🎓 RAG Educational System

A retrieval-augmented educational platform combining application services, vector search, caching, and event-driven infrastructure to support AI-assisted learning experiences.

📌 Overview

RAG Educational System is a multi-service project centered on Retrieval-Augmented Generation (RAG) for educational use cases.

The repository brings together application components with supporting infrastructure for relational data, caching, vector retrieval, and event-driven processing. Environment-specific secrets and local service data are intentionally excluded from version control.

🧩 Core Architecture

                    ┌──────────────────────┐
                    │ Educational Client   │
                    └──────────┬───────────┘
                               │
                               ▼
                    ┌──────────────────────┐
                    │ Application Services │
                    └───────┬──────┬───────┘
                            │      │
                ┌───────────┘      └────────────┐
                ▼                               ▼
        PostgreSQL 16                         Redis 7
                │                               │
                └──────────────┬────────────────┘
                               ▼
                         Qdrant Vector DB
                               │
                               ▼
                     Retrieval / RAG Layer
                               │
                               ▼
                         AI-powered answers

              Kafka provides event-driven messaging

🛠️ Technology Stack

Area	Technologies
Application	Java / Python services in the repository
Database	PostgreSQL 16
Cache	Redis 7
Vector Database	Qdrant
Messaging	Apache Kafka / Confluent Kafka
Frontend	Project frontend module
AI Architecture	Retrieval-Augmented Generation (RAG)
Infrastructure	Docker Compose

🧠 RAG Workflow

User Question
     ↓
Retrieve Relevant Knowledge
     ↓
Vector Search
     ↓
Build Context
     ↓
Generate Context-Aware Response
     ↓
Return Educational Answer

The vector database is used for semantic retrieval, while PostgreSQL provides relational persistence and Redis supports fast-access application state/cache use cases.

🏗️ Local Infrastructure

The provided Docker Compose configuration defines:

• PostgreSQL 16 on port 5432
• Redis 7 on port 6379
• Qdrant on ports 6333 and 6334
• Kafka on ports 9092 and 29092

Persistent Docker volumes are configured for PostgreSQL, Redis, and Qdrant data.

🚀 Getting Started

Prerequisites

• Git
• Docker and Docker Compose
• The runtime/tooling required by the individual application modules

1. Clone

git clone https://github.com/prashantpiyush1111/rag-educational-system.git
cd rag-educational-system

2. Configure Environment

The infrastructure uses environment variables such as DB_PASSWORD. Create the required local environment configuration without committing secrets.

3. Start Infrastructure

docker compose up -d

4. Verify Services

Check the containers with:

docker compose ps

Application-specific run commands may vary by module and should be executed from the relevant service directory.

🔐 Security & Repository Hygiene

The repository excludes local environment files, generated vector-database data, Python virtual environments, build artifacts, and infrastructure secrets from version control.

Never commit database passwords, API keys, tokens, or production environment files.

📂 Repository Organization

The repository includes separate areas for application services, a frontend, infrastructure configuration, and local development dependencies. See the source tree for the current module layout.

🎯 Project Goals

• Explore practical RAG architecture for educational applications
• Combine semantic retrieval with application/business data
• Use containerized infrastructure for reproducible local development
• Practice integration across databases, caches, vector stores, and messaging systems

🗺️ Future Direction

The architecture can be extended with richer educational workflows, stronger retrieval/evaluation pipelines, production authentication, observability, and deployment automation as the project evolves.

👨‍💻 Author

Prashant Maurya
GitHub: @prashantpiyush1111

📄 License

See the repository license for current usage terms.