Graph RAG Explained

Standard RAG systems retrieve flat text chunks from a vector database and feed them to a language model. This works well for simple factual questions, but falls apart when questions require understanding relationships between entities, questions like "How does drug X interact with drug Y through enzyme Z?" or "What's the chain of command from the CEO to the engineering team?" Graph RAG solves this by augmenting traditional vector retrieval with a knowledge graph that explicitly models entities and their relationships.

The Graph RAG pipeline has four stages. First, entity extraction identifies key entities (people, organizations, concepts, products) from your documents. Second, relationship mapping connects those entities with typed, directed edges, "works at," "reports to," "depends on," "causes." Third, these entities and relationships are stored in a graph database alongside the original vector embeddings. Fourth, during query time, the system performs both vector retrieval (finding relevant text) and graph traversal (following relationship paths) to assemble a rich context that includes not just relevant passages but the structural connections between them.

Graph RAG shines in domains with rich interconnections. Medical knowledge bases benefit enormously because diseases, symptoms, drugs, and side effects form complex relationship networks. Legal document analysis uses it to trace precedent chains and regulatory dependencies. Enterprise knowledge management uses it to connect people, projects, decisions, and documents across organizational boundaries. The article covers implementation strategies using both dedicated graph databases (Neo4j) and lightweight approaches using property graphs in standard databases, making Graph RAG accessible even for smaller projects.