Graphrag MCP

🚀 GraphRAG MCP Server

A Model Context Protocol (MCP) server for querying hybrid graph and vector database systems, integrating Neo4j (graph database) and Qdrant (vector database) to achieve powerful semantic and graph-based document retrieval.

🚀 Quick Start

GraphRAG MCP Server combines Neo4j and Qdrant, enabling seamless integration between large language models and a hybrid retrieval system. It offers semantic search, context expansion based on graph relationships, and hybrid search.

✨ Features

• Semantic Search: Conducted using sentence embeddings and Qdrant.
• Graph-based Context Expansion: Utilizing Neo4j.
• Hybrid Search: Combining both methods.
• MCP Tools and Resources: For LLM integration.
• Complete Documentation: Of Neo4j schema and Qdrant collection information.

📦 Installation

Prerequisites

• Operating System: Linux, macOS, or Windows (64-bit recommended).
• Python Version: 3.8 or higher.
• Memory Requirement: At least 4GB RAM.
• Disk Space: At least 10GB of available space.
• Dependency Management Tool: pipenv is recommended.

Quick Installation

Use the following commands for a quick installation:

git clone https://github.com/rileylemm/graphrag-mcp.git
cd graphrag-mcp
pip install -r requirements.txt

Detailed Installation Steps

1. Clone the Repository:

   git clone https://github.com/rileylemm/graphrag-mcp.git
   cd graphrag-mcp
   

2. Install Dependencies:

   pip install -r requirements.txt
   

3. Configure Environment Variables: Create a .env file and add the following content:

   NEO4J_URI=Your Neo4j connection string
   NEO4J_USER=Your Neo4j username
   NEO4J_PASSWORD=Your Neo4j password
   QDRANT_HOST=http://localhost:6333
   

4. Start the Server:

   python -m graphrag.mcp.server
   

💻 Usage Examples

Basic Usage

from mcpipy import mcp

# Connect to the MCP server
client = mcp.Client("http://localhost:8080")

# Query example
response = client.query("Query content", resource="https://graphrag.db/collection/qdrant")
print(response)

Using MCP Resources

• https://graphrag.db/schema/neo4j: Provides schema information for the Neo4j graph database.
• https://graphrag.db/collection/qdrant: Provides information for the Qdrant vector collection.

📚 Documentation

Troubleshooting

• Connection Issues: Ensure that Neo4j and Qdrant are running and accessible.
• Empty Results: Check if the document collection has been correctly indexed.
• Missing Dependencies: Run pip install -r requirements.txt to ensure all packages are installed.
• Database Authentication: Verify the credentials in the .env file.

Contribution Guidelines

Contributions are welcome! Please feel free to submit pull requests.

📄 License

This project is under the MIT License.

Copyright (c) 2025 Riley Lemm

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Acknowledgments

If you use this MCP server or adapt it for your own purposes, please provide a citation to Riley Lemm and link back to this repository (https://github.com/rileylemm/graphrag_mcp).