Retrieval & Memory · 2024
Graph & Structured Retrieval (GraphRAG)
GraphRAG builds a knowledge graph and hierarchical community summaries from a corpus so retrieval can operate over structured, aggregated entities, removing standard RAG's inability to answer global questions that span an entire dataset.
Editorial record
Plain-language summary
GraphRAG uses a language model to extract entities and relationships into a graph, clusters the graph into communities, and precomputes summaries for each community; queries are then answered by combining these community summaries rather than only retrieving isolated text chunks. This structured approach lets the system answer broad, whole-corpus sensemaking questions (such as overarching themes) that flat vector retrieval handles poorly, while keeping evidence traceable to source entities and relations.
Source record
Provenance
- Record ID
- P-327
- Record created
- 2026-07-13
- Last reviewed
- 2026-07-14
- Record version
- 2
- https://arxiv.org/abs/2404.16130
- arXiv:2404.16130
Citation caveat: Citation metadata is approximate and marked unverified in the source dataset.