GraphRAG
Createdrileylemm
Enables querying of a hybrid graph and vector database using the Model Context Protocol for enhanced document retrieval.
About
GraphRAG provides a seamless integration between large language models and a hybrid retrieval system, leveraging the strengths of both graph databases (Neo4j) and vector databases (Qdrant). It enables semantic search through document embeddings, graph-based context expansion following relationships, and hybrid search combining vector similarity with graph relationships, fully integrating with Claude and other LLMs through MCP.
Key Features
- Semantic search using sentence embeddings and Qdrant
- 2 GitHub stars
- Hybrid search combining both approaches
- Full documentation of Neo4j schema and Qdrant collection information
- Graph-based context expansion using Neo4j
- MCP tools and resources for LLM integration
Use Cases
- Hybrid search for information retrieval combining vector similarity and graph relationships
- Integration with LLMs like Claude for enhanced context
- Semantic search through documentation
