Standard RAG has a ceiling. If your query requires connecting information across multiple documents — "How did decision A lead to outcome B, which caused problem C?" — vector similarity search fails.

GraphRAG, released by Microsoft Research in 2024, solves this by building a knowledge graph from your documents before any query runs.

Why Standard RAG Fails at Multi-Hop Questions

Vector search retrieves chunks that are semantically similar to the query. But similarity ≠ relationship.

❌ "What are all the indirect effects of policy X across departments?" ❌ "Which entities are connected to both A and B?" ❌ "What's the overall theme across this entire document corpus?"

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These require traversing relationships between entities — exactly what graphs are built for.

How GraphRAG Works

Standard RAG: Document → Chunks → Embeddings → Nearest-neighbor search → Answer

GraphRAG: Document → Entity extraction (LLM) → Relationship extraction (LLM) → Knowledge graph → Community…