Advanced RAG System

Upload documents and ask AI questions about them with source citations.

🏗️ Architecture

graph TB
    User[👤 User] --> WebUI[🌐 React Frontend]
    WebUI --> API[🔌 FastAPI Backend]
    API --> Processor[📝 Document Processor]
    API --> QueryEngine[🔍 Query Engine]
    
    Processor --> Embeddings[🧠 OpenAI Embeddings]
    Embeddings --> VectorDB[(🗄️ Qdrant Vector DB)]
    
    QueryEngine --> VectorDB
    QueryEngine --> LLM[🤖 GPT-4]
    
    API --> PostgreSQL[(💾 PostgreSQL)]
    API --> Redis[(⚡ Redis Cache)]

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🚀 Quick Start

# Clone and setup
git clone <repository-url>
cd ai-rag-python
cp .env.example .env
# Edit .env with your OpenAI API key

# Start services
docker-compose up -d

# Access
# Frontend: http://localhost:3000
# API Docs: http://localhost:8000/docs

🔧 Development

Backend:

cd backend && pip install -r requirements.txt
uvicorn app.main:app --reload

Frontend:

cd frontend && npm install
npm start

📋 Key Features

• Multi-format document support (PDF, text)
• AI-powered Q&A with citations
• Semantic search via vector embeddings
• React frontend + FastAPI backend
• Production Docker deployment