Retrieving relevant information using vector similarity is the foundation of most RAG systems. While effective for many use cases, it often struggles when answers require connecting multiple pieces of information or understanding relationships across data.
GraphRAG addresses this limitation by organizing data into a knowledge graph. Instead of retrieving isolated text, it captures how entities are connected, enabling more structured reasoning.
In this article, I introduce GraphRAG through a hands-on demo project.