Term
GraphRAG
GraphRAG is a RAG variant that uses a knowledge graph in addition to vector search — answers come from linked entities and relationship paths, not just similar text.
GraphRAG — explained in detail
Classic vector RAG finds passages that are semantically similar to a question. For multi-part questions that span several documents (“Which suppliers of vendor X were also involved in case Y?”), the approach often fails — no single passage contains the full answer. GraphRAG adds a knowledge graph on top: entities (people, products, cases) and their relationships are extracted from the source documents and stored as nodes and edges. When a question comes in, the system traverses the graph and queries the vector index — the answer is built from both.
Example / Practical use
A typical flow following Microsoft and Neo4j implementations: 1) Extraction — an LLM reads the documents, identifies entities and relations, and writes them into a property-graph database (Neo4j, Memgraph, NetworkX). 2) Indexing — embeddings on node level are built in parallel. 3) Retrieval — for a query, multi-hop paths are traversed (“person → worked-at → company → sued → …”) and the resulting contexts plus vector hits are handed to the LLM. The approach shines in compliance research, legal case work, and scientific literature.
Distinction from related terms
GraphRAG complements vector RAG — it does not replace it. Vector search remains faster and cheaper for local lookups. GraphRAG pays off as soon as multi-hop reasoning or cross-document aggregation is required. Drawbacks: building and maintaining the graph is expensive, and answer quality depends directly on the quality of entity extraction. Distinction from hybrid search (BM25 + vector): hybrid combines two search methods, while GraphRAG adds a structured knowledge layer on top.
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