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    Stars

    53

    Forks

    17

    Release Date

    4/30/2025

    about 2 months ago

    Detailed Description

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    LightRAG MCP Server

    MCP server for integrating LightRAG with AI tools. Provides a unified interface for interacting with LightRAG API through the MCP protocol.

    Description

    LightRAG MCP Server is a bridge between LightRAG API and MCP-compatible clients. It allows using LightRAG (Retrieval-Augmented Generation) capabilities in various AI tools that support the MCP protocol.

    Key Features

    • Information Retrieval: Execute semantic and keyword queries to documents
    • Document Management: Upload, index, and track document status
    • Knowledge Graph Operations: Manage entities and relationships in the knowledge graph
    • Monitoring: Check LightRAG API status and document processing

    Installation

    This server is designed to be used as an MCP server and should be installed in a virtual environment using uv, not as a system-wide package.

    Development Installation

    # Create a virtual environment
    uv venv --python 3.11
    
    # Install the package in development mode
    uv pip install -e .
    

    Requirements

    • Python 3.11+
    • Running LightRAG API server

    Usage

    Important: LightRAG MCP server should only be run as an MCP server through an MCP client configuration file (mcp-config.json).

    Command Line Options

    The following arguments are available when configuring the server in mcp-config.json:

    • --host: LightRAG API host (default: localhost)
    • --port: LightRAG API port (default: 9621)
    • --api-key: LightRAG API key (optional)

    Integration with LightRAG API

    The MCP server requires a running LightRAG API server. Start it as follows:

    # Create virtual environment
    uv venv --python 3.11
    
    # Install dependencies
    uv pip install -r LightRAG/lightrag/api/requirements.txt
    
    # Start LightRAG API
    uv run LightRAG/lightrag/api/lightrag_server.py --host localhost --port 9621 --working-dir ./rag_storage --input-dir ./input --llm-binding openai --embedding-binding openai --log-level DEBUG
    

    Setting up as MCP server

    To set up LightRAG MCP as an MCP server, add the following configuration to your MCP client configuration file (e.g., mcp-config.json):

    Using uvenv (uvx):

    {
      "mcpServers": {
        "lightrag-mcp": {
          "command": "uvx",
          "args": [
            "lightrag_mcp",
            "--host",
            "localhost",
            "--port",
            "9621",
            "--api-key",
            "your_api_key"
          ]
        }
      }
    }
    

    Development

    {
      "mcpServers": {
        "lightrag-mcp": {
          "command": "uv",
          "args": [
            "--directory",
            "/path/to/lightrag_mcp",
            "run",
            "src/lightrag_mcp/main.py",
            "--host",
            "localhost",
            "--port",
            "9621",
            "--api-key",
            "your_api_key"
          ]
        }
      }
    }
    

    Replace /path/to/lightrag_mcp with the actual path to your lightrag-mcp directory.

    Available MCP Tools

    Document Queries

    • query_document: Execute a query to documents through LightRAG API

    Document Management

    • insert_document: Add text directly to LightRAG storage
    • upload_document: Upload document from file to the /input directory
    • insert_file: Add document from file directly to storage
    • insert_batch: Add batch of documents from directory
    • scan_for_new_documents: Start scanning the /input directory for new documents
    • get_documents: Get list of all uploaded documents
    • get_pipeline_status: Get status of document processing in pipeline

    Knowledge Graph Operations

    • get_graph_labels: Get labels (node and relationship types) from knowledge graph
    • create_entities: Create multiple entities in knowledge graph
    • edit_entities: Edit multiple existing entities in knowledge graph
    • delete_by_entities: Delete multiple entities from knowledge graph by name
    • delete_by_doc_ids: Delete all entities and relationships associated with multiple documents
    • create_relations: Create multiple relationships between entities in knowledge graph
    • edit_relations: Edit multiple relationships between entities in knowledge graph
    • merge_entities: Merge multiple entities into one with relationship migration

    Monitoring

    • check_lightrag_health: Check LightRAG API status

    Development

    Installing development dependencies

    uv pip install -e ".[dev]"
    

    Running linters

    ruff check src/
    mypy src/
    

    License

    MIT

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