# GraphRAG

AI Glossary · Retrieval & RAG
Canonical URL: https://aisat.uk/glossary/graphrag
Author: Lucy Gilmore, Assistant General Counsel, Product, Privacy & AI at Sprinklr | Practical Privacy, Product & AI Governance & Defensible Risk Decisions (https://www.linkedin.com/in/lucy-gilmore/)
Breadcrumb: [Glossary](https://aisat.uk/glossary) > [Retrieval & RAG](https://aisat.uk/glossary?category=rag) > GraphRAG

## TL;DR

> **GraphRAG** is a retrieval architecture that pairs vector similarity search with a structured knowledge graph of entities and relationships extracted from a document corpus. It enables language models to answer complex, multi-hop questions and synthesise themes across thousands of interconnected records.

## How GraphRAG works in practice

Standard vector RAG retrieves isolated text chunks that are semantically similar to a user's query. That works well when the answer lives inside a single paragraph, but fails on two classes of enterprise questions: multi-hop reasoning (where connecting Entity A to Entity C requires traversing an intermediate relationship in Document B) and global sense-making ('What are the top systemic supply-chain risks across all 2,000 board minutes from the past three years?').

A GraphRAG pipeline adds a knowledge-graph extraction and community-clustering layer during indexing. Using a language model or specialised NER extractor, the pipeline identifies nodes (people, companies, contracts, systems, statutes) and typed edges (`OWNS`, `SUPPLIES_TO`, `AMENDS`, `GOVERNED_BY`) across the corpus, storing them in a graph database such as Neo4j or alongside vector indexes, and optionally pre-computing hierarchical community summaries. At query time, the retriever combines vector search with graph traversal (`Cypher` or subgraph expansion) to pull both direct text chunks and the structural neighbourhood around matched entities.

For example, a London financial crime investigation unit uses GraphRAG across corporate filings, sanctions notices, and correspondence to trace beneficial ownership chains across shell companies that never appear in the same document.

## Why it matters and trade-offs

In domains defined by dense relational structure, such as anti-money laundering, pharmaceutical drug discovery, complex M&A due diligence, and enterprise dependency mapping, GraphRAG surfaces multi-document connections that flat vector retrieval consistently misses.

However, GraphRAG carries a steep indexing and maintenance price tag. Extracting entities, resolving duplicates ('Acme Ltd' vs 'Acme Holdings UK'), and computing community summaries with an LLM across a large corpus can cost ten to fifty times more in indexing tokens than standard vector embedding, and schema drift complicates incremental updates. Teams should only adopt GraphRAG after proving with an eval dataset that hybrid vector + BM25 search with reranking cannot answer their target queries.

## Related AI concepts

- [Retrieval-Augmented Generation (RAG)](https://aisat.uk/glossary/retrieval-augmented-generation): Retrieval-Augmented Generation (RAG) is an AI architecture that retrieves relevant passages from external documents or databases at query time and injects them into a language model's context window to generate cited, grounded answers.
- [Vector Database](https://aisat.uk/glossary/vector-database): A Vector Database is a specialised storage engine, such as pgvector, Qdrant, Pinecone or Weaviate, built to index high-dimensional vector embeddings using approximate nearest-neighbour graphs and execute low-latency similarity queries alongside metadata filters.
- [Hybrid Search](https://aisat.uk/glossary/hybrid-search): Hybrid Search is a retrieval pipeline that executes sparse lexical keyword search alongside dense semantic vector search and merges the candidate rankings using reciprocal rank fusion or a cross-encoder reranker.
- [Embeddings (Vector Embeddings)](https://aisat.uk/glossary/embeddings): Embeddings (Vector Embeddings) are dense arrays of floating-point numbers generated by an embedding model to represent the semantic meaning of text, code, images or audio so conceptually related items sit close together in vector space.