Get the GraphRAG Playbook

Vector RAG grabs the few paragraphs that LOOK like your question — so it can quote one line, but it can't connect two facts on different pages, and it physically can't answer a question about your whole corpus ("what are the main themes across all of this?"). GraphRAG, from Microsoft Research, reads your documents, extracts the entities and the relationships between them into a KNOWLEDGE GRAPH, clusters that graph into communities (the Leiden algorithm), and has the LLM summarize every community — so it can reason over structure instead of scattered chunks. This field guide is the whole thing, step for step: where vector RAG breaks (connecting the dots, multi-hop reasoning, and global/sensemaking questions, and exactly WHY each one fails); how GraphRAG builds the map (chunk → extract entities + relationships → merge into one graph → Leiden communities C0–C3 → community summaries); the two ways to query it (Local search for one entity, Global search as a map-reduce over the community summaries for whole-corpus questions) with the exact graphrag CLI commands; the proof from Microsoft's "From Local to Global" paper (72–83% comprehensiveness and 62–82% diversity WIN RATES vs vector RAG — not accuracy scores — and over 97% fewer tokens at the root level), with the honest caveat that vector RAG still wins on directness; a GRAPH-vs-VECTOR decision chart so you know which to reach for; the honest cost (indexing is expensive, plus LazyGraphRAG at ~0.1% of full indexing cost); and the open-source ecosystem (Microsoft GraphRAG, Neo4j, LlamaIndex, LangChain). Every claim verified against the paper and the official docs. Stop reading in the dark — give your AI the map.

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