microsoft/graphrag

Most RAG systems treat your document collection as a bag of chunks. This one builds a structured knowledge graph over your entire corpus first, indexing entities, relationships, and community clusters before a single query is answered. The result is a retrieval layer that can answer questions requiring synthesis across many documents, not just semantic similarity to a nearby paragraph. The practical upside is real: complex questions about large, interconnected corpora get genuinely better answers because the system understands how concepts relate, not just where they appear. Microsoft has invested in enterprise-grade documentation, scalable indexing pipelines, and clear hooks for compliance-sensitive deployments. The honest reservation is the setup cost. You are not dropping this into a weekend project. Indexing a large corpus takes time and compute, configuration is non-trivial, and the learning curve is steeper than simpler vector-search approaches. Teams without dedicated ML or data engineering bandwidth may find it frustrating to operationalize. -> Best for: engineering teams building internal knowledge tools or research assistants over large document collections inside compliance-conscious organizations.