GraphRAG vs Vector RAG: When Simple Vector Search Stops Being Enough

GraphRAG is not just another AI buzzword. It is part of a larger architectural shift happening inside retrieval-augmented generation systems. Most early RAG systems were built around vector search. The idea was simple: break documents into chunks, convert those chunks into embeddings, them in a vector database, and retrieve the most semantically similar chunks when a user asks a question. This works very well for direct questions. For example: What does this policy say about refunds? What are the termination clauses in this contract? Summarize this annual report section.