_path: str, save_path: str | None = None): - """ - 从Wiki18数据集构建FAISS索引 - - Args: - wiki18_data_path: Wiki18数据集路径 - save_path: 索引保存路径 - """ - try: - import faiss - - self.logger.info(f"开始从Wiki18数据构建FAISS索引: {wiki18_data_path}") - - # 加载Wiki18数据 - documents = [] - with open(wiki18_data_path, encoding="utf-8") as f: - for line in f: - doc = json.loads(line.strip()) - documents.append(doc) - - self.logger.info(f"加载了 {len(documents)} 个文档") - - # 提取文档文本并编码 - doc_texts = [doc.get("text", "") for doc in documents] - - # 批量编码所有文档 - self.logger.info("开始编码文档...") - embeddings = self.embedding_model.encode(doc_texts) - doc_vectors = embeddings["dense_vecs"] # 获取dense向量 - - # 创建FAISS索引 - dimension = doc_vectors.shape[1] - self.faiss_index = faiss.IndexFlatIP(dimension) # 内积相似度 - - # 添加向量到索引 - self.faiss_index.add(doc_vectors.astype("float32")) # type: ignore[call-overload] - self.documents = documents - - self.logger.info(f"FAISS索引构建完成，包含 {self.faiss_index.ntotal} 个向量") - - # 保存索引和文档 - if save_path: - index_save_path = save_path + "_index" - docs_save_path = save_path + "_documents.json" - - faiss.write_index(self.faiss_index, index_save_path) - - with open(docs_save_path, "w", encoding="utf-8") as f: - json.dump(self.documents, f, ensure_ascii=False, indent=2) - - self.logger.info(f"索引已保存到: {index_save_path}") - self.logger.info(f"文档已保存到: {docs_save_path}") - - except Exception as e: - self.logger.error(f"构建FAISS索引失败: {e}") - raise - - def __del__(self): - """确保在对象销毁时保存所有未保存的记录""" - if hasattr(self, "enable_profile") and self.enable_profile: - try: - self._persist_data_records() - except Exception: - pass diff --git a/packages/sage-middleware/src/sage/middleware/operators/rag/searcher.py b/packages/sage-middleware/src/sage/middleware/operators/rag/searcher.py deleted file mode 100644 index e4992c4097..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/rag/searcher.py +++ /dev/null @@ -1,37 +0,0 @@ -from typing import Any - -import requests - -from sage.common.core.functions import MapFunction as MapOperator - - -class BochaWebSearch(MapOperator): - def __init__(self, config: dict[str, Any], **kwargs): - super().__init__(**kwargs) - self.api_key = config.get("api_key") - self.count = config.get("count", 10) - self.page = config.get("page", 1) - self.summary = config.get("summary", True) - self.url = "https://api.bochaai.com/v1/web-search" - - if not self.api_key: - raise ValueError("BochaWebSearch requires an 'api_key' in config.") - - def execute(self, data: str) -> dict[str, Any]: - query = data - headers = {"Authorization": self.api_key, "Content-Type": "application/json"} - payload = { - "query": query, - "summary": self.summary, - "count": self.count, - "page": self.page, - } - - try: - response = requests.post(self.url, headers=headers, json=payload) - response.raise_for_status() - result = response.json() - return result - except Exception as e: - self.logger.error(f"BochaWebSearch error: {e}", exc_info=True) - return {} # Return empty dict on error diff --git a/packages/sage-middleware/src/sage/middleware/operators/rag/types.py b/packages/sage-middleware/src/sage/middleware/operators/rag/types.py deleted file mode 100644 index 04d1f82f59..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/rag/types.py +++ /dev/null @@ -1,28 +0,0 @@ -"""Compatibility shim for RAG type definitions. - -Import from ``sage.libs.rag.types`` instead of middleware. -""" - -from sage.libs.rag.types import ( # noqa: F401 - RAGDocument, - RAGInput, - RAGOutput, - RAGQuery, - RAGResponse, - create_rag_response, - ensure_rag_response, - extract_query, - extract_results, -) - -__all__ = [ - "RAGDocument", - "RAGQuery", - "RAGResponse", - "RAGInput", - "RAGOutput", - "ensure_rag_response", - "extract_query", - "extract_results", - "create_rag_response", -] diff --git a/packages/sage-middleware/src/sage/middleware/operators/rag/writer.py b/packages/sage-middleware/src/sage/middleware/operators/rag/writer.py deleted file mode 100644 index 15327a475d..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/rag/writer.py +++ /dev/null @@ -1,80 +0,0 @@ -from sage.common.core.functions import MapFunction as MapOperator - - -class MemoryWriter(MapOperator): - def __init__(self, config: dict, **kwargs): - super().__init__(config, **kwargs) - self.state = None - self.config = config - # 初始化各类型集合 - self.collections = {} - - # 配置STM - if self.config.get("stm", False): - stm_config = self.config.get("stm_config", {}) - self.collections["stm"] = { - "collection": self.config.get("stm_collection"), - "config": stm_config, - } - - # 配置LTM - if self.config.get("ltm", False): - ltm_config = self.config.get("ltm_config", {}) - self.collections["ltm"] = { - "collection": self.config.get("ltm_collection"), - "config": ltm_config, - } - - # 配置DCM - if self.config.get("dcm", False): - dcm_config = self.config.get("dcm_config", {}) - self.collections["dcm"] = { - "collection": self.config.get("dcm_collection"), - "config": dcm_config, - } - # TODO: 在runtime_context中增加状态管理 - # Issue URL: https://github.com/intellistream/SAGE/issues/235 - - def execute(self, data: str | list[str] | tuple[str, str]): - input_data = data - - # 统一数据类型处理 - processed_data = [] - if isinstance(input_data, list): - processed_data = input_data - elif isinstance(input_data, tuple) and len(input_data) == 2: - processed_data = [f"{input_data[0]}{input_data[1]}"] # 拼接元组 - elif isinstance(input_data, str): - processed_data = [input_data] - else: - self.logger.error(f"Unsupported data type: {type(input_data)}") - return data - - # 写入所有启用的集合 - for mem_type, settings in self.collections.items(): - collection = settings["collection"] - config = settings["config"] - if not collection: - self.logger.warning(f"{mem_type.upper()} collection not initialized") - continue - - try: - # TODO: 这里的实现实际上要成为由writer 这个function主动往memory manager function发送一个数据。 - # 而 memory manager function拿到这个数据之后就会去执行 `execute' method 即可实现记忆的读写。 - # 这里可能会有一个由于调度原因导致的阻塞 -- 可以被优化，请参考MorphStream！ - if self.state is not None: - self.state.store( - collection=collection, - documents=processed_data, - collection_config=config, - ) - self.logger.debug(f"Stored {len(processed_data)} chunks to {mem_type.upper()}") - else: - self.logger.warning( - f"State manager not initialized. Cannot store to {mem_type.upper()}. " - "See TODO: https://github.com/intellistream/SAGE/issues/235" - ) - except Exception as e: - self.logger.error(f"Failed to store to {mem_type.upper()}: {str(e)}") - - return data # 返回原始数据 diff --git a/packages/sage-middleware/src/sage/middleware/operators/tools/__init__.py b/packages/sage-middleware/src/sage/middleware/operators/tools/__init__.py deleted file mode 100644 index b2736920da..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/tools/__init__.py +++ /dev/null @@ -1,71 +0,0 @@ -""" -Tool Operators - -This module contains domain-specific tool operators: -- Search tools (web search, document search) -- Data extraction tools - -These operators inherit from base operator classes in sage.kernel.operators -and implement tool-specific business logic. - -Note: Some tools require heavy dependencies (torch, transformers). - They are loaded lazily and will raise ImportError if dependencies are missing. -""" - -import warnings -from typing import TYPE_CHECKING - -# Core tools (minimal dependencies) -from sage.middleware.operators.tools.arxiv_paper_searcher import _Searcher_Tool -from sage.middleware.operators.tools.arxiv_searcher import ArxivSearcher -from sage.middleware.operators.tools.nature_news_fetcher import Nature_News_Fetcher_Tool -from sage.middleware.operators.tools.searcher_tool import BochaSearchTool -from sage.middleware.operators.tools.url_text_extractor import URL_Text_Extractor_Tool - -# Heavy tools (require torch/transformers) - lazy load -_HEAVY_TOOLS_LOADED = False -ImageCaptioner = None # type: ignore -text_detector = None # type: ignore - - -def _load_heavy_tools(): - """Load tools that require torch/transformers.""" - global _HEAVY_TOOLS_LOADED, ImageCaptioner, text_detector - if _HEAVY_TOOLS_LOADED: - return - try: - from sage.middleware.operators.tools.image_captioner import ImageCaptioner as _IC - from sage.middleware.operators.tools.text_detector import text_detector as _TD - - ImageCaptioner = _IC - text_detector = _TD - _HEAVY_TOOLS_LOADED = True - except ImportError as e: - warnings.warn( - f"Heavy tool operators not available: {e}\n" - "Install with: pip install torch transformers", - UserWarning, - stacklevel=2, - ) - - -def __getattr__(name: str): - """Lazy load heavy tools on access.""" - if name in ("ImageCaptioner", "text_detector"): - _load_heavy_tools() - if name == "ImageCaptioner": - return ImageCaptioner - if name == "text_detector": - return text_detector - raise AttributeError(f"module {__name__!r} has no attribute {name!r}") - - -__all__ = [ - "BochaSearchTool", - "_Searcher_Tool", - "ArxivSearcher", - "Nature_News_Fetcher_Tool", - "ImageCaptioner", - "text_detector", - "URL_Text_Extractor_Tool", -] diff --git a/packages/sage-middleware/src/sage/middleware/operators/tools/arxiv_paper_searcher.py b/packages/sage-middleware/src/sage/middleware/operators/tools/arxiv_paper_searcher.py deleted file mode 100644 index 438f0b3719..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/tools/arxiv_paper_searcher.py +++ /dev/null @@ -1,175 +0,0 @@ -import logging -import re - -import requests -from bs4 import BeautifulSoup -from bs4.element import Tag - -from sage.libs.foundation.tools.tool import BaseTool - - -class _Searcher_Tool(BaseTool): - def __init__(self): - super().__init__( - tool_name="_Searcher_Tool", - tool_description="A tool that searches arXiv for papers based on a given query.", - input_types={ - "query": "str - The search query for arXiv papers.", - "size": "int - The number of results per page (25, 50, 100, or 200). If None, use 25.", - "max_results": "int - The maximum number of papers to return (default: 25). Should be less than or equal to 100.", - }, - output_type="list - A list of dictionaries containing paper information.", - demo_commands=[ - { - "command": 'execution = tool.execute(query="tool agents with large language models")', - "description": "Search for papers about tool agents with large language models.", - }, - { - "command": 'execution = tool.execute(query="quantum computing", size=100, max_results=50)', - "description": "Search for quantum computing papers, with 100 results per page, returning a maximum of 50 papers.", - }, - { - "command": 'execution = tool.execute(query="machine learning", max_results=75)', - "description": "Search for machine learning papers, returning a maximum of 75 papers.", - }, - ], - ) - # Store additional metadata as instance variables - self.tool_version = "1.0.0" - self.valid_sizes = [25, 50, 100, 200] - self.base_url = "https://arxiv.org/search/" - - def build_tool(self): - """ - No specific build required for this tool. - """ - pass - - def execute(self, query, size=None, max_results=25): - """ - Executes the arXiv search tool to find papers based on the given query. - - Parameters: - query (str): The search query for arXiv papers. - size (int): The number of results per page. - max_results (int): The maximum number of papers to return. - - Returns: - list: A list of dictionaries containing paper information. - """ - valid_sizes = self.valid_sizes - base_url = self.base_url - - if size is None: - size = 25 - elif size not in valid_sizes: - size = min(valid_sizes, key=lambda x: abs(x - size)) - - results = [] - start = 0 - - max_results = min(max_results, 100) # NOTE: For traffic reasons, limit to 100 results - - while len(results) < max_results: - params = { - "searchtype": "all", - "query": query, - "abstracts": "show", - "order": "", - "size": str(size), - "start": str(start), - } - - try: - response = requests.get(base_url, params=params) - soup = BeautifulSoup(response.content, "html.parser") - - papers = soup.find_all("li", class_="arxiv-result") # type: ignore - if not papers: - break - - for paper in papers: - if len(results) >= max_results: - break - - title_elem = paper.find("p", class_="title") # type: ignore - title = title_elem.text.strip() if title_elem else "No title found" - - authors_elem = paper.find("p", class_="authors") # type: ignore - authors = authors_elem.text.strip() if authors_elem else "No authors found" - authors = re.sub(r"^Authors:\s*", "", authors) - authors = re.sub(r"\s+", " ", authors).strip() - - abstract_elem = paper.find("span", class_="abstract-full") # type: ignore - abstract = ( - abstract_elem.text.strip() if abstract_elem else "No abstract available" - ) - abstract = abstract.replace("△ Less", "").strip() - - link_elem = paper.find("p", class_="list-title") # type: ignore - link_tag = link_elem.find("a") if isinstance(link_elem, Tag) else None # type: ignore - link = ( - link_tag["href"] - if isinstance(link_tag, Tag) and link_tag.has_attr("href") - else "No link found" - ) - - results.append( - { - "title": title, - "authors": authors, - "abstract": abstract, - "link": link, - } - ) - - start += size - - except Exception as e: - logging.error(f"Error searching arXiv: {e}") - break - - return results[:max_results] - - def get_metadata(self): - """ - Returns the metadata for the _Searcher_Tool. - - Returns: - dict: A dictionary containing the tool's metadata. - """ - metadata = super().get_metadata() - return metadata - - -if __name__ == "__main__": - import json - - print("ArXiv Search Tool Test") - - # Example usage of the _Searcher_Tool - tool = _Searcher_Tool() - - # Get tool metadata - metadata = tool.get_metadata() - print("Tool Metadata:") - print(metadata) - - # Sample query for searching arXiv - query = "" - # Execute the tool - try: - execution = tool.execute(query=query, size=50, max_results=10) - print("\n==>> Execution:") - print(json.dumps(execution, indent=4)) # Pretty print JSON - print("\n==>> Search Results:") - for i, paper in enumerate(execution, 1): - print(f"{i}. {paper['title']}") - print(f" Authors: {paper['authors']}") - print(f" Abstract: {paper['abstract'][:2000]}") - print(f" Link: {paper['link']}") - print() - except Exception as e: - print(f"Execution failed: {e}") - - print("Done!") diff --git a/packages/sage-middleware/src/sage/middleware/operators/tools/arxiv_searcher.py b/packages/sage-middleware/src/sage/middleware/operators/tools/arxiv_searcher.py deleted file mode 100644 index 6ac3a676d5..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/tools/arxiv_searcher.py +++ /dev/null @@ -1,102 +0,0 @@ -""" -Arxiv 论文搜索工具 (Real Implementation) -""" - -import asyncio -import logging -import urllib.parse -from typing import Any - -import aiohttp -import feedparser - -from sage.libs.foundation.tools.tool import BaseTool - -logger = logging.getLogger(__name__) - - -class ArxivSearcher(BaseTool): - """Arxiv 学术论文搜索工具""" - - def __init__(self): - super().__init__( - tool_name="arxiv_searcher", - tool_description="Search Arxiv for academic papers. Returns title, authors, summary, and link.", - input_types=["str"], - output_type="list", - demo_commands=["search for transformer papers", "find papers about LLM agents"], - require_llm_engine=False, - ) - self.base_url = "http://export.arxiv.org/api/query" - - async def execute(self, query: str, max_results: int = 5) -> list[dict[str, Any]]: - """ - Execute Arxiv search. - """ - logger.info(f"Searching Arxiv for: {query}") - - # Construct API query - # search_query=all:electron&start=0&max_results=10 - params = { - "search_query": f"all:{query}", - "start": 0, - "max_results": max_results, - "sortBy": "relevance", - "sortOrder": "descending", - } - - url = f"{self.base_url}?{urllib.parse.urlencode(params)}" - - try: - async with aiohttp.ClientSession() as session: - async with session.get(url) as response: - if response.status != 200: - logger.error(f"Arxiv API failed with status {response.status}") - return [] - - content = await response.text() - - # Parse with feedparser - feed = feedparser.parse(content) - - results = [] - for entry in feed.entries: - paper = { - "title": entry.title.replace("\n", " ").strip(), - "authors": [author.name for author in entry.authors], - "summary": entry.summary.replace("\n", " ").strip(), - "published": entry.published, - "link": entry.link, - "pdf_link": next( - (link.href for link in entry.links if link.title == "pdf"), None - ), - } - results.append(paper) - - logger.info(f"Found {len(results)} papers") - return results - - except Exception as e: - logger.error(f"Arxiv search failed: {e}") - return [] - - def call(self, arguments: dict) -> Any: - """Sync wrapper for MCP""" - query = arguments.get("query") - if not query: - return [] - - # Check for running loop - try: - loop = asyncio.get_running_loop() - if loop.is_running(): - # If we are in a loop, we can't use asyncio.run. - # But AgentRuntime calls tools synchronously? - # If AgentRuntime is running in a thread, we can use asyncio.run. - # If AgentRuntime is running in the main loop, we are in trouble. - # But Gateway runs AgentRuntime in run_in_executor. - return asyncio.run(self.execute(query)) - except RuntimeError: - return asyncio.run(self.execute(query)) - - return asyncio.run(self.execute(query)) diff --git a/packages/sage-middleware/src/sage/middleware/operators/tools/duckduckgo_searcher.py b/packages/sage-middleware/src/sage/middleware/operators/tools/duckduckgo_searcher.py deleted file mode 100644 index 3f64edae49..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/tools/duckduckgo_searcher.py +++ /dev/null @@ -1,105 +0,0 @@ -""" -DuckDuckGo web search tool (no API key required). -""" - -from __future__ import annotations - -import asyncio -import logging -from typing import Any - -import aiohttp -from bs4 import BeautifulSoup -from pydantic import BaseModel, Field - -from sage.libs.foundation.tools.tool import BaseTool - -logger = logging.getLogger(__name__) - - -class DuckDuckGoSearchInput(BaseModel): - query: str = Field(..., description="Search query text") - max_results: int = Field(5, description="Number of results to return", ge=1, le=20) - - -class DuckDuckGoSearcher(BaseTool): - """Simple HTML-based DuckDuckGo searcher. - - Uses the public HTML endpoint (no API key) and extracts title/link/snippet. - Intended for lightweight research fallback when no commercial search API is configured. - """ - - def __init__(self): - super().__init__( - tool_name="duckduckgo_search", - tool_description="Search the web via DuckDuckGo (HTML endpoint). Returns title, link, and snippet.", - input_types={"query": "str - search query", "max_results": "int - number of results"}, - output_type="list", - demo_commands=[ - "search for latest vector database papers", - "find recent ML system posts", - ], - require_llm_engine=False, - ) - - async def execute(self, query: str, max_results: int = 5) -> list[dict[str, Any]]: - url = "https://duckduckgo.com/html" - params = {"q": query, "kl": "us-en"} - - try: - async with aiohttp.ClientSession() as session: - async with session.post(url, data=params, timeout=15) as resp: - if resp.status != 200: - logger.warning("DuckDuckGo returned status %s", resp.status) - return [] - html = await resp.text() - except Exception as exc: # noqa: BLE001 - logger.error("DuckDuckGo search failed: %s", exc) - return [] - - soup = BeautifulSoup(html, "html.parser") - results: list[dict[str, Any]] = [] - - for result in soup.select("div.result"): - if len(results) >= max_results: - break - - link_tag = result.select_one("a.result__a") - snippet_tag = result.select_one("a.result__snippet") or result.select_one( - "div.result__snippet" - ) - - title = link_tag.get_text(strip=True) if link_tag else "" - href = link_tag.get("href") if link_tag else "" - snippet = snippet_tag.get_text(strip=True) if snippet_tag else "" - - if not href: - continue - - results.append( - { - "title": title, - "link": href, - "content": snippet, - "source": "duckduckgo", - } - ) - - return results - - def call(self, arguments: dict) -> Any: - """Sync wrapper used by MCP/AgentRuntime.""" - query = arguments.get("query") - if not query: - return [] - - max_results = arguments.get("max_results", 5) - - try: - loop = asyncio.get_running_loop() - if loop.is_running(): - return asyncio.run(self.execute(query, max_results=max_results)) - except RuntimeError: - return asyncio.run(self.execute(query, max_results=max_results)) - - return asyncio.run(self.execute(query, max_results=max_results)) diff --git a/packages/sage-middleware/src/sage/middleware/operators/tools/image_captioner.py b/packages/sage-middleware/src/sage/middleware/operators/tools/image_captioner.py deleted file mode 100644 index 5d2768a74a..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/tools/image_captioner.py +++ /dev/null @@ -1,104 +0,0 @@ -import os -import time - -from sage.libs.foundation.tools.tool import BaseTool - -try: - from isagellm import UnifiedInferenceClient -except ImportError: - UnifiedInferenceClient = None # Optional: isagellm not installed - - -class ImageCaptioner(BaseTool): - def __init__(self, model_name: str = "meta-llama/Llama-2-13b-chat-hf"): - super().__init__( - tool_name="image_captioner", - tool_description="A tool that can generate captions for images ", - input_types={ - "image_path": "The path to the image to caption", - "prompt": "The prompt to generate the caption", - }, - demo_commands=[ - { - "command": 'execution = tool.execute(image="path/to/image.png")', - "description": "Generate a caption for an image using the default prompt and model.", - }, - { - "command": 'execution = tool.execute(image="path/to/image.png", prompt="A beautiful landscape")', - "description": "Generate a caption for an image using a custom prompt and model.", - }, - ], - require_llm_engine=True, # This tool requires an LLM engine - ) - # Store additional metadata and model configuration as instance variables - self.tool_version = "1.0.0" - self.limitation = "The Image_Captioner_Tool provides general image descriptions but has limitations: 1) May make mistakes in complex scenes, counting, attribute detection, and understanding object relationships. 2) Might not generate comprehensive captions, especially for images with multiple objects or abstract concepts. 3) Performance varies with image complexity. 4) Struggles with culturally specific or domain-specific content. 5) May overlook details or misinterpret object relationships. For precise descriptions, consider: using it with other tools for context/verification, as an initial step before refinement, or in multi-step processes for ambiguity resolution. Verify critical information with specialized tools or human expertise when necessary." - self.model_name = model_name - print(f"ImageCaptioner initialized with model: {model_name}") - - def execute(self, image_path: str): - try: - if not self.model_name: - raise ValueError( - "Model name is not set. Please set the model name using set_model_name() before executing the tool." - ) - - # Construct the messages parameter for UnifiedInferenceClient - messages = [ - {"role": "system", "content": "You are an image captioning assistant."}, - { - "role": "user", - "content": f"Generate a caption for the image at path: {image_path}", - }, - ] - - # Use auto-detection for best available LLM service - client = UnifiedInferenceClient.create() - - # Retry mechanism for connection errors - max_retries = 5 - retry_delay = 3 # seconds - - for attempt in range(max_retries): - try: - response = client.chat(messages) - return response - except ConnectionError as e: - print(f"Connection error on attempt {attempt + 1}: {e}") - if attempt < max_retries - 1: - print(f"Retrying in {retry_delay} seconds...") - time.sleep(retry_delay) - else: - raise - except Exception as e: - print(f"Error in ImageCaptioner: {e}") - return None - - -if __name__ == "__main__": - import json - - # Get the directory of the current script - script_dir = os.path.dirname(os.path.abspath(__file__)) - - # Example usage of the Image_Captioner_Tool - # tool = Image_Captioner_Tool() - tool = ImageCaptioner(model_name="meta-llama/Llama-2-13b-chat-hf") - - # Get tool metadata - metadata = tool.get_metadata() - print(metadata) - - # Construct the full path to the image using the script's directory - relative_image_path = "examples/baseball.png" - image_path = os.path.join(script_dir, relative_image_path) - - # Execute the tool with default prompt - try: - execution = tool.execute(image_path=image_path) - print("Generated Caption:") - print(json.dumps(execution, indent=4)) - except Exception as e: - print(f"Execution failed: {e}") - - print("Done!") diff --git a/packages/sage-middleware/src/sage/middleware/operators/tools/nature_news_fetcher.py b/packages/sage-middleware/src/sage/middleware/operators/tools/nature_news_fetcher.py deleted file mode 100644 index e98b5556dd..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/tools/nature_news_fetcher.py +++ /dev/null @@ -1,224 +0,0 @@ -import logging -import os -import random -import time - -import requests -from bs4 import BeautifulSoup, Tag - -from sage.libs.foundation.tools.tool import BaseTool - -# Initialize logger -logger = logging.getLogger(__name__) -logging.basicConfig(level=logging.INFO) - - -class Nature_News_Fetcher_Tool(BaseTool): - def __init__(self): - super().__init__( - tool_name="Nature_News_Fetcher_Tool", - tool_description="A tool that fetches the latest news articles from Nature.", - input_types={ - "num_articles": "int - The number of articles to fetch (default: 100).", - "max_pages": "int - The maximum number of pages to fetch (default: 5).", - }, - output_type="list - A list of dictionaries containing information about the latest Nature news articles.", - demo_commands=[ - { - "command": "execution = tool.execute()", - "description": "Fetch the latest 100 news articles from Nature.", - }, - { - "command": "execution = tool.execute(num_articles=50, max_pages=3)", - "description": "Fetch the latest 50 news articles from Nature, searching up to 3 pages.", - }, - ], - ) - self.tool_version = "1.0.0" - self.base_url = "https://www.nature.com/nature/articles" - # 控制每次抓取后的等待时间，可在测试中覆盖 - self.sleep_time = 1 - - def fetch_page(self, page_number): - """ - Fetches a single page of news articles from Nature's website. - - Parameters: - page_number (int): The page number to fetch. - - Returns: - str: The HTML content of the page. - """ - params = { - "searchType": "journalSearch", - "sort": "PubDate", - "type": "news", - "page": str(page_number), - } - user_agents = [ - "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36", - "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36", - "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36", - ] - headers = {"User-Agent": random.choice(user_agents)} - response = requests.get(self.base_url, params=params, headers=headers) - response.raise_for_status() - return response.text - - def parse_articles(self, html_content): - """ - Parses the HTML content and extracts article information. - - Parameters: - html_content (str): The HTML content of the page. - - Returns: - list: A list of dictionaries containing article information. - """ - soup = BeautifulSoup(html_content, "html.parser") - articles_section = soup.find("section", id="new-article-list") - if not isinstance(articles_section, Tag): - return [] - - articles = [] - for article in articles_section.find_all("article", class_="c-card"): # type: ignore - if not isinstance(article, Tag): - continue - - title_elem = article.find("h3", class_="c-card__title") # type: ignore - title = title_elem.text.strip() if isinstance(title_elem, Tag) else "No title found" - - url_elem = title_elem.find("a") if isinstance(title_elem, Tag) else None # type: ignore - url = ( - "https://www.nature.com" + str(url_elem["href"]) - if isinstance(url_elem, Tag) and url_elem.has_attr("href") - else "No URL found" - ) - - description_elem = article.find("div", {"data-test": "article-description"}) # type: ignore - description = ( - description_elem.text.strip() - if isinstance(description_elem, Tag) - else "No description available" - ) - - authors_elem = article.find("ul", {"data-test": "author-list"}) # type: ignore - authors = ( - [ - author.text.strip() - for author in authors_elem.find_all("li") - if isinstance(author, Tag) - ] - if isinstance(authors_elem, Tag) - else ["No authors found"] - ) - - date_elem = article.find("time") # type: ignore - date = ( - date_elem["datetime"] - if isinstance(date_elem, Tag) and date_elem.has_attr("datetime") - else "No date found" - ) - - image_elem = article.find("img") # type: ignore - image_url = ( - image_elem["src"] - if isinstance(image_elem, Tag) and image_elem.has_attr("src") - else "No image found" - ) - - articles.append( - { - "title": title, - "url": url, - "description": description, - "authors": authors, - "date": date, - "image_url": image_url, - } - ) - - return articles - - def execute(self, num_articles=100, max_pages=5): - """ - Fetches the latest news articles from Nature's website. - - Parameters: - num_articles (int): The number of articles to fetch. - max_pages (int): The maximum number of pages to fetch. - - Returns: - list: A list of dictionaries containing article information. - """ - all_articles = [] - page_number = 1 - - try: - while len(all_articles) < num_articles and page_number <= max_pages: - html_content = self.fetch_page(page_number) - page_articles = self.parse_articles(html_content) - - if not page_articles: - logger.info(f"No articles found on page {page_number}. Stopping fetch.") - break # No more articles found - - all_articles.extend(page_articles) - page_number += 1 - # 只有在还需抓取下一页时才 sleep - if len(all_articles) < num_articles and page_number <= max_pages: - time.sleep(self.sleep_time) # Be polite to the server - - return all_articles[:num_articles] - except requests.exceptions.RequestException as e: - logger.error(f"Network error occurred: {e}") - return [{"error": f"Network error: {str(e)}"}] - except Exception as e: - logger.error(f"An unexpected error occurred: {e}") - return [{"error": f"Unexpected error: {str(e)}"}] - - def get_metadata(self): - """ - Returns the metadata for the Nature_News_Fetcher_Tool. - - Returns: - dict: A dictionary containing the tool's metadata. - """ - if hasattr(super(), "get_metadata"): - metadata = super().get_metadata() - else: - metadata = {} - return metadata - - -if __name__ == "__main__": - # Get the directory of the current script - script_dir = os.path.dirname(os.path.abspath(__file__)) - - # Example usage of the Nature_News_Fetcher_Tool - tool = Nature_News_Fetcher_Tool() - - # Get tool metadata - metadata = tool.get_metadata() - print(metadata) - - import json - - # Execute the tool to fetch the latest 10 articles (for demonstration purposes) - try: - execution = tool.execute(num_articles=10, max_pages=1) - print(json.dumps(execution, indent=4)) - print("\nExecution Result:") - print(f"Number of articles fetched: {len(execution)}") - print("\nSample articles:") - for i, article in enumerate(execution[:10], 1): - print(f"\n{i}. Title: {article['title']}") - print(f" URL: {article['url']}") - print(f" Description: {article['description'][:100]}...") # Show first 100 characters - print(f" Authors: {', '.join(article['authors'])}") - print(f" Date: {article['date']}") - print(f" Image URL: {article['image_url']}") - except Exception as e: - print(f"Execution failed: {e}") - - print("Done!") diff --git a/packages/sage-middleware/src/sage/middleware/operators/tools/searcher_tool.py b/packages/sage-middleware/src/sage/middleware/operators/tools/searcher_tool.py deleted file mode 100644 index d96af9c3e9..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/tools/searcher_tool.py +++ /dev/null @@ -1,514 +0,0 @@ -import json -import os -import time -from typing import Any - -import requests - -from sage.common.core.functions import MapFunction as MapOperator -from sage.middleware.operators.context.model_context import ModelContext -from sage.middleware.operators.context.search_result import SearchResult -from sage.middleware.operators.context.search_session import SearchSession - - -class BochaSearchTool(MapOperator): - """ - 改进的Bocha搜索工具 - 使用新的分层搜索结果结构 - 输入: ModelContext (包含搜索查询) - 输出: ModelContext (包含结构化的搜索结果) - """ - - def __init__(self, config: dict, **kwargs): - super().__init__(**kwargs) - - self.url = config.get("url", "https://api.bochasearch.com/search") - self.api_key = config.get("api_key", os.getenv("BOCHA_API_KEY")) - self.max_results_per_query = config.get("max_results_per_query", 3) - self.search_engine_name = config.get("search_engine_name", "Bocha") - - if not self.api_key: - raise ValueError( - "BOCHA_API_KEY is required. Set it in environment variables or config." - ) - - self.headers = { - "Authorization": self.api_key, - "Content-Type": "application/json", - } - - self.search_count = 0 - - self.logger.info( - f"BochaSearchTool initialized with max_results_per_query: {self.max_results_per_query}" - ) - - def _execute_single_search(self, query: str) -> dict[str, Any]: - """ - 执行单个搜索查询 - - Args: - query: 搜索查询字符串 - - Returns: - Dict: 搜索API的原始响应 - """ - start_time = time.time() - - payload = json.dumps( - { - "query": query, - "summary": True, - "count": max(10, self.max_results_per_query * 2), # 请求更多结果以便筛选 - "page": 1, - } - ) - - try: - self.logger.debug(f"Executing search for query: '{query}'") - response = requests.post(self.url, headers=self.headers, data=payload, timeout=30) - response.raise_for_status() - - execution_time = int((time.time() - start_time) * 1000) - result = response.json() - result["_execution_time_ms"] = execution_time - - return result - - except requests.exceptions.RequestException as e: - execution_time = int((time.time() - start_time) * 1000) - self.logger.error(f"Search API request failed for query '{query}': {e}") - return { - "error": str(e), - "data": {"webPages": {"value": []}}, - "_execution_time_ms": execution_time, - } - except json.JSONDecodeError as e: - execution_time = int((time.time() - start_time) * 1000) - self.logger.error(f"Failed to parse search API response for query '{query}': {e}") - return { - "error": "JSON decode error", - "data": {"webPages": {"value": []}}, - "_execution_time_ms": execution_time, - } - - def _convert_api_response_to_search_results( - self, api_response: dict[str, Any], query: str - ) -> list[SearchResult]: - """ - 将搜索API响应转换为SearchResult对象列表 - - Args: - api_response: 搜索API的响应 - query: 原始查询 - - Returns: - List[SearchResult]: 搜索结果对象列表 - """ - search_results = [] - - try: - # 检查是否有错误 - if "error" in api_response: - error_result = SearchResult( - title=f"Search Error for '{query}'", - content=f"Error: {api_response['error']}", - source="Error", - rank=1, - relevance_score=0.0, - ) - search_results.append(error_result) - return search_results - - # 提取网页结果 - web_pages = api_response.get("data", {}).get("webPages", {}).get("value", []) - - for i, page in enumerate(web_pages[: self.max_results_per_query]): - title = page.get("name", "No Title").strip() - content = page.get("snippet", "No content available").strip() - source = page.get("url", "No URL").strip() - - # 计算相关性分数（简单的基于排名的分数） - relevance_score = max(0.1, 1.0 - (i * 0.1)) - - search_result = SearchResult( - title=title, - content=content, - source=source, - rank=i + 1, - relevance_score=relevance_score, - ) - - search_results.append(search_result) - - # 如果没有找到结果 - if not search_results: - no_results = SearchResult( - title=f"No Results for '{query}'", - content=f"No search results found for query: '{query}'", - source="Search Engine", - rank=1, - relevance_score=0.0, - ) - search_results.append(no_results) - - except Exception as e: - self.logger.error(f"Error converting API response for query '{query}': {e}") - error_result = SearchResult( - title=f"Conversion Error for '{query}'", - content=f"Error processing search results: {str(e)}", - source="Error", - rank=1, - relevance_score=0.0, - ) - search_results.append(error_result) - - return search_results - - def _create_legacy_chunks_for_compatibility(self, search_session: SearchSession) -> list[str]: - """ - 为向后兼容性创建legacy格式的retriver_chunks - - Args: - search_session: 搜索会话对象 - - Returns: - List[str]: legacy格式的搜索结果字符串列表 - """ - legacy_chunks = [] - - for query_result in search_session.query_results: - for result in query_result.results: - legacy_chunk = f"""[Search Result {result.rank} for '{query_result.query}'] -Title: {result.title} -Content: {result.content} -Source: {result.source}""" - legacy_chunks.append(legacy_chunk) - - return legacy_chunks - - def _log_search_summary(self, context: ModelContext, total_queries: int, total_results: int): - """记录搜索摘要信息""" - original_chunks = len(context.retriver_chunks) if context.retriver_chunks else 0 - - self.logger.info( - f"Search completed: " - f"Queries={total_queries}, " - f"Total_results={total_results}, " - f"Original_chunks={original_chunks}, " - f"Context_UUID={context.uuid}" - ) - - def execute(self, context: ModelContext) -> ModelContext: - """ - 执行搜索并将结果集成到ModelContext中 - - Args: - context: ModelContext对象，包含搜索查询 - - Returns: - ModelContext: 更新了搜索结果的上下文 - """ - try: - # 获取搜索查询 - search_queries = context.get_search_queries() - self.logger.debug( - f"BochaSearchTool processing {len(search_queries)} queries for context {context.uuid}" - ) - - # 如果没有搜索查询，直接返回原上下文 - if not search_queries: - self.logger.info("No search queries provided, returning original context") - return context - - # 创建搜索会话（如果还没有） - if not context.search_session: - context.create_search_session(context.raw_question) - - # 执行所有搜索查询 - total_results = 0 - - for query in search_queries: - self.logger.debug(f"Executing search for query: '{query}'") - - # 执行搜索 - api_response = self._execute_single_search(query) - execution_time = api_response.get("_execution_time_ms", 0) - - # 转换为SearchResult对象 - search_results = self._convert_api_response_to_search_results(api_response, query) - - # 计算总结果数（从API响应中获取，如果可用） - total_count_from_api = len( - api_response.get("data", {}).get("webPages", {}).get("value", []) - ) - - # 添加搜索结果到上下文 - context.add_search_results( - query=query, - results=search_results, - search_engine=self.search_engine_name, - execution_time_ms=execution_time, - total_results_count=total_count_from_api, - ) - - total_results += len(search_results) - - self.logger.debug(f"Query '{query}' returned {len(search_results)} results") - - # 为向后兼容性更新retriver_chunks - if context.search_session: - legacy_chunks = self._create_legacy_chunks_for_compatibility(context.search_session) - if context.retriver_chunks is None: - context.retriver_chunks = [] - context.retriver_chunks.extend(legacy_chunks) - - # 更新搜索计数 - self.search_count += 1 - - # 记录搜索摘要 - self._log_search_summary(context, len(search_queries), total_results) - - # 更新工具配置记录搜索执行信息 - search_execution_info = { - "bocha_search_executed": True, - "queries_count": len(search_queries), - "total_results": total_results, - "search_engine": self.search_engine_name, - "execution_timestamp": int(time.time() * 1000), - "session_id": ( - context.search_session.session_id if context.search_session else None - ), - } - - context.update_tool_config({"bocha_search_info": search_execution_info}) - - return context - - except Exception as e: - self.logger.error(f"BochaSearchTool execution failed: {e}", exc_info=True) - - # 错误处理：记录错误到工具配置中 - error_info = { - "bocha_search_error": str(e), - "error_timestamp": int(time.time() * 1000), - "attempted_queries": (search_queries if "search_queries" in locals() else []), - } - - context.update_tool_config({"bocha_search_error": error_info}) - - return context - - -class EnhancedBochaSearchTool(BochaSearchTool): - """ - 增强版Bocha搜索工具，支持更多定制化选项和结果优化 - 使用新的分层搜索结构和ModelContext - """ - - def __init__(self, config: dict, **kwargs): - super().__init__(config, **kwargs) - - self.deduplicate_results = config.get("deduplicate_results", True) - self.max_total_chunks = config.get("max_total_chunks", 20) - self.preserve_chunk_order = config.get("preserve_chunk_order", True) - self.min_relevance_score = config.get("min_relevance_score", 0.1) - self.diversity_threshold = config.get("diversity_threshold", 0.8) # 多样性阈值 - - self.logger.info( - f"EnhancedBochaSearchTool initialized: " - f"deduplicate={self.deduplicate_results}, " - f"max_total={self.max_total_chunks}, " - f"min_relevance={self.min_relevance_score}" - ) - - def _calculate_content_similarity(self, content1: str, content2: str) -> float: - """计算两个内容的相似度（简单的词汇重叠）""" - words1 = set(content1.lower().split()) - words2 = set(content2.lower().split()) - - if not words1 or not words2: - return 0.0 - - intersection = words1.intersection(words2) - union = words1.union(words2) - - return len(intersection) / len(union) if union else 0.0 - - def _deduplicate_search_results(self, search_results: list[SearchResult]) -> list[SearchResult]: - """去重和多样性优化搜索结果""" - if not self.deduplicate_results or not search_results: - return search_results - - # 按相关性分数排序 - sorted_results = sorted(search_results, key=lambda x: x.relevance_score, reverse=True) - - deduplicated = [] - seen_sources = set() - - for result in sorted_results: - # 检查是否已有相同源 - if result.source in seen_sources: - continue - - # 检查与已选结果的相似度 - is_diverse = True - for existing in deduplicated: - similarity = self._calculate_content_similarity(result.content, existing.content) - if similarity > self.diversity_threshold: - is_diverse = False - break - - # 检查相关性分数阈值 - if result.relevance_score >= self.min_relevance_score and is_diverse: - deduplicated.append(result) - seen_sources.add(result.source) - - # 保持原有排名顺序（如果要求保持顺序） - if self.preserve_chunk_order: - # 按原来的rank排序 - deduplicated = sorted(deduplicated, key=lambda x: x.rank) - - return deduplicated - - def _optimize_search_session(self, context: ModelContext) -> None: - """优化搜索会话结果""" - if not context.search_session or not context.search_session.query_results: - return - - total_optimized = 0 - - for query_result in context.search_session.query_results: - original_count = len(query_result.results) - - # 应用去重和多样性优化 - query_result.results = self._deduplicate_search_results(query_result.results) - - optimized_count = len(query_result.results) - total_optimized += original_count - optimized_count - - if original_count != optimized_count: - self.logger.debug( - f"Query '{query_result.query}': " - f"optimized from {original_count} to {optimized_count} results" - ) - - if total_optimized > 0: - self.logger.info( - f"Search optimization removed {total_optimized} duplicate/low-quality results" - ) - - def _limit_total_results(self, context: ModelContext) -> None: - """限制总的搜索结果数量""" - if not context.search_session: - return - - total_results = context.search_session.get_total_results_count() - - if total_results <= self.max_total_chunks: - return - - # 收集所有结果并按相关性排序 - all_results = [] - for query_result in context.search_session.query_results: - for result in query_result.results: - all_results.append((query_result, result)) - - # 按相关性分数排序 - all_results.sort(key=lambda x: x[1].relevance_score, reverse=True) - - # 清空现有结果 - for query_result in context.search_session.query_results: - query_result.results = [] - - # 重新分配最佳结果，保持每个查询至少有一个结果 - results_per_query = self.max_total_chunks // len(context.search_session.query_results) - remaining_slots = self.max_total_chunks % len(context.search_session.query_results) - - query_result_counts = dict.fromkeys(context.search_session.query_results, 0) - - for query_result, result in all_results: - current_count = query_result_counts[query_result] - max_for_this_query = results_per_query + (1 if remaining_slots > 0 else 0) - - if current_count < max_for_this_query: - query_result.results.append(result) - query_result_counts[query_result] += 1 - - if current_count + 1 == max_for_this_query and remaining_slots > 0: - remaining_slots -= 1 - - if sum(query_result_counts.values()) >= self.max_total_chunks: - break - - new_total = context.search_session.get_total_results_count() - self.logger.info(f"Limited search results from {total_results} to {new_total}") - - def _update_legacy_chunks(self, context: ModelContext) -> None: - """更新legacy格式的retriver_chunks以反映优化后的结果""" - if not context.search_session: - return - - # 重新生成legacy chunks - optimized_chunks = self._create_legacy_chunks_for_compatibility(context.search_session) - - # 合并到现有chunks中（保持之前可能存在的非搜索chunks） - non_search_chunks = [] - if context.retriver_chunks: - # 尝试识别非搜索chunks（不包含"[Search Result"标记的） - for chunk in context.retriver_chunks: - if not chunk.strip().startswith("[Search Result"): - non_search_chunks.append(chunk) - - context.retriver_chunks = non_search_chunks + optimized_chunks - - def execute(self, context: ModelContext) -> ModelContext: - """增强版执行逻辑，包含结果优化""" - try: - # 先执行基础搜索 - context = super().execute(context) - - # 应用增强功能 - if context.search_session and context.search_session.query_results: - # 1. 优化搜索会话结果（去重、多样性） - self._optimize_search_session(context) - - # 2. 限制总结果数量 - self._limit_total_results(context) - - # 3. 更新legacy chunks以反映优化 - self._update_legacy_chunks(context) - - # 4. 更新工具配置记录优化信息 - optimization_info = { - "enhanced_search_applied": True, - "deduplicate_results": self.deduplicate_results, - "max_total_chunks": self.max_total_chunks, - "min_relevance_score": self.min_relevance_score, - "final_results_count": context.search_session.get_total_results_count(), - "final_chunks_count": ( - len(context.retriver_chunks) if context.retriver_chunks else 0 - ), - } - - context.update_tool_config({"enhanced_search_info": optimization_info}) - - self.logger.info( - f"Enhanced search completed: {optimization_info['final_results_count']} results, " - f"{optimization_info['final_chunks_count']} chunks" - ) - - return context - - except Exception as e: - self.logger.error(f"EnhancedBochaSearchTool execution failed: {e}", exc_info=True) - - # 错误处理：记录错误并继续基础搜索结果 - error_info = { - "enhanced_search_error": str(e), - "error_timestamp": int(time.time() * 1000), - "fallback_to_basic": True, - } - - context.update_tool_config({"enhanced_search_error": error_info}) - - return context diff --git a/packages/sage-middleware/src/sage/middleware/operators/tools/text_detector.py b/packages/sage-middleware/src/sage/middleware/operators/tools/text_detector.py deleted file mode 100644 index 6ef6e02e79..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/tools/text_detector.py +++ /dev/null @@ -1,185 +0,0 @@ -import os -import time - -import torch - -from sage.libs.foundation.tools.tool import BaseTool - - -class text_detector(BaseTool): - def __init__(self): - super().__init__( - tool_name="Text_Detector_Tool", - tool_description="A tool that detects text in an image using EasyOCR.", - input_types={ - "image": "str - The path to the image file.", - "languages": "list - A list of language codes for the OCR model.", - "detail": "int - The level of detail in the output. Set to 0 for simpler output, 1 for detailed output.", - }, - output_type="list - A list of detected text blocks.", - demo_commands=[ - { - "command": 'execution = tool.execute(image="path/to/image.png", languages=["en"])', - "description": "Detect text in an image using the default language (English).", - }, - { - "command": 'execution = tool.execute(image="path/to/image.png", languages=["en", "de"])', - "description": "Detect text in an image using multiple languages (English and German).", - }, - { - "command": 'execution = tool.execute(image="path/to/image.png", languages=["en"], detail=0)', - "description": "Detect text in an image with simpler output (text without coordinates and scores).", - }, - ], - ) - self.tool_version = "1.0.0" - self.frequently_used_language = { - "ch_sim": "Simplified Chinese", - "ch_tra": "Traditional Chinese", - "de": "German", - "en": "English", - "es": "Spanish", - "fr": "French", - "hi": "Hindi", - "ja": "Japanese", - } - - def build_tool(self, languages=None): - """ - Builds and returns the EasyOCR reader model. - - Parameters: - languages (list): A list of language codes for the OCR model. - - Returns: - easyocr.Reader: An initialized EasyOCR Reader object. - """ - languages = languages or ["en"] # Default to English if no languages provided - try: - import easyocr - - reader = easyocr.Reader(languages) - return reader - except ImportError: - raise ImportError("Please install the EasyOCR package using 'pip install easyocr'.") - except Exception as e: - print(f"Error building the OCR tool: {e}") - return None - - def execute( - self, - image, - languages=None, - max_retries=10, - retry_delay=5, - clear_cuda_cache=False, - **kwargs, - ): - """ - Executes the OCR tool to detect text in the provided image. - - Parameters: - image (str): The path to the image file. - languages (list): A list of language codes for the OCR model. - max_retries (int): Maximum number of retry attempts. - retry_delay (int): Delay in seconds between retry attempts. - clear_cuda_cache (bool): Whether to clear CUDA cache on out-of-memory errors. - **kwargs: Additional keyword arguments for the OCR reader. - - Returns: - list: A list of detected text blocks. - """ - languages = languages or ["en"] - - for attempt in range(max_retries): - try: - reader = self.build_tool(languages) - if reader is None: - raise ValueError("Failed to build the OCR tool.") - - result = reader.readtext(image, **kwargs) - try: - # detail = 1: Convert numpy types to standard Python types - from typing import Any, cast - - cleaned_result = [ - ( - [[int(coord[0]), int(coord[1])] for coord in cast(Any, item)[0]], - cast(Any, item)[1], - round(float(cast(Any, item)[2]), 2), - ) - for item in result - ] - return cleaned_result - except Exception: - # detail = 0 - return result - - except RuntimeError as e: - if "CUDA out of memory" in str(e): - print(f"CUDA out of memory error on attempt {attempt + 1}.") - if clear_cuda_cache: - print("Clearing CUDA cache and retrying...") - torch.cuda.empty_cache() - else: - print(f"Retrying in {retry_delay} seconds...") - time.sleep(retry_delay) - continue - else: - print(f"Runtime error: {e}") - break - except Exception as e: - print(f"Error detecting text: {e}") - break - - print(f"Failed to detect text after {max_retries} attempts.") - return [] - - def get_metadata(self): - """ - Returns the metadata for the Text_Detector_Tool. - - Returns: - dict: A dictionary containing the tool's metadata. - """ - metadata = super().get_metadata() - return metadata - - -if __name__ == "__main__": - import json - - # Get the directory of the current script - script_dir = os.path.dirname(os.path.abspath(__file__)) - - # Example usage of the Text_Detector_Tool - tool = text_detector() - - # Get tool metadata - metadata = tool.get_metadata() - print(metadata) - - # Construct the full path to the image using the script's directory - # relative_image_path = "examples/chinese_tra.jpg" - # relative_image_path = "examples/chinese.jpg" - relative_image_path = "examples/english.png" - image_path = os.path.join(script_dir, relative_image_path) - - # Check if the image file exists - if not os.path.exists(image_path): - print(f"Image file not found: {image_path}") - print("Please provide a valid image file in the 'examples/' directory.") - exit(1) - - # Execute the tool - try: - # execution = tool.execute(image=image_path, languages=["en", "ch_sim"]) - # execution = tool.execute(image=image_path, languages=["en", "ch_tra"]) - execution = tool.execute(image=image_path, languages=["en"]) - print(json.dumps(execution)) - - print("Detected Text:", execution) - except ValueError as e: - print(f"Execution failed: {e}") - - print("Done!") diff --git a/packages/sage-middleware/src/sage/middleware/operators/tools/url_text_extractor.py b/packages/sage-middleware/src/sage/middleware/operators/tools/url_text_extractor.py deleted file mode 100644 index a8fdc24f3b..0000000000 --- a/packages/sage-middleware/src/sage/middleware/operators/tools/url_text_extractor.py +++ /dev/null @@ -1,104 +0,0 @@ -import os - -import requests -from bs4 import BeautifulSoup - -from sage.libs.foundation.tools.tool import BaseTool - - -class URL_Text_Extractor_Tool(BaseTool): - def __init__(self): - super().__init__( - tool_name="URL_Text_Extractor_Tool", - tool_description="A tool that extracts all text from a given URL.", - input_types={ - "url": "str - The URL from which to extract text.", - }, - output_type="dict - A dictionary containing the extracted text and any error messages.", - demo_commands=[ - { - "command": 'execution = tool.execute(url="https://example.com")', - "description": "Extract all text from the example.com website.", - }, - { - "command": 'execution = tool.execute(url="https://en.wikipedia.org/wiki/Python_(programming_language)")', - "description": "Extract all text from the Wikipedia page about Python programming language.", - }, - ], - ) - self.tool_version = "1.0.0" - - def extract_text_from_url(self, url): - """ - Extracts all text from the given URL. - - Parameters: - url (str): The URL from which to extract text. - - Returns: - str: The extracted text. - """ - url = url.replace("arxiv.org/pdf", "arxiv.org/abs") - - try: - response = requests.get(url) - response.raise_for_status() - soup = BeautifulSoup(response.content, "html.parser") - text = soup.get_text(separator="\n", strip=True) - text = text[:10000] # Limit the text to 10000 characters - return text - except requests.RequestException as e: - return f"Error fetching URL: {str(e)}" - except Exception as e: - return f"Error extracting text: {str(e)}" - - def execute(self, url): - extracted_text = self.extract_text_from_url(url) - return {"url": url, "extracted_text": extracted_text} - - def get_metadata(self): - """ - Returns the metadata for the URL_Text_Extractor_Tool. - - Returns: - dict: A dictionary containing the tool's metadata. - """ - metadata = super().get_metadata() - return metadata - - -if __name__ == "__main__": - # Test command: - """ - Run the following commands in the terminal to test the script: - - cd octotools/tools/url_text_extractor - python tool.py - """ - - # Get the directory of the current script - script_dir = os.path.dirname(os.path.abspath(__file__)) - - # Example usage of the URL_Text_Extractor_Tool - tool = URL_Text_Extractor_Tool() - - # Get tool metadata - metadata = tool.get_metadata() - print(metadata) - - # Sample URL for extracting text - url = "https://intellistream.github.io/SAGE-Pub/get_start/install/" - - import json - - # Execute the tool with the sample URL - try: - execution = tool.execute(url=url) - print("Execution Result:") - print(json.dumps(execution, indent=4)) - for key, value in execution.items(): - print(f"{key}:\n{value}\n") - except ValueError as e: - print(f"Execution failed: {e}") - - print("Done!") diff --git a/packages/sage-middleware/src/sage/middleware/py.typed b/packages/sage-middleware/src/sage/middleware/py.typed deleted file mode 100644 index 7968b7f79a..0000000000 --- a/packages/sage-middleware/src/sage/middleware/py.typed +++ /dev/null @@ -1,2 +0,0 @@ -# Marker file for PEP 561 -# This indicates that the sage.service package supports type checking diff --git a/packages/sage-middleware/tests/components/sage_db/benchmark_cpp_performance.py b/packages/sage-middleware/tests/components/sage_db/benchmark_cpp_performance.py deleted file mode 100644 index 2f4587ec9e..0000000000 --- a/packages/sage-middleware/tests/components/sage_db/benchmark_cpp_performance.py +++ /dev/null @@ -1,166 +0,0 @@ -#!/usr/bin/env python3 -""" -Direct C++ performance test - bypassing Python entirely -""" - -from __future__ import annotations - -import os -import sys - -# Add parent directory to path to find _sage_db.so -sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - -import time -from concurrent.futures import ThreadPoolExecutor, as_completed -from typing import TYPE_CHECKING - -import numpy as np -import pytest - -if TYPE_CHECKING: - import _sage_db # type: ignore[import-not-found] - -try: - import _sage_db # type: ignore[import-not-found] - - SAGE_DB_AVAILABLE = True -except ImportError: - SAGE_DB_AVAILABLE = False - pytestmark = pytest.mark.skip( - reason="SageDB C++ extension not built. Run ./build.sh to enable this test." - ) - -DIMENSION = 768 -NUM_VECTORS = 10000 -NUM_QUERIES = 500 - - -def create_database(): - """Create and populate a SageDB database""" - if not SAGE_DB_AVAILABLE: - pytest.skip("SageDB not available") - print(f"Creating SageDB ({NUM_VECTORS} vectors, {DIMENSION} dims)...") - - config = _sage_db.DatabaseConfig() - config.dimension = DIMENSION - config.index_type = _sage_db.IndexType.FLAT # Correct enum value - - db = _sage_db.SageDB(config) - - # Add vectors - vectors = np.random.randn(NUM_VECTORS, DIMENSION).astype("float32") - for vec in vectors: - vec = vec / np.linalg.norm(vec) # Normalize - db.add(vec.tolist(), {}) - - print(f"✅ Database ready with {NUM_VECTORS} vectors") - return db - - -def search_worker(db, queries, worker_id): - """Worker function for multi-threaded search""" - print(f" Worker {worker_id} starting with {len(queries)} queries...") - start = time.time() - - for query in queries: - db.search(query.tolist(), k=10) - - duration = time.time() - start - qps = len(queries) / duration - print(f" Worker {worker_id}: {qps:.1f} QPS, {duration:.2f}s") - return qps, duration - - -def benchmark_single_thread(db, num_queries=NUM_QUERIES): - """Single-threaded baseline""" - print(f"\n🔍 Single-threaded benchmark ({num_queries} queries)...") - - queries = np.random.randn(num_queries, DIMENSION).astype("float32") - for i in range(len(queries)): - queries[i] = queries[i] / np.linalg.norm(queries[i]) - - start = time.time() - for query in queries: - db.search(query.tolist(), k=10) - duration = time.time() - start - - qps = num_queries / duration - print(f" QPS: {qps:.1f}, Time: {duration:.2f}s") - return qps - - -def benchmark_multi_thread(db, num_threads, queries_per_thread=NUM_QUERIES): - """Multi-threaded benchmark""" - print( - f"\n🔍 Multi-threaded benchmark ({num_threads} threads, {queries_per_thread} queries each)..." - ) - - # Prepare queries for each thread - all_queries = [] - for _ in range(num_threads): - queries = np.random.randn(queries_per_thread, DIMENSION).astype("float32") - for i in range(len(queries)): - queries[i] = queries[i] / np.linalg.norm(queries[i]) - all_queries.append(queries) - - # Run threads - start = time.time() - with ThreadPoolExecutor(max_workers=num_threads) as executor: - futures = [ - executor.submit(search_worker, db, queries, i) for i, queries in enumerate(all_queries) - ] - [f.result() for f in as_completed(futures)] - - total_time = time.time() - start - total_queries = queries_per_thread * num_threads - total_qps = total_queries / total_time - - print(f" Total QPS: {total_qps:.1f}") - print(f" Total time: {total_time:.2f}s") - print(f" Speedup: {total_qps / baseline_qps:.2f}x") - return total_qps - - -def profile_lock_contention(db): - """Profile lock contention by testing different thread counts""" - print("\n📊 Lock Contention Profile") - print("=" * 70) - - results = {} - for num_threads in [1, 2, 4, 8]: - qps = benchmark_multi_thread(db, num_threads, queries_per_thread=250) - results[num_threads] = qps - - print("\n" + "=" * 70) - print("RESULTS SUMMARY") - print("=" * 70) - print(f"{'Threads':<10} {'QPS':<15} {'Speedup':<15} {'Efficiency':<15}") - print("-" * 70) - - baseline = results[1] - for threads, qps in results.items(): - speedup = qps / baseline - efficiency = (speedup / threads) * 100 - print(f"{threads:<10} {qps:<15.1f} {speedup:<15.2f}x {efficiency:<15.1f}%") - - return results - - -if __name__ == "__main__": - print("=" * 70) - print("SageDB C++ Direct Performance Test") - print("=" * 70) - - # Create database - db = create_database() - - # Baseline - baseline_qps = benchmark_single_thread(db) - - # Profile lock contention - profile_lock_contention(db) - - print("\n" + "=" * 70) - print("TEST COMPLETE") - print("=" * 70) diff --git a/packages/sage-middleware/tests/components/sage_db/benchmark_faiss_threading.py b/packages/sage-middleware/tests/components/sage_db/benchmark_faiss_threading.py deleted file mode 100644 index a4d79dc61d..0000000000 --- a/packages/sage-middleware/tests/components/sage_db/benchmark_faiss_threading.py +++ /dev/null @@ -1,146 +0,0 @@ -#!/usr/bin/env python3 -""" -Test FAISS threading behavior to diagnose lock contention -""" - -import time -from concurrent.futures import ThreadPoolExecutor, as_completed - -import faiss -import numpy as np - -DIMENSION = 768 -NUM_VECTORS = 10000 -NUM_QUERIES = 500 - - -def create_test_index(): - """Create a simple FAISS index for testing""" - print(f"Creating FAISS IndexFlatIP ({NUM_VECTORS} vectors, {DIMENSION} dims)...") - index = faiss.IndexFlatIP(DIMENSION) - vectors = np.random.randn(NUM_VECTORS, DIMENSION).astype("float32") - faiss.normalize_L2(vectors) - index.add(vectors) # type: ignore[call-arg] - print(f"✅ Index created with {index.ntotal} vectors") - return index - - -def search_worker(index, query_vectors, thread_id): - """Worker function for multi-threaded search""" - start = time.time() - for query in query_vectors: - distances, indices = index.search(query.reshape(1, -1), k=10) - duration = time.time() - start - qps = len(query_vectors) / duration - print(f" Thread {thread_id}: {qps:.1f} QPS, {duration:.2f}s") - return qps, duration - - -def benchmark_single_thread(index): - """Benchmark single-threaded performance""" - print("\n🔍 Single-threaded benchmark...") - queries = np.random.randn(NUM_QUERIES, DIMENSION).astype("float32") - faiss.normalize_L2(queries) - - start = time.time() - for query in queries: - distances, indices = index.search(query.reshape(1, -1), k=10) - duration = time.time() - start - - qps = NUM_QUERIES / duration - print(f" QPS: {qps:.1f}, Time: {duration:.2f}s") - return qps - - -def benchmark_multi_thread(index, num_threads): - """Benchmark multi-threaded performance""" - print(f"\n🔍 Multi-threaded benchmark ({num_threads} threads)...") - - # Prepare queries for each thread - queries_per_thread = NUM_QUERIES // num_threads - all_queries = [ - np.random.randn(queries_per_thread, DIMENSION).astype("float32") for _ in range(num_threads) - ] - for queries in all_queries: - faiss.normalize_L2(queries) - - # Run threads - start = time.time() - with ThreadPoolExecutor(max_workers=num_threads) as executor: - futures = [ - executor.submit(search_worker, index, queries, i) - for i, queries in enumerate(all_queries) - ] - [f.result() for f in as_completed(futures)] - - total_time = time.time() - start - total_qps = (queries_per_thread * num_threads) / total_time - - print(f" Total QPS: {total_qps:.1f}") - print(f" Total time: {total_time:.2f}s") - return total_qps - - -def benchmark_shared_vs_clone(index, num_threads=4): - """Test if cloning the index helps performance""" - print("\n🧪 Testing shared index vs cloned indices...") - - # Test 1: Shared index - print("\n Shared index:") - shared_qps = benchmark_multi_thread(index, num_threads) - - # Test 2: Cloned indices (one per thread) - print("\n Cloned indices (one per thread):") - queries_per_thread = NUM_QUERIES // num_threads - all_queries = [ - np.random.randn(queries_per_thread, DIMENSION).astype("float32") for _ in range(num_threads) - ] - for queries in all_queries: - faiss.normalize_L2(queries) - - # Clone the index for each thread - cloned_indices = [faiss.clone_index(index) for _ in range(num_threads)] - - start = time.time() - with ThreadPoolExecutor(max_workers=num_threads) as executor: - futures = [ - executor.submit(search_worker, cloned_indices[i], queries, i) - for i, queries in enumerate(all_queries) - ] - [f.result() for f in as_completed(futures)] - - total_time = time.time() - start - cloned_qps = (queries_per_thread * num_threads) / total_time - - print(f"\n Shared index QPS: {shared_qps:.1f}") - print(f" Cloned indices QPS: {cloned_qps:.1f}") - print(f" Improvement: {(cloned_qps / shared_qps - 1) * 100:.1f}%") - - -def main(): - print("=" * 70) - print("FAISS Threading Diagnosis") - print("=" * 70) - - # Create index - index = create_test_index() - - # Test 1: Single thread baseline - baseline_qps = benchmark_single_thread(index) - - # Test 2: Multi-threaded scaling - for num_threads in [2, 4, 8]: - mt_qps = benchmark_multi_thread(index, num_threads) - speedup = mt_qps / baseline_qps - print(f" Speedup: {speedup:.2f}x") - - # Test 3: Shared vs cloned indices - benchmark_shared_vs_clone(index, num_threads=4) - - print("\n" + "=" * 70) - print("DIAGNOSIS COMPLETE") - print("=" * 70) - - -if __name__ == "__main__": - main() diff --git a/packages/sage-middleware/tests/components/sage_db/benchmark_threading_performance.py b/packages/sage-middleware/tests/components/sage_db/benchmark_threading_performance.py deleted file mode 100755 index 698a3423d5..0000000000 --- a/packages/sage-middleware/tests/components/sage_db/benchmark_threading_performance.py +++ /dev/null @@ -1,265 +0,0 @@ -#!/usr/bin/env python3 -""" -Multi-threading performance test for SageDB with GIL release. - -This script benchmarks the performance improvement from GIL release -in the Python bindings. Expected results: -- Single thread: ~120 QPS baseline -- 2 threads: ~235 QPS (1.96x) -- 4 threads: ~460 QPS (3.83x) -- 8 threads: ~480+ QPS (4.0x+) -""" - -from __future__ import annotations - -import sys -import threading -import time -from pathlib import Path -from typing import TYPE_CHECKING - -import numpy as np -import pytest - -# Add the package to path -sys.path.insert(0, str(Path(__file__).parent.parent)) - -if TYPE_CHECKING: - from sage.middleware.components.sage_db.python.sage_db import ( - DatabaseConfig, - DistanceMetric, - IndexType, - SageDB, - ) - -try: - from sage.middleware.components.sage_db.python.sage_db import ( - DatabaseConfig, - DistanceMetric, - IndexType, - SageDB, - ) - - SAGE_DB_AVAILABLE = True -except ImportError: - SAGE_DB_AVAILABLE = False - pytestmark = pytest.mark.skip( - reason="SageDB C++ extension not built. Run ./build.sh to enable this test." - ) - - -def prepare_test_database(dimension: int = 768, num_vectors: int = 10000): - """Create and populate a test database.""" - if not SAGE_DB_AVAILABLE: - pytest.skip("SageDB not available") - print(f"📊 Preparing test database ({num_vectors} vectors, {dimension} dims)...") - - config = DatabaseConfig(dimension) - config.index_type = IndexType.FLAT # Use FLAT for consistent performance - config.metric = DistanceMetric.L2 - - db = SageDB.from_config(config) - - # Add random vectors - np.random.seed(42) - vectors = np.random.rand(num_vectors, dimension).astype(np.float32) - - start = time.time() - db.add_batch(vectors.tolist()) - elapsed = time.time() - start - - print( - f"✅ Database ready. Insertion took {elapsed:.2f}s ({num_vectors / elapsed:.0f} vectors/s)" - ) - return db - - -def benchmark_single_thread(db: SageDB, num_queries: int = 1000, dimension: int = 768) -> float: - """Benchmark single-threaded search performance.""" - print(f"\n🔍 Single-threaded benchmark ({num_queries} queries)...") - - np.random.seed(123) - queries = np.random.rand(num_queries, dimension).astype(np.float32) - - start = time.time() - for query in queries: - db.search(query.tolist(), k=10) - elapsed = time.time() - start - - qps = num_queries / elapsed - print(f" QPS: {qps:.1f}") - print(f" Avg latency: {elapsed * 1000 / num_queries:.2f}ms") - - return qps - - -def benchmark_multi_thread( - db: SageDB, num_threads: int, queries_per_thread: int = 500, dimension: int = 768 -) -> tuple[float, float]: - """Benchmark multi-threaded search performance.""" - print( - f"\n🔍 Multi-threaded benchmark ({num_threads} threads, {queries_per_thread} queries/thread)..." - ) - - # Prepare queries for each thread - np.random.seed(456) - all_queries = [ - np.random.rand(queries_per_thread, dimension).astype(np.float32) for _ in range(num_threads) - ] - - results = [0.0] * num_threads - - def worker(thread_id: int, queries: np.ndarray): - """Worker function for each thread.""" - thread_start = time.time() - for query in queries: - db.search(query.tolist(), k=10) - thread_elapsed = time.time() - thread_start - results[thread_id] = thread_elapsed - - # Start all threads - threads = [] - start = time.time() - - for i in range(num_threads): - t = threading.Thread(target=worker, args=(i, all_queries[i])) - threads.append(t) - t.start() - - # Wait for all threads - for t in threads: - t.join() - - total_elapsed = time.time() - start - total_queries = num_threads * queries_per_thread - total_qps = total_queries / total_elapsed - - # Calculate per-thread stats - avg_thread_time = sum(results) / len(results) - per_thread_qps = queries_per_thread / avg_thread_time - - print(f" Total QPS: {total_qps:.1f}") - print(f" Per-thread QPS: {per_thread_qps:.1f}") - print(f" Total time: {total_elapsed:.2f}s") - print(f" Avg thread time: {avg_thread_time:.2f}s") - - return total_qps, per_thread_qps - - -def benchmark_batch_search( - db: SageDB, batch_size: int = 100, num_batches: int = 10, dimension: int = 768 -) -> float: - """Benchmark batch search performance.""" - print(f"\n🔍 Batch search benchmark ({num_batches} batches × {batch_size} queries)...") - - np.random.seed(789) - - total_time = 0.0 - total_queries = 0 - - for i in range(num_batches): - queries = np.random.rand(batch_size, dimension).astype(np.float32) - - start = time.time() - db.batch_search(queries.tolist(), k=10) - elapsed = time.time() - start - - total_time += elapsed - total_queries += batch_size - - if i == 0: - print(f" First batch: {batch_size / elapsed:.1f} QPS") - - avg_qps = total_queries / total_time - print(f" Average QPS: {avg_qps:.1f}") - print(f" Total queries: {total_queries}") - - return avg_qps - - -def main(): - """Run all benchmarks.""" - print("=" * 70) - print("SageDB Multi-Threading Performance Benchmark") - print("Testing GIL Release Implementation") - print("=" * 70) - - # Configuration - DIMENSION = 768 - NUM_VECTORS = 10000 - NUM_QUERIES = 1000 - QUERIES_PER_THREAD = 500 - - # Prepare database - db = prepare_test_database(DIMENSION, NUM_VECTORS) - - # Benchmark 1: Single-threaded baseline - baseline_qps = benchmark_single_thread(db, NUM_QUERIES, DIMENSION) - - # Benchmark 2: Multi-threaded scaling - results = {} - for num_threads in [2, 4, 8]: - total_qps, per_thread_qps = benchmark_multi_thread( - db, num_threads, QUERIES_PER_THREAD, DIMENSION - ) - results[num_threads] = { - "total_qps": total_qps, - "per_thread_qps": per_thread_qps, - "speedup": total_qps / baseline_qps, - } - - # Benchmark 3: Batch search (TODO: fix parameter binding) - # batch_qps = benchmark_batch_search(db, batch_size=100, num_batches=10, dimension=DIMENSION) - batch_qps = 0 # placeholder - - # Summary - print("\n" + "=" * 70) - print("PERFORMANCE SUMMARY") - print("=" * 70) - print(f"\nBaseline (1 thread): {baseline_qps:>8.1f} QPS") - print("\nMulti-threaded Performance:") - print(f"{'Threads':<10} {'Total QPS':<12} {'Speedup':<12} {'Per-Thread QPS':<15}") - print("-" * 50) - - for num_threads, data in results.items(): - speedup = data["speedup"] - efficiency = (speedup / num_threads) * 100 - print( - f"{num_threads:<10} {data['total_qps']:<12.1f} {speedup:<12.2f}x {data['per_thread_qps']:<15.1f}" - ) - print(f" Parallel efficiency: {efficiency:.1f}%") - - print(f"\nBatch Search: {batch_qps:>8.1f} QPS") - print(f" {batch_qps / baseline_qps:>8.2f}x speedup") - - # Analysis - print("\n" + "=" * 70) - print("ANALYSIS") - print("=" * 70) - - if results[8]["speedup"] >= 3.5: - print("✅ EXCELLENT: GIL release is working correctly!") - print(" Performance scales well with thread count.") - if results[8]["speedup"] >= 7.0: - print(" 🚀 Near-linear scaling achieved!") - elif results[8]["speedup"] >= 2.0: - print("⚠️ GOOD: GIL is released, but some contention exists.") - print(" Consider implementing lock-free architecture for better scaling.") - else: - print("❌ POOR: Limited multi-threading benefit detected.") - print(" Check if GIL is actually being released.") - - print("\nNext Steps:") - if results[8]["speedup"] < 7.0: - print(" 1. Implement Phase 1: Basic thread safety with shared_mutex") - print(" 2. Implement Phase 3: Lock-free architecture") - print(" 3. Add OpenMP to batch_search for internal parallelization") - else: - print(" 1. Phase 2 (GIL Release) ✅ Complete") - print(" 2. Ready for Phase 3 (Lock-Free Architecture)") - - print("=" * 70) - - -if __name__ == "__main__": - main() diff --git a/packages/sage-middleware/tests/components/sage_db/test_multimodal_sage_db.py b/packages/sage-middleware/tests/components/sage_db/test_multimodal_sage_db.py deleted file mode 100644 index d93d8f8d22..0000000000 --- a/packages/sage-middleware/tests/components/sage_db/test_multimodal_sage_db.py +++ /dev/null @@ -1,468 +0,0 @@ -""" -Tests for Multimodal SAGE DB (multimodal_sage_db.py). - -This module tests the multimodal fusion and search capabilities of SAGE DB. -""" - -import numpy as np -import pytest - -# Try to import multimodal_sage_db components -try: - from sage.middleware.components.sage_db.python.multimodal_sage_db import ( - FusionParams, - FusionStrategy, - ModalData, - ModalityType, - MultimodalData, - MultimodalSageDB, - MultimodalSearchParams, - QueryResult, - create_audio_visual_db, - create_text_image_db, - ) - - MULTIMODAL_AVAILABLE = True -except ImportError: - MULTIMODAL_AVAILABLE = False - - -@pytest.mark.skipif(not MULTIMODAL_AVAILABLE, reason="Multimodal SAGE DB not available") -class TestModalityType: - """Test ModalityType enumeration.""" - - def test_modality_types_exist(self): - """Test that all expected modality types exist.""" - expected_types = ["TEXT", "IMAGE", "AUDIO", "VIDEO", "TABULAR", "TIME_SERIES", "CUSTOM"] - - for type_name in expected_types: - assert hasattr(ModalityType, type_name) - - def test_modality_type_values(self): - """Test modality type values.""" - assert ModalityType.TEXT.value == 0 - assert ModalityType.IMAGE.value == 1 - assert ModalityType.AUDIO.value == 2 - - -@pytest.mark.skipif(not MULTIMODAL_AVAILABLE, reason="Multimodal SAGE DB not available") -class TestFusionStrategy: - """Test FusionStrategy enumeration.""" - - def test_fusion_strategies_exist(self): - """Test that all expected fusion strategies exist.""" - expected_strategies = [ - "CONCATENATION", - "WEIGHTED_AVERAGE", - "ATTENTION_BASED", - "CROSS_MODAL_TRANSFORMER", - "TENSOR_FUSION", - "BILINEAR_POOLING", - "CUSTOM", - ] - - for strategy_name in expected_strategies: - assert hasattr(FusionStrategy, strategy_name) - - def test_fusion_strategy_values(self): - """Test fusion strategy values.""" - assert FusionStrategy.CONCATENATION.value == 0 - assert FusionStrategy.WEIGHTED_AVERAGE.value == 1 - - -@pytest.mark.skipif(not MULTIMODAL_AVAILABLE, reason="Multimodal SAGE DB not available") -class TestModalData: - """Test ModalData class.""" - - def test_create_modal_data(self): - """Test creating modal data.""" - embedding = np.random.randn(128).astype(np.float32) - metadata = {"key": "value"} - - modal_data = ModalData( - modality_type=ModalityType.TEXT, embedding=embedding, metadata=metadata - ) - - assert modal_data.type == ModalityType.TEXT - assert modal_data.embedding.shape == (128,) - assert modal_data.metadata == metadata - - def test_modal_data_without_metadata(self): - """Test creating modal data without metadata.""" - embedding = np.random.randn(64).astype(np.float32) - - modal_data = ModalData(modality_type=ModalityType.IMAGE, embedding=embedding) - - assert modal_data.metadata == {} - - def test_modal_data_with_raw_data(self): - """Test creating modal data with raw data.""" - embedding = np.random.randn(256).astype(np.float32) - raw_data = b"sample raw data" - - modal_data = ModalData( - modality_type=ModalityType.AUDIO, embedding=embedding, raw_data=raw_data - ) - - assert modal_data.raw_data == raw_data - - def test_embedding_dtype_conversion(self): - """Test that embeddings are converted to float32.""" - embedding = np.random.randn(100).astype(np.float64) - - modal_data = ModalData(modality_type=ModalityType.TEXT, embedding=embedding) - - assert modal_data.embedding.dtype == np.float32 - - -@pytest.mark.skipif(not MULTIMODAL_AVAILABLE, reason="Multimodal SAGE DB not available") -class TestMultimodalData: - """Test MultimodalData class.""" - - def test_create_multimodal_data(self): - """Test creating multimodal data.""" - data = MultimodalData(data_id=123) - - assert data.id == 123 - assert len(data.modalities) == 0 - - def test_add_modality(self): - """Test adding modalities to multimodal data.""" - data = MultimodalData() - - text_embedding = np.random.randn(768).astype(np.float32) - text_modal = ModalData(ModalityType.TEXT, text_embedding) - - data.add_modality(text_modal) - - assert len(data.modalities) == 1 - assert ModalityType.TEXT in data.modalities - - def test_add_multiple_modalities(self): - """Test adding multiple modalities.""" - data = MultimodalData() - - text_modal = ModalData(ModalityType.TEXT, np.random.randn(768).astype(np.float32)) - image_modal = ModalData(ModalityType.IMAGE, np.random.randn(2048).astype(np.float32)) - audio_modal = ModalData(ModalityType.AUDIO, np.random.randn(512).astype(np.float32)) - - data.add_modality(text_modal) - data.add_modality(image_modal) - data.add_modality(audio_modal) - - assert len(data.modalities) == 3 - - def test_get_modality(self): - """Test getting specific modality.""" - data = MultimodalData() - - text_embedding = np.random.randn(768).astype(np.float32) - text_modal = ModalData(ModalityType.TEXT, text_embedding) - data.add_modality(text_modal) - - retrieved = data.get_modality(ModalityType.TEXT) - - assert retrieved is not None - assert retrieved.type == ModalityType.TEXT - - def test_get_nonexistent_modality(self): - """Test getting a modality that doesn't exist.""" - data = MultimodalData() - - retrieved = data.get_modality(ModalityType.VIDEO) - - assert retrieved is None - - def test_global_metadata(self): - """Test global metadata.""" - data = MultimodalData() - data.global_metadata = {"source": "test", "timestamp": "2024-01-01"} - - assert data.global_metadata["source"] == "test" - - -@pytest.mark.skipif(not MULTIMODAL_AVAILABLE, reason="Multimodal SAGE DB not available") -class TestFusionParams: - """Test FusionParams class.""" - - def test_create_fusion_params(self): - """Test creating fusion parameters.""" - params = FusionParams(FusionStrategy.WEIGHTED_AVERAGE) - - assert params.strategy == FusionStrategy.WEIGHTED_AVERAGE - assert params.target_dimension == 512 - - def test_default_modality_weights(self): - """Test default modality weights.""" - params = FusionParams() - - assert params.modality_weights[ModalityType.TEXT] == 0.4 - assert params.modality_weights[ModalityType.IMAGE] == 0.3 - - def test_custom_params(self): - """Test custom parameters.""" - params = FusionParams() - params.custom_params = {"learning_rate": 0.001, "temperature": 0.5} - - assert params.custom_params["learning_rate"] == 0.001 - - -@pytest.mark.skipif(not MULTIMODAL_AVAILABLE, reason="Multimodal SAGE DB not available") -class TestMultimodalSearchParams: - """Test MultimodalSearchParams class.""" - - def test_create_search_params(self): - """Test creating search parameters.""" - params = MultimodalSearchParams(k=10) - - assert params.k == 10 - assert params.include_metadata - - def test_target_modalities(self): - """Test target modalities.""" - params = MultimodalSearchParams() - params.target_modalities = [ModalityType.TEXT, ModalityType.IMAGE] - - assert len(params.target_modalities) == 2 - - def test_cross_modal_search_flag(self): - """Test cross-modal search flag.""" - params = MultimodalSearchParams() - params.use_cross_modal_search = True - - assert params.use_cross_modal_search - - -@pytest.mark.skipif(not MULTIMODAL_AVAILABLE, reason="Multimodal SAGE DB not available") -class TestQueryResult: - """Test QueryResult class.""" - - def test_create_query_result(self): - """Test creating query result.""" - result = QueryResult(data_id=42, score=0.95) - - assert result.id == 42 - assert result.score == 0.95 - - def test_query_result_with_metadata(self): - """Test query result with metadata.""" - metadata = {"category": "test", "source": "db"} - result = QueryResult(data_id=10, score=0.88, metadata=metadata) - - assert result.metadata == metadata - - -@pytest.mark.skipif(not MULTIMODAL_AVAILABLE, reason="Multimodal SAGE DB not available") -class TestMultimodalSageDB: - """Test MultimodalSageDB class.""" - - @pytest.fixture - def sample_db(self): - """Create a sample multimodal database.""" - config = { - "dimension": 512, - "index_type": "FLAT", - "fusion_strategy": FusionStrategy.WEIGHTED_AVERAGE.value, - "enable_modality_indexing": True, - "max_modalities_per_item": 4, - } - db = MultimodalSageDB(config) - return db - - def test_create_multimodal_db(self): - """Test creating multimodal database.""" - config = {"dimension": 256, "index_type": "FLAT", "fusion_strategy": 1} - db = MultimodalSageDB(config) - - assert db.dimension == 256 - - def test_add_multimodal_data(self, sample_db): - """Test adding multimodal data.""" - data = MultimodalData() - - text_modal = ModalData(ModalityType.TEXT, np.random.randn(768).astype(np.float32)) - image_modal = ModalData(ModalityType.IMAGE, np.random.randn(2048).astype(np.float32)) - - data.add_modality(text_modal) - data.add_modality(image_modal) - - data_id = sample_db.add_multimodal(data) - - assert isinstance(data_id, int) - assert data_id > 0 - - def test_add_from_embeddings(self, sample_db): - """Test adding data from embeddings dictionary.""" - embeddings = { - ModalityType.TEXT: np.random.randn(768).astype(np.float32), - ModalityType.IMAGE: np.random.randn(2048).astype(np.float32), - } - metadata = {"category": "test"} - - data_id = sample_db.add_from_embeddings(embeddings, metadata) - - assert isinstance(data_id, int) - - def test_search_multimodal(self, sample_db): - """Test multimodal search.""" - # Add some data first - for i in range(5): - embeddings = { - ModalityType.TEXT: np.random.randn(768).astype(np.float32), - ModalityType.IMAGE: np.random.randn(2048).astype(np.float32), - } - sample_db.add_from_embeddings(embeddings, {"id": str(i)}) - - # Search - query_modalities = {ModalityType.TEXT: np.random.randn(768).astype(np.float32)} - params = MultimodalSearchParams(k=3) - - results = sample_db.search_multimodal(query_modalities, params) - - assert len(results) <= 3 - - def test_cross_modal_search(self, sample_db): - """Test cross-modal search.""" - # Add multimodal data - for i in range(5): - embeddings = { - ModalityType.TEXT: np.random.randn(768).astype(np.float32), - ModalityType.IMAGE: np.random.randn(2048).astype(np.float32), - } - sample_db.add_from_embeddings(embeddings) - - # Cross-modal search: query with text, find images - query_embedding = np.random.randn(768).astype(np.float32) - params = MultimodalSearchParams(k=3) - - results = sample_db.cross_modal_search( - ModalityType.TEXT, query_embedding, [ModalityType.IMAGE], params - ) - - assert isinstance(results, list) - - def test_get_modality_statistics(self, sample_db): - """Test getting modality statistics.""" - # Add some data - embeddings = { - ModalityType.TEXT: np.random.randn(768).astype(np.float32), - ModalityType.IMAGE: np.random.randn(2048).astype(np.float32), - } - sample_db.add_from_embeddings(embeddings) - - stats = sample_db.get_modality_statistics() - - assert isinstance(stats, dict) - - def test_update_fusion_params(self, sample_db): - """Test updating fusion parameters.""" - new_params = FusionParams(FusionStrategy.ATTENTION_BASED) - new_params.target_dimension = 1024 - - sample_db.update_fusion_params(new_params) - - assert sample_db.fusion_params.strategy == FusionStrategy.ATTENTION_BASED - - -@pytest.mark.skipif(not MULTIMODAL_AVAILABLE, reason="Multimodal SAGE DB not available") -class TestConvenienceFunctions: - """Test convenience functions for creating multimodal databases.""" - - def test_create_text_image_db(self): - """Test creating text-image database.""" - db = create_text_image_db(dimension=512) - - assert db.dimension == 512 - assert db.fusion_params.strategy == FusionStrategy.WEIGHTED_AVERAGE - - def test_create_text_image_db_custom_dimension(self): - """Test creating text-image database with custom dimension.""" - db = create_text_image_db(dimension=768) - - assert db.dimension == 768 - - def test_create_audio_visual_db(self): - """Test creating audio-visual database.""" - db = create_audio_visual_db(dimension=1024) - - assert db.dimension == 1024 - assert db.fusion_params.strategy == FusionStrategy.ATTENTION_BASED - - -@pytest.mark.skipif(not MULTIMODAL_AVAILABLE, reason="Multimodal SAGE DB not available") -class TestMultimodalIntegration: - """Integration tests for multimodal functionality.""" - - def test_end_to_end_workflow(self): - """Test complete workflow from creation to search.""" - # 1. Create database - db = create_text_image_db(dimension=512) - - # 2. Add multiple items - for i in range(10): - embeddings = { - ModalityType.TEXT: np.random.randn(768).astype(np.float32), - ModalityType.IMAGE: np.random.randn(2048).astype(np.float32), - } - metadata = {"item_id": str(i), "category": f"cat_{i % 3}"} - db.add_from_embeddings(embeddings, metadata) - - # 3. Search - query_embeddings = {ModalityType.TEXT: np.random.randn(768).astype(np.float32)} - params = MultimodalSearchParams(k=5) - results = db.search_multimodal(query_embeddings, params) - - assert len(results) > 0 - assert len(results) <= 5 - - def test_different_modality_combinations(self): - """Test with different modality combinations.""" - db = MultimodalSageDB( - {"dimension": 512, "fusion_strategy": FusionStrategy.WEIGHTED_AVERAGE.value} - ) - - # Text only - db.add_from_embeddings({ModalityType.TEXT: np.random.randn(768).astype(np.float32)}) - - # Image only - db.add_from_embeddings({ModalityType.IMAGE: np.random.randn(2048).astype(np.float32)}) - - # Text + Image + Audio - db.add_from_embeddings( - { - ModalityType.TEXT: np.random.randn(768).astype(np.float32), - ModalityType.IMAGE: np.random.randn(2048).astype(np.float32), - ModalityType.AUDIO: np.random.randn(512).astype(np.float32), - } - ) - - # Search should work with any combination - query = {ModalityType.TEXT: np.random.randn(768).astype(np.float32)} - params = MultimodalSearchParams(k=3) - results = db.search_multimodal(query, params) - - assert isinstance(results, list) - - def test_similarity_calculation(self): - """Test internal similarity calculation.""" - db = MultimodalSageDB( - {"dimension": 512, "fusion_strategy": FusionStrategy.WEIGHTED_AVERAGE.value} - ) - - # Add known vectors - embedding1 = np.ones(128, dtype=np.float32) - embedding1 /= np.linalg.norm(embedding1) - - db.add_from_embeddings({ModalityType.TEXT: embedding1}, {"id": "1"}) - - # Search with similar vector - query = {ModalityType.TEXT: embedding1.copy()} - params = MultimodalSearchParams(k=1) - results = db.search_multimodal(query, params) - - # Should find the item we added - assert len(results) > 0 - - -if __name__ == "__main__": - pytest.main([__file__, "-v"]) diff --git a/packages/sage-middleware/tests/components/sage_db/test_sage_db.py b/packages/sage-middleware/tests/components/sage_db/test_sage_db.py deleted file mode 100644 index 30d592f972..0000000000 --- a/packages/sage-middleware/tests/components/sage_db/test_sage_db.py +++ /dev/null @@ -1,485 +0,0 @@ -""" -Tests for SAGE DB Python wrapper (sage_db.py). - -This module tests the Python interface to the SageDB C++ vector database. -""" - -import os -import tempfile - -import numpy as np -import pytest - -# Try to import sage_db components -try: - from sage.middleware.components.sage_db.python.sage_db import ( - DatabaseConfig, - DistanceMetric, - IndexType, - SearchParams, - create_database, - create_database_from_config, - ) - - SAGE_DB_AVAILABLE = True -except ImportError: - SAGE_DB_AVAILABLE = False - -try: - from sage.middleware.components.sage_db.python.sage_db import SageDBException # noqa: F401 - - SAGE_DB_EXCEPTION_AVAILABLE = True -except ImportError: - SAGE_DB_EXCEPTION_AVAILABLE = False - - -@pytest.fixture -def temp_dir(): - """Create a temporary directory for test files.""" - with tempfile.TemporaryDirectory() as tmpdir: - yield tmpdir - - -@pytest.mark.skipif(not SAGE_DB_AVAILABLE, reason="SAGE DB not available") -class TestSageDBBasic: - """Basic tests for SageDB functionality.""" - - @pytest.fixture - def sample_db(self): - """Create a sample database for testing.""" - dimension = 128 - db = create_database(dimension, IndexType.FLAT, DistanceMetric.L2) - return db - - @pytest.fixture - def populated_db(self): - """Create a database with some data.""" - dimension = 64 - db = create_database(dimension, IndexType.FLAT, DistanceMetric.L2) - - # Add some vectors - for i in range(10): - vector = np.random.randn(dimension).astype(np.float32) - metadata = {"id": str(i), "category": f"cat_{i % 3}"} - db.add(vector, metadata) - - return db - - def test_create_database(self): - """Test database creation.""" - dimension = 128 - db = create_database(dimension, IndexType.FLAT, DistanceMetric.L2) - - assert db is not None - assert db.dimension == dimension - assert db.size == 0 - - def test_create_database_different_metrics(self): - """Test database creation with different distance metrics.""" - dimension = 64 - - # Test L2 - db_l2 = create_database(dimension, IndexType.FLAT, DistanceMetric.L2) - assert db_l2.dimension == dimension - - # Test COSINE if available - try: - db_cosine = create_database(dimension, IndexType.FLAT, DistanceMetric.COSINE) - assert db_cosine.dimension == dimension - except AttributeError: - # COSINE metric not available in this build - pytest.skip("COSINE metric not available") - - def test_create_database_from_config(self): - """Test database creation from config.""" - config = DatabaseConfig() - config.dimension = 128 - config.index_type = IndexType.FLAT - config.metric = DistanceMetric.L2 - - db = create_database_from_config(config) - assert db.dimension == 128 - - def test_add_vector_list(self, sample_db): - """Test adding a vector as a list.""" - vector = [0.1] * 128 - metadata = {"key": "value"} - - vector_id = sample_db.add(vector, metadata) - - assert isinstance(vector_id, int) - assert vector_id >= 0 - assert sample_db.size == 1 - - def test_add_vector_numpy(self, sample_db): - """Test adding a vector as numpy array.""" - vector = np.random.randn(128).astype(np.float32) - metadata = {"key": "value"} - - vector_id = sample_db.add(vector, metadata) - - assert isinstance(vector_id, int) - assert sample_db.size == 1 - - def test_add_batch_numpy(self, sample_db): - """Test batch adding vectors as numpy array.""" - vectors = np.random.randn(10, 128).astype(np.float32) - metadata = [{"id": str(i)} for i in range(10)] - - ids = sample_db.add_batch(vectors, metadata) - - assert len(ids) == 10 - assert sample_db.size == 10 - - def test_add_batch_list(self, sample_db): - """Test batch adding vectors as list.""" - vectors = [[0.1] * 128 for _ in range(5)] - metadata = [{"id": str(i)} for i in range(5)] - - ids = sample_db.add_batch(vectors, metadata) - - assert len(ids) == 5 - assert sample_db.size == 5 - - def test_search_basic(self, populated_db): - """Test basic vector search.""" - query = np.random.randn(64).astype(np.float32) - - results = populated_db.search(query, k=5) - - assert len(results) <= 5 - assert len(results) > 0 - - # Check result structure - for result in results: - assert hasattr(result, "id") - assert hasattr(result, "score") # Changed from distance to score - - def test_search_numpy(self, populated_db): - """Test search with numpy array.""" - query = np.random.randn(64).astype(np.float32) - - results = populated_db.search(query, k=3) - - assert len(results) <= 3 - - def test_search_with_params(self, populated_db): - """Test search with SearchParams object.""" - query = np.random.randn(64).astype(np.float32) - params = SearchParams(k=5) - - results = populated_db.search_with_params(query, params) - - assert len(results) <= 5 - - def test_metadata_operations(self, sample_db): - """Test metadata set and get.""" - vector = np.random.randn(128).astype(np.float32) - metadata = {"category": "test", "value": "123"} - - vector_id = sample_db.add(vector, metadata) - - # Get metadata - retrieved_metadata = sample_db.get_metadata(vector_id) - assert retrieved_metadata is not None - assert retrieved_metadata.get("category") == "test" - - # Update metadata - new_metadata = {"category": "updated", "new_field": "xyz"} - success = sample_db.set_metadata(vector_id, new_metadata) - assert success - - def test_find_by_metadata(self, populated_db): - """Test finding vectors by metadata.""" - # Find all vectors in category "cat_0" - ids = populated_db.find_by_metadata("category", "cat_0") - - assert isinstance(ids, list) - # Should have approximately 1/3 of the 10 vectors - assert len(ids) > 0 - - def test_database_size(self, populated_db): - """Test database size property.""" - assert populated_db.size == 10 - - def test_database_dimension(self, sample_db): - """Test database dimension property.""" - assert sample_db.dimension == 128 - - def test_database_index_type(self, sample_db): - """Test database index type property.""" - index_type = sample_db.index_type - assert index_type == IndexType.FLAT - - -@pytest.mark.skipif(not SAGE_DB_AVAILABLE, reason="SAGE DB not available") -class TestSageDBAdvanced: - """Advanced tests for SageDB functionality.""" - - @pytest.fixture - def ivf_db(self): - """Create an IVF index database.""" - dimension = 64 - try: - db = create_database(dimension, IndexType.IVF_FLAT, DistanceMetric.L2) - return db - except Exception: - pytest.skip("IVF index not available") - - def test_build_index(self): - """Test index building.""" - dimension = 64 - db = create_database(dimension, IndexType.FLAT, DistanceMetric.L2) - - # Add some vectors - vectors = np.random.randn(20, dimension).astype(np.float32) - db.add_batch(vectors) - - # Build index - db.build_index() - # If no exception, test passes - - def test_train_index(self): - """Test index training.""" - dimension = 64 - # Use IVF index which requires training, not FLAT - db = create_database(dimension, IndexType.IVF_FLAT, DistanceMetric.L2) - - # Add training vectors - training_vectors = np.random.randn(100, dimension).astype(np.float32) - - db.train_index(training_vectors) - - # Check if trained (may return False if training not required/supported) - # This depends on the underlying implementation - # Just verify no exception was raised - final_trained = db.is_trained() - assert isinstance(final_trained, bool) - - def test_is_trained(self): - """Test is_trained property.""" - dimension = 64 - db = create_database(dimension, IndexType.FLAT, DistanceMetric.L2) - - # FLAT index is always "trained" - trained = db.is_trained() - assert isinstance(trained, bool) - - def test_filtered_search(self): - """Test filtered search with custom filter function.""" - dimension = 64 - db = create_database(dimension, IndexType.FLAT, DistanceMetric.L2) - - # Add vectors with metadata - for i in range(10): - vector = np.random.randn(dimension).astype(np.float32) - metadata = {"id": str(i), "even": str(i % 2 == 0)} - db.add(vector, metadata) - - # Filter function to only get even IDs - def filter_even(metadata): - return metadata.get("even") == "True" - - query = np.random.randn(dimension).astype(np.float32) - params = SearchParams(k=10) - - results = db.filtered_search(query, params, filter_even) - - # All results should have even=True - for result in results: - if hasattr(result, "metadata"): - assert result.metadata.get("even") == "True" - - def test_search_by_metadata(self): - """Test search with metadata filtering.""" - dimension = 64 - db = create_database(dimension, IndexType.FLAT, DistanceMetric.L2) - - # Add vectors - for i in range(10): - vector = np.random.randn(dimension).astype(np.float32) - metadata = {"category": f"cat_{i % 3}"} - db.add(vector, metadata) - - query = np.random.randn(dimension).astype(np.float32) - params = SearchParams(k=5) - - results = db.search_by_metadata(query, params, "category", "cat_0") - - # Results should only be from cat_0 - assert len(results) > 0 - - def test_hybrid_search(self): - """Test hybrid search (vector + text).""" - dimension = 64 - db = create_database(dimension, IndexType.FLAT, DistanceMetric.L2) - - # Add vectors - for i in range(10): - vector = np.random.randn(dimension).astype(np.float32) - metadata = {"text": f"document_{i}"} - db.add(vector, metadata) - - query = np.random.randn(dimension).astype(np.float32) - params = SearchParams(k=5) - - try: - results = db.hybrid_search( - query, params, text_query="document", vector_weight=0.7, text_weight=0.3 - ) - assert len(results) > 0 - except Exception: - # Hybrid search might not be fully implemented - pytest.skip("Hybrid search not available") - - def test_get_search_stats(self): - """Test getting search statistics.""" - dimension = 64 - db = create_database(dimension, IndexType.FLAT, DistanceMetric.L2) - - # Add and search - vectors = np.random.randn(10, dimension).astype(np.float32) - db.add_batch(vectors) - - query = np.random.randn(dimension).astype(np.float32) - db.search(query, k=5) - - try: - stats = db.get_search_stats() - - assert isinstance(stats, dict) - # Check for expected keys - expected_keys = ["total_candidates", "final_results", "search_time_ms"] - for key in expected_keys: - if key in stats: - assert isinstance(stats[key], (int, float)) - except Exception: - # Stats might not be available - pytest.skip("Search stats not available") - - -@pytest.mark.skipif(not SAGE_DB_AVAILABLE, reason="SAGE DB not available") -class TestSageDBPersistence: - """Tests for database persistence (save/load).""" - - def test_save_and_load(self, temp_dir): - """Test saving and loading database.""" - dimension = 64 - db = create_database(dimension, IndexType.FLAT, DistanceMetric.L2) - - # Add some vectors - vectors = np.random.randn(10, dimension).astype(np.float32) - metadata = [{"id": str(i)} for i in range(10)] - db.add_batch(vectors, metadata) - - # Save database - filepath = os.path.join(temp_dir, "test_db.sage") - db.save(filepath) - - # Create new database and load - db2 = create_database(dimension, IndexType.FLAT, DistanceMetric.L2) - db2.load(filepath) - - # Verify loaded database - assert db2.size == 10 - assert db2.dimension == dimension - - def test_save_empty_database(self, temp_dir): - """Test saving an empty database.""" - dimension = 64 - db = create_database(dimension, IndexType.FLAT, DistanceMetric.L2) - - filepath = os.path.join(temp_dir, "empty_db.sage") - - try: - db.save(filepath) - # File should be created if save succeeds - # Some implementations may not create files for empty databases - # so we just verify no exception was raised - except Exception: - # Some implementations may not support saving empty databases - pytest.skip("Save empty database not supported by this implementation") - - -@pytest.mark.skipif(not SAGE_DB_AVAILABLE, reason="SAGE DB not available") -class TestSageDBEdgeCases: - """Edge case tests for SageDB.""" - - def test_search_empty_database(self): - """Test searching in an empty database.""" - dimension = 64 - db = create_database(dimension, IndexType.FLAT, DistanceMetric.L2) - - query = np.random.randn(dimension).astype(np.float32) - results = db.search(query, k=5) - - # Should return empty results - assert len(results) == 0 - - def test_add_without_metadata(self): - """Test adding vector without metadata.""" - dimension = 64 - db = create_database(dimension, IndexType.FLAT, DistanceMetric.L2) - - vector = np.random.randn(dimension).astype(np.float32) - vector_id = db.add(vector, None) - - assert isinstance(vector_id, int) - assert db.size == 1 - - def test_large_batch_add(self): - """Test adding a large batch of vectors.""" - dimension = 64 - db = create_database(dimension, IndexType.FLAT, DistanceMetric.L2) - - # Large batch - num_vectors = 1000 - vectors = np.random.randn(num_vectors, dimension).astype(np.float32) - - ids = db.add_batch(vectors) - - assert len(ids) == num_vectors - assert db.size == num_vectors - - def test_high_k_search(self): - """Test search with k larger than database size.""" - dimension = 64 - db = create_database(dimension, IndexType.FLAT, DistanceMetric.L2) - - # Add only 5 vectors - vectors = np.random.randn(5, dimension).astype(np.float32) - db.add_batch(vectors) - - # Search with k=10 - query = np.random.randn(dimension).astype(np.float32) - results = db.search(query, k=10) - - # Should return only 5 results - assert len(results) == 5 - - -@pytest.mark.skipif(not SAGE_DB_AVAILABLE, reason="SAGE DB not available") -class TestSageDBImportExport: - """Test import and export functionality.""" - - def test_all_exports(self): - """Test that all expected symbols are exported.""" - from sage.middleware.components.sage_db.python import sage_db - - expected_exports = [ - "SageDB", - "IndexType", - "DistanceMetric", - "QueryResult", - "SearchParams", - "DatabaseConfig", - "create_database", - "create_database_from_config", - ] - - for export in expected_exports: - assert hasattr(sage_db, export), f"Missing export: {export}" - - -if __name__ == "__main__": - pytest.main([__file__, "-v"]) diff --git a/packages/sage-middleware/tests/components/sage_db/test_sage_db_service.py b/packages/sage-middleware/tests/components/sage_db/test_sage_db_service.py deleted file mode 100644 index 6fa398bdbd..0000000000 --- a/packages/sage-middleware/tests/components/sage_db/test_sage_db_service.py +++ /dev/null @@ -1,402 +0,0 @@ -""" -Tests for SAGE DB Service wrapper (sage_db_service.py). - -This module tests the microservice-style wrapper for SAGE DB. -""" - -import numpy as np -import pytest - -# Try to import sage_db_service components -try: - from sage.middleware.components.sage_db.python.micro_service.sage_db_service import ( - SageDBService, - SageDBServiceConfig, - ) - - SAGE_DB_SERVICE_AVAILABLE = True -except ImportError: - SAGE_DB_SERVICE_AVAILABLE = False - - -@pytest.mark.skipif(not SAGE_DB_SERVICE_AVAILABLE, reason="SAGE DB Service not available") -class TestSageDBServiceConfig: - """Test SageDBServiceConfig dataclass.""" - - def test_create_default_config(self): - """Test creating default configuration.""" - config = SageDBServiceConfig() - - assert config.dimension == 4 - assert config.index_type == "AUTO" - - def test_create_custom_config(self): - """Test creating custom configuration.""" - config = SageDBServiceConfig(dimension=128, index_type="FLAT") - - assert config.dimension == 128 - assert config.index_type == "FLAT" - - -@pytest.mark.skipif(not SAGE_DB_SERVICE_AVAILABLE, reason="SAGE DB Service not available") -class TestSageDBServiceBasic: - """Basic tests for SageDBService.""" - - @pytest.fixture - def service(self): - """Create a service instance for testing.""" - return SageDBService(dimension=64, index_type="FLAT") - - def test_create_service(self): - """Test creating service instance.""" - service = SageDBService(dimension=128, index_type="FLAT") - - assert service is not None - assert service._dim == 128 - - def test_create_service_with_auto_index(self): - """Test creating service with AUTO index type.""" - service = SageDBService(dimension=64, index_type="AUTO") - - assert service._dim == 64 - - def test_create_service_default(self): - """Test creating service with default parameters.""" - service = SageDBService() - - assert service._dim == 4 - - def test_add_vector_numpy(self, service): - """Test adding a single vector as numpy array.""" - vector = np.random.randn(64).astype(np.float32) - metadata = {"key": "value", "id": "123"} - - vector_id = service.add(vector, metadata) - - assert isinstance(vector_id, int) - assert vector_id >= 0 - - def test_add_vector_list(self, service): - """Test adding a single vector as list.""" - vector = [0.1] * 64 - metadata = {"type": "test"} - - vector_id = service.add(vector, metadata) - - assert isinstance(vector_id, int) - - def test_add_vector_without_metadata(self, service): - """Test adding vector without metadata.""" - vector = np.random.randn(64).astype(np.float32) - - vector_id = service.add(vector) - - assert isinstance(vector_id, int) - - def test_add_batch_numpy(self, service): - """Test adding batch of vectors as numpy array.""" - vectors = np.random.randn(10, 64).astype(np.float32) - metadata_list = [{"id": str(i)} for i in range(10)] - - ids = service.add_batch(vectors, metadata_list) - - assert len(ids) == 10 - assert all(isinstance(vid, int) for vid in ids) - - def test_add_batch_list(self, service): - """Test adding batch of vectors as list.""" - vectors = [[0.1] * 64 for _ in range(5)] - metadata_list = [{"id": str(i)} for i in range(5)] - - ids = service.add_batch(vectors, metadata_list) - - assert len(ids) == 5 - - def test_add_batch_without_metadata(self, service): - """Test adding batch without metadata.""" - vectors = np.random.randn(5, 64).astype(np.float32) - - ids = service.add_batch(vectors) - - assert len(ids) == 5 - - def test_search_numpy(self, service): - """Test searching with numpy query.""" - # Add some vectors first - vectors = np.random.randn(10, 64).astype(np.float32) - service.add_batch(vectors) - - # Search - query = np.random.randn(64).astype(np.float32) - results = service.search(query, k=5) - - assert len(results) <= 5 - assert len(results) > 0 - - def test_search_list(self, service): - """Test searching with list query.""" - # Add some vectors - vectors = np.random.randn(10, 64).astype(np.float32) - service.add_batch(vectors) - - # Search with list - query = [0.1] * 64 - results = service.search(query, k=3) - - assert len(results) <= 3 - - def test_search_result_format(self, service): - """Test that search results have correct format.""" - # Add vectors with metadata - vectors = np.random.randn(5, 64).astype(np.float32) - metadata_list = [{"category": f"cat_{i}"} for i in range(5)] - service.add_batch(vectors, metadata_list) - - # Search - query = np.random.randn(64).astype(np.float32) - results = service.search(query, k=3, include_metadata=True) - - # Check format - for result in results: - assert "id" in result - assert "score" in result - assert "metadata" in result - assert isinstance(result["id"], int) - assert isinstance(result["score"], float) - assert isinstance(result["metadata"], dict) - - def test_search_without_metadata(self, service): - """Test search without including metadata.""" - # Add vectors - vectors = np.random.randn(5, 64).astype(np.float32) - service.add_batch(vectors) - - # Search without metadata - query = np.random.randn(64).astype(np.float32) - results = service.search(query, k=3, include_metadata=False) - - # Check that metadata is empty - for result in results: - assert result["metadata"] == {} - - def test_stats(self, service): - """Test getting service statistics.""" - # Add some data - vectors = np.random.randn(10, 64).astype(np.float32) - service.add_batch(vectors) - - # Perform a search to generate stats - query = np.random.randn(64).astype(np.float32) - service.search(query, k=5) - - # Get stats - stats = service.stats() - - assert isinstance(stats, dict) - assert "size" in stats - assert "dimension" in stats - assert stats["size"] == 10 - assert stats["dimension"] == 64 - - -@pytest.mark.skipif(not SAGE_DB_SERVICE_AVAILABLE, reason="SAGE DB Service not available") -class TestSageDBServiceValidation: - """Test input validation for SageDBService.""" - - def test_add_wrong_dimension(self): - """Test that adding vector with wrong dimension raises error.""" - service = SageDBService(dimension=64) - - # Wrong dimension - vector = np.random.randn(128).astype(np.float32) - - with pytest.raises(ValueError, match="vector shape must be"): - service.add(vector) - - def test_add_wrong_shape(self): - """Test that adding vector with wrong shape raises error.""" - service = SageDBService(dimension=64) - - # 2D instead of 1D - vector = np.random.randn(1, 64).astype(np.float32) - - with pytest.raises(ValueError, match="vector shape must be"): - service.add(vector) - - def test_add_batch_wrong_dimension(self): - """Test that adding batch with wrong dimension raises error.""" - service = SageDBService(dimension=64) - - # Wrong second dimension - vectors = np.random.randn(10, 128).astype(np.float32) - - with pytest.raises(ValueError, match="vectors shape must be"): - service.add_batch(vectors) - - def test_add_batch_wrong_shape(self): - """Test that adding batch with wrong shape raises error.""" - service = SageDBService(dimension=64) - - # 1D instead of 2D - vectors = np.random.randn(64).astype(np.float32) - - with pytest.raises(ValueError, match="vectors shape must be"): - service.add_batch(vectors) - - def test_add_batch_list_wrong_dimension(self): - """Test that adding batch list with wrong dimension raises error.""" - service = SageDBService(dimension=64) - - # Wrong dimension - vectors = [[0.1] * 128 for _ in range(5)] - - with pytest.raises(ValueError, match="vectors shape must be"): - service.add_batch(vectors) - - -@pytest.mark.skipif(not SAGE_DB_SERVICE_AVAILABLE, reason="SAGE DB Service not available") -class TestSageDBServiceWorkflow: - """Integration tests for complete workflows.""" - - def test_end_to_end_workflow(self): - """Test complete workflow from creation to search.""" - # 1. Create service - service = SageDBService(dimension=128, index_type="FLAT") - - # 2. Add single vectors - for i in range(5): - vector = np.random.randn(128).astype(np.float32) - metadata = {"id": str(i), "type": "single"} - service.add(vector, metadata) - - # 3. Add batch - batch_vectors = np.random.randn(10, 128).astype(np.float32) - batch_metadata = [{"id": str(i + 5), "type": "batch"} for i in range(10)] - service.add_batch(batch_vectors, batch_metadata) - - # 4. Search - query = np.random.randn(128).astype(np.float32) - results = service.search(query, k=5) - - assert len(results) == 5 - - # 5. Check stats - stats = service.stats() - assert stats["size"] == 15 - - def test_multiple_searches(self): - """Test multiple consecutive searches.""" - service = SageDBService(dimension=64, index_type="FLAT") - - # Add data - vectors = np.random.randn(20, 64).astype(np.float32) - service.add_batch(vectors) - - # Perform multiple searches - for _ in range(5): - query = np.random.randn(64).astype(np.float32) - results = service.search(query, k=3) - assert len(results) <= 3 - - def test_incremental_additions(self): - """Test incremental addition of vectors.""" - service = SageDBService(dimension=64) - - # Add vectors incrementally - for i in range(10): - vector = np.random.randn(64).astype(np.float32) - service.add(vector, {"step": str(i)}) - - # Verify size - stats = service.stats() - assert stats["size"] == 10 - - def test_different_k_values(self): - """Test searching with different k values.""" - service = SageDBService(dimension=64) - - # Add data - vectors = np.random.randn(20, 64).astype(np.float32) - service.add_batch(vectors) - - query = np.random.randn(64).astype(np.float32) - - # Test different k values - for k in [1, 5, 10, 15, 20, 25]: - results = service.search(query, k=k) - expected_results = min(k, 20) # Can't return more than available - assert len(results) == expected_results - - def test_empty_database_search(self): - """Test searching in empty database.""" - service = SageDBService(dimension=64) - - query = np.random.randn(64).astype(np.float32) - results = service.search(query, k=5) - - # Should return empty list - assert len(results) == 0 - - -@pytest.mark.skipif(not SAGE_DB_SERVICE_AVAILABLE, reason="SAGE DB Service not available") -class TestSageDBServiceEdgeCases: - """Edge case tests for SageDBService.""" - - def test_service_with_small_dimension(self): - """Test service with very small dimension.""" - service = SageDBService(dimension=2) - - vector = np.array([0.1, 0.2], dtype=np.float32) - vector_id = service.add(vector) - - assert isinstance(vector_id, int) - - def test_service_with_large_dimension(self): - """Test service with large dimension.""" - service = SageDBService(dimension=2048) - - vector = np.random.randn(2048).astype(np.float32) - vector_id = service.add(vector) - - assert isinstance(vector_id, int) - - def test_search_with_k_zero(self): - """Test search with k=0.""" - service = SageDBService(dimension=64) - - vectors = np.random.randn(10, 64).astype(np.float32) - service.add_batch(vectors) - - query = np.random.randn(64).astype(np.float32) - results = service.search(query, k=0) - - # Should return empty results - assert len(results) == 0 - - def test_invalid_index_type(self): - """Test that invalid index type falls back to AUTO.""" - service = SageDBService(dimension=64, index_type="INVALID_TYPE") - - # Should still work (fallback to AUTO) - vector = np.random.randn(64).astype(np.float32) - vector_id = service.add(vector) - - assert isinstance(vector_id, int) - - def test_metadata_with_special_characters(self): - """Test metadata with special characters.""" - service = SageDBService(dimension=64) - - vector = np.random.randn(64).astype(np.float32) - metadata = { - "name": "Test Item", - "description": "Item with special chars: !@#$%^&*()", - "unicode": "测试中文", - } - - vector_id = service.add(vector, metadata) - assert isinstance(vector_id, int) - - -if __name__ == "__main__": - pytest.main([__file__, "-v"]) diff --git a/packages/sage-middleware/tests/components/sage_mem/toy_data.json b/packages/sage-middleware/tests/components/sage_mem/toy_data.json deleted file mode 100644 index 0016d352ba..0000000000 --- a/packages/sage-middleware/tests/components/sage_mem/toy_data.json +++ /dev/null @@ -1,471 +0,0 @@ -[ - { - "id": "doc_1", - "text": "用户在重置密码时，系统会发送一封带有验证码的邮件到注册邮箱。", - "metadata": { - "priority": "low", - "tags": "faq", - "category": "business" - } - }, - { - "id": "doc_2", - "text": "2023 年度财报显示，公司 AI 产品线的收入同比增长 45%。", - "metadata": { - "tags": "faq" - } - }, - { - "id": "doc_3", - "text": "员工入职手册规定试用期为三个月，期间享有基本福利。", - "metadata": { - "tags": "log" - } - }, - { - "id": "doc_4", - "text": "搜索引擎的倒排索引能够加速关键词检索。", - "metadata": { - "author": "Bob", - "category": "user-guide", - "priority": "medium", - "date": "2021-01-01" - } - }, - { - "id": "doc_5", - "text": "在深度学习中，梯度消失是训练 RNN 模型的常见问题。", - "metadata": { - "category": "business", - "priority": "high", - "type": "doc", - "tags": "ml" - } - }, - { - "id": "doc_6", - "text": "FAQ：如何查看订单详情？答：进入个人中心，点击订单即可查看。", - "metadata": { - "category": "nlp" - } - }, - { - "id": "doc_7", - "text": "日志：2023-05-01 服务启动成功，端口号 9000。", - "metadata": { - "date": "2021-01-01", - "category": "nlp", - "type": "doc" - } - }, - { - "id": "doc_8", - "text": "Python 的生成器可以通过 yield 实现惰性计算。", - "metadata": { - "type": "report", - "tags": "doc", - "priority": "medium", - "author": "Alice" - } - }, - { - "id": "doc_9", - "text": "在 Transformer 中，多头注意力机制提升了模型表达能力。", - "metadata": { - "priority": "high" - } - }, - { - "id": "doc_10", - "text": "公告：公司将于 2024-01-01 进行系统维护，预计停机 2 小时。", - "metadata": { - "author": "Admin" - } - }, - { - "id": "doc_11", - "text": "用户反馈：页面加载速度较慢，希望优化性能。", - "metadata": { - "type": "report", - "date": "2021-01-01", - "tags": "faq", - "author": "Bob" - } - }, - { - "id": "doc_12", - "text": "常见报错：KeyError 出现在访问不存在的字典键时。", - "metadata": { - "type": "faq", - "priority": "medium" - } - }, - { - "id": "doc_13", - "text": "知识点：卷积神经网络常用于图像分类任务。", - "metadata": { - "priority": "low", - "date": "2022-06-15", - "type": "report", - "author": "Admin" - } - }, - { - "id": "doc_14", - "text": "接口 /api/v1/register 请求耗时 320ms。", - "metadata": { - "priority": "medium", - "tags": "faq", - "type": "faq", - "author": "Alice" - } - }, - { - "id": "doc_15", - "text": "业务规则：退款申请需在下单 7 天内提交。", - "metadata": { - "priority": "medium", - "author": "Alice" - } - }, - { - "id": "doc_16", - "text": "员工福利包括年度体检与节日礼品。", - "metadata": { - "date": "2024-09-10", - "type": "doc" - } - }, - { - "id": "doc_17", - "text": "日志：磁盘剩余空间不足 10%，请及时清理。", - "metadata": { - "priority": "medium", - "tags": "system", - "author": "Bob", - "date": "2024-09-10" - } - }, - { - "id": "doc_18", - "text": "A/B 测试结果表明，版本 B 提升了 12% 的转化率。", - "metadata": { - "date": "2022-06-15", - "category": "ml", - "priority": "low", - "type": "report" - } - }, - { - "id": "doc_19", - "text": "提示：修改密码时应包含大小写字母和特殊字符。", - "metadata": { - "tags": "ai", - "priority": "medium" - } - }, - { - "id": "doc_20", - "text": "公司于 2022 年成立了新的自然语言处理研究部门。", - "metadata": { - "category": "tech", - "type": "article", - "priority": "low", - "author": "Bob" - } - }, - { - "id": "doc_21", - "text": "知识点：BERT 模型基于双向 Transformer 编码器。", - "metadata": { - "tags": "faq", - "author": "Admin" - } - }, - { - "id": "doc_22", - "text": "日志：检测到异常流量，触发防火墙策略。", - "metadata": { - "tags": "system", - "date": "2022-06-15", - "type": "report", - "author": "Bob" - } - }, - { - "id": "doc_23", - "text": "常见问题：登录时提示账号不存在，可能是输入错误。", - "metadata": { - "priority": "high", - "category": "nlp", - "tags": "system" - } - }, - { - "id": "doc_24", - "text": "公告：即日起启用新的客户工单系统。", - "metadata": { - "type": "note" - } - }, - { - "id": "doc_25", - "text": "数据库事务可确保操作的原子性与一致性。", - "metadata": { - "author": "Charlie", - "priority": "low" - } - }, - { - "id": "doc_26", - "text": "员工培训内容包括数据安全与合规要求。", - "metadata": { - "tags": "log", - "type": "note", - "category": "nlp" - } - }, - { - "id": "doc_27", - "text": "FAQ：如何联系人工客服？答：拨打 400 热线或在线咨询。", - "metadata": { - "category": "ml", - "author": "Alice" - } - }, - { - "id": "doc_28", - "text": "技术文档：安装依赖时推荐使用虚拟环境。", - "metadata": { - "type": "doc", - "tags": "nlp", - "author": "Alice" - } - }, - { - "id": "doc_29", - "text": "知识点：强化学习通过奖励信号指导智能体学习策略。", - "metadata": { - "tags": "doc", - "author": "Admin" - } - }, - { - "id": "doc_30", - "text": "日志：GPU 使用率达到 95%，可能导致任务排队。", - "metadata": { - "priority": "high", - "tags": "nlp", - "category": "policy" - } - }, - { - "id": "doc_31", - "text": "用户反馈：搜索结果相关性不高，希望改进推荐算法。", - "metadata": { - "type": "report" - } - }, - { - "id": "doc_32", - "text": "系统提示：您的会话已过期，请重新登录。", - "metadata": { - "category": "ml" - } - }, - { - "id": "doc_33", - "text": "公告：新版 APP 支持指纹与人脸识别登录。", - "metadata": { - "author": "Alice" - } - }, - { - "id": "doc_34", - "text": "知识点：K-Means 聚类是一种无监督学习方法。", - "metadata": { - "category": "nlp", - "priority": "medium", - "tags": "faq" - } - }, - { - "id": "doc_35", - "text": "常见报错：ImportError 出现在缺少依赖包时。", - "metadata": { - "tags": "faq", - "type": "log" - } - }, - { - "id": "doc_36", - "text": "FAQ：如何修改绑定手机号？答：进入安全设置进行更换。", - "metadata": { - "author": "Alice", - "date": "2024-09-10" - } - }, - { - "id": "doc_37", - "text": "日志：Kafka 队列积压超过 10000 条消息。", - "metadata": { - "date": "2023-03-22", - "type": "log" - } - }, - { - "id": "doc_38", - "text": "业务规则：优惠券不可与满减活动同时使用。", - "metadata": { - "date": "2022-06-15", - "category": "user-guide", - "type": "faq", - "tags": "nlp" - } - }, - { - "id": "doc_39", - "text": "提示：请勿在公共场所登录公司系统。", - "metadata": { - "author": "Alice", - "category": "tech", - "type": "log", - "tags": "ai" - } - }, - { - "id": "doc_40", - "text": "知识点：自注意力机制可以捕捉长距离依赖关系。", - "metadata": { - "type": "log", - "author": "Alice" - } - }, - { - "id": "doc_41", - "text": "公告：公司将举办 2024 年技术创新大赛。", - "metadata": { - "type": "article", - "date": "2023-03-22" - } - }, - { - "id": "doc_42", - "text": "用户反馈：APP 经常闪退，严重影响使用体验。", - "metadata": { - "date": "2022-06-15", - "tags": "rag", - "priority": "medium", - "author": "Admin" - } - }, - { - "id": "doc_43", - "text": "技术文档：推荐使用 Docker 部署微服务。", - "metadata": { - "type": "doc" - } - }, - { - "id": "doc_44", - "text": "日志：内存占用率超过 80%，建议优化应用。", - "metadata": { - "date": "2022-06-15", - "tags": "rag" - } - }, - { - "id": "doc_45", - "text": "FAQ：如何删除账号？答：联系客服申请注销。", - "metadata": { - "author": "Admin" - } - }, - { - "id": "doc_46", - "text": "常见问题：支付失败怎么办？答：检查余额或更换支付方式。", - "metadata": { - "tags": "log" - } - }, - { - "id": "doc_47", - "text": "知识点：迁移学习能够减少模型训练所需的数据量。", - "metadata": { - "category": "nlp", - "tags": "rag" - } - }, - { - "id": "doc_48", - "text": "系统提示：检测到新版本，请及时更新。", - "metadata": { - "tags": "log", - "date": "2024-09-10", - "priority": "low" - } - }, - { - "id": "doc_49", - "text": "公告：春节期间快递配送将有所延迟。", - "metadata": { - "type": "article", - "category": "nlp", - "priority": "high", - "author": "Alice" - } - }, - { - "id": "doc_50", - "text": "日志：Redis 连接超时，正在进行重试。", - "metadata": { - "tags": "policy", - "category": "nlp", - "priority": "medium", - "author": "Charlie" - } - }, - { - "id": "doc_51", - "text": "用户反馈：界面设计简洁明了，体验良好。", - "metadata": { - "priority": "medium", - "author": "Charlie", - "tags": "doc", - "type": "article" - } - }, - { - "id": "doc_52", - "text": "知识点：长短期记忆网络（LSTM）改进了 RNN 的长期依赖问题。", - "metadata": { - "date": "2022-06-15", - "category": "business", - "type": "article" - } - }, - { - "id": "doc_53", - "text": "提示：为确保安全，定期更换密码。", - "metadata": { - "tags": "system" - } - }, - { - "id": "doc_54", - "text": "常见报错：ValueError 通常出现在参数取值不合法时。", - "metadata": { - "type": "article" - } - }, - { - "id": "doc_55", - "text": "技术文档：前端与后端通信采用 RESTful API。", - "metadata": { - "category": "policy", - "priority": "low", - "date": "2023-03-22", - "tags": "faq" - } - } -] diff --git a/packages/sage-middleware/tests/components/test_extensions_compat.py b/packages/sage-middleware/tests/components/test_extensions_compat.py deleted file mode 100644 index 829423fcf3..0000000000 --- a/packages/sage-middleware/tests/components/test_extensions_compat.py +++ /dev/null @@ -1,458 +0,0 @@ -""" -Comprehensive tests for extensions compatibility module. - -Tests cover extension availability detection, requirement checking, -status reporting, and fallback mechanisms for optional C++ extensions. -""" - -from unittest.mock import MagicMock, patch - -import pytest - -from sage.middleware.components.extensions_compat import ( - check_extensions_availability, - get_extension_status, - is_sage_db_available, - is_sage_flow_available, - is_sage_tsdb_available, - require_sage_db, - require_sage_flow, - require_sage_tsdb, -) - - -class TestExtensionAvailabilityChecks: - """Test individual extension availability checks.""" - - def test_is_sage_db_available(self): - """Test is_sage_db_available returns boolean.""" - result = is_sage_db_available() - assert isinstance(result, bool) - - def test_is_sage_flow_available(self): - """Test is_sage_flow_available returns boolean.""" - result = is_sage_flow_available() - assert isinstance(result, bool) - - def test_is_sage_tsdb_available(self): - """Test is_sage_tsdb_available returns boolean.""" - result = is_sage_tsdb_available() - assert isinstance(result, bool) - - @patch("sage.middleware.components.extensions_compat._SAGE_DB_AVAILABLE", True) - def test_is_sage_db_available_when_true(self): - """Test is_sage_db_available returns True when available.""" - # Re-import to get the patched value - from sage.middleware.components import extensions_compat - - with patch.object(extensions_compat, "_SAGE_DB_AVAILABLE", True): - result = is_sage_db_available() - assert result is True or result is False # Just verify it doesn't crash - - @patch("sage.middleware.components.extensions_compat._SAGE_FLOW_AVAILABLE", False) - def test_is_sage_flow_available_when_false(self): - """Test is_sage_flow_available returns False when unavailable.""" - from sage.middleware.components import extensions_compat - - with patch.object(extensions_compat, "_SAGE_FLOW_AVAILABLE", False): - result = is_sage_flow_available() - assert result is True or result is False # Just verify it doesn't crash - - -class TestExtensionStatusReporting: - """Test status reporting functions.""" - - def test_get_extension_status_structure(self): - """Test get_extension_status returns expected structure.""" - status = get_extension_status() - - assert isinstance(status, dict) - assert "sage_db" in status - assert "sage_flow" in status - assert "sage_tsdb" in status - assert "total_available" in status - assert "total_extensions" in status - - def test_get_extension_status_values(self): - """Test get_extension_status values are booleans and ints.""" - status = get_extension_status() - - assert isinstance(status["sage_db"], bool) - assert isinstance(status["sage_flow"], bool) - assert isinstance(status["sage_tsdb"], bool) - assert isinstance(status["total_available"], int) - assert isinstance(status["total_extensions"], int) - - def test_get_extension_status_total_valid(self): - """Test get_extension_status totals are valid.""" - status = get_extension_status() - - # total_available should be between 0 and 3 - assert 0 <= status["total_available"] <= 3 - assert status["total_extensions"] == 3 - - def test_get_extension_status_total_matches_flags(self): - """Test total_available matches actual availability flags.""" - status = get_extension_status() - - expected_total = sum( - [ - status["sage_db"], - status["sage_flow"], - status["sage_tsdb"], - ] - ) - assert status["total_available"] == expected_total - - def test_check_extensions_availability_structure(self): - """Test check_extensions_availability returns expected structure.""" - availability = check_extensions_availability() - - assert isinstance(availability, dict) - assert "sage_db" in availability - assert "sage_flow" in availability - assert "sage_tsdb" in availability - - def test_check_extensions_availability_values(self): - """Test check_extensions_availability values are booleans.""" - availability = check_extensions_availability() - - assert isinstance(availability["sage_db"], bool) - assert isinstance(availability["sage_flow"], bool) - assert isinstance(availability["sage_tsdb"], bool) - - def test_check_extensions_availability_matches_get_status(self): - """Test check_extensions_availability matches get_extension_status.""" - status = get_extension_status() - availability = check_extensions_availability() - - assert availability["sage_db"] == status["sage_db"] - assert availability["sage_flow"] == status["sage_flow"] - assert availability["sage_tsdb"] == status["sage_tsdb"] - - -class TestRequirementChecking: - """Test requirement checking functions that raise on unavailability.""" - - def test_require_sage_db_available(self): - """Test require_sage_db returns module when available.""" - from sage.middleware.components import extensions_compat - - # Mock sagevdb import by patching availability and the import - with patch.object(extensions_compat, "_SAGE_DB_AVAILABLE", True): - with patch.dict("sys.modules", {"sagevdb": MagicMock()}): - result = require_sage_db() - assert result is not None - - def test_require_sage_db_unavailable_raises(self): - """Test require_sage_db raises when unavailable.""" - from sage.middleware.components import extensions_compat - - with patch.object(extensions_compat, "_SAGE_DB_AVAILABLE", False): - with pytest.raises(ImportError, match="SageVDB|isage-vdb"): - require_sage_db() - - @patch("sage.middleware.components.extensions_compat._SAGE_FLOW_AVAILABLE", True) - @patch("sage.middleware.components.extensions_compat._sage_flow", MagicMock()) - def test_require_sage_flow_available(self): - """Test require_sage_flow returns module when available.""" - from sage.middleware.components import extensions_compat - - with patch.object(extensions_compat, "_SAGE_FLOW_AVAILABLE", True): - with patch.object(extensions_compat, "_sage_flow", MagicMock()): - result = require_sage_flow() - assert result is not None - - @patch("sage.middleware.components.extensions_compat._SAGE_FLOW_AVAILABLE", False) - def test_require_sage_flow_unavailable_raises(self): - """Test require_sage_flow raises when unavailable.""" - from sage.middleware.components import extensions_compat - - with patch.object(extensions_compat, "_SAGE_FLOW_AVAILABLE", False): - with pytest.raises(ImportError, match="SAGE Flow"): - require_sage_flow() - - def test_require_sage_tsdb_available(self): - """Test require_sage_tsdb returns module when available.""" - from sage.middleware.components import extensions_compat - - # Mock sage_tsdb import by patching availability and the import - with patch.object(extensions_compat, "_SAGE_TSDB_AVAILABLE", True): - with patch.dict("sys.modules", {"sage_tsdb": MagicMock()}): - result = require_sage_tsdb() - assert result is not None - - @patch("sage.middleware.components.extensions_compat._SAGE_TSDB_AVAILABLE", False) - def test_require_sage_tsdb_unavailable_raises(self): - """Test require_sage_tsdb raises when unavailable.""" - from sage.middleware.components import extensions_compat - - with patch.object(extensions_compat, "_SAGE_TSDB_AVAILABLE", False): - with pytest.raises(ImportError, match="SAGE TSDB|isage-tsdb"): - require_sage_tsdb() - - -class TestErrorMessages: - """Test error message content for requirement failures.""" - - def test_require_sage_db_error_message_helpful(self): - """Test require_sage_db error message is helpful.""" - from sage.middleware.components import extensions_compat - - with patch.object(extensions_compat, "_SAGE_DB_AVAILABLE", False): - try: - require_sage_db() - except ImportError as e: - error_msg = str(e) - # SageVDB is independent PyPI package (isage-vdb) - assert "SageVDB" in error_msg or "isage-vdb" in error_msg - - def test_require_sage_flow_error_message_helpful(self): - """Test require_sage_flow error message is helpful.""" - from sage.middleware.components import extensions_compat - - with patch.object(extensions_compat, "_SAGE_FLOW_AVAILABLE", False): - try: - require_sage_flow() - except ImportError as e: - error_msg = str(e) - assert "SAGE Flow" in error_msg - - def test_require_sage_tsdb_error_message_helpful(self): - """Test require_sage_tsdb error message is helpful.""" - from sage.middleware.components import extensions_compat - - with patch.object(extensions_compat, "_SAGE_TSDB_AVAILABLE", False): - try: - require_sage_tsdb() - except ImportError as e: - error_msg = str(e) - # SageTSDB is independent PyPI package (isage-tsdb) - assert "SAGE TSDB" in error_msg or "isage-tsdb" in error_msg - - -class TestImportWarnings: - """Test that appropriate warnings are issued during import.""" - - def test_import_handles_missing_extensions_gracefully(self): - """Test that missing extensions don't crash import.""" - # This test verifies the module imports successfully - # regardless of extension availability - from sage.middleware.components import extensions_compat - - # Module should be importable - assert extensions_compat is not None - # Module should have the availability flags - assert hasattr(extensions_compat, "_SAGE_DB_AVAILABLE") - assert hasattr(extensions_compat, "_SAGE_FLOW_AVAILABLE") - assert hasattr(extensions_compat, "_SAGE_TSDB_AVAILABLE") - - def test_module_constants_initialized(self): - """Test that all module constants are initialized.""" - from sage.middleware.components import extensions_compat - - assert hasattr(extensions_compat, "_SAGE_DB_AVAILABLE") - assert hasattr(extensions_compat, "_SAGE_FLOW_AVAILABLE") - assert hasattr(extensions_compat, "_SAGE_TSDB_AVAILABLE") - - assert isinstance(extensions_compat._SAGE_DB_AVAILABLE, bool) - assert isinstance(extensions_compat._SAGE_FLOW_AVAILABLE, bool) - assert isinstance(extensions_compat._SAGE_TSDB_AVAILABLE, bool) - - -class TestExtensionModuleReferences: - """Test module reference handling through public APIs.""" - - def test_require_functions_handle_availability(self): - """Test require functions properly handle availability status.""" - from sage.middleware.components import extensions_compat - - # Test that require functions raise when not available - if not extensions_compat._SAGE_DB_AVAILABLE: - with pytest.raises(ImportError): - require_sage_db() - - if not extensions_compat._SAGE_FLOW_AVAILABLE: - with pytest.raises(ImportError): - require_sage_flow() - - if not extensions_compat._SAGE_TSDB_AVAILABLE: - with pytest.raises(ImportError): - require_sage_tsdb() - - def test_require_functions_succeed_when_available(self): - """Test require functions return module when available.""" - from sage.middleware.components import extensions_compat - - # Test that require functions return module when available - if extensions_compat._SAGE_DB_AVAILABLE: - result = require_sage_db() - assert result is not None - - if extensions_compat._SAGE_FLOW_AVAILABLE: - result = require_sage_flow() - assert result is not None - - if extensions_compat._SAGE_TSDB_AVAILABLE: - result = require_sage_tsdb() - assert result is not None - - -class TestConsistency: - """Test consistency between different status functions.""" - - def test_availability_check_consistency(self): - """Test all availability checks are consistent.""" - status = get_extension_status() - check = check_extensions_availability() - - assert is_sage_db_available() == status["sage_db"] == check["sage_db"] - assert is_sage_flow_available() == status["sage_flow"] == check["sage_flow"] - assert is_sage_tsdb_available() == status["sage_tsdb"] == check["sage_tsdb"] - - def test_total_available_consistency(self): - """Test total_available count is consistent.""" - status = get_extension_status() - - available_count = sum( - [ - is_sage_db_available(), - is_sage_flow_available(), - is_sage_tsdb_available(), - ] - ) - - assert status["total_available"] == available_count - - def test_extension_status_not_changed_by_checks(self): - """Test that checking availability doesn't change it.""" - status1 = get_extension_status() - - # Perform multiple checks - is_sage_db_available() - is_sage_flow_available() - is_sage_tsdb_available() - check_extensions_availability() - - status2 = get_extension_status() - - assert status1 == status2 - - -class TestEdgeCases: - """Test edge cases and unusual scenarios.""" - - def test_get_extension_status_is_idempotent(self): - """Test get_extension_status returns same result on multiple calls.""" - status1 = get_extension_status() - status2 = get_extension_status() - status3 = get_extension_status() - - assert status1 == status2 == status3 - - def test_all_checks_return_expected_types(self): - """Test all functions return expected types.""" - assert isinstance(is_sage_db_available(), bool) - assert isinstance(is_sage_flow_available(), bool) - assert isinstance(is_sage_tsdb_available(), bool) - assert isinstance(get_extension_status(), dict) - assert isinstance(check_extensions_availability(), dict) - - def test_require_functions_consistency(self): - """Test require functions are consistent with availability checks.""" - - # If available, require should return something - if is_sage_db_available(): - result = require_sage_db() - assert result is not None - - # If not available, require should raise - if not is_sage_flow_available(): - with pytest.raises(ImportError): - require_sage_flow() - - if not is_sage_tsdb_available(): - with pytest.raises(ImportError): - require_sage_tsdb() - - -class TestModuleInitialization: - """Test module initialization behavior.""" - - def test_module_imports_without_error(self): - """Test that module can be imported without errors.""" - # This should not raise any exceptions - from sage.middleware.components import extensions_compat - - assert extensions_compat is not None - - def test_all_public_functions_callable(self): - """Test all public functions are callable.""" - from sage.middleware.components import extensions_compat - - assert callable(extensions_compat.is_sage_db_available) - assert callable(extensions_compat.is_sage_flow_available) - assert callable(extensions_compat.is_sage_tsdb_available) - assert callable(extensions_compat.get_extension_status) - assert callable(extensions_compat.check_extensions_availability) - assert callable(extensions_compat.require_sage_db) - assert callable(extensions_compat.require_sage_flow) - assert callable(extensions_compat.require_sage_tsdb) - - def test_functions_no_required_arguments(self): - """Test that check functions have no required arguments.""" - # These should be callable with no arguments - is_sage_db_available() - is_sage_flow_available() - is_sage_tsdb_available() - get_extension_status() - check_extensions_availability() - - -class TestTypeAnnotations: - """Test that functions have proper type annotations.""" - - def test_is_sage_db_available_returns_bool(self): - """Test is_sage_db_available returns bool.""" - result = is_sage_db_available() - assert result is True or result is False - - def test_get_extension_status_returns_dict_with_int_values(self): - """Test get_extension_status returns dict with correct value types.""" - status = get_extension_status() - - assert isinstance(status["total_available"], int) - assert isinstance(status["total_extensions"], int) - assert status["total_extensions"] == 3 - - -class TestRequireReturnValues: - """Test that require functions return expected values based on availability.""" - - def test_require_functions_behavior(self): - """Test require functions return module or raise based on availability.""" - from sage.middleware.components import extensions_compat - - # Test sage_db - if extensions_compat._SAGE_DB_AVAILABLE: - result = require_sage_db() - assert result is not None # Should return something when available - else: - with pytest.raises(ImportError): - require_sage_db() # Should raise when not available - - # Test sage_flow - if extensions_compat._SAGE_FLOW_AVAILABLE: - result = require_sage_flow() - assert result is not None # Should return something when available - else: - with pytest.raises(ImportError): - require_sage_flow() # Should raise when not available - - # Test sage_tsdb - if extensions_compat._SAGE_TSDB_AVAILABLE: - result = require_sage_tsdb() - assert result is not None # Should return something when available - else: - with pytest.raises(ImportError): - require_sage_tsdb() # Should raise when not available diff --git a/packages/sage-middleware/tests/components/vector_stores/test_chroma.py b/packages/sage-middleware/tests/components/vector_stores/test_chroma.py deleted file mode 100644 index 7452757160..0000000000 --- a/packages/sage-middleware/tests/components/vector_stores/test_chroma.py +++ /dev/null @@ -1,361 +0,0 @@ -""" -Tests for ChromaDB integration module - -Tests cover: -- Client initialization (persistent and HTTP modes) -- Collection creation and retrieval -- Document addition and deletion -- Search functionality -- Error handling -""" - -from unittest.mock import Mock, patch - -import numpy as np -import pytest - - -@pytest.mark.unit -class TestChromaBackendInitialization: - """Test ChromaDB backend initialization""" - - @patch("chromadb.PersistentClient") - def test_init_persistent_client_localhost(self, mock_persistent_client): - """测试本地持久化客户端初始化""" - from sage.middleware.components.vector_stores.chroma import ChromaBackend - - # Mock PersistentClient - mock_client = Mock() - mock_collection = Mock() - mock_persistent_client.return_value = mock_client - mock_client.get_collection.return_value = mock_collection - - config = { - "host": "localhost", - "port": 8000, - "persistence_path": "/tmp/test_chroma", - "collection_name": "test_collection", - } - - backend = ChromaBackend(config) - - # Verify persistent client was created - mock_persistent_client.assert_called_once_with(path="/tmp/test_chroma") - assert backend.client == mock_client - assert backend.collection == mock_collection - - @patch("chromadb.HttpClient") - def test_init_http_client_remote(self, mock_http_client): - """测试远程HTTP客户端初始化""" - from sage.middleware.components.vector_stores.chroma import ChromaBackend - - # Mock HttpClient - mock_client = Mock() - mock_collection = Mock() - mock_http_client.return_value = mock_client - mock_client.get_collection.return_value = mock_collection - - config = { - "host": "remote-server.com", - "port": 8000, - "collection_name": "test_collection", - } - - backend = ChromaBackend(config) - - # Verify HTTP client was created - mock_http_client.assert_called_once() - assert backend.client == mock_client - - @patch("chromadb.HttpClient") - def test_init_force_http_mode(self, mock_http_client): - """测试强制HTTP模式""" - from sage.middleware.components.vector_stores.chroma import ChromaBackend - - mock_client = Mock() - mock_collection = Mock() - mock_http_client.return_value = mock_client - mock_client.get_collection.return_value = mock_collection - - config = { - "host": "localhost", - "port": 8000, - "force_http": True, - "collection_name": "test_collection", - } - - _ = ChromaBackend(config) - - # Should use HTTP client even for localhost - mock_http_client.assert_called_once() - - def test_init_missing_chromadb_dependency(self): - """测试缺少ChromaDB依赖时的错误处理""" - import sys - - from sage.middleware.components.vector_stores.chroma import ChromaBackend - - # Temporarily remove chromadb from sys.modules - chromadb_backup = sys.modules.get("chromadb") - if "chromadb" in sys.modules: - del sys.modules["chromadb"] - - # Mock the import to raise ImportError - with patch("builtins.__import__", side_effect=ImportError("chromadb not found")): - config = {"host": "localhost", "collection_name": "test"} - - with pytest.raises(ImportError, match="ChromaDB dependencies not available"): - ChromaBackend(config) - - # Restore chromadb - if chromadb_backup: - sys.modules["chromadb"] = chromadb_backup - - -@pytest.mark.unit -class TestChromaBackendCollection: - """Test ChromaDB collection operations""" - - @patch("chromadb.PersistentClient") - def test_get_existing_collection(self, mock_persistent_client): - """测试获取已存在的集合""" - from sage.middleware.components.vector_stores.chroma import ChromaBackend - - mock_client = Mock() - mock_collection = Mock() - mock_persistent_client.return_value = mock_client - mock_client.get_collection.return_value = mock_collection - - config = {"host": "localhost", "collection_name": "existing_collection"} - - backend = ChromaBackend(config) - - # Should retrieve existing collection - mock_client.get_collection.assert_called_once_with(name="existing_collection") - assert backend.collection == mock_collection - - @patch("chromadb.PersistentClient") - def test_create_new_collection(self, mock_persistent_client): - """测试创建新集合""" - from sage.middleware.components.vector_stores.chroma import ChromaBackend - - mock_client = Mock() - mock_collection = Mock() - mock_persistent_client.return_value = mock_client - - # First call fails (collection doesn't exist), second call creates it - mock_client.get_collection.side_effect = Exception("Collection not found") - mock_client.create_collection.return_value = mock_collection - - config = { - "host": "localhost", - "collection_name": "new_collection", - "metadata": {"hnsw:space": "cosine"}, - } - - backend = ChromaBackend(config) - - # Should create new collection - mock_client.create_collection.assert_called_once_with( - name="new_collection", metadata={"hnsw:space": "cosine"} - ) - assert backend.collection == mock_collection - - -@pytest.mark.unit -class TestChromaBackendDocuments: - """Test document operations""" - - @patch("chromadb.PersistentClient") - def test_add_documents_with_embeddings(self, mock_persistent_client): - """测试添加带有embeddings的文档""" - from sage.middleware.components.vector_stores.chroma import ChromaBackend - - mock_client = Mock() - mock_collection = Mock() - mock_persistent_client.return_value = mock_client - mock_client.get_collection.return_value = mock_collection - - backend = ChromaBackend({"host": "localhost", "collection_name": "test"}) - - # Prepare test data - documents = ["doc1", "doc2", "doc3"] - embeddings = [np.random.rand(768) for _ in range(3)] # list of np.ndarray - doc_ids = ["id1", "id2", "id3"] - - # Call add_documents (API: documents, embeddings, doc_ids) - result = backend.add_documents(documents=documents, embeddings=embeddings, doc_ids=doc_ids) - - # Verify collection.add was called with correct parameters - mock_collection.add.assert_called_once() - call_kwargs = mock_collection.add.call_args[1] - assert call_kwargs["documents"] == documents - assert call_kwargs["ids"] == doc_ids - assert len(call_kwargs["embeddings"]) == 3 - assert "metadatas" in call_kwargs - assert result == doc_ids - - -@pytest.mark.unit -class TestChromaBackendSearch: - """Test search functionality""" - - @patch("chromadb.PersistentClient") - def test_search_with_embedding(self, mock_persistent_client): - """测试使用embedding进行搜索""" - from sage.middleware.components.vector_stores.chroma import ChromaBackend - - mock_client = Mock() - mock_collection = Mock() - mock_persistent_client.return_value = mock_client - mock_client.get_collection.return_value = mock_collection - - # Mock search results - mock_collection.query.return_value = { - "ids": [["id1", "id2"]], - "documents": [["doc1", "doc2"]], - "distances": [[0.1, 0.3]], - "metadatas": [[{"source": "test1"}, {"source": "test2"}]], - } - - backend = ChromaBackend( - {"host": "localhost", "collection_name": "test", "use_embedding_query": True} - ) - - # Perform search (API uses query_vector, query_text, top_k) - query_vector = np.random.rand(768) - query_text = "test query" - results = backend.search(query_vector=query_vector, query_text=query_text, top_k=2) - - # Verify results - assert len(results) == 2 - assert results[0] == "doc1" - # Verify query was called - mock_collection.query.assert_called_once() - assert "query_embeddings" in mock_collection.query.call_args[1] - assert mock_collection.query.call_args[1]["n_results"] == 2 - - @patch("chromadb.PersistentClient") - def test_search_with_filter(self, mock_persistent_client): - """测试带过滤条件的搜索""" - from sage.middleware.components.vector_stores.chroma import ChromaBackend - - mock_client = Mock() - mock_collection = Mock() - mock_persistent_client.return_value = mock_client - mock_client.get_collection.return_value = mock_collection - - mock_collection.query.return_value = { - "ids": [["id1"]], - "documents": [["doc1"]], - "distances": [[0.1]], - "metadatas": [[{"category": "tech"}]], - } - - backend = ChromaBackend({"host": "localhost", "collection_name": "test"}) - - # Search (API doesn't support where filter directly) - query_vector = np.random.rand(768) - query_text = "tech" - results = backend.search(query_vector=query_vector, query_text=query_text, top_k=5) - - # Verify query was called - mock_collection.query.assert_called_once() - # Verify results (filtered to tech category) - assert len(results) == 1 - assert results[0] == "doc1" - - -@pytest.mark.unit -class TestChromaBackendUtilities: - """Test utility functions""" - - @patch("chromadb.PersistentClient") - def test_delete_collection(self, mock_persistent_client): - """测试删除集合""" - from sage.middleware.components.vector_stores.chroma import ChromaBackend - - mock_client = Mock() - mock_collection = Mock() - mock_persistent_client.return_value = mock_client - mock_client.get_collection.return_value = mock_collection - - backend = ChromaBackend({"host": "localhost", "collection_name": "test"}) - - # Delete collection - backend.delete_collection() - - # Verify client.delete_collection was called - mock_client.delete_collection.assert_called_once_with(name="test") - - @patch("chromadb.PersistentClient") - def test_get_collection_info(self, mock_persistent_client): - """测试获取集合信息""" - from sage.middleware.components.vector_stores.chroma import ChromaBackend - - mock_client = Mock() - mock_collection = Mock() - mock_persistent_client.return_value = mock_client - mock_client.get_collection.return_value = mock_collection - - # Mock collection properties - mock_collection.name = "test" - mock_collection.count.return_value = 42 - - backend = ChromaBackend({"host": "localhost", "collection_name": "test"}) - - # Get collection info - info = backend.get_collection_info() - - # Verify info structure - assert isinstance(info, dict) - assert "collection_name" in info - assert info["collection_name"] == "test" - assert "document_count" in info - assert info["document_count"] == 42 - - -@pytest.mark.integration -class TestChromaBackendIntegration: - """Integration tests with mocked ChromaDB""" - - @patch("chromadb.PersistentClient") - def test_full_workflow(self, mock_persistent_client): - """测试完整的工作流程：创建、添加、搜索、删除""" - from sage.middleware.components.vector_stores.chroma import ChromaBackend - - # Setup mocks - mock_client = Mock() - mock_collection = Mock() - mock_persistent_client.return_value = mock_client - mock_client.get_collection.side_effect = Exception("Not found") - mock_client.create_collection.return_value = mock_collection - - # Create backend - backend = ChromaBackend({"host": "localhost", "collection_name": "test"}) - - # Add documents - documents = ["test doc 1", "test doc 2"] - embeddings = [np.random.rand(768) for _ in range(2)] - doc_ids = ["id1", "id2"] - - backend.add_documents(documents=documents, embeddings=embeddings, doc_ids=doc_ids) - mock_collection.add.assert_called_once() - - # Search - mock_collection.query.return_value = { - "ids": [["id1", "id2"]], - "documents": [documents], - "distances": [[0.1, 0.2]], - "metadatas": [[{}, {}]], - } - - query_vector = np.random.rand(768) - results = backend.search(query_vector=query_vector, query_text="test", top_k=2) - assert len(results) == 2 - - mock_collection.query.assert_called_once() - - # Delete - backend.delete_collection() - mock_client.delete_collection.assert_called_once() diff --git a/packages/sage-middleware/tests/components/vector_stores/test_milvus.py b/packages/sage-middleware/tests/components/vector_stores/test_milvus.py deleted file mode 100644 index 1bad99d001..0000000000 --- a/packages/sage-middleware/tests/components/vector_stores/test_milvus.py +++ /dev/null @@ -1,123 +0,0 @@ -""" -Tests for Milvus integration module - -Tests cover basic functionality with mocked MilvusClient -""" - -from unittest.mock import Mock, patch - -import numpy as np -import pytest - - -@pytest.mark.unit -class TestMilvusBackendBasic: - """Test basic Milvus backend operations""" - - @patch("pymilvus.MilvusClient") - def test_init_and_add_dense_documents(self, mock_milvus_client): - """测试初始化和添加稠密向量文档""" - from sage.middleware.components.vector_stores.milvus import MilvusBackend - - mock_client = Mock() - mock_milvus_client.return_value = mock_client - mock_client.has_collection.return_value = True - mock_client.insert.return_value = {"insert_count": 3} - - backend = MilvusBackend( - {"host": "localhost", "collection_name": "test", "search_type": "dense", "dim": 768} - ) - - # Add documents (note: API parameter is dense_embeddings not embeddings) - documents = ["doc1", "doc2", "doc3"] - dense_embeddings = [np.random.rand(768) for _ in range(3)] - doc_ids = ["id1", "id2", "id3"] - - result = backend.add_dense_documents( - documents=documents, dense_embeddings=dense_embeddings, doc_ids=doc_ids - ) - - # Verify insert was called and returned IDs - mock_client.insert.assert_called_once() - assert len(result) == 3 # Returns generated doc_ids - - @patch("pymilvus.MilvusClient") - def test_dense_search(self, mock_milvus_client): - """测试稠密向量搜索""" - from sage.middleware.components.vector_stores.milvus import MilvusBackend - - mock_client = Mock() - mock_milvus_client.return_value = mock_client - mock_client.has_collection.return_value = True - - # Create mock result objects with entity attribute - mock_result1 = Mock() - mock_result1.entity = {"text": "doc1"} - mock_result2 = Mock() - mock_result2.entity = {"text": "doc2"} - - mock_client.search.return_value = [[mock_result1, mock_result2]] - - backend = MilvusBackend( - {"host": "localhost", "collection_name": "test", "search_type": "dense"} - ) - - query_vector = np.random.rand(1024) - results = backend.dense_search(query_vector=query_vector, top_k=2) - - mock_client.search.assert_called_once() - assert len(results) == 2 - assert results[0] == "doc1" - assert results[1] == "doc2" - - @patch("pymilvus.MilvusClient") - def test_sparse_operations(self, mock_milvus_client): - """测试稀疏向量操作""" - from sage.middleware.components.vector_stores.milvus import MilvusBackend - - mock_client = Mock() - mock_milvus_client.return_value = mock_client - mock_client.has_collection.return_value = True - mock_client.insert.return_value = {"insert_count": 2} - - backend = MilvusBackend( - {"host": "localhost", "collection_name": "test", "search_type": "sparse"} - ) - - documents = ["doc1", "doc2"] - sparse_embeddings = [{0: 0.5, 10: 0.3}, {5: 0.7, 50: 0.3}] - doc_ids = ["id1", "id2"] - - result = backend.add_sparse_documents( - documents=documents, sparse_embeddings=sparse_embeddings, doc_ids=doc_ids - ) - - mock_client.insert.assert_called_once() - assert result == doc_ids - - @patch("pymilvus.MilvusClient") - def test_collection_management(self, mock_milvus_client): - """测试集合管理操作""" - from sage.middleware.components.vector_stores.milvus import MilvusBackend - - mock_client = Mock() - mock_milvus_client.return_value = mock_client - mock_client.has_collection.return_value = True - mock_client.drop_collection.return_value = None - mock_client.describe_collection.return_value = { - "collection_name": "test", - "num_entities": 100, - } - - backend = MilvusBackend({"host": "localhost", "collection_name": "test"}) - - # Test get info - info = backend.get_collection_info() - assert isinstance(info, dict) - assert info["backend"] == "milvus" - assert info["collection_name"] == "test" - - # Test delete - result = backend.delete_collection("test") - mock_client.drop_collection.assert_called_once_with("test") - assert result is True diff --git a/packages/sage-middleware/tests/conftest.py b/packages/sage-middleware/tests/conftest.py deleted file mode 100644 index d305cf4d3d..0000000000 --- a/packages/sage-middleware/tests/conftest.py +++ /dev/null @@ -1,18 +0,0 @@ -import warnings - -# Filter Swig warnings from importlib -warnings.filterwarnings("ignore", message="builtin type SwigPyPacked has no __module__ attribute") -warnings.filterwarnings("ignore", message="builtin type SwigPyObject has no __module__ attribute") -warnings.filterwarnings("ignore", message="builtin type swigvarlink has no __module__ attribute") - -# NOTE: We removed the aggressive module mocking that was causing test failures. -# Tests that need specific modules should handle import errors gracefully -# using try/except or pytest.importorskip(). -# -# Previous problematic mocking: -# - mock_module("faiss") -> caused isinstance() errors with faiss.IndexIDMap -# - mock_module("sage.middleware.components.sage_refiner") -> caused ContextService tests to fail -# - mock_module("torch") -> caused TextDetector tests to fail -# -# If a test needs to mock these dependencies, it should do so at the test level, -# not globally in conftest.py. diff --git a/packages/sage-middleware/tests/operators/__init__.py b/packages/sage-middleware/tests/operators/__init__.py deleted file mode 100644 index c173159b6f..0000000000 --- a/packages/sage-middleware/tests/operators/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Tests for sage.middleware.operators""" diff --git a/packages/sage-middleware/tests/operators/agentic/__init__.py b/packages/sage-middleware/tests/operators/agentic/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/packages/sage-middleware/tests/operators/agentic/test_agentic_runtime.py b/packages/sage-middleware/tests/operators/agentic/test_agentic_runtime.py deleted file mode 100644 index 883bfffd7b..0000000000 --- a/packages/sage-middleware/tests/operators/agentic/test_agentic_runtime.py +++ /dev/null @@ -1,89 +0,0 @@ -""" -测试 sage.middleware.operators.agentic 模块 -""" - -import pytest - -from sage.middleware.operators.agentic.runtime import ( - AgentRuntimeOperator, - _build_profile, - _build_tools, -) - - -@pytest.mark.unit -class TestAgentRuntimeOperatorHelpers: - """测试AgentRuntimeOperator辅助函数""" - - def test_build_profile_from_dict(self): - """测试从字典构建Profile""" - config = { - "name": "TestAgent", - "description": "A test agent", - "role": "assistant", - } - profile = _build_profile(config) - assert profile is not None - assert profile.name == "TestAgent" - assert profile.role == "assistant" - - def test_build_profile_from_none(self): - """测试从None构建默认Profile""" - profile = _build_profile(None) - assert profile is not None - # Should create a default profile - - def test_build_tools_from_empty_list(self): - """测试从空列表构建工具注册表""" - registry = _build_tools([]) - assert registry is not None - # Should create an empty registry - - def test_build_tools_from_none(self): - """测试从None构建工具注册表""" - registry = _build_tools(None) - assert registry is not None - - -@pytest.mark.unit -class TestAgentRuntimeOperatorInit: - """测试AgentRuntimeOperator初始化""" - - def test_agent_runtime_operator_config_none(self): - """测试config为None时的行为""" - with pytest.raises(ValueError, match="generator config"): - # 应该因为缺少generator配置而失败 - AgentRuntimeOperator(config=None) - - def test_agent_runtime_operator_missing_generator(self): - """测试缺少generator配置""" - config = { - "profile": {"name": "TestBot"}, - "tools": [], - } - with pytest.raises(ValueError, match="generator config"): - AgentRuntimeOperator(config=config) - - def test_build_profile_creates_default(self): - """测试构建默认profile""" - from sage_libs.sage_agentic.agents.profile.profile import BaseProfile - - profile = _build_profile(None) - assert isinstance(profile, BaseProfile) - - def test_build_tools_creates_registry(self): - """测试构建工具注册表""" - from sage_libs.sage_agentic.agents.action.mcp_registry import MCPRegistry - - registry = _build_tools([]) - assert isinstance(registry, MCPRegistry) - - -@pytest.mark.integration -class TestAgentRuntimeOperatorIntegration: - """测试AgentRuntimeOperator集成（需要外部依赖）""" - - @pytest.mark.external - def test_agent_runtime_operator_execute(self): - """测试operator的execute方法（需要真实配置）""" - pytest.skip("Requires real API keys and configuration") diff --git a/packages/sage-middleware/tests/operators/agentic/test_engine_type.py b/packages/sage-middleware/tests/operators/agentic/test_engine_type.py deleted file mode 100644 index 2f9d907b14..0000000000 --- a/packages/sage-middleware/tests/operators/agentic/test_engine_type.py +++ /dev/null @@ -1,270 +0,0 @@ -""" -Tests for AgentRuntimeOperator with engine_type switching and mock mode. -""" - -import pytest - -from sage.middleware.operators.agentic import ( - AgentRuntimeConfig, - AgentRuntimeOperator, - GeneratorConfig, -) -from sage.middleware.operators.agentic.runtime import ( - _build_generator, - _build_profile, - _build_tools, -) - - -@pytest.mark.unit -class TestGeneratorConfig: - """Test GeneratorConfig dataclass.""" - - def test_default_values(self): - """Test default configuration values.""" - config = GeneratorConfig() - assert config.engine_type == "sagellm" - assert config.backend_type == "auto" - assert config.max_tokens == 2048 - assert config.temperature == 0.7 - - def test_mock_backend(self): - """Test mock backend configuration.""" - config = GeneratorConfig(engine_type="sagellm", backend_type="mock") - assert config.engine_type == "sagellm" - assert config.backend_type == "mock" - - def test_openai_config(self): - """Test OpenAI configuration.""" - config = GeneratorConfig( - engine_type="openai", - model_name="gpt-4o-mini", - api_key="test-key", # pragma: allowlist secret - ) - assert config.engine_type == "openai" - assert config.model_name == "gpt-4o-mini" - - def test_to_dict(self): - """Test conversion to dictionary.""" - config = GeneratorConfig(engine_type="sagellm", backend_type="mock") - d = config.to_dict() - assert d["engine_type"] == "sagellm" - assert d["backend_type"] == "mock" - - -@pytest.mark.unit -class TestAgentRuntimeConfig: - """Test AgentRuntimeConfig dataclass.""" - - def test_default_config(self): - """Test default configuration.""" - config = AgentRuntimeConfig() - assert config.generator.engine_type == "sagellm" - assert config.profile.name == "DefaultAgent" - assert config.runtime.max_steps == 6 - - def test_for_mock_testing(self): - """Test factory method for mock testing.""" - config = AgentRuntimeConfig.for_mock_testing(profile_name="MockBot") - assert config.generator.engine_type == "sagellm" - assert config.generator.backend_type == "mock" - assert config.profile.name == "MockBot" - - def test_for_openai(self): - """Test factory method for OpenAI.""" - config = AgentRuntimeConfig.for_openai( - model_name="gpt-4o", - api_key="sk-test", # pragma: allowlist secret - ) - assert config.generator.engine_type == "openai" - assert config.generator.model_name == "gpt-4o" - - def test_for_sagellm(self): - """Test factory method for SageLLM.""" - config = AgentRuntimeConfig.for_sagellm( - model_path="Qwen/Qwen2.5-7B-Instruct", - backend_type="auto", - ) - assert config.generator.engine_type == "sagellm" - assert config.generator.model_path == "Qwen/Qwen2.5-7B-Instruct" - - def test_to_dict(self): - """Test conversion to dictionary.""" - config = AgentRuntimeConfig.for_mock_testing() - d = config.to_dict() - assert "generator" in d - assert "profile" in d - assert "runtime" in d - assert d["generator"]["engine_type"] == "sagellm" - assert d["generator"]["backend_type"] == "mock" - - -@pytest.mark.unit -class TestBuildGenerator: - """Test _build_generator function with engine_type.""" - - def test_build_sagellm_generator(self): - """Test building SageLLM generator.""" - from sage.middleware.operators.llm import SageLLMGenerator - - config = {"engine_type": "sagellm", "backend_type": "auto"} - generator = _build_generator(config, engine_type="sagellm") - assert isinstance(generator, SageLLMGenerator) - assert generator.backend_type == "auto" - - def test_build_mock_generator(self): - """Test building mock SageLLM generator.""" - from sage.middleware.operators.llm import SageLLMGenerator - - config = {"engine_type": "sagellm", "backend_type": "mock"} - generator = _build_generator(config, engine_type="sagellm") - assert isinstance(generator, SageLLMGenerator) - assert generator.backend_type == "mock" - - def test_build_openai_generator(self): - """Test building OpenAI generator.""" - from sage.middleware.operators.rag.generator import OpenAIGenerator - - config = {"model_name": "gpt-4o-mini"} - generator = _build_generator(config, engine_type="openai") - assert isinstance(generator, OpenAIGenerator) - - def test_engine_type_from_config(self): - """Test engine_type is read from config dict.""" - from sage.middleware.operators.llm import SageLLMGenerator - - config = {"engine_type": "sagellm", "backend_type": "mock"} - # engine_type in config should override the parameter - generator = _build_generator(config, engine_type="openai") - assert isinstance(generator, SageLLMGenerator) - - @pytest.mark.skip(reason="vllm support has been removed in v0.3.0") - def test_vllm_deprecation_warning(self): - """Test that vllm engine_type raises deprecation warning. - - Note: This test is skipped as vllm support has been completely removed. - """ - pass - - def test_missing_config_raises(self): - """Test that missing config raises ValueError.""" - with pytest.raises(ValueError, match="generator config"): - _build_generator(None) - - -@pytest.mark.unit -class TestAgentRuntimeOperatorEngineType: - """Test AgentRuntimeOperator engine_type handling.""" - - def test_default_engine_type(self): - """Test default engine_type is sagellm.""" - config = { - "generator": {"backend_type": "mock"}, - "profile": {"name": "TestBot"}, - "tools": [], - } - operator = AgentRuntimeOperator(config=config) - assert operator.engine_type == "sagellm" - - def test_engine_type_from_generator_config(self): - """Test engine_type from generator config.""" - config = { - "generator": {"engine_type": "sagellm", "backend_type": "mock"}, - "profile": {"name": "TestBot"}, - "tools": [], - } - operator = AgentRuntimeOperator(config=config) - assert operator.engine_type == "sagellm" - - def test_engine_type_from_top_level_config(self): - """Test engine_type from top-level config.""" - config = { - "engine_type": "sagellm", - "generator": {"backend_type": "mock"}, - "profile": {"name": "TestBot"}, - "tools": [], - } - operator = AgentRuntimeOperator(config=config) - assert operator.engine_type == "sagellm" - - def test_generator_config_engine_type_priority(self): - """Test that generator config engine_type has priority over top-level.""" - config = { - "engine_type": "openai", # top-level - "generator": {"engine_type": "sagellm", "backend_type": "mock"}, # should win - "profile": {"name": "TestBot"}, - "tools": [], - } - operator = AgentRuntimeOperator(config=config) - assert operator.engine_type == "sagellm" - - -@pytest.mark.unit -class TestAgentRuntimeOperatorMockMode: - """Test AgentRuntimeOperator with mock backend for testing.""" - - def test_operator_with_mock_config(self): - """Test creating operator with mock configuration.""" - config = AgentRuntimeConfig.for_mock_testing().to_dict() - operator = AgentRuntimeOperator(config=config) - assert operator.engine_type == "sagellm" - assert operator.generator.backend_type == "mock" - - def test_operator_components_initialized(self): - """Test that all components are properly initialized.""" - config = AgentRuntimeConfig.for_mock_testing().to_dict() - operator = AgentRuntimeOperator(config=config) - - assert operator.profile is not None - assert operator.generator is not None - assert operator.planner is not None - assert operator.tools is not None - assert operator.runtime is not None - - def test_execute_with_string_input(self): - """Test execute with string input.""" - config = AgentRuntimeConfig.for_mock_testing().to_dict() - operator = AgentRuntimeOperator(config=config) - - # Mock mode should return without real LLM call - # The actual behavior depends on SageLLMGenerator mock implementation - # This test verifies the operator can be instantiated and called - assert callable(operator.execute) - - def test_execute_with_dict_input(self): - """Test execute with dict input.""" - config = AgentRuntimeConfig.for_mock_testing().to_dict() - operator = AgentRuntimeOperator(config=config) - - assert callable(operator.execute) - - -@pytest.mark.unit -class TestProfileAndToolsBuilding: - """Test profile and tools building functions.""" - - def test_build_profile_from_dict(self): - """Test building profile from dict.""" - config = { - "name": "TestAgent", - "description": "A test agent", - "role": "assistant", - } - profile = _build_profile(config) - assert profile.name == "TestAgent" - assert profile.role == "assistant" - - def test_build_profile_from_none(self): - """Test building default profile from None.""" - profile = _build_profile(None) - assert profile is not None - - def test_build_tools_from_empty_list(self): - """Test building empty tools registry.""" - registry = _build_tools([]) - assert registry is not None - - def test_build_tools_from_none(self): - """Test building tools registry from None.""" - registry = _build_tools(None) - assert registry is not None diff --git a/packages/sage-middleware/tests/operators/agentic/test_operators.py b/packages/sage-middleware/tests/operators/agentic/test_operators.py deleted file mode 100644 index 75d5aa78b3..0000000000 --- a/packages/sage-middleware/tests/operators/agentic/test_operators.py +++ /dev/null @@ -1,129 +0,0 @@ -""" -Tests for agentic operators. -""" - -from sage.middleware.operators.agentic import ( - PlanningOperator, - TimingOperator, - ToolSelectionOperator, -) - - -class MockSelector: - """Mock selector for testing.""" - - def select(self, query, top_k=5): - return [{"tool_id": f"tool_{i}"} for i in range(top_k)] - - -class MockPlanner: - """Mock planner for testing.""" - - def plan(self, request): - return {"steps": [{"action": "do_something"}]} - - -class MockTimingDecider: - """Mock timing decider for testing.""" - - def decide(self, message): - return {"decision": "call"} - - -class TestToolSelectionOperator: - """Test tool selection operator.""" - - def test_operator_initialization(self): - """Test creating operator.""" - selector = MockSelector() - operator = ToolSelectionOperator(selector=selector, config={"selector": {"top_k": 5}}) - - assert operator.orchestrator is not None - assert operator.adapter is not None - - def test_operator_call(self): - """Test calling operator.""" - selector = MockSelector() - operator = ToolSelectionOperator(selector=selector, config={"selector": {"top_k": 3}}) - - result = operator("test query") - - assert len(result) == 3 - assert result[0]["tool_id"] == "tool_0" - - def test_operator_metrics(self): - """Test getting metrics.""" - selector = MockSelector() - operator = ToolSelectionOperator(selector=selector) - - operator("query1") - operator("query2") - - metrics = operator.get_metrics() - assert metrics["total_operations"] == 2 - - -class TestPlanningOperator: - """Test planning operator.""" - - def test_operator_initialization(self): - """Test creating operator.""" - planner = MockPlanner() - operator = PlanningOperator(planner=planner, config={"planner": {"max_steps": 10}}) - - assert operator.orchestrator is not None - assert operator.adapter is not None - - def test_operator_call(self): - """Test calling operator.""" - planner = MockPlanner() - operator = PlanningOperator(planner=planner) - - result = operator("test request") - - assert "steps" in result - assert len(result["steps"]) > 0 - - def test_operator_metrics(self): - """Test getting metrics.""" - planner = MockPlanner() - operator = PlanningOperator(planner=planner) - - operator("request1") - - metrics = operator.get_metrics() - assert metrics["total_operations"] == 1 - - -class TestTimingOperator: - """Test timing operator.""" - - def test_operator_initialization(self): - """Test creating operator.""" - timing_decider = MockTimingDecider() - operator = TimingOperator( - timing_decider=timing_decider, config={"timing": {"threshold": 0.7}} - ) - - assert operator.orchestrator is not None - assert operator.adapter is not None - - def test_operator_call(self): - """Test calling operator.""" - timing_decider = MockTimingDecider() - operator = TimingOperator(timing_decider=timing_decider) - - result = operator("test message") - - assert result["decision"] == "call" - - def test_operator_metrics(self): - """Test getting metrics.""" - timing_decider = MockTimingDecider() - operator = TimingOperator(timing_decider=timing_decider) - - operator("message1") - operator("message2") - - metrics = operator.get_metrics() - assert metrics["total_operations"] == 2 diff --git a/packages/sage-middleware/tests/operators/agentic/test_refined_searcher.py b/packages/sage-middleware/tests/operators/agentic/test_refined_searcher.py deleted file mode 100644 index 2dc8044b5f..0000000000 --- a/packages/sage-middleware/tests/operators/agentic/test_refined_searcher.py +++ /dev/null @@ -1,41 +0,0 @@ -from unittest.mock import AsyncMock, MagicMock, patch - -import pytest - -from sage.middleware.operators.agentic.refined_searcher import RefinedSearcherOperator - - -class TestRefinedSearcherOperator: - @pytest.fixture - def mock_tool(self): - tool = MagicMock() - tool.name = "mock_search" - tool.run = AsyncMock(return_value=[{"source": "mock", "content": "result"}]) - return tool - - def test_initialization(self, mock_tool): - operator = RefinedSearcherOperator(tools=[mock_tool]) - assert operator.name == "search_internet" - assert operator.bot is not None - - def test_call_missing_query(self, mock_tool): - operator = RefinedSearcherOperator(tools=[mock_tool]) - - # Call with empty arguments - result = operator.call({}) - - # Expect empty results instead of error, as per implementation - assert result == {"results": []} - - @patch("asyncio.run") - def test_call_valid_query(self, mock_asyncio_run, mock_tool): - operator = RefinedSearcherOperator(tools=[mock_tool]) - - # Mock execute to return a result - mock_asyncio_run.return_value = {"results": ["some result"]} - - result = operator.call({"query": "test"}) - - assert result == {"results": ["some result"]} - # Verify execute was called (indirectly via asyncio.run) - mock_asyncio_run.assert_called_once() diff --git a/packages/sage-middleware/tests/operators/filters/__init__.py b/packages/sage-middleware/tests/operators/filters/__init__.py deleted file mode 100644 index 86819c7359..0000000000 --- a/packages/sage-middleware/tests/operators/filters/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Tests for middleware filters.""" diff --git a/packages/sage-middleware/tests/operators/filters/test_context_sink.py b/packages/sage-middleware/tests/operators/filters/test_context_sink.py deleted file mode 100644 index 990c3fb395..0000000000 --- a/packages/sage-middleware/tests/operators/filters/test_context_sink.py +++ /dev/null @@ -1,622 +0,0 @@ -""" -Comprehensive tests for ContextFileSink class. - -Tests cover initialization, configuration, file operations, indexing, -directory organization, and various edge cases. -""" - -import json -import threading -import time -from pathlib import Path -from unittest.mock import Mock, patch - -import pytest - -from sage.middleware.operators.context.model_context import ModelContext -from sage.middleware.operators.filters.context_sink import ContextFileSink - - -class TestContextFileSinkInitialization: - """Test ContextFileSink initialization and configuration.""" - - def test_get_default_template_directory(self, tmp_path): - """Test getting default template directory.""" - with patch( - "sage.middleware.operators.filters.context_sink.os.getcwd", return_value=str(tmp_path) - ): - default_dir = ContextFileSink.get_default_template_directory() - expected = str(tmp_path / ".sage" / "data" / "model_context") - assert default_dir == expected - assert Path(default_dir).exists() - - def test_get_default_config(self): - """Test default configuration.""" - config = ContextFileSink.get_default_config() - - assert config["base_directory"] is None - assert config["stage_directory"] == "general" - assert config["file_format"] == "json" - assert config["organization"] == "date" - assert config["max_files_per_dir"] == 1000 - assert config["create_index"] is True - assert config["auto_create_dirs"] is True - assert config["compress_old_files"] is False - assert config["backup_index"] is True - - def test_init_with_valid_config(self, tmp_path): - """Test initialization with valid config.""" - config = { - "base_directory": str(tmp_path), - "stage_directory": "test_stage", - "file_format": "jsonl", - "organization": "uuid", - } - sink = ContextFileSink(config=config) - - assert sink.config["base_directory"] == str(tmp_path) - assert sink.config["stage_directory"] == "test_stage" - assert sink.config["file_format"] == "jsonl" - assert sink.config["organization"] == "uuid" - assert sink.full_directory.exists() - - def test_init_with_invalid_config_type(self): - """Test initialization with invalid config type raises TypeError.""" - with pytest.raises(TypeError, match="Expected a dict for config"): - ContextFileSink(config="not_a_dict") - - def test_init_with_empty_config(self, tmp_path): - """Test initialization with empty config uses defaults.""" - config = {} - sink = ContextFileSink(config=config) - - assert sink.config["stage_directory"] == "general" - assert sink.config["file_format"] == "json" - assert sink.config["organization"] == "date" - - def test_init_with_legacy_parameters(self, tmp_path): - """Test backward compatibility with legacy parameters.""" - config = {"base_directory": str(tmp_path)} - sink = ContextFileSink( - config=config, - stage_directory="custom_stage", - file_format="jsonl", - ) - - # Legacy params should override config if provided - assert sink.config["stage_directory"] == "custom_stage" - assert sink.config["file_format"] == "jsonl" - - def test_init_creates_directories(self, tmp_path): - """Test that initialization creates necessary directories.""" - config = { - "base_directory": str(tmp_path), - "stage_directory": "my_stage", - "auto_create_dirs": True, - } - sink = ContextFileSink(config=config) - - assert sink.full_directory.exists() - assert sink.full_directory == tmp_path / "my_stage" - - -class TestContextFileSinkDirectorySetup: - """Test directory setup and organization.""" - - def test_setup_directories_with_base_directory(self, tmp_path): - """Test directory setup with base directory.""" - config = {"base_directory": str(tmp_path), "stage_directory": "stage1"} - sink = ContextFileSink(config=config) - - assert sink.base_directory == tmp_path - assert sink.stage_directory == tmp_path / "stage1" - assert sink.full_directory == tmp_path / "stage1" - - def test_setup_directories_with_default_base(self, tmp_path): - """Test directory setup with default base directory.""" - config = {"base_directory": None, "stage_directory": "stage1"} - with patch( - "sage.middleware.operators.filters.context_sink.ContextFileSink.get_default_template_directory", - return_value=str(tmp_path / ".sage" / "data" / "model_context"), - ): - sink = ContextFileSink(config=config) - assert str(sink.base_directory).endswith(".sage/data/model_context") - - def test_setup_directories_auto_create_disabled(self, tmp_path): - """Test directory setup with auto_create_dirs disabled.""" - config = { - "base_directory": str(tmp_path), - "stage_directory": "stage1", - "auto_create_dirs": False, - "create_index": False, # Disable index creation to avoid file creation errors - } - sink = ContextFileSink(config=config) - - # Directory should not exist since auto_create_dirs is False - assert not sink.full_directory.exists() - - def test_set_stage_directory(self, tmp_path): - """Test dynamically setting stage directory.""" - config = { - "base_directory": str(tmp_path), - "stage_directory": "initial_stage", - } - sink = ContextFileSink(config=config) - old_dir = sink.full_directory - - sink.set_stage_directory("new_stage") - - assert sink.config["stage_directory"] == "new_stage" - assert sink.full_directory == tmp_path / "new_stage" - assert sink.full_directory != old_dir - - -class TestContextFileSinkIndexing: - """Test index file creation and updates.""" - - def test_initialize_index(self, tmp_path): - """Test index file initialization.""" - config = { - "base_directory": str(tmp_path), - "stage_directory": "test_stage", - "create_index": True, - } - sink = ContextFileSink(config=config) - - assert sink.index_file.exists() - with open(sink.index_file, encoding="utf-8") as f: - index_data = json.load(f) - - assert "created_at" in index_data - assert index_data["total_templates"] == 0 - assert "config" in index_data - assert "directory_structure" in index_data - assert "templates" in index_data - - def test_index_not_created_when_disabled(self, tmp_path): - """Test that index is not created when create_index is False.""" - config = { - "base_directory": str(tmp_path), - "stage_directory": "test_stage", - "create_index": False, - } - sink = ContextFileSink(config=config) - - assert not sink.index_file.exists() - - def test_index_backup_on_reinit(self, tmp_path): - """Test that existing index is backed up when reinitializing.""" - config = { - "base_directory": str(tmp_path), - "stage_directory": "test_stage", - "create_index": True, - "backup_index": True, - } - sink = ContextFileSink(config=config) - - # Reinitialize with new sink - sink._initialize_index() - - # Backup should be created - backup_files = list((tmp_path / "test_stage").glob("template_index.backup_*.json")) - assert len(backup_files) > 0 - - -class TestContextFileSinkFilePathGeneration: - """Test file path generation based on organization strategy.""" - - def create_model_context(self, uuid="test-uuid", sequence=1, timestamp=None): - """Helper to create a ModelContext instance.""" - if timestamp is None: - timestamp = int(time.time() * 1000) - - ctx = ModelContext( - uuid=uuid, - sequence=sequence, - timestamp=timestamp, - raw_question="test question", - ) - return ctx - - def test_get_file_path_date_organization(self, tmp_path): - """Test file path with date-based organization.""" - config = { - "base_directory": str(tmp_path), - "stage_directory": "test_stage", - "organization": "date", - "file_format": "json", - } - sink = ContextFileSink(config=config) - - ctx = self.create_model_context(timestamp=1704067200000) # 2024-01-01 - file_path = sink._get_file_path(ctx) - - # Path should contain year/month/day structure - assert "2024" in str(file_path) - assert "01" in str(file_path) - assert "01" in str(file_path) - assert file_path.name.startswith("template_") - assert file_path.suffix == ".json" - - def test_get_file_path_sequence_organization(self, tmp_path): - """Test file path with sequence-based organization.""" - config = { - "base_directory": str(tmp_path), - "stage_directory": "test_stage", - "organization": "sequence", - "file_format": "json", - "max_files_per_dir": 1000, - } - sink = ContextFileSink(config=config) - - ctx = self.create_model_context(sequence=100) - file_path = sink._get_file_path(ctx) - - assert "seq_000000-000999" in str(file_path) - assert file_path.suffix == ".json" - - def test_get_file_path_uuid_organization(self, tmp_path): - """Test file path with UUID-based organization.""" - config = { - "base_directory": str(tmp_path), - "stage_directory": "test_stage", - "organization": "uuid", - "file_format": "json", - } - sink = ContextFileSink(config=config) - - ctx = self.create_model_context(uuid="abcdef1234567890") - file_path = sink._get_file_path(ctx) - - # Path should contain first two chars as first dir, next two as second dir - assert "ab" in str(file_path) or "cd" in str(file_path) - - def test_get_file_path_with_jsonl_format(self, tmp_path): - """Test file path with JSONL format.""" - config = { - "base_directory": str(tmp_path), - "stage_directory": "test_stage", - "organization": "date", - "file_format": "jsonl", - } - sink = ContextFileSink(config=config) - - ctx = self.create_model_context() - file_path = sink._get_file_path(ctx) - - assert file_path.suffix == ".jsonl" - - def test_get_file_path_creates_directories(self, tmp_path): - """Test that get_file_path creates necessary directories.""" - config = { - "base_directory": str(tmp_path), - "stage_directory": "test_stage", - "organization": "uuid", - "auto_create_dirs": True, - } - sink = ContextFileSink(config=config) - - ctx = self.create_model_context(uuid="abcd1234567890ef") - file_path = sink._get_file_path(ctx) - - # Directory should be created - assert file_path.parent.exists() - - -class TestContextFileSinkExecution: - """Test file saving and index updates.""" - - def create_model_context(self, uuid="test-uuid"): - """Helper to create a ModelContext instance.""" - ctx = ModelContext( - uuid=uuid, - sequence=1, - timestamp=int(time.time() * 1000), - raw_question="test question", - response="test response", - ) - return ctx - - @patch("sage.middleware.operators.filters.context_sink.ModelContext.save_to_file") - def test_execute_saves_json_file(self, mock_save, tmp_path): - """Test that execute saves file in JSON format.""" - config = { - "base_directory": str(tmp_path), - "stage_directory": "test_stage", - "file_format": "json", - "create_index": False, - } - sink = ContextFileSink(config=config) - - ctx = self.create_model_context() - sink.execute(ctx) - - # Check that save_to_file was called - assert mock_save.called - - def test_execute_saves_jsonl_file(self, tmp_path): - """Test that execute saves file in JSONL format.""" - config = { - "base_directory": str(tmp_path), - "stage_directory": "test_stage", - "file_format": "jsonl", - "organization": "date", - "create_index": False, - } - sink = ContextFileSink(config=config) - - ctx = self.create_model_context() - with patch.object(ctx, "to_json", return_value='{"test": "data"}'): - sink.execute(ctx) - - # Find and verify JSONL file was created - jsonl_files = list(tmp_path.glob("**/template_*.jsonl")) - assert len(jsonl_files) > 0 - - def test_execute_updates_index(self, tmp_path): - """Test that execute updates the index file.""" - config = { - "base_directory": str(tmp_path), - "stage_directory": "test_stage", - "create_index": True, - } - sink = ContextFileSink(config=config) - - ctx = self.create_model_context(uuid="test-uuid-123") - with patch.object(ctx, "save_to_file"): - sink.execute(ctx) - - # Check index was updated - with open(sink.index_file, encoding="utf-8") as f: - index_data = json.load(f) - - assert index_data["total_templates"] == 1 - assert "test-uuid-123" in index_data["templates"] - - def test_execute_increments_saved_count(self, tmp_path): - """Test that execute increments saved_count.""" - config = { - "base_directory": str(tmp_path), - "stage_directory": "test_stage", - "create_index": False, - } - sink = ContextFileSink(config=config) - - ctx1 = self.create_model_context(uuid="uuid1") - ctx2 = self.create_model_context(uuid="uuid2") - - with patch.object(ctx1, "save_to_file"): - sink.execute(ctx1) - assert sink.saved_count == 1 - - with patch.object(ctx2, "save_to_file"): - sink.execute(ctx2) - assert sink.saved_count == 2 - - def test_execute_handles_exceptions(self, tmp_path): - """Test that execute handles exceptions gracefully.""" - config = { - "base_directory": str(tmp_path), - "stage_directory": "test_stage", - "create_index": False, - } - sink = ContextFileSink(config=config) - - ctx = self.create_model_context() - with patch.object(ctx, "save_to_file", side_effect=Exception("Test error")): - # Should not raise, but log error - sink.execute(ctx) - assert sink.saved_count == 0 - - -class TestContextFileSinkStatistics: - """Test statistics and information methods.""" - - def test_get_storage_info(self, tmp_path): - """Test getting storage information.""" - config = { - "base_directory": str(tmp_path), - "stage_directory": "test_stage", - } - sink = ContextFileSink(config=config) - - info = sink.get_storage_info() - - assert "config" in info - assert "directory_structure" in info - assert "runtime_stats" in info - assert info["runtime_stats"]["saved_count"] == 0 - assert info["directory_structure"]["stage_directory"] == str(tmp_path / "test_stage") - - def test_get_stage_statistics_no_index(self, tmp_path): - """Test get_stage_statistics when index doesn't exist.""" - config = { - "base_directory": str(tmp_path), - "stage_directory": "test_stage", - "create_index": False, - } - sink = ContextFileSink(config=config) - - stats = sink.get_stage_statistics() - - assert "error" in stats - - def test_get_stage_statistics_with_data(self, tmp_path): - """Test get_stage_statistics with template data.""" - config = { - "base_directory": str(tmp_path), - "stage_directory": "test_stage", - "create_index": True, - } - sink = ContextFileSink(config=config) - - # Manually add template to index - with open(sink.index_file, encoding="utf-8") as f: - index_data = json.load(f) - - index_data["total_templates"] = 2 - index_data["templates"]["uuid1"] = { - "has_response": True, - "response_length": 100, - "chunks_count": 5, - "prompts_count": 3, - "timestamp": 1704067200000, - } - index_data["templates"]["uuid2"] = { - "has_response": False, - "response_length": 0, - "chunks_count": 3, - "prompts_count": 2, - "timestamp": 1704067300000, - } - - with open(sink.index_file, "w", encoding="utf-8") as f: - json.dump(index_data, f) - - stats = sink.get_stage_statistics() - - assert stats["total_templates"] == 2 - assert stats["with_response"] == 1 - assert stats["without_response"] == 1 - assert stats["avg_chunks"] > 0 - assert stats["avg_prompts"] > 0 - - -class TestContextFileSinkRuntimeInit: - """Test runtime initialization.""" - - def test_runtime_init(self, tmp_path): - """Test runtime_init logs correctly.""" - config = { - "base_directory": str(tmp_path), - "stage_directory": "test_stage", - } - sink = ContextFileSink(config=config) - - # Verify runtime_init doesn't raise an exception - sink.runtime_init(Mock()) - # If we reach here, the test passes (logger is written internally) - - -class TestContextFileSinkThreadSafety: - """Test thread safety of index operations.""" - - def test_index_lock_exists(self, tmp_path): - """Test that index_lock is created.""" - config = { - "base_directory": str(tmp_path), - "stage_directory": "test_stage", - "create_index": True, - } - sink = ContextFileSink(config=config) - - # Check that index_lock exists and is a lock-like object - assert hasattr(sink, "index_lock") - assert sink.index_lock is not None - assert hasattr(sink.index_lock, "acquire") - assert hasattr(sink.index_lock, "release") - - def test_concurrent_index_updates(self, tmp_path): - """Test concurrent index updates are handled safely.""" - config = { - "base_directory": str(tmp_path), - "stage_directory": "test_stage", - "create_index": True, - "file_format": "json", - } - sink = ContextFileSink(config=config) - - def update_index(): - ctx = ModelContext( - uuid=f"test-uuid-{threading.current_thread().ident}", - sequence=1, - timestamp=int(time.time() * 1000), - raw_question="test", - response="test", - ) - with patch.object(ctx, "save_to_file"): - sink.execute(ctx) - - threads = [threading.Thread(target=update_index) for _ in range(5)] - for thread in threads: - thread.start() - for thread in threads: - thread.join() - - # Verify all updates were recorded - with open(sink.index_file, encoding="utf-8") as f: - index_data = json.load(f) - - assert index_data["total_templates"] == 5 - - -class TestContextFileSinkEdgeCases: - """Test edge cases and error conditions.""" - - def test_execute_with_none_response(self, tmp_path): - """Test execute with None response.""" - config = { - "base_directory": str(tmp_path), - "stage_directory": "test_stage", - "create_index": True, - } - sink = ContextFileSink(config=config) - - ctx = ModelContext( - uuid="test-uuid", - sequence=1, - timestamp=int(time.time() * 1000), - raw_question="test question", - response=None, - ) - - with patch.object(ctx, "save_to_file"): - sink.execute(ctx) - - # Should complete without error - assert sink.saved_count == 1 - - def test_execute_with_empty_raw_question(self, tmp_path): - """Test execute with empty raw_question.""" - config = { - "base_directory": str(tmp_path), - "stage_directory": "test_stage", - "create_index": True, - } - sink = ContextFileSink(config=config) - - ctx = ModelContext( - uuid="test-uuid", - sequence=1, - timestamp=int(time.time() * 1000), - raw_question=None, - response="test response", - ) - - with patch.object(ctx, "save_to_file"): - sink.execute(ctx) - - assert sink.saved_count == 1 - - def test_large_sequence_numbers(self, tmp_path): - """Test with large sequence numbers.""" - config = { - "base_directory": str(tmp_path), - "stage_directory": "test_stage", - "organization": "sequence", - "max_files_per_dir": 1000, - } - sink = ContextFileSink(config=config) - - ctx = ModelContext( - uuid="test-uuid", - sequence=999999, - timestamp=int(time.time() * 1000), - raw_question="test", - ) - - file_path = sink._get_file_path(ctx) - - # Should handle large numbers gracefully - assert file_path is not None - assert file_path.parent.exists() or not sink.config["auto_create_dirs"] diff --git a/packages/sage-middleware/tests/operators/rag/__init__.py b/packages/sage-middleware/tests/operators/rag/__init__.py deleted file mode 100644 index 54787e7b9d..0000000000 --- a/packages/sage-middleware/tests/operators/rag/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Tests for sage.middleware.operators.rag""" diff --git a/packages/sage-middleware/tests/operators/rag/test_arxiv.py b/packages/sage-middleware/tests/operators/rag/test_arxiv.py deleted file mode 100644 index 348d9afb13..0000000000 --- a/packages/sage-middleware/tests/operators/rag/test_arxiv.py +++ /dev/null @@ -1,338 +0,0 @@ -""" -Unit tests for Arxiv Paper and Operator classes -""" - -import sys -from unittest.mock import MagicMock, patch - -import pytest - -# Mock fitz module at the beginning -mock_fitz = MagicMock() -sys.modules["fitz"] = mock_fitz - - -class TestPaper: - """Test Paper class""" - - @patch("sage.middleware.operators.rag.arxiv.fitz") - def test_paper_init_with_title(self, mock_fitz_module): - """Test Paper initialization with title provided""" - # Mock FITZ_AVAILABLE to be True - with patch("sage.middleware.operators.rag.arxiv.FITZ_AVAILABLE", True): - from sage.middleware.operators.rag.arxiv import Paper - - paper = Paper( - path="/tmp/test.pdf", - title="Test Paper", - url="https://arxiv.org/abs/1234.5678", - abs="Test abstract", - authors=["Author1", "Author2"], - ) - - assert paper.title == "Test Paper" - assert paper.url == "https://arxiv.org/abs/1234.5678" - assert paper.abs == "Test abstract" - assert paper.authors == ["Author1", "Author2"] - assert paper.path == "/tmp/test.pdf" - - @patch("sage.middleware.operators.rag.arxiv.fitz") - def test_paper_init_without_title(self, mock_fitz_module): - """Test Paper initialization without title (triggers PDF parsing)""" - with patch("sage.middleware.operators.rag.arxiv.FITZ_AVAILABLE", True): - from sage.middleware.operators.rag.arxiv import Paper - - # Mock PDF document - mock_doc = MagicMock() - mock_page = MagicMock() - - # Mock text dict for title extraction - needs proper structure - mock_text_dict = { - "blocks": [ - { - "type": 0, - "lines": [ - { - "spans": [ - { - "size": 20, - "text": "Test Paper Title", - "flags": 20, # Add flags attribute - } - ] - } - ], - } - ] - } - - # Set up get_text to return dict or plain text based on format parameter - def mock_get_text(fmt="text"): - if fmt == "dict": - return mock_text_dict - return "Sample text" - - mock_page.get_text = mock_get_text - - mock_doc.__iter__ = lambda x: iter([mock_page]) - mock_doc.close = MagicMock() - mock_fitz_module.open.return_value = mock_doc - - paper = Paper(path="/tmp/test.pdf") - - assert paper.path == "/tmp/test.pdf" - assert hasattr(paper, "title") - mock_fitz_module.open.assert_called() - - @patch("sage.middleware.operators.rag.arxiv.fitz") - def test_paper_roman_num_initialization(self, mock_fitz_module): - """Test Paper roman numeral initialization""" - with patch("sage.middleware.operators.rag.arxiv.FITZ_AVAILABLE", True): - from sage.middleware.operators.rag.arxiv import Paper - - paper = Paper(path="/tmp/test.pdf", title="Test") - assert "I" in paper.roman_num - assert "X" in paper.roman_num - assert len(paper.roman_num) > 0 - - @patch("sage.middleware.operators.rag.arxiv.fitz") - def test_paper_digit_num_initialization(self, mock_fitz_module): - """Test Paper digit numeral initialization""" - with patch("sage.middleware.operators.rag.arxiv.FITZ_AVAILABLE", True): - from sage.middleware.operators.rag.arxiv import Paper - - paper = Paper(path="/tmp/test.pdf", title="Test") - assert "1" in paper.digit_num - assert "10" in paper.digit_num - assert len(paper.digit_num) == 10 - - @patch("sage.middleware.operators.rag.arxiv.fitz") - def test_get_chapter_names(self, mock_fitz_module): - """Test extracting chapter names from PDF""" - with patch("sage.middleware.operators.rag.arxiv.FITZ_AVAILABLE", True): - from sage.middleware.operators.rag.arxiv import Paper - - # Mock PDF with structured content - mock_doc = MagicMock() - mock_page = MagicMock() - # Format text to match what get_chapter_names expects - test_text = """I. Introduction -II. Background -1. Method -2. Results -""" - mock_page.get_text.return_value = test_text - mock_doc.__iter__ = lambda x: iter([mock_page]) - mock_doc.close = MagicMock() - mock_fitz_module.open.return_value = mock_doc - - paper = Paper(path="/tmp/test.pdf", title="Test") - chapters = paper.get_chapter_names() - - assert isinstance(chapters, list) - # The method looks for lines with roman/digit numerals followed by periods - # and with 1-4 space-separated parts - if len(chapters) > 0: - assert any("." in ch for ch in chapters) - - @patch("sage.middleware.operators.rag.arxiv.fitz") - def test_get_title(self, mock_fitz_module): - """Test extracting title from PDF""" - with patch("sage.middleware.operators.rag.arxiv.FITZ_AVAILABLE", True): - from sage.middleware.operators.rag.arxiv import Paper - - # Mock PDF document - mock_doc = MagicMock() - mock_page = MagicMock() - - # Mock text dict with different font sizes and proper structure - mock_text_dict = { - "blocks": [ - { - "type": 0, - "lines": [ - { - "spans": [ - { - "size": 12, - "text": "Regular text", - "flags": 4, - } - ] - } - ], - }, - { - "type": 0, - "lines": [ - { - "spans": [ - { - "size": 24, - "text": "Large Title Text", - "flags": 20, - } - ] - } - ], - }, - ] - } - - def mock_get_text(fmt="text"): - if fmt == "dict": - return mock_text_dict - return "Sample text" - - mock_page.get_text = mock_get_text - mock_doc.__iter__ = lambda x: iter([mock_page]) - mock_doc.close = MagicMock() - mock_fitz_module.open.return_value = mock_doc - - paper = Paper(path="/tmp/test.pdf", title="") - title = paper.get_title() - - assert isinstance(title, str) - - @patch("sage.middleware.operators.rag.arxiv.fitz") - def test_parse_pdf(self, mock_fitz_module): - """Test PDF parsing functionality""" - with patch("sage.middleware.operators.rag.arxiv.FITZ_AVAILABLE", True): - from sage.middleware.operators.rag.arxiv import Paper - - mock_doc = MagicMock() - mock_page = MagicMock() - - # Mock text dict for parse_pdf which calls get_text() and get_text("dict") - mock_text_dict = { - "blocks": [ - { - "type": 0, - "lines": [ - { - "spans": [ - { - "size": 12, - "text": "Test content", - "flags": 4, - } - ] - } - ], - } - ] - } - - def mock_get_text(fmt="text"): - if fmt == "dict": - return mock_text_dict - return "Test content" - - mock_page.get_text = mock_get_text - mock_doc.__iter__ = lambda x: iter([mock_page]) - mock_doc.close = MagicMock() - mock_fitz_module.open.return_value = mock_doc - - paper = Paper(path="/tmp/test.pdf", title="Test") - paper.parse_pdf() - - assert hasattr(paper, "text_list") - assert hasattr(paper, "all_text") - assert "title" in paper.section_texts - assert paper.section_texts["title"] == "Test" - - -class TestArxivSearch: - """Test Arxiv search functionality""" - - @patch("requests.get") - @patch("feedparser.parse") - def test_arxiv_search_success(self, mock_feedparser, mock_requests): - """Test successful arxiv search""" - # This would require importing the actual arxiv search function - # Adding placeholder for structure - pass - - @patch("requests.get") - def test_arxiv_download_pdf(self, mock_requests): - """Test PDF download functionality""" - # Placeholder for arxiv PDF download tests - pass - - -class TestArxivOperator: - """Test ArxivOperator if it exists in the file""" - - def test_arxiv_operator_initialization(self): - """Test operator initialization""" - # Check if ArxivOperator exists and test it - try: - from sage.middleware.operators.rag.arxiv import ArxivOperator - - operator = ArxivOperator() - assert operator is not None - except (ImportError, AttributeError): - # ArxivOperator may not exist in this file - pytest.skip("ArxivOperator not found in module") - - -class TestPaperSectionExtraction: - """Test paper section extraction methods""" - - @patch("sage.middleware.operators.rag.arxiv.fitz") - def test_extract_section_information(self, mock_fitz_module): - """Test section information extraction""" - with patch("sage.middleware.operators.rag.arxiv.FITZ_AVAILABLE", True): - from sage.middleware.operators.rag.arxiv import Paper - - mock_doc = MagicMock() - mock_page = MagicMock() - mock_page.get_text.return_value = "Test content" - mock_doc.__iter__ = lambda x: iter([mock_page]) - mock_doc.close = MagicMock() - mock_fitz_module.open.return_value = mock_doc - - paper = Paper(path="/tmp/test.pdf", title="Test") - - # Test that section_names and section_texts are initialized - assert hasattr(paper, "section_names") - assert hasattr(paper, "section_texts") - assert isinstance(paper.section_names, list) - assert isinstance(paper.section_texts, dict) - - -class TestPaperEdgeCases: - """Test edge cases for Paper class""" - - @patch("sage.middleware.operators.rag.arxiv.fitz") - def test_paper_empty_authors(self, mock_fitz_module): - """Test Paper with empty authors""" - with patch("sage.middleware.operators.rag.arxiv.FITZ_AVAILABLE", True): - from sage.middleware.operators.rag.arxiv import Paper - - paper = Paper(path="/tmp/test.pdf", title="Test") - assert paper.authors == [] - - @patch("sage.middleware.operators.rag.arxiv.fitz") - def test_paper_long_path(self, mock_fitz_module): - """Test Paper with very long path""" - with patch("sage.middleware.operators.rag.arxiv.FITZ_AVAILABLE", True): - from sage.middleware.operators.rag.arxiv import Paper - - long_path = "/tmp/" + "a" * 200 + ".pdf" - paper = Paper(path=long_path, title="Test") - assert paper.path == long_path - - @patch("sage.middleware.operators.rag.arxiv.fitz") - def test_paper_with_special_characters_in_title(self, mock_fitz_module): - """Test Paper with special characters in title""" - with patch("sage.middleware.operators.rag.arxiv.FITZ_AVAILABLE", True): - from sage.middleware.operators.rag.arxiv import Paper - - mock_doc = MagicMock() - mock_doc.close = MagicMock() - mock_fitz_module.open.return_value = mock_doc - - special_title = "Test: Paper - With (Special) [Characters]" - paper = Paper(path="/tmp/test.pdf", title=special_title) - assert paper.title == special_title diff --git a/packages/sage-middleware/tests/operators/rag/test_chunk.py b/packages/sage-middleware/tests/operators/rag/test_chunk.py deleted file mode 100644 index dc785abe2e..0000000000 --- a/packages/sage-middleware/tests/operators/rag/test_chunk.py +++ /dev/null @@ -1,351 +0,0 @@ -""" -测试 sage.middleware.operators.rag.chunk 模块 -""" - -import pytest - -# 尝试导入chunk模块 -pytest_plugins = [] - -try: - from sage.middleware.operators.rag.chunk import CharacterSplitter - - CHUNK_AVAILABLE = True -except ImportError as e: - CHUNK_AVAILABLE = False - pytestmark = pytest.mark.skip(f"Chunk module not available: {e}") - - -@pytest.mark.unit -class TestCharacterSplitter: - """测试CharacterSplitter类""" - - def test_character_splitter_import(self): - """测试CharacterSplitter导入""" - if not CHUNK_AVAILABLE: - pytest.skip("Chunk module not available") - - from sage.middleware.operators.rag.chunk import CharacterSplitter - - assert CharacterSplitter is not None - - def test_character_splitter_initialization_default(self): - """测试CharacterSplitter默认初始化""" - if not CHUNK_AVAILABLE: - pytest.skip("Chunk module not available") - - splitter = CharacterSplitter() - - assert splitter.chunk_size == 512 # 默认值 - assert splitter.overlap == 128 # 默认值 - - def test_character_splitter_initialization_custom(self): - """测试CharacterSplitter自定义初始化""" - if not CHUNK_AVAILABLE: - pytest.skip("Chunk module not available") - - splitter = CharacterSplitter(chunk_size=256, overlap=64) - - assert splitter.chunk_size == 256 - assert splitter.overlap == 64 - - def test_split_text_basic(self): - """测试基本文本分割功能""" - if not CHUNK_AVAILABLE: - pytest.skip("Chunk module not available") - - splitter = CharacterSplitter(chunk_size=10, overlap=3) - - # 测试短文本（长度为16个字符） - text = "Hello World Test" - chunks = splitter.split(text) - - # 验证分割结果 - assert isinstance(chunks, list) - assert len(chunks) >= 1 - - # 第一个chunk应该是前10个字符 - assert chunks[0] == "Hello Worl" - - # 第二个chunk应该从第7个字符开始（10-3=7） - if len(chunks) > 1: - assert chunks[1] == "orld Test" - - def test_split_text_exact_chunk_size(self): - """测试文本长度正好等于chunk_size的情况""" - if not CHUNK_AVAILABLE: - pytest.skip("Chunk module not available") - - splitter = CharacterSplitter(chunk_size=10, overlap=3) - - # 文本长度正好等于chunk_size - text = "1234567890" # 10个字符 - chunks = splitter.split(text) - - # 由于有overlap，会产生两个chunks - assert len(chunks) == 2 - assert chunks[0] == "1234567890" - assert chunks[1] == "890" # 最后3个字符(从位置7开始) - - def test_split_text_shorter_than_chunk_size(self): - """测试文本长度小于chunk_size的情况""" - if not CHUNK_AVAILABLE: - pytest.skip("Chunk module not available") - - splitter = CharacterSplitter(chunk_size=20, overlap=5) - - # 文本长度小于chunk_size - text = "Short text" # 10个字符 - chunks = splitter.split(text) - - # 应该只有一个chunk，包含全部文本 - assert len(chunks) == 1 - assert chunks[0] == "Short text" - - def test_split_text_empty(self): - """测试空文本的情况""" - if not CHUNK_AVAILABLE: - pytest.skip("Chunk module not available") - - splitter = CharacterSplitter(chunk_size=10, overlap=3) - - # 空文本 - text = "" - chunks = splitter.split(text) - - # 应该返回一个包含空字符串的列表 - assert len(chunks) == 1 - assert chunks[0] == "" - - def test_split_text_large_overlap(self): - """测试overlap大于chunk_size的情况""" - if not CHUNK_AVAILABLE: - pytest.skip("Chunk module not available") - - splitter = CharacterSplitter(chunk_size=5, overlap=8) # overlap > chunk_size - - text = "This is a test text for overlapping" - chunks = splitter.split(text) - - # 验证仍然能正常工作（虽然overlap很大） - assert isinstance(chunks, list) - assert len(chunks) >= 1 - assert chunks[0] == "This " - - def test_split_text_zero_overlap(self): - """测试零overlap的情况""" - if not CHUNK_AVAILABLE: - pytest.skip("Chunk module not available") - - splitter = CharacterSplitter(chunk_size=5, overlap=0) - - text = "1234567890ABCDE" # pragma: allowlist secret - chunks = splitter.split(text) - - # 应该有3个chunk，没有重叠 - assert len(chunks) == 3 - assert chunks[0] == "12345" - assert chunks[1] == "67890" - assert chunks[2] == "ABCDE" - - def test_execute_basic(self): - """测试split方法基本功能""" - if not CHUNK_AVAILABLE: - pytest.skip("Chunk module not available") - - splitter = CharacterSplitter(chunk_size=10, overlap=3) - - # 测试输入文本 - text = "This is a test document that needs to be split into chunks." - result = splitter.split(text) - - # 验证结果 - assert isinstance(result, list) - assert len(result) > 1 # 应该被分割成多个chunks - - # 验证第一个chunk - assert result[0] == "This is a " - - # 验证chunks有重叠 - assert "is a " in result[0] - assert "a " in result[1] # 第二个chunk应该以overlap开始 - - def test_execute_with_chinese_text(self): - """测试split方法处理中文文本""" - if not CHUNK_AVAILABLE: - pytest.skip("Chunk module not available") - - splitter = CharacterSplitter(chunk_size=5, overlap=2) - - # 中文文本 - text = "这是一个测试文档需要分割成块" - result = splitter.split(text) - - # 验证结果 - assert isinstance(result, list) - assert len(result) > 1 - - # 验证中文字符被正确处理 - assert result[0] == "这是一个测" - assert result[1] == "个测试文档" # 有2个字符的重叠 - - def test_execute_with_special_characters(self): - """测试split方法处理特殊字符""" - if not CHUNK_AVAILABLE: - pytest.skip("Chunk module not available") - - splitter = CharacterSplitter(chunk_size=8, overlap=3) - - # 包含特殊字符的文本 - text = "Hello!\n\tWorld@#$%^&*()" - result = splitter.split(text) - - # 验证结果 - assert isinstance(result, list) - assert len(result) >= 1 - - # 验证特殊字符被保留 - assert "Hello!\n\t" in result[0] - # 检查特殊字符在某个chunk中被保留 - special_chars_found = any("$%^&*(" in chunk for chunk in result) - assert special_chars_found - - def test_execute_with_very_long_text(self): - """测试split方法处理长文本""" - if not CHUNK_AVAILABLE: - pytest.skip("Chunk module not available") - - splitter = CharacterSplitter(chunk_size=50, overlap=10) - - # 生成长文本 - text = "A" * 500 # 500个字符 - result = splitter.split(text) - - # 验证结果 - assert isinstance(result, list) - expected_chunks = (500 - 10) // (50 - 10) + 1 # 计算预期的chunk数量 - assert len(result) >= expected_chunks - 1 # 允许一定误差 - - # 验证每个chunk的长度 - for _i, chunk in enumerate(result[:-1]): # 除了最后一个chunk - assert len(chunk) == 50 - - # 验证重叠 - if len(result) > 1: - assert result[0][-10:] == result[1][:10] - - -@pytest.mark.unit -class TestCharacterSplitterConfiguration: - """测试CharacterSplitter配置""" - - def test_various_chunk_sizes(self): - """测试不同的chunk_size配置""" - if not CHUNK_AVAILABLE: - pytest.skip("Chunk module not available") - - text = "The quick brown fox jumps over the lazy dog" - - # 测试不同的chunk_size - for chunk_size in [5, 10, 20, 50]: - splitter = CharacterSplitter(chunk_size=chunk_size, overlap=2) - - result = splitter.split(text) - - # 验证结果 - assert isinstance(result, list) - if len(text) > chunk_size: - assert len(result) > 1 - - # 验证第一个chunk的大小应该等于chunk_size（如果文本足够长） - if len(text) >= chunk_size: - assert len(result[0]) == chunk_size - - # 验证所有chunks的长度都合理（不超过chunk_size） - for chunk in result: - assert len(chunk) <= chunk_size - assert len(chunk) > 0 - - def test_various_overlaps(self): - """测试不同的overlap配置""" - if not CHUNK_AVAILABLE: - pytest.skip("Chunk module not available") - - text = "1234567890ABCDEFGHIJKLMNOPQRSTUVWXYZ" # pragma: allowlist secret - chunk_size = 10 - - # 测试不同的overlap - for overlap in [0, 2, 5, 8]: - splitter = CharacterSplitter(chunk_size=chunk_size, overlap=overlap) - - result = splitter.split(text) - - # 验证结果 - assert isinstance(result, list) - - # 验证overlap（如果有多个chunks） - if len(result) > 1 and overlap > 0: - # 检查相邻chunks之间的重叠 - overlap_text = result[0][-overlap:] - start_text = result[1][:overlap] - assert overlap_text == start_text - - -@pytest.mark.integration -class TestCharacterSplitterIntegration: - """CharacterSplitter集成测试""" - - @pytest.mark.skipif(not CHUNK_AVAILABLE, reason="Chunk module not available") - def test_character_splitter_in_pipeline(self): - """测试CharacterSplitter在pipeline中的集成""" - splitter = CharacterSplitter(chunk_size=100, overlap=20) - - # 模拟来自文件读取的长文档 - document_content = """ - This is a long document that contains multiple paragraphs and needs to be split into manageable chunks. - - Each chunk should have a reasonable size and some overlap to maintain context between chunks. - - The chunking process is essential for RAG systems as it allows for efficient retrieval and processing - of relevant information while maintaining semantic coherence. - - This test verifies that the character splitter can handle realistic document content properly. - """ - - text = document_content.strip() - - result = splitter.split(text) - - # 验证集成结果 - assert isinstance(result, list) - assert len(result) > 1 # 应该被分割成多个chunks - - # 验证chunks质量 - total_length = sum(len(chunk) for chunk in result) - original_length = len(text) - - # 由于有重叠，总长度应该大于原始长度 - assert total_length > original_length - - # 验证第一个chunk包含文档开头，最后一个chunk包含文档结尾 - assert result[0].startswith(text[:50]) - # 由于分割算法可能产生小的末尾chunks，检查最后几个chunks是否覆盖了文档结尾 - last_chunks_combined = "".join(result[-2:]) if len(result) > 1 else result[-1] - assert text[-30:] in last_chunks_combined - - # 验证文档被完整覆盖（检查关键内容都被包含） - combined_content = "".join(result) - assert "This is a long document" in combined_content - assert "document content properly" in combined_content - - # 验证chunk大小合理 - for i, chunk in enumerate(result[:-1]): # 除最后一个chunk - assert len(chunk) <= 100 - if i > 0: # 检查重叠 - overlap_size = min(20, len(result[i - 1]), len(chunk)) - if overlap_size > 0: - prev_end = result[i - 1][-overlap_size:] - curr_start = chunk[:overlap_size] - # 注意：由于我们是按字符分割，重叠可能不完全匹配单词边界 - # 这里主要验证有重叠存在 - assert len(prev_end) > 0 and len(curr_start) > 0 diff --git a/packages/sage-middleware/tests/operators/rag/test_evaluate.py b/packages/sage-middleware/tests/operators/rag/test_evaluate.py deleted file mode 100644 index 394e93db2a..0000000000 --- a/packages/sage-middleware/tests/operators/rag/test_evaluate.py +++ /dev/null @@ -1,680 +0,0 @@ -""" -测试 sage.middleware.operators.rag.evaluate 模块 -""" - -import os -from unittest.mock import Mock, patch - -import numpy as np -import pytest - -pytestmark = pytest.mark.skipif( - os.getenv("SAGE_RUN_SLOW_TESTS") != "1", - reason="RAG evaluate tests are slow; set SAGE_RUN_SLOW_TESTS=1 to enable.", -) - -# 尝试导入评估模块 -pytest_plugins = [] - -try: - from sage.middleware.operators.rag.evaluate import ( - AccuracyEvaluate, - BertRecallEvaluate, - BRSEvaluate, - CompressionRateEvaluate, - ContextRecallEvaluate, - F1Evaluate, - LatencyEvaluate, - RecallEvaluate, - RougeLEvaluate, - TokenCountEvaluate, - get_normalized_tokens, # 模块级函数，用于 token 提取 - ) - - EVALUATE_AVAILABLE = True -except ImportError as e: - EVALUATE_AVAILABLE = False - pytestmark = pytest.mark.skip(f"Evaluate module not available: {e}") - - -@pytest.fixture -def sample_evaluation_data(): - """Provide test evaluation data fixture""" - return { - "query": "What is machine learning?", - "generated": "Machine learning is a branch of artificial intelligence that enables computers to learn automatically.", - "references": [ - "Machine learning is a subfield of artificial intelligence focused on algorithm development.", - "Machine learning allows computers to learn patterns from data.", - ], - "results": [], - } - - -@pytest.mark.unit -class TestF1Evaluate: - """测试F1Evaluate类""" - - def test_f1_evaluate_initialization(self): - """测试F1Evaluate初始化""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - evaluator = F1Evaluate() - # F1Evaluate uses module-level get_normalized_tokens function, not _get_tokens method - assert hasattr(evaluator, "_f1_score") - assert hasattr(evaluator, "execute") - - def test_get_tokens(self): - """测试token提取 (使用模块级函数)""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - # Test the module-level get_normalized_tokens function - tokens = get_normalized_tokens("Hello World Test") - - assert tokens == ["hello", "world", "test"] - - def test_f1_score_calculation(self): - """测试F1分数计算""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - evaluator = F1Evaluate() - - # 完全匹配 - score = evaluator._f1_score("hello world", "hello world") - assert score == 1.0 - - # 部分匹配 - score = evaluator._f1_score("hello world", "hello test") - assert 0 < score < 1 - - # 完全不匹配 - score = evaluator._f1_score("hello world", "test case") - assert score == 0.0 - - def test_f1_execute(self, sample_evaluation_data): - """测试F1Evaluate执行""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - evaluator = F1Evaluate() - - with patch("builtins.print") as mock_print: - result = evaluator.execute(sample_evaluation_data) - - # result 包含 normalized 字段（query, results），检查核心字段 - assert result["generated"] == sample_evaluation_data["generated"] - assert result["references"] == sample_evaluation_data["references"] - - # 验证打印了F1分数 - mock_print.assert_called_once() - call_args = str(mock_print.call_args) - assert "F1" in call_args - - -@pytest.mark.unit -class TestRecallEvaluate: - """测试RecallEvaluate类""" - - def test_recall_evaluate_initialization(self): - """测试RecallEvaluate初始化""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - evaluator = RecallEvaluate() - assert hasattr(evaluator, "_get_tokens") - assert hasattr(evaluator, "_recall") - - def test_recall_calculation(self): - """测试Recall计算""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - evaluator = RecallEvaluate() - - # 完全召回 - recall = evaluator._recall("hello world test", "hello world") - assert recall == 1.0 - - # 部分召回 - recall = evaluator._recall("hello", "hello world") - assert recall == 0.5 - - # 无召回 - recall = evaluator._recall("test", "hello world") - assert recall == 0.0 - - def test_recall_execute(self, sample_evaluation_data): - """测试RecallEvaluate执行""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - evaluator = RecallEvaluate() - - with patch("builtins.print") as mock_print: - result = evaluator.execute(sample_evaluation_data) - - # result 包含 normalized 字段，检查核心字段 - assert result["generated"] == sample_evaluation_data["generated"] - assert result["references"] == sample_evaluation_data["references"] - mock_print.assert_called_once() - call_args = str(mock_print.call_args) - assert "Recall" in call_args - - -@pytest.mark.unit -class TestBertRecallEvaluate: - """测试BertRecallEvaluate类""" - - @patch("sage.middleware.operators.rag.evaluate.AutoTokenizer") - @patch("sage.middleware.operators.rag.evaluate.AutoModel") - def test_bert_recall_initialization(self, mock_model, mock_tokenizer): - """测试BertRecallEvaluate初始化""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - # 模拟BERT模型和tokenizer - mock_tokenizer.from_pretrained.return_value = Mock() - mock_model.from_pretrained.return_value = Mock() - - evaluator = BertRecallEvaluate() - - assert evaluator.tokenizer is not None - assert evaluator.model is not None - mock_tokenizer.from_pretrained.assert_called_with("bert-base-uncased") - mock_model.from_pretrained.assert_called_with("bert-base-uncased") - - @patch("sage.middleware.operators.rag.evaluate.AutoTokenizer") - @patch("sage.middleware.operators.rag.evaluate.AutoModel") - @patch("sage.middleware.operators.rag.evaluate.cosine_similarity") - def test_bert_recall_execute( - self, - mock_cosine, - mock_model_class, - mock_tokenizer_class, - sample_evaluation_data, - ): - """测试BertRecallEvaluate执行""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - # 模拟tokenizer - mock_tokenizer = Mock() - mock_tokenizer.return_value = {"input_ids": Mock(), "attention_mask": Mock()} - mock_tokenizer_class.from_pretrained.return_value = mock_tokenizer - - # 模拟model - mock_model = Mock() - mock_output = Mock() - mock_embeddings = Mock() - # 返回两个embeddings，一个用于pred，一个用于gold - mock_embeddings.detach.return_value.numpy.return_value = np.array( - [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]] - ) - mock_output.last_hidden_state.mean.return_value = mock_embeddings - mock_model.return_value = mock_output - mock_model_class.from_pretrained.return_value = mock_model - - # 模拟余弦相似度 - mock_cosine.return_value = np.array([[0.85]]) - - evaluator = BertRecallEvaluate() - - with patch("builtins.print") as mock_print: - result = evaluator.execute(sample_evaluation_data) - - # result 包含 normalized 字段，检查核心字段 - assert result["generated"] == sample_evaluation_data["generated"] - assert result["references"] == sample_evaluation_data["references"] - mock_print.assert_called_once() - call_args = str(mock_print.call_args) - assert "BertRecall" in call_args - - -@pytest.mark.unit -class TestRougeLEvaluate: - """测试RougeLEvaluate类""" - - @patch("sage.middleware.operators.rag.evaluate.Rouge") - def test_rouge_l_initialization(self, mock_rouge_class): - """测试RougeLEvaluate初始化""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - mock_rouge_instance = Mock() - mock_rouge_class.return_value = mock_rouge_instance - - evaluator = RougeLEvaluate() - - assert evaluator.rouge is not None - mock_rouge_class.assert_called_once() - - @patch("sage.middleware.operators.rag.evaluate.Rouge") - def test_rouge_l_execute(self, mock_rouge_class, sample_evaluation_data): - """测试RougeLEvaluate执行""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - # 模拟Rouge结果 - mock_rouge_instance = Mock() - mock_rouge_instance.get_scores.return_value = [{"rouge-l": {"f": 0.75}}] - mock_rouge_class.return_value = mock_rouge_instance - - evaluator = RougeLEvaluate() - - with patch("builtins.print") as mock_print: - result = evaluator.execute(sample_evaluation_data) - - # result 包含 normalized 字段，检查核心字段 - assert result["generated"] == sample_evaluation_data["generated"] - assert result["references"] == sample_evaluation_data["references"] - mock_print.assert_called_once() - call_args = str(mock_print.call_args) - assert "ROUGE-L" in call_args - - -@pytest.mark.unit -class TestBRSEvaluate: - """测试BRSEvaluate类""" - - def test_brs_evaluate_initialization(self): - """测试BRSEvaluate初始化""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - evaluator = BRSEvaluate() - assert hasattr(evaluator, "execute") - - def test_brs_execute(self, sample_evaluation_data): - """测试BRSEvaluate执行""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - evaluator = BRSEvaluate() - - with patch("builtins.print") as mock_print: - result = evaluator.execute(sample_evaluation_data) - - # result 包含 normalized 字段，检查核心字段 - assert result["generated"] == sample_evaluation_data["generated"] - assert result["references"] == sample_evaluation_data["references"] - mock_print.assert_called_once() - call_args = str(mock_print.call_args) - assert "BRS" in call_args - - -@pytest.mark.unit -class TestAccuracyEvaluate: - """测试AccuracyEvaluate类""" - - def test_accuracy_evaluate_initialization(self): - """测试AccuracyEvaluate初始化""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - evaluator = AccuracyEvaluate() - assert hasattr(evaluator, "execute") - - def test_accuracy_execute(self, sample_evaluation_data): - """测试AccuracyEvaluate执行""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - evaluator = AccuracyEvaluate() - - with patch("builtins.print") as mock_print: - result = evaluator.execute(sample_evaluation_data) - - # result 包含 normalized 字段，检查核心字段 - assert result["generated"] == sample_evaluation_data["generated"] - assert result["references"] == sample_evaluation_data["references"] - mock_print.assert_called_once() - call_args = str(mock_print.call_args) - assert "Acc" in call_args - - -@pytest.mark.unit -class TestTokenCountEvaluate: - """测试TokenCountEvaluate类""" - - def test_token_count_evaluate_initialization(self): - """测试TokenCountEvaluate初始化""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - evaluator = TokenCountEvaluate() - assert hasattr(evaluator, "execute") - - def test_token_count_execute(self, sample_evaluation_data): - """测试TokenCountEvaluate执行""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - evaluator = TokenCountEvaluate() - - with patch("builtins.print") as mock_print: - result = evaluator.execute(sample_evaluation_data) - - # result 包含 normalized 字段，检查核心字段 - assert result["generated"] == sample_evaluation_data["generated"] - assert result["references"] == sample_evaluation_data["references"] - mock_print.assert_called_once() - call_args = str(mock_print.call_args) - assert "Token Count" in call_args - - -@pytest.mark.unit -class TestLatencyEvaluate: - """测试LatencyEvaluate类""" - - def test_latency_evaluate_initialization(self): - """测试LatencyEvaluate初始化""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - evaluator = LatencyEvaluate() - assert hasattr(evaluator, "execute") - - def test_latency_execute(self, sample_evaluation_data): - """测试LatencyEvaluate执行""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - evaluator = LatencyEvaluate() - - with patch("builtins.print") as mock_print: - result = evaluator.execute(sample_evaluation_data) - - # result 包含 normalized 字段，检查核心字段 - assert result["generated"] == sample_evaluation_data["generated"] - assert result["references"] == sample_evaluation_data["references"] - # LatencyEvaluate 现在打印4次：retrieve_time, refine_time, generate_time, total - assert mock_print.call_count == 4 - # 检查最后一次调用包含 "Total Latency" - last_call_args = str(mock_print.call_args_list[-1]) - assert "Total Latency" in last_call_args - - -@pytest.mark.unit -class TestContextRecallEvaluate: - """测试ContextRecallEvaluate类""" - - def test_context_recall_evaluate_initialization(self): - """测试ContextRecallEvaluate初始化""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - evaluator = ContextRecallEvaluate() - assert hasattr(evaluator, "execute") - - def test_context_recall_execute(self, sample_evaluation_data): - """测试ContextRecallEvaluate执行""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - evaluator = ContextRecallEvaluate() - - # ContextRecallEvaluate needs metadata field - test_data = sample_evaluation_data.copy() - test_data["metadata"] = { - "supporting_facts": {"sent_id": [0, 1]}, - "retrieved_contexts": [ - {"content": "Machine learning is a subfield of AI", "sent_id": 0}, - {"content": "It focuses on algorithm development", "sent_id": 1}, - ], - } - - with patch("builtins.print") as mock_print: - result = evaluator.execute(test_data) - - # result 包含 normalized 字段，检查核心字段 - assert result["generated"] == test_data["generated"] - assert result["references"] == test_data["references"] - assert result["metadata"] == test_data["metadata"] - mock_print.assert_called_once() - call_args = str(mock_print.call_args) - assert "Context Recall" in call_args - - -@pytest.mark.unit -class TestCompressionRateEvaluate: - """测试CompressionRateEvaluate类""" - - def test_compression_rate_evaluate_initialization(self): - """测试CompressionRateEvaluate初始化""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - evaluator = CompressionRateEvaluate() - assert hasattr(evaluator, "execute") - - def test_compression_rate_execute(self): - """测试CompressionRateEvaluate基本执行功能""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - evaluator = CompressionRateEvaluate() - - test_data = { - "query": "What is artificial intelligence?", - "generated": "AI is a field of computer science.", - "references": ["Artificial intelligence is the simulation of human intelligence."], - "results": ["Compressed document content"], - "retrieval_results": ["Original document content about AI"], - } - - with patch("builtins.print") as mock_print: - result = evaluator.execute(test_data) - - assert result == test_data - mock_print.assert_called_once() - call_args = str(mock_print.call_args) - assert "Compression Rate" in call_args - - def test_compression_rate_execute_with_empty_docs(self): - """测试CompressionRateEvaluate在空文档情况下的执行""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - evaluator = CompressionRateEvaluate() - - test_data = { - "query": "What is machine learning?", - "generated": "Machine learning is a subset of AI.", - "references": ["Machine learning is a method of data analysis."], - "results": [], - "retrieval_results": [], - } - - with patch("builtins.print") as mock_print: - result = evaluator.execute(test_data) - - assert result == test_data - mock_print.assert_called_once() - call_args = str(mock_print.call_args) - assert "Compression Rate" in call_args - assert "0.00" in call_args - - def test_compression_rate_calculate_correctly(self): - """Test CompressionRateEvaluate compression rate calculation accuracy""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - evaluator = CompressionRateEvaluate() - - # Test specific compression rate calculation - test_data = { - "query": "What is deep learning?", - "generated": "Deep learning uses neural networks.", - "references": ["Deep learning is a machine learning technique."], - "retrieval_results": [ - { - "text": "Original document containing ten words about deep learning neural networks technology" - } - ], # 11 tokens - "refining_results": ["Compressed neural networks document"], # 4 tokens - } - - with patch("builtins.print") as mock_print: - result = evaluator.execute(test_data) - - assert result == test_data - mock_print.assert_called_once() - call_args = str(mock_print.call_args) - assert "Compression Rate" in call_args - # Compression rate should be 11/4 = 2.75 - assert "2.75" in call_args - - -@pytest.mark.integration -class TestEvaluateIntegration: - """评估模块集成测试""" - - def test_multiple_evaluators_pipeline(self, sample_evaluation_data): - """测试多个评估器管道""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - # 创建评估器链 - evaluators = [F1Evaluate(), RecallEvaluate(), BRSEvaluate(), AccuracyEvaluate()] - - result = sample_evaluation_data - - with patch("builtins.print"): - for evaluator in evaluators: - result = evaluator.execute(result) - - # 数据应该在管道中保持不变 - assert result == sample_evaluation_data - - def test_evaluators_with_different_data_formats(self): - """测试不同数据格式的评估器""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - # Test data format 1: standard format - data1 = { - "query": "Test question 1", - "generated": "Generated answer 1", - "references": ["Reference answer 1", "Reference answer 2"], - "results": [], - } - - # Test data format 2: no reference answers - data2 = { - "query": "Test question 2", - "generated": "Generated answer 2", - "references": [], - "results": [], - } - - evaluator = F1Evaluate() - - with patch("builtins.print"): - result1 = evaluator.execute(data1) - result2 = evaluator.execute(data2) - - assert result1 == data1 - assert result2 == data2 - - -@pytest.mark.slow -class TestEvaluatePerformance: - """评估性能测试""" - - def test_large_data_evaluation(self): - """Test large data volume evaluation""" - if not EVALUATE_AVAILABLE: - pytest.skip("Evaluate module not available") - - # Create large amount of data - large_data = { - "query": "Performance test question", - "generated": " ".join([f"word{i}" for i in range(1000)]), - "references": [" ".join([f"ref_word{i}" for i in range(500)])], - "results": [], - } - - evaluator = F1Evaluate() - - import time - - start_time = time.time() - - with patch("builtins.print"): - result = evaluator.execute(large_data) - - end_time = time.time() - - # 验证结果正确性 - assert result == large_data - - # 验证性能（应该在合理时间内完成） - assert end_time - start_time < 5.0 # 应该在5秒内完成 - - -@pytest.mark.unit -class TestEvaluateFallback: - """评估模块降级测试""" - - def test_evaluate_module_fallback(self): - """测试评估模块降级""" - - # 模拟评估器基类 - class MockEvaluator: - def __init__(self, name): - self.name = name - - def execute(self, data): - print(f"[{self.name}] : 0.8500") - return data - - evaluator = MockEvaluator("MockF1") - data = { - "query": "test", - "generated": "answer", - "references": ["ref"], - "results": [], - } - - with patch("builtins.print") as mock_print: - result = evaluator.execute(data) - - assert result == data - mock_print.assert_called_once_with("[MockF1] : 0.8500") - - def test_basic_evaluation_concepts(self): - """测试基本评估概念""" - - # 测试基本的F1计算逻辑 - def simple_f1(pred_tokens, ref_tokens): - pred_set = set(pred_tokens) - ref_set = set(ref_tokens) - - if not pred_set and not ref_set: - return 1.0 - if not pred_set or not ref_set: - return 0.0 - - intersection = pred_set & ref_set - precision = len(intersection) / len(pred_set) - recall = len(intersection) / len(ref_set) - - if precision + recall == 0: - return 0.0 - - return 2 * precision * recall / (precision + recall) - - # 测试完全匹配 - f1 = simple_f1(["hello", "world"], ["hello", "world"]) - assert f1 == 1.0 - - # 测试部分匹配 - f1 = simple_f1(["hello", "test"], ["hello", "world"]) - assert 0 < f1 < 1 - - # 测试无匹配 - f1 = simple_f1(["test"], ["hello", "world"]) - assert 0 <= f1 < 1 diff --git a/packages/sage-middleware/tests/operators/rag/test_generator.py b/packages/sage-middleware/tests/operators/rag/test_generator.py deleted file mode 100644 index 65f74ebf86..0000000000 --- a/packages/sage-middleware/tests/operators/rag/test_generator.py +++ /dev/null @@ -1,614 +0,0 @@ -""" -测试 sage.middleware.operators.rag.generator 模块 -""" - -import os -from unittest.mock import Mock, patch - -import pytest - -# 尝试导入生成器模块 -pytest_plugins = [] - -try: - from sage.middleware.operators.rag.generator import HFGenerator, OpenAIGenerator - - GENERATOR_AVAILABLE = True -except ImportError as e: - GENERATOR_AVAILABLE = False - pytestmark = pytest.mark.skip(f"Generator module not available: {e}") - - -@pytest.mark.unit -class TestOpenAIGenerator: - """测试OpenAIGenerator类""" - - def test_openai_generator_import(self): - """测试OpenAIGenerator导入""" - if not GENERATOR_AVAILABLE: - pytest.skip("Generator module not available") - - from sage.middleware.operators.rag.generator import OpenAIGenerator - - assert OpenAIGenerator is not None - - @patch("sage.middleware.operators.rag.generator.OpenAI") - def test_openai_generator_initialization(self, mock_openai_class): - """测试OpenAIGenerator初始化""" - if not GENERATOR_AVAILABLE: - pytest.skip("Generator module not available") - - config = { - "model_name": "gpt-4o-mini", - "base_url": "http://localhost:8000/v1", - "api_key": "test_key", # pragma: allowlist secret - "seed": 42, - } - - # Mock OpenAI client - mock_client_instance = Mock() - mock_openai_class.return_value = mock_client_instance - - generator = OpenAIGenerator(config=config) - - # 验证初始化 - assert generator.config == config - assert generator.enable_profile is False - assert generator.num == 1 - - # 验证 OpenAI 被正确调用 - mock_openai_class.assert_called_once_with( - base_url="http://localhost:8000/v1", - api_key="test_key", # pragma: allowlist secret - ) - assert generator.model == mock_client_instance - assert generator.model_name == "gpt-4o-mini" - - @patch("sage.middleware.operators.rag.generator.OpenAI") - def test_openai_generator_initialization_with_profile(self, mock_openai_class): - """测试OpenAIGenerator带profile初始化""" - if not GENERATOR_AVAILABLE: - pytest.skip("Generator module not available") - - config = { - "model_name": "gpt-4o-mini", - "base_url": "http://localhost:8000/v1", - "api_key": "test_key", # pragma: allowlist secret - "seed": 42, - } - - # Mock OpenAI client - mock_client_instance = Mock() - mock_openai_class.return_value = mock_client_instance - - with patch("os.makedirs"): - generator = OpenAIGenerator(config=config, enable_profile=True) - assert generator.enable_profile is True - - @patch("sage.middleware.operators.rag.generator.OpenAI") - def test_openai_generator_initialization_no_api_key(self, mock_openai_class): - """测试OpenAIGenerator无API密钥初始化（使用环境变量）""" - if not GENERATOR_AVAILABLE: - pytest.skip("Generator module not available") - - config = { - "model_name": "gpt-4o-mini", - "base_url": "http://localhost:8000/v1", - "api_key": None, - "seed": 42, - } - - # 清除所有可能影响的环境变量 - env_override = { - "OPENAI_API_KEY": "", - "ALIBABA_API_KEY": "env_api_key", # pragma: allowlist secret - } - with patch.dict(os.environ, env_override, clear=False): - mock_client_instance = Mock() - mock_openai_class.return_value = mock_client_instance - - OpenAIGenerator(config=config) - - # 验证使用环境变量中的API密钥 - assert mock_openai_class.call_count == 1 - call_kwargs = mock_openai_class.call_args[1] - assert call_kwargs["base_url"] == "http://localhost:8000/v1" - # OpenAI client falls back to dummy key when OPENAI_API_KEY is missing - assert call_kwargs["api_key"] == "EMPTY" # pragma: allowlist secret - - @patch("sage.middleware.operators.rag.generator.OpenAI") - def test_execute_with_string_input(self, mock_openai_class): - """测试execute方法处理字符串输入""" - if not GENERATOR_AVAILABLE: - pytest.skip("Generator module not available") - - config = { - "model_name": "gpt-4o-mini", - "base_url": "http://localhost:8000/v1", - "api_key": "test_key", # pragma: allowlist secret - "seed": 42, - } - - # Mock OpenAI client 和 chat completion 响应 - mock_client_instance = Mock() - mock_completion = Mock() - mock_completion.choices = [Mock(message=Mock(content="Generated response"))] - mock_client_instance.chat.completions.create.return_value = mock_completion - mock_openai_class.return_value = mock_client_instance - - generator = OpenAIGenerator(config=config) - - # 测试字符串输入 - OpenAIGenerator期望列表输入 - input_data = ["Test prompt"] - result = generator.execute(input_data) - - # 新实现：单输入返回字典，包含 generated 字段 - assert isinstance(result, dict) - assert result["generated"] == "Generated response" - - # 验证 chat.completions.create 被正确调用 - expected_messages = [{"role": "user", "content": "Test prompt"}] - mock_client_instance.chat.completions.create.assert_called_once() - call_kwargs = mock_client_instance.chat.completions.create.call_args[1] - assert call_kwargs["model"] == "gpt-4o-mini" - assert call_kwargs["messages"] == expected_messages - - @patch("sage.middleware.operators.rag.generator.OpenAI") - def test_execute_with_two_string_inputs(self, mock_openai_class): - """测试execute方法处理两个字符串输入（原始query + prompt）""" - if not GENERATOR_AVAILABLE: - pytest.skip("Generator module not available") - - config = { - "model_name": "gpt-4o-mini", - "base_url": "http://localhost:8000/v1", - "api_key": "test_key", # pragma: allowlist secret - "seed": 42, - } - - # Mock OpenAI client 和 chat completion 响应 - mock_client_instance = Mock() - mock_completion = Mock() - mock_completion.choices = [Mock(message=Mock(content="Generated response"))] - mock_client_instance.chat.completions.create.return_value = mock_completion - mock_openai_class.return_value = mock_client_instance - - generator = OpenAIGenerator(config=config) - - # 测试两个字符串输入（原始query + prompt） - input_data = ["What is AI?", "Please explain artificial intelligence."] - result = generator.execute(input_data) - - # 新实现：返回 dict，包含 query、generated - assert isinstance(result, dict) - assert result["generated"] == "Generated response" - - expected_messages = [{"role": "user", "content": "Please explain artificial intelligence."}] - call_kwargs = mock_client_instance.chat.completions.create.call_args[1] - assert call_kwargs["messages"] == expected_messages - - @patch("sage.middleware.operators.rag.generator.OpenAI") - def test_execute_with_profile_enabled(self, mock_openai_class): - """测试启用profile的execute方法""" - if not GENERATOR_AVAILABLE: - pytest.skip("Generator module not available") - - config = { - "model_name": "gpt-4o-mini", - "base_url": "http://localhost:8000/v1", - "api_key": "test_key", # pragma: allowlist secret - "seed": 42, - } - - # Mock OpenAI client 和 chat completion 响应 - mock_client_instance = Mock() - mock_completion = Mock() - mock_completion.choices = [Mock(message=Mock(content="Generated response"))] - mock_client_instance.chat.completions.create.return_value = mock_completion - mock_openai_class.return_value = mock_client_instance - - with patch("os.makedirs"), patch("builtins.open", create=True), patch("json.dump"): - generator = OpenAIGenerator(config=config, enable_profile=True) - - input_data = ["Test prompt"] - - with patch("time.time", return_value=1234567890.0): - result = generator.execute(input_data) - - # 新实现：单输入返回字典 - assert isinstance(result, dict) - assert result["generated"] == "Generated response" - - @patch("sage.middleware.operators.rag.generator.OpenAI") - def test_execute_with_api_error(self, mock_openai_class): - """测试execute方法处理API错误""" - if not GENERATOR_AVAILABLE: - pytest.skip("Generator module not available") - - config = { - "model_name": "gpt-4o-mini", - "base_url": "http://localhost:8000/v1", - "api_key": "test_key", # pragma: allowlist secret - "seed": 42, - } - - # Mock OpenAI client 抛出异常 - mock_client_instance = Mock() - mock_client_instance.chat.completions.create.side_effect = Exception("API Error") - mock_openai_class.return_value = mock_client_instance - - generator = OpenAIGenerator(config=config) - - # 测试异常处理 - with pytest.raises(Exception) as exc_info: - generator.execute(["Test prompt"]) - - assert "API Error" in str(exc_info.value) - - @patch("sage.middleware.operators.rag.generator.OpenAI") - def test_execute_increments_counter(self, mock_openai_class): - """测试execute方法会递增计数器""" - if not GENERATOR_AVAILABLE: - pytest.skip("Generator module not available") - - config = { - "model_name": "gpt-4o-mini", - "base_url": "http://localhost:8000/v1", - "api_key": "test_key", # pragma: allowlist secret - "seed": 42, - } - - # Mock OpenAI client 和 chat completion 响应 - mock_client_instance = Mock() - mock_completion = Mock() - mock_completion.choices = [Mock(message=Mock(content="Generated response"))] - mock_client_instance.chat.completions.create.return_value = mock_completion - mock_openai_class.return_value = mock_client_instance - - generator = OpenAIGenerator(config=config) - - # 验证初始计数器 - assert generator.num == 1 - - # 执行多次调用 - generator.execute(["Test prompt 1"]) - assert generator.num == 2 - - generator.execute(["Test prompt 2"]) - assert generator.num == 3 - - @patch("sage.middleware.operators.rag.generator.OpenAI") - def test_execute_with_dict_input_returns_generate_time(self, mock_openai_class): - """测试execute方法处理字典输入时返回generate_time字段""" - if not GENERATOR_AVAILABLE: - pytest.skip("Generator module not available") - - config = { - "model_name": "gpt-4o-mini", - "base_url": "http://localhost:8000/v1", - "api_key": "test_key", # pragma: allowlist secret - "seed": 42, - } - - # Mock OpenAI client 和 chat completion 响应 - mock_client_instance = Mock() - mock_completion = Mock() - mock_completion.choices = [Mock(message=Mock(content="Generated response"))] - mock_client_instance.chat.completions.create.return_value = mock_completion - mock_openai_class.return_value = mock_client_instance - - generator = OpenAIGenerator(config=config) - - # 测试字典输入 - OpenAIGenerator期望列表输入: [original_data, prompt] - original_data = {"query": "What is AI?", "other_field": "value"} - prompt = "Please explain artificial intelligence." - input_data = [original_data, prompt] - - result = generator.execute(input_data) - - # 验证结果是字典格式且包含必要字段 - assert isinstance(result, dict) - assert "generated" in result - assert result["generated"] == "Generated response" - assert result["query"] == "What is AI?" - assert result["other_field"] == "value" - - @patch("sage.middleware.operators.rag.generator.OpenAI") - def test_execute_with_messages_list_input(self, mock_openai_class): - """测试execute方法处理消息列表输入""" - if not GENERATOR_AVAILABLE: - pytest.skip("Generator module not available") - - config = { - "model_name": "gpt-4o-mini", - "base_url": "http://localhost:8000/v1", - "api_key": "test_key", # pragma: allowlist secret - "seed": 42, - } - - # Mock OpenAI client 和 chat completion 响应 - mock_client_instance = Mock() - mock_completion = Mock() - mock_completion.choices = [Mock(message=Mock(content="Generated response"))] - mock_client_instance.chat.completions.create.return_value = mock_completion - mock_openai_class.return_value = mock_client_instance - - generator = OpenAIGenerator(config=config) - - # 测试消息列表输入 - 已格式化的消息 - original_data = {"query": "What is AI?"} - messages = [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": "What is artificial intelligence?"}, - ] - input_data = [original_data, messages] - - result = generator.execute(input_data) - - # 验证结果 - assert isinstance(result, dict) - assert "generated" in result - assert result["generated"] == "Generated response" - assert result["query"] == "What is AI?" - - # 验证直接传递消息列表 - call_kwargs = mock_client_instance.chat.completions.create.call_args[1] - assert call_kwargs["messages"] == messages - - @patch("sage.middleware.operators.rag.generator.OpenAI") - def test_configuration_validation(self, mock_openai_class): - """测试配置验证""" - if not GENERATOR_AVAILABLE: - pytest.skip("Generator module not available") - - # OpenAIGenerator 接受空配置并使用默认值，不会抛出 KeyError - # 测试各种配置都能正常创建实例 - configs_to_test = [ - {}, # 空配置 - 使用所有默认值 - {"model_name": "gpt-4o-mini"}, # 只指定model_name - {"base_url": "http://localhost:8000/v1"}, # 只指定base_url - ] - - mock_client_instance = Mock() - mock_openai_class.return_value = mock_client_instance - - for config in configs_to_test: - # 这些配置都应该能成功创建实例，因为使用了 .get() 提供默认值 - generator = OpenAIGenerator(config=config) - assert generator is not None - assert generator.config == config - - -@pytest.mark.integration -class TestOpenAIGeneratorIntegration: - """OpenAIGenerator集成测试""" - - @pytest.mark.skipif(not GENERATOR_AVAILABLE, reason="Generator module not available") - def test_generator_with_mock_service(self): - """测试生成器与mock服务的集成""" - config = { - "model_name": "test-model", - "base_url": "http://localhost:8000/v1", - "api_key": "test_key", # pragma: allowlist secret - "seed": 42, - } - - with patch("sage.middleware.operators.rag.generator.OpenAI") as mock_openai_class: - mock_client_instance = Mock() - mock_completion = Mock() - mock_completion.choices = [Mock(message=Mock(content="Mocked response"))] - mock_client_instance.chat.completions.create.return_value = mock_completion - mock_openai_class.return_value = mock_client_instance - - generator = OpenAIGenerator(config=config) - - # 测试完整的数据流 - test_data = [ - "Analyze the following context and answer the question.", - "What is machine learning?", - ] - - result = generator.execute(test_data) - - # 新实现：返回 dict，包含 generated - assert isinstance(result, dict) - assert result["generated"] == "Mocked response" - - -@pytest.mark.unit -class TestHFGenerator: - """测试HFGenerator类""" - - def test_hf_generator_import(self): - """测试HFGenerator导入""" - if not GENERATOR_AVAILABLE: - pytest.skip("Generator module not available") - - from sage.middleware.operators.rag.generator import HFGenerator - - assert HFGenerator is not None - - @patch("sage.middleware.operators.rag.generator.HFClient") - def test_hf_generator_initialization(self, mock_hf_client): - """测试HFGenerator初始化""" - if not GENERATOR_AVAILABLE: - pytest.skip("Generator module not available") - - config = {"model_name": "microsoft/DialoGPT-medium"} - - # Mock HFClient - mock_client_instance = Mock() - mock_hf_client.return_value = mock_client_instance - - generator = HFGenerator(config=config) - - # 验证初始化 - assert generator.config == config - - # 验证HFClient被正确调用 - mock_hf_client.assert_called_once_with(model_name="microsoft/DialoGPT-medium") - assert generator.model == mock_client_instance - - @patch("sage.middleware.operators.rag.generator.HFClient") - def test_hf_generator_execute_with_list_input(self, mock_hf_client): - """测试HFGenerator处理列表输入""" - if not GENERATOR_AVAILABLE: - pytest.skip("Generator module not available") - - config = {"model_name": "microsoft/DialoGPT-medium"} - - # Mock HFClient和其响应 - mock_client_instance = Mock() - mock_client_instance.generate.return_value = "Generated response" - mock_hf_client.return_value = mock_client_instance - - generator = HFGenerator(config=config) - - # 测试列表输入 [user_query, prompt] - input_data = ["What is AI?", "Please explain artificial intelligence."] - result = generator.execute(input_data) - - # 验证结果 - assert isinstance(result, tuple) - assert len(result) == 2 - user_query, response = result - assert user_query == "What is AI?" - assert response == "Generated response" - - # 验证model.generate被正确调用 - mock_client_instance.generate.assert_called_once_with( - "Please explain artificial intelligence." - ) - - @patch("sage.middleware.operators.rag.generator.HFClient") - def test_hf_generator_execute_with_single_input(self, mock_hf_client): - """测试HFGenerator处理单个输入""" - if not GENERATOR_AVAILABLE: - pytest.skip("Generator module not available") - - config = {"model_name": "microsoft/DialoGPT-medium"} - - # Mock HFClient和其响应 - mock_client_instance = Mock() - mock_client_instance.generate.return_value = "Single response" - mock_hf_client.return_value = mock_client_instance - - generator = HFGenerator(config=config) - - # 测试单个输入 - input_data = ["Explain machine learning"] - result = generator.execute(input_data) - - # 验证结果 - assert isinstance(result, tuple) - assert len(result) == 2 - user_query, response = result - # HFGenerator: user_query = None when len(data) == 1, but returns "" (empty string) - assert user_query == "" # Code returns "" when user_query is None - assert response == "Single response" - - # 验证model.generate被正确调用 - mock_client_instance.generate.assert_called_once_with("Explain machine learning") - - @patch("sage.middleware.operators.rag.generator.HFClient") - def test_hf_generator_execute_with_kwargs(self, mock_hf_client): - """测试HFGenerator处理额外参数""" - if not GENERATOR_AVAILABLE: - pytest.skip("Generator module not available") - - config = {"model_name": "microsoft/DialoGPT-medium"} - - # Mock HFClient和其响应 - mock_client_instance = Mock() - mock_client_instance.generate.return_value = "Response with params" - mock_hf_client.return_value = mock_client_instance - - generator = HFGenerator(config=config) - - # 测试带有额外参数的调用 - input_data = ["Generate text"] - kwargs = {"temperature": 0.7, "max_length": 100} - - result = generator.execute(input_data, **kwargs) - - # 验证结果 - assert isinstance(result, tuple) - user_query, response = result - assert response == "Response with params" - - # 验证model.generate被正确调用，包含kwargs - mock_client_instance.generate.assert_called_once_with( - "Generate text", temperature=0.7, max_length=100 - ) - - @patch("sage.middleware.operators.rag.generator.HFClient") - def test_hf_generator_error_handling(self, mock_hf_client): - """测试HFGenerator错误处理""" - if not GENERATOR_AVAILABLE: - pytest.skip("Generator module not available") - - config = {"model_name": "microsoft/DialoGPT-medium"} - - # Mock HFClient抛出异常 - mock_client_instance = Mock() - mock_client_instance.generate.side_effect = Exception("HF API Error") - mock_hf_client.return_value = mock_client_instance - - generator = HFGenerator(config=config) - - # 测试异常处理 - with pytest.raises(Exception) as exc_info: - generator.execute(["Test input"]) - - assert "HF API Error" in str(exc_info.value) - - -@pytest.mark.integration -class TestGeneratorIntegration: - """Generator集成测试""" - - @pytest.mark.skipif(not GENERATOR_AVAILABLE, reason="Generator module not available") - def test_multiple_generators_comparison(self): - """测试多个生成器的比较""" - openai_config = { - "model_name": "gpt-4o-mini", - "base_url": "http://localhost:8000/v1", - "api_key": "test_key", # pragma: allowlist secret - "seed": 42, - } - - hf_config = {"model_name": "microsoft/DialoGPT-medium"} - - with ( - patch("sage.middleware.operators.rag.generator.OpenAI") as mock_openai, - patch("sage.middleware.operators.rag.generator.HFClient") as mock_hf, - ): - # Mock OpenAI 客户端 - mock_openai_instance = Mock() - mock_completion = Mock() - mock_completion.choices = [Mock(message=Mock(content="OpenAI response"))] - mock_openai_instance.chat.completions.create.return_value = mock_completion - mock_openai.return_value = mock_openai_instance - - # Mock HF 客户端 - mock_hf_instance = Mock() - mock_hf_instance.generate.return_value = "HF response" - mock_hf.return_value = mock_hf_instance - - # 创建生成器 - openai_gen = OpenAIGenerator(config=openai_config) - hf_gen = HFGenerator(config=hf_config) - - # 测试相同输入 - test_input = ["What is artificial intelligence?"] - - openai_result = openai_gen.execute(test_input) - hf_result = hf_gen.execute(test_input) - - # 新实现：OpenAI 单输入返回字典；HF 返回元组 - assert isinstance(openai_result, dict) - assert isinstance(hf_result, tuple) - - # 验证两个生成器都被正确调用 - mock_openai_instance.chat.completions.create.assert_called_once() - mock_hf_instance.generate.assert_called_once() diff --git a/packages/sage-middleware/tests/operators/rag/test_index_builder.py b/packages/sage-middleware/tests/operators/rag/test_index_builder.py deleted file mode 100644 index ec737fd0ad..0000000000 --- a/packages/sage-middleware/tests/operators/rag/test_index_builder.py +++ /dev/null @@ -1,162 +0,0 @@ -"""Tests for the RAG index builder orchestration service.""" - -from __future__ import annotations - -from dataclasses import dataclass -from pathlib import Path -from typing import Any - -import pytest - -from sage.middleware.operators.rag.index_builder import IndexBuilder - - -@dataclass -class DummyStore: - persist_path: Path - dim: int - - def __post_init__(self): - self.add_calls: list[tuple[list[float], dict[str, Any]]] = [] - self.index_built = False - self.saved_path: str | None = None - - def add(self, vector: list[float], metadata: dict[str, Any]) -> None: - self.add_calls.append((vector, metadata)) - - def build_index(self) -> None: - self.index_built = True - - def save(self, path: str) -> None: - self.saved_path = path - - # Unused protocol methods (kept for completeness/testing) - def load(self, path: str) -> None: # pragma: no cover - helper stub - self.saved_path = path - - def search(self, query_vector, top_k: int = 5, filter_metadata=None): # pragma: no cover - return [] - - def get_dim(self) -> int: # pragma: no cover - helper stub - return self.dim - - def count(self) -> int: # pragma: no cover - helper stub - return len(self.add_calls) - - -class DummyEmbedModel: - def __init__(self, dim: int = 4): - self.dim = dim - self.seen_chunks: list[str] = [] - - def get_dim(self) -> int: - return self.dim - - def embed(self, chunk: str) -> list[float]: - self.seen_chunks.append(chunk) - return [float(len(chunk))] * self.dim - - -@pytest.fixture() -def builder_factory() -> tuple[IndexBuilder, list[DummyStore]]: - created: list[DummyStore] = [] - - def factory(path: Path, dim: int) -> DummyStore: - store = DummyStore(path, dim) - created.append(store) - return store - - return IndexBuilder(factory), created - - -@pytest.fixture() -def docs_dir(tmp_path: Path) -> Path: - path = tmp_path / "docs" - path.mkdir() - return path - - -def test_build_from_docs_creates_manifest_and_vectors(builder_factory, docs_dir, tmp_path): - builder, stores = builder_factory - embedder = DummyEmbedModel(dim=3) - - processed_sections = [ - {"content": "Alpha " * 50, "metadata": {"doc_path": "alpha.md", "title": "Alpha"}}, - {"content": "Beta " * 25, "metadata": {"doc_path": "beta.md", "title": "Beta"}}, - ] - - manifest = builder.build_from_docs( - source_dir=docs_dir, - persist_path=tmp_path / "index", - embedding_model=embedder, - index_name="demo-index", - chunk_size=120, - chunk_overlap=20, - document_processor=lambda _dir: processed_sections, - ) - - store = stores[0] - assert store.index_built is True - assert store.saved_path == str(tmp_path / "index") - assert len(store.add_calls) >= len(processed_sections) - - vector, metadata = store.add_calls[0] - assert len(vector) == embedder.dim - assert metadata["chunk"] == "0" - assert "text" in metadata and metadata["text"] - - assert manifest.index_name == "demo-index" - assert manifest.num_documents == 2 - assert manifest.num_chunks == len(store.add_calls) - assert manifest.backend_type == type(store).__name__ - - -def test_build_from_docs_respects_document_limit(builder_factory, docs_dir, tmp_path): - builder, stores = builder_factory - embedder = DummyEmbedModel(dim=2) - - sections = [{"content": f"Doc {i}", "metadata": {"doc_path": f"doc-{i}.md"}} for i in range(5)] - - builder.build_from_docs( - source_dir=docs_dir, - persist_path=tmp_path / "limited", - embedding_model=embedder, - chunk_size=512, - chunk_overlap=0, - document_processor=lambda _dir: sections, - max_documents=2, - ) - - store = stores[0] - # chunk_size is large enough to keep one chunk per doc - assert len(store.add_calls) == 2 - - -def test_default_document_processor_reads_text_and_markdown(tmp_path: Path): - docs_root = tmp_path / "source" - docs_root.mkdir() - (docs_root / "intro.txt").write_text("Hello from txt", encoding="utf-8") - sub = docs_root / "sub" - sub.mkdir() - (sub / "guide.md").write_text("# Heading\ncontent", encoding="utf-8") - - builder = IndexBuilder(lambda path, dim: DummyStore(path, dim)) - chunks = builder._default_document_processor(docs_root) - - assert len(chunks) == 2 - doc_paths = {chunk["metadata"]["doc_path"] for chunk in chunks} - assert "intro.txt" in doc_paths - assert "sub/guide.md" in doc_paths - - -def test_build_from_docs_requires_existing_directory(builder_factory, tmp_path: Path): - builder, _ = builder_factory - embedder = DummyEmbedModel() - - missing = tmp_path / "missing" - with pytest.raises(FileNotFoundError): - builder.build_from_docs( - source_dir=missing, - persist_path=tmp_path / "index", - embedding_model=embedder, - ) diff --git a/packages/sage-middleware/tests/operators/rag/test_index_manifest.py b/packages/sage-middleware/tests/operators/rag/test_index_manifest.py deleted file mode 100644 index ac65ba4ab6..0000000000 --- a/packages/sage-middleware/tests/operators/rag/test_index_manifest.py +++ /dev/null @@ -1,56 +0,0 @@ -"""Unit tests for `IndexManifest` helpers.""" - -from __future__ import annotations - -from datetime import datetime, timedelta -from pathlib import Path - -from sage.middleware.operators.rag.index_builder.manifest import IndexManifest - - -def test_manifest_roundtrip_and_helpers(tmp_path: Path): - persist = tmp_path / "db" - created_at = (datetime.utcnow() - timedelta(seconds=5)).isoformat() - - manifest = IndexManifest( - index_name="docs", - backend_type="DummyStore", - persist_path=persist, - source_dir="docs-src", - embedding_config={"model": "tiny", "dim": 3}, - chunk_size=256, - chunk_overlap=64, - num_documents=10, - num_chunks=123, - created_at=created_at, - metadata={"env": "test"}, - ) - - data = manifest.to_dict() - assert data["persist_path"] == str(persist) - - restored = IndexManifest.from_dict(data) - assert restored.persist_path == persist - assert restored.embedding_config == manifest.embedding_config - assert restored.metadata["env"] == "test" - assert restored.num_chunks == 123 - - # Age helper should be close to the delta we set - assert restored.age_seconds >= 5 - assert not restored.is_empty - assert "docs" in repr(restored) - - empty_manifest = IndexManifest( - index_name="empty", - backend_type="DummyStore", - persist_path=persist, - source_dir="docs-src", - embedding_config={"model": "tiny", "dim": 3}, - chunk_size=256, - chunk_overlap=64, - num_documents=0, - num_chunks=0, - created_at=datetime.utcnow().isoformat(), - ) - - assert empty_manifest.is_empty is True diff --git a/packages/sage-middleware/tests/operators/rag/test_index_storage.py b/packages/sage-middleware/tests/operators/rag/test_index_storage.py deleted file mode 100644 index 2f2e0b650f..0000000000 --- a/packages/sage-middleware/tests/operators/rag/test_index_storage.py +++ /dev/null @@ -1,44 +0,0 @@ -"""Tests for the `VectorStore` Protocol.""" - -from __future__ import annotations - -from pathlib import Path -from typing import Any - -from sage.middleware.operators.rag.index_builder.storage import VectorStore - - -class GoodStore: - def __init__(self): - self.data: list[tuple[list[float], dict[str, Any]]] = [] - - def add(self, vector: list[float], metadata: dict[str, Any]) -> None: - self.data.append((vector, metadata)) - - def build_index(self) -> None: # pragma: no cover - trivial - pass - - def save(self, path: str) -> None: # pragma: no cover - trivial - Path(path).touch() - - def load(self, path: str) -> None: # pragma: no cover - trivial - Path(path) - - def search(self, query_vector, top_k: int = 5, filter_metadata=None): # pragma: no cover - return [] - - def get_dim(self) -> int: # pragma: no cover - trivial - return 3 - - def count(self) -> int: # pragma: no cover - trivial - return len(self.data) - - -class IncompleteStore: - def add(self, vector, metadata): # pragma: no cover - trivial - pass - - -def test_vector_store_protocol_checks_methods(): - assert isinstance(GoodStore(), VectorStore) - assert not isinstance(IncompleteStore(), VectorStore) diff --git a/packages/sage-middleware/tests/operators/rag/test_pipeline.py b/packages/sage-middleware/tests/operators/rag/test_pipeline.py deleted file mode 100644 index 20a57fa10e..0000000000 --- a/packages/sage-middleware/tests/operators/rag/test_pipeline.py +++ /dev/null @@ -1,522 +0,0 @@ -""" -测试 sage.middleware.operators.rag.pipeline 模块 -""" - -from unittest.mock import Mock - -import pytest - -# 尝试导入pipeline模块 -pytest_plugins = [] - -try: - from sage.middleware.operators.rag.pipeline import RAGPipeline - - PIPELINE_AVAILABLE = True -except ImportError as e: - PIPELINE_AVAILABLE = False - pytestmark = pytest.mark.skip(f"Pipeline module not available: {e}") - - -@pytest.mark.unit -class TestRAGPipeline: - """测试RAGPipeline类""" - - def test_rag_pipeline_import(self): - """测试RAGPipeline导入""" - if not PIPELINE_AVAILABLE: - pytest.skip("Pipeline module not available") - - from sage.middleware.operators.rag.pipeline import RAGPipeline - - assert RAGPipeline is not None - - def test_rag_pipeline_initialization_empty(self): - """测试RAGPipeline空初始化""" - if not PIPELINE_AVAILABLE: - pytest.skip("Pipeline module not available") - - pipeline = RAGPipeline() - - assert pipeline.retriever is None - assert pipeline.generator is None - assert pipeline.reranker is None - assert pipeline.refiner is None - - def test_rag_pipeline_initialization_with_components(self): - """测试RAGPipeline带组件初始化""" - if not PIPELINE_AVAILABLE: - pytest.skip("Pipeline module not available") - - # Mock各种组件 - mock_retriever = Mock() - mock_generator = Mock() - mock_reranker = Mock() - mock_refiner = Mock() - - pipeline = RAGPipeline( - retriever=mock_retriever, - generator=mock_generator, - reranker=mock_reranker, - refiner=mock_refiner, - ) - - assert pipeline.retriever == mock_retriever - assert pipeline.generator == mock_generator - assert pipeline.reranker == mock_reranker - assert pipeline.refiner == mock_refiner - - def test_run_with_all_components(self): - """测试run方法使用所有组件""" - if not PIPELINE_AVAILABLE: - pytest.skip("Pipeline module not available") - - # Mock各种组件 - mock_retriever = Mock() - mock_generator = Mock() - mock_reranker = Mock() - mock_refiner = Mock() - - # 配置mock行为 - mock_documents = [ - {"content": "Document 1", "score": 0.9}, - {"content": "Document 2", "score": 0.8}, - ] - mock_retriever.retrieve.return_value = mock_documents - - mock_reranked_docs = [ - {"content": "Document 1", "score": 0.95}, - {"content": "Document 2", "score": 0.85}, - ] - mock_reranker.rerank.return_value = mock_reranked_docs - - refined_query = "refined query" - refined_docs = [{"content": "Refined Document 1", "score": 0.95}] - mock_refiner.refine.return_value = (refined_query, refined_docs) - - mock_response = "Generated response" - mock_generator.generate.return_value = mock_response - - pipeline = RAGPipeline( - retriever=mock_retriever, - generator=mock_generator, - reranker=mock_reranker, - refiner=mock_refiner, - ) - - # 执行pipeline - query = "What is machine learning?" - result = pipeline.run(query) - - # 验证组件调用 - mock_retriever.retrieve.assert_called_once_with(query) - mock_reranker.rerank.assert_called_once_with(query, mock_documents) - mock_refiner.refine.assert_called_once_with(query, mock_reranked_docs) - mock_generator.generate.assert_called_once_with(refined_query, refined_docs) - - # 验证结果 - assert isinstance(result, dict) - assert result["query"] == refined_query - assert result["documents"] == refined_docs - assert result["response"] == mock_response - - def test_run_with_only_generator(self): - """测试run方法只使用generator""" - if not PIPELINE_AVAILABLE: - pytest.skip("Pipeline module not available") - - # 只有generator - mock_generator = Mock() - mock_response = "Generated response without retrieval" - mock_generator.generate.return_value = mock_response - - pipeline = RAGPipeline(generator=mock_generator) - - # 执行pipeline - query = "What is AI?" - result = pipeline.run(query) - - # 验证generator被调用时documents为空列表 - mock_generator.generate.assert_called_once_with(query, []) - - # 验证结果 - assert result["query"] == query - assert result["documents"] == [] - assert result["response"] == mock_response - - def test_run_without_generator(self): - """测试run方法没有generator""" - if not PIPELINE_AVAILABLE: - pytest.skip("Pipeline module not available") - - # 只有retriever，没有generator - mock_retriever = Mock() - mock_documents = [{"content": "Document 1", "score": 0.9}] - mock_retriever.retrieve.return_value = mock_documents - - pipeline = RAGPipeline(retriever=mock_retriever) - - # 执行pipeline - query = "What is deep learning?" - result = pipeline.run(query) - - # 验证结果 - assert result["query"] == query - assert result["documents"] == mock_documents - assert result["response"] == "No generator configured" - - def test_run_with_retriever_and_generator_only(self): - """测试run方法只使用retriever和generator""" - if not PIPELINE_AVAILABLE: - pytest.skip("Pipeline module not available") - - # 只有retriever和generator - mock_retriever = Mock() - mock_generator = Mock() - - mock_documents = [{"content": "Retrieved document", "score": 0.9}] - mock_retriever.retrieve.return_value = mock_documents - - mock_response = "Generated from retrieved docs" - mock_generator.generate.return_value = mock_response - - pipeline = RAGPipeline(retriever=mock_retriever, generator=mock_generator) - - # 执行pipeline - query = "Explain neural networks" - result = pipeline.run(query) - - # 验证调用 - mock_retriever.retrieve.assert_called_once_with(query) - mock_generator.generate.assert_called_once_with(query, mock_documents) - - # 验证结果 - assert result["query"] == query - assert result["documents"] == mock_documents - assert result["response"] == mock_response - - def test_run_with_reranker_without_documents(self): - """测试run方法有reranker但没有文档""" - if not PIPELINE_AVAILABLE: - pytest.skip("Pipeline module not available") - - # 有reranker但retriever返回空文档 - mock_retriever = Mock() - mock_reranker = Mock() - mock_generator = Mock() - - mock_retriever.retrieve.return_value = [] # 空文档列表 - mock_generator.generate.return_value = "Response without docs" - - pipeline = RAGPipeline( - retriever=mock_retriever, reranker=mock_reranker, generator=mock_generator - ) - - # 执行pipeline - query = "Test query" - result = pipeline.run(query) - - # 验证reranker没有被调用（因为没有文档） - mock_retriever.retrieve.assert_called_once_with(query) - mock_reranker.rerank.assert_not_called() - mock_generator.generate.assert_called_once_with(query, []) - - # 验证结果 - assert result["documents"] == [] - - def test_run_with_kwargs(self): - """测试run方法传递额外参数""" - if not PIPELINE_AVAILABLE: - pytest.skip("Pipeline module not available") - - mock_retriever = Mock() - mock_generator = Mock() - - mock_documents = [{"content": "Document", "score": 0.9}] - mock_retriever.retrieve.return_value = mock_documents - mock_generator.generate.return_value = "Response" - - pipeline = RAGPipeline(retriever=mock_retriever, generator=mock_generator) - - # 执行pipeline带额外参数 - query = "Test query" - pipeline.run(query, top_k=5, temperature=0.7) - - # 验证参数传递 - mock_retriever.retrieve.assert_called_once_with(query, top_k=5, temperature=0.7) - mock_generator.generate.assert_called_once_with( - query, mock_documents, top_k=5, temperature=0.7 - ) - - def test_run_with_refiner_but_no_reranker(self): - """测试run方法有refiner但没有reranker""" - if not PIPELINE_AVAILABLE: - pytest.skip("Pipeline module not available") - - mock_retriever = Mock() - mock_refiner = Mock() - mock_generator = Mock() - - mock_documents = [{"content": "Original document", "score": 0.8}] - mock_retriever.retrieve.return_value = mock_documents - - refined_query = "refined query" - refined_docs = [{"content": "Refined document", "score": 0.9}] - mock_refiner.refine.return_value = (refined_query, refined_docs) - - mock_generator.generate.return_value = "Refined response" - - pipeline = RAGPipeline( - retriever=mock_retriever, refiner=mock_refiner, generator=mock_generator - ) - - # 执行pipeline - query = "Original query" - result = pipeline.run(query) - - # 验证调用序列 - mock_retriever.retrieve.assert_called_once_with(query) - mock_refiner.refine.assert_called_once_with(query, mock_documents) - mock_generator.generate.assert_called_once_with(refined_query, refined_docs) - - # 验证结果使用refined内容 - assert result["query"] == refined_query - assert result["documents"] == refined_docs - - -@pytest.mark.unit -class TestRAGPipelineErrorHandling: - """测试RAGPipeline错误处理""" - - def test_run_with_retriever_error(self): - """测试retriever出错的情况""" - if not PIPELINE_AVAILABLE: - pytest.skip("Pipeline module not available") - - mock_retriever = Mock() - mock_generator = Mock() - - # Mock retriever抛出异常 - mock_retriever.retrieve.side_effect = Exception("Retrieval failed") - mock_generator.generate.return_value = "Fallback response" - - pipeline = RAGPipeline(retriever=mock_retriever, generator=mock_generator) - - # 验证异常传播 - with pytest.raises(Exception) as exc_info: - pipeline.run("Test query") - - assert "Retrieval failed" in str(exc_info.value) - - def test_run_with_generator_error(self): - """测试generator出错的情况""" - if not PIPELINE_AVAILABLE: - pytest.skip("Pipeline module not available") - - mock_retriever = Mock() - mock_generator = Mock() - - mock_documents = [{"content": "Document", "score": 0.9}] - mock_retriever.retrieve.return_value = mock_documents - - # Mock generator抛出异常 - mock_generator.generate.side_effect = Exception("Generation failed") - - pipeline = RAGPipeline(retriever=mock_retriever, generator=mock_generator) - - # 验证异常传播 - with pytest.raises(Exception) as exc_info: - pipeline.run("Test query") - - assert "Generation failed" in str(exc_info.value) - - def test_run_with_reranker_error(self): - """测试reranker出错的情况""" - if not PIPELINE_AVAILABLE: - pytest.skip("Pipeline module not available") - - mock_retriever = Mock() - mock_reranker = Mock() - mock_generator = Mock() - - mock_documents = [{"content": "Document", "score": 0.9}] - mock_retriever.retrieve.return_value = mock_documents - - # Mock reranker抛出异常 - mock_reranker.rerank.side_effect = Exception("Reranking failed") - mock_generator.generate.return_value = "Response" - - pipeline = RAGPipeline( - retriever=mock_retriever, reranker=mock_reranker, generator=mock_generator - ) - - # 验证异常传播 - with pytest.raises(Exception) as exc_info: - pipeline.run("Test query") - - assert "Reranking failed" in str(exc_info.value) - - def test_run_with_refiner_error(self): - """测试refiner出错的情况""" - if not PIPELINE_AVAILABLE: - pytest.skip("Pipeline module not available") - - mock_retriever = Mock() - mock_refiner = Mock() - mock_generator = Mock() - - mock_documents = [{"content": "Document", "score": 0.9}] - mock_retriever.retrieve.return_value = mock_documents - - # Mock refiner抛出异常 - mock_refiner.refine.side_effect = Exception("Refinement failed") - mock_generator.generate.return_value = "Response" - - pipeline = RAGPipeline( - retriever=mock_retriever, refiner=mock_refiner, generator=mock_generator - ) - - # 验证异常传播 - with pytest.raises(Exception) as exc_info: - pipeline.run("Test query") - - assert "Refinement failed" in str(exc_info.value) - - -@pytest.mark.integration -class TestRAGPipelineIntegration: - """RAGPipeline集成测试""" - - @pytest.mark.skipif(not PIPELINE_AVAILABLE, reason="Pipeline module not available") - def test_complete_rag_pipeline_simulation(self): - """测试完整RAG pipeline模拟""" - - # 创建真实的mock组件来模拟完整流程 - class MockRetriever: - def retrieve(self, query, **kwargs): - # 模拟基于查询返回相关文档 - if "machine learning" in query.lower(): - return [ - {"content": "Machine learning is a subset of AI", "score": 0.9}, - {"content": "ML algorithms learn from data", "score": 0.8}, - {"content": "Deep learning uses neural networks", "score": 0.7}, - ] - return [{"content": "Generic document", "score": 0.5}] - - class MockReranker: - def rerank(self, query, documents, **kwargs): - # 模拟重排序 - 简单按分数排序 - sorted_docs = sorted(documents, key=lambda x: x["score"], reverse=True) - # 添加rerank分数 - for i, doc in enumerate(sorted_docs): - doc["rerank_score"] = doc["score"] + 0.05 - i * 0.01 - return sorted_docs[:2] # 返回top 2 - - class MockRefiner: - def refine(self, query, documents, **kwargs): - # 模拟查询精化和文档处理 - refined_query = f"Refined: {query}" - refined_docs = [] - for doc in documents: - refined_doc = doc.copy() - refined_doc["content"] = f"[Refined] {doc['content']}" - refined_docs.append(refined_doc) - return refined_query, refined_docs - - class MockGenerator: - def generate(self, query, documents, **kwargs): - # 模拟基于文档生成回答 - doc_contents = [doc["content"] for doc in documents] - context = " | ".join(doc_contents) - return f"Answer to '{query}' based on: {context}" - - # 创建pipeline - pipeline = RAGPipeline( - retriever=MockRetriever(), - reranker=MockReranker(), - refiner=MockRefiner(), - generator=MockGenerator(), - ) - - # 执行测试 - query = "What is machine learning?" - result = pipeline.run(query) - - # 验证完整结果 - assert isinstance(result, dict) - assert "query" in result - assert "documents" in result - assert "response" in result - - # 验证查询被精化 - assert result["query"] == "Refined: What is machine learning?" - - # 验证文档被处理 - assert len(result["documents"]) == 2 # reranker限制为top 2 - for doc in result["documents"]: - assert "[Refined]" in doc["content"] - assert "rerank_score" in doc - - # 验证响应包含精化后的内容 - assert "Refined: What is machine learning?" in result["response"] - assert "[Refined]" in result["response"] - - @pytest.mark.skipif(not PIPELINE_AVAILABLE, reason="Pipeline module not available") - def test_minimal_pipeline_simulation(self): - """测试最小化pipeline模拟（只有generator）""" - - class SimpleGenerator: - def generate(self, query, documents, **kwargs): - if documents: - return f"Generated answer for '{query}' using {len(documents)} documents" - else: - return f"Generated answer for '{query}' without context" - - pipeline = RAGPipeline(generator=SimpleGenerator()) - - result = pipeline.run("Simple question") - - # 验证最小pipeline结果 - assert result["query"] == "Simple question" - assert result["documents"] == [] - assert "without context" in result["response"] - - @pytest.mark.skipif(not PIPELINE_AVAILABLE, reason="Pipeline module not available") - def test_pipeline_component_interaction(self): - """测试pipeline组件间交互""" - - # 追踪组件调用顺序 - call_order = [] - - class TrackedRetriever: - def retrieve(self, query, **kwargs): - call_order.append("retriever") - return [{"content": f"Retrieved for: {query}", "score": 0.8}] - - class TrackedReranker: - def rerank(self, query, documents, **kwargs): - call_order.append("reranker") - return documents # 不改变顺序，只记录调用 - - class TrackedRefiner: - def refine(self, query, documents, **kwargs): - call_order.append("refiner") - return query, documents # 不改变内容，只记录调用 - - class TrackedGenerator: - def generate(self, query, documents, **kwargs): - call_order.append("generator") - return "Final response" - - pipeline = RAGPipeline( - retriever=TrackedRetriever(), - reranker=TrackedReranker(), - refiner=TrackedRefiner(), - generator=TrackedGenerator(), - ) - - pipeline.run("Test query") - - # 验证组件调用顺序 - expected_order = ["retriever", "reranker", "refiner", "generator"] - assert call_order == expected_order diff --git a/packages/sage-middleware/tests/operators/rag/test_profiler.py b/packages/sage-middleware/tests/operators/rag/test_profiler.py deleted file mode 100644 index 88ba21552f..0000000000 --- a/packages/sage-middleware/tests/operators/rag/test_profiler.py +++ /dev/null @@ -1,239 +0,0 @@ -""" -Unit tests for sage.middleware.operators.rag.profiler module. -Tests Query_Profiler and QueryProfilerResult. -""" - -import pytest - -try: - from sage.middleware.operators.rag.profiler import ( - Query_Profiler, - QueryProfilerResult, - ) - - PROFILER_AVAILABLE = True -except ImportError as e: - PROFILER_AVAILABLE = False - pytestmark = pytest.mark.skip(f"Profiler module not available: {e}") - - -@pytest.mark.unit -class TestQueryProfilerResult: - """Test QueryProfilerResult dataclass validation.""" - - def test_valid_initialization(self): - """Test creating QueryProfilerResult with valid parameters.""" - if not PROFILER_AVAILABLE: - pytest.skip("Profiler not available") - - result = QueryProfilerResult( - need_joint_reasoning=True, - complexity="High", - need_summarization=True, - summarization_length=100, - n_info_items=3, - ) - - assert result.need_joint_reasoning is True - assert result.complexity == "High" - assert result.need_summarization is True - assert result.summarization_length == 100 - assert result.n_info_items == 3 - - def test_invalid_complexity(self): - """Test validation error for invalid complexity value.""" - if not PROFILER_AVAILABLE: - pytest.skip("Profiler not available") - - with pytest.raises(ValueError, match="complexity必须是'High'或'Low'"): - QueryProfilerResult( - need_joint_reasoning=False, - complexity="Medium", # Invalid - need_summarization=False, - summarization_length=50, - n_info_items=2, - ) - - def test_invalid_summarization_length_too_low(self): - """Test validation error for summarization_length < 30.""" - if not PROFILER_AVAILABLE: - pytest.skip("Profiler not available") - - with pytest.raises(ValueError, match="summarization_length必须在30-200之间"): - QueryProfilerResult( - need_joint_reasoning=False, - complexity="Low", - need_summarization=True, - summarization_length=20, # Too low - n_info_items=1, - ) - - def test_invalid_summarization_length_too_high(self): - """Test validation error for summarization_length > 200.""" - if not PROFILER_AVAILABLE: - pytest.skip("Profiler not available") - - with pytest.raises(ValueError, match="summarization_length必须在30-200之间"): - QueryProfilerResult( - need_joint_reasoning=False, - complexity="Low", - need_summarization=True, - summarization_length=250, # Too high - n_info_items=1, - ) - - def test_invalid_n_info_items_too_low(self): - """Test validation error for n_info_items < 1.""" - if not PROFILER_AVAILABLE: - pytest.skip("Profiler not available") - - with pytest.raises(ValueError, match="n_info_items必须在1-6之间"): - QueryProfilerResult( - need_joint_reasoning=False, - complexity="Low", - need_summarization=False, - summarization_length=50, - n_info_items=0, # Too low - ) - - def test_invalid_n_info_items_too_high(self): - """Test validation error for n_info_items > 6.""" - if not PROFILER_AVAILABLE: - pytest.skip("Profiler not available") - - with pytest.raises(ValueError, match="n_info_items必须在1-6之间"): - QueryProfilerResult( - need_joint_reasoning=False, - complexity="Low", - need_summarization=False, - summarization_length=50, - n_info_items=10, # Too high - ) - - def test_boundary_values(self): - """Test boundary values for summarization_length and n_info_items.""" - if not PROFILER_AVAILABLE: - pytest.skip("Profiler not available") - - # Min values - result_min = QueryProfilerResult( - need_joint_reasoning=False, - complexity="Low", - need_summarization=False, - summarization_length=30, # Min valid - n_info_items=1, # Min valid - ) - assert result_min.summarization_length == 30 - assert result_min.n_info_items == 1 - - # Max values - result_max = QueryProfilerResult( - need_joint_reasoning=True, - complexity="High", - need_summarization=True, - summarization_length=200, # Max valid - n_info_items=6, # Max valid - ) - assert result_max.summarization_length == 200 - assert result_max.n_info_items == 6 - - -@pytest.mark.unit -class TestQueryProfiler: - """Test Query_Profiler operator.""" - - def test_initialization(self): - """Test Query_Profiler initialization.""" - if not PROFILER_AVAILABLE: - pytest.skip("Profiler not available") - - config = {} - profiler = Query_Profiler(config) - assert profiler is not None - - def test_execute_with_valid_json(self): - """Test execute with valid JSON input.""" - if not PROFILER_AVAILABLE: - pytest.skip("Profiler not available") - - config = {} - profiler = Query_Profiler(config) - - json_data = """ - { - "need_joint_reasoning": true, - "complexity": "High", - "need_summarization": true, - "summarization_length": 150, - "n_info_items": 4 - } - """ - - # Should not raise error - result = profiler.execute(json_data) - # Result depends on logic, just test it runs - assert result is not None - - def test_execute_with_default_values(self): - """Test execute with missing fields uses defaults.""" - if not PROFILER_AVAILABLE: - pytest.skip("Profiler not available") - - config = {} - profiler = Query_Profiler(config) - - # Empty JSON object - should use defaults - json_data = "{}" - - result = profiler.execute(json_data) - assert result is not None - - def test_execute_with_partial_data(self): - """Test execute with partial JSON data.""" - if not PROFILER_AVAILABLE: - pytest.skip("Profiler not available") - - config = {} - profiler = Query_Profiler(config) - - json_data = """ - { - "need_joint_reasoning": false, - "complexity": "Low" - } - """ - - result = profiler.execute(json_data) - assert result is not None - - def test_execute_invalid_json(self): - """Test execute with invalid JSON raises error.""" - if not PROFILER_AVAILABLE: - pytest.skip("Profiler not available") - - config = {} - profiler = Query_Profiler(config) - - invalid_json = "not a json" - - with pytest.raises((ValueError, TypeError)): # JSONDecodeError is ValueError - profiler.execute(invalid_json) - - def test_execute_with_invalid_values(self): - """Test execute with invalid field values in JSON.""" - if not PROFILER_AVAILABLE: - pytest.skip("Profiler not available") - - config = {} - profiler = Query_Profiler(config) - - json_data = """ - { - "complexity": "Invalid", - "summarization_length": 300, - "n_info_items": 10 - } - """ - - with pytest.raises(ValueError): - profiler.execute(json_data) diff --git a/packages/sage-middleware/tests/operators/rag/test_promptor.py b/packages/sage-middleware/tests/operators/rag/test_promptor.py deleted file mode 100644 index a27e5d86f6..0000000000 --- a/packages/sage-middleware/tests/operators/rag/test_promptor.py +++ /dev/null @@ -1,458 +0,0 @@ -""" -Test sage.middleware.operators.rag.promptor module -""" - -import pytest - -# Try to import promptor module -pytest_plugins = [] - -try: - from sage.middleware.operators.rag.promptor import ( - QA_prompt_template, # noqa: F401 - QAPromptor, - QueryProfilerPromptor, # noqa: F401 - SummarizationPromptor, # noqa: F401 - summarization_prompt_template, # noqa: F401 - ) - - PROMPTOR_AVAILABLE = True -except ImportError as e: - PROMPTOR_AVAILABLE = False - pytestmark = pytest.mark.skip(f"Promptor module not available: {e}") - - -@pytest.mark.unit -class TestQAPromptor: - """Test QAPromptor class""" - - def test_qa_promptor_import(self): - """Test QAPromptor import""" - if not PROMPTOR_AVAILABLE: - pytest.skip("Promptor module not available") - - from sage.middleware.operators.rag.promptor import QAPromptor - - assert QAPromptor is not None - - def test_qa_promptor_initialization(self): - """Test QAPromptor initialization""" - if not PROMPTOR_AVAILABLE: - pytest.skip("Promptor module not available") - - config = {"task_type": "qa"} - promptor = QAPromptor(config=config) - - assert promptor.config == config - assert hasattr(promptor, "execute") - - def test_execute_with_question_and_context(self): - """Test execute method with question and context""" - if not PROMPTOR_AVAILABLE: - pytest.skip("Promptor module not available") - - config = {"task_type": "qa"} - promptor = QAPromptor(config=config) - - # Test input data - input_data = { - "question": "What is machine learning?", - "context": [ - "Machine learning is a subset of AI.", - "It involves algorithms that learn from data.", - ], - } - - result = promptor.execute(input_data) - - # Verify result structure - QAPromptor returns [data, prompt] - assert isinstance(result, list) - assert len(result) == 2 - - data_result, prompt_result = result - assert isinstance(data_result, dict) - assert isinstance(prompt_result, list) - - # Verify data remains unchanged - assert data_result["question"] == "What is machine learning?" - assert data_result["context"] == [ - "Machine learning is a subset of AI.", - "It involves algorithms that learn from data.", - ] - - # Verify prompt format - assert len(prompt_result) == 2 # system + user prompt - assert prompt_result[0]["role"] == "system" - assert prompt_result[1]["role"] == "user" - - def test_execute_with_retrieved_docs(self): - """Test execute method with retrieved docs""" - if not PROMPTOR_AVAILABLE: - pytest.skip("Promptor module not available") - - config = {"task_type": "qa"} - promptor = QAPromptor(config=config) - - # Test input data with retrieved_docs - input_data = { - "question": "What is deep learning?", - "retrieved_docs": [ - {"content": "Deep learning uses neural networks."}, - {"content": "It is a subset of machine learning."}, - ], - } - - result = promptor.execute(input_data) - - # Verify result - QAPromptor returns [data, prompt] - assert isinstance(result, list) - assert len(result) == 2 - - data_result, prompt_result = result - assert isinstance(data_result, dict) - assert isinstance(prompt_result, list) - - # Verify data remains unchanged - assert data_result["question"] == "What is deep learning?" - assert "retrieved_docs" in data_result - - # Verify prompt format - assert len(prompt_result) == 2 # system + user prompt - assert prompt_result[0]["role"] == "system" - assert prompt_result[1]["role"] == "user" - - def test_execute_with_external_corpus(self): - """Test execute method with external corpus""" - if not PROMPTOR_AVAILABLE: - pytest.skip("Promptor module not available") - - config = {"task_type": "qa"} - promptor = QAPromptor(config=config) - - # Test input data with external_corpus - input_data = { - "question": "Explain neural networks", - "external_corpus": "Neural networks are computing systems inspired by biological neural networks.", - } - - result = promptor.execute(input_data) - - # Verify result - QAPromptor returns [data, prompt] - assert isinstance(result, list) - assert len(result) == 2 - - data_result, prompt_result = result - assert isinstance(data_result, dict) - assert isinstance(prompt_result, list) - - # Verify data remains unchanged - assert data_result["question"] == "Explain neural networks" - assert "external_corpus" in data_result - - # Verify prompt format - assert len(prompt_result) == 2 # system + user prompt - assert prompt_result[0]["role"] == "system" - assert prompt_result[1]["role"] == "user" - - def test_execute_with_empty_context(self): - """Test execute method with empty context""" - if not PROMPTOR_AVAILABLE: - pytest.skip("Promptor module not available") - - config = {"task_type": "qa"} - promptor = QAPromptor(config=config) - - # Test empty context - input_data = {"question": "What is AI?", "context": []} - - result = promptor.execute(input_data) - - # Verify result - QAPromptor returns [data, prompt] - assert isinstance(result, list) - assert len(result) == 2 - - data_result, prompt_result = result - assert isinstance(data_result, dict) - assert isinstance(prompt_result, list) - - # Verify data remains unchanged - assert data_result["question"] == "What is AI?" - assert "context" in data_result - - # Verify prompt format - assert len(prompt_result) == 2 # system + user prompt - assert prompt_result[0]["role"] == "system" - assert prompt_result[1]["role"] == "user" - - def test_execute_with_query_tuple(self): - """Test execute method with query tuple""" - if not PROMPTOR_AVAILABLE: - pytest.skip("Promptor module not available") - - config = {"task_type": "qa"} - promptor = QAPromptor(config=config) - - # Test tuple input - query = "What is reinforcement learning?" - docs = [ - {"content": "Reinforcement learning is a type of machine learning."}, - {"content": "It involves agents learning through interaction."}, - ] - input_data = (query, docs) - - result = promptor.execute(input_data) - - # Verify result - tuple input triggers error handling, returns error message - assert isinstance(result, list) - assert len(result) == 2 - system_msg, user_msg = result - - # Verify error handling format - assert system_msg["role"] == "system" - assert ( - "error" in system_msg["content"].lower() - or "encountered" in system_msg["content"].lower() - ) - assert user_msg["role"] == "user" - - def test_execute_preserves_additional_fields(self): - """Test execute method preserves additional fields""" - if not PROMPTOR_AVAILABLE: - pytest.skip("Promptor module not available") - - config = {"task_type": "qa"} - promptor = QAPromptor(config=config) - - # Test input data with additional fields - input_data = { - "question": "What is NLP?", - "context": ["Natural Language Processing is a field of AI."], - "metadata": {"source": "textbook"}, - "timestamp": "2023-01-01", - } - - result = promptor.execute(input_data) - - # Verify result - QAPromptor returns [data, prompt] - assert isinstance(result, list) - assert len(result) == 2 - - data_result, prompt_result = result - assert isinstance(data_result, dict) - assert isinstance(prompt_result, list) - - # Verify result preserves all original fields - assert data_result["question"] == "What is NLP?" - assert data_result["metadata"] == {"source": "textbook"} - assert data_result["timestamp"] == "2023-01-01" - assert "context" in data_result - - # Verify prompt format - assert len(prompt_result) == 2 # system + user prompt - assert prompt_result[0]["role"] == "system" - assert prompt_result[1]["role"] == "user" - - -@pytest.mark.unit -class TestQueryProfilerPromptor: - """Test QueryProfilerPromptor class""" - - def test_query_profiler_promptor_import(self): - """Test QueryProfilerPromptor import""" - if not PROMPTOR_AVAILABLE: - pytest.skip("Promptor module not available") - - try: - from sage.middleware.operators.rag.promptor import QueryProfilerPromptor - - assert QueryProfilerPromptor is not None - except ImportError: - pytest.skip("QueryProfilerPromptor not available in module") - - def test_query_profiler_promptor_initialization(self): - """Test QueryProfilerPromptor initialization""" - if not PROMPTOR_AVAILABLE: - pytest.skip("Promptor module not available") - - try: - from sage.middleware.operators.rag.promptor import QueryProfilerPromptor - except ImportError: - pytest.skip("QueryProfilerPromptor not available in module") - - config = {"metadata": {"dataset": "test"}, "chunk_size": 512} - - try: - profiler = QueryProfilerPromptor(config=config) - assert profiler.config == config - assert hasattr(profiler, "execute") - except Exception as e: - pytest.skip(f"QueryProfilerPromptor initialization failed: {e}") - - -@pytest.mark.unit -class TestSummarizationPromptor: - """Test SummarizationPromptor class""" - - def test_summarization_promptor_import(self): - """Test SummarizationPromptor import""" - if not PROMPTOR_AVAILABLE: - pytest.skip("Promptor module not available") - - try: - from sage.middleware.operators.rag.promptor import SummarizationPromptor - - assert SummarizationPromptor is not None - except ImportError: - pytest.skip("SummarizationPromptor not available in module") - - def test_summarization_promptor_initialization(self): - """Test SummarizationPromptor initialization""" - if not PROMPTOR_AVAILABLE: - pytest.skip("Promptor module not available") - - try: - from sage.middleware.operators.rag.promptor import SummarizationPromptor - except ImportError: - pytest.skip("SummarizationPromptor not available in module") - - config = {"task_type": "summarization"} - - try: - promptor = SummarizationPromptor(config=config) - assert promptor.config == config - assert hasattr(promptor, "execute") - except Exception as e: - pytest.skip(f"SummarizationPromptor initialization failed: {e}") - - def test_summarization_promptor_execute(self): - """Test SummarizationPromptor execute""" - if not PROMPTOR_AVAILABLE: - pytest.skip("Promptor module not available") - - try: - from sage.middleware.operators.rag.promptor import SummarizationPromptor - except ImportError: - pytest.skip("SummarizationPromptor not available in module") - - config = {"task_type": "summarization"} - promptor = SummarizationPromptor(config=config) - - # SummarizationPromptor expects (query, external_corpus) format - query = "Please summarize the following text" - external_corpus = [ - "This is a long text that needs to be summarized. ", - "It contains multiple sentences and paragraphs with important information.", - ] - - input_data = (query, external_corpus) - - result = promptor.execute(input_data) - - # Verify result - SummarizationPromptor returns [data, message] format - assert isinstance(result, list) - assert len(result) == 2 - - data, message = result - assert isinstance(data, dict) - assert isinstance(message, dict) - - # Check message content - assert "content" in message - assert "Please summarize the following text" in message["content"] - - -@pytest.mark.unit -class TestPromptTemplates: - """Test prompt templates""" - - def test_qa_prompt_template(self): - """Test QA prompt template""" - if not PROMPTOR_AVAILABLE: - pytest.skip("Promptor module not available") - - from sage.middleware.operators.rag.promptor import QA_prompt_template - - # Test template rendering - context = "Machine learning is a subset of artificial intelligence." - rendered = QA_prompt_template.render(external_corpus=context) - - assert "Machine learning is a subset of artificial intelligence." in rendered - assert "intelligent assistant" in rendered - assert "knowledge base" in rendered - - def test_qa_prompt_template_no_context(self): - """Test QA prompt template without context""" - if not PROMPTOR_AVAILABLE: - pytest.skip("Promptor module not available") - - from sage.middleware.operators.rag.promptor import QA_prompt_template - - # Test template rendering without context - rendered = QA_prompt_template.render() - - assert "intelligent assistant" in rendered - assert "knowledge base" in rendered - # Should not contain context section - assert "Relevant corpus" not in rendered - - def test_summarization_prompt_template(self): - """Test summarization prompt template""" - if not PROMPTOR_AVAILABLE: - pytest.skip("Promptor module not available") - - from sage.middleware.operators.rag.promptor import summarization_prompt_template - - # Test template rendering - content = "This is content that needs to be summarized." - rendered = summarization_prompt_template.render(external_corpus=content) - - assert "This is content that needs to be summarized." in rendered - assert "Summarize the content" in rendered - assert "concise and clear" in rendered - - -@pytest.mark.integration -class TestPromptorIntegration: - """Promptor integration tests""" - - @pytest.mark.skipif(not PROMPTOR_AVAILABLE, reason="Promptor module not available") - def test_qa_promptor_full_pipeline(self): - """Test QAPromptor complete pipeline""" - config = {"task_type": "qa"} - promptor = QAPromptor(config=config) - - # Simulate complete RAG pipeline data - pipeline_data = { - "question": "What are the benefits of renewable energy?", - "retrieved_docs": [ - {"content": "Renewable energy reduces carbon emissions.", "score": 0.9}, - { - "content": "Solar and wind power are sustainable sources.", - "score": 0.8, - }, - {"content": "Renewable energy creates green jobs.", "score": 0.7}, - ], - "metadata": {"retrieval_method": "dense", "total_docs": 100, "top_k": 3}, - } - - result = promptor.execute(pipeline_data) - - # QAPromptor returns [data, prompt] format - assert isinstance(result, list) - assert len(result) == 2 - - data, messages = result - - # Verify data section - assert isinstance(data, dict) - assert "question" in data - assert "retrieved_docs" in data - assert "metadata" in data - - # Verify messages section (OpenAI format) - assert isinstance(messages, list) - assert len(messages) >= 1 - - # Check messages content - message_content = str(messages) - assert "What are the benefits of renewable energy?" in message_content diff --git a/packages/sage-middleware/tests/operators/rag/test_reranker.py b/packages/sage-middleware/tests/operators/rag/test_reranker.py deleted file mode 100644 index a4996cf5f2..0000000000 --- a/packages/sage-middleware/tests/operators/rag/test_reranker.py +++ /dev/null @@ -1,564 +0,0 @@ -""" -测试 sage.middleware.operators.rag.reranker 模块 -""" - -from unittest.mock import Mock, patch - -import pytest -import torch - -# 尝试导入reranker模块 -pytest_plugins = [] - -try: - from sage.middleware.operators.rag.reranker import BGEReranker - - RERANKER_AVAILABLE = True -except ImportError as e: - RERANKER_AVAILABLE = False - pytestmark = pytest.mark.skip(f"Reranker module not available: {e}") - - -@pytest.mark.unit -class TestBGEReranker: - """测试BGEReranker类""" - - def test_bge_reranker_import(self): - """测试BGEReranker导入""" - if not RERANKER_AVAILABLE: - pytest.skip("Reranker module not available") - - from sage.middleware.operators.rag.reranker import BGEReranker - - assert BGEReranker is not None - - @patch("sage.middleware.operators.rag.reranker.AutoTokenizer") - @patch("sage.middleware.operators.rag.reranker.AutoModelForSequenceClassification") - @patch("torch.cuda.is_available") - def test_bge_reranker_initialization_cuda( - self, mock_cuda_available, mock_model_class, mock_tokenizer_class - ): - """测试BGEReranker CUDA初始化""" - if not RERANKER_AVAILABLE: - pytest.skip("Reranker module not available") - - # Mock CUDA可用 - mock_cuda_available.return_value = True - - # Mock tokenizer和model - mock_tokenizer = Mock() - mock_model = Mock() - mock_tokenizer_class.from_pretrained.return_value = mock_tokenizer - mock_model_class.from_pretrained.return_value = mock_model - - # 让model.to()返回同一个model对象 - mock_model.to.return_value = mock_model - - config = {"model_name": "BAAI/bge-reranker-v2-m3", "top_k": 5} - - reranker = BGEReranker(config=config) - - # 验证初始化 - assert reranker.config == config - assert reranker.device == "cuda" - assert reranker.tokenizer == mock_tokenizer - assert reranker.model == mock_model - - # 验证模型被移动到正确设备并设置为评估模式 - mock_model.to.assert_called_once_with("cuda") - mock_model.eval.assert_called_once() - - @patch("sage.middleware.operators.rag.reranker.AutoTokenizer") - @patch("sage.middleware.operators.rag.reranker.AutoModelForSequenceClassification") - @patch("torch.cuda.is_available") - def test_bge_reranker_initialization_cpu( - self, mock_cuda_available, mock_model_class, mock_tokenizer_class - ): - """测试BGEReranker CPU初始化""" - if not RERANKER_AVAILABLE: - pytest.skip("Reranker module not available") - - # Mock CUDA不可用 - mock_cuda_available.return_value = False - - # Mock tokenizer和model - mock_tokenizer = Mock() - mock_model = Mock() - mock_tokenizer_class.from_pretrained.return_value = mock_tokenizer - mock_model_class.from_pretrained.return_value = mock_model - - config = {"model_name": "BAAI/bge-reranker-v2-m3", "top_k": 3} - - reranker = BGEReranker(config=config) - - # 验证初始化 - assert reranker.device == "cpu" - mock_model.to.assert_called_once_with("cpu") - - @patch("sage.middleware.operators.rag.reranker.AutoTokenizer") - @patch("sage.middleware.operators.rag.reranker.AutoModelForSequenceClassification") - @patch("torch.cuda.is_available") - def test_load_model_success(self, mock_cuda_available, mock_model_class, mock_tokenizer_class): - """测试_load_model方法成功加载""" - if not RERANKER_AVAILABLE: - pytest.skip("Reranker module not available") - - mock_cuda_available.return_value = False - - # Mock成功加载 - mock_tokenizer = Mock() - mock_model = Mock() - mock_tokenizer_class.from_pretrained.return_value = mock_tokenizer - mock_model_class.from_pretrained.return_value = mock_model - - config = {"model_name": "BAAI/bge-reranker-v2-m3"} - BGEReranker(config=config) - - # 验证模型加载调用 - mock_tokenizer_class.from_pretrained.assert_called_once_with("BAAI/bge-reranker-v2-m3") - mock_model_class.from_pretrained.assert_called_once_with("BAAI/bge-reranker-v2-m3") - - @patch("sage.middleware.operators.rag.reranker.AutoTokenizer") - @patch("sage.middleware.operators.rag.reranker.AutoModelForSequenceClassification") - @patch("torch.cuda.is_available") - def test_load_model_failure(self, mock_cuda_available, mock_model_class, mock_tokenizer_class): - """测试_load_model方法加载失败""" - if not RERANKER_AVAILABLE: - pytest.skip("Reranker module not available") - - mock_cuda_available.return_value = False - - # Mock加载失败 - mock_tokenizer_class.from_pretrained.side_effect = Exception("Model loading failed") - - config = {"model_name": "invalid-model"} - - with pytest.raises(Exception) as exc_info: - BGEReranker(config=config) - - assert "Model loading failed" in str(exc_info.value) - - @patch("sage.middleware.operators.rag.reranker.AutoTokenizer") - @patch("sage.middleware.operators.rag.reranker.AutoModelForSequenceClassification") - @patch("torch.cuda.is_available") - @patch("torch.no_grad") - def test_execute_with_tuple_input( - self, mock_no_grad, mock_cuda_available, mock_model_class, mock_tokenizer_class - ): - """测试execute方法处理元组输入""" - if not RERANKER_AVAILABLE: - pytest.skip("Reranker module not available") - - mock_cuda_available.return_value = False - - # Mock tokenizer和model - mock_tokenizer = Mock() - mock_model = Mock() - mock_tokenizer_class.from_pretrained.return_value = mock_tokenizer - mock_model_class.from_pretrained.return_value = mock_model - - # 让model.to()返回同一个model对象 - mock_model.to.return_value = mock_model - - # Mock tokenizer调用 - mock_tokenizer.return_value = { - "input_ids": torch.tensor([[1, 2, 3], [4, 5, 6]]), - "attention_mask": torch.tensor([[1, 1, 1], [1, 1, 1]]), - } - - # Mock model输出 - 关键是logits需要正确处理 - mock_output = Mock() - mock_output.logits = torch.tensor([[2.5], [1.8]]) - mock_model.return_value = mock_output - - # Mock no_grad上下文 - mock_no_grad_context = Mock() - mock_no_grad.return_value.__enter__ = Mock(return_value=mock_no_grad_context) - mock_no_grad.return_value.__exit__ = Mock(return_value=None) - - config = {"model_name": "BAAI/bge-reranker-v2-m3", "top_k": 2} - - reranker = BGEReranker(config=config) - - # 测试输入 - 使用标准格式 - query = "What is machine learning?" - docs = [ - {"content": "Machine learning is a subset of AI", "score": 0.8}, - {"content": "Deep learning uses neural networks", "score": 0.7}, - ] - input_data = {"query": query, "results": docs} - - result = reranker.execute(input_data) - - # 验证结果 - BGEReranker 返回标准 dict 格式 - assert isinstance(result, dict) - assert result["query"] == query - assert isinstance(result["results"], list) - - @patch("sage.middleware.operators.rag.reranker.AutoTokenizer") - @patch("sage.middleware.operators.rag.reranker.AutoModelForSequenceClassification") - @patch("torch.cuda.is_available") - def test_execute_with_empty_docs( - self, mock_cuda_available, mock_model_class, mock_tokenizer_class - ): - """测试execute方法处理空文档列表""" - if not RERANKER_AVAILABLE: - pytest.skip("Reranker module not available") - - mock_cuda_available.return_value = False - - # Mock tokenizer和model - mock_tokenizer = Mock() - mock_model = Mock() - mock_tokenizer_class.from_pretrained.return_value = mock_tokenizer - mock_model_class.from_pretrained.return_value = mock_model - - config = {"model_name": "BAAI/bge-reranker-v2-m3", "top_k": 5} - - reranker = BGEReranker(config=config) - - # 测试空文档列表 - 使用标准格式 - query = "What is AI?" - docs = [] - input_data = {"query": query, "results": docs} - - result = reranker.execute(input_data) - - # 验证结果 - 返回标准 dict 格式 - assert isinstance(result, dict) - assert result["query"] == query - assert result["results"] == [] - - @patch("sage.middleware.operators.rag.reranker.AutoTokenizer") - @patch("sage.middleware.operators.rag.reranker.AutoModelForSequenceClassification") - @patch("torch.cuda.is_available") - @patch("torch.no_grad") - def test_rerank_documents_scoring( - self, mock_no_grad, mock_cuda_available, mock_model_class, mock_tokenizer_class - ): - """测试文档重排序和评分""" - if not RERANKER_AVAILABLE: - pytest.skip("Reranker module not available") - - mock_cuda_available.return_value = False - - # Mock tokenizer和model - mock_tokenizer = Mock() - mock_model = Mock() - mock_tokenizer_class.from_pretrained.return_value = mock_tokenizer - mock_model_class.from_pretrained.return_value = mock_model - - # 让model.to()返回同一个model对象 - mock_model.to.return_value = mock_model - - # Mock tokenizer返回批处理结果 - mock_tokenizer.return_value = { - "input_ids": torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), - "attention_mask": torch.tensor([[1, 1, 1], [1, 1, 1], [1, 1, 1]]), - } - - # Mock model输出 - 不同的相关性分数 - mock_output = Mock() - mock_output.logits = torch.tensor( - [[3.2], [1.1], [2.8]] - ) # 第1个最相关，第3个次之，第2个最低 - mock_model.return_value = mock_output - - # Mock no_grad上下文 - mock_no_grad_context = Mock() - mock_no_grad.return_value.__enter__ = Mock(return_value=mock_no_grad_context) - mock_no_grad.return_value.__exit__ = Mock(return_value=None) - - config = {"model_name": "BAAI/bge-reranker-v2-m3", "top_k": 3} - - reranker = BGEReranker(config=config) - - # 测试输入 - 多个文档，使用标准格式 - query = "machine learning algorithms" - docs = [ - {"content": "Random forest is a machine learning algorithm", "score": 0.6}, - {"content": "Cats are pets", "score": 0.5}, - {"content": "Neural networks are used in machine learning", "score": 0.7}, - ] - input_data = {"query": query, "results": docs} - - result = reranker.execute(input_data) - - # 验证结果 - 返回标准 dict 格式 - assert isinstance(result, dict) - assert result["query"] == query - assert len(result["results"]) == 3 - - @patch("sage.middleware.operators.rag.reranker.AutoTokenizer") - @patch("sage.middleware.operators.rag.reranker.AutoModelForSequenceClassification") - @patch("torch.cuda.is_available") - @patch("torch.no_grad") - def test_top_k_filtering( - self, mock_no_grad, mock_cuda_available, mock_model_class, mock_tokenizer_class - ): - """测试top_k过滤功能""" - if not RERANKER_AVAILABLE: - pytest.skip("Reranker module not available") - - mock_cuda_available.return_value = False - - # Mock tokenizer和model - mock_tokenizer = Mock() - mock_model = Mock() - mock_tokenizer_class.from_pretrained.return_value = mock_tokenizer - mock_model_class.from_pretrained.return_value = mock_model - - # 让model.to()返回同一个model对象 - mock_model.to.return_value = mock_model - - # Mock tokenizer返回5个文档的结果 - mock_tokenizer.return_value = { - "input_ids": torch.tensor([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]), - "attention_mask": torch.tensor([[1, 1], [1, 1], [1, 1], [1, 1], [1, 1]]), - } - - # Mock model输出 - 5个不同的分数 - mock_output = Mock() - mock_output.logits = torch.tensor([[1.0], [3.0], [2.0], [5.0], [4.0]]) - mock_model.return_value = mock_output - - # Mock no_grad上下文 - mock_no_grad_context = Mock() - mock_no_grad.return_value.__enter__ = Mock(return_value=mock_no_grad_context) - mock_no_grad.return_value.__exit__ = Mock(return_value=None) - - config = {"model_name": "BAAI/bge-reranker-v2-m3", "top_k": 3} # 只保留前3个 - - reranker = BGEReranker(config=config) - - # 测试输入 - 5个文档，使用标准格式 - query = "test query" - docs = [ - {"content": "doc1", "score": 0.1}, - {"content": "doc2", "score": 0.2}, - {"content": "doc3", "score": 0.3}, - {"content": "doc4", "score": 0.4}, - {"content": "doc5", "score": 0.5}, - ] - input_data = {"query": query, "results": docs} - - result = reranker.execute(input_data) - - # 验证结果 - 返回标准 dict 格式 - assert isinstance(result, dict) - assert result["query"] == query - assert len(result["results"]) <= 3 # 被top_k限制 - - @patch("sage.middleware.operators.rag.reranker.AutoTokenizer") - @patch("sage.middleware.operators.rag.reranker.AutoModelForSequenceClassification") - @patch("torch.cuda.is_available") - def test_execute_with_model_error( - self, mock_cuda_available, mock_model_class, mock_tokenizer_class - ): - """测试execute方法处理模型错误""" - if not RERANKER_AVAILABLE: - pytest.skip("Reranker module not available") - - mock_cuda_available.return_value = False - - # Mock tokenizer和model - mock_tokenizer = Mock() - mock_model = Mock() - mock_tokenizer_class.from_pretrained.return_value = mock_tokenizer - mock_model_class.from_pretrained.return_value = mock_model - - # Mock tokenizer抛出异常 - mock_tokenizer.side_effect = Exception("Tokenization failed") - - config = {"model_name": "BAAI/bge-reranker-v2-m3", "top_k": 5} - - reranker = BGEReranker(config=config) - - query = "test query" - docs = [{"content": "test doc", "score": 0.5}] - input_data = (query, docs) - - # 验证异常处理 - with pytest.raises(Exception) as exc_info: - reranker.execute(input_data) - - assert "Tokenization failed" in str(exc_info.value) - - -@pytest.mark.integration -class TestBGERerankerIntegration: - """BGEReranker集成测试""" - - @pytest.mark.skipif(not RERANKER_AVAILABLE, reason="Reranker module not available") - def test_reranker_full_pipeline(self): - """测试重排序器完整pipeline - 简化版本""" - - config = {"model_name": "BAAI/bge-reranker-v2-m3", "top_k": 2} - - # 使用mock来测试基本逻辑，避免复杂的tensor链式调用 - with ( - patch("sage.middleware.operators.rag.reranker.AutoTokenizer") as mock_tokenizer_class, - patch( - "sage.middleware.operators.rag.reranker.AutoModelForSequenceClassification" - ) as mock_model_class, - patch("torch.cuda.is_available") as mock_cuda_available, - patch("torch.no_grad"), - ): - mock_cuda_available.return_value = False - - # 简单的mock设置 - mock_tokenizer = Mock() - mock_model = Mock() - mock_tokenizer_class.from_pretrained.return_value = mock_tokenizer - mock_model_class.from_pretrained.return_value = mock_model - - # Mock执行过程直接返回简化结果 - def simple_execute(data): - query = data["query"] - docs = data["results"] - # 简单返回前top_k个文档 - return {"query": query, "results": docs[: config.get("top_k", 2)]} - - # 创建reranker并替换execute方法 - reranker = BGEReranker(config=config) - reranker.execute = simple_execute - - # 测试数据 - 使用标准格式 - query = "What are the applications of deep learning?" - docs = [ - { - "content": "Deep learning is used in computer vision applications.", - "score": 0.8, - }, - { - "content": "Machine learning has many applications in various fields.", - "score": 0.6, - }, - { - "content": "Deep learning enables natural language processing and speech recognition.", - "score": 0.9, - }, - ] - retrieval_output = {"query": query, "results": docs} - - result = reranker.execute(retrieval_output) - - # 验证结果 - 标准 dict 格式 - assert isinstance(result, dict) - assert result["query"] == query - assert len(result["results"]) <= 2 # top_k限制 - - # 验证文档格式 - for doc in result["results"]: - assert isinstance(doc, dict) - assert "content" in doc - - -@pytest.mark.unit -class TestLLMbasedReranker: - """Test LLMbased_Reranker class.""" - - def test_llm_reranker_import(self): - """Test LLMbased_Reranker import.""" - if not RERANKER_AVAILABLE: - pytest.skip("Reranker module not available") - - try: - from sage.middleware.operators.rag.reranker import LLMbased_Reranker - - assert LLMbased_Reranker is not None - except ImportError: - pytest.skip("LLMbased_Reranker not available") - - @patch("sage.middleware.operators.rag.reranker.AutoTokenizer") - @patch("sage.middleware.operators.rag.reranker.AutoModelForCausalLM") - @patch("torch.cuda.is_available") - def test_llm_reranker_initialization( - self, mock_cuda_available, mock_model_class, mock_tokenizer_class - ): - """Test LLMbased_Reranker initialization.""" - if not RERANKER_AVAILABLE: - pytest.skip("Reranker module not available") - - try: - from sage.middleware.operators.rag.reranker import LLMbased_Reranker - except ImportError: - pytest.skip("LLMbased_Reranker not available") - - mock_cuda_available.return_value = False - - # Mock tokenizer and model - mock_tokenizer = Mock() - mock_model = Mock() - mock_tokenizer_class.from_pretrained.return_value = mock_tokenizer - mock_model_class.from_pretrained.return_value = mock_model - mock_model.to.return_value = mock_model - - # Mock yes_loc extraction - mock_tokenizer.return_value = {"input_ids": [123]} - - config = {"top_k": 3} - - reranker = LLMbased_Reranker(config=config) - - assert reranker.config == config - assert reranker.device in ["cuda", "cpu"] - assert reranker.tokenizer == mock_tokenizer - - @patch("sage.middleware.operators.rag.reranker.AutoTokenizer") - @patch("sage.middleware.operators.rag.reranker.AutoModelForCausalLM") - @patch("torch.cuda.is_available") - def test_llm_reranker_load_model( - self, mock_cuda_available, mock_model_class, mock_tokenizer_class - ): - """Test LLMbased_Reranker model loading.""" - if not RERANKER_AVAILABLE: - pytest.skip("Reranker module not available") - - try: - from sage.middleware.operators.rag.reranker import LLMbased_Reranker - except ImportError: - pytest.skip("LLMbased_Reranker not available") - - mock_cuda_available.return_value = False - - mock_tokenizer = Mock() - mock_model = Mock() - mock_tokenizer_class.from_pretrained.return_value = mock_tokenizer - mock_model_class.from_pretrained.return_value = mock_model - mock_model.to.return_value = mock_model - mock_tokenizer.return_value = {"input_ids": [123]} - - config = {} - custom_model = "custom/reranker-model" - - LLMbased_Reranker(config=config, model_name=custom_model) - - # Verify custom model was loaded - mock_tokenizer_class.from_pretrained.assert_called_with(custom_model) - mock_model_class.from_pretrained.assert_called_with(custom_model) - - @patch("sage.middleware.operators.rag.reranker.AutoTokenizer") - @patch("sage.middleware.operators.rag.reranker.AutoModelForCausalLM") - @patch("torch.cuda.is_available") - def test_llm_reranker_load_model_failure( - self, mock_cuda_available, mock_model_class, mock_tokenizer_class - ): - """Test LLMbased_Reranker handles model loading failures.""" - if not RERANKER_AVAILABLE: - pytest.skip("Reranker module not available") - - try: - from sage.middleware.operators.rag.reranker import LLMbased_Reranker - except ImportError: - pytest.skip("LLMbased_Reranker not available") - - mock_cuda_available.return_value = False - - # Mock loading failure - mock_tokenizer_class.from_pretrained.side_effect = Exception("Load failed") - - config = {} - - with pytest.raises(RuntimeError, match="Model loading failed"): - LLMbased_Reranker(config=config) diff --git a/packages/sage-middleware/tests/operators/rag/test_retriever.py b/packages/sage-middleware/tests/operators/rag/test_retriever.py deleted file mode 100644 index d1ff46eb69..0000000000 --- a/packages/sage-middleware/tests/operators/rag/test_retriever.py +++ /dev/null @@ -1,1527 +0,0 @@ -""" -测试 sage.middleware.operators.rag.retriever 模块 - ChromaRetriever 和 BM25sRetriever -""" - -from unittest.mock import Mock, patch - -import numpy as np -import pytest - -# 尝试导入检索模块 -try: - from sage.middleware.operators.rag.retriever import ChromaRetriever - - RETRIEVER_AVAILABLE = True -except ImportError as e: - RETRIEVER_AVAILABLE = False - pytestmark = pytest.mark.skip(f"Retriever module not available: {e}") - -# 检查 pymilvus.model 是否可用（新版本） -try: - import pymilvus.model # noqa: F401 - - PYMILVUS_MODEL_AVAILABLE = True -except (ImportError, AttributeError): - PYMILVUS_MODEL_AVAILABLE = False - - -@pytest.fixture -def chroma_config(): - """ChromaRetriever测试配置""" - return { - "dimension": 384, - "top_k": 5, - "embedding": {"method": "mockembedder", "model": "test_model"}, - "chroma": { - "persist_path": "./test_vector_db", - "collection_name": "test_collection", - }, - } - - -@pytest.fixture -def sample_documents(): - """测试文档""" - return [ - {"content": "机器学习是人工智能的一个分支。", "score": 0.9, "id": "doc_1"}, - {"content": "深度学习使用神经网络。", "score": 0.8, "id": "doc_2"}, - {"content": "自然语言处理处理文本数据。", "score": 0.7, "id": "doc_3"}, - ] - - -@pytest.mark.unit -class TestChromaRetriever: - """测试ChromaRetriever类""" - - def test_import(self): - """测试导入""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - assert ChromaRetriever is not None - - @patch("sage.middleware.operators.rag.retriever.ChromaUtils") - @patch("sage.middleware.operators.rag.retriever.ChromaBackend") - @patch("sage.middleware.operators.rag.retriever.EmbeddingModel") - def test_initialization( - self, - mock_embedding_model, - mock_chroma_backend, - mock_chroma_utils, - chroma_config, - ): - """测试初始化""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - # 设置模拟 - mock_chroma_utils.check_chromadb_availability.return_value = True - mock_chroma_utils.validate_chroma_config.return_value = True - mock_embedding = Mock() - mock_embedding.get_dim.return_value = 384 - mock_embedding_model.return_value = mock_embedding - mock_chroma_backend.return_value = Mock() - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - retriever = ChromaRetriever(config=chroma_config) - assert retriever.config == chroma_config - assert retriever.backend_type == "chroma" - assert retriever.vector_dimension == 384 - assert retriever.top_k == 5 - - @patch("sage.middleware.operators.rag.retriever.ChromaUtils") - @patch("sage.middleware.operators.rag.retriever.ChromaBackend") - @patch("sage.middleware.operators.rag.retriever.EmbeddingModel") - def test_execute_string_input( - self, - mock_embedding_model, - mock_chroma_backend, - mock_chroma_utils, - chroma_config, - ): - """测试执行 - 字符串输入""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - # 设置模拟 - mock_chroma_utils.check_chromadb_availability.return_value = True - mock_chroma_utils.validate_chroma_config.return_value = True - - mock_embedding = Mock() - mock_embedding.get_dim.return_value = 384 - mock_embedding.embed.return_value = np.random.rand(384).tolist() - mock_embedding_model.return_value = mock_embedding - - mock_backend = Mock() - mock_backend.search.return_value = [ - {"content": "相关文档1", "score": 0.95, "id": "doc_1"}, - {"content": "相关文档2", "score": 0.85, "id": "doc_2"}, - ] - mock_chroma_backend.return_value = mock_backend - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - retriever = ChromaRetriever(config=chroma_config) - query = "What is artificial intelligence?" - result = retriever.execute(query) - - # 验证结果格式 - assert isinstance(result, dict) - assert "query" in result - assert "retrieval_results" in result - assert result["query"] == query - assert len(result["retrieval_results"]) == 2 - - @patch("sage.middleware.operators.rag.retriever.ChromaUtils") - @patch("sage.middleware.operators.rag.retriever.ChromaBackend") - @patch("sage.middleware.operators.rag.retriever.EmbeddingModel") - def test_execute_dict_input( - self, - mock_embedding_model, - mock_chroma_backend, - mock_chroma_utils, - chroma_config, - ): - """测试执行 - 字典输入""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - # 设置模拟 - mock_chroma_utils.check_chromadb_availability.return_value = True - mock_chroma_utils.validate_chroma_config.return_value = True - - mock_embedding = Mock() - mock_embedding.embed.return_value = np.random.rand(384).tolist() - mock_embedding_model.return_value = mock_embedding - - mock_backend = Mock() - mock_backend.search.return_value = [{"content": "相关文档", "score": 0.95, "id": "doc_1"}] - mock_chroma_backend.return_value = mock_backend - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - retriever = ChromaRetriever(config=chroma_config) - input_data = {"query": "What is machine learning?", "other_field": "value"} - result = retriever.execute(input_data) - - # 验证结果格式 - assert isinstance(result, dict) - assert "retrieval_results" in result - assert result["query"] == "What is machine learning?" - assert result["other_field"] == "value" - - @patch("sage.middleware.operators.rag.retriever.ChromaUtils") - @patch("sage.middleware.operators.rag.retriever.ChromaBackend") - @patch("sage.middleware.operators.rag.retriever.EmbeddingModel") - def test_add_documents( - self, - mock_embedding_model, - mock_chroma_backend, - mock_chroma_utils, - chroma_config, - ): - """测试添加文档""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - # 设置模拟 - mock_chroma_utils.check_chromadb_availability.return_value = True - mock_chroma_utils.validate_chroma_config.return_value = True - - mock_embedding = Mock() - mock_embedding.embed.return_value = np.random.rand(384).tolist() - mock_embedding_model.return_value = mock_embedding - - mock_backend = Mock() - mock_backend.add_documents.return_value = ["doc_1", "doc_2", "doc_3"] - mock_chroma_backend.return_value = mock_backend - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - retriever = ChromaRetriever(config=chroma_config) - - documents = ["文档1内容", "文档2内容", "文档3内容"] - doc_ids = retriever.add_documents(documents) - - assert doc_ids == ["doc_1", "doc_2", "doc_3"] - assert mock_embedding.embed.call_count == 3 - - @patch("sage.middleware.operators.rag.retriever.ChromaUtils") - @patch("sage.middleware.operators.rag.retriever.ChromaBackend") - @patch("sage.middleware.operators.rag.retriever.EmbeddingModel") - def test_error_handling( - self, - mock_embedding_model, - mock_chroma_backend, - mock_chroma_utils, - chroma_config, - ): - """测试错误处理""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - # 设置模拟 - mock_chroma_utils.check_chromadb_availability.return_value = True - mock_chroma_utils.validate_chroma_config.return_value = True - - mock_embedding = Mock() - mock_embedding.embed.side_effect = Exception("Embedding failed") - mock_embedding_model.return_value = mock_embedding - - mock_chroma_backend.return_value = Mock() - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - retriever = ChromaRetriever(config=chroma_config) - query = "测试查询" - result = retriever.execute(query) - - # 验证错误处理 - assert isinstance(result, dict) - assert result["query"] == query - assert result["retrieval_results"] == [] - - -# 尝试导入检索模块 -try: - from sage.middleware.operators.rag.retriever import MilvusDenseRetriever - - RETRIEVER_AVAILABLE = True -except ImportError as e: - RETRIEVER_AVAILABLE = False - pytestmark = pytest.mark.skip(f"Retriever module not available: {e}") - - -@pytest.fixture -def milvus_dense_config(): - """MilvusDenseRetriever测试配置""" - return { - "dimension": 384, - "top_k": 5, - "embedding": {"method": "mockembedder", "model": "test_model"}, - "milvus_dense": { - "collection_name": "test_collection", - "uri": "http://localhost:19530", - "metric_type": "COSINE", - "index_type": "HNSW", - }, - } - - -@pytest.mark.unit -class TestMilvusDenseRetriever: - """测试MilvusDenseRetriever类""" - - def test_import(self): - """测试导入""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - assert MilvusDenseRetriever is not None - - @patch("sage.middleware.operators.rag.retriever.MilvusUtils") - @patch("sage.middleware.operators.rag.retriever.MilvusBackend") - @patch("sage.middleware.operators.rag.retriever.EmbeddingModel") - def test_initialization( - self, - mock_embedding_model, - mock_milvus_backend, - mock_milvus_utils, - milvus_dense_config, - ): - """测试初始化""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - # 设置模拟 - mock_milvus_utils.check_milvus_available.return_value = True - mock_milvus_utils.validate_milvus_config.return_value = True - mock_embedding = Mock() - mock_embedding.get_dim.return_value = 384 - mock_embedding_model.return_value = mock_embedding - mock_milvus_backend.return_value = Mock() - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - retriever = MilvusDenseRetriever(config=milvus_dense_config) - assert retriever.config == milvus_dense_config - assert retriever.backend_type == "milvus" - assert retriever.vector_dimension == 384 - assert retriever.top_k == 5 - - @patch("sage.middleware.operators.rag.retriever.MilvusUtils") - @patch("sage.middleware.operators.rag.retriever.MilvusBackend") - @patch("sage.middleware.operators.rag.retriever.EmbeddingModel") - def test_execute_string_input( - self, - mock_embedding_model, - mock_milvus_backend, - mock_milvus_utils, - milvus_dense_config, - ): - """测试执行 - 字符串输入""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - # 设置模拟 - mock_milvus_utils.check_milvus_available.return_value = True - mock_milvus_utils.validate_milvus_config.return_value = True - - mock_embedding = Mock() - mock_embedding.get_dim.return_value = 384 - mock_embedding.encode.return_value = np.random.rand(384).tolist() - mock_embedding_model.return_value = mock_embedding - - mock_backend = Mock() - mock_backend.dense_search.return_value = [ - {"content": "相关文档1", "score": 0.95, "id": "doc_1"}, - {"content": "相关文档2", "score": 0.85, "id": "doc_2"}, - ] - mock_milvus_backend.return_value = mock_backend - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - retriever = MilvusDenseRetriever(config=milvus_dense_config) - - query = "What is artificial intelligence?" - result = retriever.execute(query) - - # 验证结果格式 - assert isinstance(result, dict) - assert "query" in result - assert "retrieval_results" in result - assert result["query"] == query - assert len(result["retrieval_results"]) == 2 - - # 验证调用了正确的方法 - mock_embedding.encode.assert_called_once_with(query) - mock_backend.dense_search.assert_called_once() - - @patch("sage.middleware.operators.rag.retriever.MilvusUtils") - @patch("sage.middleware.operators.rag.retriever.MilvusBackend") - @patch("sage.middleware.operators.rag.retriever.EmbeddingModel") - def test_execute_dict_input( - self, - mock_embedding_model, - mock_milvus_backend, - mock_milvus_utils, - milvus_dense_config, - ): - """测试执行 - 字典输入""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - # 设置模拟 - mock_milvus_utils.check_milvus_available.return_value = True - mock_milvus_utils.validate_milvus_config.return_value = True - - mock_embedding = Mock() - mock_embedding.encode.return_value = np.random.rand(384).tolist() - mock_embedding_model.return_value = mock_embedding - - mock_backend = Mock() - mock_backend.dense_search.return_value = [ - {"content": "相关文档", "score": 0.95, "id": "doc_1"} - ] - mock_milvus_backend.return_value = mock_backend - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - retriever = MilvusDenseRetriever(config=milvus_dense_config) - - input_data = { - "question": "What is machine learning?", - "other_field": "value", - } - result = retriever.execute(input_data) - - # 验证结果格式 - assert isinstance(result, dict) - assert "retrieval_results" in result - assert result["question"] == "What is machine learning?" - assert result["other_field"] == "value" - - @patch("sage.middleware.operators.rag.retriever.MilvusUtils") - @patch("sage.middleware.operators.rag.retriever.MilvusBackend") - @patch("sage.middleware.operators.rag.retriever.EmbeddingModel") - def test_execute_tuple_input( - self, - mock_embedding_model, - mock_milvus_backend, - mock_milvus_utils, - milvus_dense_config, - ): - """测试执行 - 元组输入""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - # 设置模拟 - mock_milvus_utils.check_milvus_available.return_value = True - mock_milvus_utils.validate_milvus_config.return_value = True - - mock_embedding = Mock() - mock_embedding.encode.return_value = np.random.rand(384).tolist() - mock_embedding_model.return_value = mock_embedding - - mock_backend = Mock() - mock_backend.dense_search.return_value = [ - {"content": "相关文档", "score": 0.95, "id": "doc_1"} - ] - mock_milvus_backend.return_value = mock_backend - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - retriever = MilvusDenseRetriever(config=milvus_dense_config) - - tuple_input = ("什么是深度学习？", "extra_data") - result = retriever.execute(tuple_input) - - # 验证结果格式 - assert isinstance(result, dict) - assert "query" in result - assert "retrieval_results" in result - assert result["query"] == "什么是深度学习？" - - @patch("sage.middleware.operators.rag.retriever.MilvusUtils") - @patch("sage.middleware.operators.rag.retriever.MilvusBackend") - @patch("sage.middleware.operators.rag.retriever.EmbeddingModel") - def test_add_documents( - self, - mock_embedding_model, - mock_milvus_backend, - mock_milvus_utils, - milvus_dense_config, - ): - """测试添加文档""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - # 设置模拟 - mock_milvus_utils.check_milvus_available.return_value = True - mock_milvus_utils.validate_milvus_config.return_value = True - - mock_embedding = Mock() - mock_embedding.embed.return_value = np.random.rand(384).tolist() - mock_embedding_model.return_value = mock_embedding - - mock_backend = Mock() - mock_backend.add_dense_documents.return_value = ["doc_1", "doc_2", "doc_3"] - mock_milvus_backend.return_value = mock_backend - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - retriever = MilvusDenseRetriever(config=milvus_dense_config) - - documents = ["文档1内容", "文档2内容", "文档3内容"] - doc_ids = retriever.add_documents(documents) - - assert doc_ids == ["doc_1", "doc_2", "doc_3"] - assert mock_embedding.embed.call_count == 3 - - # 验证调用了正确的Milvus方法 - mock_backend.add_dense_documents.assert_called_once() - call_args = mock_backend.add_dense_documents.call_args - assert len(call_args[0][0]) == 3 # documents - assert len(call_args[0][1]) == 3 # embeddings - assert len(call_args[0][2]) == 3 # doc_ids - - @patch("sage.middleware.operators.rag.retriever.MilvusUtils") - @patch("sage.middleware.operators.rag.retriever.MilvusBackend") - @patch("sage.middleware.operators.rag.retriever.EmbeddingModel") - def test_add_documents_with_custom_ids( - self, - mock_embedding_model, - mock_milvus_backend, - mock_milvus_utils, - milvus_dense_config, - ): - """测试添加文档 - 自定义ID""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - # 设置模拟 - mock_milvus_utils.check_milvus_available.return_value = True - mock_milvus_utils.validate_milvus_config.return_value = True - - mock_embedding = Mock() - mock_embedding.embed.return_value = np.random.rand(384).tolist() - mock_embedding_model.return_value = mock_embedding - - mock_backend = Mock() - mock_backend.add_dense_documents.return_value = ["custom_1", "custom_2"] - mock_milvus_backend.return_value = mock_backend - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - retriever = MilvusDenseRetriever(config=milvus_dense_config) - - documents = ["文档1内容", "文档2内容"] - custom_ids = ["custom_1", "custom_2"] - doc_ids = retriever.add_documents(documents, doc_ids=custom_ids) - - assert doc_ids == ["custom_1", "custom_2"] - - # 验证使用了自定义ID - call_args = mock_backend.add_dense_documents.call_args - assert call_args[0][2] == custom_ids - - @patch("sage.middleware.operators.rag.retriever.MilvusUtils") - @patch("sage.middleware.operators.rag.retriever.MilvusBackend") - @patch("sage.middleware.operators.rag.retriever.EmbeddingModel") - def test_error_handling_embedding_failure( - self, - mock_embedding_model, - mock_milvus_backend, - mock_milvus_utils, - milvus_dense_config, - ): - """测试错误处理 - embedding失败""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - # 设置模拟 - mock_milvus_utils.check_milvus_available.return_value = True - mock_milvus_utils.validate_milvus_config.return_value = True - - mock_embedding = Mock() - mock_embedding.encode.side_effect = Exception("Embedding failed") - mock_embedding_model.return_value = mock_embedding - - mock_milvus_backend.return_value = Mock() - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - retriever = MilvusDenseRetriever(config=milvus_dense_config) - - query = "测试查询" - result = retriever.execute(query) - - # 验证错误处理 - assert isinstance(result, dict) - assert result["query"] == query - assert result["retrieval_results"] == [] - - @patch("sage.middleware.operators.rag.retriever.MilvusUtils") - @patch("sage.middleware.operators.rag.retriever.MilvusBackend") - @patch("sage.middleware.operators.rag.retriever.EmbeddingModel") - def test_error_handling_search_failure( - self, - mock_embedding_model, - mock_milvus_backend, - mock_milvus_utils, - milvus_dense_config, - ): - """测试错误处理 - 搜索失败""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - # 设置模拟 - mock_milvus_utils.check_milvus_available.return_value = True - mock_milvus_utils.validate_milvus_config.return_value = True - - mock_embedding = Mock() - mock_embedding.encode.return_value = np.random.rand(384).tolist() - mock_embedding_model.return_value = mock_embedding - - mock_backend = Mock() - mock_backend.dense_search.side_effect = Exception("Search failed") - mock_milvus_backend.return_value = mock_backend - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - retriever = MilvusDenseRetriever(config=milvus_dense_config) - - query = "测试查询" - result = retriever.execute(query) - - # 验证错误处理 - assert isinstance(result, dict) - assert result["query"] == query - assert result["retrieval_results"] == [] - - @patch("sage.middleware.operators.rag.retriever.MilvusUtils") - @patch("sage.middleware.operators.rag.retriever.MilvusBackend") - @patch("sage.middleware.operators.rag.retriever.EmbeddingModel") - def test_configuration_methods( - self, - mock_embedding_model, - mock_milvus_backend, - mock_milvus_utils, - milvus_dense_config, - ): - """测试配置相关方法""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - # 设置模拟 - mock_milvus_utils.check_milvus_available.return_value = True - mock_milvus_utils.validate_milvus_config.return_value = True - mock_embedding_model.return_value = Mock() - - mock_backend = Mock() - mock_backend.save_config.return_value = True - mock_backend.load_config.return_value = True - mock_backend.get_collection_info.return_value = { - "name": "test_collection", - "count": 100, - } - mock_backend.delete_collection.return_value = True - mock_milvus_backend.return_value = mock_backend - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - retriever = MilvusDenseRetriever(config=milvus_dense_config) - - # 测试保存配置 - assert retriever.save_config("/path/to/config") is True - mock_backend.save_config.assert_called_with("/path/to/config") - - # 测试加载配置 - assert retriever.load_config("/path/to/config") is True - mock_backend.load_config.assert_called_with("/path/to/config") - - # 测试获取集合信息 - info = retriever.get_collection_info() - assert info["name"] == "test_collection" - assert info["count"] == 100 - - # 测试删除集合 - assert retriever.delete_collection("test_collection") is True - mock_backend.delete_collection.assert_called_with("test_collection") - - @patch("sage.middleware.operators.rag.retriever.MilvusUtils") - @patch("sage.middleware.operators.rag.retriever.MilvusBackend") - @patch("sage.middleware.operators.rag.retriever.EmbeddingModel") - def test_profile_mode( - self, - mock_embedding_model, - mock_milvus_backend, - mock_milvus_utils, - milvus_dense_config, - ): - """测试profile模式""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - # 设置模拟 - mock_milvus_utils.check_milvus_available.return_value = True - mock_milvus_utils.validate_milvus_config.return_value = True - - mock_embedding = Mock() - mock_embedding.encode.return_value = np.random.rand(384).tolist() - mock_embedding_model.return_value = mock_embedding - - mock_backend = Mock() - mock_backend.dense_search.return_value = [ - {"content": "相关文档", "score": 0.95, "id": "doc_1"} - ] - mock_milvus_backend.return_value = mock_backend - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - with patch("os.makedirs"): - with patch("builtins.open", create=True): - with patch("json.dump"): - # 启用profile模式 - retriever = MilvusDenseRetriever( - config=milvus_dense_config, enable_profile=True - ) - - query = "测试查询" - retriever.execute(query) - - # 验证profile数据被收集 - assert hasattr(retriever, "data_records") - assert hasattr(retriever, "data_base_path") - - @patch("sage.middleware.operators.rag.retriever.MilvusUtils") - def test_initialization_failure_milvus_unavailable( - self, mock_milvus_utils, milvus_dense_config - ): - """测试初始化失败 - Milvus不可用""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - mock_milvus_utils.check_milvus_available.return_value = False - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - with pytest.raises(ImportError): - MilvusDenseRetriever(config=milvus_dense_config) - - @patch("sage.middleware.operators.rag.retriever.MilvusUtils") - def test_initialization_failure_invalid_config(self, mock_milvus_utils, milvus_dense_config): - """测试初始化失败 - 无效配置""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - mock_milvus_utils.check_milvus_available.return_value = True - mock_milvus_utils.validate_milvus_config.return_value = False - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - with pytest.raises(ValueError): - MilvusDenseRetriever(config=milvus_dense_config) - - -# 尝试导入检索模块 -try: - from sage.middleware.operators.rag.retriever import MilvusSparseRetriever - - RETRIEVER_AVAILABLE = True -except ImportError as e: - RETRIEVER_AVAILABLE = False - pytestmark = pytest.mark.skip(f"Retriever module not available: {e}") - - -@pytest.fixture -def milvus_sparse_config(): - """MilvusSparseRetriever测试配置""" - return { - "top_k": 10, - "milvus_sparse": { - "collection_name": "test_sparse_collection", - "uri": "http://localhost:19530", - "metric_type": "IP", # 稀疏向量通常使用IP - "index_type": "SPARSE_INVERTED_INDEX", - }, - } - - -@pytest.fixture -def mock_sparse_embeddings(): - """模拟稀疏向量embedding结果""" - return { - "sparse": [ - {"indices": [1, 5, 10], "values": [0.8, 0.6, 0.4]}, - {"indices": [2, 8, 15], "values": [0.9, 0.5, 0.3]}, - {"indices": [3, 7, 12], "values": [0.7, 0.8, 0.2]}, - ] - } - - -@pytest.mark.unit -@pytest.mark.skipif( - not PYMILVUS_MODEL_AVAILABLE, reason="pymilvus.model not available (requires pymilvus>=2.3.0)" -) -class TestMilvusSparseRetriever: - """测试MilvusSparseRetriever类""" - - def test_import(self): - """测试导入""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - assert MilvusSparseRetriever is not None - - @patch("sage.middleware.operators.rag.retriever.MilvusUtils") - @patch("sage.middleware.operators.rag.retriever.MilvusBackend") - @patch("pymilvus.model.hybrid.BGEM3EmbeddingFunction") - def test_initialization( - self, mock_bgem3, mock_milvus_backend, mock_milvus_utils, milvus_sparse_config - ): - """测试初始化""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - # 设置模拟 - mock_milvus_utils.check_milvus_available.return_value = True - mock_milvus_utils.validate_milvus_config.return_value = True - mock_embedding = Mock() - mock_bgem3.return_value = mock_embedding - mock_milvus_backend.return_value = Mock() - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - retriever = MilvusSparseRetriever(config=milvus_sparse_config) - assert retriever.config == milvus_sparse_config - assert retriever.backend_type == "milvus" - assert retriever.top_k == 10 - assert hasattr(retriever, "embedding_model") - - @patch("sage.middleware.operators.rag.retriever.MilvusUtils") - @patch("sage.middleware.operators.rag.retriever.MilvusBackend") - @patch("pymilvus.model.hybrid.BGEM3EmbeddingFunction") - def test_execute_string_input( - self, mock_bgem3, mock_milvus_backend, mock_milvus_utils, milvus_sparse_config - ): - """测试执行 - 字符串输入""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - # 设置模拟 - mock_milvus_utils.check_milvus_available.return_value = True - mock_milvus_utils.validate_milvus_config.return_value = True - - mock_embedding = Mock() - mock_bgem3.return_value = mock_embedding - - mock_backend = Mock() - mock_backend.sparse_search.return_value = [ - {"content": "相关文档1", "score": 0.95, "id": "doc_1"}, - {"content": "相关文档2", "score": 0.85, "id": "doc_2"}, - ] - mock_milvus_backend.return_value = mock_backend - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - retriever = MilvusSparseRetriever(config=milvus_sparse_config) - - query = "什么是人工智能？" - result = retriever.execute(query) - - # 验证结果格式 - assert isinstance(result, dict) - assert "query" in result - assert "retrieval_results" in result - assert result["query"] == query - assert len(result["retrieval_results"]) == 2 - - # 验证调用了正确的方法 - 稀疏检索直接传递文本 - mock_backend.sparse_search.assert_called_once_with(query_text=query, top_k=10) - - @patch("sage.middleware.operators.rag.retriever.MilvusUtils") - @patch("sage.middleware.operators.rag.retriever.MilvusBackend") - @patch("pymilvus.model.hybrid.BGEM3EmbeddingFunction") - def test_execute_dict_input( - self, mock_bgem3, mock_milvus_backend, mock_milvus_utils, milvus_sparse_config - ): - """测试执行 - 字典输入""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - # 设置模拟 - mock_milvus_utils.check_milvus_available.return_value = True - mock_milvus_utils.validate_milvus_config.return_value = True - - mock_embedding = Mock() - mock_bgem3.return_value = mock_embedding - - mock_backend = Mock() - mock_backend.sparse_search.return_value = [ - {"content": "相关文档", "score": 0.95, "id": "doc_1"} - ] - mock_milvus_backend.return_value = mock_backend - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - retriever = MilvusSparseRetriever(config=milvus_sparse_config) - - input_data = {"question": "什么是机器学习？", "other_field": "value"} - result = retriever.execute(input_data) - - # 验证结果格式 - assert isinstance(result, dict) - assert "retrieval_results" in result - assert result["question"] == "什么是机器学习？" - assert result["other_field"] == "value" - - @patch("sage.middleware.operators.rag.retriever.MilvusUtils") - @patch("sage.middleware.operators.rag.retriever.MilvusBackend") - @patch("pymilvus.model.hybrid.BGEM3EmbeddingFunction") - def test_execute_tuple_input( - self, mock_bgem3, mock_milvus_backend, mock_milvus_utils, milvus_sparse_config - ): - """测试执行 - 元组输入""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - # 设置模拟 - mock_milvus_utils.check_milvus_available.return_value = True - mock_milvus_utils.validate_milvus_config.return_value = True - - mock_embedding = Mock() - mock_bgem3.return_value = mock_embedding - - mock_backend = Mock() - mock_backend.sparse_search.return_value = [ - {"content": "相关文档", "score": 0.95, "id": "doc_1"} - ] - mock_milvus_backend.return_value = mock_backend - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - retriever = MilvusSparseRetriever(config=milvus_sparse_config) - - tuple_input = ("什么是深度学习？", "extra_data") - result = retriever.execute(tuple_input) - - # 验证结果格式 - assert isinstance(result, dict) - assert "query" in result - assert "retrieval_results" in result - assert result["query"] == "什么是深度学习？" - - @patch("sage.middleware.operators.rag.retriever.MilvusUtils") - @patch("sage.middleware.operators.rag.retriever.MilvusBackend") - @patch("pymilvus.model.hybrid.BGEM3EmbeddingFunction") - def test_add_documents( - self, - mock_bgem3, - mock_milvus_backend, - mock_milvus_utils, - milvus_sparse_config, - mock_sparse_embeddings, - ): - """测试添加文档""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - # 设置模拟 - mock_milvus_utils.check_milvus_available.return_value = True - mock_milvus_utils.validate_milvus_config.return_value = True - - mock_embedding = Mock() - mock_embedding.encode_documents.return_value = mock_sparse_embeddings - mock_bgem3.return_value = mock_embedding - - mock_backend = Mock() - mock_backend.add_sparse_documents.return_value = ["doc_1", "doc_2", "doc_3"] - mock_milvus_backend.return_value = mock_backend - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - retriever = MilvusSparseRetriever(config=milvus_sparse_config) - - documents = ["文档1内容", "文档2内容", "文档3内容"] - doc_ids = retriever.add_documents(documents) - - assert doc_ids == ["doc_1", "doc_2", "doc_3"] - - # 验证调用了正确的方法 - mock_embedding.encode_documents.assert_called_once_with(documents) - mock_backend.add_sparse_documents.assert_called_once() - - # 验证传递了正确的稀疏向量 - call_args = mock_backend.add_sparse_documents.call_args - assert len(call_args[0][0]) == 3 # documents - assert call_args[0][1] == mock_sparse_embeddings["sparse"] # sparse embeddings - assert len(call_args[0][2]) == 3 # doc_ids - - @patch("sage.middleware.operators.rag.retriever.MilvusUtils") - @patch("sage.middleware.operators.rag.retriever.MilvusBackend") - @patch("pymilvus.model.hybrid.BGEM3EmbeddingFunction") - def test_add_documents_with_custom_ids( - self, - mock_bgem3, - mock_milvus_backend, - mock_milvus_utils, - milvus_sparse_config, - mock_sparse_embeddings, - ): - """测试添加文档 - 自定义ID""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - # 设置模拟 - mock_milvus_utils.check_milvus_available.return_value = True - mock_milvus_utils.validate_milvus_config.return_value = True - - mock_embedding = Mock() - mock_embedding.encode_documents.return_value = mock_sparse_embeddings - mock_bgem3.return_value = mock_embedding - - mock_backend = Mock() - mock_backend.add_sparse_documents.return_value = ["custom_1", "custom_2"] - mock_milvus_backend.return_value = mock_backend - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - retriever = MilvusSparseRetriever(config=milvus_sparse_config) - - documents = ["文档1内容", "文档2内容"] - custom_ids = ["custom_1", "custom_2"] - doc_ids = retriever.add_documents(documents, doc_ids=custom_ids) - - assert doc_ids == ["custom_1", "custom_2"] - - # 验证使用了自定义ID - call_args = mock_backend.add_sparse_documents.call_args - assert call_args[0][2] == custom_ids - - @patch("sage.middleware.operators.rag.retriever.MilvusUtils") - @patch("sage.middleware.operators.rag.retriever.MilvusBackend") - @patch("pymilvus.model.hybrid.BGEM3EmbeddingFunction") - def test_error_handling_search_failure( - self, mock_bgem3, mock_milvus_backend, mock_milvus_utils, milvus_sparse_config - ): - """测试错误处理 - 搜索失败""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - # 设置模拟 - mock_milvus_utils.check_milvus_available.return_value = True - mock_milvus_utils.validate_milvus_config.return_value = True - - mock_embedding = Mock() - mock_bgem3.return_value = mock_embedding - - mock_backend = Mock() - mock_backend.sparse_search.side_effect = Exception("Search failed") - mock_milvus_backend.return_value = mock_backend - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - retriever = MilvusSparseRetriever(config=milvus_sparse_config) - - query = "测试查询" - result = retriever.execute(query) - - # 验证错误处理 - assert isinstance(result, dict) - assert result["query"] == query - assert result["retrieval_results"] == [] - - @patch("sage.middleware.operators.rag.retriever.MilvusUtils") - @patch("sage.middleware.operators.rag.retriever.MilvusBackend") - @patch("pymilvus.model.hybrid.BGEM3EmbeddingFunction") - def test_error_handling_embedding_failure( - self, mock_bgem3, mock_milvus_backend, mock_milvus_utils, milvus_sparse_config - ): - """测试错误处理 - embedding失败""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - # 设置模拟 - mock_milvus_utils.check_milvus_available.return_value = True - mock_milvus_utils.validate_milvus_config.return_value = True - - mock_embedding = Mock() - mock_embedding.encode_documents.side_effect = Exception("Embedding failed") - mock_bgem3.return_value = mock_embedding - - mock_milvus_backend.return_value = Mock() - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - retriever = MilvusSparseRetriever(config=milvus_sparse_config) - - documents = ["测试文档"] - - # 验证添加文档时的错误处理 - with pytest.raises(Exception): # noqa: B017 - retriever.add_documents(documents) - - @patch("sage.middleware.operators.rag.retriever.MilvusUtils") - @patch("sage.middleware.operators.rag.retriever.MilvusBackend") - @patch("pymilvus.model.hybrid.BGEM3EmbeddingFunction") - def test_configuration_methods( - self, mock_bgem3, mock_milvus_backend, mock_milvus_utils, milvus_sparse_config - ): - """测试配置相关方法""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - # 设置模拟 - mock_milvus_utils.check_milvus_available.return_value = True - mock_milvus_utils.validate_milvus_config.return_value = True - mock_bgem3.return_value = Mock() - - mock_backend = Mock() - mock_backend.save_config.return_value = True - mock_backend.load_config.return_value = True - mock_backend.get_collection_info.return_value = { - "name": "test_sparse_collection", - "count": 100, - } - mock_milvus_backend.return_value = mock_backend - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - retriever = MilvusSparseRetriever(config=milvus_sparse_config) - - # 测试保存配置 - assert retriever.save_config("/path/to/config") is True - mock_backend.save_config.assert_called_with("/path/to/config") - - # 测试加载配置 - assert retriever.load_config("/path/to/config") is True - mock_backend.load_config.assert_called_with("/path/to/config") - - # 测试获取集合信息 - info = retriever.get_collection_info() - assert info["name"] == "test_sparse_collection" - assert info["count"] == 100 - - @patch("sage.middleware.operators.rag.retriever.MilvusUtils") - @patch("sage.middleware.operators.rag.retriever.MilvusBackend") - @patch("pymilvus.model.hybrid.BGEM3EmbeddingFunction") - def test_profile_mode( - self, mock_bgem3, mock_milvus_backend, mock_milvus_utils, milvus_sparse_config - ): - """测试profile模式""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - # 设置模拟 - mock_milvus_utils.check_milvus_available.return_value = True - mock_milvus_utils.validate_milvus_config.return_value = True - - mock_embedding = Mock() - mock_bgem3.return_value = mock_embedding - - mock_backend = Mock() - mock_backend.sparse_search.return_value = [ - {"content": "相关文档", "score": 0.95, "id": "doc_1"} - ] - mock_milvus_backend.return_value = mock_backend - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - with patch("os.makedirs"): - with patch("builtins.open", create=True): - with patch("json.dump"): - # 启用profile模式 - retriever = MilvusSparseRetriever( - config=milvus_sparse_config, enable_profile=True - ) - - query = "测试查询" - retriever.execute(query) - - # 验证profile数据被收集 - assert hasattr(retriever, "data_records") - assert hasattr(retriever, "data_base_path") - - @patch("sage.middleware.operators.rag.retriever.MilvusUtils") - def test_initialization_failure_milvus_unavailable( - self, mock_milvus_utils, milvus_sparse_config - ): - """测试初始化失败 - Milvus不可用""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - mock_milvus_utils.check_milvus_available.return_value = False - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - with pytest.raises(ImportError): - MilvusSparseRetriever(config=milvus_sparse_config) - - @patch("sage.middleware.operators.rag.retriever.MilvusUtils") - def test_initialization_failure_invalid_config(self, mock_milvus_utils, milvus_sparse_config): - """测试初始化失败 - 无效配置""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - mock_milvus_utils.check_milvus_available.return_value = True - mock_milvus_utils.validate_milvus_config.return_value = False - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - with pytest.raises(ValueError): - MilvusSparseRetriever(config=milvus_sparse_config) - - @patch("sage.middleware.operators.rag.retriever.MilvusUtils") - @patch("sage.middleware.operators.rag.retriever.MilvusBackend") - def test_embedding_model_import_failure( - self, mock_milvus_backend, mock_milvus_utils, milvus_sparse_config - ): - """测试embedding模型导入失败""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - # 设置模拟 - mock_milvus_utils.check_milvus_available.return_value = True - mock_milvus_utils.validate_milvus_config.return_value = True - mock_milvus_backend.return_value = Mock() - - # 模拟BGEM3EmbeddingFunction导入失败 - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - with patch( - "pymilvus.model.hybrid.BGEM3EmbeddingFunction", - side_effect=ImportError("BGEM3EmbeddingFunction not available"), - ): - with pytest.raises(ImportError): - MilvusSparseRetriever(config=milvus_sparse_config) - - @patch("sage.middleware.operators.rag.retriever.MilvusUtils") - @patch("sage.middleware.operators.rag.retriever.MilvusBackend") - @patch("pymilvus.model.hybrid.BGEM3EmbeddingFunction") - def test_knowledge_file_loading( - self, mock_bgem3, mock_milvus_backend, mock_milvus_utils, milvus_sparse_config - ): - """测试知识库文件加载""" - if not RETRIEVER_AVAILABLE: - pytest.skip("Retriever module not available") - - # 设置模拟 - mock_milvus_utils.check_milvus_available.return_value = True - mock_milvus_utils.validate_milvus_config.return_value = True - - mock_embedding = Mock() - mock_bgem3.return_value = mock_embedding - - mock_backend = Mock() - mock_backend.load_knowledge_from_file_sparse.return_value = 10 # 成功加载10个文档 - mock_milvus_backend.return_value = mock_backend - - # 修改配置以包含知识库文件 - config_with_knowledge = milvus_sparse_config.copy() - config_with_knowledge["milvus_sparse"]["knowledge_file"] = "/path/to/knowledge.txt" - - with patch("sage.middleware.operators.rag.retriever.MapOperator"): - with patch("os.path.exists", return_value=True): - MilvusSparseRetriever(config=config_with_knowledge) - - # 验证知识库文件被加载 - mock_backend.load_knowledge_from_file_sparse.assert_called_once_with( - "/path/to/knowledge.txt" - ) - - -# 尝试导入Wiki18FAISSRetriever -try: - from sage.middleware.operators.rag.retriever import Wiki18FAISSRetriever - - WIKI18_FAISS_AVAILABLE = True -except ImportError: - WIKI18_FAISS_AVAILABLE = False - - -@pytest.fixture -def wiki18_faiss_config(): - """Wiki18FAISSRetriever测试配置""" - return { - "top_k": 5, - "embedding": {"model": "BAAI/bge-m3", "gpu_device": 0}, - "faiss": { - "index_path": "/path/to/test/wiki18_index.index", - "documents_path": "/path/to/test/wiki18_documents.jsonl", - }, - } - - -@pytest.fixture -def sample_wiki18_documents(): - """测试Wiki18文档""" - return [ - { - "id": "1", - "title": "Machine Learning", - "contents": "Machine learning is a subset of artificial intelligence.", - "doc_size": 50, - }, - { - "id": "2", - "title": "Deep Learning", - "contents": "Deep learning uses neural networks with multiple layers.", - "doc_size": 55, - }, - ] - - -@pytest.mark.unit -class TestWiki18FAISSRetriever: - """测试Wiki18FAISSRetriever类""" - - def test_wiki18_faiss_import(self): - """测试Wiki18FAISSRetriever导入""" - if not WIKI18_FAISS_AVAILABLE: - pytest.skip("Wiki18FAISSRetriever not available") - - assert Wiki18FAISSRetriever is not None - - def test_wiki18_faiss_initialization(self, wiki18_faiss_config): - """测试Wiki18FAISSRetriever初始化""" - if not WIKI18_FAISS_AVAILABLE: - pytest.skip("Wiki18FAISSRetriever not available") - - # 简单验证配置和类的存在 - config = wiki18_faiss_config - assert "top_k" in config - assert "embedding" in config - assert "faiss" in config - assert config["top_k"] == 5 - assert config["embedding"]["model"] == "BAAI/bge-m3" - assert config["faiss"]["index_path"] == "/path/to/test/wiki18_index.index" - assert config["faiss"]["documents_path"] == "/path/to/test/wiki18_documents.jsonl" - - # 验证类可以导入 - assert Wiki18FAISSRetriever is not None - - # 验证类具有期望的方法 - assert hasattr(Wiki18FAISSRetriever, "execute") - assert hasattr(Wiki18FAISSRetriever, "__init__") - - def test_wiki18_faiss_execute_string_input(self, wiki18_faiss_config, sample_wiki18_documents): - """测试Wiki18FAISSRetriever execute方法 - 字符串输入""" - if not WIKI18_FAISS_AVAILABLE: - pytest.skip("Wiki18FAISSRetriever not available") - - # 创建模拟的retriever实例 - mock_retriever = Mock(spec=Wiki18FAISSRetriever) - mock_retriever.top_k = 5 - mock_retriever.documents = sample_wiki18_documents - - # 模拟execute方法的返回结果 - def mock_execute(query): - if isinstance(query, str): - return { - "query": query, - "retrieval_results": [ - { - "text": doc["contents"], - "similarity_score": 0.9, - "document_index": i, - "title": doc["title"], - "id": doc["id"], - } - for i, doc in enumerate(sample_wiki18_documents) - ], - } - return {"query": str(query), "retrieval_results": []} - - mock_retriever.execute = mock_execute - - # 测试字符串输入 - result = mock_retriever.execute("machine learning") - - # 验证结果 - assert "query" in result - assert "retrieval_results" in result - assert result["query"] == "machine learning" - assert len(result["retrieval_results"]) == 2 - - # 验证结果格式 - for doc in result["retrieval_results"]: - assert "text" in doc - assert "similarity_score" in doc - assert "document_index" in doc - assert "title" in doc - assert "id" in doc - - def test_wiki18_faiss_execute_dict_input(self, wiki18_faiss_config, sample_wiki18_documents): - """测试Wiki18FAISSRetriever execute方法 - 字典输入""" - if not WIKI18_FAISS_AVAILABLE: - pytest.skip("Wiki18FAISSRetriever not available") - - # 创建模拟的retriever实例 - mock_retriever = Mock(spec=Wiki18FAISSRetriever) - mock_retriever.top_k = 5 - mock_retriever.documents = sample_wiki18_documents - - # 模拟execute方法的返回结果 - def mock_execute(data): - result = data.copy() if isinstance(data, dict) else {"input": str(data)} - - # 提取查询文本 - query_text = "" - if isinstance(data, dict): - query_text = data.get("query", data.get("question", "")) - else: - query_text = str(data) - - result["query"] = query_text - result["retrieval_results"] = [ - { - "text": doc["contents"], - "similarity_score": 0.8, - "document_index": i, - "title": doc["title"], - "id": doc["id"], - } - for i, doc in enumerate(sample_wiki18_documents[:1]) # 返回第一个文档 - ] - # 新增字段以匹配统一接口 - return result - - mock_retriever.execute = mock_execute - - # 测试字典输入 - query字段 - input_data = {"query": "deep learning", "other_field": "value"} - result = mock_retriever.execute(input_data) - - # 验证结果 - assert "query" in result - assert "retrieval_results" in result - assert result["query"] == "deep learning" - assert "other_field" in result # 原始字段应保留 - assert result["other_field"] == "value" - - def test_wiki18_faiss_execute_question_field( - self, wiki18_faiss_config, sample_wiki18_documents - ): - """测试Wiki18FAISSRetriever execute方法 - question字段""" - if not WIKI18_FAISS_AVAILABLE: - pytest.skip("Wiki18FAISSRetriever not available") - - # 创建模拟的retriever实例 - mock_retriever = Mock(spec=Wiki18FAISSRetriever) - - # 模拟execute方法 - def mock_execute(data): - result = data.copy() if isinstance(data, dict) else {"input": str(data)} - - # 提取查询文本 - query_text = "" - if isinstance(data, dict): - query_text = data.get("query", data.get("question", "")) - - result["query"] = query_text - result["retrieval_results"] = [ - { - "text": sample_wiki18_documents[0]["contents"], - "similarity_score": 0.9, - "document_index": 0, - "title": sample_wiki18_documents[0]["title"], - "id": sample_wiki18_documents[0]["id"], - } - ] - return result - - mock_retriever.execute = mock_execute - - # 测试字典输入 - question字段 - input_data = {"question": "what is AI?"} - result = mock_retriever.execute(input_data) - - # 验证结果 - assert "query" in result - assert result["query"] == "what is AI?" - assert "question" in result # 原始字段应保留 - assert result["question"] == "what is AI?" - - def test_wiki18_faiss_execute_error_handling(self, wiki18_faiss_config): - """测试Wiki18FAISSRetriever execute方法错误处理""" - if not WIKI18_FAISS_AVAILABLE: - pytest.skip("Wiki18FAISSRetriever not available") - - # 创建模拟的retriever实例 - mock_retriever = Mock(spec=Wiki18FAISSRetriever) - - # 模拟execute方法处理错误情况 - def mock_execute(data): - if not data or data == "" or data is None or isinstance(data, (int, float)): - return {"query": str(data) if data is not None else "", "retrieval_results": []} - - # 正常情况 - query_text = data if isinstance(data, str) else str(data) - return {"query": query_text, "retrieval_results": []} - - mock_retriever.execute = mock_execute - - # 测试空查询 - result = mock_retriever.execute("") - assert "retrieval_results" in result - assert len(result["retrieval_results"]) == 0 - - # 测试无效输入类型 - result = mock_retriever.execute(123) - assert "retrieval_results" in result - assert len(result["retrieval_results"]) == 0 - - # 测试None输入 - result = mock_retriever.execute(None) - assert "retrieval_results" in result - assert len(result["retrieval_results"]) == 0 - - def test_wiki18_faiss_method_signature_consistency(self, wiki18_faiss_config): - """测试Wiki18FAISSRetriever方法签名一致性""" - if not WIKI18_FAISS_AVAILABLE: - pytest.skip("Wiki18FAISSRetriever not available") - - # 验证execute方法签名 - import inspect - - sig = inspect.signature(Wiki18FAISSRetriever.execute) - - # 应该有data参数 - assert "data" in sig.parameters - - # 验证参数类型注解（如果有的话） - data_param = sig.parameters["data"] - if data_param.annotation != inspect.Parameter.empty: - # 检查是否接受Union[str, Dict[str, Any]]或类似类型 - annotation_str = str(data_param.annotation) - assert "str" in annotation_str or "Any" in annotation_str - - # 验证返回类型注解（如果有的话） - if sig.return_annotation != sig.empty: - return_annotation_str = str(sig.return_annotation) - assert "Dict" in return_annotation_str or "dict" in return_annotation_str - - def test_wiki18_faiss_config_validation(self, wiki18_faiss_config): - """测试Wiki18FAISSRetriever配置验证""" - if not WIKI18_FAISS_AVAILABLE: - pytest.skip("Wiki18FAISSRetriever not available") - - # 测试必需配置字段 - assert "top_k" in wiki18_faiss_config - assert "embedding" in wiki18_faiss_config - assert "faiss" in wiki18_faiss_config - assert wiki18_faiss_config["top_k"] > 0 - assert "model" in wiki18_faiss_config["embedding"] - - # 测试faiss配置项 - faiss_config = wiki18_faiss_config["faiss"] - assert "index_path" in faiss_config - assert "documents_path" in faiss_config - assert faiss_config["index_path"] is not None - assert faiss_config["documents_path"] is not None - - def test_wiki18_faiss_missing_config_validation(self): - """测试Wiki18FAISSRetriever缺少必需配置时的验证""" - if not WIKI18_FAISS_AVAILABLE: - pytest.skip("Wiki18FAISSRetriever not available") - - # 测试缺少faiss配置的情况 - - # 测试缺少index_path的情况 - - # 测试缺少documents_path的情况 - - # 由于我们无法直接实例化（需要模拟文件系统等），这里只验证配置结构 - # 实际的验证逻辑会在_init_faiss_index方法中抛出ValueError - - def test_wiki18_faiss_search_with_no_results(self, wiki18_faiss_config): - """测试Wiki18FAISSRetriever搜索无结果的情况""" - if not WIKI18_FAISS_AVAILABLE: - pytest.skip("Wiki18FAISSRetriever not available") - - # 创建模拟的retriever实例 - mock_retriever = Mock(spec=Wiki18FAISSRetriever) - - # 模拟execute方法返回无结果 - def mock_execute(query): - return {"query": str(query), "retrieval_results": []} # 无结果 - - mock_retriever.execute = mock_execute - - # 测试搜索无结果 - result = mock_retriever.execute("nonexistent query") - - # 验证结果 - assert "query" in result - assert "retrieval_results" in result - assert result["query"] == "nonexistent query" - assert len(result["retrieval_results"]) == 0 diff --git a/packages/sage-middleware/tests/operators/rag/test_searcher.py b/packages/sage-middleware/tests/operators/rag/test_searcher.py deleted file mode 100644 index 267ef33360..0000000000 --- a/packages/sage-middleware/tests/operators/rag/test_searcher.py +++ /dev/null @@ -1,214 +0,0 @@ -""" -Unit tests for sage.middleware.operators.rag.searcher module. -Tests BochaWebSearch operator. -""" - -from unittest.mock import Mock, patch - -import pytest - -try: - from sage.middleware.operators.rag.searcher import BochaWebSearch - - SEARCHER_AVAILABLE = True -except ImportError as e: - SEARCHER_AVAILABLE = False - pytestmark = pytest.mark.skip(f"Searcher module not available: {e}") - - -@pytest.mark.unit -class TestBochaWebSearch: - """Test BochaWebSearch operator.""" - - def test_initialization_with_api_key(self): - """Test BochaWebSearch initialization with valid config.""" - if not SEARCHER_AVAILABLE: - pytest.skip("Searcher not available") - - config = { - "api_key": "test_key_12345", # pragma: allowlist secret - "count": 5, - "page": 1, - "summary": True, - } - - searcher = BochaWebSearch(config) - assert searcher.api_key == "test_key_12345" # pragma: allowlist secret - assert searcher.count == 5 - assert searcher.page == 1 - assert searcher.summary is True - assert searcher.url == "https://api.bochaai.com/v1/web-search" - - def test_initialization_without_api_key(self): - """Test BochaWebSearch initialization fails without api_key.""" - if not SEARCHER_AVAILABLE: - pytest.skip("Searcher not available") - - config = {"count": 10} - - with pytest.raises(ValueError, match="requires an 'api_key'"): - BochaWebSearch(config) - - def test_initialization_with_defaults(self): - """Test BochaWebSearch uses default values.""" - if not SEARCHER_AVAILABLE: - pytest.skip("Searcher not available") - - config = {"api_key": "test_key"} # pragma: allowlist secret - - searcher = BochaWebSearch(config) - assert searcher.count == 10 # Default - assert searcher.page == 1 # Default - assert searcher.summary is True # Default - - @patch("sage.middleware.operators.rag.searcher.requests.post") - def test_execute_successful_search(self, mock_post): - """Test execute with successful API response.""" - if not SEARCHER_AVAILABLE: - pytest.skip("Searcher not available") - - # Mock successful response - mock_response = Mock() - mock_response.json.return_value = { - "results": [ - {"title": "Result 1", "url": "http://example.com/1"}, - {"title": "Result 2", "url": "http://example.com/2"}, - ] - } - mock_response.raise_for_status = Mock() - mock_post.return_value = mock_response - - config = {"api_key": "test_key"} # pragma: allowlist secret - searcher = BochaWebSearch(config) - - result = searcher.execute("test query") - - assert "results" in result - assert len(result["results"]) == 2 - mock_post.assert_called_once() - - @patch("sage.middleware.operators.rag.searcher.requests.post") - def test_execute_with_custom_params(self, mock_post): - """Test execute sends correct payload with custom parameters.""" - if not SEARCHER_AVAILABLE: - pytest.skip("Searcher not available") - - mock_response = Mock() - mock_response.json.return_value = {"results": []} - mock_response.raise_for_status = Mock() - mock_post.return_value = mock_response - - config = { - "api_key": "custom_key", # pragma: allowlist secret - "count": 20, - "page": 2, - "summary": False, - } - searcher = BochaWebSearch(config) - - searcher.execute("custom query") - - # Verify the payload - call_args = mock_post.call_args - payload = call_args[1]["json"] - assert payload["query"] == "custom query" - assert payload["count"] == 20 - assert payload["page"] == 2 - assert payload["summary"] is False - - @patch("sage.middleware.operators.rag.searcher.requests.post") - def test_execute_api_error(self, mock_post): - """Test execute handles API errors gracefully.""" - if not SEARCHER_AVAILABLE: - pytest.skip("Searcher not available") - - # Mock API error - mock_post.side_effect = Exception("API connection failed") - - config = {"api_key": "test_key"} # pragma: allowlist secret - searcher = BochaWebSearch(config) - - result = searcher.execute("test query") - - # Should return empty dict on error - assert result == {} - - @patch("sage.middleware.operators.rag.searcher.requests.post") - def test_execute_http_error(self, mock_post): - """Test execute handles HTTP errors.""" - if not SEARCHER_AVAILABLE: - pytest.skip("Searcher not available") - - # Mock HTTP error - mock_response = Mock() - mock_response.raise_for_status.side_effect = Exception("404 Not Found") - mock_post.return_value = mock_response - - config = {"api_key": "test_key"} # pragma: allowlist secret - searcher = BochaWebSearch(config) - - result = searcher.execute("test query") - - assert result == {} - - @patch("sage.middleware.operators.rag.searcher.requests.post") - def test_execute_invalid_json_response(self, mock_post): - """Test execute handles invalid JSON response.""" - if not SEARCHER_AVAILABLE: - pytest.skip("Searcher not available") - - # Mock invalid JSON - mock_response = Mock() - mock_response.raise_for_status = Mock() - mock_response.json.side_effect = ValueError("Invalid JSON") - mock_post.return_value = mock_response - - config = {"api_key": "test_key"} # pragma: allowlist secret - searcher = BochaWebSearch(config) - - result = searcher.execute("test query") - - assert result == {} - - @patch("sage.middleware.operators.rag.searcher.requests.post") - def test_execute_with_empty_query(self, mock_post): - """Test execute with empty query string.""" - if not SEARCHER_AVAILABLE: - pytest.skip("Searcher not available") - - mock_response = Mock() - mock_response.json.return_value = {"results": []} - mock_response.raise_for_status = Mock() - mock_post.return_value = mock_response - - config = {"api_key": "test_key"} # pragma: allowlist secret - searcher = BochaWebSearch(config) - - result = searcher.execute("") - - assert "results" in result - # Check empty query was sent - call_args = mock_post.call_args - assert call_args[1]["json"]["query"] == "" - - @patch("sage.middleware.operators.rag.searcher.requests.post") - def test_authorization_header(self, mock_post): - """Test that authorization header is correctly set.""" - if not SEARCHER_AVAILABLE: - pytest.skip("Searcher not available") - - mock_response = Mock() - mock_response.json.return_value = {} - mock_response.raise_for_status = Mock() - mock_post.return_value = mock_response - - config = {"api_key": "secret_api_key"} # pragma: allowlist secret - searcher = BochaWebSearch(config) - - searcher.execute("test") - - # Verify headers - call_args = mock_post.call_args - headers = call_args[1]["headers"] - assert headers["Authorization"] == "secret_api_key" # pragma: allowlist secret - assert headers["Content-Type"] == "application/json" diff --git a/packages/sage-middleware/tests/operators/rag/test_writer.py b/packages/sage-middleware/tests/operators/rag/test_writer.py deleted file mode 100644 index d71c9c9829..0000000000 --- a/packages/sage-middleware/tests/operators/rag/test_writer.py +++ /dev/null @@ -1,269 +0,0 @@ -""" -Unit tests for sage.middleware.operators.rag.writer module. -Tests MemoryWriter operator. -""" - -from unittest.mock import Mock - -import pytest - -try: - from sage.middleware.operators.rag.writer import MemoryWriter - - WRITER_AVAILABLE = True -except ImportError as e: - WRITER_AVAILABLE = False - pytestmark = pytest.mark.skip(f"Writer module not available: {e}") - - -@pytest.mark.unit -class TestMemoryWriter: - """Test MemoryWriter operator.""" - - def test_initialization_no_memory_types(self): - """Test MemoryWriter initialization without memory types.""" - if not WRITER_AVAILABLE: - pytest.skip("Writer not available") - - config = {} - writer = MemoryWriter(config) - - assert writer.state is None - assert writer.config == {} - assert writer.collections == {} - - def test_initialization_with_stm(self): - """Test MemoryWriter initialization with STM config.""" - if not WRITER_AVAILABLE: - pytest.skip("Writer not available") - - config = { - "stm": True, - "stm_collection": "short_term_memory", - "stm_config": {"max_size": 100}, - } - writer = MemoryWriter(config) - - assert "stm" in writer.collections - assert writer.collections["stm"]["collection"] == "short_term_memory" - assert writer.collections["stm"]["config"] == {"max_size": 100} - - def test_initialization_with_ltm(self): - """Test MemoryWriter initialization with LTM config.""" - if not WRITER_AVAILABLE: - pytest.skip("Writer not available") - - config = { - "ltm": True, - "ltm_collection": "long_term_memory", - "ltm_config": {"persistence": True}, - } - writer = MemoryWriter(config) - - assert "ltm" in writer.collections - assert writer.collections["ltm"]["collection"] == "long_term_memory" - assert writer.collections["ltm"]["config"] == {"persistence": True} - - def test_initialization_with_dcm(self): - """Test MemoryWriter initialization with DCM config.""" - if not WRITER_AVAILABLE: - pytest.skip("Writer not available") - - config = { - "dcm": True, - "dcm_collection": "dynamic_context_memory", - "dcm_config": {"ttl": 3600}, - } - writer = MemoryWriter(config) - - assert "dcm" in writer.collections - assert writer.collections["dcm"]["collection"] == "dynamic_context_memory" - assert writer.collections["dcm"]["config"] == {"ttl": 3600} - - def test_initialization_with_all_memory_types(self): - """Test MemoryWriter initialization with all memory types.""" - if not WRITER_AVAILABLE: - pytest.skip("Writer not available") - - config = { - "stm": True, - "stm_collection": "stm", - "stm_config": {}, - "ltm": True, - "ltm_collection": "ltm", - "ltm_config": {}, - "dcm": True, - "dcm_collection": "dcm", - "dcm_config": {}, - } - writer = MemoryWriter(config) - - assert len(writer.collections) == 3 - assert "stm" in writer.collections - assert "ltm" in writer.collections - assert "dcm" in writer.collections - - def test_execute_with_string_input(self): - """Test execute with string input.""" - if not WRITER_AVAILABLE: - pytest.skip("Writer not available") - - config = {} - writer = MemoryWriter(config) - - result = writer.execute("test string") - - # Should return original data - assert result == "test string" - - def test_execute_with_list_input(self): - """Test execute with list input.""" - if not WRITER_AVAILABLE: - pytest.skip("Writer not available") - - config = {} - writer = MemoryWriter(config) - - input_data = ["item1", "item2", "item3"] - result = writer.execute(input_data) - - assert result == input_data - - def test_execute_with_tuple_input(self): - """Test execute with tuple input (query, context pattern).""" - if not WRITER_AVAILABLE: - pytest.skip("Writer not available") - - config = {} - writer = MemoryWriter(config) - - input_data = ("query: ", "what is AI?") - result = writer.execute(input_data) - - assert result == input_data - - def test_execute_with_unsupported_type(self): - """Test execute with unsupported data type.""" - if not WRITER_AVAILABLE: - pytest.skip("Writer not available") - - config = {} - writer = MemoryWriter(config) - - input_data = {"key": "value"} # Dict not supported - result = writer.execute(input_data) - - # Should return original data - assert result == input_data - - def test_execute_without_state_manager(self): - """Test execute without state manager (should log warning).""" - if not WRITER_AVAILABLE: - pytest.skip("Writer not available") - - config = { - "stm": True, - "stm_collection": "test_stm", - "stm_config": {}, - } - writer = MemoryWriter(config) - - # State is None by default - result = writer.execute("test data") - - # Should return original data even without state - assert result == "test data" - - def test_execute_with_state_manager(self): - """Test execute with mocked state manager.""" - if not WRITER_AVAILABLE: - pytest.skip("Writer not available") - - config = { - "stm": True, - "stm_collection": "test_stm", - "stm_config": {"max_size": 50}, - } - writer = MemoryWriter(config) - - # Mock state manager - mock_state = Mock() - writer.state = mock_state - - result = writer.execute("test document") - - # Should call state.store - mock_state.store.assert_called_once() - call_args = mock_state.store.call_args[1] - assert call_args["collection"] == "test_stm" - assert call_args["documents"] == ["test document"] - assert call_args["collection_config"] == {"max_size": 50} - - # Should return original data - assert result == "test document" - - def test_execute_with_multiple_collections(self): - """Test execute writes to multiple collections.""" - if not WRITER_AVAILABLE: - pytest.skip("Writer not available") - - config = { - "stm": True, - "stm_collection": "stm", - "stm_config": {}, - "ltm": True, - "ltm_collection": "ltm", - "ltm_config": {}, - } - writer = MemoryWriter(config) - - mock_state = Mock() - writer.state = mock_state - - writer.execute(["doc1", "doc2"]) - - # Should be called twice (once for stm, once for ltm) - assert mock_state.store.call_count == 2 - - def test_execute_with_missing_collection_name(self): - """Test execute handles missing collection name.""" - if not WRITER_AVAILABLE: - pytest.skip("Writer not available") - - config = { - "stm": True, - # stm_collection is missing - "stm_config": {}, - } - writer = MemoryWriter(config) - - mock_state = Mock() - writer.state = mock_state - - result = writer.execute("test") - - # Should not call store when collection is None - mock_state.store.assert_not_called() - assert result == "test" - - def test_execute_with_store_exception(self): - """Test execute handles store exceptions gracefully.""" - if not WRITER_AVAILABLE: - pytest.skip("Writer not available") - - config = { - "stm": True, - "stm_collection": "test_stm", - "stm_config": {}, - } - writer = MemoryWriter(config) - - mock_state = Mock() - mock_state.store.side_effect = Exception("Storage failed") - writer.state = mock_state - - # Should not raise exception - result = writer.execute("test data") - - # Should still return original data - assert result == "test data" diff --git a/packages/sage-middleware/tests/operators/tools/__init__.py b/packages/sage-middleware/tests/operators/tools/__init__.py deleted file mode 100644 index bdb28a98f2..0000000000 --- a/packages/sage-middleware/tests/operators/tools/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Tests for domain-specific tools.""" diff --git a/packages/sage-middleware/tests/operators/tools/test_arxiv_paper_searcher.py b/packages/sage-middleware/tests/operators/tools/test_arxiv_paper_searcher.py deleted file mode 100644 index eab6585554..0000000000 --- a/packages/sage-middleware/tests/operators/tools/test_arxiv_paper_searcher.py +++ /dev/null @@ -1,160 +0,0 @@ -# test_tool.py - -import pytest -import requests - -# ================================ -# 关键修改：根据您的项目结构更新 import 语句 -# 假设您的项目根目录是 /home/wxh/refactor_wxh/MemoRAG/ -# 并且您会从该根目录运行 pytest -# ================================ -from sage.middleware.operators.tools.arxiv_paper_searcher import _Searcher_Tool - - -# ================================ -# 1. Fixture: 创建可复用的工具实例 -# ================================ -@pytest.fixture -def searcher_tool(): - """ - 这是一个 Fixture，它为每个需要它的测试函数提供一个干净的、 - 全新的 _Searcher_Tool 实例。这避免了在每个测试中重复创建对象。 - """ - return _Searcher_Tool() - - -# ================================ -# 2. 基础功能测试 -# ================================ -def test_tool_initialization(searcher_tool): - """ - 测试：工具初始化时，其名称、输入/输出类型等元数据是否正确。 - 这是一个简单的“冒烟测试”，确保基础配置没问题。 - """ - assert searcher_tool.tool_name == "_Searcher_Tool" - assert "query" in searcher_tool.input_types - assert ( - searcher_tool.output_type == "list - A list of dictionaries containing paper information." - ) - assert isinstance(searcher_tool.get_metadata(), dict) - - -# ================================ -# 3. 核心功能测试（使用 Mocking） -# ================================ - - -def test_execute_success_with_mock(searcher_tool, mocker): - """ - 测试：在成功获取网络数据时，execute 方法能否正确解析并返回结果。 - 这是最重要的测试，它使用了“模拟”（Mocking）技术来伪造网络响应。 - """ - # --- 准备 (Arrange) --- - # a. 创建一个假的、简化的 arXiv HTML 响应内容，模拟真实网页结构。 - fake_html_content = """ - <html> - <body> - <li class="arxiv-result"> - <p class="title">Fake Paper Title 1</p> - <p class="authors">Authors: Dr. Fake Author One</p> - <p class="list-title"> - <a href="https://arxiv.org/abs/1111.1111">arXiv:1111.1111</a> - </p> - <span class="abstract-full">This is a fake abstract. △ Less</span> - </li> - <li class="arxiv-result"> - <p class="title">Fake Paper Title 2</p> - <p class="authors">Authors: Dr. Fake Author Two</p> - <p class="list-title"> - <a href="https://arxiv.org/abs/2222.2222">arXiv:2222.2222</a> - </p> - <span class="abstract-full">Second fake abstract. △ Less</span> - </li> - </body> - </html> - """ - - # b. 使用 mocker 来“拦截”所有 `requests.get` 的调用。 - # 我们不让它真的去访问网络，而是让它返回我们上面伪造的 HTML 数据。 - mock_response = mocker.Mock() - mock_response.content = fake_html_content.encode("utf-8") # 响应内容需要是 bytes - mocker.patch("requests.get", return_value=mock_response) - - # --- 执行 (Act) --- - results = searcher_tool.execute(query="any query", max_results=2) - - # --- 断言 (Assert) --- - # 验证返回结果的类型和数量是否正确 - assert isinstance(results, list) - assert len(results) == 2 - - # 验证第一个解析结果的每个字段是否都正确 - first_paper = results[0] - assert first_paper["title"] == "Fake Paper Title 1" - assert first_paper["authors"] == "Dr. Fake Author One" - assert first_paper["abstract"] == "This is a fake abstract." - assert first_paper["link"] == "https://arxiv.org/abs/1111.1111" - - # 验证第二个解析结果的标题 - second_paper = results[1] - assert second_paper["title"] == "Fake Paper Title 2" - - -def test_execute_handles_network_error(searcher_tool, mocker): - """ - 测试：当网络请求失败（例如超时、DNS错误）时，execute 方法能否优雅地处理。 - """ - # --- 准备 (Arrange) --- - # 模拟 requests.get 在被调用时，直接抛出一个网络异常 - mocker.patch( - "requests.get", - side_effect=requests.exceptions.RequestException("Fake Network Error"), - ) - - # --- 执行 (Act) --- - results = searcher_tool.execute(query="any query") - - # --- 断言 (Assert) --- - # 根据您代码中的 try/except 逻辑，遇到异常时应该返回一个空列表 - assert results == [] - - -def test_execute_parameter_handling(searcher_tool, mocker): - """ - 测试：execute 方法对输入参数的处理逻辑是否正确。 - 例如，它是否能将无效的 `size` 修正为最接近的有效值。 - """ - # --- 准备 (Arrange) --- - # 我们只需要一个能让循环退出的空响应即可，内容不重要 - mocker.patch("requests.get", return_value=mocker.Mock(content=b"")) - - # --- 执行 (Act) --- - # 提供一个无效的 size=60 (有效值为25, 50, 100, 200) - searcher_tool.execute(query="test", size=60) - - # --- 断言 (Assert) --- - # 检查传递给 requests.get 的参数是否被正确修正了。 - # size=60 应该被修正为最接近的有效值 50。 - # .call_args 可以获取到最后一次调用 mock 对象的参数 - called_args, called_kwargs = requests.get.call_args - assert called_kwargs["params"]["size"] == "50" - - -def test_execute_no_results_found(searcher_tool, mocker): - """ - 测试：当搜索页面成功返回，但页面上没有任何论文结果时的情况。 - """ - # --- 准备 (Arrange) --- - # 模拟一个没有任何 <li class="arxiv-result"> 的HTML响应 - empty_html_content = "<html><body><h3>Show Results</h3><p>No results found.</p></body></html>" - mocker.patch( - "requests.get", - return_value=mocker.Mock(content=empty_html_content.encode("utf-8")), - ) - - # --- 执行 (Act) --- - results = searcher_tool.execute(query="a query that finds nothing") - - # --- 断言 (Assert) --- - # 此时应该返回一个空列表 - assert results == [] diff --git a/packages/sage-middleware/tests/operators/tools/test_image_captioner.py b/packages/sage-middleware/tests/operators/tools/test_image_captioner.py deleted file mode 100644 index 900c09d913..0000000000 --- a/packages/sage-middleware/tests/operators/tools/test_image_captioner.py +++ /dev/null @@ -1,140 +0,0 @@ -# image_captioner_test.py - -# ================================ -# 重要提示：此测试文件依赖 pytest-mock 插件。 -# 如果遇到 "fixture 'mocker' not found" 的错误，请运行以下命令安装： -# pip install pytest-mock -# ================================ - - -import pytest - -# ================================ -# 关键修改：根据您的项目结构更新 import 语句 -# 假设您的源文件位于 sage.apps.lib/tools/image_captioner.py -# ================================ -from sage.middleware.operators.tools.image_captioner import ImageCaptioner - - -# ================================ -# 1. Fixture: 创建可复用的工具实例 -# ================================ -@pytest.fixture -def image_captioner_tool(): - """ - 这是一个 Fixture，它为每个需要它的测试函数提供一个干净的、 - 全新的 ImageCaptioner 实例。 - """ - return ImageCaptioner() - - -# ================================ -# 2. 核心功能测试（使用 Mocking） -# ================================ - - -def test_initialization(image_captioner_tool): - """ - 测试：工具初始化时，其元数据是否正确。 - """ - assert image_captioner_tool.tool_name == "image_captioner" - assert "image_path" in image_captioner_tool.input_types - assert image_captioner_tool.require_llm_engine is True - - -def test_execute_success(image_captioner_tool, mocker): - """ - 测试：在成功调用时，execute 方法能否正确返回大模型生成的标题。 - """ - pytest.skip("isagellm module not available - optional dependency") - - -def test_execute_model_not_set(image_captioner_tool): - """ - 测试：如果没有设置 model_name，是否会按预期抛出 ValueError。 - [修正]：源文件中的 try-except 会捕获此异常并返回 None，因此我们测试返回值为 None。 - """ - # --- 准备 (Arrange) --- - # 手动将 model_name 设置为 None 来触发错误条件 - image_captioner_tool.model_name = None - - # --- 执行 (Act) --- - result = image_captioner_tool.execute(image_path="path/to/image.png") - - # --- 断言 (Assert) --- - # 验证返回结果是 None，因为异常被捕获了 - assert result is None - - -# def test_execute_retry_on_connection_error(image_captioner_tool, mocker): -# """ -# 测试：当遇到 ConnectionError 时，重试逻辑是否能正常工作。 -# """ -# # --- 准备 (Arrange) --- -# # 模拟一个先失败后成功的场景 -# successful_caption = "Retry successful!" - -# mock_client_instance = MagicMock() -# # 使用 side_effect 来定义一个调用序列：第一次调用抛出 ConnectionError，第二次调用返回成功结果。 -# mock_client_instance.generate.side_effect = [ -# ConnectionError("Fake connection failed"), -# successful_caption -# ] - -# mocker.patch('sage.middleware.operators.tools.image_captioner.UnifiedInferenceClient.create', return_value=mock_client_instance) -# # 同样需要模拟 time.sleep，否则测试会真的暂停 -# mock_sleep = mocker.patch('time.sleep') - -# # --- 执行 (Act) --- -# result = image_captioner_tool.execute(image_path="path/to/image.png") - -# # --- 断言 (Assert) --- -# # 验证 generate 方法被调用了两次（一次失败，一次成功） -# assert mock_client_instance.generate.call_count == 2 -# # 验证 time.sleep 被调用了一次 -# mock_sleep.assert_called_once_with(3) # 检查是否按代码中的3秒来等待 -# # 验证最终返回的是成功的结果 -# assert result == successful_caption - -# def test_execute_max_retries_exceeded(image_captioner_tool, mocker): -# """ -# 测试：当重试次数用尽后，是否会最终抛出 ConnectionError。 -# [修正]：源文件中的 try-except 会捕获此异常并返回 None，因此我们测试返回值为 None。 -# """ -# # --- 准备 (Arrange) --- -# mock_client_instance = MagicMock() -# # 模拟一个总是失败的场景 -# mock_client_instance.generate.side_effect = ConnectionError("Persistent connection failure") - -# mocker.patch('sage.middleware.operators.tools.image_captioner.UnifiedInferenceClient.create', return_value=mock_client_instance) -# mock_sleep = mocker.patch('time.sleep') - -# # --- 执行 (Act) --- -# result = image_captioner_tool.execute(image_path="path/to/image.png") - -# # --- 断言 (Assert) --- -# # 验证返回结果是 None，因为最终的异常被捕获了 -# assert result is None - -# # 验证 generate 方法被调用了最大重试次数（5次） -# assert mock_client_instance.generate.call_count == 5 -# # 验证 sleep 被调用了4次（最后一次失败后不再等待） -# assert mock_sleep.call_count == 4 - -# def test_execute_handles_general_exception(image_captioner_tool, mocker): -# """ -# 测试：当遇到其他非 ConnectionError 的异常时，是否能捕获并返回 None。 -# """ -# # --- 准备 (Arrange) --- -# mock_client_instance = MagicMock() -# # 模拟一个通用的异常 -# mock_client_instance.generate.side_effect = Exception("A generic error occurred") - -# mocker.patch('sage.middleware.operators.tools.image_captioner.UnifiedInferenceClient.create', return_value=mock_client_instance) - -# # --- 执行 (Act) --- -# result = image_captioner_tool.execute(image_path="path/to/image.png") - -# # --- 断言 (Assert) --- -# # 根据代码逻辑，此时应返回 None -# assert result is None diff --git a/packages/sage-middleware/tests/operators/tools/test_nature_news_fetcher.py b/packages/sage-middleware/tests/operators/tools/test_nature_news_fetcher.py deleted file mode 100644 index 47b11bbb6a..0000000000 --- a/packages/sage-middleware/tests/operators/tools/test_nature_news_fetcher.py +++ /dev/null @@ -1,184 +0,0 @@ -# nature_news_fetcher_test.py - -# ================================ -# 重要提示：此测试文件依赖 pytest-mock 插件。 -# 如果遇到 "fixture 'mocker' not found" 的错误，请运行以下命令安装： -# pip install pytest-mock -# ================================ - - -import pytest -import requests - -# ================================ -# 关键修改：根据您的项目结构更新 import 语句 -# 假设您的源文件位于 sage.apps.lib/tools/nature_news_fetcher.py -# ================================ -from sage.middleware.operators.tools.nature_news_fetcher import Nature_News_Fetcher_Tool - - -# ================================ -# 1. Fixture: 创建可复用的工具实例 -# ================================ -@pytest.fixture -def news_fetcher_tool(): - """ - 这是一个 Fixture，为每个测试提供一个干净的 Nature_News_Fetcher_Tool 实例。 - """ - return Nature_News_Fetcher_Tool() - - -# ================================ -# 2. 辅助数据和函数 -# ================================ -def create_fake_html_page(num_articles): - """ - 一个辅助函数，用于生成包含指定数量文章的假 HTML 页面。 - """ - if num_articles == 0: - return """ - <html><body><section id='new-article-list'></section></body></html> - """ - - article_template = """ - <article class="c-card"> - <h3 class="c-card__title"> - <a href="/articles/d41586-024-000{i}-5">Fake Article Title {i}</a> - </h3> - <div data-test="article-description">Fake description {i}.</div> - <ul data-test="author-list"><li>Fake Author</li></ul> - <time datetime="2024-01-0{i}T00:00:00Z">Jan 0{i}, 2024</time> - <img src="https://fake.url/image{i}.jpg" /> - </article> - """ - articles_html = "".join([article_template.format(i=i) for i in range(1, num_articles + 1)]) - - return f""" - <html><body><section id='new-article-list'>{articles_html}</section></body></html> - """ - - -# ================================ -# 3. 核心功能测试（使用 Mocking） -# ================================ - - -def test_initialization(news_fetcher_tool): - """ - 测试：工具初始化时，其元数据是否正确。 - """ - assert news_fetcher_tool.tool_name == "Nature_News_Fetcher_Tool" - assert "num_articles" in news_fetcher_tool.input_types - - -def test_parse_articles(news_fetcher_tool): - """ - 测试：parse_articles 方法能否从给定的 HTML 中正确提取信息。 - """ - # --- 准备 (Arrange) --- - fake_html = create_fake_html_page(2) - - # --- 执行 (Act) --- - articles = news_fetcher_tool.parse_articles(fake_html) - - # --- 断言 (Assert) --- - assert len(articles) == 2 - assert articles[0]["title"] == "Fake Article Title 1" - assert articles[0]["url"] == "https://www.nature.com/articles/d41586-024-0001-5" - assert articles[1]["title"] == "Fake Article Title 2" - - -# def test_execute_success_multiple_pages(news_fetcher_tool, mocker): -# """ -# 测试：execute 方法能否成功抓取并组合多个页面的文章。 -# """ -# # --- 准备 (Arrange) --- -# # 模拟两次网络请求，第一次返回2篇文章，第二次返回1篇 -# page1_html = create_fake_html_page(2) -# page2_html = create_fake_html_page(1) - -# mock_response_page1 = MagicMock() -# mock_response_page1.text = page1_html - -# mock_response_page2 = MagicMock() -# mock_response_page2.text = page2_html - -# # 模拟 requests.get 的行为序列 -# mock_get = mocker.patch('requests.get', side_effect=[mock_response_page1, mock_response_page2]) -# mock_sleep = mocker.patch('time.sleep') - -# # --- 执行 (Act) --- -# results = news_fetcher_tool.execute(num_articles=5, max_pages=2) - -# # --- 断言 (Assert) --- -# assert len(results) == 3 # 2 + 1 -# assert results[0]['title'] == "Fake Article Title 1" -# assert results[2]['title'] == "Fake Article Title 1" # The second page's first article -# assert mock_get.call_count == 2 -# assert mock_sleep.call_count == 2 - -# def test_execute_stops_when_no_articles_found(news_fetcher_tool, mocker): -# """ -# 测试：当一个页面没有返回任何文章时，execute 是否会停止抓取。 -# """ -# # --- 准备 (Arrange) --- -# page1_html = create_fake_html_page(2) -# empty_page_html = create_fake_html_page(0) # 空页面 - -# mock_response_page1 = MagicMock(text=page1_html) -# mock_response_empty = MagicMock(text=empty_page_html) - -# mock_get = mocker.patch('requests.get', side_effect=[mock_response_page1, mock_response_empty]) -# mock_sleep = mocker.patch('time.sleep') - -# # --- 执行 (Act) --- -# results = news_fetcher_tool.execute(num_articles=10, max_pages=5) - -# # --- 断言 (Assert) --- -# assert len(results) == 2 # 只应包含第一页的结果 -# assert mock_get.call_count == 2 # 尝试了第一页和第二页 -# # [修正] sleep 只在成功抓取到文章的循环末尾调用，因此只调用1次 -# assert mock_sleep.call_count == 1 - - -def test_execute_handles_network_error(news_fetcher_tool, mocker): - """ - 测试：当 requests.get 抛出网络异常时，execute 是否能正确处理。 - """ - # --- 准备 (Arrange) --- - mocker.patch( - "requests.get", - side_effect=requests.exceptions.RequestException("Fake network error"), - ) - - # --- 执行 (Act) --- - results = news_fetcher_tool.execute() - - # --- 断言 (Assert) --- - assert len(results) == 1 - assert "error" in results[0] - assert "Network error" in results[0]["error"] - - -# def test_execute_respects_num_articles_limit(news_fetcher_tool, mocker): -# """ -# 测试：当抓取的文章数达到 num_articles 限制时，是否会停止。 -# """ -# # --- 准备 (Arrange) --- -# page1_html = create_fake_html_page(5) -# page2_html = create_fake_html_page(5) - -# mock_response_page1 = MagicMock(text=page1_html) -# mock_response_page2 = MagicMock(text=page2_html) - -# mock_get = mocker.patch('requests.get', side_effect=[mock_response_page1, mock_response_page2]) -# mock_sleep = mocker.patch('time.sleep') - -# # --- 执行 (Act) --- -# # 我们只需要7篇文章，但第一页有5篇，所以它需要去第二页 -# results = news_fetcher_tool.execute(num_articles=7, max_pages=5) - -# # --- 断言 (Assert) --- -# assert len(results) == 7 # 结果被正确截断 -# assert mock_get.call_count == 2 # 确实抓取了第二页 -# assert mock_sleep.call_count == 2 diff --git a/packages/sage-middleware/tests/operators/tools/test_text_detector.py b/packages/sage-middleware/tests/operators/tools/test_text_detector.py deleted file mode 100644 index fd5504e92b..0000000000 --- a/packages/sage-middleware/tests/operators/tools/test_text_detector.py +++ /dev/null @@ -1,167 +0,0 @@ -# text_detector_test.py - -# ================================ -# 重要提示：此测试文件依赖 pytest-mock 插件。 -# 如果遇到 "fixture 'mocker' not found" 的错误，请运行以下命令安装： -# pip install pytest-mock -# ================================ - -from unittest.mock import MagicMock - -import pytest - -# ================================ -# 关键修改：根据您的项目结构更新 import 语句 -# 源文件位于 sage.middleware.operators.tools.text_detector -# ================================ -from sage.middleware.operators.tools.text_detector import text_detector - - -# ================================ -# 1. Fixture: 创建可复用的工具实例 -# ================================ -@pytest.fixture -def detector_tool(): - """ - 这是一个 Fixture，为每个测试提供一个干净的 text_detector 实例。 - """ - return text_detector() - - -# ================================ -# 2. 核心功能测试（使用 Mocking） -# ================================ - - -def test_initialization(detector_tool): - """ - 测试：工具初始化时，其元数据是否正确。 - """ - assert detector_tool.tool_name == "Text_Detector_Tool" - assert "image" in detector_tool.input_types - - -def test_execute_success_detail_1(detector_tool, mocker): - """ - 测试：成功执行并返回详细结果（detail=1 格式）。 - """ - # --- 准备 (Arrange) --- - # 模拟 easyocr.readtext 返回的详细结果 - mock_raw_result = [ - ([[0, 0], [100, 0], [100, 20], [0, 20]], "Hello", 0.99), - ([[10, 30], [120, 30], [120, 50], [10, 50]], "World", 0.95), - ] - - # 模拟 easyocr 库和它的 Reader - mock_reader_instance = MagicMock() - mock_reader_instance.readtext.return_value = mock_raw_result - mock_easyocr = MagicMock() - mock_easyocr.Reader.return_value = mock_reader_instance - - # 因为 easyocr 是在方法内动态导入的，我们需要在 sys.modules 中模拟它 - mocker.patch.dict("sys.modules", {"easyocr": mock_easyocr}) - - # --- 执行 (Act) --- - result = detector_tool.execute(image="fake/path.png") - - # --- 断言 (Assert) --- - assert len(result) == 2 - # 检查结果是否被正确清理（例如，浮点数被四舍五入） - assert result[0] == ([[0, 0], [100, 0], [100, 20], [0, 20]], "Hello", 0.99) - assert result[1][1] == "World" - mock_reader_instance.readtext.assert_called_once_with("fake/path.png") - - -def test_execute_success_detail_0(detector_tool, mocker): - """ - 测试：成功执行并返回简单结果（detail=0 格式）。 - """ - # --- 准备 (Arrange) --- - # 模拟 easyocr.readtext 返回的简单结果 - mock_raw_result = ["Hello", "World"] - - mock_reader_instance = MagicMock() - mock_reader_instance.readtext.return_value = mock_raw_result - mock_easyocr = MagicMock() - mock_easyocr.Reader.return_value = mock_reader_instance - mocker.patch.dict("sys.modules", {"easyocr": mock_easyocr}) - - # --- 执行 (Act) --- - # 源代码中的清理逻辑会失败，然后返回原始结果 - result = detector_tool.execute(image="fake/path.png") - - # --- 断言 (Assert) --- - assert result == ["Hello", "World"] - - -def test_build_tool_import_error(detector_tool, mocker): - """ - 测试：当 easyocr 未安装时，build_tool 是否会抛出 ImportError。 - """ - # --- 准备 (Arrange) --- - # 从 sys.modules 中移除 easyocr 来模拟它未安装的情况 - mocker.patch.dict("sys.modules", {"easyocr": None}) - - # --- 执行 & 断言 (Act & Assert) --- - with pytest.raises(ImportError, match="Please install the EasyOCR package"): - detector_tool.build_tool() - - -def test_cuda_out_of_memory_with_retry_and_clear_cache(detector_tool, mocker): - """ - 测试：当遇到 CUDA out of memory 错误时，是否会重试并清理缓存。 - """ - # --- 准备 (Arrange) --- - # [修正]：直接 patch 在被测模块中使用的 torch 对象，而不是通过 sys.modules。 - # 这是因为被测模块在顶层已经导入了 torch，我们需要替换那个已经被导入的引用。 - mock_torch = mocker.patch("sage.middleware.operators.tools.text_detector.torch") - - # 模拟 easyocr，让它第一次调用抛出 CUDA 错误，第二次成功 - mock_reader_instance = MagicMock() - mock_reader_instance.readtext.side_effect = [ - RuntimeError("CUDA out of memory: Tried to allocate ..."), - [([[0, 0]], "Success", 0.9)], - ] - mock_easyocr = MagicMock() - mock_easyocr.Reader.return_value = mock_reader_instance - mocker.patch.dict("sys.modules", {"easyocr": mock_easyocr}) - - # --- 执行 (Act) --- - result = detector_tool.execute(image="fake/path.png", clear_cuda_cache=True) - - # --- 断言 (Assert) --- - assert len(result) == 1 - assert result[0][1] == "Success" - # 验证 torch.cuda.empty_cache 被调用了一次 - mock_torch.cuda.empty_cache.assert_called_once() - # 验证 readtext 被调用了两次 - assert mock_reader_instance.readtext.call_count == 2 - - -# def test_fails_after_max_retries(detector_tool, mocker): -# """ -# 测试：当错误持续发生，超过最大重试次数后，是否返回空列表。 -# """ -# # --- 准备 (Arrange) --- -# # [修正]：同样，直接 patch 在被测模块中使用的 torch 对象。 -# mocker.patch('sage.middleware.operators.tools.text_detector.torch') - -# # 模拟一个总是失败的 easyocr.readtext -# mock_reader_instance = MagicMock() -# mock_reader_instance.readtext.side_effect = RuntimeError("CUDA out of memory") -# mock_easyocr = MagicMock() -# mock_easyocr.Reader.return_value = mock_reader_instance -# mocker.patch.dict('sys.modules', {'easyocr': mock_easyocr}) - -# mock_sleep = mocker.patch('time.sleep') - -# # --- 执行 (Act) --- -# # 使用较小的重试次数以加快测试 -# result = detector_tool.execute(image="fake/path.png", max_retries=3) - -# # --- 断言 (Assert) --- -# assert result == [] -# # 验证 readtext 被调用了3次 -# assert mock_reader_instance.readtext.call_count == 3 -# # 验证 sleep 被调用了3次（因为 clear_cuda_cache=False） -# assert mock_sleep.call_count == 3 diff --git a/packages/sage-middleware/tests/operators/tools/test_tools_coverage.py b/packages/sage-middleware/tests/operators/tools/test_tools_coverage.py deleted file mode 100644 index dd85e063ad..0000000000 --- a/packages/sage-middleware/tests/operators/tools/test_tools_coverage.py +++ /dev/null @@ -1,283 +0,0 @@ -""" -Unit tests for middleware operator tools -""" - -from unittest.mock import MagicMock, patch - -import pytest - - -class TestImageCaptioner: - """Test ImageCaptioner tool""" - - def test_image_captioner_init(self): - """Test ImageCaptioner initialization""" - try: - from sage.middleware.operators.tools.image_captioner import ImageCaptioner - - captioner = ImageCaptioner() - assert captioner is not None - except (ImportError, AttributeError): - pytest.skip("ImageCaptioner not available") - - def test_image_captioner_execute(self): - """Test ImageCaptioner execute method""" - pytest.skip("isagellm module not available - optional dependency") - - -class TestArxivPaperSearcher: - """Test ArxivPaperSearcher tool""" - - def test_arxiv_paper_searcher_init(self): - """Test ArxivPaperSearcher initialization""" - try: - from sage.middleware.operators.tools.arxiv_paper_searcher import _Searcher_Tool - - searcher = _Searcher_Tool() - assert searcher is not None - except (ImportError, AttributeError): - pytest.skip("ArxivPaperSearcher not available") - - @patch("requests.get") - def test_arxiv_paper_searcher_search(self, mock_requests): - """Test ArxivPaperSearcher search functionality""" - try: - from sage.middleware.operators.tools.arxiv_paper_searcher import _Searcher_Tool - - # Mock response - mock_response = MagicMock() - mock_response.status_code = 200 - mock_response.content = b""" - <html> - <body> - <li class="arxiv-result"> - <p class="title">Test Paper</p> - <p class="authors">Authors: John Doe</p> - <span class="abstract-full">Test summary</span> - <p class="list-title"><a href="/abs/1234.5678">Link</a></p> - </li> - </body> - </html> - """ - mock_requests.return_value = mock_response - - searcher = _Searcher_Tool() - if hasattr(searcher, "execute"): - results = searcher.execute("machine learning") - assert results is not None - except (ImportError, AttributeError): - pytest.skip("ArxivPaperSearcher not available") - - -class TestTextDetector: - """Test TextDetector tool""" - - @patch("easyocr.Reader") - def test_text_detector_init(self, mock_easyocr_reader): - """Test TextDetector initialization""" - try: - from sage.middleware.operators.tools.text_detector import text_detector - - mock_easyocr_reader.return_value = MagicMock() - detector = text_detector() - assert detector is not None - except (ImportError, AttributeError): - pytest.skip("TextDetector not available") - - @patch("easyocr.Reader") - def test_text_detector_execute(self, mock_easyocr_reader): - """Test TextDetector execute method""" - try: - from sage.middleware.operators.tools.text_detector import text_detector - - # Mock OCR reader - mock_reader = MagicMock() - mock_reader.readtext.return_value = [ - ([[0, 0], [100, 0], [100, 50], [0, 50]], "Hello World", 0.95) - ] - mock_easyocr_reader.return_value = mock_reader - - detector = text_detector() - if hasattr(detector, "execute"): - result = detector.execute("test_image.jpg") - assert isinstance(result, list) - except (ImportError, AttributeError): - pytest.skip("TextDetector not available") - - -class TestUrlTextExtractor: - """Test UrlTextExtractor tool""" - - def test_url_text_extractor_init(self): - """Test UrlTextExtractor initialization""" - try: - from sage.middleware.operators.tools.url_text_extractor import URL_Text_Extractor_Tool - - extractor = URL_Text_Extractor_Tool() - assert extractor is not None - except (ImportError, AttributeError): - pytest.skip("UrlTextExtractor not available") - - @patch("requests.get") - def test_url_text_extractor_execute(self, mock_requests): - """Test UrlTextExtractor execute method""" - try: - from sage.middleware.operators.tools.url_text_extractor import URL_Text_Extractor_Tool - - # Mock response - mock_response = MagicMock() - mock_response.status_code = 200 - mock_response.text = "<html><body><p>Test content</p></body></html>" - mock_requests.return_value = mock_response - - extractor = URL_Text_Extractor_Tool() - if hasattr(extractor, "execute"): - result = extractor.execute("https://example.com") - assert result is not None - except (ImportError, AttributeError): - pytest.skip("UrlTextExtractor not available") - - -class TestNatureNewsFetcher: - """Test NatureNewsFetcher tool""" - - def test_nature_news_fetcher_init(self): - """Test NatureNewsFetcher initialization""" - try: - from sage.middleware.operators.tools.nature_news_fetcher import Nature_News_Fetcher_Tool - - fetcher = Nature_News_Fetcher_Tool() - assert fetcher is not None - except (ImportError, AttributeError): - pytest.skip("NatureNewsFetcher not available") - - @patch("requests.get") - def test_nature_news_fetcher_execute(self, mock_requests): - """Test NatureNewsFetcher execute method""" - try: - from sage.middleware.operators.tools.nature_news_fetcher import Nature_News_Fetcher_Tool - - # Mock response - mock_response = MagicMock() - mock_response.status_code = 200 - mock_response.text = """ - <html> - <body> - <article> - <h1>Test Article</h1> - <p>Test content</p> - </article> - </body> - </html> - """ - mock_requests.return_value = mock_response - - fetcher = Nature_News_Fetcher_Tool() - if hasattr(fetcher, "execute"): - result = fetcher.execute() - assert result is not None - except (ImportError, AttributeError): - pytest.skip("NatureNewsFetcher not available") - - -class TestToolsIntegration: - """Test integration between tools""" - - def test_all_tools_importable(self): - """Test that all tools can be imported""" - tools_to_test = [ - ("image_captioner", "ImageCaptioner"), - ("arxiv_paper_searcher", "_Searcher_Tool"), - ("text_detector", "text_detector"), - ("url_text_extractor", "URL_Text_Extractor_Tool"), - ("nature_news_fetcher", "Nature_News_Fetcher_Tool"), - ] - - for module_name, class_name in tools_to_test: - try: - module = __import__( - f"sage.middleware.operators.tools.{module_name}", fromlist=[class_name] - ) - assert hasattr(module, class_name) - except (ImportError, AttributeError): - # Some tools may have dependencies that aren't available - pass - - -class TestToolsErrorHandling: - """Test error handling in tools""" - - @patch("requests.get") - def test_url_text_extractor_handles_404(self, mock_requests): - """Test UrlTextExtractor handles 404 errors""" - try: - from sage.middleware.operators.tools.url_text_extractor import URL_Text_Extractor_Tool - - mock_response = MagicMock() - mock_response.status_code = 404 - mock_requests.return_value = mock_response - - extractor = URL_Text_Extractor_Tool() - if hasattr(extractor, "execute"): - # Should handle error gracefully - try: - result = extractor.execute("https://nonexistent.example.com") - # Either returns None/empty or raises exception - assert result is None or result == "" or isinstance(result, str) - except Exception: - # Exception is acceptable - pass - except (ImportError, AttributeError): - pytest.skip("UrlTextExtractor not available") - - @patch("requests.get") - def test_arxiv_searcher_handles_network_error(self, mock_requests): - """Test ArxivPaperSearcher handles network errors""" - try: - from sage.middleware.operators.tools.arxiv_paper_searcher import _Searcher_Tool - - mock_requests.side_effect = Exception("Network error") - - searcher = _Searcher_Tool() - if hasattr(searcher, "execute"): - try: - result = searcher.execute("test query") - # Should return empty or None - assert result is None or result == [] or isinstance(result, (list, str)) - except Exception: - # Exception is acceptable - pass - except (ImportError, AttributeError): - pytest.skip("ArxivPaperSearcher not available") - - -class TestToolsConfiguration: - """Test tool configuration""" - - def test_tools_have_default_config(self): - """Test that tools have default configurations""" - tool_classes = [] - - try: - from sage.middleware.operators.tools.image_captioner import ImageCaptioner - - tool_classes.append(ImageCaptioner) - except (ImportError, AttributeError): - pass - - try: - from sage.middleware.operators.tools.text_detector import text_detector - - tool_classes.append(text_detector) - except (ImportError, AttributeError): - pass - - # Each tool should be instantiable (at least try) - for tool_class in tool_classes: - try: - with patch.object(tool_class, "__init__", return_value=None): - tool = tool_class.__new__(tool_class) - assert tool is not None - except Exception: - # Some tools may require specific initialization - pass diff --git a/packages/sage-middleware/tests/operators/tools/test_url_text_extractor.py b/packages/sage-middleware/tests/operators/tools/test_url_text_extractor.py deleted file mode 100644 index 175fdadb49..0000000000 --- a/packages/sage-middleware/tests/operators/tools/test_url_text_extractor.py +++ /dev/null @@ -1,147 +0,0 @@ -# url_text_extractor_test.py - -# ================================ -# 重要提示：此测试文件依赖 pytest-mock 插件。 -# 如果遇到 "fixture 'mocker' not found" 的错误，请运行以下命令安装： -# pip install pytest-mock -# ================================ - -from unittest.mock import MagicMock - -import pytest -import requests - -# ================================ -# 关键修改：根据您的项目结构更新 import 语句 -# 假设您的源文件位于 sage.apps.lib/tools/url_text_extractor.py -# ================================ -from sage.middleware.operators.tools.url_text_extractor import URL_Text_Extractor_Tool - - -# ================================ -# 1. Fixture: 创建可复用的工具实例 -# ================================ -@pytest.fixture -def url_extractor_tool(): - """ - 这是一个 Fixture，为每个测试提供一个干净的 URL_Text_Extractor_Tool 实例。 - """ - return URL_Text_Extractor_Tool() - - -# ================================ -# 2. 核心功能测试（使用 Mocking） -# ================================ - - -def test_initialization(url_extractor_tool): - """ - 测试：工具初始化时，其元数据是否正确。 - """ - assert url_extractor_tool.tool_name == "URL_Text_Extractor_Tool" - assert "url" in url_extractor_tool.input_types - - -def test_execute_success(url_extractor_tool, mocker): - """ - 测试：在成功获取网页时，能否正确提取和格式化文本。 - """ - # --- 准备 (Arrange) --- - # 模拟一个简单的 HTML 页面 - fake_html = "<html><head><title>Test Page

Hello

World" - expected_text = "Test Page\nHello\nWorld" - - # 模拟 requests.get 的成功响应 - mock_response = MagicMock() - mock_response.content = fake_html.encode("utf-8") - # 模拟 raise_for_status() 不抛出任何异常 - mock_response.raise_for_status.return_value = None - mock_get = mocker.patch("requests.get", return_value=mock_response) - - # --- 执行 (Act) --- - result = url_extractor_tool.execute(url="https://fake-example.com") - - # --- 断言 (Assert) --- - mock_get.assert_called_once_with("https://fake-example.com") - assert "extracted_text" in result - assert result["extracted_text"] == expected_text - - -def test_arxiv_pdf_url_replacement(url_extractor_tool, mocker): - """ - 测试：是否能正确地将 arxiv.org/pdf 链接替换为 /abs 链接。 - """ - # --- 准备 (Arrange) --- - mock_get = mocker.patch("requests.get", return_value=MagicMock()) - pdf_url = "https://arxiv.org/pdf/1234.5678" - expected_abs_url = "https://arxiv.org/abs/1234.5678" - - # --- 执行 (Act) --- - url_extractor_tool.execute(url=pdf_url) - - # --- 断言 (Assert) --- - # 验证 requests.get 是用替换后的 URL 调用的 - mock_get.assert_called_once_with(expected_abs_url) - - -def test_text_truncation(url_extractor_tool, mocker): - """ - 测试：当提取的文本超过10000个字符时，是否会被正确截断。 - """ - # --- 准备 (Arrange) --- - # 创建一个超过10000个字符的假文本 - long_text = "a" * 10001 - fake_html = f"{long_text}" - - mock_response = MagicMock() - mock_response.content = fake_html.encode("utf-8") - mock_response.raise_for_status.return_value = None - mocker.patch("requests.get", return_value=mock_response) - - # --- 执行 (Act) --- - result = url_extractor_tool.execute(url="https://longtext.com") - - # --- 断言 (Assert) --- - extracted_text = result["extracted_text"] - assert len(extracted_text) == 10000 - assert extracted_text == "a" * 10000 - - -def test_network_error_handling(url_extractor_tool, mocker): - """ - 测试：当发生网络错误时，能否返回正确的错误信息。 - """ - # --- 准备 (Arrange) --- - # 模拟 requests.get 抛出网络异常 - error_message = "Fake 404 Not Found" - mocker.patch("requests.get", side_effect=requests.RequestException(error_message)) - - # --- 执行 (Act) --- - result = url_extractor_tool.execute(url="https://non-existent-url.com") - - # --- 断言 (Assert) --- - assert "extracted_text" in result - assert "Error fetching URL" in result["extracted_text"] - assert error_message in result["extracted_text"] - - -def test_general_error_handling(url_extractor_tool, mocker): - """ - 测试：当发生非网络的其他异常时，能否返回正确的错误信息。 - """ - # --- 准备 (Arrange) --- - # 模拟 BeautifulSoup 在解析时发生异常 - error_message = "Fake parsing error" - mocker.patch("requests.get", return_value=MagicMock()) # 让 get 成功 - mocker.patch( - "sage.middleware.operators.tools.url_text_extractor.BeautifulSoup", - side_effect=Exception(error_message), - ) - - # --- 执行 (Act) --- - result = url_extractor_tool.execute(url="https://badhtml.com") - - # --- 断言 (Assert) --- - assert "extracted_text" in result - assert "Error extracting text" in result["extracted_text"] - assert error_message in result["extracted_text"] diff --git a/packages/sage-middleware/tests/smoke/__init__.py b/packages/sage-middleware/tests/smoke/__init__.py deleted file mode 100644 index 2b40d8f205..0000000000 --- a/packages/sage-middleware/tests/smoke/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Smoke tests for sage-middleware package.""" diff --git a/packages/sage-middleware/tests/smoke/test_imports.py b/packages/sage-middleware/tests/smoke/test_imports.py deleted file mode 100644 index ff2e34ad9c..0000000000 --- a/packages/sage-middleware/tests/smoke/test_imports.py +++ /dev/null @@ -1,241 +0,0 @@ -""" -Import smoke tests for sage-middleware package. - -Tests are categorized into: -- Release tests: For packaged/installed environment (pip install isage-middleware) -- Develop tests: For development environment (pip install -e .) - -Run release tests: - pytest tests/smoke/test_imports.py -m release - -Run develop tests: - pytest tests/smoke/test_imports.py -m develop - -Run all smoke tests: - pytest tests/smoke/test_imports.py -""" - -import sys - -import pytest - - -def is_release_environment(): - """ - Check if we're running in a release (installed) environment. - - Returns: - bool: True if installed package, False if development mode - """ - try: - import sage.middleware - - # In development mode, __file__ will point to the source directory - # In release mode, it will point to site-packages - middleware_path = sage.middleware.__file__ - return "site-packages" in middleware_path or "dist-packages" in middleware_path - except (ImportError, AttributeError): - return False - - -# Mark tests based on environment -pytestmark = pytest.mark.smoke - - -class TestCoreImports: - """Test core sage.middleware imports.""" - - @pytest.mark.release - @pytest.mark.develop - def test_import_sage_middleware(self): - """Test importing the main sage.middleware package.""" - import sage.middleware - - assert sage.middleware is not None - assert hasattr(sage.middleware, "__version__") - - @pytest.mark.release - @pytest.mark.develop - def test_import_version(self): - """Test importing version information.""" - from sage.middleware._version import __version__ - - assert __version__ is not None - assert isinstance(__version__, str) - print(f"sage-middleware version: {__version__}") - - -class TestAgentImports: - """Test sage.middleware.agent imports.""" - - @pytest.mark.release - @pytest.mark.develop - def test_import_runtime(self): - """Test importing agent runtime.""" - from sage.middleware.operators.agent import runtime - - assert runtime is not None - - @pytest.mark.release - @pytest.mark.develop - def test_import_planning(self): - """Test importing planning modules.""" - from sage.middleware.operators.agent.planning import llm_adapter, planner_adapter, router - - assert router is not None - assert planner_adapter is not None - assert llm_adapter is not None - - -class TestComponentsImports: - """Test sage.middleware.components imports.""" - - @pytest.mark.release - @pytest.mark.develop - def test_import_components(self): - """Test importing components package.""" - import sage.middleware.components - - assert sage.middleware.components is not None - - @pytest.mark.release - @pytest.mark.develop - def test_import_extensions_compat(self): - """Test importing extensions compatibility layer.""" - from sage.middleware.components import extensions_compat - - assert extensions_compat is not None - - @pytest.mark.release - @pytest.mark.develop - def test_import_sage_db(self): - """Test importing sage_db component.""" - try: - from sage.middleware.components import sage_db - - assert sage_db is not None - print(f"sage_db backend available: {hasattr(sage_db, 'backend')}") - except ImportError as e: - pytest.skip(f"sage_db not available (requires isage-vdb): {e}") - - @pytest.mark.release - @pytest.mark.develop - def test_import_sage_flow(self): - """Test importing sage_flow component.""" - try: - from sage.middleware.components import sage_flow - - assert sage_flow is not None - print("sage_flow available") - except ImportError as e: - pytest.skip(f"sage_flow not available (requires isage-flow): {e}") - - @pytest.mark.release - @pytest.mark.develop - def test_import_sage_mem(self): - """Test importing sage_mem component.""" - try: - from sage.middleware.components import sage_mem - - assert sage_mem is not None - print("sage_mem available") - except ImportError as e: - pytest.skip(f"sage_mem not available (requires isage-neuromem): {e}") - - @pytest.mark.release - @pytest.mark.develop - def test_import_sage_refiner(self): - """Test importing sage_refiner component.""" - try: - from sage.middleware.components import sage_refiner - - assert sage_refiner is not None - print("sage_refiner available") - except ImportError as e: - pytest.skip(f"sage_refiner not available (requires isage-refiner): {e}") - - -class TestOperatorsImports: - """Test sage.middleware.operators imports.""" - - @pytest.mark.release - @pytest.mark.develop - def test_import_operators(self): - """Test importing operators package.""" - try: - import sage.middleware.operators - - assert sage.middleware.operators is not None - except ImportError as e: - pytest.skip(f"operators module structure may have changed: {e}") - - -class TestContextImports: - """Test sage.middleware.context imports.""" - - @pytest.mark.release - @pytest.mark.develop - def test_import_context(self): - """Test importing context package.""" - try: - import sage.middleware.operators.context - - assert sage.middleware.operators.context is not None - except ImportError as e: - pytest.skip(f"context module structure may have changed: {e}") - - -class TestEnvironmentInfo: - """Display environment information for debugging.""" - - @pytest.mark.release - @pytest.mark.develop - def test_environment_info(self): - """Display environment information.""" - import sage.middleware - - env_type = "RELEASE" if is_release_environment() else "DEVELOP" - print(f"\n{'=' * 60}") - print(f"Environment Type: {env_type}") - print(f"Python Version: {sys.version}") - print(f"Python Executable: {sys.executable}") - print(f"sage.middleware location: {sage.middleware.__file__}") - print(f"sage.middleware version: {sage.middleware.__version__}") - print(f"{'=' * 60}") - - -class TestReleaseOnly: - """Tests that should only run in release (installed) environment.""" - - @pytest.mark.release - def test_release_package_structure(self): - """Verify release package has correct structure.""" - import sage.middleware - - if not is_release_environment(): - pytest.skip("This test only runs in release environment") - - middleware_path = sage.middleware.__file__ - assert "site-packages" in middleware_path or "dist-packages" in middleware_path - print(f"Release package location verified: {middleware_path}") - - -class TestDevelopOnly: - """Tests that should only run in development environment.""" - - @pytest.mark.develop - def test_develop_package_structure(self): - """Verify development package has correct structure.""" - import sage.middleware - - if is_release_environment(): - pytest.skip("This test only runs in development environment") - - middleware_path = sage.middleware.__file__ - assert "src/sage/middleware" in middleware_path or "sage-middleware" in middleware_path - print(f"Development package location verified: {middleware_path}") - - -if __name__ == "__main__": - # Run tests with verbose output - pytest.main([__file__, "-v", "-s", "--tb=short"]) diff --git a/packages/sage-middleware/tests/unit/agent/planning/test_adapter.py b/packages/sage-middleware/tests/unit/agent/planning/test_adapter.py deleted file mode 100644 index aac57dd340..0000000000 --- a/packages/sage-middleware/tests/unit/agent/planning/test_adapter.py +++ /dev/null @@ -1,95 +0,0 @@ -from unittest.mock import MagicMock - -import pytest -from sage_libs.sage_agentic.agents.planning.schemas import PlanResult, PlanStep - -from sage.middleware.operators.agent.planning.planner_adapter import SageLibsPlannerAdapter - - -@pytest.fixture -def mock_planner_cls(): - return MagicMock() - - -@pytest.fixture -def adapter(mock_planner_cls): - config = MagicMock() - llm_client = MagicMock() - return SageLibsPlannerAdapter(mock_planner_cls, config, llm_client) - - -def test_plan_success(adapter): - # Mock the inner planner returning a PlanResult - mock_result = PlanResult( - steps=[ - PlanStep(id=1, action="tool1", inputs={"arg": 1}), - PlanStep(id=2, action="finish", inputs={}), - ], - final_thought="Done", - ) - adapter.planner.plan.return_value = mock_result - - tools = {"tool1": {"description": "desc", "category": "general"}} - - result = adapter.plan("sys", "query", tools) - - # Should convert tool1 to tool step - assert len(result) == 1 - assert result[0]["type"] == "tool" - assert result[0]["name"] == "tool1" - assert result[0]["arguments"] == {"arg": 1} - - -def test_plan_failure(adapter): - adapter.planner.plan.side_effect = Exception("Planning error") - - result = adapter.plan("sys", "query", {}) - - assert len(result) == 1 - assert result[0]["type"] == "reply" - assert "Planning failed" in result[0]["text"] - - -def test_plan_no_steps(adapter): - """Test that when planner returns no steps, adapter returns 'No plan generated.'""" - mock_result = PlanResult(steps=[], final_thought="Just a thought") - adapter.planner.plan.return_value = mock_result - - result = adapter.plan("sys", "query", {}) - - # Implementation returns "No plan generated." when steps is empty - assert len(result) == 1 - assert result[0]["type"] == "reply" - assert result[0]["text"] == "No plan generated." - - -def test_plan_context_passed(adapter): - """Test that context is passed to planner correctly.""" - mock_result = PlanResult(steps=[], final_thought="Done") - adapter.planner.plan.return_value = mock_result - - adapter.plan("sys", "query", {}) - - # Check if context was passed in request - call_args = adapter.planner.plan.call_args - assert call_args is not None - request = call_args[0][0] - assert "system_prompt" in request.context - assert request.context["system_prompt"] == "sys" - - -def test_plan_unknown_action(adapter): - # Step with action not in tools - mock_result = PlanResult( - steps=[PlanStep(id=1, action="unknown_action", inputs={})], final_thought="Fallback thought" - ) - adapter.planner.plan.return_value = mock_result - - tools = {"tool1": {"category": "general"}} - - result = adapter.plan("sys", "query", tools) - - # Should ignore unknown action and fallback to reply with final_thought - assert len(result) == 1 - assert result[0]["type"] == "reply" - assert result[0]["text"] == "Fallback thought" diff --git a/packages/sage-middleware/tests/unit/agent/planning/test_llm_adapter.py b/packages/sage-middleware/tests/unit/agent/planning/test_llm_adapter.py deleted file mode 100644 index 5610af5517..0000000000 --- a/packages/sage-middleware/tests/unit/agent/planning/test_llm_adapter.py +++ /dev/null @@ -1,53 +0,0 @@ -from unittest.mock import MagicMock - -import pytest - -from sage.middleware.operators.agent.planning.llm_adapter import GeneratorToClientAdapter - - -@pytest.fixture -def mock_generator(): - return MagicMock() - - -@pytest.fixture -def adapter(mock_generator): - return GeneratorToClientAdapter(mock_generator) - - -def test_chat_success(adapter, mock_generator): - # Mock generator.execute returning (usage, output) - mock_generator.execute.return_value = ({"tokens": 10}, "Hello world") - - messages = [{"role": "system", "content": "sys"}, {"role": "user", "content": "hi"}] - - response = adapter.chat(messages) - - assert response == "Hello world" - mock_generator.execute.assert_called_once() - args = mock_generator.execute.call_args[0][0] - assert args[0] == "hi" # user_query - assert args[1] == messages - - -def test_chat_no_user_msg(adapter, mock_generator): - mock_generator.execute.return_value = ({}, "output") - messages = [{"role": "system", "content": "sys"}] - - adapter.chat(messages) - - args = mock_generator.execute.call_args[0][0] - assert args[0] == "Chat request" # default - - -def test_generate(adapter, mock_generator): - mock_generator.execute.return_value = ({}, "generated text") - - result = adapter.generate("prompt") - - assert len(result) == 1 - assert result[0]["generations"][0]["text"] == "generated text" - - args = mock_generator.execute.call_args[0][0] - assert args[0] == "prompt" - assert args[1] == [{"role": "user", "content": "prompt"}] diff --git a/packages/sage-middleware/tests/unit/agent/planning/test_router.py b/packages/sage-middleware/tests/unit/agent/planning/test_router.py deleted file mode 100644 index fa6a7390d3..0000000000 --- a/packages/sage-middleware/tests/unit/agent/planning/test_router.py +++ /dev/null @@ -1,94 +0,0 @@ -from unittest.mock import MagicMock, patch - -import pytest - -from sage.middleware.operators.agent.planning.router import PlannerRouter - - -@pytest.fixture -def mock_generator(): - return MagicMock() - - -@pytest.fixture -def router(mock_generator): - # We mock the internal planners to avoid instantiating real ones - with ( - patch("sage.middleware.operators.agent.planning.router.SimpleLLMPlanner"), - patch("sage.middleware.operators.agent.planning.router.SageLibsPlannerAdapter"), - patch("sage.middleware.operators.agent.planning.router.GeneratorToClientAdapter"), - ): - # Setup mocks - # mock_simple_instance = MockSimple.return_value - # mock_adapter_instance = MockAdapter.return_value - - router = PlannerRouter(mock_generator) - - # Manually assign mocks to attributes if __init__ logic is complex or if we want specific control - # But since we patched the classes used in __init__, router.simple_planner etc are already mocks - - return router - - -def test_classify_intent_react(router): - # Mock llm_client.chat to return react strategy - router.llm_client.chat.return_value = '{"strategy": "react"}' - strategy = router._classify_intent("some query") - assert strategy == "react" - - -def test_classify_intent_simple(router): - router.llm_client.chat.return_value = '{"strategy": "simple"}' - strategy = router._classify_intent("hello") - assert strategy == "simple" - - -def test_classify_intent_fallback(router): - # If LLM returns garbage, should default to simple - router.llm_client.chat.side_effect = Exception("LLM error") - strategy = router._classify_intent("query") - assert strategy == "simple" - - -def test_plan_routing_react(router): - # Force intent to react - with patch.object(router, "_classify_intent", return_value="react"): - router.react_planner.plan.return_value = [{"type": "reply", "text": "react plan"}] - - plan = router.plan("sys", "query", {}) - - assert plan == [{"type": "reply", "text": "react plan"}] - router.react_planner.plan.assert_called_once() - router.simple_planner.plan.assert_not_called() - - -def test_plan_routing_simple(router): - with patch.object(router, "_classify_intent", return_value="simple"): - router.simple_planner.plan.return_value = [{"type": "reply", "text": "simple plan"}] - - plan = router.plan("sys", "query", {}) - - assert plan == [{"type": "reply", "text": "simple plan"}] - router.simple_planner.plan.assert_called_once() - - -def test_plan_routing_tot(router): - with patch.object(router, "_classify_intent", return_value="tot"): - router.tot_planner.plan.return_value = [{"type": "reply", "text": "tot plan"}] - - plan = router.plan("sys", "query", {}) - - assert plan == [{"type": "reply", "text": "tot plan"}] - router.tot_planner.plan.assert_called_once() - - -def test_plan_routing_hierarchical(router): - with patch.object(router, "_classify_intent", return_value="hierarchical"): - router.hierarchical_planner.plan.return_value = [ - {"type": "reply", "text": "hierarchical plan"} - ] - - plan = router.plan("sys", "query", {}) - - assert plan == [{"type": "reply", "text": "hierarchical plan"}] - router.hierarchical_planner.plan.assert_called_once() diff --git a/packages/sage-middleware/tests/unit/components/sage_mem/__init__.py b/packages/sage-middleware/tests/unit/components/sage_mem/__init__.py deleted file mode 100644 index acfc009c7d..0000000000 --- a/packages/sage-middleware/tests/unit/components/sage_mem/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""sage_mem 服务单元测试模块""" diff --git a/packages/sage-middleware/tests/unit/components/sage_mem/test_service_validation.py b/packages/sage-middleware/tests/unit/components/sage_mem/test_service_validation.py deleted file mode 100644 index 972711fd7e..0000000000 --- a/packages/sage-middleware/tests/unit/components/sage_mem/test_service_validation.py +++ /dev/null @@ -1,651 +0,0 @@ -"""服务层实现验证测试 - -验证 Service 层实现与配置定义的一致性。 -对应文档：TODO_Optimization_Task4_ServiceValidation.md - -验证覆盖： -1. VectorHashMemoryService (TiM): LSH 哈希索引 -2. HierarchicalMemoryService (MemoryBank, MemGPT, MemoryOS, LD-Agent): - - tier 结构、迁移、遗忘曲线 -3. GraphMemoryService (A-Mem, HippoRAG): - - 自动链接、同义词边、PPR 检索 -4. HybridMemoryService (Mem0): - - 多索引支持 -5. ShortTermMemoryService (SCM): - - 滑动窗口、FIFO 淘汰 -""" - -import time -import uuid - -import numpy as np -import pytest - -# Skip: Service implementation issues (Vector requirements, float() errors, etc.) -pytestmark = pytest.mark.skip(reason="Service implementation issues") - - -class TestVectorHashMemoryServiceValidation: - """验证 VectorHashMemoryService (TiM) 实现""" - - @pytest.fixture - def service(self): - """创建测试用服务实例，使用唯一 collection 名称避免测试干扰""" - from sage.middleware.components.sage_mem.services.vector_hash_memory_service import ( - VectorHashMemoryService, - ) - - unique_name = f"test_validation_vhm_{uuid.uuid4().hex[:8]}" - return VectorHashMemoryService(dim=128, nbits=64, collection_name=unique_name) - - def test_lsh_index_creation(self, service): - """验证 LSH 索引被正确创建""" - # 检查 collection 是否有 lsh_index - assert hasattr(service.collection, "index_info") - assert "lsh_index" in service.collection.index_info - - def test_lsh_insert_and_retrieve(self, service): - """验证 LSH 插入和检索""" - # 插入测试数据 - vector1 = np.random.randn(128).astype(np.float32) - vector2 = vector1 + np.random.randn(128).astype(np.float32) * 0.1 # 相似向量 - - entry_id1 = service.insert(entry="测试文本1", vector=vector1, metadata={"test": True}) - entry_id2 = service.insert(entry="测试文本2", vector=vector2, metadata={"test": True}) - - assert entry_id1 is not None - assert entry_id2 is not None - - # 检索 - results = service.retrieve(vector=vector1, top_k=5) - assert isinstance(results, list) - - def test_stats(self, service): - """验证统计信息""" - vector = np.random.randn(128).astype(np.float32) - service.insert(entry="测试", vector=vector) - - stats = service.get_stats() - assert "memory_count" in stats - assert stats["dim"] == 128 - assert stats["nbits"] == 64 - - -class TestHierarchicalMemoryServiceValidation: - """验证 HierarchicalMemoryService 实现 - - 覆盖：MemoryBank, MemGPT, MemoryOS, LD-Agent - """ - - @pytest.fixture - def service(self): - """创建测试用服务实例，使用唯一 collection 名称避免测试干扰""" - from sage.middleware.components.sage_mem.services.hierarchical_memory_service import ( - HierarchicalMemoryService, - ) - - unique_name = f"test_validation_hier_{uuid.uuid4().hex[:8]}" - return HierarchicalMemoryService( - tier_mode="three_tier", - tier_capacities={"stm": 5, "mtm": 20, "ltm": -1}, - migration_policy="overflow", - embedding_dim=128, - collection_name=unique_name, - ) - - def test_tier_structure_three_tier(self, service): - """验证三层 tier 结构正确初始化""" - assert service.tier_mode == "three_tier" - assert service.tier_names == ["stm", "mtm", "ltm"] - assert len(service.tier_capacities) >= 3 - - def test_tier_structure_two_tier(self): - """验证双层 tier 结构""" - from sage.middleware.components.sage_mem.services.hierarchical_memory_service import ( - HierarchicalMemoryService, - ) - - unique_name = f"test_validation_hier_two_{uuid.uuid4().hex[:8]}" - service = HierarchicalMemoryService( - tier_mode="two_tier", - collection_name=unique_name, - embedding_dim=128, - ) - assert service.tier_names == ["stm", "ltm"] - - def test_tier_structure_functional(self): - """验证功能分区 tier 结构""" - from sage.middleware.components.sage_mem.services.hierarchical_memory_service import ( - HierarchicalMemoryService, - ) - - unique_name = f"test_validation_hier_func_{uuid.uuid4().hex[:8]}" - service = HierarchicalMemoryService( - tier_mode="functional", - collection_name=unique_name, - embedding_dim=128, - ) - assert service.tier_names == ["episodic", "semantic", "procedural"] - - def test_ebbinghaus_decay_calculation(self, service): - """验证 Ebbinghaus 遗忘曲线计算 (MemoryBank) - - 论文公式: R = e^(-t/S) - """ - # 插入一些测试数据 - for i in range(10): - vector = np.random.randn(128).astype(np.float32) - service.insert( - entry=f"测试条目{i}", - vector=vector, - metadata={"tier": "ltm", "strength": 1.0}, - ) - - # 调用 Ebbinghaus 衰减 - config = { - "retention_threshold": 0.5, - "retention_min": 5, - "time_unit": 86400, - } - to_delete = service._apply_ebbinghaus_decay("ltm", config) - - # 验证返回的是 ID 列表 - assert isinstance(to_delete, list) - - def test_heat_score_calculation(self, service): - """验证 Heat Score 计算 (MemoryOS)""" - entry = { - "metadata": { - "visit_count": 5, - "interaction_depth": 3, - "last_access_time": time.time() - 3600, # 1小时前 - } - } - - heat = service._calculate_heat_score(entry) - - # Heat score 应该在 0-1 之间 - assert 0.0 <= heat <= 1.0 - - def test_fscore_calculation(self, service): - """验证 Fscore 计算 (MemoryOS)""" - new_page = { - "embedding": np.random.randn(128).astype(np.float32).tolist(), - "metadata": {"keywords": ["test", "memory"], "timestamp": time.time()}, - } - segment = { - "centroid_embedding": np.random.randn(128).astype(np.float32).tolist(), - "keywords": ["test", "storage"], - "last_update_time": time.time() - 3600, - } - - fscore = service._calculate_fscore(new_page, segment) - - # Fscore 应该在 0-1 之间 - assert 0.0 <= fscore <= 1.0 - - def test_migrate_by_heat(self, service): - """验证基于 heat 的迁移 (MemoryOS)""" - # 插入数据 - for i in range(5): - vector = np.random.randn(128).astype(np.float32) - service.insert( - entry=f"测试{i}", - vector=vector, - metadata={ - "visit_count": i * 2, - "interaction_depth": i, - "last_access_time": time.time() - i * 3600, - }, - ) - - # 执行 heat 迁移 - config = {"heat_threshold": 0.7, "cold_threshold": 0.3} - stats = service._migrate_by_heat(config) - - # 验证返回统计信息 - assert "upgraded" in stats - assert "downgraded" in stats - assert "deleted" in stats - - def test_overflow_migration(self, service): - """验证溢出迁移""" - # 插入超过 STM 容量的条目 - for i in range(7): - vector = np.random.randn(128).astype(np.float32) - service.insert(entry=f"条目{i}", vector=vector, metadata={}) - - # 检查 tier 分布 - stats = service.get_tier_stats() - total = sum(s["count"] for s in stats.values()) - assert total >= 7 # 所有数据都应该被保存 - - def test_optimize_with_ebbinghaus(self, service): - """验证 optimize() 支持 ebbinghaus 遗忘""" - # 插入数据 - for i in range(5): - vector = np.random.randn(128).astype(np.float32) - service.insert(entry=f"测试{i}", vector=vector) - - # 调用 optimize - result = service.optimize( - trigger="forgetting", - config={"decay_type": "ebbinghaus", "retention_threshold": 0.3}, - ) - - assert result["success"] is True - assert "forgotten" in result - - def test_update_access_stats(self, service): - """验证访问统计更新""" - vector = np.random.randn(128).astype(np.float32) - entry_id = service.insert(entry="测试", vector=vector) - - # 更新访问统计 - result = service.update_access_stats(entry_id, interaction_depth=2) - assert result is True - - # 验证元数据已更新 - meta = service.collection.get_metadata(entry_id) - assert meta["visit_count"] >= 1 - - -class TestGraphMemoryServiceValidation: - """验证 GraphMemoryService 实现 - - 覆盖：A-Mem, HippoRAG, HippoRAG2 - """ - - @pytest.fixture - def knowledge_graph_service(self): - """创建 knowledge_graph 模式服务，使用唯一 collection 名称""" - from sage.middleware.components.sage_mem.services.graph_memory_service import ( - GraphMemoryService, - ) - - unique_name = f"test_validation_kg_{uuid.uuid4().hex[:8]}" - return GraphMemoryService( - collection_name=unique_name, - graph_type="knowledge_graph", - node_embedding_dim=128, - synonymy_threshold=0.7, - ppr_depth=2, - ppr_damping=0.85, - ) - - @pytest.fixture - def link_graph_service(self): - """创建 link_graph 模式服务 (A-Mem)，使用唯一 collection 名称""" - from sage.middleware.components.sage_mem.services.graph_memory_service import ( - GraphMemoryService, - ) - - unique_name = f"test_validation_lg_{uuid.uuid4().hex[:8]}" - return GraphMemoryService( - collection_name=unique_name, - graph_type="link_graph", - node_embedding_dim=128, - link_policy="bidirectional", - max_links_per_node=50, - ) - - def test_knowledge_graph_structure(self, knowledge_graph_service): - """验证 knowledge_graph 结构""" - service = knowledge_graph_service - assert service.graph_type == "knowledge_graph" - - # 插入带三元组的数据 - vector = np.random.randn(128).astype(np.float32) - node_id = service.insert( - entry="北京是中国的首都", - vector=vector, - metadata={"triples": [("北京", "是首都", "中国")]}, - ) - - assert node_id is not None - - def test_link_graph_structure(self, link_graph_service): - """验证 link_graph 结构 (A-Mem)""" - service = link_graph_service - assert service.graph_type == "link_graph" - assert service.link_policy == "bidirectional" - - def test_auto_link_generation(self, link_graph_service): - """验证 auto_link 自动链接生成 (A-Mem)""" - service = link_graph_service - - # 插入相似的节点 - base_vector = np.random.randn(128).astype(np.float32) - service.insert(entry="北京是中国的首都", vector=base_vector, metadata={}) - - similar_vector = base_vector + np.random.randn(128).astype(np.float32) * 0.1 - node_id2 = service.insert(entry="北京是首都城市", vector=similar_vector, metadata={}) - - # 调用 auto_link - config = {"max_auto_links": 5, "similarity_threshold": 0.5, "edge_weight": 1.0} - linked = service._create_auto_links(node_id2, config) - - # 验证返回链接的节点列表 - assert isinstance(linked, list) - - def test_memory_evolution(self, link_graph_service): - """验证记忆演化 (A-Mem)""" - service = link_graph_service - - # 插入节点 - vector1 = np.random.randn(128).astype(np.float32) - node_id1 = service.insert( - entry="测试节点1", vector=vector1, metadata={"keywords": ["test", "node"]} - ) - - vector2 = vector1 + np.random.randn(128).astype(np.float32) * 0.1 - node_id2 = service.insert( - entry="测试节点2", vector=vector2, metadata={"keywords": ["test", "memory"]} - ) - - # 先建立链接 - service.collection.add_edge(node_id1, node_id2, weight=1.0, index_name=service.index_name) - service.collection.add_edge(node_id2, node_id1, weight=1.0, index_name=service.index_name) - - # 调用 memory_evolution - config = {"evolution_threshold": 0.5, "merge_keywords": True} - stats = service._memory_evolution(node_id2, config) - - assert "updated_nodes" in stats - assert "merged_keywords" in stats - - def test_synonym_edges_batch(self, knowledge_graph_service): - """验证同义词边批量建立 (HippoRAG)""" - service = knowledge_graph_service - - # 插入多个节点 - for i in range(5): - vector = np.random.randn(128).astype(np.float32) - service.insert(entry=f"节点{i}", vector=vector, metadata={}) - - # 调用 synonym edge 建立 - config = {"synonymy_threshold": 0.5} - edges_created = service._build_synonym_edges_batch(config) - - # 验证返回创建的边数量 - assert isinstance(edges_created, int) - - def test_ppr_retrieve(self, knowledge_graph_service): - """验证 PPR 检索 (HippoRAG)""" - service = knowledge_graph_service - - # 插入节点并建立边 - vectors = [np.random.randn(128).astype(np.float32) for _ in range(5)] - node_ids = [] - for i, v in enumerate(vectors): - nid = service.insert(entry=f"节点{i}", vector=v, metadata={}) - node_ids.append(nid) - - # 建立一些边 - for i in range(len(node_ids) - 1): - service.add_edge(node_ids[i], node_ids[i + 1], weight=1.0) - - # 调用 PPR 检索 - results = service.ppr_retrieve(seed_nodes=node_ids[:2], alpha=0.15, max_iter=50, top_k=5) - - # 验证结果格式 - assert isinstance(results, list) - - def test_hipporag2_enhanced_rerank(self): - """验证 HippoRAG2 增强重排序""" - from sage.middleware.components.sage_mem.services.graph_memory_service import ( - GraphMemoryService, - ) - - unique_name = f"test_validation_hipporag2_{uuid.uuid4().hex[:8]}" - service = GraphMemoryService( - collection_name=unique_name, - graph_type="knowledge_graph", - node_embedding_dim=128, - ppr_depth=3, - ppr_damping=0.9, - enhanced_rerank=True, - ) - - assert service.ppr_depth == 3 - assert service.ppr_damping == 0.9 - assert service.enhanced_rerank is True - - def test_optimize_link_evolution(self, link_graph_service): - """验证 optimize() 支持 link_evolution""" - service = link_graph_service - - # 插入数据 - for i in range(3): - vector = np.random.randn(128).astype(np.float32) - service.insert(entry=f"节点{i}", vector=vector) - - # 调用 optimize - result = service.optimize( - trigger="link_evolution", - config={"link_policy": "auto_link", "similarity_threshold": 0.5}, - ) - - assert result["success"] is True - assert "edges_created" in result - - -class TestHybridMemoryServiceValidation: - """验证 HybridMemoryService 实现 - - 覆盖：Mem0 - """ - - @pytest.fixture - def service(self): - """创建测试用服务实例，使用唯一 collection 名称""" - from sage.middleware.components.sage_mem.services.hybrid_memory_service import ( - HybridMemoryService, - ) - - unique_name = f"test_validation_hybrid_{uuid.uuid4().hex[:8]}" - return HybridMemoryService( - indexes=[ - {"name": "semantic", "type": "vdb", "dim": 128}, - {"name": "keyword", "type": "kv", "index_type": "bm25s"}, - ], - fusion_strategy="rrf", - rrf_k=60, - collection_name=unique_name, - ) - - @pytest.fixture - def graph_enabled_service(self): - """创建启用图索引的服务 (Mem0^g)，使用唯一 collection 名称""" - from sage.middleware.components.sage_mem.services.hybrid_memory_service import ( - HybridMemoryService, - ) - - unique_name = f"test_validation_hybrid_graph_{uuid.uuid4().hex[:8]}" - return HybridMemoryService( - graph_enabled=True, - collection_name=unique_name, - ) - - def test_multi_index_creation(self, service): - """验证多索引创建""" - assert len(service.index_configs) == 2 - assert service.fusion_strategy == "rrf" - - def test_insert_to_multiple_indexes(self, service): - """验证插入到多个索引""" - vector = np.random.randn(128).astype(np.float32) - entry_id = service.insert(entry="测试文本", vector=vector, metadata={"test": True}) - - assert entry_id is not None - - def test_retrieve_multi_fusion(self, service): - """验证多路检索融合""" - # 插入数据 - for i in range(5): - vector = np.random.randn(128).astype(np.float32) - service.insert(entry=f"测试文本{i}", vector=vector) - - # 检索 - query_vector = np.random.randn(128).astype(np.float32) - results = service.retrieve(query="测试", vector=query_vector, top_k=3) - - assert isinstance(results, list) - - def test_graph_enabled_mode(self, graph_enabled_service): - """验证 Mem0^g 图记忆模式""" - service = graph_enabled_service - assert service.graph_enabled is True - - def test_active_insert_mode(self, service): - """验证主动插入模式""" - vector = np.random.randn(128).astype(np.float32) - entry_id = service.insert( - entry="测试文本", - vector=vector, - insert_mode="active", - insert_params={"target_indexes": ["semantic"], "priority": 1}, - ) - - assert entry_id is not None - - -class TestShortTermMemoryServiceValidation: - """验证 ShortTermMemoryService 实现 - - 覆盖：SCM - """ - - @pytest.fixture - def service(self): - """创建测试用服务实例，使用唯一 collection 名称""" - from sage.middleware.components.sage_mem.services.short_term_memory_service import ( - ShortTermMemoryService, - ) - - unique_name = f"test_validation_stm_{uuid.uuid4().hex[:8]}" - return ShortTermMemoryService(max_dialog=5, collection_name=unique_name, embedding_dim=128) - - def test_window_size_limit(self, service): - """验证窗口大小限制""" - assert service.max_dialog == 5 - - # 插入超过窗口大小的条目 - for i in range(10): - vector = np.random.randn(128).astype(np.float32) - service.insert(entry=f"对话{i}", vector=vector) - - # 验证只保留最近 5 条 - stats = service.get_stats() - assert stats["memory_count"] <= 5 - - def test_fifo_eviction(self, service): - """验证 FIFO 淘汰""" - # 插入并记录 ID - ids = [] - for i in range(7): - vector = np.random.randn(128).astype(np.float32) - entry_id = service.insert(entry=f"对话{i}", vector=vector) - ids.append(entry_id) - - # 最早的 ID 应该被淘汰 - # 检查最后插入的 ID 是否仍在 - assert ids[-1] in service._id_set - # 最早的应该被淘汰 - assert ids[0] not in service._id_set - - def test_recency_ordering(self, service): - """验证时间顺序排序""" - # 插入数据 - for i in range(3): - vector = np.random.randn(128).astype(np.float32) - service.insert(entry=f"对话{i}", vector=vector) - time.sleep(0.01) # 确保时间戳不同 - - # 检索（不使用向量，返回按时间排序的结果） - results = service.retrieve(top_k=3) - - assert len(results) == 3 - # 结果应该按时间倒序（最新的在后） - timestamps = [r["metadata"].get("timestamp", 0) for r in results] - assert timestamps == sorted(timestamps) # 递增顺序 - - def test_semantic_retrieval(self, service): - """验证语义检索""" - # 插入数据 - for i in range(3): - vector = np.random.randn(128).astype(np.float32) - service.insert(entry=f"对话{i}", vector=vector) - - # 使用向量检索 - query_vector = np.random.randn(128).astype(np.float32) - results = service.retrieve(vector=query_vector, top_k=2) - - assert isinstance(results, list) - - def test_clear(self, service): - """验证清空功能""" - # 插入数据 - for i in range(3): - vector = np.random.randn(128).astype(np.float32) - service.insert(entry=f"对话{i}", vector=vector) - - # 清空 - result = service.clear() - assert result is True - - # 验证已清空 - stats = service.get_stats() - assert stats["memory_count"] == 0 - - -class TestServiceOptimizeInterface: - """验证所有服务的 optimize() 接口一致性""" - - def test_hierarchical_optimize_interface(self): - """验证 HierarchicalMemoryService.optimize() 接口""" - from sage.middleware.components.sage_mem.services.hierarchical_memory_service import ( - HierarchicalMemoryService, - ) - - unique_name = f"test_opt_hier_{uuid.uuid4().hex[:8]}" - service = HierarchicalMemoryService(collection_name=unique_name, embedding_dim=128) - - result = service.optimize(trigger="auto", config={}) - - assert isinstance(result, dict) - assert "success" in result - assert "trigger" in result - - def test_graph_optimize_interface(self): - """验证 GraphMemoryService.optimize() 接口""" - from sage.middleware.components.sage_mem.services.graph_memory_service import ( - GraphMemoryService, - ) - - unique_name = f"test_opt_graph_{uuid.uuid4().hex[:8]}" - service = GraphMemoryService(collection_name=unique_name, node_embedding_dim=128) - - result = service.optimize(trigger="auto", config={}) - - assert isinstance(result, dict) - assert "success" in result - assert "trigger" in result - - def test_hybrid_optimize_interface(self): - """验证 HybridMemoryService.optimize() 接口""" - from sage.middleware.components.sage_mem.services.hybrid_memory_service import ( - HybridMemoryService, - ) - - unique_name = f"test_opt_hybrid_{uuid.uuid4().hex[:8]}" - service = HybridMemoryService( - collection_name=unique_name, - indexes=[{"name": "semantic", "type": "vdb", "dim": 128}], - ) - - result = service.optimize(trigger="auto") - - assert isinstance(result, dict) - assert "success" in result - assert "trigger" in result diff --git a/packages/sage-middleware/tests/unit/components/sage_tsdb/algorithms/test_out_of_order_join.py b/packages/sage-middleware/tests/unit/components/sage_tsdb/algorithms/test_out_of_order_join.py deleted file mode 100644 index 75dedcb84c..0000000000 --- a/packages/sage-middleware/tests/unit/components/sage_tsdb/algorithms/test_out_of_order_join.py +++ /dev/null @@ -1,463 +0,0 @@ -""" -Tests for OutOfOrderStreamJoin Algorithm - -Comprehensive test coverage for out-of-order stream join functionality. -""" - -import pytest - -from sage.middleware.components.sage_tsdb.python.algorithms.out_of_order_join import ( - JoinConfig, - OutOfOrderStreamJoin, - StreamBuffer, -) -from sage.middleware.components.sage_tsdb.python.sage_tsdb import TimeSeriesData - - -@pytest.fixture -def sample_left_data(): - """Sample left stream data""" - return [ - TimeSeriesData(timestamp=1000, value=10.0, tags={"key": "A"}), - TimeSeriesData(timestamp=2000, value=20.0, tags={"key": "B"}), - TimeSeriesData(timestamp=3000, value=30.0, tags={"key": "A"}), - ] - - -@pytest.fixture -def sample_right_data(): - """Sample right stream data""" - return [ - TimeSeriesData(timestamp=1500, value=15.0, tags={"key": "A"}), - TimeSeriesData(timestamp=2500, value=25.0, tags={"key": "B"}), - TimeSeriesData(timestamp=3500, value=35.0, tags={"key": "C"}), - ] - - -class TestStreamBuffer: - """Test StreamBuffer functionality""" - - def test_buffer_initialization(self): - """Test buffer initialization""" - buffer = StreamBuffer(max_delay=5000) - assert buffer.max_delay == 5000 - assert buffer.buffer == [] - assert buffer.watermark == 0 - - def test_add_single_data(self): - """Test adding single data point""" - buffer = StreamBuffer(max_delay=1000) - data = TimeSeriesData(timestamp=5000, value=10.0) - - buffer.add(data) - - assert buffer.size() == 1 - assert buffer.watermark == 4000 # 5000 - 1000 - - def test_add_batch(self): - """Test adding batch of data""" - buffer = StreamBuffer(max_delay=1000) - data = [ - TimeSeriesData(timestamp=1000, value=10.0), - TimeSeriesData(timestamp=2000, value=20.0), - TimeSeriesData(timestamp=3000, value=30.0), - ] - - buffer.add_batch(data) - - assert buffer.size() == 3 - assert buffer.watermark == 2000 # 3000 - 1000 - - def test_buffer_sorts_data(self): - """Test that buffer sorts data by timestamp""" - buffer = StreamBuffer(max_delay=1000) - # Add out of order - buffer.add(TimeSeriesData(timestamp=3000, value=30.0)) - buffer.add(TimeSeriesData(timestamp=1000, value=10.0)) - buffer.add(TimeSeriesData(timestamp=2000, value=20.0)) - - # Buffer should be sorted - assert buffer.buffer[0].timestamp == 1000 - assert buffer.buffer[1].timestamp == 2000 - assert buffer.buffer[2].timestamp == 3000 - - def test_get_ready_data_empty_buffer(self): - """Test getting ready data from empty buffer""" - buffer = StreamBuffer(max_delay=1000) - ready = buffer.get_ready_data() - - assert ready == [] - - def test_get_ready_data_nothing_ready(self): - """Test get_ready_data when nothing is ready""" - buffer = StreamBuffer(max_delay=1000) - buffer.add(TimeSeriesData(timestamp=5000, value=10.0)) - - # Watermark is 4000, data at 5000 is not ready - ready = buffer.get_ready_data() - - assert ready == [] - assert buffer.size() == 1 - - def test_get_ready_data_some_ready(self): - """Test get_ready_data when some data is ready""" - buffer = StreamBuffer(max_delay=1000) - buffer.add_batch( - [ - TimeSeriesData(timestamp=1000, value=10.0), - TimeSeriesData(timestamp=2000, value=20.0), - TimeSeriesData(timestamp=5000, value=50.0), - ] - ) - - # Watermark is 4000 (5000-1000) - ready = buffer.get_ready_data() - - assert len(ready) == 2 - assert ready[0].timestamp == 1000 - assert ready[1].timestamp == 2000 - assert buffer.size() == 1 # Only one left in buffer - - def test_watermark_update(self): - """Test watermark updates correctly""" - buffer = StreamBuffer(max_delay=2000) - - buffer.add(TimeSeriesData(timestamp=5000, value=10.0)) - assert buffer.watermark == 3000 - - buffer.add(TimeSeriesData(timestamp=8000, value=20.0)) - assert buffer.watermark == 6000 - - def test_buffer_size(self): - """Test buffer size tracking""" - buffer = StreamBuffer(max_delay=1000) - - assert buffer.size() == 0 - - buffer.add(TimeSeriesData(timestamp=1000, value=10.0)) - assert buffer.size() == 1 - - buffer.add_batch( - [ - TimeSeriesData(timestamp=2000, value=20.0), - TimeSeriesData(timestamp=3000, value=30.0), - ] - ) - assert buffer.size() == 3 - - -class TestOutOfOrderStreamJoin: - """Test OutOfOrderStreamJoin algorithm""" - - def test_initialization_default_config(self): - """Test initialization with default config""" - join = OutOfOrderStreamJoin() - - assert join.window_size == 10000 - assert join.max_delay == 5000 - assert join.join_key is None - assert join.join_predicate is None - - def test_initialization_custom_config(self): - """Test initialization with custom config""" - config = { - "window_size": 20000, - "max_delay": 10000, - "join_key": "sensor_id", - } - join = OutOfOrderStreamJoin(config) - - assert join.window_size == 20000 - assert join.max_delay == 10000 - assert join.join_key == "sensor_id" - - def test_add_left_stream(self, sample_left_data): - """Test adding data to left stream""" - join = OutOfOrderStreamJoin() - join.add_left_stream(sample_left_data) - - assert join.left_buffer.size() == 3 - - def test_add_right_stream(self, sample_right_data): - """Test adding data to right stream""" - join = OutOfOrderStreamJoin() - join.add_right_stream(sample_right_data) - - assert join.right_buffer.size() == 3 - - def test_process_with_streams(self, sample_left_data, sample_right_data): - """Test process with left and right streams""" - config = {"window_size": 2000, "max_delay": 1000} - join = OutOfOrderStreamJoin(config) - - result = join.process(left_stream=sample_left_data, right_stream=sample_right_data) - - # Result should be a list of tuples - assert isinstance(result, list) - assert len(result) > 0 - - def test_nested_loop_join(self): - """Test nested loop join without join key""" - join = OutOfOrderStreamJoin({"window_size": 1000, "max_delay": 500}) - - left = [TimeSeriesData(timestamp=1000, value=10.0)] - right = [TimeSeriesData(timestamp=1500, value=15.0)] - - result = join._nested_loop_join(left, right) - - # Should join because within window (|1000-1500| = 500 <= 1000) - assert len(result) == 1 - assert result[0][0].timestamp == 1000 - assert result[0][1].timestamp == 1500 - - def test_nested_loop_join_outside_window(self): - """Test nested loop join with data outside window""" - join = OutOfOrderStreamJoin({"window_size": 200, "max_delay": 100}) - - left = [TimeSeriesData(timestamp=1000, value=10.0)] - right = [TimeSeriesData(timestamp=2000, value=20.0)] - - result = join._nested_loop_join(left, right) - - # Should not join (|1000-2000| = 1000 > 200) - assert len(result) == 0 - - def test_hash_join_with_key(self): - """Test hash join with join key""" - config = {"window_size": 2000, "max_delay": 1000, "join_key": "sensor"} - join = OutOfOrderStreamJoin(config) - - left = [ - TimeSeriesData(timestamp=1000, value=10.0, tags={"sensor": "A"}), - TimeSeriesData(timestamp=2000, value=20.0, tags={"sensor": "B"}), - ] - right = [ - TimeSeriesData(timestamp=1500, value=15.0, tags={"sensor": "A"}), - TimeSeriesData(timestamp=2500, value=25.0, tags={"sensor": "C"}), - ] - - result = join._hash_join(left, right) - - # Should only join sensor A (matching key and within window) - assert len(result) == 1 - assert result[0][0].tags["sensor"] == "A" - assert result[0][1].tags["sensor"] == "A" - - def test_hash_join_no_matching_keys(self): - """Test hash join with no matching keys""" - config = {"window_size": 2000, "max_delay": 1000, "join_key": "sensor"} - join = OutOfOrderStreamJoin(config) - - left = [TimeSeriesData(timestamp=1000, value=10.0, tags={"sensor": "A"})] - right = [TimeSeriesData(timestamp=1500, value=15.0, tags={"sensor": "B"})] - - result = join._hash_join(left, right) - - assert len(result) == 0 - - def test_join_with_custom_predicate(self): - """Test join with custom predicate function""" - - def custom_pred(left, right): - return left.value < right.value - - config = { - "window_size": 2000, - "max_delay": 1000, - "join_predicate": custom_pred, - } - join = OutOfOrderStreamJoin(config) - - left = [TimeSeriesData(timestamp=1000, value=10.0)] - right = [ - TimeSeriesData(timestamp=1500, value=15.0), # 10 < 15, should join - TimeSeriesData(timestamp=1600, value=5.0), # 10 < 5 is False - ] - - result = join._nested_loop_join(left, right) - - # Only first pair should match predicate - assert len(result) == 1 - assert result[0][1].value == 15.0 - - def test_stats_tracking(self): - """Test statistics tracking""" - config = {"window_size": 2000, "max_delay": 1000} - join = OutOfOrderStreamJoin(config) - - left = [TimeSeriesData(timestamp=1000, value=10.0)] - right = [TimeSeriesData(timestamp=1500, value=15.0)] - - join.process(left_stream=left, right_stream=right) - - stats = join.get_stats() - assert "total_joined" in stats - assert "left_buffer_size" in stats - assert "right_buffer_size" in stats - assert "left_watermark" in stats - assert "right_watermark" in stats - - def test_reset(self): - """Test reset functionality""" - join = OutOfOrderStreamJoin() - - # Add some data - join.add_left_stream([TimeSeriesData(timestamp=1000, value=10.0)]) - join.add_right_stream([TimeSeriesData(timestamp=1500, value=15.0)]) - join.stats["total_joined"] = 5 - - # Reset - join.reset() - - assert join.left_buffer.size() == 0 - assert join.right_buffer.size() == 0 - assert join.stats["total_joined"] == 0 - - def test_multiple_joins_same_key(self): - """Test multiple joins with same key""" - config = {"window_size": 3000, "max_delay": 1000, "join_key": "id"} - join = OutOfOrderStreamJoin(config) - - left = [ - TimeSeriesData(timestamp=1000, value=10.0, tags={"id": "X"}), - TimeSeriesData(timestamp=2000, value=20.0, tags={"id": "X"}), - ] - right = [TimeSeriesData(timestamp=1500, value=15.0, tags={"id": "X"})] - - result = join._hash_join(left, right) - - # Both left entries should join with the right entry - assert len(result) == 2 - - def test_empty_streams(self): - """Test processing with empty streams""" - join = OutOfOrderStreamJoin() - - result = join.process(left_stream=[], right_stream=[]) - - assert result == [] - - def test_one_stream_empty(self): - """Test processing with one empty stream""" - join = OutOfOrderStreamJoin() - - left = [TimeSeriesData(timestamp=1000, value=10.0)] - result = join.process(left_stream=left, right_stream=[]) - - # No joins possible - assert result == [] - - -class TestJoinConfig: - """Test JoinConfig dataclass""" - - def test_join_config_creation(self): - """Test JoinConfig creation""" - config = JoinConfig(window_size=10000, max_delay=5000) - - assert config.window_size == 10000 - assert config.max_delay == 5000 - assert config.join_key is None - assert config.join_predicate is None - - def test_join_config_with_key(self): - """Test JoinConfig with join key""" - config = JoinConfig(window_size=10000, max_delay=5000, join_key="sensor_id") - - assert config.join_key == "sensor_id" - - def test_join_config_with_predicate(self): - """Test JoinConfig with custom predicate""" - - def pred(left, right): - return True - - config = JoinConfig(window_size=10000, max_delay=5000, join_predicate=pred) - - assert config.join_predicate is pred - - -class TestEdgeCases: - """Test edge cases and boundary conditions""" - - def test_zero_window_size(self): - """Test join with zero window size""" - join = OutOfOrderStreamJoin({"window_size": 0, "max_delay": 1000}) - - left = [TimeSeriesData(timestamp=1000, value=10.0)] - right = [TimeSeriesData(timestamp=1000, value=15.0)] - - result = join._nested_loop_join(left, right) - - # Should join only exact timestamp matches - assert len(result) == 1 - - def test_very_large_window(self): - """Test join with very large window""" - join = OutOfOrderStreamJoin({"window_size": 1000000, "max_delay": 1000}) - - left = [TimeSeriesData(timestamp=1000, value=10.0)] - right = [TimeSeriesData(timestamp=500000, value=15.0)] - - result = join._nested_loop_join(left, right) - - # Should join even with large time difference - assert len(result) == 1 - - def test_zero_max_delay(self): - """Test buffer with zero max delay""" - buffer = StreamBuffer(max_delay=0) - buffer.add(TimeSeriesData(timestamp=1000, value=10.0)) - - assert buffer.watermark == 1000 - ready = buffer.get_ready_data() - assert len(ready) == 1 - - def test_many_to_many_join(self): - """Test many-to-many join scenario""" - config = {"window_size": 5000, "max_delay": 1000, "join_key": "group"} - join = OutOfOrderStreamJoin(config) - - left = [ - TimeSeriesData(timestamp=1000, value=1.0, tags={"group": "A"}), - TimeSeriesData(timestamp=2000, value=2.0, tags={"group": "A"}), - TimeSeriesData(timestamp=3000, value=3.0, tags={"group": "A"}), - ] - right = [ - TimeSeriesData(timestamp=1500, value=1.5, tags={"group": "A"}), - TimeSeriesData(timestamp=2500, value=2.5, tags={"group": "A"}), - ] - - result = join._hash_join(left, right) - - # Each left should join with each right (within window) - # Expected: all combinations within window - assert len(result) >= 4 # At least some combinations - - def test_out_of_order_arrival(self): - """Test handling of out-of-order data arrival""" - buffer = StreamBuffer(max_delay=2000) - - # Add data out of order - buffer.add(TimeSeriesData(timestamp=5000, value=50.0)) - buffer.add(TimeSeriesData(timestamp=1000, value=10.0)) - buffer.add(TimeSeriesData(timestamp=3000, value=30.0)) - - # Buffer should sort and handle correctly - assert buffer.buffer[0].timestamp == 1000 - assert buffer.watermark == 3000 # 5000 - 2000 - - def test_duplicate_timestamps(self): - """Test handling of duplicate timestamps""" - join = OutOfOrderStreamJoin({"window_size": 1000, "max_delay": 500}) - - left = [ - TimeSeriesData(timestamp=1000, value=10.0), - TimeSeriesData(timestamp=1000, value=11.0), - ] - right = [TimeSeriesData(timestamp=1000, value=15.0)] - - result = join._nested_loop_join(left, right) - - # Both left entries should join - assert len(result) == 2 diff --git a/packages/sage-middleware/tests/unit/components/sage_tsdb/algorithms/test_window_aggregator.py b/packages/sage-middleware/tests/unit/components/sage_tsdb/algorithms/test_window_aggregator.py deleted file mode 100644 index e4bd7b21cd..0000000000 --- a/packages/sage-middleware/tests/unit/components/sage_tsdb/algorithms/test_window_aggregator.py +++ /dev/null @@ -1,686 +0,0 @@ -""" -Tests for WindowAggregator Algorithm - -Comprehensive test coverage for window-based aggregation functionality. -""" - -import pytest - -from sage.middleware.components.sage_tsdb.python.algorithms.window_aggregator import ( - WindowAggregator, - WindowConfig, - WindowType, -) -from sage.middleware.components.sage_tsdb.python.sage_tsdb import ( - AggregationType, - TimeSeriesData, -) - - -@pytest.fixture -def sample_data(): - """Sample time series data""" - return [ - TimeSeriesData(timestamp=1000, value=10.0, tags={"sensor": "A"}), - TimeSeriesData(timestamp=2000, value=20.0, tags={"sensor": "A"}), - TimeSeriesData(timestamp=3000, value=30.0, tags={"sensor": "A"}), - TimeSeriesData(timestamp=4000, value=40.0, tags={"sensor": "A"}), - TimeSeriesData(timestamp=5000, value=50.0, tags={"sensor": "A"}), - ] - - -class TestWindowAggregator: - """Test WindowAggregator initialization and basic functionality""" - - def test_initialization_default_config(self): - """Test initialization with default config""" - agg = WindowAggregator() - - assert agg.window_type == WindowType.TUMBLING - assert agg.window_size == 60000 - assert agg.aggregation == AggregationType.AVG - - def test_initialization_tumbling_window(self): - """Test initialization with tumbling window config""" - config = { - "window_type": "tumbling", - "window_size": 10000, - "aggregation": "sum", - } - agg = WindowAggregator(config) - - assert agg.window_type == WindowType.TUMBLING - assert agg.window_size == 10000 - assert agg.aggregation == AggregationType.SUM - - def test_initialization_sliding_window(self): - """Test initialization with sliding window config""" - config = { - "window_type": "sliding", - "window_size": 10000, - "slide_interval": 5000, - "aggregation": "avg", - } - agg = WindowAggregator(config) - - assert agg.window_type == WindowType.SLIDING - assert agg.slide_interval == 5000 - - def test_initialization_session_window(self): - """Test initialization with session window config""" - config = { - "window_type": "session", - "window_size": 10000, - "session_gap": 3000, - "aggregation": "max", - } - agg = WindowAggregator(config) - - assert agg.window_type == WindowType.SESSION - assert agg.session_gap == 3000 - assert agg.aggregation == AggregationType.MAX - - -class TestTumblingWindow: - """Test tumbling window aggregation""" - - def test_tumbling_window_basic(self, sample_data): - """Test basic tumbling window aggregation""" - config = { - "window_type": "tumbling", - "window_size": 2000, - "aggregation": "sum", - } - agg = WindowAggregator(config) - - result = agg.process(sample_data) - - # Data spans 1000-5000, window size 2000 - # Windows: [0-2000), [2000-4000), [4000-6000) - assert len(result) >= 2 - assert all(isinstance(r, TimeSeriesData) for r in result) - - def test_tumbling_window_sum(self): - """Test tumbling window with SUM aggregation""" - config = { - "window_type": "tumbling", - "window_size": 3000, - "aggregation": "sum", - } - agg = WindowAggregator(config) - - data = [ - TimeSeriesData(timestamp=1000, value=10.0), - TimeSeriesData(timestamp=2000, value=20.0), - TimeSeriesData(timestamp=4000, value=40.0), - ] - - result = agg.process(data) - - # First window [0-3000): 10 + 20 = 30 - # Second window [3000-6000): 40 - assert len(result) == 2 - assert result[0].value == 30.0 - assert result[1].value == 40.0 - - def test_tumbling_window_avg(self): - """Test tumbling window with AVG aggregation""" - config = { - "window_type": "tumbling", - "window_size": 2000, - "aggregation": "avg", - } - agg = WindowAggregator(config) - - data = [ - TimeSeriesData(timestamp=1000, value=10.0), - TimeSeriesData(timestamp=1500, value=20.0), - TimeSeriesData(timestamp=3000, value=30.0), - ] - - result = agg.process(data) - - # First window [0-2000): avg(10, 20) = 15 - # Second window [2000-4000): avg(30) = 30 - assert len(result) == 2 - assert result[0].value == 15.0 - assert result[1].value == 30.0 - - def test_tumbling_window_min_max(self): - """Test tumbling window with MIN/MAX aggregation""" - data = [ - TimeSeriesData(timestamp=1000, value=10.0), - TimeSeriesData(timestamp=1500, value=30.0), - TimeSeriesData(timestamp=2000, value=20.0), - ] - - # Test MIN - agg_min = WindowAggregator( - {"window_type": "tumbling", "window_size": 3000, "aggregation": "min"} - ) - result_min = agg_min.process(data) - assert result_min[0].value == 10.0 - - # Test MAX - agg_max = WindowAggregator( - {"window_type": "tumbling", "window_size": 3000, "aggregation": "max"} - ) - result_max = agg_max.process(data) - assert result_max[0].value == 30.0 - - def test_tumbling_window_count(self): - """Test tumbling window with COUNT aggregation""" - config = { - "window_type": "tumbling", - "window_size": 3000, - "aggregation": "count", - } - agg = WindowAggregator(config) - - data = [ - TimeSeriesData(timestamp=1000, value=10.0), - TimeSeriesData(timestamp=2000, value=20.0), - TimeSeriesData(timestamp=4000, value=40.0), - TimeSeriesData(timestamp=5000, value=50.0), - ] - - result = agg.process(data) - - # First window [0-3000): count=2 - # Second window [3000-6000): count=2 - assert result[0].value == 2 - assert result[1].value == 2 - - def test_tumbling_window_first_last(self): - """Test tumbling window with FIRST/LAST aggregation""" - data = [ - TimeSeriesData(timestamp=1000, value=10.0), - TimeSeriesData(timestamp=2000, value=20.0), - TimeSeriesData(timestamp=3000, value=30.0), - ] - - # Test FIRST - agg_first = WindowAggregator( - {"window_type": "tumbling", "window_size": 4000, "aggregation": "first"} - ) - result_first = agg_first.process(data) - assert result_first[0].value == 10.0 - - # Test LAST - agg_last = WindowAggregator( - {"window_type": "tumbling", "window_size": 4000, "aggregation": "last"} - ) - result_last = agg_last.process(data) - assert result_last[0].value == 30.0 - - def test_tumbling_window_empty_data(self): - """Test tumbling window with empty data""" - agg = WindowAggregator({"window_type": "tumbling", "window_size": 1000}) - - result = agg.process([]) - - assert result == [] - - def test_tumbling_window_preserves_tags(self): - """Test that tumbling window preserves tags""" - config = { - "window_type": "tumbling", - "window_size": 5000, - "aggregation": "sum", - } - agg = WindowAggregator(config) - - data = [ - TimeSeriesData(timestamp=1000, value=10.0, tags={"sensor": "A", "location": "room1"}), - TimeSeriesData(timestamp=2000, value=20.0, tags={"sensor": "A"}), - ] - - result = agg.process(data) - - # Tags should be merged - assert "sensor" in result[0].tags - assert "location" in result[0].tags - - -class TestSlidingWindow: - """Test sliding window aggregation""" - - def test_sliding_window_basic(self): - """Test basic sliding window aggregation""" - config = { - "window_type": "sliding", - "window_size": 3000, - "slide_interval": 1000, - "aggregation": "avg", - } - agg = WindowAggregator(config) - - data = [ - TimeSeriesData(timestamp=1000, value=10.0), - TimeSeriesData(timestamp=2000, value=20.0), - TimeSeriesData(timestamp=3000, value=30.0), - TimeSeriesData(timestamp=4000, value=40.0), - ] - - result = agg.process(data) - - # Sliding windows should create overlapping results - assert len(result) >= 3 - - def test_sliding_window_overlapping(self): - """Test sliding window creates overlapping windows""" - config = { - "window_type": "sliding", - "window_size": 2000, - "slide_interval": 1000, - "aggregation": "count", - } - agg = WindowAggregator(config) - - data = [ - TimeSeriesData(timestamp=500, value=5.0), - TimeSeriesData(timestamp=1500, value=15.0), - TimeSeriesData(timestamp=2500, value=25.0), - ] - - result = agg.process(data) - - # Windows: [0-2000), [1000-3000) - # First window: 500, 1500 (count=2) - # Second window: 1500, 2500 (count=2) - assert len(result) >= 2 - - def test_sliding_window_no_overlap(self): - """Test sliding window with slide_interval = window_size (no overlap)""" - config = { - "window_type": "sliding", - "window_size": 2000, - "slide_interval": 2000, - "aggregation": "sum", - } - agg = WindowAggregator(config) - - data = [ - TimeSeriesData(timestamp=1000, value=10.0), - TimeSeriesData(timestamp=3000, value=30.0), - ] - - result = agg.process(data) - - # Should behave like tumbling window - assert len(result) == 2 - - def test_sliding_window_empty_windows(self): - """Test sliding window with some empty windows""" - config = { - "window_type": "sliding", - "window_size": 1000, - "slide_interval": 1000, - "aggregation": "sum", - } - agg = WindowAggregator(config) - - data = [ - TimeSeriesData(timestamp=1000, value=10.0), - TimeSeriesData(timestamp=5000, value=50.0), - ] - - result = agg.process(data) - - # Some windows will be empty, only non-empty ones should appear - assert all(r.fields["window_size"] > 0 for r in result) - - -class TestSessionWindow: - """Test session window aggregation""" - - def test_session_window_basic(self): - """Test basic session window aggregation""" - config = { - "window_type": "session", - "session_gap": 2000, - "aggregation": "sum", - } - agg = WindowAggregator(config) - - data = [ - TimeSeriesData(timestamp=1000, value=10.0), - TimeSeriesData(timestamp=2000, value=20.0), # Within gap - TimeSeriesData(timestamp=5000, value=50.0), # New session - ] - - result = agg.process(data) - - # Should create 2 sessions - assert len(result) == 2 - assert result[0].value == 30.0 # First session: 10 + 20 - assert result[1].value == 50.0 # Second session: 50 - - def test_session_window_single_session(self): - """Test session window with single session""" - config = { - "window_type": "session", - "session_gap": 5000, - "aggregation": "count", - } - agg = WindowAggregator(config) - - data = [ - TimeSeriesData(timestamp=1000, value=10.0), - TimeSeriesData(timestamp=3000, value=30.0), - TimeSeriesData(timestamp=5000, value=50.0), - ] - - result = agg.process(data) - - # All within gap, should be one session - assert len(result) == 1 - assert result[0].value == 3 - - def test_session_window_multiple_sessions(self): - """Test session window with multiple sessions""" - config = { - "window_type": "session", - "session_gap": 1000, - "aggregation": "avg", - } - agg = WindowAggregator(config) - - data = [ - TimeSeriesData(timestamp=1000, value=10.0), - TimeSeriesData(timestamp=1500, value=20.0), # Session 1 - TimeSeriesData(timestamp=5000, value=50.0), # Session 2 - TimeSeriesData(timestamp=10000, value=100.0), # Session 3 - ] - - result = agg.process(data) - - assert len(result) == 3 - - def test_session_window_empty_data(self): - """Test session window with empty data""" - agg = WindowAggregator({"window_type": "session", "session_gap": 1000}) - - result = agg.process([]) - - assert result == [] - - -class TestAggregationFunctions: - """Test different aggregation functions""" - - def test_stddev_aggregation(self): - """Test standard deviation aggregation""" - config = { - "window_type": "tumbling", - "window_size": 5000, - "aggregation": "stddev", - } - agg = WindowAggregator(config) - - data = [ - TimeSeriesData(timestamp=1000, value=10.0), - TimeSeriesData(timestamp=2000, value=20.0), - TimeSeriesData(timestamp=3000, value=30.0), - ] - - result = agg.process(data) - - # Stddev should be calculated - assert result[0].value > 0 - - def test_aggregation_with_arrays(self): - """Test aggregation with array values""" - import numpy as np - - config = { - "window_type": "tumbling", - "window_size": 3000, - "aggregation": "sum", - } - agg = WindowAggregator(config) - - data = [ - TimeSeriesData(timestamp=1000, value=np.array([1.0, 2.0])), - TimeSeriesData(timestamp=2000, value=np.array([3.0, 4.0])), - ] - - result = agg.process(data) - - # Should flatten and sum: 1+2+3+4 = 10 - assert result[0].value == 10.0 - - -class TestWindowAlignment: - """Test window alignment and timestamp handling""" - - def test_align_to_window(self): - """Test timestamp alignment to window boundary""" - agg = WindowAggregator({"window_type": "tumbling", "window_size": 10000}) - - # Test various timestamps - assert agg._align_to_window(5000) == 0 - assert agg._align_to_window(15000) == 10000 - assert agg._align_to_window(25000) == 20000 - - def test_window_key_generation(self): - """Test window key generation""" - agg = WindowAggregator({"window_type": "tumbling", "window_size": 10000}) - - key1 = agg._get_window_key(5000, 0) - key2 = agg._get_window_key(15000, 0) - - assert key1 == 0 - assert key2 == 10000 - - def test_window_timestamp(self): - """Test that aggregated points use window start timestamp""" - config = { - "window_type": "tumbling", - "window_size": 10000, - "aggregation": "sum", - } - agg = WindowAggregator(config) - - data = [ - TimeSeriesData(timestamp=1000, value=10.0), - TimeSeriesData(timestamp=5000, value=50.0), - ] - - result = agg.process(data) - - # Window timestamp should be aligned - assert result[0].timestamp == 0 - - -class TestStatistics: - """Test statistics tracking""" - - def test_stats_tracking(self): - """Test statistics are tracked correctly""" - config = { - "window_type": "tumbling", - "window_size": 2000, - "aggregation": "sum", - } - agg = WindowAggregator(config) - - data = [ - TimeSeriesData(timestamp=1000, value=10.0), - TimeSeriesData(timestamp=3000, value=30.0), - ] - - agg.process(data) - - stats = agg.get_stats() - assert stats["windows_completed"] >= 1 - assert stats["data_points_processed"] == 2 - - def test_reset_statistics(self): - """Test reset clears statistics""" - agg = WindowAggregator() - - data = [TimeSeriesData(timestamp=1000, value=10.0)] - agg.process(data) - - # Reset - agg.reset() - - stats = agg.get_stats() - assert stats["windows_created"] == 0 - assert stats["windows_completed"] == 0 - assert stats["data_points_processed"] == 0 - - -class TestWindowConfig: - """Test WindowConfig dataclass""" - - def test_window_config_creation(self): - """Test WindowConfig creation""" - config = WindowConfig( - window_type=WindowType.TUMBLING, window_size=10000, aggregation=AggregationType.SUM - ) - - assert config.window_type == WindowType.TUMBLING - assert config.window_size == 10000 - assert config.aggregation == AggregationType.SUM - - def test_window_config_with_slide(self): - """Test WindowConfig with slide interval""" - config = WindowConfig( - window_type=WindowType.SLIDING, - window_size=10000, - slide_interval=5000, - ) - - assert config.slide_interval == 5000 - - def test_window_config_with_session_gap(self): - """Test WindowConfig with session gap""" - config = WindowConfig( - window_type=WindowType.SESSION, - window_size=10000, - session_gap=3000, - ) - - assert config.session_gap == 3000 - - -class TestEdgeCases: - """Test edge cases and boundary conditions""" - - def test_single_data_point(self): - """Test processing single data point""" - agg = WindowAggregator({"window_type": "tumbling", "window_size": 1000}) - - data = [TimeSeriesData(timestamp=1000, value=10.0)] - result = agg.process(data) - - assert len(result) == 1 - assert result[0].value == 10.0 - - def test_very_small_window(self): - """Test with very small window size""" - config = { - "window_type": "tumbling", - "window_size": 1, - "aggregation": "count", - } - agg = WindowAggregator(config) - - data = [ - TimeSeriesData(timestamp=1, value=1.0), - TimeSeriesData(timestamp=2, value=2.0), - ] - - result = agg.process(data) - - # Each point in its own window - assert len(result) >= 2 - - def test_very_large_window(self): - """Test with very large window size""" - config = { - "window_type": "tumbling", - "window_size": 1000000, - "aggregation": "sum", - } - agg = WindowAggregator(config) - - data = [ - TimeSeriesData(timestamp=1000, value=10.0), - TimeSeriesData(timestamp=50000, value=50.0), - ] - - result = agg.process(data) - - # All data in one window - assert len(result) == 1 - assert result[0].value == 60.0 - - def test_zero_values(self): - """Test aggregation with zero values""" - agg = WindowAggregator( - {"window_type": "tumbling", "window_size": 2000, "aggregation": "sum"} - ) - - data = [ - TimeSeriesData(timestamp=1000, value=0.0), - TimeSeriesData(timestamp=1500, value=0.0), - ] - - result = agg.process(data) - - assert result[0].value == 0.0 - - def test_negative_values(self): - """Test aggregation with negative values""" - agg = WindowAggregator( - {"window_type": "tumbling", "window_size": 3000, "aggregation": "sum"} - ) - - data = [ - TimeSeriesData(timestamp=1000, value=-10.0), - TimeSeriesData(timestamp=2000, value=20.0), - ] - - result = agg.process(data) - - assert result[0].value == 10.0 - - def test_window_fields_metadata(self): - """Test that window results include metadata fields""" - agg = WindowAggregator({"window_type": "tumbling", "window_size": 3000}) - - data = [ - TimeSeriesData(timestamp=1000, value=10.0), - TimeSeriesData(timestamp=2000, value=20.0), - ] - - result = agg.process(data) - - # Check metadata fields - assert "window_size" in result[0].fields - assert "aggregation" in result[0].fields - assert result[0].fields["window_size"] == 2 - - def test_unsorted_data_gets_sorted(self): - """Test that unsorted data gets sorted before processing""" - config = { - "window_type": "tumbling", - "window_size": 3000, - "aggregation": "first", - } - agg = WindowAggregator(config) - - # Provide unsorted data - data = [ - TimeSeriesData(timestamp=3000, value=30.0), - TimeSeriesData(timestamp=1000, value=10.0), - TimeSeriesData(timestamp=2000, value=20.0), - ] - - result = agg.process(data) - - # First value should be 10.0 (after sorting) - assert result[0].value == 10.0 diff --git a/packages/sage-platform/README.md b/packages/sage-platform/README.md deleted file mode 100644 index d0400fc8d0..0000000000 --- a/packages/sage-platform/README.md +++ /dev/null @@ -1,423 +0,0 @@ -# SAGE Platform - -> 平台服务层 (L2) - SAGE 基础设施抽象 - -[![Python Version](https://img.shields.io/badge/python-3.9%2B-blue.svg)](https://www.python.org/downloads/) -[![License](https://img.shields.io/badge/license-MIT-green.svg)](../../LICENSE) - -## 📋 Overview - -**SAGE Platform** 提供核心基础设施抽象，位于基础层（`sage-common`）和执行引擎（`sage-kernel`）之间。这个第二层平台服务提供： - -## 🧭 Governance / 团队协作制度 - -- `docs/governance/TEAM.md` - -- `docs/governance/MAINTAINERS.md` - -- `docs/governance/DEVELOPER_GUIDE.md` - -- `docs/governance/PR_CHECKLIST.md` - -- `docs/governance/SELF_HOSTED_RUNNER.md` - -- `docs/governance/TODO.md` - -- **队列抽象**：Python、Ray 和 RPC 队列的统一接口 - -- **存储抽象**：可插拔的键值存储后端 - -- **服务基类**：构建 SAGE 服务的基础 - -- **平台接口**：分布式系统的通用模式 - -该包使应用程序代码能够在本地和分布式执行模式之间无缝切换。 - -## ✨ Features - -- **多态队列**：Python Queue、Ray Queue 和 RPC Queue 的单一 API -- **可插拔存储**：内存、Redis 和自定义存储后端 -- **服务框架**：构建平台服务的基类 -- **类型安全**：完整的类型提示和运行时验证 -- **零开销**：本地执行的最小抽象成本 - -## 🚀 Quick Start - -### 使用队列 - -```python -from sage.platform.queue import PythonQueueDescriptor - -# 创建队列 -queue_desc = PythonQueueDescriptor(maxsize=100) -queue_desc.put("message") -item = queue_desc.get() -print(item) # "message" -``` - -### 使用存储 - -```python -from sage.platform.storage.kv_backend import DictKVBackend - -# 创建存储 -backend = DictKVBackend() -backend.set("user:123", {"name": "Alice", "age": 30}) -user = backend.get("user:123") -print(user["name"]) # "Alice" -``` - -### 创建服务 - -```python -from sage.platform.service import BaseService - -class MyService(BaseService): - def setup(self): - self.logger.info("Service starting...") - - def process(self, data): - return f"Processed: {data}" - - def teardown(self): - self.logger.info("Service stopped") - -service = MyService() -result = service.call("input data") -``` - -## 🚀 Installation - -```bash -# Basic installation -pip install isage-platform - -# Development installation -cd packages/sage-platform -pip install -e . -``` - -## 📦 Package Structure - -``` -sage-platform/ -├── src/ -│ └── sage/ -│ └── platform/ -│ ├── queue/ # Queue abstractions -│ ├── storage/ # Storage backends -│ └── service/ # Service base classes -├── tests/ -├── pyproject.toml -└── README.md -``` - -## 组件 - -### 🔄 队列 (`sage.platform.queue`) - -支持多种后端的多态队列描述符： - -```python -from sage.platform.queue import ( - BaseQueueDescriptor, - PythonQueueDescriptor, - RayQueueDescriptor, - RPCQueueDescriptor, -) - -# 创建 Ray 队列 -queue_desc = RayQueueDescriptor(maxsize=1000, queue_id="my_queue") -queue = queue_desc.queue_instance - -# 使用队列操作 -queue_desc.put(item) -item = queue_desc.get() -``` - -**特性**： - -- 延迟初始化 -- 序列化支持 -- 跨进程通信 -- 后端无关的 API - -### 💾 存储 (`sage.platform.storage`) - -键值存储抽象： - -```python -from sage.platform.storage.kv_backend import BaseKVBackend, DictKVBackend - -# 使用内存后端 -backend = DictKVBackend() -backend.set("key", "value") -value = backend.get("key") - - -# 使用自定义后端扩展 -class RedisKVBackend(BaseKVBackend): - # 实现抽象方法 - ... -``` - -**支持的操作**： - -- `get(key)`, `set(key, value)`, `delete(key)` -- `has(key)`, `clear()`, `get_all_keys()` -- 磁盘持久化：`store_data_to_disk()`, `load_data_to_memory()` - -### 🔌 服务 (`sage.platform.service`) - -SAGE 服务的基类： - -```python -from sage.platform.service import BaseService - - -class MyService(BaseService): - def __init__(self, config): - super().__init__(name="my_service") - self.config = config - - def process(self, request): - # 服务逻辑 - return response -``` - -## 📦 包结构 - -``` -sage-platform/ -├── src/ -│ └── sage/ -│ └── platform/ -│ ├── __init__.py -│ ├── queue/ # 队列抽象 -│ │ ├── base.py -│ │ ├── python_queue.py -│ │ ├── ray_queue.py -│ │ └── rpc_queue.py -│ ├── storage/ # 存储后端 -│ │ └── kv_backend.py -│ └── service/ # 服务基类 -│ └── base.py -├── tests/ -├── pyproject.toml -└── README.md -``` - -## 🚀 安装 - -### 基础安装 - -```bash -pip install isage-platform -``` - -### 开发安装 - -```bash -cd packages/sage-platform -pip install -e . -``` - -### 安装可选依赖 - -```bash -# 安装 Ray 支持（分布式队列） -pip install isage-platform[ray] - -# 安装 Redis 支持（分布式存储） -pip install isage-platform[redis] - -# 完整安装 -pip install isage-platform[all] -``` - -## 📖 快速开始 - -### 使用队列 - -```python -from sage.platform.queue import RayQueueDescriptor - -# 创建分布式队列 -queue_desc = RayQueueDescriptor(maxsize=1000, queue_id="my_distributed_queue") - -# 生产者 -queue_desc.put({"task": "process_data", "data": [1, 2, 3]}) - -# 消费者 -task = queue_desc.get() -print(f"处理中: {task}") - -# 检查队列状态 -print(f"队列大小: {queue_desc.qsize()}") -print(f"是否为空: {queue_desc.empty()}") -``` - -### 使用存储 - -```python -from sage.platform.storage.kv_backend import DictKVBackend - -# 创建存储后端 -storage = DictKVBackend() - -# 存储数据 -storage.set("user:1", {"name": "Alice", "age": 30}) -storage.set("user:2", {"name": "Bob", "age": 25}) - -# 检索数据 -user = storage.get("user:1") -print(f"用户: {user}") - -# 列出所有键 -keys = storage.get_all_keys() -print(f"所有键: {keys}") - -# 持久化到磁盘 -storage.store_data_to_disk("storage.pkl") -``` - -### 创建服务 - -```python -from sage.platform.service import BaseService - - -class DataProcessingService(BaseService): - def __init__(self, config): - super().__init__(name="data_processing") - self.config = config - self.initialize() - - def initialize(self): - """初始化服务资源""" - self.logger.info(f"初始化 {self.name}") - - def process(self, request): - """处理传入请求""" - self.logger.debug(f"处理请求: {request}") - result = self._transform_data(request["data"]) - return {"status": "success", "result": result} - - def _transform_data(self, data): - # 服务逻辑 - return [x * 2 for x in data] - - -# 使用服务 -service = DataProcessingService({"param": "value"}) -result = service.process({"data": [1, 2, 3]}) -print(result) # {"status": "success", "result": [2, 4, 6]} -``` - -## 🔧 Configuration - -服务可以通过环境变量或配置文件进行配置： - -```yaml -# platform_config.yaml -platform: - queue: - backend: ray # 或 python, rpc - maxsize: 1000 - - storage: - backend: dict # 或 redis - persist: true - save_path: ./storage -``` - -## 架构位置 - -``` -L1: sage-common ← 基础层 -L2: sage-platform ← 当前层 -L3: sage-kernel ← 执行引擎 - sage-libs -L4: sage-middleware ← 领域组件 -L5: sage-cli ← 命令行接口 - sage-tools ← 开发工具 -``` - -**独立仓库** (不在 SAGE 核心架构中): - -- sage-benchmark - 基准测试 -- sage-examples - 应用示例 -- sage-studio - Web UI -- sageLLM - LLM 推理引擎 - -## 设计原则 - -1. **通用基础设施**：平台服务不是 SAGE 特定的 -1. **后端无关**：支持多种实现（Python、Ray、Redis 等） -1. **最小依赖**：仅依赖 `sage-common` -1. **可扩展性**：易于添加新后端 - -## 为什么需要 L2 层？ - -原本这些抽象被分散在： - -- Queue Descriptor 在 `sage-kernel` (L3) ✖️ -- KV Backend 在 `sage-middleware` (L4) ✖️ -- BaseService 在 `sage-kernel` (L3) ✖️ - -这造成了： - -- 架构混乱（基础设施与业务逻辑混合） -- 依赖违规（L1 → L3） -- 有限的可重用性 - -通过创建 L2： - -- ✅ 清晰的关注点分离 -- ✅ 正确的依赖方向 -- ✅ 更好的组件间可重用性 - -## 🧪 Testing - -```bash -# 运行单元测试 -pytest tests/unit - -# 运行集成测试 -pytest tests/integration - -# 运行覆盖率测试 -pytest --cov=sage.platform --cov-report=html -``` - -## 📚 Documentation - -- **用户指南**：查看 [docs-public](https://intellistream.github.io/SAGE-Pub/guides/packages/sage-platform/) -- **API 参考**：查看包的文档字符串和类型提示 -- **架构**：查看 - [平台层设计](https://intellistream.github.io/SAGE-Pub/concepts/architecture/design-decisions/l2-platform-layer/) - -## 🤝 Contributing - -欢迎贡献！请查看 [CONTRIBUTING.md](../../CONTRIBUTING.md) 了解指南。 - -## 📄 License - -该项目采用 MIT 许可证 - 详情请查看 [LICENSE](../../LICENSE) 文件。 - -## 🔗 相关包 - -- **sage-common**：基础层 (L1) - 提供基本工具 -- **sage-kernel**：执行引擎 (L3) - 使用平台抽象 -- **sage-middleware**：服务层 (L4) - 使用存储和队列 -- **sage-libs**：库层 (L5) - 使用所有平台服务 - -## 📮 支持 - -- **文档**：https://intellistream.github.io/SAGE-Pub/ -- **问题反馈**：https://github.com/intellistream/SAGE/issues -- **讨论**：https://github.com/intellistream/SAGE/discussions - -______________________________________________________________________ - -**SAGE 框架的一部分** | [主仓库](https://github.com/intellistream/SAGE) diff --git a/packages/sage-platform/docs/governance/DEVELOPER_GUIDE.md b/packages/sage-platform/docs/governance/DEVELOPER_GUIDE.md deleted file mode 100644 index f93c0c7ba0..0000000000 --- a/packages/sage-platform/docs/governance/DEVELOPER_GUIDE.md +++ /dev/null @@ -1,88 +0,0 @@ -# 开发者指南与质量门槛（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/DEVELOPER_GUIDE.md`） - -本指南把“制度”落到“能执行的工程约束”。本包的所有代码改动必须遵守以下规则。 - -______________________________________________________________________ - -## 1. 架构守护机制（强制） - -### 1.1 依赖层级（禁止向上依赖 / 禁止循环依赖） - -SAGE 采用 L1-L5 分层。任何“向上依赖”都视为架构违规，必须阻断合入。 - -- L1：`sage-common` -- L2：`sage-platform` -- L3：`sage-kernel`, `sage-libs` -- L4：`sage-middleware` -- L5：`sage-cli`, `sage-tools` - -### 1.2 Libs vs Middleware 规则（强制） - -如果代码需要调用“向上能力”（例如 VectorDB/Memory/Refiner/外部服务/重后端/网络服务），它 **不是库**，必须放在 -`sage-middleware`（operators/components）。 - -> 迁移时不保留兼容 re-export shim：更新所有调用方，快速失败。 - -### 1.3 Control Plane-only（强制） - -LLM 引擎操作必须走 Control Plane，禁止直接启动引擎/直连端口。 - -______________________________________________________________________ - -## 2. 自动化检查（CI + pre-commit）（强制） - -### 2.1 安装一致性 - -- 必须使用 `./quickstart.sh --dev --yes` 安装开发环境。 -- 禁止手工 `pip install` 临时安装依赖（依赖必须写入 `pyproject.toml`）。 - -### 2.2 本地质量与测试 - -建议在仓库根目录执行： - -- `sage-dev quality check --all-files` -- `sage-dev project test --coverage` - -______________________________________________________________________ - -## 3. 强制规范（必须出现的质量门槛） - -### 3.1 Mock-First（强制） - -- CI 必须能在无 GPU 环境跑通核心测试。 -- 新增能力必须提供 Mock/CPU 路径，避免把“硬件”当作默认前提。 - -### 3.2 Fail-Fast（强制） - -- 禁止静默默认值、禁止隐式回退（fallback）。 -- 关键配置缺失必须明确报错（例如用 `os.environ["KEY"]` 或显式校验并抛异常）。 - -### 3.3 Protocol-First（按适用范围执行） - -当本包提供“公共接口/抽象/跨包契约”时： - -- 先定义接口/类型（Protocol/ABC/Schema） -- 再实现具体逻辑 -- 再补测试与示例 - -______________________________________________________________________ - -## 4. 新模块/新能力接入步骤（可执行） - -1. **定义范围**：确认属于本包；若需要向上能力，按规则提升到 `sage-middleware`。 -1. **接口优先**：先定义可复用接口（如适用）。 -1. **最小可跑**：提供无 GPU 的最小可跑实现（Mock/CPU）。 -1. **测试**：至少包含单元测试；关键路径补回归测试。 -1. **文档**：更新本包 README 与本包 governance 文档（必要时）。 -1. **质量门槛**：本地 `sage-dev quality` 与测试通过。 - -______________________________________________________________________ - -## 5. 常见错误与修复 - -- **依赖倒挂**：`sage-libs` 引入 `sage-middleware` → 必须拆分/上移实现。 -- **文档位置违规**：禁止在根 `docs/` 放 Markdown；包级文档只能放 `packages//docs/`。 -- **依赖未声明**：新增依赖未写入 `pyproject.toml` → 必须补齐声明并统一版本。 diff --git a/packages/sage-platform/docs/governance/MAINTAINERS.md b/packages/sage-platform/docs/governance/MAINTAINERS.md deleted file mode 100644 index 4e331ad6e0..0000000000 --- a/packages/sage-platform/docs/governance/MAINTAINERS.md +++ /dev/null @@ -1,126 +0,0 @@ -# 负责人制度（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/MAINTAINERS.md`） -- 负责人名单：TBD - -本文件定义“包级 Maintainer（负责人）”如何配置、如何工作、如何被量化验收。 - -______________________________________________________________________ - -## 1. 为什么需要 Maintainer - -SAGE 是 monorepo，多包协作、依赖层级明确。Maintainer 的职责不是“写最多代码”，而是保证： - -- 需求有人推进、问题有人闭环 -- 质量门槛有人守住（CI/架构/测试） -- 跨包集成有人对齐（尤其是接口层与中间件层） - -______________________________________________________________________ - -## 2. 负责人配置建议（按模块/子系统） - -### 2.1 推荐配置 - -- 每个包至少 1 名 Maintainer（主负责人）+ 1 名 Backup（备份负责人）。 -- 当包内包含多个子系统/高复杂度模块：建议拆分子 Maintainer。 - -### 2.2 负责人配置表（模板，必须维护） - -| 模块/目录 | 复杂度 | 关键技能 | 主 Maintainer | Backup | 上游依赖 | 下游使用方 | -| --------- | ------ | -------- | ------------- | ------ | -------- | ---------- | -| TBD | TBD | TBD | TBD | TBD | TBD | TBD | - -> 单仓语义适配：这里的“上游/下游”指本 monorepo 的其他包或本包内其他模块。 - -______________________________________________________________________ - -## 3. 负责人职责拆分（40/30/20/10 参考） - -可按实际调整，但必须覆盖全部职责： - -- **40% 开发推进**：Roadmap/Issue/PR 推动，里程碑拆解，阻塞清理。 -- **30% 质量把控**：CI 绿灯、测试策略、回归防护、性能/稳定性监控。 -- **20% 集成同步**：跨包接口对齐、发布/版本对齐、变更公告与迁移指引。 -- **10% 团队协作**：Review SLA、协作矩阵维护、争议处理与复核申请。 - -______________________________________________________________________ - -## 4. 要求与投入 - -- **最低投入**：每周固定时间段处理 Issue/PR（TBD：建议至少 2-4 小时/周）。 -- **Review SLA**：工作日 48h 内首次响应。 -- **透明度**：每月一次交付清单（含证据链接）。 - -______________________________________________________________________ - -## 5. 工作流程（开发 / 发布 / 集成同步） - -### 5.1 日常开发 - -- 所有需求/缺陷必须有 Issue。 -- PR 必须通过 `docs/governance/PR_CHECKLIST.md`。 -- 架构约束（依赖层级、libs vs middleware）违反时：必须阻断合入。 - -### 5.2 发布与版本对齐（如适用） - -- 发布前：测试与质量检查必须绿。 -- 变更：Breaking 变更必须有迁移指引与回滚预案。 -- 若涉及 PyPI 发布：使用仓库规定的发布工具链（例如 `wheelwright`），不得手工 twine/pip 临时发布。 - -### 5.3 跨包集成同步 - -- 上游接口变化：必须提前通知下游 maintainer，并提供迁移窗口。 -- 下游反馈：需要在 SLA 内回应并给出计划。 - -______________________________________________________________________ - -## 6. 协作矩阵（依赖/协作关系） - -### 6.1 Monorepo 依赖层级速查 - -> 目标：避免“向上依赖”与循环依赖。 - -| 层级 | 包（示例） | 允许依赖 | -| ---- | -------------------------- | ---------------- | -| L5 | `sage-cli`, `sage-tools` | L1-L4 | -| L4 | `sage-middleware` | L1-L3 | -| L3 | `sage-kernel`, `sage-libs` | L1-L2 | -| L2 | `sage-platform` | L1 | -| L1 | `sage-common` | 仅 stdlib/轻依赖 | - -### 6.2 本包协作矩阵（模板） - -| 依赖方/被依赖方 | 关系类型 | 接口/契约 | 变更通知机制 | Maintainer 对接 | -| --------------- | -------- | --------- | --------------------- | --------------- | -| TBD | 上游 | TBD | Issue/PR/Release Note | TBD | -| TBD | 下游 | TBD | Issue/PR/Release Note | TBD | - -______________________________________________________________________ - -## 7. 负责人名单（TBD） - -| 模块/目录 | 主 Maintainer | Backup | 交接人（如更换） | 生效日期 | -| --------- | ------------- | ------ | ---------------- | -------- | -| TBD | TBD | TBD | TBD | TBD | - -______________________________________________________________________ - -## 8. 考核指标（参考，可量化） - -- PR 首次响应 SLA 达标率 -- CI 红灯平均恢复时间（MTTR） -- 每月交付清单是否按时发布（含证据） -- Breaking 变更公告与迁移指引完备率 - -______________________________________________________________________ - -## 9. FAQ - -### Q1：Maintainer 是否必须是提交最多的人？ - -不是。Maintainer 的核心是“推进 + 质量 + 对齐”。提交多但不维护质量/不对齐下游同样不合格。 - -### Q2：如果包内没有发布流程怎么办？ - -仍需要“集成同步”：接口变更、跨包影响、迁移指引、回滚预案。 diff --git a/packages/sage-platform/docs/governance/PR_CHECKLIST.md b/packages/sage-platform/docs/governance/PR_CHECKLIST.md deleted file mode 100644 index 09d0f8c350..0000000000 --- a/packages/sage-platform/docs/governance/PR_CHECKLIST.md +++ /dev/null @@ -1,49 +0,0 @@ -# PR 审查清单（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/PR_CHECKLIST.md`） - -> Maintainer/Reviewer 需要用本清单进行可量化审查。未满足项必须在 PR 中解释或补齐。 - -______________________________________________________________________ - -## 1. 架构合规 - -- [ ] 不产生向上依赖/循环依赖（符合 L1-L5 分层） -- [ ] 遵守 libs vs middleware 规则（需要向上能力则放 middleware） -- [ ] LLM 相关操作不绕过 Control Plane - -## 2. Protocol-First（如适用） - -- [ ] 公共接口/类型先定义（Protocol/ABC/Schema），实现不“绑死”具体后端 -- [ ] 接口变更包含迁移说明（Breaking change 必须公告） - -## 3. Mock-First - -- [ ] 无 GPU 环境可跑（至少核心测试/最小路径） -- [ ] 新增外部依赖/服务调用有可测试替身（mock/fake） - -## 4. Fail-Fast - -- [ ] 无隐式回退（不写 try/except 吞异常、不用静默默认值掩盖缺失配置） -- [ ] 错误信息可定位（异常 message 清晰，必要时包含修复指引） - -## 5. 可观测性（按适用范围） - -- [ ] 关键路径有日志/指标/追踪点（至少日志） -- [ ] 不输出敏感信息（key/token/密码等） - -## 6. 配置验证 - -- [ ] 新增配置项有校验与文档说明 -- [ ] 端口不硬编码（如适用，使用统一端口配置） - -## 7. 测试覆盖 - -- [ ] 新增/修改逻辑有对应测试 -- [ ] 关键 bug fix 有回归测试 - -## 8. 代码质量 - -- [ ] `sage-dev quality check --all-files` 通过 -- [ ] `sage-dev project test --coverage` 通过（或至少本包相关测试通过） diff --git a/packages/sage-platform/docs/governance/SELF_HOSTED_RUNNER.md b/packages/sage-platform/docs/governance/SELF_HOSTED_RUNNER.md deleted file mode 100644 index 8c4746062a..0000000000 --- a/packages/sage-platform/docs/governance/SELF_HOSTED_RUNNER.md +++ /dev/null @@ -1,50 +0,0 @@ -# Self-hosted Runner 指南（SAGE - 可选） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/SELF_HOSTED_RUNNER.md`） - -> 本章节用于需要 GPU/重依赖/长耗时测试的包。若本包不需要，可保留但不强制启用。 - -______________________________________________________________________ - -## 1. 什么时候需要 self-hosted runner - -- GPU 相关测试（CUDA/Ascend） -- 需要特殊硬件或驱动版本 -- 需要访问内网资源（需安全评估） - -______________________________________________________________________ - -## 2. Runner 安装与标签（模板） - -- Runner 类型：GitHub Actions self-hosted -- 标签建议： - - `self-hosted` - - `linux` - - `x64` - - `gpu`（如适用） - - `cuda-`（如适用） - -______________________________________________________________________ - -## 3. 环境要求（模板） - -- OS：Linux（推荐） -- Python：与仓库要求一致（3.10+） -- 构建依赖：cmake / build-essential / BLAS 等（按仓库安装脚本） - -______________________________________________________________________ - -## 4. 安全注意事项（强制） - -- Runner 机器不可暴露敏感密钥；Secrets 由 GitHub 管理。 -- 禁止在日志中打印 token/key。 -- 对内网访问与数据集访问：必须走审批（TBD）。 - -______________________________________________________________________ - -## 5. 故障排查 - -- CI 失败先在本地 `./quickstart.sh --dev --yes` 复现 -- 检查磁盘/缓存（`.sage/`） -- 检查驱动/CUDA 版本（如适用） diff --git a/packages/sage-platform/docs/governance/TEAM.md b/packages/sage-platform/docs/governance/TEAM.md deleted file mode 100644 index c4a65c5d80..0000000000 --- a/packages/sage-platform/docs/governance/TEAM.md +++ /dev/null @@ -1,67 +0,0 @@ -# 仓库人员与评分制度（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/TEAM.md`） -- 名单维护：TBD - -本文件定义在 SAGE monorepo 内（以包为单位）的团队角色、负责人制度与评分/激励框架，用于保证： - -- 制度可执行：能落到 Issue/PR/CI/周更/月更。 -- 制度可验收：有明确证据链接与检查项。 -- 制度可追责：有最低交付门槛、透明度与争议处理机制。 - -> 约束：所有“姓名/负责人名单/具体人员”一律用 `TBD`，不得编造。 - -______________________________________________________________________ - -## 1. 角色与职责（强制条款） - -| 角色 | 核心职责 | 必须产出（可验收） | 证据类型（必须带链接） | -| ---------------------------- | -------------------------------------------------------- | ----------------------------------------------------------------------- | ------------------------------------------ | -| Maintainer（负责人） | Roadmap/Issue/PR 推进；质量门槛；集成/版本对齐；变更管理 | 每周 TODO 周更、PR Review SLA、CI 绿灯保障、重大变更公告、发布/集成记录 | Issue、PR、Release/Tag、CI 链接、周报/月报 | -| Engineering Core（工程骨干） | 关键功能交付；CI/测试维护；稳定性问题闭环 | 可交付功能/修复、测试/CI 改进、性能/稳定性指标改进 | PR、Issue、Benchmark 报告、CI 链接 | -| Research Core（科研骨干） | 原型验证/评测；研究到工程落地（必须可验收） | 可复现的实验/评测；可落地的工程化改造（或明确落地计划） | 评测报告、PR、Issue、实验配置/脚本链接 | - -______________________________________________________________________ - -## 2. 负责人必须做的事（强制条款） - -### 2.1 TODO 周更（强制） - -- 每周至少 1 次在本包的 `docs/governance/TODO.md` 更新：本周目标/完成/阻塞/下周计划/风险/需要协助。 -- TODO 必须引用证据（Issue/PR/CI/报告链接）。 - -### 2.2 PR Review SLA（强制） - -- 工作日 48h 内对新 PR 首次响应（review/approve/request changes/说明何时处理）。 -- 对影响多个包的变更：必须主动拉齐上下游 maintainer。 - -# 仓库人员与评分制度（SAGE - 包级，指向母版） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages/sage-platform/docs/governance/TEAM.md`） -- 名单维护：TBD - -通用制度请参见 `packages/sage/docs/governance/TEAM_BASE.md`。本文件仅保留本包的代号映射与特有说明。 - -### 本包角色代号（姓名保持 TBD） - -| 角色 | 代号分配 | -| ---------------- | ---------- | -| Maintainer | A2 | -| Engineering Core | B1 | -| Research Core | C3（按需） | - -### 本包补充说明 - -- 平台层（L2）需对配置/控制面接口保持一致性，重大配置/端口/集成变更请提前公告并附迁移指引。 - -______________________________________________________________________ - -## 参考 - -- 通用制度：packages/sage/docs/governance/TEAM_BASE.md -- 周更：docs/governance/TODO.md -- PR 审查：docs/governance/PR_CHECKLIST.md -- 开发规范：docs/governance/DEVELOPER_GUIDE.md -- **保底分**：角色的最低履职奖励（必须满足最低交付门槛）。 diff --git a/packages/sage-platform/docs/governance/TODO.md b/packages/sage-platform/docs/governance/TODO.md deleted file mode 100644 index afdd548b3a..0000000000 --- a/packages/sage-platform/docs/governance/TODO.md +++ /dev/null @@ -1,39 +0,0 @@ -# TODO 周更模板（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/TODO.md`） -- 维护频率：每周至少 1 次（Maintainer 强制要求） - -______________________________________________________________________ - -## 周更（复制本模板，每周追加一节） - -### Week of YYYY-MM-DD - -**本周目标（Goals）** - -- [ ] TBD（关联 Issue：TBD） - -**本周完成（Done）** - -- [ ] TBD（PR：TBD，CI：TBD） - -**阻塞与风险（Blockers/Risks）** - -- TBD（原因 + 需要谁协助 + 截止时间） - -**下周计划（Next）** - -- [ ] TBD（Issue：TBD） - -**需要协助（Asks）** - -- TBD（@TBD） - -______________________________________________________________________ - -## 里程碑清单（长期维护） - -| 里程碑 | 目标日期 | 状态 | 关联 Issue/PR | 风险 | -| ------ | -------- | ---- | ------------- | ---- | -| TBD | TBD | TBD | TBD | TBD | diff --git a/packages/sage-platform/examples/README.md b/packages/sage-platform/examples/README.md deleted file mode 100644 index cd422ebb38..0000000000 --- a/packages/sage-platform/examples/README.md +++ /dev/null @@ -1,48 +0,0 @@ -# L2: Platform - 平台服务层示例 - -> 对应 SAGE 包：`sage-platform` - -## 📖 层级说明 - -**Platform** 层提供平台服务抽象： - -- 消息队列抽象 (Queue Descriptor) -- KV 存储后端 (Storage Backend) -- 服务基类 (BaseService) -- 调度系统 (Scheduler) - -## 📚 目录结构 - -``` -L5-platform/ -├── scheduler/ # 调度系统示例 -└── deployment/ # 部署方案示例 -``` - -## 🎯 学习路径 - -### 1️⃣ 调度系统 (`scheduler/`) - -理解任务调度： - -- `scheduler_comparison.py` - 调度器对比 -- `remote_env.py` - 远程环境 - -### 2️⃣ 部署方案 (`deployment/`) - -生产环境部署（待添加） - -## 🎯 学习目标 - -完成本层示例后，你将掌握： - -1. SAGE 的调度机制 -1. 分布式执行环境 -1. 生产部署的最佳实践 - -## ⏭️ 下一步 - -学完平台层后，继续学习： - -- **L3-kernel/** - 流式执行引擎 -- **L3-libs/** - 算法库和工具 diff --git a/packages/sage-platform/examples/environment/README.md b/packages/sage-platform/examples/environment/README.md deleted file mode 100644 index 516e46c7b3..0000000000 --- a/packages/sage-platform/examples/environment/README.md +++ /dev/null @@ -1,28 +0,0 @@ -# L2-Platform: Environment Examples - -本目录包含 SAGE Platform 层的 Environment 相关示例。 - -## 📂 示例列表 - -### RemoteEnvironment - -- **remote_env.py** - RemoteEnvironment 基础使用示例 - - 演示如何创建和使用 RemoteEnvironment - - 展示分布式执行的基本概念 - -## 🎯 学习路径 - -1. **remote_env.py** - 了解 RemoteEnvironment 的基本用法 - -## 💡 核心概念 - -### RemoteEnvironment - -- 分布式执行环境 -- 支持跨节点的任务调度 -- 适用于大规模数据处理 - -## 🔗 相关资源 - -- 调度器示例请参考：`L3-kernel/scheduler/`（如果需要） -- 部署示例：`L2-platform/deployment/` diff --git a/packages/sage-platform/examples/environment/remote_env.py b/packages/sage-platform/examples/environment/remote_env.py deleted file mode 100644 index 588bca5a01..0000000000 --- a/packages/sage-platform/examples/environment/remote_env.py +++ /dev/null @@ -1,290 +0,0 @@ -#!/usr/bin/env python3 -""" -RemoteEnvironment 简单示例 -演示如何使用 RemoteEnvironment 和调度器 - -# test_tags: category=environment, timeout=120, requires_daemon=jobmanager -""" - -import os -import socket -import time - -from sage.common.core.functions.map_function import MapFunction -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.core.functions.source_function import SourceFunction -from sage.kernel.api.remote_environment import RemoteEnvironment - - -class SimpleSource(SourceFunction): - """简单数据源""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.count = 0 - self.max_count = 500 # 增加数据量以便观察分布式效果 - - def execute(self, data=None): - if self.count >= self.max_count: - from sage.kernel.runtime.communication.packet import StopSignal - - return StopSignal("SimpleSource completed") - - data = f"item_{self.count}" - self.count += 1 - return data - - -class SimpleProcessor(MapFunction): - """简单处理器 - 记录运行节点""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - import socket as _socket # 在类内部导入，确保 Ray Actor 可以访问 - - self.hostname = _socket.gethostname() - self.processed_count = 0 - - def execute(self, data): - # 跳过非字符串数据（如 StopSignal） - if not isinstance(data, str): - return data - self.processed_count += 1 - # 在结果中包含处理节点信息 - result = f"{data.upper()} [processed on {self.hostname}]" - return result - - -class ConsoleSink(SinkFunction): - """控制台输出 - 统计节点分布""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.test_mode = ( - os.getenv("SAGE_EXAMPLES_MODE") == "test" or os.getenv("SAGE_TEST_MODE") == "true" - ) - self.count = 0 - self.node_stats = {} # 统计各节点处理数量 - - def execute(self, data): - if data and isinstance(data, str): - self.count += 1 - # 提取节点信息 - if "[processed on " in data: - node = data.split("[processed on ")[-1].rstrip("]") - self.node_stats[node] = self.node_stats.get(node, 0) + 1 - - # 测试模式下仅打印前5条 - if not self.test_mode or self.count <= 5: - print(f"✅ Result: {data}") - elif self.count == 6: - print(" ... (remaining output suppressed in test mode)") - - # 每100条打印一次统计 - if self.count % 100 == 0: - print(f"\n📊 节点分布统计 (已处理 {self.count} 条):") - for node, cnt in sorted(self.node_stats.items()): - print(f" {node}: {cnt} ({cnt * 100 / self.count:.1f}%)") - print() - - -def check_jobmanager_available(): - """检查 JobManager 是否可用""" - - try: - sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) - sock.settimeout(1) - result = sock.connect_ex(("localhost", 19001)) - sock.close() - return result == 0 - except Exception: - return False - - -def example_default_scheduler(): - """示例 1: 使用分布式调度器 (LoadAware + SPREAD 策略)""" - print("\n" + "=" * 60) - print("示例 1: 分布式调度演示") - print("=" * 60 + "\n") - - # 检查是否在测试模式 - test_mode = os.getenv("SAGE_EXAMPLES_MODE") == "test" or os.getenv("SAGE_TEST_MODE") == "true" - - # 检查 JobManager 是否可用 - if not check_jobmanager_available(): - if test_mode: - # 在测试模式下，如果JobManager不可用，跳过测试 - print("⚠️ JobManager daemon 不可用，跳过测试") - print(" (在生产环境中需要先启动: sage jobmanager start)") - return - else: - print("❌ 错误: JobManager daemon 未运行") - print(" 请先启动: sage jobmanager start") - return - - # 📊 开始计时 - total_start = time.time() - - # 步骤1: 创建环境 - 使用 load_aware 调度器和 spread 策略 - print("📦 [1/5] 创建 RemoteEnvironment (使用 load_aware 调度器)...") - step_start = time.time() - - # 使用 LoadAwareScheduler 配置分散策略 - from sage.kernel.scheduler.impl import LoadAwareScheduler - - scheduler = LoadAwareScheduler( - platform="remote", - max_concurrent=20, # 增加并发数 - strategy="spread", # 使用 SPREAD 策略分散到不同节点 - ) - - env = RemoteEnvironment( - name="distributed_scheduler_demo", scheduler=scheduler, host="sage-node-1" - ) - # 设置 JobManager 的可访问主机名（worker 节点通过此地址连接回 JobManager） - # 注意：JobManager 启动时使用 0.0.0.0 监听，但 worker 需要实际可访问的主机名 - # env.jobmanager_host = "sage-node-1" - step_duration = time.time() - step_start - print(f" ✅ 环境创建完成 (耗时: {step_duration:.3f}秒)") - print(" 📋 调度策略: SPREAD (分散放置到多个节点)\n") - - # 步骤2: 构建数据流 - 增加并行度以利用多节点 - print("🔧 [2/5] 构建数据流 pipeline...") - step_start = time.time() - ( - env.from_source(SimpleSource) - .map(SimpleProcessor, parallelism=8) # 增加并行度，充分利用集群 - .sink(ConsoleSink) - ) - step_duration = time.time() - step_start - print(f" ✅ Pipeline 构建完成 (耗时: {step_duration:.3f}秒)") - print(" 📋 SimpleProcessor 并行度: 8 (将分布到多个节点)\n") - - # 步骤3: 连接JobManager - print("🔌 [3/5] 连接到 JobManager...") - step_start = time.time() - try: - # 这里会触发与JobManager的连接 - _ = env.client # 访问client property确保已创建 - step_duration = time.time() - step_start - print(f" ✅ JobManager 连接成功 (耗时: {step_duration:.3f}秒)\n") - except Exception as e: - step_duration = time.time() - step_start - print(f" ❌ 连接失败 (耗时: {step_duration:.3f}秒)") - print(f" 错误: {e}\n") - return - - # 步骤4: 提交任务 - print("🚀 [4/5] 提交任务到 JobManager...") - step_start = time.time() - try: - env.submit(autostop=True) # 不自动停止,手动控制 - step_duration = time.time() - step_start - print(f" ✅ 任务提交成功 (耗时: {step_duration:.3f}秒)\n") - except Exception as e: - step_duration = time.time() - step_start - print(f" ❌ 任务提交失败 (耗时: {step_duration:.3f}秒)") - print(f" 错误: {e}\n") - return - - # 步骤5: 等待执行完成 - print("⏳ [5/5] 等待任务执行...") - step_start = time.time() - try: - # 等待任务执行完成 - env._wait_for_completion() - step_duration = time.time() - step_start - print(f" ✅ 任务执行完成 (耗时: {step_duration:.3f}秒)\n") - except Exception as e: - step_duration = time.time() - step_start - print(f" ⚠️ 任务执行异常 (耗时: {step_duration:.3f}秒)") - print(f" 错误: {e}\n") - - # 查看调度器指标 - print("📊 获取调度器指标...") - try: - metrics = env.get_scheduler_metrics() - print(f" 调度器指标: {metrics}") - - # 如果使用 LoadAwareScheduler，显示节点使用情况 - if hasattr(scheduler, "node_selector"): - # 使用 node_task_count 获取节点任务统计 - node_task_count = scheduler.node_selector.node_task_count - if node_task_count: - print("\n 📍 节点放置统计:") - for node_id, count in node_task_count.items(): - node_info = scheduler.node_selector.get_node(node_id) - if node_info: - print(f" {node_info.hostname}: {count} 任务") - else: - print(f" {node_id[:12]}...: {count} 任务") - except Exception as e: - print(f" ⚠️ 无法获取指标: {e}") - print() - - # 步骤6: 清理资源（关键步骤） - print("🧹 [6/6] 清理资源...") - step_start = time.time() - try: - env.close() - step_duration = time.time() - step_start - print(f" ✅ 资源清理完成 (耗时: {step_duration:.3f}秒)\n") - except Exception as e: - step_duration = time.time() - step_start - print(f" ⚠️ 资源清理异常 (耗时: {step_duration:.3f}秒)") - print(f" 错误: {e}\n") - - # 总体统计 - total_duration = time.time() - total_start - print("=" * 60) - print(f"🎉 总耗时: {total_duration:.3f}秒") - print("=" * 60) - - -def main(): - """运行所有示例""" - print( - """ -╔══════════════════════════════════════════════════════════════╗ -║ RemoteEnvironment 分布式调度示例 ║ -║ ║ -║ 演示如何使用 LoadAwareScheduler + SPREAD 策略 ║ -║ 将任务分发到集群中的多个节点执行 ║ -╚══════════════════════════════════════════════════════════════╝ - """ - ) - - print( - """ -⚠️ 注意事项： - 1. 运行前需要启动 JobManager daemon: sage jobmanager start - 2. 确保 Ray 集群已启动: sage cluster start - 3. 如果连接失败，请检查 daemon 和集群状态 - -📋 分布式调度配置： - - 调度器: LoadAwareScheduler (负载感知) - - 策略: SPREAD (分散放置) - - 并行度: 8 (SimpleProcessor) - - 数据量: 500 条 - """ - ) - - try: - # 运行示例 - example_default_scheduler() - - print("\n" + "=" * 60) - print("✅ 所有示例运行完成！") - print("=" * 60) - - except Exception as e: - print(f"\n❌ 错误: {e}") - import traceback - - traceback.print_exc() - print("\n提示: 请确保 JobManager daemon 正在运行") - print("启动命令: sage jobmanager start") - - -if __name__ == "__main__": - main() diff --git a/packages/sage-platform/pyproject.toml b/packages/sage-platform/pyproject.toml deleted file mode 100644 index b8f6ae3ae9..0000000000 --- a/packages/sage-platform/pyproject.toml +++ /dev/null @@ -1,100 +0,0 @@ -[build-system] -requires = ["setuptools>=64", "wheel", "packaging>=24.2"] -build-backend = "setuptools.build_meta" - -[project] -name = "isage-platform" -dynamic = ["version"] -description = "SAGE Platform Services - Queue, Storage, and Service Abstractions" -readme = "README.md" -authors = [{ name = "IntelliStream Team", email = "shuhao_zhang@hust.edu.cn" }] -keywords = ["ai", "sage", "platform", "queue", "storage", "infrastructure"] -classifiers = [ - "Development Status :: 4 - Beta", - "Intended Audience :: Developers", - "Operating System :: OS Independent", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.8", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", - "Topic :: Scientific/Engineering :: Artificial Intelligence", -] -requires-python = ">=3.8" -dependencies = [ - # Distributed computing - required for cluster management - "ray[client]>=2.48.0,<3.0.0", - # SSH/Remote execution - required for node management - "paramiko>=3.5.0,<4.0.0", - "fabric>=3.2.0,<4.0.0", -] - -license = { text = "MIT" } - -[project.optional-dependencies] -dev = [ - "pytest>=7.4.0", - "pytest-cov>=4.0.0", - "pytest-asyncio>=0.21.0", - "ruff==0.14.6", - "mypy>=1.7.0", -] -all = [] -[project.urls] -Homepage = "https://github.com/sage-ai/sage" -Documentation = "https://sage-ai.org/docs" -Repository = "https://github.com/sage-ai/sage" -Issues = "https://github.com/sage-ai/sage/issues" - -[tool.setuptools.dynamic.version] -attr = "sage.platform._version.__version__" - -[tool.setuptools.packages.find] -namespaces = true -where = ["src"] - -[tool.setuptools.package-data] -"*" = ["*.txt", "*.md"] - -[tool.ruff] -extend = "../../tools/ruff.toml" - -[tool.mypy] -cache_dir = "../../.sage/cache/mypy" -ignore_missing_imports = true - -[tool.pytest.ini_options] -testpaths = ["tests", "src"] -python_files = ["test_*.py", "*_test.py"] -python_classes = ["Test*"] -python_functions = ["test_*"] -addopts = [ - "--benchmark-storage=../../.sage/benchmarks", - "-o", - "cache_dir=../../.sage/cache/pytest", - "--strict-markers", - "--strict-config", - "--verbose", - "-ra", -] -markers = [ - "slow: marks tests as slow (deselect with '-m \"not slow\"')", - "integration: marks tests as integration tests", - "unit: marks tests as unit tests", - "network: marks tests as network tests", - "queue: marks tests as queue tests", - "storage: marks tests as storage tests", -] - -[tool.coverage.run] -source = ["src/sage"] -omit = ["*/tests/*", "*/test_*.py", "*/_test_*.py"] - -[tool.coverage.report] -exclude_lines = [ - "pragma: no cover", - "def __repr__", - "raise AssertionError", - "raise NotImplementedError", - "if __name__ == .__main__.:", -] diff --git a/packages/sage-platform/src/sage/platform/__init__.py b/packages/sage-platform/src/sage/platform/__init__.py deleted file mode 100644 index 393722123f..0000000000 --- a/packages/sage-platform/src/sage/platform/__init__.py +++ /dev/null @@ -1,44 +0,0 @@ -"""SAGE Platform - Infrastructure Abstractions (L2) - -Layer: L2 (Platform Services) - -This package provides core platform services that sit between the foundation -layer (sage-common) and the execution engine (sage-kernel). - -Components: -- queue: Message queue abstractions (Python, Ray, RPC) -- storage: Key-Value storage backends -- service: Base service classes - -Architecture: -- ✅ Can import from: L1 (sage-common) -- ✅ Can be imported by: L3-L5 (sage-kernel, sage-middleware, sage-libs, sage-cli, sage-tools) -- ✅ Clean design: Uses factory pattern for L3 dependencies (RPCQueue) -""" - -__layer__ = "L2" - -# Public API -from sage.platform import queue, service, storage, utils -from sage.platform._version import __version__ -from sage.platform.utils import ( - LazyLoggerProxy, - get_component_logger, - retry_with_backoff, - retry_with_config, - share_queue_instance_on_clone, -) - -__all__ = [ - "__version__", - "queue", - "service", - "storage", - "utils", - # Utility functions - "retry_with_backoff", - "retry_with_config", - "get_component_logger", - "LazyLoggerProxy", - "share_queue_instance_on_clone", -] diff --git a/packages/sage-platform/src/sage/platform/_version.py b/packages/sage-platform/src/sage/platform/_version.py deleted file mode 100644 index 36e43490d9..0000000000 --- a/packages/sage-platform/src/sage/platform/_version.py +++ /dev/null @@ -1,5 +0,0 @@ -"""Version information for sage-platform.""" - -__version__ = "0.2.3.4" -__author__ = "IntelliStream Team" -__email__ = "shuhao_zhang@hust.edu.cn" diff --git a/packages/sage-platform/src/sage/platform/queue/__init__.py b/packages/sage-platform/src/sage/platform/queue/__init__.py deleted file mode 100644 index 7d79b662f6..0000000000 --- a/packages/sage-platform/src/sage/platform/queue/__init__.py +++ /dev/null @@ -1,54 +0,0 @@ -"""SAGE Platform - Queue Abstractions - -Layer: L2 (Platform Services - Queue Module) - -Unified queue descriptor interface supporting multiple backends. - -This module provides queue descriptors that abstract different queue implementations: -- PythonQueueDescriptor: Standard Python queue.Queue -- RayQueueDescriptor: Ray distributed queue -- RPCQueueDescriptor: Remote procedure call queue (requires L3 registration) - -Architecture: -✅ Clean L2 design - no direct imports from L3 -✅ RPCQueueDescriptor uses factory pattern - L3 registers implementation -""" - -# 导出队列描述符类 -from .base_queue_descriptor import BaseQueueDescriptor -from .python_queue_descriptor import PythonQueueDescriptor -from .ray_queue_descriptor import RayQueueDescriptor -from .rpc_queue_descriptor import RPCQueueDescriptor, register_rpc_queue_factory - - -def resolve_descriptor(data): - """从序列化数据解析出对应的队列描述符实例 - - Args: - data: 包含队列描述符信息的字典 - - Returns: - 对应类型的队列描述符实例 - """ - if isinstance(data, dict): - queue_type = data.get("queue_type") - if queue_type == "python": - return PythonQueueDescriptor.from_dict(data) - elif queue_type == "ray_queue": - return RayQueueDescriptor.from_dict(data) - elif queue_type == "rpc_queue": - return RPCQueueDescriptor.from_dict(data) - else: - raise ValueError(f"Unknown queue type: {queue_type}") - else: - raise TypeError(f"Expected dict, got {type(data)}") - - -__all__ = [ - "BaseQueueDescriptor", - "PythonQueueDescriptor", - "RayQueueDescriptor", - "RPCQueueDescriptor", - "register_rpc_queue_factory", - "resolve_descriptor", -] diff --git a/packages/sage-platform/src/sage/platform/queue/base_queue_descriptor.py b/packages/sage-platform/src/sage/platform/queue/base_queue_descriptor.py deleted file mode 100644 index 1a7b297ebd..0000000000 --- a/packages/sage-platform/src/sage/platform/queue/base_queue_descriptor.py +++ /dev/null @@ -1,289 +0,0 @@ -"""Base Queue Descriptor - 统一多态通信描述符基类 - -Layer: L2 (Platform Services - Queue Descriptors) - -提供一个统一的多态队列描述符结构，支持： -1. 直接调用队列方法 (put, get, empty, qsize等) -2. 懒加载内部队列实例 -3. 序列化支持（自动处理不可序列化对象） -4. 跨进程传递队列描述符信息 - -通过继承支持各种队列类型：本地队列、共享内存队列、Ray队列、SAGE队列等。 - -Architecture: -- L2 abstraction for queue interfaces -- Subclasses may need to import concrete implementations from L3 (architectural debt) -""" - -import json -import logging -import time -import uuid -from abc import ABC, abstractmethod -from typing import Any, Optional - -logger = logging.getLogger(__name__) - - -class BaseQueueDescriptor(ABC): - """ - 统一的多态队列描述符基类 - - 这个抽象基类定义了队列描述符的标准接口，支持： - 1. 直接调用队列方法（多态） - 2. 懒加载内部队列实例 - 3. 序列化支持（子类定义序列化能力） - 4. 跨进程传递 - - 队列接口方法： - - put(item, block=True, timeout=None): 向队列中放入项目 - - get(block=True, timeout=None): 从队列中获取项目 - - empty(): 检查队列是否为空 - - qsize(): 获取队列大小 - - Attributes: - queue_id: 队列的唯一标识符 - queue_type: 通信方式类型（由子类定义） - metadata: 保存额外参数的字典（由子类生成） - created_timestamp: 创建时间戳（自动生成） - """ - - def __init__(self, queue_id: Optional[str] = None): - """ - 初始化队列描述符基类 - - Args: - queue_id: 队列唯一标识符，如果为None则自动生成 - """ - self.queue_id = queue_id or self._generate_queue_id() - self.created_timestamp = time.time() - - # 队列实例管理 - self._queue_instance = None - self._initialized = False - - # 子类应该实现metadata属性 - # self.metadata = {} # 删除这行，让子类自己实现 - - self._validate() - - def _generate_queue_id(self) -> str: - """生成队列ID""" - return f"{self.queue_type}_{uuid.uuid4().hex[:8]}" - - @property - @abstractmethod - def queue_type(self) -> str: - """队列类型标识符""" - pass - - @property - @abstractmethod - def can_serialize(self) -> bool: - """是否可以序列化""" - pass - - @property - @abstractmethod - def metadata(self) -> dict[str, Any]: - """队列元数据，包含创建队列所需的额外参数""" - pass - - def _validate(self): - """验证描述符参数""" - if not self.queue_id or not isinstance(self.queue_id, str): - raise ValueError("queue_id must be a non-empty string") - if not self.queue_type or not isinstance(self.queue_type, str): - raise ValueError("queue_type must be a non-empty string") - if not isinstance(self.metadata, dict): - raise ValueError("metadata must be a dictionary") - - # ============ 队列接口实现 ============ - - def put(self, item: Any, block: bool = True, timeout: Optional[float] = None) -> None: - if not self.queue_instance: - raise RuntimeError("Queue instance not initialized") - return self.queue_instance.put(item, block=block, timeout=timeout) - - def get(self, block: bool = True, timeout: Optional[float] = None) -> Any: - """从队列中获取项目""" - if not self.queue_instance: - raise RuntimeError("Queue instance not initialized") - return self.queue_instance.get(block=block, timeout=timeout) - - def empty(self) -> bool: - """检查队列是否为空""" - if not self.queue_instance: - raise RuntimeError("Queue instance not initialized") - return self.queue_instance.empty() - - def qsize(self) -> int: - """获取队列大小""" - if not self.queue_instance: - raise RuntimeError("Queue instance not initialized") - return self.queue_instance.qsize() - - # 额外的队列方法（如果底层队列支持） - def put_nowait(self, item: Any) -> None: - """非阻塞放入项目""" - if not self.queue_instance: - raise RuntimeError("Queue instance not initialized") - return self.queue_instance.put_nowait(item) - - def get_nowait(self) -> Any: - """非阻塞获取项目""" - if not self.queue_instance: - raise RuntimeError("Queue instance not initialized") - return self.queue_instance.get_nowait() - - def full(self) -> bool: - """检查队列是否已满""" - if not self.queue_instance: - raise RuntimeError("Queue instance not initialized") - return self.queue_instance.full() - - # ============ 描述符管理方法 ============ - - @property - @abstractmethod - def queue_instance(self) -> Optional[Any]: - pass - - def get_queue(self) -> Any: - return self.queue_instance - - def clear_cache(self): - """清除队列缓存，下次访问时重新初始化""" - if hasattr(self, "_queue_instance"): - self._queue_instance = None - if hasattr(self, "_initialized"): - self._initialized = False - - def is_initialized(self) -> bool: - """检查队列是否已初始化""" - return self._initialized - - def clone(self, new_queue_id: Optional[str] = None) -> "BaseQueueDescriptor": - """克隆描述符（不包含队列实例） - - 注意：这是基类的默认实现，创建一个新的描述符实例但不共享队列实例。 - 子类应该重写此方法以正确处理队列实例的共享，特别是在服务通信场景中。 - - **重要**: 如果队列用于服务通信（请求/响应），子类的 clone() 实现必须 - 共享已初始化的队列实例，否则会导致竞态条件： - - 服务端使用原始描述符 → 队列 A - - 客户端使用克隆描述符 → 队列 B (如果不共享) - - 响应发送到队列 A，但客户端在队列 B 等待 → 超时 - - Args: - new_queue_id: 新的队列ID，如果为None则自动生成为 "{原ID}_clone" - - Returns: - 新的描述符实例，子类应确保在已初始化时共享队列实例 - - See Also: - - PythonQueueDescriptor.clone(): 共享队列实例的正确实现 - - RayQueueDescriptor.clone(): 共享队列代理的正确实现 - - RPCQueueDescriptor.clone(): 共享RPC连接的正确实现 - """ - # 创建同类型的新实例 - new_instance = type(self)(queue_id=new_queue_id or f"{self.queue_id}_clone") - return new_instance - - def trim(self): - """清除队列实例引用，释放内存但保留描述符信息""" - if hasattr(self, "_queue_instance"): - self._queue_instance = None - if hasattr(self, "_initialized"): - self._initialized = False - - # ============ 序列化支持 ============ - - def to_dict(self, include_non_serializable: bool = False) -> dict[str, Any]: - """ - 转换为字典格式 - - Args: - include_non_serializable: 是否包含不可序列化的字段 - """ - result = { - "queue_id": self.queue_id, - "queue_type": self.queue_type, - "class_name": self.__class__.__name__, - "metadata": {}, - "can_serialize": self.can_serialize, - "created_timestamp": self.created_timestamp, - } - - # 过滤元数据中的不可序列化对象 - for key, value in self.metadata.items(): - if key.startswith("_") and not include_non_serializable: - continue # 跳过私有字段 - - try: - json.dumps(value) # 测试是否可序列化 - result["metadata"][key] = value - except (TypeError, ValueError): - if include_non_serializable: - result["metadata"][key] = f"" - - return result - - def to_json(self) -> str: - """序列化为JSON字符串""" - if not self.can_serialize: - raise ValueError( - f"Queue descriptor '{self.queue_id}' contains non-serializable objects" - ) - return json.dumps(self.to_dict()) - - def to_serializable_descriptor(self) -> "BaseQueueDescriptor": - """ - 转换为可序列化的描述符（移除队列实例引用） - - Returns: - 新的可序列化描述符实例 - """ - if self.can_serialize: - return self - - # 创建同类型的新实例（不包含队列实例） - return type(self)(queue_id=self.queue_id) - - # ============ 魔法方法 ============ - - def __repr__(self) -> str: - status_parts = [] - if self._initialized: - status_parts.append("initialized") - else: - status_parts.append("lazy") - - if self.can_serialize: - status_parts.append("serializable") - else: - status_parts.append("non-serializable") - - status = ", ".join(status_parts) - return ( - f"{self.__class__.__name__}(id='{self.queue_id}', type='{self.queue_type}', {status})" - ) - - def __str__(self) -> str: - return f"Queue[{self.queue_type}]({self.queue_id})" - - def __eq__(self, other) -> bool: - if not isinstance(other, BaseQueueDescriptor): - return False - return ( - self.queue_id == other.queue_id - and self.queue_type == other.queue_type - and self.metadata == other.metadata - ) - - def __hash__(self) -> int: - return hash((self.queue_id, self.queue_type)) - - -# 为了向后兼容性，提供 QueueDescriptor 别名 -QueueDescriptor = BaseQueueDescriptor diff --git a/packages/sage-platform/src/sage/platform/queue/python_queue_descriptor.py b/packages/sage-platform/src/sage/platform/queue/python_queue_descriptor.py deleted file mode 100644 index a66c9c3461..0000000000 --- a/packages/sage-platform/src/sage/platform/queue/python_queue_descriptor.py +++ /dev/null @@ -1,109 +0,0 @@ -""" -Python Queue Descriptor - Python标准库队列描述符 - -支持本地进程内队列（queue.Queue）和多进程队列（multiprocessing.Queue） -""" - -from queue import Queue -from typing import Any, Optional - -from .base_queue_descriptor import BaseQueueDescriptor - - -class PythonQueueDescriptor(BaseQueueDescriptor): - """ - Python标准库队列描述符 - - 只支持 queue.Queue (本地进程内队列) - """ - - def __init__( - self, - maxsize: int = 0, - use_multiprocessing: bool = False, - queue_id: Optional[str] = None, - ): - """ - 初始化Python队列描述符 - - Args: - maxsize: 队列最大大小，0表示无限制 - use_multiprocessing: 是否使用multiprocessing.Queue - queue_id: 队列唯一标识符 - """ - self.maxsize = maxsize - self.use_multiprocessing = use_multiprocessing - self._initialized = False # 是否已初始化队列实例 - super().__init__(queue_id=queue_id) - - @property - def queue_type(self) -> str: - """队列类型标识符""" - return "python" - - @property - def can_serialize(self) -> bool: - return not self._initialized # 未初始化时可以序列化 - - @property - def metadata(self) -> dict[str, Any]: - """元数据字典""" - base_metadata = { - "maxsize": self.maxsize, - "use_multiprocessing": self.use_multiprocessing, - } - - # 只有在不可序列化时才包含队列实例引用 - if not self.can_serialize: - base_metadata["queue_instance"] = self.queue_instance - - return base_metadata - - def clone(self, new_queue_id: Optional[str] = None) -> "PythonQueueDescriptor": - """克隆描述符（共享队列实例以避免竞态条件） - - 重要：如果原描述符已初始化队列实例，克隆体将共享同一个队列实例。 - 这对于服务通信至关重要 - 服务端和客户端必须使用相同的队列实例， - 否则会导致间歇性超时（竞态条件）。 - - Args: - new_queue_id: 新的队列ID，如果为None则自动生成 - - Returns: - 新的描述符实例，如果原实例已初始化则共享队列实例 - """ - # 创建同类型的新实例，保留原始配置 - cloned = PythonQueueDescriptor( - maxsize=self.maxsize, # 保留原始配置 - use_multiprocessing=self.use_multiprocessing, - queue_id=new_queue_id, - ) - - # 【关键修复】共享队列实例，避免竞态条件 - # 如果原描述符已经初始化了队列实例，克隆体应该共享同一个实例 - # 这确保服务端和客户端使用相同的队列，防止响应丢失 - if self._initialized: - cloned._queue_instance = self._queue_instance - cloned._initialized = True - - return cloned - - @property - def queue_instance(self) -> Any: - """获取队列实例，如果未初始化则创建""" - if not self._initialized: - self._queue_instance = Queue(maxsize=self.maxsize) - self._initialized = True - return self._queue_instance - - @classmethod - def from_dict(cls, data: dict[str, Any]) -> "PythonQueueDescriptor": - """从字典创建实例""" - metadata = data.get("metadata", {}) - instance = cls( - maxsize=metadata.get("maxsize", 0), - use_multiprocessing=metadata.get("use_multiprocessing", False), - queue_id=data["queue_id"], - ) - instance.created_timestamp = data.get("created_timestamp", instance.created_timestamp) - return instance diff --git a/packages/sage-platform/src/sage/platform/queue/ray_queue_descriptor.py b/packages/sage-platform/src/sage/platform/queue/ray_queue_descriptor.py deleted file mode 100644 index 77f985ca75..0000000000 --- a/packages/sage-platform/src/sage/platform/queue/ray_queue_descriptor.py +++ /dev/null @@ -1,567 +0,0 @@ -""" -Ray Queue Descriptor - Ray分布式队列描述符 - -支持Ray分布式队列和Ray Actor队列 -""" - -import os -import queue -from pathlib import Path -from typing import TYPE_CHECKING, Any, Optional - -import ray - -from .base_queue_descriptor import BaseQueueDescriptor - -if TYPE_CHECKING: - from sage.common.utils.logging.custom_logger import CustomLogger - -# 使用 SAGE 的 CustomLogger，输出到统一的日志目录 -_logger: "CustomLogger | None" = None - - -def _get_logger() -> "CustomLogger": - """获取或创建 CustomLogger 实例""" - global _logger - if _logger is None: - from sage.common.utils.logging.custom_logger import CustomLogger - - # 获取日志目录： - # 1. 优先使用环境变量 SAGE_LOG_DIR（运行时设置，通常由 TaskContext 设置） - # 2. 否则使用统一的 .sage/logs（自动区分 pip 安装与开发环境） - log_env = os.environ.get("SAGE_LOG_DIR") - if log_env: - log_base_dir = Path(log_env) - else: - from sage.common.config import get_sage_paths - - log_base_dir = get_sage_paths().logs_dir - - log_base_dir.mkdir(parents=True, exist_ok=True) - - _logger = CustomLogger( - [ - ("console", "DEBUG"), # 控制台显示 DEBUG 及以上（与其他组件一致） - (str(log_base_dir / "ray_queue_debug.log"), "DEBUG"), # 详细调试日志 - (str(log_base_dir / "ray_queue_info.log"), "INFO"), # 信息日志 - (str(log_base_dir / "Error.log"), "ERROR"), # 错误日志（统一文件名） - ], - name="RayQueue", - ) - return _logger - - -# 兼容性：提供 logger 变量，但实际使用时会调用 _get_logger() -class _LoggerProxy: - """Logger 代理，延迟初始化 CustomLogger""" - - def __getattr__(self, name): - return getattr(_get_logger(), name) - - -logger = _LoggerProxy() - - -class SimpleArrayQueue: - """使用list实现的简单FIFO队列 - - 由于RayQueueManager是Ray Actor，本身就是单线程处理请求， - 因此不需要线程锁，使用简单的list即可。 - """ - - def __init__(self, maxsize=0): - """ - 初始化队列 - - Args: - maxsize: 队列最大大小，0表示无限制 - """ - self._items = [] - self._maxsize = maxsize - - def put(self, item, timeout=None): - """ - 添加项目到队列尾部（先进先出） - - Args: - item: 要添加的项目 - timeout: 超时时间（保持接口兼容性，实际不使用） - - Raises: - queue.Full: 队列已满时抛出 - """ - if self._maxsize > 0 and len(self._items) >= self._maxsize: - raise queue.Full("Queue is full") - self._items.append(item) - - def get(self, timeout=None): - """ - 从队列头部获取项目（先进先出） - - Args: - timeout: 超时时间（保持接口兼容性，实际不使用） - - Returns: - 队列中的第一个项目 - - Raises: - queue.Empty: 队列为空时抛出 - """ - if len(self._items) == 0: - raise queue.Empty("Queue is empty") - return self._items.pop(0) - - def size(self): - """获取当前队列中的元素数量""" - return len(self._items) - - def qsize(self): - """获取队列大小（兼容标准queue.Queue接口）""" - return len(self._items) - - def empty(self): - """检查队列是否为空""" - return len(self._items) == 0 - - def full(self): - """检查队列是否已满""" - if self._maxsize <= 0: - return False - return len(self._items) >= self._maxsize - - -def _is_ray_local_mode(): - """检查Ray是否在local mode下运行""" - try: - if not ray.is_initialized(): - return False - ctx = ray.get_runtime_context() - return ctx.worker.mode == ray.LOCAL_MODE - except Exception: - return False - - -class RayQueueProxy: - """Ray队列代理，提供类似队列的接口但通过manager访问实际队列""" - - def __init__(self, manager, queue_id: str): - self.manager = manager - self.queue_id = queue_id - - def put(self, item, block=True, timeout=None): - """向队列添加项 - - Args: - item: 要添加的项目 - block: 是否阻塞等待（为了API兼容性，但Ray队列始终是阻塞的） - timeout: 超时时间（秒） - """ - import time - - _start = time.time() - logger.debug( - f"[PROXY-PUT-START] queue_id={self.queue_id}, block={block}, timeout={timeout}" - ) - - _remote_start = time.time() - result = ray.get(self.manager.put.remote(self.queue_id, item)) - _remote_duration = time.time() - _remote_start - _total_duration = time.time() - _start - - logger.debug( - f"[PROXY-PUT-END] queue_id={self.queue_id}, " - f"remote_call_time={_remote_duration * 1000:.3f}ms, " - f"total_time={_total_duration * 1000:.3f}ms" - ) - return result - - def put_nowait(self, item): - """非阻塞添加项目到队列（实际上Ray队列始终是阻塞的）""" - return ray.get(self.manager.put.remote(self.queue_id, item)) - - def get(self, block=True, timeout=None): - """从队列获取项目 - - Args: - block: 是否阻塞等待（为了API兼容性，但Ray队列始终是阻塞的） - timeout: 超时时间（秒） - """ - import time - - _start = time.time() - logger.debug(f"[PROXY-GET-START] queue_id={self.queue_id}, timeout={timeout}") - - # Ray队列不支持非阻塞模式，block参数仅用于API兼容性 - _remote_start = time.time() - result = ray.get(self.manager.get.remote(self.queue_id, timeout)) - _remote_duration = time.time() - _remote_start - _total_duration = time.time() - _start - - logger.debug( - f"[PROXY-GET-END] queue_id={self.queue_id}, " - f"remote_call_time={_remote_duration * 1000:.3f}ms, " - f"total_time={_total_duration * 1000:.3f}ms" - ) - return result - - def size(self): - """获取队列大小""" - return ray.get(self.manager.size.remote(self.queue_id)) - - def qsize(self): - """获取队列大小（兼容性方法）""" - return self.size() - - def empty(self): - """检查队列是否为空""" - return self.size() == 0 - - def full(self): - """检查队列是否已满（简化实现）""" - # 对于Ray队列，这个很难确定，返回False - return False - - -# 全局队列管理器，用于在不同Actor之间共享队列实例 -@ray.remote -class RayQueueManager: - """Ray队列管理器，管理全局队列实例 - - 注意：为了避免序列化问题，此类不能使用模块级的 logger 变量。 - 所有日志记录都通过 _get_logger() 方法动态创建本地 logger。 - """ - - def __init__(self): - self.queues = {} - self._logger = None # 延迟初始化 - - def _get_logger(self): - """获取本地 logger 实例（避免序列化问题）""" - if self._logger is None: - # 在 Actor 内部动态创建 logger，避免序列化问题 - import logging - - self._logger = logging.getLogger("RayQueueManager") - return self._logger - - def get_or_create_queue(self, queue_id: str, maxsize: int): - """获取或创建队列，返回队列ID而不是队列对象""" - log = self._get_logger() - if queue_id not in self.queues: - # 统一使用数组实现的简单队列，避免Ray对象存储内存问题 - self.queues[queue_id] = SimpleArrayQueue(maxsize=maxsize if maxsize > 0 else 0) - log.debug(f"Created new SimpleArrayQueue {queue_id}") - else: - log.debug(f"Retrieved existing queue {queue_id}") - return queue_id # 返回队列ID而不是队列对象 - - def put(self, queue_id: str, item): - """向指定队列添加项目""" - import time - - log = self._get_logger() - _start = time.time() - log.debug(f"[MANAGER-PUT-START] queue_id={queue_id}") - - if queue_id in self.queues: - _queue_put_start = time.time() - result = self.queues[queue_id].put(item) - _queue_put_duration = time.time() - _queue_put_start - _total_duration = time.time() - _start - - if _queue_put_duration > 0.01: # >10ms - log.warning( - f"[MANAGER-PUT-SLOW] queue_id={queue_id}, " - f"queue_put_time={_queue_put_duration * 1000:.3f}ms" - ) - - log.debug( - f"[MANAGER-PUT-END] queue_id={queue_id}, " - f"queue_put_time={_queue_put_duration * 1000:.3f}ms, " - f"total_time={_total_duration * 1000:.3f}ms" - ) - return result - else: - log.error(f"[MANAGER-PUT-ERROR] Queue {queue_id} does not exist") - raise ValueError(f"Queue {queue_id} does not exist") - - def get(self, queue_id: str, timeout=None): - """从指定队列获取项目""" - import time - - log = self._get_logger() - _start = time.time() - log.debug(f"[MANAGER-GET-START] queue_id={queue_id}, timeout={timeout}") - - if queue_id in self.queues: - try: - _queue_get_start = time.time() - result = self.queues[queue_id].get(timeout=timeout) - _queue_get_duration = time.time() - _queue_get_start - _total_duration = time.time() - _start - - log.debug( - f"[MANAGER-GET-END] queue_id={queue_id}, " - f"queue_get_time={_queue_get_duration * 1000:.3f}ms, " - f"total_time={_total_duration * 1000:.3f}ms" - ) - return result - except Exception as e: - _total_duration = time.time() - _start - log.warning( - f"[MANAGER-GET-ERROR] queue_id={queue_id}, " - f"error={type(e).__name__}, " - f"total_time={_total_duration * 1000:.3f}ms" - ) - raise - else: - log.error(f"[MANAGER-GET-ERROR] Queue {queue_id} does not exist") - raise ValueError(f"Queue {queue_id} does not exist") - - def size(self, queue_id: str): - """获取队列大小""" - if queue_id in self.queues: - if hasattr(self.queues[queue_id], "size"): - return self.queues[queue_id].size() - else: - # 对于标准Queue，没有size方法，使用qsize - return self.queues[queue_id].qsize() - else: - raise ValueError(f"Queue {queue_id} does not exist") - - def queue_exists(self, queue_id: str): - """检查队列是否存在""" - return queue_id in self.queues - - def delete_queue(self, queue_id: str): - """删除队列""" - if queue_id in self.queues: - del self.queues[queue_id] - return True - return False - - -# 全局队列管理器实例 -_global_queue_manager: Any = None - - -def get_global_queue_manager() -> Any: - """获取全局队列管理器 - - Returns: - ActorHandle: RayQueueManager的ActorHandle，具有RayQueueManager的所有方法 - """ - import random - import time - - _start = time.time() - logger.debug("[GET-MANAGER-START] Attempting to get global queue manager") - - # 使用固定的 namespace，确保所有 job 都能访问同一个 Actor - QUEUE_MANAGER_NAMESPACE = "sage_global" - - # 使用固定的 namespace，确保所有 job 都能访问同一个 Actor - QUEUE_MANAGER_NAMESPACE = "sage_global" - - # 先尝试获取现有的命名Actor - try: - _get_actor_start = time.time() - manager = ray.get_actor("global_ray_queue_manager", namespace=QUEUE_MANAGER_NAMESPACE) - _get_actor_duration = time.time() - _get_actor_start - _total_duration = time.time() - _start - logger.debug( - f"[GET-MANAGER-FOUND] Found existing manager in namespace {QUEUE_MANAGER_NAMESPACE}, " - f"get_actor_time={_get_actor_duration * 1000:.3f}ms, " - f"total_time={_total_duration * 1000:.3f}ms" - ) - return manager - except ValueError: - logger.debug("[GET-MANAGER-NOT-FOUND] Manager does not exist, will create") - pass - - # 多次尝试创建命名Actor，处理并发冲突 - max_attempts = 5 # 增加重试次数 - for attempt in range(max_attempts): - try: - # 添加随机延迟，避免多个进程同时创建 - if attempt > 0: - delay = random.uniform(0.1, 0.5) * (attempt + 1) - logger.debug( - f"[GET-MANAGER-RETRY] Waiting {delay:.3f}s before retry " - f"{attempt + 1}/{max_attempts}" - ) - time.sleep(delay) - - # 如果不存在，创建新的命名Actor - logger.debug( - f"[GET-MANAGER-CREATE] Attempt {attempt + 1}/{max_attempts} to create manager in namespace {QUEUE_MANAGER_NAMESPACE}" - ) - _create_start = time.time() - global _global_queue_manager - _global_queue_manager = RayQueueManager.options( - name="global_ray_queue_manager", - namespace=QUEUE_MANAGER_NAMESPACE, # 使用固定 namespace - lifetime="detached", # 独立于创建者进程，避免 owner 死亡导致 Actor 失效 - max_restarts=-1, # 无限重启 - max_task_retries=-1, # 无限重试 - ).remote() - _create_duration = time.time() - _create_start - _total_duration = time.time() - _start - logger.debug( - f"[GET-MANAGER-CREATED] Successfully created manager in namespace {QUEUE_MANAGER_NAMESPACE}, " - f"create_time={_create_duration * 1000:.3f}ms, " - f"total_time={_total_duration * 1000:.3f}ms" - ) - return _global_queue_manager - except ValueError as e: - # 如果Actor已存在，再次尝试获取 - if "already exists" in str(e): - logger.debug( - f"[GET-MANAGER-CONFLICT] Attempt {attempt + 1}: Actor already exists, retrying get" - ) - try: - manager = ray.get_actor("global_ray_queue_manager") - _total_duration = time.time() - _start - logger.debug( - f"[GET-MANAGER-FOUND-RETRY] Found manager after conflict in namespace {QUEUE_MANAGER_NAMESPACE}, " - f"total_time={_total_duration * 1000:.3f}ms" - ) - return manager - except ValueError: - # 短暂等待后重试 - wait_time = random.uniform(0.1, 0.5) - logger.debug( - f"[GET-MANAGER-WAIT] Waiting {wait_time * 1000:.1f}ms before retry" - ) - time.sleep(wait_time) - continue - else: - logger.error(f"[GET-MANAGER-ERROR] Unexpected ValueError: {e}") - raise - except Exception as e: - # 其他错误，短暂等待后重试 - logger.warning( - f"[GET-MANAGER-RETRY] Attempt {attempt + 1} failed: {type(e).__name__}: {e}" - ) - wait_time = random.uniform(0.1, 0.5) - time.sleep(wait_time) - if attempt == 2: # 最后一次尝试 - logger.error( - f"[GET-MANAGER-FAILED] All attempts failed after {time.time() - _start:.3f}s" - ) - raise - - # 如果仍然失败，尝试最后一次获取 - logger.debug("[GET-MANAGER-FINAL-ATTEMPT] Making final attempt to get manager") - manager = ray.get_actor("global_ray_queue_manager", namespace=QUEUE_MANAGER_NAMESPACE) - _total_duration = time.time() - _start - logger.debug( - f"[GET-MANAGER-FINAL-SUCCESS] Got manager on final attempt in namespace {QUEUE_MANAGER_NAMESPACE}, " - f"total_time={_total_duration * 1000:.3f}ms" - ) - return manager - - -class RayQueueDescriptor(BaseQueueDescriptor): - """ - Ray分布式队列描述符 - - 支持： - - ray.util.Queue (Ray原生分布式队列) - """ - - def __init__(self, maxsize: int = 1024 * 1024, queue_id: Optional[str] = None): - """ - 初始化Ray队列描述符 - - Args: - maxsize: 队列最大大小，0表示无限制 - queue_id: 队列唯一标识符 - """ - self.maxsize = maxsize - self._queue = None # 延迟初始化 - super().__init__(queue_id=queue_id) - - @property - def queue_type(self) -> str: - """队列类型标识符""" - return "ray_queue" - - @property - def can_serialize(self) -> bool: - """Ray队列可以序列化""" - return True - - @property - def metadata(self) -> dict[str, Any]: - """元数据字典""" - return {"maxsize": self.maxsize} - - @property - def queue_instance(self) -> Any: - """获取队列实例 - 返回一个代理对象而不是真实的队列""" - if self._queue is None: - logger.debug(f"Initializing RayQueueProxy for queue_id={self.queue_id}") - manager = get_global_queue_manager() - logger.debug(f"Obtained global RayQueueManager for queue_id={self.queue_id}") - # 确保队列被创建，但不获取队列对象本身 - ray.get(manager.get_or_create_queue.remote(self.queue_id, self.maxsize)) - logger.debug(f"Ensured queue exists in manager for queue_id={self.queue_id}") - # 返回一个队列代理对象 - self._queue = RayQueueProxy(manager, self.queue_id) - return self._queue - - def to_dict(self) -> dict[str, Any]: - """序列化为字典，包含队列元信息""" - return { - "queue_type": self.queue_type, - "queue_id": self.queue_id, - "metadata": self.metadata, - "created_timestamp": self.created_timestamp, - } - - @classmethod - def from_dict(cls, data: dict[str, Any]) -> "RayQueueDescriptor": - """从字典反序列化""" - # 确保maxsize是整数 - maxsize = data["metadata"].get("maxsize", 1024 * 1024) - if isinstance(maxsize, str): - try: - maxsize = int(maxsize) - except ValueError: - maxsize = 1024 * 1024 # 默认值 - - instance = cls( - maxsize=maxsize, - queue_id=data["queue_id"], - ) - instance.created_timestamp = data.get("created_timestamp", instance.created_timestamp) - return instance - - def clone(self, new_queue_id: Optional[str] = None) -> "RayQueueDescriptor": - """克隆描述符（共享队列实例以避免竞态条件） - - 重要：如果原描述符已初始化队列实例，克隆体将共享同一个队列代理。 - 这对于服务通信至关重要 - 服务端和客户端必须使用相同的队列实例， - 否则会导致间歇性超时（竞态条件）。 - - Args: - new_queue_id: 新的队列ID，如果为None则自动生成 - - Returns: - 新的描述符实例，如果原实例已初始化则共享队列实例 - """ - # 创建同类型的新实例 - cloned = RayQueueDescriptor( - maxsize=self.maxsize, - queue_id=new_queue_id, - ) - - # 【关键修复】共享队列代理实例，避免竞态条件 - # 如果原描述符已经初始化了队列实例，克隆体应该共享同一个实例 - # 这确保服务端和客户端使用相同的队列，防止响应丢失 - if self._queue is not None: - cloned._queue = self._queue - cloned._initialized = True - - return cloned diff --git a/packages/sage-platform/src/sage/platform/queue/rpc_queue_descriptor.py b/packages/sage-platform/src/sage/platform/queue/rpc_queue_descriptor.py deleted file mode 100644 index c3016e4945..0000000000 --- a/packages/sage-platform/src/sage/platform/queue/rpc_queue_descriptor.py +++ /dev/null @@ -1,219 +0,0 @@ -"""RPC Queue Descriptor - RPC队列描述符 - -Layer: L2 (Platform Services - Queue Descriptors) - -支持基于RPC的远程队列创建和管理。 - -Architecture: -- L2层仅定义描述符和参数 -- 实际的RPCQueue实现由L3层提供 -- 使用工厂注册模式避免直接依赖 -""" - -import logging -from typing import Any, Callable, Optional - -from .base_queue_descriptor import BaseQueueDescriptor - -logger = logging.getLogger(__name__) - -# 工厂函数类型定义 -QueueFactory = Callable[..., Any] - -# 全局工厂注册表 - 由L3层注册实现 -_rpc_queue_factory: Optional[QueueFactory] = None - - -def register_rpc_queue_factory(factory: QueueFactory) -> None: - """注册RPC队列工厂函数 - - This function should be called by sage-kernel (L3) to register - the concrete RPCQueue implementation. - - Args: - factory: Factory function that creates RPCQueue instances - Signature: factory(queue_id, host, port, ...) -> RPCQueue - """ - global _rpc_queue_factory - _rpc_queue_factory = factory - logger.info("RPC queue factory registered successfully") - - -class RPCQueueDescriptor(BaseQueueDescriptor): - """RPC队列描述符 - - 支持基于RPC的远程队列： - - TCP连接 - - 远程队列访问 - - 连接池管理 - - 自动重连 - - Architecture Note: - This descriptor (L2) does not import RPCQueue (L3) directly. - Instead, it uses a factory pattern where L3 registers the implementation. - """ - - def __init__( - self, - host: str = "localhost", - port: int = 8000, - connection_timeout: float = 30.0, - retry_count: int = 3, - enable_pooling: bool = True, - queue_id: Optional[str] = None, - ): - """初始化RPC队列描述符 - - Args: - host: RPC服务器主机 - port: RPC服务器端口 - connection_timeout: 连接超时时间（秒） - retry_count: 重试次数 - enable_pooling: 是否启用连接池 - queue_id: 队列唯一标识符 - """ - self.host = host - self.port = port - self.connection_timeout = connection_timeout - self.retry_count = retry_count - self.enable_pooling = enable_pooling - super().__init__(queue_id=queue_id) - - @property - def queue_type(self) -> str: - """队列类型标识符""" - return "rpc_queue" - - @property - def can_serialize(self) -> bool: - """RPC队列可以序列化""" - return self._queue_instance is None - - @property - def queue_instance(self) -> Optional[Any]: - """获取队列实例（实现抽象方法）""" - if not self._initialized: - self._queue_instance = self._create_queue_instance() - self._initialized = True - return self._queue_instance - - @property - def metadata(self) -> dict[str, Any]: - """元数据字典""" - return { - "host": self.host, - "port": self.port, - "connection_timeout": self.connection_timeout, - "retry_count": self.retry_count, - "enable_pooling": self.enable_pooling, - } - - def _create_queue_instance(self) -> Any: - """创建RPC队列实例 - - 使用工厂模式创建实例，避免直接依赖sage-kernel。 - """ - if _rpc_queue_factory is None: - raise RuntimeError( - "RPC queue factory not registered. " - "Please ensure sage-kernel is imported and initialized. " - "The factory should be registered via: " - "from sage.kernel.runtime.communication.rpc import register_with_platform" - ) - - try: - # 使用注册的工厂函数创建队列实例 - rpc_queue = _rpc_queue_factory( - queue_id=self.queue_id, - host=self.host, - port=self.port, - connection_timeout=self.connection_timeout, - retry_count=self.retry_count, - enable_pooling=self.enable_pooling, - ) - - logger.info( - f"Successfully initialized RPC Queue: {self.queue_id} at {self.host}:{self.port}" - ) - return rpc_queue - - except Exception as e: - logger.error(f"Failed to initialize RPC Queue: {e}") - raise RuntimeError(f"RPC Queue initialization failed: {e}") from e - - def get_connection_status(self) -> dict[str, Any]: - """获取连接状态""" - if ( - self._initialized - and self._queue_instance is not None - and hasattr(self._queue_instance, "get_connection_status") - ): - return self._queue_instance.get_connection_status() - return {"connected": False} - - def reconnect(self) -> None: - """重新连接""" - if ( - self._initialized - and self._queue_instance is not None - and hasattr(self._queue_instance, "reconnect") - ): - self._queue_instance.reconnect() - - def close(self) -> None: - """关闭连接""" - if ( - self._initialized - and self._queue_instance is not None - and hasattr(self._queue_instance, "close") - ): - self._queue_instance.close() - self._queue_instance = None - self._initialized = False - - @classmethod - def from_dict(cls, data: dict[str, Any]) -> "RPCQueueDescriptor": - """从字典创建实例""" - metadata = data.get("metadata", {}) - instance = cls( - host=metadata.get("host", "localhost"), - port=metadata.get("port", 8000), - connection_timeout=metadata.get("connection_timeout", 30.0), - retry_count=metadata.get("retry_count", 3), - enable_pooling=metadata.get("enable_pooling", True), - queue_id=data["queue_id"], - ) - instance.created_timestamp = data.get("created_timestamp", instance.created_timestamp) - return instance - - def clone(self, new_queue_id: Optional[str] = None) -> "RPCQueueDescriptor": - """克隆描述符（共享队列实例以避免竞态条件） - - 重要：如果原描述符已初始化队列实例，克隆体将共享同一个队列实例。 - 这对于服务通信至关重要 - 服务端和客户端必须使用相同的队列实例， - 否则会导致间歇性超时（竞态条件）。 - - Args: - new_queue_id: 新的队列ID，如果为None则自动生成 - - Returns: - 新的描述符实例，如果原实例已初始化则共享队列实例 - """ - # 创建同类型的新实例 - cloned = RPCQueueDescriptor( - host=self.host, - port=self.port, - connection_timeout=self.connection_timeout, - retry_count=self.retry_count, - enable_pooling=self.enable_pooling, - queue_id=new_queue_id, - ) - - # 【关键修复】共享队列实例，避免竞态条件 - # 如果原描述符已经初始化了队列实例，克隆体应该共享同一个实例 - # 这确保服务端和客户端使用相同的队列，防止响应丢失 - if self._initialized: - cloned._queue_instance = self._queue_instance - cloned._initialized = True - - return cloned diff --git a/packages/sage-platform/src/sage/platform/service/__init__.py b/packages/sage-platform/src/sage/platform/service/__init__.py deleted file mode 100644 index 7dea48e8fd..0000000000 --- a/packages/sage-platform/src/sage/platform/service/__init__.py +++ /dev/null @@ -1,22 +0,0 @@ -"""SAGE Platform - Service Abstractions - -Layer: L2 (Platform Services - Service Module) - -Base classes for SAGE services. - -This module provides the BaseService abstract class that all SAGE services -should inherit from. It provides: -- Service context integration -- Logger access -- Service-to-service communication helpers - -Architecture: -- Uses TYPE_CHECKING import for sage.kernel types (acceptable for type hints) -- Provides runtime service infrastructure -""" - -from .base_service import BaseService - -__all__ = [ - "BaseService", -] diff --git a/packages/sage-platform/src/sage/platform/service/base_service.py b/packages/sage-platform/src/sage/platform/service/base_service.py deleted file mode 100644 index 1a6f79d506..0000000000 --- a/packages/sage-platform/src/sage/platform/service/base_service.py +++ /dev/null @@ -1,148 +0,0 @@ -"""Base Service Abstract Class - -Layer: L2 (Platform Services) - -Provides the base class for all SAGE services with: -- Service context integration -- Logger management -- Service-to-service communication helpers - -Architecture Note: -- Uses TYPE_CHECKING import for ServiceContext (L3) - acceptable for type hints only -- Runtime injection of context happens through ServiceFactory -""" - -import logging -from abc import ABC -from typing import TYPE_CHECKING, Optional - -if TYPE_CHECKING: - from sage.kernel.runtime.context.service_context import ServiceContext - - -class BaseService(ABC): - """BaseService is the abstract base class for all services in SAGE. - It defines the core interface and provides access to runtime context and logger. - """ - - def __init__(self, *args, **kwargs): - """ - 初始化基础服务 - - Args: - *args: 位置参数 - **kwargs: 关键字参数 - - Note: - ctx 会在实例创建时由 ServiceFactory 自动注入， - 服务类不需要在构造函数中声明 ctx 参数 - """ - # ctx 由 ServiceFactory 在 __init__ 调用前通过 __new__ 方法注入 - if not hasattr(self, "ctx"): - self.ctx: Optional[ServiceContext] = None - self._logger = None - - @property - def logger(self): - """获取logger，优先使用ctx.logger，否则使用默认logger""" - if not hasattr(self, "_logger") or self._logger is None: - if self.ctx is None: - self._logger = logging.getLogger(self.__class__.__name__) - else: - self._logger = self.ctx.logger - return self._logger - - @property - def name(self): - """获取服务名称，如果有ctx则使用ctx.name，否则使用类名""" - if self.ctx is not None: - return self.ctx.name - return self.__class__.__name__ - - def call_service( - self, - service_name: str, - *args, - timeout: Optional[float] = None, - method: Optional[str] = None, - **kwargs, - ): - """ - 同步服务调用语法糖 - - 用法: - result = self.call_service("cache_service", key, method="get") - data = self.call_service("pipeline_name", payload) - """ - if self.ctx is None: - raise RuntimeError("Service context not initialized. Cannot access services.") - - return self.ctx.call_service(service_name, *args, timeout=timeout, method=method, **kwargs) - - def call_service_async( - self, - service_name: str, - *args, - timeout: Optional[float] = None, - method: Optional[str] = None, - **kwargs, - ): - """ - 异步服务调用语法糖 - - 用法: - future = self.call_service_async("cache_service", key, method="get") - result = future.result() # 阻塞等待结果 - - # 或者非阻塞检查 - if future.done(): - result = future.result() - """ - if self.ctx is None: - raise RuntimeError("Service context not initialized. Cannot access services.") - - return self.ctx.call_service_async( - service_name, *args, timeout=timeout, method=method, **kwargs - ) - - def setup(self): - """ - 服务初始化设置方法，在service_instance创建后调用 - 子类可以重写此方法来进行初始化设置 - """ - pass - - def cleanup(self): - """ - 服务清理方法，在服务停止时调用 - 子类可以重写此方法来进行资源清理 - """ - pass - - def start(self): - """ - 服务启动方法，在服务启动时调用 - 子类可以重写此方法来进行启动逻辑 - """ - pass - - def stop(self): - """ - 服务停止方法，在服务停止时调用 - 子类可以重写此方法来进行停止逻辑 - """ - pass - - def get_status(self) -> dict: - """获取服务状态（被动插入模式） - - 用于 PostInsert 算子查询服务内部状态，实现被动插入+状态查询模式。 - 子类应重写此方法以返回具体的待处理状态。 - - Returns: - dict: 服务状态字典，包含: - - pending_action: str | None, 待处理动作类型 - - pending_items: List[Dict], 待处理条目 - - ...其他服务特定字段 - """ - return {"pending_action": None, "pending_items": []} diff --git a/packages/sage-platform/src/sage/platform/storage/__init__.py b/packages/sage-platform/src/sage/platform/storage/__init__.py deleted file mode 100644 index f3eb133b27..0000000000 --- a/packages/sage-platform/src/sage/platform/storage/__init__.py +++ /dev/null @@ -1,63 +0,0 @@ -"""SAGE Platform - Storage Abstractions - -Layer: L2 (Platform Services - Storage Module) - -Key-Value storage backend interfaces. - -This module provides storage abstractions for key-value stores: -- BaseKVBackend: Abstract interface for KV backends -- DictKVBackend: In-memory dictionary-based implementation -- HDFSConfig: HDFS connection configuration (optional, requires pyarrow) -- HDFSFileSystem: HDFS filesystem operations (optional, requires pyarrow) - -Architecture: -- Pure L2 module, no cross-layer dependencies -- Provides backend-agnostic storage interface -- HDFS support is optional and requires additional dependencies -""" - -from .base_kv_backend import BaseKVBackend -from .dict_kv_backend import DictKVBackend - -# HDFS 支持为可选功能,需要安装 pyarrow -# HDFS support is optional and requires pyarrow installation -HDFS_AVAILABLE = False -try: - from .hdfs_config import HDFSConfig - from .hdfs_filesystem import ( - HDFSConnectionError, - HDFSConnectionPool, - HDFSError, - HDFSFileNotFoundError, - HDFSFileSystem, - HDFSIOError, - HDFSPermissionError, - ) - - HDFS_AVAILABLE = True -except ImportError: - # pyarrow 未安装,HDFS 功能不可用 - # pyarrow not installed, HDFS features unavailable - pass - -__all__ = [ - "BaseKVBackend", - "DictKVBackend", - "HDFS_AVAILABLE", -] - -# 仅在 HDFS 可用时导出 HDFS 相关类 -# Export HDFS classes only when available -if HDFS_AVAILABLE: - __all__.extend( - [ - "HDFSConfig", - "HDFSFileSystem", - "HDFSConnectionPool", - "HDFSError", - "HDFSConnectionError", - "HDFSFileNotFoundError", - "HDFSPermissionError", - "HDFSIOError", - ] - ) diff --git a/packages/sage-platform/src/sage/platform/storage/base_kv_backend.py b/packages/sage-platform/src/sage/platform/storage/base_kv_backend.py deleted file mode 100644 index e8cf795a6e..0000000000 --- a/packages/sage-platform/src/sage/platform/storage/base_kv_backend.py +++ /dev/null @@ -1,79 +0,0 @@ -"""Base Key-Value Backend Abstract Class - -Layer: L2 (Platform Services - Storage) - -Abstract base class for key-value storage backends. -Defines the interface that all KV storage implementations must follow. - -Architecture: -- Pure L2 abstraction, no dependencies on upper layers -- Provides backend-agnostic storage interface -""" - -# file: sage/core/sage.middleware.services.neuromem./storage_engine/kv_backend/base_kv_backend.py - -from abc import ABC, abstractmethod -from typing import Any - - -class BaseKVBackend(ABC): - """Abstract base class for key-value backends. - - 抽象基类，用于定义 KV 存储后端接口规范。 - """ - - @abstractmethod - def get_all_keys(self) -> list[str]: - pass - - @abstractmethod - def has(self, key: str) -> bool: - """ - Check whether the key exists. - 检查指定键是否存在。 - """ - pass - - @abstractmethod - def get(self, key: str) -> Any: - """ - Retrieve value by key. - 根据键获取对应的值。 - """ - pass - - @abstractmethod - def set(self, key: str, value: Any): - """ - Set a key-value pair. - 存储键值对。 - """ - pass - - @abstractmethod - def delete(self, key: str): - """ - Delete a key-value pair. - 删除指定键及其对应的值。 - """ - pass - - @abstractmethod - def clear(self): - """ - Clear the entire store. - 清空所有键值对。 - """ - pass - - @abstractmethod - def load_data_to_memory(self, path: str): - pass - - @abstractmethod - def store_data_to_disk(self, path: str): - pass - - @abstractmethod - def clear_disk_data(self, path: str): - pass diff --git a/packages/sage-platform/src/sage/platform/storage/dict_kv_backend.py b/packages/sage-platform/src/sage/platform/storage/dict_kv_backend.py deleted file mode 100644 index 1c7f218803..0000000000 --- a/packages/sage-platform/src/sage/platform/storage/dict_kv_backend.py +++ /dev/null @@ -1,56 +0,0 @@ -# file: sage/core/sage.middleware.services.neuromem./storage_engine/kv_backend/dict_kv_backend.py - -import json -import os -from typing import Any - -from .base_kv_backend import BaseKVBackend - - -class DictKVBackend(BaseKVBackend): - """ - In-memory KV backend using a Python dictionary. - """ - - def __init__(self): - self._store: dict[str, Any] = {} - - def has(self, key: str) -> bool: - return key in self._store - - def get(self, key: str) -> Any: - return self._store.get(key) - - def set(self, key: str, value: Any): - self._store[key] = value - - def delete(self, key: str): - self._store.pop(key, None) - - def clear(self): - self._store.clear() - - def get_all_keys(self): - """ - Get all keys in the store. - 获取所有存储的key。 - """ - return list(self._store.keys()) - - def store_data_to_disk(self, path: str): - """将当前内存数据存储为 JSON 文件""" - with open(path, "w", encoding="utf-8") as f: - # 用 ensure_ascii=False 保证 utf-8 兼容中文，indent=2 可读性高 - json.dump(self._store, f, ensure_ascii=False, indent=2) - - def load_data_to_memory(self, path: str): - """从指定 JSON 文件加载数据到内存（覆盖当前 _store）""" - if not os.path.exists(path): - raise FileNotFoundError(f"File '{path}' does not exist.") - with open(path, encoding="utf-8") as f: - self._store = json.load(f) - - def clear_disk_data(self, path: str): - """删除指定 JSON 文件""" - if os.path.exists(path): - os.remove(path) diff --git a/packages/sage-platform/src/sage/platform/storage/hdfs_config.py b/packages/sage-platform/src/sage/platform/storage/hdfs_config.py deleted file mode 100644 index b4c8095723..0000000000 --- a/packages/sage-platform/src/sage/platform/storage/hdfs_config.py +++ /dev/null @@ -1,178 +0,0 @@ -"""HDFS Configuration - -Layer: L2 (Platform Services - Storage Module) - -HDFS 连接配置类,用于管理 HDFS 集群连接参数。 -""" - -import os -from dataclasses import dataclass, field -from typing import Optional - - -@dataclass -class HDFSConfig: - """HDFS 连接配置类 / HDFS Connection Configuration - - 用于配置 HDFS 集群的连接参数,支持从环境变量、字典等多种方式初始化。 - """ - - # 核心参数 / Core Parameters - namenode_host: str = "localhost" # HDFS NameNode 主机地址 - namenode_port: int = 9000 # HDFS NameNode 端口 (默认 9000) - user: str = field(default_factory=lambda: os.getenv("USER", "hdfs")) # HDFS 用户名 - - # 存储参数 / Storage Parameters - base_path: str = "/sage" # HDFS 基础路径前缀 - replication: int = 3 # HDFS 副本数量 (建议 3-5) - block_size: int = 134217728 # HDFS 块大小 128 MB - - # 连接池参数 / Connection Pool Parameters - max_connections: int = 10 # 最大连接数 - connection_timeout: int = 30 # 连接超时时间(秒) - - # 重试参数 / Retry Parameters - retry_attempts: int = 3 # 最大重试次数 - retry_delay: float = 1.0 # 初始重试延迟(秒) - retry_backoff: float = 2.0 # 重试退避因子 (指数退避) - - # 安全参数 / Security Parameters - kerberos_enabled: bool = False # 是否启用 Kerberos 认证 - kerberos_principal: Optional[str] = None # Kerberos 主体名称 - kerberos_keytab: Optional[str] = None # Kerberos keytab 文件路径 - - # 高可用参数 / High Availability Parameters - ha_enabled: bool = False # 是否启用 HDFS HA - nameservice: Optional[str] = None # HDFS NameService ID (HA 模式) - namenodes: Optional[list[str]] = None # NameNode 节点列表 (HA 模式) - - # 性能参数 / Performance Parameters - buffer_size: int = 4096 # I/O 缓冲区大小 4 KB - read_timeout: int = 60 # 读取超时时间(秒) - write_timeout: int = 60 # 写入超时时间(秒) - - def validate(self) -> None: - """验证配置参数的有效性 / Validate configuration parameters""" - if not 1 <= self.namenode_port <= 65535: - raise ValueError(f"Invalid namenode_port: {self.namenode_port}") - if not 1 <= self.replication <= 10: - raise ValueError(f"Invalid replication: {self.replication}") - if self.block_size < 1048576: # 至少 1MB - raise ValueError(f"Invalid block_size: {self.block_size}") - if self.max_connections < 1: - raise ValueError(f"Invalid max_connections: {self.max_connections}") - if self.connection_timeout <= 0: - raise ValueError(f"Invalid connection_timeout: {self.connection_timeout}") - if self.retry_attempts < 0: - raise ValueError(f"Invalid retry_attempts: {self.retry_attempts}") - if self.retry_delay < 0: - raise ValueError(f"Invalid retry_delay: {self.retry_delay}") - if self.retry_backoff < 1.0: - raise ValueError(f"Invalid retry_backoff: {self.retry_backoff}") - if self.buffer_size < 512: - raise ValueError(f"Invalid buffer_size: {self.buffer_size}") - if self.read_timeout <= 0: - raise ValueError(f"Invalid read_timeout: {self.read_timeout}") - if self.write_timeout <= 0: - raise ValueError(f"Invalid write_timeout: {self.write_timeout}") - if self.ha_enabled: - if not self.nameservice: - raise ValueError("HA enabled but nameservice not specified") - if not self.namenodes or len(self.namenodes) == 0: - raise ValueError("HA enabled but namenodes list is empty") - if self.kerberos_enabled and not self.kerberos_principal: - raise ValueError("Kerberos enabled but principal not specified") - - @classmethod - def from_env(cls) -> "HDFSConfig": - """从环境变量创建配置 / Create configuration from environment variables""" - - def parse_bool(value: Optional[str], default: bool) -> bool: - if value is None: - return default - return value.lower() in ("true", "1", "yes") - - def parse_int(value: Optional[str], default: int) -> int: - if value is None: - return default - try: - return int(value) - except ValueError: - return default - - def parse_float(value: Optional[str], default: float) -> float: - if value is None: - return default - try: - return float(value) - except ValueError: - return default - - namenodes_str = os.getenv("HDFS_NAMENODES") - namenodes = ( - [nn.strip() for nn in namenodes_str.split(",") if nn.strip()] if namenodes_str else None - ) - - return cls( - namenode_host=os.getenv("HDFS_NAMENODE_HOST", "localhost"), - namenode_port=parse_int(os.getenv("HDFS_NAMENODE_PORT"), 9000), - user=os.getenv("HDFS_USER", os.getenv("USER", "hdfs")), - base_path=os.getenv("HDFS_BASE_PATH", "/sage"), - replication=parse_int(os.getenv("HDFS_REPLICATION"), 3), - block_size=parse_int(os.getenv("HDFS_BLOCK_SIZE"), 134217728), - max_connections=parse_int(os.getenv("HDFS_MAX_CONNECTIONS"), 10), - connection_timeout=parse_int(os.getenv("HDFS_CONNECTION_TIMEOUT"), 30), - retry_attempts=parse_int(os.getenv("HDFS_RETRY_ATTEMPTS"), 3), - retry_delay=parse_float(os.getenv("HDFS_RETRY_DELAY"), 1.0), - retry_backoff=parse_float(os.getenv("HDFS_RETRY_BACKOFF"), 2.0), - kerberos_enabled=parse_bool(os.getenv("HDFS_KERBEROS_ENABLED"), False), - kerberos_principal=os.getenv("HDFS_KERBEROS_PRINCIPAL"), - kerberos_keytab=os.getenv("HDFS_KERBEROS_KEYTAB"), - ha_enabled=parse_bool(os.getenv("HDFS_HA_ENABLED"), False), - nameservice=os.getenv("HDFS_NAMESERVICE"), - namenodes=namenodes, - buffer_size=parse_int(os.getenv("HDFS_BUFFER_SIZE"), 4096), - read_timeout=parse_int(os.getenv("HDFS_READ_TIMEOUT"), 60), - write_timeout=parse_int(os.getenv("HDFS_WRITE_TIMEOUT"), 60), - ) - - @classmethod - def from_dict(cls, config_dict: dict) -> "HDFSConfig": - """从字典创建配置 / Create configuration from dictionary""" - return cls(**config_dict) - - def to_dict(self) -> dict: - """转换为字典 / Convert to dictionary""" - return { - "namenode_host": self.namenode_host, - "namenode_port": self.namenode_port, - "user": self.user, - "base_path": self.base_path, - "replication": self.replication, - "block_size": self.block_size, - "max_connections": self.max_connections, - "connection_timeout": self.connection_timeout, - "retry_attempts": self.retry_attempts, - "retry_delay": self.retry_delay, - "retry_backoff": self.retry_backoff, - "kerberos_enabled": self.kerberos_enabled, - "kerberos_principal": self.kerberos_principal, - "kerberos_keytab": self.kerberos_keytab, - "ha_enabled": self.ha_enabled, - "nameservice": self.nameservice, - "namenodes": self.namenodes, - "buffer_size": self.buffer_size, - "read_timeout": self.read_timeout, - "write_timeout": self.write_timeout, - } - - @property - def connection_string(self) -> str: - """生成 HDFS 连接字符串 / Generate HDFS connection string - - 标准模式: "hdfs://{host}:{port}" - HA 模式: "hdfs://{nameservice}" - """ - if self.ha_enabled and self.nameservice: - return f"hdfs://{self.nameservice}" - return f"hdfs://{self.namenode_host}:{self.namenode_port}" diff --git a/packages/sage-platform/src/sage/platform/storage/hdfs_filesystem.py b/packages/sage-platform/src/sage/platform/storage/hdfs_filesystem.py deleted file mode 100644 index 09a717c864..0000000000 --- a/packages/sage-platform/src/sage/platform/storage/hdfs_filesystem.py +++ /dev/null @@ -1,543 +0,0 @@ -"""HDFS FileSystem Operations - -Layer: L2 (Platform Services - Storage Module) - -HDFS 文件系统操作封装,提供线程安全的连接池、重试机制和完整的文件操作接口。 -""" - -import logging -from collections import deque -from pathlib import Path -from threading import Lock -from typing import Any, Optional - -from sage.platform.utils import retry_with_config - -from .hdfs_config import HDFSConfig - -logger = logging.getLogger(__name__) - - -# ========== 异常定义 / Exception Definitions ========== - - -class HDFSError(Exception): - """HDFS 基础异常类 / Base HDFS exception""" - - pass - - -class HDFSConnectionError(HDFSError): - """HDFS 连接错误 / HDFS connection error""" - - pass - - -class HDFSFileNotFoundError(HDFSError): - """HDFS 文件不存在错误 / HDFS file not found error""" - - pass - - -class HDFSPermissionError(HDFSError): - """HDFS 权限错误 / HDFS permission error""" - - pass - - -class HDFSIOError(HDFSError): - """HDFS I/O 错误 / HDFS I/O error""" - - pass - - -# ========== 连接池实现 / Connection Pool Implementation ========== - - -class HDFSConnectionPool: - """HDFS 连接池 - 线程安全的连接管理 - - 管理 HDFS 连接的创建、复用和健康检查,避免频繁建立连接的开销。 - """ - - def __init__(self, config: HDFSConfig): - """初始化连接池 - - Args: - config: HDFS 配置对象 - """ - self.config = config - self._pool: deque = deque(maxlen=config.max_connections) - self._lock = Lock() - self._created_count = 0 - - logger.info(f"HDFSConnectionPool initialized: max_connections={config.max_connections}") - - def get_connection(self): - """从连接池获取连接 - - Returns: - pyarrow.fs.HadoopFileSystem: HDFS 连接对象 - """ - with self._lock: - # 尝试复用现有连接 - while self._pool: - conn = self._pool.popleft() - if self._is_connection_healthy(conn): - logger.debug("Reusing existing HDFS connection") - return conn - logger.debug("Discarding unhealthy connection") - - # 创建新连接 - return self._create_new_connection() - - def return_connection(self, conn): - """将连接返回到连接池 - - Args: - conn: HDFS 连接对象 - """ - if conn is None: - return - - with self._lock: - if len(self._pool) < self.config.max_connections: - self._pool.append(conn) - logger.debug(f"Connection returned to pool, size={len(self._pool)}") - else: - logger.debug("Pool full, discarding connection") - - def _create_new_connection(self): - """创建新的 HDFS 连接 - - Returns: - pyarrow.fs.HadoopFileSystem: 新创建的 HDFS 连接 - - Raises: - HDFSConnectionError: 连接创建失败时抛出 - """ - try: - import pyarrow.fs as pafs - - self._created_count += 1 - logger.info( - f"Creating new HDFS connection #{self._created_count}: " - f"{self.config.connection_string}" - ) - - # 创建 HDFS 连接 - fs = pafs.HadoopFileSystem( - host=self.config.namenode_host, - port=self.config.namenode_port, - user=self.config.user, - ) - - return fs - - except ImportError as e: - raise HDFSConnectionError( - "pyarrow is not installed. Please install it: pip install pyarrow" - ) from e - except Exception as e: - raise HDFSConnectionError(f"Failed to create HDFS connection: {e}") from e - - def _is_connection_healthy(self, conn) -> bool: - """检查连接是否健康 - - Args: - conn: HDFS 连接对象 - - Returns: - bool: 连接是否健康 - """ - try: - # 简单的健康检查: 尝试获取根目录信息 - conn.get_file_info(self.config.base_path) - return True - except Exception: - return False - - def close_all(self): - """关闭所有连接""" - with self._lock: - while self._pool: - self._pool.popleft() - try: - # pyarrow FileSystem 通常不需要显式关闭 - pass - except Exception as e: - logger.error(f"Error closing connection: {e}") - - logger.info("All HDFS connections closed") - - -# ========== 重试装饰器 / Retry Decorator ========== - -# 使用统一的重试装饰器 -# retry_with_config 从 self.config 读取 retry_attempts, retry_delay, retry_backoff -retry_on_failure = retry_with_config - - -# ========== HDFS 文件系统类 / HDFS FileSystem Class ========== - - -class HDFSFileSystem: - """HDFS 文件系统操作类 - - 提供完整的 HDFS 文件操作接口,包括: - - 文件读写、删除、列表 - - 目录创建、删除 - - 元数据查询 - - 文件复制、移动 - - 连接管理和自动重试 - """ - - def __init__(self, config: HDFSConfig): - """初始化 HDFS 文件系统 - - Args: - config: HDFS 配置对象 - """ - self.config = config - self.config.validate() # 验证配置 - - self._pool = HDFSConnectionPool(config) - self._fs = None - - logger.info(f"HDFSFileSystem initialized: {config.connection_string}") - - def connect(self): - """建立 HDFS 连接""" - if self._fs is None: - self._fs = self._pool.get_connection() - logger.info("HDFS connection established") - - def disconnect(self): - """断开 HDFS 连接""" - if self._fs is not None: - self._pool.return_connection(self._fs) - self._fs = None - logger.info("HDFS connection disconnected") - - def is_connected(self) -> bool: - """检查是否已连接 - - Returns: - bool: 是否已建立连接 - """ - return self._fs is not None - - def _ensure_connected(self): - """确保已连接,否则抛出异常""" - if not self.is_connected(): - raise HDFSConnectionError("Not connected to HDFS. Call connect() first.") - - def _normalize_path(self, path: str) -> str: - """规范化路径 - 添加 base_path 前缀 - - Args: - path: 原始路径 - - Returns: - str: 规范化后的完整路径 - """ - if path.startswith("/"): - return path - return f"{self.config.base_path}/{path}".replace("//", "/") - - # ========== 文件操作 / File Operations ========== - - @retry_on_failure() - def write_file(self, path: str, data: bytes, overwrite: bool = True): - """写入文件到 HDFS - - Args: - path: 文件路径 (相对于 base_path) - data: 文件数据 (字节) - overwrite: 是否覆盖已存在的文件 - - Raises: - HDFSIOError: 写入失败时抛出 - """ - self._ensure_connected() - full_path = self._normalize_path(path) - - try: - with self._fs.open_output_stream(full_path) as f: - f.write(data) - logger.info(f"File written: {full_path} ({len(data)} bytes)") - except Exception as e: - raise HDFSIOError(f"Failed to write file {full_path}: {e}") from e - - @retry_on_failure() - def read_file(self, path: str) -> bytes: - """从 HDFS 读取文件 - - Args: - path: 文件路径 (相对于 base_path) - - Returns: - bytes: 文件数据 - - Raises: - HDFSFileNotFoundError: 文件不存在时抛出 - HDFSIOError: 读取失败时抛出 - """ - self._ensure_connected() - full_path = self._normalize_path(path) - - try: - with self._fs.open_input_stream(full_path) as f: - data = f.read() - logger.info(f"File read: {full_path} ({len(data)} bytes)") - return data - except FileNotFoundError as e: - raise HDFSFileNotFoundError(f"File not found: {full_path}") from e - except Exception as e: - raise HDFSIOError(f"Failed to read file {full_path}: {e}") from e - - @retry_on_failure() - def delete_file(self, path: str): - """删除 HDFS 文件 - - Args: - path: 文件路径 (相对于 base_path) - - Raises: - HDFSFileNotFoundError: 文件不存在时抛出 - HDFSIOError: 删除失败时抛出 - """ - self._ensure_connected() - full_path = self._normalize_path(path) - - try: - self._fs.delete_file(full_path) - logger.info(f"File deleted: {full_path}") - except FileNotFoundError as e: - raise HDFSFileNotFoundError(f"File not found: {full_path}") from e - except Exception as e: - raise HDFSIOError(f"Failed to delete file {full_path}: {e}") from e - - @retry_on_failure() - def exists(self, path: str) -> bool: - """检查文件或目录是否存在 - - Args: - path: 文件/目录路径 (相对于 base_path) - - Returns: - bool: 是否存在 - """ - self._ensure_connected() - full_path = self._normalize_path(path) - - try: - info = self._fs.get_file_info(full_path) - return info.type != 0 # 0 = NotFound - except Exception: - return False - - @retry_on_failure() - def list_files(self, path: str = "/", pattern: Optional[str] = None) -> list[str]: - """列出目录下的文件 - - Args: - path: 目录路径 (相对于 base_path) - pattern: 文件名模式 (如 "*.txt", 可选) - - Returns: - list[str]: 文件路径列表 - - Raises: - HDFSFileNotFoundError: 目录不存在时抛出 - HDFSIOError: 列表失败时抛出 - """ - self._ensure_connected() - full_path = self._normalize_path(path) - - try: - selector = pafs.FileSelector(full_path, recursive=False) - file_infos = self._fs.get_file_info(selector) - - files = [info.path for info in file_infos if info.is_file] - - # 应用模式过滤 - if pattern: - from fnmatch import fnmatch - - files = [f for f in files if fnmatch(Path(f).name, pattern)] - - logger.info(f"Listed {len(files)} files in {full_path}") - return files - except FileNotFoundError as e: - raise HDFSFileNotFoundError(f"Directory not found: {full_path}") from e - except Exception as e: - raise HDFSIOError(f"Failed to list files in {full_path}: {e}") from e - - # ========== 目录操作 / Directory Operations ========== - - @retry_on_failure() - def mkdir(self, path: str, recursive: bool = True): - """创建目录 - - Args: - path: 目录路径 (相对于 base_path) - recursive: 是否递归创建父目录 - - Raises: - HDFSIOError: 创建失败时抛出 - """ - self._ensure_connected() - full_path = self._normalize_path(path) - - try: - self._fs.create_dir(full_path, recursive=recursive) - logger.info(f"Directory created: {full_path}") - except Exception as e: - raise HDFSIOError(f"Failed to create directory {full_path}: {e}") from e - - @retry_on_failure() - def rmdir(self, path: str): - """删除目录 - - Args: - path: 目录路径 (相对于 base_path) - - Raises: - HDFSFileNotFoundError: 目录不存在时抛出 - HDFSIOError: 删除失败时抛出 - """ - self._ensure_connected() - full_path = self._normalize_path(path) - - try: - self._fs.delete_dir(full_path) - logger.info(f"Directory deleted: {full_path}") - except FileNotFoundError as e: - raise HDFSFileNotFoundError(f"Directory not found: {full_path}") from e - except Exception as e: - raise HDFSIOError(f"Failed to delete directory {full_path}: {e}") from e - - # ========== 元数据操作 / Metadata Operations ========== - - @retry_on_failure() - def get_file_info(self, path: str) -> dict[str, Any]: - """获取文件/目录信息 - - Args: - path: 文件/目录路径 (相对于 base_path) - - Returns: - dict: 包含文件信息的字典 (type, size, mtime) - - Raises: - HDFSFileNotFoundError: 文件不存在时抛出 - """ - self._ensure_connected() - full_path = self._normalize_path(path) - - try: - info = self._fs.get_file_info(full_path) - return { - "path": info.path, - "type": "file" if info.is_file else "directory", - "size": info.size, - "mtime": info.mtime, - } - except FileNotFoundError as e: - raise HDFSFileNotFoundError(f"Path not found: {full_path}") from e - - @retry_on_failure() - def get_file_size(self, path: str) -> int: - """获取文件大小 - - Args: - path: 文件路径 (相对于 base_path) - - Returns: - int: 文件大小(字节) - """ - info = self.get_file_info(path) - return info["size"] - - @retry_on_failure() - def get_modification_time(self, path: str) -> float: - """获取文件修改时间 - - Args: - path: 文件路径 (相对于 base_path) - - Returns: - float: 修改时间 (Unix 时间戳) - """ - info = self.get_file_info(path) - return info["mtime"] - - # ========== 高级操作 / Advanced Operations ========== - - @retry_on_failure() - def copy(self, src: str, dst: str, overwrite: bool = False): - """复制文件 - - Args: - src: 源文件路径 (相对于 base_path) - dst: 目标文件路径 (相对于 base_path) - overwrite: 是否覆盖目标文件 - - Raises: - HDFSFileNotFoundError: 源文件不存在时抛出 - HDFSIOError: 复制失败时抛出 - """ - self._ensure_connected() - src_path = self._normalize_path(src) - dst_path = self._normalize_path(dst) - - try: - # pyarrow 不直接支持 copy,使用 read + write - data = self.read_file(src) - self.write_file(dst, data, overwrite=overwrite) - logger.info(f"File copied: {src_path} -> {dst_path}") - except Exception as e: - raise HDFSIOError(f"Failed to copy file {src_path} to {dst_path}: {e}") from e - - @retry_on_failure() - def move(self, src: str, dst: str, overwrite: bool = False): - """移动文件 - - Args: - src: 源文件路径 (相对于 base_path) - dst: 目标文件路径 (相对于 base_path) - overwrite: 是否覆盖目标文件 - - Raises: - HDFSFileNotFoundError: 源文件不存在时抛出 - HDFSIOError: 移动失败时抛出 - """ - self._ensure_connected() - src_path = self._normalize_path(src) - dst_path = self._normalize_path(dst) - - try: - self._fs.move(src_path, dst_path) - logger.info(f"File moved: {src_path} -> {dst_path}") - except Exception as e: - raise HDFSIOError(f"Failed to move file {src_path} to {dst_path}: {e}") from e - - # ========== 上下文管理器 / Context Manager ========== - - def __enter__(self): - """上下文管理器入口 - 自动连接 HDFS""" - self.connect() - return self - - def __exit__(self, exc_type, exc_val, exc_tb): - """上下文管理器出口 - 自动断开 HDFS 连接""" - self.disconnect() - return False - - -# 导入 pyarrow FileSelector (需要在模块级别) -try: - import pyarrow.fs as pafs -except ImportError: - pafs = None diff --git a/packages/sage-platform/src/sage/platform/utils.py b/packages/sage-platform/src/sage/platform/utils.py deleted file mode 100644 index 996f05a507..0000000000 --- a/packages/sage-platform/src/sage/platform/utils.py +++ /dev/null @@ -1,281 +0,0 @@ -""" -Platform Utilities - 统一的工具函数 - -Layer: L2 (Platform Services - Utilities) - -提供 sage-platform 包中常用的工具函数和装饰器。 -""" - -import logging -import os -import time -from functools import wraps -from pathlib import Path -from typing import TYPE_CHECKING, Any, Callable, Optional, TypeVar - -if TYPE_CHECKING: - from sage.common.utils.logging.custom_logger import CustomLogger - -logger = logging.getLogger(__name__) - -# Type variable for generic retry decorator -T = TypeVar("T") - - -# ============================================================================= -# 重试装饰器 -# ============================================================================= - - -def retry_with_backoff( - max_attempts: int = 3, - initial_delay: float = 1.0, - backoff_factor: float = 2.0, - exceptions: tuple = (Exception,), - on_retry: Optional[Callable[[int, Exception], None]] = None, -): - """通用重试装饰器 - 失败时自动重试，支持指数退避 - - Args: - max_attempts: 最大尝试次数（包括首次） - initial_delay: 初始重试延迟（秒） - backoff_factor: 退避因子，每次重试延迟乘以此值 - exceptions: 需要重试的异常类型元组 - on_retry: 重试时的回调函数，接收 (attempt_number, exception) - - Returns: - 装饰后的函数 - - Example: - @retry_with_backoff(max_attempts=3, initial_delay=1.0) - def fetch_data(): - ... - """ - - def decorator(func: Callable[..., T]) -> Callable[..., T]: - @wraps(func) - def wrapper(*args, **kwargs) -> T: - last_exception: Optional[Exception] = None - delay = initial_delay - - for attempt in range(max_attempts): - try: - return func(*args, **kwargs) - except exceptions as e: - last_exception = e - if attempt < max_attempts - 1: - if on_retry: - on_retry(attempt + 1, e) - else: - logger.warning( - f"Attempt {attempt + 1}/{max_attempts} failed: {e}. " - f"Retrying in {delay:.2f}s..." - ) - time.sleep(delay) - delay *= backoff_factor - else: - logger.error(f"All {max_attempts} attempts failed for {func.__name__}") - - if last_exception: - raise last_exception - raise RuntimeError("Unexpected: no exception but all attempts failed") - - return wrapper - - return decorator - - -def retry_with_config( - max_attempts_attr: str = "retry_attempts", - delay_attr: str = "retry_delay", - backoff_attr: str = "retry_backoff", -): - """从对象配置读取重试参数的装饰器 - - 用于类方法，从 self.config 读取重试配置。 - - Args: - max_attempts_attr: config 中最大尝试次数的属性名 - delay_attr: config 中初始延迟的属性名 - backoff_attr: config 中退避因子的属性名 - - Example: - class MyService: - @retry_with_config() - def fetch_data(self): - ... - """ - - def decorator(func: Callable) -> Callable: - @wraps(func) - def wrapper(self, *args, **kwargs): - config = getattr(self, "config", None) - if config is None: - # 没有配置，直接调用 - return func(self, *args, **kwargs) - - max_attempts = getattr(config, max_attempts_attr, 3) - delay = getattr(config, delay_attr, 1.0) - backoff = getattr(config, backoff_attr, 2.0) - - last_exception: Optional[Exception] = None - current_delay = delay - - for attempt in range(max_attempts): - try: - return func(self, *args, **kwargs) - except Exception as e: - last_exception = e - if attempt < max_attempts - 1: - logger.warning( - f"Attempt {attempt + 1}/{max_attempts} failed: {e}. " - f"Retrying in {current_delay:.2f}s..." - ) - time.sleep(current_delay) - current_delay *= backoff - else: - logger.error(f"All {max_attempts} attempts failed for {func.__name__}") - - if last_exception: - raise last_exception - raise RuntimeError("Unexpected: no exception but all attempts failed") - - return wrapper - - return decorator - - -# ============================================================================= -# 日志工具 -# ============================================================================= - -# 缓存的 logger 实例 -_cached_loggers: dict[str, "CustomLogger"] = {} - - -def get_component_logger( - component_name: str, - log_levels: Optional[list[tuple[str, str]]] = None, -) -> "CustomLogger": - """获取组件专用的 CustomLogger - - 使用 SAGE 的 CustomLogger，自动配置日志目录。 - - Args: - component_name: 组件名称，用于日志文件名 - log_levels: 日志级别配置列表，格式为 [(output, level), ...] - output 可以是 "console" 或文件路径 - - Returns: - CustomLogger 实例 - - Example: - logger = get_component_logger("RayQueue") - logger.info("Queue initialized") - """ - global _cached_loggers - - if component_name in _cached_loggers: - return _cached_loggers[component_name] - - from sage.common.utils.logging.custom_logger import CustomLogger - - # 获取日志目录（优先环境变量，其次统一的 SAGE 输出目录） - log_env = os.environ.get("SAGE_LOG_DIR") - if log_env: - log_base_dir = Path(log_env) - else: - # 使用 L1 的统一路径配置，兼容 pip 安装与开发环境 - from sage.common.config import get_sage_paths - - log_base_dir = get_sage_paths().logs_dir - - log_base_dir.mkdir(parents=True, exist_ok=True) - - # 默认日志配置 - if log_levels is None: - log_levels = [ - ("console", "DEBUG"), - (str(log_base_dir / f"{component_name.lower()}_debug.log"), "DEBUG"), - (str(log_base_dir / f"{component_name.lower()}_info.log"), "INFO"), - (str(log_base_dir / "Error.log"), "ERROR"), - ] - - custom_logger = CustomLogger(log_levels, name=component_name) - _cached_loggers[component_name] = custom_logger - return custom_logger - - -class LazyLoggerProxy: - """延迟初始化的 Logger 代理 - - 避免在模块导入时就初始化 CustomLogger。 - - Example: - logger = LazyLoggerProxy("MyComponent") - logger.info("This works") # Logger 在首次使用时初始化 - """ - - def __init__(self, component_name: str): - self._component_name = component_name - self._logger: Optional[CustomLogger] = None - - def _get_logger(self) -> "CustomLogger": - if self._logger is None: - self._logger = get_component_logger(self._component_name) - return self._logger - - def __getattr__(self, name: str) -> Any: - return getattr(self._get_logger(), name) - - -# ============================================================================= -# 队列描述符工具 -# ============================================================================= - - -def share_queue_instance_on_clone(clone_func: Callable) -> Callable: - """装饰器：确保 clone() 方法共享队列实例 - - 用于队列描述符的 clone() 方法，确保已初始化的队列实例被共享， - 避免服务通信中的竞态条件。 - - Example: - class MyQueueDescriptor(BaseQueueDescriptor): - @share_queue_instance_on_clone - def clone(self, new_queue_id=None): - return MyQueueDescriptor( - queue_id=new_queue_id, - ... - ) - """ - - @wraps(clone_func) - def wrapper(self, new_queue_id: Optional[str] = None): - # 调用原始 clone 方法创建新实例 - cloned = clone_func(self, new_queue_id) - - # 如果原实例已初始化，共享队列实例 - if getattr(self, "_initialized", False): - if hasattr(self, "_queue_instance"): - cloned._queue_instance = self._queue_instance - if hasattr(self, "_queue"): - cloned._queue = self._queue - cloned._initialized = True - - return cloned - - return wrapper - - -# ============================================================================= -# 导出 -# ============================================================================= - -__all__ = [ - "retry_with_backoff", - "retry_with_config", - "get_component_logger", - "LazyLoggerProxy", - "share_queue_instance_on_clone", -] diff --git a/packages/sage-platform/tests/unit/queue/test_inheritance_architecture.py b/packages/sage-platform/tests/unit/queue/test_inheritance_architecture.py deleted file mode 100644 index 110e96579a..0000000000 --- a/packages/sage-platform/tests/unit/queue/test_inheritance_architecture.py +++ /dev/null @@ -1,250 +0,0 @@ -""" -测试基于继承的队列描述符架构 - -验证 BaseQueueDescriptor 及其子类的功能完整性 -""" - -import pytest - -from sage.platform.queue import ( - BaseQueueDescriptor, - PythonQueueDescriptor, - RPCQueueDescriptor, - resolve_descriptor, -) - -# 检查Ray是否可用 -try: - import ray # noqa: F401 - - RAY_AVAILABLE = True -except ImportError: - RAY_AVAILABLE = False - - -class TestBaseQueueDescriptor: - """测试基础队列描述符""" - - def test_abstract_methods(self): - """测试抽象方法不能直接实例化""" - with pytest.raises(TypeError): - BaseQueueDescriptor() # type: ignore[abstract] - - -class TestPythonQueueDescriptor: - """测试Python队列描述符""" - - def test_local_queue_creation(self): - """测试本地队列创建""" - queue = PythonQueueDescriptor(queue_id="test_local", maxsize=10) - - assert queue.queue_id == "test_local" - assert queue.queue_type == "python" - assert queue.can_serialize is True - assert queue.metadata["maxsize"] == 10 - assert queue.metadata["use_multiprocessing"] is False - - def test_multiprocessing_queue_creation(self): - """测试多进程队列创建""" - queue = PythonQueueDescriptor(queue_id="test_mp", maxsize=20, use_multiprocessing=True) - - assert queue.queue_id == "test_mp" - assert queue.queue_type == "python" - assert queue.metadata["maxsize"] == 20 - assert queue.metadata["use_multiprocessing"] is True - - def test_queue_operations(self): - """测试队列基本操作""" - queue = PythonQueueDescriptor(queue_id="test_ops", maxsize=5) - - # 初始状态 - assert queue.empty() is True - assert queue.qsize() == 0 - - # 放入和取出 - queue.put("item1") - queue.put("item2") - - assert queue.empty() is False - assert queue.qsize() == 2 - - item1 = queue.get() - item2 = queue.get() - - assert item1 == "item1" - assert item2 == "item2" - assert queue.empty() is True - - def test_serialization(self): - """测试序列化功能""" - queue = PythonQueueDescriptor(queue_id="test_serial", maxsize=10) - - # 序列化为字典 - data = queue.to_dict() - assert data["queue_id"] == "test_serial" - assert data["queue_type"] == "python" - assert data["metadata"]["maxsize"] == 10 - - # 序列化为JSON - json_str = queue.to_json() - assert isinstance(json_str, str) - assert "test_serial" in json_str - - # 创建新的队列描述符来模拟反序列化 - restored = PythonQueueDescriptor( - queue_id=data["queue_id"], maxsize=data["metadata"]["maxsize"] - ) - assert restored.queue_id == queue.queue_id - assert restored.queue_type == queue.queue_type - - def test_clone(self): - """测试克隆功能""" - original = PythonQueueDescriptor(queue_id="original", maxsize=15) - clone = original.clone("cloned") - - assert clone.queue_id == "cloned" - assert clone.queue_type == original.queue_type - # 克隆应该保留原始配置 - assert clone.maxsize == 15 # 克隆保留原始的 maxsize 配置 - assert clone.is_initialized() is False - - def test_lazy_loading(self): - """测试懒加载功能""" - queue = PythonQueueDescriptor(queue_id="lazy_test") - - # 初始状态未初始化 - assert queue.is_initialized() is False - - # 首次使用时初始化 - queue.put("lazy_item") - assert queue.is_initialized() is True - - # 清除缓存 - queue.clear_cache() - assert queue.is_initialized() is False - - -# class TestRayQueueDescriptor: -# """测试Ray队列描述符""" - -# @pytest.mark.skipif(not RAY_AVAILABLE, reason="Ray not available") -# @patch('sage.kernel.runtime.communication.queue_descriptor.ray_queue_descriptor.Queue') -# @patch('ray.is_initialized') -# def test_ray_queue_creation(self, mock_ray_initialized, mock_ray_queue): -# """测试Ray队列创建""" -# mock_ray_initialized.return_value = True -# mock_queue_instance = MagicMock() -# mock_ray_queue.return_value = mock_queue_instance - -# queue = RayQueueDescriptor(queue_id="test_ray", maxsize=100) - -# assert queue.queue_id == "test_ray" -# assert queue.queue_type == "ray_queue" -# assert queue.metadata["maxsize"] == 100 - -# @pytest.mark.skipif(not RAY_AVAILABLE, reason="Ray not available") -# @patch('ray.init') -# @patch('ray.is_initialized') -# def test_ray_actor_queue_creation(self, mock_ray_initialized, mock_ray_init): -# """测试Ray Actor队列创建""" -# # 模拟 Ray 未初始化，需要先初始化 -# mock_ray_initialized.return_value = False -# mock_ray_init.return_value = None - -# with pytest.raises(Exception): # 期望抛出异常，因为没有初始化 Ray -# queue = RayQueueDescriptor( -# queue_id="test_actor", -# maxsize=200 -# ) - - -class TestRPCQueueDescriptor: - """测试RPC队列描述符""" - - def test_rpc_queue_creation(self): - """测试RPC队列创建""" - queue = RPCQueueDescriptor(queue_id="test_rpc", host="localhost", port=8080) - - assert queue.queue_id == "test_rpc" - assert queue.queue_type == "rpc_queue" - assert queue.metadata["host"] == "localhost" - assert queue.metadata["port"] == 8080 - - -class TestDescriptorResolution: - """测试描述符解析功能""" - - def test_resolve_python_descriptor(self): - """测试解析Python描述符""" - queue = PythonQueueDescriptor(queue_id="test_resolve") - data = queue.to_dict() - resolved = resolve_descriptor(data) - - # 解析应该返回相同类型的队列描述符 - assert resolved is not None - assert resolved.queue_id == queue.queue_id - assert resolved.queue_type == queue.queue_type - - -class TestErrorHandling: - """测试错误处理""" - - def test_invalid_queue_id(self): - """测试无效队列ID""" - # PythonQueueDescriptor 允许空字符串作为 queue_id，会自动生成 - # 这里测试传入 None 的情况 - queue = PythonQueueDescriptor(queue_id=None) - assert queue.queue_id is not None - assert len(queue.queue_id) > 0 - - def test_invalid_parameters(self): - """测试无效参数""" - # PythonQueueDescriptor 允许负数 maxsize，这里测试正常创建 - queue = PythonQueueDescriptor(queue_id="test", maxsize=-1) - assert queue.maxsize == -1 - - -if __name__ == "__main__": - # 运行测试 - test_suite = [ - TestBaseQueueDescriptor(), - TestPythonQueueDescriptor(), - TestRPCQueueDescriptor(), - TestDescriptorResolution(), - TestErrorHandling(), - ] - - print("Running inheritance-based queue descriptor tests...") - - try: - # 测试Python队列描述符 - python_tests = TestPythonQueueDescriptor() - python_tests.test_local_queue_creation() - print("✓ Python queue creation tests passed") - - python_tests.test_queue_operations() - print("✓ Python queue operations tests passed") - - python_tests.test_serialization() - print("✓ Python queue serialization tests passed") - - python_tests.test_clone() - print("✓ Python queue clone tests passed") - - python_tests.test_lazy_loading() - print("✓ Python queue lazy loading tests passed") - - # 测试错误处理 - error_tests = TestErrorHandling() - error_tests.test_invalid_queue_id() - print("✓ Error handling tests passed") - - print( - "\n🎉 All tests passed! The inheritance-based queue architecture is working correctly." - ) - - except Exception as e: - print(f"❌ Test failed: {e}") - import traceback - - traceback.print_exc() diff --git a/packages/sage-platform/tests/unit/queue/test_queue_descriptor.py b/packages/sage-platform/tests/unit/queue/test_queue_descriptor.py deleted file mode 100644 index d099a4bf44..0000000000 --- a/packages/sage-platform/tests/unit/queue/test_queue_descriptor.py +++ /dev/null @@ -1,449 +0,0 @@ -#!/usr/bin/env python3 -""" -Queue Descriptor Comprehensive Test Suite - -测试队列描述符系统的核心功能： -1. 基础队列操作 (put, get, empty, qsize) -2. 懒加载功能 -3. 序列化和反序列化 -4. 各种队列类型的创建和使用 -5. 错误处理和边界条件 -6. 多态性和继承架构 -""" - -import json -import time -from queue import Empty, Full - -import pytest - -from sage.platform.queue.base_queue_descriptor import BaseQueueDescriptor, QueueDescriptor -from sage.platform.queue.python_queue_descriptor import PythonQueueDescriptor - -# 尝试导入其他队列类型（可能不存在） -try: - from sage.platform.queue.ray_queue_descriptor import RayQueueDescriptor -except ImportError: - RayQueueDescriptor = None - -try: - from sage.platform.queue.rpc_queue_descriptor import RPCQueueDescriptor -except ImportError: - RPCQueueDescriptor = None - - -class _TestableQueueDescriptor(BaseQueueDescriptor): - """用于测试的具体队列描述符实现（添加下划线避免pytest收集）""" - - def __init__(self, queue_id=None, maxsize=0, mock_queue=None, extra_metadata=None): - self.maxsize = maxsize - self._mock_queue = mock_queue - self._extra_metadata = extra_metadata or {} - super().__init__(queue_id=queue_id) - - @property - def queue_type(self) -> str: - return "testable" - - @property - def can_serialize(self) -> bool: - return not self._initialized - - @property - def metadata(self): - base_metadata = {"maxsize": self.maxsize} - base_metadata.update(self._extra_metadata) - return base_metadata - - @property - def queue_instance(self): - if not self._initialized: - if self._mock_queue: - self._queue_instance = self._mock_queue - else: - from queue import Queue - - self._queue_instance = Queue(maxsize=self.maxsize) - self._initialized = True - return self._queue_instance - - -class TestBaseQueueDescriptor: - """测试基础队列描述符功能""" - - def test_initialization(self): - """测试初始化功能""" - # 测试自动生成queue_id - desc = _TestableQueueDescriptor() - assert desc.queue_id is not None - assert desc.queue_id.startswith("testable_") - assert len(desc.queue_id) > len("testable_") - - # 测试自定义queue_id - custom_id = "custom_test_queue" - desc2 = _TestableQueueDescriptor(queue_id=custom_id) - assert desc2.queue_id == custom_id - - # 测试时间戳 - assert desc.created_timestamp > 0 - assert abs(desc.created_timestamp - time.time()) < 1 # 1秒内创建 - - def test_queue_type_property(self): - """测试队列类型属性""" - desc = _TestableQueueDescriptor() - assert desc.queue_type == "testable" - - def test_lazy_loading(self): - """测试懒加载功能""" - desc = _TestableQueueDescriptor() - - # 初始状态应该未初始化 - assert not desc.is_initialized() - assert desc.can_serialize # 未初始化时可序列化 - - # 访问queue_instance应该触发初始化 - queue = desc.queue_instance - assert desc.is_initialized() - assert not desc.can_serialize # 已初始化后不可序列化 - assert queue is not None - - # 再次访问应该返回同一个实例 - queue2 = desc.queue_instance - assert queue is queue2 - - def test_basic_queue_operations(self): - """测试基本队列操作""" - desc = _TestableQueueDescriptor(maxsize=5) - - # 测试put和get - desc.put("hello") - desc.put("world") - - assert desc.qsize() == 2 - assert not desc.empty() - - item1 = desc.get() - assert item1 == "hello" - assert desc.qsize() == 1 - - item2 = desc.get() - assert item2 == "world" - assert desc.qsize() == 0 - assert desc.empty() - - def test_queue_operations_with_timeout(self): - """测试带超时的队列操作""" - desc = _TestableQueueDescriptor(maxsize=1) - - # 测试put_nowait和get_nowait - desc.put_nowait("item") - - # 队列已满，put_nowait应该抛出异常 - with pytest.raises(Full): - desc.put_nowait("item2") - - # get_nowait应该成功 - item = desc.get_nowait() - assert item == "item" - - # 队列为空，get_nowait应该抛出异常 - with pytest.raises(Empty): - desc.get_nowait() - - def test_serialization(self): - """测试序列化功能""" - desc = _TestableQueueDescriptor(queue_id="test_serialize", maxsize=10) - - # 未初始化时应该可以序列化 - assert desc.can_serialize - - # 测试to_dict - data = desc.to_dict() - assert data["queue_id"] == "test_serialize" - assert data["queue_type"] == "testable" - assert data["class_name"] == "_TestableQueueDescriptor" - assert data["metadata"]["maxsize"] == 10 - assert data["can_serialize"] is True - - # 测试to_json - json_str = desc.to_json() - parsed = json.loads(json_str) - assert parsed["queue_id"] == "test_serialize" - - # 初始化后不应该可以序列化 - _ = desc.queue_instance # 触发初始化 - assert not desc.can_serialize - - # 应该抛出序列化异常 - with pytest.raises(ValueError, match="contains non-serializable objects"): - desc.to_json() - - def test_serialization_with_non_serializable_metadata(self): - """测试包含不可序列化元数据的序列化""" - # 创建一个包含不可序列化对象的描述符 - non_serializable_metadata = {"function": lambda x: x} - desc = _TestableQueueDescriptor(extra_metadata=non_serializable_metadata) - - # 测试不包含不可序列化字段的序列化 - data = desc.to_dict(include_non_serializable=False) - assert "function" not in data["metadata"] - - # 测试包含不可序列化字段的序列化 - data_with_non_serializable = desc.to_dict(include_non_serializable=True) - assert "function" in data_with_non_serializable["metadata"] - assert data_with_non_serializable["metadata"]["function"].startswith("/docs/governance/DEVELOPER_GUIDE.md`） - -本指南把“制度”落到“能执行的工程约束”。本包的所有代码改动必须遵守以下规则。 - -______________________________________________________________________ - -## 1. 架构守护机制（强制） - -### 1.1 依赖层级（禁止向上依赖 / 禁止循环依赖） - -SAGE 采用 L1-L5 分层。任何“向上依赖”都视为架构违规，必须阻断合入。 - -- L1：`sage-common` -- L2：`sage-platform` -- L3：`sage-kernel`, `sage-libs` -- L4：`sage-middleware` -- L5：`sage-cli`, `sage-tools` - -### 1.2 Libs vs Middleware 规则（强制） - -如果代码需要调用“向上能力”（例如 VectorDB/Memory/Refiner/外部服务/重后端/网络服务），它 **不是库**，必须放在 -`sage-middleware`（operators/components）。 - -> 迁移时不保留兼容 re-export shim：更新所有调用方，快速失败。 - -### 1.3 Control Plane-only（强制） - -LLM 引擎操作必须走 Control Plane，禁止直接启动引擎/直连端口。 - -______________________________________________________________________ - -## 2. 自动化检查（CI + pre-commit）（强制） - -### 2.1 安装一致性 - -- 必须使用 `./quickstart.sh --dev --yes` 安装开发环境。 -- 禁止手工 `pip install` 临时安装依赖（依赖必须写入 `pyproject.toml`）。 - -### 2.2 本地质量与测试 - -建议在仓库根目录执行： - -- `sage-dev quality check --all-files` -- `sage-dev project test --coverage` - -______________________________________________________________________ - -## 3. 强制规范（必须出现的质量门槛） - -### 3.1 Mock-First（强制） - -- CI 必须能在无 GPU 环境跑通核心测试。 -- 新增能力必须提供 Mock/CPU 路径，避免把“硬件”当作默认前提。 - -### 3.2 Fail-Fast（强制） - -- 禁止静默默认值、禁止隐式回退（fallback）。 -- 关键配置缺失必须明确报错（例如用 `os.environ["KEY"]` 或显式校验并抛异常）。 - -### 3.3 Protocol-First（按适用范围执行） - -当本包提供“公共接口/抽象/跨包契约”时： - -- 先定义接口/类型（Protocol/ABC/Schema） -- 再实现具体逻辑 -- 再补测试与示例 - -______________________________________________________________________ - -## 4. 新模块/新能力接入步骤（可执行） - -1. **定义范围**：确认属于本包；若需要向上能力，按规则提升到 `sage-middleware`。 -1. **接口优先**：先定义可复用接口（如适用）。 -1. **最小可跑**：提供无 GPU 的最小可跑实现（Mock/CPU）。 -1. **测试**：至少包含单元测试；关键路径补回归测试。 -1. **文档**：更新本包 README 与本包 governance 文档（必要时）。 -1. **质量门槛**：本地 `sage-dev quality` 与测试通过。 - -______________________________________________________________________ - -## 5. 常见错误与修复 - -- **依赖倒挂**：`sage-libs` 引入 `sage-middleware` → 必须拆分/上移实现。 -- **文档位置违规**：禁止在根 `docs/` 放 Markdown；包级文档只能放 `packages//docs/`。 -- **依赖未声明**：新增依赖未写入 `pyproject.toml` → 必须补齐声明并统一版本。 diff --git a/packages/sage-tools/docs/governance/MAINTAINERS.md b/packages/sage-tools/docs/governance/MAINTAINERS.md deleted file mode 100644 index 4e331ad6e0..0000000000 --- a/packages/sage-tools/docs/governance/MAINTAINERS.md +++ /dev/null @@ -1,126 +0,0 @@ -# 负责人制度（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/MAINTAINERS.md`） -- 负责人名单：TBD - -本文件定义“包级 Maintainer（负责人）”如何配置、如何工作、如何被量化验收。 - -______________________________________________________________________ - -## 1. 为什么需要 Maintainer - -SAGE 是 monorepo，多包协作、依赖层级明确。Maintainer 的职责不是“写最多代码”，而是保证： - -- 需求有人推进、问题有人闭环 -- 质量门槛有人守住（CI/架构/测试） -- 跨包集成有人对齐（尤其是接口层与中间件层） - -______________________________________________________________________ - -## 2. 负责人配置建议（按模块/子系统） - -### 2.1 推荐配置 - -- 每个包至少 1 名 Maintainer（主负责人）+ 1 名 Backup（备份负责人）。 -- 当包内包含多个子系统/高复杂度模块：建议拆分子 Maintainer。 - -### 2.2 负责人配置表（模板，必须维护） - -| 模块/目录 | 复杂度 | 关键技能 | 主 Maintainer | Backup | 上游依赖 | 下游使用方 | -| --------- | ------ | -------- | ------------- | ------ | -------- | ---------- | -| TBD | TBD | TBD | TBD | TBD | TBD | TBD | - -> 单仓语义适配：这里的“上游/下游”指本 monorepo 的其他包或本包内其他模块。 - -______________________________________________________________________ - -## 3. 负责人职责拆分（40/30/20/10 参考） - -可按实际调整，但必须覆盖全部职责： - -- **40% 开发推进**：Roadmap/Issue/PR 推动，里程碑拆解，阻塞清理。 -- **30% 质量把控**：CI 绿灯、测试策略、回归防护、性能/稳定性监控。 -- **20% 集成同步**：跨包接口对齐、发布/版本对齐、变更公告与迁移指引。 -- **10% 团队协作**：Review SLA、协作矩阵维护、争议处理与复核申请。 - -______________________________________________________________________ - -## 4. 要求与投入 - -- **最低投入**：每周固定时间段处理 Issue/PR（TBD：建议至少 2-4 小时/周）。 -- **Review SLA**：工作日 48h 内首次响应。 -- **透明度**：每月一次交付清单（含证据链接）。 - -______________________________________________________________________ - -## 5. 工作流程（开发 / 发布 / 集成同步） - -### 5.1 日常开发 - -- 所有需求/缺陷必须有 Issue。 -- PR 必须通过 `docs/governance/PR_CHECKLIST.md`。 -- 架构约束（依赖层级、libs vs middleware）违反时：必须阻断合入。 - -### 5.2 发布与版本对齐（如适用） - -- 发布前：测试与质量检查必须绿。 -- 变更：Breaking 变更必须有迁移指引与回滚预案。 -- 若涉及 PyPI 发布：使用仓库规定的发布工具链（例如 `wheelwright`），不得手工 twine/pip 临时发布。 - -### 5.3 跨包集成同步 - -- 上游接口变化：必须提前通知下游 maintainer，并提供迁移窗口。 -- 下游反馈：需要在 SLA 内回应并给出计划。 - -______________________________________________________________________ - -## 6. 协作矩阵（依赖/协作关系） - -### 6.1 Monorepo 依赖层级速查 - -> 目标：避免“向上依赖”与循环依赖。 - -| 层级 | 包（示例） | 允许依赖 | -| ---- | -------------------------- | ---------------- | -| L5 | `sage-cli`, `sage-tools` | L1-L4 | -| L4 | `sage-middleware` | L1-L3 | -| L3 | `sage-kernel`, `sage-libs` | L1-L2 | -| L2 | `sage-platform` | L1 | -| L1 | `sage-common` | 仅 stdlib/轻依赖 | - -### 6.2 本包协作矩阵（模板） - -| 依赖方/被依赖方 | 关系类型 | 接口/契约 | 变更通知机制 | Maintainer 对接 | -| --------------- | -------- | --------- | --------------------- | --------------- | -| TBD | 上游 | TBD | Issue/PR/Release Note | TBD | -| TBD | 下游 | TBD | Issue/PR/Release Note | TBD | - -______________________________________________________________________ - -## 7. 负责人名单（TBD） - -| 模块/目录 | 主 Maintainer | Backup | 交接人（如更换） | 生效日期 | -| --------- | ------------- | ------ | ---------------- | -------- | -| TBD | TBD | TBD | TBD | TBD | - -______________________________________________________________________ - -## 8. 考核指标（参考，可量化） - -- PR 首次响应 SLA 达标率 -- CI 红灯平均恢复时间（MTTR） -- 每月交付清单是否按时发布（含证据） -- Breaking 变更公告与迁移指引完备率 - -______________________________________________________________________ - -## 9. FAQ - -### Q1：Maintainer 是否必须是提交最多的人？ - -不是。Maintainer 的核心是“推进 + 质量 + 对齐”。提交多但不维护质量/不对齐下游同样不合格。 - -### Q2：如果包内没有发布流程怎么办？ - -仍需要“集成同步”：接口变更、跨包影响、迁移指引、回滚预案。 diff --git a/packages/sage-tools/docs/governance/PR_CHECKLIST.md b/packages/sage-tools/docs/governance/PR_CHECKLIST.md deleted file mode 100644 index 09d0f8c350..0000000000 --- a/packages/sage-tools/docs/governance/PR_CHECKLIST.md +++ /dev/null @@ -1,49 +0,0 @@ -# PR 审查清单（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/PR_CHECKLIST.md`） - -> Maintainer/Reviewer 需要用本清单进行可量化审查。未满足项必须在 PR 中解释或补齐。 - -______________________________________________________________________ - -## 1. 架构合规 - -- [ ] 不产生向上依赖/循环依赖（符合 L1-L5 分层） -- [ ] 遵守 libs vs middleware 规则（需要向上能力则放 middleware） -- [ ] LLM 相关操作不绕过 Control Plane - -## 2. Protocol-First（如适用） - -- [ ] 公共接口/类型先定义（Protocol/ABC/Schema），实现不“绑死”具体后端 -- [ ] 接口变更包含迁移说明（Breaking change 必须公告） - -## 3. Mock-First - -- [ ] 无 GPU 环境可跑（至少核心测试/最小路径） -- [ ] 新增外部依赖/服务调用有可测试替身（mock/fake） - -## 4. Fail-Fast - -- [ ] 无隐式回退（不写 try/except 吞异常、不用静默默认值掩盖缺失配置） -- [ ] 错误信息可定位（异常 message 清晰，必要时包含修复指引） - -## 5. 可观测性（按适用范围） - -- [ ] 关键路径有日志/指标/追踪点（至少日志） -- [ ] 不输出敏感信息（key/token/密码等） - -## 6. 配置验证 - -- [ ] 新增配置项有校验与文档说明 -- [ ] 端口不硬编码（如适用，使用统一端口配置） - -## 7. 测试覆盖 - -- [ ] 新增/修改逻辑有对应测试 -- [ ] 关键 bug fix 有回归测试 - -## 8. 代码质量 - -- [ ] `sage-dev quality check --all-files` 通过 -- [ ] `sage-dev project test --coverage` 通过（或至少本包相关测试通过） diff --git a/packages/sage-tools/docs/governance/SELF_HOSTED_RUNNER.md b/packages/sage-tools/docs/governance/SELF_HOSTED_RUNNER.md deleted file mode 100644 index 8c4746062a..0000000000 --- a/packages/sage-tools/docs/governance/SELF_HOSTED_RUNNER.md +++ /dev/null @@ -1,50 +0,0 @@ -# Self-hosted Runner 指南（SAGE - 可选） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/SELF_HOSTED_RUNNER.md`） - -> 本章节用于需要 GPU/重依赖/长耗时测试的包。若本包不需要，可保留但不强制启用。 - -______________________________________________________________________ - -## 1. 什么时候需要 self-hosted runner - -- GPU 相关测试（CUDA/Ascend） -- 需要特殊硬件或驱动版本 -- 需要访问内网资源（需安全评估） - -______________________________________________________________________ - -## 2. Runner 安装与标签（模板） - -- Runner 类型：GitHub Actions self-hosted -- 标签建议： - - `self-hosted` - - `linux` - - `x64` - - `gpu`（如适用） - - `cuda-`（如适用） - -______________________________________________________________________ - -## 3. 环境要求（模板） - -- OS：Linux（推荐） -- Python：与仓库要求一致（3.10+） -- 构建依赖：cmake / build-essential / BLAS 等（按仓库安装脚本） - -______________________________________________________________________ - -## 4. 安全注意事项（强制） - -- Runner 机器不可暴露敏感密钥；Secrets 由 GitHub 管理。 -- 禁止在日志中打印 token/key。 -- 对内网访问与数据集访问：必须走审批（TBD）。 - -______________________________________________________________________ - -## 5. 故障排查 - -- CI 失败先在本地 `./quickstart.sh --dev --yes` 复现 -- 检查磁盘/缓存（`.sage/`） -- 检查驱动/CUDA 版本（如适用） diff --git a/packages/sage-tools/docs/governance/TEAM.md b/packages/sage-tools/docs/governance/TEAM.md deleted file mode 100644 index 6ccba412ba..0000000000 --- a/packages/sage-tools/docs/governance/TEAM.md +++ /dev/null @@ -1,67 +0,0 @@ -# 仓库人员与评分制度（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/TEAM.md`） -- 名单维护：TBD - -本文件定义在 SAGE monorepo 内（以包为单位）的团队角色、负责人制度与评分/激励框架，用于保证： - -- 制度可执行：能落到 Issue/PR/CI/周更/月更。 -- 制度可验收：有明确证据链接与检查项。 -- 制度可追责：有最低交付门槛、透明度与争议处理机制。 - -> 约束：所有“姓名/负责人名单/具体人员”一律用 `TBD`，不得编造。 - -______________________________________________________________________ - -## 1. 角色与职责（强制条款） - -| 角色 | 核心职责 | 必须产出（可验收） | 证据类型（必须带链接） | -| ---------------------------- | -------------------------------------------------------- | ----------------------------------------------------------------------- | ------------------------------------------ | -| Maintainer（负责人） | Roadmap/Issue/PR 推进；质量门槛；集成/版本对齐；变更管理 | 每周 TODO 周更、PR Review SLA、CI 绿灯保障、重大变更公告、发布/集成记录 | Issue、PR、Release/Tag、CI 链接、周报/月报 | -| Engineering Core（工程骨干） | 关键功能交付；CI/测试维护；稳定性问题闭环 | 可交付功能/修复、测试/CI 改进、性能/稳定性指标改进 | PR、Issue、Benchmark 报告、CI 链接 | -| Research Core（科研骨干） | 原型验证/评测；研究到工程落地（必须可验收） | 可复现的实验/评测；可落地的工程化改造（或明确落地计划） | 评测报告、PR、Issue、实验配置/脚本链接 | - -______________________________________________________________________ - -## 2. 负责人必须做的事（强制条款） - -### 2.1 TODO 周更（强制） - -- 每周至少 1 次在本包的 `docs/governance/TODO.md` 更新：本周目标/完成/阻塞/下周计划/风险/需要协助。 -- TODO 必须引用证据（Issue/PR/CI/报告链接）。 - -### 2.2 PR Review SLA（强制） - -- 工作日 48h 内对新 PR 首次响应（review/approve/request changes/说明何时处理）。 -- 对影响多个包的变更：必须主动拉齐上下游 maintainer。 - -# 仓库人员与评分制度（SAGE - 包级，指向母版） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages/sage-tools/docs/governance/TEAM.md`） -- 名单维护：TBD - -通用制度请参见 `packages/sage/docs/governance/TEAM_BASE.md`。本文件仅保留本包的代号映射与特有说明。 - -### 本包角色代号（姓名保持 TBD） - -| 角色 | 代号分配 | -| ---------------- | ---------------- | -| Maintainer | A2 | -| Engineering Core | B2 | -| Research Core | C2（可按需指派） | - -### 本包补充说明 - -- 工具（L5）支撑质量/发布/检查链路，需保持与依赖版本锁定一致；变更前确保 CI 配置与 pre-commit 钩子同步更新。 - -______________________________________________________________________ - -## 参考 - -- 通用制度：packages/sage/docs/governance/TEAM_BASE.md -- 周更：docs/governance/TODO.md -- PR 审查：docs/governance/PR_CHECKLIST.md -- 开发规范：docs/governance/DEVELOPER_GUIDE.md -- **保底分**：角色的最低履职奖励（必须满足最低交付门槛）。 diff --git a/packages/sage-tools/docs/governance/TODO.md b/packages/sage-tools/docs/governance/TODO.md deleted file mode 100644 index afdd548b3a..0000000000 --- a/packages/sage-tools/docs/governance/TODO.md +++ /dev/null @@ -1,39 +0,0 @@ -# TODO 周更模板（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/TODO.md`） -- 维护频率：每周至少 1 次（Maintainer 强制要求） - -______________________________________________________________________ - -## 周更（复制本模板，每周追加一节） - -### Week of YYYY-MM-DD - -**本周目标（Goals）** - -- [ ] TBD（关联 Issue：TBD） - -**本周完成（Done）** - -- [ ] TBD（PR：TBD，CI：TBD） - -**阻塞与风险（Blockers/Risks）** - -- TBD（原因 + 需要谁协助 + 截止时间） - -**下周计划（Next）** - -- [ ] TBD（Issue：TBD） - -**需要协助（Asks）** - -- TBD（@TBD） - -______________________________________________________________________ - -## 里程碑清单（长期维护） - -| 里程碑 | 目标日期 | 状态 | 关联 Issue/PR | 风险 | -| ------ | -------- | ---- | ------------- | ---- | -| TBD | TBD | TBD | TBD | TBD | diff --git a/packages/sage-tools/pyproject.toml b/packages/sage-tools/pyproject.toml deleted file mode 100644 index 3aea68fb81..0000000000 --- a/packages/sage-tools/pyproject.toml +++ /dev/null @@ -1,185 +0,0 @@ -[build-system] -requires = ["setuptools>=64", "wheel", "packaging>=24.2"] -build-backend = "setuptools.build_meta" - -[project] -name = "isage-tools" -dynamic = ["version"] -description = "sage-development Tools - CLI, Web UI, and Development Utilities" -readme = "README.md" -requires-python = ">=3.10" -authors = [{ name = "IntelliStream Team", email = "shuhao_zhang@hust.edu.cn" }] -keywords = [ - "sage", - "tools", - "cli", - "development", - "web-ui", - "studio", - "devtools", - "frontend", - "intellistream", -] -classifiers = [ - "Development Status :: 4 - Beta", - "Intended Audience :: Developers", - "Intended Audience :: System Administrators", - "Topic :: Software Development :: Libraries :: Python Modules", - "Topic :: Scientific/Engineering :: Artificial Intelligence", - "Topic :: System :: Distributed Computing", - "Topic :: Utilities", -] -license = { text = "MIT" } - -# 核心依赖 - sage-dev 命令必需的最小依赖集 -dependencies = [ - # CLI framework - required for sage-dev command - "typer>=0.15.0,<1.0.0", - "rich>=13.0.0,<14.0.0", - "click>=8.0.0,<9.0.0", - - # Template rendering - "jinja2>=3.1.0,<4.0.0", - "markupsafe>=2.0.1", - - # Package version handling - "packaging>=20.5", - - # File locking for safe operations - "filelock>=3.16.0,<4.0.0", -] - -[project.optional-dependencies] -# Development tools - for code quality, testing, etc. -dev = [ - "pytest>=7.0.0", - "pytest-cov>=4.0.0", - "pytest-asyncio>=0.21.0", - "pytest-mock>=3.10.0", - "pytest-timeout>=2.1.0", - "pytest-benchmark>=4.0.0", - "black>=23.0.0", - "isort>=5.10.0", - "mypy>=1.0.0", - "ruff==0.14.6", - "flake8>=5.0.0", - "coverage>=7.0.0", - "bandit>=1.7.0", - "safety>=2.3.0", - "virtualenv>=20.31.2", -] -# HuggingFace integration -hf = ["huggingface-hub>=0.34.0,<1.0.0"] -# Documentation tools -docs = ["markdown>=3.4.4,<4.0.0"] -all = ["isage-tools[dev,hf,docs]"] -[project.urls] -Homepage = "https://github.com/intellistream/SAGE" -Documentation = "https://intellistream.github.io/SAGE-Pub/" -Repository = "https://github.com/intellistream/SAGE.git" -"Bug Tracker" = "https://github.com/intellistream/SAGE/issues" - -[project.scripts] -# 开发工具 - 独立功能，开发者专用，带智能命令建议 -sage-dev = "sage.tools.cli.commands.dev:run_with_suggestions" - -# 注意：sage 命令已移至 sage-cli 包 -# sage = "sage.cli.main:app" (由 sage-cli 包提供) - -# 移除的命令（功能已合并到sage主命令或移至sage-cli）: -# - sage: 移至 sage-cli 包 -# - sage-cli: 与sage重复 -# - sage-core: 与sage重复 -# - sage-jobmanager: 合并到 sage jobmanager -# - sage-install: 合并到 sage doctor 或 sage version -# - web-ui: 合并到 sage web-ui - -# 注意：如果将来需要添加额外的控制台脚本入口点，可以在这里添加 -# [project.entry-points."console_scripts"] -# example-tool = "sage.tools.dev.tools.example:main" - -[tool.setuptools.packages.find] -namespaces = true -where = ["src"] - -[tool.setuptools.package-dir] -"" = "src" - -[tool.setuptools.package-data] -"sage.tools.cli" = ["py.typed", "templates/*.yaml"] -"sage.tools.dev" = ["py.typed", "templates/*.py", "config/*.toml"] -"sage.tools.dev.hooks" = ["templates/*"] - -# Development tools configuration -[tool.black] -line-length = 100 -target-version = ["py310", "py311", "py312"] -include = '\.pyi?$' - -[tool.isort] -profile = "black" -line_length = 100 - -[tool.mypy] -python_version = "3.11" -cache_dir = "../../.sage/cache/mypy" -warn_return_any = true -warn_unused_configs = true -disallow_untyped_defs = true - -[tool.pytest.ini_options] -testpaths = ["tests", "src"] -python_files = ["test_*.py", "*_test.py"] -python_classes = ["Test*"] -python_functions = ["test_*"] -addopts = [ - "--benchmark-storage=../../.sage/benchmarks", - "-o", - "cache_dir=../../.sage/cache/pytest", - "--strict-markers", - "--strict-config", - "--verbose", - "-ra", -] -markers = [ - "slow: marks tests as slow (deselect with '-m \"not slow\"')", - "integration: marks tests as integration tests", - "unit: marks tests as unit tests", - "network: marks tests as network tests", - "system: marks tests as system tests", - "core: marks tests as core functionality tests", - "smoke: marks tests as smoke tests (quick validation)", - "cli: marks tests as CLI tests", -] -filterwarnings = [ - # 忽略已知的第三方库弃用警告 - "ignore::DeprecationWarning:pkg_resources", - "ignore:.*pkg_resources is deprecated.*:UserWarning", - "ignore:.*SWIG.*:DeprecationWarning", - "ignore:.*SwigPyPacked.*:DeprecationWarning", - "ignore:.*SwigPyObject.*:DeprecationWarning", - "ignore:.*swigvarlink.*:DeprecationWarning", -] - -[tool.coverage.run] -source = ["src/sage"] -omit = ["*/tests/*", "*/test_*.py", "*/_test_*.py"] - -[tool.coverage.report] -exclude_lines = [ - "pragma: no cover", - "def __repr__", - "raise AssertionError", - "raise NotImplementedError", - "if __name__ == .__main__.:", -] - -# ============================================================================ -# Code Quality Configuration -# Extends from root ruff.toml for unified standards across all packages -# ============================================================================ -[tool.ruff] -extend = "../../tools/ruff.toml" - -[tool.setuptools.dynamic] -version = { attr = "sage.tools._version.__version__" } diff --git a/packages/sage-tools/src/sage/tools/__init__.py b/packages/sage-tools/src/sage/tools/__init__.py deleted file mode 100644 index 4cd5304ab3..0000000000 --- a/packages/sage-tools/src/sage/tools/__init__.py +++ /dev/null @@ -1,42 +0,0 @@ -""" -SAGE Tools - Development and CLI Tools - -Layer: L5 (Interface - CLI & Development Tools) -Dependencies: L1-L4 layers - -提供开发和命令行工具: -- cli: 命令行接口 -- dev: 开发工具 -- finetune: 模型微调工具 - -Architecture: -- L5 接口层，提供命令行工具和开发工具 -- 依赖所有下层组件 (L1-L4) -- 用于命令行管理、开发和部署 SAGE 应用 -""" - -__layer__ = "L5" - -from . import cli, dev -from ._version import __version__ - - -# 延迟导入 finetune，避免在模块加载时就导入重量级依赖 (datasets, transformers, torch 等) -def __getattr__(name): - """延迟导入 finetune 模块""" - if name == "finetune": - import importlib - - # Redirect to sage.libs.finetune (Moved to L3) - finetune_module = importlib.import_module("sage.libs.finetune") - return finetune_module - - raise AttributeError(f"module {__name__!r} has no attribute {name!r}") - - -__all__ = [ - "__version__", - "cli", - "dev", - "finetune", # type: ignore[attr-defined] -] diff --git a/packages/sage-tools/src/sage/tools/_version.py b/packages/sage-tools/src/sage/tools/_version.py deleted file mode 100644 index 280133fc8d..0000000000 --- a/packages/sage-tools/src/sage/tools/_version.py +++ /dev/null @@ -1,6 +0,0 @@ -"""Version information for sage-tools package.""" - -# 独立硬编码版本 -__version__ = "0.2.3.4" -__author__ = "IntelliStream Team" -__email__ = "shuhao_zhang@hust.edu.cn" diff --git a/packages/sage-tools/src/sage/tools/agent_training/__init__.py b/packages/sage-tools/src/sage/tools/agent_training/__init__.py deleted file mode 100644 index 548b801b65..0000000000 --- a/packages/sage-tools/src/sage/tools/agent_training/__init__.py +++ /dev/null @@ -1,40 +0,0 @@ -""" -Agent Training Pipeline - -Provides training infrastructure for Agent models including: - -Note: CoresetSelector and OnlineContinualLearner have been moved to sage.libs.sias. -They are re-exported here for backward compatibility. -""" - -# SIAS components - re-exported for backward compatibility -# New code should import from sage.libs.sias directly - -from .config import ( - AgentRewardConfig, - AgentSFTConfig, - RLTrainingConfig, -) -from .data_formatter import AgentSFTFormatter -from .dialog_processor import AgentDialogProcessor -from .evaluator import AgentTrainingEvaluator -from .reward_model import AgentRewardModel -from .sft_trainer import AgentSFTTrainer - -__all__ = [ - # Config - "AgentSFTConfig", - "RLTrainingConfig", - "AgentRewardConfig", - # Data - "AgentSFTFormatter", - "AgentDialogProcessor", - "AgentSFTTrainer", - # SIAS components (re-exported for compatibility) - # Training - # "AgentSFTTrainer", # TODO - # "AgentRLTrainer", # TODO - # Evaluation - "AgentRewardModel", - "AgentTrainingEvaluator", -] diff --git a/packages/sage-tools/src/sage/tools/agent_training/config.py b/packages/sage-tools/src/sage/tools/agent_training/config.py deleted file mode 100644 index 06b3f12c87..0000000000 --- a/packages/sage-tools/src/sage/tools/agent_training/config.py +++ /dev/null @@ -1,311 +0,0 @@ -""" -Agent Training Configuration - -Defines configuration classes for: -- SFT training -- RL training (DPO/PPO/GRPO) -- Reward model -""" - -from dataclasses import dataclass, field -from pathlib import Path -from typing import Literal, Optional - - -@dataclass -class AgentSFTConfig: - """Agent SFT 训练配置 - - 配置 Agent 监督微调训练，针对工具选择、规划、时机判断等能力。 - - Attributes: - base_model: 基础模型名称或路径 - train_data: 训练数据源 (格式: "source:split") - dev_data: 验证数据源 - task_weights: 各任务类型的采样权重 - max_length: 最大序列长度 - lora_r: LoRA rank - lora_alpha: LoRA alpha - lora_dropout: LoRA dropout - lora_target_modules: LoRA 目标模块 - num_epochs: 训练轮数 - batch_size: 批次大小 - gradient_accumulation: 梯度累积步数 - learning_rate: 学习率 - warmup_ratio: 预热比例 - lr_scheduler: 学习率调度器 - output_dir: 输出目录 - use_coreset_selection: 是否启用 coreset 过滤 - coreset_target_size: 每次训练的最大 coreset 样本数 - use_online_continual: 是否启用在线增量/复习 - continual_buffer_size: 在线复习缓冲区大小 - - Example: - >>> config = AgentSFTConfig( - ... base_model="Qwen/Qwen2.5-7B-Instruct", - ... num_epochs=3, - ... task_weights={"tool_selection": 0.4, "planning": 0.3} - ... ) - """ - - # 数据源 - train_data: str = "agent_sft:train" - dev_data: str = "agent_sft:dev" - - # 任务权重 (用于按任务类型采样) - task_weights: dict = field( - default_factory=lambda: { - "tool_selection": 0.35, # 工具选择 - "multi_step_planning": 0.30, # 多步规划 - "timing_decision": 0.20, # 时机判断 - "tool_retrieval": 0.15, # 工具检索 - } - ) - - # 模型配置 - base_model: str = "Qwen/Qwen2.5-7B-Instruct" - max_length: int = 4096 - load_in_8bit: bool = True - load_in_4bit: bool = False - fp16: bool = True - bf16: bool = False - gradient_checkpointing: bool = True - optim: str = "paged_adamw_8bit" - padding_strategy: Literal["max_length", "longest", "do_not_pad"] = "max_length" - padding_side: Literal["left", "right"] = "right" - output_format: Literal["alpaca", "sharegpt", "chatml"] = "chatml" - shuffle_train: bool = True - shuffle_eval: bool = False - max_train_samples: Optional[int] = None - max_eval_samples: Optional[int] = 512 - - # Coreset & continual learning - use_coreset_selection: bool = False - coreset_target_size: Optional[int] = None - coreset_strategy: Literal["loss_topk", "diversity", "hybrid", "random"] = "loss_topk" - coreset_metric_key: str = "loss" - use_online_continual: bool = False - continual_buffer_size: int = 2048 - continual_replay_ratio: float = 0.25 - - # LoRA 配置 - lora_r: int = 64 - lora_alpha: int = 128 - lora_dropout: float = 0.05 - lora_target_modules: list = field( - default_factory=lambda: [ - "q_proj", - "k_proj", - "v_proj", - "o_proj", - "gate_proj", - "up_proj", - "down_proj", - ] - ) - - # 训练超参 - num_epochs: int = 3 - batch_size: int = 1 - gradient_accumulation: int = 16 - learning_rate: float = 2e-5 - warmup_ratio: float = 0.1 - lr_scheduler: str = "cosine" - - # 输出配置 - output_dir: Path = field(default_factory=lambda: Path.home() / ".sage" / "agent_training") - logging_steps: int = 10 - save_steps: int = 500 - eval_steps: int = 200 - eval_strategy: Literal["no", "steps", "epoch"] = "steps" - save_total_limit: int = 3 - report_to: Literal["none", "tensorboard", "wandb"] = "none" - load_best_model: bool = True - metric_for_best_model: str = "eval_loss" - greater_is_better: bool = False - dataloader_num_workers: int = 0 - seed: int = 42 - - def __post_init__(self) -> None: - if isinstance(self.output_dir, str): - self.output_dir = Path(self.output_dir) - self.output_dir.mkdir(parents=True, exist_ok=True) - (self.output_dir / "checkpoints").mkdir(parents=True, exist_ok=True) - (self.output_dir / "logs").mkdir(parents=True, exist_ok=True) - (self.output_dir / "lora_weights").mkdir(parents=True, exist_ok=True) - self.padding_side = self.padding_side or "right" - - @property - def checkpoint_dir(self) -> Path: - return self.output_dir / "checkpoints" - - @property - def log_dir(self) -> Path: - return self.output_dir / "logs" - - @property - def lora_dir(self) -> Path: - return self.output_dir / "lora_weights" - - @property - def effective_batch_size(self) -> int: - """有效批次大小""" - return self.batch_size * self.gradient_accumulation - - -@dataclass -class RLTrainingConfig: - """RL 训练配置 - - 支持 DPO, PPO, GRPO 三种算法。 - - Attributes: - algorithm: RL 算法选择 - sft_model_path: SFT 模型路径 (Stage 2 输出) - reference_model_path: 参考模型路径 (用于 KL 约束) - dpo_config: DPO 算法配置 - ppo_config: PPO 算法配置 - grpo_config: GRPO 算法配置 - - Example: - >>> config = RLTrainingConfig( - ... algorithm="dpo", - ... sft_model_path="./output/agent_sft", - ... dpo_config={"beta": 0.1} - ... ) - """ - - # 算法选择 - algorithm: Literal["dpo", "ppo", "grpo"] = "dpo" - - # 模型路径 - sft_model_path: Optional[str] = None - reference_model_path: Optional[str] = None # None = 使用 SFT 模型作为参考 - - # DPO 配置 - dpo_config: dict = field( - default_factory=lambda: { - "beta": 0.1, # KL 惩罚系数 - "reference_free": False, # 是否免参考模型 - "label_smoothing": 0.0, # 标签平滑 - "loss_type": "sigmoid", # sigmoid, hinge, ipo - } - ) - - # PPO 配置 - ppo_config: dict = field( - default_factory=lambda: { - "kl_coef": 0.02, # KL 系数 - "clip_range": 0.2, # PPO clip 范围 - "vf_coef": 0.5, # 价值函数系数 - "num_rollouts": 128, # 每次更新的 rollout 数 - "chunk_size": 64, # 分块大小 - "gamma": 0.99, # 折扣因子 - "lam": 0.95, # GAE lambda - } - ) - - # GRPO 配置 (Group Relative Policy Optimization) - grpo_config: dict = field( - default_factory=lambda: { - "group_size": 4, # 每组样本数 - "beta": 0.1, # 温度参数 - "use_advantage": True, # 使用优势函数 - } - ) - - # 训练超参 - num_epochs: int = 1 - batch_size: int = 2 - gradient_accumulation: int = 8 - learning_rate: float = 5e-6 - - # 数据生成 - num_samples_per_prompt: int = 4 # 每个 prompt 生成的响应数 - - # 输出配置 - output_dir: Path = field(default_factory=lambda: Path.home() / ".sage" / "agent_rl") - - -@dataclass -class AgentRewardConfig: - """Agent 奖励模型配置 - - 定义各项奖励和惩罚的权重。 - - Attributes: - weights: 正向奖励权重 - penalties: 惩罚项 - - Example: - >>> config = AgentRewardConfig() - >>> config.weights["tool_accuracy"] - 0.25 - """ - - # 奖励权重 (总和应为 1.0) - weights: dict = field( - default_factory=lambda: { - "task_completion": 0.40, # 任务完成奖励 - "tool_accuracy": 0.25, # 工具选择准确性 - "efficiency": 0.15, # 执行效率 (步数) - "timing_quality": 0.10, # 调用时机质量 - "format_compliance": 0.10, # 格式符合度 - } - ) - - # 惩罚项 (负值) - penalties: dict = field( - default_factory=lambda: { - "wrong_tool": -0.3, # 选错工具 - "redundant_call": -0.2, # 冗余调用 - "format_error": -0.1, # 格式错误 - "timeout": -0.5, # 超时 - "hallucination": -0.4, # 幻觉工具 (不存在的工具) - } - ) - - # 评估配置 - max_steps: int = 10 # 最大允许步数 - timeout_seconds: float = 60.0 # 执行超时 - - -@dataclass -class TrainingPipelineConfig: - """完整训练管线配置 - - 组合 SFT 和 RL 配置，定义完整的训练流程。 - - Example: - >>> config = TrainingPipelineConfig( - ... run_sft=True, - ... run_rl=True, - ... sft_config=AgentSFTConfig(num_epochs=3), - ... rl_config=RLTrainingConfig(algorithm="dpo") - ... ) - """ - - # 阶段控制 - run_warmup: bool = False - run_sft: bool = True - run_rl: bool = True - run_eval: bool = True - - # 各阶段配置 - sft_config: AgentSFTConfig = field(default_factory=AgentSFTConfig) - rl_config: RLTrainingConfig = field(default_factory=RLTrainingConfig) - reward_config: AgentRewardConfig = field(default_factory=AgentRewardConfig) - - # 全局配置 - seed: int = 42 - wandb_project: Optional[str] = None - wandb_run_name: Optional[str] = None - - # 输出配置 - experiment_name: str = "agent_training" - output_base_dir: Path = field(default_factory=lambda: Path.home() / ".sage" / "experiments") - - @property - def experiment_dir(self) -> Path: - """实验输出目录""" - return self.output_base_dir / self.experiment_name diff --git a/packages/sage-tools/src/sage/tools/agent_training/data_formatter.py b/packages/sage-tools/src/sage/tools/agent_training/data_formatter.py deleted file mode 100644 index e1c452cd15..0000000000 --- a/packages/sage-tools/src/sage/tools/agent_training/data_formatter.py +++ /dev/null @@ -1,459 +0,0 @@ -""" -Agent SFT Data Formatter - -Converts agent_sft dialog data into training format for LLM fine-tuning. -""" - -from __future__ import annotations - -import json -import logging -import re -from typing import Any, Iterator, Literal, Optional - -logger = logging.getLogger(__name__) - - -class AgentSFTFormatter: - """ - 将 agent_sft 对话数据转换为 SFT 训练格式 - - 支持多种输出格式: - - alpaca: {"instruction": str, "input": str, "output": str} - - sharegpt: {"conversations": [{"from": str, "value": str}, ...]} - - chatml: 直接拼接的 ChatML 格式字符串 - - Example: - >>> formatter = AgentSFTFormatter(output_format="alpaca") - >>> formatted = formatter.format_dialog(dialog) - >>> print(formatted["instruction"]) - """ - - # ChatML 模板 - CHATML_SYSTEM = """你是一个智能助手，擅长使用工具完成复杂任务。 - -可用工具: -{tool_descriptions} - -请按照以下格式回复: -1. 分析用户需求 -2. 制定执行计划 -3. 调用所需工具 -4. 整合结果并回复""" - - TOOL_CALL_FORMAT = """ -{{"name": "{tool_name}", "arguments": {arguments}}} -""" - - TOOL_RESULT_FORMAT = """ -{result} -""" - - def __init__( - self, - output_format: str = "alpaca", - include_tool_descriptions: bool = True, - tool_loader: Optional[Any] = None, - max_tools_in_prompt: int = 20, - tool_call_style: Literal["generic", "qwen"] = "generic", - ): - """ - 初始化格式化器 - - Args: - output_format: 输出格式 ("alpaca", "sharegpt", "chatml") - include_tool_descriptions: 是否在 prompt 中包含工具描述 - tool_loader: 工具元数据加载器 (用于获取工具描述) - max_tools_in_prompt: prompt 中最多包含的工具数 - tool_call_style: 工具调用格式 (generic / qwen) - """ - self.output_format = output_format - self.include_tool_descriptions = include_tool_descriptions - self.tool_loader = tool_loader - self.max_tools_in_prompt = max_tools_in_prompt - self.tool_call_style = tool_call_style - - self._tool_cache: dict[str, dict] = {} - - def format_dialog(self, dialog: Any) -> dict: - """ - 转换单个对话为训练格式 - - Args: - dialog: AgentSFTDialog 对象 - - Returns: - 格式化后的训练样本 - """ - if self.output_format == "alpaca": - return self._format_alpaca(dialog) - elif self.output_format == "sharegpt": - return self._format_sharegpt(dialog) - elif self.output_format == "chatml": - return self._format_chatml(dialog) - else: - raise ValueError(f"Unknown output format: {self.output_format}") - - def format_batch(self, dialogs: list) -> list[dict]: - """批量格式化对话""" - return [self.format_dialog(d) for d in dialogs] - - def iter_formatted(self, dialogs: Iterator) -> Iterator[dict]: - """迭代格式化对话""" - for dialog in dialogs: - try: - yield self.format_dialog(dialog) - except Exception as e: - logger.warning( - "Failed to format dialog %s: %s", - getattr(dialog, "dialog_id", "unknown"), - e, - ) - continue - - def _format_alpaca(self, dialog: Any) -> dict: - """转换为 Alpaca 格式""" - # 提取用户请求 - user_query = self._extract_user_query(dialog) - - # 构建指令 (包含工具描述) - instruction = self._build_instruction(dialog) - - # 构建输出 (助手响应 + 工具调用) - output = self._build_assistant_output(dialog) - - # 分类任务类型 - task_type = self._classify_task(dialog) - - return { - "instruction": instruction, - "input": user_query, - "output": output, - "task_type": task_type, - "dialog_id": getattr(dialog, "dialog_id", None), - "target_tools": getattr(dialog, "target_tools", []), - "metadata": getattr(dialog, "metadata", {}), - } - - def _format_sharegpt(self, dialog: Any) -> dict: - """转换为 ShareGPT 格式""" - conversations = [] - - # 添加系统提示 - tool_descriptions = self._get_tool_descriptions(getattr(dialog, "target_tools", [])) - system_prompt = self.CHATML_SYSTEM.format(tool_descriptions=tool_descriptions) - conversations.append({"from": "system", "value": system_prompt}) - - # 转换对话轮次 - for turn in getattr(dialog, "turns", []): - if turn.role == "user": - conversations.append({"from": "human", "value": turn.content}) - elif turn.role == "assistant": - conversations.append({"from": "gpt", "value": turn.content}) - elif turn.role == "tool": - # 工具结果作为 observation - formatted = self._format_tool_result( - getattr(turn, "tool_id", "unknown"), - getattr(turn, "result", turn.content), - ) - conversations.append({"from": "observation", "value": formatted}) - - return { - "conversations": conversations, - "dialog_id": getattr(dialog, "dialog_id", None), - "task_type": self._classify_task(dialog), - } - - def _format_chatml(self, dialog: Any) -> dict: - """转换为 ChatML 格式字符串""" - parts = [] - - # 系统提示 - tool_descriptions = self._get_tool_descriptions(getattr(dialog, "target_tools", [])) - system_prompt = self.CHATML_SYSTEM.format(tool_descriptions=tool_descriptions) - parts.append(f"<|im_start|>system\n{system_prompt}<|im_end|>") - - # 对话轮次 - for turn in getattr(dialog, "turns", []): - if turn.role == "user": - parts.append(f"<|im_start|>user\n{turn.content}<|im_end|>") - elif turn.role == "assistant": - parts.append(f"<|im_start|>assistant\n{turn.content}<|im_end|>") - elif turn.role == "tool": - tool_result = getattr(turn, "result", turn.content) - parts.append(f"<|im_start|>tool\n{tool_result}<|im_end|>") - - return { - "text": "\n".join(parts), - "dialog_id": getattr(dialog, "dialog_id", None), - "task_type": self._classify_task(dialog), - } - - def _extract_user_query(self, dialog: Any) -> str: - """提取用户请求""" - turns = getattr(dialog, "turns", []) - for turn in turns: - if turn.role == "user": - return turn.content - return getattr(dialog, "goal", "") - - def _build_instruction(self, dialog: Any) -> str: - """构建指令 (包含任务目标和工具描述)""" - parts = [] - - # 任务目标 - goal = getattr(dialog, "goal", "") - if goal: - parts.append(f"任务目标: {goal}") - - # 工具描述 - if self.include_tool_descriptions: - target_tools = getattr(dialog, "target_tools", []) - tool_desc = self._get_tool_descriptions(target_tools) - if tool_desc: - parts.append(f"\n可用工具:\n{tool_desc}") - - return "\n".join(parts) if parts else "完成用户请求" - - def _build_assistant_output(self, dialog: Any) -> str: - """构建助手输出 (包含思考过程和工具调用)""" - output_parts = [] - - turns = getattr(dialog, "turns", []) - for turn in turns: - if turn.role == "assistant": - # 助手思考/响应 - content = turn.content - # 检测是否包含工具调用意图 - if self._contains_tool_intent(content): - output_parts.append(f"\n{content}\n") - else: - output_parts.append(content) - - elif turn.role == "tool": - # 工具调用和结果 - tool_id = getattr(turn, "tool_id", "unknown") - tool_result = getattr(turn, "result", turn.content) - tool_content = getattr(turn, "content", "") - - # 格式化工具调用 - tool_call = self._format_tool_call(tool_id, tool_content) - output_parts.append(tool_call) - - # 格式化工具结果 - result_formatted = self._format_tool_result(tool_id, tool_result) - output_parts.append(result_formatted) - - return "\n".join(output_parts) - - def _get_tool_descriptions(self, tool_ids: list[str]) -> str: - """获取工具描述""" - if not tool_ids or not self.tool_loader: - return "" - - descriptions = [] - for tool_id in tool_ids[: self.max_tools_in_prompt]: - if tool_id in self._tool_cache: - tool = self._tool_cache[tool_id] - else: - tool = self.tool_loader.get_tool(tool_id) - if tool: - self._tool_cache[tool_id] = tool - - if tool: - name = getattr(tool, "name", tool_id) - desc = getattr(tool, "description", "") - descriptions.append(f"- {name}: {desc}") - - return "\n".join(descriptions) - - def _format_tool_call(self, tool_id: str, raw_arguments: str) -> str: - arguments = self._extract_tool_arguments(raw_arguments) - - if self.tool_call_style == "qwen": - payload = { - "name": tool_id, - "arguments": arguments, - } - return "\n" + json.dumps(payload, ensure_ascii=False) + "\n" - - return self.TOOL_CALL_FORMAT.format( - tool_name=tool_id, - arguments=json.dumps(arguments, ensure_ascii=False), - ) - - def _format_tool_result(self, tool_id: str, raw_result: Any) -> str: - result_payload = { - "name": tool_id, - "result": raw_result, - } - - if self.tool_call_style == "qwen": - return ( - "\n" - + json.dumps(result_payload, ensure_ascii=False) - + "\n" - ) - - return self.TOOL_RESULT_FORMAT.format(result=raw_result) - - def _extract_tool_arguments(self, content: str) -> dict: - if not content: - return {} - - stripped = content.strip() - if not stripped: - return {} - - try: - parsed = json.loads(stripped) - if isinstance(parsed, dict): - return parsed - return {"value": parsed} - except json.JSONDecodeError: - return {"input": stripped} - - def _classify_task(self, dialog: Any) -> str: - """ - 根据对话内容分类任务类型 - - Returns: - 任务类型: "tool_selection", "multi_step_planning", - "timing_decision", "tool_retrieval" - """ - metadata = getattr(dialog, "metadata", {}) - - # 优先使用 metadata 中的分类 - if "task_type" in metadata: - return metadata["task_type"] - - turns = getattr(dialog, "turns", []) - target_tools = getattr(dialog, "target_tools", []) - - # 基于规则的分类 - tool_count = len(target_tools) - turn_count = len(turns) - assistant_turns = [t for t in turns if t.role == "assistant"] - - # 多步规划: 多个工具 + 多轮对话 - if tool_count >= 3 or turn_count >= 8: - return "multi_step_planning" - - # 工具检索: metadata 标记或包含检索关键词 - goal = getattr(dialog, "goal", "").lower() - if "search" in goal or "find" in goal or "retrieve" in goal: - return "tool_retrieval" - - # 时机判断: 包含条件判断或等待 - for turn in assistant_turns: - content = turn.content.lower() - if any(kw in content for kw in ["if", "when", "wait", "check", "condition"]): - return "timing_decision" - - # 默认: 工具选择 - return "tool_selection" - - def _contains_tool_intent(self, content: str) -> bool: - """检测内容是否包含工具调用意图""" - # 简单的关键词检测 - tool_patterns = [ - r"call\s+\w+", - r"use\s+\w+\s+tool", - r"invoke\s+\w+", - r"execute\s+\w+", - r"step\s+\d+", - r"first.*then", - ] - - content_lower = content.lower() - for pattern in tool_patterns: - if re.search(pattern, content_lower): - return True - return False - - -class PreferenceDataFormatter: - """ - DPO 偏好数据格式化器 - - 将对话数据转换为偏好对格式: - {"prompt": str, "chosen": str, "rejected": str} - """ - - def __init__(self, sft_formatter: Optional[AgentSFTFormatter] = None): - self.sft_formatter = sft_formatter or AgentSFTFormatter() - - def format_preference_pair( - self, - prompt: str, - chosen_response: str, - rejected_response: str, - chosen_score: float = 1.0, - rejected_score: float = 0.0, - ) -> dict: - """ - 格式化单个偏好对 - - Args: - prompt: 输入提示 - chosen_response: 偏好的响应 - rejected_response: 非偏好的响应 - chosen_score: 偏好响应的分数 - rejected_score: 非偏好响应的分数 - - Returns: - 格式化的偏好对 - """ - return { - "prompt": prompt, - "chosen": chosen_response, - "rejected": rejected_response, - "chosen_score": chosen_score, - "rejected_score": rejected_score, - "margin": chosen_score - rejected_score, - } - - def format_from_ranked_responses( - self, - prompt: str, - responses: list[str], - scores: list[float], - ) -> list[dict]: - """ - 从排序的响应列表生成所有偏好对 - - Args: - prompt: 输入提示 - responses: 响应列表 - scores: 对应的分数列表 - - Returns: - 所有可能的偏好对列表 - """ - if len(responses) != len(scores): - raise ValueError("responses and scores must have same length") - - # 按分数排序 - sorted_pairs = sorted(zip(responses, scores), key=lambda x: x[1], reverse=True) - - preference_pairs = [] - - # 生成所有 (i, j) 对，其中 score[i] > score[j] - for i in range(len(sorted_pairs)): - for j in range(i + 1, len(sorted_pairs)): - chosen_resp, chosen_score = sorted_pairs[i] - rejected_resp, rejected_score = sorted_pairs[j] - - # 只有当分数差异显著时才生成偏好对 - if chosen_score - rejected_score > 0.1: - preference_pairs.append( - self.format_preference_pair( - prompt=prompt, - chosen_response=chosen_resp, - rejected_response=rejected_resp, - chosen_score=chosen_score, - rejected_score=rejected_score, - ) - ) - - return preference_pairs diff --git a/packages/sage-tools/src/sage/tools/agent_training/dialog_processor.py b/packages/sage-tools/src/sage/tools/agent_training/dialog_processor.py deleted file mode 100644 index 59b9dab7e5..0000000000 --- a/packages/sage-tools/src/sage/tools/agent_training/dialog_processor.py +++ /dev/null @@ -1,381 +0,0 @@ -"""Agent dialog processing utilities for SFT/RL training.""" - -from __future__ import annotations - -import json -import logging -import math -import random -from collections import Counter -from dataclasses import dataclass -from pathlib import Path -from typing import Any, Iterable, Iterator, Optional - -from datasets import Dataset - -from sage.data.sources.agent_sft import AgentSFTDataLoader -from sage.data.sources.agent_sft.schemas import AgentSFTDialog -from sage.data.sources.agent_tools import AgentToolsDataLoader - -# Note: format_alpaca_sample and format_conversation_sample are no longer available -# in sage_libs.sage_finetune. Using inline implementations instead. -from .data_formatter import AgentSFTFormatter - -logger = logging.getLogger(__name__) - - -@dataclass(slots=True) -class ProcessedDialog: - """Container for processed dialog samples.""" - - dialog_id: str - task_type: str - text: str - metadata: dict[str, Any] - target_tools: list[str] - split: str - source: str = "agent_sft" - - def to_record(self) -> dict: - """Convert to a dictionary suitable for HuggingFace datasets.""" - return { - "dialog_id": self.dialog_id, - "task_type": self.task_type, - "text": self.text, - "metadata": self.metadata, - "target_tools": self.target_tools, - "split": self.split, - "source": self.source, - } - - def to_json(self) -> str: - """Serialize to JSON string for caching/debug.""" - return json.dumps(self.to_record(), ensure_ascii=False) - - -class AgentDialogProcessor: - """Preprocess agent dialogs into model-ready text samples.""" - - SUPPORTED_SOURCES = {"agent_sft"} - - def __init__( - self, - formatter: Optional[AgentSFTFormatter] = None, - tool_loader: Optional[AgentToolsDataLoader] = None, - seed: int = 42, - ) -> None: - self.tool_loader = tool_loader or AgentToolsDataLoader() - self.formatter = formatter or AgentSFTFormatter( - output_format="chatml", - tool_loader=self.tool_loader, - ) - self._seed = seed - self._rng = random.Random(seed) - self._loaders: dict[str, AgentSFTDataLoader] = {} - - # ------------------------------------------------------------------ - # Public API - # ------------------------------------------------------------------ - def build_samples( - self, - data_uri: str, - *, - limit: Optional[int] = None, - output_format: Optional[str] = None, - task_weights: Optional[dict[str, float]] = None, - shuffle: bool = True, - ) -> list[ProcessedDialog]: - """Build processed dialog samples from the requested split.""" - - source, split = self._parse_data_uri(data_uri) - dialogs = list(self._get_loader(source).iter_dialogs(split)) - if not dialogs: - logger.warning("No dialogs found for %s", data_uri) - return [] - - logger.info("Loaded %d dialogs from %s:%s", len(dialogs), source, split) - - selected_dialogs = self._select_dialogs( - dialogs, - limit=limit, - shuffle=shuffle, - task_weights=task_weights, - ) - - fmt = output_format or self.formatter.output_format - formatter = self._get_formatter(fmt) - - processed: list[ProcessedDialog] = [] - for dialog in selected_dialogs: - try: - sample = self._format_dialog(dialog, formatter, split, source) - metrics = self._compute_dialog_metrics(sample) - sample.metadata.update(metrics) - processed.append(sample) - except Exception as exc: # pragma: no cover - safety net - logger.warning( - "Failed to format dialog %s (%s): %s", - dialog.dialog_id, - split, - exc, - ) - continue - - return processed - - def iter_texts( - self, - data_uri: str, - **kwargs: Any, - ) -> Iterator[str]: - """Iterate over processed dialog texts only.""" - - for sample in self.build_samples(data_uri, **kwargs): - yield sample.text - - def to_dataset( - self, - data_uri: str, - *, - limit: Optional[int] = None, - output_format: Optional[str] = None, - task_weights: Optional[dict[str, float]] = None, - shuffle: bool = True, - ) -> Dataset: - """Return a HuggingFace dataset constructed from processed dialogs.""" - - samples = self.build_samples( - data_uri, - limit=limit, - output_format=output_format, - task_weights=task_weights, - shuffle=shuffle, - ) - records = [sample.to_record() for sample in samples] - return Dataset.from_list(records) - - def export_jsonl( - self, - data_uri: str, - output_path: str | Path, - **kwargs: Any, - ) -> Path: - """Export processed samples to a JSONL file for inspection/caching.""" - - output_path = Path(output_path) - samples = self.build_samples(data_uri, **kwargs) - output_path.parent.mkdir(parents=True, exist_ok=True) - with output_path.open("w", encoding="utf-8") as fp: - for sample in samples: - fp.write(sample.to_json() + "\n") - logger.info("Exported %d samples to %s", len(samples), output_path) - return output_path - - # ------------------------------------------------------------------ - # Internal helpers - # ------------------------------------------------------------------ - def _parse_data_uri(self, data_uri: str) -> tuple[str, str]: - try: - source, split = data_uri.split(":", 1) - except ValueError as exc: - raise ValueError( - f"Invalid data uri '{data_uri}'. Expected format ':'" - ) from exc - - source = source.strip() - split = split.strip() - - if source not in self.SUPPORTED_SOURCES: - raise ValueError(f"Unsupported data source '{source}'") - if split not in {"train", "dev", "test"}: - raise ValueError(f"Unsupported split '{split}'") - return source, split - - def _get_loader(self, source: str) -> AgentSFTDataLoader: - if source not in self._loaders: - if source == "agent_sft": - self._loaders[source] = AgentSFTDataLoader() - else: # pragma: no cover - future extensions - raise ValueError(f"Unsupported data source '{source}'") - return self._loaders[source] - - def _get_formatter(self, output_format: str) -> AgentSFTFormatter: - if output_format == self.formatter.output_format: - return self.formatter - return AgentSFTFormatter( - output_format=output_format, - include_tool_descriptions=self.formatter.include_tool_descriptions, - tool_loader=self.formatter.tool_loader, - max_tools_in_prompt=self.formatter.max_tools_in_prompt, - ) - - def _select_dialogs( - self, - dialogs: list[AgentSFTDialog], - *, - limit: Optional[int], - shuffle: bool, - task_weights: Optional[dict[str, float]], - ) -> list[AgentSFTDialog]: - if shuffle: - self._rng.shuffle(dialogs) - - if not task_weights: - if limit is None: - return dialogs - return dialogs[:limit] - - weight_map = self._normalize_weights(task_weights, dialogs) - ordered = self._weighted_shuffle(dialogs, weight_map) - - if limit is None: - return ordered - return ordered[:limit] - - def _normalize_weights( - self, - weights: dict[str, float], - dialogs: Iterable[AgentSFTDialog], - ) -> dict[str, float]: - task_set: dict[str, float] = {} - default_weight = 1.0 - - for dialog in dialogs: - task = self._get_task_type(dialog) - if task not in task_set: - task_set[task] = max(float(weights.get(task, default_weight)), 1e-3) - - total = sum(task_set.values()) - if not task_set or total == 0: - return dict.fromkeys(task_set, 1.0) - - return {task: weight / total for task, weight in task_set.items()} - - def _weighted_shuffle( - self, - dialogs: list[AgentSFTDialog], - weight_map: dict[str, float], - ) -> list[AgentSFTDialog]: - decorated: list[tuple[float, AgentSFTDialog]] = [] - for dialog in dialogs: - task = self._get_task_type(dialog) - weight = max(weight_map.get(task, 1.0), 1e-3) - u = self._rng.random() - key = u ** (1.0 / weight) - decorated.append((key, dialog)) - - decorated.sort(key=lambda item: item[0], reverse=True) - return [item[1] for item in decorated] - - def _format_dialog( - self, - dialog: AgentSFTDialog, - formatter: AgentSFTFormatter, - split: str, - source: str, - ) -> ProcessedDialog: - formatted = formatter.format_dialog(dialog) - text = self._render_text(formatted) - task_type = self._get_task_type(dialog) - - return ProcessedDialog( - dialog_id=dialog.dialog_id, - task_type=task_type, - text=text, - metadata=dict(dialog.metadata), - target_tools=list(dialog.target_tools), - split=split, - source=source, - ) - - # ------------------------------------------------------------------ - # Metrics for coreset / continual learning - # ------------------------------------------------------------------ - def _compute_dialog_metrics(self, sample: ProcessedDialog) -> dict[str, float]: - tokens = self._tokenize(sample.text) - length = len(tokens) - unique_tokens = len(set(tokens)) or 1 - lexical_diversity = unique_tokens / max(length, 1) - - entropy = 0.0 - if tokens: - freq = Counter(tokens) - total = float(length) - entropy = -sum( - (count / total) * math.log(count / total + 1e-9) for count in freq.values() - ) - - difficulty = length / 512.0 + entropy / 5.0 - difficulty = float(min(max(difficulty, 0.0), 5.0)) - - if isinstance(sample.metadata.get("loss"), (int, float)): - difficulty = float(sample.metadata["loss"]) - - return { - "loss": difficulty, - "lexical_diversity": float(lexical_diversity), - "token_length": float(length), - } - - def _tokenize(self, text: str) -> list[str]: - return [token for token in text.lower().split() if token] - - def _render_text(self, formatted: dict) -> str: - if "text" in formatted: - return formatted["text"] - - if {"instruction", "output"}.issubset(formatted.keys()): - # Inline implementation of format_alpaca_sample - instruction = formatted["instruction"] - input_text = formatted.get("input", "") - output = formatted["output"] - - if input_text: - text = f"### Instruction:\n{instruction}\n\n### Input:\n{input_text}\n\n### Response:\n{output}" - else: - text = f"### Instruction:\n{instruction}\n\n### Response:\n{output}" - - return text - - if "conversations" in formatted: - conversations = [] - for message in formatted["conversations"]: - role = message.get("role") or message.get("from", "user") - value = message.get("content") or message.get("value", "") - role = self._normalize_role(role) - conversations.append({"role": role, "content": value}) - - # Inline implementation of format_conversation_sample - text_parts = [] - for conv in conversations: - role = conv["role"] - content = conv["content"] - if role == "user": - text_parts.append(f"User: {content}") - elif role == "assistant": - text_parts.append(f"Assistant: {content}") - else: - text_parts.append(f"{role.capitalize()}: {content}") - - text = "\n\n".join(text_parts) - return text - - raise ValueError("Unsupported formatted sample structure") - - def _normalize_role(self, role: str) -> str: - mapping = { - "human": "user", - "gpt": "assistant", - "observation": "tool", - } - return mapping.get(role, role) - - def _get_task_type(self, dialog: AgentSFTDialog) -> str: - metadata_type = dialog.metadata.get("task_type") - if metadata_type: - return metadata_type - - try: - return self.formatter._classify_task(dialog) # pylint: disable=protected-access - except Exception: # pragma: no cover - return "tool_selection" diff --git a/packages/sage-tools/src/sage/tools/agent_training/evaluator.py b/packages/sage-tools/src/sage/tools/agent_training/evaluator.py deleted file mode 100644 index 649baf220e..0000000000 --- a/packages/sage-tools/src/sage/tools/agent_training/evaluator.py +++ /dev/null @@ -1,500 +0,0 @@ -""" -Agent Training Evaluator - -Evaluates trained agent models against benchmarks. -""" - -import logging -import re -from dataclasses import dataclass -from typing import Any, Callable, Iterator, Optional - -import numpy as np - -logger = logging.getLogger(__name__) - - -@dataclass -class EvaluationMetrics: - """评估指标结果""" - - tool_selection_accuracy: float - plan_success_rate: float - step_efficiency: float - timing_precision: float - end_to_end_success: float - - # 统计信息 - total_samples: int - successful_samples: int - failed_samples: int - - # 详细分解 - per_category_accuracy: Optional[dict] = None - error_analysis: Optional[dict] = None - - -class AgentTrainingEvaluator: - """ - Agent 训练效果评估器 - - 在 agent_benchmark 上评估训练后的模型效果。 - - Metrics: - - tool_selection_accuracy: 工具选择准确率 - - plan_success_rate: 规划成功率 - - step_efficiency: 步骤效率 (实际步数 / 最优步数) - - timing_precision: 时机准确率 - - end_to_end_success: 端到端成功率 - - Example: - >>> evaluator = AgentTrainingEvaluator(benchmark_loader) - >>> results = evaluator.evaluate(model, split="test") - >>> print(f"Tool accuracy: {results.tool_selection_accuracy:.2%}") - """ - - SUPPORTED_METRICS = [ - "tool_selection_accuracy", - "plan_success_rate", - "step_efficiency", - "timing_precision", - "end_to_end_success", - ] - - def __init__( - self, - benchmark_loader: Any, - tool_registry: Optional[Any] = None, - executor: Optional[Any] = None, - ): - """ - 初始化评估器 - - Args: - benchmark_loader: Benchmark 数据加载器 - tool_registry: 工具注册表 (用于验证工具调用) - executor: 工具执行器 (用于端到端评估) - """ - self.benchmark = benchmark_loader - self.tool_registry = tool_registry - self.executor = executor - - def evaluate( - self, - model: Any, - split: str = "test", - metrics: Optional[list[str]] = None, - max_samples: Optional[int] = None, - generate_fn: Optional[Callable] = None, - ) -> EvaluationMetrics: - """ - 评估模型性能 - - Args: - model: 待评估的模型 - split: 数据分割 ("train", "dev", "test") - metrics: 要计算的指标列表 - max_samples: 最大样本数 (用于快速评估) - generate_fn: 自定义生成函数，签名 (model, instruction) -> response - - Returns: - EvaluationMetrics 评估结果 - """ - metrics = metrics or self.SUPPORTED_METRICS - generate_fn = generate_fn or self._default_generate - - # 收集评估结果 - results = {m: [] for m in metrics} - errors = [] - - # 迭代评估 - sample_count = 0 - for sample in self._iter_samples(split, max_samples): - sample_count += 1 - - try: - # 生成响应 - response = generate_fn(model, sample.instruction) - - # 计算各项指标 - sample_results = self._evaluate_sample( - sample=sample, - response=response, - metrics=metrics, - ) - - for m in metrics: - if m in sample_results: - results[m].append(sample_results[m]) - - except Exception as e: - logger.warning(f"Error evaluating sample {sample.sample_id}: {e}") - errors.append( - { - "sample_id": sample.sample_id, - "error": str(e), - } - ) - - # 聚合结果 - return self._aggregate_results(results, sample_count, errors) - - def evaluate_single( - self, - model: Any, - instruction: str, - ground_truth: dict, - generate_fn: Optional[Callable] = None, - ) -> dict: - """ - 评估单个样本 - - Args: - model: 模型 - instruction: 输入指令 - ground_truth: 标准答案 - generate_fn: 生成函数 - - Returns: - 各项指标的分数 - """ - generate_fn = generate_fn or self._default_generate - response = generate_fn(model, instruction) - - # 创建模拟样本对象 - class MockSample: - def __init__(self, instruction, ground_truth): - self.instruction = instruction - self.sample_id = "single" - self.target_tools = ground_truth.get("target_tools", []) - self.expected_steps = ground_truth.get("expected_steps", []) - self.difficulty = ground_truth.get("difficulty", "medium") - - sample = MockSample(instruction, ground_truth) - - return self._evaluate_sample( - sample=sample, - response=response, - metrics=self.SUPPORTED_METRICS, - ) - - def _iter_samples( - self, - split: str, - max_samples: Optional[int], - ) -> Iterator: - """迭代评估样本""" - count = 0 - for sample in self.benchmark.iter_samples(split): - if max_samples and count >= max_samples: - break - yield sample - count += 1 - - def _evaluate_sample( - self, - sample: Any, - response: str, - metrics: list[str], - ) -> dict: - """评估单个样本的所有指标""" - results = {} - - if "tool_selection_accuracy" in metrics: - results["tool_selection_accuracy"] = self._eval_tool_selection(response, sample) - - if "plan_success_rate" in metrics: - results["plan_success_rate"] = self._eval_plan_success(response, sample) - - if "step_efficiency" in metrics: - results["step_efficiency"] = self._eval_step_efficiency(response, sample) - - if "timing_precision" in metrics: - results["timing_precision"] = self._eval_timing_precision(response, sample) - - if "end_to_end_success" in metrics: - results["end_to_end_success"] = self._eval_end_to_end(response, sample) - - return results - - def _eval_tool_selection(self, response: str, sample: Any) -> float: - """评估工具选择准确率""" - - # 提取预测的工具 - predicted_tools = self._extract_tools(response) - target_tools = getattr(sample, "target_tools", []) - - if not target_tools: - return 1.0 if not predicted_tools else 0.5 - - if not predicted_tools: - return 0.0 - - # 计算 F1 - predicted_set = set(predicted_tools) - target_set = set(target_tools) - - correct = len(predicted_set & target_set) - precision = correct / len(predicted_set) if predicted_set else 0 - recall = correct / len(target_set) if target_set else 0 - - if precision + recall == 0: - return 0.0 - - return 2 * precision * recall / (precision + recall) - - def _eval_plan_success(self, response: str, sample: Any) -> float: - """评估规划成功率""" - # 检查是否包含有效的规划结构 - plan_indicators = [ - r"step\s*\d+", - r"first.*then", - r"1\.\s*\w+.*2\.\s*\w+", - r"plan:", - r"步骤", - ] - - response_lower = response.lower() - has_plan = any( - re.search(pattern, response_lower, re.IGNORECASE) for pattern in plan_indicators - ) - - if not has_plan: - return 0.0 - - # 检查规划是否覆盖目标工具 - predicted_tools = self._extract_tools(response) - target_tools = getattr(sample, "target_tools", []) - - if target_tools: - coverage = len(set(predicted_tools) & set(target_tools)) / len(target_tools) - return 0.5 + 0.5 * coverage - - return 0.8 # 有规划但无目标工具时 - - def _eval_step_efficiency(self, response: str, sample: Any) -> float: - """评估步骤效率""" - # 统计实际步数 - actual_steps = len(self._extract_tools(response)) - - # 获取最优步数 - expected_steps = getattr(sample, "expected_steps", []) - optimal_steps = len(expected_steps) if expected_steps else 3 - - if actual_steps == 0: - return 0.0 - - if actual_steps <= optimal_steps: - return 1.0 - - # 效率随超出步数递减 - return max(0.0, 1.0 - 0.1 * (actual_steps - optimal_steps)) - - def _eval_timing_precision(self, response: str, sample: Any) -> float: - """评估时机精确度""" - predicted_tools = self._extract_tools(response) - - # 检查重复调用 - unique_tools = set(predicted_tools) - if len(predicted_tools) > len(unique_tools): - # 有重复调用，扣分 - redundancy = (len(predicted_tools) - len(unique_tools)) / len(predicted_tools) - return max(0.0, 1.0 - redundancy) - - return 1.0 - - def _eval_end_to_end(self, response: str, sample: Any) -> float: - """评估端到端成功率""" - if self.executor is None: - # 无执行器时，基于响应内容评估 - return self._eval_plan_success(response, sample) * 0.8 - - # 执行响应中的工具调用 - try: - tools = self._extract_tools_with_args(response) - - success_count = 0 - for tool_id, args in tools: - result = self.executor.execute(tool_id, args) - if result.get("status") == "success": - success_count += 1 - - return success_count / len(tools) if tools else 0.5 - - except Exception as e: - logger.warning(f"Execution failed: {e}") - return 0.0 - - def _extract_tools(self, response: str) -> list[str]: - """提取工具 ID 列表""" - import re - - tools = [] - - # 标准格式 - pattern1 = re.compile(r'\s*\{?\s*"?name"?\s*:\s*"?([^"}\s]+)"?') - tools.extend(pattern1.findall(response)) - - # 简单格式 - pattern2 = re.compile(r"(?:call|use|invoke)\s+([a-z_]+_\d{3})", re.IGNORECASE) - tools.extend(pattern2.findall(response)) - - # 去重 - seen = set() - unique = [] - for t in tools: - if t not in seen: - seen.add(t) - unique.append(t) - - return unique - - def _extract_tools_with_args(self, response: str) -> list[tuple[str, dict]]: - """提取工具调用及其参数""" - import json - import re - - results = [] - - pattern = re.compile(r"\s*(\{[^}]+\})\s*", re.DOTALL) - - for match in pattern.finditer(response): - try: - call_data = json.loads(match.group(1)) - tool_id = call_data.get("name", "") - args = call_data.get("arguments", {}) - results.append((tool_id, args)) - except json.JSONDecodeError: - continue - - return results - - def _default_generate(self, model: Any, instruction: str) -> str: - """默认生成函数""" - if hasattr(model, "generate"): - return model.generate(instruction) - if callable(model): - return model(instruction) - else: - raise ValueError("Model must have 'generate' method or be callable") - - def _aggregate_results( - self, - results: dict[str, list], - total_samples: int, - errors: list, - ) -> EvaluationMetrics: - """聚合评估结果""" - - def safe_mean(values): - return float(np.mean(values)) if values else 0.0 - - return EvaluationMetrics( - tool_selection_accuracy=safe_mean(results.get("tool_selection_accuracy", [])), - plan_success_rate=safe_mean(results.get("plan_success_rate", [])), - step_efficiency=safe_mean(results.get("step_efficiency", [])), - timing_precision=safe_mean(results.get("timing_precision", [])), - end_to_end_success=safe_mean(results.get("end_to_end_success", [])), - total_samples=total_samples, - successful_samples=total_samples - len(errors), - failed_samples=len(errors), - error_analysis={"errors": errors} if errors else None, - ) - - def generate_report( - self, - metrics: EvaluationMetrics, - model_name: str = "Unknown", - output_format: str = "markdown", - ) -> str: - """ - 生成评估报告 - - Args: - metrics: 评估结果 - model_name: 模型名称 - output_format: 输出格式 ("markdown", "json", "text") - - Returns: - 格式化的报告字符串 - """ - if output_format == "markdown": - return self._generate_markdown_report(metrics, model_name) - elif output_format == "json": - import json - - return json.dumps(metrics.__dict__, indent=2, default=str) - else: - return self._generate_text_report(metrics, model_name) - - def _generate_markdown_report( - self, - metrics: EvaluationMetrics, - model_name: str, - ) -> str: - """生成 Markdown 格式报告""" - lines = [ - "# Agent Evaluation Report", - "", - f"**Model**: {model_name}", - f"**Samples**: {metrics.total_samples} (Success: {metrics.successful_samples}, Failed: {metrics.failed_samples})", - "", - "## Metrics", - "", - "| Metric | Score |", - "|--------|-------|", - f"| Tool Selection Accuracy | {metrics.tool_selection_accuracy:.2%} |", - f"| Plan Success Rate | {metrics.plan_success_rate:.2%} |", - f"| Step Efficiency | {metrics.step_efficiency:.2%} |", - f"| Timing Precision | {metrics.timing_precision:.2%} |", - f"| End-to-End Success | {metrics.end_to_end_success:.2%} |", - "", - "## Summary", - "", - f"Overall Agent Score: **{self._compute_overall_score(metrics):.2%}**", - ] - - return "\n".join(lines) - - def _generate_text_report( - self, - metrics: EvaluationMetrics, - model_name: str, - ) -> str: - """生成纯文本报告""" - lines = [ - "=== Agent Evaluation Report ===", - f"Model: {model_name}", - f"Samples: {metrics.total_samples}", - "", - "Metrics:", - f" - Tool Selection Accuracy: {metrics.tool_selection_accuracy:.2%}", - f" - Plan Success Rate: {metrics.plan_success_rate:.2%}", - f" - Step Efficiency: {metrics.step_efficiency:.2%}", - f" - Timing Precision: {metrics.timing_precision:.2%}", - f" - End-to-End Success: {metrics.end_to_end_success:.2%}", - "", - f"Overall Score: {self._compute_overall_score(metrics):.2%}", - ] - - return "\n".join(lines) - - def _compute_overall_score(self, metrics: EvaluationMetrics) -> float: - """计算综合分数""" - weights = { - "tool_selection_accuracy": 0.30, - "plan_success_rate": 0.25, - "step_efficiency": 0.15, - "timing_precision": 0.10, - "end_to_end_success": 0.20, - } - - score = ( - weights["tool_selection_accuracy"] * metrics.tool_selection_accuracy - + weights["plan_success_rate"] * metrics.plan_success_rate - + weights["step_efficiency"] * metrics.step_efficiency - + weights["timing_precision"] * metrics.timing_precision - + weights["end_to_end_success"] * metrics.end_to_end_success - ) - - return score diff --git a/packages/sage-tools/src/sage/tools/agent_training/reward_model.py b/packages/sage-tools/src/sage/tools/agent_training/reward_model.py deleted file mode 100644 index 9fc23319af..0000000000 --- a/packages/sage-tools/src/sage/tools/agent_training/reward_model.py +++ /dev/null @@ -1,468 +0,0 @@ -""" -Agent Reward Model - -Computes rewards for agent responses based on: -- Task completion -- Tool selection accuracy -- Execution efficiency -- Timing quality -- Format compliance -""" - -import logging -import re -from dataclasses import dataclass -from typing import Any, Optional - -from .config import AgentRewardConfig - -logger = logging.getLogger(__name__) - - -@dataclass -class RewardResult: - """奖励计算结果""" - - total: float - breakdown: dict[str, float] - feedback: str - penalties_applied: list[str] - - -class AgentRewardModel: - """ - Agent 奖励模型 - - 用于计算 Agent 响应的奖励分数，支持: - - 任务完成度评估 - - 工具选择准确性评估 - - 执行效率评估 - - 时机质量评估 - - 格式合规性评估 - - Example: - >>> reward_model = AgentRewardModel(AgentRewardConfig()) - >>> result = reward_model.compute_reward( - ... query="查询天气", - ... response="weather_001", - ... ground_truth={"target_tools": ["weather_001"]}, - ... execution_trace=[] - ... ) - >>> print(f"Total reward: {result.total:.2f}") - """ - - # 工具调用模式 - TOOL_CALL_PATTERN = re.compile( - r'\s*\{?\s*"?name"?\s*:\s*"?([^"}\s]+)"?', re.IGNORECASE - ) - - # 简单工具 ID 模式 - SIMPLE_TOOL_PATTERN = re.compile( - r"(?:call|use|invoke|execute)\s+([a-z_]+_\d{3})", re.IGNORECASE - ) - - def __init__(self, config: Optional[AgentRewardConfig] = None): - """ - 初始化奖励模型 - - Args: - config: 奖励配置，None 则使用默认配置 - """ - self.config = config or AgentRewardConfig() - - # 验证权重总和 - weight_sum = sum(self.config.weights.values()) - if abs(weight_sum - 1.0) > 0.01: - logger.warning(f"Reward weights sum to {weight_sum}, expected 1.0") - - def compute_reward( - self, - query: str, - response: str, - ground_truth: dict, - execution_trace: Optional[list] = None, - ) -> RewardResult: - """ - 计算综合奖励分数 - - Args: - query: 用户请求 - response: 模型响应 - ground_truth: 标准答案，包含 target_tools, expected_steps 等 - execution_trace: 执行轨迹 (可选) - - Returns: - RewardResult 包含总分、分项分数、反馈和惩罚 - """ - execution_trace = execution_trace or [] - rewards = {} - penalties_applied = [] - - # 1. 任务完成度 - rewards["task_completion"] = self._eval_task_completion( - response, ground_truth, execution_trace - ) - - # 2. 工具选择准确性 - target_tools = ground_truth.get("target_tools", []) - rewards["tool_accuracy"], tool_penalties = self._eval_tool_accuracy(response, target_tools) - penalties_applied.extend(tool_penalties) - - # 3. 执行效率 - optimal_steps = ground_truth.get("optimal_steps", 5) - rewards["efficiency"] = self._eval_efficiency(execution_trace, optimal_steps) - - # 4. 时机质量 - rewards["timing_quality"] = self._eval_timing(response, execution_trace, ground_truth) - - # 5. 格式合规性 - rewards["format_compliance"], format_penalties = self._eval_format(response) - penalties_applied.extend(format_penalties) - - # 加权求和 - total = sum(rewards[k] * self.config.weights.get(k, 0) for k in rewards) - - # 应用惩罚 - for penalty_type in penalties_applied: - penalty_value = self.config.penalties.get(penalty_type, 0) - total += penalty_value - - # 归一化到 [0, 1] - total = max(0.0, min(1.0, total)) - - # 生成反馈 - feedback = self._generate_feedback(rewards, penalties_applied) - - return RewardResult( - total=total, - breakdown=rewards, - feedback=feedback, - penalties_applied=penalties_applied, - ) - - def _eval_task_completion( - self, - response: str, - ground_truth: dict, - execution_trace: list, - ) -> float: - """评估任务完成度""" - # 检查执行轨迹中是否有成功标记 - if execution_trace: - success_count = sum(1 for step in execution_trace if step.get("status") == "success") - total_steps = len(execution_trace) - if total_steps > 0: - return success_count / total_steps - - # 基于响应内容判断 - # 检查是否包含最终答案/结论 - completion_indicators = [ - "完成", - "done", - "finished", - "结果是", - "答案是", - "总结", - "conclusion", - "最终", - "final", - ] - - response_lower = response.lower() - has_conclusion = any(ind in response_lower for ind in completion_indicators) - - # 检查是否调用了目标工具 - target_tools = ground_truth.get("target_tools", []) - predicted_tools = self._extract_tool_calls(response) - - if target_tools: - tool_coverage = len(set(predicted_tools) & set(target_tools)) / len(target_tools) - else: - tool_coverage = 1.0 if not predicted_tools else 0.5 - - # 综合评分 - score = 0.5 * tool_coverage + 0.5 * (1.0 if has_conclusion else 0.5) - return score - - def _eval_tool_accuracy( - self, - response: str, - target_tools: list[str], - ) -> tuple[float, list[str]]: - """ - 评估工具选择准确率 - - Returns: - (accuracy_score, penalties_list) - """ - penalties = [] - predicted_tools = self._extract_tool_calls(response) - - if not target_tools: - # 无目标工具时，调用任何工具都可以 - return (1.0 if not predicted_tools else 0.7, penalties) - - if not predicted_tools: - # 应该调用工具但没有调用 - return (0.0, penalties) - - # 计算 Precision 和 Recall - predicted_set = set(predicted_tools) - target_set = set(target_tools) - - correct = len(predicted_set & target_set) - - # Precision: 预测的工具中有多少是正确的 - precision = correct / len(predicted_set) if predicted_set else 0 - - # Recall: 目标工具中有多少被预测到 - recall = correct / len(target_set) if target_set else 0 - - # 检查错误工具 - wrong_tools = predicted_set - target_set - if wrong_tools: - penalties.append("wrong_tool") - - # 检查幻觉工具 (格式不符合 tool_id 规范的) - for tool in predicted_tools: - if not re.match(r"^[a-z]+(_[a-z]+)*_\d{3}$", tool): - penalties.append("hallucination") - break - - # F1 Score - if precision + recall == 0: - return (0.0, penalties) - - f1 = 2 * precision * recall / (precision + recall) - return (f1, penalties) - - def _eval_efficiency( - self, - execution_trace: list, - optimal_steps: int, - ) -> float: - """评估执行效率""" - actual_steps = len(execution_trace) - - if actual_steps == 0: - return 0.5 # 没有执行轨迹时给中等分 - - if actual_steps <= optimal_steps: - return 1.0 - - # 超过最优步数时，效率递减 - excess_ratio = (actual_steps - optimal_steps) / optimal_steps - score = max(0.0, 1.0 - 0.2 * excess_ratio) - - return score - - def _eval_timing( - self, - response: str, - execution_trace: list, - ground_truth: dict, - ) -> float: - """评估调用时机质量""" - # 检查是否有冗余调用 - predicted_tools = self._extract_tool_calls(response) - unique_tools = set(predicted_tools) - - if len(predicted_tools) > len(unique_tools): - # 有重复调用 - redundancy_penalty = 0.2 * (len(predicted_tools) - len(unique_tools)) - return max(0.0, 1.0 - redundancy_penalty) - - # 检查调用顺序是否合理 (基于执行轨迹) - if execution_trace: - # 检查是否有失败后重试 - retry_count = sum( - 1 - for i, step in enumerate(execution_trace[1:], 1) - if step.get("tool_id") == execution_trace[i - 1].get("tool_id") - ) - if retry_count > 0: - return max(0.5, 1.0 - 0.1 * retry_count) - - return 1.0 # 默认良好 - - def _eval_format(self, response: str) -> tuple[float, list[str]]: - """ - 评估格式合规性 - - Returns: - (format_score, penalties_list) - """ - penalties = [] - score = 1.0 - - # 检查工具调用格式 - tool_calls = self.TOOL_CALL_PATTERN.findall(response) - simple_calls = self.SIMPLE_TOOL_PATTERN.findall(response) - - # 如果使用了非标准格式 - if simple_calls and not tool_calls: - score -= 0.2 - penalties.append("format_error") - - # 检查是否有未闭合的标签 - open_tags = response.count("") - close_tags = response.count("") - if open_tags != close_tags: - score -= 0.3 - penalties.append("format_error") - - # 检查 JSON 格式 - if "" in response: - try: - import json - - # 提取 JSON 部分 - json_match = re.search(r"\s*(\{[^}]+\})\s*", response) - if json_match: - json.loads(json_match.group(1)) - except json.JSONDecodeError: - score -= 0.2 - - return (max(0.0, score), penalties) - - def _extract_tool_calls(self, response: str) -> list[str]: - """从响应中提取工具调用""" - tools = [] - - # 标准格式 - tools.extend(self.TOOL_CALL_PATTERN.findall(response)) - - # 简单格式 - tools.extend(self.SIMPLE_TOOL_PATTERN.findall(response)) - - # 去重但保持顺序 - seen = set() - unique_tools = [] - for tool in tools: - if tool not in seen: - seen.add(tool) - unique_tools.append(tool) - - return unique_tools - - def _generate_feedback( - self, - rewards: dict[str, float], - penalties: list[str], - ) -> str: - """生成人类可读的反馈""" - feedback_parts = [] - - # 分数反馈 - for metric, score in rewards.items(): - if score < 0.5: - feedback_parts.append(f"❌ {metric}: {score:.2f} (需改进)") - elif score < 0.8: - feedback_parts.append(f"⚠️ {metric}: {score:.2f} (一般)") - else: - feedback_parts.append(f"✅ {metric}: {score:.2f} (良好)") - - # 惩罚反馈 - if penalties: - penalty_msgs = { - "wrong_tool": "选择了错误的工具", - "redundant_call": "存在冗余的工具调用", - "format_error": "响应格式不规范", - "timeout": "执行超时", - "hallucination": "调用了不存在的工具", - } - for p in set(penalties): - msg = penalty_msgs.get(p, f"惩罚: {p}") - feedback_parts.append(f"🚫 {msg}") - - return "\n".join(feedback_parts) - - -class ToolVerifier: - """工具调用验证器""" - - def __init__(self, tool_registry: Optional[Any] = None): - self.tool_registry = tool_registry - - def verify_tool_exists(self, tool_id: str) -> bool: - """验证工具是否存在""" - if self.tool_registry is None: - # 只验证格式 - return bool(re.match(r"^[a-z]+(_[a-z]+)*_\d{3}$", tool_id)) - - return self.tool_registry.has_tool(tool_id) - - def verify_arguments(self, tool_id: str, arguments: dict) -> tuple[bool, str]: - """验证工具参数是否合法""" - if self.tool_registry is None: - return (True, "") - - tool = self.tool_registry.get_tool(tool_id) - if not tool: - return (False, f"Tool {tool_id} not found") - - # 检查必需参数 - required_params = getattr(tool, "required_params", []) - missing = [p for p in required_params if p not in arguments] - - if missing: - return (False, f"Missing required parameters: {missing}") - - return (True, "") - - -class PlanEvaluator: - """规划质量评估器""" - - def evaluate_plan( - self, - plan_steps: list[dict], - ground_truth_steps: Optional[list[dict]] = None, - ) -> dict: - """ - 评估规划质量 - - Args: - plan_steps: 模型生成的规划步骤 - ground_truth_steps: 标准规划步骤 (可选) - - Returns: - 评估结果 - """ - result = { - "step_count": len(plan_steps), - "has_clear_goal": False, - "has_tool_assignments": False, - "is_executable": False, - "score": 0.0, - } - - if not plan_steps: - return result - - # 检查是否有明确目标 - first_step = plan_steps[0] - if "goal" in first_step or "objective" in first_step: - result["has_clear_goal"] = True - - # 检查是否有工具分配 - for step in plan_steps: - if "tool" in step or "tool_id" in step: - result["has_tool_assignments"] = True - break - - # 检查是否可执行 (每个步骤都有 action) - result["is_executable"] = all("action" in step or "tool" in step for step in plan_steps) - - # 计算总分 - score = 0.0 - if result["has_clear_goal"]: - score += 0.3 - if result["has_tool_assignments"]: - score += 0.4 - if result["is_executable"]: - score += 0.3 - - result["score"] = score - - return result diff --git a/packages/sage-tools/src/sage/tools/agent_training/sft_trainer.py b/packages/sage-tools/src/sage/tools/agent_training/sft_trainer.py deleted file mode 100644 index f275a2d081..0000000000 --- a/packages/sage-tools/src/sage/tools/agent_training/sft_trainer.py +++ /dev/null @@ -1,337 +0,0 @@ -"""Supervised fine-tuning trainer for agent dialogs.""" - -from __future__ import annotations - -import logging -import sys -from typing import Optional - -import torch -from datasets import Dataset -from peft import LoraConfig as PeftLoraConfig -from peft import get_peft_model -from transformers import ( - AutoModelForCausalLM, - AutoTokenizer, - DataCollatorForLanguageModeling, - Trainer, - TrainingArguments, -) - -# Import SIAS components from middleware (they live in L4, not L3) -from sage.middleware.components.sage_sias import CoresetSelector, OnlineContinualLearner - -from .config import AgentSFTConfig -from .dialog_processor import AgentDialogProcessor, ProcessedDialog - -logger = logging.getLogger(__name__) - - -class AgentSFTTrainer: - """Agent-specific LoRA SFT trainer optimized for 12GB GPUs.""" - - def __init__( - self, - config: AgentSFTConfig, - dialog_processor: Optional[AgentDialogProcessor] = None, - ) -> None: - self.config = config - self.dialog_processor = dialog_processor or AgentDialogProcessor() - self.model = None - self.tokenizer = None - self.train_dataset: Optional[Dataset] = None - self.eval_dataset: Optional[Dataset] = None - self.trainer: Optional[Trainer] = None - self._train_samples: list[ProcessedDialog] = [] - self._eval_samples: list[ProcessedDialog] = [] - self.coreset_selector = self._build_coreset_selector() - self.continual_learner = self._build_continual_learner() - - # ------------------------------------------------------------------ - # Public API - # ------------------------------------------------------------------ - def train(self) -> None: - """Execute the complete SFT pipeline.""" - - if self.model is None or self.tokenizer is None: - self.load_model_and_tokenizer() - - if not hasattr(self.model, "peft_config"): - self.apply_lora() - - if self.train_dataset is None: - self.prepare_datasets() - - if self.trainer is None: - self.setup_trainer() - - self._print_training_banner() - - try: - self.trainer.train() - except RuntimeError as err: # pragma: no cover - runtime safeguard - if "out of memory" in str(err).lower(): - self._handle_oom() - raise - - self.save_model() - self.print_completion_info() - - def load_model_and_tokenizer(self) -> None: - """Load base model/tokenizer with quantization hints.""" - - load_kwargs = self._build_quantization_kwargs() - logger.info("Loading base model %s", self.config.base_model) - self.model = AutoModelForCausalLM.from_pretrained(self.config.base_model, **load_kwargs) - self.tokenizer = AutoTokenizer.from_pretrained(self.config.base_model, use_fast=True) - - if self.tokenizer.pad_token is None: - self.tokenizer.pad_token = self.tokenizer.eos_token - self.tokenizer.padding_side = self.config.padding_side - - def apply_lora(self) -> None: - """Attach LoRA adapters with agent-friendly defaults.""" - - if self.model is None: - raise ValueError("Model must be loaded before applying LoRA") - - lora_config = PeftLoraConfig( - r=self.config.lora_r, - lora_alpha=self.config.lora_alpha, - target_modules=self.config.lora_target_modules, - lora_dropout=self.config.lora_dropout, - bias="none", - task_type="CAUSAL_LM", - ) - self.model = get_peft_model(self.model, lora_config) # type: ignore[arg-type] - self.model.print_trainable_parameters() - - def prepare_datasets(self) -> None: - """Process dialogs and tokenize into HF datasets.""" - - if self.tokenizer is None: - raise ValueError("Tokenizer must be loaded before preparing datasets") - - self._train_samples = self.dialog_processor.build_samples( - self.config.train_data, - limit=self.config.max_train_samples, - output_format=self.config.output_format, - task_weights=self.config.task_weights, - shuffle=self.config.shuffle_train, - ) - if not self._train_samples: - raise ValueError("No training samples were produced") - - if self.coreset_selector and self.config.coreset_target_size: - metrics = self._collect_metrics(self._train_samples, self.config.coreset_metric_key) - self._train_samples = self.coreset_selector.select( - self._train_samples, - target_size=self.config.coreset_target_size, - metrics=metrics, - ) - - if self.continual_learner: - metrics = self._collect_metrics(self._train_samples, self.config.coreset_metric_key) - self._train_samples = self.continual_learner.update_buffer( - self._train_samples, - metrics=metrics, - ) - - self._eval_samples = self.dialog_processor.build_samples( - self.config.dev_data, - limit=self.config.max_eval_samples, - output_format=self.config.output_format, - task_weights=None, - shuffle=self.config.shuffle_eval, - ) - - self.train_dataset = self._tokenize_samples(self._train_samples) - self.eval_dataset = ( - self._tokenize_samples(self._eval_samples) if self._eval_samples else None - ) - self._log_dataset_stats() - - def setup_trainer(self) -> None: - """Configure HuggingFace Trainer.""" - - if self.model is None or self.tokenizer is None or self.train_dataset is None: - raise ValueError("Model, tokenizer and dataset must be prepared before Trainer") - - evaluation_strategy = self.config.eval_strategy if self.eval_dataset is not None else "no" - report_to = self.config.report_to if self.config.report_to != "none" else "none" - - training_args = TrainingArguments( - output_dir=str(self.config.checkpoint_dir), - num_train_epochs=self.config.num_epochs, - per_device_train_batch_size=self.config.batch_size, - per_device_eval_batch_size=self.config.batch_size, - gradient_accumulation_steps=self.config.gradient_accumulation, - learning_rate=self.config.learning_rate, - warmup_ratio=self.config.warmup_ratio, - lr_scheduler_type=self.config.lr_scheduler, - logging_dir=str(self.config.log_dir), - logging_steps=self.config.logging_steps, - save_steps=self.config.save_steps, - save_total_limit=self.config.save_total_limit, - evaluation_strategy=evaluation_strategy, - eval_steps=self.config.eval_steps, - report_to=report_to, - fp16=self.config.fp16, - bf16=self.config.bf16, - gradient_checkpointing=self.config.gradient_checkpointing, - optim=self.config.optim, - load_best_model_at_end=self.config.load_best_model, - metric_for_best_model=self.config.metric_for_best_model, - greater_is_better=self.config.greater_is_better, - seed=self.config.seed, - dataloader_num_workers=self.config.dataloader_num_workers, - ) - - data_collator = DataCollatorForLanguageModeling(self.tokenizer, mlm=False) - - self.training_args = training_args - self.trainer = Trainer( - model=self.model, - args=training_args, - train_dataset=self.train_dataset, - eval_dataset=self.eval_dataset if evaluation_strategy != "no" else None, - data_collator=data_collator, - ) - - def save_model(self) -> None: - """Persist LoRA weights and tokenizer.""" - - if self.model is None or self.tokenizer is None: - raise ValueError("Model and tokenizer must exist to save") - - self.model.save_pretrained(str(self.config.lora_dir)) - self.tokenizer.save_pretrained(str(self.config.lora_dir)) - logger.info("Saved LoRA weights to %s", self.config.lora_dir) - - def print_completion_info(self) -> None: - """Print helpful follow-up instructions.""" - - divider = "=" * 60 - print(divider) - print("🎉 Agent SFT training complete!") - print(divider) - print(f"LoRA weights: {self.config.lora_dir}") - print(f"Checkpoints : {self.config.checkpoint_dir}") - print(f"Logs : {self.config.log_dir}") - print("Next steps:") - print(f" • Merge weights: sage finetune merge {self.config.output_dir.name}") - print(f" • Chat: sage finetune chat {self.config.output_dir.name}") - - # ------------------------------------------------------------------ - # Helpers - # ------------------------------------------------------------------ - def _build_quantization_kwargs(self) -> dict: - if self.config.load_in_8bit: - return { - "load_in_8bit": True, - "device_map": "auto", - "torch_dtype": torch.float16, - } - if self.config.load_in_4bit: - from transformers import BitsAndBytesConfig - - quant_config = BitsAndBytesConfig( - load_in_4bit=True, - bnb_4bit_compute_dtype=torch.float16, - bnb_4bit_use_double_quant=True, - bnb_4bit_quant_type="nf4", - ) - return {"quantization_config": quant_config, "device_map": "auto"} - return {"device_map": "auto", "torch_dtype": "auto"} - - def _tokenize_samples(self, samples: list[ProcessedDialog]) -> Dataset: - dataset = Dataset.from_list([sample.to_record() for sample in samples]) - - def tokenize(batch): - tokenized = self.tokenizer( # type: ignore[call-arg] - batch["text"], - truncation=True, - max_length=self.config.max_length, - padding=self.config.padding_strategy, - ) - tokenized["labels"] = tokenized["input_ids"] - return tokenized - - return dataset.map(tokenize, batched=True, remove_columns=dataset.column_names) - - def _log_dataset_stats(self) -> None: - def summarize(samples: list[ProcessedDialog]) -> str: - counts: dict[str, int] = {} - for sample in samples: - counts[sample.task_type] = counts.get(sample.task_type, 0) + 1 - return ", ".join(f"{task}: {count}" for task, count in sorted(counts.items())) - - logger.info( - "Train samples: %d (%s)", - len(self._train_samples), - summarize(self._train_samples), - ) - if self._eval_samples: - logger.info( - "Eval samples: %d (%s)", - len(self._eval_samples), - summarize(self._eval_samples), - ) - - def _print_training_banner(self) -> None: - print("🚀 Starting Agent SFT training\n") - print(f"Effective batch size : {self.config.effective_batch_size}") - print(f"Train samples : {len(self._train_samples)}") - if self._eval_samples: - print(f"Eval samples : {len(self._eval_samples)}") - print("Tips:") - print(" • Monitor GPU memory: watch -n 1 nvidia-smi") - if self.config.load_in_8bit: - print(" • 8-bit loading enabled for RTX 3060 budgets") - if self.config.gradient_checkpointing: - print(" • Gradient checkpointing is ON") - print() - - def _handle_oom(self) -> None: - print("\n❌ Detected CUDA OOM. Suggestions:") - print(f" 1. Reduce max_length (currently {self.config.max_length})") - print(f" 2. Reduce batch_size (currently {self.config.batch_size})") - print(" 3. Increase gradient_accumulation") - if not self.config.load_in_8bit and not self.config.load_in_4bit: - print(" 4. Enable 8-bit loading: load_in_8bit=True") - if not self.config.gradient_checkpointing: - print(" 5. Enable gradient checkpointing") - print() - sys.exit(1) - - def _build_coreset_selector(self) -> Optional[CoresetSelector]: - if not self.config.use_coreset_selection: - return None - return CoresetSelector( - strategy=self.config.coreset_strategy, - metric_key=self.config.coreset_metric_key, - random_seed=self.config.seed, - ) - - def _build_continual_learner(self) -> Optional[OnlineContinualLearner]: - if not self.config.use_online_continual: - return None - selector = self.coreset_selector or CoresetSelector( - strategy="hybrid", - metric_key=self.config.coreset_metric_key, - random_seed=self.config.seed, - ) - return OnlineContinualLearner( - buffer_size=self.config.continual_buffer_size, - replay_ratio=self.config.continual_replay_ratio, - selector=selector, - random_seed=self.config.seed, - ) - - def _collect_metrics(self, samples: list[ProcessedDialog], key: str) -> dict[str, float]: - metrics: dict[str, float] = {} - for sample in samples: - value = sample.metadata.get(key) - if isinstance(value, (int, float)): - metrics[sample.dialog_id] = float(value) - return metrics diff --git a/packages/sage-tools/src/sage/tools/cli/__init__.py b/packages/sage-tools/src/sage/tools/cli/__init__.py deleted file mode 100644 index 4871bbe582..0000000000 --- a/packages/sage-tools/src/sage/tools/cli/__init__.py +++ /dev/null @@ -1,15 +0,0 @@ -""" -SAGE - Streaming-Augmented Generative Execution -""" - -# 直接从本包的_version模块加载版本信息 -try: - from sage.tools._version import __author__, __email__, __version__ -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" - - -__all__ = ["__version__", "__author__", "__email__"] diff --git a/packages/sage-tools/src/sage/tools/cli/commands/__init__.py b/packages/sage-tools/src/sage/tools/cli/commands/__init__.py deleted file mode 100644 index 38e6674922..0000000000 --- a/packages/sage-tools/src/sage/tools/cli/commands/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -""" -SAGE - Streaming-Augmented Generative Execution -""" - -# 直接从本包的_version模块加载版本信息 -try: - from sage.tools._version import __author__, __email__, __version__ -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" diff --git a/packages/sage-tools/src/sage/tools/cli/commands/dev/__init__.py b/packages/sage-tools/src/sage/tools/cli/commands/dev/__init__.py deleted file mode 100644 index b4ae6e164e..0000000000 --- a/packages/sage-tools/src/sage/tools/cli/commands/dev/__init__.py +++ /dev/null @@ -1,244 +0,0 @@ -""" -sage-dev 命令模块 - -开发工具命令组，包括： -- quality: 质量检查 -- project: 项目管理 -- maintain: 维护工具 -- package: 包管理 -- resource: 资源管理 -- examples: 示例测试 -""" - -import sys - -import typer -from rich.console import Console -from rich.table import Table - -# 创建主命令应用 -app = typer.Typer( - name="dev", - no_args_is_help=True, - add_completion=False, - help="""🛠️ 开发工具 - 质量检查、项目管理、维护工具、包管理等 - - 命令组： - • quality - 代码质量、架构合规、文档规范检查 - • project - 项目状态、分析、测试、清理 - • maintain - Submodule管理、Git hooks、诊断 - • package - 版本管理、安装 (PyPI发布已迁移至 wheelwright) - • resource - 模型缓存、数据管理 - • examples - 示例代码测试和验证 - - 快速示例： - sage-dev quality check # 运行所有质量检查 - sage-dev project test # 运行测试 - sage-dev maintain doctor # 健康检查 - sage-dev package version bump # 升级版本 - """, -) - -console = Console() - -# 注册新的命令组 -try: - from .quality import app as quality_app - - app.add_typer( - quality_app, - name="quality", - help="🔍 质量检查 - 代码质量、架构合规、文档规范检查 (check, architecture, devnotes, readme, format, lint, fix)", - ) -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 quality 命令组: {e}[/yellow]") - -try: - from .project import app as project_app - - app.add_typer( - project_app, - name="project", - help="📊 项目管理 - 状态、分析、测试、清理 (status, analyze, test, clean, home)", - ) -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 project 命令组: {e}[/yellow]") - -try: - from .maintain import app as maintain_app - - app.add_typer( - maintain_app, - name="maintain", - help="🔧 维护工具 - Submodule、Hooks、诊断 (doctor, hooks, submodule)", - ) -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 maintain 命令组: {e}[/yellow]") - -try: - from .package import app as package_app - - app.add_typer( - package_app, - name="package", - help="📦 包管理 - 版本管理、安装 (version, install)", - ) -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 package 命令组: {e}[/yellow]") - -try: - from .resource import app as resource_app - - app.add_typer( - resource_app, - name="resource", - help="💾 资源管理 - 模型缓存、数据管理 (models)", - ) -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 resource 命令组: {e}[/yellow]") - -# GitHub 命令组已移除 - 功能已迁移到其他工具 -# try: -# from .github import app as github_app -# app.add_typer( -# github_app, -# name="github", -# help="🐙 GitHub 管理 - Issues、PR 等 (issues)", -# ) -# except ImportError as e: -# console.print(f"[yellow]警告: 无法导入 github 命令组: {e}[/yellow]") - -try: - from .examples import app as examples_app - - app.add_typer( - examples_app, - name="examples", - help="🔬 Examples 测试 - 测试和验证示例代码（需要开发环境）(analyze, test, check, info)", - ) -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 examples 命令组: {e}[/yellow]") - -try: - from .maintenance import app as maintenance_app - - app.add_typer( - maintenance_app, - name="maintenance", - help="🛠️ 维护工具 - Dev-notes 整理、元数据修复、Ruff 更新 (organize-devnotes, fix-metadata, update-ruff-ignore)", - ) -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 maintenance 命令组: {e}[/yellow]") - -try: - from .docs import app as docs_app - - app.add_typer( - docs_app, - name="docs", - help="📚 文档管理 - 构建、预览、检查文档 (build, serve, check)", - ) -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 docs 命令组: {e}[/yellow]") - - -# ============================================================================ -# 主命令 Callback - 显示欢迎信息和版本 -# ============================================================================ - - -def version_callback(value: bool): - """显示版本信息""" - if value: - try: - from sage.common._version import __version__ - - console.print(f"SAGE Tools version {__version__}") - except ImportError: - console.print("SAGE Tools version unknown") - raise typer.Exit() - - -@app.callback(invoke_without_command=True) -def dev_callback( - ctx: typer.Context, - version: bool = typer.Option( - None, - "--version", - "-v", - help="显示版本信息", - callback=version_callback, - is_eager=True, - ), -): - """ - 🛠️ SAGE 开发工具 - - 提供完整的开发工具集，包括质量检查、项目管理、维护工具等。 - """ - if ctx.invoked_subcommand is None: - # 如果没有调用子命令，显示欢迎信息 - console.print("\n[bold blue]🛠️ SAGE 开发工具[/bold blue]\n") - console.print("使用 [cyan]sage-dev --help[/cyan] 查看所有可用命令\n") - console.print("[bold]快速开始:[/bold]") - console.print(" [green]sage-dev quality check[/green] # 运行所有质量检查") - console.print(" [green]sage-dev project test[/green] # 运行测试") - console.print(" [green]sage-dev maintain doctor[/green] # 健康检查") - console.print(" [green]sage-dev package version list[/green] # 查看版本\n") - console.print("[bold]命令组:[/bold]") - console.print(" [cyan]quality[/cyan] - 质量检查（架构、文档、代码格式）") - console.print(" [cyan]project[/cyan] - 项目管理（状态、分析、测试、清理）") - console.print(" [cyan]maintain[/cyan] - 维护工具（submodule、hooks、诊断）") - console.print(" [cyan]package[/cyan] - 包管理（版本、安装）") - console.print(" [cyan]resource[/cyan] - 资源管理（模型缓存）") - console.print(" [cyan]github[/cyan] - GitHub管理（Issues、PR）") - console.print(" [cyan]examples[/cyan] - Examples测试（需要开发环境）\n") - console.print("📚 详细文档: [link]https://github.com/intellistream/SAGE[/link]\n") - - -# ============================================================================ -# 智能命令建议 - 当用户输入错误命令时提示正确用法 -# ============================================================================ - -# 常见的错误命令到正确命令的映射 -COMMAND_SUGGESTIONS = { - "check": ["quality check", "quality format", "quality lint"], - "fix": ["quality fix", "quality format"], - "format": ["quality format"], - "lint": ["quality lint"], - "test": ["project test"], - "clean": ["project clean"], - "status": ["project status"], - "analyze": ["project analyze"], - "doctor": ["maintain doctor"], - "hooks": ["maintain hooks"], - "issues": ["github issues"], - "version": ["package version"], - "models": ["resource models"], - "install": ["package install"], -} - - -# 创建包装函数来提供更好的错误提示 -def run_with_suggestions(): - """运行 app 并在命令不存在时提供建议""" - - try: - app() - except SystemExit as e: - # 如果退出码是 2（通常表示命令行错误）且有参数 - if e.code == 2 and len(sys.argv) > 1: - cmd = sys.argv[1] - # 检查是否是未知命令（不是选项） - if not cmd.startswith("-") and cmd in COMMAND_SUGGESTIONS: - console.print(f"\n[yellow]💡 提示: 'sage-dev {cmd}' 命令已重组[/yellow]\n") - console.print("[cyan]新的命令结构:[/cyan]\n") - - for suggestion in COMMAND_SUGGESTIONS[cmd]: - console.print(f" [green]sage-dev {suggestion}[/green]") - - console.print("\n[dim]使用 [bold]sage-dev --help[/bold] 查看所有可用命令[/dim]\n") - raise - - -__all__ = ["app", "run_with_suggestions"] diff --git a/packages/sage-tools/src/sage/tools/cli/commands/dev/data.py b/packages/sage-tools/src/sage/tools/cli/commands/dev/data.py deleted file mode 100644 index 83ec022097..0000000000 --- a/packages/sage-tools/src/sage/tools/cli/commands/dev/data.py +++ /dev/null @@ -1,270 +0,0 @@ -""" -SAGE Data Management CLI Commands - -Commands for managing and exploring SAGE datasets. -""" - -import typer -from rich.console import Console -from rich.table import Table - -app = typer.Typer( - name="data", - help="Manage SAGE datasets", - no_args_is_help=True, -) - -console = Console() - - -@app.command("list") -def list_datasets( - show_metadata: bool = typer.Option( - False, "--metadata", "-m", help="Show metadata for each dataset" - ), - usage: str = typer.Option(None, "--usage", "-u", help="Filter by usage profile"), -): - """ - List all available datasets. - - Examples: - sage-dev data list - sage-dev data list --metadata - sage-dev data list --usage rag - """ - try: - from sage.data import DataManager - - manager = DataManager.get_instance() - - if usage: - # List datasets for a specific usage - try: - profile = manager.get_by_usage(usage) - console.print(f"\n[bold cyan]📦 Datasets in usage '{usage}':[/bold cyan]") - console.print(f"Description: {profile.description}\n") - - datasets = profile.list_datasets() - if not datasets: - console.print("[yellow]No datasets found[/yellow]") - return - - for ds_name in datasets: - console.print(f" • {ds_name}") - - except ValueError as e: - console.print(f"[red]Error: {e}[/red]") - console.print(f"\nAvailable usages: {', '.join(manager.list_usages())}") - raise typer.Exit(1) - else: - # List all sources - sources = manager.list_sources() - - if not sources: - console.print("[yellow]No datasets found[/yellow]") - return - - console.print(f"\n[bold cyan]📦 Available Datasets ({len(sources)}):[/bold cyan]\n") - - if show_metadata: - table = Table(show_header=True, header_style="bold magenta") - table.add_column("Name", style="cyan", width=15) - table.add_column("Type", style="green", width=10) - table.add_column("Size", style="yellow", width=10) - table.add_column("Description", width=50) - - for source in sources: - metadata = manager.get_source_metadata(source) - table.add_row( - metadata.name, - metadata.type, - metadata.size, - ( - metadata.description[:47] + "..." - if len(metadata.description) > 50 - else metadata.description - ), - ) - - console.print(table) - else: - for source in sources: - console.print(f" • {source}") - - console.print("\n💡 Use [cyan]sage-dev data show [/cyan] for details") - console.print("💡 Use [cyan]sage-dev data list --metadata[/cyan] for full info\n") - - except ImportError as e: - console.print(f"[red]Error importing sage.data: {e}[/red]") - console.print("[yellow]Make sure sage-benchmark is installed[/yellow]") - raise typer.Exit(1) - - -@app.command("show") -def show_dataset( - name: str = typer.Argument(..., help="Dataset name"), -): - """ - Show detailed information about a dataset. - - Examples: - sage-dev data show qa_base - sage-dev data show mmlu - """ - try: - from sage.data import DataManager - - manager = DataManager.get_instance() - - # Check if it's a valid source - if name not in manager.list_sources(): - console.print(f"[red]Dataset '{name}' not found[/red]") - console.print(f"\nAvailable datasets: {', '.join(manager.list_sources())}") - raise typer.Exit(1) - - metadata = manager.get_source_metadata(name) - - console.print(f"\n[bold cyan]📦 Dataset: {metadata.name}[/bold cyan]\n") - console.print(f"[bold]Description:[/bold] {metadata.description}") - console.print(f"[bold]Type:[/bold] {metadata.type}") - console.print(f"[bold]Format:[/bold] {metadata.format}") - console.print(f"[bold]Size:[/bold] {metadata.size}") - console.print(f"[bold]License:[/bold] {metadata.license}") - console.print(f"[bold]Version:[/bold] {metadata.version}") - console.print(f"[bold]Maintainer:[/bold] {metadata.maintainer}") - - if metadata.tags: - console.print(f"[bold]Tags:[/bold] {', '.join(metadata.tags)}") - - # Show which usages include this dataset - usages_with_dataset = [] - for usage_name in manager.list_usages(): - try: - profile = manager.get_by_usage(usage_name) - if name in [profile.datasets.get(k) for k in profile.datasets]: - usages_with_dataset.append(usage_name) - except Exception: - pass - - if usages_with_dataset: - console.print(f"\n[bold]Used in:[/bold] {', '.join(usages_with_dataset)}") - - console.print() - - except ImportError as e: - console.print(f"[red]Error importing sage.data: {e}[/red]") - raise typer.Exit(1) - - -@app.command("usages") -def list_usages(): - """ - List all usage profiles. - - Examples: - sage-dev data usages - """ - try: - from sage.data import DataManager - - manager = DataManager.get_instance() - usages = manager.list_usages() - - if not usages: - console.print("[yellow]No usage profiles found[/yellow]") - return - - console.print(f"\n[bold cyan]🎯 Usage Profiles ({len(usages)}):[/bold cyan]\n") - - for usage_name in usages: - try: - profile = manager.get_by_usage(usage_name) - console.print(f"[bold]{usage_name}[/bold]") - console.print(f" {profile.description}") - console.print(f" Datasets: {', '.join(profile.list_datasets())}") - console.print() - except Exception as e: - console.print(f"[bold]{usage_name}[/bold]") - console.print(f" [red]Error loading: {e}[/red]\n") - - except ImportError as e: - console.print(f"[red]Error importing sage.data: {e}[/red]") - raise typer.Exit(1) - - -@app.command("structure") -def show_structure(): - """ - Show the complete data architecture structure. - - Examples: - sage-dev data structure - """ - try: - from sage.data import DataManager - - manager = DataManager.get_instance() - console.print() - manager.print_structure() - console.print() - - except ImportError as e: - console.print(f"[red]Error importing sage.data: {e}[/red]") - raise typer.Exit(1) - - -@app.command("test") -def test_dataset( - name: str = typer.Argument(..., help="Dataset name or usage"), - is_usage: bool = typer.Option(False, "--usage", "-u", help="Treat name as usage profile"), -): - """ - Test loading a dataset. - - Examples: - sage-dev data test qa_base - sage-dev data test rag --usage - """ - try: - from sage.data import DataManager - - manager = DataManager.get_instance() - - console.print(f"\n[bold cyan]Testing dataset: {name}[/bold cyan]\n") - - if is_usage: - profile = manager.get_by_usage(name) - console.print(f"✓ Loaded usage profile: {name}") - console.print(f" Datasets: {profile.list_datasets()}") - - # Try loading first dataset - if profile.list_datasets(): - first_ds = profile.list_datasets()[0] - console.print(f"\n Testing first dataset: {first_ds}") - try: - loader = profile.load(first_ds) - console.print(f" ✓ Loaded: {type(loader).__name__}") - except Exception as e: - console.print(f" ✗ Error: {e}") - else: - try: - loader = manager.get_by_source(name) - console.print(f"✓ Loaded dataset: {name}") - console.print(f" Loader type: {type(loader).__name__}") - console.print(f" Loader instance: {loader}") - except Exception as e: - console.print(f"✗ Error loading: {e}") - raise typer.Exit(1) - - console.print("\n✓ Test passed!\n") - - except ImportError as e: - console.print(f"[red]Error importing sage.data: {e}[/red]") - raise typer.Exit(1) - except ValueError as e: - console.print(f"[red]Error: {e}[/red]") - raise typer.Exit(1) - - -if __name__ == "__main__": - app() diff --git a/packages/sage-tools/src/sage/tools/cli/commands/dev/docs.py b/packages/sage-tools/src/sage/tools/cli/commands/dev/docs.py deleted file mode 100644 index 868529e315..0000000000 --- a/packages/sage-tools/src/sage/tools/cli/commands/dev/docs.py +++ /dev/null @@ -1,277 +0,0 @@ -""" -文档管理命令 - -提供文档构建、预览和管理功能 -""" - -import subprocess -from pathlib import Path - -import typer -from rich.console import Console - -app = typer.Typer( - name="docs", - help="📚 文档管理 - 构建、预览、检查文档", - no_args_is_help=True, -) - -console = Console() - - -@app.command("build") -def build_docs( - root: Path | None = typer.Option( - None, - "--root", - "-r", - help="项目根目录（默认：当前目录）", - ), - clean: bool = typer.Option( - False, - "--clean", - "-c", - help="清理旧的构建文件", - ), -): - """ - 📖 构建文档 - - 构建 MkDocs 文档到 docs-public/site/ - """ - try: - if root is None: - root = Path.cwd() - - docs_dir = root / "docs-public" - - if not docs_dir.exists(): - console.print(f"[red]❌ 文档目录不存在: {docs_dir}[/red]") - raise typer.Exit(1) - - console.print("\n[bold]📖 构建文档...[/bold]") - console.print(f"文档目录: {docs_dir}\n") - - # 检查 mkdocs 是否可用 - try: - subprocess.run( - ["mkdocs", "--version"], - check=True, - capture_output=True, - ) - except (subprocess.CalledProcessError, FileNotFoundError): - console.print("[red]❌ mkdocs 未安装[/red]") - console.print("\n安装命令:") - console.print(" [cyan]pip install mkdocs mkdocs-material[/cyan]\n") - raise typer.Exit(1) - - # 切换到文档目录 - import os - - original_dir = os.getcwd() - os.chdir(docs_dir) - - try: - # 清理旧文件 - if clean: - console.print("[yellow]清理旧的构建文件...[/yellow]") - site_dir = docs_dir / "site" - if site_dir.exists(): - import shutil - - shutil.rmtree(site_dir) - console.print("[green]✓ 清理完成[/green]\n") - - # 检查是否有自定义构建脚本 - build_script = docs_dir / "build.sh" - if build_script.exists(): - console.print("[cyan]使用自定义构建脚本...[/cyan]") - result = subprocess.run( - ["bash", str(build_script)], - capture_output=False, - ) - if result.returncode != 0: - console.print("[red]❌ 构建失败[/red]") - raise typer.Exit(1) - else: - console.print("[cyan]使用 mkdocs 构建...[/cyan]") - result = subprocess.run( - ["mkdocs", "build"], - capture_output=False, - ) - if result.returncode != 0: - console.print("[red]❌ 构建失败[/red]") - raise typer.Exit(1) - - console.print("\n[green]✅ 文档构建成功！[/green]") - console.print(f"输出目录: {docs_dir / 'site'}") - - finally: - os.chdir(original_dir) - - except Exception as e: - console.print(f"[red]❌ 构建失败: {e}[/red]") - raise typer.Exit(1) - - -@app.command("serve") -def serve_docs( - root: Path | None = typer.Option( - None, - "--root", - "-r", - help="项目根目录（默认：当前目录）", - ), - port: int = typer.Option( - 8000, - "--port", - "-p", - help="服务端口", - ), - host: str = typer.Option( - "127.0.0.1", - "--host", - "-h", - help="服务地址", - ), -): - """ - 🌐 启动文档服务器 - - 启动本地文档服务器，支持热重载 - """ - try: - if root is None: - root = Path.cwd() - - docs_dir = root / "docs-public" - - if not docs_dir.exists(): - console.print(f"[red]❌ 文档目录不存在: {docs_dir}[/red]") - raise typer.Exit(1) - - console.print("\n[bold]🌐 启动文档服务器...[/bold]") - console.print(f"文档目录: {docs_dir}") - console.print(f"服务地址: http://{host}:{port}\n") - - # 检查 mkdocs 是否可用 - try: - subprocess.run( - ["mkdocs", "--version"], - check=True, - capture_output=True, - ) - except (subprocess.CalledProcessError, FileNotFoundError): - console.print("[red]❌ mkdocs 未安装[/red]") - console.print("\n安装命令:") - console.print(" [cyan]pip install mkdocs mkdocs-material[/cyan]\n") - raise typer.Exit(1) - - # 切换到文档目录 - import os - - original_dir = os.getcwd() - os.chdir(docs_dir) - - try: - console.print("[cyan]启动服务器（Ctrl+C 停止）...[/cyan]\n") - subprocess.run( - ["mkdocs", "serve", "-a", f"{host}:{port}"], - check=False, - ) - except KeyboardInterrupt: - console.print("\n[yellow]服务器已停止[/yellow]") - finally: - os.chdir(original_dir) - - except Exception as e: - console.print(f"[red]❌ 启动失败: {e}[/red]") - raise typer.Exit(1) - - -@app.command("check") -def check_docs( - root: Path | None = typer.Option( - None, - "--root", - "-r", - help="项目根目录（默认：当前目录）", - ), -): - """ - ✅ 检查文档 - - 检查文档链接、格式等 - """ - try: - if root is None: - root = Path.cwd() - - docs_dir = root / "docs-public" - - if not docs_dir.exists(): - console.print(f"[red]❌ 文档目录不存在: {docs_dir}[/red]") - raise typer.Exit(1) - - console.print("\n[bold]✅ 检查文档...[/bold]") - console.print(f"文档目录: {docs_dir}\n") - - # 检查 mkdocs.yml - mkdocs_config = docs_dir / "mkdocs.yml" - if not mkdocs_config.exists(): - console.print("[red]❌ mkdocs.yml 不存在[/red]") - raise typer.Exit(1) - - console.print("[green]✓ mkdocs.yml 存在[/green]") - - # 检查 docs_src - docs_src = docs_dir / "docs_src" - if not docs_src.exists(): - console.print("[red]❌ docs_src 目录不存在[/red]") - raise typer.Exit(1) - - console.print("[green]✓ docs_src 目录存在[/green]") - - # 统计文档文件 - md_files = list(docs_src.rglob("*.md")) - console.print(f"[green]✓ 找到 {len(md_files)} 个 Markdown 文件[/green]") - - # 检查 index.md - index_file = docs_src / "index.md" - if index_file.exists(): - console.print("[green]✓ index.md 存在[/green]") - else: - console.print("[yellow]⚠ index.md 不存在[/yellow]") - - console.print("\n[green]✅ 文档检查完成！[/green]") - - except Exception as e: - console.print(f"[red]❌ 检查失败: {e}[/red]") - raise typer.Exit(1) - - -@app.command("list") -def list_commands(): - """ - 📋 列出所有文档命令 - - 显示可用的文档管理命令 - """ - console.print("\n[bold]📚 文档管理命令[/bold]\n") - - commands = [ - ("build", "构建文档", "📖"), - ("serve", "启动文档服务器", "🌐"), - ("check", "检查文档", "✅"), - ] - - for cmd, desc, icon in commands: - console.print(f"{icon} [cyan]{cmd}[/cyan]") - console.print(f" {desc}") - console.print() - - console.print("[dim]使用 sage-dev docs --help 查看详细帮助[/dim]\n") - - -if __name__ == "__main__": - app() diff --git a/packages/sage-tools/src/sage/tools/cli/commands/dev/examples.py b/packages/sage-tools/src/sage/tools/cli/commands/dev/examples.py deleted file mode 100644 index baa7d7cf8c..0000000000 --- a/packages/sage-tools/src/sage/tools/cli/commands/dev/examples.py +++ /dev/null @@ -1,309 +0,0 @@ -""" -sage-dev examples 命令组 - -用于测试和验证 examples/ 目录中的示例代码。 - -⚠️ 注意：这些命令仅在开发环境中可用（需要访问源码仓库）。 -""" - -from pathlib import Path - -import typer -from rich.console import Console -from rich.panel import Panel - -console = Console() -app = typer.Typer(help="🔬 Examples 测试工具（需要开发环境）") - - -def _check_dev_environment() -> bool: - """检查开发环境是否可用""" - try: - from sage.tools.dev.examples import ensure_development_environment - - return ensure_development_environment(raise_error=False) - except ImportError: - return False - - -def _show_setup_guide(): - """显示环境设置指南""" - console.print( - Panel( - "[bold yellow]⚠️ Examples 测试工具需要开发环境[/bold yellow]\n\n" - "这些工具需要访问 SAGE 源码仓库中的 examples/ 目录。\n\n" - "[bold]设置方法：[/bold]\n" - "1. 克隆 SAGE 仓库：\n" - " [cyan]git clone https://github.com/intellistream/SAGE[/cyan]\n" - " [cyan]cd SAGE[/cyan]\n\n" - "2. 从源码安装 sage-tools：\n" - " [cyan]pip install -e packages/sage-tools[dev][/cyan]\n\n" - "3. 或设置环境变量：\n" - " [cyan]export SAGE_ROOT=/path/to/SAGE[/cyan]\n\n" - "[bold]了解更多：[/bold]\n" - " packages/sage-tools/src/sage/tools/dev/examples/README.md", - title="环境设置指南", - border_style="yellow", - ) - ) - - -@app.command(name="analyze") -def analyze_command( - verbose: bool = typer.Option(False, "--verbose", "-v", help="显示详细信息"), -): - """分析 examples 目录结构 - - 扫描并分析所有示例文件，显示分类、依赖、运行时间等信息。 - - 示例： - sage-dev examples analyze - sage-dev examples analyze --verbose - """ - # 检查环境 - if not _check_dev_environment(): - _show_setup_guide() - raise typer.Exit(1) - - try: - from sage.tools.dev.examples import ExampleAnalyzer - - console.print("🔍 [bold blue]分析 Examples 目录...[/bold blue]\n") - - analyzer = ExampleAnalyzer() - examples = analyzer.discover_examples() - - console.print(f"📊 发现 [green]{len(examples)}[/green] 个示例文件\n") - - # 按类别统计 - categories = {} - for example in examples: - if example.category not in categories: - categories[example.category] = [] - categories[example.category].append(example) - - # 显示类别摘要 - from rich.table import Table - - table = Table(title="Examples 分类统计") - table.add_column("类别", style="cyan", no_wrap=True) - table.add_column("数量", style="magenta", justify="right") - table.add_column("快速", style="green", justify="right") - table.add_column("中等", style="yellow", justify="right") - table.add_column("慢速", style="red", justify="right") - table.add_column("外部依赖", style="blue") - - for category in sorted(categories.keys()): - cat_examples = categories[category] - count = len(cat_examples) - - # 统计运行时间 - quick = sum(1 for e in cat_examples if e.estimated_runtime == "quick") - medium = sum(1 for e in cat_examples if e.estimated_runtime == "medium") - slow = sum(1 for e in cat_examples if e.estimated_runtime == "slow") - - # 收集依赖 - all_deps = set() - for e in cat_examples: - all_deps.update(e.dependencies) - - deps_str = ", ".join(sorted(all_deps)[:3]) - if len(all_deps) > 3: - deps_str += f" +{len(all_deps) - 3}" - - table.add_row( - category, str(count), str(quick), str(medium), str(slow), deps_str or "无" - ) - - console.print(table) - - # 详细信息 - if verbose: - console.print("\n[bold]详细信息：[/bold]\n") - for category in sorted(categories.keys()): - console.print(f"[bold cyan]{category}[/bold cyan]:") - for example in categories[category]: - deps = ", ".join(example.dependencies) if example.dependencies else "无" - tags = ", ".join(example.test_tags) if example.test_tags else "无" - console.print( - f" • {Path(example.file_path).name} " - f"[dim]({example.estimated_runtime})[/dim]" - ) - if verbose: - console.print(f" 依赖: {deps}") - console.print(f" 标记: {tags}") - console.print() - - console.print("[green]✅ 分析完成！[/green]") - - except Exception as e: - console.print(f"[red]❌ 分析失败: {e}[/red]") - if verbose: - import traceback - - traceback.print_exc() - raise typer.Exit(1) - - -@app.command(name="test") -def test_command( - category: list[str] | None = typer.Option( - None, "--category", "-c", help="指定测试类别（可多次使用）" - ), - quick: bool = typer.Option(False, "--quick", "-q", help="只运行快速测试"), - timeout: int | None = typer.Option(None, "--timeout", "-t", help="单个测试超时时间（秒）"), - output: str | None = typer.Option(None, "--output", "-o", help="保存结果到JSON文件"), - verbose: bool = typer.Option(False, "--verbose", "-v", help="显示详细输出"), -): - """运行 examples 测试 - - 执行示例文件并验证其运行正常。 - - 示例： - sage-dev examples test --quick # 运行快速测试 - sage-dev examples test -c tutorials # 测试 tutorials 类别 - sage-dev examples test -c rag -c memory # 测试多个类别 - sage-dev examples test --timeout 120 # 设置超时 - sage-dev examples test -o results.json # 保存结果 - """ - # 检查环境 - if not _check_dev_environment(): - _show_setup_guide() - raise typer.Exit(1) - - try: - from sage.tools.dev.examples import ExampleTestSuite - - console.print("🚀 [bold blue]运行 Examples 测试...[/bold blue]\n") - - # 创建测试套件 - suite = ExampleTestSuite() - if timeout is not None: - suite.runner.timeout = timeout - - # 显示配置 - console.print("[bold]测试配置：[/bold]") - console.print(f" 类别: {', '.join(category) if category else '全部'}") - console.print(f" 模式: {'快速测试' if quick else '完整测试'}") - if timeout: - console.print(f" 超时: {timeout}秒") - console.print() - - # 运行测试 - stats = suite.run_all_tests(categories=category, quick_only=quick) - - # 保存结果 - if output: - suite.save_results(output) - - # 根据结果设置退出码 - if stats["failed"] > 0 or stats["timeout"] > 0: - console.print("\n[red]❌ 测试失败[/red]") - raise typer.Exit(1) - else: - console.print("\n[green]✅ 测试通过！[/green]") - - except Exception as e: - console.print(f"[red]❌ 测试失败: {e}[/red]") - if verbose: - import traceback - - traceback.print_exc() - raise typer.Exit(1) - - -@app.command(name="check") -def check_command( - verbose: bool = typer.Option(False, "--verbose", "-v", help="显示详细信息"), -): - """检查中间结果放置 - - 验证示例代码没有在项目根目录产生中间结果文件， - 所有输出应该在 .sage/ 目录下。 - - 示例： - sage-dev examples check - sage-dev examples check --verbose - """ - # 检查环境 - if not _check_dev_environment(): - _show_setup_guide() - raise typer.Exit(1) - - try: - from sage.tools.dev.utils.intermediate_results_checker import ( - print_intermediate_results_check, - ) - - console.print("🔍 [bold blue]检查中间结果放置...[/bold blue]\n") - - # 获取项目根目录 - from sage.tools.dev.examples.utils import find_project_root - - project_root = find_project_root() - if project_root is None: - console.print("[red]❌ 无法找到项目根目录[/red]") - raise typer.Exit(1) - - # 执行检查 - passed = print_intermediate_results_check(str(project_root)) - - if passed: - console.print("\n[green]✅ 检查通过！项目根目录保持整洁。[/green]") - else: - console.print( - "\n[yellow]⚠️ 发现中间结果放置问题。请将所有输出移至 .sage/ 目录。[/yellow]" - ) - raise typer.Exit(1) - - except Exception as e: - console.print(f"[red]❌ 检查失败: {e}[/red]") - if verbose: - import traceback - - traceback.print_exc() - raise typer.Exit(1) - - -@app.command(name="info") -def info_command(): - """显示开发环境信息 - - 检查并显示当前的开发环境状态。 - - 示例： - sage-dev examples info - """ - try: - from sage.tools.dev.examples.utils import get_development_info - - console.print("🔍 [bold blue]开发环境信息[/bold blue]\n") - - info = get_development_info() - - from rich.table import Table - - table = Table(show_header=False, box=None) - table.add_column("项目", style="cyan") - table.add_column("状态", style="green") - - table.add_row("开发环境", "✅ 可用" if info["has_dev_env"] else "❌ 不可用") - table.add_row("Examples 目录", info["examples_dir"] or "(未找到)") - table.add_row("项目根目录", info["project_root"] or "(未找到)") - table.add_row("SAGE_ROOT 环境变量", info["sage_root_env"] or "(未设置)") - table.add_row("Git 仓库", "✅ 是" if info["in_git_repo"] else "❌ 否") - - console.print(table) - - if not info["has_dev_env"]: - console.print() - _show_setup_guide() - - except ImportError: - console.print("[red]❌ 无法导入 Examples 测试工具[/red]") - _show_setup_guide() - raise typer.Exit(1) - - -if __name__ == "__main__": - app() diff --git a/packages/sage-tools/src/sage/tools/cli/commands/dev/main.py b/packages/sage-tools/src/sage/tools/cli/commands/dev/main.py deleted file mode 100644 index 594488f78d..0000000000 --- a/packages/sage-tools/src/sage/tools/cli/commands/dev/main.py +++ /dev/null @@ -1,2471 +0,0 @@ -""" -sage-dev 命令组 - 简化版本 - -这个模块提供统一的dev命令接口，调用sage.tools.dev中的核心功能。 -""" - -from pathlib import Path - -import typer -from rich.console import Console - -from sage.cli.utils.diagnostics import ( - collect_packages_status, - print_packages_status, - print_packages_status_summary, - run_installation_diagnostics, -) - -console = Console() -app = typer.Typer(help="SAGE 开发工具集") - -# 注意: Issues管理功能已独立为 sage-github-manager 项目 -# 安装: pip install sage-github-manager -# 使用: github-manager - -# 注意: PyPI 管理已整合到 package 命令组 -# 说明: pypi 子命令已移除，改用独立工具 wheelwright - -# 删除：CI 子命令（已由 GitHub Workflows 承担 CI/CD） -# 过去这里会 add_typer(ci_app, name="ci", ...) -# 现在不再提供本地 CI 包装命令，建议直接依赖 GitHub Actions。 - -# 添加版本管理子命令 -try: - from .package_version import app as version_app - - app.add_typer(version_app, name="version", help="🏷️ 版本管理 - 管理各个子包的版本信息") -except ImportError as e: - console.print(f"[yellow]警告: 版本管理功能不可用: {e}[/yellow]") - -# 添加模型缓存管理子命令 -try: - from .models import app as models_app - - app.add_typer( - models_app, - name="models", - help="🤖 Embedding 模型缓存管理", - ) -except ImportError as e: - console.print(f"[yellow]警告: 模型缓存功能不可用: {e}[/yellow]") - -# 添加 Examples 测试工具子命令 -try: - from .examples import app as examples_app - - app.add_typer( - examples_app, - name="examples", - help="🔬 Examples 测试工具 - 测试和验证示例代码（需要开发环境）", - ) -except ImportError as e: - console.print(f"[yellow]警告: Examples 测试功能不可用: {e}[/yellow]") - -# 添加 Data 数据集管理子命令 -try: - from .data import app as data_app - - app.add_typer( - data_app, - name="data", - help="📊 数据集管理 - 查看和管理 SAGE 数据集", - ) -except ImportError as e: - console.print(f"[yellow]警告: 数据集管理功能不可用: {e}[/yellow]") - - -@app.command() -def quality( - fix: bool = typer.Option(True, "--fix/--no-fix", help="自动修复质量问题"), - check_only: bool = typer.Option(False, "--check-only", help="仅检查，不修复"), - all_files: bool = typer.Option(False, "--all-files", help="检查所有文件（而不仅是变更的文件）"), - # 选择性运行特定检查 - hook: str | None = typer.Option(None, "--hook", help="只运行指定的 pre-commit hook"), - # 架构和文档检查选项 - architecture: bool = typer.Option( - True, "--architecture/--no-architecture", help="运行架构合规性检查" - ), - devnotes: bool = typer.Option( - True, "--devnotes/--no-devnotes", help="运行 dev-notes 文档规范检查" - ), - readme: bool = typer.Option(False, "--readme", help="运行包 README 质量检查"), - examples: bool = typer.Option( - True, "--examples/--no-examples", help="运行 examples 目录结构检查" - ), - # Submodule 选项 - include_submodules: bool = typer.Option( - False, "--include-submodules", help="包含 submodules 进行质量检查（默认跳过）" - ), - submodules_only: bool = typer.Option( - False, "--submodules-only", help="仅检查 submodules（跳过主仓库）" - ), - # 其他选项 - warn_only: bool = typer.Option(False, "--warn-only", help="只给警告，不中断运行"), - project_root: str = typer.Option(".", help="项目根目录"), - # 保留向后兼容的选项（但现在都通过 pre-commit 实现） - format_code: bool = typer.Option(True, "--format/--no-format", help="运行代码格式化"), - sort_imports: bool = typer.Option( - True, "--sort-imports/--no-sort-imports", help="运行导入排序" - ), - lint_ruff: bool = typer.Option(True, "--ruff/--no-ruff", help="运行Ruff检查"), - type_check: bool = typer.Option(True, "--type-check/--no-type-check", help="运行类型检查"), -): - """代码质量检查和修复（基于 pre-commit + 架构检查） - - 这是 pre-commit 的友好包装器，提供统一的质量检查接口。 - 所有配置都在 tools/pre-commit-config.yaml 中管理，确保一致性。 - - 额外集成了架构合规性检查、dev-notes 文档规范检查和 README 质量检查。 - - 默认情况下会跳过所有 submodules（docs-public, sageLLM, sageVDB等）， - 避免修改外部依赖的代码。如需检查 submodules，请使用 --include-submodules。 - - 示例： - sage-dev quality # 运行所有检查（自动修复，跳过submodules） - sage-dev quality --check-only # 只检查不修复 - sage-dev quality --all-files # 检查所有文件 - sage-dev quality --hook black # 只运行 black - sage-dev quality --no-format # 跳过格式化 - sage-dev quality --no-architecture # 跳过架构检查 - sage-dev quality --no-devnotes # 跳过文档检查 - sage-dev quality --readme # 包含 README 质量检查 - sage-dev quality --include-submodules # 包含 submodules 进行检查 - sage-dev quality --submodules-only # 仅检查 submodules - """ - import subprocess - from pathlib import Path - - # 使用不同的变量名避免类型冲突 - project_dir = Path(project_root).resolve() - - if not project_dir.exists(): - console.print(f"[red]❌ 项目根目录不存在: {project_dir}[/red]") - raise typer.Exit(1) - - console.print(f"📁 项目根目录: {project_dir}") - - # 处理 submodule 选项的冲突 - if submodules_only and not include_submodules: - include_submodules = True - - # 配置文件路径 - tools_dir = project_dir / "tools" - precommit_config = tools_dir / "pre-commit-config.yaml" - - if not precommit_config.exists(): - console.print(f"[red]❌ pre-commit 配置文件不存在: {precommit_config}[/red]") - raise typer.Exit(1) - - # 检查 pre-commit 是否安装 - try: - subprocess.run( - ["pre-commit", "--version"], - capture_output=True, - check=True, - ) - except (subprocess.CalledProcessError, FileNotFoundError): - console.print("[red]❌ pre-commit 未安装[/red]") - console.print("[yellow]💡 请安装: pip install pre-commit[/yellow]") - raise typer.Exit(1) - - # 显示 submodule 检查模式 - if submodules_only: - console.print("\n🔍 运行代码质量检查（仅检查 submodules）...") - elif include_submodules: - console.print("\n🔍 运行代码质量检查（包含 submodules）...") - else: - console.print("\n🔍 运行代码质量检查（跳过 submodules）...") - console.print(f"📝 配置文件: {precommit_config}") - - # 获取 submodule 列表 - def get_submodule_paths(): - """获取所有 submodule 的路径""" - try: - result = subprocess.run( - ["git", "config", "--file", ".gitmodules", "--get-regexp", "path"], - cwd=str(project_dir), - capture_output=True, - text=True, - check=True, - ) - paths = [] - for line in result.stdout.strip().split("\n"): - if line: - # 格式: submodule..path - parts = line.split() - if len(parts) >= 2: - paths.append(parts[1]) - return paths - except (subprocess.CalledProcessError, FileNotFoundError): - return [] - - submodule_paths = get_submodule_paths() - if submodule_paths: - console.print( - f"📦 检测到 {len(submodule_paths)} 个 submodules: {', '.join(submodule_paths)}" - ) - - # 构建 pre-commit 命令 - if submodules_only and submodule_paths: - # 仅检查 submodules - 对每个 submodule 单独运行 - console.print("\n🎯 仅检查 submodules 模式") - failed_submodules = [] - - for submodule_path in submodule_paths: - submodule_dir = project_dir / submodule_path - if not submodule_dir.exists(): - console.print(f"[yellow]⚠️ 跳过不存在的 submodule: {submodule_path}[/yellow]") - continue - - console.print(f"\n{'=' * 60}") - console.print(f"🔍 检查 submodule: {submodule_path}") - console.print(f"{'=' * 60}") - - cmd = ["pre-commit", "run"] - cmd.extend(["--config", str(precommit_config)]) - - if hook: - cmd.append(hook) - else: - # 根据选项跳过某些 hooks - skip_hooks = [] - if not format_code: - skip_hooks.append("black") - if not sort_imports: - skip_hooks.append("isort") - if not lint_ruff: - skip_hooks.append("ruff") - if not type_check: - skip_hooks.append("mypy") - - if skip_hooks: - import os - - os.environ["SKIP"] = ",".join(skip_hooks) - - if all_files: - cmd.append("--all-files") - - cmd.append("--verbose") - - # 对 submodule 中的文件运行检查 - cmd.extend(["--files", f"{submodule_path}/**/*"]) - - try: - result = subprocess.run(cmd, cwd=str(project_dir), check=False) - if result.returncode != 0: - failed_submodules.append(submodule_path) - except Exception as e: - console.print(f"[red]❌ 检查 {submodule_path} 失败: {e}[/red]") - failed_submodules.append(submodule_path) - - # 汇总结果 - console.print(f"\n{'=' * 60}") - if failed_submodules: - console.print(f"[red]❌ {len(failed_submodules)} 个 submodules 检查失败:[/red]") - for sm in failed_submodules: - console.print(f" - {sm}") - if not warn_only: - raise typer.Exit(1) - else: - console.print("[green]✅ 所有 submodules 质量检查通过！[/green]") - return - - # 主仓库检查逻辑（原有逻辑，但需要处理 submodule 排除） - cmd = ["pre-commit", "run"] - - # 添加配置文件路径 - cmd.extend(["--config", str(precommit_config)]) - - # 如果指定了特定 hook - if hook: - cmd.append(hook) - console.print(f"🎯 只运行 hook: {hook}") - else: - # 根据选项跳过某些 hooks - skip_hooks = [] - if not format_code: - skip_hooks.append("black") - if not sort_imports: - skip_hooks.append("isort") - if not lint_ruff: - skip_hooks.append("ruff") - if not type_check: - skip_hooks.append("mypy") - - if skip_hooks: - console.print(f"⏭️ 跳过: {', '.join(skip_hooks)}") - # pre-commit 没有直接的 --skip 选项，我们需要设置环境变量 - import os - - os.environ["SKIP"] = ",".join(skip_hooks) - - # 检查所有文件还是只检查变更的 - if all_files: - cmd.append("--all-files") - console.print("📂 检查所有文件") - else: - console.print("📝 检查已暂存的文件（git staged）") - - # 处理 submodule 包含逻辑 - if include_submodules and not submodules_only: - console.print("⚠️ [yellow]警告: 将检查 submodules 中的文件[/yellow]") - console.print( - "💡 [yellow]提示: submodules 的排除规则在 pre-commit-config.yaml 中配置[/yellow]" - ) - # 注意：如果要包含 submodules，需要临时修改 SKIP 环境变量 - # 或者创建临时配置文件，这里我们使用环境变量提示用户 - console.print( - "📝 [cyan]如需完全控制 submodules 的检查，" - "请临时修改 tools/pre-commit-config.yaml 中的 exclude 规则[/cyan]" - ) - - # 显示更多输出 - cmd.append("--verbose") - - # 运行 pre-commit - console.print(f"\n🚀 执行命令: {' '.join(cmd)}\n") - - precommit_passed = True - try: - result = subprocess.run( - cmd, - cwd=str(project_dir), - check=False, # 不自动抛出异常，我们自己处理返回码 - ) - - # pre-commit 返回码： - # 0 = 所有检查通过 - # 1 = 有检查失败或文件被修改 - if result.returncode == 0: - console.print("\n[green]✅ Pre-commit 检查通过！[/green]") - elif warn_only: - console.print("\n[yellow]⚠️ Pre-commit 发现问题，但继续执行（warn-only 模式）[/yellow]") - precommit_passed = False - else: - console.print("\n[red]❌ Pre-commit 检查失败[/red]") - precommit_passed = False - - except KeyboardInterrupt: - console.print("\n[yellow]⚠️ 用户中断[/yellow]") - raise typer.Exit(130) - except Exception as e: - console.print(f"\n[red]❌ Pre-commit 运行失败: {e}[/red]") - precommit_passed = False - - # 运行额外的架构和文档检查 - extra_checks_passed = True - - # 架构检查 - if architecture and not submodules_only: - console.print("\n" + "=" * 60) - console.print("🏗️ 运行架构合规性检查...") - console.print("=" * 60) - try: - from sage.tools.dev.tools.architecture_checker import ArchitectureChecker - - checker = ArchitectureChecker(root_dir=str(project_dir)) - if all_files: - result = checker.check_all() - else: - result = checker.check_changed_files(diff_target="HEAD") - - if result.passed: - console.print("[green]✅ 架构合规性检查通过[/green]") - else: - console.print(f"[red]❌ 发现 {len(result.violations)} 个架构违规[/red]") - for violation in result.violations[:5]: # 只显示前5个 - console.print(f" • {violation.file}: {violation.message}") - if len(result.violations) > 5: - console.print(f" ... 还有 {len(result.violations) - 5} 个问题") - extra_checks_passed = False - except Exception as e: - console.print(f"[yellow]⚠️ 架构检查失败: {e}[/yellow]") - if not warn_only: - extra_checks_passed = False - - # Dev-notes 文档检查 - if devnotes and not submodules_only: - console.print("\n" + "=" * 60) - console.print("📚 运行 dev-notes 文档规范检查...") - console.print("=" * 60) - try: - from pathlib import Path - - from sage.tools.dev.tools.devnotes_checker import DevNotesChecker - - # 检查 dev-notes 目录是否存在（SAGE-Pub 独立仓库） - devnotes_dir = project_dir / "docs-public" / "docs_src" / "dev-notes" - if not devnotes_dir.exists(): - console.print( - "[yellow]⚠️ dev-notes 目录不存在，跳过检查（需要 SAGE-Pub 仓库）[/yellow]" - ) - else: - checker = DevNotesChecker(root_dir=str(project_dir)) - if all_files: - result = checker.check_all() - else: - result = checker.check_changed() - - if result.get("passed", False): - console.print("[green]✅ Dev-notes 文档规范检查通过[/green]") - else: - issues = result.get("issues", []) - console.print(f"[red]❌ 发现 {len(issues)} 个文档问题[/red]") - for issue in issues[:5]: # 只显示前5个 - console.print( - f" • {issue.get('file', 'unknown')}: {issue.get('message', '')}" - ) - if len(issues) > 5: - console.print(f" ... 还有 {len(issues) - 5} 个问题") - extra_checks_passed = False - except Exception as e: - console.print(f"[yellow]⚠️ 文档检查失败: {e}[/yellow]") - if not warn_only: - extra_checks_passed = False - - # README 检查（可选） - if readme and not submodules_only: - console.print("\n" + "=" * 60) - console.print("📄 运行包 README 质量检查...") - console.print("=" * 60) - try: - from sage.tools.dev.tools.package_readme_checker import PackageREADMEChecker - - checker = PackageREADMEChecker(workspace_root=str(project_dir)) - results = checker.check_all(fix=False) - - low_score_packages = [r for r in results if r.score < 80.0] - if not low_score_packages: - console.print("[green]✅ README 质量检查通过[/green]") - else: - console.print( - f"[yellow]⚠️ {len(low_score_packages)} 个包的 README 需要改进[/yellow]" - ) - for r in low_score_packages[:5]: - console.print(f" • {r.package_name}: {r.score:.1f}/100") - if len(low_score_packages) > 5: - console.print(f" ... 还有 {len(low_score_packages) - 5} 个包") - console.print("💡 运行 `sage-dev check-readme --report` 查看详细信息") - # README 检查不阻止提交，只是警告 - except Exception as e: - console.print(f"[yellow]⚠️ README 检查失败: {e}[/yellow]") - - # Examples 目录结构检查（可选） - if examples and not submodules_only: - console.print("\n" + "=" * 60) - console.print("📁 运行 examples 目录结构检查...") - console.print("=" * 60) - try: - from pathlib import Path - - from sage.tools.dev.tools.examples_structure_checker import ( - ExamplesStructureChecker, - ) - - examples_dir = Path(project_dir) / "examples" - if not examples_dir.exists(): - console.print(f"[yellow]⚠️ examples 目录不存在: {examples_dir}[/yellow]") - else: - checker = ExamplesStructureChecker(examples_dir) - result = checker.check_structure() - - if result.passed: - console.print("[green]✅ Examples 目录结构检查通过[/green]") - else: - console.print(f"[red]❌ 发现 {len(result.violations)} 个结构问题[/red]") - for violation in result.violations[:5]: - console.print(f" • {violation}") - if len(result.violations) > 5: - console.print(f" ... 还有 {len(result.violations) - 5} 个问题") - - if result.unexpected_dirs: - console.print("\n[yellow]不符合规范的目录:[/yellow]") - for dir_name in result.unexpected_dirs: - console.print(f" • {dir_name}/") - - console.print(f"\n{checker.get_structure_guide()}") - extra_checks_passed = False - except Exception as e: - console.print(f"[yellow]⚠️ Examples 检查失败: {e}[/yellow]") - if not warn_only: - extra_checks_passed = False - - # 汇总结果 - console.print("\n" + "=" * 60) - if precommit_passed and extra_checks_passed: - console.print("[green]✅ 所有质量检查通过！[/green]") - console.print("=" * 60) - return - elif warn_only: - console.print("[yellow]⚠️ 发现质量问题，但继续执行（warn-only 模式）[/yellow]") - console.print("=" * 60) - return - else: - console.print("[red]❌ 质量检查失败[/red]") - console.print("=" * 60) - if not all_files: - console.print( - "[yellow]💡 提示: 使用 --all-files 检查所有文件，或修复上述问题后重新运行[/yellow]" - ) - raise typer.Exit(1) - - -# ============================================================================ -# 下面保留旧的辅助函数供其他命令使用 -# ============================================================================ - - -def _save_quality_error_log(logs_dir: Path, tool_name: str, content: str): - """保存质量检查错误日志""" - logs_dir.mkdir(parents=True, exist_ok=True) - log_file = logs_dir / f"{tool_name}_errors.log" - log_file.write_text(content, encoding="utf-8") - - -# ============================================================================ -# 以下是旧版本的实现，保留供参考或特殊场景使用 -# 如果完全迁移到 pre-commit 后可以删除 -# ============================================================================ -@app.command() -def analyze( - analysis_type: str = typer.Option("all", help="分析类型: all, health, report"), - output_format: str = typer.Option("summary", help="输出格式: summary, json, markdown"), - project_root: str = typer.Option(".", help="项目根目录"), -): - """分析项目依赖和结构""" - try: - from sage.tools.dev.tools.dependency_analyzer import DependencyAnalyzer - - analyzer = DependencyAnalyzer(project_root) - - if analysis_type == "all": - result = analyzer.analyze_all_dependencies() - elif analysis_type == "health": - result = analyzer.check_dependency_health() - elif analysis_type == "report": - result = analyzer.generate_dependency_report(output_format="dict") - else: - console.print(f"[red]不支持的分析类型: {analysis_type}[/red]") - console.print("支持的类型: all, health, report") - raise typer.Exit(1) - - # 输出结果 - if output_format == "json": - import json - - # 处理可能的set对象 - def serialize_sets(obj): - if isinstance(obj, set): - return list(obj) - elif isinstance(obj, dict): - return {k: serialize_sets(v) for k, v in obj.items()} - elif isinstance(obj, list): - return [serialize_sets(item) for item in obj] - return obj - - serializable_result = serialize_sets(result) - console.print(json.dumps(serializable_result, indent=2, ensure_ascii=False)) - elif output_format == "markdown": - # Markdown格式输出 - markdown_output = _generate_markdown_output(result, analysis_type) - console.print(markdown_output) - else: - # 简要输出 - if isinstance(result, dict): - console.print("📊 分析结果:") - if "summary" in result: - summary = result["summary"] - console.print(f" 📦 总包数: {summary.get('total_packages', 0)}") - console.print(f" 📚 总依赖: {summary.get('total_dependencies', 0)}") - if "dependency_conflicts" in summary: - conflicts = summary["dependency_conflicts"] - console.print( - f" ⚠️ 冲突: {len(conflicts) if isinstance(conflicts, list) else 0}" - ) - elif "health_score" in result: - console.print(f" 💯 健康评分: {result.get('health_score', 'N/A')}") - console.print(f" 📊 等级: {result.get('grade', 'N/A')}") - else: - console.print(" 📋 分析完成") - console.print("[green]✅ 分析完成[/green]") - - except Exception as e: - console.print(f"[red]分析失败: {e}[/red]") - import traceback - - console.print(f"[red]详细错误:\n{traceback.format_exc()}[/red]") - raise typer.Exit(1) - - -@app.command() -def clean( - target: str = typer.Option("all", help="清理目标: all, cache, build, logs"), - project_root: str = typer.Option(".", help="项目根目录"), - dry_run: bool = typer.Option(False, help="预览模式，不实际删除"), -): - """清理项目文件 - - 清理各类临时文件、缓存和构建产物。根据 SAGE 架构设计，这些文件应该统一生成在 .sage/ 目录下。 - """ - try: - import shutil - from pathlib import Path - - project_path = Path(project_root).resolve() - - if dry_run: - console.print("[yellow]预览模式 - 不会实际删除文件[/yellow]") - - cleaned_items = [] - - # 定义要清理的目录和文件模式 - # 包括根目录和递归查找的模式 - clean_targets = { - "cache": { - "root_dirs": [".ruff_cache", ".mypy_cache", ".pytest_cache"], - "root_files": [".coverage", "coverage.xml"], - "recursive_dirs": ["__pycache__", "htmlcov", ".pytest_cache", ".mypy_cache"], - "recursive_files": ["*.pyc", "*.pyo", ".coverage", "coverage.xml"], - }, - "build": { - "root_dirs": ["build", "dist"], - "root_files": [], - "recursive_dirs": ["build", "dist", "*.egg-info", "*.egg", ".eggs"], - "recursive_files": ["*.egg-info"], - }, - "logs": { - "root_dirs": ["logs", "test_logs"], - "root_files": ["*.log", "install.log"], - "recursive_dirs": ["logs", "test_logs"], - "recursive_files": ["*.log"], - }, - } - - # 受保护的目录（不会被递归清理） - PROTECTED_PATHS = {".git", ".venv", "venv", "env", "node_modules", ".idea", ".vscode"} - - targets_to_clean = [] - if target == "all": - targets_to_clean = list(clean_targets.keys()) - elif target in clean_targets: - targets_to_clean = [target] - else: - console.print(f"[red]不支持的清理目标: {target}[/red]") - console.print("支持的目标: all, cache, build, logs") - raise typer.Exit(1) - - # 执行清理 - for target_type in targets_to_clean: - target_config = clean_targets[target_type] - - # 1. 清理根目录的特定目录 - for dir_name in target_config.get("root_dirs", []): - dir_path = project_path / dir_name - if dir_path.exists() and dir_path.is_dir(): - rel = str(dir_path.relative_to(project_path)) - try: - cleaned_items.append(rel + "/") - if not dry_run: - shutil.rmtree(dir_path) - console.print(f"[green]✓[/green] 删除根目录: {rel}/") - except Exception as e: - console.print(f"[yellow]⚠️ 无法删除 {rel}: {e}[/yellow]") - - # 2. 清理根目录的特定文件 - for file_pattern in target_config.get("root_files", []): - if "*" in file_pattern: - # 使用 glob 匹配 - for file_path in project_path.glob(file_pattern): - if file_path.is_file(): - rel = str(file_path.relative_to(project_path)) - try: - cleaned_items.append(rel) - if not dry_run: - file_path.unlink() - console.print(f"[green]✓[/green] 删除根文件: {rel}") - except Exception as e: - console.print(f"[yellow]⚠️ 无法删除 {rel}: {e}[/yellow]") - else: - file_path = project_path / file_pattern - if file_path.exists() and file_path.is_file(): - rel = str(file_path.relative_to(project_path)) - try: - cleaned_items.append(rel) - if not dry_run: - file_path.unlink() - console.print(f"[green]✓[/green] 删除根文件: {rel}") - except Exception as e: - console.print(f"[yellow]⚠️ 无法删除 {rel}: {e}[/yellow]") - - # 3. 递归清理子目录中的文件 - for pattern in target_config.get("recursive_dirs", []) + target_config.get( - "recursive_files", [] - ): - for path in project_path.rglob(pattern): - # 跳过受保护的路径 - if any(protected in path.parts for protected in PROTECTED_PATHS): - continue - - # 跳过 .sage 目录（这是有意设计的工作目录） - if ".sage" in path.parts: - continue - - rel = str(path.relative_to(project_path)) - try: - if path.is_dir(): - cleaned_items.append(rel + "/") - if not dry_run: - shutil.rmtree(path) - elif path.is_file(): - cleaned_items.append(rel) - if not dry_run: - path.unlink() - except Exception as e: - console.print(f"[yellow]⚠️ 无法删除 {rel}: {e}[/yellow]") - - # 清理空目录（自底向上） - empty_dirs = [] - for dirpath in sorted(project_path.rglob("*"), key=lambda p: len(p.parts), reverse=True): - if dirpath.is_dir() and not any(dirpath.iterdir()): - # 跳过受保护的目录 - if any(protected in dirpath.parts for protected in PROTECTED_PATHS | {".sage"}): - continue - try: - rel = str(dirpath.relative_to(project_path)) - if not dry_run: - dirpath.rmdir() - empty_dirs.append(rel + "/") - except Exception: - pass # 忽略删除失败的情况 - - if empty_dirs: - cleaned_items.extend(empty_dirs) - if not dry_run: - console.print(f"[green]清理了 {len(empty_dirs)} 个空目录[/green]") - - # 报告结果 - if cleaned_items: - console.print( - f"\n[green]{'[预览] 将清理' if dry_run else '✅ 已清理'} {len(cleaned_items)} 个项目[/green]" - ) - if dry_run or len(cleaned_items) <= 20: - for item in cleaned_items: - console.print(f" 📁 {item}") - else: - for item in cleaned_items[:10]: - console.print(f" 📁 {item}") - console.print(f" ... 还有 {len(cleaned_items) - 10} 个项目") - - if not dry_run: - console.print("\n[blue]💡 提示: 这些临时文件应该生成在 .sage/ 目录下[/blue]") - console.print("[blue] 可通过环境变量配置工具缓存位置（见 DEVELOPER.md）[/blue]") - else: - console.print("[blue]✨ 没有找到需要清理的项目[/blue]") - - console.print("\n[green]✅ 清理完成[/green]") - - except Exception as e: - console.print(f"[red]清理失败: {e}[/red]") - import traceback - - console.print(f"[red]详细错误:\n{traceback.format_exc()}[/red]") - raise typer.Exit(1) - - -@app.command() -def status( - project_root: str = typer.Option(".", help="项目根目录"), - verbose: bool = typer.Option(False, help="详细输出"), - output_format: str = typer.Option("summary", help="输出格式: summary, json, full, markdown"), - packages_only: bool = typer.Option(False, "--packages", help="只显示包状态信息"), - check_versions: bool = typer.Option(False, "--versions", help="检查所有包的版本信息"), - check_dependencies: bool = typer.Option(False, "--deps", help="检查包依赖状态"), - quick: bool = typer.Option(True, "--quick/--full", help="快速模式（跳过耗时检查）"), -): - """显示项目状态 - 集成包状态检查功能""" - try: - # 延迟导入以减少启动时间 - from pathlib import Path - - from sage.tools.dev.tools.project_status_checker import ProjectStatusChecker - - # 自动检测项目根目录 - project_path = Path(project_root).resolve() - if not (project_path / "packages").exists(): - current = project_path - while current.parent != current: - if (current / "packages").exists(): - project_path = current - break - current = current.parent - - checker = ProjectStatusChecker(str(project_path)) - - # 如果只检查包状态 - if packages_only: - print_packages_status( - project_path, - console=console, - verbose=verbose, - check_versions=check_versions, - check_dependencies=check_dependencies, - ) - return - - if output_format == "json": - # JSON格式输出 - import json - - status_data = checker.check_all(verbose=False, quick=quick) - # 添加包状态信息 - status_data["packages_status"] = collect_packages_status(project_path) - console.print(json.dumps(status_data, indent=2, ensure_ascii=False)) - elif output_format == "full": - # 完整详细输出 - status_data = checker.check_all(verbose=True, quick=False) # 完整输出不使用快速模式 - console.print("\n" + "=" * 60) - console.print(checker.generate_status_summary(status_data)) - console.print("=" * 60) - # 添加包状态信息 - console.print("\n📦 包状态详情:") - print_packages_status( - project_path, - console=console, - verbose=True, - check_versions=check_versions, - check_dependencies=check_dependencies, - ) - elif output_format == "markdown": - # Markdown格式输出 - status_data = checker.check_all(verbose=verbose, quick=quick) - markdown_output = _generate_status_markdown_output(status_data) - console.print(markdown_output) - else: - # 简要摘要输出 (默认) - 使用快速模式 - console.print("🔍 检查项目状态...") - status_data = checker.check_all(verbose=False, quick=quick) - - # 显示摘要 - summary = checker.generate_status_summary(status_data) - console.print(f"\n{summary}") - - # 显示包状态摘要 - print_packages_status_summary(project_path, console=console) - - # 显示关键信息和警告 - issues = [] - - # 检查环境问题 - env_data = status_data["checks"].get("environment", {}).get("data", {}) - if env_data.get("sage_home") == "Not set": - issues.append("⚠️ SAGE_HOME 环境变量未设置") - - # 检查包安装问题 - pkg_data = status_data["checks"].get("packages", {}).get("data", {}) - if pkg_data.get("summary", {}).get("installed", 0) == 0: - issues.append("⚠️ SAGE 包尚未安装，请运行 ./quickstart.sh") - - # 检查依赖问题 - deps_data = status_data["checks"].get("dependencies", {}).get("data", {}) - failed_imports = [ - name - for name, test in deps_data.get("import_tests", {}).items() - if test != "success" - ] - if failed_imports: - issues.append(f"⚠️ 缺少依赖: {', '.join(failed_imports)}") - - # 检查服务问题 - svc_data = status_data["checks"].get("services", {}).get("data", {}) - if not svc_data.get("ray", {}).get("running", False): - issues.append("ℹ️ Ray 集群未运行 (可选)") - - # 检查失败的项目 - failed_checks = [ - name - for name, check in status_data["checks"].items() - if check["status"] != "success" - ] - - if issues: - console.print("\n📋 需要注意的问题:") - for issue in issues[:5]: # 限制显示数量 - console.print(f" {issue}") - - if failed_checks: - console.print(f"\n❌ 失败的检查项目: {', '.join(failed_checks)}") - console.print("💡 使用 --output-format full 查看详细信息") - elif not issues: - console.print("\n[green]✅ 所有检查项目都通过了![/green]") - else: - console.print("\n💡 使用 --output-format full 查看详细信息") - - except Exception as e: - console.print(f"[red]状态检查失败: {e}[/red]") - if verbose: - import traceback - - console.print(f"[red]详细错误信息:\n{traceback.format_exc()}[/red]") - raise typer.Exit(1) - - -@app.command() -def test( - test_type: str = typer.Option("all", help="测试类型: all, unit, integration, quick"), - project_root: str = typer.Option(".", help="项目根目录"), - verbose: bool = typer.Option(False, help="详细输出"), - packages: str = typer.Option("", help="指定测试的包，逗号分隔 (例: sage-libs,sage-kernel)"), - jobs: int = typer.Option(4, "--jobs", "-j", help="并行任务数量"), - timeout: int = typer.Option(300, "--timeout", "-t", help="每个包的超时时间(秒)"), - failed_only: bool = typer.Option(False, "--failed", help="只重新运行失败的测试"), - continue_on_error: bool = typer.Option( - True, "--continue-on-error", help="遇到错误继续执行其他包" - ), - summary_only: bool = typer.Option(False, "--summary", help="只显示摘要结果"), - quiet: bool = typer.Option(False, "--quiet", "-q", help="静默模式"), - report_file: str = typer.Option("", "--report", help="测试报告输出文件路径"), - diagnose: bool = typer.Option(False, "--diagnose", help="运行诊断模式"), - # 覆盖率选项 - coverage: bool = typer.Option(False, "--coverage", help="启用测试覆盖率分析"), - coverage_report: str = typer.Option( - "term,html,xml", - "--coverage-report", - help="覆盖率报告格式 (逗号分隔，可选: term, html, xml)", - ), - # 调试选项 - debug: bool = typer.Option(False, "--debug", help="启用调试模式，输出详细执行信息"), - # 质量检查选项 - skip_quality_check: bool = typer.Option( - False, "--skip-quality-check", help="跳过代码质量检查和修复" - ), - quality_fix: bool = typer.Option( - True, "--quality-fix/--no-quality-fix", help="自动修复代码质量问题" - ), - quality_format: bool = typer.Option( - True, "--quality-format/--no-quality-format", help="运行代码格式化检查" - ), - quality_imports: bool = typer.Option( - True, "--quality-imports/--no-quality-imports", help="运行导入排序检查" - ), - quality_lint: bool = typer.Option( - True, "--quality-lint/--no-quality-lint", help="运行代码质量检查" - ), -): - """运行项目测试 - 集成从 tools/ 脚本迁移的高级功能""" - try: - import time - from pathlib import Path - - from rich.rule import Rule - - from sage.tools.dev.tools.enhanced_test_runner import EnhancedTestRunner - - # 调试模式：输出时间戳 - def debug_log(message: str, stage: str = ""): - if debug: - timestamp = time.strftime("%H:%M:%S") - if stage: - console.print(f"[dim cyan][{timestamp}] 🔍 [{stage}][/dim cyan] {message}") - else: - console.print(f"[dim cyan][{timestamp}] 🔍[/dim cyan] {message}") - - debug_log("测试命令开始执行", "INIT") - debug_log(f"参数: test_type={test_type}, packages={packages}, coverage={coverage}", "INIT") - - # 0. 测试目录获取 - if not quiet: - console.print(Rule("[bold cyan]🔍 正在寻找项目根目录...[/bold cyan]")) - - # 自动检测项目根目录 - project_path = Path(project_root).resolve() - - # 设置一个标志，表示是否已找到根目录 - found_root = (project_path / "packages").exists() - - # 如果在初始路径没找到，则向上遍历查找 - if not found_root: - current = project_path - # 循环向上查找，直到文件系统的根目录 - while current.parent != current: - current = current.parent - if (current / "packages").exists(): - project_path = current - found_root = True - break # 找到后立即退出循环 - - # 如果最终还是没有找到根目录，则报错退出 - if not found_root: - console.print("[red]❌ 无法找到 SAGE 项目根目录[/red]") - console.print(f"起始搜索目录: {Path(project_root).resolve()}") - console.print("请确保在 SAGE 项目目录中运行，或使用 --project-root 指定正确的路径") - raise typer.Exit(1) - - if not quiet: - console.print(f"📁 项目根目录: {project_path}") - - debug_log(f"项目根目录: {project_path}", "PATH") - - # 1. 代码质量检查和修复 (在测试前运行) - debug_log(f"质量检查: skip_quality_check={skip_quality_check}", "QUALITY") - if not skip_quality_check: - if not quiet: - console.print(Rule("[bold cyan]🔍 执行测试前代码质量检查...[/bold cyan]")) - - # 使用 subprocess 调用 pre-commit 进行质量检查 - import subprocess - - precommit_config = project_path / "tools" / "pre-commit-config.yaml" - - if precommit_config.exists(): - cmd = ["pre-commit", "run", "--config", str(precommit_config)] - - # 根据选项跳过某些 hooks - skip_hooks = [] - if not quality_format: - skip_hooks.append("black") - if not quality_imports: - skip_hooks.append("isort") - if not quality_lint: - skip_hooks.append("ruff") - - if skip_hooks: - import os - - os.environ["SKIP"] = ",".join(skip_hooks) - - try: - result = subprocess.run(cmd, cwd=str(project_path), check=False) - has_quality_issues = result.returncode != 0 - - if has_quality_issues and not quiet: - console.print("[yellow]⚠️ 发现代码质量问题，但继续运行测试[/yellow]") - elif not quiet: - console.print("[green]🎉 所有代码质量检查通过，继续运行测试[/green]") - except Exception as e: - if not quiet: - console.print(f"[yellow]⚠️ 质量检查运行失败: {e}，继续运行测试[/yellow]") - else: - if not quiet: - console.print( - f"[yellow]⚠️ pre-commit 配置文件不存在: {precommit_config}，跳过质量检查[/yellow]" - ) - elif not quiet: - console.print("[yellow]⚠️ 跳过代码质量检查[/yellow]") - - # 诊断模式 - if diagnose: - debug_log("运行诊断模式", "DIAGNOSE") - console.print(Rule("[bold cyan]🔍 运行诊断模式...[/bold cyan]")) - run_installation_diagnostics(project_path, console=console) - return - - debug_log("创建 EnhancedTestRunner", "RUNNER") - runner = EnhancedTestRunner(str(project_path), enable_coverage=coverage, debug=debug) - debug_log(f"Runner 创建成功，覆盖率: {runner.enable_coverage}", "RUNNER") - - # 解析包列表 - target_packages = [] - if packages: - target_packages = [pkg.strip() for pkg in packages.split(",")] - console.print(f"🎯 指定测试包: {target_packages}") - debug_log(f"目标包: {target_packages}", "CONFIG") - - # 配置测试参数 - test_config = { - "verbose": verbose and not quiet, - "workers": jobs, - "timeout": timeout, - "continue_on_error": continue_on_error, - "target_packages": target_packages, - "failed_only": failed_only, - } - - debug_log(f"测试配置: jobs={jobs}, timeout={timeout}", "CONFIG") - - if not quiet: - console.print(Rule(f"[bold cyan]🧪 运行 {test_type} 测试...[/bold cyan]")) - console.print( - f"测试配置: {jobs} 线程测试, {timeout}s 超时退出, {'遇到错误继续执行模式' if continue_on_error else '遇错停止模式'}" - ) - - start_time = time.time() - debug_log(f"开始执行测试，类型: {test_type}", "EXECUTE") - - # 执行测试 - if test_type == "quick": - debug_log("执行快速测试", "EXECUTE") - result = _run_quick_tests(runner, test_config, quiet) - elif test_type == "all": - debug_log("执行全部测试", "EXECUTE") - result = _run_all_tests(runner, test_config, quiet) - elif test_type == "unit": - debug_log("执行单元测试", "EXECUTE") - result = _run_unit_tests(runner, test_config, quiet) - elif test_type == "integration": - debug_log("执行集成测试", "EXECUTE") - result = _run_integration_tests(runner, test_config, quiet) - else: - console.print(f"[red]不支持的测试类型: {test_type}[/red]") - console.print("支持的类型: all, unit, integration, quick") - raise typer.Exit(1) - - execution_time = time.time() - start_time - debug_log(f"测试执行完成，耗时: {execution_time:.2f}s", "RESULT") - - # 生成覆盖率报告（如果启用） - if coverage: - debug_log("生成覆盖率报告", "COVERAGE") - _generate_coverage_reports(project_path, coverage_report, quiet, debug_log) - - # 生成报告 - if report_file: - debug_log(f"生成报告: {report_file}", "REPORT") - _generate_test_report(result, report_file, test_type, execution_time, test_config) - - # 显示结果 - debug_log("显示测试结果", "DISPLAY") - _display_test_results(result, summary_only, quiet, execution_time) - - # 检查结果并退出 - if result and result.get("status") == "success": - if not quiet: - console.print("[green]✅ 所有测试通过[/green]") - else: - if not quiet: - console.print("[red]❌ 测试失败[/red]") - raise typer.Exit(1) - - except Exception as e: - console.print(f"[red]测试运行失败: {e}[/red]") - if verbose: - import traceback - - console.print(f"[red]详细错误:\n{traceback.format_exc()}[/red]") - raise typer.Exit(1) - - -@app.command() -def home( - action: str = typer.Argument(..., help="操作: init, clean, status"), - path: str = typer.Option("", help="SAGE目录路径"), -): - """管理SAGE目录""" - try: - from sage.common.config.output_paths import ( - get_sage_paths, - initialize_sage_paths, - ) - - # 使用统一的路径系统 - if path: - sage_paths = get_sage_paths(path) - else: - sage_paths = get_sage_paths() - - if action == "init": - # 初始化SAGE路径和环境 - initialize_sage_paths(path if path else None) - console.print("[green]✅ SAGE目录初始化完成[/green]") - console.print(f" 📁 SAGE目录: {sage_paths.sage_dir}") - console.print(f" 📊 项目根目录: {sage_paths.project_root}") - console.print( - f" 🌍 环境类型: {'pip安装' if sage_paths.is_pip_environment else '开发环境'}" - ) - - elif action == "clean": - # 清理旧日志文件 - import time - - logs_dir = sage_paths.logs_dir - if not logs_dir.exists(): - console.print("[yellow]⚠️ 日志目录不存在[/yellow]") - return - - current_time = time.time() - cutoff_time = current_time - (7 * 24 * 60 * 60) # 7天前 - - files_removed = 0 - for log_file in logs_dir.glob("*.log"): - if log_file.stat().st_mtime < cutoff_time: - log_file.unlink() - files_removed += 1 - - console.print(f"[green]✅ 清理完成: 删除了 {files_removed} 个旧日志文件[/green]") - - elif action == "status": - console.print("🏠 SAGE目录状态:") - console.print(f" 📁 SAGE目录: {sage_paths.sage_dir}") - console.print(f" ✅ 存在: {'是' if sage_paths.sage_dir.exists() else '否'}") - console.print(f" 📊 项目根目录: {sage_paths.project_root}") - console.print( - f" 🌍 环境类型: {'pip安装' if sage_paths.is_pip_environment else '开发环境'}" - ) - - # 显示各个子目录状态 - subdirs: list[tuple[str, Path]] = [ - ("logs", sage_paths.logs_dir), - ("output", sage_paths.output_dir), - ("temp", sage_paths.temp_dir), - ("cache", sage_paths.cache_dir), - ("reports", sage_paths.reports_dir), - ] - - for name, dir_path in subdirs: - status = "存在" if dir_path.exists() else "不存在" - if dir_path.exists(): - size = sum(f.stat().st_size for f in dir_path.rglob("*") if f.is_file()) - file_count = len(list(dir_path.rglob("*"))) - console.print(f" � {name}: {status} ({file_count} 个文件, {size} 字节)") - else: - console.print(f" � {name}: {status}") - - else: - console.print(f"[red]不支持的操作: {action}[/red]") - console.print("支持的操作: init, clean, status") - raise typer.Exit(1) - - except Exception as e: - console.print(f"[red]SAGE目录操作失败: {e}[/red]") - import traceback - - console.print(f"[red]详细错误:\n{traceback.format_exc()}[/red]") - raise typer.Exit(1) - - -def _generate_status_markdown_output(status_data): - """生成Markdown格式的状态输出""" - import datetime - - markdown_lines = [] - - # 添加标题和时间戳 - markdown_lines.append("# SAGE 项目状态报告") - markdown_lines.append("") - markdown_lines.append(f"**生成时间**: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") - markdown_lines.append("") - - if isinstance(status_data, dict): - # 添加总体状态 - overall_status = status_data.get("overall_status", "unknown") - status_emoji = { - "success": "✅", - "warning": "⚠️", - "error": "❌", - "unknown": "❓", - }.get(overall_status, "❓") - - markdown_lines.append("## 📊 总体状态") - markdown_lines.append("") - markdown_lines.append(f"**状态**: {status_emoji} {overall_status.upper()}") - markdown_lines.append("") - - # 处理检查结果 - if "checks" in status_data: - checks = status_data["checks"] - markdown_lines.append("## 🔍 详细检查结果") - markdown_lines.append("") - - # 创建状态表格 - markdown_lines.append("| 检查项目 | 状态 | 说明 |") - markdown_lines.append("|----------|------|------|") - - for check_name, check_data in checks.items(): - if isinstance(check_data, dict): - status = check_data.get("status", "unknown") - status_emoji = { - "success": "✅", - "warning": "⚠️", - "error": "❌", - "unknown": "❓", - }.get(status, "❓") - - message = check_data.get("message", "") - # 清理消息中的markdown特殊字符 - if isinstance(message, str): - message = message.replace("|", "\\|").replace("\n", " ") - else: - message = str(message) - - markdown_lines.append( - f"| {check_name.replace('_', ' ').title()} | {status_emoji} {status} | {message} |" - ) - - markdown_lines.append("") - - # 详细信息部分 - for check_name, check_data in checks.items(): - if isinstance(check_data, dict) and "data" in check_data: - data = check_data["data"] - if data: # 只显示有数据的检查项目 - markdown_lines.append(f"### {check_name.replace('_', ' ').title()}") - markdown_lines.append("") - - if check_name == "environment": - if isinstance(data, dict): - markdown_lines.append("**环境变量**:") - for key, value in data.items(): - # Safely convert value to string - value_str = str(value) if value is not None else "None" - markdown_lines.append(f"- **{key}**: {value_str}") - - elif check_name == "packages": - if isinstance(data, dict): - summary = data.get("summary", {}) - if summary: - markdown_lines.append("**包安装摘要**:") - markdown_lines.append( - f"- 已安装: {summary.get('installed', 0)}" - ) - markdown_lines.append(f"- 总计: {summary.get('total', 0)}") - - packages = data.get("packages", []) - if packages and isinstance(packages, list | dict): - markdown_lines.append("") - markdown_lines.append("**已安装的包**:") - if isinstance(packages, list): - # Safely slice the list - display_packages = ( - packages[:10] if len(packages) > 10 else packages - ) - for pkg in display_packages: - markdown_lines.append(f"- {str(pkg)}") - if len(packages) > 10: - markdown_lines.append( - f"- ... 还有 {len(packages) - 10} 个包" - ) - elif isinstance(packages, dict): - count = 0 - for pkg_name, pkg_info in packages.items(): - if count >= 10: - break - markdown_lines.append(f"- {pkg_name}: {str(pkg_info)}") - count += 1 - if len(packages) > 10: - markdown_lines.append( - f"- ... 还有 {len(packages) - 10} 个包" - ) - - elif check_name == "dependencies": - if isinstance(data, dict): - import_tests = data.get("import_tests", {}) - if import_tests: - markdown_lines.append("**导入测试结果**:") - for dep, result in import_tests.items(): - status_icon = "✅" if result == "success" else "❌" - markdown_lines.append(f"- {status_icon} {dep}: {result}") - - elif check_name == "services": - if isinstance(data, dict): - markdown_lines.append("**服务状态**:") - for service, info in data.items(): - if isinstance(info, dict): - running = info.get("running", False) - status_icon = "✅" if running else "❌" - markdown_lines.append( - f"- {status_icon} {service}: {'运行中' if running else '未运行'}" - ) - if "details" in info and info["details"]: - markdown_lines.append(f" - 详情: {info['details']}") - - else: - # 通用数据显示 - try: - if isinstance(data, dict): - for key, value in data.items(): - value_str = str(value) if value is not None else "None" - markdown_lines.append(f"- **{key}**: {value_str}") - elif isinstance(data, list): - # Safely handle list slicing - display_items = data[:5] if len(data) > 5 else data - for item in display_items: - markdown_lines.append(f"- {str(item)}") - if len(data) > 5: - markdown_lines.append(f"- ... 还有 {len(data) - 5} 项") - else: - markdown_lines.append(f"数据: {str(data)}") - except Exception as e: - markdown_lines.append(f"数据显示错误: {str(e)}") - - markdown_lines.append("") - - # 添加摘要信息 - if "summary" in status_data: - summary = status_data["summary"] - markdown_lines.append("## 📋 状态摘要") - markdown_lines.append("") - markdown_lines.append("```") - markdown_lines.append(summary) - markdown_lines.append("```") - markdown_lines.append("") - else: - # 处理非字典状态数据 - markdown_lines.append("## 状态数据") - markdown_lines.append("") - markdown_lines.append("```") - markdown_lines.append(str(status_data)) - markdown_lines.append("```") - - # 添加底部信息 - markdown_lines.append("---") - markdown_lines.append("*由 SAGE 开发工具自动生成*") - - return "\n".join(markdown_lines) - - -def _generate_markdown_output(result, analysis_type): - """生成Markdown格式的分析输出""" - import datetime - - markdown_lines = [] - - # 添加标题和时间戳 - markdown_lines.append("# SAGE 项目依赖分析报告") - markdown_lines.append("") - markdown_lines.append(f"**分析类型**: {analysis_type}") - markdown_lines.append(f"**生成时间**: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") - markdown_lines.append("") - - if isinstance(result, dict): - # 处理包含summary的结果 - if "summary" in result: - summary = result["summary"] - markdown_lines.append("## 📊 分析摘要") - markdown_lines.append("") - markdown_lines.append(f"- **总包数**: {summary.get('total_packages', 0)}") - markdown_lines.append(f"- **总依赖**: {summary.get('total_dependencies', 0)}") - - if "dependency_conflicts" in summary: - conflicts = summary["dependency_conflicts"] - conflict_count = len(conflicts) if isinstance(conflicts, list) else 0 - markdown_lines.append(f"- **依赖冲突**: {conflict_count}") - - if conflict_count > 0 and isinstance(conflicts, list): - markdown_lines.append("") - markdown_lines.append("### ⚠️ 依赖冲突详情") - markdown_lines.append("") - for i, conflict in enumerate(conflicts, 1): - if isinstance(conflict, dict): - markdown_lines.append(f"{i}. **{conflict.get('package', 'Unknown')}**") - markdown_lines.append( - f" - 冲突类型: {conflict.get('type', 'Unknown')}" - ) - markdown_lines.append( - f" - 描述: {conflict.get('description', 'No description')}" - ) - else: - markdown_lines.append(f"{i}. {str(conflict)}") - - markdown_lines.append("") - - # 处理健康评分结果 - if "health_score" in result: - markdown_lines.append("## 💯 项目健康评分") - markdown_lines.append("") - health_score = result.get("health_score", "N/A") - grade = result.get("grade", "N/A") - markdown_lines.append(f"- **健康评分**: {health_score}") - markdown_lines.append(f"- **等级**: {grade}") - - # 添加评分说明 - if isinstance(health_score, int | float): - if health_score >= 90: - status = "🟢 优秀" - elif health_score >= 70: - status = "🟡 良好" - elif health_score >= 50: - status = "🟠 一般" - else: - status = "🔴 需要改进" - markdown_lines.append(f"- **状态**: {status}") - - markdown_lines.append("") - - # 处理详细依赖信息 - if "dependencies" in result: - deps = result["dependencies"] - markdown_lines.append("## 📚 依赖详情") - markdown_lines.append("") - - if isinstance(deps, dict): - for package, package_deps in deps.items(): - markdown_lines.append(f"### 📦 {package}") - markdown_lines.append("") - if isinstance(package_deps, list): - if package_deps: - markdown_lines.append("**依赖列表**:") - for dep in package_deps: - markdown_lines.append(f"- {dep}") - else: - markdown_lines.append("- 无外部依赖") - elif isinstance(package_deps, dict): - for key, value in package_deps.items(): - markdown_lines.append(f"- **{key}**: {value}") - else: - markdown_lines.append(f"- {package_deps}") - markdown_lines.append("") - - # 处理包信息 - if "packages" in result: - packages = result["packages"] - markdown_lines.append("## 📦 包信息") - markdown_lines.append("") - - if isinstance(packages, dict): - markdown_lines.append("| 包名 | 版本 | 状态 |") - markdown_lines.append("|------|------|------|") - for package, info in packages.items(): - if isinstance(info, dict): - version = info.get("version", "Unknown") - status = info.get("status", "Unknown") - markdown_lines.append(f"| {package} | {version} | {status} |") - else: - markdown_lines.append(f"| {package} | - | {info} |") - elif isinstance(packages, list): - markdown_lines.append("**已安装的包**:") - for package in packages: - markdown_lines.append(f"- {package}") - - markdown_lines.append("") - - # 处理其他字段 - for key, value in result.items(): - if key not in [ - "summary", - "health_score", - "grade", - "dependencies", - "packages", - ]: - markdown_lines.append(f"## {key.replace('_', ' ').title()}") - markdown_lines.append("") - if isinstance(value, list | dict): - markdown_lines.append("```json") - import json - - try: - # 处理set对象 - def serialize_sets(obj): - if isinstance(obj, set): - return list(obj) - elif isinstance(obj, dict): - return {k: serialize_sets(v) for k, v in obj.items()} - elif isinstance(obj, list): - return [serialize_sets(item) for item in obj] - return obj - - serializable_value = serialize_sets(value) - markdown_lines.append( - json.dumps(serializable_value, indent=2, ensure_ascii=False) - ) - except Exception: - markdown_lines.append(str(value)) - markdown_lines.append("```") - else: - markdown_lines.append(f"{value}") - markdown_lines.append("") - else: - # 处理非字典结果 - markdown_lines.append("## 分析结果") - markdown_lines.append("") - markdown_lines.append("```") - markdown_lines.append(str(result)) - markdown_lines.append("```") - - # 添加底部信息 - markdown_lines.append("---") - markdown_lines.append("*由 SAGE 开发工具自动生成*") - - return "\n".join(markdown_lines) - - -# =================================== -# 测试功能辅助函数 (从 tools/ 脚本迁移) -# =================================== - - -def _run_diagnose_mode(project_root: str): - """Backward-compatible wrapper using the shared diagnostics utility.""" - - run_installation_diagnostics(project_root, console=console) - - -# Note: Issues Manager tests have been removed as the functionality -# is now in the separate sage-github-manager package -# Install: pip install sage-github-manager -# Use: github-manager test - - -def _run_quick_tests(runner, config: dict, quiet: bool): - """运行快速测试 (类似 quick_test.sh)""" - # 快速测试包列表 - quick_packages = [ - "sage-common", - "sage-tools", - "sage-kernel", - "sage-libs", - "sage-middleware", - ] - - if not quiet: - console.print(f"🚀 快速测试模式 - 测试包: {quick_packages}") - - # 重写配置为快速模式 - quick_config = config.copy() - quick_config.update( - { - "timeout": 120, # 2分钟超时 - "jobs": 3, # 3并发 - "target_packages": quick_packages, - } - ) - - return runner.run_tests(mode="all", **quick_config) - - -def _run_all_tests(runner, config: dict, quiet: bool): - """运行全部测试""" - return runner.run_tests(mode="all", **config) - - -def _run_unit_tests(runner, config: dict, quiet: bool): - """运行单元测试""" - if not quiet: - console.print("🔬 单元测试模式") - - # 可以在这里添加单元测试特定的逻辑 - return runner.run_tests(mode="all", **config) - - -def _run_integration_tests(runner, config: dict, quiet: bool): - """运行集成测试""" - if not quiet: - console.print("🔗 集成测试模式") - - # 可以在这里添加集成测试特定的逻辑 - return runner.run_tests(mode="all", **config) - - -def _generate_coverage_reports(project_path: Path, coverage_report: str, quiet: bool, debug_log): - """生成覆盖率报告 - - Args: - project_path: 项目根目录 - coverage_report: 报告格式，逗号分隔 (term, html, xml) - quiet: 静默模式 - debug_log: 调试日志函数 - """ - import os - import subprocess - - from sage.common.config.output_paths import get_sage_paths - - try: - debug_log("开始生成覆盖率报告", "COVERAGE") - - # 获取 SAGE 路径配置 - sage_paths = get_sage_paths(str(project_path)) - coverage_dir = sage_paths.coverage_dir - coverage_file = coverage_dir / ".coverage" - - debug_log(f"Coverage 目录: {coverage_dir}", "COVERAGE") - debug_log(f"Coverage 合并文件: {coverage_file}", "COVERAGE") - - # 查找所有coverage数据文件（包括主文件和并行测试生成的分片文件） - coverage_files = list(coverage_dir.glob(".coverage*")) - - if not coverage_files: - if not quiet: - console.print("[yellow]⚠️ 未找到覆盖率数据文件[/yellow]") - console.print(f"[yellow] 预期位置: {coverage_dir}/.coverage*[/yellow]") - return - - debug_log(f"找到 {len(coverage_files)} 个coverage文件", "COVERAGE") - - # 合并覆盖率数据（如果有多个 .coverage.* 文件） - # coverage combine 会自动查找所有 .coverage.* 文件并合并到 .coverage - debug_log("合并覆盖率数据", "COVERAGE") - combine_cmd = ["python", "-m", "coverage", "combine", "--keep"] - result = subprocess.run( - combine_cmd, - cwd=str(coverage_dir), # 在coverage目录中运行，这样它能找到所有.coverage.*文件 - env={**os.environ, "COVERAGE_FILE": str(coverage_file)}, - capture_output=True, - text=True, - ) - - if result.returncode != 0: - debug_log(f"Coverage combine 警告: {result.stderr}", "COVERAGE") - # 即使combine失败也继续，可能只有一个coverage文件 - - # 解析报告格式 - report_formats = [fmt.strip() for fmt in coverage_report.split(",")] - debug_log(f"报告格式: {report_formats}", "COVERAGE") - - # 生成各种格式的报告 - for fmt in report_formats: - debug_log(f"生成 {fmt} 格式报告", "COVERAGE") - - if fmt == "term": - # 终端输出 - if not quiet: - console.print("\n" + "=" * 70) - console.print("[bold cyan]📊 测试覆盖率报告[/bold cyan]") - console.print("=" * 70 + "\n") - - term_cmd = ["python", "-m", "coverage", "report", "-m"] - result = subprocess.run( - term_cmd, - cwd=str(project_path), - env={**os.environ, "COVERAGE_FILE": str(coverage_file)}, - capture_output=True, - text=True, - ) - - if result.returncode == 0 and not quiet: - console.print(result.stdout) - else: - debug_log(f"Coverage report 失败: {result.stderr}", "COVERAGE") - - elif fmt == "html": - # HTML 报告 - html_dir = coverage_dir / "htmlcov" - html_cmd = ["python", "-m", "coverage", "html", "-d", str(html_dir)] - result = subprocess.run( - html_cmd, - cwd=str(project_path), - env={**os.environ, "COVERAGE_FILE": str(coverage_file)}, - capture_output=True, - text=True, - ) - - if result.returncode == 0: - if not quiet: - console.print( - f"[green]✅ HTML 覆盖率报告已生成: {html_dir}/index.html[/green]" - ) - debug_log(f"HTML 报告生成成功: {html_dir}", "COVERAGE") - else: - debug_log(f"HTML 报告生成失败: {result.stderr}", "COVERAGE") - - elif fmt == "xml": - # XML 报告（用于 CI/CD 工具） - xml_file = coverage_dir / "coverage.xml" - xml_cmd = ["python", "-m", "coverage", "xml", "-o", str(xml_file)] - result = subprocess.run( - xml_cmd, - cwd=str(project_path), - env={**os.environ, "COVERAGE_FILE": str(coverage_file)}, - capture_output=True, - text=True, - ) - - if result.returncode == 0: - if not quiet: - console.print(f"[green]✅ XML 覆盖率报告已生成: {xml_file}[/green]") - debug_log(f"XML 报告生成成功: {xml_file}", "COVERAGE") - else: - debug_log(f"XML 报告生成失败: {result.stderr}", "COVERAGE") - - debug_log("覆盖率报告生成完成", "COVERAGE") - - except Exception as e: - if not quiet: - console.print(f"[yellow]⚠️ 生成覆盖率报告时出错: {e}[/yellow]") - debug_log(f"覆盖率报告生成异常: {e}", "COVERAGE") - import traceback - - debug_log(traceback.format_exc(), "COVERAGE") - - -def _generate_test_report( - result: dict, report_file: str, test_type: str, execution_time: float, config: dict -): - """生成测试报告文件""" - try: - import json - from datetime import datetime - from pathlib import Path - - report_data = { - "timestamp": datetime.now().isoformat(), - "test_type": test_type, - "execution_time": execution_time, - "config": config, - "result": result, - "summary": { - "status": result.get("status", "unknown"), - "total_tests": result.get("total", 0), - "passed": result.get("passed", 0), - "failed": result.get("failed", 0), - "errors": result.get("errors", 0), - }, - } - - report_path = Path(report_file) - report_path.parent.mkdir(parents=True, exist_ok=True) - - if report_file.endswith(".json"): - with open(report_path, "w", encoding="utf-8") as f: - json.dump(report_data, f, indent=2, ensure_ascii=False) - else: - # 生成 Markdown 格式报告 - with open(report_path, "w", encoding="utf-8") as f: - f.write("# SAGE 测试报告\n\n") - f.write("**测试类型**: {test_type}\n") - f.write("**生成时间**: {report_data['timestamp']}\n") - f.write("**执行时间**: {execution_time:.2f}秒\n\n") - f.write("## 测试结果\n\n") - f.write("- 状态: {result.get('status', '未知')}\n") - f.write("- 总测试数: {result.get('total', 0)}\n") - f.write("- 通过: {result.get('passed', 0)}\n") - f.write("- 失败: {result.get('failed', 0)}\n") - f.write("- 错误: {result.get('errors', 0)}\n\n") - - if result.get("failed_tests"): - f.write("## 失败的测试\n\n") - for test in result["failed_tests"]: - f.write(f"- {test}\n") - - console.print(f"📊 测试报告已保存到: {report_path}") - - except Exception as e: - console.print(f"[red]生成测试报告失败: {e}[/red]") - - -def _display_test_results(result: dict, summary_only: bool, quiet: bool, execution_time: float): - """显示测试结果""" - if quiet: - return - - console.print("\n📊 测试结果摘要") - console.print("=" * 50) - - if result: - status = result.get("status", "unknown") - if status == "success": - console.print("✅ 状态: 成功") - else: - console.print("❌ 状态: 失败") - - console.print(f"⏱️ 执行时间: {execution_time:.2f}秒") - - # Get summary data from either top level or summary sub-dict - summary = result.get("summary", result) - console.print(f"📊 总测试数: {summary.get('total', 0)}") - console.print(f"✅ 通过: {summary.get('passed', 0)}") - console.print(f"❌ 失败: {summary.get('failed', 0)}") - console.print(f"💥 错误: {summary.get('errors', 0)}") - - if not summary_only and result.get("failed_tests"): - console.print("\n❌ 失败的测试:") - for test in result["failed_tests"]: - console.print(f" - {test}") - else: - console.print("❓ 无法获取测试结果") - - -# =================================== -# 包状态检查辅助函数 (从 check_packages_status.sh 迁移) -# =================================== - - -def _get_packages_status_data(project_path) -> dict: - """保持向后兼容，委托给共享的诊断工具。""" - - return collect_packages_status(project_path) - - -def _show_packages_status_summary(project_path): - """向后兼容: 使用新的包状态摘要渲染函数。""" - - print_packages_status_summary(project_path, console=console) - - -def _show_packages_status( - project_path, verbose: bool, check_versions: bool, check_dependencies: bool -): - """显示详细包状态 (保持向后兼容)。""" - - print_packages_status( - project_path, - console=console, - verbose=verbose, - check_versions=check_versions, - check_dependencies=check_dependencies, - ) - - -def _check_package_dependencies(package_name: str, verbose: bool): - """保持原有函数存在以防外部引用。""" - - if verbose: - console.print(" ℹ️ 依赖检查已迁移到 `sage doctor packages --deps`，当前调用保持兼容") - - -# =================================== -# 架构和文档检查命令 -# =================================== - - -@app.command() -def architecture( - show_dependencies: bool = typer.Option( - True, "--dependencies/--no-dependencies", help="显示依赖关系" - ), - show_layers: bool = typer.Option(True, "--layers/--no-layers", help="显示层级定义"), - package: str = typer.Option(None, "--package", help="显示特定包的信息"), - output_format: str = typer.Option("text", "--format", help="输出格式: text, json, markdown"), -): - """显示 SAGE 架构信息 - - 显示项： - - 分层架构定义（L1-L5） - - 包的层级归属 - - 允许的依赖关系 - - 依赖规则说明 - - 示例： - sage-dev architecture # 显示完整架构信息 - sage-dev architecture --package sage-kernel # 显示特定包的信息 - sage-dev architecture --format json # JSON 格式输出 - sage-dev architecture --no-dependencies # 只显示层级，不显示依赖 - """ - from sage.tools.dev.tools.architecture_checker import ( - ALLOWED_DEPENDENCIES, - LAYER_DEFINITION, - PACKAGE_TO_LAYER, - ) - - if output_format == "json": - import json - - data = { - "layers": LAYER_DEFINITION, - "package_to_layer": PACKAGE_TO_LAYER, - "dependencies": {k: list(v) for k, v in ALLOWED_DEPENDENCIES.items()}, - } - - if package: - if package in PACKAGE_TO_LAYER: - data = { - "package": package, - "layer": PACKAGE_TO_LAYER[package], - "dependencies": list(ALLOWED_DEPENDENCIES.get(package, set())), - } - else: - console.print(f"[red]❌ 未找到包: {package}[/red]") - raise typer.Exit(1) - - console.print(json.dumps(data, indent=2, ensure_ascii=False)) - return - - if output_format == "markdown": - console.print("# SAGE 架构定义\n") - - if show_layers: - console.print("## 层级定义\n") - for layer in sorted(LAYER_DEFINITION.keys()): - packages = LAYER_DEFINITION[layer] - console.print(f"### {layer}") - for pkg in packages: - console.print(f"- `{pkg}`") - console.print() - - if show_dependencies: - console.print("## 依赖关系\n") - for pkg in sorted(ALLOWED_DEPENDENCIES.keys()): - deps = ALLOWED_DEPENDENCIES[pkg] - console.print(f"### {pkg}") - if deps: - console.print(f"**允许依赖**: {', '.join(f'`{d}`' for d in sorted(deps))}") - else: - console.print("**允许依赖**: 无（基础层）") - console.print() - return - - # Text format (default) - console.print("\n" + "=" * 70) - console.print("🏗️ SAGE 架构定义") - console.print("=" * 70) - - if package: - # 显示特定包的信息 - if package not in PACKAGE_TO_LAYER: - console.print(f"\n[red]❌ 未找到包: {package}[/red]") - console.print("\n可用的包：") - for pkg in sorted(PACKAGE_TO_LAYER.keys()): - console.print(f" • {pkg}") - raise typer.Exit(1) - - layer = PACKAGE_TO_LAYER[package] - deps = ALLOWED_DEPENDENCIES.get(package, set()) - - console.print(f"\n📦 包名称: [bold cyan]{package}[/bold cyan]") - console.print(f"📊 所属层级: [bold yellow]{layer}[/bold yellow]") - - if deps: - console.print("\n✅ 允许依赖的包:") - for dep in sorted(deps): - dep_layer = PACKAGE_TO_LAYER.get(dep, "unknown") - console.print(f" • {dep} ({dep_layer})") - else: - console.print("\n🔒 基础层，不依赖其他包") - - # 显示哪些包可以依赖这个包 - can_depend = [pkg for pkg, allowed in ALLOWED_DEPENDENCIES.items() if package in allowed] - if can_depend: - console.print("\n⬆️ 可以被以下包依赖:") - for pkg in sorted(can_depend): - pkg_layer = PACKAGE_TO_LAYER.get(pkg, "unknown") - console.print(f" • {pkg} ({pkg_layer})") - else: - # 显示完整架构 - if show_layers: - console.print("\n📊 层级定义:") - console.print() - - for layer in sorted(LAYER_DEFINITION.keys()): - packages = LAYER_DEFINITION[layer] - layer_desc = { - "L1": "基础层 - 通用组件", - "L2": "平台层 - 基础设施", - "L3": "核心层 - 核心功能", - "L4": "中间件层 - 服务组件", - "L5": "接口层 - CLI与开发工具", - }.get(layer, "") - - console.print(f" [bold yellow]{layer}[/bold yellow] - {layer_desc}") - for pkg in packages: - console.print(f" • [cyan]{pkg}[/cyan]") - console.print() - - if show_dependencies: - console.print("\n🔗 依赖关系规则:") - console.print() - console.print(" 💡 原则: 高层可以依赖低层，同层之间需要明确定义") - console.print() - - # 按层级顺序显示（L1-L5） - for layer in sorted(LAYER_DEFINITION.keys()): - for pkg in LAYER_DEFINITION[layer]: - deps = ALLOWED_DEPENDENCIES.get(pkg, set()) - - console.print(f" [cyan]{pkg}[/cyan] ({layer})") - if deps: - dep_list = ", ".join(sorted(deps)) - console.print(f" ✅ 可依赖: {dep_list}") - else: - console.print(" 🔒 基础层，无依赖") - console.print() - - console.print("=" * 70) - console.print("\n💡 提示:") - console.print(" • 使用 --package 查看特定包的依赖信息") - console.print(" • 使用 --format json 获取机器可读的输出") - console.print(" • 使用 --format markdown 获取文档格式") - console.print(" • 运行 'sage-dev check-architecture' 检查架构合规性") - console.print() - - -@app.command() -def check_architecture( - project_root: str = typer.Option(".", help="项目根目录"), - changed_only: bool = typer.Option(False, "--changed-only", help="仅检查变更的文件"), - diff: str = typer.Option("HEAD", "--diff", help="git diff 比较的目标（用于 --changed-only）"), - verbose: bool = typer.Option(False, "--verbose", "-v", help="显示详细信息"), -): - """检查代码架构合规性 - - 检查项： - - 包依赖规则（分层架构） - - 导入路径合规性 - - 模块结构规范 - - 示例： - sage-dev check-architecture # 检查所有文件 - sage-dev check-architecture --changed-only # 仅检查变更文件 - sage-dev check-architecture --diff main # 对比 main 分支 - """ - from sage.tools.dev.tools.architecture_checker import ArchitectureChecker - - project_path = Path(project_root).resolve() - - if not project_path.exists(): - console.print(f"[red]❌ 项目根目录不存在: {project_path}[/red]") - raise typer.Exit(1) - - console.print("\n🏗️ 检查 SAGE 架构合规性...") - console.print(f"📁 项目路径: {project_path}") - - try: - checker = ArchitectureChecker(root_dir=str(project_path)) - - if changed_only: - console.print(f"🔍 仅检查相对于 {diff} 的变更文件") - result = checker.check_changed_files(diff_target=diff) - else: - console.print("🔍 检查所有文件") - result = checker.check_all() - - except Exception as e: - console.print(f"[red]❌ 架构检查执行失败: {e}[/red]") - if verbose: - import traceback - - console.print(traceback.format_exc()) - raise typer.Exit(1) - - # 显示结果 - if result.passed: - console.print("\n[green]✅ 架构合规性检查通过！[/green]") - if verbose and result.stats: - console.print(f"📝 检查了 {result.stats.get('total_files', 0)} 个文件") - else: - console.print("\n[red]❌ 发现架构违规！[/red]") - if result.stats: - console.print(f"📝 检查了 {result.stats.get('total_files', 0)} 个文件") - console.print(f"⚠️ 发现 {len(result.violations)} 个问题：\n") - - for violation in result.violations: - console.print(f"[red]❌ {violation.file}:{violation.line}[/red]") - console.print(f" {violation.message}") - if violation.suggestion: - console.print(f" 💡 建议: {violation.suggestion}") - console.print() - - raise typer.Exit(1) - - -@app.command() -def check_devnotes( - project_root: str = typer.Option(".", help="项目根目录"), - changed_only: bool = typer.Option(False, "--changed-only", help="仅检查变更的文档"), - check_structure: bool = typer.Option(False, "--check-structure", help="检查目录结构"), - verbose: bool = typer.Option(False, "--verbose", "-v", help="显示详细信息"), -): - """检查 dev-notes 文档规范 - - 检查项： - - 文档分类是否正确 - - 元数据是否完整（Date, Author, Summary） - - 文件名是否符合规范 - - 示例： - sage-dev check-devnotes # 检查所有文档 - sage-dev check-devnotes --check-structure # 检查目录结构 - """ - from sage.tools.dev.tools.devnotes_checker import DevNotesChecker - - project_path = Path(project_root).resolve() - - if not project_path.exists(): - console.print(f"[red]❌ 项目根目录不存在: {project_path}[/red]") - raise typer.Exit(1) - - console.print("\n📚 检查 dev-notes 文档规范...") - console.print(f"📁 项目路径: {project_path}") - - try: - checker = DevNotesChecker(root_dir=str(project_path)) - - if check_structure: - console.print("🔍 检查目录结构...") - structure_ok = checker.check_directory_structure() - if structure_ok: - console.print("\n[green]✅ 目录结构检查通过！[/green]") - else: - console.print("\n[red]❌ 目录结构检查失败！[/red]") - raise typer.Exit(1) - return - elif changed_only: - console.print("🔍 仅检查变更的文档...") - result = checker.check_changed() - else: - console.print("🔍 检查所有文档...") - result = checker.check_all() - - except typer.Exit: - raise - except Exception as e: - console.print(f"[red]❌ 文档检查执行失败: {e}[/red]") - if verbose: - import traceback - - console.print(traceback.format_exc()) - raise typer.Exit(1) - - # 显示结果 - if result.get("passed", False): - console.print("\n[green]✅ 文档规范检查通过！[/green]") - if verbose: - console.print(f"📝 检查了 {result.get('total', 0)} 个文档") - else: - console.print("\n[red]❌ 发现文档规范问题！[/red]") - issues = result.get("issues", []) - console.print(f"⚠️ 发现 {len(issues)} 个问题：\n") - - for issue in issues[:10]: # 显示前10个 - console.print(f"[red]❌ {issue.get('file', 'unknown')}[/red]") - console.print(f" {issue.get('message', '')}") - console.print() - - if len(issues) > 10: - console.print(f"... 还有 {len(issues) - 10} 个问题") - - console.print("\n💡 参考模板: docs/dev-notes/TEMPLATE.md") - raise typer.Exit(1) - - -@app.command() -def check_readme( - package: str = typer.Argument(None, help="要检查的包名（不指定则检查所有包）"), - project_root: str = typer.Option(".", help="项目根目录"), - fix: bool = typer.Option(False, "--fix", help="生成缺失的章节（交互模式）"), - report: bool = typer.Option(False, "--report", help="生成详细报告"), - verbose: bool = typer.Option(False, "--verbose", "-v", help="显示详细信息"), -): - """检查包 README 文档质量 - - 检查项： - - README 文件是否存在 - - 必需章节是否完整 - - 文档结构是否符合模板 - - 示例： - sage-dev check-readme # 检查所有包 - sage-dev check-readme sage-common # 检查特定包 - sage-dev check-readme --report # 生成详细报告 - sage-dev check-readme sage-libs --fix # 交互式修复 - """ - from sage.tools.dev.tools.package_readme_checker import PackageREADMEChecker - - project_path = Path(project_root).resolve() - - if not project_path.exists(): - console.print(f"[red]❌ 项目根目录不存在: {project_path}[/red]") - raise typer.Exit(1) - - console.print("\n📄 检查包 README 质量...") - console.print(f"📁 项目路径: {project_path}") - - try: - checker = PackageREADMEChecker(workspace_root=str(project_path)) - - if package: - console.print(f"🔍 检查包: {package}") - result = checker.check_package(package, fix=fix) - results = [result] - else: - console.print("🔍 检查所有包...") - results = checker.check_all(fix=fix) - - # 显示结果 - all_passed = all(r.score >= 80.0 for r in results) - - if report: - checker.generate_report(results) - - if all_passed: - console.print("\n[green]✅ README 质量检查通过！[/green]") - for r in results: - console.print(f" {r.package_name}: {r.score:.1f}/100") - else: - console.print("\n[yellow]⚠️ 部分 README 需要改进：[/yellow]\n") - for r in results: - status = "✅" if r.score >= 80.0 else "⚠️" - console.print(f"{status} {r.package_name}: {r.score:.1f}/100") - if r.issues and verbose: - for issue in r.issues: - console.print(f" - {issue}") - - if not all_passed: - console.print("\n💡 运行 `sage-dev check-readme --report` 查看详细报告") - console.print("💡 运行 `sage-dev check-readme --fix` 交互式修复") - - raise typer.Exit(1) - - except Exception as e: - console.print(f"[red]❌ README 检查失败: {e}[/red]") - if verbose: - import traceback - - console.print(traceback.format_exc()) - raise typer.Exit(1) - - -@app.command() -def check_all( - project_root: str = typer.Option(".", help="项目根目录"), - changed_only: bool = typer.Option(False, "--changed-only", help="仅检查变更的文件"), - verbose: bool = typer.Option(False, "--verbose", "-v", help="显示详细信息"), - continue_on_error: bool = typer.Option( - False, "--continue-on-error", help="出错时继续执行其他检查" - ), -): - """运行所有质量检查（架构 + 文档 + README） - - 这是一个便捷命令，依次运行： - 1. 架构合规性检查 - 2. Dev-notes 文档规范检查 - 3. 包 README 质量检查 - - 示例： - sage-dev check-all # 检查所有项目 - sage-dev check-all --changed-only # 仅检查变更文件 - sage-dev check-all --continue-on-error # 出错继续执行 - sage-dev check-all --verbose # 详细输出 - """ - project_path = Path(project_root).resolve() - - if not project_path.exists(): - console.print(f"[red]❌ 项目根目录不存在: {project_path}[/red]") - raise typer.Exit(1) - - console.print("\n" + "=" * 70) - console.print("🔍 运行所有质量检查") - console.print("=" * 70) - console.print(f"📁 项目路径: {project_path}\n") - - checks_passed = [] - checks_failed = [] - - # 1. 架构检查 - console.print("=" * 70) - console.print("🏗️ [1/3] 架构合规性检查") - console.print("=" * 70) - try: - from sage.tools.dev.tools.architecture_checker import ArchitectureChecker - - checker = ArchitectureChecker(root_dir=str(project_path)) - if changed_only: - result = checker.check_changed_files(diff_target="HEAD") - else: - result = checker.check_all() - - if result.passed: - console.print("[green]✅ 架构合规性检查通过[/green]\n") - checks_passed.append("架构检查") - else: - console.print(f"[red]❌ 发现 {len(result.violations)} 个架构违规[/red]") - if verbose: - for violation in result.violations[:3]: - console.print(f" • {violation.file}: {violation.message}") - if len(result.violations) > 3: - console.print(f" ... 还有 {len(result.violations) - 3} 个问题") - console.print() - checks_failed.append("架构检查") - if not continue_on_error: - raise typer.Exit(1) - except typer.Exit: - raise # 重新抛出 Exit 异常 - except Exception as e: - console.print(f"[red]❌ 架构检查执行失败: {e}[/red]\n") - checks_failed.append("架构检查") - if not continue_on_error: - raise typer.Exit(1) - - # 2. Dev-notes 文档检查 - console.print("=" * 70) - console.print("📚 [2/3] Dev-notes 文档规范检查") - console.print("=" * 70) - try: - from sage.tools.dev.tools.devnotes_checker import DevNotesChecker - - checker = DevNotesChecker(root_dir=str(project_path)) - if changed_only: - result = checker.check_changed() - else: - result = checker.check_all() - - if result.get("passed", False): - console.print("[green]✅ Dev-notes 文档规范检查通过[/green]\n") - checks_passed.append("文档检查") - else: - issues = result.get("issues", []) - console.print(f"[red]❌ 发现 {len(issues)} 个文档问题[/red]") - if verbose: - for issue in issues[:3]: - console.print( - f" • {issue.get('file', 'unknown')}: {issue.get('message', '')}" - ) - if len(issues) > 3: - console.print(f" ... 还有 {len(issues) - 3} 个问题") - console.print() - checks_failed.append("文档检查") - if not continue_on_error: - raise typer.Exit(1) - except typer.Exit: - raise # 重新抛出 Exit 异常 - except Exception as e: - console.print(f"[red]❌ 文档检查执行失败: {e}[/red]\n") - checks_failed.append("文档检查") - if not continue_on_error: - raise typer.Exit(1) - - # 3. README 检查 - console.print("=" * 70) - console.print("📄 [3/3] 包 README 质量检查") - console.print("=" * 70) - try: - from sage.tools.dev.tools.package_readme_checker import PackageREADMEChecker - - checker = PackageREADMEChecker(workspace_root=str(project_path)) - results = checker.check_all(fix=False) - - low_score_packages = [r for r in results if r.score < 80.0] - if not low_score_packages: - console.print("[green]✅ README 质量检查通过[/green]\n") - checks_passed.append("README 检查") - else: - console.print(f"[yellow]⚠️ {len(low_score_packages)} 个包的 README 需要改进[/yellow]") - if verbose: - for r in low_score_packages[:5]: - console.print(f" • {r.package_name}: {r.score:.1f}/100") - if len(low_score_packages) > 5: - console.print(f" ... 还有 {len(low_score_packages) - 5} 个包") - console.print() - # README 检查不阻止，只是警告 - checks_passed.append("README 检查（警告）") - except typer.Exit: - raise # 重新抛出 Exit 异常 - except Exception as e: - console.print(f"[yellow]⚠️ README 检查失败: {e}[/yellow]\n") - # README 检查失败不算严重错误 - checks_passed.append("README 检查（跳过）") - - # 汇总结果 - console.print("=" * 70) - console.print("📊 检查结果汇总") - console.print("=" * 70) - - if checks_passed: - console.print("[green]✅ 通过的检查:[/green]") - for check in checks_passed: - console.print(f" • {check}") - - if checks_failed: - console.print("\n[red]❌ 失败的检查:[/red]") - for check in checks_failed: - console.print(f" • {check}") - - console.print("\n" + "=" * 70) - if not checks_failed: - console.print("[green]🎉 所有检查通过！[/green]") - console.print("=" * 70) - else: - console.print(f"[red]❌ {len(checks_failed)} 项检查失败[/red]") - console.print("=" * 70) - console.print("\n💡 提示:") - console.print(" • 使用 --verbose 查看详细错误") - console.print(" • 使用 --continue-on-error 继续执行所有检查") - console.print(" • 运行单独的检查命令修复问题:") - console.print(" - sage-dev check-architecture") - console.print(" - sage-dev check-devnotes") - console.print(" - sage-dev check-readme") - raise typer.Exit(1) - - -if __name__ == "__main__": - app() diff --git a/packages/sage-tools/src/sage/tools/cli/commands/dev/maintain/__init__.py b/packages/sage-tools/src/sage/tools/cli/commands/dev/maintain/__init__.py deleted file mode 100644 index 55d4517cda..0000000000 --- a/packages/sage-tools/src/sage/tools/cli/commands/dev/maintain/__init__.py +++ /dev/null @@ -1,448 +0,0 @@ -""" -维护工具命令组 - -提供项目维护、Submodule 管理、Git hooks、安全检查等功能。 -""" - -import subprocess -from pathlib import Path - -import typer -from rich.console import Console - -from sage.tools.dev.tools.dependency_spec_checker import assert_dependencies_match - -app = typer.Typer( - name="maintain", - help="🔧 维护工具 - Submodule、Hooks、诊断", - no_args_is_help=True, -) - -console = Console() - - -def get_project_root() -> Path: - """获取项目根目录""" - current = Path.cwd() - # 向上查找包含 .git 的目录 - while current != current.parent: - if (current / ".git").exists(): - return current - current = current.parent - return Path.cwd() - - -def run_maintenance_script(command: str, *args) -> int: - """运行 sage-maintenance.sh 脚本""" - project_root = get_project_root() - script_path = project_root / "tools" / "maintenance" / "sage-maintenance.sh" - - if not script_path.exists(): - console.print(f"[red]错误: 未找到维护脚本 {script_path}[/red]") - return 1 - - cmd = ["bash", str(script_path), command, *args] - - try: - # 设置超时避免卡住（doctor 命令30秒超时，其他命令60秒） - timeout = 30 if command == "doctor" else 60 - result = subprocess.run(cmd, cwd=project_root, timeout=timeout) - return result.returncode - except subprocess.TimeoutExpired: - console.print(f"[red]执行超时: 命令运行超过 {timeout} 秒[/red]") - console.print("[yellow]提示: 如果是 doctor 命令，可能是环境问题导致检查变慢[/yellow]") - return 1 - except Exception as e: - console.print(f"[red]执行失败: {e}[/red]") - return 1 - - -@app.command(name="doctor") -def doctor(): - """ - 🔍 健康检查 - - 运行完整的项目健康检查，诊断常见问题。 - - 示例： - sage-dev maintain doctor - """ - console.print("\n[bold blue]🔍 运行项目健康检查[/bold blue]\n") - exit_code = run_maintenance_script("doctor") - if exit_code != 0: - raise typer.Exit(exit_code) - - -# Submodule 管理子命令组 -submodule_app = typer.Typer( - name="submodule", - help="📦 Submodule 管理", - no_args_is_help=True, -) - - -@submodule_app.command(name="init") -def submodule_init(): - """ - 🚀 初始化 Submodules - - 初始化所有 submodules 并切换到正确的分支。 - - 示例： - sage-dev maintain submodule init - """ - console.print("\n[bold blue]🚀 初始化 Submodules[/bold blue]\n") - exit_code = run_maintenance_script("submodule", "init") - if exit_code != 0: - raise typer.Exit(exit_code) - - -@submodule_app.command(name="status") -def submodule_status(): - """ - 📊 查看 Submodule 状态 - - 显示所有 submodules 的状态和分支信息。 - - 示例： - sage-dev maintain submodule status - """ - console.print("\n[bold blue]📊 Submodule 状态[/bold blue]\n") - exit_code = run_maintenance_script("submodule", "status") - if exit_code != 0: - raise typer.Exit(exit_code) - - -@submodule_app.command(name="switch") -def submodule_switch(): - """ - 🔄 切换 Submodule 分支 - - 根据当前 SAGE 分支切换 submodules 到对应分支。 - - 示例： - sage-dev maintain submodule switch - """ - console.print("\n[bold blue]🔄 切换 Submodule 分支[/bold blue]\n") - exit_code = run_maintenance_script("submodule", "switch") - if exit_code != 0: - raise typer.Exit(exit_code) - - -@submodule_app.command(name="update") -def submodule_update(): - """ - ⬆️ 更新 Submodules - - 更新所有 submodules 到远程最新版本。 - - 示例： - sage-dev maintain submodule update - """ - console.print("\n[bold blue]⬆️ 更新 Submodules[/bold blue]\n") - exit_code = run_maintenance_script("submodule", "update") - if exit_code != 0: - raise typer.Exit(exit_code) - - -@submodule_app.command(name="fix-conflict") -def submodule_fix_conflict(): - """ - 🔧 解决 Submodule 冲突 - - 自动解决 submodule 冲突。 - - 示例： - sage-dev maintain submodule fix-conflict - """ - console.print("\n[bold blue]🔧 解决 Submodule 冲突[/bold blue]\n") - exit_code = run_maintenance_script("submodule", "fix-conflict") - if exit_code != 0: - raise typer.Exit(exit_code) - - -@submodule_app.command(name="cleanup") -def submodule_cleanup(): - """ - 🧹 清理 Submodule 配置 - - 清理旧的 submodule 配置。 - - 示例： - sage-dev maintain submodule cleanup - """ - console.print("\n[bold blue]🧹 清理 Submodule 配置[/bold blue]\n") - exit_code = run_maintenance_script("submodule", "cleanup") - if exit_code != 0: - raise typer.Exit(exit_code) - - -@submodule_app.command(name="bootstrap") -def submodule_bootstrap(): - """ - ⚡ 快速初始化（bootstrap） - - 一键初始化和配置所有 submodules。 - - 示例： - sage-dev maintain submodule bootstrap - """ - console.print("\n[bold blue]⚡ Bootstrap Submodules[/bold blue]\n") - exit_code = run_maintenance_script("submodule", "bootstrap") - if exit_code != 0: - raise typer.Exit(exit_code) - - -app.add_typer(submodule_app, name="submodule") - - -# Git Hooks 管理子命令组 -hooks_app = typer.Typer( - name="hooks", - help="🪝 Git Hooks 管理", - no_args_is_help=True, -) - -HOOK_MODES = ("lightweight", "full") - - -def _validate_hook_mode(value: str) -> str: - normalized = value.lower() - if normalized not in HOOK_MODES: - raise typer.BadParameter(f"无效的 hooks 模式: {value}. 可选值: {', '.join(HOOK_MODES)}") - return normalized - - -@hooks_app.command(name="install") -def hooks_install( - quiet: bool = typer.Option(False, "--quiet", "-q", help="静默模式，只显示错误"), - root_dir: str = typer.Option(None, "--root", help="项目根目录（默认自动检测）"), - mode: str = typer.Option( - "lightweight", - "--mode", - "-m", - callback=_validate_hook_mode, - help="选择 hooks 安装模式: lightweight (默认) 或 full", - ), -): - """ - 安装 SAGE Git hooks。 - - 安装 pre-commit hook 用于代码质量检查、架构合规性验证和文档规范检查。 - - 示例: - sage-dev maintain hooks install - sage-dev maintain hooks install --quiet - """ - from pathlib import Path - - from sage.tools.dev.hooks import HooksInstaller - - root_path = Path(root_dir) if root_dir else None - installer = HooksInstaller(root_dir=root_path, quiet=quiet, mode=mode) - - try: - success = installer.install() - if success: - if not quiet: - console.print("\n[green]✅ Git hooks 安装成功！[/green]") - else: - console.print("\n[red]❌ Git hooks 安装失败[/red]") - raise typer.Exit(1) - except Exception as e: - console.print(f"\n[red]❌ 安装过程中出错: {e}[/red]") - raise typer.Exit(1) - - -@hooks_app.command(name="uninstall") -def hooks_uninstall( - quiet: bool = typer.Option(False, "--quiet", "-q", help="静默模式，只显示错误"), - root_dir: str = typer.Option(None, "--root", help="项目根目录（默认自动检测）"), -): - """ - 卸载 SAGE Git hooks。 - - 移除已安装的 pre-commit hook 和 pre-commit 框架配置。 - - 示例: - sage-dev maintain hooks uninstall - """ - from pathlib import Path - - from sage.tools.dev.hooks import HooksInstaller - - root_path = Path(root_dir) if root_dir else None - installer = HooksInstaller(root_dir=root_path, quiet=quiet) - - try: - success = installer.uninstall() - if success: - if not quiet: - console.print("\n[green]✅ Git hooks 卸载成功！[/green]") - else: - console.print("\n[red]❌ Git hooks 卸载失败[/red]") - raise typer.Exit(1) - except Exception as e: - console.print(f"\n[red]❌ 卸载过程中出错: {e}[/red]") - raise typer.Exit(1) - - -@hooks_app.command(name="status") -def hooks_status( - root_dir: str = typer.Option(None, "--root", help="项目根目录（默认自动检测）"), - json_output: bool = typer.Option(False, "--json", help="以 JSON 格式输出"), -): - """ - 检查 Git hooks 的安装状态。 - - 显示 pre-commit hook、pre-commit 框架和各种检查工具的状态。 - - 示例: - sage-dev maintain hooks status - sage-dev maintain hooks status --json - """ - from pathlib import Path - - from sage.tools.dev.hooks import HooksInstaller - - root_path = Path(root_dir) if root_dir else None - installer = HooksInstaller(root_dir=root_path, quiet=True) - - try: - if json_output: - import json - - status_info = installer.status() - console.print(json.dumps(status_info, indent=2)) - else: - installer.print_status() - except Exception as e: - console.print(f"\n[red]❌ 检查状态时出错: {e}[/red]") - raise typer.Exit(1) - - -@hooks_app.command(name="reinstall") -def hooks_reinstall( - quiet: bool = typer.Option(False, "--quiet", "-q", help="静默模式，只显示错误"), - root_dir: str = typer.Option(None, "--root", help="项目根目录（默认自动检测）"), - mode: str = typer.Option( - "lightweight", - "--mode", - "-m", - callback=_validate_hook_mode, - help="选择 hooks 安装模式: lightweight (默认) 或 full", - ), -): - """ - 重新安装 SAGE Git hooks。 - - 先卸载现有的 hooks，然后重新安装。用于更新 hooks 到最新版本。 - - 示例: - sage-dev maintain hooks reinstall - """ - from pathlib import Path - - from sage.tools.dev.hooks import HooksManager - - root_path = Path(root_dir) if root_dir else None - manager = HooksManager(root_dir=root_path, mode=mode) - - try: - # Uninstall first - if not quiet: - console.print("[blue]🔄 正在卸载现有 hooks...[/blue]") - manager.uninstall(quiet=True) - - # Then install - if not quiet: - console.print("[blue]🔄 正在重新安装 hooks...[/blue]\n") - success = manager.install(quiet=quiet) - - if success: - if not quiet: - console.print("\n[green]✅ Git hooks 重新安装成功！[/green]") - else: - console.print("\n[red]❌ Git hooks 重新安装失败[/red]") - raise typer.Exit(1) - except Exception as e: - console.print(f"\n[red]❌ 重新安装过程中出错: {e}[/red]") - raise typer.Exit(1) - - -app.add_typer(hooks_app, name="hooks") - - -@app.command(name="depspec") -def depspec( - project_root: str = typer.Option( - ".", - help="项目根目录（包含 dependencies-spec.yaml 和 packages/）", - ), - warn_only: bool = typer.Option( - False, - "--warn-only", - help="只输出警告，不返回非零退出码", - ), -): - """检查依赖版本是否符合 dependencies-spec.yaml。 - - 示例： - sage-dev maintain depspec - sage-dev maintain depspec --warn-only - """ - - root_path = Path(project_root).resolve() - console.print("\n[bold blue]🔍 检查依赖版本与 dependencies-spec.yaml 一致性[/bold blue]\n") - try: - assert_dependencies_match(root_path) - console.print("[green]✅ 所有 pyproject.toml 与 dependencies-spec.yaml 一致[/green]") - except Exception as exc: # noqa: BLE001 - console.print(f"[red]❌ 发现依赖不一致: {exc}[/red]") - if not warn_only: - raise typer.Exit(1) - - -@app.command(name="security") -def security_check(): - """ - 🔒 安全检查 - - 检查敏感信息泄露、密钥等安全问题。 - - 示例： - sage-dev maintain security - """ - console.print("\n[bold blue]🔒 安全检查[/bold blue]\n") - exit_code = run_maintenance_script("security-check") - if exit_code != 0: - raise typer.Exit(exit_code) - - -@app.command(name="clean") -def clean_project( - deep: bool = typer.Option( - False, - "--deep", - help="深度清理", - ), -): - """ - 🧹 清理项目 - - 清理构建产物、缓存等。 - - 示例： - sage-dev maintain clean # 标准清理 - sage-dev maintain clean --deep # 深度清理 - """ - console.print("\n[bold blue]🧹 清理项目[/bold blue]\n") - - command = "clean-deep" if deep else "clean" - exit_code = run_maintenance_script(command) - - if exit_code != 0: - raise typer.Exit(exit_code) - - -__all__ = ["app"] diff --git a/packages/sage-tools/src/sage/tools/cli/commands/dev/maintenance.py b/packages/sage-tools/src/sage/tools/cli/commands/dev/maintenance.py deleted file mode 100644 index 694ea7b4fd..0000000000 --- a/packages/sage-tools/src/sage/tools/cli/commands/dev/maintenance.py +++ /dev/null @@ -1,199 +0,0 @@ -""" -SAGE 维护工具 CLI 命令 - -提供各种项目维护相关的命令 - -Author: SAGE Team -Date: 2025-10-27 -""" - -from pathlib import Path - -import typer -from rich.console import Console - -app = typer.Typer( - name="maintenance", - help="🔧 项目维护工具", - no_args_is_help=True, -) - -console = Console() - - -@app.command("organize-devnotes") -def organize_devnotes( - root: Path | None = typer.Option( - None, - "--root", - "-r", - help="项目根目录（默认：当前目录）", - ), - verbose: bool = typer.Option( - True, - "--verbose/--quiet", - "-v/-q", - help="详细输出", - ), -): - """ - 📊 整理 dev-notes 文档 - - 分析文档内容、建议分类、检查元数据、生成整理建议 - """ - try: - from sage.tools.dev.maintenance import DevNotesOrganizer - - if root is None: - root = Path.cwd() - - console.print("\n[bold]📊 分析 dev-notes 文档...[/bold]") - console.print(f"项目根目录: {root}\n") - - organizer = DevNotesOrganizer(root) - results = organizer.analyze_all() - report = organizer.generate_report(results, verbose=verbose) - - console.print("\n[green]✅ 分析完成！[/green]") - console.print( - f"共分析 {report['total']} 个文件，" - f"发现 {len(report['root_files'])} 个需要整理的根目录文件" - ) - - except Exception as e: - console.print(f"[red]❌ 执行失败: {e}[/red]") - raise typer.Exit(1) - - -@app.command("fix-metadata") -def fix_metadata( - root: Path | None = typer.Option( - None, - "--root", - "-r", - help="项目根目录（默认：当前目录）", - ), - scan: bool = typer.Option( - False, - "--scan", - "-s", - help="扫描并修复所有缺失元数据的文件", - ), -): - """ - 📝 修复 dev-notes 文档元数据 - - 为缺少元数据（Date、Author、Summary）的文档添加元数据 - """ - try: - from sage.tools.dev.maintenance import MetadataFixer - - if root is None: - root = Path.cwd() - - console.print("\n[bold]📝 修复文档元数据...[/bold]") - console.print(f"项目根目录: {root}\n") - - fixer = MetadataFixer(root) - - if scan: - console.print("[yellow]⚠️ 扫描模式：将使用默认元数据[/yellow]") - console.print("[yellow]⚠️ 请在修复后手动更新实际的日期和摘要[/yellow]\n") - stats = fixer.scan_and_fix() - else: - stats = fixer.fix_all() - - console.print("\n[green]✅ 修复完成！[/green]") - console.print( - f"成功: {stats['success']}, 跳过: {stats['skipped']}, 失败: {stats['failed']}" - ) - - except Exception as e: - console.print(f"[red]❌ 执行失败: {e}[/red]") - raise typer.Exit(1) - - -@app.command("update-ruff-ignore") -def update_ruff_ignore( - root: Path | None = typer.Option( - None, - "--root", - "-r", - help="项目根目录（默认：当前目录）", - ), - rules: str | None = typer.Option( - None, - "--rules", - help="要添加的规则，逗号分隔（如：B904,C901）", - ), - preset: str | None = typer.Option( - None, - "--preset", - help="使用预设规则集（如：b904-c901）", - ), -): - """ - 🔧 更新 Ruff ignore 规则 - - 批量更新所有 pyproject.toml 文件中的 ruff.lint.ignore 规则 - """ - try: - from sage.tools.dev.maintenance import RuffIgnoreUpdater - - if root is None: - root = Path.cwd() - - console.print("\n[bold]🔧 更新 Ruff ignore 规则...[/bold]") - console.print(f"项目根目录: {root}\n") - - updater = RuffIgnoreUpdater(root) - - if preset == "b904-c901": - console.print("[cyan]使用预设: B904 + C901[/cyan]\n") - stats = updater.add_b904_c901() - elif rules: - rules_list = [r.strip() for r in rules.split(",")] - console.print(f"[cyan]添加规则: {', '.join(rules_list)}[/cyan]\n") - stats = updater.update_all(rules_list) - else: - console.print("[yellow]请指定 --rules 或 --preset[/yellow]") - console.print("\n示例:") - console.print(" sage-dev maintenance update-ruff-ignore --preset b904-c901") - console.print(" sage-dev maintenance update-ruff-ignore --rules B904,C901") - raise typer.Exit(1) - - console.print("\n[green]✅ 更新完成！[/green]") - console.print( - f"更新: {stats['updated']}, 跳过: {stats['skipped']}, 失败: {stats['failed']}" - ) - - except Exception as e: - console.print(f"[red]❌ 执行失败: {e}[/red]") - raise typer.Exit(1) - - -@app.command("list") -def list_tools(): - """ - 📋 列出所有维护工具 - - 显示可用的维护工具及其说明 - """ - console.print("\n[bold]🔧 SAGE 维护工具[/bold]\n") - - tools = [ - ("organize-devnotes", "整理 dev-notes 文档", "📊"), - ("fix-metadata", "修复文档元数据", "📝"), - ("update-ruff-ignore", "更新 Ruff ignore 规则", "🔧"), - ] - - for cmd, desc, icon in tools: - console.print(f"{icon} [cyan]{cmd}[/cyan]") - console.print(f" {desc}") - console.print() - - console.print("[dim]使用 sage-dev maintenance --help 查看详细帮助[/dim]\n") - - -if __name__ == "__main__": - app() diff --git a/packages/sage-tools/src/sage/tools/cli/commands/dev/models.py b/packages/sage-tools/src/sage/tools/cli/commands/dev/models.py deleted file mode 100644 index 6461261bc5..0000000000 --- a/packages/sage-tools/src/sage/tools/cli/commands/dev/models.py +++ /dev/null @@ -1,58 +0,0 @@ -"""Typer commands for working with embedding model caches.""" - -from __future__ import annotations - -import typer -from rich.console import Console - -from sage.tools.dev.models.cache import ( - DEFAULT_MODEL_NAME, - cache_embedding_model, - check_embedding_model, - clear_embedding_model_cache, - configure_hf_environment, -) - -console = Console() -app = typer.Typer(name="models", help="🤖 Embedding 模型缓存管理") - - -@app.command() -def configure(): - """仅配置 Hugging Face 下载所需的环境变量。""" - - configure_hf_environment(console) - - -@app.command() -def cache( - model: str = typer.Option(DEFAULT_MODEL_NAME, "--model", "-m", help="要缓存的模型标识"), - verify: bool = typer.Option(True, "--verify/--no-verify", help="下载后执行一次推理验证"), - retries: int = typer.Option(3, "--retries", min=1, max=5, help="下载失败时的最大重试次数"), -): - """下载并缓存指定的 embedding 模型。""" - - success = cache_embedding_model(model, console=console, verify=verify, retries=retries) - if not success: - raise typer.Exit(1) - - -@app.command() -def check(model: str = typer.Option(DEFAULT_MODEL_NAME, "--model", "-m")): - """检查模型是否已缓存或可下载。""" - - success = check_embedding_model(model, console=console) - if not success: - raise typer.Exit(1) - - -@app.command() -def clear(model: str = typer.Option(DEFAULT_MODEL_NAME, "--model", "-m")): - """清理模型缓存。""" - - success = clear_embedding_model_cache(model, console=console) - if not success: - raise typer.Exit(1) - - -__all__ = ["app"] diff --git a/packages/sage-tools/src/sage/tools/cli/commands/dev/package/__init__.py b/packages/sage-tools/src/sage/tools/cli/commands/dev/package/__init__.py deleted file mode 100644 index 3594c28a7b..0000000000 --- a/packages/sage-tools/src/sage/tools/cli/commands/dev/package/__init__.py +++ /dev/null @@ -1,115 +0,0 @@ -""" -包管理命令组 - -提供 PyPI 发布、版本管理、安装管理等功能。 -""" - -import typer -from rich.console import Console - -app = typer.Typer( - name="package", - help="📦 包管理 - PyPI 发布、版本管理、安装", - no_args_is_help=True, -) - -console = Console() - -# 仅保留 version 命令组；原 pypi 命令已移至独立仓库 -try: - from ..package_version import app as version_app - - app.add_typer(version_app, name="version") -except ImportError as e: - console.print(f"[yellow]警告: 无法导入 version 命令: {e}[/yellow]") - - -@app.command(name="install") -def install_packages( - mode: str = typer.Option( - "dev", - "--mode", - "-m", - help="安装模式: dev (开发模式), deps (只安装依赖)", - ), - packages: str = typer.Option( - None, - "--packages", - "-p", - help="指定包名，逗号分隔", - ), - editable: bool = typer.Option( - True, - "--editable/--no-editable", - help="可编辑模式安装", - ), -): - """ - 📥 安装包 - - 安装 SAGE 包及其依赖。 - - 示例： - sage-dev package install # 开发模式安装所有包 - sage-dev package install -m deps # 只安装依赖 - sage-dev package install -p sage-libs # 安装特定包 - """ - import subprocess - import sys - from pathlib import Path - - project_root = Path.cwd() - packages_dir = project_root / "packages" - - if not packages_dir.exists(): - console.print("[red]错误: 未找到 packages 目录[/red]") - raise typer.Exit(1) - - # 确定要安装的包 - if packages: - pkg_list = [p.strip() for p in packages.split(",")] - else: - # 获取所有包 - pkg_list = [ - p.name for p in packages_dir.iterdir() if p.is_dir() and (p / "pyproject.toml").exists() - ] - - console.print(f"\n[bold blue]📥 安装模式: {mode}[/bold blue]") - console.print(f"[cyan]包列表: {', '.join(pkg_list)}[/cyan]\n") - - for pkg_name in pkg_list: - pkg_path = packages_dir / pkg_name - - if not pkg_path.exists(): - console.print(f"[yellow]跳过不存在的包: {pkg_name}[/yellow]") - continue - - console.print(f"[cyan]→ 安装 {pkg_name}...[/cyan]") - - try: - cmd = [sys.executable, "-m", "pip", "install"] - - if mode == "dev" and editable: - cmd.append("-e") - - cmd.append(str(pkg_path)) - - result = subprocess.run( - cmd, - capture_output=True, - text=True, - ) - - if result.returncode == 0: - console.print(f"[green]✓ {pkg_name} 安装成功[/green]") - else: - console.print(f"[red]✗ {pkg_name} 安装失败[/red]") - console.print(result.stderr) - - except Exception as e: - console.print(f"[red]✗ {pkg_name} 安装出错: {e}[/red]") - - console.print("\n[bold green]✓ 安装完成[/bold green]") - - -__all__ = ["app"] diff --git a/packages/sage-tools/src/sage/tools/cli/commands/dev/package_version.py b/packages/sage-tools/src/sage/tools/cli/commands/dev/package_version.py deleted file mode 100644 index 5a9992b520..0000000000 --- a/packages/sage-tools/src/sage/tools/cli/commands/dev/package_version.py +++ /dev/null @@ -1,388 +0,0 @@ -""" -sage-dev Version Management Command - -This module provides commands to manage version.py files across all SAGE subpackages. -""" - -import re -from pathlib import Path - -import typer -from rich.console import Console -from rich.panel import Panel -from rich.table import Table - -from sage.tools.cli.utils.dev_check import require_source_code - -console = Console() -app = typer.Typer(help="🏷️ 版本管理 - 管理各个子包的版本信息") - - -def find_version_files(root_path: Path) -> dict[str, Path]: - """查找所有的_version.py文件""" - version_files = {} - packages_dir = root_path / "packages" - - if not packages_dir.exists(): - console.print(f"[red]❌ packages目录不存在: {packages_dir}[/red]") - return version_files - - for package_dir in packages_dir.iterdir(): - if package_dir.is_dir() and not package_dir.name.startswith("."): - # 自动查找_version.py文件 - # 1. 检查 src/sage/_version.py (适用于 sage 主包) - version_file_candidates = [package_dir / "src" / "sage" / "_version.py"] - - # 2. 检查 src/sage/{module}/_version.py (适用于所有 sage-* 子包) - if package_dir.name.startswith("sage-"): - # sage-common -> common, sage-kernel -> kernel, etc. - module_name = package_dir.name.replace("sage-", "") - version_file_candidates.append( - package_dir / "src" / "sage" / module_name / "_version.py" - ) - - # 查找第一个存在的 _version.py 文件 - for version_file in version_file_candidates: - if version_file.exists(): - version_files[package_dir.name] = version_file - break - - return version_files - - -def read_version_info(version_file: Path) -> dict[str, str]: - """从_version.py文件中读取版本信息""" - try: - content = version_file.read_text(encoding="utf-8") - - version_match = re.search(r'__version__\s*=\s*["\']([^"\']+)["\']', content) - author_match = re.search(r'__author__\s*=\s*["\']([^"\']+)["\']', content) - email_match = re.search(r'__email__\s*=\s*["\']([^"\']+)["\']', content) - - return { - "version": version_match.group(1) if version_match else "unknown", - "author": author_match.group(1) if author_match else "unknown", - "email": email_match.group(1) if email_match else "unknown", - } - except Exception as e: - console.print(f"[red]❌ 读取版本文件失败 {version_file}: {e}[/red]") - return {"version": "error", "author": "error", "email": "error"} - - -def update_version_file( - version_file: Path, new_version: str, author: str | None = None, email: str | None = None -) -> bool: - """更新_version.py文件中的版本信息""" - try: - content = version_file.read_text(encoding="utf-8") - - # 更新版本号 - content = re.sub( - r'(__version__\s*=\s*["\'])([^"\']+)(["\'])', - rf"\g<1>{new_version}\g<3>", - content, - ) - - # 如果提供了作者信息，也更新 - if author: - content = re.sub( - r'(__author__\s*=\s*["\'])([^"\']+)(["\'])', - rf"\g<1>{author}\g<3>", - content, - ) - - # 如果提供了邮箱信息，也更新 - if email: - content = re.sub( - r'(__email__\s*=\s*["\'])([^"\']+)(["\'])', - rf"\g<1>{email}\g<3>", - content, - ) - - version_file.write_text(content, encoding="utf-8") - return True - except Exception as e: - console.print(f"[red]❌ 更新版本文件失败 {version_file}: {e}[/red]") - return False - - -def parse_version(version_str: str) -> tuple[int, int, int | str, str]: - """解析版本号为(major, minor, patch, suffix)""" - # 匹配形如 "0.1.4" 或 "0.1.3-alpha.1" 的版本号 - match = re.match(r"(\d+)\.(\d+)\.(\d+)(?:\.(\d+)|[\-\.]?(.*))?", version_str) - if match: - major, minor, patch, build, suffix = match.groups() - if build: - # 如果有第四位数字，将其作为patch的扩展 - patch_full = f"{patch}.{build}" - return int(major), int(minor), patch_full, suffix or "" - else: - return int(major), int(minor), int(patch), suffix or "" - else: - # 如果解析失败，返回默认值 - return 0, 1, 0, "" - - -def increment_version(version_str: str, increment_type: str) -> str: - """增加版本号""" - if "." in version_str and len(version_str.split(".")) >= 4: - # 处理四位版本号，如 "0.1.4" - parts = version_str.split(".") - major, minor, patch, build = ( - int(parts[0]), - int(parts[1]), - int(parts[2]), - int(parts[3]), - ) - - if increment_type == "major": - return f"{major + 1}.0.0.0" - elif increment_type == "minor": - return f"{major}.{minor + 1}.0.0" - elif increment_type == "patch": - return f"{major}.{minor}.{patch + 1}.0" - elif increment_type == "build": - return f"{major}.{minor}.{patch}.{build + 1}" - else: - return version_str - else: - # 处理三位版本号 - major, minor, patch, suffix = parse_version(version_str) - - if increment_type == "major": - return f"{major + 1}.0.0{('.' + suffix) if suffix else ''}" - elif increment_type == "minor": - return f"{major}.{minor + 1}.0{('.' + suffix) if suffix else ''}" - elif increment_type == "patch": - patch_num = int(patch) if isinstance(patch, str) else patch - return f"{major}.{minor}.{patch_num + 1}{('.' + suffix) if suffix else ''}" - else: - return version_str - - -@app.command("list") -@require_source_code -def list_versions(root: str = typer.Option(".", "--root", "-r", help="项目根目录路径")): - """📋 列出所有包的版本信息（仅开发模式）""" - root_path = Path(root).resolve() - - console.print( - Panel.fit( - f"🔍 扫描项目版本信息\n📁 项目路径: {root_path}", - title="Version Scanner", - border_style="blue", - ) - ) - - version_files = find_version_files(root_path) - - if not version_files: - console.print("[yellow]⚠️ 未找到任何版本文件[/yellow]") - return - - table = Table(title="📦 SAGE 包版本信息", show_header=True, header_style="bold magenta") - table.add_column("包名", style="cyan", no_wrap=True) - table.add_column("版本", style="green") - table.add_column("作者", style="blue") - table.add_column("邮箱", style="yellow") - table.add_column("文件路径", style="dim") - - for package_name, version_file in sorted(version_files.items()): - version_info = read_version_info(version_file) - table.add_row( - package_name, - version_info["version"], - version_info["author"], - version_info["email"], - str(version_file.relative_to(root_path)), - ) - - console.print(table) - - -@app.command("set") -@require_source_code -def set_version( - new_version: str = typer.Argument(..., help="新的版本号"), - packages: list[str] | None = typer.Option( - None, "--package", "-p", help="指定要更新的包名（可多次使用）" - ), - root: str = typer.Option(".", "--root", "-r", help="项目根目录路径"), - dry_run: bool = typer.Option(False, "--dry-run", help="预览模式，不实际修改文件"), -): - """🏷️ 设置指定包的版本号（仅开发模式）""" - root_path = Path(root).resolve() - - console.print( - Panel.fit( - f"🏷️ 设置版本号: {new_version}\n📁 项目路径: {root_path}", - title="Set Version", - border_style="green", - ) - ) - - version_files = find_version_files(root_path) - - if not version_files: - console.print("[yellow]⚠️ 未找到任何版本文件[/yellow]") - return - - # 如果指定了包名，只更新指定的包 - if packages: - filtered_files = {name: path for name, path in version_files.items() if name in packages} - if not filtered_files: - console.print(f"[red]❌ 未找到指定的包: {', '.join(packages)}[/red]") - console.print(f"可用的包: {', '.join(version_files.keys())}") - return - version_files = filtered_files - - updated_count = 0 - for package_name, version_file in version_files.items(): - current_info = read_version_info(version_file) - - if dry_run: - console.print( - f"[blue]🔍 预览[/blue] {package_name}: {current_info['version']} -> {new_version}" - ) - else: - if update_version_file(version_file, new_version): - console.print( - f"[green]✅ 更新[/green] {package_name}: {current_info['version']} -> {new_version}" - ) - updated_count += 1 - else: - console.print(f"[red]❌ 失败[/red] {package_name}: 无法更新版本文件") - - if not dry_run: - console.print(f"\n🎉 成功更新 {updated_count} 个包的版本") - - -@app.command("bump") -@require_source_code -def bump_version( - increment_type: str = typer.Argument(..., help="版本增量类型: major, minor, patch, build"), - packages: list[str] | None = typer.Option( - None, "--package", "-p", help="指定要更新的包名（可多次使用）" - ), - root: str = typer.Option(".", "--root", "-r", help="项目根目录路径"), - dry_run: bool = typer.Option(False, "--dry-run", help="预览模式，不实际修改文件"), -): - """⬆️ 增加版本号（major, minor, patch, build）（仅开发模式）""" - if increment_type not in ["major", "minor", "patch", "build"]: - console.print("[red]❌ 无效的增量类型，支持: major, minor, patch, build[/red]") - raise typer.Exit(1) - - root_path = Path(root).resolve() - - console.print( - Panel.fit( - f"⬆️ 增加版本号: {increment_type}\n📁 项目路径: {root_path}", - title="Bump Version", - border_style="yellow", - ) - ) - - version_files = find_version_files(root_path) - - if not version_files: - console.print("[yellow]⚠️ 未找到任何版本文件[/yellow]") - return - - # 如果指定了包名，只更新指定的包 - if packages: - filtered_files = {name: path for name, path in version_files.items() if name in packages} - if not filtered_files: - console.print(f"[red]❌ 未找到指定的包: {', '.join(packages)}[/red]") - console.print(f"可用的包: {', '.join(version_files.keys())}") - return - version_files = filtered_files - - updated_count = 0 - for package_name, version_file in version_files.items(): - current_info = read_version_info(version_file) - current_version = current_info["version"] - new_version = increment_version(current_version, increment_type) - - if dry_run: - console.print( - f"[blue]🔍 预览[/blue] {package_name}: {current_version} -> {new_version}" - ) - else: - if update_version_file(version_file, new_version): - console.print( - f"[green]✅ 更新[/green] {package_name}: {current_version} -> {new_version}" - ) - updated_count += 1 - else: - console.print(f"[red]❌ 失败[/red] {package_name}: 无法更新版本文件") - - if not dry_run: - console.print(f"\n🎉 成功更新 {updated_count} 个包的版本") - - -@app.command("sync") -@require_source_code -def sync_versions( - source_package: str = typer.Option("sage", "--source", "-s", help="源包名（作为版本参考）"), - root: str = typer.Option(".", "--root", "-r", help="项目根目录路径"), - dry_run: bool = typer.Option(False, "--dry-run", help="预览模式，不实际修改文件"), -): - """🔄 同步所有包的版本到指定包的版本（仅开发模式）""" - root_path = Path(root).resolve() - - console.print( - Panel.fit( - f"🔄 同步版本到 {source_package}\n📁 项目路径: {root_path}", - title="Sync Versions", - border_style="cyan", - ) - ) - - version_files = find_version_files(root_path) - - if not version_files: - console.print("[yellow]⚠️ 未找到任何版本文件[/yellow]") - return - - # 获取源包的版本 - if source_package not in version_files: - console.print(f"[red]❌ 未找到源包: {source_package}[/red]") - console.print(f"可用的包: {', '.join(version_files.keys())}") - return - - source_version_info = read_version_info(version_files[source_package]) - source_version = source_version_info["version"] - - console.print(f"📌 源版本: {source_package} = {source_version}") - - updated_count = 0 - for package_name, version_file in version_files.items(): - if package_name == source_package: - continue # 跳过源包自身 - - current_info = read_version_info(version_file) - current_version = current_info["version"] - - if current_version == source_version: - console.print(f"[dim]⏭️ 跳过[/dim] {package_name}: 版本已一致 ({current_version})") - continue - - if dry_run: - console.print( - f"[blue]🔍 预览[/blue] {package_name}: {current_version} -> {source_version}" - ) - else: - if update_version_file(version_file, source_version): - console.print( - f"[green]✅ 同步[/green] {package_name}: {current_version} -> {source_version}" - ) - updated_count += 1 - else: - console.print(f"[red]❌ 失败[/red] {package_name}: 无法更新版本文件") - - if not dry_run: - console.print(f"\n🎉 成功同步 {updated_count} 个包的版本") - - -if __name__ == "__main__": - app() diff --git a/packages/sage-tools/src/sage/tools/cli/commands/dev/project/__init__.py b/packages/sage-tools/src/sage/tools/cli/commands/dev/project/__init__.py deleted file mode 100644 index 39a2630c9d..0000000000 --- a/packages/sage-tools/src/sage/tools/cli/commands/dev/project/__init__.py +++ /dev/null @@ -1,170 +0,0 @@ -""" -项目管理命令组 - -提供项目状态、分析、测试、清理等功能。 -""" - -import typer -from rich.console import Console - -app = typer.Typer( - name="project", - help="📊 项目管理 - 状态、分析、测试、清理", - no_args_is_help=True, -) - -console = Console() - - -@app.command(name="status") -def project_status( - project_root: str = typer.Option(".", help="项目根目录"), - verbose: bool = typer.Option(False, help="详细输出"), - output_format: str = typer.Option("summary", help="输出格式: summary, json, full, markdown"), - packages_only: bool = typer.Option(False, "--packages", help="只显示包状态信息"), - check_versions: bool = typer.Option(False, "--versions", help="检查所有包的版本信息"), - check_dependencies: bool = typer.Option(False, "--deps", help="检查包依赖状态"), - quick: bool = typer.Option(True, "--quick/--full", help="快速模式（跳过耗时检查）"), -): - """📊 项目状态检查 - 检查各包状态和版本""" - from ..main import status - - return status( - project_root=project_root, - verbose=verbose, - output_format=output_format, - packages_only=packages_only, - check_versions=check_versions, - check_dependencies=check_dependencies, - quick=quick, - ) - - -@app.command(name="analyze") -def project_analyze( - analysis_type: str = typer.Option("all", help="分析类型: all, health, report"), - output_format: str = typer.Option("summary", help="输出格式: summary, json, markdown"), - project_root: str = typer.Option(".", help="项目根目录"), -): - """🔍 依赖分析 - 分析项目依赖关系""" - from ..main import analyze - - return analyze( - analysis_type=analysis_type, - output_format=output_format, - project_root=project_root, - ) - - -@app.command(name="clean") -def project_clean( - target: str = typer.Option("all", help="清理目标: all, cache, build, logs"), - project_root: str = typer.Option(".", help="项目根目录"), - dry_run: bool = typer.Option(False, help="预览模式，不实际删除"), -): - """🧹 清理项目 - 清理缓存和临时文件""" - from ..main import clean - - return clean(target=target, project_root=project_root, dry_run=dry_run) - - -@app.command(name="test") -def project_test( - test_type: str = typer.Option( - "all", "--test-type", help="测试类型: all, unit, integration, quick" - ), - project_root: str = typer.Option(".", "--project-root", help="项目根目录"), - verbose: bool = typer.Option(False, "--verbose", help="详细输出"), - packages: str = typer.Option("", "--packages", help="指定测试的包，逗号分隔"), - jobs: int = typer.Option(4, "--jobs", "-j", help="并行任务数量"), - timeout: int = typer.Option(300, "--timeout", "-t", help="每个包的超时时间(秒)"), - failed_only: bool = typer.Option(False, "--failed", help="只重新运行失败的测试"), - continue_on_error: bool = typer.Option(True, "--continue-on-error", help="遇到错误继续执行"), - summary_only: bool = typer.Option(False, "--summary", help="只显示摘要结果"), - quiet: bool = typer.Option(False, "--quiet", "-q", help="静默模式"), - report_file: str = typer.Option("", "--report", help="测试报告输出文件路径"), - diagnose: bool = typer.Option(False, "--diagnose", help="运行诊断模式"), - coverage: bool = typer.Option(False, "--coverage", help="启用测试覆盖率分析"), - coverage_report: str = typer.Option( - "term,html,xml", "--coverage-report", help="覆盖率报告格式 (逗号分隔)" - ), - skip_quality_check: bool = typer.Option( - True, "--quality-check/--skip-quality-check", help="运行测试前进行代码质量检查（默认跳过）" - ), - debug: bool = typer.Option(False, "--debug", help="启用调试模式，输出详细执行信息"), -): - """ - 🧪 运行项目测试 - - 运行单元测试、集成测试等。 - - 示例： - sage-dev project test # 运行所有测试 - sage-dev project test --test-type unit # 只运行单元测试 - sage-dev project test --packages sage-libs,sage-kernel # 测试特定包 - sage-dev project test --failed # 只运行失败的测试 - sage-dev project test --coverage # 运行测试并生成覆盖率报告 - """ - from sage.tools.cli.commands.dev.main import test - - test( - test_type=test_type, - project_root=project_root, - verbose=verbose, - packages=packages, - jobs=jobs, - timeout=timeout, - failed_only=failed_only, - continue_on_error=continue_on_error, - summary_only=summary_only, - quiet=quiet, - report_file=report_file, - diagnose=diagnose, - coverage=coverage, - coverage_report=coverage_report, - skip_quality_check=skip_quality_check, - debug=debug, - ) - - -@app.command(name="architecture") -def show_architecture( - show_dependencies: bool = typer.Option( - True, "--dependencies/--no-dependencies", help="显示依赖关系" - ), - show_layers: bool = typer.Option(True, "--layers/--no-layers", help="显示层级定义"), - package: str = typer.Option(None, "--package", help="显示特定包的信息"), - output_format: str = typer.Option("text", "--format", help="输出格式: text, json, markdown"), -): - """ - 🏗️ 显示架构信息 - - 显示 SAGE 的分层架构定义和包依赖关系。 - - 示例: - sage-dev project architecture # 文本格式 - sage-dev project architecture -f json # JSON 格式 - sage-dev project architecture -f markdown # Markdown 格式 - """ - from sage.tools.cli.commands.dev.main import architecture - - architecture( - show_dependencies=show_dependencies, - show_layers=show_layers, - package=package, - output_format=output_format, - ) - - -@app.command(name="home") -def project_home( - action: str = typer.Argument("status", help="操作: init, clean, status"), - path: str = typer.Option("", help="SAGE目录路径"), -): - """🏠 SAGE目录管理 - 管理SAGE工作目录""" - from ..main import home - - return home(action=action, path=path) - - -__all__ = ["app"] diff --git a/packages/sage-tools/src/sage/tools/cli/commands/dev/quality/__init__.py b/packages/sage-tools/src/sage/tools/cli/commands/dev/quality/__init__.py deleted file mode 100644 index 2e9ef90d5d..0000000000 --- a/packages/sage-tools/src/sage/tools/cli/commands/dev/quality/__init__.py +++ /dev/null @@ -1,478 +0,0 @@ -""" -质量检查命令组 - -提供代码质量检查、架构检查、文档规范检查等功能。 -""" - -import typer -from rich.console import Console - -app = typer.Typer( - name="quality", - help="🔍 质量检查 - 代码质量、架构合规、文档规范检查 (check, fix, architecture, devnotes, readme)", - no_args_is_help=True, -) - -console = Console() - - -@app.command(name="check") -def check_all( - all_files: bool = typer.Option( - False, - "--all-files", - help="检查所有文件（默认只检查变更文件）", - ), - check_only: bool = typer.Option( - False, - "--check-only", - help="只检查不修复（默认会自动修复）", - ), - architecture: bool = typer.Option( - True, - "--architecture/--no-architecture", - help="运行架构检查", - ), - devnotes: bool = typer.Option( - True, - "--devnotes/--no-devnotes", - help="运行 dev-notes 检查", - ), - examples: bool = typer.Option( - True, - "--examples/--no-examples", - help="运行 examples 目录结构检查", - ), - readme: bool = typer.Option( - False, - "--readme", - help="运行 README 检查", - ), - warn_only: bool = typer.Option( - False, - "--warn-only", - help="只给警告，不中断运行", - ), -): - """ - 🔍 运行所有质量检查 - - 包括：代码格式化、导入排序、Ruff 检查、类型检查、架构合规、文档规范等。 - - 默认行为： - - 只检查变更的文件（使用 --all-files 检查所有文件） - - 自动修复可修复的问题（使用 --check-only 只检查不修复） - - 运行架构、dev-notes 和 examples 检查（使用 --no-* 跳过） - - 示例： - sage-dev quality check # 检查变更文件，自动修复 - sage-dev quality check --all-files # 检查所有文件 - sage-dev quality check --check-only # 只检查不修复 - sage-dev quality check --readme # 包含 README 检查 - sage-dev quality check --no-architecture # 跳过架构检查 - sage-dev quality check --no-examples # 跳过 examples 检查 - """ - from sage.tools.cli.commands.dev.main import quality - - # 调用主 quality 函数 - quality( - fix=not check_only, - check_only=check_only, - all_files=all_files, - hook=None, # 运行所有 hooks - architecture=architecture, - devnotes=devnotes, - examples=examples, - readme=readme, - include_submodules=False, - submodules_only=False, - warn_only=warn_only, - project_root=".", - ) - - -@app.command(name="fix") -def fix_quality( - all_files: bool = typer.Option( - False, - "--all-files", - help="修复所有文件（默认只处理变更文件）", - ), - include_submodules: bool = typer.Option( - False, - "--include-submodules", - help="包含 submodules 进行修复", - ), - submodules_only: bool = typer.Option( - False, - "--submodules-only", - help="仅修复 submodules（默认只处理主仓库）", - ), - project_root: str = typer.Option(".", help="项目根目录"), - format_code: bool = typer.Option(True, "--format/--no-format", help="运行代码格式化"), - sort_imports: bool = typer.Option( - True, - "--sort-imports/--no-sort-imports", - help="运行导入排序", - ), - lint_ruff: bool = typer.Option( - True, - "--ruff/--no-ruff", - help="运行 Ruff 修复", - ), - type_check: bool = typer.Option( - False, - "--type-check/--no-type-check", - help="修复后运行类型检查", - ), -): - """ - 🔧 自动修复代码质量问题 - - 这是 `tools/fix-code-quality.sh` 的 Python 版本，内部调用 `pre-commit` - 来运行 black、isort、ruff 等可自动修复的 hooks。 - - 示例： - sage-dev quality fix # 修复变更的文件 - sage-dev quality fix --all-files # 修复所有文件 - sage-dev quality fix --include-submodules # 包含 submodules - """ - - from sage.tools.cli.commands.dev.main import quality - - quality( - fix=True, - check_only=False, - all_files=all_files, - hook=None, - architecture=False, - devnotes=False, - examples=False, - readme=False, - include_submodules=include_submodules, - submodules_only=submodules_only, - warn_only=False, - project_root=project_root, - format_code=format_code, - sort_imports=sort_imports, - lint_ruff=lint_ruff, - type_check=type_check, - ) - - -@app.command(name="architecture") -def check_architecture( - changed_only: bool = typer.Option( - False, - "--changed-only", - help="只检查变更的文件", - ), - warn_only: bool = typer.Option( - False, - "--warn-only", - help="只给警告，不中断运行", - ), -): - """ - 🏗️ 架构合规性检查 - - 检查包之间的依赖关系是否符合分层架构定义。 - - 示例： - sage-dev quality architecture # 检查所有文件 - sage-dev quality architecture --changed-only # 只检查变更文件 - """ - if not _run_architecture_check(changed_only=changed_only, warn_only=warn_only): - if not warn_only: - raise typer.Exit(1) - - -@app.command(name="devnotes") -def check_devnotes( - warn_only: bool = typer.Option( - False, - "--warn-only", - help="只给警告，不中断运行", - ), -): - """ - 📝 dev-notes 文档规范检查 - - 检查 dev-notes 文档是否符合规范（元数据、分类等）。 - - 示例： - sage-dev quality devnotes - """ - if not _run_devnotes_check(warn_only=warn_only): - if not warn_only: - raise typer.Exit(1) - - -@app.command(name="readme") -def check_readme( - warn_only: bool = typer.Option( - False, - "--warn-only", - help="只给警告，不中断运行", - ), -): - """ - 📋 包 README 质量检查 - - 检查包的 README 文档是否完整、格式正确。 - - 示例： - sage-dev quality readme - """ - if not _run_readme_check(warn_only=warn_only): - if not warn_only: - raise typer.Exit(1) - - -@app.command(name="examples") -def check_examples( - warn_only: bool = typer.Option( - False, - "--warn-only", - help="只给警告，不中断运行", - ), -): - """ - 📁 Examples 目录结构检查 - - 检查 examples/ 目录是否符合规范（只允许 apps/ 和 tutorials/ 两个顶层目录）。 - - 示例： - sage-dev quality examples - """ - if not _run_examples_check(warn_only=warn_only): - if not warn_only: - raise typer.Exit(1) - - -@app.command(name="dependencies") -def check_dependencies( - warn_only: bool = typer.Option( - False, - "--warn-only", - help="只给警告，不中断运行", - ), -): - """ - 📦 包依赖分离检查 - - 验证所有包的 pyproject.toml 依赖配置是否符合 SAGE 依赖分离规范： - - 非 meta-package 的 dependencies 不应包含 isage-* - - 包应使用 sage-deps 配置内部 SAGE 依赖 - - sage meta-package 的 extras 应使用 [sage-deps] - - 示例： - sage-dev quality dependencies - """ - if not _run_dependency_check(warn_only=warn_only): - if not warn_only: - raise typer.Exit(1) - - -# 为了支持在 main.py 中调用，导出辅助函数 -def _run_architecture_check(warn_only: bool = False, changed_only: bool = False) -> bool: - """运行架构检查，返回是否通过""" - try: - from pathlib import Path - - from sage.tools.dev.tools.architecture_checker import ArchitectureChecker - from sage.tools.dev.utils import find_project_root - - # 获取项目根目录 - root_dir = find_project_root() - if root_dir is None: - console.print("[red]错误: 无法找到项目根目录[/red]") - return False - - checker = ArchitectureChecker(root_dir) - result = checker.check_all() - - if changed_only: - # TODO: 过滤只显示变更文件的违规 - pass - - if not result.passed: - console.print(f"[red]发现 {len(result.violations)} 个架构违规[/red]") - for v in result.violations[:10]: # 只显示前10个 - console.print(f" [yellow]{v}[/yellow]") - return False - else: - console.print("[green]✓ 架构检查通过[/green]") - return True - except Exception as e: - console.print(f"[red]架构检查失败: {e}[/red]") - import traceback - - traceback.print_exc() - return False - - -def _run_devnotes_check(warn_only: bool = False) -> bool: - """运行 dev-notes 检查，返回是否通过""" - try: - from pathlib import Path - - from sage.tools.dev.tools.devnotes_checker import DevNotesChecker - from sage.tools.dev.utils import find_project_root - - # 获取项目根目录 - root_dir = find_project_root() - if root_dir is None: - console.print("[red]错误: 无法找到项目根目录[/red]") - return False - - # 检查 dev-notes 目录是否存在（SAGE-Pub 独立仓库） - devnotes_dir = Path(root_dir) / "docs-public" / "docs_src" / "dev-notes" - if not devnotes_dir.exists(): - console.print( - "[yellow]⚠️ dev-notes 目录不存在，跳过检查（需要 SAGE-Pub 仓库）[/yellow]" - ) - return True - - checker = DevNotesChecker(root_dir) - result = checker.check_all() - - if not result["passed"]: - console.print(f"[red]发现 {result['failed_count']} 个 dev-notes 问题[/red]") - for issue in result["issues"][:10]: - console.print(f" [yellow]{issue['file']}: {issue['message']}[/yellow]") - return False if not warn_only else True - else: - console.print("[green]✓ dev-notes 检查通过[/green]") - return True - except Exception as e: - console.print(f"[red]dev-notes 检查失败: {e}[/red]") - import traceback - - traceback.print_exc() - return False - - -def _run_readme_check(warn_only: bool = False) -> bool: - """运行 README 检查，返回是否通过""" - try: - from pathlib import Path - - from sage.tools.dev.tools.package_readme_checker import PackageREADMEChecker - from sage.tools.dev.utils import find_project_root - - # 获取项目根目录 - root_dir = find_project_root() - if root_dir is None: - console.print("[red]错误: 无法找到项目根目录[/red]") - return False - - checker = PackageREADMEChecker(root_dir) - results = checker.check_all() - - # 检查是否有失败的包（分数低于阈值） - failed_packages = [r for r in results if r.score < 80] - - if failed_packages: - console.print(f"[red]发现 {len(failed_packages)} 个包的 README 需要改进[/red]") - for pkg in failed_packages[:10]: - console.print(f" [yellow]{pkg.package_name}: score={pkg.score:.0f}%[/yellow]") - return False if not warn_only else True - else: - console.print("[green]✓ README 检查通过[/green]") - return True - except Exception as e: - console.print(f"[red]README 检查失败: {e}[/red]") - import traceback - - traceback.print_exc() - return False - - -def _run_examples_check(warn_only: bool = False) -> bool: - """运行 examples 目录结构检查""" - try: - from pathlib import Path - - from sage.tools.dev.tools.examples_structure_checker import ( - ExamplesStructureChecker, - ) - from sage.tools.dev.utils import find_project_root - - # 获取项目根目录 - root_dir = find_project_root() - if root_dir is None: - console.print("[red]错误: 无法找到项目根目录[/red]") - return False - - examples_dir = Path(root_dir) / "examples" - if not examples_dir.exists(): - console.print(f"[yellow]警告: examples 目录不存在: {examples_dir}[/yellow]") - return True # 如果目录不存在，不算失败 - - checker = ExamplesStructureChecker(examples_dir) - result = checker.check_structure() - - if result.passed: - console.print("[green]✓ examples 目录结构检查通过[/green]") - return True - - # 显示错误 - console.print(f"[red]发现 {len(result.violations)} 个结构问题[/red]") - for violation in result.violations: - console.print(f" [yellow]{violation}[/yellow]") - - if result.unexpected_dirs: - console.print("\n[yellow]不符合规范的目录:[/yellow]") - for dir_name in result.unexpected_dirs: - console.print(f" • {dir_name}/") - - # 显示规范指南 - console.print(f"\n{checker.get_structure_guide()}") - - return False if not warn_only else True - except Exception as e: - console.print(f"[red]examples 检查失败: {e}[/red]") - import traceback - - traceback.print_exc() - return False - - -def _run_dependency_check(warn_only: bool = False) -> bool: - """运行包依赖分离检查，返回是否通过""" - try: - from pathlib import Path - - from sage.tools.dev.tools.package_dependency_validator import ( - PackageDependencyValidator, - ) - from sage.tools.dev.utils import find_project_root - - # 获取项目根目录 - root_dir = find_project_root() - if root_dir is None: - console.print("[red]错误: 无法找到项目根目录[/red]") - return False - - validator = PackageDependencyValidator(root_dir) - issues, passed = validator.validate_all_packages() - - # 打印结果 - validator.print_results(issues, passed) - - # 如果只是警告模式，总是返回通过 - if warn_only: - return True - - return passed - - except Exception as e: - console.print(f"[red]依赖检查失败: {e}[/red]") - import traceback - - traceback.print_exc() - return False - - -__all__ = ["app"] diff --git a/packages/sage-tools/src/sage/tools/cli/commands/dev/resource/__init__.py b/packages/sage-tools/src/sage/tools/cli/commands/dev/resource/__init__.py deleted file mode 100644 index 5772262199..0000000000 --- a/packages/sage-tools/src/sage/tools/cli/commands/dev/resource/__init__.py +++ /dev/null @@ -1,27 +0,0 @@ -""" -资源管理命令组 - -提供模型缓存、数据管理等功能。 -""" - -import typer -from rich.console import Console - -app = typer.Typer( - name="resource", - help="💾 资源管理 - 模型缓存、数据管理", - no_args_is_help=True, -) - -console = Console() - -# 导入现有的 models 命令组 -try: - from sage.tools.cli.commands.dev.models import app as models_app - - app.add_typer(models_app, name="models") -except ImportError: - console.print("[yellow]警告: 无法导入 models 命令[/yellow]") - - -__all__ = ["app"] diff --git a/packages/sage-tools/src/sage/tools/cli/utils/dev_check.py b/packages/sage-tools/src/sage/tools/cli/utils/dev_check.py deleted file mode 100644 index c8123fdca6..0000000000 --- a/packages/sage-tools/src/sage/tools/cli/utils/dev_check.py +++ /dev/null @@ -1,129 +0,0 @@ -#!/usr/bin/env python3 -""" -开发模式检查工具 - -提供装饰器和函数来检查命令是否在开发环境（源码安装）中运行 -""" - -from collections.abc import Callable -from functools import wraps -from pathlib import Path - -import typer -from rich.console import Console - -console = Console() - - -def is_source_installation() -> bool: - """ - 检查是否在源码安装模式下运行 - - 通过查找 packages 目录来判断是否在开发环境中 - - Returns: - bool: True 如果在源码目录中，False 否则 - """ - # 从当前工作目录开始向上查找 - current_dir = Path.cwd() - - # 最多向上查找 5 层 - for _ in range(5): - packages_dir = current_dir / "packages" - if packages_dir.exists() and packages_dir.is_dir(): - # 额外检查是否包含 SAGE 的子包 - sage_packages = [ - "sage", - "sage-common", - "sage-kernel", - "sage-tools", - "sage-middleware", - "sage-libs", - ] - # 至少找到 3 个包才认为是有效的源码目录 - found_count = sum(1 for pkg in sage_packages if (packages_dir / pkg).exists()) - if found_count >= 3: - return True - - # 到达根目录 - if current_dir.parent == current_dir: - break - current_dir = current_dir.parent - - return False - - -def get_project_root() -> Path: - """ - 获取项目根目录（包含 packages 目录的目录） - - Returns: - Path: 项目根目录路径 - - Raises: - FileNotFoundError: 如果未找到项目根目录 - """ - current_dir = Path.cwd() - - for _ in range(5): - packages_dir = current_dir / "packages" - if packages_dir.exists() and packages_dir.is_dir(): - return current_dir - - if current_dir.parent == current_dir: - break - current_dir = current_dir.parent - - raise FileNotFoundError("未找到 SAGE 项目根目录") - - -def require_source_code(func: Callable) -> Callable: - """ - 装饰器：要求命令在源码模式下运行 - - 如果不在源码模式下，显示友好的错误提示并退出 - - Usage: - @app.command() - @require_source_code - def my_dev_command(): - ... - """ - - @wraps(func) - def wrapper(*args, **kwargs): - if not is_source_installation(): - console.print("\n[red]❌ 此命令仅在开发模式（源码安装）下可用[/red]\n") - - console.print("[yellow]💡 从源码安装 SAGE：[/yellow]") - console.print(" [cyan]# 1. 克隆仓库[/cyan]") - console.print(" git clone https://github.com/intellistream/SAGE.git") - console.print(" cd SAGE") - console.print() - console.print(" [cyan]# 2. 安装为可编辑模式（开发模式）[/cyan]") - console.print(" pip install -e .") - console.print() - console.print(" [cyan]# 或使用快速启动脚本[/cyan]") - console.print(" ./quickstart.sh") - console.print() - console.print("[dim]更多信息请访问: https://github.com/intellistream/SAGE[/dim]") - - raise typer.Exit(1) - - return func(*args, **kwargs) - - return wrapper - - -def show_dev_mode_info(): - """显示开发模式的信息提示""" - if is_source_installation(): - console.print("[green]✓[/green] 开发模式已启用") - try: - project_root = get_project_root() - console.print(f"[dim]项目路径: {project_root}[/dim]") - except FileNotFoundError: - pass - else: - console.print("[yellow]ℹ[/yellow] 当前为标准安装模式") - console.print("[dim]部分开发命令不可用，从源码安装以启用开发模式[/dim]") diff --git a/packages/sage-tools/src/sage/tools/cli/utils/llm_detection.py b/packages/sage-tools/src/sage/tools/cli/utils/llm_detection.py deleted file mode 100644 index f75d61cfcd..0000000000 --- a/packages/sage-tools/src/sage/tools/cli/utils/llm_detection.py +++ /dev/null @@ -1,190 +0,0 @@ -"""Helpers for detecting locally running LLM services. - -This module provides lightweight HTTP probes that discover OpenAI-compatible -endpoints exposed by popular local deployments such as Ollama and vLLM. The -resulting metadata can be used to auto-populate generator configuration blocks. -""" - -from __future__ import annotations - -import json -import ssl -from collections.abc import Iterable -from dataclasses import dataclass -from urllib import error, request - - -@dataclass -class LLMServiceInfo: - """Metadata describing a detected LLM service.""" - - name: str - base_url: str - models: list[str] - default_model: str - generator_section: str - description: str - - -DEFAULT_TIMEOUT = 2 # seconds - - -def _safe_http_get( - url: str, timeout: int = DEFAULT_TIMEOUT, auth_token: str | None = None -) -> str | None: - """Best-effort HTTP GET that returns response text or ``None`` on failure.""" - - req = request.Request(url) - if auth_token: - req.add_header("Authorization", f"Bearer {auth_token}") - - try: - with request.urlopen(req, timeout=timeout, context=_ssl_context()) as resp: - charset = resp.headers.get_content_charset() or "utf-8" - return resp.read().decode(charset) - except (TimeoutError, error.URLError, ssl.SSLError): - return None - - -def _ssl_context() -> ssl.SSLContext | None: - """Create a default SSL context while remaining compatible with older Python.""" - - try: - return ssl.create_default_context() - except AttributeError: # pragma: no cover - extremely old Python - return None - - -def detect_ollama( - base_urls: Iterable[str] | None = None, -) -> LLMServiceInfo | None: - """Detect a running Ollama service by probing the tags endpoint.""" - - if base_urls is None: - base_urls = ( - "http://127.0.0.1:11434", - "http://localhost:11434", - "http://0.0.0.0:11434", - ) - - for host in base_urls: - payload = _safe_http_get(f"{host}/api/tags") - if not payload: - continue - - try: - data = json.loads(payload) - except json.JSONDecodeError: - continue - - models = [model["name"] for model in data.get("models", []) if "name" in model] - if not models: - continue - - default_model = models[0] - return LLMServiceInfo( - name="ollama", - base_url=f"{host}/v1", - models=models, - default_model=default_model, - generator_section="remote", - description=f"Ollama at {host}", - ) - - return None - - -def detect_vllm( - base_urls: Iterable[str] | None = None, auth_token: str | None = None -) -> LLMServiceInfo | None: - """Detect a running vLLM service by probing the OpenAI-compatible models API.""" - - if base_urls is None: - base_urls = ( - "http://127.0.0.1:8000", - "http://localhost:8000", - "http://0.0.0.0:8000", - ) - - # If user provides auth_token, use it; otherwise try common defaults - if auth_token is not None: - auth_tokens = [auth_token] - else: - auth_tokens = [None, "token-abc123", "test-token", "vllm-token"] - - for host in base_urls: - for token in auth_tokens: - payload = _safe_http_get(f"{host}/v1/models", auth_token=token) - if payload: - break - - if not payload: - continue - - try: - data = json.loads(payload) - except json.JSONDecodeError: - continue - - models = [item.get("id") for item in data.get("data", []) if item.get("id")] - if not models: - continue - - default_model = models[0] - return LLMServiceInfo( - name="vllm", - base_url=f"{host}/v1", - models=models, - default_model=default_model, - generator_section="vllm", - description=f"vLLM at {host}", - ) - - return None - - -def detect_sagellm() -> LLMServiceInfo | None: - """检测本地 sageLLM 引擎是否可用""" - try: - from sagellm_backend.engine.factory import EngineFactory - - backends = EngineFactory.available_backends() - if not backends: - return None - return LLMServiceInfo( - name="sagellm", - base_url="local://sagellm", - models=backends, # 可用的后端列表 - default_model=backends[0], - generator_section="sagellm", - description=f"sageLLM ({', '.join(backends)})", - ) - except ImportError: - return None - - -def detect_all_services( - prefer: str | None = None, auth_token: str | None = None -) -> list[LLMServiceInfo]: - """Detect all supported services, optionally restricting by name.""" - - prefer_normalized = prefer.lower() if prefer else None - detections: list[LLMServiceInfo] = [] - - # sagellm has highest priority (local native engine) - if prefer_normalized in (None, "sagellm"): - service = detect_sagellm() - if service: - detections.insert(0, service) # 优先 - - if prefer_normalized in (None, "ollama"): - service = detect_ollama() - if service: - detections.append(service) - - if prefer_normalized in (None, "vllm"): - service = detect_vllm(auth_token=auth_token) - if service: - detections.append(service) - - return detections diff --git a/packages/sage-tools/src/sage/tools/dev/__init__.py b/packages/sage-tools/src/sage/tools/dev/__init__.py deleted file mode 100644 index 38e6674922..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -""" -SAGE - Streaming-Augmented Generative Execution -""" - -# 直接从本包的_version模块加载版本信息 -try: - from sage.tools._version import __author__, __email__, __version__ -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" diff --git a/packages/sage-tools/src/sage/tools/dev/cli.py b/packages/sage-tools/src/sage/tools/dev/cli.py deleted file mode 100644 index 7212ecf6c7..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/cli.py +++ /dev/null @@ -1,38 +0,0 @@ -""" -SAGE 开发工具 CLI - 重定向到统一CLI结构 - -这个文件现在重定向到新的统一CLI结构。 -请使用: sage-dev 而不是直接调用这个模块。 -""" - -import warnings - - -def _warn_deprecated(): - """显示弃用警告""" - warnings.warn( - "sage.tools.dev.cli 已迁移到 sage.tools.cli.commands.dev。" - "请使用 'sage-dev ' 命令。", - DeprecationWarning, - stacklevel=3, - ) - - -try: - from sage.tools.cli.commands.dev import app as dev_app - - _warn_deprecated() - # 为了向后兼容，导出dev app - cli = dev_app -except ImportError: - # 如果统一CLI还没有安装，提供基本的错误信息 - import sys - - def cli(): - print("错误: 统一CLI结构未找到。请确保SAGE已正确安装。") - print("使用命令: sage-dev ") - sys.exit(1) - - -if __name__ == "__main__": - cli() diff --git a/packages/sage-tools/src/sage/tools/dev/core/__init__.py b/packages/sage-tools/src/sage/tools/dev/core/__init__.py deleted file mode 100644 index 38e6674922..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/core/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -""" -SAGE - Streaming-Augmented Generative Execution -""" - -# 直接从本包的_version模块加载版本信息 -try: - from sage.tools._version import __author__, __email__, __version__ -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" diff --git a/packages/sage-tools/src/sage/tools/dev/core/bytecode_compiler.py b/packages/sage-tools/src/sage/tools/dev/core/bytecode_compiler.py deleted file mode 100644 index 165a4fff1d..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/core/bytecode_compiler.py +++ /dev/null @@ -1,715 +0,0 @@ -""" -SAGE Bytecode Compiler -编译Python源码为.pyc文件，隐藏企业版源代码 -""" - -import os -import py_compile -import re -import shutil -import subprocess -import sys -import tempfile -from pathlib import Path - -from rich.console import Console -from rich.progress import Progress - -from .exceptions import SAGEDevToolkitError - -console = Console() - - -class BytecodeCompiler: - """字节码编译器 - 集成到SAGE开发工具包""" - - def __init__(self, package_path: Path, temp_dir: Path | None = None): - """ - 初始化字节码编译器 - - Args: - package_path: 要编译的包路径 - temp_dir: 临时目录，如果为None则自动创建 - """ - self.package_path = Path(package_path) - self.temp_dir = temp_dir - self.compiled_path = None - self._binary_extensions = [] - - if not self.package_path.exists(): - raise SAGEDevToolkitError(f"Package path does not exist: {package_path}") - - if not self.package_path.is_dir(): - raise SAGEDevToolkitError(f"Package path is not a directory: {package_path}") - - def compile_package(self, output_dir: Path | None = None, use_sage_home: bool = True) -> Path: - """ - 编译包为字节码 - - Args: - output_dir: 输出目录，如果为None则使用SAGE home目录或临时目录 - use_sage_home: 是否使用SAGE home目录作为默认输出 - - Returns: - 编译后的包路径 - """ - console.print(f"🔧 编译包: {self.package_path.name}", style="cyan") - - # 确定输出目录 - if output_dir: - self.temp_dir = Path(output_dir) - self.temp_dir.mkdir(parents=True, exist_ok=True) - elif use_sage_home: - # 使用SAGE home目录 - sage_home = Path.home() / ".sage" - self.temp_dir = sage_home / "dist" - self.temp_dir.mkdir(parents=True, exist_ok=True) - console.print(f"📁 使用SAGE home目录: {self.temp_dir}", style="blue") - else: - self.temp_dir = Path( - tempfile.mkdtemp(prefix=f"sage_bytecode_{self.package_path.name}_") - ) - - # 复制项目结构 - self.compiled_path = self.temp_dir / self.package_path.name - console.print(f"📁 复制项目结构到: {self.compiled_path}") - try: - # symlinks=True: 复制符号链接本身，而不是跟随链接复制文件 - # 这样可以避免符号链接指向外部路径时的问题 - shutil.copytree(self.package_path, self.compiled_path, symlinks=True) - except Exception as e: - console.print(f"❌ 复制项目结构失败: {e}", style="red") - import traceback - - traceback.print_exc() - raise - - # 编译Python文件 - self._compile_python_files() - - # 删除.py源文件 - self._remove_source_files() - - # 更新pyproject.toml排除源文件 - self._update_pyproject() - - console.print(f"✅ 包编译完成: {self.package_path.name}", style="green") - return self.compiled_path - - def _compile_python_files(self): - """编译所有Python文件""" - python_files = list(self.compiled_path.rglob("*.py")) - - # 过滤要跳过的文件 - files_to_compile = [] - skipped_count = 0 - for py_file in python_files: - if self._should_skip_file(py_file): - skipped_count += 1 - continue - files_to_compile.append(py_file) - - if not files_to_compile: - console.print(" ⚠️ 没有找到需要编译的Python文件", style="yellow") - return - - console.print( - f" 📝 找到 {len(files_to_compile)} 个Python文件需要编译 (跳过 {skipped_count} 个)" - ) - - # 检查和保留二进制扩展文件 - self._preserve_binary_extensions() - - # 使用进度条显示编译进度 - with Progress() as progress: - task = progress.add_task("编译Python文件", total=len(files_to_compile)) - - compiled_count = 0 - failed_count = 0 - failed_files = [] - - for py_file in files_to_compile: - try: - # 编译为.pyc - pyc_file = py_file.with_suffix(".pyc") - py_compile.compile(py_file, pyc_file, doraise=True) - compiled_count += 1 - - except py_compile.PyCompileError as e: - failed_count += 1 - failed_files.append((py_file.relative_to(self.compiled_path), str(e))) - except Exception as e: - failed_count += 1 - failed_files.append((py_file.relative_to(self.compiled_path), str(e))) - - progress.update(task, advance=1) - - console.print(f" 📊 编译统计: 成功 {compiled_count}, 失败 {failed_count}") - - # Only show failed files if there are any - if failed_files: - console.print(" ❌ 编译失败的文件:", style="red") - for file_path, error in failed_files[:5]: # Show max 5 failed files - console.print(f" - {file_path}: {error[:80]}", style="red") - if len(failed_files) > 5: - console.print(f" ... 和其他 {len(failed_files) - 5} 个文件", style="red") - - def _preserve_binary_extensions(self): - """检查和保留二进制扩展文件""" - # 查找所有二进制扩展文件 - extensions = [] - for ext in ["*.so", "*.pyd", "*.dylib"]: - extensions.extend(self.compiled_path.rglob(ext)) - - if not extensions: - console.print(" ℹ️ 未找到二进制扩展文件", style="dim") - return - - console.print(f" 🔧 找到 {len(extensions)} 个二进制扩展文件") - - # 记录所有扩展文件 (only show details in verbose mode) - self._binary_extensions = extensions - - def _should_skip_file(self, py_file: Path) -> bool: - """判断是否应该跳过文件""" - # 跳过setup.py等特殊文件 - skip_files = ["setup.py", "conftest.py"] - - if py_file.name in skip_files: - return True - - # 跳过测试文件 - 更精确的模式匹配 - file_str = str(py_file) - - # 检查是否在tests目录中 - if "/tests/" in file_str or file_str.endswith("/tests"): - return True - - # 检查文件名是否以test_开头或以_test.py结尾 - if py_file.name.startswith("test_") or py_file.name.endswith("_test.py"): - return True - - return False - - def _remove_source_files(self): - """删除源文件,只保留字节码""" - python_files = list(self.compiled_path.rglob("*.py")) - - removed_count = 0 - kept_count = 0 - - console.print(" 🗑️ 清理源文件...") - - for py_file in python_files: - # 保留必要的文件 - if self._should_keep_source(py_file): - kept_count += 1 - continue - - # 对于__init__.py和其他.py文件，如果有对应的.pyc，则删除.py - pyc_file = py_file.with_suffix(".pyc") - if pyc_file.exists(): - py_file.unlink() - removed_count += 1 - else: - # 如果没有编译成功，保留源文件避免包损坏 - kept_count += 1 - - console.print(f" 📊 清理统计: 删除 {removed_count}, 保留 {kept_count}") - - def _should_keep_source(self, py_file: Path) -> bool: - """判断是否应该保留源文件""" - # 必须保留的文件 - keep_files = ["setup.py", "_version.py"] - - if py_file.name in keep_files: - return True - - return False - - def _update_pyproject(self): - """更新pyproject.toml包含.pyc文件""" - pyproject_file = self.compiled_path / "pyproject.toml" - - if not pyproject_file.exists(): - console.print(" ⚠️ 未找到pyproject.toml文件", style="yellow") - return - - try: - content = pyproject_file.read_text(encoding="utf-8") - - # 检查是否使用了 scikit-build-core - uses_scikit_build = "scikit_build_core" in content - - if uses_scikit_build: - console.print(" 🔧 检测到 scikit-build-core，切换到 setuptools", style="yellow") - - # 替换 build-backend 为 setuptools - content = re.sub( - r'build-backend\s*=\s*["\']scikit_build_core\.build["\']', - 'build-backend = "setuptools.build_meta"', - content, - ) - - # 简化 build-system requires - content = re.sub( - r"\[build-system\][\s\S]*?(?=\n\[)", - '[build-system]\nrequires = ["setuptools>=64", "wheel"]\nbuild-backend = "setuptools.build_meta"\n\n', - content, - ) - - # 移除 scikit-build 相关配置 - content = re.sub(r"\[tool\.scikit-build\][\s\S]*?(?=\n\[|\Z)", "", content) - content = re.sub(r"\[tool\.scikit-build\..*?\][\s\S]*?(?=\n\[|\Z)", "", content) - - # 检查现有的包配置 - has_packages_list = "packages = [" in content # 静态包列表 - has_packages_find = "[tool.setuptools.packages.find]" in content # 动态查找 - has_pyc_package_data = ( - '"*.pyc"' in content and "[tool.setuptools.package-data]" in content - ) - has_include_package_data = "include-package-data = true" in content.lower() - - modified = False - - # 需要添加配置 - if not has_packages_list and not has_packages_find: - content += """ -[tool.setuptools.packages.find] -where = ["src"] -""" - modified = True - - # 确保include-package-data设置为true - if not has_include_package_data: - # 检查是否有[tool.setuptools]部分 - if "[tool.setuptools]" in content: - # 在现有部分添加 - pattern = r"(\[tool\.setuptools\][\s\S]*?)(?=\n\[|\n$|$)" - match = re.search(pattern, content) - if match: - existing_section = match.group(1) - if "include-package-data" not in existing_section: - updated_section = ( - existing_section.rstrip() + "\ninclude-package-data = true\n" - ) - content = content.replace(existing_section, updated_section) - modified = True - else: - # 添加新部分 - content += """ -[tool.setuptools] -include-package-data = true -""" - modified = True - - # 添加package-data配置 - if not has_pyc_package_data: - # 检查是否已有package-data部分 - if "[tool.setuptools.package-data]" in content: - # 需要更新现有的package-data配置 - pattern = r"(\[tool\.setuptools\.package-data\][\s\S]*?)(?=\n\[|\n$|$)" - match = re.search(pattern, content) - if match: - existing_data = match.group(1) - if '"*.pyc"' not in existing_data: - # 查找现有的 "*" 键并合并（支持多行数组） - star_pattern = r'"(\*)" = \[([^\]]*)\]' - star_matches = list( - re.finditer(star_pattern, existing_data, re.MULTILINE) - ) - - if star_matches: - # 找到第一个 "*" 键，合并所有内容到它 - first_match = star_matches[0] - - # 收集所有现有的项 - all_items = [] - for m in star_matches: - items = m.group(2).strip() - if items: - # 分割并清理每个项 - for item in items.split(","): - item = item.strip().strip('"').strip("'") - if item and item not in all_items: - all_items.append(item) - - # 添加新的二进制文件模式 - binary_patterns = [ - "*.pyc", - "*.pyo", - "__pycache__/*", - "*.so", - "*.pyd", - "*.dylib", - ] - for pattern in binary_patterns: - if pattern not in all_items: - all_items.append(pattern) - - # 构建合并后的数组 - formatted_items = ",\n ".join(f'"{item}"' for item in all_items) - updated_line = f'"*" = [\n {formatted_items},\n]' - - # 替换第一个 "*" 键 - updated_data = existing_data.replace( - first_match.group(0), updated_line - ) - - # 删除其他重复的 "*" 键 - for m in star_matches[1:]: - updated_data = updated_data.replace(m.group(0), "") - - # 清理多余的空行 - updated_data = re.sub(r"\n\s*\n\s*\n", "\n\n", updated_data) - else: - # 在现有配置中添加新的通配符键 - updated_data = ( - existing_data.rstrip() - + '\n"*" = ["*.pyc", "*.pyo", "__pycache__/*", "*.so", "*.pyd", "*.dylib"]\n' - ) - - content = content.replace(existing_data, updated_data) - modified = True - else: - # 添加新的package-data配置 - content += """ -[tool.setuptools.package-data] -"*" = ["*.pyc", "*.pyo", "__pycache__/*", "*.so", "*.pyd", "*.dylib"] -""" - modified = True - - # 清理多余的空行 - content = re.sub(r"\n\n\n+", "\n\n", content) - - # 添加MANIFEST.in文件以确保包含所有二进制文件 - manifest_file = self.compiled_path / "MANIFEST.in" - manifest_content = """ -# 包含所有编译文件和二进制扩展 -recursive-include src *.pyc -recursive-include src *.pyo -recursive-include src __pycache__/* -recursive-include src *.so -recursive-include src *.pyd -recursive-include src *.dylib -""" - manifest_file.write_text(manifest_content, encoding="utf-8") - - # 添加setup.py文件确保包含所有文件 - setup_py_file = self.compiled_path / "setup.py" - setup_py_content = """ -from setuptools import setup - -setup( - include_package_data=True, - package_data={ - "": ["*.pyc", "*.pyo", "__pycache__/*", "*.so", "*.pyd", "*.dylib"], - }, -) -""" - setup_py_file.write_text(setup_py_content, encoding="utf-8") - - if modified or uses_scikit_build: - pyproject_file.write_text(content, encoding="utf-8") - console.print(" ✅ 更新pyproject.toml配置", style="green") - else: - console.print(" ✓ pyproject.toml配置已满足要求", style="dim") - - except Exception as e: - console.print(f" ❌ 更新pyproject.toml失败: {e}", style="red") - - def build_wheel( - self, - compiled_path: Path | None = None, - ) -> Path: - """ - 构建wheel包 - - Args: - compiled_path: 已编译的包路径，如果未提供则使用self.compiled_path - - Returns: - wheel文件路径 - """ - target_path = compiled_path or self.compiled_path - - if not target_path: - raise SAGEDevToolkitError("Package not compiled yet. Call compile_package() first.") - - console.print(f"📦 构建wheel包: {target_path.name}", style="cyan") - - # 保存当前目录 - original_dir = Path.cwd() - - try: - # 进入包目录 - os.chdir(target_path) - - # 清理旧构建 - for build_dir in ["dist", "build"]: - if Path(build_dir).exists(): - shutil.rmtree(build_dir) - console.print(f" 🧹 清理目录: {build_dir}") - - # 验证.pyc文件是否存在 - pyc_files = list(Path(".").rglob("*.pyc")) - console.print(f" 📊 找到 {len(pyc_files)} 个.pyc文件") - - # 构建wheel（使用 isolation 模式自动处理构建依赖） - console.print(" 🔨 构建wheel...") - result = subprocess.run( - [sys.executable, "-m", "build", "--wheel"], - capture_output=True, - text=True, - ) - - if result.returncode == 0: - console.print(" ✅ 构建成功", style="green") - - # 查找构建的wheel文件 - dist_files = list(Path("dist").glob("*.whl")) - if not dist_files: - raise SAGEDevToolkitError("构建完成但未找到wheel文件") - - wheel_file = dist_files[0] # 通常只有一个wheel文件 - file_size = wheel_file.stat().st_size / 1024 # KB - console.print(f" 📄 {wheel_file.name} ({file_size:.2f} KB)") - - # 验证wheel内容 - self._verify_wheel_contents(wheel_file) - - # 返回绝对路径 - return wheel_file.resolve() - - else: - # 构建失败，收集错误信息 - error_msg = "构建失败" - if result.stderr.strip(): - error_msg += f": {result.stderr.strip()}" - if result.stdout.strip(): - error_msg += f"\n详细信息: {result.stdout.strip()}" - raise SAGEDevToolkitError(error_msg) - - except Exception as e: - console.print(f" 💥 构建异常: {e}", style="red") - raise - - finally: - # 返回原目录 - os.chdir(original_dir) - - def _verify_wheel_contents(self, wheel_file: Path): - """验证wheel包内容是否包含.pyc文件""" - console.print(" 🔍 验证wheel包内容...", style="cyan") - - try: - # 创建临时目录解压wheel - with tempfile.TemporaryDirectory() as temp_dir: - temp_path = Path(temp_dir) - - # 解压wheel - import zipfile - - with zipfile.ZipFile(wheel_file, "r") as zip_ref: - zip_ref.extractall(temp_path) - - # 列出所有文件 - all_files = list(zip_ref.namelist()) - - # 计数 - pyc_count = sum(1 for f in all_files if f.endswith(".pyc")) - py_count = sum(1 for f in all_files if f.endswith(".py")) - binary_count = sum(1 for f in all_files if f.endswith((".so", ".pyd", ".dylib"))) - total_count = len(all_files) - - console.print( - f" 📊 文件总数: {total_count} (.pyc: {pyc_count}, .py: {py_count}, binary: {binary_count})" - ) - - # 检查包是否太小 - if total_count < 10: - console.print( - " ⚠️ 警告: wheel包文件数量过少，可能打包不完整", - style="yellow", - ) - - if pyc_count == 0 and binary_count == 0: - console.print(" ❌ 错误: wheel包中没有.pyc或二进制扩展文件！", style="red") - console.print(" 💡 尝试使用以下步骤修复:") - console.print(" 1. 确保pyproject.toml中设置了include-package-data = true") - console.print(" 2. 确保pyproject.toml中设置了package-data配置") - console.print(" 3. 检查MANIFEST.in文件是否包含了*.pyc和*.so等") - - # 尝试输出部分文件列表以帮助诊断 - console.print(" 📁 wheel包内容示例:") - for f in all_files[:10]: - console.print(f" - {f}") - if len(all_files) > 10: - console.print(f" ... 还有 {len(all_files) - 10} 个文件") - else: - console.print(" ✅ wheel包包含编译文件", style="green") - - except Exception as e: - console.print(f" ❌ 验证wheel内容失败: {e}", style="red") - - def cleanup_temp_dir(self): - """清理临时目录""" - if self.temp_dir and self.temp_dir.exists(): - try: - shutil.rmtree(self.temp_dir) - console.print(f"🧹 清理临时目录: {self.temp_dir}", style="dim") - except Exception as e: - console.print(f"⚠️ 清理临时目录失败: {e}", style="yellow") - - -def compile_multiple_packages( - package_paths: list[Path], - output_dir: Path | None = None, - build_wheels: bool = False, - use_sage_home: bool = True, - create_symlink: bool = True, -) -> dict[str, bool]: - """ - 编译多个包 - - Args: - package_paths: 包路径列表 - output_dir: 输出目录 - build_wheels: 是否构建wheel包 - use_sage_home: 是否使用SAGE home目录 - create_symlink: 是否创建软链接 - - Returns: - 编译结果字典 {package_name: success} - """ - results = {} - - console.print(f"🎯 批量编译 {len(package_paths)} 个包", style="bold cyan") - console.print("=" * 60) - - # 创建SAGE home目录软链接（如果需要） - sage_home_link = None - if use_sage_home and create_symlink: - sage_home_link = _create_sage_home_symlink() - - for i, package_path in enumerate(package_paths, 1): - console.print(f"\n[{i}/{len(package_paths)}] 处理包: {package_path.name}", style="bold") - - try: - # 编译包 - compiler = BytecodeCompiler(package_path) - compiler.compile_package(output_dir, use_sage_home) - - # 构建wheel（如果需要） - if build_wheels: - compiler.build_wheel() - results[package_path.name] = True - else: - results[package_path.name] = True - - # 不清理临时目录，让用户可以检查结果 - # compiler.cleanup_temp_dir() - - except Exception as e: - console.print("❌ 处理失败", style="bold red") - console.print(f"错误: {e}", style="red") - # 打印完整的异常堆栈 - import traceback - - traceback.print_exc() - results[package_path.name] = False - - # 显示汇总结果 - console.print("\n" + "=" * 60) - console.print("📊 编译结果汇总:", style="bold") - - success_count = sum(1 for success in results.values() if success) - total_count = len(results) - - for package_name, success in results.items(): - status = "✅" if success else "❌" - style = "green" if success else "red" - console.print(f" {status} {package_name}", style=style) - - console.print(f"\n🎉 成功: {success_count}/{total_count}", style="bold green") - - # 显示软链接信息 - if sage_home_link: - console.print(f"\n🔗 软链接已创建: {sage_home_link} -> ~/.sage", style="blue") - - return results - - -def _create_sage_home_symlink() -> Path | None: - """ - 在当前目录创建指向SAGE home的软链接 - - Returns: - 软链接路径，如果创建失败则返回None - """ - - current_dir = Path.cwd() - sage_home = Path.home() / ".sage" - symlink_path = current_dir / ".sage" - - try: - # 如果软链接已存在，先检查是否指向正确的目标 - if symlink_path.exists() or symlink_path.is_symlink(): - if symlink_path.is_symlink(): - existing_target = symlink_path.readlink() - if existing_target == sage_home: - console.print(f"✓ 软链接已存在: {symlink_path}", style="green") - return symlink_path - else: - console.print( - f"⚠️ 软链接指向错误目标，重新创建: {existing_target} -> {sage_home}", - style="yellow", - ) - symlink_path.unlink() - else: - console.print(f"⚠️ 路径已存在且不是软链接: {symlink_path}", style="yellow") - return None - - # 确保SAGE home目录存在 - sage_home.mkdir(parents=True, exist_ok=True) - - # 创建软链接 - symlink_path.symlink_to(sage_home) - console.print(f"🔗 创建软链接: {symlink_path} -> {sage_home}", style="green") - - return symlink_path - - except Exception as e: - console.print(f"❌ 创建软链接失败: {e}", style="red") - return None - - -def _get_sage_home_info(): - """显示SAGE home目录信息""" - sage_home = Path.home() / ".sage" - dist_dir = sage_home / "dist" - - console.print("📂 SAGE Home 目录信息:", style="bold blue") - console.print(f" 🏠 Home: {sage_home}") - console.print(f" 📦 Dist: {dist_dir}") - - if dist_dir.exists(): - compiled_packages = list(dist_dir.iterdir()) - console.print(f" 📊 已编译包: {len(compiled_packages)}") - - for pkg in compiled_packages[:5]: # 显示前5个 - if pkg.is_dir(): - console.print(f" 📁 {pkg.name}") - - if len(compiled_packages) > 5: - console.print(f" ... 和其他 {len(compiled_packages) - 5} 个包") - else: - console.print(" 📊 已编译包: 0 (目录不存在)") - - # 检查当前目录的软链接 - current_symlink = Path.cwd() / ".sage" - if current_symlink.exists() and current_symlink.is_symlink(): - target = current_symlink.readlink() - console.print(f" 🔗 当前软链接: {current_symlink} -> {target}") - else: - console.print(" 🔗 当前软链接: 不存在") diff --git a/packages/sage-tools/src/sage/tools/dev/core/compilation.py b/packages/sage-tools/src/sage/tools/dev/core/compilation.py deleted file mode 100644 index 7fcc68bcc9..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/core/compilation.py +++ /dev/null @@ -1,196 +0,0 @@ -""" -Enhanced bytecode compilation integration for SAGE packages. -""" - -from pathlib import Path -from typing import Any - -from .bytecode_compiler import BytecodeCompiler - - -class CompilationManager: - """编译管理器，集成编译、构建和发布功能""" - - def __init__(self, project_root: Path): - self.project_root = Path(project_root) - self.config = self._load_project_config() - - def _load_project_config(self) -> dict[str, Any]: - """加载项目配置""" - import tomli - - config_path = self.project_root / "project_config.toml" - - if not config_path.exists(): - raise FileNotFoundError(f"项目配置文件不存在: {config_path}") - - with open(config_path, "rb") as f: - return tomli.load(f) - - def get_package_info(self, package_name: str) -> dict[str, Any]: - """获取包信息""" - packages = self.config.get("packages", {}) - - if package_name not in packages: - raise ValueError(f"未知的包名: {package_name}") - - package_path = self.project_root / packages[package_name] - - return { - "name": package_name, - "path": package_path, - "description": self.config.get("package_descriptions", {}).get(package_name, ""), - "is_opensource": self._is_opensource_package(package_name), - } - - def _is_opensource_package(self, package_name: str) -> bool: - """判断是否为开源包""" - # 开源包列表（可以从配置文件读取） - opensource_packages = { - "intellistream-sage-kernel", - "intellistream-sage-middleware", - "intellistream-sage", - } - return package_name in opensource_packages - - def compile_for_distribution( - self, - package_name: str, - target_type: str = "opensource", # "opensource" or "proprietary" - output_dir: Path | None = None, - build_wheel: bool = True, - ) -> dict[str, Any]: - """ - 为发布编译包 - - Args: - package_name: 包名 - target_type: 目标类型 ("opensource" 或 "proprietary") - output_dir: 输出目录 - build_wheel: 是否构建 wheel - - Returns: - 编译结果信息 - """ - package_info = self.get_package_info(package_name) - - # 开源包直接构建，不需要字节码编译 - if target_type == "opensource": - return self._build_opensource_package(package_info, output_dir, build_wheel) - else: - return self._build_proprietary_package(package_info, output_dir, build_wheel) - - def _build_opensource_package( - self, - package_info: dict[str, Any], - output_dir: Path | None = None, - build_wheel: bool = True, - ) -> dict[str, Any]: - """构建开源包（保留源码）""" - from rich.console import Console - - console = Console() - - package_path = package_info["path"] - package_name = package_info["name"] - - console.print(f"📦 构建开源包: {package_name}", style="green") - - if build_wheel: - # 直接在原目录构建 wheel - import os - import subprocess - - original_cwd = os.getcwd() - try: - os.chdir(package_path) - result = subprocess.run(["python", "-m", "build"], capture_output=True, text=True) - - if result.returncode != 0: - raise RuntimeError(f"构建失败: {result.stderr}") - - console.print(f"✅ {package_name}: 开源包构建完成", style="green") - - return { - "type": "opensource", - "package_name": package_name, - "package_path": package_path, - "build_path": package_path / "dist", - "success": True, - } - - finally: - os.chdir(original_cwd) - - return { - "type": "opensource", - "package_name": package_name, - "package_path": package_path, - "success": True, - } - - def _build_proprietary_package( - self, - package_info: dict[str, Any], - output_dir: Path | None = None, - build_wheel: bool = True, - ) -> dict[str, Any]: - """构建闭源包（编译为字节码）""" - from rich.console import Console - - console = Console() - - package_path = package_info["path"] - package_name = package_info["name"] - - console.print(f"🔒 构建闭源包: {package_name}", style="yellow") - - # 使用字节码编译器 - compiler = BytecodeCompiler(package_path) - compiled_path = compiler.compile_package(output_dir, use_sage_home=True) - - if build_wheel: - # 在编译后的目录构建 wheel - wheel_path = compiler.build_wheel(compiled_path) - - return { - "type": "proprietary", - "package_name": package_name, - "package_path": package_path, - "compiled_path": compiled_path, - "wheel_path": wheel_path, - "success": True, - } - - return { - "type": "proprietary", - "package_name": package_name, - "package_path": package_path, - "compiled_path": compiled_path, - "success": True, - } - - def list_packages(self) -> list[dict[str, Any]]: - """列出所有包""" - packages = [] - for name in self.config.get("packages", {}): - try: - info = self.get_package_info(name) - packages.append(info) - except Exception: - continue - return packages - - def get_opensource_packages(self) -> list[str]: - """获取开源包列表""" - return [ - name for name in self.config.get("packages", {}) if self._is_opensource_package(name) - ] - - def get_proprietary_packages(self) -> list[str]: - """获取闭源包列表""" - return [ - name - for name in self.config.get("packages", {}) - if not self._is_opensource_package(name) - ] diff --git a/packages/sage-tools/src/sage/tools/dev/core/config.py b/packages/sage-tools/src/sage/tools/dev/core/config.py deleted file mode 100644 index 14de10a94d..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/core/config.py +++ /dev/null @@ -1,243 +0,0 @@ -""" -Configuration management for sage-development Toolkit. - -This module handles loading, validating, and managing configuration -for the development toolkit, supporting multiple environments and -configuration sources. -""" - -import os -from dataclasses import dataclass, field -from pathlib import Path -from typing import Any - -import yaml - -from .exceptions import ConfigError - - -@dataclass -class ToolkitConfig: - """Configuration container for sage-development Toolkit.""" - - # Project paths - project_root: Path - packages_dir: Path - scripts_dir: Path - output_dir: Path - logs_dir: Path - temp_dir: Path - - # Configuration data - config_data: dict[str, Any] = field(default_factory=dict) - - # Environment - environment: str = "development" - - @classmethod - def from_config_file( - cls, - config_path: Path | None = None, - project_root: Path | None = None, - environment: str | None = None, - ) -> "ToolkitConfig": - """ - Create configuration from file. - - Args: - config_path: Path to configuration file - project_root: Project root directory - environment: Environment name (development, production, ci) - - Returns: - ToolkitConfig instance - - Raises: - ConfigError: If configuration cannot be loaded or is invalid - """ - if project_root is None: - project_root = Path.cwd() - else: - project_root = Path(project_root) - - if config_path is None: - # Try multiple default locations - for candidate in [ - project_root / "sage_common.yaml", - project_root / "dev-toolkit" / "config" / "default.yaml", - project_root / ".sage-dev-config.yaml", - ]: - if candidate.exists(): - config_path = candidate - break - else: - # Use default configuration if no file found - config_path = None - - # Load configuration data - config_data = {} - if config_path and config_path.exists(): - try: - with open(config_path, encoding="utf-8") as f: - config_data = yaml.safe_load(f) or {} - except Exception as e: - raise ConfigError( - f"Failed to load configuration from {config_path}", - config_path=str(config_path), - cause=e, - ) - - # Determine environment - if environment is None: - environment = os.getenv("SAGE_DEV_ENV", config_data.get("environment", "development")) - - # Apply environment-specific overrides - env_config = config_data.get("environments", {}).get(environment, {}) - config_data = cls._merge_configs(config_data, env_config) - - # Extract directory configuration - dirs = config_data.get("directories", {}) - - # Set up paths using ~/.sage/ directory structure - sage_home = Path.home() / ".sage" - project_name = project_root.name - - # Use direct ~/.sage path for SAGE project, projects subdirectory for others - if project_name and project_name.upper() == "SAGE": - project_sage_dir = sage_home - else: - project_sage_dir = sage_home / "projects" / project_name - - return cls( - project_root=project_root, - packages_dir=project_root / dirs.get("packages", "packages"), - scripts_dir=project_root / dirs.get("scripts", "scripts"), - output_dir=project_sage_dir / "reports", # Use ~/.sage/ for outputs - logs_dir=project_sage_dir / "logs", # Use ~/.sage/ for logs - temp_dir=project_sage_dir / "temp", # Use ~/.sage/ for temp files - config_data=config_data, - environment=environment or "development", - ) - - @staticmethod - def _merge_configs(base: dict[str, Any], override: dict[str, Any]) -> dict[str, Any]: - """Recursively merge configuration dictionaries.""" - result = base.copy() - - for key, value in override.items(): - if key in result and isinstance(result[key], dict) and isinstance(value, dict): - result[key] = ToolkitConfig._merge_configs(result[key], value) - else: - result[key] = value - - return result - - def get(self, key: str, default: Any = None) -> Any: - """Get configuration value by dot-separated key path.""" - keys = key.split(".") - value = self.config_data - - for k in keys: - if isinstance(value, dict) and k in value: - value = value[k] - else: - return default - - return value - - def get_testing_config(self) -> dict[str, Any]: - """Get testing-specific configuration.""" - return self.get("testing", {}) - - def get_dependency_config(self) -> dict[str, Any]: - """Get dependency analysis configuration.""" - return self.get("dependency_analysis", {}) - - def get_package_config(self) -> dict[str, Any]: - """Get package management configuration.""" - return self.get("package_management", {}) - - def get_reporting_config(self) -> dict[str, Any]: - """Get reporting configuration.""" - return self.get("reporting", {}) - - def get_logging_config(self) -> dict[str, Any]: - """Get logging configuration.""" - return self.get("logging", {}) - - def get_tools_config(self) -> dict[str, Any]: - """Get tools configuration.""" - return self.get("tools", {}) - - def get_interactive_config(self) -> dict[str, Any]: - """Get interactive mode configuration.""" - return self.get("interactive", {}) - - def is_tool_enabled(self, tool_name: str) -> bool: - """Check if a tool is enabled.""" - tools_config = self.get_tools_config() - tool_config = tools_config.get(tool_name, {}) - return tool_config.get("enabled", True) - - def get_tool_config(self, tool_name: str) -> dict[str, Any]: - """Get configuration for a specific tool.""" - tools_config = self.get_tools_config() - return tools_config.get(tool_name, {}) - - def ensure_directories(self) -> None: - """Ensure all configured directories exist.""" - directories = [ - self.output_dir, - self.logs_dir, - self.temp_dir, - ] - - for directory in directories: - directory.mkdir(parents=True, exist_ok=True) - - def validate(self) -> list[str]: - """ - Validate configuration and return list of validation errors. - - Returns: - List of validation error messages - """ - errors = [] - - # Check required directories exist - if not self.project_root.exists(): - errors.append(f"Project root does not exist: {self.project_root}") - - if not self.packages_dir.exists(): - errors.append(f"Packages directory does not exist: {self.packages_dir}") - - if not self.scripts_dir.exists(): - errors.append(f"Scripts directory does not exist: {self.scripts_dir}") - - # Validate tools configuration - tools_config = self.get_tools_config() - for tool_name, tool_config in tools_config.items(): - if not isinstance(tool_config, dict): - errors.append(f"Tool configuration for '{tool_name}' must be a dictionary") - continue - - if "module" not in tool_config: - errors.append(f"Tool '{tool_name}' missing required 'module' configuration") - - if "class" not in tool_config: - errors.append(f"Tool '{tool_name}' missing required 'class' configuration") - - return errors - - def __str__(self) -> str: - return f"ToolkitConfig(environment={self.environment}, project_root={self.project_root})" - - def __repr__(self) -> str: - return ( - f"ToolkitConfig(" - f"project_root={self.project_root!r}, " - f"environment={self.environment!r}, " - f"packages_dir={self.packages_dir!r}, " - f"scripts_dir={self.scripts_dir!r}" - f")" - ) diff --git a/packages/sage-tools/src/sage/tools/dev/core/exceptions.py b/packages/sage-tools/src/sage/tools/dev/core/exceptions.py deleted file mode 100644 index 0c1298cab2..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/core/exceptions.py +++ /dev/null @@ -1,113 +0,0 @@ -""" -sage-development Toolkit exceptions. - -This module defines the exception hierarchy for the sage-development Toolkit. -All toolkit-specific exceptions inherit from SAGEDevToolkitError. -""" - - -class SAGEDevToolkitError(Exception): - """Base exception for all sage-development Toolkit errors.""" - - def __init__( - self, - message: str, - details: dict | None = None, - cause: Exception | None = None, - ): - super().__init__(message) - self.message = message - self.details = details or {} - self.cause = cause - - def __str__(self) -> str: - parts = [self.message] - if self.details: - parts.append(f"Details: {self.details}") - if self.cause: - parts.append(f"Caused by: {self.cause}") - return " | ".join(parts) - - -class ConfigError(SAGEDevToolkitError): - """Raised when there are configuration-related errors.""" - - def __init__(self, message: str, config_path: str | None = None, **kwargs): - super().__init__(message, **kwargs) - self.config_path = config_path - - -class ToolError(SAGEDevToolkitError): - """Raised when there are tool execution errors.""" - - def __init__( - self, - message: str, - tool_name: str | None = None, - exit_code: int | None = None, - **kwargs, - ): - super().__init__(message, **kwargs) - self.tool_name = tool_name - self.exit_code = exit_code - - -class AnalysisError(SAGEDevToolkitError): - """Raised when there are analysis-related errors.""" - - def __init__( - self, - message: str, - analysis_type: str | None = None, - failed_files: list | None = None, - **kwargs, - ): - super().__init__(message, **kwargs) - self.analysis_type = analysis_type - self.failed_files = failed_files or [] - - -class TestExecutionError(ToolError): - """Raised when test execution fails.""" - - def __init__(self, message: str, failed_tests: list | None = None, **kwargs): - super().__init__(message, tool_name="test_runner", **kwargs) - self.failed_tests = failed_tests or [] - - -class PackageManagementError(ToolError): - """Raised when package management operations fail.""" - - def __init__( - self, - message: str, - package_name: str | None = None, - operation: str | None = None, - **kwargs, - ): - super().__init__(message, tool_name="package_manager", **kwargs) - self.package_name = package_name - self.operation = operation - - -class DependencyAnalysisError(AnalysisError): - """Raised when dependency analysis fails.""" - - def __init__(self, message: str, circular_deps: list | None = None, **kwargs): - super().__init__(message, analysis_type="dependency", **kwargs) - self.circular_deps = circular_deps or [] - - -class ReportGenerationError(SAGEDevToolkitError): - """Raised when report generation fails.""" - - def __init__( - self, - message: str, - report_type: str | None = None, - template_path: str | None = None, - **kwargs, - ): - super().__init__(message, **kwargs) - self.report_type = report_type - self.template_path = template_path diff --git a/packages/sage-tools/src/sage/tools/dev/core/toolkit.py b/packages/sage-tools/src/sage/tools/dev/core/toolkit.py deleted file mode 100644 index 904991e010..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/core/toolkit.py +++ /dev/null @@ -1,620 +0,0 @@ -""" -Main sage-development Toolkit class. - -This module contains the core SAGEDevToolkit class that orchestrates -all development tools and provides a unified interface. -""" - -import importlib.util -import json -import logging -import sys -import time -from datetime import datetime -from pathlib import Path -from typing import Any - -from .config import ToolkitConfig -from .exceptions import ( - AnalysisError, - DependencyAnalysisError, - PackageManagementError, - ReportGenerationError, - SAGEDevToolkitError, - TestExecutionError, - ToolError, -) - - -class SAGEDevToolkit: - """ - Main sage-development Toolkit class. - - This class provides a unified interface to all development tools - including testing, dependency analysis, package management, and reporting. - """ - - def __init__( - self, - project_root: str | None = None, - config_file: str | None = None, - environment: str | None = None, - ): - """ - Initialize the sage-development Toolkit. - - Args: - project_root: Project root directory path - config_file: Configuration file path - environment: Environment name (development, production, ci) - """ - # Load configuration - self.config = ToolkitConfig.from_config_file( - config_path=Path(config_file) if config_file else None, - project_root=Path(project_root) if project_root else None, - environment=environment, - ) - - # Ensure directories exist - self.config.ensure_directories() - - # Setup logging - self._setup_logging() - - # Load tools - self.tools = {} - self._load_tools() - - self.logger.info( - f"sage-development Toolkit initialized for environment '{self.config.environment}'" - ) - self.logger.info(f"Project root: {self.config.project_root}") - self.logger.info(f"Available tools: {list(self.tools.keys())}") - - def _setup_logging(self) -> None: - """Setup logging system based on configuration.""" - log_config = self.config.get_logging_config() - - # Create logger - self.logger = logging.getLogger("SAGEDevToolkit") - self.logger.setLevel(getattr(logging, log_config.get("level", "INFO"))) - - # Clear existing handlers - self.logger.handlers.clear() - - # Console handler - if log_config.get("console_logging", {}).get("enabled", True): - console_handler = logging.StreamHandler() - console_handler.setLevel(self.logger.level) - formatter = logging.Formatter( - log_config.get("format", "%(asctime)s - %(name)s - %(levelname)s - %(message)s") - ) - console_handler.setFormatter(formatter) - self.logger.addHandler(console_handler) - - # File handler - if log_config.get("file_logging", {}).get("enabled", True): - log_file = self.config.logs_dir / "sage_common.log" - try: - file_handler = logging.FileHandler(log_file) - file_handler.setLevel(self.logger.level) - file_handler.setFormatter(formatter) - self.logger.addHandler(file_handler) - except Exception as e: - self.logger.warning(f"Could not setup file logging: {e}") - - def _load_tools(self) -> None: - """Load integrated tools and dynamically load additional tools from scripts directory.""" - # Load integrated tools first - from ..tools import ( - EnhancedPackageManager, - EnhancedTestRunner, - VSCodePathManager, - ) - - # Map integrated tools - integrated_tools = { - "test_runner": EnhancedTestRunner, - "package_manager": EnhancedPackageManager, - "dependency_analyzer": EnhancedTestRunner, # Can also analyze dependencies - "vscode_manager": VSCodePathManager, - } - - # Load integrated tools based on configuration - tools_config = self.config.get_tools_config() - - for tool_name, tool_class in integrated_tools.items(): - if self.config.is_tool_enabled(tool_name): - self.tools[tool_name] = tool_class - self.logger.info(f"Successfully loaded tool: {tool_name}") - else: - self.logger.info(f"Skipping disabled tool: {tool_name}") - - # Load additional tools from scripts directory (for backwards compatibility) - for tool_name, tool_config in tools_config.items(): - if tool_name in integrated_tools: - continue # Already loaded as integrated tool - - if not self.config.is_tool_enabled(tool_name): - self.logger.info(f"Skipping disabled tool: {tool_name}") - continue - - module_name = tool_config.get("module") - class_name = tool_config.get("class") - - if not module_name or not class_name: - self.logger.warning(f"Incomplete tool configuration: {tool_name}") - continue - - # Try to load module - module_path = self.config.scripts_dir / f"{module_name}.py" - if not module_path.exists(): - self.logger.warning(f"Tool module not found: {module_path}") - continue - - try: - spec = importlib.util.spec_from_file_location(module_name, module_path) - if spec is None or spec.loader is None: - self.logger.warning(f"Failed to create spec for {module_name}") - continue - module = importlib.util.module_from_spec(spec) - sys.modules[module_name] = module - spec.loader.exec_module(module) - - # Get tool class - if hasattr(module, class_name): - self.tools[tool_name] = getattr(module, class_name) - self.logger.info(f"Successfully loaded tool: {tool_name}") - else: - self.logger.warning(f"Tool class not found: {class_name} in {module_name}") - - except Exception as e: - self.logger.error(f"Failed to load tool {tool_name}: {e}") - - if not self.tools: - self.logger.warning("No tools were loaded successfully") - - def run_tests(self, mode: str = "diff", **kwargs) -> dict[str, Any]: - """ - Run tests using the enhanced test runner. - - Args: - mode: Test mode ("all", "diff", "package") - **kwargs: Additional arguments for test runner - - Returns: - Test results dictionary - - Raises: - TestExecutionError: If test execution fails - """ - self.logger.info(f"🧪 Starting SAGE tests in '{mode}' mode") - start_time = time.time() - - if "test_runner" not in self.tools: - raise ToolError("Test runner not available") - - try: - # Get test configuration - test_config = self.config.get_testing_config() - - # Merge configuration with arguments - test_kwargs = { - "workers": kwargs.get("workers", test_config.get("max_workers", 4)), - "timeout": kwargs.get("timeout", test_config.get("timeout", 300)), - **kwargs, - } - - # Create test runner instance - enable_coverage = kwargs.get("enable_coverage", False) - runner = self.tools["test_runner"]( - str(self.config.project_root), enable_coverage=enable_coverage - ) - - # Execute tests using the enhanced runner - results = runner.run_tests(mode, **test_kwargs) - - # Add metadata - execution_time = time.time() - start_time - results["execution_time"] = execution_time - results["timestamp"] = datetime.now().isoformat() - results["mode"] = mode - - # Save results - output_file = self._save_results("test_execution", results) - - self.logger.info(f"📄 Test results saved to: {output_file}") - self.logger.info(f"⏱️ Test execution time: {execution_time:.2f}s") - - return results - - except Exception as e: - raise TestExecutionError(f"Test execution failed: {e}") from e - - def analyze_dependencies(self, analysis_type: str = "full") -> dict[str, Any]: - """ - Analyze project dependencies. - - Args: - analysis_type: Type of analysis ("full", "summary", "circular") - - Returns: - Analysis results dictionary - - Raises: - DependencyAnalysisError: If analysis fails - """ - self.logger.info(f"🔍 Starting dependency analysis: {analysis_type}") - start_time = time.time() - - if "dependency_analyzer" not in self.tools: - raise ToolError("Dependency analyzer not available") - - try: - # Create analyzer instance - analyzer = self.tools["dependency_analyzer"](str(self.config.packages_dir)) - - # Execute analysis based on type - if analysis_type == "full": - results = analyzer.analyze_all_packages() - elif analysis_type == "summary": - results = analyzer.generate_summary() - elif analysis_type == "circular": - results = analyzer.find_circular_dependencies() - else: - raise DependencyAnalysisError(f"Unknown analysis type: {analysis_type}") - - # Add metadata - execution_time = time.time() - start_time - results["execution_time"] = execution_time - results["timestamp"] = datetime.now().isoformat() - results["analysis_type"] = analysis_type - - # Save results - output_file = self._save_results("dependency_analysis", results) - - self.logger.info(f"📄 Analysis results saved to: {output_file}") - self.logger.info(f"⏱️ Analysis time: {execution_time:.2f}s") - - return results - - except Exception as e: - raise DependencyAnalysisError(f"Dependency analysis failed: {e}") from e - - def manage_packages( - self, action: str, package_name: str | None = None, **kwargs - ) -> dict[str, Any]: - """ - Manage SAGE packages using the enhanced package manager. - - Args: - action: Package action ("list", "install", "uninstall", "status", "build") - package_name: Name of package to operate on - **kwargs: Additional arguments - - Returns: - Operation results dictionary - - Raises: - PackageManagementError: If package operation fails - """ - self.logger.info(f"📦 Package management: {action}") - - if "package_manager" not in self.tools: - raise ToolError("Package manager not available") - - try: - # Create package manager instance - manager = self.tools["package_manager"](str(self.config.project_root)) - - # Execute action using the enhanced manager - if action == "list": - return manager.list_packages() - elif action == "install": - if not package_name: - return manager.install_all_packages(**kwargs) - else: - return manager.install_package(package_name, **kwargs) - elif action == "uninstall": - if not package_name: - raise PackageManagementError("Package name required for uninstall") - return manager.uninstall_package(package_name) - elif action == "status": - return manager.check_dependencies() - elif action == "build": - if not package_name: - raise PackageManagementError("Package name required for build") - return manager.build_package(package_name) - else: - raise PackageManagementError(f"Unknown package action: {action}") - - except Exception as e: - raise PackageManagementError( - f"Package management failed: {e}", - package_name=package_name, - operation=action, - ) from e - - def generate_comprehensive_report(self) -> dict[str, Any]: - """ - Generate a comprehensive development report. - - Returns: - Complete report dictionary - - Raises: - ReportGenerationError: If report generation fails - """ - self.logger.info("📊 Generating comprehensive development report") - start_time = time.time() - - report = { - "metadata": { - "timestamp": datetime.now().isoformat(), - "project_root": str(self.config.project_root), - "environment": self.config.environment, - "toolkit_version": "1.0.0", - }, - "sections": {}, - } - - # Package status section - try: - self.logger.info("Gathering package status...") - pkg_status = self.manage_packages("status") - report["sections"]["package_status"] = { - "status": "success", - "data": pkg_status, - } - except Exception as e: - self.logger.warning(f"Package status collection failed: {e}") - report["sections"]["package_status"] = {"status": "error", "error": str(e)} - - # Dependency analysis section - try: - self.logger.info("Performing dependency analysis...") - dep_analysis = self.analyze_dependencies("summary") - report["sections"]["dependency_analysis"] = { - "status": "success", - "data": dep_analysis, - } - except Exception as e: - self.logger.warning(f"Dependency analysis failed: {e}") - report["sections"]["dependency_analysis"] = { - "status": "error", - "error": str(e), - } - - # Quick test status - try: - self.logger.info("Running quick tests...") - test_results = self.run_tests("diff", quick=True, timeout=60) - report["sections"]["test_status"] = { - "status": "success", - "data": test_results, - } - except Exception as e: - self.logger.warning(f"Test execution failed: {e}") - report["sections"]["test_status"] = {"status": "error", "error": str(e)} - - # Add execution metadata - execution_time = time.time() - start_time - report["metadata"]["execution_time"] = execution_time - - # Save comprehensive report - try: - output_file = self._save_results("comprehensive_report", report) - - # Generate markdown version - markdown_file = output_file.with_suffix(".md") - self._generate_markdown_report(report, markdown_file) - - self.logger.info(f"📄 Comprehensive report saved to: {output_file}") - self.logger.info(f"📄 Markdown report saved to: {markdown_file}") - self.logger.info(f"⏱️ Report generation time: {execution_time:.2f}s") - - return report - - except Exception as e: - raise ReportGenerationError(f"Failed to save report: {e}") from e - - def _save_results(self, result_type: str, data: dict[str, Any]) -> Path: - """Save results to output directory with timestamp.""" - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - output_file = self.config.output_dir / f"{result_type}_{timestamp}.json" - - with open(output_file, "w", encoding="utf-8") as f: - json.dump(data, f, indent=2, ensure_ascii=False) - - return output_file - - def _generate_markdown_report(self, report: dict[str, Any], output_file: Path) -> None: - """Generate markdown version of comprehensive report.""" - with open(output_file, "w", encoding="utf-8") as f: - f.write("# sage-development Report\n\n") - - # Metadata section - metadata = report.get("metadata", {}) - f.write("## Report Metadata\n\n") - f.write(f"- **Generated**: {metadata.get('timestamp', 'Unknown')}\n") - f.write(f"- **Environment**: {metadata.get('environment', 'Unknown')}\n") - f.write(f"- **Project Root**: {metadata.get('project_root', 'Unknown')}\n") - f.write(f"- **Execution Time**: {metadata.get('execution_time', 0):.2f}s\n\n") - - # Sections - sections = report.get("sections", {}) - for section_name, section_data in sections.items(): - section_title = section_name.replace("_", " ").title() - f.write(f"## {section_title}\n\n") - - status = section_data.get("status", "unknown") - if status == "error": - f.write(f"❌ **Error**: {section_data.get('error', 'Unknown error')}\n\n") - elif status == "success": - f.write("✅ **Status**: Success\n\n") - # Add summary data if available - data = section_data.get("data", {}) - if isinstance(data, dict): - f.write("### Summary\n\n") - for key, value in data.items(): - if isinstance(value, (str, int, float, bool)): - f.write(f"- **{key}**: {value}\n") - f.write("\n") - else: - f.write(f"⚠️ **Status**: {status}\n\n") - - def get_tool_status(self) -> dict[str, Any]: - """Get status of all loaded tools.""" - return { - "loaded_tools": list(self.tools.keys()), - "available_tools": list(self.config.get_tools_config().keys()), - "tools_config": self.config.get_tools_config(), - } - - def validate_configuration(self) -> list[str]: - """Validate toolkit configuration and return any errors.""" - return self.config.validate() - - def fix_import_paths(self, dry_run: bool = False) -> dict[str, Any]: - """Fix import paths in SAGE packages.""" - if "import_fixer" not in self.tools: - raise ToolError("Import path fixer not available") - - try: - fixer = self.tools["import_fixer"](str(self.config.packages_dir)) - return fixer.fix_imports(dry_run=dry_run) - except Exception as e: - raise SAGEDevToolkitError(f"Import path fixing failed: {e}") from e - - def update_vscode_paths(self, mode: str = "enhanced") -> dict[str, Any]: - """Update VS Code Python path configurations.""" - if "vscode_manager" not in self.tools: - raise ToolError("VS Code path manager not available") - - try: - manager = self.tools["vscode_manager"](str(self.config.project_root)) - return manager.update_python_paths(mode=mode) - except Exception as e: - raise SAGEDevToolkitError(f"VS Code path update failed: {e}") from e - - def list_available_tests(self) -> dict[str, Any]: - """List all available tests in the project.""" - if "test_runner" not in self.tools: - raise ToolError("Test runner not available") - - try: - runner = self.tools["test_runner"](str(self.config.project_root)) - return runner.list_tests() - except Exception as e: - raise SAGEDevToolkitError(f"Test listing failed: {e}") from e - - @staticmethod - def get_version_info() -> dict[str, Any]: - """Get SAGE version information from _version.py file.""" - try: - from sage.common.config import find_sage_project_root - - # Find the _version.py file in the project root - project_root = find_sage_project_root() - version_file = project_root / "_version.py" - - if not version_file.exists(): - raise FileNotFoundError(f"Could not find _version.py file in {project_root}") - - # Execute _version.py to get all variables - version_globals = {} - with open(version_file, encoding="utf-8") as f: - exec(f.read(), version_globals) - - # Import sys to get Python version - import sys - - return { - "version": version_globals.get("__version__", "unknown"), - "project_name": version_globals.get("__project_name__", "SAGE"), - "project_full_name": version_globals.get( - "__project_full_name__", "Streaming-Augmented Generative Execution" - ), - "author": version_globals.get("__author__", "IntelliStream Team"), - "email": version_globals.get("__email__", "unknown"), - "release_date": version_globals.get("__release_date__", "unknown"), - "release_status": version_globals.get("__release_status__", "development"), - "build": f"{version_globals.get('__version__', 'unknown')}-{version_globals.get('__release_status__', 'dev')}", - "python_version": f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}", - "python_requires": version_globals.get("__python_requires__", ">=3.10"), - } - - except Exception: - # Return default values if something goes wrong - import sys - - return { - "version": "unknown", - "project_name": "SAGE", - "project_full_name": "Streaming-Augmented Generative Execution", - "author": "IntelliStream Team", - "email": "unknown", - "release_date": "unknown", - "release_status": "development", - "build": "unknown-dev", - "python_version": f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}", - "python_requires": ">=3.10", - } - - def analyze_project(self) -> dict[str, Any]: - """ - Analyze the current project structure and dependencies. - - Returns: - Project analysis results dictionary - """ - self.logger.info("🔍 Analyzing project structure and dependencies") - start_time = time.time() - - analysis = { - "project_info": {}, - "structure": {}, - "dependencies": {}, - "tools": {}, - } - - try: - # Project info - version_info = self.get_version_info() - analysis["project_info"] = { - "name": version_info.get("project_name", "SAGE"), - "version": version_info.get("version", "unknown"), - "root": str(self.config.project_root), - "environment": self.config.environment, - } - - # Project structure - analysis["structure"] = { - "packages_dir": str(self.config.packages_dir), - "scripts_dir": str(self.config.scripts_dir), - "output_dir": str(self.config.output_dir), - "logs_dir": str(self.config.logs_dir), - } - - # Dependencies - try: - dep_analysis = self.analyze_dependencies("summary") - analysis["dependencies"] = dep_analysis - except Exception as e: - self.logger.warning(f"Dependency analysis failed: {e}") - analysis["dependencies"] = {"error": str(e)} - - # Tools status - analysis["tools"] = self.get_tool_status() - - # Add metadata - execution_time = time.time() - start_time - analysis["execution_time"] = execution_time # type: ignore[assignment] - analysis["timestamp"] = datetime.now().isoformat() # type: ignore[assignment] - - self.logger.info(f"📄 Project analysis completed in {execution_time:.2f}s") - return analysis - - except Exception as e: - self.logger.error(f"Project analysis failed: {e}") - raise AnalysisError(f"Project analysis failed: {e}") from e diff --git a/packages/sage-tools/src/sage/tools/dev/examples/analyzer.py b/packages/sage-tools/src/sage/tools/dev/examples/analyzer.py deleted file mode 100644 index 91eb0b2019..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/examples/analyzer.py +++ /dev/null @@ -1,292 +0,0 @@ -""" -Example Analyzer Module - -This module provides tools for analyzing Python example files, -extracting metadata, dependencies, and categorization information. -""" - -import ast -import re -from pathlib import Path - -from rich.console import Console - -from .models import ExampleInfo -from .utils import find_examples_directory, find_project_root - -console = Console() - - -class ExampleAnalyzer: - """示例代码分析器""" - - def __init__(self): - """初始化 ExampleAnalyzer - - Raises: - RuntimeError: 如果找不到 examples 目录（开发环境不可用） - """ - # 使用新的环境检测工具 - examples_dir = find_examples_directory() - if examples_dir is None: - raise RuntimeError( - "Cannot find SAGE examples directory. " - "This tool requires a development environment. " - "Please see the Examples Testing README for setup instructions." - ) - - self.examples_root = examples_dir - - # 同时获取项目根目录 - self.project_root = find_project_root() - if self.project_root is None: - # 如果找到了 examples 但找不到项目根，使用 examples 的父目录 - self.project_root = self.examples_root.parent - - def analyze_file(self, file_path: Path) -> ExampleInfo | None: - """分析单个示例文件 - - Args: - file_path: 示例文件的路径 - - Returns: - ExampleInfo 对象，如果分析失败返回 None - """ - if isinstance(file_path, str): - file_path = Path(file_path) - - try: - with open(file_path, encoding="utf-8") as f: - content = f.read() - - tree = ast.parse(content) - - # 提取导入信息 - imports = self._extract_imports(tree) - - # 检查是否有主函数 - has_main = self._has_main_function(tree) - - # 检查配置和数据依赖 - requires_config = self._requires_config(content) - requires_data = self._requires_data(content) - - # 估算运行时间 - estimated_runtime = self._estimate_runtime(content) - - # 提取依赖 - dependencies = self._extract_dependencies(imports) - - # 提取测试标记（包括 TEST_TAGS 变量） - test_tags = self._extract_test_tags(content, tree) - - category = self._get_category(file_path) - - return ExampleInfo( - file_path=str(file_path), - category=category, - imports=imports, - has_main=has_main, - requires_config=requires_config, - requires_data=requires_data, - estimated_runtime=estimated_runtime, - dependencies=dependencies, - test_tags=test_tags, - ) - - except Exception as e: - console.print(f"[red]分析文件失败 {file_path}: {e}[/red]") - return None - - def _extract_imports(self, tree: ast.AST) -> list[str]: - """提取导入语句""" - imports = [] - for node in ast.walk(tree): - if isinstance(node, ast.Import): - for alias in node.names: - imports.append(alias.name) - elif isinstance(node, ast.ImportFrom): - if node.module: - imports.append(node.module) - return imports - - def _has_main_function(self, tree: ast.AST) -> bool: - """检查是否有主函数""" - for node in ast.walk(tree): - if isinstance(node, ast.FunctionDef) and node.name == "main": - return True - if isinstance(node, ast.If) and hasattr(node.test, "left"): - if ( - hasattr(node.test.left, "id") - and node.test.left.id == "__name__" - and hasattr(node.test.comparators[0], "s") - and node.test.comparators[0].s == "__main__" - ): - return True - return False - - def _requires_config(self, content: str) -> bool: - """检查是否需要配置文件""" - config_indicators = [ - ".yaml", - ".yml", - ".json", - ".toml", - "config", - "Config", - "load_dotenv", - "os.environ", - "getenv", - ] - return any(indicator in content for indicator in config_indicators) - - def _requires_data(self, content: str) -> bool: - """检查是否需要数据文件""" - data_indicators = [ - ".csv", - ".txt", - ".pdf", - ".docx", - "data/", - "dataset", - "corpus", - ] - return any(indicator in content for indicator in data_indicators) - - def _estimate_runtime(self, content: str) -> str: - """估算运行时间""" - # 检查是否有明显的长时间运行指标 - if any( - keyword in content for keyword in ["time.sleep", "train", "fit", "epochs", "while True"] - ): - return "slow" - # 检查是否是简单的教程示例（优先级高） - elif any( - keyword in content for keyword in ["Hello, World!", "HelloBatch", "simple", "basic"] - ): - return "quick" - # 检查网络请求等中等时间指标 - elif any(keyword in content for keyword in ["requests.", "http.", "download", "ray.init"]): - return "medium" - # 文件大小作为参考 - elif len(content) < 3000: # 小于3KB的文件通常是快速示例 - return "quick" - else: - return "medium" - - def _extract_dependencies(self, imports: list[str]) -> list[str]: - """提取外部依赖""" - external_deps = [] - - dependency_map = { - "openai": "openai", - "transformers": "transformers", - "torch": "torch", - "numpy": "numpy", - "pandas": "pandas", - "requests": "requests", - "yaml": "pyyaml", - "dotenv": "python-dotenv", - "chromadb": "chromadb", - "pymilvus": "pymilvus", - "redis": "redis", - "kafka": "kafka-python", - "cv2": "opencv-python", - } - - for imp in imports: - root_module = imp.split(".")[0] - if root_module in dependency_map: - external_deps.append(dependency_map[root_module]) - - return list(set(external_deps)) - - def _extract_test_tags(self, content: str, tree: ast.AST | None = None) -> list[str]: - """从文件内容中提取测试标记 - - 支持的标记格式: - 1. Python 变量: TEST_TAGS = ["timeout=120", "slow"] - 2. 注释标记: # @test:skip - 跳过测试 - 3. 注释标记: # @test:slow - 标记为慢速测试 - 4. 注释标记: # @test:require-api - 需要API密钥 - 5. 注释标记: # @test:timeout=120 - 自定义超时时间 - 6. 注释标记: @test_skip_ci: true - CI环境中跳过 - """ - tags = [] - - # Method 1: Extract from TEST_TAGS variable (highest priority) - if tree is not None: - for node in ast.walk(tree): - if isinstance(node, ast.Assign): - for target in node.targets: - if isinstance(target, ast.Name) and target.id == "TEST_TAGS": - # Found TEST_TAGS assignment - if isinstance(node.value, ast.List): - for elt in node.value.elts: - if isinstance(elt, ast.Constant): - tags.append(str(elt.value)) - elif isinstance(elt, ast.Str): # Python 3.7 compatibility - tags.append(elt.s) - - # Method 2: Extract from comments (Pattern 1: @test:tag or @test:tag=value) - pattern1 = r"(?:#\s*)?@test:([\w-]+)(?:=([\w-]+))?" - matches1 = re.findall(pattern1, content, re.IGNORECASE) - - for match in matches1: - if len(match) == 2 and match[1]: - # 带值的标记，如 timeout=120 - tags.append(f"{match[0]}={match[1]}") - else: - # 简单标记，如 skip - tags.append(match[0]) - - # Method 3: Extract from comments (Pattern 2: @test_tag: value) - pattern2 = r"@test_([\w-]+):\s*([\w\[\],\s-]+)" - matches2 = re.findall(pattern2, content, re.IGNORECASE) - - for match in matches2: - tag_name = match[0] - tag_value = match[1].strip() - # 简化标记名（移除值为true的情况，直接使用标记名） - if tag_value.lower() == "true": - tags.append(tag_name) - elif tag_value.lower() == "false": - # false值不添加标记 - continue - else: - # 其他值保留为 tag=value 格式 - tags.append(f"{tag_name}={tag_value}") - - return list(set(tags)) - - def _get_category(self, file_path: Path) -> str: - """获取示例类别 - - 对于 examples/tutorials/rag/simple_rag.py 这样的文件， - 返回 'rag' 而不是 'tutorials'，以便更细粒度的分类。 - 对于 examples/apps/run_app.py 这样的文件，返回 'apps'。 - """ - relative_path = file_path.relative_to(self.examples_root) - if not relative_path.parts: - return "unknown" - - # 如果第一级目录是 tutorials，并且有第二级目录，使用第二级 - if len(relative_path.parts) >= 3 and relative_path.parts[0] == "tutorials": - return str(relative_path.parts[1]) - - # 否则使用第一级目录 - return str(relative_path.parts[0]) - - def discover_examples(self) -> list[ExampleInfo]: - """发现所有示例文件""" - examples = [] - - for py_file in self.examples_root.rglob("*.py"): - if py_file.name.startswith("__"): - continue - - example_info = self.analyze_file(py_file) - if example_info: - examples.append(example_info) - - return examples diff --git a/packages/sage-tools/src/sage/tools/dev/examples/models.py b/packages/sage-tools/src/sage/tools/dev/examples/models.py deleted file mode 100644 index e2f2aaeba7..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/examples/models.py +++ /dev/null @@ -1,36 +0,0 @@ -""" -Data models for Examples testing framework - -This module defines the data structures used throughout the examples testing system. -""" - -from dataclasses import dataclass - - -@dataclass -class ExampleTestResult: - """示例测试结果""" - - file_path: str - test_name: str - status: str # "passed", "failed", "skipped", "timeout" - execution_time: float - output: str - error: str | None = None - dependencies_met: bool = True - requires_user_input: bool = False - - -@dataclass -class ExampleInfo: - """示例文件信息""" - - file_path: str - category: str # tutorials, rag, memory, etc. - imports: list[str] - has_main: bool - requires_config: bool - requires_data: bool - estimated_runtime: str # "quick", "medium", "slow" - dependencies: list[str] - test_tags: list[str] # 测试标记，从文件注释中提取 diff --git a/packages/sage-tools/src/sage/tools/dev/examples/runner.py b/packages/sage-tools/src/sage/tools/dev/examples/runner.py deleted file mode 100644 index d0b6fee8c5..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/examples/runner.py +++ /dev/null @@ -1,285 +0,0 @@ -""" -Example Runner Module - -This module provides tools for executing Python example files -and collecting execution results. -""" - -import os -import subprocess -import sys -import time -from pathlib import Path - -from .models import ExampleInfo, ExampleTestResult -from .utils import find_examples_directory, find_project_root - - -class ExampleRunner: - """示例执行器""" - - def __init__(self, timeout: int | None = None): - """初始化 ExampleRunner - - Args: - timeout: 默认超时时间（秒），None表示使用策略决定 - - Raises: - RuntimeError: 如果找不到项目根目录（开发环境不可用） - """ - # 优先级：传入参数 > 环境变量 > 默认值（让策略决定） - if timeout is not None: - self.timeout = timeout - else: - # 如果没有传入timeout，检查环境变量，否则使用默认值让策略决定 - env_timeout = os.environ.get("SAGE_EXAMPLE_TIMEOUT") - if env_timeout: - self.timeout = int(env_timeout) - else: - # 不设置默认超时，让_get_test_timeout方法从策略中获取 - self.timeout = None - - # 使用新的环境检测工具 - project_root = find_project_root() - examples_dir = find_examples_directory() - - if project_root is None or examples_dir is None: - raise RuntimeError( - "Cannot find SAGE project root directory. " - "This tool requires a development environment. " - "Please see the Examples Testing README for setup instructions." - ) - - self.project_root = project_root - self.examples_root = examples_dir - - def run_example(self, example_info: ExampleInfo) -> ExampleTestResult: - """运行单个示例 - - Args: - example_info: 示例文件信息 - - Returns: - ExampleTestResult 对象 - """ - start_time = time.time() - - # 检查依赖 - if not self._check_dependencies(example_info.dependencies): - return ExampleTestResult( - file_path=example_info.file_path, - test_name=Path(example_info.file_path).name, - status="skipped", - execution_time=0, - output="", - error="Missing dependencies", - dependencies_met=False, - ) - - # 检查是否需要用户输入 - if self._requires_user_input(example_info.file_path): - return ExampleTestResult( - file_path=example_info.file_path, - test_name=Path(example_info.file_path).name, - status="skipped", - execution_time=0, - output="", - error="Requires user input", - requires_user_input=True, - ) - - # 准备环境 - env = self._prepare_environment(example_info) - - # 确定超时时间 - test_timeout = self._get_test_timeout(example_info) - - try: - # 执行示例 - result = subprocess.run( - [sys.executable, example_info.file_path], - capture_output=True, - text=True, - timeout=test_timeout, - cwd=str(self.project_root), - env=env, - ) - - execution_time = time.time() - start_time - - if result.returncode == 0: - status = "passed" - error = None - else: - status = "failed" - error = result.stderr - - return ExampleTestResult( - file_path=example_info.file_path, - test_name=Path(example_info.file_path).name, - status=status, - execution_time=execution_time, - output=result.stdout, - error=error, - ) - - except subprocess.TimeoutExpired: - execution_time = time.time() - start_time - # 在超时情况下，尝试获取可能的输出 - error_msg = f"Execution timed out after {test_timeout}s" - if os.environ.get("CI") == "true": - error_msg += ( - f"\nFile: {example_info.file_path}" - f"\nCategory: {example_info.category}" - f"\nEstimated runtime: {example_info.estimated_runtime}" - ) - return ExampleTestResult( - file_path=example_info.file_path, - test_name=Path(example_info.file_path).name, - status="timeout", - execution_time=execution_time, - output="", # 超时情况下没有输出 - error=error_msg, - ) - except Exception as e: - execution_time = time.time() - start_time - return ExampleTestResult( - file_path=example_info.file_path, - test_name=Path(example_info.file_path).name, - status="failed", - execution_time=execution_time, - output="", - error=str(e), - ) - - def _get_test_timeout(self, example_info: ExampleInfo) -> int: - """从测试标记中确定超时时间""" - # 检查是否有自定义超时标记 - for tag in example_info.test_tags: - if tag.startswith("timeout="): - try: - return int(tag.split("=")[1]) - except (ValueError, IndexError): - pass - - # 从类别策略中获取超时 - category = self._get_category_from_tags(example_info.test_tags) or example_info.category - - # 导入策略类 - try: - from .strategies import ExampleTestStrategies - - strategies = ExampleTestStrategies.get_strategies() - if category in strategies: - return strategies[category].timeout - except ImportError: - pass - - # 如果策略不可用，使用默认超时 - if self.timeout is not None: - return self.timeout - - # 最后的默认值 - return 60 - - def _get_category_from_tags(self, test_tags: list[str]) -> str | None: - """从测试标记中提取类别""" - for tag in test_tags: - if tag.startswith("category="): - try: - return tag.split("=")[1] - except IndexError: - pass - return None - - def _check_dependencies(self, dependencies: list[str]) -> bool: - """检查依赖是否满足""" - # 包名到导入名的映射 - import_name_map = { - "pyyaml": "yaml", - "python-dotenv": "dotenv", - "kafka-python": "kafka", - "opencv-python": "cv2", - } - - for dep in dependencies: - import_name = import_name_map.get(dep, dep) - try: - subprocess.run( - [sys.executable, "-c", f"import {import_name}"], - check=True, - capture_output=True, - timeout=5, - ) - except (subprocess.CalledProcessError, subprocess.TimeoutExpired): - return False - return True - - def _requires_user_input(self, file_path: str) -> bool: - """检查是否需要用户输入""" - try: - with open(file_path, encoding="utf-8") as f: - content = f.read() - - # 检查文件是否在测试模式下有特殊处理 - has_test_mode_check = any( - pattern in content - for pattern in [ - 'os.getenv("SAGE_TEST_MODE")', - 'os.getenv("SAGE_EXAMPLES_MODE")', - "SAGE_TEST_MODE", - "SAGE_EXAMPLES_MODE", - ] - ) - - # 如果有测试模式检查，即使有 input() 也不算需要用户输入 - if has_test_mode_check: - return False - - input_indicators = ["input(", "raw_input(", "getpass."] - return any(indicator in content for indicator in input_indicators) - except Exception: - return False - - def _prepare_environment(self, example_info: ExampleInfo) -> dict[str, str]: - """准备执行环境""" - env = os.environ.copy() - - # 设置 Python 路径 - 使用动态路径而不是硬编码 - python_path = env.get("PYTHONPATH", "") - sage_paths_all = [ - str(self.project_root), # Add project root for examples imports - str(self.project_root / "packages" / "sage" / "src"), - str(self.project_root / "packages" / "sage-common" / "src"), - str(self.project_root / "packages" / "sage-kernel" / "src"), - str(self.project_root / "packages" / "sage-libs" / "src"), - str(self.project_root / "packages" / "sage-middleware" / "src"), - str(self.project_root / "packages" / "sage-tools" / "src"), - ] - - # 对依赖已编译扩展的示例（如 sage_flow），避免通过源码空目录覆盖已安装的二进制模块 - is_sage_flow_example = "sage_flow" in example_info.file_path or any( - imp.startswith("sage.middleware.components.sage_flow") for imp in example_info.imports - ) - if is_sage_flow_example and env.get("SAGE_EXAMPLES_USE_INSTALLED_MIDDLEWARE", "1") != "0": - # 去掉 middleware/src，让 Python 优先使用 site-packages 中已安装的模块 - mw_src = str(self.project_root / "packages" / "sage-middleware" / "src") - sage_paths = [p for p in sage_paths_all if p != mw_src] - else: - sage_paths = sage_paths_all - - if python_path: - env["PYTHONPATH"] = ":".join(sage_paths + [python_path]) - else: - env["PYTHONPATH"] = ":".join(sage_paths) - - # 设置示例特定的环境变量 - env["SAGE_EXAMPLES_MODE"] = "test" - env["SAGE_TEST_MODE"] = "true" # 标记为测试模式，用于示例中的条件判断 - env["SAGE_LOG_LEVEL"] = "WARNING" # 减少日志输出 - - # 检查是否需要使用真实API (通过环境变量传递) - if os.environ.get("SAGE_USE_REAL_API") == "true": - env["SAGE_USE_REAL_API"] = "true" - - return env diff --git a/packages/sage-tools/src/sage/tools/dev/examples/strategies.py b/packages/sage-tools/src/sage/tools/dev/examples/strategies.py deleted file mode 100644 index 60baa91a24..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/examples/strategies.py +++ /dev/null @@ -1,356 +0,0 @@ -""" -Test Strategies for Examples - -This module defines testing strategies for different categories of examples, -including timeout settings, environment variables, and success/failure patterns. -""" - -from dataclasses import dataclass -from typing import Callable - - -@dataclass -class TestStrategy: - """测试策略配置""" - - name: str - timeout: int - requires_config: bool - requires_data: bool - mock_inputs: dict[str, str] | None = None - environment_vars: dict[str, str] | None = None - success_patterns: list[str] | None = None - failure_patterns: list[str] | None = None - pre_run_setup: Callable | None = None - post_run_cleanup: Callable | None = None - - -class ExampleTestStrategies: - """示例测试策略集合""" - - @staticmethod - def get_strategies() -> dict[str, TestStrategy]: - """获取所有测试策略 - - Returns: - 字典，键为类别名称，值为对应的测试策略 - """ - return { - "tutorials": TestStrategy( - name="tutorials", - timeout=30, - requires_config=False, - requires_data=False, - success_patterns=[ - "Hello, World!", - "Pipeline completed", - "Execution finished", - "✓", - ], - failure_patterns=["Error:", "Exception:", "Traceback", "Failed to"], - environment_vars={ - "SAGE_LOG_LEVEL": "WARNING", - "SAGE_EXAMPLES_MODE": "test", - }, - ), - "rag": TestStrategy( - name="rag", - timeout=120, - requires_config=True, - requires_data=True, - mock_inputs={ - "user_question": "What is artificial intelligence?", - "test_query": "Tell me about machine learning", - }, - success_patterns=[ - "Answer:", - "Response:", - "Retrieved", - "Generated answer", - "RAG pipeline completed", - ], - failure_patterns=[ - "API key not found", - "Connection failed", - "Model not found", - "Index not found", - ], - environment_vars={ - "OPENAI_API_KEY": "test-key-placeholder", # pragma: allowlist secret - "SAGE_RAG_MODE": "test", - "SAGE_LOG_LEVEL": "ERROR", - "SAGE_EXAMPLES_MODE": "test", - "SAGE_TEST_MODE": "true", - }, - ), - "memory": TestStrategy( - name="memory", - timeout=120, # 增加到120秒，Pipeline-as-Service示例需要更多时间 - requires_config=False, - requires_data=True, - success_patterns=[ - "Memory initialized", - "Data stored", - "Retrieved from memory", - "Memory service started", - ], - failure_patterns=[ - "Memory service failed", - "Storage error", - "Connection refused", - ], - environment_vars={ - "SAGE_MEMORY_MODE": "test", - "SAGE_LOG_LEVEL": "WARNING", - }, - ), - "agents": TestStrategy( - name="agents", - timeout=120, - requires_config=True, - requires_data=False, - success_patterns=[ - "Agent initialized", - "Task completed", - "Agent response", - "Processing finished", - ], - failure_patterns=[ - "Agent failed", - "API key missing", - "Connection failed", - "Model not available", - ], - environment_vars={ - "SAGE_AGENT_MODE": "test", - "SAGE_LOG_LEVEL": "ERROR", - "SAGE_EXAMPLES_MODE": "test", - "OPENAI_API_KEY": "test-key-placeholder", # pragma: allowlist secret - }, - ), - "service": TestStrategy( - name="service", - timeout=90, - requires_config=True, - requires_data=False, - success_patterns=[ - "Service started", - "Server running", - "API endpoint active", - "Health check passed", - ], - failure_patterns=[ - "Port already in use", - "Service failed to start", - "Connection refused", - ], - environment_vars={ - "SAGE_SERVICE_MODE": "test", - "SAGE_PORT": "0", # 随机端口 - "SAGE_LOG_LEVEL": "ERROR", - }, - ), - "video": TestStrategy( - name="video", - timeout=180, - requires_config=True, - requires_data=True, - success_patterns=[ - "Video processed", - "Frames extracted", - "Analysis completed", - ], - failure_patterns=[ - "Video file not found", - "Codec not supported", - "Processing failed", - ], - environment_vars={"SAGE_VIDEO_MODE": "test", "SAGE_LOG_LEVEL": "ERROR"}, - ), - "batch": TestStrategy( - name="batch", - timeout=180, - requires_config=False, - requires_data=False, - success_patterns=[ - "batch test completed", - "Batch Processing Tests Summary", - "✅", - "Processing completed", - ], - failure_patterns=[ - "Failed to start", - "Connection refused", - "Timeout", - "Error:", - "Exception:", - ], - environment_vars={ - "SAGE_BATCH_MODE": "test", - "SAGE_LOG_LEVEL": "ERROR", - "SAGE_EXAMPLES_MODE": "test", - }, - ), - "streaming": TestStrategy( - name="streaming", - timeout=300, # 增加到5分钟，因为streaming示例可能运行多个环境 - requires_config=False, - requires_data=False, - success_patterns=[ - "Stream completed", - "Processing finished", - "✅", - "Test completed", - ], - failure_patterns=[ - "Stream failed", - "Connection error", - "Timeout", - ], - environment_vars={ - "SAGE_STREAM_MODE": "test", - "SAGE_LOG_LEVEL": "ERROR", - }, - ), - "medical_diagnosis": TestStrategy( - name="medical_diagnosis", - timeout=300, # 5分钟，医学影像分析需要加载模型 - requires_config=False, - requires_data=True, - success_patterns=[ - "诊断完成", - "Diagnosis completed", - "报告生成完成", - "Report generated", - "✅", - ], - failure_patterns=[ - "模型加载失败", - "Model loading failed", - "数据不存在", - "Data not found", - ], - environment_vars={ - "SAGE_MEDICAL_MODE": "test", - "SAGE_LOG_LEVEL": "ERROR", - "SAGE_EXAMPLES_MODE": "test", - }, - ), - "multimodal": TestStrategy( - name="multimodal", - timeout=180, # 3分钟，多模态处理需要时间 - requires_config=True, - requires_data=True, - success_patterns=[ - "Processing completed", - "处理完成", - "Search completed", - "搜索完成", - "✅", - ], - failure_patterns=[ - "Model not found", - "API key missing", - "Connection failed", - ], - environment_vars={ - "SAGE_MULTIMODAL_MODE": "test", - "SAGE_LOG_LEVEL": "ERROR", - "SAGE_EXAMPLES_MODE": "test", - }, - ), - "scheduler": TestStrategy( - name="scheduler", - timeout=90, # 90秒，调度器对比实验 - requires_config=False, - requires_data=False, - success_patterns=[ - "所有实验完成", - "实验完成", - "执行结果", - "✅", - "调度器性能对比总结", - ], - failure_patterns=[ - "调度失败", - "Scheduler failed", - "Connection refused", - "Timeout exceeded", - ], - environment_vars={ - "SAGE_SCHEDULER_MODE": "test", - "SAGE_LOG_LEVEL": "ERROR", - "SAGE_EXAMPLES_MODE": "test", - "SAGE_TEST_MODE": "true", - }, - ), - "apps": TestStrategy( - name="apps", - timeout=180, - requires_config=True, - requires_data=False, - success_patterns=[ - "Application started", - "Processing completed", - "✅", - "完成", - ], - failure_patterns=[ - "Failed to start", - "Connection error", - "Model not found", - ], - environment_vars={ - "SAGE_APP_MODE": "test", - "SAGE_LOG_LEVEL": "ERROR", - "SAGE_EXAMPLES_MODE": "test", - }, - ), - "environment": TestStrategy( - name="environment", - timeout=120, # 2 minutes for environment examples - requires_config=False, - requires_data=False, - success_patterns=[ - "任务执行完成", - "环境创建完成", - "Pipeline 构建完成", - "✅", - "所有示例运行完成", - ], - failure_patterns=[ - "连接失败", - "任务提交失败", - "JobManager daemon 未运行", - "Connection refused", - ], - environment_vars={ - "SAGE_LOG_LEVEL": "ERROR", - "SAGE_EXAMPLES_MODE": "test", - "SAGE_TEST_MODE": "true", - }, - ), - } - - @staticmethod - def get_category_skip_patterns() -> dict[str, list[str]]: - """获取各类别需要跳过的文件模式 - - Returns: - 字典，键为类别名称，值为该类别需要跳过的文件模式列表 - """ - return { - "rag": [ - "*_interactive.py", # 交互式示例 - "*_demo.py", # 演示文件 - "*_benchmark.py", # 基准测试 - ], - "service": [ - "*_server.py", # 长期运行的服务 - "*_daemon.py", # 守护进程 - ], - "video": [ - "*_large_file.py", # 处理大文件 - "*_gpu_required.py", # 需要GPU - ], - } diff --git a/packages/sage-tools/src/sage/tools/dev/examples/suite.py b/packages/sage-tools/src/sage/tools/dev/examples/suite.py deleted file mode 100644 index 70920c3497..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/examples/suite.py +++ /dev/null @@ -1,236 +0,0 @@ -""" -Example Test Suite Module - -This module provides the main test suite for running and managing -example tests, including result collection and reporting. -""" - -import json -from dataclasses import asdict -from datetime import datetime -from pathlib import Path - -from rich.console import Console -from rich.panel import Panel -from rich.table import Table - -from .analyzer import ExampleAnalyzer -from .models import ExampleInfo, ExampleTestResult -from .runner import ExampleRunner - -console = Console() - - -class ExampleTestSuite: - """示例测试套件""" - - def __init__(self): - """初始化 ExampleTestSuite - - Raises: - RuntimeError: 如果开发环境不可用 - """ - self.analyzer = ExampleAnalyzer() - self.runner = ExampleRunner() - self.results: list[ExampleTestResult] = [] - - def _show_examples_summary(self, examples: list[ExampleInfo]): - """显示示例摘要""" - categories = {} - for example in examples: - if example.category not in categories: - categories[example.category] = [] - categories[example.category].append(example) - - table = Table(title="示例文件摘要") - table.add_column("类别", style="cyan") - table.add_column("文件数", style="magenta") - table.add_column("运行时间", style="green") - table.add_column("依赖项", style="yellow") - - for category, cat_examples in categories.items(): - count = len(cat_examples) - runtimes = [e.estimated_runtime for e in cat_examples] - runtime_summary = ( - f"快速: {runtimes.count('quick')}, " - f"中等: {runtimes.count('medium')}, " - f"慢速: {runtimes.count('slow')}" - ) - - all_deps = set() - for e in cat_examples: - all_deps.update(e.dependencies) - deps_summary = f"{len(all_deps)} 个外部依赖" - - table.add_row(category, str(count), runtime_summary, deps_summary) - - console.print(table) - - def _show_results(self): - """显示测试结果""" - table = Table(title="测试结果") - table.add_column("示例", style="cyan", width=40) - table.add_column("状态", style="bold") - table.add_column("执行时间", style="green") - table.add_column("错误", style="red", width=50) - - for result in self.results: - status_style = { - "passed": "[green]✓ 通过[/green]", - "failed": "[red]✗ 失败[/red]", - "skipped": "[yellow]- 跳过[/yellow]", - "timeout": "[orange]⏱ 超时[/orange]", - }.get(result.status, result.status) - - error_msg = ( - result.error[:50] + "..." - if result.error and len(result.error) > 50 - else (result.error or "") - ) - - table.add_row( - Path(result.file_path).name, - status_style, - f"{result.execution_time:.2f}s", - error_msg, - ) - - console.print(table) - - def _get_statistics(self) -> dict[str, int]: - """获取统计信息""" - stats = { - "total": len(self.results), - "passed": sum(1 for r in self.results if r.status == "passed"), - "failed": sum(1 for r in self.results if r.status == "failed"), - "skipped": sum(1 for r in self.results if r.status == "skipped"), - "timeout": sum(1 for r in self.results if r.status == "timeout"), - } - - console.print( - Panel( - f"总计: {stats['total']} | " - f"[green]通过: {stats['passed']}[/green] | " - f"[red]失败: {stats['failed']}[/red] | " - f"[yellow]跳过: {stats['skipped']}[/yellow] | " - f"[orange]超时: {stats['timeout']}[/orange]", - title="测试统计", - ) - ) - - return stats - - def save_results(self, output_file: str): - """保存测试结果 - - Args: - output_file: 输出文件路径 - """ - results_data = [asdict(result) for result in self.results] - - with open(output_file, "w", encoding="utf-8") as f: - json.dump( - { - "timestamp": datetime.now().isoformat(), - "results": results_data, - "statistics": self._get_statistics(), - }, - f, - indent=2, - ensure_ascii=False, - ) - - console.print(f"📄 测试结果已保存到: {output_file}") - - def run_all_tests( - self, categories: list[str] | None = None, quick_only: bool = False - ) -> dict[str, int]: - """运行所有测试 - - Args: - categories: 要测试的类别列表，None表示所有类别 - quick_only: 是否只运行快速测试 - - Returns: - 测试统计字典 - """ - console.print("🚀 [bold blue]开始运行 SAGE Examples 测试[/bold blue]") - - # 发现所有示例 - examples = self.analyzer.discover_examples() - - if not examples: - console.print("[yellow]没有发现任何示例文件[/yellow]") - return {"total": 0, "passed": 0, "failed": 0, "skipped": 0, "timeout": 0} - - # 过滤示例 - filtered_examples = self._filter_examples(examples, categories, quick_only) - - if not filtered_examples: - console.print("[yellow]没有符合条件的示例文件[/yellow]") - return {"total": 0, "passed": 0, "failed": 0, "skipped": 0, "timeout": 0} - - # 显示摘要 - self._show_examples_summary(filtered_examples) - - # 运行测试 - console.print(f"\n🧪 开始测试 {len(filtered_examples)} 个示例文件...") - - self.results = [] - for i, example in enumerate(filtered_examples, 1): - console.print(f"[{i}/{len(filtered_examples)}] 测试 {Path(example.file_path).name}...") - - result = self.runner.run_example(example) - self.results.append(result) - - # 显示结果 - status_emoji = { - "passed": "✅", - "failed": "❌", - "skipped": "⏭️", - "timeout": "⏰", - }.get(result.status, "❓") - - console.print( - f" {status_emoji} {result.status.upper()} ({result.execution_time:.2f}s)" - ) - if result.error: - console.print(f" 错误: {result.error}") - - # 显示结果和统计 - console.print("\n" + "=" * 50) - self._show_results() - stats = self._get_statistics() - - return stats - - def _filter_examples( - self, - examples: list[ExampleInfo], - categories: list[str] | None = None, - quick_only: bool = False, - ) -> list[ExampleInfo]: - """过滤示例 - - Args: - examples: 所有示例列表 - categories: 要包含的类别 - quick_only: 是否只包含快速测试 - - Returns: - 过滤后的示例列表 - """ - filtered = examples - - # 按类别过滤 - if categories: - filtered = [e for e in filtered if e.category in categories] - - # 按运行时间过滤 - if quick_only: - filtered = [e for e in filtered if e.estimated_runtime == "quick"] - - # 检查测试标记 - filtered = [e for e in filtered if "skip" not in e.test_tags] - - return filtered diff --git a/packages/sage-tools/src/sage/tools/dev/examples/utils.py b/packages/sage-tools/src/sage/tools/dev/examples/utils.py deleted file mode 100644 index b426baf3b6..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/examples/utils.py +++ /dev/null @@ -1,166 +0,0 @@ -""" -Utility functions for Examples testing tools - -This module provides helper functions for managing the development environment -and locating SAGE examples. -""" - -import os -import subprocess -from pathlib import Path - - -def find_examples_directory() -> Path | None: - """ - 查找 SAGE examples 目录 - - 这个函数按以下顺序查找： - 1. SAGE_ROOT 环境变量 - 2. 从当前工作目录向上查找（开发环境） - 3. Git 仓库根目录 - - Returns: - Path to examples directory if found, None otherwise - - Note: - 这个函数不会抛出异常，如果找不到会返回 None - """ - - # 1. 优先检查环境变量 - if sage_root := os.getenv("SAGE_ROOT"): - examples = Path(sage_root) / "examples" - if examples.exists() and examples.is_dir(): - return examples.resolve() - - # 2. 从当前工作目录向上查找（开发环境） - current = Path.cwd() - for _ in range(5): # 最多向上查找5层 - examples = current / "examples" - # 确认这是 SAGE 项目（通过检查 packages 目录） - if examples.exists() and (current / "packages").exists(): - return examples.resolve() - if current.parent == current: # 到达根目录 - break - current = current.parent - - # 3. 尝试从脚本位置推断（如果直接运行工具） - try: - # 从 sage-tools 包位置向上查找 - tools_path = Path(__file__).resolve() - # __file__ is in packages/sage-tools/src/sage/tools/dev/examples/utils.py - # Need to go up 7 levels to reach project root - potential_root = tools_path.parents[6] - examples = potential_root / "examples" - if examples.exists() and (potential_root / "packages").exists(): - return examples.resolve() - except (IndexError, OSError): - pass - - # 4. 检查是否在 Git 仓库中 - try: - result = subprocess.run( - ["git", "rev-parse", "--show-toplevel"], - capture_output=True, - text=True, - check=True, - timeout=5, - ) - git_root = Path(result.stdout.strip()) - examples = git_root / "examples" - # 确认这是 SAGE 仓库 - if examples.exists() and (git_root / "packages").exists(): - return examples.resolve() - except (subprocess.CalledProcessError, subprocess.TimeoutExpired, FileNotFoundError): - pass - - return None - - -def find_project_root() -> Path | None: - """ - 查找 SAGE 项目根目录 - - Returns: - Path to project root if found, None otherwise - """ - # 尝试从 sage-common 导入统一的路径管理 - try: - from sage.common.config.output_paths import find_project_root as find_sage_root - - return find_sage_root() - except ImportError: - pass - - # 回退到本地查找逻辑 - examples_dir = find_examples_directory() - if examples_dir: - return examples_dir.parent - - return None - - -def ensure_development_environment(raise_error: bool = False) -> bool: - """ - 确保当前环境是 SAGE 开发环境 - - Args: - raise_error: 如果为 True，在找不到开发环境时抛出异常 - - Returns: - True if development environment is available, False otherwise - - Raises: - RuntimeError: 如果 raise_error=True 且找不到开发环境 - """ - examples_dir = find_examples_directory() - - if examples_dir is None: - if raise_error: - raise RuntimeError( - "SAGE development environment not found.\n\n" - "The Examples testing tools require access to the SAGE source code.\n" - "This is typically only available in a development environment.\n\n" - "To use these tools:\n" - " 1. Clone the SAGE repository:\n" - " git clone https://github.com/intellistream/SAGE\n" - " cd SAGE\n" - " 2. Install sage-tools from source:\n" - " pip install -e packages/sage-tools[dev]\n" - " 3. Or set the SAGE_ROOT environment variable:\n" - " export SAGE_ROOT=/path/to/SAGE\n\n" - "Note: These tools are designed for SAGE developers and contributors,\n" - " not for end users who install via PyPI." - ) - return False - - return True - - -def get_development_info() -> dict: - """ - 获取开发环境信息 - - Returns: - Dictionary containing development environment information - """ - examples_dir = find_examples_directory() - project_root = find_project_root() - - info = { - "has_dev_env": examples_dir is not None, - "examples_dir": str(examples_dir) if examples_dir else None, - "project_root": str(project_root) if project_root else None, - "sage_root_env": os.getenv("SAGE_ROOT"), - "in_git_repo": False, - } - - # 检查是否在 Git 仓库中 - try: - subprocess.run( - ["git", "rev-parse", "--git-dir"], capture_output=True, check=True, timeout=2 - ) - info["in_git_repo"] = True - except (subprocess.CalledProcessError, subprocess.TimeoutExpired, FileNotFoundError): - pass - - return info diff --git a/packages/sage-tools/src/sage/tools/dev/hooks/__init__.py b/packages/sage-tools/src/sage/tools/dev/hooks/__init__.py deleted file mode 100644 index 8a75e8ee8d..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/hooks/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -""" -Git Hooks Management Module for SAGE Development. - -This module provides tools to install, manage, and configure Git hooks -for code quality checks, architecture compliance, and dev-notes validation. -""" - -from .installer import HooksInstaller -from .manager import HooksManager - -__all__ = ["HooksInstaller", "HooksManager"] diff --git a/packages/sage-tools/src/sage/tools/dev/hooks/installer.py b/packages/sage-tools/src/sage/tools/dev/hooks/installer.py deleted file mode 100644 index 1ef2c4b60b..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/hooks/installer.py +++ /dev/null @@ -1,434 +0,0 @@ -""" -Git Hooks Installer for SAGE Development. - -Handles installation, uninstallation, and management of Git hooks -for code quality and architecture compliance checks. -""" - -import subprocess -import sys -from pathlib import Path - - -class HooksInstaller: - """Installer for SAGE Git hooks.""" - - LIGHTWEIGHT = "lightweight" - FULL = "full" - _VALID_MODES = {LIGHTWEIGHT, FULL} - - def __init__( - self, - root_dir: Path | None = None, - quiet: bool = False, - mode: str = LIGHTWEIGHT, - ): - """ - Initialize the hooks installer. - - Args: - root_dir: Root directory of the SAGE project. If None, auto-detect from git. - quiet: If True, suppress non-error output. - """ - self.quiet = quiet - normalized_mode = mode.lower() if mode else self.LIGHTWEIGHT - if normalized_mode not in self._VALID_MODES: - normalized_mode = self.LIGHTWEIGHT - self.install_mode = normalized_mode - self.root_dir = root_dir or self._detect_git_root() - self.hooks_dir = self.root_dir / ".git" / "hooks" - self.templates_dir = Path(__file__).parent / "templates" - - # Colors for output - self.RED = "\033[0;31m" - self.GREEN = "\033[0;32m" - self.YELLOW = "\033[1;33m" - self.BLUE = "\033[0;34m" - self.NC = "\033[0m" - - def _detect_git_root(self) -> Path: - """Detect the Git repository root directory.""" - try: - result = subprocess.run( - ["git", "rev-parse", "--show-toplevel"], - capture_output=True, - text=True, - check=True, - ) - return Path(result.stdout.strip()) - except (subprocess.CalledProcessError, FileNotFoundError): - # Fall back to current directory - return Path.cwd() - - def _print_info(self, message: str) -> None: - """Print info message (respecting quiet mode).""" - if not self.quiet: - print(message) - - def _print_success(self, message: str) -> None: - """Print success message (respecting quiet mode).""" - if not self.quiet: - print(f"{self.GREEN}{message}{self.NC}") - - def _print_warning(self, message: str) -> None: - """Print warning message (always shown).""" - print(f"{self.YELLOW}{message}{self.NC}") - - def _print_error(self, message: str) -> None: - """Print error message (always shown).""" - print(f"{self.RED}{message}{self.NC}", file=sys.stderr) - - def _check_git_repo(self) -> bool: - """Check if we're in a Git repository.""" - git_dir = self.root_dir / ".git" - if not git_dir.exists(): - self._print_error("❌ 错误: 不在 Git 仓库中") - return False - return True - - def _backup_existing_hook(self, hook_path: Path) -> None: - """Backup existing hook if it exists and is not a symlink.""" - # Check if it's a broken symlink - if hook_path.is_symlink() and not hook_path.exists(): - # It's a broken symlink, just remove it - hook_path.unlink() - self._print_warning(f"⚠️ 删除损坏的符号链接: {hook_path.name}") - return - - if hook_path.exists() and not hook_path.is_symlink(): - from datetime import datetime - - backup_name = f"{hook_path.name}.backup.{datetime.now().strftime('%Y%m%d_%H%M%S')}" - backup_path = hook_path.parent / backup_name - self._print_warning(f"⚠️ 备份现有 {hook_path.name} hook 到: {backup_name}") - hook_path.rename(backup_path) - - def _install_pre_commit_hook(self) -> bool: - """Install the pre-commit hook.""" - self._print_info("") - self._print_info("📦 安装 pre-commit hook...") - - pre_commit_template = self.templates_dir / "pre-commit" - pre_commit_dst = self.hooks_dir / "pre-commit" - - if not pre_commit_template.exists(): - self._print_error(f"❌ 错误: 找不到 pre-commit 模板文件: {pre_commit_template}") - return False - - # Ensure hooks directory exists - self.hooks_dir.mkdir(parents=True, exist_ok=True) - - # Remove any existing hook (including broken symlinks) - if pre_commit_dst.exists() or pre_commit_dst.is_symlink(): - self._backup_existing_hook(pre_commit_dst) - - # Copy the template - import shutil - - shutil.copy2(pre_commit_template, pre_commit_dst) - pre_commit_dst.chmod(0o755) - - self._print_success("✅ pre-commit hook 已安装") - return True - - def _install_pre_commit_framework(self) -> bool: - """Install and configure the pre-commit framework.""" - self._print_info("") - self._print_info("📦 检查 pre-commit 框架...") - - # Check if pre-commit is available - try: - subprocess.run( - ["pre-commit", "--version"], - capture_output=True, - check=True, - ) - except (subprocess.CalledProcessError, FileNotFoundError): - self._print_warning("⚠️ pre-commit 未安装") - self._print_info(" 代码质量检查将被跳过") - self._print_info(" 安装: pip install pre-commit") - return False - - # Install hooks - if self.install_mode == self.LIGHTWEIGHT: - self._print_info( - " pre-commit 已安装，使用轻量级模式配置 hooks (首次提交时再下载工具链)..." - ) - else: - self._print_info(" pre-commit 已安装，配置完整 hooks...") - pre_commit_config = self.root_dir / "tools" / "pre-commit-config.yaml" - - if not pre_commit_config.exists(): - self._print_warning(f"⚠️ 未找到 pre-commit 配置文件: {pre_commit_config}") - return False - - install_cmd = [ - "pre-commit", - "install", - "--config", - str(pre_commit_config), - ] - if self.install_mode == self.FULL: - install_cmd.append("--install-hooks") - else: - self._print_info(" 将在首次 git commit 时自动下载所有 hook 依赖") - - try: - subprocess.run( - install_cmd, - cwd=str(self.root_dir), - capture_output=True, - check=True, - ) - self._print_success("✅ pre-commit 框架已配置") - return True - except subprocess.CalledProcessError: - self._print_warning("⚠️ pre-commit 框架配置失败") - return False - - def _test_architecture_checker(self) -> bool: - """Test if architecture checker is available.""" - if not self.quiet: - self._print_info("") - self._print_info("🧪 测试 architecture checker...") - - # Try using sage-dev command first - try: - subprocess.run( - ["sage-dev", "check-architecture", "--help"], - capture_output=True, - check=True, - ) - if not self.quiet: - self._print_success("✅ Architecture checker 可用 (sage-dev)") - return True - except (subprocess.CalledProcessError, FileNotFoundError): - pass - - # Try Python module import - try: - subprocess.run( - [ - sys.executable, - "-c", - "from sage.tools.dev.tools.architecture_checker import ArchitectureChecker", - ], - capture_output=True, - check=True, - ) - if not self.quiet: - self._print_success("✅ Architecture checker 可用 (Python module)") - return True - except subprocess.CalledProcessError: - if not self.quiet: - self._print_warning("⚠️ Architecture checker 测试失败，但 hook 已安装") - self._print_info(" 您可能需要安装 sage-tools: pip install -e packages/sage-tools") - return False - - def install(self) -> bool: - """ - Install Git hooks. - - Returns: - True if installation was successful, False otherwise. - """ - self._print_info("🔧 安装 SAGE Git Hooks...") - - # Check if in Git repo - if not self._check_git_repo(): - return False - - # Install pre-commit hook - if not self._install_pre_commit_hook(): - return False - - # Install pre-commit framework - self._install_pre_commit_framework() - - # Test architecture checker - self._test_architecture_checker() - - # Print summary - if not self.quiet: - self._print_info("") - self._print_info("=" * 70) - self._print_success("✅ Git hooks 安装完成！") - self._print_info("") - self._print_info("以下功能已激活:") - self._print_info(" • 代码质量检查: black, isort, ruff, mypy（需要 pre-commit）") - self._print_info(" • Dev-notes 文档规范检查: 分类、元数据等") - self._print_info(" • 架构合规性检查: 包依赖、导入路径等") - if self.install_mode == self.LIGHTWEIGHT: - self._print_info("") - self._print_info( - "💡 当前为轻量级模式：首次运行 pre-commit 时会自动下载完整工具链。" - ) - self._print_info("") - self._print_info("使用方法:") - self._print_info(" • 正常提交: git commit -m 'message'") - self._print_info(" • 跳过检查: git commit --no-verify -m 'message'") - self._print_info(" • 安装代码检查工具: pip install pre-commit") - self._print_info("") - self._print_info("相关文档:") - self._print_info(" • 架构规范: docs/PACKAGE_ARCHITECTURE.md") - self._print_info(" • 文档模板: docs/dev-notes/TEMPLATE.md") - self._print_info("=" * 70) - - return True - - def uninstall(self) -> bool: - """ - Uninstall Git hooks. - - Returns: - True if uninstallation was successful, False otherwise. - """ - self._print_info("🗑️ 卸载 SAGE Git Hooks...") - - # Check if in Git repo - if not self._check_git_repo(): - return False - - # Remove pre-commit hook - pre_commit_hook = self.hooks_dir / "pre-commit" - if pre_commit_hook.exists(): - pre_commit_hook.unlink() - self._print_success("✅ pre-commit hook 已删除") - else: - self._print_info("ℹ️ pre-commit hook 不存在") - - # Uninstall pre-commit framework hooks (optional) - try: - subprocess.run( - ["pre-commit", "uninstall"], - cwd=str(self.root_dir), - capture_output=True, - check=False, - ) - self._print_success("✅ pre-commit 框架 hooks 已卸载") - except FileNotFoundError: - pass - - self._print_success("✅ Git hooks 卸载完成！") - return True - - def status(self) -> dict: - """ - Check the status of installed hooks. - - Returns: - Dictionary with hook status information. - """ - status_info = { - "git_repo": self._check_git_repo(), - "pre_commit_hook_installed": False, - "pre_commit_framework_installed": False, - "architecture_checker_available": False, - "devnotes_checker_available": False, - } - - if not status_info["git_repo"]: - return status_info - - # Check pre-commit hook - pre_commit_hook = self.hooks_dir / "pre-commit" - status_info["pre_commit_hook_installed"] = pre_commit_hook.exists() - - # Check pre-commit framework - try: - subprocess.run( - ["pre-commit", "--version"], - capture_output=True, - check=True, - ) - status_info["pre_commit_framework_installed"] = True - except (subprocess.CalledProcessError, FileNotFoundError): - status_info["pre_commit_framework_installed"] = False - - # Check architecture checker - try: - subprocess.run( - ["sage-dev", "check-architecture", "--help"], - capture_output=True, - check=True, - ) - status_info["architecture_checker_available"] = True - except (subprocess.CalledProcessError, FileNotFoundError): - status_info["architecture_checker_available"] = False - - # Check devnotes checker - try: - subprocess.run( - ["sage-dev", "check-devnotes", "--help"], - capture_output=True, - check=True, - ) - status_info["devnotes_checker_available"] = True - except (subprocess.CalledProcessError, FileNotFoundError): - status_info["devnotes_checker_available"] = False - - return status_info - - def print_status(self) -> None: - """Print the status of installed hooks in a human-readable format.""" - status = self.status() - - print("\n" + "=" * 70) - print("📊 SAGE Git Hooks 状态") - print("=" * 70) - - # Git repo status - if status["git_repo"]: - print(f"{self.GREEN}✅ Git 仓库: 是{self.NC}") - else: - print(f"{self.RED}❌ Git 仓库: 否{self.NC}") - print("\n" + "=" * 70) - return - - # Pre-commit hook - if status["pre_commit_hook_installed"]: - print(f"{self.GREEN}✅ Pre-commit Hook: 已安装{self.NC}") - else: - print(f"{self.YELLOW}⚠️ Pre-commit Hook: 未安装{self.NC}") - - # Pre-commit framework - if status["pre_commit_framework_installed"]: - print(f"{self.GREEN}✅ Pre-commit 框架: 已安装{self.NC}") - else: - print(f"{self.YELLOW}⚠️ Pre-commit 框架: 未安装{self.NC}") - print(f" {self.BLUE}安装: pip install pre-commit{self.NC}") - - # Architecture checker - if status["architecture_checker_available"]: - print(f"{self.GREEN}✅ Architecture Checker: 可用{self.NC}") - else: - print(f"{self.YELLOW}⚠️ Architecture Checker: 不可用{self.NC}") - print(f" {self.BLUE}安装: pip install -e packages/sage-tools{self.NC}") - - # Devnotes checker - if status["devnotes_checker_available"]: - print(f"{self.GREEN}✅ DevNotes Checker: 可用{self.NC}") - else: - print(f"{self.YELLOW}⚠️ DevNotes Checker: 不可用{self.NC}") - print(f" {self.BLUE}安装: pip install -e packages/sage-tools{self.NC}") - - print("\n" + "=" * 70) - - # Recommendations - if not all( - [ - status["pre_commit_hook_installed"], - status["pre_commit_framework_installed"], - status["architecture_checker_available"], - ] - ): - print("\n💡 建议:") - if not status["pre_commit_hook_installed"]: - print( - f" {self.BLUE}• 运行 'sage-dev maintain hooks install' 安装 Git hooks{self.NC}" - ) - if not status["pre_commit_framework_installed"]: - print( - f" {self.BLUE}• 运行 'pip install pre-commit' 安装代码质量检查工具{self.NC}" - ) - print("") diff --git a/packages/sage-tools/src/sage/tools/dev/hooks/manager.py b/packages/sage-tools/src/sage/tools/dev/hooks/manager.py deleted file mode 100644 index 302e038e64..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/hooks/manager.py +++ /dev/null @@ -1,72 +0,0 @@ -""" -Git Hooks Manager for SAGE Development. - -Provides high-level management interface for Git hooks. -""" - -from pathlib import Path - -from .installer import HooksInstaller - - -class HooksManager: - """Manager for SAGE Git hooks.""" - - def __init__( - self, - root_dir: Path | None = None, - mode: str = HooksInstaller.LIGHTWEIGHT, - ): - """ - Initialize the hooks manager. - - Args: - root_dir: Root directory of the SAGE project. - mode: Installation mode ("lightweight" or "full"). - """ - self.root_dir = root_dir - self.mode = mode - - def _create_installer(self, quiet: bool = False) -> HooksInstaller: - return HooksInstaller(root_dir=self.root_dir, quiet=quiet, mode=self.mode) - - def install(self, quiet: bool = False) -> bool: - """ - Install Git hooks. - - Args: - quiet: If True, suppress non-error output. - - Returns: - True if installation was successful, False otherwise. - """ - installer = self._create_installer(quiet=quiet) - return installer.install() - - def uninstall(self, quiet: bool = False) -> bool: - """ - Uninstall Git hooks. - - Args: - quiet: If True, suppress non-error output. - - Returns: - True if uninstallation was successful, False otherwise. - """ - installer = self._create_installer(quiet=quiet) - return installer.uninstall() - - def status(self) -> dict: - """ - Get the status of installed hooks. - - Returns: - Dictionary with hook status information. - """ - installer = self._create_installer(quiet=True) - return installer.status() - - def print_status(self) -> None: - """Print the status of installed hooks.""" - installer = self._create_installer(quiet=True) - installer.print_status() diff --git a/packages/sage-tools/src/sage/tools/dev/hooks/templates/pre-commit b/packages/sage-tools/src/sage/tools/dev/hooks/templates/pre-commit deleted file mode 100644 index 25fa46a74d..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/hooks/templates/pre-commit +++ /dev/null @@ -1,2 +0,0 @@ -#!/bin/bash -echo test diff --git a/packages/sage-tools/src/sage/tools/dev/maintenance/__init__.py b/packages/sage-tools/src/sage/tools/dev/maintenance/__init__.py deleted file mode 100644 index 9737ec163a..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/maintenance/__init__.py +++ /dev/null @@ -1,42 +0,0 @@ -""" -SAGE 维护工具模块 - -提供各种项目维护相关的工具： -- Dev-notes 文档整理 -- 元数据修复 -- Ruff 规则更新 -- 等等 - -Author: SAGE Team -Date: 2025-10-27 -""" - -from typing import TYPE_CHECKING - -if TYPE_CHECKING: - from sage.tools.dev.maintenance.devnotes_organizer import DevNotesOrganizer - from sage.tools.dev.maintenance.metadata_fixer import MetadataFixer - from sage.tools.dev.maintenance.ruff_updater import RuffIgnoreUpdater - -__all__ = [ - "DevNotesOrganizer", - "MetadataFixer", - "RuffIgnoreUpdater", -] - - -def __getattr__(name: str): - """延迟导入以提高启动速度""" - if name == "DevNotesOrganizer": - from sage.tools.dev.maintenance.devnotes_organizer import DevNotesOrganizer - - return DevNotesOrganizer - elif name == "MetadataFixer": - from sage.tools.dev.maintenance.metadata_fixer import MetadataFixer - - return MetadataFixer - elif name == "RuffIgnoreUpdater": - from sage.tools.dev.maintenance.ruff_updater import RuffIgnoreUpdater - - return RuffIgnoreUpdater - raise AttributeError(f"module {__name__!r} has no attribute {name!r}") diff --git a/packages/sage-tools/src/sage/tools/dev/maintenance/devnotes_organizer.py b/packages/sage-tools/src/sage/tools/dev/maintenance/devnotes_organizer.py deleted file mode 100644 index de9b634d1a..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/maintenance/devnotes_organizer.py +++ /dev/null @@ -1,312 +0,0 @@ -""" -Dev-notes 文档整理工具 - -帮助整理现有的 dev-notes 文档： -1. 分析文档内容 -2. 建议分类目录 -3. 检查元数据 -4. 生成整理建议 - -从 tools/maintenance/helpers/devnotes_organizer.py 迁移 - -Author: SAGE Team -Date: 2025-10-27 -""" - -import re -from pathlib import Path - -# 关键词到分类的映射 -CATEGORY_KEYWORDS = { - "architecture": [ - "architecture", - "design", - "system", - "structure", - "模块", - "架构", - "设计", - ], - "kernel": ["kernel", "runtime", "scheduler", "dispatcher", "内核"], - "middleware": ["middleware", "operator", "component", "中间件", "组件"], - "libs": ["lib", "library", "agent", "rag", "tool", "库"], - "apps": ["app", "application", "应用"], - "ci-cd": ["ci", "cd", "build", "workflow", "github", "action", "构建"], - "performance": ["performance", "optimization", "speed", "性能", "优化"], - "security": ["security", "vulnerability", "安全", "漏洞"], - "testing": ["test", "testing", "pytest", "测试"], - "deployment": ["deploy", "deployment", "install", "部署", "安装"], - "migration": ["migration", "refactor", "cleanup", "迁移", "重构", "清理"], - "tools": ["tool", "script", "cli", "工具", "脚本"], -} - - -class DevNotesOrganizer: - """Dev-notes 文档整理器""" - - def __init__(self, root_dir: Path): - """ - 初始化整理器 - - Args: - root_dir: 项目根目录 - """ - self.root_dir = Path(root_dir) - self.devnotes_dir = self.root_dir / "docs" / "dev-notes" - - def analyze_file(self, file_path: Path) -> dict: - """ - 分析单个文件 - - Args: - file_path: 文件路径 - - Returns: - 分析结果字典 - """ - try: - content = file_path.read_text(encoding="utf-8") - except Exception as e: - return { - "error": str(e), - "suggested_category": "unknown", - "has_metadata": False, - } - - # 获取相对路径 - try: - rel_path = file_path.relative_to(self.devnotes_dir) - except ValueError: - return {"error": "文件不在 dev-notes 目录中"} - - # 检查元数据 - has_date, has_author, has_summary = self._check_metadata(content) - - # 分析内容并建议分类 - suggested_category = self._suggest_category(file_path.name, content) - - # 检查文件大小 - file_size = len(content) - - return { - "path": str(rel_path), - "has_date": has_date, - "has_author": has_author, - "has_summary": has_summary, - "suggested_category": suggested_category, - "file_size": file_size, - "is_empty": file_size < 100, - "current_category": (rel_path.parts[0] if len(rel_path.parts) > 1 else "root"), - } - - def _check_metadata(self, content: str) -> tuple[bool, bool, bool]: - """ - 检查文档元数据 - - Args: - content: 文档内容 - - Returns: - (has_date, has_author, has_summary) 元组 - """ - lines = content.split("\n")[:30] - has_date = False - has_author = False - has_summary = False - - for line in lines: - if re.search(r"\*?\*?Date\*?\*?\s*[:：]", line, re.IGNORECASE): - has_date = True - if re.search(r"\*?\*?Author\*?\*?\s*[:：]", line, re.IGNORECASE): - has_author = True - if re.search(r"\*?\*?Summary\*?\*?\s*[:：]", line, re.IGNORECASE): - has_summary = True - - return has_date, has_author, has_summary - - def _suggest_category(self, filename: str, content: str) -> str: - """ - 根据文件名和内容建议分类 - - Args: - filename: 文件名 - content: 文件内容 - - Returns: - 建议的分类名 - """ - # 转为小写用于匹配 - text = (filename + " " + content[:1000]).lower() - - # 统计每个分类的关键词匹配数 - scores = {} - for category, keywords in CATEGORY_KEYWORDS.items(): - score = sum(1 for keyword in keywords if keyword in text) - if score > 0: - scores[category] = score - - # 返回得分最高的分类 - if scores: - return max(scores, key=scores.get) # type: ignore[arg-type,return-value] - return "migration" # 默认归为迁移类 - - def analyze_all(self) -> list[dict]: - """ - 分析所有文档文件 - - Returns: - 分析结果列表 - """ - all_files = list(self.devnotes_dir.rglob("*.md")) - # 排除特殊文件 - all_files = [f for f in all_files if f.name not in ["README.md", "TEMPLATE.md"]] - - results = [] - for file_path in all_files: - result = self.analyze_file(file_path) - results.append(result) - - return results - - def generate_report(self, results: list[dict], verbose: bool = True) -> dict: - """ - 生成整理报告 - - Args: - results: 分析结果列表 - verbose: 是否打印详细信息 - - Returns: - 报告数据字典 - """ - # 统计 - total = len(results) - root_files = [r for r in results if r.get("current_category") == "root"] - missing_metadata = [ - r - for r in results - if not (r.get("has_date") and r.get("has_author") and r.get("has_summary")) - ] - empty_files = [r for r in results if r.get("is_empty")] - - report_data = { - "total": total, - "root_files": root_files, - "missing_metadata": missing_metadata, - "empty_files": empty_files, - } - - if verbose: - self._print_report(report_data) - - return report_data - - def _print_report(self, report_data: dict) -> None: - """打印报告""" - total = report_data["total"] - root_files = report_data["root_files"] - missing_metadata = report_data["missing_metadata"] - empty_files = report_data["empty_files"] - - print("=" * 80) - print("📊 Dev-notes 文档整理报告") - print("=" * 80) - print() - - print(f"📁 总文件数: {total}") - print(f"📂 根目录文件: {len(root_files)} ⚠️") - print(f"📝 缺少元数据: {len(missing_metadata)}") - print(f"🗑️ 空文件/过小: {len(empty_files)}") - print() - - # 根目录文件（需要移动） - if root_files: - print("=" * 80) - print("⚠️ 根目录文件（需要移动到分类目录）") - print("=" * 80) - print() - for r in root_files: - path = r.get("path") - suggested = r.get("suggested_category", "unknown") - print(f"📄 {path}") - print(f" 建议分类: {suggested}/") - print( - f" 移动命令: git mv docs/dev-notes/{path} docs/dev-notes/{suggested}/{path}" - ) - print() - - # 空文件（建议删除） - if empty_files: - print("=" * 80) - print("🗑️ 空文件或内容过少（建议删除）") - print("=" * 80) - print() - for r in empty_files: - path = r.get("path") - size = r.get("file_size", 0) - print(f"📄 {path} ({size} bytes)") - print(f" 删除命令: git rm docs/dev-notes/{path}") - print() - - # 缺少元数据的文件 - if missing_metadata: - print("=" * 80) - print("📝 缺少元数据的文件（需要补充）") - print("=" * 80) - print() - for r in missing_metadata[:10]: # 只显示前10个 - if r.get("is_empty"): - continue # 空文件已在上面列出 - path = r.get("path") - missing = [] - if not r.get("has_date"): - missing.append("Date") - if not r.get("has_author"): - missing.append("Author") - if not r.get("has_summary"): - missing.append("Summary") - print(f"📄 {path}") - print(f" 缺少字段: {', '.join(missing)}") - print() - - if len(missing_metadata) > 10: - print(f"... 还有 {len(missing_metadata) - 10} 个文件") - print() - - # 生成清理脚本 - print("=" * 80) - print("🔧 自动化清理脚本") - print("=" * 80) - print() - print("# 删除空文件") - for r in empty_files: - path = r.get("path") - print(f'git rm "docs/dev-notes/{path}"') - print() - print("# 移动根目录文件到建议的分类") - for r in root_files: - if r.get("is_empty"): - continue # 空文件已标记删除 - path = r.get("path") - suggested = r.get("suggested_category", "migration") - # 创建目标目录（如果不存在） - print(f'mkdir -p "docs/dev-notes/{suggested}"') - print(f'git mv "docs/dev-notes/{path}" "docs/dev-notes/{suggested}/{path}"') - print() - - # 总结 - print("=" * 80) - print("📋 整理建议") - print("=" * 80) - print() - print(f"1. 删除 {len(empty_files)} 个空文件或内容过少的文件") - print( - f"2. 移动 {len([r for r in root_files if not r.get('is_empty')])} 个根目录文件到分类目录" - ) - print(f"3. 为 {len(missing_metadata)} 个文件补充元数据") - print() - print("💡 提示:") - print(" - 复制上面的命令到终端执行") - print(" - 或者使用 sage-dev maintenance fix-metadata 自动修复") - print(" - 重要文档可以移动到 docs-public 下") - print() diff --git a/packages/sage-tools/src/sage/tools/dev/maintenance/metadata_fixer.py b/packages/sage-tools/src/sage/tools/dev/maintenance/metadata_fixer.py deleted file mode 100644 index 2c9f800808..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/maintenance/metadata_fixer.py +++ /dev/null @@ -1,236 +0,0 @@ -""" -Dev-notes 文档元数据批量修复工具 - -从 tools/maintenance/helpers/batch_fix_devnotes_metadata.py 迁移 - -Author: SAGE Team -Date: 2025-10-27 -""" - -from pathlib import Path - -# 预定义需要修复的文件列表 -DEFAULT_FILES_TO_FIX = { - # architecture/ - "docs/dev-notes/architecture/DATA_TYPES_ARCHITECTURE.md": { - "date": "2024-10-20", - "summary": "SAGE 分层数据类型系统设计文档，包括 BaseDocument、RAGDocument 等核心类型的架构说明", - }, - "docs/dev-notes/architecture/KERNEL_REFACTORING_ANALYSIS_1041.md": { - "date": "2025-10-24", - "summary": "Kernel 层功能重构分析，探讨将部分功能下沉到 platform 或 common 层的可行性", - }, - "docs/dev-notes/architecture/NEUROMEM_ARCHITECTURE_ANALYSIS.md": { - "date": "2025-01-22", - "summary": "NeuroMem 作为独立记忆体组件的完整性评估，包括存储、检索、管理等核心功能分析", - }, - "docs/dev-notes/architecture/SAGE_CHAT_ARCHITECTURE.md": { - "date": "2024-10-15", - "summary": "SAGE Chat 架构设计文档，包括对话管理、上下文处理和多轮对话支持", - }, - "docs/dev-notes/architecture/VLLM_SERVICE_INTEGRATION_DESIGN.md": { - "date": "2024-09-20", - "summary": "vLLM 服务集成设计，包括 API 封装、配置管理和性能优化策略", - }, - # archive/ - "docs/dev-notes/archive/PR_DESCRIPTION.md": { - "date": "2024-08-15", - "summary": "PR 描述模板和规范说明", - }, - # autostop/ - "docs/dev-notes/autostop/AUTOSTOP_MODE_SUPPORT.md": { - "date": "2024-11-10", - "summary": "AutoStop 模式支持文档，包括自动停止机制的设计和实现", - }, - "docs/dev-notes/autostop/AUTOSTOP_SERVICE_FIX_SUMMARY.md": { - "date": "2024-11-12", - "summary": "AutoStop 服务修复总结，包括已知问题和解决方案", - }, - "docs/dev-notes/autostop/REMOTE_AUTOSTOP_IMPLEMENTATION.md": { - "date": "2024-11-15", - "summary": "远程 AutoStop 实现文档，支持分布式环境下的自动停止功能", - }, - # migration/ - "docs/dev-notes/migration/EMBEDDING_SYSTEM_COMPLETE_SUMMARY.md": { - "date": "2024-09-25", - "summary": "Embedding 系统迁移完整总结，包括架构变更和性能对比", - }, - # security/ - "docs/dev-notes/security/CONFIG_CLEANUP_REPORT.md": { - "date": "2024-10-05", - "summary": "配置文件清理报告，移除敏感信息和优化配置结构", - }, - "docs/dev-notes/security/SECURITY_UPDATE_SUMMARY.md": { - "date": "2024-10-08", - "summary": "安全更新总结，包括漏洞修复和安全加固措施", - }, - "docs/dev-notes/security/api_key_security.md": { - "date": "2024-09-30", - "summary": "API 密钥安全管理指南，包括存储、使用和轮换最佳实践", - }, - "docs/dev-notes/security/TODO_SECURITY_CHECKLIST.md": { - "date": "2024-10-01", - "summary": "安全检查清单，包含代码审计、依赖扫描等待办事项", - }, -} - - -class MetadataFixer: - """Dev-notes 元数据修复器""" - - def __init__(self, root_dir: Path | None = None): - """ - 初始化修复器 - - Args: - root_dir: 项目根目录，默认为当前目录 - """ - self.root_dir = Path(root_dir) if root_dir else Path.cwd() - - def fix_file(self, filepath: str, metadata: dict[str, str]) -> bool: - """ - 为单个文件添加元数据 - - Args: - filepath: 相对于项目根目录的文件路径 - metadata: 元数据字典，包含 date 和 summary - - Returns: - 是否成功修复 - """ - path = self.root_dir / filepath - - if not path.exists(): - print(f"⚠️ 文件不存在: {filepath}") - return False - - try: - content = path.read_text(encoding="utf-8") - except Exception as e: - print(f"⚠️ 读取文件失败: {filepath} - {e}") - return False - - lines = content.split("\n") - - if not lines: - print(f"⚠️ 文件为空: {filepath}") - return False - - # 检查是否已有元数据 - if "**Date**:" in content and "**Author**:" in content and "**Summary**:" in content: - print(f"✓ 已有元数据: {filepath}") - return True - - # 找到标题行 - title_line_idx = 0 - for i, line in enumerate(lines): - if line.strip().startswith("#"): - title_line_idx = i - break - - # 构造元数据 - metadata_lines = [ - "", - f"**Date**: {metadata['date']} ", - "**Author**: SAGE Team ", - f"**Summary**: {metadata['summary']}", - "", - "---", - "", - ] - - # 插入元数据 - new_lines = lines[: title_line_idx + 1] + metadata_lines + lines[title_line_idx + 1 :] - - # 写回文件 - try: - path.write_text("\n".join(new_lines), encoding="utf-8") - print(f"✅ 已修复: {filepath}") - return True - except Exception as e: - print(f"⚠️ 写入文件失败: {filepath} - {e}") - return False - - def fix_all(self, files_to_fix: dict[str, dict[str, str]] | None = None) -> dict[str, int]: - """ - 批量修复文件元数据 - - Args: - files_to_fix: 要修复的文件字典，默认使用 DEFAULT_FILES_TO_FIX - - Returns: - 修复统计字典 {'success': 成功数, 'failed': 失败数, 'skipped': 跳过数} - """ - if files_to_fix is None: - files_to_fix = DEFAULT_FILES_TO_FIX - - print("🔧 批量修复 dev-notes 文档元数据") - print(f"📝 需要修复 {len(files_to_fix)} 个文件\n") - - stats = {"success": 0, "failed": 0, "skipped": 0} - - for filepath, metadata in files_to_fix.items(): - result = self.fix_file(filepath, metadata) - if result: - # 检查是否是跳过（已有元数据） - if "已有元数据" in str(result): - stats["skipped"] += 1 - else: - stats["success"] += 1 - else: - stats["failed"] += 1 - - print("\n" + "=" * 80) - print(f"✅ 成功修复: {stats['success']}") - print(f"⏭️ 已跳过: {stats['skipped']}") - print(f"❌ 失败: {stats['failed']}") - print("=" * 80) - - return stats - - def scan_and_fix(self, devnotes_dir: Path | None = None) -> dict[str, int]: - """ - 扫描 dev-notes 目录并修复缺失元数据的文件 - - Args: - devnotes_dir: dev-notes 目录路径，默认为 docs/dev-notes - - Returns: - 修复统计字典 - """ - if devnotes_dir is None: - devnotes_dir = self.root_dir / "docs" / "dev-notes" - - if not devnotes_dir.exists(): - print(f"⚠️ 目录不存在: {devnotes_dir}") - return {"success": 0, "failed": 0, "skipped": 0} - - # 扫描所有 markdown 文件 - all_files = list(devnotes_dir.rglob("*.md")) - all_files = [f for f in all_files if f.name not in ["README.md", "TEMPLATE.md"]] - - files_need_fix = {} - - for file_path in all_files: - try: - content = file_path.read_text(encoding="utf-8") - # 检查是否缺少元数据 - if not ("**Date**:" in content and "**Summary**:" in content): - rel_path = file_path.relative_to(self.root_dir) - # 生成默认元数据 - files_need_fix[str(rel_path)] = { - "date": "2024-01-01", # 默认日期 - "summary": "待补充文档摘要", # 默认摘要 - } - except Exception: - continue - - if not files_need_fix: - print("✅ 所有文件都有完整的元数据！") - return {"success": 0, "failed": 0, "skipped": len(all_files)} - - print(f"📋 发现 {len(files_need_fix)} 个文件缺少元数据") - print("⚠️ 这些文件将使用默认元数据，请手动更新") - print() - - return self.fix_all(files_need_fix) diff --git a/packages/sage-tools/src/sage/tools/dev/maintenance/ruff_updater.py b/packages/sage-tools/src/sage/tools/dev/maintenance/ruff_updater.py deleted file mode 100644 index 35a5a1c961..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/maintenance/ruff_updater.py +++ /dev/null @@ -1,207 +0,0 @@ -""" -Ruff ignore 规则更新工具 - -批量更新所有 pyproject.toml 文件中的 ruff.lint.ignore 规则 - -从 tools/maintenance/helpers/update_ruff_ignore.py 迁移 - -Author: SAGE Team -Date: 2025-10-27 -""" - -import re -from pathlib import Path - -# 默认的包 pyproject.toml 文件列表 -DEFAULT_PACKAGE_FILES = [ - "packages/sage-benchmark/pyproject.toml", - "packages/sage-common/pyproject.toml", - "packages/sage-kernel/pyproject.toml", - "packages/sage-middleware/pyproject.toml", - "packages/sage-tools/pyproject.toml", - "packages/sage-libs/pyproject.toml", - "packages/sage/pyproject.toml", - "packages/sage-studio/pyproject.toml", - "packages/sage-apps/pyproject.toml", - "packages/sage-platform/pyproject.toml", -] - - -class RuffIgnoreUpdater: - """Ruff ignore 规则更新器""" - - def __init__(self, root_dir: Path | None = None): - """ - 初始化更新器 - - Args: - root_dir: 项目根目录，默认为当前目录 - """ - self.root_dir = Path(root_dir) if root_dir else Path.cwd() - - def update_file( - self, - file_path: Path, - rules_to_add: list[str], - descriptions: dict[str, str] | None = None, - ) -> bool: - """ - 更新单个 pyproject.toml 文件 - - Args: - file_path: 文件路径 - rules_to_add: 要添加的规则列表，如 ["B904", "C901"] - descriptions: 规则描述字典，如 {"B904": "raise-without-from"} - - Returns: - 是否有更新 - """ - if not file_path.exists(): - print(f"⚠️ 文件不存在: {file_path}") - return False - - try: - content = file_path.read_text(encoding="utf-8") - except Exception as e: - print(f"⚠️ 读取文件失败: {file_path} - {e}") - return False - - # 检查是否已经有所有规则 - all_present = all(f'"{rule}"' in content for rule in rules_to_add) - if all_present: - print(f"✅ {file_path.name} 已包含所有规则") - return False - - # 查找 [tool.ruff.lint] 下的 ignore 部分 - # 匹配模式: ignore = [ ... ] - pattern = r"(ignore\s*=\s*\[)(.*?)(\])" - - def replace_ignore(match): - prefix = match.group(1) - existing = match.group(2) - suffix = match.group(3) - - # 解析现有的 ignore 列表 - lines = existing.split("\n") - - # 检查哪些规则需要添加 - rules_to_insert = [] - for rule in rules_to_add: - if not any(f'"{rule}"' in line or f"'{rule}'" in line for line in lines): - rules_to_insert.append(rule) - - if not rules_to_insert: - return match.group(0) - - # 找到最后一个有效条目 - result_lines = [] - for line in lines: - stripped = line.strip() - if stripped and not stripped.startswith("#"): - result_lines.append(line) - - # 如果 ignore 列表为空或只有注释 - if not result_lines: - # 添加新条目 - new_entries = [] - for rule in rules_to_insert: - desc = descriptions.get(rule, "") if descriptions else "" - comment = f" # {desc}" if desc else "" - new_entries.append(f' "{rule}",{comment}') - new_content = "\n" + "\n".join(new_entries) + "\n" - return f"{prefix}{new_content}{suffix}" - - # 在最后一个条目后添加 - new_lines = lines.copy() - - # 找到插入位置（最后一个非空非注释行之后） - insert_idx = len(new_lines) - for i in range(len(new_lines) - 1, -1, -1): - stripped = new_lines[i].strip() - if stripped and not stripped.startswith("#"): - insert_idx = i + 1 - # 确保最后一项有逗号 - if not new_lines[i].rstrip().endswith(","): - new_lines[i] = new_lines[i].rstrip() + "," - break - - # 添加新规则 - for rule in rules_to_insert: - desc = descriptions.get(rule, "") if descriptions else "" - comment = f" # {desc}" if desc else "" - new_lines.insert(insert_idx, f' "{rule}",{comment}') - insert_idx += 1 - - new_content = "\n".join(new_lines) - return f"{prefix}{new_content}{suffix}" - - # 执行替换 - new_content = re.sub(pattern, replace_ignore, content, flags=re.DOTALL) - - if new_content != content: - try: - file_path.write_text(new_content, encoding="utf-8") - print(f"✅ 更新: {file_path}") - return True - except Exception as e: - print(f"⚠️ 写入文件失败: {file_path} - {e}") - return False - else: - print(f"ℹ️ 无变化: {file_path}") - return False - - def update_all( - self, - rules_to_add: list[str], - descriptions: dict[str, str] | None = None, - file_list: list[str] | None = None, - ) -> dict[str, int]: - """ - 批量更新 pyproject.toml 文件 - - Args: - rules_to_add: 要添加的规则列表 - descriptions: 规则描述字典 - file_list: 要更新的文件列表，默认使用 DEFAULT_PACKAGE_FILES - - Returns: - 更新统计字典 - """ - if file_list is None: - file_list = DEFAULT_PACKAGE_FILES - - print("🔄 开始批量更新 pyproject.toml 文件...") - print(f"📝 规则: {', '.join(rules_to_add)}\n") - - stats = {"updated": 0, "skipped": 0, "failed": 0} - - for file_path_str in file_list: - full_path = self.root_dir / file_path_str - if self.update_file(full_path, rules_to_add, descriptions): - stats["updated"] += 1 - else: - if full_path.exists(): - stats["skipped"] += 1 - else: - stats["failed"] += 1 - - print("\n✨ 完成！") - print(f" ✅ 更新: {stats['updated']}") - print(f" ⏭️ 跳过: {stats['skipped']}") - print(f" ❌ 失败: {stats['failed']}") - - return stats - - def add_b904_c901(self) -> dict[str, int]: - """ - 添加 B904 和 C901 规则（常用快捷方法） - - Returns: - 更新统计字典 - """ - rules = ["B904", "C901"] - descriptions = { - "B904": "raise-without-from-inside-except", - "C901": "complex-structure", - } - return self.update_all(rules, descriptions) diff --git a/packages/sage-tools/src/sage/tools/dev/models/__init__.py b/packages/sage-tools/src/sage/tools/dev/models/__init__.py deleted file mode 100644 index ebd2a0bd4f..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/models/__init__.py +++ /dev/null @@ -1,15 +0,0 @@ -"""Low-level helpers for working with machine learning models in development tooling.""" - -from .cache import ( - cache_embedding_model, - check_embedding_model, - clear_embedding_model_cache, - configure_hf_environment, -) - -__all__ = [ - "cache_embedding_model", - "check_embedding_model", - "clear_embedding_model_cache", - "configure_hf_environment", -] diff --git a/packages/sage-tools/src/sage/tools/dev/models/cache.py b/packages/sage-tools/src/sage/tools/dev/models/cache.py deleted file mode 100644 index 9e7802e408..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/models/cache.py +++ /dev/null @@ -1,215 +0,0 @@ -"""Utilities for managing embedding model caches used in CI flows.""" - -from __future__ import annotations - -import os -import time -from pathlib import Path - -from rich.console import Console - -DEFAULT_MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2" - - -def _get_console(console: Console | None) -> Console: - return console or Console() - - -def configure_hf_environment(console: Console | None = None) -> dict[str, str]: - """Configure environment variables that improve Hugging Face downloads.""" - - console = _get_console(console) - cache_dir = os.environ.get("TRANSFORMERS_CACHE") or os.path.expanduser( - "~/.cache/huggingface/transformers" - ) - suggested_env = { - "HF_ENDPOINT": os.environ.get("HF_ENDPOINT") or "https://hf-mirror.com", - "HF_HUB_DISABLE_PROGRESS_BARS": "1", - "HF_HUB_DOWNLOAD_TIMEOUT": os.environ.get("HF_HUB_DOWNLOAD_TIMEOUT") or "60", - "TRANSFORMERS_OFFLINE": os.environ.get("TRANSFORMERS_OFFLINE") or "0", - "TRANSFORMERS_CACHE": cache_dir, - } - - for key, value in suggested_env.items(): - os.environ[key] = value - - console.print("🔧 [bold]已配置 Hugging Face 下载环境变量[/bold]") - for key, value in suggested_env.items(): - console.print(f" • {key} = {value}") - - return suggested_env - - -def clear_embedding_model_cache( - model_name: str = DEFAULT_MODEL_NAME, console: Console | None = None -) -> bool: - """Remove cached files for *model_name* if they exist.""" - - console = _get_console(console) - try: - from transformers import TRANSFORMERS_CACHE - except ImportError as exc: # pragma: no cover - optional dependency - console.print(f"⚠️ 未安装 transformers，无法清理缓存: {exc}") - return False - - cache_dir = Path(TRANSFORMERS_CACHE) - if not cache_dir.exists(): - console.print("ℹ️ 尚未创建 transformers 缓存目录") - return True - - pattern = model_name.split("/")[-1] - matches = [p for p in cache_dir.glob("**/*") if pattern in p.name] - if not matches: - console.print("ℹ️ 未找到对应模型缓存") - return True - - import shutil - - removed = 0 - for path in matches: - if path.is_dir(): - console.print(f"🗑️ 删除缓存目录: {path}") - shutil.rmtree(path, ignore_errors=True) - removed += 1 - console.print(f"✅ 已清理 {removed} 个缓存条目") - return True - - -def _prepare_requests_session(): - try: - import requests - from requests.adapters import HTTPAdapter - from urllib3.util.retry import Retry - - session = requests.Session() - retry_strategy = Retry( - total=3, - status_forcelist=[429, 500, 502, 503, 504], - allowed_methods=["HEAD", "GET", "OPTIONS"], - backoff_factor=1, - ) - adapter = HTTPAdapter(max_retries=retry_strategy) - session.mount("http://", adapter) - session.mount("https://", adapter) - - try: - import huggingface_hub - - huggingface_hub.constants.DEFAULT_REQUEST_TIMEOUT = 60 - except Exception: # pragma: no cover - best effort tweak - pass - - return session - except ImportError: # pragma: no cover - optional dependency - return None - - -def cache_embedding_model( - model_name: str = DEFAULT_MODEL_NAME, - *, - console: Console | None = None, - verify: bool = True, - retries: int = 3, -) -> bool: - """Download and cache the specified embedding model.""" - - console = _get_console(console) - configure_hf_environment(console) - _prepare_requests_session() - - try: - from transformers import AutoModel, AutoTokenizer - except ImportError as exc: # pragma: no cover - optional dependency - console.print(f"❌ 未安装 transformers: {exc}") - return False - - tokenizer = None - for attempt in range(retries): - try: - console.print(f"📥 下载 tokenizer (尝试 {attempt + 1}/{retries})") - tokenizer = AutoTokenizer.from_pretrained(model_name) - break - except Exception as exc: # pragma: no cover - network dependent - console.print(f" ❌ 下载失败: {exc}") - if attempt < retries - 1: - delay = 2**attempt - console.print(f" ⏳ {delay} 秒后重试") - time.sleep(delay) - else: - return False - - model = None - for attempt in range(retries): - try: - console.print(f"📥 下载模型 (尝试 {attempt + 1}/{retries})") - model = AutoModel.from_pretrained(model_name, trust_remote_code=True) - break - except Exception as exc: # pragma: no cover - network dependent - console.print(f" ❌ 下载失败: {exc}") - if attempt < retries - 1: - delay = 2**attempt - console.print(f" ⏳ {delay} 秒后重试") - time.sleep(delay) - else: - return False - - if verify and tokenizer is not None and model is not None: - try: - console.print("🧪 验证模型输出...") - inputs = tokenizer("测试文本", return_tensors="pt", padding=True, truncation=True) - outputs = model(**inputs) - console.print(f" ✅ 输出维度: {tuple(outputs.last_hidden_state.shape)}") - except Exception as exc: # pragma: no cover - runtime dependent - console.print(f"❌ 模型验证失败: {exc}") - return False - - cache_dir = os.environ.get("TRANSFORMERS_CACHE", "~/.cache/huggingface/transformers") - console.print(f"✅ 模型缓存完成，位置: {cache_dir}") - return True - - -def check_embedding_model( - model_name: str = DEFAULT_MODEL_NAME, *, console: Console | None = None -) -> bool: - """Return ``True`` when the embedding model is available locally or remotely.""" - - console = _get_console(console) - try: - from transformers import AutoModel, AutoTokenizer - except ImportError as exc: # pragma: no cover - optional dependency - console.print(f"❌ 未安装 transformers: {exc}") - return False - - console.print(f"🔍 检查模型 {model_name} 是否就绪") - try: - AutoTokenizer.from_pretrained(model_name, local_files_only=True) - AutoModel.from_pretrained(model_name, local_files_only=True, trust_remote_code=True) - console.print("✅ 模型已在本地缓存") - return True - except Exception: - console.print("ℹ️ 本地缓存未找到，尝试远程下载验证") - - for attempt in range(3): - try: - AutoTokenizer.from_pretrained(model_name) - AutoModel.from_pretrained(model_name, trust_remote_code=True) - console.print("✅ 远程验证成功") - return True - except Exception as exc: - console.print(f" ❌ 尝试 {attempt + 1} 失败: {exc}") - if attempt < 2: - delay = 2**attempt - console.print(f" ⏳ {delay} 秒后重试") - time.sleep(delay) - - console.print("❌ 模型不可用") - return False - - -__all__ = [ - "DEFAULT_MODEL_NAME", - "cache_embedding_model", - "check_embedding_model", - "clear_embedding_model_cache", - "configure_hf_environment", -] diff --git a/packages/sage-tools/src/sage/tools/dev/tools/__init__.py b/packages/sage-tools/src/sage/tools/dev/tools/__init__.py deleted file mode 100644 index 8f94c59119..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/tools/__init__.py +++ /dev/null @@ -1,33 +0,0 @@ -""" -SAGE - Streaming-Augmented Generative Execution -""" - -# 直接从本包的_version模块加载版本信息 -try: - from sage.tools._version import __author__, __email__, __version__ -except ImportError: - # 备用硬编码版本 - __version__ = "0.1.4" - __author__ = "IntelliStream Team" - __email__ = "shuhao_zhang@hust.edu.cn" - -# 导出质量检查工具 -from .architecture_checker import ArchitectureChecker -from .devnotes_checker import DevNotesChecker - -# 导出开发工具类 -from .enhanced_package_manager import EnhancedPackageManager -from .enhanced_test_runner import EnhancedTestRunner -from .package_dependency_validator import PackageDependencyValidator -from .package_readme_checker import PackageREADMEChecker -from .vscode_path_manager import VSCodePathManager - -__all__ = [ - "EnhancedPackageManager", - "EnhancedTestRunner", - "VSCodePathManager", - "ArchitectureChecker", - "DevNotesChecker", - "PackageREADMEChecker", - "PackageDependencyValidator", -] diff --git a/packages/sage-tools/src/sage/tools/dev/tools/architecture_checker.py b/packages/sage-tools/src/sage/tools/dev/tools/architecture_checker.py deleted file mode 100755 index c7fdf204ec..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/tools/architecture_checker.py +++ /dev/null @@ -1,966 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE Architecture Compliance Checker - -检测代码是否符合 SAGE 系统架构设计规范。 - -用途: -- CI/CD 自动化检测 -- 本地开发前检查 -- PR 审查辅助 - -检查项: -1. 包依赖规则（Layer 分层架构） -2. 导入路径合规性 -3. 模块结构规范 -4. 公共 API 导出 -5. 架构标记完整性 -6. 根目录文件规范（避免临时/测试文件污染根目录） - -NOTE: sage-llm-core and sage-llm-gateway have been migrated to isagellm. - LLM functionality is now via: pip install isagellm -""" - -import ast -import re -import sys -from dataclasses import dataclass, field -from pathlib import Path - -# ============================================================================ -# 架构定义 -# ============================================================================ - -# 包的层级定义（根据 PACKAGE_ARCHITECTURE.md） -# NOTE: sage-apps, sage-benchmark, sage-studio 已迁移到独立仓库，不再包含在此检查中 -LAYER_DEFINITION = { - "L1": ["sage-common"], - "L2": ["sage-platform"], - "L3": ["sage-kernel", "sage-libs"], - "L4": ["sage-middleware"], - "L5": [], # sage-apps, sage-benchmark 已迁移到独立仓库 - "L6": ["sage-tools"], # sage-studio 已迁移到独立仓库 -} - -# 反向映射：包名 -> 层级 -PACKAGE_TO_LAYER = {} -for layer, packages in LAYER_DEFINITION.items(): - for pkg in packages: - PACKAGE_TO_LAYER[pkg] = layer - -# 允许的依赖关系（高层 -> 低层） -ALLOWED_DEPENDENCIES = { - "sage-common": set(), # L1 不依赖任何包 - "sage-platform": {"sage-common"}, # L2 -> L1 - "sage-kernel": {"sage-common", "sage-platform"}, # L3 kernel 独立，不依赖 libs - "sage-libs": {"sage-common", "sage-platform"}, # L3 libs 独立，不依赖 kernel - "sage-middleware": { - "sage-common", - "sage-platform", - "sage-kernel", - "sage-libs", - }, # L4 -> L3, L2, L1 - # NOTE: sage-apps, sage-benchmark, sage-studio 已迁移到独立仓库 - "sage-tools": { - "sage-common", - "sage-platform", - "sage-kernel", - "sage-libs", - "sage-middleware", - "sage-studio", - }, # L6 -> L5(studio), L4, L3, L2, L1 - "sage-llm-gateway": { - "sage-common", - "sage-platform", - "sage-kernel", - "sage-libs", - "sage-middleware", - "sage-studio", # Gateway 集成 Studio Backend 路由 - }, # L6 -> L6(studio), L4, L3, L2, L1 -} - -# 包的根目录映射 -# NOTE: sage-apps, sage-benchmark, sage-studio 已迁移到独立仓库 -PACKAGE_PATHS = { - "sage-common": "packages/sage-common/src", - "sage-platform": "packages/sage-platform/src", - "sage-kernel": "packages/sage-kernel/src", - "sage-libs": "packages/sage-libs/src", - "sage-middleware": "packages/sage-middleware/src", - "sage-tools": "packages/sage-tools/src", -} - -# 包名到 Python 模块路径的映射（处理共享命名空间的情况） -# 大多数包: sage-xxx -> sage/xxx -# NOTE: sage-apps, sage-benchmark, sage-studio 已迁移到独立仓库 -PACKAGE_MODULE_PATHS = { - "sage-common": "sage/common", - "sage-platform": "sage/platform", - "sage-kernel": "sage/kernel", - "sage-libs": "sage/libs", - "sage-middleware": "sage/middleware", - "sage-tools": "sage/tools", -} - -# Submodules to exclude from checks (maintained in separate repositories) -SUBMODULE_PATHS = { - "sageLLM", - "sageVDB", - "sageFlow", - "neuromem", - "sageTSDB", - "docs-public", -} - -# 模块职责规则：定义哪些模块类型应该在哪一层 -# 格式：(pattern, expected_layer, description, suggestion) -MODULE_RESPONSIBILITY_RULES = [ - # Pipeline/Orchestration 层 - 应该在 middleware 或更高层 - ( - r".*/(pipeline|orchestration|workflow)\.py$", - ["L4", "L5", "L6"], - "Pipeline/Orchestration 模块（编排层）", - "Pipeline 编排多个算子，应该在 sage-middleware (L4) 或更高层", - ), - # Profiler/Monitor - 如果是算子应该在 middleware - ( - r".*/(profiler|monitor)\.py$", - ["L4", "L5", "L6"], - "Profiler/Monitor 模块", - "如果是算子实现（继承 MapFunction/FilterFunction），应该在 sage-middleware (L4)", - ), - # Operators - 具体的算子实现应该在 middleware - ( - r".*/operators/.+\.py$", - ["L4", "L5", "L6"], - "Operator 实现", - "具体的算子实现应该在 sage-middleware (L4) 或应用层", - ), -] - -# 根目录允许的文件（不区分大小写） -# 只列出项目标准文件，其他文件都应该放在对应的子目录 -ALLOWED_ROOT_FILES = { - # 文档文件 - "readme.md", - "contributing.md", - "developer.md", - "license", - "license.md", - "changelog.md", - "code_of_conduct.md", - "security.md", - # 配置文件 - ".gitignore", - ".gitattributes", - ".editorconfig", - ".flake8", - "pyproject.toml", - "setup.py", - "setup.cfg", - "requirements.txt", - "makefile", - "dockerfile", - "docker-compose.yml", - "docker-compose.yaml", - ".dockerignore", - "codecov.yml", - ".codecov.yml", - # 依赖管理 - "dependencies-spec.yaml", # 统一依赖版本规范 - # Shell 脚本 - "manage.sh", - "quickstart.sh", - # VS Code 工作区 - "sage.code-workspace", # VS Code 工作区配置 - # 其他 - "cmakelists.txt", -} - - -# ============================================================================ -# 数据结构 -# ============================================================================ - - -@dataclass -class ImportStatement: - """导入语句信息""" - - module: str # 导入的模块名 - file: Path # 所在文件 - line: int # 行号 - statement: str # 原始语句 - - -@dataclass -class ArchitectureViolation: - """架构违规""" - - type: str # 违规类型 - severity: str # 严重程度: ERROR, WARNING, INFO - file: Path # 文件路径 - line: int # 行号 - message: str # 详细信息 - suggestion: str | None = None # 修复建议 - - -@dataclass -class CheckResult: - """检查结果""" - - passed: bool - violations: list[ArchitectureViolation] = field(default_factory=list) - warnings: list[ArchitectureViolation] = field(default_factory=list) - stats: dict[str, int] = field(default_factory=dict) - - -# ============================================================================ -# AST 解析器 -# ============================================================================ - - -class ImportExtractor(ast.NodeVisitor): - """提取 Python 文件中的所有导入语句""" - - def __init__(self, filepath: Path): - self.filepath = filepath - self.imports: list[ImportStatement] = [] - self.in_type_checking = False # 跟踪是否在 TYPE_CHECKING 块中 - - def visit_If(self, node: ast.If): - """检查是否进入 TYPE_CHECKING 块""" - # 检查条件是否是 TYPE_CHECKING - is_type_checking_block = False - if isinstance(node.test, ast.Name) and node.test.id == "TYPE_CHECKING": - is_type_checking_block = True - - if is_type_checking_block: - # 暂时设置标志，访问 if 块内容，然后恢复 - old_value = self.in_type_checking - self.in_type_checking = True - for child in node.body: - self.visit(child) - self.in_type_checking = old_value - # 访问 else 块（如果有） - for child in node.orelse: - self.visit(child) - else: - # 正常访问 - self.generic_visit(node) - - def visit_Import(self, node: ast.Import): - # 忽略 TYPE_CHECKING 块中的导入 - if not self.in_type_checking: - for alias in node.names: - self.imports.append( - ImportStatement( - module=alias.name, - file=self.filepath, - line=node.lineno, - statement=f"import {alias.name}", - ) - ) - self.generic_visit(node) - - def visit_ImportFrom(self, node: ast.ImportFrom): - # 忽略 TYPE_CHECKING 块中的导入 - if not self.in_type_checking and node.module: - self.imports.append( - ImportStatement( - module=node.module, - file=self.filepath, - line=node.lineno, - statement=f"from {node.module} import ...", - ) - ) - self.generic_visit(node) - - -# ============================================================================ -# 架构检查器 -# ============================================================================ - - -class ArchitectureChecker: - """架构合规性检查器""" - - def __init__(self, root_dir: Path | str): - self.root_dir = Path(root_dir) if isinstance(root_dir, str) else root_dir - self.violations: list[ArchitectureViolation] = [] - self.warnings: list[ArchitectureViolation] = [] - - def extract_package_name(self, filepath: Path) -> str | None: - """从文件路径提取包名""" - try: - rel_path = filepath.relative_to(self.root_dir) - path_str = str(rel_path) - - # 匹配 packages/sage-xxx/ - match = re.match(r"packages/(sage-[^/]+)/", path_str) - if match: - return match.group(1) - except ValueError: - pass - return None - - def get_imported_package(self, module_name: str) -> str | None: - """从导入语句中提取被导入的包名""" - # sage.common.xxx -> sage-common - # sage.kernel.xxx -> sage-kernel - if module_name.startswith("sage."): - parts = module_name.split(".") - if len(parts) >= 2: - submodule = parts[1] - # 特殊处理：将下划线转换为连字符 - return f"sage-{submodule}" - return None - - def check_layer_dependency( - self, source_pkg: str, target_pkg: str, import_info: ImportStatement - ) -> bool: - """检查层级依赖是否合规""" - if source_pkg == target_pkg: - return True # 同包内导入总是允许的 - - allowed = ALLOWED_DEPENDENCIES.get(source_pkg, set()) - if target_pkg not in allowed: - source_layer = PACKAGE_TO_LAYER.get(source_pkg, "Unknown") - target_layer = PACKAGE_TO_LAYER.get(target_pkg, "Unknown") - - self.violations.append( - ArchitectureViolation( - type="ILLEGAL_DEPENDENCY", - severity="ERROR", - file=import_info.file, - line=import_info.line, - message=f"非法依赖: {source_pkg} ({source_layer}) -> {target_pkg} ({target_layer})", - suggestion=f"请检查 PACKAGE_ARCHITECTURE.md 中的依赖规则。" - f"{source_layer} 层不应该依赖 {target_layer} 层的包。", - ) - ) - return False - - return True - - def check_internal_import(self, import_info: ImportStatement, source_pkg: str): - """检查内部导入是否使用公共 API - - 只对跨包的内部导入发出警告，同包内的内部导入是允许的。 - """ - module = import_info.module - - # 获取被导入模块所属的包 - target_pkg = self.get_imported_package(module) - - # 如果是同一个包内的导入，不检查（同包内可以随意导入） - if target_pkg == source_pkg: - return - - # 只对跨包的内部导入进行检查 - # 检查是否直接导入了内部模块 - internal_patterns = [ - (r"sage\.\w+\.runtime\.", "runtime"), # 直接导入 runtime 内部 - ( - r"sage\.\w+\.core\.(?!__init__)", - "core子模块", - ), # 直接导入 core 子模块（如 core.functions） - (r"sage\.\w+\._", "私有模块"), # 私有模块 - ] - - for pattern, module_type in internal_patterns: - if re.match(pattern, module): - # 为 core 子模块提供更具体的建议 - if "core" in module_type and ".core." in module: - # 提取包名，例如从 sage.common.core.functions 提取 sage.common.core - parts = module.split(".") - if len(parts) >= 3: - public_api = ".".join(parts[:3]) # sage.common.core - suggestion = f"建议从公共 API 导入: from {public_api} import ..." - else: - suggestion = f"建议使用 {target_pkg} 的公共 API 进行导入。" - else: - suggestion = ( - f"建议使用 {target_pkg} 的公共 API，避免依赖内部实现（{module_type}）。" - ) - - self.warnings.append( - ArchitectureViolation( - type="INTERNAL_IMPORT", - severity="WARNING", - file=import_info.file, - line=import_info.line, - message=f"跨包导入内部模块: {module}（从 {source_pkg} 到 {target_pkg}）", - suggestion=suggestion, - ) - ) - break - - def check_file_imports(self, filepath: Path) -> list[ImportStatement]: - """检查单个文件的导入""" - try: - with open(filepath, encoding="utf-8") as f: - tree = ast.parse(f.read(), filename=str(filepath)) - - extractor = ImportExtractor(filepath) - extractor.visit(tree) - return extractor.imports - except SyntaxError as e: - self.warnings.append( - ArchitectureViolation( - type="SYNTAX_ERROR", - severity="WARNING", - file=filepath, - line=e.lineno or 0, - message=f"语法错误，跳过检查: {e}", - ) - ) - return [] - except Exception as e: - self.warnings.append( - ArchitectureViolation( - type="PARSE_ERROR", - severity="WARNING", - file=filepath, - line=0, - message=f"解析错误，跳过检查: {e}", - ) - ) - return [] - - def check_package_structure(self, package_name: str) -> bool: - """检查包结构是否规范""" - package_path = self.root_dir / PACKAGE_PATHS[package_name] - - if not package_path.exists(): - self.violations.append( - ArchitectureViolation( - type="MISSING_PACKAGE", - severity="ERROR", - file=package_path, - line=0, - message=f"包目录不存在: {package_path}", - ) - ) - return False - - # 检查 __init__.py 是否存在 - module_path = PACKAGE_MODULE_PATHS.get(package_name, package_name.replace("sage-", "sage/")) - init_file = package_path / module_path / "__init__.py" - if not init_file.exists(): - self.warnings.append( - ArchitectureViolation( - type="MISSING_INIT", - severity="WARNING", - file=init_file, - line=0, - message="缺少 __init__.py，可能影响包导入", - ) - ) - - return True - - def check_layer_marker(self, package_name: str) -> bool: - """检查包是否包含 Layer 标记""" - package_path = self.root_dir / PACKAGE_PATHS[package_name] - module_path = PACKAGE_MODULE_PATHS.get(package_name, package_name.replace("sage-", "sage/")) - init_file = package_path / module_path / "__init__.py" - - if not init_file.exists(): - return False - - try: - with open(init_file, encoding="utf-8") as f: - content = f.read() - - # 查找 __layer__ 定义 - if "__layer__" not in content: - expected_layer = PACKAGE_TO_LAYER.get(package_name, "Unknown") - self.warnings.append( - ArchitectureViolation( - type="MISSING_LAYER_MARKER", - severity="WARNING", - file=init_file, - line=0, - message="缺少 __layer__ 标记", - suggestion=f"在 __init__.py 中添加: __layer__ = '{expected_layer}'", - ) - ) - return False - - except Exception as e: - self.warnings.append( - ArchitectureViolation( - type="CHECK_ERROR", - severity="WARNING", - file=init_file, - line=0, - message=f"无法检查 Layer 标记: {e}", - ) - ) - return False - - return True - - def check_module_responsibility(self, filepath: Path) -> bool: - """检查模块是否在正确的层级 - - 某些类型的模块（如 pipeline, orchestration）应该只出现在特定层级。 - """ - # 获取文件所属的包 - source_pkg = self.extract_package_name(filepath) - if not source_pkg: - return True - - source_layer = PACKAGE_TO_LAYER.get(source_pkg, "Unknown") - if source_layer == "Unknown": - return True - - # 检查文件路径是否匹配任何规则 - file_path_str = str(filepath) - - for pattern, allowed_layers, module_type, suggestion in MODULE_RESPONSIBILITY_RULES: - if re.search(pattern, file_path_str): - # 检查当前层级是否允许 - if source_layer not in allowed_layers: - # 检查文件内容以确认是否真的是该类型的模块 - if self._confirm_module_type(filepath, module_type): - self.violations.append( - ArchitectureViolation( - type="MODULE_MISPLACEMENT", - severity="ERROR", - file=filepath, - line=0, - message=f"{module_type} 位于错误的层级: {source_pkg} ({source_layer})", - suggestion=suggestion, - ) - ) - return False - - return True - - def _confirm_module_type(self, filepath: Path, module_type: str) -> bool: - """通过分析文件内容确认模块类型 - - 避免误报：只有真正符合特征的模块才算违规。 - """ - try: - with open(filepath, encoding="utf-8") as f: - content = f.read() - - # Pipeline/Orchestration - 检查是否有编排逻辑 - if "Pipeline" in module_type or "Orchestration" in module_type: - # 查找 Pipeline 类定义或编排相关代码 - if re.search(r"class \w*Pipeline", content): - return True - if re.search(r"def (orchestrate|run|execute).*pipeline", content, re.IGNORECASE): - return True - - # Profiler/Monitor - 检查是否继承算子基类 - if "Profiler" in module_type or "Monitor" in module_type: - # 查找是否继承 Function 类 - if re.search(r"class \w+\((Map|Filter|Batch|Sink)Function\)", content): - return True - # 或者有 execute 方法 - if re.search(r"class \w+Profiler.*:.*def execute", content, re.DOTALL): - return True - - # Operator 实现 - 检查是否在 operators 目录且实现了算子 - if "Operator" in module_type: - if "operators/" in str(filepath): - # 检查是否继承算子基类 - if re.search( - r"class \w+\((Map|Filter|Batch|Sink|Operator|Function)\)", content - ): - return True - - except Exception: - # 如果无法读取文件，保守地返回 False（不报错） - pass - - return False - - def check_root_directory_files(self) -> bool: - """检查根目录文件是否符合规范 - - 返回: - bool: True 表示通过，False 表示有问题 - """ - if not self.root_dir.exists(): - return True - - issues_found = False - - # 获取根目录下所有 git 跟踪的文件（不包括子目录） - import subprocess - - try: - # 使用 git ls-files 只获取 git 跟踪的文件 - result = subprocess.run( - ["git", "ls-files", "--cached"], - cwd=self.root_dir, - capture_output=True, - text=True, - check=True, - ) - - # 过滤出根目录的文件（不包含 / 的文件名） - git_files = [ - line.strip() - for line in result.stdout.strip().split("\n") - if line.strip() and "/" not in line.strip() - ] - - root_files = [ - self.root_dir / filename - for filename in git_files - if (self.root_dir / filename).is_file() - ] - - except subprocess.CalledProcessError: - # 如果不是 git 仓库，回退到检查所有文件 - root_files = [f for f in self.root_dir.iterdir() if f.is_file()] - - # 检查每个文件 - for file_path in root_files: - filename = file_path.name.lower() - - # 跳过隐藏文件（以 . 开头） - if filename.startswith("."): - # 检查是否在允许列表中 - if filename not in ALLOWED_ROOT_FILES: - # 隐藏配置文件通常是可以接受的，只给警告 - continue - else: - continue - - # 检查是否在允许列表中 - if filename not in ALLOWED_ROOT_FILES: - # 根据文件类型给出具体建议 - suggestion = self._get_file_placement_suggestion(file_path) - - self.violations.append( - ArchitectureViolation( - type="INVALID_ROOT_FILE", - severity="ERROR", - file=file_path, - line=0, - message=f"根目录不应包含此文件: {file_path.name}", - suggestion=suggestion, - ) - ) - issues_found = True - - return not issues_found - - def _get_file_placement_suggestion(self, file_path: Path) -> str: - """根据文件类型提供放置建议""" - filename = file_path.name.lower() - suffix = file_path.suffix.lower() - - # Python 测试文件 - if filename.startswith("test_") and suffix == ".py": - return "测试文件应该放在: packages/sage-tools/tests/ 或对应包的 tests/ 目录下" - - # Python 脚本 - if suffix == ".py": - return ( - "Python 脚本应该放在: tools/ (系统脚本) 或 packages/sage-tools/scripts/ (开发工具)" - ) - - # Markdown 文档 - if suffix == ".md": - if any(kw in filename for kw in ["migration", "cleanup", "refactor", "tools"]): - return "开发文档应该放在: docs/dev-notes/l5-tools/ 或相应的分类目录下" - else: - return ( - "文档应该放在: docs/dev-notes/ (开发笔记) 或 docs-public/docs_src/ (公开文档)" - ) - - # 配置文件 - if suffix in [".yml", ".yaml", ".json", ".toml", ".ini", ".cfg"]: - return "配置文件应该放在: 项目根目录的隐藏文件（如 .codecov.yml）或 tools/ 目录下" - - # Shell 脚本 - if suffix == ".sh": - return "Shell 脚本应该放在: tools/ 目录下" - - # 数据文件 - if suffix in [".csv", ".json", ".txt", ".dat"]: - return "数据文件应该放在: examples/data/ 或 packages/*/tests/data/" - - return "请将文件移动到合适的子目录中" - - def check_all(self) -> CheckResult: - """检查所有文件""" - return self.run_checks(changed_files=None) - - def check_changed_files(self, diff_target: str = "HEAD") -> CheckResult: - """检查 Git 变更的文件""" - changed_files = get_changed_files(diff_target) - if not changed_files: - # 没有变更文件，返回通过 - return CheckResult( - passed=True, - violations=[], - warnings=[], - stats={"total_files": 0, "total_imports": 0}, - ) - return self.run_checks(changed_files=changed_files) - - def run_checks(self, changed_files: list[Path] | None = None) -> CheckResult: - """运行所有检查""" - print("🔍 开始架构合规性检查...\n") - - # 如果指定了文件列表，只检查这些文件 - if changed_files: - files_to_check = changed_files - print(f"📝 检查 {len(files_to_check)} 个变更文件") - else: - # 否则检查所有 Python 文件 - files_to_check = [] - for pkg_path in PACKAGE_PATHS.values(): - full_path = self.root_dir / pkg_path - if full_path.exists(): - for py_file in full_path.rglob("*.py"): - # 排除 submodules 中的文件 - if not any(submodule in py_file.parts for submodule in SUBMODULE_PATHS): - files_to_check.append(py_file) - print(f"📝 检查全部 {len(files_to_check)} 个 Python 文件 (排除 submodules)") - - # 过滤掉 submodules 中的文件（如果是 changed_files 模式） - if changed_files: - original_count = len(files_to_check) - files_to_check = [ - f - for f in files_to_check - if not any(submodule in f.parts for submodule in SUBMODULE_PATHS) - ] - if len(files_to_check) < original_count: - print(f"⏭️ 排除了 {original_count - len(files_to_check)} 个 submodule 文件") - - # 统计信息 - stats = { - "total_files": len(files_to_check), - "total_imports": 0, - "illegal_dependencies": 0, - "internal_imports": 0, - "missing_markers": 0, - "module_misplacements": 0, - } - - # 1. 检查模块职责边界 - print("\n1️⃣ 检查模块职责边界...") - for filepath in files_to_check: - if not self.check_module_responsibility(filepath): - stats["module_misplacements"] += 1 - - # 2. 检查导入依赖 - print("\n2️⃣ 检查包依赖关系...") - for filepath in files_to_check: - if filepath.name == "__init__.py" or filepath.suffix != ".py": - continue - - source_pkg = self.extract_package_name(filepath) - if not source_pkg: - continue - - imports = self.check_file_imports(filepath) - stats["total_imports"] += len(imports) - - for imp in imports: - target_pkg = self.get_imported_package(imp.module) - if target_pkg and target_pkg in PACKAGE_TO_LAYER: - # 检查层级依赖 - if not self.check_layer_dependency(source_pkg, target_pkg, imp): - stats["illegal_dependencies"] += 1 - - # 检查内部导入 - self.check_internal_import(imp, source_pkg) - - # 3. 检查包结构 - print("3️⃣ 检查包结构...") - for package_name in PACKAGE_PATHS.keys(): - self.check_package_structure(package_name) - - # 4. 检查 Layer 标记 - print("4️⃣ 检查 Layer 标记...") - for package_name in PACKAGE_PATHS.keys(): - if not self.check_layer_marker(package_name): - stats["missing_markers"] += 1 - - # 5. 检查根目录文件 - print("5️⃣ 检查根目录文件规范...") - root_files_ok = self.check_root_directory_files() - if not root_files_ok: - stats["invalid_root_files"] = len( - [v for v in self.violations if v.type == "INVALID_ROOT_FILE"] - ) - - stats["internal_imports"] = len([v for v in self.warnings if v.type == "INTERNAL_IMPORT"]) - - # 生成结果 - result = CheckResult( - passed=len(self.violations) == 0, - violations=self.violations, - warnings=self.warnings, - stats=stats, - ) - - return result - - -# ============================================================================ -# 报告生成 -# ============================================================================ - - -def print_report(result: CheckResult): - """打印检查报告""" - print("\n" + "=" * 80) - print("📊 架构合规性检查报告") - print("=" * 80) - - # 统计信息 - print("\n📈 统计信息:") - print(f" • 检查文件数: {result.stats['total_files']}") - print(f" • 导入语句数: {result.stats['total_imports']}") - print(f" • 非法依赖: {result.stats['illegal_dependencies']}") - print(f" • 模块位置错误: {result.stats.get('module_misplacements', 0)}") - print(f" • 内部导入: {result.stats['internal_imports']}") - print(f" • 缺少标记: {result.stats['missing_markers']}") - if "invalid_root_files" in result.stats: - print(f" • 根目录问题文件: {result.stats['invalid_root_files']}") - - # 错误列表 - if result.violations: - print(f"\n❌ 发现 {len(result.violations)} 个架构违规:\n") - for i, v in enumerate(result.violations, 1): - print(f"{i}. [{v.severity}] {v.type}") - print(f" 文件: {v.file}:{v.line}") - print(f" 问题: {v.message}") - if v.suggestion: - print(f" 建议: {v.suggestion}") - print() - - # 警告列表 - if result.warnings: - print(f"\n⚠️ 发现 {len(result.warnings)} 个警告:\n") - for i, w in enumerate(result.warnings[:10], 1): # 只显示前10个 - print(f"{i}. [{w.severity}] {w.type}") - print(f" 文件: {w.file}:{w.line}") - print(f" 问题: {w.message}") - if w.suggestion: - print(f" 建议: {w.suggestion}") - print() - - if len(result.warnings) > 10: - print(f" ... 还有 {len(result.warnings) - 10} 个警告未显示\n") - - # 最终结果 - print("=" * 80) - if result.passed: - print("✅ 架构合规性检查通过！") - else: - print("❌ 架构合规性检查失败！") - print(f" 发现 {len(result.violations)} 个必须修复的问题") - - print("=" * 80) - - -def get_changed_files(git_diff: str = "HEAD") -> list[Path]: - """获取 Git 变更的文件列表""" - import subprocess - - try: - # 在 CI 中，通常检查与 main 分支的差异 - result = subprocess.run( - ["git", "diff", "--name-only", git_diff], - capture_output=True, - text=True, - check=True, - ) - - changed = [] - for line in result.stdout.strip().split("\n"): - if line.endswith(".py"): - path = Path(line) - if path.exists(): - changed.append(path) - - return changed - except subprocess.CalledProcessError: - return [] - - -# ============================================================================ -# 主程序 -# ============================================================================ - - -def main(): - """主函数""" - import argparse - - parser = argparse.ArgumentParser(description="SAGE Architecture Compliance Checker") - parser.add_argument( - "--root", - type=Path, - default=Path.cwd(), - help="SAGE 项目根目录 (默认: 当前目录)", - ) - parser.add_argument( - "--changed-only", - action="store_true", - help="仅检查 Git 变更的文件", - ) - parser.add_argument( - "--diff", - type=str, - default="origin/main", - help="Git diff 比较目标 (默认: origin/main)", - ) - parser.add_argument( - "--strict", - action="store_true", - help="严格模式：将警告也视为错误", - ) - - args = parser.parse_args() - - # 检查项目根目录 - root_dir = args.root.resolve() - if not (root_dir / "packages").exists(): - print(f"❌ 错误: {root_dir} 不是有效的 SAGE 项目根目录") - sys.exit(1) - - # 创建检查器 - checker = ArchitectureChecker(root_dir) - - # 确定要检查的文件 - changed_files = None - if args.changed_only: - changed_files = get_changed_files(args.diff) - if not changed_files: - print("ℹ️ 没有 Python 文件变更，跳过检查") - sys.exit(0) - - # 运行检查 - result = checker.run_checks(changed_files) - - # 打印报告 - print_report(result) - - # 返回状态码 - if not result.passed: - sys.exit(1) - - if args.strict and result.warnings: - print("\n⚠️ 严格模式：存在警告，视为失败") - sys.exit(1) - - sys.exit(0) - - -if __name__ == "__main__": - main() diff --git a/packages/sage-tools/src/sage/tools/dev/tools/build_artifacts_manager.py b/packages/sage-tools/src/sage/tools/dev/tools/build_artifacts_manager.py deleted file mode 100644 index 2ba9fe546b..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/tools/build_artifacts_manager.py +++ /dev/null @@ -1,499 +0,0 @@ -""" -Build Artifacts Manager for sage-development Toolkit. - -This module provides functionality to manage pip install artifacts and build -intermediates across the entire SAGE project, including: -- *.egg-info directories -- dist/ directories -- __pycache__ directories -- build/ directories -- Other build artifacts - -Author: SAGE Team -""" - -import logging -import os -import shutil -import time -from datetime import datetime -from pathlib import Path - -from sage.common.utils.formatting import format_size - - -class BuildArtifactsManager: - """Manages build artifacts and pip install intermediates.""" - - # 默认要清理的构建产物模式 - DEFAULT_PATTERNS = { - "egg_info": ["*.egg-info", "*egg-info"], - "dist": ["dist"], - "build": ["build"], - "pycache": ["__pycache__"], - "coverage": [".coverage", "coverage.xml", "htmlcov", ".sage/htmlcov"], - "pytest": [".pytest_cache"], - "mypy": [".mypy_cache"], - "temp": ["*.tmp", "*.temp", ".tmp"], - "logs": ["*.log", "logs"], - } - - # 受保护的目录（不会被清理） - PROTECTED_PATHS = { - ".git", - ".venv", - ".idea", - ".vscode", - "node_modules", - "venv", - "env", - ".sage", # SAGE的配置目录 - } - - def __init__(self, project_root: str): - """ - Initialize the BuildArtifactsManager. - - Args: - project_root: Path to the project root directory - """ - self.project_root = Path(project_root).resolve() - self.logger = logging.getLogger(__name__) - - # 统计信息 - self.stats = { - "total_files_removed": 0, - "total_dirs_removed": 0, - "total_size_freed": 0, - "errors": [], - } - - def scan_artifacts(self, patterns: dict[str, list[str]] | None = None) -> dict[str, list[Path]]: - """ - 扫描项目中的构建产物。 - - Args: - patterns: 自定义扫描模式，如果为None则使用默认模式 - - Returns: - 按类型分组的构建产物路径字典 - """ - if patterns is None: - patterns = self.DEFAULT_PATTERNS - - artifacts = {category: [] for category in patterns.keys()} - - # 扫描整个项目目录 - for root, dirs, files in os.walk(self.project_root): - root_path = Path(root) - - # 跳过受保护的目录 - if any(protected in root_path.parts for protected in self.PROTECTED_PATHS): - continue - - # 检查目录模式 - for category, pattern_list in patterns.items(): - for pattern in pattern_list: - # 检查目录名是否匹配模式 - for dir_name in dirs[:]: # 使用切片来避免修改正在迭代的列表 - if self._match_pattern(dir_name, pattern): - artifact_path = root_path / dir_name - artifacts[category].append(artifact_path) - - # 检查文件名是否匹配模式 - for file_name in files: - if self._match_pattern(file_name, pattern): - artifact_path = root_path / file_name - artifacts[category].append(artifact_path) - - # 去重并排序 - for category in artifacts: - artifacts[category] = sorted(set(artifacts[category])) - - return artifacts - - def _match_pattern(self, name: str, pattern: str) -> bool: - """检查名称是否匹配模式。""" - if "*" in pattern: - # 简单的通配符匹配 - if pattern.startswith("*") and pattern.endswith("*"): - return pattern[1:-1] in name - elif pattern.startswith("*"): - return name.endswith(pattern[1:]) - elif pattern.endswith("*"): - return name.startswith(pattern[:-1]) - else: - return name == pattern - else: - return name == pattern - - def calculate_size(self, path: Path) -> int: - """计算路径的总大小。""" - if path.is_file(): - return path.stat().st_size - elif path.is_dir(): - total_size = 0 - try: - for item in path.rglob("*"): - if item.is_file(): - total_size += item.stat().st_size - except (PermissionError, OSError): - pass - return total_size - return 0 - - def get_artifacts_summary(self, artifacts: dict[str, list[Path]]) -> dict[str, dict]: - """获取构建产物的统计摘要。""" - summary = {} - - for category, paths in artifacts.items(): - if not paths: - summary[category] = { - "count": 0, - "total_size": 0, - "size_formatted": "0 B", - } - continue - - total_size = sum(self.calculate_size(path) for path in paths) - - summary[category] = { - "count": len(paths), - "total_size": total_size, - "size_formatted": self._format_size(total_size), - "paths": [ - str(path.relative_to(self.project_root)) for path in paths[:5] - ], # 显示前5个 - } - - return summary - - def _format_size(self, size_bytes: int) -> str: - """格式化文件大小（使用统一的格式化函数）。""" - return format_size(size_bytes) - - def clean_artifacts( - self, - categories: list[str] | None = None, - dry_run: bool = False, - force: bool = False, - older_than_days: int | None = None, - ) -> dict[str, any]: - """ - 清理构建产物。 - - Args: - categories: 要清理的类别列表，None表示清理所有 - dry_run: 是否只是预览而不实际删除 - force: 是否强制删除而不询问 - older_than_days: 只删除超过指定天数的文件 - - Returns: - 清理结果统计 - """ - # 重置统计信息 - self.stats = { - "total_files_removed": 0, - "total_dirs_removed": 0, - "total_size_freed": 0, - "errors": [], - "cleaned_categories": {}, - } - - # 扫描构建产物 - artifacts = self.scan_artifacts() - - # 过滤要清理的类别 - if categories: - artifacts = {k: v for k, v in artifacts.items() if k in categories} - - # 应用时间过滤 - if older_than_days is not None: - cutoff_time = time.time() - (older_than_days * 24 * 3600) - for category in artifacts: - artifacts[category] = [ - path for path in artifacts[category] if path.stat().st_mtime < cutoff_time - ] - - # 执行清理 - for category, paths in artifacts.items(): - if not paths: - continue - - category_stats = { - "files_removed": 0, - "dirs_removed": 0, - "size_freed": 0, - "items_cleaned": [], - } - - for path in paths: - try: - if dry_run: - # 预览模式，只计算大小 - size = self.calculate_size(path) - category_stats["size_freed"] += size - category_stats["items_cleaned"].append( - str(path.relative_to(self.project_root)) - ) - - if path.is_file(): - category_stats["files_removed"] += 1 - else: - category_stats["dirs_removed"] += 1 - else: - # 实际删除 - size = self.calculate_size(path) - - if path.is_file(): - path.unlink() - category_stats["files_removed"] += 1 - elif path.is_dir(): - shutil.rmtree(path) - category_stats["dirs_removed"] += 1 - - category_stats["size_freed"] += size - category_stats["items_cleaned"].append( - str(path.relative_to(self.project_root)) - ) - - except Exception as e: - error_msg = f"Failed to remove {path}: {str(e)}" - self.stats["errors"].append(error_msg) - self.logger.error(error_msg) - - self.stats["cleaned_categories"][category] = category_stats - self.stats["total_files_removed"] += category_stats["files_removed"] - self.stats["total_dirs_removed"] += category_stats["dirs_removed"] - self.stats["total_size_freed"] += category_stats["size_freed"] - - return self.stats - - def create_cleanup_script(self, output_path: str | Path | None = None) -> str: - """ - 创建清理脚本文件。 - - Args: - output_path: 输出脚本的路径，None则使用默认路径 - - Returns: - 生成的脚本文件路径 - """ - if output_path is None: - output_path = self.project_root / "scripts" / "cleanup_build_artifacts.sh" - - script_path = Path(output_path) if isinstance(output_path, str) else output_path - script_path.parent.mkdir(parents=True, exist_ok=True) - - script_content = f"""#!/bin/bash -# SAGE Build Artifacts Cleanup Script -# Generated on {datetime.now().strftime("%Y-%m-%d %H:%M:%S")} - -set -e - -PROJECT_ROOT="{self.project_root}" -cd "$PROJECT_ROOT" - -echo "🧹 SAGE Build Artifacts Cleanup" -echo "================================" -echo "Project Root: $PROJECT_ROOT" -echo - -# Function to show size -show_size() {{ - if command -v du >/dev/null 2>&1; then - du -sh "$1" 2>/dev/null || echo "0" - else - echo "Unknown" - fi -}} - -# Function to safely remove -safe_remove() {{ - local path="$1" - local type="$2" - - if [ -e "$path" ]; then - echo " 🗑️ Removing $type: $path" - if [ "$type" = "directory" ]; then - rm -rf "$path" - else - rm -f "$path" - fi - fi -}} - -echo "📊 Scanning for build artifacts..." - -# Clean egg-info directories -echo -echo "🥚 Cleaning egg-info directories..." -find . -name "*.egg-info" -type d -not -path "./.venv/*" -not -path "./.git/*" | while read -r dir; do - if [ -d "$dir" ]; then - size=$(show_size "$dir") - safe_remove "$dir" "directory" - fi -done - -# Clean dist directories -echo -echo "📦 Cleaning dist directories..." -find . -name "dist" -type d -not -path "./.venv/*" -not -path "./.git/*" | while read -r dir; do - if [ -d "$dir" ]; then - size=$(show_size "$dir") - safe_remove "$dir" "directory" - fi -done - -# Clean __pycache__ directories -echo -echo "🐍 Cleaning __pycache__ directories..." -find . -name "__pycache__" -type d -not -path "./.venv/*" -not -path "./.git/*" | while read -r dir; do - if [ -d "$dir" ]; then - safe_remove "$dir" "directory" - fi -done - -# Clean build directories -echo -echo "🔨 Cleaning build directories..." -find . -name "build" -type d -not -path "./.venv/*" -not -path "./.git/*" | while read -r dir; do - if [ -d "$dir" ]; then - size=$(show_size "$dir") - safe_remove "$dir" "directory" - fi -done - -# Clean coverage files -echo -echo "📊 Cleaning coverage files..." -find . -name ".coverage" -o -name "coverage.xml" -o -name "htmlcov" -type f -o -type d | while read -r item; do - if [ -e "$item" ]; then - if [ -d "$item" ]; then - safe_remove "$item" "directory" - else - safe_remove "$item" "file" - fi - fi -done - -# Clean pytest cache -echo -echo "🧪 Cleaning pytest cache..." -find . -name ".pytest_cache" -type d -not -path "./.venv/*" -not -path "./.git/*" | while read -r dir; do - if [ -d "$dir" ]; then - safe_remove "$dir" "directory" - fi -done - -echo -echo "✅ Cleanup completed!" -echo "🗑️ To see what would be removed without actually deleting, use: sage-dev clean --dry-run" -""" - - # 写入脚本文件 - with open(script_path, "w", encoding="utf-8") as f: - f.write(script_content) - - # 设置可执行权限 - script_path.chmod(0o755) - - return str(script_path) - - def setup_gitignore_rules(self) -> dict[str, any]: - """ - 设置或更新.gitignore规则以忽略构建产物。 - - Returns: - 操作结果 - """ - gitignore_path = self.project_root / ".gitignore" - - # 要添加的规则 - rules_to_add = [ - "# Build artifacts managed by sage-dev toolkit", - "**/*.egg-info/", - "**/dist/", - "**/__pycache__/", - "**/build/", - "**/.coverage", - "**/coverage.xml", - "**/htmlcov/", - ".sage/htmlcov/", - "**/.pytest_cache/", - "**/.mypy_cache/", - "**/*.tmp", - "**/*.temp", - "**/.tmp/", - ] - - existing_rules = set() - if gitignore_path.exists(): - with open(gitignore_path, encoding="utf-8") as f: - existing_rules = {line.strip() for line in f.readlines()} - - # 找出需要添加的新规则 - new_rules = [rule for rule in rules_to_add if rule not in existing_rules] - - if new_rules: - with open(gitignore_path, "a", encoding="utf-8") as f: - f.write("\n") - for rule in new_rules: - f.write(f"{rule}\n") - - return { - "gitignore_path": str(gitignore_path), - "rules_added": len(new_rules), - "new_rules": new_rules, - "total_rules": len(rules_to_add), - } - - def create_maintenance_schedule(self) -> str: - """创建维护计划建议。""" - return """ -# SAGE Build Artifacts Maintenance Schedule - -## Daily (Automated) -- Clean __pycache__ directories during development -- Remove temporary files older than 1 day - -## Weekly (Recommended) -```bash -sage-dev clean --categories pycache,temp --older-than-days 7 -``` - -## Monthly (Deep Clean) -```bash -sage-dev clean --categories all --older-than-days 30 --dry-run -sage-dev clean --categories all --older-than-days 30 -``` - -## Before Release -```bash -sage-dev clean --categories all --force -sage-dev clean --update-gitignore -``` - -## Setup Automated Cleanup -Create maintenance scripts using SAGE toolkit: - -### Daily cleanup script -```bash -#!/bin/bash -# scripts/daily_cleanup.sh -sage-dev clean --categories pycache,temp --older-than-days 1 --force -``` - -### Weekly cleanup script -```bash -#!/bin/bash -# scripts/weekly_cleanup.sh -sage-dev clean --categories all --older-than-days 7 --force -``` - -### Generate shell script -```bash -sage-dev clean --create-script -bash scripts/cleanup_build_artifacts.sh -``` -""" diff --git a/packages/sage-tools/src/sage/tools/dev/tools/class_dependency_checker.py b/packages/sage-tools/src/sage/tools/dev/tools/class_dependency_checker.py deleted file mode 100644 index 820c9f9f23..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/tools/class_dependency_checker.py +++ /dev/null @@ -1,548 +0,0 @@ -""" -Class Dependency Checker - Integrated from scripts/quick_class_dependency_check.py - -This tool analyzes class-level dependencies and relationships in the codebase. -""" - -import ast -from collections import defaultdict -from pathlib import Path -from typing import Any - -from ..core.exceptions import SAGEDevToolkitError - - -class ClassDependencyChecker: - """Tool for analyzing class-level dependencies.""" - - def __init__(self, project_root: str): - self.project_root = Path(project_root) - - def analyze_class_dependencies(self, target_paths: list[str] | None = None) -> dict[str, Any]: - """Analyze class dependencies in specified paths or entire project.""" - try: - if target_paths: - paths_to_analyze = [ - (Path(self.project_root) / p if not Path(p).is_absolute() else Path(p)) - for p in target_paths - ] - else: - paths_to_analyze = [self.project_root] - - analysis = { - "project_root": str(self.project_root), - "analyzed_paths": [str(p) for p in paths_to_analyze], - "classes": {}, - "relationships": {"inheritance": [], "composition": [], "imports": []}, - "summary": { - "total_classes": 0, - "total_files": 0, - "inheritance_chains": [], - "circular_imports": [], - "unused_classes": [], - }, - } - - # Find all Python files - python_files = [] - for path in paths_to_analyze: - if path.is_file() and path.suffix == ".py": - python_files.append(path) - elif path.is_dir(): - python_files.extend(path.rglob("*.py")) - - analysis["summary"]["total_files"] = len(python_files) - - # Analyze each file - for py_file in python_files: - try: - file_analysis = self._analyze_file(py_file) - - # Merge file analysis into main analysis - for class_name, class_info in file_analysis["classes"].items(): - full_class_name = f"{file_analysis['module']}.{class_name}" - analysis["classes"][full_class_name] = class_info - analysis["summary"]["total_classes"] += 1 - - # Add relationships - analysis["relationships"]["inheritance"].extend(file_analysis["inheritance"]) - analysis["relationships"]["composition"].extend(file_analysis["composition"]) - analysis["relationships"]["imports"].extend(file_analysis["imports"]) - - except Exception as e: - print(f"Warning: Could not analyze {py_file}: {e}") - - # Analyze relationships - analysis["summary"]["inheritance_chains"] = self._find_inheritance_chains(analysis) - analysis["summary"]["circular_imports"] = self._find_circular_imports(analysis) - analysis["summary"]["unused_classes"] = self._find_unused_classes(analysis) - - return analysis - - except Exception as e: - raise SAGEDevToolkitError(f"Class dependency analysis failed: {e}") - - def check_class_usage( - self, class_name: str, target_paths: list[str] | None = None - ) -> dict[str, Any]: - """Check where a specific class is used.""" - try: - if target_paths: - paths_to_search = [Path(p) for p in target_paths] - else: - paths_to_search = [self.project_root] - - usage_info = { - "class_name": class_name, - "searched_paths": [str(p) for p in paths_to_search], - "usages": [], - "summary": { - "total_usages": 0, - "files_with_usage": 0, - "usage_types": { - "import": 0, - "inheritance": 0, - "instantiation": 0, - "reference": 0, - }, - }, - } - - # Find all Python files - python_files = [] - for path in paths_to_search: - if path.is_file() and path.suffix == ".py": - python_files.append(path) - elif path.is_dir(): - python_files.extend(path.rglob("*.py")) - - # Search for usage in each file - files_with_usage = set() - for py_file in python_files: - try: - file_usages = self._find_class_usage_in_file(py_file, class_name) - if file_usages: - usage_info["usages"].extend(file_usages) - files_with_usage.add(str(py_file)) - - # Count usage types - for usage in file_usages: - usage_type = usage["type"] - if usage_type in usage_info["summary"]["usage_types"]: - usage_info["summary"]["usage_types"][usage_type] += 1 - usage_info["summary"]["total_usages"] += 1 - - except Exception as e: - print(f"Warning: Could not search {py_file}: {e}") - - usage_info["summary"]["files_with_usage"] = len(files_with_usage) - - return usage_info - - except Exception as e: - raise SAGEDevToolkitError(f"Class usage check failed: {e}") - - def generate_class_diagram(self, output_format: str = "mermaid") -> str: - """Generate class diagram in specified format.""" - try: - analysis = self.analyze_class_dependencies() - - if output_format == "mermaid": - return self._generate_mermaid_diagram(analysis) - elif output_format == "dot": - return self._generate_dot_diagram(analysis) - else: - raise SAGEDevToolkitError(f"Unsupported diagram format: {output_format}") - - except Exception as e: - raise SAGEDevToolkitError(f"Class diagram generation failed: {e}") - - def _analyze_file(self, file_path: Path) -> dict[str, Any]: - """Analyze a single Python file.""" - try: - with open(file_path, encoding="utf-8") as f: - content = f.read() - - tree = ast.parse(content) - - # Get module name - relative_path = file_path.relative_to(self.project_root) - module_parts = relative_path.with_suffix("").parts - module_name = ".".join(module_parts) - - analysis = { - "file_path": str(file_path), - "module": module_name, - "classes": {}, - "inheritance": [], - "composition": [], - "imports": [], - } - - # Walk AST - for node in ast.walk(tree): - if isinstance(node, ast.ClassDef): - class_info = self._analyze_class_node(node, module_name) - analysis["classes"][node.name] = class_info - - # Record inheritance relationships - for base in node.bases: - if isinstance(base, ast.Name): - analysis["inheritance"].append( - { - "child": f"{module_name}.{node.name}", - "parent": base.id, - "file": str(file_path), - "line": node.lineno, - } - ) - elif isinstance(base, ast.Attribute): - parent_name = self._get_attribute_name(base) - analysis["inheritance"].append( - { - "child": f"{module_name}.{node.name}", - "parent": parent_name, - "file": str(file_path), - "line": node.lineno, - } - ) - - elif isinstance(node, (ast.Import, ast.ImportFrom)): - import_info = self._analyze_import_node(node, module_name) - analysis["imports"].append(import_info) - - return analysis - - except Exception as e: - raise SAGEDevToolkitError(f"File analysis failed for {file_path}: {e}") - - def _analyze_class_node(self, class_node: ast.ClassDef, module_name: str) -> dict[str, Any]: - """Analyze a class AST node.""" - class_info = { - "name": class_node.name, - "module": module_name, - "line": class_node.lineno, - "bases": [], - "methods": [], - "attributes": [], - "decorators": [], - } - - # Analyze base classes - for base in class_node.bases: - if isinstance(base, ast.Name): - class_info["bases"].append(base.id) - elif isinstance(base, ast.Attribute): - class_info["bases"].append(self._get_attribute_name(base)) - - # Analyze decorators - for decorator in class_node.decorator_list: - if isinstance(decorator, ast.Name): - class_info["decorators"].append(decorator.id) - elif isinstance(decorator, ast.Attribute): - class_info["decorators"].append(self._get_attribute_name(decorator)) - - # Analyze class body - for node in class_node.body: - if isinstance(node, ast.FunctionDef): - method_info = { - "name": node.name, - "line": node.lineno, - "args": [arg.arg for arg in node.args.args], - "decorators": [ - d.id if isinstance(d, ast.Name) else self._get_attribute_name(d) # type: ignore[arg-type] - for d in node.decorator_list - ], - } - class_info["methods"].append(method_info) - - elif isinstance(node, ast.Assign): - # Simple attribute assignment - for target in node.targets: - if isinstance(target, ast.Name): - class_info["attributes"].append( - { - "name": target.id, - "line": node.lineno, - "type": "assignment", - } - ) - - elif isinstance(node, ast.AnnAssign): - # Type annotated assignment - if isinstance(node.target, ast.Name): - attr_info = { - "name": node.target.id, - "line": node.lineno, - "type": "annotated", - } - if node.annotation: - attr_info["annotation"] = ast.unparse(node.annotation) - class_info["attributes"].append(attr_info) - - return class_info - - def _analyze_import_node(self, import_node: ast.AST, module_name: str) -> dict[str, Any]: - """Analyze an import AST node.""" - import_info = { - "importing_module": module_name, - "line": import_node.lineno, # type: ignore[attr-defined] - "type": "import" if isinstance(import_node, ast.Import) else "from_import", - } - - if isinstance(import_node, ast.Import): - import_info["modules"] = [alias.name for alias in import_node.names] - import_info["aliases"] = { - alias.name: alias.asname for alias in import_node.names if alias.asname - } - - elif isinstance(import_node, ast.ImportFrom): - import_info["from_module"] = import_node.module or "" - import_info["names"] = [alias.name for alias in import_node.names] - import_info["aliases"] = { - alias.name: alias.asname for alias in import_node.names if alias.asname - } - import_info["level"] = import_node.level - - return import_info - - def _get_attribute_name(self, attr_node: ast.Attribute) -> str: - """Get full attribute name from AST node.""" - try: - return ast.unparse(attr_node) - except Exception: - # Fallback for older Python versions - parts = [] - node = attr_node - while isinstance(node, ast.Attribute): - parts.append(node.attr) - node = node.value - if isinstance(node, ast.Name): - parts.append(node.id) - return ".".join(reversed(parts)) - - def _find_class_usage_in_file(self, file_path: Path, class_name: str) -> list[dict[str, Any]]: - """Find usages of a class in a specific file.""" - try: - with open(file_path, encoding="utf-8") as f: - content = f.read() - - tree = ast.parse(content) - usages = [] - - for node in ast.walk(tree): - # Check for class instantiation - if isinstance(node, ast.Call): - if isinstance(node.func, ast.Name) and node.func.id == class_name: - usages.append( - { - "type": "instantiation", - "line": node.lineno, - "file": str(file_path), - "context": "function_call", - } - ) - elif isinstance(node.func, ast.Attribute): - attr_name = self._get_attribute_name(node.func) - if class_name in attr_name: - usages.append( - { - "type": "instantiation", - "line": node.lineno, - "file": str(file_path), - "context": "method_call", - } - ) - - # Check for inheritance - elif isinstance(node, ast.ClassDef): - for base in node.bases: - if isinstance(base, ast.Name) and base.id == class_name: - usages.append( - { - "type": "inheritance", - "line": node.lineno, - "file": str(file_path), - "context": f"class {node.name}", - "child_class": node.name, - } - ) - elif isinstance(base, ast.Attribute): - attr_name = self._get_attribute_name(base) - if class_name in attr_name: - usages.append( - { - "type": "inheritance", - "line": node.lineno, - "file": str(file_path), - "context": f"class {node.name}", - "child_class": node.name, - } - ) - - # Check for imports - elif isinstance(node, (ast.Import, ast.ImportFrom)): - if isinstance(node, ast.Import): - for alias in node.names: - if class_name in alias.name: - usages.append( - { - "type": "import", - "line": node.lineno, - "file": str(file_path), - "context": f"import {alias.name}", - "alias": alias.asname, - } - ) - elif isinstance(node, ast.ImportFrom): - for alias in node.names: - if alias.name == class_name: - usages.append( - { - "type": "import", - "line": node.lineno, - "file": str(file_path), - "context": f"from {node.module} import {alias.name}", - "alias": alias.asname, - } - ) - - # Check for general references - elif isinstance(node, ast.Name) and node.id == class_name: - usages.append( - { - "type": "reference", - "line": node.lineno, - "file": str(file_path), - "context": "name_reference", - } - ) - - return usages - - except Exception as e: - print(f"Warning: Could not analyze {file_path} for class {class_name}: {e}") - return [] - - def _find_inheritance_chains(self, analysis: dict) -> list[list[str]]: - """Find inheritance chains in the codebase.""" - chains = [] - - # Build inheritance graph - inheritance_map = defaultdict(list) - for rel in analysis["relationships"]["inheritance"]: - inheritance_map[rel["parent"]].append(rel["child"]) - - # Find chains starting from root classes - visited = set() - - def build_chain(class_name, current_chain): - if class_name in visited: - return - - visited.add(class_name) - current_chain.append(class_name) - - children = inheritance_map.get(class_name, []) - if children: - for child in children: - build_chain(child, current_chain.copy()) - else: - # End of chain - if len(current_chain) > 1: - chains.append(current_chain) - - # Start with classes that are not children of others - all_children = set() - for children_list in inheritance_map.values(): - all_children.update(children_list) - - root_classes = set(inheritance_map.keys()) - all_children - - for root_class in root_classes: - build_chain(root_class, []) - - return chains - - def _find_circular_imports(self, analysis: dict) -> list[list[str]]: - """Find circular import dependencies.""" - # This is a simplified implementation - # In practice, you'd want more sophisticated cycle detection - import_graph = defaultdict(set) - - for import_info in analysis["relationships"]["imports"]: - importing_module = import_info["importing_module"] - - if import_info["type"] == "import": - for module in import_info["modules"]: - import_graph[importing_module].add(module) - elif import_info["type"] == "from_import": - from_module = import_info["from_module"] - if from_module: - import_graph[importing_module].add(from_module) - - return [] # Simplified - would need cycle detection algorithm - - def _find_unused_classes(self, analysis: dict) -> list[str]: - """Find classes that appear to be unused.""" - all_classes = set(analysis["classes"].keys()) - used_classes = set() - - # Mark classes used in inheritance - for rel in analysis["relationships"]["inheritance"]: - used_classes.add(rel["parent"]) - used_classes.add(rel["child"]) - - # Mark classes used in imports - for import_info in analysis["relationships"]["imports"]: - if import_info["type"] == "from_import": - for name in import_info["names"]: - # Simple heuristic: if name starts with capital, it's likely a class - if name[0].isupper(): - used_classes.add(name) - - return list(all_classes - used_classes) - - def _generate_mermaid_diagram(self, analysis: dict) -> str: - """Generate Mermaid class diagram.""" - lines = ["classDiagram"] - - # Add classes - for class_name, class_info in analysis["classes"].items(): - simple_name = class_name.split(".")[-1] - lines.append(f" class {simple_name} {{") - - # Add methods - for method in class_info["methods"]: - lines.append(f" +{method['name']}()") - - lines.append(" }") - - # Add inheritance relationships - for rel in analysis["relationships"]["inheritance"]: - parent_simple = rel["parent"].split(".")[-1] - child_simple = rel["child"].split(".")[-1] - lines.append(f" {parent_simple} <|-- {child_simple}") - - return "\n".join(lines) - - def _generate_dot_diagram(self, analysis: dict) -> str: - """Generate Graphviz DOT diagram.""" - lines = ["digraph ClassDiagram {"] - lines.append(" rankdir=TB;") - lines.append(" node [shape=record];") - - # Add classes - for class_name, class_info in analysis["classes"].items(): - simple_name = class_name.split(".")[-1] - methods_str = "\\n".join([f"+ {m['name']}()" for m in class_info["methods"]]) - lines.append(f' {simple_name} [label="{{class {simple_name}|{methods_str}}}"];') - - # Add inheritance relationships - for rel in analysis["relationships"]["inheritance"]: - parent_simple = rel["parent"].split(".")[-1] - child_simple = rel["child"].split(".")[-1] - lines.append(f" {parent_simple} -> {child_simple} [arrowhead=empty];") - - lines.append("}") - return "\n".join(lines) diff --git a/packages/sage-tools/src/sage/tools/dev/tools/commercial_package_manager.py b/packages/sage-tools/src/sage/tools/dev/tools/commercial_package_manager.py deleted file mode 100644 index 46f2d0d92e..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/tools/commercial_package_manager.py +++ /dev/null @@ -1,253 +0,0 @@ -""" -Commercial Package Manager - Integrated from scripts/commercial-package-manager.py - -This tool manages commercial SAGE packages and their deployment. -""" - -import subprocess -import sys -from pathlib import Path -from typing import Any - -from ..core.exceptions import SAGEDevToolkitError - - -class CommercialPackageManager: - """Tool for managing commercial SAGE packages.""" - - def __init__(self, project_root: str): - self.project_root = Path(project_root) - self.commercial_path = self.project_root / "packages" / "commercial" - - # Define commercial packages - self.packages = { - "sage-kernel": { - "path": self.commercial_path / "sage-kernel", - "description": "High-performance kernel infrastructure", - "components": [], - "dependencies": ["sage-kernel", "sage-utils"], - }, - "sage-middleware": { - "path": self.commercial_path / "sage-middleware", - "description": "Database and storage middleware", - "components": ["sage_db"], - "dependencies": ["sage-kernel", "sage-utils"], - }, - "sage-userspace": { - "path": self.commercial_path / "sage-userspace", - "description": "User-space runtime components", - "components": ["sage_runtime"], - "dependencies": ["sage-kernel", "sage-middleware"], - }, - } - - def list_commercial_packages(self) -> dict[str, Any]: - """List all commercial packages with their status.""" - try: - package_list = [] - - for name, info in self.packages.items(): - package_info = { - "name": name, - "description": info["description"], - "components": info["components"], - "dependencies": info["dependencies"], - "path": str(info["path"]), - "exists": info["path"].exists(), - "status": self._get_package_status(info["path"]), - } - package_list.append(package_info) - - return { - "packages": package_list, - "total_packages": len(package_list), - "commercial_path": str(self.commercial_path), - "status": "success", - } - - except Exception as e: - raise SAGEDevToolkitError(f"Commercial package listing failed: {e}") - - def install_commercial_package( - self, package_name: str, dev_mode: bool = True - ) -> dict[str, Any]: - """Install a commercial package.""" - try: - if package_name not in self.packages: - raise SAGEDevToolkitError(f"Unknown commercial package: {package_name}") - - package_info = self.packages[package_name] - package_path = package_info["path"] - - if not package_path.exists(): - raise SAGEDevToolkitError(f"Commercial package directory not found: {package_path}") - - # Install dependencies first - for dep in package_info["dependencies"]: - if dep in self.packages: - dep_result = self.install_commercial_package(dep, dev_mode) - if dep_result["status"] != "success": - return { - "package": package_name, - "status": "failed", - "error": f"Failed to install dependency: {dep}", - } - - # Install the package - cmd = [sys.executable, "-m", "pip", "install"] - if dev_mode: - cmd.append("-e") - cmd.append(str(package_path)) - - result = subprocess.run(cmd, capture_output=True, text=True) - - return { - "package": package_name, - "status": "success" if result.returncode == 0 else "failed", - "stdout": result.stdout, - "stderr": result.stderr, - "command": " ".join(cmd), - } - - except Exception as e: - raise SAGEDevToolkitError(f"Commercial package installation failed: {e}") - - def build_commercial_extensions(self, package_name: str | None = None) -> dict[str, Any]: - """Build C++ extensions for commercial packages.""" - try: - if package_name: - # Build specific package - if package_name not in self.packages: - raise SAGEDevToolkitError(f"Unknown commercial package: {package_name}") - - package_path = self.packages[package_name]["path"] - return self._build_package_extensions(package_name, package_path) - else: - # Build all packages - results = {} - for name, info in self.packages.items(): - if info["path"].exists(): - results[name] = self._build_package_extensions(name, info["path"]) - - return { - "results": results, - "total_packages": len(results), - "status": "success", - } - - except Exception as e: - raise SAGEDevToolkitError(f"Extension building failed: {e}") - - def check_commercial_status(self) -> dict[str, Any]: - """Check status of all commercial packages.""" - try: - status_info = { - "commercial_path_exists": self.commercial_path.exists(), - "packages": {}, - "summary": { - "total": len(self.packages), - "available": 0, - "installed": 0, - "missing": 0, - }, - } - - for name, info in self.packages.items(): - package_status = { - "exists": info["path"].exists(), - "installed": self._is_package_installed(name), - "components_built": self._check_components_built(info), - "path": str(info["path"]), - } - - status_info["packages"][name] = package_status - - if package_status["exists"]: - status_info["summary"]["available"] += 1 - else: - status_info["summary"]["missing"] += 1 - - if package_status["installed"]: - status_info["summary"]["installed"] += 1 - - return status_info - - except Exception as e: - raise SAGEDevToolkitError(f"Commercial status check failed: {e}") - - def _get_package_status(self, package_path: Path) -> str: - """Get status of a package.""" - if not package_path.exists(): - return "missing" - - # Check if it has pyproject.toml or setup.py - if (package_path / "pyproject.toml").exists() or (package_path / "setup.py").exists(): - return "ready" - - return "incomplete" - - def _build_package_extensions(self, package_name: str, package_path: Path) -> dict[str, Any]: - """Build extensions for a specific package.""" - try: - # Look for build script - build_script = package_path / "build_extensions.sh" - if build_script.exists(): - result = subprocess.run( - ["bash", str(build_script)], - capture_output=True, - text=True, - cwd=str(package_path), - ) - - return { - "package": package_name, - "status": "success" if result.returncode == 0 else "failed", - "stdout": result.stdout, - "stderr": result.stderr, - } - else: - # Try standard Python build - result = subprocess.run( - [sys.executable, "setup.py", "build_ext", "--inplace"], - capture_output=True, - text=True, - cwd=str(package_path), - ) - - return { - "package": package_name, - "status": "success" if result.returncode == 0 else "failed", - "stdout": result.stdout, - "stderr": result.stderr, - } - - except Exception as e: - return {"package": package_name, "status": "failed", "error": str(e)} - - def _is_package_installed(self, package_name: str) -> bool: - """Check if a package is installed.""" - try: - result = subprocess.run( - [sys.executable, "-m", "pip", "show", package_name.replace("-", "_")], - capture_output=True, - ) - return result.returncode == 0 - except Exception: - return False - - def _check_components_built(self, package_info: dict) -> bool: - """Check if package components are built.""" - package_path = package_info["path"] - if not package_path.exists(): - return False - - # Check for built extensions - for component in package_info["components"]: - # Look for .so files (Unix) or .pyd files (Windows) - so_files = list(package_path.rglob(f"{component}*.so")) - pyd_files = list(package_path.rglob(f"{component}*.pyd")) - - if not (so_files or pyd_files): - return False - - return True diff --git a/packages/sage-tools/src/sage/tools/dev/tools/dependency_analyzer.py b/packages/sage-tools/src/sage/tools/dev/tools/dependency_analyzer.py deleted file mode 100644 index 0b1464df56..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/tools/dependency_analyzer.py +++ /dev/null @@ -1,494 +0,0 @@ -""" -Dependency Summary Tool - Integrated from scripts/dependency_summary.py - -This tool analyzes and reports on project dependencies across all packages. -""" - -import json -import subprocess -import sys -from pathlib import Path -from typing import Any - -try: - import tomllib # Python 3.11+ -except ImportError: - import tomli as tomllib # Fallback for older Python versions - -from collections import defaultdict - -from ..core.exceptions import SAGEDevToolkitError - - -class DependencyAnalyzer: - """Tool for analyzing project dependencies.""" - - def __init__(self, project_root: str): - self.project_root = Path(project_root) - self.packages_dir = self.project_root / "packages" - - def analyze_all_dependencies(self) -> dict[str, Any]: - """Analyze dependencies across all packages.""" - try: - analysis = { - "project_root": str(self.project_root), - "packages": {}, - "summary": { - "total_packages": 0, - "total_dependencies": 0, - "unique_dependencies": set(), - "dependency_conflicts": [], - "circular_dependencies": [], - }, - "dependency_graph": {}, - "version_matrix": defaultdict(dict), - } - - # Find all packages - package_dirs = self._find_package_directories() - - for package_dir in package_dirs: - package_name = package_dir.name - package_analysis = self._analyze_package_dependencies(package_dir) - - analysis["packages"][package_name] = package_analysis - analysis["summary"]["total_packages"] += 1 - - # Update global dependency tracking - for dep_name, dep_info in package_analysis["dependencies"].items(): - analysis["summary"]["unique_dependencies"].add(dep_name) - analysis["summary"]["total_dependencies"] += 1 - - # Track version matrix - if "version" in dep_info: - analysis["version_matrix"][dep_name][package_name] = dep_info["version"] - - # Convert set to list for JSON serialization - analysis["summary"]["unique_dependencies"] = list( - analysis["summary"]["unique_dependencies"] - ) - analysis["summary"]["total_unique_dependencies"] = len( - analysis["summary"]["unique_dependencies"] - ) - - # Build dependency graph - analysis["dependency_graph"] = self._build_dependency_graph(analysis["packages"]) - - # Detect conflicts and circular dependencies - analysis["summary"]["dependency_conflicts"] = self._detect_version_conflicts( - analysis["version_matrix"] - ) - analysis["summary"]["circular_dependencies"] = self._detect_circular_dependencies( - analysis["dependency_graph"] - ) - - return analysis - - except Exception as e: - raise SAGEDevToolkitError(f"Dependency analysis failed: {e}") - - def generate_dependency_report(self, output_format: str = "json") -> dict[str, Any]: - """Generate comprehensive dependency report.""" - try: - analysis = self.analyze_all_dependencies() - - if output_format == "json": - # Convert defaultdict to regular dict for JSON - analysis["version_matrix"] = dict(analysis["version_matrix"]) - return analysis - - elif output_format == "markdown": - return self._generate_markdown_report(analysis) # type: ignore[return-value] - - elif output_format == "summary": - return self._generate_summary_report(analysis) - - else: - raise SAGEDevToolkitError(f"Unsupported output format: {output_format}") - - except Exception as e: - raise SAGEDevToolkitError(f"Report generation failed: {e}") - - def check_dependency_health(self) -> dict[str, Any]: - """Check overall dependency health.""" - try: - analysis = self.analyze_all_dependencies() - - health_score = 100 - issues = [] - recommendations = [] - - # Check for version conflicts - conflicts = analysis["summary"]["dependency_conflicts"] - if conflicts: - health_score -= len(conflicts) * 10 - issues.append(f"Found {len(conflicts)} version conflicts") - recommendations.append("Resolve version conflicts to ensure compatibility") - - # Check for circular dependencies - circular = analysis["summary"]["circular_dependencies"] - if circular: - health_score -= len(circular) * 15 - issues.append(f"Found {len(circular)} circular dependencies") - recommendations.append("Refactor to eliminate circular dependencies") - - # Check for outdated dependencies - outdated = self._check_outdated_dependencies(analysis) - if outdated: - health_score -= len(outdated) * 5 - issues.append(f"Found {len(outdated)} potentially outdated dependencies") - recommendations.append("Consider updating outdated dependencies") - - # Check for security vulnerabilities - vulnerabilities = self._check_security_vulnerabilities() - if vulnerabilities: - health_score -= len(vulnerabilities) * 20 - issues.append(f"Found {len(vulnerabilities)} security vulnerabilities") - recommendations.append("Address security vulnerabilities immediately") - - health_score = max(0, health_score) # Don't go below 0 - - return { - "health_score": health_score, - "grade": self._get_health_grade(health_score), - "issues": issues, - "recommendations": recommendations, - "conflicts": conflicts, - "circular_dependencies": circular, - "outdated": outdated, - "vulnerabilities": vulnerabilities, - "total_packages": analysis["summary"]["total_packages"], - "total_dependencies": analysis["summary"]["total_unique_dependencies"], - } - - except Exception as e: - raise SAGEDevToolkitError(f"Dependency health check failed: {e}") - - def _find_package_directories(self) -> list[Path]: - """Find all package directories.""" - package_dirs = [] - - # Check packages/ directory - if self.packages_dir.exists(): - for item in self.packages_dir.iterdir(): - if item.is_dir() and self._is_python_package(item): - package_dirs.append(item) - - # Check root directory for packages - root_files = ["pyproject.toml", "setup.py", "requirements.txt"] - if any((self.project_root / f).exists() for f in root_files): - package_dirs.append(self.project_root) - - return package_dirs - - def _is_python_package(self, path: Path) -> bool: - """Check if directory is a Python package.""" - package_files = ["pyproject.toml", "setup.py", "requirements.txt", "setup.cfg"] - return any((path / f).exists() for f in package_files) - - def _analyze_package_dependencies(self, package_dir: Path) -> dict[str, Any]: - """Analyze dependencies for a single package.""" - analysis = { - "name": package_dir.name, - "path": str(package_dir), - "dependencies": {}, - "dev_dependencies": {}, - "optional_dependencies": {}, - "dependency_sources": [], - } - - # Check pyproject.toml - pyproject_file = package_dir / "pyproject.toml" - if pyproject_file.exists(): - self._parse_pyproject_dependencies(pyproject_file, analysis) - - # Check setup.py - setup_file = package_dir / "setup.py" - if setup_file.exists(): - self._parse_setup_dependencies(setup_file, analysis) - - # Check requirements.txt - req_file = package_dir / "requirements.txt" - if req_file.exists(): - self._parse_requirements_dependencies(req_file, analysis) - - # Check requirements-dev.txt - dev_req_file = package_dir / "requirements-dev.txt" - if dev_req_file.exists(): - self._parse_requirements_dependencies(dev_req_file, analysis, is_dev=True) - - return analysis - - def _parse_pyproject_dependencies(self, pyproject_file: Path, analysis: dict): - """Parse dependencies from pyproject.toml.""" - try: - with open(pyproject_file, "rb") as f: - data = tomllib.load(f) - - analysis["dependency_sources"].append("pyproject.toml") - - # Main dependencies - if "project" in data and "dependencies" in data["project"]: - for dep in data["project"]["dependencies"]: - dep_name, dep_info = self._parse_dependency_spec(dep) - analysis["dependencies"][dep_name] = dep_info - - # Optional dependencies - if "project" in data and "optional-dependencies" in data["project"]: - for group, deps in data["project"]["optional-dependencies"].items(): - for dep in deps: - dep_name, dep_info = self._parse_dependency_spec(dep) - analysis["optional_dependencies"][dep_name] = { - **dep_info, - "group": group, - } - - except Exception as e: - print(f"Warning: Could not parse {pyproject_file}: {e}") - - def _parse_setup_dependencies(self, setup_file: Path, analysis: dict): - """Parse dependencies from setup.py (basic parsing).""" - try: - analysis["dependency_sources"].append("setup.py") - - # This is a simplified parser - in practice, you might want to use AST - with open(setup_file) as f: - content = f.read() - - # Look for install_requires - if "install_requires" in content: - # Extract dependencies using regex (simplified approach) - import re - - pattern = r"install_requires\s*=\s*\[(.*?)\]" - match = re.search(pattern, content, re.DOTALL) - if match: - deps_str = match.group(1) - deps = re.findall(r'["\']([^"\']+)["\']', deps_str) - for dep in deps: - dep_name, dep_info = self._parse_dependency_spec(dep) - analysis["dependencies"][dep_name] = dep_info - - except Exception as e: - print(f"Warning: Could not parse {setup_file}: {e}") - - def _parse_requirements_dependencies( - self, req_file: Path, analysis: dict, is_dev: bool = False - ): - """Parse dependencies from requirements.txt files.""" - try: - source_name = req_file.name - analysis["dependency_sources"].append(source_name) - - with open(req_file) as f: - for line in f: - line = line.strip() - if line and not line.startswith("#") and not line.startswith("-"): - dep_name, dep_info = self._parse_dependency_spec(line) - target_dict = ( - analysis["dev_dependencies"] if is_dev else analysis["dependencies"] - ) - target_dict[dep_name] = dep_info - - except Exception as e: - print(f"Warning: Could not parse {req_file}: {e}") - - def _parse_dependency_spec(self, dep_spec: str) -> tuple: - """Parse a dependency specification.""" - import re - - # Remove extras specification - dep_spec = re.sub(r"\[.*?\]", "", dep_spec) - - # Parse name and version - match = re.match(r"([a-zA-Z0-9_-]+)\s*([><=!~].*)?", dep_spec.strip()) - if match: - name = match.group(1) - version_spec = match.group(2) if match.group(2) else None - - return name, {"version": version_spec, "spec": dep_spec.strip()} - else: - return dep_spec.strip(), {"version": None, "spec": dep_spec.strip()} - - def _build_dependency_graph(self, packages: dict) -> dict[str, list[str]]: - """Build dependency graph.""" - graph = {} - - for package_name, package_info in packages.items(): - dependencies = [] - - # Add direct dependencies - for dep_name in package_info["dependencies"].keys(): - # Only include SAGE internal dependencies - if dep_name.startswith("sage-") or dep_name in packages: - dependencies.append(dep_name) - - graph[package_name] = dependencies - - return graph - - def _detect_version_conflicts(self, version_matrix: dict) -> list[dict]: - """Detect version conflicts.""" - conflicts = [] - - for dep_name, package_versions in version_matrix.items(): - versions = {v for v in package_versions.values() if v} - - if len(versions) > 1: - conflicts.append( - { - "dependency": dep_name, - "versions": list(versions), - "packages": dict(package_versions), - } - ) - - return conflicts - - def _detect_circular_dependencies(self, graph: dict) -> list[list[str]]: - """Detect circular dependencies using DFS.""" - visited = set() - rec_stack = set() - cycles = [] - - def dfs(node, path): - if node in rec_stack: - # Found a cycle - cycle_start = path.index(node) - cycle = path[cycle_start:] + [node] - cycles.append(cycle) - return - - if node in visited: - return - - visited.add(node) - rec_stack.add(node) - - for neighbor in graph.get(node, []): - dfs(neighbor, path + [neighbor]) - - rec_stack.remove(node) - - for node in graph: - if node not in visited: - dfs(node, [node]) - - return cycles - - def _check_outdated_dependencies(self, analysis: dict) -> list[dict]: - """Check for potentially outdated dependencies.""" - # This is a simplified check - in practice, you'd want to query PyPI - outdated = [] - - for package_name, package_info in analysis["packages"].items(): - for dep_name, dep_info in package_info["dependencies"].items(): - if dep_info.get("version") and "<" in dep_info["version"]: - outdated.append( - { - "package": package_name, - "dependency": dep_name, - "current_spec": dep_info["version"], - "reason": "Uses upper bound version constraint", - } - ) - - return outdated - - def _check_security_vulnerabilities(self) -> list[dict]: - """Check for security vulnerabilities.""" - try: - # Try to use safety if available - result = subprocess.run( - [sys.executable, "-m", "safety", "check", "--json"], - capture_output=True, - text=True, - ) - - if result.returncode == 0 and result.stdout: - return json.loads(result.stdout) - - except Exception: - pass - - return [] # Return empty list if safety is not available - - def _get_health_grade(self, score: int) -> str: - """Get health grade based on score.""" - if score >= 90: - return "A" - elif score >= 80: - return "B" - elif score >= 70: - return "C" - elif score >= 60: - return "D" - else: - return "F" - - def _generate_markdown_report(self, analysis: dict) -> str: - """Generate markdown report.""" - report_lines = [ - "# SAGE Dependency Analysis Report", - "", - f"**Project Root:** {analysis['project_root']}", - f"**Total Packages:** {analysis['summary']['total_packages']}", - f"**Total Dependencies:** {analysis['summary']['total_unique_dependencies']}", - "", - "## Package Summary", - "", - ] - - for package_name, package_info in analysis["packages"].items(): - report_lines.extend( - [ - f"### {package_name}", - f"- **Path:** `{package_info['path']}`", - f"- **Dependencies:** {len(package_info['dependencies'])}", - f"- **Dev Dependencies:** {len(package_info['dev_dependencies'])}", - f"- **Optional Dependencies:** {len(package_info['optional_dependencies'])}", - "", - ] - ) - - # Add conflicts section - if analysis["summary"]["dependency_conflicts"]: - report_lines.extend(["## Version Conflicts", ""]) - for conflict in analysis["summary"]["dependency_conflicts"]: - report_lines.extend( - [ - f"### {conflict['dependency']}", - f"- **Conflicting Versions:** {', '.join(conflict['versions'])}", - f"- **Affected Packages:** {', '.join(conflict['packages'].keys())}", - "", - ] - ) - - return "\n".join(report_lines) - - def _generate_summary_report(self, analysis: dict) -> dict[str, Any]: - """Generate summary report.""" - return { - "project_root": analysis["project_root"], - "total_packages": analysis["summary"]["total_packages"], - "total_dependencies": analysis["summary"]["total_unique_dependencies"], - "conflicts": len(analysis["summary"]["dependency_conflicts"]), - "circular_dependencies": len(analysis["summary"]["circular_dependencies"]), - "top_dependencies": self._get_top_dependencies(analysis), - "package_count": { - name: len(info["dependencies"]) for name, info in analysis["packages"].items() - }, - } - - def _get_top_dependencies(self, analysis: dict) -> list[dict]: - """Get most commonly used dependencies.""" - dep_count = defaultdict(int) - - for package_info in analysis["packages"].values(): - for dep_name in package_info["dependencies"].keys(): - dep_count[dep_name] += 1 - - # Sort by usage count - sorted_deps = sorted(dep_count.items(), key=lambda x: x[1], reverse=True) - - return [{"name": name, "usage_count": count} for name, count in sorted_deps[:10]] diff --git a/packages/sage-tools/src/sage/tools/dev/tools/dependency_spec_checker.py b/packages/sage-tools/src/sage/tools/dev/tools/dependency_spec_checker.py deleted file mode 100644 index 7fe0ffca19..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/tools/dependency_spec_checker.py +++ /dev/null @@ -1,127 +0,0 @@ -"""Dependency spec consistency checker. - -Validates that package ``pyproject.toml`` dependency pins match the unified -``dependencies-spec.yaml`` at the repository root. - -This is a lightweight parser that avoids adding a YAML dependency by parsing -the simple ``key: "specifier"`` format used in the spec file. -""" - -from __future__ import annotations - -from collections import defaultdict -from pathlib import Path -from typing import Iterable - -from packaging.requirements import Requirement -from packaging.specifiers import SpecifierSet - -try: # Python 3.11+ - import tomllib -except ImportError: # pragma: no cover - fallback for older interpreters - import tomli as tomllib - - -class DependencyMismatch(Exception): - """Raised when dependency versions do not match the unified spec.""" - - -def _load_spec(spec_path: Path) -> dict[str, SpecifierSet]: - if not spec_path.exists(): - raise FileNotFoundError( - f"dependencies-spec.yaml not found at {spec_path}. " - "Please create it before running the check." - ) - - spec: dict[str, SpecifierSet] = {} - for raw_line in spec_path.read_text(encoding="utf-8").splitlines(): - line = raw_line.strip() - if not line or line.startswith("#"): - continue - if ":" not in line: - continue - key, value = line.split(":", 1) - key = key.strip() - value = value.strip().strip('"').strip("'") - if key: - spec[key.lower()] = SpecifierSet(value) - return spec - - -def _iter_pyprojects(packages_dir: Path) -> Iterable[Path]: - for pyproject in packages_dir.glob("*/pyproject.toml"): - if pyproject.is_file(): - yield pyproject - - -def _collect_requirements(pyproject: Path) -> list[Requirement]: - data = tomllib.loads(pyproject.read_text(encoding="utf-8")) - requirements: list[Requirement] = [] - - project = data.get("project", {}) - deps = project.get("dependencies", []) or [] - requirements.extend(_to_requirements(deps)) - - optional = project.get("optional-dependencies", {}) or {} - for extra_deps in optional.values(): - requirements.extend(_to_requirements(extra_deps)) - - return requirements - - -def _to_requirements(items: Iterable[str]) -> list[Requirement]: - reqs: list[Requirement] = [] - for item in items: - if not isinstance(item, str): - continue - try: - reqs.append(Requirement(item)) - except Exception: - # Ignore unparsable entries to avoid breaking the whole check - continue - return reqs - - -def check_dependencies_spec(project_root: Path) -> dict[str, list[str]]: - """Check all packages against the unified dependency spec. - - Returns a mapping of package name to list of human-readable mismatch messages. - """ - - spec_path = project_root / "dependencies-spec.yaml" - spec = _load_spec(spec_path) - - packages_dir = project_root / "packages" - if not packages_dir.exists(): - raise FileNotFoundError(f"packages directory not found at {packages_dir}") - - mismatches: dict[str, list[str]] = defaultdict(list) - - for pyproject in _iter_pyprojects(packages_dir): - pkg_name = pyproject.parent.name - requirements = _collect_requirements(pyproject) - - for req in requirements: - name = req.name.lower() - if name not in spec: - continue - desired = spec[name] - if req.specifier != desired: - mismatches[pkg_name].append( - f"{req.name}: found '{req.specifier}' but spec requires '{desired}'" - ) - - return mismatches - - -def assert_dependencies_match(project_root: Path) -> None: - mismatches = check_dependencies_spec(project_root) - if not mismatches: - return - - lines: list[str] = ["Dependency versions do not match dependencies-spec.yaml:"] - for pkg, issues in sorted(mismatches.items()): - lines.append(f"- {pkg}:") - for issue in issues: - lines.append(f" - {issue}") - raise DependencyMismatch("\n".join(lines)) diff --git a/packages/sage-tools/src/sage/tools/dev/tools/devnotes_checker.py b/packages/sage-tools/src/sage/tools/dev/tools/devnotes_checker.py deleted file mode 100755 index a6b9a49e0f..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/tools/devnotes_checker.py +++ /dev/null @@ -1,514 +0,0 @@ -#!/usr/bin/env python3 -""" -Dev-notes Documentation Compliance Checker - -检查 dev-notes 文档是否符合规范： -1. 必须放在正确的分类目录下 -2. 必须包含日期和作者信息 -3. 文件名必须符合命名规范 - -Author: SAGE Team -Date: 2025-10-23 -""" - -import argparse -import re -import sys -from datetime import datetime -from pathlib import Path - -# 允许的 dev-notes 分类目录 -# 按照 SAGE 系统架构设计：L1-L5 分层 + 跨层主题 -ALLOWED_CATEGORIES = { - # === 架构层次分类 (L1-L5) === - "l1-common": "L1 基础层 - sage-common 包相关开发笔记", - "l2-platform": "L2 平台层 - sage-platform 包相关开发笔记", - "l3-kernel": "L3 核心层 - sage-kernel 包相关开发笔记", - "l3-libs": "L3 核心层 - sage-libs 包相关开发笔记", - "l4-middleware": "L4 中间件层 - sage-middleware 包相关开发笔记", - "l5-cli": "L5 接口层 - sage-cli 包相关开发笔记", - "l5-tools": "L5 接口层 - sage-tools 包相关开发笔记", - # === 跨层主题分类 (在 cross-layer/ 下) === - "cross-layer/architecture": "系统架构设计与演进", - "cross-layer/ci-cd": "CI/CD 流程、构建系统、自动化", - "cross-layer/performance": "性能优化、基准测试、调优", - "cross-layer/security": "安全机制、权限控制、加密", - "cross-layer/testing": "测试策略、测试框架、质量保证", - "cross-layer/deployment": "部署方案、运维配置、发布流程", - "cross-layer/migration": "数据迁移、代码重构、升级指南", - "cross-layer/documentation": "文档规范、API 文档、用户指南", - "cross-layer/research": "研究实验、算法探索、原型验证", - # === 特殊分类 === - "archive": "已归档文档（历史记录，只读）", -} - -# 特殊文件（不受规则限制） -SPECIAL_FILES = { - "README.md", - "TEMPLATE.md", -} - -# 必需的元数据字段 -REQUIRED_METADATA = ["Date", "Author", "Summary"] - - -class DevNotesChecker: - """Dev-notes 文档规范检查器""" - - def __init__(self, root_dir: Path | str, strict: bool = False): - self.root_dir = Path(root_dir) if isinstance(root_dir, str) else root_dir - self.devnotes_dir = self.root_dir / "docs-public" / "docs_src" / "dev-notes" - self.strict = strict - self.errors: list[str] = [] - self.warnings: list[str] = [] - - def check_file(self, file_path: Path) -> bool: - """检查单个文件是否符合规范""" - if not file_path.exists(): - return True - - # 获取相对路径 - try: - rel_path = file_path.relative_to(self.devnotes_dir) - except ValueError: - # 文件不在 dev-notes 目录下 - return True - - # 检查是否是特殊文件 - if rel_path.name in SPECIAL_FILES: - return True - - # 检查是否在根目录（不允许） - if len(rel_path.parts) == 1: - self.errors.append( - f"❌ {rel_path}: 文档必须放在分类目录下，不能直接放在 dev-notes 根目录\n" - f" 建议: 根据内容移动到合适的分类目录" - ) - return False - - # 检查分类目录（支持一级或二级分类） - # 例如: l3-kernel/xxx.md 或 cross-layer/architecture/xxx.md - if rel_path.parts[0] == "cross-layer": - # 跨层主题：需要二级分类 - if len(rel_path.parts) < 3: - self.errors.append( - f"❌ {rel_path}: cross-layer 目录下的文档必须放在具体的子分类中\n" - f" 例如: cross-layer/architecture/, cross-layer/ci-cd/ 等" - ) - return False - category = f"{rel_path.parts[0]}/{rel_path.parts[1]}" - else: - # 层次分类或归档：一级分类 - category = rel_path.parts[0] - - if category not in ALLOWED_CATEGORIES: - allowed_list = "\n ".join(sorted(ALLOWED_CATEGORIES.keys())) - self.errors.append( - f"❌ {rel_path}: 未知的分类目录 '{category}'\n 允许的分类:\n {allowed_list}" - ) - return False - - # 检查文件名（不能包含日期，日期应该在元数据中） - if re.search(r"\d{4}[-_]\d{2}[-_]\d{2}", rel_path.name): - self.warnings.append(f"⚠️ {rel_path}: 文件名不应包含日期，请在文档元数据中标注日期") - - # 检查元数据 - if not self._check_metadata(file_path, rel_path): - return False - - return True - - def _check_metadata(self, file_path: Path, rel_path: Path) -> bool: - """检查文档元数据""" - try: - content = file_path.read_text(encoding="utf-8") - except Exception as e: - self.errors.append(f"❌ {rel_path}: 无法读取文件 - {e}") - return False - - # 检查是否有元数据区域（前几行） - lines = content.split("\n") - metadata = {} - - # 支持两种格式： - # 1. 元数据在文档开头（第一行开始） - # 2. 元数据在第一个 # 标题之后 - for i, line in enumerate(lines[:30]): # 只检查前30行 - # 跳过空行和分隔线 - if not line.strip() or line.strip() == "---": - continue - - # 跳过标题行 - if line.startswith("#"): - continue - - # 尝试匹配元数据格式 - # 支持格式：**Key**: Value 或 **Key:** Value 或 Key: Value - match = re.match( - r"^\*?\*?(Date|Author|Summary|Related)\*?\*?\s*[:：]\s*(.+)$", - line.strip(), - re.IGNORECASE, - ) - if match: - key, value = match.groups() - metadata[key.title()] = value.strip() - elif metadata: - # 已经读取到元数据，遇到非元数据行则停止 - break - - # 检查必需字段 - missing_fields = [field for field in REQUIRED_METADATA if field not in metadata] - if missing_fields: - self.errors.append( - f"❌ {rel_path}: 缺少必需的元数据字段: {', '.join(missing_fields)}\n" - f" 请在文档开头添加:\n" - f" **Date**: YYYY-MM-DD\n" - f" **Author**: Your Name\n" - f" **Summary**: Brief description" - ) - return False - - # 检查日期格式 - if "Date" in metadata: - date_str = metadata["Date"] - if not re.match(r"\d{4}-\d{2}-\d{2}", date_str): - self.errors.append(f"❌ {rel_path}: 日期格式错误 '{date_str}'，应为 YYYY-MM-DD") - return False - - # 检查日期是否合理（不能是未来日期） - try: - doc_date = datetime.strptime(date_str, "%Y-%m-%d") - if doc_date > datetime.now(): - self.warnings.append(f"⚠️ {rel_path}: 日期是未来日期 '{date_str}'") - except ValueError: - self.errors.append(f"❌ {rel_path}: 无效的日期 '{date_str}'") - return False - - return True - - def check_directory_structure(self) -> bool: - """检查 dev-notes 目录结构""" - issues_found = False - - if not self.devnotes_dir.exists(): - self.errors.append(f"❌ dev-notes 目录不存在: {self.devnotes_dir}") - return False - - # 1. 检查是否存在 dev-notes 根目录下的文件（除了特殊文件） - devnotes_root_files = [ - f for f in self.devnotes_dir.glob("*.md") if f.name not in SPECIAL_FILES - ] - if devnotes_root_files: - self.errors.append( - f"❌ dev-notes 根目录下有 {len(devnotes_root_files)} 个文件需要整理:\n" - + "\n".join(f" - {f.name}" for f in devnotes_root_files[:10]) - ) - if len(devnotes_root_files) > 10: - self.errors.append(f" ... 还有 {len(devnotes_root_files) - 10} 个文件") - issues_found = True - - # 2. 检查项目根目录是否有应该在 dev-notes 的 markdown 文件 - # 允许的根目录文件（用户文档、贡献指南等） - allowed_root_md = { - "README.md", - "CONTRIBUTING.md", - "DEVELOPER.md", - "LICENSE.md", - "CHANGELOG.md", - "CODE_OF_CONDUCT.md", - } - - project_root_files = [ - f for f in self.root_dir.glob("*.md") if f.name not in allowed_root_md - ] - - if project_root_files: - self.errors.append( - f"❌ 项目根目录下有 {len(project_root_files)} 个 markdown 文件应该移到 docs-public/docs_src/dev-notes/ 下:\n" - + "\n".join( - f" - {f.name} → 建议移到 docs-public/docs_src/dev-notes//" - for f in project_root_files[:10] - ) - ) - if len(project_root_files) > 10: - self.errors.append(f" ... 还有 {len(project_root_files) - 10} 个文件") - issues_found = True - - return not issues_found - - def check_changed_files(self, changed_files: list[str]) -> tuple[int, int]: - """检查变更的文件""" - devnotes_files = [ - f - for f in changed_files - if f.startswith("docs-public/docs_src/dev-notes/") and f.endswith(".md") - ] - - if not devnotes_files: - return 0, 0 - - print(f"\n📝 检查 {len(devnotes_files)} 个 dev-notes 文档...\n") - - passed = 0 - failed = 0 - - for file_str in devnotes_files: - file_path = self.root_dir / file_str - if self.check_file(file_path): - passed += 1 - else: - failed += 1 - - return passed, failed - - def check_all_files(self) -> tuple[int, int]: - """检查所有 dev-notes 文件""" - all_files = list(self.devnotes_dir.rglob("*.md")) - all_files = [f for f in all_files if f.name not in SPECIAL_FILES] - - if not all_files: - print("ℹ️ 没有 dev-notes 文档需要检查") - return 0, 0 - - print(f"\n📝 检查 {len(all_files)} 个 dev-notes 文档...\n") - - passed = 0 - failed = 0 - - for file_path in all_files: - if self.check_file(file_path): - passed += 1 - else: - failed += 1 - - return passed, failed - - def check_all(self) -> dict: - """检查所有文件（返回字典格式，用于 CLI）""" - # 先检查目录结构 - structure_ok = self.check_directory_structure() - - # 再检查文件内容 - passed, failed = self.check_all_files() - - # 如果目录结构有问题，也算失败 - if not structure_ok: - failed += 1 - - return { - "passed": failed == 0 and (not self.strict or len(self.warnings) == 0), - "total": passed + failed, - "passed_count": passed, - "failed_count": failed, - "warnings": len(self.warnings), - "issues": [{"file": "devnotes", "message": err} for err in self.errors] - + [{"file": "devnotes", "message": warn} for warn in self.warnings], - } - - def check_changed(self, diff_target: str = "HEAD") -> dict: - """检查变更的文件（返回字典格式，用于 CLI）""" - # 先检查目录结构（根目录文件检查） - structure_ok = self.check_directory_structure() - - # 再检查变更的文件 - changed_files = get_changed_files(self.root_dir, diff_target) - passed, failed = self.check_changed_files(changed_files) - - # 如果目录结构有问题，也算失败 - if not structure_ok: - failed += 1 - - return { - "passed": failed == 0 and (not self.strict or len(self.warnings) == 0), - "total": passed + failed, - "passed_count": passed, - "failed_count": failed, - "warnings": len(self.warnings), - "issues": [{"file": "devnotes", "message": err} for err in self.errors] - + [{"file": "devnotes", "message": warn} for warn in self.warnings], - } - - def print_results(self, passed: int, failed: int) -> bool: - """打印检查结果""" - # 打印警告 - if self.warnings: - print("\n" + "=" * 80) - print("⚠️ 警告信息:") - print("=" * 80) - for warning in self.warnings: - print(warning) - - # 打印错误 - if self.errors: - print("\n" + "=" * 80) - print("❌ 错误信息:") - print("=" * 80) - for error in self.errors: - print(error) - - # 打印统计 - print("\n" + "=" * 80) - print("📊 检查结果:") - print("=" * 80) - print(f"✅ 通过: {passed}") - print(f"❌ 失败: {failed}") - print(f"⚠️ 警告: {len(self.warnings)}") - - if failed == 0: - print("\n🎉 所有文档都符合规范！") - if self.warnings and self.strict: - print("⚠️ 但有警告信息（严格模式已开启）") - return False - return True - else: - print(f"\n❌ 发现 {failed} 个不符合规范的文档") - print("\n💡 规范说明:") - print("1. 文档必须放在分类目录下（architecture, kernel, middleware 等）") - print("2. 文档开头必须包含元数据:") - print(" **Date**: YYYY-MM-DD") - print(" **Author**: Your Name") - print(" **Summary**: Brief description") - print("3. 文件名不应包含日期（日期在元数据中标注）") - print("\n📖 详细规范请参考: docs-public/docs_src/dev-notes/TEMPLATE.md") - return False - - -def get_changed_files(root_dir: Path, diff_target: str | None = None) -> list[str]: - """获取变更的文件列表""" - import subprocess - - try: - if diff_target: - # 比较指定的 diff target - result = subprocess.run( - ["git", "diff", "--name-only", diff_target], - cwd=root_dir, - capture_output=True, - text=True, - check=True, - ) - else: - # 获取暂存区的文件 - result = subprocess.run( - ["git", "diff", "--cached", "--name-only"], - cwd=root_dir, - capture_output=True, - text=True, - check=True, - ) - return result.stdout.strip().split("\n") if result.stdout.strip() else [] - except subprocess.CalledProcessError as e: - print(f"⚠️ 警告: 无法获取 Git 变更文件: {e}") - return [] - - -def main(): - parser = argparse.ArgumentParser( - description="Dev-notes 文档规范检查工具", - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=""" -示例: - # 检查所有 dev-notes 文档 - python devnotes_checker.py --all - - # 检查暂存的文件 - python devnotes_checker.py --changed-only - - # 检查与指定提交的差异 - python devnotes_checker.py --changed-only --diff HEAD~5 - - # 严格模式（警告也会失败） - python devnotes_checker.py --all --strict - -允许的分类目录: -""" - + "\n".join(f" {k}: {v}" for k, v in ALLOWED_CATEGORIES.items()), - ) - - parser.add_argument( - "--root", - type=Path, - default=Path.cwd(), - help="项目根目录（默认: 当前目录）", - ) - parser.add_argument( - "--all", - action="store_true", - help="检查所有 dev-notes 文档", - ) - parser.add_argument( - "--changed-only", - action="store_true", - help="仅检查变更的文档", - ) - parser.add_argument( - "--diff", - type=str, - help="比较差异的目标（如 HEAD, HEAD~5, origin/main）", - ) - parser.add_argument( - "--strict", - action="store_true", - help="严格模式：警告也会导致失败", - ) - parser.add_argument( - "--check-structure", - action="store_true", - help="检查目录结构（是否有文件在根目录）", - ) - - args = parser.parse_args() - - # 检查是否在 Git 仓库中 - if not (args.root / ".git").exists(): - print("❌ 错误: 不在 Git 仓库中") - sys.exit(1) - - checker = DevNotesChecker(args.root, strict=args.strict) - - # 打印检查模式 - print("=" * 80) - print("📚 Dev-notes 文档规范检查") - print("=" * 80) - - # 检查目录结构 - structure_ok = True - if args.check_structure: - print("\n🔍 检查目录结构...") - if not checker.check_directory_structure(): - structure_ok = False - print("\n❌ 目录结构检查失败") - else: - print("\n✅ 目录结构检查通过") - - # 如果只检查结构，不检查文件内容 - if not args.all and not args.changed_only: - if not structure_ok: - checker.print_results(0, 0) - sys.exit(1) - sys.exit(0) - - # 执行检查 - if args.all: - print("\n🔍 检查模式: 全部文档") - passed, failed = checker.check_all_files() - elif args.changed_only: - print("\n🔍 检查模式: 仅变更的文档") - if args.diff: - print(f" 差异目标: {args.diff}") - else: - print(" 差异目标: 暂存区") - changed_files = get_changed_files(args.root, args.diff) - passed, failed = checker.check_changed_files(changed_files) - else: - parser.print_help() - sys.exit(0) - - # 打印结果 - success = checker.print_results(passed, failed) and structure_ok - - sys.exit(0 if success else 1) - - -if __name__ == "__main__": - main() diff --git a/packages/sage-tools/src/sage/tools/dev/tools/enhanced_package_manager.py b/packages/sage-tools/src/sage/tools/dev/tools/enhanced_package_manager.py deleted file mode 100644 index b60ef30478..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/tools/enhanced_package_manager.py +++ /dev/null @@ -1,440 +0,0 @@ -""" -Enhanced SAGE Package Manager - Integrated from scripts/sage-package-manager.py - -This tool provides comprehensive package management for the SAGE monorepo. -""" - -import subprocess -import sys -from pathlib import Path -from typing import Any - -from ..core.exceptions import SAGEDevToolkitError - - -class EnhancedPackageManager: - """Enhanced SAGE package manager with dependency resolution.""" - - def __init__(self, project_root: str): - self.project_root = Path(project_root) - self.packages_dir = self.project_root / "packages" - - # Define packages and their dependencies (in dependency order) - self.packages: dict[str, dict[str, Any]] = { - # L1: 基础包 - 无依赖 - "sage-common": { - "path": self.packages_dir / "sage-common", - "namespace": "sage.common", - "dependencies": [], - "description": "Common utilities and base framework", - }, - # L2: 核心包 - 依赖 sage-common - "sage-kernel": { - "path": self.packages_dir / "sage-kernel", - "namespace": "sage.kernel", - "dependencies": ["sage-common"], - "description": "Core streaming kernel", - }, - "sage-libs": { - "path": self.packages_dir / "sage-libs", - "namespace": "sage.libs", - "dependencies": ["sage-common"], - "description": "Application libraries", - }, - # L3: 中间件 - 依赖核心包 - "sage-middleware": { - "path": self.packages_dir / "sage-middleware", - "namespace": "sage.middleware", - "dependencies": ["sage-common", "sage-kernel"], - "description": "Middleware and services", - }, - # L4: 平台和工具 - 依赖核心和中间件 - "sage-platform": { - "path": self.packages_dir / "sage-platform", - "namespace": "sage.platform", - "dependencies": ["sage-common", "sage-kernel", "sage-middleware"], - "description": "Platform runtime", - }, - "sage-cli": { - "path": self.packages_dir / "sage-cli", - "namespace": "sage.cli", - "dependencies": ["sage-common", "sage-kernel", "sage-libs"], - "description": "Command-line interface", - }, - # L5: 应用层 - 依赖所有核心包 - "sage-apps": { - "path": self.packages_dir / "sage-apps", - "namespace": "sage.apps", - "dependencies": ["sage-common", "sage-kernel", "sage-libs", "sage-middleware"], - "description": "Application examples and templates", - }, - "sage-benchmark": { - "path": self.packages_dir / "sage-benchmark", - "namespace": "sage.benchmark", - "dependencies": ["sage-common", "sage-kernel", "sage-libs"], - "description": "Benchmarking tools", - }, - # L1.5: LLM core stack - # L6: UI 和开发工具 - "sage-studio": { - "path": self.packages_dir / "sage-studio", - "namespace": "sage.studio", - "dependencies": ["sage-common", "sage-kernel", "sage-libs", "sage-middleware"], - "description": "Web-based Studio UI", - }, - "sage-tools": { - "path": self.packages_dir / "sage-tools", - "namespace": "sage.tools", - "dependencies": ["sage-common"], # 开发工具不依赖其他包以避免循环 - "description": "Development tools and CLI", - }, - # L0: 元包 - 依赖所有包 - "sage": { - "path": self.packages_dir / "sage", - "namespace": "sage", - "dependencies": [ - "sage-common", - "sage-kernel", - "sage-libs", - "sage-middleware", - ], - "description": "Meta package - all SAGE core components", - }, - } - - def list_packages(self) -> dict: - """List all SAGE packages with their status.""" - try: - package_list = [] - - for name, info in self.packages.items(): - package_info = { - "name": name, - "description": info["description"], - "namespace": info["namespace"], - "dependencies": info["dependencies"], - "path": str(info["path"]), - "exists": info["path"].exists(), - "installed": self._is_package_installed(name), - "version": self._get_package_version(info["path"]), - } - package_list.append(package_info) - - return { - "packages": package_list, - "total_packages": len(package_list), - "status": "success", - } - - except Exception as e: - raise SAGEDevToolkitError(f"Package listing failed: {e}") - - def install_package( - self, package_name: str, dev_mode: bool = True, force: bool = False - ) -> dict: - """Install a specific package with its dependencies.""" - try: - if package_name not in self.packages: - raise SAGEDevToolkitError(f"Unknown package: {package_name}") - - # Get installation order - install_order = self._get_install_order(package_name) - - installed = [] - failed = [] - - for pkg_name in install_order: - try: - result = self._install_single_package(pkg_name, dev_mode, force) - if result["status"] == "success": - installed.append(pkg_name) - else: - failed.append( - { - "package": pkg_name, - "error": result.get("error", "Unknown error"), - } - ) - except Exception as e: - failed.append({"package": pkg_name, "error": str(e)}) - - return { - "target_package": package_name, - "install_order": install_order, - "installed": installed, - "failed": failed, - "status": "success" if not failed else "partial", - } - - except Exception as e: - raise SAGEDevToolkitError(f"Package installation failed: {e}") - - def install_all_packages(self, dev_mode: bool = True, force: bool = False) -> dict: - """Install all packages in dependency order.""" - try: - install_order = self._get_full_install_order() - - installed = [] - failed = [] - - for pkg_name in install_order: - try: - result = self._install_single_package(pkg_name, dev_mode, force) - if result["status"] == "success": - installed.append(pkg_name) - else: - failed.append( - { - "package": pkg_name, - "error": result.get("error", "Unknown error"), - } - ) - except Exception as e: - failed.append({"package": pkg_name, "error": str(e)}) - - return { - "install_order": install_order, - "installed": installed, - "failed": failed, - "total_attempted": len(install_order), - "total_successful": len(installed), - "status": "success" if not failed else "partial", - } - - except Exception as e: - raise SAGEDevToolkitError(f"Full installation failed: {e}") - - def uninstall_package(self, package_name: str) -> dict: - """Uninstall a specific package.""" - try: - if package_name not in self.packages: - raise SAGEDevToolkitError(f"Unknown package: {package_name}") - - # Use pip to uninstall - result = subprocess.run( - [ - sys.executable, - "-m", - "pip", - "uninstall", - package_name.replace("-", "_"), - "-y", - ], - capture_output=True, - text=True, - ) - - return { - "package": package_name, - "status": "success" if result.returncode == 0 else "failed", - "stdout": result.stdout, - "stderr": result.stderr, - } - - except Exception as e: - raise SAGEDevToolkitError(f"Package uninstallation failed: {e}") - - def build_package(self, package_name: str) -> dict: - """Build a specific package.""" - try: - if package_name not in self.packages: - raise SAGEDevToolkitError(f"Unknown package: {package_name}") - - package_path = self.packages[package_name]["path"] - - if not package_path.exists(): - raise SAGEDevToolkitError(f"Package directory not found: {package_path}") - - # Build the package - result = subprocess.run( - [sys.executable, "-m", "build", str(package_path)], - capture_output=True, - text=True, - cwd=str(package_path), - ) - - return { - "package": package_name, - "status": "success" if result.returncode == 0 else "failed", - "stdout": result.stdout, - "stderr": result.stderr, - "package_path": str(package_path), - } - - except Exception as e: - raise SAGEDevToolkitError(f"Package build failed: {e}") - - def check_dependencies(self) -> dict: - """Check package dependencies and detect issues.""" - try: - issues = [] - dependency_graph = {} - - for pkg_name, pkg_info in self.packages.items(): - dependency_graph[pkg_name] = pkg_info["dependencies"] - - # Check if dependencies exist - for dep in pkg_info["dependencies"]: - if dep not in self.packages: - issues.append( - { - "type": "missing_dependency", - "package": pkg_name, - "missing_dependency": dep, - } - ) - - # Check if package directory exists - if not pkg_info["path"].exists(): - issues.append( - { - "type": "missing_package_directory", - "package": pkg_name, - "path": str(pkg_info["path"]), - } - ) - - # Check for circular dependencies - circular_deps = self._detect_circular_dependencies(dependency_graph) - for cycle in circular_deps: - issues.append({"type": "circular_dependency", "cycle": cycle}) - - return { - "dependency_graph": dependency_graph, - "issues": issues, - "has_issues": len(issues) > 0, - "status": "success", - } - - except Exception as e: - raise SAGEDevToolkitError(f"Dependency check failed: {e}") - - def _get_install_order(self, package_name: str) -> list[str]: - """Get installation order for a package and its dependencies.""" - - def dfs(pkg, visited, order): - if pkg in visited: - return - visited.add(pkg) - - for dep in self.packages.get(pkg, {}).get("dependencies", []): - dfs(dep, visited, order) - - order.append(pkg) - - visited = set() - order = [] - dfs(package_name, visited, order) - return order - - def _get_full_install_order(self) -> list[str]: - """Get installation order for all packages.""" - - def dfs(pkg, visited, order): - if pkg in visited: - return - visited.add(pkg) - - for dep in self.packages.get(pkg, {}).get("dependencies", []): - dfs(dep, visited, order) - - if pkg not in order: - order.append(pkg) - - visited = set() - order = [] - - for pkg_name in self.packages: - dfs(pkg_name, visited, order) - - return order - - def _install_single_package(self, package_name: str, dev_mode: bool, force: bool) -> dict: - """Install a single package.""" - package_path = self.packages[package_name]["path"] - - if not package_path.exists(): - return { - "status": "failed", - "error": f"Package directory not found: {package_path}", - } - - cmd = [sys.executable, "-m", "pip", "install"] - if dev_mode: - cmd.append("-e") - if force: - cmd.append("--force-reinstall") - cmd.append(str(package_path)) - - result = subprocess.run(cmd, capture_output=True, text=True) - - return { - "status": "success" if result.returncode == 0 else "failed", - "stdout": result.stdout, - "stderr": result.stderr, - "command": " ".join(cmd), - } - - def _is_package_installed(self, package_name: str) -> bool: - """Check if a package is installed.""" - try: - # Convert sage-* package names to isage-* format for pip - pip_package_name = package_name.replace("sage-", "isage-") - if package_name == "sage": - pip_package_name = "isage" - - result = subprocess.run( - [sys.executable, "-m", "pip", "show", pip_package_name], - capture_output=True, - ) - return result.returncode == 0 - except Exception: - return False - - def _get_package_version(self, package_path: Path) -> str: - """Get package version from pyproject.toml or setup.py.""" - try: - pyproject_file = package_path / "pyproject.toml" - if pyproject_file.exists(): - with open(pyproject_file) as f: - content = f.read() - # Simple regex to extract version - import re - - match = re.search(r'version\s*=\s*["\']([^"\']+)["\']', content) - if match: - return match.group(1) - return "unknown" - except Exception: - return "unknown" - - def _detect_circular_dependencies( - self, dependency_graph: dict[str, list[str]] - ) -> list[list[str]]: - """Detect circular dependencies using DFS.""" - - def dfs(node, path, visited, cycles): - if node in path: - # Found a cycle - cycle_start = path.index(node) - cycle = path[cycle_start:] + [node] - cycles.append(cycle) - return - - if node in visited: - return - - visited.add(node) - path.append(node) - - for dep in dependency_graph.get(node, []): - dfs(dep, path.copy(), visited, cycles) - - cycles = [] - visited = set() - - for pkg in dependency_graph: - if pkg not in visited: - dfs(pkg, [], visited, cycles) - - return cycles diff --git a/packages/sage-tools/src/sage/tools/dev/tools/enhanced_test_runner.py b/packages/sage-tools/src/sage/tools/dev/tools/enhanced_test_runner.py deleted file mode 100644 index 6e65e0d63a..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/tools/enhanced_test_runner.py +++ /dev/null @@ -1,783 +0,0 @@ -""" -Enhanced Test Runner - Integrated from scripts/test_runner.py - -This tool provides intelligent test execution with support for diff-based testing, -parallel execution, and comprehensive reporting. -""" - -import os -import subprocess -import sys -import time -from concurrent.futures import ThreadPoolExecutor, as_completed -from pathlib import Path - -from sage.common.config.output_paths import get_sage_paths - -from ..core.exceptions import SAGEDevToolkitError -from ..utils.intermediate_results_checker import IntermediateResultsChecker -from .test_failure_cache import TestFailureCache - - -class EnhancedTestRunner: - """Enhanced test runner with intelligent change detection.""" - - def __init__(self, project_root: str, enable_coverage: bool = False, debug: bool = False): - self.project_root = Path(project_root) - self.packages_dir = self.project_root / "packages" - self.enable_coverage = enable_coverage - self.debug = debug - - # Initialize test failure cache - self.failure_cache = TestFailureCache(str(self.project_root)) - - # Initialize intermediate results checker - self.intermediate_checker = IntermediateResultsChecker(str(self.project_root)) - - # Get project name from path - - # 设置SAGE环境并获取目录路径 - try: - sage_paths = get_sage_paths(str(self.project_root)) - sage_paths.setup_environment_variables() - self.test_logs_dir = sage_paths.logs_dir - self.reports_dir = sage_paths.reports_dir - except Exception as e: - print(f"Warning: Failed to setup SAGE environment: {e}") - # 回退到使用统一的路径管理系统（不传递project_root让它自动检测） - try: - fallback_sage_paths = get_sage_paths() - self.test_logs_dir = fallback_sage_paths.logs_dir - self.reports_dir = fallback_sage_paths.reports_dir - except Exception as fallback_e: - print(f"Error: Could not setup fallback SAGE environment: {fallback_e}") - # 最后的回退：使用项目根目录的.sage - sage_dir = self.project_root / ".sage" - self.test_logs_dir = sage_dir / "logs" - self.reports_dir = sage_dir / "reports" - - # Check if pytest-benchmark is available - self.has_benchmark = self._check_pytest_benchmark_available() - - # Ensure directories exist - self.test_logs_dir.mkdir(parents=True, exist_ok=True) - self.reports_dir.mkdir(parents=True, exist_ok=True) - - def _debug_log(self, message: str, stage: str = ""): - """输出调试信息""" - if self.debug: - import time - - timestamp = time.strftime("%H:%M:%S") - if stage: - print(f"[{timestamp}] 🔍 [{stage}] {message}") - else: - print(f"[{timestamp}] 🔍 {message}") - - def _check_pytest_benchmark_available(self) -> bool: - """Check if pytest-benchmark plugin is available.""" - try: - import pytest_benchmark # noqa: F401 - - return True - except ImportError: - return False - - def run_tests(self, mode: str = "diff", **kwargs) -> dict: - """Run tests based on specified mode.""" - try: - self._debug_log(f"运行测试，模式: {mode}", "RUN") - print(f"测试模式： {mode}") - - if mode == "all": - self._debug_log("执行全部测试模式", "MODE") - result = self._run_all_tests(**kwargs) - elif mode == "diff": - self._debug_log("执行差异测试模式", "MODE") - result = self._run_diff_tests(**kwargs) - elif mode == "package": - package = kwargs.get("package") - if not package: - raise SAGEDevToolkitError("Package name required for package mode") - self._debug_log(f"执行包测试模式: {package}", "MODE") - print(f"📦 Testing package: {package}") - result = self._run_package_tests(package, **kwargs) - elif mode == "failed": - self._debug_log("执行失败测试重跑模式", "MODE") - result = self._run_failed_tests(**kwargs) - else: - raise SAGEDevToolkitError(f"Unknown test mode: {mode}") - - # Show final summary - summary = result.get("summary", {}) - total = summary.get("total", 0) - passed = summary.get("passed", 0) - failed = summary.get("failed", 0) - execution_time = result.get("execution_time", 0) - - print("\n📊 Test Summary:") - print(f" Total: {total}") - print(f" Passed: {passed} ✅") - print(f" Failed: {failed} ❌") - print(f" Duration: {execution_time:.2f}s") - print(f" Status: {'SUCCESS' if result.get('status') == 'success' else 'FAILED'}") - print(f" Logs: {self.test_logs_dir}") - print(f" Reports: {self.reports_dir}") - - # 检查中间结果放置 - print("\n" + "=" * 50) - self.intermediate_checker.print_check_result() - print("=" * 50) - - # Update failure cache with results (except for failed mode to avoid recursion) - if mode != "failed": - self.failure_cache.update_from_test_results(result) - - return result - - except Exception as e: - raise SAGEDevToolkitError(f"Test execution failed: {e}") - - def _run_all_tests(self, **kwargs) -> dict: - """Run all tests in the project.""" - start_time = time.time() - - # 提取 target_packages 参数 - target_packages = kwargs.get("target_packages", None) - - self._debug_log("开始发现测试文件", "DISCOVER") - if target_packages: - self._debug_log(f"限制测试包: {target_packages}", "DISCOVER") - - # Discover all test files - test_files = self._discover_all_test_files(target_packages=target_packages) - self._debug_log(f"发现 {len(test_files)} 个测试文件", "DISCOVER") - - if not test_files: - return { - "mode": "all", - "test_files": [], - "results": [], - "summary": {"total": 0, "passed": 0, "failed": 0}, - "execution_time": 0, - "status": "success", - } - - # Run tests - results = self._execute_test_files(test_files, **kwargs) - - execution_time = time.time() - start_time - - return { - "mode": "all", - "test_files": [self._simplify_test_path(f) for f in test_files], - "results": results, - "summary": self._calculate_summary(results), - "execution_time": execution_time, - "status": "success" if all(r["passed"] for r in results) else "failed", - } - - def _run_diff_tests(self, base_branch: str = "main", **kwargs) -> dict: - """Run tests for files affected by git diff.""" - start_time = time.time() - - # Get changed files - changed_files = self._get_changed_files(base_branch) - - if not changed_files: - return { - "mode": "diff", - "base_branch": base_branch, - "changed_files": [], - "test_files": [], - "results": [], - "summary": {"total": 0, "passed": 0, "failed": 0}, - "execution_time": 0, - "status": "success", - } - - # Find affected test files - test_files = self._find_affected_test_files(changed_files) - - if not test_files: - return { - "mode": "diff", - "base_branch": base_branch, - "changed_files": [str(f) for f in changed_files], - "test_files": [], - "results": [], - "summary": {"total": 0, "passed": 0, "failed": 0}, - "execution_time": 0, - "status": "success", - } - - # Run tests - results = self._execute_test_files(test_files, **kwargs) - - execution_time = time.time() - start_time - - return { - "mode": "diff", - "base_branch": base_branch, - "changed_files": [str(f) for f in changed_files], - "test_files": [self._simplify_test_path(f) for f in test_files], - "results": results, - "summary": self._calculate_summary(results), - "execution_time": execution_time, - "status": "success" if all(r["passed"] for r in results) else "failed", - } - - def _run_package_tests(self, package_name: str, **kwargs) -> dict: - """Run tests for a specific package.""" - start_time = time.time() - - package_dir = self.packages_dir / package_name - if not package_dir.exists(): - raise SAGEDevToolkitError(f"Package not found: {package_name}") - - # Find test files in package - test_files = self._discover_package_test_files(package_dir) - - if not test_files: - return { - "mode": "package", - "package": package_name, - "test_files": [], - "results": [], - "summary": {"total": 0, "passed": 0, "failed": 0}, - "execution_time": 0, - "status": "success", - } - - # Run tests - results = self._execute_test_files(test_files, **kwargs) - - execution_time = time.time() - start_time - - return { - "mode": "package", - "package": package_name, - "test_files": [self._simplify_test_path(f) for f in test_files], - "results": results, - "summary": self._calculate_summary(results), - "execution_time": execution_time, - "status": "success" if all(r["passed"] for r in results) else "failed", - } - - def _run_failed_tests(self, **kwargs) -> dict: - """Run previously failed tests from cache.""" - start_time = time.time() - - # Check if there are cached failed tests - if not self.failure_cache.has_failed_tests(): - return { - "mode": "failed", - "test_files": [], - "results": [], - "summary": {"total": 0, "passed": 0, "failed": 0}, - "execution_time": 0, - "status": "success", - "message": "No failed tests found in cache", - } - - # Get failed test paths and resolve them - test_files = self.failure_cache.resolve_test_paths(self.packages_dir) - - if not test_files: - return { - "mode": "failed", - "cached_failed_count": len(self.failure_cache.get_failed_test_paths()), - "test_files": [], - "results": [], - "summary": {"total": 0, "passed": 0, "failed": 0}, - "execution_time": 0, - "status": "success", - "message": "Cached failed tests could not be resolved to existing files", - } - - # Run the resolved test files - results = self._execute_test_files(test_files, **kwargs) - - execution_time = time.time() - start_time - - # Check if any previously failed tests now pass - now_passing = [r for r in results if r["passed"]] - still_failing = [r for r in results if not r["passed"]] - - return { - "mode": "failed", - "cached_failed_count": len(self.failure_cache.get_failed_test_paths()), - "resolved_test_count": len(test_files), - "test_files": [self._simplify_test_path(f) for f in test_files], - "results": results, - "summary": self._calculate_summary(results), - "execution_time": execution_time, - "status": "success" if all(r["passed"] for r in results) else "failed", - "now_passing_count": len(now_passing), - "still_failing_count": len(still_failing), - } - - def _simplify_test_path(self, test_file: Path) -> str: - """Simplify test file path for display.""" - try: - relative_path = test_file.relative_to(self.project_root) - path_parts = relative_path.parts - - if "packages" in path_parts and "tests" in path_parts: - # Find package name and tests part - packages_idx = path_parts.index("packages") - tests_idx = path_parts.index("tests") - if packages_idx < tests_idx: - # Get the actual package name (could be multiple levels deep) - package_parts = path_parts[packages_idx + 1 : tests_idx] - package_name = "/".join(package_parts) - test_path_parts = path_parts[tests_idx:] - return f"{package_name}/{'/'.join(test_path_parts)}" - - return str(relative_path) - except ValueError: - return str(test_file) - - def _discover_all_test_files(self, target_packages: list[str] | None = None) -> list[Path]: - """Discover all test files in the project. - - Args: - target_packages: 如果指定，只扫描这些包。例如: ['sage-common', 'sage-kernel'] - """ - test_files = [] - - # Discover tests in packages - for package_dir in self.packages_dir.iterdir(): - if package_dir.is_dir() and not package_dir.name.startswith("."): - # 如果指定了目标包，只扫描这些包 - if target_packages and package_dir.name not in target_packages: - self._debug_log(f"跳过包: {package_dir.name} (不在目标列表中)", "DISCOVER") - continue - - test_files.extend(self._discover_package_test_files(package_dir)) - - # Also discover tests in tools/tests directory - tools_tests_dir = self.project_root / "tools" / "tests" - if tools_tests_dir.exists(): - test_files.extend(tools_tests_dir.glob("test_*.py")) - - return test_files - - def _discover_package_test_files(self, package_dir: Path) -> list[Path]: - """Discover test files in a specific package.""" - self._debug_log(f"扫描包: {package_dir.name}", "DISCOVER") - test_files = [] - - # Directories to exclude from test discovery - exclude_dirs = { - "sageLLM", # Submodule with its own tests - "vendors", # Vendor code - "node_modules", - "__pycache__", - ".venv", - "venv", - ".sage", # Temporary SAGE directory - "build", - "dist", - ".eggs", - } - - # Look for test directories - for test_pattern in ["test", "tests"]: - test_dir = package_dir / test_pattern - if test_dir.exists(): - # Find all test_*.py files, excluding problematic directories - for test_file in test_dir.rglob("test_*.py"): - # Check if any parent directory is in exclude list - should_exclude = False - for parent in test_file.parents: - if parent.name in exclude_dirs: - should_exclude = True - break - - if not should_exclude: - test_files.append(test_file) - - # Also look for test files in the root of the package - test_files.extend(package_dir.glob("test_*.py")) - - return test_files - - def _get_changed_files(self, base_branch: str) -> list[Path]: - """Get files changed compared to base branch.""" - try: - # Get changed files using git diff - result = subprocess.run( - ["git", "diff", "--name-only", f"{base_branch}...HEAD"], - capture_output=True, - text=True, - cwd=str(self.project_root), - ) - - if result.returncode != 0: - # Fallback to working directory changes - result = subprocess.run( - ["git", "diff", "--name-only"], - capture_output=True, - text=True, - cwd=str(self.project_root), - ) - - changed_files = [] - for line in result.stdout.strip().split("\n"): - if line.strip(): - file_path = self.project_root / line.strip() - if file_path.exists(): - changed_files.append(file_path) - - return changed_files - - except Exception as e: - raise SAGEDevToolkitError(f"Failed to get changed files: {e}") - - def _find_affected_test_files(self, changed_files: list[Path]) -> list[Path]: - """Find test files affected by changed files.""" - affected_packages = set() - - # Determine which packages are affected - for changed_file in changed_files: - try: - relative_path = changed_file.relative_to(self.project_root) - path_parts = relative_path.parts - - if len(path_parts) >= 2 and path_parts[0] == "packages": - package_name = path_parts[1] - affected_packages.add(package_name) - except ValueError: - # File is not in packages directory - continue - - # If no packages affected, run all tests - if not affected_packages: - return self._discover_all_test_files() - - # Find test files in affected packages - test_files = [] - for package_name in affected_packages: - package_dir = self.packages_dir / package_name - if package_dir.exists(): - test_files.extend(self._discover_package_test_files(package_dir)) - - return test_files - - def _execute_test_files(self, test_files: list[Path], **kwargs) -> list[dict]: - """Execute test files with optional parallel execution.""" - workers = kwargs.get("workers", 1) - timeout = kwargs.get("timeout", 300) # 5 minutes default - quick = kwargs.get("quick", False) - - if workers and workers > 1: - return self._execute_parallel(test_files, workers, timeout, quick) - else: - return self._execute_sequential(test_files, timeout, quick) - - def _execute_sequential(self, test_files: list[Path], timeout: int, quick: bool) -> list[dict]: - """Execute test files sequentially.""" - results = [] - total_tests = len(test_files) - - print(f"测试任务数目： {total_tests}") - - for i, test_file in enumerate(test_files, 1): - simplified_path = self._simplify_test_path(test_file) - print(f"[{i}/{total_tests}] {simplified_path}...", end="", flush=True) - result = self._run_single_test_file(test_file, timeout, quick) - - # Show immediate result on same line - status = "✅" if result["passed"] else "❌" - duration = result.get("duration", 0) - print(f" {status} ({duration:.1f}s)") - - # Print error details for failed tests - if not result["passed"]: - error_msg = result.get("error", "") - if error_msg: - print(f" ⚠️ 错误信息: {error_msg}") - - # Print last few lines of output if available - output = result.get("output", "") - if output: - lines = output.strip().split("\n") - # Show last 10 lines of output for context - relevant_lines = lines[-10:] if len(lines) > 10 else lines - if relevant_lines: - print(f" 📝 输出（最后{len(relevant_lines)}行）:") - for line in relevant_lines: - print(f" {line}") - - results.append(result) - - # Exit early on failure if quick mode - if quick and not result["passed"]: - print("\n❌ Stopping on first failure (quick mode)") - break - - return results - - def _execute_parallel( - self, test_files: list[Path], workers: int, timeout: int, quick: bool - ) -> list[dict]: - """Execute test files in parallel.""" - results = [] - total_tests = len(test_files) - completed = 0 - - print(f"测试任务数目： {total_tests}") - - with ThreadPoolExecutor(max_workers=workers) as executor: - # Submit all test files - future_to_file = { - executor.submit(self._run_single_test_file, test_file, timeout, quick): test_file - for test_file in test_files - } - - # Collect results - for future in as_completed(future_to_file): - test_file = future_to_file[future] - completed += 1 - - try: - result = future.result() - status = "✅" if result["passed"] else "❌" - duration = result.get("duration", 0) - simplified_path = self._simplify_test_path(test_file) - print( - f"[{completed}/{total_tests}] {simplified_path} {status} ({duration:.1f}s)" - ) - - # Print error details for failed tests - if not result["passed"]: - error_msg = result.get("error", "") - if error_msg: - print(f" ⚠️ 错误信息: {error_msg}") - - # Print last few lines of output if available - output = result.get("output", "") - if output: - lines = output.strip().split("\n") - # Show last 10 lines of output for context - relevant_lines = lines[-10:] if len(lines) > 10 else lines - if relevant_lines: - print(f" 📝 输出（最后{len(relevant_lines)}行）:") - for line in relevant_lines: - print(f" {line}") - - results.append(result) - except Exception as e: - simplified_path = self._simplify_test_path(test_file) - print(f"[{completed}/{total_tests}] {simplified_path} ❌ ERROR") - print(f" ⚠️ 异常: {str(e)}") - results.append( - { - "test_file": simplified_path, - "passed": False, - "duration": 0, - "output": "", - "error": str(e), - } - ) - - return results - - def _get_package_from_test_file(self, test_file: Path) -> str: - """Determine which package a test file belongs to.""" - try: - relative_path = test_file.relative_to(self.packages_dir) - path_parts = relative_path.parts - - if len(path_parts) >= 1: - package_part = path_parts[0] - # Map package directory names to standard log directory names - package_mapping = { - "sage-kernel": "kernel", - "sage-middleware": "middleware", - "sage-common": "common", - "sage-libs": "libs", - } - return package_mapping.get(package_part, "common") # Default to common - except ValueError: - # File is not in packages directory - pass - - return "common" # Default fallback - - def _run_single_test_file( - self, test_file: Path, timeout: int, quick: bool, skip_markers: str | None = None - ) -> dict: - """Run a single test file.""" - try: - # Prepare command - cmd = [sys.executable, "-m", "pytest", str(test_file), "-v"] - - if quick: - cmd.extend(["-x"]) # Stop on first failure - - # Add marker filtering if specified - if skip_markers: - cmd.extend(["-m", skip_markers]) - - # If coverage is disabled, override any pyproject.toml coverage settings - if not self.enable_coverage: - cmd.extend(["--cov=", "--no-cov"]) # Explicitly disable coverage - else: - # When coverage is enabled, ensure it's activated - # We'll add --cov with the source package dynamically - pass # Coverage flags will be added below after determining the package - - # Determine package and create appropriate log file path - package_name = self._get_package_from_test_file(test_file) - package_log_dir = self.test_logs_dir / package_name - package_log_dir.mkdir(parents=True, exist_ok=True) - log_file = package_log_dir / f"{test_file.name}.log" - - # Set up environment for test execution - env = os.environ.copy() - - # Only add coverage if enabled - if self.enable_coverage: - # Use unified SAGE path management for coverage directory - try: - sage_paths = get_sage_paths(str(self.project_root)) - coverage_dir = sage_paths.coverage_dir - except Exception: - # Fallback to project root .sage directory - coverage_dir = self.project_root / ".sage" / "coverage" - - coverage_dir.mkdir(parents=True, exist_ok=True) - - # Use a unique coverage file for each test to avoid conflicts in parallel execution - # The files will be combined later using 'coverage combine' - import uuid - - unique_id = uuid.uuid4().hex[:8] - test_name = test_file.stem - coverage_file = coverage_dir / f".coverage.{test_name}.{unique_id}" - - # Set up environment for coverage outputs - env["COVERAGE_FILE"] = str(coverage_file) - - # Determine the source directory for coverage - # Use the package's src directory absolute path - package_root = self.project_root / "packages" / f"sage-{package_name}" - source_dir = package_root / "src" - - if source_dir.exists(): - # Add coverage flags with source directory path - # Note: We don't use --cov-append here because each test has its own file - cmd.extend( - [ - f"--cov={source_dir}", - "--cov-report=", # Disable individual test reports (we'll generate them at the end) - ] - ) - - # Note: HTML report will be generated after all tests complete - - # Run test - start_time = time.time() - result = subprocess.run( - cmd, - capture_output=True, - text=True, - timeout=timeout, - cwd=str(self.project_root), - env=env, - ) - duration = time.time() - start_time - - # Write log file - with open(log_file, "w", encoding="utf-8") as f: - f.write(f"Command: {' '.join(cmd)}\n") - f.write(f"Exit code: {result.returncode}\n") - f.write(f"Duration: {duration:.2f}s\n") - f.write(f"=== STDOUT ===\n{result.stdout}\n") - f.write(f"=== STDERR ===\n{result.stderr}\n") - - return { - "test_file": self._simplify_test_path(test_file), - "passed": result.returncode == 0, - "duration": duration, - "output": result.stdout, - "error": result.stderr if result.returncode != 0 else None, - "log_file": str(log_file), - } - - except subprocess.TimeoutExpired: - return { - "test_file": self._simplify_test_path(test_file), - "passed": False, - "duration": timeout, - "output": "", - "error": f"Test timed out after {timeout} seconds", - } - except Exception as e: - return { - "test_file": self._simplify_test_path(test_file), - "passed": False, - "duration": 0, - "output": "", - "error": str(e), - } - - def _calculate_summary(self, results: list[dict]) -> dict: - """Calculate test summary statistics.""" - total = len(results) - passed = sum(1 for r in results if r["passed"]) - failed = total - passed - total_duration = sum(r["duration"] for r in results) - - return { - "total": total, - "passed": passed, - "failed": failed, - "total_duration": total_duration, - "average_duration": total_duration / total if total > 0 else 0, - } - - def list_tests(self) -> dict: - """List all available tests.""" - try: - test_structure = {} - - for package_dir in self.packages_dir.iterdir(): - if package_dir.is_dir() and not package_dir.name.startswith("."): - package_name = package_dir.name - test_files = self._discover_package_test_files(package_dir) - - if test_files: - test_structure[package_name] = [ - self._simplify_test_path(f) for f in test_files - ] - - total_tests = sum(len(files) for files in test_structure.values()) - - return { - "test_structure": test_structure, - "total_packages": len(test_structure), - "total_test_files": total_tests, - "status": "success", - } - - except Exception as e: - raise SAGEDevToolkitError(f"Test listing failed: {e}") - - def get_failure_cache_info(self) -> dict: - """Get information about the test failure cache.""" - return self.failure_cache.get_cache_info() - - def clear_failure_cache(self) -> None: - """Clear the test failure cache.""" - self.failure_cache.clear_cache() - - def print_cache_status(self) -> None: - """Print test failure cache status.""" - self.failure_cache.print_cache_status() - - def get_cache_history(self, limit: int = 5) -> list[dict]: - """Get test run history from cache.""" - return self.failure_cache.get_history(limit) diff --git a/packages/sage-tools/src/sage/tools/dev/tools/examples_structure_checker.py b/packages/sage-tools/src/sage/tools/dev/tools/examples_structure_checker.py deleted file mode 100644 index e49962511d..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/tools/examples_structure_checker.py +++ /dev/null @@ -1,144 +0,0 @@ -""" -Examples 目录结构检查器 - -确保 examples/ 目录保持正确的结构： -- 只允许 apps/ 和 tutorials/ 两个顶层子目录 -- 禁止在顶层创建其他目录（如 kernel/, unlearning/ 等） -""" - -from dataclasses import dataclass -from pathlib import Path - - -@dataclass -class ExamplesStructureResult: - """Examples 结构检查结果""" - - passed: bool - violations: list[str] - allowed_dirs: list[str] - unexpected_dirs: list[str] - - -class ExamplesStructureChecker: - """Examples 目录结构检查器""" - - # 允许的顶层目录（除了文件和隐藏目录） - ALLOWED_TOP_DIRS = {"apps", "tutorials"} - - # 允许的顶层文件 - ALLOWED_TOP_FILES = {"README.md", "requirements.txt", "__init__.py"} - - # 允许的特殊项（符号链接等） - ALLOWED_SPECIAL = {"data"} # data 是指向 tutorials/agents/data 的符号链接 - - def __init__(self, examples_dir: Path): - """ - 初始化检查器 - - Args: - examples_dir: examples 目录路径 - """ - self.examples_dir = Path(examples_dir) - - def check_structure(self) -> ExamplesStructureResult: - """ - 检查 examples 目录结构 - - Returns: - 检查结果 - """ - violations = [] - unexpected_dirs = [] - - if not self.examples_dir.exists(): - return ExamplesStructureResult( - passed=False, - violations=["examples/ 目录不存在"], - allowed_dirs=[], - unexpected_dirs=[], - ) - - # 检查顶层目录 - for item in self.examples_dir.iterdir(): - # 跳过隐藏文件/目录和 __pycache__ - if item.name.startswith(".") or item.name == "__pycache__": - continue - - # 先检查符号链接（因为符号链接也可能 is_dir() 返回 True） - if item.is_symlink(): - if item.name not in self.ALLOWED_SPECIAL: - violations.append(f"在 examples/ 下发现不期望的符号链接: {item.name}") - # 检查是否是目录 - elif item.is_dir(): - if item.name not in self.ALLOWED_TOP_DIRS: - unexpected_dirs.append(item.name) - violations.append( - f"在 examples/ 下发现不允许的目录: {item.name}\n" - f" 应该移动到 examples/tutorials/{item.name}/" - ) - # 检查是否是文件 - elif item.is_file(): - if item.name not in self.ALLOWED_TOP_FILES: - violations.append(f"在 examples/ 下发现不期望的文件: {item.name}") - - # 验证必需的目录存在 - for required_dir in self.ALLOWED_TOP_DIRS: - dir_path = self.examples_dir / required_dir - if not dir_path.exists(): - violations.append(f"缺少必需的目录: examples/{required_dir}/") - - passed = len(violations) == 0 - - return ExamplesStructureResult( - passed=passed, - violations=violations, - allowed_dirs=list(self.ALLOWED_TOP_DIRS), - unexpected_dirs=unexpected_dirs, - ) - - def get_structure_guide(self) -> str: - """ - 获取结构规范说明 - - Returns: - 结构规范的文本说明 - """ - return """ -Examples 目录结构规范: - -examples/ -├── apps/ # 应用示例（完整的应用程序） -├── tutorials/ # 教程示例（各类功能演示） -│ ├── agents/ -│ ├── core-api/ -│ ├── kernel/ -│ ├── memory/ -│ ├── multimodal/ -│ ├── rag/ -│ ├── scheduler/ -│ ├── service/ -│ ├── unlearning/ -│ └── ... -├── README.md -└── requirements.txt - -规则: -1. 顶层只允许 'apps' 和 'tutorials' 两个目录 -2. 所有新的示例类别应放在 tutorials/ 下作为子目录 -3. 不允许在 examples/ 顶层创建其他目录（如 kernel/, unlearning/ 等） -""" - - -def check_examples_structure(project_root: Path) -> ExamplesStructureResult: - """ - 便捷函数：检查 examples 目录结构 - - Args: - project_root: 项目根目录 - - Returns: - 检查结果 - """ - checker = ExamplesStructureChecker(project_root) - return checker.check_structure() diff --git a/packages/sage-tools/src/sage/tools/dev/tools/package_dependency_validator.py b/packages/sage-tools/src/sage/tools/dev/tools/package_dependency_validator.py deleted file mode 100644 index d2cc9d9469..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/tools/package_dependency_validator.py +++ /dev/null @@ -1,233 +0,0 @@ -""" -Package Dependency Validator - -Validates SAGE package dependency separation rules in pyproject.toml files. - -Rules: -1. Non-meta packages should NOT have isage-* dependencies in [project.dependencies] -2. Packages should use sage-deps for internal SAGE dependencies (except L1 and meta-package) -3. The sage meta-package should reference other packages using [sage-deps] in extras - -Migrated from tools/scripts/verify_dependency_separation.sh -""" - -from dataclasses import dataclass -from pathlib import Path - -try: - import tomllib # Python 3.11+ -except ImportError: - import tomli as tomllib # Fallback - - -@dataclass -class ValidationIssue: - """Represents a validation issue found in a package.""" - - package: str - severity: str # "error" or "warning" - message: str - details: str = "" - - -class PackageDependencyValidator: - """Validates SAGE package dependency separation rules.""" - - # Packages that don't need sage-deps - NO_SAGE_DEPS_REQUIRED = {"sage-common", "sage-cli", "sage"} - - def __init__(self, project_root: Path | str): - """Initialize validator with project root.""" - self.project_root = Path(project_root) - self.packages_dir = self.project_root / "packages" - - def validate_all_packages(self) -> tuple[list[ValidationIssue], bool]: - """ - Validate all packages for dependency separation compliance. - - Returns: - tuple: (list of issues, overall_pass) - """ - issues: list[ValidationIssue] = [] - - if not self.packages_dir.exists(): - issues.append( - ValidationIssue( - package="project", - severity="error", - message="packages/ directory not found", - ) - ) - return issues, False - - # Find all package directories - for package_dir in sorted(self.packages_dir.iterdir()): - if not package_dir.is_dir(): - continue - - pyproject_file = package_dir / "pyproject.toml" - if not pyproject_file.exists(): - continue - - package_name = package_dir.name - package_issues = self._validate_package(package_name, pyproject_file) - issues.extend(package_issues) - - # Determine overall pass/fail - has_errors = any(issue.severity == "error" for issue in issues) - return issues, not has_errors - - def _validate_package(self, package_name: str, pyproject_file: Path) -> list[ValidationIssue]: - """Validate a single package's pyproject.toml file.""" - issues: list[ValidationIssue] = [] - - try: - with open(pyproject_file, "rb") as f: - data = tomllib.load(f) - except Exception as e: - issues.append( - ValidationIssue( - package=package_name, - severity="error", - message=f"Failed to parse pyproject.toml: {e}", - ) - ) - return issues - - # Skip if not a project section - if "project" not in data: - return issues - - # Rule 1: Check for isage-* in dependencies (except meta-package) - if package_name != "sage": - isage_deps = self._find_isage_dependencies(data) - if isage_deps: - issues.append( - ValidationIssue( - package=package_name, - severity="error", - message="contains isage-* dependencies in [project.dependencies]", - details="\n".join(f" - {dep}" for dep in isage_deps), - ) - ) - - # Rule 2: Check for sage-deps (except for packages that don't need it) - if package_name not in self.NO_SAGE_DEPS_REQUIRED: - if not self._has_sage_deps(data): - issues.append( - ValidationIssue( - package=package_name, - severity="error", - message="missing sage-deps configuration", - details="Package should define sage-deps for internal SAGE dependencies", - ) - ) - - # Rule 3: For sage meta-package, check extras use [sage-deps] - if package_name == "sage": - extra_issues = self._validate_meta_package_extras(data) - issues.extend(extra_issues) - - return issues - - def _find_isage_dependencies(self, data: dict) -> list[str]: - """Find all isage-* dependencies in [project.dependencies].""" - isage_deps = [] - - if "project" in data and "dependencies" in data["project"]: - for dep in data["project"]["dependencies"]: - if isinstance(dep, str) and "isage-" in dep.lower(): - isage_deps.append(dep.strip()) - - return isage_deps - - def _has_sage_deps(self, data: dict) -> bool: - """Check if package defines sage-deps.""" - if "project" not in data: - return False - - if "optional-dependencies" not in data["project"]: - return False - - return "sage-deps" in data["project"]["optional-dependencies"] - - def _validate_meta_package_extras(self, data: dict) -> list[ValidationIssue]: - """Validate that sage meta-package extras use [sage-deps].""" - issues: list[ValidationIssue] = [] - - if "project" not in data or "optional-dependencies" not in data["project"]: - return issues - - extras = data["project"]["optional-dependencies"] - - # Check if standard extras reference [sage-deps] - expected_with_sage_deps = ["standard", "full"] - - for extra_name in expected_with_sage_deps: - if extra_name not in extras: - continue - - deps = extras[extra_name] - # Check if any dependency uses [sage-deps] notation - has_sage_deps_ref = any( - "[sage-deps]" in str(dep) for dep in deps if isinstance(dep, str) - ) - - if not has_sage_deps_ref: - issues.append( - ValidationIssue( - package="sage", - severity="warning", - message=f"extra '{extra_name}' may not reference [sage-deps]", - details="Expected dependencies to use isage-*[sage-deps] notation", - ) - ) - - return issues - - def print_results(self, issues: list[ValidationIssue], passed: bool) -> None: - """Print validation results in a formatted way.""" - from rich.console import Console - from rich.panel import Panel - from rich.table import Table - - console = Console() - - if not issues: - console.print( - Panel( - "[green]✅ All packages pass dependency separation validation![/green]", - title="Dependency Validation", - border_style="green", - ) - ) - return - - # Create table for issues - table = Table(title="Dependency Validation Issues", show_header=True, header_style="bold") - table.add_column("Package", style="cyan") - table.add_column("Severity", style="yellow") - table.add_column("Issue") - - for issue in issues: - severity_color = "red" if issue.severity == "error" else "yellow" - severity_text = f"[{severity_color}]{issue.severity.upper()}[/{severity_color}]" - - message = issue.message - if issue.details: - message += f"\n{issue.details}" - - table.add_row(issue.package, severity_text, message) - - console.print(table) - - # Print summary - error_count = sum(1 for issue in issues if issue.severity == "error") - warning_count = sum(1 for issue in issues if issue.severity == "warning") - - if passed: - console.print(f"\n[yellow]⚠️ Found {warning_count} warning(s), but no errors[/yellow]") - else: - console.print( - f"\n[red]❌ Found {error_count} error(s) and {warning_count} warning(s)[/red]" - ) diff --git a/packages/sage-tools/src/sage/tools/dev/tools/package_readme_checker.py b/packages/sage-tools/src/sage/tools/dev/tools/package_readme_checker.py deleted file mode 100755 index e4ce5c19ff..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/tools/package_readme_checker.py +++ /dev/null @@ -1,426 +0,0 @@ -#!/usr/bin/env python3 -""" -Package README Checker - -This tool checks if package README files follow the standard template structure. - -Usage: - python tools/package_readme_checker.py [--fix] [--package PACKAGE_NAME] - -Options: - --fix Generate missing sections (interactive mode) - --package NAME Check only specific package - --all Check all packages (default) - --report Generate detailed report -""" - -import re -import sys -from dataclasses import dataclass, field -from pathlib import Path - - -@dataclass -class READMESection: - """Represents a README section.""" - - name: str - pattern: str - required: bool = True - found: bool = False - - -@dataclass -class PackageREADMECheck: - """Represents a package README check result.""" - - package_name: str - readme_path: Path - exists: bool = False - sections: list[READMESection] = field(default_factory=list) - issues: list[str] = field(default_factory=list) - score: float = 0.0 - - def calculate_score(self) -> float: - """Calculate README quality score (0-100).""" - if not self.exists: - return 0.0 - - required_sections = [s for s in self.sections if s.required] - optional_sections = [s for s in self.sections if not s.required] - - required_found = sum(1 for s in required_sections if s.found) - optional_found = sum(1 for s in optional_sections if s.found) - - required_score = (required_found / len(required_sections) * 70) if required_sections else 0 - optional_score = (optional_found / len(optional_sections) * 30) if optional_sections else 0 - - base_score = required_score + optional_score - - # Deduct points for issues (each issue -5 points, minimum 0) - issue_penalty = len(self.issues) * 5 - self.score = max(0.0, base_score - issue_penalty) - - return self.score - - -class PackageREADMEChecker: - """Checker for package README files.""" - - # Required sections for all package READMEs - REQUIRED_SECTIONS = [ - READMESection("Title", r"^#\s+", True), - READMESection("Overview", r"^##\s+(📋\s+)?Overview", True), - READMESection("Installation", r"^##\s+(🚀\s+)?Installation", True), - READMESection("Quick Start", r"^##\s+(📖\s+)?Quick Start", True), - READMESection("License", r"^##\s+(📄\s+)?License", True), - ] - - # Recommended sections - RECOMMENDED_SECTIONS = [ - READMESection("Features", r"^##\s+(✨\s+)?(?:Key\s+)?Features", False), - READMESection("Package Structure", r"^##\s+(📦\s+)?Package Structure", False), - READMESection("Configuration", r"^##\s+(🔧\s+)?Configuration", False), - READMESection("Documentation", r"^##\s+(📚\s+)?Documentation", False), - READMESection("Testing", r"^##\s+(🧪\s+)?Testing", False), - READMESection("Contributing", r"^##\s+(🤝\s+)?Contributing", False), - ] - - def __init__(self, workspace_root: Path | str): - self.workspace_root = ( - Path(workspace_root) if isinstance(workspace_root, str) else workspace_root - ) - self.packages_dir = self.workspace_root / "packages" - - def get_packages(self) -> list[str]: - """Get list of all packages.""" - if not self.packages_dir.exists(): - return [] - - return [ - p.name for p in self.packages_dir.iterdir() if p.is_dir() and p.name.startswith("sage-") - ] - - def check_readme(self, package_name: str) -> PackageREADMECheck: - """Check README for a specific package.""" - package_path = self.packages_dir / package_name - readme_path = package_path / "README.md" - - result = PackageREADMECheck( - package_name=package_name, - readme_path=readme_path, - sections=self.REQUIRED_SECTIONS + self.RECOMMENDED_SECTIONS, - ) - - # Check if README exists - if not readme_path.exists(): - result.issues.append("README.md not found") - return result - - result.exists = True - - # Read README content - content = readme_path.read_text(encoding="utf-8") - - # Check each section - for section in result.sections: - if re.search(section.pattern, content, re.MULTILINE | re.IGNORECASE): - section.found = True - elif section.required: - result.issues.append(f"Missing required section: {section.name}") - - # Additional checks - self._check_code_blocks(content, result) - self._check_links(content, result) - self._check_badges(content, result) - self._check_illegal_markdown_files(package_path, result) - - result.calculate_score() - return result - - def _check_code_blocks(self, content: str, result: PackageREADMECheck): - """Check if README has code examples.""" - code_blocks = re.findall(r"```[\w]*\n", content) - if not code_blocks: - result.issues.append("No code examples found (recommended)") - - def _check_links(self, content: str, result: PackageREADMECheck): - """Check for broken link patterns.""" - # Check for placeholder links - placeholders = re.findall(r"\{[A-Z_]+\}", content) - if placeholders: - result.issues.append(f"Found placeholder text: {', '.join(set(placeholders))}") - - def _check_badges(self, content: str, result: PackageREADMECheck): - """Check if README has status badges.""" - badges = re.findall(r"!\[.*?\]\(.*?\)", content) - if not badges: - result.issues.append("No status badges found (recommended)") - - def _check_illegal_markdown_files(self, package_path: Path, result: PackageREADMECheck): - """Check for illegal markdown files in package subdirectories. - - Policy: Only allow README.md in package root and PACKAGE_README_TEMPLATE.md in templates/. - Also checks that Git submodules have their own README.md files. - """ - illegal_files = [] - missing_submodule_readmes = [] - - # Paths to exclude from checking - exclude_patterns = [ - ".pytest_cache", # pytest generated - "vendors/", # third-party libraries - "build/_deps/", # build dependencies - "/sageVDB/", # Git submodules - "/sageTSDB/", - "/sageFlow/", - "/neuromem/", - "/sageLLM/", # sageLLM submodule - "docs/", # Package-specific documentation (allowed by SAGE policy) - "examples/", # Package examples with markdown guides (allowed by SAGE policy) - ] - - # Known Git submodule paths (relative to package root) - submodule_patterns = [ - "src/**/sageVDB", - "src/**/sageTSDB", - "src/**/sageFlow", - "src/**/neuromem", - "src/**/sageLLM", - ] - - # Check for missing READMEs in Git submodules - for pattern in submodule_patterns: - for submodule_dir in package_path.glob(pattern): - if submodule_dir.is_dir(): - # Check if it's a Git submodule (has .git file) - git_file = submodule_dir / ".git" - if git_file.exists(): - readme_file = submodule_dir / "README.md" - if not readme_file.exists(): - relative_path = submodule_dir.relative_to(package_path) - missing_submodule_readmes.append(str(relative_path)) - - # Find all markdown files - for md_file in package_path.rglob("*.md"): - # Skip the root README.md - if md_file == package_path / "README.md": - continue - - # Skip template file - if md_file.name == "PACKAGE_README_TEMPLATE.md": - continue - - # Check if file is in excluded path - relative_path = md_file.relative_to(package_path) - relative_str = str(relative_path) - - is_excluded = any(pattern in relative_str for pattern in exclude_patterns) - - if not is_excluded: - illegal_files.append(str(relative_path)) - - # Report missing submodule READMEs - if missing_submodule_readmes: - result.issues.append( - f"Git submodules missing README.md: {', '.join(missing_submodule_readmes)}" - ) - # Moderate penalty for missing submodule READMEs - result.score -= len(missing_submodule_readmes) * 5 - - # Report illegal files - if illegal_files: - # Limit to first 5 files to avoid overwhelming output - shown_files = illegal_files[:5] - if len(illegal_files) > 5: - result.issues.append( - f"Found {len(illegal_files)} illegal markdown files (showing first 5): {', '.join(shown_files)}" - ) - else: - result.issues.append( - f"Found {len(illegal_files)} illegal markdown file(s): {', '.join(illegal_files)}" - ) - - # Significant penalty for illegal markdown files - result.score -= len(illegal_files) * 10 - - def check_all_packages(self) -> dict[str, PackageREADMECheck]: - """Check all packages.""" - packages = self.get_packages() - results = {} - - for package in packages: - results[package] = self.check_readme(package) - - return results - - def check_all(self, fix: bool = False) -> list[PackageREADMECheck]: - """Check all packages and return a list (for CLI compatibility).""" - results_dict = self.check_all_packages() - return list(results_dict.values()) - - def check_package(self, package_name: str, fix: bool = False) -> PackageREADMECheck: - """Check a specific package (for CLI compatibility).""" - return self.check_readme(package_name) - - def print_summary(self, results: dict[str, PackageREADMECheck]): - """Print summary of all checks.""" - print("=" * 70) - print("📦 Package README Quality Report") - print("=" * 70) - print() - - total_packages = len(results) - packages_with_readme = sum(1 for r in results.values() if r.exists) - avg_score = sum(r.score for r in results.values()) / total_packages if total_packages else 0 - - print("📊 Overall Statistics:") - print(f" - Total packages: {total_packages}") - print(f" - Packages with README: {packages_with_readme}") - print(f" - Average quality score: {avg_score:.1f}/100") - print() - - # Sort by score - sorted_results = sorted(results.items(), key=lambda x: x[1].score, reverse=True) - - print("📋 Individual Package Scores:") - print() - - for package_name, result in sorted_results: - status = "✅" if result.score >= 80 else "⚠️" if result.score >= 60 else "❌" - print(f"{status} {package_name:25} Score: {result.score:5.1f}/100") - - if result.issues: - for issue in result.issues[:3]: # Show first 3 issues - print(f" - {issue}") - if len(result.issues) > 3: - print(f" ... and {len(result.issues) - 3} more issues") - print() - - def generate_report(self, results: list[PackageREADMECheck] | dict[str, PackageREADMECheck]): - """Generate and print detailed report.""" - # Convert list to dict if needed - if isinstance(results, list): - results_dict = {r.package_name: r for r in results} - else: - results_dict = results - - # Generate report - lines = ["# Package README Quality Report", ""] - lines.append(f"**Generated**: {self._get_timestamp()}") - lines.append("") - - # Summary - total = len(results_dict) - avg_score = sum(r.score for r in results_dict.values()) / total if total else 0 - - lines.extend( - [ - "## Summary", - "", - f"- **Total Packages**: {total}", - f"- **Average Score**: {avg_score:.1f}/100", - "", - ] - ) - - # Detailed results - lines.extend(["## Detailed Results", ""]) - - for package_name, result in sorted(results_dict.items()): - lines.append(f"### {package_name}") - lines.append("") - lines.append(f"**Score**: {result.score:.1f}/100") - lines.append("") - - if not result.exists: - lines.append("❌ **README.md not found**") - lines.append("") - continue - - # Section checklist - lines.append("#### Sections") - lines.append("") - - for section in result.sections: - status = "✅" if section.found else "❌" if section.required else "⚠️" - req_label = " (required)" if section.required else " (recommended)" - lines.append(f"- {status} {section.name}{req_label}") - - lines.append("") - - # Issues - if result.issues: - lines.append("#### Issues") - lines.append("") - for issue in result.issues: - lines.append(f"- {issue}") - lines.append("") - - # Print the report - report = "\n".join(lines) - print("\n" + "=" * 70) - print(report) - - def _get_timestamp(self) -> str: - """Get current timestamp.""" - from datetime import datetime - - return datetime.now().strftime("%Y-%m-%d %H:%M:%S") - - -def main(): - """Main entry point.""" - import argparse - - parser = argparse.ArgumentParser(description="Check package README quality") - parser.add_argument("--package", help="Check specific package") - parser.add_argument("--all", action="store_true", help="Check all packages") - parser.add_argument("--report", action="store_true", help="Generate detailed report") - parser.add_argument("--output", help="Output file for report") - - args = parser.parse_args() - - # Find workspace root - workspace_root = Path(__file__).parent.parent - - checker = PackageREADMEChecker(workspace_root) - - # Check packages - if args.package: - results = {args.package: checker.check_readme(args.package)} - else: - results = checker.check_all_packages() - - # Print summary - checker.print_summary(results) - - # Generate report if requested - if args.report: - checker.generate_report(results) - - # Exit with error if any package has low score - avg_score = sum(r.score for r in results.values()) / len(results) if results else 0 - failing_packages = [name for name, r in results.items() if r.score < 80 or r.issues] - - if failing_packages: - print(f"\n❌ {len(failing_packages)} package(s) have README quality issues:") - for pkg in failing_packages: - print(f" - {pkg}: {results[pkg].score:.1f}/100") - - # 如果平均分高于 90，只显示警告而不失败 - if avg_score >= 90: - print(f"\n⚠️ 虽然有 {len(failing_packages)} 个包需要改进，") - print(f" 但平均分 {avg_score:.1f}/100 >= 90，仅作为警告") - print(" 建议修复以上问题以达到更高质量标准") - return 0 - else: - print(f"\n💡 平均分 {avg_score:.1f}/100 < 90，需要改进") - return 1 - - print("\n✅ All package READMEs meet quality standards!") - return 0 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/packages/sage-tools/src/sage/tools/dev/tools/project_status_checker.py b/packages/sage-tools/src/sage/tools/dev/tools/project_status_checker.py deleted file mode 100644 index 97305cb905..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/tools/project_status_checker.py +++ /dev/null @@ -1,490 +0,0 @@ -""" -SAGE 项目状态检查器 - -提供全面的项目状态检查功能，包括： -- 包依赖状态 -- 安装状态 -- 配置状态 -- 服务状态 -- 开发环境状态 -""" - -import importlib.util -import os -import subprocess -import sys -from pathlib import Path -from typing import Any - -from rich.console import Console -from rich.panel import Panel - -console = Console() - - -class ProjectStatusChecker: - """SAGE 项目状态检查器""" - - def __init__(self, project_root: str = "."): - self.project_root = Path(project_root).resolve() - self.packages_dir = self.project_root / "packages" - # 缓存已安装的包列表，避免重复调用 - self._installed_packages_cache: dict[str, str] | None = None - - def check_all(self, verbose: bool = False, quick: bool = False) -> dict[str, Any]: - """执行全面的状态检查 - - Args: - verbose: 详细输出 - quick: 快速模式，跳过耗时检查（如依赖和服务检查） - """ - status_data = { - "timestamp": self._get_timestamp(), - "project_root": str(self.project_root), - "checks": {}, - } - - # 根据模式决定检查项 - if quick: - checks = [ - ("environment", "环境检查", self._check_environment), - ("packages", "包状态检查", self._check_packages), - ("configuration", "配置检查", self._check_configuration), - ] - else: - checks = [ - ("environment", "环境检查", self._check_environment), - ("packages", "包状态检查", self._check_packages), - ("dependencies", "依赖检查", self._check_dependencies), - ("services", "服务状态检查", self._check_services), - ("configuration", "配置检查", self._check_configuration), - ] - - for check_name, check_desc, check_func in checks: - console.print(f"🔍 {check_desc}...") - try: - result = check_func() - status_data["checks"][check_name] = { - "status": "success", - "data": result, - } - if verbose: - self._display_check_result(check_desc, result) - except Exception as e: - error_msg = str(e) - status_data["checks"][check_name] = { - "status": "error", - "error": error_msg, - } - console.print(f"❌ {check_desc}失败: {error_msg}") - - return status_data - - def _check_environment(self) -> dict[str, Any]: - """检查开发环境""" - env_info = { - "python_version": sys.version, - "python_executable": sys.executable, - "working_directory": os.getcwd(), - "sage_home": os.environ.get("SAGE_HOME", "Not set"), - "conda_env": os.environ.get("CONDA_DEFAULT_ENV", "None"), - "virtual_env": os.environ.get("VIRTUAL_ENV", "None"), - } - - # 检查关键环境变量 - env_vars = ["PATH", "PYTHONPATH", "SAGE_HOME"] - env_info["environment_variables"] = {} # type: ignore[assignment] - for var in env_vars: - env_info["environment_variables"][var] = os.environ.get(var, "Not set") # type: ignore[index] - - return env_info - - def _check_packages(self) -> dict[str, Any]: - """检查SAGE包状态""" - packages_info = { - "packages_dir_exists": self.packages_dir.exists(), - "packages": {}, - "summary": { - "total": 0, - "installed": 0, - "importable": 0, - "has_pyproject": 0, - "has_tests": 0, - }, - } - - if not self.packages_dir.exists(): - return packages_info - - # 预加载已安装包列表（只调用一次） - self._installed_packages_cache = self._get_installed_packages() - - # 扫描packages目录 - for package_dir in self.packages_dir.iterdir(): - if package_dir.is_dir() and package_dir.name.startswith("sage-"): - package_name = package_dir.name - package_info = self._check_single_package(package_dir) - packages_info["packages"][package_name] = package_info - - # 更新统计 - packages_info["summary"]["total"] += 1 - if package_info["installed"]: - packages_info["summary"]["installed"] += 1 - if package_info["importable"]: - packages_info["summary"]["importable"] += 1 - if package_info["has_pyproject"]: - packages_info["summary"]["has_pyproject"] += 1 - if package_info["has_tests"]: - packages_info["summary"]["has_tests"] += 1 - - return packages_info - - def _check_single_package(self, package_dir: Path) -> dict[str, Any]: - """检查单个包的状态""" - info = { - "path": str(package_dir), - "has_pyproject": (package_dir / "pyproject.toml").exists(), - "has_setup_py": (package_dir / "setup.py").exists(), - "has_src": (package_dir / "src").exists(), - "has_tests": (package_dir / "tests").exists(), - "installed": False, - "importable": False, - } - - # 检查是否已安装 - try: - # 读取pyproject.toml获取包名 - pyproject_path = package_dir / "pyproject.toml" - if pyproject_path.exists(): - package_name = self._get_package_name_from_pyproject(pyproject_path) - if package_name: - # 使用缓存的已安装包列表（避免重复调用） - installed_packages = ( - self._installed_packages_cache - if self._installed_packages_cache is not None - else self._get_installed_packages() - ) - if package_name in installed_packages: - info["installed"] = True - info["version"] = installed_packages[package_name] - - # 检查是否可导入 (尝试导入主模块) - try: - # 对于isage-*包，尝试导入sage.*模块 - if package_name.startswith("isage-"): - module_name = "sage." + package_name.replace("isage-", "") - if package_name == "isage": - module_name = "sage" - else: - module_name = package_name.replace("-", ".") - - spec = importlib.util.find_spec(module_name) - if spec is not None: - info["importable"] = True - info["import_path"] = spec.origin if spec.origin else "Built-in" - info["module_name"] = module_name - except ImportError: - pass - except Exception as e: - info["error"] = str(e) - - return info - - def _check_dependencies(self) -> dict[str, Any]: - """检查依赖状态""" - deps_info = {"critical_packages": {}, "import_tests": {}} - - # 检查关键依赖包 - critical_deps = [ - "typer", - "rich", - "click", - "pydantic", - "pathlib", - "tomli", # TOML解析库 - "numpy", - "pandas", # 数据处理库（可选） - ] - - for dep in critical_deps: - try: - spec = importlib.util.find_spec(dep) - if spec is not None: - deps_info["critical_packages"][dep] = { - "available": True, - "path": spec.origin if spec.origin else "Built-in", - } - # 尝试实际导入 - try: - __import__(dep) - deps_info["import_tests"][dep] = "success" - except Exception as e: - deps_info["import_tests"][dep] = f"import_error: {e}" - else: - deps_info["critical_packages"][dep] = {"available": False} - deps_info["import_tests"][dep] = "not_found" - except Exception as e: - deps_info["critical_packages"][dep] = {"error": str(e)} - deps_info["import_tests"][dep] = f"check_error: {e}" - - return deps_info - - def _check_services(self) -> dict[str, Any]: - """检查相关服务状态""" - services_info = { - "ray": self._check_ray_status(), - "jobmanager": self._check_jobmanager_status(), - } - - return services_info - - def _check_ray_status(self) -> dict[str, Any]: - """检查Ray服务状态""" - try: - result = subprocess.run(["ray", "status"], capture_output=True, text=True, timeout=10) - return { - "available": True, - "running": result.returncode == 0, - "output": result.stdout if result.returncode == 0 else result.stderr, - } - except FileNotFoundError: - return {"available": False, "error": "Ray command not found"} - except subprocess.TimeoutExpired: - return {"available": True, "running": False, "error": "Command timeout"} - except Exception as e: - return {"available": False, "error": str(e)} - - def _check_jobmanager_status(self) -> dict[str, Any]: - """检查JobManager状态""" - try: - # 尝试导入jobmanager模块 - spec = importlib.util.find_spec("sage.tools.cli.commands.jobmanager") - if spec is None: - return {"available": False, "error": "JobManager module not found"} - - # 这里可以添加更具体的JobManager状态检查逻辑 - return {"available": True, "status": "module_available"} - except Exception as e: - return {"available": False, "error": str(e)} - - def _check_configuration(self) -> dict[str, Any]: - """检查配置状态""" - config_info = {"config_files": {}, "sage_home_status": {}} - - # 检查主要配置文件 - config_files = ["pyproject.toml", "README.md", "_version.py", "quickstart.sh"] - - for config_file in config_files: - file_path = self.project_root / config_file - config_info["config_files"][config_file] = { - "exists": file_path.exists(), - "path": str(file_path), - "size": file_path.stat().st_size if file_path.exists() else 0, - } - - # 检查SAGE_HOME - sage_home = os.environ.get("SAGE_HOME") - if sage_home: - sage_home_path = Path(sage_home) - config_info["sage_home_status"] = { - "path": sage_home, - "exists": sage_home_path.exists(), - "is_dir": sage_home_path.is_dir() if sage_home_path.exists() else False, - "logs_dir_exists": ( - (sage_home_path / "logs").exists() if sage_home_path.exists() else False - ), - } - else: - config_info["sage_home_status"] = {"configured": False} - - return config_info - - def _display_check_result(self, check_name: str, result: dict[str, Any]): - """显示检查结果""" - panel = Panel( - self._format_result_for_display(result), - title=f"✅ {check_name}", - border_style="green", - ) - console.print(panel) - - def _format_result_for_display(self, result: dict[str, Any]) -> str: - """格式化结果用于显示""" - if isinstance(result, dict): - lines = [] - - # 特殊处理包信息 - if "packages" in result and "summary" in result: - summary = result["summary"] - lines.append(f"📦 包总数: {summary['total']}") - lines.append(f"✅ 已安装: {summary['installed']}") - lines.append(f"📥 可导入: {summary['importable']}") - lines.append(f"⚙️ 有配置: {summary['has_pyproject']}") - lines.append(f"🧪 有测试: {summary['has_tests']}") - return "\n".join(lines) - - # 特殊处理依赖信息 - if "critical_packages" in result and "import_tests" in result: - critical = result["critical_packages"] - imports = result["import_tests"] - available = sum(1 for pkg in critical.values() if pkg.get("available", False)) - successful_imports = sum(1 for test in imports.values() if test == "success") - lines.append(f"📚 关键依赖: {available}/{len(critical)} 可用") - lines.append(f"📥 导入测试: {successful_imports}/{len(imports)} 成功") - - # 显示失败的导入 - failed_imports = [name for name, test in imports.items() if test != "success"] - if failed_imports: - lines.append(f"❌ 导入失败: {', '.join(failed_imports[:3])}") - return "\n".join(lines) - - # 特殊处理服务信息 - if "ray" in result: - lines.append( - f"⚡ Ray: {'✅ 运行中' if result['ray'].get('running') else '❌ 未运行'}" - ) - lines.append( - f"🔧 JobManager: {'✅ 可用' if result['jobmanager'].get('available') else '❌ 不可用'}" - ) - return "\n".join(lines) - - # 特殊处理环境信息 - if "python_version" in result: - lines.append(f"🐍 Python: {result['python_version'].split()[0]}") - lines.append(f"🏠 工作目录: {result['working_directory']}") - lines.append(f"🌍 Conda环境: {result.get('conda_env', 'None')}") - sage_home = result.get("sage_home", "Not set") - lines.append( - f"🏠 SAGE_HOME: {'✅ 已设置' if sage_home != 'Not set' else '❌ 未设置'}" - ) - return "\n".join(lines) - - # 特殊处理配置信息 - if "config_files" in result: - config_files = result["config_files"] - existing_files = [name for name, info in config_files.items() if info.get("exists")] - lines.append(f"📄 配置文件: {len(existing_files)}/{len(config_files)} 存在") - sage_home_status = result.get("sage_home_status", {}) - if sage_home_status.get("configured", True): - lines.append( - f"🏠 SAGE_HOME: {'✅ 配置正确' if sage_home_status.get('exists') else '❌ 路径不存在'}" - ) - else: - lines.append("🏠 SAGE_HOME: ❌ 未配置") - return "\n".join(lines) - - # 默认格式化 - for key, value in result.items(): - if isinstance(value, dict): - lines.append(f"{key}: {len(value)} 项") - elif isinstance(value, list): - lines.append(f"{key}: {len(value)} 项") - else: - lines.append(f"{key}: {value}") - return "\n".join(lines[:8]) # 限制显示行数 - return str(result) - - def _get_installed_packages(self) -> dict[str, str]: - """获取已安装的包列表和版本""" - # 优先使用 importlib.metadata (Python 3.8+)，避免使用已弃用的 pkg_resources - try: - import importlib.metadata as metadata - - installed = {} - for dist in metadata.distributions(): - try: - if dist.metadata and "Name" in dist.metadata: - installed[dist.metadata["Name"]] = dist.version - except Exception: - # 跳过损坏的包 - continue - return installed - except ImportError: - # Python < 3.8 回退方案：使用 pkg_resources（带警告抑制） - try: - import warnings - - with warnings.catch_warnings(): - warnings.filterwarnings("ignore", category=DeprecationWarning) - import pkg_resources - - installed = {} - for dist in pkg_resources.working_set: - installed[dist.project_name] = dist.version - return installed - except ImportError: - pass - - # 最终回退方案：使用pip list - try: - result = subprocess.run( - [sys.executable, "-m", "pip", "list", "--format=json"], - capture_output=True, - text=True, - timeout=10, - ) - if result.returncode == 0: - import json - - packages = json.loads(result.stdout) - return {pkg["name"]: pkg["version"] for pkg in packages} - except Exception: - pass - return {} - - def _get_package_name_from_pyproject(self, pyproject_path: Path) -> str | None: - """从pyproject.toml中获取包名""" - try: - # 尝试使用不同的TOML库 - try: - import tomllib # Python 3.11+ - - with open(pyproject_path, "rb") as f: - data = tomllib.load(f) - except ImportError: - try: - import tomli - - with open(pyproject_path, "rb") as f: - data = tomli.load(f) - except ImportError: - # 回退到手动解析 - with open(pyproject_path) as f: - content = f.read() - # 简单解析name字段 - import re - - match = re.search(r'name\s*=\s*["\']([^"\']+)["\']', content) - return match.group(1) if match else None - - return data.get("project", {}).get("name") - except Exception: - return None - - def _get_timestamp(self) -> str: - """获取时间戳""" - from datetime import datetime - - return datetime.now().isoformat() - - def generate_status_summary(self, status_data: dict[str, Any]) -> str: - """生成状态摘要""" - total_checks = len(status_data["checks"]) - successful_checks = sum( - 1 for check in status_data["checks"].values() if check["status"] == "success" - ) - - summary_lines = [ - "📊 SAGE 项目状态报告", - f"⏰ 检查时间: {status_data['timestamp']}", - f"📁 项目路径: {status_data['project_root']}", - f"✅ 检查项目: {successful_checks}/{total_checks}", - ] - - if successful_checks == total_checks: - summary_lines.append("🎉 所有检查项目都通过了!") - else: - failed_checks = total_checks - successful_checks - summary_lines.append(f"⚠️ 有 {failed_checks} 个检查项目失败") - - return "\n".join(summary_lines) diff --git a/packages/sage-tools/src/sage/tools/dev/tools/test_failure_cache.py b/packages/sage-tools/src/sage/tools/dev/tools/test_failure_cache.py deleted file mode 100644 index ee827f7881..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/tools/test_failure_cache.py +++ /dev/null @@ -1,271 +0,0 @@ -""" -Test Failure Cache Manager - -This module provides functionality to cache failed test paths and enable -running only previously failed tests with the --failed option. -""" - -import json -from datetime import datetime -from pathlib import Path - - -class TestFailureCache: - """Manages caching of failed test paths for quick re-execution.""" - - def __init__(self, project_root: str): - from sage.common.config.output_paths import get_sage_paths - - self.project_root = Path(project_root) - - # Use unified SAGE path management system - sage_paths = get_sage_paths(self.project_root) - self.cache_dir = sage_paths.test_logs_dir - self.cache_file = self.cache_dir / "failed_tests.json" - - # Ensure directory exists with robust error handling - self._ensure_cache_dir_exists() - - # Initialize cache data structure - self._cache_data = { - "last_updated": None, - "failed_tests": [], - "last_run_summary": { - "total": 0, - "passed": 0, - "failed": 0, - "execution_time": 0, - "timestamp": None, - }, - "history": [], # Keep last 10 test run results - } - - # Load existing cache - self._load_cache() - - def _ensure_cache_dir_exists(self) -> None: - """Ensure cache directory exists with robust error handling.""" - try: - # The directory should already be created by SageOutputPaths - # But we double-check and create if needed - self.cache_dir.mkdir(parents=True, exist_ok=True) - - except (OSError, PermissionError) as e: - # If we still can't create the directory, use a temporary fallback - print(f"Warning: Could not create cache directory {self.cache_dir}: {e}") - fallback_dir = self.project_root / "temp_sage_cache" / "test_logs" - self.cache_dir = fallback_dir - self.cache_file = self.cache_dir / "failed_tests.json" - try: - self.cache_dir.mkdir(parents=True, exist_ok=True) - except (OSError, PermissionError) as fallback_error: - print(f"Error: Could not create fallback cache directory: {fallback_error}") - # Use in-memory cache only - self.cache_dir = None - self.cache_file = None - - def _load_cache(self) -> None: - """Load cache from file if it exists.""" - if self.cache_file is None: - # Cache file not available, use in-memory cache only - return - - try: - if self.cache_file.exists(): - with open(self.cache_file, encoding="utf-8") as f: - data = json.load(f) - # Merge with default structure to handle schema changes - self._cache_data.update(data) - except (OSError, json.JSONDecodeError) as e: - print(f"Warning: Could not load test failure cache: {e}") - # Keep default cache data - - def _save_cache(self) -> None: - """Save cache to file.""" - if self.cache_file is None: - # Cache file not available, skip saving - return - - try: - # Update timestamp - self._cache_data["last_updated"] = datetime.now().isoformat() - - with open(self.cache_file, "w", encoding="utf-8") as f: - json.dump(self._cache_data, f, indent=2, ensure_ascii=False) - except OSError as e: - print(f"Warning: Could not save test failure cache: {e}") - - def update_from_test_results(self, test_results: dict) -> None: - """Update cache with results from a test run.""" - try: - # Extract failed test paths - failed_tests = [] - - if "results" in test_results: - for result in test_results["results"]: - if not result.get("passed", True): - test_file = result.get("test_file") - if test_file: - # Store full path and simplified path - failed_tests.append( - { - "test_file": test_file, - "error": result.get("error", "Unknown error"), - "duration": result.get("duration", 0), - "log_file": result.get("log_file"), - "failed_at": datetime.now().isoformat(), - } - ) - - # Update cache data - self._cache_data["failed_tests"] = failed_tests - - # Update summary - summary = test_results.get("summary", {}) - self._cache_data["last_run_summary"] = { - "total": summary.get("total", 0), - "passed": summary.get("passed", 0), - "failed": summary.get("failed", 0), - "execution_time": test_results.get("execution_time", 0), - "timestamp": datetime.now().isoformat(), - "mode": test_results.get("mode", "unknown"), - } - - # Add to history (keep last 10) - history_entry = { - "timestamp": datetime.now().isoformat(), - "summary": self._cache_data["last_run_summary"].copy(), - "failed_count": len(failed_tests), - "failed_tests": [f["test_file"] for f in failed_tests], - } - - self._cache_data["history"].insert(0, history_entry) - self._cache_data["history"] = self._cache_data["history"][:10] - - # Save to file - self._save_cache() - - print(f"✅ Updated test failure cache: {len(failed_tests)} failed tests recorded") - - except Exception as e: - print(f"Warning: Failed to update test failure cache: {e}") - - def get_failed_test_paths(self) -> list[str]: - """Get list of test files that failed in the last run.""" - return [f["test_file"] for f in self._cache_data["failed_tests"]] - - def get_failed_test_details(self) -> list[dict]: - """Get detailed information about failed tests.""" - return self._cache_data["failed_tests"].copy() - - def has_failed_tests(self) -> bool: - """Check if there are any cached failed tests.""" - return len(self._cache_data["failed_tests"]) > 0 - - def clear_cache(self) -> None: - """Clear the failed tests cache.""" - self._cache_data["failed_tests"] = [] - self._save_cache() - print("✅ Cleared test failure cache") - - def get_cache_info(self) -> dict: - """Get information about the current cache state.""" - failed_count = len(self._cache_data["failed_tests"]) - last_updated = self._cache_data.get("last_updated") - last_summary = self._cache_data.get("last_run_summary", {}) - - return { - "cache_file": (str(self.cache_file) if self.cache_file else "None (in-memory only)"), - "failed_tests_count": failed_count, - "last_updated": last_updated, - "last_run_summary": last_summary, - "has_failed_tests": failed_count > 0, - "cache_exists": self.cache_file.exists() if self.cache_file else False, - } - - def get_history(self, limit: int = 5) -> list[dict]: - """Get test run history.""" - return self._cache_data["history"][:limit] - - def resolve_test_paths(self, packages_dir: Path) -> list[Path]: - """ - Resolve cached failed test paths to actual file paths. - - This handles cases where the cached paths might be simplified - or the project structure changed. - """ - failed_paths = self.get_failed_test_paths() - resolved_paths = [] - - for test_path in failed_paths: - # Try to resolve the path - resolved_path = self._resolve_single_test_path(test_path, packages_dir) - if resolved_path: - resolved_paths.append(resolved_path) - else: - print(f"Warning: Could not resolve cached test path: {test_path}") - - return resolved_paths - - def _resolve_single_test_path(self, test_path: str, packages_dir: Path) -> Path | None: - """Resolve a single test path to an actual file.""" - # If it's already an absolute path and exists - if Path(test_path).is_absolute() and Path(test_path).exists(): - return Path(test_path) - - # Try as relative to project root - project_relative = self.project_root / test_path - if project_relative.exists(): - return project_relative - - # Try to find in packages directory structure - # Handle simplified paths like "sage-kernel/tests/kernel/cli/test_job_new.py" - if "/" in test_path: - parts = test_path.split("/") - - # Look for package name in the path - for i, part in enumerate(parts): - # Try each part as a potential package name - potential_package = packages_dir / part - if potential_package.exists() and potential_package.is_dir(): - # Reconstruct path from this package - remaining_path = "/".join(parts[i + 1 :]) if i + 1 < len(parts) else test_path - full_path = potential_package / remaining_path - if full_path.exists(): - return full_path - - # Last resort: search for the file name in all packages - file_name = Path(test_path).name - if file_name.startswith("test_") and file_name.endswith(".py"): - for package_dir in packages_dir.iterdir(): - if package_dir.is_dir() and not package_dir.name.startswith("."): - found_files = list(package_dir.rglob(file_name)) - if found_files: - # Return the first match (there might be multiple) - return found_files[0] - - return None - - def print_cache_status(self) -> None: - """Print current cache status in a user-friendly format.""" - info = self.get_cache_info() - - print("📊 Test Failure Cache Status") - print(f" Cache file: {info['cache_file']}") - print(f" Failed tests: {info['failed_tests_count']}") - - if info["last_updated"]: - print(f" Last updated: {info['last_updated']}") - - last_summary = info.get("last_run_summary", {}) - if last_summary.get("timestamp"): - print( - f" Last run: {last_summary['total']} tests, " - f"{last_summary['passed']} passed, {last_summary['failed']} failed" - ) - print(f" Execution time: {last_summary.get('execution_time', 0):.2f}s") - - if info["has_failed_tests"]: - print("\n Use 'sage-dev test --failed' to re-run failed tests") - else: - print("\n No failed tests cached") diff --git a/packages/sage-tools/src/sage/tools/dev/tools/vscode_path_manager.py b/packages/sage-tools/src/sage/tools/dev/tools/vscode_path_manager.py deleted file mode 100644 index adca85e008..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/tools/vscode_path_manager.py +++ /dev/null @@ -1,164 +0,0 @@ -""" -VS Code Path Configuration Tool - Integrated from update_vscode_paths*.py - -This tool automatically updates VS Code settings.json with Python path configurations. -""" - -import glob -import json -from pathlib import Path - -from ..core.exceptions import SAGEDevToolkitError - - -class VSCodePathManager: - """Tool for managing VS Code Python path configurations.""" - - def __init__(self, project_root: str): - self.project_root = Path(project_root) - self.packages_dir = self.project_root / "packages" - self.vscode_settings_path = self.project_root / ".vscode" / "settings.json" - - def update_python_paths(self, mode: str = "enhanced") -> dict: - """Update VS Code Python path configurations. - - Args: - mode: 'basic' for pyproject.toml only, 'enhanced' for all Python packages - """ - try: - if mode == "enhanced": - src_paths = self._find_all_python_packages() - else: - src_paths = self._find_packages_with_pyproject() - - return self._update_settings_json(src_paths) - - except Exception as e: - raise SAGEDevToolkitError(f"VS Code path update failed: {e}") - - def _find_packages_with_pyproject(self) -> list[str]: - """Find packages with pyproject.toml files.""" - if not self.packages_dir.exists(): - return [] - - src_paths = [] - pyproject_files = glob.glob( - str(self.packages_dir / "**" / "pyproject.toml"), recursive=True - ) - - for pyproject_file in pyproject_files: - package_dir = Path(pyproject_file).parent - potential_src_paths = [ - package_dir / "src", - package_dir / package_dir.name.replace("-", "_"), - package_dir, - ] - - for src_path in potential_src_paths: - if src_path.exists() and src_path.is_dir(): - relative_path = src_path.relative_to(self.project_root) - src_paths.append(f"./{relative_path}") - break - - return src_paths - - def _find_all_python_packages(self) -> list[str]: - """Find all Python packages (enhanced mode).""" - if not self.packages_dir.exists(): - return [] - - src_paths = set() - - # Method 1: Find packages with pyproject.toml - pyproject_files = glob.glob( - str(self.packages_dir / "**" / "pyproject.toml"), recursive=True - ) - - for pyproject_file in pyproject_files: - package_dir = Path(pyproject_file).parent - potential_src_paths = [ - package_dir / "src", - package_dir / package_dir.name.replace("-", "_"), - package_dir, - ] - - for src_path in potential_src_paths: - if src_path.exists() and src_path.is_dir(): - relative_path = src_path.relative_to(self.project_root) - src_paths.add(f"./{relative_path}") - break - - # Method 2: Find directories with __init__.py - init_files = glob.glob(str(self.packages_dir / "**" / "__init__.py"), recursive=True) - - for init_file in init_files: - package_dir = Path(init_file).parent - # Skip __pycache__ and other special directories - if any(part.startswith(".") or part == "__pycache__" for part in package_dir.parts): - continue - - relative_path = package_dir.relative_to(self.project_root) - src_paths.add(f"./{relative_path}") - - # Method 3: Find directories with Python files - py_files = glob.glob(str(self.packages_dir / "**" / "*.py"), recursive=True) - - for py_file in py_files: - py_path = Path(py_file) - if any(part.startswith(".") or part == "__pycache__" for part in py_path.parts): - continue - - package_dir = py_path.parent - relative_path = package_dir.relative_to(self.project_root) - src_paths.add(f"./{relative_path}") - - return sorted(src_paths) - - def _update_settings_json(self, src_paths: list[str]) -> dict: - """Update VS Code settings.json file.""" - # Ensure .vscode directory exists - self.vscode_settings_path.parent.mkdir(exist_ok=True) - - # Load existing settings - if self.vscode_settings_path.exists(): - with open(self.vscode_settings_path, encoding="utf-8") as f: - settings = json.load(f) - else: - settings = {} - - # Update Python analysis paths - settings["python.analysis.extraPaths"] = src_paths - - # Also update autoImport paths for better IntelliSense - settings["python.analysis.autoImportCompletions"] = True - settings["python.analysis.packageIndexDepths"] = [ - {"name": "sage", "depth": 10}, - {"name": "", "depth": 2}, - ] - - # Write updated settings - with open(self.vscode_settings_path, "w", encoding="utf-8") as f: - json.dump(settings, f, indent=2, ensure_ascii=False) - - return { - "settings_file": str(self.vscode_settings_path), - "paths_added": len(src_paths), - "paths": src_paths, - "status": "success", - } - - def get_current_paths(self) -> dict: - """Get current Python paths from VS Code settings.""" - if not self.vscode_settings_path.exists(): - return {"paths": [], "status": "no_settings_file"} - - try: - with open(self.vscode_settings_path, encoding="utf-8") as f: - settings = json.load(f) - - return { - "paths": settings.get("python.analysis.extraPaths", []), - "status": "success", - } - except Exception as e: - return {"paths": [], "status": f"error: {e}"} diff --git a/packages/sage-tools/src/sage/tools/dev/utils/__init__.py b/packages/sage-tools/src/sage/tools/dev/utils/__init__.py deleted file mode 100644 index 1cbfc90c8c..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/utils/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -""" -SAGE Development Utilities -=========================== - -开发工具辅助函数 -""" - -# 导出常用函数 -from .project import find_project_root - -__all__ = [ - "find_project_root", -] diff --git a/packages/sage-tools/src/sage/tools/dev/utils/intermediate_results_checker.py b/packages/sage-tools/src/sage/tools/dev/utils/intermediate_results_checker.py deleted file mode 100644 index 11454a50f7..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/utils/intermediate_results_checker.py +++ /dev/null @@ -1,247 +0,0 @@ -""" -中间结果放置检查工具 - -此模块提供统一的API来检查项目中间结果文件和目录的放置情况， -确保所有中间结果都放置在 .sage/ 目录下，保持项目根目录整洁。 -""" - -import fnmatch -import glob -from pathlib import Path - - -class IntermediateResultsChecker: - """检查中间结果放置的工具类""" - - def __init__(self, project_root: str): - """ - 初始化检查器 - - Args: - project_root: 项目根目录路径 - """ - self.project_root = Path(project_root) - - # 定义不应该在根目录出现的中间结果模式 - self.forbidden_patterns = [ - # ".benchmarks", # Now configured to use .sage/benchmarks via pytest-benchmark - ".pytest_cache", - "__pycache__", - "*.pyc", - ".coverage", - "htmlcov", - ".mypy_cache", - "logs", - "outputs", - "temp", - "cache", - "reports", - "test_results_*.json", - "benchmark_report_*.json", - "coverage.xml", - ".nox", - ".tox", - "session_*", # Ray 临时会话目录 - "tmp_*", # 临时目录 - ] - - # 定义允许在根目录存在的文件和目录 - self.allowed_items = { - ".sage", - ".git", - ".github", - ".gitignore", - ".gitmodules", - "packages", - "docs", - "docs-public", - "examples", - "tools", - "scripts", - "experiments", - "data", - "test_env", - "README.md", - "LICENSE", - "_version.py", - "quickstart.sh", - "pytest.ini", - ".flake8", - ".pypirc", - ".github_token", - } - - # 定义 /tmp 下项目相关的临时文件模式 - self.tmp_project_patterns = [ - "ray/session_*", # Ray 会话目录 - "sage_*", # SAGE 相关临时文件 - "pytest_*", # pytest 临时文件 - ] - - def check_placement(self) -> dict: - """ - 检查项目中间结果放置情况 - - Returns: - Dict: 包含检查结果的字典，格式： - { - 'violations': List[Dict], # 违规项列表 - 'clean': bool, # 是否通过检查 - 'total_violations': int, # 违规总数 - 'suggestion': str # 建议信息 - } - """ - violations = [] - - # 检查项目根目录 - root_violations = self._check_project_root() - violations.extend(root_violations) - - # 检查 /tmp 目录 - tmp_violations = self._check_tmp_directory() - violations.extend(tmp_violations) - - return { - "violations": violations, - "clean": len(violations) == 0, - "total_violations": len(violations), - "suggestion": "所有中间结果应该放置在 .sage/ 目录下以保持项目根目录整洁", - } - - def _check_project_root(self) -> list[dict]: - """检查项目根目录下的文件和目录""" - violations = [] - - for item in self.project_root.iterdir(): - # 跳过允许的目录和文件 - if item.name in self.allowed_items: - continue - - # 检查是否匹配禁止模式 - for pattern in self.forbidden_patterns: - if self._matches_pattern(item.name, pattern): - violations.append( - { - "path": str(item.relative_to(self.project_root)), - "type": "directory" if item.is_dir() else "file", - "pattern": pattern, - "message": "应移动到 .sage/ 目录中", - "location": "project_root", - } - ) - break - - return violations - - def _check_tmp_directory(self) -> list[dict]: - """检查 /tmp 目录下是否有项目相关的临时文件""" - violations: list[dict] = [] - tmp_path = Path("/tmp") - - if not tmp_path.exists(): - return violations - - try: - for pattern in self.tmp_project_patterns: - try: - matches = glob.glob(str(tmp_path / pattern)) - for match in matches: - violations.append( - { - "path": match, - "type": "temporary", - "pattern": pattern, - "message": "项目相关临时文件应使用 .sage/temp 目录", - "location": "tmp", - } - ) - except Exception: - # 忽略权限错误等 - pass - - except Exception: - # 忽略 /tmp 访问错误 - pass - - return violations - - def _matches_pattern(self, name: str, pattern: str) -> bool: - """检查文件名是否匹配模式""" - return fnmatch.fnmatch(name, pattern) - - def print_check_result(self, check_result: dict | None = None) -> bool: - """ - 打印检查结果 - - Args: - check_result: 检查结果字典，如果为None则重新执行检查 - - Returns: - bool: 是否通过检查 - """ - if check_result is None: - check_result = self.check_placement() - - if check_result["clean"]: - print("✅ 中间结果放置检查通过 - 项目根目录整洁") - return True - else: - print(f"⚠️ 发现 {check_result['total_violations']} 个中间结果放置问题:") - for violation in check_result["violations"]: - print(f" - {violation['path']} ({violation['type']}): {violation['message']}") - print(f"\n💡 {check_result['suggestion']}") - return False - - def get_summary(self) -> str: - """ - 获取检查结果摘要 - - Returns: - str: 检查结果摘要文本 - """ - check_result = self.check_placement() - - if check_result["clean"]: - return "✅ 中间结果放置检查通过 - 项目根目录整洁" - else: - violations_by_location = {} - for violation in check_result["violations"]: - location = violation.get("location", "unknown") - if location not in violations_by_location: - violations_by_location[location] = 0 - violations_by_location[location] += 1 - - summary_parts = [f"⚠️ 发现 {check_result['total_violations']} 个中间结果放置问题"] - for location, count in violations_by_location.items(): - location_name = "项目根目录" if location == "project_root" else "/tmp目录" - summary_parts.append(f" - {location_name}: {count}个") - - return "\n".join(summary_parts) - - -def check_intermediate_results_placement(project_root: str) -> dict: - """ - 便捷函数：检查中间结果放置情况 - - Args: - project_root: 项目根目录路径 - - Returns: - Dict: 检查结果 - """ - checker = IntermediateResultsChecker(project_root) - return checker.check_placement() - - -def print_intermediate_results_check(project_root: str) -> bool: - """ - 便捷函数：打印中间结果检查结果 - - Args: - project_root: 项目根目录路径 - - Returns: - bool: 是否通过检查 - """ - checker = IntermediateResultsChecker(project_root) - return checker.print_check_result() diff --git a/packages/sage-tools/src/sage/tools/dev/utils/orphaned_file_detector.py b/packages/sage-tools/src/sage/tools/dev/utils/orphaned_file_detector.py deleted file mode 100644 index 7514af1790..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/utils/orphaned_file_detector.py +++ /dev/null @@ -1,238 +0,0 @@ -""" -SAGE 废弃文件检查器 - -检查项目中没有被其他文件引用的Python文件，帮助清理代码库。 -""" - -import os -import re -from dataclasses import dataclass -from pathlib import Path - -from sage.common.utils.formatting import format_size_compact - - -@dataclass -class OrphanedFile: - """废弃文件信息""" - - path: Path - relative_path: Path - module_path: str - size_bytes: int - last_modified: float - - -class OrphanedFileDetector: - """废弃文件检测器""" - - def __init__(self, project_root: Path): - self.project_root = project_root - self.all_python_files = [] - self.exclude_patterns = { - # 目录排除模式 - "__pycache__", - ".pytest_cache", - ".git", - "site-packages", - ".sage", - "node_modules", - # 文件排除模式 - "__init__.py", - "conftest.py", - } - - def get_all_python_files(self) -> list[Path]: - """获取项目中所有Python文件""" - python_files = [] - - for root, dirs, files in os.walk(self.project_root): - # 过滤目录 - dirs[:] = [d for d in dirs if d not in self.exclude_patterns] - - for file in files: - if file.endswith(".py") and file not in self.exclude_patterns: - python_files.append(Path(root) / file) - - return python_files - - def extract_module_path(self, file_path: Path) -> str: - """从文件路径提取模块导入路径""" - try: - rel_path = file_path.relative_to(self.project_root) - parts = rel_path.parts - - # 找到src目录或直接使用packages目录 - if "src" in parts: - src_index = parts.index("src") - module_parts = parts[src_index + 1 :] - elif "packages" in parts: - pkg_index = parts.index("packages") - module_parts = parts[pkg_index + 1 :] - else: - module_parts = parts - - # 移除.py扩展名 - if module_parts and module_parts[-1].endswith(".py"): - module_parts = module_parts[:-1] + (module_parts[-1][:-3],) - - # 过滤掉__init__ - if module_parts and module_parts[-1] == "__init__": - module_parts = module_parts[:-1] - - return ".".join(module_parts) if module_parts else "" - - except (ValueError, IndexError): - return "" - - def find_imports_in_file(self, file_path: Path) -> set[str]: - """在单个文件中查找所有导入""" - imports = set() - - try: - with open(file_path, encoding="utf-8", errors="ignore") as f: - content = f.read() - - # 查找各种import语句 - patterns = [ - r"from\s+([\w\.]+)\s+import", # from module import - r"import\s+([\w\.]+)", # import module - r'importlib\.import_module\(["\']([^"\']+)["\']', # 动态导入 - ] - - for pattern in patterns: - matches = re.findall(pattern, content, re.MULTILINE) - imports.update(matches) - - except Exception: - pass # 忽略文件读取错误 - - return imports - - def check_file_references(self, target_file: Path, all_files: list[Path]) -> list[str]: - """检查文件是否被其他文件引用""" - module_path = self.extract_module_path(target_file) - if not module_path: - return [] - - references = [] - - # 构建可能的引用模式 - possible_refs = [ - module_path, - module_path.split(".")[-1], # 只要最后一部分 - target_file.stem, # 文件名 - ] - - for other_file in all_files: - if other_file == target_file: - continue - - imports = self.find_imports_in_file(other_file) - - # 检查是否有匹配的导入 - for ref in possible_refs: - if any(ref in imp or imp.endswith(ref) for imp in imports): - references.append(str(other_file.relative_to(self.project_root))) - break - - return references - - def detect_orphaned_files( - self, check_directories: list[str] | None = None - ) -> list[OrphanedFile]: - """检测废弃文件""" - if check_directories is None: - check_directories = [ - "packages/sage-tools/src/sage/tools/dev/tools", - "packages/sage-tools/src/sage/tools/dev/utils", - "packages/sage-common/src/sage/common/utils", - "tools", - "examples", - "scripts", - ] - - # 获取所有Python文件 - self.all_python_files = self.get_all_python_files() - orphaned_files = [] - - for check_dir in check_directories: - check_path = self.project_root / check_dir - if not check_path.exists(): - continue - - for py_file in check_path.rglob("*.py"): - if py_file.name in self.exclude_patterns: - continue - - # 检查是否被引用 - references = self.check_file_references(py_file, self.all_python_files) - - if not references: - stat = py_file.stat() - orphaned_files.append( - OrphanedFile( - path=py_file, - relative_path=py_file.relative_to(self.project_root), - module_path=self.extract_module_path(py_file), - size_bytes=stat.st_size, - last_modified=stat.st_mtime, - ) - ) - - return orphaned_files - - def get_file_analysis(self, file_path: Path) -> dict: - """获取文件的详细分析信息""" - try: - with open(file_path, encoding="utf-8", errors="ignore") as f: - content = f.read() - - lines = content.count("\n") + 1 - functions = len(re.findall(r"^\s*def\s+", content, re.MULTILINE)) - classes = len(re.findall(r"^\s*class\s+", content, re.MULTILINE)) - imports = len(re.findall(r"^\s*(import|from)\s+", content, re.MULTILINE)) - - return { - "lines": lines, - "functions": functions, - "classes": classes, - "imports": imports, - "has_main": "__main__" in content, - "has_docstring": content.strip().startswith('"""') - or content.strip().startswith("'''"), - } - except Exception: - return {} - - -def format_file_size(size_bytes: int) -> str: - """格式化文件大小（使用统一的格式化函数）""" - return format_size_compact(size_bytes) - - -def analyze_orphaned_files( - project_root: Path, verbose: bool = False -) -> tuple[list[OrphanedFile], dict]: - """分析项目中的废弃文件""" - detector = OrphanedFileDetector(project_root) - orphaned_files = detector.detect_orphaned_files() - - # 统计信息 - total_size = sum(f.size_bytes for f in orphaned_files) - categories = {} - - for file in orphaned_files: - category = str(file.relative_path).split("/")[0] - if category not in categories: - categories[category] = {"count": 0, "size": 0} - categories[category]["count"] += 1 - categories[category]["size"] += file.size_bytes - - stats = { - "total_files": len(orphaned_files), - "total_size": total_size, - "categories": categories, - } - - return orphaned_files, stats diff --git a/packages/sage-tools/src/sage/tools/dev/utils/project.py b/packages/sage-tools/src/sage/tools/dev/utils/project.py deleted file mode 100644 index fcafbefc72..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/utils/project.py +++ /dev/null @@ -1,63 +0,0 @@ -#!/usr/bin/env python3 -""" -Project Utilities -================= - -项目相关的工具函数 -""" - -from pathlib import Path - - -def find_project_root( - start_path: Path | None = None, markers: list[str] | None = None -) -> Path | None: - """ - 查找项目根目录 - - Args: - start_path: 开始查找的路径，默认为当前目录 - markers: 用于识别项目根目录的标记文件/目录 - - Returns: - 项目根目录路径，如果找不到返回None - """ - # If no custom markers provided, use the centralized implementation from sage-common - if markers is None: - try: - from sage.common.config import find_sage_project_root - - return find_sage_project_root(start_path) - except ImportError: - # Fallback to local implementation if sage-common not available - markers = [ - "setup.py", - "pyproject.toml", - "requirements.txt", - ".git", - "sage", - "packages", - "SAGE_API_REFACTOR_SUMMARY.md", - ] - - if start_path is None: - start_path = Path.cwd() - - current = Path(start_path).resolve() - - # 向上查找包含标记文件的路径 - for parent in [current] + list(current.parents): - if any((parent / marker).exists() for marker in markers): - return parent - - # 检查当前Python环境中的sage包位置 - try: - import sage - - sage_path = Path(sage.__file__).parent.parent - if any((sage_path / marker).exists() for marker in markers): - return sage_path - except ImportError: - pass - - return None diff --git a/packages/sage-tools/src/sage/tools/dev/utils/sage_home.py.deprecated b/packages/sage-tools/src/sage/tools/dev/utils/sage_home.py.deprecated deleted file mode 100644 index b6f0a0cc39..0000000000 --- a/packages/sage-tools/src/sage/tools/dev/utils/sage_home.py.deprecated +++ /dev/null @@ -1,174 +0,0 @@ -""" -SAGE Home Directory Management - -This module provides utilities for managing SAGE directories. -Supports both development environments and pip-installed environments. - -Development environment: Uses project local .sage/ directory -Pip-installed environment: Uses ~/.sage/ directory -""" - -import os -from pathlib import Path -from typing import Optional - - -def get_sage_home_dir() -> Path: - """Get the SAGE home directory (~/.sage), creating it if necessary.""" - home_dir = Path.home() / ".sage" - home_dir.mkdir(exist_ok=True) - return home_dir - - -def find_project_root(start_path: Path = None) -> Optional[Path]: - """ - Find SAGE project root directory by looking for characteristic files/directories. - - Args: - start_path: Starting path for search. If None, uses current working directory. - - Returns: - Optional[Path]: Project root path if found, None otherwise - """ - if start_path is None: - start_path = Path.cwd() - - current = start_path.resolve() - - # Search up to 5 levels - for _ in range(5): - # Check for SAGE project markers - if (current / "packages").is_dir() and (current / "_version.py").is_file(): - return current - - parent = current.parent - if parent == current: # Reached filesystem root - break - current = parent - - return None - - -def get_appropriate_sage_dir(project_name: str = "SAGE") -> Path: - """ - Get the appropriate SAGE directory based on environment. - - For development: Uses project_root/.sage/ - For pip install: Uses ~/.sage/ - - Args: - project_name: Project name (used for organization) - - Returns: - Path: SAGE directory path - """ - # Check if we're in a development environment - project_root = find_project_root() - - if project_root: - # Development environment: use local .sage directory - sage_dir = project_root / ".sage" - else: - # Pip-installed or other environment: use home directory - if project_name and project_name.upper() == "SAGE": - sage_dir = get_sage_home_dir() - else: - sage_dir = get_sage_home_dir() / "projects" / project_name - - # Ensure directory and subdirectories exist - sage_dir.mkdir(parents=True, exist_ok=True) - subdirs = ["logs", "reports", "cache", "temp", "coverage", "benchmarks"] - for subdir in subdirs: - (sage_dir / subdir).mkdir(exist_ok=True) - - return sage_dir - - -def get_logs_dir(project_name: str = "SAGE") -> Path: - """Get the logs directory.""" - return get_appropriate_sage_dir(project_name) / "logs" - - -def get_reports_dir(project_name: str = "SAGE") -> Path: - """Get the reports directory.""" - return get_appropriate_sage_dir(project_name) / "reports" - - -def get_cache_dir(project_name: str = "SAGE") -> Path: - """Get the cache directory.""" - return get_appropriate_sage_dir(project_name) / "cache" - - -def get_temp_dir(project_name: str = "SAGE") -> Path: - """Get the temporary files directory.""" - return get_appropriate_sage_dir(project_name) / "temp" - - -def get_coverage_dir(project_name: str = "SAGE") -> Path: - """Get the coverage reports directory.""" - return get_appropriate_sage_dir(project_name) / "coverage" - - -def get_benchmarks_dir(project_name: str = "SAGE") -> Path: - """Get the benchmarks directory.""" - return get_appropriate_sage_dir(project_name) / "benchmarks" - - -def get_ray_temp_dir(project_name: str = "SAGE") -> Path: - """Get Ray temporary files directory.""" - ray_dir = get_temp_dir(project_name) / "ray" - ray_dir.mkdir(parents=True, exist_ok=True) - return ray_dir - - -def setup_sage_environment(project_name: str = "SAGE") -> dict: - """ - Set up SAGE environment variables and directories. - - Returns: - dict: Dictionary with created directory paths - """ - sage_dir = get_appropriate_sage_dir(project_name) - - # Set environment variables for other tools - os.environ["SAGE_HOME"] = str(sage_dir) - os.environ["SAGE_LOGS_DIR"] = str(get_logs_dir(project_name)) - os.environ["SAGE_TEMP_DIR"] = str(get_temp_dir(project_name)) - - # Ray-specific environment - ray_temp_dir = get_ray_temp_dir(project_name) - os.environ["RAY_TMPDIR"] = str(ray_temp_dir) - - return { - "sage_dir": sage_dir, - "logs_dir": get_logs_dir(project_name), - "reports_dir": get_reports_dir(project_name), - "cache_dir": get_cache_dir(project_name), - "temp_dir": get_temp_dir(project_name), - "coverage_dir": get_coverage_dir(project_name), - "benchmarks_dir": get_benchmarks_dir(project_name), - "ray_temp_dir": ray_temp_dir, - } - - -# Deprecated functions - kept for backward compatibility -def get_project_sage_dir(project_name: str = "SAGE") -> Path: - """Deprecated: Use get_appropriate_sage_dir() instead.""" - import warnings - warnings.warn( - "get_project_sage_dir() is deprecated. Use get_appropriate_sage_dir() instead.", - DeprecationWarning, - stacklevel=2 - ) - return get_appropriate_sage_dir(project_name) - - -def _get_sage_dir_for_project(project_name: str = "SAGE") -> Path: - """Deprecated: Use get_appropriate_sage_dir() instead.""" - import warnings - warnings.warn( - "_get_sage_dir_for_project() is deprecated. Use get_appropriate_sage_dir() instead.", - DeprecationWarning, - stacklevel=2 - ) - return get_appropriate_sage_dir(project_name) diff --git a/packages/sage-tools/src/sage/tools/templates/PACKAGE_README_TEMPLATE.md b/packages/sage-tools/src/sage/tools/templates/PACKAGE_README_TEMPLATE.md deleted file mode 100644 index cefad091ff..0000000000 --- a/packages/sage-tools/src/sage/tools/templates/PACKAGE_README_TEMPLATE.md +++ /dev/null @@ -1,149 +0,0 @@ -# {PACKAGE_NAME} - -> {BRIEF_DESCRIPTION} - -[![Python Version](https://img.shields.io/badge/python-3.9%2B-blue.svg)](https://www.python.org/downloads/) -[![License](https://img.shields.io/badge/license-MIT-green.svg)](../../LICENSE) - -## 📋 Overview - -{DETAILED_OVERVIEW} - -## ✨ Key Features - -- **Feature 1**: Description -- **Feature 2**: Description -- **Feature 3**: Description - -## 📦 Package Structure - -``` -{PACKAGE_NAME}/ -├── src/ -│ └── sage/ -│ └── {module_name}/ -│ ├── __init__.py -│ ├── core/ # Core functionality -│ ├── utils/ # Utility functions -│ └── ... -├── tests/ -│ ├── unit/ -│ └── integration/ -├── docs/ # Package-specific documentation -├── examples/ # Usage examples (optional) -├── README.md -├── pyproject.toml -└── setup.py -``` - -## 🚀 Installation - -### Basic Installation - -```bash -pip install {package_name} -``` - -### Development Installation - -```bash -cd packages/{package_name} -pip install -e . -``` - -### With Optional Dependencies - -```bash -# Install with extra features -pip install {package_name}[extra1,extra2] -``` - -## 📖 Quick Start - -### Basic Usage - -```python -from sage.{module_name} import SomeClass - -# Example usage -obj = SomeClass() -result = obj.do_something() -``` - -### Advanced Example - -```python -# More complex usage example -from sage.{module_name} import AdvancedFeature - -# Configuration -config = { - "option1": "value1", - "option2": "value2" -} - -# Initialize and use -feature = AdvancedFeature(config) -result = feature.process() -``` - -## 🔧 Configuration - -Configuration can be provided through: - -- Environment variables -- Configuration files (YAML/TOML) -- Direct API parameters - -### Example Configuration - -```yaml -# config.yaml -{module_name}: - setting1: value1 - setting2: value2 -``` - -## 📚 Documentation - -- **User Guide**: See [docs-public](%7BDOC_LINK%7D) -- **API Reference**: See [API docs](%7BAPI_DOC_LINK%7D) -- **Examples**: See [examples/](%7BEXAMPLES_LINK%7D) - -## 🧪 Testing - -```bash -# Run unit tests -pytest tests/unit - -# Run integration tests -pytest tests/integration - -# Run all tests with coverage -pytest --cov=sage.{module_name} --cov-report=html -``` - -## 🤝 Contributing - -Contributions are welcome! Please see [CONTRIBUTING.md](../../CONTRIBUTING.md) for guidelines. - -## 📄 License - -This project is licensed under the MIT License - see the [LICENSE](../../LICENSE) file for details. - -## 🔗 Related Packages - -- **sage-kernel**: Core computation engine -- **sage-common**: Common utilities -- **sage-libs**: Library of reusable components -- _(Add other related packages)_ - -## 📮 Support - -- **Documentation**: https://intellistream.github.io/SAGE-Pub/ -- **Issues**: https://github.com/intellistream/SAGE/issues -- **Discussions**: https://github.com/intellistream/SAGE/discussions - -______________________________________________________________________ - -**Part of the SAGE Framework** | [Main Repository](https://github.com/intellistream/SAGE) diff --git a/packages/sage-tools/src/sage/tools/web_ui/app.py b/packages/sage-tools/src/sage/tools/web_ui/app.py deleted file mode 100644 index 762da43d3e..0000000000 --- a/packages/sage-tools/src/sage/tools/web_ui/app.py +++ /dev/null @@ -1,380 +0,0 @@ -""" -SAGE Frontend FastAPI Application - -This module provides the main FastAPI application for the SAGE Web UI. -""" - -import json -import os -from pathlib import Path - -import uvicorn -from fastapi import FastAPI, HTTPException -from fastapi.responses import HTMLResponse -from pydantic import BaseModel - -from sage.common.config.ports import SagePorts - - -def _load_version(): - """加载版本信息""" - try: - # 尝试从本地包的版本文件加载 - from sage.common import __version__ - - return __version__ - except ImportError: - # 如果本地版本文件不存在，返回默认值 - return "0.1.3" - - -def _get_sage_dir(): - """获取 SAGE 目录路径""" - # 首先检查环境变量 - env_dir = os.environ.get("SAGE_OUTPUT_DIR") - if env_dir: - sage_dir = Path(env_dir) - else: - # 检查是否在开发环境中 - current_dir = Path.cwd() - if (current_dir / "packages" / "sage-common").exists(): - sage_dir = current_dir / ".sage" - else: - sage_dir = Path.home() / ".sage" - - sage_dir.mkdir(parents=True, exist_ok=True) - return sage_dir - - -# Pydantic 模型定义 -class Job(BaseModel): - jobId: str - name: str - isRunning: bool - nthreads: str - cpu: str - ram: str - startTime: str - duration: str - nevents: int - minProcessTime: int - maxProcessTime: int - meanProcessTime: int - latency: int - throughput: int - ncore: int - periodicalThroughput: list[int] - periodicalLatency: list[int] - totalTimeBreakdown: dict - schedulerTimeBreakdown: dict - operators: list[dict] - - -class OperatorInfo(BaseModel): - id: int - name: str - description: str - code: str - isCustom: bool - - -# 创建 FastAPI 应用 -app = FastAPI( - title="SAGE Web UI", - description="SAGE Framework Web 管理界面，提供 API 文档、系统监控和基础管理功能", - version=_load_version(), - docs_url="/docs", - redoc_url="/redoc", -) - - -def _read_sage_data_from_files(): - """从 .sage 目录的文件中读取实际的 SAGE 数据""" - sage_dir = _get_sage_dir() - data = {"jobs": [], "operators": [], "pipelines": []} - - try: - # 读取作业信息 - states_dir = sage_dir / "states" - if states_dir.exists(): - for job_file in states_dir.glob("*.json"): - try: - with open(job_file) as f: - job_data = json.load(f) - data["jobs"].append(job_data) - except Exception as e: - print(f"Error reading job file {job_file}: {e}") - - # 读取操作符信息 - operators_file = sage_dir / "output" / "operators.json" - if operators_file.exists(): - try: - with open(operators_file) as f: - operators_data = json.load(f) - data["operators"] = operators_data - except Exception as e: - print(f"Error reading operators file: {e}") - - # 读取管道信息 - pipelines_file = sage_dir / "output" / "pipelines.json" - if pipelines_file.exists(): - try: - with open(pipelines_file) as f: - pipelines_data = json.load(f) - data["pipelines"] = pipelines_data - except Exception as e: - print(f"Error reading pipelines file: {e}") - - except Exception as e: - print(f"Error reading SAGE data: {e}") - - return data - - -# 创建 FastAPI 应用 -app = FastAPI( - title="SAGE Web UI", - description="SAGE Framework Web 管理界面，提供 API 文档、系统监控和基础管理功能", - version=_load_version(), - docs_url="/docs", - redoc_url="/redoc", -) - - -@app.get("/", response_class=HTMLResponse) -async def root(): - """根路径，返回欢迎页面""" - return """ - - - - SAGE Web UI - - - -
-

🌟 欢迎使用 SAGE Web UI

-

SAGE (Streaming-Augmented Generative Execution) Framework Web 管理界面

-

提供 API 文档、系统监控和基础管理功能

-
- 📚 API 文档 - 📖 ReDoc - 🏥 健康检查 - ℹ️ API 信息 -
-
- - - """ - - -@app.get("/health") -async def health_check(): - """健康检查端点""" - return { - "status": "healthy", - "service": "SAGE Web UI", - "version": _load_version(), - "timestamp": "2025-09-01", - } - - -@app.get("/api/info") -async def api_info(): - """API 信息端点""" - return { - "name": "SAGE Web UI API", - "version": _load_version(), - "description": "SAGE Framework Web 管理界面 API", - "author": "IntelliStream Team", - "repository": "https://github.com/intellistream/SAGE", - } - - -@app.get("/api/jobs/all", response_model=list[Job]) -async def get_all_jobs(): - """获取所有作业信息""" - try: - sage_data = _read_sage_data_from_files() - jobs = sage_data.get("jobs", []) - - # 如果没有实际数据，返回一些示例数据（用于开发） - if not jobs: - jobs = [ - { - "jobId": "job_001", - "name": "RAG问答管道", - "isRunning": True, - "nthreads": "4", - "cpu": "80%", - "ram": "2GB", - "startTime": "2025-08-18 10:30:00", - "duration": "00:45:12", - "nevents": 1000, - "minProcessTime": 10, - "maxProcessTime": 500, - "meanProcessTime": 150, - "latency": 200, - "throughput": 800, - "ncore": 4, - "periodicalThroughput": [750, 800, 820, 785, 810], - "periodicalLatency": [180, 200, 190, 210, 195], - "totalTimeBreakdown": { - "totalTime": 2712000, - "serializeTime": 50000, - "persistTime": 100000, - "streamProcessTime": 2500000, - "overheadTime": 62000, - }, - "schedulerTimeBreakdown": { - "overheadTime": 50000, - "streamTime": 2600000, - "totalTime": 2712000, - "txnTime": 62000, - }, - "operators": [ - { - "id": 1, - "name": "FileSource", - "numOfInstances": 1, - "throughput": 800, - "latency": 50, - "explorationStrategy": "greedy", - "schedulingGranularity": "batch", - "abortHandling": "rollback", - "numOfTD": 10, - "numOfLD": 5, - "numOfPD": 2, - "lastBatch": 999, - "downstream": [2], - } - ], - } - ] - - return jobs - except Exception as e: - raise HTTPException(status_code=500, detail=f"获取作业信息失败: {str(e)}") - - -@app.get("/api/operators", response_model=list[OperatorInfo]) -async def get_operators(): - """获取所有操作符信息""" - try: - sage_data = _read_sage_data_from_files() - operators = sage_data.get("operators", []) - - # 如果没有实际数据，返回一些示例数据 - if not operators: - operators = [ - { - "id": 1, - "name": "FileSource", - "description": "从文件读取数据的源操作符", - "code": "class FileSource:\n def __init__(self, file_path):\n self.file_path = file_path\n \n def read_data(self):\n with open(self.file_path, 'r') as f:\n return f.read()", - "isCustom": True, - }, - { - "id": 2, - "name": "SimpleRetriever", - "description": "简单的检索操作符", - "code": "class SimpleRetriever:\n def __init__(self, top_k=5):\n self.top_k = top_k\n \n def retrieve(self, query):\n return query[:self.top_k]", - "isCustom": True, - }, - ] - - return operators - except Exception as e: - raise HTTPException(status_code=500, detail=f"获取操作符信息失败: {str(e)}") - - -@app.get("/api/pipelines") -async def get_pipelines(): - """获取所有管道信息""" - try: - sage_data = _read_sage_data_from_files() - pipelines = sage_data.get("pipelines", []) - - # 如果没有实际数据，返回一些示例数据 - if not pipelines: - pipelines = [ - { - "id": "pipeline_001", - "name": "示例RAG管道", - "description": "演示RAG问答系统的数据处理管道", - "status": "running", - "operators": [ - { - "id": "source1", - "type": "FileSource", - "config": {"file_path": "/data/documents.txt"}, - }, - { - "id": "retriever1", - "type": "SimpleRetriever", - "config": {"top_k": 5}, - }, - { - "id": "sink1", - "type": "TerminalSink", - "config": {"format": "json"}, - }, - ], - "connections": [ - {"from": "source1", "to": "retriever1"}, - {"from": "retriever1", "to": "sink1"}, - ], - } - ] - - return {"pipelines": pipelines} - except Exception as e: - raise HTTPException(status_code=500, detail=f"获取管道信息失败: {str(e)}") - - -def start_server(host: str = "127.0.0.1", port: int | None = None, reload: bool = False): - """启动服务器""" - if port is None: - port = SagePorts.GATEWAY_DEFAULT - uvicorn.run( - "sage.tools.web_ui.app:app", - host=host, - port=port, - reload=reload, - ) - - -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser(description="SAGE Web UI Server") - parser.add_argument("--host", default="127.0.0.1", help="Host to bind") - parser.add_argument( - "--port", - type=int, - default=SagePorts.GATEWAY_DEFAULT, - help="Port to bind", - ) - parser.add_argument("--reload", action="store_true", help="Enable auto-reload") - args = parser.parse_args() - - uvicorn.run( - "sage.tools.web_ui.app:app", - host=args.host, - port=args.port, - reload=args.reload, - ) diff --git a/packages/sage-tools/src/sage/tools/web_ui/package-lock.json b/packages/sage-tools/src/sage/tools/web_ui/package-lock.json deleted file mode 100644 index b58418f6cc..0000000000 --- a/packages/sage-tools/src/sage/tools/web_ui/package-lock.json +++ /dev/null @@ -1,328 +0,0 @@ -{ - "name": "SAGE", - "lockfileVersion": 3, - "requires": true, - "packages": { - "": { - "dependencies": { - "dagre-compound": "^0.0.13", - "ng-zorro-antd": "^20.3.1" - } - }, - "node_modules/@angular/animations": { - "version": "20.3.1", - "resolved": "https://registry.npmjs.org/@angular/animations/-/animations-20.3.1.tgz", - 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-Tests for sage-dev CLI commands: -- docs: Documentation management (build, serve, check) -""" diff --git a/packages/sage-tools/tests/cli/commands/dev/test_docs.py b/packages/sage-tools/tests/cli/commands/dev/test_docs.py deleted file mode 100644 index 69f472f614..0000000000 --- a/packages/sage-tools/tests/cli/commands/dev/test_docs.py +++ /dev/null @@ -1,116 +0,0 @@ -""" -测试 docs 命令 -""" - -import os -from pathlib import Path -from unittest.mock import MagicMock, patch - -import pytest -from typer.testing import CliRunner - -from sage.tools.cli.commands.dev.docs import app - - -@pytest.mark.cli -class TestDocsCommands: - """测试 docs 命令组""" - - def setup_method(self): - """设置测试""" - self.runner = CliRunner() - - @patch("subprocess.run") - @patch.object(Path, "cwd") - def test_build_command_success(self, mock_cwd, mock_run): - """测试 build 命令成功""" - # Mock当前目录 - mock_cwd.return_value = Path("/fake/project") - mock_run.return_value = MagicMock(returncode=0, stderr="", stdout="Building...") - - result = self.runner.invoke(app, ["build"]) - - # 不应该崩溃（即使docs-public不存在） - assert isinstance(result.exit_code, int) - # 验证输出包含相关信息（可能是中文或英文） - assert len(result.stdout) > 0 - - @patch("subprocess.run") - def test_serve_command(self, mock_run): - """测试 serve 命令""" - mock_run.return_value = MagicMock(returncode=0) - - result = self.runner.invoke(app, ["serve"]) - - # 命令应该尝试运行 - assert isinstance(result.exit_code, int) - - @patch("subprocess.run") - def test_serve_with_custom_port(self, mock_run): - """测试自定义端口""" - mock_run.return_value = MagicMock(returncode=0) - - result = self.runner.invoke(app, ["serve", "--port", "9000"]) - - assert isinstance(result.exit_code, int) - - def test_check_command_basic(self): - """测试 check 命令基本功能""" - result = self.runner.invoke(app, ["check"]) - - # 应该能运行（即使没有docs-public目录） - assert isinstance(result.exit_code, int) - # 输出应该包含相关信息 - assert len(result.stdout) > 0 - - -@pytest.mark.cli -class TestDocsCheckWithRealFiles: - """使用真实文件测试 check 命令""" - - def setup_method(self): - self.runner = CliRunner() - self.original_cwd = Path.cwd() - - def test_check_in_sage_project(self): - """在SAGE项目中测试check命令""" - # 切换到SAGE根目录（如果存在） - sage_root = Path(__file__).parents[5] # 测试文件 -> ... -> SAGE根目录 - - if (sage_root / "docs-public").exists(): - os.chdir(sage_root) - try: - result = self.runner.invoke(app, ["check"]) - assert result.exit_code == 0 - # 应该找到一些文件 - assert "file" in result.stdout.lower() or "found" in result.stdout.lower() - finally: - os.chdir(self.original_cwd) - else: - pytest.skip("docs-public directory not found") - - -@pytest.mark.cli -class TestDocsEdgeCases: - """测试边缘情况""" - - def setup_method(self): - self.runner = CliRunner() - - @patch("subprocess.run") - def test_mkdocs_not_installed(self, mock_run): - """测试 mkdocs 未安装""" - mock_run.side_effect = FileNotFoundError("mkdocs not found") - - result = self.runner.invoke(app, ["build"]) - - # 应该处理错误 - assert isinstance(result.exit_code, int) - - def test_help_commands(self): - """测试help命令""" - result = self.runner.invoke(app, ["--help"]) - assert result.exit_code == 0 - assert "build" in result.stdout - assert "serve" in result.stdout - assert "check" in result.stdout diff --git a/packages/sage-tools/tests/cli/test_chat_pipeline.py b/packages/sage-tools/tests/cli/test_chat_pipeline.py deleted file mode 100644 index 5f3bf4b669..0000000000 --- a/packages/sage-tools/tests/cli/test_chat_pipeline.py +++ /dev/null @@ -1,231 +0,0 @@ -import itertools -from pathlib import Path -from typing import Any - -import pytest - -from sage.cli.commands.apps import chat as chat_module -from sage.cli.commands.apps import pipeline as pipeline_builder - - -def test_looks_like_pipeline_request_detection(): - assert chat_module._looks_like_pipeline_request("请帮我构建一个大模型应用") - assert chat_module._looks_like_pipeline_request("build an LLM pipeline for retrieval") - assert not chat_module._looks_like_pipeline_request("SAGE 是什么？") - - -@pytest.fixture() -def fake_generator(monkeypatch): - plan = { - "pipeline": { - "name": "demo", - "description": "test", - "version": "1.0.0", - "type": "local", - }, - "source": { - "class": "sage.benchmark.benchmark_rag.implementations.rag_simple.SimpleQuestionSource", - "params": {"questions": ["hi"]}, - }, - "stages": [ - { - "id": "generator", - "kind": "map", - "class": "sage.benchmark.benchmark_rag.implementations.rag_simple.SimplePromptor", - "params": {}, - } - ], - "sink": { - "class": "sage.benchmark.benchmark_rag.implementations.rag_simple.SimpleTerminalSink", - "params": {}, - }, - "services": [], - "monitors": [], - } - - instances = [] - - class DummyGenerator: - def __init__(self, config): - self.config = config - self.calls: list[tuple[dict[str, Any], dict[str, Any] | None, str | None]] = [] - - def generate( - self, - requirements: dict[str, Any], - previous_plan: dict[str, Any] | None = None, - feedback: str | None = None, - ) -> dict[str, Any]: - self.calls.append((requirements, previous_plan, feedback)) - return plan - - def factory(config): - instance = DummyGenerator(config) - instances.append(instance) - return instance - - monkeypatch.setattr(pipeline_builder, "PipelinePlanGenerator", factory) - monkeypatch.setattr(pipeline_builder, "render_pipeline_plan", lambda plan: None) - monkeypatch.setattr(pipeline_builder, "preview_pipeline_plan", lambda plan: None) - - executed: dict[str, Any] = {} - - def fake_execute(plan_obj, autostop=True, host=None, port=None, console_override=None): - executed.update( - { - "plan": plan_obj, - "autostop": autostop, - "host": host, - "port": port, - } - ) - return "job-1234" - - saves = [] - - def fake_save(plan_obj, output, overwrite): - target = output or Path("demo.yaml") - target.parent.mkdir(parents=True, exist_ok=True) - target.write_text("pipeline: demo\n", encoding="utf-8") - saves.append((plan_obj, target, overwrite)) - return target - - monkeypatch.setattr(pipeline_builder, "execute_pipeline_plan", fake_execute) - monkeypatch.setattr(pipeline_builder, "save_pipeline_plan", fake_save) - - monkeypatch.setattr(chat_module, "load_domain_contexts", lambda limit=4: ("默认上下文",)) - monkeypatch.setattr(chat_module, "get_default_knowledge_base", lambda: None) - - return { - "plan": plan, - "instances": instances, - "saves": saves, - "executed": executed, - } - - -def test_pipeline_chat_coordinator_handles_flow(monkeypatch, tmp_path, fake_generator): - prompts = itertools.chain( - [ - # 场景模板相关 - # "qa", # 模板选择（由 confirm 返回 False 跳过） - # 需求收集 - "DemoPipeline", # 名称 - "构建一个问答应用", # 目标 - "文档知识库", # 数据来源 - "实时", # 延迟 - "", # 约束 - # 保存路径 - str(tmp_path / "demo.yaml"), - ] - ) - prompt_iter = iter(prompts) - - def fake_prompt(message, default=None, **kwargs): - try: - return next(prompt_iter) - except StopIteration: - return default or "" - - # 调整 confirm 顺序：不使用模板、配置满意、保存文件、立即运行、autostop - confirms = iter([False, True, True, True]) - - def fake_confirm(message, default=False, **kwargs): - try: - return next(confirms) - except StopIteration: - return default - - monkeypatch.setattr(chat_module.typer, "prompt", fake_prompt) - monkeypatch.setattr(chat_module.typer, "confirm", fake_confirm) - - coordinator = chat_module.PipelineChatCoordinator("mock", "mock-model", None, None) - handled = coordinator.handle("请帮我构建一个大模型应用，包含知识检索") - - assert handled is True - assert fake_generator["instances"], "Generator should be instantiated" - instance = fake_generator["instances"][0] - assert instance.calls, "generate should be invoked" - requirements = instance.calls[0][0] - assert requirements["data_sources"] == ["文档知识库"] - assert fake_generator["saves"], "plan should be saved" - saved_path = fake_generator["saves"][0][1] - assert saved_path.exists() - assert fake_generator["executed"].get("plan") == fake_generator["plan"] - assert fake_generator["executed"].get("autostop") is True - - -def test_scenario_templates(): - """测试场景模板功能""" - # 测试获取模板 - qa_template = chat_module._get_scenario_template("qa") - assert qa_template is not None - assert qa_template["name"] == "问答助手" - assert "data_sources" in qa_template - - # 测试不存在的模板 - invalid = chat_module._get_scenario_template("nonexistent") - assert invalid is None - - -def test_validate_pipeline_config(): - """测试配置验证功能""" - # 有效配置 - valid_plan = { - "pipeline": { - "name": "test", - "type": "local", - }, - "source": { - "class": "test.Source", - "params": {}, - }, - "sink": { - "class": "test.Sink", - "params": {}, - }, - "stages": [ - { - "id": "stage1", - "kind": "map", - "class": "test.Stage", - } - ], - } - is_valid, errors = chat_module._validate_pipeline_config(valid_plan) - assert is_valid - assert len(errors) == 0 - - # 无效配置 - 缺少必需字段 - invalid_plan = { - "pipeline": {"name": "test"}, # 缺少 type - "source": {}, # 缺少 class - } - is_valid, errors = chat_module._validate_pipeline_config(invalid_plan) - assert not is_valid - assert len(errors) > 0 - assert any("type" in err for err in errors) - assert any("class" in err for err in errors) - - -def test_normalize_list_field(): - """测试列表字段规范化""" - # 逗号分隔 - result = chat_module._normalize_list_field("a,b,c") - assert result == ["a", "b", "c"] - - # 中文逗号 - result = chat_module._normalize_list_field("文档，数据库，API") - assert result == ["文档", "数据库", "API"] - - # 混合分隔符 - result = chat_module._normalize_list_field("a,b;c/d") - assert result == ["a", "b", "c", "d"] - - # 空字符串 - result = chat_module._normalize_list_field("") - assert result == [] - - # 带空格 - result = chat_module._normalize_list_field(" a , b ") - assert result == ["a", "b"] diff --git a/packages/sage-tools/tests/cli/test_pipeline_builder.py b/packages/sage-tools/tests/cli/test_pipeline_builder.py deleted file mode 100644 index 3b1c792d87..0000000000 --- a/packages/sage-tools/tests/cli/test_pipeline_builder.py +++ /dev/null @@ -1,268 +0,0 @@ -import sys -import zipfile -from pathlib import Path - -import yaml -from typer.testing import CliRunner - -from sage.cli.commands.apps.pipeline_domain import load_domain_contexts -from sage.cli.commands.apps.pipeline_knowledge import ( - PipelineKnowledgeBase, - build_query_payload, -) -from sage.cli.main import app - -runner = CliRunner() - - -def test_pipeline_builder_mock_non_interactive(tmp_path): - output_path = tmp_path / "demo.yaml" - - result = runner.invoke( - app, - [ - "pipeline", - "build", - "--backend", - "mock", - "--no-knowledge", - "--name", - "QA Helper", - "--goal", - "构建一个问答流程", - "--non-interactive", - "--output", - str(output_path), - ], - catch_exceptions=False, - ) - - assert result.exit_code == 0, result.output - assert output_path.exists() - - data = yaml.safe_load(output_path.read_text(encoding="utf-8")) - assert data["pipeline"]["name"] == "qa-helper" - assert data["stages"], "stages should not be empty" - classes = [stage["class"] for stage in data["stages"]] - assert "sage.benchmark.benchmark_rag.implementations.rag_simple.SimpleGenerator" in classes - assert ( - data["source"]["class"] - == "sage.benchmark.benchmark_rag.implementations.rag_simple.SimpleQuestionSource" - ) - assert ( - data["sink"]["class"] - == "sage.benchmark.benchmark_rag.implementations.rag_simple.SimpleTerminalSink" - ) - - -def test_pipeline_builder_missing_fields_non_interactive(tmp_path): - result = runner.invoke( - app, - [ - "pipeline", - "build", - "--backend", - "mock", - "--no-knowledge", - "--non-interactive", - "--output", - str(tmp_path / "config.yaml"), - ], - ) - - assert result.exit_code != 0 - assert result.exception is not None - assert "必须提供" in str(result.exception) - - -def test_load_domain_contexts_provides_examples(): - contexts = load_domain_contexts(limit=2) - assert contexts, "context loader should yield at least one snippet" - joined = "\n".join(contexts) - assert "Pipeline" in joined or "SAGE" in joined - - -def test_pipeline_knowledge_base_retrieval(tmp_path): - docs_dir = tmp_path / "docs-public" / "docs_src" - docs_dir.mkdir(parents=True) - (docs_dir / "builder.md").write_text( - "SAGE Pipeline Builder 支持多阶段配置", - encoding="utf-8", - ) - - examples_dir = tmp_path / "examples" / "config" - examples_dir.mkdir(parents=True) - (examples_dir / "demo.yaml").write_text( - """ -pipeline: - name: demo - description: test -stages: - - id: retriever - class: sage.libs.rag.retriever.SimpleRetriever - summary: demo retriever -sink: - class: sage.libs.io.TerminalSink -""".strip(), - encoding="utf-8", - ) - - libs_dir = tmp_path / "packages" / "sage-libs" / "src" / "sage" / "libs" - libs_dir.mkdir(parents=True, exist_ok=True) - (libs_dir / "demo.py").write_text( - '"""Simple pipeline components."""\nclass DemoStage:\n """Demo stage for tests."""', - encoding="utf-8", - ) - - kb = PipelineKnowledgeBase(project_root=tmp_path, max_chunks=200) - results = kb.search("retriever component", top_k=3) - assert results, "knowledge base should return results" - assert any("retriever" in item.text for item in results) - - -def test_pipeline_knowledge_base_remote_download(tmp_path, monkeypatch): - # Produce a fake remote docs archive - docs_src = tmp_path / "remote" / "docs_src" - docs_src.mkdir(parents=True) - (docs_src / "guide.md").write_text("# 指南\nPipeline Builder 远程文档", encoding="utf-8") - - zip_path = tmp_path / "docs.zip" - with zipfile.ZipFile(zip_path, "w") as zf: - for file_path in docs_src.rglob("*"): - arcname = file_path.relative_to(tmp_path / "remote") - zf.write(file_path, arcname=str(arcname)) - - monkeypatch.setenv("SAGE_PIPELINE_DOCS_URL", zip_path.as_uri()) - monkeypatch.setenv("SAGE_PIPELINE_DOWNLOAD_DOCS", "1") - monkeypatch.setenv("SAGE_OUTPUT_DIR", str(tmp_path / ".sage")) - - from sage.common.config.output_paths import get_sage_paths - - get_sage_paths.cache_clear() - - kb = PipelineKnowledgeBase(project_root=Path("/nonexistent"), allow_download=True) - results = kb.search("远程文档", top_k=2) - assert any("远程文档" in item.text for item in results) - - -def test_build_query_payload_includes_feedback(): - payload = build_query_payload( - {"goal": "test", "name": "demo"}, - previous_plan={"stages": [{"id": "retriever", "class": "Demo"}]}, - feedback="需要加速", - ) - assert "retriever" in payload - assert "需要加速" in payload - - -def test_pipeline_run_success(tmp_path, monkeypatch): - module_dir = tmp_path / "demo_components" - module_dir.mkdir() - (module_dir / "__init__.py").write_text("", encoding="utf-8") - component_file = module_dir / "ops.py" - component_file.write_text( - """ -from sage.common.core.functions.batch_function import BatchFunction -from sage.common.core.functions.map_function import MapFunction -from sage.common.core.functions.sink_function import SinkFunction - - -class DemoBatch(BatchFunction): - def __init__(self, **kwargs): - super().__init__(**kwargs) - self._emitted = False - - def execute(self): - if self._emitted: - return None - self._emitted = True - return "hello" - - -class UpperCase(MapFunction): - def execute(self, value): - return str(value).upper() - - -class CollectSink(SinkFunction): - collected = [] - - def execute(self, value): - self.collected.append(value) - return value - """, - encoding="utf-8", - ) - - sys.path.insert(0, str(tmp_path)) - - config_path = tmp_path / "pipeline.yaml" - config_path.write_text( - """ -pipeline: - name: demo-pipeline - type: local -source: - id: demo-source - kind: batch - class: demo_components.ops.DemoBatch - params: {} -stages: - - id: uppercase - kind: map - class: demo_components.ops.UpperCase - params: {} -sink: - id: collector - kind: sink - class: demo_components.ops.CollectSink - params: {} -services: [] -""", - encoding="utf-8", - ) - - submitted: dict[str, object] = {} - - def fake_submit(self, autostop: bool = False): - submitted["autostop"] = autostop - submitted["functions"] = [ - getattr(transformation, "function_class", None) for transformation in self.pipeline - ] - return "uuid-demo" - - monkeypatch.setattr( - "sage.kernel.api.local_environment.LocalEnvironment.submit", - fake_submit, - ) - monkeypatch.setattr( - "sage.kernel.api.local_environment.LocalEnvironment._wait_for_completion", - lambda self: None, - ) - - try: - result = runner.invoke( - app, - ["pipeline", "run", str(config_path)], - catch_exceptions=False, - ) - - assert result.exit_code == 0, result.stdout - assert submitted["autostop"] is True - function_names = [fn.__name__ for fn in submitted["functions"] if fn] - assert "DemoBatch" in function_names - assert "UpperCase" in function_names - assert "CollectSink" in function_names - finally: - sys.path.remove(str(tmp_path)) - - -def test_pipeline_run_missing_file(): - result = runner.invoke( - app, - ["pipeline", "run", "/nonexistent/pipeline.yaml"], - catch_exceptions=False, - ) - - assert result.exit_code != 0 - assert "❌" in result.stdout diff --git a/packages/sage-tools/tests/conftest.py b/packages/sage-tools/tests/conftest.py deleted file mode 100644 index 356fadb6c1..0000000000 --- a/packages/sage-tools/tests/conftest.py +++ /dev/null @@ -1,53 +0,0 @@ -""" -pytest配置文件 -""" - -import sys -from pathlib import Path - -import pytest - -from sage.common.config import find_sage_project_root - -# 添加项目根目录到Python路径 -project_root = find_sage_project_root() -sys.path.insert(0, str(project_root)) - - -@pytest.fixture -def project_root_path(): - """项目根目录路径""" - return project_root - - -@pytest.fixture -def sage_tools_path(): - """sage-tools包路径""" - return Path(__file__).parent.parent - - -@pytest.fixture -def test_data_dir(): - """测试数据目录""" - return Path(__file__).parent / "data" - - -# 配置pytest标记 -def pytest_configure(config): - """配置pytest""" - config.addinivalue_line("markers", "unit: 单元测试") - config.addinivalue_line("markers", "integration: 集成测试") - config.addinivalue_line("markers", "cli: CLI测试") - config.addinivalue_line("markers", "slow: 慢速测试") - config.addinivalue_line("markers", "quick: 快速测试") - - -def pytest_collection_modifyitems(config, items): - """修改测试收集项""" - # 为CLI相关的测试文件自动添加cli标记 - for item in items: - if "cli" in str(item.fspath): - item.add_marker(pytest.mark.cli) - if "integration" in str(item.fspath): - item.add_marker(pytest.mark.integration) - item.add_marker(pytest.mark.slow) diff --git a/packages/sage-tools/tests/dev/__init__.py b/packages/sage-tools/tests/dev/__init__.py deleted file mode 100644 index 955a103e37..0000000000 --- a/packages/sage-tools/tests/dev/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Dev tools tests.""" diff --git a/packages/sage-tools/tests/dev/maintenance/__init__.py b/packages/sage-tools/tests/dev/maintenance/__init__.py deleted file mode 100644 index 2be7bcab01..0000000000 --- a/packages/sage-tools/tests/dev/maintenance/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -""" -Maintenance module tests - -Tests for sage.tools.dev.maintenance modules: -- DevNotesOrganizer: Dev-notes organization and analysis -- MetadataFixer: Automated metadata fixing for documentation -- RuffIgnoreUpdater: Ruff ignore rules management -""" diff --git a/packages/sage-tools/tests/dev/maintenance/test_devnotes_organizer.py b/packages/sage-tools/tests/dev/maintenance/test_devnotes_organizer.py deleted file mode 100644 index ff7c790cd8..0000000000 --- a/packages/sage-tools/tests/dev/maintenance/test_devnotes_organizer.py +++ /dev/null @@ -1,221 +0,0 @@ -""" -测试 DevNotesOrganizer -""" - -import tempfile -from pathlib import Path - -import pytest - -from sage.tools.dev.maintenance import DevNotesOrganizer - - -@pytest.fixture -def temp_project(): - """创建临时项目结构""" - with tempfile.TemporaryDirectory() as tmpdir: - root = Path(tmpdir) - devnotes = root / "docs" / "dev-notes" - devnotes.mkdir(parents=True) - - # 创建测试文件 - (devnotes / "test1.md").write_text( - """# Test 1 - -**Date**: 2024-01-01 -**Author**: Test -**Summary**: Test summary - -This is a test architecture document. -""" - ) - - (devnotes / "test2.md").write_text( - """# Test 2 - -No metadata here. -This is about migration and refactoring. -""" - ) - - # 创建分类目录 - (devnotes / "architecture").mkdir() - (devnotes / "architecture" / "design.md").write_text( - """# Design Doc - -**Date**: 2024-01-01 -**Author**: Test -**Summary**: Design - -Architecture design document. -""" - ) - - yield root - - -@pytest.mark.unit -class TestDevNotesOrganizer: - """测试 DevNotesOrganizer 类""" - - def test_init(self, temp_project): - """测试初始化""" - organizer = DevNotesOrganizer(temp_project) - assert organizer.root_dir == temp_project - assert organizer.devnotes_dir == temp_project / "docs" / "dev-notes" - - def test_check_metadata(self, temp_project): - """测试元数据检查""" - organizer = DevNotesOrganizer(temp_project) - - # 有完整元数据的文件 - content1 = """# Test -**Date**: 2024-01-01 -**Author**: Test -**Summary**: Summary -""" - has_date, has_author, has_summary = organizer._check_metadata(content1) - assert has_date - assert has_author - assert has_summary - - # 缺少元数据的文件 - content2 = """# Test -No metadata -""" - has_date, has_author, has_summary = organizer._check_metadata(content2) - assert not has_date - assert not has_author - assert not has_summary - - def test_suggest_category(self, temp_project): - """测试分类建议""" - organizer = DevNotesOrganizer(temp_project) - - # 架构相关 - assert ( - organizer._suggest_category( - "ARCHITECTURE_DESIGN.md", "This is about system architecture and design" - ) - == "architecture" - ) - - # 迁移相关 - assert ( - organizer._suggest_category("MIGRATION.md", "This is about migration and refactor") - == "migration" - ) - - # 测试相关 - assert organizer._suggest_category("TEST_GUIDE.md", "Testing guide") == "testing" - - def test_analyze_file(self, temp_project): - """测试文件分析""" - organizer = DevNotesOrganizer(temp_project) - - # 分析有元数据的文件 - file1 = temp_project / "docs" / "dev-notes" / "test1.md" - result1 = organizer.analyze_file(file1) - - assert result1["path"] == "test1.md" - assert result1["has_date"] - assert result1["has_author"] - assert result1["has_summary"] - assert result1["suggested_category"] == "architecture" - assert result1["current_category"] == "root" - assert not result1["is_empty"] - - # 分析缺少元数据的文件 - file2 = temp_project / "docs" / "dev-notes" / "test2.md" - result2 = organizer.analyze_file(file2) - - assert result2["path"] == "test2.md" - assert not result2["has_date"] - assert not result2["has_author"] - assert not result2["has_summary"] - assert result2["suggested_category"] == "migration" - - def test_analyze_all(self, temp_project): - """测试分析所有文件""" - organizer = DevNotesOrganizer(temp_project) - results = organizer.analyze_all() - - # 应该找到3个文件（test1.md, test2.md, architecture/design.md） - assert len(results) == 3 - - # 检查路径 - paths = {r["path"] for r in results} - assert "test1.md" in paths - assert "test2.md" in paths - assert "architecture/design.md" in paths - - def test_generate_report(self, temp_project): - """测试生成报告""" - organizer = DevNotesOrganizer(temp_project) - results = organizer.analyze_all() - - # 生成报告（不打印） - report = organizer.generate_report(results, verbose=False) - - assert "total" in report - assert "root_files" in report - assert "missing_metadata" in report - assert "empty_files" in report - - assert report["total"] == 3 - assert len(report["root_files"]) == 2 # test1.md, test2.md - - def test_empty_file_detection(self, temp_project): - """测试空文件检测""" - # 创建空文件 - empty_file = temp_project / "docs" / "dev-notes" / "empty.md" - empty_file.write_text("# Empty\n") - - organizer = DevNotesOrganizer(temp_project) - result = organizer.analyze_file(empty_file) - - assert result["is_empty"] # 少于100字节 - - -@pytest.mark.unit -class TestDevNotesOrganizerEdgeCases: - """测试边缘情况""" - - def test_nonexistent_directory(self): - """测试不存在的目录""" - organizer = DevNotesOrganizer(Path("/nonexistent")) - results = organizer.analyze_all() - # 应该返回空列表而不是报错 - assert results == [] - - def test_file_outside_devnotes(self, temp_project): - """测试dev-notes目录外的文件""" - organizer = DevNotesOrganizer(temp_project) - outside_file = temp_project / "docs" / "other.md" - outside_file.parent.mkdir(exist_ok=True) - outside_file.write_text("# Other") - - result = organizer.analyze_file(outside_file) - assert "error" in result - - def test_unicode_content(self, temp_project): - """测试Unicode内容""" - devnotes = temp_project / "docs" / "dev-notes" - unicode_file = devnotes / "unicode.md" - unicode_file.write_text( - """# 中文标题 - -**Date**: 2024-01-01 -**Author**: 测试作者 -**Summary**: 这是一个包含中文的测试文档 - -内容包含各种Unicode字符：🎉 ✅ 📝 -""" - ) - - organizer = DevNotesOrganizer(temp_project) - result = organizer.analyze_file(unicode_file) - - assert result["has_date"] - assert result["has_author"] - assert result["has_summary"] diff --git a/packages/sage-tools/tests/dev/maintenance/test_metadata_fixer.py b/packages/sage-tools/tests/dev/maintenance/test_metadata_fixer.py deleted file mode 100644 index 9328181af7..0000000000 --- a/packages/sage-tools/tests/dev/maintenance/test_metadata_fixer.py +++ /dev/null @@ -1,207 +0,0 @@ -""" -测试 MetadataFixer -""" - -import tempfile -from pathlib import Path - -import pytest - -from sage.tools.dev.maintenance import MetadataFixer - - -@pytest.fixture -def temp_project(): - """创建临时项目结构""" - with tempfile.TemporaryDirectory() as tmpdir: - root = Path(tmpdir) - devnotes = root / "docs" / "dev-notes" - devnotes.mkdir(parents=True) - - # 创建测试文件 - (devnotes / "complete.md").write_text( - """# Complete Document - -**Date**: 2024-01-01 -**Author**: Test Author -**Summary**: This document has complete metadata - -Content here. -""" - ) - - (devnotes / "missing_date.md").write_text( - """# Missing Date - -**Author**: Test Author -**Summary**: Missing date field - -Content here. -""" - ) - - (devnotes / "missing_all.md").write_text( - """# No Metadata - -Just content, no metadata at all. -""" - ) - - (devnotes / "partial.md").write_text( - """# Partial - -**Date**: 2024-01-01 - -Missing author and summary. -""" - ) - - yield root - - -@pytest.mark.unit -class TestMetadataFixer: - """测试 MetadataFixer 类""" - - def test_init(self, temp_project): - """测试初始化""" - fixer = MetadataFixer(temp_project) - assert fixer.root_dir == temp_project - - def test_fix_file_with_metadata(self, temp_project): - """测试为文件添加元数据""" - fixer = MetadataFixer(temp_project) - - # 创建测试文件 - file_path = "test_new.md" - full_path = temp_project / file_path - full_path.write_text("# Test\n\nContent") - - # 修复文件 - metadata = {"date": "2024-01-01", "summary": "Test summary"} - result = fixer.fix_file(file_path, metadata) - - assert result is True - - # 验证内容 - content = full_path.read_text() - assert "**Date**: 2024-01-01" in content - assert "**Author**: SAGE Team" in content - assert "**Summary**: Test summary" in content - - def test_fix_file_already_has_metadata(self, temp_project): - """测试已有元数据的文件""" - fixer = MetadataFixer(temp_project) - file_path = "docs/dev-notes/complete.md" - - metadata = {"date": "2024-01-01", "summary": "Test"} - result = fixer.fix_file(file_path, metadata) - - # 应该跳过（返回True但实际没修改） - assert result is True - - def test_fix_file_not_found(self, temp_project): - """测试文件不存在""" - fixer = MetadataFixer(temp_project) - - metadata = {"date": "2024-01-01", "summary": "Test"} - result = fixer.fix_file("nonexistent.md", metadata) - - assert result is False - - def test_fix_all_with_default_files(self, temp_project): - """测试批量修复（使用默认文件列表）""" - fixer = MetadataFixer(temp_project) - - # 使用自定义文件列表（避免使用硬编码的默认列表） - files_to_fix = { - "docs/dev-notes/missing_date.md": { - "date": "2024-01-01", - "summary": "Test document", - } - } - - stats = fixer.fix_all(files_to_fix) - - assert "success" in stats or "failed" in stats - assert isinstance(stats, dict) - - def test_scan_and_fix(self, temp_project): - """测试扫描并修复""" - fixer = MetadataFixer(temp_project) - devnotes_dir = temp_project / "docs" / "dev-notes" - - stats = fixer.scan_and_fix(devnotes_dir) - - assert "success" in stats - assert "failed" in stats - assert "skipped" in stats - - def test_fix_empty_file(self, temp_project): - """测试空文件""" - fixer = MetadataFixer(temp_project) - - # 创建空文件 - empty_file = temp_project / "empty.md" - empty_file.write_text("") - - metadata = {"date": "2024-01-01", "summary": "Test"} - result = fixer.fix_file("empty.md", metadata) - - # 实际实现会成功（添加元数据到空文件） - # 但会给出警告 - assert result is True or result is False # 接受任一结果 - - -@pytest.mark.unit -class TestMetadataFixerEdgeCases: - """测试边缘情况""" - - def test_scan_empty_devnotes_directory(self): - """测试扫描空的dev-notes目录""" - with tempfile.TemporaryDirectory() as tmpdir: - root = Path(tmpdir) - devnotes = root / "docs" / "dev-notes" - devnotes.mkdir(parents=True) - - fixer = MetadataFixer(root) - stats = fixer.scan_and_fix(devnotes) - - # 空目录应该返回统计信息 - assert "success" in stats - assert "skipped" in stats - - def test_fix_with_unicode(self, temp_project): - """测试包含Unicode的文件""" - devnotes = temp_project / "docs" / "dev-notes" - unicode_file = devnotes / "unicode.md" - unicode_file.write_text("# 中文标题\n\n内容") - - fixer = MetadataFixer(temp_project) - metadata = {"date": "2024-01-01", "summary": "中文摘要测试"} - - # 使用相对路径 - rel_path = unicode_file.relative_to(temp_project) - result = fixer.fix_file(str(rel_path), metadata) - - assert result is True - - # 验证内容 - content = unicode_file.read_text() - assert "**Date**: 2024-01-01" in content - assert "**Summary**: 中文摘要测试" in content - - def test_scan_finds_incomplete_files(self, temp_project): - """测试扫描找到缺失元数据的文件""" - # 创建一个缺失元数据的文件 - devnotes = temp_project / "docs" / "dev-notes" - incomplete = devnotes / "incomplete.md" - incomplete.write_text("# Incomplete\n\nNo metadata here") - - fixer = MetadataFixer(temp_project) - stats = fixer.scan_and_fix(devnotes) - - # 应该找到并尝试修复 - assert isinstance(stats, dict) - total_processed = stats.get("success", 0) + stats.get("failed", 0) + stats.get("skipped", 0) - assert total_processed > 0 diff --git a/packages/sage-tools/tests/dev/maintenance/test_ruff_updater.py b/packages/sage-tools/tests/dev/maintenance/test_ruff_updater.py deleted file mode 100644 index e6fc55f799..0000000000 --- a/packages/sage-tools/tests/dev/maintenance/test_ruff_updater.py +++ /dev/null @@ -1,218 +0,0 @@ -""" -测试 RuffIgnoreUpdater -""" - -import tempfile -from pathlib import Path - -import pytest - -from sage.tools.dev.maintenance import RuffIgnoreUpdater - - -@pytest.fixture -def temp_project(): - """创建临时项目结构""" - with tempfile.TemporaryDirectory() as tmpdir: - root = Path(tmpdir) - - # 创建pyproject.toml（基础版） - (root / "pyproject.toml").write_text( - """[project] -name = "test-project" - -[tool.ruff.lint] -select = ["E", "F", "W"] -ignore = [] -""" - ) - - # 创建子包的pyproject.toml（已有ignore） - subpkg = root / "packages" / "subpkg" - subpkg.mkdir(parents=True) - (subpkg / "pyproject.toml").write_text( - """[project] -name = "subpkg" - -[tool.ruff.lint] -select = ["E", "F"] -ignore = ["E501"] -""" - ) - - # 创建无ruff配置的pyproject.toml - no_ruff = root / "packages" / "no-ruff" - no_ruff.mkdir(parents=True) - (no_ruff / "pyproject.toml").write_text( - """[project] -name = "no-ruff" -version = "1.0.0" -""" - ) - - yield root - - -@pytest.mark.unit -class TestRuffIgnoreUpdater: - """测试 RuffIgnoreUpdater 类""" - - def test_init(self, temp_project): - """测试初始化""" - updater = RuffIgnoreUpdater(temp_project) - assert updater.root_dir == temp_project - - def test_update_file_add_new_rules(self, temp_project): - """测试添加新规则到空ignore""" - updater = RuffIgnoreUpdater(temp_project) - file_path = temp_project / "pyproject.toml" - - result = updater.update_file(file_path, ["F841"]) - - assert result is True - - # 验证文件被更新 - content = file_path.read_text() - assert "F841" in content - - def test_update_file_add_to_existing(self, temp_project): - """测试添加到已有ignore列表""" - updater = RuffIgnoreUpdater(temp_project) - file_path = temp_project / "packages" / "subpkg" / "pyproject.toml" - - result = updater.update_file(file_path, ["F841"]) - - assert result is True - - # 验证两个都在 - content = file_path.read_text() - assert "E501" in content # 原有的 - assert "F841" in content # 新添加的 - - def test_update_file_already_exists(self, temp_project): - """测试规则已存在的情况""" - updater = RuffIgnoreUpdater(temp_project) - file_path = temp_project / "packages" / "subpkg" / "pyproject.toml" - - result = updater.update_file(file_path, ["E501"]) - - # 不应该更新（已存在） - assert result is False - - def test_update_file_not_found(self, temp_project): - """测试文件不存在""" - updater = RuffIgnoreUpdater(temp_project) - file_path = temp_project / "nonexistent" / "pyproject.toml" - - result = updater.update_file(file_path, ["F841"]) - - assert result is False - - def test_update_all(self, temp_project): - """测试批量更新""" - updater = RuffIgnoreUpdater(temp_project) - - # 使用自定义文件列表 - file_list = [ - "pyproject.toml", - "packages/subpkg/pyproject.toml", - ] - - stats = updater.update_all(["F841"], file_list=file_list) - - # 检查统计信息 - assert "updated" in stats - assert "skipped" in stats - assert "failed" in stats - - def test_update_with_descriptions(self, temp_project): - """测试带描述的更新""" - updater = RuffIgnoreUpdater(temp_project) - file_path = temp_project / "pyproject.toml" - - descriptions = {"F841": "unused-variable"} - result = updater.update_file(file_path, ["F841"], descriptions) - - assert result is True - - # 验证内容 - content = file_path.read_text() - assert "F841" in content - # 注释可能被添加 - # assert "unused-variable" in content # 取决于实现 - - def test_add_b904_c901_helper(self, temp_project): - """测试快捷方法""" - updater = RuffIgnoreUpdater(temp_project) - stats = updater.add_b904_c901() - - assert isinstance(stats, dict) - assert "updated" in stats - - -@pytest.mark.unit -class TestRuffIgnoreUpdaterEdgeCases: - """测试边缘情况""" - - def test_updater_with_empty_project(self): - """测试空项目""" - with tempfile.TemporaryDirectory() as tmpdir: - root = Path(tmpdir) - updater = RuffIgnoreUpdater(root) - - # 使用不存在的文件列表 - stats = updater.update_all(["F841"], file_list=["nonexistent.toml"]) - - # 应该都失败 - assert stats["failed"] > 0 or stats["updated"] == 0 - - def test_update_multiple_rules(self, temp_project): - """测试添加多个规则""" - updater = RuffIgnoreUpdater(temp_project) - file_path = temp_project / "pyproject.toml" - - rules = ["F841", "E501", "W503"] - result = updater.update_file(file_path, rules) - - assert result is True - - content = file_path.read_text() - - # 所有规则都应该在 - for rule in rules: - assert rule in content - - def test_update_with_existing_rules(self, temp_project): - """测试部分规则已存在""" - updater = RuffIgnoreUpdater(temp_project) - file_path = temp_project / "packages" / "subpkg" / "pyproject.toml" - - # E501 已存在，F841 不存在 - rules = ["E501", "F841"] - result = updater.update_file(file_path, rules) - - # 应该仍然更新（添加F841） - # 注意：这取决于实际实现 - assert result is True or result is False # 接受任一结果 - - def test_no_ignore_section(self, temp_project): - """测试没有ignore部分的文件""" - no_ignore = temp_project / "packages" / "no-ignore" - no_ignore.mkdir(parents=True) - (no_ignore / "pyproject.toml").write_text( - """[project] -name = "no-ignore" - -[tool.ruff.lint] -select = ["E", "F"] -# No ignore section -""" - ) - - updater = RuffIgnoreUpdater(temp_project) - file_path = no_ignore / "pyproject.toml" - - result = updater.update_file(file_path, ["F841"]) - - # 可能会失败（没有ignore section）或成功添加 - assert isinstance(result, bool) diff --git a/packages/sage-tools/tests/dev/tools/__init__.py b/packages/sage-tools/tests/dev/tools/__init__.py deleted file mode 100644 index 955a103e37..0000000000 --- a/packages/sage-tools/tests/dev/tools/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Dev tools tests.""" diff --git a/packages/sage-tools/tests/examples/demo_examples_testing.py b/packages/sage-tools/tests/examples/demo_examples_testing.py deleted file mode 100644 index 5a54196a1a..0000000000 --- a/packages/sage-tools/tests/examples/demo_examples_testing.py +++ /dev/null @@ -1,185 +0,0 @@ -#!/usr/bin/env python3 -""" -Demo: Using SAGE Examples Testing Tools - -This script demonstrates how to use the Examples testing framework -and handles the development environment requirement gracefully. -""" - -from rich.console import Console - -console = Console() - - -def check_environment(): - """检查并报告开发环境状态""" - console.print("\n[bold blue]🔍 Checking Development Environment[/bold blue]\n") - - try: - from sage.tools.dev.examples.utils import get_development_info - - info = get_development_info() - - console.print("Development Environment Info:") - console.print(f" Has Dev Environment: {'✅ Yes' if info['has_dev_env'] else '❌ No'}") - console.print(f" Examples Directory: {info['examples_dir'] or '(not found)'}") - console.print(f" Project Root: {info['project_root'] or '(not found)'}") - console.print(f" SAGE_ROOT env: {info['sage_root_env'] or '(not set)'}") - console.print(f" In Git Repo: {'✅ Yes' if info['in_git_repo'] else '❌ No'}") - - return info["has_dev_env"] - - except ImportError as e: - console.print(f"[red]❌ Failed to import tools: {e}[/red]") - return False - - -def demo_analysis(): - """演示示例分析功能""" - console.print("\n[bold blue]📊 Examples Analysis Demo[/bold blue]\n") - - try: - from sage.tools.dev.examples import ExampleAnalyzer - - analyzer = ExampleAnalyzer() - examples = analyzer.discover_examples() - - console.print(f"Found [green]{len(examples)}[/green] examples\n") - - # 按类别统计 - categories = {} - for example in examples: - if example.category not in categories: - categories[example.category] = [] - categories[example.category].append(example) - - console.print("[bold]Examples by Category:[/bold]") - for category, cat_examples in sorted(categories.items()): - console.print(f" • {category}: {len(cat_examples)} files") - - # 显示一些示例细节 - if examples: - console.print("\n[bold]Sample Example Details:[/bold]") - sample = examples[0] - console.print(f" File: {sample.file_path}") - console.print(f" Category: {sample.category}") - console.print(f" Runtime: {sample.estimated_runtime}") - console.print(f" Dependencies: {', '.join(sample.dependencies) or 'none'}") - console.print(f" Test tags: {', '.join(sample.test_tags) or 'none'}") - - return True - - except RuntimeError as e: - console.print(f"[yellow]⚠️ {e}[/yellow]") - return False - except Exception as e: - console.print(f"[red]❌ Error: {e}[/red]") - import traceback - - traceback.print_exc() - return False - - -def demo_quick_test(): - """演示快速测试""" - console.print("\n[bold blue]🧪 Quick Test Demo[/bold blue]\n") - - try: - from sage.tools.dev.examples import ExampleTestSuite - - suite = ExampleTestSuite() - - console.print("Running quick tests on tutorials category...\n") - - stats = suite.run_all_tests(categories=["tutorials"], quick_only=True) - - console.print("\n[bold]Test Results:[/bold]") - console.print(f" Total: {stats['total']}") - console.print(f" [green]Passed: {stats['passed']}[/green]") - console.print(f" [red]Failed: {stats['failed']}[/red]") - console.print(f" [yellow]Skipped: {stats['skipped']}[/yellow]") - console.print(f" [orange]Timeout: {stats['timeout']}[/orange]") - - if stats["total"] > 0: - pass_rate = stats["passed"] / stats["total"] * 100 - console.print(f"\n Pass Rate: [bold]{pass_rate:.1f}%[/bold]") - - return True - - except RuntimeError as e: - console.print(f"[yellow]⚠️ {e}[/yellow]") - return False - except Exception as e: - console.print(f"[red]❌ Error: {e}[/red]") - import traceback - - traceback.print_exc() - return False - - -def show_setup_guide(): - """显示设置指南""" - console.print("\n[bold yellow]📚 Setup Guide[/bold yellow]\n") - console.print("To use Examples testing tools, you need a development environment:\n") - console.print("[bold]Option 1: Clone Repository[/bold]") - console.print(" git clone https://github.com/intellistream/SAGE") - console.print(" cd SAGE") - console.print(" pip install -e packages/sage-tools[dev]") - console.print("\n[bold]Option 2: Set SAGE_ROOT[/bold]") - console.print(" export SAGE_ROOT=/path/to/your/SAGE") - console.print("\n[bold]Then you can use:[/bold]") - console.print(" sage-dev examples analyze") - console.print(" sage-dev examples test --quick") - console.print(" python this_demo.py") - console.print() - - -def main(): - """主函数""" - console.print("[bold cyan]=" * 60 + "[/bold cyan]") - console.print("[bold cyan]SAGE Examples Testing Tools - Demo[/bold cyan]") - console.print("[bold cyan]=" * 60 + "[/bold cyan]") - - # 检查环境 - has_dev_env = check_environment() - - if not has_dev_env: - console.print("\n[yellow]⚠️ Development environment not available[/yellow]") - show_setup_guide() - console.print("[blue]ℹ️ This is expected if you installed via PyPI[/blue]") - console.print("[blue]ℹ️ Examples testing is only for SAGE developers[/blue]") - return - - console.print("\n[green]✅ Development environment is ready![/green]") - - # 运行演示 - console.print("\n" + "=" * 60) - - # 1. 分析示例 - if demo_analysis(): - console.print("\n[green]✅ Analysis completed[/green]") - - # 2. 运行快速测试（可选，注释掉以加快演示） - # Uncomment to run actual tests: - # console.print("\n" + "=" * 60) - # if demo_quick_test(): - # console.print("\n[green]✅ Tests completed[/green]") - - console.print("\n" + "=" * 60) - console.print("\n[bold green]🎉 Demo completed successfully![/bold green]\n") - console.print("For more information:") - console.print(" sage-dev examples --help") - console.print(" See: packages/sage-tools/src/sage/tools/dev/examples/README.md") - console.print() - - -if __name__ == "__main__": - try: - main() - except KeyboardInterrupt: - console.print("\n[yellow]⚠️ Demo interrupted by user[/yellow]") - except Exception as e: - console.print(f"\n[red]❌ Unexpected error: {e}[/red]") - import traceback - - traceback.print_exc() diff --git a/packages/sage-tools/tests/examples/strategies.py b/packages/sage-tools/tests/examples/strategies.py deleted file mode 100644 index 134cbafe8f..0000000000 --- a/packages/sage-tools/tests/examples/strategies.py +++ /dev/null @@ -1,538 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE Examples 专用测试配置和策略 -为不同类型的示例定义特定的测试策略 -""" - -from dataclasses import dataclass -from pathlib import Path -from typing import Any, Callable - -# 导入项目根目录查找函数 -from test_examples import find_project_root - - -@dataclass -class TestStrategy: - """测试策略配置""" - - name: str - timeout: int - requires_config: bool - requires_data: bool - mock_inputs: dict[str, str] | None = None - environment_vars: dict[str, str] | None = None - success_patterns: list[str] | None = None - failure_patterns: list[str] | None = None - pre_run_setup: Callable | None = None - post_run_cleanup: Callable | None = None - - -class ExampleTestStrategies: - """示例测试策略集合""" - - @staticmethod - def get_strategies() -> dict[str, TestStrategy]: - """获取所有测试策略""" - return { - "tutorials": TestStrategy( - name="tutorials", - timeout=30, - requires_config=False, - requires_data=False, - success_patterns=[ - "Hello, World!", - "Pipeline completed", - "Execution finished", - "✓", - ], - failure_patterns=["Error:", "Exception:", "Traceback", "Failed to"], - environment_vars={ - "SAGE_LOG_LEVEL": "WARNING", - "SAGE_EXAMPLES_MODE": "test", - }, - ), - "rag": TestStrategy( - name="rag", - timeout=120, - requires_config=True, - requires_data=True, - mock_inputs={ - "user_question": "What is artificial intelligence?", - "test_query": "Tell me about machine learning", - }, - success_patterns=[ - "Answer:", - "Response:", - "Retrieved", - "Generated answer", - "RAG pipeline completed", - ], - failure_patterns=[ - "API key not found", - "Connection failed", - "Model not found", - "Index not found", - ], - environment_vars={ - "OPENAI_API_KEY": "test-key-placeholder", # pragma: allowlist secret - "SAGE_RAG_MODE": "test", - "SAGE_LOG_LEVEL": "ERROR", - "SAGE_EXAMPLES_MODE": "test", - "SAGE_TEST_MODE": "true", - }, - ), - "memory": TestStrategy( - name="memory", - timeout=60, - requires_config=False, - requires_data=True, - success_patterns=[ - "Memory initialized", - "Data stored", - "Retrieved from memory", - "Memory service started", - ], - failure_patterns=[ - "Memory service failed", - "Storage error", - "Connection refused", - ], - environment_vars={ - "SAGE_MEMORY_MODE": "test", - "SAGE_LOG_LEVEL": "WARNING", - }, - ), - "agents": TestStrategy( - name="agents", - timeout=120, - requires_config=True, - requires_data=False, - success_patterns=[ - "Agent initialized", - "Task completed", - "Agent response", - "Processing finished", - ], - failure_patterns=[ - "Agent failed", - "API key missing", - "Connection failed", - "Model not available", - ], - environment_vars={ - "SAGE_AGENT_MODE": "test", - "SAGE_LOG_LEVEL": "ERROR", - "SAGE_EXAMPLES_MODE": "test", - "OPENAI_API_KEY": "test-key-placeholder", # pragma: allowlist secret - }, - ), - "service": TestStrategy( - name="service", - timeout=90, - requires_config=True, - requires_data=False, - success_patterns=[ - "Service started", - "Server running", - "API endpoint active", - "Health check passed", - ], - failure_patterns=[ - "Port already in use", - "Service failed to start", - "Connection refused", - ], - environment_vars={ - "SAGE_SERVICE_MODE": "test", - "SAGE_PORT": "0", # 随机端口 - "SAGE_LOG_LEVEL": "ERROR", - }, - ), - "video": TestStrategy( - name="video", - timeout=180, - requires_config=True, - requires_data=True, - success_patterns=[ - "Video processed", - "Frames extracted", - "Analysis completed", - ], - failure_patterns=[ - "Video file not found", - "Codec not supported", - "Processing failed", - ], - environment_vars={"SAGE_VIDEO_MODE": "test", "SAGE_LOG_LEVEL": "ERROR"}, - ), - "batch": TestStrategy( - name="batch", - timeout=180, - requires_config=False, - requires_data=False, - success_patterns=[ - "batch test completed", - "Batch Processing Tests Summary", - "✅", - "Processing completed", - ], - failure_patterns=[ - "Failed to start", - "Connection refused", - "Timeout", - "Error:", - "Exception:", - ], - environment_vars={ - "SAGE_BATCH_MODE": "test", - "SAGE_LOG_LEVEL": "ERROR", - "SAGE_EXAMPLES_MODE": "test", - }, - ), - "streaming": TestStrategy( - name="streaming", - timeout=300, # 增加到5分钟，因为streaming示例可能运行多个环境 - requires_config=False, - requires_data=False, - success_patterns=[ - "Stream completed", - "Processing finished", - "✅", - "Test completed", - ], - failure_patterns=[ - "Stream failed", - "Connection error", - "Timeout", - ], - environment_vars={ - "SAGE_STREAM_MODE": "test", - "SAGE_LOG_LEVEL": "ERROR", - }, - ), - "medical_diagnosis": TestStrategy( - name="medical_diagnosis", - timeout=300, # 5分钟，医学影像分析需要加载模型 - requires_config=False, - requires_data=True, - success_patterns=[ - "诊断完成", - "Diagnosis completed", - "报告生成完成", - "Report generated", - "✅", - ], - failure_patterns=[ - "模型加载失败", - "Model loading failed", - "数据不存在", - "Data not found", - ], - environment_vars={ - "SAGE_MEDICAL_MODE": "test", - "SAGE_LOG_LEVEL": "ERROR", - "SAGE_EXAMPLES_MODE": "test", - }, - ), - "multimodal": TestStrategy( - name="multimodal", - timeout=180, # 3分钟，多模态处理需要时间 - requires_config=True, - requires_data=True, - success_patterns=[ - "Processing completed", - "处理完成", - "Search completed", - "搜索完成", - "✅", - ], - failure_patterns=[ - "Model not found", - "API key missing", - "Connection failed", - ], - environment_vars={ - "SAGE_MULTIMODAL_MODE": "test", - "SAGE_LOG_LEVEL": "ERROR", - "SAGE_EXAMPLES_MODE": "test", - }, - ), - "scheduler": TestStrategy( - name="scheduler", - timeout=90, # 90秒，调度器对比实验 - requires_config=False, - requires_data=False, - success_patterns=[ - "所有实验完成", - "实验完成", - "执行结果", - "✅", - "调度器性能对比总结", - ], - failure_patterns=[ - "调度失败", - "Scheduler failed", - "Connection refused", - "Timeout exceeded", - ], - environment_vars={ - "SAGE_SCHEDULER_MODE": "test", - "SAGE_LOG_LEVEL": "ERROR", - "SAGE_EXAMPLES_MODE": "test", - "SAGE_TEST_MODE": "true", - }, - ), - } - - @staticmethod - def get_category_skip_patterns() -> dict[str, list[str]]: - """获取各类别需要跳过的文件模式""" - return { - "rag": [ - "*_interactive.py", # 交互式示例 - "*_demo.py", # 演示文件 - "*_benchmark.py", # 基准测试 - ], - "service": ["*_server.py", "*_daemon.py"], # 长期运行的服务 # 守护进程 - "video": ["*_large_file.py", "*_gpu_required.py"], # 处理大文件 # 需要GPU - } - - @staticmethod - def get_mock_data_generators() -> dict[str, Callable]: - """获取模拟数据生成器""" - return { - "rag": ExampleTestStrategies._generate_rag_mock_data, - "memory": ExampleTestStrategies._generate_memory_mock_data, - "video": ExampleTestStrategies._generate_video_mock_data, - } - - @staticmethod - def _generate_rag_mock_data() -> dict[str, Any]: - """生成RAG测试的模拟数据""" - return { - "documents": """ - Document 1: Artificial Intelligence (AI) is the simulation of human intelligence in machines. - Document 2: Machine Learning is a subset of AI that learns from data. - Document 3: Deep Learning uses neural networks with multiple layers. - """, - "queries": [ - "What is AI?", - "How does machine learning work?", - "Explain deep learning", - ], - } - - @staticmethod - def _generate_memory_mock_data() -> dict[str, Any]: - """生成内存测试的模拟数据""" - return { - "test_data": "This is test data for memory storage", - "metadata": {"source": "test", "type": "example"}, - } - - @staticmethod - def _generate_video_mock_data() -> dict[str, str]: - """生成视频测试的模拟数据""" - # 创建一个简单的测试视频文件路径 - return { - "video_path": "/tmp/test_video.mp4", - "frame_count": "10", - "duration": "1.0", - } - - -class ExampleTestFilters: - """示例测试过滤器""" - - @staticmethod - def should_skip_file(file_path: Path, category: str, example_info=None) -> tuple[bool, str]: - """判断是否应该跳过某个文件的测试 - - Args: - file_path: 文件路径 - category: 文件类别 - example_info: 示例信息对象（包含test_tags） - - Returns: - (should_skip, reason): 是否跳过和跳过原因 - """ - import os - - # 检查文件内的测试标记 - if example_info and hasattr(example_info, "test_tags"): - # 检查跳过标记 - if "skip" in example_info.test_tags: - return True, "文件包含 @test:skip 标记" - - # 检查 CI 环境下的跳过标记 - is_ci = os.environ.get("CI") == "true" or os.environ.get("GITHUB_ACTIONS") == "true" - if is_ci: - # 检查 skip_ci 标记（支持 skip_ci 或 skip_ci=true） - for tag in example_info.test_tags: - if tag == "skip_ci" or tag.startswith("skip_ci="): - return True, "文件包含 @test_skip_ci 标记，在 CI 环境中跳过" - - # 检查需要API密钥的标记 - if "require-api" in example_info.test_tags: - return True, "需要API密钥，在测试环境中跳过" - - # 检查需要用户交互的标记 - if "interactive" in example_info.test_tags: - return True, "需要用户交互，自动测试中跳过" - - # 检查不稳定测试标记 - if "unstable" in example_info.test_tags: - return True, "标记为不稳定测试，跳过" - - # 检查需要GPU的标记 - if "gpu" in example_info.test_tags: - return True, "需要GPU支持，在测试环境中跳过" - - return False, "" - - @staticmethod - def estimate_test_priority(file_path: Path, category: str) -> int: - """估算测试优先级 (1=高, 2=中, 3=低)""" - # 基础教程最高优先级 - if category == "tutorials": - if "hello_world" in file_path.name: - return 1 - return 2 - - # RAG示例中等优先级 - if category == "rag": - if "simple" in file_path.name: - return 2 - return 3 - - # 其他类别默认低优先级 - return 3 - - -class ExampleEnvironmentManager: - """示例执行环境管理器""" - - def __init__(self): - self.temp_files = [] - self.temp_dirs = [] - - def setup_category_environment(self, category: str) -> dict[str, str]: - """为特定类别设置环境""" - strategy = ExampleTestStrategies.get_strategies().get(category) - if not strategy: - return {} - - env_vars = strategy.environment_vars.copy() if strategy.environment_vars else {} - - # 添加通用测试环境变量 - env_vars.update( - { - "SAGE_TEST_MODE": "true", - "SAGE_EXAMPLES_TEST": "true", - "PYTHONPATH": self._get_sage_python_path(), - } - ) - - # 为需要配置的类别创建临时配置文件 - if strategy.requires_config: - config_path = self._create_temp_config(category) - env_vars["SAGE_CONFIG_PATH"] = str(config_path) - - # 为需要数据的类别创建模拟数据 - if strategy.requires_data: - data_path = self._create_temp_data(category) - env_vars["SAGE_DATA_PATH"] = str(data_path) - - return env_vars - - def _get_sage_python_path(self) -> str: - """获取SAGE的Python路径""" - try: - project_root = find_project_root() - sage_paths = [ - str(project_root / "packages" / "sage" / "src"), - str(project_root / "packages" / "sage-common" / "src"), - str(project_root / "packages" / "sage-kernel" / "src"), - str(project_root / "packages" / "sage-libs" / "src"), - str(project_root / "packages" / "sage-middleware" / "src"), - str(project_root / "packages" / "sage-tools" / "src"), - ] - return ":".join(sage_paths) - except FileNotFoundError: - # 如果找不到项目根目录，返回空字符串或抛出错误 - raise FileNotFoundError("Cannot find SAGE project root directory for Python path setup") - - def _create_temp_config(self, category: str) -> Path: - """创建临时配置文件""" - import tempfile - - import yaml - - config_data = {"test_mode": True, "log_level": "WARNING", "category": category} - - if category == "rag": - config_data.update( - { - "llm": { - "provider": "mock", - "model": "test-model", - "api_key": "test-key", # pragma: allowlist secret - }, - "embedding": {"provider": "mock", "model": "test-embedding"}, - "retriever": {"type": "mock", "top_k": 3}, - } - ) - - temp_file = tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) - yaml.dump(config_data, temp_file) - temp_file.close() - - self.temp_files.append(temp_file.name) - return Path(temp_file.name) - - def _create_temp_data(self, category: str) -> Path: - """创建临时数据目录和文件""" - import tempfile - - temp_dir = tempfile.mkdtemp(prefix=f"sage_test_{category}_") - self.temp_dirs.append(temp_dir) - - data_generators = ExampleTestStrategies.get_mock_data_generators() - if category in data_generators: - mock_data = data_generators[category]() - - # 根据类别创建对应的数据文件 - if category == "rag": - docs_file = Path(temp_dir) / "documents.txt" - with open(docs_file, "w") as f: - f.write(mock_data["documents"]) - - elif category == "memory": - data_file = Path(temp_dir) / "test_data.json" - import json - - with open(data_file, "w") as f: - json.dump(mock_data, f) - - return Path(temp_dir) - - def cleanup(self): - """清理临时文件和目录""" - import os - import shutil - - for temp_file in self.temp_files: - try: - os.unlink(temp_file) - except Exception: - pass - - for temp_dir in self.temp_dirs: - try: - shutil.rmtree(temp_dir) - except Exception: - pass - - self.temp_files.clear() - self.temp_dirs.clear() diff --git a/packages/sage-tools/tests/pypi/__init__.py b/packages/sage-tools/tests/pypi/__init__.py deleted file mode 100644 index 93ec8eaf74..0000000000 --- a/packages/sage-tools/tests/pypi/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# PyPI验证测试模块 diff --git a/packages/sage-tools/tests/pypi/test_install_modes.py b/packages/sage-tools/tests/pypi/test_install_modes.py deleted file mode 100644 index ad8e6fe3a6..0000000000 --- a/packages/sage-tools/tests/pypi/test_install_modes.py +++ /dev/null @@ -1,95 +0,0 @@ -#!/usr/bin/env python3 -""" -测试本地包安装模式的脚本 -""" - -import os -import sys - -import pytest - - -@pytest.mark.parametrize( - "mode,description", - [ - ("dev", "开发模式 - 开发工具"), - ("all", "完整安装 - 所有功能"), - ], -) -def test_install_mode(mode, description): - """测试单个安装模式 - 简化版本，只测试包的可用性""" - print(f"\n=== 测试 {mode} 模式: {description} ===") - - # 获取项目根目录 - script_dir = os.path.dirname(os.path.abspath(__file__)) - project_root = os.path.abspath(os.path.join(script_dir, "../../../..")) - - # 检查必需的包是否存在 - packages_to_check = [ - "packages/sage-common", - "packages/sage-kernel", - "packages/sage-tools", - "packages/sage-middleware", - "packages/sage-libs", - ] - - # 检查所有必需的包目录是否存在且有pyproject.toml - for package in packages_to_check: - package_path = os.path.join(project_root, package) - pyproject_path = os.path.join(package_path, "pyproject.toml") - - assert os.path.exists(package_path), f"包目录不存在: {package}" - assert os.path.exists(pyproject_path), f"pyproject.toml 不存在: {package}" - - print(f"✅ 检查通过: {package}") - - # 检查主包 - main_package_path = os.path.join(project_root, "packages/sage") - main_pyproject_path = os.path.join(main_package_path, "pyproject.toml") - - assert os.path.exists(main_package_path), "主包目录不存在: packages/sage" - assert os.path.exists(main_pyproject_path), "主包 pyproject.toml 不存在" - - print("✅ 检查通过: packages/sage") - - # 检查主包的pyproject.toml中是否包含对应的安装模式 - with open(main_pyproject_path, encoding="utf-8") as f: - content = f.read() - assert f"{mode} = [" in content, f"{mode} 安装模式未在pyproject.toml中定义" - - print(f"✅ {mode} 安装模式已定义") - print(f"✅ {mode} 模式配置检查成功") - - -def main(): - """主测试函数""" - modes = { - "dev": "开发模式 - 开发工具", - "all": "完整安装 - 所有功能", - } - - results = {} - - for mode, desc in modes.items(): - try: - test_install_mode(mode, desc) - results[mode] = True - except AssertionError as e: - print(f"❌ {mode} 模式测试失败: {e}") - results[mode] = False - except Exception as e: - print(f"❌ {mode} 模式测试过程中出现异常: {e}") - results[mode] = False - - print("\n=== 测试结果汇总 ===") - for mode, success in results.items(): - status = "✅ 成功" if success else "❌ 失败" - print(f"{mode}: {status}") - - # 如果有失败的测试，退出码为1 - if not all(results.values()): - sys.exit(1) - - -if __name__ == "__main__": - main() diff --git a/packages/sage-tools/tests/pypi/test_install_modes.sh b/packages/sage-tools/tests/pypi/test_install_modes.sh deleted file mode 100644 index e2530191f6..0000000000 --- a/packages/sage-tools/tests/pypi/test_install_modes.sh +++ /dev/null @@ -1,59 +0,0 @@ -#!/bin/bash -# PyPI 安装模式测试脚本 -# 测试 pip install isage[] 的各种选项是否正常工作 - -set -e - -echo "🔍 测试 PyPI 安装模式配置..." -echo "========================================" - -# 切换到项目根目录 -SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" -cd "$SCRIPT_DIR" - -# 测试函数 -test_install_mode() { - local mode="$1" - local description="$2" - - echo "" - echo "📦 测试安装模式: $mode" - echo " 描述: $description" - - # 构建正确的pip命令 - local pip_cmd - if [ -z "$mode" ]; then - pip_cmd="pip install -e packages/sage --dry-run" - echo " 命令: $pip_cmd" - else - pip_cmd="pip install -e \"packages/sage[$mode]\" --dry-run" - echo " 命令: pip install -e packages/sage[$mode] --dry-run" - fi - - if eval "$pip_cmd" 2>/dev/null; then - echo " ✅ $mode 模式验证通过" - return 0 - else - echo " ❌ $mode 模式验证失败" - return 1 - fi -} - -# 测试所有安装模式 -echo "测试主包 (isage) 的安装模式:" - -# 基本安装模式 (对应quickstart.sh的模式) -test_install_mode "minimal" "最小安装 (仅sage-common, sage-kernel)" -test_install_mode "standard" "标准安装 (包含所有核心组件)" -test_install_mode "dev" "开发者安装 (标准安装 + 开发工具)" - -echo "" -echo "========================================" -echo "🎉 PyPI 安装模式测试完成！" - -# 显示使用示例 -echo "" -echo "💡 使用示例:" -echo " pip install isage[minimal] # 最小安装 (仅核心组件)" -echo " pip install isage[standard] # 标准安装 (所有核心组件)" -echo " pip install isage[dev] # 开发者安装 (标准 + 开发工具)" diff --git a/packages/sage-tools/tests/pypi/test_testpypi_install.sh b/packages/sage-tools/tests/pypi/test_testpypi_install.sh deleted file mode 100755 index d88cb12dea..0000000000 --- a/packages/sage-tools/tests/pypi/test_testpypi_install.sh +++ /dev/null @@ -1,130 +0,0 @@ -#!/bin/bash -# TestPyPI安装测试脚本 -# 用于验证从TestPyPI安装SAGE的完整流程 - -set -e # 遇到错误立即退出 - -# 颜色定义 -GREEN='\033[0;32m' -BLUE='\033[0;34m' -YELLOW='\033[1;33m' -RED='\033[0;31m' -NC='\033[0m' # No Color - -echo -e "${BLUE}🧪 TestPyPI安装测试${NC}" -echo "================================" - -# 1. 创建测试环境 -TEST_ENV="testpypi_test_$$" -echo -e "\n${BLUE}📁 创建测试环境: ${TEST_ENV}${NC}" -python -m venv "${TEST_ENV}" - -# 激活环境 -if [[ "$OSTYPE" == "msys" || "$OSTYPE" == "win32" ]]; then - source "${TEST_ENV}/Scripts/activate" -else - source "${TEST_ENV}/bin/activate" -fi - -echo -e "${GREEN}✅ 测试环境创建成功${NC}" - -# 2. 升级pip -echo -e "\n${BLUE}📦 升级pip...${NC}" -pip install --upgrade pip --quiet - -# 3. 从TestPyPI安装SAGE -echo -e "\n${BLUE}📥 从TestPyPI安装SAGE（包含依赖）...${NC}" -echo -e "${YELLOW}安装命令:${NC}" -echo "pip install --index-url https://test.pypi.org/simple/ \\" -echo " --extra-index-url https://pypi.org/simple/ \\" -echo " isage" - -pip install --index-url https://test.pypi.org/simple/ \ - --extra-index-url https://pypi.org/simple/ \ - isage - -if [ $? -eq 0 ]; then - echo -e "${GREEN}✅ SAGE安装成功${NC}" -else - echo -e "${RED}❌ SAGE安装失败${NC}" - deactivate - rm -rf "${TEST_ENV}" - exit 1 -fi - -# 4. 验证安装 -echo -e "\n${BLUE}🔍 验证安装...${NC}" - -# 检查版本 -echo -e "\n${YELLOW}检查版本:${NC}" -if sage --version; then - echo -e "${GREEN}✅ sage命令可用${NC}" -else - echo -e "${RED}❌ sage命令不可用${NC}" - deactivate - rm -rf "${TEST_ENV}" - exit 1 -fi - -# 测试导入 -echo -e "\n${YELLOW}测试核心导入:${NC}" -python -c " -import sage -print(f'✅ SAGE版本: {sage.__version__}') - -from sage.core.api.local_environment import LocalEnvironment -print('✅ LocalEnvironment导入成功') - -from sage.libs.io_utils.source import FileSource -from sage.libs.io_utils.sink import TerminalSink -print('✅ IO工具导入成功') - -from sage.common.utils.logging.custom_logger import CustomLogger -print('✅ 日志工具导入成功') -" - -if [ $? -eq 0 ]; then - echo -e "${GREEN}✅ 所有导入测试通过${NC}" -else - echo -e "${RED}❌ 导入测试失败${NC}" - deactivate - rm -rf "${TEST_ENV}" - exit 1 -fi - -# 5. 测试基本功能 -echo -e "\n${YELLOW}测试基本功能:${NC}" -python -c " -from sage.core.api.local_environment import LocalEnvironment -from sage.libs.io_utils.source import FileSource -from sage.libs.io_utils.sink import TerminalSink - -# 创建环境 -env = LocalEnvironment( - name='test_env', - source=FileSource('./test.txt'), - sink=TerminalSink() -) -print('✅ 环境创建成功') -print(f' 环境名称: {env.name}') -" - -if [ $? -eq 0 ]; then - echo -e "${GREEN}✅ 基本功能测试通过${NC}" -else - echo -e "${RED}❌ 基本功能测试失败${NC}" - deactivate - rm -rf "${TEST_ENV}" - exit 1 -fi - -# 6. 清理 -echo -e "\n${BLUE}🧹 清理测试环境...${NC}" -deactivate -rm -rf "${TEST_ENV}" - -# 7. 总结 -echo -e "\n${GREEN}🎉 TestPyPI安装测试完成！${NC}" -echo "================================" -echo -e "${GREEN}✅ 所有测试通过${NC}" -echo -e "${YELLOW}💡 可以安全发布到正式PyPI${NC}" diff --git a/packages/sage-tools/tests/pypi/validate_pip_fast.py b/packages/sage-tools/tests/pypi/validate_pip_fast.py deleted file mode 100644 index 06d8611e18..0000000000 --- a/packages/sage-tools/tests/pypi/validate_pip_fast.py +++ /dev/null @@ -1,576 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE PyPI发布准备快速验证脚本 - -这是一个快速的PyPI发布准备验证脚本，专门用于验证代码是否准备好发布到PyPI。 -主要功能： -1. 验证wheel包能正确构建 -2. 模拟用户pip install过程 -3. 验证安装后核心功能正常 -4. 确保发布到PyPI后用户能正常使用 - -主要改进： -1. 更快的安装过程 -2. 并行化测试 -3. 更好的进度显示 -4. 跳过耗时的测试项 -""" - -import argparse -import os -import shutil -import subprocess -import sys -import threading -import time -from pathlib import Path - - -class FastPipValidator: - """快速PyPI发布准备验证器""" - - def __init__(self, test_dir: str | None = None, skip_wheel: bool = False): - # 查找SAGE项目根目录 - current_file = Path(__file__).resolve() - # 从 packages/sage-tools/tests/pypi/test_pip_validate_fast.py 找到项目根目录 - self.project_root = ( - current_file.parent.parent.parent.parent.parent - ) # pypi -> tests -> sage-tools -> packages -> SAGE - - # 如果没有指定test_dir，则在.sage目录下创建 - if test_dir: - self.test_dir = Path(test_dir) - else: - sage_config_dir = self.project_root / ".sage" / "temp" - sage_config_dir.mkdir(parents=True, exist_ok=True) - self.test_dir = sage_config_dir / f"pip_test_{int(time.time())}" - - self.venv_dir = self.test_dir / "test_env" - - # 验证项目根目录 - if not (self.project_root / "packages" / "sage").exists(): - # 如果不在标准位置，向上查找 - check_dir = current_file.parent - while check_dir.parent != check_dir: - if (check_dir / "packages" / "sage").exists(): - self.project_root = check_dir - break - check_dir = check_dir.parent - - self.python_exe = None - self.pip_exe = None - self.skip_wheel = skip_wheel - - # 测试结果 - self.results = { - "environment_setup": False, - "wheel_build": False, - "package_installation": False, - "basic_imports": False, - "core_functionality": False, - "cli_availability": False, - "cleanup": False, - } - - def run_command( - self, - cmd: list[str], - cwd: Path | None = None, - capture_output: bool = True, - timeout: int = 300, - env: dict | None = None, - ) -> tuple[int, str, str]: - """运行命令并返回结果""" - try: - # 如果没有指定环境变量，使用当前环境 - if env is None: - env = os.environ.copy() - - result = subprocess.run( - cmd, - cwd=cwd or self.test_dir, - capture_output=capture_output, - text=True, - timeout=timeout, - env=env, - ) - return result.returncode, result.stdout, result.stderr - except subprocess.TimeoutExpired: - return -1, "", f"Command timed out after {timeout}s" - except Exception as e: - return -1, "", str(e) - - def show_progress(self, message: str, duration: float = 0): - """显示进度动画""" - if duration <= 0: - print(f" {message}") - return - - chars = ["⠋", "⠙", "⠹", "⠸", "⠼", "⠴", "⠦", "⠧", "⠇", "⠏"] - start_time = time.time() - i = 0 - - while time.time() - start_time < duration: - print(f"\r {chars[i % len(chars)]} {message}", end="", flush=True) - time.sleep(0.1) - i += 1 - - print(f"\r ✅ {message}") - - def setup_test_environment(self) -> bool: - """设置测试环境""" - print("\n🔧 设置测试环境...") - - try: - # 创建测试目录 - self.test_dir.mkdir(parents=True, exist_ok=True) - - # 创建虚拟环境（使用--system-site-packages加速） - print(" 📦 创建虚拟环境...") - returncode, stdout, stderr = self.run_command( - [ - sys.executable, - "-m", - "venv", - str(self.venv_dir), - "--system-site-packages", - ], - timeout=60, - ) - - if returncode != 0: - print(f" ❌ 创建虚拟环境失败: {stderr}") - return False - - # 设置Python和pip路径 - if sys.platform == "win32": - self.python_exe = self.venv_dir / "Scripts" / "python.exe" - self.pip_exe = self.venv_dir / "Scripts" / "pip.exe" - else: - self.python_exe = self.venv_dir / "bin" / "python" - self.pip_exe = self.venv_dir / "bin" / "pip" - - # 快速升级pip（只升级必要组件） - print(" 📦 配置pip...") - returncode, stdout, stderr = self.run_command( - [ - str(self.python_exe), - "-m", - "pip", - "install", - "--upgrade", - "pip", - "--quiet", - ], - timeout=60, - ) - - print(" ✅ 虚拟环境设置完成") - self.results["environment_setup"] = True - return True - - except Exception as e: - print(f" ❌ 设置测试环境失败: {e}") - return False - - def build_or_find_wheel(self) -> Path | None: - """构建或查找wheel包""" - if self.skip_wheel: - print("\n📦 查找现有wheel包...") - else: - print("\n🔨 快速构建wheel包...") - - try: - # 查找sage包目录 - sage_package_dir = self.project_root / "packages" / "sage" - if not sage_package_dir.exists(): - print(f" ❌ sage包目录不存在: {sage_package_dir}") - return None - - dist_dir = sage_package_dir / "dist" - - if not self.skip_wheel: - # 快速清理和构建 - if dist_dir.exists(): - shutil.rmtree(dist_dir) - - print(" 🔨 执行快速构建...") - returncode, stdout, stderr = self.run_command( - [sys.executable, "setup.py", "bdist_wheel", "--quiet"], - cwd=sage_package_dir, - timeout=300, - ) - - if returncode != 0: - print(f" ❌ 构建wheel包失败: {stderr}") - return None - - # 查找wheel文件 - if not dist_dir.exists(): - print(f" ❌ dist目录不存在: {dist_dir}") - return None - - wheel_files = list(dist_dir.glob("*.whl")) - if not wheel_files: - print(" ❌ 未找到wheel包文件") - return None - - wheel_file = wheel_files[0] - print(f" ✅ 找到wheel包: {wheel_file.name}") - self.results["wheel_build"] = True - return wheel_file - - except Exception as e: - print(f" ❌ 处理wheel包失败: {e}") - return None - - def install_package(self, wheel_file: Path) -> bool: - """快速安装包""" - print("\n📥 安装SAGE包...") - - try: - print(f" 📦 安装: {wheel_file.name}") - - # 显示安装进度 - def install_with_progress(): - return self.run_command( - [ - str(self.pip_exe), - "install", - str(wheel_file), - "--quiet", - "--no-deps", - ], - timeout=300, - ) - - # 使用进度显示 - result_container = [None] - - def run_install(): - result_container[0] = install_with_progress() - - install_thread = threading.Thread(target=run_install) - install_thread.daemon = True - install_thread.start() - - # 显示进度 - chars = ["⠋", "⠙", "⠹", "⠸", "⠼", "⠴", "⠦", "⠧", "⠇", "⠏"] - i = 0 - while install_thread.is_alive(): - print(f"\r {chars[i % len(chars)]} 安装中...", end="", flush=True) - time.sleep(0.1) - i += 1 - - install_thread.join() - print("\r" + " " * 20 + "\r", end="", flush=True) - - returncode, stdout, stderr = result_container[0] - - if returncode != 0: - print(f" ❌ 安装失败: {stderr}") - return False - - # 快速验证安装 - print(f" 🔍 使用Python路径: {self.python_exe}") - test_cmd = [ - str(self.python_exe), - "-c", - "import sage; print(f'SAGE {sage.__version__} 安装成功')", - ] - print(f" 🔍 执行命令: {' '.join(test_cmd)}") - - # 创建干净的环境变量，移除PYTHONPATH避免导入冲突 - clean_env = os.environ.copy() - clean_env.pop("PYTHONPATH", None) # 移除PYTHONPATH - - returncode, stdout, stderr = self.run_command(test_cmd, env=clean_env) - - print(f" 🔍 返回码: {returncode}") - print(f" 🔍 标准输出: {stdout}") - print(f" 🔍 标准错误: {stderr}") - - if returncode != 0: - print(f" ❌ 验证安装失败: {stderr}") - - # 添加额外的诊断信息 - print(" 🔧 运行诊断...") - diag_returncode, diag_stdout, diag_stderr = self.run_command( - [ - str(self.python_exe), - "-c", - "import sys, os; print(f'工作目录: {os.getcwd()}'); print('Python路径:'); [print(f' {p}') for p in sys.path]; import sage; print(f'sage文件: {sage.__file__}'); print(f'sage属性: {dir(sage)}')", - ], - env=clean_env, - ) - print(f" 🔍 诊断输出: {diag_stdout}") - if diag_stderr.strip(): - print(f" 🔍 诊断错误: {diag_stderr}") - - return False - - print(f" ✅ {stdout.strip()}") - self.results["package_installation"] = True - return True - - except Exception as e: - print(f" ❌ 安装包失败: {e}") - return False - - def test_basic_imports(self) -> bool: - """测试核心导入""" - print("\n🔍 测试核心导入...") - - # 核心导入测试（简化版） - test_script = """ -import sys -try: - import sage - from sage.kernel.api.local_environment import LocalEnvironment - from sage.libs.foundation.io.source import FileSource - from sage.libs.foundation.io.sink import TerminalSink - from sage.common.utils.logging.custom_logger import CustomLogger - print("✅ 所有核心模块导入成功") - sys.exit(0) -except ImportError as e: - print(f"❌ 导入失败: {e}") - sys.exit(1) -""" - - try: - # 创建测试脚本 - test_file = self.test_dir / "test_imports.py" - with open(test_file, "w", encoding="utf-8") as f: - f.write(test_script) - - # 运行测试 - returncode, stdout, stderr = self.run_command( - [str(self.python_exe), str(test_file)], timeout=30 - ) - - if returncode == 0: - print(f" {stdout.strip()}") - self.results["basic_imports"] = True - return True - else: - print(f" {stderr.strip()}") - return False - - except Exception as e: - print(f" ❌ 导入测试异常: {e}") - return False - - def test_core_functionality(self) -> bool: - """测试核心功能""" - print("\n⚙️ 测试核心功能...") - - # 核心功能测试（简化版） - test_script = """ -from sage.kernel.api.local_environment import LocalEnvironment -from sage.common.core.functions.batch_function import BatchFunction -from sage.common.core.functions.sink_function import SinkFunction - -# 测试环境创建 -env = LocalEnvironment("test_env") -print("✅ 环境创建成功") - -# 测试基本函数 -class TestBatch(BatchFunction): - def __init__(self): - super().__init__() - self.count = 0 - - def execute(self): - if self.count < 2: - self.count += 1 - return f"data_{self.count}" - return None - -class TestSink(SinkFunction): - def __init__(self): - super().__init__() - self.received = [] - - def execute(self, data): - self.received.append(data) - -# 简单的数据流测试 -batch = TestBatch() -sink = TestSink() - -while True: - data = batch.execute() - if data is None: - break - sink.execute(data) - -if len(sink.received) == 2: - print("✅ 数据流测试成功") - print(f"处理数据: {sink.received}") -else: - print(f"❌ 数据流测试失败: 预期2条，实际{len(sink.received)}条") - exit(1) -""" - - try: - # 创建测试脚本 - test_file = self.test_dir / "test_functionality.py" - with open(test_file, "w", encoding="utf-8") as f: - f.write(test_script) - - # 运行测试 - returncode, stdout, stderr = self.run_command( - [str(self.python_exe), str(test_file)], timeout=30 - ) - - if returncode == 0: - print(f" {stdout.strip()}") - self.results["core_functionality"] = True - return True - else: - print(f" ❌ 功能测试失败: {stderr}") - return False - - except Exception as e: - print(f" ❌ 功能测试异常: {e}") - return False - - def test_cli_availability(self) -> bool: - """测试CLI可用性""" - print("\n🔧 测试CLI可用性...") - - try: - # 测试sage模块是否支持命令行调用 - returncode, stdout, stderr = self.run_command( - [ - str(self.python_exe), - "-c", - "import sage; print('✅ SAGE模块CLI支持正常')", - ], - timeout=10, - ) - - if returncode == 0: - print(f" {stdout.strip()}") - self.results["cli_availability"] = True - return True - else: - print(f" ❌ CLI测试失败: {stderr}") - return False - - except Exception as e: - print(f" ❌ CLI测试异常: {e}") - return False - - def run_fast_validation(self) -> bool: - """运行快速发布准备验证""" - print("🚀 SAGE PyPI发布准备快速验证") - print("=" * 50) - - start_time = time.time() - - # 运行测试步骤 - steps = [ - ("环境设置", self.setup_test_environment), - ("包构建", lambda: self.build_or_find_wheel() is not None), - ("包安装", lambda: self.install_package(self.build_or_find_wheel())), - ("导入测试", self.test_basic_imports), - ("功能测试", self.test_core_functionality), - ("CLI测试", self.test_cli_availability), - ] - - all_passed = True - completed_steps = 0 - - for step_name, step_func in steps: - try: - if step_func(): - completed_steps += 1 - else: - all_passed = False - break - except Exception as e: - print(f" ❌ {step_name} 异常: {e}") - all_passed = False - break - - # 计算测试时间 - end_time = time.time() - duration = end_time - start_time - - print("\n" + "=" * 50) - print("📊 快速发布准备验证结果:") - - for test_name, passed in self.results.items(): - if test_name == "cleanup": - continue - status = "✅ 通过" if passed else "❌ 失败" - print(f" {test_name}: {status}") - - print(f"⏱️ 验证时间: {duration:.1f}秒") - print(f"📈 完成步骤: {completed_steps}/{len(steps)}") - - if all_passed: - print("\n🎉 快速发布准备验证通过！") - print("📦 SAGE核心功能可以正常工作") - print("🚀 建议运行完整验证确认发布准备") - return True - else: - print("\n⚠️ 快速发布准备验证失败") - print("🔧 建议运行完整验证以获取详细信息") - return False - - def cleanup(self) -> bool: - """清理测试环境""" - print("\n🧹 清理测试环境...") - - try: - if self.test_dir.exists(): - shutil.rmtree(self.test_dir) - print(" ✅ 测试目录已清理") - else: - print(" ℹ️ 测试目录不存在，无需清理") - - self.results["cleanup"] = True - return True - - except Exception as e: - print(f" ❌ 清理失败: {e}") - return False - - -def main(): - parser = argparse.ArgumentParser(description="SAGE PyPI发布准备快速验证脚本") - parser.add_argument("--test-dir", type=str, help="指定测试目录（可选）") - parser.add_argument( - "--skip-wheel", action="store_true", help="跳过wheel构建，使用现有的wheel包" - ) - parser.add_argument( - "--cleanup", action="store_true", default=True, help="测试完成后清理临时文件" - ) - - args = parser.parse_args() - - # 创建验证器 - validator = FastPipValidator(args.test_dir, args.skip_wheel) - - try: - success = validator.run_fast_validation() - - # 清理 - if args.cleanup: - validator.cleanup() - - sys.exit(0 if success else 1) - - except KeyboardInterrupt: - print("\n⚠️ 验证被用户中断") - validator.cleanup() - sys.exit(1) - except Exception as e: - print(f"\n❌ 验证过程中发生异常: {e}") - sys.exit(1) - - -if __name__ == "__main__": - main() diff --git a/packages/sage-tools/tests/pypi/validate_pip_install_complete.py b/packages/sage-tools/tests/pypi/validate_pip_install_complete.py deleted file mode 100644 index cdef1c521e..0000000000 --- a/packages/sage-tools/tests/pypi/validate_pip_install_complete.py +++ /dev/null @@ -1,1258 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE PyPI发布准备完整验证脚本 - -这个脚本提供完整的PyPI发布准备验证，模拟用户从PyPI安装isage[dev]后的完整流程，确保： -1. wheel包构建正常 -2. PyPI发布流程正常 -3. 用户pip install isage[dev]过程正常（包含所有子包） -4. 安装后基本导入功能正常（验证所有子包） -5. 核心组件能正常工作 -6. sage命令行工具可用 -7. sage-dev开发工具正常 -8. 示例代码能正常运行 -9. 所有测试都能通过 - -测试范围： -- isage[dev] 完整安装模式 -- 包含所有子包：common, kernel, middleware, libs, tools -- 验证所有核心功能和API - -使用方法: - python test_pip_install_complete.py [选项] - -参数: - --cleanup-only: 仅清理之前的测试环境，不运行完整测试 - --test-dir: 指定测试目录 - --skip-wheel: 跳过wheel构建，使用现有的wheel包 -""" - -import argparse -import shutil -import subprocess -import sys -import time -from pathlib import Path - - -class CompletePipInstallTester: - """完整的PyPI发布准备验证器""" - - def __init__( - self, - test_dir: str | None = None, - skip_wheel: bool = False, - use_conda_env: bool = False, - ): - # 查找SAGE项目根目录 - current_file = Path(__file__).resolve() - # 从 packages/sage-tools/tests/pypi/test_pip_install_complete.py 找到项目根目录 - self.project_root = ( - current_file.parent.parent.parent.parent.parent - ) # pypi -> tests -> sage-tools -> packages -> SAGE - - # 如果没有指定test_dir，则在.sage目录下创建 - if test_dir: - self.test_dir = Path(test_dir) - else: - sage_config_dir = self.project_root / ".sage" / "temp" - sage_config_dir.mkdir(parents=True, exist_ok=True) - self.test_dir = sage_config_dir / f"pip_complete_test_{int(time.time())}" - - self.venv_dir = self.test_dir / "test_env" - self.use_conda_env = use_conda_env - - # 验证项目根目录 - if not (self.project_root / "packages" / "sage").exists(): - # 如果不在标准位置，向上查找 - check_dir = current_file.parent - while check_dir.parent != check_dir: - if (check_dir / "packages" / "sage").exists(): - self.project_root = check_dir - break - check_dir = check_dir.parent - - # 根据是否使用conda环境设置Python路径 - if use_conda_env: - # 使用系统Python（假设是conda环境） - self.python_exe = Path(sys.executable) - self.pip_exe = Path(sys.executable).parent / "pip" - self.sage_exe = Path(sys.executable).parent / "sage" - else: - self.python_exe = None - self.pip_exe = None - self.sage_exe = None - - self.skip_wheel = skip_wheel - - # 测试结果 - self.results = { - "environment_setup": False, - "wheel_build": False, - "package_installation": False, - "basic_imports": False, - "core_components": False, - "cli_tools": False, - "dev_tools": False, - "example_execution": False, - "unit_tests": False, - "cleanup": False, - } - - def run_command( - self, - cmd: list[str], - cwd: Path | None = None, - capture_output: bool = True, - check: bool = False, - timeout: int = 300, - stream_output: bool = False, - ) -> tuple[int, str, str]: - """运行命令并返回结果""" - try: - if stream_output: - # 实时输出模式 - process = subprocess.Popen( - cmd, - cwd=cwd or self.test_dir, - stdout=subprocess.PIPE, - stderr=subprocess.STDOUT, - text=True, - bufsize=1, - universal_newlines=True, - ) - - output_lines = [] - - while True: - try: - # 使用 poll() 检查进程是否完成 - if process.poll() is not None: - break - - # 读取输出行 - line = process.stdout.readline() - if line: - output_lines.append(line.rstrip()) - print(f" {line.rstrip()}") # 实时显示输出 - else: - time.sleep(0.1) - - except KeyboardInterrupt: - process.terminate() - return -1, "", "Command interrupted by user" - - # 获取剩余输出 - remaining_output, _ = process.communicate() - if remaining_output: - for line in remaining_output.splitlines(): - if line.strip(): - output_lines.append(line.rstrip()) - print(f" {line.rstrip()}") - - return process.returncode, "\n".join(output_lines), "" - else: - # 标准模式 - result = subprocess.run( - cmd, - cwd=cwd or self.test_dir, - capture_output=capture_output, - text=True, - check=check, - timeout=timeout, - ) - return result.returncode, result.stdout, result.stderr - - except subprocess.CalledProcessError as e: - return e.returncode, e.stdout, e.stderr - except subprocess.TimeoutExpired: - return -1, "", f"Command timed out after {timeout}s" - - def setup_test_environment(self) -> bool: - """设置测试环境""" - print("\n🔧 设置测试环境...") - - if self.use_conda_env: - print(" 📦 使用现有conda环境进行测试...") - - # 即使使用conda环境，也需要创建测试目录用于存放临时文件 - self.test_dir.mkdir(parents=True, exist_ok=True) - - # 验证Python可用性 - returncode, stdout, stderr = self.run_command([str(self.python_exe), "--version"]) - if returncode != 0: - print(f" ❌ Python验证失败: {stderr}") - return False - - print(f" ✅ 使用现有环境: {stdout.strip()}") - - # 检查是否是conda环境 - returncode, stdout, stderr = self.run_command( - [ - str(self.python_exe), - "-c", - "import sys; print('conda' if 'conda' in sys.executable.lower() else 'other')", - ] - ) - if returncode == 0 and "conda" in stdout.lower(): - print(" ✅ 检测到conda环境") - else: - print(" ⚠️ 未检测到conda环境，使用系统Python") - - self.results["environment_setup"] = True - return True - - try: - # 创建测试目录 - self.test_dir.mkdir(parents=True, exist_ok=True) - - # 创建虚拟环境 - print(" 📦 创建虚拟环境...") - returncode, stdout, stderr = self.run_command( - [sys.executable, "-m", "venv", str(self.venv_dir)] - ) - - if returncode != 0: - print(f" ❌ 创建虚拟环境失败: {stderr}") - return False - - # 设置Python和pip路径 - if sys.platform == "win32": - self.python_exe = self.venv_dir / "Scripts" / "python.exe" - self.pip_exe = self.venv_dir / "Scripts" / "pip.exe" - self.sage_exe = self.venv_dir / "Scripts" / "sage.exe" - else: - self.python_exe = self.venv_dir / "bin" / "python" - self.pip_exe = self.venv_dir / "bin" / "pip" - self.sage_exe = self.venv_dir / "bin" / "sage" - - # 升级pip - print(" 📦 升级pip...") - returncode, stdout, stderr = self.run_command( - [str(self.python_exe), "-m", "pip", "install", "--upgrade", "pip"] - ) - - if returncode != 0: - print(f" ⚠️ 升级pip警告: {stderr}") - - # 验证虚拟环境 - returncode, stdout, stderr = self.run_command([str(self.python_exe), "--version"]) - if returncode != 0: - print(f" ❌ Python验证失败: {stderr}") - return False - - print(f" ✅ 虚拟环境创建成功: {stdout.strip()}") - self.results["environment_setup"] = True - return True - - except Exception as e: - print(f" ❌ 设置测试环境失败: {e}") - return False - - def build_all_packages(self) -> bool: - """构建所有SAGE包""" - print("\n� 构建所有SAGE包...") - - packages = [ - "sage-common", - "sage-kernel", - "sage-middleware", - "sage-libs", - "sage-tools", - "sage-apps", - "sage-benchmark", - "sage-studio", - "sage", - ] - built_packages = [] - - for package in packages: - package_dir = self.project_root / "packages" / package - if not package_dir.exists(): - print(f" ⚠️ 跳过不存在的包: {package}") - continue - - print(f" 🔨 构建包: {package}") - - # 清理旧的构建 - dist_dir = package_dir / "dist" - build_dir = package_dir / "build" - if dist_dir.exists(): - shutil.rmtree(dist_dir) - if build_dir.exists(): - shutil.rmtree(build_dir) - - # 构建包 - returncode, stdout, stderr = self.run_command( - [str(self.python_exe), "-m", "build"], - cwd=package_dir, - timeout=300, - ) - - if returncode != 0: - print(f" ❌ 构建包 {package} 失败: {stderr}") - return False - - # 检查生成的wheel文件 - wheel_files = list(dist_dir.glob("*.whl")) - if wheel_files: - built_packages.append((package, wheel_files[0])) - print(f" ✅ 成功构建: {wheel_files[0].name}") - else: - print(f" ❌ 未找到wheel文件: {package}") - return False - - # 创建本地PyPI索引目录 - local_pypi_dir = self.test_dir / "local_pypi" - local_pypi_dir.mkdir(exist_ok=True) - - print(f" 📦 创建本地PyPI索引: {local_pypi_dir}") - - # 复制所有wheel文件到本地PyPI目录 - for _package, wheel_file in built_packages: - shutil.copy2(wheel_file, local_pypi_dir) - print(f" 📦 添加到本地索引: {wheel_file.name}") - - self.local_pypi_dir = local_pypi_dir - print(f" ✅ 本地PyPI索引创建完成，包含 {len(built_packages)} 个包") - return True - - def build_wheel_packages(self) -> bool: - """构建wheel包""" - if self.skip_wheel: - print("\n📦 跳过wheel构建（使用现有包）...") - self.results["wheel_build"] = True - return True - - print("\n🔨 构建wheel包...") - - try: - # 先安装build工具 - print(" 🔧 安装构建工具...") - returncode, stdout, stderr = self.run_command( - [str(self.python_exe), "-m", "pip", "install", "build"], - timeout=300, - ) - - if returncode != 0: - print(f" ⚠️ 安装build工具警告: {stderr}") - - # 构建所有包 - success = self.build_all_packages() - if not success: - return False - - self.results["wheel_build"] = True - return True - - except Exception as e: - print(f" ❌ 构建过程异常: {e}") - return False - - def install_package(self) -> bool: - """安装SAGE包""" - print("\n📥 安装SAGE包...") - - try: - # 使用本地PyPI索引安装完整的SAGE开发环境 - if not hasattr(self, "local_pypi_dir"): - print(" ❌ 本地PyPI索引未创建") - return False - - print(f" 📦 从本地索引安装: {self.local_pypi_dir}") - print(" 🔧 安装完整开发环境 isage[dev]，包含所有子包和依赖...") - - # 安装包，显示详细输出 - print(" 🔧 开始安装...") - print( - " 📝 安装命令:", - f"pip install --find-links {self.local_pypi_dir} --prefer-binary isage[dev]", - ) - - # 直接安装完整的开发环境，包含所有子包： - # - isage[standard] (minimal + middleware + libs) - # - 所有开发工具和依赖 - returncode, stdout, stderr = self.run_command( - [ - str(self.pip_exe), - "install", - "--find-links", - str(self.local_pypi_dir), - "--prefer-binary", # 优先使用二进制包 - "--verbose", # 显示详细信息 - "isage[dev]", # 安装完整开发环境，验证所有子包 - ], - timeout=600, # 增加超时时间，因为dev模式包含更多包 - stream_output=True, # 实时显示输出 - ) - - if returncode != 0: - print(f" ❌ 安装失败: {stderr}") - return False - - # 验证安装 - 使用更robust的版本检测方法 - version_check_code = """ -try: - import sage - version = None - - # 尝试多种方式获取版本信息 - if hasattr(sage, '__version__'): - version = sage.__version__ - elif hasattr(sage, '_version') and hasattr(sage._version, '__version__'): - version = sage._version.__version__ - - # 尝试从子包获取版本 - if not version: - try: - import sage.common - if hasattr(sage.common, '__version__'): - version = sage.common.__version__ - except ImportError: - pass - - # 尝试使用importlib.metadata（Python 3.8+的标准方式） - if not version: - # 尝试不同的包名 - package_names = ['isage', 'sage', 'sage-common', 'sage-kernel'] - for pkg_name in package_names: - try: - import importlib.metadata - version = importlib.metadata.version(pkg_name) - break - except (ImportError, Exception): - try: - import pkg_resources - version = pkg_resources.get_distribution(pkg_name).version - break - except (ImportError, Exception): - continue - - if version: - print(f"SAGE version: {version}") - else: - print("SAGE installed but version not accessible") -except Exception as e: - print(f"Import failed: {e}") - raise -""" - returncode, stdout, stderr = self.run_command( - [ - str(self.python_exe), - "-c", - version_check_code, - ] - # 移除cwd参数，在pip安装环境中使用默认工作目录 - ) - - if returncode != 0: - print(f" ❌ 验证安装失败: {stderr}") - return False - - print(f" ✅ 安装成功: {stdout.strip()}") - self.results["package_installation"] = True - return True - - except Exception as e: - print(f" ❌ 安装包失败: {e}") - return False - - def test_basic_imports(self) -> bool: - """测试基本导入功能""" - print("\n🔍 测试基本导入...") - - test_imports = [ - # 核心包 - 使用更robust的版本访问方法 - ( - "sage", - """import sage; -version = 'unknown' -# 尝试多种方式获取版本信息 -try: - if hasattr(sage, '__version__'): - version = sage.__version__ - elif hasattr(sage, '_version'): - if hasattr(sage._version, '__version__'): - version = sage._version.__version__ - - # 如果仍然是unknown，尝试从子包获取 - if version == 'unknown': - try: - import sage.common - if hasattr(sage.common, '__version__'): - version = sage.common.__version__ - except ImportError: - pass - - # 最后尝试使用importlib.metadata - if version == 'unknown': - # 尝试不同的包名 - package_names = ['isage', 'sage', 'sage-common', 'sage-kernel'] - for pkg_name in package_names: - try: - import importlib.metadata - version = importlib.metadata.version(pkg_name) - break - except (ImportError, Exception): - try: - import pkg_resources - version = pkg_resources.get_distribution(pkg_name).version - break - except (ImportError, Exception): - continue - -except Exception: - pass -print(f'SAGE {version} loaded')""", - ), - ("sage.common", "import sage.common; print('sage.common imported')"), - ("sage.core", "import sage.core; print('sage.core imported')"), - ("sage.libs", "import sage.libs; print('sage.libs imported')"), - ( - "sage.middleware", - "import sage.middleware; print('sage.middleware imported')", - ), - ("sage.tools", "import sage.tools; print('sage.tools imported')"), - # 核心API - ( - "LocalEnvironment", - "from sage.kernel.api.local_environment import LocalEnvironment; print('LocalEnvironment imported')", - ), - ( - "BatchFunction", - "from sage.common.core.functions.batch_function import BatchFunction; print('BatchFunction imported')", - ), - ( - "SinkFunction", - "from sage.common.core.functions.sink_function import SinkFunction; print('SinkFunction imported')", - ), - # Libs组件 (RAG, 数据源等) - ( - "FileSource", - "from sage.libs.foundation.io.source import FileSource; print('FileSource imported')", - ), - ( - "TerminalSink", - "from sage.libs.foundation.io.sink import TerminalSink; print('TerminalSink imported')", - ), - ( - "OpenAIGenerator", - "from sage.middleware.operators.rag.generator import OpenAIGenerator; print('OpenAIGenerator imported')", - ), - # Tools组件 - ( - "CustomLogger", - "from sage.common.utils.logging.custom_logger import CustomLogger; print('CustomLogger imported')", - ), - ( - "SAGEDevToolkit", - "from sage.tools.dev.core.toolkit import SAGEDevToolkit; print('SAGEDevToolkit imported')", - ), - ] - - failed_imports = [] - - for module_name, import_stmt in test_imports: - try: - returncode, stdout, stderr = self.run_command( - [str(self.python_exe), "-c", import_stmt] - # 移除cwd参数，在pip安装环境中使用默认工作目录 - ) - - if returncode == 0: - print(f" ✅ {module_name}: {stdout.strip()}") - else: - print(f" ❌ {module_name}: {stderr.strip()}") - failed_imports.append((module_name, stderr.strip())) - - except Exception as e: - print(f" ❌ {module_name}: {e}") - failed_imports.append((module_name, str(e))) - - success = len(failed_imports) == 0 - self.results["basic_imports"] = success - - if success: - print(" 🎉 所有基本导入成功") - else: - print(f" ⚠️ {len(failed_imports)} 个导入失败") - - return success - - def test_core_components(self) -> bool: - """测试核心组件功能""" - print("\n⚙️ 测试核心组件...") - - test_script = ''' -import sys -import traceback - -def test_component(name, test_code): - try: - exec(test_code) - print(f"✅ {name} 测试通过") - return True - except Exception as e: - print(f"❌ {name} 测试失败: {e}") - return False - -success_count = 0 - -# 测试LocalEnvironment -if test_component("LocalEnvironment", """ -from sage.kernel.api.local_environment import LocalEnvironment -env = LocalEnvironment('test_env') -print(f" 环境创建: {env.name}") -"""): - success_count += 1 - -# 测试BatchFunction -if test_component("BatchFunction", """ -from sage.common.core.functions.batch_function import BatchFunction - -class TestBatchFunction(BatchFunction): - def __init__(self): - super().__init__() - self.counter = 0 - - def execute(self): - if self.counter < 3: - result = f"data_{self.counter}" - self.counter += 1 - return result - return None - -func = TestBatchFunction() -results = [] -while True: - data = func.execute() - if data is None: - break - results.append(data) -print(f" 批处理函数执行: {len(results)} 条数据") -"""): - success_count += 1 - -# 测试SinkFunction -if test_component("SinkFunction", """ -from sage.common.core.functions.sink_function import SinkFunction - -class TestSinkFunction(SinkFunction): - def __init__(self): - super().__init__() - self.received = [] - - def execute(self, data): - self.received.append(data) - -sink = TestSinkFunction() -sink.execute("test_data") -print(f" 接收函数执行: {len(sink.received)} 条数据") -"""): - success_count += 1 - -# 测试CustomLogger -if test_component("CustomLogger", """ -from sage.common.utils.logging.custom_logger import CustomLogger -import tempfile -import os - -with tempfile.TemporaryDirectory() as temp_dir: - log_file = os.path.join(temp_dir, "test.log") - logger = CustomLogger(outputs=[("console", "INFO"), (log_file, "DEBUG")], name='test_logger') - logger.info("测试日志消息") - print(f" 日志系统创建: {logger.name}") -"""): - success_count += 1 - -print(f"\\n🎯 核心组件测试结果: {success_count}/4 通过") -if success_count == 4: - print("🎉 所有核心组件测试通过！") -else: - print("⚠️ 部分核心组件测试失败") - -sys.exit(0 if success_count == 4 else 1) -''' - - try: - # 创建临时测试脚本 - test_file = self.test_dir / "test_core.py" - with open(test_file, "w", encoding="utf-8") as f: - f.write(test_script) - - # 运行测试 - returncode, stdout, stderr = self.run_command( - [str(self.python_exe), str(test_file)] - # 移除cwd参数，在pip安装环境中使用默认工作目录 - ) - - print(stdout) - - success = returncode == 0 - self.results["core_components"] = success - - if success: - print(" ✅ 核心组件测试通过") - else: - print(f" ❌ 核心组件测试失败: {stderr}") - - return success - - except Exception as e: - print(f" ❌ 核心组件测试异常: {e}") - self.results["core_components"] = False - return False - - def test_cli_tools(self) -> bool: - """测试命令行工具""" - print("\n🔧 测试命令行工具...") - - try: - # 测试sage命令是否可用 - if self.sage_exe.exists(): - print(" ✅ sage命令行工具已安装") - - # 测试sage --version - returncode, stdout, stderr = self.run_command( - [str(self.sage_exe), "--version"], - cwd=self.test_dir, # 使用测试目录作为工作目录，避免依赖项目根目录 - ) - - if returncode == 0: - print(f" ✅ sage --version: {stdout.strip()}") - else: - print(f" ⚠️ sage --version 失败: {stderr}") - - # 测试sage --help - returncode, stdout, stderr = self.run_command( - [str(self.sage_exe), "--help"], - cwd=self.test_dir, # 使用测试目录作为工作目录，避免依赖项目根目录 - ) - - if returncode == 0: - print(" ✅ sage --help 正常") - else: - print(f" ⚠️ sage --help 失败: {stderr}") - - else: - print(" ⚠️ sage命令行工具未找到，尝试python -m sage") - - # 尝试python -m sage - returncode, stdout, stderr = self.run_command( - [str(self.python_exe), "-m", "sage", "--version"], - cwd=self.test_dir, # 使用测试目录作为工作目录，避免依赖项目根目录 - ) - - if returncode == 0: - print(f" ✅ python -m sage --version: {stdout.strip()}") - else: - print(f" ⚠️ python -m sage 也不可用: {stderr}") - - # 测试sage模块导入中的命令行接口 - cli_test = """ -try: - import sage - print("✅ sage模块导入成功") - - # 检查是否有CLI相关的属性 - if hasattr(sage, '__main__'): - print("✅ sage模块支持命令行调用") - else: - print("⚠️ sage模块不支持命令行调用") - -except Exception as e: - print(f"❌ sage模块导入失败: {e}") -""" - - returncode, stdout, stderr = self.run_command( - [str(self.python_exe), "-c", cli_test] - # 移除cwd参数，在pip安装环境中使用默认工作目录 - ) - - print(stdout) - - # 如果基本导入成功，认为CLI工具测试通过 - success = "sage模块导入成功" in stdout - self.results["cli_tools"] = success - - if success: - print(" ✅ 命令行工具测试通过") - else: - print(" ❌ 命令行工具测试失败") - - return success - - except Exception as e: - print(f" ❌ 命令行工具测试异常: {e}") - self.results["cli_tools"] = False - return False - - def test_dev_tools(self) -> bool: - """测试开发工具""" - print("\n👨‍💻 测试开发工具...") - - try: - # 测试sage.tools模块 - dev_test = """ -try: - # 测试开发工具导入 - from sage.tools.dev.core.toolkit import SAGEDevToolkit - print("✅ SAGEDevToolkit 导入成功") - - # 创建工具包实例 - toolkit = SAGEDevToolkit("./test_project") - print("✅ SAGEDevToolkit 实例创建成功") - - # 测试项目分析功能 - result = toolkit.analyze_project() - print(f"✅ 项目分析完成: {type(result)}") - -except ImportError as e: - print(f"⚠️ 开发工具模块导入失败（这在pip安装版本中是正常的）: {e}") -except Exception as e: - print(f"❌ 开发工具测试失败: {e}") - -# 测试基本开发相关功能 -try: - from sage.common.utils.logging.custom_logger import CustomLogger - logger = CustomLogger(outputs=[("console", "INFO")], name="dev_test") - logger.info("开发工具日志测试") - print("✅ 开发日志功能正常") -except Exception as e: - print(f"❌ 开发日志功能失败: {e}") - -print("🎉 开发工具测试完成") -""" - - returncode, stdout, stderr = self.run_command( - [str(self.python_exe), "-c", dev_test] - # 移除cwd参数，在pip安装环境中使用默认工作目录 - ) - - print(stdout) - - # 开发工具可能在pip安装版本中不完整，这是正常的 - # 只要基本的日志功能正常就认为通过 - success = "开发日志功能正常" in stdout - self.results["dev_tools"] = success - - if success: - print(" ✅ 开发工具测试通过") - else: - print(" ⚠️ 开发工具测试部分功能不可用（pip安装版本中正常）") - # 对于pip安装版本，开发工具不完整是可以接受的 - self.results["dev_tools"] = True - success = True - - return success - - except Exception as e: - print(f" ❌ 开发工具测试异常: {e}") - self.results["dev_tools"] = False - return False - - def test_example_execution(self) -> bool: - """测试示例代码执行""" - print("\n🚀 测试示例执行...") - - # 创建一个简单但完整的示例 - example_script = ''' -""" -完整的SAGE流水线示例 -测试从数据源到数据接收的完整流程 -""" - -from sage.kernel.api.local_environment import LocalEnvironment -from sage.common.core.functions.batch_function import BatchFunction -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.utils.logging.custom_logger import CustomLogger -import tempfile -import os - -# 设置日志 -logger = CustomLogger(outputs=[("console", "INFO")], name="example_test") - -class DataSource(BatchFunction): - """数据源：生成测试数据""" - - def __init__(self, data_list): - super().__init__() - self.data_list = data_list - self.index = 0 - logger.info(f"数据源初始化，包含 {len(data_list)} 条数据") - - def execute(self): - if self.index >= len(self.data_list): - logger.info("数据源已耗尽") - return None - - data = self.data_list[self.index] - self.index += 1 - logger.info(f"生成数据: {data}") - return data - -class DataProcessor(BatchFunction): - """数据处理器：处理数据""" - - def __init__(self, source): - super().__init__() - self.source = source - - def execute(self): - data = self.source.execute() - if data is None: - return None - - # 简单的数据处理 - processed = f"processed_{data}" - logger.info(f"处理数据: {data} -> {processed}") - return processed - -class DataSink(SinkFunction): - """数据接收器：收集处理后的数据""" - - def __init__(self): - super().__init__() - self.results = [] - logger.info("数据接收器初始化") - - def execute(self, data): - self.results.append(data) - logger.info(f"接收数据: {data}") - -def main(): - """主函数：执行完整的数据流水线""" - try: - # 创建执行环境 - env = LocalEnvironment("example_env") - logger.info(f"创建执行环境: {env.name}") - - # 准备测试数据 - test_data = ["apple", "banana", "cherry", "date", "elderberry"] - - # 创建组件 - source = DataSource(test_data) - processor = DataProcessor(source) - sink = DataSink() - - # 执行流水线 - logger.info("开始执行流水线...") - - while True: - data = processor.execute() - if data is None: - break - sink.execute(data) - - # 验证结果 - expected_count = len(test_data) - actual_count = len(sink.results) - - logger.info(f"流水线执行完成: 期望 {expected_count} 条，实际 {actual_count} 条") - - if actual_count == expected_count: - print("✅ 示例执行成功！") - print(f"📊 处理数据: {actual_count} 条") - print("📝 处理结果:") - for i, result in enumerate(sink.results, 1): - print(f" {i}. {result}") - print("🎉 SAGE PyPI安装验证完成！") - return True - else: - print(f"❌ 数据处理不完整: 期望 {expected_count}，实际 {actual_count}") - return False - - except Exception as e: - logger.error(f"示例执行失败: {e}") - import traceback - traceback.print_exc() - return False - -if __name__ == "__main__": - success = main() - exit(0 if success else 1) -''' - - try: - # 创建示例脚本 - example_file = self.test_dir / "test_example.py" - with open(example_file, "w", encoding="utf-8") as f: - f.write(example_script) - - # 运行示例 - returncode, stdout, stderr = self.run_command( - [str(self.python_exe), str(example_file)], - timeout=60, - # 移除cwd参数，在pip安装环境中使用默认工作目录 - ) - - print(stdout) - - success = returncode == 0 and "示例执行成功" in stdout - self.results["example_execution"] = success - - if success: - print(" ✅ 示例执行成功") - else: - print(f" ❌ 示例执行失败: {stderr}") - - return success - - except Exception as e: - print(f" ❌ 示例执行异常: {e}") - self.results["example_execution"] = False - return False - - def test_unit_tests(self) -> bool: - """测试简单的单元测试""" - print("\n🧪 测试单元测试运行...") - - # 创建简单的单元测试 - unit_test = ''' -""" -简单的SAGE单元测试 -验证核心功能是否正常工作 -""" - -import unittest -from sage.kernel.api.local_environment import LocalEnvironment -from sage.common.core.functions.batch_function import BatchFunction -from sage.common.core.functions.sink_function import SinkFunction -from sage.common.utils.logging.custom_logger import CustomLogger - -class TestSageCore(unittest.TestCase): - """SAGE核心功能测试""" - - def test_local_environment_creation(self): - """测试LocalEnvironment创建""" - env = LocalEnvironment("test_env") - self.assertIsNotNone(env) - self.assertEqual(env.name, "test_env") - - def test_batch_function_inheritance(self): - """测试BatchFunction继承""" - - class TestBatch(BatchFunction): - def execute(self): - return "test_data" - - batch = TestBatch() - self.assertIsNotNone(batch) - self.assertEqual(batch.execute(), "test_data") - - def test_sink_function_inheritance(self): - """测试SinkFunction继承""" - - class TestSink(SinkFunction): - def __init__(self): - super().__init__() - self.data = None - - def execute(self, data): - self.data = data - - sink = TestSink() - sink.execute("test_data") - self.assertEqual(sink.data, "test_data") - - def test_custom_logger_creation(self): - """测试CustomLogger创建""" - logger = CustomLogger(outputs=[("console", "INFO")], name="test_logger") - self.assertIsNotNone(logger) - self.assertEqual(logger.name, "test_logger") - -if __name__ == "__main__": - # 运行测试 - unittest.main(verbosity=2) -''' - - try: - # 创建单元测试文件 - test_file = self.test_dir / "test_units.py" - with open(test_file, "w", encoding="utf-8") as f: - f.write(unit_test) - - # 运行单元测试 - returncode, stdout, stderr = self.run_command( - [str(self.python_exe), str(test_file)], - timeout=60, - # 移除cwd参数，在pip安装环境中使用默认工作目录 - ) - - # unittest的输出可能在stdout或stderr中 - full_output = stdout + stderr - print(full_output) - - # 修复判断逻辑：检查返回码和输出（包括stderr） - success = returncode == 0 and ("OK" in full_output or "Ran 4 tests" in full_output) - self.results["unit_tests"] = success - - if success: - print(" ✅ 单元测试通过") - else: - print(f" ❌ 单元测试失败 (返回码: {returncode})") - if stderr: - print(f" 错误输出: {stderr[:200]}") - if stdout: - print(f" 标准输出: {stdout[:200]}") - if returncode == 0: - print(" 调试信息: 返回码为0但未找到成功标识") - print(f" 完整输出: {repr(full_output[:300])}") - - return success - - except Exception as e: - print(f" ❌ 单元测试异常: {e}") - self.results["unit_tests"] = False - return False - - def cleanup(self) -> bool: - """清理测试环境""" - print("\n🧹 清理测试环境...") - - try: - if self.test_dir.exists(): - shutil.rmtree(self.test_dir) - print(f" ✅ 测试目录已清理: {self.test_dir}") - else: - print(" ℹ️ 测试目录不存在，无需清理") - - self.results["cleanup"] = True - return True - - except Exception as e: - print(f" ❌ 清理失败: {e}") - return False - - def run_all_tests(self) -> bool: - """运行所有发布准备验证测试""" - test_mode = "conda环境完整验证" if self.use_conda_env else "虚拟环境完整安装" - print("🧪 开始SAGE PyPI发布准备完整验证") - print(f"📦 测试模式: {test_mode}") - print("🔍 验证范围: 所有子包 (common, kernel, middleware, libs, tools)") - print("=" * 60) - - start_time = time.time() - - # 运行测试步骤 - if self.use_conda_env: - # conda环境模式：使用现有环境，但仍然需要构建和安装最新包 - steps = [ - ("环境设置", self.setup_test_environment), - ("构建wheel包", self.build_wheel_packages), - ("包安装", self.install_package), - ("基本导入", self.test_basic_imports), - ("核心组件", self.test_core_components), - ("命令行工具", self.test_cli_tools), - ("开发工具", self.test_dev_tools), - ("示例执行", self.test_example_execution), - ("单元测试", self.test_unit_tests), - ] - else: - # 虚拟环境模式：完整流程 - steps = [ - ("环境设置", self.setup_test_environment), - ("构建wheel包", self.build_wheel_packages), - ("包安装", self.install_package), - ("基本导入", self.test_basic_imports), - ("核心组件", self.test_core_components), - ("命令行工具", self.test_cli_tools), - ("开发工具", self.test_dev_tools), - ("示例执行", self.test_example_execution), - ("单元测试", self.test_unit_tests), - ] - - all_passed = True - completed_steps = 0 - - for step_name, step_func in steps: - print(f"\n📋 执行测试步骤 ({completed_steps + 1}/{len(steps)}): {step_name}") - try: - if step_func(): - print(f" ✅ {step_name} 通过") - completed_steps += 1 - else: - print(f" ❌ {step_name} 失败") - all_passed = False - except Exception as e: - print(f" ❌ {step_name} 异常: {e}") - all_passed = False - - # 计算测试时间 - end_time = time.time() - duration = end_time - start_time - - print("\n" + "=" * 60) - print("📊 测试结果汇总:") - - for test_name, passed in self.results.items(): - if test_name == "cleanup": - continue # 跳过cleanup结果显示 - status = "✅ 通过" if passed else "❌ 失败" - print(f" {test_name}: {status}") - - print(f"⏱️ 总测试时间: {duration:.2f}秒") - print(f"📈 完成步骤: {completed_steps}/{len(steps)}") - - if all_passed: - print("\n🎉 所有发布准备验证测试通过！") - print("📦 SAGE已准备好发布到PyPI") - print("✨ 用户pip install isage[dev]后将获得完整功能") - return True - else: - print("\n⚠️ 部分发布准备验证测试失败") - print("🔧 建议在发布到PyPI前修复这些问题") - return False - - def run_cleanup_only(self) -> bool: - """仅运行清理""" - print("🧹 仅执行清理操作") - return self.cleanup() - - -def main(): - parser = argparse.ArgumentParser( - description="SAGE PyPI完整安装测试脚本 - 测试 isage[dev] 完整开发环境" - ) - parser.add_argument("--cleanup-only", action="store_true", help="仅清理之前的测试环境") - parser.add_argument("--test-dir", type=str, help="指定测试目录（可选）") - parser.add_argument( - "--skip-wheel", action="store_true", help="跳过wheel构建，使用现有的wheel包" - ) - parser.add_argument( - "--use-conda-env", - action="store_true", - help="在现有conda环境中进行验证，避免重复下载依赖", - ) - - args = parser.parse_args() - - # 创建测试器 - tester = CompletePipInstallTester(args.test_dir, args.skip_wheel, args.use_conda_env) - - try: - if args.cleanup_only: - success = tester.run_cleanup_only() - else: - success = tester.run_all_tests() - # 运行完测试后不自动清理，方便调试 - if not success: - print(f"\n💡 测试环境保留在: {tester.test_dir}") - print("💡 可以手动检查或重新运行测试") - - sys.exit(0 if success else 1) - - except KeyboardInterrupt: - print("\n⚠️ 测试被用户中断") - tester.cleanup() - sys.exit(1) - except Exception as e: - print(f"\n❌ 测试过程中发生异常: {e}") - import traceback - - traceback.print_exc() - sys.exit(1) - - -if __name__ == "__main__": - main() diff --git a/packages/sage-tools/tests/run_pytest.py b/packages/sage-tools/tests/run_pytest.py deleted file mode 100755 index 866a9853fa..0000000000 --- a/packages/sage-tools/tests/run_pytest.py +++ /dev/null @@ -1,162 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE CLI Test Runner (pytest-based) - -A comprehensive pytest-based test runner for SAGE CLI tools. -Provides organized test execution with multiple test categories and detailed reporting. -""" - -import argparse -import subprocess -import sys - - -def run_pytest( - test_pattern: str = ".", - markers: list[str] | None = None, - verbose: bool = True, - capture: str = "no", - coverage: bool = False, - output_file: str | None = None, - exitfirst: bool = False, -) -> int: - """ - Run pytest with specified parameters. - - Args: - test_pattern: Test pattern or directory to run - markers: pytest markers to filter tests - verbose: Enable verbose output - capture: Capture mode ("no", "sys", "fd") - coverage: Enable coverage reporting - output_file: Output file for results - exitfirst: Exit on first failure - - Returns: - Exit code from pytest - """ - cmd = ["python", "-m", "pytest"] - - # Add test pattern - cmd.append(test_pattern) - - # Add verbosity - if verbose: - cmd.append("-v") - - # Add capture mode - cmd.extend(["-s" if capture == "no" else f"--capture={capture}"]) - - # Add markers - if markers: - for marker in markers: - cmd.extend(["-m", marker]) - - # Add coverage - if coverage: - cmd.extend(["--cov=sage.tools", "--cov-report=term-missing"]) - - # Add output file - if output_file: - cmd.extend(["--junitxml", output_file]) - - # Exit on first failure - if exitfirst: - cmd.append("-x") - - print(f"Running: {' '.join(cmd)}") - return subprocess.run(cmd).returncode - - -def main(): - """Main test runner function.""" - parser = argparse.ArgumentParser( - description="SAGE CLI Test Runner (pytest-based)", - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=""" -Test Categories: - unit - Unit tests (fast, isolated) - integration - Integration tests (slower, with real services) - cli - CLI command tests - slow - Long-running tests - quick - Quick smoke tests - -Examples: - python run_pytest.py # Run all tests - python run_pytest.py --unit # Run only unit tests - python run_pytest.py --cli --verbose # Run CLI tests with verbose output - python run_pytest.py --pattern test_dev # Run tests in test_dev directory - python run_pytest.py --coverage # Run with coverage reporting - """, - ) - - # Test selection - parser.add_argument( - "--pattern", - "-p", - default=".", - help="Test pattern or directory to run (default: all tests)", - ) - - # Test categories - parser.add_argument("--unit", action="store_true", help="Run unit tests") - parser.add_argument("--integration", action="store_true", help="Run integration tests") - parser.add_argument("--cli", action="store_true", help="Run CLI tests") - parser.add_argument("--slow", action="store_true", help="Run slow tests") - parser.add_argument("--quick", action="store_true", help="Run quick tests") - - # Output options - parser.add_argument("--verbose", "-v", action="store_true", help="Verbose output") - parser.add_argument("--quiet", "-q", action="store_true", help="Quiet output") - parser.add_argument( - "--capture", - choices=["no", "sys", "fd"], - default="no", - help="Capture mode for output", - ) - parser.add_argument("--coverage", action="store_true", help="Enable coverage reporting") - parser.add_argument("--output", "-o", help="Output file for results (JUnit XML)") - - # Special test runs - parser.add_argument("--failed", action="store_true", help="Run only failed tests from last run") - parser.add_argument("--exitfirst", "-x", action="store_true", help="Exit on first failure") - - args = parser.parse_args() - - # Build markers list - markers = [] - if args.unit: - markers.append("unit") - if args.integration: - markers.append("integration") - if args.cli: - markers.append("cli") - if args.slow: - markers.append("slow") - if args.quick: - markers.append("quick") - - # Special handling for failed tests - if args.failed: - cmd = ["python", "-m", "pytest", "--lf"] - if args.verbose: - cmd.append("-v") - print(f"Running: {' '.join(cmd)}") - return subprocess.run(cmd).returncode - - # Run pytest - exit_code = run_pytest( - test_pattern=args.pattern, - markers=markers if markers else None, - verbose=args.verbose and not args.quiet, - capture=args.capture, - coverage=args.coverage, - output_file=args.output, - exitfirst=args.exitfirst, - ) - - return exit_code - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/packages/sage-tools/tests/templates/test_catalog.py b/packages/sage-tools/tests/templates/test_catalog.py deleted file mode 100644 index e469ba1433..0000000000 --- a/packages/sage-tools/tests/templates/test_catalog.py +++ /dev/null @@ -1,53 +0,0 @@ -from sage.cli import templates - - -def test_template_ids_cover_examples(): - template_ids = set(templates.list_template_ids()) - assert { - "rag-simple-demo", - "hello-world-batch", - "hello-world-log", - "rag-multimodal-fusion", - }.issubset(template_ids) - - -def test_multimodal_template_pipeline_plan_uses_real_components(): - template = templates.get_template("rag-multimodal-fusion") - plan = template.pipeline_plan() - - source_class = plan["source"]["class"] - stage_classes = [stage["class"] for stage in plan["stages"]] - - assert ( - source_class - == "sage.benchmark.benchmark_rag.implementations.qa_multimodal_fusion.MultimodalQuestionSource" - ) - assert ( - "sage.benchmark.benchmark_rag.implementations.qa_multimodal_fusion.MultimodalFusionRetriever" - in stage_classes - ) - assert "sage.middleware.operators.rag.generator.OpenAIGenerator" in stage_classes - assert plan["sink"]["class"] == "sage.libs.io.sink.TerminalSink" - - -def test_match_templates_scores_chinese_support_requests(): - matches = templates.match_templates( - { - "goal": "构建客户支持知识助手", - "description": "需要针对客服 ticket 自动回答", - }, - top_k=2, - ) - - assert matches, "模板匹配应返回候选" - assert matches[0].template.id == "rag-simple-demo" - assert matches[0].score > 0 - - -def test_render_prompt_mentions_example_path(): - template = templates.get_template("hello-world-batch") - snippet = template.render_prompt(0.5) - - assert template.example_path in snippet - assert template.title in snippet - assert "默认Pipeline" in snippet diff --git a/packages/sage-tools/tests/templates/test_pipeline_blueprints.py b/packages/sage-tools/tests/templates/test_pipeline_blueprints.py deleted file mode 100644 index 5cacf7a65e..0000000000 --- a/packages/sage-tools/tests/templates/test_pipeline_blueprints.py +++ /dev/null @@ -1,59 +0,0 @@ -from sage.cli.templates import pipeline_blueprints - - -def test_match_blueprints_surface_relevant_candidate(): - requirements = { - "goal": "构建客户支持知识助手", - "description": "需要针对客服 ticket 自动回答", - } - - matches = pipeline_blueprints.match_blueprints(requirements) - assert matches, "should return at least one blueprint" - top_blueprint, score = matches[0] - assert top_blueprint.id == "rag-simple-demo" - assert score > 0 - - -def test_build_mock_pipeline_plan_uses_blueprint_components(): - blueprint = pipeline_blueprints.select_blueprint({"goal": "运行hello world批处理示例"}) - plan = pipeline_blueprints.build_pipeline_plan(blueprint, {"goal": "运行hello world批处理示例"}) - - assert plan["source"]["class"] == "examples.tutorials.hello_world.HelloBatch" - classes = [stage["class"] for stage in plan["stages"]] - assert "examples.tutorials.hello_world.UpperCaseMap" in classes - assert plan["sink"]["class"] in { - "examples.tutorials.hello_world.PrintSink", - "sage.libs.io.sink.PrintSink", - } - notes = plan.get("notes") or [] - assert notes, "blueprint plan should include descriptive notes" - - -def test_render_blueprint_prompt_contains_metadata(): - blueprint = pipeline_blueprints.select_blueprint({"goal": "ops 告警分析"}) - snippet = pipeline_blueprints.render_blueprint_prompt(blueprint, 0.75) - - assert "Blueprint" in snippet - assert blueprint.title in snippet - assert blueprint.id in snippet - assert "主要组件" in snippet - - -def test_multimodal_blueprint_references_real_components(): - candidates = [ - blueprint - for blueprint in pipeline_blueprints.BLUEPRINT_LIBRARY - if blueprint.id == "rag-multimodal-fusion" - ] - assert candidates, "multimodal blueprint should be registered" - blueprint = candidates[0] - assert blueprint.source.class_path == ( - "sage.benchmark.benchmark_rag.implementations.qa_multimodal_fusion.MultimodalQuestionSource" - ) - stage_classes = [stage.class_path for stage in blueprint.stages] - assert ( - "sage.benchmark.benchmark_rag.implementations.qa_multimodal_fusion.MultimodalFusionRetriever" - in stage_classes - ) - assert "sage.middleware.operators.rag.generator.OpenAIGenerator" in stage_classes - assert blueprint.sink.class_path == "sage.libs.io.sink.TerminalSink" diff --git a/packages/sage-tools/tests/test_agent_training_components.py b/packages/sage-tools/tests/test_agent_training_components.py deleted file mode 100644 index 6a7527e5ea..0000000000 --- a/packages/sage-tools/tests/test_agent_training_components.py +++ /dev/null @@ -1,161 +0,0 @@ -"""Unit tests for agent training utilities.""" - -from __future__ import annotations - -import json -from types import SimpleNamespace - -import pytest - -# Import SIAS components from middleware -from sage.middleware.components.sage_sias import CoresetSelector, OnlineContinualLearner -from sage.tools.agent_training.data_formatter import AgentSFTFormatter -from sage.tools.agent_training.dialog_processor import AgentDialogProcessor, ProcessedDialog - - -def _make_turn(role: str, **kwargs): - payload = {"role": role} - payload.update(kwargs) - return SimpleNamespace(**payload) - - -def _make_dialog(turns, **kwargs): - return SimpleNamespace( - dialog_id=kwargs.get("dialog_id", "dlg-test"), - goal=kwargs.get("goal", "test tool call"), - target_tools=kwargs.get("target_tools", ["web_search"]), - metadata=kwargs.get("metadata", {}), - turns=turns, - ) - - -def _make_sample(dialog_id: str, loss: float) -> ProcessedDialog: - return ProcessedDialog( - dialog_id=dialog_id, - task_type="tool_selection", - text=f"dialog {dialog_id}", - metadata={"loss": loss}, - target_tools=["tool"], - split="train", - source="agent_sft", - ) - - -@pytest.mark.unit -def test_formatter_emits_qwen_tool_payloads(): - assistant_turn = _make_turn( - "assistant", - content="call web_search tool with plan first", - ) - tool_turn = _make_turn( - "tool", - tool_id="web_search", - content='{"query": "weather in hangzhou"}', - result={"hits": 3}, - ) - user_turn = _make_turn("user", content="查一下杭州天气") - - dialog = _make_dialog( - [user_turn, assistant_turn, tool_turn], - dialog_id="dlg-qwen", - ) - - formatter = AgentSFTFormatter( - output_format="alpaca", - include_tool_descriptions=False, - tool_call_style="qwen", - ) - - formatted = formatter.format_dialog(dialog) - output = formatted["output"] - - assert "" in output - assert "" in output - - call_payload = json.loads(output.split("\n", 1)[1].split("\n", 1)[0]) - assert call_payload["name"] == "web_search" - assert call_payload["arguments"]["query"] == "weather in hangzhou" - - response_payload = json.loads( - output.split("\n", 1)[1].split("\n", 1)[0] - ) - assert response_payload["name"] == "web_search" - assert response_payload["result"] == {"hits": 3} - - -@pytest.mark.unit -def test_coreset_selector_picks_highest_loss_samples(): - samples = [ - _make_sample("dlg1", 0.1), - _make_sample("dlg2", 0.7), - _make_sample("dlg3", 0.4), - ] - - selector = CoresetSelector(strategy="loss_topk", metric_key="loss", random_seed=0) - selected = selector.select(samples, target_size=2, metrics=None) - - assert {sample.dialog_id for sample in selected} == {"dlg2", "dlg3"} - - -@pytest.mark.unit -def test_online_continual_learner_replays_buffer_samples(): - samples = [ - _make_sample("dlg1", 0.1), - _make_sample("dlg2", 0.8), - _make_sample("dlg3", 0.5), - _make_sample("dlg4", 0.9), - ] - - selector = CoresetSelector(strategy="loss_topk", metric_key="loss", random_seed=0) - learner = OnlineContinualLearner( - buffer_size=3, - replay_ratio=0.5, - selector=selector, - random_seed=0, - ) - - batch1 = learner.update_buffer(samples[:2], metrics={"dlg1": 0.1, "dlg2": 0.8}) - assert {dialog.dialog_id for dialog in batch1} == {"dlg1", "dlg2"} - assert {dialog.dialog_id for dialog in learner.buffer_snapshot()} == {"dlg1", "dlg2"} - - batch2 = learner.update_buffer( - samples[2:], - metrics={"dlg1": 0.1, "dlg2": 0.8, "dlg3": 0.5, "dlg4": 0.9}, - ) - - # replay_ratio=0.5 => expect 1 replay when two new samples are provided - assert len(batch2) == 3 - new_ids = {dialog.dialog_id for dialog in samples[2:]} - replay_ids = {dialog.dialog_id for dialog in batch2 if dialog.dialog_id not in new_ids} - assert len(replay_ids) == 1 - - # Buffer should keep the top-3 loss dialogs (dlg2, dlg3, dlg4) - assert {dialog.dialog_id for dialog in learner.buffer_snapshot()} == {"dlg2", "dlg3", "dlg4"} - - -@pytest.mark.unit -def test_dialog_processor_emits_metrics_for_coreset(): - from unittest.mock import MagicMock, patch - - # Mock AgentToolsDataLoader to avoid data directory dependency - mock_loader = MagicMock() - mock_loader.tool_definitions = {} # Empty tool definitions - - with patch( - "sage.tools.agent_training.dialog_processor.AgentToolsDataLoader", return_value=mock_loader - ): - processor = AgentDialogProcessor() - sample = ProcessedDialog( - dialog_id="dlg-metrics", - task_type="tool_selection", - text="plan tool call plan tool call", - metadata={"loss": 0.5}, - target_tools=["web_search"], - split="train", - source="agent_sft", - ) - - metrics = processor._compute_dialog_metrics(sample) # pylint: disable=protected-access - assert metrics["loss"] == pytest.approx(0.5) - assert metrics["token_length"] > 0 - assert 0 < metrics["lexical_diversity"] <= 1 diff --git a/packages/sage-tools/tests/test_basic.py b/packages/sage-tools/tests/test_basic.py deleted file mode 100644 index ac861afb6f..0000000000 --- a/packages/sage-tools/tests/test_basic.py +++ /dev/null @@ -1,62 +0,0 @@ -""" -SAGE Common基础测试 - -这是一个基本的测试文件，用于确保sage-common包的基本功能正常。 -""" - -import os -import sys - -import pytest - -# 添加src目录到Python路径 -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "src")) - - -def test_sage_common_import(): - """测试sage.common模块是否能正常导入""" - try: - import sage.common # noqa: F401 - - assert True - except ImportError as e: - pytest.fail(f"Failed to import sage.common: {e}") - - -def test_sage_common_version(): - """测试sage.common是否有版本信息""" - import sage.common - - # 检查是否有__version__属性或者能正常导入 - assert hasattr(sage.common, "__path__") - - -def test_sage_common_structure(): - """测试sage.common的基本结构""" - import sage.common - - # 确保模块结构正确 - assert sage.common.__name__ == "sage.common" - - -@pytest.mark.unit -class TestSageCommonBasic: - """SAGE Common基础测试类""" - - def test_module_exists(self): - """测试模块存在""" - import sage.common - - assert sage.common is not None - - def test_package_structure(self): - """测试包结构""" - import sage.common - - # 检查包路径 - assert hasattr(sage.common, "__path__") - assert len(sage.common.__path__) > 0 - - -if __name__ == "__main__": - pytest.main([__file__]) diff --git a/packages/sage-tools/tests/test_cli/__init__.py b/packages/sage-tools/tests/test_cli/__init__.py deleted file mode 100644 index 4fb743ed0d..0000000000 --- a/packages/sage-tools/tests/test_cli/__init__.py +++ /dev/null @@ -1,31 +0,0 @@ -""" -CLI测试模块 - -这个模块包含所有CLI相关的测试： - -1. test_main.py - CLI主模块的单元测试 (pytest) -2. test_commands_full.py - 完整的CLI命令功能测试 -3. test_smoke.py - 冒烟测试，快速验证核心功能 -4. runner.py - Rich 汇总报告的命令分组集成测试入口 - -测试层次： -- Unit Tests (test_main.py): 测试单个组件和函数 -- Integration Tests (test_commands_full.py): 测试完整的命令流程 -- Smoke Tests (test_smoke.py): 快速验证核心功能是否可用 -- CLI Suites (runner.py): 针对 Typer 命令分组的隔离执行与模拟依赖 - -运行方式： -```bash -# 快速冒烟测试 (2-3分钟) -python test_cli/test_smoke.py - -# 完整功能测试 (10-15分钟) -python test_cli/test_commands_full.py - -# pytest单元测试 -pytest test_cli/test_main.py -v - -# Rich 集成测试 -python packages/sage-tools/tests/test_cli/runner.py -``` -""" diff --git a/packages/sage-tools/tests/test_cli/chat_suite.py b/packages/sage-tools/tests/test_cli/chat_suite.py deleted file mode 100644 index 445a49d65b..0000000000 --- a/packages/sage-tools/tests/test_cli/chat_suite.py +++ /dev/null @@ -1,84 +0,0 @@ -"""Test cases for ``sage chat`` command group.""" - -from __future__ import annotations - -import tempfile -from pathlib import Path -from types import SimpleNamespace -from unittest.mock import patch - -from .helpers import CLITestCase - -CHAT_MODULE = "sage.cli.commands.apps.chat" - - -def _patch_noop(name: str): - return lambda: patch(f"{CHAT_MODULE}.{name}", return_value=None) - - -def _patch_resolve_index_root(): - temp_dir = Path(tempfile.mkdtemp(prefix="sage_chat_cli_index_")) - return patch(f"{CHAT_MODULE}.resolve_index_root", return_value=temp_dir) - - -def _patch_default_source_dir(): - temp_dir = Path(tempfile.mkdtemp(prefix="sage_chat_cli_docs_")) - return patch(f"{CHAT_MODULE}.default_source_dir", return_value=temp_dir) - - -def _patch_load_manifest(): - fake = SimpleNamespace( - db_path=Path("/tmp/fake.sagevdb"), - created_at="2025-01-01T00:00:00Z", - source_dir="/tmp/docs", - num_documents=10, - num_chunks=120, - embedding={"method": "hash", "params": {"dim": 384}}, - chunk_size=800, - chunk_overlap=160, - ) - return patch(f"{CHAT_MODULE}.load_manifest", return_value=fake) - - -def _patch_ingest_source(): - return patch(f"{CHAT_MODULE}.ingest_source", return_value=None) - - -def collect_cases() -> list[CLITestCase]: - def check_missing_model(result): - assert result.exit_code == 2 - assert "--embedding-model" in result.stderr - - return [ - CLITestCase("sage chat --help", ["chat", "--help"]), - CLITestCase( - "sage chat ingest default", - ["chat", "ingest", "--index", "ci-test"], - patch_factories=[ - _patch_noop("ensure_sage_db"), - _patch_default_source_dir(), - _patch_resolve_index_root(), - _patch_ingest_source(), - ], - ), - CLITestCase( - "sage chat ingest needs model", - ["chat", "ingest", "--embedding-method", "openai"], - patch_factories=[ - _patch_noop("ensure_sage_db"), - _patch_default_source_dir(), - _patch_resolve_index_root(), - ], - expected_exit_code=2, - check=check_missing_model, - ), - CLITestCase( - "sage chat show manifest", - ["chat", "show", "--index", "ci-test"], - patch_factories=[ - _patch_noop("ensure_sage_db"), - _patch_resolve_index_root(), - _patch_load_manifest(), - ], - ), - ] diff --git a/packages/sage-tools/tests/test_cli/cluster_suite.py b/packages/sage-tools/tests/test_cli/cluster_suite.py deleted file mode 100644 index 11947c3007..0000000000 --- a/packages/sage-tools/tests/test_cli/cluster_suite.py +++ /dev/null @@ -1,138 +0,0 @@ -"""Test cases for ``sage cluster`` command group.""" - -from __future__ import annotations - -from types import SimpleNamespace -from unittest.mock import patch - -from sage.cli.main import app as sage_app - -from .helpers import CLITestCase, FakeConfigManager - - -def _patch(target: str, **kwargs): - return lambda: patch(target, **kwargs) - - -def _patch_cluster_config() -> list: - manager = FakeConfigManager() - return [ - _patch( - "sage.tools.cli.commands.cluster.get_config_manager", - return_value=manager, - ) - ] - - -def _patch_time_sleep(): - return _patch("sage.cli.commands.platform.cluster.time.sleep", return_value=None) - - -def _patch_start_dependencies(): - return [ - _patch("sage.cli.commands.platform.cluster.start_head", return_value=None), - _patch("sage.cli.commands.platform.cluster.start_workers", return_value=None), - _patch( - "sage.cli.commands.platform.cluster.get_config_manager", - return_value=FakeConfigManager(), - ), - _patch_time_sleep(), - ] - - -def _patch_stop_dependencies(): - return [ - _patch("sage.cli.commands.platform.cluster.stop_workers", return_value=None), - _patch("sage.cli.commands.platform.cluster.stop_head", return_value=None), - _patch_time_sleep(), - ] - - -def _patch_status_dependencies(): - manager = FakeConfigManager() - manager.add_worker_ssh_host("worker-node-1") - return [ - _patch( - "sage.cli.commands.platform.cluster.get_config_manager", - return_value=manager, - ), - _patch("sage.cli.commands.platform.cluster.status_head", return_value=None), - _patch("sage.cli.commands.platform.cluster.status_workers", return_value=None), - ] - - -def _patch_deploy_dependencies(): - deployment = SimpleNamespace(deploy_to_all_workers=lambda: (2, 2)) - return [ - _patch( - "sage.cli.commands.platform.cluster.DeploymentManager", - return_value=deployment, - ) - ] - - -def _patch_scale_dependencies(): - return [ - _patch("sage.cli.commands.platform.cluster.add_worker", return_value=None), - _patch("sage.cli.commands.platform.cluster.remove_worker", return_value=None), - ] - - -def collect_cases() -> list[CLITestCase]: - patches = _patch_cluster_config() - - return [ - CLITestCase( - "sage cluster info", - ["cluster", "info"], - app=sage_app, - patch_factories=patches, - ), - CLITestCase( - "sage cluster version", - ["cluster", "version"], - app=sage_app, - ), - CLITestCase( - "sage cluster start", - ["cluster", "start"], - app=sage_app, - patch_factories=_patch_start_dependencies(), - ), - CLITestCase( - "sage cluster stop", - ["cluster", "stop"], - app=sage_app, - patch_factories=_patch_stop_dependencies(), - ), - CLITestCase( - "sage cluster restart", - ["cluster", "restart"], - app=sage_app, - patch_factories=_patch_start_dependencies() + _patch_stop_dependencies(), - ), - CLITestCase( - "sage cluster status", - ["cluster", "status"], - app=sage_app, - patch_factories=_patch_status_dependencies(), - ), - CLITestCase( - "sage cluster deploy", - ["cluster", "deploy"], - app=sage_app, - patch_factories=_patch_deploy_dependencies(), - ), - CLITestCase( - "sage cluster scale add", - ["cluster", "scale", "add", "worker:22"], - app=sage_app, - patch_factories=_patch_scale_dependencies(), - ), - CLITestCase( - "sage cluster scale remove", - ["cluster", "scale", "remove", "worker:22"], - app=sage_app, - patch_factories=_patch_scale_dependencies(), - ), - ] diff --git a/packages/sage-tools/tests/test_cli/config_suite.py b/packages/sage-tools/tests/test_cli/config_suite.py deleted file mode 100644 index 2ca8e65ca5..0000000000 --- a/packages/sage-tools/tests/test_cli/config_suite.py +++ /dev/null @@ -1,58 +0,0 @@ -"""Test cases for ``sage config`` command group.""" - -from __future__ import annotations - -from collections.abc import Callable -from unittest.mock import patch - -from sage.cli.main import app as sage_app - -from .helpers import CLITestCase, FakeConfigManager - - -def _patch_config_manager(factory: Callable[[], FakeConfigManager]): - def _factory(): - manager = factory() - return patch("sage.tools.cli.config_manager.get_config_manager", return_value=manager) - - return _factory - - -def collect_cases() -> list[CLITestCase]: - def make_show_manager() -> FakeConfigManager: - manager = FakeConfigManager() - manager.save_config(manager.load_config()) - return manager - - init_manager = FakeConfigManager() - init_called = {"value": False} - - def custom_init(): - init_called["value"] = True - init_manager.save_config(init_manager.load_config()) - - init_manager.init_config = custom_init # type: ignore[assignment] - - def check_init(result): - assert init_called["value"], "Config init should have been invoked" - - return [ - CLITestCase( - "sage config show", - ["config", "show"], - app=sage_app, - patch_factories=[_patch_config_manager(make_show_manager)], - ), - CLITestCase( - "sage config init --force", - ["config", "init", "--force"], - app=sage_app, - patch_factories=[ - lambda: patch( - "sage.tools.cli.config_manager.get_config_manager", - return_value=init_manager, - ) - ], - check=check_init, - ), - ] diff --git a/packages/sage-tools/tests/test_cli/dev_suite.py b/packages/sage-tools/tests/test_cli/dev_suite.py deleted file mode 100644 index 1762f12422..0000000000 --- a/packages/sage-tools/tests/test_cli/dev_suite.py +++ /dev/null @@ -1,47 +0,0 @@ -"""Test cases for ``sage-dev`` command group.""" - -from __future__ import annotations - -import shutil -import tempfile -from pathlib import Path - -from sage.cli.main import app as sage_app - -from .helpers import CLITestCase - -_TEMP_DIRS: list[Path] = [] - - -def _create_sample_project() -> Path: - temp_dir = Path(tempfile.mkdtemp(prefix="sage_cli_dev_")) - _TEMP_DIRS.append(temp_dir) - - pkg_src = temp_dir / "packages" / "demo" / "src" / "sage" / "demo" - pkg_src.mkdir(parents=True, exist_ok=True) - (pkg_src / "__init__.py").write_text("__all__ = []\n", encoding="utf-8") - version_file = pkg_src.parent / "_version.py" - version_file.write_text( - "__version__ = '0.1.0'\n__author__ = 'Test'\n__email__ = 'test@example.com'\n", - encoding="utf-8", - ) - - return temp_dir - - -def cleanup(): - for path in _TEMP_DIRS: - shutil.rmtree(path, ignore_errors=True) - _TEMP_DIRS.clear() - - -def collect_cases() -> list[CLITestCase]: - project_root = _create_sample_project() - - return [ - CLITestCase( - "sage-dev version list", - ["dev", "version", "list", "--root", str(project_root)], - app=sage_app, - ), - ] diff --git a/packages/sage-tools/tests/test_cli/docs_suite.py b/packages/sage-tools/tests/test_cli/docs_suite.py deleted file mode 100644 index 904d382b88..0000000000 --- a/packages/sage-tools/tests/test_cli/docs_suite.py +++ /dev/null @@ -1,67 +0,0 @@ -"""Test cases for ``sage docs`` command group.""" - -from __future__ import annotations - -from pathlib import Path -from unittest.mock import patch - -from .helpers import CLITestCase - -DOCS_MODULE = "sage.cli.commands.platform.docs" - -_DOCS_DIR = Path("/tmp/docs-public") - - -def _patch_common(success: bool = True): - patches = [ - patch(f"{DOCS_MODULE}.find_docs_dir", return_value=_DOCS_DIR), - patch(f"{DOCS_MODULE}.check_mkdocs_installed", return_value=success), - ] - return patches - - -def _patch_subprocess(success: bool = True): - def runner(*args, **kwargs): # pragma: no cover - simple stub - if not success: - raise RuntimeError("subprocess error") - return None - - return patch(f"{DOCS_MODULE}.subprocess.run", side_effect=runner) - - -def collect_cases() -> list[CLITestCase]: - return [ - CLITestCase("sage docs --help", ["docs", "--help"]), - CLITestCase( - "sage docs serve", - ["docs", "serve", "--port", "9000", "--no-open"], - patch_factories=[ - *_patch_common(), - _patch_subprocess(), - ], - ), - CLITestCase( - "sage docs serve missing mkdocs", - ["docs", "serve"], - patch_factories=[*_patch_common(success=False)], - expected_exit_code=1, - ), - CLITestCase( - "sage docs build", - ["docs", "build", "--strict", "--output", "site"], - patch_factories=[ - *_patch_common(), - _patch_subprocess(), - ], - ), - CLITestCase( - "sage docs install-deps", - ["docs", "install-deps"], - patch_factories=[_patch_subprocess()], - ), - CLITestCase( - "sage docs info", - ["docs", "info"], - patch_factories=[*_patch_common()], - ), - ] diff --git a/packages/sage-tools/tests/test_cli/doctor_suite.py b/packages/sage-tools/tests/test_cli/doctor_suite.py deleted file mode 100644 index 2913e17f9b..0000000000 --- a/packages/sage-tools/tests/test_cli/doctor_suite.py +++ /dev/null @@ -1,13 +0,0 @@ -"""Test cases for ``sage doctor`` command.""" - -from __future__ import annotations - -from sage.cli.main import app as sage_app - -from .helpers import CLITestCase - - -def collect_cases() -> list[CLITestCase]: - return [ - CLITestCase("sage doctor", ["doctor"], app=sage_app), - ] diff --git a/packages/sage-tools/tests/test_cli/embedding_suite.py b/packages/sage-tools/tests/test_cli/embedding_suite.py deleted file mode 100644 index 53e892a9b8..0000000000 --- a/packages/sage-tools/tests/test_cli/embedding_suite.py +++ /dev/null @@ -1,88 +0,0 @@ -"""Test cases for ``sage embedding`` command group.""" - -from __future__ import annotations - -from unittest.mock import patch - -from .helpers import CLITestCase - -EMBED_MODULE = "sage.cli.commands.apps.embedding" - -SAMPLE_MODELS = { - "hash": { - "display_name": "Hash", - "requires_api_key": False, - "requires_download": False, - "default_dimension": 384, - "examples": ["hash"], - }, - "openai": { - "display_name": "OpenAI", - "requires_api_key": True, - "requires_download": False, - "default_dimension": 1536, - "examples": ["text-embedding-3-small"], - }, -} - - -def _patch_list_models(): - return patch(f"{EMBED_MODULE}.list_embedding_models", return_value=SAMPLE_MODELS) - - -def _patch_check_availability(): - payload = { - "status": "available", - "message": "ok", - "action": "none", - } - return patch(f"{EMBED_MODULE}.check_model_availability", return_value=payload) - - -def _patch_get_embedder(): - class DummyEmbedder: - def __init__(self): - self.calls = 0 - - def embed(self, text: str): - self.calls += 1 - return [0.1, 0.2, 0.3] - - def __str__(self) -> str: # pragma: no cover - repr only - return "DummyEmbedder" - - def factory(*args, **kwargs): - return DummyEmbedder() - - return patch(f"{EMBED_MODULE}.get_embedding_model", side_effect=factory) - - -def collect_cases() -> list[CLITestCase]: - return [ - CLITestCase("sage embedding --help", ["embedding", "--help"]), - CLITestCase( - "sage embedding list simple", - ["embedding", "list", "--format", "simple"], - patch_factories=[_patch_list_models()], - ), - CLITestCase( - "sage embedding list json api-only", - ["embedding", "list", "--format", "json", "--api-key-only"], - patch_factories=[_patch_list_models()], - ), - CLITestCase( - "sage embedding check", - ["embedding", "check", "hash"], - patch_factories=[_patch_check_availability(), _patch_list_models()], - ), - CLITestCase( - "sage embedding test", - ["embedding", "test", "hash", "--text", "hi"], - patch_factories=[_patch_get_embedder()], - ), - CLITestCase( - "sage embedding benchmark", - ["embedding", "benchmark", "hash", "mockembedder"], - patch_factories=[_patch_get_embedder()], - ), - ] diff --git a/packages/sage-tools/tests/test_cli/extensions_suite.py b/packages/sage-tools/tests/test_cli/extensions_suite.py deleted file mode 100644 index 3afdab2d92..0000000000 --- a/packages/sage-tools/tests/test_cli/extensions_suite.py +++ /dev/null @@ -1,48 +0,0 @@ -"""Test cases for ``sage extensions`` commands.""" - -from __future__ import annotations - -import builtins -from types import SimpleNamespace -from unittest.mock import patch - -from sage.cli.main import app as sage_app - -from .helpers import CLITestCase - - -def _patch_extensions_import(): - real_import = builtins.__import__ - - def fake_import(name, *args, **kwargs): - extensions = { - "sage.middleware.components.sage_db.python._sage_db", - "sage.middleware.components.sage_flow.python._sage_flow", - } - if name in extensions: - return SimpleNamespace() - return real_import(name, *args, **kwargs) - - return patch("builtins.__import__", side_effect=fake_import) - - -def collect_cases() -> list[CLITestCase]: - return [ - CLITestCase( - "sage extensions status", - ["extensions", "status"], - app=sage_app, - patch_factories=[_patch_extensions_import], - ), - CLITestCase( - "sage test cpp-extensions", - ["test", "cpp-extensions"], - app=sage_app, - patch_factories=[ - lambda: patch( - "sage.tools.cli.commands.extensions.check_extension_import", - return_value=True, - ) - ], - ), - ] diff --git a/packages/sage-tools/tests/test_cli/head_suite.py b/packages/sage-tools/tests/test_cli/head_suite.py deleted file mode 100644 index 00cdc3171e..0000000000 --- a/packages/sage-tools/tests/test_cli/head_suite.py +++ /dev/null @@ -1,103 +0,0 @@ -"""Test cases for ``sage head`` command group.""" - -from __future__ import annotations - -import tempfile -from pathlib import Path -from unittest.mock import patch - -from sage.cli.main import app as sage_app - -from .helpers import CLITestCase, FakeCompletedProcess, FakeConfigManager - - -def _make_config_manager() -> FakeConfigManager: - manager = FakeConfigManager() - temp_dir = Path(tempfile.mkdtemp(prefix="sage_head_cli_")) - log_dir = temp_dir / "logs" - log_dir.mkdir(parents=True, exist_ok=True) - (log_dir / "head.log").write_text("log\n", encoding="utf-8") - - # Update underlying config so accessor returns these paths - manager._config["head"]["log_dir"] = str(log_dir) - manager._config["head"]["temp_dir"] = str(temp_dir / "tmp") - manager._config["remote"]["ray_command"] = "ray" - manager._config["remote"]["conda_env"] = "sage" - return manager - - -def _patch(target: str, **kwargs): - return lambda: patch(target, **kwargs) - - -def _patch_head_dependencies() -> list: - manager = _make_config_manager() - - return [ - _patch( - "sage.tools.cli.commands.head.get_config_manager", - return_value=manager, - ), - _patch( - "sage.cli.commands.platform.head.get_config_manager", - return_value=manager, - ), - _patch( - "sage.cli.commands.platform.head.subprocess.run", - return_value=FakeCompletedProcess(stdout="ok", returncode=0), - ), - ] - - -def _patch_time_sleep(): - return _patch("sage.cli.commands.platform.head.time.sleep", return_value=None) - - -def _start_stop_patches() -> list: - return _patch_head_dependencies() + [_patch_time_sleep()] - - -def collect_cases() -> list[CLITestCase]: - patches = _patch_head_dependencies() - - return [ - CLITestCase( - "sage head status", - ["head", "status"], - app=sage_app, - patch_factories=patches, - ), - CLITestCase( - "sage head version", - ["head", "version"], - app=sage_app, - ), - CLITestCase( - "sage head start", - ["head", "start"], - app=sage_app, - patch_factories=_start_stop_patches(), - ), - CLITestCase( - "sage head stop", - ["head", "stop"], - app=sage_app, - patch_factories=_start_stop_patches(), - ), - CLITestCase( - "sage head restart", - ["head", "restart"], - app=sage_app, - patch_factories=[ - _patch("sage.cli.commands.platform.head.stop_head", return_value=None), - _patch("sage.cli.commands.platform.head.start_head", return_value=None), - _patch_time_sleep(), - ], - ), - CLITestCase( - "sage head logs", - ["head", "logs", "--lines", "5"], - app=sage_app, - patch_factories=patches, - ), - ] diff --git a/packages/sage-tools/tests/test_cli/helpers.py b/packages/sage-tools/tests/test_cli/helpers.py deleted file mode 100644 index 98bb1b1dd4..0000000000 --- a/packages/sage-tools/tests/test_cli/helpers.py +++ /dev/null @@ -1,261 +0,0 @@ -"""Utility helpers for SAGE CLI integration tests. - -This module provides a small framework around :class:`typer.testing.CliRunner` -so individual CLI test scripts can focus on describing the commands they want to -validate instead of wiring mocks and assertion plumbing repeatedly. -""" - -from __future__ import annotations - -import copy -import tempfile -from collections.abc import Callable, Iterable, Sequence -from contextlib import AbstractContextManager, ExitStack -from dataclasses import dataclass, field -from pathlib import Path -from typing import Any - -from typer import Typer -from typer.testing import CliRunner - -from sage.cli.main import app as sage_app - -# Type alias for factory functions that return context managers (e.g. mocks). -PatchFactory = Callable[[], AbstractContextManager[Any]] -CheckCallable = Callable[["CLITestResult"], None] - - -@dataclass -class CLITestCase: - """Represents a single CLI invocation that should succeed.""" - - name: str - args: Sequence[str] - app: Typer = sage_app - patch_factories: Sequence[PatchFactory] = field(default_factory=tuple) - env: dict[str, str] | None = None - expected_exit_code: int = 0 - check: CheckCallable | None = None - - -@dataclass -class CLITestResult: - """Outcome of executing a :class:`CLITestCase`.""" - - case: CLITestCase - exit_code: int - stdout: str - stderr: str - exception: BaseException | None - - @property - def ok(self) -> bool: - return self.exit_code == self.case.expected_exit_code and self.exception is None - - -@dataclass -class CLIRunSummary: - """Summary returned by :func:`run_cases` or the CLI test runner.""" - - results: list[CLITestResult] - - @property - def success(self) -> bool: - return all(result.ok for result in self.results) - - @property - def failures(self) -> list[CLITestResult]: - return [result for result in self.results if not result.ok] - - -class FakeConfigManager: - """In-memory drop-in replacement for :class:`ConfigManager`.""" - - def __init__(self, config: dict | None = None): - self._temp_dir = Path(tempfile.mkdtemp(prefix="sage_cli_config_")) - self.config_path = self._temp_dir / "config.yaml" - self._config = copy.deepcopy(config or default_config()) - self._config.setdefault("workers_ssh_hosts", []) - - def load_config(self) -> dict: - return copy.deepcopy(self._config) - - def save_config(self, config: dict): - self._config = copy.deepcopy(config) - - def init_config(self): - self.save_config(default_config()) - - @property - def config(self) -> dict: - return copy.deepcopy(self._config) - - def get_head_config(self) -> dict: - return copy.deepcopy(self._config.get("head", {})) - - def get_worker_config(self) -> dict: - return copy.deepcopy(self._config.get("worker", {})) - - def get_ssh_config(self) -> dict: - return copy.deepcopy(self._config.get("ssh", {})) - - def get_remote_config(self) -> dict: - return copy.deepcopy(self._config.get("remote", {})) - - def get_workers_ssh_hosts(self) -> list[tuple[str, int]]: - hosts = self._config.get("workers_ssh_hosts", []) - if isinstance(hosts, list): - return [(item["host"], item.get("port", 22)) for item in hosts] - return [] - - def add_worker_ssh_host(self, host: str, port: int = 22) -> bool: - hosts = self._config.setdefault("workers_ssh_hosts", []) - for item in hosts: - if item["host"] == host and item.get("port", 22) == port: - return False - hosts.append({"host": host, "port": port}) - return True - - def remove_worker_ssh_host(self, host: str, port: int = 22) -> bool: - hosts = self._config.setdefault("workers_ssh_hosts", []) - original_len = len(hosts) - hosts[:] = [ - item for item in hosts if not (item["host"] == host and item.get("port", 22) == port) - ] - return len(hosts) < original_len - - def get_worker_config_path(self) -> Path: - return self._temp_dir - - -def default_config() -> dict: - """Return a representative configuration dictionary for tests.""" - - return { - "head": { - "host": "127.0.0.1", - "head_port": 6379, - "dashboard_port": 8265, - "dashboard_host": "127.0.0.1", - "temp_dir": "/tmp/ray_head", - "log_dir": "/tmp/sage_head_logs", - "ray_command": "ray", - "conda_env": "sage", - }, - "worker": { - "bind_host": "127.0.0.1", - "temp_dir": "/tmp/ray_worker", - "log_dir": "/tmp/sage_worker_logs", - }, - "ssh": { - "user": "sage", - "key_path": "~/.ssh/id_rsa", - "connect_timeout": 5, - "workers": [ - {"host": "worker-node-1", "port": 22}, - {"host": "worker-node-2", "port": 2200}, - ], - }, - "remote": { - "sage_home": "/opt/sage", - "python_path": "/opt/conda/bin/python", - "ray_command": "ray", - "conda_env": "sage", - }, - "monitor": {"refresh_interval": 5}, - "jobmanager": {"timeout": 10, "retry_attempts": 2}, - } - - -def run_case(case: CLITestCase) -> CLITestResult: - """Execute a single CLI test case.""" - - runner = CliRunner() - with ExitStack() as stack: - for factory in case.patch_factories: - stack.enter_context(factory()) - result = runner.invoke(case.app, list(case.args), env=case.env) - - cli_result = CLITestResult( - case=case, - exit_code=result.exit_code, - stdout=result.stdout, - stderr=result.stderr or "", - exception=result.exception, - ) - - if case.check is not None: - case.check(cli_result) - - return cli_result - - -def run_cases(cases: Iterable[CLITestCase]) -> CLIRunSummary: - results = [run_case(case) for case in cases] - return CLIRunSummary(results=results) - - -class FakeCompletedProcess: - """Simple stand-in for :class:`subprocess.CompletedProcess`.""" - - def __init__(self, stdout: str = "", stderr: str = "", returncode: int = 0): - self.stdout = stdout - self.stderr = stderr - self.returncode = returncode - - -class DummyProcess: - """A lightweight object emulating a running process for CLI tests.""" - - def __init__(self, pid: int = 1234, cmd: list[str] | None = None): - self.pid = pid - self._cmd = cmd or ["python", "-m", "sage"] - - # Methods used by psutil during CLI commands - def terminate(self): - return None - - def kill(self): - return None - - def wait(self, timeout: int | None = None): - return 0 - - def cpu_percent(self): - return 0.5 - - def memory_info(self): - return type("Mem", (), {"rss": 10 * 1024 * 1024})() - - def create_time(self): - import time - - return time.time() - 30 - - def cmdline(self): - return list(self._cmd) - - def oneshot(self): - outer_self = self - - class _Oneshot: - def __enter__(self): - return outer_self - - def __exit__(self, exc_type, exc, tb): - return False - - return _Oneshot() - - -__all__ = [ - "CLITestCase", - "CLITestResult", - "CLIRunSummary", - "FakeCompletedProcess", - "FakeConfigManager", - "DummyProcess", - "default_config", - "run_case", - "run_cases", -] diff --git a/packages/sage-tools/tests/test_cli/inference_suite.py b/packages/sage-tools/tests/test_cli/inference_suite.py deleted file mode 100644 index 742ca84afb..0000000000 --- a/packages/sage-tools/tests/test_cli/inference_suite.py +++ /dev/null @@ -1,137 +0,0 @@ -"""Test cases for ``sage inference`` command group.""" - -from __future__ import annotations - -import tempfile -from pathlib import Path -from unittest.mock import MagicMock, patch - -from sage.cli.main import app as sage_app - -from .helpers import CLITestCase, DummyProcess - -MODULE = "sage.cli.commands.apps.inference" - - -def _fake_process(pid: int = 4321) -> MagicMock: - proc = MagicMock() - proc.pid = pid - proc.wait.return_value = 0 - proc.terminate.return_value = None - proc.kill.return_value = None - return proc - - -def _patch_temp_paths(*, write_log: bool = False): - temp_dir = Path(tempfile.mkdtemp(prefix="sage_inference_cli_")) - pid_file = temp_dir / "inference.pid" - config_file = temp_dir / "inference.json" - log_file = temp_dir / "logs" / "inference.log" - log_file.parent.mkdir(parents=True, exist_ok=True) - if write_log: - log_file.write_text("line1\nline2\n", encoding="utf-8") - - return [ - lambda: patch(f"{MODULE}.PID_FILE", pid_file), - lambda: patch(f"{MODULE}.CONFIG_FILE", config_file), - lambda: patch(f"{MODULE}.LOG_FILE", log_file), - ] - - -def _patch_psutil_process(): - return lambda: patch( - f"{MODULE}.psutil.Process", - side_effect=lambda pid: DummyProcess(pid=pid), - ) - - -def _patch_running_pid(pid: int | None): - return lambda: patch(f"{MODULE}._get_running_pid", return_value=pid) - - -def _patch_port_in_use(result: bool): - return lambda: patch(f"{MODULE}._is_port_in_use", return_value=result) - - -def _patch_popen(): - return lambda: patch(f"{MODULE}.subprocess.Popen", return_value=_fake_process()) - - -def _patch_health_response(): - return lambda: patch( - f"{MODULE}._test_api_health", - return_value={ - "status": "success", - "backends": { - "llm": {"healthy": True}, - "embedding": {"healthy": True}, - }, - }, - ) - - -def _patch_load_config(config: dict): - return lambda: patch(f"{MODULE}._load_config", return_value=config) - - -def collect_cases() -> list[CLITestCase]: - return [ - CLITestCase( - "sage inference start background", - ["inference", "start", "--background", "--port", "8100"], - app=sage_app, - patch_factories=_patch_temp_paths() - + [ - _patch_running_pid(None), - _patch_port_in_use(False), - _patch_popen(), - lambda: patch(f"{MODULE}._save_pid"), - lambda: patch(f"{MODULE}._save_config"), - ], - ), - CLITestCase( - "sage inference stop force", - ["inference", "stop", "--force"], - app=sage_app, - patch_factories=_patch_temp_paths() - + [ - _patch_running_pid(9001), - _patch_psutil_process(), - ], - ), - CLITestCase( - "sage inference status json", - ["inference", "status", "--json"], - app=sage_app, - patch_factories=_patch_temp_paths() - + [ - _patch_running_pid(1234), - _patch_psutil_process(), - _patch_port_in_use(True), - _patch_health_response(), - _patch_load_config({"port": 8000, "llm_model": "demo"}), - ], - ), - CLITestCase( - "sage inference config json", - ["inference", "config", "--output", "json"], - app=sage_app, - patch_factories=[ - _patch_load_config( - { - "host": "0.0.0.0", - "port": 8000, - "llm_model": "demo-llm", - "embedding_model": "demo-embed", - "scheduling_policy": "adaptive", - } - ) - ], - ), - CLITestCase( - "sage inference logs tail", - ["inference", "logs", "-n", "1"], - app=sage_app, - patch_factories=_patch_temp_paths(write_log=True), - ), - ] diff --git a/packages/sage-tools/tests/test_cli/job_suite.py b/packages/sage-tools/tests/test_cli/job_suite.py deleted file mode 100644 index 1ee86033f1..0000000000 --- a/packages/sage-tools/tests/test_cli/job_suite.py +++ /dev/null @@ -1,208 +0,0 @@ -"""Test cases for the ``sage job`` command group.""" - -from __future__ import annotations - -from collections.abc import Callable -from types import SimpleNamespace -from unittest.mock import patch - -from sage.cli.main import app as sage_app - -from .helpers import CLITestCase - - -def _build_fake_client() -> SimpleNamespace: - jobs = [ - { - "uuid": "job-demo-uuid", - "name": "demo-job", - "status": "running", - "start_time": "2025-01-01 00:00:00", - "runtime": "00:05:00", - } - ] - - def list_jobs() -> dict[str, object]: - return {"status": "success", "jobs": jobs} - - def get_job_status(uuid: str) -> dict[str, object]: - return { - "status": "success", - "job_status": { - "uuid": uuid, - "name": "demo-job", - "status": "running", - "start_time": "2025-01-01 00:00:00", - "runtime": "00:05:00", - }, - } - - def health_check() -> dict[str, object]: - return { - "status": "success", - "daemon_status": { - "socket_service": "running", - "actor_name": "demo", - "namespace": "default", - }, - } - - def get_server_info() -> dict[str, object]: - return { - "status": "success", - "server_info": { - "session_id": "session-1", - "log_base_dir": "/tmp/sage", - "environments_count": 1, - "jobs": jobs, - }, - } - - return SimpleNamespace( - list_jobs=list_jobs, - get_job_status=get_job_status, - health_check=health_check, - get_server_info=get_server_info, - pause_job=lambda uuid: {"status": "stopped", "message": "stopped"}, - continue_job=lambda uuid: {"status": "running", "message": "resumed"}, - delete_job=lambda uuid, force=False: {"status": "success", "message": "deleted"}, - cleanup_all_jobs=lambda: {"status": "success", "message": "all cleaned"}, - ) - - -def _patch_cli_client() -> list[Callable[[], object]]: - fake_client = _build_fake_client() - - def fake_connect(self): # type: ignore[override] - self.client = fake_client - self.connected = True - return True - - def fake_ensure_connected(self): # type: ignore[override] - if not getattr(self, "connected", False): - fake_connect(self) - return True - - return [ - lambda: patch( - "sage.tools.cli.commands.job.JobManagerCLI.connect", - fake_connect, - ), - lambda: patch( - "sage.tools.cli.commands.job.JobManagerCLI.ensure_connected", - fake_ensure_connected, - ), - lambda: patch( - "sage.tools.cli.commands.job.typer.confirm", - return_value=True, - ), - lambda: patch( - "sage.tools.cli.commands.job.cli._resolve_job_identifier", - return_value="job-demo-uuid", - ), - ] - - -def collect_cases() -> list[CLITestCase]: - patches = _patch_cli_client() - - return [ - CLITestCase( - "sage job list", - ["job", "list"], - app=sage_app, - patch_factories=patches, - ), - CLITestCase( - "sage job status", - ["job", "status", "job-demo-uuid"], - app=sage_app, - patch_factories=patches, - ), - CLITestCase( - "sage job health", - ["job", "health"], - app=sage_app, - patch_factories=patches, - ), - CLITestCase( - "sage job info", - ["job", "info"], - app=sage_app, - patch_factories=patches, - ), - CLITestCase( - "sage job stop", - ["job", "stop", "job-demo-uuid", "--force"], - app=sage_app, - patch_factories=patches, - ), - CLITestCase( - "sage job continue", - ["job", "continue", "job-demo-uuid", "--force"], - app=sage_app, - patch_factories=patches, - ), - CLITestCase( - "sage job delete", - ["job", "delete", "job-demo-uuid", "--force"], - app=sage_app, - patch_factories=patches, - ), - CLITestCase( - "sage job cleanup", - ["job", "cleanup", "--force"], - app=sage_app, - patch_factories=patches, - ), - CLITestCase( - "sage job show", - ["job", "show", "job-demo-uuid"], - app=sage_app, - patch_factories=patches, - ), - CLITestCase( - "sage job pause alias", - ["job", "pause", "job-demo-uuid", "--force"], - app=sage_app, - patch_factories=patches, - ), - CLITestCase( - "sage job resume alias", - ["job", "resume", "job-demo-uuid", "--force"], - app=sage_app, - patch_factories=patches, - ), - CLITestCase( - "sage job monitor", - ["job", "monitor", "--refresh", "0"], - app=sage_app, - patch_factories=patches - + [ - lambda: patch( - "sage.tools.cli.commands.job.time.sleep", - side_effect=KeyboardInterrupt, - ), - lambda: patch( - "sage.tools.cli.commands.job.os.system", - return_value=0, - ), - ], - ), - CLITestCase( - "sage job watch", - ["job", "watch", "job-demo-uuid", "--refresh", "0"], - app=sage_app, - patch_factories=patches - + [ - lambda: patch( - "sage.tools.cli.commands.job.time.sleep", - side_effect=KeyboardInterrupt, - ), - lambda: patch( - "sage.tools.cli.commands.job.os.system", - return_value=0, - ), - ], - ), - ] diff --git a/packages/sage-tools/tests/test_cli/jobmanager_suite.py b/packages/sage-tools/tests/test_cli/jobmanager_suite.py deleted file mode 100644 index 16a7d928b5..0000000000 --- a/packages/sage-tools/tests/test_cli/jobmanager_suite.py +++ /dev/null @@ -1,113 +0,0 @@ -"""Test cases for ``sage jobmanager`` command group.""" - -from __future__ import annotations - -from types import SimpleNamespace -from unittest.mock import patch - -from sage.cli.main import app as sage_app - -from .helpers import CLITestCase - - -def _fake_sudo_manager() -> SimpleNamespace: - return SimpleNamespace( - ensure_sudo_access=lambda: True, - has_sudo_access=lambda: True, - get_cached_password=lambda: "secret", - ) - - -def _patch_sudo_manager(): - return lambda: patch( - "sage.cli.commands.platform.jobmanager.create_sudo_manager", - return_value=_fake_sudo_manager(), - ) - - -def _patch_controller(method: str, *, return_value=True): - return lambda: patch( - f"sage.cli.commands.platform.jobmanager.JobManagerController.{method}", - return_value=return_value, - ) - - -def _status_patch(): - return lambda: patch( - "sage.cli.commands.platform.jobmanager.JobManagerController.status", - return_value={ - "health": {"status": "success"}, - "processes": [], - "port_occupied": False, - }, - ) - - -def collect_cases() -> list[CLITestCase]: - base_patches = [_patch_sudo_manager()] - - return [ - CLITestCase( - "sage jobmanager status", - ["jobmanager", "status"], - app=sage_app, - patch_factories=base_patches + [_status_patch()], - ), - CLITestCase( - "sage jobmanager version", - ["jobmanager", "version"], - app=sage_app, - ), - CLITestCase( - "sage jobmanager start", - ["jobmanager", "start"], - app=sage_app, - patch_factories=base_patches + [_patch_controller("start")], - ), - CLITestCase( - "sage jobmanager start foreground force", - [ - "jobmanager", - "start", - "--foreground", - "--force", - "--no-wait", - "--host", - "0.0.0.0", - "--port", - "19005", - ], - app=sage_app, - patch_factories=base_patches + [_patch_controller("start")], - ), - CLITestCase( - "sage jobmanager stop", - ["jobmanager", "stop"], - app=sage_app, - patch_factories=base_patches + [_patch_controller("stop_gracefully")], - ), - CLITestCase( - "sage jobmanager stop force", - ["jobmanager", "stop", "--force"], - app=sage_app, - patch_factories=base_patches + [_patch_controller("force_kill")], - ), - CLITestCase( - "sage jobmanager restart", - ["jobmanager", "restart"], - app=sage_app, - patch_factories=base_patches + [_patch_controller("restart")], - ), - CLITestCase( - "sage jobmanager restart force", - ["jobmanager", "restart", "--force", "--no-wait"], - app=sage_app, - patch_factories=base_patches + [_patch_controller("restart")], - ), - CLITestCase( - "sage jobmanager kill", - ["jobmanager", "kill"], - app=sage_app, - patch_factories=base_patches + [_patch_controller("force_kill")], - ), - ] diff --git a/packages/sage-tools/tests/test_cli/llm_suite.py b/packages/sage-tools/tests/test_cli/llm_suite.py deleted file mode 100644 index 5881e19302..0000000000 --- a/packages/sage-tools/tests/test_cli/llm_suite.py +++ /dev/null @@ -1,144 +0,0 @@ -"""Test cases for the ``sage llm`` command group.""" - -from __future__ import annotations - -from types import SimpleNamespace -from unittest.mock import patch - -from sage.cli.main import app as sage_app -from sage.middleware.operators import SageLLMGenerator - -from .helpers import CLITestCase - - -def _raise_not_implemented(*_args, **_kwargs): - raise NotImplementedError("placeholder") - - -def collect_cases() -> list[CLITestCase]: - fake_info = SimpleNamespace( - model_id="demo/model", - revision="main", - path="/tmp/demo", - size_bytes=1024, - size_mb=1.0, - last_used_iso="2024-01-01T00:00:00", - tags=["text"], - ) - - return [ - CLITestCase( - "sage llm model show --json", - ["llm", "model", "show", "--json"], - app=sage_app, - patch_factories=[ - lambda: patch( - "sage.common.model_registry.sagellm_registry.list_models", - return_value=[fake_info], - ) - ], - ), - CLITestCase( - "sage llm run (stub)", - ["llm", "run", "--model", "demo/model"], - app=sage_app, - patch_factories=[ - lambda: patch( - "sage.middleware.operators.llm.sagellm_generator.SageLLMGenerator", - return_value=SimpleNamespace( - setup=lambda: None, - execute=lambda *_a, **_k: "hi", - cleanup=lambda: None, - ), - ), - lambda: patch( - "sage.tools.cli.commands.llm.typer.prompt", - return_value="", - ), - ], - ), - CLITestCase( - "sage llm model download", - ["llm", "model", "download", "--model", "demo/model"], - app=sage_app, - patch_factories=[ - lambda: patch( - "sage.common.model_registry.sagellm_registry.download_model", - return_value=fake_info, - ) - ], - ), - CLITestCase( - "sage llm model delete", - ["llm", "model", "delete", "--model", "demo/model", "--yes"], - app=sage_app, - patch_factories=[ - lambda: patch( - "sage.common.model_registry.sagellm_registry.delete_model", - return_value=None, - ) - ], - ), - CLITestCase( - "sage llm fine-tune (stub)", - [ - "llm", - "fine-tune", - "--base-model", - "demo/model", - "--dataset", - "data.json", - "--output", - "out", - ], - app=sage_app, - patch_factories=[ - lambda: patch( - "sage.middleware.operators.llm.sagellm_generator.SageLLMGenerator", - return_value=SimpleNamespace( - fine_tune=_raise_not_implemented, - setup=lambda: None, - cleanup=lambda: None, - ), - ) - ], - ), - ] - - -# --------------------------------------------------------------------------- -# SageLLMGenerator unit tests (GPU-free, CI-compatible) -# --------------------------------------------------------------------------- - - -class TestSageLLMGeneratorMockMode: - """Test SageLLMGenerator with mock backend (no GPU required).""" - - def test_sagellm_generator_mock_mode(self): - """SageLLMGenerator should work in mock mode without GPU.""" - generator = SageLLMGenerator(backend_type="mock") - result = generator.execute("test prompt") - # Mock backend returns dict with 'text' and 'usage' keys - assert result is not None - assert isinstance(result, dict) - assert "text" in result - assert "usage" in result - # Check that output contains generated text - assert isinstance(result["text"], str) - assert len(result["text"]) > 0 - - def test_sagellm_generator_mock_dict_input(self): - """SageLLMGenerator mock mode should handle dict inputs.""" - generator = SageLLMGenerator(backend_type="mock") - result = generator.execute({"prompt": "test prompt", "options": {"max_tokens": 100}}) - assert result is not None - assert isinstance(result, dict) - assert "text" in result - assert len(result["text"]) > 0 - - def test_sagellm_generator_default_config(self): - """SageLLMGenerator should have sensible defaults.""" - generator = SageLLMGenerator(backend_type="mock") - assert generator.backend_type == "mock" - # device_map should default to "auto" - assert generator.device_map == "auto" diff --git a/packages/sage-tools/tests/test_cli/pipeline_suite.py b/packages/sage-tools/tests/test_cli/pipeline_suite.py deleted file mode 100644 index 674451a614..0000000000 --- a/packages/sage-tools/tests/test_cli/pipeline_suite.py +++ /dev/null @@ -1,104 +0,0 @@ -"""Test cases for ``sage pipeline`` command group.""" - -from __future__ import annotations - -from pathlib import Path -from unittest.mock import MagicMock, patch - -from .helpers import CLITestCase - -PIPELINE_MODULE = "sage.cli.commands.apps.pipeline" - - -def _patch_builder_dependencies(): - patches = [ - patch(f"{PIPELINE_MODULE}.load_domain_contexts", return_value=[]), - patch(f"{PIPELINE_MODULE}.load_custom_contexts", return_value=[]), - patch(f"{PIPELINE_MODULE}.get_default_knowledge_base", return_value=None), - ] - return patches - - -def _patch_plan_generation(): - mock_generator = MagicMock() - mock_plan = { - "pipeline": {"name": "demo", "type": "local"}, - "stages": [{"class": "A"}], - "sink": {"class": "B"}, - } - mock_generator.generate.side_effect = [mock_plan] - patches = [ - patch(f"{PIPELINE_MODULE}.PipelinePlanGenerator", return_value=mock_generator), - patch(f"{PIPELINE_MODULE}._render_plan"), - patch(f"{PIPELINE_MODULE}._plan_to_yaml", return_value="pipeline: demo"), - patch(f"{PIPELINE_MODULE}._preview_yaml"), - patch(f"{PIPELINE_MODULE}._save_plan", return_value=Path("/tmp/demo.yaml")), - ] - return patches - - -def _patch_execute_plan(): - return patch(f"{PIPELINE_MODULE}.execute_pipeline_plan", return_value=None) - - -def _patch_pipeline_file(plan: dict | None = None): - target = plan or { - "pipeline": {"name": "ci", "type": "local"}, - "stages": [{"class": "X"}], - "sink": {"class": "Y"}, - } - return patch(f"{PIPELINE_MODULE}._load_pipeline_file", return_value=target) - - -def _patch_kb_search(): - fake_chunk = MagicMock() - fake_chunk.text = "Sample chunk" - fake_chunk.kind = "doc" - fake_chunk.score = 0.9 - fake_chunk.vector = [0.1] * 5 - - fake_kb = MagicMock() - fake_kb.search.return_value = [fake_chunk] - - return patch(f"{PIPELINE_MODULE}.PipelineKnowledgeBase", return_value=fake_kb) - - -def collect_cases() -> list[CLITestCase]: - build_patches = _patch_builder_dependencies() + _patch_plan_generation() - - return [ - CLITestCase("sage pipeline --help", ["pipeline", "--help"]), - CLITestCase( - "sage pipeline build non-interactive", - [ - "pipeline", - "build", - "--name", - "demo", - "--goal", - "demo goal", - "--backend", - "mock", - "--non-interactive", - "--no-knowledge", - ], - patch_factories=[lambda p=p: p for p in build_patches], - ), - CLITestCase( - "sage pipeline run", - ["pipeline", "run", "demo.yaml"], - patch_factories=[_patch_pipeline_file(), _patch_execute_plan()], - ), - CLITestCase( - "sage pipeline analyze-embedding", - ["pipeline", "analyze-embedding", "question"], - patch_factories=[_patch_kb_search()], - ), - CLITestCase( - "sage pipeline create-embedding", - ["pipeline", "create-embedding", "--template", "rag"], - patch_factories=[ - patch(f"{PIPELINE_MODULE}.generate_embedding_pipeline", return_value={}) - ], - ), - ] diff --git a/packages/sage-tools/tests/test_cli/runner.py b/packages/sage-tools/tests/test_cli/runner.py deleted file mode 100644 index d72712eaba..0000000000 --- a/packages/sage-tools/tests/test_cli/runner.py +++ /dev/null @@ -1,157 +0,0 @@ -"""Aggregate runner for SAGE CLI command tests.""" - -from __future__ import annotations - -import importlib.util -import sys -from collections.abc import Iterable, Sequence -from dataclasses import dataclass -from pathlib import Path -from types import ModuleType - -from rich.console import Console -from rich.table import Table - -THIS_DIR = Path(__file__).resolve().parent -SUITE_PACKAGE = "sage_cli_suite" - -try: # pragma: no cover - import fallback for direct execution - from .helpers import CLIRunSummary, CLITestCase, run_cases -except ImportError: # pragma: no cover - if __package__ in (None, ""): - sys.path.insert(0, str(THIS_DIR)) - from helpers import ( - CLIRunSummary, # type: ignore[no-redef] - CLITestCase, - run_cases, - ) - else: # pragma: no cover - raise -SUITE_FILES: Sequence[str] = ( - "version_suite.py", - "config_suite.py", - "llm_suite.py", - "chat_suite.py", - "embedding_suite.py", - "pipeline_suite.py", - "inference_suite.py", - "studio_suite.py", - "docs_suite.py", - "stack_suite.py", - "doctor_suite.py", - "dev_suite.py", - "extensions_suite.py", - # studio_suite.py moved to sage-studio package - "job_suite.py", - "jobmanager_suite.py", - "worker_suite.py", - "cluster_suite.py", - "head_suite.py", -) - -console = Console() - - -@dataclass -class CLITestRun: - cases: list[CLITestCase] - summary: CLIRunSummary - - @property - def success(self) -> bool: - return self.summary.success - - -def _ensure_package_namespace() -> None: - if SUITE_PACKAGE not in sys.modules: - package_module = ModuleType(SUITE_PACKAGE) - package_module.__path__ = [str(THIS_DIR)] # type: ignore[attr-defined] - sys.modules[SUITE_PACKAGE] = package_module - - helpers_module = sys.modules.get(f"{SUITE_PACKAGE}.helpers") - if helpers_module is None: - base_helpers = sys.modules.get("helpers") - if base_helpers is not None: - sys.modules[f"{SUITE_PACKAGE}.helpers"] = base_helpers - - -def _load_module_from_path(path: Path) -> ModuleType: - _ensure_package_namespace() - module_name = f"{SUITE_PACKAGE}.{path.stem}" - spec = importlib.util.spec_from_file_location(module_name, str(path)) - if spec is None or spec.loader is None: - raise ImportError(f"Unable to load module from {path}") - module = importlib.util.module_from_spec(spec) - module.__package__ = SUITE_PACKAGE - sys.modules[module_name] = module - spec.loader.exec_module(module) # type: ignore[assignment] - return module - - -def _load_modules() -> tuple[list[ModuleType], list[CLITestCase]]: - modules: list[ModuleType] = [] - cases: list[CLITestCase] = [] - - for filename in SUITE_FILES: - path = THIS_DIR / filename - module = _load_module_from_path(path) - modules.append(module) - if hasattr(module, "collect_cases"): - module_cases = list(module.collect_cases()) # type: ignore[attr-defined] - cases.extend(module_cases) - - return modules, cases - - -def run_all_cli_tests(*, quiet: bool = False) -> CLITestRun: - modules, cases = _load_modules() - - summary = run_cases(cases) - - for module in modules: - cleanup = getattr(module, "cleanup", None) - if callable(cleanup): - cleanup() - - if not quiet: - _render_summary(summary) - - return CLITestRun(cases=cases, summary=summary) - - -def _render_summary(summary: CLIRunSummary) -> None: - table = Table(title="SAGE CLI Test Summary") - table.add_column("Case", style="cyan") - table.add_column("Exit Code", justify="right") - table.add_column("Status", style="green") - - for result in summary.results: - status = "PASS" if result.ok else "FAIL" - style = "green" if result.ok else "red" - table.add_row(result.case.name, str(result.exit_code), f"[{style}]{status}[/{style}]") - - console.print(table) - - if summary.failures: - console.print("[red]❌ CLI tests failed. Detailed outputs:[/red]") - for failure in summary.failures: - console.print(f"\n[red]Case:[/red] {failure.case.name}") - if failure.exception: - console.print(f"[red]Exception:[/red] {failure.exception!r}") - if failure.stdout: - console.print("[yellow]stdout:[/yellow]") - console.print(failure.stdout) - if failure.stderr: - console.print("[yellow]stderr:[/yellow]") - console.print(failure.stderr) - else: - console.print("[green]✅ All CLI tests passed[/green]") - - -def main(argv: Iterable[str] | None = None) -> int: - run = run_all_cli_tests(quiet=False) - return 0 if run.success else 1 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/packages/sage-tools/tests/test_cli/studio_suite.py b/packages/sage-tools/tests/test_cli/studio_suite.py deleted file mode 100644 index 74f4803b63..0000000000 --- a/packages/sage-tools/tests/test_cli/studio_suite.py +++ /dev/null @@ -1,144 +0,0 @@ -"""Test cases for ``sage studio`` command group.""" - -from __future__ import annotations - -from types import MethodType -from unittest.mock import patch - -from .helpers import CLITestCase - -STUDIO_MODULE = "sage.cli.commands.apps.studio" - - -def _make_manager(**overrides): - class FakeManager: - def __init__(self): - self.config = {"host": "127.0.0.1", "port": 5173} - - def list_finetuned_models(self): - return [] - - def is_running(self): - return False - - def load_config(self): - return self.config - - def start(self, **kwargs): - return True - - def stop(self): - return True - - def status(self): - return None - - def logs(self, **kwargs): - return None - - def install(self): - return True - - def build(self): - return True - - def clean(self): # type: ignore[override] - return True - - def clean_frontend_cache(self): - return True - - def run_npm_command(self, args): - return True - - manager = FakeManager() - for name, value in overrides.items(): - if callable(value): - setattr(manager, name, MethodType(value, manager)) - else: - setattr(manager, name, value) - return manager - - -def _patch_manager(**overrides): - return patch(f"{STUDIO_MODULE}.studio_manager", _make_manager(**overrides)) - - -def _patch_webbrowser_open(): - return patch("webbrowser.open", return_value=True) - - -def collect_cases() -> list[CLITestCase]: - return [ - CLITestCase("sage studio --help", ["studio", "--help"]), - CLITestCase( - "sage studio start", - ["studio", "start", "--host", "0.0.0.0", "--port", "9000", "--no-llm"], - patch_factories=[_patch_manager()], - ), - CLITestCase( - "sage studio start list finetuned", - ["studio", "start", "--list-finetuned"], - patch_factories=[ - _patch_manager( - list_finetuned_models=lambda self: [ - { - "name": "demo", - "type": "llm", - "base_model": "qwen", - "path": "/tmp/model", - "completed_at": "2025-01-01", - } - ] - ) - ], - ), - CLITestCase( - "sage studio stop", - ["studio", "stop"], - patch_factories=[_patch_manager()], - ), - CLITestCase( - "sage studio restart", - ["studio", "restart", "--no-clean", "--no-llm"], - patch_factories=[_patch_manager()], - ), - CLITestCase( - "sage studio status", - ["studio", "status"], - patch_factories=[_patch_manager()], - ), - CLITestCase( - "sage studio logs", - ["studio", "logs", "--backend", "--gateway"], - patch_factories=[_patch_manager()], - ), - CLITestCase( - "sage studio install", - ["studio", "install"], - patch_factories=[_patch_manager()], - ), - CLITestCase( - "sage studio build", - ["studio", "build"], - patch_factories=[_patch_manager()], - ), - CLITestCase( - "sage studio open", - ["studio", "open"], - patch_factories=[ - _patch_manager(is_running=lambda self: True), - _patch_webbrowser_open(), - ], - ), - CLITestCase( - "sage studio clean", - ["studio", "clean"], - patch_factories=[_patch_manager()], - ), - CLITestCase( - "sage studio npm", - ["studio", "npm", "run", "build"], - patch_factories=[_patch_manager()], - ), - ] diff --git a/packages/sage-tools/tests/test_cli/test_commands_full.py b/packages/sage-tools/tests/test_cli/test_commands_full.py deleted file mode 100644 index 4d98807eac..0000000000 --- a/packages/sage-tools/tests/test_cli/test_commands_full.py +++ /dev/null @@ -1,226 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE 开发工具 CLI 命令完整测试 - -测试所有dev命令的功能，确保它们能正常工作。 -使用pytest格式符合测试标准。 -""" - -import json -import subprocess -import sys -from pathlib import Path -from typing import Any - -import pytest - -from sage.common.config import find_sage_project_root - -# 项目根目录 -PROJECT_ROOT = find_sage_project_root() -DEV_CLI_MODULE = "sage.tools.cli.commands.dev.main" - - -def run_command( - command: list[str], timeout: int = 30, project_root: Path = PROJECT_ROOT -) -> dict[str, Any]: - """运行命令并返回结果""" - try: - result = subprocess.run( - command, cwd=project_root, capture_output=True, text=True, timeout=timeout - ) - return { - "success": result.returncode == 0, - "stdout": result.stdout, - "stderr": result.stderr, - "returncode": result.returncode, - } - except subprocess.TimeoutExpired: - return { - "success": False, - "stdout": "", - "stderr": f"Command timed out after {timeout} seconds", - "returncode": -1, - } - except Exception as e: - return {"success": False, "stdout": "", "stderr": str(e), "returncode": -1} - - -@pytest.mark.cli -@pytest.mark.integration -class TestCLICommandsFull: - """完整的CLI命令测试""" - - def test_main_cli_help(self): - """测试主CLI帮助""" - result = run_command([sys.executable, "-m", DEV_CLI_MODULE, "--help"]) - assert result["success"], f"CLI help failed: {result['stderr']}" - # dev CLI 显示开发工具帮助 - assert "开发工具" in result["stdout"] or "dev" in result["stdout"].lower() - - def test_dev_help(self): - """测试dev命令帮助""" - result = run_command([sys.executable, "-m", DEV_CLI_MODULE, "--help"]) - assert result["success"], f"Dev help failed: {result['stderr']}" - assert "开发工具" in result["stdout"] - - def test_sage_dev_help(self): - """测试sage-dev帮助""" - result = run_command(["sage-dev", "--help"]) - # sage-dev 可能不在PATH中，允许失败 - if not result["success"]: - pytest.skip("sage-dev command not available in PATH") - assert "开发工具" in result["stdout"] - - def test_status_command_summary(self): - """测试status命令 - summary格式""" - result = run_command( - [ - sys.executable, - "-m", - DEV_CLI_MODULE, - "status", - "--output-format", - "summary", - "--quick", - ], - timeout=120, # 增加超时时间到120秒 - ) - assert result["success"], f"Status summary failed: {result['stderr']}" - assert "状态报告" in result["stdout"] - - def test_status_command_json(self): - """测试status命令 - JSON格式""" - result = run_command( - [ - sys.executable, - "-m", - DEV_CLI_MODULE, - "status", - "--output-format", - "json", - ], - timeout=120, # 增加超时时间，status 命令较慢 - ) - assert result["success"], f"Status JSON failed: {result['stderr']}" - # 验证JSON格式 - 跳过调试输出，找到实际的JSON - lines = result["stdout"].strip().split("\n") - json_lines = [] - json_started = False - for line in lines: - if line.strip().startswith("{"): - json_started = True - if json_started: - json_lines.append(line) - - if json_lines: - json_text = "\n".join(json_lines) - try: - # Try to parse the JSON - data = json.loads(json_text) - # Verify it has the expected structure - assert "timestamp" in data - assert "checks" in data - assert isinstance(data["checks"], dict) - except json.JSONDecodeError: - # If JSON parsing fails due to control characters, just check basic structure - assert "timestamp" in json_text - assert "checks" in json_text - assert "{" in json_text and "}" in json_text - else: - pytest.fail("No JSON found in status output") - - def test_status_command_markdown(self): - """测试status命令 - Markdown格式""" - result = run_command( - [ - sys.executable, - "-m", - DEV_CLI_MODULE, - "status", - "--output-format", - "markdown", - ] - ) - assert result["success"], f"Status markdown failed: {result['stderr']}" - assert "# SAGE 项目状态报告" in result["stdout"] - - def test_analyze_command_basic(self): - """测试analyze命令 - 基本分析""" - result = run_command( - [ - sys.executable, - "-m", - DEV_CLI_MODULE, - "analyze", - "--analysis-type", - "all", - ] - ) - # 分析命令可能需要更长时间，允许某些错误 - if result["success"]: - assert "分析" in result["stdout"] - else: - # 如果失败，检查是否是预期的错误 - assert result["returncode"] in [ - 0, - 1, - ], f"Unexpected return code: {result['returncode']}" - - def test_clean_command_dry_run(self): - """测试clean命令（预览模式）""" - result = run_command( - [ - sys.executable, - "-m", - DEV_CLI_MODULE, - "clean", - "--dry-run", - ] - ) - assert result["success"], f"Clean dry-run failed: {result['stderr']}" - assert "预览" in result["stdout"] - - @pytest.mark.slow - def test_import_functionality(self): - """测试关键模块导入功能""" - modules_to_test = [ - DEV_CLI_MODULE, - "sage.tools.dev.tools.project_status_checker", - "sage.tools.dev.tools.dependency_analyzer", - ] - - for module in modules_to_test: - result = run_command([sys.executable, "-c", f"import {module}; print('OK')"]) - assert result["success"], f"Failed to import {module}: {result['stderr']}" - assert "OK" in result["stdout"] - - @pytest.mark.slow - def test_home_command_status(self): - """测试home命令状态""" - result = run_command( - [ - sys.executable, - "-m", - DEV_CLI_MODULE, - "home", - "status", - ] - ) - assert result["success"], f"Home status failed: {result['stderr']}" - # 检查SAGE目录状态输出中的关键信息 - assert "SAGE目录" in result["stdout"] or "SAGE目录状态" in result["stdout"] - - def test_test_command_basic(self): - """测试test命令基本功能""" - # 这个测试可能耗时较长，所以我们只测试命令能够启动 - # 实际的测试运行在其他地方验证 - result = run_command( - [ - sys.executable, - "-c", - f"from {DEV_CLI_MODULE} import test; print('Test command importable')", - ] - ) - assert result["success"], f"Test command import failed: {result['stderr']}" - assert "Test command importable" in result["stdout"] diff --git a/packages/sage-tools/tests/test_cli/test_llm_config.py b/packages/sage-tools/tests/test_cli/test_llm_config.py deleted file mode 100644 index bd212752f1..0000000000 --- a/packages/sage-tools/tests/test_cli/test_llm_config.py +++ /dev/null @@ -1,136 +0,0 @@ -#!/usr/bin/env python3 -"""Tests for the `sage config llm auto` command.""" - -from pathlib import Path - -import yaml -from typer.testing import CliRunner - -from sage.cli.commands.platform.config import app as config_app -from sage.cli.utils.llm_detection import LLMServiceInfo - -runner = CliRunner() - - -def _write_config(path: Path, content: str) -> None: - path.write_text(content, encoding="utf-8") - - -def test_auto_updates_generator_remote(monkeypatch, tmp_path): - """Ensure the command updates generator.remote with detected service info.""" - - config_path = tmp_path / "config.yaml" - _write_config( - config_path, - """ - generator: - remote: - method: "openai" - api_key: "" - """, - ) - - detection = LLMServiceInfo( - name="ollama", - base_url="http://127.0.0.1:11434/v1", - models=["llama3", "llama2"], - default_model="llama3", - generator_section="remote", - description="Ollama test instance", - ) - - monkeypatch.setattr( - "sage.cli.commands.platform.llm_config.detect_all_services", - lambda prefer=None, auth_token=None: [detection], - ) - - result = runner.invoke( - config_app, - [ - "llm", - "auto", - "--config-path", - str(config_path), - "--yes", - ], - ) - - assert result.exit_code == 0, result.stdout - - updated = yaml.safe_load(config_path.read_text(encoding="utf-8")) - generator = updated["generator"]["remote"] - assert generator["base_url"] == detection.base_url - assert generator["model_name"] == detection.default_model - assert generator["method"] == "openai" - - backup_path = config_path.with_suffix(".yaml.bak") - assert backup_path.exists() - - -def test_auto_updates_specific_section(monkeypatch, tmp_path): - """The user can override the generator section and model name.""" - - config_path = tmp_path / "pipeline.yaml" - _write_config(config_path, "generator: {}\n") - - detection = LLMServiceInfo( - name="ollama", - base_url="http://127.0.0.1:11434/v1", - models=["llama3"], - default_model="llama3", - generator_section="remote", - description="Ollama test instance", - ) - - monkeypatch.setattr( - "sage.cli.commands.platform.llm_config.detect_all_services", - lambda prefer=None, auth_token=None: [detection], - ) - - result = runner.invoke( - config_app, - [ - "llm", - "auto", - "--config-path", - str(config_path), - "--section", - "sagellm", - "--model-name", - "custom-model", - "--yes", - ], - ) - - assert result.exit_code == 0, result.stdout - - updated = yaml.safe_load(config_path.read_text(encoding="utf-8")) - generator = updated["generator"]["sagellm"] - assert generator["base_url"] == detection.base_url - assert generator["model_name"] == "custom-model" - - -def test_auto_handles_missing_services(monkeypatch, tmp_path): - """When no services are detected the command exits with error.""" - - config_path = tmp_path / "config.yaml" - _write_config(config_path, "generator: {}\n") - - monkeypatch.setattr( - "sage.cli.commands.platform.llm_config.detect_all_services", - lambda prefer=None, auth_token=None: [], - ) - - result = runner.invoke( - config_app, - [ - "llm", - "auto", - "--config-path", - str(config_path), - "--yes", - ], - ) - - assert result.exit_code != 0 - assert "未检测到支持的本地 LLM 服务" in result.stdout diff --git a/packages/sage-tools/tests/test_cli/test_main.py b/packages/sage-tools/tests/test_cli/test_main.py deleted file mode 100644 index 84d6230814..0000000000 --- a/packages/sage-tools/tests/test_cli/test_main.py +++ /dev/null @@ -1,94 +0,0 @@ -""" -CLI主模块测试 -""" - -import os - -import pytest -from typer.testing import CliRunner - -pytestmark = pytest.mark.skipif( - os.getenv("SAGE_RUN_SLOW_TESTS") != "1", - reason="CLI dev command tests can be slow; set SAGE_RUN_SLOW_TESTS=1 to enable.", -) - -from sage.tools.cli.commands.dev import app - - -@pytest.mark.cli -class TestCLIMain: - """CLI主模块测试""" - - def setup_method(self): - """测试前设置""" - self.runner = CliRunner() - - def test_cli_help(self): - """测试CLI帮助""" - result = self.runner.invoke(app, ["--help"]) - assert result.exit_code == 0 - # app is sage.tools.cli.commands.dev:app, already at dev level - assert "质量检查" in result.stdout or "project" in result.stdout - - def test_dev_help(self): - """测试dev命令帮助 - 实际测试project子命令""" - result = self.runner.invoke(app, ["project", "--help"]) - assert result.exit_code == 0 - assert "项目管理" in result.stdout or "status" in result.stdout - - -@pytest.mark.cli -@pytest.mark.unit -class TestDevCommands: - """dev命令测试""" - - def setup_method(self): - """测试前设置""" - self.runner = CliRunner() - - def test_dev_project_status(self): - """测试project status命令""" - result = self.runner.invoke(app, ["project", "status"]) - assert result.exit_code == 0 - assert "状态报告" in result.stdout or "状态" in result.stdout - - def test_dev_project_status_json(self): - """测试project status JSON输出""" - result = self.runner.invoke(app, ["project", "status", "--output-format", "json"]) - assert result.exit_code == 0 - # 应该包含JSON结构 - assert "timestamp" in result.stdout or "{" in result.stdout - - def test_dev_project_status_full(self): - """测试project status详细输出""" - result = self.runner.invoke(app, ["project", "status", "--output-format", "full"]) - assert result.exit_code == 0 - assert "检查" in result.stdout or "状态" in result.stdout - - def test_dev_project_analyze(self): - """测试project analyze命令""" - result = self.runner.invoke(app, ["project", "analyze"]) - # 分析命令可能需要更长时间，允许某些失败 - assert result.exit_code in [0, 1] - if result.exit_code == 0: - assert "分析" in result.stdout or "状态" in result.stdout - - def test_dev_project_analyze_health(self): - """测试project analyze健康检查""" - result = self.runner.invoke(app, ["project", "analyze", "--analysis-type", "health"]) - assert result.exit_code in [0, 1] - if result.exit_code == 0: - assert "分析" in result.stdout or "健康" in result.stdout - - def test_dev_project_clean_dry_run(self): - """测试project clean dry-run""" - result = self.runner.invoke(app, ["project", "clean", "--dry-run"]) - assert result.exit_code == 0 - assert "预览" in result.stdout or "清理" in result.stdout - - def test_dev_project_home_status(self): - """测试project home status""" - result = self.runner.invoke(app, ["project", "home", "status"]) - assert result.exit_code == 0 - # 检查SAGE目录状态输出中的关键信息 - assert "SAGE目录" in result.stdout or "SAGE" in result.stdout diff --git a/packages/sage-tools/tests/test_cli/test_smoke.py b/packages/sage-tools/tests/test_cli/test_smoke.py deleted file mode 100644 index 95a03c4fcc..0000000000 --- a/packages/sage-tools/tests/test_cli/test_smoke.py +++ /dev/null @@ -1,90 +0,0 @@ -#!/usr/bin/env python3 -""" -SAGE CLI 冒烟测试 (Smoke Test) - -这是一个轻量级的快速验证测试，只测试最关键的核心功能： -1. CLI能否正常启动 -2. 核心命令是否可访问 -3. 基本功能是否工作 - -与 test_commands_full.py 的区别： -- Smoke Test: 快速验证，2-3分钟，关键路径 -- Full Test: 详细测试，可能10-15分钟，覆盖所有功能 - -使用pytest格式符合测试标准。 -""" - -import subprocess -import sys - -import pytest - -from sage.common.config import find_sage_project_root - - -def get_project_root(): - """获取项目根目录""" - return find_sage_project_root() - - -def run_command_simple(cmd_list, timeout=20): - """运行命令并返回成功状态""" - try: - result = subprocess.run( - cmd_list, - capture_output=True, - text=True, - timeout=timeout, - cwd=get_project_root(), - ) - return result.returncode == 0, result.stdout, result.stderr - except subprocess.TimeoutExpired: - return False, "", f"Command timed out after {timeout}s" - except Exception as e: - return False, "", str(e) - - -@pytest.mark.cli -@pytest.mark.smoke -class TestCLISmoke: - """CLI冒烟测试 - 快速验证核心功能""" - - def test_cli_startup(self): - """测试CLI启动""" - success, stdout, stderr = run_command_simple( - [sys.executable, "-m", "sage.tools.cli.commands.dev.main", "--help"] - ) - assert success, f"CLI startup failed: {stderr}" - # dev CLI 显示开发工具帮助 - assert "开发工具" in stdout or "dev" in stdout.lower() - - def test_dev_command_help(self): - """测试dev命令""" - success, stdout, stderr = run_command_simple( - [sys.executable, "-m", "sage.tools.cli.commands.dev.main", "--help"] - ) - assert success, f"Dev command failed: {stderr}" - assert "开发工具" in stdout or "dev" in stdout.lower() - - def test_status_check(self): - """测试基本状态检查""" - success, stdout, stderr = run_command_simple( - [ - sys.executable, - "-m", - "sage.tools.cli.commands.dev.main", - "status", - "--quick", - ], - timeout=120, # 增加超时到120秒 - ) - assert success, f"Status check failed: {stderr}" - assert "状态报告" in stdout or "status" in stdout.lower() - - def test_backwards_compatibility(self): - """测试向后兼容性""" - success, stdout, stderr = run_command_simple(["sage-dev", "--help"]) - if not success: - # sage-dev可能不在PATH中，这是可以接受的 - pytest.skip("sage-dev command not available in PATH - this is acceptable") - assert "开发工具" in stdout or "dev" in stdout.lower() diff --git a/packages/sage-tools/tests/test_cli/version_suite.py b/packages/sage-tools/tests/test_cli/version_suite.py deleted file mode 100644 index 38e32ed6e5..0000000000 --- a/packages/sage-tools/tests/test_cli/version_suite.py +++ /dev/null @@ -1,14 +0,0 @@ -"""Validation cases for ``sage version`` commands.""" - -from __future__ import annotations - -from sage.cli.main import app as sage_app - -from .helpers import CLITestCase - - -def collect_cases() -> list[CLITestCase]: - return [ - CLITestCase("sage --version callback", ["--version"], app=sage_app), - CLITestCase("sage version show", ["version", "show"], app=sage_app), - ] diff --git a/packages/sage-tools/tests/test_cli/worker_suite.py b/packages/sage-tools/tests/test_cli/worker_suite.py deleted file mode 100644 index 700eb4504b..0000000000 --- a/packages/sage-tools/tests/test_cli/worker_suite.py +++ /dev/null @@ -1,151 +0,0 @@ -"""Test cases for ``sage worker`` command group.""" - -from __future__ import annotations - -import tempfile -from pathlib import Path -from unittest.mock import patch - -from sage.cli.main import app as sage_app - -from .helpers import CLITestCase, FakeConfigManager - - -def _setup_manager() -> FakeConfigManager: - manager = FakeConfigManager() - temp_dir = Path(tempfile.mkdtemp(prefix="sage_worker_cli_")) - log_dir = temp_dir / "logs" - log_dir.mkdir(parents=True, exist_ok=True) - manager._config["worker"]["log_dir"] = str(log_dir) - manager._config["worker"]["temp_dir"] = str(temp_dir / "tmp") - manager._config["remote"]["ray_command"] = "ray" - manager._config["remote"]["conda_env"] = "sage" - manager._config.setdefault("ssh", {})["user"] = "sage" - manager._config["ssh"]["key_path"] = str(temp_dir / "id_rsa") - manager._config["ssh"]["connect_timeout"] = 5 - manager._config["workers_ssh_hosts"] = [("host1", 22)] - return manager - - -def _patch(target: str, **kwargs): - return lambda: patch(target, **kwargs) - - -def _patch_config_manager() -> list: - manager = _setup_manager() - return [ - _patch( - "sage.tools.cli.commands.worker.get_config_manager", - return_value=manager, - ), - _patch( - "sage.cli.commands.platform.worker.get_config_manager", - return_value=manager, - ), - ] - - -def _patch_subprocess(): - return _patch("sage.cli.commands.platform.worker.subprocess.run") - - -def _patch_execute_remote(success: bool = True): - return _patch( - "sage.cli.commands.platform.worker.execute_remote_command", - return_value=success, - ) - - -def _patch_time_sleep(): - return _patch("sage.cli.commands.platform.worker.time.sleep", return_value=None) - - -def collect_cases() -> list[CLITestCase]: - patches = _patch_config_manager() - - return [ - CLITestCase( - "sage worker list", - ["worker", "list"], - app=sage_app, - patch_factories=patches, - ), - CLITestCase( - "sage worker version", - ["worker", "version"], - app=sage_app, - ), - CLITestCase( - "sage worker start", - ["worker", "start"], - app=sage_app, - patch_factories=patches - + [ - _patch_execute_remote(), - _patch_time_sleep(), - ], - ), - CLITestCase( - "sage worker stop", - ["worker", "stop"], - app=sage_app, - patch_factories=patches + [_patch_execute_remote()], - ), - CLITestCase( - "sage worker stop force", - ["worker", "stop", "--force"], - app=sage_app, - patch_factories=patches + [_patch_execute_remote()], - ), - CLITestCase( - "sage worker status", - ["worker", "status"], - app=sage_app, - patch_factories=patches + [_patch_subprocess()], - ), - CLITestCase( - "sage worker add", - ["worker", "add", "host1:22"], - app=sage_app, - patch_factories=patches - + [ - _patch( - "sage.cli.commands.platform.worker.add_worker", - return_value=None, - ) - ], - ), - CLITestCase( - "sage worker remove", - ["worker", "remove", "host1:22"], - app=sage_app, - patch_factories=patches - + [ - _patch( - "sage.cli.commands.platform.worker.remove_worker", - return_value=None, - ) - ], - ), - CLITestCase( - "sage worker config", - ["worker", "config"], - app=sage_app, - patch_factories=patches, - ), - CLITestCase( - "sage worker deploy", - ["worker", "deploy"], - app=sage_app, - patch_factories=[ - _patch( - "sage.cli.commands.platform.worker.DeploymentManager", - return_value=type( - "DM", - (), - {"deploy_to_all_workers": lambda self: (1, 1)}, - )(), - ) - ], - ), - ] diff --git a/packages/sage-tools/tests/test_dev/test_dependency_analyzer.py b/packages/sage-tools/tests/test_dev/test_dependency_analyzer.py deleted file mode 100644 index 1f670577bf..0000000000 --- a/packages/sage-tools/tests/test_dev/test_dependency_analyzer.py +++ /dev/null @@ -1,111 +0,0 @@ -""" -依赖分析器单元测试 -""" - -import os - -import pytest - -pytestmark = pytest.mark.skipif( - os.getenv("SAGE_RUN_SLOW_TESTS") != "1", - reason="Dependency analyzer tests are slow; set SAGE_RUN_SLOW_TESTS=1 to enable.", -) - -from sage.common.config import find_sage_project_root -from sage.tools.dev.tools.dependency_analyzer import DependencyAnalyzer - - -@pytest.fixture -def project_root(): - """获取项目根目录的fixture""" - return find_sage_project_root() - - -@pytest.mark.unit -class TestDependencyAnalyzer: - """依赖分析器测试""" - - def test_init(self, project_root): - """测试初始化""" - analyzer = DependencyAnalyzer(str(project_root)) - assert analyzer.project_root.is_absolute() - assert analyzer.packages_dir.exists() - - def test_analyze_all_dependencies(self, project_root): - """测试分析所有依赖""" - analyzer = DependencyAnalyzer(str(project_root)) - result = analyzer.analyze_all_dependencies() - - assert "project_root" in result - assert "packages" in result - assert "summary" in result - assert "dependency_graph" in result - - summary = result["summary"] - assert "total_packages" in summary - assert "total_dependencies" in summary - - def test_check_dependency_health(self, project_root): - """测试依赖健康检查""" - analyzer = DependencyAnalyzer(str(project_root)) - result = analyzer.check_dependency_health() - - assert "health_score" in result - assert "grade" in result - assert "issues" in result - assert "recommendations" in result - - def test_generate_dependency_report(self, project_root): - """测试生成依赖报告""" - analyzer = DependencyAnalyzer(str(project_root)) - result = analyzer.generate_dependency_report() - - assert isinstance(result, dict) - # 报告应该包含分析结果 - assert len(result) > 0 - - def test_find_package_directories(self, project_root): - """测试查找包目录""" - analyzer = DependencyAnalyzer(str(project_root)) - packages = analyzer._find_package_directories() - - assert isinstance(packages, list) - # 应该找到一些包 - assert len(packages) > 0 - - # 每个包都应该是目录 - for pkg in packages: - assert pkg.is_dir() - - def test_is_python_package(self, project_root): - """测试Python包识别""" - analyzer = DependencyAnalyzer(str(project_root)) - - # 测试真实的包目录 - packages_dir = analyzer.packages_dir - if packages_dir.exists(): - for pkg_dir in packages_dir.iterdir(): - if pkg_dir.is_dir() and pkg_dir.name.startswith("sage-"): - assert analyzer._is_python_package(pkg_dir) is True - - def test_parse_dependency_spec(self, project_root): - """测试依赖规格解析""" - analyzer = DependencyAnalyzer(str(project_root)) - - # 测试简单包名 - name, info = analyzer._parse_dependency_spec("requests") - assert name == "requests" - assert isinstance(info, dict) - assert info["spec"] == "requests" - - # 测试带版本的包名 - name, info = analyzer._parse_dependency_spec("requests>=2.25.0") - assert name == "requests" - assert isinstance(info, dict) - assert "requests" in info["spec"] - - # 测试复杂版本规格 - name, info = analyzer._parse_dependency_spec("numpy>=1.20.0,<2.0.0") - assert name == "numpy" - assert isinstance(info, dict) - assert "numpy" in info["spec"] diff --git a/packages/sage-tools/tests/test_dev/test_hooks.py b/packages/sage-tools/tests/test_dev/test_hooks.py deleted file mode 100644 index 5161085b78..0000000000 --- a/packages/sage-tools/tests/test_dev/test_hooks.py +++ /dev/null @@ -1,312 +0,0 @@ -""" -Tests for Git Hooks Installer and Manager. -""" - -from pathlib import Path -from unittest.mock import MagicMock, Mock, patch - -from sage.tools.dev.hooks import HooksInstaller, HooksManager - - -class TestHooksInstaller: - """Test HooksInstaller class.""" - - def test_init_with_root_dir(self, tmp_path: Path) -> None: - """Test installer initialization with explicit root directory.""" - installer = HooksInstaller(root_dir=tmp_path, quiet=True) - assert installer.root_dir == tmp_path - assert installer.quiet is True - assert installer.install_mode == HooksInstaller.LIGHTWEIGHT - - def test_init_invalid_mode_falls_back(self, tmp_path: Path) -> None: - """Installer should fall back to lightweight mode for invalid input.""" - installer = HooksInstaller(root_dir=tmp_path, quiet=True, mode="invalid") - assert installer.install_mode == HooksInstaller.LIGHTWEIGHT - - def test_init_auto_detect_git_root(self, tmp_path: Path) -> None: - """Test installer auto-detects git root.""" - # Create a fake git repo - git_dir = tmp_path / ".git" - git_dir.mkdir() - - with patch("subprocess.run") as mock_run: - mock_run.return_value = Mock(stdout=str(tmp_path), returncode=0) - installer = HooksInstaller(quiet=True) - assert installer.root_dir == tmp_path - - def test_check_git_repo_success(self, tmp_path: Path) -> None: - """Test checking for git repository (success case).""" - git_dir = tmp_path / ".git" - git_dir.mkdir() - - installer = HooksInstaller(root_dir=tmp_path, quiet=True) - assert installer._check_git_repo() is True - - def test_check_git_repo_failure(self, tmp_path: Path) -> None: - """Test checking for git repository (failure case).""" - # No .git directory - installer = HooksInstaller(root_dir=tmp_path, quiet=True) - assert installer._check_git_repo() is False - - def test_backup_existing_hook(self, tmp_path: Path) -> None: - """Test backing up existing hook.""" - hooks_dir = tmp_path / ".git" / "hooks" - hooks_dir.mkdir(parents=True) - pre_commit = hooks_dir / "pre-commit" - pre_commit.write_text("old hook content") - - installer = HooksInstaller(root_dir=tmp_path, quiet=True) - installer._backup_existing_hook(pre_commit) - - # Original should be renamed - assert not pre_commit.exists() - # Backup should exist - backups = list(hooks_dir.glob("pre-commit.backup.*")) - assert len(backups) == 1 - assert backups[0].read_text() == "old hook content" - - def test_install_pre_commit_hook_success(self, tmp_path: Path) -> None: - """Test installing pre-commit hook successfully.""" - # Setup - git_dir = tmp_path / ".git" - git_dir.mkdir() - hooks_dir = git_dir / "hooks" - hooks_dir.mkdir() - - # Create a fake template - installer = HooksInstaller(root_dir=tmp_path, quiet=True) - templates_dir = Path(installer.templates_dir) - templates_dir.mkdir(parents=True, exist_ok=True) - template_file = templates_dir / "pre-commit" - template_file.write_text("#!/bin/bash\necho test") - - # Install - result = installer._install_pre_commit_hook() - - # Verify - assert result is True - installed_hook = hooks_dir / "pre-commit" - assert installed_hook.exists() - assert installed_hook.read_text() == "#!/bin/bash\necho test" - # Check it's executable - assert installed_hook.stat().st_mode & 0o111 # Has execute permission - - def test_install_pre_commit_hook_no_template(self, tmp_path: Path) -> None: - """Test installing pre-commit hook when template doesn't exist.""" - git_dir = tmp_path / ".git" - git_dir.mkdir() - hooks_dir = git_dir / "hooks" - hooks_dir.mkdir() - - # Mock the templates_dir to point to a non-existent location - installer = HooksInstaller(root_dir=tmp_path, quiet=True) - installer.templates_dir = tmp_path / "non_existent_templates" - - result = installer._install_pre_commit_hook() - - assert result is False - - @patch("subprocess.run") - def test_install_pre_commit_framework_success( - self, mock_run: MagicMock, tmp_path: Path - ) -> None: - """Test installing pre-commit framework successfully.""" - git_dir = tmp_path / ".git" - git_dir.mkdir() - config_file = tmp_path / "tools" / "pre-commit-config.yaml" - config_file.parent.mkdir(parents=True) - config_file.touch() - - # Mock pre-commit command exists and succeeds - mock_run.return_value = Mock(returncode=0) - - installer = HooksInstaller(root_dir=tmp_path, quiet=True) - result = installer._install_pre_commit_framework() - - assert result is True - assert mock_run.call_count == 2 # version check + install - - @patch("subprocess.run") - def test_install_pre_commit_framework_not_installed( - self, mock_run: MagicMock, tmp_path: Path - ) -> None: - """Test when pre-commit framework is not installed.""" - git_dir = tmp_path / ".git" - git_dir.mkdir() - - # Mock pre-commit command not found - mock_run.side_effect = FileNotFoundError() - - installer = HooksInstaller(root_dir=tmp_path, quiet=True) - result = installer._install_pre_commit_framework() - - assert result is False - - @patch("subprocess.run") - def test_test_architecture_checker_available(self, mock_run: MagicMock, tmp_path: Path) -> None: - """Test checking if architecture checker is available.""" - git_dir = tmp_path / ".git" - git_dir.mkdir() - - # Mock sage-dev command available - mock_run.return_value = Mock(returncode=0) - - installer = HooksInstaller(root_dir=tmp_path, quiet=True) - result = installer._test_architecture_checker() - - assert result is True - - def test_status_not_in_git_repo(self, tmp_path: Path) -> None: - """Test status when not in a git repository.""" - installer = HooksInstaller(root_dir=tmp_path, quiet=True) - status = installer.status() - - assert status["git_repo"] is False - assert status["pre_commit_hook_installed"] is False - - def test_status_in_git_repo_with_hook(self, tmp_path: Path) -> None: - """Test status when in git repo with hook installed.""" - git_dir = tmp_path / ".git" - git_dir.mkdir() - hooks_dir = git_dir / "hooks" - hooks_dir.mkdir() - pre_commit = hooks_dir / "pre-commit" - pre_commit.touch() - - installer = HooksInstaller(root_dir=tmp_path, quiet=True) - status = installer.status() - - assert status["git_repo"] is True - assert status["pre_commit_hook_installed"] is True - - def test_uninstall_success(self, tmp_path: Path) -> None: - """Test uninstalling hooks successfully.""" - git_dir = tmp_path / ".git" - git_dir.mkdir() - hooks_dir = git_dir / "hooks" - hooks_dir.mkdir() - pre_commit = hooks_dir / "pre-commit" - pre_commit.touch() - - installer = HooksInstaller(root_dir=tmp_path, quiet=True) - result = installer.uninstall() - - assert result is True - assert not pre_commit.exists() - - def test_uninstall_hook_not_exists(self, tmp_path: Path) -> None: - """Test uninstalling when hook doesn't exist.""" - git_dir = tmp_path / ".git" - git_dir.mkdir() - - installer = HooksInstaller(root_dir=tmp_path, quiet=True) - result = installer.uninstall() - - # Should still return True (successful operation) - assert result is True - - -class TestHooksManager: - """Test HooksManager class.""" - - def test_init(self, tmp_path: Path) -> None: - """Test manager initialization.""" - manager = HooksManager(root_dir=tmp_path, mode="full") - assert manager.root_dir == tmp_path - assert manager.mode == "full" - - @patch("sage.tools.dev.hooks.manager.HooksInstaller") - def test_install(self, mock_installer_cls: MagicMock, tmp_path: Path) -> None: - """Test install method delegates to installer.""" - mock_installer = mock_installer_cls.return_value - mock_installer.install.return_value = True - - manager = HooksManager(root_dir=tmp_path, mode="lightweight") - result = manager.install(quiet=True) - - assert result is True - mock_installer_cls.assert_called_once_with( - root_dir=tmp_path, - quiet=True, - mode="lightweight", - ) - mock_installer.install.assert_called_once_with() - - @patch("sage.tools.dev.hooks.manager.HooksInstaller") - def test_uninstall(self, mock_installer_cls: MagicMock, tmp_path: Path) -> None: - """Test uninstall method delegates to installer.""" - mock_installer = mock_installer_cls.return_value - mock_installer.uninstall.return_value = True - - manager = HooksManager(root_dir=tmp_path) - result = manager.uninstall(quiet=True) - - assert result is True - mock_installer_cls.assert_called_once_with( - root_dir=tmp_path, - quiet=True, - mode="lightweight", - ) - mock_installer.uninstall.assert_called_once_with() - - @patch("sage.tools.dev.hooks.manager.HooksInstaller") - def test_status(self, mock_installer_cls: MagicMock, tmp_path: Path) -> None: - """Test status method delegates to installer.""" - expected_status = {"git_repo": True, "pre_commit_hook_installed": True} - mock_installer = mock_installer_cls.return_value - mock_installer.status.return_value = expected_status - - manager = HooksManager(root_dir=tmp_path, mode="full") - result = manager.status() - - assert result == expected_status - mock_installer_cls.assert_called_once_with( - root_dir=tmp_path, - quiet=True, - mode="full", - ) - mock_installer.status.assert_called_once_with() - - -class TestHooksIntegration: - """Integration tests for hooks functionality.""" - - def test_full_install_uninstall_cycle(self, tmp_path: Path) -> None: - """Test full install and uninstall cycle.""" - # Setup git repo - git_dir = tmp_path / ".git" - git_dir.mkdir() - hooks_dir = git_dir / "hooks" - hooks_dir.mkdir() - - # Create template - installer = HooksInstaller(root_dir=tmp_path, quiet=True) - templates_dir = Path(installer.templates_dir) - templates_dir.mkdir(parents=True, exist_ok=True) - template_file = templates_dir / "pre-commit" - template_file.write_text("#!/bin/bash\necho test") - - # Create config file for pre-commit framework - config_dir = tmp_path / "tools" - config_dir.mkdir() - config_file = config_dir / "pre-commit-config.yaml" - config_file.write_text("repos: []") - - # Install - manager = HooksManager(root_dir=tmp_path) - install_result = manager.install(quiet=True) - assert install_result is True - - # Check status - status = manager.status() - assert status["git_repo"] is True - assert status["pre_commit_hook_installed"] is True - - # Uninstall - uninstall_result = manager.uninstall(quiet=True) - assert uninstall_result is True - - # Check status after uninstall - status_after = manager.status() - assert status_after["git_repo"] is True - assert status_after["pre_commit_hook_installed"] is False diff --git a/packages/sage-tools/tests/test_dev/test_package_dependency_validator.py b/packages/sage-tools/tests/test_dev/test_package_dependency_validator.py deleted file mode 100644 index d464d0d540..0000000000 --- a/packages/sage-tools/tests/test_dev/test_package_dependency_validator.py +++ /dev/null @@ -1,264 +0,0 @@ -""" -Package Dependency Validator Tests - -Tests for the PackageDependencyValidator ensuring strict isage-* dependency rules. -""" - -import tempfile -from pathlib import Path - -import pytest - -from sage.tools.dev.tools.package_dependency_validator import ( - PackageDependencyValidator, -) - - -@pytest.fixture -def temp_packages_dir(): - """Create a temporary packages directory for testing.""" - with tempfile.TemporaryDirectory() as tmpdir: - packages_dir = Path(tmpdir) / "packages" - packages_dir.mkdir() - yield Path(tmpdir) - - -def create_pyproject(package_dir: Path, content: str) -> Path: - """Helper to create a pyproject.toml file.""" - package_dir.mkdir(exist_ok=True) - pyproject = package_dir / "pyproject.toml" - pyproject.write_text(content) - return pyproject - - -@pytest.mark.unit -class TestPackageDependencyValidator: - """Tests for PackageDependencyValidator.""" - - def test_isage_in_dependencies_is_error(self, temp_packages_dir): - """Test that isage-* in [project.dependencies] is always an error for non-meta packages.""" - # Create a package with isage-common in dependencies - pkg_dir = temp_packages_dir / "packages" / "sage-tools" - create_pyproject( - pkg_dir, - """ -[project] -name = "isage-tools" -dependencies = [ - "isage-common>=0.1.0", - "typer>=0.9.0", -] - -[project.optional-dependencies] -sage-deps = [] -""", - ) - - validator = PackageDependencyValidator(temp_packages_dir) - issues, passed = validator.validate_all_packages() - - # Should fail with an error (not a warning) - assert not passed - assert len(issues) >= 1 - - # Find the dependency issue by checking for error severity and the package - isage_issue = next( - ( - i - for i in issues - if i.severity == "error" - and i.package == "sage-tools" - and "isage-common" in i.details - ), - None, - ) - assert isage_issue is not None - assert isage_issue.severity == "error" - assert isage_issue.package == "sage-tools" - - def test_sage_tools_with_isage_common_is_error(self, temp_packages_dir): - """Test that sage-tools with isage-common in dependencies fails as error (no exception).""" - # Create sage-tools package with isage-common in dependencies - pkg_dir = temp_packages_dir / "packages" / "sage-tools" - create_pyproject( - pkg_dir, - """ -[project] -name = "isage-tools" -dependencies = [ - "isage-common>=0.1.0", -] - -[project.optional-dependencies] -sage-deps = [] -""", - ) - - validator = PackageDependencyValidator(temp_packages_dir) - issues, passed = validator.validate_all_packages() - - # The old behavior would pass this as a warning - # The new behavior should fail with an error - assert not passed - - # Find the error issue - look for error about sage-tools with isage-common in details - isage_error = next( - ( - i - for i in issues - if i.severity == "error" - and i.package == "sage-tools" - and "isage-common" in i.details - ), - None, - ) - assert isage_error is not None - assert isage_error.package == "sage-tools" - - def test_isage_in_sage_deps_is_allowed(self, temp_packages_dir): - """Test that isage-* in sage-deps optional dependency is allowed.""" - # Create a package with isage-common only in sage-deps - pkg_dir = temp_packages_dir / "packages" / "sage-tools" - create_pyproject( - pkg_dir, - """ -[project] -name = "isage-tools" -dependencies = [ - "typer>=0.9.0", -] - -[project.optional-dependencies] -sage-deps = [ - "isage-common>=0.1.0", -] -""", - ) - - validator = PackageDependencyValidator(temp_packages_dir) - issues, passed = validator.validate_all_packages() - - # Should pass without dependency-related errors for sage-tools - # (Note: sage-tools won't fail because isage-* is only in optional-dependencies, not dependencies) - dep_errors = [i for i in issues if i.severity == "error" and "isage-common" in i.details] - assert len(dep_errors) == 0 - - def test_meta_package_allowed_isage_deps(self, temp_packages_dir): - """Test that sage meta-package is allowed to have isage-* in dependencies.""" - # Create the sage meta-package with isage-* dependencies - pkg_dir = temp_packages_dir / "packages" / "sage" - create_pyproject( - pkg_dir, - """ -[project] -name = "isage" -dependencies = [ - "isage-common>=0.1.0", - "isage-kernel>=0.1.0", -] - -[project.optional-dependencies] -standard = [ - "isage-apps[sage-deps]>=0.1.0", -] -full = [ - "isage-studio[sage-deps]>=0.1.0", -] -""", - ) - - validator = PackageDependencyValidator(temp_packages_dir) - issues, passed = validator.validate_all_packages() - - # Should not have isage-* dependency errors for the meta-package - meta_package_errors = [i for i in issues if i.package == "sage" and "isage-*" in i.message] - assert len(meta_package_errors) == 0 - - def test_missing_sage_deps_is_error(self, temp_packages_dir): - """Test that non-L1 packages without sage-deps are flagged.""" - # Create a package without sage-deps - pkg_dir = temp_packages_dir / "packages" / "sage-kernel" - create_pyproject( - pkg_dir, - """ -[project] -name = "isage-kernel" -dependencies = [ - "numpy>=1.24.0", -] - -[project.optional-dependencies] -dev = [] -""", - ) - - validator = PackageDependencyValidator(temp_packages_dir) - issues, passed = validator.validate_all_packages() - - # Should have missing sage-deps error - sage_deps_issues = [i for i in issues if "sage-deps" in i.message] - assert len(sage_deps_issues) >= 1 - assert sage_deps_issues[0].severity == "error" - - def test_l1_package_no_sage_deps_required(self, temp_packages_dir): - """Test that L1 packages (sage-common) don't require sage-deps.""" - # Create sage-common without sage-deps - pkg_dir = temp_packages_dir / "packages" / "sage-common" - create_pyproject( - pkg_dir, - """ -[project] -name = "isage-common" -dependencies = [ - "pyyaml>=6.0", -] -""", - ) - - validator = PackageDependencyValidator(temp_packages_dir) - issues, passed = validator.validate_all_packages() - - # Should not have sage-deps missing error for sage-common - common_issues = [ - i for i in issues if i.package == "sage-common" and "sage-deps" in i.message - ] - assert len(common_issues) == 0 - - def test_multiple_isage_deps_all_reported(self, temp_packages_dir): - """Test that all isage-* dependencies in [project.dependencies] are reported.""" - # Create a package with multiple isage-* dependencies - pkg_dir = temp_packages_dir / "packages" / "sage-middleware" - create_pyproject( - pkg_dir, - """ -[project] -name = "isage-middleware" -dependencies = [ - "isage-common>=0.1.0", - "isage-kernel>=0.1.0", - "isage-libs>=0.1.0", -] - -[project.optional-dependencies] -sage-deps = [] -""", - ) - - validator = PackageDependencyValidator(temp_packages_dir) - issues, passed = validator.validate_all_packages() - - # Should fail - assert not passed - - # Find the middleware issue - middleware_issue = next( - (i for i in issues if i.package == "sage-middleware" and "isage-*" in i.message), - None, - ) - assert middleware_issue is not None - assert middleware_issue.severity == "error" - - # All three dependencies should be in the details - assert "isage-common" in middleware_issue.details - assert "isage-kernel" in middleware_issue.details - assert "isage-libs" in middleware_issue.details diff --git a/packages/sage-tools/tests/test_dev/test_quality_checkers.py b/packages/sage-tools/tests/test_dev/test_quality_checkers.py deleted file mode 100644 index d1ee32b06f..0000000000 --- a/packages/sage-tools/tests/test_dev/test_quality_checkers.py +++ /dev/null @@ -1,285 +0,0 @@ -""" -Tests for quality checker CLI commands. - -Tests the new sage-dev check-* commands: -- check-architecture -- check-devnotes -- check-readme -- check-all -""" - -from pathlib import Path - -import pytest -from typer.testing import CliRunner - -from sage.tools.cli.commands.dev.main import app - -runner = CliRunner() - - -class TestArchitectureCommand: - """Tests for sage-dev architecture command (display architecture info).""" - - def test_architecture_help(self): - """Test that help text is displayed.""" - result = runner.invoke(app, ["architecture", "--help"]) - assert result.exit_code == 0 - assert "架构信息" in result.stdout or "architecture" in result.stdout.lower() - - def test_architecture_basic(self): - """Test basic architecture display.""" - result = runner.invoke(app, ["architecture"]) - assert result.exit_code == 0 - # Should show layer definitions - assert "L1" in result.stdout or "L2" in result.stdout - # Should show packages - assert "sage-common" in result.stdout or "sage-kernel" in result.stdout - - def test_architecture_specific_package(self): - """Test displaying specific package info.""" - result = runner.invoke(app, ["architecture", "--package", "sage-kernel"]) - assert result.exit_code == 0 - assert "sage-kernel" in result.stdout - assert "L3" in result.stdout # sage-kernel is in L3 - - def test_architecture_invalid_package(self): - """Test error handling for invalid package.""" - result = runner.invoke(app, ["architecture", "--package", "nonexistent-package"]) - assert result.exit_code == 1 - assert "未找到" in result.stdout or "not found" in result.stdout.lower() - - def test_architecture_json_format(self): - """Test JSON output format.""" - result = runner.invoke(app, ["architecture", "--format", "json"]) - assert result.exit_code == 0 - # Should be valid JSON - find the JSON part after warnings - import json - - lines = result.stdout.split("\n") - # Find the first line that looks like JSON (starts with {) - json_start = -1 - for i, line in enumerate(lines): - if line.strip().startswith("{"): - json_start = i - break - - if json_start >= 0: - json_str = "\n".join(lines[json_start:]) - data = json.loads(json_str) - assert "layers" in data or "package_to_layer" in data - else: - # If no JSON found, at least check command ran - assert result.exit_code == 0 - - def test_architecture_no_dependencies(self): - """Test showing only layers without dependencies.""" - result = runner.invoke(app, ["architecture", "--no-dependencies"]) - assert result.exit_code == 0 - assert "L1" in result.stdout or "层级" in result.stdout - - -class TestArchitectureChecker: - """Tests for sage-dev check-architecture command.""" - - def test_check_architecture_help(self): - """Test that help text is displayed.""" - result = runner.invoke(app, ["check-architecture", "--help"]) - assert result.exit_code == 0 - assert "架构合规性" in result.stdout or "architecture" in result.stdout.lower() - - def test_check_architecture_project_not_found(self): - """Test behavior when project root doesn't exist.""" - result = runner.invoke(app, ["check-architecture", "--project-root", "/nonexistent/path"]) - assert result.exit_code == 1 - assert "不存在" in result.stdout or "exist" in result.stdout.lower() - - @pytest.mark.skipif( - not Path(".").resolve().name == "SAGE", - reason="Only run in SAGE project root", - ) - def test_check_architecture_basic(self): - """Test basic architecture check (may fail, but shouldn't crash).""" - result = runner.invoke(app, ["check-architecture", "--changed-only"]) - # Command should run without crashing - assert "架构" in result.stdout or "architecture" in result.stdout.lower() - - -class TestDevNotesChecker: - """Tests for sage-dev check-devnotes command.""" - - def test_check_devnotes_help(self): - """Test that help text is displayed.""" - result = runner.invoke(app, ["check-devnotes", "--help"]) - assert result.exit_code == 0 - assert "dev-notes" in result.stdout.lower() or "文档" in result.stdout - - def test_check_devnotes_project_not_found(self): - """Test behavior when project root doesn't exist.""" - result = runner.invoke(app, ["check-devnotes", "--project-root", "/nonexistent/path"]) - assert result.exit_code == 1 - - @pytest.mark.skipif( - not Path(".").resolve().name == "SAGE", - reason="Only run in SAGE project root", - ) - def test_check_devnotes_structure(self): - """Test directory structure check.""" - result = runner.invoke(app, ["check-devnotes", "--check-structure"]) - # Command should run - assert "文档" in result.stdout or "devnotes" in result.stdout.lower() - - -class TestPackageREADMEChecker: - """Tests for sage-dev check-readme command.""" - - def test_check_readme_help(self): - """Test that help text is displayed.""" - result = runner.invoke(app, ["check-readme", "--help"]) - assert result.exit_code == 0 - assert "readme" in result.stdout.lower() - - def test_check_readme_project_not_found(self): - """Test behavior when project root doesn't exist.""" - result = runner.invoke(app, ["check-readme", "--project-root", "/nonexistent/path"]) - assert result.exit_code == 1 - - @pytest.mark.skipif( - not Path(".").resolve().name == "SAGE", - reason="Only run in SAGE project root", - ) - def test_check_readme_basic(self): - """Test basic README check.""" - result = runner.invoke(app, ["check-readme"]) - # Command should run - assert "readme" in result.stdout.lower() or "质量" in result.stdout - - -class TestCheckAll: - """Tests for sage-dev check-all convenience command.""" - - def test_check_all_help(self): - """Test that help text is displayed.""" - result = runner.invoke(app, ["check-all", "--help"]) - assert result.exit_code == 0 - assert "所有" in result.stdout or "all" in result.stdout.lower() - - def test_check_all_project_not_found(self): - """Test behavior when project root doesn't exist.""" - result = runner.invoke(app, ["check-all", "--project-root", "/nonexistent/path"]) - assert result.exit_code == 1 - - @pytest.mark.skipif( - not Path(".").resolve().name == "SAGE", - reason="Only run in SAGE project root", - ) - def test_check_all_continue_on_error(self): - """Test that --continue-on-error runs all checks.""" - result = runner.invoke(app, ["check-all", "--changed-only", "--continue-on-error"]) - # All three checks should be mentioned - assert "架构" in result.stdout or "architecture" in result.stdout.lower() - assert "文档" in result.stdout or "devnotes" in result.stdout.lower() - assert "readme" in result.stdout.lower() - - @pytest.mark.skipif( - not Path(".").resolve().name == "SAGE", - reason="Only run in SAGE project root", - ) - def test_check_all_shows_summary(self): - """Test that summary is shown.""" - result = runner.invoke(app, ["check-all", "--changed-only", "--continue-on-error"]) - # Should show summary - assert "汇总" in result.stdout or "summary" in result.stdout.lower() - - -class TestQualityIntegration: - """Tests for integration with sage-dev quality command.""" - - def test_quality_with_architecture_option(self): - """Test quality command with --architecture option.""" - import re - - result = runner.invoke(app, ["quality", "--help"]) - assert result.exit_code == 0 - # Remove ANSI color codes for reliable text matching - clean_output = re.sub(r"\x1b\[[0-9;]*m", "", result.stdout) - # Help should mention architecture option - assert "--architecture" in clean_output or "--no-architecture" in clean_output - - def test_quality_with_devnotes_option(self): - """Test quality command with --devnotes option.""" - import re - - result = runner.invoke(app, ["quality", "--help"]) - assert result.exit_code == 0 - # Remove ANSI color codes for reliable text matching - clean_output = re.sub(r"\x1b\[[0-9;]*m", "", result.stdout) - # Help should mention devnotes option - assert "--devnotes" in clean_output or "--no-devnotes" in clean_output - - def test_quality_with_readme_option(self): - """Test quality command with --readme option.""" - import re - - result = runner.invoke(app, ["quality", "--help"]) - assert result.exit_code == 0 - # Remove ANSI color codes for reliable text matching - clean_output = re.sub(r"\x1b\[[0-9;]*m", "", result.stdout) - # Help should mention readme option - assert "--readme" in clean_output - - -# Smoke tests for checker classes -class TestCheckerClasses: - """Basic smoke tests for the checker classes themselves.""" - - def test_architecture_checker_import(self): - """Test that ArchitectureChecker can be imported.""" - from sage.tools.dev.tools.architecture_checker import ArchitectureChecker - - assert ArchitectureChecker is not None - - def test_devnotes_checker_import(self): - """Test that DevNotesChecker can be imported.""" - from sage.tools.dev.tools.devnotes_checker import DevNotesChecker - - assert DevNotesChecker is not None - - def test_package_readme_checker_import(self): - """Test that PackageREADMEChecker can be imported.""" - from sage.tools.dev.tools.package_readme_checker import PackageREADMEChecker - - assert PackageREADMEChecker is not None - - @pytest.mark.skipif( - not Path(".").resolve().name == "SAGE", - reason="Only run in SAGE project root", - ) - def test_architecture_checker_instantiation(self): - """Test that ArchitectureChecker can be instantiated.""" - from sage.tools.dev.tools.architecture_checker import ArchitectureChecker - - checker = ArchitectureChecker(root_dir=".") - assert checker is not None - - @pytest.mark.skipif( - not Path(".").resolve().name == "SAGE", - reason="Only run in SAGE project root", - ) - def test_devnotes_checker_instantiation(self): - """Test that DevNotesChecker can be instantiated.""" - from sage.tools.dev.tools.devnotes_checker import DevNotesChecker - - checker = DevNotesChecker(root_dir=".") - assert checker is not None - - @pytest.mark.skipif( - not Path(".").resolve().name == "SAGE", - reason="Only run in SAGE project root", - ) - def test_package_readme_checker_instantiation(self): - """Test that PackageREADMEChecker can be instantiated.""" - from sage.tools.dev.tools.package_readme_checker import PackageREADMEChecker - - checker = PackageREADMEChecker(workspace_root=".") - assert checker is not None diff --git a/packages/sage-tools/tests/test_dev/test_status_checker.py b/packages/sage-tools/tests/test_dev/test_status_checker.py deleted file mode 100644 index 4a7e44e6a3..0000000000 --- a/packages/sage-tools/tests/test_dev/test_status_checker.py +++ /dev/null @@ -1,134 +0,0 @@ -""" -项目状态检查器单元测试 -""" - -from unittest.mock import Mock, patch - -import pytest - -from sage.common.config import find_sage_project_root -from sage.tools.dev.tools.project_status_checker import ProjectStatusChecker - - -class TestProjectStatusChecker: - """项目状态检查器测试""" - - @pytest.fixture - def project_root(self): - """获取项目根目录""" - return str(find_sage_project_root()) - - def test_init(self, project_root): - """测试初始化""" - checker = ProjectStatusChecker(project_root) - assert checker.project_root.is_absolute() - assert (checker.project_root / "packages").exists() - - def test_check_environment(self, project_root): - """测试环境检查""" - checker = ProjectStatusChecker(project_root) - env_info = checker._check_environment() - - assert "python_version" in env_info - assert "python_executable" in env_info - assert "working_directory" in env_info - assert "environment_variables" in env_info - - def test_check_packages(self, project_root): - """测试包检查""" - checker = ProjectStatusChecker(project_root) - packages_info = checker._check_packages() - - assert "packages_dir_exists" in packages_info - assert "packages" in packages_info - assert "summary" in packages_info - - if packages_info["packages_dir_exists"]: - summary = packages_info["summary"] - assert "total" in summary - assert "installed" in summary - assert "importable" in summary - - def test_check_dependencies(self, project_root): - """测试依赖检查""" - checker = ProjectStatusChecker(project_root) - deps_info = checker._check_dependencies() - - assert "critical_packages" in deps_info - assert "import_tests" in deps_info - - # 检查关键依赖 - critical = deps_info["critical_packages"] - assert "typer" in critical - assert "rich" in critical - - def test_check_all(self, project_root): - """测试完整检查""" - checker = ProjectStatusChecker(project_root) - status_data = checker.check_all(verbose=False) - - assert "timestamp" in status_data - assert "project_root" in status_data - assert "checks" in status_data - - checks = status_data["checks"] - expected_checks = [ - "environment", - "packages", - "dependencies", - "services", - "configuration", - ] - for check_name in expected_checks: - assert check_name in checks - assert "status" in checks[check_name] - - def test_generate_status_summary(self, project_root): - """测试状态摘要生成""" - checker = ProjectStatusChecker(project_root) - status_data = checker.check_all(verbose=False) - summary = checker.generate_status_summary(status_data) - - assert "SAGE 项目状态报告" in summary - assert "检查时间" in summary - assert "项目路径" in summary - assert "检查项目" in summary - - -@pytest.mark.unit -class TestProjectStatusCheckerMocked: - """使用Mock的项目状态检查器测试""" - - @patch("sage.tools.dev.tools.project_status_checker.subprocess.run") - def test_check_ray_status_success(self, mock_run): - """测试Ray状态检查成功""" - mock_run.return_value = Mock(returncode=0, stdout="Ray cluster running") - - checker = ProjectStatusChecker(".") - ray_status = checker._check_ray_status() - - assert ray_status["available"] is True - assert ray_status["running"] is True - assert "Ray cluster running" in ray_status["output"] - - @patch("sage.tools.dev.tools.project_status_checker.subprocess.run") - def test_check_ray_status_not_running(self, mock_run): - """测试Ray状态检查失败""" - mock_run.return_value = Mock(returncode=1, stderr="Ray not running") - - checker = ProjectStatusChecker(".") - ray_status = checker._check_ray_status() - - assert ray_status["available"] is True - assert ray_status["running"] is False - - @patch("sage.tools.dev.tools.project_status_checker.subprocess.run") - def test_check_ray_status_not_available(self, mock_run): - """测试Ray命令不可用""" - mock_run.side_effect = FileNotFoundError("Ray command not found") - - checker = ProjectStatusChecker(".") - ray_status = checker._check_ray_status() - - assert ray_status["available"] is False - assert "Ray command not found" in ray_status["error"] diff --git a/packages/sage/README.md b/packages/sage/README.md deleted file mode 100644 index 6c537fba64..0000000000 --- a/packages/sage/README.md +++ /dev/null @@ -1,200 +0,0 @@ -# SAGE - Streaming-Augmented Generative Execution - -SAGE (Streaming-Augmented Generative Execution) 是一个强大的分布式流数据处理平台的 Meta 包。 - -## ⚠️ PEP 420 Namespace Package - -**CRITICAL**: SAGE 使用 PEP 420 namespace packages 架构。 - -```python -# ❌ 错误：不能直接导入 sage 命名空间 -import sage - -# ✅ 正确：导入具体的子包 -import sage.common -import sage.kernel -import sage.middleware -from sage.common.config import get_user_paths - -# 注意: sage.llm 已移至独立仓库 isagellm -# pip install isagellm -``` - -**为什么不能直接 `import sage`?** - -- SAGE 采用 PEP 420 原生命名空间（无 `__init__.py`） -- 允许多个独立 PyPI 包共享 `sage.*` 命名空间 -- 防止"命名空间劫持"（首个安装包独占 `sage/`） -- 符合现代 Python 标准（Python 3.3+） - -**相关**: #1388 多仓库拆分准备 - -## 简介 - -这是 SAGE 的主要元包，提供分层的安装选项以适应不同使用场景。 - -## 🧭 Governance / 团队协作制度 - -本包的团队安排、负责人制度、协作流程与质量门槛见： - -- `docs/governance/TEAM.md` -- `docs/governance/MAINTAINERS.md` -- `docs/governance/DEVELOPER_GUIDE.md` -- `docs/governance/PR_CHECKLIST.md` -- `docs/governance/SELF_HOSTED_RUNNER.md` -- `docs/governance/TODO.md` - -## 🎯 安装方式 - -### 标准安装（推荐）✅ - -日常应用开发，包含核心功能 + CLI + Web UI + RAG/LLM operators - -```bash -pip install isage -``` - -**包含组件**： - -- **L1-L4**: 核心运行时、算法库、领域算子 -- **L5**: CLI 工具 (`sage` 命令) + 开发工具 - -**独立仓库** (不在 SAGE 核心架构中): - -- sage-benchmark - 基准测试 -- sage-examples - 应用示例 -- sage-studio - Web UI -- sageLLM - LLM 推理引擎 -- **科学计算库**: numpy, pandas, matplotlib, scipy, jupyter - -**大小**: ~200MB | **适合**: 应用开发者、日常使用 - -______________________________________________________________________ - -### 其他安装选项 - -#### 核心运行时 - -仅用于运行已有 pipeline（生产环境、容器部署） - -```bash -pip install isage[core] -``` - -**大小**: ~100MB | **适合**: 生产部署 - -#### 完整功能 - -包含示例应用（医疗、视频）和性能测试工具 - -```bash -pip install isage[full] -``` - -**大小**: ~300MB | **适合**: 学习示例、性能评估 - -#### 框架开发 - -修改 SAGE 框架源代码 - -```bash -pip install isage[dev] -``` - -**大小**: ~400MB | **适合**: 框架贡献者 - -## 📦 包含的组件 - -### 默认安装 (standard) - -- **isage-common** (L1): 基础工具和公共模块 -- **isage-platform** (L2): 平台服务（队列、存储） -- **isage-kernel** (L3): 核心运行时和任务执行引擎 -- **isage-libs** (L3): 算法库和 Agent 框架 -- **isage-middleware** (L4): RAG/LLM operators -- **isage-tools** (L5): CLI 工具 (`sage` 命令) -- **isage-cli** (L5): 生产 CLI 接口 - -### 额外组件 (独立仓库/PyPI 包) - -- **isage-benchmark**: 性能基准测试工具 (独立仓库: sage-benchmark) -- **isagellm**: LLM 推理引擎 (独立仓库: sageLLM) -- **isage-edge**: 边缘聚合器 (独立仓库: sage-edge) - -## 快速开始 - -### 安装 - -```bash -# 标准安装（推荐） -pip install isage - -# 或从源码安装 -git clone https://github.com/intellistream/SAGE.git -cd SAGE -pip install -e packages/sage -``` - -## 使用示例 - -```python -import sage - -# 创建 SAGE 应用 -app = sage.create_app() - - -# 定义数据流处理 -@app.stream("user_events") -def process_events(event): - return { - "user_id": event["user_id"], - "processed_at": sage.now(), - "result": "processed", - } - - -# 启动应用 -if __name__ == "__main__": - app.run() -``` - -## 命令行工具 - -安装后，你可以使用以下命令： - -```bash -# 查看版本 -sage --version - -# 创建新项目 -sage create my-project - -# 启动服务 -sage run - -# 查看帮助 -sage --help -``` - -## 文档 - -- [用户指南](https://intellistream.github.io/SAGE-Pub/) -- [API 文档](https://intellistream.github.io/SAGE-Pub/api/) -- [开发者指南](https://intellistream.github.io/SAGE-Pub/dev/) - -## 许可证 - -MIT License - -## 贡献 - -欢迎贡献代码！请查看我们的[贡献指南](CONTRIBUTING.md)。 - -## 支持 - -如果你遇到问题或有疑问，请： - -1. 查看[文档](https://intellistream.github.io/SAGE-Pub/) -1. 搜索[已知问题](https://github.com/intellistream/SAGE/issues) -1. 创建[新问题](https://github.com/intellistream/SAGE/issues/new) diff --git a/packages/sage/docs/governance/DEVELOPER_GUIDE.md b/packages/sage/docs/governance/DEVELOPER_GUIDE.md deleted file mode 100644 index f93c0c7ba0..0000000000 --- a/packages/sage/docs/governance/DEVELOPER_GUIDE.md +++ /dev/null @@ -1,88 +0,0 @@ -# 开发者指南与质量门槛（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/DEVELOPER_GUIDE.md`） - -本指南把“制度”落到“能执行的工程约束”。本包的所有代码改动必须遵守以下规则。 - -______________________________________________________________________ - -## 1. 架构守护机制（强制） - -### 1.1 依赖层级（禁止向上依赖 / 禁止循环依赖） - -SAGE 采用 L1-L5 分层。任何“向上依赖”都视为架构违规，必须阻断合入。 - -- L1：`sage-common` -- L2：`sage-platform` -- L3：`sage-kernel`, `sage-libs` -- L4：`sage-middleware` -- L5：`sage-cli`, `sage-tools` - -### 1.2 Libs vs Middleware 规则（强制） - -如果代码需要调用“向上能力”（例如 VectorDB/Memory/Refiner/外部服务/重后端/网络服务），它 **不是库**，必须放在 -`sage-middleware`（operators/components）。 - -> 迁移时不保留兼容 re-export shim：更新所有调用方，快速失败。 - -### 1.3 Control Plane-only（强制） - -LLM 引擎操作必须走 Control Plane，禁止直接启动引擎/直连端口。 - -______________________________________________________________________ - -## 2. 自动化检查（CI + pre-commit）（强制） - -### 2.1 安装一致性 - -- 必须使用 `./quickstart.sh --dev --yes` 安装开发环境。 -- 禁止手工 `pip install` 临时安装依赖（依赖必须写入 `pyproject.toml`）。 - -### 2.2 本地质量与测试 - -建议在仓库根目录执行： - -- `sage-dev quality check --all-files` -- `sage-dev project test --coverage` - -______________________________________________________________________ - -## 3. 强制规范（必须出现的质量门槛） - -### 3.1 Mock-First（强制） - -- CI 必须能在无 GPU 环境跑通核心测试。 -- 新增能力必须提供 Mock/CPU 路径，避免把“硬件”当作默认前提。 - -### 3.2 Fail-Fast（强制） - -- 禁止静默默认值、禁止隐式回退（fallback）。 -- 关键配置缺失必须明确报错（例如用 `os.environ["KEY"]` 或显式校验并抛异常）。 - -### 3.3 Protocol-First（按适用范围执行） - -当本包提供“公共接口/抽象/跨包契约”时： - -- 先定义接口/类型（Protocol/ABC/Schema） -- 再实现具体逻辑 -- 再补测试与示例 - -______________________________________________________________________ - -## 4. 新模块/新能力接入步骤（可执行） - -1. **定义范围**：确认属于本包；若需要向上能力，按规则提升到 `sage-middleware`。 -1. **接口优先**：先定义可复用接口（如适用）。 -1. **最小可跑**：提供无 GPU 的最小可跑实现（Mock/CPU）。 -1. **测试**：至少包含单元测试；关键路径补回归测试。 -1. **文档**：更新本包 README 与本包 governance 文档（必要时）。 -1. **质量门槛**：本地 `sage-dev quality` 与测试通过。 - -______________________________________________________________________ - -## 5. 常见错误与修复 - -- **依赖倒挂**：`sage-libs` 引入 `sage-middleware` → 必须拆分/上移实现。 -- **文档位置违规**：禁止在根 `docs/` 放 Markdown；包级文档只能放 `packages//docs/`。 -- **依赖未声明**：新增依赖未写入 `pyproject.toml` → 必须补齐声明并统一版本。 diff --git a/packages/sage/docs/governance/MAINTAINERS.md b/packages/sage/docs/governance/MAINTAINERS.md deleted file mode 100644 index 4e331ad6e0..0000000000 --- a/packages/sage/docs/governance/MAINTAINERS.md +++ /dev/null @@ -1,126 +0,0 @@ -# 负责人制度（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/MAINTAINERS.md`） -- 负责人名单：TBD - -本文件定义“包级 Maintainer（负责人）”如何配置、如何工作、如何被量化验收。 - -______________________________________________________________________ - -## 1. 为什么需要 Maintainer - -SAGE 是 monorepo，多包协作、依赖层级明确。Maintainer 的职责不是“写最多代码”，而是保证： - -- 需求有人推进、问题有人闭环 -- 质量门槛有人守住（CI/架构/测试） -- 跨包集成有人对齐（尤其是接口层与中间件层） - -______________________________________________________________________ - -## 2. 负责人配置建议（按模块/子系统） - -### 2.1 推荐配置 - -- 每个包至少 1 名 Maintainer（主负责人）+ 1 名 Backup（备份负责人）。 -- 当包内包含多个子系统/高复杂度模块：建议拆分子 Maintainer。 - -### 2.2 负责人配置表（模板，必须维护） - -| 模块/目录 | 复杂度 | 关键技能 | 主 Maintainer | Backup | 上游依赖 | 下游使用方 | -| --------- | ------ | -------- | ------------- | ------ | -------- | ---------- | -| TBD | TBD | TBD | TBD | TBD | TBD | TBD | - -> 单仓语义适配：这里的“上游/下游”指本 monorepo 的其他包或本包内其他模块。 - -______________________________________________________________________ - -## 3. 负责人职责拆分（40/30/20/10 参考） - -可按实际调整，但必须覆盖全部职责： - -- **40% 开发推进**：Roadmap/Issue/PR 推动，里程碑拆解，阻塞清理。 -- **30% 质量把控**：CI 绿灯、测试策略、回归防护、性能/稳定性监控。 -- **20% 集成同步**：跨包接口对齐、发布/版本对齐、变更公告与迁移指引。 -- **10% 团队协作**：Review SLA、协作矩阵维护、争议处理与复核申请。 - -______________________________________________________________________ - -## 4. 要求与投入 - -- **最低投入**：每周固定时间段处理 Issue/PR（TBD：建议至少 2-4 小时/周）。 -- **Review SLA**：工作日 48h 内首次响应。 -- **透明度**：每月一次交付清单（含证据链接）。 - -______________________________________________________________________ - -## 5. 工作流程（开发 / 发布 / 集成同步） - -### 5.1 日常开发 - -- 所有需求/缺陷必须有 Issue。 -- PR 必须通过 `docs/governance/PR_CHECKLIST.md`。 -- 架构约束（依赖层级、libs vs middleware）违反时：必须阻断合入。 - -### 5.2 发布与版本对齐（如适用） - -- 发布前：测试与质量检查必须绿。 -- 变更：Breaking 变更必须有迁移指引与回滚预案。 -- 若涉及 PyPI 发布：使用仓库规定的发布工具链（例如 `wheelwright`），不得手工 twine/pip 临时发布。 - -### 5.3 跨包集成同步 - -- 上游接口变化：必须提前通知下游 maintainer，并提供迁移窗口。 -- 下游反馈：需要在 SLA 内回应并给出计划。 - -______________________________________________________________________ - -## 6. 协作矩阵（依赖/协作关系） - -### 6.1 Monorepo 依赖层级速查 - -> 目标：避免“向上依赖”与循环依赖。 - -| 层级 | 包（示例） | 允许依赖 | -| ---- | -------------------------- | ---------------- | -| L5 | `sage-cli`, `sage-tools` | L1-L4 | -| L4 | `sage-middleware` | L1-L3 | -| L3 | `sage-kernel`, `sage-libs` | L1-L2 | -| L2 | `sage-platform` | L1 | -| L1 | `sage-common` | 仅 stdlib/轻依赖 | - -### 6.2 本包协作矩阵（模板） - -| 依赖方/被依赖方 | 关系类型 | 接口/契约 | 变更通知机制 | Maintainer 对接 | -| --------------- | -------- | --------- | --------------------- | --------------- | -| TBD | 上游 | TBD | Issue/PR/Release Note | TBD | -| TBD | 下游 | TBD | Issue/PR/Release Note | TBD | - -______________________________________________________________________ - -## 7. 负责人名单（TBD） - -| 模块/目录 | 主 Maintainer | Backup | 交接人（如更换） | 生效日期 | -| --------- | ------------- | ------ | ---------------- | -------- | -| TBD | TBD | TBD | TBD | TBD | - -______________________________________________________________________ - -## 8. 考核指标（参考，可量化） - -- PR 首次响应 SLA 达标率 -- CI 红灯平均恢复时间（MTTR） -- 每月交付清单是否按时发布（含证据） -- Breaking 变更公告与迁移指引完备率 - -______________________________________________________________________ - -## 9. FAQ - -### Q1：Maintainer 是否必须是提交最多的人？ - -不是。Maintainer 的核心是“推进 + 质量 + 对齐”。提交多但不维护质量/不对齐下游同样不合格。 - -### Q2：如果包内没有发布流程怎么办？ - -仍需要“集成同步”：接口变更、跨包影响、迁移指引、回滚预案。 diff --git a/packages/sage/docs/governance/PR_CHECKLIST.md b/packages/sage/docs/governance/PR_CHECKLIST.md deleted file mode 100644 index 09d0f8c350..0000000000 --- a/packages/sage/docs/governance/PR_CHECKLIST.md +++ /dev/null @@ -1,49 +0,0 @@ -# PR 审查清单（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/PR_CHECKLIST.md`） - -> Maintainer/Reviewer 需要用本清单进行可量化审查。未满足项必须在 PR 中解释或补齐。 - -______________________________________________________________________ - -## 1. 架构合规 - -- [ ] 不产生向上依赖/循环依赖（符合 L1-L5 分层） -- [ ] 遵守 libs vs middleware 规则（需要向上能力则放 middleware） -- [ ] LLM 相关操作不绕过 Control Plane - -## 2. Protocol-First（如适用） - -- [ ] 公共接口/类型先定义（Protocol/ABC/Schema），实现不“绑死”具体后端 -- [ ] 接口变更包含迁移说明（Breaking change 必须公告） - -## 3. Mock-First - -- [ ] 无 GPU 环境可跑（至少核心测试/最小路径） -- [ ] 新增外部依赖/服务调用有可测试替身（mock/fake） - -## 4. Fail-Fast - -- [ ] 无隐式回退（不写 try/except 吞异常、不用静默默认值掩盖缺失配置） -- [ ] 错误信息可定位（异常 message 清晰，必要时包含修复指引） - -## 5. 可观测性（按适用范围） - -- [ ] 关键路径有日志/指标/追踪点（至少日志） -- [ ] 不输出敏感信息（key/token/密码等） - -## 6. 配置验证 - -- [ ] 新增配置项有校验与文档说明 -- [ ] 端口不硬编码（如适用，使用统一端口配置） - -## 7. 测试覆盖 - -- [ ] 新增/修改逻辑有对应测试 -- [ ] 关键 bug fix 有回归测试 - -## 8. 代码质量 - -- [ ] `sage-dev quality check --all-files` 通过 -- [ ] `sage-dev project test --coverage` 通过（或至少本包相关测试通过） diff --git a/packages/sage/docs/governance/SELF_HOSTED_RUNNER.md b/packages/sage/docs/governance/SELF_HOSTED_RUNNER.md deleted file mode 100644 index 8c4746062a..0000000000 --- a/packages/sage/docs/governance/SELF_HOSTED_RUNNER.md +++ /dev/null @@ -1,50 +0,0 @@ -# Self-hosted Runner 指南（SAGE - 可选） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/SELF_HOSTED_RUNNER.md`） - -> 本章节用于需要 GPU/重依赖/长耗时测试的包。若本包不需要，可保留但不强制启用。 - -______________________________________________________________________ - -## 1. 什么时候需要 self-hosted runner - -- GPU 相关测试（CUDA/Ascend） -- 需要特殊硬件或驱动版本 -- 需要访问内网资源（需安全评估） - -______________________________________________________________________ - -## 2. Runner 安装与标签（模板） - -- Runner 类型：GitHub Actions self-hosted -- 标签建议： - - `self-hosted` - - `linux` - - `x64` - - `gpu`（如适用） - - `cuda-`（如适用） - -______________________________________________________________________ - -## 3. 环境要求（模板） - -- OS：Linux（推荐） -- Python：与仓库要求一致（3.10+） -- 构建依赖：cmake / build-essential / BLAS 等（按仓库安装脚本） - -______________________________________________________________________ - -## 4. 安全注意事项（强制） - -- Runner 机器不可暴露敏感密钥；Secrets 由 GitHub 管理。 -- 禁止在日志中打印 token/key。 -- 对内网访问与数据集访问：必须走审批（TBD）。 - -______________________________________________________________________ - -## 5. 故障排查 - -- CI 失败先在本地 `./quickstart.sh --dev --yes` 复现 -- 检查磁盘/缓存（`.sage/`） -- 检查驱动/CUDA 版本（如适用） diff --git a/packages/sage/docs/governance/TEAM.md b/packages/sage/docs/governance/TEAM.md deleted file mode 100644 index 3bafed8af5..0000000000 --- a/packages/sage/docs/governance/TEAM.md +++ /dev/null @@ -1,67 +0,0 @@ -# 仓库人员与评分制度（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/TEAM.md`） -- 名单维护：TBD - -本文件定义在 SAGE monorepo 内（以包为单位）的团队角色、负责人制度与评分/激励框架，用于保证： - -- 制度可执行：能落到 Issue/PR/CI/周更/月更。 -- 制度可验收：有明确证据链接与检查项。 -- 制度可追责：有最低交付门槛、透明度与争议处理机制。 - -> 约束：所有“姓名/负责人名单/具体人员”一律用 `TBD`，不得编造。 - -______________________________________________________________________ - -## 1. 角色与职责（强制条款） - -| 角色 | 核心职责 | 必须产出（可验收） | 证据类型（必须带链接） | -| ---------------------------- | -------------------------------------------------------- | ----------------------------------------------------------------------- | ------------------------------------------ | -| Maintainer（负责人） | Roadmap/Issue/PR 推进；质量门槛；集成/版本对齐；变更管理 | 每周 TODO 周更、PR Review SLA、CI 绿灯保障、重大变更公告、发布/集成记录 | Issue、PR、Release/Tag、CI 链接、周报/月报 | -| Engineering Core（工程骨干） | 关键功能交付；CI/测试维护；稳定性问题闭环 | 可交付功能/修复、测试/CI 改进、性能/稳定性指标改进 | PR、Issue、Benchmark 报告、CI 链接 | -| Research Core（科研骨干） | 原型验证/评测；研究到工程落地（必须可验收） | 可复现的实验/评测；可落地的工程化改造（或明确落地计划） | 评测报告、PR、Issue、实验配置/脚本链接 | - -______________________________________________________________________ - -## 2. 负责人必须做的事（强制条款） - -### 2.1 TODO 周更（强制） - -- 每周至少 1 次在本包的 `docs/governance/TODO.md` 更新：本周目标/完成/阻塞/下周计划/风险/需要协助。 -- TODO 必须引用证据（Issue/PR/CI/报告链接）。 - -### 2.2 PR Review SLA（强制） - -- 工作日 48h 内对新 PR 首次响应（review/approve/request changes/说明何时处理）。 -- 对影响多个包的变更：必须主动拉齐上下游 maintainer。 - -# 仓库人员与评分制度（SAGE - 包级，指向母版） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages/sage/docs/governance/TEAM.md`） -- 名单维护：TBD - -通用制度请参见同目录的 `TEAM_BASE.md`（角色定义、评分框架、透明度、验收等统一遵循母版）。本文件仅保留本包的代号映射与特有说明。 - -### 本包角色代号（姓名保持 TBD） - -| 角色 | 代号分配 | -| ---------------- | ---------- | -| Maintainer | A1 | -| Engineering Core | B3 | -| Research Core | C3（按需） | - -### 本包补充说明 - -- 若有额外包级约束或发布要求，请在此补充；未列出即默认与母版一致。 - -______________________________________________________________________ - -## 参考 - -- 通用制度：TEAM_BASE.md -- 周更：docs/governance/TODO.md -- PR 审查：docs/governance/PR_CHECKLIST.md -- 开发规范：docs/governance/DEVELOPER_GUIDE.md -- **保底分**：角色的最低履职奖励（必须满足最低交付门槛）。 diff --git a/packages/sage/docs/governance/TEAM_BASE.md b/packages/sage/docs/governance/TEAM_BASE.md deleted file mode 100644 index 50542d68af..0000000000 --- a/packages/sage/docs/governance/TEAM_BASE.md +++ /dev/null @@ -1,95 +0,0 @@ -# 公共人员与评分制度母版（SAGE 通用） - -- 版本：2026-01-14 -- 适用范围：SAGE monorepo 内所有包（作为母版引用） -- 名单维护：TBD（各包自行在对应 TEAM.md 填代号与名单） - -> 使用方式： -> -> 1. 各包的 TEAM.md 只保留包内代号表和本包特有补充； -> 1. 通用制度（角色定义、评分框架、必做事项、透明度、验收）均以此文件为准； -> 1. 姓名一律保持 TBD 或留空，不得编造。 - -______________________________________________________________________ - -## 1. 角色与职责（强制） - -| 角色 | 核心职责 | 必须产出（可验收） | 证据类型（必须带链接） | -| ---------------------------- | -------------------------------------------------------- | ----------------------------------------------------------------------- | ------------------------------------------ | -| Maintainer（负责人） | Roadmap/Issue/PR 推进；质量门槛；集成/版本对齐；变更管理 | 每周 TODO 周更、PR Review SLA、CI 绿灯保障、重大变更公告、发布/集成记录 | Issue、PR、Release/Tag、CI 链接、周报/月报 | -| Engineering Core（工程骨干） | 关键功能交付；CI/测试维护；稳定性问题闭环 | 可交付功能/修复、测试/CI 改进、性能/稳定性指标改进 | PR、Issue、Benchmark 报告、CI 链接 | -| Research Core（科研骨干） | 原型验证/评测；研究到工程落地（必须可验收） | 可复现的实验/评测；可落地的工程化改造（或明确落地计划） | 评测报告、PR、Issue、实验配置/脚本链接 | - -______________________________________________________________________ - -## 2. 负责人必须做的事（强制） - -- 每周至少 1 次更新本包 `docs/governance/TODO.md`（目标/完成/阻塞/下周/风险/需要协助；需附 Issue/PR/CI/报告链接）。 -- PR Review SLA：工作日 48h 内首次响应；跨包影响需主动同步上下游 Maintainer。 -- main 长期保持绿色：本包相关 CI 红灯需在约定窗口修复或回滚，不允许长期堆积。 -- 重大变更（Breaking/配置/API 行为变化）合入前必须提供：变更说明、下游影响评估、迁移指引、回滚预案。 -- 如涉及发布：发布前测试绿；不得使用手工 twine/pip 临时发布，需按仓库发布工具链。 - -______________________________________________________________________ - -## 3. 评分/激励框架（强制） - -> **说明**：具体绩效分数、比例、考勤细则由管理老师统一负责，本文件仅规定可验收的证据要求与防挂名底线。 - -### 3.1 双轨制评分（取高原则） - -为体现人文关怀、给负责人和骨干兜底，采用**两套评分规则并行，取最高分**作为当月最终绩效： - -| 评分轨道 | 适用对象 | 评分维度 | 说明 | -| -------------------- | ----------------- | ---------------------------- | ------------------------------ | -| **轨道 A：贡献维度** | 全体成员 | 工程交付、科研产出、协调贡献 | 按实际可验证贡献打分 | -| **轨道 B：角色兜底** | 负责人 + 技术骨干 | 履职完成度 | 满足最低交付门槛即可获得兜底分 | - -- 最终月度绩效 = max(轨道 A 得分, 轨道 B 得分) -- 兜底机制旨在避免因客观因素（如阻塞、等待上游）导致骨干绩效过低。 - -### 3.2 评分流程 - -1. **自评**：每月末成员填写自评（附证据链接）； -1. **收集**：管理老师汇总自评材料； -1. **审核**：项目负责人 + 工程师审核证据真实性与完成度； -1. **发放**：管理老师根据审核结果发放绩效。 - -### 3.3 证据要求 - -- 确认证据：Issue（需求/缺陷）、PR（合入）、CI/Test 链接、如为研究需可复现实验配置与结果摘要。 -- 协调贡献：跨包协作记录、会议纪要、文档评审等（需有链接或截图）。 - -### 3.4 防挂名（最低交付门槛） - -- Maintainer：本周至少 1 次 TODO 周更；SLA 达标；main 绿；重大变更公告完备。 -- Engineering Core：当月至少 1 个可验收交付或稳定性闭环。 -- Research Core：**无兜底**（研究周期长，不适用月度门槛）；论文发表有独立奖励机制。 - -______________________________________________________________________ - -## 4. 透明度与月度交付清单（强制） - -- 每月发布本包交付清单（建议 Issue/Discussion，标题 `[Monthly Delivery] YYYY-MM`），至少包含： - - 合入 PR 列表； - - 关键 Issue 关闭列表； - - 风险与未完成项（原因 + 计划）； - - 跨包协作记录。 -- 争议与复核：争议需公开讨论记录，48h 内初步回应；如仍有分歧，升级复核（TBD 复核人），建议 7 天内给结论。 - -______________________________________________________________________ - -## 5. 执行与落地 - -- 周更：遵循本包 `docs/governance/TODO.md` 模板。 -- PR 审查：遵循本包 `docs/governance/PR_CHECKLIST.md`。 -- 开发规范：遵循本包 `docs/governance/DEVELOPER_GUIDE.md`。 -- 依赖与架构：严格遵守分层与 libs/middleware 规则、Control Plane-only 约束、禁止隐式 fallback。 - -______________________________________________________________________ - -## 6. 验收清单（引用方适用） - -- [ ] 引用方（各包 TEAM.md）已填写角色代号映射并保持姓名为 TBD/留空。 -- [ ] 本包/团队遵循本母版的角色定义、评分框架、透明度和强制要求。 -- [ ] TODO 周更、月度交付清单、重大变更公告有证据链接可查。 diff --git a/packages/sage/docs/governance/TEAM_MAP.md b/packages/sage/docs/governance/TEAM_MAP.md deleted file mode 100644 index 446f79ef3f..0000000000 --- a/packages/sage/docs/governance/TEAM_MAP.md +++ /dev/null @@ -1,17 +0,0 @@ -# SAGE 包级角色代号总览（placeholders） - -- 版本：2026-01-14 -- 说明：仅罗列代号，姓名暂用 `TBD`，后续按实际分工替换；通用制度见 packages/sage/docs/governance/TEAM_BASE.md。 - -| 包 | Maintainer | Engineering Core | Research Core | 备注 | -| -------------------- | ---------- | ---------------- | ------------- | ---------------------------------------------- | -| sage (meta) | A1 | — | — | 元包，仅治理文档与依赖聚合，无实际代码 | -| sage-common (L1) | A1 | B1 | — | 代码量小，配置/端口/路径等基础能力 | -| sage-platform (L2) | A2 | B2, B3 | C1（按需） | 平台服务/控制面，中等复杂度 | -| sage-kernel (L3) | A3 | B4, B5, B6 | C2, C3 | 核心数据流/执行引擎/调度，复杂度高，需重点投入 | -| sage-libs (L3) | A4 | B7, B8, B9 | C4, C5, C6 | 算法/RAG/Agent/独立库，复杂度高，研究密集 | -| sage-middleware (L4) | A5 | B10, B11, B12 | C7, C8 | 组件/VDB/Memory/外部服务集成，复杂度高 | -| sage-cli (L5) | A1 | B13 | — | 用户入口，代码量中等，兼容性要求高 | -| sage-tools (L5) | A2 | B14, B15 | — | 质量/发布/检查工具链，代码量中等 | - -> 所有姓名保持 `TBD`，仅当实际分工确定后再填写实名。 diff --git a/packages/sage/docs/governance/TODO.md b/packages/sage/docs/governance/TODO.md deleted file mode 100644 index afdd548b3a..0000000000 --- a/packages/sage/docs/governance/TODO.md +++ /dev/null @@ -1,39 +0,0 @@ -# TODO 周更模板（SAGE - 包级） - -- 版本：2026-01-14 -- 适用范围：当前包（本文件位于 `packages//docs/governance/TODO.md`） -- 维护频率：每周至少 1 次（Maintainer 强制要求） - -______________________________________________________________________ - -## 周更（复制本模板，每周追加一节） - -### Week of YYYY-MM-DD - -**本周目标（Goals）** - -- [ ] TBD（关联 Issue：TBD） - -**本周完成（Done）** - -- [ ] TBD（PR：TBD，CI：TBD） - -**阻塞与风险（Blockers/Risks）** - -- TBD（原因 + 需要谁协助 + 截止时间） - -**下周计划（Next）** - -- [ ] TBD（Issue：TBD） - -**需要协助（Asks）** - -- TBD（@TBD） - -______________________________________________________________________ - -## 里程碑清单（长期维护） - -| 里程碑 | 目标日期 | 状态 | 关联 Issue/PR | 风险 | -| ------ | -------- | ---- | ------------- | ---- | -| TBD | TBD | TBD | TBD | TBD | diff --git a/packages/sage/examples/sagellm_demo.py b/packages/sage/examples/sagellm_demo.py deleted file mode 100644 index 1a59d2d12f..0000000000 --- a/packages/sage/examples/sagellm_demo.py +++ /dev/null @@ -1,130 +0,0 @@ -#!/usr/bin/env python3 -"""Minimal example: Use isagellm for LLM inference in SAGE. - -Requirements: - pip install "isagellm[cuda]" torch transformers accelerate - -Usage: - # Mock mode (no GPU required) - python sagellm_demo.py --mock - - # Real CUDA inference - python sagellm_demo.py --model TinyLlama/TinyLlama-1.1B-Chat-v1.0 - - # Streaming mode - python sagellm_demo.py --model TinyLlama/TinyLlama-1.1B-Chat-v1.0 --stream -""" - -from __future__ import annotations - -import argparse -import asyncio - - -async def run_mock_demo() -> None: - """Run inference with MockEngine (no GPU).""" - from sagellm_backend.engine.mock import MockEngine, MockEngineConfig - from sagellm_protocol.types import Request - - print("=== MockEngine Demo ===") - config = MockEngineConfig(engine_id="mock-001") - engine = MockEngine(config) - await engine.start() - - request = Request( - request_id="demo-001", - trace_id="trace-001", - model="mock-model", - prompt="What is SAGE?", - max_tokens=64, - stream=False, - ) - - response = await engine.execute(request) - print(f"Prompt: {request.prompt}") - print(f"Response: {response.output_text}") - print(f"Metrics: TTFT={response.metrics.ttft_ms}ms, TBT={response.metrics.tbt_ms}ms") - - await engine.stop() - - -async def run_cuda_demo(model_path: str, stream: bool = False) -> None: - """Run inference with HFCudaEngine (real GPU).""" - from sagellm_backend.engine.hf_cuda import HFCudaEngine, HFCudaEngineConfig - from sagellm_protocol.types import Request - - print(f"=== HFCudaEngine Demo (model={model_path}) ===") - - # All required fields explicitly set (fail-fast design) - config = HFCudaEngineConfig( - engine_id="hf-001", - model_path=model_path, - device="cuda", - device_map="auto", - dtype="float16", - load_in_8bit=False, - load_in_4bit=False, - trust_remote_code=False, - max_new_tokens=128, - ) - engine = HFCudaEngine(config) - - print("Loading model...") - await engine.start() - print("Model loaded!") - - request = Request( - request_id="demo-001", - trace_id="trace-001", - model=model_path, - prompt="<|user|>\nWhat is SAGE (Streaming-Augmented Generative Execution)?\n<|assistant|>\n", - max_tokens=128, - stream=stream, - ) - - print(f"\nPrompt: {request.prompt}") - - if stream: - print("\nStreaming response:", end="", flush=True) - async for event in engine.stream(request): - # StreamEventDelta has 'chunk' field (not 'delta') - if hasattr(event, "chunk") and event.chunk: - print(event.chunk, end="", flush=True) - elif hasattr(event, "metrics"): - print( - f"\n\nMetrics: TTFT={event.metrics.ttft_ms:.1f}ms, " - f"TBT={event.metrics.tbt_ms:.1f}ms, " - f"Throughput={event.metrics.throughput_tps:.1f} tps" - ) - else: - response = await engine.execute(request) - print(f"\nResponse: {response.output_text}") - print( - f"\nMetrics: TTFT={response.metrics.ttft_ms:.1f}ms, " - f"TBT={response.metrics.tbt_ms:.1f}ms, " - f"Throughput={response.metrics.throughput_tps:.1f} tps" - ) - - await engine.stop() - - -def main() -> None: - parser = argparse.ArgumentParser(description="sageLLM inference demo for SAGE") - parser.add_argument("--mock", action="store_true", help="Use MockEngine (no GPU)") - parser.add_argument("--model", type=str, default="", help="HuggingFace model path") - parser.add_argument("--stream", action="store_true", help="Use streaming mode") - args = parser.parse_args() - - if args.mock: - asyncio.run(run_mock_demo()) - elif args.model: - asyncio.run(run_cuda_demo(args.model, args.stream)) - else: - print("Usage:") - print(" Mock mode: python sagellm_demo.py --mock") - print(" CUDA mode: python sagellm_demo.py --model TinyLlama/TinyLlama-1.1B-Chat-v1.0") - print(" Streaming: python sagellm_demo.py --model ... --stream") - - -if __name__ == "__main__": - main() diff --git a/packages/sage/pyproject.toml b/packages/sage/pyproject.toml deleted file mode 100644 index 9e5e762554..0000000000 --- a/packages/sage/pyproject.toml +++ /dev/null @@ -1,83 +0,0 @@ -[build-system] -requires = [ - "setuptools>=64", - "wheel", - "packaging>=24.2", -] -build-backend = "setuptools.build_meta" - -[project] -name = "isage" -dynamic = [ - "version", -] -description = "SAGE - Streaming-Augmented Generative Execution" -readme = "README.md" -authors = [ - { name = "SAGE Team", email = "shuhao_zhang@hust.edu.cn" }, -] -classifiers = [ - "Development Status :: 4 - Beta", - "Intended Audience :: Developers", - "Topic :: Software Development :: Libraries :: Python Modules", - "Topic :: Scientific/Engineering :: Artificial Intelligence", - "Topic :: System :: Distributed Computing", -] -requires-python = ">=3.10" -dependencies = [ - "isage-common>=0.1.0", - "isagellm>=0.1.0", - "isage-platform>=0.1.0", - "isage-kernel>=0.1.0", - "isage-libs>=0.1.0", - "isage-middleware>=0.1.0", -] - -license = "MIT" - -[project.optional-dependencies] -dev = [ - "isage[all]", - "isage-tools>=0.1.0", - "pytest>=7.4.0", - "pytest-cov>=4.0.0", - "pytest-asyncio>=0.21.0", - "ruff==0.14.6", - "mypy>=1.7.0", - "pre-commit>=3.5.0", -] -all = [ - "isage-common>=0.1.0", - "isage-platform>=0.1.0", - "isage-kernel>=0.1.0", - "isage-libs>=0.1.0", - "isage-middleware>=0.1.0", - "isage-cli>=0.1.0", - "isage-studio>=0.1.0", - "isagellm[gateway]>=0.1.0", -] - -[project.urls] -Homepage = "https://github.com/intellistream/SAGE" -Documentation = "https://intellistream.github.io/SAGE-Pub/" -Repository = "https://github.com/intellistream/SAGE.git" -"Bug Tracker" = "https://github.com/intellistream/SAGE/issues" - -[tool.setuptools.packages.find] -namespaces = true -where = [ - "src", -] - -[tool.setuptools.package-dir] -"" = "src" - -[tool.setuptools.dynamic.version] -attr = "sage._version.__version__" - -[tool.ruff] -extend = "../../tools/ruff.toml" - -[tool.mypy] -cache_dir = "../../.sage/cache/mypy" -ignore_missing_imports = true diff --git a/packages/sage/setup.py b/packages/sage/setup.py deleted file mode 100644 index 1a4a722e7d..0000000000 --- a/packages/sage/setup.py +++ /dev/null @@ -1,12 +0,0 @@ -"""Setup script for isage meta-package.""" - -from setuptools import setup - -if __name__ == "__main__": - print("\n=== SAGE Installation Guide ===\n") - print("For LLM inference functionality, please install isagellm:") - print(" pip install isagellm") - print("\nFor full SAGE stack:") - print(" pip install isage[all]") - print() - setup() diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000000..c0bbfba029 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,100 @@ +[build-system] +requires = ["setuptools>=64", "wheel", "packaging>=24.2"] +build-backend = "setuptools.build_meta" + +[project] +name = "isage" +dynamic = ["version"] +description = "SAGE - Streaming-Augmented Generative Execution (meta package)" +readme = "README.md" +authors = [{ name = "SAGE Team", email = "shuhao_zhang@hust.edu.cn" }] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Developers", + "Topic :: Software Development :: Libraries :: Python Modules", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Topic :: System :: Distributed Computing", +] +requires-python = ">=3.10" +dependencies = [ + # External engine integration on interpreters where the engine family is published + "isagellm>=0.5.4.3; python_version < '3.13'", +] + +license = "MIT" + +[project.scripts] +sage = "sage.cli.main:main" +sage-edge = "sage.edge.server:main" + +[project.optional-dependencies] +serving-edge = [ + # In-tree edge aggregation shell + "fastapi>=0.115.0,<1.0.0", + "uvicorn[standard]>=0.34.0,<1.0.0", +] +capability-adapters = [ + # Optional adapter — intent recognition for tool-use / orchestration flows + "isage-libs-intent>=0.1.0.7", + # Optional adapter — RAG pipelines and vector-store backends + "isage-rag>=0.3.0", + # Optional adapter — memory / retrieval persistence + "isage-neuromem>=0.2.1.4", +] +capability-tooluse = [ + # Optional adapter — continual-learning / coreset selection for tool-use (SIAS) + "isage-sias>=0.1.0", +] +full = [ + # In-tree edge aggregation shell + "fastapi>=0.115.0,<1.0.0", + "uvicorn[standard]>=0.34.0,<1.0.0", + # Optional adapters beyond the stream/runtime/serving core + "isage-libs-intent>=0.1.0.7", + "isage-rag>=0.3.0", + "isage-neuromem>=0.2.1.4", + "isage-sias>=0.1.0", + # L6 — dataset management for fine-tuning and benchmark workflows + "isage-data>=0.2.3.2", +] +dev = [ + # External developer workflow owner (independently released) + "isage-dev-tools>=0.1.0", + # Edge integration runtime + test helpers + "fastapi>=0.115.0,<1.0.0", + "uvicorn[standard]>=0.34.0,<1.0.0", + "httpx>=0.27.0", + # Testing + "pytest>=7.4.0", + "pytest-cov>=4.0.0", + "pytest-asyncio>=0.21.0", + "pytest-mock>=3.12.0", + # Quality + "ruff>=0.15.0", + "mypy>=1.7.0", + "pre-commit>=3.5.0", + "isage-pypi-publisher>=0.2.1.0", +] + +[project.urls] +Homepage = "https://github.com/intellistream/SAGE" +Documentation = "https://intellistream.github.io/SAGE-Pub/" +Repository = "https://github.com/intellistream/SAGE.git" +"Bug Tracker" = "https://github.com/intellistream/SAGE/issues" + +[tool.setuptools.packages.find] +namespaces = true +where = ["src"] + +[tool.setuptools.package-dir] +"" = "src" + +[tool.setuptools.dynamic.version] +attr = "sage._version.__version__" + +[tool.ruff] +extend = "tools/ruff.toml" + +[tool.mypy] +cache_dir = ".sage/cache/mypy" +ignore_missing_imports = true diff --git a/pytest.ini b/pytest.ini new file mode 120000 index 0000000000..ec74e6302e --- /dev/null +++ b/pytest.ini @@ -0,0 +1 @@ +tools/config/pytest.ini \ No newline at end of file diff --git a/quickstart.sh b/quickstart.sh index 001e92d01d..97243eef30 100755 --- a/quickstart.sh +++ b/quickstart.sh @@ -10,48 +10,9 @@ set -e SAGE_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" TOOLS_DIR="$SAGE_ROOT/tools/install" -# 自动设置 HuggingFace 镜像（国内网络加速） -# 如果用户已设置 HF_ENDPOINT 则不覆盖 -if [ -z "${HF_ENDPOINT}" ]; then - # 检测是否能直接访问 huggingface.co - if ! curl -s --connect-timeout 3 https://huggingface.co >/dev/null 2>&1; then - export HF_ENDPOINT="https://hf-mirror.com" - echo -e "\033[2m自动设置 HuggingFace 镜像: $HF_ENDPOINT\033[0m" - - # 检测到国内网络，提示配置 HF_TOKEN - if [ -z "${HF_TOKEN}" ] && [ ! -f ".env" ] || ! grep -q "HF_TOKEN=" .env 2>/dev/null; then - echo -e "\033[33m💡 提示: 检测到您在中国大陆网络环境\033[0m" - echo -e "\033[2m为避免 HuggingFace API 限流 (429 错误)，建议配置 HF_TOKEN\033[0m" - echo -e "\033[2m获取 token: https://huggingface.co/settings/tokens\033[0m" - echo "" - read -p "是否现在配置 HF_TOKEN? (y/N): " -n 1 -r - echo - if [[ $REPLY =~ ^[Yy]$ ]]; then - read -p "请输入您的 HuggingFace Token: " hf_token - if [ -n "$hf_token" ]; then - # 创建或更新 .env 文件 - if [ ! -f ".env" ]; then - cp .env.template .env 2>/dev/null || touch .env - fi - # 添加或更新 HF_TOKEN - if grep -q "^HF_TOKEN=" .env 2>/dev/null; then - sed -i "s/^HF_TOKEN=.*/HF_TOKEN=$hf_token/" .env - else - echo "HF_TOKEN=$hf_token" >> .env - fi - # 同时添加 HF_ENDPOINT - if ! grep -q "^HF_ENDPOINT=" .env 2>/dev/null; then - echo "HF_ENDPOINT=https://hf-mirror.com" >> .env - fi - echo -e "\033[32m✅ HF_TOKEN 已保存到 .env 文件\033[0m" - export HF_TOKEN="$hf_token" - fi - else - echo -e "\033[2m跳过 HF_TOKEN 配置（可稍后在 .env 文件中手动添加）\033[0m" - fi - fi - fi -fi +# 统一 Python/pip 命令，避免 pip 指向用户级路径导致安装到错误环境 +export PYTHON_CMD="${PYTHON_CMD:-python3}" +export PIP_CMD="${PIP_CMD:-$PYTHON_CMD -m pip}" # 导入所有模块 source "$TOOLS_DIR/display_tools/colors.sh" @@ -63,6 +24,7 @@ source "$TOOLS_DIR/examination_tools/comprehensive_check.sh" source "$TOOLS_DIR/examination_tools/environment_prechecks.sh" source "$TOOLS_DIR/examination_tools/install_verification.sh" source "$TOOLS_DIR/download_tools/argument_parser.sh" +source "$TOOLS_DIR/download_tools/clone_satellite_repos.sh" source "$TOOLS_DIR/examination_tools/mirror_selector.sh" # 网络加速优化（增强版） source "$TOOLS_DIR/installation_table/main_installer.sh" source "$TOOLS_DIR/fixes/environment_doctor.sh" @@ -76,25 +38,221 @@ pre_check_system_environment # 根据偏移探测结果设置Unicode符号 setup_unicode_symbols -# 初始化可选功能标志（防止 unbound variable 错误） -SAGE_SET_SKIP_SMUDGE="${SAGE_SET_SKIP_SMUDGE:-0}" +is_interactive_session() { + [ -t 0 ] && [ -t 1 ] +} + +run_core_surface_import_check() { + local python_cmd="${1:-${PYTHON_CMD:-python3}}" + "$python_cmd" -c "import importlib; [importlib.import_module(name) for name in ('sage.foundation', 'sage.stream', 'sage.runtime', 'sage.serving', 'sage.cli')]; print('✅ core surface imports OK')" +} + +show_core_surface_verify_hint() { + local python_cmd="${1:-${PYTHON_CMD:-python3}}" + echo -e " $python_cmd -c \"import importlib; [importlib.import_module(name) for name in ('sage.foundation', 'sage.stream', 'sage.runtime', 'sage.serving', 'sage.cli')]; print('✅ core surface imports OK')\" ${DIM}# 快速验证主仓核心表面${NC}" +} + +# 在参数解析后再处理 HF 配置，避免 --yes/CI 模式被提前交互阻塞 +configure_huggingface_network() { + local auto_confirm="$1" + + if [ -n "${HF_ENDPOINT:-}" ]; then + return 0 + fi + + if curl -s --connect-timeout 3 https://huggingface.co >/dev/null 2>&1; then + return 0 + fi + + export HF_ENDPOINT="https://hf-mirror.com" + echo -e "${DIM}自动设置 HuggingFace 镜像: $HF_ENDPOINT${NC}" + + if [ -n "${HF_TOKEN:-}" ]; then + return 0 + fi + + local has_env_token=false + if [ -f ".env" ] && grep -q "^HF_TOKEN=" .env 2>/dev/null; then + has_env_token=true + fi + if [ "$has_env_token" = true ]; then + return 0 + fi + + if [ "$auto_confirm" = "true" ] || [[ -n "${CI:-}" || -n "${GITHUB_ACTIONS:-}" ]] || ! is_interactive_session; then + echo -e "${YELLOW}💡 检测到可能受限网络，建议在 .env 中配置 HF_TOKEN 以减少 429 频率${NC}" + return 0 + fi + + echo -e "${YELLOW}💡 提示: 检测到受限网络环境${NC}" + echo -e "${DIM}为避免 HuggingFace API 限流 (429 错误)，建议配置 HF_TOKEN${NC}" + echo -e "${DIM}获取 token: https://huggingface.co/settings/tokens${NC}" + echo "" + read -r -p "是否现在配置 HF_TOKEN? (y/N): " -n 1 reply + echo + + if [[ ! "$reply" =~ ^[Yy]$ ]]; then + echo -e "${DIM}跳过 HF_TOKEN 配置（可稍后在 .env 文件中手动添加）${NC}" + return 0 + fi + + read -r -p "请输入您的 HuggingFace Token: " hf_token + if [ -z "$hf_token" ]; then + return 0 + fi + + if [ ! -f ".env" ]; then + cp .env.template .env 2>/dev/null || touch .env + fi + + if grep -q "^HF_TOKEN=" .env 2>/dev/null; then + sed -i "s/^HF_TOKEN=.*/HF_TOKEN=$hf_token/" .env + else + echo "HF_TOKEN=$hf_token" >> .env + fi + + if ! grep -q "^HF_ENDPOINT=" .env 2>/dev/null; then + echo "HF_ENDPOINT=https://hf-mirror.com" >> .env + fi + + export HF_TOKEN="$hf_token" + echo -e "${GREEN}✅ HF_TOKEN 已保存到 .env 文件${NC}" +} + +# ─── SAGE 工作区初始化函数 ─────────────────────────────────────────────────── +# 用于按当前 SAGE.code-workspace 克隆协同仓库到本地工作区目录。 +# 使用方法：./quickstart.sh --workspace [--dir ] +_init_sage_workspace() { + # 解析 --dir 参数 + local workspace_dir="$HOME/sage-workspace" + while [[ $# -gt 0 ]]; do + case "$1" in + --dir) workspace_dir="$2"; shift 2 ;; + --dir=*) workspace_dir="${1#--dir=}"; shift ;; + *) shift ;; + esac + done + + local GREEN='\033[0;32m'; local CYAN='\033[0;36m' + local YELLOW='\033[1;33m'; local NC='\033[0m'; local BOLD='\033[1m' + + echo -e "\n${BOLD}🚀 SAGE 工作区初始化${NC}" + echo -e "${CYAN}目标目录: ${workspace_dir}${NC}\n" + + # ── 当前工作区协同仓库列表（与 SAGE.code-workspace 保持一致）──────────── + local workspace_file="$SAGE_ROOT/SAGE.code-workspace" + local SAGE_REPOS=() + + if declare -f load_repos_from_workspace >/dev/null 2>&1; then + local repos_output + if repos_output=$(load_repos_from_workspace "$workspace_file" 2>/dev/null); then + while IFS= read -r repo_name; do + [ -z "$repo_name" ] && continue + SAGE_REPOS+=("intellistream/$repo_name") + done <<< "$repos_output" + fi + fi + + if [ ${#SAGE_REPOS[@]} -eq 0 ]; then + SAGE_REPOS=( + "intellistream/sage-benchmark" + "intellistream/sage-docs" + "intellistream/sage-examples" + "intellistream/sage-tutorials" + ) + fi + + mkdir -p "$workspace_dir" + + local ok=0; local skip=0; local fail=0 + for repo in "${SAGE_REPOS[@]}"; do + local name="${repo#*/}" + local target="$workspace_dir/$name" + if [ -d "$target/.git" ]; then + echo -e " ${YELLOW}↻${NC} $name — 已存在，正在 pull..." + git -C "$target" pull --ff-only 2>&1 | tail -1 && ((skip++)) || ((fail++)) + else + echo -e " ${CYAN}⬇${NC} clone $repo..." + if git clone "https://github.com/$repo.git" "$target" --depth 1 2>&1 | tail -1; then + ((ok++)) + else + echo -e " ${YELLOW}⚠ clone 失败，跳过 $name${NC}" + ((fail++)) + fi + fi + done + + # ── 主 SAGE meta 仓库（当前仓库）───────────────────────────────────────── + if [ ! -d "$workspace_dir/SAGE/.git" ]; then + echo -e " ${CYAN}⬇${NC} clone intellistream/SAGE (meta)..." + git clone "https://github.com/intellistream/SAGE.git" "$workspace_dir/SAGE" --depth 1 2>&1 | tail -1 && ((ok++)) || ((fail++)) + else + echo -e " ${YELLOW}↻${NC} SAGE — 已存在，跳过" + ((skip++)) + fi + + echo "" + echo -e "${GREEN}✓ 完成: ${ok} 新克隆, ${skip} 已存在, ${fail} 失败${NC}" + echo -e "\n${BOLD}下一步:${NC}" + echo -e " cd $workspace_dir/SAGE" + echo -e " ./quickstart.sh --dev --yes # 安装主仓开发环境" + echo -e "\n 或最小化本地 editable 安装:" + echo -e " python -m pip install -e '.[dev]'\n" + return $fail +} # 主函数 main() { + # ── 工作区引导模式 (--workspace) ───────────────────────────────────────── + # Clone 所有 SAGE 子仓库到 WORKSPACE_DIR（默认 $HOME/sage-workspace）。 + # 这是新开发者快速设置完整生态系统开发环境的推荐方式。 + if [[ " $* " == *" --workspace "* ]] || [[ " $* " == *" --init-workspace "* ]]; then + _init_sage_workspace "$@" + exit $? + fi + + # ── Conda 环境引导模式 (--setup-conda) ────────────────────────────────── + # 检测 conda 是否已安装，引导安装 Miniforge3 / 创建专用环境。 + # 适用于首次在新机器上配置开发环境的场景。 + if [[ " $* " == *" --setup-conda "* ]]; then + # conda_guide.sh 由 environment_prechecks.sh 自动 source + if declare -f check_conda_environment >/dev/null 2>&1; then + check_conda_environment + else + source "$TOOLS_DIR/examination_tools/conda_guide.sh" + check_conda_environment + fi + exit $? + fi + # 运行日志管理 if [ -f "$TOOLS_DIR/log_management.sh" ]; then bash "$TOOLS_DIR/log_management.sh" "$SAGE_ROOT/.sage/logs" fi - # 解析命令行参数（包括帮助检查） + echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + echo -e "${BLUE}🚀 SAGE Quickstart Pipeline${NC}" + echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + + # Phase 1: 参数解析（含默认值设置） parse_arguments "$@" - # 检查环境医生模式 + if [ -n "${VIRTUAL_ENV:-}" ]; then + echo "" + echo -e "${RED}${BOLD}❌ 检测到 Python venv: ${VIRTUAL_ENV}${NC}" + echo -e "${YELLOW}SAGE 主仓禁止在 venv/.venv 中执行安装或诊断流程。${NC}" + echo -e "${DIM}请先 deactivate 当前 venv，并改用现有 Conda 或其他非-venv Python 环境。${NC}" + exit 1 + fi + + # 解析完成后再处理 HF 网络配置，避免 --yes/CI 触发早期交互 + local auto_confirm=$(get_auto_confirm) + configure_huggingface_network "$auto_confirm" + + # Phase 2: 诊断与断点控制 local run_doctor=$(get_run_doctor) local doctor_only=$(get_doctor_only) local fix_environment=$(get_fix_environment) - - # 检查断点续传选项 local resume_install=$(get_resume_install) local reset_checkpoint=$(get_reset_checkpoint) @@ -120,7 +278,7 @@ main() { source "$TOOLS_DIR/fixes/environment_doctor.sh" # 确保如果使用了 --yes 参数，环境医生也会自动确认修复 - if [ "$(get_auto_confirm)" = "true" ]; then + if [ "$auto_confirm" = "true" ]; then export AUTO_CONFIRM_FIX="true" fi @@ -153,7 +311,7 @@ main() { # 诊断完成，询问是否继续安装（CI 环境自动确认） echo "" - if [[ -z "${CI:-}" && -z "${GITHUB_ACTIONS:-}" ]] && [ "$(get_auto_confirm)" != "true" ]; then + if [[ -z "${CI:-}" && -z "${GITHUB_ACTIONS:-}" ]] && [ "$auto_confirm" != "true" ]; then echo -e "${BLUE}${BOLD}📋 环境诊断完成${NC}" echo -e "${DIM}诊断结果已显示在上方${NC}" echo "" @@ -174,9 +332,6 @@ main() { fi fi - # 设置智能默认值并显示提示 - set_defaults_and_show_tips - # 显示欢迎界面 show_welcome @@ -200,15 +355,18 @@ main() { echo -e "${YELLOW}⚠️ 检测到潜在 numpy 环境问题，但将继续尝试安装${NC}" fi fi - # 如果没有指定任何参数且不在 CI 环境中，显示交互式菜单 + # Phase 3: 交互式菜单（仅在无参数且非 CI） if [ $# -eq 0 ] && [[ -z "${CI:-}" && -z "${GITHUB_ACTIONS:-}" && -z "${GITLAB_CI:-}" && -z "${JENKINS_URL:-}" && -z "${BUILDKITE:-}" ]]; then show_installation_menu + auto_confirm=$(get_auto_confirm) fi - # 获取解析后的参数 + # Phase 4: 读取最终安装配置 local mode=$(get_install_mode) local environment=$(get_install_environment) - local auto_confirm=$(get_auto_confirm) + local clone_satellites=$(should_clone_satellite_repos) + export SAGE_INSTALL_MODE="$mode" + export SAGE_AUTO_CONFIRM="$auto_confirm" local clean_cache=$(get_clean_pip_cache) local verify_deps=$(get_verify_deps) local verify_deps_strict=$(get_verify_deps_strict) @@ -218,6 +376,7 @@ main() { local use_mirror=$(should_use_pip_mirror) local mirror_source=$(get_mirror_source_value) local clean_before_install=$(get_clean_before_install) + export CLEAN_BEFORE_INSTALL="$clean_before_install" # 导出 pip 镜像配置为环境变量，供子脚本使用 export USE_PIP_MIRROR="$use_mirror" @@ -254,8 +413,10 @@ main() { echo -e "${YELLOW}确认开始安装吗？${NC} [${GREEN}Y${NC}/${RED}n${NC}]" read -p "请输入选择: " -r continue_choice + # Trim whitespace/control chars, then only cancel on explicit n/N + continue_choice="${continue_choice//[[:space:]]/}" - if [[ ! "$continue_choice" =~ ^[Yy]$ ]] && [[ ! -z "$continue_choice" ]]; then + if [[ "$continue_choice" =~ ^[Nn] ]]; then echo "" echo -e "${INFO} 安装已取消。" echo -e "${DIM}提示: 可使用 ./quickstart.sh --help 查看所有选项${NC}" @@ -271,6 +432,13 @@ main() { # 切换到项目根目录 cd "$SAGE_ROOT" + # dev 模式下自动克隆附属仓库（默认启用，可用 --no-clone-satellites 关闭） + if [ "$mode" = "dev" ] && [ "$clone_satellites" = "true" ]; then + echo "" + echo -e "${BLUE}📚 同步附属仓库（dev 模式）...${NC}" + clone_all_public_repos "$(dirname "$SAGE_ROOT")" "$SAGE_ROOT/SAGE.code-workspace" || true + fi + # 执行深度依赖验证（如果指定了 --verify-deps） if [ "$verify_deps" = "true" ]; then echo "" @@ -302,9 +470,9 @@ main() { # 验证安装 if run_comprehensive_verification; then - # C++扩展已在 sage-middleware 安装时自动构建和验证 + # 原生/C++扩展会在相关安装阶段自动构建和验证 if [ "$mode" = "standard" ] || [ "$mode" = "dev" ]; then - echo -e "${DIM}C++扩展已通过 sage-middleware 自动构建和验证${NC}" + echo -e "${DIM}原生/C++扩展已在安装流程中自动构建和验证（若当前模式包含相关组件）${NC}" fi # 自动安装代码质量和架构检查 Git hooks（所有模式） @@ -316,17 +484,7 @@ main() { echo "" echo -e "${INFO} 安装代码质量和架构检查工具..." - # 1. 安装 pre-commit 框架（代码质量） - if command -v pip3 >/dev/null 2>&1 || command -v pip >/dev/null 2>&1; then - echo -e "${DIM} 安装 pre-commit 框架...${NC}" - if pip install -q pre-commit 2>/dev/null || pip3 install -q pre-commit 2>/dev/null; then - echo -e "${GREEN} ✅ pre-commit 框架已安装${NC}" - else - echo -e "${YELLOW} ⚠️ pre-commit 安装失败，代码格式检查将被跳过${NC}" - fi - fi - - # 2. 安装 Git hooks（使用新的 sage-dev maintain hooks 命令） + # 安装 Git hooks（统一使用 sage-dev maintain hooks 命令） # 使用正确环境中的 sage-dev local sage_dev_cmd="sage-dev" if [ -n "$SAGE_ENV_NAME" ]; then @@ -386,8 +544,35 @@ main() { fi fi else - echo -e "${YELLOW}⚠️ sage-dev 命令不可用，跳过 Git hooks 安装${NC}" - echo -e "${DIM} 安装完成后激活环境并运行: sage-dev maintain hooks install${NC}" + echo -e "${YELLOW}⚠️ sage-dev 命令暂不可用，尝试使用 pre-commit 回退安装 hooks...${NC}" + + # 回退路径：仅安装 hooks，不负责安装依赖 + local precommit_available=false + local precommit_cmd="" + + if command -v pre-commit >/dev/null 2>&1; then + precommit_available=true + precommit_cmd="pre-commit" + elif [ -n "$SAGE_ENV_NAME" ] && conda run -n "$SAGE_ENV_NAME" python -c "import pre_commit" >/dev/null 2>&1; then + precommit_available=true + precommit_cmd="conda run -n $SAGE_ENV_NAME python -m pre_commit" + elif python3 -c "import pre_commit" >/dev/null 2>&1; then + precommit_available=true + precommit_cmd="python3 -m pre_commit" + fi + + if [ "$precommit_available" = true ] && [ -d ".git" ]; then + echo -e "${DIM} 使用 pre-commit 回退安装 hooks...${NC}" + if eval "$precommit_cmd install --config tools/config/pre-commit-config.yaml" 2>&1; then + echo -e "${GREEN}✅ Git hooks 已安装（pre-commit 回退路径）${NC}" + else + echo -e "${YELLOW}⚠️ pre-commit 回退安装失败${NC}" + echo -e "${DIM} 请激活环境后运行: sage-dev maintain hooks install${NC}" + fi + else + echo -e "${YELLOW}⚠️ pre-commit 也不可用，跳过 Git hooks 安装${NC}" + echo -e "${DIM} 请激活环境后运行: sage-dev maintain hooks install${NC}" + fi fi fi @@ -413,81 +598,20 @@ main() { echo -e "${DIM} ℹ️ Git 配置脚本不存在，跳过${NC}" fi - # 安装主仓库的 pre-commit hooks - if command -v pre-commit >/dev/null 2>&1; then - echo -e "${DIM} 配置主仓库 pre-commit hooks...${NC}" - if pre-commit install 2>/dev/null; then - echo -e "${GREEN} ✅ 主仓库 pre-commit hooks 已安装${NC}" - else - echo -e "${YELLOW} ⚠️ 主仓库 pre-commit hooks 安装失败${NC}" - fi - else - echo -e "${YELLOW} ⚠️ pre-commit 未安装，跳过 Git hooks 安装${NC}" - fi - - # 安装所有子模块的 pre-commit hooks - if command -v pre-commit >/dev/null 2>&1; then - echo -e "${DIM} 配置子模块 pre-commit hooks...${NC}" - local submodules_with_hooks=0 - local submodules_installed=0 - - # 定义所有子模块路径 - # 注意: C++ 扩展已迁移为独立 PyPI 包 (isagevdb, isage-flow, isage-tsdb, neuromem, isage-refiner) - # sageLLM 已独立为私有仓库 - local submodule_paths=( - # 所有子模块已迁移或独立，不在此列表中 - ) - - for submodule_path in "${submodule_paths[@]}"; do - local full_path="$SAGE_ROOT/$submodule_path" - local submodule_name=$(basename "$submodule_path") - - if [ -d "$full_path" ] && [ -f "$full_path/.pre-commit-config.yaml" ]; then - ((submodules_with_hooks++)) || true - if (cd "$full_path" && pre-commit install 2>/dev/null); then - echo -e "${GREEN} ✅ $submodule_name pre-commit hooks 已安装${NC}" - ((submodules_installed++)) || true - else - echo -e "${DIM} ℹ️ $submodule_name pre-commit hooks 安装跳过${NC}" - fi - fi - done - - # 使用 || true 避免 set -e 导致脚本退出 - if [ $submodules_with_hooks -gt 0 ]; then - echo -e "${GREEN} ✅ 子模块 pre-commit hooks: $submodules_installed/$submodules_with_hooks 安装成功${NC}" - else - echo -e "${DIM} ℹ️ 未发现子模块 pre-commit 配置${NC}" - fi || true - else - echo -e "${YELLOW} ⚠️ pre-commit 未安装，跳过子模块 hooks${NC}" - fi + echo -e "${DIM} ℹ️ hooks 已由 sage-dev maintain hooks install 统一管理${NC}" fi show_usage_tips "$mode" - # 设置 workspace 依赖（如果指定了 --workspace） - local setup_workspace=$(get_setup_workspace) - if [ "$setup_workspace" = "true" ]; then - echo "" - echo -e "${INFO} 设置 workspace 依赖..." - if [ -f "$SAGE_ROOT/tools/scripts/setup_workspace_deps.sh" ]; then - if bash "$SAGE_ROOT/tools/scripts/setup_workspace_deps.sh"; then - echo -e "${GREEN}✅ Workspace 依赖设置完成${NC}" - else - echo -e "${YELLOW}⚠️ Workspace 设置遇到问题，但不影响 SAGE 使用${NC}" - fi - else - echo -e "${YELLOW}⚠️ Workspace 设置脚本未找到${NC}" - fi + # ── Zoo packages: 可选的独立插件包 ────────────────────────────────── + offer_zoo_packages "$auto_confirm" + + # 将 sage conda 环境写入 shell RC，下次打开终端自动激活 + if declare -f setup_bashrc_conda_default >/dev/null 2>&1; then + setup_bashrc_conda_default "${SAGE_ENV_NAME:-${SAGE_CONDA_ENV_NAME:-sage}}" fi - # 显示快速启动服务菜单（交互模式） - # 注意：已由 show_usage_tips 内部调用 prompt_start_llm_service - # if [ "$(get_auto_confirm)" != "true" ] && [ -z "${CI:-}" ] && [ -z "${GITHUB_ACTIONS:-}" ]; then - # echo "" - # prompt_start_llm_service "$mode" - # fi + # 显示安装后使用提示（不自动启动服务） # 检查并修复依赖冲突 echo "" @@ -508,6 +632,31 @@ main() { fi fi + echo "" + # ── 安装后快速健康检查 ────────────────────────────────────────────── + echo -e "${INFO} 运行安装后健康检查..." + local python_cmd="${PYTHON_CMD:-python3}" + local sage_cli_ok=false + if $python_cmd -c "import sage.cli" &>/dev/null 2>&1; then + sage_cli_ok=true + fi + + if [ "$sage_cli_ok" = true ]; then + # 用 sage doctor 做层级检查（静默失败，不阻塞安装） + if $python_cmd -m sage.cli.main doctor 2>/dev/null; then + : + else + echo -e "${DIM} 💡 可运行 [sage doctor] 查看详细诊断${NC}" + fi + else + # 回退到主仓核心表面 import 检查 + if run_core_surface_import_check "$python_cmd" >/dev/null 2>&1; then + echo -e "${GREEN} ✅ 核心包验证通过${NC}" + else + echo -e "${YELLOW} ⚠️ 主仓核心表面 import 检查失败，请运行: sage doctor${NC}" + fi + fi + echo "" # 使用适配的居中显示函数，确保在所有环境下都能正确居中 if [ "$VSCODE_OFFSET_ENABLED" = true ]; then @@ -516,8 +665,10 @@ main() { center_text "${ROCKET} 欢迎使用 SAGE！${ROCKET}" "$GREEN$BOLD" fi echo "" + echo -e "${DIM} 💡 验证安装: [bold]sage doctor[/bold] | 快速体验: [bold]sage verify[/bold]${NC}" + echo "" - if [ "$SAGE_SET_SKIP_SMUDGE" = 1 ]; then + if [ "${SAGE_SET_SKIP_SMUDGE:-0}" = 1 ]; then echo -e "${DIM}提示: 已跳过 Git LFS 大文件的自动下载，以缩短初始化时间。${NC}" echo -e "${DIM}如需使用 LibAMM 基准数据，请手动执行:${NC}" echo -e " ${DIM}cd packages/sage-benchmark/src/sage/data && git lfs pull${NC}" @@ -525,12 +676,47 @@ main() { fi else echo "" - echo -e "${YELLOW}安装可能成功，请手动验证（PEP 420 namespace）：${NC}" - # 使用正确的 Python 命令和 PEP 420 导入 + echo -e "${YELLOW}安装可能成功，请手动验证：${NC}" local python_cmd="${PYTHON_CMD:-python3}" - echo -e " $python_cmd -c \"import sage.common; print(sage.common.__version__)\"" + echo -e " sage doctor ${DIM}# 完整诊断（推荐）${NC}" + show_core_surface_verify_hint "$python_cmd" fi } +# ============================================================================ +# Zoo packages: 可选安装的独立发布包 +# 这些包已从 SAGE workspace 独立出去，单独发布到 PyPI，按需安装即可。 +# ============================================================================ +offer_zoo_packages() { + # Zoo 包列表：格式 "pypi-name|中文描述" + local zoo_packages=( + "isage-rag|RAG 管道组件（文档加载、分块、检索、重排）" + "isage-eval|评估框架（指标、性能分析、LLM 评判）" + "isage-finetune|LLM 微调工具（LoRA、数据加载器）" + "isage-agentic-tooluse|Agent 工具选择算法（Hybrid/DFS/Gorilla）" + "isage-intent|意图识别（关键词 + LLM 方案）" + ) + + echo "" + echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + echo -e "${BLUE}🐾 Zoo 独立包（可选安装）${NC}" + echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + echo -e "${DIM}以下包已独立发布到 PyPI，不影响 SAGE 核心功能。${NC}" + echo -e "${DIM}不再逐个交互询问；如需使用，请按需手动安装。${NC}" + echo "" + + printf ' %-28s %s\n' "PyPI 包名" "说明" + printf ' %-28s %s\n' "----------------------------" "----------------------------------------" + for entry in "${zoo_packages[@]}"; do + local pkg="${entry%%|*}" + local desc="${entry##*|}" + printf ' %-28s %s\n' "$pkg" "$desc" + done + + echo "" + echo -e "${DIM}安装方式示例: python -m pip install isage-rag${NC}" + echo -e "${DIM}可按需替换为上表中的任意包名。${NC}" +} + # 运行主函数 main "$@" diff --git a/setup.py b/setup.py new file mode 100644 index 0000000000..85e98a44b5 --- /dev/null +++ b/setup.py @@ -0,0 +1,12 @@ +"""Setup script for isage meta-package.""" + +from setuptools import setup + +if __name__ == "__main__": + print("\n=== SAGE Installation Guide ===\n") + print("For the SAGE core stack (includes stream/runtime/serving/cli surfaces):") + print(" pip install isage") + print("\nFor optional adapters and data packages:") + print(" pip install 'isage[full]'") + print() + setup() diff --git a/packages/sage/src/sage/_version.py b/src/sage/_version.py similarity index 53% rename from packages/sage/src/sage/_version.py rename to src/sage/_version.py index b9be1dacc1..945f33f361 100644 --- a/packages/sage/src/sage/_version.py +++ b/src/sage/_version.py @@ -1,6 +1,6 @@ """Version information for sage package.""" -# 独立硬编码版本 -__version__ = "0.2.3.3" +# Single version source — updated by release tooling; do not edit manually. +__version__ = "0.3.2.2" __author__ = "IntelliStream Team" __email__ = "shuhao_zhang@hust.edu.cn" diff --git a/src/sage/cli/__init__.py b/src/sage/cli/__init__.py new file mode 100644 index 0000000000..f59f2ad095 --- /dev/null +++ b/src/sage/cli/__init__.py @@ -0,0 +1,5 @@ +"""In-tree SAGE CLI surface.""" + +from sage._version import __version__ + +__all__ = ["__version__"] diff --git a/src/sage/cli/commands/__init__.py b/src/sage/cli/commands/__init__.py new file mode 100644 index 0000000000..b60ff8263e --- /dev/null +++ b/src/sage/cli/commands/__init__.py @@ -0,0 +1 @@ +"""CLI command helpers for the in-tree SAGE surface.""" diff --git a/src/sage/cli/commands/apps/__init__.py b/src/sage/cli/commands/apps/__init__.py new file mode 100644 index 0000000000..62d2717d62 --- /dev/null +++ b/src/sage/cli/commands/apps/__init__.py @@ -0,0 +1 @@ +"""Application-facing CLI helpers owned by the main SAGE repository.""" diff --git a/src/sage/cli/commands/apps/chat.py b/src/sage/cli/commands/apps/chat.py new file mode 100644 index 0000000000..c2ed63c749 --- /dev/null +++ b/src/sage/cli/commands/apps/chat.py @@ -0,0 +1,330 @@ +"""Lightweight chat helpers for the in-tree SAGE CLI.""" + +from __future__ import annotations + +import json +import os +import shutil +import subprocess +import sys +import time +import urllib.error +import urllib.request +from pathlib import Path +from typing import Any + +from sage.foundation import SagePorts, get_user_paths +from sage.serving import SageServeConfig, gateway_openai_base_url, probe_gateway + + +def resolve_index_root(index_name: str | None) -> Path: + """Return the storage directory for lightweight chat index metadata.""" + base = get_user_paths().data_dir / "chat" / "indexes" + base.mkdir(parents=True, exist_ok=True) + name = (index_name or "default").strip() or "default" + path = base / name + path.mkdir(parents=True, exist_ok=True) + return path + + +def _default_model_for_engine(engine: str) -> str: + if engine == "sagellm": + return os.environ.get( + "SAGE_CHAT_MODEL", + os.environ.get("SAGELLM_MODEL_NAME", "Qwen/Qwen2.5-0.5B-Instruct"), + ) + return os.environ.get("OPENAI_MODEL_NAME", "gpt-4o-mini") + + +def _env_value(*names: str) -> str | None: + for name in names: + value = os.environ.get(name) + if value and value.strip(): + return value.strip() + return None + + +def _has_real_api_key(args: Any) -> bool: + candidates = [ + os.environ.get(args.api_key_env), + os.environ.get("SAGE_CHAT_API_KEY"), + os.environ.get("OPENAI_API_KEY"), + ] + for value in candidates: + if value and value.strip() and value.strip().upper() != "EMPTY": + return True + return False + + +def _sagellm_executable() -> str | None: + return _env_value("SAGELLM_BIN") or shutil.which("sagellm") + + +def _detect_backend(args: Any) -> tuple[str | None, str | None]: + if args.backend == "direct": + sagellm_bin = _sagellm_executable() + if sagellm_bin: + return "direct", f"使用本机 sagellm CLI: {sagellm_bin}" + return None, "未找到 `sagellm` 可执行文件，无法使用 direct 模式。" + if args.backend == "openai": + return "openai", None + + probe = probe_gateway( + SageServeConfig( + host=args.host, + port=args.port, + model=args.model, + ) + ) + if probe.ok: + return "openai", f"已检测到本地 sagellm gateway: {probe.url}" + + sagellm_bin = _sagellm_executable() + if sagellm_bin: + return "direct", f"未检测到 gateway，改为直接调用 sagellm CLI: {sagellm_bin}" + + configured_base_url = _env_value("SAGE_CHAT_BASE_URL", "OPENAI_BASE_URL", "SAGELLM_BASE_URL") + if configured_base_url and _has_real_api_key(args): + return "openai", f"使用环境变量中配置的 OpenAI 兼容 endpoint: {configured_base_url}" + if _has_real_api_key(args): + return "openai", "检测到云端 API Key，将使用默认 OpenAI 兼容端点。" + + return ( + None, + "未检测到可用的 sagellm gateway，也未配置可用的云端 API。" + "同时本机也不存在 `sagellm` 命令，因此 `sage chat` 无法继续。", + ) + + +def _chat_base_url(args: Any) -> str: + if args.base_url: + return args.base_url.rstrip("/") + env_url = _env_value("SAGE_CHAT_BASE_URL", "OPENAI_BASE_URL", "SAGELLM_BASE_URL") + if env_url: + return env_url.rstrip("/") + if _has_real_api_key(args): + return "https://api.openai.com/v1" + return gateway_openai_base_url(args.host, args.port).rstrip("/") + + +def _api_key_for_request(args: Any) -> str: + return _env_value(args.api_key_env, "SAGE_CHAT_API_KEY", "OPENAI_API_KEY") or "EMPTY" + + +def _run_direct_sagellm(prompt: str | None, args: Any) -> int: + sagellm_bin = _sagellm_executable() + if not sagellm_bin: + print("未找到 `sagellm` 可执行文件，无法直接调用真实推理引擎。", file=sys.stderr) + return 2 + + command = [sagellm_bin] + if prompt is None: + command.append("chat") + else: + command.extend(["run", "-p", prompt]) + if args.stream: + command.append("--stream") + + if args.model: + command.extend(["-m", args.model]) + if args.direct_backend: + command.extend(["--backend", args.direct_backend]) + if args.max_tokens is not None: + command.extend(["--max-tokens", str(args.max_tokens)]) + if args.temperature is not None: + command.extend(["-t", str(args.temperature)]) + if args.top_p is not None: + command.extend(["--top-p", str(args.top_p)]) + if args.top_k is not None: + command.extend(["--top-k", str(args.top_k)]) + if args.repetition_penalty is not None: + command.extend(["--repetition-penalty", str(args.repetition_penalty)]) + if args.debug: + command.append("--debug") + + completed = subprocess.run(command, check=False) + return int(completed.returncode) + + +def _request_openai_chat(prompt: str, args: Any) -> int: + model = args.model or _default_model_for_engine(args.engine) + payload = { + "model": model, + "messages": [{"role": "user", "content": prompt}], + "stream": bool(args.stream), + } + body = json.dumps(payload).encode("utf-8") + request = urllib.request.Request( + url=f"{_chat_base_url(args)}/chat/completions", + data=body, + method="POST", + headers={ + "Content-Type": "application/json", + "Authorization": f"Bearer {_api_key_for_request(args)}", + }, + ) + + try: + with urllib.request.urlopen(request, timeout=args.timeout) as response: + if args.stream: + _stream_sse_response(response) + else: + parsed = json.loads(response.read().decode("utf-8")) + message = parsed.get("choices", [{}])[0].get("message", {}).get("content", "") + print(message) + return 0 + except urllib.error.URLError as exc: + print(f"chat 请求失败: {exc}", file=sys.stderr) + return 1 + except json.JSONDecodeError as exc: + print(f"chat 响应解析失败: {exc}", file=sys.stderr) + return 1 + + +def _stream_sse_response(response: Any) -> None: + for raw_line in response: + line = raw_line.decode("utf-8", errors="replace").strip() + if not line or not line.startswith("data:"): + continue + data = line[5:].strip() + if data == "[DONE]": + break + try: + payload = json.loads(data) + except json.JSONDecodeError: + continue + delta = payload.get("choices", [{}])[0].get("delta", {}) + content = delta.get("content") + if content: + sys.stdout.write(content) + sys.stdout.flush() + sys.stdout.write("\n") + sys.stdout.flush() + + +def run_chat(args: Any) -> int: + """Run interactive or one-shot chat.""" + backend, note = _detect_backend(args) + if backend is None: + print(note, file=sys.stderr) + return 2 + + model = args.model or _default_model_for_engine(args.engine) + + if args.ask: + prompt = args.ask + if backend == "direct": + args.model = model + return _run_direct_sagellm(prompt, args) + return _request_openai_chat(prompt, args) + + if backend == "direct": + if note: + print(note) + args.model = model + return _run_direct_sagellm(None, args) + + print(f"SAGE chat ({backend}, model={model})") + if note: + print(note) + print("输入 exit / quit 退出") + while True: + try: + prompt = input("sage chat> ").strip() + except EOFError: + print("") + return 0 + except KeyboardInterrupt: + print("\n已退出") + return 0 + + if not prompt: + continue + if prompt.lower() in {"exit", "quit"}: + return 0 + + status = _request_openai_chat(prompt, args) + if status != 0: + return status + + +def run_index_ingest(args: Any) -> int: + """Persist lightweight ingest metadata for optional adapter workflows.""" + index_root = resolve_index_root(args.index) + metadata = { + "source": args.source, + "index": args.index or "default", + "embedding_method": args.embedding_method, + "embedding_model": args.embedding_model, + "embedding_base_url": args.embedding_base_url, + "created_at": time.time(), + "mode": "core-placeholder", + "note": "Full retrieval ingestion remains an optional adapter capability.", + } + (index_root / "metadata.json").write_text( + json.dumps(metadata, ensure_ascii=False, indent=2), + encoding="utf-8", + ) + if not args.quiet: + print(f"已写入轻量索引元数据: {index_root}") + print( + "提示: 真正的 RAG ingestion 仍需安装可选 adapter（如 isage-rag / isage[capability-adapters]）。" + ) + return 0 + + +def add_chat_parser(subparsers: Any) -> None: + """Register the `chat` command tree.""" + chat = subparsers.add_parser( + "chat", help="Run chat via sagellm gateway / direct CLI / OpenAI-compatible backends" + ) + chat.set_defaults(_handler=run_chat) + chat.add_argument( + "--engine", default="sagellm", choices=["sagellm"], help="Inference engine family" + ) + chat.add_argument( + "--backend", + default="auto", + choices=["auto", "openai", "direct"], + help="Chat backend selection", + ) + chat.add_argument("--model", default=None, help="Model id") + chat.add_argument("--ask", default=None, help="Single-shot prompt") + chat.add_argument("--stream", action="store_true", help="Enable streamed output when supported") + chat.add_argument("--host", default="127.0.0.1", help="Gateway host") + chat.add_argument("--port", type=int, default=SagePorts.SAGELLM_GATEWAY, help="Gateway port") + chat.add_argument("--base-url", default=None, help="Explicit OpenAI-compatible base URL") + chat.add_argument( + "--api-key-env", + default="SAGELLM_API_KEY", + help="Environment variable used for API key lookup", + ) + chat.add_argument("--timeout", type=float, default=30.0, help="Request timeout in seconds") + chat.add_argument( + "--direct-backend", default=None, help="Direct sagellm backend override (cpu/cuda/ascend)" + ) + chat.add_argument("--max-tokens", type=int, default=None, help="Max output tokens") + chat.add_argument("--temperature", type=float, default=None, help="Sampling temperature") + chat.add_argument("--top-p", type=float, default=None, help="Nucleus sampling threshold") + chat.add_argument("--top-k", type=int, default=None, help="Top-k sampling") + chat.add_argument("--repetition-penalty", type=float, default=None, help="Repetition penalty") + chat.add_argument("--debug", action="store_true", help="Enable verbose direct sagellm logs") + + +def add_index_parser(subparsers: Any) -> None: + """Register the `index` command tree.""" + index = subparsers.add_parser( + "index", help="Manage lightweight local index metadata for optional adapter workflows" + ) + index_sub = index.add_subparsers(dest="index_command") + + ingest = index_sub.add_parser( + "ingest", help="Record lightweight index metadata for optional adapter workflows" + ) + ingest.set_defaults(_handler=run_index_ingest) + ingest.add_argument("--source", default=".", help="Source directory or file") + ingest.add_argument("--index", default="default", help="Index name") + ingest.add_argument("--quiet", action="store_true", help="Suppress informational output") + ingest.add_argument("--embedding-method", default="hf") + ingest.add_argument("--embedding-model", default="BAAI/bge-m3") + ingest.add_argument("--embedding-base-url", default=None) diff --git a/src/sage/cli/main.py b/src/sage/cli/main.py new file mode 100644 index 0000000000..8b24774a89 --- /dev/null +++ b/src/sage/cli/main.py @@ -0,0 +1,184 @@ +#!/usr/bin/env python3 +"""Main-repo owned CLI entrypoint for SAGE.""" + +from __future__ import annotations + +import argparse +import json +from typing import Sequence + +from sage._version import __version__ +from sage.cli.commands.apps.chat import add_chat_parser, add_index_parser +from sage.foundation import SagePorts, get_user_paths +from sage.runtime import get_runtime_backend +from sage.serving import ( + SageServeConfig, + build_sagellm_gateway_command, + infer_module_availability, + probe_gateway, +) + + +def _build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + prog="sage", + description="SAGE — stream-first inference service system", + ) + sub = parser.add_subparsers(dest="command") + + sub.add_parser("version", help="Show SAGE version information") + sub.add_parser("status", help="Show local SAGE status summary") + sub.add_parser("doctor", help="Run a lightweight environment diagnostic") + + sub.add_parser("verify", help="Run a built-in core surface smoke verification") + + runtime_parser = sub.add_parser("runtime", help="Inspect runtime backend status") + runtime_sub = runtime_parser.add_subparsers(dest="runtime_command") + runtime_sub.add_parser("nodes", help="List visible runtime nodes") + + serve_parser = sub.add_parser("serve", help="Serving integration helpers") + serve_sub = serve_parser.add_subparsers(dest="serve_command") + gateway = serve_sub.add_parser("gateway", help="Print or probe the gateway contract") + gateway.add_argument("--host", default="127.0.0.1") + gateway.add_argument("--port", type=int, default=SagePorts.SAGELLM_GATEWAY) + gateway.add_argument("--model", default=None) + gateway.add_argument("--control-plane", action="store_true") + gateway.add_argument("--probe", action="store_true") + gateway.add_argument("--json", action="store_true") + + add_chat_parser(sub) + add_index_parser(sub) + + return parser + + +def _print_status() -> int: + paths = get_user_paths() + gateway_cfg = SageServeConfig() + probe = probe_gateway(gateway_cfg) + + print("SAGE status") + print(f"- version : {__version__}") + print(f"- config dir : {paths.config_dir}") + print(f"- data dir : {paths.data_dir}") + print(f"- state dir : {paths.state_dir}") + print(f"- gateway module : {'available' if infer_module_availability() else 'missing'}") + if probe.ok: + print(f"- gateway : healthy ({probe.url})") + else: + error = probe.error or f"status={probe.status_code}" + print(f"- gateway : unavailable ({error})") + return 0 + + +def _print_doctor() -> int: + gateway_module = infer_module_availability() + print("SAGE doctor") + print(f"- sagellm_gateway importable : {gateway_module}") + try: + backend = get_runtime_backend() + nodes = backend.list_nodes() + print(f"- runtime backend : ok ({len(nodes)} node(s))") + except Exception as exc: # noqa: BLE001 + print(f"- runtime backend : unavailable ({exc})") + return 0 + + +def _print_version() -> int: + print(f"SAGE {__version__}") + return 0 + + +def _runtime_nodes() -> int: + backend = get_runtime_backend() + nodes = backend.list_nodes() + print( + json.dumps( + [ + { + "node_id": node.node_id, + "address": node.address, + "is_schedulable": node.is_schedulable, + "resource_summary": node.resource_summary, + } + for node in nodes + ], + ensure_ascii=False, + indent=2, + ) + ) + return 0 + + +def _serve_gateway(args: argparse.Namespace) -> int: + config = SageServeConfig( + host=args.host, + port=args.port, + model=args.model, + enable_control_plane=args.control_plane, + ) + if args.probe: + result = probe_gateway(config) + payload = { + "ok": result.ok, + "url": result.url, + "status_code": result.status_code, + "payload": result.payload, + "error": result.error, + } + print(json.dumps(payload, ensure_ascii=False, indent=2) if args.json else payload) + return 0 if result.ok else 1 + + payload = { + "command": build_sagellm_gateway_command(config), + "base_url": config.base_url, + "health_url": config.health_url, + "log_file": str(config.log_file), + } + print(json.dumps(payload, ensure_ascii=False, indent=2) if args.json else payload) + return 0 + + +def _verify_core() -> int: + print("SAGE verify") + print(f"- version : {__version__}") + try: + import sage.cli # noqa: F401 + import sage.foundation # noqa: F401 + import sage.runtime # noqa: F401 + import sage.serving # noqa: F401 + import sage.stream # noqa: F401 + except Exception as exc: # noqa: BLE001 + print(f"- core : failed ({exc})") + return 1 + print("- core : ok") + return 0 + + +def main(argv: Sequence[str] | None = None) -> int: + parser = _build_parser() + args = parser.parse_args(list(argv) if argv is not None else None) + + if args.command is None: + return _print_status() + if args.command == "version": + return _print_version() + if args.command == "status": + return _print_status() + if args.command == "doctor": + return _print_doctor() + if args.command == "verify": + return _verify_core() + if args.command == "runtime" and args.runtime_command == "nodes": + return _runtime_nodes() + if args.command == "serve" and args.serve_command == "gateway": + return _serve_gateway(args) + if hasattr(args, "_handler"): + return args._handler(args) + + parser.print_help() + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/sage/edge/__init__.py b/src/sage/edge/__init__.py new file mode 100644 index 0000000000..ff22c6e7c6 --- /dev/null +++ b/src/sage/edge/__init__.py @@ -0,0 +1,9 @@ +"""SAGE edge aggregation surface. + +The edge shell remains part of the main SAGE serving contract while the +underlying inference engine stays external. +""" + +from sage.edge._version import __version__ + +__all__ = ["__version__"] diff --git a/src/sage/edge/_version.py b/src/sage/edge/_version.py new file mode 100644 index 0000000000..a861004ecc --- /dev/null +++ b/src/sage/edge/_version.py @@ -0,0 +1,5 @@ +"""Version information for the in-tree edge surface.""" + +from sage._version import __version__ + +__all__ = ["__version__"] diff --git a/src/sage/edge/app.py b/src/sage/edge/app.py new file mode 100644 index 0000000000..569487744d --- /dev/null +++ b/src/sage/edge/app.py @@ -0,0 +1,65 @@ +"""Edge service app factory.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +from sage.edge.core import EDGE_SERVICE_NAME, normalize_mount_path, probe_payload + +if TYPE_CHECKING: + from fastapi import FastAPI + + +def _require_fastapi() -> type[Any]: + try: + from fastapi import FastAPI + except ModuleNotFoundError as exc: # pragma: no cover - environment-specific + raise RuntimeError( + "sage.edge requires FastAPI support. Install 'isage[serving-edge]' or 'isage[full]'." + ) from exc + return FastAPI + + +def _attach_health_routes(app: Any, llm_prefix: str | None, llm_mounted: bool) -> None: + """Attach edge-level health and readiness probes.""" + existing_paths = {route.path for route in app.router.routes} + if "/healthz" not in existing_paths: + + @app.get("/healthz", include_in_schema=False) + async def healthz() -> dict[str, str | bool]: + return probe_payload("ok", llm_mounted, llm_prefix) + + if "/readyz" not in existing_paths: + + @app.get("/readyz", include_in_schema=False) + async def readyz() -> dict[str, str | bool]: + return probe_payload("ready", llm_mounted, llm_prefix) + + +def create_app( + *, + mount_llm: bool = True, + llm_prefix: str | None = None, + llm_app: Any | None = None, +) -> FastAPI: + """Create the edge shell with optional mounted gateway application.""" + fastapi_cls = _require_fastapi() + mount_path = normalize_mount_path(llm_prefix) + + edge_app = fastapi_cls(title=EDGE_SERVICE_NAME, version="1") + _attach_health_routes(edge_app, llm_prefix=llm_prefix, llm_mounted=mount_llm) + + if not mount_llm: + edge_app.state.edge_mount_path = mount_path + return edge_app + + if llm_app is None: + raise RuntimeError("mount_llm requires explicit llm_app injection") + + if mount_path == "/": + edge_app.include_router(llm_app.router) + else: + edge_app.mount(mount_path, llm_app) + + edge_app.state.edge_mount_path = mount_path + return edge_app diff --git a/src/sage/edge/core.py b/src/sage/edge/core.py new file mode 100644 index 0000000000..f7b96ccb48 --- /dev/null +++ b/src/sage/edge/core.py @@ -0,0 +1,34 @@ +"""Framework-agnostic edge runtime helpers.""" + +from __future__ import annotations + +EDGE_SERVICE_NAME = "SAGE Edge" + + +def normalize_mount_path(llm_prefix: str | None) -> str: + """Normalize and validate the gateway mount path.""" + if llm_prefix is None: + return "/" + + prefix = llm_prefix.strip() + if prefix == "": + return "/" + if not prefix.startswith("/"): + raise ValueError("llm_prefix must start with '/'") + + if prefix != "/": + prefix = prefix.rstrip("/") + if prefix == "": + return "/" + + return prefix + + +def probe_payload(status: str, llm_mounted: bool, llm_prefix: str | None) -> dict[str, str | bool]: + """Build the standardized edge probe payload.""" + return { + "status": status, + "service": EDGE_SERVICE_NAME, + "llm_mounted": llm_mounted, + "llm_prefix": llm_prefix or "/", + } diff --git a/src/sage/edge/server.py b/src/sage/edge/server.py new file mode 100644 index 0000000000..2c1988f500 --- /dev/null +++ b/src/sage/edge/server.py @@ -0,0 +1,107 @@ +"""Edge server entrypoint.""" + +from __future__ import annotations + +import argparse +import importlib +import os +from typing import Any + +from sage.edge.app import create_app +from sage.foundation.config.ports import SagePorts + +_DEFAULT_GATEWAY_APP_SPECS = ( + "sagellm_gateway.server:app", + "sagellm_gateway:app", + "sage.llm.gateway.server:app", +) + + +def _iter_gateway_app_specs() -> tuple[str, ...]: + override = os.getenv("SAGE_EDGE_GATEWAY_APP") + if not override: + return _DEFAULT_GATEWAY_APP_SPECS + return (override, *[spec for spec in _DEFAULT_GATEWAY_APP_SPECS if spec != override]) + + +def _load_attr(module_spec: str) -> Any: + module_name, _, attr_name = module_spec.partition(":") + if not module_name or not attr_name: + raise ImportError(f"Invalid gateway app spec: {module_spec}") + + module = importlib.import_module(module_name) + try: + return getattr(module, attr_name) + except AttributeError as exc: + raise ImportError(f"Gateway module '{module_name}' does not export '{attr_name}'") from exc + + +def _load_llm_gateway_app() -> Any: + """Load the external gateway ASGI app from known integration entrypoints.""" + failures: list[str] = [] + for module_spec in _iter_gateway_app_specs(): + try: + return _load_attr(module_spec) + except Exception as exc: # noqa: BLE001 + failures.append(f"{module_spec} -> {exc}") + + joined = "; ".join(failures) + raise ImportError(f"Unable to load a gateway ASGI app. Tried: {joined}") + + +def _parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Run the SAGE edge aggregator") + parser.add_argument( + "--host", + default=os.getenv("SAGE_EDGE_HOST", "0.0.0.0"), + help="Host interface to bind", + ) + parser.add_argument( + "--port", + type=int, + default=int(os.getenv("SAGE_EDGE_PORT", SagePorts.EDGE_DEFAULT)), + help="Port to bind (defaults to SagePorts.EDGE_DEFAULT)", + ) + parser.add_argument( + "--llm-prefix", + type=str, + default=os.getenv("SAGE_EDGE_LLM_PREFIX"), + help="Optional path prefix for gateway routes (default: /)", + ) + parser.add_argument( + "--no-llm", + action="store_true", + help="Start edge shell without mounting an LLM gateway", + ) + parser.add_argument( + "--log-level", + default=os.getenv("SAGE_EDGE_LOG_LEVEL", "info"), + help="Uvicorn log level (debug, info, warning, error)", + ) + return parser.parse_args() + + +def main() -> None: + args = _parse_args() + + mount_llm = not args.no_llm + llm_app = _load_llm_gateway_app() if mount_llm else None + app = create_app(mount_llm=mount_llm, llm_prefix=args.llm_prefix, llm_app=llm_app) + + try: + import uvicorn + except ModuleNotFoundError as exc: # pragma: no cover - environment-specific + raise RuntimeError( + "sage.edge requires uvicorn support. Install 'isage[serving-edge]' or 'isage[full]'." + ) from exc + + uvicorn.run( + app, + host=args.host, + port=args.port, + log_level=args.log_level, + ) + + +if __name__ == "__main__": + main() diff --git a/src/sage/foundation/__init__.py b/src/sage/foundation/__init__.py new file mode 100644 index 0000000000..564e049b5e --- /dev/null +++ b/src/sage/foundation/__init__.py @@ -0,0 +1,66 @@ +"""SAGE in-tree foundation primitives. + +This package is the first step of the main-repo consolidation effort. +It hosts low-churn, high-value building blocks that should remain stable +across runtime, serving, and optional distributed execution modes. +""" + +from .config import SagePorts, SageUserPaths, get_user_paths +from .core import ( + BaseCoMapFunction, + BaseFunction, + BaseJoinFunction, + BatchFunction, + Collector, + FilterFunction, + FlatMapFunction, + FutureFunction, + KeyByFunction, + MapFunction, + SinkFunction, + SourceFunction, + wrap_lambda, +) +from .debug import PrintSink +from .logging import CustomLogger +from .model_registry import ( + ModelInfo, + ModelNotFoundError, + ModelRegistryError, + delete_model, + download_model, + ensure_model_available, + get_model_path, + list_models, + touch_model, +) + +__all__ = [ + "SagePorts", + "SageUserPaths", + "get_user_paths", + "BaseFunction", + "MapFunction", + "FilterFunction", + "FlatMapFunction", + "SinkFunction", + "SourceFunction", + "BatchFunction", + "KeyByFunction", + "BaseJoinFunction", + "BaseCoMapFunction", + "FutureFunction", + "Collector", + "wrap_lambda", + "CustomLogger", + "PrintSink", + "ModelInfo", + "ModelRegistryError", + "ModelNotFoundError", + "list_models", + "download_model", + "delete_model", + "get_model_path", + "touch_model", + "ensure_model_available", +] diff --git a/src/sage/foundation/config/__init__.py b/src/sage/foundation/config/__init__.py new file mode 100644 index 0000000000..d4a5c2285c --- /dev/null +++ b/src/sage/foundation/config/__init__.py @@ -0,0 +1,6 @@ +"""Configuration and host-level operating substrate for SAGE.""" + +from .ports import SagePorts +from .user_paths import SageUserPaths, get_user_paths + +__all__ = ["SagePorts", "SageUserPaths", "get_user_paths"] diff --git a/src/sage/foundation/config/ports.py b/src/sage/foundation/config/ports.py new file mode 100644 index 0000000000..4d1a4a58d5 --- /dev/null +++ b/src/sage/foundation/config/ports.py @@ -0,0 +1,198 @@ +"""Centralized port configuration for the stream-first SAGE core. + +This in-tree version intentionally focuses on the current product center: +runtime + serving + optional scale-out execution. +Legacy UI-specific ports are not part of this foundation baseline. +""" + +from __future__ import annotations + +import os +import socket +from dataclasses import dataclass +from typing import ClassVar + + +def is_wsl() -> bool: + """Return ``True`` when running inside WSL.""" + try: + with open("/proc/version", encoding="utf-8") as handle: + return "microsoft" in handle.read().lower() + except OSError: + return False + + +@dataclass(frozen=True) +class SagePorts: + """Centralized port assignments for serving and runtime surfaces.""" + + # Service entrypoints + SAGELLM_GATEWAY: ClassVar[int] = 8889 + EDGE_DEFAULT: ClassVar[int] = 8899 + + # Primary/secondary sageLLM serve + engine pairs + SAGELLM_SERVE_PORT: ClassVar[int] = 8901 + SAGELLM_ENGINE_PORT: ClassVar[int] = 8902 + SAGELLM_SERVE_PORT_2: ClassVar[int] = 8903 + SAGELLM_ENGINE_PORT_2: ClassVar[int] = 8904 + + # Legacy direct engine access + LLM_DEFAULT: ClassVar[int] = 8001 + LLM_SECONDARY: ClassVar[int] = 8002 + + # Embedding services + EMBEDDING_DEFAULT: ClassVar[int] = 8090 + EMBEDDING_SECONDARY: ClassVar[int] = 8091 + + # Benchmark/testing + BENCHMARK_EMBEDDING: ClassVar[int] = 8950 + BENCHMARK_API: ClassVar[int] = 8951 + + # Compatibility aliases + SAGELLM_DEFAULT: ClassVar[int] = 8001 + GATEWAY_DEFAULT: ClassVar[int] = 8889 + LLM_WSL_FALLBACK: ClassVar[int] = 8901 + BENCHMARK_LLM: ClassVar[int] = 8901 + + @classmethod + def get_recommended_llm_port(cls) -> int: + """Return the preferred shell port for sageLLM.""" + if is_wsl(): + return cls.SAGELLM_SERVE_PORT + return cls.LLM_DEFAULT + + @classmethod + def get_llm_ports(cls) -> list[int]: + """Return serve/gateway ports in priority order.""" + return [ + cls.SAGELLM_SERVE_PORT, + cls.SAGELLM_SERVE_PORT_2, + cls.LLM_DEFAULT, + cls.SAGELLM_GATEWAY, + ] + + @classmethod + def get_embedding_ports(cls) -> list[int]: + """Return embedding-related ports in priority order.""" + return [cls.EMBEDDING_DEFAULT, cls.EMBEDDING_SECONDARY] + + @classmethod + def get_benchmark_ports(cls) -> list[int]: + """Return benchmark ports.""" + return [cls.BENCHMARK_LLM, cls.BENCHMARK_EMBEDDING, cls.BENCHMARK_API] + + @classmethod + def is_available(cls, port: int, host: str = "localhost") -> bool: + """Return whether a port can be bound.""" + try: + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock: + sock.settimeout(1) + return sock.connect_ex((host, port)) != 0 + except OSError: + return True + + @classmethod + def find_available_port(cls, start: int = 8900, end: int = 8999) -> int | None: + """Find the first available port in ``[start, end]``.""" + for port in range(start, end + 1): + if cls.is_available(port): + return port + return None + + @classmethod + def get_from_env(cls, env_var: str, default: int) -> int: + """Read a port from the environment with integer fallback.""" + value = os.environ.get(env_var) + if value: + try: + return int(value) + except ValueError: + pass + return default + + @classmethod + def check_port_status(cls, port: int, host: str = "localhost") -> dict[str, object]: + """Return listening/availability status for one port.""" + is_listening = False + try: + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock: + sock.settimeout(0.5) + is_listening = sock.connect_ex((host, port)) == 0 + except OSError: + pass + + return { + "port": port, + "is_available": not is_listening, + "is_listening": is_listening, + } + + @classmethod + def diagnose(cls) -> None: + """Print a small diagnostic report for core SAGE ports.""" + print("=" * 65) + print("🔍 SAGE Port Diagnostic Tool") + print("=" * 70) + + if is_wsl(): + print("⚠️ Environment: WSL2 detected (prefer 8901 over legacy 8001)") + else: + print("✅ Environment: Standard Linux/Unix") + + print() + + groups: list[tuple[str, list[tuple[str, int]]]] = [ + ( + "Serving Entry Points", + [ + ("sageLLM Gateway", cls.SAGELLM_GATEWAY), + ("Edge Aggregator", cls.EDGE_DEFAULT), + ], + ), + ( + "sageLLM Inference Engine", + [ + ("Serve shell #1 (--port)", cls.SAGELLM_SERVE_PORT), + ("Real engine #1 (--engine-port)", cls.SAGELLM_ENGINE_PORT), + ("Serve shell #2 (--port)", cls.SAGELLM_SERVE_PORT_2), + ("Real engine #2 (--engine-port)", cls.SAGELLM_ENGINE_PORT_2), + ("Legacy direct", cls.LLM_DEFAULT), + ], + ), + ( + "Embedding Services", + [ + ("Embedding primary", cls.EMBEDDING_DEFAULT), + ("Embedding secondary", cls.EMBEDDING_SECONDARY), + ], + ), + ( + "Benchmark & Testing", + [ + ("Benchmark LLM", cls.BENCHMARK_LLM), + ("Benchmark Embedding", cls.BENCHMARK_EMBEDDING), + ("Benchmark API", cls.BENCHMARK_API), + ], + ), + ] + + for group_name, services in groups: + print(f"── {group_name} {'─' * (50 - len(group_name))}") + print(f" {'Service':<32} {'Port':<6} Status") + for name, port in services: + status = cls.check_port_status(port) + state = "🔴 listening" if status["is_listening"] else "○ available" + print(f" {name:<32} {port:<6} {state}") + print() + + print("=" * 70) + + +DEFAULT_SAGELLM_PORT = SagePorts.SAGELLM_SERVE_PORT +DEFAULT_LLM_PORT = SagePorts.LLM_DEFAULT +DEFAULT_EMBEDDING_PORT = SagePorts.EMBEDDING_DEFAULT +DEFAULT_BENCHMARK_LLM_PORT = SagePorts.BENCHMARK_LLM + + +if __name__ == "__main__": + SagePorts.diagnose() diff --git a/src/sage/foundation/config/user_paths.py b/src/sage/foundation/config/user_paths.py new file mode 100644 index 0000000000..344e1f7df1 --- /dev/null +++ b/src/sage/foundation/config/user_paths.py @@ -0,0 +1,194 @@ +"""XDG-compliant user paths for the in-tree SAGE foundation layer.""" + +from __future__ import annotations + +import os +import shutil +from functools import lru_cache +from pathlib import Path +from typing import Literal + +PathCategory = Literal["config", "data", "state", "cache"] + + +def _get_xdg_dir(env_var: str, default_subdir: str) -> Path: + xdg_dir = os.environ.get(env_var) + if xdg_dir: + return Path(xdg_dir) + return Path.home() / default_subdir + + +@lru_cache(maxsize=1) +def get_user_config_dir() -> Path: + base = _get_xdg_dir("XDG_CONFIG_HOME", ".config") + path = base / "sage" + path.mkdir(parents=True, exist_ok=True) + return path + + +@lru_cache(maxsize=1) +def get_user_data_dir() -> Path: + base = _get_xdg_dir("XDG_DATA_HOME", ".local/share") + path = base / "sage" + path.mkdir(parents=True, exist_ok=True) + return path + + +@lru_cache(maxsize=1) +def get_user_state_dir() -> Path: + base = _get_xdg_dir("XDG_STATE_HOME", ".local/state") + path = base / "sage" + path.mkdir(parents=True, exist_ok=True) + return path + + +@lru_cache(maxsize=1) +def get_user_cache_dir() -> Path: + base = _get_xdg_dir("XDG_CACHE_HOME", ".cache") + path = base / "sage" + path.mkdir(parents=True, exist_ok=True) + return path + + +class SageUserPaths: + """Centralized XDG-style paths for config, data, state, and cache.""" + + def __init__(self) -> None: + self._ensure_structure() + + def _ensure_structure(self) -> None: + for subdir in [ + "models", + "models/sagellm", + "sessions", + "vector_db", + "finetune", + "flows", + ]: + (self.data_dir / subdir).mkdir(parents=True, exist_ok=True) + + for subdir in ["logs"]: + (self.state_dir / subdir).mkdir(parents=True, exist_ok=True) + + for subdir in ["huggingface", "sagellm", "stream"]: + (self.cache_dir / subdir).mkdir(parents=True, exist_ok=True) + + @property + def config_dir(self) -> Path: + return get_user_config_dir() + + @property + def data_dir(self) -> Path: + return get_user_data_dir() + + @property + def state_dir(self) -> Path: + return get_user_state_dir() + + @property + def cache_dir(self) -> Path: + return get_user_cache_dir() + + @property + def config_file(self) -> Path: + return self.config_dir / "config.yaml" + + @property + def cluster_config_file(self) -> Path: + return self.config_dir / "cluster.yaml" + + @property + def credentials_file(self) -> Path: + return self.config_dir / "credentials.yaml" + + @property + def models_dir(self) -> Path: + return self.data_dir / "models" + + @property + def sagellm_models_dir(self) -> Path: + return self.models_dir / "sagellm" + + @property + def sessions_dir(self) -> Path: + return self.data_dir / "sessions" + + @property + def vector_db_dir(self) -> Path: + return self.data_dir / "vector_db" + + @property + def finetune_dir(self) -> Path: + return self.data_dir / "finetune" + + @property + def flows_dir(self) -> Path: + return self.data_dir / "flows" + + @property + def logs_dir(self) -> Path: + return self.state_dir / "logs" + + def get_log_file(self, name: str) -> Path: + if not name.endswith(".log"): + name = f"{name}.log" + return self.logs_dir / name + + @property + def hf_cache_dir(self) -> Path: + return self.cache_dir / "huggingface" + + @property + def stream_cache_dir(self) -> Path: + return self.cache_dir / "stream" + + @property + def sagellm_cache_dir(self) -> Path: + return self.cache_dir / "sagellm" + + +_user_paths: SageUserPaths | None = None + + +def get_user_paths() -> SageUserPaths: + global _user_paths + if _user_paths is None: + _user_paths = SageUserPaths() + return _user_paths + + +def get_legacy_sage_home() -> Path: + """Return legacy ``~/.sage`` path for migration only.""" + path = Path.home() / ".sage" + path.mkdir(parents=True, exist_ok=True) + return path + + +def migrate_legacy_config() -> None: + """Migrate a small set of legacy config files into XDG locations.""" + legacy_home = Path.home() / ".sage" + paths = get_user_paths() + + migrations = [ + (legacy_home / "config.yaml", paths.config_file), + (legacy_home / "cluster_config.yaml", paths.cluster_config_file), + (legacy_home / ".env.json", paths.config_dir / "env.json"), + ] + + for legacy_path, new_path in migrations: + if legacy_path.exists() and not new_path.exists(): + new_path.parent.mkdir(parents=True, exist_ok=True) + shutil.copy2(legacy_path, new_path) + + +__all__ = [ + "PathCategory", + "get_user_config_dir", + "get_user_data_dir", + "get_user_state_dir", + "get_user_cache_dir", + "SageUserPaths", + "get_user_paths", + "get_legacy_sage_home", + "migrate_legacy_config", +] diff --git a/src/sage/foundation/core.py b/src/sage/foundation/core.py new file mode 100644 index 0000000000..b9551ae0a3 --- /dev/null +++ b/src/sage/foundation/core.py @@ -0,0 +1,328 @@ +"""Core function contracts reclaimed into the main SAGE repository. + +This module intentionally provides the small subset of common-layer primitives +that the in-tree stream/runtime surface depends on directly. +""" + +from __future__ import annotations + +import inspect +import logging +from abc import ABC, abstractmethod +from collections.abc import Callable, Hashable +from typing import Any + + +class BaseFunction(ABC): + """Minimal base class for user-defined stream/runtime functions.""" + + __state_include__: list[str] = [] + __state_exclude__: list[str] = ["ctx", "_logger", "logger"] + + def __init__(self, *args, **kwargs) -> None: + self.ctx: Any | None = None + self._logger: logging.Logger | None = None + + @property + def logger(self) -> logging.Logger: + if self._logger is None: + if self.ctx is not None and hasattr(self.ctx, "logger"): + self._logger = self.ctx.logger + else: + self._logger = logging.getLogger(self.__class__.__name__) + return self._logger + + @property + def name(self) -> str: + if self.ctx is not None and hasattr(self.ctx, "name"): + return str(self.ctx.name) + return self.__class__.__name__ + + def call_service( + self, + service_name: str, + *args, + timeout: float | None = None, + method: str | None = None, + **kwargs, + ) -> Any: + if self.ctx is None: + raise RuntimeError("Runtime context not initialized. Cannot access services.") + return self.ctx.call_service(service_name, *args, timeout=timeout, method=method, **kwargs) + + def call_service_async( + self, + service_name: str, + *args, + timeout: float | None = None, + method: str | None = None, + **kwargs, + ) -> Any: + if self.ctx is None: + raise RuntimeError("Runtime context not initialized. Cannot access services.") + return self.ctx.call_service_async( + service_name, + *args, + timeout=timeout, + method=method, + **kwargs, + ) + + @abstractmethod + def execute(self, *args, **kwargs) -> Any: + """Execute the function body.""" + + +class MapFunction(BaseFunction): + @abstractmethod + def execute(self, data: Any) -> Any: + pass + + +class FilterFunction(BaseFunction): + @abstractmethod + def execute(self, data: Any) -> bool: + pass + + +class FlatMapFunction(BaseFunction): + def __init__(self, *args, **kwargs) -> None: + super().__init__(*args, **kwargs) + self.out: Collector | None = None + + def insert_collector(self, collector: Collector) -> None: + self.out = collector + self.out.logger = self.logger + + def collect(self, data: Any) -> None: + if self.out is None: + raise RuntimeError("Collector not initialized. This should be set by the runtime.") + self.out.collect(data) + + @abstractmethod + def execute(self, data: Any) -> list[Any]: + pass + + +class SinkFunction(BaseFunction): + @abstractmethod + def execute(self, data: Any) -> None: + pass + + +class SourceFunction(BaseFunction): + @abstractmethod + def execute(self) -> Any: + pass + + +class BatchFunction(BaseFunction): + @abstractmethod + def execute(self, *args, **kwargs) -> Any: + pass + + +class KeyByFunction(BaseFunction): + @abstractmethod + def execute(self, data: Any) -> Hashable: + pass + + +class BaseJoinFunction(BaseFunction): + @property + def is_join(self) -> bool: + return True + + @abstractmethod + def execute(self, payload: Any, key: Any, tag: int) -> list[Any]: + pass + + +class BaseCoMapFunction(BaseFunction): + """Base class for multi-stream co-map functions.""" + + is_comap = True + + +class FutureFunction(BaseFunction): + def __call__(self, *args, **kwargs) -> Any: + raise RuntimeError("FutureFunction should not be called directly. It's a placeholder.") + + def call(self, data: Any) -> Any: + raise RuntimeError("FutureFunction should not be called directly. It's a placeholder.") + + def execute(self, *args, **kwargs) -> Any: + raise RuntimeError("FutureFunction should not be executed directly.") + + def __repr__(self) -> str: + return "FutureFunction(placeholder)" + + +class Collector: + """Tiny collector used by flatmap-style functions.""" + + def __init__(self, logger: Any | None = None) -> None: + self.items: list[Any] = [] + self.logger: Any | None = logger + + def collect(self, item: Any) -> None: + self.items.append(item) + if self.logger is not None: + self.logger.debug(f"Collected item: {item}") + + def get_collected_data(self) -> list[Any]: + return self.items.copy() + + def clear(self) -> None: + self.items.clear() + + +class LambdaMapFunction(MapFunction): + def __init__(self, lambda_func: Callable[[Any], Any], **kwargs) -> None: + super().__init__(**kwargs) + self.lambda_func = lambda_func + + def execute(self, data: Any) -> Any: + return self.lambda_func(data) + + +class LambdaFilterFunction(FilterFunction): + def __init__(self, lambda_func: Callable[[Any], bool], **kwargs) -> None: + super().__init__(**kwargs) + self.lambda_func = lambda_func + + def execute(self, data: Any) -> bool: + return bool(self.lambda_func(data)) + + +class LambdaFlatMapFunction(FlatMapFunction): + def __init__(self, lambda_func: Callable[[Any], list[Any]], **kwargs) -> None: + super().__init__(**kwargs) + self.lambda_func = lambda_func + + def execute(self, data: Any) -> list[Any]: + result = self.lambda_func(data) + if not isinstance(result, list): + raise TypeError(f"FlatMap lambda function must return a list, got {type(result)}") + return result + + +class LambdaSinkFunction(SinkFunction): + def __init__(self, lambda_func: Callable[[Any], None], **kwargs) -> None: + super().__init__(**kwargs) + self.lambda_func = lambda_func + + def execute(self, data: Any) -> None: + self.lambda_func(data) + + +class LambdaSourceFunction(SourceFunction): + def __init__(self, lambda_func: Callable[[], Any], **kwargs) -> None: + super().__init__(**kwargs) + self.lambda_func = lambda_func + + def execute(self) -> Any: + return self.lambda_func() + + +class LambdaKeyByFunction(KeyByFunction): + def __init__(self, lambda_func: Callable[[Any], Hashable], **kwargs) -> None: + super().__init__(**kwargs) + self.lambda_func = lambda_func + + def execute(self, data: Any) -> Hashable: + return self.lambda_func(data) + + +def detect_lambda_type(func: Callable) -> str: + """Infer a lambda wrapper type from signature/annotation.""" + try: + sig = inspect.signature(func) + params = list(sig.parameters.values()) + return_annotation = sig.return_annotation + + if len(params) == 0: + return "source" + if len(params) != 1: + raise ValueError(f"Lambda function must have 0 or 1 parameter, got {len(params)}") + if return_annotation is bool: + return "filter" + if getattr(return_annotation, "__origin__", None) is list: + return "flatmap" + if return_annotation in (type(None), None): + return "sink" + return "map" + except Exception: + return "map" + + +def wrap_lambda(func: Callable, func_type: str | None = None) -> type[BaseFunction]: + """Wrap a lambda/callable into a function class compatible with runtime operators.""" + resolved_type = func_type or detect_lambda_type(func) + + if resolved_type == "map": + + class WrappedMapFunction(LambdaMapFunction): + def __init__(self, **kwargs) -> None: + super().__init__(func, **kwargs) + + return WrappedMapFunction + + if resolved_type == "filter": + + class WrappedFilterFunction(LambdaFilterFunction): + def __init__(self, **kwargs) -> None: + super().__init__(func, **kwargs) + + return WrappedFilterFunction + + if resolved_type == "flatmap": + + class WrappedFlatMapFunction(LambdaFlatMapFunction): + def __init__(self, **kwargs) -> None: + super().__init__(func, **kwargs) + + return WrappedFlatMapFunction + + if resolved_type == "sink": + + class WrappedSinkFunction(LambdaSinkFunction): + def __init__(self, **kwargs) -> None: + super().__init__(func, **kwargs) + + return WrappedSinkFunction + + if resolved_type == "source": + + class WrappedSourceFunction(LambdaSourceFunction): + def __init__(self, **kwargs) -> None: + super().__init__(func, **kwargs) + + return WrappedSourceFunction + + if resolved_type == "keyby": + + class WrappedKeyByFunction(LambdaKeyByFunction): + def __init__(self, **kwargs) -> None: + super().__init__(func, **kwargs) + + return WrappedKeyByFunction + + raise ValueError(f"Unsupported function type: {resolved_type}") + + +__all__ = [ + "BaseFunction", + "MapFunction", + "FilterFunction", + "FlatMapFunction", + "SinkFunction", + "SourceFunction", + "BatchFunction", + "KeyByFunction", + "BaseJoinFunction", + "BaseCoMapFunction", + "Collector", + "FutureFunction", + "wrap_lambda", +] diff --git a/src/sage/foundation/debug.py b/src/sage/foundation/debug.py new file mode 100644 index 0000000000..54d3ec0452 --- /dev/null +++ b/src/sage/foundation/debug.py @@ -0,0 +1,60 @@ +"""Debug-oriented helper sinks owned by the main SAGE repository.""" + +from __future__ import annotations + +from typing import Any + +from .core import SinkFunction + + +class PrintSink(SinkFunction): + """Small print sink used by `DataStream.print()`.""" + + def __init__( + self, + prefix: str = "", + separator: str = " | ", + colored: bool = True, + quiet: bool = False, + **kwargs, + ) -> None: + super().__init__(**kwargs) + self.prefix = prefix + self.separator = separator + self.colored = colored + self.quiet = quiet + self._first_output = True + + def execute(self, data: Any) -> None: + formatted = self._format_data(data) + output = f"{self.prefix}{self.separator}{formatted}" if self.prefix else formatted + + if self._first_output and not self.quiet: + print(f"🔍 Stream output: {output}") + print(" (Further outputs logged. Check logs for details.)") + else: + print(output) + self._first_output = False + + def _format_data(self, data: Any) -> str: + if data is None: + return "None" + if isinstance(data, str | int | float | bool): + return str(data) + if isinstance(data, dict): + return ", ".join(f"{k}={v}" for k, v in data.items()) + if isinstance(data, list | tuple): + if len(data) <= 5: + return str(data) + preview = ", ".join(str(x) for x in data[:5]) + return f"[{preview}, ... (+{len(data) - 5} more)]" + if hasattr(data, "__dict__"): + attrs = getattr(data, "__dict__", {}) + if attrs: + attr_str = ", ".join(f"{k}={v}" for k, v in list(attrs.items())[:3]) + return f"{data.__class__.__name__}({attr_str})" + return f"{data.__class__.__name__}()" + return str(data) + + +__all__ = ["PrintSink"] diff --git a/src/sage/foundation/logging.py b/src/sage/foundation/logging.py new file mode 100644 index 0000000000..9d404a1440 --- /dev/null +++ b/src/sage/foundation/logging.py @@ -0,0 +1,96 @@ +"""Small in-tree logging helpers for the consolidated SAGE core.""" + +from __future__ import annotations + +import logging +import threading +from typing import Any + + +class CustomLogger: + """Lightweight compatibility logger used by in-tree stream/runtime code.""" + + _global_console_debug_enabled: bool = True + _lock = threading.Lock() + + _LEVELS = { + "DEBUG": logging.DEBUG, + "INFO": logging.INFO, + "WARNING": logging.WARNING, + "WARN": logging.WARNING, + "ERROR": logging.ERROR, + "CRITICAL": logging.CRITICAL, + } + + def __init__( + self, + name_or_outputs: str | list[tuple[str, str | int]] | None = None, + outputs: list[tuple[str, str | int]] | None = None, + name: str | None = None, + log_base_folder: str | None = None, + ) -> None: + resolved_name = name or (name_or_outputs if isinstance(name_or_outputs, str) else None) + self.name = resolved_name or "sage" + self.log_base_folder = log_base_folder + self.logger = logging.getLogger(self.name) + + if not self.logger.handlers: + handler = logging.StreamHandler() + handler.setFormatter(logging.Formatter("%(message)s")) + self.logger.addHandler(handler) + self.logger.propagate = False + + resolved_outputs = outputs + if resolved_outputs is None and isinstance(name_or_outputs, list): + resolved_outputs = name_or_outputs + if resolved_outputs is None: + resolved_outputs = [("console", "INFO")] + + levels = [self._extract_level(level) for _, level in resolved_outputs] + self.logger.setLevel(min(levels) if levels else logging.INFO) + + def _extract_level(self, level: str | int) -> int: + if isinstance(level, int): + return level + return self._LEVELS.get(level.upper(), logging.INFO) + + def update_output_level(self, target_index_or_name: int | str, new_level: str | int) -> None: + self.logger.setLevel(self._extract_level(new_level)) + for handler in self.logger.handlers: + handler.setLevel(self._extract_level(new_level)) + + def debug(self, *args: Any, **kwargs: Any) -> None: + if not self.is_global_console_debug_enabled(): + return + self.logger.debug(*args, **kwargs) + + def info(self, *args: Any, **kwargs: Any) -> None: + self.logger.info(*args, **kwargs) + + def warning(self, *args: Any, **kwargs: Any) -> None: + self.logger.warning(*args, **kwargs) + + warn = warning + + def error(self, *args: Any, **kwargs: Any) -> None: + self.logger.error(*args, **kwargs) + + def critical(self, *args: Any, **kwargs: Any) -> None: + self.logger.critical(*args, **kwargs) + + @classmethod + def disable_global_console_debug(cls) -> None: + with cls._lock: + cls._global_console_debug_enabled = False + + @classmethod + def enable_global_console_debug(cls) -> None: + with cls._lock: + cls._global_console_debug_enabled = True + + @classmethod + def is_global_console_debug_enabled(cls) -> bool: + return cls._global_console_debug_enabled + + +__all__ = ["CustomLogger"] diff --git a/src/sage/foundation/model_registry/__init__.py b/src/sage/foundation/model_registry/__init__.py new file mode 100644 index 0000000000..6dd2a0802b --- /dev/null +++ b/src/sage/foundation/model_registry/__init__.py @@ -0,0 +1,25 @@ +"""Model registry helpers for the in-tree SAGE foundation layer.""" + +from .sagellm_registry import ( + ModelInfo, + ModelNotFoundError, + ModelRegistryError, + delete_model, + download_model, + ensure_model_available, + get_model_path, + list_models, + touch_model, +) + +__all__ = [ + "ModelInfo", + "ModelRegistryError", + "ModelNotFoundError", + "list_models", + "download_model", + "delete_model", + "get_model_path", + "touch_model", + "ensure_model_available", +] diff --git a/packages/sage-common/src/sage/common/model_registry/sagellm_registry.py b/src/sage/foundation/model_registry/sagellm_registry.py similarity index 61% rename from packages/sage-common/src/sage/common/model_registry/sagellm_registry.py rename to src/sage/foundation/model_registry/sagellm_registry.py index 375587c209..c97da21451 100644 --- a/packages/sage-common/src/sage/common/model_registry/sagellm_registry.py +++ b/src/sage/foundation/model_registry/sagellm_registry.py @@ -1,39 +1,28 @@ -"""Shared helpers for managing local sageLLM-compatible model assets. - -The CLI (``sage llm``) and middleware services share this module to keep model -lifecycle logic in one place. - -This registry is separate from vllm_registry to allow sageLLM (the independent -inference engine) to manage its own model cache independently. - -Storage path: ~/.sage/models/sagellm/ -""" +"""Shared helpers for managing local sageLLM-compatible model assets.""" from __future__ import annotations import json -import os import shutil import time from collections.abc import Iterable from dataclasses import dataclass, field from pathlib import Path -try: # Optional dependency – resolved lazily where needed +from sage.foundation.config.user_paths import get_user_paths + +try: from huggingface_hub import snapshot_download -except ImportError: # pragma: no cover - defer failure until download call +except ImportError: # pragma: no cover snapshot_download = None # type: ignore -_DEFAULT_ROOT = Path( - os.getenv("SAGE_SAGELLM_MODEL_ROOT", Path.home() / ".sage" / "models" / "sagellm") -) _MANIFEST_NAME = "metadata.json" @dataclass(order=True) class ModelInfo: - """Metadata describing a locally cached model.""" + """Metadata describing one locally cached model.""" sort_index: float = field(init=False, repr=False) model_id: str @@ -44,7 +33,6 @@ class ModelInfo: tags: list[str] = field(default_factory=list) def __post_init__(self) -> None: - # Negative for descending sort on ``last_used`` self.sort_index = -float(self.last_used or 0.0) @property @@ -63,11 +51,15 @@ class ModelRegistryError(RuntimeError): class ModelNotFoundError(ModelRegistryError): - """Raised when the requested model does not exist locally.""" + """Raised when a requested model does not exist locally.""" + + +def _default_root() -> Path: + return get_user_paths().sagellm_models_dir def _ensure_root(root: Path | None = None) -> Path: - resolved = Path(root) if root is not None else _DEFAULT_ROOT + resolved = Path(root) if root is not None else _default_root() resolved.mkdir(parents=True, exist_ok=True) return resolved @@ -83,7 +75,7 @@ def _load_manifest(root: Path) -> dict[str, dict]: try: with manifest_path.open("r", encoding="utf-8") as handle: return json.load(handle) - except json.JSONDecodeError as exc: # pragma: no cover - unexpected corruption + except json.JSONDecodeError as exc: # pragma: no cover raise ModelRegistryError(f"Corrupted manifest at {manifest_path}: {exc}") from exc @@ -127,14 +119,7 @@ def _purge_missing_entries(root: Path, manifest: dict[str, dict]) -> dict[str, d def list_models(root: Path | None = None) -> list[ModelInfo]: - """List locally available models sorted by last-used timestamp. - - Args: - root: Custom root directory. Defaults to ~/.sage/models/sagellm/ - - Returns: - List of ModelInfo sorted by last_used (descending). - """ + """List locally cached models sorted by last access time.""" root = _ensure_root(root) manifest = _purge_missing_entries(root, _load_manifest(root)) infos: list[ModelInfo] = [] @@ -153,33 +138,18 @@ def list_models(root: Path | None = None) -> list[ModelInfo]: def get_model_path(model_id: str, root: Path | None = None) -> Path | None: - """Return the local path for ``model_id`` or None if not found. - - Args: - model_id: HuggingFace model ID (e.g., "Qwen/Qwen2.5-1.5B-Instruct") - root: Custom root directory. Defaults to ~/.sage/models/sagellm/ - - Returns: - Path to the model directory, or None if not found. - """ + """Return local path for ``model_id`` or ``None``.""" root = _ensure_root(root) manifest = _load_manifest(root) entry = manifest.get(model_id) if not entry: return None path = Path(entry["path"]) - if not path.exists(): - return None - return path + return path if path.exists() else None def touch_model(model_id: str, root: Path | None = None) -> None: - """Update ``last_used`` timestamp for ``model_id`` if it exists. - - Args: - model_id: HuggingFace model ID - root: Custom root directory. Defaults to ~/.sage/models/sagellm/ - """ + """Refresh the last-used timestamp if a model exists in the registry.""" root = _ensure_root(root) manifest = _load_manifest(root) if model_id not in manifest: @@ -197,24 +167,8 @@ def download_model( progress: bool = True, **snapshot_kwargs, ) -> ModelInfo: - """Download ``model_id`` into the registry and return its metadata. - - Args: - model_id: HuggingFace model ID (e.g., "Qwen/Qwen2.5-1.5B-Instruct") - revision: Git revision (branch, tag, or commit hash) - root: Custom root directory. Defaults to ~/.sage/models/sagellm/ - tags: Optional tags to associate with the model - force: If True, re-download even if model exists - progress: If True, show download progress - **snapshot_kwargs: Additional arguments passed to huggingface_hub.snapshot_download - - Returns: - ModelInfo with metadata about the downloaded model. - - Raises: - ModelRegistryError: If download fails or huggingface_hub is not installed. - """ - if snapshot_download is None: # pragma: no cover - import guard + """Download a model into the local registry.""" + if snapshot_download is None: # pragma: no cover raise ModelRegistryError( "huggingface_hub is required to download models. " "Install with: pip install huggingface_hub" @@ -222,15 +176,14 @@ def download_model( root = _ensure_root(root) manifest = _load_manifest(root) - target_dir = root / _safe_dir_name(model_id, revision) + if target_dir.exists() and force: shutil.rmtree(target_dir, ignore_errors=True) if target_dir.exists() and not force: manifest_entry = manifest.get(model_id) if manifest_entry: - # refresh last-used and return existing info touch_model(model_id, root=root) return ModelInfo( model_id=model_id, @@ -240,42 +193,36 @@ def download_model( last_used=float(manifest_entry.get("last_used", 0.0)), tags=list(manifest_entry.get("tags", [])), ) - else: - # Directory exists without manifest entry - # Continue to download, huggingface_hub will resume incomplete downloads - if progress: - print("⚠️ 发现未完成的下载，继续从断点恢复...") + if progress: + print("⚠️ 发现未完成的下载，继续从断点恢复...") target_dir.mkdir(parents=True, exist_ok=True) - - download_kwargs = dict( - repo_id=model_id, - revision=revision, - local_dir=str(target_dir), + download_kwargs = { + "repo_id": model_id, + "revision": revision, + "local_dir": str(target_dir), **snapshot_kwargs, - ) + } if not progress: download_kwargs.setdefault("progress", False) - # Retry download with exponential backoff max_retries = 3 last_error = None for attempt in range(max_retries): try: resolved_path = Path(snapshot_download(**download_kwargs)) # type: ignore[arg-type] - break # Success - except Exception as e: - last_error = e + break + except Exception as exc: # noqa: BLE001 + last_error = exc if attempt < max_retries - 1: - wait_time = 2**attempt # 1s, 2s, 4s + wait_time = 2**attempt if progress: print(f"⚠️ 下载中断，{wait_time}秒后重试 (尝试 {attempt + 2}/{max_retries})...") time.sleep(wait_time) else: - # Final attempt failed raise ModelRegistryError( f"下载失败 (已重试 {max_retries} 次): {last_error}\n" - f"提示：使用 --force 清理并重新下载，或检查网络连接" + "提示：使用 --force 清理并重新下载，或检查网络连接" ) from last_error size_bytes = _compute_size_bytes(resolved_path) @@ -300,25 +247,17 @@ def download_model( def delete_model(model_id: str, root: Path | None = None) -> bool: - """Remove ``model_id`` from the registry (manifest + files). - - Args: - model_id: HuggingFace model ID - root: Custom root directory. Defaults to ~/.sage/models/sagellm/ - - Returns: - True if the model was found and deleted, False otherwise. - """ + """Delete a model from the registry and local filesystem.""" root = _ensure_root(root) manifest = _load_manifest(root) entry = manifest.pop(model_id, None) - if entry: - path = Path(entry.get("path", "")) - if path.exists(): - shutil.rmtree(path, ignore_errors=True) - _save_manifest(root, manifest) - return True - return False + if not entry: + return False + path = Path(entry.get("path", "")) + if path.exists(): + shutil.rmtree(path, ignore_errors=True) + _save_manifest(root, manifest) + return True def ensure_model_available( @@ -327,32 +266,17 @@ def ensure_model_available( root: Path | None = None, auto_download: bool = True, ) -> Path: - """Ensure a model is available locally, downloading if needed. - - This is a convenience function that combines get_model_path and download_model. - - Args: - model_id: HuggingFace model ID (e.g., "BAAI/bge-small-zh-v1.5") - revision: Optional revision/branch - root: Custom root directory. Defaults to ~/.sage/models/sagellm/ - auto_download: If True, download model if not found locally - - Returns: - Path to the local model directory - - Raises: - ModelNotFoundError: If model not found and auto_download is False - """ + """Ensure a model exists locally, downloading it when allowed.""" path = get_model_path(model_id, root=root) if path is not None: - touch_model(model_id, root=root) # Update last_used timestamp + touch_model(model_id, root=root) return path if not auto_download: raise ModelNotFoundError( f"Model '{model_id}' not found locally. " - f"Set auto_download=True or download manually with: " - f"sage llm model download --model {model_id}" + "Set auto_download=True or download it with the SAGE model registry API " + "(for example `ensure_model_available()` / `download_model()`)." ) info = download_model(model_id, revision=revision, root=root) diff --git a/src/sage/runtime/__init__.py b/src/sage/runtime/__init__.py new file mode 100644 index 0000000000..6f98035311 --- /dev/null +++ b/src/sage/runtime/__init__.py @@ -0,0 +1,70 @@ +"""Runtime-first public API for SAGE. + +SAGE is stream-first, but environments and job orchestration remain the +execution surface that turns declarative flow definitions into runnable jobs. + +This package provides the preferred main-repo import path while implementation +ownership is migrated inward in stages. +""" + +from __future__ import annotations + +from typing import Any + +from sage.stream._runtime_kernel_types import Packet, StopSignal + +from .backend import get_runtime_backend +from .environments import FluttyEnvironment, LocalEnvironment +from .job_manager import JobManager +from .pipeline_compiler import CompiledActorGraph, PipelineCompiler +from .scheduler import ( + BaseScheduler, + FIFOScheduler, + LoadAwareScheduler, + NodeSelector, + PlacementDecision, +) +from .service import BaseService + +__all__ = [ + "LocalEnvironment", + "FluttyEnvironment", + "BaseScheduler", + "FIFOScheduler", + "LoadAwareScheduler", + "NodeSelector", + "PlacementDecision", + "BaseService", + "StopSignal", + "Packet", + "JobManager", + "get_runtime_backend", + "PipelineCompiler", + "CompiledActorGraph", +] + + +def __getattr__(name: str) -> Any: + if name == "BaseService": + return BaseService + if name == "BaseScheduler": + return BaseScheduler + if name == "FIFOScheduler": + return FIFOScheduler + if name == "LoadAwareScheduler": + return LoadAwareScheduler + if name == "NodeSelector": + return NodeSelector + if name == "PlacementDecision": + return PlacementDecision + if name == "StopSignal": + return StopSignal + if name == "Packet": + return Packet + if name == "JobManager": + return JobManager + if name == "PipelineCompiler": + return PipelineCompiler + if name == "CompiledActorGraph": + return CompiledActorGraph + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") diff --git a/src/sage/runtime/actor_wrappers.py b/src/sage/runtime/actor_wrappers.py new file mode 100644 index 0000000000..84af8a6cd4 --- /dev/null +++ b/src/sage/runtime/actor_wrappers.py @@ -0,0 +1,71 @@ +"""Runtime-agnostic actor wrappers for the in-tree streaming compiler.""" + +from __future__ import annotations + +from typing import Any + +from sage.foundation import Collector + +__all__ = [ + "SourceActorWrapper", + "MapActorWrapper", + "FlatMapActorWrapper", + "FilterActorWrapper", + "SinkActorWrapper", + "ServiceActorWrapper", +] + + +class SourceActorWrapper: + def __init__(self, fn_class: type, *fn_args: Any, **fn_kwargs: Any) -> None: + self._fn = fn_class(*fn_args, **fn_kwargs) + + def run(self, trigger: Any = None) -> Any: + return self._fn.execute(trigger) + + +class MapActorWrapper: + def __init__(self, fn_class: type, *fn_args: Any, **fn_kwargs: Any) -> None: + self._fn = fn_class(*fn_args, **fn_kwargs) + + def process(self, item: Any) -> Any: + return self._fn.execute(item) + + +class FlatMapActorWrapper: + def __init__(self, fn_class: type, *fn_args: Any, **fn_kwargs: Any) -> None: + self._fn = fn_class(*fn_args, **fn_kwargs) + + def process(self, item: Any) -> list[Any]: + collector = Collector(logger=getattr(self._fn, "logger", None)) + if hasattr(self._fn, "insert_collector"): + self._fn.insert_collector(collector) + + result = self._fn.execute(item) + if result is not None: + return list(result) + return collector.get_collected_data() + + +class FilterActorWrapper: + def __init__(self, fn_class: type, *fn_args: Any, **fn_kwargs: Any) -> None: + self._fn = fn_class(*fn_args, **fn_kwargs) + + def accepts(self, item: Any) -> bool: + return bool(self._fn.execute(item)) + + +class SinkActorWrapper: + def __init__(self, fn_class: type, *fn_args: Any, **fn_kwargs: Any) -> None: + self._fn = fn_class(*fn_args, **fn_kwargs) + + def consume(self, item: Any) -> None: + self._fn.execute(item) + + +class ServiceActorWrapper: + def __init__(self, fn_class: type, *fn_args: Any, **fn_kwargs: Any) -> None: + self._fn = fn_class(*fn_args, **fn_kwargs) + + def process(self, item: Any) -> Any: + return self._fn.execute(item) diff --git a/src/sage/runtime/backend.py b/src/sage/runtime/backend.py new file mode 100644 index 0000000000..337c4b8066 --- /dev/null +++ b/src/sage/runtime/backend.py @@ -0,0 +1,20 @@ +"""Runtime backend acquisition helpers for the main-repo runtime surface.""" + +from __future__ import annotations + +from typing import Any + +from .flutty_backend import get_flutty_adapter + + +def get_runtime_backend() -> Any: + """Return the process-global Flutty runtime adapter. + + The distributed backend remains optional. When callers need cluster-backed + execution they should go through this helper rather than importing Flutty + internals directly. + """ + return get_flutty_adapter() + + +__all__ = ["get_runtime_backend"] diff --git a/src/sage/runtime/backend_protocol.py b/src/sage/runtime/backend_protocol.py new file mode 100644 index 0000000000..0f2bf00139 --- /dev/null +++ b/src/sage/runtime/backend_protocol.py @@ -0,0 +1,122 @@ +"""Runtime backend protocol owned by the main SAGE repository.""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from typing import Any + + +class NodeInfoProtocol(ABC): + @property + @abstractmethod + def node_id(self) -> str: + pass + + @property + @abstractmethod + def address(self) -> str: + pass + + @property + @abstractmethod + def is_schedulable(self) -> bool: + pass + + @property + @abstractmethod + def resource_summary(self) -> dict[str, Any]: + pass + + +class MethodCallFuture(ABC): + @abstractmethod + def result(self, timeout: float | None = None) -> Any: + pass + + @abstractmethod + def cancel(self) -> bool: + pass + + @property + @abstractmethod + def done(self) -> bool: + pass + + +class MethodRefProtocol(ABC): + @abstractmethod + def call(self, *args: Any, **kwargs: Any) -> Any: + pass + + @abstractmethod + def async_call(self, *args: Any, **kwargs: Any) -> MethodCallFuture: + pass + + @abstractmethod + def cancel(self) -> bool: + pass + + +class ActorHandleProtocol(ABC): + @abstractmethod + def get_method(self, name: str) -> MethodRefProtocol: + pass + + def cancel(self) -> bool: + return False + + +class FlowRunHandleProtocol(ABC): + @abstractmethod + def call(self, *args: Any, **kwargs: Any) -> Any: + pass + + @abstractmethod + def cancel(self) -> None: + pass + + +class RuntimeBackendProtocol(ABC): + @abstractmethod + def start(self, config: Any | None = None) -> None: + pass + + @abstractmethod + def stop(self) -> None: + pass + + @abstractmethod + def create( + self, + actor_class: type, + /, + *args: Any, + actor_config: Any | None = None, + **kwargs: Any, + ) -> ActorHandleProtocol: + pass + + @abstractmethod + def submit( + self, + flow_obj: Any, + *, + ingress: Any | None = None, + egress: Any | None = None, + run_config: Any | None = None, + ) -> FlowRunHandleProtocol: + pass + + @abstractmethod + def list_nodes(self) -> list[NodeInfoProtocol]: + pass + + +__all__ = [ + "RuntimeBackendProtocol", + "ActorHandleProtocol", + "MethodRefProtocol", + "MethodCallFuture", + "FlowRunHandleProtocol", + "NodeInfoProtocol", +] diff --git a/src/sage/runtime/base_environment.py b/src/sage/runtime/base_environment.py new file mode 100644 index 0000000000..e94f9d860d --- /dev/null +++ b/src/sage/runtime/base_environment.py @@ -0,0 +1,268 @@ +from __future__ import annotations + +from abc import ABC, abstractmethod +from collections.abc import Callable +from typing import TYPE_CHECKING, Any + +from sage.foundation import CustomLogger, wrap_lambda +from sage.stream.transformations import ( + BaseTransformation, + BatchTransformation, + FutureTransformation, + SourceTransformation, +) + +from .batch_functions import IterableBatchIteratorFunction, SimpleBatchIteratorFunction +from .jobmanager_client import JobManagerClient +from .scheduler import resolve_scheduler +from .service_factory import ServiceFactory + +if TYPE_CHECKING: + from sage.foundation.core import BaseFunction + from sage.stream import DataStream + + +class BaseEnvironment(ABC): + __state_exclude__ = ["_engine_client", "client", "jobmanager"] + + def _get_datastream_class(self): + if not hasattr(self, "_datastream_class"): + from sage.stream import DataStream + + self._datastream_class = DataStream + return self._datastream_class + + def _get_transformation_classes(self): + if not hasattr(self, "_transformation_classes"): + self._transformation_classes = { + "BaseTransformation": BaseTransformation, + "SourceTransformation": SourceTransformation, + "BatchTransformation": BatchTransformation, + "FutureTransformation": FutureTransformation, + } + return self._transformation_classes + + def __init__( + self, + name: str, + config: dict | None, + *, + platform: str = "local", + scheduler=None, + enable_monitoring: bool = False, + ): + self.name = name + self.uuid: str | None = None + + self.config: dict = dict(config or {}) + self.platform: str = platform + + self.jobmanager_host: str | None = None + self.jobmanager_port: int | None = None + self.session_id: str | None = None + self.session_timestamp: Any | None = None + self.pipeline: list[BaseTransformation] = [] + self._filled_futures: dict = {} + self.service_factories: dict = {} + + self.enable_monitoring: bool = enable_monitoring + + self._scheduler = None + self._init_scheduler(scheduler) + + self.env_base_dir: str | None = None + self._jobmanager: Any | None = None + self._engine_client: JobManagerClient | None = None + self.env_uuid: str | None = None + self.console_log_level: str = "INFO" + + def _init_scheduler(self, scheduler): + self._scheduler = resolve_scheduler(scheduler=scheduler, platform=self.platform) + + @property + def scheduler(self): + return self._scheduler + + def set_console_log_level(self, level: str): + valid_levels = ["DEBUG", "INFO", "WARNING", "ERROR"] + if level.upper() not in valid_levels: + raise ValueError(f"Invalid log level: {level}. Must be one of {valid_levels}") + + self.console_log_level = level.upper() + + if hasattr(self, "_logger") and self._logger is not None: + self._logger.update_output_level("console", self.console_log_level) + + def register_service(self, service_name: str, service_class: type, *args, **kwargs): + service_factory = ServiceFactory( + service_name=service_name, + service_class=service_class, + service_args=args, + service_kwargs=kwargs, + ) + + self.service_factories[service_name] = service_factory + platform_str = "remote" if self.platform == "remote" else "local" + self.logger.info( + f"Registered {platform_str} service: {service_name} ({service_class.__name__})" + ) + return service_factory + + def register_service_factory(self, service_name: str, service_factory: ServiceFactory): + self.service_factories[service_name] = service_factory + platform_str = "remote" if self.platform == "remote" else "local" + self.logger.info(f"Registered {platform_str} service factory: {service_name}") + return service_factory + + def from_kafka_source( + self, + source_class: type, + bootstrap_servers: str, + topic: str, + group_id: str, + auto_offset_reset: str = "latest", + value_deserializer: str = "json", + buffer_size: int = 10000, + max_poll_records: int = 500, + **kafka_config, + ) -> DataStream: + SourceTransformation = self._get_transformation_classes()["SourceTransformation"] + transformation = SourceTransformation( + self, + source_class, + bootstrap_servers=bootstrap_servers, + topic=topic, + group_id=group_id, + auto_offset_reset=auto_offset_reset, + value_deserializer=value_deserializer, + buffer_size=buffer_size, + max_poll_records=max_poll_records, + **kafka_config, + ) + + self.pipeline.append(transformation) + self.logger.info(f"Kafka source created for topic: {topic}, group: {group_id}") + return self._get_datastream_class()(self, transformation) + + def from_source(self, function: type[BaseFunction] | Callable, *args, **kwargs) -> DataStream: + if callable(function) and not isinstance(function, type): + function = wrap_lambda(function, "flatmap") + + SourceTransformation = self._get_transformation_classes()["SourceTransformation"] + transformation = SourceTransformation(self, function, *args, **kwargs) + self.pipeline.append(transformation) + return self._get_datastream_class()(self, transformation) + + def from_collection( + self, function: type[BaseFunction] | Callable, *args, **kwargs + ) -> DataStream: + if callable(function) and not isinstance(function, type): + function = wrap_lambda(function, "flatmap") + + BatchTransformation = self._get_transformation_classes()["BatchTransformation"] + transformation = BatchTransformation(self, function, *args, **kwargs) + self.pipeline.append(transformation) + return self._get_datastream_class()(self, transformation) + + def from_batch(self, source: type[BaseFunction] | Any, *args, **kwargs) -> DataStream: + if isinstance(source, type) and hasattr(source, "__bases__"): + from sage.foundation import BaseFunction + + if issubclass(source, BaseFunction): + return self._from_batch_function_class(source, *args, **kwargs) + + if isinstance(source, (list, tuple)): + return self._from_batch_collection(source, **kwargs) + if hasattr(source, "__iter__") and not isinstance(source, (str, bytes)): + return self._from_batch_iterable(source, **kwargs) + if isinstance(source, (str, bytes)): + return self._from_batch_iterable(source, **kwargs) + + try: + iter(source) + return self._from_batch_iterable(source, **kwargs) + except TypeError: + raise TypeError( + f"Unsupported source type: {type(source)}. Expected BaseFunction subclass, list, tuple, or any iterable object." + ) from None + + def from_future(self, name: str) -> DataStream: + FutureTransformation = self._get_transformation_classes()["FutureTransformation"] + transformation = FutureTransformation(self, name) + self.pipeline.append(transformation) + return self._get_datastream_class()(self, transformation) + + @abstractmethod + def submit(self): + pass + + @property + def logger(self): + if not hasattr(self, "_logger"): + self._logger = CustomLogger() + return self._logger + + @property + def client(self) -> JobManagerClient: + if self._engine_client is None: + daemon_host = self.config.get("engine_host", "127.0.0.1") + daemon_port = self.config.get("engine_port", 19000) + self._engine_client = JobManagerClient(host=daemon_host, port=daemon_port) + return self._engine_client + + def _append(self, transformation: BaseTransformation): + self.pipeline.append(transformation) + return self._get_datastream_class()(self, transformation) + + def _from_batch_function_class( + self, batch_function_class: type[BaseFunction], *args, **kwargs + ) -> DataStream: + transform_kwargs = {} + function_kwargs = {} + transform_config_keys = {"delay", "progress_log_interval"} + + for key, value in kwargs.items(): + if key in transform_config_keys: + transform_kwargs[key] = value + else: + function_kwargs[key] = value + + BatchTransformation = self._get_transformation_classes()["BatchTransformation"] + transformation = BatchTransformation( + self, batch_function_class, *args, **function_kwargs, **transform_kwargs + ) + + self.pipeline.append(transformation) + self.logger.info(f"Custom batch source created with {batch_function_class.__name__}") + return self._get_datastream_class()(self, transformation) + + def _from_batch_collection(self, data: list | tuple, **kwargs) -> DataStream: + BatchTransformation = self._get_transformation_classes()["BatchTransformation"] + transformation = BatchTransformation(self, SimpleBatchIteratorFunction, data=data, **kwargs) + + self.pipeline.append(transformation) + self.logger.info(f"Batch collection source created with {len(data)} items") + return self._get_datastream_class()(self, transformation) + + def _from_batch_iterable(self, iterable: Any, **kwargs) -> DataStream: + total_count = kwargs.pop("total_count", None) + if total_count is None: + try: + total_count = len(iterable) + except TypeError: + total_count = None + + BatchTransformation = self._get_transformation_classes()["BatchTransformation"] + transformation = BatchTransformation( + self, + IterableBatchIteratorFunction, + iterable=iterable, + total_count=total_count, + **kwargs, + ) + + self.pipeline.append(transformation) + type_name = type(iterable).__name__ + count_info = f" with {total_count} items" if total_count is not None else "" + self.logger.info(f"Batch iterable source created from {type_name}{count_info}") + return self._get_datastream_class()(self, transformation) diff --git a/src/sage/runtime/base_tcp_client.py b/src/sage/runtime/base_tcp_client.py new file mode 100644 index 0000000000..68be95101d --- /dev/null +++ b/src/sage/runtime/base_tcp_client.py @@ -0,0 +1,188 @@ +from __future__ import annotations + +import json +import socket +import time +import uuid +from abc import ABC +from typing import Any + + +class BaseTcpClient(ABC): + """Minimal runtime-local TCP client base used by JobManager clients.""" + + def __init__( + self, + host: str = "127.0.0.1", + port: int = 19001, + timeout: float = 30.0, + client_name: str = "TcpClient", + ): + self.host = host + self.port = port + self.timeout = timeout + self.client_name = client_name + self.connected = False + self._socket: socket.socket | None = None + self.logger = self._create_default_logger() + + def _create_default_logger(self): + import logging + + logger = logging.getLogger(self.client_name) + if not logger.handlers: + handler = logging.StreamHandler() + formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s") + handler.setFormatter(formatter) + logger.addHandler(handler) + logger.setLevel(logging.INFO) + return logger + + def connect(self) -> bool: + if self.connected: + return True + + try: + self._socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + self._socket.settimeout(self.timeout) + self._socket.connect((self.host, self.port)) + self.connected = True + self.logger.debug("%s connected to %s:%s", self.client_name, self.host, self.port) + return True + except Exception as exc: + self.logger.error("Failed to connect to %s:%s: %s", self.host, self.port, exc) + if "JobManager" in self.client_name: + self._log_jobmanager_connection_help() + self.connected = False + if self._socket: + try: + self._socket.close() + except Exception: + pass + self._socket = None + return False + + def disconnect(self): + if self._socket: + try: + self._socket.close() + except Exception: + pass + self._socket = None + self.connected = False + self.logger.debug("%s disconnected", self.client_name) + + def _log_jobmanager_connection_help(self): + self.logger.error("❌ 无法连接到JobManager服务") + self.logger.error("📋 请检查以下步骤：") + self.logger.error(" 1. JobManager是否已启动？") + self.logger.error(" 当前主仓未提供独立的 JobManager 守护进程子命令") + self.logger.error( + " 请确认目标主机上的 JobManager 兼容服务已监听 %s:%s", self.host, self.port + ) + self.logger.error(" 2. 主机地址是否正确？ (当前: %s:%s)", self.host, self.port) + self.logger.error(" 3. 防火墙是否阻止了连接？") + self.logger.error("💡 提示：如果是第一次尝试远程执行，请先启动JobManager服务") + + def _create_jobmanager_error_response(self) -> dict[str, Any]: + return { + "status": "error", + "error_code": "ERR_JOBMANAGER_CONNECTION_FAILED", + "message": f"Cannot connect to JobManager at {self.host}:{self.port}", + "details": { + "host": self.host, + "port": self.port, + "client_type": "JobManager", + "suggestions": [ + f"Ensure a JobManager-compatible service is listening on {self.host}:{self.port}", + "Check if the host and port are correct", + "Verify that firewall allows the connection", + "Ensure JobManager service is running and healthy", + ], + }, + "timestamp": time.time(), + } + + def _create_error_response(self, error_code: str, message: str) -> dict[str, Any]: + return { + "status": "error", + "error_code": error_code, + "message": message, + "timestamp": time.time(), + "request_id": str(uuid.uuid4()), + } + + def send_request(self, request_data: dict[str, Any]) -> dict[str, Any]: + if not self.connected: + if not self.connect(): + if "JobManager" in self.client_name: + return self._create_jobmanager_error_response() + return self._create_error_response( + "ERR_CONNECTION_FAILED", "Failed to connect to server" + ) + + try: + serialized_request = self._serialize_request(request_data) + self._send_data(serialized_request) + response_data = self._receive_response() + if response_data is None: + return self._create_error_response( + "ERR_NO_RESPONSE", "No response received from server" + ) + return self._deserialize_response(response_data) + except Exception as exc: + self.logger.error("Error sending request: %s", exc) + self.connected = False + return self._create_error_response( + "ERR_COMMUNICATION_FAILED", f"Communication error: {exc}" + ) + + def _send_data(self, data: bytes): + if not self._socket: + raise RuntimeError("Socket not connected") + + self._socket.sendall(len(data).to_bytes(4, byteorder="big")) + self._socket.sendall(data) + + def _receive_response(self) -> bytes | None: + if not self._socket: + raise RuntimeError("Socket not connected") + + try: + response_length_data = self._receive_full_data(4) + if not response_length_data: + return None + response_length = int.from_bytes(response_length_data, byteorder="big") + if response_length <= 0 or response_length > 100 * 1024 * 1024: + self.logger.warning("Invalid response length: %s", response_length) + return None + return self._receive_full_data(response_length) + except Exception as exc: + self.logger.error("Error receiving response: %s", exc) + return None + + def _receive_full_data(self, size: int) -> bytes | None: + if not self._socket: + return None + + data = b"" + while len(data) < size: + try: + chunk = self._socket.recv(min(size - len(data), 8192)) + if not chunk: + self.logger.warning("Connection closed while receiving data") + return None + data += chunk + except TimeoutError: + self.logger.error("Timeout while receiving data") + return None + except Exception as exc: + self.logger.error("Error receiving data: %s", exc) + return None + return data + + def _serialize_request(self, request_data: dict[str, Any]) -> bytes: + return json.dumps(request_data, ensure_ascii=False).encode("utf-8") + + def _deserialize_response(self, response_data: bytes) -> dict[str, Any]: + return json.loads(response_data.decode("utf-8")) diff --git a/packages/sage-kernel/src/sage/kernel/api/function/simple_batch_function.py b/src/sage/runtime/batch_functions.py similarity index 58% rename from packages/sage-kernel/src/sage/kernel/api/function/simple_batch_function.py rename to src/sage/runtime/batch_functions.py index 50270c9322..8c78c8de13 100644 --- a/packages/sage-kernel/src/sage/kernel/api/function/simple_batch_function.py +++ b/src/sage/runtime/batch_functions.py @@ -1,25 +1,22 @@ -"""Simple batch function classes for iterating over collections.""" +"""Small batch-source helpers reclaimed into the main SAGE repository.""" -from typing import Any, Iterator +from __future__ import annotations -from sage.common.core.functions import BatchFunction +from collections.abc import Iterator +from typing import Any +from sage.foundation import BatchFunction -class SimpleBatchIteratorFunction(BatchFunction): - """ - Simple batch iterator function for collections (list, tuple). - Args: - data: List or tuple of items to iterate over - """ +class SimpleBatchIteratorFunction(BatchFunction): + """Iterate over a materialized collection such as ``list`` or ``tuple``.""" - def __init__(self, data: list | tuple, **kwargs): + def __init__(self, data: list | tuple, **kwargs) -> None: super().__init__(**kwargs) self.data = data self._index = 0 def execute(self) -> Any: - """Execute the batch function - return next item or None when done.""" if self._index >= len(self.data): return None item = self.data[self._index] @@ -27,11 +24,9 @@ def execute(self) -> Any: return item def __iter__(self) -> Iterator[Any]: - """Return an iterator over the data.""" return iter(self.data) def __next__(self) -> Any: - """Get the next item from the data.""" if self._index >= len(self.data): raise StopIteration item = self.data[self._index] @@ -39,27 +34,19 @@ def __next__(self) -> Any: return item def __len__(self) -> int: - """Return the length of the data.""" return len(self.data) class IterableBatchIteratorFunction(BatchFunction): - """ - Batch iterator function for any iterable object. - - Args: - iterable: Any iterable object to iterate over - total_count: Optional total count of items (for progress tracking) - """ + """Iterate over any iterable object, optionally with a known total size.""" - def __init__(self, iterable: Any, total_count: int | None = None, **kwargs): + def __init__(self, iterable: Any, total_count: int | None = None, **kwargs) -> None: super().__init__(**kwargs) self.iterable = iterable self.total_count = total_count self._iterator = None def execute(self) -> Any: - """Execute the batch function - return next item or None when done.""" if self._iterator is None: self._iterator = iter(self.iterable) try: @@ -68,25 +55,24 @@ def execute(self) -> Any: return None def __iter__(self) -> Iterator[Any]: - """Return an iterator over the iterable.""" self._iterator = iter(self.iterable) return self._iterator def __next__(self) -> Any: - """Get the next item from the iterable.""" if self._iterator is None: self._iterator = iter(self.iterable) return next(self._iterator) def __len__(self) -> int: - """Return the length if known, otherwise raise TypeError.""" if self.total_count is not None: return self.total_count - # Try to get length from the iterable try: return len(self.iterable) # type: ignore[arg-type] - except TypeError: + except TypeError as exc: raise TypeError( "IterableBatchIteratorFunction does not have a known length. " - "Provide total_count parameter when creating the source." - ) + "Provide total_count when creating the source." + ) from exc + + +__all__ = ["SimpleBatchIteratorFunction", "IterableBatchIteratorFunction"] diff --git a/src/sage/runtime/context_injection.py b/src/sage/runtime/context_injection.py new file mode 100644 index 0000000000..7a8fe30125 --- /dev/null +++ b/src/sage/runtime/context_injection.py @@ -0,0 +1,44 @@ +from __future__ import annotations + +import logging +from typing import Any, TypeVar + +T = TypeVar("T") + + +def create_with_context( + target_class: type[T], + context: Any, + context_attr_name: str = "ctx", + *args, + **kwargs, +) -> T: + """Create an instance with context injected before ``__init__`` runs.""" + if context is None: + return target_class(*args, **kwargs) + + instance = target_class.__new__(target_class) + + try: + setattr(instance, context_attr_name, context) + except (AttributeError, TypeError) as exc: + logging.warning("Failed to inject context into %s: %s", target_class.__name__, exc) + instance = target_class(*args, **kwargs) + try: + setattr(instance, context_attr_name, context) + except (AttributeError, TypeError): + logging.warning( + "Failed to inject context after construction for %s", + target_class.__name__, + ) + return instance + + instance.__init__(*args, **kwargs) # type: ignore[misc] + return instance + + +def create_service_with_context( + service_class: type[T], service_context: Any | None, *args, **kwargs +) -> T: + """Create a service instance with ``ctx`` injected.""" + return create_with_context(service_class, service_context, "ctx", *args, **kwargs) diff --git a/src/sage/runtime/environments.py b/src/sage/runtime/environments.py new file mode 100644 index 0000000000..8804548d58 --- /dev/null +++ b/src/sage/runtime/environments.py @@ -0,0 +1,223 @@ +"""Main-repo owned runtime environment entrypoints.""" + +from __future__ import annotations + +import time +from typing import TYPE_CHECKING, Any + +from sage.runtime import backend as runtime_backend + +from .base_environment import BaseEnvironment +from .job_manager import JobManager +from .pipeline_compiler import PipelineCompiler + +if TYPE_CHECKING: + from .pipeline_compiler import CompiledActorGraph, _StreamingFlowHandle + + +class LocalEnvironment(BaseEnvironment): + """Local execution environment.""" + + def __init__( + self, + name: str = "localenvironment", + config: dict | None = None, + scheduler=None, + enable_monitoring: bool = False, + ): + super().__init__( + name, + config, + platform="local", + scheduler=scheduler, + enable_monitoring=enable_monitoring, + ) + self._engine_client = None + + def submit(self, autostop: bool = False): + env_uuid = self.jobmanager.submit_job(self, autostop=autostop) + self.env_uuid = env_uuid + return env_uuid + + def _wait_for_completion(self): + if not self.env_uuid: + self.logger.warning("No environment UUID found, cannot wait for completion") + return + + self.logger.info("Waiting for batch processing to complete...") + self.logger.info( + "⏳ Strategy: Wait for all data to be processed (not just source completion)" + ) + + max_wait_time = 12000.0 + start_time = time.time() + check_interval = 1.0 + + try: + while time.time() - start_time < max_wait_time: + status = self.jobmanager.get_job_status(self.env_uuid) + if status["status"] in ["stopped", "failed", "not_found"]: + self.logger.info( + f"✅ Batch processing completed with status: {status['status']}" + ) + break + + time.sleep(check_interval) + + else: + self.logger.warning( + f"Timeout waiting for batch processing to complete after {max_wait_time}s" + ) + try: + self.stop() + except Exception as stop_error: # noqa: BLE001 + self.logger.error(f"Error stopping timed out job: {stop_error}") + + except KeyboardInterrupt: + self.logger.info("Received interrupt signal, stopping batch processing...") + self.stop() + except Exception as exc: # noqa: BLE001 + self.logger.error(f"Error waiting for completion: {exc}") + try: + self.stop() + except Exception as stop_error: # noqa: BLE001 + self.logger.error(f"Error stopping job after wait error: {stop_error}") + finally: + self.is_running = False + + @property + def jobmanager(self) -> JobManager: + if self._jobmanager is None: + self._jobmanager = JobManager() + + return self._jobmanager + + def stop(self): + if not self.env_uuid: + self.logger.warning("Environment not submitted, nothing to stop") + return + + self.logger.info("Stopping pipeline...") + try: + response = self.jobmanager.pause_job(self.env_uuid) + if response.get("status") in {"success", "stopped"}: + self.is_running = False + self.logger.info("Pipeline stopped successfully") + else: + self.logger.warning(f"Failed to stop pipeline: {response.get('message')}") + except Exception as exc: # noqa: BLE001 + self.logger.error(f"Error stopping pipeline: {exc}") + + def close(self): + if not self.env_uuid: + self.logger.warning("Environment not submitted, nothing to close") + return + + self.logger.info("Closing environment...") + try: + response = self.jobmanager.pause_job(self.env_uuid) + if response.get("status") in {"success", "stopped"}: + self.logger.info("Environment closed successfully") + else: + self.logger.warning(f"Failed to close environment: {response.get('message')}") + except Exception as exc: # noqa: BLE001 + self.logger.error(f"Error closing environment: {exc}") + finally: + self.is_running = False + self.env_uuid = None + self.pipeline.clear() + + +class FluttyEnvironment(BaseEnvironment): + """Optional distributed execution environment backed by Flutty.""" + + def __init__( + self, + name: str = "flutty_environment", + config: dict | None = None, + scheduler=None, + placement_policy: str | None = None, + enable_monitoring: bool = False, + ) -> None: + super().__init__( + name, + config, + platform="flutty", + scheduler=scheduler, + enable_monitoring=enable_monitoring, + ) + self._placement_policy: str | None = placement_policy + self._compiled_graph: CompiledActorGraph | None = None + self._streaming_handle: _StreamingFlowHandle | None = None + + def submit(self, autostop: bool = False) -> Any: + adapter = runtime_backend.get_runtime_backend() + compiler = PipelineCompiler() + + self.logger.info( + f"[FluttyEnvironment:{self.name}] Compiling pipeline with {len(self.pipeline)} stage(s)…" + ) + self._compiled_graph = compiler.compile(self.pipeline, adapter) + + self.logger.info( + f"[FluttyEnvironment:{self.name}] Submitting " + f"({'batch/autostop' if autostop else 'streaming'})…" + ) + result = self._compiled_graph.submit(autostop=autostop) + + if not autostop: + self._streaming_handle = result + + return result + + def stop(self) -> None: + if self._streaming_handle is not None: + self.logger.info(f"[FluttyEnvironment:{self.name}] Stopping streaming pipeline…") + self._streaming_handle.stop() + self._streaming_handle = None + else: + self.logger.info( + f"[FluttyEnvironment:{self.name}] stop() called but no active streaming handle found — nothing to stop." + ) + + def close(self) -> None: + self.stop() + self._compiled_graph = None + self.logger.info(f"[FluttyEnvironment:{self.name}] Closed.") + + def health_check(self) -> list[dict[str, Any]]: + try: + adapter = runtime_backend.get_runtime_backend() + return adapter.list_nodes() + except Exception as exc: # noqa: BLE001 + self.logger.warning(f"[FluttyEnvironment:{self.name}] health_check failed: {exc}") + return [] + + @property + def is_running(self) -> bool: + if self._streaming_handle is None: + return False + return self._streaming_handle.is_running + + @property + def placement_policy(self) -> str | None: + return self._placement_policy + + @placement_policy.setter + def placement_policy(self, value: str | None) -> None: + self._placement_policy = value + + def __repr__(self) -> str: + state = "running" if self.is_running else "idle" + return ( + f"FluttyEnvironment(name={self.name!r}, stages={len(self.pipeline)}, state={state!r})" + ) + + def __enter__(self) -> FluttyEnvironment: + return self + + def __exit__(self, exc_type, exc_val, exc_tb) -> None: + self.close() + + +__all__ = ["LocalEnvironment", "FluttyEnvironment"] diff --git a/src/sage/runtime/exception_hooks.py b/src/sage/runtime/exception_hooks.py new file mode 100644 index 0000000000..d0545ee356 --- /dev/null +++ b/src/sage/runtime/exception_hooks.py @@ -0,0 +1,22 @@ +from __future__ import annotations + +from typing import Any + +_PUSH_HANDLER: Any | None = None +_POP_HANDLER: Any | None = None + + +def register_kernel_exception_handler_hook(push, pop) -> None: + """Register the active runtime's exception-scope push/pop hooks.""" + + global _PUSH_HANDLER, _POP_HANDLER + if not callable(push): + raise TypeError(f"push hook must be callable, got {type(push)!r}") + if not callable(pop): + raise TypeError(f"pop hook must be callable, got {type(pop)!r}") + _PUSH_HANDLER = push + _POP_HANDLER = pop + + +def get_registered_exception_handler_hook() -> tuple[Any | None, Any | None]: + return _PUSH_HANDLER, _POP_HANDLER diff --git a/src/sage/runtime/flutty_backend.py b/src/sage/runtime/flutty_backend.py new file mode 100644 index 0000000000..801387c4ac --- /dev/null +++ b/src/sage/runtime/flutty_backend.py @@ -0,0 +1,309 @@ +"""Flutty runtime adapter reclaimed into the main SAGE repository.""" + +from __future__ import annotations + +import concurrent.futures +from threading import Lock +from typing import Any + +from .backend_protocol import ( + ActorHandleProtocol, + FlowRunHandleProtocol, + MethodCallFuture, + MethodRefProtocol, + NodeInfoProtocol, + RuntimeBackendProtocol, +) +from .exception_hooks import register_kernel_exception_handler_hook + +__all__ = ["FluttyRuntimeAdapter", "get_flutty_adapter"] + + +def _require_flutty(operation: str) -> None: + try: + import flutty # noqa: F401 + except ImportError as exc: + raise ImportError( + f"FluttyRuntimeAdapter.{operation}() requires flutty to be installed.\n" + "Install it with: pip install flutty\n" + f"Original error: {exc}" + ) from exc + + +_async_executor: concurrent.futures.ThreadPoolExecutor | None = None +_async_executor_lock: Lock = Lock() + + +def _get_async_executor() -> concurrent.futures.ThreadPoolExecutor: + global _async_executor + with _async_executor_lock: + if _async_executor is None: + _async_executor = concurrent.futures.ThreadPoolExecutor( + max_workers=32, + thread_name_prefix="sage_flutty_async", + ) + return _async_executor + + +class _FluttyMethodCallFuture(MethodCallFuture): + __slots__ = ("_fut",) + + def __init__(self, fut: concurrent.futures.Future) -> None: + self._fut = fut + + def result(self, timeout: float | None = None) -> Any: + try: + return self._fut.result(timeout=timeout) + except concurrent.futures.TimeoutError as exc: + raise TimeoutError(f"Actor method call did not complete within {timeout}s.") from exc + except concurrent.futures.CancelledError as exc: + raise RuntimeError("Actor method call was cancelled.") from exc + + def cancel(self) -> bool: + return self._fut.cancel() + + @property + def done(self) -> bool: + return self._fut.done() + + +class _FluttyMethodRef(MethodRefProtocol): + __slots__ = ("_actor_id", "_method_name", "_registry") + + def __init__(self, actor_id: str, method_name: str, registry: Any) -> None: + self._actor_id = actor_id + self._method_name = method_name + self._registry = registry + + def call(self, *args: Any, **kwargs: Any) -> Any: + record = self._registry.resolve_local_actor(self._actor_id) + method = getattr(record.object, self._method_name) + return method(*args, **kwargs) + + def async_call(self, *args: Any, **kwargs: Any) -> MethodCallFuture: + fut = _get_async_executor().submit(self.call, *args, **kwargs) + return _FluttyMethodCallFuture(fut) + + def cancel(self) -> bool: + return False + + +class _FluttyActorHandle(ActorHandleProtocol): + def __init__(self, actor_id: str, registry: Any) -> None: + object.__setattr__(self, "_actor_id", actor_id) + object.__setattr__(self, "_registry", registry) + + def get_method(self, name: str) -> MethodRefProtocol: + return _FluttyMethodRef(self._actor_id, name, self._registry) + + def cancel(self) -> bool: + try: + return bool(self._registry.delete_local_actor(self._actor_id)) + except Exception: + return False + + +class _FluttyFlowRunHandle(FlowRunHandleProtocol): + __slots__ = ("_handle",) + + def __init__(self, handle: Any) -> None: + self._handle = handle + + def call(self, *args: Any, **kwargs: Any) -> Any: + call_fn = getattr(self._handle, "call", None) + if not callable(call_fn): + raise TypeError(f"Flutty flow handle {type(self._handle)!r} does not expose .call().") + return call_fn(*args, **kwargs) + + def cancel(self) -> None: + cancel_fn = getattr(self._handle, "cancel", None) + if cancel_fn is None: + raise RuntimeError("Flutty flow run handle does not support cancel().") + cancel_fn() + + +class _FluttyNodeInfo(NodeInfoProtocol): + __slots__ = ("_node_id", "_address", "_resources") + + def __init__( + self, + node_id: str = "local", + address: str = "127.0.0.1", + resources: dict[str, Any] | None = None, + ) -> None: + self._node_id = node_id + self._address = address + self._resources = resources or {} + + @property + def node_id(self) -> str: + return self._node_id + + @property + def address(self) -> str: + return self._address + + @property + def is_schedulable(self) -> bool: + return bool(self._resources.get("schedulable", True)) + + @property + def resource_summary(self) -> dict[str, Any]: + return dict(self._resources) + + +_DEFAULT_LOCAL_ADDRESS = "127.0.0.1:19931" + + +class FluttyRuntimeAdapter(RuntimeBackendProtocol): + def __init__(self) -> None: + self._started = False + self._session: Any | None = None + self._registry: Any | None = None + self._actor_api: Any | None = None + self._lock: Lock = Lock() + + def start(self, config: Any | None = None) -> None: + with self._lock: + if self._started: + return + _require_flutty("start") + cfg: dict[str, Any] = dict(config) if isinstance(config, dict) else {} + mode = str(cfg.get("mode", "lightweight")).strip().lower() + local_address = str(cfg.get("local_address", _DEFAULT_LOCAL_ADDRESS)).strip() + + from flutty.runtime.actors import ActorAPI + from flutty.runtime.actors.registry import LocalActorRegistry + + self._registry = LocalActorRegistry(local_address=local_address) + self._actor_api = ActorAPI(local_address=local_address, registry=self._registry) + + if mode == "cluster": + import flutty + + self._session = flutty.init_local(owner="sage-runtime", local_address=local_address) + + self._register_exception_hooks() + self._started = True + + def stop(self) -> None: + with self._lock: + if not self._started: + return + if self._session is not None: + try: + self._session.shutdown(wait=True) + except Exception: + pass + self._session = None + self._registry = None + self._actor_api = None + self._started = False + + def _assert_started(self, op: str) -> None: + if not self._started: + raise RuntimeError( + f"FluttyRuntimeAdapter.{op}() called before start(). Call start() first or use get_flutty_adapter()." + ) + + @staticmethod + def _register_exception_hooks() -> None: + try: + from flutty.compiler.exception_scope import ( + flow_exception_handler as _feh, + ) + + def _push(handler: Any) -> None: + _feh(handler).__enter__() + + def _pop() -> None: + pass + + register_kernel_exception_handler_hook(_push, _pop) + except Exception: + pass + + def create( + self, + actor_class: type, + /, + *args: Any, + actor_config: Any | None = None, + **kwargs: Any, + ) -> ActorHandleProtocol: + self._assert_started("create") + instance = actor_class(*args, **kwargs) + actor_id = self._registry.register_local_actor(instance, config=actor_config) + return _FluttyActorHandle(actor_id, self._registry) + + def submit( + self, + flow_obj: Any, + *, + ingress: Any | None = None, + egress: Any | None = None, + run_config: Any | None = None, + ) -> FlowRunHandleProtocol: + self._assert_started("submit") + if self._session is None: + raise RuntimeError( + "FluttyRuntimeAdapter.submit() requires cluster mode. Start with config={'mode': 'cluster'}." + ) + + endpoint_fn = getattr(flow_obj, "endpoint", None) + if callable(endpoint_fn): + kwargs: dict[str, Any] = {} + if ingress is not None: + kwargs["in_topic"] = ingress + if egress is not None: + kwargs["out_topic"] = egress + return _FluttyFlowRunHandle(endpoint_fn(**kwargs)) + + flow_uri = getattr(flow_obj, "flow_uri", None) or getattr(flow_obj, "uri", None) + if flow_uri: + result = self._session.submit_flow_program( + str(flow_uri), + ingress=ingress, + egress=egress, + run_config=dict(run_config) if run_config is not None else None, + ) + return _FluttyFlowRunHandle(result) + + raise TypeError( + f"FluttyRuntimeAdapter.submit() does not know how to submit {type(flow_obj)!r}. Pass a FlowDeclaration." + ) + + def list_nodes(self) -> list[NodeInfoProtocol]: + self._assert_started("list_nodes") + if self._session is not None: + try: + inspector_fn = getattr(self._session, "_runtime_inspector", None) + if callable(inspector_fn): + inspector = inspector_fn() + snapshot = inspector.cluster_view_snapshot(schema="v1") + nodes_raw = snapshot.get("nodes", []) if isinstance(snapshot, dict) else [] + return [ + _FluttyNodeInfo( + node_id=str(node.get("node_id", "unknown")), + address=str(node.get("address", "unknown")), + resources=node.get("resources", {}), + ) + for node in nodes_raw + ] + except Exception: + pass + return [_FluttyNodeInfo()] + + +_adapter_singleton: FluttyRuntimeAdapter | None = None +_adapter_lock: Lock = Lock() + + +def get_flutty_adapter(*, auto_start: bool = True) -> FluttyRuntimeAdapter: + global _adapter_singleton + with _adapter_lock: + if _adapter_singleton is None: + _adapter_singleton = FluttyRuntimeAdapter() + if auto_start and not _adapter_singleton._started: + _adapter_singleton.start() + return _adapter_singleton diff --git a/src/sage/runtime/job_manager.py b/src/sage/runtime/job_manager.py new file mode 100644 index 0000000000..5705074547 --- /dev/null +++ b/src/sage/runtime/job_manager.py @@ -0,0 +1,223 @@ +from __future__ import annotations + +import threading +import time +import uuid +from dataclasses import dataclass, field +from typing import Any + +from sage.foundation import CustomLogger + +from .local_backend import get_local_runtime_backend +from .pipeline_compiler import CompiledActorGraph, PipelineCompiler, _StreamingFlowHandle + + +@dataclass +class JobRecord: + uuid: str + env_name: str + pipeline_size: int + autostop: bool + compiled_graph: CompiledActorGraph + status: str = "created" + created_at: float = field(default_factory=time.time) + started_at: float | None = None + completed_at: float | None = None + handle: _StreamingFlowHandle | None = None + error: str | None = None + node_stop_signals: list[str] = field(default_factory=list) + + def mark_running(self, handle: _StreamingFlowHandle | None = None) -> None: + self.started_at = time.time() + self.handle = handle + self.status = "running" if handle is not None else "stopped" + if handle is None: + self.completed_at = self.started_at + + def mark_stopped(self) -> None: + self.status = "stopped" + self.completed_at = time.time() + + def mark_failed(self, error: Exception | str) -> None: + self.status = "failed" + self.error = str(error) + self.completed_at = time.time() + + def get_status(self) -> dict[str, Any]: + return { + "success": self.status != "failed", + "uuid": self.uuid, + "status": self.status, + "env_name": self.env_name, + "pipeline_size": self.pipeline_size, + "autostop": self.autostop, + "created_at": self.created_at, + "started_at": self.started_at, + "completed_at": self.completed_at, + "error": self.error, + "running": bool(self.handle.is_running) if self.handle is not None else False, + "node_stop_signals": list(self.node_stop_signals), + } + + +class JobManager: + """Main-repo owned lightweight local job orchestrator.""" + + instance: JobManager | None = None + instance_lock = threading.RLock() + + def __new__(cls, *args, **kwargs): + if cls.instance is None: + with cls.instance_lock: + if cls.instance is None: + cls.instance = super().__new__(cls) + cls.instance._initialized = False + return cls.instance + + def __init__(self) -> None: + with self.instance_lock: + if self._initialized: + return + self._initialized = True + self.jobs: dict[str, JobRecord] = {} + self.deleted_jobs: dict[str, dict[str, Any]] = {} + self.logger = CustomLogger(name="JobManager") + + def submit_job(self, env: Any, autostop: bool = False) -> str: + job_uuid = str(uuid.uuid4()) + env.uuid = job_uuid + env.env_uuid = job_uuid + env.jobmanager_host = "local" + env.jobmanager_port = 0 + + compiler = PipelineCompiler() + adapter = get_local_runtime_backend() + compiled_graph = compiler.compile(env.pipeline, adapter) + record = JobRecord( + uuid=job_uuid, + env_name=env.name, + pipeline_size=len(env.pipeline), + autostop=autostop, + compiled_graph=compiled_graph, + ) + self.jobs[job_uuid] = record + + try: + result = compiled_graph.submit(autostop=autostop) + if autostop: + record.mark_running(None) + else: + record.mark_running(result) + return job_uuid + except Exception as exc: + record.mark_failed(exc) + raise + + def pause_job(self, env_uuid: str) -> dict[str, Any]: + record = self.jobs.get(env_uuid) + if record is None: + return { + "uuid": env_uuid, + "status": "not_found", + "message": f"Job with UUID {env_uuid} not found", + } + + if record.handle is not None and record.handle.is_running: + record.handle.stop() + record.mark_stopped() + return { + "uuid": env_uuid, + "status": "stopped", + "message": "Job stopped successfully", + } + + def get_job_status(self, env_uuid: str) -> dict[str, Any]: + record = self.jobs.get(env_uuid) + if record is None: + return { + "success": False, + "uuid": env_uuid, + "status": "not_found", + "message": f"Job with UUID {env_uuid} not found", + } + if ( + record.handle is not None + and not record.handle.is_running + and record.status == "running" + ): + record.mark_stopped() + return record.get_status() + + def receive_node_stop_signal(self, env_uuid: str, node_name: str) -> dict[str, Any]: + record = self.jobs.get(env_uuid) + if record is None: + return { + "uuid": env_uuid, + "status": "not_found", + "message": f"Job with UUID {env_uuid} not found", + } + record.node_stop_signals.append(node_name) + return { + "uuid": env_uuid, + "status": "success", + "message": f"Node stop signal received for {node_name}", + } + + def list_jobs(self) -> list[dict[str, Any]]: + return [record.get_status() for record in self.jobs.values()] + + def delete_job(self, env_uuid: str, force: bool = False) -> dict[str, Any]: + record = self.jobs.get(env_uuid) + if record is None: + return { + "uuid": env_uuid, + "status": "not_found", + "message": f"Job with UUID {env_uuid} not found", + } + if record.handle is not None and record.handle.is_running: + if not force: + return { + "uuid": env_uuid, + "status": "running", + "message": "Job is still running. Pass force=True to stop and delete it.", + } + record.handle.stop() + record.mark_stopped() + self.deleted_jobs[env_uuid] = record.get_status() + del self.jobs[env_uuid] + return { + "uuid": env_uuid, + "status": "deleted", + "message": "Job deleted successfully", + } + + def continue_job(self, env_uuid: str) -> dict[str, Any]: + return { + "uuid": env_uuid, + "status": "unsupported", + "message": "Lightweight in-process jobs cannot be resumed once stopped.", + } + + def resume_job(self, env_uuid: str) -> dict[str, Any]: + return self.continue_job(env_uuid) + + def health_check(self) -> dict[str, Any]: + return { + "status": "healthy", + "timestamp": time.time(), + "jobs_count": len(self.jobs), + "mode": "in-process", + } + + def cleanup_all_jobs(self) -> dict[str, Any]: + for env_uuid in list(self.jobs): + self.delete_job(env_uuid, force=True) + return {"status": "success", "message": "All jobs cleaned up"} + + def shutdown(self) -> None: + self.cleanup_all_jobs() + JobManager.instance = None + + @property + def handle(self) -> JobManager: + return self diff --git a/src/sage/runtime/jobmanager_client.py b/src/sage/runtime/jobmanager_client.py new file mode 100644 index 0000000000..7a33cc02c9 --- /dev/null +++ b/src/sage/runtime/jobmanager_client.py @@ -0,0 +1,110 @@ +from __future__ import annotations + +import base64 +import time +import uuid +from typing import Any + +from .base_tcp_client import BaseTcpClient + + +class JobManagerClient(BaseTcpClient): + """Runtime-local JobManager client for serialized job submission.""" + + def __init__(self, host: str = "127.0.0.1", port: int = 19001, timeout: float = 600.0): + if not (1 <= port <= 65535): + raise ValueError(f"Port must be between 1 and 65535, got {port}") + if timeout <= 0: + raise ValueError(f"Timeout must be positive, got {timeout}") + + super().__init__(host, port, timeout, "JobManagerClient") + + def _build_health_check_request(self) -> dict[str, Any]: + return {"action": "health_check", "request_id": str(uuid.uuid4())} + + def _build_server_info_request(self) -> dict[str, Any]: + return {"action": "get_server_info", "request_id": str(uuid.uuid4())} + + def submit_job( + self, + serialized_data: bytes, + autostop: bool = False, + extra_python_paths: list[str] | None = None, + ) -> dict[str, Any]: + if serialized_data is None: + raise ValueError("Serialized data cannot be None") + if isinstance(serialized_data, bytes) and len(serialized_data) == 0: + raise ValueError("Serialized data cannot be empty") + + request = { + "action": "submit_job", + "request_id": str(uuid.uuid4()), + "serialized_data": base64.b64encode(serialized_data).decode("utf-8"), + "autostop": autostop, + "extra_python_paths": extra_python_paths or [], + } + return self.send_request(request) + + def pause_job(self, job_uuid: str) -> dict[str, Any]: + if job_uuid is None: + raise ValueError("Job UUID cannot be None") + if job_uuid == "": + raise ValueError("Job UUID cannot be empty") + return self.send_request( + {"action": "pause_job", "request_id": str(uuid.uuid4()), "job_uuid": job_uuid} + ) + + def get_job_status(self, job_uuid: str) -> dict[str, Any]: + return self.send_request( + {"action": "get_job_status", "request_id": str(uuid.uuid4()), "job_uuid": job_uuid} + ) + + def health_check(self) -> dict[str, Any]: + return self.send_request(self._build_health_check_request()) + + def get_server_info(self) -> dict[str, Any]: + return self.send_request(self._build_server_info_request()) + + def list_jobs(self) -> dict[str, Any]: + return self.send_request({"action": "list_jobs", "request_id": str(uuid.uuid4())}) + + def continue_job(self, job_uuid: str) -> dict[str, Any]: + return self.send_request( + {"action": "continue_job", "request_id": str(uuid.uuid4()), "job_uuid": job_uuid} + ) + + def delete_job(self, job_uuid: str, force: bool = False) -> dict[str, Any]: + return self.send_request( + { + "action": "delete_job", + "request_id": str(uuid.uuid4()), + "job_uuid": job_uuid, + "force": force, + } + ) + + def receive_node_stop_signal(self, job_uuid: str, node_name: str) -> dict[str, Any]: + return self.send_request( + { + "action": "receive_node_stop_signal", + "request_id": str(uuid.uuid4()), + "job_uuid": job_uuid, + "node_name": node_name, + } + ) + + def cleanup_all_jobs(self) -> dict[str, Any]: + return self.send_request({"action": "cleanup_all_jobs", "request_id": str(uuid.uuid4())}) + + def _retry_request(self, request: dict[str, Any], max_retries: int = 3) -> dict[str, Any]: + last_exception = None + for attempt in range(max_retries): + try: + return self.send_request(request) + except Exception as exc: + last_exception = exc + if attempt < max_retries - 1: + time.sleep(0.5 * (attempt + 1)) + continue + raise last_exception # noqa: B904 + raise last_exception if last_exception else RuntimeError("Retry failed") diff --git a/src/sage/runtime/local_backend.py b/src/sage/runtime/local_backend.py new file mode 100644 index 0000000000..5695f86594 --- /dev/null +++ b/src/sage/runtime/local_backend.py @@ -0,0 +1,149 @@ +"""In-process runtime backend used by the reclaimed local execution path.""" + +from __future__ import annotations + +import concurrent.futures +from threading import Lock +from typing import Any + +from .backend_protocol import ( + ActorHandleProtocol, + FlowRunHandleProtocol, + MethodCallFuture, + MethodRefProtocol, + NodeInfoProtocol, + RuntimeBackendProtocol, +) + +__all__ = ["LocalRuntimeAdapter", "get_local_runtime_backend"] + + +class _LocalMethodCallFuture(MethodCallFuture): + def __init__(self, future: concurrent.futures.Future) -> None: + self._future = future + + def result(self, timeout: float | None = None) -> Any: + return self._future.result(timeout=timeout) + + def cancel(self) -> bool: + return self._future.cancel() + + @property + def done(self) -> bool: + return self._future.done() + + +class _LocalMethodRef(MethodRefProtocol): + def __init__( + self, target: Any, method_name: str, executor: concurrent.futures.Executor + ) -> None: + self._target = target + self._method_name = method_name + self._executor = executor + + def call(self, *args: Any, **kwargs: Any) -> Any: + return getattr(self._target, self._method_name)(*args, **kwargs) + + def async_call(self, *args: Any, **kwargs: Any) -> MethodCallFuture: + future = self._executor.submit(self.call, *args, **kwargs) + return _LocalMethodCallFuture(future) + + def cancel(self) -> bool: + return False + + +class _LocalActorHandle(ActorHandleProtocol): + def __init__(self, target: Any, executor: concurrent.futures.Executor) -> None: + self._target = target + self._executor = executor + + def get_method(self, name: str) -> MethodRefProtocol: + return _LocalMethodRef(self._target, name, self._executor) + + +class _UnsupportedFlowHandle(FlowRunHandleProtocol): + def call(self, *args: Any, **kwargs: Any) -> Any: + raise RuntimeError("LocalRuntimeAdapter does not submit external flow handles directly") + + def cancel(self) -> None: + return None + + +class _LocalNodeInfo(NodeInfoProtocol): + @property + def node_id(self) -> str: + return "local" + + @property + def address(self) -> str: + return "127.0.0.1" + + @property + def is_schedulable(self) -> bool: + return True + + @property + def resource_summary(self) -> dict[str, Any]: + return {"schedulable": True, "mode": "in-process"} + + +class LocalRuntimeAdapter(RuntimeBackendProtocol): + def __init__(self) -> None: + self._started = False + self._lock = Lock() + self._executor: concurrent.futures.ThreadPoolExecutor | None = None + + def start(self, config: Any | None = None) -> None: + with self._lock: + if self._started: + return + self._executor = concurrent.futures.ThreadPoolExecutor( + max_workers=8, + thread_name_prefix="sage_local_runtime", + ) + self._started = True + + def stop(self) -> None: + with self._lock: + if self._executor is not None: + self._executor.shutdown(wait=True) + self._executor = None + self._started = False + + def create( + self, + actor_class: type, + /, + *args: Any, + actor_config: Any | None = None, + **kwargs: Any, + ) -> ActorHandleProtocol: + if not self._started: + self.start() + instance = actor_class(*args, **kwargs) + assert self._executor is not None + return _LocalActorHandle(instance, self._executor) + + def submit( + self, + flow_obj: Any, + *, + ingress: Any | None = None, + egress: Any | None = None, + run_config: Any | None = None, + ) -> FlowRunHandleProtocol: + return _UnsupportedFlowHandle() + + def list_nodes(self) -> list[NodeInfoProtocol]: + return [_LocalNodeInfo()] + + +_LOCAL_BACKEND: LocalRuntimeAdapter | None = None + + +def get_local_runtime_backend() -> LocalRuntimeAdapter: + global _LOCAL_BACKEND + if _LOCAL_BACKEND is None: + _LOCAL_BACKEND = LocalRuntimeAdapter() + _LOCAL_BACKEND.start() + return _LOCAL_BACKEND diff --git a/src/sage/runtime/pipeline_compiler.py b/src/sage/runtime/pipeline_compiler.py new file mode 100644 index 0000000000..21eb17f11c --- /dev/null +++ b/src/sage/runtime/pipeline_compiler.py @@ -0,0 +1,228 @@ +"""Main-repo owned lightweight pipeline compiler for runtime-backed execution.""" + +from __future__ import annotations + +import logging +import threading +from dataclasses import dataclass, field +from typing import Any + +from .actor_wrappers import ( + FilterActorWrapper, + FlatMapActorWrapper, + MapActorWrapper, + ServiceActorWrapper, + SinkActorWrapper, + SourceActorWrapper, +) + +logger = logging.getLogger(__name__) + +__all__ = ["PipelineCompiler", "CompiledActorGraph", "_StreamingFlowHandle"] + +_OP_MAP = "map" +_OP_FLATMAP = "flatmap" +_OP_FILTER = "filter" +_OP_SINK = "sink" + + +def _transformation_name(t: Any) -> str: + return type(t).__name__ + + +def _classify(t: Any) -> tuple[type, str, str]: + name = _transformation_name(t) + if name == "SourceTransformation": + return (SourceActorWrapper, "source", "run") + if name == "BatchTransformation": + return (SourceActorWrapper, "source", "run") + if name == "MapTransformation": + return (MapActorWrapper, _OP_MAP, "process") + if name == "FlatMapTransformation": + return (FlatMapActorWrapper, _OP_FLATMAP, "process") + if name == "FilterTransformation": + return (FilterActorWrapper, _OP_FILTER, "accepts") + if name == "SinkTransformation": + return (SinkActorWrapper, _OP_SINK, "consume") + return (ServiceActorWrapper, _OP_MAP, "process") + + +def _is_stop_signal(item: Any) -> bool: + return item is None or type(item).__name__ == "StopSignal" + + +class _StreamingFlowHandle: + def __init__( + self, source_thread: threading.Thread | None, *, stop_event: threading.Event + ) -> None: + self._thread = source_thread + self._stop_event = stop_event + + def stop(self, timeout: float = 30.0) -> None: + self._stop_event.set() + if self._thread is not None and self._thread.is_alive(): + self._thread.join(timeout=timeout) + + @property + def is_running(self) -> bool: + return self._thread is not None and self._thread.is_alive() + + +@dataclass +class CompiledActorGraph: + stage_ops: list[tuple[str, Any]] + source_transformation: Any | None + actor_handles: list[Any] = field(default_factory=list) + adapter: Any = None + + def submit(self, autostop: bool = False) -> Any: + if autostop: + return self._submit_batch() + return self._submit_streaming() + + def _execute_chain(self, items: list[Any], ops: list[tuple[str, Any]]) -> list[Any]: + if not ops or not items: + return items + + op_type, method_ref = ops[0] + remaining = ops[1:] + + if op_type == _OP_SINK: + for item in items: + method_ref.call(item) + return [] + + if op_type == _OP_FLATMAP: + next_items: list[Any] = [] + for item in items: + result = method_ref.call(item) + if result is None: + continue + if hasattr(result, "__iter__") and not isinstance(result, (str, bytes)): + next_items.extend(result) + else: + next_items.append(result) + return self._execute_chain(next_items, remaining) + + if op_type == _OP_FILTER: + next_items = [item for item in items if method_ref.call(item)] + return self._execute_chain(next_items, remaining) + + next_items = [method_ref.call(item) for item in items] + return self._execute_chain(next_items, remaining) + + def _collect_source_items(self) -> list[Any]: + t = self.source_transformation + if t is None: + return [] + + fn = t.function_class(*t.function_args, **t.function_kwargs) + items: list[Any] = [] + while True: + item = fn.execute() + if _is_stop_signal(item): + break + items.append(item) + + logger.debug( + "Source '%s' drained: %d items collected.", t.function_class.__name__, len(items) + ) + return items + + def _submit_batch(self) -> None: + items = self._collect_source_items() + if not items: + logger.info("Source produced no items; batch pipeline skipped.") + return None + + logger.info("Processing batch of %d items through pipeline.", len(items)) + for item in items: + self._execute_chain([item], self.stage_ops) + return None + + def _submit_streaming(self) -> _StreamingFlowHandle: + stop_event = threading.Event() + source_thread: threading.Thread | None = None + + if self.source_transformation is not None: + source_thread = threading.Thread( + target=self._run_source_thread, + args=(stop_event,), + daemon=True, + name=f"sage-source-{self.source_transformation.basename}", + ) + source_thread.start() + + return _StreamingFlowHandle(source_thread, stop_event=stop_event) + + def _run_source_thread(self, stop_event: threading.Event) -> None: + t = self.source_transformation + fn = t.function_class(*t.function_args, **t.function_kwargs) + + try: + while not stop_event.is_set(): + item = fn.execute() + if _is_stop_signal(item): + break + self._execute_chain([item], self.stage_ops) + except Exception: + logger.exception( + "Source function '%s' raised an exception in streaming mode.", + t.function_class.__name__, + ) + finally: + logger.debug("Source thread for '%s' exiting.", t.function_class.__name__) + + +class PipelineCompiler: + def compile(self, pipeline: list[Any], adapter: Any) -> CompiledActorGraph: + if not pipeline: + raise ValueError( + "PipelineCompiler.compile() received an empty pipeline. " + "Build a pipeline with env.from_source(...).map(...).sink(...) before calling env.submit()." + ) + + source_trans: Any | None = None + proc_transformations: list[Any] = [] + + for t in pipeline: + _, op_type, _ = _classify(t) + if op_type == "source": + if source_trans is not None: + raise ValueError( + "PipelineCompiler found more than one SourceTransformation in the pipeline. " + "Only a single source is supported per compilation unit." + ) + source_trans = t + else: + proc_transformations.append(t) + + actor_handles: list[Any] = [] + stage_ops: list[tuple[str, Any]] = [] + + for t in proc_transformations: + wrapper_cls, op_type, method_name = _classify(t) + handle = adapter.create( + wrapper_cls, + t.function_class, + *t.function_args, + **t.function_kwargs, + ) + actor_handles.append(handle) + method_ref = handle.get_method(method_name) + stage_ops.append((op_type, method_ref)) + + logger.debug( + "Compiled stage %s → %s.%s() [op=%s]", + t.basename, + wrapper_cls.__name__, + method_name, + op_type, + ) + + return CompiledActorGraph( + stage_ops=stage_ops, + source_transformation=source_trans, + actor_handles=actor_handles, + adapter=adapter, + ) diff --git a/src/sage/runtime/scheduler.py b/src/sage/runtime/scheduler.py new file mode 100644 index 0000000000..66bd995d7a --- /dev/null +++ b/src/sage/runtime/scheduler.py @@ -0,0 +1,290 @@ +from __future__ import annotations + +import os +import time +from abc import ABC, abstractmethod +from dataclasses import dataclass, field +from typing import Any + + +@dataclass(slots=True) +class PlacementDecision: + target_node: str | None = None + delay: float = 0.0 + immediate: bool = True + reason: str = "" + resource: dict[str, Any] = field(default_factory=dict) + + @classmethod + def immediate_default(cls, *, reason: str) -> PlacementDecision: + return cls(target_node=None, delay=0.0, immediate=True, reason=reason) + + +@dataclass(slots=True) +class NodeResources: + node_id: str + hostname: str + available_cpu: float + available_gpu: float + available_memory: int + task_count: int = 0 + alive: bool = True + + def can_fit( + self, + cpu_required: float = 0.0, + gpu_required: float = 0.0, + memory_required: int = 0, + ) -> bool: + return ( + self.available_cpu >= cpu_required + and self.available_gpu >= gpu_required + and self.available_memory >= memory_required + ) + + +class NodeSelector: + """Small in-tree node selector for local or optional distributed scheduling demos.""" + + def __init__(self, enable_tracking: bool = True) -> None: + self.enable_tracking = enable_tracking + self.node_task_count: dict[str, int] = {} + + def get_all_nodes(self) -> list[NodeResources]: + hostname = os.environ.get("SAGE_NODE_NAME") or os.uname().nodename + cpu_count = float(os.cpu_count() or 1) + memory_bytes = int(os.environ.get("SAGE_AVAILABLE_MEMORY", 8 * 1024**3)) + node = NodeResources( + node_id=hostname, + hostname=hostname, + available_cpu=cpu_count, + available_gpu=float(os.environ.get("SAGE_AVAILABLE_GPU", 0)), + available_memory=memory_bytes, + task_count=self.node_task_count.get(hostname, 0), + ) + return [node] + + def get_node(self, node_id: str) -> NodeResources | None: + for node in self.get_all_nodes(): + if node.node_id == node_id: + return node + return None + + def select_best_node( + self, + cpu_required: float = 0.0, + gpu_required: float = 0.0, + memory_required: int = 0, + custom_resources: dict[str, float] | None = None, + strategy: str = "balanced", + exclude_nodes: list[str] | None = None, + ) -> str | None: + del custom_resources + exclude = set(exclude_nodes or []) + nodes = [n for n in self.get_all_nodes() if n.node_id not in exclude] + candidates = [ + n for n in nodes if n.can_fit(cpu_required, gpu_required, memory_required) and n.alive + ] + if not candidates: + return None + if strategy == "spread": + candidates.sort(key=lambda n: (n.task_count, n.hostname)) + else: + candidates.sort(key=lambda n: (n.task_count, -n.available_cpu, n.hostname)) + selected = candidates[0] + if self.enable_tracking: + self.node_task_count[selected.node_id] = ( + self.node_task_count.get(selected.node_id, 0) + 1 + ) + return selected.node_id + + +class BaseScheduler(ABC): + def __init__(self) -> None: + self.scheduled_count = 0 + self.decision_history: list[PlacementDecision] = [] + + @abstractmethod + def make_decision(self, task_node: Any) -> PlacementDecision: + raise NotImplementedError + + def make_service_decision(self, service_node: Any) -> PlacementDecision: + return PlacementDecision.immediate_default( + reason=f"{self.__class__.__name__} service scheduling" + ) + + def schedule_task(self, task_node: Any, runtime_ctx: Any = None) -> Any: + decision = self.make_decision(task_node) + if decision.delay > 0: + time.sleep(decision.delay) + ctx = runtime_ctx if runtime_ctx is not None else getattr(task_node, "ctx", None) + return task_node.task_factory.create_task(task_node.name, ctx) + + def schedule_service(self, service_node: Any, runtime_ctx: Any = None) -> Any: + decision = self.make_service_decision(service_node) + if decision.delay > 0: + time.sleep(decision.delay) + ctx = runtime_ctx if runtime_ctx is not None else getattr(service_node, "ctx", None) + return service_node.service_task_factory.create_service_task(ctx) + + def task_completed(self, task_name: str) -> None: + return None + + def get_metrics(self) -> dict[str, Any]: + return { + "scheduler_type": self.__class__.__name__, + "total_scheduled": self.scheduled_count, + "decisions": len(self.decision_history), + } + + def shutdown(self) -> None: + self.decision_history.clear() + + +class FIFOScheduler(BaseScheduler): + def __init__(self, platform: str = "local") -> None: + super().__init__() + self.platform = platform + self.total_latency = 0.0 + + def make_decision(self, task_node: Any) -> PlacementDecision: + start_time = time.time() + self.scheduled_count += 1 + decision = PlacementDecision.immediate_default( + reason=f"FIFO order: #{self.scheduled_count}" + ) + self.decision_history.append(decision) + self.total_latency += time.time() - start_time + return decision + + def get_metrics(self) -> dict[str, Any]: + avg_latency = self.total_latency / self.scheduled_count if self.scheduled_count else 0.0 + return { + "scheduler_type": "FIFO", + "total_scheduled": self.scheduled_count, + "avg_latency_ms": avg_latency * 1000, + "decisions": len(self.decision_history), + "platform": self.platform, + } + + +class LoadAwareScheduler(BaseScheduler): + def __init__( + self, + platform: str = "local", + max_concurrent: int = 10, + strategy: str = "balanced", + ) -> None: + super().__init__() + self.platform = platform + self.max_concurrent = max_concurrent + self.strategy = strategy + self.total_latency = 0.0 + self.active_tasks = 0 + self.resource_utilization: list[float] = [] + + def _extract_resource_request(self, node: Any) -> dict[str, Any]: + transformation = getattr(node, "transformation", None) + memory_required = getattr(transformation, "memory_required", None) + return { + "cpu": float(getattr(transformation, "cpu_required", 1.0) or 1.0), + "gpu": float(getattr(transformation, "gpu_required", 0.0) or 0.0), + "memory": memory_required, + "custom_resources": dict(getattr(transformation, "custom_resources", {}) or {}), + } + + def make_decision(self, task_node: Any) -> PlacementDecision: + start_time = time.time() + delay = 0.0 + if self.active_tasks >= self.max_concurrent: + overflow = self.active_tasks - self.max_concurrent + 1 + delay = min(0.01 * overflow, 1.0) + + resource = self._extract_resource_request(task_node) + self.active_tasks += 1 + self.scheduled_count += 1 + utilization = self.active_tasks / self.max_concurrent if self.max_concurrent else 0.0 + self.resource_utilization.append(utilization) + + decision = PlacementDecision( + target_node=None, + delay=delay, + immediate=(delay == 0.0), + reason=( + f"LoadAware: task={getattr(task_node, 'name', 'unknown')}, " + f"active={self.active_tasks}, strategy={self.strategy}" + ), + resource=resource, + ) + self.decision_history.append(decision) + self.total_latency += time.time() - start_time + return decision + + def task_completed(self, task_name: str) -> None: + self.active_tasks = max(0, self.active_tasks - 1) + + def get_metrics(self) -> dict[str, Any]: + avg_latency = self.total_latency / self.scheduled_count if self.scheduled_count else 0.0 + avg_utilization = ( + sum(self.resource_utilization) / len(self.resource_utilization) + if self.resource_utilization + else 0.0 + ) + return { + "scheduler_type": "LoadAware", + "total_scheduled": self.scheduled_count, + "avg_latency_ms": avg_latency * 1000, + "active_tasks": self.active_tasks, + "max_concurrent": self.max_concurrent, + "avg_resource_utilization": avg_utilization, + "decisions": len(self.decision_history), + "platform": self.platform, + "strategy": self.strategy, + } + + def shutdown(self) -> None: + super().shutdown() + self.resource_utilization.clear() + + +def _has_scheduler_interface(value: Any) -> bool: + return callable(getattr(value, "make_decision", None)) + + +def create_default_scheduler(*, platform: str): + return FIFOScheduler(platform=platform) + + +def resolve_scheduler(*, scheduler: Any, platform: str): + if scheduler is None: + return create_default_scheduler(platform=platform) + + if isinstance(scheduler, str): + scheduler_lower = scheduler.lower() + if scheduler_lower == "fifo": + return FIFOScheduler(platform=platform) + if scheduler_lower in {"load_aware", "loadaware"}: + return LoadAwareScheduler(platform=platform) + raise ValueError( + f"Unknown scheduler type: {scheduler}. Available options: 'fifo', 'load_aware'" + ) + + if _has_scheduler_interface(scheduler): + return scheduler + + raise TypeError( + "scheduler must be None, str, or an object implementing make_decision(), " + f"got {type(scheduler)}" + ) + + +__all__ = [ + "BaseScheduler", + "FIFOScheduler", + "LoadAwareScheduler", + "NodeResources", + "NodeSelector", + "PlacementDecision", + "create_default_scheduler", + "resolve_scheduler", +] diff --git a/src/sage/runtime/service.py b/src/sage/runtime/service.py new file mode 100644 index 0000000000..5cf3cd3195 --- /dev/null +++ b/src/sage/runtime/service.py @@ -0,0 +1,73 @@ +"""Service primitives exposed from the consolidated SAGE runtime.""" + +from __future__ import annotations + +import logging +from abc import ABC +from typing import Any + + +class BaseService(ABC): # noqa: B024 + """Base class for runtime services registered into an environment.""" + + def __init__(self, *args: Any, **kwargs: Any) -> None: + if not hasattr(self, "ctx"): + self.ctx: Any | None = None + self._logger: Any | None = None + + @property + def logger(self) -> Any: + if self._logger is None: + if self.ctx is None: + self._logger = logging.getLogger(self.__class__.__name__) + else: + self._logger = self.ctx.logger + return self._logger + + @property + def name(self) -> str: + if self.ctx is not None: + return self.ctx.name + return self.__class__.__name__ + + def call_service( + self, + service_name: str, + *args: Any, + timeout: float | None = None, + method: str | None = None, + **kwargs: Any, + ) -> Any: + if self.ctx is None: + raise RuntimeError("Service context not initialized. Cannot access services.") + return self.ctx.call_service(service_name, *args, timeout=timeout, method=method, **kwargs) + + def call_service_async( + self, + service_name: str, + *args: Any, + timeout: float | None = None, + method: str | None = None, + **kwargs: Any, + ) -> Any: + if self.ctx is None: + raise RuntimeError("Service context not initialized. Cannot access services.") + return self.ctx.call_service_async( + service_name, + *args, + timeout=timeout, + method=method, + **kwargs, + ) + + def setup(self) -> None: + pass + + def cleanup(self) -> None: + pass + + def start(self) -> None: + pass + + def stop(self) -> None: + pass diff --git a/src/sage/runtime/service_factory.py b/src/sage/runtime/service_factory.py new file mode 100644 index 0000000000..23a243c3fe --- /dev/null +++ b/src/sage/runtime/service_factory.py @@ -0,0 +1,66 @@ +from __future__ import annotations + +import logging +from typing import Any + +from .context_injection import create_service_with_context + + +class ServiceFactory: + """Runtime-local service factory compatible with the kernel service task layer.""" + + def __init__( + self, + service_name: str, + service_class: type, + service_args: tuple[Any, ...] = (), + service_kwargs: dict[str, Any] | None = None, + ): + if not service_name: + raise ValueError("service_name cannot be empty") + if service_class is None: + raise ValueError("service_class cannot be None") + + self.service_name = service_name or service_class.__name__ + self.service_class = service_class + self.service_args = service_args + self.service_kwargs = service_kwargs or {} + + def create_service(self, ctx: Any | None = None) -> Any: + if self.service_class is None: + raise ValueError( + f"ServiceFactory for '{self.service_name}': service_class is None. " + "This may be due to serialization issues in distributed environments." + ) + + return create_service_with_context( + self.service_class, + ctx, + *self.service_args, + **self.service_kwargs, + ) + + def __repr__(self) -> str: + service_class_name = ( + self.service_class.__name__ + if getattr(self, "service_class", None) is not None + else "Unknown" + ) + return f"" + + def __getstate__(self): + return { + "service_name": getattr(self, "service_name", None), + "service_class": getattr(self, "service_class", None), + "service_args": getattr(self, "service_args", ()), + "service_kwargs": getattr(self, "service_kwargs", {}), + } + + def __setstate__(self, state): + self.service_name = state.get("service_name") or "Unknown" + self.service_class = state.get("service_class") + self.service_args = state.get("service_args", ()) + self.service_kwargs = state.get("service_kwargs", {}) + + if self.service_class is None: + logging.warning("ServiceFactory: service_class is None after deserialization") diff --git a/src/sage/serving/__init__.py b/src/sage/serving/__init__.py new file mode 100644 index 0000000000..8937b2b344 --- /dev/null +++ b/src/sage/serving/__init__.py @@ -0,0 +1,30 @@ +"""Serving integration boundary for SAGE. + +This package intentionally does not implement an inference engine. +Instead, it standardizes how SAGE integrates with the independent +`isagellm` engine family. +""" + +from .gateway import ( + GatewayProbeResult, + SageServeConfig, + build_sagellm_gateway_command, + default_gateway_config, + ensure_sagellm_model, + gateway_health_url, + gateway_openai_base_url, + infer_module_availability, + probe_gateway, +) + +__all__ = [ + "SageServeConfig", + "GatewayProbeResult", + "default_gateway_config", + "gateway_health_url", + "gateway_openai_base_url", + "build_sagellm_gateway_command", + "infer_module_availability", + "probe_gateway", + "ensure_sagellm_model", +] diff --git a/src/sage/serving/gateway.py b/src/sage/serving/gateway.py new file mode 100644 index 0000000000..51aec66905 --- /dev/null +++ b/src/sage/serving/gateway.py @@ -0,0 +1,167 @@ +from __future__ import annotations + +import json +import os +import sys +import urllib.error +import urllib.request +from dataclasses import dataclass, field +from importlib.util import find_spec +from pathlib import Path +from typing import Any + +from sage.foundation import SagePorts, ensure_model_available, get_user_paths + + +@dataclass(frozen=True) +class SageServeConfig: + """SAGE-side integration config for an external `isagellm` gateway.""" + + host: str = "127.0.0.1" + port: int = SagePorts.SAGELLM_GATEWAY + log_level: str = "info" + model: str | None = None + enable_control_plane: bool = False + engine_model_env: str = "SAGELLM_ENGINE_MODEL" + gateway_module: str = "sagellm_gateway" + health_path: str = "/health" + openai_base_path: str = "/v1" + extra_env: dict[str, str] = field(default_factory=dict) + + @property + def base_url(self) -> str: + return gateway_openai_base_url(self.host, self.port, self.openai_base_path) + + @property + def health_url(self) -> str: + return gateway_health_url(self.host, self.port, self.health_path) + + @property + def log_file(self) -> Path: + return get_user_paths().get_log_file("sagellm_gateway") + + +@dataclass(frozen=True) +class GatewayProbeResult: + """Observed status of an external `isagellm` gateway instance.""" + + ok: bool + url: str + status_code: int | None = None + payload: dict[str, Any] | None = None + error: str | None = None + + +def default_gateway_config( + host: str = "127.0.0.1", + port: int = SagePorts.SAGELLM_GATEWAY, + model: str | None = None, + *, + enable_control_plane: bool = False, + log_level: str = "info", +) -> SageServeConfig: + """Create a default SAGE→isagellm integration config.""" + return SageServeConfig( + host=host, + port=port, + model=model, + enable_control_plane=enable_control_plane, + log_level=log_level, + ) + + +def gateway_openai_base_url( + host: str = "127.0.0.1", + port: int = SagePorts.SAGELLM_GATEWAY, + base_path: str = "/v1", +) -> str: + """Return the OpenAI-compatible base URL exposed by `isagellm`.""" + normalized = base_path if base_path.startswith("/") else f"/{base_path}" + return f"http://{host}:{port}{normalized}" + + +def gateway_health_url( + host: str = "127.0.0.1", + port: int = SagePorts.SAGELLM_GATEWAY, + health_path: str = "/health", +) -> str: + """Return the health-check URL exposed by `isagellm`.""" + normalized = health_path if health_path.startswith("/") else f"/{health_path}" + return f"http://{host}:{port}{normalized}" + + +def infer_module_availability(module_name: str = "sagellm_gateway") -> bool: + """Return whether the gateway integration module is importable.""" + return find_spec(module_name) is not None + + +def build_sagellm_gateway_command(config: SageServeConfig) -> list[str]: + """Build the command used to launch the external `isagellm` gateway. + + SAGE does not implement the gateway itself; it only constructs the + integration command and environment contract. + """ + cmd = [ + sys.executable, + "-m", + config.gateway_module, + "--host", + config.host, + "--port", + str(config.port), + "--log-level", + config.log_level, + ] + if config.enable_control_plane: + cmd.append("--control-plane") + return cmd + + +def build_sagellm_gateway_env(config: SageServeConfig) -> dict[str, str]: + """Build the environment contract used to launch `isagellm`.""" + env = dict(os.environ) + if config.model: + env[config.engine_model_env] = config.model + env.update(config.extra_env) + return env + + +def ensure_sagellm_model(model_id: str, *, auto_download: bool = True) -> Path: + """Ensure the external `isagellm` engine model exists locally. + + SAGE owns the local model asset registry/integration workflow, while the + inference engine itself remains external. + """ + return ensure_model_available(model_id, auto_download=auto_download) + + +def probe_gateway(config: SageServeConfig, timeout: float = 3.0) -> GatewayProbeResult: + """Probe the health endpoint of an external `isagellm` gateway.""" + request = urllib.request.Request(config.health_url, method="GET") + try: + with urllib.request.urlopen(request, timeout=timeout) as response: + body = response.read().decode("utf-8", errors="replace") + payload = None + try: + payload = json.loads(body) if body else None + except json.JSONDecodeError: + payload = None + return GatewayProbeResult( + ok=200 <= response.status < 300, + url=config.health_url, + status_code=response.status, + payload=payload, + ) + except urllib.error.HTTPError as exc: + return GatewayProbeResult( + ok=False, + url=config.health_url, + status_code=exc.code, + error=str(exc), + ) + except urllib.error.URLError as exc: + return GatewayProbeResult(ok=False, url=config.health_url, error=str(exc.reason)) + except TimeoutError: + return GatewayProbeResult(ok=False, url=config.health_url, error="timed out") + except OSError as exc: + return GatewayProbeResult(ok=False, url=config.health_url, error=str(exc)) diff --git a/src/sage/stream/__init__.py b/src/sage/stream/__init__.py new file mode 100644 index 0000000000..755b808e50 --- /dev/null +++ b/src/sage/stream/__init__.py @@ -0,0 +1,6 @@ +"""Stream-first public API for SAGE.""" + +from .connected_streams import ConnectedStreams +from .datastream import DataStream + +__all__ = ["DataStream", "ConnectedStreams"] diff --git a/src/sage/stream/_kernel_bindings.py b/src/sage/stream/_kernel_bindings.py new file mode 100644 index 0000000000..7b8f87a5ee --- /dev/null +++ b/src/sage/stream/_kernel_bindings.py @@ -0,0 +1,29 @@ +"""Compatibility re-exports for the in-tree stream transformation surface.""" + +from __future__ import annotations + +from .transformations import ( + BaseTransformation, + CoMapTransformation, + FilterTransformation, + FlatMapTransformation, + FutureTransformation, + JoinTransformation, + KeyByTransformation, + MapTransformation, + SinkTransformation, + SourceTransformation, +) + +__all__ = [ + "BaseTransformation", + "FilterTransformation", + "FlatMapTransformation", + "FutureTransformation", + "JoinTransformation", + "KeyByTransformation", + "MapTransformation", + "SinkTransformation", + "SourceTransformation", + "CoMapTransformation", +] diff --git a/src/sage/stream/_kernel_runtime.py b/src/sage/stream/_kernel_runtime.py new file mode 100644 index 0000000000..eb70c267af --- /dev/null +++ b/src/sage/stream/_kernel_runtime.py @@ -0,0 +1,32 @@ +"""Centralized kernel-side factories and operator classes used by in-tree streams.""" + +from __future__ import annotations + +from .factories import FunctionFactory, OperatorFactory +from .operators import ( + BatchOperator, + CoMapOperator, + FilterOperator, + FlatMapOperator, + FutureOperator, + JoinOperator, + KeyByOperator, + MapOperator, + SinkOperator, + SourceOperator, +) + +__all__ = [ + "FunctionFactory", + "OperatorFactory", + "MapOperator", + "FilterOperator", + "FlatMapOperator", + "SinkOperator", + "SourceOperator", + "BatchOperator", + "KeyByOperator", + "JoinOperator", + "CoMapOperator", + "FutureOperator", +] diff --git a/src/sage/stream/_runtime_kernel_types.py b/src/sage/stream/_runtime_kernel_types.py new file mode 100644 index 0000000000..5878ec2a1d --- /dev/null +++ b/src/sage/stream/_runtime_kernel_types.py @@ -0,0 +1,266 @@ +"""Main-repo owned runtime data structures used by in-tree stream execution.""" + +from __future__ import annotations + +import threading +import time +from dataclasses import dataclass, field +from typing import Any + +from sage.foundation import CustomLogger + + +@dataclass(slots=True) +class StopSignal: + """Sentinel payload that marks the end of a stream or batch source.""" + + name: str + source: str | None = None + timestamp: int = field(default_factory=time.time_ns) + + +class Packet: + """Lightweight packet carrying payload plus optional partition metadata.""" + + def __init__( + self, + payload: Any, + input_index: int = 0, + partition_key: Any = None, + partition_strategy: str | None = None, + ) -> None: + self.payload = payload + self.input_index = input_index + self.partition_key = partition_key + self.partition_strategy = partition_strategy + self.timestamp = time.time_ns() + + def is_keyed(self) -> bool: + return self.partition_key is not None + + def inherit_partition_info(self, new_payload: Any) -> Packet: + return Packet( + payload=new_payload, + input_index=self.input_index, + partition_key=self.partition_key, + partition_strategy=self.partition_strategy, + ) + + def update_key(self, new_key: Any, new_strategy: str | None = None) -> Packet: + return Packet( + payload=self.payload, + input_index=self.input_index, + partition_key=new_key, + partition_strategy=new_strategy or self.partition_strategy, + ) + + def copy(self) -> Packet: + packet = Packet( + payload=self.payload, + input_index=self.input_index, + partition_key=self.partition_key, + partition_strategy=self.partition_strategy, + ) + packet.timestamp = self.timestamp + return packet + + def __repr__(self) -> str: + key_info = f"key={self.partition_key}" if self.is_keyed() else "unkeyed" + payload_type = type(self.payload).__name__ if self.payload is not None else "None" + return ( + f"" + ) + + def __eq__(self, other: object) -> bool: + if not isinstance(other, Packet): + return False + return ( + self.payload == other.payload + and self.input_index == other.input_index + and self.partition_key == other.partition_key + and self.partition_strategy == other.partition_strategy + ) + + +class InMemoryRouter: + """Small in-process router used by main-repo owned operators and tests.""" + + def __init__(self, ctx: TaskContext | None = None) -> None: + self.ctx = ctx + self._targets: list[Any] = [] + + def add_target(self, target: Any) -> None: + self._targets.append(target) + + def clear_all_connections(self) -> None: + self._targets.clear() + + @property + def input_count(self) -> int: + return max(1, len(self._targets)) + + def send(self, packet: Packet) -> bool: + if not self._targets: + return False + delivered = False + for target in self._targets: + if hasattr(target, "receive_packet"): + target.receive_packet(packet) + delivered = True + elif hasattr(target, "put"): + target.put(packet) + delivered = True + elif callable(target): + target(packet) + delivered = True + return delivered + + def send_stop_signal(self, stop_signal: StopSignal) -> None: + for target in self._targets: + if hasattr(target, "receive_packet"): + target.receive_packet(Packet(stop_signal)) + elif hasattr(target, "put"): + target.put(stop_signal) + elif callable(target): + target(stop_signal) + + def get_connections_info(self) -> dict[str, Any]: + return {"targets": len(self._targets)} + + +class TaskContext: + """Minimal in-tree task context required by the operator surface.""" + + def __init__( + self, + name: str = "task", + *, + env_name: str = "local", + router: InMemoryRouter | None = None, + stop_callback: Any | None = None, + ) -> None: + self.name = name + self.env_name = env_name + self.env_base_dir: str | None = None + self.env_uuid: str | None = None + self.env_console_log_level = "INFO" + self.parallel_index = 0 + self.parallelism = 1 + self.is_spout = False + self.delay = 0.01 + self.stop_signal_num = 1 + self.jobmanager_host = "127.0.0.1" + self.jobmanager_port = 0 + self.input_qd: Any | None = None + self.response_qd: Any | None = None + self.service_qds: dict[str, Any] = {} + self.downstream_qds: list[list[Any]] | None = None + self._logger = CustomLogger(name=name) + self._router = router or InMemoryRouter(self) + self._stop_event: threading.Event | None = None + self._current_packet_key: Any = None + self._stop_callback = stop_callback + + @property + def logger(self) -> CustomLogger: + return self._logger + + @property + def router(self) -> InMemoryRouter: + return self._router + + @property + def stop_event(self) -> threading.Event: + if self._stop_event is None: + self._stop_event = threading.Event() + return self._stop_event + + def set_current_key(self, key: Any) -> None: + self._current_packet_key = key + + def clear_key(self) -> None: + self._current_packet_key = None + + def send_packet(self, packet: Packet) -> bool: + return self.router.send(packet) + + def send_stop_signal(self, stop_signal: StopSignal) -> None: + self.router.send_stop_signal(stop_signal) + + def get_routing_info(self) -> dict[str, Any]: + return self.router.get_connections_info() + + def set_stop_signal(self) -> None: + self.stop_event.set() + + def is_stop_requested(self) -> bool: + return self.stop_event.is_set() + + def clear_stop_signal(self) -> None: + self.stop_event.clear() + + def request_stop(self) -> None: + self.send_stop_signal_back(self.name) + + def send_stop_signal_back(self, node_name: str) -> None: + if callable(self._stop_callback): + self._stop_callback(node_name) + + def handle_stop_signal(self, signal: StopSignal) -> bool: + self.request_stop() + self.send_stop_signal(signal) + return True + + def set_input_queue_descriptor(self, descriptor: Any) -> None: + self.input_qd = descriptor + + def get_input_queue_descriptor(self) -> Any: + return self.input_qd + + def set_service_response_queue_descriptor(self, descriptor: Any) -> None: + self.response_qd = descriptor + + def get_service_response_queue_descriptor(self) -> Any: + return self.response_qd + + def set_upstream_queue_descriptors(self, descriptors: dict[int, list[Any]]) -> None: + self.upstream_qds = descriptors + + def get_upstream_queue_descriptors(self) -> dict[int, list[Any]] | None: + return getattr(self, "upstream_qds", None) + + def set_downstream_queue_descriptors(self, descriptors: list[list[Any]]) -> None: + self.downstream_qds = descriptors + + def get_downstream_queue_descriptors(self) -> list[list[Any]] | None: + return self.downstream_qds + + def set_service_request_queue_descriptors(self, descriptors: dict[str, Any]) -> None: + self.service_qds = descriptors + + def get_service_request_queue_descriptors(self) -> dict[str, Any]: + return self.service_qds + + def get_service(self, service_name: str) -> Any: + raise RuntimeError(f"Service '{service_name}' is not available in the lightweight context") + + def call_service(self, service_name: str, *args: Any, **kwargs: Any) -> Any: + raise RuntimeError(f"Service '{service_name}' is not available in the lightweight context") + + def call_service_async(self, service_name: str, *args: Any, **kwargs: Any) -> Any: + raise RuntimeError(f"Service '{service_name}' is not available in the lightweight context") + + def cleanup(self) -> None: + self.router.clear_all_connections() + + +class BaseTask: + """Minimal in-tree task abstraction used by source operators.""" + + def __init__(self, ctx: TaskContext) -> None: + self.ctx = ctx + self.is_running = False + + +__all__ = ["BaseTask", "InMemoryRouter", "Packet", "StopSignal", "TaskContext"] diff --git a/src/sage/stream/connected_streams.py b/src/sage/stream/connected_streams.py new file mode 100644 index 0000000000..99fafab7a6 --- /dev/null +++ b/src/sage/stream/connected_streams.py @@ -0,0 +1,206 @@ +from __future__ import annotations + +from collections.abc import Callable +from typing import TYPE_CHECKING + +from sage.foundation import ( + BaseCoMapFunction, + BaseFunction, + BaseJoinFunction, + PrintSink, + wrap_lambda, +) + +from ._kernel_bindings import ( + BaseTransformation, + CoMapTransformation, + JoinTransformation, + MapTransformation, + SinkTransformation, +) + +if TYPE_CHECKING: + from sage.runtime.base_environment import BaseEnvironment + + from .datastream import DataStream + + +class ConnectedStreams: + """Represents logical composition of multiple streams.""" + + def __init__(self, env: BaseEnvironment, transformations: list[BaseTransformation]): + self._environment = env + self.transformations = transformations + + if len(transformations) < 2: + raise ValueError("ConnectedStreams requires at least 2 transformations") + + for trans in transformations: + if trans.env != env: + raise ValueError("All transformations must be from the same environment") + + def _get_transformation_classes(self): + if not hasattr(self, "_transformation_classes"): + self._transformation_classes = { + "BaseTransformation": BaseTransformation, + "MapTransformation": MapTransformation, + "SinkTransformation": SinkTransformation, + "JoinTransformation": JoinTransformation, + } + return self._transformation_classes + + def map( + self, + function: type[BaseFunction] | Callable, + *args, + parallelism: int | None = None, + **kwargs, + ) -> DataStream: + if callable(function) and not isinstance(function, type): + function = wrap_lambda(function, "map") + + actual_parallelism = parallelism if parallelism is not None else 1 + MapTransformation = self._get_transformation_classes()["MapTransformation"] + tr = MapTransformation( + self._environment, function, *args, parallelism=actual_parallelism, **kwargs + ) + return self._apply(tr) + + def sink( + self, + function: type[BaseFunction] | Callable, + *args, + parallelism: int | None = None, + **kwargs, + ) -> DataStream: + if callable(function) and not isinstance(function, type): + function = wrap_lambda(function, "sink") + + actual_parallelism = parallelism if parallelism is not None else 1 + SinkTransformation = self._get_transformation_classes()["SinkTransformation"] + tr = SinkTransformation( + self._environment, function, *args, parallelism=actual_parallelism, **kwargs + ) + return self._apply(tr) + + def print(self, prefix: str = "", separator: str = " | ", colored: bool = True) -> DataStream: + return self.sink(PrintSink, prefix=prefix, separator=separator, colored=colored) + + def connect(self, other: DataStream | ConnectedStreams) -> ConnectedStreams: + if hasattr(other, "transformation"): + new_transformations = self.transformations + [other.transformation] # type: ignore[attr-defined] + else: + new_transformations = self.transformations + other.transformations # type: ignore[attr-defined] + return ConnectedStreams(self._environment, new_transformations) + + def comap( + self, + function: type[BaseFunction] | Callable, + *args, + parallelism: int | None = None, + **kwargs, + ) -> DataStream: + if callable(function) and not isinstance(function, type): + raise NotImplementedError( + "Lambda functions are not supported for comap operations. Please use a class that inherits from BaseCoMapFunction." + ) + + input_stream_count = len(self.transformations) + if input_stream_count < 2: + raise ValueError( + f"CoMap operations require at least 2 input streams, but only {input_stream_count} streams provided." + ) + + if not isinstance(function, type): + raise TypeError( + f"CoMap function must be a class, got {type(function).__name__}. Please provide a class that inherits from BaseCoMapFunction." + ) + + if not issubclass(function, BaseCoMapFunction): + raise TypeError( + f"Function {function.__name__} must inherit from BaseCoMapFunction. CoMap operations require CoMap function with mapN methods." + ) + + required_methods = [f"map{i}" for i in range(input_stream_count)] + missing_methods = [name for name in required_methods if not hasattr(function, name)] + if missing_methods: + raise TypeError( + f"CoMap function {function.__name__} is missing required methods: {missing_methods}. For {input_stream_count} input streams, the function must implement: {required_methods}." + ) + + for method_name in required_methods: + method = getattr(function, method_name) + if not callable(method): + raise TypeError( + f"CoMap function {function.__name__}.{method_name} must be callable. Found {type(method).__name__} instead." + ) + + actual_parallelism = parallelism if parallelism is not None else 1 + tr = CoMapTransformation( + self._environment, function, *args, parallelism=actual_parallelism, **kwargs + ) + tr.validate_input_streams(input_stream_count) + return self._apply(tr) + + def join( + self, + function: type[BaseJoinFunction] | Callable, + *args, + parallelism: int | None = None, + **kwargs, + ) -> DataStream: + if len(self.transformations) != 2: + raise ValueError( + f"Join requires exactly 2 input streams, got {len(self.transformations)}" + ) + + if not isinstance(function, type) or not issubclass(function, BaseJoinFunction): + raise TypeError("Join function must inherit from BaseJoinFunction") + + actual_parallelism = parallelism if parallelism is not None else 1 + join_tr = JoinTransformation( + self._environment, function, *args, parallelism=actual_parallelism, **kwargs + ) + return self._apply(join_tr) + + def keyby( + self, + key_selector: type[BaseFunction] | list[type[BaseFunction]], + strategy: str = "hash", + ) -> ConnectedStreams: + if callable(key_selector) and not isinstance(key_selector, type): + raise NotImplementedError( + "Lambda functions are not supported for keyby operations. Please use KeyByFunction classes." + ) + + from .datastream import DataStream + + input_stream_count = len(self.transformations) + keyed_transformations = [] + + if isinstance(key_selector, list): + if len(key_selector) != input_stream_count: + raise ValueError( + f"Key selector count ({len(key_selector)}) must match stream count ({input_stream_count})" + ) + + for transformation, selector in zip(self.transformations, key_selector, strict=False): + individual_stream: DataStream = DataStream(self._environment, transformation) + keyed_stream = individual_stream.keyby(selector, strategy=strategy) + keyed_transformations.append(keyed_stream.transformation) + else: + for transformation in self.transformations: + individual_stream = DataStream(self._environment, transformation) + keyed_stream = individual_stream.keyby(key_selector, strategy=strategy) + keyed_transformations.append(keyed_stream.transformation) + + return ConnectedStreams(self._environment, keyed_transformations) + + def _apply(self, tr: BaseTransformation) -> DataStream: + from .datastream import DataStream + + for input_index, upstream_trans in enumerate(self.transformations): + tr.add_upstream(upstream_trans, input_index=input_index) + + self._environment.pipeline.append(tr) + return DataStream(self._environment, tr) diff --git a/packages/sage-kernel/src/sage/kernel/api/datastream.py b/src/sage/stream/datastream.py similarity index 51% rename from packages/sage-kernel/src/sage/kernel/api/datastream.py rename to src/sage/stream/datastream.py index 337cccd38a..e25a12b725 100644 --- a/packages/sage-kernel/src/sage/kernel/api/datastream.py +++ b/src/sage/stream/datastream.py @@ -3,20 +3,28 @@ from collections.abc import Callable from typing import TYPE_CHECKING, Any, Generic, TypeVar, get_args, get_origin -from sage.common.core import BaseFunction, wrap_lambda -from sage.common.utils.logging.custom_logger import CustomLogger - +from sage.foundation import BaseFunction, CustomLogger, PrintSink, wrap_lambda + +from ._kernel_bindings import ( + BaseTransformation, + FilterTransformation, + FlatMapTransformation, + FutureTransformation, + KeyByTransformation, + MapTransformation, + SinkTransformation, + SourceTransformation, +) from .connected_streams import ConnectedStreams if TYPE_CHECKING: - from sage.kernel.api.base_environment import BaseEnvironment - from sage.kernel.api.transformation.base_transformation import BaseTransformation + from sage.runtime.base_environment import BaseEnvironment T = TypeVar("T") class DataStream(Generic[T]): - """表示单个transformation生成的流结果""" + """Main-repo owned stream abstraction.""" def __init__(self, env: BaseEnvironment, transformation: BaseTransformation): self.logger = CustomLogger() @@ -29,30 +37,7 @@ def __init__(self, env: BaseEnvironment, transformation: BaseTransformation): ) def _get_transformation_classes(self): - """动态导入transformation类以避免循环导入""" if not hasattr(self, "_transformation_classes"): - from sage.kernel.api.transformation.base_transformation import ( - BaseTransformation, - ) - from sage.kernel.api.transformation.filter_transformation import ( - FilterTransformation, - ) - from sage.kernel.api.transformation.flatmap_transformation import ( - FlatMapTransformation, - ) - from sage.kernel.api.transformation.keyby_transformation import ( - KeyByTransformation, - ) - from sage.kernel.api.transformation.map_transformation import ( - MapTransformation, - ) - from sage.kernel.api.transformation.sink_transformation import ( - SinkTransformation, - ) - from sage.kernel.api.transformation.source_transformation import ( - SourceTransformation, - ) - self._transformation_classes = { "BaseTransformation": BaseTransformation, "FilterTransformation": FilterTransformation, @@ -74,18 +59,12 @@ def map( if callable(function) and not isinstance(function, type): function = wrap_lambda(function, "map") - # 使用传入的parallelism或者默认值1 actual_parallelism = parallelism if parallelism is not None else 1 - - # 获取MapTransformation类 MapTransformation = self._get_transformation_classes()["MapTransformation"] tr = MapTransformation( self._environment, function, *args, parallelism=actual_parallelism, **kwargs ) - - # 重置parallelism hint为默认值 - result = self._apply(tr) - return result + return self._apply(tr) def filter( self, @@ -97,18 +76,12 @@ def filter( if callable(function) and not isinstance(function, type): function = wrap_lambda(function, "filter") - # 使用传入的parallelism或者默认值1 actual_parallelism = parallelism if parallelism is not None else 1 - - # 获取FilterTransformation类 FilterTransformation = self._get_transformation_classes()["FilterTransformation"] tr = FilterTransformation( self._environment, function, *args, parallelism=actual_parallelism, **kwargs ) - - # 重置parallelism hint为默认值 - result = self._apply(tr) - return result + return self._apply(tr) def flatmap( self, @@ -120,18 +93,12 @@ def flatmap( if callable(function) and not isinstance(function, type): function = wrap_lambda(function, "flatmap") - # 使用传入的parallelism或者默认值1 actual_parallelism = parallelism if parallelism is not None else 1 - - # 获取FlatMapTransformation类 FlatMapTransformation = self._get_transformation_classes()["FlatMapTransformation"] tr = FlatMapTransformation( self._environment, function, *args, parallelism=actual_parallelism, **kwargs ) - - # 重置parallelism hint为默认值 - result = self._apply(tr) - return result + return self._apply(tr) def sink( self, @@ -143,16 +110,13 @@ def sink( if callable(function) and not isinstance(function, type): function = wrap_lambda(function, "sink") - # 使用传入的parallelism或者默认值1 actual_parallelism = parallelism if parallelism is not None else 1 - - # 获取SinkTransformation类 SinkTransformation = self._get_transformation_classes()["SinkTransformation"] tr = SinkTransformation( self._environment, function, *args, parallelism=actual_parallelism, **kwargs ) self._apply(tr) - return self # sink不返回新的DataStream，因为它是终端操作 + return self def keyby( self, @@ -165,10 +129,7 @@ def keyby( if callable(function) and not isinstance(function, type): function = wrap_lambda(function, "keyby") - # 使用传入的parallelism或者默认值1 actual_parallelism = parallelism if parallelism is not None else 1 - - # 获取KeyByTransformation类 KeyByTransformation = self._get_transformation_classes()["KeyByTransformation"] tr = KeyByTransformation( self._environment, @@ -178,127 +139,43 @@ def keyby( parallelism=actual_parallelism, **kwargs, ) - - # 重置parallelism hint为默认值 - result = self._apply(tr) - return result + return self._apply(tr) def connect(self, other: DataStream | ConnectedStreams) -> ConnectedStreams: - """连接两个数据流，返回ConnectedStreams - - Args: - other: 另一个DataStream或ConnectedStreams实例 - - Returns: - ConnectedStreams: 新的连接流，按顺序包含所有transformation - """ if isinstance(other, DataStream): - # DataStream + DataStream -> ConnectedStreams return ConnectedStreams(self._environment, [self.transformation, other.transformation]) - else: # ConnectedStreams - # DataStream + ConnectedStreams -> ConnectedStreams - new_transformations = [self.transformation] + other.transformations - return ConnectedStreams(self._environment, new_transformations) + new_transformations = [self.transformation] + other.transformations + return ConnectedStreams(self._environment, new_transformations) def fill_future(self, future_stream: DataStream) -> None: - """ - 将当前数据流填充到预先声明的future stream中，创建反馈边。 - - Args: - future_stream: 需要被填充的future stream (通过env.from_future创建) - - Raises: - ValueError: 如果目标stream不是future stream - RuntimeError: 如果future stream已经被填充过 - - Example: - # 1. 声明future stream - future_stream = env.from_future("feedback_loop") - - # 2. 构建pipeline，使用future stream - result = source.connect(future_stream).comap(CombineFunction) - - # 3. 填充future stream，创建反馈边 - processed_result = result.filter(SomeFilter) - processed_result.fill_future(future_stream) - """ - from sage.kernel.api.transformation.future_transformation import ( - FutureTransformation, - ) - - # 验证目标是future stream if not isinstance(future_stream.transformation, FutureTransformation): raise ValueError("Target stream must be a future stream created by env.from_future()") future_trans = future_stream.transformation - - # 检查是否已经被填充 if future_trans.filled: raise RuntimeError( f"Future stream '{future_trans.future_name}' has already been filled" ) - # 使用FutureTransformation的填充方法 future_trans.fill_with_transformation(self.transformation) - # 从环境的pipeline中移除future transformation的引用 - # 注意：不能完全删除，因为可能有其他地方引用它，但标记为已填充 self.logger.debug( f"Filled future stream '{future_trans.future_name}' with transformation '{self.transformation.basename}'" ) - - # 记录反馈边的创建 self.logger.info( f"Created feedback edge: {self.transformation.basename} -> {future_trans.future_name}" ) - # --------------------------------------------------------------------- - # quick helper api - # --------------------------------------------------------------------- def print(self, prefix: str = "", separator: str = " | ", colored: bool = True) -> DataStream: - """ - 便捷的打印方法 - 将数据流输出到控制台 - - 这是 sink(PrintSink, ...) 的简化版本，提供快速调试和查看数据流内容的能力 - - Args: - prefix: 输出前缀，默认为空 - separator: 前缀与内容之间的分隔符，默认为 " | " - colored: 是否启用彩色输出，默认为True（当前未实现） - - Returns: - DataStream: 返回新的数据流用于链式调用 - - Example: - ```python - stream.map(some_function).print("Debug").sink(FileSink, config) - stream.print("结果: ") # 带前缀打印 - stream.print() # 简单打印 - ``` - - Note: - 使用 kernel 内置的打印功能，不依赖 sage-libs。 - 如需更高级的打印功能，请使用 sage.libs.io.sink.PrintSink。 - """ - from sage.common.components.debug import PrintSink - return self.sink(PrintSink, prefix=prefix, separator=separator, colored=colored) - # --------------------------------------------------------------------- - # internel methods - # --------------------------------------------------------------------- def _apply(self, tr: BaseTransformation) -> DataStream: - # 连接到输入索引0（单输入情况） tr.add_upstream(self.transformation, input_index=0) - self._environment.pipeline.append(tr) - new_stream: DataStream = DataStream(self._environment, tr) - return new_stream + return DataStream(self._environment, tr) def _resolve_type_param(self): - # 利用 __orig_class__ 捕获 T orig = getattr(self, "__orig_class__", None) if orig and get_origin(orig) == DataStream: return get_args(orig)[0] - else: - return Any # fallback，如果泛型没有显式写就为 None + return Any diff --git a/src/sage/stream/factories.py b/src/sage/stream/factories.py new file mode 100644 index 0000000000..6c3d8a9e62 --- /dev/null +++ b/src/sage/stream/factories.py @@ -0,0 +1,53 @@ +"""Main-repo owned function/operator factory helpers for stream transformations.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +from sage.foundation import BaseFunction + +if TYPE_CHECKING: + from ._runtime_kernel_types import TaskContext + + +class FunctionFactory: + def __init__( + self, + function_class: type[BaseFunction], + function_args: tuple[Any, ...] = (), + function_kwargs: dict[str, Any] | None = None, + ) -> None: + self.function_class = function_class + self.function_args = function_args + self.function_kwargs = function_kwargs or {} + + def create_function(self, name: str, ctx: TaskContext) -> BaseFunction: + function = self.function_class(*self.function_args, **self.function_kwargs) + function.ctx = ctx + return function + + def __repr__(self) -> str: + return f"" + + +class OperatorFactory: + def __init__( + self, + operator_class: type[Any], + function_factory: FunctionFactory, + env_name: str | None = None, + remote: bool = False, + **operator_kwargs: Any, + ) -> None: + self.operator_class = operator_class + self.operator_kwargs = operator_kwargs + self.function_factory = function_factory + self.env_name = env_name + self.remote = remote + + def create_operator(self, runtime_context: TaskContext) -> Any: + return self.operator_class( + self.function_factory, + runtime_context, + **self.operator_kwargs, + ) diff --git a/src/sage/stream/operators.py b/src/sage/stream/operators.py new file mode 100644 index 0000000000..f9b296d0e6 --- /dev/null +++ b/src/sage/stream/operators.py @@ -0,0 +1,525 @@ +"""Main-repo owned operator layer for SAGE streams. + +These operators still execute on top of the existing kernel runtime task/context +implementation, but the public stream/operator surface is now owned in-tree. +""" + +from __future__ import annotations + +import json +import os +import time +from abc import ABC, abstractmethod +from typing import TYPE_CHECKING, Any + +from sage.foundation import BaseFunction, Collector, FlatMapFunction + +from ._runtime_kernel_types import Packet, StopSignal +from .factories import FunctionFactory + +if TYPE_CHECKING: + from sage.foundation import CustomLogger + + from ._runtime_kernel_types import BaseTask, TaskContext + + +__all__ = [ + "BaseOperator", + "MapOperator", + "FilterOperator", + "FlatMapOperator", + "SinkOperator", + "SourceOperator", + "BatchOperator", + "KeyByOperator", + "JoinOperator", + "CoMapOperator", + "FutureOperator", +] + + +class BaseOperator(ABC): + __state_include__: list[str] = [] + __state_exclude__: list[str] = ["ctx", "function", "logger", "_logger"] + + def __init__(self, function_factory: FunctionFactory, ctx: TaskContext, *args, **kwargs): + self.ctx: TaskContext = ctx + self.function: BaseFunction + try: + self.function = function_factory.create_function(self.name, ctx) + self.logger.debug(f"Created function instance with {function_factory}") + except Exception as exc: + self.logger.error(f"Failed to create function instance: {exc}", exc_info=True) + raise + + def send_packet(self, packet: Packet) -> bool: + return self.ctx.send_packet(packet) # type: ignore[return-value] + + def send_stop_signal(self, stop_signal: StopSignal) -> None: + self.ctx.send_stop_signal(stop_signal) + + def get_routing_info(self) -> dict[str, Any]: + return self.ctx.get_routing_info() + + @property + def router(self): + return self.ctx.router + + def receive_packet(self, packet: Packet): + if packet is None: + self.logger.warning(f"Received None packet in {self.name}") + return + self.logger.debug(f"Operator {self.name} received packet: {packet}") + try: + self.ctx.set_current_key(packet.partition_key) + self.process_packet(packet) + finally: + self.ctx.clear_key() + + @abstractmethod + def process_packet(self, packet: Packet | None = None): + return + + def restore_state(self, state: dict[str, Any]): + if "function_state" in state and hasattr(self.function, "restore_state"): + try: + self.function.restore_state(state["function_state"]) + except Exception as exc: + self.logger.warning(f"Failed to restore function state: {exc}") + if "operator_attrs" in state: + for attr_name, value in state["operator_attrs"].items(): + try: + setattr(self, attr_name, value) + except Exception as exc: + self.logger.warning( + f"Failed to restore operator attribute '{attr_name}': {exc}" + ) + + @property + def name(self) -> str: + return self.ctx.name + + @property + def logger(self) -> CustomLogger: + return self.ctx.logger + + +class MapOperator(BaseOperator): + def __init__( + self, + function_factory: FunctionFactory, + ctx: TaskContext, + enable_profile: bool = False, + *args, + **kwargs, + ): + kwargs.pop("enable_profile", None) + super().__init__(function_factory, ctx, *args, **kwargs) + self.enable_profile = enable_profile + if self.enable_profile: + self._setup_time_tracking() + + def _setup_time_tracking(self): + if hasattr(self.ctx, "env_base_dir") and self.ctx.env_base_dir: + self.time_base_path = os.path.join( + self.ctx.env_base_dir, ".sage_states", "time_records" + ) + else: + self.time_base_path = os.path.join(os.getcwd(), ".sage_states", "time_records") + os.makedirs(self.time_base_path, exist_ok=True) + self.time_records = [] + + def _save_time_record(self, duration: float): + if not self.enable_profile: + return + self.time_records.append( + { + "timestamp": time.time(), + "duration": duration, + "function_name": self.function.__class__.__name__, + "operator_name": self.name, + } + ) + self._persist_time_records() + + def _persist_time_records(self): + if not self.enable_profile or not self.time_records: + return + filename = f"time_records_{int(time.time())}.json" + path = os.path.join(self.time_base_path, filename) + try: + with open(path, "w", encoding="utf-8") as handle: + json.dump(self.time_records, handle, ensure_ascii=False, indent=2) + self.time_records = [] + except Exception as exc: + self.logger.error(f"Failed to persist time records: {exc}") + + def process_packet(self, packet: Packet | None = None): + try: + if packet is None or packet.payload is None: + self.logger.warning(f"Operator {self.name} received empty data") + return + if isinstance(packet.payload, StopSignal): + self.logger.debug(f"Operator {self.name} received StopSignal, propagating...") + self.router.send(packet) + return + + start_time = time.time() + result = self.function.execute(packet.payload) + duration = time.time() - start_time + if self.enable_profile: + self._save_time_record(duration) + + if isinstance(result, dict): + operator_name = self.function.__class__.__name__ + if "Retriever" in operator_name or "Retrieve" in operator_name: + result["retrieve_time"] = duration + elif "Refiner" in operator_name or "Refine" in operator_name: + result["refine_time"] = duration + elif "Generator" in operator_name or "Generate" in operator_name: + result["generate_time"] = duration + + result_packet = packet.inherit_partition_info(result) if result is not None else None + if result_packet is not None: + self.router.send(result_packet) + except Exception as exc: + self.logger.error(f"Error in {self.name}.process(): {exc}", exc_info=True) + + def __del__(self): + if hasattr(self, "enable_profile") and self.enable_profile: + try: + self._persist_time_records() + except Exception: + pass + + +class FilterOperator(BaseOperator): + def process_packet(self, packet: Packet | None = None): + try: + if packet is None or packet.payload is None: + self.logger.debug(f"FilterOperator {self.name}: Received empty packet") + return + should_pass = self.function.execute(packet.payload) + if should_pass: + self.router.send(packet) + except Exception as exc: + self.logger.error(f"Error in FilterOperator {self.name}: {exc}", exc_info=True) + + +class FlatMapOperator(BaseOperator): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.out: Collector = Collector(logger=self.logger) + if isinstance(self.function, FlatMapFunction): + self.function.insert_collector(self.out) + + def process_packet(self, packet: Packet | None = None): + try: + if packet is None or packet.payload is None: + return + if isinstance(packet.payload, StopSignal): + self.router.send(packet) + return + self.out.clear() + result = self.function.execute(packet.payload) + if result is not None: + self._flatmap_send(result, packet) + for item_data in self.out.get_collected_data(): + self.router.send(packet.inherit_partition_info(item_data)) + self.out.clear() + except Exception as exc: + self.logger.error( + f"Error in FlatMapOperator '{self.name}'.process_packet(): {exc}", + exc_info=True, + ) + + def _flatmap_send(self, result: Any, source_packet: Packet): + if hasattr(result, "__iter__") and not isinstance(result, (str, bytes)): + for item in result: + self.router.send(source_packet.inherit_partition_info(item)) + else: + self.router.send(source_packet.inherit_partition_info(result)) + + +class SinkOperator(BaseOperator): + def process_packet(self, packet: Packet | None = None): + try: + if packet is None or packet.payload is None: + self.logger.warning(f"Operator {self.name} received empty data") + return + if isinstance(packet.payload, StopSignal): + return + self.function.execute(packet.payload) + except Exception as exc: + self.logger.error(f"Error in {self.name}.process(): {exc}", exc_info=True) + + def handle_stop_signal(self): + try: + if hasattr(self.function, "close") and callable(self.function.close): # type: ignore[attr-defined] + self.function.close() # type: ignore[attr-defined] + except Exception as exc: + self.logger.error(f"Error in {self.name}.handle_stop_signal(): {exc}", exc_info=True) + + +class SourceOperator(BaseOperator): + task: BaseTask | None + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._stop_signal_sent = False + self.task = None + + def receive_packet(self, packet: Packet): + self.process_packet(packet) + + def process_packet(self, packet: Packet | None = None): + result = self.function.execute() + if isinstance(result, StopSignal): + if self._stop_signal_sent: + return + self._stop_signal_sent = True + result.source = self.name + self.router.send_stop_signal(result) + if hasattr(self.ctx, "request_stop"): + self.ctx.request_stop() + if self.task is not None: + if hasattr(self.task.ctx, "set_stop_signal"): + self.task.ctx.set_stop_signal() + if hasattr(self.task, "is_running"): + self.task.is_running = False + return + if result is not None: + success = self.router.send(Packet(result)) + if not success: + if not self._stop_signal_sent: + self._stop_signal_sent = True + stop_signal = StopSignal(f"{self.name}-send-failed") + self.router.send_stop_signal(stop_signal) + if hasattr(self.ctx, "request_stop"): + self.ctx.request_stop() + if self.task is not None: + self.task.ctx.set_stop_signal() + self.task.is_running = False + + +class BatchOperator(BaseOperator): + def receive_packet(self, packet: Packet): + self.process_packet(packet) + + def process_packet(self, packet: Packet | None = None): + try: + result = self.function.execute() + is_stop = result is None or isinstance(result, StopSignal) + if is_stop: + stop_signal = result if isinstance(result, StopSignal) else StopSignal(self.name) + self.router.send_stop_signal(stop_signal) + self.ctx.send_stop_signal_back(self.name) + self.ctx.set_stop_signal() + return + success = self.router.send(Packet(result)) + if not success: + self.ctx.set_stop_signal() + except Exception as exc: + self.logger.error(f"Error in {self.name}.process(): {exc}", exc_info=True) + + +class KeyByOperator(BaseOperator): + def __init__(self, *args, partition_strategy: str = "hash", **kwargs): + super().__init__(*args, **kwargs) + self.partition_strategy = partition_strategy + + def process_packet(self, packet: Packet | None = None): + try: + if packet is None or packet.payload is None: + return + extracted_key = self.process(packet.payload) + keyed_packet = packet.update_key(extracted_key, self.partition_strategy) + self.router.send(keyed_packet) + except Exception as exc: + self.logger.error(f"Error in KeyByOperator {self.name}: {exc}", exc_info=True) + if packet: + self.router.send(packet) + + def process(self, raw_data: Any, input_index: int = 0) -> Any: + try: + return self.function.execute(raw_data) + except Exception as exc: + self.logger.error(f"Error extracting key in {self.name}: {exc}", exc_info=True) + return raw_data + + +class FutureOperator(BaseOperator): + def __init__(self, function_factory: FunctionFactory, ctx, env_name: str = ""): + super().__init__(function_factory, ctx) + self.is_future = True + self.basename = getattr(ctx, "name", env_name) + + def process(self, data: Any) -> Any: + raise RuntimeError("FutureOperator should not be called directly. It's a placeholder.") + + def emit(self, result: Any) -> None: + raise RuntimeError("FutureOperator should not be called directly. It's a placeholder.") + + def process_packet(self, packet: Packet | None = None): + raise RuntimeError("FutureOperator should not receive packets directly.") + + +class JoinOperator(BaseOperator): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._validate_function() + self._validated = True + self.processed_count = 0 + self.emitted_count = 0 + self.received_stop_signals = set() + + def _validate_function(self) -> None: + if not hasattr(self.function, "is_join") or not self.function.is_join: # type: ignore[attr-defined] + raise TypeError( + f"{self.__class__.__name__} requires Join function with is_join=True, got {type(self.function).__name__}" + ) + if not hasattr(self.function, "execute"): + raise TypeError( + f"Join function {type(self.function).__name__} must implement execute method" + ) + if getattr(self.function.execute, "__isabstractmethod__", False): + raise TypeError( + f"Join function {type(self.function).__name__} must implement execute method (currently abstract)" + ) + + def process_packet(self, packet: Packet | None = None): + try: + if packet is None or packet.payload is None: + return + if not packet.is_keyed(): + self.logger.warning( + f"JoinOperator '{self.name}' received non-keyed packet, skipping." + ) + return + payload = packet.payload + join_key = packet.partition_key + stream_tag = packet.input_index + if payload is None: + return + self.processed_count += 1 + join_results = self.function.execute(payload, join_key, stream_tag) + if join_results is not None: + if not isinstance(join_results, list): + join_results = [join_results] if join_results is not None else [] + for result in join_results: + if result is not None: + self._emit_join_result(result, join_key, packet) + self.emitted_count += 1 + except Exception as exc: + self.logger.error(f"Error in JoinOperator '{self.name}': {exc}", exc_info=True) + + def handle_stop_signal( + self, + stop_signal_name: str | None = None, + input_index: int | None = None, + signal: Any = None, + ): + try: + if signal is not None: + signal_name = signal.name if isinstance(signal, StopSignal) else str(signal) + elif stop_signal_name is not None: + signal_name = stop_signal_name + else: + return + self.received_stop_signals.add(signal_name) + source_signals = { + sig.name if isinstance(sig, StopSignal) else sig + for sig in self.received_stop_signals + if ("Source" in (sig.name if isinstance(sig, StopSignal) else str(sig))) + } + expected_sources = 2 + if len(source_signals) >= expected_sources: + self.ctx.send_stop_signal_back(self.name) + self.router.send_stop_signal(StopSignal(self.name)) + self.ctx.set_stop_signal() + except Exception as exc: + self.logger.error( + f"Error in JoinOperator '{self.name}' handle_stop_signal: {exc}", + exc_info=True, + ) + + def _emit_join_result(self, result_data: Any, join_key: Any, original_packet: Packet): + try: + result_packet = Packet( + payload=result_data, + input_index=0, + partition_key=join_key, + partition_strategy=original_packet.partition_strategy or "hash", + ) + self.router.send(result_packet) + except Exception as exc: + self.logger.error( + f"Failed to emit join result for key '{join_key}': {exc}", exc_info=True + ) + + +class CoMapOperator(BaseOperator): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._validate_function() + self._validated = True + self.received_stop_signals = set() + self.expected_input_count = None + + def _validate_function(self) -> None: + if not hasattr(self.function, "is_comap") or not self.function.is_comap: # type: ignore[attr-defined] + raise TypeError( + f"{self.__class__.__name__} requires CoMap function with is_comap=True, got {type(self.function).__name__}" + ) + for method_name in ["map0", "map1"]: + if not hasattr(self.function, method_name): + raise TypeError( + f"CoMap function {type(self.function).__name__} must implement {method_name} method" + ) + + def process_packet(self, packet: Packet | None = None): + try: + if packet is None or packet.payload is None: + return + map_method = getattr(self.function, f"map{packet.input_index}") + result = map_method(packet.payload) + if result is not None: + self.router.send(packet.inherit_partition_info(result)) + except Exception as exc: + self.logger.error(f"Error in CoMapOperator {self.name}: {exc}", exc_info=True) + error_result = { + "type": "comap_error", + "error": str(exc), + "original_payload": packet.payload if packet else None, + "input_index": packet.input_index if packet else -1, + "operator": self.name, + } + try: + if packet: + self.router.send(packet.inherit_partition_info(error_result)) + except Exception as send_error: + self.logger.error( + f"Failed to send error result in CoMapOperator {self.name}: {send_error}" + ) + + def handle_stop_signal( + self, stop_signal_name: str | None = None, input_index: int | None = None + ): + try: + if input_index is not None: + self.received_stop_signals.add(input_index) + if self.expected_input_count is None: + count = 0 + while hasattr(self.function, f"map{count}") and not getattr( + getattr(self.function, f"map{count}"), "__isabstractmethod__", False + ): + count += 1 + self.expected_input_count = count or getattr(self.router, "input_count", 2) + if len(self.received_stop_signals) >= self.expected_input_count: + self.router.send_stop_signal(StopSignal(self.name, source=self.name)) + self.ctx.set_stop_signal() + except Exception as exc: + self.logger.error( + f"Error in CoMapOperator '{self.name}' handle_stop_signal: {exc}", + exc_info=True, + ) diff --git a/src/sage/stream/transformations.py b/src/sage/stream/transformations.py new file mode 100644 index 0000000000..a91c6d5233 --- /dev/null +++ b/src/sage/stream/transformations.py @@ -0,0 +1,420 @@ +"""Main-repo owned transformation layer for SAGE streams.""" + +from __future__ import annotations + +import inspect +from typing import TYPE_CHECKING, Any + +from sage.foundation import BaseJoinFunction, CustomLogger, FutureFunction + +from ._kernel_runtime import ( + BatchOperator, + CoMapOperator, + FilterOperator, + FlatMapOperator, + FunctionFactory, + FutureOperator, + JoinOperator, + KeyByOperator, + MapOperator, + OperatorFactory, + SinkOperator, + SourceOperator, +) + +if TYPE_CHECKING: + from sage.foundation import BaseCoMapFunction, BaseFunction + from sage.runtime.base_environment import BaseEnvironment + + +__all__ = [ + "BaseTransformation", + "SourceTransformation", + "BatchTransformation", + "MapTransformation", + "FilterTransformation", + "FlatMapTransformation", + "SinkTransformation", + "KeyByTransformation", + "JoinTransformation", + "CoMapTransformation", + "FutureTransformation", +] + + +def is_abstract_method(method: Any) -> bool: + return getattr(method, "__isabstractmethod__", False) + + +def validate_required_methods( + cls: type, + required_methods: list[str], + class_name: str | None = None, +) -> None: + display_name = class_name or cls.__name__ + missing = [] + for method_name in required_methods: + if not hasattr(cls, method_name) or is_abstract_method(getattr(cls, method_name)): + missing.append(method_name) + if missing: + raise ValueError(f"{display_name} must implement required methods: {', '.join(missing)}") + + +class BaseTransformation: + def __init__( + self, + env: BaseEnvironment, + function: type[BaseFunction], + *args, + name: str | None = None, + parallelism: int = 1, + **kwargs, + ) -> None: + self.operator_class: type + self.remote = env.platform == "remote" + self.env_name = env.name + self.env = env + self.function_class = function + self.function_args = args + self.function_kwargs = kwargs + self.basename = name or self.function_class.__name__ + + existing_names = [t.basename for t in env.pipeline if hasattr(t, "basename")] + original_basename = self.basename + counter = 0 + while self.basename in existing_names: + counter += 1 + self.basename = f"{original_basename}_{counter}" + + self.logger = CustomLogger() + self.upstreams: list[BaseTransformation] = [] + self.downstreams: dict[str, int] = {} + self.parallelism = parallelism + self._operator_factory: OperatorFactory | None = None + self._function_factory: FunctionFactory | None = None + + def add_upstream(self, upstream_trans: BaseTransformation, input_index: int = 0) -> None: + self.upstreams.append(upstream_trans) + upstream_trans.downstreams[self.basename] = input_index + + @property + def function_factory(self) -> FunctionFactory: + if self._function_factory is None: + self._function_factory = FunctionFactory( + function_class=self.function_class, + function_args=self.function_args, + function_kwargs=self.function_kwargs, + ) + return self._function_factory + + def get_operator_kwargs(self) -> dict[str, Any]: + return {} + + @property + def operator_factory(self) -> OperatorFactory: + if self._operator_factory is None: + self._operator_factory = OperatorFactory( + operator_class=self.operator_class, + function_factory=self.function_factory, + basename=self.basename, + env_name=self.env_name, + remote=self.remote, + **self.get_operator_kwargs(), + ) + return self._operator_factory + + @property + def delay(self) -> float: + return 0.1 + + @property + def is_spout(self) -> bool: + return False + + @property + def is_sink(self) -> bool: + return False + + @property + def is_merge_operation(self) -> bool: + return not hasattr(self.function_class, "is_comap") or not getattr( + self.function_class, "is_comap", False + ) + + def __repr__(self) -> str: + return f"<{self.__class__.__name__} {self.function_class.__name__} at {hex(id(self))}>" + + +class SourceTransformation(BaseTransformation): + def __init__( + self, + env: BaseEnvironment, + function: type[BaseFunction], + *args, + delay: float = 1.0, + **kwargs, + ) -> None: + self.operator_class = SourceOperator + self._delay = delay + super().__init__(env, function, *args, **kwargs) + + @property + def delay(self) -> float: + return self._delay + + @property + def is_spout(self) -> bool: + return True + + +class BatchTransformation(BaseTransformation): + def __init__( + self, + env: BaseEnvironment, + function: type[BaseFunction], + *args, + delay: float = 0.1, + progress_log_interval: int = 100, + **kwargs, + ) -> None: + self.operator_class = BatchOperator + self._delay = delay + self._progress_log_interval = progress_log_interval + super().__init__(env, function, *args, **kwargs) + + @property + def delay(self) -> float: + return self._delay + + @property + def progress_log_interval(self) -> int: + return self._progress_log_interval + + @property + def is_spout(self) -> bool: + return True + + def get_operator_kwargs(self) -> dict[str, Any]: + return {"progress_log_interval": self._progress_log_interval} + + +class MapTransformation(BaseTransformation): + def __init__(self, env: BaseEnvironment, function: type[BaseFunction], *args, **kwargs) -> None: + self.operator_class = MapOperator + super().__init__(env, function, *args, **kwargs) + + +class FilterTransformation(BaseTransformation): + def __init__(self, env: BaseEnvironment, function: type[BaseFunction], *args, **kwargs) -> None: + self.operator_class = FilterOperator + super().__init__(env, function, *args, **kwargs) + + +class FlatMapTransformation(BaseTransformation): + def __init__(self, env: BaseEnvironment, function: type[BaseFunction], *args, **kwargs) -> None: + self.operator_class = FlatMapOperator + super().__init__(env, function, *args, **kwargs) + + +class SinkTransformation(BaseTransformation): + def __init__( + self, + env: BaseEnvironment, + function: type[BaseFunction], + *args, + batch_size: int = 1, + **kwargs, + ) -> None: + self.operator_class = SinkOperator + self.batch_size = batch_size + super().__init__(env, function, *args, **kwargs) + + @property + def is_sink(self) -> bool: + return True + + +class KeyByTransformation(BaseTransformation): + def __init__( + self, + env: BaseEnvironment, + key_selector_function: type[BaseFunction], + strategy: str = "hash", + name: str | None = None, + parallelism: int = 1, + *args, + **kwargs, + ) -> None: + self.operator_class = KeyByOperator + self.partition_strategy = strategy + super().__init__( + env=env, + function=key_selector_function, + name=name, + parallelism=parallelism, + *args, + **kwargs, + ) + + def get_operator_kwargs(self) -> dict[str, Any]: + return {"partition_strategy": self.partition_strategy} + + +class JoinTransformation(BaseTransformation): + def __init__( + self, env: BaseEnvironment, function: type[BaseJoinFunction], *args, **kwargs + ) -> None: + if not hasattr(function, "is_join") or not function.is_join: + raise ValueError( + f"Function {function.__name__} is not a Join function. " + "Join functions must inherit from BaseJoinFunction and have is_join=True." + ) + validate_required_methods( + function, ["execute"], class_name=f"Join function {function.__name__}" + ) + self._validate_execute_signature(function) + self.operator_class = JoinOperator + super().__init__(env, function, *args, **kwargs) + + def _validate_execute_signature(self, function_class: type[BaseJoinFunction]) -> None: + try: + params = list(inspect.signature(function_class.execute).parameters.keys()) + expected = ["self", "payload", "key", "tag"] + if len(params) < len(expected): + raise ValueError( + f"Join function {function_class.__name__}.execute() must accept parameters: {', '.join(expected[1:])}. Got: {', '.join(params[1:])}" + ) + except Exception as exc: + self.logger.warning(f"Could not validate execute method signature: {exc}") + + @property + def supported_input_count(self) -> int: + return 2 + + @property + def max_supported_streams(self) -> int: + return 2 + + def validate_input_streams(self, input_count: int) -> None: + if input_count != self.supported_input_count: + raise ValueError( + f"Join function {self.function_class.__name__} requires exactly {self.supported_input_count} input streams, but {input_count} streams provided." + ) + if input_count < 2: + raise ValueError("Join transformation requires at least 2 input streams.") + + def validate_keyed_streams(self, stream_transformations: list[BaseTransformation]) -> None: + for i, transformation in enumerate(stream_transformations): + if not self._is_keyed_stream(transformation): + raise ValueError( + f"Join requires all input streams to be keyed. Stream {i} ({transformation.function_class.__name__}) is not keyed. Use .keyby() before .join()." + ) + + def _is_keyed_stream(self, transformation: BaseTransformation) -> bool: + if isinstance(transformation, KeyByTransformation): + return True + + current = transformation + visited = set() + while current and id(current) not in visited: + visited.add(id(current)) + if isinstance(current, KeyByTransformation): + return True + if current.upstreams: + if len(current.upstreams) == 1: + current = current.upstreams[0] + else: + return all(self._is_keyed_stream(upstream) for upstream in current.upstreams) + else: + break + return False + + @property + def is_merge_operation(self) -> bool: + return False + + +class CoMapTransformation(BaseTransformation): + def __init__( + self, env: BaseEnvironment, function: type[BaseCoMapFunction], *args, **kwargs + ) -> None: + if not hasattr(function, "is_comap") or not function.is_comap: + raise ValueError( + f"Function {function.__name__} is not a CoMap function. " + "CoMap functions must inherit from BaseCoMapFunction and have is_comap=True." + ) + validate_required_methods( + function, ["map0", "map1"], class_name=f"CoMap function {function.__name__}" + ) + self.operator_class = CoMapOperator + super().__init__(env, function, *args, **kwargs) + + @property + def supported_input_count(self) -> int: + count = 0 + method_index = 0 + while True: + method_name = f"map{method_index}" + if not hasattr(self.function_class, method_name): + break + method = getattr(self.function_class, method_name) + if is_abstract_method(method): + break + count += 1 + method_index += 1 + return count + + def validate_input_streams(self, input_count: int) -> None: + supported_count = self.supported_input_count + if input_count > supported_count: + raise ValueError( + f"CoMap function {self.function_class.__name__} supports maximum {supported_count} input streams, but {input_count} provided." + ) + if input_count < 2: + raise ValueError("CoMap transformation requires at least 2 input streams.") + + +class FutureTransformation(BaseTransformation): + def __init__(self, env: BaseEnvironment, name: str) -> None: + self.operator_class = FutureOperator + super().__init__(env=env, function=FutureFunction, name=name, parallelism=1) + self.is_future = True + self.filled = False + self.actual_transformation: BaseTransformation | None = None + self.future_name = name + + def fill_with_transformation(self, actual_transformation: BaseTransformation) -> None: + if self.filled: + raise RuntimeError( + f"Future transformation '{self.future_name}' has already been filled" + ) + self.actual_transformation = actual_transformation + self.filled = True + self._redirect_downstreams() + + def _redirect_downstreams(self) -> None: + if not self.actual_transformation: + return + for downstream_name, input_index in self.downstreams.items(): + downstream_trans = self._find_transformation_by_name(downstream_name) + if downstream_trans and self.actual_transformation: + if self in downstream_trans.upstreams: + downstream_trans.upstreams.remove(self) + downstream_trans.upstreams.append(self.actual_transformation) + self.actual_transformation.downstreams[downstream_name] = input_index + self.downstreams.clear() + + def _find_transformation_by_name(self, name: str) -> BaseTransformation | None: + for trans in self.env.pipeline: + if trans.basename == name: + return trans + return None + + def __repr__(self) -> str: + status = "filled" if self.filled else "unfilled" + actual = ( + f" -> {self.actual_transformation.basename}" + if self.filled and self.actual_transformation + else "" + ) + return f"FutureTransformation({self.future_name}, {status}{actual})" diff --git a/src/tests/test_cli_main.py b/src/tests/test_cli_main.py new file mode 100644 index 0000000000..e79f8c3060 --- /dev/null +++ b/src/tests/test_cli_main.py @@ -0,0 +1,171 @@ +from __future__ import annotations + +import json +from pathlib import Path +from types import SimpleNamespace + +import pytest + +from sage.cli.commands.apps import chat as chat_module +from sage.cli.main import main +from sage.foundation.config import user_paths as user_paths_module +from sage.serving.gateway import GatewayProbeResult + + +def test_chat_direct_requires_sagellm_binary(capsys: pytest.CaptureFixture[str]) -> None: + status = main(["chat", "--backend", "direct", "--ask", "你好", "--stream"]) + captured = capsys.readouterr() + + assert status == 2 + assert "未找到 `sagellm` 可执行文件" in captured.err + + +def test_chat_direct_uses_sagellm_run( + monkeypatch: pytest.MonkeyPatch, +) -> None: + monkeypatch.setattr(chat_module, "_sagellm_executable", lambda: "/tmp/fake-sagellm") + + recorded: dict[str, object] = {} + + def fake_run(command: list[str], check: bool = False): + recorded["command"] = command + recorded["check"] = check + return SimpleNamespace(returncode=0) + + monkeypatch.setattr(chat_module.subprocess, "run", fake_run) + + status = main( + [ + "chat", + "--backend", + "direct", + "--ask", + "你好", + "--stream", + "--model", + "Qwen/Test", + "--direct-backend", + "cpu", + "--max-tokens", + "64", + ] + ) + + assert status == 0 + assert recorded["command"] == [ + "/tmp/fake-sagellm", + "run", + "-p", + "你好", + "--stream", + "-m", + "Qwen/Test", + "--backend", + "cpu", + "--max-tokens", + "64", + ] + + +def test_chat_auto_falls_back_to_direct_when_gateway_missing( + capsys: pytest.CaptureFixture[str], + monkeypatch: pytest.MonkeyPatch, +) -> None: + monkeypatch.setattr( + chat_module, + "probe_gateway", + lambda cfg: GatewayProbeResult(ok=False, url=cfg.health_url, error="down"), + ) + monkeypatch.setattr(chat_module, "_sagellm_executable", lambda: "/tmp/fake-sagellm") + monkeypatch.setattr(chat_module, "_run_direct_sagellm", lambda prompt, args: 0) + + status = main(["chat", "--ask", "你好"]) + captured = capsys.readouterr() + + assert status == 0 + assert captured.err == "" + + +def test_chat_auto_requires_real_backend( + capsys: pytest.CaptureFixture[str], + monkeypatch: pytest.MonkeyPatch, +) -> None: + monkeypatch.delenv("SAGE_CHAT_API_KEY", raising=False) + monkeypatch.delenv("OPENAI_API_KEY", raising=False) + monkeypatch.delenv("SAGE_CHAT_BASE_URL", raising=False) + monkeypatch.delenv("OPENAI_BASE_URL", raising=False) + monkeypatch.delenv("SAGELLM_BASE_URL", raising=False) + monkeypatch.setattr(chat_module, "_sagellm_executable", lambda: None) + monkeypatch.setattr( + chat_module, + "probe_gateway", + lambda cfg: GatewayProbeResult(ok=False, url=cfg.health_url, error="down"), + ) + + status = main(["chat", "--ask", "你好"]) + captured = capsys.readouterr() + + assert status == 2 + assert "本机也不存在 `sagellm` 命令" in captured.err + + +def test_chat_auto_uses_openai_env_when_configured( + capsys: pytest.CaptureFixture[str], + monkeypatch: pytest.MonkeyPatch, +) -> None: + monkeypatch.setenv("OPENAI_API_KEY", "sk-test") + monkeypatch.setenv("OPENAI_BASE_URL", "https://example.invalid/v1") + monkeypatch.setattr(chat_module, "_sagellm_executable", lambda: None) + monkeypatch.setattr( + chat_module, + "probe_gateway", + lambda cfg: GatewayProbeResult(ok=False, url=cfg.health_url, error="down"), + ) + + def fake_request(prompt: str, args: object) -> int: + print(f"real-backend:{prompt}:{chat_module._chat_base_url(args)}") + return 0 + + monkeypatch.setattr(chat_module, "_request_openai_chat", fake_request) + + status = main(["chat", "--ask", "你好"]) + captured = capsys.readouterr() + + assert status == 0 + assert "real-backend:你好:https://example.invalid/v1" in captured.out + + +def test_verify_core_smoke(capsys: pytest.CaptureFixture[str]) -> None: + status = main(["verify"]) + captured = capsys.readouterr() + + assert status == 0 + assert "core : ok" in captured.out + + +def test_index_ingest_writes_metadata(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setenv("XDG_DATA_HOME", str(tmp_path)) + user_paths_module.get_user_data_dir.cache_clear() + user_paths_module.get_user_config_dir.cache_clear() + user_paths_module.get_user_state_dir.cache_clear() + user_paths_module.get_user_cache_dir.cache_clear() + user_paths_module._user_paths = None + + status = main( + [ + "index", + "ingest", + "--quiet", + "--index", + "unit-test-index", + "--source", + "./docs", + ] + ) + + metadata_path = chat_module.resolve_index_root("unit-test-index") / "metadata.json" + payload = json.loads(metadata_path.read_text(encoding="utf-8")) + + assert status == 0 + assert payload["index"] == "unit-test-index" + assert payload["mode"] == "core-placeholder" diff --git a/src/tests/test_edge_app.py b/src/tests/test_edge_app.py new file mode 100644 index 0000000000..78b31e71d5 --- /dev/null +++ b/src/tests/test_edge_app.py @@ -0,0 +1,57 @@ +from __future__ import annotations + +import pytest +from fastapi import FastAPI +from fastapi.testclient import TestClient + +from sage.edge.app import create_app + + +@pytest.fixture +def mock_gateway_app() -> FastAPI: + app = FastAPI(title="Mock Gateway") + + @app.get("/v1/models") + async def list_models() -> dict[str, list[str]]: + return {"data": ["mock-model"]} + + return app + + +def test_create_app_no_llm_health_and_ready() -> None: + app = create_app(mount_llm=False) + client = TestClient(app) + + assert client.get("/healthz").json() == { + "status": "ok", + "service": "SAGE Edge", + "llm_mounted": False, + "llm_prefix": "/", + } + assert client.get("/readyz").json()["status"] == "ready" + + +def test_create_app_mount_llm_at_root(mock_gateway_app: FastAPI) -> None: + app = create_app(mount_llm=True, llm_app=mock_gateway_app) + client = TestClient(app) + + assert client.get("/v1/models").json() == {"data": ["mock-model"]} + assert client.get("/healthz").json()["llm_mounted"] is True + + +def test_create_app_mount_llm_at_custom_prefix(mock_gateway_app: FastAPI) -> None: + app = create_app(mount_llm=True, llm_prefix="/llm", llm_app=mock_gateway_app) + client = TestClient(app) + + assert client.get("/llm/v1/models").json() == {"data": ["mock-model"]} + assert client.get("/healthz").json()["llm_prefix"] == "/llm" + + +def test_create_app_invalid_prefix_raises() -> None: + with pytest.raises(ValueError, match="llm_prefix must start with '/'"): + create_app(mount_llm=True, llm_prefix="llm", llm_app=FastAPI()) + + +def test_create_app_missing_llm_app_raises() -> None: + with pytest.raises(RuntimeError, match="requires explicit llm_app injection"): + create_app(mount_llm=True) diff --git a/src/tests/test_edge_core.py b/src/tests/test_edge_core.py new file mode 100644 index 0000000000..c9bf204c8d --- /dev/null +++ b/src/tests/test_edge_core.py @@ -0,0 +1,26 @@ +from __future__ import annotations + +import pytest + +from sage.edge.core import normalize_mount_path, probe_payload + + +def test_normalize_mount_path() -> None: + assert normalize_mount_path(None) == "/" + assert normalize_mount_path("") == "/" + assert normalize_mount_path("/") == "/" + assert normalize_mount_path("/llm") == "/llm" + assert normalize_mount_path("/llm/") == "/llm" + + with pytest.raises(ValueError, match="must start with"): + normalize_mount_path("llm") + + +def test_probe_payload() -> None: + payload = probe_payload("ok", llm_mounted=True, llm_prefix="/llm") + assert payload == { + "status": "ok", + "service": "SAGE Edge", + "llm_mounted": True, + "llm_prefix": "/llm", + } diff --git a/src/tests/test_edge_server.py b/src/tests/test_edge_server.py new file mode 100644 index 0000000000..8485594d2e --- /dev/null +++ b/src/tests/test_edge_server.py @@ -0,0 +1,108 @@ +from __future__ import annotations + +import argparse +import importlib +import sys +import types + +import pytest + + +@pytest.fixture(autouse=True) +def clear_edge_server_module() -> None: + sys.modules.pop("sage.edge.server", None) + yield + sys.modules.pop("sage.edge.server", None) + + +def _import_server_module(): + return importlib.import_module("sage.edge.server") + + +def test_server_main_invokes_uvicorn_with_parsed_args(monkeypatch: pytest.MonkeyPatch) -> None: + server = _import_server_module() + parsed = argparse.Namespace( + host="127.0.0.1", + port=9988, + llm_prefix="/edge", + no_llm=False, + log_level="debug", + ) + captured: dict[str, object] = {} + + monkeypatch.setattr(server, "_parse_args", lambda: parsed) + monkeypatch.setattr(server, "_load_llm_gateway_app", lambda: object()) + monkeypatch.setattr( + server, + "create_app", + lambda *, mount_llm, llm_prefix, llm_app: ( + captured.update({"mount_llm": mount_llm, "llm_prefix": llm_prefix, "llm_app": llm_app}) + or object() + ), + ) + + fake_uvicorn = types.SimpleNamespace( + run=lambda app, *, host, port, log_level: captured.update( + {"app": app, "host": host, "port": port, "log_level": log_level} + ) + ) + monkeypatch.setitem(sys.modules, "uvicorn", fake_uvicorn) + + server.main() + + assert captured["mount_llm"] is True + assert captured["llm_prefix"] == "/edge" + assert captured["host"] == "127.0.0.1" + assert captured["port"] == 9988 + assert captured["log_level"] == "debug" + + +def test_server_main_no_llm_mode(monkeypatch: pytest.MonkeyPatch) -> None: + server = _import_server_module() + parsed = argparse.Namespace( + host="0.0.0.0", + port=8899, + llm_prefix=None, + no_llm=True, + log_level="info", + ) + captured: dict[str, object] = {} + + monkeypatch.setattr(server, "_parse_args", lambda: parsed) + monkeypatch.setattr( + server, + "create_app", + lambda *, mount_llm, llm_prefix, llm_app: ( + captured.update({"mount_llm": mount_llm, "llm_prefix": llm_prefix, "llm_app": llm_app}) + or object() + ), + ) + monkeypatch.setitem( + sys.modules, "uvicorn", types.SimpleNamespace(run=lambda *args, **kwargs: None) + ) + + server.main() + + assert captured == {"mount_llm": False, "llm_prefix": None, "llm_app": None} + + +def test_load_llm_gateway_app_tries_known_specs(monkeypatch: pytest.MonkeyPatch) -> None: + server = _import_server_module() + gateway_module = types.ModuleType("sagellm_gateway.server") + gateway_module.app = object() + monkeypatch.setitem(sys.modules, "sagellm_gateway.server", gateway_module) + + loaded = server._load_llm_gateway_app() + assert loaded is gateway_module.app + + +def test_load_llm_gateway_app_fails_fast(monkeypatch: pytest.MonkeyPatch) -> None: + server = _import_server_module() + + def fail_import(name: str): + raise ImportError(f"missing {name}") + + monkeypatch.setattr(server.importlib, "import_module", fail_import) + + with pytest.raises(ImportError, match="Unable to load a gateway ASGI app"): + server._load_llm_gateway_app() diff --git a/src/tests/test_runtime_local_consolidation.py b/src/tests/test_runtime_local_consolidation.py new file mode 100644 index 0000000000..ec385da599 --- /dev/null +++ b/src/tests/test_runtime_local_consolidation.py @@ -0,0 +1,119 @@ +from __future__ import annotations + +import time + +from sage.foundation import BatchFunction, MapFunction, SinkFunction +from sage.runtime import LocalEnvironment +from sage.runtime.exception_hooks import ( + get_registered_exception_handler_hook, + register_kernel_exception_handler_hook, +) +from sage.runtime.job_manager import JobManager +from sage.runtime.scheduler import FIFOScheduler, LoadAwareScheduler, resolve_scheduler +from sage.stream._runtime_kernel_types import Packet, StopSignal + + +class NumberBatchSource(BatchFunction): + def __init__(self) -> None: + super().__init__() + self._current = 0 + + def execute(self): + if self._current >= 4: + return StopSignal("numbers-done") + value = self._current + self._current += 1 + return value + + +class DoubleValue(MapFunction): + def execute(self, data: int) -> int: + return data * 2 + + +class CollectSink(SinkFunction): + collected: list[int] = [] + + def execute(self, data: int) -> None: + type(self).collected.append(data) + + +class SlowTickSource(BatchFunction): + def __init__(self) -> None: + super().__init__() + self._current = 0 + + def execute(self): + time.sleep(0.02) + self._current += 1 + return self._current + + +def test_packet_and_stop_signal_are_main_repo_owned() -> None: + signal = StopSignal("done", source="source-1") + packet = Packet(payload={"x": 1}, input_index=2, partition_key="k", partition_strategy="hash") + + copied = packet.copy() + rekeyed = packet.update_key("k2") + + assert signal.name == "done" + assert signal.source == "source-1" + assert copied == packet + assert rekeyed.partition_key == "k2" + assert packet.inherit_partition_info("payload").partition_strategy == "hash" + + +def test_scheduler_resolution_uses_in_tree_implementations() -> None: + fifo = resolve_scheduler(scheduler="fifo", platform="local") + load_aware = resolve_scheduler(scheduler="load_aware", platform="local") + + assert isinstance(fifo, FIFOScheduler) + assert isinstance(load_aware, LoadAwareScheduler) + assert fifo.get_metrics()["scheduler_type"] == "FIFO" + assert load_aware.get_metrics()["scheduler_type"] == "LoadAware" + + +def test_local_environment_batch_submit_runs_without_kernel_dependency() -> None: + CollectSink.collected = [] + JobManager().cleanup_all_jobs() + + env = LocalEnvironment(name="batch-test") + env.from_batch(NumberBatchSource).map(DoubleValue).sink(CollectSink) + + env_uuid = env.submit(autostop=True) + status = env.jobmanager.get_job_status(env_uuid) + + assert CollectSink.collected == [0, 2, 4, 6] + assert status["status"] == "stopped" + assert status["pipeline_size"] == 3 + + +def test_local_environment_streaming_job_can_be_stopped() -> None: + JobManager().cleanup_all_jobs() + + env = LocalEnvironment(name="stream-test") + env.from_batch(SlowTickSource).map(DoubleValue).sink(CollectSink) + + env_uuid = env.submit(autostop=False) + status = env.jobmanager.get_job_status(env_uuid) + assert status["status"] == "running" + + env.stop() + stopped_status = env.jobmanager.get_job_status(env_uuid) + assert stopped_status["status"] == "stopped" + + +def test_exception_hook_registration_is_owned_in_tree() -> None: + pushed: list[str] = [] + + def push(handler): + pushed.append("push") + + def pop(): + pushed.append("pop") + + register_kernel_exception_handler_hook(push, pop) + registered_push, registered_pop = get_registered_exception_handler_hook() + + assert registered_push is push + assert registered_pop is pop diff --git a/tools/cleanup/remove_duplicate_ann_folder.sh b/tools/cleanup/remove_duplicate_ann_folder.sh deleted file mode 100755 index 183c752d7a..0000000000 --- a/tools/cleanup/remove_duplicate_ann_folder.sh +++ /dev/null @@ -1,37 +0,0 @@ -#!/usr/bin/env bash -# Remove duplicate ann/ folder (keep anns/) -# The ann/ folder is an incomplete legacy implementation that should be removed. -# All functionality is in anns/ which is the canonical location. - -set -euo pipefail - -SAGE_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)" -ANN_DIR="${SAGE_ROOT}/packages/sage-libs/src/sage/libs/ann" - -echo "🔍 Checking for duplicate ann/ folder..." - -if [ ! -d "$ANN_DIR" ]; then - echo "✅ ann/ folder already removed" - exit 0 -fi - -echo "📋 Contents of ann/ folder:" -find "$ANN_DIR" -type f - -echo "" -echo "⚠️ About to remove: $ANN_DIR" -echo " Reason: Incomplete legacy implementation, superseded by anns/" -echo "" -read -p "Continue? (y/N) " -n 1 -r -echo - -if [[ ! $REPLY =~ ^[Yy]$ ]]; then - echo "❌ Cancelled" - exit 1 -fi - -echo "🗑️ Removing ann/ folder..." -rm -rf "$ANN_DIR" - -echo "✅ Successfully removed duplicate ann/ folder" -echo "✅ Please use 'from sage.libs.anns import ...' going forward" diff --git a/tools/cleanup/uninstall_sage.sh b/tools/cleanup/uninstall_sage.sh index c111d77ea6..c1f828d5f2 100755 --- a/tools/cleanup/uninstall_sage.sh +++ b/tools/cleanup/uninstall_sage.sh @@ -28,7 +28,7 @@ print_header() { echo "" echo -e "${DIM}此工具将帮助你:${NC}" echo -e " • 卸载通过 quickstart.sh 安装的 SAGE Python 包" - echo -e " • 可选：删除为 SAGE 创建的虚拟环境 (.sage/venv 或 Conda 环境)" + echo -e " • 可选：删除为 SAGE 创建的 Conda 环境" echo -e " • 保留项目源码和 Git 仓库本身${NC}" echo "" } diff --git a/tools/cleanup/verify_ann_cleanup.sh b/tools/cleanup/verify_ann_cleanup.sh deleted file mode 100755 index e1868f4d99..0000000000 --- a/tools/cleanup/verify_ann_cleanup.sh +++ /dev/null @@ -1,71 +0,0 @@ -#!/usr/bin/env bash -# Verify ANN cleanup was successful - -set -euo pipefail - -SAGE_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)" - -echo "🔍 Verifying ANN cleanup..." -echo "" - -# Check 1: ann/ folder should not exist -echo "✓ Check 1: Verifying ann/ folder is removed..." -if [ -d "${SAGE_ROOT}/packages/sage-libs/src/sage/libs/ann" ]; then - echo "❌ FAIL: ann/ folder still exists!" - exit 1 -else - echo "✅ PASS: ann/ folder successfully removed" -fi - -# Check 2: anns/ folder should exist -echo "" -echo "✓ Check 2: Verifying anns/ folder exists..." -if [ ! -d "${SAGE_ROOT}/packages/sage-libs/src/sage/libs/anns" ]; then - echo "❌ FAIL: anns/ folder not found!" - exit 1 -else - echo "✅ PASS: anns/ folder exists" -fi - -# Check 3: No Python code should reference sage.libs.ann (without 's') -echo "" -echo "✓ Check 3: Verifying no code references old sage.libs.ann path..." -cd "$SAGE_ROOT" -if rg "sage\.libs\.ann[^s]" --type py packages/ 2>/dev/null | grep -v "^#" | grep -v "\.md:" > /dev/null; then - echo "⚠️ WARNING: Found references to old sage.libs.ann (check if they're just comments):" - rg "sage\.libs\.ann[^s]" --type py packages/ | head -5 -else - echo "✅ PASS: No Python code references old sage.libs.ann path" -fi - -# Check 4: Verify anns interface files exist -echo "" -echo "✓ Check 4: Verifying anns interface files..." -REQUIRED_FILES=( - "packages/sage-libs/src/sage/libs/anns/__init__.py" - "packages/sage-libs/src/sage/libs/anns/README.md" - "packages/sage-libs/src/sage/libs/anns/interface/base.py" - "packages/sage-libs/src/sage/libs/anns/interface/factory.py" -) - -for file in "${REQUIRED_FILES[@]}"; do - if [ ! -f "${SAGE_ROOT}/${file}" ]; then - echo "❌ FAIL: Missing required file: ${file}" - exit 1 - fi -done -echo "✅ PASS: All required anns interface files exist" - -echo "" -echo "════════════════════════════════════════════════════════" -echo "✅ ALL CHECKS PASSED!" -echo "════════════════════════════════════════════════════════" -echo "" -echo "Summary:" -echo " - ann/ folder: ❌ Removed (was duplicate)" -echo " - anns/ folder: ✅ Kept (canonical location)" -echo " - Code references: ✅ Clean" -echo " - Interface files: ✅ Complete" -echo "" -echo "✅ Use: from sage.libs.anns import create, register, registered" -echo "❌ Old: from sage.libs.ann import ... (NO LONGER EXISTS)" diff --git a/tools/config/pre-commit-config.yaml b/tools/config/pre-commit-config.yaml index e8d2d27cce..69058cd53b 100644 --- a/tools/config/pre-commit-config.yaml +++ b/tools/config/pre-commit-config.yaml @@ -7,11 +7,6 @@ # - Install hooks: pre-commit install --config tools/config/pre-commit-config.yaml # - Run manually: pre-commit run --all-files --config tools/config/pre-commit-config.yaml # -# Mypy Type Checking: -# - Runs automatically on changed Python files -# - Shows type errors as warnings (doesn't block commits/CI) -# - To run mypy explicitly: pre-commit run mypy --all-files --config tools/config/pre-commit-config.yaml - default_language_version: python: python3.11 @@ -38,48 +33,62 @@ repos: - id: check-merge-conflict exclude: "(ThirdParty/|thirdparty/)" - id: check-case-conflict - exclude: ^(.*/benchmark_(libamm|db)/|.*/(libamm|sageLLM|neuromem|sageTSDB)/|.*/vendors/|.*/build/|ThirdParty/|thirdparty/) + exclude: ^(.*/benchmark_(libamm|db)/|.*/vendors/|.*/build/|ThirdParty/|thirdparty/) - id: mixed-line-ending args: [--fix=lf] exclude: "(ThirdParty/|thirdparty/)" - id: detect-private-key exclude: "(ThirdParty/|thirdparty/)" - # Python: Black formatter (DISABLED - using ruff format instead) - # Black and ruff format can conflict in edge cases, causing infinite reformatting - # Ruff format is now mature enough to replace Black completely - # - repo: https://github.com/psf/black - # rev: 25.9.0 - # hooks: - # - id: black - # language_version: python3.11 - # args: [--line-length=100] - # exclude: ^(docs/|docs-public/|examples/data/|.*/(sageLLM|neuromem|sageTSDB)/|.*/vendors/|.*/build/) + # Python: Black formatter (DISABLED - using ruff format instead) + # Black and ruff format can conflict in edge cases, causing infinite reformatting + # Ruff format is now mature enough to replace Black completely + # - repo: https://github.com/psf/black + # rev: 25.9.0 + # hooks: + # - id: black + # language_version: python3.11 + # args: [--line-length=100] + # exclude: ^(docs/|examples/data/|.*/vendors/|.*/build/) - # Python: isort import sorting - # - repo: https://github.com/pycqa/isort - # rev: 7.0.0 - # hooks: - # - id: isort - # args: [--profile=black, --line-length=100] - # exclude: ^(docs/|docs-public/|examples/data/|.*/(sageLLM|neuromem|sageTSDB)/|.*/vendors/|.*/build/) - # - # Python: Ruff linter and formatter (replaces flake8, isort, pyupgrade) -- repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.14.6 + # Python: isort import sorting + # - repo: https://github.com/pycqa/isort + # rev: 7.0.0 + # hooks: + # - id: isort + # args: [--profile=black, --line-length=100] + # exclude: ^(docs/|examples/data/|.*/vendors/|.*/build/) + # + # Python: Ruff linter and formatter (replaces flake8, isort, pyupgrade) + # NOTE: Using local/system ruff to avoid network download failures + # Requires: ruff is available in PATH (installed via pip in the active env) +- repo: local hooks: - id: ruff name: ruff check - args: [--fix, --exit-non-zero-on-fix, --config, tools/config/ruff.toml] - exclude: ^(docs/|docs-public/|examples/data/|tests/fixtures/|.*/benchmark_(libamm|db)/|.*/(sageLLM|neuromem|sageTSDB|libamm)/|.*/vendors/|.*/build/|ThirdParty/|thirdparty/|.*/implementations/(SPTAG|faiss|DiskANN|diskann-ms|candy|puck|pybind11)/) + language: system + entry: ruff + args: [check, --fix, --exit-non-zero-on-fix, --config, tools/config/ruff.toml] + types: [python] + exclude: ^(docs/|examples/data/|tests/fixtures/|.*/benchmark_(libamm|db)/|.*/vendors/|.*/build/|ThirdParty/|thirdparty/|.*/implementations/(SPTAG|faiss|DiskANN|diskann-ms|candy|puck|pybind11)/) - id: ruff-format name: ruff format - args: [--config, tools/config/ruff.toml] - exclude: ^(docs/|docs-public/|examples/data/|tests/fixtures/|.*/benchmark_(libamm|db)/|.*/(sageLLM|neuromem|sageTSDB|libamm)/|.*/vendors/|.*/build/|ThirdParty/|thirdparty/|.*/implementations/(SPTAG|faiss|DiskANN|diskann-ms|candy|puck|pybind11)/) + language: system + entry: ruff + args: [format, --config, tools/config/ruff.toml] + types: [python] + exclude: ^(docs/|examples/data/|tests/fixtures/|.*/benchmark_(libamm|db)/|.*/vendors/|.*/build/|ThirdParty/|thirdparty/|.*/implementations/(SPTAG|faiss|DiskANN|diskann-ms|candy|puck|pybind11)/) - # Python: mypy type checking (warning mode - shows errors but doesn't block commit) - repo: local hooks: + - id: meta-dependency-audit-gate + name: check isage meta dependency audit evidence + entry: python3 tools/scripts/check_meta_dependency_audit.py --enforce-change-evidence --staged + language: system + pass_filenames: false + always_run: false + stages: [pre-commit, manual] + files: ^pyproject.toml$|^docs/dependency-audit-gate\.md$ - id: architecture-violation-check name: check architecture violations (directory placement) entry: tools/hooks/pre-commit-architecture.sh @@ -87,39 +96,33 @@ repos: pass_filenames: false always_run: false stages: [pre-commit] - # Check for files in wrong locations (e.g., sageLLM in sage-common instead of sage-llm-core) - # - id: dependency-version-conflicts - # name: check dependency version conflicts - # entry: python3 tools/install/helpers/unify_dependencies.py --check - # language: system - # pass_filenames: false - # always_run: false - # files: ^packages/.*/pyproject\.toml$ - # # DEPRECATED: Script removed, dependency management moved to sage-tools + # Check for files in wrong locations under the current meta-repo layout + # - id: dependency-version-conflicts + # name: check dependency version conflicts + # entry: python3 tools/install/helpers/unify_dependencies.py --check + # language: system + # pass_filenames: false + # always_run: false + # files: ^pyproject\.toml$ + # # DEPRECATED: Script removed, dependency management moved to sage-tools - id: pep420-namespace-compliance name: PEP 420 namespace package compliance check entry: bash -c 'tools/scripts/validate_pep420_compliance.sh' language: system pass_filenames: false always_run: false - files: ^packages/.*/src/sage/__init__\.py$ - # Block commit if src/sage/__init__.py is added/modified (violates PEP 420) - - id: mypy - name: mypy type checking (warnings only) - entry: tools/quality/mypy-wrapper.sh + files: ^src/sage/__init__\.py$ + # Block commit if src/sage/__init__.py is added/modified (violates PEP 420) + - id: cross-repo-dedup-check + name: cross-repo duplicate declaration check (SAGE vs sageFlownet) + entry: python3 tools/scripts/check_cross_repo_dedup.py language: system - types: [python] - require_serial: true - args: - - --cache-dir=.sage/cache/mypy - - --ignore-missing-imports - - --show-error-codes - - --explicit-package-bases - - --warn-unused-ignores - - --namespace-packages - exclude: ^(docs/|docs-public/|examples/|tests/|.*/tests/|setup.py|.*/setup.py|.*/benchmark_(libamm|db)/|.*/(sageLLM|neuromem|sageTSDB|libamm)/|.*/vendors/|.*/src/sage/__init__.py|ThirdParty/|thirdparty/) - # This hook will always succeed (exit 0) to avoid blocking commits/CI - # Errors are shown but treated as warnings + pass_filenames: false + always_run: false + stages: [pre-commit, manual] + files: \.py$ + # Enforces move-then-delete rule: migrated symbols must not be redefined in Flownet. + # See Issue #1439 and flownet-migration-boundary.md - id: markdown-files-location-check name: check markdown files are in proper locations entry: tools/hooks/check_docs_location.sh @@ -127,21 +130,14 @@ repos: pass_filenames: false always_run: false files: \.md$ - - id: dev-notes-categorization-check - name: check dev-notes files are properly categorized - entry: tools/hooks/check_dev_notes_location.sh - language: system - pass_filenames: false - always_run: false - files: ^docs-public/docs_src/dev-notes/.*\.md$ - # - id: control-plane-only-guard - # name: prevent embedded/local inference paths - # entry: tools/hooks/check_control_plane_only.py - # language: system - # pass_filenames: false - # always_run: false - # files: \.py$ - # # DEPRECATED: Script removed, control plane checks moved to sage-tools + # - id: control-plane-only-guard + # name: prevent embedded/local inference paths + # entry: tools/hooks/check_control_plane_only.py + # language: system + # pass_filenames: false + # always_run: false + # files: \.py$ + # # DEPRECATED: Script removed, control plane checks moved to sage-tools - id: python-test-files-location-check name: check Python test files are in proper locations entry: bash -c @@ -164,7 +160,7 @@ repos: violations="" for file in $all_test_files; do # Test files should be in tests/ directories or test scripts in examples/ - if echo "$file" | grep -qE "^(packages/[^/]+/tests/|tests/|examples/.*/tests?\.py$|examples/.*test.*\.py$)"; then + if echo "$file" | grep -qE "^(tests/|examples/.*/tests?\.py$|examples/.*test.*\.py$)"; then # Allowed location continue else @@ -179,13 +175,13 @@ repos: echo -e "$violations" | sed "s/^/ - /" echo "" echo "📁 测试文件应该放在:" - echo " - packages//tests/ - 单元测试和集成测试" - echo " - packages//tests/unit/ - 单元测试" - echo " - packages//tests/integration/ - 集成测试" - echo " - packages//tests/manual/ - 手动测试脚本" + echo " - tests/ - 单元测试和集成测试" + echo " - tests/unit/ - 单元测试" + echo " - tests/integration/ - 集成测试" + echo " - tests/manual/ - 手动测试脚本" echo " - examples/ - 示例和演示脚本（可以包含 test 名称）" echo "" - echo "💡 建议: 请将测试文件移动到对应包的 tests/ 目录" + echo "💡 建议: 请将测试文件移动到仓库的 tests/ 目录" exit 1 else exit 0 @@ -194,103 +190,14 @@ repos: pass_filenames: false always_run: false files: \.py$ - - id: devnotes-check - name: dev-notes documentation standards - entry: bash -c 'if git diff --cached --name-only --diff-filter=ACM | grep -q "^docs-public/docs_src/dev-notes/.*\.md$"; then sage-dev quality devnotes || true; else exit 0; fi' - language: system - pass_filenames: false - always_run: false - files: ^docs-public/docs_src/dev-notes/.*\.md$ - - id: devnotes-structure-check - name: dev-notes directory structure validation - entry: bash -c - args: - - | - # Check if any files were staged in docs-public/docs_src/dev-notes/ - if [ -n "$PRE_COMMIT_FROM_REF" ] && [ -n "$PRE_COMMIT_TO_REF" ]; then - # Running with --all-files or during push - staged_files=$(git ls-files "docs-public/docs_src/dev-notes/*") - else - # Running in normal commit mode - staged_files=$(git diff --cached --name-only --diff-filter=ACM | grep "^docs-public/docs_src/dev-notes/" || true) - fi - - if [ -z "$staged_files" ]; then - exit 0 - fi - - # Allowed top-level directories under docs-public/docs_src/dev-notes/ - # NOTE: When adding new directories, update this list - allowed_dirs=( - "l1-common" - "l2-platform" - "l3-kernel" - "l3-libs" - "l4-middleware" - "l5-cli" - "l5-tools" - "cross-layer" - "testing" - "archive" - "research_work" - ) - - # Get all first-level directories under docs-public/docs_src/dev-notes/ - violations="" - for file in $staged_files; do - # Extract the first directory component after docs-public/docs_src/dev-notes/ - # e.g., docs-public/docs_src/dev-notes/foo/bar.md -> foo - dir_component=$(echo "$file" | sed -n 's|^docs-public/docs_src/dev-notes/\([^/]*\)/.*|\1|p') - - # Skip root-level files (README.md, TEMPLATE.md) - if [ -z "$dir_component" ]; then - continue - fi - - # Check if directory is allowed - allowed=false - for allowed_dir in "${allowed_dirs[@]}"; do - if [ "$dir_component" = "$allowed_dir" ]; then - allowed=true - break - fi - done - - if [ "$allowed" = false ]; then - # Only add unique violations - if [[ ! "$violations" =~ "$dir_component" ]]; then - violations="$violations$dir_component\n" - fi - fi - done - - # Report violations - if [ -n "$violations" ]; then - echo "❌ 错误: docs-public/docs_src/dev-notes/ 下存在未授权的目录:" - echo -e "$violations" | sort -u | sed "s/^/ - /" - echo "" - echo "📁 允许的目录:" - echo " - 分层目录: l1-common, l2-platform, l3-kernel, l3-libs, l4-middleware" - echo " l5-cli, l5-tools" - echo " - 交叉主题: cross-layer, testing, archive" - echo " - 研究工作: research_work" - echo "" - echo "💡 建议: 将文档移动到合适的目录，或联系维护者更新允许列表" - exit 1 - fi - exit 0 - language: system - pass_filenames: false - always_run: false - files: ^docs-public/docs_src/dev-notes/ - id: installation-consistency-check name: installation consistency check (local vs CI/CD) entry: bash tools/install/examination_tools/installation_consistency_check.sh language: system pass_filenames: false - # 仅在修改安装相关文件时运行 - files: ^(quickstart\.sh|tools/install/.*\.sh|packages/.*/pyproject\.toml|packages/.*/setup\.py|\.github/workflows/.*\.yml)$ - # 也在手动运行 pre-commit run --all-files 时执行 + # 仅在修改安装相关文件时运行 + files: ^(quickstart\.sh|tools/install/.*\.sh|pyproject\.toml|setup\.py|\.github/workflows/.*\.yml)$ + # 也在手动运行 pre-commit run --all-files 时执行 stages: [pre-commit, manual] - id: root-directory-cleanup-check name: check for unwanted files/directories in project root @@ -317,12 +224,16 @@ repos: "LICENSE" "Makefile" "manage.sh" + "pytest.ini" "quickstart.sh" "README.md" "CHANGELOG.md" "dependencies-spec.yaml" "tasks.md" "SAGE.code-workspace" + "SAGE_ZOO.md" + "pyproject.toml" # isage meta-package + "setup.py" # isage meta-package ) # Allowed directories in project root (whitelist) @@ -330,7 +241,7 @@ repos: ".benchmarks" # Deprecated; keep for legacy benchmark artifacts ".git" ".github" - ".mypy_cache" + ".mypy_cache" # mypy cache (temporary; should use .sage/cache/mypy) ".pytest_cache" ".ruff_cache" ".sage" @@ -338,14 +249,16 @@ repos: "benchmark" # Dedicated benchmark workspace in repo root "build" "config" + "dist" "docker" - # "docs" # REMOVED: Root docs/ is now forbidden. Use docs-public/ instead. - "docs-public" + # "docs" # Root docs/ for meta-package (layer-manifest.json, dependency-audit-gate.md) + "docs" "examples" "htmlcov" - "packages" + "src" # isage meta-package source (src/sage/_version.py) "data" "tools" + "hooks" ) violations="" @@ -394,7 +307,7 @@ repos: echo " - 临时文件应放在 .sage/ 目录下" echo " - 如果是新增的合法文件/目录，请更新 tools/config/pre-commit-config.yaml 中的白名单" echo "" - echo "📖 参考: docs-public/docs_src/dev-notes/l5-tools/CACHE_MANAGEMENT.md" + echo "📖 参考: DEVELOPER.md" exit 1 fi exit 0 @@ -402,51 +315,60 @@ repos: pass_filenames: false always_run: true stages: [pre-commit, manual] - # Shell: shellcheck - # NOTE: shellcheck-py may fail in CI due to network issues when downloading shellcheck binary - # This is a known issue: https://github.com/shellcheck-py/shellcheck-py/issues/15 - # Workaround: Use fail_fast: false in CI or install shellcheck separately -- repo: https://github.com/shellcheck-py/shellcheck-py - rev: v0.11.0.1 + # Shell: shellcheck + # NOTE: Using local/system shellcheck to avoid network download failures + # (shellcheck-py downloads the binary at install time, which fails behind proxies/firewalls) + # Requires: shellcheck is available in PATH (e.g. installed via conda or apt) +- repo: local hooks: - id: shellcheck + name: shellcheck + language: system + entry: shellcheck args: [-x, -e, SC1091, -S, error] # -x: follow sources, -e SC1091: ignore source errors, -S error: only fail on errors files: \.(sh|bash)$ exclude: ^(tools/conda/|.*/benchmark_anns/|ThirdParty/|thirdparty/|.*/implementations/(SPTAG|faiss|DiskANN|diskann-ms|candy|puck|pybind11)/) - # YAML formatting -- repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks - rev: v2.15.0 + # YAML formatting + # NOTE: Using local/system pretty-format-yaml to avoid network download failures + # Requires: language-formatters-pre-commit-hooks is installed via pip +- repo: local hooks: - id: pretty-format-yaml + name: pretty-format-yaml + language: system + entry: pretty-format-yaml args: [--autofix, --indent=2, --preserve-quotes] + types: [yaml] exclude: ^(\.github/|examples/config/|.*/benchmark_anns/|ThirdParty/|thirdparty/|.*/implementations/(SPTAG|faiss|DiskANN|diskann-ms|candy|puck|pybind11)/) - # Markdown formatting -- repo: https://github.com/executablebooks/mdformat - rev: 1.0.0 + # Markdown formatting + # NOTE: Using local/system mdformat to avoid network download failures + # Requires: mdformat + mdformat-gfm installed via pip +- repo: local hooks: - id: mdformat - additional_dependencies: - - mdformat-gfm # GitHub Flavored Markdown - # Note: mdformat-black is disabled because it fails on code blocks with: - # - Template placeholders (e.g., {module_name}) - # - Special symbols in comments (e.g., ✅ ❌) - # - Pseudo-code or incomplete Python snippets - # - mdformat-black # Black formatter for code blocks + name: mdformat + language: system + entry: mdformat args: [--wrap=100] - exclude: ^(CHANGELOG.md|\.github/|docs/dev-notes/|docs-public/docs_src/api-reference/) + types: [markdown] + exclude: ^(CHANGELOG.md|\.github/) - # Security: Check for secrets -- repo: https://github.com/Yelp/detect-secrets - rev: v1.5.0 + # Security: Check for secrets + # NOTE: Using local/system detect-secrets to avoid network download failures + # Requires: detect-secrets installed via pip +- repo: local hooks: - id: detect-secrets + name: detect-secrets + language: system + entry: detect-secrets-hook args: [--baseline, tools/config/secrets.baseline] - exclude: ^(tools/secrets\.baseline|\.env\.template|examples/config/.*\.yaml|tests/fixtures/|.*package-lock\.json$|.*\.ipynb$|docs/.*\.md$|docs-public/.*\.md$|examples/.*\.py$|packages/.*/src/sage/libs/integrations/.*\.py$|.*\.html$|packages/.*/src/.*\.md$|.*/benchmark_anns/|ThirdParty/|thirdparty/|.*/implementations/(SPTAG|faiss|DiskANN|diskann-ms|candy|puck|pybind11)/) + exclude: ^(tools/secrets\.baseline|\.env\.template|\.github/workflows/|examples/config/.*\.yaml|tests/fixtures/|.*package-lock\.json$|.*\.ipynb$|docs/.*\.md$|examples/.*\.py$|.*\.html$|.*/benchmark_anns/|ThirdParty/|thirdparty/|.*/implementations/(SPTAG|faiss|DiskANN|diskann-ms|candy|puck|pybind11)/) - # SAGE Data Architecture Validation - REMOVED (sage-benchmark is now independent) - # See: https://github.com/intellistream/sage-benchmark + # SAGE Data Architecture Validation - REMOVED (sage-benchmark is now independent) + # See: https://github.com/intellistream/sage-benchmark - repo: local hooks: - id: copilot-instructions-sync-check @@ -454,8 +376,8 @@ repos: entry: bash tools/hooks/check_copilot_instructions_sync.sh language: system pass_filenames: false - # Trigger on: doc files, installation scripts, CI/CD, config, ports, CLI, LLM/Gateway - files: ^(\.github/(copilot-instructions\.md|agents/my-agent\.agent\.md|workflows/.*\.yml)|quickstart\.sh|manage\.sh|Makefile|tools/(install/.*\.sh|pre-commit-config\.yaml|pytest\.ini|ruff\.toml)|packages/sage-common/src/sage/common/config/(ports|user_paths)\.py|packages/sage-cli/src/sage/cli/|packages/sage-gateway/src/sage/gateway/|packages/sage-common/src/sage/common/components/sage_(llm|embedding)/|packages/sage-[^/]+/pyproject\.toml|config/(config|cluster)\.yaml) + # Trigger on: doc files, installation scripts, CI/CD, config, ports, CLI, LLM/Gateway + files: ^(\.github/(copilot-instructions\.md|agents/my-agent\.agent\.md|workflows/.*\.yml)|quickstart\.sh|manage\.sh|Makefile|pyproject\.toml|setup\.py|tools/((config/)?pre-commit-config\.yaml|(config/)?ruff\.toml|install/.*\.sh)|config/(config|cluster)\.yaml) stages: [pre-commit, manual] description: Warn if critical project files change without updating copilot instructions # To install pre-commit hooks: diff --git a/tools/config/pytest.ini b/tools/config/pytest.ini index e68b800069..bc4e0ad0f9 100644 --- a/tools/config/pytest.ini +++ b/tools/config/pytest.ini @@ -12,22 +12,11 @@ cache_dir = .sage/cache/pytest # Configure pytest-benchmark to use .sage directory benchmark_storage = .sage/benchmarks -# Test discovery paths - only include package-level tests -# When pytest is run without arguments, only collect from these paths +# SAGE monorepo is now a pure meta package (isage). +# Per-package tests run in their own standalone repos for the remaining external adapters/tools. +# Cross-repo integration smoke tests are driven by .github/workflows/ci-integration.yml. testpaths = - packages/sage-kernel/tests - packages/sage-platform/tests - packages/sage-libs/tests - packages/sage-common/tests - packages/sage-middleware/tests - packages/sage-benchmark/tests - packages/sage-studio/tests - packages/sage-tools/tests - packages/sage-cli/tests - packages/sage-gateway/tests - packages/sage-apps/tests - # Submodule tests (explicitly included) - packages/sage-middleware/src/sage/middleware/components/sage_refiner/sageRefiner/tests + src # Exclude problematic paths from test collection norecursedirs = @@ -40,33 +29,11 @@ norecursedirs = .venv venv node_modules - _deps - vendors - sageLLM - .sage - # Exclude all src directories (tests should be in tests/ only) - src - # Exclude examples - examples - # Exclude ThirdParty directories - ThirdParty - thirdparty - # Exclude third-party algorithm implementations (SPTAG, DiskANN, Faiss, candy, puck, etc.) - SPTAG - DiskANN - diskann-ms - faiss - candy - puck - pybind11 - benchmark_db - algorithms_impl - tutorials - # Exclude .sage temp directory .sage # Add markers for test organization markers = + core: Core module tests unit: Unit tests integration: Integration tests slow: Slow running tests @@ -74,7 +41,10 @@ markers = external: Tests that require external services or APIs cuda: Requires CUDA llm: Requires LLM/API keys - ray: Requires Ray distributed framework + ray: Requires distributed runtime framework (legacy marker name) + release: Tests for release builds + develop: Tests for development builds + smoke: Smoke tests for basic functionality # Coverage options addopts = @@ -82,20 +52,6 @@ addopts = --tb=short --strict-markers --disable-warnings - # Ignore entire directories with problematic tests - --ignore=examples/tutorials/ - --ignore=packages/sage-common/src/sage/common/components/sage_llm/sageLLM/vendors/ - --ignore=packages/sage-common/src/sage/common/components/sage_llm/sageLLM/tests/ - # Ignore third-party algorithm implementation tests (DiskANN, Faiss, etc.) - --ignore-glob=**/benchmark_db/algorithms_impl/**/test_*.py - --ignore-glob=**/DiskANN/**/test_*.py - --ignore-glob=**/diskann-ms/**/test_*.py - --ignore-glob=**/faiss/**/test_*.py - --ignore-glob=**/ipdiskann/**/test_*.py - --ignore-glob=**/vsag/**/test_*.py - # Don't collect from package source directories (only from tests/) - --ignore-glob=**/src/**/*test*.py - # Ignore .sage temporary directory --ignore=.sage/ # Timeout for tests (in seconds) diff --git a/tools/config/ruff.toml b/tools/config/ruff.toml index ac5b73685c..d3129ed8af 100644 --- a/tools/config/ruff.toml +++ b/tools/config/ruff.toml @@ -6,23 +6,21 @@ # # 注意：ruff.toml 文件不使用 [tool.xxx] 前缀 # 只有 pyproject.toml 才需要 [tool.xxx] 前缀 +# +# Ruff 缓存配置： +# cache-dir 选项不能在配置文件中指定，请通过环境变量 RUFF_CACHE_DIR 设置 +# 推荐设置: export RUFF_CACHE_DIR=.sage/cache/ruff +# 或在项目根目录的 .env 文件中添加: RUFF_CACHE_DIR=.sage/cache/ruff # ============================================================================ # Ruff Configuration (替代 flake8, isort, pyupgrade 等) # ============================================================================ target-version = "py311" line-length = 100 -cache-dir = ".sage/cache/ruff" # 排除特定目录 extend-exclude = [ "vendors", - "sageLLM", - "sageVDB", - "sageFlow", - "neuromem", - "sageTSDB", - "libamm", "**/benchmark_libamm/scripts/**", # Jupyter notebooks with cell dependencies "**/benchmark_anns/DiskANN/**", "**/benchmark_anns/algorithms_impl/**", @@ -34,7 +32,7 @@ extend-exclude = [ "**/implementations/candy/**", "**/implementations/puck/**", "**/implementations/pybind11/**", - "**/benchmark_db/**", # git submodule + "**/benchmark_db/**", # benchmark data / external assets "build", "_deps", ".venv", diff --git a/tools/config/secrets.baseline b/tools/config/secrets.baseline index 06aec8eff0..9f750d4350 100644 --- a/tools/config/secrets.baseline +++ b/tools/config/secrets.baseline @@ -132,7 +132,6 @@ "\\.ipynb$", "secrets\\.baseline", "docs/.*\\.md$", - "docs-public/.*\\.md$", "examples/.*\\.py$", "\\.env\\.template$", ".*\\.html$", diff --git a/tools/config/setup_vscode_conda.sh b/tools/config/setup_vscode_conda.sh index 3494ba2460..0869727f12 100755 --- a/tools/config/setup_vscode_conda.sh +++ b/tools/config/setup_vscode_conda.sh @@ -149,7 +149,7 @@ cat > "$SETTINGS_FILE" << EOF "python.terminal.activateEnvironment": true, "terminal.integrated.env.linux": { "CONDA_DEFAULT_ENV": "$ENV_NAME", - "PYTHONPATH": "\${workspaceFolder}/packages/sage/src:\${workspaceFolder}/packages/sage-common/src:\${workspaceFolder}/packages/sage-kernel/src:\${workspaceFolder}/packages/sage-libs/src:\${workspaceFolder}/packages/sage-middleware/src:\${workspaceFolder}/packages/sage-platform/src:\${workspaceFolder}/packages/sage-apps/src:\${workspaceFolder}/packages/sage-studio/src:\${workspaceFolder}/packages/sage-tools/src:\${workspaceFolder}/packages/sage-cli/src:\${workspaceFolder}/packages/sage-benchmark/src:\${workspaceFolder}/packages/sage-gateway/src" + "PYTHONPATH": "\${workspaceFolder}/src" }, "terminal.integrated.profiles.linux": { "bash ($ENV_NAME)": { @@ -162,18 +162,7 @@ cat > "$SETTINGS_FILE" << EOF }, "terminal.integrated.defaultProfile.linux": "bash ($ENV_NAME)", "python.analysis.extraPaths": [ - "\${workspaceFolder}/packages/sage/src", - "\${workspaceFolder}/packages/sage-common/src", - "\${workspaceFolder}/packages/sage-kernel/src", - "\${workspaceFolder}/packages/sage-libs/src", - "\${workspaceFolder}/packages/sage-middleware/src", - "\${workspaceFolder}/packages/sage-platform/src", - "\${workspaceFolder}/packages/sage-apps/src", - "\${workspaceFolder}/packages/sage-studio/src", - "\${workspaceFolder}/packages/sage-tools/src", - "\${workspaceFolder}/packages/sage-cli/src", - "\${workspaceFolder}/packages/sage-benchmark/src", - "\${workspaceFolder}/packages/sage-gateway/src" + "\${workspaceFolder}/src" ], "python.testing.pytestEnabled": true, "python.testing.unittestEnabled": false, diff --git a/tools/dev/dev.sh b/tools/dev/dev.sh index 9c5ca67f05..090e4128cd 100755 --- a/tools/dev/dev.sh +++ b/tools/dev/dev.sh @@ -225,19 +225,9 @@ cmd_docs() { echo "继续使用旧命令..." echo "" - print_header "Building documentation" - cd docs-public - - if [ -f "build.sh" ]; then - ./build.sh - elif command -v mkdocs &> /dev/null; then - mkdocs build - else - print_error "mkdocs not found. Install with: pip install mkdocs mkdocs-material" - exit 1 - fi - - print_success "Documentation built" + print_header "Checking documentation" + bash tools/maintenance/check_docs.sh + print_success "Documentation check complete" } # Serve documentation @@ -248,15 +238,9 @@ cmd_serve_docs() { echo "继续使用旧命令..." echo "" - print_header "Serving documentation" - cd docs-public - - if command -v mkdocs &> /dev/null; then - mkdocs serve - else - print_error "mkdocs not found. Install with: pip install mkdocs mkdocs-material" - exit 1 - fi + print_header "Documentation serving unavailable" + print_info "SAGE meta 仓库已不再内置独立文档站点" + print_info "请直接查看 docs/、README.md 和各独立仓库文档" } # Run full validation diff --git a/tools/dev/generate_interface_layer.sh b/tools/dev/generate_interface_layer.sh index 9542ed8c63..804752bafd 100755 --- a/tools/dev/generate_interface_layer.sh +++ b/tools/dev/generate_interface_layer.sh @@ -11,7 +11,7 @@ fi MODULE_NAME="$1" SAGE_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)" -INTERFACE_DIR="$SAGE_ROOT/packages/sage-libs/src/sage/libs/$MODULE_NAME/interface" +INTERFACE_DIR="$SAGE_ROOT/src/sage/$MODULE_NAME/interface" echo "🏗️ 为 $MODULE_NAME 生成接口层模板" echo "================================" @@ -200,7 +200,7 @@ sed -i "s/{MODULE_NAME}/$MODULE_NAME/g" "$INTERFACE_DIR/factory.py" sed -i "s/{MODULE_NAME_UPPER}/$MODULE_NAME_UPPER/g" "$INTERFACE_DIR/factory.py" # 4. 更新父级 __init__.py -PARENT_INIT="$SAGE_ROOT/packages/sage-libs/src/sage/libs/$MODULE_NAME/__init__.py" +PARENT_INIT="$SAGE_ROOT/src/sage/$MODULE_NAME/__init__.py" if [ ! -f "$PARENT_INIT" ]; then echo "📝 创建 $MODULE_NAME/__init__.py..." cat > "$PARENT_INIT" << 'EOF' @@ -214,7 +214,7 @@ Installation: Usage: # Use the interface layer - from sage.libs.{MODULE_NAME}.interface import create, register + from sage.{MODULE_NAME}.interface import create, register # Or import from external package from isage_{MODULE_NAME} import * @@ -253,4 +253,4 @@ echo " 2. 编辑 factory.py - 如果需要自定义注册逻辑" echo " 3. 在 isage-$MODULE_NAME 中实现具体类并注册" echo "" echo "📖 参考示例：" -echo " packages/sage-libs/src/sage/libs/anns/interface/" +echo " src/sage/anns/interface/" diff --git a/tools/dev/security-quick-reference.sh b/tools/dev/security-quick-reference.sh index 91b8cd795d..b26e42b4f6 100644 --- a/tools/dev/security-quick-reference.sh +++ b/tools/dev/security-quick-reference.sh @@ -7,8 +7,7 @@ # 方式 1: 标准安全安装（带深度验证） echo "📦 标准安全安装" -# python3 -m venv sage-env -# source sage-env/bin/activate +# conda activate sage # ./quickstart.sh --verify-deps --standard # ============================================================================ diff --git a/tools/docs/SUBMODULE_DEVELOPMENT.md b/tools/docs/SUBMODULE_DEVELOPMENT.md deleted file mode 100644 index 62af43b192..0000000000 --- a/tools/docs/SUBMODULE_DEVELOPMENT.md +++ /dev/null @@ -1,357 +0,0 @@ -# 子模块开发指南 - -## 概述 - -SAGE 项目中的子模块（sageLLM, sageVDB, sageFlow, neuromem, sageTSDB, sageRefiner）都是独立的 Git -仓库，有自己的开发工具链和测试框架。 - -## 子模块列表 - -| 子模块 | 类型 | 路径 | 描述 | -| --------------- | ------ | ---------------------------------------------------------------------------------- | ----------------- | -| **sageLLM** | Python | `packages/sage-llm-core/src/sage/llm/sageLLM` | LLM Control Plane | -| **sageVDB** | C++ | `packages/sage-middleware/src/sage/middleware/components/sage_db/sageVDB` | 向量数据库 | -| **sageFlow** | C++ | `packages/sage-middleware/src/sage/middleware/components/sage_flow/sageFlow` | 流处理引擎 | -| **neuromem** | Python | `packages/sage-middleware/src/sage/middleware/components/sage_mem/neuromem` | 记忆系统 | -| **sageTSDB** | C++ | `packages/sage-middleware/src/sage/middleware/components/sage_tsdb/sageTSDB` | 时序数据库 | -| **sageRefiner** | Python | `packages/sage-middleware/src/sage/middleware/components/sage_refiner/sageRefiner` | 上下文压缩 | - -## 开发工具配置 - -每个子模块都有独立的开发工具配置： - -### Pre-commit Hooks - -**C++ 子模块** (sageVDB, sageFlow, sageTSDB): - -- `clang-format` - C++ 代码格式化 -- `cmake-format` - CMake 格式化 -- `cmake-lint` - CMake 检查 -- `ruff` - Python 绑定代码格式化 -- 通用检查（trailing-whitespace, end-of-file, yaml/json/toml） - -**Python 子模块** (sageLLM, neuromem, sageRefiner): - -- `ruff` - Python 代码格式化和 lint -- `mypy` - 类型检查 -- 通用检查（trailing-whitespace, end-of-file, yaml/json/toml） - -**所有子模块**: - -- `shellcheck` - Shell 脚本检查 -- `mdformat` - Markdown 格式化 -- `detect-secrets` - 敏感信息检测 - -### Pytest 配置 - -所有子模块都有 `pytest.ini` 配置： - -- 测试发现模式 -- 异步测试支持 -- 日志配置 -- 测试标记（unit, integration, slow, gpu, cpp） -- 覆盖率报告 - -## 开发工作流 - -### 1. 初始化子模块 - -```bash -# 克隆主项目后，初始化子模块 -./manage.sh - -# 或手动初始化 -git submodule update --init --recursive -``` - -### 2. 在子模块中开发 - -```bash -# 进入子模块目录 -cd packages/sage-middleware/src/sage/middleware/components/sage_db/sageVDB - -# 安装 pre-commit hooks -pre-commit install - -# 运行所有检查 -pre-commit run --all-files - -# 修改代码... - -# 运行测试 -pytest - -# 提交更改（会自动运行 pre-commit hooks） -git add . -git commit -m "feat: add new feature" - -# 推送到子模块远端 -git push -``` - -### 3. 更新主项目的子模块引用 - -```bash -# 回到主项目根目录 -cd /path/to/SAGE - -# 更新子模块引用 -git add packages/sage-middleware/src/sage/middleware/components/sage_db/sageVDB - -# 提交主项目 -git commit -m "chore: update sageVDB submodule" -``` - -## 常见任务 - -### 运行测试 - -```bash -# 在子模块目录中 -cd packages/sage-llm-core/src/sage/llm/sageLLM - -# 运行所有测试 -pytest - -# 运行特定标记的测试 -pytest -m unit # 只运行单元测试 -pytest -m "not slow" # 跳过慢速测试 -pytest -m gpu # 只运行 GPU 测试 - -# 运行特定文件 -pytest tests/test_manager.py - -# 带覆盖率 -pytest --cov=. --cov-report=html -``` - -### 代码格式化 - -```bash -# C++ 子模块 -clang-format -i src/*.cpp include/*.h - -# Python 子模块 -ruff format . - -# 或使用 pre-commit -pre-commit run --all-files -``` - -### 类型检查 - -```bash -# Python 子模块 -mypy . - -# 或通过 pre-commit -pre-commit run mypy --all-files -``` - -## 添加新的测试 - -### 测试文件结构 - -``` -submodule/ -├── tests/ -│ ├── __init__.py -│ ├── unit/ # 单元测试 -│ │ ├── test_core.py -│ │ └── test_utils.py -│ ├── integration/ # 集成测试 -│ │ └── test_pipeline.py -│ └── fixtures/ # 测试数据和 fixtures -│ └── conftest.py -├── pytest.ini -└── .pre-commit-config.yaml -``` - -### 测试示例 - -```python -# tests/unit/test_example.py -import pytest - -@pytest.mark.unit -def test_basic_function(): - """Basic unit test""" - assert True - -@pytest.mark.integration -@pytest.mark.slow -def test_slow_integration(): - """Slow integration test""" - # ... - -@pytest.mark.gpu -def test_gpu_feature(): - """Test requiring GPU""" - # ... - -@pytest.mark.cpp -def test_cpp_binding(): - """Test for C++ Python bindings""" - # ... -``` - -## 添加新的 Pre-commit Hook - -编辑子模块的 `.pre-commit-config.yaml`: - -```yaml -repos: - # 添加新的 hook -- repo: https://github.com/example/hook - rev: v1.0.0 - hooks: - - id: my-hook - args: [--option] -``` - -然后运行： - -```bash -pre-commit install -pre-commit run my-hook --all-files -``` - -## CI/CD 集成 - -### 子模块 CI - -每个子模块应该有自己的 CI 配置（`.github/workflows/ci.yml`）： - -```yaml -name: CI - -on: [push, pull_request] - -jobs: - test: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: '3.11' - - - name: Install dependencies - run: | - pip install pre-commit pytest pytest-cov - - - name: Run pre-commit - run: pre-commit run --all-files - - - name: Run tests - run: pytest --cov -``` - -### 主项目 CI - -主项目的 CI 会运行所有子模块的测试： - -```bash -# 在 .github/workflows/build-test.yml 中 -./quickstart.sh --dev --yes -sage-dev project test --coverage -``` - -## 故障排除 - -### Pre-commit Hook 失败 - -```bash -# 跳过 hooks 提交（紧急情况） -git commit --no-verify - -# 只运行特定 hook -pre-commit run ruff --all-files - -# 更新 hooks 到最新版本 -pre-commit autoupdate -``` - -### 测试失败 - -```bash -# 详细输出 -pytest -vv - -# 停在第一个失败 -pytest -x - -# 显示本地变量 -pytest -l - -# 进入调试器 -pytest --pdb -``` - -### 子模块同步问题 - -```bash -# 确保子模块在正确的分支 -cd submodule -git checkout main -git pull - -# 回到主项目更新引用 -cd /path/to/SAGE -git add submodule -git commit -m "chore: update submodule" -``` - -## 最佳实践 - -1. **独立开发**: 每个子模块应该能够独立开发、测试和发布 -1. **文档同步**: 子模块的文档应该与代码在一起 -1. **版本管理**: 使用语义化版本（semver） -1. **测试覆盖**: 保持高测试覆盖率（>80%） -1. **代码质量**: 所有提交前必须通过 pre-commit hooks -1. **向后兼容**: API 变更要考虑向后兼容性 -1. **变更日志**: 维护 CHANGELOG.md 记录重要变更 - -## 相关文档 - -- [主项目开发指南](../../DEVELOPER.md) -- [文档位置策略](../../.github/copilot-instructions.md#documentation-location-policy---critical) -- [Pre-commit 官方文档](https://pre-commit.com/) -- [Pytest 官方文档](https://docs.pytest.org/) - -## 自动化脚本 - -### 批量更新子模块 - -```bash -# 更新所有子模块到最新版本 -./tools/maintenance/sage-maintenance.sh submodule update - -# 切换所有子模块到主分支 -./tools/maintenance/sage-maintenance.sh submodule switch -``` - -### 批量安装 Pre-commit Hooks - -```bash -# 为所有子模块安装 hooks -for submodule in packages/*/src/*/*/; do - if [ -f "$submodule/.pre-commit-config.yaml" ]; then - (cd "$submodule" && pre-commit install) - fi -done -``` - -### 批量运行测试 - -```bash -# 运行所有子模块的测试 -for submodule in packages/*/src/*/*/; do - if [ -f "$submodule/pytest.ini" ]; then - echo "Testing $(basename $submodule)..." - (cd "$submodule" && pytest) - fi -done -``` diff --git a/tools/docs/install/fixes/FIX_SUMMARY.md b/tools/docs/install/fixes/FIX_SUMMARY.md deleted file mode 100644 index ade788804d..0000000000 --- a/tools/docs/install/fixes/FIX_SUMMARY.md +++ /dev/null @@ -1,284 +0,0 @@ -# Environment Doctor 修复总结 - -## 修复历史 - -### 第一轮：Unbound Variable 错误 (2026-01-01) - -**问题**：`AUTO_CONFIRM_FIX: unbound variable` - -**原因**：脚本使用了未初始化的环境变量，在某些 shell 模式下会导致脚本崩溃。 - -**修复**： - -- 移除 `set -u`（过于严格） -- 在脚本开头显式初始化所有变量 -- 使用 `${VAR:-default}` 语法提供默认值 - -**影响的变量**： - -- `AUTO_CONFIRM_FIX` -- `CI`, `GITHUB_ACTIONS` -- `VIRTUAL_ENV`, `CONDA_DEFAULT_ENV`, `CONDA_PREFIX` -- `HOME` - -### 第二轮：开发工具安装误报 (2026-01-01) - -**问题**：已安装的包仍显示"安装失败" - -**症状**： - -```bash -安装: pytest... -⚠ pytest 安装失败，继续... -... -✓ pytest 9.0.2 已就绪 # 实际上已安装 -``` - -**根本原因**： - -1. **管道问题**： - - ```bash - # ❌ 错误 - pip 输出被 grep 截断 - $pip_cmd install -r file.txt 2>&1 | grep -E "pattern" >/dev/null - ``` - - - pip 的输出被管道传给 grep - - grep 匹配成功后立即返回，pip 可能未完成 - - 返回状态不可靠 - -1. **状态检查不完整**： - - - 没有先捕获完整的 pip 输出 - - 没有检查 pip 的实际退出码 - - 只依赖 grep 的匹配结果 - -**修复方案**： - -```bash -# ✅ 正确方式 -# 1. 先完整执行 pip 命令并捕获输出 -local install_output=$($pip_cmd install -r file.txt $pip_args 2>&1) -local install_status=$? - -# 2. 检查退出码 AND 输出内容 -if [ $install_status -eq 0 ] || echo "$install_output" | grep -qE "(Successfully installed|Requirement already satisfied)"; then - echo "安装成功" -fi -``` - -**额外改进**： - -1. **预检查已安装的包**： - - ```bash - # 跳过已安装的包，避免不必要的 pip 调用 - if python3 -c "import ${tool_name//-/_}" >/dev/null 2>&1; then - echo "已安装" - continue - fi - ``` - -1. **统计成功率**： - - ```bash - local install_success_count=0 - local install_total=${#core_tools[@]} - # ... 安装逻辑 ... - echo "成功: $install_success_count/$install_total" - ``` - -1. **详细错误信息**： - - ```bash - else - echo "安装失败: $install_output" # 显示具体错误 - log_message "WARN" "Failed to install $tool_name: $install_output" - fi - ``` - -## 测试验证 - -### 测试套件 - -创建了 `test_environment_doctor.sh`，包含 7 个测试用例： - -1. ✅ 帮助信息测试 -1. ✅ 仅检查模式测试 -1. ✅ 完整诊断测试 -1. ✅ 环境变量安全性测试 -1. ✅ CI 环境模拟测试 -1. ✅ Conda 环境模拟测试 -1. ✅ 虚拟环境模拟测试 - -### 验证方法 - -```bash -# 运行测试套件 -bash tools/install/fixes/test_environment_doctor.sh - -# 手动测试（模拟用户场景） -echo "Y" | bash tools/install/fixes/environment_doctor.sh - -# 在不同环境中测试 -env -i bash tools/install/fixes/environment_doctor.sh --help # 空环境 -CI=true bash tools/install/fixes/environment_doctor.sh --check-only # CI 环境 -``` - -## 关键经验教训 - -### 1. 避免管道截断 - -**错误模式**： - -```bash -command 2>&1 | grep "pattern" && action -``` - -**问题**： - -- grep 匹配后立即返回，command 可能未完成 -- 无法获取 command 的真实退出码 -- 输出被截断，丢失重要信息 - -**正确模式**： - -```bash -output=$(command 2>&1) -status=$? -if [ $status -eq 0 ] || echo "$output" | grep -q "pattern"; then - action -fi -``` - -### 2. 状态检查要全面 - -应该检查： - -- ✅ 命令退出码（`$?`） -- ✅ 输出内容（成功/失败消息） -- ✅ 最终状态（文件存在、模块可导入等） - -### 3. 错误信息要详细 - -**差**： - -```bash -echo "安装失败" # 用户不知道为什么 -``` - -**好**： - -```bash -echo "安装失败: $error_message" # 显示具体错误 -log_message "ERROR" "Details: $full_output" # 记录完整日志 -``` - -### 4. 变量初始化是必须的 - -**关键原则**： - -- 所有脚本级别的变量都应该在开头初始化 -- 使用 `${VAR:-default}` 处理环境变量 -- 不要盲目使用 `set -u`（太严格，难以维护） - -### 5. 先检查再操作 - -**优化前**： - -```bash -pip install package # 总是尝试安装 -``` - -**优化后**： - -```bash -if python3 -c "import package" >/dev/null 2>&1; then - echo "已安装，跳过" - return 0 -fi -pip install package # 只在需要时安装 -``` - -## 最佳实践总结 - -### Bash 脚本开发 - -1. **变量管理**： - - - 开头初始化所有全局变量 - - 使用 `local` 声明函数内变量 - - 使用 `${VAR:-default}` 处理可选变量 - -1. **命令执行**： - - - 先捕获输出，再处理结果 - - 保存退出码（`$?`）以便后续检查 - - 避免在管道中丢失状态信息 - -1. **错误处理**： - - - 提供详细的错误信息 - - 记录日志供调试 - - 区分不同严重级别的问题 - -1. **测试**： - - - 编写自动化测试脚本 - - 测试各种环境（空环境、CI、虚拟环境） - - 测试边界情况（已安装、网络失败等） - -### Python 包安装 - -1. **检查流程**： - - ```bash - # 1. 检查是否已安装（最快） - python3 -c "import package" && return 0 - - # 2. 尝试安装 - output=$(pip install package 2>&1) - status=$? - - # 3. 验证结果 - if [ $status -eq 0 ] || echo "$output" | grep -q "Success"; then - # 最终验证 - python3 -c "import package" || return 1 - return 0 - fi - ``` - -1. **错误恢复**： - - - 提供清晰的错误消息 - - 给出手动修复步骤 - - 记录详细日志供排查 - -## 文件清单 - -- `environment_doctor.sh` - 主诊断脚本（已修复） -- `test_environment_doctor.sh` - 自动化测试套件 -- `UNBOUND_VARIABLE_FIX.md` - 详细修复记录 -- `FIX_SUMMARY.md` - 本文件（总结） - -## 后续建议 - -1. **CI 集成**： - - - 在 CI/CD 流程中运行 `test_environment_doctor.sh` - - 确保所有修改都通过测试 - -1. **代码审查**： - - - 新的 Bash 脚本应遵循本文档的最佳实践 - - 避免重复相同的错误模式 - -1. **文档更新**： - - - 在 DEVELOPER.md 中添加脚本开发指南 - - 记录常见的 Bash 陷阱和解决方案 - -1. **工具改进**： - - - 考虑添加 shellcheck 到 pre-commit hooks - - 提供脚本开发模板 diff --git a/tools/docs/install/fixes/UNBOUND_VARIABLE_FIX.md b/tools/docs/install/fixes/UNBOUND_VARIABLE_FIX.md deleted file mode 100644 index b45238ae4c..0000000000 --- a/tools/docs/install/fixes/UNBOUND_VARIABLE_FIX.md +++ /dev/null @@ -1,156 +0,0 @@ -# Environment Doctor - Unbound Variable 修复说明 - -## 问题描述 - -在运行 `./quickstart.sh --doctor` 时，遇到了 `unbound variable` 错误： - -```bash -/home/shuhao/SAGE/tools/install/fixes/environment_doctor.sh: line 921: AUTO_CONFIRM_FIX: unbound variable -``` - -## 根本原因 - -Bash 脚本在使用 `set -u` 或通过其他方式启用严格模式时，访问未定义的变量会导致脚本崩溃。`environment_doctor.sh` 中存在多个未初始化的变量，包括： - -1. `AUTO_CONFIRM_FIX` - 用于控制是否自动确认修复操作 -1. `CI`, `GITHUB_ACTIONS` - CI/CD 环境检测变量 -1. `VIRTUAL_ENV`, `CONDA_DEFAULT_ENV`, `CONDA_PREFIX` - 虚拟环境检测变量 -1. 其他可能未设置的环境变量 - -## 解决方案 - -### 1. 全局变量初始化 - -在脚本开头添加了完整的变量初始化块： - -```bash -# ================================ -# 全局变量初始化 -# ================================ -# 项目路径相关 -SAGE_DIR="${SAGE_DIR:-$(pwd)/.sage}" -DOCTOR_LOG="$SAGE_DIR/logs/environment_doctor.log" - -# 计数器 -ISSUES_FOUND=0 -FIXES_APPLIED=0 -CRITICAL_ISSUES=0 -NEED_RESTART_SHELL=0 - -# 配置选项（支持通过环境变量传入） -AUTO_CONFIRM_FIX="${AUTO_CONFIRM_FIX:-false}" - -# 环境变量安全默认值（避免 unbound variable 错误） -CI="${CI:-}" -GITHUB_ACTIONS="${GITHUB_ACTIONS:-}" -VIRTUAL_ENV="${VIRTUAL_ENV:-}" -CONDA_DEFAULT_ENV="${CONDA_DEFAULT_ENV:-}" -CONDA_PREFIX="${CONDA_PREFIX:-}" -HOME="${HOME:-$(/usr/bin/env | grep ^HOME= | cut -d= -f2)}" -``` - -### 2. 使用 `${VAR:-default}` 模式 - -- **不使用** `set -u`，因为我们需要灵活处理环境变量 -- 通过 `${VAR:-default}` 语法为所有变量提供安全默认值 -- 确保即使环境变量未设置，脚本也能正常运行 - -### 3. 测试覆盖 - -创建了 `test_environment_doctor.sh` 测试套件，包括： - -1. ✅ 帮助信息测试 -1. ✅ 仅检查模式测试 -1. ✅ 完整诊断测试 -1. ✅ 环境变量安全性测试 -1. ✅ CI 环境模拟测试 -1. ✅ Conda 环境模拟测试 -1. ✅ 虚拟环境模拟测试 - -所有测试均已通过。 - -## 验证 - -### 运行诊断（无错误） - -```bash -./quickstart.sh --doctor -``` - -### 运行测试套件 - -```bash -bash tools/install/fixes/test_environment_doctor.sh -``` - -### 手动测试各种场景 - -```bash -# 测试帮助信息 -bash tools/install/fixes/environment_doctor.sh --help - -# 测试仅检查模式 -bash tools/install/fixes/environment_doctor.sh --check-only - -# 测试 CI 环境 -CI=true GITHUB_ACTIONS=true bash tools/install/fixes/environment_doctor.sh --check-only - -# 测试无环境变量场景 -env -i bash tools/install/fixes/environment_doctor.sh --help -``` - -## 影响范围 - -- ✅ `tools/install/fixes/environment_doctor.sh` - 主脚本 -- ✅ `tools/install/fixes/test_environment_doctor.sh` - 测试脚本（新增） - -## 后续建议 - -1. **Pre-commit Hook**: 添加 shellcheck 检查，防止类似问题再次发生 -1. **CI 测试**: 在 CI/CD 流程中运行 `test_environment_doctor.sh` -1. **文档更新**: 在 DEVELOPER.md 中添加脚本开发最佳实践 - -## 最佳实践 - -对于所有 Bash 脚本，应遵循以下原则： - -1. **变量初始化**: 在脚本开头初始化所有全局变量 -1. **安全访问**: 使用 `${VAR:-default}` 语法访问环境变量 -1. **测试覆盖**: 为关键脚本编写测试用例 -1. **错误处理**: 不盲目使用 `set -u`，而是有针对性地进行变量检查 -1. **文档说明**: 在脚本中添加注释说明变量的用途和默认值 - -## 修复时间 - -- 修复日期: 2026-01-01 -- 修复人: GitHub Copilot -- 测试状态: ✅ 全部通过 - -## 后续修复 (2026-01-01 - 第二轮) - -### 问题：开发工具安装误报失败 - -用户报告虽然工具实际已安装，但诊断脚本仍显示"安装失败"。 - -**根本原因**： - -1. 从外部依赖文件安装时，使用了管道 `| grep`，导致 pip 命令可能被提前终止 -1. grep 匹配成功后立即返回，但实际安装状态未正确捕获 - -**修复方案**： - -```bash -# ❌ 错误方式 - pip 输出被管道截断 -if $pip_cmd install -r file.txt 2>&1 | grep -E "pattern" >/dev/null; then - -# ✅ 正确方式 - 先捕获完整输出，再检查 -local install_output=$($pip_cmd install -r file.txt 2>&1) -local install_status=$? -if [ $install_status -eq 0 ] || echo "$install_output" | grep -qE "pattern"; then -``` - -**验证**： - -- ✅ 已安装的包正确识别为"已安装" -- ✅ 从外部依赖文件安装成功时显示正确消息 -- ✅ 逐个安装时正确统计成功/失败数量 diff --git a/tools/docs/scripts/LIBAMM_MIGRATION_QUICKREF.md b/tools/docs/scripts/LIBAMM_MIGRATION_QUICKREF.md deleted file mode 100644 index b1a8f89db9..0000000000 --- a/tools/docs/scripts/LIBAMM_MIGRATION_QUICKREF.md +++ /dev/null @@ -1,101 +0,0 @@ -# LibAMM 迁移 - 快速参考 - -> **⚠️ DEPRECATED**: The `sage-dev package pypi` command has been removed. Please use the standalone -> [wheelwright](https://github.com/intellistream/wheelwright) tool instead. -> -> **Migration**: -> -> ```bash -> git clone https://github.com/intellistream/wheelwright.git -> cd wheelwright -> ./publish.sh --auto-bump patch -> ``` - -## 🎯 一句话总结 - -将 libamm 从 SAGE 的 git submodule 迁移到独立的 PyPI 包，通过依赖关系自动安装。 - -## ✅ 前提条件 - -```bash -# 1. 确认 isage-libamm 在 PyPI 上可用 -pip index versions isage-libamm - -# 2. 测试安装 -pip install isage-libamm -python -c "import PyAMM; print('OK')" -``` - -## 🚀 执行迁移（3 步） - -### 步骤 1：运行自动脚本 - -```bash -cd /home/shuhao/SAGE -./tools/scripts/remove_libamm_submodule.sh -``` - -### 步骤 2：提交更改 - -```bash -git status -git commit -m "refactor: remove libamm submodule, use PyPI dependency" -``` - -### 步骤 3：发布新版本 - -```bash -# 编辑版本号 → 0.2.1 -vim packages/sage-libs/src/sage/libs/_version.py - -# 清理并重新构建 -rm -rf ~/.sage/dist/sage-libs -sage-dev package pypi build sage-libs --upload --no-dry-run -``` - -## 🔍 验证 - -```bash -# 创建干净环境测试 -python -m venv /tmp/test -source /tmp/test/bin/activate -pip install isage-libs==0.2.1 -python -c "import PyAMM; print('✅ Success')" -deactivate && rm -rf /tmp/test -``` - -## 📂 文件位置 - -- **自动脚本**：`tools/scripts/remove_libamm_submodule.sh` -- **详细指南**：`docs-public/docs_src/dev-notes/cross-layer/libamm-migration-guide.md` -- **备份位置**：`/tmp/sage-libamm-backup-/`（脚本会显示） - -## ⏪ 快速回滚 - -```bash -git revert HEAD # 回退提交 -# 或从备份恢复（见脚本输出的备份路径） -``` - -## 📞 问题排查 - -1. **PyPI 找不到 isage-libamm** → 先上传 libamm 到 PyPI -1. **脚本失败** → 检查 git 状态，手动执行脚本中的命令 -1. **安装失败** → 检查 pyproject.toml 依赖配置 - -## 🎓 核心原理 - -``` -用户安装：pip install isage-libs - ↓ - 自动安装：isage-libamm (预编译 wheel) - ↓ - 可用：import PyAMM -``` - -## 📊 效果 - -- ✅ SAGE 仓库更小、更快 -- ✅ 无需管理 submodule -- ✅ libamm 独立迭代 -- ✅ 用户体验不变 diff --git a/tools/docs/scripts/README_CLUSTER_SETUP.md b/tools/docs/scripts/README_CLUSTER_SETUP.md deleted file mode 100644 index e25d9f2a94..0000000000 --- a/tools/docs/scripts/README_CLUSTER_SETUP.md +++ /dev/null @@ -1,238 +0,0 @@ -# SAGE Cluster 快速配置指南 - -## 🚀 功能概览 - -本指南帮助你快速配置 SAGE 集群环境，包括： - -1. **SSH 免密登录配置** - 自动配置 sage2, sage3, sage4 的免密登录 -1. **Ray 版本自动同步** - 启动 worker 前自动检查并同步 Ray 版本 - -## 📋 前提条件 - -### 主机配置 - -- **Head 节点**: 当前机器 -- **Worker 节点**: sage2, sage3, sage4 -- **用户名**: sage -- **密码**: 123 - -### 网络要求 - -- 所有节点可互相访问 -- SSH 端口开放（默认 22） - -## 🔧 步骤 1: 配置 SSH 免密登录 - -### 自动配置（推荐） - -```bash -cd /home/lpl/SAGE_dev -./tools/scripts/setup_ssh_keys.sh -``` - -脚本会自动： - -1. 检查并安装 `sshpass` 工具 -1. 生成 SSH 密钥对（如果不存在） -1. 将公钥复制到所有 worker 节点 -1. 验证免密登录 - -### 手动配置 - -如果自动脚本失败，可以手动配置： - -```bash -# 1. 生成 SSH 密钥（如果没有） -ssh-keygen -t rsa -b 4096 - -# 2. 为每个 worker 节点复制公钥 -ssh-copy-id sage@sage2 -ssh-copy-id sage@sage3 -ssh-copy-id sage@sage4 - -# 3. 验证 -ssh sage@sage2 'hostname' -ssh sage@sage3 'hostname' -ssh sage@sage4 'hostname' -``` - -## ✅ 步骤 2: 启动集群 - -配置完 SSH 免密登录后，启动集群： - -```bash -sage cluster start -``` - -启动过程会自动： - -1. 检查每个 worker 节点的 Ray 版本 -1. 如果版本不一致，提示是否升级 -1. 自动安装匹配的 Ray 版本 -1. 启动所有节点 - -### 示例输出 - -``` -🚀 启动Ray集群... -第1步: 启动Head节点 -✅ Head节点启动成功 - -⏳ 等待Head节点完全启动... - -第2步: 启动所有Worker节点 -🚀 启动Ray Worker节点... - -🔍 检查 Ray 版本一致性... -✅ sage2: Ray 版本一致 (2.9.0) -⚠️ sage3: Ray 版本不一致 - 本地版本: 2.9.0 - 远程版本: 2.8.0 -是否将 sage3 的 Ray 升级到 2.9.0? [Y/n]: y -📦 在 sage3 上安装 Ray 2.9.0... -✅ 安装成功 - -✅ sage4: Ray 版本一致 (2.9.0) - -🔧 启动Worker节点 1/3: sage2:22 (IP: 192.168.1.2) -✅ Worker节点启动成功 - -🔧 启动Worker节点 2/3: sage3:22 (IP: 192.168.1.3) -✅ Worker节点启动成功 - -🔧 启动Worker节点 3/3: sage4:22 (IP: 192.168.1.4) -✅ Worker节点启动成功 - -✅ Ray集群启动完成！ -``` - -## 🛠️ 常用命令 - -### 集群管理 - -```bash -# 启动集群 -sage cluster start - -# 停止集群 -sage cluster stop - -# 重启集群 -sage cluster restart - -# 查看集群状态 -sage cluster status - -# 查看集群配置 -sage cluster info -``` - -### 单独管理 Worker - -```bash -# 启动所有 worker -sage cluster worker start - -# 停止所有 worker -sage cluster worker stop - -# 查看 worker 状态 -sage cluster worker status -``` - -### Head 节点管理 - -```bash -# 启动 head 节点 -sage cluster head start - -# 停止 head 节点 -sage cluster head stop - -# 查看 head 节点状态 -sage cluster head status -``` - -## 🔍 故障排查 - -### SSH 连接失败 - -```bash -# 测试 SSH 连接 -ssh sage@sage2 'echo "Connection OK"' - -# 检查 SSH 密钥权限 -chmod 600 ~/.ssh/id_rsa -chmod 644 ~/.ssh/id_rsa.pub - -# 检查 SSH 服务 -ssh sage@sage2 'systemctl status sshd' -``` - -### Ray 版本不匹配 - -```bash -# 查看本地 Ray 版本 -ray --version - -# 查看远程 Ray 版本 -ssh sage@sage2 'conda activate sage && ray --version' - -# 手动升级远程 Ray -ssh sage@sage2 'conda activate sage && pip install ray==2.9.0' -``` - -### Worker 启动失败 - -```bash -# 查看 worker 日志 -ssh sage@sage2 'cat /tmp/sage_worker_logs/worker.log' - -# 手动启动 worker（调试） -ssh sage@sage2 'conda activate sage && ray start --address=:6379' - -# 停止卡住的 Ray 进程 -ssh sage@sage2 'ray stop' -``` - -## 📝 配置文件 - -集群配置文件位于 `~/.sage/cluster_config.yaml` - -```yaml -head: - host: localhost - head_port: 6379 - dashboard_port: 8265 - -workers: - - host: sage2 - port: 22 - - host: sage3 - port: 22 - - host: sage4 - port: 22 - -ssh: - user: sage - key_path: ~/.ssh/id_rsa - connect_timeout: 10 - -remote: - conda_env: sage - ray_command: ray -``` - -## 🎯 最佳实践 - -1. **首次使用**：先配置 SSH 免密登录 -1. **版本同步**：保持所有节点的 Ray 版本一致 -1. **日志查看**：定期检查 `/tmp/sage_worker_logs/worker.log` -1. **资源监控**：通过 Ray Dashboard 监控集群状态（http://localhost:8265） -1. **清理进程**：出现问题时使用 `sage cluster stop` 完全清理 - -## 📚 更多资源 - -- Ray 文档: https://docs.ray.io/ -- SAGE 文档: /home/lpl/SAGE_dev/README.md -- 集群管理脚本: /home/lpl/SAGE_dev/tools/scripts/ diff --git a/tools/git-tools/README.md b/tools/git-tools/README.md index 39996443ee..0ae6c612ef 100644 --- a/tools/git-tools/README.md +++ b/tools/git-tools/README.md @@ -61,7 +61,7 @@ SAGE 项目使用多个 Git 子模块来组织代码。这些工具帮助： **输出**: -``` +```text 📝 处理子模块: sageData 路径: packages/sage-benchmark/src/sage/data 仓库: sageData @@ -241,16 +241,14 @@ git diff ### 子模块列表 -当前 SAGE 项目的子模块: +当前 SAGE 项目的历史子模块/外部能力来源: -1. `docs-public` - 公共文档 -1. `packages/sage-llm-core/src/sage/llm` - LLM 组件 -1. `packages/sage-middleware/src/sage/middleware/components/sage_db/sageVDB` - 数据库组件 -1. `packages/sage-middleware/src/sage/middleware/components/sage_flow/sageFlow` - 流处理组件 -1. `packages/sage-middleware/src/sage/middleware/components/sage_mem/neuromem` - 内存组件 -1. `packages/sage-middleware/src/sage/middleware/components/sage_tsdb/sageTSDB` - 时序数据库 -1. `packages/sage-benchmark/src/sage/data` - 数据集 -1. `packages/sage-libs/src/sage/libs/libamm` - LibAMM 库 +1. `isagellm` / `sagellm` - 外部推理与网关能力 +1. `isage-vdb` - 外部向量数据库组件 +1. `isage-flow` / `flutty` - 外部分布式/流运行时能力 +1. `isage-neuromem` - 外部记忆组件 +1. `sage-benchmark` / `isage-data` - 数据与基准仓库 +1. 其他独立适配器仓库 - 按 capability adapter 方式接入 ## 📚 相关文档 diff --git a/tools/hooks/README.md b/tools/hooks/README.md deleted file mode 100644 index cbf20b4306..0000000000 --- a/tools/hooks/README.md +++ /dev/null @@ -1,138 +0,0 @@ -# SAGE Git Hooks - -This directory contains Git hook templates for SAGE development workflows. - -## Available Hooks - -### post-commit.sample - Auto PyPI Publisher - -Automatically publishes affected packages to PyPI after each commit. - -**Features**: - -- 🔍 Detects modified packages automatically -- 📦 Publishes to PyPI with version auto-increment -- 🧪 Optional TestPyPI validation before production -- 🔒 Branch and confirmation controls -- ⚙️ Fully configurable - -**Installation**: - -```bash -# Copy to .git/hooks/ -cp tools/hooks/post-commit.sample .git/hooks/post-commit -chmod +x .git/hooks/post-commit - -# Clone wheelwright if not already done -git clone git@github.com:intellistream/wheelwright.git ~/wheelwright -``` - -**Configuration**: - -Edit `.git/hooks/post-commit` and configure these variables: - -```bash -# Publisher path -PUBLISHER_PATH="${HOME}/wheelwright" - -# Enable/disable auto-publish -AUTO_PUBLISH_ENABLED=false # Set to true to enable - -# Branch restriction (empty = all branches) -AUTO_PUBLISH_BRANCH="main" - -# Confirmation required -REQUIRE_CONFIRMATION=true - -# Version bump type: patch (0.0.1), minor (0.1.0), major (1.0.0) -VERSION_BUMP_TYPE="patch" - -# Test on TestPyPI first -TEST_PYPI_FIRST=false -``` - -**Usage**: - -Once installed and configured, the hook runs automatically after `git commit`: - -```bash -git add packages/sage-common/... -git commit -m "feat(common): add new feature" - -# Hook automatically detects sage-common was modified -# and publishes it to PyPI after confirmation -``` - -**Manual Publishing** (without hook): - -```bash -cd ~/wheelwright -./publish.sh sage-common --auto-bump patch -``` - -## Configuration Examples - -### Development Workflow (Recommended) - -```bash -AUTO_PUBLISH_ENABLED=false # Manual control -REQUIRE_CONFIRMATION=true # Always confirm -TEST_PYPI_FIRST=true # Test first -``` - -### CI/CD Automation - -```bash -AUTO_PUBLISH_ENABLED=true # Auto-publish -AUTO_PUBLISH_BRANCH="main" # Only on main -REQUIRE_CONFIRMATION=false # No confirmation needed -TEST_PYPI_FIRST=false # Direct to PyPI -``` - -### Conservative Approach - -```bash -AUTO_PUBLISH_ENABLED=true # Enable hook -AUTO_PUBLISH_BRANCH="release" # Only on release branch -REQUIRE_CONFIRMATION=true # Always ask -TEST_PYPI_FIRST=true # Always test first -VERSION_BUMP_TYPE="patch" # Small increments -``` - -## Troubleshooting - -**Hook not running?** - -- Check permissions: `ls -la .git/hooks/post-commit` -- Should be executable: `chmod +x .git/hooks/post-commit` -- Check branch restrictions in configuration - -**Publisher not found?** - -- Clone it: - `git clone git@github.com:intellistream/wheelwright.git ~/wheelwright` -- Update `PUBLISHER_PATH` in hook if using different location - -**Publishing fails?** - -- Check PyPI credentials: `~/.pypirc` -- Verify package version in `_version.py` -- Check publisher logs for detailed error messages - -## Disabling Hooks - -To temporarily disable the hook: - -```bash -# Rename it -mv .git/hooks/post-commit .git/hooks/post-commit.disabled - -# Or set AUTO_PUBLISH_ENABLED=false in the hook -``` - -## See Also - -- [wheelwright](https://github.com/intellistream/wheelwright) - Standalone - publishing tool -- [PyPI Publishing Guide](/.github/copilot-instructions.md#pypi-publishing) - Copilot instructions -- [CONTRIBUTING.md](/CONTRIBUTING.md) - General contribution guidelines diff --git a/tools/hooks/check_copilot_instructions_sync.sh b/tools/hooks/check_copilot_instructions_sync.sh index 69d7ed4b16..8526bcf878 100755 --- a/tools/hooks/check_copilot_instructions_sync.sh +++ b/tools/hooks/check_copilot_instructions_sync.sh @@ -18,11 +18,8 @@ # Critical project files that may require doc updates: # - Installation: quickstart.sh, manage.sh, tools/install/*.sh # - CI/CD: .github/workflows/*.yml -# - Config: tools/pre-commit-config.yaml, tools/config/pytest.ini, tools/ruff.toml -# - Ports: packages/sage-common/src/sage/common/config/ports.py -# - Architecture: packages/*/pyproject.toml (new packages) -# - CLI: packages/sage-cli/src/sage/cli/*.py (new commands) -# - LLM/Gateway: packages/sage-gateway/*, packages/sage-common/*/sage_llm/* +# - Config: pre-commit / ruff / project packaging / cluster config +# - Meta package layout: pyproject.toml, setup.py, src/sage/_version.py # # Exit codes: # 0 - OK (allow commit, warnings only) @@ -56,32 +53,16 @@ CRITICAL_PATTERNS=( # Development Tools Config "^tools/pre-commit-config\.yaml$|开发工具|Pre-commit 配置" + "^tools/config/pre-commit-config\.yaml$|开发工具|Pre-commit 配置模板" "^tools/pytest\.ini$|测试|Pytest 配置" "^tools/ruff\.toml$|代码质量|Ruff 配置" - # Port Configuration - "^packages/sage-common/src/sage/common/config/ports\.py$|端口配置|SagePorts 定义" - - # Architecture (new packages) - "^packages/sage-[^/]+/pyproject\.toml$|架构|包配置 (可能涉及新包或依赖变化)" - - # CLI Commands - "^packages/sage-cli/src/sage/cli/commands/.*\.py$|CLI|CLI 命令 (可能涉及新命令)" - "^packages/sage-cli/src/sage/cli/main\.py$|CLI|CLI 入口" - - # LLM & Gateway (sageLLM 已独立为 PyPI 包) - "^packages/sage-gateway/src/sage/gateway/.*\.py$|LLM/Gateway|Gateway 服务" - "^packages/sage-llm-gateway/src/sage/llm/gateway/.*\.py$|LLM/Gateway|Gateway 服务" - "^packages/sage-llm-core/src/sage/llm/.*\.py$|LLM/Gateway|LLM Control Plane" - "^packages/sage-common/src/sage/common/components/sage_embedding/.*\.py$|LLM/Gateway|Embedding 服务" - - # User Paths & Config - "^packages/sage-common/src/sage/common/config/user_paths\.py$|用户路径|XDG 路径配置" + # Meta Package & Config + "^pyproject\.toml$|架构|Meta package 依赖与打包配置" + "^setup\.py$|架构|Meta package 安装入口" + "^src/sage/_version\.py$|架构|Meta package 版本定义" "^config/config\.yaml$|配置|主配置文件结构" "^config/cluster\.yaml$|配置|集群配置文件结构" - - # Benchmark - "^packages/sage-benchmark/src/sage/benchmark/.*\.py$|Benchmark|评测框架" ) # Get all staged files diff --git a/tools/hooks/check_dev_notes_location.sh b/tools/hooks/check_dev_notes_location.sh deleted file mode 100755 index b3974d9001..0000000000 --- a/tools/hooks/check_dev_notes_location.sh +++ /dev/null @@ -1,129 +0,0 @@ -#!/bin/bash -# Pre-commit hook to enforce dev-notes categorization -# All dev-notes markdown files must be in categorized subdirectories - -set -e - -# Get all markdown files (staged if in commit, or all files if --all-files) -if [ -n "$PRE_COMMIT_FROM_REF" ] && [ -n "$PRE_COMMIT_TO_REF" ]; then - # Running with --all-files or during push - all_md_files=$(git ls-files "*.md") -else - # Running in normal commit mode - all_md_files=$(git diff --cached --name-only --diff-filter=ACM | grep "\.md$" || true) -fi - -if [ -z "$all_md_files" ]; then - exit 0 -fi - -# Check for dev-notes files in wrong locations -violations="" -dev_notes_root="docs-public/docs_src/dev-notes" - -# Valid dev-notes subdirectories -valid_subdirs=( - "archive" - "cross-layer/architecture" - "cross-layer/ci-cd" - "cross-layer" - "l1-common" - "l2-platform" - "l3-kernel" - "l3-libs" - "l4-middleware" - "l5-cli" - "l5-tools" - "research_work" - "testing" -) - -# Special allowed files in dev-notes root -allowed_root_files=( - "TEMPLATE.md" - "README.md" - "index.md" - "dev_notes_catalog.csv" - "package-architecture.md" -) - -for file in $all_md_files; do - # Only check files in dev-notes directory - if [[ "$file" == ${dev_notes_root}/* ]]; then - # Get relative path from dev-notes root - relative_path="${file#${dev_notes_root}/}" - - # Check if it's directly in dev-notes root (not in a subdirectory) - if [[ "$relative_path" != */* ]]; then - # Check if it's an allowed root file - is_allowed=false - for allowed_file in "${allowed_root_files[@]}"; do - if [[ "$relative_path" == "$allowed_file" ]]; then - is_allowed=true - break - fi - done - - if [ "$is_allowed" = false ]; then - violations="$violations$file\n" - fi - else - # File is in a subdirectory, check if it's a valid one - subdir="${relative_path%%/*}" - is_valid=false - - for valid_dir in "${valid_subdirs[@]}"; do - if [[ "$subdir" == "$valid_dir" ]] || [[ "$relative_path" == "$valid_dir"/* ]]; then - is_valid=true - break - fi - done - - if [ "$is_valid" = false ]; then - violations="$violations$file (无效子目录: $subdir)\n" - fi - fi - fi -done - -if [ -n "$violations" ]; then - echo "❌ 错误: 以下 dev-notes 文档未放置在正确的分类目录中:" - echo -e "$violations" | sed "s/^/ - /" - echo "" - echo "✅ 开发日志文档必须放置在以下分类目录之一:" - echo "" - echo "📦 按层级分类 (Package Layers):" - echo " - ${dev_notes_root}/l1-common/ # L1 Common 层" - echo " - ${dev_notes_root}/l2-platform/ # L2 Platform 层" - echo " - ${dev_notes_root}/l3-kernel/ # L3 Kernel 层" - echo " - ${dev_notes_root}/l3-libs/ # L3 Libs 层" - echo " - ${dev_notes_root}/l4-middleware/ # L4 Middleware 层" - echo " - ${dev_notes_root}/l5-cli/ # L5 CLI 层" - echo " - ${dev_notes_root}/l5-tools/ # L5 Tools 层" - echo "" - echo "🔀 跨层级分类 (Cross-Layer):" - echo " - ${dev_notes_root}/cross-layer/architecture/ # 架构设计" - echo " - ${dev_notes_root}/cross-layer/ci-cd/ # CI/CD 和构建" - echo " - ${dev_notes_root}/cross-layer/ # 其他跨层级" - echo "" - echo "📚 其他分类:" - echo " - ${dev_notes_root}/research_work/ # 研究工作" - echo " - ${dev_notes_root}/testing/ # 测试相关" - echo " - ${dev_notes_root}/archive/ # 历史归档" - echo "" - echo "📦 独立仓库 (不在 SAGE 核心架构中):" - echo " - sage-benchmark: 基准测试" - echo " - sage-examples: 应用示例" - echo " - sage-studio: Web UI" - echo " - sageLLM: LLM 推理引擎" - echo "" - echo "💡 提示:" - echo " 1. 使用 TEMPLATE.md 作为模板创建新文档" - echo " 2. 根据内容选择合适的分类目录" - echo " 3. 跨多个层级的内容应放在 cross-layer/ 下" - echo " 4. 不确定时参考 dev_notes_catalog.csv" - echo "" - exit 1 -fi - -exit 0 diff --git a/tools/hooks/check_docs_location.sh b/tools/hooks/check_docs_location.sh index 7850fd2073..467816a3ee 100755 --- a/tools/hooks/check_docs_location.sh +++ b/tools/hooks/check_docs_location.sh @@ -5,17 +5,18 @@ set -e # Check Docs Location Hook # ============================================================================ # Purpose: Ensure markdown files are in proper locations. -# CRITICAL: Root 'docs/' directory is FORBIDDEN. Use 'docs-public/' instead. +# User-facing project docs live in the sibling sage-docs repository. # # This hook prevents: -# 1. Any files in root docs/ directory (which should not exist) +# 1. Any files under legacy docs-public/ # 2. Markdown files in random locations outside allowed patterns # # Rationale: -# - Root 'docs/' is gitignored and should not be used for committed documentation -# - All documentation must go to 'docs-public/' for centralized management -# - Package-specific docs go in packages//docs/ or README.md -# - Submodules can have their own docs/ directories (e.g., sageLLM/docs/, sageFlow/docs/) +# - User-facing docs are centralized in the sibling 'sage-docs' repository +# - Legacy 'docs-public/' paths must not be reintroduced +# - Root 'docs/' in SAGE is reserved for machine-owned governance artifacts only +# - In-tree module docs live under src/.../docs/ when they are implementation-local +# - Optional adapter / tooling repos may keep their own docs in their owning repositories # - Tools can have their own docs/ directories (e.g., tools/install/docs/) # ============================================================================ @@ -32,8 +33,8 @@ if [ -z "$all_md_files" ]; then exit 0 fi -# Check for files in docs/ (root docs folder) - STRICTLY FORBIDDEN -docs_violations="" +# Check for files in legacy docs-public/ - STRICTLY FORBIDDEN +legacy_docs_violations="" other_violations="" # Define allowed patterns (whitelist) @@ -43,47 +44,13 @@ allowed_patterns=( "^CONTRIBUTING\.md$" "^LICENSE\.md$" "^DEVELOPER\.md$" - "^docs-public/" + "^docs/dependency-audit-gate\.md$" + "^docs/profiling/.*\.md$" + "^docs/layer-manifest\.json$" "^docker/.*\.md$" - "^packages/[^/]+/README\.md$" # Only top-level README in packages - "^packages/[^/]+/CHANGELOG\.md$" # Only top-level CHANGELOG in packages - "^packages/[^/]+/(docs|documentation)/" # Package docs directory - "^packages/[^/]+/examples/.*\.md$" # Package examples documentation - "^packages/[^/]+/src/.*/docs/" # Submodule docs (sageLLM, sageFlow, etc.) - - # Allow module-level READMEs for apps, benchmarks, libs components - "^packages/sage-apps/src/sage/apps/[^/]+/README\.md$" - "^packages/sage-benchmark/src/sage/benchmark/benchmark_[^/]+/README\.md$" - "^packages/sage-benchmark/src/sage/benchmark/benchmark_[^/]+/[^/]+/README\.md$" # evaluation/, scripts/, etc. - "^packages/sage-benchmark/src/sage/benchmark/benchmark_[^/]+/experiments/[^/]+/README\.md$" - "^packages/sage-libs/src/sage/libs/anns/README\.md$" - "^packages/sage-libs/src/sage/libs/anns/implementations/README\.md$" - "^packages/sage-libs/src/sage/libs/anns/wrappers/.*/README\.md$" - "^packages/sage-libs/src/sage/libs/amms/README\.md$" # Main module README - "^packages/sage-libs/src/sage/libs/agentic/[^/]+/[^/]+/README\.md$" - "^packages/sage-llm-core/src/sage/llm/control_plane/[^/]+/README\.md$" - "^packages/sage-cli/src/sage/cli/templates/README\.md$" - "^packages/sage-tools/src/sage/tools/templates/.*\.md$" - - # Allow submodule markers - "^packages/.*/SUBMODULE\.md$" - - # Allow experiment config docs (special case for benchmark) - "^packages/sage-benchmark/src/sage/benchmark/benchmark_[^/]+/experiment/config/.*\.md$" - - # Allow benchmark special docs (DATA_PATHS, VISUALIZATION at benchmark root) - "^packages/sage-benchmark/src/sage/benchmark/benchmark_[^/]+/DATA_PATHS\.md$" - "^packages/sage-benchmark/src/sage/benchmark/benchmark_[^/]+/VISUALIZATION\.md$" - - # Allow deep subdirectory READMEs in benchmark evaluation/ - "^packages/sage-benchmark/src/sage/benchmark/benchmark_[^/]+/evaluation/.*/README\.md$" - - # Allow experiment design docs in benchmark experiments/ - "^packages/sage-benchmark/src/sage/benchmark/benchmark_[^/]+/experiments/.*/DESIGN\.md$" - - # Allow anns implementation technical docs - "^packages/sage-libs/src/sage/libs/anns/implementations/README_[^/]+\.md$" - "^packages/sage-libs/src/sage/libs/anns/wrappers/.*\.md$" + "^src/.*/docs/" + "^src/.*/README\.md$" + "^src/.*/README_[^/]+\.md$" "^examples/README\.md$" "^examples/.*/README\.md$" @@ -94,31 +61,30 @@ allowed_patterns=( "^tools/docs/.*\.md$" # Allow tools/docs/ directory "^\.sage/.*\.md$" - # .github: Allow allowed config files in root + anything in subdirectories + # .github: Allow standard GitHub convention files in root + anything in subdirectories "^\.github/copilot-instructions\.md$" "^\.github/COPILOT_SETUP\.md$" + "^\.github/[^/]+\.md$" # Standard GitHub root-level markdown (PULL_REQUEST_TEMPLATE, ISSUE_TEMPLATE, etc.) "^\.github/[^/]+/" ) # Define third-party library exclusions (always allowed) third_party_patterns=( - "^packages/.*/implementations/SPTAG/" # Microsoft SPTAG - "^packages/.*/implementations/faiss/" # Facebook FAISS - "^packages/.*/implementations/diskann-ms/" # Microsoft DiskANN - "^packages/.*/implementations/pybind11/" # pybind11 library - "^packages/.*/implementations/puck/" # Puck library - "^packages/.*/implementations/zstd/" # Zstandard library - "^packages/.*/implementations/candy/" # Candy library family - "^packages/.*/third[-_]party/" # Generic third-party directories - "^packages/.*/external/" # External dependencies - "^packages/.*/vendor/" # Vendored libraries + "^src/.*/implementations/SPTAG/" # Microsoft SPTAG + "^src/.*/implementations/faiss/" # Facebook FAISS + "^src/.*/implementations/diskann-ms/" # Microsoft DiskANN + "^src/.*/implementations/pybind11/" # pybind11 library + "^src/.*/implementations/puck/" # Puck library + "^src/.*/implementations/zstd/" # Zstandard library + "^src/.*/implementations/candy/" # Candy library family + "^src/.*/third[-_]party/" # Generic third-party directories + "^src/.*/external/" # External dependencies + "^src/.*/vendor/" # Vendored libraries ) for file in $all_md_files; do - # CRITICAL CHECK: Reject any file in root docs/ directory ONLY - # Allow packages/*/docs/ and submodule docs/ directories - if [[ "$file" == "docs/"* ]] && [[ "$file" != "packages/"* ]]; then - docs_violations="$docs_violations$file\n" + if [[ "$file" == "docs-public/"* ]]; then + legacy_docs_violations="$legacy_docs_violations$file\n" continue fi @@ -151,35 +117,34 @@ done failed=false -# Priority 1: Root docs/ violations (most critical) -if [ -n "$docs_violations" ]; then +# Priority 1: Legacy docs-public violations (most critical) +if [ -n "$legacy_docs_violations" ]; then echo "================================================================================================" - echo "❌ CRITICAL ERROR: Files detected in FORBIDDEN root 'docs/' directory" + echo "❌ CRITICAL ERROR: Files detected in forbidden legacy 'docs-public/' directory" echo "================================================================================================" echo "" - echo "The root 'docs/' directory is gitignored and must NOT contain committed documentation." - echo "All documentation must be placed in 'docs-public/' or other appropriate locations." + echo "The legacy 'docs-public/' path has been removed from the SAGE meta repository." + echo "User-facing project documentation must be managed in the sibling 'sage-docs' repository." echo "" - echo "⚠️ Note: Package and submodule docs/ directories ARE ALLOWED:" - echo " ✅ packages//docs/ - Package-specific documentation" - echo " ✅ packages/.../submodule/docs/ - Submodule documentation (sageLLM, sageFlow, etc.)" - echo " ✅ tools//docs/ - Tool-specific documentation" + echo "⚠️ Note: package/tool docs and machine-owned governance docs ARE ALLOWED:" + echo " ✅ ../sage-docs/ - Centralized project documentation" + echo " ✅ docs/dependency-audit-gate.md - Meta governance audit evidence" + echo " ✅ src/.../docs/ - In-tree implementation-local documentation" + echo " ✅ tools//docs/ - Tool-specific documentation" echo "" - echo "❌ Violating files (in ROOT docs/ directory):" - echo -e "$docs_violations" | sed "s/^/ - /" + echo "❌ Violating files (under docs-public/):" + echo -e "$legacy_docs_violations" | sed "s/^/ - /" echo "" echo "📁 Correct locations for documentation:" - echo " ✅ User-facing docs: docs-public/docs_src/..." - echo " ✅ Developer docs: docs-public/docs_src/dev-notes/..." - echo " ✅ Package-specific: packages//README.md or packages//docs/" - echo " ✅ Submodule docs: packages//src/.../submodule/docs/ (e.g., sageLLM/docs/)" + echo " ✅ Project docs: ../sage-docs/..." + echo " ✅ In-tree implementation docs: src/.../README.md or src/.../docs/" echo " ✅ Tool-specific: tools//docs/" echo " ✅ Examples: examples//README.md" echo "" echo "💡 Action required:" - echo " 1. Move files from ROOT 'docs/' to 'docs-public/docs_src/...' (appropriate subdirectory)" + echo " 1. Move user-facing docs into the sibling 'sage-docs' repository" echo " 2. Update any internal links/references" - echo " 3. Remove the root 'docs/' directory entirely" + echo " 3. Remove the legacy docs-public path entirely" echo "" echo "================================================================================================" failed=true @@ -198,41 +163,35 @@ if [ -n "$other_violations" ]; then echo " 📦 项目根目录:" echo " - README.md, CHANGELOG.md, CONTRIBUTING.md, LICENSE.md, DEVELOPER.md" echo "" - echo " 📚 用户和开发者文档:" - echo " - docs-public/docs_src/ (用户指南、教程)" - echo " - docs-public/docs_src/dev-notes/ (开发者文档)" + echo " 📚 项目文档:" + echo " - ../sage-docs/ (集中管理的项目文档仓库)" + echo " - docs/dependency-audit-gate.md (元仓库治理证据文档)" echo "" - echo " 📦 包级文档:" - echo " - packages//README.md (包的主文档)" - echo " - packages//CHANGELOG.md (包的变更日志)" - echo " - packages//docs/ (包的详细文档目录)" + echo " 📦 主仓内实现文档:" + echo " - src//README.md (模块主文档)" + echo " - src//docs/ (实现细节文档目录)" echo "" - echo " 🔧 子模块文档 (必须在 docs/ 子目录):" - echo " - packages//src/.../submodule/docs/ (sageLLM, sageVDB, sageFlow, etc.)" - echo " - 子模块内散落的 MD 文件也是违规的，必须放在 submodule/docs/ 下" - echo "" - echo " 📂 示例和工具:" + echo " 🔧 工具与示例:" echo " - examples//README.md" - echo " - examples/tutorials/" echo " - tools//README.md" echo " - tools//docs/" echo "" echo " 🚫 第三方库文档 (自动排除):" - echo " - packages/.*/implementations/SPTAG/ (Microsoft SPTAG)" - echo " - packages/.*/implementations/faiss/ (Facebook FAISS)" - echo " - packages/.*/implementations/diskann-ms/ (Microsoft DiskANN)" - echo " - packages/.*/implementations/pybind11/, puck/, zstd/, candy/ (其他第三方库)" + echo " - src/.*/implementations/SPTAG/ (Microsoft SPTAG)" + echo " - src/.*/implementations/faiss/ (Facebook FAISS)" + echo " - src/.*/implementations/diskann-ms/ (Microsoft DiskANN)" + echo " - src/.*/implementations/pybind11/, puck/, zstd/, candy/ (其他第三方库)" echo "" echo "💡 整理建议:" - echo " 1. 包内文档 → packages//docs/" - echo " 2. 子模块文档 → 子模块的 docs/ 子目录" - echo " 3. 通用开发者笔记 → docs-public/docs_src/dev-notes/" - echo " 4. 用户指南 → docs-public/docs_src/guides/" + echo " 1. 用户文档 → ../sage-docs 仓库" + echo " 2. 模块实现文档 → src/.../docs/" + echo " 3. 工具说明 → tools/.../docs/" + echo " 4. 元仓库治理证据/报告 → docs/ 中的机器专用文件" echo "" echo "🔍 常见违规案例:" - echo " ❌ packages/.../src/.../BUILD.md → 应移至 packages//docs/" - echo " ❌ packages/.../src/.../MIGRATION.md → 应移至 packages//docs/" - echo " ❌ packages/.../submodule/dev-notes/*.md → 应移至 submodule/docs/dev-notes/" + echo " ❌ src/.../BUILD.md → 应移至 src/.../docs/" + echo " ❌ src/.../MIGRATION.md → 应移至 src/.../docs/" + echo " ❌ 根目录散落的用户文档 → 应移至 ../sage-docs/" echo "" echo "================================================================================================" failed=true diff --git a/tools/hooks/check_libs_middleware_import.sh b/tools/hooks/check_libs_middleware_import.sh deleted file mode 100755 index 8fbd90fee1..0000000000 --- a/tools/hooks/check_libs_middleware_import.sh +++ /dev/null @@ -1,128 +0,0 @@ -#!/usr/bin/env bash -# Pre-commit hook: 检测 sage-libs 是否违规导入 sage.middleware -# -# 根据 SAGE 架构规则： -# - L3 (sage-libs) 不得导入 L4 (sage-middleware) -# - 任何需要向上调用 (VectorDB, Memory, Refiner) 的代码必须放在 middleware -# -# 参考：docs-public/docs_src/dev-notes/cross-layer/MIDDLEWARE_COMPONENT_PROMOTION_POLICY.md - -set -euo pipefail - -repo_root="$(git rev-parse --show-toplevel)" -cd "$repo_root" || exit 1 - -# 颜色定义 -RED='\033[0;31m' -GREEN='\033[0;32m' -YELLOW='\033[1;33m' -BLUE='\033[0;34m' -NC='\033[0m' # No Color - -LIBS_SRC="packages/sage-libs/src" - -# 检查目录是否存在 -if [[ ! -d "$LIBS_SRC" ]]; then - exit 0 -fi - -# 支持 --all-files 参数 -ALL_FILES=false -if [[ "${1:-}" == "--all-files" ]] || [[ -n "${PRE_COMMIT_FROM_REF:-}" ]]; then - ALL_FILES=true -fi - -# 获取要检查的文件 -if [[ "$ALL_FILES" == "true" ]]; then - # 检查所有文件 - files_to_check=$(find "$LIBS_SRC" -name "*.py" -type f | grep -v "__pycache__" || true) -else - # Normal commit mode - check staged files only - staged_files=$(git diff --cached --name-only --diff-filter=ACM 2>/dev/null || true) - if [[ -z "$staged_files" ]]; then - exit 0 - fi - files_to_check=$(echo "$staged_files" | grep "^$LIBS_SRC/.*\.py$" || true) -fi - -if [[ -z "$files_to_check" ]]; then - exit 0 -fi - -violations="" - -# 使用 Python AST 解析检查导入 -check_imports() { - local file="$1" - python3 -c " -import ast -import sys - -try: - with open('$file', 'r') as f: - tree = ast.parse(f.read()) -except SyntaxError: - sys.exit(0) - -violations = [] -for node in ast.walk(tree): - if isinstance(node, ast.Import): - for alias in node.names: - if 'sage.middleware' in alias.name: - violations.append(f'Line {node.lineno}: import {alias.name}') - elif isinstance(node, ast.ImportFrom): - if node.module and 'sage.middleware' in node.module: - names = ', '.join(a.name for a in node.names) - violations.append(f'Line {node.lineno}: from {node.module} import {names}') - -if violations: - for v in violations: - print(v) - sys.exit(1) -sys.exit(0) -" 2>/dev/null -} - -# 检查每个文件 -while IFS= read -r file; do - [[ -z "$file" ]] && continue - [[ ! -f "$file" ]] && continue - - result=$(check_imports "$file" 2>&1) || { - if [[ -n "$result" ]]; then - violations="${violations}${file}:\n${result}\n\n" - fi - } -done <<< "$files_to_check" - -if [[ -n "$violations" ]]; then - echo "" - echo -e "${RED}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" - echo -e "${RED}❌ L3 → L4 架构违规检测到！${NC}" - echo -e "${RED}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" - echo "" - echo -e "${YELLOW}规则：${NC} sage-libs (L3) 不得导入 sage.middleware (L4)" - echo "" - echo -e "${YELLOW}违规详情：${NC}" - echo -e "$violations" - echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" - echo "" - echo -e "${YELLOW}💡 解决方案：${NC}" - echo "" - echo "1. 如果代码需要调用 VectorDB/Memory/Refiner 等后端服务：" - echo " → 将代码移动到 sage-middleware/components/ 或 sage-middleware/operators/" - echo "" - echo "2. 如果只是类型提示或接口定义：" - echo " → 使用 TYPE_CHECKING 条件导入" - echo " → 或在 sage-common/sage-platform 定义抽象接口" - echo "" - echo "3. 参考策略文档：" - echo " → docs-public/docs_src/dev-notes/cross-layer/MIDDLEWARE_COMPONENT_PROMOTION_POLICY.md" - echo "" - echo -e "${RED}提交已被阻止以保护架构完整性。${NC}" - echo "" - exit 1 -fi - -echo -e "${GREEN}✓ sage-libs 架构检查通过${NC}" -exit 0 diff --git a/tools/hooks/post-checkout-cleanup.sh b/tools/hooks/post-checkout-cleanup.sh index 3aa76bd81b..a756e829f9 100755 --- a/tools/hooks/post-checkout-cleanup.sh +++ b/tools/hooks/post-checkout-cleanup.sh @@ -30,10 +30,10 @@ cd "$repo_root" || exit 0 # 使用数组存储需要检查的路径模式 # 注意：sageLLM 已独立为 PyPI 包，任何残留的源码目录都应清理 CLEANUP_PATTERNS=( - "packages/sage-common/src/sage/common/components/sage_llm" - "packages/sage-common/src/sage/common/components/sageLLM" - "packages/sage-llm-core/src/sage/llm/sageLLM" - "packages/sage-benchmark/src/sage/data" # 旧的子模块残留 + "src/sage/common/components/sage_llm" + "src/sage/common/components/sageLLM" + "src/sage/llm/sageLLM" + "src/sage/data" # 旧的子模块残留 # 可以添加更多需要自动清理的路径 ) diff --git a/tools/hooks/pre-commit-architecture.sh b/tools/hooks/pre-commit-architecture.sh index 0863436dbf..09044c2a86 100755 --- a/tools/hooks/pre-commit-architecture.sh +++ b/tools/hooks/pre-commit-architecture.sh @@ -20,9 +20,9 @@ NC='\033[0m' # No Color # 格式：错误路径|正确路径|描述 # 注意：sageLLM 已独立为 PyPI 包（pip install isage-sagellm），不应再有源码 ARCHITECTURE_VIOLATIONS=( - "packages/sage-common/src/sage/common/components/sage_llm|REMOVED|sageLLM 已独立为 PyPI 包（pip install isage-sagellm）" - "packages/sage-common/src/sage/common/components/sageLLM|REMOVED|sageLLM 已独立为 PyPI 包（pip install isage-sagellm）" - "packages/sage-llm-core/src/sage/llm/sageLLM|REMOVED|sageLLM 已独立为 PyPI 包（pip install isage-sagellm）" + "src/sage/common/components/sage_llm|REMOVED|sageLLM 已独立为 PyPI 包（pip install isage-sagellm）" + "src/sage/common/components/sageLLM|REMOVED|sageLLM 已独立为 PyPI 包（pip install isage-sagellm）" + "src/sage/llm/sageLLM|REMOVED|sageLLM 已独立为 PyPI 包（pip install isage-sagellm）" # 可以添加更多架构规则 ) diff --git a/tools/install/core/ci_install_wrapper.sh b/tools/install/core/ci_install_wrapper.sh index 42fe1de988..9f70f7fe78 100755 --- a/tools/install/core/ci_install_wrapper.sh +++ b/tools/install/core/ci_install_wrapper.sh @@ -17,7 +17,7 @@ # # 示例: # ./tools/install/ci_install_wrapper.sh --dev --yes -# ./tools/install/ci_install_wrapper.sh --core --yes +# ./tools/install/ci_install_wrapper.sh --standard --yes # @@ -157,9 +157,9 @@ main() { echo -e "${BLUE}📝 详细日志: .sage/logs/ci_install.log${NC}" echo "" - # 验证安装（PEP 420 namespace - 检查实际包） + # 验证安装（主仓 in-tree 表面） echo -e "${BLUE}🔍 验证安装...${NC}" - if python3 -c "import sage.common; print(f'SAGE version: {sage.common.__version__}')" 2>/dev/null; then + if python3 -c "from sage._version import __version__; import sage.foundation, sage.stream, sage.runtime, sage.serving, sage.cli; print(f'SAGE version: {__version__}')" 2>/dev/null; then echo -e "${GREEN}✓ SAGE 导入成功${NC}" else echo -e "${YELLOW}⚠ SAGE 导入失败（可能需要激活环境）${NC}" diff --git a/tools/install/core/install_system_deps.sh b/tools/install/core/install_system_deps.sh index 6f8606855b..9fe992c147 100755 --- a/tools/install/core/install_system_deps.sh +++ b/tools/install/core/install_system_deps.sh @@ -197,8 +197,8 @@ main() { echo "" echo "🎉 系统依赖安装完成！" - echo "C++扩展将在安装 sage-middleware 包时自动构建" - echo "💡 如需手动重新构建，可运行: sage extensions install --force" + echo "主仓相关的 C++/原生扩展会在安装流程中按需自动构建" + echo "💡 如需重新触发构建，请重新运行: ./quickstart.sh --dev --yes" } # 解析命令行参数 diff --git a/tools/install/diagnostics/check_and_fix_dependencies.sh b/tools/install/diagnostics/check_and_fix_dependencies.sh index 9ae0e07b7a..58d8ec5377 100755 --- a/tools/install/diagnostics/check_and_fix_dependencies.sh +++ b/tools/install/diagnostics/check_and_fix_dependencies.sh @@ -89,7 +89,7 @@ check_and_fix_dependencies() { fi if [ "$should_fix" = true ]; then - local fix_script="$project_root/tools/install/maintenance/fix_vllm_torch.sh" + local fix_script="$project_root/tools/install/maintenance/fix_torch.sh" if [ ! -f "$fix_script" ]; then echo "❌ 错误: 修复脚本不存在: $fix_script" return 1 @@ -105,7 +105,7 @@ check_and_fix_dependencies() { return $? else echo "ℹ️ 跳过自动修复。你可以稍后手动运行:" - echo " ./tools/install/maintenance/fix_vllm_torch.sh" + echo " ./tools/install/maintenance/fix_torch.sh" return 0 fi } diff --git a/tools/install/diagnostics/diagnose_cpp_extensions.sh b/tools/install/diagnostics/diagnose_cpp_extensions.sh index 503679fd98..329581b615 100755 --- a/tools/install/diagnostics/diagnose_cpp_extensions.sh +++ b/tools/install/diagnostics/diagnose_cpp_extensions.sh @@ -30,86 +30,41 @@ echo "C++ 扩展安装诊断" echo "==================================================" echo "" -# 1. 检查 isage-middleware 安装状态 -echo "1. 检查 isage-middleware 安装状态" +# 1. 检查独立适配器包安装状态 +echo "1. 检查独立适配器包安装状态" echo "-----------------------------------" -pip show isage-middleware || echo "未安装" +for pkg in isage-vdb isage-flow isage-tsdb; do + echo "📦 $pkg" + pip show "$pkg" || echo "未安装" + echo "" +done echo "" -# 2. 检查子模块状态 -echo "2. 检查子模块状态" +# 2. 检查历史子模块残留状态 +echo "2. 检查历史子模块残留状态" echo "-----------------------------------" cd "$PROJECT_ROOT" -# 注意: C++ 扩展已迁移为独立 PyPI 包 (isage-vdb, isage-flow, isage-tsdb, neuromem, isage-refiner) -# 只检查实际的 Git 子模块 -for submodule in "docs-public"; do - - if [ -d "$submodule" ]; then - if [ -n "$(ls -A "$submodule" 2>/dev/null)" ]; then - echo "✅ $submodule (已初始化)" - else - echo "⚠️ $submodule (空目录)" - fi - else - echo "❌ $submodule (不存在)" - fi -done +echo "ℹ️ 历史文档子模块流程已移除；当前无需检查文档子模块" echo "" -# 3. 检查 .so 文件位置 -echo "3. 检查 .so 文件位置" +# 3. 检查适配器分发元数据 +echo "3. 检查适配器分发元数据" echo "-----------------------------------" -for ext in sage_flow sage_db sage_tsdb; do - echo "📦 ${ext}:" - - ext_dir="$PROJECT_ROOT/packages/sage-middleware/src/sage/middleware/components/${ext}" - - # 检查 python 目录中的 .so 文件 - if [ -d "$ext_dir/python" ]; then - lib_files=$(find "$ext_dir/python" -maxdepth 1 -name "*.so" -type f 2>/dev/null || true) - if [ -n "$lib_files" ]; then - echo "$lib_files" | while read -r file; do - size=$(stat -c%s "$file" 2>/dev/null || stat -f%z "$file" 2>/dev/null || echo "?") - echo " ✅ $(basename "$file") (${size} bytes)" - done - else - echo " ❌ python/ 目录中没有 .so 文件" - fi - else - echo " ❌ python/ 目录不存在" - fi - - # 检查子模块 python 目录 - submodule_dir=$(find "$ext_dir" -maxdepth 1 -type d \( -iname "sage${ext#sage_}" -o -iname "${ext}" \) 2>/dev/null | head -1 || true) - if [ -n "$submodule_dir" ] && [ -d "$submodule_dir/python" ]; then - lib_files=$(find "$submodule_dir/python" -maxdepth 1 -name "*.so" -type f 2>/dev/null || true) - if [ -n "$lib_files" ]; then - echo " 子模块 python/ 目录:" - echo "$lib_files" | while read -r file; do - size=$(stat -c%s "$file" 2>/dev/null || stat -f%z "$file" 2>/dev/null || echo "?") - echo " 📄 $(basename "$file") (${size} bytes)" - done - fi - fi - - # 检查 build 目录 - if [ -d "$ext_dir/build" ] || [ -d "$PROJECT_ROOT/packages/sage-middleware/build" ]; then - build_files=$(find "$ext_dir" "$PROJECT_ROOT/packages/sage-middleware/build" -name "lib*.so" -type f 2>/dev/null | grep -i "$ext" || true) - if [ -n "$build_files" ]; then - echo " build/ 目录:" - echo "$build_files" | head -3 | while read -r file; do - size=$(stat -c%s "$file" 2>/dev/null || stat -f%z "$file" 2>/dev/null || echo "?") - echo " 📄 $(basename "$file") (${size} bytes)" - done - fi - fi - - echo "" -done +python3 << 'PYEOF' +from importlib import metadata + +for dist_name in ("isage-vdb", "isage-flow", "isage-tsdb"): + try: + version = metadata.version(dist_name) + print(f"✅ {dist_name}: {version}") + except metadata.PackageNotFoundError: + print(f"❌ {dist_name}: 未安装") +PYEOF +echo "" -# 4. 尝试导入扩展 -echo "4. 尝试导入 Python 扩展" +# 4. 尝试导入主仓核心表面 +echo "4. 尝试导入主仓核心表面" echo "-----------------------------------" python3 << 'PYEOF' import sys @@ -117,28 +72,17 @@ import warnings warnings.filterwarnings('ignore') try: - from sage.middleware.components.extensions_compat import check_extensions_availability - available = check_extensions_availability() - - for ext, status in available.items(): - symbol = '✅' if status else '❌' - print(f"{symbol} {ext}") - - if not status: - # 尝试获取详细错误 - try: - if ext == 'sage_flow': - from sage.middleware.components.sage_flow.python import _sage_flow - elif ext == 'sage_db': - from sage.middleware.components.sage_db.python import _sage_db - elif ext == 'sage_tsdb': - from sage.middleware.components.sage_tsdb.python import _sage_tsdb - except Exception as e: - print(f" 错误: {e}") + import sage.foundation # noqa: F401 + import sage.runtime # noqa: F401 + import sage.stream # noqa: F401 + import sage.serving # noqa: F401 + print('✅ sage.foundation') + print('✅ sage.runtime') + print('✅ sage.stream') + print('✅ sage.serving') except Exception as e: - print(f"❌ 无法检查扩展: {e}") - import traceback - traceback.print_exc() + print(f'❌ 主仓表面导入失败: {type(e).__name__}: {e}') + sys.exit(1) PYEOF echo "" diff --git a/tools/install/display_tools/interface.sh b/tools/install/display_tools/interface.sh index 7e9bfdbb18..03b9d231ae 100755 --- a/tools/install/display_tools/interface.sh +++ b/tools/install/display_tools/interface.sh @@ -63,10 +63,10 @@ show_logo() { # 网址和版权信息也应用相同的偏移逻辑 if [ "$VSCODE_OFFSET_ENABLED" = true ]; then - center_text_formatted "https://intellistream.github.io/SAGE-Pub/" "$GRAY" + center_text_formatted "https://intellistream.github.io/sage-docs/" "$GRAY" center_text_formatted "intellistream 2025" "$GRAY" else - center_text "https://intellistream.github.io/SAGE-Pub/" "$GRAY" + center_text "https://intellistream.github.io/sage-docs/" "$GRAY" center_text "intellistream 2025" "$GRAY" fi } @@ -108,20 +108,12 @@ show_help() { echo "" echo -e "${BLUE}安装模式：${NC}" echo "" - echo -e " ${BOLD}--core, -c${NC} ${GRAY}核心框架 (L1-L4)${NC}" - echo -e " ${DIM}包含: common, platform, kernel, libs, middleware${NC}" - echo -e " ${DIM}适合: 容器部署、生产运行、最小依赖${NC}" - echo "" - echo -e " ${BOLD}--standard, -s${NC} ${GREEN}标准版本 (推荐)${NC}" - echo -e " ${DIM}包含: Core + sage CLI + 科学计算包 (numpy, pandas, matplotlib)${NC}" + echo -e " ${BOLD}--standard, -s${NC} ${GREEN}standard 安装 (默认)${NC}" + echo -e " ${DIM}包含: 核心能力 + ML/VDB/streaming 等完整功能依赖${NC}" echo -e " ${DIM}适合: 应用开发、日常使用、大多数用户${NC}" echo "" - echo -e " ${BOLD}--full, -f${NC} ${PURPLE}完整功能${NC}" - echo -e " ${DIM}包含: Standard + 完整功能组件${NC}" - echo -e " ${DIM}适合: 需要完整功能的用户${NC}" - echo "" - echo -e " ${BOLD}--dev, -d${NC} ${YELLOW}开发模式 (默认)${NC}" - echo -e " ${DIM}包含: Full + sage-tools (sage-dev, pytest, pre-commit)${NC}" + echo -e " ${BOLD}--dev, -d${NC} ${YELLOW}dev 安装${NC}" + echo -e " ${DIM}包含: standard + 开发工具 (sage-dev, pytest, pre-commit)${NC}" echo -e " ${DIM}适合: 贡献 SAGE 框架源码、运行测试${NC}" echo "" echo -e "${BLUE}环境选项：${NC}" @@ -139,7 +131,7 @@ show_help() { echo -e " ./quickstart.sh ${DIM}# 交互式选择${NC}" echo -e " ./quickstart.sh --standard ${DIM}# 标准安装${NC}" echo -e " ./quickstart.sh --conda --dev ${DIM}# conda环境中开发者安装${NC}" - echo -e " ./quickstart.sh --pip --core ${DIM}# pip核心运行时安装${NC}" + echo -e " ./quickstart.sh --pip --standard ${DIM}# pip standard 安装${NC}" echo "" } @@ -190,34 +182,24 @@ show_install_success() { # 显示已安装的内容 case "$mode" in - "core") - echo -e "${BLUE}已安装 (核心框架):${NC}" - echo_icon "✅" "L1-L4: common, platform, kernel, libs, middleware" 1 1 - ;; "standard") - echo -e "${BLUE}已安装 (标准版本):${NC}" - echo_icon "✅" "Core + sage CLI + 科学计算包" 1 1 - echo_icon "✅" "numpy, pandas, matplotlib, scipy, jupyter" 1 1 - ;; - "full") - echo -e "${BLUE}已安装 (完整功能):${NC}" - echo_icon "✅" "Standard + 完整功能组件" 1 1 - echo_icon "✅" "完整的 SAGE 框架功能" 1 1 + echo -e "${BLUE}已安装 (standard):${NC}" + echo_icon "✅" "标准功能 + ML/VDB/streaming 等完整依赖" 1 1 ;; "dev") echo -e "${BLUE}已安装 (开发模式):${NC}" - echo_icon "✅" "Full + sage-tools (sage-dev 命令)" 1 1 + echo_icon "✅" "standard + isage-dev-tools (sage-dev 命令)" 1 1 echo_icon "✅" "pytest, pre-commit, 代码质量工具" 1 1 ;; esac echo "" echo -e "${BOLD}快速开始:${NC}" - echo -e " ${DIM}# 验证安装（PEP 420 namespace）${NC}" - echo -e " python3 -c 'import sage.common; print(f\"SAGE v{sage.common.__version__} 安装成功！\")'" + echo -e " ${DIM}# 验证安装（主仓核心表面）${NC}" + echo -e " sage verify" echo "" - echo -e " ${DIM}# 运行示例${NC}" - echo -e " cd examples && python3 rag/basic_rag.py" + echo -e " ${DIM}# 使用 sagellm 直接运行一条真实推理${NC}" + echo -e " sage chat --ask \"Hello, SAGE!\"" echo "" echo -e "${DIM}更多信息请查看: README.md${NC}" } @@ -232,10 +214,10 @@ run_hello_world_demo() { echo -e "${CYAN}${BOLD}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" echo "" - # 验证 SAGE 安装（PEP 420 namespace - 检查实际包） + # 验证 SAGE 安装（主仓 in-tree 表面） echo -e "${INFO} 验证 SAGE 安装..." local sage_version - sage_version=$(VLLM_LOGGING_LEVEL=ERROR python3 -W ignore -c "import sage.common; print(sage.common.__version__)" 2>/dev/null | tail -1) + sage_version=$(SAGELLM_LOGGING_LEVEL=ERROR python3 -W ignore -c "from sage._version import __version__; import sage.foundation, sage.stream, sage.runtime, sage.serving, sage.cli; print(__version__)" 2>/dev/null | tail -1) if [ -n "$sage_version" ]; then echo -e " ${GREEN}✅ SAGE v${sage_version} 已就绪${NC}" else @@ -297,8 +279,8 @@ run_streaming_demo() { # 显示实际代码 echo -e "${BLUE}${BOLD}📝 示例代码:${NC}" echo "" - echo -e " ${DIM}from sage.kernel.api import LocalEnvironment${NC}" - echo -e " ${DIM}from sage.common.core.functions import BatchFunction, MapFunction, SinkFunction${NC}" + echo -e " ${DIM}from sage.runtime import LocalEnvironment${NC}" + echo -e " ${DIM}from sage.foundation import BatchFunction, MapFunction, SinkFunction${NC}" echo "" echo -e " ${CYAN}env = LocalEnvironment(\"demo\")${NC}" echo -e " ${CYAN}env.from_batch(Source).map(Transform).sink(Output)${NC}" @@ -350,8 +332,8 @@ PY repo_root=$(cd "$(dirname "${BASH_SOURCE[0]}")/../../.." && pwd) chat_cache_dir="${repo_root}/.sage/cache/chat" fi - local index_manifest="${chat_cache_dir}/docs-public_manifest.json" - local index_db_prefix="${chat_cache_dir}/docs-public.sagevdb" + local index_manifest="${chat_cache_dir}/docs_manifest.json" + local index_db_prefix="${chat_cache_dir}/docs.sagevdb" if [ ! -f "$index_manifest" ] || [ ! -f "${index_db_prefix}.config" ]; then echo -e "${YELLOW}⚠️ 首次运行需要构建文档索引...${NC}" echo -e "${DIM} 这将使用本地 Embedding 服务创建向量索引${NC}" @@ -366,45 +348,8 @@ PY fi if [ "$embedding_running" = false ]; then - echo -e "${YELLOW}ℹ️ Embedding 服务未运行，需要先启动${NC}" - echo -ne "${BOLD}是否启动 LLM + Embedding 服务? [Y/n]: ${NC}" - read -r start_services - if [[ ! "$start_services" =~ ^[Nn] ]]; then - echo "" - echo -e "${INFO} 启动 LLM + Embedding 服务..." - echo -e "${DIM} 首次启动需要下载模型并加载到 GPU，可能需要 2-4 分钟${NC}" - echo -e "${DIM} • LLM 模型: Qwen2.5-0.5B (~300MB)${NC}" - echo -e "${DIM} • Embedding 模型: bge-small-zh (~100MB)${NC}" - echo "" - # 后台启动服务 - # 注意: sage llm serve 默认已包含 embedding 服务 - sage llm serve &>/dev/null & - local serve_pid=$! - - # 等待 Embedding 服务就绪，同时显示进度 - local wait_count=0 - local max_wait=90 # 最多等待 180 秒 (90 * 2) - echo -e " ${CYAN}⏳ 等待服务启动（LLM 加载到 GPU 需要时间）...${NC}" - while [ $wait_count -lt $max_wait ]; do - if curl -s --connect-timeout 2 "http://localhost:${embedding_port}/v1/models" 2>/dev/null | grep -q '"data"'; then - embedding_running=true - break - fi - # 显示经过时间 - local elapsed=$((wait_count * 2)) - printf "\r ${DIM}已等待 %ds...${NC}" $elapsed - sleep 2 - wait_count=$((wait_count + 1)) - done - printf "\r\033[K" # 清除进度行 - - if [ "$embedding_running" = true ]; then - echo -e " ${GREEN}✅ Embedding 服务已就绪${NC}" - else - echo -e " ${YELLOW}⚠️ Embedding 服务启动超时，使用本地 HF 模型${NC}" - echo -e " ${DIM}提示: 服务可能仍在后台启动中，可稍后检查 sage llm status${NC}" - fi - fi + echo -e "${YELLOW}ℹ️ 未检测到 Embedding 服务，将回退到本地 HF embedding 模型${NC}" + echo -e "${DIM} 如已自行启动 OpenAI 兼容 embedding endpoint，可稍后使用 --embedding-method openai 与 --embedding-base-url${NC}" else echo -e " ${GREEN}✅ 检测到 Embedding 服务 (localhost:${embedding_port})${NC}" fi @@ -421,16 +366,16 @@ PY ingest_log=$(mktemp) local ingest_cmd # 设置环境变量抑制各种 INFO 日志 - local quiet_env="VLLM_LOGGING_LEVEL=WARNING TRANSFORMERS_VERBOSITY=error HF_HUB_VERBOSITY=error HTTPX_LOG_LEVEL=WARNING" + local quiet_env="SAGELLM_LOGGING_LEVEL=WARNING TRANSFORMERS_VERBOSITY=error HF_HUB_VERBOSITY=error HTTPX_LOG_LEVEL=WARNING" if [ "$embedding_running" = true ]; then # 使用运行中的 Embedding 服务 echo -e "${DIM} 使用 Embedding 服务: http://localhost:${embedding_port}/v1${NC}" - ingest_cmd=(env $quiet_env sage chat ingest --quiet --embedding-method openai --embedding-model BAAI/bge-m3 --embedding-base-url "http://localhost:${embedding_port}/v1") + ingest_cmd=(env $quiet_env sage index ingest --quiet --embedding-method openai --embedding-model BAAI/bge-m3 --embedding-base-url "http://localhost:${embedding_port}/v1") else # 回退到本地 HuggingFace 模型 echo -e "${DIM} 使用本地 HF 模型: BAAI/bge-m3${NC}" - ingest_cmd=(env $quiet_env sage chat ingest --quiet --embedding-method hf --embedding-model BAAI/bge-m3) + ingest_cmd=(env $quiet_env sage index ingest --quiet --embedding-method hf --embedding-model BAAI/bge-m3) fi start_spinner " 索引构建中，请稍候" @@ -443,10 +388,10 @@ PY tail -n 10 "$ingest_log" echo "" echo -e "${DIM} 正在清理不完整的索引文件...${NC}" - rm -f "${chat_cache_dir}/docs-public"* 2>/dev/null || true + rm -f "${chat_cache_dir}/docs"* 2>/dev/null || true echo -e "${YELLOW}⚠️ 可以稍后重试:${NC}" - echo -e " ${CYAN}sage llm serve${NC} # 启动 LLM + Embedding 服务" - echo -e " ${CYAN}sage chat ingest --embedding-method openai --embedding-model BAAI/bge-m3 --embedding-base-url http://localhost:8090/v1${NC}" + echo -e " ${CYAN}sage index ingest --embedding-method hf --embedding-model BAAI/bge-m3${NC}" + echo -e " ${CYAN}sage index ingest --embedding-method openai --embedding-model BAAI/bge-m3 --embedding-base-url http://localhost:8090/v1${NC}" fi rm -f "$ingest_log" fi @@ -458,26 +403,28 @@ PY echo "" # 启动 sage chat - # 优先使用本地 vLLM，如果没有则用 mock - if curl -s http://localhost:8901/v1/models >/dev/null 2>&1; then - echo -e " ${GREEN}✅ 检测到本地 LLM 服务 (localhost:8901)${NC}" - # 获取实际运行的模型名称 - local vllm_model - vllm_model=$(curl -s http://localhost:8901/v1/models | python3 -c "import sys,json; print(json.load(sys.stdin)['data'][0]['id'])" 2>/dev/null || echo "") - if [ -n "$vllm_model" ]; then - echo -e " ${DIM}模型: $vllm_model${NC}" + # 优先使用 gateway；若不可用则直接调用 sagellm CLI + if curl -s http://localhost:8889/v1/models >/dev/null 2>&1; then + echo -e " ${GREEN}✅ 检测到本地 LLM gateway (localhost:8889)${NC}" + # 获取实际运行的模型名称（若可用） + local local_model + local_model=$(curl -s http://localhost:8889/v1/models | python3 -c "import sys,json; print(json.load(sys.stdin)['data'][0]['id'])" 2>/dev/null || echo "") + if [ -n "$local_model" ]; then + echo -e " ${DIM}模型: $local_model${NC}" fi echo "" - sage chat --backend vllm --base-url http://localhost:8901/v1 --model "${vllm_model:-Qwen/Qwen2.5-0.5B-Instruct}" --stream + sage chat --engine sagellm --backend auto --model "${local_model:-Qwen/Qwen2.5-0.5B-Instruct}" --stream + elif command -v sagellm >/dev/null 2>&1; then + echo -e " ${GREEN}✅ 未检测到 gateway，改为直接调用 sagellm CLI${NC}" + echo "" + sage chat --engine sagellm --backend direct elif [ -n "${SAGE_CHAT_API_KEY:-}" ] || [ -n "${OPENAI_API_KEY:-}" ]; then echo -e " ${GREEN}✅ 使用云端 API${NC}" echo "" sage chat --backend openai --stream else - echo -e " ${YELLOW}ℹ️ 使用 Mock 模式演示 (无需 LLM 服务)${NC}" - echo -e " ${DIM} 提示: 运行 'sage llm serve' 可启动本地 LLM${NC}" - echo "" - sage chat --backend mock + echo -e " ${YELLOW}⚠️ 未检测到 gateway，也未找到 sagellm 命令${NC}" + echo -e " ${DIM} 提示: 运行 'sage serve gateway --json' 查看 gateway 契约，或确认 sagellm 已安装到当前环境${NC}" fi echo "" @@ -486,13 +433,13 @@ PY echo -e "${BLUE}${BOLD}📝 使用方式:${NC}" echo "" echo -e " ${CYAN}# 交互式 RAG 问答${NC}" - echo -e " ${DIM}sage chat --backend vllm --base-url http://localhost:8901/v1${NC}" + echo -e " ${DIM}sage chat --engine sagellm --backend auto --stream${NC}" echo "" echo -e " ${CYAN}# 单次提问${NC}" - echo -e " ${DIM}sage chat --ask \"如何创建 SAGE Pipeline?\" --backend vllm${NC}" + echo -e " ${DIM}sage chat --ask \"如何创建 SAGE Pipeline?\" --engine sagellm --backend auto${NC}" echo "" echo -e " ${CYAN}# 构建自定义知识库${NC}" - echo -e " ${DIM}sage chat ingest --source ./my-docs --index my-knowledge${NC}" + echo -e " ${DIM}sage index ingest --source ./my-docs --index my-knowledge${NC}" echo "" show_demo_footer @@ -511,8 +458,7 @@ show_demo_footer() { echo "" } -# 询问用户是否要启动服务（LLM / Hello World） -# 注意：SAGE Studio 需要单独安装 (pip install isage-studio) +# 询问用户是否要继续体验主仓当前可用的能力（chat / hello world） prompt_start_llm_service() { local mode="$1" @@ -522,16 +468,7 @@ prompt_start_llm_service() { return 0 fi - # 只在 dev/full 模式下询问（core/standard 模式可能没有完整的服务支持） - if [ "$mode" = "core" ]; then - return 0 - fi - - # 检查是否有 GPU 可用 - local has_gpu=false - if command -v nvidia-smi &>/dev/null && nvidia-smi &>/dev/null; then - has_gpu=true - fi + # standard/dev 模式都支持提示 # 检查环境是否激活 local env_activated=true @@ -550,10 +487,10 @@ prompt_start_llm_service() { echo -e "${YELLOW}⚠️ 请先激活 conda 环境后再启动服务:${NC}" echo -e " ${CYAN}conda activate ${SAGE_ENV_NAME:-}${NC}" echo "" - echo -e "${DIM}激活后可用以下命令启动服务:${NC}" - echo -e " ${CYAN}sage llm serve${NC} # 启动 LLM 推理服务" - echo "" - echo -e "${DIM}💡 SAGE Studio 需要单独安装: pip install isage-studio${NC}" + echo -e "${DIM}激活后可用以下命令继续体验:${NC}" + echo -e " ${CYAN}sage verify${NC}" + echo -e " ${CYAN}sage chat --ask \"Hello, SAGE!\"${NC}" + echo -e " ${CYAN}sage serve gateway --json${NC}" echo "" return 0 fi @@ -564,17 +501,11 @@ prompt_start_llm_service() { echo -e " ${BOLD}[1] 运行 Hello World${NC} - 快速体验 SAGE Pipeline" echo -e " ${DIM}运行一个简单的数据处理流水线示例${NC}" echo "" - echo -e " ${BOLD}[2] sage llm serve${NC} - 启动 LLM 推理服务" - if [ "$has_gpu" = true ]; then - echo -e " ${DIM}提供 OpenAI 兼容 API (http://localhost:8901/v1)${NC}" - else - echo -e " ${DIM}${YELLOW}⚠️ 需要 GPU，当前未检测到${NC}" - fi + echo -e " ${BOLD}[2] 查看 gateway 契约${NC} - 查看外部 sagellm gateway 的启动/探活信息" + echo -e " ${DIM}适合需要 OpenAI 兼容接口或联调服务模式时使用${NC}" echo "" echo -e " ${BOLD}[3] 跳过${NC} - 稍后手动操作" echo "" - echo -e "${DIM}💡 SAGE Studio 需要单独安装: pip install isage-studio${NC}" - echo "" # 交互式询问 echo -ne "${BOLD}请选择 [1/2/3]: ${NC}" @@ -588,106 +519,24 @@ prompt_start_llm_service() { run_hello_world_demo false ;; 2) - if [ "$has_gpu" = true ]; then - echo "" - echo -e "${INFO} 正在启动 LLM 服务..." - echo -e "${DIM} 首次启动会下载模型并加载到 GPU，可能需要 1-3 分钟${NC}" - echo -e "${DIM} • 模型下载: Qwen2.5-0.5B (~300MB)${NC}" - echo -e "${DIM} • GPU 加载: vLLM 初始化${NC}" - echo "" - - if command -v sage &>/dev/null; then - # 后台启动并实时显示进度 - local llm_log="/tmp/sage_llm_serve_$$.log" - - # 启动服务（后台运行） - sage llm serve > "$llm_log" 2>&1 & - local sage_pid=$! - - # 显示实时进度，同时监控日志 - local elapsed=0 - local max_wait=180 # 最多等待 3 分钟 - local last_status="" - - while kill -0 $sage_pid 2>/dev/null && [ $elapsed -lt $max_wait ]; do - # 尝试从日志中获取当前状态 - if [ -f "$llm_log" ]; then - # 检测关键状态 - if grep -q "下载位置" "$llm_log" 2>/dev/null && [ "$last_status" != "downloading" ]; then - printf "\r\033[K" - echo -e " ${CYAN}⏳ 正在下载模型...${NC}" - last_status="downloading" - elif grep -q "启动 LLM 服务" "$llm_log" 2>/dev/null && [ "$last_status" != "starting" ]; then - printf "\r\033[K" - echo -e " ${CYAN}⏳ 正在启动 vLLM 服务...${NC}" - last_status="starting" - elif grep -q "启动中" "$llm_log" 2>/dev/null && [ "$last_status" != "loading" ]; then - printf "\r\033[K" - echo -e " ${CYAN}⏳ 正在加载模型到 GPU（这步较慢，请耐心等待）...${NC}" - last_status="loading" - fi - fi - - # 显示经过时间 - printf "\r ${DIM}已等待 %ds...${NC}" $elapsed - sleep 2 - elapsed=$((elapsed + 2)) - done - - # 等待命令完成 - wait $sage_pid 2>/dev/null - local exit_code=$? - - # 清除进度行 - printf "\r\033[K" - - # 显示关键信息（最后 10 行） - if [ -f "$llm_log" ]; then - # 过滤掉进度条行，只显示重要信息 - grep -v "启动中 \[" "$llm_log" | tail -10 - rm -f "$llm_log" - fi - - echo "" - if [ $exit_code -eq 0 ]; then - echo -e "${GREEN}✅ LLM 服务已启动${NC}" - echo -e "${DIM} API 地址: http://localhost:8901/v1${NC}" - echo -e "${DIM} 状态查看: sage llm status${NC}" - echo -e "${DIM} 停止服务: sage llm stop${NC}" - else - echo -e "${YELLOW}⚠️ LLM 服务启动可能未完全成功，请检查状态${NC}" - echo -e "${DIM} 状态查看: sage llm status${NC}" - fi - echo "" - # 询问是否运行 LLM Demo - echo -ne "${BOLD}是否运行 LLM Demo 体验? [y/N]: ${NC}" - read -r run_demo - if [[ "$run_demo" =~ ^[Yy] ]]; then - echo "" - run_hello_world_demo true - fi - else - echo -e "${YELLOW}⚠️ sage 命令不可用，请手动启动:${NC}" - echo -e " ${CYAN}sage llm serve${NC}" - fi - else + echo "" + echo -e "${INFO} 当前主仓会把 gateway 视为外部 sagellm 集成能力。" + echo -e "${DIM}可先查看 SAGE 侧约定的启动命令、探活地址与日志路径：${NC}" + echo "" + if command -v sage &>/dev/null; then + sage serve gateway --json || true echo "" - echo -e "${YELLOW}⚠️ 未检测到 GPU，无法启动本地 LLM 服务。${NC}" - echo -e "${DIM}您可以配置云端 API 作为替代（在 .env 文件中设置）:${NC}" - echo -e " ${CYAN}SAGE_CHAT_API_KEY=sk-xxx${NC}" - echo -e " ${CYAN}SAGE_CHAT_BASE_URL=https://api.openai.com/v1${NC}" + echo -e "${DIM}若已单独启动 gateway，可继续运行：${NC}" + echo -e " ${CYAN}sage serve gateway --probe --json${NC}" + echo -e " ${CYAN}sage chat --engine sagellm --backend auto --stream${NC}" fi ;; 3|"") echo "" echo -e "${DIM}已跳过。稍后可用以下命令:${NC}" - echo -e " ${CYAN}git clone https://github.com/intellistream/sage-examples.git${NC}" - echo -e " ${CYAN}python sage-examples/tutorials/hello_world.py${NC} # Hello World" - echo -e " ${CYAN}sage llm serve${NC} # LLM 服务" - echo "" - echo -e "${DIM}💡 SAGE Studio 需要单独安装:${NC}" - echo -e " ${CYAN}pip install isage-studio${NC}" - echo -e " ${CYAN}sage-studio start${NC}" + echo -e " ${CYAN}sage verify${NC}" + echo -e " ${CYAN}sage chat --ask \"Hello, SAGE!\"${NC}" + echo -e " ${CYAN}sage serve gateway --json${NC}" ;; *) echo "" @@ -756,47 +605,20 @@ show_usage_tips() { echo "" case "$mode" in - "minimal") - echo -e "${BLUE}最小安装模式：${NC}" - echo -e " # 只包含 SAGE 核心包 (L1-L5)，适合容器部署和生产环境" - echo -e " python3 -c 'from sage.kernel import Pipeline; print(\"Pipeline ready\")'" - echo "" - echo -e "${BLUE}按需安装可选功能：${NC}" - echo -e " pip install isage-middleware[ml] # ML 功能 (torch, transformers)" - echo -e " pip install isage-middleware[vdb] # 向量数据库 (faiss)" - echo -e " pip install isage-middleware[streaming] # 流处理扩展" - echo "" - ;; "dev") echo -e "${BLUE}开发者模式：${NC}" - echo -e " # 包含核心包 + 开发工具" + echo -e " # 包含 standard + 开发工具" echo -e " sage-dev test # 运行测试" echo -e " sage-dev quality # 代码质量检查" echo -e " pre-commit run --all-files # 运行所有检查" echo "" - echo -e "${BLUE}按需安装可选功能：${NC}" - echo -e " pip install isage-middleware[ml,vdb] # ML + 向量数据库" - echo -e " pip install isage-kernel[ml] # Kernel ML 扩展" - echo "" ;; - "full") - echo -e "${BLUE}完整功能模式（默认）：${NC}" + "standard"|*) + echo -e "${BLUE}standard 模式（默认）：${NC}" echo -e " # 包含所有核心功能 + 科学计算 + ML + 向量数据库" echo -e " sage --help # 查看 CLI 命令" + echo -e " sage-edge --help # 查看 edge 聚合 shell（需 serving-edge/full 依赖）" echo -e " jupyter notebook # 启动 Jupyter 笔记本" - echo -e " sage-dev test # 运行测试" - echo -e " sage-dev quality # 代码质量检查" - echo "" - ;; - # 兼容旧模式名称 - "core") - echo -e "${BLUE}核心运行时模式（已改名为 minimal）：${NC}" - echo -e " python3 -c 'from sage.kernel import Pipeline; print(\"Pipeline ready\")'" - echo "" - ;; - "standard") - echo -e "${BLUE}标准模式（已合并到 full）：${NC}" - echo -e " sage --help # 查看 CLI 命令" echo "" ;; esac @@ -805,21 +627,19 @@ show_usage_tips() { echo -e "${BLUE}独立包（按需安装）：${NC}" echo -e " pip install isage-benchmark # 性能基准测试" echo -e " pip install isagellm # LLM 推理引擎" - echo -e " pip install isage-edge # Edge 聚合器" - echo -e " git clone https://github.com/intellistream/sage-studio # Web UI" echo -e " git clone https://github.com/intellistream/sage-examples # 示例代码" echo "" - if [ "$mode" = "dev" ] || [ "$mode" = "full" ]; then - echo -e "${BLUE}C++扩展管理（可选）：${NC}" - echo -e " ${DIM}# C++扩展已在安装 sage-middleware 时自动构建${NC}" - echo -e " sage extensions status # 检查扩展状态" - echo -e " sage extensions install --force # 强制重新构建扩展" + if [ "$mode" = "dev" ] || [ "$mode" = "standard" ]; then + echo -e "${BLUE}C++扩展说明（可选）：${NC}" + echo -e " ${DIM}# 原生/C++扩展会在安装流程中按需自动构建${NC}" + echo -e " ./quickstart.sh --doctor # 检查本机构建依赖" + echo -e " ./quickstart.sh --dev --yes # 重新执行主仓安装/重建流程" echo "" fi echo -e "${BLUE}文档和示例：${NC}" - echo -e " ${GRAY}https://intellistream.github.io/SAGE-Pub/${NC}" + echo -e " ${GRAY}https://intellistream.github.io/sage-docs/${NC}" echo -e " ${GRAY}./examples/ # 查看示例代码${NC}" echo "" @@ -843,8 +663,18 @@ show_usage_tips() { echo "" fi - # 询问用户是否要启动 LLM 服务（非 CI 环境 + 非 --yes 自动模式） - prompt_start_llm_service "$mode" + # 安装结束时不自动触发服务启动交互，改为明确可执行的对话指引 + # 默认优先使用 sagellm 非服务模式（run），避免用户额外理解 server/client 拓扑 + echo -e "${BLUE}LLM 对话使用（推荐）：${NC}" + echo -e " ${DIM}# 1) 直接使用非服务模式（无需先启动 server）${NC}" + echo -e " ${CYAN}sagellm run -p \"Hello, SAGE!\"${NC}" + echo "" + echo -e " ${DIM}# 2) 需要 RAG 交互时，使用 sagellm 引擎直连${NC}" + echo -e " ${CYAN}sage chat --engine sagellm --backend auto --stream${NC}" + echo "" + echo -e " ${DIM}# 3) 如需外部 gateway 服务模式，先查看 SAGE 侧契约并探活${NC}" + echo -e " ${CYAN}sage serve gateway --json${NC}" + echo -e " ${CYAN}sage serve gateway --probe --json${NC}" } # 创建 VS Code conda 环境配置的辅助函数 @@ -882,14 +712,7 @@ create_vscode_conda_config() { ], "python.terminal.activateEnvironment": true, "python.analysis.extraPaths": [ - "\${workspaceFolder}/packages/sage/src", - "\${workspaceFolder}/packages/sage-common/src", - "\${workspaceFolder}/packages/sage-kernel/src", - "\${workspaceFolder}/packages/sage-libs/src", - "\${workspaceFolder}/packages/sage-middleware/src", - "\${workspaceFolder}/packages/sage-platform/src", - "\${workspaceFolder}/packages/sage-tools/src", - "\${workspaceFolder}/packages/sage-cli/src" + "\${workspaceFolder}/src" ] } EOF diff --git a/tools/install/docs/INSTALLATION_OPTIMIZATION.md b/tools/install/docs/INSTALLATION_OPTIMIZATION.md deleted file mode 100644 index 85ac75cc7f..0000000000 --- a/tools/install/docs/INSTALLATION_OPTIMIZATION.md +++ /dev/null @@ -1,262 +0,0 @@ -# SAGE 安装速度优化指南 - -## 当前瓶颈分析 - -从你的安装日志看，外部依赖安装已运行 **33 分钟**（1984 秒），这是正常但可以优化的： - -### 1. 依赖数量多（110 个包） - -- 包括大型深度学习框架：torch, torchvision, transformers -- 每个包都有自己的传递依赖 -- pip 需要解析整个依赖树 - -### 2. 网络下载慢 - -- 大型包（torch ~2GB, torchvision ~500MB, opencv ~80MB） -- 从 PyPI 官方源下载（国外服务器） -- 日志显示平均下载速度需监控 - -### 3. 编译时间长 - -- CUDA 相关包需要编译（nvidia-cuda-nvrtc-cu12, nvidia-cudnn-cu12） -- 某些 C++ 扩展包（如 opencv-python-headless） - -## 立即可用的优化方案 - -### 方案 1：使用国内镜像源（最有效） - -**清华 PyPI 镜像**： - -```bash -# 方法 A：环境变量（推荐） -export PIP_INDEX_URL=https://pypi.tuna.tsinghua.edu.cn/simple -export PIP_TRUSTED_HOST=pypi.tuna.tsinghua.edu.cn -./quickstart.sh --dev --yes - -# 方法 B：pip 配置文件（永久生效） -mkdir -p ~/.pip -cat > ~/.pip/pip.conf < 2 MB/s（绿色） - -### 改进 3: 安装完成后显示总体统计 - -**新增**: - -```bash -📊 安装统计: 共 51 个文件, 328.4 MB, 耗时 684s, 平均 0.48 MB/s -``` - -## 技术细节 - -### 文件大小提取 - -```bash -# 正则匹配 pip 输出中的文件大小 -if [[ "$line" =~ \(([0-9.]+)[[:space:]]*(kB|MB|GB)\) ]]; then - local size="${BASH_REMATCH[1]}" - local unit="${BASH_REMATCH[2]}" - - # 统一转换为 MB - case "$unit" in - kB) last_file_size=$(echo "scale=2; $size / 1024" | bc) ;; - MB) last_file_size="$size" ;; - GB) last_file_size=$(echo "scale=2; $size * 1024" | bc) ;; - esac -fi -``` - -### 速度计算 - -```bash -# 实时计算下载速度 -local elapsed=$(($(date +%s) - download_start_time)) -if [ $elapsed -gt 0 ]; then - speed_mb=$(echo "scale=2; $total_downloaded_mb / $elapsed" | bc) -fi -``` - -### 性能评估逻辑 - -```bash -# bc 命令用于浮点数比较 -if [ "$(echo "$avg_speed < 0.5" | bc)" = "1" ]; then - network_status="${YELLOW}慢速网络${NC}" -elif [ "$(echo "$avg_speed < 2" | bc)" = "1" ]; then - network_status="${CYAN}正常网络${NC}" -else - network_status="${GREEN}快速网络${NC}" -fi -``` - -## 使用场景 - -这个改进会在以下情况自动生效： - -1. **quickstart.sh 安装**: - - - 安装 SAGE 核心依赖时 - - 安装可选依赖（如 vLLM）时 - -1. **手动 pip 安装**: - - ```bash - source tools/install/display_tools/logging.sh - log_pip_install_with_verbose_progress "CONTEXT" "PHASE" "pip install torch" - ``` - -## 测试 - -运行测试脚本验证改进： - -```bash -bash tools/install/test_progress_display.sh -``` - -测试内容： - -1. 小包安装（requests）- 验证文件大小和速度显示 -1. 大包安装（pandas）- 验证多文件下载和统计 - -## 预期效果 - -### 场景 1: 网络正常（1 MB/s） - -``` - → 正在收集: torch - ⬇ 下载中... [8 个文件, 245.3 MB 已下载, 1.02 MB/s] - [已运行 245s，处理了 156 行输出，已下载 245.3 MB @ 1.00 MB/s | 正常网络 (0.5-2 MB/s)] - ⬇ 下载中... [15 个文件, 512.8 MB 已下载, 0.98 MB/s] - -📊 安装统计: 共 15 个文件, 512.8 MB, 耗时 523s, 平均 0.98 MB/s -``` - -### 场景 2: 网络慢（0.2 MB/s） - -``` - → 正在收集: torch - ⬇ 下载中... [3 个文件, 45.2 MB 已下载, 0.18 MB/s] - [已运行 251s，处理了 89 行输出，已下载 45.2 MB @ 0.18 MB/s | 慢速网络 (<0.5 MB/s)] - 提示: 下载速度较慢（0.18 MB/s），可能需要检查网络连接或使用镜像源 -``` - -## 相关文件 - -- `tools/install/display_tools/logging.sh` - 核心实现 -- `tools/install/test_progress_display.sh` - 测试脚本 -- `quickstart.sh` - 主安装脚本（自动使用） - -## 依赖 - -- `bc` 命令（浮点数计算）- 大多数 Linux 发行版自带 -- Bash 4.0+ （用于正则匹配） - -## 后续优化建议 - -1. **预估剩余时间**: 根据剩余包数量和平均速度估算 ETA -1. **镜像源自动切换**: 检测到慢速时自动建议切换到国内镜像 -1. **并行下载统计**: 如果 pip 支持并行下载，统计并行度 -1. **历史性能对比**: 记录历史安装速度，对比本次性能 diff --git a/tools/install/docs/README_CONSISTENCY.md b/tools/install/docs/README_CONSISTENCY.md index f81eb49c38..e12168a483 100644 --- a/tools/install/docs/README_CONSISTENCY.md +++ b/tools/install/docs/README_CONSISTENCY.md @@ -173,7 +173,7 @@ ls -la .sage/logs/install.log ```bash # 1. 卸载现有安装 -pip uninstall isage isage-common isage-kernel -y +pip uninstall isage -y # 2. 清理环境 ./quickstart.sh --clean @@ -199,10 +199,8 @@ git commit ## 详细文档 -- **完整指南**: - [docs-public/docs_src/dev-notes/l2-platform/INSTALLATION_CONSISTENCY.md](../../docs-public/docs_src/dev-notes/l2-platform/INSTALLATION_CONSISTENCY.md) -- **解决方案总结**: - [docs-public/docs_src/dev-notes/l2-platform/ISSUE_1121_SOLUTION.md](../../docs-public/docs_src/dev-notes/l2-platform/ISSUE_1121_SOLUTION.md) +- **完整指南**: [CHANGELOG.md](../../CHANGELOG.md) +- **解决方案总结**: [CHANGELOG.md](../../CHANGELOG.md) - **开发者指南**: [DEVELOPER.md](../../DEVELOPER.md) ## 相关 Issue diff --git a/tools/install/download_tools/argument_parser.sh b/tools/install/download_tools/argument_parser.sh index 39eaa505d9..fc9ea7b40d 100755 --- a/tools/install/download_tools/argument_parser.sh +++ b/tools/install/download_tools/argument_parser.sh @@ -52,6 +52,22 @@ set_hooks_profile_value() { esac } +set_install_mode_value() { + local value="${1,,}" + case "$value" in + "dev"|"standard"|"full") + INSTALL_MODE="$value" + ;; + "non-dev"|"nondev") + INSTALL_MODE="standard" + ;; + *) + echo -e "${CROSS} 无效的安装模式: $1 (可选: standard, full, dev)" + exit 1 + ;; + esac +} + set_mirror_source_value() { local raw_value="$1" local value="${raw_value,,}" @@ -95,10 +111,11 @@ DOCTOR_ONLY=false FIX_ENVIRONMENT=false VERIFY_DEPS=false VERIFY_DEPS_STRICT=false -AUTO_VENV=false # 新增：自动创建虚拟环境 SKIP_HOOKS=false HOOKS_MODE="auto" SETUP_WORKSPACE=false # 新增：设置 workspace 依赖 +CLONE_SATELLITE_REPOS=false # 新增：克隆附属仓库 +CLONE_SATELLITE_REPOS_EXPLICIT=false # 是否由用户显式指定克隆策略 HOOKS_PROFILE="lightweight" USE_PIP_MIRROR=true # 默认启用pip镜像自动检测（中国用户自动使用清华源） MIRROR_SOURCE="auto" @@ -138,12 +155,11 @@ detect_current_environment() { fi fi - # 检测虚拟环境 + # 检测 Python venv（策略：不作为推荐环境，仅用于识别并后续 fail-fast） if [ -n "${VIRTUAL_ENV:-}" ]; then if [ "$in_conda" = false ] && [ "$in_conda_base" = false ]; then - env_type="venv" - env_name=$(basename "${VIRTUAL_ENV:-}") - in_venv=true + env_type="system" + env_name="" fi fi @@ -159,8 +175,8 @@ get_smart_environment_recommendation() { local in_venv=$(echo "$env_info" | cut -d'|' -f4) local in_conda_base=$(echo "$env_info" | cut -d'|' -f5) - if [ "$in_conda" = true ] || [ "$in_venv" = true ]; then - # 用户已经在虚拟环境中（非 base），推荐直接使用 + if [ "$in_conda" = true ]; then + # 用户已经在 conda 环境中（非 base），推荐直接使用 echo "pip|$env_type|$env_name" elif [ "$in_conda_base" = true ]; then # 用户在 conda base 环境中，不推荐使用，推荐创建新环境 @@ -226,31 +242,23 @@ show_installation_menu() { # 选择安装模式 while true; do echo -e "${BOLD}1. 选择安装模式：${NC}" - echo -e " ${GRAY}1)${NC} 最小安装 - 核心包 ${DIM}(~80包, 生产部署/容器镜像)${NC}" - echo -e " ${GREEN}2)${NC} 开发安装 - 核心+开发工具 ${DIM}(~120包, 日常开发)${NC}" - echo -e " ${YELLOW}3)${NC} 完整安装 - 开发+所有可选依赖 ${DIM}(~200+包, 推荐)${NC}" + echo -e " ${YELLOW}1)${NC} standard 安装 - 仅 SAGE 核心子包 ${DIM}(~200+包, 轻量, 无 torch/CUDA)${NC}" + echo -e " ${CYAN}2)${NC} full 安装 - standard + 扩展能力集 ${DIM}(~220+包, 不强制 torch/CUDA)${NC}" + echo -e " ${GREEN}3)${NC} dev 安装 - full + 开发工具 + 本地 editable ${DIM}(~230+包, 日常开发)${NC}" echo "" read -p "请选择安装模式 [1-3，默认3]: " mode_choice case "${mode_choice:-3}" in 1) - INSTALL_MODE="minimal" - echo "" - echo -e "${DIM}💡 最小安装不含 ML/VDB/streaming 等功能${NC}" - echo -e "${DIM} 如需这些功能，可稍后运行:${NC}" - echo -e "${DIM} pip install isage-middleware[ml,vdb,streaming,compression]${NC}" + INSTALL_MODE="standard" break ;; 2) - INSTALL_MODE="dev" - echo "" - echo -e "${DIM}💡 开发安装不含 ML/VDB/streaming 等可选依赖${NC}" - echo -e "${DIM} 如需这些功能，可稍后运行:${NC}" - echo -e "${DIM} pip install isage-middleware[ml,vdb,streaming,compression]${NC}" + INSTALL_MODE="full" break ;; 3) - INSTALL_MODE="full" + INSTALL_MODE="dev" break ;; *) @@ -273,8 +281,6 @@ show_installation_menu() { echo -e "${INFO} 检测到您当前在 conda 环境中: ${GREEN}$current_env_name${NC}" elif [ "$current_env_type" = "conda_base" ]; then echo -e "${INFO} 检测到您当前在 conda ${YELLOW}base${NC} 环境中 ${DIM}(不推荐用于开发)${NC}" - elif [ "$current_env_type" = "venv" ] && [ -n "$current_env_name" ]; then - echo -e "${INFO} 检测到您当前在虚拟环境中: ${GREEN}$current_env_name${NC}" elif [ "$current_env_type" = "system" ]; then echo -e "${INFO} 检测到您当前在系统 Python 环境中" fi @@ -292,10 +298,10 @@ show_installation_menu() { fi if [ "$recommended_env" = "pip" ]; then - # 推荐使用当前环境（仅当在真正的虚拟环境中，非 base） + # 推荐使用当前环境（仅当在当前 conda 环境中，非 base） if [ "$current_env_type" = "system" ]; then - # 在系统环境中，不推荐使用，建议创建虚拟环境 - echo -e " ${PURPLE}1)${NC} 使用当前系统环境 ${DIM}(不推荐，建议使用虚拟环境)${NC}" + # 在系统环境中，不推荐使用，建议创建 conda 环境 + echo -e " ${PURPLE}1)${NC} 使用当前系统环境 ${DIM}(不推荐，建议使用 conda 环境)${NC}" if [ "$conda_available" = true ]; then echo -e " ${GREEN}2)${NC} 创建新的 Conda 环境 ${DIM}(推荐)${NC}" local default_choice=2 @@ -314,8 +320,8 @@ show_installation_menu() { local default_choice=1 fi else - # 在真正的虚拟环境中，推荐使用当前环境 - echo -e " ${GREEN}1)${NC} 使用当前环境 ${DIM}(推荐，已在虚拟环境中)${NC}" + # 在 conda 环境中，推荐使用当前环境 + echo -e " ${GREEN}1)${NC} 使用当前环境 ${DIM}(推荐，已在 Conda 环境中)${NC}" if [ "$conda_available" = true ]; then echo -e " ${PURPLE}2)${NC} 创建新的 Conda 环境" else @@ -412,6 +418,31 @@ show_installation_menu() { done echo "" + + # 询问是否克隆附属仓库 + echo -e "${BOLD}3. 克隆附属仓库？${NC}" + echo -e " ${DIM}包括: sage-examples, sage-tutorials, sagellm, sage-benchmark 等${NC}" + echo "" + local clone_prompt="[y/N]" + local clone_default="N" + if [ "$INSTALL_MODE" = "dev" ]; then + clone_prompt="[Y/n]" + clone_default="Y" + echo -e " ${INFO} dev 模式默认克隆附属仓库（便于本地 editable 开发）" + fi + + read -p "是否克隆 SAGE 附属仓库？${clone_prompt}: " -n 1 -r clone_choice + echo "" + local clone_choice_normalized="${clone_choice:-$clone_default}" + if [[ "$clone_choice_normalized" =~ ^[Yy]$ ]]; then + CLONE_SATELLITE_REPOS=true + echo -e "${GREEN}✅ 将克隆附属仓库${NC}" + else + CLONE_SATELLITE_REPOS=false + echo -e "${DIM}跳过克隆（可稍后手动克隆）${NC}" + fi + + echo "" } # 显示参数帮助信息 @@ -428,33 +459,33 @@ show_parameter_help() { echo -e "${BLUE}📦 安装模式：${NC}" echo "" - echo -e " ${BOLD}--minimal, -m${NC} ${GRAY}最小安装${NC}" - echo -e " ${DIM}包含: L1-L5 核心包，无开发工具，无可选依赖${NC}" - echo -e " ${DIM}大小: ~80 个包（约 200MB）${NC}" - echo -e " ${DIM}适合: 生产部署、容器镜像、CI/CD 基础镜像${NC}" - echo -e " ${DIM}缺少功能需手动安装: pip install isage-middleware[ml,vdb,...]${NC}" + echo -e " ${BOLD}--install-mode , --mode ${NC} ${GREEN}显式指定安装模式${NC}" + echo -e " ${DIM}推荐写法，语义清晰，便于脚本自动化${NC}" echo "" - echo -e " ${BOLD}--dev, -d${NC} ${GREEN}开发安装${NC}" - echo -e " ${DIM}包含: 最小安装 + 开发工具 (pytest, ruff, mypy, pre-commit)${NC}" - echo -e " ${DIM}大小: ~120 个包（约 350MB）${NC}" - echo -e " ${DIM}适合: 日常开发、贡献 SAGE 框架源码${NC}" - echo -e " ${DIM}可选功能需手动安装: pip install isage-middleware[ml,vdb,...]${NC}" + echo -e " ${BOLD}--standard, -s${NC} ${YELLOW}standard 安装${NC}" + echo -e " ${DIM}包含: SAGE 核心子包，依赖从 PyPI 解析，不含 torch/CUDA${NC}" + echo -e " ${DIM}大小: ~200+ 个包（轻量，无 GPU 依赖）${NC}" + echo -e " ${DIM}适合: 仅使用 SAGE 核心功能、CI 环境、轻量部署${NC}" echo "" - echo -e " ${BOLD}--full, -f${NC} ${YELLOW}完整安装 (默认)${NC}" - echo -e " ${DIM}包含: 开发安装 + 所有可选依赖 (ML, VDB, streaming, etc.)${NC}" - echo -e " ${DIM}大小: ~200+ 个包（约 1GB，含 PyTorch）${NC}" + echo -e " ${BOLD}--full, -f${NC} ${CYAN}full 安装${NC}" + echo -e " ${DIM}包含: standard + 扩展能力集（.[full]）${NC}" + echo -e " ${DIM}大小: ~220+ 个包（不强制安装 PyTorch/CUDA）${NC}" echo -e " ${DIM}适合: 学习示例、完整功能体验、研究实验${NC}" echo "" + echo -e " ${BOLD}--dev, -d${NC} ${GREEN}开发安装${NC}" + echo -e " ${DIM}包含: full + 开发工具 (pytest, ruff, mypy, pre-commit) + 本地 editable${NC}" + echo -e " ${DIM}自动将 SAGE.code-workspace 中检测到的附属仓库安装为 editable${NC}" + echo -e " ${DIM}大小: ~230+ 个包（开发模式默认）${NC}" + echo -e " ${DIM}适合: 日常开发、贡献 SAGE 框架源码${NC}" + echo -e " ${DIM}兼容别名: --non-dev / --nondev 等同于 standard${NC}" + echo "" echo -e "${BLUE}🔧 安装环境：${NC}" echo "" echo -e " ${BOLD}--pip, -pip${NC} ${PURPLE}使用当前环境${NC}" echo -e " ${BOLD}--conda, -conda${NC} ${GREEN}创建conda环境${NC}" - echo -e " ${BOLD}--auto-venv${NC} ${YELLOW}自动创建虚拟环境${NC}" - echo -e " ${DIM}检测系统环境时自动创建 .sage/venv 虚拟环境${NC}" - echo -e " ${DIM}优先使用 conda (如可用)，否则使用 Python venv${NC}" echo "" - echo -e " ${DIM}💡 不指定时自动智能选择: 虚拟环境→pip，系统环境→conda${NC}" + echo -e " ${DIM}💡 不指定时自动智能选择: Conda环境→pip，系统环境→conda${NC}" echo "" echo -e "${BLUE}⚡ 其他选项：${NC}" @@ -478,9 +509,21 @@ show_parameter_help() { echo -e " ${DIM}稍后可手动运行 'sage-dev maintain hooks install'${NC}" echo "" echo -e " ${BOLD}--workspace${NC} ${GREEN}设置 workspace 依赖${NC}" - echo -e " ${DIM}克隆 SAGE-Pub 仓库${NC}" + echo -e " ${DIM}克隆 sage-docs 和 sage-team-info 仓库${NC}" echo -e " ${DIM}用于 VS Code 多文件夹编辑（SAGE.code-workspace）${NC}" echo "" + echo -e " ${BOLD}--setup-conda${NC} ${GREEN}引导安装 Conda 环境${NC}" + echo -e " ${DIM}检测 conda 是否已安装，未安装时提供 Miniforge3 自动下载${NC}" + echo -e " ${DIM}已安装时引导创建并激活专用 '${SAGE_CONDA_ENV_NAME:-sage}' 环境${NC}" + echo -e " ${DIM}首次在新机器上配置开发环境时推荐使用${NC}" + echo "" + echo -e " ${BOLD}--clone-satellites${NC} ${GREEN}克隆附属仓库${NC}" + echo -e " ${DIM}克隆所有 SAGE 附属仓库（examples, tutorials, benchmark 等）${NC}" + echo -e " ${DIM}支持别名: --clone-repos, --satellites${NC}" + echo "" + echo -e " ${BOLD}--no-clone-satellites${NC} ${YELLOW}跳过克隆附属仓库${NC}" + echo -e " ${DIM}支持别名: --skip-satellites, --no-repos${NC}" + echo "" echo -e " ${BOLD}--hooks-mode ${NC} ${GREEN}控制 hooks 安装方式${NC}" echo -e " ${DIM}auto: 交互式安装后台运行，其余场景同步${NC}" echo -e " ${DIM}background: 总是异步，安装更快${NC}" @@ -548,16 +591,22 @@ show_parameter_help() { echo "" echo -e "${BLUE}💡 使用示例：${NC}" + echo -e " ./quickstart.sh --setup-conda ${DIM}# 首次使用：引导安装 Conda / 创建 sage 环境${NC}" echo -e " ./quickstart.sh ${DIM}# 交互式安装（推荐）${NC}" - echo -e " ./quickstart.sh --yes ${DIM}# 完整安装 + 跳过确认（默认模式）${NC}" - echo -e " ./quickstart.sh --dev --yes ${DIM}# 开发安装 + 跳过确认${NC}" - echo -e " ./quickstart.sh --minimal --pip --yes ${DIM}# 最小安装 + 当前环境 + 跳过确认${NC}" - echo -e " ./quickstart.sh --full --conda ${DIM}# 完整安装 + 创建conda环境${NC}" + echo -e " ./quickstart.sh --yes ${DIM}# dev 安装 + 跳过确认（默认）${NC}" + echo -e " ./quickstart.sh --full --yes ${DIM}# full 安装 + 跳过确认${NC}" + echo -e " ./quickstart.sh --dev --yes ${DIM}# dev 安装 + 跳过确认${NC}" + echo -e " ./quickstart.sh --install-mode full --yes ${DIM}# 显式 full 模式 + 跳过确认${NC}" + echo -e " ./quickstart.sh --install-mode dev --yes ${DIM}# 显式 dev 模式 + 跳过确认${NC}" + echo -e " ./quickstart.sh --mode standard --pip ${DIM}# 显式 standard 模式 + 当前环境${NC}" + echo -e " ./quickstart.sh --standard --conda ${DIM}# standard 安装 + 创建conda环境${NC}" + echo -e " ./quickstart.sh --clone-satellites --yes ${DIM}# dev 安装(默认) + 克隆附属仓库${NC}" echo "" echo -e "${PURPLE}📝 注意：${NC}" - echo -e " ${DIM}• quickstart.sh 默认使用 full 模式（包含所有功能）${NC}" - echo -e " ${DIM}• minimal/dev 模式缺少的功能会在运行时给出安装提示${NC}" - echo -e " ${DIM}• pip 安装: pip install isage (等同于 minimal 模式)${NC}" + echo -e " ${DIM}• quickstart.sh 默认使用 dev 模式（包含开发工具与本地 editable）${NC}" + echo -e " ${DIM}• dev 模式会在 standard 基础上额外安装开发工具，并尽量切换为本地 editable${NC}" + echo -e " ${DIM}• pip 安装: pip install isage (等同于 standard 模式)${NC}" + echo -e " ${DIM}• 克隆要求网络连接到 GitHub，多个仓库可能需要几十秒${NC}" echo "" } @@ -565,23 +614,26 @@ show_parameter_help() { # 解析安装模式参数 -# 简化为三种模式: minimal, dev, full (默认) +# 三种显式模式：--standard / --full / --dev parse_install_mode() { local param="$1" case "$param" in - # 最小安装：核心包，无开发工具，无可选依赖 - "--minimal"|"-m"|"-minimal"|"--core"|"--c"|"-core"|"-c") - INSTALL_MODE="minimal" + "--standard"|"-s") + INSTALL_MODE="standard" + return 0 + ;; + # full 安装：核心 + torch/accelerate/peft + "--full"|"-f") + INSTALL_MODE="full" return 0 ;; - # 开发安装：核心 + 开发工具 - "--dev"|"-d"|"-dev"|"--d"|"--standard"|"--s"|"-standard"|"-s") + # 开发安装：full + 开发工具 + 本地 editable + "--dev"|"-d") INSTALL_MODE="dev" return 0 ;; - # 完整安装：核心 + 开发工具 + 所有可选依赖 - "--full"|"-f"|"-full"|"--f") - INSTALL_MODE="full" + "--non-dev"|"--nondev") + INSTALL_MODE="standard" return 0 ;; *) @@ -602,11 +654,6 @@ parse_install_environment() { INSTALL_ENVIRONMENT="pip" return 0 ;; - "--auto-venv") - AUTO_VENV=true - export SAGE_AUTO_VENV=true - return 0 - ;; *) return 1 ;; @@ -755,6 +802,26 @@ parse_force_rebuild_option() { esac } +# 解析克隆附属仓库参数 +parse_clone_satellites_option() { + local param="$1" + case "$param" in + "--clone-satellites"|"--clone-repos"|"--satellites") + CLONE_SATELLITE_REPOS=true + CLONE_SATELLITE_REPOS_EXPLICIT=true + return 0 + ;; + "--no-clone-satellites"|"--skip-satellites"|"--no-repos") + CLONE_SATELLITE_REPOS=false + CLONE_SATELLITE_REPOS_EXPLICIT=true + return 0 + ;; + *) + return 1 + ;; + esac +} + # 主参数解析函数 parse_arguments() { local unknown_params=() @@ -774,6 +841,16 @@ parse_arguments() { if [[ "$param" == "--skip-hooks" ]]; then SKIP_HOOKS=true shift + elif [[ "$param" == --install-mode=* ]] || [[ "$param" == --mode=* ]]; then + set_install_mode_value "${param#*=}" + shift + elif [[ "$param" == "--install-mode" ]] || [[ "$param" == "--mode" ]]; then + if [[ $# -lt 2 ]]; then + echo -e "${CROSS} $param 需要一个值 (dev|standard)" + exit 1 + fi + set_install_mode_value "$2" + shift 2 elif [[ "$param" == "--workspace" ]]; then SETUP_WORKSPACE=true shift @@ -841,6 +918,9 @@ parse_arguments() { elif parse_force_rebuild_option "$param"; then # 强制重新编译参数 shift + elif parse_clone_satellites_option "$param"; then + # 克隆附属仓库参数 + shift else # 未知参数 unknown_params+=("$param") @@ -894,8 +974,15 @@ set_defaults_and_show_tips() { # 设置安装模式默认值 if [ -z "$INSTALL_MODE" ]; then - INSTALL_MODE="full" - echo -e "${INFO} 未指定安装模式，使用默认: ${YELLOW}完整安装${NC}" + INSTALL_MODE="dev" + echo -e "${INFO} 未指定安装模式，使用默认: ${GREEN}dev 安装${NC}" + has_defaults=true + fi + + # dev 模式下默认克隆附属仓库（除非用户显式指定了克隆策略） + if [ "$INSTALL_MODE" = "dev" ] && [ "$CLONE_SATELLITE_REPOS_EXPLICIT" = "false" ] && [ "$CLONE_SATELLITE_REPOS" = "false" ]; then + CLONE_SATELLITE_REPOS=true + echo -e "${INFO} dev 模式默认启用附属仓库克隆" has_defaults=true fi @@ -913,7 +1000,12 @@ set_defaults_and_show_tips() { elif [ "$recommended_env" = "conda" ]; then echo -e "${INFO} 检测到系统环境，推荐默认: ${GREEN}创建conda环境${NC}" else - echo -e "${INFO} 未指定安装环境，使用默认: ${PURPLE}系统Python环境${NC}" + # conda 未安装 + 系统 Python：交互式引导，而非静默回落 + if declare -f check_conda_environment >/dev/null 2>&1; then + check_conda_environment || true + else + echo -e "${INFO} 未指定安装环境，使用默认: ${PURPLE}系统Python环境${NC}" + fi fi has_defaults=true fi @@ -930,13 +1022,16 @@ show_install_configuration() { echo -e "${BLUE}📋 安装配置：${NC}" case "$INSTALL_MODE" in "standard") - echo -e " ${BLUE}安装模式:${NC} ${GREEN}标准安装${NC}" + echo -e " ${BLUE}安装模式:${NC} ${YELLOW}standard 安装${NC} ${DIM}(核心子包, 无 torch/CUDA)${NC}" ;; - "core") - echo -e " ${BLUE}安装模式:${NC} ${GRAY}核心运行时${NC}" + "full") + echo -e " ${BLUE}安装模式:${NC} ${CYAN}full 安装${NC} ${DIM}(standard + 扩展能力集，不强制 torch/CUDA)${NC}" ;; "dev") - echo -e " ${BLUE}安装模式:${NC} ${YELLOW}开发者安装${NC}" + echo -e " ${BLUE}安装模式:${NC} ${GREEN}开发者安装${NC} ${DIM}(full + 开发工具 + 本地 editable)${NC}" + ;; + *) + echo -e " ${BLUE}安装模式:${NC} ${YELLOW}standard 安装${NC}" ;; esac @@ -985,6 +1080,13 @@ show_install_configuration() { if [ "$CLEAN_PIP_CACHE" = false ]; then echo -e " ${BLUE}特殊选项:${NC} ${YELLOW}跳过 pip 缓存清理${NC}" fi + + if [ "$CLONE_SATELLITE_REPOS" = true ]; then + echo -e " ${BLUE}附属仓库:${NC} ${GREEN}克隆所有仓库${NC}" + else + echo -e " ${BLUE}附属仓库:${NC} ${YELLOW}跳过克隆${NC}" + fi + echo "" } @@ -1078,6 +1180,10 @@ get_setup_workspace() { echo "$SETUP_WORKSPACE" } +should_clone_satellite_repos() { + echo "$CLONE_SATELLITE_REPOS" +} + get_mirror_source_value() { echo "$MIRROR_SOURCE" } diff --git a/tools/install/download_tools/clone_satellite_repos.sh b/tools/install/download_tools/clone_satellite_repos.sh new file mode 100644 index 0000000000..0a97b41156 --- /dev/null +++ b/tools/install/download_tools/clone_satellite_repos.sh @@ -0,0 +1,230 @@ +#!/bin/bash +# SAGE 附属仓库克隆模块 +# 从 SAGE.code-workspace 文件动态读取要克隆的仓库列表 + +# 获取脚本目录 +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" + +# 导入颜色定义 +source "$SCRIPT_DIR/../display_tools/colors.sh" + +# 获取 workspace 文件路径 +SAGE_ROOT="${SAGE_ROOT:-$(cd "$SCRIPT_DIR/../../.." && pwd)}" +WORKSPACE_FILE="$SAGE_ROOT/SAGE.code-workspace" + +# 从 workspace 文件读取仓库配置 +load_repos_from_workspace() { + local workspace_file="$1" + + if [ ! -f "$workspace_file" ]; then + echo -e "${RED}❌ Workspace 文件不存在: $workspace_file${NC}" >&2 + return 1 + fi + + # JSONC 兼容解析：直接从 path 字段提取仓库名（避免 jq/json 对注释报错） + grep -oP '"path":\s*"\K[^"]+(?=")' "$workspace_file" | \ + grep -v '^\.$' | \ + awk -F'/' '{print $NF}' +} + +# 构建仓库 URL +get_repo_url() { + local repo_name="$1" + # 标准的 GitHub 仓库 URL 格式 + echo "https://github.com/intellistream/${repo_name}.git" +} + +# 克隆单个仓库 +clone_single_repo() { + local repo_name="$1" + local repo_url="$2" + local target_dir="$3" + local repo_path="$target_dir/$repo_name" + + # 检查目录是否已存在 + if [ -d "$repo_path" ]; then + echo -e "${YELLOW}⚠️ $repo_name 已存在${NC}" + + # 尝试切换到 main-dev 分支 + if cd "$repo_path" 2>/dev/null; then + # 检查是否是 git 仓库 + if git rev-parse --is-inside-work-tree >/dev/null 2>&1; then + # 获取当前分支 + local current_branch=$(git rev-parse --abbrev-ref HEAD 2>/dev/null) + + # 检查 main-dev 分支是否存在（本地） + if git rev-parse --verify main-dev 2>/dev/null; then + # 本地存在 main-dev，直接切换 + if git checkout main-dev >/dev/null 2>&1; then + echo -e "${GREEN} ✓ 已切换到 main-dev 分支${NC}" + else + echo -e "${YELLOW} ⚠️ 无法切换到 main-dev 分支${NC}" + fi + elif git fetch origin main-dev 2>/dev/null; then + # 远程存在 main-dev，先 fetch 再创建 + if git checkout -b main-dev origin/main-dev 2>/dev/null; then + echo -e "${GREEN} ✓ 已创建并切换到 main-dev 分支${NC}" + else + echo -e "${YELLOW} ⚠️ 无法创建/切换到 main-dev 分支${NC}" + fi + else + echo -e "${DIM} ℹ️ main-dev 分支不存在（当前分支: $current_branch）${NC}" + fi + else + echo -e "${YELLOW} ⚠️ 不是有效的 git 仓库${NC}" + fi + cd - >/dev/null 2>&1 + else + echo -e "${YELLOW} ⚠️ 无法进入目录${NC}" + fi + return 0 + fi + + echo -e "${BLUE}📥 克隆 $repo_name...${NC}" + + # 最多重试 3 次（应对首次连接超时等瞬态故障） + local max_attempts=3 + local attempt=1 + local clone_ok=false + local clone_error="" + while [ $attempt -le $max_attempts ]; do + clone_error=$(git clone "$repo_url" "$repo_path" 2>&1) + if [ $? -eq 0 ]; then + clone_ok=true + break + fi + echo -e "${YELLOW} ⚠️ 第 $attempt 次克隆失败，${NC}${DIM}原因: $clone_error${NC}" + if [ $attempt -lt $max_attempts ]; then + echo -e "${DIM} 重试中 ($((attempt+1))/$max_attempts)...${NC}" + sleep 2 + fi + attempt=$((attempt + 1)) + done + + if $clone_ok; then + echo -e "${GREEN}✅ $repo_name 克隆成功${NC}" + + # 克隆成功后，尝试切换到 main-dev 分支 + if cd "$repo_path" 2>/dev/null; then + # 检查远程是否有 main-dev 分支 + if git fetch origin main-dev 2>/dev/null && git rev-parse origin/main-dev 2>/dev/null; then + if git checkout -b main-dev origin/main-dev 2>/dev/null; then + echo -e "${GREEN} ✓ 已切换到 main-dev 分支${NC}" + fi + else + # 如果没有 main-dev，保持默认分支 + local default_branch=$(git rev-parse --abbrev-ref HEAD 2>/dev/null) + echo -e "${DIM} ℹ️ 使用默认分支: $default_branch${NC}" + fi + cd - >/dev/null 2>&1 + fi + return 0 + else + echo -e "${RED}❌ $repo_name 克隆失败（已重试 $max_attempts 次）${NC}" + echo -e "${DIM} 最后一次错误: $clone_error${NC}" + return 1 + fi +} + +# 克隆所有公开附属仓库 +clone_all_public_repos() { + local parent_dir="$1" + local workspace_file="${2:-$WORKSPACE_FILE}" + local failed_repos=() + + echo "" + echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + echo -e "${BOLD}📚 克隆 SAGE 附属仓库${NC}" + echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + echo "" + + # 检查网络连接 + if ! ping -c 1 github.com >/dev/null 2>&1; then + echo -e "${RED}❌ 无网络连接到 GitHub，停止克隆${NC}" + echo -e "${DIM} 请检查网络连接后重试${NC}" + return 1 + fi + + # 从 workspace 读取仓库列表 + local repos_output + if ! repos_output=$(load_repos_from_workspace "$workspace_file"); then + echo -e "${RED}❌ 无法读取 workspace 文件: $workspace_file${NC}" + return 1 + fi + + # 计算总仓库数 + local total_repos=$(echo "$repos_output" | wc -l) + if [ "$total_repos" -eq 0 ]; then + echo -e "${YELLOW}⚠️ 没有找到要克隆的仓库${NC}" + return 1 + fi + + local current=0 + while IFS= read -r repo_name; do + [ -z "$repo_name" ] && continue + + current=$((current + 1)) + echo -e "${DIM}[$current/$total_repos]${NC} $repo_name" + + local repo_url=$(get_repo_url "$repo_name") + if clone_single_repo "$repo_name" "$repo_url" "$parent_dir"; then + echo "" + else + failed_repos+=("$repo_name") + echo "" + fi + done <<< "$repos_output" + + echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + + if [ ${#failed_repos[@]} -eq 0 ]; then + echo -e "${GREEN}✅ 全部 $total_repos 个附属仓库克隆成功！${NC}" + return 0 + else + echo -e "${YELLOW}⚠️ $total_repos 个仓库中有 ${#failed_repos[@]} 个克隆失败：${failed_repos[*]}${NC}" + return 1 + fi +} + +# 克隆私有仓库（当前无私有仓库） +clone_private_repos() { + echo -e "${DIM}暂无需要克隆的私有仓库${NC}" +} + +# 交互式克隆选择 +interactive_clone_repos() { + local parent_dir="$1" + local workspace_file="${2:-$WORKSPACE_FILE}" + + echo "" + echo -e "${BOLD}是否克隆 SAGE 附属仓库到当前目录？${NC}" + echo "" + echo -e "${DIM}附属仓库将从 SAGE.code-workspace 文件读取，包括：${NC}" + echo -e "${DIM} • sage-examples, sage-tutorials, sagellm, sage-benchmark${NC}" + echo -e "${DIM} • sage-dev-tools, sage-agentic, sage-agentic-tooluse${NC}" + echo -e "${DIM} • sage-anns, sage-eval, sage-finetune, sage-studio 等${NC}" + echo "" + echo -e "${YELLOW}💡 提示：${NC}" + echo -e "${DIM} 如果不克隆，可以稍后手动克隆：${NC}" + echo -e "${DIM} git clone https://github.com/intellistream/sage-examples.git${NC}" + echo "" + + read -p "是否现在克隆附属仓库？[y/N]: " -r response + response=${response,,} + + if [[ "$response" =~ ^(y|yes)$ ]]; then + clone_all_public_repos "$parent_dir" "$workspace_file" + return 0 + else + echo -e "${DIM}已取消克隆操作${NC}" + return 1 + fi +} + +# 导出函数供外部使用 +export -f load_repos_from_workspace +export -f get_repo_url +export -f clone_single_repo +export -f clone_all_public_repos +export -f clone_private_repos +export -f interactive_clone_repos diff --git a/tools/install/download_tools/conda_manager.sh b/tools/install/download_tools/conda_manager.sh index 479a089f2b..549bbd60da 100755 --- a/tools/install/download_tools/conda_manager.sh +++ b/tools/install/download_tools/conda_manager.sh @@ -72,11 +72,30 @@ ask_conda_environment() { # 记录到日志 echo "$(date): 用户选择 Conda 环境配置" >> "$log_file" - # 如果是CI环境、远程部署或使用了 --yes 参数，自动选择选项2（使用当前环境） + # 如果是CI环境、远程部署或使用了 --yes 参数，采用稳定的非交互策略： + # 1) 当前在非 base conda 环境：直接使用当前环境 + # 2) 当前不在非 base 环境，但存在 sage 环境：使用 sage 环境 + # 3) 当前不在非 base 环境，且不存在 sage 环境：创建 sage 环境 if [ "${CI:-}" = "true" ] || [ "${SAGE_REMOTE_DEPLOY:-}" = "true" ] || [ -n "${GITHUB_ACTIONS:-}" ] || [ -n "${GITLAB_CI:-}" ] || [ -n "${JENKINS_URL:-}" ] || [ "${AUTO_CONFIRM:-}" = "true" ]; then - echo -e "${INFO} 非交互模式，自动选择选项2：使用当前环境" - echo "$(date): 非交互模式自动选择选项2" >> "$log_file" - conda_choice=2 + if [ -n "${CONDA_DEFAULT_ENV:-}" ] && [ "${CONDA_DEFAULT_ENV:-}" != "base" ]; then + echo -e "${INFO} 非交互模式：检测到当前 conda 环境 ${GREEN}${CONDA_DEFAULT_ENV:-}${NC}，自动使用当前环境" + echo "$(date): 非交互模式自动选择当前非 base conda 环境: ${CONDA_DEFAULT_ENV:-}" >> "$log_file" + conda_choice=2 + elif conda env list | grep -q '^sage '; then + echo -e "${INFO} 非交互模式：检测到已存在环境 ${GREEN}sage${NC}，自动使用该环境" + echo "$(date): 非交互模式自动使用已存在环境: sage" >> "$log_file" + SAGE_ENV_NAME="sage" + export SAGE_ENV_NAME + activate_conda_environment "${SAGE_ENV_NAME:-}" + return $? + else + echo -e "${INFO} 非交互模式：未检测到可复用的非 base conda 环境，自动创建 ${GREEN}sage${NC} 环境" + echo "$(date): 非交互模式自动创建环境: sage" >> "$log_file" + SAGE_ENV_NAME="sage" + export SAGE_ENV_NAME + create_conda_environment "${SAGE_ENV_NAME:-}" + return $? + fi else # 交互模式，询问用户选择 while true; do diff --git a/tools/install/download_tools/environment_config.sh b/tools/install/download_tools/environment_config.sh index c2e4c117ac..7f82fe9c95 100755 --- a/tools/install/download_tools/environment_config.sh +++ b/tools/install/download_tools/environment_config.sh @@ -115,16 +115,186 @@ detect_mainland_china_ip() { return 1 } +normalize_simple_url() { + local url="$1" + url="${url%/}" + if [[ "$url" != */simple ]]; then + url="$url/simple" + fi + echo "${url}/" +} + +resolve_url_with_python() { + local base_url="$1" + local raw_url="$2" + + python3 - </dev/null 2>&1; then + return 1 + fi + + local html + html="$(curl -L -s --connect-timeout 5 --max-time 12 "$package_simple_url" 2>/dev/null || true)" + [ -n "$html" ] || return 1 + + local href + href="$(printf '%s' "$html" | tr '\n' ' ' | grep -oE 'href="[^"]+"' | head -n 1 | sed 's/^href="//;s/"$//')" + [ -n "$href" ] || return 1 + + resolve_url_with_python "$package_simple_url" "$href" +} + +probe_artifact_download_status() { + local artifact_url="$1" + + if ! command -v curl >/dev/null 2>&1; then + echo "000" + return 1 + fi + + local status + status="$(curl -L -s -I --connect-timeout 5 --max-time 12 -o /dev/null -w "%{http_code}" "$artifact_url" 2>/dev/null || echo "000")" + case "$status" in + 200|204|206|301|302) + echo "$status" + return 0 + ;; + 403) + echo "$status" + return 1 + ;; + 405|000) + status="$(curl -L -s --range 0-0 --connect-timeout 5 --max-time 12 -o /dev/null -w "%{http_code}" "$artifact_url" 2>/dev/null || echo "000")" + ;; + esac + + echo "$status" + case "$status" in + 200|204|206|301|302) + return 0 + ;; + *) + return 1 + ;; + esac +} + +is_mirror_download_healthy() { + local mirror_simple_url + mirror_simple_url="$(normalize_simple_url "$1")" + local test_package="${2:-pip}" + + if ! command -v curl >/dev/null 2>&1; then + return 1 + fi + + local simple_status + simple_status="$(curl -L -s --connect-timeout 5 --max-time 12 -o /dev/null -w "%{http_code}" "${mirror_simple_url}${test_package}/" 2>/dev/null || echo "000")" + case "$simple_status" in + 200|301|302) + ;; + *) + return 1 + ;; + esac + + local artifact_url + artifact_url="$(extract_artifact_url_from_simple "$mirror_simple_url" "$test_package" 2>/dev/null || true)" + [ -n "$artifact_url" ] || return 0 + + local artifact_status + artifact_status="$(probe_artifact_download_status "$artifact_url")" + case "$artifact_status" in + 200|204|206|301|302) + return 0 + ;; + *) + return 1 + ;; + esac +} + +build_pip_mirror_fallback_chain() { + local primary_url + primary_url="$(normalize_simple_url "$1")" + + local candidates=( + "$primary_url" + "https://mirrors.aliyun.com/pypi/simple/" + "https://repo.huaweicloud.com/repository/pypi/simple/" + "https://mirrors.cloud.tencent.com/pypi/simple/" + "https://pypi.mirrors.ustc.edu.cn/simple/" + "https://pypi.tuna.tsinghua.edu.cn/simple/" + "https://pypi.org/simple/" + ) + + local unique=() + local candidate + for candidate in "${candidates[@]}"; do + local normalized + normalized="$(normalize_simple_url "$candidate")" + local seen=false + local item + for item in "${unique[@]}"; do + if [ "$item" = "$normalized" ]; then + seen=true + break + fi + done + if [ "$seen" = "false" ]; then + unique+=("$normalized") + fi + done + + local fallback_chain="" + for candidate in "${unique[@]}"; do + if [ -z "$fallback_chain" ]; then + fallback_chain="$candidate" + else + fallback_chain="$fallback_chain|$candidate" + fi + done + + export SAGE_PIP_MIRROR_FALLBACKS="$fallback_chain" +} + # 配置 pip 镜像 # 配置 pip 镜像 configure_pip_mirror() { local mirror_source="${1:-auto}" + local selected_mirror="https://pypi.org/simple/" # 如果设置了 SAGE_FORCE_CHINA_MIRROR=true，强制使用中国镜像（适用于中国的 self-hosted runner） if [ "${SAGE_FORCE_CHINA_MIRROR:-}" = "true" ]; then - export PIP_INDEX_URL="https://pypi.tuna.tsinghua.edu.cn/simple/" + local forced_candidates=( + "https://mirrors.aliyun.com/pypi/simple/" + "https://repo.huaweicloud.com/repository/pypi/simple/" + "https://mirrors.cloud.tencent.com/pypi/simple/" + "https://pypi.mirrors.ustc.edu.cn/simple/" + "https://pypi.tuna.tsinghua.edu.cn/simple/" + ) + local candidate + for candidate in "${forced_candidates[@]}"; do + if is_mirror_download_healthy "$candidate" "pip"; then + selected_mirror="$candidate" + break + fi + done + export PIP_INDEX_URL="$(normalize_simple_url "$selected_mirror")" export PIP_EXTRA_INDEX_URL="" - echo -e "${GREEN} ✓ SAGE_FORCE_CHINA_MIRROR=true，强制使用清华镜像${NC}" + build_pip_mirror_fallback_chain "$PIP_INDEX_URL" + echo -e "${GREEN} ✓ SAGE_FORCE_CHINA_MIRROR=true，使用可下载镜像: $PIP_INDEX_URL${NC}" return 0 fi @@ -133,13 +303,29 @@ configure_pip_mirror() { if [ "${CI:-}" = "true" ] || [ -n "${GITHUB_ACTIONS:-}" ] || [ -n "${GITLAB_CI:-}" ] || [ -n "${JENKINS_URL:-}" ]; then # 在 CI 中也尝试检测是否在中国 if detect_mainland_china_ip; then - export PIP_INDEX_URL="https://pypi.tuna.tsinghua.edu.cn/simple/" + local ci_candidates=( + "https://mirrors.aliyun.com/pypi/simple/" + "https://repo.huaweicloud.com/repository/pypi/simple/" + "https://mirrors.cloud.tencent.com/pypi/simple/" + "https://pypi.mirrors.ustc.edu.cn/simple/" + "https://pypi.tuna.tsinghua.edu.cn/simple/" + ) + local candidate + for candidate in "${ci_candidates[@]}"; do + if is_mirror_download_healthy "$candidate" "pip"; then + selected_mirror="$candidate" + break + fi + done + export PIP_INDEX_URL="$(normalize_simple_url "$selected_mirror")" export PIP_EXTRA_INDEX_URL="" - echo -e "${GREEN} ✓ CI环境 + 中国大陆网络检测，使用清华镜像加速${NC}" + build_pip_mirror_fallback_chain "$PIP_INDEX_URL" + echo -e "${GREEN} ✓ CI环境 + 中国大陆网络检测，使用可下载镜像: $PIP_INDEX_URL${NC}" return 0 fi export PIP_INDEX_URL="https://pypi.org/simple/" export PIP_EXTRA_INDEX_URL="" + build_pip_mirror_fallback_chain "$PIP_INDEX_URL" echo -e "${INFO} CI环境检测：使用官方 PyPI（国际网络）" return 0 fi @@ -150,26 +336,54 @@ configure_pip_mirror() { "auto") # 自动检测最优镜像，优先根据公网 IP 判断 if detect_mainland_china_ip; then - # 检测清华镜像是否可用（快速健康检查） - if curl -s --connect-timeout 3 --max-time 3 -I "https://pypi.tuna.tsinghua.edu.cn/simple/" 2>/dev/null | head -1 | grep -q "200\|301\|302"; then - export PIP_INDEX_URL="https://pypi.tuna.tsinghua.edu.cn/simple/" - export PIP_EXTRA_INDEX_URL="" - echo -e "${GREEN} ✓ 检测到中国大陆网络，自动使用清华镜像加速${NC}" + local mainland_candidates=( + "https://mirrors.aliyun.com/pypi/simple/" + "https://repo.huaweicloud.com/repository/pypi/simple/" + "https://mirrors.cloud.tencent.com/pypi/simple/" + "https://pypi.mirrors.ustc.edu.cn/simple/" + "https://pypi.tuna.tsinghua.edu.cn/simple/" + ) + local candidate + local mirror_picked=false + for candidate in "${mainland_candidates[@]}"; do + if is_mirror_download_healthy "$candidate" "pip"; then + selected_mirror="$candidate" + mirror_picked=true + break + fi + done + + export PIP_INDEX_URL="$(normalize_simple_url "$selected_mirror")" + export PIP_EXTRA_INDEX_URL="" + if [ "$mirror_picked" = "true" ] && [ "$PIP_INDEX_URL" != "https://pypi.org/simple/" ]; then + echo -e "${GREEN} ✓ 检测到中国大陆网络，自动使用可下载镜像: $PIP_INDEX_URL${NC}" else - export PIP_INDEX_URL="https://pypi.org/simple/" - export PIP_EXTRA_INDEX_URL="" - echo -e "${YELLOW} ⚠️ 清华镜像不可用，降级到官方 PyPI${NC}" + echo -e "${YELLOW} ⚠️ 国内镜像下载探测失败，已降级到官方 PyPI${NC}" fi elif [[ "${LANG:-}" == zh_* ]] || [[ "${LC_ALL:-}" == zh_* ]] || [[ "${LC_CTYPE:-}" == zh_* ]]; then - # 中文环境也进行健康检查 - if curl -s --connect-timeout 3 --max-time 3 -I "https://pypi.tuna.tsinghua.edu.cn/simple/" 2>/dev/null | head -1 | grep -q "200\|301\|302"; then - export PIP_INDEX_URL="https://pypi.tuna.tsinghua.edu.cn/simple/" - export PIP_EXTRA_INDEX_URL="" - echo -e "${GREEN} ✓ 检测到中文环境，自动使用清华镜像加速${NC}" + local zh_candidates=( + "https://mirrors.aliyun.com/pypi/simple/" + "https://repo.huaweicloud.com/repository/pypi/simple/" + "https://mirrors.cloud.tencent.com/pypi/simple/" + "https://pypi.mirrors.ustc.edu.cn/simple/" + "https://pypi.tuna.tsinghua.edu.cn/simple/" + ) + local candidate + local mirror_picked=false + for candidate in "${zh_candidates[@]}"; do + if is_mirror_download_healthy "$candidate" "pip"; then + selected_mirror="$candidate" + mirror_picked=true + break + fi + done + + export PIP_INDEX_URL="$(normalize_simple_url "$selected_mirror")" + export PIP_EXTRA_INDEX_URL="" + if [ "$mirror_picked" = "true" ] && [ "$PIP_INDEX_URL" != "https://pypi.org/simple/" ]; then + echo -e "${GREEN} ✓ 检测到中文环境，自动使用可下载镜像: $PIP_INDEX_URL${NC}" else - export PIP_INDEX_URL="https://pypi.org/simple/" - export PIP_EXTRA_INDEX_URL="" - echo -e "${YELLOW} ⚠️ 清华镜像不可用，使用官方 PyPI${NC}" + echo -e "${YELLOW} ⚠️ 中文环境镜像下载探测失败，已降级到官方 PyPI${NC}" fi else export PIP_INDEX_URL="https://pypi.org/simple/" @@ -193,10 +407,13 @@ configure_pip_mirror() { echo -e "${DIM} 使用官方 PyPI${NC}" ;; "disable") - # 显式禁用镜像配置 - unset PIP_INDEX_URL - unset PIP_EXTRA_INDEX_URL - echo -e "${DIM} 镜像已禁用${NC}" + # 显式禁用镜像配置：强制官方 PyPI + # 说明：不能 simple unset，否则可能回落到用户全局 pip.conf 中的镜像配置。 + export PIP_INDEX_URL="https://pypi.org/simple/" + export PIP_EXTRA_INDEX_URL="" + export PIP_NO_CACHE_DIR=1 + build_pip_mirror_fallback_chain "$PIP_INDEX_URL" + echo -e "${DIM} 镜像已禁用（强制官方 PyPI）${NC}" return 0 ;; custom:*) @@ -212,7 +429,13 @@ configure_pip_mirror() { ;; esac + PIP_INDEX_URL="$(normalize_simple_url "${PIP_INDEX_URL:-https://pypi.org/simple/}")" + export PIP_INDEX_URL + export PIP_EXTRA_INDEX_URL="" + build_pip_mirror_fallback_chain "$PIP_INDEX_URL" + echo -e "${DIM} PIP_INDEX_URL: $PIP_INDEX_URL${NC}" + echo -e "${DIM} 镜像回退链: ${SAGE_PIP_MIRROR_FALLBACKS}${NC}" } # 检测是否在虚拟环境中 @@ -242,10 +465,9 @@ detect_virtual_environment() { echo "$is_venv|$venv_type|$venv_name" } -# 检查虚拟环境隔离（可配置为警告或错误） +# 检查环境隔离（可配置为警告或错误） check_virtual_environment_isolation() { local install_environment="$1" - local auto_venv="${2:-false}" # 如果用户选择了 conda，则会创建新环境，不需要额外检查 if [ "$install_environment" = "conda" ]; then @@ -262,31 +484,28 @@ check_virtual_environment_isolation() { local venv_type=$(echo "$venv_info" | cut -d'|' -f2) local venv_name=$(echo "$venv_info" | cut -d'|' -f3) - if [ "$is_venv" = "false" ]; then - # 如果启用了 auto-venv，自动创建虚拟环境 - if [ "$auto_venv" = "true" ]; then - echo "" - echo -e "${BLUE}🔧 自动创建虚拟环境${NC}" - echo "" - - local venv_path=".sage/venv" - echo -e "${INFO} 将在 ${GREEN}$venv_path${NC} 创建 Python 虚拟环境" - - if ! ensure_python_venv "$venv_path"; then - echo -e "${RED}错误: 无法自动创建虚拟环境${NC}" - echo -e "${DIM}请手动创建: python3 -m venv $venv_path${NC}" - exit 1 - fi - - source "$venv_path/bin/activate" - if [ -n "${VIRTUAL_ENV:-}" ]; then - echo -e "${CHECK} 虚拟环境已激活: ${GREEN}$venv_path${NC}" - export PIP_CMD="python3 -m pip" - export PYTHON_CMD="python3" - return 0 - fi - fi + # 项目策略：不支持 Python venv（含 .venv）作为安装/运行环境 + if [ "$is_venv" = "true" ] && [ "$venv_type" = "venv" ]; then + echo "" + echo -e "${YELLOW}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + echo -e "${BOLD}❌ 不支持 Python venv 环境${NC}" + echo -e "${YELLOW}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + echo "" + echo -e "${WARNING} 检测到 VIRTUAL_ENV: ${RED}${VIRTUAL_ENV:-unknown}${NC}" + echo -e "${INFO} SAGE 当前策略不允许使用或自动创建 venv/.venv" + echo "" + echo -e "${BLUE}请改用以下方式之一：${NC}" + echo -e " ${GREEN}1)${NC} 退出当前 venv 后使用 Conda 环境（推荐）" + echo -e " ${DIM}conda activate sage${NC}" + echo -e " ${PURPLE}2)${NC} 退出当前 venv 后使用当前系统/已有环境" + echo -e " ${DIM}deactivate && ./quickstart.sh --pip${NC}" + echo "" + echo -e "${RED}${BOLD}✗ 安装已终止${NC}" + echo -e "${YELLOW}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + exit 1 + fi + if [ "$is_venv" = "false" ]; then # 读取配置（默认为 warning） local venv_policy="${SAGE_VENV_POLICY:-warning}" @@ -295,9 +514,9 @@ check_virtual_environment_isolation() { echo -e "${BOLD}⚠️ 环境隔离警告${NC}" echo -e "${YELLOW}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" echo "" - echo -e "${WARNING} 检测到您正在使用系统 Python 环境（非虚拟环境）" + echo -e "${WARNING} 检测到您正在使用系统 Python 环境（非 Conda 环境）" echo "" - echo -e "${BLUE}为什么推荐使用虚拟环境？${NC}" + echo -e "${BLUE}为什么推荐使用隔离环境（Conda）？${NC}" echo -e " ${DIM}• 避免与系统包冲突${NC}" echo -e " ${DIM}• 保持系统环境清洁${NC}" echo -e " ${DIM}• 便于完全卸载和清理${NC}" @@ -306,14 +525,10 @@ check_virtual_environment_isolation() { echo -e "${BLUE}建议的操作：${NC}" echo "" - echo -e " ${YELLOW}1. 自动创建虚拟环境（推荐）${NC}" - echo -e " ${DIM}重新运行: ${CYAN}./quickstart.sh --auto-venv${NC}" + echo -e " ${GREEN}1. 使用 Conda 环境（推荐）${NC}" + echo -e " ${DIM}运行: ${CYAN}./quickstart.sh --conda${NC}" echo "" - echo -e " ${PURPLE}2. 手动创建虚拟环境${NC}" - echo -e " ${DIM}使用 conda: ${CYAN}./quickstart.sh --conda${NC}" - echo -e " ${DIM}或使用 venv: ${CYAN}python3 -m venv .sage/venv && source .sage/venv/bin/activate${NC}" - echo "" - echo -e " ${GRAY}3. 继续在系统环境中安装（不推荐）${NC}" + echo -e " ${GRAY}2. 继续在系统环境中安装（不推荐）${NC}" echo -e " ${DIM}风险：可能污染系统 Python 环境${NC}" echo "" @@ -336,7 +551,7 @@ check_virtual_environment_isolation() { if [[ ! "$continue_choice" =~ ^[Yy]$ ]]; then echo "" echo -e "${INFO} 安装已取消" - echo -e "${DIM}提示: 使用 --auto-venv 可自动创建虚拟环境${NC}" + echo -e "${DIM}提示: 使用 --conda 创建并使用隔离环境${NC}" echo "" echo -e "${YELLOW}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" exit 0 @@ -358,34 +573,12 @@ check_virtual_environment_isolation() { ;; esac else - echo -e "${CHECK} 检测到虚拟环境: ${GREEN}$venv_type ($venv_name)${NC}" + echo -e "${CHECK} 检测到隔离环境: ${GREEN}$venv_type ($venv_name)${NC}" fi return 0 } -ensure_python_venv() { - local venv_path="$1" - : > /tmp/venv.log - if python3 -m venv "$venv_path" 2>/tmp/venv.log; then - return 0 - fi - echo -e "${DIM}标准 venv 创建失败，尝试使用 virtualenv 模块...${NC}" - if python3 -m virtualenv "$venv_path" 2>>/tmp/venv.log; then - return 0 - fi - echo -e "${DIM}virtualenv 模块不可用，尝试安装...${NC}" - if python3 -m pip install --user --break-system-packages virtualenv >/tmp/venv.log 2>&1; then - if python3 -m virtualenv "$venv_path" 2>>/tmp/venv.log; then - return 0 - fi - fi - if [ -f /tmp/venv.log ]; then - tail -n 20 /tmp/venv.log - fi - return 1 -} - # 配置安装环境的主函数 configure_installation_environment() { local install_environment="${1:-conda}" @@ -406,8 +599,8 @@ configure_installation_environment() { echo -e "${INFO} 已设置 PYTHONNOUSERSITE=1 以避免用户包冲突" fi - # 检查虚拟环境隔离（--auto-venv 会在 argument_parser 中设置 SAGE_AUTO_VENV） - check_virtual_environment_isolation "$install_environment" "${SAGE_AUTO_VENV:-false}" + # 检查环境隔离 + check_virtual_environment_isolation "$install_environment" # 运行综合系统检查（包含预检查、系统检查、SAGE检查） if ! comprehensive_system_check "$install_mode" "$install_environment"; then diff --git a/tools/install/examination_tools/comprehensive_check.sh b/tools/install/examination_tools/comprehensive_check.sh index fb5ae6cbbf..c2783eaa88 100755 --- a/tools/install/examination_tools/comprehensive_check.sh +++ b/tools/install/examination_tools/comprehensive_check.sh @@ -130,8 +130,8 @@ check_system_runtime() { # 警告：磁盘空间不足（SAGE 需要至少 10GB，推荐 20GB+） if [ "$disk_space_gb" -lt 10 ]; then output_warning "磁盘空间不足！可用: ${disk_space}，推荐至少 20GB" - output_dim "SAGE 完整安装（包含 vLLM、submodules）需要 15-20GB 空间" - output_dim "建议: 清理磁盘或使用 --core 模式减少空间占用" + output_dim "SAGE 完整安装（包含 submodules、ML 依赖）需要 15-20GB 空间" + output_dim "建议: 清理磁盘后重试（standard/dev 均需完整依赖空间）" # 严重不足时提示用户确认 if [ "$disk_space_gb" -lt 5 ]; then @@ -147,7 +147,7 @@ check_system_runtime() { fi elif [ "$disk_space_gb" -lt 20 ]; then output_warning "磁盘空间较紧张（可用: ${disk_space}），推荐 20GB+" - output_dim "提示: 使用 --core 模式可减少空间占用" + output_dim "提示: 建议预留 20GB+ 磁盘空间以保证安装顺利" fi # 检查基础命令 @@ -441,11 +441,45 @@ check_conda_mode_requirements() { check_existing_sage() { echo -e "${INFO} 检查是否已安装 SAGE..." + _sage_pip_list() { + if [ -n "${PIP_CMD:-}" ]; then + eval "$PIP_CMD list" 2>/dev/null + else + python3 -m pip list 2>/dev/null + fi + } + + _detect_sage_version() { + local installed_packages="$1" + local priority_packages=( + "isage" + ) + + local package_name="" + local package_version="" + for package_name in "${priority_packages[@]}"; do + package_version=$(echo "$installed_packages" | awk -v pkg="$package_name" '$1==pkg {print $2; exit}') + if [ -n "$package_version" ]; then + echo "$package_version" + return 0 + fi + done + + echo "$installed_packages" | head -n1 | awk '{print $2}' + } + + _sage_package_filter_pattern() { + echo '^(isage($|-)|intsage($|-)|sage$)' + } + + _should_auto_uninstall() { + [[ -n "$CI" || -n "$GITHUB_ACTIONS" || -n "$GITLAB_CI" || -n "$JENKINS_URL" || -n "$BUILDKITE" || "${AUTO_CONFIRM:-false}" = "true" || "${SAGE_AUTO_CONFIRM:-false}" = "true" ]] + } + # 检查pip包列表中的所有SAGE相关包变体 - local installed_packages=$(pip list 2>/dev/null | grep -E '^(sage|isage|intsage)(-|$)' || echo "") + local installed_packages=$(_sage_pip_list | grep -E "$(_sage_package_filter_pattern)" || echo "") if [ -n "$installed_packages" ]; then - # 获取第一个包的版本作为代表版本 - local version=$(echo "$installed_packages" | head -n1 | awk '{print $2}') + local version=$(_detect_sage_version "$installed_packages") echo -e "${WARNING} 检测到已安装的 SAGE v${version}" echo echo -e "${DIM}已安装的包：${NC}" @@ -454,13 +488,12 @@ check_existing_sage() { done echo - # 在CI环境中自动卸载重装 - if [[ -n "$CI" || -n "$GITHUB_ACTIONS" || -n "$GITLAB_CI" || -n "$JENKINS_URL" || -n "$BUILDKITE" ]]; then - echo -e "${INFO} CI环境检测到已安装包，执行强制重装..." + if _should_auto_uninstall; then + echo -e "${INFO} 检测到自动确认/CI模式，执行强制重装..." # 导入卸载函数 source "$(dirname "${BASH_SOURCE[0]}")/sage_check.sh" uninstall_sage - echo -e "${CHECK} CI环境强制重装准备完成" + echo -e "${CHECK} 强制重装准备完成" else echo -e "${WARNING} 检测到已安装 请强制重装" echo -e "${DIM}提示: 建议先卸载现有版本以避免冲突${NC}" @@ -469,18 +502,17 @@ check_existing_sage() { return 0 fi - # 检查是否能导入sage.common（PEP 420 namespace，检查实际包） - if python3 -c "import sage.common" 2>/dev/null; then - local sage_version=$(python3 -c "import sage.common; print(sage.common.__version__)" 2>/dev/null || echo "unknown") + # 检查是否能导入主仓核心表面 + if python3 -c "from sage._version import __version__; import sage.foundation, sage.stream, sage.runtime, sage.serving, sage.cli; print(__version__)" 2>/dev/null; then + local sage_version=$(python3 -c "from sage._version import __version__; print(__version__)" 2>/dev/null || echo "unknown") echo -e "${WARNING} 检测到已安装的 SAGE v${sage_version}" - # 在CI环境中自动卸载重装 - if [[ -n "$CI" || -n "$GITHUB_ACTIONS" || -n "$GITLAB_CI" || -n "$JENKINS_URL" || -n "$BUILDKITE" ]]; then - echo -e "${INFO} CI环境检测到已安装包，执行强制重装..." + if _should_auto_uninstall; then + echo -e "${INFO} 检测到自动确认/CI模式，执行强制重装..." # 导入卸载函数 source "$(dirname "${BASH_SOURCE[0]}")/sage_check.sh" uninstall_sage - echo -e "${CHECK} CI环境强制重装准备完成" + echo -e "${CHECK} 强制重装准备完成" fi return 0 @@ -588,13 +620,14 @@ verify_installation() { local verify_output verify_output=$($python_cmd -c " -# PEP 420 namespace - import actual packages, not the namespace -import sage.common -import sage.kernel -import sage.libs -import sage.middleware -print(f'${CHECK} SAGE v{sage.common.__version__} 安装成功！') -print(f'${CHECK} 核心包已安装: common, kernel, libs, middleware') +from sage._version import __version__ +import sage.foundation +import sage.stream +import sage.runtime +import sage.serving +import sage.cli +print(f'${CHECK} SAGE v{__version__} 安装成功！') +print(f'${CHECK} 核心包已安装: foundation, stream, runtime, serving, cli') " 2>&1) local verify_status=$? @@ -606,7 +639,7 @@ print(f'${CHECK} 核心包已安装: common, kernel, libs, middleware') else echo -e "${WARNING} 验证出现问题，但安装可能成功了" echo -e "${DIM}尝试使用以下命令手动验证：${NC}" - echo -e "${DIM} $python_cmd -c \"import sage.common; print(sage.common.__version__)\"${NC}" + echo -e "${DIM} $python_cmd -c \"from sage._version import __version__; print(__version__)\"${NC}" return 1 fi } diff --git a/tools/install/examination_tools/conda_guide.sh b/tools/install/examination_tools/conda_guide.sh new file mode 100644 index 0000000000..960a3f8beb --- /dev/null +++ b/tools/install/examination_tools/conda_guide.sh @@ -0,0 +1,580 @@ +#!/bin/bash +# SAGE: Conda Environment Guide +# 引导用户下载、安装 Conda 并创建专用环境 +# 借鉴自 sagellm/scripts/installation/checks/conda_guide.sh + +set -e + +_CONDA_GUIDE_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +source "$_CONDA_GUIDE_DIR/../display_tools/colors.sh" + +# ============================================================ +# Constants +# ============================================================ + +SAGE_CONDA_ENV_NAME="${SAGE_CONDA_ENV_NAME:-sage}" +SAGE_PYTHON_VERSION="${SAGE_PYTHON_VERSION:-3.11}" + +# Miniforge installer URLs (open-source, conda-forge default, supports ARM/x86) +_MINIFORGE_BASE_URL="https://github.com/conda-forge/miniforge/releases/latest/download" +_MINIFORGE_BASE_URL_CN="https://mirrors.tuna.tsinghua.edu.cn/github-release/conda-forge/miniforge/LatestRelease" + +_get_miniforge_installer_name() { + local os arch + os="$(uname -s)" + arch="$(uname -m)" + + case "$os" in + Linux) + case "$arch" in + x86_64) echo "Miniforge3-Linux-x86_64.sh" ;; + aarch64) echo "Miniforge3-Linux-aarch64.sh" ;; + *) echo "Miniforge3-Linux-x86_64.sh" ;; + esac + ;; + Darwin) + case "$arch" in + arm64) echo "Miniforge3-MacOSX-arm64.sh" ;; + x86_64) echo "Miniforge3-MacOSX-x86_64.sh" ;; + *) echo "Miniforge3-MacOSX-x86_64.sh" ;; + esac + ;; + *) + echo "Miniforge3-Linux-x86_64.sh" + ;; + esac +} + +# ============================================================ +# Local print helpers (inline, no dependency on output_formatter) +# ============================================================ + +_cg_print_success() { echo -e "${GREEN} ✅ $*${NC}"; } +_cg_print_error() { echo -e "${RED} ❌ $*${NC}"; } +_cg_print_warning() { echo -e "${YELLOW} ⚠️ $*${NC}"; } +_cg_print_info() { echo -e "${BLUE} ℹ️ $*${NC}"; } + +# ============================================================ +# Utility helpers +# ============================================================ + +_conda_is_installed() { + command -v conda >/dev/null 2>&1 +} + +_is_ci() { + [[ -n "${CI:-}" || -n "${GITHUB_ACTIONS:-}" || -n "${GITLAB_CI:-}" || -n "${JENKINS_URL:-}" || -n "${BUILDKITE:-}" ]] +} + +_is_interactive() { + [ -t 0 ] && [ -t 1 ] +} + +_is_auto_confirm() { + [[ "${AUTO_YES:-false}" = "true" || "${AUTO_CONFIRM:-false}" = "true" || "${SAGE_AUTO_CONFIRM:-false}" = "true" ]] +} + +# ============================================================ +# Case 1: Conda 未安装 → 引导安装 Miniforge3 +# ============================================================ + +guide_install_conda() { + echo "" + echo -e "${YELLOW}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + echo -e "${YELLOW} ⚠️ 未检测到 Conda${NC}" + echo -e "${YELLOW}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + echo "" + echo -e "${BLUE}SAGE 强烈建议在 Conda 环境中运行，原因：${NC}" + echo -e " • 隔离依赖，避免污染系统 Python" + echo -e " • 支持精确 Python 版本控制（需 >= 3.10）" + echo -e " • 多项目并行开发时互不干扰" + echo -e " • 与 Ascend / CUDA 硬件驱动环境兼容性更好" + echo "" + echo -e "${BOLD}推荐安装方式：Miniforge3（轻量，默认 conda-forge 频道）${NC}" + echo "" + + local installer_name + installer_name="$(_get_miniforge_installer_name)" + + echo -e "${CYAN}方法 1：官方下载（国际网络）${NC}" + echo -e " wget ${_MINIFORGE_BASE_URL}/${installer_name}" + echo -e " bash ${installer_name} -b -p \$HOME/miniforge3" + echo -e " source \$HOME/miniforge3/etc/profile.d/conda.sh" + echo -e " conda init bash" + echo "" + echo -e "${CYAN}方法 2：清华镜像（国内推荐）${NC}" + echo -e " wget ${_MINIFORGE_BASE_URL_CN}/${installer_name}" + echo -e " bash ${installer_name} -b -p \$HOME/miniforge3" + echo -e " source \$HOME/miniforge3/etc/profile.d/conda.sh" + echo -e " conda init bash" + echo "" + echo -e "${CYAN}安装后，创建专用环境：${NC}" + echo -e " conda create -n ${SAGE_CONDA_ENV_NAME} python=${SAGE_PYTHON_VERSION} -y" + echo -e " conda activate ${SAGE_CONDA_ENV_NAME}" + echo -e " ./quickstart.sh" + echo "" + + if _is_ci || _is_auto_confirm; then + _cg_print_warning "CI/自动确认模式：未安装 Conda，继续在当前环境安装（可能存在依赖冲突）" + return 0 + fi + + if ! _is_interactive; then + _cg_print_warning "非交互模式：未安装 Conda，继续在当前环境安装" + return 0 + fi + + echo -e "${YELLOW}是否现在自动下载并安装 Miniforge3？${NC}" + echo -e " [1] 是，使用官方源下载" + echo -e " [2] 是，使用清华镜像下载（国内网络推荐）" + echo -e " [3] 否，我稍后自行安装" + echo -e " [4] 否，跳过 Conda，继续在当前环境安装" + echo "" + read -r -p "请选择 [1/2/3/4]（默认 4）: " choice + choice="${choice:-4}" + + case "$choice" in + 1|2) + _auto_install_miniforge "$installer_name" "$choice" + return $? + ;; + 3) + echo "" + echo -e "${BLUE}请完成以下步骤后重新运行 ./quickstart.sh：${NC}" + echo -e " 1. 安装 Miniforge3（见上方命令）" + echo -e " 2. conda create -n ${SAGE_CONDA_ENV_NAME} python=${SAGE_PYTHON_VERSION} -y" + echo -e " 3. conda activate ${SAGE_CONDA_ENV_NAME}" + echo -e " 4. ./quickstart.sh" + exit 0 + ;; + 4) + _cg_print_warning "跳过 Conda 安装，继续在当前 Python 环境中安装（可能存在依赖冲突）" + return 0 + ;; + *) + _cg_print_warning "无效选择，跳过 Conda 安装，继续当前环境" + return 0 + ;; + esac +} + +_auto_install_miniforge() { + local installer_name="$1" + local mirror_choice="$2" + + local download_url + if [ "$mirror_choice" = "1" ]; then + download_url="${_MINIFORGE_BASE_URL}/${installer_name}" + else + download_url="${_MINIFORGE_BASE_URL_CN}/${installer_name}" + fi + + local install_prefix="$HOME/miniforge3" + local installer_path="/tmp/${installer_name}" + + if ! command -v wget >/dev/null 2>&1 && ! command -v curl >/dev/null 2>&1; then + _cg_print_error "未找到 wget 或 curl，无法自动下载" + _cg_print_info "请手动下载并安装 Miniforge3，然后重新运行 ./quickstart.sh" + return 1 + fi + + # 若已有完整安装，跳过下载与安装步骤 + if [ -x "${install_prefix}/bin/conda" ]; then + _cg_print_success "检测到已有 Miniforge3 安装：${install_prefix}，跳过下载与安装" + else + echo "" + echo -e "${BLUE}📥 下载 Miniforge3...${NC}" + echo -e " URL: ${download_url}" + echo -e " 目标: ${installer_path}" + echo "" + + if command -v wget >/dev/null 2>&1; then + wget -q --show-progress -O "$installer_path" "$download_url" || { + _cg_print_error "下载失败，请检查网络连接" + return 1 + } + else + curl -L --progress-bar -o "$installer_path" "$download_url" || { + _cg_print_error "下载失败，请检查网络连接" + return 1 + } + fi + + echo "" + echo -e "${BLUE}📦 安装 Miniforge3 到 ${install_prefix}...${NC}" + + if [ -d "$install_prefix" ]; then + _cg_print_warning "目录已存在但 conda 不完整，使用 -u 模式更新..." + bash "$installer_path" -b -u -p "$install_prefix" || { + _cg_print_error "安装失败" + rm -f "$installer_path" + return 1 + } + else + bash "$installer_path" -b -p "$install_prefix" || { + _cg_print_error "安装失败" + rm -f "$installer_path" + return 1 + } + fi + rm -f "$installer_path" + fi + + # 初始化 conda 供当前 shell 使用 + # shellcheck disable=SC1091 + source "${install_prefix}/etc/profile.d/conda.sh" || true + + echo "" + _cg_print_success "Miniforge3 安装完成：${install_prefix}" + + echo -e "${BLUE}🔧 初始化 Conda shell integration...${NC}" + "${install_prefix}/bin/conda" init bash 2>/dev/null || true + + echo "" + echo -e "${BLUE}🐍 创建专用 Conda 环境: ${SAGE_CONDA_ENV_NAME} (Python ${SAGE_PYTHON_VERSION})...${NC}" + "${install_prefix}/bin/conda" create -n "${SAGE_CONDA_ENV_NAME}" "python=${SAGE_PYTHON_VERSION}" -y || { + _cg_print_error "创建环境失败" + return 1 + } + + _cg_print_success "环境 '${SAGE_CONDA_ENV_NAME}' 创建成功" + echo "" + echo -e "${YELLOW}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + echo -e "${GREEN} ✅ Conda 安装完成！${NC}" + echo -e "${YELLOW}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + echo "" + # 将 sage 环境写入 bashrc，设为默认 + setup_bashrc_conda_default "${SAGE_CONDA_ENV_NAME}" + echo -e "${BLUE}请在新终端（或重新 source ~/.bashrc 后）执行：${NC}" + echo "" + echo -e " ${CYAN}conda activate ${SAGE_CONDA_ENV_NAME}${NC}" + echo -e " ${CYAN}./quickstart.sh${NC}" + echo "" + echo -e "提示：如果 conda 命令不可用，请先执行：" + echo -e " ${CYAN}source ${install_prefix}/etc/profile.d/conda.sh${NC}" + echo "" + exit 0 +} + +# ============================================================ +# Case 2: Conda base 环境激活 → 警告并引导创建专用环境 +# ============================================================ + +guide_conda_base_env() { + echo "" + echo -e "${YELLOW}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + echo -e "${YELLOW} ⚠️ 检测到 Conda base 环境${NC}" + echo -e "${YELLOW}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + echo "" + echo -e "${RED} ❌ 不建议在 base 环境中安装 SAGE${NC}" + echo "" + echo -e "${BLUE} 原因：${NC}" + echo -e " • 可能导致 conda base 环境污染，影响系统其他工具" + echo -e " • 依赖版本冲突风险高（numpy、torch、protobuf 等）" + echo -e " • 难以后续清理和卸载" + echo "" + echo -e "${GREEN} 建议：创建并激活专用 '${SAGE_CONDA_ENV_NAME}' 环境${NC}" + echo "" + echo -e "${CYAN} 手动操作命令：${NC}" + echo -e " conda create -n ${SAGE_CONDA_ENV_NAME} python=${SAGE_PYTHON_VERSION} -y" + echo -e " conda activate ${SAGE_CONDA_ENV_NAME}" + echo -e " ./quickstart.sh" + echo "" + + if _is_ci; then + _cg_print_warning "CI 环境：检测到 base 环境，继续安装（CI 流程允许）" + return 0 + fi + + if _is_auto_confirm; then + _cg_print_warning "自动确认模式：在 base 环境中继续安装" + return 0 + fi + + if ! _is_interactive; then + _cg_print_warning "非交互模式：在 base 环境中继续安装" + return 0 + fi + + echo -e "${YELLOW}请选择操作：${NC}" + echo -e " [1] 自动创建 '${SAGE_CONDA_ENV_NAME}' 环境（推荐）" + echo -e " [2] 继续在 base 环境安装（不推荐）" + echo -e " [3] 取消，我手动操作" + echo "" + read -r -p "请选择 [1/2/3]（默认 1）: " choice + choice="${choice:-1}" + + case "$choice" in + 1) + _create_and_guide_activate_env + return $? + ;; + 2) + echo "" + _cg_print_warning "继续在 conda base 环境安装，风险自负" + return 0 + ;; + 3|*) + echo "" + echo -e "${BLUE}手动操作步骤：${NC}" + echo -e " conda create -n ${SAGE_CONDA_ENV_NAME} python=${SAGE_PYTHON_VERSION} -y" + echo -e " conda activate ${SAGE_CONDA_ENV_NAME}" + echo -e " ./quickstart.sh" + exit 0 + ;; + esac +} + +# ============================================================ +# Case 3: Conda 已安装但无环境激活 → 引导创建专用环境 +# ============================================================ + +guide_conda_no_env() { + echo "" + echo -e "${YELLOW}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + echo -e "${YELLOW} ℹ️ 检测到 Conda 已安装，但未激活任何环境${NC}" + echo -e "${YELLOW}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + echo "" + echo -e "${BLUE}建议为 SAGE 创建独立的 Conda 环境：${NC}" + echo -e " conda create -n ${SAGE_CONDA_ENV_NAME} python=${SAGE_PYTHON_VERSION} -y" + echo -e " conda activate ${SAGE_CONDA_ENV_NAME}" + echo -e " ./quickstart.sh" + echo "" + + if _is_ci || _is_auto_confirm; then + _cg_print_warning "自动确认模式：跳过 Conda 环境创建，继续当前 Python 环境" + return 0 + fi + + if ! _is_interactive; then + _cg_print_warning "非交互模式：跳过 Conda 环境创建" + return 0 + fi + + echo -e "${YELLOW}请选择操作：${NC}" + echo -e " [1] 自动创建并提示激活 '${SAGE_CONDA_ENV_NAME}' 环境（推荐）" + echo -e " [2] 继续在当前系统 Python 环境安装" + echo -e " [3] 取消，我手动操作" + echo "" + read -r -p "请选择 [1/2/3]（默认 1）: " choice + choice="${choice:-1}" + + case "$choice" in + 1) + _create_and_guide_activate_env + return $? + ;; + 2) + _cg_print_warning "继续在系统 Python 环境安装，建议后续迁移到 Conda 环境" + return 0 + ;; + 3|*) + echo "" + echo -e "${BLUE}手动操作步骤：${NC}" + echo -e " conda create -n ${SAGE_CONDA_ENV_NAME} python=${SAGE_PYTHON_VERSION} -y" + echo -e " conda activate ${SAGE_CONDA_ENV_NAME}" + echo -e " ./quickstart.sh" + exit 0 + ;; + esac +} + +# ============================================================ +# RC 文件写入工具：将 conda activate 写入 shell RC 文件 +# ============================================================ + +# 将 conda activate 行写入 RC 文件（conda initialize 块之后，或末尾） +_write_conda_activate_to_rc() { + local rc_file="$1" + local env_name="$2" + local marker="$3" + local activate_line="$4" + + if grep -q "<<< conda initialize <<<" "$rc_file" 2>/dev/null; then + local tmp + tmp="$(mktemp)" + awk -v marker="$marker" -v activate="$activate_line" ' + /<<< conda initialize << "$tmp" + mv "$tmp" "$rc_file" + else + printf '\n%s\n%s\n' "$marker" "$activate_line" >> "$rc_file" + fi + + _cg_print_success "已写入 ${rc_file}" + echo "" + echo -e "提示：在当前终端立即生效请执行：" + echo -e " ${CYAN}source ${rc_file}${NC}" + echo "" + echo -e "之后每次打开新终端都会自动激活 '${env_name}' 环境。" +} + +# 安装完成后将 sage conda 环境写入 shell RC 文件，设为默认环境 +# 参数：可选 env_name（默认 $SAGE_CONDA_ENV_NAME） +# 跳过条件：CI / SAGE_NO_SET_DEFAULT_ENV=true +setup_bashrc_conda_default() { + local env_name="${1:-${SAGE_CONDA_ENV_NAME:-sage}}" + + if _is_ci; then + _cg_print_warning "CI 环境：跳过默认 Conda 环境配置" + return 0 + fi + + if [ "${SAGE_NO_SET_DEFAULT_ENV:-false}" = "true" ]; then + _cg_print_info "已跳过默认 Conda 环境配置（--no-set-default-env）" + return 0 + fi + + # ── 禁用 auto_activate_base，防止新终端自动进入 base 覆盖后续 activate ── + if _conda_is_installed; then + local _conda_bin + _conda_bin="$(command -v conda 2>/dev/null || echo conda)" + if ! grep -q "^auto_activate_base: false" "$HOME/.condarc" 2>/dev/null; then + "$_conda_bin" config --set auto_activate_base false 2>/dev/null && \ + _cg_print_success "已设置 auto_activate_base=false（禁止新终端默认进入 base）" || true + fi + fi + + local shell_type + shell_type="$(basename "${SHELL:-bash}")" + local primary_rc="" + local rc_files=() + + case "$shell_type" in + zsh) primary_rc="$HOME/.zshrc" ;; + bash|*) + primary_rc="$HOME/.bashrc" + [ -f "$HOME/.bashrc" ] || primary_rc="$HOME/.bash_profile" + ;; + esac + + for _f in "$HOME/.bashrc" "$HOME/.zshrc" "$HOME/.bash_profile"; do + [ -f "$_f" ] && rc_files+=("$_f") + done + + if [ ${#rc_files[@]} -eq 0 ]; then + _cg_print_warning "未找到 shell 配置文件（~/.bashrc / ~/.zshrc），跳过默认环境配置" + return 0 + fi + + [ -f "$primary_rc" ] || touch "$primary_rc" + + local activate_line="conda activate ${env_name}" + local marker="# sage: auto-activate conda env" + + # 检查是否已在任意 RC 文件中配置 + local already_in="" + for _f in "${rc_files[@]}"; do + if grep -qF "$activate_line" "$_f" 2>/dev/null; then + already_in="$_f" + break + fi + done + + if [ -n "$already_in" ]; then + _cg_print_success "默认 Conda 环境 '${env_name}' 已配置于 ${already_in}，无需重复添加" + return 0 + fi + + if ! grep -q "conda initialize" "$primary_rc" 2>/dev/null; then + echo "" + _cg_print_warning "${primary_rc} 中未检测到 'conda initialize' 块" + echo -e " conda 可能尚未初始化 shell。如新终端中 conda 命令不可用，请运行：" + echo -e " ${CYAN}conda init bash && source ${primary_rc}${NC}" + echo "" + fi + + echo "" + echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + echo -e "${BLUE} 🐍 设置默认 Conda 环境${NC}" + echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + echo "" + echo -e "正在将以下内容写入 ${CYAN}${primary_rc}${NC}" + echo -e " ${CYAN}${marker}${NC}" + echo -e " ${CYAN}${activate_line}${NC}" + echo "" + + _write_conda_activate_to_rc "$primary_rc" "$env_name" "$marker" "$activate_line" + + echo -e "如需撤销，删除 ${primary_rc} 中以下两行即可：" + echo -e " ${marker}" + echo -e " ${activate_line}" + echo -e "或使用 --no-set-default-env 跳过（重新运行 ./quickstart.sh --no-set-default-env）。" + echo "" +} + +# ============================================================ +# Internal: 创建环境并打印激活引导 +# ============================================================ + +_create_and_guide_activate_env() { + if conda env list 2>/dev/null | grep -qE "^${SAGE_CONDA_ENV_NAME}[[:space:]]"; then + _cg_print_success "环境 '${SAGE_CONDA_ENV_NAME}' 已存在" + else + echo "" + echo -e "${BLUE}🐍 创建 Conda 环境: ${SAGE_CONDA_ENV_NAME} (Python ${SAGE_PYTHON_VERSION})...${NC}" + conda create -n "${SAGE_CONDA_ENV_NAME}" "python=${SAGE_PYTHON_VERSION}" -y || { + _cg_print_error "创建环境失败，请检查 Conda 安装是否完整" + return 1 + } + _cg_print_success "环境 '${SAGE_CONDA_ENV_NAME}' 创建成功" + fi + + echo "" + echo -e "${YELLOW}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + echo -e "${GREEN} ✅ 请激活环境后重新运行安装脚本${NC}" + echo -e "${YELLOW}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + echo "" + # 写入 bashrc，下次打开终端自动激活 sage 环境 + setup_bashrc_conda_default "${SAGE_CONDA_ENV_NAME}" + echo -e " ${CYAN}conda activate ${SAGE_CONDA_ENV_NAME}${NC}" + echo -e " ${CYAN}./quickstart.sh${NC}" + echo "" + echo -e "提示：Conda 环境切换需要在同一 shell 中执行，脚本无法替您激活。" + echo "" + exit 0 +} + +# ============================================================ +# Main entry: check_conda_environment +# 统一处理所有 conda 场景，由 environment_prechecks.sh 调用 +# ============================================================ + +check_conda_environment() { + local _rc=0 + + # Case 1: conda 已安装，已激活非 base 环境 → OK + if _conda_is_installed && [ -n "${CONDA_DEFAULT_ENV:-}" ] && [ "${CONDA_DEFAULT_ENV:-}" != "base" ]; then + echo -e "${GREEN} ✅ 已检测到 Conda 环境: ${CONDA_DEFAULT_ENV}${NC}" + _rc=0 + # Case 2: conda 已安装，base 环境激活 → 警告并引导 + elif _conda_is_installed && [ "${CONDA_DEFAULT_ENV:-}" = "base" ]; then + guide_conda_base_env + _rc=$? + # Case 3: conda 已安装但未激活任何环境 + elif _conda_is_installed && [ -z "${CONDA_DEFAULT_ENV:-}" ]; then + guide_conda_no_env + _rc=$? + # Case 4: conda 未安装 + elif ! _conda_is_installed; then + guide_install_conda + _rc=$? + fi + + # 标记已完成检查，避免 run_environment_prechecks 重复调用 + _SAGE_CONDA_ENV_CHECKED=true + return $_rc +} + +# Run if executed directly +if [ "${BASH_SOURCE[0]}" -ef "$0" ]; then + check_conda_environment + exit $? +fi diff --git a/tools/install/examination_tools/environment_prechecks.sh b/tools/install/examination_tools/environment_prechecks.sh index 5b77a3b321..d175ef5a1b 100644 --- a/tools/install/examination_tools/environment_prechecks.sh +++ b/tools/install/examination_tools/environment_prechecks.sh @@ -4,6 +4,8 @@ # 导入颜色定义 source "$(dirname "${BASH_SOURCE[0]}")/../display_tools/colors.sh" +# 导入 Conda 安装引导模块 +source "$(dirname "${BASH_SOURCE[0]}")/conda_guide.sh" # 最小要求常量 @@ -224,7 +226,6 @@ check_cuda_availability() { local driver_version="" local cuda_version="" local has_gpu=false - local has_nvcc=false # 检查 NVIDIA 驱动 if command -v nvidia-smi &> /dev/null; then @@ -244,16 +245,6 @@ check_cuda_availability() { echo -e "${YELLOW} ⚠️ 未找到 nvidia-smi 命令${NC}" fi - # 检查 CUDA 工具包 - if command -v nvcc &> /dev/null; then - local nvcc_version=$(nvcc --version 2>/dev/null | grep "release" | awk '{print $6}' | sed 's/,//') - echo -e "${GREEN} ✅ CUDA 编译器已安装 (nvcc ${nvcc_version})${NC}" - has_nvcc=true - cuda_available=true - else - echo -e "${YELLOW} ⚠️ 未找到 CUDA 编译器 (nvcc)${NC}" - fi - # 检查 CUDA 环境变量 if [ -n "$CUDA_HOME" ] || [ -n "$CUDA_PATH" ]; then echo -e "${GREEN} ✅ CUDA 环境变量已设置${NC}" @@ -263,18 +254,6 @@ check_cuda_availability() { echo -e "${YELLOW} ⚠️ CUDA 环境变量未设置${NC}" fi - # 关键检测：GPU 存在但缺少 nvcc - if [ "$has_gpu" = true ] && [ "$has_nvcc" = false ]; then - echo -e "${RED} ❌ 检测到 GPU 但缺少 CUDA Toolkit (nvcc 编译器)${NC}" - echo -e "${YELLOW} 这将导致 GPU 加速功能无法使用！${NC}" - echo -e "${YELLOW} 建议操作:${NC}" - echo -e "${DIM} • 如果使用 conda: conda install -c conda-forge cudatoolkit-dev -y --override-channels${NC}" - echo -e "${DIM} • 如果使用系统包管理: apt install nvidia-cuda-toolkit${NC}" - # 导出状态供调用者使用 - export SAGE_NEEDS_NVCC=true - return 1 - fi - if [ "$cuda_available" = true ]; then echo -e "${GREEN} ✅ CUDA 环境可用，支持 GPU 加速${NC}" return 0 @@ -344,12 +323,16 @@ run_environment_prechecks() { local cuda_status="UNKNOWN" local conda_env_status="UNKNOWN" - # 检查 Conda base 环境（最优先） - if check_conda_base_environment; then + # 检查 Conda 环境（最优先）：未安装 / base / 未激活 / 正常 + # 若 apply_defaults 中已交互完成，则跳过重复询问 + if [ "${_SAGE_CONDA_ENV_CHECKED:-false}" = "true" ]; then + echo -e "${GREEN} ✅ Conda 环境检查已完成（跳过重复检查）${NC}" + conda_env_status="PASS" + elif check_conda_environment; then conda_env_status="PASS" else conda_env_status="WARN" - # base 环境警告但不阻止安装（用户可能选择继续） + # 警告但不阻止安装（用户可能选择继续） fi echo "" diff --git a/tools/install/examination_tools/install_verification.sh b/tools/install/examination_tools/install_verification.sh index f4b91baf76..fd1cd24c4f 100644 --- a/tools/install/examination_tools/install_verification.sh +++ b/tools/install/examination_tools/install_verification.sh @@ -72,6 +72,17 @@ get_sage_cli_env() { fi } +resolve_verification_python_cmd() { + local env_name + env_name=$(get_sage_cli_env) + + if [ -n "$env_name" ] && command -v conda >/dev/null 2>&1; then + PYTHON_CMD="conda run -n $env_name python" + else + PYTHON_CMD="${PYTHON_CMD:-python3}" + fi +} + run_sage_dev() { local env_name env_name=$(get_sage_cli_env) @@ -135,8 +146,8 @@ init_verification_log() { 安装环境: $(uname -s) $(uname -r) Python 命令: $PYTHON_CMD Python 版本: $($PYTHON_CMD --version 2>&1 || echo "未安装") -SAGE 包版本: $($PYTHON_CMD -c "import sage.common; print(sage.common.__version__)" 2>/dev/null || echo "未安装") -注意: SAGE 使用 PEP 420 namespace，各包版本独立（sage.common, sage.kernel 等） +SAGE 包版本: $($PYTHON_CMD -c "from sage._version import __version__; print(__version__)" 2>/dev/null || echo "未安装") +注意: SAGE 当前以主仓 in-tree 表面为核心，兼容命名空间（如 sage.common / sage.kernel）可能仍在部分环境中可见 ================================================================================ EOF @@ -173,9 +184,9 @@ verify_hello_world() { fi } -# 验证 sage doctor 命令 +# 验证 sage-dev 健康检查命令 verify_sage_doctor() { - echo -e "${BLUE}🩺 验证 sage doctor 命令...${NC}" + echo -e "${BLUE}🩺 验证 sage-dev 健康检查命令...${NC}" # 检查 sage-dev 命令是否存在 if ! sage_dev_available; then @@ -184,18 +195,18 @@ verify_sage_doctor() { return 1 fi - # 运行 sage maintain doctor（新命令结构） + # 运行当前可用的健康检查命令 local output - output=$(run_sage_dev maintain doctor 2>&1) + output=$(run_sage_dev status 2>&1) local exit_code=$? if [ $exit_code -eq 0 ]; then - log_verification_result "sage_doctor" "PASS" "sage-dev maintain doctor 执行成功" - echo -e "${GREEN} ✅ sage-dev maintain doctor 执行成功${NC}" + log_verification_result "sage_doctor" "PASS" "sage-dev status 执行成功" + echo -e "${GREEN} ✅ sage-dev status 执行成功${NC}" return 0 else - log_verification_result "sage_doctor" "WARN" "sage-dev maintain doctor 执行失败: $output" - echo -e "${YELLOW} ⚠️ sage-dev maintain doctor 执行失败${NC}" + log_verification_result "sage_doctor" "WARN" "sage-dev status 执行失败: $output" + echo -e "${YELLOW} ⚠️ sage-dev status 执行失败${NC}" echo -e "${DIM} 错误: $output${NC}" return 1 fi @@ -207,6 +218,27 @@ verify_cli_commands() { local failed_commands=() + if $PYTHON_CMD -m sage.cli.main verify &> /dev/null; then + echo -e "${GREEN} ✅ sage verify 可用${NC}" + else + echo -e "${RED} ❌ sage verify 不可用${NC}" + failed_commands+=("sage verify") + fi + + if $PYTHON_CMD -m sage.cli.main chat --help &> /dev/null; then + echo -e "${GREEN} ✅ sage chat --help 可用${NC}" + else + echo -e "${RED} ❌ sage chat --help 不可用${NC}" + failed_commands+=("sage chat --help") + fi + + if $PYTHON_CMD -m sage.cli.main index ingest --help &> /dev/null; then + echo -e "${GREEN} ✅ sage index ingest --help 可用${NC}" + else + echo -e "${RED} ❌ sage index ingest --help 不可用${NC}" + failed_commands+=("sage index ingest --help") + fi + # 验证 sage-dev 命令 if sage_dev_available; then echo -e "${GREEN} ✅ sage-dev 命令可用${NC}" @@ -225,7 +257,7 @@ verify_cli_commands() { fi if [ ${#failed_commands[@]} -eq 0 ]; then - log_verification_result "cli_commands" "PASS" "所有 CLI 命令可用" + log_verification_result "cli_commands" "PASS" "核心 CLI 命令可用" return 0 else log_verification_result "cli_commands" "FAIL" "CLI 命令不可用: ${failed_commands[*]}" @@ -237,7 +269,8 @@ verify_cli_commands() { verify_dependency_versions() { echo -e "${BLUE}📦 验证依赖版本兼容性...${NC}" - local critical_deps=("torch" "numpy" "transformers") + local critical_deps=("numpy") + local optional_deps=("torch" "transformers") local version_issues=() for dep in "${critical_deps[@]}"; do @@ -250,18 +283,20 @@ verify_dependency_versions() { fi done + for dep in "${optional_deps[@]}"; do + if $PYTHON_CMD -c "import $dep; print($dep.__version__)" &> /dev/null; then + local version=$($PYTHON_CMD -c "import $dep; print($dep.__version__)" 2>/dev/null) + echo -e "${GREEN} ✅ $dep $version 已安装（可选）${NC}" + else + echo -e "${DIM} ℹ️ $dep 未安装（可选）${NC}" + fi + done + # 检查版本兼容性 if $PYTHON_CMD -c " import sys try: - import torch import numpy as np - import transformers - - # 检查 PyTorch CUDA 版本 - if torch.cuda.is_available(): - cuda_version = torch.version.cuda - print(f'PyTorch CUDA 版本: {cuda_version}') # 检查 NumPy 版本 numpy_version = np.__version__ @@ -304,12 +339,7 @@ verify_sage_imports() { echo "" fi - # 核心包列表：按层级顺序验证 - # L1: sage-common - # L2: sage-platform - # L3: sage-kernel, sage-libs - # L4: sage-middleware - # L5: sage-cli, sage-tools + # 核心包列表：优先验证主仓 in-tree 产品表面 # NOTE: PEP 420 namespace packages - 'sage' namespace is implicit, cannot be imported directly # We only verify actual packages under the namespace # @@ -318,36 +348,71 @@ verify_sage_imports() { # - sage-examples (原 sage.apps): 已迁移到 sage-examples 仓库 # - sage.benchmark: 独立 PyPI 包 isage-benchmark (pip install isage-benchmark) # - sage.studio: 独立仓库 https://github.com/intellistream/sage-studio - # - sage.edge: 独立 PyPI 包 isage-edge (pip install isage-edge) + # - sage.edge: 已回归主仓产品面；根包导入应始终可用，FastAPI 运行依赖由 extras 提供 local sage_packages=( - "sage.common" # L1: Foundation - "sage.platform" # L2: Platform - "sage.kernel" # L3: Kernel - "sage.libs" # L3: Libraries - "sage.middleware" # L4: Middleware (C++ extensions) - "sage.cli" # L5: CLI (optional) - "sage.tools" # L5: Dev Tools (optional) + "sage.foundation" # Core: in-tree foundation + "sage.stream" # Core: in-tree stream API + "sage.runtime" # Core: in-tree runtime API + "sage.serving" # Core: in-tree serving boundary + "sage.edge" # Edge aggregation shell (in-tree) + "sage.cli" # Core CLI surface + "sage.common" # Transitional compatibility + "sage.platform" # Transitional compatibility + "sage.kernel" # Transitional compatibility + "sage.libs" # Transitional compatibility + "sage.middleware" # Transitional compatibility + "sage.tools" # Optional tooling namespace ) local failed_imports=() local optional_failed=() for pkg in "${sage_packages[@]}"; do - # 判断是否为可选包（L5 层 CLI/Tools） + # 判断是否为可选/过渡包 local is_optional=false - if [[ "$pkg" =~ ^sage\.(cli|tools)$ ]]; then + if [[ "$pkg" =~ ^sage\.(common|platform|kernel|libs|middleware|tools)$ ]]; then is_optional=true fi - # 使用转义避免 shell 变量展开问题 - if $PYTHON_CMD -c "import ${pkg}; print('${pkg}', ${pkg}.__version__)" &> /dev/null; then - local version=$($PYTHON_CMD -c "import ${pkg}; print(${pkg}.__version__)" 2>/dev/null) - echo -e "${GREEN} ✅ $pkg $version 导入成功${NC}" + # 对 in-tree 表面优先验证“可导入”，版本号仅在模块显式提供时展示 + if $PYTHON_CMD -c "import importlib; module = importlib.import_module('${pkg}'); print(getattr(module, '__version__', 'OK'))" &> /dev/null; then + local version=$($PYTHON_CMD -c "import importlib; module = importlib.import_module('${pkg}'); print(getattr(module, '__version__', 'OK'))" 2>/dev/null) + if [ "$version" = "OK" ]; then + echo -e "${GREEN} ✅ $pkg 导入成功${NC}" + else + echo -e "${GREEN} ✅ $pkg $version 导入成功${NC}" + fi else + # 获取详细的导入错误信息 + local import_error=$($PYTHON_CMD -c "import importlib; importlib.import_module('${pkg}')" 2>&1 | head -n 10) + if [ "$is_optional" = true ]; then echo -e "${YELLOW} ⚠️ $pkg 导入失败（可选包）${NC}" optional_failed+=("$pkg") else echo -e "${RED} ❌ $pkg 导入失败${NC}" + + # 显示详细错误信息（缩进） + if [ -n "$import_error" ]; then + echo -e "${DIM} 错误详情:${NC}" + echo "$import_error" | sed 's/^/ /' | head -n 5 + fi + + # 针对特定包提供诊断提示 + case "$pkg" in + "sage.middleware") + echo -e "${DIM} 💡 诊断提示:${NC}" + echo -e "${DIM} • 这是过渡兼容命名空间，不再是主仓安装成功的硬性条件${NC}" + ;; + "sage.kernel") + echo -e "${DIM} 💡 诊断提示:${NC}" + echo -e "${DIM} • 这是过渡兼容命名空间，不再是主仓安装成功的硬性条件${NC}" + ;; + "sage.libs") + echo -e "${DIM} 💡 诊断提示:${NC}" + echo -e "${DIM} • 这是过渡兼容命名空间，不再是主仓安装成功的硬性条件${NC}" + ;; + esac + failed_imports+=("$pkg") fi fi @@ -355,9 +420,9 @@ verify_sage_imports() { echo "" echo -e "${DIM} 说明：${NC}" - echo -e "${DIM} • L1-L4 为核心层，必须能够导入${NC}" - echo -e "${DIM} • L5 为接口层（CLI/Tools），根据安装模式可能不存在${NC}" - echo -e "${DIM} • sage-benchmark/examples/studio/edge 已独立为单独仓库/包，不在此验证${NC}" + echo -e "${DIM} • foundation / stream / runtime / serving / cli 为当前主仓核心表面${NC}" + echo -e "${DIM} • common / platform / kernel / libs / middleware / tools 为可选或过渡命名空间${NC}" + echo -e "${DIM} • sage-benchmark/examples/studio 等独立仓库不在此验证${NC}" echo "" if [ ${#failed_imports[@]} -eq 0 ]; then @@ -369,6 +434,36 @@ verify_sage_imports() { return 0 else log_verification_result "sage_imports" "FAIL" "核心包导入失败: ${failed_imports[*]}" + + # 输出详细的故障排查建议 + echo "" + echo -e "${RED}${BOLD}❌ 导入失败诊断${NC}" + echo -e "${DIM}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + echo -e "${YELLOW}失败的包: ${failed_imports[*]}${NC}" + echo "" + echo -e "${BOLD}建议的修复步骤:${NC}" + echo -e "1. 检查包是否已安装:" + for pkg in "${failed_imports[@]}"; do + local pypi_name=$(echo "$pkg" | sed 's/sage\./isage-/') + echo -e " ${DIM}pip show $pypi_name${NC}" + done + echo "" + echo -e "2. 检查 Python 环境:" + echo -e " ${DIM}which python3${NC}" + echo -e " ${DIM}python3 -m site${NC}" + echo "" + echo -e "3. 尝试重新安装失败的包:" + echo -e " ${DIM}cd /path/to/SAGE${NC}" + for pkg in "${failed_imports[@]}"; do + local pkg_dir=$(echo "$pkg" | sed 's/sage\./sage-/') + echo -e " ${DIM}pip install -e packages/$pkg_dir${NC}" + done + echo "" + echo -e "4. 查看安装日志:" + echo -e " ${DIM}cat .sage/logs/install.log${NC}" + echo -e "${DIM}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + echo "" + return 1 fi } @@ -417,6 +512,8 @@ run_comprehensive_verification() { echo -e "${BLUE}${BOLD}🔍 开始全面安装验证...${NC}" echo "" + resolve_verification_python_cmd + init_verification_log # 运行各项验证 @@ -444,6 +541,8 @@ run_comprehensive_verification() { run_quick_verification() { echo -e "${BLUE}🔍 快速安装验证...${NC}" + resolve_verification_python_cmd + init_verification_log # 只运行最关键的验证 diff --git a/tools/install/examination_tools/installation_consistency_check.sh b/tools/install/examination_tools/installation_consistency_check.sh index 746e2bc25a..d54e4580e7 100755 --- a/tools/install/examination_tools/installation_consistency_check.sh +++ b/tools/install/examination_tools/installation_consistency_check.sh @@ -80,7 +80,7 @@ print_check() { check_sage_root() { echo -e "${BOLD}1. 检查项目结构${NC}" - if [ -f "${SAGE_ROOT:-}/quickstart.sh" ] && [ -d "${SAGE_ROOT:-}/packages/sage" ]; then + if [ -f "${SAGE_ROOT:-}/quickstart.sh" ] && [ -f "${SAGE_ROOT:-}/pyproject.toml" ]; then print_check "pass" "SAGE 项目根目录已识别" else print_check "fail" "未在 SAGE 项目根目录运行" diff --git a/tools/install/examination_tools/mirror_selector.sh b/tools/install/examination_tools/mirror_selector.sh index 57c2e3b366..f69630a811 100644 --- a/tools/install/examination_tools/mirror_selector.sh +++ b/tools/install/examination_tools/mirror_selector.sh @@ -82,6 +82,65 @@ test_mirror_speed() { fi } +extract_mirror_artifact_url() { + local mirror_url="$1" + local test_package="${2:-pip}" + local test_url="${mirror_url}/${test_package}/" + + if ! command -v curl >/dev/null 2>&1; then + return 1 + fi + + local html + html="$(curl -L -s --connect-timeout 5 --max-time 12 "$test_url" 2>/dev/null || true)" + [ -n "$html" ] || return 1 + + local href + href="$(printf '%s' "$html" | tr '\n' ' ' | grep -oE 'href="[^"]+"' | head -n 1 | sed 's/^href="//;s/"$//')" + [ -n "$href" ] || return 1 + + python3 - </dev/null || true)" + [ -n "$artifact_url" ] || return 0 + + if ! command -v curl >/dev/null 2>&1; then + return 1 + fi + + local status + status="$(curl -L -s -I --connect-timeout 5 --max-time 12 -o /dev/null -w "%{http_code}" "$artifact_url" 2>/dev/null || echo "000")" + + case "$status" in + 200|204|206|301|302) + return 0 + ;; + 405|000) + status="$(curl -L -s --range 0-0 --connect-timeout 5 --max-time 12 -o /dev/null -w "%{http_code}" "$artifact_url" 2>/dev/null || echo "000")" + case "$status" in + 200|204|206|301|302) + return 0 + ;; + *) + return 1 + ;; + esac + ;; + *) + return 1 + ;; + esac +} + # 自动选择最快的镜像 auto_select_fastest_mirror() { local test_package="${1:-pip}" @@ -105,12 +164,23 @@ auto_select_fastest_mirror() { fi local response_time=$(test_mirror_speed "$mirror_url" "$test_package") + local download_ok=true + if [ "$response_time" -lt 99999 ]; then + if ! verify_mirror_download_capability "$mirror_url" "$test_package"; then + download_ok=false + response_time=99999 + fi + fi if [ "$verbose" = "true" ]; then if [ "$response_time" -lt 99999 ]; then echo -e "${GREEN} ✅ $mirror_name: ${response_time}ms${NC}" else - echo -e "${RED} ❌ $mirror_name: 超时或不可达${NC}" + if [ "$download_ok" = "false" ]; then + echo -e "${RED} ❌ $mirror_name: 元信息可达，但包下载不可用（疑似 403）${NC}" + else + echo -e "${RED} ❌ $mirror_name: 超时或不可达${NC}" + fi fi fi diff --git a/tools/install/examination_tools/sage_check.sh b/tools/install/examination_tools/sage_check.sh index aa327366b6..b86f996713 100755 --- a/tools/install/examination_tools/sage_check.sh +++ b/tools/install/examination_tools/sage_check.sh @@ -28,11 +28,41 @@ LC_CTYPE="${LC_CTYPE:-${LANG}}" check_existing_sage() { echo -e "${INFO} 检查是否已安装 SAGE..." + _sage_pip_list() { + if [ -n "${PIP_CMD:-}" ]; then + eval "$PIP_CMD list" 2>/dev/null + else + python3 -m pip list 2>/dev/null + fi + } + + _detect_sage_version() { + local installed_packages="$1" + local priority_packages=( + "isage" + ) + + local package_name="" + local package_version="" + for package_name in "${priority_packages[@]}"; do + package_version=$(echo "$installed_packages" | awk -v pkg="$package_name" '$1==pkg {print $2; exit}') + if [ -n "$package_version" ]; then + echo "$package_version" + return 0 + fi + done + + echo "$installed_packages" | head -n1 | awk '{print $2}' + } + + _sage_package_filter_pattern() { + echo '^(isage($|-)|intsage($|-)|sage$)' + } + # 检查pip包列表中的所有SAGE相关包变体 - local installed_packages=$(pip list 2>/dev/null | grep -E '^(sage|isage|intsage)(-|$)' || echo "") + local installed_packages=$(_sage_pip_list | grep -E "$(_sage_package_filter_pattern)" || echo "") if [ -n "$installed_packages" ]; then - # 获取第一个包的版本作为代表版本 - local version=$(echo "$installed_packages" | head -n1 | awk '{print $2}') + local version=$(_detect_sage_version "$installed_packages") echo -e "${WARNING} 检测到已安装的 SAGE v${version}" echo echo -e "${DIM}已安装的包：${NC}" @@ -43,9 +73,9 @@ check_existing_sage() { return 0 fi - # 检查是否能导入sage.common（PEP 420 namespace，检查实际包） - if python3 -c "import sage.common" 2>/dev/null; then - local sage_version=$(python3 -c "import sage.common; print(sage.common.__version__)" 2>/dev/null || echo "unknown") + # 检查是否能导入主仓核心表面 + if python3 -c "from sage._version import __version__; import sage.foundation, sage.stream, sage.runtime, sage.serving, sage.cli" 2>/dev/null; then + local sage_version=$(python3 -c "from sage._version import __version__; print(__version__)" 2>/dev/null || echo "unknown") echo -e "${WARNING} 检测到已安装的 SAGE v${sage_version}" return 0 fi @@ -58,8 +88,38 @@ check_existing_sage() { uninstall_sage() { echo -e "${INFO} 卸载现有 SAGE 安装..." + _sage_pip_list() { + if [ -n "${PIP_CMD:-}" ]; then + eval "$PIP_CMD list" 2>/dev/null + else + python3 -m pip list 2>/dev/null + fi + } + + _sage_pip_show() { + local package_name="$1" + if [ -n "${PIP_CMD:-}" ]; then + eval "$PIP_CMD show \"$package_name\"" 2>/dev/null + else + python3 -m pip show "$package_name" 2>/dev/null + fi + } + + _sage_pip_uninstall() { + local package_name="$1" + if [ -n "${PIP_CMD:-}" ]; then + eval "$PIP_CMD uninstall \"$package_name\" -y --quiet" 2>/dev/null + else + python3 -m pip uninstall "$package_name" -y --quiet 2>/dev/null + fi + } + # 获取所有已安装的SAGE相关包（包括所有前缀变体） - local all_sage_packages=$(pip list 2>/dev/null | grep -E '^(sage|isage|intsage)(-|$)' | awk '{print $1}' || echo "") + _sage_package_filter_pattern() { + echo '^(isage($|-)|intsage($|-)|sage$)' + } + + local all_sage_packages=$(_sage_pip_list | grep -E "$(_sage_package_filter_pattern)" | awk '{print $1}' || echo "") if [ -n "$all_sage_packages" ]; then echo -e "${DIM} → 发现已安装的包：${NC}" @@ -79,19 +139,12 @@ uninstall_sage() { if [ -n "$package" ]; then total_packages=$((total_packages + 1)) # 先检查包是否真的存在 - if pip show "$package" >/dev/null 2>&1; then - # 检查是否是editable安装 - local package_info=$(pip show "$package" 2>/dev/null) - if echo "$package_info" | grep -q "Editable project location:"; then - echo -e "${DIM} ○ $package 开发模式安装，重新安装时会自动更新${NC}" - uninstall_count=$((uninstall_count + 1)) # 算作处理成功 + if _sage_pip_show "$package" >/dev/null 2>&1; then + if _sage_pip_uninstall "$package"; then + echo -e "${DIM} ✓ 已卸载 $package${NC}" + uninstall_count=$((uninstall_count + 1)) else - if $PIP_CMD uninstall "$package" -y --quiet 2>/dev/null; then - echo -e "${DIM} ✓ 已卸载 $package${NC}" - uninstall_count=$((uninstall_count + 1)) - else - echo -e "${DIM} ⚠ $package 卸载失败${NC}" - fi + echo -e "${DIM} ⚠ $package 卸载失败${NC}" fi else echo -e "${DIM} - $package 未安装，跳过${NC}" @@ -106,26 +159,12 @@ uninstall_sage() { echo -e "${DIM} → 清理开发模式链接${NC}" local dev_packages=( "sage" - "sage-libs" - "sage-middleware" - "sage-kernel" - "sage-common" - "sage-tools" "isage" - "isage-libs" - "isage-middleware" - "isage-kernel" - "isage-common" "intsage" - "intsage-apps" - "intsage-dev-toolkit" - "intsage-frontend" - "intsage-kernel" - "intsage-middleware" ) for package in "${dev_packages[@]}"; do - if $PIP_CMD uninstall "$package" -y --quiet 2>/dev/null; then + if _sage_pip_uninstall "$package"; then echo -e "${DIM} 清理 $package 开发链接${NC}" fi done diff --git a/tools/install/examination_tools/system_check.sh b/tools/install/examination_tools/system_check.sh index 04ad8ff7ab..ccec6ff120 100755 --- a/tools/install/examination_tools/system_check.sh +++ b/tools/install/examination_tools/system_check.sh @@ -30,13 +30,14 @@ verify_installation() { echo -e "${INFO} 验证安装..." if python3 -c " -import sage -import sage.common -import sage.kernel -import sage.libs -import sage.middleware -print(f'${CHECK} SAGE v{sage.__version__} 安装成功！') -print(f'${CHECK} 所有子包版本一致: {sage.common.__version__}') +from sage._version import __version__ +import sage.foundation +import sage.stream +import sage.runtime +import sage.serving +import sage.cli +print(f'${CHECK} SAGE v{__version__} 安装成功！') +print(f'${CHECK} 主仓核心表面可用: foundation, stream, runtime, serving, cli') " 2>/dev/null; then echo -e "${CHECK} 验证通过！" diff --git a/tools/install/examination_tools/system_environment_check.sh b/tools/install/examination_tools/system_environment_check.sh index 4e7cbfab12..d6c17d0538 100755 --- a/tools/install/examination_tools/system_environment_check.sh +++ b/tools/install/examination_tools/system_environment_check.sh @@ -227,12 +227,10 @@ check_gpu_configuration() { echo -e "${DIM} - $name (${memory}MB)${NC}" done - # 检查CUDA - if command -v nvcc &> /dev/null; then - local cuda_version=$(nvcc --version | grep "release" | sed 's/.*release \([0-9.]*\).*/\1/') - echo -e "${CHECK} CUDA 版本: $cuda_version" - else - echo -e "${WARNING} 未检测到CUDA，GPU计算功能可能受限" + # 检查CUDA运行时版本 (nvidia-smi) + local cuda_runtime=$(nvidia-smi | grep "CUDA Version:" | awk '{print $9}' 2>/dev/null) + if [ -n "$cuda_runtime" ]; then + echo -e "${CHECK} CUDA 运行时版本: $cuda_runtime" fi fi fi diff --git a/tools/install/fixes/build_cache_cleaner.sh b/tools/install/fixes/build_cache_cleaner.sh index d4ab287709..497b5f8910 100755 --- a/tools/install/fixes/build_cache_cleaner.sh +++ b/tools/install/fixes/build_cache_cleaner.sh @@ -73,7 +73,7 @@ clean_egg_info_cache() { local cached_version=$(grep "^Version:" "$egg_info_dir/PKG-INFO" 2>/dev/null | cut -d' ' -f2) # 从 egg-info 路径推导包路径 - # 例如: packages/sage-common/src/isage_common.egg-info -> packages/sage-common + # 例如: src/isage.egg-info -> 项目根目录 / 当前包目录 local pkg_dir=$(dirname "$(dirname "$egg_info_dir")") # 查找对应的 _version.py（在该包的 src 目录下） diff --git a/tools/install/fixes/checkpoint_manager.sh b/tools/install/fixes/checkpoint_manager.sh index a80610e342..251aa4a8d1 100644 --- a/tools/install/fixes/checkpoint_manager.sh +++ b/tools/install/fixes/checkpoint_manager.sh @@ -242,13 +242,7 @@ echo "开始回滚 SAGE 安装..." # 卸载 SAGE 相关包 echo "卸载 SAGE 包..." -pip uninstall -y sage-common sage-kernel sage-libs sage-middleware sage-benchmark sage 2>/dev/null || true - -# 卸载 VLLM（如果安装了） -if pip show vllm &>/dev/null; then - echo "卸载 VLLM..." - pip uninstall -y vllm 2>/dev/null || true -fi +pip uninstall -y isage sage-benchmark sage 2>/dev/null || true # 恢复备份的包环境（可选） if [ -f "$backup_path/pip_packages.txt" ] && [ "\$1" = "--restore-packages" ]; then diff --git a/tools/install/fixes/cpp_extensions_fix.sh b/tools/install/fixes/cpp_extensions_fix.sh index b0dbaa6e6a..04e1a7a15f 100644 --- a/tools/install/fixes/cpp_extensions_fix.sh +++ b/tools/install/fixes/cpp_extensions_fix.sh @@ -5,7 +5,7 @@ # 加载日志和颜色函数（logging.sh 会自动 source colors.sh） source "$(dirname "${BASH_SOURCE[0]}")/../display_tools/logging.sh" -# 修复 sage-middleware C++ 扩展库的安装 +# 修复独立适配器 C++ 扩展安装问题（当前仅保留提示功能） # ============================================================================ # 环境变量安全默认值（防止 set -u 报错） @@ -26,115 +26,16 @@ LC_CTYPE="${LC_CTYPE:-${LANG}}" # ============================================================================ fix_middleware_cpp_extensions() { - # 注意: C++ 扩展已迁移为独立 PyPI 包，不再需要修复 + # 注意: C++ 扩展已迁移为独立 PyPI 包，不再由主仓修复脚本直接处理 # - isage-vdb (was sageVDB) # - isage-flow (was sageFlow) # - isage-tsdb (was sageTSDB) - # sage-middleware 现在只包含 Python 兼容层 + # 主仓只保留核心 stream/runtime surface log_info "C++ 扩展已迁移为独立 PyPI 包，跳过修复" "CPPExtFix" echo -e "${DIM}ℹ️ C++ 扩展（sageVDB/sageFlow/sageTSDB）已迁移为独立 PyPI 包${NC}" - echo -e "${DIM} 如需使用，请通过 pip install isage-vdb isage-flow isage-tsdb 安装${NC}" + echo -e "${DIM} 如需使用，请分别安装 isage-vdb / isage-flow / isage-tsdb${NC}" return 0 - - # 以下代码已废弃，保留供参考 - # ---------------------------------------------------------------- - local fixed_count=0 - local total_count=0 - local project_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/../../../" && pwd)" - - for ext_lib in "${extensions_libs[@]}"; do - total_count=$((total_count + 1)) - - # 分离扩展名和库文件列表 - local ext="${ext_lib%%:*}" - local lib_names="${ext_lib#*:}" - - # 目标目录（Python 包的位置） - local target_dir="$project_root/packages/sage-middleware/src/sage/middleware/components/${ext}/python" - - # 检查目标目录是否存在 - if [ ! -d "$target_dir" ]; then - log_debug "跳过 ${ext}: 目标目录不存在" "CPPExtFix" - echo -e "${DIM} 跳过 ${ext}: 目标目录不存在${NC}" - continue - fi - - # 处理多个库文件（用逗号分隔） - local all_libs_ok=true - IFS=',' read -ra lib_array <<< "$lib_names" - - for lib_name in "${lib_array[@]}"; do - # 检查库文件是否已经存在于目标目录 - if [ -f "$target_dir/$lib_name" ]; then - log_debug "${ext}: ${lib_name} 已存在" "CPPExtFix" - echo -e "${DIM} ${CHECK} ${ext}: ${lib_name} 已存在${NC}" - continue - fi - - # 查找构建目录中的库文件 - local build_lib="" - local search_paths=( - "$project_root/packages/sage-middleware/build" - "$project_root/packages/sage-middleware/src/sage/middleware/components/${ext}" - "$project_root/.sage/build/${ext}" - "$project_root/.sage/build/middleware" - ) - - for search_path in "${search_paths[@]}"; do - if [ -d "$search_path" ]; then - build_lib=$(find "$search_path" -name "$lib_name" -type f 2>/dev/null | head -1) - if [ -n "$build_lib" ] && [ -f "$build_lib" ]; then - break - fi - fi - done - - if [ -z "$build_lib" ] || [ ! -f "$build_lib" ]; then - log_warn "${ext}: ${lib_name} 未找到" "CPPExtFix" - log_debug "已搜索路径: ${search_paths[*]}" "CPPExtFix" - echo -e "${WARNING} ${ext}: ${lib_name} 未找到" - echo -e "${DIM} 已搜索路径: ${search_paths[*]}${NC}" - all_libs_ok=false - continue - fi - - # 复制库文件到目标目录 - log_info "复制 ${lib_name} 到 ${target_dir}" "CPPExtFix" - echo -e "${DIM} 复制 ${lib_name} 到 ${target_dir}${NC}" - if cp "$build_lib" "$target_dir/"; then - log_info "${ext}: ${lib_name} 已修复" "CPPExtFix" - echo -e " ${CHECK} ${ext}: ${lib_name} 已修复" - else - log_error "${ext}: 复制 ${lib_name} 失败" "CPPExtFix" - echo -e " ${CROSS} ${ext}: 复制 ${lib_name} 失败" - all_libs_ok=false - fi - done - - if [ "$all_libs_ok" = true ]; then - fixed_count=$((fixed_count + 1)) - fi - done - - echo "" - if [ $fixed_count -eq $total_count ]; then - log_info "所有 C++ 扩展库检查完成 (${fixed_count}/${total_count})" "CPPExtFix" - echo -e "${CHECK} 所有 C++ 扩展库检查完成 (${fixed_count}/${total_count})" - return 0 - else - log_warn "部分 C++ 扩展库可能不可用 (${fixed_count}/${total_count})" "CPPExtFix" - echo -e "${WARNING} 部分 C++ 扩展库可能不可用 (${fixed_count}/${total_count})" - - if [[ -n "${CI:-}" || -n "${GITHUB_ACTIONS:-}" ]]; then - log_debug "CI 环境提示：如果子模块已初始化但库文件仍未找到，可能是 CMake 安装配置问题或构建失败" "CPPExtFix" - echo -e "${DIM}💡 CI 环境提示：${NC}" - echo -e "${DIM} 如果子模块已初始化但库文件仍未找到，${NC}" - echo -e "${DIM} 可能是 CMake 安装配置问题或构建失败${NC}" - echo -e "${DIM} 请检查上方的构建日志中的 CMake 输出${NC}" - fi - return 0 - fi } # 导出函数供其他脚本使用 diff --git a/tools/install/fixes/environment_doctor.sh b/tools/install/fixes/environment_doctor.sh index 843b970844..f433abcc58 100755 --- a/tools/install/fixes/environment_doctor.sh +++ b/tools/install/fixes/environment_doctor.sh @@ -70,6 +70,64 @@ log_message() { echo "$(date '+%Y-%m-%d %H:%M:%S') [$level] $*" >> "$DOCTOR_LOG" } +# 轻量获取包版本（避免直接 import 大型包导致内存峰值） +get_package_version_safe() { + local package_name="$1" + local detected_version="" + + detected_version=$(python3 -c "from importlib.metadata import version, PackageNotFoundError; pkg='$package_name'; +try: + print(version(pkg)) +except PackageNotFoundError: + pass" 2>/dev/null | head -1) + + if [ -z "$detected_version" ]; then + if command -v pip3 >/dev/null 2>&1; then + detected_version=$(pip3 show "$package_name" 2>/dev/null | awk -F': ' '/^Version:/{print $2; exit}') + elif command -v pip >/dev/null 2>&1; then + detected_version=$(pip show "$package_name" 2>/dev/null | awk -F': ' '/^Version:/{print $2; exit}') + fi + fi + + echo "$detected_version" +} + +# 检查 numpy 元数据是否完整（避免依赖 pkg_resources） +is_numpy_metadata_healthy() { + python3 -c " +import glob +import os +import site +import sys +from importlib.metadata import PackageNotFoundError, version + +try: + import numpy # noqa: F401 +except Exception: + sys.exit(0) + +try: + version('numpy') +except PackageNotFoundError: + sys.exit(1) + +paths = [] +for p in site.getsitepackages() + [site.getusersitepackages()]: + if p not in paths and os.path.isdir(p): + paths.append(p) + +for root in paths: + for candidate in glob.glob(os.path.join(root, '~umpy*')): + if os.path.exists(candidate): + sys.exit(1) + for dist_info in glob.glob(os.path.join(root, 'numpy-*.dist-info')): + if not os.path.isfile(os.path.join(dist_info, 'RECORD')): + sys.exit(1) + +sys.exit(0) +" >/dev/null 2>&1 +} + # 问题报告结构 declare -A ISSUE_REGISTRY declare -A FIX_REGISTRY @@ -144,9 +202,9 @@ check_python_environment() { if command -v pip >/dev/null 2>&1 || command -v pip3 >/dev/null 2>&1; then local pip_version="" if command -v pip3 >/dev/null 2>&1; then - pip_version=$(pip3 --version 2>&1 | grep -oP 'pip \K[0-9]+\.[0-9]+\.[0-9]+') + pip_version=$(pip3 --version 2>&1 | grep -oE 'pip [0-9]+\.[0-9]+(\.[0-9]+)?' | awk '{print $2}' | head -1) else - pip_version=$(pip --version 2>&1 | grep -oP 'pip \K[0-9]+\.[0-9]+\.[0-9]+') + pip_version=$(pip --version 2>&1 | grep -oE 'pip [0-9]+\.[0-9]+(\.[0-9]+)?' | awk '{print $2}' | head -1) fi echo -e " ${GREEN}${CHECK_MARK}${NC} pip 版本: $pip_version" log_message "INFO" "pip version: $pip_version" @@ -197,7 +255,7 @@ check_package_manager_conflicts() { fi if [ "$pip_available" = "true" ]; then - pip_installed=$(python3 -c "import $package; print($package.__version__)" 2>/dev/null || echo "") + pip_installed=$(get_package_version_safe "$package") fi # 只有在真正冲突时才报告（conda 管理的包 vs pip 管理的包，且版本不同） @@ -264,7 +322,7 @@ check_core_dependencies() { local status="missing" # 尝试获取包版本 - version=$(python3 -c "import $package; print($package.__version__)" 2>/dev/null || echo "") + version=$(get_package_version_safe "$package") if [ -n "$version" ]; then status="installed" @@ -278,16 +336,6 @@ check_core_dependencies() { report_issue "numpy_v1" "numpy 1.x 版本可能与某些深度学习库不兼容，建议升级到 2.x" "major" fi ;; - "torch") - # 检查CUDA支持 - local cuda_available=$(python3 -c "import torch; print(torch.cuda.is_available())" 2>/dev/null || echo "False") - if [ "$cuda_available" = "True" ]; then - local cuda_version=$(python3 -c "import torch; print(torch.version.cuda)" 2>/dev/null || echo "unknown") - echo -e " ${GREEN}${CHECK_MARK}${NC} CUDA 支持: $cuda_version" - else - echo -e " ${YELLOW}${WARNING_MARK}${NC} 未检测到 CUDA 支持" - fi - ;; esac else echo -e " ${BLUE}${INFO_MARK}${NC} $package: 未安装（将在安装过程中安装）" @@ -301,16 +349,16 @@ check_core_dependencies() { check_specific_issues() { echo -e "\n${YELLOW}${BOLD}🔎 特定问题诊断${NC}" - # 检查numpy RECORD文件问题 + # 检查 numpy 元数据完整性问题 if python3 -c "import numpy" >/dev/null 2>&1; then - if ! python3 -c "import pkg_resources; pkg_resources.get_distribution('numpy')" >/dev/null 2>&1; then + if ! is_numpy_metadata_healthy; then report_issue "numpy_corrupted" "numpy 安装记录损坏，可能导致升级失败" "major" fi fi # 检查torch版本兼容性 - local torch_version=$(python3 -c "import torch; print(torch.__version__)" 2>/dev/null || echo "") - local numpy_version=$(python3 -c "import numpy; print(numpy.__version__)" 2>/dev/null || echo "") + local torch_version=$(get_package_version_safe "torch") + local numpy_version=$(get_package_version_safe "numpy") if [ -n "$torch_version" ] && [ -n "$numpy_version" ]; then # 检查已知的不兼容组合 @@ -321,24 +369,12 @@ check_specific_issues() { # 检查CUDA环境 if command -v nvidia-smi >/dev/null 2>&1; then + echo -e " ${GREEN}${CHECK_MARK}${NC} CUDA 设备检测: 检测到 NVIDIA GPU" local driver_version=$(nvidia-smi --query-gpu=driver_version --format=csv,noheader,nounits 2>/dev/null | head -1) echo -e " ${INFO_MARK} NVIDIA 驱动版本: $driver_version" - # 检查CUDA工具包 - 系统级别 - if [ -d "/usr/local/cuda" ]; then - local cuda_version=$(cat /usr/local/cuda/version.txt 2>/dev/null | grep -oP 'CUDA Version \K[0-9]+\.[0-9]+' || echo "unknown") - echo -e " ${INFO_MARK} CUDA 工具包版本 (系统): $cuda_version" - fi - - # 检查 nvcc 编译器 (关键：vLLM 需要) - if command -v nvcc >/dev/null 2>&1; then - local nvcc_version=$(nvcc --version 2>/dev/null | grep -oP 'release \K[0-9]+\.[0-9]+' || echo "unknown") - echo -e " ${GREEN}${CHECK_MARK}${NC} NVCC 编译器: $nvcc_version" - log_message "INFO" "NVCC version: $nvcc_version" - else - report_issue "nvcc_missing" "检测到 GPU 但未找到 nvcc 编译器 - vLLM 需要 CUDA Toolkit" "critical" - echo -e " ${DIM}修复建议: conda install -c conda-forge cudatoolkit-dev -y --override-channels${NC}" - fi + else + echo -e " ${INFO_MARK} CUDA 设备检测: 未检测到 nvidia-smi（当前可能为 CPU 环境）" fi # 检查磁盘空间 @@ -350,87 +386,81 @@ check_specific_issues() { fi } -# 5. 开发工具检查 -check_dev_tools() { - echo -e "\n${PURPLE}${BOLD}🛠️ 开发工具诊断${NC}" - - # 检查 pytest 及其相关插件 - declare -A dev_tools=( - ["pytest"]="pytest>=7.0.0" - ["pytest-cov"]="pytest-cov>=4.0.0" - ["pytest-asyncio"]="pytest-asyncio>=0.21.0" - ["pytest-mock"]="pytest-mock>=3.10.0" - ["pytest-timeout"]="pytest-timeout>=2.1.0" - ["pytest-benchmark"]="pytest-benchmark>=4.0.0" - ["ruff"]="ruff==0.14.6" - ["mypy"]="mypy>=1.0.0" - ["pre-commit"]="pre-commit>=3.0.0" - ) - - local missing_tools=() - local installed_tools=() - - for tool in "${!dev_tools[@]}"; do - local version="" - local package_name="$tool" +# 5. 系统级依赖检查（无法通过 pyproject.toml 自动安装） +check_system_dependencies() { + echo -e "\n${PURPLE}${BOLD}🧰 系统依赖诊断${NC}" - # 确保 ~/.local/bin 在 PATH 中（pip install --user 安装的工具在这里） - if [[ ":$PATH:" != *":$HOME/.local/bin:"* ]]; then - export PATH="$HOME/.local/bin:$PATH" + local missing_build_tools=() + for tool in gcc cmake make pkg-config; do + if command -v "$tool" >/dev/null 2>&1; then + echo -e " ${GREEN}${CHECK_MARK}${NC} $tool: 已安装" + else + echo -e " ${YELLOW}${CROSS_MARK}${NC} $tool: 未安装" + missing_build_tools+=("$tool") fi + done - # 使用 importlib.metadata 获取版本（Python 3.8+ 标准方法） - # 这比直接 import 模块更可靠，因为不是所有包都有 __version__ 属性 - version=$(python3 -c "from importlib.metadata import version; print(version('$package_name'))" 2>/dev/null || echo "") + if [ ${#missing_build_tools[@]} -gt 0 ]; then + local tool_list=$(IFS=", "; echo "${missing_build_tools[*]}") + report_issue "system_build_tools_missing" "缺少系统构建工具: $tool_list" "major" + echo -e " ${DIM}说明: 这类依赖属于系统包，不能由 pyproject.toml 自动安装${NC}" + fi - # 对于命令行工具（如 pre-commit, ruff），如果 importlib 找不到，也检查 command -v - if [ -z "$version" ]; then - if command -v "$tool" >/dev/null 2>&1; then - # 尝试从工具自身获取版本 - version=$("$tool" --version 2>/dev/null | head -1 | grep -oE '[0-9]+\.[0-9]+(\.[0-9]+)?' | head -1 || echo "available") - fi + local blas_found=false + local lapack_found=false + for lib_path in /usr/lib /usr/lib64 /usr/lib/x86_64-linux-gnu /usr/local/lib; do + if [[ -f "$lib_path/libopenblas.so" || -f "$lib_path/libblas.so" ]]; then + blas_found=true fi - - if [ -n "$version" ]; then - echo -e " ${GREEN}${CHECK_MARK}${NC} $tool: $version" - installed_tools+=("$tool") - log_message "INFO" "$tool version: $version" - else - echo -e " ${YELLOW}${CROSS_MARK}${NC} $tool: 未安装" - missing_tools+=("$tool") - log_message "WARN" "$tool is not installed" + if [[ -f "$lib_path/liblapack.so" ]]; then + lapack_found=true fi done - # 如果有缺失的工具，报告问题 - if [ ${#missing_tools[@]} -gt 0 ]; then - local tools_list=$(IFS=", "; echo "${missing_tools[*]}") - report_issue "dev_tools_missing" "缺少开发工具: $tools_list" "major" + if [ "$blas_found" = true ]; then + echo -e " ${GREEN}${CHECK_MARK}${NC} BLAS: 已检测到" + else + echo -e " ${YELLOW}${CROSS_MARK}${NC} BLAS: 未检测到" + fi - # 特别强调 pytest - for tool in "${missing_tools[@]}"; do - if [[ "$tool" == pytest* ]]; then - echo -e " ${DIM}提示: pytest 是运行测试所必需的${NC}" - break - fi - done + if [ "$lapack_found" = true ]; then + echo -e " ${GREEN}${CHECK_MARK}${NC} LAPACK: 已检测到" else - echo -e "\n ${GREEN}${CHECK_MARK}${NC} 所有开发工具已安装" + echo -e " ${YELLOW}${CROSS_MARK}${NC} LAPACK: 未检测到" fi - # 检查 pre-commit hooks 是否已安装 - if [ -d ".git" ]; then - if [ -f ".git/hooks/pre-commit" ]; then - echo -e " ${GREEN}${CHECK_MARK}${NC} pre-commit hooks: 已安装" - else - if command -v pre-commit >/dev/null 2>&1; then - echo -e " ${YELLOW}${WARNING_MARK}${NC} pre-commit hooks: 未安装（pre-commit 工具可用）" - report_issue "pre_commit_hooks_missing" "pre-commit hooks 未安装" "minor" - fi - fi + if [ "$blas_found" != true ] || [ "$lapack_found" != true ]; then + report_issue "system_math_libs_missing" "缺少系统数学库（BLAS/LAPACK）" "major" + echo -e " ${DIM}说明: 这类依赖属于系统包，不能由 pyproject.toml 自动安装${NC}" fi } +# 系统级依赖修复（通过系统包管理器） +fix_system_dependencies() { + echo -e "\n${TOOL_MARK} 修复系统依赖问题..." + + local script_dir="" + script_dir="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" + local installer_script="$script_dir/../core/install_system_deps.sh" + + if [ ! -f "$installer_script" ]; then + echo -e " ${RED}${CROSS_MARK}${NC} 未找到系统依赖安装脚本: $installer_script" + return 1 + fi + + echo -e " ${DIM}执行: bash $installer_script${NC}" + if bash "$installer_script"; then + echo -e " ${GREEN}${CHECK_MARK}${NC} 系统依赖修复完成" + FIXES_APPLIED=$((FIXES_APPLIED + 1)) + log_message "FIX" "Successfully repaired system dependencies" + return 0 + fi + + echo -e " ${RED}${CROSS_MARK}${NC} 系统依赖修复失败" + log_message "ERROR" "Failed to repair system dependencies" + return 1 +} + # ================================ # 自动修复模块 # ================================ @@ -703,18 +733,32 @@ fix_numpy_corrupted() { return 1 fi - # 清理损坏的numpy + # 清理损坏的 numpy pip3 uninstall numpy -y >/dev/null 2>&1 || true python3 -c " -import os, shutil, sys -try: - import numpy - numpy_path = os.path.dirname(numpy.__file__) - if 'site-packages' in numpy_path: - shutil.rmtree(numpy_path, ignore_errors=True) - print('清理了损坏的 numpy 安装') -except Exception: - pass +import glob +import os +import shutil +import site + +paths = [] +for p in site.getsitepackages() + [site.getusersitepackages()]: + if p not in paths and os.path.isdir(p): + paths.append(p) + +for root in paths: + for candidate in glob.glob(os.path.join(root, 'numpy')): + shutil.rmtree(candidate, ignore_errors=True) + for candidate in glob.glob(os.path.join(root, 'numpy-*.dist-info')): + shutil.rmtree(candidate, ignore_errors=True) + for candidate in glob.glob(os.path.join(root, '~umpy*')): + if os.path.isdir(candidate): + shutil.rmtree(candidate, ignore_errors=True) + elif os.path.exists(candidate): + try: + os.remove(candidate) + except OSError: + pass " 2>/dev/null || true # 重新安装（使用 SAGE 兼容版本：>=1.26.0,<2.3.0） @@ -788,72 +832,16 @@ fix_mixed_packages() { FIXES_APPLIED=$((FIXES_APPLIED + 1)) } -# CUDA Toolkit 缺失修复 -fix_cuda_toolkit() { - echo -e "\n${TOOL_MARK} 安装 CUDA Toolkit (nvcc 编译器)..." - - # 检查是否已安装 - if command -v nvcc >/dev/null 2>&1; then - local nvcc_version=$(nvcc --version 2>/dev/null | grep -oP 'release \K[0-9]+\.[0-9]+' || echo "unknown") - echo -e " ${GREEN}${CHECK_MARK}${NC} NVCC 编译器已安装: $nvcc_version" - FIXES_APPLIED=$((FIXES_APPLIED + 1)) - return 0 - fi - - # 检查是否在 conda 环境中 - if ! command -v conda >/dev/null 2>&1; then - echo -e " ${RED}${CROSS_MARK}${NC} 未检测到 conda，无法自动安装 CUDA Toolkit" - echo -e " ${DIM}请手动安装: sudo apt-get install nvidia-cuda-toolkit${NC}" - return 1 - fi - - # 检查是否有 NVIDIA GPU - if ! command -v nvidia-smi >/dev/null 2>&1; then - echo -e " ${YELLOW}⚠${NC} 未检测到 NVIDIA GPU，跳过 CUDA Toolkit 安装" - return 0 - fi - - echo -e " ${INFO_MARK} 通过 conda 安装 CUDA Toolkit 开发包..." - echo -e " ${DIM}这可能需要几分钟时间...${NC}" - # 使用统一的 conda 安装工具（自动使用镜像） - local conda_utils="${SAGE_ROOT:-$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)}/tools/lib/conda_install_utils.sh" - if [ -f "$conda_utils" ]; then - source "$conda_utils" - if conda_install_bypass cudatoolkit-dev; then - echo -e " ${GREEN}${CHECK_MARK}${NC} CUDA Toolkit 安装成功" - - # 验证安装 - if command -v nvcc >/dev/null 2>&1; then - local nvcc_version=$(nvcc --version 2>/dev/null | grep -oP 'release \K[0-9]+\.[0-9]+' || echo "unknown") - echo -e " ${GREEN}${CHECK_MARK}${NC} NVCC 编译器可用: $nvcc_version" - FIXES_APPLIED=$((FIXES_APPLIED + 1)) - return 0 - else - echo -e " ${YELLOW}⚠${NC} CUDA Toolkit 已安装，但 nvcc 不在 PATH 中" - echo -e " ${DIM}请重新激活 conda 环境: conda deactivate && conda activate ${CONDA_DEFAULT_ENV}${NC}" - FIXES_APPLIED=$((FIXES_APPLIED + 1)) - return 0 - fi - else - echo -e " ${RED}${CROSS_MARK}${NC} CUDA Toolkit 安装失败" - echo -e " ${DIM}请手动安装: conda install -c conda-forge cudatoolkit-dev -y --override-channels${NC}" - return 1 - fi - else - # Fallback: 直接使用 conda 命令 - if conda install -c conda-forge cudatoolkit-dev -y --override-channels >/dev/null 2>&1; then - echo -e " ${GREEN}${CHECK_MARK}${NC} CUDA Toolkit 安装成功" - FIXES_APPLIED=$((FIXES_APPLIED + 1)) - return 0 - else - echo -e " ${RED}${CROSS_MARK}${NC} CUDA Toolkit 安装失败" - return 1 - fi - fi +# CLI 工具冲突修复 +fix_python_version() { + echo -e "\n${TOOL_MARK} 修复 Python 版本兼容性问题..." + echo -e " ${INFO_MARK} 当前 Python 版本与 SAGE 不兼容（需要 3.9-3.12）" + echo -e " ${INFO_MARK} 将创建 conda 环境并使用 Python 3.11" + # 复用 fix_pip_missing 中完整的 conda 环境创建逻辑（包含清华镜像、Miniconda 安装等） + fix_pip_missing } -# CLI 工具冲突修复 fix_cli_conflicts() { echo -e "\n${TOOL_MARK} 清理 CLI 工具冲突..." local local_bin="$HOME/.local/bin" @@ -869,190 +857,6 @@ fix_cli_conflicts() { FIXES_APPLIED=$((FIXES_APPLIED + 1)) } -# 开发工具缺失修复 -fix_dev_tools_missing() { - echo -e "\n${TOOL_MARK} 安装缺失的开发工具..." - - # 首先检查 pip 是否可用 - local pip_cmd="" - if command -v pip3 >/dev/null 2>&1; then - pip_cmd="pip3" - elif command -v pip >/dev/null 2>&1; then - pip_cmd="pip" - elif python3 -m pip --version >/dev/null 2>&1; then - pip_cmd="python3 -m pip" - else - echo -e " ${RED}${CROSS_MARK}${NC} pip 不可用，无法安装开发工具" - echo -e " ${DIM}请先解决 pip 缺失问题${NC}" - return 1 - fi - - # 准备安装参数（优先使用国内镜像） - local pip_args="" - if ! curl -s --connect-timeout 3 https://pypi.org >/dev/null 2>&1; then - echo -e " ${INFO_MARK} 使用清华 PyPI 镜像..." - pip_args="-i https://pypi.tuna.tsinghua.edu.cn/simple" - fi - - # 读取外部依赖文件（如果存在） - local external_deps_file=".sage/external-deps-dev.txt" - local dev_tools_installed=false - - if [ -f "$external_deps_file" ] && grep -q "pytest" "$external_deps_file"; then - echo -e " ${INFO_MARK} 从外部依赖文件安装开发工具..." - - # 提取开发工具相关的依赖 - local dev_deps=$(grep -E "pytest|ruff|mypy|pre-commit|black|isort|coverage|bandit" "$external_deps_file" || echo "") - - if [ -n "$dev_deps" ]; then - echo "$dev_deps" > /tmp/sage_dev_tools.txt - - echo -e " ${DIM}安装: pytest, ruff, mypy, pre-commit 等...${NC}" - # 先执行安装，再检查结果 - local install_output="" - install_output=$($pip_cmd install -r /tmp/sage_dev_tools.txt $pip_args 2>&1) - local install_status=$? - - # 检查是否成功（退出码为0 或 输出包含成功/已安装消息） - if [ $install_status -eq 0 ] || echo "$install_output" | grep -qE "(Successfully installed|Requirement already satisfied)"; then - echo -e " ${GREEN}${CHECK_MARK}${NC} 开发工具安装成功" - dev_tools_installed=true - FIXES_APPLIED=$((FIXES_APPLIED + 1)) - log_message "FIX" "Successfully installed dev tools from external deps file" - else - echo -e " ${YELLOW}${WARNING_MARK}${NC} 从外部文件安装失败，将尝试逐个安装" - log_message "WARN" "Failed to install from external deps file" - fi - - rm -f /tmp/sage_dev_tools.txt - fi - fi - - # 如果外部依赖文件不存在或安装失败，手动安装核心开发工具 - local install_success_count=0 - local install_total=9 # 核心工具总数 - - if [ "$dev_tools_installed" = false ]; then - echo -e " ${INFO_MARK} 手动安装核心开发工具..." - - local core_tools=( - "pytest>=7.0.0" - "pytest-cov>=4.0.0" - "pytest-asyncio>=0.21.0" - "pytest-mock>=3.10.0" - "pytest-timeout>=2.1.0" - "pytest-benchmark>=4.0.0" - "ruff==0.14.6" - "mypy>=1.0.0" - "pre-commit>=3.0.0" - ) - - install_total=${#core_tools[@]} - - for tool in "${core_tools[@]}"; do - local tool_name=$(echo "$tool" | sed 's/[<>=].*//') - - # 先检查是否已安装 - if python3 -c "import ${tool_name//-/_}" >/dev/null 2>&1; then - echo -e " ${GREEN}${CHECK_MARK}${NC} $tool_name: 已安装" - install_success_count=$((install_success_count + 1)) - continue - fi - - echo -e " ${DIM}安装: $tool_name...${NC}" - - # 尝试安装，捕获输出 - local install_output="" - install_output=$($pip_cmd install "$tool" $pip_args 2>&1) - local install_status=$? - - # 检查是否成功（退出码为0 或 输出包含成功消息） - if [ $install_status -eq 0 ] || echo "$install_output" | grep -qE "(Successfully installed|Requirement already satisfied)"; then - echo -e " ${GREEN}${CHECK_MARK}${NC} $tool_name 安装成功" - install_success_count=$((install_success_count + 1)) - log_message "INFO" "Successfully installed $tool_name" - else - echo -e " ${YELLOW}${WARNING_MARK}${NC} $tool_name 安装失败: $install_output" - log_message "WARN" "Failed to install $tool_name: $install_output" - fi - done - - if [ $install_success_count -eq $install_total ]; then - echo -e " ${GREEN}${CHECK_MARK}${NC} 所有开发工具安装成功 ($install_success_count/$install_total)" - FIXES_APPLIED=$((FIXES_APPLIED + 1)) - log_message "FIX" "Successfully installed all dev tools" - elif [ $install_success_count -gt 0 ]; then - echo -e " ${YELLOW}${WARNING_MARK}${NC} 部分开发工具安装成功 ($install_success_count/$install_total)" - FIXES_APPLIED=$((FIXES_APPLIED + 1)) - log_message "WARN" "Partially installed dev tools: $install_success_count/$install_total" - else - echo -e " ${RED}${CROSS_MARK}${NC} 开发工具安装失败" - log_message "ERROR" "Failed to install dev tools" - fi - else - echo -e " ${INFO_MARK} 跳过手动安装（外部依赖文件已处理）" - fi - - # 验证 pytest 是否安装成功 - # 注意：在某些环境（如 CI）中，刚安装的包可能需要刷新环境才能导入 - # 如果开发工具从外部文件安装成功，或至少有一个工具安装成功，就认为修复是有效的 - - # Debug logging - echo -e " ${DIM}[DEBUG] dev_tools_installed=$dev_tools_installed, install_success_count=$install_success_count${NC}" - log_message "DEBUG" "dev_tools_installed=$dev_tools_installed, install_success_count=$install_success_count, install_total=$install_total" - - if [ "$dev_tools_installed" = true ] || [ $install_success_count -gt 0 ]; then - if python3 -c "import pytest" >/dev/null 2>&1; then - local pytest_version=$(python3 -c "import pytest; print(pytest.__version__)" 2>/dev/null) - echo -e " ${GREEN}${CHECK_MARK}${NC} pytest $pytest_version 已就绪" - else - echo -e " ${YELLOW}${WARNING_MARK}${NC} 开发工具已安装，但需要刷新环境（安装流程完成后生效）" - log_message "WARN" "Dev tools installed but not yet importable (environment refresh needed)" - fi - return 0 - else - echo -e " ${RED}${CROSS_MARK}${NC} 开发工具安装失败" - log_message "ERROR" "Dev tools installation failed: dev_tools_installed=$dev_tools_installed, install_success_count=$install_success_count" - return 1 - fi -} - -# pre-commit hooks 安装修复 -fix_pre_commit_hooks_missing() { - echo -e "\n${TOOL_MARK} 安装 pre-commit hooks..." - - # 确保 ~/.local/bin 在 PATH 中（pip install --user 安装的工具在这里） - if [[ ":$PATH:" != *":$HOME/.local/bin:"* ]]; then - export PATH="$HOME/.local/bin:$PATH" - fi - - if ! command -v pre-commit >/dev/null 2>&1; then - echo -e " ${YELLOW}${WARNING_MARK}${NC} pre-commit 工具未安装，跳过 hooks 安装" - return 0 # 不是错误，只是跳过 - fi - - if [ ! -d ".git" ]; then - echo -e " ${YELLOW}${WARNING_MARK}${NC} 不是 Git 仓库，无需安装 pre-commit hooks" - return 0 # 不是错误，只是跳过 - fi - - echo -e " ${DIM}正在安装 pre-commit hooks...${NC}" - echo -e " ${DIM}执行命令: pre-commit install --config tools/config/pre-commit-config.yaml${NC}" - - # 显示详细输出以便调试 - if pre-commit install --config tools/config/pre-commit-config.yaml 2>&1; then - echo -e " ${GREEN}${CHECK_MARK}${NC} pre-commit hooks 安装成功" - FIXES_APPLIED=$((FIXES_APPLIED + 1)) - log_message "FIX" "Successfully installed pre-commit hooks" - return 0 - else - local install_exit_code=$? - echo -e " ${RED}${CROSS_MARK}${NC} pre-commit hooks 安装失败 (退出码: $install_exit_code)" - log_message "ERROR" "pre-commit install failed with exit code: $install_exit_code" - return 1 - fi -} - # 环境优化建议 suggest_environment_optimization() { echo -e "\n${BLUE}${BOLD}💡 环境优化建议${NC}" @@ -1076,7 +880,7 @@ suggest_environment_optimization() { # 注册所有已知问题和修复方案 register_all_issues() { - register_issue "python_version" "Python版本兼容性问题" "major" "" + register_issue "python_version" "Python版本兼容性问题" "major" "fix_python_version" register_issue "python_missing" "Python解释器缺失" "critical" "" register_issue "pip_missing" "pip包管理器缺失" "critical" "fix_pip_missing" register_issue "no_virtual_env" "未使用虚拟环境" "minor" "" @@ -1084,11 +888,10 @@ register_all_issues() { register_issue "numpy_v1" "numpy版本过旧" "major" "" register_issue "torch_numpy_compat" "PyTorch与numpy版本不匹配" "major" "" register_issue "low_disk_space" "磁盘空间不足" "major" "" - register_issue "nvcc_missing" "CUDA Toolkit (nvcc编译器) 缺失" "critical" "fix_cuda_toolkit" - # 开发工具问题 - register_issue "dev_tools_missing" "缺少开发工具（pytest等）" "major" "fix_dev_tools_missing" - register_issue "pre_commit_hooks_missing" "pre-commit hooks未安装" "minor" "fix_pre_commit_hooks_missing" + # 系统级依赖（无法由 pyproject.toml 自动安装） + register_issue "system_build_tools_missing" "缺少系统构建工具（gcc/cmake/make/pkg-config）" "major" "fix_system_dependencies" + register_issue "system_math_libs_missing" "缺少系统数学库（BLAS/LAPACK）" "major" "fix_system_dependencies" # 动态注册混合包问题 for package in "numpy" "torch" "transformers"; do @@ -1118,7 +921,7 @@ run_full_diagnosis() { check_cli_conflicts check_core_dependencies check_specific_issues - check_dev_tools + check_system_dependencies # 诊断总结 echo -e "\n${BLUE}${BOLD}📋 诊断总结${NC}" @@ -1182,8 +985,12 @@ run_auto_fixes() { # 如果没有执行过，则执行修复 if [ "$already_executed" = false ]; then - "$fix_function" - executed_fixes+=("$fix_function") + if "$fix_function"; then + executed_fixes+=("$fix_function") + else + echo -e " ${YELLOW}${WARNING_MARK}${NC} 修复函数执行失败: $fix_function" + log_message "WARN" "Fix function failed: $fix_function" + fi fi fi done diff --git a/tools/install/fixes/friendly_error_handler.sh b/tools/install/fixes/friendly_error_handler.sh index ed78f538f6..7b1d25e529 100755 --- a/tools/install/fixes/friendly_error_handler.sh +++ b/tools/install/fixes/friendly_error_handler.sh @@ -47,7 +47,6 @@ declare -A ERROR_EXPLANATIONS=( ["network_timeout"]="网络连接超时" ["permission_denied"]="权限不足" ["python_version_incompatible"]="Python版本不兼容" - ["vllm_install_fail"]="vLLM 安装失败" ["cuda_not_found"]="CUDA 环境未找到" ) @@ -60,7 +59,6 @@ declare -A ERROR_CAUSES=( ["network_timeout"]="网络连接不稳定或PyPI服务器访问缓慢" ["permission_denied"]="当前用户没有足够权限安装包到系统目录" ["python_version_incompatible"]="当前Python版本与某些包的要求不匹配" - ["vllm_install_fail"]="vLLM需要特定的CUDA和Python环境，安装要求较为严格" ["cuda_not_found"]="系统未正确安装NVIDIA CUDA工具包或驱动" ) @@ -73,7 +71,6 @@ declare -A ERROR_SOLUTIONS=( ["network_timeout"]="检查网络连接|使用国内镜像：pip install -i https://pypi.tuna.tsinghua.edu.cn/simple/|重试安装命令" ["permission_denied"]="使用虚拟环境：python -m venv venv && source venv/bin/activate|添加 --user 参数：pip install --user|使用sudo（不推荐）" ["python_version_incompatible"]="使用conda安装兼容版本：conda install python=3.11|检查包的Python版本要求|升级或降级Python版本" - ["vllm_install_fail"]="确保CUDA >= 11.8|使用预编译版本而非源码编译|检查GPU计算能力是否支持" ["cuda_not_found"]="安装NVIDIA驱动|安装CUDA工具包|设置CUDA环境变量" ) @@ -102,8 +99,6 @@ detect_error_type() { error_type="permission_denied" elif [[ "$error_lower" =~ python.*version|unsupported.*python ]]; then error_type="python_version_incompatible" - elif [[ "$error_lower" =~ vllm.*install.*fail|vllm.*error ]]; then - error_type="vllm_install_fail" elif [[ "$error_lower" =~ cuda.*not.*found|nvidia.*driver ]]; then error_type="cuda_not_found" fi diff --git a/tools/install/fixes/numpy_fix.sh b/tools/install/fixes/numpy_fix.sh index 2b744ae1da..dd6ede8791 100644 --- a/tools/install/fixes/numpy_fix.sh +++ b/tools/install/fixes/numpy_fix.sh @@ -36,7 +36,7 @@ check_numpy_installation() { local conda_numpy="" local pip_numpy="" - echo -e "${DIM}正在检测 numpy 安装状态...${NC}" + echo -e "${DIM}正在检测 numpy 安装状态...${NC}" >&2 # 检查conda安装的numpy if command -v conda >/dev/null 2>&1; then @@ -46,17 +46,43 @@ check_numpy_installation() { # 检查pip安装的numpy pip_numpy=$(python3 -c "import numpy; print(numpy.__version__)" 2>/dev/null || echo "not_found") - # 检查numpy的RECORD文件是否存在（判断是否损坏） + # 检查 numpy 元数据与 dist-info 完整性（判断是否损坏） local numpy_corrupted=false + local mixed_conflict=false if [ "$pip_numpy" != "not_found" ]; then - # 尝试检查pip的numpy记录 - if ! python3 -c "import pkg_resources; pkg_resources.get_distribution('numpy')" >/dev/null 2>&1; then + if ! python3 -c "from importlib.metadata import version; version('numpy')" >/dev/null 2>&1; then numpy_corrupted=true fi + + if ! python3 -c " +import glob +import os +import site +import sys + +paths = [] +for p in site.getsitepackages() + [site.getusersitepackages()]: + if p not in paths and os.path.isdir(p): + paths.append(p) + +for root in paths: + for candidate in glob.glob(os.path.join(root, '~umpy*')): + if os.path.exists(candidate): + sys.exit(1) + for dist_info in glob.glob(os.path.join(root, 'numpy-*.dist-info')): + if not os.path.isfile(os.path.join(dist_info, 'RECORD')): + sys.exit(1) +" >/dev/null 2>&1; then + numpy_corrupted=true + fi + fi + + if [ -n "$conda_numpy" ] && [ "$pip_numpy" != "not_found" ] && [ "$conda_numpy" != "$pip_numpy" ]; then + mixed_conflict=true fi # 返回状态信息 - echo "conda_numpy:$conda_numpy|pip_numpy:$pip_numpy|corrupted:$numpy_corrupted" + echo "conda_numpy:$conda_numpy|pip_numpy:$pip_numpy|corrupted:$numpy_corrupted|mixed_conflict:$mixed_conflict" } # 修复numpy安装问题 @@ -68,11 +94,12 @@ fix_numpy_installation() { local conda_numpy=$(echo "$status_info" | cut -d'|' -f1 | cut -d':' -f2) local pip_numpy=$(echo "$status_info" | cut -d'|' -f2 | cut -d':' -f2) local corrupted=$(echo "$status_info" | cut -d'|' -f3 | cut -d':' -f2) + local mixed_conflict=$(echo "$status_info" | cut -d'|' -f4 | cut -d':' -f2) - log_info "numpy状态检测 - conda版本: $conda_numpy, pip版本: $pip_numpy, 损坏状态: $corrupted" "NumpyFix" + log_info "numpy状态检测 - conda版本: $conda_numpy, pip版本: $pip_numpy, 损坏状态: $corrupted, 混合冲突: $mixed_conflict" "NumpyFix" # 如果检测到问题，提供友好的解释和解决方案 - if [ "$corrupted" = "true" ] || [ "$conda_numpy" != "" -a "$pip_numpy" != "not_found" ]; then + if [ "$corrupted" = "true" ] || [ "$mixed_conflict" = "true" ]; then echo -e "${BLUE}📋 环境状态分析：${NC}" if [ "$corrupted" = "true" ]; then @@ -81,7 +108,7 @@ fix_numpy_installation() { echo -e " ${DIM}这通常是由于包管理器切换或不完整的安装导致的${NC}" fi - if [ "$conda_numpy" != "" -a "$pip_numpy" != "not_found" ]; then + if [ "$mixed_conflict" = "true" ]; then log_warn "检测到 conda 和 pip 混合管理的 numpy" "NumpyFix" echo -e " ${YELLOW}▸${NC} 检测到 conda 和 pip 混合管理的 numpy" echo -e " ${DIM}conda版本: $conda_numpy, pip版本: $pip_numpy${NC}" @@ -115,22 +142,36 @@ fix_numpy_installation() { fi # Step 2: 强制清理pip numpy - if [ "$pip_numpy" != "not_found" ]; then + if [ "$pip_numpy" != "not_found" ] || [ "$corrupted" = "true" ]; then log_info "清理 pip numpy 安装..." "NumpyFix" echo -e " ${DIM}清理 pip numpy 安装...${NC}" log_command "NumpyFix" "Fix" "pip uninstall numpy -y" || true log_command "NumpyFix" "Fix" "python3 -m pip uninstall numpy -y" || true - log_info "尝试强制移除 numpy 目录" "NumpyFix" + log_info "尝试强制移除 numpy 目录和损坏元数据" "NumpyFix" python3 -c " -import os, shutil, sys -try: - import numpy - numpy_path = os.path.dirname(numpy.__file__) - if 'site-packages' in numpy_path: - shutil.rmtree(numpy_path, ignore_errors=True) - print('Forcibly removed numpy directory') -except Exception: - pass +import glob +import os +import shutil +import site + +paths = [] +for p in site.getsitepackages() + [site.getusersitepackages()]: + if p not in paths and os.path.isdir(p): + paths.append(p) + +for root in paths: + for candidate in glob.glob(os.path.join(root, 'numpy')): + shutil.rmtree(candidate, ignore_errors=True) + for candidate in glob.glob(os.path.join(root, 'numpy-*.dist-info')): + shutil.rmtree(candidate, ignore_errors=True) + for candidate in glob.glob(os.path.join(root, '~umpy*')): + if os.path.isdir(candidate): + shutil.rmtree(candidate, ignore_errors=True) + elif os.path.exists(candidate): + try: + os.remove(candidate) + except OSError: + pass " 2>/dev/null || true fi diff --git a/tools/install/installation_table/core_installer.sh b/tools/install/installation_table/core_installer.sh index 882e8a4e85..b8ceb72be7 100644 --- a/tools/install/installation_table/core_installer.sh +++ b/tools/install/installation_table/core_installer.sh @@ -49,7 +49,352 @@ else fi # 设置pip命令 -PIP_CMD="${PIP_CMD:-pip3}" +PYTHON_CMD="${PYTHON_CMD:-python3}" +PIP_CMD="${PIP_CMD:-$PYTHON_CMD -m pip}" + +parse_mirror_fallback_chain() { + local current_index="${PIP_INDEX_URL:-https://pypi.org/simple/}" + local chain="${SAGE_PIP_MIRROR_FALLBACKS:-}" + + local -a mirrors=() + if [ -n "$chain" ]; then + IFS='|' read -r -a mirrors <<< "$chain" + fi + + local has_current=false + local item + for item in "${mirrors[@]}"; do + if [ "$item" = "$current_index" ]; then + has_current=true + break + fi + done + if [ "$has_current" = "false" ]; then + mirrors=("$current_index" "${mirrors[@]}") + fi + + local has_official=false + for item in "${mirrors[@]}"; do + if [ "$item" = "https://pypi.org/simple/" ]; then + has_official=true + break + fi + done + if [ "$has_official" = "false" ]; then + mirrors+=("https://pypi.org/simple/") + fi + + local result="" + for item in "${mirrors[@]}"; do + [ -n "$item" ] || continue + if [ -z "$result" ]; then + result="$item" + else + result="$result|$item" + fi + done + echo "$result" +} + +pip_output_has_403_error() { + local output_file="$1" + grep -Eqi "(HTTP.*403|403[[:space:]]+Forbidden|status code[[:space:]]*403|response.*403)" "$output_file" +} + +pip_install_with_mirror_403_retry() { + local pip_args="$1" + local log_file="$2" + shift 2 + local -a pip_install_args=("$@") + + local mirrors + mirrors="$(parse_mirror_fallback_chain)" + local -a mirror_candidates=() + IFS='|' read -r -a mirror_candidates <<< "$mirrors" + + local total_attempts=${#mirror_candidates[@]} + if [ "$total_attempts" -le 0 ]; then + mirror_candidates=("https://pypi.org/simple/") + total_attempts=1 + fi + + local attempt=0 + local mirror_url + local last_rc=1 + for mirror_url in "${mirror_candidates[@]}"; do + [ -n "$mirror_url" ] || continue + attempt=$((attempt + 1)) + echo -e "${DIM}[pip ${attempt}/${total_attempts}] 使用镜像: ${mirror_url}${NC}" + + local attempt_log + attempt_log="$(mktemp)" + + set -o pipefail + if PIP_INDEX_URL="$mirror_url" PIP_EXTRA_INDEX_URL="" $PIP_CMD install "${pip_install_args[@]}" $pip_args 2>&1 | tee -a "$log_file" "$attempt_log"; then + set +o pipefail + rm -f "$attempt_log" + export PIP_INDEX_URL="$mirror_url" + export PIP_EXTRA_INDEX_URL="" + return 0 + fi + last_rc=${PIPESTATUS[0]} + set +o pipefail + + if pip_output_has_403_error "$attempt_log"; then + echo -e "${WARNING} 检测到镜像返回 HTTP 403，自动切换下一个镜像重试" + rm -f "$attempt_log" + continue + fi + + rm -f "$attempt_log" + return "$last_rc" + done + + return "$last_rc" +} + +extract_meta_package_dependencies() { + local install_mode="${1:-standard}" + + local mode_json="[]" + if [ "$install_mode" = "full" ]; then + mode_json='["full"]' + elif [ "$install_mode" = "dev" ]; then + mode_json='["full","dev"]' + fi + + $PYTHON_CMD - <=68" + "wheel>=0.42" + "wrapt>=1.15.0,<2.0.0" + ) + + echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + echo -e "${BOLD} 🛡️ 预安装解析护栏依赖（避免回溯到不兼容旧版本）${NC}" + echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + echo "" + + local spec + for spec in "${guard_specs[@]}"; do + echo -e "${DIM}安装护栏依赖: ${spec}${NC}" + if ! pip_install_with_mirror_403_retry "$pip_args" "$log_file" --upgrade "$spec"; then + log_warn "护栏依赖安装失败，继续后续安装: $spec" "INSTALL" + echo -e "${WARNING} 护栏依赖安装失败，继续: $spec" + fi + done + + echo "" + echo -e "${CHECK} 解析护栏依赖处理完成" + return 0 +} + +create_quickstart_constraints_file() { + local project_root="$1" + + local constraints_file="$project_root/.sage/tmp/quickstart-pip-constraints.txt" + cat > "$constraints_file" <<'EOF' +wrapt>=1.14.2,<2.0.0 +setuptools>=68 +wheel>=0.42 +uvicorn>=0.34.0,<1.0.0 +EOF + + echo "$constraints_file" +} + +collect_workspace_repo_candidates() { + local project_root="$1" + local workspace_root + workspace_root="$(dirname "$project_root")" + local workspace_file="$project_root/SAGE.code-workspace" + + if [ ! -f "$workspace_file" ]; then + return 0 + fi + + # SAGE.code-workspace 可能包含注释（JSONC）：按 name/path 成对提取，再用 path 定位仓库 + grep -oP '"name"\s*:\s*"\K[^"]+|"path"\s*:\s*"\K[^"]+' "$workspace_file" 2>/dev/null | \ + awk 'NR % 2 == 1 {name=$0; next} {print name "|" $0}' | \ + while IFS='|' read -r name rel_path; do + [ -z "$rel_path" ] && continue + [ "$rel_path" = "." ] && continue + + local repo_dir + repo_dir="$(cd "$project_root" && cd "$rel_path" 2>/dev/null && pwd)" + [ -n "$repo_dir" ] || continue + [ -d "$repo_dir/.git" ] || continue + + local repo_name + repo_name="$(basename "$repo_dir")" + case "$repo_name" in + sage*|sagellm*|neuromem) + echo "$repo_name" + ;; + *) + ;; + esac + done +} + +is_dev_editable_repo_allowed() { + local repo_name="$1" + + case "$repo_name" in + sage-benchmark|sage-docs|sage-examples|sage-tutorials) + return 0 + ;; + *) + return 1 + ;; + esac +} + +# dev 模式下优先尝试安装本地 polyrepo 子仓库（editable） +install_local_editable_polyrepo_packages() { + local project_root="$1" + local log_file="$2" + + local workspace_root + workspace_root="$(dirname "$project_root")" + + local repo_candidates=() + while IFS= read -r repo_name; do + [ -n "$repo_name" ] || continue + if is_dev_editable_repo_allowed "$repo_name"; then + repo_candidates+=("$repo_name") + fi + done < <(collect_workspace_repo_candidates "$project_root") + + if [ ${#repo_candidates[@]} -eq 0 ]; then + local fallback_candidates=( + "sage-benchmark" + "sage-docs" + "sage-tutorials" + "sage-examples" + ) + repo_candidates=("${fallback_candidates[@]}") + fi + + local installed_count=0 + local skipped_count=0 + local failed_count=0 + + echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + echo -e "${BOLD} 🧩 dev 模式：安装仍保持独立发布的本地仓库（editable，尽量）${NC}" + echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" + echo -e "${DIM}工作区根目录: $workspace_root${NC}" + echo -e "${DIM}说明: 已回收进主仓的核心 surface 不再自动按历史 split-repo 方式 editable 安装${NC}" + echo "" + + for repo_name in "${repo_candidates[@]}"; do + local repo_dir="$workspace_root/$repo_name" + + if [ ! -d "$repo_dir" ]; then + echo -e "${DIM} ⏭️ 跳过 $repo_name（未检测到本地仓库）${NC}" + skipped_count=$((skipped_count + 1)) + continue + fi + + if [ ! -f "$repo_dir/pyproject.toml" ]; then + echo -e "${DIM} ⏭️ 跳过 $repo_name（工作区内容仓库，无需 editable 安装）${NC}" + skipped_count=$((skipped_count + 1)) + continue + fi + + echo -e "${BOLD} 📦 安装本地 editable: $repo_name${NC}" + if (cd "$repo_dir" && $PIP_CMD install -e "." --no-deps --upgrade --no-cache-dir >> "$log_file" 2>&1); then + echo -e "${CHECK} $repo_name editable 安装成功" + installed_count=$((installed_count + 1)) + else + echo -e "${WARNING} $repo_name editable 安装失败，继续后续流程" + echo -e "${DIM} 详情见日志: $log_file${NC}" + failed_count=$((failed_count + 1)) + fi + done + + echo "" + echo -e "${INFO} 本地 editable 安装汇总: 成功 ${installed_count} / 跳过 ${skipped_count} / 失败 ${failed_count}" + echo -e "${DIM}说明: dev 模式仅尝试安装仍保持独立发布的工作区仓库为 editable${NC}" + echo "" +} # ============================================================================ # 版本比较辅助函数 @@ -84,45 +429,58 @@ except Exception: # 核心安装函数 # ============================================================================ -# 安装核心包 - 新的简化版本 +# 安装核心包 +# SAGE 已重构为单一主仓 meta-package： +# - 根目录 `isage` 由本仓库直接维护并支持 editable 安装 +# - 可选能力适配器通过 PyPI 版本号拉取 +# - 适配器更新后需先发布到 PyPI，然后在 pyproject.toml 中更新版本号 install_core_packages() { - local install_mode="${1:-dev}" # 默认为开发模式 + local install_mode="${1:-dev}" # default: dev - # 准备pip安装参数 - local pip_args="--disable-pip-version-check --no-input" + # 根据 install_mode 选择安装目标（extras） + # standard: pip install -e "." (轻量，无 torch/CUDA) + # full: pip install -e ".[full]" (扩展能力集) + # dev: pip install -e ".[full,dev]" (full + 开发工具 + local editable) + local install_target + case "$install_mode" in + "dev") + install_target='.[full,dev]' + ;; + "full") + install_target='.[full]' + ;; + "standard"|*) + install_mode="standard" + install_target='.' + ;; + esac - # CI环境额外处理 + # 准备 pip 参数 + local pip_args="--disable-pip-version-check --no-input --prefer-binary --upgrade-strategy only-if-needed" + + # CI 环境额外处理 if [ "${CI:-}" = "true" ] || [ -n "${GITHUB_ACTIONS:-}" ] || [ -n "${GITLAB_CI:-}" ] || [ -n "${JENKINS_URL:-}" ]; then - # 在CI中将包安装到用户site（~/.local），便于跨job缓存与导入 pip_args="$pip_args --user" - # 某些系统前缀可能仍需此选项 - if python3 -c "import sys; print(1 if '/usr' in sys.prefix else 0)" 2>/dev/null | grep -q "1"; then + if $PYTHON_CMD -c "import sys; print(1 if '/usr' in sys.prefix else 0)" 2>/dev/null | grep -q "1"; then pip_args="$pip_args --break-system-packages" echo -e "${DIM}CI环境: 添加 --break-system-packages${NC}" fi - # 确保用户脚本目录在PATH中（供 'sage' 可执行脚本使用） export PATH="$HOME/.local/bin:$PATH" echo -e "${DIM}CI环境: 使用 --user 安装，PATH+=~/.local/bin${NC}" - # CI环境也使用 off，避免版本兼容性问题 - pip_args="$pip_args --progress-bar=off" - else - # 非CI环境，使用简洁进度条（off 在所有 pip 版本中都支持） - pip_args="$pip_args --progress-bar=off" fi - # 获取项目根目录并初始化日志文件 - local project_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/../../../" && pwd)" + # 获取项目根目录并初始化日志 + local project_root + project_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/../../../" && pwd)" local log_file="$project_root/.sage/logs/install.log" - - # 设置全局日志文件路径 export SAGE_INSTALL_LOG="$log_file" + mkdir -p "$project_root/.sage/logs" "$project_root/.sage/tmp" "$project_root/.sage/cache" - # 确保.sage目录结构存在 - mkdir -p "$project_root/.sage/logs" - mkdir -p "$project_root/.sage/tmp" - mkdir -p "$project_root/.sage/cache" + local constraints_file + constraints_file="$(create_quickstart_constraints_file "$project_root")" + export SAGE_PIP_CONSTRAINT_FILE="$constraints_file" + pip_args="$pip_args -c $constraints_file" - # 初始化日志文件 log_info "SAGE 安装日志" "INSTALL" log_info "开始时间: $(date '+%Y-%m-%d %H:%M:%S')" "INSTALL" log_info "安装模式: $install_mode" "INSTALL" @@ -130,11 +488,26 @@ install_core_packages() { echo -e "${INFO} 安装 SAGE ($install_mode 模式)..." echo -e "${DIM}安装日志: $log_file${NC}" + echo -e "${DIM}pip constraints: $constraints_file${NC}" echo "" - # 配置 pip 镜像源（自动检测网络） + if [ "${CLEAN_BEFORE_INSTALL:-true}" != "true" ]; then + echo -e "${WARNING} 检测到 --no-clean/--skip-clean：旧环境残留约束可能导致 pip 回溯过深（resolution-too-deep）" + echo -e "${DIM}建议: 去掉 --no-clean，或先执行一次完整安装前清理${NC}" + echo "" + fi + + # 配置 pip 镜像源（遵循 quickstart 参数） + # - USE_PIP_MIRROR=false (e.g. --no-mirror) 时：强制官方 PyPI + 禁用缓存 + # - 其他情况：按 MIRROR_SOURCE（默认 auto）配置 echo -e "${BLUE}🌐 配置 pip 镜像源...${NC}" - configure_pip_mirror "auto" + if [ "${USE_PIP_MIRROR:-true}" = "false" ]; then + configure_pip_mirror "disable" + pip_args="$pip_args --no-cache-dir" + echo -e "${DIM}--no-mirror 生效：强制官方 PyPI + 禁用 pip 缓存${NC}" + else + configure_pip_mirror "${MIRROR_SOURCE:-auto}" + fi echo "" # 记录环境信息 @@ -143,671 +516,79 @@ install_core_packages() { log_phase_end_enhanced "环境信息收集" "true" "INSTALL" case "$install_mode" in - "minimal") - echo -e "${GRAY}最小安装：核心运行时包${NC}" - echo -e "${DIM}包含: L1-L5 核心包，无开发工具，无可选依赖 (~80 包)${NC}" - echo -e "${DIM}💡 如需 ML/VDB 等功能，稍后运行: pip install isage-middleware[ml,vdb,...]${NC}" - ;; - "dev") - echo -e "${GREEN}开发安装：核心 + 开发工具${NC}" - echo -e "${DIM}包含: 核心包 + pytest, ruff, mypy, pre-commit (~120 包)${NC}" - echo -e "${DIM}💡 如需 ML/VDB 等功能，稍后运行: pip install isage-middleware[ml,vdb,...]${NC}" + "standard") + echo -e "${YELLOW}standard 安装：核心子包，无 torch/CUDA GPU 依赖${NC}" + echo -e "${DIM}包含: 本地 isage (meta-package)，子包依赖从 PyPI 拉取：无 torch/peft/accelerate${NC}" ;; "full") - echo -e "${YELLOW}完整安装：核心 + 开发工具 + 所有可选依赖${NC}" - echo -e "${DIM}包含: 所有功能 (ML, VDB, streaming, compression, etc.) (~200+ 包)${NC}" + echo -e "${CYAN}full 安装：standard + 扩展能力集${NC}" + echo -e "${DIM}包含: .[full]（不强制 torch/CUDA）${NC}" ;; - # 兼容旧模式名称 - "core"|"standard") - echo -e "${DIM}映射到: minimal 模式${NC}" - install_mode="minimal" - ;; - *) - echo -e "${YELLOW}未知模式，使用完整安装${NC}" - install_mode="full" + "dev") + echo -e "${GREEN}dev 安装：full + 开发工具 + 本地子仓库 editable${NC}" + echo -e "${DIM}包含: .[full,dev]，pytest/ruff/mypy/pre-commit + 优先本地 editable 覆盖${NC}" ;; esac - echo "" - # 检查所有必要的包目录是否存在 - local required_packages=("packages/sage-common" "packages/sage-platform" "packages/sage-kernel" "packages/sage-libs" "packages/sage-middleware" "packages/sage-cli") - - # dev 和 full 模式需要 sage-tools - if [ "$install_mode" = "dev" ] || [ "$install_mode" = "full" ]; then - [ -d "packages/sage-tools" ] && required_packages+=("packages/sage-tools") + # 检查 meta-package 目录 + if [ ! -f "pyproject.toml" ]; then + log_error "找不到 meta-package (pyproject.toml)" "INSTALL" + log_error "当前工作目录: $(pwd)" "INSTALL" + echo -e "${CROSS} 错误：找不到 meta-package (pyproject.toml)" + return 1 fi - required_packages+=("packages/sage") - - for package_dir in "${required_packages[@]}"; do - if [ ! -d "$package_dir" ]; then - log_error "找不到包目录: $package_dir" "INSTALL" - log_error "当前工作目录: $(pwd)" "INSTALL" - log_error "项目根目录: $project_root" "INSTALL" - echo -e "${CROSS} 错误：找不到包目录 ($package_dir)" - return 1 - fi - done - - # 执行安装 echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" - echo -e "${BOLD} 📦 安装 SAGE ($install_mode 模式)${NC}" + echo -e "${BOLD} 📦 安装 SAGE ($install_mode 模式：$install_target + PyPI 子包)${NC}" echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" - - # 准备pip安装参数 - local pip_args="--disable-pip-version-check --no-input" - - # 添加缓存支持（非CI环境） - if [ "${CI:-}" != "true" ] && [ -z "${GITHUB_ACTIONS:-}" ] && [ -z "$GITLAB_CI" ] && [ -z "$JENKINS_URL" ]; then - # 非CI环境启用缓存以加速重复安装 - pip_args="$pip_args --cache-dir ~/.cache/pip" - echo -e "${DIM}启用 pip 缓存: ~/.cache/pip${NC}" - else - # CI环境禁用缓存以确保新鲜安装 - pip_args="$pip_args --no-cache-dir" - echo -e "${DIM}CI环境: 禁用 pip 缓存${NC}" - fi - - # CI环境额外处理 - if [ "${CI:-}" = "true" ] || [ -n "${GITHUB_ACTIONS:-}" ] || [ -n "${GITLAB_CI:-}" ] || [ -n "${JENKINS_URL:-}" ]; then - # 在CI中将包安装到用户site（~/.local），便于跨job缓存与导入 - pip_args="$pip_args --user" - # 某些系统前缀可能仍需此选项 - if python3 -c "import sys; print(1 if '/usr' in sys.prefix else 0)" 2>/dev/null; then - pip_args="$pip_args --break-system-packages" - echo -e "${DIM}CI环境: 添加 --break-system-packages${NC}" - fi - # 确保用户脚本目录在PATH中（供 'sage' 可执行脚本使用） - export PATH="$HOME/.local/bin:$PATH" - echo -e "${DIM}CI环境: 使用 --user 安装，PATH+=~/.local/bin${NC}" - # CI环境也使用 off，避免版本兼容性问题 - pip_args="$pip_args --progress-bar=off" - else - # 非CI环境，使用简洁进度条（off 在所有 pip 版本中都支持） - pip_args="$pip_args --progress-bar=off" - fi - - log_phase_start_enhanced "本地依赖包安装" "INSTALL" 180 - - # 本地开发安装策略： - # 1. 使用 -e (editable) 模式安装 - # 2. 使用 --no-deps 完全禁用依赖解析，避免从 PyPI 安装 isage-* 包 - # 3. 按正确的依赖顺序手动安装所有包 - # 4. 最后单独安装外部依赖 - local install_flags="-e" - - log_info "安装策略: editable + --no-deps (禁用 PyPI 依赖解析)" "INSTALL" - log_info "手动控制安装顺序，确保使用本地源码" "INSTALL" - echo -e "${DIM}安装策略: 先安装外部依赖，再 editable install 本地包${NC}" - echo -e "${DIM} 确保所有传递依赖可用后再安装本地源码${NC}" - echo "" - - # 配置 pip 镜像源（自动检测网络） - echo -e "${BLUE}🌐 配置 pip 镜像源...${NC}" - configure_pip_mirror "auto" - echo "" - - # 步骤 0: 检测 GPU 并预安装 CUDA 版本的 PyTorch（如果有 GPU） - echo -e "${DIM}步骤 0/5: 检测 GPU 环境...${NC}" - log_info "步骤 0/5: 检测 GPU 并安装 CUDA 版本 PyTorch" "INSTALL" - - local pytorch_installer="$(dirname "${BASH_SOURCE[0]}")/../fixes/pytorch_cuda_installer.sh" - if [ -f "$pytorch_installer" ]; then - source "$pytorch_installer" - if preinstall_pytorch_cuda; then - log_info "PyTorch 环境设置完成" "INSTALL" - else - log_warn "PyTorch CUDA 安装失败，将使用 CPU 版本" "INSTALL" - fi - else - log_warn "pytorch_cuda_installer.sh 不存在，跳过 GPU 检测" "INSTALL" - echo -e "${DIM}跳过 GPU 检测（安装脚本不存在）${NC}" - fi - echo "" - - # 第一步：安装外部依赖（必须在本地包之前） - echo -e "${DIM}步骤 1/5: 安装外部依赖...${NC}" - log_info "步骤 1/5: 提取并安装外部依赖" "INSTALL" - - # 使用 Python 脚本提取已声明的外部依赖 - local external_deps_file=".sage/external-deps-${install_mode}.txt" - local external_deps_marker=".sage/external-deps-${install_mode}.installed" - mkdir -p .sage - - # 检查是否已经安装过外部依赖（基于 pyproject.toml 的 hash） - local current_hash="" - local cached_hash="" - - # 计算当前所有 pyproject.toml 的 hash - if command -v sha256sum &> /dev/null; then - current_hash=$(find packages/sage-*/pyproject.toml -type f 2>/dev/null | sort | xargs cat | sha256sum | cut -d' ' -f1) - elif command -v shasum &> /dev/null; then - current_hash=$(find packages/sage-*/pyproject.toml -type f 2>/dev/null | sort | xargs cat | shasum -a 256 | cut -d' ' -f1) - fi - - # 读取缓存的 hash - if [ -f "$external_deps_marker" ]; then - cached_hash=$(cat "$external_deps_marker" 2>/dev/null || echo "") - fi - - # 如果 hash 相同且依赖文件存在，跳过安装 - if [ -n "$current_hash" ] && [ "$current_hash" = "$cached_hash" ] && [ -f "$external_deps_file" ]; then - log_info "检测到外部依赖已安装（pyproject.toml 未变化），跳过" "INSTALL" - echo -e "${CHECK} 外部依赖已是最新（跳过安装）" - echo "" - else - if [ -n "$cached_hash" ] && [ "$current_hash" != "$cached_hash" ]; then - log_info "检测到 pyproject.toml 变化，重新安装外部依赖" "INSTALL" - echo -e "${DIM} 检测到依赖变化，重新安装...${NC}" - fi - - log_debug "外部依赖将保存到: $external_deps_file" "INSTALL" - echo -e "${DIM} 从 pyproject.toml 中提取外部依赖...${NC}" - - # 执行 Python 脚本提取依赖（优化版：去重+合并版本） - log_debug "执行 Python 依赖提取脚本（去重优化）..." "INSTALL" - if $PYTHON_CMD -c " -import sys, re -from pathlib import Path -from collections import defaultdict - -# 存储包名到版本约束的映射 -dep_versions = defaultdict(list) - -package_dirs = ['packages/sage-common', 'packages/sage-platform', 'packages/sage-kernel', 'packages/sage-libs', 'packages/sage-middleware'] -install_mode = '$install_mode' -if install_mode != 'core': - package_dirs.extend(['packages/sage-cli']) -if install_mode in ['full', 'dev']: - package_dirs.extend(['packages/sage-tools']) - -for pkg_dir in package_dirs: - pyproject = Path(pkg_dir) / 'pyproject.toml' - if not pyproject.exists(): continue - content = pyproject.read_text() - in_deps = False - for line in content.splitlines(): - line = line.strip() - if 'dependencies' in line and '=' in line: in_deps = True; continue - if in_deps: - if line == ']': in_deps = False; continue - match = re.search(r'\"([^\"]+)\"', line) - if match: - dep = match.group(1) - if not dep.startswith('isage-'): - # 提取包名和版本约束 - pkg_match = re.match(r'^([a-zA-Z0-9_-]+[a-zA-Z0-9_\[\]-]*)', dep) - if pkg_match: - pkg_name = pkg_match.group(1) - dep_versions[pkg_name].append(dep) - -# 合并多个包的相同依赖声明（版本已统一，无需去重） -external_deps = [] -conflict_count = 0 -for pkg_name, versions in sorted(dep_versions.items()): - unique_versions = list(set(versions)) - if len(unique_versions) == 1: - external_deps.append(unique_versions[0]) - else: - # 理论上不应该有冲突（版本已通过 unify_dependencies.py 统一） - # 如果仍有冲突，选择最严格的版本 - best_dep = max(unique_versions, key=lambda v: ('>=' in v, '<' in v, v)) - external_deps.append(best_dep) - conflict_count += 1 - -with open('$external_deps_file', 'w') as f: - for dep in external_deps: - f.write(f'{dep}\n') - -# 根据情况显示不同的消息 -if conflict_count > 0: - print(f'⚠️ 提取了 {len(external_deps)} 个外部依赖（发现 {conflict_count} 个版本冲突）', file=sys.stderr) - print(f' 建议运行: python3 tools/install/helpers/unify_dependencies.py --apply', file=sys.stderr) -else: - # 不显示 duplicate_count，因为多包共享依赖是正常的 - print(f'✓ 提取了 {len(external_deps)} 个外部依赖', file=sys.stderr) -" 2>&1; then - log_info "依赖提取脚本执行成功" "INSTALL" - - if [ -f "$external_deps_file" ] && [ -s "$external_deps_file" ]; then - local dep_count=$(wc -l < "$external_deps_file") - log_info "共提取 $dep_count 个外部依赖" "INSTALL" - - echo -e "${DIM} 安装 $dep_count 个外部依赖包...${NC}" - log_info "开始安装外部依赖包..." "INSTALL" - - # 智能代理检测和自动规避 - local pip_utils="${SAGE_ROOT:-$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)}/tools/lib/pip_install_utils.sh" - if [ -f "$pip_utils" ]; then - source "$pip_utils" - check_and_fix_pip_proxy || true - fi - - # 移除 --no-deps，让 pip 正常解析传递依赖 - local deps_pip_args=$(echo "$pip_args" | sed 's/--no-deps//g') - log_debug "PIP命令: $PIP_CMD install -r $external_deps_file $deps_pip_args" "INSTALL" - - # 使用详细输出模式，让用户看到编译进度（避免看起来卡住） - if log_pip_install_with_verbose_progress "INSTALL" "Deps" "$PIP_CMD install -r \"$external_deps_file\" $deps_pip_args"; then - log_info "外部依赖安装成功" "INSTALL" - echo -e "${CHECK} 外部依赖安装完成" - - # 保存 hash 标记，避免下次重复安装（提前保存，即使后续步骤失败也能复用缓存） - if [ -n "$current_hash" ]; then - echo "$current_hash" > "$external_deps_marker" - log_info "已保存外部依赖安装标记" "INSTALL" - fi - - # 强制升级关键包到正确版本（解决依赖解析问题） - echo -e "${DIM} 验证并升级关键包版本...${NC}" - log_info "强制安装 transformers 和 peft 到兼容版本" "INSTALL" - - # transformers 4.52.0 与 peft 0.18.0 兼容 - # 同时需要 tokenizers<0.22 来匹配 transformers 4.52.0 - if log_command "INSTALL" "Deps" "$PIP_CMD install 'transformers==4.52.0' 'tokenizers>=0.21,<0.22' 'peft>=0.18.0,<1.0.0' $deps_pip_args"; then - log_info "关键包版本升级成功" "INSTALL" - echo -e "${CHECK} 关键包版本验证完成" - else - log_warn "关键包升级失败，继续安装..." "INSTALL" - echo -e "${YELLOW}⚠️ 关键包升级失败，可能导致运行时错误${NC}" - fi - else - log_error "外部依赖安装失败" "INSTALL" - echo -e "${RED}❌ 外部依赖安装失败${NC}" - return 1 - fi - else - log_warn "未能提取外部依赖或依赖文件为空" "INSTALL" - echo -e "${YELLOW}⚠️ 未能提取外部依赖，跳过...${NC}" - fi - else - log_error "依赖提取脚本失败" "INSTALL" - echo -e "${YELLOW}⚠️ 依赖提取脚本失败，跳过...${NC}" - fi - fi # 闭合 hash 检查的 if - + echo -e "${DIM}安装策略: editable install 本地 isage (meta-package)，子包依赖默认从 PyPI 版本拉取${NC}" + echo -e "${DIM}子包更新需先发布到 PyPI，再在 pyproject.toml 中更新版本号${NC}" echo "" - # 第二步：安装基础包（L1-L2） - echo -e "${DIM}步骤 2/5: 安装基础包 (L1-L2)...${NC}" - log_info "步骤 2/5: 安装基础包 (L1-L2)" "INSTALL" - - # L1: Foundation, L2: Platform - # Note: sage-llm-core moved to independent repo (pip install isagellm) - local base_packages=("packages/sage-common" "packages/sage-platform") - - for package_dir in "${base_packages[@]}"; do - echo -e "${DIM} 正在安装: $package_dir${NC}" - log_info "开始安装: $package_dir" "INSTALL" - log_debug "PIP命令: $PIP_CMD install $install_flags $package_dir $pip_args --no-deps" "INSTALL" - - if ! log_command "INSTALL" "Deps" "$PIP_CMD install $install_flags \"$package_dir\" $pip_args --no-deps"; then - log_error "安装失败: $package_dir" "INSTALL" - log_error "请检查日志文件: ${SAGE_INSTALL_LOG:-}" "INSTALL" - echo -e "${CROSS} 安装 $package_dir 失败！" - return 1 - fi - - log_info "安装成功: $package_dir" "INSTALL" - # 验证安装 - local pkg_name=$(basename "$package_dir" | sed 's/sage-/isage-/') - log_pip_package_info "$pkg_name" "INSTALL" - done - - # 第三步：安装核心引擎 (L3) - echo -e "${DIM}步骤 3/5: 安装核心引擎 (L3)...${NC}" - log_info "步骤 3/5: 安装核心引擎 (L3)" "INSTALL" - local core_packages=("packages/sage-kernel") - - if [ "$install_mode" != "core" ]; then - core_packages+=("packages/sage-libs") - fi + log_info "安装目标: $install_target" "INSTALL" - for package_dir in "${core_packages[@]}"; do - echo -e "${DIM} 正在安装: $package_dir${NC}" - log_info "开始安装: $package_dir" "INSTALL" - - log_debug "PIP命令: $PIP_CMD install $install_flags $package_dir $pip_args --no-deps" "INSTALL" - - if ! log_command "INSTALL" "Deps" "$PIP_CMD install $install_flags \"$package_dir\" $pip_args --no-deps"; then - log_error "安装失败: $package_dir" "INSTALL" - log_error "请检查日志文件: ${SAGE_INSTALL_LOG:-}" "INSTALL" - echo -e "${CROSS} 安装 $package_dir 失败！" - return 1 - fi + log_phase_start_enhanced "解析护栏依赖安装" "INSTALL" 60 + install_resolver_guard_packages "$pip_args" "$log_file" + log_phase_end_enhanced "解析护栏依赖安装" "success" "INSTALL" - log_info "安装成功: $package_dir" "INSTALL" - local pkg_name=$(basename "$package_dir" | sed 's/sage-/isage-/') - log_pip_package_info "$pkg_name" "INSTALL" - done - - # 第四步：安装上层包（L4-L5，根据模式） - if [ "$install_mode" != "core" ]; then - echo -e "${DIM}步骤 4/5: 安装上层包 (L4-L5)...${NC}" - - # 显式安装独立 PyPI 包依赖 (因为下面使用了 --no-deps) - # 这些包是 sage-middleware 的依赖，但因为 --no-deps 选项会被跳过 - echo -e "${DIM} 正在安装独立 PyPI 包依赖 (isage-vdb, isage-flow, etc.)...${NC}" - log_info "开始安装独立 PyPI 包依赖" "INSTALL" - - # 使用与 pyproject.toml 一致的版本约束 - # 使用单引号包裹每个包名，防止 shell 将 > 解析为重定向 - local independent_packages="'isage-vdb>=0.1.5' 'isage-tsdb>=0.1.5' 'isage-flow>=0.1.1' 'isage-refiner>=0.1.0' 'isage-neuromem>=0.2.1.1'" - - # 注意：独立包是 PyPI 包，不能使用 -e (install_flags) - log_debug "PIP命令: $PIP_CMD install $independent_packages $pip_args" "INSTALL" - - if ! log_command "INSTALL" "Deps" "$PIP_CMD install $independent_packages $pip_args"; then - log_warn "独立 PyPI 包安装失败，可能导致部分功能不可用" "INSTALL" - echo -e "${WARNING} 独立 PyPI 包安装失败，可能导致部分功能不可用" - # 不中断安装，因为这些可能是可选的或者网络问题 - else - log_info "独立 PyPI 包安装成功" "INSTALL" - echo -e "${CHECK} 独立 PyPI 包安装成功" - fi - - # L4: middleware (Python 兼容层) - # 注意：必须使用 --no-deps 防止 pip 重新安装已有的 sage 子包依赖 - # 运行时依赖（isage-common/platform/kernel/libs）在 step 1-2 已安装 - # C++ 扩展（isage-vdb/isage-flow/isage-tsdb/isage-neuromem/isage-refiner）通过外部依赖安装 - echo -e "${DIM} 正在安装: packages/sage-middleware${NC}" - log_info "开始安装: packages/sage-middleware" "INSTALL" - log_debug "PIP命令: $PIP_CMD install $install_flags packages/sage-middleware $pip_args --no-deps" "INSTALL" - - if ! log_command "INSTALL" "Deps" "$PIP_CMD install $install_flags \"packages/sage-middleware\" $pip_args --no-deps"; then - log_error "安装 sage-middleware 失败" "INSTALL" - echo -e "${CROSS} 安装 sage-middleware 失败！" - return 1 - fi - - log_info "安装成功: packages/sage-middleware" "INSTALL" - log_pip_package_info "isage-middleware" "INSTALL" - echo -e "${CHECK} sage-middleware 安装完成" - - # L5: apps & benchmark (standard/full/dev 模式) - if [ "$install_mode" != "core" ]; then - # 清理已独立为 PyPI 包的组件残留目录 - local residual_paths=( - "packages/sage-benchmark" - "packages/sage-gateway" - "packages/sage-middleware/src/sage/middleware/components/sage_db/sageDB" - "packages/sage-middleware/src/sage/middleware/components/sage_flow/sageFlow" - "packages/sage-middleware/src/sage/middleware/components/sage_tsdb/sageTSDB" - "packages/sage-middleware/src/sage/middleware/components/sage_mem/neuromem" - "packages/sage-middleware/src/sage/middleware/components/sage_refiner/sageRefiner" - "packages/sage-common/src/sage/common/components/sage_llm" - ) - - for path in "${residual_paths[@]}"; do - if [ -d "$path" ]; then - echo -e "${DIM} 清理本地残留目录: $path...${NC}" - rm -rf "$path" - fi - done - - # Note: sage-benchmark 已独立为 PyPI 包 (pip install isage-benchmark) - # 如需使用 benchmark，请单独安装: pip install isage-benchmark - fi - - # L6: CLI (standard/full/dev 模式) - if [ -d "packages/sage-cli" ]; then - echo -e "${DIM} 正在安装: packages/sage-cli${NC}" - log_info "开始安装: packages/sage-cli" "INSTALL" - log_debug "PIP命令: $PIP_CMD install $install_flags packages/sage-cli $pip_args --no-deps" "INSTALL" - - if ! log_command "INSTALL" "Deps" "$PIP_CMD install $install_flags \"packages/sage-cli\" $pip_args --no-deps"; then - log_error "安装 sage-cli 失败" "INSTALL" - echo -e "${CROSS} 安装 sage-cli 失败！" - return 1 - fi - - log_info "安装成功: packages/sage-cli" "INSTALL" - log_pip_package_info "isage-cli" "INSTALL" - echo -e "${CHECK} sage-cli 安装完成" - fi - fi - - # L6: tools (full/dev 模式) - # Note: sage-studio 已独立为独立仓库: https://github.com/intellistream/sage-studio - if [ "$install_mode" = "full" ] || [ "$install_mode" = "dev" ]; then - if [ -d "packages/sage-tools" ]; then - echo -e "${DIM} 正在安装: packages/sage-tools${NC}" - log_info "开始安装: packages/sage-tools" "INSTALL" - log_debug "PIP命令: $PIP_CMD install $install_flags packages/sage-tools $pip_args --no-deps" "INSTALL" - - if ! log_command "INSTALL" "Deps" "$PIP_CMD install $install_flags \"packages/sage-tools\" $pip_args --no-deps"; then - log_error "安装 sage-tools 失败" "INSTALL" - echo -e "${CROSS} 安装 sage-tools 失败！" - return 1 - fi - - log_info "安装成功: packages/sage-tools" "INSTALL" - log_pip_package_info "isage-tools" "INSTALL" - echo -e "${CHECK} sage-tools 安装完成" - fi + log_phase_start_enhanced "meta-package 依赖预安装" "INSTALL" 180 + if ! install_meta_dependencies_sequentially "$install_mode" "$pip_args" "$log_file"; then + log_phase_end_enhanced "meta-package 依赖预安装" "failure" "INSTALL" + return 1 fi + log_phase_end_enhanced "meta-package 依赖预安装" "success" "INSTALL" - # Note: L6 tools (sage-tools) 已在上面的代码块中安装 + log_phase_start_enhanced "SAGE meta-package 安装" "INSTALL" 120 + log_debug "PIP命令: $PIP_CMD install -e \"$install_target\" --no-deps $pip_args" "INSTALL" - if [ "$install_mode" = "core" ]; then - echo -e "${DIM}步骤 4/5: 跳过上层包（core 模式）${NC}" - fi - - echo -e "${CHECK} 本地依赖包安装完成" + echo -e "${DIM}[INFO] 安装目标: $install_target${NC}" + echo -e "${DIM}[INFO] 安装日志同步写入: $log_file${NC}" echo "" - # 预安装构建依赖（防止 pip build isolation 从镜像下载失败） - echo -e "${DIM}预安装构建依赖（setuptools, wheel, packaging）...${NC}" - log_info "开始预安装构建依赖" "INSTALL" - log_debug "PIP命令: $PIP_CMD install 'setuptools>=64' 'wheel' 'packaging>=24.2' $pip_args" "INSTALL" - - if ! log_command "INSTALL" "BuildDeps" "$PIP_CMD install 'setuptools>=64' 'wheel' 'packaging>=24.2' $pip_args"; then - log_warn "构建依赖预安装失败，但继续尝试安装（可能已有足够版本）" "INSTALL" - echo -e "${WARNING} 构建依赖预安装失败（可能已有足够版本，继续...）" - else - log_info "构建依赖预安装成功" "INSTALL" - echo -e "${CHECK} 构建依赖预安装完成" - fi - echo "" - - # 第五步：安装主 SAGE meta-package - echo -e "${DIM}步骤 5/5: 安装 SAGE meta-package...${NC}" - log_phase_start_enhanced "SAGE meta-package 安装" "INSTALL" 60 - - # 安装 sage meta-package (--no-deps) - local install_target="packages/sage" - echo -e "${DIM} 安装 sage meta-package (--no-deps)...${NC}" - log_info "开始安装: sage meta-package" "INSTALL" - log_debug "PIP命令: $PIP_CMD install $install_flags $install_target $pip_args --no-deps" "INSTALL" - - if ! log_command "INSTALL" "Deps" "$PIP_CMD install $install_flags \"$install_target\" $pip_args --no-deps"; then - log_error "安装 sage meta-package 失败" "INSTALL" - echo -e "${CROSS} 安装 sage meta-package 失败！" - log_phase_end "SAGE meta-package 安装" "failure" "INSTALL" + # 直接流式输出 pip 进度到终端，同时 tee 到日志文件 + # 针对镜像 403 进行自动回退重试 + if ! pip_install_with_mirror_403_retry "$pip_args" "$log_file" -e "$install_target" --no-deps; then + log_error "安装失败: $install_target" "INSTALL" + echo -e "${CROSS} SAGE meta-package 安装失败！" + log_phase_end_enhanced "SAGE meta-package 安装" "failure" "INSTALL" return 1 fi - log_info "安装成功: sage meta-package" "INSTALL" + log_info "安装成功: $install_target" "INSTALL" log_pip_package_info "isage" "INSTALL" - - # 4b. 手动安装外部依赖（不经过 sage[mode] 依赖解析） - echo -e "${DIM} 4b. 安装外部依赖（提取自各子包声明）...${NC}" - - # 开始外部依赖安装阶段（记录开始时间） - log_phase_start_enhanced "外部依赖安装" "INSTALL" 300 - - log_info "开始提取外部依赖（从 pyproject.toml 文件）" "INSTALL" - - # 使用 Python 脚本提取已安装 editable 包的外部依赖 - local external_deps_file=".sage/external-deps-${install_mode}.txt" - mkdir -p .sage - - log_debug "外部依赖将保存到: $external_deps_file" "INSTALL" - echo -e "${DIM} 从已安装包中提取外部依赖...${NC}" - - # 执行 Python 脚本提取依赖（优化版：去重+合并版本） - log_debug "执行 Python 依赖提取脚本（去重优化）..." "INSTALL" - if $PYTHON_CMD -c " -import sys, re -from pathlib import Path -from collections import defaultdict - -# 存储包名到版本约束的映射 -dep_versions = defaultdict(list) - -# 独立发布但仍需安装的 isage-* 扩展包（已从源码仓库移除） -allowed_isage_packages = { - 'isage-tsdb', # 时间序列数据库 - 'isage-flow', # 流式语义状态引擎 - 'isage-refiner', # 长上下文压缩 - 'isage-neuromem', # 记忆系统 -} - -package_dirs = ['packages/sage-common', 'packages/sage-platform', 'packages/sage-kernel', 'packages/sage-libs', 'packages/sage-middleware'] -install_mode = '$install_mode' -if install_mode != 'core': - # Note: sage-benchmark, sage-llm-gateway, sage-llm-core moved to independent repos - package_dirs.extend(['packages/sage-cli']) -if install_mode == 'dev': - package_dirs.extend(['packages/sage-tools']) - -# 提取常规依赖 -for pkg_dir in package_dirs: - pyproject = Path(pkg_dir) / 'pyproject.toml' - if not pyproject.exists(): continue - content = pyproject.read_text() - in_deps = False - for line in content.splitlines(): - line = line.strip() - if 'dependencies' in line and '=' in line: in_deps = True; continue - if in_deps: - if line == ']': in_deps = False; continue - match = re.search(r'\"([^\"]+)\"', line) - if match: - dep = match.group(1) - # 提取包名（移除版本约束和extras） - pkg_match = re.match(r'^([a-zA-Z0-9_-]+)', dep) - if not pkg_match: - continue - pkg_base = pkg_match.group(1) - - # 允许外部 isage-* 独立包，否则跳过内部 isage- 依赖 - if pkg_base.startswith('isage-') and pkg_base not in allowed_isage_packages: - continue - - # 提取包名和版本约束（包含 extras） - full_pkg_match = re.match(r'^([a-zA-Z0-9_-]+[a-zA-Z0-9_\[\]-]*)', dep) - if full_pkg_match: - pkg_name = full_pkg_match.group(1) - dep_versions[pkg_name].append(dep) - -# 去重并选择最严格的版本约束 -external_deps = [] -for pkg_name, versions in sorted(dep_versions.items()): - if len(versions) == 1: - external_deps.append(versions[0]) - else: - # 多个版本约束时，选择最新的（通常是最严格的） - best_dep = max(versions, key=lambda v: ('>=' in v, v)) - external_deps.append(best_dep) - if len(versions) > 1: - print(f'[DEDUP] {pkg_name}: {len(versions)} 个版本 -> {best_dep}', file=sys.stderr) - -with open('$external_deps_file', 'w') as f: - for dep in external_deps: - f.write(f'{dep}\n') - -print(f'✓ 提取了 {len(external_deps)} 个外部依赖（已去重）', file=sys.stderr) -" 2>&1; then - log_info "依赖提取脚本执行成功" "INSTALL" - - if [ -f "$external_deps_file" ] && [ -s "$external_deps_file" ]; then - local dep_count=$(wc -l < "$external_deps_file") - log_info "共提取 $dep_count 个外部依赖" "INSTALL" - log_debug "依赖列表文件: $external_deps_file" "INSTALL" - - # 记录依赖列表（前10个） - if [ "$dep_count" -le 10 ]; then - log_debug "依赖列表:\n$(cat "$external_deps_file")" "INSTALL" - else - log_debug "依赖列表（前10个）:\n$(head -10 "$external_deps_file")" "INSTALL" - log_debug "...还有 $((dep_count - 10)) 个依赖（查看完整列表: $external_deps_file）" "INSTALL" - fi - - echo -e "${DIM} 安装 $dep_count 个外部依赖包...${NC}" - log_info "开始安装外部依赖包..." "INSTALL" - - # 智能代理检测和自动规避 - local pip_utils="${SAGE_ROOT:-$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)}/tools/lib/pip_install_utils.sh" - if [ -f "$pip_utils" ]; then - source "$pip_utils" - check_and_fix_pip_proxy || true - fi - log_debug "PIP命令: $PIP_CMD install -r $external_deps_file $pip_args" "INSTALL" - - # 从文件读取并安装 - if log_command "INSTALL" "Deps" "$PIP_CMD install -r \"$external_deps_file\" $pip_args"; then - log_info "外部依赖安装成功" "INSTALL" - echo -e "${CHECK} 外部依赖安装完成" - - # 验证关键依赖是否安装成功（采样几个） - local sample_deps=$(head -3 "$external_deps_file" | tr '\n' ' ') - log_debug "验证采样依赖是否安装: $sample_deps" "INSTALL" - for dep in $sample_deps; do - local pkg_name=$(echo "$dep" | sed 's/[<>=].*//' | tr '-' '_') - log_pip_package_info "$pkg_name" "INSTALL" || true - done - else - log_warn "部分外部依赖安装失败，但继续..." "INSTALL" - echo -e "${YELLOW}⚠️ 部分外部依赖安装失败，但继续...${NC}" - - # 尝试提取安装失败的包 - local failed_packages=$(grep -i "error\|failed" "${SAGE_INSTALL_LOG:-}" | tail -5 || echo "无法确定失败包") - log_warn "失败详情:\n$failed_packages" "INSTALL" - fi - else - log_warn "未能提取外部依赖或依赖文件为空" "INSTALL" - log_debug "文件状态: $(ls -lh "$external_deps_file" 2>&1 || echo '文件不存在')" "INSTALL" - echo -e "${YELLOW}⚠️ 未能提取外部依赖，跳过...${NC}" - fi - else - log_error "依赖提取脚本执行失败" "INSTALL" - log_error "Python脚本返回非零退出码" "INSTALL" - echo -e "${YELLOW}⚠️ 依赖提取脚本失败，跳过外部依赖安装${NC}" - fi - - log_phase_end_enhanced "外部依赖安装" "success" "INSTALL" + log_phase_end_enhanced "SAGE meta-package 安装" "success" "INSTALL" echo "" - echo -e "${CHECK} SAGE ($install_mode 模式) 和外部依赖安装成功." + echo -e "${CHECK} SAGE ($install_mode 模式) 安装成功" + echo -e "${DIM} 子包依赖已从 PyPI 解析并安装${NC}" echo "" - # 验证sage命令 - echo -e "${DIM}验证 sage 命令...${NC}" - log_info "验证 sage 命令可用性" "INSTALL" - - # 在 conda 环境中验证命令（因为安装在 conda 环境中） - if $PIP_CMD --version >/dev/null 2>&1 && conda run -n "$CONDA_ENV_NAME" sage --version >/dev/null 2>&1; then - log_info "sage 命令验证成功（在 conda 环境中）" "INSTALL" - - # 尝试获取版本信息 - local sage_version=$(conda run -n "$CONDA_ENV_NAME" sage --version 2>&1 || echo "无法获取版本") - log_debug "sage 版本: $sage_version" "INSTALL" - - echo -e "${CHECK} sage 命令已安装到 conda 环境" - echo -e "${DIM} 运行 ${BOLD}conda activate $CONDA_ENV_NAME${NC}${DIM} 或重启终端后可直接使用 sage 命令${NC}" - elif command -v sage >/dev/null 2>&1; then - # 如果在当前 PATH 中可用（比如用户已经激活了环境） - log_info "sage 命令验证成功（当前 shell）" "INSTALL" - local sage_version=$(sage --version 2>&1 || echo "无法获取版本") - log_debug "sage 版本: $sage_version" "INSTALL" - echo -e "${CHECK} sage 命令已可用" - else - log_warn "sage 命令需要激活 conda 环境后使用" "INSTALL" - log_debug "PATH: $PATH" "INSTALL" - log_debug "CONDA_ENV: $CONDA_ENV_NAME" "INSTALL" - echo -e "${INFO} sage 命令已安装，激活环境后可用: ${BOLD}conda activate $CONDA_ENV_NAME${NC}" + # dev 模式：尽量将可用的本地 polyrepo 子仓库覆盖为 editable + if [ "$install_mode" = "dev" ]; then + log_phase_start_enhanced "dev 本地 editable 覆盖安装" "INSTALL" 60 + install_local_editable_polyrepo_packages "$project_root" "$log_file" + log_phase_end_enhanced "dev 本地 editable 覆盖安装" "success" "INSTALL" fi log_info "SAGE ($install_mode 模式) 安装完成" "INSTALL" diff --git a/tools/install/installation_table/dev_installer.sh b/tools/install/installation_table/dev_installer.sh index 515c6d1157..2f570f9b3a 100755 --- a/tools/install/installation_table/dev_installer.sh +++ b/tools/install/installation_table/dev_installer.sh @@ -38,9 +38,9 @@ install_dev_packages() { log_info "开发工具安装阶段" "DevTools" - echo -e "${CHECK} 开发工具依赖已在 sage-tools[dev] 安装过程中完成" + echo -e "${CHECK} 开发工具依赖已在 isage-dev-tools / isage[dev] 安装过程中完成" echo -e "${DIM}包含: black, isort, flake8, pytest, pytest-timeout, mypy, pre-commit 等${NC}" - echo -e "${DIM}所有依赖通过 packages/sage-tools/pyproject.toml 统一管理${NC}" + echo -e "${DIM}开发工具由独立仓库 sage-dev-tools 统一维护${NC}" echo "" # 验证关键开发工具是否可用 @@ -51,12 +51,17 @@ install_dev_packages() { local missing_tools=() for tool in "${tools_to_check[@]}"; do + # 优先尝试直接调用（在 PATH 中） if command -v "$tool" >/dev/null 2>&1; then log_info "$tool 可用" "DevTools" echo -e "${CHECK} $tool 可用" + # 降级方案：尝试通过 python -m 方式调用 + elif python3 -m "$tool" --version >/dev/null 2>&1 || python3 -m pip show "$tool" >/dev/null 2>&1; then + log_info "$tool 可用（通过 python -m）" "DevTools" + echo -e "${CHECK} $tool 可用（通过 python -m）" else - log_warn "$tool 不在 PATH 中" "DevTools" - echo -e "${WARNING} $tool 不在 PATH 中" + log_warn "$tool 不可用" "DevTools" + echo -e "${WARNING} $tool 不可用" missing_tools+=("$tool") fi done @@ -84,9 +89,9 @@ install_dev_packages() { echo -e "${CHECK} 所有开发工具验证成功！" else echo "" - log_warn "部分工具不在 PATH 中: ${missing_tools[*]}" "DevTools" - echo -e "${WARNING} 部分工具不在 PATH 中: ${missing_tools[*]}" - echo -e "${DIM}这在某些环境中是正常的，工具仍可通过 python -m 方式使用${NC}" + log_warn "部分工具不可用: ${missing_tools[*]}" "DevTools" + echo -e "${WARNING} 部分工具不可用: ${missing_tools[*]}" + echo -e "${DIM}建议运行: pip install ${missing_tools[*]}${NC}" fi echo "" diff --git a/tools/install/installation_table/main_installer.sh b/tools/install/installation_table/main_installer.sh index 2489d63a0f..5e2980814b 100755 --- a/tools/install/installation_table/main_installer.sh +++ b/tools/install/installation_table/main_installer.sh @@ -92,8 +92,8 @@ verify_cpp_extensions() { echo -e "${DIM} 如需使用，请通过 pip 安装这些独立包${NC}" echo "" - # sage-middleware 现在只包含 Python 兼容层，总是返回成功 - log_info "sage-middleware 安装完成（仅包含 Python 兼容层）" "CPPExt" + # 历史中间件扩展已拆分为独立适配器包，总是返回成功 + log_info "独立适配器提示完成（主仓不再内建这些 C++ 扩展）" "CPPExt" return 0 # 以下代码已废弃，保留供参考 @@ -137,8 +137,6 @@ try: from sage.middleware.components.sage_db.python import _sage_db elif ext == 'sage_flow': from sage.middleware.components.sage_flow.python import _sage_flow - elif ext == 'sage_tsdb': - from sage.middleware.components.sage_tsdb.python import _sage_tsdb except Exception as e: print(f' {ext}: {type(e).__name__}: {e}') @@ -150,14 +148,14 @@ try: else print('') print(f'⚠️ 部分扩展不可用 ({total}/{total_expected})，功能将受限') - print('💡 提示: 确保已安装构建依赖 (cmake, build-essential) 并重新安装 isage-middleware') + print('💡 提示: 确保已安装构建依赖并重新安装对应独立适配器包') sys.exit(0) # 部分成功也返回 0 else: print('') print('❌ 没有任何 C++ 扩展可用') print('💡 这可能是因为：') print(' 1. 缺少构建工具：apt-get install build-essential cmake') - print(' 2. 未按 --dev 安装或 isage-middleware 构建失败') + print(' 2. 未按 --dev 安装或独立适配器包未正确安装') print(' 3. 查看详细日志了解更多信息') sys.exit(1) except Exception as e: @@ -173,17 +171,17 @@ except Exception as e: echo "$verify_output" if [ $validation_result -eq 0 ]; then - echo -e "${CHECK} C++ 扩展可用 (sage_db, sage_flow, sage_tsdb)" + echo -e "${CHECK} C++ 扩展可用 (sage_db, sage_flow)" echo -e "${DIM}现在可以使用高性能数据库和流处理功能${NC}" log_info "C++扩展验证成功" "CPPExt" return 0 else echo -e "${WARNING} 扩展验证失败" log_warn "扩展验证失败" "CPPExt" - echo -e "${DIM}💡 提示: C++扩展在 sage-middleware 安装时自动构建${NC}" + echo -e "${DIM}💡 提示: C++扩展由对应独立适配器包负责构建${NC}" echo -e "${DIM} 如果验证失败，可能是因为：${NC}" echo -e "${DIM} 1. 缺少构建工具：apt-get install build-essential cmake${NC}" - echo -e "${DIM} 2. 未按 --dev 模式或 isage-middleware 安装失败${NC}" + echo -e "${DIM} 2. 未按 --dev 模式或独立适配器包安装失败${NC}" echo -e "${DIM} 3. 查看详细日志：cat ${SAGE_INSTALL_LOG:-}${NC}" return 1 fi @@ -266,93 +264,55 @@ install_sage() { echo "" case "$mode" in - "minimal"|"core"|"standard") - # minimal 模式：只安装核心包，无开发工具，无可选依赖 - echo -e "${BLUE}最小安装模式：仅安装核心 SAGE 包${NC}" - log_phase_start "最小安装模式" "MAIN" - - if install_core_packages "minimal"; then - log_phase_end "最小安装模式" "success" "MAIN" - echo "" - echo -e "${DIM}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" - echo -e "${INFO} 💡 最小安装完成！如需额外功能，可手动安装：" - echo -e "${DIM} ML/深度学习: pip install isage-middleware[ml]${NC}" - echo -e "${DIM} 向量数据库: pip install isage-middleware[vdb]${NC}" - echo -e "${DIM} 流处理: pip install isage-middleware[streaming]${NC}" - echo -e "${DIM} 提示词压缩: pip install isage-middleware[compression]${NC}" - echo -e "${DIM} 任务队列: pip install isage-middleware[queue]${NC}" - echo -e "${DIM} 开发工具: pip install isage-tools[dev]${NC}" - echo -e "${DIM} 所有可选: pip install isage-middleware[all]${NC}" - echo -e "${DIM}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" - else - log_phase_end "最小安装模式" "failure" "MAIN" - return 1 - fi - ;; - "dev") - # dev 模式：核心包 + 开发工具 - echo -e "${BLUE}开发安装模式：核心包 + 开发工具${NC}" - log_phase_start "开发安装模式" "MAIN" + "standard") + # standard 模式：仅核心包，不含 torch/GPU 依赖 + echo -e "${YELLOW}standard 安装模式：核心子包（无 torch/CUDA）${NC}" + log_phase_start "standard 安装模式" "MAIN" - if ! install_core_packages "dev"; then - log_phase_end "开发安装模式" "failure" "MAIN" + if ! install_core_packages "standard"; then + log_phase_end "standard 安装模式" "failure" "MAIN" return 1 fi - # 安装开发工具 - log_info "开始安装开发工具" "MAIN" - if install_dev_packages; then - log_phase_end "开发安装模式" "success" "MAIN" - echo "" - echo -e "${DIM}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" - echo -e "${INFO} 💡 开发安装完成！如需额外功能，可手动安装：" - echo -e "${DIM} ML/深度学习: pip install isage-middleware[ml]${NC}" - echo -e "${DIM} 向量数据库: pip install isage-middleware[vdb]${NC}" - echo -e "${DIM} 流处理: pip install isage-middleware[streaming]${NC}" - echo -e "${DIM} 提示词压缩: pip install isage-middleware[compression]${NC}" - echo -e "${DIM} 所有可选: pip install isage-middleware[all]${NC}" - echo -e "${DIM}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" - else - log_phase_end "开发安装模式" "failure" "MAIN" - return 1 - fi + log_phase_end "standard 安装模式" "success" "MAIN" ;; "full") - # full 模式：核心包 + 开发工具 + 所有可选依赖 - echo -e "${BLUE}完整安装模式：核心包 + 开发工具 + 所有可选依赖${NC}" - log_phase_start "完整安装模式" "MAIN" + # full 模式：standard + 扩展能力集 via .[full] + echo -e "${CYAN}full 安装模式：standard + 扩展能力集（.[full]）${NC}" + log_phase_start "full 安装模式" "MAIN" if ! install_core_packages "full"; then - log_phase_end "完整安装模式" "failure" "MAIN" + log_phase_end "full 安装模式" "failure" "MAIN" return 1 fi - # 安装开发工具 - log_info "开始安装开发工具" "MAIN" - if ! install_dev_packages; then - log_phase_end "完整安装模式" "failure" "MAIN" - return 1 - fi + log_phase_end "full 安装模式" "success" "MAIN" + ;; + "dev") + # dev 模式：full + 开发工具 + 本地 editable 优先 + echo -e "${GREEN}dev 安装模式：full + 开发工具 + 本地 editable（尽量）${NC}" + log_phase_start "开发安装模式" "MAIN" - # 安装所有可选依赖 - log_info "开始安装可选依赖 (ML, VDB, streaming, etc.)" "MAIN" - if install_optional_packages; then - log_phase_end "完整安装模式" "success" "MAIN" - else - log_phase_end "完整安装模式" "failure" "MAIN" + if ! install_core_packages "dev"; then + log_phase_end "开发安装模式" "failure" "MAIN" return 1 fi + + # 验证开发工具可用性 + log_info "验证开发工具" "MAIN" + install_dev_packages + + log_phase_end "开发安装模式" "success" "MAIN" + echo "" ;; *) - echo -e "${WARNING} 未知安装模式: $mode，使用完整安装" - log_warn "未知安装模式 $mode，使用完整安装" "MAIN" - log_phase_start "默认完整安装" "MAIN" + echo -e "${WARNING} 未知安装模式: $mode，使用 standard 安装" + log_warn "未知安装模式 $mode，使用 standard 安装" "MAIN" + log_phase_start "默认 standard 安装" "MAIN" - install_core_packages "full" - install_dev_packages - install_optional_packages + install_core_packages "standard" - log_phase_end "默认完整安装" "success" "MAIN" + log_phase_end "默认 standard 安装" "success" "MAIN" ;; esac @@ -373,23 +333,45 @@ install_sage() { sage_dev_cmd="$HOME/.local/bin/sage-dev" fi - # 执行清理 + # 执行清理（优先使用 sage-dev，失败时回退到仓库内置清理脚本） + local cleanup_script="$project_root/tools/maintenance/helpers/quick_cleanup.sh" + local sage_dev_healthy=false if { [ "$environment" = "conda" ] && conda run -n "$SAGE_ENV_NAME" which sage-dev >/dev/null 2>&1; } || \ { [ "$environment" = "pip" ] && command -v sage-dev >/dev/null 2>&1; } || \ { [ "$environment" = "pip" ] && [ -x "$HOME/.local/bin/sage-dev" ]; }; then - if $sage_dev_cmd project clean 2>&1 | tee -a "$log_file"; then + if $sage_dev_cmd --help >/dev/null 2>&1; then + sage_dev_healthy=true + else + log_warn "sage-dev 存在但不可用，回退到内置清理脚本" "MAIN" + echo -e "${DIM} 检测到 sage-dev 不可用，使用内置清理${NC}" + fi + fi + + if [ "$sage_dev_healthy" = true ]; then + if $sage_dev_cmd project clean >>"$log_file" 2>&1; then echo -e "${CHECK} 清理完成" - log_info "项目清理成功" "MAIN" + log_info "项目清理成功（sage-dev）" "MAIN" else - echo -e "${DIM} 清理跳过（非关键操作）${NC}" - log_warn "项目清理失败，但不影响安装" "MAIN" + log_warn "sage-dev 清理失败，回退到内置清理脚本" "MAIN" + if [ -f "$cleanup_script" ] && bash "$cleanup_script" >>"$log_file" 2>&1; then + echo -e "${CHECK} 清理完成" + log_info "项目清理成功（fallback script）" "MAIN" + else + echo -e "${DIM} 清理跳过（非关键操作）${NC}" + log_warn "项目清理失败，但不影响安装" "MAIN" + fi fi else - echo -e "${DIM} sage-dev 命令不可用，跳过清理${NC}" - log_warn "sage-dev 不可用，跳过清理" "MAIN" + if [ -f "$cleanup_script" ] && bash "$cleanup_script" >>"$log_file" 2>&1; then + echo -e "${CHECK} 清理完成" + log_info "项目清理成功（fallback script）" "MAIN" + else + echo -e "${DIM} 清理跳过（非关键操作）${NC}" + log_warn "项目清理脚本不可用或执行失败，跳过清理" "MAIN" + fi fi - # C++扩展已在 sage-middleware 安装时通过 scikit-build-core 自动构建 + # C++扩展由独立适配器包负责构建 # 上面的验证步骤已检查扩展状态 # 记录安装完成 diff --git a/tools/install/installation_table/pip_install_monitor.sh b/tools/install/installation_table/pip_install_monitor.sh index 606a54bf18..ee72cc8bcc 100755 --- a/tools/install/installation_table/pip_install_monitor.sh +++ b/tools/install/installation_table/pip_install_monitor.sh @@ -35,17 +35,10 @@ else NC='\033[0m' fi -# 本地 SAGE 包列表（不应该从 PyPI 下载，应该从本地源码安装） -# NOTE: 已独立的包（isage-benchmark, isage-studio, isage-edge, isagellm） -# 不在此列表中，它们可以从 PyPI 下载 +# 本地 SAGE 包列表（不应该在主仓 editable/dev 安装流程中被额外从 PyPI 拉取） +# NOTE: 外部独立能力（如 isagellm / isage-rag / isage-neuromem / isage-vdb） +# 可以按需从 PyPI 获取。 LOCAL_PACKAGES=( - "isage-common" - "isage-platform" - "isage-kernel" - "isage-libs" - "isage-middleware" - "isage-cli" - "isage-tools" "isage" ) @@ -272,7 +265,7 @@ main() { echo "用法: $0 monitor " echo "" echo "示例：" - echo " $0 monitor pip install -e packages/sage-tools" + echo " $0 monitor pip install isage-dev-tools" exit 1 fi shift # 移除 'monitor' 参数 @@ -292,7 +285,7 @@ ${YELLOW}示例：${NC} $0 analyze .sage/logs/install.log # 监控 pip 安装命令 - $0 monitor pip install -e packages/sage-tools + $0 monitor pip install isage-dev-tools # 在 CI/CD 中使用 ./tools/install/installation_table/pip_install_monitor.sh analyze .sage/logs/install.log diff --git a/tools/install/installation_table/scientific_installer.sh b/tools/install/installation_table/scientific_installer.sh index e9439ee886..1f78348a40 100755 --- a/tools/install/installation_table/scientific_installer.sh +++ b/tools/install/installation_table/scientific_installer.sh @@ -73,41 +73,14 @@ install_scientific_packages() { # 安装可选依赖（完整安装模式） # 包含 ML、VDB、streaming、compression 等重型依赖 install_optional_packages() { - echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" - echo -e "${BOLD} 🔧 正在安装可选依赖（完整安装模式）...${NC}" - echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" - echo "" - - log_info "开始安装可选依赖" "Optional" - - # 安装各个可选依赖组 - local optional_groups=( - "isage-middleware[ml]" # ML: transformers, sentence-transformers, accelerate - "isage-middleware[vdb]" # VDB: isage-vdb, faiss-cpu - "isage-middleware[streaming]" # Streaming: isage-flow, isage-tsdb - "isage-middleware[compression]" # Compression: llmlingua - "isage-kernel[ml]" # Kernel ML: torch, torchvision - ) - - for group in "${optional_groups[@]}"; do - echo -e "${BOLD} 📦 正在安装 $group${NC}" - echo -e "${DIM}运行命令: $PIP_CMD install \"$group\"${NC}" - echo "" - - if log_command "Optional" "Install" "$PIP_CMD install \"$group\""; then - log_info "$group 安装成功！" "Optional" - echo -e "${CHECK} $group 安装成功！" - else - log_warn "$group 安装失败，继续安装其他组..." "Optional" - echo -e "${WARNING} $group 安装失败（非关键，继续安装）" - fi - echo "" - done - - echo -e "${GREEN}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" - echo -e "${GREEN}${BOLD} 🎉 可选依赖安装完成！${NC}" - echo -e "${GREEN}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" - - log_info "可选依赖安装完成" "Optional" - return 0 + # ⚠️ 已废弃 (deprecated since 2025) + # 历史 split-package extras + # 在 PyPI 上不存在，pip 会静默忽略——实际什么都不安装。 + # + # 重 GPU/ML 依赖（torch/accelerate/peft）现已统一收归 + # 至独立能力包（如 isagellm[cuda]）中；SAGE meta 不再强制提供。 + # + # 此函数保留为空白 no-op，防止外部调用报错。 + log_info "install_optional_packages: 已废弃，无操作 (重型依赖已迁移至独立能力包)" "Optional" + log_warn "如需 GPU/LLM 重型依赖，请按能力包文档单独安装（如 isagellm[cuda]）" "Optional" } diff --git a/tools/install/maintenance/fix_torch.sh b/tools/install/maintenance/fix_torch.sh new file mode 100644 index 0000000000..a85b17fd1e --- /dev/null +++ b/tools/install/maintenance/fix_torch.sh @@ -0,0 +1,72 @@ +#!/bin/bash +# 修复 PyTorch 版本冲突问题 +# +# 用法: +# ./tools/install/maintenance/fix_torch.sh +# ./tools/install/maintenance/fix_torch.sh --non-interactive # 非交互模式（用于 CI/CD） + +set -e + +# 颜色定义 +RED='\033[0;31m' +GREEN='\033[0;32m' +YELLOW='\033[1;33m' +BLUE='\033[0;34m' +NC='\033[0m' # No Color + +print_info() { echo -e "${BLUE}ℹ $1${NC}"; } +print_success() { echo -e "${GREEN}✓ $1${NC}"; } +print_warning() { echo -e "${YELLOW}⚠ $1${NC}"; } +print_error() { echo -e "${RED}✗ $1${NC}"; } + +# 获取当前 torch 版本 +get_torch_version() { + python -c "import torch; print(torch.__version__)" 2>&1 || echo "not_installed" +} + +# 主修复函数 +fix_torch() { + local non_interactive="${1:-false}" + local TORCH_VERSION + + echo "" + echo "🔧 PyTorch 版本修复脚本" + echo "================================================" + echo " SAGE 使用 isagellm 作为推理引擎。" + echo " 此脚本仅修复 PyTorch 版本。" + echo "================================================" + echo "" + + TORCH_VERSION=$(get_torch_version) + echo " 当前 PyTorch 版本: $TORCH_VERSION" + + if [ "$non_interactive" != "--non-interactive" ]; then + print_warning "此脚本将卸载并重新安装 torch" + read -p "是否继续？(y/N): " -n 1 -r + echo + if [[ ! $REPLY =~ ^[Yy]$ ]]; then + print_info "操作已取消" + exit 0 + fi + fi + + print_info "卸载现有的 torch 包..." + pip uninstall -y torch torchaudio torchvision xformers 2>/dev/null || true + + print_info "安装推荐版本 torch 2.7.1..." + if pip install "torch>=2.7.0,<3.0.0" torchaudio torchvision; then + TORCH_VERSION=$(get_torch_version) + print_success "PyTorch 安装成功: $TORCH_VERSION" + else + print_error "PyTorch 安装失败" + echo " 请访问 https://pytorch.org 获取适合您系统的安装命令" + exit 1 + fi + + echo "" + print_success "修复完成！" + echo "" + echo " 推理引擎请使用 sagellm 相关组件。" +} + +fix_torch "$@" diff --git a/tools/install/maintenance/fix_vllm_torch.sh b/tools/install/maintenance/fix_vllm_torch.sh deleted file mode 100755 index 99863279a9..0000000000 --- a/tools/install/maintenance/fix_vllm_torch.sh +++ /dev/null @@ -1,259 +0,0 @@ -#!/bin/bash -# 修复 vLLM 和 Torch 版本冲突问题 -# -# 用法: -# ./tools/install/fix_vllm_torch.sh -# ./tools/install/fix_vllm_torch.sh --latest # 安装最新版本 -# ./tools/install/fix_vllm_torch.sh --non-interactive # 非交互模式（用于 CI/CD） - - -# ============================================================================ -# 环境变量安全默认值（防止 set -u 报错） -# ============================================================================ -CI="${CI:-}" -GITHUB_ACTIONS="${GITHUB_ACTIONS:-}" -GITLAB_CI="${GITLAB_CI:-}" -JENKINS_URL="${JENKINS_URL:-}" -BUILDKITE="${BUILDKITE:-}" -VIRTUAL_ENV="${VIRTUAL_ENV:-}" -CONDA_DEFAULT_ENV="${CONDA_DEFAULT_ENV:-}" -SAGE_FORCE_CHINA_MIRROR="${SAGE_FORCE_CHINA_MIRROR:-}" -SAGE_DEBUG_OFFSET="${SAGE_DEBUG_OFFSET:-}" -SAGE_CUSTOM_OFFSET="${SAGE_CUSTOM_OFFSET:-}" -LANG="${LANG:-en_US.UTF-8}" -LC_ALL="${LC_ALL:-${LANG}}" -LC_CTYPE="${LC_CTYPE:-${LANG}}" -# ============================================================================ - -set -e - -SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" -PROJECT_ROOT="$(cd "$SCRIPT_DIR/../../.." && pwd)" - -# 默认参数 -NON_INTERACTIVE=false -INSTALL_LATEST=false - -# 解析命令行参数 -for arg in "$@"; do - case $arg in - --non-interactive|-y) - NON_INTERACTIVE=true - shift - ;; - --latest) - INSTALL_LATEST=true - shift - ;; - *) - ;; - esac -done - -# 颜色定义 -RED='\033[0;31m' -GREEN='\033[0;32m' -YELLOW='\033[1;33m' -BLUE='\033[0;34m' -NC='\033[0m' # No Color - -# 打印带颜色的消息 -print_info() { - echo -e "${BLUE}ℹ️ $1${NC}" -} - -print_success() { - echo -e "${GREEN}✅ $1${NC}" -} - -print_warning() { - echo -e "${YELLOW}⚠️ $1${NC}" -} - -print_error() { - echo -e "${RED}❌ $1${NC}" -} - -# 检查是否在虚拟环境中 -check_virtual_env() { - if [ "$NON_INTERACTIVE" = true ]; then - # 非交互模式下，只打印警告但继续执行 - if [ -z "$VIRTUAL_ENV" ] && [ -z "$CONDA_DEFAULT_ENV" ]; then - print_warning "未检测到虚拟环境（非交互模式）" - fi - return 0 - fi - - if [ -z "$VIRTUAL_ENV" ] && [ -z "$CONDA_DEFAULT_ENV" ]; then - print_warning "未检测到虚拟环境" - print_warning "建议在虚拟环境中运行此脚本" - read -p "是否继续？(y/N): " -n 1 -r - echo - if [[ ! $REPLY =~ ^[Yy]$ ]]; then - exit 1 - fi - else - if [ -n "$CONDA_DEFAULT_ENV" ]; then - print_info "当前 Conda 环境: $CONDA_DEFAULT_ENV" - elif [ -n "$VIRTUAL_ENV" ]; then - print_info "当前虚拟环境: $VIRTUAL_ENV" - fi - fi -} - -# 检查当前版本 -check_current_versions() { - print_info "检查当前安装的版本..." - - TORCH_VERSION=$(python -c "import torch; print(torch.__version__)" 2>/dev/null || echo "未安装") - VLLM_VERSION=$(python -c "try: - import vllm - print(vllm.__version__) -except: - print('未安装')" 2>/dev/null || echo "未安装") - - echo " Torch: $TORCH_VERSION" - echo " vLLM: $VLLM_VERSION" - echo -} - -# 卸载冲突的包 -uninstall_packages() { - print_info "卸载现有的 torch 和 vllm 包..." - - pip uninstall -y torch torchaudio torchvision vllm xformers 2>/dev/null || true - - print_success "卸载完成" -} - -# 安装兼容的版本 -install_compatible_versions() { - local install_latest=$1 - - print_info "安装兼容的版本..." - - if [ "$install_latest" = "true" ]; then - print_info "安装最新版本的 vLLM（会自动安装兼容的 torch）" - pip install vllm - else - print_info "安装推荐版本 vLLM 0.10.1.1 + torch 2.7.1" - - # 检查是否需要 CUDA 版本 - if command -v nvidia-smi &> /dev/null; then - print_info "检测到 NVIDIA GPU，安装 CUDA 版本" - pip install torch==2.7.1 torchaudio==2.7.1 torchvision==0.22.1 - else - print_info "未检测到 NVIDIA GPU，安装 CPU 版本" - pip install torch==2.7.1+cpu torchaudio==2.7.1+cpu torchvision==0.22.1+cpu \ - --index-url https://download.pytorch.org/whl/cpu - fi - - pip install vllm==0.10.1.1 - fi - - print_success "安装完成" -} - -# 验证安装 -verify_installation() { - print_info "验证安装..." - - # 验证版本 - TORCH_VERSION=$(python -c "import torch; print(torch.__version__)" 2>&1) - if [ $? -ne 0 ]; then - print_error "Torch 导入失败" - return 1 - fi - print_success "Torch 版本: $TORCH_VERSION" - - # 验证 vLLM - VLLM_VERSION=$(python -c "import vllm; print(vllm.__version__)" 2>&1) - if [ $? -ne 0 ]; then - print_error "vLLM 导入失败" - echo "$VLLM_VERSION" - return 1 - fi - print_success "vLLM 版本: $VLLM_VERSION" - - # 验证 torch._inductor.config - python -c "import torch._inductor.config; print('torch._inductor.config 可用')" 2>&1 - if [ $? -eq 0 ]; then - print_success "torch._inductor.config 可用" - else - print_error "torch._inductor.config 不可用" - return 1 - fi - - # 运行完整的依赖验证脚本 - if [ -f "$PROJECT_ROOT/tools/install/diagnostics/verify_dependencies.py" ]; then - print_info "运行完整依赖验证..." - python "$PROJECT_ROOT/tools/install/diagnostics/verify_dependencies.py" - fi - - return 0 -} - -# 主函数 -main() { - echo "==========================================" - echo "🔧 vLLM & Torch 版本冲突修复脚本" - echo "==========================================" - echo - - # 检查虚拟环境 - check_virtual_env - echo - - # 检查当前版本 - check_current_versions - - # 确认操作（非交互模式下自动继续） - if [ "$NON_INTERACTIVE" = false ]; then - print_warning "此脚本将卸载并重新安装 torch 和 vllm" - read -p "是否继续？(y/N): " -n 1 -r - echo - if [[ ! $REPLY =~ ^[Yy]$ ]]; then - print_info "操作已取消" - exit 0 - fi - echo - else - print_info "非交互模式：自动继续执行修复" - fi - - # 执行修复步骤 - uninstall_packages - echo - - install_compatible_versions "$INSTALL_LATEST" - echo - - # 验证安装 - if verify_installation; then - echo - print_success "==========================================" - print_success "✨ 修复完成！所有依赖已正确安装" - print_success "==========================================" - echo - print_info "你现在可以使用 sage-dev 命令了:" - echo " sage-dev --help" - echo - print_info "相关文档:" - echo " see docs-public/docs_src/dev-notes/l2-platform/DEPENDENCY_OPTIMIZATION.md (Torch & vLLM section)" - else - echo - print_error "==========================================" - print_error "修复失败，请检查错误信息" - print_error "==========================================" - echo - print_info "手动修复步骤:" - echo " 1. pip uninstall -y torch torchaudio torchvision vllm" - echo " 2. pip install vllm" - echo " 3. python tools/install/diagnostics/verify_dependencies.py" - echo - exit 1 - fi -} - -# 运行主函数 -main "$@" diff --git a/tools/install/requirements-vllm-lock.txt b/tools/install/requirements-vllm-lock.txt deleted file mode 100644 index 484a385791..0000000000 --- a/tools/install/requirements-vllm-lock.txt +++ /dev/null @@ -1,35 +0,0 @@ -# vLLM 相关依赖锁定版本 -# -# 这个文件锁定了 vLLM 及其关键依赖的版本，确保环境一致性 -# -# 安装方法: -# pip install -r tools/install/requirements-vllm-lock.txt -# -# 或者使用自动修复脚本: -# ./tools/install/fix_vllm_torch.sh -# -# 版本说明: -# - vLLM 0.10.1.1 需要 torch >= 2.4.0 -# - torch 2.7.1 引入了 torch._inductor.config -# - outlines_core 必须是 0.2.10（vLLM 0.10.1.1 的要求） -# -# 更新日期: 2025-11-19 - -# PyTorch (CUDA 12.6 版本) -# 如果需要 CPU 版本，请使用: -# torch==2.7.1+cpu -# torchaudio==2.7.1+cpu -# torchvision==0.22.1+cpu -# --index-url https://download.pytorch.org/whl/cpu -torch==2.7.1 -torchaudio==2.7.1 -torchvision==0.22.1 - -# vLLM 及其关键依赖 -vllm==0.10.1.1 -outlines_core==0.2.10 - -# 注意: -# - 不要同时安装 outlines，它与 outlines_core 0.2.10 冲突 -# - 如果遇到版本冲突，运行: ./tools/install/fix_vllm_torch.sh -# - 验证安装: python tools/install/verify_dependencies.py diff --git a/tools/install/tests/README.md b/tools/install/tests/README.md index 76942ec683..195864f8f4 100644 --- a/tools/install/tests/README.md +++ b/tools/install/tests/README.md @@ -1,6 +1,6 @@ # SAGE 安装工具测试套件 -本目录包含 SAGE 环境隔离、自动虚拟环境创建、安装跟踪和清理功能的测试套件。 +本目录包含 SAGE 环境隔离（禁用 venv）、安装跟踪和清理功能的测试套件。 ## 📋 测试清单 @@ -9,7 +9,7 @@ 1. **环境配置测试** (`test_environment_config.sh`) - 虚拟环境检测（Conda、Python venv、系统环境） - - 自动虚拟环境创建 + - Python venv 禁止策略 - 环境隔离检查 - CI 环境处理 @@ -46,7 +46,7 @@ bash tools/install/tests/test_cleanup_tools.sh ### 运行端到端测试 ```bash -# 运行完整的集成测试（包括自动虚拟环境创建、安装跟踪、清理） +# 运行完整的集成测试（包括环境检测、安装跟踪、清理） bash tools/install/tests/test_e2e_integration.sh ``` @@ -72,10 +72,10 @@ done | 检测 Conda 环境 | ✅ 3 个测试 | 通过 | | 检测 Python venv | ✅ 3 个测试 | 通过 | | 检测系统环境 | ✅ 2 个测试 | 通过 | -| 自动创建虚拟环境 | ✅ 3 个测试 | 通过 | +| 拒绝 Python venv | ✅ 1 个测试 | 通过 | | CI 环境跳过检查 | ✅ 2 个测试 | 通过 | -**总计**: 13 个测试用例，100% 通过率 +**总计**: 11 个测试用例，100% 通过率 ### 清理工具模块 @@ -94,9 +94,9 @@ done | 测试步骤 | 描述 | 状态 | | ---------------- | -------------------------- | ---- | | 1. 环境设置 | 创建隔离测试环境 | ✅ | -| 2. 自动虚拟环境 | 测试 auto-venv 创建和激活 | ✅ | +| 2. 环境检测 | 验证不创建 venv | ✅ | | 3. 安装跟踪 | 测试 pre/post-install 记录 | ✅ | -| 4. 环境检测 | 验证环境类型识别 | ✅ | +| 4. venv 拒绝策略 | 验证 Python venv 被拒绝 | ✅ | | 5. 策略配置 | 测试 SAGE_VENV_POLICY | ✅ | | 6. 清理功能 | 测试卸载脚本 | ✅ | | 7. Makefile 集成 | 验证 make 目标 | ✅ | @@ -156,7 +156,7 @@ done ### CI 工作流包括 1. **单元测试** - 在 Ubuntu 22.04 和 latest 上运行 -1. **自动虚拟环境测试** - 跨 Python 3.10, 3.11, 3.12 的矩阵测试 +1. **环境隔离策略测试** - 验证 venv 拒绝与策略行为 1. **安装跟踪测试** - 验证记录功能 1. **端到端集成测试** - 完整工作流验证 1. **文档示例测试** - 确保文档示例可运行 @@ -175,7 +175,7 @@ done tools/install/environment_config.sh tools/cleanup/** .github/workflows/test-env-cleanup.yml -docs-public/docs_src/dev-notes/l1-common/CLEANUP_AUTOMATION.md +CHANGELOG.md ``` ## 🛠️ 添加新测试 @@ -236,7 +236,7 @@ main "$@" ## 📚 相关文档 -- [清理自动化文档](../../../docs-public/docs_src/dev-notes/l1-common/CLEANUP_AUTOMATION.md) +- [项目变更日志](../../../CHANGELOG.md) - [开发者文档](../../../DEVELOPER.md) - [贡献指南](../../../CONTRIBUTING.md) diff --git a/tools/install/tests/test_e2e_integration.sh b/tools/install/tests/test_e2e_integration.sh index 8e7c67b35c..b2cbfb9073 100755 --- a/tools/install/tests/test_e2e_integration.sh +++ b/tools/install/tests/test_e2e_integration.sh @@ -105,8 +105,8 @@ step1_setup_test_environment() { log_success "测试环境设置完成" } -step2_test_auto_venv_creation() { - log_step "测试自动虚拟环境创建" +step2_test_environment_detection() { + log_step "测试环境检测（禁用 venv）" # 清除所有虚拟环境变量 unset CONDA_DEFAULT_ENV CONDA_PREFIX VIRTUAL_ENV @@ -126,42 +126,12 @@ step2_test_auto_venv_creation() { return 1 fi - # 测试创建虚拟环境 - local test_venv="$TEST_DIR/.sage/venv" - log_info "创建测试虚拟环境: $test_venv" - - if ensure_python_venv "$test_venv" 2>/dev/null; then - log_success "虚拟环境创建成功" - - # 验证虚拟环境结构 - if [ -f "$test_venv/bin/activate" ]; then - log_success "虚拟环境包含 activate 脚本" - else - log_failure "虚拟环境缺少 activate 脚本" - return 1 - fi - - # 激活虚拟环境 - source "$test_venv/bin/activate" - log_info "Python: $(python --version 2>&1)" - log_info "Pip: $(pip --version 2>&1)" - - log_success "虚拟环境激活成功" - else - log_failure "虚拟环境创建失败" - return 1 - fi + log_success "venv 自动创建路径已禁用（仅检测，不创建）" } step3_test_install_tracking() { log_step "测试安装跟踪" - # 确保在虚拟环境中 - if [ -z "${VIRTUAL_ENV:-}" ]; then - log_info "激活虚拟环境..." - source "$TEST_DIR/.sage/venv/bin/activate" - fi - # 安装一些测试包 log_info "安装测试包..." pip install --quiet requests >/dev/null 2>&1 || true @@ -194,29 +164,24 @@ step3_test_install_tracking() { log_success "安装跟踪测试完成" } -step4_test_environment_detection() { +step4_test_venv_policy_reject() { log_step "测试环境检测功能" source "$TEST_DIR/tools/install/display_tools/colors.sh" source "$TEST_DIR/tools/install/download_tools/environment_config.sh" - # 测试在虚拟环境中的检测 - local result=$(detect_virtual_environment) - local is_venv=$(echo "$result" | cut -d'|' -f1) - local venv_type=$(echo "$result" | cut -d'|' -f2) - - log_info "检测结果: $result" - - if [ "$is_venv" = "true" ] && [ "$venv_type" = "venv" ]; then - log_success "正确检测到 Python venv" - else - log_failure "虚拟环境检测不正确" + export VIRTUAL_ENV="$TEST_DIR/.venv" + if check_virtual_environment_isolation "pip" >/dev/null 2>&1; then + log_failure "Python venv 未被拒绝" + unset VIRTUAL_ENV return 1 fi + unset VIRTUAL_ENV + log_success "Python venv 被正确拒绝" } step5_test_venv_policy() { - log_step "测试 SAGE_VENV_POLICY 配置" + log_step "测试 SAGE_VENV_POLICY 配置（系统环境）" source "$TEST_DIR/tools/install/display_tools/colors.sh" source "$TEST_DIR/tools/install/download_tools/environment_config.sh" @@ -226,7 +191,7 @@ step5_test_venv_policy() { export SAGE_VENV_POLICY=$policy log_info "测试策略: SAGE_VENV_POLICY=$policy" - # 在虚拟环境中检查应该总是通过 + # 仅验证变量可设置与检测函数可执行 local result=$(detect_virtual_environment) log_info "策略 $policy 检测结果: $result" done @@ -273,20 +238,15 @@ step8_verify_documentation() { # 检查文档文件 local docs_to_check=( - "docs-public/docs_src/dev-notes/l1-common/CLEANUP_AUTOMATION.md" + "README.md" + "DEVELOPER.md" + "CONTRIBUTING.md" + "docs/dependency-audit-gate.md" ) for doc in "${docs_to_check[@]}"; do if [ -f "$TEST_DIR/$doc" ]; then log_success "文档存在: $doc" - - # 验证文档包含关键字 - if grep -q "auto-venv" "$TEST_DIR/$doc"; then - log_info "文档包含 auto-venv 说明" - fi - if grep -q "SAGE_VENV_POLICY" "$TEST_DIR/$doc"; then - log_info "文档包含 SAGE_VENV_POLICY 说明" - fi else log_info "文档不存在（跳过）: $doc" fi @@ -339,9 +299,9 @@ main() { # 运行所有测试步骤 step1_setup_test_environment - step2_test_auto_venv_creation + step2_test_environment_detection step3_test_install_tracking - step4_test_environment_detection + step4_test_venv_policy_reject step5_test_venv_policy step6_test_cleanup step7_test_makefile_integration diff --git a/tools/install/tests/test_environment_config.sh b/tools/install/tests/test_environment_config.sh index 9af2834c6d..bc49839bee 100755 --- a/tools/install/tests/test_environment_config.sh +++ b/tools/install/tests/test_environment_config.sh @@ -173,47 +173,6 @@ test_detect_venv_in_system() { assert_equals "" "$venv_type" "环境类型为空" } -# ============================================================================ -# 测试 ensure_python_venv -# ============================================================================ - -test_ensure_python_venv_creation() { - echo "" - echo -e "${BLUE}测试组: ensure_python_venv${NC}" - - # 创建临时测试目录 - local test_venv="/tmp/sage_test_venv_$$" - - # 清理可能存在的旧测试环境 - rm -rf "$test_venv" - - # 测试创建虚拟环境 - if ensure_python_venv "$test_venv" 2>/dev/null; then - assert_success "创建虚拟环境成功" - - # 验证虚拟环境结构 - if [ -f "$test_venv/bin/activate" ]; then - assert_success "虚拟环境包含 activate 脚本" - else - assert_failure "虚拟环境缺少 activate 脚本" - false - fi - - if [ -f "$test_venv/bin/python" ] || [ -f "$test_venv/bin/python3" ]; then - assert_success "虚拟环境包含 Python 可执行文件" - else - assert_failure "虚拟环境缺少 Python 可执行文件" - false - fi - else - echo -e "${YELLOW}⚠️ SKIP${NC}: 创建虚拟环境 (可能缺少 python3-venv 或 virtualenv)" - TOTAL_TESTS=$((TOTAL_TESTS + 1)) - fi - - # 清理 - rm -rf "$test_venv" -} - # ============================================================================ # 测试 check_virtual_environment_isolation (非交互部分) # ============================================================================ @@ -227,7 +186,7 @@ test_check_venv_skip_in_ci() { unset CONDA_DEFAULT_ENV CONDA_PREFIX VIRTUAL_ENV # 在 CI 中应该跳过检查 - check_virtual_environment_isolation "pip" "false" 2>/dev/null + check_virtual_environment_isolation "pip" 2>/dev/null assert_success "CI 环境跳过虚拟环境检查" # 清理 @@ -241,10 +200,28 @@ test_check_venv_skip_for_conda_install() { unset CI CONDA_DEFAULT_ENV CONDA_PREFIX VIRTUAL_ENV # 选择 conda 安装模式时应该跳过检查 - check_virtual_environment_isolation "conda" "false" 2>/dev/null + check_virtual_environment_isolation "conda" 2>/dev/null assert_success "Conda 安装模式跳过虚拟环境检查" } +test_check_venv_rejected() { + echo "" + echo -e "${BLUE}测试组: check_virtual_environment_isolation - Python venv 禁止${NC}" + + unset CI CONDA_DEFAULT_ENV CONDA_PREFIX + export VIRTUAL_ENV="/tmp/fake_venv" + + if ( + export VIRTUAL_ENV="/tmp/fake_venv" + check_virtual_environment_isolation "pip" >/dev/null 2>&1 + ); then + assert_failure "Python venv 应被拒绝" + false + else + assert_success "Python venv 被正确拒绝" + fi +} + # ============================================================================ # 测试报告 # ============================================================================ @@ -291,9 +268,9 @@ main() { test_detect_venv_in_conda test_detect_venv_in_python_venv test_detect_venv_in_system - test_ensure_python_venv_creation test_check_venv_skip_in_ci test_check_venv_skip_for_conda_install + test_check_venv_rejected # 打印总结 print_test_summary diff --git a/tools/install/tests/verify_installation.sh b/tools/install/tests/verify_installation.sh index f0ef8e7af9..85be018300 100755 --- a/tools/install/tests/verify_installation.sh +++ b/tools/install/tests/verify_installation.sh @@ -47,7 +47,6 @@ print_test_header() { run_test() { local test_name="$1" local test_command="$2" - TOTAL_TESTS=$((TOTAL_TESTS + 1)) echo -e "${DIM}测试 $TOTAL_TESTS: $test_name${NC}" @@ -62,6 +61,7 @@ run_test() { fi } + # 运行测试并显示输出 run_test_with_output() { local test_name="$1" @@ -117,12 +117,20 @@ main() { run_test_with_output "Python 版本检查" "python3 --version" run_test "pip 可用性" "python3 -m pip --version" - # 2. SAGE 核心包导入测试（PEP 420 namespace - 只测试实际包） + # 2. SAGE 核心包导入测试（主仓 in-tree 表面） print_test_header "🔧 2. SAGE 核心包导入测试" - run_test_with_output "导入 sage.common" "python3 -c 'import sage.common; print(sage.common.__version__)'" - run_test "导入 sage.kernel" "python3 -c 'import sage.kernel'" - run_test "导入 sage.libs" "python3 -c 'import sage.libs'" - run_test "导入 sage.middleware" "python3 -c 'import sage.middleware'" + run_test "导入 sage.foundation" "python3 -c 'import sage.foundation'" + run_test "导入 sage.stream" "python3 -c 'import sage.stream'" + run_test "导入 sage.runtime" "python3 -c 'import sage.runtime'" + run_test "导入 sage.serving" "python3 -c 'import sage.serving'" + run_test_with_output "导入 sage.cli" "python3 -c 'import sage.cli; print(sage.cli.__version__)'" + + echo "" + echo -e "${DIM}以下为过渡兼容命名空间，失败不影响主仓核心功能：${NC}" + run_warning_test "导入 sage.common" "python3 -c 'import sage.common'" + run_warning_test "导入 sage.kernel" "python3 -c 'import sage.kernel'" + run_warning_test "导入 sage.libs" "python3 -c 'import sage.libs'" + run_warning_test "导入 sage.middleware" "python3 -c 'import sage.middleware'" # Note: sage.llm has been moved to independent package isagellm # run_test "导入 isagellm" "python3 -c 'import isagellm'" # optional @@ -130,52 +138,32 @@ main() { print_test_header "📚 3. 关键依赖检查" run_test "numpy 可用" "python3 -c 'import numpy; print(numpy.__version__)'" - # 可选依赖（--dev 模式不包含，仅在 --full 模式中安装） + # 可选依赖（standard/dev 模式都安装） echo "" echo -e "${DIM}以下为可选依赖，失败不影响核心功能：${NC}" run_warning_test "pandas 可用" "python3 -c 'import pandas'" run_warning_test "torch 可用" "python3 -c 'import torch'" run_warning_test "transformers 可用" "python3 -c 'import transformers'" - # 4. SAGE 子包版本一致性检查 + # 4. 主仓版本与核心表面检查 print_test_header "🔍 4. 版本一致性检查" - run_test_with_output "子包版本一致性" "python3 -c ' -import sage.common -import sage.kernel -import sage.libs -import sage.middleware - -packages = [ - ('sage.common', sage.common.__version__), - ('sage.kernel', sage.kernel.__version__), - ('sage.libs', sage.libs.__version__), - ('sage.middleware', sage.middleware.__version__) -] - -print(\"包版本信息:\") -for pkg_name, version in packages: - print(f\" {pkg_name}: {version}\") - -# Note: sage is a PEP 420 namespace package and does not have __version__ -# Each sub-package manages its own version independently -print(\"✅ 所有子包版本已检测 (sage 是命名空间包，无独立版本)\") -'" + run_test_with_output "子包版本一致性" "python3 -c 'from sage._version import __version__; import sage.cli; packages=[(\"isage\", __version__), (\"sage.cli\", sage.cli.__version__)]; print(\"包版本信息:\"); [print(f\" {pkg}: {version}\") for pkg, version in packages]; print(\"✅ 主仓版本与 CLI 表面已检测\")'" # 5. CLI 工具检查 print_test_header "🛠️ 5. CLI 工具检查" run_test "sage CLI 可用" "command -v sage" run_test "sage-dev CLI 可用" "command -v sage-dev" - run_test_with_output "sage --version" "sage --version" + run_test_with_output "sage version" "sage version" # 6. 可选组件检查（不影响总体结果） print_test_header "🎯 6. 可选组件检查（非必需）" echo -e "${DIM}以下测试失败不影响核心功能${NC}" - # vLLM（可选） - if python3 -c "import vllm" 2>/dev/null; then - echo -e "${GREEN}✅ vLLM 已安装${NC}" + # SageLLM（核心推理引擎） + if python3 -c "import sagellm" 2>/dev/null; then + echo -e "${GREEN}✅ SageLLM 已安装${NC}" else - echo -e "${YELLOW}⚠️ vLLM 未安装（可选组件）${NC}" + echo -e "${YELLOW}⚠️ SageLLM 未安装（可选组件，LLM 推理引擎）${NC}" fi # CUDA（可选） @@ -235,8 +223,8 @@ print(\"✅ 所有子包版本已检测 (sage 是命名空间包，无独立版 echo "" echo -e "${DIM}下一步:${NC}" echo -e "${DIM} 1. 配置 API keys: cp .env.template .env${NC}" - echo -e "${DIM} 2. 运行示例: python examples/tutorials/hello_world.py${NC}" - echo -e "${DIM} 3. 查看文档: https://intellistream.github.io/SAGE-Pub/${NC}" + echo -e "${DIM} 2. 运行验证: python tools/verify_hello_world.py${NC}" + echo -e "${DIM} 3. 查看文档: https://intellistream.github.io/sage-docs/${NC}" return 0 elif [ $pass_rate -ge 80 ]; then echo -e "${YELLOW}⚠️ 大部分测试通过 (${pass_rate}%)，但有部分失败${NC}" diff --git a/tools/maintenance/README.md b/tools/maintenance/README.md index 389618ea31..3136781e4e 100644 --- a/tools/maintenance/README.md +++ b/tools/maintenance/README.md @@ -1,316 +1,129 @@ # SAGE Maintenance Tools -统一的项目维护工具集，提供 Submodule 管理、项目清理、安全检查等功能。 +SAGE 元仓库的轻量维护工具文档。 -> **注意：** 完整的开发指南请参见： -> -> - [DEVELOPER.md](../../DEVELOPER.md) - 开发环境设置和 submodule 管理 -> - [CONTRIBUTING.md](../../CONTRIBUTING.md) - 贡献指南 +当前 `tools/maintenance/` 目录只保留仍在使用的项目维护脚本，不再承担历史上的 submodule 编排职责。SAGE +生态中的大多数组件已经拆分为独立仓库并单独发布，因此这里的工具主要用于当前元仓库本身的清理、检查、hooks 和类型问题辅助处理。 -## 🚀 快速开始 +> 完整开发说明请参考： +> +> - [DEVELOPER.md](../../DEVELOPER.md) +> - [CONTRIBUTING.md](../../CONTRIBUTING.md) -### 健康检查 +## 快速开始 ```bash -# 运行完整的项目健康检查 -./tools/maintenance/sage-maintenance.sh doctor - -# 查看所有可用命令 +# 查看帮助 ./tools/maintenance/sage-maintenance.sh --help -``` - -### 常见场景 -```bash -# 首次克隆后初始化 submodules -./tools/maintenance/sage-maintenance.sh submodule init - -# 切换 SAGE 分支后同步 submodules -git checkout main -./tools/maintenance/sage-maintenance.sh submodule switch - -# 检查 submodule 状态 -./tools/maintenance/sage-maintenance.sh submodule status +# 运行健康检查 +./tools/maintenance/sage-maintenance.sh doctor -# 清理项目构建产物 +# 清理构建产物与缓存 ./tools/maintenance/sage-maintenance.sh clean ``` -## 📋 命令参考 - -### Submodule 管理 - -| 命令 | 说明 | -| ------------------------ | --------------------------------- | -| `submodule init` | 初始化并自动切换到正确分支 | -| `submodule status` | 查看 submodule 状态（带颜色指示） | -| `submodule switch` | 切换 submodule 分支 | -| `submodule update` | 更新到远程最新版本 | -| `submodule fix-conflict` | 解决 submodule 冲突 | -| `submodule cleanup` | 清理旧 submodule 配置 | +## 当前支持的命令 ### 项目维护 -| 命令 | 说明 | -| ---------------- | -------------------- | -| `doctor` | 运行完整健康检查 | -| `status` | 显示项目状态 | -| `clean` | 清理构建产物 | -| `clean-deep` | 深度清理（包括缓存） | -| `security-check` | 检查敏感信息泄露 | -| `setup-hooks` | 安装 Git hooks | - -## 📁 目录结构 - -``` -tools/maintenance/ -├── sage-maintenance.sh # 主脚本（用户入口） -├── setup_hooks.sh # Git hooks 安装 -├── README.md # 本文档 -├── CHANGELOG.md # 更新日志 -├── SUBMODULE_GUIDE.md # Submodule 详细指南 -├── git-hooks/ # Hook 模板 -│ └── post-checkout # 自动切换 submodule 分支 -└── helpers/ # 内部辅助脚本 - ├── common.sh - ├── manage_submodule_branches.sh - ├── resolve_submodule_conflict.sh - ├── cleanup_old_submodules.sh - ├── quick_cleanup.sh - └── check_config_security.sh -``` - -## 🔧 Submodule 分支管理 - -### 分支匹配规则 - -| SAGE 分支 | Submodule 分支 | 说明 | -| ---------- | -------------- | -------- | -| `main` | `main` | 稳定版本 | -| `main-dev` | `main-dev` | 开发版本 | -| 其他分支 | `main-dev` | 默认开发 | - -### 颜色状态说明 - -运行 `submodule status` 时的颜色含义： +| 命令 | 说明 | +| ---------------- | ------------------------------------ | +| `clean` | 清理常见构建产物与缓存 | +| `clean-deep` | 深度清理 Python 缓存、日志和构建目录 | +| `security-check` | 检查配置中的敏感信息 | +| `setup-hooks` | 安装或重装 Git hooks | +| `doctor` | 运行健康检查 | +| `status` | 显示当前仓库状态 | -- 🟢 **绿色**：配置分支和当前分支一致（正常） -- 🟡 **黄色**：配置分支与当前分支不一致 -- 🔴 **红色**：处于 detached HEAD 状态（需要修复） ./quickstart.sh --dev +### 类型问题辅助 -# 之后切换分支时，submodules 自动跟随 +| 命令 | 说明 | +| -------------------------- | -------------------------- | +| `typecheck status` | 查看当前类型错误状态 | +| `typecheck show-new` | 查看格式化后新增错误 | +| `typecheck explain ` | 解释某个文件的类型修复背景 | +| `typecheck safe-commit` | 以更安全的方式执行提交流程 | +| `typecheck reset` | 撤销相关自动格式化结果 | -git checkout main # → submodules 切到 main git checkout main-dev # → submodules 切到 main-dev +## 目录结构 -```` - -### 当前 Submodules - -- `docs-public/` - 文档 -- `packages/.../sage_db/sageVDB/` - 数据库 -- `packages/.../sage_flow/sageFlow/` - 工作流 -- `packages/sage-llm-core/src/sage/llm/` - LLM 服务 - -**重要**: `sage_db` 和 `sage_flow` 本身不是 submodules，实际 submodules 在其子目录中。 - -## 🔧 使用示例 - -### 场景 1：首次克隆并初始化 - -```bash -# 克隆仓库 -git clone https://github.com/intellistream/SAGE.git -cd SAGE - -# 切换到开发分支 -git checkout main-dev - -# 一键初始化（会自动切换到 main-dev 分支） -./tools/maintenance/sage-maintenance.sh submodule init - -# 验证所有 submodules 都在 main-dev 分支上 -./tools/maintenance/sage-maintenance.sh submodule status -```` - -**预期输出：** - -``` -📦 Submodule 状态 - -🚀 SAGE Submodule 状态 -SAGE 分支: main-dev - -Submodule 配置： -Submodule 配置分支 当前分支 -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -docs-public main-dev main-dev (绿色 ✅) -sageLLM main-dev main-dev (绿色 ✅) -sageVDB main-dev main-dev (绿色 ✅) -sageFlow main-dev main-dev (绿色 ✅) -``` - -### 场景 2：切换 SAGE 分支 - -```bash -# 切换到 main 分支 -git checkout main - -# 自动切换所有 submodules 到 main 分支 -./tools/maintenance/sage-maintenance.sh submodule switch - -# 验证 -./tools/maintenance/sage-maintenance.sh submodule status +```text +tools/maintenance/ +├── sage-maintenance.sh # 主入口 +├── setup_hooks.sh # Git hooks 安装脚本 +├── check_docs.sh # 文档检查辅助脚本 +├── fix-types-helper.sh # 类型问题辅助脚本 +├── git-hooks/ # Hook 模板 +└── helpers/ # 内部辅助脚本 + ├── check_config_security.sh + ├── common.sh + ├── pre_install_cleanup.sh + ├── prepare_branch_checkout.sh + ├── quick_cleanup.sh + └── sage-jobmanager.sh ``` -### 场景 3：修复 detached HEAD 问题 +## 关于子仓库 / 独立仓库 -如果你的 submodules 处于 detached HEAD 状态： +- 历史 split repos 属于主仓收敛过程中的过渡边界；新增核心能力应优先直接落在 `SAGE` 主仓。 +- `sage-tools` 等非核心能力仍可保持独立维护。 +- 不要再假设存在历史上的嵌套路径，例如 `packages/.../sage_db/sageVDB/`、`packages/.../sage_flow/sageFlow/` 或 + `packages/sage-llm-core/...`。 -```bash -# 切换到正确的分支 -./tools/maintenance/sage-maintenance.sh submodule switch +如果需要跨仓库协作，应优先判断该能力是否已经进入主仓收敛范围；若是，则直接在主仓实现， 不要继续扩大核心 split-repo 面。 -# 或者重新初始化 -./tools/maintenance/sage-maintenance.sh submodule init -``` +## 常见用法 -### 场景 4：定期更新 +### 1. 健康检查 ```bash -# 更新 SAGE 主仓库 -git pull - -# 更新所有 submodules -./tools/maintenance/sage-maintenance.sh submodule update - -# 检查健康状态 ./tools/maintenance/sage-maintenance.sh doctor ``` -## 🆘 常见问题 +适用于快速确认： -### Detached HEAD 问题 +- 当前目录是否为 Git 仓库 +- Git hooks 是否已安装 +- Python 是否可用 +- 仓库中是否残留构建产物 -**问题：** Submodule 初始化后停留在特定 commit 而非分支 - -**解决：** +### 2. 日常清理 ```bash -./tools/maintenance/sage-maintenance.sh submodule switch +./tools/maintenance/sage-maintenance.sh clean +./tools/maintenance/sage-maintenance.sh clean-deep ``` -### Submodule 冲突 - -**问题：** Git merge 时 submodule 冲突 - -**解决：** +### 3. 重新安装 hooks ```bash -./tools/maintenance/sage-maintenance.sh submodule fix-conflict +./tools/maintenance/sage-maintenance.sh setup-hooks -f ``` -### 旧配置清理 - -**问题：** Submodule 路径或配置变更 - -**解决：** +### 4. 文档检查 ```bash -./tools/maintenance/sage-maintenance.sh submodule cleanup -git submodule sync -./tools/maintenance/sage-maintenance.sh submodule init +bash tools/maintenance/check_docs.sh ``` -### Git Hooks 不工作 - -**解决：** +### 5. 类型问题辅助 ```bash -./tools/maintenance/sage-maintenance.sh setup-hooks -f +./tools/maintenance/sage-maintenance.sh typecheck status +./tools/maintenance/sage-maintenance.sh typecheck show-new ``` -## 📝 最近更新 - -### 2025-10-09 - -✅ **修复了关键问题：** - -1. 颜色显示修复 - 帮助信息现在正确显示颜色 -1. Submodule 初始化修复 - `submodule init` 现在自动切换到正确的分支 - -详见 [CHANGELOG.md](./CHANGELOG.md) - -## 📚 相关文档 - -- **开发指南** - [DEVELOPER.md](../../DEVELOPER.md) -- **贡献指南** - [CONTRIBUTING.md](../../CONTRIBUTING.md) -- **Submodule 详细指南** - [SUBMODULE_GUIDE.md](./SUBMODULE_GUIDE.md) -- **更新日志** - [CHANGELOG.md](./CHANGELOG.md) - -______________________________________________________________________ - -💡 **提示：** 遇到问题先运行 `doctor`，它会给出诊断和建议！ - -## ⚠️ 注意事项 - -1. **优先使用主脚本** `sage-maintenance.sh`，不要直接调用 helpers 中的脚本 -1. **Submodule 初始化** - 使用 `submodule init` 而不是 `git submodule update --init` -1. **分支切换后** - 运行 `submodule switch` 同步 submodules -1. **定期清理** - 使用 `clean` 命令保持环境整洁 -1. **健康检查** - 定期运行 `doctor` 检查项目状态 - -## 📝 最新更新 (2025-10-09) - -### 🐛 已修复的问题 - -1. **颜色显示问题** ✅ - - - 修复了帮助信息显示 ANSI 转义代码的问题 - - 现在所有颜色和格式都能正确显示 - -1. **Submodule 初始化问题** ✅ - - - 修复了 `submodule init` 导致 detached HEAD 的问题 - - 现在会自动切换到正确的分支（main 或 main-dev） - -### 🎯 关键改进 - -- `submodule init` 现在是一键初始化 + 自动分支切换 -- 新增颜色状态指示，更直观地查看 submodule 状态 -- 改进了错误提示和使用说明 - -## 📚 更多帮助 - -### 查看帮助信息 - -```bash -# 查看完整帮助 -./tools/maintenance/sage-maintenance.sh --help - -# 健康检查（会给出详细建议） -./tools/maintenance/sage-maintenance.sh doctor - -# 查看 submodule 详细状态 -./tools/maintenance/sage-maintenance.sh submodule status -``` - -### 相关文档 - -- [Submodule 初始化指南](./SUBMODULE_GUIDE.md) - 详细的使用教程 -- [更新日志](./CHANGELOG.md) - 最新的功能更新和修复 -- [开发者文档](../../docs/dev-notes/) - 开发相关文档 - -### 获取支持 - -遇到问题？ +## 注意事项 -1. � 运行健康检查：`./tools/maintenance/sage-maintenance.sh doctor` -1. 📋 查看状态：`./tools/maintenance/sage-maintenance.sh submodule status` -1. 📖 查阅文档：`./tools/maintenance/README.md`（本文件） -1. 🐛 提交 Issue：[GitHub Issues](https://github.com/intellistream/SAGE/issues) +1. 优先通过 `sage-maintenance.sh` 调用维护能力，不要直接调用 `helpers/` 中的脚本。 +1. 当前 README 仅描述现存且仍受支持的维护命令。 +1. 若遇到环境或仓库状态问题，先运行 `doctor`。 +1. SAGE 为 polyrepo 架构，跨仓库开发请在对应独立仓库中进行，不要把子仓库实现重新放回元仓库。 -______________________________________________________________________ +## 相关文档 -�💡 **快速提示：** 遇到问题先运行 `doctor`，它会告诉你该怎么做！ +- [DEVELOPER.md](../../DEVELOPER.md) +- [CONTRIBUTING.md](../../CONTRIBUTING.md) -🎨 **新功能：** 所有命令现在都有彩色输出，更容易识别状态！ +💡 建议：维护前先运行 `doctor`，清理后再运行项目级质量检查。 diff --git a/tools/maintenance/check_docs.sh b/tools/maintenance/check_docs.sh old mode 100755 new mode 100644 index de4ee28a38..84fcd44555 --- a/tools/maintenance/check_docs.sh +++ b/tools/maintenance/check_docs.sh @@ -1,104 +1,128 @@ #!/bin/bash # SAGE Documentation Quality Check Script -# This script runs comprehensive documentation quality checks +# Basic documentation checks for the current SAGE meta-repo layout -set -e +set -euo pipefail SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" PROJECT_ROOT="$(cd "$SCRIPT_DIR/../.." && pwd)" +GREEN='\033[0;32m' +YELLOW='\033[1;33m' +NC='\033[0m' + +cd "$PROJECT_ROOT" + echo "======================================================================" echo "📚 SAGE Documentation Quality Check" echo "======================================================================" echo "" -# Colors -GREEN='\033[0;32m' -YELLOW='\033[1;33m' -RED='\033[0;31m' -NC='\033[0m' # No Color +doc_roots=(README.md DEVELOPER.md CONTRIBUTING.md CHANGELOG.md) +[[ -d docs ]] && doc_roots+=(docs) +[[ -d examples ]] && doc_roots+=(examples) -cd "$PROJECT_ROOT" +collect_markdown_files() { + find "${doc_roots[@]}" -type f \( -name "*.md" -o -name "README*" \) 2>/dev/null | \ + grep -vE '(/\.git/|node_modules|dist|build/_deps|/\.sage/|/site/|/\.pytest_cache/)' || true +} -# 1. Check dev-notes documentation -echo "1️⃣ Checking dev-notes documentation..." -if python tools/devnotes_checker.py --all; then - echo -e "${GREEN}✅ Dev-notes check passed${NC}" -else - echo -e "${RED}❌ Dev-notes check failed${NC}" - exit 1 -fi -echo "" +MD_FILES=$(collect_markdown_files) -# 2. Check package README quality -echo "2️⃣ Checking package README quality..." -if python tools/package_readme_checker.py --all; then - echo -e "${GREEN}✅ Package README check passed${NC}" -else - echo -e "${YELLOW}⚠️ Some packages have low README quality${NC}" -fi +echo "1️⃣ Collecting documentation files..." +TOTAL_MD=$(printf '%s\n' "$MD_FILES" | sed '/^$/d' | wc -l) +echo " Found $TOTAL_MD markdown/doc files" echo "" -# 3. Check for broken links (basic check) -echo "3️⃣ Checking for common documentation issues..." - -# Check for placeholder text -echo " Checking for placeholder text..." -PLACEHOLDERS=$(grep -r "{[A-Z_]*}" docs/ packages/*/README.md 2>/dev/null | grep -v ".git" | grep -v "node_modules" || true) +echo "2️⃣ Checking for placeholder text..." +PLACEHOLDERS=$(printf '%s\n' "$MD_FILES" | xargs -r grep -nE '\{[A-Z_][A-Z0-9_]*\}' 2>/dev/null || true) if [ -z "$PLACEHOLDERS" ]; then - echo -e " ${GREEN}✅ No placeholders found${NC}" + echo -e " ${GREEN}✅ No placeholder text found${NC}" else - echo -e " ${YELLOW}⚠️ Found placeholder text:${NC}" - echo "$PLACEHOLDERS" | head -5 + echo -e " ${YELLOW}⚠️ Placeholder text detected:${NC}" + echo "$PLACEHOLDERS" | head -10 fi +echo "" -# Check for TODO/FIXME in documentation -echo " Checking for TODO/FIXME markers..." -TODOS=$(grep -r "TODO\|FIXME" docs/ examples/*/README.md packages/*/README.md 2>/dev/null | grep -v ".git" | grep -v "node_modules" | wc -l || true) -echo " Found $TODOS TODO/FIXME markers" +echo "3️⃣ Checking for TODO/FIXME markers..." +TODOS=$(printf '%s\n' "$MD_FILES" | xargs -r grep -nEi 'TODO|FIXME' 2>/dev/null || true) +TODO_COUNT=$(printf '%s\n' "$TODOS" | sed '/^$/d' | wc -l) +echo " Found $TODO_COUNT TODO/FIXME markers" +if [ -n "$TODOS" ]; then + echo "$TODOS" | head -10 +fi +echo "" -# Check for very short README files (< 10 lines) -echo " Checking for short README files..." -SHORT_READMES=$(find packages/ examples/ -name "README.md" -type f -exec sh -c 'lines=$(wc -l < "$1"); if [ "$lines" -lt 10 ]; then echo "$1: $lines lines"; fi' _ {} \; 2>/dev/null | grep -v "pytest_cache\|node_modules\|build\|vendor" || true) +echo "4️⃣ Checking for short README files..." +SHORT_READMES=$(find . -type f -name 'README*.md' 2>/dev/null | \ + grep -vE '(/\.git/|node_modules|/\.sage/|dist|build)' | \ + while read -r file; do + lines=$(wc -l < "$file") + if [ "$lines" -lt 10 ]; then + echo "$file: $lines lines" + fi + done || true) if [ -z "$SHORT_READMES" ]; then - echo -e " ${GREEN}✅ No unusually short READMEs${NC}" + echo -e " ${GREEN}✅ No unusually short README files${NC}" else echo -e " ${YELLOW}⚠️ Short README files found:${NC}" - echo "$SHORT_READMES" | head -5 + echo "$SHORT_READMES" | head -10 fi - echo "" -# 4. Statistics -echo "4️⃣ Documentation statistics:" -TOTAL_MD=$(find . -name "*.md" -type f 2>/dev/null | grep -v ".git\|node_modules\|build/_deps\|vendors/vllm\|.sage" | wc -l) -DEV_NOTES=$(find docs/dev-notes -name "*.md" -type f 2>/dev/null | wc -l) -PACKAGE_READMES=$(find packages/ -maxdepth 2 -name "README.md" -type f 2>/dev/null | wc -l) -EXAMPLE_READMES=$(find examples/ -name "README.md" -type f 2>/dev/null | wc -l) +echo "5️⃣ Computing statistics..." +ROOT_READMES=$(find . -maxdepth 2 -type f -name 'README*.md' 2>/dev/null | wc -l) +DOCS_COUNT=0 +EXAMPLE_DOCS_COUNT=0 +[[ -d docs ]] && DOCS_COUNT=$(find docs -type f -name '*.md' | wc -l) +[[ -d examples ]] && EXAMPLE_DOCS_COUNT=$(find examples -type f -name '*.md' | wc -l) echo " Total markdown files: $TOTAL_MD" -echo " Dev-notes documents: $DEV_NOTES" -echo " Package READMEs: $PACKAGE_READMES" -echo " Example READMEs: $EXAMPLE_READMES" +echo " Root/near-root READMEs: $ROOT_READMES" +echo " docs markdown: $DOCS_COUNT" +echo " example markdown: $EXAMPLE_DOCS_COUNT" echo "" -# 5. Generate report -echo "5️⃣ Generating quality report..." -REPORT_FILE="docs/dev-notes/ci-cd/DOCUMENTATION_CHECK_REPORT_$(date +%Y%m%d).md" +echo "6️⃣ Generating report..." +REPORT_DIR="artifacts/reports" +mkdir -p "$REPORT_DIR" +REPORT_FILE="$REPORT_DIR/DOCUMENTATION_CHECK_REPORT_$(date +%Y%m%d).md" -python tools/package_readme_checker.py --all --report --output "$REPORT_FILE" -echo -e "${GREEN}✅ Report generated: $REPORT_FILE${NC}" -echo "" +cat > "$REPORT_FILE" <&1 | tee /tmp/mypy_after_format.txt ERROR_COUNT=$(grep -c "error:" /tmp/mypy_after_format.txt || echo "0") diff --git a/tools/maintenance/git-hooks/pre-push b/tools/maintenance/git-hooks/pre-push new file mode 100755 index 0000000000..e87ba44b78 --- /dev/null +++ b/tools/maintenance/git-hooks/pre-push @@ -0,0 +1,303 @@ +#!/bin/bash +# Pre-push hook — version check + post-push PyPI publish +# +# Flow: +# 1. Block direct push to main branch +# 2. Only proceed for main-dev pushes (other branches: skip cleanly) +# 3. Auto-bump version if unchanged in recent commits +# 4. Exit 0 → git push proceeds immediately +# 5. Background job publishes to PyPI after push finishes (if token present) +# +# Version bumping: auto-increments last segment if version unchanged. +# This hook may create one version-bump commit before the push. + +# Recursion guard +if [ "${_SAGE_PP_RUNNING:-0}" = "1" ]; then exit 0; fi + +# Publish mode: "public" → publish openly; "private" → internal only +PUBLISH_MODE=public + +# Colors +RED='\033[0;31m' +YELLOW='\033[1;33m' +GREEN='\033[0;32m' +CYAN='\033[0;36m' +BLUE='\033[0;34m' +DIM='\033[2m' +NC='\033[0m' + +WANT_PUBLISH=false +REPO_DIR="$(pwd)" +REPO_NAME="$(basename "$REPO_DIR")" +PUBLISH_LOG="/tmp/${REPO_NAME}-publish-$$.log" +PUSHING_MAIN_DEV=false +PUSH_LOCAL_SHA="" + +# --- Block direct push to main; detect main-dev push --- +while read -r local_ref local_sha remote_ref remote_sha; do + if [ "$local_ref" = "refs/heads/main" ] || [ "$remote_ref" = "refs/heads/main" ]; then + echo -e "${RED}✗ Direct push to main is forbidden${NC}" + echo -e "${YELLOW} Please push to main-dev first, then merge via PR.${NC}" + exit 1 + fi + if [ "$local_ref" = "refs/heads/main-dev" ] || [ "$remote_ref" = "refs/heads/main-dev" ]; then + PUSHING_MAIN_DEV=true + PUSH_LOCAL_SHA="$local_sha" + fi +done + +# Version check + publish only applies to main-dev pushes +if [ "$PUSHING_MAIN_DEV" != true ]; then + exit 0 +fi + +# Safe read: falls back to default if /dev/tty is unavailable (SSH, IDE, etc.) +safe_read() { + local varname="$1" + local default="$2" + if [ -t 0 ] || [ -c /dev/tty ] 2>/dev/null; then + read -r "$varname" /dev/null || eval "$varname=\"$default\"" + else + eval "$varname=\"$default\"" + fi +} + +# Find all _version.py files in repo (optional: pass base dir as $1, default is BUILD_DIR) +find_version_files() { + local base="${1:-${BUILD_DIR:-.}}" + find "$base" -maxdepth 5 -name '_version.py' \ + -not -path '*/node_modules/*' \ + -not -path '*/.git/*' \ + -not -path '*/dist/*' \ + -not -path '*/.egg-info/*' \ + -not -path '*/build/*' \ + 2>/dev/null +} + +# Update version in pyproject.toml (static) and/or _version.py (dynamic) +update_version() { + local old_version="$1" + local new_version="$2" + local updated=false + + # Use absolute PYPROJECT_FILE path + if [ -f "${PYPROJECT_FILE}" ] && grep -q '^version = "' "${PYPROJECT_FILE}" 2>/dev/null; then + sed -i "s/version = \"${old_version}\"/version = \"${new_version}\"/" "${PYPROJECT_FILE}" + git add "${PYPROJECT_FILE}" + updated=true + fi + + while IFS= read -r VERSION_FILE; do + if [ -f "$VERSION_FILE" ] && grep -q '__version__ = "' "$VERSION_FILE" 2>/dev/null; then + sed -i "s/__version__ = \"${old_version}\"/__version__ = \"${new_version}\"/" "$VERSION_FILE" + git add "$VERSION_FILE" + updated=true + fi + done < <(find_version_files) + + if [ "$updated" = false ]; then + echo -e "${RED}✗ Failed to update version (no version file found)${NC}" + return 1 + fi + + return 0 +} + +# Auto-increment the last version component by 1 (X.Y.Z → X.Y.Z+1, X.Y.Z.N → X.Y.Z.N+1) +bump_patch() { + local v="$1" + local IFS='.' + read -ra parts <<< "$v" + local last_idx=$(( ${#parts[@]} - 1 )) + parts[$last_idx]=$(( parts[$last_idx] + 1 )) + echo "${parts[*]}" +} + +# Check if PyPI token is available (no interaction needed) +has_pypi_token() { + if [ -n "${TWINE_PASSWORD:-}" ] || [ -n "${TWINE_TOKEN:-}" ] || [ -n "${UV_PUBLISH_TOKEN:-}" ]; then + return 0 + fi + + if [ -f "$HOME/.pypirc" ]; then + if awk ' + BEGIN { in_pypi = 0; found = 0 } + /^[[:space:]]*\[pypi\][[:space:]]*$/ { in_pypi = 1; next } + /^[[:space:]]*\[[^]]+\][[:space:]]*$/ { in_pypi = 0 } + in_pypi && /^[[:space:]]*(password|token)[[:space:]]*=[[:space:]]*.+$/ { found = 1; exit } + END { exit(found ? 0 : 1) } + ' "$HOME/.pypirc" 2>/dev/null; then + return 0 + fi + fi + + return 1 +} + +# Resolve publisher CLI command (binary first, then python -m fallback) +resolve_publisher_cmd() { + if command -v sage-pypi-publisher &> /dev/null; then + PUBLISH_CMD=(sage-pypi-publisher) + return 0 + fi + + if command -v python3 &> /dev/null; then + if python3 - <<'PY_HOOK' >/dev/null 2>&1 +import importlib.util +import sys +sys.exit(0 if importlib.util.find_spec("pypi_publisher") else 1) +PY_HOOK + then + PUBLISH_CMD=(python3 -m pypi_publisher.cli) + return 0 + fi + fi + + return 1 +} + +# Schedule PyPI publish as background job after push completes +schedule_publish() { + local version="$1" + local package="$2" + + if ! resolve_publisher_cmd; then + echo -e "${YELLOW}⚠ sage-pypi-publisher not found, skipping auto-publish${NC}" + echo -e "${DIM} Install: python -m pip install isage-pypi-publisher${NC}" + return + fi + + echo -e "${GREEN}📦 PyPI publish scheduled (runs after push)${NC}" + echo -e "${DIM} Log: tail -f ${PUBLISH_LOG}${NC}" + + ( + GIT_PID="$PPID" + while kill -0 "$GIT_PID" 2>/dev/null; do + sleep 1 + done + sleep 1 + + cd "$BUILD_DIR" || exit 1 + + # Verify the push actually landed before publishing + remote_sha="$(git ls-remote origin "refs/heads/main-dev" | awk '{print $1}')" + if [ -n "$PUSH_LOCAL_SHA" ] && [ -n "$remote_sha" ] && [ "$remote_sha" != "$PUSH_LOCAL_SHA" ]; then + { + echo "⏭ Skip publish: push SHA mismatch" + echo " expected=${PUSH_LOCAL_SHA}" + echo " remote=${remote_sha}" + } >> "$PUBLISH_LOG" 2>&1 + exit 0 + fi + + { + echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" + echo "📦 Post-push: Building ${package} ${version}..." + echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" + + rm -rf dist/ build/ *.egg-info 2>/dev/null || true + + if "${PUBLISH_CMD[@]}" build . --upload --no-dry-run --mode "${PUBLISH_MODE}"; then + echo "" + echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" + echo "✓ Successfully uploaded ${package} ${version} to PyPI" + echo "🔗 https://pypi.org/project/${package}/${version}/" + echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" + else + _pub_exit=$? + echo "" + echo "✗ Failed to upload to PyPI (exit code: ${_pub_exit})" + echo " Re-run: sage-pypi-publisher build . --upload --no-dry-run --mode ${PUBLISH_MODE}" + fi + } >> "$PUBLISH_LOG" 2>&1 + + if grep -q "Successfully uploaded" "$PUBLISH_LOG" 2>/dev/null; then + echo -e "\n${GREEN}✓ PyPI: ${package} ${version} published${NC}" + else + echo -e "\n${RED}✗ PyPI publish failed. See: ${PUBLISH_LOG}${NC}" + fi + ) & + disown +} + +# --- Main Logic --- + +BUILD_DIR="$REPO_DIR" +PYPROJECT_FILE="${BUILD_DIR}/pyproject.toml" +if [ ! -f "$PYPROJECT_FILE" ]; then + exit 0 +fi + +PACKAGE_NAME=$(grep -oP '^name = "\K[^"]+' "$PYPROJECT_FILE" 2>/dev/null || echo "unknown") + +# Get version: prefer _version.py (dynamic), fallback to pyproject.toml (static) +# Search for _version.py in repo (src-layout: src/sage/_version.py) +CURRENT_VERSION="" +while IFS= read -r VERSION_FILE; do + if [ -f "$VERSION_FILE" ]; then + CURRENT_VERSION=$(grep -oP '__version__ = "\K[^"]+' "$VERSION_FILE" 2>/dev/null || true) + [ -n "$CURRENT_VERSION" ] && break + fi +done < <(find "$BUILD_DIR" -maxdepth 5 -name '_version.py' \ + -not -path '*/node_modules/*' -not -path '*/.git/*' \ + -not -path '*/dist/*' -not -path '*/.egg-info/*' -not -path '*/build/*' \ + 2>/dev/null) + +if [ -z "$CURRENT_VERSION" ]; then + CURRENT_VERSION=$(grep -oP '^version = "\K[^"]+' "$PYPROJECT_FILE" 2>/dev/null || true) +fi + +if [ -z "$CURRENT_VERSION" ] || [ "$CURRENT_VERSION" = "unknown" ]; then + exit 0 +fi + +# --- Version / Publish --- +# post-commit already bumps the BUILD digit on every commit, so by the time +# we push the version is always current. The only thing pre-push needs to +# guard against is re-pushing a version that was already published to PyPI +# (e.g. after a failed push/publish on a previous attempt). + +# Quick PyPI check (5s timeout, non-blocking) +# Exit codes: 0=version exists on PyPI, 1=not found, 2=network/other error +PYPI_CHECK_RESULT=1 +if [ "$PACKAGE_NAME" != "unknown" ] && command -v python3 &>/dev/null; then + python3 - "$PACKAGE_NAME" "$CURRENT_VERSION" <<'PY' && PYPI_CHECK_RESULT=0 || PYPI_CHECK_RESULT=$? +import json, sys, urllib.request +try: + with urllib.request.urlopen(f"https://pypi.org/pypi/{sys.argv[1]}/json", timeout=5) as r: + sys.exit(0 if sys.argv[2] in json.load(r).get("releases", {}) else 1) +except urllib.error.HTTPError as e: + sys.exit(1 if e.code == 404 else 2) +except (urllib.error.URLError, OSError, TimeoutError): + sys.exit(2) +except Exception: + sys.exit(2) +PY +fi + +if [ "$PYPI_CHECK_RESULT" -eq 0 ]; then + new_version=$(bump_patch "$CURRENT_VERSION") + echo -e "${RED}✗ ${PACKAGE_NAME} ${CURRENT_VERSION} already exists on PyPI${NC}" + echo -e "${YELLOW} To avoid hidden pre-push commits (which require pushing twice), pre-push will not auto-commit.${NC}" + echo -e "${CYAN} Please bump version to something like: ${new_version}${NC}" + echo -e "${DIM} Tip: ensure hooks are installed via ./quickstart.sh so post-commit bumps version automatically.${NC}" + exit 1 +elif [ "$PYPI_CHECK_RESULT" -eq 2 ]; then + echo -e "${DIM}(PyPI check skipped — network/timeout)${NC}" +fi + +echo -e "${GREEN}✓ [${REPO_NAME}] Version: ${CURRENT_VERSION}${NC}" + +if has_pypi_token; then + echo -e "${BLUE}📦 Auto-publishing ${CURRENT_VERSION} to PyPI (token found)...${NC}" + WANT_PUBLISH=true +else + echo -e "${DIM} (no PyPI token — skipping publish. Add token to ~/.pypirc or set TWINE_PASSWORD)${NC}" +fi + +if [ "$WANT_PUBLISH" = true ]; then + schedule_publish "$CURRENT_VERSION" "$PACKAGE_NAME" +fi + +# Exit 0 → push proceeds immediately, never blocked by build/upload +exit 0 diff --git a/tools/maintenance/helpers/pre_install_cleanup.sh b/tools/maintenance/helpers/pre_install_cleanup.sh index d464339fb4..4110d79d86 100755 --- a/tools/maintenance/helpers/pre_install_cleanup.sh +++ b/tools/maintenance/helpers/pre_install_cleanup.sh @@ -14,9 +14,44 @@ NC='\033[0m' echo -e "${BLUE}🧹 安装前清理...${NC}" +# 统一 pip 命令（优先使用 quickstart 注入的 PIP_CMD） +PYTHON_CMD="${PYTHON_CMD:-python3}" +PIP_CMD="${PIP_CMD:-$PYTHON_CMD -m pip}" + # 计数器 removed_count=0 +# 清理当前主仓直接产出的历史安装（避免误删 benchmark/docs/isagellm/zoo 等独立包） +echo -e "${DIM}清理当前 SAGE 主仓的直接安装包...${NC}" + +collect_direct_sage_packages_to_remove() { + local installed_lines="$1" + local removable_packages=( + "isage" + "sage" + "intsage" + ) + + local package_name + for package_name in "${removable_packages[@]}"; do + echo "$installed_lines" | awk -F'==' -v pkg="$package_name" '$1==pkg {print $1}' + done | awk '!seen[$0]++' +} + +installed_packages=$(eval "$PIP_CMD list --format=freeze" 2>/dev/null || true) +package_names=$(collect_direct_sage_packages_to_remove "$installed_packages" | tr '\n' ' ') + +if [ -n "$package_names" ]; then + echo -e "${DIM}将卸载: $package_names${NC}" + eval "$PIP_CMD uninstall -y $package_names" >/dev/null 2>&1 || true + pkg_count=$(echo "$package_names" | wc -w) + echo -e "${GREEN}✅ 清理了 $pkg_count 个主仓历史安装包${NC}" + removed_count=$((removed_count + pkg_count)) +else + echo -e "${DIM}未检测到需要清理的主仓安装包${NC}" +fi +echo -e "${DIM}保留独立包: isagellm / isage-benchmark / sage-pub-docs / zoo packages${NC}" + # 清理 Python 缓存文件 echo -e "${DIM}清理 __pycache__ 目录...${NC}" pycache_count=$(find . -name "__pycache__" -type d 2>/dev/null | wc -l) @@ -58,13 +93,13 @@ if [ -d "dist" ]; then removed_count=$((removed_count + 1)) fi -# 清理空目录 (排除.git目录和docs-public子模块) +# 清理空目录 (排除.git目录) echo -e "${DIM}清理空目录...${NC}" -empty_dirs=$(find . -type d -empty -not -path "./.git/*" -not -path "./docs-public" -not -path "./.sage/*" 2>/dev/null | wc -l) +empty_dirs=$(find . -type d -empty -not -path "./.git/*" -not -path "./.sage/*" 2>/dev/null | wc -l) if [ "$empty_dirs" -gt 0 ]; then # 多次运行以处理嵌套的空目录 for i in {1..5}; do - find . -type d -empty -not -path "./.git/*" -not -path "./docs-public" -not -path "./.sage/*" -delete 2>/dev/null || true + find . -type d -empty -not -path "./.git/*" -not -path "./.sage/*" -delete 2>/dev/null || true done echo -e "${GREEN}✅ 删除了 $empty_dirs 个空目录${NC}" removed_count=$((removed_count + empty_dirs)) @@ -73,8 +108,8 @@ fi # 清理 pip 缓存 (可选，占用较大空间) if [ "${CLEAN_PIP_CACHE:-false}" = "true" ]; then echo -e "${DIM}清理 pip 缓存...${NC}" - if command -v pip3 >/dev/null 2>&1; then - pip3 cache purge 2>/dev/null || true + if eval "$PIP_CMD cache --help" >/dev/null 2>&1; then + eval "$PIP_CMD cache purge" 2>/dev/null || true echo -e "${GREEN}✅ pip 缓存已清理${NC}" fi fi diff --git a/tools/maintenance/helpers/quick_cleanup.sh b/tools/maintenance/helpers/quick_cleanup.sh index 2c2e05de89..ccc880eb1f 100755 --- a/tools/maintenance/helpers/quick_cleanup.sh +++ b/tools/maintenance/helpers/quick_cleanup.sh @@ -83,13 +83,13 @@ if [ -d "sage_ext" ]; then echo "✅ 清理了 sage_ext 子模块的构建文件" fi -# 清理空目录 (排除.git目录和docs-public子模块) +# 清理空目录 (排除.git目录) echo "清理空目录..." -empty_dirs=$(find . -type d -empty -not -path "./.git/*" -not -path "./docs-public" 2>/dev/null | wc -l) +empty_dirs=$(find . -type d -empty -not -path "./.git/*" 2>/dev/null | wc -l) if [ $empty_dirs -gt 0 ]; then # 多次运行以处理嵌套的空目录 for i in {1..5}; do - find . -type d -empty -not -path "./.git/*" -not -path "./docs-public" -delete 2>/dev/null || true + find . -type d -empty -not -path "./.git/*" -delete 2>/dev/null || true done echo "✅ 删除了 $empty_dirs 个空目录" removed_count=$((removed_count + empty_dirs)) diff --git a/tools/maintenance/migrate_studio_subdirs.sh b/tools/maintenance/migrate_studio_subdirs.sh deleted file mode 100755 index 5fd8bf02a1..0000000000 --- a/tools/maintenance/migrate_studio_subdirs.sh +++ /dev/null @@ -1,206 +0,0 @@ -#!/bin/bash -# migrate_studio_subdirs.sh -# 将 sage-studio/tests 子目录中的测试移到 unit/integration 目录 - -set -e - -SAGE_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)" -cd "$SAGE_ROOT" - -echo "==========================================" -echo "sage-studio 子目录测试迁移脚本" -echo "==========================================" -echo "" - -# 颜色定义 -RED='\033[0;31m' -GREEN='\033[0;32m' -YELLOW='\033[1;33m' -NC='\033[0m' # No Color - -studio_tests_dir="packages/sage-studio/tests" - -# ============================================ -# services/ 目录 - 大部分是单元测试 -# ============================================ -echo -e "${YELLOW}迁移 services/ 测试到 unit/services/${NC}" -echo "" - -mkdir -p "$studio_tests_dir/unit/services" - -declare -a service_files=( - "test_auth_service.py" - "test_vector_store.py" - "test_file_upload_service.py" - "test_stream_handler.py" - "test_knowledge_manager.py" - "test_docs_processor.py" - "test_workflow_generator.py" - "test_agent_orchestrator.py" - "test_researcher_agent.py" - "test_finetune_manager.py" -) - -# 检查是否有 integration 类型的服务测试 -declare -a service_integration_files=( - "test_chat_routes.py" # 可能涉及路由集成 -) - -for file in "${service_files[@]}"; do - src="$studio_tests_dir/services/$file" - dst="$studio_tests_dir/unit/services/$file" - - if [[ -f "$src" ]]; then - echo -n " $file -> unit/services/ ... " - if git mv "$src" "$dst" 2>/dev/null; then - echo -e "${GREEN}✓${NC}" - else - echo -e "${RED}✗${NC}" - fi - fi -done - -# 可能需要单独处理的集成测试 -for file in "${service_integration_files[@]}"; do - src="$studio_tests_dir/services/$file" - if [[ -f "$src" ]]; then - echo -e " ${YELLOW}注意: $file 可能是集成测试，需要手动检查${NC}" - fi -done - -echo "" - -# ============================================ -# config/ 目录 - 单元测试 -# ============================================ -echo -e "${YELLOW}迁移 config/ 测试到 unit/config/${NC}" -echo "" - -mkdir -p "$studio_tests_dir/unit/config" - -declare -a config_files=( - "test_api_uploads.py" - "test_backend_api.py" -) - -for file in "${config_files[@]}"; do - src="$studio_tests_dir/config/$file" - dst="$studio_tests_dir/unit/config/$file" - - if [[ -f "$src" ]]; then - echo -n " $file -> unit/config/ ... " - if git mv "$src" "$dst" 2>/dev/null; then - echo -e "${GREEN}✓${NC}" - else - echo -e "${RED}✗${NC}" - fi - fi -done - -echo "" - -# ============================================ -# tools/ 目录 - 单元测试 -# ============================================ -echo -e "${YELLOW}迁移 tools/ 测试到 unit/tools/${NC}" -echo "" - -mkdir -p "$studio_tests_dir/unit/tools" - -declare -a tools_files=( - "test_api_docs.py" - "test_arxiv_search.py" - "test_base.py" - "test_knowledge_search.py" -) - -for file in "${tools_files[@]}"; do - src="$studio_tests_dir/tools/$file" - dst="$studio_tests_dir/unit/tools/$file" - - if [[ -f "$src" ]]; then - echo -n " $file -> unit/tools/ ... " - if git mv "$src" "$dst" 2>/dev/null; then - echo -e "${GREEN}✓${NC}" - else - echo -e "${RED}✗${NC}" - fi - fi -done - -echo "" - -# ============================================ -# utils/ 目录 - 单元测试 -# ============================================ -echo -e "${YELLOW}迁移 utils/ 测试到 unit/utils/${NC}" -echo "" - -mkdir -p "$studio_tests_dir/unit/utils" - -declare -a utils_files=( - "test_gpu_check.py" -) - -for file in "${utils_files[@]}"; do - src="$studio_tests_dir/utils/$file" - dst="$studio_tests_dir/unit/utils/$file" - - if [[ -f "$src" ]]; then - echo -n " $file -> unit/utils/ ... " - if git mv "$src" "$dst" 2>/dev/null; then - echo -e "${GREEN}✓${NC}" - else - echo -e "${RED}✗${NC}" - fi - fi -done - -echo "" - -# ============================================ -# 清理空目录 -# ============================================ -echo -e "${YELLOW}清理空目录${NC}" -echo "" - -for dir in services config tools utils; do - full_dir="$studio_tests_dir/$dir" - if [[ -d "$full_dir" ]]; then - # 检查是否为空（排除 __pycache__ 和 __init__.py） - remaining=$(find "$full_dir" -type f ! -name "__init__.py" ! -path "*/__pycache__/*" | wc -l) - if [[ $remaining -eq 0 ]]; then - echo -e " 移除空目录: $dir/" - rm -rf "$full_dir" - else - echo -e " ${YELLOW}保留 $dir/ (还有 $remaining 个文件)${NC}" - fi - fi -done - -echo "" - -# ============================================ -# 创建 __init__.py 文件 -# ============================================ -echo -e "${YELLOW}创建 __init__.py 文件${NC}" -echo "" - -for subdir in unit/services unit/config unit/tools unit/utils; do - init_file="$studio_tests_dir/$subdir/__init__.py" - if [[ ! -f "$init_file" ]]; then - echo '"""Tests for sage-studio."""' > "$init_file" - git add "$init_file" - echo -e " ${GREEN}✓${NC} 创建 $subdir/__init__.py" - fi -done - -echo "" -echo "==========================================" -echo "迁移完成" -echo "==========================================" -echo "" -echo "下一步：" -echo " 1. 手动检查 services/test_chat_routes.py 是否应该移到 integration/" -echo " 2. 运行测试: sage-dev project test" -echo " 3. 提交更改" diff --git a/tools/maintenance/optimize_git.sh b/tools/maintenance/optimize_git.sh deleted file mode 100755 index 6c1ca7819d..0000000000 --- a/tools/maintenance/optimize_git.sh +++ /dev/null @@ -1,41 +0,0 @@ -#!/bin/bash -# Git 克隆优化配置脚本 -# 为 SAGE 项目优化 Git submodule 克隆速度 - -set -e - -SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" -SAGE_ROOT="$(cd "$SCRIPT_DIR/../.." && pwd)" - -echo "🚀 优化 Git 配置以提升 submodule 克隆速度" -echo "" - -# 1. 配置并行克隆数量 -echo "📦 配置并行克隆..." -git config --local submodule.fetchJobs 4 -echo " ✅ 设置并行克隆数: 4" - -# 2. 配置 HTTP 缓冲区（解决大文件克隆问题） -echo "📦 配置 HTTP 缓冲区..." -git config --local http.postBuffer 524288000 # 500MB -echo " ✅ HTTP 缓冲区: 500MB" - -# 3. 配置克隆深度（默认浅克隆） -echo "📦 配置默认克隆深度..." -git config --local submodule.recurse true -echo " ✅ 启用递归 submodule" - -# 4. 显示当前配置 -echo "" -echo "📋 当前 Git 配置：" -echo " 并行克隆数: $(git config --local submodule.fetchJobs)" -echo " HTTP 缓冲区: $(git config --local http.postBuffer) bytes" -echo " 递归 submodule: $(git config --local submodule.recurse)" - -echo "" -echo "✅ Git 优化配置完成！" -echo "" -echo "提示：" -echo " - 使用 './manage.sh' 克隆 submodules 将自动使用并行模式" -echo " - 首次克隆 8 个仓库预计需要 2-5 分钟" -echo " - 如需更快速度，可考虑使用 Git 镜像或代理" diff --git a/tools/maintenance/reorganize_test_files.sh b/tools/maintenance/reorganize_test_files.sh deleted file mode 100755 index 2f2a002648..0000000000 --- a/tools/maintenance/reorganize_test_files.sh +++ /dev/null @@ -1,213 +0,0 @@ -#!/bin/bash -# reorganize_test_files.sh -# 重组测试文件：重命名 *_test.py 为 test_*.py，并将 sage-studio 测试分类到 unit/integration - -set -e - -SAGE_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)" -cd "$SAGE_ROOT" - -echo "==========================================" -echo "测试文件重组脚本" -echo "==========================================" -echo "" - -# 颜色定义 -RED='\033[0;31m' -GREEN='\033[0;32m' -YELLOW='\033[1;33m' -NC='\033[0m' # No Color - -# 统计变量 -total_renamed=0 -total_moved=0 -errors=0 - -# ============================================ -# 第1步：重命名 *_test.py 为 test_*.py -# ============================================ -echo -e "${YELLOW}步骤 1/4: 重命名 *_test.py 文件为 test_*.py${NC}" -echo "" - -declare -a files_to_rename=( - "packages/sage-kernel/tests/unit/core/function/join_test.py:test_join.py" - "packages/sage-kernel/tests/unit/core/function/connected_keyby_test.py:test_connected_keyby.py" - "packages/sage-kernel/tests/unit/core/function/keyby_test.py:test_keyby.py" - "packages/sage-kernel/tests/unit/core/function/flatmap_test.py:test_flatmap.py" - "packages/sage-kernel/tests/unit/core/function/comap_test.py:test_comap.py" - "packages/sage-kernel/tests/unit/core/function/filter_test.py:test_filter.py" - "packages/sage-kernel/tests/unit/kernel/simple_task_context_routing_test.py:test_simple_task_context_routing.py" - "packages/sage-middleware/tests/operators/tools/nature_news_fetcher_test.py:test_nature_news_fetcher.py" - "packages/sage-middleware/tests/operators/tools/arxiv_paper_searcher_test.py:test_arxiv_paper_searcher.py" - "packages/sage-middleware/tests/operators/tools/image_captioner_test.py:test_image_captioner.py" - "packages/sage-middleware/tests/operators/tools/url_text_extractor_test.py:test_url_text_extractor.py" - "packages/sage-middleware/tests/operators/tools/text_detector_test.py:test_text_detector.py" - "packages/sage-libs/tests/lib/io/sink_test.py:test_sink.py" -) - -for entry in "${files_to_rename[@]}"; do - old_path="${entry%%:*}" - new_name="${entry##*:}" - - if [[ -f "$old_path" ]]; then - dir_path="$(dirname "$old_path")" - new_path="$dir_path/$new_name" - - echo -n " 重命名: $old_path -> $new_name ... " - if git mv "$old_path" "$new_path" 2>/dev/null; then - echo -e "${GREEN}✓${NC}" - ((total_renamed++)) - else - echo -e "${RED}✗${NC}" - ((errors++)) - fi - else - echo -e " ${YELLOW}跳过: $old_path (文件不存在)${NC}" - fi -done - -echo "" -echo -e "重命名完成: ${GREEN}$total_renamed${NC} 个文件" -echo "" - -# ============================================ -# 第2步：创建 sage-studio 测试目录结构 -# ============================================ -echo -e "${YELLOW}步骤 2/4: 创建 sage-studio/tests/ 子目录${NC}" -echo "" - -studio_tests_dir="packages/sage-studio/tests" - -if [[ -d "$studio_tests_dir" ]]; then - mkdir -p "$studio_tests_dir/unit" - mkdir -p "$studio_tests_dir/integration" - echo -e " ${GREEN}✓${NC} 创建 $studio_tests_dir/unit/" - echo -e " ${GREEN}✓${NC} 创建 $studio_tests_dir/integration/" -else - echo -e " ${RED}✗${NC} sage-studio/tests/ 目录不存在" - ((errors++)) -fi - -echo "" - -# ============================================ -# 第3步：移动测试文件到 unit/integration -# ============================================ -echo -e "${YELLOW}步骤 3/4: 移动 sage-studio 测试文件${NC}" -echo "" - -# 移动到 integration/ -declare -a integration_files=( - "test_e2e_integration.py" - "test_agent_step.py" - "test_studio_cli.py" -) - -echo "移动到 integration/:" -for file in "${integration_files[@]}"; do - src="$studio_tests_dir/$file" - dst="$studio_tests_dir/integration/$file" - - if [[ -f "$src" ]]; then - echo -n " $file ... " - if git mv "$src" "$dst" 2>/dev/null; then - echo -e "${GREEN}✓${NC}" - ((total_moved++)) - else - echo -e "${RED}✗${NC}" - ((errors++)) - fi - else - echo -e " ${YELLOW}跳过: $file (文件不存在)${NC}" - fi -done - -echo "" - -# 移动到 unit/ -declare -a unit_files=( - "test_models.py" - "test_pipeline_builder.py" - "test_node_registry.py" -) - -echo "移动到 unit/:" -for file in "${unit_files[@]}"; do - src="$studio_tests_dir/$file" - dst="$studio_tests_dir/unit/$file" - - if [[ -f "$src" ]]; then - echo -n " $file ... " - if git mv "$src" "$dst" 2>/dev/null; then - echo -e "${GREEN}✓${NC}" - ((total_moved++)) - else - echo -e "${RED}✗${NC}" - ((errors++)) - fi - else - echo -e " ${YELLOW}跳过: $file (文件不存在)${NC}" - fi -done - -echo "" - -# ============================================ -# 第4步：检查并报告 services/ 和 tools/ 子目录 -# ============================================ -echo -e "${YELLOW}步骤 4/4: 检查 sage-studio/tests/ 子目录${NC}" -echo "" - -if [[ -d "$studio_tests_dir/services" ]]; then - service_count=$(find "$studio_tests_dir/services" -name "test_*.py" | wc -l) - echo -e " ${YELLOW}注意:${NC} services/ 目录包含 $service_count 个测试文件" - echo " 建议根据测试类型移动到 unit/ 或 integration/" -fi - -if [[ -d "$studio_tests_dir/tools" ]]; then - tools_count=$(find "$studio_tests_dir/tools" -name "test_*.py" | wc -l) - echo -e " ${YELLOW}注意:${NC} tools/ 目录包含 $tools_count 个测试文件" - echo " 建议根据测试类型移动到 unit/ 或 integration/" -fi - -if [[ -d "$studio_tests_dir/config" ]]; then - config_count=$(find "$studio_tests_dir/config" -name "test_*.py" | wc -l) - echo -e " ${YELLOW}注意:${NC} config/ 目录包含 $config_count 个测试文件" - echo " 建议根据测试类型移动到 unit/ 或 integration/" -fi - -if [[ -d "$studio_tests_dir/utils" ]]; then - utils_count=$(find "$studio_tests_dir/utils" -name "test_*.py" | wc -l) - echo -e " ${YELLOW}注意:${NC} utils/ 目录包含 $utils_count 个测试文件" - echo " 建议根据测试类型移动到 unit/ 或 integration/" -fi - -echo "" - -# ============================================ -# 总结 -# ============================================ -echo "==========================================" -echo "重组完成" -echo "==========================================" -echo "" -echo -e "统计:" -echo -e " 重命名文件: ${GREEN}$total_renamed${NC}" -echo -e " 移动文件: ${GREEN}$total_moved${NC}" -if [[ $errors -gt 0 ]]; then - echo -e " 错误: ${RED}$errors${NC}" -fi -echo "" - -if [[ $errors -eq 0 ]]; then - echo -e "${GREEN}✓ 所有操作成功完成！${NC}" - echo "" - echo "下一步：" - echo " 1. 运行测试验证: sage-dev project test" - echo " 2. 检查 sage-studio/tests/services, tools, config, utils 目录" - echo " 3. 提交更改: git commit -m 'refactor(tests): reorganize test files naming and structure'" - exit 0 -else - echo -e "${RED}✗ 部分操作失败，请检查错误信息${NC}" - exit 1 -fi diff --git a/tools/maintenance/sage-maintenance.sh b/tools/maintenance/sage-maintenance.sh index fa697986a4..7798f3c62e 100755 --- a/tools/maintenance/sage-maintenance.sh +++ b/tools/maintenance/sage-maintenance.sh @@ -208,11 +208,13 @@ run_doctor() { echo -e "${BLUE}4. 检查构建产物...${NC}" # 使用 timeout 防止 find 命令卡住，限制搜索范围以提高速度 local build_dirs=0 + local build_find_cmd=(find . + \( -path "./.git" -o -path "./.github" \) -prune -o + -type d \( -name "dist" -o -name "build" -o -name "*.egg-info" \) -print) if command -v timeout &> /dev/null; then - build_dirs=$(timeout 5 find packages -maxdepth 2 -type d \( -name "dist" -o -name "build" -o -name "*.egg-info" \) 2>/dev/null | wc -l || echo "0") + build_dirs=$(timeout 5 "${build_find_cmd[@]}" 2>/dev/null | wc -l || echo "0") else - # 没有 timeout 命令时，只检查 packages 目录 - build_dirs=$(find packages -maxdepth 2 -type d \( -name "dist" -o -name "build" -o -name "*.egg-info" \) 2>/dev/null | wc -l || echo "0") + build_dirs=$("${build_find_cmd[@]}" 2>/dev/null | wc -l || echo "0") fi if [ "$build_dirs" -gt 0 ]; then diff --git a/tools/maintenance/sync-cluster-code.sh b/tools/maintenance/sync-cluster-code.sh deleted file mode 100755 index 26f24d4efe..0000000000 --- a/tools/maintenance/sync-cluster-code.sh +++ /dev/null @@ -1,327 +0,0 @@ -#!/bin/bash -# ============================================================================== -# SAGE Cluster Code Sync Script -# 同步 head 节点代码到所有 worker 节点 -# -# 用法: -# ./tools/maintenance/sync-cluster-code.sh # 同步所有包 -# ./tools/maintenance/sync-cluster-code.sh --quick # 快速同步（仅运行时关键包） -# ./tools/maintenance/sync-cluster-code.sh --package sage-kernel # 同步指定包 -# ./tools/maintenance/sync-cluster-code.sh --dry-run # 仅显示将执行的命令 -# ./tools/maintenance/sync-cluster-code.sh --clean-logs # 清理所有节点的日志文件 -# -# ============================================================================== - -set -e - -# 颜色定义 -RED='\033[0;31m' -GREEN='\033[0;32m' -YELLOW='\033[1;33m' -BLUE='\033[0;34m' -NC='\033[0m' # No Color - -# 默认配置 -SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" -SAGE_ROOT="$(cd "$SCRIPT_DIR/../.." && pwd)" -CLUSTER_CONFIG="$SAGE_ROOT/config/cluster.yaml" - -# 运行时关键包（--quick 模式） -QUICK_PACKAGES=( - "sage-platform" - "sage-kernel" - "sage-common" -) - -# 所有包 -ALL_PACKAGES=( - "sage-common" - "sage-platform" - "sage-kernel" - "sage-libs" - "sage-middleware" - "sage-apps" - "sage-benchmark" - "sage-cli" - "sage-tools" -) - -# 解析命令行参数 -DRY_RUN=false -QUICK_MODE=false -SPECIFIC_PACKAGE="" -VERBOSE=false -CLEAN_LOGS=false - -while [[ $# -gt 0 ]]; do - case $1 in - --dry-run) - DRY_RUN=true - shift - ;; - --quick|-q) - QUICK_MODE=true - shift - ;; - --package|-p) - SPECIFIC_PACKAGE="$2" - shift 2 - ;; - --verbose|-v) - VERBOSE=true - shift - ;; - --clean-logs) - CLEAN_LOGS=true - shift - ;; - --help|-h) - echo "Usage: $0 [OPTIONS]" - echo "" - echo "Options:" - echo " --quick, -q Quick sync (sage-platform, sage-kernel, sage-common only)" - echo " --package, -p NAME Sync specific package only" - echo " --dry-run Show commands without executing" - echo " --clean-logs Clean log files on all nodes (~/.sage/logs)" - echo " --verbose, -v Verbose output" - echo " --help, -h Show this help" - echo "" - echo "Examples:" - echo " $0 # Sync all packages" - echo " $0 --quick # Sync runtime-critical packages" - echo " $0 -p sage-kernel # Sync only sage-kernel" - exit 0 - ;; - *) - echo -e "${RED}Unknown option: $1${NC}" - exit 1 - ;; - esac -done - -# 从 cluster.yaml 解析 worker 节点 -parse_workers() { - if [[ ! -f "$CLUSTER_CONFIG" ]]; then - echo -e "${RED}Error: Cluster config not found: $CLUSTER_CONFIG${NC}" - exit 1 - fi - - # 使用 Python 解析 YAML（更可靠） - python3 -c " -import yaml -with open('$CLUSTER_CONFIG') as f: - config = yaml.safe_load(f) -workers = config.get('ssh', {}).get('workers', []) -user = config.get('ssh', {}).get('user', 'sage') -for w in workers: - print(f\"{user}@{w['host']}\") -" -} - -# 清理单个节点的日志文件 -clean_logs_on_node() { - local node="$1" - local log_dir="~/SAGE/.sage/logs" - local cmd="ssh $node 'rm -rf $log_dir/*' 2>/dev/null" - - if $DRY_RUN; then - echo " [DRY-RUN] $cmd" - return 0 - else - if eval "$cmd"; then - return 0 - else - return 1 - fi - fi -} - -# 清理所有节点的日志 -clean_all_logs() { - echo -e "${BLUE}╔════════════════════════════════════════════════╗${NC}" - echo -e "${BLUE}║ SAGE Cluster Log Cleanup ║${NC}" - echo -e "${BLUE}╚════════════════════════════════════════════════╝${NC}" - echo "" - - if $DRY_RUN; then - echo -e "${YELLOW}[DRY-RUN MODE - No changes will be made]${NC}" - fi - echo "" - - # 获取 worker 节点列表 - echo -e "${BLUE}Parsing cluster configuration...${NC}" - local workers - workers=$(parse_workers) - - if [[ -z "$workers" ]]; then - echo -e "${RED}Error: No worker nodes found in $CLUSTER_CONFIG${NC}" - exit 1 - fi - - local worker_count=$(echo "$workers" | wc -l) - echo -e "Found ${GREEN}$worker_count${NC} worker node(s)" - echo "" - - local success_count=0 - local fail_count=0 - - for node in $workers; do - local hostname=$(echo "$node" | cut -d@ -f2) - echo -ne "${BLUE}Cleaning logs on $hostname...${NC} " - - if clean_logs_on_node "$node"; then - echo -e "${GREEN}✓${NC}" - success_count=$((success_count + 1)) - else - echo -e "${RED}✗${NC}" - fail_count=$((fail_count + 1)) - fi - done - - echo "" - echo -e "${BLUE}════════════════════════════════════════════════${NC}" - if [[ $fail_count -eq 0 ]]; then - echo -e "${GREEN}✅ Log cleanup completed: $success_count/$worker_count nodes${NC}" - else - echo -e "${YELLOW}⚠ Log cleanup completed with errors: $success_count/$worker_count nodes succeeded${NC}" - exit 1 - fi -} - -# 同步单个包到单个节点 -sync_package() { - local node="$1" - local package="$2" - local src_base="$SAGE_ROOT/packages/$package/src/sage" - local dst_base="~/SAGE/packages/$package/src/sage" - - if [[ ! -d "$src_base" ]]; then - if $VERBOSE; then - echo -e " ${YELLOW}⚠ Skipping $package (src dir not found)${NC}" - fi - return 0 - fi - - # 同步 sage/ 目录下的所有子目录（不包括 __init__.py） - local synced=false - for subdir in "$src_base"/*/; do - if [[ -d "$subdir" ]]; then - local dirname=$(basename "$subdir") - local cmd="scp -rq $subdir $node:$dst_base/" - - if $DRY_RUN; then - echo " [DRY-RUN] $cmd" - else - if $VERBOSE; then - echo " Syncing: $package/src/sage/$dirname/" - fi - if ! eval "$cmd" 2>/dev/null; then - return 1 - fi - fi - synced=true - fi - done - - if ! $synced; then - if $VERBOSE; then - echo -e " ${YELLOW}⚠ Skipping $package (no subdirs)${NC}" - fi - fi - return 0 -} - -# 主函数 -main() { - # 如果只是清理日志，执行清理并退出 - if $CLEAN_LOGS; then - clean_all_logs - exit 0 - fi - - echo -e "${BLUE}╔════════════════════════════════════════════════╗${NC}" - echo -e "${BLUE}║ SAGE Cluster Code Sync ║${NC}" - echo -e "${BLUE}╚════════════════════════════════════════════════╝${NC}" - echo "" - - # 确定要同步的包 - local packages_to_sync=() - - if [[ -n "$SPECIFIC_PACKAGE" ]]; then - packages_to_sync=("$SPECIFIC_PACKAGE") - echo -e "${YELLOW}Mode: Single package ($SPECIFIC_PACKAGE)${NC}" - elif $QUICK_MODE; then - packages_to_sync=("${QUICK_PACKAGES[@]}") - echo -e "${YELLOW}Mode: Quick sync (${#QUICK_PACKAGES[@]} packages)${NC}" - else - packages_to_sync=("${ALL_PACKAGES[@]}") - echo -e "${YELLOW}Mode: Full sync (${#ALL_PACKAGES[@]} packages)${NC}" - fi - - if $DRY_RUN; then - echo -e "${YELLOW}[DRY-RUN MODE - No changes will be made]${NC}" - fi - echo "" - - # 获取 worker 节点列表 - echo -e "${BLUE}Parsing cluster configuration...${NC}" - local workers - workers=$(parse_workers) - - if [[ -z "$workers" ]]; then - echo -e "${RED}Error: No worker nodes found in $CLUSTER_CONFIG${NC}" - exit 1 - fi - - local worker_count=$(echo "$workers" | wc -l) - echo -e "Found ${GREEN}$worker_count${NC} worker node(s)" - echo "" - - # 同步到每个节点 - local success_count=0 - local fail_count=0 - - for node in $workers; do - local hostname=$(echo "$node" | cut -d@ -f2) - echo -e "${BLUE}━━━ Syncing to $hostname ━━━${NC}" - - local node_success=true - for package in "${packages_to_sync[@]}"; do - if ! sync_package "$node" "$package"; then - echo -e " ${RED}✗ Failed: $package${NC}" - node_success=false - else - if $VERBOSE || $DRY_RUN; then - echo -e " ${GREEN}✓ $package${NC}" - fi - fi - done - - if $node_success; then - echo -e " ${GREEN}✓ Done${NC}" - success_count=$((success_count + 1)) - else - echo -e " ${RED}✗ Some packages failed${NC}" - fail_count=$((fail_count + 1)) - fi - echo "" - done - - # 总结 - echo -e "${BLUE}════════════════════════════════════════════════${NC}" - if [[ $fail_count -eq 0 ]]; then - echo -e "${GREEN}✅ Sync completed: $success_count/$worker_count nodes${NC}" - echo -e "${GREEN} Packages synced: ${#packages_to_sync[@]}${NC}" - else - echo -e "${YELLOW}⚠ Sync completed with errors: $success_count/$worker_count nodes succeeded${NC}" - exit 1 - fi - - if ! $DRY_RUN; then - echo "" - echo -e "${YELLOW}💡 Tip: Restart Ray cluster to apply changes:${NC}" - echo -e " sage cluster restart" - fi -} - -main diff --git a/tools/maintenance/upgrade_to_0.2.0.sh b/tools/maintenance/upgrade_to_0.2.0.sh deleted file mode 100755 index 7d80940877..0000000000 --- a/tools/maintenance/upgrade_to_0.2.0.sh +++ /dev/null @@ -1,165 +0,0 @@ -#!/bin/bash -# 批量升级所有 SAGE 包到 0.2.0 -# 将版本管理从中心化改为各包独立管理 - -set -e - -REPO_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)" -cd "$REPO_ROOT" - -echo "🔄 升级所有 SAGE 包到 0.2.0..." - -# 定义所有需要更新的包 -packages=( - "sage-common" - "sage-llm-core" - "sage-platform" - "sage-kernel" - "sage-libs" - "sage-middleware" - "sage-apps" - "sage-benchmark" - "sage-cli" - "sage-tools" - "sage-studio" - "sage-llm-gateway" - "sage-edge" - "sage" -) - -# 更新每个包的 _version.py 文件 -for pkg in "${packages[@]}"; do - version_file="" - - # 确定版本文件路径 - case "$pkg" in - "sage-common") - version_file="packages/sage-common/src/sage/common/_version.py" - ;; - "sage-llm-core") - version_file="packages/sage-llm-core/src/sage/llm/_version.py" - ;; - "sage-platform") - version_file="packages/sage-platform/src/sage/platform/_version.py" - ;; - "sage-kernel") - version_file="packages/sage-kernel/src/sage/kernel/_version.py" - ;; - "sage-libs") - version_file="packages/sage-libs/src/sage/libs/_version.py" - ;; - "sage-middleware") - version_file="packages/sage-middleware/src/sage/middleware/_version.py" - ;; - "sage-apps") - version_file="packages/sage-apps/src/sage/apps/_version.py" - ;; - "sage-benchmark") - version_file="packages/sage-benchmark/src/sage/benchmark/_version.py" - ;; - "sage-cli") - version_file="packages/sage-cli/src/sage/cli/_version.py" - ;; - "sage-tools") - version_file="packages/sage-tools/src/sage/tools/_version.py" - ;; - "sage-studio") - version_file="packages/sage-studio/src/sage/studio/_version.py" - ;; - "sage-llm-gateway") - version_file="packages/sage-llm-gateway/src/sage/llm/gateway/_version.py" - ;; - "sage-edge") - # sage-edge 已独立: https://github.com/intellistream/sage-edge - ;; - "sage") - version_file="packages/sage/src/sage/_version.py" - ;; - esac - - if [ -n "$version_file" ] && [ -f "$version_file" ]; then - echo " 📝 更新 $pkg: $version_file" - - # 使用 sed 更新版本号（支持多种版本格式） - sed -i 's/__version__ = "0\.1\.10\.7"/__version__ = "0.2.0"/' "$version_file" - sed -i 's/__version__ = "0\.1\.0"/__version__ = "0.2.0"/' "$version_file" - sed -i 's/__version__ = "0\.1\.[0-9]\+"/__version__ = "0.2.0"/' "$version_file" - - # 验证更新 - if grep -q '__version__ = "0.2.0"' "$version_file"; then - echo " ✅ $pkg 版本已更新到 0.2.0" - else - echo " ⚠️ $pkg 版本文件格式可能不标准，请手动检查" - fi - else - echo " ⚠️ 未找到 $pkg 的版本文件: $version_file" - fi -done - -echo "" -echo "🔧 移除中心化版本依赖..." -echo "" - -# 修复各包的 sage/__init__.py，使其不再依赖 sage.common._version -namespace_init_files=( - "packages/sage-kernel/src/sage/__init__.py" - "packages/sage-libs/src/sage/__init__.py" - "packages/sage-platform/src/sage/__init__.py" - "packages/sage-benchmark/src/sage/__init__.py" - "packages/sage-studio/src/sage/__init__.py" - "packages/sage-tools/src/sage/__init__.py" - "packages/sage-middleware/src/sage/__init__.py" - "packages/sage-apps/src/sage/__init__.py" - "packages/sage-cli/src/sage/__init__.py" -) - -for init_file in "${namespace_init_files[@]}"; do - if [ -f "$init_file" ]; then - echo " 🔧 修复 $init_file..." - - # 将 sage.common._version 导入改为尝试导入但不强制依赖 - # 这样即使 sage-common 不在，命名空间包也能工作 - sed -i 's/from sage\.common\._version import/__version__ = "unknown"; __author__ = "IntelliStream Team"; __email__ = "shuhao_zhang@hust.edu.cn" # from sage.common._version import/' "$init_file" - - echo " ✅ 已修复" - fi -done - -echo "" -echo "✅ 版本更新完成！" -echo "" -echo "📋 验证更新结果：" -echo "" - -# 验证所有版本文件 -for pkg in "${packages[@]}"; do - version_file="" - - case "$pkg" in - "sage-common") version_file="packages/sage-common/src/sage/common/_version.py" ;; - "sage-llm-core") version_file="packages/sage-llm-core/src/sage/llm/_version.py" ;; - "sage-platform") version_file="packages/sage-platform/src/sage/platform/_version.py" ;; - "sage-kernel") version_file="packages/sage-kernel/src/sage/kernel/_version.py" ;; - "sage-libs") version_file="packages/sage-libs/src/sage/libs/_version.py" ;; - "sage-middleware") version_file="packages/sage-middleware/src/sage/middleware/_version.py" ;; - "sage-apps") version_file="packages/sage-apps/src/sage/apps/_version.py" ;; - "sage-benchmark") version_file="packages/sage-benchmark/src/sage/benchmark/_version.py" ;; - "sage-cli") version_file="packages/sage-cli/src/sage/cli/_version.py" ;; - "sage-tools") version_file="packages/sage-tools/src/sage/tools/_version.py" ;; - "sage-studio") version_file="packages/sage-studio/src/sage/studio/_version.py" ;; - "sage-llm-gateway") version_file="packages/sage-llm-gateway/src/sage/llm/gateway/_version.py" ;; - "sage-edge") echo "sage-edge 已独立，请访问 https://github.com/intellistream/sage-edge"; continue ;; - "sage") version_file="packages/sage/src/sage/_version.py" ;; - esac - - if [ -f "$version_file" ]; then - version=$(grep '__version__' "$version_file" | head -1) - printf " %-20s %s\n" "$pkg:" "$version" - fi -done - -echo "" -echo "🎯 下一步：" -echo " 1. 提交这些更改: git add -A && git commit -m 'chore: upgrade all packages to 0.2.0'" -echo " 2. 重新安装: ./quickstart.sh --dev --yes" -echo "" diff --git a/tools/pre-commit-config.yaml b/tools/pre-commit-config.yaml index c8306af826..a9939aafd5 100644 --- a/tools/pre-commit-config.yaml +++ b/tools/pre-commit-config.yaml @@ -7,11 +7,6 @@ # - Install hooks: pre-commit install --config tools/config/pre-commit-config.yaml # - Run manually: pre-commit run --all-files --config tools/config/pre-commit-config.yaml # -# Mypy Type Checking: -# - Runs automatically on changed Python files -# - Shows type errors as warnings (doesn't block commits/CI) -# - To run mypy explicitly: pre-commit run mypy --all-files --config tools/config/pre-commit-config.yaml - default_language_version: python: python3.11 @@ -26,110 +21,108 @@ repos: - id: end-of-file-fixer exclude: '(\.svg$|ThirdParty/|thirdparty/)' - id: check-yaml - args: [--unsafe] # Allow custom YAML tags + args: [--unsafe] # Allow custom YAML tags exclude: ^(.*/benchmark_anns/|ThirdParty/|thirdparty/|.*/implementations/(SPTAG|faiss|DiskANN)/) - id: check-json - exclude: '(ThirdParty/|thirdparty/)' + exclude: "(ThirdParty/|thirdparty/)" - id: check-toml - exclude: '(ThirdParty/|thirdparty/)' + exclude: "(ThirdParty/|thirdparty/)" - id: check-added-large-files - args: ['--maxkb=1000'] - exclude: '(ThirdParty/|thirdparty/)' + args: ["--maxkb=1000"] + exclude: "(ThirdParty/|thirdparty/)" - id: check-merge-conflict - exclude: '(ThirdParty/|thirdparty/)' + exclude: "(ThirdParty/|thirdparty/)" - id: check-case-conflict - exclude: ^(.*/benchmark_(libamm|db)/|.*/(libamm|sageLLM|neuromem|sageTSDB)/|.*/vendors/|.*/build/|ThirdParty/|thirdparty/) + exclude: ^(.*/benchmark_(libamm|db)/|.*/vendors/|.*/build/|ThirdParty/|thirdparty/) - id: mixed-line-ending args: [--fix=lf] - exclude: '(ThirdParty/|thirdparty/)' + exclude: "(ThirdParty/|thirdparty/)" - id: detect-private-key - exclude: '(ThirdParty/|thirdparty/)' + exclude: "(ThirdParty/|thirdparty/)" - # Python: Black formatter (DISABLED - using ruff format instead) - # Black and ruff format can conflict in edge cases, causing infinite reformatting - # Ruff format is now mature enough to replace Black completely -# - repo: https://github.com/psf/black -# rev: 25.9.0 -# hooks: -# - id: black -# language_version: python3.11 -# args: [--line-length=100] -# exclude: ^(docs/|docs-public/|examples/data/|.*/(sageLLM|neuromem|sageTSDB)/|.*/vendors/|.*/build/) + # Python: Black formatter (DISABLED - using ruff format instead) + # Black and ruff format can conflict in edge cases, causing infinite reformatting + # Ruff format is now mature enough to replace Black completely + # - repo: https://github.com/psf/black + # rev: 25.9.0 + # hooks: + # - id: black + # language_version: python3.11 + # args: [--line-length=100] + # exclude: ^(docs/|examples/data/|.*/vendors/|.*/build/) - # Python: isort import sorting -# - repo: https://github.com/pycqa/isort -# rev: 7.0.0 -# hooks: -# - id: isort -# args: [--profile=black, --line-length=100] -# exclude: ^(docs/|docs-public/|examples/data/|.*/(sageLLM|neuromem|sageTSDB)/|.*/vendors/|.*/build/) -# - # Python: Ruff linter and formatter (replaces flake8, isort, pyupgrade) -- repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.14.6 + # Python: isort import sorting + # - repo: https://github.com/pycqa/isort + # rev: 7.0.0 + # hooks: + # - id: isort + # args: [--profile=black, --line-length=100] + # exclude: ^(docs/|examples/data/|.*/vendors/|.*/build/) + # + # Python: Ruff linter and formatter (replaces flake8, isort, pyupgrade) + # NOTE: Using local/system ruff to avoid network download failures + # Requires: ruff is available in PATH (installed via pip in the active env) +- repo: local hooks: - id: ruff name: ruff check - args: [--fix, --exit-non-zero-on-fix, --config, tools/config/ruff.toml] - exclude: ^(docs/|docs-public/|examples/data/|tests/fixtures/|.*/benchmark_(libamm|db)/|.*/(sageLLM|neuromem|sageTSDB|libamm)/|.*/vendors/|.*/build/|ThirdParty/|thirdparty/|.*/implementations/(SPTAG|faiss|DiskANN|diskann-ms|candy|puck|pybind11)/) + language: system + entry: ruff + args: [check, --fix, --exit-non-zero-on-fix, --config, tools/config/ruff.toml] + types: [python] + exclude: ^(docs/|examples/data/|tests/fixtures/|.*/benchmark_(libamm|db)/|.*/vendors/|.*/build/|ThirdParty/|thirdparty/|.*/implementations/(SPTAG|faiss|DiskANN|diskann-ms|candy|puck|pybind11)/) - id: ruff-format name: ruff format - args: [--config, tools/config/ruff.toml] - exclude: ^(docs/|docs-public/|examples/data/|tests/fixtures/|.*/benchmark_(libamm|db)/|.*/(sageLLM|neuromem|sageTSDB|libamm)/|.*/vendors/|.*/build/|ThirdParty/|thirdparty/|.*/implementations/(SPTAG|faiss|DiskANN|diskann-ms|candy|puck|pybind11)/) + language: system + entry: ruff + args: [format, --config, tools/config/ruff.toml] + types: [python] + exclude: ^(docs/|examples/data/|tests/fixtures/|.*/benchmark_(libamm|db)/|.*/vendors/|.*/build/|ThirdParty/|thirdparty/|.*/implementations/(SPTAG|faiss|DiskANN|diskann-ms|candy|puck|pybind11)/) -# Python: mypy type checking (warning mode - shows errors but doesn't block commit) - repo: local hooks: - - id: architecture-violation-check - name: check architecture violations (directory placement) - entry: tools/hooks/pre-commit-architecture.sh + - id: meta-dependency-audit-gate + name: check isage meta dependency audit evidence + entry: python3 tools/scripts/check_meta_dependency_audit.py --enforce-change-evidence --staged language: system pass_filenames: false always_run: false - stages: [pre-commit] - # Check for files in wrong locations (e.g., sageLLM in sage-common instead of sage-llm-core) - - id: libs-middleware-import-check - name: check sage-libs does not import sage.middleware (L3→L4 violation) - entry: tools/hooks/check_libs_middleware_import.sh + stages: [pre-commit, manual] + files: ^pyproject.toml$|^docs/dependency-audit-gate\.md$ + - id: architecture-violation-check + name: check architecture violations (directory placement) + entry: tools/hooks/pre-commit-architecture.sh language: system pass_filenames: false always_run: false - files: ^packages/sage-libs/src/.*\.py$ stages: [pre-commit] - # Enforces architecture rule: L3 (sage-libs) must NOT import L4 (sage.middleware) - # See: docs-public/docs_src/dev-notes/cross-layer/MIDDLEWARE_COMPONENT_PROMOTION_POLICY.md - # - id: dependency-version-conflicts - # name: check dependency version conflicts - # entry: python3 tools/install/helpers/unify_dependencies.py --check - # language: system - # pass_filenames: false - # always_run: false - # files: ^packages/.*/pyproject\.toml$ - # # DEPRECATED: Script removed, dependency management moved to sage-tools + # Check for files in wrong locations under the current meta-repo layout + # - id: dependency-version-conflicts + # name: check dependency version conflicts + # entry: python3 tools/install/helpers/unify_dependencies.py --check + # language: system + # pass_filenames: false + # always_run: false + # files: ^pyproject\.toml$ + # # DEPRECATED: Script removed, dependency management moved to sage-tools - id: pep420-namespace-compliance name: PEP 420 namespace package compliance check entry: bash -c 'tools/scripts/validate_pep420_compliance.sh' language: system pass_filenames: false always_run: false - files: ^packages/.*/src/sage/__init__\.py$ - # Block commit if src/sage/__init__.py is added/modified (violates PEP 420) - - id: mypy - name: mypy type checking (warnings only) - entry: tools/quality/mypy-wrapper.sh + files: ^src/sage/__init__\.py$ + # Block commit if src/sage/__init__.py is added/modified (violates PEP 420) + - id: cross-repo-dedup-check + name: cross-repo duplicate declaration check (SAGE vs sageFlownet) + entry: python3 tools/scripts/check_cross_repo_dedup.py language: system - types: [python] - require_serial: true - args: - - --cache-dir=.sage/cache/mypy - - --ignore-missing-imports - - --show-error-codes - - --explicit-package-bases - - --warn-unused-ignores - - --namespace-packages - exclude: ^(docs/|docs-public/|examples/|tests/|.*/tests/|setup.py|.*/setup.py|.*/benchmark_(libamm|db)/|.*/(sageLLM|neuromem|sageTSDB|libamm)/|.*/vendors/|.*/src/sage/__init__.py|ThirdParty/|thirdparty/) - # This hook will always succeed (exit 0) to avoid blocking commits/CI - # Errors are shown but treated as warnings + pass_filenames: false + always_run: false + stages: [pre-commit, manual] + files: \.py$ + # Enforces move-then-delete rule: migrated symbols must not be redefined in Flownet. + # See Issue #1439 and flownet-migration-boundary.md - id: markdown-files-location-check name: check markdown files are in proper locations entry: tools/hooks/check_docs_location.sh @@ -137,21 +130,14 @@ repos: pass_filenames: false always_run: false files: \.md$ - - id: dev-notes-categorization-check - name: check dev-notes files are properly categorized - entry: tools/hooks/check_dev_notes_location.sh - language: system - pass_filenames: false - always_run: false - files: ^docs-public/docs_src/dev-notes/.*\.md$ - # - id: control-plane-only-guard - # name: prevent embedded/local inference paths - # entry: tools/hooks/check_control_plane_only.py - # language: system - # pass_filenames: false - # always_run: false - # files: \.py$ - # # DEPRECATED: Script removed, control plane checks moved to sage-tools + # - id: control-plane-only-guard + # name: prevent embedded/local inference paths + # entry: tools/hooks/check_control_plane_only.py + # language: system + # pass_filenames: false + # always_run: false + # files: \.py$ + # # DEPRECATED: Script removed, control plane checks moved to sage-tools - id: python-test-files-location-check name: check Python test files are in proper locations entry: bash -c @@ -174,7 +160,7 @@ repos: violations="" for file in $all_test_files; do # Test files should be in tests/ directories or test scripts in examples/ - if echo "$file" | grep -qE "^(packages/[^/]+/tests/|tests/|examples/.*/tests?\.py$|examples/.*test.*\.py$)"; then + if echo "$file" | grep -qE "^(tests/|examples/.*/tests?\.py$|examples/.*test.*\.py$)"; then # Allowed location continue else @@ -189,13 +175,13 @@ repos: echo -e "$violations" | sed "s/^/ - /" echo "" echo "📁 测试文件应该放在:" - echo " - packages//tests/ - 单元测试和集成测试" - echo " - packages//tests/unit/ - 单元测试" - echo " - packages//tests/integration/ - 集成测试" - echo " - packages//tests/manual/ - 手动测试脚本" + echo " - tests/ - 单元测试和集成测试" + echo " - tests/unit/ - 单元测试" + echo " - tests/integration/ - 集成测试" + echo " - tests/manual/ - 手动测试脚本" echo " - examples/ - 示例和演示脚本（可以包含 test 名称）" echo "" - echo "💡 建议: 请将测试文件移动到对应包的 tests/ 目录" + echo "💡 建议: 请将测试文件移动到仓库的 tests/ 目录" exit 1 else exit 0 @@ -204,110 +190,14 @@ repos: pass_filenames: false always_run: false files: \.py$ - - id: devnotes-check - name: dev-notes documentation standards - entry: bash -c 'if git diff --cached --name-only --diff-filter=ACM | grep -q "^docs-public/docs_src/dev-notes/.*\.md$"; then sage-dev quality devnotes || true; else exit 0; fi' - language: system - pass_filenames: false - always_run: false - files: ^docs-public/docs_src/dev-notes/.*\.md$ - - id: depspec-consistency - name: check dependencies match dependencies-spec.yaml - entry: bash -c 'sage-dev maintain depspec' - language: system - pass_filenames: false - always_run: false - files: ^(packages/.*/pyproject\.toml$|dependencies-spec\.yaml$) - - id: devnotes-structure-check - name: dev-notes directory structure validation - entry: bash -c - args: - - | - # Check if any files were staged in docs-public/docs_src/dev-notes/ - if [ -n "$PRE_COMMIT_FROM_REF" ] && [ -n "$PRE_COMMIT_TO_REF" ]; then - # Running with --all-files or during push - staged_files=$(git ls-files "docs-public/docs_src/dev-notes/*") - else - # Running in normal commit mode - staged_files=$(git diff --cached --name-only --diff-filter=ACM | grep "^docs-public/docs_src/dev-notes/" || true) - fi - - if [ -z "$staged_files" ]; then - exit 0 - fi - - # Allowed top-level directories under docs-public/docs_src/dev-notes/ - # NOTE: When adding new directories, update this list - allowed_dirs=( - "l1-common" - "l2-platform" - "l3-kernel" - "l3-libs" - "l4-middleware" - "l5-cli" - "l5-tools" - "cross-layer" - "testing" - "archive" - "research_work" - ) - - # Get all first-level directories under docs-public/docs_src/dev-notes/ - violations="" - for file in $staged_files; do - # Extract the first directory component after docs-public/docs_src/dev-notes/ - # e.g., docs-public/docs_src/dev-notes/foo/bar.md -> foo - dir_component=$(echo "$file" | sed -n 's|^docs-public/docs_src/dev-notes/\([^/]*\)/.*|\1|p') - - # Skip root-level files (README.md, TEMPLATE.md) - if [ -z "$dir_component" ]; then - continue - fi - - # Check if directory is allowed - allowed=false - for allowed_dir in "${allowed_dirs[@]}"; do - if [ "$dir_component" = "$allowed_dir" ]; then - allowed=true - break - fi - done - - if [ "$allowed" = false ]; then - # Only add unique violations - if [[ ! "$violations" =~ "$dir_component" ]]; then - violations="$violations$dir_component\n" - fi - fi - done - - # Report violations - if [ -n "$violations" ]; then - echo "❌ 错误: docs-public/docs_src/dev-notes/ 下存在未授权的目录:" - echo -e "$violations" | sort -u | sed "s/^/ - /" - echo "" - echo "📁 允许的目录:" - echo " - 分层目录: l1-common, l2-platform, l3-kernel, l3-libs, l4-middleware" - echo " l5-cli, l5-tools" - echo " - 交叉主题: cross-layer, testing, archive" - echo " - 研究工作: research_work" - echo "" - echo "💡 建议: 将文档移动到合适的目录，或联系维护者更新允许列表" - exit 1 - fi - exit 0 - language: system - pass_filenames: false - always_run: false - files: ^docs-public/docs_src/dev-notes/ - id: installation-consistency-check name: installation consistency check (local vs CI/CD) entry: bash tools/install/examination_tools/installation_consistency_check.sh language: system pass_filenames: false - # 仅在修改安装相关文件时运行 - files: ^(quickstart\.sh|tools/install/.*\.sh|packages/.*/pyproject\.toml|packages/.*/setup\.py|\.github/workflows/.*\.yml)$ - # 也在手动运行 pre-commit run --all-files 时执行 + # 仅在修改安装相关文件时运行 + files: ^(quickstart\.sh|tools/install/.*\.sh|pyproject\.toml|setup\.py|\.github/workflows/.*\.yml)$ + # 也在手动运行 pre-commit run --all-files 时执行 stages: [pre-commit, manual] - id: root-directory-cleanup-check name: check for unwanted files/directories in project root @@ -334,13 +224,16 @@ repos: "LICENSE" "Makefile" "manage.sh" + "pytest.ini" "quickstart.sh" "README.md" "CHANGELOG.md" "dependencies-spec.yaml" - "dependencies-spec.yaml" "tasks.md" "SAGE.code-workspace" + "SAGE_ZOO.md" + "pyproject.toml" # isage meta-package + "setup.py" # isage meta-package ) # Allowed directories in project root (whitelist) @@ -348,7 +241,7 @@ repos: ".benchmarks" # Deprecated; keep for legacy benchmark artifacts ".git" ".github" - ".mypy_cache" + ".mypy_cache" # mypy cache (temporary; should use .sage/cache/mypy) ".pytest_cache" ".ruff_cache" ".sage" @@ -357,13 +250,14 @@ repos: "build" "config" "docker" - # "docs" # REMOVED: Root docs/ is now forbidden. Use docs-public/ instead. - "docs-public" + # "docs" # Root docs/ only for machine-owned governance artifacts + "docs" "examples" "htmlcov" - "packages" + "src" # isage meta-package source (src/sage/_version.py) "data" "tools" + "hooks" ) violations="" @@ -412,7 +306,7 @@ repos: echo " - 临时文件应放在 .sage/ 目录下" echo " - 如果是新增的合法文件/目录，请更新 tools/config/pre-commit-config.yaml 中的白名单" echo "" - echo "📖 参考: docs/dev-notes/l5-tools/CACHE_MANAGEMENT.md" + echo "📖 参考: DEVELOPER.md" exit 1 fi exit 0 @@ -420,51 +314,60 @@ repos: pass_filenames: false always_run: true stages: [pre-commit, manual] - # Shell: shellcheck - # NOTE: shellcheck-py may fail in CI due to network issues when downloading shellcheck binary - # This is a known issue: https://github.com/shellcheck-py/shellcheck-py/issues/15 - # Workaround: Use fail_fast: false in CI or install shellcheck separately -- repo: https://github.com/shellcheck-py/shellcheck-py - rev: v0.11.0.1 + # Shell: shellcheck + # NOTE: Using local/system shellcheck to avoid network download failures + # (shellcheck-py downloads the binary at install time, which fails behind proxies/firewalls) + # Requires: shellcheck is available in PATH (e.g. installed via conda or apt) +- repo: local hooks: - id: shellcheck - args: [-x, -e, SC1091, -S, error] # -x: follow sources, -e SC1091: ignore source errors, -S error: only fail on errors + name: shellcheck + language: system + entry: shellcheck + args: [-x, -e, SC1091, -S, error] # -x: follow sources, -e SC1091: ignore source errors, -S error: only fail on errors files: \.(sh|bash)$ exclude: ^(tools/conda/|.*/benchmark_anns/|ThirdParty/|thirdparty/|.*/implementations/(SPTAG|faiss|DiskANN|diskann-ms|candy|puck|pybind11)/) - # YAML formatting -- repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks - rev: v2.15.0 + # YAML formatting + # NOTE: Using local/system pretty-format-yaml to avoid network download failures + # Requires: language-formatters-pre-commit-hooks is installed via pip +- repo: local hooks: - id: pretty-format-yaml + name: pretty-format-yaml + language: system + entry: pretty-format-yaml args: [--autofix, --indent=2, --preserve-quotes] + types: [yaml] exclude: ^(\.github/|examples/config/|.*/benchmark_anns/|ThirdParty/|thirdparty/|.*/implementations/(SPTAG|faiss|DiskANN|diskann-ms|candy|puck|pybind11)/) - # Markdown formatting -- repo: https://github.com/executablebooks/mdformat - rev: 1.0.0 + # Markdown formatting + # NOTE: Using local/system mdformat to avoid network download failures + # Requires: mdformat + mdformat-gfm installed via pip +- repo: local hooks: - id: mdformat - additional_dependencies: - - mdformat-gfm # GitHub Flavored Markdown - # Note: mdformat-black is disabled because it fails on code blocks with: - # - Template placeholders (e.g., {module_name}) - # - Special symbols in comments (e.g., ✅ ❌) - # - Pseudo-code or incomplete Python snippets - # - mdformat-black # Black formatter for code blocks + name: mdformat + language: system + entry: mdformat args: [--wrap=100] - exclude: ^(CHANGELOG.md|\.github/|docs/dev-notes/|docs-public/docs_src/api-reference/) + types: [markdown] + exclude: ^(CHANGELOG.md|\.github/) - # Security: Check for secrets -- repo: https://github.com/Yelp/detect-secrets - rev: v1.5.0 + # Security: Check for secrets + # NOTE: Using local/system detect-secrets to avoid network download failures + # Requires: detect-secrets installed via pip +- repo: local hooks: - id: detect-secrets + name: detect-secrets + language: system + entry: detect-secrets-hook args: [--baseline, tools/config/secrets.baseline] - exclude: ^(tools/secrets\.baseline|\.env\.template|examples/config/.*\.yaml|tests/fixtures/|.*package-lock\.json$|.*\.ipynb$|docs/.*\.md$|docs-public/.*\.md$|examples/.*\.py$|packages/.*/src/sage/libs/integrations/.*\.py$|.*\.html$|packages/.*/src/.*\.md$|.*/benchmark_anns/|ThirdParty/|thirdparty/|.*/implementations/(SPTAG|faiss|DiskANN|diskann-ms|candy|puck|pybind11)/) + exclude: ^(tools/secrets\.baseline|\.env\.template|\.github/workflows/|examples/config/.*\.yaml|tests/fixtures/|.*package-lock\.json$|.*\.ipynb$|docs/.*\.md$|examples/.*\.py$|.*\.html$|.*/benchmark_anns/|ThirdParty/|thirdparty/|.*/implementations/(SPTAG|faiss|DiskANN|diskann-ms|candy|puck|pybind11)/) - # SAGE Data Architecture Validation - REMOVED (sage-benchmark is now independent) - # See: https://github.com/intellistream/sage-benchmark + # SAGE Data Architecture Validation - REMOVED (sage-benchmark is now independent) + # See: https://github.com/intellistream/sage-benchmark - repo: local hooks: - id: copilot-instructions-sync-check @@ -472,11 +375,10 @@ repos: entry: bash tools/hooks/check_copilot_instructions_sync.sh language: system pass_filenames: false - # Trigger on: doc files, installation scripts, CI/CD, config, ports, CLI, LLM/Gateway - files: ^(\.github/(copilot-instructions\.md|agents/my-agent\.agent\.md|workflows/.*\.yml)|quickstart\.sh|manage\.sh|Makefile|tools/(install/.*\.sh|pre-commit-config\.yaml|pytest\.ini|ruff\.toml)|packages/sage-common/src/sage/common/config/(ports|user_paths)\.py|packages/sage-cli/src/sage/cli/|packages/sage-gateway/src/sage/gateway/|packages/sage-common/src/sage/common/components/sage_(llm|embedding)/|packages/sage-[^/]+/pyproject\.toml|config/(config|cluster)\.yaml) + # Trigger on: doc files, installation scripts, CI/CD, config, ports, CLI, LLM/Gateway + files: ^(\.github/(copilot-instructions\.md|agents/my-agent\.agent\.md|workflows/.*\.yml)|quickstart\.sh|manage\.sh|Makefile|pyproject\.toml|setup\.py|tools/((config/)?pre-commit-config\.yaml|(config/)?ruff\.toml|install/.*\.sh)|config/(config|cluster)\.yaml) stages: [pre-commit, manual] description: Warn if critical project files change without updating copilot instructions - # To install pre-commit hooks: # pre-commit install # diff --git a/tools/profiling/README.md b/tools/profiling/README.md new file mode 100644 index 0000000000..d0d50e8342 --- /dev/null +++ b/tools/profiling/README.md @@ -0,0 +1,153 @@ +# SAGE Hot-Path Profiling Infrastructure + +> Issue: [#1467](https://github.com/intellistream/SAGE/issues/1467)\ +> Feeds: [#1468](https://github.com/intellistream/SAGE/issues/1468) · +> [#1469](https://github.com/intellistream/SAGE/issues/1469) + +## Overview + +This directory contains the profiling workloads and tooling to: + +1. Identify the **actual** hot paths before any C++ porting work +1. Collect cProfile `.prof` files for deep-dive analysis +1. (Optionally) generate py-spy / perf flame graph SVGs +1. Generate a Markdown report suitable for `docs/profiling/` + +## Hot-Path Candidates + +| Surface | Path | Suspected Cost Driver | +| ------------------------------ | ----------------------------- | ---------------------------------------------------------- | +| `sage.runtime` | `scheduler/` | Task dispatch loop — `make_decision()` called per-operator | +| `sage.stream` / `sage.runtime` | `runtime/communication/` | `Packet` construction + routing / serialization overhead | +| `isage-privacy` | `sage_privacy/dp_unlearning/` | NumPy noise generation over large vector batches | +| Historical ingestion path | `foundation/io/` | Batch assembly + JSON decode in streaming ingestion | + +## Quick Start + +```bash +# From SAGE repo root +cd /path/to/SAGE + +# 1. Run all profilers (default workload sizes, ~60s) +python tools/profiling/cprofile_runner.py + +# 2. Heavier workloads for more accurate numbers +python tools/profiling/cprofile_runner.py --heavy + +# 3. Single path +python tools/profiling/cprofile_runner.py --path dp_unlearning + +# 4. Generate Markdown report from JSON results +python tools/profiling/report_generator.py + +# 5. Full flame-graph pipeline (installs py-spy if missing) +bash tools/profiling/flame_graph.sh --install +``` + +## Directory Layout + +```text +tools/profiling/ +├── README.md # This file +├── cprofile_runner.py # Main cProfile orchestrator +├── report_generator.py # JSON → Markdown report +├── flame_graph.sh # py-spy + perf SVG collector +├── workloads/ +│ ├── workload_scheduler.py # scheduler bench (historical kernel vs in-tree runtime) +│ ├── workload_communication.py # packet / communication bench +│ ├── workload_dp_unlearning.py # isage-privacy DP unlearning bench +│ └── workload_foundation_io.py # historical foundation/io bench +└── reports/ # ← generated, not committed (gitignored) + ├── hot_path_summary.txt + ├── hot_path_report.md # Copy to docs/profiling/ when ready + ├── full_summary.json + ├── scheduler.prof + ├── communication.prof + ├── dp_unlearning.prof + ├── foundation_io.prof + └── flamegraph/ + ├── scheduler.svg + ├── dp_unlearning.svg + └── ... +``` + +## Interpreting Results + +### cProfile Tables + +The `cprofile_runner.py` outputs `pstats` tables sorted by cumulative time. Focus on: + +- Lines where `tottime` (exclusive CPU time) is large → these are the leaves consuming real time +- Functions called millions of times with `percall` > 1µs → candidates for C++ inlining + +### Verdict Table + +After all paths run, a verdict table is printed: + +```text +🔴 HIGH → C++ port directly benefits (>2× threshold) +🟡 MED → profile deeper with larger data or real traffic +🟢 LOW → Python overhead is acceptable, not worth porting +``` + +### Viewing .prof Files + +```bash +# Interactive browser (requires snakeviz) +pip install snakeviz +snakeviz tools/profiling/reports/dp_unlearning.prof + +# DOT / SVG call graph +pip install gprof2dot +gprof2dot -f pstats tools/profiling/reports/dp_unlearning.prof \ + | dot -Tsvg -o dp_unlearning_callgraph.svg +``` + +### Viewing py-spy Flame Graphs + +Open the `.svg` output in a browser: + +```bash +xdg-open tools/profiling/reports/flamegraph/dp_unlearning_flamegraph.svg +``` + +The `.svg` interactive flame graph can be panned/zoomed with the mouse. For Speedscope format +(`.svg` labeled speedscope): + +```bash +npx speedscope tools/profiling/reports/flamegraph/dp_unlearning.svg +``` + +## Thresholds & Priorities + +Thresholds are defined in `report_generator.py` `_THRESHOLDS`. Adjust after the first real-traffic +measurement: + +| Metric | Default threshold | Rationale | +| --------------------- | ----------------: | ----------------------------------- | +| `pkt_construct_us` | 0.2 µs | 5 M pkt/s target → 200 ns budget | +| `msgpack_ser_us` | 1.0 µs | Network latency dominated otherwise | +| `rr_per_decision_us` | 0.5 µs | 2 M tasks/s throughput | +| `perturb_ms_per_call` | 5 ms | Unlearning batch of 5 k vectors | +| `comp_ms_per_call` | 50 ms | Neighbor update budget | + +## Committing Results + +```bash +# After run, copy to docs/ +mkdir -p docs/profiling +cp tools/profiling/reports/hot_path_report.md docs/profiling/ +git add docs/profiling/hot_path_report.md +git commit -m "docs: add hot-path profiling report (closes #1467)" +``` + +The `.prof` / `.svg` files are large binaries — optionally upload to the GitHub issue as attachments +rather than committing. + +## Updating P6 Priority + +After running, update issues #1468 / #1469 based on the 🔴 findings: + +- Paths consistently 🔴 HIGH → immediate C++ scope +- Paths 🟡 MED → deferred, measure again with synthetic Flownet traffic +- Paths 🟢 LOW → removed from C++ scope diff --git a/tools/profiling/cprofile_runner.py b/tools/profiling/cprofile_runner.py new file mode 100644 index 0000000000..e88d6b85fd --- /dev/null +++ b/tools/profiling/cprofile_runner.py @@ -0,0 +1,289 @@ +#!/usr/bin/env python3 +""" +SAGE Hot-Path Profiler — cProfile + pstats runner +=================================================== + +Profiles the four candidate hot paths and emits: + 1. Sorted cProfile text tables (stdout + tool/profiling/reports/*.txt) + 2. pstats-compatible binary dumps (reports/*.prof) — loadable by snakeviz / gprof2dot + +Usage +----- + # All paths, default workload sizes + python tools/profiling/cprofile_runner.py + + # Single path with custom sizes + python tools/profiling/cprofile_runner.py --path scheduler --iterations 100000 + + # All paths, heavy mode + python tools/profiling/cprofile_runner.py --heavy + + # Only write .prof files (no stdout tables) + python tools/profiling/cprofile_runner.py --quiet + +Outputs +------- + tools/profiling/reports/ + hot_path_summary.txt — top-20 functions per path, side-by-side + scheduler.prof + communication.prof + dp_unlearning.prof + foundation_io.prof + full_summary.json — machine-readable timing data + +Viewing flame graphs from .prof files (requires snakeviz or gprof2dot): + snakeviz tools/profiling/reports/dp_unlearning.prof + gprof2dot -f pstats tools/profiling/reports/dp_unlearning.prof | dot -Tsvg -o flamegraph.svg +""" + +from __future__ import annotations + +import argparse +import cProfile +import io +import json +import pstats +import sys +import time +from pathlib import Path + +# --------------------------------------------------------------------------- +# Make workloads importable when run from any cwd +# --------------------------------------------------------------------------- +_TOOLS_PROFILING = Path(__file__).resolve().parent +sys.path.insert(0, str(_TOOLS_PROFILING)) + +from workloads.workload_communication import run_communication_workload # noqa: E402 +from workloads.workload_dp_unlearning import run_dp_unlearning_workload # noqa: E402 +from workloads.workload_foundation_io import run_foundation_io_workload # noqa: E402 +from workloads.workload_scheduler import run_scheduler_workload # noqa: E402 + +REPORTS_DIR = _TOOLS_PROFILING / "reports" +REPORTS_DIR.mkdir(parents=True, exist_ok=True) + + +# --------------------------------------------------------------------------- +# Configuration profiles +# --------------------------------------------------------------------------- + +DEFAULT_CONFIG = { + "scheduler": {"iterations": 50_000}, + "communication": {"iterations": 100_000, "payload_size": 1024}, + "dp_unlearning": {"num_vectors": 5_000, "dim": 128, "iterations": 10}, + "foundation_io": {"batch_size": 256, "iterations": 10_000}, +} + +HEAVY_CONFIG = { + "scheduler": {"iterations": 500_000}, + "communication": {"iterations": 1_000_000, "payload_size": 4096}, + "dp_unlearning": {"num_vectors": 20_000, "dim": 256, "iterations": 50}, + "foundation_io": {"batch_size": 512, "iterations": 100_000}, +} + + +# --------------------------------------------------------------------------- +# Profiling helpers +# --------------------------------------------------------------------------- + + +def profile_fn(fn, kwargs: dict, label: str, top_n: int = 20) -> tuple[dict, str]: + """ + Run `fn(**kwargs)` under cProfile. + + Returns + ------- + (timing_dict, pstats_text) + """ + prof = cProfile.Profile() + t0 = time.perf_counter() + prof.enable() + result = fn(**kwargs) + prof.disable() + wall_time = time.perf_counter() - t0 + + # Write binary .prof file + prof_path = REPORTS_DIR / f"{label}.prof" + prof.dump_stats(str(prof_path)) + + # Generate pstats text + buf = io.StringIO() + ps = pstats.Stats(prof, stream=buf).sort_stats(pstats.SortKey.CUMULATIVE) + ps.print_stats(top_n) + pstats_text = buf.getvalue() + + timing = result if isinstance(result, dict) else {} + timing["wall_time_s"] = wall_time + return timing, pstats_text + + +def _section(title: str, width: int = 72) -> str: + bar = "=" * width + return f"\n{bar}\n {title}\n{bar}\n" + + +# --------------------------------------------------------------------------- +# Summary table helpers +# --------------------------------------------------------------------------- + + +_CPP_VERDICT = { + # threshold: µs/op → "worth C++ porting" + # These are illustrative; update after actual measurement. + "scheduler_rr": ("rr_per_decision_us", 0.5), + "scheduler_pri": ("pri_per_decision_us", 1.0), + "comm_construct": ("pkt_construct_us", 0.2), + "comm_ser": ("msgpack_ser_us", 1.0), + "comm_deser": ("msgpack_deser_us", 1.0), + "comm_route": ("route_us", 0.1), + "dp_perturb": ("perturb_ms_per_call", 5.0), + "dp_comp": ("comp_ms_per_call", 50.0), + "dp_engine": ("engine_ms_per_call", 100.0), + "io_batch": ("batch_assembly_us", 2.0), + "io_json": ("json_roundtrip_us", 10.0), + "io_queue_enq": ("queue_enq_us", 0.5), +} + + +def _verdict(value: float, threshold: float) -> str: + if value >= threshold * 2: + return "🔴 HIGH (C++ recommended)" + if value >= threshold: + return "🟡 MED (profile more)" + return "🟢 LOW (skip C++)" + + +def build_verdict_table(all_results: dict[str, dict]) -> str: + """Produce a human-readable verdict table from all timing results.""" + flat = {} + for section_results in all_results.values(): + flat.update(section_results) + + lines = [ + "┌─────────────────────────────┬─────────────────┬──────────────────────────────┐", + "│ Hot Path │ Measured (µs) │ Verdict │", + "├─────────────────────────────┼─────────────────┼──────────────────────────────┤", + ] + for key, (metric, threshold) in _CPP_VERDICT.items(): + val = flat.get(metric) + if val is None: + continue + # Convert ms to µs for display if metric is in ms + disp_val = val * 1000 if "ms" in metric else val + verdict = _verdict(val, threshold) + lines.append(f"│ {key:<27} │ {disp_val:>13.2f}µs │ {verdict:<28} │") + lines.append("└─────────────────────────────┴─────────────────┴──────────────────────────────┘") + return "\n".join(lines) + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- + +ALL_PATHS = ("scheduler", "communication", "dp_unlearning", "foundation_io") + +WORKLOADS = { + "scheduler": (run_scheduler_workload, "iterations"), + "communication": (run_communication_workload, None), + "dp_unlearning": (run_dp_unlearning_workload, None), + "foundation_io": (run_foundation_io_workload, None), +} + + +def run( + paths: list[str], + config: dict[str, dict], + top_n: int = 20, + quiet: bool = False, +) -> dict[str, dict]: + all_results: dict[str, dict] = {} + report_lines: list[str] = [] + + for path in paths: + fn, _ = WORKLOADS[path] + kwargs = config.get(path, {}) + label_map = { + "dp_unlearning": "dp_unlearning", + } + label = label_map.get(path, path) + + print(_section(f"Profiling: {path}")) + timing, pstats_text = profile_fn(fn, kwargs, label) + all_results[path] = timing + + section_header = _section(f"cProfile top-{top_n}: {path}") + report_lines.append(section_header) + report_lines.append(pstats_text) + + if not quiet: + print(pstats_text[:3000]) # avoid terminal flood + + verdict = build_verdict_table(all_results) + report_lines.append(_section("Hot-Path Verdict")) + report_lines.append(verdict) + report_lines.append("") + + print(_section("Hot-Path Verdict")) + print(verdict) + + # Write summary text report + summary_txt = REPORTS_DIR / "hot_path_summary.txt" + summary_txt.write_text("\n".join(report_lines), encoding="utf-8") + print(f"\n[profiler] Text report → {summary_txt}") + + # Write JSON + summary_json = REPORTS_DIR / "full_summary.json" + summary_json.write_text(json.dumps(all_results, indent=2, default=str), encoding="utf-8") + print(f"[profiler] JSON summary → {summary_json}") + print(f"[profiler] .prof files → {REPORTS_DIR}/") + + return all_results + + +def main() -> None: + parser = argparse.ArgumentParser( + description="SAGE hot-path cProfile runner", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=__doc__, + ) + parser.add_argument( + "--path", + choices=list(ALL_PATHS), + help="Profile only this path (default: all)", + ) + parser.add_argument( + "--heavy", + action="store_true", + help="Use larger workload sizes", + ) + parser.add_argument( + "--iterations", + type=int, + default=None, + help="Override iterations for all paths", + ) + parser.add_argument( + "--top-n", + type=int, + default=20, + help="Number of functions in pstats output (default: 20)", + ) + parser.add_argument( + "--quiet", + action="store_true", + help="Suppress pstats stdout, only write files", + ) + args = parser.parse_args() + + config = HEAVY_CONFIG if args.heavy else DEFAULT_CONFIG + + if args.iterations: + for key in config: + config[key]["iterations"] = args.iterations + + paths = [args.path] if args.path else list(ALL_PATHS) + + run(paths, config, top_n=args.top_n, quiet=args.quiet) + + +if __name__ == "__main__": + main() diff --git a/tools/profiling/flame_graph.sh b/tools/profiling/flame_graph.sh new file mode 100644 index 0000000000..f1cdea3771 --- /dev/null +++ b/tools/profiling/flame_graph.sh @@ -0,0 +1,250 @@ +#!/usr/bin/env bash +# SAGE flame-graph collector — uses py-spy (SVG) + perf (Linux kernel stacks) +# +# Usage: +# bash tools/profiling/flame_graph.sh [PATH] [OPTIONS] +# +# Arguments: +# PATH One of: scheduler communication dp_unlearning foundation_io all +# Default: all +# +# Options: +# --duration N py-spy sampling duration in seconds (default: 30) +# --rate N py-spy sampling rate in Hz (default: 100) +# --no-pyspy Skip py-spy (even if installed) +# --perf Also collect Linux perf mixed-stack flame graph (requires root) +# --install pip-install py-spy before running +# +# Outputs written to tools/profiling/reports/flamegraph/ +# .svg — py-spy flame graph +# _perf.svg — perf + stackcollapse flame graph (Linux only, --perf) +# +# Dependencies: +# py-spy>=0.3 (pip install py-spy) +# FlameGraph scripts (cloned automatically if missing, --perf only) +# perf + linux-tools (--perf only, requires sudo) +# +# Notes: +# - py-spy flame graphs work without root on modern kernels (SYS_PTRACE cap) +# - perf mixed-mode requires sudo and kernel.perf_event_paranoid <= 1 +# - On WSL2, use py-spy only (perf has limited support) + +set -euo pipefail + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +REPORTS_DIR="$SCRIPT_DIR/reports/flamegraph" +WORKLOADS_DIR="$SCRIPT_DIR/workloads" + +mkdir -p "$REPORTS_DIR" + +# ---------------------------------------------------------------- +# Defaults +# ---------------------------------------------------------------- +SELECTED_PATH="${1:-all}" +DURATION=30 +RATE=100 +USE_PYSPY=true +USE_PERF=false +DO_INSTALL=false + +for arg in "${@:2}"; do + case "$arg" in + --duration=*) DURATION="${arg#*=}" ;; + --rate=*) RATE="${arg#*=}" ;; + --no-pyspy) USE_PYSPY=false ;; + --perf) USE_PERF=true ;; + --install) DO_INSTALL=true ;; + esac +done + +# ---------------------------------------------------------------- +# Colors +# ---------------------------------------------------------------- +RED='\033[0;31m'; YELLOW='\033[1;33m'; GREEN='\033[0;32m'; NC='\033[0m' + +log() { echo -e "${GREEN}[flame_graph]${NC} $*"; } +warn() { echo -e "${YELLOW}[flame_graph] WARN:${NC} $*"; } +err() { echo -e "${RED}[flame_graph] ERROR:${NC} $*"; } + +# ---------------------------------------------------------------- +# Install py-spy if requested +# ---------------------------------------------------------------- +if $DO_INSTALL; then + log "Installing py-spy..." + python -m pip install --quiet py-spy +fi + +# ---------------------------------------------------------------- +# Check py-spy availability +# ---------------------------------------------------------------- +PYSPY_CMD="" +if $USE_PYSPY; then + if command -v py-spy &>/dev/null; then + PYSPY_CMD="py-spy" + elif python -m py_spy --version &>/dev/null 2>&1; then + PYSPY_CMD="python -m py_spy" + else + warn "py-spy not found. Install with: pip install py-spy" + warn "Falling back to cProfile runner only." + USE_PYSPY=false + fi +fi + +# ---------------------------------------------------------------- +# Workload scripts mapped by path name +# ---------------------------------------------------------------- +declare -A WORKLOAD_SCRIPTS=( + ["scheduler"]="$WORKLOADS_DIR/workload_scheduler.py" + ["communication"]="$WORKLOADS_DIR/workload_communication.py" + ["dp_unlearning"]="$WORKLOADS_DIR/workload_dp_unlearning.py" + ["foundation_io"]="$WORKLOADS_DIR/workload_foundation_io.py" +) + +declare -A WORKLOAD_ARGS=( + ["scheduler"]="--iterations 500000" + ["communication"]="--iterations 1000000 --payload-size 1024" + ["dp_unlearning"]="--vectors 10000 --dim 128 --iterations 30" + ["foundation_io"]="--batch-size 256 --iterations 100000" +) + +# ---------------------------------------------------------------- +# Determine which paths to profile +# ---------------------------------------------------------------- +if [[ "$SELECTED_PATH" == "all" ]]; then + PATHS=("scheduler" "communication" "dp_unlearning" "foundation_io") +else + PATHS=("$SELECTED_PATH") +fi + +# ---------------------------------------------------------------- +# py-spy flame graph collector +# ---------------------------------------------------------------- +collect_pyspy() { + local path="$1" + local script="${WORKLOAD_SCRIPTS[$path]}" + local wargs="${WORKLOAD_ARGS[$path]}" + local svg_out="$REPORTS_DIR/${path}.svg" + + log "py-spy flame graph: ${path} → ${svg_out}" + + # Run workload in background, capture PID + python "$script" $wargs & + local pid=$! + + $PYSPY_CMD record \ + --pid "$pid" \ + --output "$svg_out" \ + --duration "$DURATION" \ + --rate "$RATE" \ + --format speedscope \ + 2>/dev/null || true + + # Also produce a raw SVG (flamegraph format) + $PYSPY_CMD record \ + --pid "$pid" \ + --output "${REPORTS_DIR}/${path}_flamegraph.svg" \ + --duration "$DURATION" \ + --rate "$RATE" \ + 2>/dev/null || true + + wait "$pid" 2>/dev/null || true + log "py-spy done: ${svg_out}" +} + +# ---------------------------------------------------------------- +# perf + FlameGraph (Linux, requires root) +# ---------------------------------------------------------------- +FLAMEGRAPH_DIR="/tmp/FlameGraph" + +ensure_flamegraph_scripts() { + if [[ ! -d "$FLAMEGRAPH_DIR" ]]; then + log "Cloning FlameGraph scripts..." + git clone --depth=1 https://github.com/brendangregg/FlameGraph "$FLAMEGRAPH_DIR" + fi +} + +collect_perf() { + local path="$1" + local script="${WORKLOAD_SCRIPTS[$path]}" + local wargs="${WORKLOAD_ARGS[$path]}" + local perf_data="$REPORTS_DIR/${path}.perf.data" + local svg_out="$REPORTS_DIR/${path}_perf.svg" + + if ! command -v perf &>/dev/null; then + warn "perf not available — skipping perf collection for ${path}" + return + fi + if [[ $EUID -ne 0 ]]; then + warn "perf mixed-mode requires root — skipping ${path}" + return + fi + + ensure_flamegraph_scripts + log "perf flame graph: ${path} → ${svg_out}" + + perf record \ + -F "$RATE" \ + -g \ + -e cpu-clock \ + -o "$perf_data" \ + -- python "$script" $wargs + + perf script -i "$perf_data" \ + | "$FLAMEGRAPH_DIR/stackcollapse-perf.pl" \ + | "$FLAMEGRAPH_DIR/flamegraph.pl" > "$svg_out" + + log "perf done: ${svg_out}" +} + +# ---------------------------------------------------------------- +# cProfile fallback (always runs to produce .prof files) +# ---------------------------------------------------------------- +collect_cprofile() { + local path="$1" + log "cProfile: ${path}" + python "$SCRIPT_DIR/cprofile_runner.py" \ + --path "$path" \ + --heavy \ + --quiet 2>&1 || warn "cprofile_runner.py failed for ${path}" +} + +# ---------------------------------------------------------------- +# Main loop +# ---------------------------------------------------------------- +for path in "${PATHS[@]}"; do + if [[ -z "${WORKLOAD_SCRIPTS[$path]+x}" ]]; then + err "Unknown path: '$path'. Valid: ${!WORKLOAD_SCRIPTS[*]}" + exit 1 + fi + + log "=== ${path} ===" + collect_cprofile "$path" + + if $USE_PYSPY; then + collect_pyspy "$path" + fi + + if $USE_PERF; then + collect_perf "$path" + fi +done + +# ---------------------------------------------------------------- +# Generate Markdown report +# ---------------------------------------------------------------- +if [[ -f "$SCRIPT_DIR/reports/full_summary.json" ]]; then + log "Generating Markdown report..." + python "$SCRIPT_DIR/report_generator.py" || warn "report_generator failed" +fi + +echo "" +log "=== Done ===" +log "Reports : $REPORTS_DIR" +log "Flamegraphs: $REPORTS_DIR/*.svg" +log "" +log "Open SVG in browser: xdg-open $REPORTS_DIR/dp_unlearning_flamegraph.svg" +log "View .prof in snakeviz: snakeviz $SCRIPT_DIR/reports/dp_unlearning.prof" +log "" +log "Commit results:" +log " cp $SCRIPT_DIR/reports/hot_path_report.md docs/profiling/" +log " git add docs/profiling/hot_path_report.md" diff --git a/tools/profiling/report_generator.py b/tools/profiling/report_generator.py new file mode 100644 index 0000000000..41a34965cb --- /dev/null +++ b/tools/profiling/report_generator.py @@ -0,0 +1,254 @@ +#!/usr/bin/env python3 +""" +SAGE Profiling Report Generator +================================ + +Converts the cProfile runner JSON output into a Markdown report suitable +for committing into docs/profiling/ or the GitHub wiki. + +Usage +----- + python tools/profiling/report_generator.py + python tools/profiling/report_generator.py --json path/to/full_summary.json + python tools/profiling/report_generator.py --output docs/profiling/hot_path_report.md + +The report includes: + - Executive summary table (hot path vs. cost vs. verdict) + - Per-path detailed analysis + - Recommendations for which paths to prioritize for C++ porting (#1468/#1469) +""" + +from __future__ import annotations + +import argparse +import json +import math +from datetime import datetime +from pathlib import Path + +_REPORTS_DIR = Path(__file__).resolve().parent / "reports" +_DEFAULT_JSON = _REPORTS_DIR / "full_summary.json" +_DEFAULT_MD = _REPORTS_DIR / "hot_path_report.md" + + +# --------------------------------------------------------------------------- +# Decision thresholds (µs/op). Values derived from SAGE perf targets. +# --------------------------------------------------------------------------- + +_THRESHOLDS: dict[str, tuple[str, float, str]] = { + # (metric_key, threshold_µs, description) + "Scheduler — RoundRobin decision": ("rr_per_decision_us", 0.5, "sage.runtime.scheduler"), + "Scheduler — Priority decision": ("pri_per_decision_us", 1.0, "sage.runtime.scheduler"), + "Comm — Packet construction": ("pkt_construct_us", 0.2, "sage.stream._runtime_kernel_types"), + "Comm — msgpack serialize": ("msgpack_ser_us", 1.0, "sage.runtime communication path"), + "Comm — msgpack deserialize": ("msgpack_deser_us", 1.0, "sage.runtime communication path"), + "Comm — Key routing": ("route_us", 0.1, "sage.stream._runtime_kernel_types"), + "DP — Vector perturbation": ("perturb_ms_per_call", 5_000.0, "sage.libs.unlearning"), + "DP — Neighbor compensation": ("comp_ms_per_call", 50_000.0, "sage.libs.unlearning"), + "DP — Full engine call": ("engine_ms_per_call", 100_000.0, "sage.libs.unlearning"), + "IO — Batch assembly": ("batch_assembly_us", 2.0, "sage.foundation / stream I/O"), + "IO — JSON roundtrip": ("json_roundtrip_us", 10.0, "sage.foundation / stream I/O"), + "IO — Queue enqueue": ("queue_enq_us", 0.5, "sage.foundation / stream I/O"), +} + + +def _verdict(val_us: float, threshold_us: float) -> str: + if val_us >= threshold_us * 2: + return "🔴 **HIGH** — C++ port recommended" + if val_us >= threshold_us: + return "🟡 **MED** — profile deeper" + return "🟢 LOW — skip for now" + + +def _fmt_us(val: float | None) -> str: + if val is None or (isinstance(val, float) and math.isnan(val)): + return "—" + if val >= 1_000_000: + return f"{val / 1_000_000:.1f}s" + if val >= 1_000: + return f"{val / 1_000:.2f}ms" + return f"{val:.2f}µs" + + +def build_report(data: dict, run_date: str | None = None) -> str: + run_date = run_date or datetime.now().strftime("%Y-%m-%d %H:%M") + + # Flatten all timing values + flat: dict[str, float] = {} + for section in data.values(): + if isinstance(section, dict): + flat.update(section) + + # Convert ms-named metrics to µs for comparison + def _to_us(metric: str, val: float) -> float: + # Only convert ms→µs when the metric name explicitly ends with _ms or contains _ms_ + # Avoid false positive on "msgpack" which contains "ms" as a substring. + if metric.endswith("_ms") or "_ms_" in metric or metric.endswith("_ms_per_call"): + return val * 1000 + return val + + lines: list[str] = [] + + lines += [ + "# SAGE Hot-Path Profiling Report", + "", + f"> Generated: {run_date} ", + "> Issue: [#1467](https://github.com/intellistream/SAGE/issues/1467) ", + "> Feeds into: [#1468](https://github.com/intellistream/SAGE/issues/1468) (runtime C++) · " + "[#1469](https://github.com/intellistream/SAGE/issues/1469) (algorithm / I/O C++)", + "", + "---", + "", + "## Executive Summary", + "", + "| Hot Path | Package | Measured | Threshold | Verdict |", + "|----------|---------|----------|-----------|---------|", + ] + + high_paths: list[str] = [] + med_paths: list[str] = [] + low_paths: list[str] = [] + + for label, (metric, threshold_us, pkg) in _THRESHOLDS.items(): + raw_val = flat.get(metric) + if raw_val is None: + disp = "—" + verdict = "❓ no data" + else: + val_us = _to_us(metric, raw_val) + disp = _fmt_us(val_us) + verdict = _verdict(val_us, threshold_us) + if "HIGH" in verdict: + high_paths.append(f"- **{label}** ({pkg}) — {disp}") + elif "MED" in verdict: + med_paths.append(f"- {label} ({pkg}) — {disp}") + else: + low_paths.append(f"- {label} ({pkg}) — {disp}") + + lines.append(f"| {label} | `{pkg}` | {disp} | {_fmt_us(threshold_us)} | {verdict} |") + + lines += [ + "", + "---", + "", + "## Recommendations", + "", + "### 🔴 Worth C++-porting (HIGH priority)", + ] + lines += high_paths or ["- *(none exceeded threshold)*"] + lines += [ + "", + "### 🟡 Profile deeper before deciding (MED priority)", + ] + lines += med_paths or ["- *(none in medium range)*"] + lines += [ + "", + "### 🟢 Not worth porting yet (LOW / skip)", + ] + lines += low_paths or ["- *(all paths below threshold)*"] + + # Per-section detail + lines += [ + "", + "---", + "", + "## Per-Path Detail", + "", + ] + + section_labels = { + "scheduler": "### Scheduler / Task Dispatch (`sage.runtime.scheduler`)", + "communication": "### Communication / Packet Serialization (`sage.stream` / `sage.runtime`)", + "dp_unlearning": "### DP Unlearning (`isage-privacy/sage_privacy.dp_unlearning`)", + "foundation_io": "### Foundation I/O (`sage.foundation` / stream I/O)", + } + + for section, header in section_labels.items(): + section_data = data.get(section) + if not section_data: + continue + lines.append(header) + lines.append("") + lines.append("| Metric | Value |") + lines.append("|--------|-------|") + for k, v in section_data.items(): + if k.startswith("_") or k in ("iterations",): + continue + if isinstance(v, float) and math.isnan(v): + lines.append(f"| `{k}` | — |") + elif isinstance(v, float): + lines.append(f"| `{k}` | {v:.4f} |") + else: + lines.append(f"| `{k}` | {v} |") + lines.append("") + + lines += [ + "---", + "", + "## How to Re-run", + "", + "```bash", + "# From SAGE repo root", + "python tools/profiling/cprofile_runner.py", + "", + "# Heavy mode (larger workloads)", + "python tools/profiling/cprofile_runner.py --heavy", + "", + "# Single path", + "python tools/profiling/cprofile_runner.py --path dp_unlearning", + "", + "# Flame graph via py-spy (requires: pip install py-spy)", + "bash tools/profiling/flame_graph.sh dp_unlearning", + "```", + "", + "## Notes", + "", + "- Measurements are wall-clock (perf_counter), not CPU-only.", + "- DP Unlearning benchmarks include NumPy allocation; " + "C++ target should reuse pre-allocated buffers.", + "- Communication benchmarks include stub Packet (no real network); " + "validate with actual Flownet traffic.", + "- See `.prof` files in `tools/profiling/reports/` for deep dives " + "with `snakeviz` or `gprof2dot`.", + "", + ] + + return "\n".join(lines) + + +def main() -> None: + parser = argparse.ArgumentParser(description="Generate profiling Markdown report") + parser.add_argument( + "--json", + default=str(_DEFAULT_JSON), + help=f"Path to full_summary.json (default: {_DEFAULT_JSON})", + ) + parser.add_argument( + "--output", + default=str(_DEFAULT_MD), + help=f"Output Markdown path (default: {_DEFAULT_MD})", + ) + args = parser.parse_args() + + json_path = Path(args.json) + if not json_path.exists(): + print( + f"[report] {json_path} not found — run cprofile_runner.py first.\n" + f" python tools/profiling/cprofile_runner.py" + ) + raise SystemExit(1) + + with open(json_path, encoding="utf-8") as f: + data = json.load(f) + + report = build_report(data) + + out_path = Path(args.output) + out_path.parent.mkdir(parents=True, exist_ok=True) + out_path.write_text(report, encoding="utf-8") + print(f"[report] Markdown report → {out_path}") + print("[report] Copy to docs/profiling/ or GitHub wiki when ready.") + + +if __name__ == "__main__": + main() diff --git a/tools/profiling/reports/.gitignore b/tools/profiling/reports/.gitignore new file mode 100644 index 0000000000..80cc151f98 --- /dev/null +++ b/tools/profiling/reports/.gitignore @@ -0,0 +1,6 @@ +# Generated profiling artifacts — large, machine-specific +*.prof +flamegraph/ +full_summary.json +hot_path_summary.txt +hot_path_report.md diff --git a/tools/profiling/workloads/__init__.py b/tools/profiling/workloads/__init__.py new file mode 100644 index 0000000000..6cd1c09e89 --- /dev/null +++ b/tools/profiling/workloads/__init__.py @@ -0,0 +1 @@ +# profiling workloads package diff --git a/tools/profiling/workloads/workload_communication.py b/tools/profiling/workloads/workload_communication.py new file mode 100644 index 0000000000..3432e5b2ad --- /dev/null +++ b/tools/profiling/workloads/workload_communication.py @@ -0,0 +1,237 @@ +""" +Profiling Workload — SAGE Runtime Packet / Communication Path +============================================================== + +Measures the cost of: + 1. Packet construction (Python object creation + timestamp) + 2. msgpack serialization/deserialization (Python-layer overhead) + 3. Router message dispatch (packet routing decision) + +These are the hot paths in inter-operator communication at high throughput. + +Run standalone: + python workload_communication.py [--iterations 100000] [--payload-size 1024] +""" + +from __future__ import annotations + +import argparse +import math +import struct +import time +from typing import Any + +# --------------------------------------------------------------------------- +# Try to import real Packet; fall back to a stub +# --------------------------------------------------------------------------- + + +def _try_import_packet(): + try: + from sage.stream._runtime_kernel_types import Packet # noqa: PLC0415 + + return Packet + except Exception: # noqa: BLE001 + return None + + +class _StubPacket: + """Stub matching the real Packet interface.""" + + __slots__ = ( + "payload", + "input_index", + "partition_key", + "partition_strategy", + "timestamp", + ) + + def __init__( + self, + payload: Any, + input_index: int = 0, + partition_key: Any = None, + partition_strategy: str | None = None, + ): + self.payload = payload + self.input_index = input_index + self.partition_key = partition_key + self.partition_strategy = partition_strategy + self.timestamp = time.time_ns() + + def is_keyed(self) -> bool: + return self.partition_key is not None + + def inherit_partition_info(self, new_payload: Any) -> _StubPacket: + return _StubPacket( + payload=new_payload, + input_index=self.input_index, + partition_key=self.partition_key, + partition_strategy=self.partition_strategy, + ) + + +# --------------------------------------------------------------------------- +# msgpack helpers +# --------------------------------------------------------------------------- + + +def _get_msgpack(): + try: + import msgpack # noqa: PLC0415 + + return msgpack + except ImportError: + return None + + +def _manual_serialize(data: dict) -> bytes: + """Fallback: struct-based binary packing (no msgpack dependency).""" + payload_bytes = str(data.get("payload", "")).encode() + return struct.pack( + f"!BIi{len(payload_bytes)}s", + data.get("input_index", 0), + len(data.get("partition_key") or b""), + data.get("timestamp", 0) & 0x7FFFFFFF, + payload_bytes, + ) + + +# --------------------------------------------------------------------------- +# Individual benchmarks +# --------------------------------------------------------------------------- + + +def bench_packet_construction( + Packet, # noqa: N803 + iterations: int, + payload: Any, +) -> float: + """Return total seconds for `iterations` Packet constructions.""" + t0 = time.perf_counter() + for i in range(iterations): + Packet( + payload=payload, + input_index=i % 8, + partition_key=i % 64, + ) + return time.perf_counter() - t0 + + +def bench_msgpack_roundtrip(msgpack, iterations: int, payload_size: int) -> tuple[float, float]: + """Return (ser_seconds, deser_seconds) for `iterations` msgpack roundtrips.""" + data = { + "payload": b"x" * payload_size, + "input_index": 0, + "partition_key": 42, + "timestamp": time.time_ns(), + } + packer = msgpack.Packer(use_bin_type=True) + + # Serialization + t0 = time.perf_counter() + serialized = None + for _ in range(iterations): + serialized = packer.pack(data) + ser_total = time.perf_counter() - t0 + + # Deserialization + t0 = time.perf_counter() + for _ in range(iterations): + msgpack.unpackb(serialized, raw=False) + deser_total = time.perf_counter() - t0 + + return ser_total, deser_total + + +def bench_key_routing(Packet, iterations: int) -> float: # noqa: N803 + """Simulate partition key hashing / routing decision.""" + packets = [ + Packet(payload=i, input_index=i % 4, partition_key=f"user:{i % 1000}") + for i in range(min(1000, iterations)) + ] + num_partitions = 16 + t0 = time.perf_counter() + for i in range(iterations): + pkt = packets[i % len(packets)] + # Simulate hash-routing decision (as router.py would do) + if pkt.is_keyed(): + _ = hash(pkt.partition_key) % num_partitions + return time.perf_counter() - t0 + + +# --------------------------------------------------------------------------- +# Main runner +# --------------------------------------------------------------------------- + + +def run_communication_workload( + iterations: int = 100_000, payload_size: int = 1024 +) -> dict[str, float]: + """ + Run all communication benchmarks and return timing stats. + + Returns + ------- + dict with timing keys (seconds and µs/op) + """ + Packet = _try_import_packet() or _StubPacket # noqa: N806 + if Packet is _StubPacket: + print("[comm] in-tree Packet not available — running with stub Packet") + else: + print("[comm] Using real sage.stream._runtime_kernel_types.Packet") + + msgpack = _get_msgpack() + + payload = b"x" * payload_size + + # 1. Packet construction + pkt_total = bench_packet_construction(Packet, iterations, payload) + + # 2. msgpack roundtrip + if msgpack: + ser_total, deser_total = bench_msgpack_roundtrip(msgpack, iterations, payload_size) + else: + print("[comm] msgpack not installed — skipping serialization bench") + ser_total = deser_total = math.nan + + # 3. Key routing + route_total = bench_key_routing(Packet, iterations) + + results = { + "iterations": iterations, + "payload_size_bytes": payload_size, + "pkt_construct_total_s": pkt_total, + "pkt_construct_us": pkt_total / iterations * 1e6, + "msgpack_ser_total_s": ser_total, + "msgpack_ser_us": (ser_total / iterations * 1e6) if not math.isnan(ser_total) else math.nan, + "msgpack_deser_total_s": deser_total, + "msgpack_deser_us": (deser_total / iterations * 1e6) + if not math.isnan(deser_total) + else math.nan, + "route_total_s": route_total, + "route_us": route_total / iterations * 1e6, + } + + def _fmt(val: float, unit: str = "µs") -> str: + return f"{val:.3f} {unit}" if not math.isnan(val) else "N/A" + + print( + f"[comm] Packet construction : {_fmt(results['pkt_construct_us'])} /pkt" + f" (total={pkt_total:.3f}s)" + ) + print( + f"[comm] msgpack serialize : {_fmt(results['msgpack_ser_us'])} /msg" + f" (payload={payload_size}B)" + ) + print(f"[comm] msgpack deserialize : {_fmt(results['msgpack_deser_us'])} /msg") + print(f"[comm] Key routing : {_fmt(results['route_us'])} /pkt") + return results + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Communication profiling workload") + parser.add_argument("--iterations", type=int, default=100_000) + parser.add_argument("--payload-size", type=int, default=1024) + args = parser.parse_args() + run_communication_workload(args.iterations, args.payload_size) diff --git a/tools/profiling/workloads/workload_dp_unlearning.py b/tools/profiling/workloads/workload_dp_unlearning.py new file mode 100644 index 0000000000..664d2ce41f --- /dev/null +++ b/tools/profiling/workloads/workload_dp_unlearning.py @@ -0,0 +1,334 @@ +""" +Profiling Workload — isage-privacy DP Unlearning (sage_privacy.dp_unlearning) +============================================================================= + +Measures the hot loops in differential-privacy unlearning: + 1. Vector perturbation — NumPy noise generation over embedding batches + 2. Privacy accountant — epsilon/delta budget accumulation + 3. Neighbor compensation — graph-walk update after unlearning + 4. Full engine run — end-to-end unlearn() call + +This workload uses the real sage_privacy classes when available, and falls +back to equivalent NumPy-based stubs that mirror the actual computation. + +Run standalone: + python workload_dp_unlearning.py [--vectors 5000] [--dim 128] [--iterations 10] +""" + +from __future__ import annotations + +import argparse +import time +from typing import Any + +import numpy as np + +# --------------------------------------------------------------------------- +# Try to import real classes +# --------------------------------------------------------------------------- + + +def _try_import_unlearning(): + try: + from sage_privacy.dp_unlearning.base_mechanism import ( # noqa: PLC0415 + SimpleLaplaceMechanism, + ) + from sage_privacy.dp_unlearning.neighbor_compensation import ( # noqa: PLC0415 + NeighborCompensation, + ) + from sage_privacy.dp_unlearning.privacy_accountant import ( # noqa: PLC0415 + PrivacyAccountant, + ) + from sage_privacy.dp_unlearning.unlearning_engine import ( # noqa: PLC0415 + UnlearningEngine, + ) + from sage_privacy.dp_unlearning.vector_perturbation import ( # noqa: PLC0415 + VectorPerturbation, + ) + + return ( + SimpleLaplaceMechanism, + VectorPerturbation, + PrivacyAccountant, + NeighborCompensation, + UnlearningEngine, + True, # is_real + ) + except Exception: # noqa: BLE001 + return None + + +# --------------------------------------------------------------------------- +# Stubs mirroring the real computation +# --------------------------------------------------------------------------- + + +class _StubLaplace: + """Stub mirroring SimpleLaplaceMechanism interface.""" + + def __init__(self, epsilon: float = 1.0, sensitivity: float = 1.0): + self.epsilon = epsilon + self.sensitivity = sensitivity + + def compute_noise(self) -> float: + scale = self.sensitivity / self.epsilon + return float(np.random.laplace(0, scale)) + + def add_noise(self, data: np.ndarray) -> np.ndarray: + scale = self.sensitivity / self.epsilon + return data + np.random.laplace(0, scale, data.shape).astype(data.dtype) + + def get_privacy_cost(self) -> tuple[float, float]: + return (self.epsilon, 1e-5) + + +class _StubPerturbation: + """Stub mirroring VectorPerturbation.perturb_single_vector() interface.""" + + def __init__(self, mechanism: Any): + self.mechanism = mechanism + + def perturb_single_vector(self, vector: np.ndarray, strategy: str = "uniform") -> np.ndarray: + return self.mechanism.add_noise(vector) + + +class _StubAccountant: + """Stub mirroring PrivacyAccountant (total_epsilon_budget, total_delta_budget, composition_type).""" + + def __init__( + self, + total_epsilon_budget: float = 10.0, + total_delta_budget: float = 1e-5, + composition_type: str = "basic", + ): + self._budget_eps = total_epsilon_budget + self._budget_delta = total_delta_budget + self._spent = 0.0 + + def record_operation( + self, + epsilon: float, + delta: float, + operation: str = "unlearn", + mechanism: str = "laplace", + metadata: dict | None = None, + ) -> bool: + self._spent += epsilon + return True + + def can_afford(self, epsilon: float) -> bool: + return self._spent + epsilon <= self._budget_eps + + def get_remaining_budget(self) -> tuple[float, float]: + return (max(0.0, self._budget_eps - self._spent), self._budget_delta) + + +class _StubNeighborCompensation: + """Stub mirroring NeighborCompensation.compute_compensation() interface.""" + + def __init__(self, k_neighbors: int = 10): + self.k_neighbors = k_neighbors + + def compute_compensation( + self, + original_vector: np.ndarray, + perturbed_vector: np.ndarray, + neighbor_vector: np.ndarray, + neighbor_similarity: float, + ) -> np.ndarray: + delta = perturbed_vector - original_vector + return neighbor_vector + neighbor_similarity * delta * 0.1 + + +class _StubEngine: + """Stub mirroring UnlearningEngine.unlearn_vectors() interface.""" + + def __init__( + self, + epsilon: float = 1.0, + delta: float = 1e-5, + total_budget_epsilon: float = 10.0, + total_budget_delta: float = 1e-4, + enable_compensation: bool = True, + ): + self._mech = _StubLaplace(epsilon) + self._perturb = _StubPerturbation(self._mech) + self._acct = _StubAccountant(total_budget_epsilon, total_budget_delta) + self._comp = _StubNeighborCompensation() + self._enable_compensation = enable_compensation + + def unlearn_vectors( + self, + vectors_to_forget: np.ndarray, + vector_ids_to_forget: list[str], + all_vectors: np.ndarray | None = None, + all_vector_ids: list[str] | None = None, + perturbation_strategy: str = "uniform", + return_compensated_neighbors: bool = False, + ) -> Any: + eps, delta = self._mech.get_privacy_cost() + if not self._acct.can_afford(eps): + return None + perturbed = np.array([self._perturb.perturb_single_vector(v) for v in vectors_to_forget]) + self._acct.record_operation(eps, delta, "unlearn", "laplace") + return perturbed + + +# --------------------------------------------------------------------------- +# Individual benchmarks +# --------------------------------------------------------------------------- + + +def bench_vector_perturbation( + Perturb: Any, Mech: Any, vectors: np.ndarray, iterations: int, is_real: bool +) -> float: + """Return total seconds for `iterations` batch perturbation passes.""" + mech = Mech(epsilon=1.0) if is_real else Mech(epsilon=1.0) + p = Perturb(mech) + sample = vectors[: min(100, len(vectors))] # perturb a 100-vec batch per call + t0 = time.perf_counter() + for _ in range(iterations): + for v in sample: + p.perturb_single_vector(v) + return time.perf_counter() - t0 + + +def bench_privacy_accounting(Acct: Any, iterations: int, is_real: bool) -> float: + # Budget must exceed iterations × per-call cost; add 10× headroom + eps_per_call, delta_per_call = 0.01, 1e-6 + a = Acct( + total_epsilon_budget=eps_per_call * iterations * 10, + total_delta_budget=delta_per_call * iterations * 10, + ) + t0 = time.perf_counter() + for _ in range(iterations): + a.record_operation(eps_per_call, delta_per_call, "unlearn", "laplace") + a.get_remaining_budget() + return time.perf_counter() - t0 + + +def bench_neighbor_compensation( + Comp: Any, vectors: np.ndarray, unlearn_mask: np.ndarray, iterations: int, is_real: bool +) -> float: + c = Comp() + forget_vec = vectors[unlearn_mask][0] + perturbed_vec = forget_vec + np.random.randn(*forget_vec.shape).astype(np.float32) * 0.1 + neighbor_vec = vectors[~unlearn_mask][0] + t0 = time.perf_counter() + for _ in range(iterations): + c.compute_compensation( + original_vector=forget_vec, + perturbed_vector=perturbed_vec, + neighbor_vector=neighbor_vec, + neighbor_similarity=0.85, + ) + return time.perf_counter() - t0 + + +def bench_full_engine( + Engine: Any, vectors: np.ndarray, num_iterations: int, is_real: bool +) -> float: + if is_real: + engine = Engine(epsilon=2.0, delta=1e-5, total_budget_epsilon=500.0) + else: + engine = Engine(epsilon=2.0, delta=1e-5, total_budget_epsilon=500.0) + n = max(1, len(vectors) // 20) # unlearn 5% each round + ids_all = [str(i) for i in range(len(vectors))] + t0 = time.perf_counter() + for i in range(num_iterations): + idx = np.arange(i * n % len(vectors), min(i * n % len(vectors) + n, len(vectors))) + engine.unlearn_vectors( + vectors_to_forget=vectors[idx], + vector_ids_to_forget=[ids_all[j] for j in idx], + all_vectors=vectors, + all_vector_ids=ids_all, + ) + return time.perf_counter() - t0 + + +# --------------------------------------------------------------------------- +# Main runner +# --------------------------------------------------------------------------- + + +def run_dp_unlearning_workload( + num_vectors: int = 5_000, + dim: int = 128, + iterations: int = 10, +) -> dict[str, float]: + """ + Run DP unlearning benchmarks and return timing stats. + """ + real = _try_import_unlearning() + if real: + Mech, Perturb, Acct, Comp, Engine, is_real = real + print("[dp_unlearn] Using real sage_privacy.dp_unlearning classes") + else: + Mech, Perturb, Acct, Comp, Engine, is_real = ( + _StubLaplace, + _StubPerturbation, + _StubAccountant, + _StubNeighborCompensation, + _StubEngine, + False, + ) + print("[dp_unlearn] sage.libs not available — running synthetic stubs") + + rng = np.random.default_rng(42) + vectors = rng.standard_normal((num_vectors, dim)).astype(np.float32) + unlearn_n = max(1, num_vectors // 20) + unlearn_mask = np.zeros(num_vectors, dtype=bool) + unlearn_mask[:unlearn_n] = True + + # 1. Vector perturbation (batch of 100 vectors × iterations) + perturb_total = bench_vector_perturbation(Perturb, Mech, vectors, iterations, is_real) + + # 2. Privacy accounting + acct_total = bench_privacy_accounting(Acct, iterations * 100, is_real) + + # 3. Neighbor compensation + comp_total = bench_neighbor_compensation(Comp, vectors, unlearn_mask, iterations, is_real) + + # 4. Full engine + engine_total = bench_full_engine(Engine, vectors, iterations, is_real) + + throughput_vecs = num_vectors * iterations / engine_total if engine_total > 0 else 0 + + results = { + "num_vectors": num_vectors, + "dim": dim, + "iterations": iterations, + "perturb_total_s": perturb_total, + "perturb_ms_per_call": perturb_total / iterations * 1e3, + "acct_total_s": acct_total, + "acct_us_per_call": acct_total / (iterations * 100) * 1e6, + "comp_total_s": comp_total, + "comp_ms_per_call": comp_total / iterations * 1e3, + "engine_total_s": engine_total, + "engine_ms_per_call": engine_total / iterations * 1e3, + "engine_vectors_per_sec": throughput_vecs, + } + + print( + f"[dp_unlearn] Perturbation : {results['perturb_ms_per_call']:.2f} ms/call" + f" ({num_vectors} vecs, dim={dim})" + ) + print(f"[dp_unlearn] Acct update : {results['acct_us_per_call']:.2f} µs/call") + print( + f"[dp_unlearn] Neighbor comp : {results['comp_ms_per_call']:.2f} ms/call" + f" (unlearn={unlearn_n} vecs)" + ) + print( + f"[dp_unlearn] Full engine : {results['engine_ms_per_call']:.2f} ms/call" + f" ({throughput_vecs:.0f} vecs/s)" + ) + return results + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="DP Unlearning profiling workload") + parser.add_argument("--vectors", type=int, default=5_000) + parser.add_argument("--dim", type=int, default=128) + parser.add_argument("--iterations", type=int, default=10) + args = parser.parse_args() + run_dp_unlearning_workload(args.vectors, args.dim, args.iterations) diff --git a/tools/profiling/workloads/workload_foundation_io.py b/tools/profiling/workloads/workload_foundation_io.py new file mode 100644 index 0000000000..821341f83e --- /dev/null +++ b/tools/profiling/workloads/workload_foundation_io.py @@ -0,0 +1,210 @@ +""" +Profiling Workload — SAGE Foundation / Stream I/O +================================================= + +Measures the hot paths in streaming data ingestion: + 1. BatchFunction execution overhead (Python call + data copy) + 2. Streaming queue throughput (enqueue/dequeue tight loop) + 3. Batch assembly — accumulate N items then flush + 4. JSON decode path (common text payload) + +Run standalone: + python workload_foundation_io.py [--batch-size 256] [--iterations 10000] +""" + +from __future__ import annotations + +import argparse +import io +import json +import queue +import time +from typing import Any + +# --------------------------------------------------------------------------- +# Try to import real sage.libs.foundation.io / batch classes +# --------------------------------------------------------------------------- + + +def _try_import_foundation(): + try: + from sage.libs.foundation.io.batch import BatchFunction # noqa: PLC0415 + + return BatchFunction + except Exception: # noqa: BLE001 + return None + + +# --------------------------------------------------------------------------- +# Stubs +# --------------------------------------------------------------------------- + + +class _StubBatchFunction: + """Mimics sage.libs.foundation.io.batch.BatchFunction behavior.""" + + def __init__(self, fn, batch_size: int = 64): + self._fn = fn + self._batch_size = batch_size + self._buffer: list = [] + + def add(self, item: Any) -> list | None: + self._buffer.append(item) + if len(self._buffer) >= self._batch_size: + batch = self._buffer + self._buffer = [] + return self._fn(batch) + return None + + def flush(self) -> list | None: + if self._buffer: + batch = self._buffer + self._buffer = [] + return self._fn(batch) + return None + + +# --------------------------------------------------------------------------- +# Individual benchmarks +# --------------------------------------------------------------------------- + + +def bench_batch_assembly(BatchFn, batch_size: int, iterations: int) -> tuple[float, int]: # noqa: N803 + """Return (total_seconds, num_batches_flushed).""" + batches_flushed = 0 + + def _noop(batch: list) -> list: + return batch + + bf = BatchFn(_noop, batch_size=batch_size) + t0 = time.perf_counter() + for i in range(iterations): + result = bf.add({"id": i, "value": i * 1.5}) + if result is not None: + batches_flushed += 1 + bf.flush() + return time.perf_counter() - t0, batches_flushed + + +def bench_queue_throughput(iterations: int) -> tuple[float, float]: + """ + Return (enqueue_sec, dequeue_sec) for a stdlib queue.Queue. + This models the in-process queue that separates source and operator threads. + """ + q: queue.Queue = queue.Queue(maxsize=0) + payload = b"x" * 256 # 256-byte payload + + t0 = time.perf_counter() + for i in range(iterations): + q.put_nowait(payload) + enq_total = time.perf_counter() - t0 + + t0 = time.perf_counter() + for _ in range(iterations): + q.get_nowait() + deq_total = time.perf_counter() - t0 + + return enq_total, deq_total + + +def bench_json_decode(iterations: int, payload_size: int = 512) -> float: + """Return total seconds for `iterations` JSON decode+encode cycles.""" + record = { + "id": 12345, + "text": "a" * payload_size, + "tags": ["nlp", "rag", "streaming"], + "score": 0.9875, + } + raw = json.dumps(record).encode() + + t0 = time.perf_counter() + for _ in range(iterations): + obj = json.loads(raw) + _ = json.dumps(obj).encode() + return time.perf_counter() - t0 + + +def bench_file_io(iterations: int, record_size: int = 512) -> tuple[float, float]: + """Return (write_sec, read_sec) against an in-memory buffer (BytesIO).""" + data = b"x" * record_size + buf = io.BytesIO() + + t0 = time.perf_counter() + for _ in range(iterations): + buf.write(data) + write_total = time.perf_counter() - t0 + + buf.seek(0) + t0 = time.perf_counter() + for _ in range(iterations): + chunk = buf.read(record_size) + if not chunk: + buf.seek(0) + read_total = time.perf_counter() - t0 + + return write_total, read_total + + +# --------------------------------------------------------------------------- +# Main runner +# --------------------------------------------------------------------------- + + +def run_foundation_io_workload(batch_size: int = 256, iterations: int = 10_000) -> dict[str, float]: + """ + Run Foundation I/O benchmarks and return timing stats. + """ + BatchFn = _try_import_foundation() or _StubBatchFunction # noqa: N806 + if BatchFn is _StubBatchFunction: + print("[foundation_io] sage.libs not available — running stubs") + else: + print("[foundation_io] Using real sage.libs.foundation.io.batch.BatchFunction") + + # 1. Batch assembly + batch_total, batches_flushed = bench_batch_assembly(BatchFn, batch_size, iterations) + + # 2. Queue throughput + enq_total, deq_total = bench_queue_throughput(iterations) + + # 3. JSON decode/encode + json_total = bench_json_decode(iterations) + + # 4. File I/O (BytesIO proxy) + write_total, read_total = bench_file_io(iterations) + + results = { + "batch_size": batch_size, + "iterations": iterations, + "batch_assembly_total_s": batch_total, + "batch_assembly_us": batch_total / iterations * 1e6, + "batches_flushed": batches_flushed, + "queue_enq_total_s": enq_total, + "queue_enq_us": enq_total / iterations * 1e6, + "queue_deq_total_s": deq_total, + "queue_deq_us": deq_total / iterations * 1e6, + "json_roundtrip_total_s": json_total, + "json_roundtrip_us": json_total / iterations * 1e6, + "file_write_total_s": write_total, + "file_write_us": write_total / iterations * 1e6, + "file_read_total_s": read_total, + "file_read_us": read_total / iterations * 1e6, + } + + print( + f"[foundation_io] Batch assembly : {results['batch_assembly_us']:.2f} µs/item" + f" (batch={batch_size}, flushed={batches_flushed})" + ) + print(f"[foundation_io] Queue enqueue : {results['queue_enq_us']:.2f} µs/item") + print(f"[foundation_io] Queue dequeue : {results['queue_deq_us']:.2f} µs/item") + print(f"[foundation_io] JSON roundtrip : {results['json_roundtrip_us']:.2f} µs/record") + print(f"[foundation_io] BytesIO write : {results['file_write_us']:.2f} µs/record") + print(f"[foundation_io] BytesIO read : {results['file_read_us']:.2f} µs/record") + return results + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Foundation I/O profiling workload") + parser.add_argument("--batch-size", type=int, default=256) + parser.add_argument("--iterations", type=int, default=10_000) + args = parser.parse_args() + run_foundation_io_workload(args.batch_size, args.iterations) diff --git a/tools/profiling/workloads/workload_scheduler.py b/tools/profiling/workloads/workload_scheduler.py new file mode 100644 index 0000000000..bc520f09f5 --- /dev/null +++ b/tools/profiling/workloads/workload_scheduler.py @@ -0,0 +1,158 @@ +""" +Profiling Workload — SAGE Runtime Scheduler / Task Dispatch +============================================================ + +Simulates heavy task-dispatch traffic to measure scheduler hot path: + - make_decision() loop (N decisioned tasks) + - PlacementDecision construction overhead + - SchedulerPolicy lookup / strategy selection cost + +Run standalone: + python workload_scheduler.py [--iterations 50000] +""" + +from __future__ import annotations + +import argparse +import time +from dataclasses import dataclass, field +from typing import Any + +# --------------------------------------------------------------------------- +# Lightweight stubs that mirror the real in-tree runtime API so this workload +# can be executed even when the local SAGE package is not installed. When the real +# package is available the real classes are used instead. +# --------------------------------------------------------------------------- + + +def _try_import_real(): + """Return (Scheduler, PlacementDecision, TaskNode stubs or real impls).""" + try: + from sage.runtime.scheduler import ( # noqa: PLC0415 + FIFOScheduler, + LoadAwareScheduler, + PlacementDecision, + ) + + return PlacementDecision, FIFOScheduler, LoadAwareScheduler + except Exception: # noqa: BLE001 + return None + + +# --------------------------------------------------------------------------- +# Synthetic stubs (used when package is absent / import fails) +# --------------------------------------------------------------------------- + + +@dataclass +class _FakeTaskNode: + name: str + parallelism: int = 1 + cpu_required: float = 1.0 + memory_required: str = "1GB" + priority: int = 0 + metadata: dict[str, Any] = field(default_factory=dict) + + +@dataclass +class _FakePlacementDecision: + node_name: str + assigned_workers: list[str] + delay: float = 0.0 + metadata: dict[str, Any] = field(default_factory=dict) + + +class _FakeRoundRobinScheduler: + def __init__(self, worker_count: int = 4): + self._workers = [f"worker-{i}" for i in range(worker_count)] + self._idx = 0 + + def make_decision(self, node: _FakeTaskNode) -> _FakePlacementDecision: + assigned = self._workers[self._idx % len(self._workers)] + self._idx += 1 + return _FakePlacementDecision( + node_name=node.name, + assigned_workers=[assigned], + ) + + +class _FakePriorityScheduler: + def __init__(self, worker_count: int = 4): + self._workers = [f"worker-{i}" for i in range(worker_count)] + + def make_decision(self, node: _FakeTaskNode) -> _FakePlacementDecision: + # Simulate priority-based selection with a sort + chosen = sorted(self._workers, key=lambda w: hash(node.name + w))[0] + return _FakePlacementDecision( + node_name=node.name, + assigned_workers=[chosen], + ) + + +# --------------------------------------------------------------------------- +# Workload +# --------------------------------------------------------------------------- + + +def run_scheduler_workload(iterations: int = 50_000) -> dict[str, float]: + """ + Drive the scheduler in a tight loop and return timing stats. + + Returns + ------- + dict with keys: rr_total_s, rr_per_decision_us, pri_total_s, pri_per_decision_us + """ + _real = _try_import_real() + if _real: + _Decision, RRScheduler, PriScheduler = _real + print("[scheduler] Using real in-tree SAGE scheduler classes") + else: + RRScheduler = _FakeRoundRobinScheduler # type: ignore[assignment] + PriScheduler = _FakePriorityScheduler # type: ignore[assignment] + print("[scheduler] in-tree scheduler not available — running synthetic stub") + + tasks = [ + _FakeTaskNode(name=f"op-{i}", parallelism=(i % 8) + 1, priority=i % 5) + for i in range(min(1000, iterations)) + ] + + # --- Round-robin --- + rr = RRScheduler() + t0 = time.perf_counter() + for i in range(iterations): + node = tasks[i % len(tasks)] + rr.make_decision(node) + rr_total = time.perf_counter() - t0 + + # --- Priority-based --- + pri = PriScheduler() + t0 = time.perf_counter() + for i in range(iterations): + node = tasks[i % len(tasks)] + pri.make_decision(node) + pri_total = time.perf_counter() - t0 + + results = { + "rr_total_s": rr_total, + "rr_per_decision_us": rr_total / iterations * 1e6, + "pri_total_s": pri_total, + "pri_per_decision_us": pri_total / iterations * 1e6, + "iterations": iterations, + } + + print( + f"[scheduler] RoundRobin : {results['rr_per_decision_us']:.3f} µs/decision" + f" ({iterations:,} iters, total={rr_total:.3f}s)" + ) + print( + f"[scheduler] Priority : {results['pri_per_decision_us']:.3f} µs/decision" + f" ({iterations:,} iters, total={pri_total:.3f}s)" + ) + return results + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Scheduler profiling workload") + parser.add_argument("--iterations", type=int, default=50_000) + args = parser.parse_args() + run_scheduler_workload(args.iterations) diff --git a/tools/quality/fix-code-quality.sh b/tools/quality/fix-code-quality.sh index b3584ab235..3adfcbb91d 100755 --- a/tools/quality/fix-code-quality.sh +++ b/tools/quality/fix-code-quality.sh @@ -8,7 +8,7 @@ REPO_ROOT="$(cd "$(dirname "$0")/.." && pwd)" if ! command -v sage-dev >/dev/null 2>&1; then echo "❌ 未检测到 sage-dev 命令" - echo "➡️ 请先安装/激活开发环境: pip install -e packages/sage-tools" + echo "➡️ 请先安装/激活开发环境: pip install 'isage[dev]'" exit 1 fi diff --git a/tools/scripts/add_test_mode_to_examples.py b/tools/scripts/add_test_mode_to_examples.py deleted file mode 100644 index 8d70f1434e..0000000000 --- a/tools/scripts/add_test_mode_to_examples.py +++ /dev/null @@ -1,353 +0,0 @@ -#!/usr/bin/env python3 -""" -add_test_mode_to_examples.py - -为 SAGE examples 添加测试模式支持的辅助脚本。 - -Usage: - python tools/scripts/add_test_mode_to_examples.py [--dry-run] [--file ] - -Examples: - # 扫描所有 examples - python tools/scripts/add_test_mode_to_examples.py --dry-run - - # 为特定文件添加测试模式 - python tools/scripts/add_test_mode_to_examples.py --file packages/sage-libs/examples/some_example.py -""" - -import argparse -import sys -from pathlib import Path - - -def has_test_mode_support(file_path: Path) -> bool: - """检查文件是否已支持测试模式。""" - content = file_path.read_text(encoding="utf-8") - return "SAGE_TEST_MODE" in content or "SAGE_EXAMPLES_MODE" in content - - -def has_main_block(file_path: Path) -> bool: - """检查文件是否有 if __name__ == "__main__" 块。""" - content = file_path.read_text(encoding="utf-8") - return 'if __name__ == "__main__"' in content - - -def analyze_example(file_path: Path) -> dict: - """分析 example 文件，返回建议。""" - result = { - "path": str(file_path), - "has_test_mode": has_test_mode_support(file_path), - "has_main_block": has_main_block(file_path), - "needs_test_mode": False, - "difficulty": "unknown", - "suggestion": "", - } - - content = file_path.read_text(encoding="utf-8") - - # 如果已有测试模式支持 - if result["has_test_mode"]: - result["suggestion"] = "✅ Already has test mode support" - return result - - # 检查是否需要 API keys - needs_api = any( - keyword in content - for keyword in [ - "openai", - "OpenAI", - "api_key", - "API_KEY", - "llm", - "LLM", - "embeddings", - ] - ) - - # 检查是否有复杂的外部依赖 - complex_deps = any( - keyword in content - for keyword in [ - "ray.init", - "redis", - "docker", - "kubernetes", - "spark", - ] - ) - - # 判断难度 - if not result["has_main_block"]: - result["difficulty"] = "hard" - result["suggestion"] = "⚠️ No main block - needs major refactoring" - elif complex_deps: - result["difficulty"] = "hard" - result["suggestion"] = "⚠️ Complex dependencies - needs careful handling" - elif needs_api: - result["difficulty"] = "medium" - result["suggestion"] = "💡 Needs API key handling in test mode" - result["needs_test_mode"] = True - else: - result["difficulty"] = "easy" - result["suggestion"] = "✨ Easy - just add test mode check" - result["needs_test_mode"] = True - - return result - - -def generate_test_mode_template(file_path: Path) -> str: - """生成测试模式代码模板。""" - return ''' -# Add this near the top of the file, after imports -def is_test_mode() -> bool: - """Check if running in test mode.""" - return ( - os.getenv("SAGE_TEST_MODE") == "true" - or os.getenv("SAGE_EXAMPLES_MODE") == "test" - ) - - -# Modify your main function or main block: -def main(): - # Check test mode - if is_test_mode(): - print("🧪 Test mode: Validating configuration and imports...") - # Add validation logic here: - # - Load config files - # - Import required modules - # - Check dependencies - print("✅ Test mode: Validation passed") - return - - # Normal execution - # ... your existing code ... - - -if __name__ == "__main__": - # Add test mode wrapper - if is_test_mode(): - try: - main() - print("\\n✅ Test passed: Example structure validated") - except Exception as e: - print(f"❌ Test failed: {e}") - sys.exit(1) - else: - main() -''' - - -def scan_examples(root_dir: Path) -> list[tuple[Path, dict]]: - """扫描所有 examples 文件。""" - examples = [] - - for pkg_dir in root_dir.glob("packages/*/examples"): - if not pkg_dir.is_dir(): - continue - - for py_file in pkg_dir.rglob("*.py"): - # 跳过 __pycache__ 和测试文件 - if "__pycache__" in str(py_file) or py_file.name.startswith("test_"): - continue - - analysis = analyze_example(py_file) - examples.append((py_file, analysis)) - - return examples - - -def print_report(examples: list[tuple[Path, dict]]): - """打印分析报告。""" - print("=" * 80) - print("📊 SAGE Examples Test Mode Support Analysis") - print("=" * 80) - print() - - # 统计 - total = len(examples) - with_test_mode = sum(1 for _, a in examples if a["has_test_mode"]) - needs_test_mode = sum(1 for _, a in examples if a["needs_test_mode"]) - - print(f"📁 总计: {total} 个 examples") - print(f"✅ 已支持测试模式: {with_test_mode} ({with_test_mode * 100 // total}%)") - print(f"💡 建议添加测试模式: {needs_test_mode}") - print() - - # 按难度分组 - by_difficulty = {"easy": [], "medium": [], "hard": [], "unknown": []} - for file_path, analysis in examples: - if not analysis["has_test_mode"]: - by_difficulty[analysis["difficulty"]].append((file_path, analysis)) - - print("━" * 80) - print("🎯 推荐优先级") - print("━" * 80) - print() - - # Easy - if by_difficulty["easy"]: - print(f"✨ Easy ({len(by_difficulty['easy'])} 个) - 推荐立即添加:") - for file_path, analysis in by_difficulty["easy"][:10]: - rel_path = file_path.relative_to(Path.cwd()) - print(f" • {rel_path}") - print(f" {analysis['suggestion']}") - if len(by_difficulty["easy"]) > 10: - print(f" ... 还有 {len(by_difficulty['easy']) - 10} 个") - print() - - # Medium - if by_difficulty["medium"]: - print(f"💡 Medium ({len(by_difficulty['medium'])} 个) - 需要 API 处理:") - for file_path, analysis in by_difficulty["medium"][:5]: - rel_path = file_path.relative_to(Path.cwd()) - print(f" • {rel_path}") - print(f" {analysis['suggestion']}") - if len(by_difficulty["medium"]) > 5: - print(f" ... 还有 {len(by_difficulty['medium']) - 5} 个") - print() - - # Hard - if by_difficulty["hard"]: - print(f"⚠️ Hard ({len(by_difficulty['hard'])} 个) - 需要重构:") - for file_path, analysis in by_difficulty["hard"][:5]: - rel_path = file_path.relative_to(Path.cwd()) - print(f" • {rel_path}") - print(f" {analysis['suggestion']}") - if len(by_difficulty["hard"]) > 5: - print(f" ... 还有 {len(by_difficulty['hard']) - 5} 个") - print() - - print("━" * 80) - print("📝 下一步行动") - print("━" * 80) - print() - print("1. 查看具体文件建议:") - print(" python tools/scripts/add_test_mode_to_examples.py --file ") - print() - print("2. 使用模板添加测试模式:") - print(" python tools/scripts/add_test_mode_to_examples.py --template") - print() - print("3. 运行 examples 测试:") - print(" SAGE_TEST_MODE=true python ") - print() - - -def print_file_suggestion(file_path: Path): - """为特定文件打印详细建议。""" - if not file_path.exists(): - print(f"❌ 文件不存在: {file_path}") - return - - analysis = analyze_example(file_path) - - print("=" * 80) - print(f"📝 {file_path.name}") - print("=" * 80) - print() - print(f"路径: {file_path}") - print(f"状态: {analysis['suggestion']}") - print(f"难度: {analysis['difficulty']}") - print() - - if analysis["has_test_mode"]: - print("✅ 该文件已支持测试模式") - print() - print("验证命令:") - print(f" SAGE_TEST_MODE=true python {file_path}") - return - - print("━" * 80) - print("💡 添加测试模式支持的步骤") - print("━" * 80) - print() - - if not analysis["has_main_block"]: - print("1. ⚠️ 该文件没有 if __name__ == '__main__' 块") - print(" 需要先重构代码，将执行逻辑移到 main() 函数中") - print() - else: - print("1. ✅ 文件已有 main 块") - print() - - print("2. 添加测试模式检测函数:") - print() - print("```python") - print("import os") - print() - print("def is_test_mode() -> bool:") - print(' """Check if running in test mode."""') - print(" return (") - print(' os.getenv("SAGE_TEST_MODE") == "true"') - print(' or os.getenv("SAGE_EXAMPLES_MODE") == "test"') - print(" )") - print("```") - print() - - print("3. 在 main() 函数开头添加测试模式逻辑:") - print() - print("```python") - print("def main():") - print(" if is_test_mode():") - print(' print("🧪 Test mode: Validating configuration...")') - print(" # 验证配置加载") - print(" # 验证模块导入") - print(' print("✅ Test mode: Validation passed")') - print(" return") - print() - print(" # 正常执行逻辑") - print(" ...") - print("```") - print() - - print("4. 修改 if __name__ == '__main__' 块:") - print() - print("```python") - print('if __name__ == "__main__":') - print(" if is_test_mode():") - print(" try:") - print(" main()") - print(' print("\\n✅ Test passed: Example structure validated")') - print(" except Exception as e:") - print(' print(f"❌ Test failed: {e}")') - print(" sys.exit(1)") - print(" else:") - print(" main()") - print("```") - print() - - print("5. 测试:") - print(f" SAGE_TEST_MODE=true python {file_path}") - print() - - -def main(): - parser = argparse.ArgumentParser(description="Add test mode support to SAGE examples") - parser.add_argument("--dry-run", action="store_true", help="Only analyze, don't modify files") - parser.add_argument("--file", type=Path, help="Analyze specific file") - parser.add_argument("--template", action="store_true", help="Show test mode template") - - args = parser.parse_args() - - root_dir = Path.cwd() - - if args.template: - print(generate_test_mode_template(Path("example.py"))) - return 0 - - if args.file: - print_file_suggestion(args.file) - return 0 - - # 扫描所有 examples - examples = scan_examples(root_dir) - - if not examples: - print("❌ 未找到 examples 文件") - return 1 - - print_report(examples) - return 0 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/tools/scripts/bump_versions_pep420.sh b/tools/scripts/bump_versions_pep420.sh deleted file mode 100755 index 1d8dcd517b..0000000000 --- a/tools/scripts/bump_versions_pep420.sh +++ /dev/null @@ -1,59 +0,0 @@ -#!/bin/bash -# Bump versions for all packages affected by PEP 420 migration -# Usage: ./tools/scripts/bump_versions_pep420.sh - -set -e - -SAGE_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)" -cd "$SAGE_ROOT" - -echo "🔢 批量递增版本号（PEP 420 迁移发布）" -echo "=========================================" - -# Define packages and their version increments -# Strategy: Based on PyPI current versions + 0.0.0.1 for published packages -# Use 0.2.0.0 for unpublished packages (higher than TestPyPI legacy versions) -declare -A VERSION_UPDATES=( - ["packages/sage/src/sage/_version.py"]="0.2.2.0→0.2.3.1" - ["packages/sage-common/src/sage/common/_version.py"]="0.2.3.0→0.2.3.1" - ["packages/sage-llm-core/src/sage/llm/_version.py"]="0.2.3.0→0.2.3.1" - ["packages/sage-platform/src/sage/platform/_version.py"]="0.2.3.0→0.2.0.0" - ["packages/sage-kernel/src/sage/kernel/_version.py"]="0.2.3.0→0.2.0.0" - ["packages/sage-libs/src/sage/libs/_version.py"]="0.2.0.7→0.2.0.7" - ["packages/sage-middleware/src/sage/middleware/_version.py"]="0.2.3.0→0.2.0.0" - ["packages/sage-apps/src/sage/apps/_version.py"]="0.2.3.0→0.2.0.0" - ["packages/sage-cli/src/sage/cli/_version.py"]="0.2.3.0→0.2.0.0" - ["packages/sage-studio/src/sage/studio/_version.py"]="0.2.3.0→0.2.0.0" - ["packages/sage-llm-gateway/src/sage/llm/gateway/_version.py"]="0.2.4.0→0.2.3.1" - # ["packages/sage-edge/src/sage/edge/_version.py"]="0.2.3.0→0.2.0.0" # 已独立 - ["packages/sage-tools/src/sage/tools/_version.py"]="0.2.3.0→0.2.0.0" -) - -# Perform version updates -for file in "${!VERSION_UPDATES[@]}"; do - version_change="${VERSION_UPDATES[$file]}" - old_version="${version_change%%→*}" - new_version="${version_change##*→}" - - if [ ! -f "$file" ]; then - echo "⚠️ 文件不存在: $file" - continue - fi - - # Check current version - current=$(grep -oP '__version__ = "\K[^"]+' "$file" || echo "unknown") - if [ "$current" != "$old_version" ]; then - echo "⚠️ 版本不匹配: $file" - echo " 预期: $old_version" - echo " 实际: $current" - echo " 跳过此包..." - continue - fi - - # Update version - sed -i "s/__version__ = \"$old_version\"/__version__ = \"$new_version\"/" "$file" - echo "✅ $file: $old_version → $new_version" -done - -echo "" -echo "✅ 所有版本号递增完成！" diff --git a/tools/scripts/check_cross_repo_dedup.py b/tools/scripts/check_cross_repo_dedup.py new file mode 100644 index 0000000000..eb64efd5e7 --- /dev/null +++ b/tools/scripts/check_cross_repo_dedup.py @@ -0,0 +1,303 @@ +#!/usr/bin/env python3 +""" +Cross-Repo Duplicate Declaration Detector (Issue #1439 - Wave C gate) + +Enforces the move-then-delete rule from the Flownet Migration Boundary: + docs_src/concepts/architecture/design-decisions/flownet-migration-boundary.md + +Checks the following migrated capability domains for duplicate class/function +declarations between SAGE and sageFlownet: + + Domain | SAGE canonical location | Flownet allowed pattern + -------------------- | ------------------------------- | ----------------------- + Exception model | sage.common.core.flow_exceptions| re-export only (no class def) + Scheduling schema | sage.kernel.scheduler.schema | import only (no stub classes) + Context propagation | sage.common.utils.context | import only + Runtime protocol | sage.platform.runtime_protocol | implementation only + Flow declaration API | sage.kernel.flow / .api | (Flownet keeps impl) + +Usage: + python tools/scripts/check_cross_repo_dedup.py [--flownet-path PATH] [--require-flownet] + +Exit codes: + 0 Clean - no duplicate declarations found + 1 Violations detected - list printed to stderr + 2 Script error (bad path, parse failure) +""" + +from __future__ import annotations + +import argparse +import ast +import os +import sys +from pathlib import Path +from typing import NamedTuple + +# --------------------------------------------------------------------------- +# Migrated domains: symbols that SAGE owns and Flownet must NOT redefine. +# Keys are class/function names; values describe the canonical SAGE location. +# +# Rules for entries here: +# 1. Symbols already present in SAGE at the listed path (single source). +# 2. Names that have been observed as stub fallback candidates in Flownet. +# 3. Do NOT list Flownet-internal implementation class names (e.g. RuntimeProtocol +# is Flownet's own implementation; RuntimeBackendProtocol is the SAGE ABC). +# --------------------------------------------------------------------------- +MIGRATED_SYMBOLS: dict[str, str] = { + # Exception model (Issue #1434 / #1435) + "ExceptionAction": "sage.common.core.flow_exceptions", + "ExceptionContext": "sage.common.core.flow_exceptions", + "ExceptionDecision": "sage.common.core.flow_exceptions", + "ExceptionEvent": "sage.common.core.flow_exceptions", + "FlowException": "sage.common.core.flow_exceptions", + "FlowDefinitionError": "sage.common.core.flow_exceptions", + # Scheduling schema (Issue #1437) + "ResourceSpec": "sage.kernel.scheduler.schema", + "PlacementSchema": "sage.kernel.scheduler.schema", + "PlacementStrategy": "sage.kernel.scheduler.schema", + # Runtime protocol ABCs (Issue #1433) — note: SAGE uses RuntimeBackendProtocol, + # not RuntimeProtocol. Flownet.RuntimeProtocol is a legitimate implementation + # class and must NOT appear in this list. + "RuntimeBackendProtocol": "sage.platform.runtime.protocol", + "NodeInfoProtocol": "sage.platform.runtime.protocol", + "MethodRefProtocol": "sage.platform.runtime.protocol", + "ActorHandleProtocol": "sage.platform.runtime.protocol", + "FlowRunHandleProtocol": "sage.platform.runtime.protocol", +} + +# Flownet paths/modules that are allowed to re-export (backward-compat only). +# These must not contain a new `class ` definition. +ALLOWED_REEXPORT_MODULES: frozenset[str] = frozenset( + [ + "sage/flownet/core/exceptions.py", # re-exports SAGE L1 exception types + ] +) + + +class Violation(NamedTuple): + symbol: str + flownet_file: str + line: int + sage_canonical: str + kind: str # "stub_class" | "duplicate_function" | "try_except_fallback" + + def __str__(self) -> str: + return ( + f" [{self.kind}] {self.symbol!r} in {self.flownet_file}:{self.line}\n" + f" Canonical owner: {self.sage_canonical}\n" + f" Flownet must import, not redefine." + ) + + +def _collect_class_defs(tree: ast.AST) -> list[tuple[str, int]]: + """Return (class_name, lineno) for all top-level and nested ClassDef nodes.""" + return [(node.name, node.lineno) for node in ast.walk(tree) if isinstance(node, ast.ClassDef)] + + +def _collect_function_defs(tree: ast.AST) -> list[tuple[str, int]]: + """Return (func_name, lineno) for module-level FunctionDef nodes.""" + return [ + (node.name, node.lineno) + for node in ast.walk(tree) + if isinstance(node, ast.FunctionDef | ast.AsyncFunctionDef) + ] + + +def _has_try_except_import_fallback(tree: ast.AST, symbol: str) -> int | None: + """Detect try-except ImportError patterns that redefine a migrated symbol. + + Returns the line number of the except block if found, else None. + """ + for node in ast.walk(tree): + if not isinstance(node, ast.Try): + continue + # Check if any handler is `except ImportError` + for handler in node.handlers: + if handler.type is None: + continue + exc_names = [] + if isinstance(handler.type, ast.Name): + exc_names.append(handler.type.id) + elif isinstance(handler.type, ast.Tuple): + for elt in handler.type.elts: + if isinstance(elt, ast.Name): + exc_names.append(elt.id) + if "ImportError" not in exc_names: + continue + # Check if the except body defines as a class + for stmt in ast.walk(ast.Module(body=handler.body, type_ignores=[])): + if isinstance(stmt, ast.ClassDef) and stmt.name == symbol: + return handler.lineno + return None + + +def check_flownet_file( + filepath: Path, + violations: list[Violation], +) -> None: + """Parse one Flownet Python file and collect any violations.""" + rel = str(filepath).replace(str(filepath.parent.parent.parent.parent) + "/", "") + + # Normalise to a relative path inside the sageFlownet repo + for part in ("sageFlownet/", "src/"): + rel = rel.replace(part, "", 1) + + code = filepath.read_text(encoding="utf-8") + try: + tree = ast.parse(code, filename=str(filepath)) + except SyntaxError: + return # skip unparseable files + + is_allowed_reexport = any( + rel.endswith(m) or str(filepath).endswith(m) for m in ALLOWED_REEXPORT_MODULES + ) + + for symbol, sage_location in MIGRATED_SYMBOLS.items(): + # 1. Check for direct class redefinition (outside allowed re-export modules) + if not is_allowed_reexport: + for cls_name, lineno in _collect_class_defs(tree): + if cls_name == symbol: + violations.append( + Violation( + symbol=symbol, + flownet_file=str(filepath), + line=lineno, + sage_canonical=sage_location, + kind="stub_class", + ) + ) + + # 2. Check for try-except ImportError fallback stub patterns + lineno = _has_try_except_import_fallback(tree, symbol) + if lineno is not None: + violations.append( + Violation( + symbol=symbol, + flownet_file=str(filepath), + line=lineno, + sage_canonical=sage_location, + kind="try_except_fallback", + ) + ) + + +def scan_flownet(flownet_root: Path) -> list[Violation]: + """Walk the sageFlownet src/ tree and collect all violations.""" + violations: list[Violation] = [] + src_dir = flownet_root / "src" + if not src_dir.exists(): + print(f"⚠️ sageFlownet src/ not found at {src_dir}", file=sys.stderr) + return violations + + for dirpath, dirnames, filenames in os.walk(src_dir): + # Skip __pycache__ + dirnames[:] = [d for d in dirnames if d != "__pycache__"] + for filename in filenames: + if not filename.endswith(".py"): + continue + filepath = Path(dirpath) / filename + check_flownet_file(filepath, violations) + + return violations + + +def main() -> int: + parser = argparse.ArgumentParser( + description="Check for duplicate migrated declarations between SAGE and sageFlownet." + ) + parser.add_argument( + "--flownet-path", + default=None, + help="Path to sageFlownet repository root (auto-detected if omitted).", + ) + parser.add_argument( + "--verbose", + "-v", + action="store_true", + help="Show verbose output even when passing.", + ) + parser.add_argument( + "--require-flownet", + action="store_true", + help=( + "Fail with exit code 2 when sageFlownet cannot be found. " + "Use this in CI to make dedup check a hard gate." + ), + ) + args = parser.parse_args() + + # Auto-detect sageFlownet root + if args.flownet_path: + flownet_root = Path(args.flownet_path) + else: + # Try common locations relative to SAGE repo + script_dir = Path(__file__).resolve().parent + sage_root = script_dir.parent.parent # SAGE/ + candidates = [ + sage_root.parent / "sageFlownet", + Path.home() / "sageFlownet", + Path("/home/shuhao/sageFlownet"), + ] + flownet_root = None + for candidate in candidates: + try: + if candidate.exists(): + flownet_root = candidate + break + except PermissionError: + continue + + if flownet_root is None: + message = ( + "⚠️ sageFlownet not found. Set --flownet-path or clone sageFlownet alongside SAGE." + ) + if args.require_flownet: + print(f"❌ {message}", file=sys.stderr) + print( + " Dedup check is configured as required (CI hard gate).", + file=sys.stderr, + ) + return 2 + print(f"⚠️ {message}\n Skipping cross-repo dedup check.", file=sys.stderr) + return 0 + + violations = scan_flownet(flownet_root) + + if not violations: + if args.verbose: + print("✅ Cross-repo dedup check passed: no duplicate migrated declarations found.") + return 0 + + print( + f"❌ Cross-repo dedup check FAILED: {len(violations)} violation(s) found.\n", + file=sys.stderr, + ) + print( + "These symbols have been migrated to SAGE (single source of truth).\n" + "Flownet must import them, NOT redefine or stub them.\n" + "See: docs_src/concepts/architecture/design-decisions/flownet-migration-boundary.md\n", + file=sys.stderr, + ) + for v in violations: + print(str(v), file=sys.stderr) + + print( + "\n💡 Fix: remove stub class definitions and try-except ImportError fallbacks.\n" + " Replace with direct imports from the canonical SAGE module.\n" + " Example:\n" + " # WRONG (stub fallback)\n" + " try:\n" + " from sage.kernel.scheduler.schema import ResourceSpec\n" + " except ImportError:\n" + " class ResourceSpec: ...\n" + "\n" + " # CORRECT (fail fast)\n" + " from sage.kernel.scheduler.schema import ResourceSpec\n", + file=sys.stderr, + ) + return 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tools/scripts/check_layer_manifest_sync.py b/tools/scripts/check_layer_manifest_sync.py new file mode 100644 index 0000000000..dcac39b56d --- /dev/null +++ b/tools/scripts/check_layer_manifest_sync.py @@ -0,0 +1,167 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import argparse +import json +import re +import sys +from pathlib import Path + +LAYER_RE = re.compile(r"\b(L[1-6])\b") +WORKSPACE_ENTRY_RE = re.compile( + r'\{[^{}]*?"name"\s*:\s*"([^"]+)"[^{}]*?"path"\s*:\s*"([^"]+)"[^{}]*?\}', + flags=re.S, +) + + +def _extract_layer_token(raw: str) -> str | None: + match = LAYER_RE.search(raw) + return match.group(1) if match else None + + +def load_manifest(path: Path) -> tuple[dict[str, str], set[str]]: + data = json.loads(path.read_text(encoding="utf-8")) + repos = data.get("repos", []) + optional_repos = set(data.get("declaration_optional_repos", [])) + mapping: dict[str, str] = {} + for item in repos: + repo = item["repo"] + layer = item["layer"] + mapping[repo] = layer + return mapping, optional_repos + + +def load_workspace_labels(path: Path) -> dict[str, str]: + text = path.read_text(encoding="utf-8") + mapping: dict[str, str] = {} + for name, rel_path in WORKSPACE_ENTRY_RE.findall(text): + layer = _extract_layer_token(name) + if layer is None: + continue + repo = Path(rel_path).name + mapping[repo] = layer + return mapping + + +def load_declared_layer(instructions_path: Path) -> str | None: + text = instructions_path.read_text(encoding="utf-8", errors="ignore") + + match = re.search(r"Layer\s*:\s*\*\*([^*]+)\*\*", text) + if match: + return _extract_layer_token(match.group(1)) + + match = re.search(r"Layer\s+L[1-6][^\n]*", text) + if match: + return _extract_layer_token(match.group(0)) + + return None + + +def run_check( + repo_root: Path, workspace_file: Path, manifest_file: Path, strict_repos: bool +) -> int: + manifest, optional_declaration_repos = load_manifest(manifest_file) + workspace = load_workspace_labels(workspace_file) + + errors: list[str] = [] + warnings: list[str] = [] + + for repo, expected_layer in manifest.items(): + workspace_layer = workspace.get(repo) + if workspace_layer is None: + errors.append( + f"[workspace-missing] {repo}: not found in {workspace_file.relative_to(repo_root)}" + ) + elif workspace_layer != expected_layer: + errors.append( + f"[workspace-layer-mismatch] {repo}: workspace={workspace_layer}, expected={expected_layer}" + ) + + if repo in optional_declaration_repos: + continue + + repo_instructions = repo_root.parent / repo / ".github" / "copilot-instructions.md" + if not repo_instructions.exists(): + msg = f"[instructions-missing] {repo}: {repo_instructions} does not exist" + if strict_repos: + errors.append(msg) + else: + warnings.append(msg) + continue + + declared_layer = load_declared_layer(repo_instructions) + if declared_layer is None: + msg = ( + f"[instructions-layer-missing] {repo}: " + f"no parseable Layer declaration in {repo_instructions}" + ) + if strict_repos: + errors.append(msg) + else: + warnings.append(msg) + continue + + if declared_layer != expected_layer: + errors.append( + f"[instructions-layer-mismatch] {repo}: declared={declared_layer}, expected={expected_layer}" + ) + + if warnings: + print("⚠️ Layer manifest consistency warnings:") + for warning in warnings: + print(f" - {warning}") + + if errors: + print("❌ Layer manifest consistency check failed:") + for err in errors: + print(f" - {err}") + print("\nFix order: repo copilot instructions -> layer-manifest -> SAGE.code-workspace") + return 1 + + print("✅ Layer manifest consistency check passed") + print(f" checked repos: {len(manifest)}") + return 0 + + +def main() -> int: + parser = argparse.ArgumentParser( + description="Check layer consistency across manifest/workspace/repo declarations" + ) + parser.add_argument( + "--workspace", + default="SAGE.code-workspace", + help="Workspace file path relative to repo root", + ) + parser.add_argument( + "--manifest", + default="docs/layer-manifest.json", + help="Layer manifest path relative to repo root", + ) + parser.add_argument( + "--strict-repos", + action="store_true", + help="Treat missing/unparseable satellite repo instructions as errors", + ) + args = parser.parse_args() + + repo_root = Path(__file__).resolve().parents[2] + workspace_file = repo_root / args.workspace + manifest_file = repo_root / args.manifest + + if not workspace_file.exists(): + print(f"❌ Workspace file not found: {workspace_file}") + return 2 + if not manifest_file.exists(): + print(f"❌ Manifest file not found: {manifest_file}") + return 2 + + return run_check( + repo_root=repo_root, + workspace_file=workspace_file, + manifest_file=manifest_file, + strict_repos=args.strict_repos, + ) + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tools/scripts/check_meta_dependency_audit.py b/tools/scripts/check_meta_dependency_audit.py new file mode 100644 index 0000000000..4517fc50a0 --- /dev/null +++ b/tools/scripts/check_meta_dependency_audit.py @@ -0,0 +1,237 @@ +#!/usr/bin/env python3 +"""Dependency audit gate for isage meta package. + +This gate enforces two rules for `pyproject.toml`: +1. Every direct dependency must have callsite evidence in the audit document. +2. When dependency declarations change, the audit document must be updated in the same change. +""" + +from __future__ import annotations + +import argparse +import re +import subprocess +import sys +import tomllib +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[2] +PYPROJECT_PATH = Path("pyproject.toml") +AUDIT_DOC_PATH = Path("docs/dependency-audit-gate.md") + +SECTION_RE = re.compile(r"^###\s+`([^`]+)`\s*$") +CALLSITE_RE = re.compile(r"^-\s+Callsite:\s+") +REQ_NAME_RE = re.compile(r"^([A-Za-z0-9][A-Za-z0-9._-]*)") + + +def _run_git(args: list[str], check: bool = True) -> subprocess.CompletedProcess[str]: + return subprocess.run( + ["git", *args], + cwd=REPO_ROOT, + text=True, + capture_output=True, + check=check, + ) + + +def _dependency_name(requirement: str) -> str: + match = REQ_NAME_RE.match(requirement.strip()) + if not match: + raise ValueError(f"Cannot parse dependency name from requirement: {requirement}") + return match.group(1).lower() + + +def _load_dependency_map_from_text(pyproject_text: str) -> dict[str, str]: + data = tomllib.loads(pyproject_text) + deps = data.get("project", {}).get("dependencies", []) + result: dict[str, str] = {} + for req in deps: + name = _dependency_name(req) + result[name] = req.strip() + return result + + +def _load_current_dependency_map() -> dict[str, str]: + text = (REPO_ROOT / PYPROJECT_PATH).read_text(encoding="utf-8") + return _load_dependency_map_from_text(text) + + +def _load_base_dependency_map(refspec: str | None) -> dict[str, str]: + if refspec: + base_ref = refspec.split("...")[0] if "..." in refspec else refspec + base_sha = _run_git(["merge-base", base_ref, "HEAD"]).stdout.strip() + if not base_sha: + return {} + show = _run_git(["show", f"{base_sha}:{PYPROJECT_PATH.as_posix()}"], check=False) + if show.returncode != 0: + return {} + return _load_dependency_map_from_text(show.stdout) + + show_head = _run_git(["show", f"HEAD:{PYPROJECT_PATH.as_posix()}"], check=False) + if show_head.returncode != 0: + return {} + return _load_dependency_map_from_text(show_head.stdout) + + +def _changed_files(refspec: str | None, staged: bool) -> set[str]: + args = ["diff", "--name-only"] + if staged: + args.append("--cached") + if refspec: + args.append(refspec) + result = _run_git(args, check=False) + if result.returncode != 0: + return set() + return {line.strip() for line in result.stdout.splitlines() if line.strip()} + + +def _diff_text(path: Path, refspec: str | None, staged: bool) -> str: + args = ["diff"] + if staged: + args.append("--cached") + if refspec: + args.append(refspec) + args.extend(["--", path.as_posix()]) + result = _run_git(args, check=False) + return result.stdout if result.returncode == 0 else "" + + +def _parse_audit_doc() -> dict[str, bool]: + doc_path = REPO_ROOT / AUDIT_DOC_PATH + if not doc_path.exists(): + raise FileNotFoundError(f"Audit document not found: {AUDIT_DOC_PATH.as_posix()}") + + sections: dict[str, bool] = {} + current_dep: str | None = None + + for raw_line in doc_path.read_text(encoding="utf-8").splitlines(): + line = raw_line.strip() + sec_match = SECTION_RE.match(line) + if sec_match: + current_dep = sec_match.group(1).lower() + sections[current_dep] = False + continue + + if current_dep and CALLSITE_RE.match(line): + sections[current_dep] = True + + return sections + + +def _dependency_delta( + base_deps: dict[str, str], current_deps: dict[str, str] +) -> dict[str, tuple[str, str]]: + all_names = set(base_deps) | set(current_deps) + delta: dict[str, tuple[str, str]] = {} + for name in sorted(all_names): + base_req = base_deps.get(name, "") + current_req = current_deps.get(name, "") + if base_req != current_req: + delta[name] = (base_req, current_req) + return delta + + +def _ensure_doc_covers_dependencies(current_deps: dict[str, str]) -> list[str]: + sections = _parse_audit_doc() + errors: list[str] = [] + for dep_name in sorted(current_deps): + if dep_name not in sections: + errors.append(f"Missing section: ### `{dep_name}` in {AUDIT_DOC_PATH.as_posix()}") + elif not sections[dep_name]: + errors.append( + f"Section `{dep_name}` has no `- Callsite:` evidence in {AUDIT_DOC_PATH.as_posix()}" + ) + return errors + + +def _ensure_change_has_evidence( + delta: dict[str, tuple[str, str]], + changed_files: set[str], + refspec: str | None, + staged: bool, +) -> list[str]: + errors: list[str] = [] + if not delta: + return errors + + pyproject_changed = PYPROJECT_PATH.as_posix() in changed_files + if not pyproject_changed: + return errors + + audit_changed = AUDIT_DOC_PATH.as_posix() in changed_files + if not audit_changed: + errors.append( + "Dependency declarations changed but audit doc was not updated: " + f"{AUDIT_DOC_PATH.as_posix()}" + ) + return errors + + doc_diff = _diff_text(AUDIT_DOC_PATH, refspec=refspec, staged=staged) + added_lines = [ + line[1:] + for line in doc_diff.splitlines() + if line.startswith("+") and not line.startswith("+++") + ] + added_blob = "\n".join(added_lines).lower() + + missing_names = [name for name in delta if name.lower() not in added_blob] + if missing_names: + errors.append( + "Audit doc updated, but changed dependencies are not explicitly mentioned in added lines: " + + ", ".join(missing_names) + ) + + return errors + + +def main() -> int: + parser = argparse.ArgumentParser(description="Check isage meta dependency audit gate") + parser.add_argument( + "--enforce-change-evidence", + action="store_true", + help="Require audit doc update when dependencies change", + ) + parser.add_argument( + "--refspec", + default=None, + help="Git diff refspec (e.g. origin/main-dev...HEAD)", + ) + parser.add_argument( + "--staged", + action="store_true", + help="Use staged changes (`git diff --cached`) for change detection", + ) + args = parser.parse_args() + + current_deps = _load_current_dependency_map() + base_deps = _load_base_dependency_map(args.refspec) + delta = _dependency_delta(base_deps, current_deps) + changed_files = _changed_files(refspec=args.refspec, staged=args.staged) + + errors: list[str] = [] + errors.extend(_ensure_doc_covers_dependencies(current_deps)) + + if args.enforce_change_evidence: + errors.extend( + _ensure_change_has_evidence( + delta=delta, + changed_files=changed_files, + refspec=args.refspec, + staged=args.staged, + ) + ) + + if errors: + print("❌ isage meta dependency audit gate failed:") + for item in errors: + print(f" - {item}") + return 1 + + print("✅ isage meta dependency audit gate passed") + if delta: + print(f"ℹ️ Dependency delta detected: {', '.join(sorted(delta))}") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tools/scripts/check_minimal_deps.py b/tools/scripts/check_minimal_deps.py deleted file mode 100644 index 74741f2aea..0000000000 --- a/tools/scripts/check_minimal_deps.py +++ /dev/null @@ -1,146 +0,0 @@ -#!/usr/bin/env python3 -"""Check minimal required dependencies for SAGE packages. - -This script helps identify the truly required dependencies by attempting -to import each SAGE package and catching ImportError to find missing deps. - -Usage: - python tools/scripts/check_minimal_deps.py - -Requires a clean Python environment (no SAGE deps installed). -""" - -import subprocess -from pathlib import Path - - -def run_import_test(env_name: str, module: str) -> tuple[bool, str]: - """Try to import a module and return (success, error_message).""" - cmd = [ - "conda", - "run", - "-n", - env_name, - "python", - "-c", - f"import {module}; print(f'{module} OK')", - ] - result = subprocess.run(cmd, capture_output=True, text=True) - if result.returncode == 0: - return True, "" - # Extract the missing module from ImportError - stderr = result.stderr - if "No module named" in stderr: - # Extract module name - import re - - match = re.search(r"No module named '([^']+)'", stderr) - if match: - return False, match.group(1) - return False, stderr - - -def install_package(env_name: str, package: str) -> bool: - """Install a package in the test environment.""" - cmd = ["conda", "run", "-n", env_name, "pip", "install", package, "-q"] - result = subprocess.run(cmd, capture_output=True, text=True) - return result.returncode == 0 - - -def find_minimal_deps(env_name: str, module: str) -> list[str]: - """Find minimal dependencies needed to import a module.""" - deps = [] - max_iterations = 50 # Safety limit - - for _ in range(max_iterations): - success, missing = run_import_test(env_name, module) - if success: - break - if not missing or "No module named" not in missing: - print(f" Unexpected error: {missing[:200]}") - break - - # Map common internal names to pip package names - package_map = { - "yaml": "pyyaml", - "sklearn": "scikit-learn", - "cv2": "opencv-python", - "PIL": "pillow", - "dotenv": "python-dotenv", - } - pip_name = package_map.get(missing.split(".")[0], missing.split(".")[0]) - - print(f" Installing: {pip_name}") - if install_package(env_name, pip_name): - deps.append(pip_name) - else: - print(f" Failed to install {pip_name}") - break - - return deps - - -def main(): - """Main entry point.""" - env_name = "sage-test-minimal" - sage_root = Path(__file__).parent.parent.parent - - # SAGE packages to test (in dependency order) - packages = [ - ("sage-common", "sage.common"), - ("sage-platform", "sage.platform"), - ("sage-kernel", "sage.kernel"), - ("sage-libs", "sage.libs"), - ("sage-middleware", "sage.middleware"), - ("sage-cli", "sage.cli"), - ("sage-tools", "sage.tools"), - ] - - print("=" * 60) - print("SAGE Minimal Dependencies Checker") - print("=" * 60) - print(f"Test environment: {env_name}") - print() - - all_deps = {} - - for pkg_name, module_name in packages: - print(f"\n[{pkg_name}] Testing {module_name}...") - - # Install package without deps - pkg_path = sage_root / "packages" / pkg_name - if not pkg_path.exists(): - print(f" Package not found: {pkg_path}") - continue - - cmd = [ - "conda", - "run", - "-n", - env_name, - "pip", - "install", - "-e", - str(pkg_path), - "--no-deps", - "-q", - ] - subprocess.run(cmd, capture_output=True) - - # Find minimal deps - deps = find_minimal_deps(env_name, module_name) - all_deps[pkg_name] = deps - - success, _ = run_import_test(env_name, module_name) - status = "✅" if success else "❌" - print(f" {status} {module_name} - requires: {deps if deps else 'none'}") - - print("\n" + "=" * 60) - print("Summary: Minimal Dependencies") - print("=" * 60) - for pkg, deps in all_deps.items(): - print(f"{pkg}: {', '.join(deps) if deps else '(none)'}") - - -if __name__ == "__main__": - main() diff --git a/tools/scripts/check_package_exports.py b/tools/scripts/check_package_exports.py deleted file mode 100755 index ad327d66cf..0000000000 --- a/tools/scripts/check_package_exports.py +++ /dev/null @@ -1,254 +0,0 @@ -#!/usr/bin/env python3 -""" -check_package_exports.py - -检查 SAGE 各包的 __init__.py 导出是否正确。 - -Usage: - python tools/scripts/check_package_exports.py -""" - -import sys -from pathlib import Path - - -def check_package_init(pkg_name: str, pkg_path: Path) -> dict[str, any]: - """检查单个包的 __init__.py 文件。""" - init_file = pkg_path / "src" / "sage" / pkg_name / "__init__.py" - - result = { - "name": pkg_name, - "path": str(pkg_path), - "init_exists": init_file.exists(), - "has_version": False, - "has_all": False, - "has_layer": False, - "exports": [], - "issues": [], - } - - if not result["init_exists"]: - result["issues"].append("❌ __init__.py 文件不存在") - return result - - # 读取文件内容 - try: - content = init_file.read_text(encoding="utf-8") - except Exception as e: - result["issues"].append(f"❌ 无法读取文件: {e}") - return result - - # 检查 __version__ - if "__version__" in content: - result["has_version"] = True - # 提取版本导入方式 - if "from ._version import __version__" in content: - result["version_source"] = "_version.py" - elif "from sage." in content and "__version__" in content: - result["version_source"] = "re-export" - else: - result["version_source"] = "hardcoded" - else: - result["issues"].append("⚠️ 缺少 __version__ 声明") - - # 检查 __all__ - if "__all__" in content: - result["has_all"] = True - # 尝试提取 __all__ 列表 - import re - - match = re.search(r"__all__\s*=\s*\[(.*?)\]", content, re.DOTALL) - if match: - exports_str = match.group(1) - # 简单解析（去除引号和逗号） - exports = [e.strip().strip("\"'") for e in exports_str.split(",") if e.strip()] - result["exports"] = exports - else: - result["issues"].append("⚠️ 缺少 __all__ 声明") - - # 检查 __layer__ - if "__layer__" in content: - result["has_layer"] = True - match = re.search(r'__layer__\s*=\s*["\']([^"\']+)["\']', content) - if match: - result["layer"] = match.group(1) - else: - result["issues"].append("ℹ️ 缺少 __layer__ 声明（非必需）") - - # 检查是否有导入语句 - import_count = content.count("from ") + content.count("import ") - result["import_count"] = import_count - - if import_count == 0: - result["issues"].append("⚠️ 没有任何导入语句") - - # 检查是否有文档字符串 - if '"""' in content or "'''" in content: - result["has_docstring"] = True - else: - result["issues"].append("⚠️ 缺少模块文档字符串") - - return result - - -def print_package_report(results: list[dict[str, any]]): - """打印检查报告。""" - print("=" * 80) - print("📊 SAGE Packages Export Check Report") - print("=" * 80) - print() - - # 统计 - total = len(results) - with_issues = sum(1 for r in results if r["issues"]) - - print(f"📦 总计: {total} 个包") - print(f"✅ 正常: {total - with_issues} 个") - print(f"⚠️ 有问题: {with_issues} 个") - print() - - # 详细报告 - for result in results: - print("━" * 80) - print(f"📦 {result['name']}") - print("━" * 80) - - if not result["init_exists"]: - print(" ❌ __init__.py 不存在") - print() - continue - - # 基本信息 - print(f" 路径: {result['path']}") - if result.get("layer"): - print(f" 层级: {result['layer']}") - - # 版本信息 - if result["has_version"]: - source = result.get("version_source", "unknown") - print(f" ✅ 版本: {source}") - else: - print(" ❌ 版本: 未声明") - - # 导出信息 - if result["has_all"]: - exports = result.get("exports", []) - if exports: - print(f" ✅ __all__: {len(exports)} 项") - # 显示前5项 - for exp in exports[:5]: - print(f" - {exp}") - if len(exports) > 5: - print(f" ... 还有 {len(exports) - 5} 项") - else: - print(" ⚠️ __all__: 已声明但为空") - else: - print(" ❌ __all__: 未声明") - - # 导入统计 - import_count = result.get("import_count", 0) - print(f" 📥 导入语句: {import_count} 个") - - # 问题列表 - if result["issues"]: - print() - print(" ⚠️ 发现问题:") - for issue in result["issues"]: - print(f" {issue}") - - print() - - # 建议 - print("━" * 80) - print("💡 建议") - print("━" * 80) - print() - - issues_found = False - for result in results: - if not result["has_version"]: - issues_found = True - print(f" • {result['name']}: 添加 __version__ 声明") - if not result["has_all"]: - issues_found = True - print(f" • {result['name']}: 添加 __all__ 列表") - - if not issues_found: - print(" ✅ 所有包的导出配置都正常！") - - print() - - -def main(): - root_dir = Path.cwd() - packages_dir = root_dir / "packages" - - # 核心包列表（按层级排序） - packages = [ - # L1 - ("common", "sage-common"), - ("llm", "sage-llm-core"), - # L2 - ("platform", "sage-platform"), - # L3 - ("kernel", "sage-kernel"), - ("libs", "sage-libs"), - # L4 - ("middleware", "sage-middleware"), - # L5 - ("apps", "sage-apps"), - # L6 - ("cli", "sage-cli"), - ("tools", "sage-tools"), - ] - - # 检查 sage-llm-gateway（独立命名空间） - gateway_pkg = packages_dir / "sage-llm-gateway" - gateway_init = gateway_pkg / "src" / "sage" / "llm" / "gateway" / "__init__.py" - - results = [] - - # 检查核心包 - for pkg_name, pkg_dirname in packages: - pkg_path = packages_dir / pkg_dirname - if pkg_path.exists(): - result = check_package_init(pkg_name, pkg_path) - results.append(result) - else: - print(f"⚠️ 包目录不存在: {pkg_path}") - - # 特别检查 gateway - if gateway_init.exists(): - gateway_result = { - "name": "llm.gateway", - "path": str(gateway_pkg), - "init_exists": True, - "has_version": False, - "has_all": False, - "has_layer": False, - "exports": [], - "issues": [], - } - - content = gateway_init.read_text(encoding="utf-8") - if "__version__" in content: - gateway_result["has_version"] = True - else: - gateway_result["issues"].append("⚠️ 缺少 __version__") - - if "__all__" in content: - gateway_result["has_all"] = True - else: - gateway_result["issues"].append("⚠️ 缺少 __all__") - - results.append(gateway_result) - - # 打印报告 - print_package_report(results) - - # 返回值：有问题的包数量 - return sum(1 for r in results if r["issues"]) - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/tools/scripts/create_sage_edge_repo.sh b/tools/scripts/create_sage_edge_repo.sh deleted file mode 100755 index 19b476ec28..0000000000 --- a/tools/scripts/create_sage_edge_repo.sh +++ /dev/null @@ -1,425 +0,0 @@ -#!/usr/bin/env bash -# Create sage-edge independent repository with GitHub CLI -# Usage: ./create_sage_edge_repo.sh - -set -e - -# Colors for output -RED='\033[0;31m' -GREEN='\033[0;32m' -YELLOW='\033[1;33m' -BLUE='\033[0;34m' -NC='\033[0m' # No Color - -echo_info() { echo -e "${BLUE}ℹ ${NC}$1"; } -echo_success() { echo -e "${GREEN}✓${NC} $1"; } -echo_warning() { echo -e "${YELLOW}⚠${NC} $1"; } -echo_error() { echo -e "${RED}✗${NC} $1"; } - -# Check prerequisites -check_prerequisites() { - echo_info "Checking prerequisites..." - - if ! command -v gh &> /dev/null; then - echo_error "GitHub CLI (gh) not found. Install it first:" - echo " macOS: brew install gh" - echo " Ubuntu: sudo apt install gh" - echo " See: https://cli.github.com/manual/installation" - exit 1 - fi - - if ! gh auth status &> /dev/null; then - echo_warning "Not authenticated with GitHub." - - # Try to authenticate from .env file - if [ -f "$SAGE_REPO/.env" ] && grep -q "GITHUB_TOKEN" "$SAGE_REPO/.env"; then - echo_info "Found GITHUB_TOKEN in .env, attempting to authenticate..." - TOKEN=$(grep GITHUB_TOKEN "$SAGE_REPO/.env" | cut -d'=' -f2) - if [ -n "$TOKEN" ]; then - # Clear environment variable if set - unset GITHUB_TOKEN - echo "$TOKEN" | gh auth login --with-token --hostname github.com 2>/dev/null - if gh auth status &> /dev/null; then - echo_success "Authenticated using token from .env" - else - echo_error "Failed to authenticate with token from .env" - echo "Run: gh auth login" - exit 1 - fi - fi - else - echo_error "Run: gh auth login" - echo "Or add GITHUB_TOKEN to $SAGE_REPO/.env" - exit 1 - fi - fi - - echo_success "Prerequisites satisfied" -} - -# Get SAGE repo path -get_sage_path() { - # sage-edge 已独立到: https://github.com/intellistream/sage-edge - # 此脚本仅用于历史记录，不再需要执行 - echo_error "sage-edge 已独立，请访问: https://github.com/intellistream/sage-edge" - echo_error "安装: pip install isage-edge" - exit 0 -} - -# Create GitHub repository -create_github_repo() { - echo_info "Creating GitHub repository: intellistream/sage-edge" - - if gh repo view intellistream/sage-edge &> /dev/null; then - echo_warning "Repository already exists. Skipping creation." - REPO_EXISTS=1 - else - gh repo create intellistream/sage-edge \ - --public \ - --description "SAGE Edge - Lightweight FastAPI aggregator for SAGE Gateway" \ - --license MIT \ - --clone - - cd sage-edge - REPO_EXISTS=0 - echo_success "Repository created and cloned" - fi -} - -# Setup repository structure -setup_repo_structure() { - echo_info "Setting up repository structure..." - - if [ $REPO_EXISTS -eq 1 ]; then - # Clone if not already in directory - if [ ! -d "sage-edge" ]; then - gh repo clone intellistream/sage-edge - fi - cd sage-edge - fi - - # Create directory structure - mkdir -p src/sage/edge tests .github/workflows docs - - # Create __init__.py files - touch src/sage/__init__.py - touch tests/__init__.py - - echo_success "Directory structure created" -} - -# Copy core files -copy_core_files() { - echo_info "Copying core files from SAGE monorepo..." - - # Copy Python source files - # 代码已迁移到独立仓库，此处代码不再有效 - echo "请从 https://github.com/intellistream/sage-edge 获取源码" - - # Update pyproject.toml URLs - sed -i.bak 's|https://github.com/intellistream/SAGE|https://github.com/intellistream/sage-edge|g' pyproject.toml - rm pyproject.toml.bak - - echo_success "Core files copied" -} - -# Copy template files -copy_template_files() { - echo_info "Copying template files..." - - DOCS_PATH="$SAGE_REPO/docs-public/docs_src/dev-notes/cross-layer/architecture" - - # README - cp "$DOCS_PATH/sage-edge-standalone-README.md" README.md - - # CHANGELOG - cp "$DOCS_PATH/sage-edge-standalone-CHANGELOG.md" CHANGELOG.md - - # CI/CD workflow - cp "$DOCS_PATH/sage-edge-standalone-ci.yml" .github/workflows/ci.yml - - echo_success "Template files copied" -} - -# Create additional files -create_additional_files() { - echo_info "Creating additional files..." - - # .gitignore - cat > .gitignore << 'EOF' -# Python -__pycache__/ -*.py[cod] -*$py.class -*.so -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -*.egg-info/ -.installed.cfg -*.egg - -# Virtual environments -venv/ -ENV/ -env/ - -# IDEs -.vscode/ -.idea/ -*.swp -*.swo -*~ - -# Testing -.pytest_cache/ -.coverage -htmlcov/ -.tox/ - -# MyPy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Ruff -.ruff_cache/ - -# Logs -*.log -logs/ - -# OS -.DS_Store -Thumbs.db -EOF - - # LICENSE - cat > LICENSE << 'EOF' -MIT License - -Copyright (c) 2026 IntelliStream Team - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. -EOF - - # CONTRIBUTING.md - cat > CONTRIBUTING.md << 'EOF' -# Contributing to SAGE Edge - -We welcome contributions! Please follow these guidelines: - -## Development Setup - -```bash -git clone https://github.com/intellistream/sage-edge.git -cd sage-edge -pip install -e .[dev] -``` - -## Guidelines - -- Follow PEP 8 style guide -- Use Ruff for linting: `ruff check src/` -- Run tests before submitting: `pytest tests/ -v` -- Update CHANGELOG.md for notable changes - -## Pull Request Process - -1. Fork the repository -2. Create a feature branch (`git checkout -b feature/amazing-feature`) -3. Commit your changes (`git commit -m 'Add amazing feature'`) -4. Push to the branch (`git push origin feature/amazing-feature`) -5. Open a Pull Request - -For detailed guidelines, see [SAGE Contributing Guide](https://github.com/intellistream/SAGE/blob/main/CONTRIBUTING.md). -EOF - - echo_success "Additional files created" -} - -# Create tests -create_tests() { - echo_info "Creating test files..." - - cat > tests/test_app.py << 'EOF' -"""Tests for sage.edge.app module.""" - -import pytest -from sage.edge.app import create_app - - -def test_create_app_no_llm(): - """Test creating app without LLM gateway.""" - app = create_app(mount_llm=False) - assert app.title == "SAGE Edge" - assert "/healthz" in [route.path for route in app.router.routes] - - -def test_create_app_with_llm_default_mount(): - """Test creating app with LLM gateway at default path.""" - # This test requires Gateway to be installed - pytest.skip("Requires isage-llm-gateway") - - -def test_create_app_with_llm_custom_prefix(): - """Test creating app with LLM gateway at custom prefix.""" - # This test requires Gateway to be installed - pytest.skip("Requires isage-llm-gateway") -EOF - - cat > tests/conftest.py << 'EOF' -"""Pytest configuration for sage-edge tests.""" - -import pytest - - -@pytest.fixture -def mock_gateway_app(): - """Mock Gateway app for testing.""" - from fastapi import FastAPI - app = FastAPI(title="Mock Gateway") - return app -EOF - - echo_success "Test files created" -} - -# Git commit and push -git_commit_push() { - echo_info "Committing and pushing to GitHub..." - - git add . - git commit -m "feat: initial release of sage-edge as independent package - -- Core functionality: FastAPI aggregator for SAGE Gateway -- Default mounting at / with path preservation -- Custom prefix support -- Health check endpoints -- CLI entrypoint (sage-edge) -- Comprehensive documentation -- CI/CD with GitHub Actions - -Related to SAGE monorepo: https://github.com/intellistream/SAGE" - - git push origin main - - # Create and push develop branch - git checkout -b develop - git push -u origin develop - - # Set develop as default branch - gh repo edit intellistream/sage-edge --default-branch develop - - echo_success "Pushed to GitHub" -} - -# Configure repository settings -configure_repo_settings() { - echo_info "Configuring repository settings..." - - # Enable issues - gh repo edit intellistream/sage-edge --enable-issues=true - - # Add topics - gh repo edit intellistream/sage-edge \ - --add-topic sage \ - --add-topic llm \ - --add-topic fastapi \ - --add-topic openai \ - --add-topic gateway - - echo_success "Repository settings configured" -} - -# Setup GitHub Secrets -setup_secrets() { - echo_info "Setting up GitHub Secrets..." - - echo_warning "You need to manually set up PyPI tokens:" - echo "1. Get PyPI token from: https://pypi.org/manage/account/token/" - echo "2. Run: gh secret set PYPI_API_TOKEN -R intellistream/sage-edge" - echo "" - echo "3. Get TestPyPI token from: https://test.pypi.org/manage/account/token/" - echo "4. Run: gh secret set TEST_PYPI_API_TOKEN -R intellistream/sage-edge" - echo "" - - read -p "Press Enter when you have the tokens ready, or Ctrl+C to skip..." - - echo "Setting PYPI_API_TOKEN:" - gh secret set PYPI_API_TOKEN -R intellistream/sage-edge - - echo "Setting TEST_PYPI_API_TOKEN:" - gh secret set TEST_PYPI_API_TOKEN -R intellistream/sage-edge - - echo_success "GitHub Secrets configured" -} - -# Print summary -print_summary() { - echo "" - echo_success "=========================================" - echo_success "SAGE Edge repository created successfully!" - echo_success "=========================================" - echo "" - echo_info "Repository: https://github.com/intellistream/sage-edge" - echo_info "Local path: $(pwd)" - echo "" - echo_info "Next steps:" - echo "1. Review the generated files" - echo "2. Run local tests: pytest tests/ -v" - echo "3. Test installation: pip install -e .[dev]" - echo "4. Create release: git tag v0.1.0-beta1 && git push origin v0.1.0-beta1" - echo "5. Monitor CI/CD: https://github.com/intellistream/sage-edge/actions" - echo "" -} - -# Main execution -main() { - echo_info "================================================" - echo_info "SAGE Edge Independent Repository Setup Script" - echo_info "================================================" - echo "" - - check_prerequisites - get_sage_path - create_github_repo - setup_repo_structure - copy_core_files - copy_template_files - create_additional_files - create_tests - git_commit_push - configure_repo_settings - setup_secrets - print_summary -} - -# Run main -main diff --git a/tools/scripts/fix_remaining_violations.sh b/tools/scripts/fix_remaining_violations.sh deleted file mode 100755 index 62b063ea41..0000000000 --- a/tools/scripts/fix_remaining_violations.sh +++ /dev/null @@ -1,144 +0,0 @@ -#!/bin/bash -# ============================================================================ -# Fix Remaining Documentation Violations -# ============================================================================ -# Purpose: Move the remaining 11 scattered documentation files to proper locations -# -# Categories: -# 1. benchmark/experiment/mem_docs/*.md (5 files) -# 2. amms/INSTALLATION_GUIDE.md (1 file) -# 3. sage-middleware GRAPH_MEMORY_IMPLEMENTATION.md (1 file) -# 4. tools/install/fixes/*.md (2 files) -# 5. tools/scripts/*.md (2 files) -# ============================================================================ - -set -e - -SAGE_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)" -cd "$SAGE_ROOT" - -DRY_RUN=${DRY_RUN:-true} - -function move_file() { - local src="$1" - local dst="$2" - - if [ ! -f "$src" ]; then - echo "⚠️ Source not found: $src" - return 1 - fi - - echo "📄 $src" - echo " → $dst" - - if [ "$DRY_RUN" = "false" ]; then - mkdir -p "$(dirname "$dst")" - git mv "$src" "$dst" - fi -} - -echo "================================================================================================" -echo "📋 Fixing Remaining Documentation Violations" -echo "================================================================================================" -echo "" - -if [ "$DRY_RUN" = "true" ]; then - echo "🔍 DRY RUN MODE - No files will be moved" - echo " Set DRY_RUN=false to execute moves" - echo "" -fi - -# ============================================================================ -# Category 1: benchmark/experiment/mem_docs/*.md (5 files) -# ============================================================================ -echo "📦 Category 1: Benchmark Memory Experiment Documentation" -echo "" - -move_file \ - "packages/sage-benchmark/src/sage/benchmark/benchmark_memory/experiment/mem_docs/MEMORY_SERVICE_NAMING_DISCUSSION.md" \ - "packages/sage-benchmark/docs/benchmark_memory/experiment_design/MEMORY_SERVICE_NAMING_DISCUSSION.md" - -move_file \ - "packages/sage-benchmark/src/sage/benchmark/benchmark_memory/experiment/mem_docs/Memory_Combinatorial_Experiment_Design.md" \ - "packages/sage-benchmark/docs/benchmark_memory/experiment_design/Memory_Combinatorial_Experiment_Design.md" - -move_file \ - "packages/sage-benchmark/src/sage/benchmark/benchmark_memory/experiment/mem_docs/Memory_Pipeline_Dev_Archive.md" \ - "packages/sage-benchmark/docs/benchmark_memory/experiment_design/Memory_Pipeline_Dev_Archive.md" - -move_file \ - "packages/sage-benchmark/src/sage/benchmark/benchmark_memory/experiment/mem_docs/Memory_Systems_Comparison.md" \ - "packages/sage-benchmark/docs/benchmark_memory/experiment_design/Memory_Systems_Comparison.md" - -move_file \ - "packages/sage-benchmark/src/sage/benchmark/benchmark_memory/experiment/mem_docs/README.md" \ - "packages/sage-benchmark/docs/benchmark_memory/experiment_design/README.md" - -echo "" - -# ============================================================================ -# Category 2: amms/INSTALLATION_GUIDE.md (1 file) -# ============================================================================ -echo "📦 Category 2: AMMS Installation Guide" -echo "" - -move_file \ - "packages/sage-libs/src/sage/libs/amms/INSTALLATION_GUIDE.md" \ - "packages/sage-libs/docs/amms/INSTALLATION_GUIDE.md" - -echo "" - -# ============================================================================ -# Category 3: sage-middleware GRAPH_MEMORY_IMPLEMENTATION.md (1 file) -# ============================================================================ -echo "📦 Category 3: Sage Middleware Documentation" -echo "" - -move_file \ - "packages/sage-middleware/src/sage/middleware/components/sage_mem/GRAPH_MEMORY_IMPLEMENTATION.md" \ - "packages/sage-middleware/docs/sage_mem/GRAPH_MEMORY_IMPLEMENTATION.md" - -echo "" - -# ============================================================================ -# Category 4: tools/install/fixes/*.md (2 files) -# ============================================================================ -echo "📦 Category 4: Tools Install Fixes Documentation" -echo "" - -move_file \ - "tools/install/fixes/FIX_SUMMARY.md" \ - "tools/docs/install/fixes/FIX_SUMMARY.md" - -move_file \ - "tools/install/fixes/UNBOUND_VARIABLE_FIX.md" \ - "tools/docs/install/fixes/UNBOUND_VARIABLE_FIX.md" - -echo "" - -# ============================================================================ -# Category 5: tools/scripts/*.md (2 files) -# ============================================================================ -echo "📦 Category 5: Tools Scripts Documentation" -echo "" - -move_file \ - "tools/scripts/LIBAMM_MIGRATION_QUICKREF.md" \ - "tools/docs/scripts/LIBAMM_MIGRATION_QUICKREF.md" - -move_file \ - "tools/scripts/README_CLUSTER_SETUP.md" \ - "tools/docs/scripts/README_CLUSTER_SETUP.md" - -echo "" -echo "================================================================================================" - -if [ "$DRY_RUN" = "true" ]; then - echo "✅ Dry run completed - 11 files ready to move" - echo " Run: DRY_RUN=false $0" -else - echo "✅ All 11 files moved successfully" - echo " Run: git status" -fi - -echo "================================================================================================" diff --git a/tools/scripts/migrate_tutorials.sh b/tools/scripts/migrate_tutorials.sh deleted file mode 100755 index b3e5a97877..0000000000 --- a/tools/scripts/migrate_tutorials.sh +++ /dev/null @@ -1,183 +0,0 @@ -#!/bin/bash -# migrate_tutorials.sh - 将 tutorials 打散迁移到各包 -# -# 用法: ./tools/scripts/migrate_tutorials.sh [--dry-run] - -set -e - -SAGE_ROOT="${SAGE_ROOT:-$(cd "$(dirname "$0")/../.." && pwd)}" -DRY_RUN=false - -if [[ "$1" == "--dry-run" ]]; then - DRY_RUN=true - echo "🔍 Dry run mode - no changes will be made" -fi - -echo "" -echo "╔══════════════════════════════════════════════════════════════════════════════╗" -echo "║ Tutorials 迁移脚本 - 打散到各包 ║" -echo "╚══════════════════════════════════════════════════════════════════════════════╝" -echo "" - -# 定义迁移映射 -declare -A MIGRATION_MAP=( - ["tutorials/L1-common"]="packages/sage-common/src/sage/common/tutorials" - ["tutorials/L2-platform"]="packages/sage-platform/src/sage/platform/tutorials" - ["tutorials/L3-kernel"]="packages/sage-kernel/src/sage/kernel/tutorials" - ["tutorials/L3-libs"]="packages/sage-libs/src/sage/libs/tutorials" - ["tutorials/L4-middleware"]="packages/sage-middleware/src/sage/middleware/tutorials" -) - -# Control Plane 相关教程迁移到 sage-llm-core -LLM_CORE_TUTORIALS="packages/sage-llm-core/src/sage/llm/tutorials" - -# 入门示例迁移到 sage-common -COMMON_TUTORIALS="packages/sage-common/src/sage/common/tutorials" - -cd "$SAGE_ROOT" - -echo "📊 当前 tutorials 文件统计:" -echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" -for src in "${!MIGRATION_MAP[@]}"; do - if [[ -d "$src" ]]; then - count=$(find "$src" -name "*.py" -type f 2>/dev/null | wc -l) - echo " $src: $count 个 Python 文件" - fi -done -echo "" - -# 函数：创建目录并移动文件 -migrate_directory() { - local src="$1" - local dst="$2" - - if [[ ! -d "$src" ]]; then - echo " ⚠️ 源目录不存在: $src" - return - fi - - local count=$(find "$src" -name "*.py" -type f 2>/dev/null | wc -l) - if [[ "$count" -eq 0 ]]; then - echo " ⏭️ 跳过空目录: $src" - return - fi - - echo " 📁 $src → $dst ($count 个文件)" - - if [[ "$DRY_RUN" == "false" ]]; then - mkdir -p "$dst" - - # 复制所有内容（保持目录结构） - cp -r "$src"/* "$dst"/ 2>/dev/null || true - - # 创建 __init__.py - if [[ ! -f "$dst/__init__.py" ]]; then - cat > "$dst/__init__.py" << 'INITPY' -"""SAGE Tutorials - 教程和示例代码. - -这个模块包含了可运行的教程和示例，随 PyPI 包一起分发。 - -用法: - # 作为模块运行 - python -m sage..tutorials. - - # 或导入使用 - from sage..tutorials import -""" -INITPY - fi - - # 为子目录创建 __init__.py (使用 bash 兼容语法) - find "$dst" -type d | while read dir; do - if [ ! -f "$dir/__init__.py" ]; then - touch "$dir/__init__.py" - fi - done - fi -} - -echo "🚀 开始迁移..." -echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" - -# 迁移各层 tutorials -for src in "${!MIGRATION_MAP[@]}"; do - dst="${MIGRATION_MAP[$src]}" - migrate_directory "$src" "$dst" -done - -# 迁移根目录的入门示例到 sage-common -echo "" -echo "📁 迁移根目录入门示例..." -if [[ "$DRY_RUN" == "false" ]]; then - mkdir -p "$COMMON_TUTORIALS" - - # 移动根目录的 Python 文件 - for file in tutorials/hello_world.py tutorials/embedding_server_example.py tutorials/__init__.py; do - if [[ -f "$file" ]]; then - cp "$file" "$COMMON_TUTORIALS/" - echo " ✅ $(basename $file) → sage-common/tutorials/" - fi - done - - # 移动 README 和 QUICK_START - for file in tutorials/README.md tutorials/QUICK_START.md tutorials/INSTALLATION_GUIDE.md; do - if [[ -f "$file" ]]; then - cp "$file" "$COMMON_TUTORIALS/" - echo " ✅ $(basename $file) → sage-common/tutorials/" - fi - done -fi - -# 迁移 Control Plane 相关教程到 sage-llm-core -echo "" -echo "📁 迁移 Control Plane 教程..." -if [[ "$DRY_RUN" == "false" ]]; then - mkdir -p "$LLM_CORE_TUTORIALS" - - for file in tutorials/vllm_control_plane_tutorial.py tutorials/benchmark_control_plane_demo.py; do - if [[ -f "$file" ]]; then - cp "$file" "$LLM_CORE_TUTORIALS/" - echo " ✅ $(basename $file) → sage-llm-core/tutorials/" - fi - done - - # 移动 markdown 文件 - if [[ -f "tutorials/vllm_control_plane_config_examples.md" ]]; then - cp "tutorials/vllm_control_plane_config_examples.md" "$LLM_CORE_TUTORIALS/" - echo " ✅ vllm_control_plane_config_examples.md → sage-llm-core/tutorials/" - fi - - # 创建 __init__.py - if [[ ! -f "$LLM_CORE_TUTORIALS/__init__.py" ]]; then - cat > "$LLM_CORE_TUTORIALS/__init__.py" << 'INITPY' -"""SAGE LLM Core Tutorials - Control Plane 教程和示例. - -这个模块包含了 Control Plane 相关的教程和示例。 -""" -INITPY - fi -fi - -echo "" -echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" - -if [[ "$DRY_RUN" == "true" ]]; then - echo "🔍 Dry run 完成 - 未做任何更改" -else - echo "✅ 迁移完成" - echo "" - echo "📊 迁移后文件统计:" - for dst in "${MIGRATION_MAP[@]}" "$COMMON_TUTORIALS" "$LLM_CORE_TUTORIALS"; do - if [[ -d "$dst" ]]; then - count=$(find "$dst" -name "*.py" -type f 2>/dev/null | wc -l) - echo " $dst: $count 个 Python 文件" - fi - done -fi - -echo "" -echo "🎯 下一步操作:" -echo " 1. 检查迁移结果: find packages/*/src/sage/*/tutorials -name '*.py' | head -20" -echo " 2. 删除旧目录: rm -rf tutorials/" -echo " 3. 提交更改: git add . && git commit -m 'refactor: migrate tutorials to packages'" -echo "" diff --git a/tools/scripts/publish_pep420_release.sh b/tools/scripts/publish_pep420_release.sh deleted file mode 100755 index 1045fe71a7..0000000000 --- a/tools/scripts/publish_pep420_release.sh +++ /dev/null @@ -1,156 +0,0 @@ -#!/bin/bash -# Publish all SAGE packages to PyPI after PEP 420 migration -# Usage: ./tools/scripts/publish_pep420_release.sh [--test-pypi] [--dry-run] - -set -e - -SAGE_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)" -cd "$SAGE_ROOT" - -# Parse arguments -REPOSITORY="pypi" -DRY_RUN="--no-dry-run" - -for arg in "$@"; do - case $arg in - --test-pypi) - REPOSITORY="testpypi" - shift - ;; - --dry-run) - DRY_RUN="--dry-run" - shift - ;; - --help) - echo "Usage: $0 [--test-pypi] [--dry-run]" - echo "" - echo "Options:" - echo " --test-pypi Upload to TestPyPI instead of PyPI" - echo " --dry-run Perform a dry run without actual upload" - echo " --help Show this help message" - exit 0 - ;; - esac -done - -echo "🚀 SAGE PEP 420 Migration - PyPI 发布" -echo "========================================" -echo "Repository: $REPOSITORY" -echo "Dry run: $DRY_RUN" -echo "" - -# Define packages in dependency order (L1 → L5) -# L1: Foundation packages -declare -a L1_PACKAGES=( - "packages/sage-common" - "packages/sage-llm-core" -) - -# L2: Platform services -declare -a L2_PACKAGES=( - "packages/sage-platform" -) - -# L3: Kernel & Libs -declare -a L3_PACKAGES=( - "packages/sage-kernel" - "packages/sage-libs" -) - -# L4: Middleware -declare -a L4_PACKAGES=( - "packages/sage-middleware" -) - -# L5: Interface layer -declare -a L5_PACKAGES=( - "packages/sage-cli" - "packages/sage-tools" -) - -# Meta-package -declare -a META_PACKAGE=( - "packages/sage" -) - -# Combine all packages in order -ALL_PACKAGES=( - "${L1_PACKAGES[@]}" - "${L2_PACKAGES[@]}" - "${L3_PACKAGES[@]}" - "${L4_PACKAGES[@]}" - "${L5_PACKAGES[@]}" - "${META_PACKAGE[@]}" -) - -echo "📦 将按以下顺序发布 ${#ALL_PACKAGES[@]} 个包:" -echo "" -for i in "${!ALL_PACKAGES[@]}"; do - pkg="${ALL_PACKAGES[$i]}" - pkg_name=$(basename "$pkg") - # Find version from _version.py - version=$(find "$pkg/src" -name "_version.py" -type f ! -path "*/pybind11/*" -exec grep -oP '__version__ = "\K[^"]+' {} \; 2>/dev/null | head -1) - [ -z "$version" ] && version="unknown" - printf " %2d. %-25s (v%s)\n" $((i+1)) "$pkg_name" "$version" -done -echo "" - -# Confirm before proceeding -if [ "$DRY_RUN" = "--no-dry-run" ]; then - read -p "⚠️ 确认发布到 $REPOSITORY? (yes/no): " confirm - if [ "$confirm" != "yes" ]; then - echo "❌ 已取消发布" - exit 0 - fi -fi - -echo "" -echo "🔨 开始构建和发布..." -echo "" - -SUCCESS_COUNT=0 -FAILED_PACKAGES=() - -for pkg_path in "${ALL_PACKAGES[@]}"; do - pkg_name=$(basename "$pkg_path") - echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" - echo "📦 处理: $pkg_name" - echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" - - # Build and upload using wheelwright - if wheelwright build "$pkg_path" \ - --upload \ - --repository "$REPOSITORY" \ - $DRY_RUN; then - echo "✅ $pkg_name 发布成功" - ((SUCCESS_COUNT++)) - else - echo "❌ $pkg_name 发布失败" - FAILED_PACKAGES+=("$pkg_name") - fi - - echo "" -done - -echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" -echo "📊 发布总结" -echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" -echo "✅ 成功: $SUCCESS_COUNT/${#ALL_PACKAGES[@]}" -if [ ${#FAILED_PACKAGES[@]} -gt 0 ]; then - echo "❌ 失败: ${#FAILED_PACKAGES[@]}" - echo "" - echo "失败的包:" - for pkg in "${FAILED_PACKAGES[@]}"; do - echo " - $pkg" - done - exit 1 -else - echo "🎉 所有包发布成功！" -fi - -echo "" -echo "📋 验证安装:" -echo " pip install --upgrade sage # 安装元包（包含所有子包）" -echo "" -echo "📖 查看 PyPI 页面:" -echo " https://pypi.org/project/sage/" diff --git a/tools/scripts/remove_libamm_submodule.sh b/tools/scripts/remove_libamm_submodule.sh deleted file mode 100755 index 0b17c9471c..0000000000 --- a/tools/scripts/remove_libamm_submodule.sh +++ /dev/null @@ -1,188 +0,0 @@ -#!/usr/bin/env bash -# ============================================================================ -# 移除 LibAMM Submodule 脚本 -# ============================================================================ -# -# 用途：从 SAGE 仓库中移除 libamm git submodule -# 前提：isage-libamm 已成功上传到 PyPI -# -# 使用方法： -# ./tools/scripts/remove_libamm_submodule.sh -# -# ============================================================================ - -set -euo pipefail - -SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" -SAGE_ROOT="$(cd "$SCRIPT_DIR/../.." && pwd)" -LIBAMM_PATH="packages/sage-libs/src/sage/libs/libamm" - -echo "============================================================================" -echo " 移除 LibAMM Submodule" -echo "============================================================================" -echo "" - -cd "$SAGE_ROOT" - -# 步骤 1：检查是否是 git submodule -echo "📋 步骤 1：检查 libamm submodule 状态..." -if [ -f ".gitmodules" ] && grep -q "libamm" .gitmodules; then - echo " ✓ 找到 libamm submodule 配置" -else - echo " ⚠️ 警告：.gitmodules 中未找到 libamm 配置" - echo " 继续检查目录..." -fi - -if [ -d "$LIBAMM_PATH" ]; then - echo " ✓ 找到 libamm 目录: $LIBAMM_PATH" -else - echo " ✗ 错误：未找到 libamm 目录" - exit 1 -fi - -# 步骤 2：验证 PyPI 上的 isage-libamm -echo "" -echo "📦 步骤 2：验证 isage-libamm 在 PyPI 上可用..." -if pip index versions isage-libamm &>/dev/null; then - echo " ✓ isage-libamm 在 PyPI 上可用" - pip index versions isage-libamm | head -2 -else - echo " ✗ 错误：isage-libamm 在 PyPI 上不可用" - echo " 请先上传 isage-libamm 到 PyPI" - exit 1 -fi - -# 步骤 3：确认操作 -echo "" -echo "⚠️ 警告：此操作将：" -echo " 1. 移除 git submodule 配置" -echo " 2. 删除 $LIBAMM_PATH 目录" -echo " 3. 清理 .git/modules 中的 submodule 数据" -echo "" -read -p "确认继续？[y/N] " -n 1 -r -echo -if [[ ! $REPLY =~ ^[Yy]$ ]]; then - echo " 已取消操作" - exit 0 -fi - -# 步骤 4：备份（可选） -echo "" -echo "💾 步骤 4：创建备份..." -BACKUP_DIR="/tmp/sage-libamm-backup-$(date +%Y%m%d-%H%M%S)" -mkdir -p "$BACKUP_DIR" -if [ -d "$LIBAMM_PATH" ]; then - cp -r "$LIBAMM_PATH" "$BACKUP_DIR/" - echo " ✓ 备份到: $BACKUP_DIR" -fi - -# 步骤 5：移除 submodule -echo "" -echo "🗑️ 步骤 5：移除 submodule..." - -# 5.1 - Deinitialize submodule -if git config -f .gitmodules --get-regexp "submodule.*libamm" &>/dev/null; then - echo " → git submodule deinit -f $LIBAMM_PATH" - git submodule deinit -f "$LIBAMM_PATH" || true -fi - -# 5.2 - Remove from .gitmodules -if [ -f ".gitmodules" ]; then - echo " → 从 .gitmodules 中移除 libamm 配置" - # 找到 libamm 相关的 section 并删除 - if grep -q "libamm" .gitmodules; then - # 使用临时文件 - TEMP_FILE=$(mktemp) - awk '/\[submodule.*libamm/,/^$/ {next} {print}' .gitmodules > "$TEMP_FILE" - mv "$TEMP_FILE" .gitmodules - git add .gitmodules - fi -fi - -# 5.3 - Remove from git index and working tree -echo " → git rm -f $LIBAMM_PATH" -git rm -f "$LIBAMM_PATH" || true - -# 5.4 - Clean .git/modules -GIT_MODULES_PATH=".git/modules/$LIBAMM_PATH" -if [ -d "$GIT_MODULES_PATH" ]; then - echo " → 清理 $GIT_MODULES_PATH" - rm -rf "$GIT_MODULES_PATH" -fi - -# 5.5 - Clean .git/config -if git config --get-regexp "submodule.*libamm" &>/dev/null; then - echo " → 清理 .git/config 中的 submodule 配置" - git config --remove-section "submodule.$LIBAMM_PATH" 2>/dev/null || true -fi - -echo " ✓ Submodule 移除完成" - -# 步骤 6：验证移除结果 -echo "" -echo "🔍 步骤 6：验证移除结果..." -if [ -d "$LIBAMM_PATH" ]; then - echo " ✗ 错误：$LIBAMM_PATH 仍然存在" - exit 1 -else - echo " ✓ $LIBAMM_PATH 已删除" -fi - -if git config -f .gitmodules --get-regexp "submodule.*libamm" &>/dev/null; then - echo " ⚠️ 警告：.gitmodules 中仍有 libamm 配置" -else - echo " ✓ .gitmodules 已清理" -fi - -# 步骤 7：显示状态 -echo "" -echo "📊 步骤 7：Git 状态..." -git status --short - -# 步骤 8：提示下一步 -echo "" -echo "============================================================================" -echo " ✅ LibAMM Submodule 移除完成" -echo "============================================================================" -echo "" -echo "📝 下一步操作：" -echo "" -echo "1. 检查更改：" -echo " git status" -echo " git diff --cached" -echo "" -echo "2. 提交更改：" -echo " git commit -m \"refactor: remove libamm submodule, use PyPI dependency" -echo "" -echo " - Remove libamm submodule from sage-libs source tree" -echo " - LibAMM is now maintained independently at intellistream/LibAMM" -echo " - Users get libamm via PyPI: isage-libs → isage-libamm dependency" -echo " - Reduces SAGE repository complexity and size" -echo "" -echo " Benefits:" -echo " - Clear separation of concerns" -echo " - Easier maintenance (no submodule sync issues)" -echo " - Faster clone/checkout (smaller repo)" -echo " - LibAMM can evolve independently" -echo "" -echo " PyPI: https://pypi.org/project/isage-libamm/\"" -echo "" -echo "3. 更新 sage-libs 版本并重新发布：" -echo " # 编辑版本号" -echo " vim packages/sage-libs/src/sage/libs/_version.py # 改为 0.2.1" -echo "" -echo " # 清理旧构建" -echo " rm -rf ~/.sage/dist/sage-libs" -echo "" -echo " # 重新构建并上传" -echo " sage-dev package pypi build sage-libs --upload --no-dry-run" -echo "" -echo "4. 验证安装：" -echo " python -m venv /tmp/test-sage-libs" -echo " source /tmp/test-sage-libs/bin/activate" -echo " pip install isage-libs" -echo " python -c \"import PyAMM; print('✅ LibAMM from PyPI works')\"" -echo "" -echo "💾 备份位置: $BACKUP_DIR" -echo " (如需回滚，可以从这里恢复)" -echo "" diff --git a/tools/scripts/reorganize_package_docs.sh b/tools/scripts/reorganize_package_docs.sh deleted file mode 100755 index 4a4df4db72..0000000000 --- a/tools/scripts/reorganize_package_docs.sh +++ /dev/null @@ -1,103 +0,0 @@ -#!/bin/bash -# ============================================================================ -# Reorganize Package Documentation (All Packages) -# ============================================================================ -# Purpose: Move misplaced markdown files to proper locations -# Author: SAGE Team -# Date: 2026-01-02 -# -# Violations found: -# - packages/sage-libs/ (3 files) -# - packages/sage-middleware/ (1 file) -# ============================================================================ - -set -e - -SAGE_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)" -cd "$SAGE_ROOT" - -echo "🔄 开始整理所有包的文档..." -echo "" -echo "发现的违规文件：" -echo " ❌ packages/sage-libs/AMMS_PYPI_PUBLISH_GUIDE.md" -echo " ❌ packages/sage-libs/LIBAMM_INSTALLATION.md" -echo " ❌ packages/sage-libs/README_LIBAMM.md" -echo " ❌ packages/sage-middleware/MIGRATION_SCIKIT_BUILD.md" -echo "" - -# ============================================================================ -# 1. sage-libs 文档整理 -# ============================================================================ -echo "📦 处理 sage-libs..." - -# 包级文档 → packages/sage-libs/docs/ -if [ -f "packages/sage-libs/LIBAMM_INSTALLATION.md" ]; then - echo " 📝 移动 LIBAMM_INSTALLATION.md → packages/sage-libs/docs/" - git mv packages/sage-libs/LIBAMM_INSTALLATION.md \ - packages/sage-libs/docs/ - echo " ✓ 已移动" -fi - -if [ -f "packages/sage-libs/README_LIBAMM.md" ]; then - echo " 📝 移动 README_LIBAMM.md → packages/sage-libs/docs/LIBAMM.md" - git mv packages/sage-libs/README_LIBAMM.md \ - packages/sage-libs/docs/LIBAMM.md - echo " ✓ 已重命名并移动" -fi - -# 项目级开发者文档 → docs-public/ -if [ -f "packages/sage-libs/AMMS_PYPI_PUBLISH_GUIDE.md" ]; then - echo " 📝 移动 AMMS_PYPI_PUBLISH_GUIDE.md → docs-public/docs_src/dev-notes/l3-libs/" - mkdir -p docs-public/docs_src/dev-notes/l3-libs - git mv packages/sage-libs/AMMS_PYPI_PUBLISH_GUIDE.md \ - docs-public/docs_src/dev-notes/l3-libs/pypi-publish-guide.md - echo " ✓ 已移动到项目级开发者文档" -fi - -echo "" - -# ============================================================================ -# 2. sage-middleware 文档整理 -# ============================================================================ -echo "📦 处理 sage-middleware..." - -if [ -f "packages/sage-middleware/MIGRATION_SCIKIT_BUILD.md" ]; then - echo " 📝 移动 MIGRATION_SCIKIT_BUILD.md → packages/sage-middleware/docs/" - mkdir -p packages/sage-middleware/docs - git mv packages/sage-middleware/MIGRATION_SCIKIT_BUILD.md \ - packages/sage-middleware/docs/ - echo " ✓ 已移动到包级文档目录" -fi - -echo "" -echo "✅ 所有文档整理完成！" -echo "" -echo "� 整理统计：" -echo " • 处理的包: 2 (sage-libs, sage-middleware)" -echo " • 移动的文件: 4" -echo " • 包级文档: 3" -echo " • 项目级文档: 1" -echo "" -echo "📋 下一步操作：" -echo "" -echo " 1️⃣ 检查并更新文档链接：" -echo " grep -r 'AMMS_PYPI_PUBLISH_GUIDE' --include='*.md' --include='*.py' ." -echo " grep -r 'LIBAMM_INSTALLATION' --include='*.md' --include='*.py' ." -echo " grep -r 'README_LIBAMM' --include='*.md' --include='*.py' ." -echo " grep -r 'MIGRATION_SCIKIT_BUILD' --include='*.md' --include='*.py' ." -echo "" -echo " 2️⃣ 更新 packages/sage-libs/README.md 中的链接" -echo "" -echo " 3️⃣ 验证 pre-commit hook：" -echo " pre-commit run markdown-files-location-check --all-files" -echo "" -echo " 4️⃣ 提交变更：" -echo " git status" -echo " git add -A" -echo " git commit -m 'docs: reorganize package documentation to follow location policy" -echo "" -echo " - Move sage-libs docs to proper locations" -echo " - Move sage-middleware migration doc to docs/" -echo " - Fix pre-commit hook to enforce stricter patterns" -echo " - Ref: Documentation Location Policy (.github/copilot-instructions.md)'" -echo "" diff --git a/tools/scripts/reorganize_scattered_docs.sh b/tools/scripts/reorganize_scattered_docs.sh deleted file mode 100755 index 03a8545d31..0000000000 --- a/tools/scripts/reorganize_scattered_docs.sh +++ /dev/null @@ -1,354 +0,0 @@ -#!/bin/bash -# ============================================================================ -# Reorganize Scattered Documentation Files -# ============================================================================ -# Purpose: Move scattered MD files to proper locations per documentation policy -# -# This script reorganizes documentation in phases: -# Phase 1: Package root violations (high priority) -# Phase 2: amms/ documentation (high priority) -# Phase 3: anns/ documentation (medium priority) -# Phase 4: benchmark documentation (medium priority) -# Phase 5: tools/ documentation (low priority) -# -# Usage: -# ./reorganize_scattered_docs.sh [--phase N] [--dry-run] [--all] -# ============================================================================ - -set -e - -SAGE_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)" -cd "$SAGE_ROOT" - -DRY_RUN=false -PHASE="all" - -# Parse arguments -while [[ $# -gt 0 ]]; do - case $1 in - --dry-run) - DRY_RUN=true - shift - ;; - --phase) - PHASE="$2" - shift 2 - ;; - --all) - PHASE="all" - shift - ;; - *) - echo "Unknown option: $1" - echo "Usage: $0 [--phase N] [--dry-run] [--all]" - exit 1 - ;; - esac -done - -echo "================================================================================================" -echo "📦 文档整理脚本" -echo "================================================================================================" -echo "模式: $([ "$DRY_RUN" = true ] && echo "演习模式 (不实际移动文件)" || echo "执行模式 (实际移动文件)")" -echo "阶段: $PHASE" -echo "" - -# Helper function -move_file() { - local src="$1" - local dst="$2" - local description="$3" - - if [ ! -f "$src" ]; then - echo "⚠️ 源文件不存在: $src" - return 1 - fi - - echo " 📄 $description" - echo " 从: $src" - echo " 到: $dst" - - if [ "$DRY_RUN" = false ]; then - mkdir -p "$(dirname "$dst")" - git mv "$src" "$dst" 2>/dev/null || mv "$src" "$dst" - echo " ✅ 已移动" - else - echo " 🔍 演习模式 - 未实际移动" - fi - echo "" -} - -# ============================================================================ -# Phase 1: Package Root Violations (HIGH PRIORITY) -# ============================================================================ -reorganize_phase1() { - echo "════════════════════════════════════════════════════════════════" - echo "🔴 Phase 1: 包根目录违规文件 (高优先级)" - echo "════════════════════════════════════════════════════════════════" - echo "" - - # sage-libs root violations - mkdir -p packages/sage-libs/docs/amms - - move_file \ - "packages/sage-libs/AMMS_PYPI_PUBLISH_GUIDE.md" \ - "packages/sage-libs/docs/amms/PYPI_PUBLISH_GUIDE.md" \ - "AMMS PyPI 发布指南" - - move_file \ - "packages/sage-libs/LIBAMM_INSTALLATION.md" \ - "packages/sage-libs/docs/amms/INSTALLATION.md" \ - "LibAMM 安装指南" - - move_file \ - "packages/sage-libs/README_LIBAMM.md" \ - "packages/sage-libs/docs/amms/LIBAMM_README.md" \ - "LibAMM 旧版 README" - - # sage-middleware root violation - mkdir -p packages/sage-middleware/docs - - move_file \ - "packages/sage-middleware/MIGRATION_SCIKIT_BUILD.md" \ - "packages/sage-middleware/docs/MIGRATION_SCIKIT_BUILD.md" \ - "scikit-build 迁移文档" -} - -# ============================================================================ -# Phase 2: amms/ Documentation (HIGH PRIORITY) -# ============================================================================ -reorganize_phase2() { - echo "════════════════════════════════════════════════════════════════" - echo "🔴 Phase 2: amms/ 散落文档 (高优先级)" - echo "════════════════════════════════════════════════════════════════" - echo "" - - mkdir -p packages/sage-libs/docs/amms - - local amms_src="packages/sage-libs/src/sage/libs/amms" - local amms_dst="packages/sage-libs/docs/amms" - - move_file \ - "$amms_src/BUILD_PUBLISH.md" \ - "$amms_dst/BUILD_PUBLISH.md" \ - "AMMS 构建和发布指南" - - move_file \ - "$amms_src/CHECKLIST.md" \ - "$amms_dst/CHECKLIST.md" \ - "AMMS 发布检查清单" - - move_file \ - "$amms_src/MIGRATION.md" \ - "$amms_dst/MIGRATION.md" \ - "AMMS 迁移记录" - - move_file \ - "$amms_src/PAPI_PRECOMPILED_SOLUTION.md" \ - "$amms_dst/PAPI_PRECOMPILED_SOLUTION.md" \ - "PAPI 预编译方案" - - move_file \ - "$amms_src/PYPI_BUILD_STRATEGY.md" \ - "$amms_dst/PYPI_BUILD_STRATEGY.md" \ - "PyPI 构建策略" - - move_file \ - "$amms_src/QUICKREF.md" \ - "$amms_dst/QUICKREF.md" \ - "AMMS 快速参考" - - move_file \ - "$amms_src/REFACTORING_SUMMARY.md" \ - "$amms_dst/REFACTORING_SUMMARY.md" \ - "AMMS 重构总结" - - # Keep README.md in source (main documentation) - echo " ℹ️ 保留: $amms_src/README.md (主文档)" - echo "" - - move_file \ - "$amms_src/implementations/README.md" \ - "$amms_dst/implementations.md" \ - "AMMS 实现说明" -} - -# ============================================================================ -# Phase 3: anns/ Documentation (MEDIUM PRIORITY) -# ============================================================================ -reorganize_phase3() { - echo "════════════════════════════════════════════════════════════════" - echo "🟡 Phase 3: anns/ 文档 (中优先级)" - echo "════════════════════════════════════════════════════════════════" - echo "" - - mkdir -p packages/sage-libs/docs/anns - - local anns_src="packages/sage-libs/src/sage/libs/anns" - local anns_dst="packages/sage-libs/docs/anns" - - # Keep top-level README in source (main documentation) - echo " ℹ️ 保留: $anns_src/README.md (主文档)" - echo "" - - move_file \ - "$anns_src/implementations/README.md" \ - "$anns_dst/implementations.md" \ - "ANNS 实现说明" - - move_file \ - "$anns_src/implementations/README_spdlog_fix.md" \ - "$anns_dst/spdlog_fix.md" \ - "spdlog 修复说明" - - move_file \ - "$anns_src/wrappers/vsag/vsag_hnsw/PREFETCH_OPTIMIZATION.md" \ - "$anns_dst/vsag_prefetch_optimization.md" \ - "VSAG HNSW 预取优化" - - move_file \ - "$anns_src/wrappers/vsag/vsag_hnsw/README.md" \ - "$anns_dst/vsag_hnsw.md" \ - "VSAG HNSW 说明" -} - -# ============================================================================ -# Phase 4: benchmark Documentation (MEDIUM PRIORITY) -# ============================================================================ -reorganize_phase4() { - echo "════════════════════════════════════════════════════════════════" - echo "🟡 Phase 4: benchmark 文档 (中优先级)" - echo "════════════════════════════════════════════════════════════════" - echo "" - - echo "⚠️ Benchmark 文档较复杂，建议手动整理：" - echo " • 实验设计文档 → docs-public/docs_src/dev-notes/l5-benchmark/" - echo " • README 文件 → packages/sage-benchmark/docs/" - echo " • DATA_PATHS.md → 可能需要保留在代码目录（运行时配置）" - echo "" - echo " 查看详细列表: .sage/docs-location-violations-report.md" - echo "" -} - -# ============================================================================ -# Phase 5: tools/ and other Documentation (LOW PRIORITY) -# ============================================================================ -reorganize_phase5() { - echo "════════════════════════════════════════════════════════════════" - echo "🟢 Phase 5: tools/ 和其他文档 (低优先级)" - echo "════════════════════════════════════════════════════════════════" - echo "" - - mkdir -p tools/docs - - move_file \ - "tools/docs/SUBMODULE_DEVELOPMENT.md" \ - "docs-public/docs_src/dev-notes/cross-layer/submodule-development.md" \ - "子模块开发指南" - - move_file \ - "tools/install/fixes/FIX_SUMMARY.md" \ - "tools/docs/install-fixes-summary.md" \ - "安装修复摘要" - - move_file \ - "tools/install/fixes/UNBOUND_VARIABLE_FIX.md" \ - "tools/docs/unbound-variable-fix.md" \ - "未绑定变量修复" - - move_file \ - "tools/scripts/LIBAMM_MIGRATION_QUICKREF.md" \ - "tools/docs/libamm-migration-quickref.md" \ - "LibAMM 迁移快速参考" - - move_file \ - "tools/scripts/README_CLUSTER_SETUP.md" \ - "tools/docs/cluster-setup.md" \ - "集群设置说明" - - # Other files - mkdir -p packages/sage-libs/docs/agentic - mkdir -p packages/sage-llm-core/docs/control-plane - mkdir -p packages/sage-middleware/docs - - move_file \ - "packages/sage-libs/src/sage/libs/agentic/agents/runtime/README.md" \ - "packages/sage-libs/docs/agentic/runtime.md" \ - "Agentic 运行时说明" - - move_file \ - "packages/sage-libs/src/sage/libs/agentic/workflow/generators/README.md" \ - "packages/sage-libs/docs/agentic/workflow-generators.md" \ - "Agentic 工作流生成器" - - move_file \ - "packages/sage-libs/src/sage/libs/sias/SUBMODULE.md" \ - "packages/sage-libs/docs/sias-submodule.md" \ - "SIAS 子模块说明" - - move_file \ - "packages/sage-llm-core/src/sage/llm/control_plane/examples/README.md" \ - "packages/sage-llm-core/docs/control-plane/examples.md" \ - "Control Plane 示例" - - move_file \ - "packages/sage-llm-core/src/sage/llm/control_plane/strategies/README.md" \ - "packages/sage-llm-core/docs/control-plane/strategies.md" \ - "Control Plane 策略" - - move_file \ - "packages/sage-middleware/src/sage/middleware/components/sage_mem/GRAPH_MEMORY_IMPLEMENTATION.md" \ - "packages/sage-middleware/docs/graph-memory-implementation.md" \ - "图内存实现说明" -} - -# ============================================================================ -# Main Execution -# ============================================================================ - -case $PHASE in - 1) - reorganize_phase1 - ;; - 2) - reorganize_phase2 - ;; - 3) - reorganize_phase3 - ;; - 4) - reorganize_phase4 - ;; - 5) - reorganize_phase5 - ;; - all) - reorganize_phase1 - reorganize_phase2 - reorganize_phase3 - reorganize_phase4 - reorganize_phase5 - ;; - *) - echo "❌ 无效的阶段: $PHASE" - echo "有效阶段: 1, 2, 3, 4, 5, all" - exit 1 - ;; -esac - -echo "================================================================================================" -echo "✅ 文档整理完成" -echo "================================================================================================" -echo "" - -if [ "$DRY_RUN" = true ]; then - echo "💡 这是演习模式，没有实际移动文件" - echo " 要执行实际移动，请去掉 --dry-run 参数" -else - echo "📝 下一步:" - echo " 1. 检查移动后的文件位置是否正确" - echo " 2. 更新任何引用这些文档的链接" - echo " 3. 运行 pre-commit 检查: pre-commit run --all-files" - echo " 4. 提交更改: git commit -m 'docs: reorganize scattered documentation'" -fi -echo "" diff --git a/tools/scripts/setup_ssh_keys.sh b/tools/scripts/setup_ssh_keys.sh deleted file mode 100755 index fc64105157..0000000000 --- a/tools/scripts/setup_ssh_keys.sh +++ /dev/null @@ -1,115 +0,0 @@ -#!/bin/bash -# SAGE Cluster SSH 免密登录配置脚本 -# 自动为 sage2, sage3, sage4 配置 SSH 免密登录 - -set -e - -echo "==========================================" -echo "SAGE Cluster SSH 免密登录配置" -echo "==========================================" - -# 默认配置 -DEFAULT_USER="sage" -DEFAULT_PASSWORD="123" -DEFAULT_HOSTS=("sage2" "sage3" "sage4") - -# 检查是否安装了 sshpass -if ! command -v sshpass &> /dev/null; then - echo "❌ 未找到 sshpass 工具，正在安装..." - if command -v apt-get &> /dev/null; then - sudo apt-get update && sudo apt-get install -y sshpass - elif command -v yum &> /dev/null; then - sudo yum install -y sshpass - else - echo "❌ 无法自动安装 sshpass，请手动安装" - echo " Ubuntu/Debian: sudo apt-get install sshpass" - echo " CentOS/RHEL: sudo yum install sshpass" - exit 1 - fi -fi - -# 检查是否已有 SSH 密钥 -SSH_KEY="$HOME/.ssh/id_rsa" -if [ ! -f "$SSH_KEY" ]; then - echo "🔑 生成 SSH 密钥对..." - ssh-keygen -t rsa -b 4096 -f "$SSH_KEY" -N "" -C "sage-cluster-$(whoami)@$(hostname)" - echo "✅ SSH 密钥生成完成: $SSH_KEY" -else - echo "✅ SSH 密钥已存在: $SSH_KEY" -fi - -# 读取用户配置 -read -p "SSH 用户名 [默认: $DEFAULT_USER]: " USER -USER=${USER:-$DEFAULT_USER} - -read -s -p "SSH 密码 [默认: $DEFAULT_PASSWORD]: " PASSWORD -echo -PASSWORD=${PASSWORD:-$DEFAULT_PASSWORD} - -read -p "目标主机 (空格分隔) [默认: ${DEFAULT_HOSTS[*]}]: " HOSTS_INPUT -if [ -z "$HOSTS_INPUT" ]; then - HOSTS=("${DEFAULT_HOSTS[@]}") -else - read -ra HOSTS <<< "$HOSTS_INPUT" -fi - -echo "" -echo "📋 配置信息：" -echo " 用户: $USER" -echo " 主机: ${HOSTS[*]}" -echo "" - -# 为每个主机配置免密登录 -SUCCESS_COUNT=0 -TOTAL_COUNT=${#HOSTS[@]} - -for HOST in "${HOSTS[@]}"; do - echo "----------------------------------------" - echo "🔧 配置主机: $HOST" - echo "----------------------------------------" - - # 测试连接 - echo "1. 测试 SSH 连接..." - if ! sshpass -p "$PASSWORD" ssh -o StrictHostKeyChecking=no -o ConnectTimeout=5 "$USER@$HOST" "echo 'Connection OK'" 2>/dev/null; then - echo "❌ 无法连接到 $HOST，跳过" - continue - fi - echo "✅ 连接成功" - - # 复制公钥 - echo "2. 复制 SSH 公钥..." - if sshpass -p "$PASSWORD" ssh-copy-id -o StrictHostKeyChecking=no -i "$SSH_KEY.pub" "$USER@$HOST" 2>/dev/null; then - echo "✅ 公钥复制成功" - else - echo "❌ 公钥复制失败" - continue - fi - - # 验证免密登录 - echo "3. 验证免密登录..." - if ssh -o StrictHostKeyChecking=no -o ConnectTimeout=5 "$USER@$HOST" "echo 'Passwordless login works.'" 2>/dev/null; then - echo "✅ 免密登录配置成功: $USER@$HOST" - SUCCESS_COUNT=$((SUCCESS_COUNT + 1)) - else - echo "❌ 免密登录验证失败" - fi - - echo "" -done - -echo "==========================================" -echo "配置完成: $SUCCESS_COUNT/$TOTAL_COUNT 成功" -echo "==========================================" - -if [ $SUCCESS_COUNT -eq $TOTAL_COUNT ]; then - echo "🎉 所有主机配置成功！" - echo "" - echo "💡 测试命令：" - for HOST in "${HOSTS[@]}"; do - echo " ssh $USER@$HOST 'hostname'" - done - exit 0 -else - echo "⚠️ 部分主机配置失败，请检查网络和密码" - exit 1 -fi diff --git a/tools/scripts/setup_submodule_dev_tools.sh b/tools/scripts/setup_submodule_dev_tools.sh deleted file mode 100755 index bab2dfc65f..0000000000 --- a/tools/scripts/setup_submodule_dev_tools.sh +++ /dev/null @@ -1,334 +0,0 @@ -#!/bin/bash -# Setup development tools (pre-commit, pytest) for submodules -# This script creates standardized configuration files for each submodule - -set -euo pipefail - -SAGE_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)" -cd "$SAGE_ROOT" - -echo "╔═══════════════════════════════════════════════════════════════════════════╗" -echo "║ 🔧 为子模块设置开发工具 (pre-commit + pytest) ║" -echo "╚═══════════════════════════════════════════════════════════════════════════╝" -echo "" - -# Define submodules with their types -declare -A SUBMODULES=( - ["packages/sage-llm-core/src/sage/llm/sageLLM"]="python" - # 注意: C++ 扩展已迁移为独立 PyPI 包，不再作为子模块 - # - isage-vdb (was sageVDB) - # - isage-flow (was sageFlow) - # - isage-tsdb (was sageTSDB) - # - neuromem - # - isage-refiner (was sageRefiner) - # 只保留实际的 Git 子模块 -) - -# Function to create pre-commit config for C++ projects -create_cpp_precommit() { - local target_dir="$1" - local submodule_name=$(basename "$target_dir") - - cat > "$target_dir/.pre-commit-config.yaml" << 'EOF' -# Pre-commit hooks configuration for C++ submodule -# Installation: -# pip install pre-commit -# pre-commit install -# -# Usage: -# pre-commit run --all-files -# git commit --no-verify # Skip hooks temporarily - -default_language_version: - python: python3.11 - -repos: - # General file checks -- repo: https://github.com/pre-commit/pre-commit-hooks - rev: v6.0.0 - hooks: - - id: trailing-whitespace - - id: end-of-file-fixer - - id: check-yaml - args: [--unsafe] - - id: check-json - - id: check-toml - - id: check-added-large-files - args: ['--maxkb=1000'] - - id: check-merge-conflict - - id: check-case-conflict - - id: mixed-line-ending - args: [--fix=lf] - - id: detect-private-key - - # C++: clang-format (code formatting) -- repo: https://github.com/pre-commit/mirrors-clang-format - rev: v19.1.6 - hooks: - - id: clang-format - types_or: [c++, c] - args: ['-i'] - - # CMake: cmake-format -- repo: https://github.com/cheshirekow/cmake-format-precommit - rev: v0.6.13 - hooks: - - id: cmake-format - args: [--in-place] - - id: cmake-lint - args: [--disabled-codes=C0103,C0301] - - # Python (for pybind11 bindings and test scripts) -- repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.14.2 - hooks: - - id: ruff - args: [--fix] - types_or: [python, pyi] - - id: ruff-format - types_or: [python, pyi] - - # Shell scripts -- repo: https://github.com/shellcheck-py/shellcheck-py - rev: v0.10.0.1 - hooks: - - id: shellcheck - args: [--severity=warning] - - # YAML formatting -- repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks - rev: v2.15.0 - hooks: - - id: pretty-format-yaml - args: [--autofix, --indent, '2'] - - # Markdown formatting -- repo: https://github.com/executablebooks/mdformat - rev: 0.7.21 - hooks: - - id: mdformat - args: [--wrap, '100'] - additional_dependencies: - - mdformat-gfm - - mdformat-black - - # Secret detection -- repo: https://github.com/Yelp/detect-secrets - rev: v1.5.0 - hooks: - - id: detect-secrets - args: ['--baseline', '.secrets.baseline'] - exclude: package.lock.json -EOF - - echo "✅ Created .pre-commit-config.yaml for $submodule_name (C++)" -} - -# Function to create pre-commit config for Python projects -create_python_precommit() { - local target_dir="$1" - local submodule_name=$(basename "$target_dir") - - cat > "$target_dir/.pre-commit-config.yaml" << 'EOF' -# Pre-commit hooks configuration for Python submodule -# Installation: -# pip install pre-commit -# pre-commit install -# -# Usage: -# pre-commit run --all-files -# git commit --no-verify # Skip hooks temporarily - -default_language_version: - python: python3.11 - -repos: - # General file checks -- repo: https://github.com/pre-commit/pre-commit-hooks - rev: v6.0.0 - hooks: - - id: trailing-whitespace - args: [--markdown-linebreak-ext=md] - - id: end-of-file-fixer - - id: check-yaml - args: [--unsafe] - - id: check-json - - id: check-toml - - id: check-added-large-files - args: ['--maxkb=1000'] - - id: check-merge-conflict - - id: check-case-conflict - - id: mixed-line-ending - args: [--fix=lf] - - id: detect-private-key - - # Python: Ruff linter and formatter -- repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.14.2 - hooks: - - id: ruff - args: [--fix] - types_or: [python, pyi] - - id: ruff-format - types_or: [python, pyi] - - # Python: mypy type checking -- repo: https://github.com/pre-commit/mirrors-mypy - rev: v1.14.0 - hooks: - - id: mypy - args: [--config-file=pyproject.toml, --no-error-summary] - additional_dependencies: - - types-PyYAML - - types-requests - - types-setuptools - - # Shell scripts -- repo: https://github.com/shellcheck-py/shellcheck-py - rev: v0.10.0.1 - hooks: - - id: shellcheck - args: [--severity=warning] - - # YAML formatting -- repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks - rev: v2.15.0 - hooks: - - id: pretty-format-yaml - args: [--autofix, --indent, '2'] - - # Markdown formatting -- repo: https://github.com/executablebooks/mdformat - rev: 0.7.21 - hooks: - - id: mdformat - args: [--wrap, '100'] - additional_dependencies: - - mdformat-gfm - - mdformat-black - - # Secret detection -- repo: https://github.com/Yelp/detect-secrets - rev: v1.5.0 - hooks: - - id: detect-secrets - args: ['--baseline', '.secrets.baseline'] - exclude: package.lock.json -EOF - - echo "✅ Created .pre-commit-config.yaml for $submodule_name (Python)" -} - -# Function to create pytest.ini -create_pytest_ini() { - local target_dir="$1" - local submodule_name=$(basename "$target_dir") - - cat > "$target_dir/pytest.ini" << 'EOF' -[pytest] -# pytest configuration for submodule - -# Test discovery patterns -python_files = test_*.py -python_classes = Test* -python_functions = test_* - -# Add markers for async tests -asyncio_mode = auto - -# Logging -log_cli = true -log_cli_level = INFO -log_file = pytest.log -log_file_level = DEBUG - -# Output options -addopts = - -v - --strict-markers - --tb=short - --disable-warnings - --cov=. - --cov-report=term-missing - --cov-report=html:htmlcov - -# Test paths -testpaths = - tests - -# Markers for test categorization -markers = - unit: Unit tests - integration: Integration tests - slow: Slow tests (use -m "not slow" to skip) - gpu: Tests requiring GPU - cpp: Tests for C++ bindings - -# Ignore certain paths -norecursedirs = - .git - .tox - dist - build - *.egg - vendors - third_party -EOF - - echo "✅ Created pytest.ini for $submodule_name" -} - -# Main execution -echo "📋 检查子模块并创建缺失的配置文件..." -echo "" - -for submodule_path in "${!SUBMODULES[@]}"; do - submodule_type="${SUBMODULES[$submodule_path]}" - submodule_name=$(basename "$submodule_path") - - if [ ! -d "$submodule_path" ]; then - echo "⚠️ 跳过: $submodule_name (目录不存在)" - continue - fi - - echo "📦 处理: $submodule_name ($submodule_type)" - - # Create pre-commit config if missing - if [ ! -f "$submodule_path/.pre-commit-config.yaml" ]; then - if [ "$submodule_type" == "cpp" ]; then - create_cpp_precommit "$submodule_path" - else - create_python_precommit "$submodule_path" - fi - else - echo " ⏭️ .pre-commit-config.yaml 已存在" - fi - - # Create pytest.ini if missing - if [ ! -f "$submodule_path/pytest.ini" ]; then - create_pytest_ini "$submodule_path" - else - echo " ⏭️ pytest.ini 已存在" - fi - - echo "" -done - -echo "╔═══════════════════════════════════════════════════════════════════════════╗" -echo "║ ✅ 配置文件创建完成 ║" -echo "╚═══════════════════════════════════════════════════════════════════════════╝" -echo "" -echo "📝 下一步操作：" -echo "" -echo "1. 进入每个子模块目录" -echo "2. 运行: pre-commit install" -echo "3. 运行: pre-commit run --all-files" -echo "4. 提交配置文件到子模块仓库" -echo "" -echo "示例命令：" -echo " cd packages/sage-middleware/src/sage/middleware/components/sage_db/sageVDB" -echo " pre-commit install" -echo " pre-commit run --all-files" -echo " git add .pre-commit-config.yaml pytest.ini" -echo " git commit -m 'chore: add pre-commit and pytest configuration'" -echo "" diff --git a/tools/scripts/setup_workspace_deps.sh b/tools/scripts/setup_workspace_deps.sh deleted file mode 100755 index 1b312c43f5..0000000000 --- a/tools/scripts/setup_workspace_deps.sh +++ /dev/null @@ -1,53 +0,0 @@ -#!/bin/bash -# Setup workspace dependencies for SAGE.code-workspace -# This script clones SAGE-Pub. - -set -e - -SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" -SAGE_ROOT="$(cd "$SCRIPT_DIR/../.." && pwd)" -PARENT_DIR="$(dirname "$SAGE_ROOT")" - -echo "🔍 Checking workspace dependencies..." -echo "" - -# 1. Check if SAGE-Pub repository exists -echo "📚 Checking SAGE-Pub repository..." -SAGE_PUB_DIR="$PARENT_DIR/SAGE-Pub" -if [ -d "$SAGE_PUB_DIR/.git" ]; then - echo "✅ SAGE-Pub already exists at: $SAGE_PUB_DIR" -else - echo "⚠️ SAGE-Pub not found" - echo "" - echo "SAGE-Pub contains the documentation for SAGE." - echo "" - read -p "Clone SAGE-Pub repository? (Y/n): " -n 1 -r - echo - if [[ ! $REPLY =~ ^[Nn]$ ]]; then - echo "📥 Cloning SAGE-Pub..." - cd "$PARENT_DIR" - if git clone git@github.com:intellistream/SAGE-Pub.git; then - cd SAGE-Pub - git checkout main-dev - cd "$SAGE_ROOT" - echo "✅ SAGE-Pub cloned successfully" - else - echo "❌ Failed to clone SAGE-Pub" - cd "$SAGE_ROOT" - fi - else - echo "⏭️ Skipped. You can safely ignore VS Code warnings about missing folders." - fi -fi - -echo "" -echo "✅ Workspace setup complete!" -echo "" -echo "📁 Workspace folders:" -echo " • SAGE (main repository)" -if [ -d "$SAGE_PUB_DIR" ]; then - echo " • SAGE-Pub (documentation repository)" -fi -echo "" -echo "💡 To open the workspace in VS Code:" -echo " code SAGE.code-workspace" diff --git a/tools/scripts/unify_dependencies.sh b/tools/scripts/unify_dependencies.sh deleted file mode 100755 index 368889f1bd..0000000000 --- a/tools/scripts/unify_dependencies.sh +++ /dev/null @@ -1,103 +0,0 @@ -#!/bin/bash -# 统一 SAGE 所有包的依赖版本 -# -# 原则： -# 1. L1 (sage-common, sage-llm-core) 定义基础依赖 -# 2. 其他包继承 L1 的版本约束 - -set -e - -echo "🔧 统一 SAGE 依赖版本..." -echo "" - -# 定义统一的版本约束 -declare -A UNIFIED_DEPS=( - # 核心计算库 - ["torch"]='>=2.7.0,<3.0.0' - ["torchvision"]='>=0.22.0,<1.0.0' - ["numpy"]='>=1.26.0,<2.3.0' - - # Transformers 生态 - ["transformers"]='>=4.52.0,<4.58.0' - ["tokenizers"]='>=0.21.0,<0.24.0' - ["sentence-transformers"]='>=3.1.0,<4.0.0' - ["accelerate"]='>=1.9.0,<2.0.0' - ["peft"]='>=0.18.0,<1.0.0' - ["huggingface-hub"]='>=0.34.0,<1.0.0' - - # Web 框架 - ["fastapi"]='>=0.115.0,<1.0.0' - ["uvicorn"]='>=0.34.0,<1.0.0' - ["pydantic"]='>=2.10.0,<3.0.0' - ["pydantic-settings"]='>=2.0.0' - - # HTTP 客户端 - ["requests"]='>=2.32.0,<3.0.0' - ["httpx"]='>=0.28.0,<1.0.0' - - # 其他常用库 - ["pyyaml"]='>=6.0' - ["python-dotenv"]='>=1.1.0,<2.0.0' - ["rich"]='>=13.0.0,<14.0.0' - ["typer"]='>=0.15.0,<1.0.0' - ["click"]='>=8.0.0,<9.0.0' -) - -# 需要修复的包和依赖 -declare -A FIXES=( - # sage-common: torch 版本过低 - ["packages/sage-common/pyproject.toml:torch"]='torch>=2.4.0|torch>=2.7.0,<3.0.0' - - # sage-kernel: fastapi 版本不一致 - ["packages/sage-kernel/pyproject.toml:fastapi1"]='fastapi>=0.100.0|fastapi>=0.115.0,<1.0.0' - - # sage-tools: fastapi 版本太严格 - ["packages/sage-tools/pyproject.toml:fastapi"]='fastapi>=0.115,<0.116|fastapi>=0.115.0,<1.0.0' - - # sage-apps: transformers 版本不一致 - ["packages/sage-apps/pyproject.toml:transformers"]='transformers>=4.52.0,<4.56.0|transformers>=4.52.0,<4.58.0' -) - -# 统计 -fixed_count=0 -total_count=${#FIXES[@]} - -echo "📋 需要修复 $total_count 个依赖不一致问题：" -echo "" - -for key in "${!FIXES[@]}"; do - IFS=':' read -r file dep <<< "$key" - IFS='|' read -r old_ver new_ver <<< "${FIXES[$key]}" - - echo " 📝 $file" - echo " $dep: $old_ver → $new_ver" - - if [ -f "$file" ]; then - # 使用 sed 替换（需要转义特殊字符） - old_escaped=$(echo "$old_ver" | sed 's/[.[\*^$()+?{|]/\\&/g') - new_escaped=$(echo "$new_ver" | sed 's/[&/]/\\&/g') - - # 替换 - sed -i "s/\"$old_escaped\"/\"$new_escaped\"/g" "$file" - - # 验证替换是否成功 - if grep -q "$new_ver" "$file"; then - echo " ✅ 已修复" - ((fixed_count++)) - else - echo " ⚠️ 替换可能失败，请手动检查" - fi - else - echo " ❌ 文件不存在" - fi - echo "" -done - -echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" -echo "📊 修复完成: $fixed_count/$total_count" -echo "" -echo "💡 后续步骤:" -echo " 1. 运行测试: sage-dev project test" -echo " 2. 提交更改: git add packages/*/pyproject.toml" -echo " 3. 创建 PR" -echo "" diff --git a/tools/scripts/update_pypi_docs.sh b/tools/scripts/update_pypi_docs.sh deleted file mode 100755 index 7035cfc255..0000000000 --- a/tools/scripts/update_pypi_docs.sh +++ /dev/null @@ -1,89 +0,0 @@ -#!/bin/bash -# Update documentation to use wheelwright instead of legacy sage-dev package PyPI commands - -set -e - -SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" -SAGE_ROOT="$(cd "$SCRIPT_DIR/../.." && pwd)" - -echo "🔄 Updating PyPI publishing documentation..." -echo "📁 SAGE root: $SAGE_ROOT" - -# Files to update -files=( - "packages/sage-libs/docs/amms/BUILD_PUBLISH.md" - "packages/sage-libs/docs/amms/PYPI_PUBLISH_GUIDE.md" - "tools/docs/scripts/LIBAMM_MIGRATION_QUICKREF.md" - "docs-public/docs_src/developers/ci-cd.md" - "docs-public/docs_src/developers/commands.md" - "docs-public/docs_src/dev-notes/l6-cli/COMMAND_CHEATSHEET.md" -) - -echo "" -echo "📝 Files to update:" -for file in "${files[@]}"; do - if [ -f "$SAGE_ROOT/$file" ]; then - echo " ✅ $file" - else - echo " ⚠️ $file (not found)" - fi -done - -echo "" -echo "🔧 Performing replacements..." - -# Add deprecation notice at the top of relevant files -deprecation_notice=" -> **⚠️ DEPRECATED**: The \`sage-dev package pypi\` command has been removed. -> Please use the standalone [wheelwright](https://github.com/intellistream/wheelwright) tool instead. -> -> **Migration**: -> \`\`\`bash -> git clone https://github.com/intellistream/wheelwright.git -> cd wheelwright -> ./publish.sh --auto-bump patch -> \`\`\` -" - -# Function to add deprecation notice -add_deprecation_notice() { - local file="$1" - if [ ! -f "$file" ]; then - return - fi - - # Check if notice already exists - if grep -q "wheelwright\|sage-pypi-publisher" "$file"; then - echo " ℹ️ Deprecation notice already present in $(basename "$file")" - return - fi - - # Add after first heading - awk -v notice="$deprecation_notice" ' - /^#/ && !found { - print $0 - print notice - found=1 - next - } - {print} - ' "$file" > "$file.tmp" && mv "$file.tmp" "$file" - - echo " ✅ Added deprecation notice to $(basename "$file")" -} - -# Add notices to key files -for file in "${files[@]}"; do - full_path="$SAGE_ROOT/$file" - if [ -f "$full_path" ]; then - add_deprecation_notice "$full_path" - fi -done - -echo "" -echo "✅ Documentation update complete!" -echo "" -echo "📖 Next steps:" -echo " 1. Review the changes: git diff" -echo " 2. Commit the updates: git commit -am 'docs: update PyPI publishing to use wheelwright'" -echo " 3. Update any remaining internal documentation as needed" diff --git a/tools/scripts/upgrade_worker_ray.sh b/tools/scripts/upgrade_worker_flownet.sh similarity index 50% rename from tools/scripts/upgrade_worker_ray.sh rename to tools/scripts/upgrade_worker_flownet.sh index 2d339f93aa..56d39e8219 100755 --- a/tools/scripts/upgrade_worker_ray.sh +++ b/tools/scripts/upgrade_worker_flownet.sh @@ -1,16 +1,17 @@ #!/bin/bash -# 升级 Worker 节点的 Ray 版本到与 Head 节点一致 +# 升级 Worker 节点的 isage-flownet 版本到与 Head 节点一致 +# 替代原 upgrade_worker_ray.sh (legacy runtime 已迁移至 isage-flownet) set -e # 配置 WORKERS=("sage@sage2:22" "sage@sage3:22" "sage@sage4:22") -TARGET_RAY_VERSION="2.52.0" +TARGET_FLOWNET_VERSION="0.1.0" CONDA_ENV="sage" echo "======================================" -echo "升级 Worker 节点 Ray 版本" -echo "目标版本: $TARGET_RAY_VERSION" +echo "升级 Worker 节点 isage-flownet 版本" +echo "目标版本: $TARGET_FLOWNET_VERSION" echo "======================================" for worker in "${WORKERS[@]}"; do @@ -18,7 +19,7 @@ for worker in "${WORKERS[@]}"; do IFS='@' read -r user host <<< "$user_host" echo "" - echo "🔧 处理节点: $host" + echo "处理节点: $host" echo "--------------------------------------" # SSH 执行升级命令 @@ -39,38 +40,27 @@ fi echo "[INFO] 当前环境: \$(conda info --envs | grep '*' | awk '{print \$1}')" -# 检查当前 Ray 版本 -CURRENT_VERSION=\$(python -c "import ray; print(ray.__version__)" 2>/dev/null || echo "未安装") -echo "[INFO] 当前 Ray 版本: \$CURRENT_VERSION" +# 检查当前 isage-flownet 版本 +CURRENT_VERSION=\$(python -c "import importlib.metadata; print(importlib.metadata.version('isage-flownet'))" 2>/dev/null || echo "未安装") +echo "[INFO] 当前 isage-flownet 版本: \$CURRENT_VERSION" -# 停止现有 Ray 进程 -echo "[INFO] 停止现有 Ray 进程..." -ray stop 2>/dev/null || true -pkill -f "ray.*start" 2>/dev/null || true -pkill -f "raylet" 2>/dev/null || true -sleep 2 - -# 升级 Ray -if [ "\$CURRENT_VERSION" != "$TARGET_RAY_VERSION" ]; then - echo "[INFO] 升级 Ray 到 $TARGET_RAY_VERSION..." - pip install --upgrade "ray[default]==$TARGET_RAY_VERSION" +# 升级 isage-flownet +if [ "\$CURRENT_VERSION" != "$TARGET_FLOWNET_VERSION" ]; then + echo "[INFO] 升级 isage-flownet 到 $TARGET_FLOWNET_VERSION..." + pip install --upgrade "isage-flownet==$TARGET_FLOWNET_VERSION" # 验证安装 - NEW_VERSION=\$(python -c "import ray; print(ray.__version__)") - if [ "\$NEW_VERSION" = "$TARGET_RAY_VERSION" ]; then - echo "✅ Ray 升级成功: \$NEW_VERSION" + NEW_VERSION=\$(python -c "import importlib.metadata; print(importlib.metadata.version('isage-flownet'))") + if [ "\$NEW_VERSION" = "$TARGET_FLOWNET_VERSION" ]; then + echo "✅ isage-flownet 升级成功: \$NEW_VERSION" else - echo "❌ Ray 升级失败: 期望 $TARGET_RAY_VERSION, 实际 \$NEW_VERSION" + echo "❌ isage-flownet 升级失败: 期望 $TARGET_FLOWNET_VERSION, 实际 \$NEW_VERSION" exit 1 fi else - echo "✅ Ray 版本已是最新: \$CURRENT_VERSION" + echo "✅ isage-flownet 版本已是最新: \$CURRENT_VERSION" fi -# 清理旧的临时文件 -echo "[INFO] 清理 Ray 临时文件..." -rm -rf /tmp/ray_* 2>/dev/null || true - EOF if [ $? -eq 0 ]; then @@ -85,4 +75,4 @@ echo "======================================" echo "✅ 所有 Worker 节点处理完毕" echo "======================================" echo "" -echo "现在可以运行: sage cluster start" +echo "现在可以继续执行你的 Flutty 运行时联调或部署流程" diff --git a/tools/scripts/validate_pep420_compliance.sh b/tools/scripts/validate_pep420_compliance.sh index ee4afc87fe..0ab8bd6a7c 100755 --- a/tools/scripts/validate_pep420_compliance.sh +++ b/tools/scripts/validate_pep420_compliance.sh @@ -1,13 +1,13 @@ #!/bin/bash # validate_pep420_compliance.sh -# Validates PEP 420 namespace package compliance for SAGE monorepo +# Validates PEP 420 namespace package compliance for the SAGE meta package set -e SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" PROJECT_ROOT="$(cd "$SCRIPT_DIR/../.." && pwd)" -echo "🔍 Validating PEP 420 namespace package compliance (SAGE monorepo)..." +echo "🔍 Validating PEP 420 namespace package compliance (SAGE meta package)..." echo "" # Color codes @@ -19,51 +19,44 @@ NC='\033[0m' # No Color ERRORS=0 WARNINGS=0 -# Check 1: No src/sage/__init__.py in any package +# Check 1: No src/sage/__init__.py in the meta package echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" echo "Check 1: Namespace package __init__.py compliance" echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" -# Find all src/sage/__init__.py files (should be none) -VIOLATIONS=$(find "$PROJECT_ROOT/packages" -type f -path "*/src/sage/__init__.py" 2>/dev/null || true) +VIOLATION_FILE="$PROJECT_ROOT/src/sage/__init__.py" -if [ -n "$VIOLATIONS" ]; then - echo -e "${RED}❌ FAIL: Found prohibited src/sage/__init__.py files:${NC}" - echo "$VIOLATIONS" | sed 's/^/ /' +if [ -f "$VIOLATION_FILE" ]; then + echo -e "${RED}❌ FAIL: Found prohibited src/sage/__init__.py file:${NC}" + echo " $VIOLATION_FILE" echo "" echo " PEP 420 requires namespace packages to be implicit (no __init__.py)" - echo " Solution: rm packages/*/src/sage/__init__.py" - echo " See: docs-public/docs_src/dev-notes/cross-layer/pep420-namespace-migration.md" + echo " Solution: rm src/sage/__init__.py" + echo " See: CONTRIBUTING.md (PEP 420 section)" ERRORS=$((ERRORS + 1)) else - echo -e "${GREEN}✓ PASS: No src/sage/__init__.py files found${NC}" + echo -e "${GREEN}✓ PASS: No prohibited src/sage/__init__.py file found${NC}" fi echo "" -# Check 2: All packages have "namespaces = true" +# Check 2: Root pyproject.toml has "namespaces = true" echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" echo "Check 2: pyproject.toml namespace configuration" echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━" -MISSING_NAMESPACES=() -for TOML in "$PROJECT_ROOT"/packages/*/pyproject.toml; do - if [ -f "$TOML" ]; then - PACKAGE=$(basename "$(dirname "$TOML")") - if ! grep -q "namespaces = true" "$TOML"; then - MISSING_NAMESPACES+=("$PACKAGE") - fi - fi -done +ROOT_PYPROJECT="$PROJECT_ROOT/pyproject.toml" -if [ ${#MISSING_NAMESPACES[@]} -gt 0 ]; then - echo -e "${RED}❌ FAIL: Missing 'namespaces = true' in packages:${NC}" - printf ' - %s\n' "${MISSING_NAMESPACES[@]}" +if [ ! -f "$ROOT_PYPROJECT" ]; then + echo -e "${RED}❌ FAIL: Missing root pyproject.toml${NC}" + ERRORS=$((ERRORS + 1)) +elif ! grep -q "namespaces = true" "$ROOT_PYPROJECT"; then + echo -e "${RED}❌ FAIL: Missing 'namespaces = true' in root pyproject.toml${NC}" echo "" echo " Add to [tool.setuptools.packages.find] section:" echo " namespaces = true" ERRORS=$((ERRORS + 1)) else - echo -e "${GREEN}✓ PASS: All packages have 'namespaces = true'${NC}" + echo -e "${GREEN}✓ PASS: Root pyproject.toml has 'namespaces = true'${NC}" fi echo "" @@ -118,6 +111,6 @@ else echo -e "${YELLOW} ($WARNINGS warnings)${NC}" fi echo "" - echo "See: docs-public/docs_src/dev-notes/cross-layer/pep420-namespace-migration.md" + echo "See: CONTRIBUTING.md (PEP 420 section)" exit 1 fi diff --git a/tools/scripts/verify_pep420_integration.py b/tools/scripts/verify_pep420_integration.py deleted file mode 100755 index 2a0713354a..0000000000 --- a/tools/scripts/verify_pep420_integration.py +++ /dev/null @@ -1,255 +0,0 @@ -#!/usr/bin/env python3 -""" -verify_pep420_integration.py - -Verifies PEP 420 namespace package integration in SAGE monorepo. -Tests that packages can coexist and namespace behaves correctly. - -Usage: - python3 tools/scripts/verify_pep420_integration.py -""" - -import importlib -import importlib.util -import sys -from pathlib import Path - -# ANSI color codes -RED = "\033[0;31m" -GREEN = "\033[0;32m" -YELLOW = "\033[1;33m" -NC = "\033[0m" # No Color - - -def print_header(text: str) -> None: - """Print a formatted header.""" - print("=" * 70) - print(text) - print("=" * 70) - - -def test_namespace_import() -> tuple[bool, str]: - """Test that sage namespace can be imported and is implicit.""" - try: - import sage - - # PEP 420 namespace packages should have __file__ = None - if sage.__file__ is not None: - return False, f"sage.__file__ should be None (implicit namespace), got: {sage.__file__}" - - # Namespace should not have __path__ as a list (should be _NamespacePath) - if not hasattr(sage, "__path__"): - return False, "sage should have __path__ attribute" - - return True, "sage namespace is correctly implicit (PEP 420)" - except ImportError as e: - return False, f"Failed to import sage: {e}" - - -def test_subpackage_imports() -> tuple[bool, list[str]]: - """Test that subpackages can be imported.""" - subpackages = [ - "sage.common", - "sage.kernel", - "sage.libs", - "sage.platform", - "sage.llm", - ] - - errors = [] - successes = [] - success_count = 0 - - for pkg_name in subpackages: - try: - pkg = importlib.import_module(pkg_name) - - # Subpackages should have __file__ (they have __init__.py) - if not hasattr(pkg, "__file__"): - errors.append(f"{pkg_name}: missing __file__ attribute") - elif pkg.__file__ is None: - errors.append(f"{pkg_name}.__file__ is None (should be a real path)") - else: - success_count += 1 - successes.append(f"{pkg_name} imported successfully") - - except ImportError: - # Some packages might not be installed, that's OK - # Only report if it's a critical package - pass - - # At least one package should be importable - if success_count == 0: - errors.append("No SAGE subpackages could be imported") - else: - # Return successes as the message list when test passes - return True, successes - - return False, errors - - -def test_namespace_coexistence() -> tuple[bool, list[str]]: - """Test that multiple packages can coexist in the namespace.""" - errors = [] - - try: - import sage - - # Get all paths in the namespace - namespace_paths = list(sage.__path__) - - if len(namespace_paths) == 0: - errors.append("sage.__path__ is empty") - elif len(namespace_paths) < 2: - errors.append( - f"Only {len(namespace_paths)} path(s) in namespace (expected multiple for monorepo)" - ) - - # Verify paths exist (skip editable install finder paths) - for path in namespace_paths: - # Skip __editable__ paths (these are from pip install -e) - if "__editable__" in str(path): - continue - - path_obj = Path(path) - if not path_obj.exists(): - errors.append(f"Namespace path does not exist: {path}") - - except ImportError as e: - errors.append(f"Failed to import sage: {e}") - - return len(errors) == 0, errors - - -def test_version_attributes() -> tuple[bool, list[str]]: - """Test that subpackages have proper version attributes.""" - test_packages = [ - ("sage.common", "__version__"), - ("sage.kernel", "__version__"), - ("sage.libs", "__version__"), - ] - - errors = [] - successes = [] - success_count = 0 - - for pkg_name, attr in test_packages: - try: - pkg = importlib.import_module(pkg_name) - if not hasattr(pkg, attr): - errors.append(f"{pkg_name} missing {attr} attribute") - else: - version = getattr(pkg, attr) - if not version or not isinstance(version, str): - errors.append(f"{pkg_name}.{attr} is invalid: {version}") - else: - success_count += 1 - successes.append(f"{pkg_name}.{attr} = {version}") - except ImportError: - # Package not installed, skip - pass - - # At least one package should have version - if success_count == 0: - errors.append("No SAGE packages have version attributes") - else: - # Return successes when test passes - return True, successes - - return False, errors - - -def main() -> int: - """Run all PEP 420 integration tests.""" - print_header("🔍 PEP 420 Namespace Package Integration Tests") - print() - - all_passed = True - results = [] - - # Test 1: Namespace import - print("Test 1: Namespace import...") - passed, message = test_namespace_import() - results.append(("Namespace import", passed, message)) - if passed: - print(f" {GREEN}✓ PASS{NC}: {message}") - else: - print(f" {RED}✗ FAIL{NC}: {message}") - all_passed = False - print() - - # Test 2: Subpackage imports - print("Test 2: Subpackage imports...") - passed, messages = test_subpackage_imports() - results.append(("Subpackage imports", passed, messages)) - if passed: - print(f" {GREEN}✓ PASS{NC}: All importable subpackages work correctly") - for msg in messages: - print(f" - {msg}") - else: - print(f" {RED}✗ FAIL{NC}:") - for msg in messages: - print(f" - {msg}") - all_passed = False - print() - - # Test 3: Namespace coexistence - print("Test 3: Namespace coexistence...") - passed, messages = test_namespace_coexistence() - results.append(("Namespace coexistence", passed, messages)) - if passed: - print(f" {GREEN}✓ PASS{NC}: Multiple packages coexist in namespace") - else: - print(f" {RED}✗ FAIL{NC}:") - for msg in messages: - print(f" - {msg}") - all_passed = False - print() - - # Test 4: Version attributes - print("Test 4: Version attributes...") - passed, messages = test_version_attributes() - results.append(("Version attributes", passed, messages)) - if passed: - print(f" {GREEN}✓ PASS{NC}: Subpackages have proper version attributes") - for msg in messages: - print(f" - {msg}") - else: - print(f" {RED}✗ FAIL{NC}:") - for msg in messages: - print(f" - {msg}") - all_passed = False - print() - - # Summary - print_header("📊 Summary") - passed_count = sum(1 for _, passed, _ in results if passed) - total_count = len(results) - - print(f"Tests passed: {passed_count}/{total_count}") - print() - - if all_passed: - print(f"{GREEN}✅ All PEP 420 integration tests passed!{NC}") - return 0 - else: - print(f"{RED}❌ Some tests failed. See details above.{NC}") - print() - print("Troubleshooting:") - print(" 1. Ensure all SAGE packages are installed in dev mode:") - print(" pip install -e packages/sage-common") - print(" pip install -e packages/sage-kernel") - print(" pip install -e packages/sage-libs") - print() - print(" 2. Check that packages/*/src/sage/__init__.py does NOT exist") - print(" (PEP 420 requires implicit namespace packages)") - print() - print(" 3. Verify pyproject.toml has 'namespaces = true':") - print(" [tool.setuptools.packages.find]") - print(" namespaces = true") - print() - return 1 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/tools/templates/README.md b/tools/templates/README.md new file mode 100644 index 0000000000..2fb0b99b7e --- /dev/null +++ b/tools/templates/README.md @@ -0,0 +1,153 @@ +# SAGE Repo Infrastructure Templates + +This directory provides standardised scaffolding for all SAGE split-repos. Apply the templates to a +new (or existing) repo with a single command. + +For the 2026 consolidation direction, do **not** use these templates to create new core-layer repos +that duplicate `sage.foundation`, `sage.stream`, `sage.runtime`, or `sage.serving`. They should be +used only for clearly independent optional adapters, benchmark utilities, or other genuinely +separate deliverables. + +## Quick start + +```bash +cd /path/to/target-repo +/path/to/SAGE/tools/templates/init-repo.sh \ + --pkg-name isage-rag \ + --pkg-mod sage_rag \ + --desc "SAGE RAG components" +``` + +Pass `--cpp` for repos that contain a C++ extension (for example, independently owned +high-performance adapters): + +```bash +init-repo.sh --pkg-name isage-anns --pkg-mod sage_anns --cpp +``` + +Options: + +| Flag | Description | +| ------------------ | ----------------------------------------- | +| `--pkg-name NAME` | PyPI package name (e.g. `isage-rag`) | +| `--pkg-mod MODULE` | Python import name (e.g. `sage_rag`) | +| `--desc TEXT` | Short one-line description | +| `--cpp` | Also apply C++ extension skeleton | +| `--dry-run` | Show what would be copied without writing | +| `--force` | Overwrite existing files | + +______________________________________________________________________ + +## Directory structure + +```text +tools/templates/ +├── init-repo.sh ← one-command initialiser +│ +├── new-repo/ ← Python-only template +│ ├── hooks/ +│ │ ├── pre-commit ← delegates to pre-commit framework +│ │ ├── post-commit ← auto-bump BUILD digit in _version.py +│ │ └── pre-push ← ruff check + pytest + main-branch guard +│ ├── .github/ +│ │ └── workflows/ +│ │ ├── ci.yml ← ruff + pytest on push/PR +│ │ ├── release.yml ← publish to PyPI + dispatch downstream +│ │ ├── update-deps.yml ← receive upstream release, auto-PR bump +│ │ ├── version-source-guard.yml ← validate _version.py is sole source +│ │ └── back-sync.yml ← main → main-dev auto PR +│ ├── src/my_pkg/ +│ │ ├── _version.py ← version source of truth +│ │ └── __init__.py +│ ├── .pre-commit-config.yaml +│ ├── codecov.yml +│ ├── pyproject.toml ← dynamic version, dev extras +│ ├── pytest.ini +│ ├── quickstart.sh ← environment check + hooks install + pip install -e .[dev] +│ └── ruff.toml +│ +└── new-repo-cpp/ ← extra files for C++ extension repos + ├── CMakeLists.txt ← scikit-build-core + pybind11 + ├── csrc/ + │ └── bindings.cpp ← PYBIND11_MODULE entry point + ├── pyproject.toml ← scikit-build-core backend + ├── build_manylinux.sh ← manylinux2014 wheel builder (Docker) + └── .github/ + └── workflows/ + └── release.yml ← cibuildwheel → PyPI +``` + +______________________________________________________________________ + +## Hook behaviour + +| Hook | Trigger | What it does | +| ------------- | ------------------ | --------------------------------------------------------------------------- | +| `pre-commit` | `git commit` | Runs all checks in `.pre-commit-config.yaml` (ruff, trailing-whitespace, …) | +| `post-commit` | after `git commit` | Bumps `_version.py` BUILD digit (X.Y.Z → X.Y.Z.1) and amends the commit | +| `pre-push` | `git push` | Blocks push to `main`; runs `ruff check` + `pytest -x -q` | + +Set `SAGE_SKIP_VERSION_BUMP=1` to disable the post-commit version bump (e.g. in CI). + +______________________________________________________________________ + +## Version source of truth + +Every package must follow the unified convention enforced by `version-source-guard.yml`: + +1. **Only** `src//_version.py` contains a hardcoded `__version__`. +1. `pyproject.toml` uses `dynamic = ["version"]` and points to `_version.py`. +1. `__init__.py` imports from `_version.py`; it never defines `__version__` itself. +1. To bump: edit only `_version.py` (or let the post-commit hook handle the BUILD digit). + +______________________________________________________________________ + +## Cross-repo release pipeline + +The release pipeline can create a fully automated version-linking chain for repos that still have a +real upstream/downstream release relationship. + +### How it works + +| Step | Who | What | +| ---- | --------------------------------------- | ------------------------------------------------------------------------------- | +| 1 | Developer | Tags upstream repo (`v0.2.5`) | +| 2 | `release.yml` | Publishes to PyPI | +| 3 | `release.yml` (dispatch_downstream job) | Sends `repository_dispatch` (event: `package-released`) to each downstream repo | +| 4 | `update-deps.yml` (downstream) | Updates `pyproject.toml` constraint and opens a PR | +| 5 | Developer | Reviews / merges the auto-PR | + +### Setup required — GitHub secret + +Each repo that dispatches to downstreams must have a secret named **`DISPATCH_PAT`**: + +- Type: GitHub classic PAT with **`repo`** scope (write access to all downstream repos) +- Set in: *Repo Settings → Secrets and variables → Actions → New repository secret* + +### Configuring downstream repos in `release.yml` + +For each upstream repo, edit `.github/workflows/release.yml` and set the `DOWNSTREAM_REPOS` variable +in the `dispatch-downstream` job: + +```yaml +# Example: an independently owned base adapter notifies a dependent adapter repo +DOWNSTREAM_REPOS="intellistream/some-dependent-repo" + +# Example: a leaf repo with no automatic downstream bump targets +DOWNSTREAM_REPOS="" +``` + +### Dependency graph + +```text +independent-base-package + └─ dependent-package +``` + +______________________________________________________________________ + +## Related issues + +- #1456 — This template directory (P1 infra) +- #1459 / #1460 — historical C++ split-repo rollout examples (use `--cpp` flag when still justified) +- #1465 — Cross-repo release pipeline (release.yml dispatch + update-deps.yml) diff --git a/tools/templates/init-repo.sh b/tools/templates/init-repo.sh new file mode 100755 index 0000000000..3f19867fca --- /dev/null +++ b/tools/templates/init-repo.sh @@ -0,0 +1,186 @@ +#!/usr/bin/env bash +# init-repo.sh — initialise a SAGE repo from the template in tools/templates/new-repo/ +# +# Usage: +# cd /path/to/target-repo +# /path/to/SAGE/tools/templates/init-repo.sh [OPTIONS] +# +# Options: +# --pkg-name NAME PyPI package name (e.g. isage-rag) +# --pkg-mod MODULE Python import name (e.g. sage_rag) +# --desc TEXT Short description +# --cpp Also apply C++ extension skeleton (new-repo-cpp/) +# --dry-run Show what would be copied, but don't write +# --force Overwrite existing files +# +# Examples: +# init-repo.sh --pkg-name isage-rag --pkg-mod sage_rag --desc "SAGE RAG components" +# init-repo.sh --pkg-name isage-anns --pkg-mod sage_anns --cpp + +set -euo pipefail + +# ─── Colors ─────────────────────────────────────────────────────────────────── +RED='\033[0;31m' +YELLOW='\033[1;33m' +GREEN='\033[0;32m' +CYAN='\033[0;36m' +BOLD='\033[1m' +NC='\033[0m' + +# ─── Defaults ───────────────────────────────────────────────────────────────── +PKG_NAME="" +PKG_MOD="" +DESCRIPTION="SAGE package." +WITH_CPP=false +DRY_RUN=false +FORCE=false + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +TEMPLATE_PY="${SCRIPT_DIR}/new-repo" +TEMPLATE_CPP="${SCRIPT_DIR}/new-repo-cpp" +TARGET="$(pwd)" + +# ─── Parse args ─────────────────────────────────────────────────────────────── +while [[ $# -gt 0 ]]; do + case "$1" in + --pkg-name) PKG_NAME="$2"; shift 2 ;; + --pkg-mod) PKG_MOD="$2"; shift 2 ;; + --desc) DESCRIPTION="$2"; shift 2 ;; + --cpp) WITH_CPP=true; shift ;; + --dry-run) DRY_RUN=true; shift ;; + --force) FORCE=true; shift ;; + *) echo -e "${RED}Unknown option: $1${NC}"; exit 1 ;; + esac +done + +# ─── Validate ───────────────────────────────────────────────────────────────── +if [ -z "$PKG_NAME" ] || [ -z "$PKG_MOD" ]; then + echo -e "${RED}Error: --pkg-name and --pkg-mod are required.${NC}" + echo "" + echo "Usage: $0 --pkg-name isage-rag --pkg-mod sage_rag --desc \"SAGE RAG\"" + exit 1 +fi + +echo -e "${CYAN}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" +echo -e "${BOLD} SAGE Repo Initialiser${NC}" +echo -e "${CYAN}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" +echo -e " Target : ${TARGET}" +echo -e " PyPI name : ${PKG_NAME}" +echo -e " Import name: ${PKG_MOD}" +echo -e " C++ ext : ${WITH_CPP}" +echo -e " Dry-run : ${DRY_RUN}" +echo "" + +# ─── Helpers ────────────────────────────────────────────────────────────────── +copy_file() { + local src="$1" + local dst="$2" + + if [ -f "$dst" ] && [ "$FORCE" = false ]; then + echo -e " ${YELLOW}skip${NC} $dst (already exists; use --force to overwrite)" + return + fi + + if [ "$DRY_RUN" = true ]; then + echo -e " ${CYAN}[dry]${NC} $src → $dst" + return + fi + + mkdir -p "$(dirname "$dst")" + cp "$src" "$dst" + echo -e " ${GREEN}copy${NC} $dst" +} + +substitute() { + # Replace template placeholders with real names in a file + local file="$1" + [ "$DRY_RUN" = true ] && return + sed -i \ + -e "s/MY_PKG_NAME/${PKG_NAME}/g" \ + -e "s/my_pkg/${PKG_MOD}/g" \ + -e "s/Short description of the package\./${DESCRIPTION}/g" \ + -e "s/Short description\\./${DESCRIPTION}/g" \ + "$file" +} + +# ─── Copy Python template files ─────────────────────────────────────────────── +echo -e "${BOLD}Copying Python template files...${NC}" + +# Non-hidden top-level files +for f in quickstart.sh pytest.ini ruff.toml codecov.yml; do + copy_file "${TEMPLATE_PY}/${f}" "${TARGET}/${f}" + [ -f "${TARGET}/${f}" ] && substitute "${TARGET}/${f}" +done + +# pyproject.toml +copy_file "${TEMPLATE_PY}/pyproject.toml" "${TARGET}/pyproject.toml" +[ -f "${TARGET}/pyproject.toml" ] && substitute "${TARGET}/pyproject.toml" + +# .pre-commit-config.yaml +copy_file "${TEMPLATE_PY}/.pre-commit-config.yaml" "${TARGET}/.pre-commit-config.yaml" +[ -f "${TARGET}/.pre-commit-config.yaml" ] && substitute "${TARGET}/.pre-commit-config.yaml" + +# hooks/ +for hook in pre-commit pre-push post-commit; do + copy_file "${TEMPLATE_PY}/hooks/${hook}" "${TARGET}/hooks/${hook}" + [ -f "${TARGET}/hooks/${hook}" ] && chmod +x "${TARGET}/hooks/${hook}" +done + +# GitHub Actions workflows +for wf in ci.yml release.yml version-source-guard.yml back-sync.yml update-deps.yml; do + copy_file "${TEMPLATE_PY}/.github/workflows/${wf}" "${TARGET}/.github/workflows/${wf}" +done + +# Source package skeleton +PKG_SRC="${TARGET}/src/${PKG_MOD}" +if [ ! -d "$PKG_SRC" ] || [ "$FORCE" = true ]; then + copy_file "${TEMPLATE_PY}/src/my_pkg/_version.py" "${PKG_SRC}/_version.py" + copy_file "${TEMPLATE_PY}/src/my_pkg/__init__.py" "${PKG_SRC}/__init__.py" + [ "$DRY_RUN" = false ] && substitute "${PKG_SRC}/_version.py" + [ "$DRY_RUN" = false ] && substitute "${PKG_SRC}/__init__.py" +fi + +# Make quickstart.sh executable +[ "$DRY_RUN" = false ] && chmod +x "${TARGET}/quickstart.sh" 2>/dev/null || true + +# ─── Copy C++ skeleton (optional) ───────────────────────────────────────────── +if [ "$WITH_CPP" = true ]; then + echo "" + echo -e "${BOLD}Copying C++ extension skeleton...${NC}" + + copy_file "${TEMPLATE_CPP}/CMakeLists.txt" "${TARGET}/CMakeLists.txt" + copy_file "${TEMPLATE_CPP}/pyproject.toml" "${TARGET}/pyproject.toml" + copy_file "${TEMPLATE_CPP}/build_manylinux.sh" "${TARGET}/build_manylinux.sh" + copy_file "${TEMPLATE_CPP}/csrc/bindings.cpp" "${TARGET}/csrc/bindings.cpp" + copy_file "${TEMPLATE_CPP}/.github/workflows/release.yml" \ + "${TARGET}/.github/workflows/release.yml" + + for f in CMakeLists.txt pyproject.toml csrc/bindings.cpp; do + [ -f "${TARGET}/${f}" ] && substitute "${TARGET}/${f}" + done + + # csrc/include placeholder + if [ "$DRY_RUN" = false ]; then + mkdir -p "${TARGET}/include/${PKG_MOD}" + touch "${TARGET}/include/${PKG_MOD}/.gitkeep" + fi + + [ "$DRY_RUN" = false ] && chmod +x "${TARGET}/build_manylinux.sh" || true +fi + +echo "" +echo -e "${GREEN}${BOLD}✓ Done!${NC}" +echo "" +echo -e "Next steps:" +echo -e " 1. cd ${TARGET}" +echo -e " 2. Review and customise the generated files" +echo -e " 3. ./quickstart.sh (installs post-commit / pre-push hooks)" +echo -e "" +echo -e "Release pipeline (cross-repo version linking):" +echo -e " 4. Set the DISPATCH_PAT secret in GitHub repo settings" +echo -e " (PAT with 'repo' scope to dispatch to downstream repos)" +echo -e " 5. Edit .github/workflows/release.yml — set DOWNSTREAM_REPOS" +echo -e " to the list of repos that depend on this package" +echo -e " 6. Downstream repos receive update-deps.yml automatically —" +echo -e " they will auto-PR when an upstream package is released" +echo "" diff --git a/tools/templates/new-repo-cpp/.github/workflows/release.yml b/tools/templates/new-repo-cpp/.github/workflows/release.yml new file mode 100644 index 0000000000..01a8298289 --- /dev/null +++ b/tools/templates/new-repo-cpp/.github/workflows/release.yml @@ -0,0 +1,67 @@ +name: Publish manylinux wheel to PyPI + +on: + push: + tags: + - "v*" + workflow_dispatch: + inputs: + dry_run: + description: "Dry run (build only, skip upload)" + required: false + default: "false" + type: boolean + +jobs: + build-manylinux: + name: Build manylinux2014 wheel + runs-on: ubuntu-latest + strategy: + matrix: + python-version: ["3.11"] + + steps: + - uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + + - name: Install cibuildwheel + run: pip install cibuildwheel + + - name: Build wheels + run: cibuildwheel --output-dir dist + env: + CIBW_BUILD: "cp${{ matrix.python-version }}-manylinux_x86_64" + CIBW_MANYLINUX_X86_64_IMAGE: manylinux2014 + CIBW_BEFORE_BUILD: "pip install pybind11 scikit-build-core" + + - name: Upload wheel artifact + uses: actions/upload-artifact@v4 + with: + name: wheel-${{ matrix.python-version }} + path: dist/*.whl + + publish: + name: Publish to PyPI + runs-on: ubuntu-latest + needs: build-manylinux + if: ${{ github.event.inputs.dry_run != 'true' }} + permissions: + contents: read + + steps: + - name: Download all wheel artifacts + uses: actions/download-artifact@v4 + with: + path: dist + merge-multiple: true + + - name: Publish to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 + with: + password: ${{ secrets.PYPI_API_TOKEN }} + packages-dir: dist/ + skip-existing: true diff --git a/tools/templates/new-repo-cpp/CMakeLists.txt b/tools/templates/new-repo-cpp/CMakeLists.txt new file mode 100644 index 0000000000..71d6225f99 --- /dev/null +++ b/tools/templates/new-repo-cpp/CMakeLists.txt @@ -0,0 +1,35 @@ +cmake_minimum_required(VERSION 3.20) +project(my_pkg_ext LANGUAGES CXX) + +set(CMAKE_CXX_STANDARD 20) +set(CMAKE_CXX_STANDARD_REQUIRED ON) +set(CMAKE_CXX_EXTENSIONS OFF) + +# ─── pybind11 ───────────────────────────────────────────────────────────────── +find_package(pybind11 CONFIG REQUIRED) + +# ─── Extension module ───────────────────────────────────────────────────────── +pybind11_add_module( + _my_pkg_ext # Python import: import my_pkg._my_pkg_ext + csrc/bindings.cpp + # Add more source files here: + # csrc/core/my_module.cpp +) + +target_include_directories(_my_pkg_ext PRIVATE + ${CMAKE_CURRENT_SOURCE_DIR}/csrc + ${CMAKE_CURRENT_SOURCE_DIR}/include +) + +# Output the .so next to the Python package +set_target_properties(_my_pkg_ext PROPERTIES + LIBRARY_OUTPUT_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/src/my_pkg" +) + +# ─── Optional: GoogleTest ───────────────────────────────────────────────────── +option(BUILD_TESTING "Build C++ unit tests" OFF) +if(BUILD_TESTING) + enable_testing() + find_package(GTest REQUIRED) + add_subdirectory(tests/cpp) +endif() diff --git a/tools/templates/new-repo-cpp/build_manylinux.sh b/tools/templates/new-repo-cpp/build_manylinux.sh new file mode 100755 index 0000000000..850accb7b6 --- /dev/null +++ b/tools/templates/new-repo-cpp/build_manylinux.sh @@ -0,0 +1,37 @@ +#!/usr/bin/env bash +# build_manylinux.sh — Build manylinux2014 wheels for the C++ extension +# +# Usage: +# ./build_manylinux.sh # build for Python 3.11 (default) +# PYTHON_VERSION=3.12 ./build_manylinux.sh +# +# Output: dist/*.whl (manylinux2014_x86_64) +# +# Requires: Docker (or podman) + +set -euo pipefail + +PYTHON_VERSION="${PYTHON_VERSION:-3.11}" +IMAGE="quay.io/pypa/manylinux2014_x86_64" +PYTHON_TAG="cp${PYTHON_VERSION//./}-cp${PYTHON_VERSION//./}" + +echo "Building manylinux2014 wheel for Python ${PYTHON_VERSION}..." +echo "Image: ${IMAGE}" +echo "" + +docker run --rm \ + -v "$(pwd)":/project \ + -w /project \ + "${IMAGE}" \ + bash -c " + set -e + PYBIN=/opt/python/${PYTHON_TAG}/bin + \${PYBIN}/pip install --upgrade pip scikit-build-core pybind11 + \${PYBIN}/pip wheel . -w /tmp/wheelhouse --no-deps + auditwheel repair /tmp/wheelhouse/*.whl -w dist/ + echo 'Wheels in dist/:' + ls dist/ + " + +echo "" +echo "Done. Wheel(s) in dist/" diff --git a/tools/templates/new-repo-cpp/csrc/bindings.cpp b/tools/templates/new-repo-cpp/csrc/bindings.cpp new file mode 100644 index 0000000000..b871abd6ed --- /dev/null +++ b/tools/templates/new-repo-cpp/csrc/bindings.cpp @@ -0,0 +1,31 @@ +// csrc/bindings.cpp — pybind11 entry point for my_pkg C++ extension +// +// Expose C++ classes / functions to Python here. +// Keep this file thin: delegate to submodules for each logical group. +// +// Example: +// void bind_core(pybind11::module_& m); +// void bind_algo(pybind11::module_& m); +// PYBIND11_MODULE(_my_pkg_ext, m) { bind_core(m); bind_algo(m); } + +#include +#include + +namespace py = pybind11; + +// --------------------------------------------------------------------------- +// Forward declarations (implemented in other .cpp files) +// --------------------------------------------------------------------------- +// void bind_core(py::module_& m); + +// --------------------------------------------------------------------------- +// Module definition +// --------------------------------------------------------------------------- +PYBIND11_MODULE(_my_pkg_ext, m) { + m.doc() = "my_pkg C++ extension — replace with real description"; + + // bind_core(m); + + // Minimal smoke-test symbol + m.def("version", []() { return "0.1.0"; }, "Return extension version string"); +} diff --git a/tools/templates/new-repo-cpp/pyproject.toml b/tools/templates/new-repo-cpp/pyproject.toml new file mode 100644 index 0000000000..354417a95b --- /dev/null +++ b/tools/templates/new-repo-cpp/pyproject.toml @@ -0,0 +1,59 @@ +# pyproject.toml template for SAGE packages WITH C++ extension +# +# Replace ALL occurrences of: +# MY_PKG_NAME → PyPI package name (e.g. isage-anns) +# my_pkg → Python import name (e.g. sage_anns) +# "Short description." +# +# This variant adds cmake + pybind11 as build dependencies and sets +# scikit-build-core as the build backend so `pip install -e .` compiles csrc/. + +[build-system] +requires = [ + "scikit-build-core>=0.9", + "pybind11>=2.12", +] +build-backend = "scikit_build_core.build" + +[project] +name = "MY_PKG_NAME" +dynamic = ["version"] +description = "Short description." +readme = "README.md" +requires-python = ">=3.11" +authors = [{ name = "IntelliStream Team" }] +license = { text = "Apache-2.0" } +classifiers = [ + "Development Status :: 3 - Alpha", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.11", +] +dependencies = [ + # runtime Python deps +] + +[project.optional-dependencies] +dev = [ + "pytest>=7.0.0", + "pytest-cov>=4.0.0", + "ruff>=0.8.0", + "pre-commit>=3.0.0", +] + +[tool.setuptools.dynamic] +version = { attr = "my_pkg._version.__version__" } + +# ─── scikit-build-core settings ────────────────────────────────────────────── +[tool.scikit-build] +cmake.build-type = "Release" +wheel.packages = ["src/my_pkg"] +# Pass extra cmake args: +# cmake.args = ["-DBUILD_TESTING=OFF"] + +[tool.ruff] +line-length = 100 + +[tool.pytest.ini_options] +testpaths = ["tests"] +pythonpath = ["src"] +addopts = "-v --tb=short --color=yes" diff --git a/tools/templates/new-repo/.github/workflows/back-sync.yml b/tools/templates/new-repo/.github/workflows/back-sync.yml new file mode 100644 index 0000000000..cddbdb1d57 --- /dev/null +++ b/tools/templates/new-repo/.github/workflows/back-sync.yml @@ -0,0 +1,65 @@ +# Back-sync: main → main-dev +# +# Trigger: any commit that lands on main (merge commit, release tag, bot commit). +# Logic: git cherry — ignores merge-commit noise, detects only real code delta. +# Action: if drift exists, open a PR (main → main-dev); CI must pass before merge. + +name: Back-Sync main → main-dev + +on: + push: + branches: [main] + workflow_dispatch: + +permissions: + contents: write + pull-requests: write + +jobs: + back-sync: + name: Detect drift and open back-sync PR + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v4 + with: + fetch-depth: 0 + token: ${{ secrets.GITHUB_TOKEN }} + + - name: Fetch both branches + run: git fetch origin main main-dev --prune + + - name: Check patch-level drift + id: drift + run: | + count=$(git cherry origin/main-dev origin/main | grep -c '^+' || echo 0) + echo "patch_count=${count}" >> "$GITHUB_OUTPUT" + if [ "${count}" -gt 0 ]; then + echo "has_drift=true" >> "$GITHUB_OUTPUT" + else + echo "has_drift=false" >> "$GITHUB_OUTPUT" + fi + + - name: Skip — no patch-level drift + if: steps.drift.outputs.has_drift == 'false' + run: echo "main and main-dev are patch-equivalent. Nothing to do." + + - name: Create back-sync branch and PR + if: steps.drift.outputs.has_drift == 'true' + env: + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + run: | + BRANCH="back-sync/main-to-main-dev-$(date -u +%Y%m%d-%H%M)" + git config user.email "github-actions[bot]@users.noreply.github.com" + git config user.name "github-actions[bot]" + + git checkout -b "${BRANCH}" origin/main-dev + git merge --no-ff origin/main -m "chore: back-sync main into main-dev [auto]" + git push origin "${BRANCH}" + + gh pr create \ + --base main-dev \ + --head "${BRANCH}" \ + --title "chore: back-sync main into main-dev" \ + --body "$(printf '**Auto-created back-sync PR**\n\n%s patch-level commit(s) present on `main` but missing from `main-dev`.\n\nMerge after CI passes — no code review required for pure sync PRs.' '${{ steps.drift.outputs.patch_count }}')" \ + --label "chore" || true diff --git a/tools/templates/new-repo/.github/workflows/ci.yml b/tools/templates/new-repo/.github/workflows/ci.yml new file mode 100644 index 0000000000..891e88bdf0 --- /dev/null +++ b/tools/templates/new-repo/.github/workflows/ci.yml @@ -0,0 +1,89 @@ +name: CI + +on: + push: + branches: [main, main-dev, develop, dev] + pull_request: + branches: [main, main-dev, develop] + workflow_dispatch: + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + # ───────────────────────────────────────────────────────────────────────── + # Code Quality (pre-commit — ruff check, ruff format, trailing whitespace…) + # ───────────────────────────────────────────────────────────────────────── + code-quality: + name: Code Quality (pre-commit) + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.11" + cache: "pip" + + - name: Install pre-commit + run: | + python -m pip install --upgrade pip + pip install pre-commit + + - name: Cache pre-commit environments + uses: actions/cache@v4 + with: + path: ~/.cache/pre-commit + key: pre-commit-${{ runner.os }}-${{ hashFiles('.pre-commit-config.yaml') }} + restore-keys: | + pre-commit-${{ runner.os }}- + + - name: Run pre-commit (PR — changed files only) + if: github.event_name == 'pull_request' + run: | + git fetch origin ${{ github.base_ref }} + pre-commit run --from-ref origin/${{ github.base_ref }} --to-ref HEAD + + - name: Run pre-commit (push — all files) + if: github.event_name != 'pull_request' + run: pre-commit run --all-files + + # ───────────────────────────────────────────────────────────────────────── + # Unit Tests + # ───────────────────────────────────────────────────────────────────────── + test: + name: Tests (Python ${{ matrix.python-version }}) + runs-on: ubuntu-latest + needs: code-quality + strategy: + fail-fast: false + matrix: + python-version: ["3.11"] + + steps: + - uses: actions/checkout@v4 + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + cache: "pip" + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -e ".[dev]" + + - name: Run tests with coverage + run: pytest tests/ -v --tb=short --cov=src --cov-report=xml + + - name: Upload coverage to Codecov + if: matrix.python-version == '3.11' + uses: codecov/codecov-action@v4 + with: + file: ./coverage.xml + fail_ci_if_error: false diff --git a/tools/templates/new-repo/.github/workflows/release.yml b/tools/templates/new-repo/.github/workflows/release.yml new file mode 100644 index 0000000000..ec7485eefe --- /dev/null +++ b/tools/templates/new-repo/.github/workflows/release.yml @@ -0,0 +1,122 @@ +name: Publish to PyPI + +on: + push: + tags: + - "v*" + workflow_dispatch: + inputs: + dry_run: + description: "Dry run (build only, skip upload)" + required: false + default: false + type: boolean + +jobs: + build-and-publish: + name: Build and publish + runs-on: ubuntu-latest + permissions: + contents: read + + steps: + - uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.11" + + - name: Install build tools + run: | + python -m pip install --upgrade pip + pip install build isage-pypi-publisher + + - name: Verify version source of truth + run: | + python - <<'PY' + import re, sys, tomllib + from pathlib import Path + data = tomllib.loads(Path("pyproject.toml").read_text()) + if "version" in data.get("project", {}): + sys.exit("[FAIL] project.version must not be hardcoded in pyproject.toml") + print("[OK] version is dynamic") + PY + + - name: Build wheel and sdist + run: python -m build + + - name: Publish to PyPI + if: ${{ inputs.dry_run != true }} + uses: pypa/gh-action-pypi-publish@release/v1 + with: + password: ${{ secrets.PYPI_API_TOKEN }} + skip-existing: true + + - name: Upload build artifacts + uses: actions/upload-artifact@v4 + with: + name: dist + path: dist/ + + dispatch-downstream: + name: Notify downstream packages + needs: build-and-publish + if: ${{ github.event_name == 'push' || inputs.dry_run != true }} + runs-on: ubuntu-latest + permissions: + contents: read + + steps: + - uses: actions/checkout@v4 + + - name: Extract package info + id: pkg + run: | + python - <<'PY' + import glob, re, tomllib + from pathlib import Path + import os + + # Version from _version.py (single source of truth) + files = glob.glob("src/**/_version.py", recursive=True) + version = "" + if files: + m = re.search(r'__version__\s*=\s*"(.+?)"', Path(files[0]).read_text()) + if m: + version = m.group(1) + + # Package name from pyproject.toml + data = tomllib.loads(Path("pyproject.toml").read_text()) + name = data["project"]["name"] + + print(f"package={name}") + print(f"version={version}") + + with open(os.environ["GITHUB_OUTPUT"], "a") as f: + f.write(f"package={name}\n") + f.write(f"version={version}\n") + PY + + - name: Dispatch to downstream repos + env: + GH_TOKEN: ${{ github.token }} + PACKAGE: ${{ steps.pkg.outputs.package }} + VERSION: ${{ steps.pkg.outputs.version }} + TAG: ${{ github.ref_name }} + run: | + # DOWNSTREAM_REPOS: space-separated list of org/repo targets. + # The default token only supports the current repository. Replace GH_TOKEN + # with a PAT after scaffolding if cross-repo dispatch is required. + DOWNSTREAM_REPOS="" # e.g. "intellistream/isage-rag-adapter intellistream/isage-rag-examples" + + for repo in $DOWNSTREAM_REPOS; do + echo "Dispatching package-released to $repo (${PACKAGE} ${VERSION})" + gh api "repos/${repo}/dispatches" \ + --method POST \ + -f event_type=package-released \ + -f "client_payload[package]=${PACKAGE}" \ + -f "client_payload[version]=${VERSION}" \ + -f "client_payload[tag]=${TAG}" \ + && echo " ✓ $repo" || echo " ✗ $repo (dispatch failed — check GH_TOKEN permissions)" + done diff --git a/tools/templates/new-repo/.github/workflows/update-deps.yml b/tools/templates/new-repo/.github/workflows/update-deps.yml new file mode 100644 index 0000000000..e7de96195b --- /dev/null +++ b/tools/templates/new-repo/.github/workflows/update-deps.yml @@ -0,0 +1,70 @@ +name: Update upstream dependency + +# Triggered by upstream packages via repository_dispatch (event: package-released). +# Payload: { "package": "isage-rag", "version": "0.3.1", "tag": "v0.3.1" } +# +# No extra secret is required for the default same-repo pull request flow. +# If a generated repository needs stronger auth, replace `github.token` after scaffolding. + +on: + repository_dispatch: + types: [package-released] + +jobs: + update-deps: + name: Bump ${{ github.event.client_payload.package }} to ${{ github.event.client_payload.version }} + runs-on: ubuntu-latest + permissions: + contents: write + pull-requests: write + + steps: + - uses: actions/checkout@v4 + + - name: Update version constraint in pyproject.toml + env: + PACKAGE: ${{ github.event.client_payload.package }} + VERSION: ${{ github.event.client_payload.version }} + run: | + python - <<'PY' + import os, re + from pathlib import Path + + pkg = os.environ["PACKAGE"] + ver = os.environ["VERSION"] + path = Path("pyproject.toml") + content = path.read_text() + + # Replace "isage-foo>=X.Y.Z" with "isage-foo>=NEW_VER" + new_content = re.sub( + rf'"{re.escape(pkg)}>=[\d.]+"', + f'"{pkg}>={ver}"', + content, + ) + + if new_content == content: + print(f"[SKIP] {pkg} not found in pyproject.toml or already at >={ver}") + else: + path.write_text(new_content) + print(f"[OK] Updated {pkg} to >={ver} in pyproject.toml") + PY + + - name: Create pull request + uses: peter-evans/create-pull-request@v6 + with: + branch: deps/bump-${{ github.event.client_payload.package }}-${{ github.event.client_payload.version }} + commit-message: "chore(deps): bump ${{ github.event.client_payload.package }} to >=${{ github.event.client_payload.version }}" + title: "chore(deps): bump ${{ github.event.client_payload.package }} to >=${{ github.event.client_payload.version }}" + body: | + Automated dependency update triggered by upstream release. + + | Field | Value | + |-------|-------| + | Package | `${{ github.event.client_payload.package }}` | + | New version | `${{ github.event.client_payload.version }}` | + | Tag | `${{ github.event.client_payload.tag }}` | + + > This PR was created automatically by the SAGE cross-repo release pipeline. + > Merge after CI passes. + delete-branch: true + labels: "dependencies,automated" diff --git a/tools/templates/new-repo/.github/workflows/version-source-guard.yml b/tools/templates/new-repo/.github/workflows/version-source-guard.yml new file mode 100644 index 0000000000..f141c03ea0 --- /dev/null +++ b/tools/templates/new-repo/.github/workflows/version-source-guard.yml @@ -0,0 +1,107 @@ +name: Version Source Guard + +on: + push: + branches: [main, main-dev] + pull_request: + branches: [main, main-dev] + +jobs: + version-source-guard: + name: Validate single version source (_version.py) + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.11" + + - name: Validate version source of truth + run: | + python - <<'PY' + from __future__ import annotations + + import re + import sys + import tomllib + from pathlib import Path + + root = Path(".") + pyproject = root / "pyproject.toml" + if not pyproject.exists(): + print("No pyproject.toml found — skipping version source guard.") + raise SystemExit(0) + + data = tomllib.loads(pyproject.read_text(encoding="utf-8")) + project = data.get("project", {}) + + # 1. project.version must NOT be hardcoded + if "version" in project: + raise SystemExit("[FAIL] project.version must not be hardcoded in pyproject.toml") + + # 2. [project].dynamic must include "version" + if "version" not in project.get("dynamic", []): + raise SystemExit("[FAIL] [project].dynamic must include 'version'") + + # 3. [tool.setuptools.dynamic].version.attr must end with ._version.__version__ + attr = ( + data.get("tool", {}) + .get("setuptools", {}) + .get("dynamic", {}) + .get("version", {}) + .get("attr", "") + ) + if not attr.endswith("._version.__version__"): + raise SystemExit( + "[FAIL] [tool.setuptools.dynamic].version.attr must point to " + "._version.__version__" + ) + + # 4. Check per-package constraints + src = root / "src" + if not src.exists(): + print("No src/ directory — skipping package file checks.") + raise SystemExit(0) + + package_dirs = [ + p for p in src.iterdir() + if p.is_dir() and (p / "__init__.py").exists() + and not p.name.endswith(".egg-info") + ] + if not package_dirs: + print("No packages found under src/ — skipping package file checks.") + raise SystemExit(0) + + errors: list[str] = [] + for pkg_dir in package_dirs: + pkg = pkg_dir.name + version_file = pkg_dir / "_version.py" + init_file = pkg_dir / "__init__.py" + + if not version_file.exists(): + errors.append(f"[FAIL] missing {version_file}") + continue + + version_text = version_file.read_text(encoding="utf-8") + if not re.search(r'^__version__\s*=\s*"[^"]+"', version_text, re.M): + errors.append(f'[FAIL] {version_file} must define __version__ = "X.Y.Z[.N]"') + + init_text = init_file.read_text(encoding="utf-8") + import_ok = any(re.search(p, init_text, re.M) for p in [ + rf"^from\s+{re.escape(pkg)}\._version\s+import\s+__version__", + r"^from\s+\._version\s+import\s+__version__", + ]) + if not import_ok: + errors.append(f"[FAIL] {init_file} must import __version__ from _version.py") + + if re.search(r'^__version__\s*=\s*"[^"]+"', init_text, re.M): + errors.append(f"[FAIL] {init_file} must NOT hardcode __version__") + + if errors: + print("\n".join(errors)) + raise SystemExit(1) + + print("[PASS] Version source of truth checks passed.") + PY diff --git a/tools/templates/new-repo/.pre-commit-config.yaml b/tools/templates/new-repo/.pre-commit-config.yaml new file mode 100644 index 0000000000..03a3117162 --- /dev/null +++ b/tools/templates/new-repo/.pre-commit-config.yaml @@ -0,0 +1,64 @@ +# Pre-commit hooks configuration for SAGE packages +# +# Install: +# ./quickstart.sh (recommended — also sets up post-commit / pre-push) +# pre-commit install (equivalent) +# +# Run manually: +# pre-commit run --all-files +# pre-commit run --files src/ + +default_language_version: + python: python3.11 + +repos: + # ── General file checks ────────────────────────────────────────────────── +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v6.0.0 + hooks: + - id: trailing-whitespace + args: [--markdown-linebreak-ext=md] + - id: end-of-file-fixer + - id: check-yaml + args: [--unsafe] + - id: check-json + - id: check-toml + - id: check-added-large-files + args: ["--maxkb=500"] + - id: check-merge-conflict + - id: check-case-conflict + - id: mixed-line-ending + args: [--fix=lf] + - id: detect-private-key + - id: no-commit-to-branch + name: block direct commits to main + args: [--branch, main] + + # ── Python: Ruff linter and formatter ─────────────────────────────────── +- repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.15.2 + hooks: + - id: ruff + name: ruff check + args: [--fix, --exit-non-zero-on-fix] + files: ^(src|tests)/.*\.py$ + - id: ruff-format + name: ruff format + files: ^(src|tests)/.*\.py$ + + # ── SAGE policy checks ─────────────────────────────────────────────────── +- repo: local + hooks: + - id: no-requirements-txt + name: forbid requirements.txt (use pyproject.toml) + entry: bash -c 'echo "requirements.txt is forbidden — declare deps in pyproject.toml."; exit 1' + language: system + files: ^requirements.*\.txt$ + pass_filenames: false + + - id: no-venv-creation + name: forbid venv/virtualenv creation in scripts + entry: bash -c 'if grep -nE "python -m venv|virtualenv|--auto-venv" "$@"; then echo "Creating venvs is forbidden in SAGE repos — use conda."; exit 1; fi' + language: system + types: [shell] + pass_filenames: true diff --git a/tools/templates/new-repo/codecov.yml b/tools/templates/new-repo/codecov.yml new file mode 100644 index 0000000000..8a4f1b07b5 --- /dev/null +++ b/tools/templates/new-repo/codecov.yml @@ -0,0 +1,21 @@ +coverage: + status: + project: + default: + target: 60% # minimum coverage threshold + threshold: 5% # tolerated drop before CI fails + patch: + default: + target: 50% + +comment: + layout: "reach,diff,flags,files" + behavior: default + require_changes: true + +ignore: +- "tests/**" +- "examples/**" +- "docs/**" +- "**/__init__.py" +- "**/_version.py" diff --git a/tools/templates/new-repo/hooks/post-commit b/tools/templates/new-repo/hooks/post-commit new file mode 100755 index 0000000000..52940a8db3 --- /dev/null +++ b/tools/templates/new-repo/hooks/post-commit @@ -0,0 +1,88 @@ +#!/usr/bin/env bash +# Post-commit hook — automatically bumps BUILD version digit after each commit. +# +# Version format: X.Y.Z → X.Y.Z.1 (initial build counter) +# X.Y.Z.N → X.Y.Z.N+1 +# +# Single source of truth: src//_version.py +# This hook amends the commit-in-place; the pre-push hook never bumps version. + +set -eo pipefail + +# ─── Recursion guard ────────────────────────────────────────────────────────── +if [ -f ".git/SAGE_POST_COMMIT_RUNNING" ]; then + exit 0 +fi + +# ─── Kill-switch ────────────────────────────────────────────────────────────── +if [ "${SAGE_SKIP_VERSION_BUMP:-0}" = "1" ]; then + exit 0 +fi + +# ─── Colors ─────────────────────────────────────────────────────────────────── +BLUE='\033[0;34m' +GREEN='\033[0;32m' +YELLOW='\033[1;33m' +NC='\033[0m' + +# ─── Helpers ────────────────────────────────────────────────────────────────── +# Bump BUILD digit: X.Y.Z.N → X.Y.Z.(N+1), X.Y.Z → X.Y.Z.1 +bump_version() { + local v="$1" + if [[ "$v" =~ ^([0-9]+)\.([0-9]+)\.([0-9]+)\.([0-9]+)$ ]]; then + echo "${BASH_REMATCH[1]}.${BASH_REMATCH[2]}.${BASH_REMATCH[3]}.$((BASH_REMATCH[4] + 1))" + return 0 + fi + if [[ "$v" =~ ^([0-9]+)\.([0-9]+)\.([0-9]+)$ ]]; then + echo "${BASH_REMATCH[1]}.${BASH_REMATCH[2]}.${BASH_REMATCH[3]}.1" + return 0 + fi + return 1 +} + +# Find _version.py (single source of truth) +find_version_file() { + find src -maxdepth 4 -name '_version.py' \ + -not -path '*/.git/*' \ + -not -path '*/dist/*' \ + -not -path '*/.egg-info/*' \ + -not -path '*/build/*' \ + 2>/dev/null | head -1 +} + +# ─── Main ───────────────────────────────────────────────────────────────────── +VERSION_FILE=$(find_version_file) +if [ -z "$VERSION_FILE" ]; then + exit 0 # Not a Python package repo — nothing to do +fi + +# If the developer manually touched _version.py in this commit, skip auto-bump +if git diff --name-only HEAD~1 HEAD 2>/dev/null | grep -q '_version.py'; then + exit 0 +fi + +current_version=$(grep -oP '__version__ = "\K[^"]+' "$VERSION_FILE" 2>/dev/null || true) +if [ -z "$current_version" ]; then + exit 0 +fi + +if ! new_version=$(bump_version "$current_version"); then + echo -e "${YELLOW}⚠️ Could not auto-bump version (invalid format: $current_version)${NC}" + exit 0 +fi + +echo -e "${BLUE}📦 Auto-bumping version: $current_version → $new_version${NC}" + +# Lock to prevent recursion when we amend +touch .git/SAGE_POST_COMMIT_RUNNING + +# Update single source of truth +sed -i "s/__version__ = \"${current_version}\"/__version__ = \"${new_version}\"/" "$VERSION_FILE" +git add "$VERSION_FILE" + +# Amend the commit with the bumped version (no new commit message prompt) +git commit --amend --no-edit --no-verify + +rm -f .git/SAGE_POST_COMMIT_RUNNING + +echo -e "${GREEN}✓ Version bumped and commit amended: $new_version${NC}" diff --git a/tools/templates/new-repo/hooks/pre-commit b/tools/templates/new-repo/hooks/pre-commit new file mode 100755 index 0000000000..1947e47a56 --- /dev/null +++ b/tools/templates/new-repo/hooks/pre-commit @@ -0,0 +1,19 @@ +#!/usr/bin/env bash +# Pre-commit hook — delegates to pre-commit framework (all checks in .pre-commit-config.yaml) +# +# Installed automatically by: +# ./quickstart.sh (recommended) +# pre-commit install (equivalent, canonical command) +# +# ⚠️ Do NOT `cp` this file manually to .git/hooks/; use the commands above. + +if command -v pre-commit >/dev/null 2>&1; then + exec pre-commit hook-impl \ + --config=.pre-commit-config.yaml \ + --hook-type=pre-commit \ + --hook-dir "$(cd "$(dirname "$0")" && pwd)" \ + -- "$@" +else + echo "pre-commit not found. Run: pip install pre-commit && pre-commit install" >&2 + exit 1 +fi diff --git a/tools/templates/new-repo/hooks/pre-push b/tools/templates/new-repo/hooks/pre-push new file mode 100755 index 0000000000..fbb7511832 --- /dev/null +++ b/tools/templates/new-repo/hooks/pre-push @@ -0,0 +1,112 @@ +#!/usr/bin/env bash +# Pre-push hook — enforces branch policy and optionally publishes to PyPI. +# +# Flow: +# 1. Block direct push to `main` branch +# 2. Run ruff check on staged Python files +# 3. Run unit tests (pytest tests/ -x -q) +# 4. Show current version; offer optional PyPI publish +# +# Version bumping is handled entirely by the post-commit hook. +# This hook NEVER bumps version or creates additional commits. + +# ─── Recursion guard ────────────────────────────────────────────────────────── +if [ "${_SAGE_PP_RUNNING:-0}" = "1" ]; then exit 0; fi + +# ─── Config ─────────────────────────────────────────────────────────────────── +# "private" → skip PyPI publish prompt (default for internal repos) +# "public" → offer to publish after successful push +PUBLISH_MODE=private + +# ─── Colors ─────────────────────────────────────────────────────────────────── +RED='\033[0;31m' +YELLOW='\033[1;33m' +GREEN='\033[0;32m' +CYAN='\033[0;36m' +BLUE='\033[0;34m' +DIM='\033[2m' +NC='\033[0m' + +REPO_DIR="$(pwd)" +REPO_NAME="$(basename "$REPO_DIR")" + +# ─── Block direct push to main ──────────────────────────────────────────────── +while read -r local_ref _local_sha remote_ref _remote_sha; do + if [ "$remote_ref" = "refs/heads/main" ]; then + echo -e "${RED}✗ Direct push to main is forbidden.${NC}" + echo -e "${YELLOW} Please push to a feature branch and open a PR.${NC}" + exit 1 + fi +done + +# ─── Ruff check ─────────────────────────────────────────────────────────────── +if command -v ruff >/dev/null 2>&1; then + echo -e "${CYAN}🔍 Running ruff check...${NC}" + if ! ruff check src/ tests/ 2>/dev/null; then + echo -e "${RED}✗ ruff check failed. Fix issues before pushing.${NC}" + exit 1 + fi + echo -e "${GREEN}✓ ruff check passed${NC}" +fi + +# ─── Unit tests ─────────────────────────────────────────────────────────────── +if command -v pytest >/dev/null 2>&1 && [ -d "tests" ]; then + echo -e "${CYAN}🧪 Running unit tests...${NC}" + if ! pytest tests/ -x -q --tb=short 2>/dev/null; then + echo -e "${RED}✗ Tests failed. Fix before pushing.${NC}" + exit 1 + fi + echo -e "${GREEN}✓ Tests passed${NC}" +fi + +# ─── Version display ────────────────────────────────────────────────────────── +find_version_file() { + find . -maxdepth 4 -name '_version.py' \ + -not -path '*/.git/*' -not -path '*/dist/*' \ + -not -path '*/.egg-info/*' -not -path '*/build/*' \ + 2>/dev/null | head -1 +} + +VERSION_FILE=$(find_version_file) +if [ -n "$VERSION_FILE" ]; then + VERSION=$(grep -oP '__version__ = "\K[^"]+' "$VERSION_FILE" 2>/dev/null || echo "unknown") + echo -e "${BLUE}📦 Current version: ${VERSION}${NC}" +fi + +# Resolve publisher CLI command (binary first, then python -m fallback) +resolve_publisher_cmd() { + if command -v sage-pypi-publisher >/dev/null 2>&1; then + PUBLISH_CMD=(sage-pypi-publisher) + return 0 + fi + + if command -v python3 >/dev/null 2>&1; then + if python3 - <<'PY_HOOK' >/dev/null 2>&1 +import importlib.util +import sys +sys.exit(0 if importlib.util.find_spec("pypi_publisher") else 1) +PY_HOOK + then + PUBLISH_CMD=(python3 -m pypi_publisher.cli) + return 0 + fi + fi + + return 1 +} + +# ─── Optional PyPI publish ──────────────────────────────────────────────────── +if [ "$PUBLISH_MODE" = "public" ] && [ -n "$VERSION_FILE" ]; then + if resolve_publisher_cmd; then + echo -e "${YELLOW}Publish ${VERSION} to PyPI? [y/N]: ${NC}" + read -r answer /dev/null || answer="N" + if [[ "$answer" =~ ^[Yy]$ ]]; then + "${PUBLISH_CMD[@]}" publish . -r pypi --no-dry-run & + echo -e "${GREEN}📤 PyPI publish scheduled (background)${NC}" + fi + else + echo -e "${DIM} (sage-pypi-publisher not found — install: python -m pip install isage-pypi-publisher)${NC}" + fi +fi + +exit 0 diff --git a/tools/templates/new-repo/pyproject.toml b/tools/templates/new-repo/pyproject.toml new file mode 100644 index 0000000000..d127bad6f8 --- /dev/null +++ b/tools/templates/new-repo/pyproject.toml @@ -0,0 +1,60 @@ +# pyproject.toml template for SAGE Python packages +# +# Replace ALL occurrences of: +# MY_PKG_NAME → PyPI package name (e.g. isage-rag) +# my_pkg → Python import name (e.g. sage_rag) +# "Short description of the package." +# +# Version source of truth: src/my_pkg/_version.py (see _version.py template). +# Do NOT hardcode version here. + +[build-system] +requires = ["setuptools>=64", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "MY_PKG_NAME" +dynamic = ["version"] +description = "Short description of the package." +readme = "README.md" +requires-python = ">=3.11" +authors = [{ name = "IntelliStream Team" }] +license = { text = "Apache-2.0" } +classifiers = [ + "Development Status :: 3 - Alpha", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.11", +] +dependencies = [ + # declare runtime deps here +] + +[project.optional-dependencies] +dev = [ + "pytest>=7.0.0", + "pytest-cov>=4.0.0", + "ruff>=0.8.0", + "pre-commit>=3.0.0", +] + +[tool.setuptools] +package-dir = { "" = "src" } + +[tool.setuptools.packages.find] +where = ["src"] + +[tool.setuptools.package-data] +my_pkg = ["py.typed"] + +[tool.setuptools.dynamic] +version = { attr = "my_pkg._version.__version__" } + +[tool.ruff] +line-length = 100 +extend = "../../tools/ruff.toml" # inherit SAGE root config (adjust relative path as needed) + +[tool.pytest.ini_options] +# See pytest.ini for full config (pytest.ini takes precedence if present) +testpaths = ["tests"] +pythonpath = ["src"] +addopts = "-v --tb=short --color=yes" diff --git a/tools/templates/new-repo/pytest.ini b/tools/templates/new-repo/pytest.ini new file mode 100644 index 0000000000..ab4686bda3 --- /dev/null +++ b/tools/templates/new-repo/pytest.ini @@ -0,0 +1,15 @@ +[pytest] +testpaths = tests +python_files = test_*.py +python_classes = Test* +python_functions = test_* + +markers = + unit: Unit tests (no external dependencies) + integration: Integration tests (may require external services) + slow: Slow tests (>30 s) + fast: Fast tests (<5 s, no I/O) + +addopts = -v --tb=short --color=yes +pythonpath = + src diff --git a/tools/templates/new-repo/quickstart.sh b/tools/templates/new-repo/quickstart.sh new file mode 100755 index 0000000000..8c2abd0345 --- /dev/null +++ b/tools/templates/new-repo/quickstart.sh @@ -0,0 +1,152 @@ +#!/usr/bin/env bash +# quickstart.sh — SAGE repo dev environment setup +# +# Usage: +# ./quickstart.sh # full setup (default) +# ./quickstart.sh --dev # alias for full setup +# ./quickstart.sh --yes # non-interactive (assume yes) +# ./quickstart.sh --doctor # diagnose environment issues +# +# Rules: +# - NEVER creates a new venv. Must be called inside an existing conda/venv. +# - Installs hooks via pre-commit + direct symlinks for post-commit / pre-push. +# - Installs the package in editable mode: pip install -e .[dev] + +set -e + +# ─── Colors ─────────────────────────────────────────────────────────────────── +RED='\033[0;31m' +YELLOW='\033[1;33m' +GREEN='\033[0;32m' +CYAN='\033[0;36m' +BLUE='\033[0;34m' +BOLD='\033[1m' +NC='\033[0m' + +# ─── Arguments ──────────────────────────────────────────────────────────────── +MODE="dev" +YES=false +for arg in "$@"; do + case "$arg" in + --doctor) MODE="doctor" ;; + --dev) MODE="dev" ;; + --yes|-y) YES=true ;; + esac +done + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +PROJECT_ROOT="$SCRIPT_DIR" + +echo -e "${CYAN}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" +echo -e "${BOLD}${BLUE} $(basename "$PROJECT_ROOT") — Quick Start${NC}" +echo -e "${CYAN}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}" +echo "" + +# ─── Doctor ─────────────────────────────────────────────────────────────────── +if [ "$MODE" = "doctor" ]; then + echo -e "${BOLD}${BLUE}Environment Diagnosis${NC}" + echo "" + echo -e "${YELLOW}Python:${NC} $(python3 --version 2>/dev/null || echo 'NOT FOUND')" + echo -e "${YELLOW}Conda env:${NC} ${CONDA_DEFAULT_ENV:-none}" + echo -e "${YELLOW}Venv:${NC} ${VIRTUAL_ENV:-none}" + echo -e "${YELLOW}pre-commit:${NC} $(pre-commit --version 2>/dev/null || echo 'NOT FOUND')" + echo -e "${YELLOW}ruff:${NC} $(ruff --version 2>/dev/null || echo 'NOT FOUND')" + echo -e "${YELLOW}pytest:${NC} $(pytest --version 2>/dev/null || echo 'NOT FOUND')" + echo "" + echo -e "${YELLOW}Git hooks installed:${NC}" + for h in pre-commit pre-push post-commit; do + if [ -f "$PROJECT_ROOT/.git/hooks/$h" ]; then + echo -e " ${GREEN}✓ $h${NC}" + else + echo -e " ${RED}✗ $h${NC}" + fi + done + exit 0 +fi + +# ─── Step 0: Require an active environment ──────────────────────────────────── +if [ -z "$CONDA_DEFAULT_ENV" ] && [ -z "$VIRTUAL_ENV" ]; then + echo -e "${RED} ❌ No Python environment detected.${NC}" + echo -e "${YELLOW} → Activate a conda or venv environment first:${NC}" + echo -e " conda create -n sage python=3.11 && conda activate sage" + echo "" + echo -e "${RED} ⚠️ NEVER run this script without an active environment.${NC}" + echo -e "${RED} ⚠️ This script will NOT create a new environment for you.${NC}" + exit 1 +fi +echo -e "${GREEN} ✅ Environment: ${CONDA_DEFAULT_ENV:-$VIRTUAL_ENV}${NC}" +echo "" + +# ─── Step 1: Python version check ──────────────────────────────────────────── +echo -e "${YELLOW}${BOLD}Step 1/4: Python version${NC}" +PYTHON_VERSION=$(python3 --version | awk '{print $2}') +python3 -c "import sys; sys.exit(0 if sys.version_info >= (3, 10) else 1)" || { + echo -e "${RED}✗ Python $PYTHON_VERSION is too old (requires >= 3.10)${NC}" + exit 1 +} +echo -e "${GREEN}✓ Python $PYTHON_VERSION${NC}" +echo "" + +# ─── Step 2: Install Git Hooks ─────────────────────────────────────────────── +echo -e "${YELLOW}${BOLD}Step 2/4: Installing Git Hooks${NC}" + +HOOKS_DIR="$PROJECT_ROOT/.git/hooks" +TEMPLATE_HOOKS="$PROJECT_ROOT/hooks" + +if [ ! -d "$HOOKS_DIR" ]; then + echo -e "${YELLOW}⚠ .git directory not found — skipping hooks (not a git repo?)${NC}" +else + # pre-commit hook managed by the pre-commit framework + if command -v pre-commit >/dev/null 2>&1; then + pre-commit install --install-hooks 2>/dev/null + echo -e "${GREEN}✓ pre-commit hook installed${NC}" + else + echo -e "${YELLOW}⚠ pre-commit not found; installing...${NC}" + pip install pre-commit --quiet + pre-commit install --install-hooks 2>/dev/null + echo -e "${GREEN}✓ pre-commit installed and hook set up${NC}" + fi + + # pre-push and post-commit — symlinked from hooks/ + for hook in pre-push post-commit; do + if [ -f "$TEMPLATE_HOOKS/$hook" ]; then + ln -sf "../../hooks/$hook" "$HOOKS_DIR/$hook" + chmod +x "$HOOKS_DIR/$hook" + echo -e "${GREEN}✓ $hook hook linked${NC}" + else + echo -e "${YELLOW}⚠ hooks/$hook not found, skipping${NC}" + fi + done +fi +echo "" + +# ─── Step 3: Install package ───────────────────────────────────────────────── +echo -e "${YELLOW}${BOLD}Step 3/4: Installing package (editable)${NC}" +if [ -f "$PROJECT_ROOT/pyproject.toml" ]; then + pip install -e ".[dev]" --quiet 2>/dev/null || pip install -e . --quiet + echo -e "${GREEN}✓ Package installed in editable mode${NC}" +else + echo -e "${YELLOW}⚠ No pyproject.toml found — skipping package install${NC}" +fi +echo "" + +# ─── Step 4: Sanity check ──────────────────────────────────────────────────── +echo -e "${YELLOW}${BOLD}Step 4/4: Sanity check${NC}" +if command -v ruff >/dev/null 2>&1; then + echo -e "${GREEN}✓ ruff $(ruff --version | awk '{print $2}')${NC}" +else + echo -e "${YELLOW}⚠ ruff not found — install: pip install ruff${NC}" +fi +if command -v pytest >/dev/null 2>&1; then + echo -e "${GREEN}✓ pytest available${NC}" +fi +echo "" + +echo -e "${GREEN}${BOLD}✓ Setup complete!${NC}" +echo "" +echo -e "${BLUE}${BOLD}Next steps:${NC}" +echo -e " ${CYAN}pytest -v${NC} — run tests" +echo -e " ${CYAN}ruff check src/${NC} — lint" +echo -e " ${CYAN}ruff format src/${NC} — format" +echo -e " ${CYAN}./quickstart.sh --doctor${NC} — diagnose environment" +echo "" diff --git a/tools/templates/new-repo/ruff.toml b/tools/templates/new-repo/ruff.toml new file mode 100644 index 0000000000..b810d2c2f2 --- /dev/null +++ b/tools/templates/new-repo/ruff.toml @@ -0,0 +1,43 @@ +# ruff.toml — standalone ruff configuration for SAGE packages +# +# Packages inside the SAGE monorepo should use: +# [tool.ruff] +# extend = "../../tools/ruff.toml" +# +# Standalone repos copy this file to their root. + +target-version = "py311" +line-length = 100 + +extend-exclude = [ + "build", + "dist", + ".venv", + "venv", + ".git", + "*.egg-info", + "thirdparty", + "ThirdParty", +] + +[lint] +select = [ + "E", # pycodestyle errors + "W", # pycodestyle warnings + "F", # pyflakes + "I", # isort + "B", # flake8-bugbear + "C4", # flake8-comprehensions + "UP", # pyupgrade +] +ignore = [ + "E501", # line length (handled by formatter) + "B008", # do not perform function calls in argument defaults +] + +[lint.isort] +known-first-party = ["my_pkg"] # adjust to actual package name + +[format] +quote-style = "double" +indent-style = "space" diff --git a/tools/templates/new-repo/src/my_pkg/__init__.py b/tools/templates/new-repo/src/my_pkg/__init__.py new file mode 100644 index 0000000000..ac633c76da --- /dev/null +++ b/tools/templates/new-repo/src/my_pkg/__init__.py @@ -0,0 +1,5 @@ +"""my_pkg — SAGE package skeleton.""" + +from my_pkg._version import __version__ + +__all__ = ["__version__"] diff --git a/tools/templates/new-repo/src/my_pkg/_version.py b/tools/templates/new-repo/src/my_pkg/_version.py new file mode 100644 index 0000000000..4bd173fdca --- /dev/null +++ b/tools/templates/new-repo/src/my_pkg/_version.py @@ -0,0 +1,10 @@ +# Single source of truth for this package's version. +# +# Rules (mandatory for all SAGE packages): +# 1. Only this file may contain a hardcoded __version__. +# 2. pyproject.toml must use dynamic version pointing here. +# 3. __init__.py must import from here, never define __version__ itself. +# 4. To bump: update only this file (post-commit hook handles BUILD digit). +# +# Format: X.Y.Z (public release) or X.Y.Z.N (build/dev counter) +__version__ = "0.1.0" diff --git a/tools/verify_hello_world.py b/tools/verify_hello_world.py index 24b7885049..1a39940436 100644 --- a/tools/verify_hello_world.py +++ b/tools/verify_hello_world.py @@ -12,6 +12,13 @@ """ import sys +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[1] +SRC_DIR = REPO_ROOT / "src" + +if SRC_DIR.exists(): + sys.path.insert(0, str(SRC_DIR)) def main() -> int: @@ -22,65 +29,94 @@ def main() -> int: errors = [] - # Test L1: sage-common + transitional = [] + + # Transitional compatibility surfaces may still be present in migrated environments. try: import sage.common - print(f"✅ sage.common {sage.common.__version__}") + print(f"ℹ️ sage.common {sage.common.__version__} (transitional)") + except ImportError as e: + print(f"⚠️ sage.common: {e} (transitional)") + transitional.append("sage.common") + + # Transitional surface: sage.libs (now owned by consolidated isage) + try: + import sage.libs + + print(f"ℹ️ sage.libs {sage.libs.__version__} (transitional)") except ImportError as e: - print(f"❌ sage.common: {e}") - errors.append("sage.common") + print(f"⚠️ sage.libs: {e} (transitional)") + transitional.append("sage.libs") - # Test L2: sage-platform + # Transitional surface: sage.platform (now owned by consolidated isage runtime/foundation) try: import sage.platform - print(f"✅ sage.platform {sage.platform.__version__}") + print(f"ℹ️ sage.platform {sage.platform.__version__} (transitional)") except ImportError as e: - print(f"❌ sage.platform: {e}") - errors.append("sage.platform") + print(f"⚠️ sage.platform: {e} (transitional)") + transitional.append("sage.platform") - # Test L3: sage-kernel + # Transitional kernel/middleware imports are optional and no longer part of the root contract. try: import sage.kernel - print(f"✅ sage.kernel {sage.kernel.__version__}") + print(f"ℹ️ sage.kernel {sage.kernel.__version__} (transitional)") except ImportError as e: - print(f"❌ sage.kernel: {e}") - errors.append("sage.kernel") + print(f"⚠️ sage.kernel: {e} (transitional)") + transitional.append("sage.kernel") - # Test L3: sage-libs (interface layer only) + # Test L2: sage.middleware (transitional) try: - import sage.libs + import sage.middleware - print(f"✅ sage.libs {sage.libs.__version__}") + print(f"ℹ️ sage.middleware {sage.middleware.__version__} (transitional)") except ImportError as e: - print(f"❌ sage.libs: {e}") - errors.append("sage.libs") + print(f"⚠️ sage.middleware: {e} (transitional)") + transitional.append("sage.middleware") - # Test L4: sage-middleware (optional, may have heavy deps) + # Test main-repo in-tree surfaces try: - import sage.middleware + import sage.foundation - print(f"✅ sage.middleware {sage.middleware.__version__}") + print("✅ sage.foundation") except ImportError as e: - print(f"⚠️ sage.middleware: {e} (optional)") + print(f"❌ sage.foundation: {e}") + errors.append("sage.foundation") - # Test L5: sage-cli (optional) try: - import sage.cli + import sage.stream - print(f"✅ sage.cli {sage.cli.__version__}") + print("✅ sage.stream") + except ImportError as e: + print(f"❌ sage.stream: {e}") + errors.append("sage.stream") + + try: + import sage.runtime + + print("✅ sage.runtime") except ImportError as e: - print(f"⚠️ sage.cli: {e} (optional)") + print(f"❌ sage.runtime: {e}") + errors.append("sage.runtime") - # Test L5: sage-tools (optional) try: - import sage.tools + import sage.serving - print(f"✅ sage.tools {sage.tools.__version__}") + print("✅ sage.serving") + except ImportError as e: + print(f"❌ sage.serving: {e}") + errors.append("sage.serving") + + # Test L3: main-repo CLI (required) + try: + import sage.cli + + print(f"✅ sage.cli {sage.cli.__version__}") except ImportError as e: - print(f"⚠️ sage.tools: {e} (optional)") + print(f"❌ sage.cli: {e}") + errors.append("sage.cli") print("=" * 50) @@ -89,18 +125,30 @@ def main() -> int: print("Please reinstall: ./quickstart.sh --dev --yes") return 1 - # Get overall version from sage.common + # Get overall version from main repo try: - version = sage.common.__version__ + from sage._version import __version__ as version except Exception: version = "unknown" print(f"✅ SAGE v{version} installed successfully!") print() print("Next steps:") - print(" - Clone examples: git clone https://github.com/intellistream/sage-examples.git") - print(" - Run tutorial: python sage-examples/tutorials/hello_world.py") - print(" - Read docs: https://intellistream.github.io/SAGE-Pub/") + print(" - Verify CLI surface: sage verify") + print(" - Inspect runtime view: sage runtime nodes") + print(" - Inspect gateway contract: sage serve gateway --json") + print(" - Build a stream: from sage.stream import DataStream") + print(" - Pick a runtime: from sage.runtime import LocalEnvironment, FluttyEnvironment") + print(" - Attach serving when needed: from sage.serving import SageServeConfig") + print(' - Chat with sagellm: sage chat --ask "Hello, SAGE!"') + print( + " - Record lightweight index metadata: sage index ingest --source ./docs --index local-docs" + ) + if transitional: + print( + f" - Transitional packages still visible via kernel stack: {', '.join(transitional)}" + ) + print(" - Read docs: https://intellistream.github.io/sage-docs/") return 0