GraphRAG

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

1. Document Processing: Extract entities and relationships
2. Graph Construction: Build knowledge graph from documents
3. Community Detection: Identify topical clusters
4. Hierarchical Summarization: Create multi-level summaries
5. 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