GraphRAG,
explained.

Standard RAG works by similarity: embed the query, retrieve the top-k most similar chunks, and let the model answer from them. For a local question — one whose answer sits in a specific place — this is perfect. The relevant passage is similar to the query, so it gets retrieved, and the model reads it off.

But a global question is different in kind. "What are the recurring themes?" "How do these findings relate across the whole report?" "Summarize the main tensions in this corpus." The answer to these isn't in any passage — it's an emergent property of many passages. Retrieve the top-k most similar chunks and you get a handful that happen to match the query's words, covering a small slice of what you'd need to synthesize. No amount of better embedding fixes this: the information is distributed, and top-k retrieval is inherently local.

GraphRAG's indexing does more than embed chunks. An LLM reads every chunk and extracts entities and the relationships between them — people, organizations, concepts, and how they connect. Stitched together across the corpus, these become a knowledge graph: nodes are entities, edges are relationships, and an entity mentioned in many places becomes a well-connected hub.

This graph captures something the flat pile of chunks never did: structure. Two facts stated in different documents about the same entity are now linked through that entity's node. The corpus stops being a bag of passages and becomes a network you can traverse, cluster, and reason over globally — which is exactly what the local, similarity-only view was missing.

A knowledge graph over a large corpus is itself too big to read. So GraphRAG runs community detection (the Leiden algorithm) to partition the graph into communities — clusters of densely interconnected entities that tend to correspond to coherent topics or themes. This is done hierarchically, giving communities at multiple levels of granularity. An LLM then writes a summary of each community ahead of time.

The community summaries are the key artifact. Instead of a million raw chunks, you now have a manageable set of topic-level digests that collectively cover the entire corpus. A global question can be answered by consulting these, not by hoping the right handful of chunks were similar enough to retrieve.

To answer a global query, GraphRAG uses map-reduce over the community summaries. Map: generate a partial answer from each relevant community summary in parallel. Reduce: combine those partial answers into a single, comprehensive response. Because the summaries span the whole corpus, the final answer draws on all of it — not the fraction that a top-k retriever would surface.

If a global answer is spread over R relevant passages and you retrieve only k < R, plain RAG covers just min(k,R)/R of it — for R=10, k=3, that's 30%. GraphRAG's map-reduce over community summaries covers the whole corpus by construction. The paper found this yields substantially more comprehensive and diverse answers to global sense-making questions, judged head-to-head against vector RAG. The runnable version below makes the coverage gap concrete.

GraphRAG buys global reach at a real cost, so it complements rather than replaces vector RAG:

The practical pattern is a hybrid: route local, fact-lookup queries to cheap vector RAG, and global, sense-making queries to GraphRAG's community summaries. The graph earns its expensive index precisely on the questions plain retrieval can't touch.

GraphRAG named and attacked a real limitation that practitioners kept hitting: retrieval-augmented systems that were great at fact lookup and useless at "help me understand this whole body of text." By reintroducing structure — a knowledge graph and its communities — it gave RAG a way to reason over a corpus as a whole, and its open-source release made the approach widely reproducible.

The deeper point is about representation. Flat vector retrieval treats a corpus as an unordered set of passages, which is why it can only ever answer locally. Imposing a graph — entities, relationships, communities — restores the connective tissue between passages, and connective tissue is exactly what global questions are about. It's a reminder that how you structure knowledge determines which questions you can answer, and that similarity search, powerful as it is, is only one such structure.