mcp-lightrag

# Blog on this project https://daybreak.hashnode.dev/mcp-server-setup-part-1-building-a-local-rag-with-deepseek-for-enhanced-security-and-compliance # Multi-Contextual Processing (MCP) Server This repository contains a Multi-Contextual Processing (MCP) server implementation that uses the DeepSeek-R1 model for processing and querying documents using a RAG framework - lightRAG, it can manipulate both text and PDF files. The server supports multiple query modes and provides a RESTful API interface. ## Features - **Multiple Query Modes**: - Naive Search: Direct document search - Local Search: Context-aware local search - Global Search: Comprehensive document search - Hybrid Search: Combines local and global search strategies - **PDF Support**: Built-in support for processing PDF documents using pdfplumber - **FastAPI Integration**: Modern, fast web framework for building APIs - **Async Processing**: Asynchronous document processing and querying - **Error Handling**: Robust error handling and retry mechanisms - **Health Monitoring**: Built-in health check endpoint ## Prerequisites - Python 3.12 or higher - Virtual environment (recommended) - DeepSeek API key - Ollama running locally (for embeddings) ## Installation 1. Clone the repository: ```bash git clone <repository-url> cd <repository-name> ``` 2. Create and activate a virtual environment: ```bash python -m venv .venv source .venv/bin/activate # On Windows, use `.venv\Scripts\activate` ``` 3. Install dependencies: ```bash pip install -r requirements.txt ``` ## Configuration 1. Set up environment variables: ```bash export DEEPSEEK_API_KEY=your_api_key_here ``` 2. Ensure Ollama is running locally (for embeddings): ```bash # Check if Ollama is running curl http://localhost:11434/api/tags ``` ## Usage 1. Start the server: ```bash python mcp_server.py ``` 2. The server will be available at `http://localhost:8000` ### API Endpoints - `POST /insert`: Insert a new document ```bash curl -X POST "http://localhost:8000/insert" \ -H "Content-Type: application/json" \ -d '{"content": "Your document content here"}' ``` - `POST /query`: Query the documents ```bash curl -X POST "http://localhost:8000/query" \ -H "Content-Type: application/json" \ -d '{"query": "Your query here", "mode": "naive"}' ``` - `GET /modes`: List available query modes ```bash curl "http://localhost:8000/modes" ``` - `GET /health`: Check server health ```bash curl "http://localhost:8000/health" ``` ### Processing PDF Files You can process PDF files using the provided script: ```python import pdfplumber pdf_path = "./your_document.pdf" pdf_text = "" with pdfplumber.open(pdf_path) as pdf: for page in pdf.pages: pdf_text += page.extract_text() + "\n" # The text can then be processed by the RAG system ``` ## Dependencies - fastapi>=0.104.1: Web framework - uvicorn>=0.24.0: ASGI server - pydantic>=2.4.2: Data validation - tenacity>=8.2.3: Retry mechanism - python-dotenv>=1.0.0: Environment variable management - httpx>=0.25.1: HTTP client - numpy<2.0.0: Numerical computations - scipy>=1.8.0,<1.14.0: Scientific computations - tqdm>=4.66.1: Progress bars - pdfplumber>=0.10.3: PDF processing ## API Documentation Once the server is running, you can access the interactive API documentation: - Swagger UI: `http://localhost:8000/docs` - ReDoc: `http://localhost:8000/redoc` ## Error Handling The server includes comprehensive error handling: - API connection issues - PDF processing errors - Query execution errors - Invalid mode selections ## Contributing 1. Fork the repository 2. Create your feature branch 3. Commit your changes 4. Push to the branch 5. Create a new Pull Request ## License [Add your license information here] ## Contact [Add your contact information here]