Do You Really Need GraphRAG? A Practitioner’s Guide Beyond the Hype

GraphRAG offers significant advantages over naïve RAG but introduces additional complexity, making it crucial to determine when it is truly necessary. It is most beneficial for applications involving long documents, queries requiring cross-document context, and the need to optimize search in a large corpus. For effective implementation, design principles suggest focusing on key entities in a star graph to manage complexity and building the system iteratively. The proposed retrieval pipeline uses the graph to classify queries and filter for relevant documents, rather than generating answers directly from the graph data, balancing graph-based retrieval with the detailed context in source documents.