iflow-mcp_rileylemm_graphrag-mcp

# GraphRAG MCP Server A Model Context Protocol server for querying a hybrid graph and vector database system, combining Neo4j (graph database) and Qdrant (vector database) for powerful semantic and graph-based document retrieval. ## Overview GraphRAG MCP provides a seamless integration between large language models and a hybrid retrieval system that leverages the strengths of both graph databases (Neo4j) and vector databases (Qdrant). This enables: - Semantic search through document embeddings - Graph-based context expansion following relationships - Hybrid search combining vector similarity with graph relationships - Full integration with Claude and other LLMs through MCP This project follows the [Model Context Protocol](https://github.com/modelcontextprotocol/python-sdk) specification, making it compatible with any MCP-enabled client. ## Features - **Semantic search** using sentence embeddings and Qdrant - **Graph-based context expansion** using Neo4j - **Hybrid search** combining both approaches - **MCP tools and resources** for LLM integration - Full documentation of Neo4j schema and Qdrant collection information ## Prerequisites - Python 3.12+ - Neo4j running on localhost:7687 (default configuration) - Qdrant running on localhost:6333 (default configuration) - Document data indexed in both databases ## Installation ### Quick Start 1. Clone this repository: ```bash git clone https://github.com/rileylemm/graphrag_mcp.git cd graphrag_mcp ``` 2. Install dependencies with uv: ```bash uv install ``` 3. Configure your database connections in the `.env` file: ``` # Neo4j Configuration NEO4J_URI=bolt://localhost:7687 NEO4J_USER=neo4j NEO4J_PASSWORD=password # Qdrant Configuration QDRANT_HOST=localhost QDRANT_PORT=6333 QDRANT_COLLECTION=document_chunks ``` 4. Run the server: ```bash uv run main.py ``` ### Detailed Setup Guide For a detailed guide on setting up the underlying hybrid database sy…