What is GraphRAG?
GraphRAG (Graph Retrieval-Augmented Generation) is a RAG approach that represents entities and relationships as a graph, then retrieves graph and text evidence to support questions that benefit from connected context.
Quick Facts
| Full Name | Graph Retrieval-Augmented Generation |
|---|---|
| Created | Popularized in recent years with LLM architecture evolution |
How It Works
GraphRAG augments chunk-and-vector retrieval with a graph of entities, relations, and sometimes community summaries. A pipeline may extract entity-relation triples during ingestion, resolve and link entities, group related nodes, and retrieve local graph neighborhoods or broader summaries at query time. It can be valuable for multi-hop questions, relationship exploration, and corpus-level synthesis, but its extraction quality, entity resolution, update cost, and graph retrieval policy must be evaluated against a simpler RAG baseline. It does not by itself guarantee factual answers or replace source citations and answer-faithfulness checks.
Key Characteristics
- Versioned entity and relation candidates with source spans and confidence
- Graph storage and traversal selected for the query and operational workload
- Optional community summaries that must retain links to source evidence
- Hybrid text, vector, and graph retrieval under identical access constraints
- Potential gains for measured multi-hop and corpus-synthesis query slices
- Extraction, resolution, refresh, deletion, and governance costs beyond a vector baseline
Common Use Cases
- Multi-hop questions whose relevant relations are evaluated against a vector baseline
- Corpus synthesis with source-linked summaries and abstention for missing evidence
- Risk investigation where graph links remain reviewable evidence rather than automated decisions
- Scientific discovery support with provenance and human validation of extracted claims
- Enterprise knowledge retrieval with tenant, object, retention, and deletion controls
Example
Loading code...Frequently Asked Questions
What is the difference between GraphRAG and Naive RAG?
A chunk-and-vector baseline retrieves text by lexical or embedding similarity. GraphRAG also indexes entities and relationships, allowing retrieval to expand through connected evidence or summaries. The graph is useful only if it improves a measured query slice, such as multi-hop or corpus-synthesis questions; extraction errors, updates, permissions, and operational cost can outweigh the benefit for simpler fact lookup.
Is the cost of building GraphRAG high?
It can cost more than vector RAG because extraction, resolution, graph storage, summary generation, refresh, and deletion lineage add work. The actual cost depends on corpus size, model, batching, cache hit rate, change frequency, and query traffic. Compare ingestion cost, query latency, evidence coverage, answer faithfulness, and maintenance burden with a vector baseline before adopting it.
What is Hybrid Search in GraphRAG?
Hybrid GraphRAG can retrieve lexical or vector text candidates alongside graph neighborhoods, paths, or summaries, then validate, deduplicate, and cite the underlying evidence. Apply tenant, object, purpose, freshness, and retention controls before all retrieval paths are fused. A graph-generated summary is a retrieval aid, not proof on its own.
How should GraphRAG be evaluated?
Evaluate it against a versioned vector or hybrid baseline using labeled query slices: single-hop facts, multi-hop relations, corpus summaries, entity ambiguity, updates, deletions, and unauthorized content. Measure retrieval recall and precision, citation coverage, answer faithfulness, abstention, extraction and resolution errors, latency, cost, and operational recovery.