pageIndex-rag-chat

An Agentic RAG Web UI system built on the ReAct paradigm with PageIndex, visualizing the document retrieval and reasoning process

Compared with the official website

official

Quick Start

dependencies:
pip install -r requirements.txt

start the service
python main.py

visit http://127.0.0.1:8000/

✨ Core Features

• 🚀 FastAPI-Powered: The service is built on FastAPI, easily integrable into your own applications
• 🗺️ TOC-Driven Navigation: Abandon blind vector matching; the Agent reads the global outline and accurately locates target chapters based on logical relevance
• 🕵️‍♂️ ReAct Thinking Engine: Empowers the model with autonomous decision-making capabilities, loading content on demand to significantly reduce Token consumption
• ⚖️ Dynamic Evaluation & Reflection: Automatically assesses the validity of extracted text, dynamically builds a high-quality local knowledge base, and filters out irrelevant noise
• 🔌 Plug & Play Simplicity: Natively adapts to the _structure.json format output by PageIndex, lightweight and ready-to-use, compatible with all OpenAI-compatible APIs

Note: The core PageIndex code in this project has been modified to adapt to OpenAI-compatible interfaces, configured in the .env file (the original project only supports the ChatGPT API)

.env
OPENAI_API_KEY=your_openai_api_key
OPENAI_MODEL=model_name
OPENAI_BASE_URL=base_url
## 🛠️Core Architecture
User Query 
   │
   ▼
[Load Document Outline (TOC)] ──────┐
   │                                 │
   ▼                                 ▼
Thought-Agent ◄──────────────── [Current Knowledge Base]
   │ (Autonomously decide next action)      ▲
   ▼                                       │
Call Tool (get_texts)                      │
   │                                       │
   ▼                                       │
Judge-Agent ───────────────────────────────┘
  (Evaluate result validity and summarize experience)
   │
   ▼ (Triggered when sufficient information is collected)
Call Tool (get_answer) 
   │
   ▼
Generate Final Answer

📌 Todo List

• Support multi-document Q&A capabilities
• Package as MCP interface
• Integrate database storage capabilities
• Connect to Ollama/VLLM to achieve full local deployment