GraphRAG
GraphRAG is a retrieval-augmented-generation variant that builds a knowledge graph from the source corpus — extracting entities, relationships, and community clusters — and uses the graph structure as retrieval context alongside or in place of raw document chunks. Microsoft Research's 2024 work popularized the term and the reference implementation. The approach helps most on queries that require synthesizing relationships across documents, such as "how are X and Y connected" or "summarize what we know about Z across the whole corpus", where chunk-based retrieval tends to return disconnected fragments that individually look relevant but collectively fail to tell the story. The cost is upfront indexing — building and maintaining the graph is materially more work than chunk-and-embed — which is justified when the query mix skews toward cross-document reasoning rather than lookup.
Example
A competitive-intelligence team indexes five years of analyst reports with plain RAG; the assistant handles single-fact questions well but struggles with "which of our competitors have partnered with the same cloud providers over the last three years". They rebuild the index as GraphRAG, with entities for companies and cloud providers and edges for partnership events. The same cross-document question now returns a structured walk through the graph plus the supporting passages — an answer the chunk-based pipeline could not assemble regardless of retrieval settings.
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