← All posts

What Is Graph RAG? A 5-Minute Explainer

Graph RAG explained in plain English: what it is, the problem it solves, the three pieces, when you actually need it, and answers to the most common questions.

By Tulasinath Reddy · November 12, 2025 · Tags: Graph RAG Knowledge Graph Architecture

If you've used a chatbot that confidently invented a fact and then cited a real document, you've hit the limit of plain vector search. Graph RAG is the fix: a retrieval architecture that augments vector similarity with a knowledge graph of entities and their relationships, so the system can reason across documents instead of just matching keywords.

This is the 5-minute version. If you want the deep technical comparison, read Graph RAG vs Vector RAG after this.

The one-sentence definition

Graph RAG is retrieval-augmented generation that uses a knowledge graph of extracted entities and relationships alongside vector embeddings, so queries can traverse connections that vector similarity alone misses.

The problem it solves

Vector RAG ranks document chunks by how similar their embeddings are to your query. That works perfectly for questions whose answer lives in one paragraph. It fails the moment the answer is composed from facts in multiple documents — multi-hop questions, entity-aware filtering, anything that needs the system to know that X is_owned_by Y is_located_in Z.

The classic failure mode: the model retrieves three chunks that each mention part of the answer, then hallucinates the connection between them. Graph RAG fixes this at the retrieval layer instead of hoping the LLM will figure it out.

The three pieces

  1. Vector index — the same chunk embeddings any RAG system has. Handles semantic similarity for definition-style questions.
  2. Knowledge graph — entities (people, products, places, concepts) extracted from your documents at ingest, plus typed relationships (:authored, :reviewed_by, :depends_on). Stored in a graph database like FalkorDB or Neo4j.
  3. Fusion layer — at query time, both indexes return candidate chunks; a reranker scores the combined set using semantic similarity AND graph-path relevance, then hands the top-K to the LLM.

When you actually need it

You need graph RAG when:

  • Your questions span multiple documents ("who reviewed the PR that broke v2.3?")
  • Your corpus has dense entity relationships (org charts, codebases, supply chains, scientific papers)
  • Vector RAG's recall is high but its answers are still wrong — the LLM is hallucinating because it can't see the connections
  • You need citations that point to specific relationships, not just text spans

You probably don't need it for a glossary chatbot or a small (under ~5k documents) prose-heavy corpus. Vector RAG is great at the easy half of the problem.

Frequently asked questions

How is graph RAG different from a knowledge graph?

A knowledge graph is just the data structure — nodes and typed edges. Graph RAG is the retrieval pattern that uses a knowledge graph as one of its two indexes (the other being vectors). You can build a knowledge graph and never use it for RAG; graph RAG specifically wires it into the LLM's retrieval pipeline.

Do I need to write a graph schema by hand?

No. Modern entity extraction (LLM-driven, at ingest time) builds the graph automatically from your documents. The graph is a derived index that gets regenerated as your corpus changes. Schema-on-read, not schema-on-write.

Is graph RAG slower than vector RAG?

Marginally — typically 10–30ms extra per query for the graph traversal and rerank step. For most production loads that's invisible next to LLM latency.

Does it cost more?

Ingest is more expensive (you pay for the entity-extraction pass). Per-query cost is often lower, because graph RAG returns fewer, more relevant chunks — so you spend fewer tokens in the LLM call.

What's the difference between graph RAG and Microsoft GraphRAG?

Microsoft's GraphRAG is one specific open-source implementation, focused on entity-community detection and hierarchical summarization. The broader graph RAG pattern includes many implementations — Cognee, Mem0, LangChain's graph extractor, VertexRAG, hand-rolled stacks. Same idea; different engineering choices.

Try it without the setup

The fastest way to see graph RAG in action on your own content: paste any text into our free Entity Extractor — it builds and visualizes the knowledge graph in real time. If the resulting graph is dense (lots of nodes and edges per paragraph), your corpus is graph-shaped and you're losing recall to vector-only RAG today.

If you want to go all the way: the VertexRAG API runs the full hybrid stack — vector index plus graph plus fusion — behind a single REST call.

Read more on the VertexRAG blog or try the Graph RAG API.