GraphRAG (knowledge graph RAG)
Also: GraphRAG, knowledge graph retrieval
What is GraphRAG (knowledge graph RAG)?
GraphRAG is a retrieval approach that extracts entities and relationships from documents into a knowledge graph, then answers questions by traversing those connections rather than only matching similar text.
What GraphRAG (knowledge graph RAG) means
Standard RAG finds chunks that look like the question. GraphRAG builds a graph first: during ingestion, a model extracts entities (people, products, accounts, clauses) and the relationships between them, and stores those as nodes and edges, often with community summaries at several levels. At query time the system retrieves not just matching chunks but the connected neighbourhood: which policy references which regulation, which supplier supplies which parts to which site.
This suits questions that span documents or need multi-hop reasoning: "which customers are affected by the change to clause 7 in the Q2 contract template" cannot be answered by any single chunk. It also supports summary questions over a whole corpus ("what are the main themes in this year's complaints") that similarity search handles badly.
GraphRAG is not a replacement for vector retrieval; production systems usually combine the two. It is also considerably more expensive to build and maintain, because extraction runs an LLM over every document and the graph must be updated as sources change. For single-document lookup questions it adds cost without accuracy.
Who it really matters to
- CTO / Head of Engineering: GraphRAG is a significant architecture and cost decision; it is justified by the question types, not by the appeal of a graph.
- Data lead: Entity extraction quality decides everything; the graph inherits every extraction error, so an eval on extraction is required.
- Compliance officer: Relationship-aware retrieval can answer impact questions (which contracts cite this regulation) that matter in audits.
- CFO: Ingestion cost scales with corpus size times extraction passes; budget it as a distinct line from vector indexing.
Why it exists
Similarity search assumes the answer sits in one passage that resembles the question. Many business questions are relational: they depend on how things connect across documents, systems and time. GraphRAG exists to make those connections retrievable so a model can reason over them. The trade-off is real: extraction is LLM-heavy and imperfect, graphs drift as documents change, and the added machinery is wasted on the majority of questions that a well-tuned hybrid search already answers. Adopt it after measuring where vector retrieval fails, not before.
Where it is applied
- Contract intelligence for a BFSI firm: which agreements, counterparties and obligations are affected by a regulatory change
- Root-cause analysis over a SaaS product's incident reports, tickets and release notes linked by component
- Supplier, part and site relationships for a logistics or manufacturing procurement assistant
- Drug, interaction and protocol relationships in a hospital's clinical reference assistant
- Curriculum prerequisite graphs so an education assistant can explain what a learner must cover before a module
Is GraphRAG (knowledge graph RAG) a skill?
Technique / practiceAn advanced retrieval technique combining LLM-based entity extraction with graph storage and traversal. Eazyware applies it selectively within Retrieval & Knowledge Engineering engagements, only where evals show multi-hop or corpus-wide questions failing under hybrid search.
Eazyware service that covers it: Retrieval & Knowledge Engineering. Starting prices are on the pricing page.
Frequently asked questions
When does GraphRAG beat ordinary RAG?
When questions need facts from several documents joined by a relationship, or ask for a summary across the whole corpus. For "what does this policy say about X" questions, ordinary hybrid retrieval is cheaper and just as accurate.
How much more does GraphRAG cost to run?
Ingestion is the expensive part, since every document passes through an extraction model, and re-ingestion is needed when sources change. Query cost is modest. Expect a multiple of plain vector indexing cost, sized on your corpus during discovery.