alexUXUI/lang-rag

rag biult with langchain

Basic RAG (Retrieval Augmented Generation) API

A FastAPI-based service that provides document processing, question answering, and real-time chat capabilities using RAG (Retrieval Augmented Generation) techniques. The service can process PDF documents, generate FAQs, and provide interactive chat sessions with context-aware responses.

Features

• PDF document processing and text extraction
• Document chunking and vector storage
• FAQ generation from documents
• Real-time WebSocket chat interface
• Session management for multiple chat instances
• Context-aware responses using RAG
• Interactive web interface for testing

Prerequisites

• Python 3.8+
• Ollama (for local LLM support)
• OpenAI API key (for embeddings)

Installation

1. Clone the repository:

git clone <repository-url>
cd basic-rag

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Set up environment variables: Create a .env file in the root directory with:

OPENAI_API_KEY=your_openai_api_key

Project Structure

basic-rag/
├── src/
│   ├── api/                 # API implementation
│   │   ├── core/           # Core configuration
│   │   ├── models/         # Pydantic models
│   │   ├── routes/         # API routes
│   │   └── services/       # API-specific services
│   ├── services/           # Core intelligent services
│   │   ├── chat/          # Chat functionality
│   │   ├── common/        # Shared utilities
│   │   ├── config/        # Configuration management
│   │   ├── document_processing/  # PDF and text processing
│   │   ├── faq_generation/      # FAQ generation logic
│   │   └── query_processing/    # Query handling and RAG
│   ├── static/             # Static files (HTML, CSS)
│   └── main.py             # Main application entry
├── tests/                  # Test files
├── data/                   # Data directory for PDFs
├── requirements.txt        # Main dependencies
└── requirements-test.txt   # Test dependencies

Running the API

1. Start the FastAPI server:

# From the project root directory
python run.py

The server will start at http://localhost:8000

2. Access the API documentation:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

3. Test the WebSocket interface:

• Open http://localhost:8000/static/test.html in your browser
• Create a session with a PDF file
• Connect to the WebSocket
• Start chatting with the document

API Endpoints

HTTP Endpoints

• POST /sessions/{session_id}: Create a new chat session
• DELETE /sessions/{session_id}: Delete a chat session
• POST /chat: Send a chat message (HTTP)
• POST /upload: Upload a PDF document
• POST /faq: Generate FAQs from a document

WebSocket Endpoint

• ws://localhost:8000/ws/chat/{session_id}: WebSocket connection for real-time chat

Running Tests

1. Install test dependencies:

pip install -r tests/requirements-test.txt

2. Run the test suite:

python tests/test_websocket.py

Development

Running Services Independently

To run specific services for development or testing:

1. Document Processing:

python -m src.services.document_processing.processor

2. FAQ Generation:

python -m src.services.faq_generation.processor

3. Chat Service:

python -m src.services.chat.processor

Adding New Features

1. Create new models in src/api/models/
2. Add business logic in src/services/ (for core services) or src/api/services/ (for API-specific services)
3. Create new routes in src/api/routes/
4. Update tests in the tests/ directory

Code Style

The project follows PEP 8 guidelines. Use a code formatter like black for consistent formatting:

pip install black
black src/ tests/

Troubleshooting

Common Issues

1. WebSocket Connection Issues

   • Ensure the server is running
   • Check if the session ID is valid
   • Verify the PDF file exists and is accessible

2. PDF Processing Errors

   • Check if the PDF file is not corrupted
   • Verify file permissions
   • Ensure the file path is correct

3. API Key Issues

   • Verify your OpenAI API key is set in .env
   • Check if the key is valid and has sufficient credits

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.