Graph Retrieval-Augmented Generation (Graph RAG) is a RAG variant that structures knowledge as a graph—entities, relations, and linked passages—so an LLM retrieves and conditions on connected subgraphs rather than isolated chunks. By querying k-hop neighborhoods and relation paths, Graph RAG enables multi-hop reasoning, disambiguation, and consistent, cited answers grounded in structured context.
What is Graph Retrieval-Augmented Generation (Graph RAG)?
Graph RAG augments the standard retrieve-then-generate loop with graph construction and graph-aware retrieval. Corpora are processed to extract nodes (entities, claims, sections) and edges (relation triples, citations, hyperlinks, semantic similarity). At query time, the system builds a query graph, expands k-hop neighborhoods, and assembles an evidence subgraph using algorithms like path finding, Personalized PageRank, or community detection. Retrieved nodes/edges are serialized for the LLM as triples, path narratives, or graph-structured JSON, often mixed with canonical passages. Compared with vanilla RAG, this reduces context duplication, preserves topology (who/what/when/how links), and improves multi-step QA and conflict checking—all with higher indexing and retrieval complexity.
Why it matters and where it’s used
Graph RAG supports problems that require stitching facts across documents: compliance and policy reasoning, threat intelligence fusion, biomedical discovery (gene–disease–drug pathways), supply-chain and bill-of-materials queries, incident/root-cause analysis, and product knowledge with many cross-references. Advantages include better grounding for multi-hop questions, controllable citations via path traces, explainability through graph paths, and easier updates by editing nodes/edges rather than retraining.
Examples
- Compliance: Traverse policies → controls → evidence to answer “Which controls satisfy clause X, and where is evidence stored?”
- Threat intel: Link indicators → campaigns → actors to justify an alert triage decision with cited paths.
- Biomedical: Surface k-hop gene–pathway–drug relations with supporting abstracts for hypothesis generation.
- Product docs: Follow feature → API → changelog links to resolve version-specific behavior.
FAQs
- How is it different from standard RAG? It retrieves connected subgraphs and relation paths, not just top-k chunks, enabling multi-hop reasoning and better disambiguation.
- Do I need a knowledge graph upfront? You can build one from text with NER/RE, citation/link mining, or use hybrid graphs that combine vector similarity edges with symbolic triples.
- Which stores work? Graph databases (e.g., Neo4j, Neptune, TigerGraph) or vector DBs with graph features; hybrids pair a vector index with a graph store.
- How do you format context? Serialize triples (subject–predicate–object), path summaries, and the most relevant passages; keep prompts schema-consistent.
- Pitfalls? Entity resolution drift, noisy edges, prompt budget blow-ups from large k-hop expansions, and latency from path search and reranking. Use pruning, confidence thresholds, and caching.