GraphRAG
Microsoft's approach to RAG that uses knowledge graphs to enhance retrieval. GraphRAG builds structured representations of documents enabling better context understanding and multi-hop reasoning for complex queries.
About this tool
Overview
GraphRAG is Microsoft's knowledge graph-enhanced approach to Retrieval Augmented Generation, addressing limitations of traditional vector-only RAG through structured knowledge representation.
How It Works
- Document Processing: Extract entities and relationships
- Graph Construction: Build knowledge graph from documents
- Community Detection: Identify topical clusters
- Hierarchical Summarization: Create multi-level summaries
- Graph-Enhanced Retrieval: Query using graph structure
Advantages Over Traditional RAG
Better Context:
- Understand document structure
- Capture relationships
- Multi-hop reasoning
Complex Queries:
- Answer questions requiring synthesis
- Handle multi-document reasoning
- Support exploratory queries
Improved Coverage:
- Community summaries for broad queries
- Fine-grained retrieval for specific questions
Use Cases
- Complex document analysis
- Enterprise knowledge bases
- Research literature review
- Multi-document summarization
- Investigative queries
Components
- Entity extraction (NER)
- Relationship extraction
- Graph database (Neo4j compatible)
- Vector embeddings for semantic search
- LLM for synthesis
Availability
Open-source: microsoft/graphrag on GitHub
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Information
Websitemicrosoft.github.io
PublishedMar 20, 2026
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