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Graph RAG(GRAG)
Knowledge graph-enhanced RAG using entity relationships and community detection for global sensemaking queries
In 30 seconds
- What
- Builds an entity knowledge graph from documents, detects communities within it, then answers queries by summarizing relevant communities rather than retrieving individual chunks.
- When to use
- Answering broad analytical questions across large document collections where entity relationships and thematic groupings matter more than keyword matching.
- Watch out
- Graph construction and community detection are computationally expensive; quality degrades sharply if entity extraction or relationship identification fails early.
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Graph RAG: Overview
Knowledge graph-enhanced RAG using entity relationships and community detection for global sensemaking queries
- Entity knowledge graph construction
- Community detection algorithms
- Global query processing
- Multi-hop reasoning paths
- Hierarchical summarization
- Relationship-aware retrieval
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References
The papers, specifications, and repositories this pattern is based on.
- From Local to Global: A Graph RAG Approach to Query-Focused Summarization (Edge et al., 2024)arXiv:2404.16130
- GraphRAG: Unlocking LLM Discovery on Narrative Private Data (Microsoft Research, 2024)
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020)arXiv:2005.11401
- From Louvain to Leiden: guaranteeing well-connected communitiesarXiv:1810.08473
- Fast Unfolding of Communities in Large Networks (Blondel et al., 2008)arXiv:0803.0476
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