GraphRAG

Sweet spot: a team working with a corpus where the interesting questions are thematic, not factual — "what are the main themes in this novel?", "what are the unresolved issues in this ticket backlog?", "how do these concepts connect?" GraphRAG's community-summaries layer genuinely changes what RAG can answer on this class of query. For research labs, narrative analytics, and long-document QA, the improvement is real. Failure modes. For plain factual lookup, GraphRAG is expensive overkill — vanilla RAG will be cheaper and just as good. The cost structure surprises people: index builds are orders of magnitude more expensive than simple embeddings, and frequent corpus changes multiply that cost. Production deployment still requires significant engineering — out of the box, GraphRAG is a research pipeline, not a hosted service. What to pilot. Pick one question that flat RAG cannot answer well on your corpus — something like "what are the three main themes in this collection?" Build the GraphRAG index and ask that question. If the answer is qualitatively better than what your existing RAG produces, the investment is justified for thematic questions. If the answer is similar or worse, the corpus may not benefit from graph structure and you should stay with flat retrieval.

GraphRAG is Microsoft Research's open-source framework that augments RAG with an automatically-constructed knowledge graph. Instead of retrieving flat chunks by semantic similarity, GraphRAG extracts entities and relationships from the source corpus during ingestion, clusters them into "communities" at multiple hierarchical levels, and generates community summaries. Queries then retrieve at the appropriate level of abstraction — specific chunks for fact questions, community summaries for thematic questions. The academic motivation: on corpora where the value is in connections across many documents (a long novel, a set of meeting transcripts, an issue tracker), flat semantic retrieval fails because no single chunk has the thematic answer. Community summaries give the model that thematic layer explicitly. The accompanying paper shows meaningful gains on summarisation-style questions over long-form corpora. GraphRAG is released as a Python package and a self-hostable pipeline. It is ingestion-heavy: a full index build on a medium corpus is minutes to hours and costs real money (tens to hundreds of dollars in LLM calls for the extraction and summary phases). Query-time is fast. The output is persisted as a set of parquet files you can query offline. MIT-licensed, actively maintained, and a notable reference implementation of the "graph-enhanced RAG" design pattern. A commercial hosted version exists via Azure AI Search.