GraphRAG – Knowledge Graph Retrieval-Augmented Generation Cheat Sheet

GraphRAG is an advanced retrieval-augmented generation paradigm that combines knowledge graphs with large language models to address the limitations of standard vector-based RAG. Unlike traditional RAG, which retrieves text chunks via semantic similarity, GraphRAG extracts a structured knowledge graph from documents—capturing entities, relationships, and communities—then uses graph traversal and community summaries to power both local (entity-focused) and global (dataset-wide) reasoning. This enables multi-hop inference, explainable provenance, and improved accuracy on complex queries where answers live in connections, not content. Key to GraphRAG's value is its two-stage architecture: an indexing pipeline that constructs the graph (entity extraction → relationship detection → community clustering → summary generation), and a retrieval pipeline that traverses or queries the graph at inference time. Trade-offs include higher indexing costs (10–100x token usage vs. vanilla RAG) and increased latency, but where relational reasoning matters—finance, healthcare, legal compliance—GraphRAG consistently outperforms embedding-only approaches by 35–46% on multi-hop benchmarks.