GraphRAG (Graph-based Retrieval-Augmented Generation) is a RAG approach that retrieves and reasons over a knowledge graph—entities and relationships—so an LLM can generate answers grounded not only in documents but also in explicit links between concepts.
What is GraphRAG?
Traditional RAG retrieves text chunks based on embedding similarity, then asks the model to answer using those passages. GraphRAG adds a structured layer: information is represented as nodes (entities such as people, products, events) and edges (relationships such as “works_at”, “depends_on”, “causes”). A query is mapped to relevant entities and subgraphs (via entity linking, graph traversal, hybrid search, or graph embeddings). The system then assembles a “graph context” (triples, neighborhood summaries, and supporting source snippets) and feeds it to the LLM. This helps the model follow multi-hop connections and maintain consistency in complex domains.
Where it’s used and why it matters
GraphRAG is used in enterprise search, customer support, compliance, and engineering knowledge bases—anywhere questions require joining facts across multiple documents (e.g., “Which services are affected if this dependency changes?”). By encoding relationships explicitly, GraphRAG can improve traceability and reduce hallucinations, especially for questions needing multi-step reasoning. It also supports explainability: the retrieved subgraph can be shown as evidence. The main costs are building and maintaining the graph, entity resolution, and keeping graph updates in sync with source data.
Examples
- Dependency impact analysis: traverse service→library→runtime edges to produce an incident summary.
- People/org intelligence: connect employee→team→project→doc nodes to answer “who owns this?”
- Hybrid retrieval: combine vector search for passages with graph traversal for related entities.
- Graph embeddings: retrieve similar subgraphs using learned representations.
FAQs
Is GraphRAG a replacement for vector RAG? Usually it’s complementary. Many implementations use vector search to find candidate documents, then build/expand a subgraph around linked entities.
Do I need a dedicated graph database? Not strictly. You can store triples in a relational store, but graph databases (or libraries) simplify traversal and neighborhood queries.
What makes GraphRAG better for multi-hop questions? Multi-hop questions require chaining relationships; the graph makes those chains explicit and easier to retrieve than relying on coincidental co-occurrence in text chunks.
How do you evaluate GraphRAG? Measure answer correctness and faithfulness, but also retrieval quality: entity linking accuracy, subgraph relevance, and whether cited edges and sources truly support the generated claims.
